SER&
United States
Environmental Protection
Agency
Economic, Environmental,
and Benefits Analysis of the
Final Metal Products &
Machinery Rule
Printed on paper containing at least 30% postconsumer recovered fiber.
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U.S. Environmental Protection Agency
Office of Water (4303T)
1200 Pennsylvania Avenue, NW
Washington, DC 20460
EPA-821-B-03-002
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Economic and Environmental Benefits Analysis Document For The
Final Effluent Limitations
Guidelines and Standards
For The
Metal Products & Machinery
Point Source Category
EPA-821-B-03-002
Christine Todd Whitman
Administrator
G. Tracy Mehan, III
Assistant Administrator, Office of Water
Geoffrey H. Grubbs
Director, Office of Science and Technology
Sheila E. Frace
Director, Engineering and Analysis Division
Nicolaas Bouwes
Chief, Economic and Environmental Assessment Branch
William Anderson
Technical Coordinator
Lynne Tudor
Economist
James C. Covington, III
Economist
February 2003
U.S. Environmental Protection Agency
Office of Water
Washington, DC 20460
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ACKNOWLEDGMENTS AND DISCLAIMER
The Agency would like to acknowledge the contributions of William Anderson,
James C. Covington, III, Lynne Tudor, Lynn Zipf, andNicolass Bouwes to development of
this Economic and Environmental Benefits Analysis document. In addition, EPA
acknowledges the contribution of Abt Associates, Westat, Eastern Research Group, and
Science Application International Corporation.
Neither the United States government nor any of its employees, contractors,
subcontractors, or other employees makes any warranty, expressed or implied, or assumes
any legal liability or responsibility for any third party's use of, or the results of such use of,
any information, apparatus, product, or process discussed in this report, or represents that its
use by such a third party would not infringe on privately owned rights. References to
proprietary technologies are not intended to be an endorsement by the Agency.
Questions or comments regarding this economic document should be addressed to:
Mr. James C. Covington, III.
Economist
Engineering and Analysis Division (4303T)
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, N.W.
Washington, DC 20460
(202) 566 - 1034
covington.james@epa.gov
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MP&M EEBA Table of Contents
Table of Contents
EXECUTIVE SUMMARY
ES.l Overview of Facilities Evaluated for Regulation under the MP&M Point Source Category and Its Effluent
Discharges ES-1
ES.2 Description of the Final Rule ES-3
ES.3 Economic Impacts and Social Costs of the Final Rule ES-3
ES.3.1 Economic Impacts ES-4
ES.3.2 Social Costs ES-7
ES.4 National Benefits of the Final Rule ES-8
ES.4.1 Reduced Human Health Risk ES-10
ES.4.2 Ecological, Recreational, and Nonuser Benefits ES-14
ES.4.3 POTW Impacts ES-15
ES.4.4 Total Estimated Benefits of the Final MP&M Rule ES-16
ES.5 National Benefits-Costs Comparison ES-16
ES.6 Ohio Case Study ES-18
ES.6.1 Benefits ES-18
ES.6.2 Social Costs ES-20
ES.6.3 Comparing Monetized Benefits and Costs ES-20
PART I: INTRODUCTION AND BACKGROUND INFORMATION
Chapter 1: Introduction
1.1 Purpose 1-1
1.2 Introduction 1-1
1.3 Readers' Aids 1-3
Chapter 2: The MP<&M Industry and the Need for Regulation
2.1 Overview of Facilities Evaluated for Regulation under the MP&M Point Source Category 2-1
2.2 MP&M Discharges and the Need for Regulation 2-3
2.2.1 Baseline MP&M Discharges for Regulated Facilities 2-4
2.2.2 Discharges under the MP&M Regulation 2-4
2.3 Addressing Market Imperfections 2-5
2.4 Overlap with Other Effluent Guidelines 2-6
2.5 Meeting Legislative and Litigation-Based Requirements 2-9
Glossary 2-11
Acronyms 2-13
Chapter 3: Profile of the MP<&M Industries
3.1 Data Sources 3-2
3.2 Overview of the MP&M Industry and Industry Trends 3-3
3.2.1 Aerospace 3-7
3.2.2 Aircraft 3-7
3.2.3 Electronic Equipment 3-7
3.2.4 Hardware 3-8
3.2.5 Household Equipment 3-8
3.2.6 Instruments 3-9
3.2.7 Iron and Steel 3-9
3.2.8 Job Shops 3-9
3.2.9 Mobile Industrial Equipment 3-9
3.2.10 Motor Vehicle and Bus & Truck 3-10
3.2.11 Office Machine 3-10
3.2.12 Ordnance 3-10
3.2.13 Precious Metals and Jewelry 3-11
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3.2.14 Printed Wiring Boards 3-11
3.2.15 Railroad 3-11
3.2.16 Ships and Boats 3-11
3.2.17 Stationary Industrial Equipment 3-12
3.3 Characteristics of MP&M Manufacturing Sectors 3-12
3.3.1 Domestic Production 3-13
3.3.2 Industry/Market Structure 3-18
3.3.3 Financial Condition and Performance 3-24
3.4 Characteristics of MP&M Non-Manufacturing Sectors 3-25
3.4.1 Domestic Production 3-25
3.4.2 Industry Structure and Competitiveness 3-28
3.5 Characteristics of All MP&M Sectors 3-30
3.5.1. Eight-firm Concentration Ratio 3-30
3.5.2 Risk Normalized Return on Assets 3-31
3.6 Characteristics of MP&M Facilities 3-32
Glossary 3-38
Acronyms 3-40
References 3-41
Chapter 4: Regulatory Options
4.1 Subcategorization 4-1
4.2 Technology Options 4-3
4.3 BPT/BAT Options for Direct Dischargers 4-3
4.4 PSES Options for Indirect Dischargers 4-3
4.5 NSPS and PSNS Options for New Sources 4-4
4.6 Summary of the Final Rule and Regulatory Alternatives 4-4
Glossary 4-5
Acronyms 4-6
PART II: COSTS AND ECONOMIC IMPACTS
Chapter 5: Facility Impact Analysis
5.1 Data Sources 5-2
5.2 Methodology 5-2
5.2.1 Converting Engineering Compliance Costs and Survey Financial Data to Current Year Dollar Values 5-3
5.2.2 Market-level Impacts and Cost Pass-through Analysis 5-4
5.2.3 Impact Measures for Private Facilities 5-5
5.2.4 Impact Measures for Railroad Line Maintenance Facilities 5-12
5.2.5 Impact Measures for Government-owned Facilities 5-12
5.3 Results 5-14
5.3.1 Baseline Closures 5-14
5.3.2 Price Increases 5-15
5.3.3 Overview of Impacts 5-16
5.3.4 Results for Indirect Dischargers 5-18
5.3.5 Results for Direct Dischargers 5-19
5.3.6 Results for Private Facilities 5-20
5.3.7 Results for Government-Owned Facilities 5-21
Glossary 5-25
Acronyms 5-26
References 5-27
Chapter 6: Employment Effects
6.1 Job Losses Due to Closures 6-2
6.2 Job Gains Due to Compliance Requirements 6-3
6.3 Net Effects on Employment 6-5
Glossary 6-7
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Acronym 6-8
References 6-9
Chapter 7: Government and Community Impact Analysis
7.1 Impacts on Governments 7-1
7.1.1 Impacts on Governments that Operate MP&M Facilities 7-1
7.1.2 POTW Administrative Costs 7-2
7.2 Community Impacts of Facility Closures 7-6
Glossary 7-7
Acronyms 7-8
Chapter 8: Foreign Trade Impacts
8.1 Data sources 8-1
8.2 Methodology 8-2
8.3 Results 8-3
References 8-5
Chapter 9: Firm Level, New Source and Industry Impacts
9.1 Firm Level Impacts 9-1
9.1.1 Sources 9-1
9.1.2 Methodology 9-2
9.1.3 Results 9-2
9.2 New Source Impacts 9-3
9.2.1 Methodology 9-4
9.2.2 Results 9-5
9.3 Industry Level Impacts 9-7
Glossary 9-9
Acronyms 9-10
References 9-11
Chapter 10: Small Entity Impact Analysis
10.1 Defining Small Entities 10-2
10.2 Methodology 10-4
10.3 Results 10-4
10.3.1 Number of Affected Small Entities 10-4
10.3.2 Impacts on Facilities Owned by Small Entities 10-5
10.3.3 Impacts on Small Firms 10-6
10.4 Consideration of Small Entity Impacts in Developing the Final Rule 10-7
Glossary 10-8
Acronyms 10-9
References 10-10
Chapter 11: Social Costs
11.1 Components of Social Costs 11-1
11.2 Resource Costs of Compliance 11-2
11.3 POTW Administration Costs 11-4
11.4 Social Costs of Unemployment 11-5
11.4.1 Social Cost of Worker Dislocation 11-5
11.4.2 Cost of Administering Unemployment Benefits Programs 11-6
11.4.3 Total Cost of Unemployment 11-6
11.5 Total Social Costs 11-7
Glossary 11-8
References 11-9
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MP&M EEBA Table of Contents
PART ill: BENEFITS
Chapter 12: Benefit Overview
12.1 MP&M Pollutants 12-2
12.1.1 Characteristics of MP&M Pollutants 12-2
12.1.2 Effects of MP&M Pollutants on Human Health 12-3
12.1.3 Environmental Effects of MP&M Pollutants 12-7
12.1.4 Effects of MP&M Pollutants on Economic Productivity 12-8
12.2 Linking the Regulation to Beneficial Outcomes 12-9
12.3 Qualitative and Quantitative Benefits Assessment 12-11
12.3.1 Overview of Benefit Categories 12-11
12.3.2 Human Health Benefits 12-13
12.3.3 Ecological Benefits 12-13
12.3.4 Economic Productivity Benefits 12-14
12.3.5 Methods for Valuing Benefit Events 12-14
Glossary 12-16
Acronyms 12-19
References 12-20
Chapter 13: Human Health Benefits
13.1 Methodology & Data Sources 13-2
13.1.1 Cancer from Fish Consumption 13-3
13.1.2 Cancer from Drinking Water Consumption 13-8
13.1.3 Exposures above Non-cancer Health Thresholds 13-10
13.1.4 Human Health AWQC 13-14
13.2 Results 13-17
13.2.1 Fish Consumption Cancer Results 13-17
13.2.2 Drinking Water Consumption Cancer Results 13-19
13.2.3 Non-cancer Health Threshold Results 13-19
13.2.4 Human Health AWQC Results 13-21
13.3 Limitations and Uncertainties 13-22
13.3.1 Sample Design & Analysis of Benefits by Location of Occurrence 13-22
13.3.2 In-Waterway Concentrations of MP&M Pollutants 13-23
13.3.3 Joint Effects of Pollutants 13-23
13.3.4 Background Concentrations of MP&M Pollutants 13-23
13.3.5 Downstream Effects 13-24
13.3.6 Exposed Fishing Population 13-24
13.3.7 Treatment of Cancer Latency 13-25
13.3.8 Treatment of Cessation Lag 13-25
13.3.9 Use of Mean Individual Exposure Parameters 13-26
13.3.10 Cancer Potency Factors 13-26
Glossary 13-27
Acronyms 13-28
References 13-29
Chapter 14: Lead-Related Benefits
14.1 Overview of Lead-Related Health Effects 14-2
14.1.1 Children Under Age One 14-3
14.1.2 Children Between the Ages of One and Seven 14-3
14.1.3 Adults 14-4
14.2 Health Benefits to Children 14-4
14.2.1 PbB Distribution of Exposed Children 14-5
14.2.2 Relationship Between PbB Levels and IQ 14-12
14.2.3 Value of Children's Intelligence 14-12
14.2.4 Value of Additional Educational Resources 14-14
14.2.5 Changes in Neonatal Mortality 14-17
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14.3 Adult Health Benefits 14-17
14.3.1 Estimating Changes in Adult PbB Distribution Levels 14-20
14.3.2 Male Health Benefits 14-22
14.3.3 Female Health Benefits 14-26
14.4 Lead-Related Benefit Results 14-28
14.4.1 Preschool Age Children Lead-Related Benefit Results 14-28
14.4.2 Adult Lead-Related Benefit Results 14-29
14.5 Limitations and Uncertainties 14-31
14.5.1 Excluding Older Children 14-31
14.5.2 Compensatory Education Costs 14-32
14.5.3 Dose-Response Relationships 14-32
14.5.4 Absorption Function for Ingested Lead in Fish Tissue 14-32
14.5.5 Economic Valuation 14-33
Glossary 14-35
Acronyms 14-38
References 14-39
Chapter 15: Recreational Benefits
15.1 Ecological Improvements from the MP&M Regulation 15-3
15.1.1 Overview of Ecological Improvements 15-3
15.1.2 Quantification of Ecological Improvements 15-3
15.1.3 Benefiting Reaches 15-4
15.1.4 Geographic Characteristics of MP&M Reaches 15-6
15.2 Valuing Economic Recreational Benefits 15-6
15.2.1 Transferring Values from Surface Water Valuation Studies 15-6
15.2.2 Recreational Fishing 15-9
15.2.3 Wildlife Viewing 15-13
15.2.4 Recreational Boating 15-17
15.2.5 Nonuse Benefits 15-20
15.3 Summary of Recreational Benefits 15-20
15.4 Limitations and Uncertainties Associated with Estimating Recreational Benefits 15-22
Glossary 15-26
Acronyms 15-28
References 15-29
Chapter 16: POTW Benefits
16.1 Reduced Interference with POTW Operations 16-2
16.2 Assessing Benefits from Reduced Sludge Contamination 16-2
16.2.1 Data Sources 16-2
16.2.2 Sludge Generation, Treatment, and Disposal Practices 16-4
16.2.3 Overview of Improved Sludge Quality Benefits 16-7
16.2.4 Sludge Use/Disposal Costs and Practices 16-8
16.2.5 Quantifying Sludge Benefits 16-10
16.3 Estimated Savings in Sludge Use/Disposal Costs 16-15
16.4 Methodology Limitations 16-16
Glossary 16-18
Acronyms 16-19
References 16-20
Chapter 17: Environmental Justice <& Protection of Children
17.1 Demographic Characteristics of Populations Living in the Counties Near MP&M Facilities 17-1
17.2 Protection of Children from Environmental Health and Safety Risks 17-3
Glossary 17-4
Reference 17-5
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PART IV: COMPARISON OF COSTS AND BENEFITS
Chapter 18: MP<&M Benefit/Cost Comparison
18.1 Estimating National Level Benefits and Costs 18-1
18.2 Social Costs 18-2
18.3 Benefits 18-2
18.4 Comparing Monetized Benefits and Costs 18-2
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
19.1 Estimated Social Costs 19-1
19.1.1 Compliance Costs for MP&M Facilities 19-1
19.1.2 Government Administrative Costs 19-2
19.1.3 Cost of Unemployment 19-2
19.1.4 Total Social Costs 19-3
19.2 Estimated Benefits 19-4
19.2.1 Human Health Benefits 19-4
19.2.2 Recreational Benefits 19-5
19.2.3 Avoided Sewage Sludge Disposal or Use Costs 19-6
19.2.4 Total Monetized Benefits 19-7
19.3 Comparison of Estimated Benefits and Costs 19-7
Glossary 19-10
Acronym 19-11
PART V: OHIO CASE STUDY
Chapter 20: Baseline Conditions in Ohio
20.1 Overview of Ohio's Geography, Population, and Economy 20-2
20.2 Profile of MP&M Facilities in Ohio 20-3
20.3 Ohio's Water Resources 20-6
20.3.1 Aquatic Life Use 20-8
20.3.2 Water Recreation in Ohio 20-11
20.3.3 Commercial Fishing in Ohio 20-12
20.3.4 Surface Water Withdrawals 20-12
20.4 Surface Water Quality in Ohio 20-12
20.4.1 Use Attainment in Streams and Rivers in Ohio 20-13
20.4.2 Lake Erie and Other Lakes Use Attainment 20-13
20.4.3 Causes and Sources of Use Non-Attainment in Ohio 20-14
20.5 Effects of Water Quality Impairments on Water Resource Services 20-15
20.5.1 Effect of Water Quality Impairment on Life Support for Animals and Plants 20-15
20.5.2 Effect of Water Quality Impairment on Recreational Services 20-17
20.6 Presence and Distribution of Endangered and Threatened Species in Ohio 20-18
20.6.1 E&T Fish 20-19
20.6.2 E&T Mollusks 20-19
20.6.3 Other Aquatic E&T Species 20-20
Glossary 20-24
Acronyms 20-27
References 20-28
Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model RUM Analysis
21.1 Methodology 21-2
21.1.1 Overview 21-2
21.1.2 Modeling the Site Choice Decision 21-3
21.1.3 Modeling Trip Participation 21-6
21.1.4 Calculating Welfare Changes from Water Quality Improvements 21-9
21.1.5 Extrapolating Results to the State Level 21-10
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21.2 Data 21-10
21.2.1 The Ohio Data 21-11
21.2.2 Estimating the Price of Visits to Sites 21-14
21.2.3 Site Characteristics 21-14
21.3 Site Choice Model Estimates 21-17
21.3.1 Fishing Model 21-18
21.3.2 Boating Model 21-19
21.3.3 Swimming Model 21-20
21.3.4 Viewing (Near-water Activity) Model 21-20
21.4 Trip Participation Model 21-20
21.5 Estimating Benefits from Reduced MP&M Discharges in Ohio 21-23
21.5.1 Benefiting Reaches in Ohio 21-23
21.5.2 Estimating Recreational Benefits in Ohio 21-24
21.6 Limitations and Uncertainty 21-25
21.6.1 One-State Approach 21-25
21.6.2 Including One-Day Trips Only 21-26
21.6.3 Nonuse Benefits 21-26
21.6.4 Potential Sources of Survey Bias 21-26
Glossary 21-28
Acronyms 21-30
References 21-31
Chapter 22: MP<&M Benefit-Cost Analysis in Ohio
22.1 Benefits of the Final Regulation 22-1
22.1.1 Human Health Benefits (Other than Lead) 22-2
22.1.2 Lead-Related Benefits 22-3
22.1.3 Economic Productivity Benefits 22-4
22.1.4 Total Monetized Benefits 22-4
22.2 Social Costs of the Final Regulation 22-5
22.2.1 Baseline and Post-Compliance Closures 22-5
22.2.2 Compliance Costs for MP&M Facilities 22-6
22.2.3 Total Social Costs 22-7
22.3 Comparison of Monetized Benefits and Costs in Ohio 22-7
Glossary 22-8
Acronyms 22-9
APPENDICES
Appendix A: Detailed Economic Impact Analysis Information
A.I MP&M SIC and NAICS Codes A-l
A.1.1 SIC Codes by Sector A-l
A.1.2 Bridge Between NAICS and SIC codes A-7
A.2 Annual Establishment "Births" and "Deaths" in MP&M Industries A-26
A.3 Description of MP&M Surveys A-28
A.3.1 Screener Surveys A-28
A.3.2 Ohio Screener Surveys A-28
A.3.3 Detailed MP&M Industry Surveys A-28
A.3.4 Iron and Steel Survey A-29
A.3.5 Municipality Survey A-29
A.3.6 Federal Facility Survey A-29
A.3.7 POTW Survey A-29
References A-31
Appendix B: Cost Pass-Through Analysis
B.I The Choice of Sector-Specific CPT Coefficients B-l
B.2 Econometric Analysis B-2
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B.2.1 Framework B-3
B.2.2 Data Used to Estimate the Regression Equation B-4
B.2.3 Regression Results B-6
B.3 Market Structure Analysis B-9
B.3.1 Measures Descriptions B-9
B.3.2 Results B-13
B.4 Validation of Econometrically-Estimated CPT Coefficients B-16
B.4.1 Other Metal Products B-17
B.4.2 Job Shops B-17
B..3 Motor Vehicle B-18
B.4.4 Aircraft B-18
B.4.5 Mobile Industrial Equipment B-18
B.4.6 Aerospace B-18
B.5 Adjusting Estimates of Compliance CPT Potential B-18
Attachment B.A: Selected Review of CPT Literature B-20
B.A.I Ashenfelter et al. (1998), "Identifying the Firm-Specific Cost Pass-Through Rate." B-20
B.A.2 Exchange Rate Pass-Through B-20
B.A.3 Tax Pass-Through B-20
B.A.4 Studies Cited B-20
Acronyms B-22
Appendix C: Summary of Moderate Impact Thresholds by Sector
C.I Developing Threshold Values forPre-Tax Return on Assets (PTRA) C-l
C.2 Developing Threshold Values for Interest Coverage Ratio (ICR) C-2
C.3 Summary of Results C-4
References C-5
Appendix b: Estimating Capital Outlays for MP<&M discounted Cash Flow Analyses
D.I Analytic Concepts Underlying Analysis of Capital Outlays D-2
D.2 Specifying Variables for the Analysis D-4
D.3 Selecting the Regression Analysis Dataset D-7
D.4 Specification of Models to be Tested D-8
D.4.1 Linear Model Specification D-9
D.4.2 Log-Linear Model Specification D-10
D.4.3 Sensitivity Analysis D-12
D.5 Model Validation D-12
Attachment D.A: Bibliography of Literature Reviewed for this Analysis D-17
Attachment D.B: Historical Variables Contained in the Value Line Investment Survey Dataset D-18
Appendix E: Calculation of Capital Cost Components
E.I Calculation of One-Time Capital Cost Components E-l
Appendix F: Administrative Costs
F.I Effluent Guidelines Permitting Requirements F-l
F.I.I NPDES Basic Industrial Permit Program F-l
F. 1.2 Pretreatment Program F-2
F.2 POTW Administrative Cost Methodology F-2
F.2.1 Data Sources F-2
F.2.2 Overview of Methodology F-3
F.3 Unit Costs of Permitting Activities F-4
F.3.1 Permit Application and Issuance F-4
F.3.2 Inspection F-7
F.3.3 Monitoring F-7
F.3.4 Enforcement F-9
F.3.5 Repermitting F-10
F.4 POTW Administrative Costs by Option F-10
Appendix F Exhibits F-12
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References F-25
Appendix &- Extrapolation Methods
G.I Using Raking to Adjust MP&M Facility Sample Weights G-2
G. 1.1 Data Sources G-2
G.I.2 Raking Adjustment G-3
G.2 Methodology for Developing Sample-Weighted Estimates for Sites with More Than One MP&M Facility G-7
G.3 Methodology for Extrapolation of Ohio Case Study Results to the National Level G-13
G.3.1 Change in Pollutant Loads G-14
G.3.2 Level of Recreational Activities on Reaches Affected by MP&M Discharges G-14
G.3.3 Differences in Household Income G-14
G.4 Results G-15
Glossary G-17
Appendix H: Fate and Transport Model for bW and Ohio Analyses
H.I Model Equations H-l
H.2 Model Assumptions H-3
H.2.1 Steady Flow Conditions Exist Within the Stream or River Reach H-3
H.2.2 Longitudinal Dispersion of the Pollutant Is Negligible H-3
H.2.3 Flow Geometry, Suspension of Solids, and Reaction Rates Are Constant Within a River Reach H-4
H.3 Hydrologic Linkages H-4
H.4 Associating Risk with Exposed Populations H-4
H.5 Data Sources H-4
H.5.1 Pollutant Loading Data Used in the Drinking Water Risk Analysis H-4
H.5.2 Pollutant Loading Data Used in the Ohio Case Study Analysis H-4
Glossary H-8
Acronyms H-9
References H-10
Appendix I: Environmental Assessment
I.I MP&M Pollutant Characterization 1-4
1.1.1 Identifying MP&M Pollutants 1-4
1.1.2 Physical-Chemical Characteristics and Toxicity Data of MP&M Pollutants 1-9
1.1.3 Grouping MP&M Pollutants Based on Risk to Aquatic Receptors 1-21
1.1.4 Assumptions and Limitations 1-23
1.2. Methodology 1-23
1.2.1 Sample Set Data Analysis and National Extrapolation 1-23
1.2.2 Water Quality Modeling 1-23
1.2.3 Impact of Indirect Discharging Facilities on POTW Operations 1-25
1.2.4 Assumptions and Limitations 1-27
1.3 Data Sources 1-28
1.3.1 Facility-Specific Data 1-28
1.3.2 Water Body-Specific Data 1-28
1.3.3 Information Used to Evaluate POTW Operations 1-29
1.4 Results 1-33
1.4.1 Human Health Impacts 1-34
1.4.2 Aquatic Life Effects 1-37
1.4.3 POTW Effects 1-41
Glossary 1-44
Acronyms 1-48
References 1-49
Appendix J: Special distribution of MP<&M Facilities and Recreational User
Populations
Table J.I Distribution of MP&M Facilities and Participants of Water Based Recreation by State J-2
Figure J.I Cumulative Distribution of Facilities and Participants J-4
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Appendix K: Selecting WPT Values for Benefits Transfer
K.I Desvousges et al., 1 987. Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study
for the Monongahela River K-2
K.2 Farber and Griner, 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis K-3
K.3 Jakus etal., 1997. Do Sportfish Consumption Advisories Affect Reservoir Anglers' Site Choice? K-5
K.4 Lant and Roberts, 1990. Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure .... K-6
K.5 Audrey Lyke, 1993. Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case
Study K-7
K.6 Montgomery and Needelman, 1997. The Welfare Effects of Toxic Contamination in Freshwater Fish K-8
K.7 Phaneuf et al., 1998. Valuing Water Quality Improvements Using Revealed Preference Methods When Corner
Solutions are Present K-8
Glossary K-10
Acronyms K-11
References K-12
Appendix L: Parameters Used in the IEUBK Model
Table B-l: Description of Parameters Used in the IEUBK Lead Model L-l
Appendix M: Sensitivity Analysis of Lead-Related Benefits
M.I Values for Quantified Lead-Related Health Effects M-l
M.2 Lead-Related Benefit Results M-2
M.2.1 Preschool Age Children Lead-Related Benefits M-2
M.2.2 Adult Lead-Related Benefits M-3
Appendix N: Analysis of the National Demand for Water-Based Recreation Survey
N.I Background Information N-l
N.2 Data Analysis N-2
N.3 Participation in Water-Based Recreation by Activity Type N-2
N.4 Allocation of Trips by Water Body Type N-ll
N.5 One-Way Travel Distance N-l6
N.6 Individual Expenditures per Trip N-l9
N.7 Distribution of Direct Costs for Single-day Trips N-22
N.8 Profile of Boating Trips N-27
N.9 Profile of Fishing Trips N-30
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MP&M EEBA
Executive Summary
Executive Summary
INTRODUCTION
EPA is promulgating effluent limitations guidelines and
standards for the Metal Products and Machinery (MP&M)
industry. This document presents EPA's economic and
environmental analyses supporting the final rule. The
Executive Summary provides an overview of the costs and
benefits of the regulation.
Overall, EPA finds that the final rule has modest economic
impacts and benefits. The estimated social cost of the final
rule is $13.8 million annually (2001$). The total benefits that
can be valued in dollar terms in the categories traditionally
analyzed for effluent guidelines range from around $1.0 to
$1.5 million annually (2001$), based on alternative
extrapolation methods.
EPA recognizes that estimates of both costs and benefits are
uncertain. To supplement the national level analysis
performed for the final MP&M regulation, EPA conducted a
more detailed case study of the expected State-level costs
and benefits of the MP&M rule in Ohio. In contrast to the
national-level analysis, the more detailed case study analysis
finds that the final regulation would achieve benefits
substantially exceeding estimated social costs. Comparing the midpoint estimate of social costs ($62,232) with the midpoint
estimate of monetizable benefits ($930,408) for Ohio, EPA estimates a net benefit of the final MP&M rule for Ohio is
$868,178 (2001$).
EPA notes that effluent limitations guidelines for the MP&M industry are technology-based. EPA is neither required to
demonstrate environmental benefits of its technology-based rules, nor is it required to consider receiving water quality in
setting technology-based effluent limitations guidelines and standards. EPA considers benefits as one of the factors that the
Agency evaluates.
Detailed descriptions of the analytic methodologies and results are presented in the Economic, Environmental, and Benefits
Assessment for the Final Metal Products and Machinery Rule (EEBA). In addition, the EEBA presents costs, benefits, and
economic impacts for alternatives to the final rule that were considered by EPA.
EXECUTIVE SUMMARY CONTENTS
ES.l Overview of Facilities Evaluated for
Regulation under the MP&M Point Source
Category and Its Effluent Discharges ES-1
ES.2 Description of the Final Rule ES-3
ES.3 Economic Impacts and Social Costs of the
Final Rule ES-3
ES.3.1 Economic Impacts ES-4
ES.3.2 Social Costs ES-7
ES.4 National Benefits of the Final Rule ES-8
ES.4.1 Reduced Human Health Risk ES-10
ES.4.2 Ecological, Recreational, and
Nonuser Benefits ES-14
ES.4.3 Reduced POTW Impacts ES-15
ES.4.4 Total Estimated Benefits of the
Final MP&M Rule ES-16
ES.5 National Benefit-Costs Comparison ES-16
ES.6 Ohio Case Study ES-18
ES.6.1 Benefits ES-18
ES.6.2 Social Costs ES-20
ES. 6.3 Comparing Monetized Benefits
and Costs ES-20
ES. l OVERVIEW OF FACILITIES EVALUATED FOR REGULATION UNDER THE MP<&M
POINT SOURCE CATEGORY AND ITS EFFLUENT &ISCHARSES
The MP&M Point Source Category regulates oily operations process wastewater discharges to surface waters from existing or
new industrial facilities (including facilities owned and operated by federal, state, or local governments) engaged in
manufacturing, rebuilding, or maintenance of metal parts, products, or machines for use in the sixteen Metal Product &
Machinery (MP&M) industrial sectors. Please note the underlined language in the previous sentence as a facility maybe
subject to the MP&M effluent guidelines even if it is not in one of the MP&M industrial sectors. For example, EPA considers
a facility performing machining part of the "Bus & Truck" MP&M industrial sector if it manufactures metal parts for truck
trailers. Process wastewater means wastewater as defined at 40 CFR parts 122 and 401, and includes wastewater from air
pollution control devices (see 40 CFR 438.2(g)). Oily operations are listed at 40 CFR 438.2(g) and defined in Appendix B to
Part 438 (see also Section 4 of the TDD).
ES-1
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MP&M EEBA
Executive Summary
According to Statistics of U.S. Business, 1996, approximately 638,696 establishments operate in the MP&M industry sectors.
Based on information in the MP&M survey database, approximately 44,000 facilities meet the definition of an MP&M
facility. These 44,000 facilities include approximately 41,000 indirect dischargers (i.e., facilities discharging effluent to a
publicly-owned sewage treatment works or POTWs) and 3,000 direct dischargers (i.e., facilities discharging effluent directly
to a waterway under a NPDES permit).
Table ES.l reports the estimated number of MP&M facilities and total discharge flow (before final rule implementation) by
type of facility. The largest number of sites, approximately 22,000, perform "rebuilding/maintenance only" and account for
approximately 6 percent of the total estimated discharge flow for the industry. "Manufacturing only" contains the next largest
number of facilities (15,400) and accounts, by far, for the largest percentage of the total estimated discharge flow for the
industry (82 percent).
Table ES.l: Number of MP&M Facilities and
Total Discharge Flow by Type of Facility
Type of Facility
Manufacturing &
Rebuilding/Maintenance
Manufacturing only
Rebuilding/Maintenance
only
Total
Number of
Facilities
6,600
15,400
22,000
44,000
Total Estimated
Discharge Flow
(million gal/yr)
9,400
64,100
4,700
78,200
Percent of
Facilities
15.0%
35.0%
50.0%
100.0%
^^^ ^^,
Percent of Total
Discharge Flow
12.0%
82.0%
6.0%
100.0%
Source: U.S. EPA analysis. See Section 4 of the Technical Development Document for the final rule.
Of the 43,858 water discharging facilities, 3,593 are predicted to close in the baseline, leaving 40,265 existing MP&M
facilities that EPA estimates could be regulated.1 After accounting for subcategory and discharger class exclusions, EPA
estimates that the final rule will regulate 2,382 of these facilities, all of which are direct dischargers. These regulated
facilities represent 5.9 percent of the 40,265 facilities that could be potentially regulated.
Table ES.2 summarizes information on the total number of MP&M facilities that were evaluated for the final rule, the number
operating in the baseline, and the number and percent of facilities that will be regulated under the final rule. As reported in
Table ES.2, no indirect dischargers are subject to the final regulation. The rule will regulate 2,382 direct dischargers in the
Oily Wastes subcategory.
Table ES.2: Number of MP&M Facilities Evaluated for the Final Rule
and Regulated under the Final Rule
Discharge Status
Direct dischargers
Indirect dischargers
All dischargers
MP&M
Facilities
2,739
41,162
43,858
Operating in
the Baseline
2,641
37,652
40,265
Regulated
under the Final
Rule
2,382
0
2,382
Percent of Facilities
Operating in the Baseline
that are Regulated
90%
0%
6%
Source: U.S. EPA analysis.
1 These are facilities that are predicted to close due to weak financial performance under baseline conditions, i.e., in the absence of
the final rule. EPA does not attribute the costs or the reduced discharges resulting from these baseline closures to the final rule, and
therefore excludes these facilities from its analyses of the rule's impacts. Baseline closures account for differences between the universe of
facilities discussed in this report and the universe discussed in the Technical Development Document.
ES-2
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MP&M EEBA Executive Summary
Several aspects of the MP&M industries as a whole and part of those industries evaluated for regulation under the final rule
are important in understanding the need for the regulation, the likely distribution and occurrence of benefits, and the
framework of the economic analysis for the regulation.
Facilities in the relevant MP&M industries are located in every state, with a particular concentration in the heavy industrial
regions along the Gulf Coast, both East and West Coasts and the Great Lakes Region. Moreover, MP&M facilities are
frequently located in highly populated regions. Based on an analysis of in-scope sample facilities, around 35% of these
facilities discharge to reaches located adjacent to counties with populations of at least 500 thousand people.
Discharges of these pollutants to surface waters and POTWs have a number of adverse effects, including degradation of
aquatic habitats, reduced survivability and diversity of native aquatic life, and increased human health risk through the
consumption of contaminated fish and water.
Many MP&M facilities evaluated for the final regulation produce goods and services that serve multiple market sectors. It is
not possible to associate regulatory costs and benefits to particular sectors, because EPA is not able to link regulated
processes to specific sectors for facilities operating in multiple sectors. As a result, EPA's cost and economic impact analyses
are disaggregated by type of facility but not by sector.
ES.2 DESCRIPTION OF THE FINAL RULE
In order to address variations between products, raw materials processed, and other factors that result in distinctly different
effluent characteristics, EPA proposed eight groupings called "subcategories" for the January 2001 proposal and June 2002
Notice of Data Availability (NOD A). EPA retained this subcategory structure for evaluating options for the final rule.
Regulation of a category using subcategories allows each subcategory to have a uniform set of effluent limitations that take
into account technological achievability and economic impacts unique to that subcategory (see Section 6 of the TDD). For
the final rule, EPA is establishing limitations and standards only for direct dischargers in the Oily Wastes subcategory. The
other seven subcategories (General Metals, Metal Finishing Job Shops, Non Chromium Anodizing, Printed Wiring Board,
Railroad Line Maintenance, Shipbuilding Dry Docks, and Steel Forming & Finishing) were considered for regulation at
proposal and for some of the alternative regulatory options, but are not further regulated under the final rule.
EPA is establishing BPT pH limitations and daily maximum limitations for two pollutants, oil and grease as hexane
extractable material (O&G (as HEM)) and total suspended solids (TSS), for direct dischargers in the Oily Wastes subcategory
based on the proposed technology option (Option 6). The technology requirements include the following treatment measures:
(1) in-process flow control and pollution prevention; and (2) oil-water separation by chemical emulsion breaking and
skimming (see Section 9 of the TDD). This technology is available technology readily applicable to all facilities in the Oily
Wastes subcategory. Approximately 42% of the direct discharging facilities in the Oily Wastes subcategory currently employ
this technology already.
EPA is promulgating BCT equivalent to BPT for facilities in the Oily Wastes subcategory and has decided not to establish
BAT limitations. EPA is promulgating NSPS for new direct dischargers in the Oily Wastes subcategory at the BPT and BCT
levels.
ES.3 ECONOMIC IMPACTS AND SOCIAL COSTS OF THE FINAL RULE
EPA assessed the economic impacts and social costs of the final rule using detailed financial and technical data from the
MP&M surveys (see Section 3 of the TDD). Engineering analyses of these facilities identified the pollution prevention and
treatment systems needed to comply with the final rule and other regulatory alternatives. The estimated capital and annual
operating and maintenance costs of these systems, incremental to the costs of systems already in place, represent the
2 EPA is not able to characterize the location characteristics of all potentially-regulated MP&M facilities at the national level
precisely, because the MP&M survey design was not intended to provide national results by location characteristics.
ES-3
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MP&M EEBA Executive Summary
compliance costs of the rule.3 EPA analyzed the financial performance of the facilities evaluated for regulation under pre-
regulation conditions (the baseline) and as subject to regulatory requirements. The Agency used a variety of measures to
assess the economic impacts resulting from the final rule, both for the regulated MP&M facilities and for the firms and
governments that own the facilities. The economic impact analysis also considered impacts for small entities in particular,
and impacts on employment, foreign trade and communities. The results of the analyses for sample facilities were
extrapolated using survey sample weights for each facility, to provide national-level results.
ES.3.1 Economic Impacts
Overall, EPA found the economic impact of the final rule to be modest. The following are EPA's findings for different
categories of impacts.
a. Facility impacts
The facility impact analysis assesses how facilities will be affected financially by the final rule. Key outputs of the facility
impact analysis include expected facility closures in the MP&M industries, associated losses in employment, and the number
of facilities experiencing financial stress short of closure ("moderate impacts"). EPA performed economic impact analyses
for three categories of facilities, using different methodologies to evaluate each of the groups. The three groups are:
»• Private MP&M Facilities. This group includes privately-owned facilities that do not perform railroad line
maintenance and are not owned by governments. This major category includes private businesses in a wide range of
sectors or industries, including facilities that manufacture and rebuild railroad equipment. Only facilities that repair
railroad track and equipment along the railroad line are not included.
> Railroad line maintenance facilities maintain and repair railroad track, equipment and vehicles.
•• Government-owned facilities include MP&M facilities operated by municipalities, State agencies and other public
sector entities such as State universities. Many of these facilities repair, rebuild, and maintain buses, trucks, cars,
utility vehicles (e.g., snow plows and street cleaners), and light machinery.
The specific methodology used to assess impacts differed for each of the three types of MP&M facilities. Forprivate MP&M
facilities, EPA established thresholds for measures of financial performance and compared the facilities' performance before
and after compliance with each regulatory option with these thresholds. Impacts were measured at the operating company
level for railroad line maintenance facilities, since firms are unlikely to keep track of financial performance at the facility level
for these sites. For governments, EPA compared compliance costs with facilities' baseline costs of service, and assessed the
impact of the compliance costs on the government's taxpayers and on its ability to finance compliance costs by issuing debt.
EPA identified facilities that are financially weak and might be expected to close under baseline conditions. Of the estimated
43,858 discharging facilities, 8.2 percent or 3,593 facilities were assessed as baseline closures. The 3,593 baseline closures
include 3,511 indirect dischargers, or 8.5 percent of indirect dischargers, and 98 direct dischargers, or 3.6 percent of direct
dischargers. These facilities were excluded from the post-compliance analysis of regulatory impacts.
Table ES .3 summarizes the facility-level economic impact of the final rule. EPA estimates that the final rule will cause no
facilities to close or to incur moderate financial stress short of closure. The final rule excludes all indirect discharging
facilities and two percent of the direct discharging facilities from requirements.
3 The annual equivalent of capital and other one-time costs is calculated by annualizing costs at a seven percent discount rate over an
estimated 15 year equipment life. Annual compliance costs are annualized capital costs plus annual operating and maintenance (O&M)
costs.
ES-4
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MP&M EEBA
Executive Summary
Table ES.3: Regulatory Impacts for All Facilities, Final Rule, National Estimates
Number of facilities operating in the baseline: total
private MP&M and railroad line maintenance
government-owned
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline excluded or below
cutoffs
Number of facilities operating subject to regulatory requirements
Number of regulatory closures
Percent of facilities operating in the baseline that are regulatory
closures
Number of facilities experiencing moderate impacts
Percent of facilities operating in the baseline that experience
moderate impacts
Total"
40,265
36,480
3,785
37,883
94.1%
2,382
0
0.0%
0
0.0%
Direct
2,641
2,183
458
259
9.8%
2,382
0
0.0%
0
0.0%
Indirect
37,652
34,325
3,327
37,652
100.0%
0
0
0.0%
0
0.0%
a The total number of facilities does not sum to the number of facilities by subcategory because some facilities
have an indirect and direct discharging operation within the same facility.
Source: U.S. EPA analysis.
Table ES.4 summarizes impacts for government-owned facilities in particular. Under the final rule, 88 percent of the
government-owned facilities are excluded from requirements by subcategory exclusions. The compliance costs of the final
rule do not result in significant budgetary impacts for any of the governments that operate MP&M facilities.
Table ES.4: Regulatory Impacts for Government -Owned
Final Rule, National Estimates
Number of government-owned facilities operating in the baseline
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline excluded
Number of facilities operating subject to regulatory requirements
Number of facilities experiencing significant budgetary impacts"
Percent of facilities operating in the baseline that experience
significant budgetary impacts
Facilities,
3,785
3,327
88%
458
0
0%
" A government is judged to experience major budgetary impacts if (1) any of its
MP&M facilities incur compliance costs exceeding 1% of baseline cost of service
and (2) the government fails both the taxpayer impact and debt impact tests.
Source: U.S. EPA analysis.
b. Firm-level impacts
EPA examined the impacts of the final rule on firms that own MP&M facilities, as well as on the financial condition of the
facilities themselves. A firm that owns multiple MP&M facilities could experience adverse financial impacts at the firm level
if its facilities are among those that incur significant impacts at the facility level. The firm-level analysis is also used to
assess impacts on small firms, as required by the Regulatory Flexibility Act.
EPA compared compliance costs with revenue at the firm level as a measure of the relative burden of compliance costs. EPA
applied this analysis only to MP&M facilities owned by private entities. EPA estimated firm-level compliance costs by
summing costs for all facilities owned by the same firm that responded to the survey plus estimated compliance costs for
ES-5
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MP&M EEBA
Executive Summary
additional facilities for which respondents submitted voluntary information. The Agency was not able to estimate the national
numbers of firms that own MP&M facilities precisely, because the sample weights based on the survey design represent
numbers of facilities rather than firms. Most MP&M facilities (26,472 of 36,480, or 73 percent) are single-facility firms,
however. These firms can be analyzed using the survey weights. In addition, from survey responses, EPA identified 389
sample facilities that are owned by 276 multi-facility firms. It is not known how many multi-facility firms exist at the national
level, so EPA included these 276 firms in the firm-level analysis without extrapolation to the national level.
Table ES.5 shows the results of the firm-level analysis. The results represent a total of 26,748 MP&M firms (26,472
owning 26,861 facilities (26,472 owned by single-facility firms + 389 owned by multi-facility firms).
276),
Table ES.5: Firm Level Before-Tax Annual Compliance Costs
as a Percent of Annual Revenues
Number of
Firms in the
Analysis"
26,748
Number and Percent with Before-Tax Annual Compliance Costs/Annual
Revenues Equal to:
0%
Number %
25,722 96.2%
>0%and
-------
MP&M EEBA
Executive Summary
terms of the estimated percentages of new facilities that would incur costs exceeding specified revenue thresholds, to decide
whether to issue revised new source limits for the various industry subcategories and discharger classes. From this analysis,
EPA concluded that the promulgation of revised new source limits for the Oily Wastes direct discharger subcategory would
not create a barrier to entry and this information, in part, underlies EPA's decision to promulgate new source limits for this
subcategory as part of the final regulation.
g. Impacts on small entities
Table ES.6 shows the total number of facilities operating in the baseline and the number owned by small entities. Overall,
approximately 73 percent of all MP&M facilities are owned by small entities. However, subcategory exclusions in the final
rule will exclude approximately 95 percent of the facilities owned by small entities.
Table ES.6: Number and Percent of MP&M Facilities Owned by Small Entities
Type of Facility
Private MP&Ma
Government-owned
Total"
Number of Facilities of
all Sizes Operating in
the Baseline
36,480
3,785
40,265
Number of Facilities
Owned by Small
Entities
27,418
1,962
29,380
Percent of Facilities
Owned by Small
Entities
75%
52%
73%
a Excludes baseline closures
Source: U.S. EPA analysis.
EPA assessed impacts on small entities by comparing compliance costs to revenues for the small entities at the firm level and
by analyzing the facility impact analysis results for facilities owned by small firms. These analyses indicate that no facilities
will incur costs exceeding 1% of revenues, and only 1,019 facilities owned by small firms will incur any costs at all. None of
these facilities incur moderate impacts or close as a result of the final rule.
EPA estimates that 1,962 facilities are owned by small governments (those with populations less than 50,000). The
subcategory exclusions in the final rule exclude 1,682 of these small government-owned MP&M facilities. Thus, the final
rule covers 280 small government-owned facilities. Of these facilities, only 140 incur costs, and the average annual cost per
facility is less than $30,000. All of the 140 facilities have costs less than 3 percent of baseline cost of service. EPA estimated
no significant impacts for any of these facilities or the governments that own them, based on the analysis of change in site cost
of service, impact on taxpayers, and impact on government debt levels. The total compliance cost for all the small
government-owned facilities incurring costs under the final rule is $3.5 million.
ES.3.2 Social Costs
The social costs of the final rule represent the value of society's resources used to comply with and administer the rule. EPA
estimated three categories of social cost for the final regulation:
•• the cost of society's economic resources used to comply with the final regulation,
•• the cost to governments of administering the final regulation, and
•• the social costs of unemployment resulting from the regulation.
Resource costs of compliance are the value of society's productive resources including labor, equipment, and materials
expended to achieve the reductions in effluent discharges required by the final rule. The social costs of these resources are
generally higher than the cost burden to facilities because facilities are able to deduct the costs from their taxable income and
may offset some of the costs through increased prices to customers. The costs to society, however, are the full value of the
resources used, whether they are paid for by the regulated facilities, by all taxpayers in the form of lost tax revenues, or passed
on to customers through increased prices.
ES-7
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MPAM EEBA
Executive Summary
The main cost to government from administering the regulation is the cost to POTWs for writing permits, and for compliance
monitoring and enforcement activities. POTWs could incur costs in writing new permits for previously unpermitted facilities,
and writing revised permits for some facilities earlier than would otherwise be required. Because the final regulation excludes
all indirect dischargers from coverage, EPA expects that the final rule will not increase POTW administrative costs.
The loss of jobs from facility closures would represent a social cost of the regulation. From its facility impact analysis, EPA
estimates that no facilities will close as a result of the regulation. Accordingly, EPA estimates a zero cost of unemployment
for the final rule. EPA did not recognize possible savings in unemployment-related costs from jobs created by the rule as a
negative cost (benefit) of the regulation.
From this analysis EPA estimated a total annual social cost of $13.8 million annually (2001$) for the final rule (see Table
ES.7). All of this cost results from the estimated resource cost of compliance.
Table ES.7: Total Social Cost: Final Rule
(millions, 2001$)
Social Cost Categories
Resource cost of compliance expenditures
Costs to POTWs of administering the rule
Social costs of unemployment
Total Social Cost
Final Rule
$13.8
$0.0
$0.0
$13.8
Source: U.S. EPA analysis.
ES.4 NATIONAL BENEFITS OF THE FINAL RULE
The final regulation will reduce MP&M industry pollutant discharges to the nation's surface waters with a number of
consequent benefits to society, including:
»• improved quality of freshwater, estuarine, and marine ecosystems;
»• increased survivability and diversity of aquatic and terrestrial wildlife; and
•• reduced risks to human health through consumption of fish or water taken from affected waterways.
Table ES.8 shows EPA estimates for reduction in pollutant discharges to U.S. waters under the final rule. Loadings are shown
both in pounds of pollutant and in toxic-weighted pound equivalents. The latter measure reflects the relative toxicity of the
various toxic pollutants. The regulation would result in a 80 percent reduction in total toxic-weighted pollutant Ibs.
equivalent per year. The estimated toxic weighted pollutant reductions range from 87 percent for priority metal pollutants to 1
percent for arsenic. Reductions in pounds of pollutants (not toxic-weighted) range from 93 percent for oil and grease (O&G)
to 5 percent for arsenic. As shown in Table ES.8, the final rule achieves modest reductions for arsenic, organics, biological
oxygen demand (BOD), and chemical oxygen demand (COD), and significant reductions for toxic metals, other inorganics,
bulk pollutants, and oil and grease.
ES-i
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MP&M EEBA
Executive Summary
Table ES.8: Summary of Discharges by Pollutant Type for Regulated MP&M Facilities"
Pollutant Category
\ \
„ . _ . IT,, j a. T- i T, i i Percent Reduction Due to the
Current Releases : Releases under the Final Rule : „. , „ ,
Final Rule
: :
Pounds 1 Pounds Eq. j Pounds 1 Pounds Eq. j Pounds 1 Pounds Eq.
Priority Pollutants
Metals
Organic s
Arsenic
Cyanide (CN)
Nonconventional Pollutants
Metals
Organic s
Other inorganics
Bulk pollutants
Conventional Pollutants
BOD
COD
O&G
TSS
Total
794
336
22
0
25,863
2,159
fc
2,334
h
335,679
263,419
523,440
428,137
160,695
2,756
58
75
0
417
45
L
0.2
L
3,351
153
268
21
0
16,428
1,038
L
1,301
h
167,295
165,567
488,697
28,955
73,769
351
45
74
0
158
39
fc
0.1
fc
667
80.7%
20.2%
4.5%
-
36.5%
51.9%
L
44.3%
h
50.2%
37.1%
6.6%
93.2%
54.1%
87.3%
22.4%
1.3%
-
62.1%
13.3%
L
50.0%
L
80.1%
a Includes only direct discharging facilities in the Oily Wastes subcategory that continue to operate in the baseline and that are subject
to the final rule.
Source: U.S. EPA analysis.
EPA assessed the benefits from the expected pollutant reductions in three broad classes: human health, ecological, and
productivity benefits.4 EPA was able to assess benefits within these three classes with varying degrees of completeness and
rigor. Where possible, EPA quantified the expected effects and estimated monetary values. Some benefit categories could
not be monetized because of data limitations and a limited understanding of how society values certain water quality changes.
EPA used sample facility data to estimate national benefits from the regulation. The Agency extrapolated findings from the
sample facility analyses to the national level using two extrapolation methods: (1) traditional extrapolation and (2)
post-stratification extrapolation. EPA traditionally uses a standard linear weighting technique (i.e., traditional extrapolation)
to estimate national compliance costs, changes in pollutant removals, and national-level benefits of environmental regulations.
However, using sample weights that are based only on facility-specific (e.g., engineering) characteristics without including
non-facility factors can lead to a conditional bias in the estimation of national-level benefits. In particular, this approach
omits consideration of important non-facility factors that influence the occurrence and size of benefits. Non-facility factors
that are likely to affect the occurrence and size of benefits from reduced sample facility discharges and that are not reflected
in the standard stratification and sample-weighting approach include the receiving water body type and size and the size of
population residing in the vicinity of a sample facility. To address omission of these important non-facility factors (i.e., water
body type and size, affected population, and co-occurrence MP&M discharges) in designing the MP&M facilities sample,
EPA adjusted sampling weights through post-stratification using two variables: (1) receiving water body type and size and
(2) the size of the population residing in the vicinity of the sample facility. The Agency used a commonly used post-
4 EPA evaluated two productivity measures: (1) the reduction in pollutant interference at POTWs, and (2) pass-through of pollutants
into the sludge, which limits options for POTW disposal of sewage sludge. Because the final rule only regulates direct discharges and thus
does not affect POTW operations, productivity benefits were evaluated for alternative options only.
ES-9
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MP&M EEBA Executive Summary
stratification method called "raking" to adjust original sample weights to reflect these benefits characteristics. Appendix G of
this report provides detail on extrapolation methods used in this analysis.
To supplement the national-level analysis performed for the final MP&M regulation, EPA also conducted a detailed case
study of the expected state-level costs and benefits of the MP&M rule in Ohio. For several reasons, EPA judges that the Ohio
case study is more robust than the national benefit analyses that EPA undertakes in support of effluent guideline development.
These reasons include: (1) use of more detailed data on MP&M facilities than is possible at the national level; (2) use of more
detailed and accurate water quality data than are usually available; (3) more accurate accounting for the presence and effect of
multiple discharges to the same reach; (4) inclusion of data on non-MP&M discharges in the baseline and post compliance;
(5) use of a first-order decay model to estimate in-stream concentrations in downstream water bodies; and (6) inclusion of an
additional recreational benefit category (swimming) in the analysis. The Ohio case study analysis is presented in Chapters 20,
21, and 22 of this report.
ES.4.1 Reduced Human Health Risk
EPA estimates that the final rule will prevent discharge of 18 pounds per year of carcinogens and 119 pounds per year of lead.
Also, the final rule will prevent discharge of an additional 6,900 pounds of 76 pollutants of concern that are known to cause
adverse human health effects. These reduced pollutant discharges from MP&M facilities are expected to result in reduced risk
of illness from consumption of contaminated fish, shellfish, and water.
EPA analyzed the following measures of health-related benefits: reduced cancer risk from fish and water consumption;
reduced risk of non-cancer toxic effects from fish and water consumption; lead-related health effects to children and adults;
and reduced occurrence of in-waterway pollutant concentrations in excess of levels of concern. The levels of concern include
human health-based ambient water quality criteria (AWQC) or documented toxic effect levels for those chemicals not covered
by water quality criteria. Although some health effects are relatively well understood and can be quantified and monetized in
a benefits analysis (e.g., cancer), others are less well understood, and may not be assessed with the same rigor or at all (e.g.,
systemic health effects). The Agency therefore monetized only two of these health benefits: (1) changes in the incidence of
cancer from fish and water consumption, and (2) changes in adverse health effects in children and adults from reduced lead
exposure.
The national-level analysis of human health benefits finds negligible monetized benefits from the final rule. However, because
of significant simplifications in the national level analysis, this finding should be recognized as potentially having substantial
error and should therefore be interpreted with caution. In particular, the national-level analysis: (1) is based only on limited
information on MP&M facilities at the national level; (2) accounts in only a very limited way for the presence and effect of
joint discharges on the same reach; (3) omits data on non-MP&M discharges in the baseline and post compliance; and (4)
omits consideration of the downstream effects of pollutant discharges.
In contrast to the national-level analysis, the methods and data used for the Ohio case study address a number of these analytic
weaknesses. This more rigorous analysis finds that the final regulation would achieve $0.5 million (2001$) in health-related
benefits in the state of Ohio alone. EPA estimates that this analysis provides a more accurate, albeit lower-bound, estimate of
health-related benefits than indicated by the simpler national-level analysis. Moreover, given (1) that Ohio represents only
about 6 percent of the total MP&M facility population and (2) that a substantial share of the total MP&M facility population
is located in other states with similar water body and population characteristics (e.g., the states of Illinois, Indiana, Michigan,
Pennsylvania), it is reasonable to expect that additional human health benefits would be estimated for the remainder of the
country if EPA were able to apply this more rigorous approach at the national level. Accordingly, EPA judges that the final
rule's human health benefits are higher than its social costs.
a. Benefits from reduced incidence of cancer cases
EPA assessed changes in the incidence of cancer cases from consumption of MP&M pollutants in fish tissue and drinking
water. The methodology for assessing human health benefits from reduced cancer incidence is presented in Chapter 1 3 of this
report. The Agency valued changes in incidence of cancer cases using a willingness-to-pay (WTP) of $6.5 million (2001$)
for avoiding premature mortality. This estimate of the value of a statistical life saved is recommended in EPA's Guidelines for
Preparing Economic Analysis. This estimate does not include estimates of WTP to avoid morbidity prior to death.
EPA estimated aggregate cancer risk from contaminated drinking water for populations served by drinking water intakes on
water bodies to which MP&M facilities discharge. EPA based this analysis on six carcinogenic pollutants for which drinking
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water criteria have not been published.5 This analysis excludes seven carcinogens for which drinking water criteria have been
published. EPA assumed that public drinking water treatment systems will remove these pollutants from the public water
supply. To the extent that treatment for these seven pollutants may cause incidental removals of the chemicals without criteria,
the analysis may overstate cancer-related benefits.
Calculated in-stream concentrations provide the basis for estimating changes in cancer risk for populations served by affected
drinking water intakes. EPA estimates that baseline MP&M discharges from in-scope facilities are associated with virtually
zero annual cancer cases. The national-level analysis finds that the final regulation would lead to a marginal reduction in these
cancer cases resulting from consumption of contaminated drinking water; correspondingly, monetary benefits estimated from
reduced consumption of contaminated drinking water are essentially zero.
EPA also estimated cancer risk from the consumption of contaminated fish for recreational and subsistence anglers and their
families. EPA based this analysis on thirteen carcinogenic pollutants found in MP&M effluent discharges. Estimated
contaminant concentrations in fish tissue are a function of predicted in-stream pollutant concentrations and pollutant
bioconcentration factors. EPA used data on numbers of licensed fishermen by state and county, presence of fish consumption
advisories, number of fishing trips per person per year, and average household size to estimate the affected population of
recreational and subsistence anglers and their families. The analysis uses different fish consumption rates for recreational and
subsistence anglers to estimate the change in cancer risk among these populations.
EPA estimated that baseline MP&M discharges from in-scope facilities are associated with 0.03 annual cancer cases. The
national-level analysis shows that the final option would lead to a marginal reduction in cancer cases among recreational and
subsistence angler populations. The monetary benefits estimated from consumption of less contaminated fish by these
populations are essentially negligible.
The findings from the national analysis of changes in cancer risk for the final rule differ from the Ohio case study results.
Based on the Ohio case study, the final option is expected to eliminate 0.01 cancer cases annually in the State of Ohio alone.
This reduction translates into $14,500 (2001$) in annual benefits due to reduced cancer risk from consumption of
contaminated fish tissue and drinking water (see Chapter 22 of this report for detail).
The difference in the findings of the national and Ohio analyses results primarily from more comprehensive information on
MP&M and non-MP&M facility discharges used in the Ohio case study analysis. The national-level analysis accounts only
for the pollutant exposures from MP&M sample facilities, hi contrast, the Ohio case study approach accounts for a broader
baseline of pollutant exposure, including more thorough and detailed coverage of discharges from MP&M facilities and also
estimated exposures from non-MP&M sources. As a result, the Ohio case study analysis more accurately reflects baseline
health risk conditions.
b. Reductions in systemic health effects
The final rule can potentially achieve a wide range of non-cancer human health benefits (e.g., systemic effects, reproductive
toxicity, and developmental toxicity) from reduced contamination of fish tissue and drinking water sources. The common
approach for assessing the risk of non-cancer health effects from the ingestion of a pollutant is to calculate a hazard quotient
by dividing an individual's oral exposure to the pollutant, expressed as a pollutant dose in milligrams per kilogram body
weight per day (mg/kg-day), by the pollutant's oral reference dose (RfD). An RfD is defined as an estimate (with uncertainty
spanning perhaps an order of magnitude) of a daily oral exposure that likely would not result in the occurrence of adverse
health effects in humans, including sensitive individuals, during a lifetime. A hazard quotient less than one means that the
pollutant dose to which an individual is exposed is less than the RfD, and, therefore, presumed to be without appreciable risk
of adverse human health effects. EPA guidance for assessing exposures to mixtures of pollutants recommends calculating a
hazard index (HI) by summing the individual hazard quotients for those pollutants in the mixture that affect the same target
organ or system (e.g., the kidneys, the respiratory system). HI values are interpreted similarly to hazard quotients; values
below one are generally considered to suggest that exposures are not likely to result in appreciable risk of adverse health
effects during a lifetime, and values above one are generally cause for concern, although an HI greater than one does not
necessarily suggest a likelihood of adverse effects. Chapter 13 of this report provides a detailed discussion of the
methodology for assessing changes in systemic health effects associated with this rule.
5 EPA included n-nitrosodimethylamine (NDMA) in its assessment of the baseline incidence of cancer cases. However, the Agency
did not consider NDMA pollutant reductions in its benefits analysis due to limited wastewater sampling for that pollutant.
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To evaluate the potential benefits of reducing the in-stream concentrations of 76 pollutants that cause non-cancer health
effects, EPA estimated target organ-specific His for drinking water and fish ingestion exposures in both the baseline and
post-compliance scenarios. Specifically, EPA calculated target-organ specific His for pollutants predicted in each MP&M
discharge reach; as a result, a separate HI was calculated for each target organ/exposure pathway (fish consumption and
drinking water)/reach combination. EPA then combined estimates of the numbers of individuals in the exposed populations
with the His for the populations to determine how many individuals might be expected to realize reduced risk of non-cancer
health effects in the post-compliance scenario.
The results of EPA's analysis suggest that hazard indices for individuals in the exposed populations may decrease after
facilities comply with today's rule. Increases in the percentage of exposed populations that would be exposed to no risk of
non-cancer adverse human health effects due to the MP&M discharges occur in both the fish and drinking water analyses. The
shift to lower hazard indices should be considered in conjunction with the finding that the hazard indices for incremental
exposures to pollutants discharged by MP&M facilities (for which reference doses are available) are less than one in the
baseline analysis for the entire population associated with sample facilities. Whether the incremental shifts in hazard indices
are significant in reducing absolute risks of non-cancer adverse human health effects is uncertain and will depend on the
magnitude of contaminant exposures for a given population from risk sources not accounted for in this analysis.
c. Benefits from reduced exposure to lead
EPA performed a separate analysis of benefits from reduced exposure to lead. This analysis differs from the analysis of
non-cancer adverse human health effects from exposure to other MP&M pollutants because it is based on dose-response
functions tied to specific health endpoints to which monetary values can be applied. Chapter 14 of this report presents the
methodology for assessing benefits from reduced exposure to lead.
Many lead-related adverse health effects are relatively common and are chronic in nature. These effects include, but are not
limited to, hypertension, coronary heart disease, and impaired cognitive function. Lead is harmful to individuals of all ages,
but the effects of lead on children are of particular concern. Children's rapid rate of development makes them more
susceptible to neurobehavioral effects from lead exposure. The neurobehavioral effects on children from lead exposure
include hyperactivity, behavioral and attention difficulties, delayed mental development, and motor and perceptual skill
deficits.
This analysis assessed benefits of reduced lead exposure from consumption of contaminated fish tissue to three sensitive
populations: (1) preschool age children; (2) pregnant women; and (3) adult men and women. This analysis uses blood-lead
levels as a biomarker of lead exposure. EPA estimated baseline and post-compliance blood lead levels in the exposed
populations and then used changes in these levels to estimate benefits in the form of avoided health damages.
EPA assessed neurobehavioral effects on children based on a dose response relationship for IQ decrements. Avoided
neurological and cognitive damages are expressed as changes in overall IQ levels, including reduced incidence of extremely
low IQ scores (<70, or two standard deviations below the mean) and reduced incidence of blood-lead levels above 20 mg/dL.
The analysis uses the value of compensatory education that an individual would otherwise need and the impact of an
additional IQ point on individuals' future earnings to value the avoided neurological and cognitive damages. The
national-level analyses shows that implementation of the final option would not result in any changes in IQ loss across all
exposed children. The final option does not reduce occurrences of extremely low IQ scores (<70) or incidences of blood-lead
levels above 20 mg/dL.
Prenatal exposure to lead is an important route of exposure. Fetal exposure to lead in utero due to maternal blood-lead levels
may result in several adverse health effects, including decreased gestational age, reduced birth weight, late fetal death,
neurobehavioral deficits in infants, and increased infant mortality. To assess benefits to pregnant women, EPA estimated
changes in the risk of infant mortality due to changes in maternal blood-lead levels during pregnancy. The national-level
analysis shows that the final option does not result in changes in maternal blood lead levels during pregnancy and as a result
does not reduce neonatal mortality.
The national-level analysis finds no benefits to children from reduced exposure to lead. However, as for the cancer risk
analysis previously discussed, these findings differ from the more comprehensive analysis used in the Ohio case study. Using
the more rigorous case study approach, EPA estimates that the final regulation will yield annual lead-related benefits for
children in Ohio of $422,113 (2001$). This benefit value includes three components. First, reduced lead exposure is estimated
to reduce neonatal mortality by 0.024 cases annually with an annual value of $162,094 (2001$). Second, reduced lead
exposure will avoid the loss of an estimated 26.96 IQ points among preschool children in Ohio, which translates into
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MP&M EEBA Executive Summary
$253,934 (2001 $) per year in benefits. Third, the annually avoided costs of compensatory education from incidence of IQ
below 70 and blood-lead levels above 20 g/dL among children amounts to approximately $5,345 (2001$).
Lead exposure has been shown to have adverse effects on the health of adults as well as children. The health effects in adults
that EPA quantified all derive from lead's effects on blood pressure. Quantified health effects include increased incidence of
hypertension (estimated for males only), initial coronary heart disease (CUD), strokes (initial cerebrovascular accidents and
atherothrombotic brain infarctions), and premature mortality. This analysis does not include other health effects associated
with elevated blood pressure and other adult health effects of lead, including nervous system disorders, anemia, and possible
cancer effects. EPA used cost of illness estimates (i.e., medical costs and lost work time) to estimate monetary value of
reduced incidence of hypertension, initial CHD, and strokes. EPA then used the value of a statistical life saved to value
changes in risk of premature mortality. The national-level analysis finds that the final rule will achieve no lead-related health
benefits among adults.
Again, the national analysis results differ from the Ohio case study results. Using the case study approach, EPA estimates that
the final regulation will achieve total lead-related benefits among Ohio adults of $117,393 (2001$). This value includes
benefits from reduced hypertension among adult males: a reduction of an estimated 9.4 cases annually, with benefits of
approximately $10,670 (2001$). In addition, reducing the incidence of initial CHD, strokes, and premature mortality among
adult males and females in Ohio would result in estimated benefits of $963, $2,115, and $103,645, respectively (see Chapter
22 of this report for detail).
Based on the national-level benefits analysis, EPA found that total benefits from reduced exposure to lead, for both children
and adults, are negligible under the final rule. However, based on the Ohio case study findings, benefits for children and
adults from reduced lead-related health effects of the final rule are estimated to total approximately $0.5 million (2001$)
annually in the state of Ohio alone (see Section H of today's final rule for detail). As in the cancer risk analysis, the difference
in the national and Ohio-based findings stems primarily from more comprehensive information on MP&M and non-MP&M
facility discharges used in Ohio.
d. Exceedances of human health-based AWQC
EPA also estimated the effect of MP&M facility discharges on the occurrence of pollutant concentrations in affected
waterways that exceed human health-based AWQCs. In a conceptual sense, this analysis and its findings are not additive to
the preceding analyses of change in cancer or lead-related health risks but are another way of quantitatively characterizing the
same possible benefit categories. This analysis compares the estimated baseline and post-compliance in-stream pollutant
concentrations in affected waterways to ambient water criteria for protection of human health. The comparison included
AWQC for protection of human health through consumption of organisms and consumption of water and organisms. Pollutant
concentrations in excess of these values indicate potential risks to human health.
EPA estimates that in-stream concentrations of 4 pollutants (i.e., arsenic, iron, manganese, and n-nitrosodimethylamine) will
exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of
baseline MP&M pollutant discharges. EPA estimates that 23% of human health AWQC exceedances are caused by
n-nitrosodimethylamine (NDMA). EPA did not consider NDMA pollutant reductions in its benefits analyses due to limited
wastewater sampling data for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of
concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms
on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values.
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ES.4.2 Ecological, Recreational, and Nonuser Benefits
EPA expects the MP&M rule to improve aquatic species habitats by reducing concentrations of toxic contaminants such as
aluminum, cadmium, copper, lead, mercury, silver, and zinc in water. These improvements should enhance the quality and
value of water-based recreation, such as fishing, swimming, wildlife viewing, camping, waterfowl hunting, and boating. The
benefits from improved water-based recreation would be seen as increases in the increased value participants derive from a
day of recreation and the increased number of days that consumers of water-based recreation choose to visit the cleaner
waterways. This analysis measures the economic benefit to society from water quality improvements based on the increased
monetary value of recreational opportunities resulting from those improvements
a. Reduced aquatic life impacts
EPA quantified the ecological improvements of the final regulation by comparing estimates of in-waterway concentrations of
pollutants discharged by MP&M facilites with AWQC values for protection of aquatic species. Pollutant concentrations in
excess of acute and/or chronic AWQC limits for protection of aquatic life indicate potential adverse impacts to aquatic
species. EPA estimates that baseline in-stream concentrations of 9 pollutants (i.e., aluminum, cadmium, copper, lead,
manganese, mercury, nickel, silver, and zinc) will exceed the acute and chronic criterion for aquatic life in 353 reaches
nationwide. The final rule eliminates concentrations in excess of aquatic life AWQCs on nine of these reaches. EPA's
analysis shows that none of the receiving reaches exceeding chronic or acute aquatic life AWQC at the baseline discharge
level will experience partial water quality improvements from reduced occurrence of AWQC exceedances for some
pollutants.
b. Recreational benefits
EPA assessed the recreational benefits from reduced occurrence of pollutant concentrations exceeding aquatic life and/or
human health AWQC values. Combining its findings from both the aquatic life and human health AWQC exceedance
analyses, EPA found that 394 stream reaches exceed chronic or acute aquatic life AWQC and/or human health AWQC values
at baseline discharge levels. The Agency estimates that the final rule will eliminate exceedances on nine of these discharge
reaches, leaving 384 reaches with concentrations of one or more pollutants exceeding AWQC limits. None of these 384
reaches will experience partial water quality improvements from reduced occurrence of some pollutant concentrations in
excess of AWQC limits.
EPA attached a monetary value to reduced exceedances based on increased values for three water-based recreation activities
(fishing, wildlife viewing, and boating) and on nonuser values. EPA applied a benefits transfer approach to estimate the total
WTP, including both use and nonuse values, for improvements in surface water quality. This approach builds upon a review
and analysis of the surface water valuation literature.
EPA first estimated the baseline value of water-based recreation for benefiting reaches, based on per-reach estimates of:
> annual person-days of water-based recreation, and
*• per-day values of water-based recreation.
The baseline per-day values of water-based recreation are based on studies by Walsh et. al (1 992) and Bergstrom and Cordell
(1991). The studies provide values per recreation day for a wide range of water-based activities, including fishing, boating,
wildlife viewing, waterfowl hunting, camping, and picnicking. The mean values per recreational fishing, boating, and wildlife
viewing day used in this analysis are $42.12, $48.30 and $26.28 (2001$), respectively. Applying facility weights and
summing over all benefiting reaches provides a total baseline value for a given recreational activity for MP&M reaches
expected to benefit from the elimination of pollutant concentrations in excess of AWQC limits.
EPA then applied the percentage change in the recreational value of water resources implied by surface water valuation
studies to estimate changes in values for all MP&M reaches in which the regulation eliminates AWQC exceedances by one or
more MP&M pollutants. The Agency selected eight of the most comparable studies and calculated the changes in recreation
values from water quality improvements (as percentage of the baseline) implied by those studies. Sources of estimates
included Lyke (1993), Jakus et al. (1997), Montgomery and Needleman (1997), Paneuf et al. (1998), Desvousges et al.
(1987), Lant and Roberts (1990), Farber and Griner (2000), and Tudor et al. (2002). EPA's reasoning for selecting each study
is discussed in detail in Chapter 15 of this report. EPA took a simple mean of point estimates from all applicable studies to
derive a central tendency value for percentage change in the water resource values due to water quality improvements. These
studies yielded estimates of increased recreational value from water quality improvements expected from reduced MP&M
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MP&M EEBA
Executive Summary
discharges of 12, 9, and 18 percent for fishing, boating, and wildlife viewing respectively. Using all possible applicable
valuation studies in developing a benefits transfer approach to valuing changes in the recreational value of water resources
from reduced MP&M discharges, makes unit values more likely to be nationally representative, and avoids the potential bias
inherent in using a single study to make estimates at the national level.
Table ES.9 presents the estimated national recreational benefits of the final rule (2001$). The estimated increased value of
recreational activities to users of water-based recreation is $537,197, $202,691, and $259,949 annually for fishing, boating,
and wildlife viewing, respectively. The recreational activities considered in this analysis are stochastically independent; EPA
calculated the total user value of enhanced water-based recreation opportunities by summing over the three recreation
categories. The estimated increase in the total user value is $999,838 annually.
EPA also estimated non-market nonuser benefits. These non-market nonuser benefits are not associated with current use of
the affected ecosystem or habitat; instead, they arise from the value society places on improved water quality independent of
planned uses or based on expected future use. Past studies have shown that nonuser values are a sizable component of the
total economic value of water resources. EPA estimated average changes in nonuser value to equal one-half of the recreational
use benefits based on study by A. Fisher and R. Raucher (1984). The estimated increase in nonuse value is $499,91 9 (2001 $).
A recent literature review finds that nonuse benefits are, on average, 1.9 to 2.5 times all use values, rather than 0.5 times
recreational benefits alone as EPA has traditionally assumed for its nonuse benefit estimates (T. Brown, 1993). EPA's method
for estimating nonuse benefits from water quality improvements resulting from reduced MP&M discharges is therefore likely
to understate the true value of nonuse benefits.
Table ES.9: Estimated Recreational Benefits from Reduced
MP&M Discharges (thousands, 2001$)
Recreational Activity
Recreational fishing
Recreational boating
Wildlife viewing and near- water recreation
Total recreational use benefits (fishing + boating + wildlife viewing)
Nonuser benefits (1A of total recreational use)
Total Recreational Benefits (2001$)
Traditional
Extrapolation
$537
$203
$260
$1,000
$500
$1,500
Post-Stratification
Extrapolation
$350
$132
$169
$651
$326
$977
Source: U.S. EPA analysis.
The recreational trips corresponding to the three activities considered in this analysis are stochastically independent; EPA
calculated the total value of enhanced water-based recreation opportunities by summing the three recreation categories and
nonuser value. The resulting increase in the value of water resources to consumers of water-based recreation and nonusers is
$1,500 thousand (2001$) annually under the traditional extrapolation method and $977 thousand (2001 $) annually under the
post-stratification extrapolation method.
ES.4.3 POTW Impacts
The final rule only regulates direct dischargers. Therefore, the selected option does not affect POTW operation. For the
alternative policy options that consider both direct and indirect dischargers, EPA evaluated two productivity measures
associated with MP&M pollutants. The first measure is the reduction in pollutant interference at publicly-owned treatment
works (POTWs). The second measure is pass-through of pollutants into the sludge, which limits options for POTW disposal
of sewage sludge. These analyses are presented in Chapter 1 6 of this report.
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MP&M EEBA Executive Summary
ES.4.4 Total Estimated Benefits of the Final MP<&M Rule
Using the traditional extrapolation method, EPA estimates total benefits for the five monetized categories of approximately
$1,500,000 (2001$) annually. This value understates the total benefits of the rule because the benefits analysis omits
significant sources of benefits to society. Examples of benefit categories not reflected in this estimate include non-cancer
health benefits other than benefits from reduced exposure to lead; other water-dependent recreational benefits, such as
swimming and waterskiing benefits to recreational users from reduced concentration of conventional pollutants and
nonconventional pollutants such as TKN; and reduced cost of drinking water treatment for the pollutants with drinking water
criteria. In addition, as noted in the prior discussion, although the national-level benefits analysis finds negligible benefits
from reduced health risk, the more rigorous analytic approach used for the Ohio case study found material health-related
benefits approximately $0.5 million in the state of Ohio alone.
ES.5 NATIONAL BENEFITS-COSTS COMPARISON
The comparison of benefits and for the final rule is inevitably incomplete because EPA cannot value all of the benefits
resulting from the final rule in dollar terms. A comparison of benefits and costs is thus limited by the lack of a comprehensive
benefits valuation and also by uncertainties in the estimates. Bearing these limitations in mind, EPA presents a summary
comparison of benefits and costs for the final rule in Table ES.10. The estimated social cost of the final rule is $13.8 million
annually (2001$). The total benefits that can be valued in dollar terms in the categories traditionally analyzed for effluent
guidelines range from $977,000 to $1,500,000 annually (2001$), based on the alternative extrapolation methods.
As previously noted, EPA used more detailed information and a more comprehensive analytic method to estimate expected
benefits of the final rule for the state of Ohio. This more rigorous analysis was undertaken to address certain issues in the
national-level analysis and to supplement the national-level analysis performed for the final rule. The following section
presents this analysis. The Ohio case study showed that the more rigorous analytic approach leads to a different conclusion
from that found in the simpler, national-level analysis approach in particular, that the estimated state-level benefits exceed
the estimated state-level cost. As previously discussed, given (1) that Ohio accounts for only about 6 percent of total MP&M
facilities, and (2) that other states with substantial numbers of MP&M facilities have similar population and water body
characteristics to Ohio, EPA estimates that use of the more rigorous approach nationally would yield a higher estimate of
national benefits. On this basis, the Agency estimates that national benefits from the final rule maybe comparable to its social
costs.
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Executive Summary
Table ES.10: Comparison of National Annual Monetizable Benefits to Social Costs
(thousands, 2001$)
Benefit and Cost Categories
Benefit Categories
Reduced Cancer Risk from Fish Consumption
Reduced Cancer Risk from Water Consumption
Reduced Risk from Exposure to Lead
Enhanced Water-Based Recreation
Nonuse Benefits
Total Monetized Benefits
Traditional Extrapolation
$0
$0
$0
$1,000
$500
$1,500
Post-Stratification
Extrapolation
$0
$0
$0
$651
$326
$977
Cost Categories
Resource Costs of Compliance
Costs of Administering the Final Regulation
Social Costs of Unemployment
Total Monetized Costs
Net Monetized Benefits (Benefits Minus Costs)
$13,825
$0
$0
$13,825
($12,325)
$13,825
$0
$0
$13,825
($12,847)
Source: U.S. EPA analysis.
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ES.6 OHIO CASE STUDY
Part V of this report presents a detailed case study of the expected state-level costs and benefits of the MP&M rule in Ohio.
The case study assesses the costs and benefits of the final rule for facilities and water bodies located in Ohio. Ohio is among
the ten states with the largest numbers of MP&M facilities. The state has a diverse water resource base and a more extensive
water quality ecological database than many other states. EPA gathered data on MP&M facilities and on Ohio's baseline water
quality conditions and water-based recreation activities to support the case study analysis. These data characterize current
water quality conditions, water quality changes expected from the regulation, and the expected welfare changes from water
quality improvements at water bodies affected by MP&M discharges. The case study also estimates the social costs of the
final rule for facilities in Ohio and compares estimated social costs and benefits for the state.
The case study analysis supplements the national-level analysis performed for the final MP&M regulation in two important
ways. First, the analysis used improved data and methods to determine MP&M pollutant discharges from both MP&M
facilities and other sources. In particular, EPA administered 1,600 screener questionnaires to augment information on the
Ohio's MP&M facilities. The Agency also used information from the sampled MP&M facilities to estimate discharge
characteristics of non-sampled MP&M facilities, as described in Appendix H of the EEBA report. The Agency assigned
discharge characteristics to all non-MP&M industrial direct discharges based on the information provided in the EPA's
Permit Compliance System (PCS) database. Second, the analysis used an original travel cost study to value four recreational
uses of water resources affected by the regulation: swimming, fishing, boating, and near-water activities. The added detail
provides a more complete and reliable analysis of water quality changes from reduced MP&M discharges. The study provides
more complete estimates of changes in human welfare resulting from reduced health risk, enhanced recreational opportunities,
and improved economic productivity.
EPA estimated human health benefits from reduced MP&M dischargers in Ohio using similar methodologies to those used for
the national-level analysis. These methodologies are presented in Chapter 13 and 14 of the EEBA report.
The case study analysis of recreational benefits combines water quality modeling with a random utility model (RUM) to
assess how changes in water quality from the regulation will affect consumers' valuation of water resources. The RUM
analysis addresses a wide range of pollutant types and effects, including water quality measures not often addressed in past
recreational benefits studies. In particular, the model supports a more complete analysis of recreational benefits from
reductions in nutrients and toxic pollutants (i.e., priority pollutants and nonconventional pollutants with toxic effects).
EPA subjected this study to a formal peer review by experts in the natural resource valuation field. The peer review concluded
that EPA had done a competent job, especially given the available data. As requested by the Agency, peer reviewers provided
suggestions for further improvements in the analysis. Since the proposed rule analysis, the Agency made changes to the Ohio
model and conducted additional sensitivity analyses suggested by the reviewers. The peer review report and EPA's response
to peer reviewers' comments, along with the revised model, are in the docket for the rule.
ES.6.1 Benefits
The use of an original RUM in this case study allows the Agency to address limitations inherent in benefits transfer used in
the analysis of recreational benefits at the national level. The use of benefits transfer often requires additional assumptions
because water quality changes evaluated in the available recreation demand studies are only roughly comparable with the
water quality measures evaluated for a particular rule. The RUM model estimates the effects of the specific water quality
characteristics analyzed for the final MP&M regulation, such as presence of AWQC exceedances and concentrations of the
nonconventional pollutant Total Kjeldahl Nitrogen (TKN). EPA estimates that this direct link between the water quality
characteristics analyzed for the rule and the characteristics valued in the RUM analysis reduces uncertainty in benefit
estimates and makes the analysis of recreational benefits more robust.
The final MP&M regulation affects a broad range of pollutants, some of which are toxic to human and aquatic life but are not
directly observable (i.e., priority and nonconventional pollutants). These unobservable toxic pollutants degrade aquatic
habitats, decrease the size and abundance offish and other aquatic species, increase fish deformities, and change watershed
species composition. Changes in toxic pollutant concentrations may therefore affect recreationists' valuation of water
resources, even if consumers are unaware of changes in ambient pollutant concentrations.
The study used data from the National Demand Survey for Water-Based Recreation (NDS), conducted by U.S. EPA and the
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Executive Summary
National Forest Service, to examine the effects of in-stream pollutant concentrations on consumers' decisions to visit a
particular water body. The analysis estimated baseline and post-compliance water quality at recreation sites actually visited by
the surveyed consumers and at all other sites within the consumers' choice set, visited or not. The RUM analysis of consumer
behavior then estimated the effect of ambient water quality and other site characteristics on the total number of trips taken for
different water-based recreation activities and the allocation of these trips among particular recreational sites. The RUM
analysis is a travel cost model, in which the cost to travel to a particular recreational site represents the "price" of a visit.
EPA modeled two consumer decisions: (1) how many water-based recreational trips to take during the recreational season (the
trip participation model); and (2) which recreation site to choose (the site choice model). Combining the trip participation
model's prediction of trips under the baseline and post-compliance scenarios and the site choice model's per-trip welfare
measure provides a measure of total welfare. EPA calculated each individual's seasonal welfare gain for each recreation
activity from post-compliance water quality changes, and then used Census data to aggregate the estimated welfare change to
the State level. The sum of estimated welfare changes over the four recreation activities yielded estimates of total welfare
gain.
EPA estimated other components of benefits in Ohio using similar methodologies to those used for the national-level analysis.
In addition to the RUM study of recreational benefits, other analytical improvements included the following: (1) use of more
detailed data on MP&M facilities, obtained from the 1,600 additional surveys; (2) use of data on non-MP&M discharges to
estimate current baseline conditions in the state, and (3) use of a first-order decay model to estimate in-stream concentrations
in the Ohio water bodies in the baseline and post-compliance.
Appendix H of this report describes the water quality model used in this analysis and the approach and data sources used to
estimate total pollutant loadings from all industrial and municipal sources to Ohio's water bodies. The Agency has concluded
that the added level of detail results in more robust benefit estimates.
Summing the monetary values over all benefit categories yields total monetized benefits of $930,408 (2001$) annually for the
final rule, as shown in Table ES.l 1. Although more comprehensive than the national benefits analysis, the case study benefit
estimates still omit important mechanisms by which society is likely to benefit from the final rule. Examples of benefit
categories not reflected in the monetized benefits include non-cancer health benefits (other than lead-related benefits) and
reduced costs of drinking water treatment.
Table ES.ll: Annual Benefits from Reduced MP&M
(thousands, 2001$)
Benefit Category
1 . Reduced cancer risk:
Fish consumption
Water consumption
2. Reduced risk from exposure to lead:
Children
Adults
3. Enhanced fishing
4. Enhanced swimming
5. Enhanced boating
4. Enhanced wildlife viewing
5. Nonuse benefits (1A recreational use benefits)
Total Monetized Benefits
Discharges in Ohio
Mean Annual Benefits
$15
$0
$422
$117
$153
$10
$0
$88
$125
$930
Source: U.S. EPA analysis.
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MP&M EEBA
Executive Summary
ES.6.2 Social Costs
EPA also estimated the social costs of the final rule for MP&M facilities in Ohio. EPA developed engineering estimates of
compliance costs for each Ohio facility, and annualized costs using a seven percent discount rate over a 1 5-year period.
Estimating the frequency of baseline closures is necessary to assess the costs of regulation. Facilities assessed as baseline
closures are not expected to incur compliance costs under the final MP&M regulation. The screener data collected for Ohio
facilities did not provide financial data to perform an after-tax cash flow or net present value test, as done in the national
analysis. EPA therefore used data from the national analysis to estimate the percentage of facilities assessed as baseline
closures. EPA assumed that the frequency of baseline closures among Ohio facilities would be the same as that estimated in
the national analysis for facilities with the same discharge status, subcategory, and flow category. For example, two percent
of direct Oily Wastes facilities discharging less than one million gallons per year close in the baseline in the national data set;
this same percentage is assumed for Ohio screener indirect dischargers in that flow and size category. EPA reduced the total
estimated costs for screener facilities, by analysis category, based on the fraction of facilities assessed as baseline closures.
EPA used the same methods as used in the national social cost analysis to estimate other components of social costs for the
Ohio case study. Table ES.12 shows the total estimated social costs of the final rule for Ohio facilities.
Table ES.12: Annual Social Costs for Ohio
Final Option
(thousands, 2001$, costs annualized
Component of Social Costs
Resource value of compliance costs
Government administrative costs
Social cost of unemployment
Total Social Cost
Facilities:
it 7%)
Final Rule
$62
$0
$0
$62
Source: U.S. EPA analysis.
ES.6.3 Comparing Monetized Benefits and Costs
The Ohio case study shows substantial net positive benefits associated with the MP&M regulation. EPA estimates the social
cost in Ohio of the final regulation to be $62 thousand annually ($2001). The sum total of benefits that can be valued in dollar
terms is $930 thousand annually ($2001). Comparing the midpoint estimate of social costs ($62 thousand) with the midpoint
estimate of monetizable benefits ($930 thousand) results in a net social benefit of $868 thousand. This represents a partial
cost-benefit comparison because not all of the benefits resulting from the regulation can be valued in dollar terms (e.g.,
changes in systemic health risk).
For the reasons previously discussed, EPA judges that the analytic approach and detailed data used for the Ohio case study
provide a more robust and accurate benefits estimate than the data and approach used for the national-level analysis.
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MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction
Chapter 1: Introduction
INTRODUCTION
CHAPTER CONTENTS
1.1 Purpose 1-1
1.2 Organization 1-1
1.3 Readers' Aids 1-3
The U.S. Environmental Protection Agency (EPA) is
promulgating effluent limitations guidelines and standards for
the Metal Products and Machinery (MP&M) Point Source
Category, under Sections 301, 304, 306, 307 and 501 of the
Clean Water Act. EPA has determined that the final rule is
not likely to result in aggregate costs to the economy that exceed $100 million annually. The Agency therefore found that the
final regulation is not a "significant regulatory action" as defined by Executive Order 12866 (58 FR 51735, October 4, 1 993).
l. l PURPOSE
This Economic, Environmental, and Benefits Analysis report (EEBA) presents EPA's economic and benefits analyses for the
final MP&M regulation. These analyses supported EPA in developing the final regulation and in meeting the requirements of
the following statutes and executive orders:
*• Executive Order 12866 "Regulatory Planning and Review", which requires analysis of costs, benefits, and economic
impacts of the final rule and regulatory alternatives;
•• Unfunded Mandates Reform Act (UMRA), which requires evaluation of impacts on governments, among other
requirements;
*• Regulatory Flexibility Act as amended by the Small Business Regulatory Enforcement Fairness Act of 1996
(RFA/SBREFA), which requires consideration of the rule's impact on small firms and governments;
•• Executive Order 12898 "Federal Actions to Address Environmental Justice in Minority Populations and Low-
Income Populations"; and
*• Executive Order 13084 " Protection of Children from Environmental Health Risks and Safety Risks".
1.2 ORGANIZATION
This report is organized in five major parts, 22 chapters, and 14 appendices, as follows:
Part I "Introduction and Background Information" (Chapters 1 though 4) describes the need for the regulation, provides a
profile of the MP&M industry, and describes regulatory options evaluated and selected by the Agency for the final rule.
Part II "Costs and Economic Impacts" (Chapters 5 through 11) presents EPA's analysis of the economic impacts and social
costs of the final rule. Chapter 5 presents the analysis of costs and impacts at the facility level. Chapters 6 through 9 present
analyses of other types of economic impacts that derive from the facility-level analysis, including impacts on employment,
governments (for EPA's analyses under UMRA), communities, foreign trade, firms, and new facilities. Chapter 10 provides
an analysis of impacts on small firms and governments, as required by RFA/SBREFA. Finally, Chapter 11 presents the social
costs of the final rule.
Part III "Benefits" (Chapters 12 through 17) provides EPA's analysis ofthe environmental impacts and benefits of the final
rule. Chapter 12 provides an overview ofthe benefits expected from the rule. Chapters 1 3 through 1 6 present EPA's
analyses of different components ofthe benefits analysis. These include human health benefits (except for lead-related)
(Chapter 13), lead-related benefits (Chapter 14), recreational benefits (Chapter 15), and benefits to POTWs (Chapter 16).
Chapter 17 presents an analysis ofthe environmental justice effects ofthe final rule, as required by Executive Order 12898.
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MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction
Part IV "Comparison of Costs and Benefits" (Chapters 18 and 19) compares the social costs and benefits for the final rule
(Chapter 1 8) and for other regulatory alternatives evaluated by the Agency for the final rule (Chapter 19).
Part V " Ohio Case Study" (Chapters 20 through 22) provides a detailed case study of the final rule's costs and benefits for
the State of Ohio. This case study includes a more detailed and complete analysis of benefits, based on more complete
information on the number and location of MP&M facilities and the characteristics of affected waters than was available for
the national analyses. The case study also includes an original travel cost study to value recreational uses affected by the final
rule. EPA believes that the case study provides more robust results because it avoids the uncertainties that result from the
need to extrapolate sample facility results to the national level. The results of the case study generally confirm the overall
results of the national analysis.
Appendices to this report provide additional material in support of the analyses described in the chapters, including the
following:
*• Appendix A: supporting material for the profile of the MP&M industries in Chapter 3;
*• Appendix B: description of the cost pass-through analysis;
*• Appendix C: description of the moderate impact analysis;
*• Appendix D: description of the methodology used to estimate capital outlays as part of the facility impact analysis;
*• Appendix E: description of the calculation of capital cost components;
*• Appendix F: description of the methodology used to estimate POTW administrative costs;
•• Appendix G: summary of the method used to extrapolate sample facility results to the national level;
•• Appendix H: description of fate and transport model for drinking water and Ohio analyses;
•• Appendix I: discussion of methodologies and results of the environmental assessment analysis;
•• Appendix J: analyses of spatial distribution of MP&M facility location and benefiting population;
•• Appendix K: description of the surface water valuation studies and specific values selected for assessing recreational
benefits from the final regulation;
•• Appendix L: description of parameters in the IEUBK lead model;
•• Appendix M: sensitivity analysis of lead related benefits; and
•• Appendix N: analysis of the national demand for water-based recreation survey (NDS).
The docket for the final rule, located at U.S. EPA Headquarters, provides additional supporting documentation, including:
•• copies of the literature cited in the report;
*• documentation of the financial and economic portions of the MP&M Section 308 surveys;
»• memorandums documenting supplementary analyses undertaken in support of regulation development, but that are
not included in the EEBA; and
•• datasets, spreadsheets, and programs used to perform the analyses.
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MP&M EEBA Part I: Introduction and Background Information Chapter 1: Introduction
1.3 READERS'AIDS
Each chapter includes a chapter-specific table of contents. A list of references is provided at the end of each chapter.
Glossaries and lists of acronyms are also provided at the end of the chapters, and the first usage of items listed in them are
denoted in the text with the following formats:
> Glossary indicates that a term is defined in the chapter glossary, and
»• Acronym indicates that the acronym is included in the chapter list of acronyms.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
Chapter 2: The MP&M Industry and
the Need for Regulation
INTRODUCTION
The Metal Products and Machinery (MP&M) effluent
guidelines establish limitations and standards only for direct
dischargers in the Oily Wastes subcategory (40 CFR 438,
Subpart A). EPA establishes industrial subcategories based
on a number of considerations (see Chapter 4 and Section 6
of the TDD). EPA evaluated seven other subcategories for
the final rule: general metals, metal finishing job shops, non
chromium anodizing, printed wiring board, railroad line
maintenance, shipbuilding dry docks, and steel forming and
finishing. EPA evaluated a number of options for these seven
subcategories. Based on these analyses, EPA did not
establish or revise limitations or standards for facilities in
these seven subcategories.
CHAPTER CONTENTS
2.1 Overview of Facilities Evaluated for Regulation under
the MP&M Point Source Category 2-1
2.2 MP&M Discharges and the Need For Regulation .. 2-3
2.2.1 Baseline MP&M Discharges for Regulated
Facilities 2-4
2.2.2 Discharges under the MP&M Regulation . 2-4
2.3 Addressing Market Imperfections 2-5
2.4 Overlap with Other Effluent Guidelines 2-6
2.5 Meeting Legislative and Litigation-Based
Requirements 2-9
Glossary 2-11
Acronyms 2-13
The facilities regulated under this rule produce, manufacture, rebuild, or maintain metal parts, products, or machines for use
in sixteen different industrial sectors. These industrial sectors include: hardware, aircraft, aerospace, ordnance, electronic
equipment, stationary industrial equipment, mobile industrial equipment, buses and trucks, motor vehicles, household
equipment, instruments, office machines, railroads, ships and boats, precious metals and jewelry, and miscellaneous metal
products. Most of the subcategories above serve multiple markets. EPA evaluated options that would have covered facilities
in three additional industrial sectors: printed wiring boards, metal finishing job shops, and iron and steel. The final regulation
does not cover facilities in these sectors.
This chapter provides an overview of the MP&M industry evaluated for the final rule and presents the pollutant discharges
from MP&M facilities subject to the final regulation. The chapter also reviews the reasons why EPA is regulating the
industry's effluent discharges including the need to reduce pollutant discharges from the MP&M industry, the issue of
addressing market imperfections, other effluent guidelines that may overlap in coverage of the MP&M industry sectors
evaluated for the final rule, and requirements that stem from the Clean Water Act (CWA) and litigation.
2. l OVERVIEW OF FACILITIES EVALUATED FOR REGULATION UNDER THE MP<&M POINT
SOURCE CATEGORY
The MP&M Point Source Category regulates oily operations process wastewater discharges to surface waters from existing or
new industrial facilities (including facilities owned and operated by Federal, State, or local governments) engaged in
manufacturing, rebuilding, or maintenance of metal parts, products, or machines for use in the sixteen Metal Product &
Machinery (MP&M) industrial sectors listed above. Please note the underlined language in the previous sentence as a facility
may be subject to the MP&M effluent guidelines even if it is not in one of the MP&M industry sectors. For example, EPA
considers a facility performing machining part of the "Bus & Truck" MP&M industry sector if it manufactures metal parts for
truck trailers. Process wastewater means wastewater as defined at 40 CFR parts 122 and 401, and includes wastewater from
air pollution control devices (see 40 CFR 438.2(g)). Oily operations are listed at 40 CFR 438.2(g) and defined in Appendix B
to Part 438 (see also Section 4 of the TDD).
As defined for this document, MP&M facilities: (1) produce metal parts, products, or machines for use in one of the 19
industry sectors evaluated for coverage in the MP&M point source category; (2) use operations in one of the eight regulatory
subcategories evaluated for coverage in the MP&M point source category; and (3) discharge process wastewater, either
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
directly or indirectly, to surface waters. MP&M facilities frequently produce products for multiple sectors and subcategories.
As referred to in this document, MP&M facilities represent only a portion of all facilities in the industry sectors, since some
facilities may perform operations that are not covered by one of the subcategories (i.e., part assembly or plastic molding), and
some may not generate or discharge process wastewater.
According to Statistics of U.S. Business, 1996, approximately 638,696 establishments operate in the MP&M industry sectors.
Based on information in the MP&M survey database, approximately 44,000 facilities meet the definition of an MP&M
facility. These 44,000 facilities include approximately 41,000 indirect dischargers (i.e., facilities discharging effluent to a
publicly-owned sewage treatment works or POTWs) that would be subject to Pretreatment Standards for
Existing Sources (PSES). The remaining 3,000 direct dischargers (i.e., they discharge effluent directly to a waterway
under a National Pollutant Discharge Elimination System (NPDES) permit) and would thus be subject to Best Available
Technology Economically Achievable (BAT) and Best Practicable Control Technology Currently Available
(BPT) requirements.
Table 2.1 reports the estimated number of MP&M facilities and total discharge flow (before final rule implementation) by
type of facility. The largest number of sites, approximately 22,000, perform "rebuilding/maintenance only" and account for
approximately 6 percent of the total estimated discharge flow for the industry. "Manufacturing only" contains the next largest
number of facilities (15,400) and accounts, by far, for the largest percentage of the total estimated discharge flow for the
industry (82 percent).
Table 2.1: Number of MP&M Facilities and
Total Discharge Flow by Type of Facility
Type of Facility
Manufacturing &
Rebuilding/Maintenance
Manufacturing Only
Rebuilding/Maintenance
Only
Total
Number of
Facilities
6,600
15,400
22,000
44,000
Total Estimated
Discharge Flow
(million gal/yr)
9,400
64,100
4,700
78,200
Percent of
Facilities
15.0%
35.0%
50.0%
100.0%
Percent of Total
Discharge Flow
12.0%
82.0%
6.0%
100.0%
Source: U.S. EPA analysis. See Section 4 of the Technical Development Document for the final
rule.
Of the 43,858 water discharging facilities, 3,593 are predicted to close in the baseline, leaving 40,265 existing MP&M
facilities that EPA estimates could be regulated.1 After accounting for subcategory and discharger class exclusions, EPA
estimates that the final rule will regulate 2,382 of these facilities, all of which are direct dischargers. These regulated
facilities represent 5.9 percent of the 40,265 facilities that could be potentially regulated.
Table 2.2 summarizes information on the total number of MP&M facilities that were evaluated for the final rule, and the
number that will be regulated under the final rule. As reported in Table 2.2, no indirect dischargers are subject to the final
regulation. The rule will regulate 2,382 direct dischargers in the Oily Wastes subcategory. The rule excludes direct
dischargers in the General Metals, Metal Finishing Job Shops, Non -Chromium Anodizing, Printed Wiring Board, Railroad
Line Maintenance, Shipbuilding Dry Docks, and Steel Forming and Finishing subcategories (214 facilities, 12 facilities, 0
facilities, 8 facilities, 6 facilities, 6 facilities, and 13 facilities, respectively) .
1 These are facilities that are predicted to close due to weak financial performance under baseline conditions, i.e., in the absence of
the final rule. EPA does not attribute the costs or the reduced discharges resulting from these baseline closures to the final rule, and
therefore excludes these facilities from its analyses of the rule's impacts. Baseline closures account for differences between the universe of
facilities discussed in this report and the universe discussed in the Technical Development Document.
2 EPA excluded 3,511 indirect and 98 direct dischargers predicted to close in the baseline.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
Table 2.2: MP&M Facilities by Subcategory and Discharger Class, and
Facilities Regulated Under the Final Rule"
Subcategory
General Metals
Metal Finishing Job Shop
Non-Chromium Anodizing
Printed Wiring Board
Steel Forming & Finishing
Oily Waste
Railroad Line Maintenance
Shipbuilding Dry Dock
All Categories
Indirect Dischargers
'
Evaluated for
Regulation
(# of facilities)
10,244
1,479
93
600
12
24,394
820
Q
37,652
Regulated under
Final Rule
(# of facilities)
0
0
0
0
0
0
0
0
0
Direct Dischargers
Evaluated for
Regulation
(# of facilities)
214
12
0
13
2,382
6
6
2,641
Regulated under
Final Rule
(# of facilities)
0
0
0
0
0
2,382
0
0
2,382
a Excludes facilities that close in the baseline.
Source: U.S. EPA analysis.
2.2 MP<&M &ISCHARSES AND THE NEED FOR REGULATION
EPA is regulating the MP&M industry because the industry releases substantial quantities of pollutants, including toxic
pollutant compounds (priority and nonconventional metals and organics) and conventional pollutants such as total
suspended solids (TSS) and oil and grease (O&G). These MP&M industry pollutants are generally controlled by
straightforward and widely-used treatment system technologies such as chemical precipitation and clarification (frequently
referred to as the "lime and settle" process).3
Discharges of these pollutants to surface waters and POTWs have a number of adverse effects, including degradation of
aquatic habitats, reduced survivability and diversity of native aquatic life, and increased human health risk through the
consumption of contaminated fish and water. In addition, many of these pollutants volatilize into the air, disrupt biological
wastewater treatment systems, and contaminate sewage sludge.
Metal constituents are of particular concern because of the large amounts present in MP&M effluents. Unlike some organic
compounds and other wastes that are metabolized in activated sludge systems to relatively innocuous constituents, metals are
chemical elements and cannot be eliminated. Moreover, in solution, some metals have a high affinity for biological uptake.
Depending on site-specific conditions, metals form insoluble inorganic and organic complexes that partition to sewage sludge
at POTWs or underlying sediment in aquatic ecosystems. The accumulated metal constituents can return to a bioavailable
form upon land application of sewage sludge; dredging and resuspension of sediment; or as a result of seasonal, natural, or
induced alteration of sediment chemistry.
Benefits of reducing metal and other pollutant loads to the environment from MP&M facilities include reduced risk of cancer
and systemic human health risks, improved recreation opportunities (e.g., fishing , swimming, boating, and other near-water
recreational activities), improved aquatic and benthic habitats, and less costly sewage sludge disposal and increased beneficial
use of the sludge.4
3 See Chapter 12 and Appendix I for more detailed information on the pollutants of concern in the MP&M industry.
4 Sewage sludge is also called biosolids.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
The goal of the MP&M regulation is to reduce pollutant discharges and to eliminate or reduce the level of risk and harm
caused by them. These pollutant discharges and their harmful consequences are the externalities that the MP&M regulation
addresses, as discussed in Section 2.3.
2.2.1 Baseline MP<&M discharges for Regulated Facilities
Table 2.3 provides an overview of the discharges from MP&M facilities that are regulated under the final rule. Loadings are
defined as toxic-weighted loadings. This measure weights quantities of different pollutants by a measure of their relative
toxicity. Toxic-weighted loadings measures the relative toxic effects of discharges containing different mixtures of pollutants.
MP&M discharges also contain conventional pollutants with little or no toxic effects but that can have substantial adverse
environmental impacts, such as O&G and some components of TSS.
Table 2.3: Toxic-Weighted Discharges for Direct Discharging Facilities in the Oily
Wastes Subcategory (Pounds Equivalent)"
# of Regulated Facilities
Baseline Discharges
Average Baseline Loadings per Facility
Remaining Discharges Under Final Rule
Average Discharges Under Final Rule per Facility
Discharge Reductions Achieved by Final Rule
2,382
3,351
1.41
668
0.28
2,683
a Discharges discussed in this table are total discharges from the facility, and do not reflect POTW pollutant
removals. EPA believes it is appropriate to analyze wastewater discharges disregarding POTW removals because
indirect discharges present environmental risks that are not fully addressed by POTW treatment. The MP&M
industry releases 89 pollutants that cause inhibition problems at POTWs and an additional 35 pollutants that
volatilize to form hazardous air pollutants (HAPs) that may present a threat to human health or the environment.
Other MP&M pollutants are found in POTW sludge. Only eight of these pollutants have land application
pollutant criteria that limit the uses of sludge.
b Excludes discharges from facilities that are projected to close in the baseline (327 Ibs-equiv., or an average of
4.4 Ibs-equiv. per closing facility).
Source: U.S. EPA analysis.
As reported in Table 2.3, direct dischargers in the Oily Wastes subcategory currently release a total of 3,351 toxic weighted
pounds per year, an average of 1.41 toxic weighted pounds per facility. After implementation of the final rule, EPA estimates
that Oily Wastes direct dischargers will release only 668 toxic weighted pounds per year, an average of 0.28 toxic pounds per
facility. EPA estimates that the final rule will reduce pollutant discharges by approximately 2,683 toxic weighted pounds per
year.
2.2.2 Discharges under the MP&M Regulation
Reductions in toxic loadings result from treatment of effluents and pollution prevention at facilities that are subject to the
regulation. Table 2.4 shows baseline and post-regulation loadings by type of pollutant, both as unweighted pounds and on a
toxic-weighted basis, for facilities that are regulated under the final rule. The final rule eliminates 80.1 percent of the baseline
toxic-weighted loadings from the facilities that are regulated, including 83.7 percent of the priority pollutants (87.3 percent
of metals, 22.4 percent of organics, and 1.3 percent of arsenic) and 57.4 percent of the nonconventional pollutants (62.1
percent ofmetals, 13.3 percent of organics, and 50.0 percent of "other inorganics"). The final rule also eliminates substantial
fractions of the baseline discharges of conventional pollutants from the regulated facilities, including 6.6 percent of
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
chemical oxygen demand (COD), 37.1 percent of biological oxygen demand (BOD), 93.2 percent of oil and grease
(O&G), and 54.1 percent of total suspended solids (TSS).5
Table 2.4: Summary of Discharges by Pollutant Type for Facilities Regulated under the Final Rule"
Pollutant Category
i j
Current Releases ! Releases under the Final Rule I Final Rule Reductions
: : : : :
: : : : :
Pounds ! Pounds Eq. j Pounds j Pounds Eq. ! Pounds I Pounds Eq.
Priority Pollutants
Metals
Organic s
Arsenic
Cyanide (CN)
Nonconventianal Pollutants
Metals
Organic s
Other Inorganics
Bulk Pollutants
Conventional Pollutants
BOD
COD
O&G
TSS
794
336
22
0
25,863
2,159
2,334
335,679
263,419
523,440
428,137
160,695
2,756
58
75
0
417
45
0.2
153
268
21
0
16,428
1,038
1,301
167,295
165,567
488,697
28,955
73,769
351
45
74
0
158
39
0.1
641
68
1
0
9,435
1,121
1,033
168,384
97,852
34,743
399,182
86,926
2,405
13
1
0
259
6
0.1
a Discharges discussed in this table are facility discharges and do not account for POTW removals. EPA believes it is appropriate to
analyze wastewater discharges disregarding POTW removals because indirect discharges present environmental risks that are not fully
addressed by POTW treatment. The MP&M industry releases 89 pollutants that cause inhibition problems at POTWs and an additional
35 pollutants that volatilize to form hazardous air pollutants (HAPs) that may present a threat to human health or the environment.
Other MP&M pollutants released by the industry are found in POTW sludge. Only eight of these pollutants have land application
pollutant criteria that limit the uses of sludge.
Source: U.S. EPA analysis.
2.3 ADDRESSING MARKET IMPERFECTIONS
Environmental legislation in general, and the CWA and the MP&M regulation in particular, seek to correct imperfections -
uncompensated environmental externalities - in the functioning of the market economy. In manufacturing, rebuilding, and
repairing metal products and machinery, MP&M facilities release pollutants that increase risks to human health and aquatic
life and cause other environmental harm without accounting for the consequences of these actions on other parties (sometimes
referred to as third parties) who do not directly participate in the business transactions of the business entities.
5 It is not possible to provide an overall estimate of total pollutant pounds removed, because overlap among some of the pollutant
categories would result in double-counting if the categories were summed. For example, TSS may include some of the priority pollutant
and nonconventional metals discharges. Use of the toxic-weighted loadings avoids this double-counting, but does not include conventional
pollutants.
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
These costs are not borne by the responsible entities and are therefore external to the production and pricing decisions of the
responsible entity.
A profit-maximizing firm or a cost-minimizing government-owned facility will ignore these costs when deciding how much to
produce and how to produce it. In addition, the externality is uncompensated because no party is compensated for the adverse
consequences of the pollution releases.
When these external costs are not accounted for in the production and pricing decisions of the responsible entities, their
decisions will yield a mix and quantity of goods and services in the economy, and an allocation of economic resources to
production activities, that are less than optimal, hi particular, the quantity of pollution and related environmental harm caused
by the activities of the responsible entities will, in general, exceed socially optimal levels. As a result, society will not
maximize total social welfare.
In addition, adverse distributional effects may accompany the uncompensated environmental externalities. If the
distribution of pollution and environmental harm is not random among the U.S. population, but instead is concentrated among
certain population subgroups based on socio-economic or other demographic characteristics, then the uncompensated
environmental externalities may produce undesirable transfers of economic welfare among subgroups of the population. See
Chapter 17: Environmental Justice and Protection of Children for more information.
The goal of environmental legislation and implementing regulations, including the final MP&M rule that is the subject of this
EEBA, is to correct these environmental externalities by requiring businesses and other polluting entities to reduce their
pollution and environmental harm. Congress, in enacting the authorizing legislation, and EPA, in promulgating the
implementing regulations, act on behalf of society to achieve a mix of goods and services and a level of pollution that more
nearly approximates socially optimal levels. As a result, the mix and quantity of goods and services provided by the
economy, the allocation of economic resources to those activities, and the quantity of pollution and environmental harm
accompanying those activities will yield higher economic welfare to society.
Requiring polluting entities to reduce levels of pollution and environmental harm is one approach to addressing the problem
of environmental externalities. This approach imposes costs on the polluting entities in the form of compliance costs incurred
to reduce pollution to allowed levels. A polluting entity will either incur the costs of meeting the regulatory limits or will
determine that compliance is not in its best financial interest and will cease the pollution-generating activities. This approach
to addressing the problem of environmental externalities will generally result in improved economic efficiency and net
welfare gains for society if the cost of reducing the pollution and environmental harm activities is less than the value of
benefits to society from the reduced pollution and environmental harm.
It is theoretically possible to correct the market imperfection by means other than direct regulation. For example, negotiation
and/or litigation could achieve an optimal allocation of economic resources and mix of production activities within the
economy. However, the transaction costs of assembling the affected parties and involving them in the negotiation/litigation
process, as well as the public goods character of the improvement sought by negotiation or litigation, make this approach
impractical.
2.4 OVERLAP WITH OTHER EFFLUENT GUIDELINES
EPA has previously promulgated effluent guidelines regulations for 13 metals-related industries. In some instances, these
industries may perform operations that are found in MP&M facilities. These effluent guidelines are:
•• Electroplating (40 CFR Part 413),
*• Iron & Steel Manufacturing (40 CFR Part 420),
> Nonferrous Metals Manufacturing (40 CFR Part 421),
•• Ferroalloy Manufacturing (40 CFR Part 424),
•• Metal Finishing (40 CFR Part 433),
•• Battery Manufacturing (40 CFR Part 461),
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
•• Metal Molding & Casting (40 CFR Part 464),
•• Coil Coating (40 CFR Part 465),
•• Porcelain Enameling (40 CFR Part 466),
•• Aluminum Forming (40 CFR Part 467),
•• Copper Forming (40 CFR Part 468),
•• Electrical & Electronic Components (40 CFR Part 469), and
> Nonferrous Metals Forming & Metal Powders (40 CFR Part 471).
In 1986, the Agency reviewed coverage of these regulations and identified a significant number of metals processing facilities
discharging wastewater that these 13 regulations did not cover. From this review, EPA performed a more detailed analysis of
these unregulated sites and identified the discharge of significant amounts of pollutants. This analysis resulted in the
formation of the "Machinery Manufacturing and Rebuilding" (MM&R) point source category. In 1992, EPA changed the
name of the category to "Metal Products and Machinery" (M P& M) to clarify coverage of the category (57 FR 19748).
Only direct dischargers in the Oily Wastes subcategory will be regulated under the final regulations for 40 CFR Part 38.
Table 2.5 shows the MP&M subcategories and the coverages that apply to each. EPA does not intend this table to be
exhaustive, but rather to provide a general overview of the applicability of the Electroplating, Metal Finishing, and Metal
Products & Machinery effluent guidelines.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
Table 2.5: Coverage by MP&M Subcategory
1
Subcategory
General Metals (including
Continuous Electroplaters)
Metal Finishing Job Shops
Non-Chromium Anodizersa
Printed Wiring Board
(Printed Circuit Board)
Steel Forming & Finishing
Wire Drawing"
Oily Waste"
Railroad Line Maintenance15
Continue Coverage under 40 CFR Part 413
(Electroplating)
Existing indirect dischargers currently covered
by Part 413.
Existing indirect dischargers currently covered
by Part 413.
Existing indirect dischargers that are currently
covered by 41 3.
Existing indirect dischargers that are currently
covered by 41 3.
N/A
N/A
N/A
Continue Coverage under 40 CFR Part 433
(Metal Finishing)
New and existing direct and indirect dischargers
currently covered by Part 433.
New and existing direct and indirect dischargers
currently covered by Part 433.
New and existing direct and indirect dischargers
currently covered by Part 433.
New and existing direct and indirect dischargers
currently covered by Part 433.
N/A
N/A
N/A
Coverage under 40 CFR Part 438
(Metal Products & Machinery)
None
None
None
None
None
All new and existing direct dischargers under this
Subcategory. (See 438.20)
None
Shipbuilding Dry Docks" I N/A ! N/A ! None
a These facilities will continue to be subject to Part 420.
" There are no national categorical pretreatment standards for these facilities.
Source: U.S. EPA analysis.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 2: The MP&M Industry and the Need for Regulation
Figure 2.1 illustrates the relationship among the various metals industries effluent guidelines.
Figure 2.1: Metals Industries Effluent Guidelines Covered Under 40CFR
I
ion ad Steel (430)
NarfeircusMetils(421) FannUajE (434)
ion £ Steel (420)
Metal Molding and Ccatirg (4.54)
Ainu ion Fomiig (4S7)
Copper Faming (468)
Nonfenous Foiming (471)
EIJIIJ unai M:jjLUtidimjLg AaaittHf,
, Manatmmc * & inrtict FiTLBliig
rtal F'riidj cts and
iVu-Hilary Indjstry
(Manufacturing,
Ftt'j Idiiii,
and fiepa rj
Surface
Metal Fiiihing(433)
E]sctnphting(4B)
f ^ i i \
Source: U.S. EPA analysis.
2.5 MEETING LEGISLATIVE AND LITIGATION-BASED REQUIREMENTS
EPA's effluent limitations guidelines and standards for the MP&M industry are under authority of the CWA, Sections 301,
304, 306, 307, and 501. These CWA sections require the EPA Administrator to publish limitations and guidelines for
controlling industrial effluent discharges consistent with the overall CWA objective to "restore and maintain the chemical,
physical, and biological integrity of the Nation's waters." EPA's MP&M industry regulation responds to these requirements.
In addition, the MP&M regulation responds to the requirements of a consent decree entered by the Agency as a result of
litigation. Section 304(m) of the CWA (33 U.S.C. 1314(m)), added by the Water Quality Act of 1987, required EPA to
establish schedules for (i) reviewing and revising existing effluent limitations guidelines and standards, and (ii) promulgating
new effluent guidelines. On January 2, 1990, EPA published an Effluent Guidelines Plan (55 FR 80), in which schedules
were established for developing new and revised effluent guidelines for several industry categories. One of the industries for
which the Agency established a schedule was the Machinery Manufacturing and Rebuilding Category (MM&R).6
The name was changed to Metal Products and Machinery (MP&M) in 1992 to avoid confusion over what was covered by the rule.
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
The Natural Resources Defense Council, Inc. (NRDC) and Public Citizen, Inc. challenged the Effluent Guidelines Plan in a
suit filed in U.S. District Court for the District of Columbia (NRDC et al v. Reilly, Civ. No. 89-2980). The plaintiffs charged
that EPA's plan did not meet the requirements of Section 304(m). A Consent Decree in this litigation was entered by the
Court on January 31, 1992. This plan required, among other things, that EPA propose effluent guidelines for the MP&M
category by November, 1994 and take final action on these effluent guidelines by May, 1996. EPA filed a motion with the
Court on September 28, 1994, requesting an extension until March 31, 1995, for the EPA Administrator to sign the proposed
regulation and a subsequent four month extension for signature of the final regulation in September 1996. EPA published a
proposal entitled, "Effluent Limitations Guidelines, Pretreatment Standards, and New Source Performance Standards: Metal
Products and Machinery" (60 FR 28210) on May 30, 1995.
EPA initially divided the industry into two phases based on industry sector, as the Agency believed that would make the
regulation more manageable. The Phase I proposal included the following industry sectors: Aerospace; Aircraft; Electronic
Equipment; Hardware; Mobile Industrial Equipment; Ordnance; and Stationary Industrial Equipment. At that time, EPA
planned to propose a rule for the Phase II sectors approximately three years after the MP&M Phase I proposal.
EPA received over 4,000 pages of public comment on the Phase I proposal. One area where commenters from all stakeholder
groups (i.e., industry, environmental groups, and regulators) were in agreement was that EPA should not divide the MP&M
industry into two separate regulations. Commenters raised concerns regarding the regulation of similar facilities with
different compliance schedules and potentially different limitations for similar processes based solely on whether the facilities
were in a Phase I or Phase II sector. Furthermore, a large number of facilities performed work in multiple sectors. In such
cases, permit writers and control authorities (e.g., POTWs) would need to decide which MP&M rule (Phase I or 2) applied to
a facility.
Based on these comments, EPA decided to combine the two phases of the regulation into one proposal. EPA published a
proposal entitled, "Effluent Limitations Guidelines, Pretreatment Standards, and New Source Performance Standards for the
Metal Products and Machinery Point Source Category" (66 FR 424) on January 3, 2001. The proposal published in January
2001 completely replaced the 1995 proposal.
On June 5, 2002, EPA published a Notice of Data Availability (NODA) (67 FR 38752). IntheNODA, EPA discussed major
issues raised in comments on the 2001 proposal; suggested revisions to the technical and economic methodologies used to
estimate compliance costs, pollutant loadings, and economic and environmental impacts; presented the results of these
suggested methodology changes and incorporation of new (or revised) data; and summarized the Agency's thinking on how
these results could affect the Agency's final decisions.
This report addresses the 304(m) decree as amended, requiring the MP&M rules to be promulgated by February 14, 2003.
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
GLOSSARY
Best Available Technology Economically Achievable: Effluent limitations for direct dischargers, addressing priority
and nonconventional pollutants. BAT is based on the best existing economically achievable performance of plants in the
industrial subcategory or category. Factors considered in assessing BAT include the cost of achieving BAT effluent
reductions, the age of equipment and facilities involved, the processes employed, engineering aspects of the control
technology, potential process changes, non-water quality environmental impacts (including energy requirements), economic
achievability, and such factors as the Administrator deems appropriate. The Agency may base BAT limitations upon effluent
reductions attainable through changes in a facility's processes and operations. Where existing performance is uniformly
inadequate, EPA may base BAT upon technology transferred from a different subcategory within an industry or from another
industrial category.
Best Practicable Control Technology Currently Available: Effluent limitations for direct discharging facilities,
addressing conventional, toxic, and nonconventional pollutants. In specifying BPT, EPA considers the cost of achieving
effluent reductions in relation to the effluent reduction benefits. The Agency also considers the age of the equipment and
facilities, the processes employed and any required process changes, engineering aspects of the control technologies, non-
water quality environmental impacts (including energy requirements), and such other factors as the Agency deems
appropriate. Limitations are traditionally based on the average of the best performances of facilities within the industry of
various ages, sizes, processes, or other common characteristics. Where existing performance is uniformly inadequate, EPA
may require higher levels of control than currently in place in an industrial category if the Agency determines that the
technology can be practically applied.
bioavailable: Degree of ability to be absorbed and ready to interact in organism metabolism.
(http://www.epa.gov/OCEPAterms)
biological oxygen demand: A measure of the amount of oxygen consumed in the biological processes that break down
organic matter in water. The greater the BOD, the greater the degree of pollution.
(http://www.epa.gov/OCEPAterms/bterms.html)
chemical oxygen demand: A measure of the oxygen required to oxidize all compounds, both organic and inorganic, in
water. (http://www.epa.gov/OCEPAterms/cterms.html)
Clean Water Act: Act passed by the U.S. Congress to control water pollution. Formerly referred to as the Federal Water
Pollution Control Act of 1972 or Federal Water Pollution Control Act Amendments of 1972 (Public Law 92-500), 33 U.S.C.
1251 et. seq., as amended by: Public Law 96-483; Public Law 97-117; Public Laws 95-217, 97-117, 97-440, and 100-04.
conventional pollutants: Statutorily listed pollutants understood well by scientists. These may be in the form of organic
waste, sediment, acid, bacteria, viruses, nutrients, oil and grease, or heat. (http://www.epa.gov/OCEPAterms)
distributional effects: Occurs when the distribution of pollution and environmental harm is not random among the U.S.
population, but instead is concentrated among certain population subgroups based on socio-economic or other demographic
characteristics, then the uncompensated environmental externalities may produce undesirable transfers of economic welfare
among subgroups of the population.
externalities: Costs or benefits of market transactions that are not reflected in the prices buyers and sellers use to make
their decisions. An externality is a by-product of the production or consumption of a good or service that affects someone not
immediately involved in the transaction.
(http://www.enmu.edu/users/biced/home/glossary.html)
A type of market failure that causes inefficiency.
(http://www.amosweb.com/cgi-bin/gls_dsp.pl?term=externalities)
MP&M facilities: MP&M facilities are defined on the basis of three considerations: (1) they produce metal parts, products,
or machines for use in one of the 19 industry sectors evaluated for coverage in the MP&M point source category; (2) they use
operations in one of the eight regulatory subcategories evaluated for coverage in the MP&M point source category; and (3)
they discharge process wastewater, either directly or indirectly, to surface waters. In this document, the term "MP&M
facilities" refers to all facilities meeting the above definition, regardless of whether a facility's industrial sector, subcategory,
or discharger category is covered by the final regulation. If the MP&M facilities are referred to as "regulated" facilities or
facilities "subject to the final regulation ", the use of the qualifier "regulated" or "subject to the final regulation " restricts
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
the definition to include only those facilities in the industry sectors, subcategory, and discharger category covered by the final
regulation.
MP&M industry: The facilities and markets comprising the 19 industry sectors evaluated for coverage in the MP&M point
source category. In this document, the term "MP&M industry" refers to the full 19 industry sectors, regardless of whether an
industry sector is covered by the final regulation. If the MP&M industry is referred to as the regulated MP&M industry, the
use of the qualifier "regulated" restricts the definition to only the industry sectors, subcategory, and discharger category
covered by the final regulation.
nonconventional pollutants: Any pollutant not statutorily listed or which is poorly understood by the scientific
community.
(http://www.epa.gov/OCEPAterms)
oil and grease (O&G): These organic substances may include hydrocarbons, fats, oils, waxes and high-molecular fatty
acids. Oil and grease may produce sludge solids that are difficult to process, (http://www.epa.gov/owmitnet/reg.htm)
Pretreatment Standards for Existing Sources (PSES): Categorical pretreatment standards for existing indirect
dischargers, designed to prevent the discharge of pollutants that pass through, interfere with, or are otherwise incompatible
with the operation of POTWs. Standards are technology-based and analogous to BAT effluent limitations guidelines.
priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
sediment, groundwater or soil samples.
publicly-owned treatment works: A treatment works for municipal sewage or liquid industrial wastes that is owned by a
State or municipality.
socially optimal level: Situation in which it is impossible to make any individual better off without making someone else
worse off. Also referred to as Pareto optimal.
social welfare: The sum of the welfare of all participants in the society; measured by the sum of consumer surplus --the
value consumers derive from goods and services less the price they have to pay for the goods and services -- and producers'
surplus -- the revenue received by producers of goods and services less their costs of producing the goods and services.
third parties: Those affected by a by-product of the production or consumption of a good or service that are not
immediately involved in the transaction.
total suspended solids: A measure of the suspended solids in wastewater, effluent, or water bodies, determined by tests
for "total suspended non-filterable solids."
(http://www.epa.gov/OCEPAterms/tterms.html).
toxic-weighted pollutants: This measure weights quantities of different pollutants in effluents by a measure of their
relative toxicity. Toxic-weighted loadings measures the relative toxic effects of discharges containing different mixtures of
pollutants.
uncompensated: Where parties damaged by externalities receive no compensation for accepting the damage.
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
ACRONYMS
BAT: Best Available Technology Economically Achievable
BPT: Best Practicable Control Technology Currently Available
BOD: biological oxygen demand
COD: chemical oxygen demand
CWA: Clean Water Act
MM&R: Machinery Manufacturing and Rebuilding
MP&M : Metal Products and Machinery
NPDES: National Pollutant Discharge Elimination System
NRDC: Natural Resources Defense Council
O&G: oil and grease
POTW: publicly-owned treatment works
PSES: Pretreatment Standards for Existing Sources
TSS: total suspended solids
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MP&M EEBA Part I: Introduction and Background Information Chapter 2: The MP&M Industry and the Need for Regulation
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Chapter 3: Profile of the MP&M
Industry Sectors
INTRODUCTION
The final MP&M rule will apply to facilities that
manufacture, rebuild, or maintain metal parts, products or
machines to be used in a large number of industrial sectors.
Manufacturing is the series of unit operations necessary to
produce metal products, and is generally performed in a
production environment. Rebuilding/maintenance is the
series of unit operations necessary to disassemble used metal
products into components, replace the components or
subassemblies or restore them to original function, and
reassemble the metal product. These operations are intended
to keep metal products in operating condition and can be
performed in either a production or a non-production
environment. Manufacturing and rebuilding/maintenance
activities often occur at the same facilities.
The MP&M industry encompasses a large number of
industries that manufacture intermediate and final goods,
support transportation and other vehicle services, and repair
and maintain products and equipment. The health of the
MP&M industry is generally tied to the overall economic
performance of the economy. The MP&M industry includes
manufacturing and non-manufacturing industries defined by
224 4-digit Standard Industrial Classification (SIC)
codes, which are grouped into nineteen industry sectors.1 Of
the 224 SIC codes, 174 are manufacturing (SICs 20 through
39) and 50 are non-manufacturing. All nineteen sectors
include manufacturing industries, and eleven include non-
manufacturing industries as well.
Although EPA evaluated regulatory options that would have
covered facilities operating in any of the nineteen sectors, the
final regulation covers facilities operating only in sixteen of
those sectors.
CHAPTER CONTENTS
3.1 Data Sources 3-2
3.2 Overview of the MP&M Industry and Industry
Trends 3-3
3.2.1 Aerospace 3-7
3.2.2 Aircraft 3-7
3.2.3 Electronic Equipment 3-7
3.2.4 Hardware 3-8
3.2.5 Household Equipment 3-8
3.2.6 Instruments 3-9
3.2.7 Iron and Steel 3-9
3.2.8 Job Shops 3-9
3.2.9 Mobile Industrial Equipment 3-9
3.2.10 Motor Vehicle and Bus & Truck 3-10
3.2.11 Office Machine 3-10
3.2.12 Ordnance 3-10
3.2.13 Precious Metals and Jewelry 3-11
3.2.14 Printed Wiring Boards 3-11
3.2.15 Railroad 3-11
3.2.16 Ships and Boats 3-11
3.2.17 Stationary Industrial Equipment 3-12
3.3 Characteristics of MP&M Manufacturing Sectors 3-12
3.3.1 Domestic Production 3-13
3.3.2 Industry/Market Structure 3-18
3.3.3 Financial Condition and Performance ..3-24
3.4 Characteristics of MP&M Non-Manufacturing
Sectors 3-25
3.4.1 Domestic Production 3-25
3.4.2 Industry Structure and Competitiveness . 3-28
3.5 Characteristics of All MP&M Sectors 3-30
3.5.1 Eight-firm Concentration Ratio 3-30
3.5.2 Risk Normalized Return on Assets 3-31
3.6 Characteristics of MP&M Facilities 3-32
Glossary 3-38
Acronyms 3-40
References . 3-41
This chapter provides a profile of the industry sectors that were evaluated for coverage by the MP&M rule. The profile
focuses on the economic characteristics of the sectors and the facilities within the sectors, which may affect the rule's
financial and economic impacts. It presents and interprets a wide variety of data associated with production, market structure,
and competitiveness, for each sector and for the MP&M industry as a whole.
Appendix A lists the nineteen sectors and their associated 4-digit SIC codes.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
3. l &ATA SOURCES
This profile presents data from the Economic Censuses, Statistics of U.S. Businesses (SUSB), Annual Survey of
Manufacturers (ASM), U.S. Industry and Trade Outlook, EPA's Sector Notebooks, and other sources, to characterize the
MP&M sectors, including both dischargers and non-dischargers.
The years 1988 and 1 996 were chosen as the years for which data are presented because these are the base analysis years,
respectively, for the MP&M Phase 1 sectors survey and the Phase 2 survey. In the cases when data for those years were not
available, data from other years were used.
This profile relies on industries defined by SICs, both because data collection for the MP&M sectors was defined by SICs and
to allow use of historical data. The Census Bureau switched to use of the new North American Industry Classification
System (NAICS) codes starting with the 1997 Economic Censuses. Data classified by NAICS code were converted to SIC
format before being included in the profile. The conversion used a bridge containing the percentage of each NAICS code that
needed to be assigned to each SIC code. For a detailed discussion of the bridge, see Appendix A.
The Agency used survey data to characterize the facilities within the MP&M sectors that are potentially subject to the rule
because they discharge process wastewater from MP&M operations. The survey provides data such as discharge type, small
business status, sources of revenues, and financial performance.
The survey requested information on the sectors from which each facility derives its revenues. Many facilities derive
revenues from more than one sector. It is therefore difficult to link facility characteristics to a specific sector. Data on the
potentially-regulated facilities are therefore summarized by the regulatory subcategories rather than by sectors.
All monetary values are shown in real 2001 dollars. EPA used the Producer Price Index (PPI) for industrial commodities
as a conversion tool. A PPI is an index that measures price changes, from the perspective of the seller, of a collection of
goods and services that are important inputs for a specific industry or for the economy as a whole. This chapter uses the PPI
for industrial commodities to inflate nominal values to real values. Later chapters include PPI's that are sector specific.
These PPI's are derived from the average of the PPI's for each component industry SIC code, weighted by industry output.
Table 3-1 shows the PPI values for the relevant years for which prices were deflated. The PPI for industrial commodities
increased slightly every year between 1988 and 1996. Total inflation for industrial commodities from 1988 to 1996 was
19.8%.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.1:
Year
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Producer Price Index for ]
Producer Price Index (PPI)
100.0
105.0
108.9
109.6
110.4
111.9
113.5
118.1
119.8
120.1
117.4
119.0
126.8
127.7
Industrial Commodities
Percent Change
n/a
5.0%
3.8%
0.6%
0.8%
1.4%
1.4%
4.0%
1.4%
0.3%
-2.3%
1.4%
6.6%
0.7%
Source: Bureau of Labor Statistics, Producer Price Index.
3.2 OVERVIEW OF THE MP<&M INDUSTRY AND INDUSTRY TRENDS
This section provides a general overview of the MP&M industry. It describes the individual MP&M industry sectors,
provides basic economic information about MP&M manufacturers, and summarizes recent industry trends.
Figure 3-1 shows that MP&M facilities are located in every state. A few MP&M sectors such as shipbuilding are
concentrated geographically. Transportation-related MP&M facilities are found throughout the country. Overall, MP&M
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
facilities are most concentrated in the heavy industrial regions along the Gulf Coast, both the East and West Coasts, and the
Great Lakes Region (New York, Pennsylvania, Ohio, Indiana, Illinois, and Michigan).
Figure 3.1: Number of MP&M Facilities by State in 1992
Number of MP&M Facilities
| | 361-2,572
2,927 - 6,370
6,907 - 0,025
0,993 - 14,656
15,150-68,359
Source: Department of Commerce, Bureau of the Census, Census of Manufacturers, Census of Transportation, Census of Wholesale
Trade, Census of Retail Trade, Census of Service Industries, 1992.
Table 3.2 lists the MP&M sectors and provides a brief description of the products and services produced by each. Appendix
A provides a more detailed list of the 4-digit SIC codes in each sector.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.2: MP&M Sector Definitions
Sector
Sector Description
Aerospace Metal parts or products such as missiles, space vehicles, satellites and associated launching
equipment.
*
Aircraft Metal parts or products including all types of aircraft for public, private or commercial use. Includes
aircraft parts and equipment as well as aircraft maintenance activities.
*
Bus and Truck Metal parts or products including freight trucks and trailers as well as public, private and commercial
buses. Includes all associated equipment including equipment specific to truck and bus terminals.
Includes bus and truck maintenance activities.
+
Electronic Equipment Metal parts or products including general electronic components such as tubes, capacitors, and
transformers, as well as finished electronic equipment such as televisions, radios, and telephones.
*
Hardware Metal parts or products such as tools, cutlery, valves and tubing, dies, springs, sheet metal, drums, and
heat treating equipment.
*
Household Equipment Metal parts or products including appliances such as refrigerators, laundry equipment, lighting
equipment, cooking equipment, and vacuum cleaners. Non-communication type radios and
televisions are included in this sector.
+
Iron and Steel Sites engaged in iron or steel manufacturing, forming and finishing.
*
Instruments Metal parts or products such as laboratory and medical equipment, measuring devices, environmental
and process controls, optical equipment, surgical and dental equipment, and pens.
*
Metal Finishing Job Shop Facilities with more than 50 percent of their revenues coming from work on products not owned by
the site. While there are SIC codes associated with some Metal Finishing Job Shops, they sell to a
variety of markets and are not a market in and of themselves.
+
Mobile Industrial Equipment Metal parts or products including tractors and other farm equipment, construction machinery and
equipment, mining machinery and equipment, industrial cranes and hoists, and tracked military
vehicles.
+
Motor Vehicle Metal parts or products including private passenger vehicles and associated parts and accessories such
as automobiles, motorcycles, utility trailers and recreational vehicles, and mobile homes.
*
Office Machines Metal parts or products including office computer equipment, storage devices, printers, photocopiers
and associated parts and accessories.
*
Ordnance Metal parts or products including all small arms, artillery, and ammunition with the exception of
missiles (aerospace). Does not include the chemical processing or the manufacture of explosives.
*
Other Metal Products Metal parts or products including products and machinery not categorized into the other sectors (e.g.,
sporting goods, musical instruments).
*
Precious Metals and Jewelry Metal parts or products including jewelry, silverware, trophies, and clocks as well as all associated
parts and accessories.
*
Printed Wiring Boards Metal parts or products including printed wiring boards and printed circuit boards.
*
Railroad Metal parts or products including railcars, locomotives and associated parts and accessories as well as
track, switching and terminal stations.
*
Ships and Boats Metal parts or products including ships and boats for military, freight, and private recreation. Includes
submarines, ferries, tug boats, barges, yachts, and other recreational boats as well as all parts and
accessories. Also includes rebuilding and maintenance activities performed at marinas, dry docks, and
other on shore activities specifically related to ships and boats.
*•
Stationary Industrial Metal parts or products including all industrial machinery, such as turbines, oil field machinery,
Equipment elevators and moving stairways, conveying equipment, chemical process industry equipment, pumps,
compressors, blowers, industrial ovens, vending machines, commercial laundry equipment,
commercial refrigeration and heating equipment, welding apparatus, motors, and generators.
Source: U.S. EPA analysis.
Table 3.3 shows output by sector for manufacturers, non-manufacturers, and all MP&M firms. Output is a good indicator of
the overall size of a market. In 1997, MP&M firms accounted for more than $2.8 trillion in output. Motor vehicles were the
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
largest single MP&M sector, accounting for 43 percent of all MP&M output. Ordnance is the smallest sector, with 0.2
percent of MP&M output.
The MP&M manufacturing and non-manufacturing sectors differ in several important ways. The manufacturing sector
accounted for $1.6 trillion in output, equal to 57 percent of the total MP&M output. The non-manufacturing sector accounted
for $1.2 trillion, or 43 percent of MP&M output. Although MP&M non-manufacturers' revenues were nearly $400 billion
smaller than manufacturers' revenues, the MP&M non-manufacturers had three times as many facilities as the MP&M
manufacturers. Also, although manufacturing output was relatively evenly divided among the different sectors, more than 86
percent of non-manufacturing output came from the motor vehicle and bus and truck sectors.
Table 3.3: MP&M Output and Share in 1997° (millions, 2001$)
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shop °
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Wiring Boards
Railroad d
Ships and Boats
Stationary Industrial Equipment
Total MP&M
Percent of total
Manufacturers
Output b
20,115.1
105,163.8
15,118.4
145,886.9
189,145.5
102,242.3
141,548.0
20,403.0
15,360.2
54,704.7
366,448.7
119,783.0
5,778.8
60,249.6
9,760.7
10,400.7
8,412.6
18,081.1
227,053.7
1,635,656.8
56.7%
Share
1.2%
6.4%
0.9%
8.9%
11.6%
6.3%
8.7%
1.2%
0.9%
3.3%
22.4%
7.3%
0.4%
3.7%
0.6%
0.6%
0.5%
1.1%
13.9%
100.0%
Non-Manufacturers
Output b
9,935.9
209,316.1
2,847.7
7,401.9
870,450.5
30,929.9
22,040.7
367.4
30,727.9
37,383.0
29,747.1
1,251,148.1
43.3%
Share
0.8%
16.7%
0.2%
0.6%
69.6%
2.5%
1.8%
0.0%
2.5%
3.0%
2.4%
100.0%
Sector Total
Output b
20,115.1
115,099.7
224,434.5
145,886.9
189,145.5
105,090.0
148,949.9
20,403.0
15,360.2
54,704.7
1,236,899.2
150,712.9
5,778.8
82,290.3
10,128.1
10,400.7
39,140.5
55,464.1
256,800.8
2,886,804.9
100.0%
Share
0.7%
4.0%
7.8%
5.1%
6.6%
3.6%
5.2%
0.7%
0.5%
1.9%
42.8%
5.2%
0.2%
2.9%
0.4%
0.4%
1.4%
1.9%
8.9%
100.0%
a Data for 1996 were not available, so economic census data from 1997 were used.
b Value of shipments for manufacturing industries; total sales for retail and wholesale trade; total receipts for service industries; total
revenue for transportation.
° Includes facilities in two SICs that are defined specifically as job shops (SICs 3471 and 3479.) Facilities reporting in other sectors
may also operate as job shops, so these data are likely to understate the true output of MP&M job shops.
d Non-manufacturing railroad data are estimated based on 1992 data.
Source: Department of Commerce, Bureau of the Census, Census of Manufacturers, Census of Transportation, Census of Wholesale
Trade, Census of Retail Trade, Census of Service Industries, 1997.
The following sections describe the MP&M sectors and briefly discuss recent industry trends in each sector. The discussion is
based on 2001 Value Line Industry Reports, U.S. Industry and Trade Outlook 2000 (DRI-McGraw Hill), EPA's Sector
Notebooks, and other sources.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
3.2.1 Aerospace
The aerospace industry includes original equipment manufacturers (OEM) and facilities that rebuild and repair aerospace
equipment. The industry serves both military and commercial end-uses such as space vehicles for commercial communication
satellites, although military applications dominate. Its products include guided missiles, space vehicles, and associated
propulsion units and parts. The assembly of aerospace products draws on numerous other industries, including plastics,
rubber, fabricated metals, metal casing, glass, textile, and electronic components. Aerospace products are typically produced
by a prime contractor and several tiers of subcontractors. Final assembly is performed by relatively few facilities, only a
small number compared with the numerous subassembly and parts manufacturers. Aerospace manufacturing is extremely
capital intensive.
The U.S. aerospace industry has consolidated substantially in recent years, due to declines in defense spending. The number
of facilities and firms as well as sector value of shipments and employment decreased from 1988 to 1996 in the US.
Growth in the industry is expected to come from lower cost air-to-air missiles, with strong focus on increasing efficiency in
production by reducing costs. Consumer demand has also grown for direct-to-home television, voice and data transmission,
and other satellite services, which have increased the commercial demand for space vehicles needed to launch satellites.
The aerospace industry exports a substantial share of its output. Many North American and European governments with large
defense budgets have been seeking to reduce their military budgets, while governments in South America (with smaller
budgets) have been maintaining or increasing their defense spending. Substantial consolidation has occurred in the European
aerospace industry, which has become more competitive with U.S. companies (U.S. EPA 1997; DRI/McGraw Hill 2000).
3.2.2 Aircraft
Trends in the aircraft sector are heavily influenced by changes in industry structure and in the international political-economic
arena. Although new aircraft production increased substantially in 1998 and 1999, production weakened in 2001 because of
the economic slowdown and then plummeted following the September 11th terrorist attacks. Airlines have reacted to falling
ticket sales by cutting scheduled flights, reducing personnel, and delaying or cancelling investment in new aircraft.
During the 1990's, there was substantial restructuring through mergers and consolidation in the aircraft manufacturing
industry, including producers of both aircraft and aircraft parts nationally and internationally. Firms focused on improving
efficiency through cost cutting efforts such as reduced staffing. In addition, there is a growing trend for U.S. producers to
outsource many aircraft parts to firms in other nations, in order to bring down costs and compete internationally.
In addition to aircraft manufacturing, this sector includes rebuilding and repair of aircraft at manufacturers' facilities or at
airports. The aircraft maintenance and repair industry has slowed with the post 9/11 decline in passenger travel.
3.2.3 Electronic Equipment
The electronic equipment sector can be divided into two general groups of industries: microelectronics manufacturers and
telecommunications equipment manufacturers.
Microelectronics industries manufacture a wide range of products, from electronic connectors to integrated circuit panels.
These products are used as material inputs in many industries such as automotive, telecommunications, aerospace, computer,
and medical equipment. Although the microelectronics industry covers a diverse array of products, producers, and end-uses,
some general trends have been evident in the industry. A strong increase in the use of microelectronic products in industries
throughout the economy has led to rapid growth in microelectronics manufacturing over the past two decades. Although the
US is a major producer of consumer electronics, Japan is the world's leading producer of consumer electronics, and U.S.
firms face strong international competition for cutting edge technological advances in their products. Due to the high skill
level necessary in the development of products, there is considerable competition for skilled labor. The recent economic
slowdown has led to lowered demand for end-products that incorporate microelectronics. In response, the microelectronics
2 The rule regulates wastewater generated from washing vehicles only when it occurs as a preparatory step prior to performing an
MP&M unit operation (e.g., prior to disassembly to perform engine maintenance or rebuilding). The rule does not cover the washing of
cars, aircraft, or other vehicles when it is performed only for aesthetic/cosmetic purposes.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
industry has reduced capacity and laid off workers to reduce costs. Despite this decline, microelectronics continue to be an
increasingly necessary component of the global economy.
Telecommunication industries focus on the production of network equipment, fiber optics, and wireless communication
equipment. Much of the growth in the industry has come from the increasing use of fiber optics and wireless end-user
devices. The telecommunications industries experienced rapid growth in the nineties; however, industry activity slowed
considerably with the collapse of the telecommunications bubble. Telecom firms have reacted by cutting employees, reducing
costs, and selling off portions of their firms. Most have continued their R&D efforts.
3.2.4 Hardware
The hardware sector consists of many different industries, which can be generally classified into three groups: building
hardware, conventional hardware, and tooling hardware.
Building hardware consists of a group of industries that manufacture metal building products, including fabricated structural
metal, sheet metalwork, and architectural metalwork. This group of industries grew rapidly throughout the 1990's. The
building products industry as a whole saw record sales in 1998 and again in 1999. Much of this growth is attributed to large
highway projects funded by the Transportation Efficiency Act for the 21st Century.
Conventional hardware includes products such as screws, industrial fasteners, and valves and hose fittings. The products
produced in this industry are used in the production of manufactured goods. Trends in this industry, therefore, generally
reflect trends in other manufacturing industries. One of the most important industries influencing conventional hardware is
the auto industry. Hardware producers have experienced pressures from end users such as auto makers to reduce costs. The
industry faces a continued trend of consolidation of firms and increasing global pressure from countries with low labor costs.
Domestic producers of screws and industrial fasteners saw growth in the real value of shipments due to the strong U.S.
economy in the late nineties.
The tooling hardware sector also contains a variety of different industries that produce various types of tools for different
uses. Because these industries also face continued globalization, many of them are impacted by changes in the global
economy. The decline in Asian markets in 1998 and 1999 resulted in a sharp decline in the value of shipments for the
machine tooling industries. Prior to the 1998 financial crisis, value of shipments were increasing annually. The market for
the power-driven segment of hand tools has increased, however, despite troubled overseas markets.
3.2.5 Household Equipment
There are three general groups of industries included in the household equipment sector: household furniture, household
appliances, and plumbing equipment. Generally speaking, factors that affect this sector are consistent across these three
groups. Low unemployment and increased disposable income stimulated growth in each of these industries in the nineties.
However, because purchases of household equipment are relatively expensive and discretionary, consumers cut back spending
in the recent recession. All three household equipment industries face international competition, as imports account for a
substantial share of domestic consumption.
Metal furniture accounts for 20 percent of the household furniture industry. Metal components are increasingly being added
to non-metal furniture. For example, there is a trend to increase the functionality of non-metal furniture by equipping
recliners with heat and massage. This could increase the industry's reliance on metal parts. The industry has integrated
vertically, as large manufacturers have begun to open their own retail stores in an effort to differentiate their products.
There are two groups of household appliance manufacturers. Major appliances such as washing machines and refrigerators
are produced by relatively few firms. Smaller appliances are characterized by little product differentiation but considerable
price competition and are manufactured by a larger number of companies.
Finally, a significant characteristic of the plumbing equipment market is the extent of U.S. dependence on foreign imports.
While the U.S. construction market has grown at a record pace in the past few years, increasing demand for plumbing
equipment, much of the demand has been served by imports and this industry has a trade deficit.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
3.2.6 Instruments
The instruments sector is characterized by a diverse array of technologically advanced products and intense global
competition among many firms of varying sizes. The sector can be generally divided into industrial measuring and testing
instruments, and medical instruments.
In the industrial measuring industry, producers of laboratory instruments are typically integrated firms who have consolidated
and reduced costs in response to pressures from medical and pharmaceutical customers. Producers of measuring devices are
also facing pressures to consolidate. These firms have been hurt by low commodity prices during the past few years, which
have led to reduced investment in measuring equipment by fuel and grain producers. Sales should rebound, however, if Asian
economies and fuel prices continue to grow. Small companies still dominate the electronic test equipment industry, which is
characterized by a high degree of product differentiation. Most of these firms are not large enough to export products.
Sales for medical devices increased steadily throughout the 1990's, while employment remained relatively constant. The
industry has historically been characterized by many small to mid-size firms and intense competition for technological
innovation. Efforts to bring down health care costs is one of the primary challenges facing this industry. Pressure to reduce
costs has reduced insurance companies' willingness-to-pay for new equipment. As the population ages, however, demand for
medical services and devices is expected to grow. The industry will likely continue to grow in the next few years, but at a
slower pace than it has grown historically..
3.2.7 Iron and Steel
The basic iron and steel industry is regulated under 40 CFR 420, and primary iron and steel works, blast furnaces and rolling
mills are not affected by the MP&M rule. The MP&M rule will regulate facilities that perform MP&M operations or cold
forming operations on steel wire, rod, bar, pipe, or tube. This subcategory does not include facilities that perform those
operations on base materials other than steel, nor does it include wastewater from cold forming, electroplating or continuous
hot dip coating of steel sheet, strip, or plates or wastewater from performing any hot steel forming operations.
Events in the global steel industry in the past few years have had significant and possibly far reaching impacts on domestic
producers. In 1998, the industry experienced a global steel crisis. This crisis was caused in part by the Asian financial crisis,
which triggered a sharp decline in imports of steel by major steel importing countries of Asia. This led to a flood of steel
imports into U.S., and U.S. steel imports rose 33 percent in 1998. The situation was made worse by global overcapacity
largely derived from producers in Russia and Latin America.
This flood of steel into the U.S. and Europe led to rapidly declining steel prices in both regions. Excess inventories that
accrued during the surge of imports hurt domestic producers. The "unfair" trading prices resulted in over 20 nations taking
formal trade protection actions such as import duties and price floors. The US Congress determined that foreign steel was
being sold in the US at unfair prices, and reacted by enacting anti-dumping tariffs. The slowdown in the US economy has
also negatively affected the steel industry. Most steel firms are being forced to focus on rationalizing capacity and cutting
costs.
3.2.8 Job Shops
MP&M metal finishing job shops are defined as those facilities with more than 50 percent of their revenues coming from
products not owned by the site. While there are specific SIC codes associated with some Metal Finishing Job Shops, they sell
to a variety of markets and are not a market in and of themselves.
3.2.9 Mobile Industrial Equipment
Mobile industrial equipment includes a number of different industries that produce machinery for different purposes,
including construction, farming, and mining. Growth in the construction equipment industry is typically tied to economic
factors such as housing starts, employment, and consumer confidence. Shipments of construction equipment rose steadily
during much of the 1990's. The 1998 Transportation EquityAct for the 21st Century was expected to stimulate further
spending by federal, state, and local governments. However, the current recession has forced many industry buyers to cut
back or cancel orders.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
The farm and mining machinery industries both have been suffering from low commodity prices. Both industries experienced
growth in shipments throughout much of the 1990's, but were hit in 1999 by low prices. Farm equipment was hit hardest as
the real value of shipments fell by 38 percent in 1999. Output is expected to continue to decrease until grain surpluses decline
and agricultural prices rise. However, the consolidation of farms has also had a significant in impact on this industry. With
the increase in farm size, there is growing dependence upon mechanization to farm more acres per farm.
3.2.10 Motor Vehicle and Bus <& Truck
The major trend in the motor vehicle and bus and truck industries is the continual consolidation of firms into highly capital
intensive globalized manufacturers. Motor vehicle manufacturers are no longer constrained within national boundaries, as
mergers and joint ventures include some of the largest firms from different countries. Many foreign owned manufacturers
have facilities located in the U.S., and relative production costs and exchange rates play a greater role in determining the
location of production facilities than the national identity of parent companies.
Manufacturers have increasingly standardized the design of motor vehicles and their parts. These changes have resulted in
much less product differentiation among manufacturers, but also in greater product quality. However, greater product quality
has resulted in a consistently sharp increase in price over the past three decades. This price increase may have reached its
pinnacle in the mid-nineties, since prices declined in 1998 and 1999. Industry output for automobiles increased 1.3 percent
between 1996 and 2000. Although the current recession has hurt car prices, manufacturers have used incentives such as zero
percent financing to maintain sales volume.
3.2.11 Office Machine
The office machine sector experienced rapid growth in the nineties that reversed itself with the downturn in the economy.
The industry experienced 7.8 percent growth in the real value of shipments between 1996 and 2000. While this growth was
accomplished with only a 1.3 percent increase in total employment, production employment increased by 5.4 percent. The
relative difference between total and production employment can be attributed to increasing reliance on the Internet for sales,
thereby reducing the need for non-production sales staff. Despite this increase in production employment, the industry
remained extremely capital intensive. The recent weakness of the US economy hit the office machine sector hard with
business purchases of computers and computer accessories falling significantly.
Firms in the office machine sector have undergone mergers and acquisitions to bring down costs in order to compete. Firms
often rely on joint venturing agreements, and sometimes form alliances with past competitors to produce complementary
components of new technologies. Consolidation also allows firms to diversify, providing a range of products such as PCs,
software, and information technology to protect against the strong competition in the market for any one product. Firms have
also increasingly outsourced production to electronics manufacturers more equipped to increase production and take
advantage of economies of size, while the original firms utilize their resources for research and development of new
technologies to stay competitive.
Globalization is an important trend in this industry as machine components are produced in different countries. Despite the
trend toward a globalized market, the U.S. has held a negative trade balance for over a decade.
3.2.12 Ordnance
The ordnance sector includes firms that manufacture small arms, including grenade launchers and heavy field machine guns;
artillery, including naval, aircraft, anti-aircraft, tank, coast, and field artillery; and ammunition, including bullets, bombs,
mines, torpedoes, grenades, depth charges, and chemical warfare projectiles. It does not include the chemical processing or
manufacture of explosives. Overall, the industry has a high ratio of value added to total sales.
The ordnance sector has contracted significantly since the end of the Cold War. Decreases in US government military
spending have caused significant declines in ordnance production, leading to lower industry shipments and cutbacks in
employment. Foreign customers, including foreign governments, buy over 80 percent of the ordnance manufactured in the
US. Although shipments of military weaponry have declined, sales of small arms have increased in the US over the past few
years. Recent military actions by the US will likely result in government weapons purchases that will benefit the industry.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
3.2.13 Precious Metals and Jewelry
Domestic production in the precious metals and jewelry industry is dominated by many small firms with low capital intensity,
mostly concentrated in the northeast US. It is influenced by trends in consumer behavior, the retail market, and global
competition. Devaluation in the price of gold due to declining world prices has benefitted the industry because it reduces the
cost of making jewelry. Increased disposable income fueled strong consumer spending on precious metals and costume
jewelry in the nineties, but this trend has weakened with the recent economic downturn.
Increases in spending have not always translated into gains for domestic producers. The lowering of tariffs has resulted in a
steady increase in imports of costume jewelry, as labor-intensive production is often less expensive in developing countries.
Domestic producers have also been hurt by the strong U.S. dollar, which makes U.S. exports more expensive. Another
challenge comes from the retail market, which has put strong pressure on producers to bring down prices in order to compete.
These challenges include consolidation of retailers, giving them greater purchasing power, increased Internet and television
home shopping, and a decrease in the number of wholesalers.
3.2.14 Printed Wiring Boards
Printed wiring boards (also referred to as printed circuit boards) are the physical structures on which electronic components
such as semiconductor and capacitors are mounted. Computers and communications are the largest uses for printed wiring
boards. In addition, printed wiring boards are used in a wide array of other products, including toys, radios, television sets,
electronic wiring in cars, guided-missile and airborne electronic equipment, biotechnology, medical devices, digital imaging
equipment, and industrial control equipment. While some producers of PWBs produce them for their own use, most
manufacturers are independent firms that sell PWBs to the open market. The majority of PWB manufacturers are small firms.
The domestic PWB industry experienced considerable growth throughout the 1990's. Real industry output grew nine percent
from 1996 to 2000. Growth was spurred by continual growth in end-use markets. In addition to the increased in value of
shipments, U.S. firms saw a 5.6 percent increase in average hourly earnings and a 16.3 percent increase in capital
expenditures over the same period. However, demand in the PC, telecommunications, and electronics sectors has
weakened recently. In parallel, there is growing international competitive pressure for PWB makers to reduce production
costs. Consequently, many of the larger PWB firms are looking to relocate offshore.
3.2.15 Railroad
Railroad service consists of both freight and passenger service. In the past few years, railroad companies have been focusing
on improving the efficiency of their lines and services. There has been a continued trend toward consolidation of major
freight railroads. Consequently, companies have reduced the number of lines and focused attention on increasing the capacity
of fewer lines. Railroads have also begun to focus on guaranteeing deliveries at specific times, which will allow them to
compete with the trucking industry.
Since the 1980's railroad traffic increased by 50 percent, while the line network decreased by 39 percent. This was
accomplished by increasing capital expenditures for equipment such as new locomotives with greater horsepower, installation
of double tracks, and increases in the capacity of non-railroad owned freight cars. Consequently, freight service in the
nineties saw the first increase in operating revenue since 1984, although this was coupled with sharp decreases in
employment. Passenger service has undergone similar changes to increase efficiency by adding new locomotives and
beginning a transition to high speed train service. Total industry output increased 7.6 percent per year from 1988 to 1996.
Although transportation volume is sensitive to the generally poor macroeconomic situation, railways have succeeded in
cutting costs to maintain earnings.
3.2.16 Ships and Boats
Ship manufacturing experienced continual declines throughout the 1990's. Despite efforts by the Federal Government to
stimulate investment in converting the industry from production of military ships to merchant ships, the U.S. Navy remains
the primary customer of shipbuilders. The U.S. Navy dramatically reduced its orders for new vessels since the end of the
Cold War, and has decommissioned many ships and submarines. The Navy decreased its fleet by 208 ships from 1985 to
1998. Although the Navy plans to add 66 new ships through construction and conversion from 2000 through 2004, this
represents a decline of over 60 percent in the procurement of new ships since the 1980's. The ship building industry was
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
helped by the Oil Pollution Act of 1990, which required all oil tankers entering U.S. ports to have double hulls. General
economic woes and instability in the Middle East are expected to hurt the ships and boats industry.
This sector also manufactures recreational boats, with sales that reflect overall trends in recreational expenditures. The U.S.
boat building business is the world's leading supplier of recreational craft. Despite this, rapid growth in the market for
smaller personal water craft (e.g., jet skis) has led to an increase in imports of boats.
3.2.17 Stationary Industrial Equipment
The stationary industrial equipment sector includes firms that manufacture machinery and machinery parts used for oil, paper,
and food production, printing and packaging, as well as heaters and air conditioners, electric generating equipment, and motor
generators. These industries also produce large metal-working machines used in making parts for other industries.
The industries supplying oil and gas production, paper production, and printing machinery were affected by similar global
factors, and consequently followed similar trends. Low petroleum prices affected oil production in 1998. Natural gas
production was influenced by the low oil prices, which put pressure on the gas industry to reduce costs in order to compete.
These factors led to a decline of 38 percent in real value of shipments for oil production equipment manufacturers in 1998 and
1999. The price of petroleum increased in 1999 and 2000 and machinery shipments rebounded by 9.2 percent. However,
natural gas prices fell in 2001, hurting the industry.
Paper manufacturing equipment has suffered from events overseas. Although the U.S. has seen a decline in the production of
paper throughout the latter half of the 1990's, the U.S. remains the largest producer of paper manufacturing machinery. The
industry therefore relies heavily on exports to sustain growth. With struggling economies overseas, the industry saw a decline
in value of shipments from 1996 to 2000. Printing machinery manufacturers realized strong growth during the first half of the
1990's due to increased demand for new digital presses, but a decline in exports resulted in slower growth for the later half of
the decade. Global events did not have such an impact on manufacturers of packaging machinery, as the U.S. is not only the
leading producer of this equipment but also its leading end-user.
A variety of industries manufacture equipment used to produce energy or to power equipment. Refrigeration, air
conditioning, and heating equipment sales tend to follow growth in housing starts and construction of new office buildings. A
number of factors contributed to strong growth in this industry throughout the 1990's including record housing starts, record
heat in the summer of 1999, replacement of chlorofluorocarbon (CFC) air conditioning units, and a large percentage of new
homes being built with central air conditioning. With 66 percent of the existing air conditioners containing CFC technology
still in operation, replacement of these machines provides an opportunity for growth in this industry in the future.
Manufacturers of turbines, transformers, and switchboards, all of which are used for the production of electricity, saw
considerable growth in the late 1990s as the domestic economy grew. This strength has been limited by the recent recession.
A number of advanced technologies have been developed to meet the demands of a deregulated industry. These new
technologies are capable of producing electricity from smaller facilities at competitive costs. Implementation of these
technologies is not expected to take place for a few years, however, until the effects of deregulation become clearer.
3.3 CHARACTERISTICS OF MP<&M MANUFACTURING SECTORS
The data in these analyses come primarily from the Annual Survey of Manufacturers and the Small Business Administration,
although some data from the 1997 Census were used for important economic indicators that were not available in 1996. The
multi-year analyses presented in this section cover a nine year period from 1988 to 1996, the base years for the original Phase
1 and Phase 2 survey data. Although ideally data would have been presented for the ten year period from 1 987 to 1996, OMB
reclassified a number of 4-digit SIC industries in 1987. This made it difficult to compare SIC codes before and after this
reclassification and resulted in incomplete data in the Annual Survey of Manufacturers for many SIC codes in 1987. Because
the data were incomplete in 1987, 1988 was chosen as the first year of the time series. With the exception of data for non-
manufacturing sectors, single-year data focus on the year 1996, the base analysis year for the overall MP&M regulatory
analysis. Because the Annual Survey of Manufacturers does not include data for non-manufacturing sectors, single-year data
for these sectors are for 1997, the most recent year of the Economic Census.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
3.3.1 Domestic Production
a. Output
The two most common measures of manufacturing output are value of shipments (VOS) and value added (VA).
Historical trends in these measures provide insight into the overall economic health of an industry. Value of shipments is the
sum of the receipts a manufacturer earns from the sale of its outputs. It is an indicator of the overall size of a market or the
size of a firm in relation to its market or competitors. Value added is the difference between the value of shipments and the
value of purchased non-labor inputs used to make the products sold. It is used to measure the value of production activity in a
particular industry. The ratio of VA to VOS is an indicator of the importance of the industry's contribution to the total value
of the product. A ratio close to zero indicates that the value of the input materials is much more important than the value of
industry processing. A ratio close to one indicates that industry processing is the primary source of value in the product.
Table 3.4 presents Department of Commerce data on VOS and VA for the MP&M manufacturing sectors during the period
from 1988 to 1996. VOS for the entire MP&M manufacturing sector grew from 1.27 trillion dollars in 1988 to 1.51 trillion
dollars in 1996, for an average annual growth rate of 2.1 percent. VA for the entire industry grew at a slower annual rate of
1.5 percent, from 638 billion dollars to 720 billion dollars. In comparison, US GDP grew at 2.6 percent per year over the
same period.
Value added as a percent of value of shipments for the MP&M manufacturing industries as a whole was 48 percent in 1996.
This indicates that 48 percent of the value of their output was the result of MP&M processing and 52 percent was the cost of
purchased inputs. In general, MP&M processing is important to the value of MP&M output products.
Growth in the individual sectors was generally consistent with the overall trend in MP&M manufacturing of slow positive
growth. Fourteen of the nineteen sectors had positive growth in VOS, and thirteen had positive growth in VA. Railroad
equipment manufacturers enjoyed the largest average annual growth of 7.6 percent in VOS. Electronic equipment
experienced the next largest average growth, with annual growth in VOS averaging 5.1 percent. Only the aerospace and
ordnance industries experienced a large decline in VOS and VA over this period. Aerospace VOS declined 7.6 percent per
year and ordnance VOS declined 7.3 percent per year. Both decreases were attributable to cutbacks in government defense
spending at the end of the Cold War.
VA as a percent of VOS for the individual sectors varied substantially from the manufacturing average of 48 percent. The
ordnance sector had the highest ratio of VA to VOS, at 67.6 percent, and the instrument and printed wiring board sectors also
had high ratios. In these sectors, industry processing is the most important part of the value of the finished product. Sectors
with low ratios of VA to VOS included the iron and steel sector, railroad sector, bus and truck sector and especially the motor
vehicle sector, for which VA as a percent of VOS was equal to only 33.7 percent. The value of input materials was the most
important contributor to the value of products in these sectors.
b. Number of facilities and firms
The number of facilities and firms in an industry is an indicator of industry size and structure. Changes in the number of firms
and facilities can indicate whether or not the industry is experiencing growth, and changes in the ratio of facilities to firms can
indicate whether an industry is becoming more integrated.
This profile uses SUSB data to assess the number of firms and facilities in the MP&M manufacturing sector. The SUSB did
not begin its survey until 1989, and it did not include firms in its survey until a year later. Thus, facilities data are presented
in 1989 and firm data are presented in 1990.
Table 3.5 shows the number of MP&M manufacturing facilities in 1989 and 1996 and the number of firms in 1990 and 1996.
Overall, the number of firms grew 2.1 percent annually and the number of facilities grew 1.4 percent annually over this
period. By 1996, there were 144,603 manufacturing firms and 153,354 facilities. The average number of facilities per firm
was relatively constant, with only a minor decrease from 1.07 in 1990 to 1.06 in 1996. Most MP&M manufacturers are
single-facility firms.
Trends in the individual manufacturing sectors were generally consistent with overall trends in manufacturing. The aerospace
industry was the only MP&M manufacturing sector to experience significant downsizing during this period, with firms and
facilities decreasing annually by 4.1 and 4.2 percent, respectively. The iron and steel industry experienced a more modest
decrease in number of firms and facilities. The number of firms and facilities in the printed wiring board sector grew the
fastest, by a little over five percent annually.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.4: Real Value of Shipments and Value Added: MP&M Manufacturing Sectors (millions, 2001$)
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shops
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Wiring Boards
Railroad
Value of Industry Shipments
r
1988
35,991
101,554
9,843
85,498
152,597
87,764
118,322
19,396
11,733
45,150
315,641
86,352
10,241
58,809
10,790
10,162
4,195
1996
19,111
88,897
14,362
127,347
180,756
98,763
136,377
19,963
14,927
56,159
387,547
110,084
5,567
63,995
9,242
11,408
7,533
Average
Annual
Growth
Rate
-7.6%
-1.7%
4.8%
5.1%
2.1%
1.5%
1.8%
0.4%
3.1%
2.8%
2.6%
3.1%
-7.3%
1.1%
-1.9%
1.5%
7.6%
Value Added by Manufacture
p
1988
24,167
51,692
3,622
48,862
82,644
42,595
78,160
7,228
6,967
21,356
107,025
43,008
6,631
36,039
5,018
5,927
1,893
Ships and Boats 18,802! 16,666! -1.5% j 10,086
1996
10,645
48,204
5,513
67,071
98,674
45,551
89,052
7,103
8,307
24,302
130,627
43,849
3,761
37,431
4,403
6,997
2,761
Average
Annual
Growth
Rate
-9.7%
-0.9%
5.4%
4.0%
2.2%
0.8%
1.6%
-0.2%
2.2%
1.6%
2.5%
0.2%
-6.8%
0.5%
-1.6%
2.1%
4.8%
Value Added as
a % of Value of
Shipments in
1996
55.7%
54.2%
38.4%
52.7%
54.6%
46.1%
65.3%
35.6%
55.7%
43.3%
33.7%
39.8%
67.6%
58.5%
47.6%
61.3%
36.7%
8,424 | -2.2% | 50.5%
Stationary Industrial Equipment ! 176,961! 236,213! 3.7% I 97,388! 125,443 I 3.2% I 53.1%
Total ! 1,359,801 ! 1,604,916 ! 2.1% I 680,309 ! 768,118 1 1.5% I 47.9%
US GDP ! 7,189,924 ! 8,821,069 ! 2.6% n/a n/a I n/a I n/a
Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers; Economagic
Horizontal integration varied substantially across sectors. The railroad sector, with 1.41 facilities per firm in 1996, was the
most horizontally integrated, but the iron and steel and aerospace sectors also had high numbers of facilities per firm. The
precious metals and jewelry sector had nearly a one to one ratio between facilities and firms, indicating a very low level of
horizontal integration.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.5: Number of Firms and Facilities: MP<&M Manufacturing Sectors
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shop
Mobile Industrial
Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and
Jewelry
Printed Wiring Boards
Railroad
Ships and Boats
Stationary Industrial
Equipment
Total
Number of Firms
r
1990
109
1,428
889
5,649
34,984
6,787
7,963
597
4,798
3,318
4,991
1,828
340
11,517
3,719
1,034
147
2,511
35,231
127,840
1996
85
1,486
953
6,180
37,832
7,563
9,730
583
5,280
3,341
6,044
2,002
421
13,819
3,867
1,452
152
3,195
40,618
144,603
Average
Annual
Growth
Rate
-4.1%
0.7%
1.2%
1.5%
1.3%
1.8%
3.4%
-0.4%
1.6%
0.1%
3.2%
1.5%
3.6%
3.1%
0.7%
5.8%
0.6%
4.1%
2.4%
2.1%
Number of Facilities
r
1989
143
1,633
1,016
6,396
37,861
7,914
8,959
784
5,104
3,606
5,977
2,050
385
12,069
3,870
1,046
180
2,708
37,261
138,962
1996
106
1,691
1,040
6,693
40,044
8,303
10,552
770
5,549
3,591
7,024
2,087
442
14,198
3,892
1,530
215
3,310
42,317
153,354
Average
Annual
Growth
Rate
-4.2%
0.5%
0.3%
0.7%
0.8%
0.7%
2.4%
-0.3%
1.2%
-0.1%
2.3%
0.3%
2.0%
2.3%
0.1%
5.6%
2.6%
2.9%
1.8%
1.4%
Facilities per
Firm
1990"
1.33
1.16
1.11
1.08
1.06
1.11
1.10
1.30
1.04
1.07
1.17
1.07
1.09
1.03
1.01
1.06
1.27
1.05
1.04
1.07
1996
1.25
1.14
1.09
1.08
1.06
1.10
1.08
1.32
1.05
1.07
1.16
1.04
1.05
1.03
1.01
1.05
1.41
1.04
1.04
1.06
a Calculated using data from 1990 for facilities and firms.
Source: Small Business Administration, Statistics of U.S. Businesses.
c. Employment
Employment is a measure of the level and trend of activity in an industry. While employment growth generally signals
economic strength in an industry, strong productivity growth and scale economies can yield growth in revenues that exceeds
growth in employment. Changing patterns of labor utilization relative to output are particularly important in understanding
how regulatory requirements may translate into job losses both in aggregate and at the community level. This profile presents
DOC data on employment for 1988 and 1996.
Table 3.6 shows that employment in the MP&M manufacturing sectors as a whole decreased modestly between 1988 and
1997. Over those years, total employment dropped from 7.98 million to 7.55 million, an average decline of 0.7 percent
annually. To put this in perspective, VOS for the entire MP&M manufacturing sector grew about 2.1% annually over the
same period of time, signaling that growth in output has been driven by increases in capital expenditures and labor
productivity, not by increases in employment.
Although total MP&M industry employment declined over the analysis period, not all sectors experienced employment
declines. Employment grew or stayed constant in ten of the nineteen sectors. However, while a number of sectors evidenced
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
large percentage and absolute losses in employment, no sectors showed large percentage gains and only two showed large
absolute gains in employment . Employment shrank by 11.6 percent annually in the aerospace sector, 9.1 percent in the
ordnance sector, and 5.6 percent in the aircraft sector, due to cutbacks in defense spending following the Cold War. The
greatest absolute decline occurred in the aircraft sector, which lost almost 220,000 jobs. The largest percentage increase in
employment was in the railroad sector, which gained just 2.1 percent annually. The largest absolute increase in employment
over the nine years was in the stationary industrial equipment sector, which gained 127,100 jobs.
Table 3.6: Employment: MP&M Manufacturing Sectors
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shops
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Wiring Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
Total
Number of Employees
1988
223,700
596,600
63,900
602,500
1,246,200
584,900
886,500
65,500
123,300
232,400
928,000
329,800
86,500
368,100
87,100
80,900
25,900
182,900
1,269,800
7,984,500
1996
81,000
376,800
67,700
604,800
1,307,600
570,600
753,800
67,900
129,200
232,600
974,000
259,100
40,200
361,400
65,800
88,300
30,600
141,300
1,396,900
7,549,600
Average Annual
Growth Rate
-11.9%
-5.6%
0.7%
0.0%
0.6%
-0.3%
-2.0%
0.5%
0.6%
0.0%
0.6%
-3.0%
-9.1%
-0.2%
-3.4%
1.1%
2.1%
-3.2%
1.2%
-0.7%
Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
d. Capital expenditures
Capital expenditures are an indicator of production characteristics and market structure. Capital expenditures are the amount
of money spent annually on capital, which includes equipment, machinery, vehicles, software, buildings, intellectual rights, or
any other permanent addition to a firm. Capital does not refer to input materials that are consumed in the course of
production. New capital expenditures are needed to modernize, expand, and replace a firm's existing production capacity to
meet growing demand or to stay current with new regulations or changing technology.
An industry with high capital stock compared to its employee payroll is considered capital intensive: its production relies
more heavily on machinery, software, and other forms of capital than on labor. An industry with high capital requirements
can have significant barriers to entry for new firms, making the market less competitive.
Table 3.7 presents DOC data on new capital expenditures by MP&M manufacturing sector.
Table 3.7: New Capital Expenditures: MP&M Manufacturing Sectors
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shops
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Wiring Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
Total
Capital Expenditures
(millions, 2001$)
1988
1,310
3,015
161
3,118
3,517
2,150
4,002
420
353
1,121
5,697
3,044
196
1,768
93
430
78
483
4,333
35,288
1996
522
2,156
213
4,482
5,624
2,616
4,832
623
772
1,121
12,840
3,109
91
1,999
156
624
103
374
7,222
49,480
Average Annual
Growth Rate
-10.9%
-4.1%
3.6%
4.6%
6.0%
2.5%
2.4%
5.0%
10.3%
0.0%
10.7%
0.3%
-9.2%
1.5%
6.7%
4.8%
3.6%
-3.1%
6.6%
4.3%
Capital Expenditures per Facility
(2001$)
1988/89
9,160,839
1,846,295
158,465
487,492
92,892
271,670
446,702
535,714
69,161
310,871
953,154
1,484,878
509,091
146,491
24,031
411,090
433,333
178,360
116,288
253,940
1996
4,924,528
1,274,985
204,808
669,655
140,446
315,067
457,923
809,091
139,124
312,169
1,828,018
1,489,698
205,882
140,794
40,082
407,843
479,070
112,991
170,664
322,652
Change from
1988/89 to 1996
-4,236,311
-571,310
46,343
182,163
47,553
43,396
11,221
273,377
69,963
1,299
874,864
4,820
-303,209
-5,697
16,051
-3,247
45,736
-65,369
54,376
68,712
Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers.
In general, the MP&M manufacturing sector is relatively capital intensive. In 1988, manufacturing capital expenditures were
38.3 billion dollars. They increased by 4.3 percent annually to reach a total yearly investment in capital of 49.5 billion dollars
in 1996. Average yearly capital expenditures per firm increased from $254,000 in 1988 to $353,000 in 1996.
For the most part, changes in capital investment from 1988 to 1996 in the individual manufacturing sectors followed the trend
for the MP&M manufacturing sectors as a whole. Capital expenditures in the job shop and motor vehicle sectors grew at over
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
10% annually. The only sectors for which spending on new capital declined were aerospace, aircraft, ordnance, and ships and
boats.
There was large variation in capital expenditures across the MP&M sectors. A few industries stood out as being extremely
capital intensive. Aerospace firms spent an average of 4.8 million dollars on capital per firm in 1996, and the aircraft, motor
vehicle, and office machine sectors each spent more than one million dollars per firm in 1996. The precious metal and
jewelry sector had the lowest levels of capital investment, with only $40,000 spent per firm in 1996.
3.3.2 Industry/Market Structure
A number of factors play an important role in determining market structure for an industry, including the barriers that firms
face in entering and exiting the market, the degree to which firms in the market are vertically and horizontally integrated, and
the extent to which markets have been globalized. The following sections discuss these factors.
a. Facility size
Facility size is an indicator of economies of scale. The presence of many large facilities can indicate that there are advantages
to building on a larger scale, such as dividing labor more efficiently, utilizing equipment more effectively, or getting bulk
discounts.
Table 3.8 shows 1997 Census data on the distribution of manufacturing facilities and VOS by employment size category and
MP&M sector. The MP&M industry is characterized by a large number of small facilities. The Census data indicate that, in
1997, 98.6 percent of all facilities in the MP&M industry employed less than 500 employees. Those facilities, however,
accounted for only 59 percent of the total value of shipments from the manufacturing industries. The 1.4 percent of facilities
with 500 or more employees generated 41 percent of the total VOS from the manufacturing industries. These large facilities
are likely to enjoy substantial economies of scale.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.8: Number of Facilities and Value of Shipments by Employment Size Category:
MP&M Manufacturing Sectors in 1997
Sector
Aerospace
Aircraft
Bus & Truck
Electronic
Equipment
Hardware
Household
Equipment
Instruments
Iron and Steel
Job Shop
Mobile Industrial
Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal
Products
Precious Metals
and Jewelry
Printed Wiring
Boards
Railroad
Ships and Boats
Stationary
Industrial
Equipment
Total
Number of Facilities
Itol9
33
898
658
3,450
26,065
4,958
6,741
278
3,701
2,116
4,004
1,408
298
11,265
3,250
801
81
3,755
30,513
104,274
20 to
99
23
441
335
2,086
11,854
2,274
2,667
292
1,654
990
1,874
485
77
2,375
480
412
71
469
8,555
37,413
100 to
499
20
270
154
1,019
2,686
1,110
1,103
205
199
383
1,206
218
40
611
100
156
54
179
2,344
12,056
500 to
2,499
13
72
18
205
189
191
253
13
6
90
324
73
17
68
12
20
13
25
343
1,947
2,500
or
more
10
32
1
21
0
11
20
0
0
9
79
21
2
3
0
0
1
6
12
227
Value of Shipments (millions, 2001$)
' ""
I to 19
42
716
675
2,945
20,669
3,745
5,592
528
2,103
1,937
3,495
1,553
168
5,382
1,777
526
99
1,314
18,793
72,061
20 to 99
991
10,087
8,013
37,794
79,456
40,843
44,850
11,072
5,610
17,351
54,221
16,551
1,472
21,579
3,436
4,322
2,659
5,714
86,348
452,366
100 to
499
15,993
74,417
573
37,178
3
7,314
21,168
0
0
8,913
231,896
59,499
935
7,473
0
0
1,222
6,414
10,837
483,834
500 to
2,499
185
2,744
2,366
14,258
69,005
13,747
17,124
4,530
7,273
7,219
13,404
4,842
522
12,511
2,864
2,022
678
2,036
50,039
227,368
2,500 or
more
2,916
17,913
3,357
54,660
25,619
37,067
52,680
4,254
343
26,684
119,088
35,440
2,686
16,812
1,666
3,334
3,714
2,404
69,921
480,558
Source: Department of Commerce, Bureau of the Census, Census of Manufacturers, 1997.
Although the majority of MP&M industry facilities are small, the distribution of facilities by employment size category varies
substantially among the 19 MP&M sectors. The aerospace, aircraft, motor vehicle, and railroad sectors all had proportionally
high numbers of large facilities. The aerospace sector, in particular, had large economies of scale, with 23 percent of its
facilities employing 500 or more employees. The hardware, job shop, other metal products, precious metal, and ships and
boats sectors had proportionally large numbers of small facilities. At least 93 percent of facilities in each of these sectors had
less than 100 employees.
b. Firm size
This profile uses firm employment size as an indicator of market power and barriers to entry. If the largest firms in an
industry own disproportionately many facilities or control a large portion of industry output, then they may have significant
market power. These firms can use their large production capacities to control and exploit markets. The presence of many
large firms in an industry can also indicate that there are barriers to entry into that industry, such as capital requirements or
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
economies of scale, that give existing firms in the industry a competitive advantage. EPA used 1 996 SUSB data to assess the
competitiveness of the MP&M manufacturing industries.
Table 3.9 presents the distribution of manufacturing firms, facilities, and VOS by firm employment size and MP&M sector.
Overall, most MP&M manufacturing firms were small, but the firms that were big owned many facilities and had
disproportionately high receipts. In 1996, 138,492 firms, equal to 96 percent of manufacturers, had fewer than 500
employees. These small businesses owned 92 percent of all facilities but had total sales of only 418.3 billion dollars, equal to
28 percent of total estimated receipts. In 1996, 6,111 firms had 500 or more employees. These firms owned eight percent of
all facilities but had estimated receipts of 1.08 trillion dollars, equal to 72 percent of the total for manufacturers. It is likely
that there are significant economies of scale in the MP&M manufacturing industries.
Although MP&M manufacturing firms tend to be small, firm size varies significantly among individual sectors. The
aerospace, iron and job shops, and railroad sectors had proportionally high numbers of large facilities, hi the aerospace
sector, 50 percent of facilities were owned by firms with 500 or more employees, and 38 percent of firms had 500 or more
employees. In contrast, over 98 percent of firms in the job shop, other metal products, precious metals, and ships and boats
sectors had less than 500 employees.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.9: Number of Firms, Facilities, and Estimated Receipts by Firm Employment Size Category, 1996:
MP&M Manufacturing Sectors
Sector
Aerospace
Aircraft
Bus & Truck
Electronic
Equipment
Hardware
Household
Equipment
Instruments
Iron and Steel
Job Shops
Mobile Industrial
Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal
Products
Precious Metals and
Jewelry
Printed Wiring
Boards
Railroad
Ships and Boats
Stationary
Industrial
Equipment
Total
Firms
r
1 to 99
51
1,209
805
4,936
34,162
6,408
8,273
362
4,945
2,875
4,950
1,662
358
13,097
3,747
1,250
99
3,003
37,669
:f
129,861
100 to
499
2
135
92
681
2,345
665
727
108
240
263
614
167
25
492
86
137
24
137
1,691
^
8,631
500 or
more
32
142
56
563
1,325
490
730
113
95
203
480
173
38
230
34
65
29
55
1,258
L J
6,111
Facilities
1 to 99
51
1,212
810
4,977
34,398
6,455
8,320
368
5,001
2,898
4,987
1,668
358
13,152
3,753
1,258
101
3,012
37,835
h
130,614
100 to
499
2
158
107
786
2,968
791
842
153
338
319
724
180
28
602
89
150
30
165
2,002
^
10,434
500 or
more
53
321
123
930
2,678
1,057
1,390
249
210
374
1,313
239
56
444
50
122
84
133
2,480
j,
12,306
Estimated Receipts
(millions, 2001$)
'
1 to 99
n/a
2,453
2,269
12,156
66,557
12,799
17,248
2,015
7,157
6,668
11,314
5,373
329
13,568
3,559
2,231
326
2,699
52,700
>,
221,420
100 to
499
n/a
2,840
2,638
17,353
42,561
17,412
16,243
4,426
3,487
6,321
21,376
7,535
453
10,738
2,019
2,402
496
2,954
35,623
:f
196,877
500 or
more
19,029
93,860
6,702
81,615
61,295
66,409
96,894
12,737
3,097
32,129
366,635
64,424
4,213
30,677
2,148
5,769
6,271
11,501
119,210
h
1,084,612
Source: Small Business Administration, Statistics of U.S. Businesses, 1996.
c. Foreign trade
This profile uses two measures of foreign competitiveness: export dependence and import penetration. Export
dependence is the share of value of shipments that is exported. Import penetration is the share of domestic consumption met
by imports. For both measures, a high value indicates a relatively high openness to foreign markets and global competition.
This openness has benefits, including providing domestic consumers with a wider selection of products and services at lower
prices, and allowing domestic producers to make profits in foreign markets. It can have costs, too, if imports to domestic
consumers are unreliable or if foreign competition drives down prices for domestic producers. This profile uses 1996 data
from the Department of Commerce to illustrate trends in foreign trade.
Table 3.10 shows that overall, the U.S. is an importer of MP&M manufactured goods, with net imports of 75.7 billion dollars
in 1996. In general, MP&M industry sectors face global competition, as illustrated by the number of sectors that had both a
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
high export dependence and import penetration. For example, in the precious metals sector, roughly 77 percent of U.S.
consumption was met by imports, while almost 23 percent of U.S. production was sold as exports.
Although overall the US has a large trade deficit in MP&M manufactured goods and services, the US was a net exporter in six
of the eighteen sectors for which balance of trade data was available. Eighty one percent of production in the ordnance sector
and 67 percent of production in the aircraft sector was consumed overseas. The aircraft sector had the highest absolute net
exports, valued at 27.26 billion dollars. A few sectors, especially aerospace, ships and boats, iron and steel, and bus and truck,
were relatively closed to global competition, with low levels of imports and exports. Foreign imports had the highest relative
importance in the precious metals, office machine, and household equipment sectors. The motor vehicle sector had the
highest absolute net imports, valued at 63.12 billion dollars.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.10: Trade Statistics, 1996: MP&M Manufacturing Sectors
Sector
(a)
Aerospace
Aircraft
Bus & Truck
Electronic
Equipment
Hardware
Household
Equipment
Instruments
Iron and Steel
Job Shopd
Mobile Industrial
Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal
Products
Precious Metals and
Jewelry
Printed Circuit
Boards
Railroad
Ships and Boats
Stationary Industrial
Equipment
Total'
Value of
Imports
(millions,
2001$)
(b)
143
14,015
^
410
31,478
26,753
40,697
18,990
937
n/a
10,775
124,203
67,082
647
25,282
15,839
2,667
1,208
1,081
38,809
421,015
Value of
Exports
(millions,
2001$)
(c)
143
41,278
L
436
30,615
20,560
16,809
31,462
263
n/a
16,634
61,015
47,783
2,792
11,243
4,607
1,947
773
1,080
55,835
345,274
Value of
Shipments
(millions, 2001$)
(d)
19,111
88,896
^
14,362
127,347
180,756
98,762
136,376
19,963
14,927
56,159
387,546
110,084
5,566
63,996
9,243
11,408
7,533
16,666
236,213
1,604,915
Implied
Domestic
Consumption"
(e)
19,112
61,633
14,335
128,211
186,949
122,650
123,904
20,637
n/a
50,300
450,735
129,384
3,421
78,035
20,474
12,127
7,969
16,666
219,187
1,680,656
Import
Penetrationb
(0
0.7%
22.7%
^
2.9%
24.6%
14.3%
33.2%
15.3%
4.5%
n/a
21.4%
27.6%
51.8%
18.9%
32.4%
77.4%
22.0%
15.2%
6.5%
17.7%
25.1%
Export
Dependence'
(B)
0.7%
67.0%
>,
3.0%
23.9%
11.0%
13.7%
25.4%
1.3%
n/a
33.1%
13.5%
36.9%
81.6%
14.4%
22.5%
16.1%
9.7%
6.5%
25.5%
20.5%
a Implied domestic consumption based on value of shipments, imports, and exports [column d + column b - column c].
b Import penetration based on implied domestic consumption and imports [column b / column e].
° Export dependence based on value of shipments and exports [column c / column d].
d As explained in the text, job shops include only two SICs specific to job shops, and not facilities in other SICs that may be operating
as job shops.
° Components may not sum to totals due to rounding.
Source: Department of Commerce, Bureau of the Census.
d. Establishment births and deaths
The number of firms starting up and closing each year reflects the competitiveness of an industry. Industries with high
numbers of these "births" and "deaths" relative to the total number of firms in the industry are likely to have low barriers to
entry or exit. These industries are likely to be competitive. Industries with low number of births and deaths are more likely to
have significant barriers to entry and exit, such as capital requirements or economies of scale, that make the industries less
competitive. As discussed in previous sections, firms in less competitive industries can manipulate prices to generate profits,
while firms in more competitive industries have little control over prices. This profile presents SUSB data from 1989 to
3-23
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
1997 on establishment births and deaths. These data are only available by three digit SIC code, making it impossible to
calculate sector specific birth and deaths rates. However, data for the MP&M industry as a whole are presented.
The MP&M manufacturing sector has an annual birth rate of 8.1 percent and an annual death rate of 7.8 percent, indicating
that in general the MP&M manufacturing sector is relatively competitive. Three digit SIC industry birth and death rates are
much more variable, ranging from 4 percent to up to 15 percent. For a more complete discussion, along with the three digit
SIC birth and death rates, see Appendix A.
3.3.3 Financial Condition and Performance
Operating margin is a measure of industry financial performance. Operating margin is defined as VOS less annual payroll
and cost of materials, as a percent of VOS, and thus measures pre-tax operating profitability before capital- and financing-
related charges. Firms with higher operating margins have more cushion against operating losses as a consequence of
fluctuating input prices, and thus are likely to be more stable.
Table 3.11 presents DOC data on operating margins for each MP&M manufacturing industry for the years 1988 and 1996, as
well as the change in operating margin between the two years. In 1996, the average operating margin for the MP&M sectors
was 29.6 percent. This was a slight increase from 1988, when the average operating margin for the MP&M manufacturers
was 28.0 percent. Ten MP&M manufacturing sectors experienced increases in their operating margins during this time
period, while nine industries experienced decreases.
Instruments, other metal products, and ordnance were the most profitable sectors, according to this measure, with operating
margins around 40 percent. The iron and steel, motor vehicle, railroad, and ships and boats sectors had the lowest operating
margins, all near 22 percent. The greatest increases in operating margin occurred in the aircraft, ordnance, and bus & truck
industries, which all gained between five and six percent. The greatest decrease occurred in the aerospace industry, which
lost 3.5 percent.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.11: Operating Margin": MP&M Manufacturing Sectors in 1988 and 1996
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shops
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Wiring Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
All MP&M Manufacturers"
1988
32.4%
20.6%
18.5%
32.0%
27.5%
29.7%
37.1%
23.2%
31.8%
27.9%
20.9%
31.7%
34.3%
41.9%
27.9%
37.2%
22.4%
23.2%
29.4%
28.0%
1996
28.9%
26.7%
24.4%
33.8%
29.7%
29.5%
41.1%
22.9%
30.8%
28.2%
22.4%
30.2%
39.6%
40.4%
28.2%
36.8%
22.1%
22.5%
31.5%
29.6%
Change in Operating
Margin
-3.5%
6.1%
5.9%
1.8%
2.2%
-0.2%
4.0%
-0.3%
-1.0%
0.3%
1.5%
-1.5%
5.3%
-1.5%
0.3%
-0.4%
-0.3%
-0.7%
2.1%
1.6%
a Operating Margin is calculated as (value of shipments - cost of materials - payroll)/value of shipments.
b Weighted average by VOS.
Source: Department of Commerce, Bureau of the Census, Annual Survey of Manufacturers.
3.4 CHARACTERISTICS OF MP<&M NON-MANUFACTURING SECTORS
Eleven of the 18 MP&M sectors include non-manufacturing industries. The non-manufacturing activities are defined by 50
four-digit SIC codes: 26 transportation SIC codes, 18 service SIC codes, five retail trade SIC codes, and one wholesale trade
SIC code. MP&M facilities may perform both manufacturing and non-manufacturing activities.
The analyses presented in this section cover 1997 only, because the Census does not collect data annually for non-
manufacturing SICs as it does for manufacturers in the Annual Survey of Manufacturers. The profile is based on data from
the 1997 Censuses of Transportation, Communications, and Utilities; Service Industries; Retail Trade; and Wholesale Trade.
3.4.1 Domestic Production
a. Output
This profile uses sales and receipts as a measure of output. The sum of the receipts a manufacturer earns from the sale of its
outputs is an indicator of the overall size of a market or the size of a firm in relation to its market or competitors. EPA used
Department of Commerce data to assess sales and receipts for the MP&M non-manufacturing sectors.
Table 3.12 shows sales and receipts by sector for MP&M non-manufacturers. The MP&M nonmanufacturing sector
generated 1.25 trillion dollars in sales and receipts in 1997. Motor vehicle repair and maintenance, with sales and receipts of
870 billion dollars, accounted for almost 70 percent of total sales and receipts. Bus and truck, with sales and receipts of 209
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
billion dollars, accounted for another 17 percent. These two vehicle sectors made up 87% of total non-manufacturing output.
The smallest sector was precious metals and jewelry, which accounted for only 367 million dollars in sales and receipts.
Table 3.12: Sales/Receipts: MP&M Non-Manufacturing Sectors in 1997
(millions, 2001$)
Sector
Aircraft
Bus & Truck
Household Equipment
Instruments
Motor Vehicle
Office Machine
Other Metal Products
Precious Metals and Jewelry
Railroadb
Ships and Boats
Stationary Industrial Equipment
Total
Output a | Share
9,935.9 | 0.8%
209,316.1 | 16.7%
2,848 | 0.2%
7,402 I 0.6%
:
870,451 69.6%
30,930 2.5%
22,041 1.8%
367 0.029%
30,728 2.5%
37,383 3.0%
29,747 2.4%
1,251,148 100.0%
a Total sales for retail and wholesale trade, total receipts for service industries, total revenue for
transportation.
b Railroad sales/receipts is estimated from 1992 data.
Source: Department of Commerce, Bureau of the Census, Census of Transportation, Census of
Wholesale Trade, Census of Retail Trade, Census of Service Industries, 1997.
b. Number of facilities and firms
The number of facilities and firms in an industry is an indicator of the size and structure of an industry. Increases and
decreases in the number of firms and facilities can indicate whether an industry is growing or shrinking, and changes in the
ratio of facilities and firms can indicate whether an industry is becoming more integrated and concentrated. This profile uses
SBA data to assess the number of facilities and firms in the non-manufacturing sector from 1989 to 1996. The SBA changed
its survey to include firms in 1990, but data on the number of firms are not available from this source in 1989.
Table 3.13 shows the number of facilities and firms in the MP&M non-manufacturing sectors in 1989/1990 and 1996, with
average annual growth rates. The number of firms and facilities grew from 1989 to 1996 in all of the sectors. The average
number of facilities per firm shrank slightly over this time period, from 1.13 to 1.11, due to the fact that the number of firms
in the non-manufacturing sector grew at 4.5 percent per year while the number of facilities grew at only 3.6 percent per year.
In general, most MP&M non-manufacturers are single facility firms.
Although the number of facilities and firms increased for all of the sectors over this time period, not all industries grew at the
same rate. The number of facilities in the other metal products sector grew at only 0.6 percent annually, and the number of
facilities in the stationary industrial equipment and instruments sectors grew at 1.3 percent annually. In contrast, the number
of facilities in the office machine sector grew by 20.2 percent annually and the number of firms in the office machine sector
grew by 23.7 percent annually.
Concentration varied across the sectors. Stationary industrial equipment was the most concentrated sector, with an average of
1.45 facilities per firm in 1996. The other metal products, household equipment, and office machine sectors were the least
concentrated sectors, with only 1.04, 1.06, and 1.07 facilities per firm, respectively.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.13: Number of Firms and Facilities: MP<&M Non-Manufacturing Sectors
Sector
Number of Firms Number of Facilities j Facilities per Firm
! Average 1
Average Annual i
1990 1996 Annual 1989 1996 ^ ' 1989/90 i 1996
' /^ *u. T. * i ' Growth •
: Growth Rate : : _
• Rate :
: : : : : : :
Aircraft | 2,024 | 3,281 j 8.4% | 2,463 j 4,062 | 7.4% j 1.22 | 1.24
Bus & Truck 74,719 | 113,840 j 7.3% | 88,128 j 127,675 | 5.4% j 1.18 | 1.12
Household Equipment
Instruments
Motor Vehicle
Office Machine
Other Metal Products
Precious Metals and Jewelry
Railroad"
Ships and Boats
Stationary Industrial Equipment
Total
3,234
7,214
183,986
9,206
32,865
1,379
n/a
5,739
14,672
335,038
3,706
7,444
213,355
32,916
36,290
1,625
n/a
8,290
15,075
435,822
2.3%
0.5%
2.5%
23.7%
1.7%
2.8%
n/a
6.3%
0.5%
4.5%
3,367
8,365
203,592
9,714
34,683
1,535
n/a
6,561
20,880
379,288
3,935
9,185
234,542
35,150
37,902
1,838
n/a
9,262
21,791
485,342
2.3%
1.3%
2.0%
20.2%
1.3%
2.6%
n/a
5.0%
0.6%
3.6%
1.04
1.16
1.11
1.06
1.06
1.11
n/a
1.14
1.42
1.13
1.06
1.23
1.10
1.07
1.04
1.13
n/a
1.12
1.45
1.11
a The railroad sector has only two non-manufacturing SIC codes, both of which were excluded from the 1997 Census. Thus no data on
railroads is available.
Source: Small Business Administration, Statistics of U.S. Businesses.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
c. Employment
Employment is a measure of the level and trend of activity in an industry. Payroll is a measure of the skill level of employees
and of their value to the production process. While employment growth is often correlated with economic strength in an
industry, strong productivity growth and scale economies can result in growth in revenues that could not be predicted from
employment trends alone. Trends in labor utilization relative to output are important in understanding how regulatory
requirements may translate into job losses both in aggregate and at the community level.
Table 3.14 shows DOC data on employment and payroll for the non-manufacturing MP&M sectors in 1997. Total
employment for the non-manufacturing sector was 5.99 million, and total payroll was $201 billion. Average yearly
pay/employee was $33,610.
The majority of total employment came from the motor vehicle and bus and truck sectors. The motor vehicle sector had 2.6
million employees, and the bus and truck sector had 2.1 million employees. Together these two sectors accounted for over 78
percent of total employment in the non-manufacturing sector. The precious metals and jewelry sector had the lowest
employment, with only 5,599 employees.
Workers in a few industries were highly compensated. The railroad sector paid its workers $58,851 per year, and the office
machine sector paid $56,092 per year. On the other extreme, workers in the precious metals and jewelry sector earned only
$20,121 per year.
Table 3.14: Employment and Payroll, 1997: MP&M Non-Manufacturing Sectors
Sector
Aircraft
Bus & Truck
Household Equipment
Instruments
Motor Vehicle
Office Machine
Other Metal Products
Precious Metals and Jewelry
Railroad
Ships and Boats
Stationary Industrial Equipment
Total
Employment
121,210
2,106,432
25,455
76,970
2,622,049
235,332
226,069
5,599
197,421
178,560
198,735
5,993,832
Share
2.0%
35.1%
0.4%
1.3%
43.7%
3.9%
3.8%
0.1%
3.3%
3.0%
3.3%
100.0%
Payroll
(thousands, 2001$)
3,286,985
65,643,990
935,661
2,530,404
83,223,310
13,200,240
6,981,264
112,659
11,618,460
7,221,006
6,701,350
201,455,328.90
Share
1.6%
32.6%
0.5%
1.3%
41.3%
6.6%
3.5%
0.1%
5.8%
3.6%
3.3%
100.0%
Pay/Employee
27,118
31,164
36,757
32,875
31,740
56,092
30,881
20,121
58,851
40,440
33,720
33,610.44
Source: Department of Commerce, Census of Transportation, Census of Wholesale Trade, Census of Retail Trade, Census
of Service Industries, 1997.
3.4.2 Industry Structure and Competitiveness
A number of factors play an important role in determining market structure for an industry, including the barriers that firms
face in entering and exiting the market, the degree to which firms in the market are vertically and horizontally integrated, and
the extent to which markets have been globalized. This profile shows facility size and firm size as measures of industry
structure and competitiveness in the MP&M non-manufacturing sector.
a. Facility size
Facility size is an indicator of economies of scale. The presence of many large facilities in an industry can indicate that there
are advantages to building on a larger scale, such as dividing labor more efficiently, utilizing equipment more effectively, or
getting bulk discounts. EPA used data from the 1997 Census to assess facility size for manufacturing facilities.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Non-manufacturing facilities tend to be small. There were 255,602 non-manufacturing facilities, or 52.9 percent, that
employed 4 employees or less. These facilities accounted for 7 percent of sales and receipts in the non-manufacturing
MP&M sectors. Facilities with less than 20 employees accounted for 88 percent of all non-manufacturing facilities but
generated only 24 percent of non-manufacturing revenues. Facilities with more than 100 employees employed less than one
percent of total employees, but generated 17 percent of total revenues. Non-manufacturing MP&M facilities appear to
experience significant economies of scale.
Although the individual non-manufacturing sectors tended to have small facilities, there was some variation between sectors
in facility size. The aircraft sector and the ships and boats sector had relatively large facilities, probably because these sectors
are involved with large-scale transportation. For both sectors, 6.3 percent of facilities had more than 100 employees. In
contrast, the other metal products and precious metals and jewelry sectors had mostly small facilities. Ninety four percent of
facilities in the other metal products sector and 96 percent of facilities in the precious metals and jewelry sector had less than
20 employees.
Table 3.15 presents the number of facilities and total sales by facility employment size category for each category.
Table 3.15: Number of Facilities and Sales/Receipts by Facility Employment Size Category:
MP&M Non-Manufacturing Sectors in 1997
Sector
Aircraft
Bus & Truck
Household Equipment
Instruments
Motor Vehicle
Office Machine
Other Metal Products
Precious Metals and
Jewelry
Railroada
Ships and Boats
Stationary Industrial
Equipment
Total
Number of Facilities
Oto4
1,936
67,959
1,886
5,535
126,505
16,849
21,564
790
n/a
2,605
9,974
255,602
5 to 9
936
24,548
735
1,737
58,372
3,619
7,585
215
n/a
930
7,601
106,277
10 to 19
720
19,355
456
988
28,184
2,186
3,813
88
n/a
848
3,601
60,238
20 to 99
870
21,294
305
711
23,021
1,935
2,136
41
n/a
1,046
2,084
53,443
100 or
more
299
3,573
37
131
2,548
408
138
2
n/a
366
134
7,635
Sales/Receipts (miHions, $2001)
Oto4
381
15,924
358
1,017
41,209
3,598
3,810
109
n/a
2,578
3,183
72,168
5 to 9
482
16,044
411
936
45,438
2,592
3,856
81
n/a
1,580
5,487
76,907
10 to 19
879
24,189
551
1,064
62,030
3,327
4,444
70
n/a
2,161
6,000
104,714
20 to 99
2,767
75,663
1,072
2,473
388,395
9,285
7,178
88
n/a
9,535
9,321
505,778
100 or
more
5,433
81,036
457
1,917
180,200
12,146
2,118
19
n/a
19,380
3,189
305,894
a The non-manufacturing railroad sector is comprised of two SIC codes, both of which were excluded from the 1997 Census.
Source: Department of Commerce, Bureau of the Census, Census of Transportation, Census of Wholesale Trade, Census of Retail
Trade, Census of Service Industries, 1997.
b. Firm size
This profile uses firm employment size as an indicator of market power and barriers to entry. The distribution of facilities and
output by firm size can indicate that the firms in an industry have market power. If the largest firms own disproportionately
many facilities, in which case they are considered horizontally integrated, or if the largest firms control a large portion of
industry output, then they may have significant market power. These firms can use their large capacities to control and
exploit markets. The presence of many large firms in an industry can also indicate that there are barriers to entry into that
industry, such as capital requirements or economies of scale, that give existing firms in the industry a competitive advantage.
Table 3.16 presents SUSB data on numbers of firms and facilities with estimated receipts by firm employment size category
in 1996 for MP&M non-manufacturers. In general, although the majority of MP&M non-manufacturing firms were small, the
larger firms owned many facilities and had disproportionately large market shares. The vast majority of non-manufacturing
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
firms - 427,173 firms or about 98 percent of non-manufacturers - employed fewer than 100 employees. However, these firms
owned only 90 percent of all facilities and earned 610 billion dollars, only 58 percent of all revenues. The 2,338 firms with
500 or more employees, equal to 0.54 percent of all non-manufacturers, owned 6.5 percent of all facilities and generated 207
billion dollars, equal to 19.8 percent of total revenue.
Firm size in the individual MP&M non-manufacturing sectors is relatively similar to the trends in the non-manufacturing
sector as a whole. At least 94 percent of the firms in every sector had less than 100 employees. Although firm size varies
little by sector, there were larger variations in receipts by firm size. The aircraft, instruments, and ships and boats sectors
each had a small percentage of firms that controlled a large share of the market. In the aircraft sector, the largest 2.35 percent
of firms generated 60.4 percent of total revenues. In the instruments sector, the largest 1.2 percent of firms generated 50.2
percent of total revenues. In the ships and boats sector, the largest 2.6 percent of firms generated 57.4 percent of total
revenues.
Table 3.16: Number of Firms, Facilities, and Estimated Receipts by Firm Employment Size Category, 1996:
MP&M Non-Manufacturing Sectors
Sector
Aircraft
Bus & Truck
Household Equipment
Instruments
Motor Vehicle
Office Machine
Other Metal Products
Precious Metals and Jewelry
Railroad
Ships and Boats
Stationary Industrial Equipment
Total
1
I
Firms
i
| 100 to | 500 or |
Ito99 ! 499 ! more 1
3,124] 80 ] 77 1
111,038] 2,001 1 801 1
3,669J 19J 18J
7,277 1 76 1 91 1
209,814J 3,010] 531 1
32,428J 290J 198J
35,788J 284J 218J
1,615] 6J 4J
n/aj n/aj n/aj
7,833 1 243 1 214J
14,587] 302J 186J
427,173 1 6,311 1 2,338 1
Facilities
3,189] 139J
112,751 1 4,334J
3,700 1 23 1
7,536J 206J
216,707J 7,119J
32,745 1 759 1
36,205 1 567 1
1,661 1 105J
n/aj n/aj
8,OOOJ 519J
16,331 1 1,359J
438,825 1 15,130 1
500 or
more
734
10,590
212
1,443
10,716
1,646
1,130
72
n/a
743
4,101
31,387
i Estimated Receipts
2001$)
lto" ! 499
2,717J
79,331 1
2,032 1
465,989J
14,787 1
16,749 1
269 1
n/a I
9,087 1
15,422 1
609,502 1
1,264
23,943
275
562
186,083
4,800
2,308
0
n/a
6,122
4,606
229,963
(millions,
1 more
6,071
66,113
873
3,715
87,113
10,565
4,610
o
n/a
20,493
7,739
207,294
a The non-manufacturing railroad sector is comprised of two SIC codes, both of which were excluded from the 1997 Census.
Source: Small Business Administration, Statistics of U.S. Businesses.
3.5 CHARACTERISTICS OF ALL MP<&M SECTORS
This section presents additional market structure data for the MP&M industry as a whole. It includes eight-firm concentration
data and risk-normalized return on assets (ROA) data as measures of industry competitiveness.
3.5.1 Eight-firm Concentration Ratio
The eight-firm concentration ratio (8-firm CR) is a measure of the degree to which the largest firms in an industry have
market power. It is defined as the percentage of the value of total industry shipments that is produced by the top eight firms
of a given industry. In general, an industry with a high 8-firm CR are likely to have larger entry and exit barriers and to be
less competitive. Firms in this kind of industry have less incentive to compete and more ability to manipulate prices to
increase their profits. It is more difficult for firms in a competitive, less concentrated industry to manipulate prices. This
profile presents 8-firm CR data from the 1992 Census.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.17 shows the 8-firm CR for each sector in 1992. The aerospace and aircraft sectors were particularly concentrated,
with the largest eight firms in each sector producing 92 percent and 85 percent of industry shipments, respectively. The motor
vehicle, ordnance, and railroad sectors were also relatively concentrated. The job shop and hardware industries were the least
concentrated, with only 19 percent and 25 percent of output, respectively, being produced by the eight largest firms.
Table 3.17: Eight-firm Concentration Ratio, 1992
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shop
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Circuit Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
! 8-firm Concentration Ratio
j Value j Rank3
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
92.29 | 19
85.3 | 18
42.51 | 7
47.27 | 9
24.52 | 2
54.22 | 10
44.2 | 8
41.87 | 6
19.26 | 1
58.56J 13
77.30 | 17
61.38 | 14
76.90J 16
54.27 | 11
35.0 | 4
35.0 | 3
71.00 | 15
58.20 | 12
| 41.16J 5
a Rank is a comparison within the MP&M manufacturing sectors only. A rank of 1
indicates the lowest level of concentration.
Source: Department of Commerce, Bureau of the Census.
3.5.2 Risk Normalized Return on Assets
Firms' abilities to enter and exit markets determine, in part, the competitiveness of an industry. If significant barriers to entry
exist, potential entrants may be dissuaded and existing firms may enjoy market power. If few barriers to entry exist, existing
firms are more likely to face competition for market share via price and other competitive tactics. Some important entry
barriers for the MP&M industry are large capital requirements, economies of scale, and brand name recognition. Although
data on barriers to entry are limited, the available data show that market power exists in some sectors.
EPA used the risk normalized return on assets as an indicator of the existence of entry or exit barriers for each industry 3. A
firm's return on assets is the profit the firm earns from investing in assets. Normally, firms in riskier industries tend to have
higher ROA's. However, barriers to entry or exit can allow firms to achieve higher ROA's than would be predicted from their
3 The risk normalized ROA only assigns MP&M industry sectors relative rankings and does not imply that they face high
or low barriers to competition in absolute terms.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
risk level. The risk normalized return on assets measures the additional profit that firms earn above and beyond what their
risk level predicts. EPA used data from Marketguide.com to calculate a risk normalized ROA. The agency calculated risk
normalized ROA by dividing each firm's ROA by its asset beta (a measure of the relative riskiness of the firm's common
stock) and averaging over the five-year period from 1996 to 2000.
The electronic equipment, printed circuit board, and office machine industries had the lowest risk normalized ROA's,
indicating relatively weaker barriers to entry or exit for these industries. The instrument, other metal products, mobile
industrial equipment, and motor vehicle industries had the highest ROA's. These industries are likely to have significant
barriers to entry and exit.
Table 3.18 presents the average risk normalized return on assets for the period from 1996 to 2001, based on data from
Marketeuide.com.
Table 3.18: Average Risk Normalized
Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shop
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious Metals and Jewelry
Printed Circuit Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
Return
i
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
on Assets, 1996 to 2001
Risk-Normalized ROA (%)
Value Rank
13.19 8
16.15 13
12.31 7
7.21 1
17.18 15
12.02 5
19.64 18
11.38 4
13.44 9
18.13 17
18.10 16
9.58 3
12.30 6
26.60 19
14.43 10
7.50 2
14.62 11
16.11 12
16.78 14
Source: www.marketguide.com
3.6 CHARACTERISTICS OF MP<&M FACILITIES
This section uses survey data to characterize MP&M facilities. It includes data on facility revenue sources, discharge type,
small business status, market type, and financial performance. These data are organized according to MP&M regulation
subcategories based on unit operations performed and the nature of the waste generated. EPA determined that a basis exists
for dividing the MP&M category into the following subcategories: General Metals, Non-Chromium Anodizing, Metal
Finishing Job Shops, Printed Wiring Boards, Steel Forming and Finishing, Oily Wastes, Railroad Line Maintenance, and
Shipbuilding Dry Dock. EPA did not generally define subcategories in terms of industrial sectors because many facilities
perform operations covered by multiple sectors and, as a result, the industrial sectors are too broad for subcategorization.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
Table 3.19 shows the national number of MP&M facilities that sell products to different combinations of sectors. The table
shows that many MP&M facilities operate in multiple market sectors. Almost every combination of sectors shows overlap,
and some MP&M facilities report revenues from three or more sectors.
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MPAM EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MPAM Industry Sectors
Table 3.19: Overlap of Sectors M
Sector
Aerospace
Aircraft
Bus and Truck
Electronic
Equipment
Hardware
Household
Equipment
Instrument
Mobile Industrial
Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal
Products
Precious and Non-
Precious Metals
Printed Circuit
Boards
Raikoad
Ship and Boat
Stationary
Industrial
Equipment
Unknown
>
§
IX
^
1
1,828
L
0
129
1,327
345
289
1,046
47
157
265
132
289
47
160
> 16
102
1,169
Aircraft
L
2,350
169
1,318
399
317
1,126
116
220
349
119
321
47
164
61
0
1,255
Bus and Truck
L
5,574
824
914
477
398
1,511
1,790
198
52
457
0
0
95
237
714
Electronic Equipment
L
4,073
1,129
898
1,680
704
619
622
204
850
36
164
86
146
1,818
Hardware
L
7,075
1,600
678
738
823
515
86
1,450
47
160
143
191
1,151
Household Equipment
L
2,635
610
417
678
477
77
1,393
24
160
67
156
687
Instrument
L
4,965
404
524
356
202
475
47
4
69
104
1,293
Mobile Industrial Equipment
L
2,467
1,089
159
80
438
12
0
124
138
688
Motor Vehicle
L
13,853
223
153
695
36
0
154
245
530
Office Machine
L
1,088
89
329
36
375
91
138
486
Ordnance
L
481
36
0
0
58
25
130
Other Metal Products
L
5,359
92
160
81
78
469
Precious Non-Precious Metals
L
1,651
0
12
12
39
Printed Circuit Boards
L
1,229
26
0
164
Raikoad
L
1,132
48
109
Ships and Boats
L
1,366
324
P
o"
p
$
a*
ix
P"
w
hQ
^'
CD
£
L
4,907
Unknown
L
583
Source: U.S. EPA analysis.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
The remainder of this profile focuses on MP&M industry facilities that discharge effluent. Out of a total population of
638,696 MP&M industry establishments reported in the Statistics of U.S. Businesses for 1996, approximately seven percent,
or 43,858 facilities, are effluent dischargers as identified by the MP&M surveys.
Figure 3.2 shows the breakdown of MP&M facilities by discharge type. Of the effluent dischargers, 41,119 (94 percent) are
indirect dischargers, meaning that they discharge into a sewer or a POTW, and 2,699 (6 percent) are direct dischargers that
discharge directly into a surface water body. The remaining 40 facilities are both direct and indirect dischargers.
Figure 3.2: Facilities by Discharge Type
Both Direct and
Induect
40
Source: U.S. EPA analysis.
Figure 3.3 shows facilities by revenue source. Local governments or municipalities operate 3,785 facilities (9 percent). The
remaining 40,073 facilities are privately owned. Of these, 17,428 facilities (40 percent) are rebuilding and maintenance
facilities and 20,172 facilities (46 percent) are manufacturing facilities.
Figure 3.3: Facilities by Revenue Source
Both Manufacturing and
Unknown Rebuilding and Maintenance
'-'
Rebuilding and Maintenance
Source: U.S. EPA analysis.
Figure 3.4 shows facilities by small entity status. Small Business Administration (SBA) thresholds were used to estimate the
number of facilities that are likely to be owned by small businesses, as defined by the SBA. Using the methodology detailed in
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
the Small Entity Impact Analysis (see Chapter 10), EPA determined that 32,179 facilities (73 percent) are owned by small or
potentially small entities.
Figure 3.4: Facilities by Small Entity Status
Fac Hit ies owned by large
entities 11,679
Facilities owned by potentially
small entities
32,179
Source: U.S. EPA analysis.
Figure 3.5 indicates that MP&M facilities derive approximately 22 percent of their revenues from export sales. Almost 78
percent of MP&M revenues come from domestic non-government sources. Government purchases account for a very small
share of MP&M revenues overall.
Figure 3.5: 1996 Facility Revenues by Market Type (billions, 2001$)
Export data were not available for Iron and Steel surveys.
Source: U.S. EPA analysis.
To characterize baseline financial performance across regulation subcategories, EPA used Pre-Tax Return on Assets
(PTRA) as a measure of industry profitability. PTRA measures the return, before tax, to total capital that company
management achieves from its deployed capital assets. Unlike the ROA measure noted above in section 3.5.2, the PTRA
reported in this discussion is not adjusted for risk.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
Table 3.20 shows that the printed wiring board subcategory has the highest median PTRA (13.4 percent ) of all the
subcategories. The shipbuilding drydock subcategory has the lowest median PTRA (2.5 percent). The median PTRA for all of
the MP&M facilities is 11.1 percent.
Table 3.20:
Subcategory
Shipbuilding Drydock
General Metals
Steel Forming & Finishing
Metal Finishing Job Shops
Non-Chromium Anodizer
Oily Wastes
Printed Wiring Boards
Railroad Line Maintenance"
Financial Performance
Median Pre-Tax Return on Assets (PTRA)
2.5%
11.5%
9.1%
9.2%
9.0%
9.6%
13.4%
n/a
a PTRA data was not available for railroad line maintenance because these facilities were treated
as cost-centers in the survey analysis.
Source: U.S. EPA analysis.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
GLOSSARY
capital expenditures: expenditures for permanent additions and major alterations to facilities and equipment, as well as
replacements and additions to capacity, which are ordinarily depreciated. Reported capital expenditures include work done on
contract and expenditures for assets leased from other concerns through capital leases. Expenditures for land and cost of
maintenance and repairs charged as current operating expenses are excluded
concentration ratio: the percentage of output from a given industry that is produced by the largest firms in that industry.
For example, the eight firm concentration ratio measures the percentage of output that is produced by the eight largest firms in
an industry. The concentration ratio is a measure of industry competitiveness.
employment: total number of full-time equivalent employees, including production workers and non-production workers.
export dependence: the share of shipments by domestic producers that is exported; calculated by dividing the value of
exports by the value of domestic shipments.
import penetration: the share of all consumption in the U.S. that is provided by imports; calculated by dividing imports by
reported or apparent domestic consumption (the latter calculated as domestic value of shipments minus exports plus imports).
manufacturing: series of unit operations necessary to produce metal products; generally performed in a production
environment.
North American Industry Classification System: classification system adopted beginning in 1997 to replace SIC
codes. NAICS codes will be used throughout North American and allow for greater comparability with the International
Standard Industrial Classification System (ISIC), which is developed and maintained by the United Nations. The new system
also better reflects the structure of today's economy, including the growth of the service sectors and new technologies.
nominal values: dollar values expressed in current dollars.
operating margin: measure of the relationship between input costs and the value of production, as an indicator of financial
performance and condition. Everything else being equal, industries and firms with lower operating margins will generally have
less flexibility to absorb the costs associated with a regulation than those with higher operating margins. Operating margins
were calculated in this profile by subtracting the cost of materials and total payroll from the value of shipments. Operating
margin is only an approximate measure of profitability, since it does not consider capital costs and other costs. It is used to
examine trends in revenues compared with production costs within an industry; it should not be used for cross-industry
comparisons of financial performance.
pre-tax return on assets (PTRA): the ratio of cash operating income (net income plus depreciation) to the book value of
total assets. This ratio is a measure of facility profitability.
producer price index (PPI): a family of indexes thatmeasures the average change overtime in selling prices received by
domestic producers of goods and services (Bureau of Labor Statistics, PPI Overview). Used in this profile to convert nominal
values into real dollar values.
real values: nominal values normalized using a price index to express values in a single year's dollars. Removes the effects
of price inflation when evaluating trends in dollar measures.
rebuilding/maintenance: unit operations necessary to disassemble used metal products into components, replace the
components or subassemblies or restore them to original function, and reassemble the metal product. These operations are
intended to keep metal products in operating condition and can be performed in either a production or a non-production
environment.
return on assets: the profit the firm earns from investing in assets. Generally firms in riskier industries have higher returns
on assets. A risk normalized return on assets (RNRO A) measures the additional profit that firms earn above and beyond what
their risk level predicts. The RNROA is a measure of industry competitiveness.
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
Standard Industrial Classification: classification system used for all establishment-based Federal economic statistics
classified by industry. Each establishment is assigned a 4-digit SIC code based on its principal product, or service. Last
revised in 1987 and currently being replaced by the NAICS.
value added: measure of manufacturing activity, derived by subtracting the cost of purchased inputs (materials, supplies,
containers, fuel, purchased electricity, contract work, and contract labor) from the value of shipments (products manufactured
plus receipts for services rendered), and adjusted by the addition of value added by merchandising operations (i.e., the
difference between the sales value and the cost of merchandise sold without further manufacture, processing, or assembly) plus
the net change in finished goods and work-in-process between the beginning-and end-of-year inventories. Value added avoids
the duplication in value of shipments as a measure of economic activity that results from the use of products of some
establishments as materials by others. Value added is considered to be the best value measure available for comparing the
relative economic importance of manufacturing among industries and geographic areas.
value of shipments: net selling values of all products shipped as well as miscellaneous receipts. Includes all items made
by or for an establishment from materials owned by it, whether sold, transferred to other plants of the same company, or
shipped on consignment. Value of shipments is a measure of the dollar value of production, and is often used as a proxy for
revenues. This profile uses value of shipments to indicate the size of a market and how the size differs from year to year, and
to calculate operating margins.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 3: Profile of the MP&M Industry Sectors
ACRONYMS
NAICS: North American Industry Classification System
PPI: producer price index
PTRA: pre-tax return on assets
ROA: return on assets
SIC: Standard Industrial Classification
VA: value added
VOS: value of shipments
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MP&M EEBA Part I: Introduction and Background Information Chapter 3: Profile of the MP&M Industry Sectors
REFERENCES
DRI/McGraw-Hill and U.S. Department of Commerce, International Trade Administration. 2000. U.S. Industry and Trade
Outlook.
Marketguide.com. 1992. Risk Normalized Return on Assets, http://www.marketguide.com.
U.S. Bureau of Labor Statistics. 2000. Producer Price Index.
U.S. Department of Commerce. 1992. Bureau of the Census. Census of Manufacturers, Census of Transportation, Census of
Wholesale Trade, Census of Retail Trade, Census of Service Industries.
U.S. Department of Commerce. 1996. Bureau of the Census. Annual Survey of Manufacturers.
U.S. Department of Commerce. 2000. Bureau of the Census. Foreign Trade Data.
U.S. Environmental Protection Agency. 1995a. Profile of the Electronics and Computer Industry. EPA Office of Compliance
Sector Notebook Project. EPA 310-R-95-002. http://es.epa.gov/oeca/sector/index.html
U.S. Environmental Protection Agency. 1995b. Profile of the Fabricated Metal Products Industry. EPA Office of
Compliance Sector Notebook Project. EPA 310-R-95-007. http://es.epa.gov/oeca/sector/index.html
U.S. Environmental Protection Agency. 1995c. Profile of the Iron and Steel Industry. EPA Office of Compliance Sector
Notebook Project. EPA 310-R-95-005. http://es.epa.gov/oeca/sector/index.html
U.S. Environmental Protection Agency. 1995d. Profile of the Motor Vehicle Assembly Industry. EPA Office of Compliance
Sector Notebook Project. EPA 310-R-95-009. http://es.epa.gov/oeca/sector/index.html
U.S. Environmental Protection Agency. 1995e. Printed Wiring Board Industry and Use Cluster Profile. Design for the
Environment Printed Wiring Board Project. EPA 744-R-95-005. http://www.epa.gov/opptintr/dfe/pwb/techreports/usecluster
U.S. Environmental Protection Agency. 1997. Profile of the Shipbuilding and Repair Industry. EPA Office of Compliance
Sector Notebook Project. EPA 310-R-97-008. http://es.epa.gov/oeca/sector/index.html
U.S. Environmental Protection Agency. 1998. Profile of the Aerospace Industry. EPA Office of Compliance Sector
Notebook Project. EPA 310-R-98-001. http://es.epa.gov/oeca/sector/index.html
U.S. Small Business Administration. Statistics of U.S. Businesses. http://www.sba.gov/advo/stats/int_data.html
Value Line Investment Survey. Industry Reports. Value Line Publishing, Inc: December 2001.
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THIS PAGE INTENTIONALLY LEFT BLANK
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MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options
Chapter 4: Regulatory Options
INTRODUCTION CH/(PTER CoNTENTS
The preamble for the final rule describes the regulatory
options considered by EPA for the final MP&M effluent
guidelines. This chapter provides a brief summary of the
subcategories and the regulatory options.
4.1 SUBCATESORIZATION
4.1 Subcategorization 4-1
4.2 Technology Options 4-3
4.3 BPT/BAT Options for Direct Dischargers 4-3
4.4 PSES Options for Indirect Dischargers 4-3
4.5 NSPS and PSNS Options for New Sources 4-4
4.6 Summary of the Final Rule and Regulatory
Alternatives 4-4
Glossary 4-5
Acronyms 4-6
EPA may divide a point source category into subcategories
to address variations in products, raw materials, processes,
and other factors that result in distinctly different effluent characteristics. Defining subcategories makes it possible to
establish effluent limitations that take into account technological achievability and economic impacts unique to each
subcategory. EPA considered the following factors in defining MP&M subcategories:
*• unit operation, >• nature of the waste generated,
> activity, > economic impacts,
»• raw materials, >• treatment costs,
*• products, •• total energy requirements,
> size of site, *• air pollution control methods,
> location, >• solid waste generation and disposal, and
»• age, •• publicly-owned treatment work (POTW)
burden.
In a way similar to the proposed rule, EPA established subcategories for the final MP&M rule based on unit operations
performed. The subcategories are defined in part based on the type of wastewater that facilities discharge, including:
facilities that discharge wastewaters with high metals content, with or without oil and grease (O&G): and
> facilities that discharge wastewaters containing mainly O&G, with limited metals and other associated organic
constituents.
The subcategories identified by EPA in each group are:
Metal-bearing (with or without O&G):
*• General Metals,
> Metal Finishing Job Shops,
> Non-Chromium Anodizing,
> Printed Wiring Board,
•• Steel Forming & Finishing; and
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MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options
Oil-bearing only:
+ Oily Wastes,
> Railroad Line Maintenance, and
*• Shipbuilding Dry Docks.
For the final rule, EPA is establishing limitations and standards only for direct dischargers in the Oily Wastes subcategory.
The other subcategories were considered at proposal and for some of the alternative regulatory options but are not further
regulated under the final rule. Section VLB of the preamble accompanying the final rule describes the basis for defining these
subcategories. The following are brief summaries of each subcategory:
General Metals: The General Metals subcategory includes facilities that perform operations that generate metal-bearing
wastewater.1 At a minimum, wastewater at these sites requires metals removal and may also require the preliminary treatment
steps of oil/water separation, chromium reduction, and cyanide destruction. For example, wastewater generated from most
manufacturing operations and heavy rebuilding operations (e.g., aircraft/aerospace, bus/truck, railroad, ship, industrial
equipment) would be regulated under the General Metals subcategory as well as sites performing surface finishing operations
at a captive shop (i.e., not a metal finishing job shop) including continuous electroplating as defined in today's rule.
Metal Finishing Job Shops: These facilities must perform one or more of the six operations regulated by the existing Metal
Finishing (40 CFR 433) and Electroplating (40 CFR 413) effluent guidelines, and must meet the definition of a job shop.
The six metal finishing operations are electroplating, electroless plating, anodizing, coating, chemical etching and milling, and
printed circuit board manufacture. A job shop is a facility that owns no more than 50 percent of the materials undergoing
metal finishing. EPA proposes to regulate Printed Wiring Board facilities that are job shops under this subcategory, but is
seeking comment on this proposal.
Non Chromium Anodizing: This subcategory includes facilities that perform aluminum anodizing without the use of chromic
acid or dichromate sealants. The wastewater generated at these facilities contains very low levels of metals (except for
aluminum) and toxic organic pollutants.
Printed Wiring Board: These facilities manufacture, maintain, and repair printed wiring boards (i.e., circuit boards), not
including job shops. They perform some unique operations, including applying, developing, and stripping of photoresist;
lead/tin soldering; and wave soldering.
Steel Forming & Finishing: This subcategory applies to facilities that perform MP&M operations or cold forming operations
on steel wire, rod, bar, pipe, or tube. Other operations on steel, including any hot forming operations for steel, or cold
forming, electroplating, or continuous hot dip coating of other steel products, will be regulated under the revisions to the
existing Iron and Steel Manufacturing effluent guidelines (40 CFR 420).
Oily Wastes: The Oily Wastes subcategory is a "catch-all" for sites that discharge wastewater exclusively from oily
operations and are not otherwise covered by the Railroad Line Maintenance or Shipbuilding Dry Dock subcategory. Oily
operations for the this subcategory are defined in today's final rule as: alkaline cleaning for oil removal, aqueous or solvent
degreasing, corrosion preventative coating (as specified in § 438.21(b)); floor cleaning; grinding; heat treating; deformation
by impact or pressure; machining; painting (spray or brush); steam cleaning; and testing (such as hydrostatic, dye penetrant,
ultrasonic, magnetic flux); iron phosphate conversion coating; abrasive blasting, alkaline treatment without cyanide;
assembly/disassembly; tumbling/barrel finishing/mass finishing/vibratory finishing; burnishing; electrical discharge
machining; polishing, thermal cutting; washing of final products; welding; wet air pollution control for organic constituents;
adhesive bonding; and calibration.
Railroad Line Maintenance: This is one of two specific subcategories that discharge only oil-bearing wastewaters (as
defined above for the Oily Wastes subcategory). The Railroad Line Maintenance subcategory includes facilities that
discharge from performing routine cleaning and light maintenance on railroad engines, cars, car-wheel trucks, or similar parts
or machines. Facilities engaged in the manufacture, overhaul or heavy maintenance of railroad engines, cars, car-wheel
1 These sites may also perform operations that generate oil-bearing wastewater.
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MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options
trucks, or similar parts or machines are not covered hy this subcategory and depending on the operations performed may be
covered by either the General Metals or Oily Wastes subcategory.
Shipbuilding Dry Docks: This is the second of two specific subcategories that discharge only oil-bearing wastewaters (as
defined above for the Oily Wastes subcategory). The Shipbuilding Dry Dock subcategory applies to discharges of process
wastewater generated in or on dry docks and similar structures, such as graving docks, building ways, marine railways and lift
barges at shipbuilding facilities (or shipyards). When generated by operations from within a dry dock or similar structure, this
subcategory covers process wastewater generated inside and outside the vessel (including bilge water) and wastewater
generated from barnacle removal conducted as preparation for ship maintenance, rebuilding or repair. Wastewaters generated
from other operations at shipyards are not included in this subcategory.
4.2 TECHNOLOGY OPTIONS
EPA defined specific effluent limitations guidelines and standards for consideration in developing the regulation based on a
statistical analysis of the performance of several wastewater treatment technology options. This analysis is described in
Section 9 of the Technical Development Document and the Statistical Support Document.
EPA is establishing BPT pH limitations and daily maximum limitations for two pollutants, oil and grease as hexane
extractable material (O&G (as HEM)) and total suspended solids (TSS), for direct dischargers in the Oily Wastes subcategory
based on the proposed technology option (Option 6). The technology requirements include the following treatment measures:
(1) in-process flow control and pollution prevention; and (2) oil-water separation by chemical emulsion breaking and
skimming (see Section 9 of the TDD). This technology is available technology readily applicable to all facilities in the Oily
Wastes subcategory. Approximately 42% of the direct discharging facilities in the Oily Wastes subcategory currently employ
this technology already.
4.3 BPT/BAT OPTIONS FOR DIRECT fclSCHARSERS
EPA selected the Best Practicable Control Technology Currently Available (BPT) for direct dischargers in each
subcategory based on the average of the best performances within the industry from operations of various ages, sizes,
processes, and other characteristics. The Agency considered the cost of these treatment technologies relative to the effluent
reductions achieved to assess the cost-reasonableness of these limitations. EPA then considered application of the Best
Available Technology Economically Achievable (BAT) for priority andnonconventional pollutants and Best
Conventional Pollutant Control Technology (BCT) for conventional pollutants. EPA is promulgating BCT equivalent
to BPT for facilities in the Oily Wastes subcategory and has decided not to establish BAT limitations.
Table 4.1 shows the technology basis for the selected option for BPT, BCT and BAT for the Oily Wastes subcategory.
Table 4. 1 : Selected Options
Subcategory
For oil-bearing wastes
Oily Wastes
i
:
:
For Direct Dischargers: BPT, BCT and BAT
BPT Option | BCT/BAT
6 BCT = 6
I BAT not promulgated
Source: U.S. EPA analysis.
4.4 PSES OPTIONS FOR INDIRECT
EPA considered Pretreatment Standards for Existing Sources (PSES) options for regulating existing indirect
dischargers under today's final rule. EPA has selected no further regulation for indirect dischargers in all of the defined
subcategories.
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MP&M EEBA Part I: Introduction and Background Information
Chapter 4: Regulatory Options
Wastewater discharges to POTWs from facilities in all subcategories will continue to be regulated by local limits, general
pretreatment standards, and 40 CFR 413 or 433, as appropriate.
4.5 NSPS AND PSNS OPTIONS FOR NEW SOURCES
EPA is promulgating New Source Performance Standards (NSPS) for new direct dischargers in the Oily Wastes
subcategory at the BPT and BCT levels. New facilities have the opportunity to incorporate the best available demonstrated
technologies, including process changes, in-plant controls, and end-of-pipe treatment technologies, without the cost of
retrofitting. EPA considered the same technologies discussed previously for BPT/BAT and PSES as the basis for new source
technology. In addition, because new sites may be able to install pollution prevention and pollution control technologies more
cost-effectively then existing sources, the Agency strongly considered more advanced treatment options. EPA is not
promulgating Pretreatment Standards for New Sources (PSNS) for new indirect dischargers.
Table 4.2 lists the technology options and exclusions for new direct and indirect dischargers.
Table 4.2: Options For New Direct Dischargers (NSPS) and
Indirect Dischargers (PSNS)
Subcategory
For oil-bearing wastes
Oily Wastes
NSPS
Technology
Option
I 6
PSNS Technology
Option
No further regulation
Source: U.S. EPA analysis.
4.6 SUMMARY OF THE FINAL RULE AND REGULATORY ALTERNATIVES
The following describes the final rule and the three alternative regulatory options considered by EPA:
•• Final Rule: technology Option 6 applied only to direct dischargers in the Oily Wastes subcategory;
»• NOD'A/Proposal Option: applies limitations and standards for direct dischargers in all eight MP&M subcategories
and pretreatment standards for all indirect dischargers in three subcategories (i.e., Metal Finishing Job Shops, Printed
Wiring Board, and Steel Forming & Finishing); pretreatment standards for facilities above a certain wastewater flow
volume in two subcategories (i.e., General Metals and Oily Wastes); and no national pretreatment standards for
facilities in three subcategories (i.e., Non-Chromium Anodizing, Railroad Line Maintenance, and Shipbuilding Dry
Docks);
*• Direct Dischargers + 413 to 433 Upgrade Option: applies the same technology requirements for direct dischargers
as the final rule and includes new requirements for indirect dischargers in the General Metals, Printed Wiring Board,
and Metal Finishing Job Shops subcategories currently regulated under the Electroplating regulations (40 CFR 413);
and
•• Direct Dischargers + 413plus 50% Local Limits Upgrade Option: applies the same technology requirements for
direct dischargers as the final rule and includes new requirements for indirect dischargers in the General Metals,
Printed Wiring Board, and Metal Finishing Job Shops subcategories currently regulated under the Electroplating
regulations (40 CFR 413) and also includes new requirements for indirect dischargers in the General Metals
subcategory that are currently regulated by local limits or general pretreatment standards.
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MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options
GLOSSARY
Best Practicable Control Technology Currently Available (BPT): effluent limitations for direct discharging
facilities, addressing conventional, toxic, and nonconventional pollutants. In specifying BPT, EPA considers the cost of
achieving effluent reductions in relation to the effluent reduction benefits. The Agency also considers the age of the
equipment and facilities, the processes employed and any required process changes, engineering aspects of the control
technologies, non-water quality environmental impacts (including energy requirements), and such other factors as the Agency
deems appropriate. Limitations are traditionally based on the average of the best performances of facilities within the industry
of various ages, sizes, processes, or other common characteristics. Where existing performance is uniformly inadequate, EPA
may require higher levels of control than currently in place in an industrial category if the Agency determines that the
technology can be practically applied.
Best Available Technology Economically Achievable (BAT): effluent limitations for direct dischargers, addressing
priority and nonconventional pollutants. BAT is based on the best existing economically achievable performance of plants in
the industrial subcategory or category. Factors considered in assessing BAT include the cost of achieving BAT effluent
reductions, the age of equipment and facilities involved, the processes employed, engineering aspects of the control
technology, potential process changes, non-water quality environmental impacts (including energy requirements), economic
achievability, and such factors as the Administrator deems appropriate. The Agency may base BAT limitations upon effluent
reductions attainable through changes in a facility's processes and operations. Where existing performance is uniformly
inadequate, EPA may base BAT upon technology transferred from a different subcategory within an industry or from another
industrial category.
Best Conventional Pollutant Control Technology (BCT): effluent limitations for direct discharging facilities,
addressing conventional pollutants. Conventional pollutants include biochemical oxygen demand (BOD5), total suspended
solids (TSS), fecal coliform, pH, and O&G. BCT is the equivalent of Best Available Technology (BAT) for control of
conventional pollutants. EPA evaluates the reasonableness of BCT candidate technologies by applying a two-part cost test:
(1) the POTW test, and (2) the industry cost-effectiveness test, hi the POTW test, EPA calculates the cost per pound of
conventional pollutant removed by industrial dischargers to upgrade from BPT to a BCT candidate technology, and then
compares this cost to the POTW cost per pound for similar pollutant load reductions. The upgrade cost to industry must be
less than the POTW benchmark of $0.25 per pound (in 1976 dollars). In the industry cost-effectiveness test, the ratio of the
incremental BPT to BCT cost divided by the BPT cost for the industry must be less than 1.29 (i.e., the cost increase must be
less than 29 percent).
Job Shop: Facilities with more than 50 percent of their revenues coming from work on products not owned by the site.
While there are SIC codes associated with some Metal Finishing Job Shops, they sell to a variety of markets and are not a
market in and of themselves.
New Source Performance Standards (NSPS): effluent limitations for new direct dischargers based on the best
available demonstrated control technology. NSPS represents the greatest degree of effluent reduction attainable through the
application of the best available demonstrated control technology for all pollutants (i.e., conventional, nonconventional, and
priority pollutants). In establishing NSPS, EPA considers the cost of achieving the effluent reduction and any non-water
quality environmental impacts and energy requirements.
Pretreatment Standards for Existing Sources (PSES): categorical pretreatment standards for existing indirect
dischargers, designed to prevent the discharge of pollutants that pass through, interfere with, or are otherwise incompatible
with the operation of POTWs. Standards are technology-based and analogous to BAT effluent limitations guidelines.
Pretreatment Standards for New Sources (PSNS): pretreatment standards for new indirect dischargers, designed to
prevent discharges of pollutants that pass through, interfere with, or are otherwise incompatible with the operation of POTWs.
Addresses all pollutants (i.e., conventional, nonconventional, and priority pollutants). Based on the same factors as are
considered in promulgating NSPS.
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MP&M EEBA Part I: Introduction and Background Information Chapter 4: Regulatory Options
ACRONYMS
BAT: Best Available Technology Economically Achievable
BCT: Best Conventional Pollutant Control Technology
BPT: Best Practicable Control Technology Currently Available
NSPS: New Source Performance Standards
O&G: oil and grease
POTW: publicly-owned treatment works
PSES: Pretreatment Standards for Existing Sources
PSNS: Pretreatment Standards for New Sources
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Chapter 5: Facility Impact Analysis
INTRODUCTION
The facility impact analysis assesses whether the MP&M
effluent guidelines are likely to impose severe or moderate
economic and financial impacts on MP&M facilities. EPA
undertook the facility impact analysis to aid in assessing the
rule's economic achievability. Severe impacts are facility
closures and the associated losses in jobs, earnings, and output
at facilities that close due to the rule. EPA also assessed
moderate economic impacts to support its evaluation of
regulatory options and to understand better the rule's
economic impacts. Moderate impacts are adverse changes
in a facility's financial position that are not threatening to its
short-term viability.
The options considered for regulation would have affected
three major categories of MP&M facilities: privately-owned,
railroad line maintenance, and government-owned facilities.
EPA developed separate analytic methodologies to assess
economic and financial impacts for each type of facility:
CHAPTER CONTENTS
5.1 Data Sources 5-2
5.2 Methodology 5-2
5.2.1 Converting Engineering Compliance Costs and
Survey Financial Data to Current Year Dollar
Values 5-3
5.2.2 Market-level Impacts and Cost Pass-Through
Analysis 5-4
5.2.3 Impact Measures for Private Facilities 5-5
5.2.4 Impact Measures for Railroad Line
Maintenance Facilities 5-12
5.2.5 Impact Measures for Government-Owned
Facilities 5-12
5.3 Results 5-14
5.3.1 Baseline Closures 5-14
5.3.2 Price Increases 5-15
5.3.3 Overview of Impacts 5-16
5.3.4 Results for Indirect Dischargers 5-18
5.3.5 Results for Direct Dischargers 5-19
5.3.6 Results for Private Facilities 5-20
5.3.7 Results for Government-Owned Facilities .. 5-21
Glossary 5-25
Acronyms 5-26
References 5-27
1. Private MP&M facilities: This group includes all
privately-owned facilities that do not perform
railroad line maintenance. This major category of
facilities operates in various subcategories and
includes private businesses in a wide range of sectors
or industries, including facilities that manufacture and rebuild railroad equipment. Only facilities that repair railroad
track and equipment along the railroad line are excluded. There are 39,248 private MP&M facilities other than
railroad line maintenance facilities nationally that may be affected by the rule, representing 89.5 percent of the
43,858 facilities that discharge process wastewater from MP&M activities.
2. Railroad line maintenance facilities: Railroad line maintenance facilities maintain and repair railroad track and
vehicles. EPA administered a separate economic and financial survey to these facilities and applied a different
impact analysis methodology than that used for other private facilities. This methodology used the same impact tests
as used for other private facilities but applied these tests to the aggregate of maintenance facilities owned by a single
railroad company instead of to individual facilities. There are 826 railroad line maintenance facilities in the analysis,
representing 1.9 percent of all facilities in the analysis.
3. Government-owned facilities: Government-owned facilities include MP&M facilities operated by
municipalities, state agencies and other public sector entities such as state universities. Many of these facilities
repair, rebuild, and maintain buses, trucks, cars, utility vehicles (e.g., snow plows and street cleaners), and light
machinery. Government-owned facilities operate in two major subcategories: General Metals and Oily Waste.
There are 3,785 government-owned facilities in the analysis, representing 8.6 percent of the total.
The specific methodology used to assess impacts differs for each of the three types of MP&M facilities. In each case, EPA
established thresholds for measures of financial performance and compared performance before and after compliance with
each regulatory option to these thresholds.
This chapter describes the methodology used to assess facility-level economic impacts for the three types of facilities, and
then presents the results of the analyses.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
5.1 bATA SOURCES
The economic impact analyses rely on data provided in the financial portion of the detailed questionnaires distributed to
MP&M facilities by EPA under the authority of Section 308 of the Clean Water Act. The surveys were conducted in two
phases, covering different MP&M industry sectors in each phase. The Phase I survey covered seven industry sectors and
reported data for respondent's fiscal years 1987 to 1989. The Phase II survey covered an additional ten industry sectors (all
remaining MP&M sectors except Iron and Steel, which was the subject of a separate survey) and reported data for fiscal years
1994 to 1996.1 EPA administered each survey to a random stratified sample of facilities and assigned each facility a sample
weight based on the stratification process and the number of facilities surveyed, so that sample-weighted results would
represent all potentially-affected MP&M facilities at the national level. The results of the impact analyses for the sample
facilities were extrapolated to the national level using these facility sample weights.
The survey financial data for private businesses included three years of facility and parent firm income statements and balance
sheets, and the composition of revenues by MP&M business sector to which the facility's goods and services are sold.
Data for railroad line maintenance facilities came from a modified version of the Phase II survey administered to railroad
operating companies. The questionnaire was modified because railroad operating companies generally do not monitor
financial performance or collect financial data at the facility level for their numerous line maintenance facilities. The railroad
operating companies reported the number of line maintenance facilities in each operating unit, and provided both operating
company and parent firm financial data. They also provided technical data for each line maintenance facility.
Data for facilities in the Iron and Steel sector came from a 1997 Section 308 survey of iron and steel facilities. This survey
requested financial data generally similar to that collected by the MP&M surveys, including income statements and balance
sheets for fiscal years 1995-1997 for the facility and the parent firm.
Government-owned MP&M facilities provided data in the Phase II Section 308 survey of municipal and other government
agency facilities. This survey requested information on fiscal year 1996 sources and amounts of revenue and debt levels for
both the government entity and their MP&M facilities, and demographic data for the population served by the government
entity.
In addition to the survey data, a number of secondary sources were used to characterize economic and financial conditions in
the industries subject to the MP&M effluent guidelines. Secondary sources used in the analyses include:
•• Department of Commerce economic census and survey data, including the Censuses of Manufactures, Annual
Surveys of Manufactures, and international trade data;
> the Benchmark Input-Output Tables of the United States, published by the U.S. Department of Commerce's Bureau
of Economic Analysis;
*• price index series from the Bureau of Labor Statistics, Department of Labor;
*• U.S. Industry and Trade Outlook, published by McGraw-Hill and the U.S. Department of Commerce; and
*• industry trade publications.
5.2 METHODOLOGY
The facility impact analysis starts with compliance cost estimates from the EPA engineering analysis and then calculates how
these compliance costs would affect the financial condition of MP&M facilities. EPA first eliminated from the analysis those
facilities showing materially inadequate financial performance in the baseline, that is, in the absence of the rule. EPA judged
these facilities, which are referred to as baseline closures, to be at substantial risk of financial failure regardless of any
1 Appendix A provides a detailed description of the surveys and describes how EPA combined data from different surveys.
2 EPA made several changes in the facility impact methodology between proposal and final regulation. These changes, which to a
large degree address comments on the proposal impact methodology, are documented in the Notice of Data Availability (reference).
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
financial burdens that may result from the MP&M rule. Second, for the remaining facilities, EPA evaluated how compliance
costs would likely affect facility financial health. In this analysis of compliance cost impact, EPA accounted for potential
price increases that may help facilities recover compliance costs. EPA's estimate of potential price increases was based on a
cost pass-through analysis, which used historical input and output price changes for the years 1982 through 1991 to
estimate how prices might change in response to regulation-induced production cost increases. A facility is identified as a
regulatory closure if it would have operated under baseline conditions but would fall below an acceptable financial
performance level when subject to the new regulatory requirements.
EPA also identified private MP&M facilities that would likely incur moderate impacts from the rule but that are not expected
to close as a result of the rule. The test of moderate impacts examined two financial ratios pre-tax return on assets and
interest coverage ratio calculated on a baseline and post-compliance basis. Incremental moderate impacts are attributed to
the rule if both financial ratios exceeded threshold values in the baseline (i.e., no moderate impacts in the baseline), but at
least one financial ratio fell below the threshold value in the post-compliance case.
5.2.1 Converting Engineering Compliance Costs and Survey Financial bata to Current
Year Dollar Values
The facility survey data underlying the facility financial impact analysis are based on the periods 1987-1989 (Phase I) and
1994-1996 (Phase II). The estimates of costs for complying with the MP&M regulation were developed, however, in dollars
of the year 1996, the baseline year of the MP&M regulatory analysis.3 To ensure consistent impact analyses, EPA aligned
facility financial data and compliance cost estimates in dollars of the same year. In addition, for understanding the
significance of the rule's potential costs in today's economy, EPA further brought all dollar values forward to 2001. EPA
used the following procedures to perform these adjustments.
EPA used the Construction Cost Index (CCI) to convert compliance cost estimates into 2001 constant dollar equivalents.
The CCI is a price index that engineers often use to estimate costs associated with building, installing, and operating waste
treatment equipment and facilities. The CCI includes the costs of labor and building materials in 20 major cities. Table 5.1
shows CCI values from 1996 to 2001.
Table 5.
Year
1996
1997
1998
1999
2000
2001
1 : Constructioi
Value
5620
5825
5920
6060
6221
6342
i Cost Index
% Change
3.6%
1.6%
2.4%
2.7%
1.9%
Source: Engineering News-Record
EPA used the Producer Price Index (PPI) to bring MP&M survey financial data to the current year. The PPI measures
average changes in selling prices that domestic producers receive for their output.4 EPA used sector-specific PPI averages to
3 The engineering cost estimates are described in the Technical Development Document accompanying this rule.
4 EPA used the PPI to bring all financial statement values forward to 2001. EPA understands that the PPI is an output price index
and that operating statement costs and balance sheet values may not change over time in the same way as output prices (and revenue).
However, in adjusting financial statement values from the original survey data years to the current year, EPA's purpose is to bring the
statement values forward to the present while preserving the cost and financial structure relationships as reflected in the original income
statements and balance sheets. Accordingly, use of a single index is appropriate for this adjustment and EPA judged the industry-specific
PPI values as a better basis for this adjustment than other non-industry specific measures of inflation.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
update financial data from Phase I and Phase II survey respondents to 1996, the base year of the analysis. EPA applied an
aggregate PPI to update from 1 996 to 2001 dollars.
Table 5.2 shows aggregate PPI values for all finished goods. Prices increased by 6.6 (1 35.7/127.3) percent from 1996 to
2001, and by 32.3 percent from 1987 to 2001 (135.7/102.6).
Table 5.2: Producer Price Index
Industrial Commodities
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Value
102.6
106.3
111.6
115.8
116.5
117.4
119.0
120.7
125.5
127.3
127.7
124.8
126.5
134.8
135.7
% Change
3.6%
5.0%
3.8%
0.6%
0.8%
1.4%
1.4%
4.0%
1.4%
0.3%
-2.3%
1.4%
6.6%
0.7%
Source: Bureau of Labor Statistics
5.2.2 Market-level Impacts and Cost Pass-Through Analysis
Increased costs from the regulation can be expected to affect industry-level prices and output. Changes in prices and output in
turn determine the distribution of economic impacts among directly- and indirectly-affected industries and their customers and
suppliers. The facilities and industries directly affected by the final rule might ultimately experience little adverse impact, for
example, if they are able to recover most or all of their added costs by raising prices to their customers or by lowering the
prices paid to their suppliers and without a material reduction on the production quantity sold. Some regulated facilities and
companies could even be better off financially as a result of the rule, if they benefit from industry-wide product price
increases and incur no or relatively-low compliance costs (e.g., if they already have treatment in place). Understanding
impacts at the industry level is therefore important to understanding who bears the impacts of the rule.
The MP&M effluent guidelines affect facilities in a wide range of industries, and some of those industries produce a diverse
slate of products that are sold in multiple industrial sectors. Detailed partial equilibrium modeling of product-level market
dynamics in each of the affected industries was therefore not feasible. EPA instead used a combination of quantitative and
qualitative methods to estimate a proportion of compliance costs that might be recovered through price increases in each
MP&M sector. This cost pass-through analysis provided sector-specific coefficients that were applied to total compliance
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
costs in each sector to estimate percentage changes in prices and revenues. EPA then evaluated facility-level impacts
assuming that all analyzed facilities in each sector benefit from the same percentage increase in prices and revenues.
The estimated cost pass-through potential for each sector reflects an econometric analysis of historical pricing and cost trends
in each MP&M industry sector, coupled with a qualitative market structure analysis. The market structure factors include:
> market power based on horizontal and vertical integration;
> extent of competition from foreign suppliers (in both domestic and export markets);
> barriers to competition, as indicated by above-normal, risk-adjusted profitability; and
> the long-term growth trend in the industry.
EPA developed cost pass-through coefficients that indicate the percentage of compliance costs that EPA expects firms subject
to regulation to recover from customers through increased revenues.6 This approach may either overstate or understate the
true changes in revenue for any one particular facility, depending on the diversity of products produced by the facility and the
percentage of competitors in each product market that incur compliance costs.
This approach to estimating market-level adjustments is a simplification because it does not simultaneously estimate changes
in prices and output. Instead, EPA estimated price changes and then estimated changes in output based on predicted closures,
taking into account the effect of the predicted price increases on facilities' financial performance. It is difficult to assess how
this simplified approach might affect the estimated economic impacts of the rule. However, EPA does not believe that the
overall impact analysis results are highly sensitive to the potential biases introduced by this approach.
5.2.3 Impact Measures for Private Facilities
a. Test of severe impacts
The analysis of severe impacts estimates the number of facilities that could potentially close due to the regulation. EPA
predicted that a facility will close if compliance costs cause the facility's overall financial performance and resulting implied
financial value to fall below a specified threshold level. Compliance costs are determined by the type and number of
processes that a facility performs, the characteristics of its wastewaters, and the level of treatment performed in the baseline.
EPA took the number and type of processes and pollutants produced into account when subcategorizing the industry.
However, EPA was not able to link estimated compliance costs to specific products. Nor was EPA able to link facility
financial performance to specific products. It was therefore not possible to conduct an impacts analysis at the product level.
In particular, the analysis does not consider output reductions short of closure for example, closing one of several
production lines/processes or continuing to produce the same products at a reduced level. It is quite possible that a facility
with no or relatively low compliance costs for most processes could choose to out-source products made using a process that
had significant compliance costs associated with it, instead of performing the process in-house. This is particularly true if it is
a process that is performed infrequently. It is also possible that firms with multiple facilities could consolidate similar
processes at individual facilities to reduce their compliance costs. These situations are not considered in this economic
impact analysis. Numerous compliance responses are available to firms and facilities that EPA is unable to model. In
addition, the analysis of severe impacts does not attempt to forecast future business circumstances for a facility and thus does
not account for potential improvements in business outlook that might strengthen a facility's ability to afford compliance
outlays and thus prevent a potential closure decision. Because of these unknowns, estimated severe impacts are worst case
and are likely to be overstated. In addition, the relationship between the compliance costs associated with the specific
processes performed, specific products made from these processes, and the multiple industrial sectors to which these products
are sold, is unknown and can not be accounted for in this analysis.
5 EPA also performed an analysis in which complying facilities are assumed to pass none of their compliance costs through to
consumers (zero-cost pass-through analysis). The results of this analysis are in the in the Record to the final rule (see Section 25.3.2, DCN
37070).
6 Appendix B provides a detailed description of the cost pass-through analysis.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
The assessment of severe impacts for MP&M private facilities7 is based on the change in the facility's estimated business
value, as determined from a discounted present value analysis of baseline cash flow and the change in cash flow resulting
from regulatory compliance. If the estimated discounted cash flow value of the facility is positive before considering the
effects of regulatory compliance but becomes negative as a result of compliance outlays, then the facility is considered a
regulatory closure. In this impact test, the estimated ongoing business value of the facility is compared with a threshold value
of zero for the closure decision: as long as the discounted cash flow value of the facility is greater than zero, the business is
earning its cost of invested capital and continuation of the business is warranted. If the discounted cash flow value of the
facility is less than zero in the baseline or becomes less than zero as a result of compliance outlays, then the business will not
earn its cost of invested capital and the business owners will be better off financially by terminating the business. As noted in
earlier discussion, facilities for which EPA estimated a negative baseline value were considered baseline closures and were
not tested for additional adverse impacts from regulatory compliance.
In an alternative, theoretically more accurate, formulation of this concept, business owners would compare the discounted
cash flow value of the facility with the value that the facility's assets would bring in liquidation. In this case, the estimated
ongoing business value would be compared with a value that may be different from zero: liquidation value could be
positive or negative. When liquidation value is positive, business owners might benefit financially by terminating a business
and seeking its liquidation value even when the ongoing business value is positive but less than the estimated liquidation
value. With negative liquidation value which generally would result from business termination liabilities (e.g., site clean-
up) the opposite result could occur: business owners may find it financially advantageous to remain in business even though
the business earns less than its cost of invested capital if the liquidation value of the business is "more negative", and thus
less in value, than the ongoing business based on the discounted cash flow analysis. EPA attempted to implement this
alternative impact test formulation. However, liquidation values were unavailable for over 75 percent of sample facilities.
Moreover, EPA judges that the liquidation value estimates are substantially speculative and subject to considerable error. For
these reasons, EPA decided against using liquidation value for comparison with ongoing business value in the closure test.
The cash flow concept used in calculating ongoing business value for the closure analysis is free cash floW available to all
capital. Free cash flow is the cash available to the providers of capital both equity owners and creditors on an after-tax
basis from business operations, and takes into account the cash required for ongoing replacement of the facility's capital
equipment. Free cash flow is discounted at an estimated after-tax total cost of capital to yield the estimated business value
of the facility. Details of the calculation of free cash flow and the discounting of free cash flow to yield the facility's
estimated value are explained in the following sections.
»»» Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test
Calculation of baseline free cash flow and performance of the baseline closure test involved the following steps:
1. Average survey income statement data over response years and convert to 2001 dollars: EPA averaged income
statement data over the years for which survey respondents reported data. For example, if a facility reported income
statement data for 1995, 1996, and 1997', then a simple average was calculated for the three reported years. Reported
values were brought forward from the initial reporting period to 1996 using MP&M sector-specific PPI adjustment
factors and then from 1996 to 2001 using an aggregate PPI value as described above.
2. Calculate after-tax income excluding the effects of financial structure: The questionnaire responses include a calculation
of after-tax income in accord with conventional accounting principles. However, this calculation reflects the financial
structure of the business, which may include debt financing and thus interest charges against income. Because the cash
flow concept to be discounted in the business value analysis is cash flow available to all capital, it is necessary to restate
after-tax income to exclude the effects of debt financing, or on a before-interest basis. This restatement involves: (1)
increasing after-tax income by the amount of interest charges and (2) increasing taxes (and thereby reducing after-tax
income) by the amount of tax reduction provided by interest deductibility. This adjustment amounts to adding tax-
adjusted interest expense to after-tax income and yields an estimate of after-tax income independent of capital structure
or financing effects. In calculating the tax adjustment for interest, EPA used a combined federal/state corporate income
tax rate of 39 percent, which reflects a combination of an approximate average state rate of 7.5 percent and a federal rate
of 34 percent with state taxes deductible from federal income tax liability. After-tax income, before interest, was
calculated as follows:
7 As opposed to non-business, government entities.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
ATI-5/ = ATI + I-Tlor (5.1)
ATI-5/ = ATI + (1 -t)I
where:
ATI-5/ = after-tax income before interest;
ATI = after-tax income from baseline financial statement;
I = interest charge from baseline financial statement; and
T = estimated combined federal-state tax rate of 39 percent.
3. Calculate after-tax cash flow from operations, before interest, by adjusting income for non-cash charges: The
calculation of after-tax income may include a non-cash charge for depreciation (and potentially amortization). To
calculate after-tax cash floW (A TCP) from operations, it is therefore necessary to add back any depreciation charge to
the calculation of after-tax income, before interest. Cash flow, before interest, was calculated as follows:
ATCF-5/= ATI-5/+D (5.2a)
where:
ATCF-5/ = after-tax cash flow before interest;
ATI-5/ = after-tax income before interest; and
D = baseline depreciation.
4. Calculate free cashflow by adjusting after-tax cash flow from operations for ongoing capital equipment outlays: The
measure of after-tax cash flow from the previous step, cash flow from operations, reflects the cash receipts and outlays
from ordinary business operations and includes no allowance for replacement of the facility's existing capital equipment.
However, to sustain ongoing operations, a business must expend cash for capital replacement. Accordingly, to
understand the true cash flow of a business and thus provide a conceptually valid cash flow measure for business
valuation, it is necessary to reduce cash flow from operations by an allowance for capital replacement. For the
calculation of free cash flow, EPA estimated baseline capital outlays from a regression analysis of capital expenditures by
public firms in the MP&M business sectors over a 10-year period (details of this analysis and estimation framework are
contained in Appendix D). Free cash flow is calculated as follows:
FCF = ATCF-5/- CAPEX (5.2b)
where:
FCF = free cash flow
ATCF-5/ = after-tax cash flow before interest; and
CAPEX = estimated baseline capital outlays.
Or on a more detailed accounting statement basis:
FCF = REV - TC - T - Tl - CAPEX (5.2c)
where:
FCF = free cash flow
REV = revenue
TC = total operating costs, excluding interest, depreciation, and taxes
T = baseline income tax
T = estimated combined federal-state tax rate of 39 percent;
I = interest charge from baseline financial statement; and
CAPEX = estimated annual baseline capital outlays.
This calculation of free cash flow is based on a static representation of a facility's business. Revenue and expenses are
not projected forward and the analysis of the business assumes, in effect, that the facility's business will continue in the
future absent the effects of regulation exactly as reflected in the baseline financial statements provided in the survey
questionnaire. Consistent with this framework, the estimation of free cash flow includes no adjustment for changes in
working capital, which might ordinarily be included in the capital outlay adjustment to operating cash flow.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
5. Calculate baseline facility value as the present value of free cashflow over a 15-year analysis horizon: To calculate
baseline business value, EPA discounted free cash flow over a 15-year period at an estimated real (i.e., excluding the
effects of inflation), after-tax cost of capital of 7 percent. The use of 15 years as the discounting horizon reflects the
expected useful life of capital equipment to be installed for MP&M regulation compliance. Facility baseline business
value is calculated as follows:
VALUE = FCF
t=o(l + CoC)'
where:
VALUE = estimated baseline business value of the facility
FCF = free cash flow
CoC = after-tax cost-of-capital; and
t = year index, t = 0-14 (15-year discounting horizon).
In the present value calculation, yearly cash flows accrue at the beginning of the year. As a result, the first year of cash
flows is not discounted i.e., t = 0 for the first year of the analysis and cash flows in the fifteenth and final year of the
analysis period are discounted over a 14-year period i.e., t = 14 in the final year of the analysis.
As explained above, EPA considered a facility to be a baseline closure if its estimated business value was negative before
incurring regulatory compliance costs. Baseline closures were not tested for adverse impact in the post-compliance impact
analysis.
»»» Calculation of Post-Compliance Free Cash Flow and Performance of Post-Compliance Closure Test
For the post-compliance closure analysis, EPA recalculated annual free cash flow accounting for changes in revenue,
operating costs, and taxes that are estimated to result from compliance-related outlays. EPA combined the post-compliance
free cash flow value and the estimated compliance capital outlay in the present value framework to calculate business value on
a post-compliance basis.
Calculation of post-compliance free cash flow and performance of the post-compliance closure test involved the following
steps:
1 . Adjust baseline annual free cashflow to reflect compliance revenue and expense effects: Compliance-related effects on
annual free cash flow include compliance operating and maintenance (O&M) expenses, post-compliance change in
revenue (from the compliance cost pass-through analysis), and change in taxes. The change in taxes includes: (1)
the tax effect of compliance expense and revenue changes and (2) the tax effect from depreciation of compliance capital
outlays. For calculating the tax effect of depreciation, EPA assumed that compliance capital outlays would be
depreciated for tax purposes on a 15-year straight-line schedule. Post-compliance free cash flow was calculated as
follows:
FCFPC= FCFM + AREV - ATC - t(AREV - ATC - AD) (5.4)
where:
FCFj,c = post-compliance free cash flow;
FCFBL = baseline free cash flow, as calculated above;
AREV = post-compliance change in revenue, as calculated in the cost pass-through analysis;
ATC = change in total facility operating costs (excluding interest, depreciation and taxes), calculated as operating
and maintenance costs of compliance;
T = marginal tax rate for calculating compliance-related tax effects (combined federal-state tax rate of 39
percent); and
AD = change in depreciation expense, calculated as compliance capital outlay (CC) divided by 15.
The operating and maintenance cost of compliance (ATC, above) is the change in costs estimated to result from operating
and maintaining pollution controls adopted to comply with effluent guidelines. Operating costs include the costs of
monitoring.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
2. Limit tax adjustment to not exceed taxes as reported in baseline financial statement: The tax effect of compliance will
generally be to reduce tax liability. That is, in the prior formulation, the term t(AREV - ATC - AD), which is the tax
effect of compliance, will generally be negative as the increase in revenue will be less than compliance-related operating
expenses and compliance equipment depreciation: (ATC + AD) > AREV. As a result, in the free cash flow calculation,
the tax adjustment will generally increase cash flow and business value and, all else equal, will reduce the likelihood that
a facility will fail the post-compliance closure test. However, the extent to which a facility will realize this contribution
to cash flow depends on its tax circumstances. In particular, some businesses may not be paying sufficient taxes in the
baseline to take full benefit of the implied tax reduction at the facility level unless the unused tax loss can be transferred
to other, profitable business units in the firm, these businesses will not be able to use fully the implied tax reduction on a
current basis. Also, the marginal tax rate for businesses with relatively lower pre-tax income may be less than the
assumed 39 percent rate used in the analysis. While businesses may be able to carry forward tax losses to reduce taxes in
later years, EPA recognizes that the implied cash flow benefit from tax reduction may not be fully realized, particularly in
circumstances involving single-facility firms. To be conservative in its analysis, EPA therefore limited the amount of tax
reduction from compliance outlays to be no greater than the amount of tax paid by facilities in the baseline financial
statement. The analysis effectively assumes that facilities will not be able to offset an implicit negative tax liability
against positive tax liability elsewhere in its operations or to carry forward (or back) the negative income and its implicit
negative tax liability to other positive income/positive tax liability operating periods. On average, this approach will
overstate impacts on facilities, because some MP&M businesses may be able to benefit from tax reductions that exceed
facility baseline taxes, especially if the facility is owned by a multiple-site firm. Accordingly, EPA constrained the tax
effect term in the free cash flow calculation, [-t(AREV - ATC - AD)] as specified above, to be no greater than baseline
financial statement tax liability, T.
3. Calculate post-compliance facility value, including post-compliance free cashflow and the compliance capital outlay:
As in the baseline analysis, EPA calculated post-compliance facility value as the present value of free cash flow and
accounting for the compliance capital outlay as an undiscounted cash outlay in the first analysis period. Facility post-
compliance business value was calculated as follows:
14 pep
VALUEpr = E ^— - CC (5'5)
t=o(i + cocy
where:
VALUEj,c = estimated post-compliance business value of the facility
FCFj,c = estimated post-compliance free cash flow
CoC = after-tax cost-of-capital;
t = year index, t = 0-14 (15-year discounting horizon); and
CC = compliance capital outlay.
EPA considered a facility to be a post-compliance closure if its estimated business value was positive in the baseline but
became negative after adjusting for compliance-related cost, revenue and tax effects. In addition to tallying closure impacts in
terms of the number of estimated facility closures, EPA also measured the significance of closures in terms of losses in
employment and output. Employment losses equal the number of employees reported by closure facilities in survey
responses; output losses equal total revenue reported for regulatory closure facilities. EPA estimated national results by
multiplying facility results by facility sample weights.
b. Test of moderate impacts
EPA also conducted an analysis of financial stress short of closure to identify the rule's moderate impacts. Facilities
experiencing moderate impacts are not projected to close due to the MP&M effluent guidelines. The rule, however, might
reduce their financial performance to the point where they might have difficulty obtaining financing for future investments.
The analysis of moderate impacts examined two financial measures:
Pre-Tax Return on Assets (PTRA). ratio of pre-tax operating income earnings before interest and taxes (EBIT) to
assets. This ratio measures the operating performance and profitability of a business' assets independent of financial structure
and tax circumstances. PTRA is a comprehensive measure of a firm's economic and financial performance. If a firm cannot
sustain a competitive PTRA on a post-compliance basis, it may have difficulty financing its investments, including the outlay
for compliance equipment.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
Interest Coverage Ratio (ICR): ratio of pre-tax operating cash flow earnings before interest, taxes, and depreciation
(EBITDA) to interest expense. This ratio measures the facility's ability to service its debt on the basis of current, ongoing
financial performance and to borrow for capital investments. Investors and creditors will be concerned about a firm whose
operating cash flow does not comfortably exceed its contractual obligations. The greater the ICR, the greater the firm's ability
to meet interest payments, and, generally speaking, the greater the firm's credit-carrying ability. ICR also provides a measure
of the amount of cash flow available for equity after interest payments.
Creditors and equity investors review the above two measures as criteria to determine whether and under what terms they will
finance a business. PTRA and ICR also provide insight into a firm's ability to generate funds for compliance investments
from internally-generated equity, i.e., from after-tax cash flow. The measures are defined as follows:
Pre-Tax Return on Assets
PTRA = ME (5-6)
TA
where:
PTRA = pre-tax return on assets,
EBIT = pre-tax operating income, or earnings before interest and taxes, and
TA = total assets.
Or, stated in terms of MP&M income statement accounts,
(5.7)
PTRA= -
TA
where:
PTRA = pre-tax return on assets;
REV = revenue;
TC = total operating costs (excluding interest, taxes, and depreciation/amortization);
D = depreciation; and
TA = total assets.
Interest Coverage Ratio
ICR = EBITDA (5.8)
I
where:
ICR = interest coverage ratio;
EBITDA = pre-tax operating cash flow, or earnings before interest, taxes, and depreciation (and amortization)
and
I = interest expense.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Or, stated in terms of MP&M income statement accounts,
ICR =
REV - TC
I
(5.9)
where:
PTRA = pre-tax return on assets;
REV = revenue;
TC = total operating costs (excluding interest, taxes, and depreciation/amortization); and
TA = total assets.
Including the effects of MP&M compliance costs, post-compliance PTRA and ICR are:
= [(REV + AREV) - (TC + ATC + D + AD)]
(TA + CC)
(5.10)
[(REV + AREV) - (TC + ATC)]
(1 + AI)
(5.11)
where:
PTRA
ICRpc
AREV
ATC
AD
CC
AI
pc
pre-tax return on assets, post-compliance;
interest coverage ratio, post-compliance;
post-compliance change in revenue, as calculated in the cost pass-through analysis;
change in total facility operating costs (excluding interest, depreciation and taxes), calculated as operating
and maintenance costs of compliance;
change in depreciation expense, calculated as compliance capital outlay (CC) divided by 15;
compliance capital outlay (assuming all of the outlay would be capitalized and reported as an addition to
assets on the balance sheet); and
incremental interest expense from financing of compliance capital outlay. As a simplifying, conservative
assumption, incremental interest expense is calculated assuming that the compliance capital outlay is fully
debt financed at the overall real cost-of-capital of 7 percent. The annual incremental interest value is
calculated as the annualized value of interest payments over 15 years, assuming a constant annual payment
of principal and interest.
For evaluating MP&M facilities according to the moderate impact measures, EPA compared baseline and post-compliance
PTRA and ICR to MP&M sector-specific thresholds that were developed from data compiled by Risk Management
Association, Inc. (RMA). RMA compiles and reports financial statement information by industry as provided by member
commercial lending institutions. The threshold values represent the 25th percentile values of PTRA and ICR for statements
received by RMA for the eight years from 1994 to 2001 within relevant industries. EPA developed MP&M sector-level
values by weighting and summing the RMA industry values according to the definition of MP&M sectors (see Appendix C
for details of moderate impact threshold development and sector-specific threshold values). Thresholds by sector ranged from
0 to 3.1 percent for PTRA and from 1.4 to 2.9 for ICR. Because the financial statements received by RMA are for businesses
applying for credit from member institutions, the data don't represent a random sample. In particular, the RMA data will
likely exclude representation from the financially weakest businesses, which are unlikely to be seeking credit. As a result,
EPA views the threshold values as being relatively conservative and likely to overstate the occurrence of moderate impacts.
Both measures are important to financial success and firms' ability to attract capital. Facilities failing at least one of the
moderate impact measures in the baseline were deemed to be already experiencing moderate financial weakness and were not
tested for additional financial impact in the moderate impact analysis. Facilities that passed both moderate impact tests in the
baseline but failed one or both threshold comparisons, post-compliance, were considered to incur moderate financial impacts,
short of closure, as a result of the MP&M regulation.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
5.2.4 Impact Measures for Railroad Line Maintenance Facilities
The proposed MP&M rule would have applied to some railroad facilities that maintain and repair railroad track and that
perform similar operations on railroad and other vehicles. Railroad representatives indicated during data collection that the
industry does not collect or monitor significant financial data at the facility level. These discussions led EPA to administer a
modified version of the survey to railroad operating units and to perform the primary economic impact analysis at the
operating unit level.
The analysis of impacts for railroad line maintenance facilities uses the same measures of impact as for other private MP&M
facilities, but applies these measures for the railroad operating unit as a whole. Compliance costs for each railroad are the
sum of compliance costs at each MP&M railroad line maintenance facility identified by the operating company.
5.2.5 Impact Measures for Government-Owned Facilities
Government-owned MP&M facilities include all facilities owned by government entities that discharge process wastewater
from MP&M activities. Most government-owned facilities that fall under the MP&M rule provide or support transportation
services. These facilities repair, rebuild, and maintain buses, trucks, cars, utility vehicles (e.g., snow-plows and street
cleaners), and light machinery. The MP&M profile describes government-owned facilities in detail.
Each government subject to the MP&M effluent guidelines at its facilities has a number of choices, which include:
> contracting out the service to a private provider or other governmental agency,
•• discontinuing these services altogether, or
*• paying for compliance and continuing operations.
The impact analysis does not predict how the government will respond. The analysis evaluates only whether a community
incurring compliance costs and continuing operations under the rule would incur a severe burden. A government may choose
a different option and avoid some of the budgetary impacts estimated here.
EPA evaluated impacts for government-owned facilities by using three tests. A government that fails all three tests is likely to
suffer severe adverse impacts as a result of the rule. The first test is applied at the facility level, and the other two tests are
applied at the government level.
a. Impacts on site-level cost of service test
The impacts on site-level cost of service test considers whether a government-owned facility's compliance costs exceed one
percent or more of its total baseline cost of service. This test is similar to the test used to assess impacts on private facilities
and firms, which compares costs to post-compliance revenues. The facility will likely absorb compliance costs within its
current budget if those costs do not exceed one percent of the total. Compliance costs in this scenario will not significantly
impact the municipal budget. Costs in excess of one percent do not, in and of itself, indicate that a budgetary impact will
occur, but only that additional analysis should be performed to determine if there is an impact.
EPA calculated the ratio of compliance costs to cost of service, Rc, for each government-owned facility as follows:
TACC
i
'"Baseline
where:
Rc = ^^. (5.12)
Rc = ratio of compliance costs to cost of service,
TACC = total annualized compliance cost for the facility, and
^Baseline = total baseline cost of service at the facility.
A facility whose Rc is equal to or greater than one percent fails this test.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
b. Impacts on taxpayers test
The impacts on taxpayers test evaluates the significance of compliance costs to the people served by the government. A
government will fail this test if the ratio of total annualized pollution control costs per household to median household income
exceeds one percent, post-compliance. Post-compliance pollution control costs include all pollution control costs (for
whatever purpose) reported by the government in the baseline plus the sum of MP&M effluent guideline compliance costs at
all MP&M facilities owned by the government. This test closely follows the methodology developed for EPA's Water Quality
Standards Workbook (EPA, 1995).
The survey requests information about current municipal expenditures on pollution control. Total annualized
compliance costs (TACC) for each government-owned facility is the sum of costs and an amortized capital cost. The sum
of TACC at all MP&M facilities for each government, plus baseline municipal expenditures on pollution control, yields a
post-compliance total annualized pollution control cost. EPA divided total annualized pollution control costs by the number
of households to calculate an average cost per household. The questionnaire also asks for median household income in the
geographic area served by the responding government.
EPA calculated a ratio of compliance costs to median household income, RH, for each government as follows:
(5.13)
MHI
where:
RH = ratio of total annualized pollution control cost to median household income,
CBPC = total baseline municipal expenditures on pollution control, and
TACCj = total annualized compliance cost for government-owned facility i,
MHI = median household income for the government jurisdiction.
Governments that incur compliance costs that cause this ratio to exceed one percent fail this test. Governments that fail this
test in the baseline as well as post-compliance are not judged to experience major budgetary impacts attributable to the rule. If
the rule causes an increase in this ratio to above one percent, then EPA concludes that the rule might present a burden to the
taxpayers that support the affected government. The calculation is a conservative estimate of the impact on taxpayers because
it does not take into account the fact that non-residential taxpayers (businesses) will bear some of the tax burden or that some
costs might be recovered in fees.
This test is used in EPA's Economic Guidance for Water Quality Standards. This guidance is used by States and EPA
Regions to assess economic factors in setting or revising water quality standards. The guidance includes as a screening
measure of economic impact, average total pollution control cost per household divided by median household income. A
value less than one percent indicates that a community would incur "little economic impact".8
c. Impacts on government debt test
The impacts on government debt test assesses the government's ability to finance compliance with the rule by issuing debt. A
government must be able to finance capital compliance costs in addition to meeting ongoing compliance costs. Governments
often finance capital compliance costs by issuing debt. This criterion tests each government's capacity to issue debt by
examining the ratio of post-compliance debt service costs to the government's total revenue. This measure is analogous to the
interest coverage ratio for private firms.
The ratio of debt service costs to revenue, RD, for each government is:
8 Source: EPA's Economic Guidance for Water Quality Standards: Workbook (1995) (Chapter 2 "Evaluating Substantial Impacts:
Public Sector Entities"). Values between one and two percent indicate potential "mid-range economic impact." Governments with values
above one percent are subject to further analysis to determine whether a significant economic impact would in fact occur.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
(5.14)
* DB+ C*
D" TRB
where:
RD = debt-to-revenue ratio;
DB = baseline municipal debt service costs (principal payments and interest);
Ck = annualized capital cost of compliance, summed over all government-owned facilities in each government;
and
TRB = baseline municipal revenue.
EPA judged that debt service costs above 25 percent of revenues might impede a government's ability to issue debt in the
future and present a burden on the budget.
This criterion is used in EPA's MUNIPAY model. This model is used in enforcement cases to assess whether municipalities
(e.g., towns, villages, cities, counties, and public utilities) can afford to pay a specific level of compliance costs, Superfund
cleanup contributions, or penalties. The model's affordability assessment limits the amount of debt that can finance these
costs, capping the debt service ratio at 25 percent.9 A higher ratio "may reduce the confidence of creditors that the
municipality can repay its debt on time." The MUNIPAY manual states that this value slightly exceeds the "warning marks"
found in the public finance and management literature.
5.3 RESULTS
This section presents the results of the facility impacts analyses. The first section presents the results of the baseline closure
analysis. The subsequent sections report the results of the analyses for the rule and the three other regulatory options that
EPA analyzed. Section 5.3.2 presents the predicted price increases. Section 5.3.3 presents an overview of impacts for all
MP&M facilities, and then results are provided for indirect dischargers (Section 5.3.4), direct dischargers (Section 5.3.5),
private facilities (Section 5.3.6), and government-owned facilities (Section 5.3.7). Section 5.3.8 provides results by
subcategory.
5.3.1 Baseline Closures
Table 5.3 shows the results of the baseline closure analysis by subcategory. EPA estimated that a total of 3,593 facilities have
a negative business value before incurring regulatory compliance costs. These facilities are projected to close in the baseline
and are not considered in the analysis of impacts attributable to the regulation.
Appendix A provides information on typical average closure rates in the MP&M industry sectors. Census data show that
over 10,000 facilities, or almost eight percent of all facilities in these industries, close annually. The number of baseline
closures predicted in this analysis is consistent with this typical closure rate.
9 Source: EPA Office of Compliance and Enforcement Assurance, MUNIPA Y User's Manual, September 1999, p. 4-14.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.3: Summary of Baseline Closures
Subcategory
General Metals
Metal Finishing Job Shops
Non-Chromium Anodizing
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Dock
All Subcategoriesa
Total Number of
Dischargers
11,364
1,542
122
29,185
848
826
14
43,858
Number of Baseline
Closures
880
50
29
2,409
239
0
0
3,593
Percent Closing in
the Baseline
7.7%
3.2%
23.8%
8.3%
28.2%
0.0%
0.0%
8.2%
Number Operating
in the Baseline
10,484
1,491
93
27,776
609
826
14
40,265
a The total number of facilities does not sum to the number of facilities by subcategory because some facilities operate in more
than one subcategory and have an indirect and direct discharging operation within the same facility.
Source: U.S. EPA analysis
5.3.2 Price Increases
The price increases predicted for the final rule and alternative regulatory options are shown in Table 5.4. The percentage
price increases are small, falling well below one-half of one percent for all sectors under the final rule.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.4: Cost Pass-Through Analysis:
Percentage Price Increases by Regulatory Option and Sector
Sector
Aerospace
Aircraft
Bus and Truck
Electronic Equipment
Hardware
Household Equipment
Instrument
Iron and Steel
Job Shop
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Precious and Non-Precious Metals
Printed Circuit Board
Railroad
Ships and Boats
Stationary Industrial Equipment
Option I: Selected
Option (Directs
Only)
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
Option II:
Proposed/NODA
Option
0.04%
0.03%
0.06%
0.04%
0.08%
0.02%
0.08%
0.20%
0.61%
0.16%
0.07%
0.00%
0.12%
0.04%
0.03%
0.00%
0.02%
0.03%
0.05%
Option III: Directs
+ 413 to 433
Upgrade
0.00%
0.00%
0.00%
0.00%
0.01%
0.00%
0.00%
0.00%
0.09%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.01%
Option IV: Directs
+ All to 433
Upgrade
0.00%
0.01%
0.01%
0.00%
0.01%
0.00%
0.01%
0.00%
0.09%
0.01%
0.00%
0.00%
0.00%
0.01%
0.00%
0.00%
0.00%
0.00%
0.01%
Source: U.S. EPA analysis
5.3.3 Overview of Impacts
Table 5.5 provides an overview of the numbers of facilities closing and experiencing moderate economic impacts, by
regulatory option. These national estimates include all types of dischargers (direct and indirect) and types of facilities (private
MP&M, railroad line maintenance, and government-owned facilities.)
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.5: Regulatory Impacts for All Facilities by Option, National Estimates
Number of facilities operating in the baseline: total
private MP&M and railroad line maintenance
government-owned
Number of facilities below low flow cutoffs
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline
excluded or below cutoffs
Number of facilities operating subject to regulatory
requirements
Number of regulatory closures
Percent of facilities operating in the baseline that are
regulatory closures
Number of facilities experiencing moderate impacts
Percent of facilities operating in the baseline that
experience moderate impacts
Option I:
Selected Option
(Directs Only)
40,265
36,480
3,785
37,883
94.1%
2,382
0
0.0%
0
0.0%
Option II:
Proposed/NODA
Option"
60,253
54,526
5,727
51,502
136
85.7%
8,615
785
9.1%
257
3.0%
Option III:
Directs + 413 to
433 Upgrade
40,265
36,480
3,785
36,820
91.4%
3,445
120
3.5%
37
1.1%
Option IV:
Directs + All to
433 Upgrade
40,265
36,480
3,785
36,339
90.3%
3,926
120
3.1%
49
1.2%
a The total number of facilities reported for the Proposed/NODA Option (Option II) analysis differs from the facility count reported for
the final rule and Options III and IV. After deciding in July 2002 to not consider the NODA option as the basis for the final rule, EPA
performed no more analysis on the NODA option, including not updating facility counts and related analyses for the change in
subcategory and discharge status classifications. These differences in facility counts by regulatory option appear in subsequent tables.
Source: U.S. EPA analysis.
Table 5.5 shows that the final rule substantially reduces facility-level impacts as compared to the three alternative regulatory
options considered by EPA. None of the facilities that continue to operate in the baseline close or experience moderate
impacts due to the final rule. The large difference in results between the final rule and other options stems largely from the
exclusion from regulatory requirements of over 94 percent of facilities that continue to operate in the baseline: the final rule
excludes from regulatory requirements all indirect dischargers and direct dischargers in all subcategories except for Oily
Wastes. Significantly larger numbers of facilities are projected to close under the Proposed/NODA Option and 433 Upgrade
Options (785 and 120 facilities, respectively). See Chapter 4 for a discussion of the options and subcategory exclusions.
Table 5.6 shows the estimated burden on facilities from regulatory compliance by option, discharge status, and subcategory.
The estimated burden includes annualized compliance costs and any estimated increase in facility revenue as a result of the
regulation, and, for private facilities, reflects the effects of taxes on compliance costs and revenue. These compliance costs
therefore represent the total after-tax cash flow impact on regulated facilities.
Table 5.6: Total Annualized Facility" After-tax Compliance Costs
by Subcategory, Discharge Status, and Regulatory Option
(millions, 2001$)
Subcategory
General Metals
Option I:
Selected Option
(Directs Only)
Direct
$0.0
Indirect
$0.0
Option II:
Proposed/NODA
Option
Direct
$267.6
Indirect
$476.7
Option III:
Directs + 413 to 433
Upgrade
Direct
$0.0
Indirect
$16.5
Optic
Directs +
Up?
Direct
$0.0
3nIV:
All to 433
rade
Indirect
$46.5
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Metal Finishing Job Shop
Non-Chromium Anodizing
Oily Waste
Printed Wiring Board
Railroad Line Maintenance
Shipbuilding Dry Dock
Steel Forming & Finishing*
All Categories: Annual Costs
All Categories: Number of
Facilities Operating Post-
Compliance Subject to
Requirements
Total Costs to Industry by Option,
Directs + Indirects
$0.0
$0.0
$11.9
$0.0
$0.0
$0.0
$11.9
2,382
$1
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
0
1.9
$2.9
$23.1
$29.0
*tn T
4>U.2
$0.6
$2.4
$25.5
$351.2
4,143
$1,1
$139.9
$0.0
$72.3
$106.4
$0.0
$0.0
$16.1
$811.4
3,688
62.5
$0.0
$0.0
$12.0
$0.0
$0.0
$0.0
$12.0
2,382
$5
*to <->
4>o.2
$0.0
$0.0
$15.0
$0.0
$0.0
$39.7
1,063
1.7
$0.0
$0.0
$12.1
$0.0
$0.0
$0.0
$12.1
2,382
*t2
4>0
$8.2
$0.0
$0.0
$15.0
$0.0
$0.0
$69.7
1,544
1.8
a This table reflects after-tax cash flow impacts to facilities and does not represent the cost of society from regulatory compliance.
Chapter 11 discusses the social costs of the final rule and the other options. The estimates in this table exclude baseline and
regulatory closures, and are after-tax.
b The Steel Forming & Finishing subcategory was removed from the MP&M universe after deciding not to consider the
Proposed/NODA Option (Option II) for the final rule. As a result, compliance costs are included in the Steel Forming &
Finishing subcategory for Option n only.
Source: U.S. EPA analysis.
Oily Wastes direct dischargers account for the total compliance costs of $11.9 million under the final rule. Total compliance
costs incurred by facilities that continue to operate post-compliance are almost 100 times higher under the Proposed/NODA
Option than under the final rule, over four times higher under the Directs and 413 to 433 Upgrade Option than under the final
rule, and almost seven times higher under the Directs and All to 433 Upgrade Option than under the final rule.
5.3.4 Results for Indirect dischargers
The sum of facilities individually identified as indirect and direct dischargers exceeds the total of all facilities as identified in
Table 5.5, above. Some facilities operate in more than one subcategory, and some have both an indirect and direct
discharging operation in the same facility. Facilities with both indirect and direct discharging operations are reported in the
tables for both discharge categories: Table 5.7, for indirect dischargers, and Table 5.8, for direct dischargers.
Table 5.7 summarizes the results of the facility impact analysis for indirect dischargers, including both private businesses and
government-owned facilities.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.7: Regulatory Impacts for Indirect Dischargers by Option, National Estimates
Number of facilities operating in the baseline: total
private MP&M and railroad line maintenance
government-owned
Number of facilities below low flow cutoffs
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline
excluded or below cutoffs
Number of facilities operating in the baseline
subject to regulatory requirements
Number of regulatory closures
Percent of facilities operating in the baseline and
subject to regulatory requirements that are
regulatory closures
Number of facilities experiencing moderate
impacts
Percent of facilities operating in the baseline and
subject to regulatory requirements that experience
moderate impacts
Option I:
Selected Option
(Directs Only)
37,652
34,325
3,327
37,652
100.0%
0
0
0.0%
0
0.0%
Option II:
Proposed/
NODA Option
56,071
51,066
5,005
51,502
136
92.1%
4,433
746
16.8%
228
5.1%
Option III: Directs
+ 413 to 433
Upgrade
37,652
34,325
3,327
36,589
97.2%
1,063
120
11.3%
37
3.5%
Option IV:
Directs + All to
433 Upgrade
37,652
34,325
3,327
36,108
95.9%
1,544
120
7.8%
49
3.2%
Source: U.S. EPA analysis.
Indirect discharging facilities account for over 93 percent of water discharging MP&M facilities as a whole. However,
because all indirect discharging are excluded from regulatory requirements under the final rule, EPA estimates that no
indirect dischargers will incur impacts under the final rule.
5.3.5 Results for birect dischargers
Table 5.8 summarizes the facility impact results for direct dischargers, which represent approximately seven percent of all
facilities that continue to operate in the baseline. In addition, most operating direct dischargers are subject to requirements
under the final rule: only 10 percent are excluded from requirements as a result of subcategory exclusions. As shown in the
table, EPA estimates that no direct dischargers will close or incur moderate impacts as a result of the final rule's requirements.
Impacts on direct dischargers are the same under the 433 Upgrade Option impacts as under the final rule, since these Options
apply the same requirements to the same universe of facilities. The Proposed/NODA Option would have yielded more
regulatory closures and moderate impacts than the final rule and 433 Upgrade Options.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.8: Regulatory Impacts on Direct Dischargers by Option, National Estimates
Option I: j Option II: j Option III: j Option IV:
| Selected Option j Proposed/NODA j Directs + 413 j Directs + All to
(Directs Only) j Option j to 433 Upgrade j 433 Upgrade
Number of facilities operating in the baseline
private MP&M and railroad line maintenance
government-owned
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline with
subcategory exclusions
Number of facilities operating in the baseline subject
to regulatory requirements
Number of regulatory closures
Percent of facilities operating in the baseline and
subject to regulatory requirements that are regulatory
closures
Number of facilities experiencing moderate impacts
Percent of facilities operating in the baseline that
experience moderate impacts
2,641
2,183
458
259
9.8%
2,382
0
0.0%
0
0.0%
4,182
3,459
722
0
0.0%
4,182
39
0.9%
28
0.7%
2,641
2,183
458
259
9.8%
2,382
0
0.0%
0
0.0%
2,641
2,183
458
259
9.8%
2,382
0
0.0%
0
0.0%
Source: U.S. EPA analysis.
5.3.6 Results for Private Facilities
Table 5.9 provides the facility impact analysis results for privately-owned facilities, including Railroad Line Maintenance
facilities. Because privately-owned facilities account for over 90 percent of all MP&M facilities operating in the baseline,
these results are similar to the results reported for all MP&M facilities in Table 5.5. Almost 95 percent of facilities operating
post-compliance are excluded from requirements under the final rule, due to the subcategory exclusions for all indirect
dischargers and all direct dischargers except for Oily Wastes.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.9: Regulatory Impacts for Private Facilities by Option, National Estimates
Number of facilities operating in the baseline
Number of facilities below low flow cutoffs
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline
excluded or below cutoffs
Number of facilities operating in the baseline
subject to regulatory requirements
Number of regulatory closures
Percent of facilities operating in the baseline and
subject to regulatory requirements that are
regulatory closures
Number of facilities experiencing moderate
impacts
Percent of facilities operating in the baseline and
subject to regulatory requirements that experience
moderate impacts
Option I:
Selected Option
(Directs Only)
36,480
34,556
94.7%
1,924
0
0.0%
0
0.0%
Option II:
Proposed/NODA
Option
54,526
46,582
136
0.2%
7,808
785
10.1%
257
3.3%
Option III:
Directs + 413 to
433 Upgrade
36,480
33,123
90.8%
3,357
120
3.6%
37
1.1%
Option IV:
Directs + All to
433 Upgrade
36,480
32,745
89.8%
3,735
120
3.2%
49
1.3%
Source: U.S. EPA analysis.
5.3.7 Results for Government-Owned Facilities
Table 5.10 provides facility impact analysis results for government-owned facilities. The 3,785 government-owned facilities
that continue to operate in the baseline represent over 9 percent of all MP&M facilities operating in the baseline. As
discussed above, instead of a closure test, the impact analysis for government-owned facilities assesses whether the rule would
impose major budgetary impacts on these facilities and the governments that own them.
Under the final rule, 88 percent of government-owned facilities would be excluded from requirements because they qualify
for subcategory exclusions. EPA's analysis indicates that none of the options would impose major budgetary impacts on the
governments operating the facilities.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.10: Regulatory Impacts for Government -Owned Facilities by Option, National Estimates
Number of government-owned facilities operating
in the baseline & post-regulation
Number of facilities below low flow cutoffs
Number of facilities with subcategory exclusions
Percent of facilities operating in the baseline
excluded or below cutoffs
Number of facilities operating subject to regulatory
requirements
Number of facilities experiencing significant
budgetary impacts"
Percent of facilities operating in the baseline that
experience significant budgetary impacts
Option I:
Selected Option
(Directs Only)
3,785
3,327
87.9%
458
0
0%
Option II:
Proposed/NODA
Option
5,727
4,920
0
85.9%
807
0
0%
Option III:
Directs + 413
to 433 Upgrade
3,785
3,327
87.9%
458
0
0%
Option IV:
Directs + All to
433 Upgrade
3,785
3,305
87.3%
480
0
0%
a A government is judged to experience major budgetary impacts if (1) any of its facilities incur compliance costs exceeding 1% of
baseline cost of service and (2) the governmental unit fails both the taxpayers impact and government debt impact tests.
Source: U.S. EPA analysis.
Tables 5.11 and 5.12 provide additional detail on the results of the facility impact analysis for government-owned facilities.
Table 5.11 shows the number of government-owned facilities by type and size of government, and the number that fall below
relevant flow cutoffs under the final rule.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.11: Number of Government- Owned Facilities
by Type and Size of Government Entity
: i : i :
Regional
Municipal State County Governmental i
Government Government Government Authority Total
Large Governments (population> 50,000)
# of regulated
government entities
# of government
entities with exclusions
# of regulated
government entities
# of government
entities with exclusions
# of regulated
government entities
# of government
entities with exclusions
Total
26
592
Smal
280
1,470
306
2,062
2,368
129
248
/ Governments (pop
0
0
All Goverm
129
248
377
23
758
ulation <= 50,000)
0
212
nents
23
970
993
0
46
0
0
0
46
46
178
1,645
280
1,682
458
3,327
3,785
Source: U.S. EPA analysis of Municipal Survey.
Table 5.12 provides additional information on the results of the three tests performed in the government impact analysis. The
vast majority of facilities, 95.7 percent, are estimated to incur costs less than one percent of their baseline cost of service.
EPA assumes that these facilities (and their owning governments) can absorb compliance costs within their current budgets
with no material burden. The remaining 162 facilities, or 4.3 percent of government-owned facilities, incur costs exceeding
one percent of their baseline costs of service. Although EPA estimates that these facilities will incur costs exceeding the one
percent no-impact threshold, whether these costs represent a material burden to the owning government depends on the
magnitude of costs at the government level. To understand whether this higher facility-level cost would constitute a
significant burden, EPA estimated the total of compliance costs incurred by a government for all of its affected MP&M
facilities and assessed the impact of these costs under the two tests outlined above: the taxpayer impact test and the
government debt service impact test. For the final rule, EPA estimated that none of the governments with facilities incurring
costs greater than one percent of baseline values would fail either of the two government-level impacts tests.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
Table 5.12: Impacts on Governments of MP&M Facility Compliance Costs by Size of Government
Owned by Small
Governments
Number of government-owned MP&M
facilities affected
1,
962
i i
I Number j Percent
: :
:
Number and percent of governments 0 0.0%
failing all three budgetary impact criteria I
Owned by Large
Governments
1,823
Number
0
All Government-
Owned Facilities
3,785
i i
Percent i Number 1 Percent
: :
0.0% | 0 | 0.0%
: i
: :
: :
Individual Test Results: number and percent of failures
Compliance costs > one percent of
baseline cost of service test
Impacts on taxpayers test
Impacts on government debt test
140
0
0
7.1%
0.0%
0.0%
22
0
0
1.2%
0.0%
0.0%
162
0
0
4.3%
0.0%
0.0%
Source: U.S. EPA analysis.
That no governments incur budgetary impacts at the government level is not surprising. The MP&M activities regulated
under the final rule typically represent a very small portion of governments' budgets. Even a significant percentage increase
in the cost of MP&M activities (as measured by the comparison of post-regulation costs to baseline costs) is unlikely to
present any serious burden on the budgets of the affected governments.
Moreover, the costs to government-owned facilities are quite low. The large majority (3,327 or 88 percent) of the 3,785
government-owned facilities are excluded from the final rule. Of the 458 government facilities remaining under regulation,
183 facilities incur no costs, and 275 incur annualized costs averaging $32,674.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
GLOSSARY
after-tax cash flow (A TCP): After-tax cash flow available to equity.
baseline closures: Facilities showing inadequate financial performance in the baseline, that is, in the absence of the rule.
These facilities closures would have occurred with or without the rule.
Construction Cost Index (CCI): Measures how much it cost to purchase a hypothetical package of goods and services
compared to what is was in the base year. It applies to general construction costs. The CCI can be used where labor costs are
a high proportion of total costs. The CCI uses 200 hours of common labor, multiplied by the 20-city average rate for wages
and fringe benefits, (http://www.enr.com/cost/costfaq.asp)
cost of capital: Costs incurred for a firm to obtain financing from all capital sources including, in particular, equity and
debt.
cost pass-through analysis: Calculates the percentage of compliance costs that EPA expects firms subject to regulation
to recover from customers through increased revenues.
facility: A contiguous set of buildings or machinery on a piece of land under common ownership.
free cash flow. Cash flow generated by the company that is available to all providers of the company's capital, both
creditors and shareholders.
government-owned facility: Includes facilities operated by municipalities, state agencies and other public sector entities
such as state universities.
interest coverage ratio (ICR): Ratio of cash operating income to interest expenses. This ratio measures the facility's
ability to service its debt and borrow for capital investments.
liquidation value: Net amount that could be realized by selling the assets of a firm after paying the debt.
(http://www.duke.edu/~charvey/Classes/wpg)
moderate impacts: Adverse changes in a facility's financial position that are not threatening to its short-term viability.
operating and maintenance (O&M): Costs estimated to result from operating and maintaining pollution controls
adopted to comply with effluent guidelines. Operating costs include the costs of monitoring.
pre-tax return on assets (PTRA):Raiio of cash operating income to assets. This ratio measures facility profitability.
private MP&M facility: Includes all privately-owned facilities that do not perform railroad line maintenance.
Producer Price Index (PPI): A family of indexes that measures the average change over time in the selling prices
received by domestic producers of goods and services. PPI's measure price change from the perspective of the seller. This
contrasts with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's
perspective. Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution
costs. (http://stats.bls.gov/ppifaq.htmSl)
railroad line maintenance facility: Facilities that maintain and repair railroad track and other vehicles.
regulatory closure: A facility that is predicted to close because it can not afford the costs of complying with the rule.
severe impacts: Facility closures and the associated losses in jobs, earnings, and output at facilities that close due to the
rule.
total annualized compliance cost (TACC): Sum of annual operating and maintenance costs and the annualized
equivalent of one-time costs, calculated over 15 years assuming a seven percent discount rate.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 5: Facility Impact Analysis
ACRONYMS
A TCP: after-tax cash flow
CCI: construction cost index
ICR: interest coverage ratio
O&M: operation and maintenance
PPI: producer price index
PTRA: pre-tax return on assets
TACC: total annualized compliance cost
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
REFERENCES
Brealey, Richard A. and Stewart C. Myers. 1996. Principles of Corporate Finance, 5th edition. New York: McGraw-Hill.
McKinsey & Company, Inc. et al. 2000. Valuation: Measuring and Managing the Value of Companies, 3rd edition. New
York: John Wiley & Sons, Inc.
U.S. Bureau of Labor Statistics, Producer Price Index Revision-Current Series. On-line database at
http://stats.bls.gov/ppihome.htm.
U.S. Department of Commerce, Bureau of the Census, Annual Survey of Manufactures. On-line database at
http://www.census.gov/prod/www/abs/industry.html.
U.S. Environmental Protection Agency (U.S. EPA). 1995. Interim Economic Guidance for Water Quality Standards
Workbook. Office of Water, Economics and Statistical Analysis Branch. March.
U.S. Environmental Protection Agency (U.S. EPA). 2000. Technical Development Document for the Proposed Effluent
Limitations Guidelines and Standards for the Metal Products & Machinery Point Source Category. EPA 82 l-B-00-005.
December.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 5: Facility Impact Analysis
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 6: Employment Effects
Chapter 6: Employment Effects
CHAPTER CONTENTS
6.1 Job Losses Due to Closures 6-2
6.2 Job Gains Due to Compliance Requirements .... 6-3
6.3 Net Effects on Employment 6-5
Glossary 6-7
Acronym 6-8
References 6-9
INTRODUCTION
This chapter discusses the employment effects associated
with the final rule and the alternative regulatory options
considered by EPA. The MP&M regulation can generate
both positive and negative impacts on employment. Any
facility closures induced by the rule would result in reduced
demand for labor and compliance activities at facilities that
close, but would also increase employment requirements in
facilities that remain open and continue to operate. The
regulation could also create a demand for compliance-related equipment and installation, which would also generate new
employment requirements.
EPA assumed that any estimated facility closures would result in the loss of full-time equivalents (FTEs).
The MP&M rule may affect overall employment in three ways.
Direct labor requirements. Direct labor requirements are job losses from closures and job gains from manufacturing,
installing, and operating compliance-related equipment. Direct labor requirements also include labor to implement the rule's
pollution prevention activities.1
Indirect labor requirements. Compliance expenditures may increase employment in industries doing business with
compliance equipment and service providers. Economists refer to these as linked industries. For example, a firm that
manufactures a treatment system will purchase pumps, pipes, and other intermediate goods and services from other firms and
sectors of the economy. Employment in these linked industries increases when treatment equipment manufacturers purchase
goods and services from them. Closures of MP&M facilities can also lead to reduced requirements for inputs to MP&M
industry products, and therefore indirect job losses in the supplier industries.
Induced labor requirements. Increased direct and indirect labor employment also increases spending on consumer-
oriented service and retail businesses. Economists refer to the additional labor demand in the businesses patronized by people
working in the direct and indirect labor industries as "induced" labor requirements. Conversely, people who are laid off from
MP&M facilities that close due to the rule may spend less, reducing employment in sectors providing consumer services and
products.
EPA is not including a total estimate of indirect and induced job gains and losses, however, because the magnitude of losses
and gains is very small at the national level and occur across all states. The job gains after the first three years are expected to
be approximately two jobs per year, without any regulation-related losses. The low magnitude of these gains means that it is
highly unlikely that there will be any material secondary and induced impacts from the regulation.
Because EPA estimates that no facility closures will occur under the final rule, EPA expects that the rule will cause no job
losses. However, EPA estimates that the regulation will increase employment, with the manufacture and installation of
compliance equipment causing a short-term gain in direct employment of 20 FTEs. In addition, EPA estimates that operation
and maintenance of compliance equipment will cause a continuing direct requirement for two FTEs per year. The net effect
on direct employment of the regulation is an estimated increase of 47 FTE-years, a measure that reflects both the number
and duration of jobs gained. This number represents an average gain of three FTEs per year over the 15 year analysis period.
Although EPA expects no job losses under the final rule, EPA considered other regulatory options that would likely have
caused facility closures and job losses. The following sections of this chapter review first the job losses from facility closures
under the alternative regulatory options, and second the expected job gains from compliance equipment installation and
See the Technical Development Document for more information on compliance costs.
6-1
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 6: Employment Effects
operation for both the final rule and the alternative options. The last section discusses net impacts on employment and the
expected timing of those effects.
6.1 JOB LOSSES &UE TO CLOSURES
As discussed, EPA estimates that the final rule will cause no facility closures and thus no job losses. However, EPA
considered other regulatory options that would likely cause facility closures and job losses. To calculate job losses for these
options, EPA assumed that all employees working at closing facilities will lose their jobs, and that one-third of the facilities
estimated to close do so in each of the first three years after promulgation of the option. The §308 surveys provide the number
of employees at each facility, expressed in FTEs. The job losses attributable to an option are simply the sum of employment
at the plants estimated to close. EPA did not analyze the job losses that would occur if facilities cut production or ceased
production of products that required certain processes instead of closing. Table 6.1 shows the total employment and
estimated job losses by subcategory due to facility closures under the alternative regulatory options and as a percent of the
total employment in the baseline.
Table 6.1: Job Losses for the Alternative Regulatory Options by Subcategory; Final Rule
Subcategory
General Metals
Metal Finishing Job Shop
Non-Chromium Anodizing
Oily Waste
Printed Wiring Board
Railroad Rebuildersa
Shipbuilding Dry Dock
Steel Forming and Finishing15
All Subcategories
Total
Employment
in the
Baseline
3,641,623
63,083
13,464
3,143,544
110,644
n/a
994
21,753
6,973,352
Option I:
Selected
Option
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
Option II:
NODA/Proposed
Option
Estimated _ ,
T v. T Total
Job Losses T ,
Jobs
7,895 0.2%
19,072 30.2%
0 0.0%
104 0.0%
3,998 3.6%
n/a n/a
0 0.0%
1,660 7.6%
32,729 0.5%
Option III:
413 to 433 Upgrade
Option
r ^. t , % of
Estimated _ . ,
Total
Job Losses T ,
Jobs
6,087 0.2%
1,425 2.3%
0 0.0%
0 0.0%
363 0.3%
n/a n/a
0 0.0%
7,874 0.1%
Option IV:
All to 433 Upgrade
Option
r ^. t , % of
Estimated _ . .
Total
Job Losses T ,
Jobs
6,087 0.2%
1,425 2.3%
0 0.0%
0 0.0%
363 0.3%
n/a n/a
0 0.0%
7,874 0.1%
a Employment is only available at the firm level for the Railraod Rebuilders subcategory.
b The Steel Forming & Finishing subcategory was removed from the MP&M universe after deciding not to consider the
Proposed/NODA Option (Option II) for the final rule. As a result, estimated job losses are included in the Steel Forming & Finishing
subcategory for Option II only. Accordingly, the employment from this subcategory is not included in the total.
Source: U.S. EPA analysis.
Job losses under the Proposed/NODA Option equal 0.5 percent of total employment at water discharging MP&M facilities
and 0.1 percent under the 433 Upgrade Options. The metal finishing job shop subcategory accounts for 19,072 of the job
losses under the Proposed/NODA Option or over 58 percent of the total 32,729 estimated job losses. The subcategories with
the highest percent of job losses under the Proposed/NODA Option are the Metal Finishing Job Shops (30.2 percent of total
employment in the subcategory), Steel Forming and Finishing (7.6 percent) and Printed Wiring Boards (3.6 percent). Job
2 EPA's analysis considers employment losses only for facility closures. As discussed in Chapter 5, firms may consider a range of
responses in structuring a compliance strategy, including consolidation and/or transfer of production among facilities to minimize the
financial burden of compliance. In some instances, these actions could result in employment losses in some facilities and possible
increases in others. Because of the complexity of these decisions, EPA's analysis cannot consider the full range of such compliance
responses and does not consider the potential employment effects - negative or positive - associated with these compliance options.
6-2
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects
losses under the 433 Upgrade Options are estimated in the General Metals, Metal Finishing Job Shops, and Printed Wiring
Boards subcategories of 6,087, 1,425, and 363 employees, respectively.
6.2 JOB SAINS &UE TO COMPLIANCE REQUIREMENTS
Direct labor requirements arise from the employment necessary to manufacture, install, and operate equipment that MP&M
facilities need to comply with the final rule, as well as pollution prevention activities undertaken to comply with the
regulation. The following sections discuss labor requirements associated with manufacturing compliance equipment,
equipment installation, and operation, respectively. This section provides a detailed analysis for the final rule only. A
summary of the net job gains due to compliance with the alternative regulatory options is presented at the end of the section.
Some more detail on the compliance costs that went into calculating job gains under the alternative regulatory options is
available in Appendix E.
a. Direct labor requirements for manufacturing compliance equipment
EPA estimated the direct labor requirements for manufacturing wastewater treatment systems using three steps:
•• Calculate the cost of compliance equipment;
»• Estimate the share of the cost of compliance equipment due to labor inputs. This estimate shows how much money
goes to employees of equipment manufacturers; and
•• Convert the dollars spent on manufacturing employees to a full-time employment equivalent (FTE), based on a
yearly labor cost.
»»» Compliance cost
EPA used the total one-time capital costs estimated by the engineers to calculate the purchase cost paid to manufacturers of
compliance equipment. The estimated one-time direct capital equipment cost is $3.1 million for the final regulatory option.
Appendix E explains in more detail how this value was calculated.
*»* Labor share
The Bureau of Economic Analysis (BEA) calculates direct requirements coefficients that measure how many dollars of
each input are purchased to produce a dollar of a given output. EPA used requirements coefficients for BEA Sector 40, the
"Heating, Plumbing, and Fabricated Structural Metal Products Industry," for the employment analysis. MP&M project
engineers identified BEA Sector 40 as the industrial sector that most nearly matches the businesses that would make, install,
and operate waste treatment systems for MP&M facilities complying with the rule. The inputs into Sector 40 production
include intermediate goods, materials, and services, as well as labor.
BEA's direct requirements table shows that every dollar of Sector 40 output delivered to final demand requires $0.30632
expended to compensate Sector 40 employees. Multiplying labor's share of output value (30.63 percent) by the value of
compliance equipment purchases for the regulation ($3.1 million) yields the labor cost of manufacturing treatment system
equipment: $0.9 million.
»»» FTE jobs
EPA converted the total labor cost to the number of FTE-equivalent jobs by dividing the total labor cost by an estimated
yearly labor cost per FTE employee. EPA used the hourly labor rate used in the engineering cost analysis - $29.67 per hour
in 1996 dollars. The $29.67 per hour rate includes fringe benefits (e.g., holidays, vacation, and various insurances) and
payroll taxes. EPA adjusted this amount to 2001 dollars using the Bureau of Labor Statistics Employment Cost Index for
manufacturing of durable goods, to provide an hourly rate in 2001$ of $34.69. The gross 2001 dollar annual labor cost per
FTE position for a 2,000-hour work year is $69,373. EPA estimated that one-time spending on manufacturing treatment
system equipment would require 14 FTEs (941 (in thousands) / 69.4). EPA assumed that one-third of facilities come into
compliance in each of the first three years, therefore, one-third of these FTEs (5) would be associated with equipment
purchases in each of the first three years after promulgation of the rule.
3 See "Benchmark Input-Output Accounts for the U.S. Economy, 1992," in Survey of Current Business, July 1997, U.S. Department
of Commerce, Bureau of Economic Analysis.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 6: Employment Effects
b. Direct labor requirements for installing treatment systems
EPA's method for estimating the direct labor requirements to install treatment system equipment parallels its method for
analyzing the labor requirements for equipment manufacturing.
»»» Compliance cost
EPA used the total one-time capital costs estimated by the engineers to calculate the cost of installation. The estimated one-
time cost of installation labor is $0.5 million for the final regulatory option. Appendix E explains in more detail how this
value was calculated.
*»* Labor share
One hundred percent of the installation is a labor cost.
»»» FT E jobs
EPA used the loaded hourly labor cost of $34.69 per hour and 2,000 hours per year to convert labor costs to numbers of FTE
jobs. Complying facilities will require an estimated 7 FTEs (455 (in thousands) / 69.4) to install the equipment needed to
comply with the regulation. This corresponds to 2 FTEs in each of the first three years after promulgation of the rule.
c. Direct labor requirements for operating and maintaining treatment systems
MP&M project engineers estimated that labor costs represent one percent of total compliance operating and maintenance
(O&M) costs. For the final rule, the labor cost of O&M is $0.1 million per year (2001$), corresponding to 2 FTEs (131 (in
thousands) / 69.4). EPA assumed that one-third of facilities come into compliance in each of the first three years after
promulgation of the rule. Therefore, one-third of these FTEs (1) would have operating maintenance requirements in the first
year, two-thirds of these FTEs (1) would have operating maintenance requirements in the second year, and all of these FTEs
(2) would have operating maintenance requirements in the third year when all facilities reach compliance.
d. Total direct labor requirements
The total direct labor requirement for complying with the MP&M rule is the sum of the direct labor requirements of
manufacturing, installing, and operating treatment systems. Table 6.2 summarizes the direct labor requirements from
compliance expenditures under the regulation. These requirements include total one-time expenditures to manufacture and
install compliance equipment equal to 20 FTEs, and continuing requirements for operating and maintenance of 2 FTEs per
year.
Table 6.2: Total Direct Labor Requirements of the
National Estimates (millions, 2001$, before
: : :
1 _ , _ 1 Labor 1
i Total Cost i
Share
: : :
One-time compliance cost $3.6 \
Capital equipment manufacturing
Installation labor
Annual operating and maintenance cost
$3.1
$0.5
$13.1
30.6%
100.0%
1.0%
Final Rule,
tax)
Total _,__, „
T u /-. * FTEs"
Labor Cost j
$1.4 \ 20
$0.9
$0.5
$0.1
14
7
2
* Number of jobs calculated on the basis of an average annual labor cost of $69,373 which assumes an
average hourly wage of $34.69 and 2,000 hours per labor-year.
Source: U.S. EPA analysis, Bureau of Labor Statistics, Bureau of Economic Analysis.
Table 6.3 summarizes the total direct labor requirements from compliance expenditures under the final rule and alternative
regulatory options.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 6: Employment Effects
Table 6.3: Total Direct Labor Requirements of
Option
Option I: Selected Option
Option II: NODA/Proposed Option
Option III: 413 to 433 Upgrade Option
Option IV: All to 433 Upgrade Option
the Final Rule and
Alternative Regulatory Options
One-time manufacturing and installation of compliance
equipment
Manufacturing
14
2,467
294
457
Installation labor
7
1,195
142
221
Total
20
3,662
436
678
Annual operating
and maintenance
2
215
8
13
Source: U.S. EPA analysis, Bureau of Labor Statistics, Bureau of Economic Analysis.
Requirements under the Proposed/NODA Option include total one-time expenditures to manufacture and install compliance
equipment equal to 3,662 FTEs, and continuing requirements for operating and maintenance of 215 FTEs per year. EPA
expects the 413 to 433 Upgrade Option and the All to 433 Upgrade Option to require 436 and 678 one-time FTEs and 8 and
13 continuing FTEs per year, respectively.
6.3 NET EFFECTS ON EMPLOYMENT
The timing and duration of employment changes resulting from the rule or the alternative options depend on the type of
employment demands and the condition of the economy at the time those demands occur. The increased employment
resulting from facilities' purchase and installation of compliance equipment will be short-term and is expected to occur in the
early years of implementation. However, the increased employment needed to operate and maintain compliance systems will
persist, presumably for the life of the compliance equipment. For job losses that might accompany the alternative options, the
duration of unemployment would depend on labor demand in the economy and specifically in the locations at which facilities
close, and the skill level of those individuals becoming unemployed.
Table 6.4 reports the estimated level and timing of direct employment impacts of the final rule. The estimates assume that:
(1) facilities come into compliance or close over a three year period, (2) displaced workers are out of work for one year on
average, and (3) the requirements to operate and maintain compliance systems continue for 15 years. As shown in Table 6.4,
the final rule results in a small increase in employment in all years of the analysis period. Summing employment each year
over the 15 year analysis period indicates that the regulation would increase direct labor requirements by 47 "FTE-years", or
an average gain of 3 FTEs per year. The comparable estimates for the alternative options (shown in Table 6.5) include the
effect of job losses from facility closures.
The industries in which employment changes are expected to occur also depend on the type of employment demands under
the rule. Increases in employment for operation and maintenance of compliance equipment are expected to occur in the
MP&M industries. In addition, because the MP&M industry, itself, is likely to be a manufacturer of compliance equipment, a
material portion of the increase in employment for producing and installing compliance equipment is likely to occur in the
MP&M industries. Accordingly, a substantial part of the total employment increase will likely occur in the MP&M
industries. Still, on balance, the impact on total employment - both in the economy as a whole and in the MP&M industries -
is expected to be very small. The average net gain of 3 FTEs for the final rule equals a negligible percent of total employment
in the MP&M facilities potentially subject to the rule and even less compared with total 1996 employment in the industries
that make up the larger MP&M industry.4
EPA did not consider the possible effects of excess capacity or underemployment in the equipment manufacturing and
installation industries, and assumed that all compliance requirements would result in proportional changes in employment.
Total employment in the potentially regulated MP&M facilities is 6,973,352 FTEs, as reported in the Section 308 surveys.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 6: Employment Effects
Table 6.4: Estimated Final Rule Net Direct Employment Impacts over 15 Years
(number of FTEs per year and total FTE-years)
Year
1
2
3
4
5
6
1
8
9
10
11
12
13
14
15
Total FTEs
over 15 years
One-Time
Manufacturing
& Installation"
7
7
7
20
Annual O&M"
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
26
Closures
0
0
0
0
Net Change in
Employment
7
8
9
2
2
2
2
2
2
2
2
2
2
2
2
47
a Assumes that one-third of facilities come into compliance in each of 3 years.
Source: U.S. EPA analysis.
Table 6.5 presents the estimated direct employment impact of the final rule and the alternative options. As discussed earlier,
the final rule would increase direct labor requirements over the 15 year period by an estimated 47 FTEs; however under each
of the alternative regulatory options, direct labor requirements would decrease. The total estimated net decrease in direct
labor requirements under the NOD A/Proposed Option of 26,060 FTEs is driven by the 32,729 job losses from estimated
facility closures under the option. The 7,874 job losses from projected facility closures under the 433 Upgrade Options result
in anet decrease in direct labor requirements under the 413 to 433 Upgrade Option of 7,319 FTEs and the All to 433 Upgrade
Option of 7,011 FTEs.
Table 6.5: Estimated 15 Year Net Employment Effects for the
Final Rule and Alternative Regulatory Options
Option
Option I: Selected Option
Option II: NODA/Proposed Option
Option III: 413 to 433 Upgrade Option
Option IV: All to 433 Upgrade Option
Net Change in Employment (FTEs)
47
(26,060)
(7,319)
(7,011)
Source: U.S. EPA Analysis.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects
GLOSSARY
direct labor requirements: employment losses resulting from lost MP&M output caused by the rule and employment
gains caused by compliance expenditures resulting from the rule in the directly-affected industries.
full-time equivalent (FTE): hours of employment equivalent to one full-time job.
FTE-year: one year of full-time employment.
indirect labor requirements: changes in employment in industries that supply directly affected industries resulting from
increased purchases or reduced output in the directly affected industries.
induced labor requirements: changes in employment in industries providing goods and services to people whose
employment is directly or indirectly affected by the rule.
linked industries: industries that sell goods and services to or purchase output from a directly-affected industry.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects
ACRONYM
FTE: full-time equivalent
-------
MP&M EEBA Part II: Costs and Economic Impacts Chapter 6: Employment Effects
REFERENCES
Apogee Research, Inc. 1992. A Report on Clean Water Investment and Job Creation, prepared for National Utility
Contractors Association.
U.S. Bureau of Labor Statistics. 2000. Employment Cost Index - Historical Listing. July 27.
http://stats.bls.gov/ecthome.htm.
U.S. Department of Commerce. 1992 and 1997. Bureau of Economic Analysis, Regional Multipliers: A User Handbook for
the Regional Input-Output Modeling System (RIMSII), Second Edition and Third Edition. Washington, D.C.
U.S. Department of Commerce. 1 997. Bureau of Economic Analysis, The 1992 Benchmark Input-Output Accounts of the
United States.
U.S. Environmental Protection Agency (U.S. EPA), Office of Water. 1993. Job Creation Fact Sheet, internal document.
February.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Sovernment and Community Impact Analysis
Chapter 7: Government and
Community Impact Analysis
INTRODUCTION CHARTER CQNTENTS
7.1 Impacts on Governments 7-1
In this chapter, EPA examines how the final MP&M rule and ?>L1 Impacts Qn Govemments that Operate
MP&M Facilities 7-1
7.1.2 POTW Administrative Costs 7-2
7.2 Community Impacts of Facility Closures 7-6
Glossary 7-7
Acronyms 7-8
alternatives for regulation considered by EPA might affect the
economic welfare of communities, where communities are
defined as States, counties and metropolitan areas.
Communities may suffer adverse impacts from a rule in two
ways. First, local governments may incur costs to comply
with the rule, if they operate MP&M facilities, or to
administer the rule. Second, communities may be affected if
MP&M facility closures resulting from the rule affect the health of their local economies.
This analysis was undertaken in part to meet potential requirements of the Unfunded Mandates Reform Act (UMRA).
However, the final rule does not contain a Federal mandate under UMRA because the rule will not result in expenditures of
$100 million or more for State, local, and tribal governments, in the aggregate, or the private sector in any one year. Thus, the
final rule is not subject to the requirements of the UMRA sections 202 and 205. Although the final rule does not contain a
Federal mandate under UMRA, this chapter summarizes the impacts of the final rule on State and local governments as part of
its decision-making process.
7.1 IMPACTS ON GOVERNMENTS
The analysis considered two effects on governments:1
*• Government-owned MP&M facilities may be subject to the regulation, and therefore incur compliance costs; and
•• Municipalities that own publicly-owned treatment works (POTWs) that receive influent from MP&M facilities
subject to the rule may incur costs to implement the regulation. These include costs of permitting MP&M facilities
that have not been previously permitted, and repermitting some MP&M facilities with existing permits earlier than
would otherwise be required.
7.1.1 Impacts on Governments that Operate MP<&M Facilities
Chapter 5 presented EPA's analysis of the final rule's impacts on government-owned MP&M facilities and on the
governments that own them. The analysis shows that the final rule imposes only limited costs on government-owned
facilities, because 3,327 (88 percent) of the 3,785 facilities are not subject to this regulation (121 General Metals facilities and
3,206 Oily Wastes facilities.) Thus, the final rule applies to 458 government owned facilities.
An estimated 162 government-owned facilities (4.3 percent of the total) would incur costs under the final rule exceeding one
percent of their baseline cost of service. Therefore, 96.3 percent of the government-owned facilities either incur no costs or
are likely to be able to absorb the added costs within their existing budgets. None of the affected governments incur costs that
1 A third potential cost would be implementation cost for direct dischargers. However, all direct dischargers regulated under the final
rule (and any alternative options considered) must already have NPDES permits in the baseline. EPA therefore does not expect
governments to incur incremental administrative costs as a result of this rule for direct dischargers, because governments will incorporate
the new standards into existing NPDES permits.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Sovernment and Community Impact Analysis
cause them to exceed the thresholds for impacts on taxpayers or for government debt burden. EPA therefore has concluded
that the final rule will not impose budgetary burdens on any of the governments that own MP&M facilities.
7.1.2 POTW Administrative Costs
The selected option excludes all indirect dischargers from MP&M regulation. Therefore, there are no POTW administrative
costs associated with the final rule. However, under some of the alternative regulatory options considered, State and local
governments would incur implementation costs for indirect dischargers. This section describes the administrative activities
involved and presents estimates of their costs.
EPA is able to estimate total costs to POTWs, but is not able to estimate the costs to any one POTW, since it is not possible to
determine which POTWs receive discharges from the regulated MP&M facilities. EPA is also not able to assess budgetary
impacts on community-owned POTWs, since available data do not provide estimates of financial characteristics for the
specific POTWs receiving effluent affected by this rule. The relatively low POTW permitting costs per facility and the
potential cost savings estimated in this section, however, suggest that impacts on individual POTWs, if any, would be minor.
a. Permitting activities
The General Pretreatment Regulations (40 CFR Part 403) establish procedures, responsibilities, and requirements for EPA,
States, local governments, and industry to control pollutant discharges to POTWs. Under the Pretreatment Regulations,
POTWs or approved States implement categorical pretreatment standards (i.e., PSES and PSNS).
Discharges from an MP&M facility to a POTW may already be permitted in the baseline.2 For example, industrial users
subject to another Categorical Pretreatment Standard would have a discharge permit. Other significant industrial users (SIU)
that are typically permitted by POTWs include industrial users that:
*• discharge an average of 25,000 gallons per day or more of process wastewater to a POTW,
> contribute a process waste stream which makes up 5 percent or more of the average dry weather hydraulic or organic
capacity of the POTW treatment plant, or
> have a reasonable potential for adversely affecting the POTW's operation or for violating any pretreatment standard.
Since all indirect dischargers have been excluded from the final rule, EPA expects no POTW administrative costs to be
associated with the rule. Under the alternative options, which include indirect dischargers, EPA expects no increase in
permitting costs for facilities that already hold a permit in the baseline. However, governments will incur additional
permitting costs forunpermitted facilities (under the NODA/Proposal option only) and to accelerate repermitting for some
indirect dischargers that currently hold permits. On the other hand, some administrative costs might decrease. For example,
control authorities would no longer have to repermit facilities that are estimated to close as the result of the regulatory options
considered. Communities that own POTWs that must issue permits might therefore experience a change in costs as a result of
some of the alternative regulatory options considered.
b. Data sources
EPA collected information from POTWs to support development of the MP&M effluent guideline. Of 150 surveys mailed,
EPA received responses to 147, for a 98 percent response rate. The POTW Survey asked respondents to provide information
on administrative permitting costs for indirect dischargers, sewage sludge use and disposal costs and practices, and general
information (including number of permitted users and number of known MP&M dischargers). The administrative cost
information included the number of hours required to complete specific permitting and repermitting, inspection, monitoring,
and enforcement activities. Respondents were also asked to provide an average labor cost for all staff involved in permitting
activities. EPA used the survey responses on administrative costs to estimate a range of costs incurred by POTWs to permit a
single MP&M facility.
EPA also used the data provided in the Association of Metropolitan Sewerage Agencies (AMSA) survey to verify and, in
some cases, supplement its own analyses of POTW administrative costs of the final MP&M rule. AMSA provided EPA with
2 Under the General Pretreatment Program, a facility's discharges may be controlled through a "permit, order or similar means". For
simplicity, this report refers to the control mechanism as a permit.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Sovernment and Community Impact Analysis
comments on the proposed MP&M rule and supplemented these comments with a spreadsheet database. The database
contains data from an AMSA formulated survey and covers responses from 176 POTWs, representing 66 pretreatment
programs. The AMSA survey was conducted to verify data from EPA's survey of POTWs and therefore included similar,
although fewer, variables compared to EPA's survey. Elements EPA verified using the AMSA survey include: (1) the
estimated number of indirect dischargers and (2) the unit costs of certain permitting activities, including permit
implementation, sampling, and sample analysis. Elements EPA added to its analysis using the AMSA data include: (1)
screening costs for POTWs that do not currently operate under a pretreatment program and (2) management oversight costs
associated with implementing the MP&M regulation.
c. Methodology
EPA estimated the annualized costs of permitting indirect dischargers under the different regulatory options using the
following steps:
»• Determine the number and characteristics of indirect dischargers that will be permitted under each
regulatory option. Only the NOD A/Proposal option includes costs for permitting an MP&M facility for the first
time. The final rule does not cover indirect dischargers while the other regulatory options only regulate those
indirect dischargers that already hold permits in the baseline. For the NODA/Proposal option, EPA determined how
many new permits would be issued. The NODA/Proposal option only requires concentration-based permits, no
mass-based permits. In addition, EPA determined the number of facilities that currently hold a permit and that
would have to be repermitted sooner than would otherwise be the case.
> Use the data from the POTW Survey to determine a high, middle, and low hourly burden for permitting a
single facility. EPA defined the low and high estimates of hours such that 90% of the POTW responses fell above
the low value and 90% of responses fell below the high value. The median value is used to define the middle hourly
burden.
»• Use the data from the POTW Survey to determine the average frequency of performing certain
administrative functions. For administrative functions that are not performed at all facilities, survey data were used
to calculate the portion of facilities requiring these functions. For example, the survey data show that on average
38.5% of facilities submit a non-compliance report.
»• Multiply the per-facility burden estimate by the average hourly wage. EPA determined a high, middle, and low
dollar cost of administering the rule for a single facility by multiplying the per-facility hour burden by the average
hourly wage. The POTW Survey reported an average hourly labor rate of $39.33 ($2001) for staff involved in
permitting. This is a fully-loaded cost, including salaries and fringe benefits.
*• Calculate the annualized cost of administering the rule. The number of facilities, hourly burden estimate,
frequency estimates, and hourly wage estimates are all combined to determine the total cost of administering the rule.
The type of administrative activities required varies over time and the total administrative cost is calculated over a 15
year time period. EPA calculated the present value of total costs using a seven percent discount rate, and then
annualized the present value using the same seven percent discount rate.
d. Unit costs of permitting activities
EPA estimated unit costs for the following permitting activities:
»• Permit application and issuance: developing and issuing concentration-based permits at previously unpermitted
facilities; providing technical guidance; and conducting public and evidentiary hearings;
•• Inspection: inspecting facilities both for the initial permit development and to assess subsequent compliance;
»• Monitoring: sampling and analyzing permittee's effluent; reviewing and recording permittee's compliance self-
monitoring reports; receiving, processing, and acting on a permittee's non-compliance reports; and reviewing a
permittee's compliance schedule report for permittees in compliance and permittees not in compliance;
*• Enforcement: issuing administrative orders and administrative fines; and
*• Repermitting.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 7: Sovernment and Community Impact Analysis
EPA believes that these functions constitute the bulk of the required administrative activities. To these costs, EPA added a
provision for managerial oversight of 25 percent.3 There are other relatively minor or infrequent administrative functions
(e.g., providing technical guidance to permittees in years other than the first year of the permit, or repermitting a facility in
significant non-compliance), but their costs are likely to be insignificant compared to the estimated costs for the five major
categories outlined above. EPA also added a cost for identifying facilities to be permitted for POTWs that do not currently
operate under a Pretreatment Program. EPA estimates this cost to be approximately $0.8 million. This cost only applies to
the NODA/Proposal Option since facilities subject to the upgrade options already hold permits.
Table 7.1 provides a summary of the estimated unit costs for each permitting activity. Appendix F provides a detailed
discussion of these unit costs.
Table 7.1: Sovernment Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs
Administrative Activity
Develop and issue a
concentration-based permit at a
previously unpermitted facility
Develop and issue a mass-based
permit at a previously unpermitted
facility
Develop and issue a mass-based
permit at a facility holding a
concentration-based permit
Provide technical guidance to a
permittee on permit compliance
Conduct a public or evidentiary
hearing
Inspect facility for permit
development
Inspect facility for compliance
assessment
Sample and analyze permittee's
effluent
Percent of facilities for which
activity is required
100% of unpermitted facilities
(applicable to NODA/Proposal option only)
100% of MP&M facilities being issued a
new mass-based permit
(estimates used for the proposed rule)
100% of MP&M facilities with permit
conversion
(estimates used for the proposed rule)
100% of MP&M facilities being issued a
new concentration-based permit
(applicable to NODA/Proposal option only)
100% of MP&M facilities being issued a
new mass-based permit
(estimates used for the proposed rule)
3.2% of MP&M facilities being issued a
new mass-based or concentration-based
permit
(applicable to NODA/Proposal option only)
100% of MP&M facilities being issued a
new permit
(applicable to NODA/Proposal option only)
100% of MP&M facilities being issued a
new permit
(applicable to NODA/Proposal option only)
100% of MP&M facilities being issued a
new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
One time
One time
One time
One time
One time
One time
L
One Time
Annual
Annual
Typical hours and costs (2001$)
Low
4.0 hours;
$122
4.0 hours;
$122
2.0 hours;
$61
1.5 hour;
$46
2.0 hours;
$61
2.0 hours;
$61
2.2 hours;
$66
2.0 hours;
$61
1.0 hour;
$30
Median
10.0
hours;
$304
13.0
hours;
$396
8.0 hours;
$243
4.0 hours;
$122
4.0 hours;
$122
8.0 hours;
$243
5.0 hours;
$152
3.3 hours;
$101
3.0 hours;
$91
High
40.0 hours;
$1,217
40.0 hours;
$1,217
20.0 hours;
$608 year
12.0 hours;
$365
12.0 hours;
$365
40.0 hours;
$1,217
L
12.0 hours;
$365
10.0 hours;
$304
17.7 hours;
$537
,
3 The 25 percent oversight cost provision is based on comments and data received from the Association of Metropolitan Sewerage
Agencies (AMSA).
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 7: Sovernment and Community Impact Analysis
Table 7.1: Sovernment Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs
....... ..... Percent of facilities for which
Administrative Activity ..... . ,
activity is required
Review and enter data from 1 1 00% of MP&M facilities being issued a
permittee's compliance self- j new permit
monitoring reports I (applicable to NODA/Proposal option only)
Receive, process and act on a 1 38.5% of all indirect dischargers receiving a
permittee's non-compliance 1 new permit
reports I (applicable to NODA/Proposal option only)
Review a compliance schedule I Meeting milestones: 16.0% of all facilities
report 1 issued a new permit - 94% of the 17% who
1 have compliance milestones
I (applicable to NODA/Proposal option only)
i
I Not meeting milestones: 1% of all facilities
1 issued a new permit - 6% of the 17% who
1 have compliance milestones
I (applicable to NODA/Proposal option only)
Minor enforcement action e.g., j 7% of MP&M facilities being issued a new
issue an administrative order I permit
1 (applicable to NODA/Proposal option only)
Minor enforcement action, e.g., I 7% of MP&M facilities being issued a new
impose an administrative fine I permit
! (applicable to NODA/Proposal option only)
Repermit 1 100% of MP&M facilities being issued a
I new permit
! (applicable to NODA/Proposal option only)
Frequency
of activity
2 reports per
year
5 times per
year
2 reports per
year
L
2 reports per
year
L
Annual
Annual
Every 5 years
Typical hours and costs (2001$)
Low
0.5 hours;
$15
1.0 hour;
$30
0.5 hours;
$15
L
1.0 hours;
$30
L
1 .0 hour;
$30
1 .0 hour;
$30
1 .0 hour;
$30
Median
1 .0 hour;
$30
2.0 hours;
$61
1 .0 hour;
$30
L
2.0 hours;
$61
L
3. 7 hours;
$112
5.0 hours;
$152
4.0 hours;
$122
High
4.0 hours;
$122
6.0 hours;
$183
2.7 hours;
$81
L
6.0 hours;
$183
L
12.0 hours;
$365
24.0 hours;
$730
20.0 hours;
$608
Source: U.S. EPA analysis of POTW Survey responses.
z. Results
Table 7.2 summarizes the number of facilities permitted and the estimated POTW permitting costs for the final rule and the
alternative options considered. Appendix F presents detailed calculations of permitting costs for these regulatory options.
The results presented in Table 7.2 reflect three effects of the regulatory options on the cost of permitting indirect dischargers:
(1) incremental costs from permitting currently unpermitted facilities that require a new permit for the first time
(NODA/Proposal option only); (2) incremental costs from repermitting some facilities that currently hold a permit earlier than
would otherwise be the case (within three years rather than within five years); and (3) cost savings from facilities that close as
a result of the regulation and no longer require repermitting.
The first part of the table shows the incremental number of facilities requiring a new permit, requiring early repermitting, or
estimated to close as a result of the rule. The second part of the table presents the resulting change in permitting costs. Costs
are calculated by multiplying the incremental number of facilities in each year by the unit hours and cost per facility for those
activities. All facilities are assumed to receive a permit within a three-year compliance period. Some facilities with existing
permits are repermitted sooner than they otherwise would be on the normal five-year permitting cycle. The cost analysis
calculates incremental costs by subtracting the costs of repermitting these facilities on a five-year schedule from the costs of
repermitting all such facilities within three years. EPA assumes that the required initial permitting activities will be equally
divided over the three-year period. The analysis also calculates the net change in the number of facilities requiring permitting
by subtracting the number of facilities that close due to the rule from the number of facilities that will require new permits
under each regulatory option.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 7: Sovernment and Community Impact Analysis
Table 7.2: POTW Permitting Costs by Regulatory Option
Number of facilities permitted:
New concentration-based permit
New mass-based permit"
Conversion of existing
concentration-based to a mass-
based permit"
Repermitted within 3 rather than
5 years
Regulatory closures (no longer
requiring permits)15
I: Selected
Option
n/a
n/a
n/a
n/a
n/a
II: NODA/Proposal
Option
103
0
0
1,434
722
III: Directs + 413 to
433 Upgrade
0
0
0
382
120
IV: Directs +
413+50%LL Upgrade
0
0
0
566
120
POTW permitting costs over 15 years (2001$):
Net present value
Low
Medium
High
Annualized (at 7%)
Low
Medium
High
Maximum costs in any one year
Low
Medium
High
n/a
n/a
n/a
($422,000)
($1,802,000)
($9,357,000)
($46,000)
($198,000)
($1,027,000)
$1,023,000
$1,022,000
$991,000
($238,000)
($509,000)
($1,982,000)
($26,000)
($56,000)
($218,000)
($6,000)
($4,000)
$1,000
($236,000)
($501,000)
($1,940,000)
($26,000)
($55,000)
($213,000)
($3,000)
$6,000
$48,000
a EPA does not require mass-based permits under any of the option considered for the final rule.
b Some facilities with existing permits will no longer require permitting due to regulatory closures.
Source: U.S. EPA analysis.
Because indirect dischargers were excluded from the final regulation, EPA expects no additional POTW administrative costs
from the final rule. Each of the three alternative regulatory options considered would result in reduced POTW regulatory
costs. These cost savings result from regulatory closures (i.e., facilities that currently hold a permit and would have required
repermitting in the baseline, but that will no longer require repermitting under the regulatory options). The cost savings as a
result of regulatory closures outweigh the additional costs associated with issuing new permits (under the NODA/Proposal
option only) and repermitting on an accelerated, three-year schedule. Estimated annualized cost savings to POTWs for the
three alternative regulatory options range between $0.04 and $1.0 million under the NODA/Proposal option, and between
$0.03 and $0.2 million under the Directs + 413 to 433 Upgrade option and the Directs + 413+50%LL Upgrade option (all
costs in ($2001).
7.2 COMMUNITY IMPACTS OF FACILITY CLOSURES
EPA considered the potential for adverse impact of regulation-induced changes in employment on communities where
MP&M facilities are located. Because EPA anticipates no facility closures and associated employment losses from the final
regulation, EPA expects no employment-related impacts on communities in which MP&M facilities operate. See Chapter 6
for further discussion of potential employment effects.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: Sovernment and Community Impact Analysis
GLOSSARY
publicly-owned treatment works (POTW): a treatment works as defined by section 212 of the Clean Water Act, which
is owned by a State or municipality. This definition includes any devices or systems used in the storage, treatment, recycling,
and reclamation of municipal sewage or industrial wastes of a liquid nature.
(http://www.epa.gov/owm/permits/pretreat/final99.pdf)
Unfunded Mandates Reform Act (UMRA): Title II of the Unfunded Mandates Reform Act of 1995 (UMRA), Public
Law 104-4, establishes requirements for Federal agencies to assess the effects of their regulatory actions on State, local, and
Tribal governments and the private sector. Under §202 of the UMRA, EPA generally must prepare a written statement,
including a cost-benefit analysis, for proposed and final rules with "Federal mandates" that may result in expenditures to
State, local, and Tribal governments, in the aggregate, or to the private sector, of $ 100 million or more in any one year.
Before promulgating an EPA rule for which a written statement is needed, §205 of the UMRA generally requires EPA to
identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-effective or least
burdensome alternative that achieves the objectives of the rule.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 7: government and Community Impact Analysis
ACRONYMS
POTW: publicly-owned treatment works
UMRA: Unfunded Mandates Reform Act
-------
MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts
Chapter 8: Foreign Trade Impacts
8.1 Data Sources 8-1
8.2 Methodology 8-2
8.3 Results 8-3
References 8-5
INTRODUCTION
EPA assessed the likely impacts on foreign trade as a result of
the final rule and the alternatives considered for regulation as
part of the analysis of the rule's effect on the national
economy. Changes in the balance of trade have the potential
to affect currency exchange rates, money supply, interest
rates, inflation, capital flows and labor migration. The
MP&M industry sectors include a substantial portion of the nation's economy, and significant impacts on the balance of trade
in these industries could affect the overall economy.
As part of the facility impact analysis in Chapter 5, EPA assessed potential price increases and output losses that may result
from the rule. EPA assessed the impact of these market-level changes on the U.S. balance of trade using information
provided by MP&M private facility surveys on the source of competition in domestic and foreign markets. The trade analysis
allocates the value of changes in output for each facility that is projected to close due to the rule to exports, imports or
domestic sales, based on the predominant source of competition in each market reported in the surveys.
EPA's analysis predicts no foreign trade impacts as a result of the final rule because no facility closures are expected. This
analysis does not account for factors such as price increases from the rule or the response of foreign producers to the rule, but
EPA believes that these factors will have a negligible effect on the U.S. balance of trade. This chapter analyzes the impact on
foreign trade of the alternative regulatory options for which closures are predicted.
8. l &ATA SOURCES
The assessment of foreign trade impacts is based on the facility closure analysis in Chapter 5. The revenue from any closing
facilities is assumed to be lost output attributable to the rule.
The analysis uses survey responses to determine whether a closed facility's revenues are more likely to be replaced by either
domestic or foreign producers. Question 5 in the Phase I §308 survey asked respondents to identify their "major source of
competition" in each of three markets: local/regional, national, and international. Question 8 in the Phase II survey asked
respondents to identify their "most significant source of competition" in domestic and international markets. Respondents
selected one of the following possible responses:
*• domestic firms,
*• foreign firms,
> no competition in this market, and
*• do not operate in this market.
During the process of clarifying survey answers with respondents, EPA found that most respondents who did not select any of
the sources of competition said that they did not participate in the relevant market. Therefore, if a respondent did not answer
the question regarding the most important source of competition in the domestic or international markets, EPA classified the
facility as not operating in the respective market (domestic or foreign).
The analysis also uses survey responses to determine revenues from exports. The Phase I §308 survey reported the
percentage of revenues earned from domestic customers and from overseas markets. EPA used export share and total
revenues for each facility to calculate export and domestic revenues. The Phase II survey asked respondents to report
8-1
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts
revenues from MP&M exports. EPA then calculated domestic sales by subtracting export revenues from total revenues for
each facility.
The Iron & Steel survey did not report comparable information on the source of competition in domestic and foreign markets.
EPA relied on published trade statistics for the products produced by facilities in the Steel Forming & Finishing subcategory
to assess potential impacts on trade for these facilities.
EPA obtained 1996 import and export data from the Bureau of the Census, Foreign Trade Division for those commodities
determined to be MP&M-related. The data included imports and exports by all facilities in relevant industries, including both
dischargers and non-dischargers.
8.2 METHODOLOGY
The effect of an increase in domestic production costs on the foreign trade balance is influenced by a variety of factors,
including:
> the extent to which domestic producers attempt to raise prices to recover costs,
*• the price elasticity of demand in both domestic and export markets,
> the likely pricing and supply response of foreign producers, and
*• trends in currency exchange rates.
EPA did not attempt to simultaneously model changes in prices, output, and sales in domestic and foreign markets for the
products and services produced by the MP&M industry sectors. As in the facility impact analysis described in Chapter 5, the
trade analysis relies on a sequential analysis that assesses price increases and then predicts output adjustments based on
closures. EPA used facilities' own assessments of their competitive status relative to foreign producers, as reported in the
survey, to assess impacts of these output adjustments on the balance of trade.
EPA expects that foreign firms would replace some but not all of the output from any closing facilities. Domestic firms that
remain open or enter the market might also win customers that used to buy from the closing facility. Revenues lost by closing
facilities are assigned to domestic or foreign producers as follows:
*• Lost exports: If a closing facility stated that most of its international competition came from foreign firms, then EPA
assigned the facility's export revenues to foreign firms. U.S. exports would therefore decline by the amount of the
closing facility's exports. If the facility identified domestic businesses as its greatest source of competition in foreign
markets, then EPA assigned the closing facility's export revenues to other domestic firms. Closures of these
facilities, which reported relatively low foreign competition for exports, will have no impact on U.S. exports under
the expected scenario.
> Increased imports: If a closing facility identified foreign producers as the main source of domestic sector
competition, then EPA assigned the facility's lost domestic revenues to foreign firms. Imports would increase by the
same amount. If other domestic businesses posed the strongest competition, then EPA assigned the closing facility's
domestic sales to other U.S. producers, and imports would remain constant.
The survey data collected for the Steel Forming and Finishing facilities did not provide export data. EPA assumed that the
ratio of exports to value of shipments for any closing facilities in the analysis was the same as the ratio for the industry as a
whole.
From the estimated changes in exports and imports, EPA calculated the net trade impact (reduction in exports plus increase in
imports) and compared this value to baseline trade levels for (1) all commodities and (2) MP&M sector commodities, only.
8-2
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 8: Foreign Trade Impacts
8.3 RESULTS
Chapter 3 provides an overview of exports, imports and the balance of trade in the MP&M industry sectors. U.S. MP&M
producers as a group exported products with a value of $345 billion in 1 996. Imports to the U.S. of the same products in
1996 totaled $421 billion, resulting in an overall net MP&M commodity trade deficit of $76 billion. Some MP&M sectors
contribute to a positive commodity trade balance (e.g. aircraft, with a $27 billion positive balance in 1996). In other sectors,
substantially more products are imported than exported (e.g. motor vehicles, with a net negative balance of $63 billion.)
Table 8.1, below, summarizes the estimated impact of the final rule and alternative options on the U.S. balance of trade for all
commodities. Because EPA's analysis indicates that the final rule will cause no facility closures, EPA expects that the final
regulation will not affect the balance of trade. As shown in the table, the other regulatory options would have a negligible
impact on U.S. imports, exports, and the national trade balance. Option II (NODA option) results in the most closures and
thus the largest trade impacts. However, even in this option, projected imports increase by only $85 million, or slightly more
than one hundredth of one percent of baseline imports, and exports decrease by only $55 million, less than one hundredth of
one percent of baseline exports. The net result for the NODA option is an insignificant 0.08 percent decline in the national
balance of trade.
Table 8.1: Estimated National Impacts on Total U.S. Foreign Trade
(millions, 2001$)
Baseline
Change due to the ruleb
Change due to the rule
0}
Post-compliance
% Change from baseline
1996 Exports
$666,321
Option I: Selected
n/a
ytion II: Proposed/NC
($55)
$666,266
1996 Imports
$847,767
Option
n/a
IDA Option
$85
$847,852
Trade Balance"
($181,446)
n/a
($141)
($181,587)
: :
-0.008% 0.010% 0.078%
: :
Option III: 413 to 433 Upgrade Option
Change due to the rule
Post-compliance
% Change from baseline
: :
$0 $22 ($22)
$666,321 $847,789 ($181,468)
0% 0.0026% 0.012%
: :
Option IV: All to 433 Upgrade Option
Change due to the rule
Post-compliance
% Change from baseline
$0 $22 ($22)
$666,321 $847,789 ($181,468)
0% | 0.0026% | 0.012%
a Trade balance is equal to exports minus imports.
b There were no regulatory closures in the selected option, and so this analysis predicts no
foreign trade impacts.
Source: Bureau of Census and U.S. EPA analysis.
Table 8.2 shows regulatory impacts on foreign trade in MP&M industry commodities. As noted above, EPA estimates that
the final rule will cause no closures and thus have no foreign trade impacts. In the other options, the projected changes in
exports and imports represent only an insignificant percentage of commodity trade in the MP&M industry sectors. The
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 8: Foreign Trade Impacts
largest impacts occur in Option II (NODA Option), but even these impacts result in only a 0.2 percent decline in the net trade
balance in these industries.
Table 8.2: Estimated National Impacts on MP&M-Related Foreign Trade
(millions, 2001$)
Baseline
Change due to the rule6
0
Change due to the rule
Post-compliance
% Change from baseline
Opt
Change due to the rule
Post-compliance
% Change from baseline
Op
Change due to the rule
Post-compliance
% Change from baseline
1996 Exports
$345,274
Option I: Selected
n/a
ption II: Proposed/NC
($55)
$345,219
-0.016%
ion III: 413 to 433 Up
$0
$345,274
0%
tion IV: All to 433 Up
$0
$345,274
0%
1996 Imports
$421,015
Option
n/a
IDA Option
$85
$421,100
0.020%
grade Option
$22
$421,037
0.005%
grade Option
$22
$421,037
0.005%
Trade Balance"
($75,741)
n/a
($141)
($75,882)
0.186%
($22)
($75,763)
0.030%
($22)
($75,763)
0.030%
a Trade balance is equal to exports minus imports.
b There were no regulatory closures in the selected option, and so this analysis predicts no
foreign trade impacts.
Source: Bureau of Census and U.S. EPA analysis.
The analysis of trade impacts does not explicitly account for responses to price increases caused by the rule, as noted
previously. However, EPA expects little change in exports and imports as a result of the minimal price increases predicted for
the final rule. The estimated price increases are less than one half of one percent in all sectors (see Table 5.4 in Chapter 5).
Annual rates of inflation for the United States' major trading partners are generally well above the projected increases in
MP&M prices, and price increases in the projected range are not likely to materially affect the terms of U.S. trade in MP&M
products.1
1 The following are 1990-98 annual inflation rates, as measured by the GDP implicit deflator, for nine of the U.S.' s top ten trading
partners: Canada 1.4%, Mexico 19.5%, Japan 0.2%, China 9.7%, Germany 2.2%, United Kingdom 3.0%, Republic of Korea 6.4%, France
1.7%, and Singapore 2.1%. The annual change in the U.S. GDP deflator over the same period is 1.9% (Data were not reported for
Taiwan.) World Bank, 2000 World Development Indicators, Table 4.16.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts
REFERENCES
U.S. Department of Commerce, Bureau of the Census. 1997. Current Industrial Reports: Steel Mill Products.
U.S. Department of Commerce, Bureau of the Census, Foreign Trade Division. 1996. FT900.
http://www.census.gov/foreign-trade/Press-Release/96_press_releases/Final_Revisions_1996/.
World Bank, 2000 World Development Indicators.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 8: Foreign Trade Impacts
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
Chapter 9: Firm Level, New Source,
and Industry Impacts
INTRODUCTION
The previous chapters assessed impacts on MP&M
facilities, on governments and communities, and on the U.S.
balance of trade. This chapter considers impacts on private
businesses in more detail, by addressing three categories of
impacts. First, the analysis of impacts on firms builds on
the facility impact analysis to assess whether firms that own
multiple facilities are likely to incur more significant
impacts than indicated by the facility impact analysis.
Second, the new source facility impact analysis considers
whether the final rule might impose disproportionate
burdens on new sources relative to existing sources, and
thereby pose a barrier to new entry. Third, this chapter
discusses potential industry-level impacts of the final rule.
9.1 Firm Level Impacts 9-1
9.1.1 Sources 9-1
9.1.2 Methodology 9-2
9.1.3 Results 9-2
9.2 New Source Impacts 9-3
9.2.2 Methodology 9-4
9.2.3 Results 9-5
9.3 Industry Level Impacts 9-7
Glossary 9-9
Acronyms 9-10
References 9-11
9.1 FIRM LEVEL IMPACTS
EPA analyzed economic impacts on firms for the following reasons:
»• Impacts may be more significant at the firm level than at the facility level if a firm owns a number of facilities that
incur significant costs. To the extent allowed by the available data, the analysis therefore looks at the combined
effect of the facility compliance costs for all facilities owned by a given firm.
> A firm-level analysis is needed to assess impacts on small businesses, as required by the Regulatory Flexibility Act
and SBREFA. Certain findings from the firm-level analysis are used in the small business impact analysis presented
in the following Chapter 10.
9.1.1 Sources
The firm-level analysis begins from the results of the facility-level analysis presented in Chapter 5, supplemented by firm-
level information provided by the MP&M facility surveys and publically available information.
EPA was not able to conduct a rigorous national analysis of firm-level impacts because the sample frame used to provide
national estimates from surveyed facilities reflects the population of facilities rather than firms. EPA therefore analyzed
impacts for a hybrid dataset of MP&M firms that includes both national estimates (for single-facility firms) and sample firms
(for multiple-facility firms), The Agency believes that the analysis of firm-level impacts presented in this chapter provides a
useful indication of national firm-level impacts, however, for two reasons:
»• Most MP&M facilities are single-facility firms. The survey facility sample weights can be used to extrapolate to the
national number of firms for these single-site firms.
»• EPA requested voluntary information in the Phase II detailed questionnaires on other MP&M facilities owned by the
firms responding to the survey for a sampled facility. EPA aggregated multiple-facility compliance costs to the firm-
level by including costs for all surveyed facilities and, for the Phase II survey, facilities identified in voluntary
responses.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
It is unlikely that firm-level impacts would be material among all MP&M firms in the nation, if this partial analysis does not
indicate significant impacts among the firms identified in this analysis.
9.1.2 Methodology
The various surveys asked respondents to provide firm-level revenues for the parent firm. For single-facility firms, firm
revenue and compliance costs are identical to those for the facility. For firms that own more than one sample facility,
compliance costs are the sum of costs for all facilities reported on in the survey.
In Part V of the detailed Phase II questionnaire, respondents had the option to submit additional voluntary data for other
MP&M facilities owned by the same parent firm. EPA did not perform a detailed engineering analysis to develop detailed
estimates of compliance costs for these facilities; however, EPA used the detailed estimates of compliance costs to estimate
costs for these additional facilities. EPA assumed that these additional facilities would have the same average compliance
costs as facilities in the same subcategory, flow range, and discharge type for which detailed cost estimates were developed.
EPA then grouped together all facilities with a common parent firm from the Phase I, Phase II and Iron and Steel surveys.
For each firm in the analysis, firm-level compliance cost is:
= -
where:
CCflrm = firm-level compliance cost
CCj = compliance cost for surveyed facility i owned by the firm
Firm-level compliance costs were compared to firm revenues. EPA judged that firms with compliance costs less than one
percent of revenues would not be materially affected by the regulation. EPA identified firms as subject to potentially more
serious impacts if their compliance costs exceeded three percent of revenues.
All firm-level data were inflated to 2001 dollars using the Producer Price Index (PPI), as described in Chapter 5.
9.1.3 Results
As noted in the introduction, the Agency was not able to estimate the national numbers of firms that own MP&M facilities
precisely, because the sample weights based on the survey design represent numbers of facilities rather than firms. EPA
assumed that the national facilities that are represented by the 307 sample single-site firms that remain open in the baseline are
also all single-site firms. Based on this assumption, EPA estimated that 26,472 of 36,480 (or 73 percent) of private MP&M
facilities nationwide are single-facility firms.
In addition, from the survey responses, EPA identified 389 sample facilities that are owned by 276 multi-facility firms. It is
not known how many multi-facility firms exist at the national level, so EPA included these 276 firms in the firm-level analysis
without extrapolation to the national level.
The combined set of 26,748 firms (26,472 national-level single-facility firms plus 276 sample multi-facility firms) provided
the basis for the firm-level analysis. This total does not represent a valid national total for the number of affected MP&M
firms. Nonetheless, this analysis provides a reasonable indication of likely firm -level impacts, given the large number of
single-facility firms and the use of Part V facility data to supplement the sample facility data for multi-facility firms.
Table 9.1 presents the number of firms in the firm-level analysis. Of the 26,472 facilities that are single-facility firms, 25,297
are owned by potentially small firms. Of the 276 firms that own more than one sample facility, 85 are potentially small firms.
9-2
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 9: Firm Level, New Source, and Industry Impacts
Table 9.1: Number of Privately Owned Facilities and Firms by Firm Type and Size"
National number of single-facility
firms (304 unique sample firms)
Sample multi -facility firms
Number of firms in the firm-level
analysis
Total Firms
26,472
276
26,748
^^ ^,
Owned by a small firm
25,297
85
25,382
Owned by a large firm
1,175
191
1,366
a Excludes firms whose only facilities close in the baseline.
Source: U.S. EPA analysis.
Table 9.2 presents estimated firm-level impacts of the MP&M rule. None of the firms in the analysis incur after-tax
compliance costs greater than 1 percent of annual revenues. Of the 1,027 firms that incur any costs at all, none close or incur
moderate impacts as a result of the rule.
Table 9.2: Firm-level After-Tax Annual Compliance Costs
as a Percent of Annual Revenues
Firm Type
Single-site
Multi-site
Total
Number of
Firms in the
Analysis3
26,472
276
26,748
Number and Percent with After-Tax Annual Compliance Costs/Annual
Revenues Equal to:
0% (no costs)
Number
25,453
269
25,722
%
96.2%
97.5%
96.2%
Between 0% and 1%
Number
1,019
8
1,027
%
3.8%
2.9%
3.8%
>1%
Number
0
0
0
%
0.0%
0.0%
0.0%
a Single-site firms whose only MP&M facilities close in the baseline are excluded. To be conservative, EPA
included compliance costs for facilities that are owned by multi-site firms but predicted to be baseline
closures in the facility impact analysis.
Source: U.S. EPA analysis.
This analysis is likely to overstate costs at the firm level because it does not consider the actions a multi-facility firm might
take to reduce its compliance costs under the final rule. These include transferring functions among facilities to consolidate
wet processes and take advantage of scale economies in wastewater treatment.
9.2 NEW SOURCE IMPACTS
This section assesses the impacts of New Source Performance Standards (NSPS) and Pretreatment Standards for
New Sources (PSNS) limitations on new direct and indirect MP&M dischargers. EPA examines the impact of these
regulations on new dischargers to determine whether new source limitations may pose sufficient financial burden on new
facilities to constitute a material barrier to entry of new establishments into the MP&M industry sectors. The first section
summarizes the framework for assessing new source impacts and the second section reviews the findings from our analysis.
Disproportionate regulatory burdens for new sources could cause adverse industry-level outcomes in the long-run in several
ways:
* Imposing more significant costs on new facilities can make existing sources more competitive than new sources,
causing barriers to new entry.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
»• Barriers to entry may increase the market power of existing firms and could discourage competition over time, with
resulting losses in market efficiencies.
> Creating a competitive advantage for existing facilities may hinder technological innovation, with resulting losses in
productivity.
9.2.1 Methodology
EPA used the existing facility database, sample-weighted, as the basis for the new source analysis. This assumes that future
entrants to the industry will look the same as indicated by the sample of facilities in the existing facility database.
To assess the potential impact of new source limitations, EPA assessed compliance costs for two cases: (1) the capital and
operating cost of compliance systems for a new facility built in compliance with existing new source discharge limits ("current
limits"), and (2) the capital and operating cost of compliance systems for a new facility built in compliance with discharge
limits under consideration ("revised limits"), which would be more stringent than the current new source limits. The
estimated capital costs for these cases account for the lower cost of a new-construction installation compared to retrofit
construction at existing facilities. These compliance cost estimates are described in detail in the Technical Development
Document. For analyzing the additional cost burden of meeting new limits, EPA calculated the incremental cost of
compliance as the cost of meeting the revised limits less the cost of meeting current limits.
As noted above, EPA based its analysis of new source limits on the economic and financial information for the sample of
facilities in the existing facility database. The new source analysis excludes sample facilities that are projected to close or to
experience moderate economic impacts in the baseline, since the economic characteristics of these financially-weak facilities
are unlikely to be representative of new facilities. In addition, EPA excluded some sample facilities from the analysis because
of issues in the engineering estimation of compliance costs.
The analysis assumes that new sources would benefit from price increases resulting from the final rule for existing sources in
the same way as existing sources. EPA therefore increased the average baseline revenue for new facilities by the average
percentage price increase estimated for existing facilities in each subcategory/discharge category, to calculate post-regulation
revenues for new sources. This effect of this adjustment on new facility revenue is minor.
To test of financial burden of revised limits and whether this burden might pose a material barrier to entry for new
establishments, EPA compared the incremental total annualized cost, after-tax, with facility revenue (cost-to-revenue ratio).
EPA classified the results in ranges as follows, fraction of sample-weighted facilities with cost-to-revenue ratio of less than
one percent, one to three percent, three to five percent, and greater than five percent.
Table 9.3 shows the total number of privately owned MP&M facilities in the survey sample, the number of existing facilities
excluded from the new source analysis, and the number of existing facilities used in this analysis.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 9: Firm Level, New Source, and Industry Impacts
Table 9.3: Number of Existing Facilities Used in New Source Analysis
Subcategory
General Metals
MF Job Shop
Non-Chromium Anodizer
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Dock
Discharger
Type
Direct
Indirect
Direct
Indirect
Direct0
Direct
Indirect
Direct
Indirect
Direct
Direct
All Subcategories
Total Number of
Private MP&M
Facilities"
888
10,419
12
1,530
122
2,108
23,292
8
840
6
6
39,230
Number of Existing
Facilities Excluded
from New Source
Analysis*
181
1,824
0
165
29
936
6,148
0
288
0
0
9,571
Number of Existing
Facilities Included in
New Source Analysis
707
8,594
12
1,365
93
1,172
17,144
8
552
6
6
29,659
a EPA did not estimate new source impacts for municipal operations because "barrier to entry1 is not a relevant consideration.
b EPA excluded an existing facility from the new source analysis either because it was financially weak in the baseline or
because the engineers were unable to accurately estimate compliance costs.
° For the analysis of new source limit impacts on the direct discharge Non-Chromium Anodizer category, EPA used sample
facility information for indirect dischargers. The final sample facility database contained no observations for direct
dischargers.
Source: U.S. EPA analysis.
9.2.2 Results
Table 9.4 summarizes (1) the currently applicable discharge limit or technology option for new sources in each subcategory
and discharge status, and (2) the alternative discharge limits or technology option that EPA considered in assessing whether
revised new source discharge limits would constitute a barrier to entry. See Preamble Section VI and the Technical
Development Document for discussion of the specific discharge limits and technology options that EPA considered for
revised new source discharge limits.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 9: Firm Level, New Source, and Industry Impacts
Table 9.4: Current New Source Requirements and Potential Revised New Source Requirements
Subcategory
General Metals
MF Job Shops
Non-Chromium Anodizer
Oily Waste
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Dock
Discharge
Type
Direct
Indirect
Direct
Indirect
Direct
Direct
Indirect
Direct
Indirect
Direct
Direct
Current New Source
Requirements
40 CFR 433
40 CFR 433
40 CFR 433
40 CFR 433
40 CFR 433
Estimated existing baseline
Estimated existing baseline
40 CFR 433
40 CFR 433
Option 6
Option 10
Revised New Source
Requirements
"Modified" Option 2,
(Two-Stage Precipitation)
Option 2
"Modified" Option 2,
(Two-Stage Precipitation)
Option 2
Option 2
Option 6
Option 6
Option 2
Option 2
Option 10
Option 8
Source: U.S. EPA analysis.
Table 9.5 reports the estimated percentages of new facilities incurring cost-to-revenue impacts of: (1) less than one percent,
(2) one to three percent, (3) three to five percent, and (4) greater than five percent. As discussed earlier, these estimates are
based on estimated incremental new source compliance costs compared to revenues for existing facilities in the MP&M
survey universe.
From this analysis, EPA found that revised new source limits would create a barrier to entry for direct discharging facilities in
the General Metals, Metal Finishing Job Shops, and Non-Chromium Anodizer subcategories and indirect discharging
facilities in the General Metals, Metal Finishing Job Shops, Printed Wiring Board, and Oily Wastes subcategories. On the
basis of this finding, EPA decided against issuing revised new source discharge limits for these subcategories. The new
source analysis indicated that revised new source limits would not create a barrier to entry for direct discharging facilities in
the Oily Wastes, Printed Wiring Board, and Railroad Rebuilders subcategories. This finding supported EPA's decision to
promulgate new source limits for the Oily Wastes direct discharger subcategory. Although the economic analysis did not
indicate a barrier to entry for the Printed Wiring Board and Railroad Rebuilders direct dischargers subcategories, EPA
decided against issuing new source limits for these subcategories based on other technical considerations as discussed in
Preamble Section VI.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 9: Firm Level, New Source, and Industry Impacts
Table 9.5: Estimated Percentages of New Facilities by Cost-to-Revenue Impact Ranges
Subcategory
General Metals
MF Job Shop
Non-Chromium Anodizer
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Dock
Discharger
Type
Direct
Indirect
Direct
Indirect
Direct
Direct
Indirect
Direct
Indirect
Direct
Direct
After-Tax Compliance Costs as a Percent of Revenue
p
5%
2%
1%
100%
6%
49%
0%
0%
0%
5%
0%
0%
Source: U.S. EPA analysis.
9.3 INDUSTRY LEVEL IMPACTS
Potential industry-level impacts include price increases, reduced competitiveness within the domestic industry and in world
markets, and reduced rates of innovation. EPA did not perform a sector-specific analysis for several reasons:
*• Sector-level impacts are complicated by the large number of product and service markets included in the MP&M
category (e.g., over 200 SICs and three activities - manufacturing, rebuilding, and repair).
*• Revenue and cost information is not available on a product by product basis, so it is impossible to link price
increases to individual products, and
> Many MP&M facilities derive revenue from multiple industry sectors.
EPA's analysis of facility- and firm-level impacts suggests, however, that material industry-level impacts are unlikely in any
of the affected sectors.
The Agency does not expect any industry level impacts from the MP&M regulation because of: (1) the low number of
facilities that will have costs, (2) the absence of regulatory closures, and (3) the absence of moderate impacts. Of the
estimated 89,000 facilities performing MP&M activities, slightly over half, or about 45,000, do not discharge water and thus
will not be affected by the rule. An additional 3,593 discharge water but are expected to close in the baseline. Of the
remaining 40,265 facilities that do discharge water and remain open in the baseline, EPA estimates that only 1,380 will incur
costs under the final rule. That so few MP&M industry facilities incur costs results from the rule's subcategory exclusions
and low-flow cutoffs.
As discussed in Chapter 5, EPA estimates that no facilities will close or incur moderate impacts as a result of the final
regulation. Given no regulatory closures or moderate impacts, EPA concludes that the final rule is unlikely to impose
significant costs on a substantial number of facilities in the MP&M industry as a whole or at the subcategory level.
Chapter 5 also presented information on the prices increases predicted to occur in each industry sector due to the final rule.
Table 5.4 in Chapter 5 presented EPA's estimates of price increases by sector. Projected price increases are less than one half
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
of one percent for all sectors. Price increases of these magnitudes are unlikely to impose burdens on customers of the
regulated facilities or substantially affect MP&M producers' position relative to competitive products (e.g., products made
with plastics) or foreign producers. Price increases may affect only some components of a product. In these cases, prices to
end-users would rise even less than the amounts detailed in Chapter 5.
EPA does not expect the final rule to affect the rate of technological innovation in the MP&M industry. Innovation impacts
could result if the rule discouraged new entry, contributed to increased concentration in the affected industries, or specified
the use of particular technologies. The following factors suggest that these conditions do not apply for the final rule:
> EPA's analysis of new source impacts presented in the previous section suggests that the final rule will not affect
entry of new businesses in the regulated sectors. The final rule will increase the investment required to build a new
facility somewhat. However, the increased capital costs are generally small relative to the overall financial resources
of the MP&M facilities, as indicated by the results of the facility impact analysis. In addition, the low flow cutoffs
applicable to a large number of MP&M facilities reduce the potential impacts of large capital requirements on small
facilities.
•• Given the small fraction of facilities regulated in each sector, and absence of closures of moderate impacts for the
final regulation, EPA does not expect the rule to increase concentration in any of the MP&M sectors.
*• The rule does not require the use of specific production or pollution control processes or technologies. Rather, it
specifies a performance standard, based on levels of pollutants in wastewaters that have been shown to be achievable
by available technologies. Facilities have the flexibility to achieve these limitations using a variety of approaches,
which is likely to encourage rather than discourage innovation in production and pollution control processes.
The final rule may affect the relative competitive position of different firms and facilities in those sectors that incur costs.
Facilities that may benefit from the rule include those that: (1) do not discharge wastewater, (2) are eligible for the
subcategory exclusions and low-flow cutoffs, (3) already have treatment in place, or (4) can more easily make process
changes to reduce pollutant loads.
Facilities that have little or no treatment in place and that discharge substantial pollutant loads may become less competitive.
The final rule may level the competitive playing field for facilities that have taken steps to reduce their environmental
impacts, relative to facilities that have avoided investments to reduce or eliminate pollutant discharges. EPA views these
effects as beneficial, given that the final regulation does not have significant impacts on the industry as a whole, and as long
as the rule does not disproportionately impact small entities as a group (impacts on small entities are addressed in the next
chapter).
-------
MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
GLOSSARY
new source: Any building, structure, facility, or installation from which there is or may be a discharge of pollutants, the
construction of which commenced after promulgation of standards of performance under Section 306 of the Clean Water Act
which are applicable to such source; and which (1) is constructed at a site at which no other source is located; (2) totally
replaces the process or production equipment that causes the discharge of pollutants at an existing source; or (3) consists of
processes that are substantially independent of an existing source at the same site.
New Source Performance Standards (NSPS): effluent limitations for new direct dischargers based on the best
available demonstrated control technology. NSPS represents the greatest degree of effluent reduction attainable through the
application of the best available demonstrated control technology for all pollutants (i.e., conventional, nonconventional, and
priority pollutants). In establishing NSPS, EPA considers the cost of achieving the effluent reduction and any non-water
quality environmental impacts and energy requirements.
Pretreatment Standards for New Sources (PSNS): pretreatment standards for new indirect dischargers, designed to
prevent discharges of pollutants that pass through, interfere with, or are otherwise incompatible with the operation of POTWs.
Addresses all pollutants (i.e., conventional, nonconventional, and priority pollutants). Based on the same factors as are
considered in promulgating NSPS.
Producer Price Index (PPI): a family of indexes that measures the average change over time in the selling prices received
by domestic producers of goods and services. PPI's measure price change from the perspective of the seller. This contrasts
with other measures, such as the Consumer Price Index (CPI), that measure price change from the purchaser's perspective.
Sellers' and purchasers' prices may differ due to government subsidies, sales and excise taxes, and distribution costs.
(http://stats.bls.gov/ppifaq.htmSl)
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 9: Firm Level, New Source, and Industry Impacts
ACRONYMS
NSPS: New Source Performance Standards
PPI: Producer Price Index
PSNS: Pretreatment Standards for New Sources
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
REFERENCES
U.S. Bureau of Labor Statistics, Producer Price Index Revision-Current Series. On-line database at
http://stats.bls.gov/ppihome.htm.
U.S. Environmental Protection Agency (U.S. EPA). 2003. Technical Development Document for the Final Effluent
Limitations Guidelines and Standards for the Metal Products & Machinery Point Source Category. February.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 9: Firm Level, New Source, and Industry Impacts
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
Chapter 10: Small Entity Impact
Assessment
INTRODUCTION
The Regulatory Flexibility Act (RFA), as amended by the
Small Business Regulatory Enforcement Fairness Act
(SBREFA), requires EPA to consider the economic impacts
a rule will have on small entities. RFA/SBREFA requires
an agency to prepare a Regulatory Flexibility Analysis
for any rule subject to notice and comment rulemaking
requirements, unless the Agency certifies that the rule will not
have a significant economic impact on a substantial number
of small entities (Small Business Regulation Enforcement
Fairness Act of 1996, P.L. 104-121, Section 243).
CHAPTER CONTENTS
10.1 Defining Small Entities 10-2
10.2 Methodology 10-4
10.3 Results 10-4
10.3.1 Number of Affected Small Entities 10-4
10.3.2 Impacts on Facilities Owned by Small
Entities 10-5
10.3.3 Impacts on Small Firms 10-6
10.4 Consideration of Small Entity Impacts in
Developing of the Final Rule 10-7
Glossary 10-8
Acronyms 10-9
References 10-10
The economic analysis prepared for the 1995 MP&M Phase I
proposal indicated that large numbers of small facilities could
be impacted by the rule and that a significant number of publically-owned treatment works (POTWs) would also be affected
by the rule.
EPA addressed this issue by crafting the final rule to exclude as many small facilities as possible while still covering as much
of the pollutant discharge as possible. With this in mind, EPA sought, from the beginning, to design a combined phase
regulation that would not unreasonably burden small entities.
To ensure that all small entities were considered in developing the MP&M regulation, EPA developed, administered, and
analyzed questionnaires for all entities that could potentially be affected, including: privately- and government-owned
facilities that would have to comply with the regulation, and POTWs that receive MP&M discharges. The Agency balanced
several factors when defining the final rule, including:
•• the predominance of small entities in the MP&M industry,
•• the pounds of pollutants discharged by large and small facilities,
»• the toxicity of the pollutants discharged by large and small facilities,
»• the need for additional reduction in effluent discharges from the MP&M industry,
»• the need to achieve these reductions without imposing unreasonable burdens on small entities, and
> the need to minimize burden on POTWs.
Given the large number of small entities that could be affected by the final rule, EPA undertook detailed analyses of potential
small entity impacts and carefully considered the findings from this analysis in defining the final rule. From these assessments
and based on the coverage and requirements of the final rule, EPA concluded that the final rule will not have a significant
economic impact on a substantial number of small entities. EPA has therefore not prepared a Regulatory Flexibility Analysis.
The following sections of this chapter describe the methodology and results of EPA's small entity impact assessment, and
discuss EPA's consideration of small entity impacts in designing the rule.
10-1
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
10.1 bEFiNiNe SMALL ENTITIES
EPA identified small entities using Small Business Administration (SBA) size threshold guidelines.1 These thresholds define
the minimum firm-level employment or revenue size, by industry (four-digit SIC codes), below which a business qualifies as a
small business under SBA guidelines. The SBA guidelines also set a threshold for small public sector entities. A small
government is one that serves a population of 50,000 or less. MP&M facilities were determined to be owned by a small
entity if the parent firm or government fell below the SBA threshold.
The SBA guidelines for businesses use either employment or revenue to measure size, depending on the specific four-digit
SIC industry. Manufacturing industries generally have employment size thresholds, while non-manufacturing industries
typically have revenue size thresholds. EPA used employment-based thresholds for the manufacturing portion of each
MP&M sector, and separate non-manufacturing thresholds for sectors that include non-manufacturing activities (e.g.,
maintenance and repair).
EPA selected the SBA threshold occurring most frequently among each sector's four-digit SIC codes as the sector threshold.
Table 10.1 presents the resulting employment size thresholds for manufacturers.
Table 10. 1 : Small Business MP&M Sector
Thresholds for Manufacturers
MP&M Sector
Aerospace
Aircraft
Bus and Truck
Electronic Equipment
Hardware
Household Equipment
Instrument
Job Shop
Mobile Industrial Equipment
Motor Vehicle
Office Machine
Ordnance
Other Metal Products
Employees
1,000
1,000
500
750
500
500
500
500
500
500
1,000
1,000
500
Precious and Non.Precious Metals 500
Printed Circuit Board
Railroad
Ship and Boat
Stationary Industrial Equipment
Steel Forming & Finishing
500
1,000
1,000
500
1,000
Source: SBA and U.S. EPA analysis.
1 The SBA website provides the most recent size thresholds at http://www.sba.gov/regulations/siccodes.
2 The SBA thresholds for four-digit SICs were not used directly because the Phase II §308 survey reports revenues by MP&M sector
but does not report facility SIC codes.
10-2
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
Table 10.2 presents the employment size thresholds for non-manufacturers, which are based on revenue except for the
railroad sector. Some sectors do not have non-manufacturing industries and do not appear in this table.
Table 10.2: Small Business MP&M Sector
Thresholds for Non- Manufacturers
MP&M Sector
Aircraft
Bus and Truck
Household Equipment
Instrument
Motor Vehicle"
Office Machine
Other Metal Products
Precious and Non-Precious Metals
Railroad
Ship and Boat"
Stationary Industrial Equipment
Revenue
$5,000,000
$5,000,000
$5,000,000
$5,000,000
$5,000,000
$18,000,000
$5,000,000
$5,000,000
1,500"
$5,000,000
$5,000,000
a Also has a threshold of 100 employees.
b Employees.
° Also has a threshold of 500 employees.
Source: SBA and U.S. EPA analysis.
EPA classified facilities as manufacturing or non-manufacturing and selected an MP&M sector threshold based on the sector
from which they received the most revenue, as reported in the §308 surveys.3 EPA then compared the firm-level employment
or revenue for the firm owning each facility to the appropriate manufacturing or non-manufacturing threshold for that sector.
The Phase II survey asked each respondent to provide firm-level employment and revenue data. The Phase I survey also
asked for firm-level revenue but not for firm employment. This omission did not matter in the case of single facility
businesses, where the facility's reported employment is the firm-level employment. For multiple-facility firms in the Phase I
survey, EPA estimated firm-level employment by assuming that the number of employees per revenue dollar for the firm was
the same as the employees per dollar at the facility. Thus,
= E,
R*
(10.1)
'facility
R
facility
where:
E
E
Rfl
Rf,
facility
firm-level employment,
facility-level employment,
firm-level revenue, and
facility-level revenue.
EPA identified facilities operated by governments that serve a population of 50,000 or fewer as being operated by small
government entities. The §308 municipal survey responses provided population data in most cases, which EPA supplemented
using the Bureau of the Census online 1990 Population Census database (Bureau of the Census.)
3 The §308 MP&M surveys did not collect firm-level revenues by sector and therefore cannot be used to assign a unique sector to
each firm. The assignment of a threshold was therefore based on the facility-level revenues by sector.
10-3
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
10.2 METHODOLOGY
EPA used several impact measures for its small entity impact analysis. First, EPA reviewed the results of the facility impact
analyses described in Chapter 5 according to business size to determine whether facilities owned by small entities are
disproportionately subject to moderate impacts at the facility level. Second, EPA calculated the ratio of annualized
compliance costs to facility revenues and examined the distribution of this ratio for facilities owned by small versus large
firms.
The analysis excluded facilities that the facility impact analysis identifies as baseline failures (see Chapter 5).
10.3 RESULTS
10.3.1 Number of Affected Small Entities
There are an estimated 40,265 MP&M facilities nationwide (excluding baseline closures). A large number of these facilities
are owned by small entities, based on SBA thresholds. Table 10.3 shows the total number of facilities operating in the
baseline and the number owned by small entities. Overall, 73 percent of all MP&M facilities are owned by small entities.
Table 10.3: Number and Percent of MP&M Facilities Owned by Small Entities
Type of Facility
Owned by small business
Owned by small government
Total owned by small
entities3
Number of Facilities of
all Sizes Operating
in the Baseline
36,480
3,785
40,265
Number of Facilities
Owned by Small
Entities
27,418
1,962
29,380
Percent of Facilities
Owned by Small
Entities
75%
52%
73%
a Excludes baseline closures.
Source: U.S. EPA analysis.
EPA has limited the scope of the final rule to MP&M facilities performing oily operations. Table 10.4 shows that only a
small percentage (five percent) of small entities are potentially subject to regulation. The final rule excludes a large
percentage (95 percent) of small entity-owned MP&M facilities from regulation.
Table 10.4: Percent of Facilities Owned by Small Entities Excluded under the Final Option
Type of Facility
Owned by small business
Owned by small government
Total owned by small entities
Number of Facilities
Operating in the Baseline
27,418
1,962
29,380
Number of Facilities Not
Subject to the Final Rule
26,368
1,682
28,050
Percentage of Facilities Not
Subject to the Final Rule
96%
86%
95%
^^^
Source: U.S. EPA analysis.
10-4
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
10.3.2 Impacts on Facilities Owned by Small Entities
The facility impact analysis findings provide the first measure EPA used to assess impacts on facilities owned by small
entities. No facilities, small or large, are projected to close or experience moderate impacts as a result of the final rule. A
second approach to assessing small entity impacts - based on a comparison of compliance costs to post-compliance
revenues - indicates that no facilities will incur costs exceeding 1 percent of revenues, and only 1,019 facilities owned by
small private businesses will incur any costs at all. This corresponds to 3.7 percent of the facilities owned by small private
businesses that operate in the baseline.
Table 10.5 summarizes the results of the facility impact analysis for facilities owned by small entities for the final rule and the
options considered by EPA.
Table 10.5: Closures and Moderate Impact
Number of facilities operating in the baseline
Number of facilities excluded from option
Percent excluded
Number of facilities with closures
Facilities with closures as a percent of facilities operating in the
baseline
Facilities with closures as a percent of regulated facilities
Number of facilities with moderate impacts
Facilities with moderate impacts as a percent of facilities operating
in the baseline
Facilities with moderate impacts as a percent of regulated facilities
s for Facilities
Final Option
29,380
28,050
95.5%
0
0.0%
0.0%
0
0.0%
0.0%
Owned by Sir
Option II
29,380
23,893
81.3%
813
2.8%
14.8%
0
0.0%
0.0%
tall Entities
Option III
29,380
27,118
92.3%
109
0.4%
4.8%
37
0.1%
1.6%
Option IV
29,380
26,907
91.6%
109
0.4%
4.4%
37
0.1%
1.5%
Source: U.S. EPA analysis.
In summary, no facilities owned by small entities that operate in the baseline are expected to close or experience moderate
impacts under the final rule.
Table 10.6 shows the results of the second approach to assessing small entity impacts, based on a comparison of compliance
costs with facility revenues. EPA conducted this analysis only for MP&M facilities owned by private entities (i.e., businesses,
but not governments), because of the low level of impacts on all sizes of governments.
10-5
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
Table 10
Discharge
Status
Direct
Indirect
Total"
6: After-Tax Annual Compliance Costs as a Percent of Annual revenues under the Final Option for
Facilities Owned by Private Small Businesses"
Number of Facilities
Owned by Small
Private Businesses
Operating in the
Baseline
1,168
26,253
27,418
Number and
Percent of Facilities
Owned by Small
Businesses that are
Not Regulated
Number
119
26,253
26,368
Number and Percent of Facilities Owned by Small Businesses with
After-Tax Annual Compliance Costs/Annual Revenues Equal to:
., _ . 1 More than 0% and 1 _ ., n/
No Cost • ,-.,„/ s Overl%
• less than 1% •
% | Number | % j Number | % j Number | %
13.9%
100.0%
96.2%
31
0
31
2.5%
0.0%
0.1%
1,019
0
1,019
83.6%
0.0%
3.7%
0
0
0
0.0%
0.0%
0.0%
a Includes only facilities that remain open in the baseline.
b The sum of the number of direct and indirect dischargers does not add up to the total because some facilities are both indirect and
direct dischargers.
Source: U.S. EPA analysis.
Of the facilities owned by small entities that operate in the baseline, 96.2 percent are not regulated under the final rule.
Another 0.1 percent are regulated but do not incur costs. The remaining 3.7 percent incur compliance costs but none incur
after-tax annualized costs exceeding 1 percent of annual revenue. These results are consistent with the finding that no
facilities owned by small business will close or experience moderate financial impacts.
10.3.3 Impacts on Small Firms
EPA also performed a firm-level analysis in which it compared compliance costs with revenue at the firm level as a measure
of compliance cost burden. EPA applied this analysis only for facilities owned by private entities (i.e., businesses, but not
governments). Table 10.7 shows the results of this comparison. The Agency was not able to estimate national numbers of
firms that own MP&M facilities precisely, because the sample weights based on the survey design represent numbers of
facilities rather than firms. Most of the facilities owned by small firms (25,297 of 27,578, or 92 percent) are single-facility
firms, however. These single-facility firms can be analyzed using sample weights. In addition, 85 small multi-facility firms
own at least one sample facility. These firms are included in the analysis but with a sample weight of one, since it is not
known how many sample firms these 85 small firms represent. The results shown in Table 10.7 therefore represent a total of
25,382 small MP&M firms (25,297 + 85).
Table 10.7: Firm Level Before-Tax Annual Compliance Costs as a Percent of
Annual Revenues for Private Small Businesses
Number of
Small Firms in
the Analysis"
25,382
Number and Percent w
0% (no costs)
Number %
24,363 95.99%
ith Before-Tax Annual Compliance Costs/Annual
Revenues Equal to:
>0% and <1%
Number %
1,019 4.01%
Over 1%
Number %
0 0%
a Firms whose only MP&M facilities close in the baseline are excluded.
Source: U.S. EPA analysis.
The vast majority, 96 percent, of the small businesses in the analysis incur no costs due to the rule. The remaining 4 percent,
equal to 1,019 firms, incur before-tax compliance costs of less than 1% of their after-tax revenues. Of these 1,019 small
firms, none were reported in the facility impact analysis to experience moderate impacts due to the final rule.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
10.4 CONSIDERATION OF SMALL ENTITY IMPACTS IN &EVELOPIN& THE FINAL RULE
EPA gave special consideration to impacts on small entities in defining the final regulation. In particular, EPA attempted to
minimize impacts on small entities while at the same time meeting Clean Water Act objectives of reducing pollutant
discharges to the nation's waterways. The final rule minimizes impacts on small entities primarily by excluding all indirect
dischargers and direct dischargers in all subcategories except Oily Wastes.
Table 10.8 shows the number and percentage of facilities owned by small versus large entities that are projected to close or
experience moderate impacts under the final and alternative regulatory options analyzed by EPA in developing the final
regulation.
Table 10.8: Percent of Facilities Estimated to Close or Experience Moderate Impacts by Owning Entity
Size Class and by Regulatory Option
Regulatory Option
and Type of Facility
Owned by Small Entities
Owned by Large Entities
Total
Owned by Small Entities
Owned by Large Entities
Total
Owned by Small Entities
Owned by Large Entities
Total
Owned by Small Entities
Owned by Large Entities
Total
Number of Facilities
Subject to
Regulation
1,330
1,052
2,382
5,487
2,863
8,350
2,262
1,182
3,444
2,473
1,453
3,926
Projected to
Close
Final Regul
0
0
0
Opti
813
0
813
Optu
109
0
109
Opti
109
0
109
Percent
Closing
itory Option
0.0%
0.0%
0.0%
on 11
14.8%
0.0%
9.7%
mill
4.8%
0.0%
0.5%
mlV
4.4%
0.0%
2.8%
^^^ ^^,
Experiencing
Moderate Impacts
0
0
0
0
0
0
37
0
37
37
12
49
^^^ ^^,
Percent with
Moderate Impacts
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.6%
0.0%
1.1%
1.5%
0.8%
1.2%
Source: U.S. EPA analysis.
As reported in the table, the final rule avoids entirely the more material impacts on small entities that likely would have
occurred under the alternative regulatory options.
In addition to avoiding impacts in the regulated community, the final rule, by excluding indirect discharging facilities from
revised limits, also eliminated the potential additional burden to POTWs, including small POTWs, from issuance of new and
revised permits. Chapter 11 and Appendix F discuss POTW administrative activities and costs under the four regulatory
options.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment
GLOSSARY
Regulatory Flexibility Analysis: an evaluation of the impact of a rule and alternative regulatory options on small entities.
small entity: a business, government or non-profit organization defined as small for EPA's RFA/SBREFA evaluation.
small business: a business with employment or revenue below the threshold specified by the Small Business
Administration for each 4-digit SIC.
small government: a government that serves a population of 50,000 or less, as defined by the Small Business
Administration.
-------
MP&M EEBA Part II: Costs and Economic Impacts
Chapter 10: Small Entity Impact Assessment
ACRONYMS
POTW: Publicly-owned treatment works
RFA: Regulatory Flexibility Act
SB A: Small Business Administration
SBREFA: Small Business Regulatory Enforcement Fairness Act
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 10: Small Entity Impact Assessment
REFERENCES
U.S. Department of Commerce, Bureau of the Census. Statistics of U.S. Businesses.
U.S. Small Business Administration, http://www.sba.gov/regulations/siccodes.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 11: Social Costs
Chapter 11: Social Costs
INTRODUCTION
This chapter presents EPA's estimates of the regulation's
costs to society. Previous chapters described the economic
impacts of the final rule in terms of facility closures and
moderate financial impacts, employment losses, community
impacts, international trade effects, financial impacts on firms
owning MP&M facilities, and impacts on small entities. The
economic impact analyses were based on the estimated costs
to MP&M facilities of complying with the regulation. These
costs of labor, equipment, material, and other economic
resources needed for regulatory compliance are also the major
component of the cost to society of the regulation. Other
components of social costs include costs to governments
administering the regulation, and the social costs associated
with unemployment resulting from facility closures.
CHAPTER CONTENTS
11.1 Components of Social Costs 11-1
11.2 Resource Costs of Compliance 11-2
11.3 POTW Administration Costs 11-4
11.4 Social Costs of Unemployment 11-5
11.4.1 Social Cost of Worker Dislocation .... 11-5
11.4.2 Cost of Administering Unemployment
Benefits Programs 11-6
11.4.3 Total Cost of Unemployment 11-6
11.5 Total Social Costs 11-7
Glossary 11-8
References 11-9
Section 11.1 provides an overview of the three components of social cost analyzed for this regulation: the cost of society's
economic resources used to comply with the rule; the cost to governments of administering the rule; and the social costs of
unemployment resulting from the rule. The next three sections discuss each of these three components of social cost in more
detail. The last section, Section 11.5, summarizes the estimated total social costs.
11.1 COMPONENTS OF SOCIAL COSTS
The social costs of regulatory actions are the opportunity costs to society of employing scarce resources in pollution
prevention and pollution control activities. The social costs of regulation include both monetary and non-monetary outlays
made by society. Monetary outlays include the resource costs of compliance, government administrative costs, and other
adjustment costs, such as the cost of relocating displaced workers. Non-monetary outlays, some of which can be assigned
monetary values, include losses in consumers' and producers' surplus in affected product markets, the adverse effects of
involuntary unemployment, possible loss of time (e.g., delays in investment decisions), and possible adverse impacts on the
rate of innovation.
To assess the MP&M regulation's social costs, EPA relied first on the estimated costs to MP&M facilities for the labor,
equipment, material, and other economic resources needed to comply with the regulation. The compliance costs used to
estimate total social costs differ from those used to assess facility- and firm-level economic impacts in their consideration of
taxes and revenue effects. In the facility and firm impact analysis, compliance costs are measured as they affect the financial
performance of regulated facilities and firms. The analyses therefore explicitly consider the tax deductibility of compliance
expenditures.1 In the analysis of costs to society, however, these compliance costs are considered on a before-tax basis. In
general, because tax deductibility reduces the burden of compliance expenditures to private firms, the estimated compliance
costs are greater from the perspective of society than from the perspective of private industry. In addition, the analysis of the
regulation's impact on regulated facilities and firms accounted for potential recovery of compliance costs through output price
increases. The assessment of social cost ignores these potential cost offsets because, like taxes, they represent only a transfer
of compliance costs from the complying entity and not a true reduction in compliance cost.
Social costs also include lost producers' and consumers' surplus that result from reduction in the quantity of goods and
services produced. Lost producers' surplus is measured as the difference between revenues earned and the cost of
production for the lost production. Lost consumers' surplus is the difference between the price paid by consumers for the
lost production and the maximum amount they would have been willing to pay for those goods and services.
Costs incurred by government facilities are not adjusted for taxes, since these facilities are not subject to income taxes.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs
Accurate calculation of lost producers' and consumers' surplus requires knowledge of market supply and demand
characteristics for each affected industry. EPA was not able to conduct an industry-specific partial equilibrium analysis of
changes in market prices and output, both because of the very large number of markets involved and because it was not
possible to link compliance costs to specific products at multi-sector facilities.
EPA's assessment of social cost includes two additional cost elements: the cost to governments of administering permitting
and compliance monitoring activities under the regulation, and the social costs associated with unemployment that may result
from facility closures. The unemployment-related costs include the cost of administering unemployment programs for
workers who are projected to lose employment (but not the cost of unemployment benefits, which are a transfer payment
within society); and an estimate of the amount that workers would be willing to pay to avoid involuntary unemployment.
11.2 RESOURCE COSTS OF COMPLIANCE
This section reviews the resource costs of compliance for the final rule and the costs for the alternative regulatory options
considered by EPA. The resource costs of compliance are the value of society's productive resources - including labor,
equipment, and materials - expended to achieve the reductions in effluent discharges required by the regulation. The social
costs of these resources are higher than the financial burden borne by facilities because facilities are able to deduct the costs
from their taxable income and may be able to recover some of the costs through price increases to customers. The costs to
society, however, are the full value of the resources used, whether paid for by the regulated facilities, by taxpayers in the form
of lost tax revenues, or by customers through increased prices. EPA included no costs for facilities assessed as baseline
closures.
EPA estimated after-tax annualized compliance costs of $11.9 million for the final regulation (see Chapter 5: Facility Impact
Analysis, Table 5.6). The estimated social value of these compliance costs, however, is $13.8 million, as shown in
Table 11-1. This amount represents the value to society of the resources that would be used to comply with the rule.
For the alternative regulatory options, EPA's estimates included compliance costs both for facilities estimated to close
because of the rule and for facilities estimated to continue operating under the regulation. This approach results in an upper-
bound estimate of the social costs of compliance, since the lost value incurred by closing facilities is presumably less than the
estimated cost of compliance.
Under the Proposed/NODA Option, annual compliance costs amount to $1,111.4 million for indirect dischargers and $508.9
million for direct dischargers (2001$). The total annualized compliance costs are $1,620.3 million, or approximately 117
times the compliance costs under the final rule. This cost increase results from including additional subcategories under the
Proposed/NODA Option. General Metals indirect dischargers, which are excluded from the final regulation, account for
approximately 40 percent of the total compliance costs under the Proposed/NODA Option.
Under the Directs + 413 to 433 Upgrade Option, annual compliance costs amount to $83.0 million for indirect dischargers
and $13.8 million for direct dischargers (2001$). The total annualized compliance costs are $96.8 million, or approximately
7 times the final rule's compliance costs. This cost increase results from requiring indirect dischargers that currently comply
with the standards of 413 to upgrade to 433 standards. General Metals facilities, which are excluded from the final regulation,
account for approximately 44 percent of the total compliance costs under this option.
Under the Directs + All to 433 Upgrade Option, annual compliance costs amount to $124.4 million for indirect dischargers
and $13.8 million for direct dischargers (2001$). The total annualized compliance costs are $138.2 million, or approximately
10 times the compliance costs under the final rule. This cost increase results from requiring general metals facilities that
currently comply local limit standards to upgrade to 433 standards. General Metals facilities, which are excluded from the
final regulation, account for approximately 61 percent of the total compliance costs under this option.
2 Including costs for regulatory closures yields an estimate of social costs assuming that every facility continued to operate post-
regulation. Calculating costs as if all facilities continue operating will overstate social costs if some facilities find it more economical to
close than comply with the regulation.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 11: Social Costs
Table 11.1: Resource Value of Compliance Costs
(millions, 2001$)
i i j
Subcategory Indirect ! Direct 1 Total
te J : : :
Option I: Selected Option (Directs Only)
General Metals
MF Job Shop
Non Chromium Anodizing
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Docks
Total
General Metals
MF Job Shop
Non Chromium Anodizing
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Docks
Steel Forming & Finishing
Total
Opti
General Metals
MF Job Shop
Non Chromium Anodizing
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Docks
Total
Opt
General Metals
MF Job Shop
Non Chromium Anodizing
Oily Wastes
Printed Wiring Boards
Railroad Rebuilders
Shipbuilding Dry Docks
Total
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
Option II: Proposed/1
$652.9
$185.2
$0
$92.8
$157.9
$0.0
$0.0
$22.6
$1,111.4
on III: Directs + 413 to
$42.4
$17.1
$0.0
$0.0
$23.5
$0.0
$0.0
$83.0
'on IV: Directs + All to <
$83.8
$17.1
$0.0
$0.0
$23.5
$0.0
$0.0
$124.4
$0.0
$0.0
$0.0
$13.8
$0.0
$0.0
$0.0
$13.8
VODA Option
$396.1
$4.6
$38.0
$35.9
$0.3
$0.7
$3.2
$30.1
$508.9
433 Upgrade Option
$0.0
$0.0
$0.0
$13.8
$0.0
$0.0
$0.0
$13.8
f33 Upgrade Option
$0.0
$0.0
$0.0
$13.8
$0.0
$0.0
$0.0
$13.8
$0.0
$0.0
$0.0
$13.8
$0.0
$0.0
$0.0
$13.8
$1,049.0
$189.8
$38.0
$128.7
$158.2
$0.7
$3.2
$52.7
$1,620.3
$42.4
$17.1
$0.0
$13.8
$23.5
$0.0
$0.0
$96.8
$83.8
$17.1
$0.0
$13.8
$23.5
$0.0
$0.0
$138.2
Source: U.S. EPA analysis.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 11: Social Costs
11.3 POTW ADMINISTRATION COSTS
This section discusses the POTW administrative costs of the final rule and the costs of the alternatives considered by EPA.
EPA estimates that the final rule will not increase POTW administrative costs. EPA expects no increase in permitting costs
for facilities that already hold a permit in the baseline. However, governments will incur additional permitting costs from (1)
permitting of unpermitted facilities (under the NODA/Proposal option only) and (2) acceleration of repermitting for some
indirect dischargers that currently hold permits. The alternative regulatory options may also cause some administrative costs
to decrease. For example, control authorities will no longer have to repermit facilities that are estimated to close as a result of
the MP&M rule.
Table 11.2 shows the number of facilities requiring a new permit under the four options considered for the final rule. Only
the NODA/Proposal option would require POTWs to issue new concentration-based permits for the first time. None of the
options considered would require a new mass-based permit or a conversion from a concentration-based to a mass-based
permit. The table also shows the number of facilities that will require early repermitting (within three years rather than within
five years), the number of estimated regulatory closures, and the total number of facilities that are expected to require permits
under the different regulatory options.
Table 11.2: Permitting Requirements for Regulatory Alternatives
(number of indirect discharging facilities)
Permitting required:
New concentration-based permit
New mass-based permit"
Convert from existing concentration-based
to mass-based"
Repermit within 3 rather than 5 years
Regulatory closures (no longer requiring
permits)15
Number of facilities operating
post-regulation requiring a permit
Option I:
Selected Option
n/a
n/a
n/a
n/a
n/a
n/a
Option II:
NODA/Proposal
Option
103
0
0
1,434
722
3,687
Option III:
Directs + 413 to
433 Upgrade
0
0
0
382
120
954
Option IV:
Directs + All to
433 Upgrade
0
0
0
566
120
1,414
a EPA does not require mass-based permits under any of the option considered for the final rule.
b Some facilities with existing permits will no longer require permitting due to regulatory closures.
Source: U.S. EPA analysis.
Table 11.3 below presents the estimated permitting costs to governments of administering the final rule and alternative
options. Chapter 7: Government and Community Impact Analysis describes the methodology used to estimate these
administrative costs.
Because the final regulation excludes from coverage all indirect dischargers, EPA estimates that the final rule will not
increase POTW administrative costs. Each of the three alternative regulatory options considered would result in reduced
POTW regulatory costs. These cost savings result from regulatory closures (i.e., facilities that currently hold a permit and
would have required repermitting in the baseline, but that will no longer require repermitting under the regulatory options).
The cost savings from regulatory closures outweigh the additional costs for issuing new permits (under the NODA/Proposal
option only) and repermitting on an accelerated, three-year schedule. Estimated annualized cost savings to POTWs for the
three alternative regulatory options range between $0.05 and $1.0 million under the NODA/Proposal option, and between
$0.03 and $0.2 million under the Directs + 413 to 433 Upgrade option and the Directs + 413+50%LL Upgrade option (all
costs in ($2001).
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 11: Social Costs
Table 11.3: Annualized Government Administrative Costs by Regulatory Option
($2001)
Option
Option I: Selected Option (Directs Only)
Option II: Proposed/NODA Option
Option III: Directs + 413 to 433 Upgrade Option
Option IV: Directs + All to 433 Upgrade Option
Low
n/a
($46,000)
($26,000)
($26,000)
Medium
n/a
($198,000)
($56,000)
($55,000)
High
n/a
($1,027,000)
($218,000)
($213,000)
Source: U.S. EPA analysis.
11.4 SOCIAL COSTS OF UNEMPLOYMENT
This section discusses the social costs of unemployment associated with the final rule and the alternatives EPA considered.
The loss of jobs from facility closures would represent a social cost of the regulation. However, from its facility impact
analysis, EPA estimates that no facilities will close as a result of the regulation. EPA did not recognize possible savings in
unemployment-related costs from jobs created by the rule as a negative cost (benefit) of the regulation. Accordingly, EPA
estimates a zero cost of unemployment for the final rule.
Chapter 6: Employment Effects discusses the effects of the alternative regulatory options on employment, including the jobs
potentially lost due to facility closures and the jobs potentially created by expenditures to comply. This section estimates the
social cost of the estimated changes in employment. EPA considered two components of the social cost of unemployment:
*• The cost of worker dislocation (exclusive of cash benefits) to unemployed individuals, as measured by their
willingness to pay to avoid unemployment; and
> The additional cost to governments to administer unemployment benefits programs.
11.4.1 Social Cost of Worker dislocation
EPA calculated the cost of worker dislocation based on an estimate of the value that workers would pay to avoid involuntary
job losses. The amount that workers would pay to avoid a job loss was derived from hedonic studies of the compensation
premium required by workers to accept jobs with a higher probability of unemployment. This framework has been used in the
past to impute a trade-off between wages and job security (Topel, 1984; Adams, 1985; Anderson and Chandran, 1987).
Specifically, this estimate approximates a one-time willingness-to-pay to avoid an involuntary episode of unemployment and
reflects all monetary and non-monetary impacts of involuntary unemployment incurred by the worker. It does not include any
offsets to the cost of unemployment, such as unemployment compensation or the value of increased leisure time.
Studies by Topel (1984) and Adams (1 985) suggest that the compensation premium for accepting a one percent increase in
the annual probability of unemployment is in the range of 2.5 percentto 3.3 percent of the base compensation value. To
illustrate this finding, assume that a worker is presented with a choice between two employment opportunities: one with
compensation of $30,000 per year and an annual unemployment probability of zero, and a second otherwise equivalent
opportunity but with an annual unemployment probability of one percent. For the worker to accept the second opportunity, his
or her compensation must be at least 2.5 to 3.3 percent greater than the $30,000 offered for the first opportunity, or at least
$30,750 to $30,990 (depending on the percentage premium used). In this case, the dollar premium required to accept the
additional one percent annual probability of unemployment is $750 to $990.
For analyzing the unemployment-related costs of the MP&M regulation, the hypothetical choice is assumed to be between an
employment opportunity with a zero percent annual probability of unemployment and a second opportunity with a 100
percent annual probability of unemployment. In this case, the one-time premium for accepting the employment opportunity
with the 100 percent probability of employment is assumed to be 250 to 330 percent of the compensation for the otherwise
11-5
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs
comparable employment opportunity with the assumed zero probability of employment.3 To estimate the premium for an
increase in the probability of unemployment requires an estimate of the average compensation to workers in the MP&M
industry. EPA calculated an average annual compensation for MP&M industry production workers of $38,309 (2001$)4
Accordingly, the annual compensation premium for a one percentage point increase in the annual probability of
unemployment would be $958 to $1,264 and the cost of a 100 percent probability event would be $95,772 to $126,420
(2001$). This calculation assumes that the cost of a certainty unemployment event is directly proportional to the increase in
probability from the low probability event (i.e., one percent) on which the calculation is based.
Chapter 6: Employment Effects presents EPA's estimate that as many as 32,729 jobs might be lost due to facility closures
under the Proposed/NODA Option. Multiplying these 32,729 job losses by the estimated range of willingness-to-pay values
for avoiding unemployment results in a total cost of unemployment for the Proposed/NODA Option of $3.1 billion to $4.1
billion (2001 $). EPA annualized these values over a 15-year period at a 7 percent rate, yielding an annualized cost of $344 to
$454 million. These values are the annualized amounts over a 15-year period that workers would be willing to pay to avoid
the job losses projected to result from compliance with the Proposed/NODA Option.
EPA estimates that as many as 7,874 jobs might be lost due to facility closures under the Directs + 413 to 433 Upgrade
Option and the Directs + All to 433 Upgrade Option. Multiplying these 7,874 job losses by the estimated range of
willingness-to-pay values for avoiding unemployment results in a total cost of unemployment for both of the 433 Upgrade
Options of $754 million to $995 million (2001$). EPA annualized these values over a 15-year period at a 7 percent rate,
yielding an annualized cost of $83 to $109 million.
11.4.2 Cost of Administering Unemployment Benefits Programs
Unemployment as the result of regulation also imposes costs on society through the additional administrative burdens placed
on the unemployment system. The cost of unemployment benefits per se is not a social cost but instead a transfer payment
within society from taxpayers to the unemployed. Administrative costs include the cost of processing unemployment claims,
retraining workers, and placing workers in new jobs. Data obtained from the Interstate Conference of Employment Security
Agencies indicated that the cost of administering an initial unemployment claim over the period 1991-1993 averaged $93.25
(1991$-1993$). These costs included total Federal and State funding for administering unemployment benefit programs but
not the cost of the benefits themselves. Inflating this estimate to 2001 dollars using the BLS Employment Cost Index for and
Local Government workers yields a value of $122 per claim.5 Based on this estimate, EPA assumed that the cost of
administering unemployment programs would amount to approximately $122 per job loss. Multiplying this figure by the
estimated loss of 32,729 jobs under the Proposed/NODA Option yields an additional $4.0 million in social costs. EPA
annualized this value over the 15-year analysis period at a 7 percent rate to yield an annual cost of approximately $438,027
(2001$). Multiplying the per job loss estimate of the cost of administering unemployment by the estimated loss of 7,874 jobs
under the 433 Upgrade Options yields almost an additional $960,000 in social costs. EPA annualized these values over the
15-year analysis period at a 7 percent rate to yield an annual cost of $105,000 under the 433 Upgrade Options.
11.4.3 Total Cost of Unemployment
As mentioned above, EPA did not estimate a cost of unemployment for the final rule because no job loss is expected. As
shown in Table 11.4 below, the 32,729 estimated job losses at facility closures under the Proposed/NODA Option have an
estimated social cost of $345 million to $455 million (2001$). The 7,874 estimated job losses at facility closures under the
433 Upgrade Options have an estimated social cost of $83 million to $109 million (2001$).
3 This analysis has a considerable artificiality in that a worker would not realistically be presented with this choice. The artificiality of
the choice in turn underscores the very strong assumption in the analysis. That is, that the cost of an unemployment event can be estimated
by linearly extrapolating the premium estimated for small percentage differences in the probability of unemployment to a circumstance in
which the probability of unemployment is 100 percent. An investigation of literature on unemployment failed to find an alternative method
for estimating unemployment costs. This analytic issue warrants further research.
4 Calculated the total payroll ($407.7 billion) /total employment (12.2 million) in MP&M SIC codes based on data obtained from the
1997 Economic Censuses which is $33,508. Inflated this estimate to 2001 dollars using the BLS Seasonally Adjusted Employment Cost
Index (ECI) for Private Industry Manufacturing - 1997 (4th Qtr): 135.4, 2001 (4th Qtr): 154.8.
5 BLS, 2000. Table la: Employment Cost Index (Compensation), State and Local Government: 1992 (December): 118.5, 1999
(December): 144.2.
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MP&M EEBA Part II: Costs and Economic Impacts
Chapter 11: Social Costs
Table 11.4
Social Cost of Unemployment Categories
Employment Loss in Closing Facilities
Annualized Worker Dislocation Cost
Low Unit Cost
(based on 2.5 percent premium)
High Unit Cost
(based on 3.3 percent premium)
Annualized Unemployment Administration
Cost (million 2001$)
Total Annual Social Costs of Unemployment
(millions, 2001$)
Option I: Option II: | Option III: | Option IV:
Selected Option Proposed/NODA j Directs + 413 to j Directs + All to
(Directs Only) j Option j 433 Upgrade 433 Upgrade
: :
n/a 1 32,729 1
: 7 :
I
7,874 | 7,874
n/a $344.16
n/a $454.29
n/a $0.44
$82.80 $82.80
$109.30 $109.30
$0.11 $0.11
Sum, Worker Dislocation and Unemployment Administration Costs (based on employment loss in closing facilities)
Low Value n/a $344.60
High Value n/a $454.73
$82.91 | $82.91
:
$109.40 | $109.40
Source: U.S. EPA analysis.
11.5 TOTAL SOCIAL COSTS
Summing across the final rule's social cost components results in a total social cost estimate of $13.8 million annually
(2001$), as shown in Table 11.5. The total social costs of the Proposed/NODA Option range between $2.0 billion and $2.1
billion. The total social costs for the Directs + 413 to 433 Upgrade Option range between $1 80 million and $206 million.
The total social costs for the Directs + All to 433 Upgrade Option range between $221 million and $247 million.
Table 11.5: Total Social Cost (millions, 2001$)
Option I:
Social Cost Categories Selected Optio
(Directs Only)
I
Resource cost of compliance expenditures j $13.8
Costs to POTWs of administering the rule | $0.0
:
Social costs of unemployment $0.0
Total Social Cost j $13.8
Option II: Option III:
Proposed/NODA Directs + 413 to
i Option 433 Upgrade
* v f v
Low High Low High
$1,620.3 $96.8
: :
($0.05) ($1.0) 1 ($0.03)
$344.6 $454.7 j $82.9
| $1,964.8 $2,074.0 j $179.7
Option IV:
Directs + All to
433 Upgrade
Low High
$1:
($0.2) 1 ($0.03)
$109.4 | $82.9
:
$206.0
$221.1
S8.2
($0.2)
$109.4
$247.4
Source: U.S. EPA analysis.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs
GLOSSARY
consumers' surplus: the value that consumers derive from goods and services above the price they have to pay to obtain
the goods and services.
opportunity cost: the lost value of alternative uses of resources (capital, labor and raw materials) used in pollution control
activ ities.
producers' surplus: the difference between what producers' earn on their output and the economic costs of producing that
output, including a normal return on capital.
social costs: the costs incurred by society as a whole as a result of the final rule; does not include costs that are simply
transfers among parties but that do not represent a net cost overall.
11-&
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs
REFERENCES
Adams, James D. 1985. Permanent differences in unemployment and permanent wage differentials. Quarterly Journal of
Econometrics 100, no. 1, 29-56.
Anderson, Donald W. and Ram V. Chandran. 1987. Market estimates of worker dislocation costs. Economics Letters 24,
381-384.
Interstate Conference of Employment Security Agencies. Employment Security Funding Survey 1991 -1993. Presented in
Christopher Van Atten, "Cost of Unemployment," memorandum to the MP&M Record, March 16, 1995.
Topel, Robert H. 1984. Equilibrium earnings, turnover, and unemployment: new evidence. Journal of Labor Economics 2
no. 4:500-522.
U.S. Bureau of Labor Statistics. 2000. Employment Cost Index, Historical Listing (June 1989=100).
http://stats.bls.gov/ecthome.htm. July 27.
U.S. Department of Labor. 1995. State Unemployment Claims 1989-1993. Presented in Christopher Van Atten, "Cost of
Unemployment", memorandum to the MP&M Record, March 16.
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MP&M EEBA Part II: Costs and Economic Impacts Chapter 11: Social Costs
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MP&M EEBA Part III: Benefits
Chapter 12: Benefit Overview
Chapter 12: Benefit Overview
INTRODUCTION
Part III of the EEBA assesses the benefits to society from the
reduced effluent discharges that will result from the MP&M
industry regulations. EPA expects that benefits will accrue
to society in several broad categories, including reduced
health risks, enhanced environmental quality, and increased
productivity in economic activities that are adversely
affected by MP&M industry discharges.
This chapter provides a discussion of the pollutants of
concern (POCs). their effect on human health, their
environmental effects, a framework for understanding the
benefits likely to be achieved by the MP&M regulation, and
a qualitative discussion of those benefits. The following
chapters quantify and estimate the economic value of these
benefit categories. Appendices I and H provide further
information on environmental effects of MP&M pollutants
and water quality models used to assess these effects.
CHAPTER CONTENTS
12.1 MP&M Pollutants
12.1.1 Characteristics of MP&M Pollutants
12.1.2 Effects of MP&M Pollutants on Human
Health
12.1.3 Environmental Effects of MP&M
Pollutants
12.1.4 Effects of MP&M Pollutants on Economic
Productivity
12.2 Linking the Regulation to Beneficial Outcomes . .
12.3 Qualitative and Quantitative Benefits Assessment
12.3.1 Overview of Benefit Categories
12.3.2 Human Health Benefits
12.3.3 Ecological Benefits
12.3.4 Economic Productivity Benefits
12.3.5 Methods for Valuing Benefit Events ....
Glossary
Acronyms
References
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12-8
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EPA estimated national benefits expected to accrue from the regulation on the basis of sample facility data. The Agency
extrapolated findings from the sample facility analyses to the national level using two alternative extrapolation methods: (1)
traditional extrapolation and (2) post-stratification extrapolation. The traditional extrapolation approach relies on sample
facility weights that were developed based on information about the economic and technical characteristics of the regulated
community. This extrapolation approach does not incorporate information that could significantly affect the occurrence and
distribution of regulatory benefits, such as characteristics of the receiving water body and the size of the population that may
benefit from reduced pollutant discharges. EPA recognizes that using a traditional extrapolation method to estimate national
level benefits may lead to a large degree of uncertainty in benefits estimates. Thus, EPA also used an alternative set of
sampling weights, based on a post-sampling stratification method, to calculate alternative national estimates of benefits. EPA
adjusted the original sample weights using two variables that are likely to affect the occurrence and size of benefits associated
with reduced discharges from sample MP&M facilities: receiving water body type and size, and the size of the population
residing in the vicinity of the sample facility. The following chapters present two sets of estimates of benefits expected to
accrue from the MP&M regulation based on both traditional and post-stratification extrapolation approaches. Appendix G of
this report provides detailed information on extrapolation methods.
In addition, the Agency used the Ohio case study results to develop a third estimate of the monetary value of national
benefits.1 EPA extrapolated the Ohio case study results to the national level based on three key factors that affect the
occurrence and magnitude of benefits: (1) the estimated change in the MP&M pollutant loadings, (2) the level of recreational
activities on the reaches affected by MP&M discharges, and (3) state level income. The Agency recognizes that this method is
not rigorous for extrapolation to the national level. Therefore, EPA used this method only as a sensitivity analysis (see
Appendix G of this report for detail).
EPA notes that effluent limitations guidelines for the MP&M industry are technology-based. EPA is not required to
demonstrate environmental benefits of its technology-based rules. It is well established that EPA is not required to consider
receiving water quality in setting technology-based effluent limitations guidelines and standards. Weyerhaeuser v. Costle,
590 F. 2nd 1011, 1043 (D.C. Cir. 1978) ("The Senate Committee declared that '[t]he use of any river, lake, stream or ocean as
a waste treatment system is unacceptable' regardless of the measurable impact of the waste on the body of water in question.
Legislative History at 1425 (Senate Report). The Conference Report states that the Act 'specifically bans pollution dilution as
See Chapter 21 for a detailed discussion the Ohio case study.
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
an alternative to treatment.' " Id. at 284). In establishing effluent limitations and standards, EPA considers benefits as one of
the factors that the Agency evaluates.
12.1 MP<&M POLLUTANTS
EPA defines three general categories of pollutants: priority or toxic pollutants; nonconventional pollutants; and conventional
pollutants. Priority pollutants (PPs) are defined as any of 126 named pollutants.2 Conventional pollutants include
biological oxygen demand (BOD), total suspended solids (TSS), oil and grease (O&G), pH, and anything else
the Administrator defines as a conventional pollutant. Nonconventionals are a catch-all category that includes everything that
is not in the two previously described categories. The naming system is somewhat confusing in that some nonconventional
pollutants may be as "toxic" as, or more "toxic" than some of the PPs.
MP&M effluents contain a variety of priority, nonconventional, and conventional pollutants. The release of these pollutants
to our nation's surface water degrades aquatic environments, alters aquatic habitats, and affects the diversity and abundance of
aquatic life. It also increases the health risks to humans who ingest contaminated surface waters or eat contaminated fish and
shellfish (U.S. EPA, 1997). A number of the pollutants commonly found in MP&M effluents also inhibit biological
wastewater treatment systems or accumulate in sewage sludge or sediment.
Metals are a particular concern because of their prevalence in MP&M effluents. Metals are inorganic compounds, generally
non-volatile (with the notable exception of mercury), and cannot be broken down by biodegradation processes. Metals can
accumulate in biological tissues, sequester into sewage sludge in publicly-owned treatment works (POTWs), and
contaminate soils and sediments when released to the environment. Sediments contaminated with metals become resuspended
by dredging, boat propellers, water currents or wave action, and storm events, releasing metals back into the water column.
Metals can also become biologically available and enter terrestrial food chains once the sludge is applied on land. Sludges
with high concentrations of metals are therefore unsuitable for land application. Some metals are quite toxic even when
present at relatively low levels.
Some of the inorganic POCs found in MP&M effluents are also natural constituents of water, including potassium, calcium,
magnesium, iron, chlorine, fluoride, sulfate, phosphates, silica, and a number of trace metals such as copper and zinc.
Human and ecological exposure and risk from environmental releases of MP&M pollutants depend on chemical-specific
properties, the mechanism and medium of release, and site-specific environmental conditions. Chemical-specific properties
include toxicological effects on living organisms, hydrophobicity/lipophilicity, reactivity and persisistence. These
properties are described in sections 12.1.1 through 12.1.4.
12.1.1 Characteristics of MP<&M Pollutants
EPA sampled MP&M facilities nationwide to assess the concentrations of pollutants in MP&M effluents. The Agency
collected samples of raw wastewater from MP&M facilities and applied standard water analysis protocols to identify and
quantify the pollutant levels in each sample. EPA used these analytical data, along with selection criteria, to identify 132
contaminants of potential concern.3
EPA then evaluated the potential environmental fate and transport of these pollutants and their toxicity to humans and aquatic
receptors. Fate of the MP&M pollutants was estimated based on the propensity of those pollutants to volatilize, adsorb onto
sediments, bioconcentrate, and biodegrade. Table I.I in Appendix I lists MP&M pollutants and provides data on human
health concerns, and fate and effects.
EPA used various data sources to evaluate pollutant-specific fate and toxicity. To evaluate potential human health effects, the
Agency relied on reference doses (RfDs) and cancer potency slope factors (SFs), human health-based water
2 The Agency originally had 129 PPs, but 3 have been dropped from the list bringing the number of PPs to 126.
3 EPA originally identified 150 MP&M POCs. Of these 150 POCs, the Agency estimated loadings for 132 pollutants for the phase 2
proposal and NODA. The benefits analysis presented in this chapter and the following chapters was based on 132 pollutants for which
loadings are available. The final regulation covers only the Oily Wastes subcategory and benefit reductions were estimated for 122
pollutants.
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
quality criteria (WQC), maximum contaminant levels (MCLs) for drinking water protection and other drinking water
related criteria, and hazardous air pollutant (HAP) and PP lists. Appendix 1.1.2 provides short descriptions and
definitions for each of these measures of human health effects.
To evaluate potential fate and effects in aquatic environments, the Agency relied on measures of acute and chronic
toxicity to aquatic species, bioconcentration factors for aquatic species, Henry's Law constants (to estimate volatility),
adsorption coefficients (Koc) (to estimate association with bottom sediments), and biodegradation half-lives (to
estimate the removal of chemicals via microbial metabolism).
The data sources used in the assessment include EPA ambient water quality criteria (A WQC) documents and updates,
EPA's Assessment Tools for the Evaluation of Risk (ASTER), the AQUatic Information REtrieval System
(AQUIRE). and the Environmental Research Laboratory-Duluth fathead minnow database, EPA's Integrated
Risk Information System (IRIS), EPA's Health Effects Assessment Summary Tables (HEAST), EPA's 1991 and
1993 Superfund Chemical Data Matrix (SCDM). Syracuse Research Corporation's CHEMFATE and BIODEG
databases, EPA and other government reports, scientific literature, and other primary and secondary data sources.
To ensure that the assessment is as comprehensive as possible, EPA also obtained data on chemicals for which
physical-chemical properties and/or toxicity data were not available from the sources listed above. To the extent possible,
EPA estimated values for the chemicals using the quantitative structure-activity relationship (QSAR) model
incorporated in ASTER, and for some physical-chemical properties, used published linear regression correlation equations.
12.1.2 Effects of MP<&M Pollutants on Human Health
Individuals are potentially exposed to MP&M pollutants released to the aquatic environment via consumption of
contaminated fish. Populations served by drinking water utilities located downstream of effluent discharges from MP&M
facilities are also exposed to MP&M pollutants via contaminated drinking water. Many of these pollutants may increase risks
to human health.
Based on the available human health toxicity data for the 132 POCs presented in Table I.I (Appendix I), EPA found that:4
>• 76 pollutants are human systemic toxicants;
* 13 pollutants with published SFs are classified as known, probable, or possible human carcinogens when ingested via
drinking water or food. Lead is also classified as a possible human carcinogen in IRIS but EPA has not developed a
SF for it (U.S. EPA, 1998/99d);
•• 36 pollutants have drinking water criteria (27 with enforceable health-based MCLs, 7 with secondary MCLs for
taste or aesthetics, and 2 with action levels for treatment);
*• 35 pollutants are designated as HAPs in wastewater;
*• 43 pollutants are identified as PPs; and
*• 76 pollutants have human health-based water quality criteria (WQC) to protect against the ingestion of water and
organisms or organisms only (see Chapter 13, Table 13.3).
The carcinogens identified by EPA in MP&M effluent samples include known (A), probable (Bl and B2) and possible (C)
human carcinogens. These pollutants are associated with the development of cancers in the spleen, liver, kidney, lung,
bladder, and skin, among others. These pollutants and target organs are shown in Table 12.1.
4 Facilities in the Oily Wastes subcategory discharge: 75 of the 76 systemic toxicants; all 13 human carcinogens; all 36 pollutants with
drinking water criteria; all 35 pollutants designated as HAPs; 41 of the 43 priority pollutants; and 75 of the 76 pollutants that have human
health-based water quality criteria. Of the 132 POCs evaluated, facilities in the Oily Wastes subcategory do not discharge the following 10
pollutants: amenable cyanide, boron, cadmium, cyanide, phosphate, sodium, sulfide, total dissolved solids, weak-acid dissociable cyanide,
and ziram/cymate.
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MPAM EEBA Part III: Benefits
Chapter 12: Benefit Overview
Table 12.1: Human Carcinogens Evaluated, Weight-of -Evidence Classifications, and Target Organs
CAS Number
62533
7440382
117817
75003
75092
75354
123911
78591
62759
86306
127184
79016
67663
Carcinogen
Aniline
Arsenic
Bis(2-ethylhexyl) phthalate
Chloroethane a
Dichloromethane
Dichloroethene, 1,1-
Dioxane, 1,4-
Isophorone
Nitrosodimethylamine, N-
Nitrosodiphenylamine, N-
Tetrachloroethene
Trichloroethene a
Trichloromethane
Weight-of-Evidence
Classification
B2
A
B2
B2
C
B2
C
B2
B2
B2
B2
Target Organs
Spleen
Liver, kidneys, lungs, bladder, skin
Liver
Liver, lungs
Inconclusive b
Liver, nasal cavity, gall bladder
Preputial gland
Liver, lungs, skin, seminal vesicle,
lymphatic/hematopoetic system
Bladder tumors, reticulum cell
sarcomas
Liver
Kidneys
A = Human Carcinogen
Bl = Probable Human Carcinogen (limited human data)
B2 = Probable Human Carcinogen (animal data only)
C = Possible Human Carcinogen
a Pollutant has been withdrawn from the IRIS database for additional study.
b There is equivocal evidence for the oral route of exposure. This chemical is likely a systemic carcinogen via inhalation.
Target organs include: kidney, pancreas, skin, mammary gland, and blood forming elements (lymphoma and leukemia).
Source: U.S. Environmental Protection Agency verified (IRIS) or provisional (HEAST) (U.S. EPA (1998/99d), U.S. EPA
(1997)).
Non-carcinogenic hazards associated with pollutants in MP&M effluent include systemic effects (e.g., impairment or loss of
neurological, respiratory, reproductive, circulatory, or immunological functions), organ-specific toxicity (e.g., kidney, small
intestines, blood, testes, liver, stomach, thyroid), fetal effects (e.g., increased fetal mortality, decreased birth weight), other
effects (e.g., lethargy, cataracts, weight loss, hyperactivity), and mortality. These effects are listed by pollutant in Table 12.2.
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MPAM EEBA Part III: Benefits
Chapter 12: Benefit Overview
Table 12.2: MP&M Pollutants Exhibiting Systemic and Other Non-Cancer Human Health Effects"
CAS Number
83329
67641
98862
107028
7429905
120127
7440360
7440382
7440393
65850
100516
7440417
92524
117817
7440428
85687
7440439
75150
108907
75003
7440473
18540299
7440484
7440508
95487
106445
57125
75354
75092
68122
105679
Toxicant
Acenaphthene
Acetone
Acetophenone
Acrolein
Aluminum
Anthracene
Antimony
Arsenic
Barium
Benzoic acid
Benzyl alcohol
Beryllium
Biphenyl
Bis(2-ethylhexyl)
phthalate
Boron
Butyl benzyl phthalate
Cadmium
Carbon disulfide
Chlorobenzene
Chloroethane
Chromium
Chromium-hexavalent
Cobalt
f Copper
Cresol, o-
Cresol, p-
Cyanide
Dichloroethene, 1,1-
Dichloromethane
Dimethylformamide,
N,N-
Dimethylphenol, 2,4-
RfD Target Organ and Effects
Liver, hepatotoxicity
Increased liver and kidney weights, nephrotoxicity
General toxicity
Cardiovascular toxicity0
Renal failure, intestinal contraction interference, adverse neurological effects'1
General toxicity
Longevity, blood glucose, cholesterol
Hyperpigmentation, keratosis and possible vascular complications
Increased kidney weight
General toxicity
Forestomach, epithelial hyperplasia
Small intestinal lesions
Kidney damage
Increased relative liver weight
Testicular atrophy, spermatogenic arrest
Significantly increased liver-to-body and liver-to-brain weight
Significant proteinuria (protein in urine)
Fetal toxicity, malformations
Histopathologic changes in liver
General toxicity
Renal tubular necrosis (kidney tissue decay)d
Reduced water consumption
Heart effects"
Gastrointestinal effects, liver necrosis"
Decreased body weight and neurotoxicity
Central nervous system hypoactivity and respiratory system distress
Weight loss, thyroid effects and myelin degeneration
Toxic effects on kidneys, spleen, lungs"; hepatic lesions
Liver toxicity
Liver and gastrointestinal system effects
Clinical signs (lethargy, prostration, and ataxia) and hematological changes
84742 ! Di-n-butyl phthalate j Increased mortality
51285 ! Dinitrophenol, 2,4- j Cataract formation
606202 ! Dinitrotoluene, 2,6- j Mortality, central nervous system neurotoxicity, blood heinz bodies and
I methemoglobinemia, bile duct hyperplasia, kidney histopathology
117840 ! Di-n-octyl phthalate j Kidney and liver increased weights, increased liver enzymes
122394 ! Diphenylamine j Decreased body weight, and increased liver and kidney weights
100414 ! Ethylbenzene j Liver and kidney toxicity
206440 ! Fluoranthene j Nephropathy, increased liver weights, hemato logical alterations, clinical effects
86737 j Fluorene j Decreased red blood cell count, packed cell volume and hemoglobin
16984488 j Fluoride j Objectionable dental fluorosis (soft, mottled teeth)
591786 ! Hexanone, 2- j Hepatotoxicity and nephrotoxcity"
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MPAM EEBA Part III: Benefits
Chapter 12: Benefit Overview
Table 12.2: MP&M Pollutants Exhibiting Systemic and Other Non-Cancer Human Health Effects"
CAS Number
7439896
78831
78591
7439965
78933
108101
80626
91576
7439987
91203
7440020
100027
59507
108952
129000
110861
7782492
7440224
100425
127184
7440280
7440315
7440326
108883
79016
75694
67663
7440622
108383
179601231
95476
136777612
7440666
137304
Toxicant
Iron
Isobutyl alcohol
Isophorone
Manganese
Methyl ethyl ketone
Methyl isobutyl ketone
Methyl methacrylate
Methylnaphthalene, 2-
Molybdenum
Naphthalene
Nickel
Nitrophenol, 4-
Parachlorometacresol
Phenol
Pyrene
Pyridine
Selenium
Silver
Styrene
Tetrachloroethene
Thallium
Tin
Titanium
Toluene
Trichloroethene
Trichlorofluoromethane
Trichloromethane
Vanadium
Xylene, m-
Xylene, m- & p- (c)
Xylene, o-
Xylene, o- & p- (c)
Zinc
Ziram \ Cymate
RfD Target Organ and Effects
Liver pathology, diabetes mellitus, endocrine disturbance, and cardiovascular effects"
Hypoactivity and ataxia
Kidney pathology
Central nervous system effects
Decreased fetal birth weight
Lethargy, increased liver and kidney weights and urinary protein
Increased kidney to body weight ratio
Increased uric acid
Decreased body weight
Decreased body and organ weights
Reduced fetal body weight
Kidney effects (renal tubular pathology, decreased kidney weights)
Increased liver weight
Clinical selenosis (hair or nail loss)
Argyria (skin discoloration)
Red blood cell and liver effects
Liver toxicity, weight gain
Liver toxicity, gastroenteritis, degeneration of peripheral and central nervous system"
Kidney and liver lesions
Considered to be physiologically inert"
Changes in liver and kidney weights
Bone marrow, central nervous system, liver, kidneys4
Histopathology and mortality
Fatty cyst formation in liver
Kidney and central nervous system effects'5
Central nervous system hyperactivity, decreased body weight
Central nervous system hyperactivity, decreased body weight
47% decrease in erythrocyte superoxide dismutase (ESOD) concentration in adult human
females after 10 weeks of zinc exposure
a Chemicals with EPA verified (IRIS) or provisional (HEAST, or other Agency document)) human health-based RfDs, referred to as
"systemic toxicants" (U.S. EPA (1998/99d), U.S. EPA (1997)).
b RfD based on ano-observed-adverse-effect level (NOAEL). Health effects summarized from Amdur, M.O., Doul, J., andKlaassen, C.D.,
eds. Cassarett and Doul's Toxicology, 4th edition, 1991.
" Target organ and effects summarized from Amdur, M.O., Doul, J., and Klaassen, C.D., eds. Cassarett and Doul's Toxicology, 5th edition,
1996.
d Target organ and effects summarized fromWexler, P., ed. Encyclopedia of Toxicology, Volumes 1-3, 1998.
Source: U.S. EPA analysis.
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
12.1.3 Environmental Effects of MP<&M Pollutants
Ecological impacts of MP&M pollutants include acute and chronic toxicity to aquatic receptors by dozens of pollutants
present in MP&M effluents, uptake of certain pollutants into aquatic food webs, sub-lethal effects on metabolic and
reproductive functions, habitat degradation from turbidity, eutrophication, dissolved oxygen depletion, and loss of prey
organisms. Metals are of particular concern to this regulation because they (1) do not volatilize, (2) do not biodegrade, (3)
can be toxic to plants, invertebrates and fish, (4) adsorb to sediments and (5) bioconcentrate in biological tissues.
EPA obtained the environmental fate and toxicity information for the 132 MP&M POCs. Table I.I in Appendix I shows the
environmental fate and toxicity of each MP&M pollutant.5 EPA found that:
*• 56 pollutants are not volatile or are only slightly volatile (all metals were assumed to be non-volatile except for
mercury);
•• 57 pollutants have moderate to high adsorption potentials (all metals were assumed to have high adsorption potential
except for nickel);
*• 42 pollutants have moderate to high bioconcentration factors;
*• 62 pollutants biodegrade slowly or are resistant to biodegradation altogether (all metals were assumed to be resistant
to biodegradation);
»• For freshwater environments, 32 pollutants have acute toxicities to aquatic life that range from moderate to high, and
33 pollutants have chronic toxicities that range from moderate to high;
> For saltwater environments, 20 pollutants have acute toxicities to aquatic life that range from moderate to high, and
23 pollutants have chronic toxicities that range from moderate to high.
The available information shows that dozens of the MP&M POCs have the potential to pose significant hazards to the aquatic
environment when released to receiving waters. A number of pollutants are of particular concern because of their combined
toxicity and fate. These include several polyaromatic hydrocarbons (acenaphthene, anthracene, 3,6-dimethyl-phenanthrene,
fluoranthene, phenanthrene, and pyrene), several metals (aluminum, cadmium, copper, mercury, and selenium) and several
phthalates (di-n-octyl phthalate, butyl benzyl phthalate, and di-n-butyl phthalate). Other pollutants are of concern chiefly
because of their toxicity (arsenic, cyanide, chromium, lead, nickel, silver, and zinc) or their fate (bis(2-ethylhexyl)phthalate,
bromo-2-chlorobenzene, bromo-3-chlorobenzene, dibenzofuran, dibenzothiophene, diphenylamine, long-chained petroleum
hydrocarbons, 1-methylfluorene, N-nitrosodiphenylamine, and several metals).
The available fate and toxicity data indicate that many MP&M pollutants tend (1) to be "toxic", (2) to not readily volatilize
from the water column, (3) to adsorb to sediments, (4) to bioconcentrate in aquatic organisms, and (5) do not biodegrade.
Such pollutants accumulate in sediments and reach concentrations which can impair benthic communities. Pollutants that
have accumulated in sediments can be released back into the water column because sediments act as long-term sinks. The
pollutants can also enter soils and reach high levels over time if present in sewage sludge that is applied to land. The
tendency of these pollutants to resist biodegradation and to bioconcentrate in biological tissue also causes them to be taken up
into aquatic food chains where they can affect predators or humans who consume fish and shellfish (U.S. EPA, 1998).
The toxicity data also indicate that a sizable number of the POCs in MP&M effluents have toxicities that result in lethal or
sub-lethal responses in aquatic receptors, including algae, vascular plants, invertebrates, fish, and amphibians. Responses
include death, which may occur within a matter of hours to days, or longer-term sub-lethal responses (such as reproductive
failure or growth impairment) that manifest themselves over weeks, months, or even years. The effects of toxic chemicals are
not shared equally among exposed species: sensitive species are typically more affected than species that are more resistant.
Hence, toxic conditions could selectively remove sensitive species from receiving waters. Such a pattern is of particular
concern to threatened and endangered (T&E) species, which may already be close to extinction. Aquatic receptors are
exposed to many different toxicants at the same time, which may have additive effects. The EPA assessment is based on a
Note that EPA was unable to obtain fate or toxicity data for a substantial number of POCs.
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
chemical-by-chemical approach and therefore does not consider additive effects. This approach may understate the benefits
of the rule.
EPA also did not evaluate the potential fate and effects of the four conventional pollutants (BOD, pH, O&G, TSS) and several
other pollutants, including Total Petroleum Hydrocarbon (TPH), Total Kjeldahl Nitrogen (TKN), phosphorus, and
chemical oxygen demand (COD), which may nonetheless adversely affect aquatic environments.6'7
Effluents with high levels of BOD or COD consume large amounts of dissolved oxygen in a short time, causing surface waters
to become oxygen-depleted, thereby killing or excluding aquatic life (U.S. EPA, 1986). At current discharge levels, MP&M
facilities discharge 1.1 million pounds of BOD per year.
Low pH (high acidity) water can be lethal to aquatic organisms; sensitive species offish and invertebrates are eliminated from
surface waters at pH's between 6.0 and 6.5 (U.S. EPA, 1999).
O&G and TPH can have lethal effects on fish by coating gill surfaces and causing asphyxia, depleting dissolved oxygen levels
due to excessive BOD, and impairing stream re-aeration due to the presence of surface films. Compounds present in O&G or
TPH can also be detrimental to waterfowl by affecting the buoyancy and insulating capacity of their feathers (U.S. EPA,
1998). At current discharge levels, MP&M facilities discharge 553,481 pounds per year of O&G, including 67,427 pounds a
year of TPH.
TSS increases the turbidity of surface water and impairs underwater visibility and transparency, thereby inhibiting
photosynthesis by diminishing the amount of sunlight that reaches algae or submerged aquatic plants. TSS also causes a
general degradation of aquatic habitats by increasing the rate of sedimentation, which smothers eggs, covers aquatic plants,
and affects benthic invertebrates (U.S. EPA, 1998).
High input of nitrogen in estuarine and marine systems or phosphorus in freshwater systems can increase primary productivity
and result in eutrophication. Such a process overloads surface waters with algae and reduces the transparency of the water
column. The excess algae sink to the bottom and decompose at the end of their life cycle. This process consumes large
amounts of dissolved oxygen and can turn surface waters anoxic (U.S. EPA, 1998; U.S. EPA, 1995).
12.1 A Effects of MP<&M Pollutants on Economic Productivity
Most MP&M pollutants associated with adverse health effects are subject to drinking water criteria. Thus, MP&M discharges
to surface water can increase the cost of municipal water treatment by requiring investment in chemical treatment and
filtration. Public water treatment systems must comply with drinking water criteria MCLs and secondary standards.
Compliance may require treatment to reduce the levels of regulated pollutants below their MCLs. Capital investment and
operating and maintenance (O&M) costs associated with treatment technologies can be substantial. To the extent that the
MP&M regulation reduces the concentration of MP&M pollutants in source waters to values that are below pollutant-specific
drinking water criteria, public drinking water systems will accrue benefits in the form of reduced water treatment costs.
Releases of MP&M pollutants to surface waters may also increase treatment costs of irrigation water and industrial water.
Releases of large quantities or high concentrations of toxic pollutants in MP&M effluents may interfere with POTW processes
(e.g., inhibiting microbial degradation), reduce the treatment efficiency or capacity of POTW s, and reduce disposal options
for the sludge. In addition, toxic pollutants present in the effluent discharges may pass through a POTW and adversely affect
receiving water quality, or may contaminate sludges generated during primary or secondary wastewater treatment. EPA
expects no changes in the current status of POTW processes or disposal options for the sludge at POTWs receiving effluent
discharges from MP&M facilities associated with the MP&M rule since all indirect dischargers have been excluded from the
final option. EPA, however, analyzed changes in interferences of POTW operations and contamination of sewage sludge at
6 TKN is defined as the total of organic and ammonia nitrogen. It is determined in the same manner as organic nitrogen, except that
the ammonia is not driven off before the digestion step.
7 EPA, however, considered environmental effects of TKN in the Ohio case study. EPA evaluated the impact of in-stream TKN
concentrations on recreational value of fishing, boating, swimming, and wildlife viewing sites. For detail see Chapter 21 of this report.
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
POTWs receiving effluent discharges from MP&M facilities for the alternative regulatory options which include indirect
dischargers.
12.2 LINKING THE REGULATION TO BENEFICIAL OUTCOMES
This section describes the linkages between promulgation of a regulation and the expected benefits to society. As indicated in
Figure 12.1, the benefits of the MP&M regulation occur from a chain of events. These events include: (1) Agency publication
of the regulation, (2) industry changes in production processes and/or treatment systems, (3) reductions in pollutant
discharges, (4) changes in water quality, (5) changes in ecosystem attributes and sewage sludge quality, (6) changes in human
responses, and (7) changes in human health and ecological risk. The first two events reflect the institutional and technical
aspects of the regulation. The benefit analysis begins with the third event, the changes in the pollutant content of effluent
discharges.
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MPAM EEBA Part III: Benefits
Chapter 12: Benefit Overview
Figure 12.1: Chain of Events in a Benefits Analysis
1. EPA Publication of
Regulation
2. Changes in
Production Processes
and/or Treatment
3. Reductions in
Pollutant Discharges
4. Changes in
Ambient Water
Quality
(Pollutant
Concentrations &
Aquatic Habitat)
5. Change in Aquatic
Ecosystem
(e.g., Increased Fish
Populations &
Diversity & Reduced
Bioaccumulation)
6. Change in Level of
Demand & Value of
Fishery
(e.g., Recreational &
Other Benefit
Categories)
7. Potential Change in
Health Risk
(e.g., from
Consumption of Fish
Caught)
Source: U.S. EPA analysis.
In event four, changes in pollutant discharges translate into improvements in water and sludge quality. In event five, these
improvements in turn affect in-stream and near-stream biota (e.g., increased diversity of aquatic species and size of species
populations) and sludge disposal options. Finally, human effects and the related valuation of benefits occur in events six and
seven. For example, improvements to recreational fisheries and enhanced enjoyment by recreational anglers is connected to
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
improved water quality and the value of reduced risk to human health. These linkages are the basis of the benefits analysis
presented in this and the following chapters.
12.3 QUALITATIVE AND QUANTITATIVE BENEFITS ASSESSMENT
A benefit assessment defines and quantifies the types of improvements to human health and ecological receptors that can be
expected from reducing the amount of MP&M pollutants released to the environment. The following sections provide an
overview of the concepts and analytic approaches involved in the benefits assessment. The first section describes the general
categories of benefits expected to result from the regulation and the level of analysis undertaken for them. The following
three sections review, within the broad categories of benefits likely to be achieved by the MP&M regulation, the specific
benefits that are evaluated in this analysis. Finally, Section 12.3.5 summarizes methods for attaching values to some of the
benefit measures. Chapters 13 through 16 present the quantitative assessment of benefits.
12.3.1 Overview of Benefit Categories
The benefits of reduced MP&M discharges may be classified in three broad categories: human health, ecological, and
economic productivity benefits. Table 12.3 summarizes the different types of benefits that fall in each of these categories.
Each category is comprised of a number of more narrowly defined benefit categories. EPA expects that the MP&M
regulation will provide benefits to society in all of these categories. EPA was not able to bring the same depth of analysis to
all of these categories, however, because of imperfect understanding of the link between discharge reductions and benefit
categories, and how society values some of the benefit events. EPA was able to quantify and monetize some benefits,
quantify but not monetize other benefits, and assess still other benefits only qualitatively.
In addition to the national-level benefits analysis, the Agency conducted a case study in the state of Ohio to provide in-depth
analysis of the regulation's expected benefits. The Ohio case study improves on the national analysis in two ways. First, the
analysis uses improved data and methods to address co-occurrence of MP&M facility benefits and other-source contributions
of MP&M pollutants in the same locations. Second, the analysis of recreational benefits is based on original travel cost
models of resource valuation in a random utility framework. The analysis values changes in the value of water resources for
four recreational activities -- fishing, boating, swimming, and near-water recreation. Due to data limitations, only three of
these four activities were valued at the national-level benefits analysis.
To provide perspective on the extent to which this regulatory impact assessment was able to comprehensively analyze the
benefits, Table 12.3 summarizes the specific benefits within each of the three broad benefit categories that are expected to
accrue from the MP&M regulation and the level of analysis applied to each category. As shown in Table 12.3, only a few of
the relevant benefit categories can be both quantified and monetized.
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MPAM EEBA Part III: Benefits
Chapter 12: Benefit Overview
Table 12.3: Level of Analysis Performed for Specific Benefit Categories
Benefit Category
Human He
Reduced cancer risk due to ingestion of chemically-contaminated
fish and unregulated pollutants in drinking water
Reduced non-cancer adverse health effects (e.g. reproductive,
immunological, neurological, circulatory, or respiratory toxicity)
due to ingestion of chemically-contaminated fish and unregulated
pollutants in drinking water
Reduced non-cancer adverse health effects from exposure to lead
from consumption of chemically-contaminated fish
Reduced cancer risk and non-cancer adverse health effects from
exposure to unregulated pollutants in chemically-contaminated
sewage sludgea
Reduced health hazards from exposure to contaminants in waters
used recreationally (e.g., swimming)
Ecologic
Reduced risk to aquatic life
Enhanced water-based recreation including fishing, boating, and
near- water (wildlife viewing) activities
Other enhanced water-based recreation such as swimming,
waterskiing, and white water rafting
Increased aesthetic benefits such as enhancement of adjoining site
amenities (e.g. residing, working, traveling, and owning property
near the water)
Nonuser value (i.e., existence, option, and bequest value)
Reduced contamination of sediments
Reduced non-point source nitrogen contamination of water if
sewage sludge is used as a substitute for chemical fertilizer on
agricultural landa
Satisfaction of a public preference for beneficial use of sewage
sludge"
Quantified
and
Monetized
alth Benefits
X
X
al Benefits
X
X
Quantified
but Not Monetized
X
X
Qualitative
X
X
X
X
X
X
X
Economic Productivity Benefits
Reduced sewage sludge disposal costs X
Reduced management practice and record-keeping costs of sewage 1 X
sludge that meets exceptional quality criteria3
Reduced interference with POTW operations3 X
Benefits to tourism industries from increased participation in water- 1 X
based recreation
j ^ ^
Improved commercial fisheries yields X
Improved crop yield (the organic matter in land-applied sewage X
sludge increases soil's water retention)3
Avoidance of costly siting processes for more controversial sewage 1 X
sludge disposal methods (e.g., incinerators) because of greater use !
of land application3
Reduced water treatment costs for municipal drinking water, X
irrigation water, and industrial process and cooling water 1 1 1
3 These benefit categories are not applicable to the final rule since all indirect dischargers have been excluded from the selected
option. EPA, however, analyzed these benefit categories for the alternative regulatory options which include indirect dischargers.
Source: U.S. EPA analysis.
Each category of benefits and the level of analysis applied to this category are discussed in greater detail below.
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
12.3.2 Human Health Benefits
Reduced pollutant discharges to the nation's waterways will generate human health benefits by several mechanisms. The
most important and readily analyzed benefits stem from reduced risk of illness associated with the consumption of water, fish,
shellfish, and other aquatic organisms that is taken from waterways affected by MP&M discharges. Human health benefits
are typically analyzed by estimating the change in the expected number of adverse human health events in the exposed
population resulting from a reduction in effluent discharges. While some health effects such as cancer are relatively well
understood and thus may be quantified in a benefits analysis, others are less well characterized and cannot be assessed with
the same rigor or at all.
EPA analyzed the following direct measures of change in risk to human health: incidence of cancer from fish and water
consumption; reduced risk of non-cancer toxic effects from fish and water consumption; and lead-related health effects to
children and adults. EPA was able to monetize only two of the three measures (cancer-related and lead-related health risks).
Incidence of cancer was translated into an expected number of avoided mortality events and, on that basis, monetized. Lead
impacts to children were evaluated in terms of potential intellectual impairment as measured by estimated changes in IQ.
Changes in adverse health effects to adults from lead exposure were measured in terms of reduced risk of hypertension, non-
fatal coronary heart disease, non-fatal strokes, and mortality.
EPA also quantified but did not monetize the expected reduction of pollutant concentrations in excess of health-based AWQC
limits. This benefit measure was obtained by comparing in-waterway pollutant concentrations to toxic effect levels.
In concept, the value of these health effects to society is the monetary value that society is willing to pay to avoid the health
effects, or the amount that society would need to be compensated to accept increases in the number of adverse health events.
"Willingness-to-pay" (WTP) values are generally considered to provide a fairly comprehensive measure of society's
valuation of the human and financial costs of illness associated with the costs of health care, losses in income, and pain and
suffering of affected individuals and of their family and friends.
In some cases, available economic research provides little empirical data for society's WTP to avoid certain health effects.
One component of the cost of an illness estimates the direct medical costs of treating a health condition (e.g., hypertension),
and can be used to value changes in health risk from reduced exposure to toxic pollutants such as lead. These estimates
represent only one component of society's WTP to avoid adverse health effects and therefore produce a partial measure of the
value of reduced exposure to MP&M pollutants. Employed alone, these monetized effects will significantly underestimate
society's WTP.
12.3.3 Ecological Benefits
EPA expects that the ecological benefits from the regulation will include protection of fresh- and saltwater plants,
invertebrates, fish, and amphibians, as well as terrestrial wildlife and birds that prey on aquatic organisms exposed to MP&M
pollutants. The regulation will reduce the presence and discharge of various pollutants and will enhance or protect aquatic
ecosystems currently under stress. The drop in pollutant loading is expected to reestablish productive ecosystems in damaged
waterways and to protect resident species, including T&E species. EPA also expects that the regulation will enhance the
general health offish and invertebrate populations, increase their propagation to waters currently impaired, and expand
fisheries for both commercial and recreational purposes. Improvements in water quality will also favor increased recreational
activities such as swimming, boating, fishing, and water skiing. Finally, the Agency expects that the regulation will augment
nonuse values (e.g., option, existence, and bequest values) of the affected water resources.
It is frequently difficult to quantify and attach economic values to ecological benefits. The difficulty results from imperfect
understanding of the relationship between changes in effluent discharges and the specific ecological changes, lack of water
quality monitoring data for most locations, and time lags between water quality changes and changes in species population
and composition. In addition, it is difficult to attach monetary values to these ecological changes because they often do not
occur in markets in which prices or costs are readily observed. As such, ecological benefits may be loosely classified as
nonmarket benefits. This classification can be further divided into nonmarket use benefits and nonmarket nonuse benefits.
Nonmarket use benefits stem from improvements in ecosystems and habitats, which in turn lead to enhanced human use and
enjoyment of these areas. For example, reduced discharges may lead to increased recreational use and enjoyment of affected
waterways in such activities as fishing, swimming, boating, hunting or near-water activities such as bird watching. In some
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
cases, it may be possible to quantify and attach partial economic values to ecological benefits using market values (e.g., an
increase in tourism or boat rentals associated with improved recreational fishing opportunities); in this case, these benefit
events might better be classified as economic productivity related events, which are discussed below. Economic markets,
however, do not provide enough information to fully capture the value of these benefits. Such markets capture only related
expenditures made by recreationists (e.g., food and lodging) and do not capture the value placed on the experience itself. A
variety of nonmarket valuation techniques can be used to capture the value placed on the resource in question. These
techniques include hedonic valuation (wage-risk studies) and travel cost methods (TCM), stated preferences methods (i.e.,
contingent valuation (CV). contingent rating (CR). contingent activity (CA). benefits transfer, and averting
behavior models.
Nonmarket nonuse benefits are not associated with current use of the affected ecosystem or habitat, but rather arise from (1)
the realization of the improvement in the affected ecosystem or habitat resulting from reduced effluent discharges and (2) the
value that individuals place on the potential for use sometime in the future. Nonmarket nonuse benefits may also be
manifested by other valuation mechanisms, such as cultural valuation, philanthropy, and bequest valuation. It is often
extremely difficult to quantify the relationship between changes in discharges and the improvements in societal well-being
associated with such valuation mechanisms. That these valuation mechanisms exist, however, is indisputable, as evidenced,
for example, by society's willingness to contribute to organizations whose mission is to purchase and preserve lands or
habitats to avert development.
12.3.4 Economic Productivity Benefits
Reduced pollutant discharges may also benefit economic productivity. First, economic productivity benefits may accrue from
reduced treatment costs of drinking water, irrigation water, and industrial use water. Reduced pollutant concentrations in
public water systems source water to levels at or below MCLs or secondary standards could reduce ongoing treatment costs
and avoid the need to invest in treatment technologies in the future. Reduced pollutant discharges may also reduce sediment
dredging costs. Contaminated sediments may contribute substantially to contamination of aquatic biota and to human
exposure of human health toxicants. Controlling point source discharges of toxic pollutants can prevent sediment
contamination and eliminate the need for future remediation (i.e., dredging) of contaminated sediments.
Other economic productivity gains may result from improved tourism opportunities in areas affected by MP&M discharges.
Improved aquatic species survival may contribute to increased commercial fishing yield. When such economic productivity
effects can be identified and quantified, they are generally straightforward to value because they involve market commodities
for which prices or unit costs are readily available.
Economic productivity gains may also occur through reduced costs to public sewage systems (POTWs) for managing and
disposing of the sludge (i.e., biosolids) from treating effluent discharges. For example, higher quality sludge may be applied
to agricultural land or otherwise beneficially used rather than being incinerated or disposed of in landfills. POTWs may also
incur lower costs because of lower record keeping requirements. Under the final regulatory option, EPA expects no POTW
productivity gains since all indirect dischargers have been excluded from the final regulatory option.
12.3.5 Methods for Valuing Benefit Events
Some of the benefits expected from the MP&M regulation will manifest themselves in economic markets through changes in
price, cost, or quantity of market-valued activities. For benefits endpoints traded in markets, such as increased yields from
commercial fisheries, benefits can be measured by market prices or market-based factor pricing. Competitive prices can be
used also to measure avoided cost type of benefits. For example, reduced pollutant loadings to public water supplies may
lower costs of drinking treatment. Market prices can be used also to value direct medical costs of illnesses associated with
exposure to pollutants. For this analysis, EPA used medical costs associated with treating hypertension, coronary heart
disease, and stroke to estimate benefits from reduced exposure to lead (see Chapter 14). The estimated values can be used as
minimum measures of the benefits associated with reduced cases of these illnesses.
In other cases, benefits involve activities or sources of value that either do not involve economic markets or involve them only
indirectly. Methods used to value such benefits are described briefly below:
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MP&M EEBA Part III: Benefits Chapter 12: Benefit Overview
a. Wage-risk approach.
The wage-risk approach uses regression estimates of the wage premium associated with greater risks of death on the job to
estimate the amount that persons are willing to pay to avoid death. Benefit values based on this approach are used as part of
the basis for valuing reduced cancer cases due to fish consumption in Chapter 13.
b. Travel cost method
The TCM uses information on costs incurred by people in traveling to a site and in using the site to estimate a demand curve
for that site. The demand curve is then used to estimate the "consumer surplus" associated with the use of the site, that is, the
value that consumers receive from the site over and above the costs that they incur in using it. Consumer surplus is an
estimate of the net benefits of the resource to the people using that resource. For example, if the resource is a recreational
fishing site, the TCM can be used to value the recreational fishing experience. The Agency used an original travel cost study
to value benefits from enhanced water-based recreation in Ohio (see Part V: Chapter 21). The analysis of recreational
benefits in Chapter 15 uses a meta-analysis of water-based recreation studies (including TCM studies) to derive the baseline
and post-compliance values of water-based recreation activities (including fishing, boating, and wildlife viewing) and to
estimate benefits to consumers of water-based recreation from improved water quality resulting from reduced MP&M
dischargers.
c. Contingent valuation
In the CV method, surveys are conducted to elicit individuals' WTP for a particular good, such as a fishery, or clean water.
CV is more broadly applicable than TCM. Like TCM, CV can be used to estimate the consumer surplus associated with
recreational fisheries. CV can also be used to estimate less tangible values, such as how much people care about a clean
environment. Values from both the CV approach and the wage-risk approach support the estimated value of avoided death
that is used to monetize reduced cancer cases from consumption of contaminated fish (Chapter 13). Similarly to the TCM
studies, CV studies are used in a meta-analysis to derive the baseline and post-compliance values of water-based recreation
activities (including fishing, boating, and wildlife viewing) and to estimate benefits from improved opportunities for water-
based recreation from reduced MP&M dischargers (Chapter 15).
d. Benefits transfer
When time and resource constraints preclude primary research, benefit assessment based on benefits transfer from existing
studies is used. This approach involves extrapolating benefit findings for one analytic situation to another. The relevant
study situations are defined by type of environmental resource (e.g., fishery), policy variable(s), and the characteristics of user
populations. The benefits transfer approach is used to monetize several benefit categories, including changes in the incidence
of cancer cases (Chapter 13) and the national-level benefits from enhanced water-based recreation (Chapter 15).
The techniques described above form the basis of the benefits methodologies described in Chapters 13,14, and 15.
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MPAM EEBA Part III: Benefits Chapter 12: Benefit Overview
GLOSSARY
acute toxicity: the ability of a substance to cause severe biological harm or death soon after a single exposure or dose.
Also, any poisonous effect resulting from a single short-term exposure to a toxic substance. (See: chronic toxicity, toxicity.)
(http://www.epa.gov/OCEPAterms/aterms.html)
adsorption coefficients (Koc): represents the ratio of the target chemical absorbed per unit weight of organic carbon in
the soil or sediment to the concentration of that same chemical in solution at equilibrium.
ambient water quality criteria (AWQC): AWQC present scientific data and guidance of the environmental effects of
pollutants which can be useful to derive regulatory requirements based on considerations of water quality impacts; these
criteria are not rules and do not have regulatory impact (U.S. EPA. 1986. Quality Criteria for Water 1986. U.S.
Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC. EPA 440/5-86-001).
AQUatic Information REtrieval System (AQUIRE): aweb-based ecotoxicity database maintained by EPA's
Mid-Continent Ecology Division (MED) which summarizes ecotoxicity data retrieved from the literature.
(http://www.epa.gov/med/databases/databases.htmlSaquire) (U.S. EPA, 1998/99b)
Assessment Tools for the Evaluation of Risk (ASTER): an ecological risk assessment tool developed by EPA's
Mid-Continent Ecology Division (MED); ASTER integrates information from the AQUIRE toxic effects database and the
QSAR system (a structure activity-based expert system) to estimate ecotoxicity, chemical properties, biodegradation and
environmental partitioning, (http://www.epa.gov/med/databases/aster.html) (U.S. EPA, 1998/99c)
avoided cost: costs that are likely to be incurred in the future if current conditions still prevail at the time, but which will
be avoided if particular actions are taken now to change the status quo.
benthic: relating to the bottom of a body of water; living on, or near, the bottom of a water body.
BIODEG: a web-based biodegradation database developed by Syracuse Research Corporation.
(http://esc.syrres.com/efdb/BIODGSUM.HTM) (Syracuse Research Corporation, 1999)
biodegradation half-lives: represents the number of days a compound takes to be degraded to half of its starting
concentration under prescribed laboratory conditions.
biological oxygen demand (BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose
organic material in an aquatic environment.
cancer potency slope factor (SF): a plausible upper-bound estimate of the probability of a response per unit intake of a
chemical over a lifetime. The slope factor is used to estimate an upper-bound probability of an individual developing cancer
as a result of a lifetime of exposure to a particular level of a potential carcinogen.
CHEMFATE: a web-based chemical fate database developed by Syracuse Research Corporation.
(http://esc.syrres.com/efdb/Chemfate.htm) (Syracuse Research Corporation, 1999)
chemical oxygen demand (COD): a measure of the oxygen required to oxidize all compounds, both organic and
inorganic, in water. (http://www.epa.gov/OCEPAterms/cterms.html)
chronic toxicity: the capacity of a substance to cause long-term poisonous health effects in humans, animals, fish, and
other organisms. (http://www.epa.gov/OCEPAterms/cterms.html)
contingent activity: one of the stated preference methods (see: contingent valuation and contingent activity). Survey
respondents are asked how their behavior would change in response to a proposed change in one or more attributes of an
activity (e.g., cost of the activity, site accessibility, or site attractiveness). Given responses to this type of question, and given
information about incremental travel costs and value of time, a revealed preference method can be used to estimate the value
of change.
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MPAM EEBA Part III: Benefits Chapter 12: Benefit Overview
contingent rating: one of the stated preference methods (see: contingent valuation and contingent activity). Survey
respondents are asked to rate several alternatives on an ad hoc utility scale (e.g., 1 to 10). The choice set of alternatives
usually includes the environmental effect to be valued, substitutes for the effect, and a good with a monetary price to act as a
threshold. Based on the respondent's rating of the environmental effect and the threshold good, and the monetary price of the
threshold good, the value of the environmental effect can be determined.
contingent valuation (CV): a method used to determine a value for a particular event, where people are asked what they
are willing to pay for a benefit and/or are willing to receive in compensation for tolerating a cost. Personal valuations for
increases or decreases in the quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit
valuations or bids that are close to what would be revealed if an actual market existed.
(http://www.damagevaluation.com/glossary.htm)
Environmental Research Laboratory-Duluth fathead minnow database: a database developed by EPA's
Mid-Continent Ecology Division (MED) which provides data on the acute toxicity of hundreds of industrial organic
compounds to the fathead minnow. (http://www.eoa.gov/med/databases/fathead_minnow.html) (U.S. EPA, 1998/99a)
hazardous air pollutant (HA P): compounds that EPA believes may represent an unacceptable risk to human health if
present in the air.
Health Effects Assessment Summary Tables (HEAST): a comprehensive listing of provisional human health risk
assessment data relative to oral and inhalation routes for chemicals of interest to EPA. Unlike data in IRIS, HEAST entries
have received insufficient review to be recognized as high quality, Agency-wide consensus information. (U.S. EPA. 1997.
Health Effects Assessment Table; FY 1997 Update. EPA-540-R-97-036)
Henry's Law constant: a numeric value which relates the equilibrium partial pressure of a gaseous substance in the
atmosphere above a liquid solution to the concentration of the same substance in the liquid solution.
human health-based water quality criteria (WQC): human health-based criteria are based on specific levels of
pollutants that would make the water harmful if used for drinking, swimming, farming, fish production, or industrial processes
(see ambient water quality criteria (AWQC)). (http://www.epa.gov/OCEPAterms/wterms.html).
hydrophobicity: having a strong aversion to water. (http://www.epa.gov/OCEPAterms/hterms.html)
Integrated Risk Information System (IRIS): IRIS is an electronic database with information on human health effects of
various chemicals. IRIS provides consistent information on chemical substances for use in risk assessments, decision-making
and regulatory activities.
lipophilicity: having a strong attraction to oils
maximum contaminant levels (MCLs): the maximum permissible level of a contaminant in water delivered to any user
of a public system. MCLs are enforceable standards.
(http://www.epa.gov/OCEPAterms/mterms.html)
metals: inorganic compounds, generally non-volatile, and which cannot be broken down by biodegradation processes. They
are a particular concern because of their prevalence in MP&M effluents. Metals can accumulate in biological tissues,
sequester into sewage sludge in POTWs, and contaminate soils and sediments when released to the environment. Some metals
are quite toxic even when present at relatively low levels.
microbial metabolism: biochemical reactions occurring in living microorganisms such as bacteria, algae, diatoms,
plankton, and fungi. POTWs make use of bacterial metabolism for wastewater treatment purposes. This process is inhibited
by the presence of toxins such as metals and cyanide because these pollutants kill bacteria.
oil and grease (O&G): organic substances that may include hydrocarbons, fats, oils, waxes, and high-molecular fatty
acids. Oil and grease may produce sludge solids that are difficult to process, (http://www.epa.gov/owmitnet/reg.htm)
pH: an expression of the intensity of the basic or acid condition of a liquid; natural waters usually have a pH between 6.5
and 8.5. (http://www.epa.gov/OCEPAterms/pterms.html)
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MPAM EEBA Part III: Benefits Chapter 12: Benefit Overview
pollutants of concern (POCs): are the 150 contaminants identified by EPA as being of potential concern for this rule and
which are currently being discharged by MP&M facilities.
priority pollutant (PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
sediment, groundwater, or soil samples.
publicly-owned treatment works (POTWs): a treatment works, as defined by section 212 of the Act, that is owned by a
State or municipality. This definition includes any devices or systems used in the storage, treatment, recycling, and
reclamation of municipal sewage or industrial wastes of a liquid nature. It also includes sewers, pipes, or other conveyances
only if they convey wastewater to a POTW Treatment Plant, (http://www.epa.gov/owm/permits/pretreat/final99.pdf)
quantitative structure-activity relationship (QSAR) model: an expert system which uses a large database of
measured physicochemical properties such as melting point, vapor pressure, and water solubility to estimate the fate and
effect of a specific chemical based on its molecular structure, (http://www.epa.gov/med/databases/aster.html) (U.S. EPA,
1998/99)
reference doses (RfDs): chemical concentrations expressed in mg of pollutant/kg body weight/day, that, if not exceeded,
are expected to protect an exposed population, including sensitive groups such as young children or pregnant women.
secondary MCLs: human health-based drinking water criteria to assess the health hazards associated with the presence of
certain toxic chemicals in drinking water. SMCLs are established for taste or aesthetic effects.
Superfund Chemical Data Matrix (SCDM): a source for factor values and benchmark values applied when evaluating
potential National Priorities List (NPL) sites using the Hazard Ranking System (HRS).
(http://www.epa.gov/superfund/resources/scdm/index.htm).
suspended solids: small particles of solid pollutants that float on the surface of, or are suspended in, water bodies.
(http://www.epa.gov/OCEPAterms/sterms.html)
systemic toxicants: chemicals that EPA believes can cause significant non-carcinogenic health effects when present in the
human body above chemical-specific toxicity thresholds.
threatened and endangered (T&E): animals, birds, fish, plants, or other living organisms threatened with extinction by
anthropogenic (man-caused) or other natural changes in their environment. Requirements for declaring a species endangered
are contained in the Endangered Species Act.
Total Petroleum Hydrocarbon (TPH): a general measure of the amount of crude oil or petroleum product present in an
environmental media (e.g., soil, water, or sediments). While it provides a measure of the overall concentration of petroleum
hydrocarbons present, TPH does not distinguish between different types of petroleum hydrocarbons.
Total Kjeldahl Nitrogen (TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as
organic nitrogen, except that the ammonia is not driven off before the digestion step.
total suspended solids (TSS): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by
tests for "total suspended non-filterable solids." (See: suspended solids.)
(http://www.epa.gov/OCEPAterms/tterms.html)
travel cost method (TCM): method to determine the value of an event by evaluating expenditures of recreators. Travel
costs are used as a proxy for price in deriving demand curves for the recreation site.
(http://www.damagevaluation.com/glossary.htm)
uptake: the movement of one or more chemicals into an organism via ingestion, inhalation, and/or through the skin.
vascular plants: plants that are composed of, orprovided with, vessels or ducts that convey fluids, (www.infoplease.com)
willingness-to-pay (WTP): maximum amount of money one would give up to buy some good.
(http://www.damagevaluation.com/glossary.htm)
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MPAM EEBA Part III: Benefits Chapter 12: Benefit Overview
ACRONYMS
AQUIRE: AQUatic Information REtrieval System
ASTER: Assessment Tools for the Evaluation of Risk
AWQC: ambient water quality criteria
BIODEG: biodegradation
BOD: biological oxygen demand
CA: contingent activity
CHEMFA TE: chemical fate
CR: contingent rating
CV: contingent valuation
COD: chemical oxygen demand
HAP: hazardous air pollutant
HE A S T: Health Effects Assessment Summary Tables
IRIS: Integrated Risk Information System
Koc: adsorption coefficient
MCL: maximum contaminant level
O&G: oil and grease
POC: pollutant of concern
POTW: publicly-owned treatment work
PP: priority pollutant
QSAR: quantitative structure-activity relationship
RfD: reference dose
SCDM: Superfund Chemical Data Matrix
SF: cancer potency slope factor
threatened and endangered
travel cost method
Total Kjeldahl Nitrogen
Total Petroleum Hydrocarbon
total suspended solids
human health-based water quality criteria
willingness-to-pay
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MPAM EEBA Part III: Benefits Chapter 12: Benefit Overview
REFERENCES
Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1991. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 4th
ed. New York, NY: McGraw-Hill Inc.
Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1 996. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 5th
ed. New York, NY: McGraw-Hill Inc.
Syracuse Research Corporation (BIODEG, CHEMFATE). 1999. Syracuse Research Corporation's Environmental Fate
Databases. Syracuse, NY: Syracuse Research Corporation. http://esc.syrres.com/efdb/BIODGSUM.HTM and
http://esc.syrres.com/efdb/Chemfate.htm.
U.S. Environmental Protection Agency. (U.S. EPA). 1980. Ambient water quality criteria documents. Washington, DC:
Office of Water, U.S. EPA. EPA 440/5-80 Series. Also refers to any update of criteria documents (EPA 440/5-85 and EPA
440/5-87 Series) or any Federal Register notices of proposed criteria or criteria corrections, and EPA 822-Z-99-001. The
most recent National Recommended Water Quality Criteria used in this report were published in the Federal Register on
December 10, 1998.
U.S. Environmental Protection Agency. (U.S. EPA). 1986. Ambient Water Quality Criteria for Dissolved Oxygen. EPA
440/5-86-003.
U.S. Environmental Protection Agency. (U.S. EPA). 1995. Proceedings of the First Gulf of Mexico Hypoxia Management
Conference. EPA-55-R-97-001.
U.S. Environmental Protection Agency. (U.S. EPA). 1997. Health Effects Assessment Summary Tables (HEAST).
Washington, DC: Office of Research and Development and Office of Emergency and Remedial Response, U.S. EPA.
U.S. Environmental Protection Agency. (U.S. EPA). 1998. National Water Quality Inventory. 1996Report to Congress.
EPA841-R-97-008.
U.S. Environmental Protection Agency. (U.S. EPA). 1998/99a. QSAR. Duluth, MN: Environmental Research Laboratory,
U.S. EPA.
U.S. Environmental Protection Agency. (U.S. EPA). 1998/99b. Aquatic Toxicity Information Retrieval (AQUIRE)
Database. Mid-Continent Ecology Division (MED), Duluth, MN. U.S. Environmental Protection Agency. Database
retrieval @ http://www.epa.gov/ecotox/.
U.S. Environmental Protection Agency. (U.S. EPA). 1998/99c. Assessment Tools for Evaluation of Risk (ASTER)
Database. Duluth, MN: Environmental Research Laboratory, U.S. EPA. 1998 Database retrieval.
U.S. Environmental Protection Agency. (U.S. EPA). 1998/99d. Integrated Risk Information System (IRIS). Washington,
DC: U.S. EPA. 1998 Database retrieval @ http://www.epa.gov/iris/.
U.S. Environmental Protection Agency. (U.S. EPA). 1999. Progress Report on the EPA Acid Rain Program. U.S. EPA
Office of Air and Radiation. EPA 430-R-99-011.
Wexler, P., ed. 1998. Encyclopedia of Toxicology, Volumes 1-3.
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MP&M EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Chapter 13: Human Health Benefits
INTRODUCTION
EPA expects that the final MP&M regulation will yield a
range of human health benefits by reducing effluent
discharges to waterways used for fishing or drinking water.
This chapter analyzes four categories of expected human
health benefits. The first two categories involve reductions in
cancer cases from two exposure pathways: consumption of
contaminated fish tissue and ingestion of contaminated
drinking water for the exposed population. EPA evaluated
the expected annual reduction in cancer cases in the exposed
population and the associated monetary value of avoiding
those cancer cases.
EPA quantified, but did not monetize, two additional
measures of human health-related benefits. The first is the
changes in fish consumption and drinking water exposures to
non-cancer causing pollutants measured against non-cancer
health effect reference doses (RfDs), an indicator of non-
cancer health risk. The second benefit measure is the change
in occurrence of pollutant concentrations that are estimated to
exceed human health-based ambient water quality
criteria (AWQC).
EPA also quantified and monetized changes in health risk to
adults and children from reduced exposure to lead. This
analysis is presented in Chapter 14.
CHAPTER CONTENTS
13.1 Methodology & Data Sources 13-2
13.1.1 Cancer from Fish Consumption 13-2
13.1.2 Cancer from Drinking Water Consumption 13-8
13.1.3 Exposures above Non-cancer Health
Thresholds 13-10
13.1.4 Human Health AWQC 13-14
13.2 Results 13-17
13.2.1 Fish Consumption Cancer Results 13-17
13.2.2 Drinking Water Consumption Cancer
Results 13-19
13.2.3 Non-cancer Health Threshold Results .. 13-19
13.2.4 Human Health AWQC Results 13-21
13.3 Limitations and Uncertainties 13-22
13.3.1 Sample Design & Analysis of Benefits by
Location of Occurrence 13-22
13.3.2 hi-Waterway Concentrations of MP&M
Pollutants 13-23
13.3.3 Joint Effects of Pollutants 13-23
13.3.4 Background Concentrations of MP&M
Pollutants 13-23
13.3.5 Downstream Effects 13-24
13.3.6 Exposed Fishing Population 13-24
13.3.7 Treatment of Cancer Latency 13-25
13.3.8 Treatment of Cessation Lag 13-25
13.3.9 Use of Mean Individual Exposure
Parameters 13-26
13.3.10 Cancer Potency Factors 13-26
Glossary 13-27
Acronyms 13-28
References . . , 13-29
The health-related measures were estimated for the baseline
and for the final option for all of the benefit categories
analyzed. In addition, EPA estimated health benefits for alternative options which EPA considered for the MP&M regulation.
The reduction in the health-related measures (i.e., number of annual cancer cases) from baseline to the post-compliance case
is the estimated benefit of the MP&M regulation. As discussed in Chapter 12, EPA estimated national benefits for the
regulation based on sample facility data. The Agency extrapolated findings from the sample facility analyses to the national
level using two alternative extrapolation methods: (1) traditional extrapolation and (2) post-stratification extrapolation.
Appendix G provides detailed information on the extrapolation approaches used in this analysis.
EPA estimated that, for combined recreational and subsistence angler populations, the final option would lead to a marginal
reduction in cancer cases. The total monetized human health benefits from reduced cancer cases from both the fish
consumption and drinking water pathways are essentially negligible (i.e., $90 per year based on the traditional extrapolation
and $134 per year based on the post-stratification extrapolation (2001 $)).
Benefits will also be realized in the form of reductions in non-cancer human health effects (e.g., systemic effects, reproductive
toxicity, and developmental toxicity) from reduced contamination of fish tissue and drinking water sources. For this analysis,
EPA estimates the numbers of individuals in the exposed populations who might be expected to realize reduced risk of
non-cancer health effects in the post-compliance scenario. To evaluate the potential benefits of reducing the in-stream
concentrations of 76 pollutants that cause non-cancer health effects, EPA estimated target organ-specific hazard indices (HI)
for drinking water and fish ingestion exposures in both the baseline and post-compliance scenarios. HI values below one are
generally considered to suggest that exposures are not likely to result in appreciable risk of adverse health effects during a
lifetime, and values above one are generally cause for concern, although an HI greater than one does not necessarily suggest a
likelihood of adverse effects.
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The results of EPA's analysis suggest that the incremental risk of non-cancer effects from pollutants discharged by MP&M
facilities alone is quite low. This analysis found that His for the entire population associated with sample facilities is less than
one in the baseline. The results of EPA's analysis of the post-compliance scenario indicate that hazard indices for individuals
in the exposed population may decrease after facilities comply with the MP&M regulation. Increases in the percentage of
exposed populations that would be exposed to no risk of non-cancer adverse human health effects due to the MP&M
discharges occur in both the fish and drinking water analyses. Whether the incremental shifts in His are significant in reducing
absolute risks of non-cancer adverse human health effects is uncertain and will depend on the magnitude of contaminant
exposures for a given population from risk sources not accounted for in this analysis.
Finally, EPA analyzed the effect of the final regulation on occurrence of pollutant concentrations resulting from MP&M
discharges that exceed human health-based AWQC. EPA estimated that, as the result of baseline MP&M pollutant
discharges, in-stream concentrations exceed human health-based AWQC in 78 and 112 receiving reaches nationwide based on
the traditional extrapolation and post-stratification extrapolation, respectively. EPA estimated that none of these exceedances
will be eliminated under the final option.
13.1 METHODOLOGY <& &ATA SOURCES
Individuals are potentially exposed to pollutants from MP&M facilities via consumption of contaminated fish tissue and
drinking water. Potential human health effects include cancer and non-cancer health effects. Risks such as skin, lung, liver,
kidney, and bladder cancer and leukemia are associated with exposure to 13 MP&M pollutants (see Table 13.1). Non-cancer
health effects are associated with exposure to 76 MP&M pollutants. These effects include increased blood pressure,
gastrointestinal effects, liver and kidney toxicity, cardiovascular and central nervous system effects, and decreased birth
weight (see Table 13.2).
This section summarizes the methodology for estimating national benefits for three benefit categories:
1. reduced incidence of cancer from consumption of fish taken from waterways affected by MP&M industry discharges,
2. reduced incidence of cancer from ingestion of water taken from waterways affected by MP&M industry discharges,
and
3. reduced occurrence of pollutant concentrations resulting from MP&M discharges that exceed human health-based
AWQC.
This analysis does not include all possible human health benefits and does not provide a comprehensive estimate of the total
human health benefits associated with the final MP&M rule. Analyses of health benefits are not possible for a significant
number of the pollutants whose discharges will be reduced under the post-compliance scenario due to the lack of data on a
quantitative relationship between ingestion rate and the potential health effects associated with these chemicals.
Beyond these important limitations, the methodologies used to assess the human health benefits involve significant
simplifications and uncertainties. Elements of the analysis involving significant simplifications and uncertainties include the
following: sample design and analysis of benefits by location of occurrence; estimation of in-waterway concentrations of
MP&M pollutants; consideration of the joint effects of pollutants; consideration of background concentrations of MP&M
pollutants; consideration of downstream effects; and estimation of the exposed fishing population. Section 13.3 provides
more detail on limitations and uncertainties associated with the human health benefits analyses. Whether these simplifications
and uncertainties, taken together, are likely to lead to an understatement or overstatement of the estimated economic values
for the human health benefits that were analyzed is not known.
13.1.1 Cancer from Fish Consumption
The analysis of reduced annual occurrence of cancer in exposed populations via the fish consumption pathway involves three
analytic steps:
»• estimating the reduced annual risk of incurring cancer per exposed individual;
»• estimating the population that would be expected to benefit from reduced contamination of fish; and
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
*• calculating the change in the number of cancer events in the exposed population.
Each step is discussed in detail below.
a. Estimating change in individual cancer risk
The estimated incremental risk to an individual of developing cancer is based on four factors:'
*• the quantity of carcinogenic chemicals that MP&M facilities discharge to waterways,
> the rate at which the discharged chemicals accumulate in fish tissue,
> the cancer effect of the chemicals, and
»• the rate of personal consumption of contaminated fish.
For each sample MP&M facility and the waterway to which it discharges, EPA calculated the incremental cancer risk to four
population classes with different fish consumption rates: children in families that participate in recreational angling, children
in families that participate in subsistence angling, adults in families that participate in recreational angling, and adults in
families that participate in subsistence angling. EPA calculated the incremental cancer risk values for baseline (i.e., before
regulation) pollutant discharges and for post-compliance discharges based on the policy options considered in the final rule
analysis. The following discussion summarizes the incremental cancer risk calculations.
EPA calculated the in-waterway pollutant concentrations for each reach receiving discharges from an MP&M facility using a
simplified dilution model for all chemicals for which a quantitative relationship between ingestion rate and the annual
probability of developing cancer has been estimated. A "reach" is a specific length of river, lake shoreline, or marine
coastline, and an "MP&M reach" is one to which an MP&M facility discharges. This analysis considered only the discharge
reach and did not estimate concentrations below the initial MP&M reach. The water quality model used for calculating in-
waterway pollutant concentrations accounts for the dilution characteristics of different water body types (i.e., streams,
estuaries, and lakes). It does not account for other fate processes, such as chemical degradation or photolysis. The estimated
pollutant concentrations reflect the average pollutant concentrations in the reach to which a facility discharges. For additional
details on the calculation of waterway concentrations, see Appendix I.
The incremental cancer risk associated with each pollutant was calculated based on the estimated concentration of the
pollutant in the affected waterway, the assumed uptake of the pollutant into fish flesh, the daily rate offish ingestion, and the
cancer risk factor for each pollutant. The formula for calculating the risk to an individual from consumption of a given
chemical is as follows:
Cx CF, xBCFx CR *EF*ED
Risk = *SF ns 1)
BW*LT*CF2 ( '
where:
Risk = incremental risk of incurring cancer from fish consumption (change in probability);
C = pollutant concentrations in surface water (ng/1);
CF( = conversion factor, micrograms to milligrams (0.001 mg/fig);
BCF = bioconcentration factor of pollutant in fish (I/kg);
CR = human consumption rate of fish (kg/day);
EF = exposure frequency (365 days/year);
ED = exposure duration (years);
BW = human body weight (70 kg for adults and 30 kg for children under 18);
1 The risk value is referred to as the incremental risk because it is the incremental lifetime probability that an individual will develop
cancer above and beyond the baseline probability posed by all other extant factors that contribute to a risk of developing cancer.
2 A reach is a length of river, shoreline, or coastline with relatively uniform water flow characteristics. Thus, it is reasonable to
assume that pollutant dischargers have a relatively uniform effect on concentrations within a reach.
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MPAM EEBA Part III: Benefits Chapter 13: Human Health Benefits
LT = human lifetime (years);
CF2 = conversion factor, years to days (365 days/year); and
SF = pollutant cancer potency factor (mg/kg/day)"1.
The pollutants analyzed and their cancer potency factors are presented in Table 13.1. EPA used the relationship outlined
above to estimate lifetime risk values for individuals in subsistence and recreational fishing households. The risks to
recreational and subsistence households are estimated over two lifetime segments. Specifically, children living in recreational
fishing households are assumed to consume 7.27 grams per day (0.007 kg/day) of freshwater/estuarine fish over an 18-year
period (ages 0 to 18). Adults are assumed to consume 17.5 grams per day (0.018 kg/day) of freshwater/estuarine fish over a
52-year period (ages 18 to 70). Risks for individuals living in recreational and subsistence fishing households differ in the
assumed consumption rates. Children living in subsistence fishing households are assumed to consume 60.58 grams per day
(0.061 kg/day) of freshwater/estuarine fish over an 18-year period (ages 0-18). Adults in subsistence households are assumed
to consume 142.4 grams per day (0.142 kg/day) of freshwater/estuarine fish over a 52-year period (ages 18 to 70). The total
lifetime incremental risk for these households is calculated by summing the risks for both lifetime segments.
Fish consumption rates for adults are taken from the Methodology for Deriving Ambient Water Quality Criteria for the
Protection of Human Health (EPA, 2000a). Both these rates, 142.4 g/day for adult subsistence anglers and 17.5g/day for
adult recreational anglers, are used for the specific sub-population that they represent. EPA was not able to break the data
supporting these rates down by gender or age group for use in this analysis.
EPA has determined that the fish consumption rate of 142.4 g/day for adult subsistence anglers falls within the range of the
arithmetic mean of adult subsistence angler studies representative of the United States (EPA, 1998). The value represents the
average consumption rate for this population of anglers. It represents uncooked, fresh and estuarine finfish and shellfish.
This rate is reported on an uncooked basis because pollutant concentration data is reported on an uncooked weight basis.
Similarly, the fish consumption rate of 17.5 g/day falls within the average consumption rate for adult recreational anglers.
This rate also represents uncooked, fresh and estuarine finfish and shellfish.
Fish consumption rates for children in recreational angling households are based on West et al. (1989) in the Exposure
Factors Handbook (EPA 1997c). This study has the most specific data for this population group and cites an intake of 7.27
grams/day of freshwater and estuarine fish for children in recreational angling households. For children in subsistence
angling households, the consumption rate was extrapolated from the 7.27 grams/day rate for children in recreational angling
households using the proportional relationship between consumption rates for adult subsistence and recreational anglers
(142.4 grams/day divided by 17.5 grams/day). The consumption rate for children in subsistence angling households is
calculated to be 60.58 grams/day.
Currently, data on marine fish consumption rates for recreational anglers and subsistence anglers are not readily available.
Given that there are few marine reaches affected by the MP&M effluent guideline, EPA decided to use the fresh and
estuarine fish consumption rates in lieu of marine fish consumption rates. This may result in underestimation of benefits,
however, it may also be argued that few subsistence fishers eat fresh/estuarine fish and marine fish at the same rate.
3 For detail see memorandum Fish Consumption Rates by Lynn Zipf (EPA, 2002).
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Chapter 13: Human Health Benefits
Table 13.1: Cancer Potency Factors for MP&M Pollutants
CAS Number
62533
62759
67663
75003
75092
75354
78591
79016
86306
117817
123911
127184
7440382
Regulated Pollutant
Aniline
Nitrosodimethylamine, N-
Trichloromethane
Chloroethane
Dichloromethane
Dichloroethene, 1,1-
Isophorone
Trichloroethene
Nitrosodiphenylamine, N-
Bis(2-ethylhexyl) phthalate
Dioxane, 1,4-
Tetrachloroethene
Arsenic
Cancer Potency Factor
(mg/kg/day)a
0.0057
51
0.0061
0.0029
0.0075
0.6
0.00095
0.011
0.0049
0.014
0.011
0.052
1.5
Drinking Water
Criterion?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
a The cancer potency factor is the incremental probability of developing cancer over a lifetime resulting from
ingestion of the indicated chemical at the rate of one milligram per day per kilogram of body mass. For the
incremental rates of exposure in this analysis and assuming reasonable background chemical exposures, the
potency factor may be reasonably assumed to be a linear constant.
Source: U.S. EPA (1998/99); U.S. EPA (1997a).
The pollutant-specific risks to recreational and subsistence anglers from MP&M facility discharges were then summed across
pollutants for each type of angler, to obtain incremental risks for each population group from each facility's discharge. EPA
developed separate estimates of cancer risk for each combination of angler type and facility discharging at least one pollutant
with a cancer risk factor. The total change in probability of developing cancer from exposure to more than one MP&M
pollutant is assumed to be the sum of the incremental risk effects from each pollutant: that is, the effects of the individual
pollutants are assumed to be linearly additive.4 The annual increased risk of cancer was estimated by dividing the increased
lifetime risk values by 70 (an estimate of lifetime).
b. Estimating the affected population
The population exposed to contaminated fish and thus expected to benefit from reduced discharges includes recreational and
subsistence anglers who fish the affected reaches, as well as members of such anglers' households. The geographic area from
which anglers would travel to fish a reach is assumed to include only those counties that abut a given reach. This assumption
is based on the finding in the 1991 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation that 65 percent
of anglers travel less than 50 miles to fish (U.S. Department of the Interior, 1993). The average diameter of the counties
abutting the reaches receiving discharges from the sample MP&M facilities is approximately 20 miles. Given that counties
may have different shapes and that the road distance to the fishing site is likely to be greater than a straight line, the MP&M
approach is likely to account for the majority of anglers that are likely to fish the affected reach. It is, however, likely to
4 Note that the assumption of linear additivity of cancer risk effects applies not only to the combination of pollutants from a single
facility but also to the combined effects of multiple facility discharges. When more than one MP&M facility discharges to the same
affected waterway a circumstance found to occur with some frequency in the sample facility data the combination of the multiple
facility discharges may be accounted for by simply analyzing the effects of each facility independently. The cancer effects from multiple
facilities can be aggregated to estimate cancer cases in the exposed population.
5 The exposed, and thus potentially benefiting, population would also include a category of "all other individuals" who consume
freshwater and estuarine fish. Although these individuals are expected to have a much lower average daily consumption rate than anglers
in the adjacent counties, they nevertheless would likely receive some benefit from reduced exposure to pollutants through fish
consumption. This analysis omits this consumption category and the associated benefit estimate.
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
introduce a downward bias into the estimate of the affected population. Given that anglers tend to travel farther to visit sites of
very good or exceptional quality, the magnitude of this bias will depend on the fishing quality of the affected sites.
Estimating the number of persons fishing a reach involved the following steps:
»• estimating the licensed fishing population in counties abutting MP&M reaches;
> estimating the population of subsistence fishermen in counties abutting MP&M reaches;
> estimating the fraction of the total fishing population in counties abutting an MP&M reach that fish the MP&M reach
and, from that fraction, the size of population expected to fish each MP&M reach;
•• adjusting the calculated fishing populations for the presence of fish advisories; and
•• including family members in the exposed population estimates.
*»* Estimating the licensed fishing population in counties abutting MP&M reaches
The number of fishing licenses sold in counties abutting MP&M reaches is assumed to approximate the number of anglers
residing in the abutting counties. EPA excluded the nonresident, one-day, and three-day license categories from the total
number of licenses used in this analysis. Data on fishing licenses are not available for every state in which MP&M facilities
are located. EPA used state-level data to estimate the number of fishing licenses per county for those states for which county-
level data were not assembled. Total state licenses were apportioned to counties based on the ratio of total population in the
county abutting a discharge reach to total state population. Where an MP&M reach spans more than one county, fishing
licenses were summed across all counties abutting the discharge reach. Where a reach lies in more than one state, EPA
separately calculated the number of licenses for the abutting county(ies) based on the fishing license and county population
data for the respective states.
EPA's analysis does not account for recreational anglers who do not purchase licenses as required by law. This may result in a
significant underestimate of the fishing population at risk from exposure to MP&M pollutants. For example, the 1996
National Survey of Fishing, Hunting, and Wildlife-Associated Recreation found that 34 percent of the anglers (16 years of
age and older) did not have licenses (U.S. Department of the Interior, 1996).
*»* Estimating the population of subsistence fishermen in counties abutting MP&M reaches
Although fishing licenses may be sold to subsistence fishermen, many of these individuals do not purchase fishing licenses.
The extent of subsistence fishing in the U.S. or in individual states is not generally known. For this analysis, EPA assumed
that the number of subsistence fishermen would be an additional 5 percent of the licensed fishing population.6 That is, after
estimating the licensed fishing population in counties abutting MP&M reaches, EPA added 5 percent to this value as the
estimated number of subsistence fishermen.7
*»* Estimating the population fishing an MP&M reach
EPA assumed that fishing activity among anglers residing within counties abutting a discharge reach is distributed evenly
among all reach miles within those counties. Thus, the number of anglers who fish an MP&M reach was estimated by
computing the length of the reach as a percentage of total reach miles within corresponding counties and multiplying the
estimated ratio by the total fishing population in counties abutting the reach.
*»* Adjusting for fish advisories
For MP&M reaches where fish advisories are in place (typically due to non-MP&M regulated pollutants such as dioxin and
mercury), EPA assumed that some proportion of anglers would adhere to the advisory and not fish those reaches (U.S. EPA,
1999a). Past studies suggest that anglers have a high, although not complete, level of awareness offish advisories. These
studies further suggest that while anglers may change their behavior in response to fish consumption advisories, they do not
necessarily refrain from fishing in these reaches or consuming fish taken from reaches under an advisory. For example,
6 It is important to estimate recreational and subsistence populations separately because fish consumption rates for subsistence anglers
are considerably higher than those for recreational anglers.
7 The environmental justice analysis presented in Chapter 17 of this report shows that the percent of residents living below the
poverty level in the counties affected by MP&M discharges ranges from 7.4 to 25.2. Thus, the assumption that subsistence anglers are an
additional 5% of the licensed fishing population is likely to provide a reasonable estimate of the subsistence anglers population.
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studies conducted by Helton et al (1986), Knuth and Velicer (1990), Silverman (1990), West et al. (1989), Connelly, Knuth,
and Bisogni (1992), and Connelly and Knuth (1993) indicate that 50 to 87 percent of anglers surveyed were aware of state
fish advisories on water bodies where they fish.
These studies also indicate that only 10 to 34 percent of anglers who were aware of advisories modified their fishing behavior
in response by no longer fishing a particular location, changing the location in which they fish, or taking fewer fishing trips.
However, 13 to 68 percent of anglers who were aware of advisories changed their consumption or preparation habits in
response to advisories. The study by Knuth and Velicer (1990) also found some confusion among anglers regarding which
waters were under advisory: 37 percent of fishermen actually fishing in waters under advisory reported that they were fishing
in uncontaminated waters.
On the basis of these data, EPA assumed that recreational fishing activity would be 20 percent less on reaches subject to an
advisory than would otherwise be estimated. EPA also assumed that fish advisories do not affect fishing participation by
subsistence anglers; thus, no adjustment was made to the estimates of the subsistence fishing population based on the presence
offish advisories.
The assumed 20 percent decrease in recreational fishing could lead to either an overestimate or underestimate of the risk
associated with consumption of contaminated fish. For one thing, anglers who change locations may simply be switching to
other locations where advisories are in place and therefore maintain or increase their current risk. Also, those who continue to
fish contaminated waters may change their consumption and preparation habits to minimize the risks. Data on the specific fish
advisories was pulled from EPA's on-line Listing of Fish and Wildlife Advisories (U.S. EPA, 1999a).
»»» Including family members in the exposed population estimates
EPA assumed that, in addition to anglers themselves, families of anglers would also consume fish taken from waters affected
by MP&M facility discharges. Therefore, for each MP&M reach, EPA multiplied the estimated numbers of recreational and
subsistence anglers fishing the affected reaches by 2.65, the size of the average U.S. household in 1996 based on Current
Population Reports, (U.S. Bureau of the Census, 1997). These calculations yielded the household populations of recreational
and subsistence anglers who are estimated to consume fish from the reach to which the MP&M facility discharges, either
directly or indirectly through a POTW. EPA expects that family members will benefit from reduced MP&M industry
discharges by consuming fish that has lower levels of pollutant contamination.
c. Calculating the change in the number of cancer events in the exposed population
EPA calculated the number of cancer cases associated with the pollutant discharges (baseline and post-compliance) from each
facility by multiplying the incremental cancer risk value for the two population classes times the estimated sizes of the
population classes living near the facility. The product of the incremental risk value and the population size yields the number
of annual cancer events in the given population class estimated to result from consumption of fish taken from waterways
affected by MP&M pollutant discharges. Summing the values for the recreational and subsistence fishing household classes
yields the total number of cancer cases associated with the sample facility discharges. Because the number of cancer cases
apply to sample facilities, EPA extrapolated the sample results to the total MP&M population by multiplying the result
obtained for each sample facility by its sample weight and summing the sample-weighted facility results. The formula
follows:
(13.2)
where:
TCCfc = total national estimate of annual cancer cases associated with consumption of contaminated fish tissue
(baseline or post-compliance);
Wtj = facility sample weight i (i = 1 to N facilities, where N is the number of facilities in the sample);
POPisprt = exposed population in recreational fishing households for the reach to which facility i discharges (with
adjustments as indicated for the presence offish consumption advisories);
POPisbst = exposed population in subsistence fishing households for the reach to which facility i discharges;
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Riskisprt = incremental cancer risk from fish consumption in the recreational fishing household population
associated with MP&M pollutant discharges from facility z; and
Riskisbst = incremental cancer risk from fish consumption in the subsistence fishing household population
associated with MP&M pollutant discharges from facility z.
These values were calculated for the baseline and post-compliance discharge cases. The difference is the number of cancer
cases estimated to be avoided annually through the fish consumption pathway as a result of the final regulation.
13.1.2 Cancer from brinking Water Consumption
The analysis of reduced cancer incidence via the drinking water pathway involves three analytical steps that are largely
parallel to those performed for the fish consumption pathway:
> estimating cancer risk to an exposed individual from consumption of contaminated drinking water,
> estimating the population that would benefit, and
•• calculating the change in the number of cancer events in the exposed population.
The major differences in the analysis for the drinking water pathway involve the identification of the exposed population and
the analysis of pollutant discharge effects in both the reach to which a facility discharges and reaches downstream of the
discharge point.
a. Estimating cancer risk from drinking water consumption
Estimating cancer risk from consumption of drinking water affected by MP&M discharges requires calculating in-waterway
pollutant concentrations in locations where drinking water treatment systems draw water for public consumption. This
analysis involves three elements:
> estimating in-waterway pollutant concentrations for each pollutant in the reach to which a facility directly or
indirectly discharges. The method and formulas for this calculation are identical to those described for the analysis
of cancer effects for the fish consumption pathway.
> estimating the pollutant concentrations over a distance of 500 kilometers downstream from each facility's discharge
reach, using an exponential decay model in which pollution concentrations diminish below the initial point of
discharge (e.g., dilution, adsorption, partitioning, volatilization, and hydrolysis). Methods used to calculate
downstream pollutant concentrations are described in more detail in Appendix H.
•• identifying the location of any drinking water intakes in the initial and downstream reaches where pollutant
concentrations were calculated and assigning pollutant concentration values to each relevant intake point. The
EPA's Safe Drinking Water Information System (SDWIS) file in the Risk Screening Environmental Indicator
(RSEI) model provided information on drinking water intakes (U.S. EPA, 1999b).
Estimated pollutant concentrations at each drinking water intake determines cancer risk. EPA assumed drinking water
treatment systems will reduce concentrations to below adverse effect thresholds for all chemicals for which EPA has
published a drinking water criterion. Therefore, pollutants examined in the MP&M drinking water analysis include only six
carcinogens for which current drinking water criteria are not available. See Table 13.1 for a list of the pollutants, their cancer
potency factors, and drinking water criteria.
The formula for calculating the incremental risk to an individual resulting from the discharge of a given pollutant from a given
facility at reaches with a known public drinking water intake is as follows:
C x CF, x CR * EF x ED
Risk = x SF (13 3)
BW x LT x CF2 l ' '
where:
Risk = incremental risk of incurring cancer from drinking water consumption (change in probability), calculated at
each drinking water intake within 500 km of the initial discharge point;
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
C = pollutant concentration in surface water in the reach with an intake (ng/1);
CF( = conversion factor, micrograms to milligrams (0.001 mg/fig);
CR = human consumption rate of water (1.24 I/day);
EF = exposure frequency (350 days/year);
ED = exposure duration (70 years);
BW = human body weight (70 kg);
LT = human lifetime (70 years);
CF2 = conversion factor (365 days/year); and
SF = pollutant cancer potency factor (mg/kg/day)"1.
The consumption rate of 1.24 liters per day used in this analysis to represent the average daily consumption of drinking water
by a person in the United States is taken from Estimated Per Capita Water Ingestion in the United States (EPA, 2000b). As
recommended in the Exposure Factors Handbook (1997c), EPA uses an exposure frequency of 350 days per year to estimate
the increased risk of cancer from consuming drinking water supplied by drinking water systems with intakes on local surface
water bodies.
The incremental individual risk from each facility's pollutants are then summed over pollutants at each drinking water intake
to calculate the incremental risk at each intake resulting from pollutant discharges by each upstream facility. The findings
carried forward to the next step include the incremental cancer risk for each combination of facility and associated drinking
water intake(s).
To estimate the annual increased risk of cancer in consumers served by drinking water intakes affected by MP&M discharges,
the lifetime risk values were then divided by 70 years (an estimate of lifetime). These values were calculated for both the
baseline and post-compliance discharge cases.
b. Estimating the benefiting population
The exposed population for each combination of discharging facility and drinking water intake is the general population
served by the drinking water system for which the drinking water intake was identified. Safe Drinking Water Information
System (SDWIS) file in the Risk Screening Environmental Indicator (RSEI) model provided information on drinking water
intakes.
c. Calculating the changes in the number of cancer events
EPA calculated the number of cancer cases for baseline and post-compliance pollutant discharges for each combination of
facility and affected drinking water intake by multiplying the incremental cancer risk value times the population served by the
water system drawing water at the drinking water intake.
The total number of cancer cases associated with the facility discharges is the sum of cancer cases over all drinking water
intakes. EPA extrapolated the sample results to the total MP&M population by multiplying the result for each sample facility
by its sample weight and summing the sample-weighted facility results. Because incremental cancer effects are assumed to be
linearly additive, cancer-risk effects are aggregated over facilities and drinking water intakes by simple addition of the effects
calculated separately for each combination of facility and drinking water intake. The formula follows:
N M
TCC** = £ E Wti X (POPij X Riski) (13.4)
' j
where:
TCCdw = total national estimate of cancer cases associated with consumption of chemically-contaminated drinking water
(baseline or post-compliance);
Wtj = facility sample weight i (i = 1 to jV facilities);
POPjj = population exposed to discharges by facility i at drinking water intake y (j = 1 to M water supply intakes); and
Risky = incremental cancer risk for discharges by facility i at drinking water intakey'.
EPA calculated these values for the baseline and post-compliance discharge cases. The difference in the values is the number
of drinking water associated cancer cases estimated to be avoided annually by reduced MP&M industry discharges.
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Chapter 13: Human Health Benefits
13.1.3 Exposures above Non-cancer Health Thresholds
Exposed populations are also at risk of developing non-cancer health problems (including systemic, reproductive,
immunological, neurological, or circulatory problems) from fish ingestion and water consumption. The common approach for
assessing the risk of non-cancer health effects from the ingestion of a pollutant is to calculate a hazard quotient by dividing an
individual's oral exposure to the pollutant, expressed as a pollutant dose in milligrams per kilogram body weight per day
(mg/kg/day), by the pollutant's oral reference dose (RfD). An RfD is defined as an estimate (with uncertainty spanning
perhaps an order of magnitude) of a daily oral exposure that likely would not result in the occurrence of adverse health effects
in humans, including sensitive individuals, during a lifetime. Toxicologists typically establish an RfD by applying uncertainty
factors to the lowest- or no observed adverse effect level (NOAEL) for the critical toxic effect of a pollutant. A hazard
quotient less than one means that the pollutant dose to which an individual is exposed is less than the RfD, and, therefore,
presumed to be without appreciable risk of adverse human health effects. A hazard quotient greater than one means that the
pollutant dose is greater than the RfD. RfDs are available for 77 of the 132 MP&M pollutants of concern. The pollutants
analyzed and their RfDs are listed in Table 13.2.
Table 13.2: RfDs for
s-i AC :
. , , i Regulated Pollutant
Number I
83329 jAcenaphthene
67641 ! Acetone
98862 jAcetophenone
107028 jAcrolein
7429905 JAluminum
120127 I Anthracene
7440360 ! Antimony
7440382 ! Arsenic
7440393 ! Barium
65850 IBenzoicacid
100516 ! Benzyl alcohol
7440417 ! Beryllium
92524 JBiphenyl
!Bis(2-ethylhexyl)
117817 jphthalate
7440428 ! Boron
85687 | Butyl benzyl phthalate
7440439 ! Cadmium
75150 ! Carbon disulfide
108907 ! Chlorobenzene
75003 | Chloroethane
7440473 ! Chromium
1 8540299 ! Chromium hexavalent
7440484 ! Cobalt
7440508 ! Copper
95487 ! Cresol, o-
RfD
(mg/kg/day)
0.060
0.100
0.100
0.020
1.000
0.300
0.000
0.000
0.070
4.000
0.300
0.002
0.050
0.020
0.090
0.200
0.001
0.100
0.020
0.400
1.500
0.003
0.060
0.040
0.050
Drinking Water
Criterion? "
No
No
No
No
Yes
No
Yes
Yes
Yes
MP&M Pollutants
! Target Organ and Effects
; Liver toxicity
; Increased liver and kidney weights; nephrotoxicity
1 General toxicity
1 Cardiovascular toxicity1'
; Renal failure, intestinal contraction interference, adverse
! neurological effects"
; Longevity, blood glucose, cholesterol
; Hyperpigmentation, keratosis and possible vascular
I complications
; Increased kidney weight
No
No
Yes
No
Yes
No
No
Yes
No
No
; Forestomach, epithelial hyperplasia
; Small intestinal lesions
; Kidney damage
; Increased relative liver weight
; Testicular atrophy, spermatogenic arrest
; Significantly increased liver-to-body weight and liver-to-brain
1 weight ratios
; Significant proteinuria (protein in urine)
; Fetal toxicity, malformations
; Histopathologic changes in liver
No
Yes
Yes
No
Yes
No
1 Renal tubular necrosis (kidney tissue decay)"
1 Reduced water consumption
1 Heart effects"
1 Gastrointestinal effects, liver necrosis"
1 Decreased body weights and neuro toxicity.
13-10
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MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.2: RfDs for
CAS
XT ^ j Regulated Pollutant
Number •
:
:
:
106445 jCresol, p-
j, ...„.
57125 1 Cyanide
75354 JDichloroethene, 1,1-
75092 | Dichloromethane
60297 JDiethyl ether
1 Dimethylformamide,
68122 |N,N-
:
:
:
105679 JDimethylphenol, 2,4-
84742 | Di-n-butyl phthalate
51285 iDinitrophenol, 2,4-
:
:
:
:
:
:
606202 JDinitrotoluene, 2,6-
:
:
:
1 1 7840 | Di-n-octyl phthalate
122394 1 Diphenylamine
100414 | Ethylbenzene
:
:
:
206440 | Fluoranthene
:
:
:
86737 | Fluorene
1 6984488 1 Fluoride
:
591786 iHexanone, 2-
:
:
:
:
7439896 jlron
78831 | Isobutyl alcohol
I
78591 1 Isophorone
7439965 | Manganese
78933 | Methyl ethyl ketone
:
:
:
108101 | Methyl isobutyl ketone
80626 | Methyl methacrylate
91576 | Methylnaphthalene, 2-
7439987 | Molybdenum
91203 | Naphthalene
7440020 | Nickel
100027 j Nitrophenol, 4-
59507 j Parachlorometacresol
:
108952 | Phenol
7723140 | Phosphorus (elemental)
RfD
(mg/kg/day)
0.005
0.020
0.009
0.060
0.200
0.100
0.020
0.100
0.002
0.001
L
0.020
0.025
0.100
0.040
0.040
0.060
0.040
0.300
0.300
0.200
0.140
0.600
0.080
1.400
0.020
0.005
0.020
0.020
0.008
2.000
0.600
0.000
Drinking Water
Criterion? "
No
Yes
Yes
Yes
No
No
No
No
No
No
L
No
No
Yes
No
No
Yes
No
Yes
No
No
Yes
No
No
No
No
No
No
Yes
No
MP&M Pollutants
Target Organ and Effects
! Central nervous system hypoactivity and respiratory system
! distress
j Weight loss, thyroid effects and myelin degeneration
! Toxic effects on kidneys, spleen, lungs"; hepatic lesions
! Liver toxicity
! Depressed body weights
j Liver and gastrointestinal system effects
! Clinical signs (lethargy, prostration, and ataxia) and
j hematological changes
! Increased mortality
! Cataract formation
! Mortality, central nervous system neurotoxicity, blood heinz
I bodies and methemoglobinemia, bile duct hyperplasia, kidney
jhistopathology
! Kidney and liver increased weights, liver increased SCOT and
| SGPT activity
! Decreased body weight, and increased liver and kidney weights
! Liver and kidney toxicity
! Nephropathy, increased liver weights, hematological
! alterations, clinical effects
! Decreased red blood cell count, packed cell volume and
! hemoglobin
! Objectionable dental fluorosis (soft, mottled teeth)
! Hypatotoxicity and nephrotoxcityd
! Liver, diabetes mellitus, endocrine disturbance, and
! cardiovascular effects'1
j Hypoactivity and ataxia
! Kidney pathology
! Central nervous system effects
! Decreased fetal birth weight
! Lethargy, increased liver and kidney weights and urinary
! protein
! Increased kidney to body weight ratio
j Increased uric acid
j Decreased body weight
j Decreased body and organ weights
No
No
No
j Reduced fetal body weight in rats
j Parturition mortality; forelimb hair loss
13-11
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MP&M EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.2: RfDs for
CAS
XT ^ j Regulated Pollutant
Number •
:
:
:
129000 IPyrene
i.....
110861 jPyridine
7782492 | Selenium
7440224 | Silver
100425 jstyrene
:
127184 JTetrachloroethene
:
:
7440280 | Thallium
7440315 JTin
7440326 | Titanium
108883 | Toluene
790 1 6 | Trichloroethene
:
75694 1 Trichlorofluoromethane
:
:
67663 I Trichloromethane
7440622 | Vanadium
108383 IXylene,m-
179601 23 ijxylene, m- & p-*
95476 JXylene, o-
136777612JXylene, o- & p-*
:
:
:
:
:
:
7440666 jZinc
I
I
137304 jZiram \Cymate
RfD
(mg/kg/day)
0.030
0.001
0.005
0.005
0.200
0.010
0.000
0.600
4.000
0.200
0.006
0.300
0.010
0.007
2.000
2.000
2.000
2.000
0.300
L
0.020
Drinking Water
Criterion? "
No
No
Yes
Yes
Yes
Yes
Yes
No
MP&M Pollutants
Target Organ and Effects
I Kidney effects (renal tubular pathology, decreased kidney
! weights)
I Increased liver weight
! Clinical selenosis (hair or nail loss)
I Argyria (skin discoloration)
! Red blood cell and liver effects
I Liver toxicity, weight gain
! Liver toxicity, gastroenteritis, degeneration of peripheral and
I central nervous system*
I Kidney and liver lesions
No
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
L
No
! Changes in liver and kidney weights
! Bone marrow, central nervous system, liver, kidneys'1
! Survival and histopathology
I Fatty cyst formation in liver
! Kidney and central nervous system effects'5
! Central nervous system hyperactivity, decreased body weight
! Central nervous system hyperactivity, decreased body weight
! 47% decrease in erythrocyte superoxide dismutase (ESOD)
I concentration in adult human females after 10 weeks of zinc
! exposure
a "Yes"= there is a published drinking water criterion for a given chemical.
b Reference dose based on a no observed adverse effect level (NOAEL). Health effects summarized from Amdur, M.O.; Doul, J.; and
Klaassen, C.D.,eds. 1991. Cassarett andDoul's Toxicology, 4th edition.
° Target organ and effects summarized from Wexler, P., ed. 1998. Encyclopedia of Toxicology, Volumes 1-3.
d Target organ and effects summarized from Amdur, M.O.; Doul, J.; and Klaassen, C.D.,eds. 1996. Cassarett and Doul's Toxicology,
5th edition.
Source: U.S. EPA (1998/99); U.S. EPA (1997a).
EPA guidance for assessing exposures to mixtures of pollutants recommends calculating a hazard index (HI) by summing the
individual hazard quotients for those pollutants in the mixture that affect the same target organ or system (e.g., the kidneys,
the respiratory system). For example, for three liver toxicants discharged from an MP&M facility (pollutant A with a hazard
index of 0.10, pollutant B with a hazard index of 0.05, and pollutant C with a hazard index of 0.15), the combined hazard
index is 0.30. HI values are interpreted similarly to hazard quotients; values below one are generally considered to suggest
that exposures are not likely to result in appreciable risk of adverse health effects during a lifetime, and values above one are
generally cause for concern, although an HI greater than one does not necessarily suggest a likelihood of adverse effects.
To evaluate the potential benefits of reducing the in-stream concentrations of 76 pollutants that cause non-cancer health
effects, EPA estimated target organ-specific His for drinking water and fish ingestion exposures in both the baseline and
post-compliance scenarios. HI is calculated for each discharge reach associated with one or more MP&M sample facilities by
dividing the estimated ingestion rate of each pollutant by the RfD value for the pollutant. The formula follows:
13-12
-------
MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
DCRk
where:
HI = hazard index for the pollutants discharged from a facility and ingested by a specific consumption pathway;
DCRk = estimated daily consumption rate per kilogram of body mass for pollutant k via a specific consumption
pathway (mg/kg/day);
RfDk = reference dose for pollutant k (mg/kg/day); and
K = number of pollutants affecting a given organ or system.
Daily consumption rate (DCR) per kilogram of body mass for pollutant k is estimated as follows:
C x CF, x CR x BCF
DCR, = - - - (13.6)
* BW
where:
DCRk = estimated daily consumption rate per kilogram of body mass for pollutant k via a specific consumption
pathway (mg/kg/day);
C = pollutant concentration in surface water in the MP&M reach (fig/1);
CF( = conversion factor, micrograms to milligrams (0.001 mg/ng);
CR = human consumption rate of water (mg/day);
BCF = bioconcentration factor for pollutant k;
BW = human body weight (kg).
These His are calculated separately for the fish and water consumption pathways. The fish consumption pathway was further
divided into recreational and subsistence fish consumption rates. The procedures and formulas for estimating the in-waterway
concentrations and ingestion of pollutants by exposed populations are the same as those used for the fish consumption and
drinking water cancer analyses. The only exception is that the analysis of non-cancer health pathways was performed for the
discharge reach only and not for reaches downstream, due to time and resource constraints. As a result, this analysis
underestimates populations exposed to non -cancer risks via drinking water pathways
EPA then combined estimates of the numbers of individuals in the exposed populations with the His for the populations to
determine how many individuals might be expected to realize reduced risk of non-cancer health effects in the post-compliance
scenario. The basis for identifying exposed populations is the same as that described for the analysis of reduced incidence of
cancer via the fish consumption and drinking water consumption pathways.8 The shift in populations from a higher to a
lower HI value from the baseline to post-compliance cases is the quantitative measure of benefits from this analysis. This
analysis was limited in two primary ways:
> First, hazard indices estimated in this analysis may understate the actual potential for adverse health effects because this
analysis considers contributions to non-cancer risk resulting only from MP&M facility discharges, and does not take into
account other sources of exposure to MP&M pollutants or other chemicals that may contribute to an aggregate non-
cancer risk. The net result is that the analysis understates the numerical value estimated for His, but the incremental
change in His between the baseline and the final option would remain the same. EPA therefore evaluated potential
incremental changes in non-cancer health risks over the entire range of hazard indices, including hazard indices below
one.
•• Second, EPA used mean individual exposure parameters and not the distribution of exposure parameters to estimate
hazard indices for the populations affected by MP&M discharges.
The results from the non-cancer health risk analysis apply to sample discharge locations only. Analytic tractability issues
prevented this analysis from being conducted on a sample-weighted national basis. EPA did not monetize these benefits.
8 The exposed populations for the drinking water consumption pathway are those associated with drinking water intakes only in a
facility's discharge reach.
13-13
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MP&M EEBA Part III: Benefits
Chapter 13: Human Health Benefits
13.1.4 Human Health AWQC
EPA used another approach to quantify reductions in health risk from the final MP&M regulation, based on the extent to
which reduced MP&M discharges would decrease the occurrence of pollutant concentrations in affected waterways that
exceed human health-based AWQC. This analysis provides a measure of the change in cancer and non-cancer health risk by
comparing the number of discharge reaches exceeding health-based AWQC for regulated pollutants due to MP&M activities
in the baseline to the number exceeding AWQC under the final option.
AWQC are set at levels to protect human health through ingestion of aquatic organisms and ingestion of water and aquatic
organisms. Accordingly, reducing the frequency at which human health-based AWQC are exceeded should translate into
reduced risk to human health. This measure should be viewed as an indirect indicator of reduced risk to human health,
because it does not reflect the size of the exposed population and is not tied to changes in human health riskper se?
EPA estimated the baseline concentrations of all MP&M pollutants for each reach to which one or more MP&M facilities
discharge. The calculation of concentrations used the same in-waterway dilution and mixing model described in the analysis
of cancer risk for the fish consumption pathway. The baseline concentrations were compared with human health-based
AWQC values. (See Table 13.3 for a list of MP&M pollutants with AWQC values.) Reaches in which concentrations of one
or more pollutants were estimated to exceed an AWQC value were identified as exceeding AWQC limits in the baseline.
This analysis was repeated using the post-compliance discharge values for the final option. Reaches estimated to have
concentrations in excess of AWQC in the baseline but not in the post-compliance case were assessed as having substantial
water quality improvements relative to human health-based criteria as a result of regulation. EPA deems such water quality
improvements to be indicative of reduced risk to human health. Although not explicitly accounted for in this analysis, human
health risk reductions are also likely to occur wherever in-waterway concentrations are reduced, regardless of whether or not
they are reduced to levels below AWQC.
Table 13.3: MP&M Pollutants with Human Health-Based AWQC
1 :
:
XTCAf Pollutant
Number j
:
:
:
:
Human Health-Based
AWQC (ug/1)
Organisms j Water &
Only ! Organisms
Target Organ and Effects"
83329 | Acenaphthene 2700 j 1200 j Liver, hepatotoxicity
67641
98862
107028
7429905
62533
120127
7440360
7440382
7440393
65850
100516
7440417
92524
Acetone
Acetophenone
Acrolein
Aluminum
Aniline
Anthracene
Antimony
Arsenic
Barium
Benzoic acid
Benzyl alcohol
Beryllium
Biphenyl
2800000
98000
1000
47000
95
6800
4300
0.16
2900000
810000
1100
1200
3500
3400
410
20000
5.8
4100
14
0.02
1000
130000
10000
66
720
Increased liver and kidney weights; nephrotoxicity
General toxicity
Cardiovascular toxicity0
Renal failure, intestinal contraction interference, adverse
neurological effects'1
Spleen and body cavity
No observed effects
Longevity, blood glucose, cholesterol
Liver, kidneys, lungs, bladder, and skin
Increased kidney weight
No observed adverse effects
Forestomach, epithelial hyperplasia
Small intestinal lesions
Kidney damage
The following chapter uses this same information in part as a direct indicator of improved water quality.
13-14
-------
MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.3: MP&M Pollutants with Human Health-Based AWQC
CAS
Number
117817
85687
7440439
75150
108907
75003
1854029
9
7440473
7440508
106445
95487
57125
117840
84742
75354
75092
60297
131113
68122
105679
51285
606202
123911
122394
100414
206440
86737
591786
7439896
Pollutant
Bis(2-ethylhexyl)
phthalate
Butyl benzyl phthalate
Cadmium
Carbon disulfide
Chlorobenzene
Chloroethane
Chromium hexavalent
Chromium
Copper
Cresol, p-
Cresol, o-
Cyanide
Di-n-octyl phthalate
Di-n-butyl phthalate
Dichloroethene, 1,1-
Dichloromethane
Diethyl ether
Dimethyl phthalate
Dimethylformamide,
N,N-
Dimethylphenol, 2,4-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,6-
Dioxane, 1,4-
Diphenylamine
Ethylbenzene
Fluoranthene
Fluorene
Hexanone, 2-
Iron
Human Health-Based
AWQC (ug/1)
1
Organisms
Only
5.9
5200
84
94000
21000
520
2000
1000000
1200
3100
30000
220000
39
12000
3.2
1600
770000
2900000
220000000
2300
14000
900
:f
2400
1000
29000
370
14000
65000
Water &
Organisms
1.8
3000
14
3400
680
12
100
50000
650
170
1700
700
37
2700
0.057
4.7
6900
310000
3500
540
70
34
L J
3.2
470
3100
300
1300
1400
300
, ,
Target Organ and Effects"
Liver
Significantly increased liver-to-body weight and liver-to-brain
weight ratios
Significant proteinuria (protein in urine)
Fetal toxicity, malformations
Histopathologic changes in liver
Reduced water consumption
Renal tubular necrosis (kidney tissue decay)d
Gastrointestinal effects, liver necrosis'1
Central nervous system hypoactivity and respiratory system
distress
Decreased body weights and neuro toxicity.
Weight loss, thyroid effects and myelin degeneration
Kidney and liver increased weights, liver increased SGOT and
SGPT activity
Increased mortality
Inconclusive
Liver, lungs
Depressed body weights
Liver and gastrointestinal system effects
Clinical signs (lethargy, prostration, and ataxia) and
hematological changes
Cataract formation
Mortality, central nervous system neurotoxicity, blood heinz
bodies and methemoglobinemia, bile duct hyperplasia, kidney
histopathology
Liver, nasal cavity, gall bladder
Decreased body weight gain, and increased liver and kidney
weights
Liver and kidney toxicity
Nephropathy, increased liver weights, hematological alterations,
clinical effects
Decreased red blood cell count, packed cell volume and
hemoglobin
Hypatotoxicity and nephrotoxcityb
Liver, diabetes mellitus, endocrine disturbance, and
cardiovascular effects"
.
13-15
-------
MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.3: MP&M Pollutants with Human Health-Based AWQC
CAS
Number
78831
78591
7439965
7439976
80626
78933
108101
91576
91203
7440020
100027
62759
86306
59507
108952
7723140
129000
110861
7782492
7440224
100425
127184
7440280
108883
79016
75694
67663
108383
1367776
12
95476
1796012
31
7440666
Pollutant
Isobutyl alcohol
Isophorone
Manganese
Mercury
Methyl methacrylate
Methyl ethyl ketone
Methyl isobutyl ketone
Methylnaphthalene, 2-
Naphthalene
Nickel
Nitrophenol, 4-
Nitrosodimethylamine,
N-
Nitrosodiphenylamine,
N-
Parachlorometacresol
Phenol
Phosphorus (elemental)
Pyrene
Pyridine
Selenium
Silver
Styrene
Tetrachloroethene
Thallium
Toluene
Trichloroethene
Trichlorofluoromethane
Trichloromethane
Xylene, m-
Xylene, o- & p- (c)
Xylene, o-
Xylene, m- & p- (c)
Zinc
L
Human Health-Based
AWQC (ug/1)
'
Organisms
Only
1500000
2600
100
0.051
2300000
6500000
360000
84
21000
4600
1100
8.1
16
270000
4600000
2.2
290
5400
11000
110000
160000
3500
6.5
200000
92
66000
470
100000
100000
100000
100000
69000
,
Water &
Organisms
10000
36
50
0.05
48000
21000
2800
75
680
610
220
0.00069
5
56000
21000
0.53
230
35
170
170
6700
320
1.8
6800
3.1
9100
5.7
42000
42000
42000
42000
9100
, ,
Target Organ and Effects3
Hypoactivity and ataxia
Preputial gland
Central nervous system effects
Increased kidney to body weight ratio
Decreased fetal birth weight
Lethargy, increased liver and kidney weights and urinary protein
Decreased body weight
Decreased body and organ weights
Tumors observed at multiple sites
Bladder tumors, reticulum cell sarcomas
Reduced fetal body weight in rats
Parturition mortality; forelimb hair loss
Kidney effects (renal tubular pathology, decreased kidney
weights)
Increased liver weight
Clinical selenosis (hair or nail loss)
Argyria (skin discoloration)
Red blood cell and liver effects
Liver toxicity, weight gain
Liver toxicity, gastroenteritis, degeneration of peripheral and
central nervous system
Changes in liver and kidney weights
Survival and histopathology
Kidneys
Central nervous system hyperactivity, decreased body weight
Central nervous system hyperactivity, decreased body weight
47% decrease in erythrocyte superoxide dismutase (ESOD)
concentration in adult human females after 10 weeks of zinc
exposure
13-16
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MP&M EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.3: MP&M Pollutants with Human Health-Based AWQC
CAS
Number
137304
Pollutant
Ziram \ Cymate
Human Health-Based
AWQC (ug/1)
'
Organisms
Only
220000000
Water &
Organisms
700
Target Organ and Effects"
a Information on target organs are not available for some pollutants.
b Reference dose based on a NOAEL. Health effects summarized from Amdur, M.O.; Doul, J.; andKlaassen, C.D.,eds. 1991.
Cassarett and Doul's Toxicology, 4th edition/
° Target organ and effects summarized from Amdur, M.O.; Doul, J.; and Klaassen, C.D.,eds., C.D., ed. 1996. Cassarett and Doul's
Toxicology, 5th edition.
d Target organ and effects summarized from Wexler, P., ed. 1998. Encyclopedia of Toxicology, Volumes 1-3.
Source: U.S. EPA (1980); U.S. EPA (1997a); U.S. EPA (1998/99).
13.2 RESULTS
EPA estimated the monetary value to society associated with reduced cancer risk from consumption offish and drinking water
affected by MP&M pollutant discharges. Little information is available about dose-response relationships for non-cancer
health outcomes or about the monetary value of avoiding such health outcomes. As a result, EPA was unable to assign
monetary values to the estimated reductions in non-cancer health risks. Such non-cancer health risks include systemic,
reproductive, immunological, neurological, and circulatory problems. Although EPA was unable to assign monetary values to
the latter two benefit measures for this regulation, the quantitative analyses of these events provide additional insight into the
human health-related benefits likely to result from the final regulation.
The following sections present the findings from the analysis of each of the benefit measures.
13.2.1 Fish Consumption Cancer Results
Table 13.4 shows the estimated changes in incidence of cancer cases from consumption of MP&M pollutants in fish tissue
and drinking water from regulatory compliance by option. The national-level analysis finds that the final regulation and the
433 Upgrade Options would lead to a marginal reduction in cancer cases resulting from consumption of contaminated fish
tissue; correspondingly, monetary benefits estimated from reduced consumption of contaminated fish are negligible under all
of these three regulatory alternatives. In contrast, the estimated reductions in carcinogen loadings under the Proposed/NODA
Option would result in $3.68 million (2001$) in benefits to recreational and subsistence anglers.
13-17
-------
MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.4: Estimated Avoided Cancer Cases and
Value of Annual Benefits for the Final Option and Regulatory Alternatives" b
Option
Final Option: Traditional Extrapolation
Final Option: Post-Stratification Extrapolation
Proposed/NODA Option6
Directs + 413 to 433 Upgrade Option
Directs + All to 433 Upgrade Option
Fish Consumption
r
Avoided
Cancer
Cases per
Year
1.38E-05
2.05E-05
0.57
1.38E-05
2.6E-05
Mean Value
of Benefit
(2001$)'
$90
$134
$3,684,973
$90
$169
Drinking Water'
f
Avoided
Cancer
Cases per
Year
0
0
0.001
0
0
Mean Value
of Benefit
(2001$)"
$0
$0
$6,536
$0
$0
a In this analysis, EPA did not consider reductions in discharges of one carcinogen n-nitrosodimethylanine (NDMA)
due to the low number of detected values for that pollutant.
b Regulatory alternatives are based on the Traditional Extrapolation.
° Avoided cancer cases via the drinking water consumption pathway were not included for pollutants with drinking
water criteria. EPA has published a drinking water criterion for seven of the 13 carcinogens and it is assumed that
drinking water treatment systems will reduce concentrations of these chemicals to below adverse effect thresholds.
d Estimated value of one avoided cancer case (2001$): $6.5 million.
° The estimated benefits of the Proposed/NODA Option are not directly comparable to the final option alternatives.
The total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported
for the final rule and the two upgrade options. After deciding in July 2002 not to consider the NO DA option as the
basis for the final rule, EPA performed no more analysis on the NODA option, including not updating facility counts
and related analyses for the change in subcategory and discharge status classifications.
Source: U.S. EPA analysis.
The valuation of benefits is based on estimates of society's willingness-to-pay to avoid the risk of cancer-related premature
mortality. Although it is not certain that all cancer cases will result in death, avoided cancer cases are valued on the basis of
avoided mortality to provide a conservative estimate of benefits.
In this analysis, EPA used the $6.5 million estimate of the value of a statistical life saved (VSL) recommended in the
Guidelines for Preparing Economic Analysis (EPA, 2000c). EPA based this value on its review and analysis of 26 policy-
relevant value of life studies (EPA, 1997b). The reviewed studies used hedonic wage and contingent valuation analyses in
labor markets to estimate the amounts that individuals would either be willing to pay to avoid slight increases in the risk of
mortality, or would need to be compensated to accept a slight increase in risk of mortality.10 EPA associated the
willingness-to-pay (WTP) values estimated in these studies with small changes in the probability of mortality. To estimate
a WTP value for avoiding certain or high probability mortality events, EPA extrapolated the smaller value to that for a 100
percent probability event.11 The Agency used the resulting estimates of the value of a "statistical life saved" in regulatory
analyses to value regulatory effects that are expected to reduce the incidence of mortality.
The monetary value of a statistical life saved used in this analysis corresponds to the value of unforeseen instant death with no
significant period of morbidity. Because a long period of morbidity usually precedes death from cancer, the value of an
avoided cancer case may be underestimated. Therefore, the estimated value of the human health benefit of the final regulation
may be understated.
10 The question analyzed in these studies is: How much more must a worker be paid to accept an occupation with a slightly higher
risk of mortality?
11 These estimates, however, do not represent the willingness-to-pay to avoid the certainty of death.
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
13.2.2 Drinking Water Consumption Cancer Results
Table 13.4 also shows the number of cancer cases estimated to be avoided for each pollutant analyzed for drinking water
populations. The national-level analysis finds that the final regulation and the 433 Upgrade Options would lead to a marginal
reduction in cancer cases resulting from consumption of contaminated drinking water; correspondingly, monetary benefits
estimated from reduced consumption of contaminated drinking water are essentially zero under all of these three regulatory
alternatives. As shown in Table 13.4, the Proposed/NODA Option would eliminate approximately 0.001 cancer cases per
year. Annual monetary benefits from reduced cancer risk for the Proposed/NODA Option are estimated at $6,536 (2001$).
As noted in the preceding sections, EPA has established drinking water criteria for seven carcinogens. EPA assumes that
public drinking water treatment systems will reduce these seven pollutants in the public water supply to levels that are
protective of human health. To the extent that the final regulation reduces the concentration of MP&M pollutants to values
that are below pollutant-specific drinking water criteria, public drinking water systems will accrue benefits in the form of
reduced water treatment costs. EPA was not able to quantify such cost savings at the national level, however.
Public drinking water supply systems that currently employ various treatment technologies may also reduce concentrations of
the six unregulated pollutants to the levels that are protective of human health. However, the Agency does not have
information on specific treatment technologies used by the drinking water systems affected by MP&M discharges. It is not
feasible to assess whether the technologies employed by the affected drinking water systems reduce concentrations of MP&M
pollutants that don't have the published drinking water criteria without collecting detailed information on the affected
drinking water systems. Thus, this analysis conservatively assumes that public water supply systems do not monitor pollutants
that don't have published drinking water criteria and, as result, these pollutants may be passed through the affected drinking
water supply systems.
13.2.3 Non-cancer Health Threshold Results
Table 13.5 summarizes baseline and post-compliance distributions of non-cancer health hazard indices and associated
population estimates for each exposed population group for the final option. The shift in populations from higher to lower
hazard score values between the baseline and post-compliance cases is the measure of benefit from reduced non-cancer health
hazards.
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MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.5: Change in Risk of Non-cancer Health Hazards from Reduced Exposure to MP&M Pollutants:
Distribution of Hazard Indices"
Range of
Ratios
Ratio = 0.00
0.00 - 10 6
10 6 - 10 3
10 3- 1.00
Score > 1.00
Totals
Ratio = 0.00
0.00 - 10 6
10 6 - 10 3
10 3- 1.00
Score > 1.00
Totals
Ratio = 0.00
0.00 - 10 6
10 6 - 10 3
10 3- 1.00
Score > 1.00
Totals
Ratio = 0.00
0.00 - 10 6
10 6 - 10 3
10 3- 1.00
Score > 1.00
Totals
Fish Consumption
Baseline
Population
0
121,814
680,301
217,201
0
1,019,316
0
872,003
2,221,724
1,054,627
40,630
4,188,984
0
121,814
680,301
217,201
0
1,019,316
0
121,814
680,301
217,201
0
1,019,316
Percent
0%
11.95%
66.73%
21.31%
0%
100%
0%
20.82%
53.04%
25.18%
0.97%
100%
0.0%
11.95%
66.73%
21.31%
0%
100%
0.0%
11.95%
66.73%
21.31%
0%
100%
Post-Compliance
:
Population j Percent
Final Option
122,865 | 12.05%
103,103 | 10.12%
578,122 | 56.72%
215,226 | 21.11%
0 | 0%
1,019,316 | 100%
Proposed/NODA Opti
342,040 | 8.17%
796,003 | 19.01%
2,310,376 | 55.16%
737,312 | 17.60%
3,253 | 0.08%
4,188,984 | 100%
Directs + 413 to 433 Upgrac
169,106 | 16.59%
91,255 | 8.96%
559,690 | 54.91%
199,265 | 19.54%
0 | 0%
1,019,316 | 100%
Directs + All to 433 Upgrad
169,106 | 16.59%
91,255 | 8.96%
563,526 | 55.28%
195,429 | 19.17%
0 | 0%
1,019,316 | 100%
Drinking Water Consumption
Baseline
Population
39,822,464
1,029,333
0
0
0
40,851,797
on"
0
36,552,343
2,783,100
0
0
39,335,442
le Option
39,822,464
1,029,333
0
0
0
40,851,797
e Option
39,822,464
1,029,333
0
0
0
40,851,797
Percent
97.48%
2.52%
0%
0%
0%
100
0%
92.93%
7.07%
0%
0%
100%
97.48%
2.52%
0%
0%
0%
100%
97.48%
2.52%
0%
0%
0%
100%
Post-Compliance
Population
40,723,280
128,517
0
0
0
40,851,797
4,308,352
33,667,164
1,359,927
0
0
39,335,442
40,723,280
128,517
0
0
0
40,851,797
40,723,280
128,517
0
0
0
40,851,797
Percent
99.69%
0.31%
0%
0%
0%
100%
10.95%
85.59%
3.46%
0%
0%
100%
99.69%
0.31%
0%
0%
0%
100%
99.69%
0.31%
0%
0%
0%
100%
a This analysis addresses only 76 of 132 chemicals of concern, excludes background exposures, and is based only on sample facility
discharges and associated populations. The exposed population values are not national estimates of the populations that would benefit
by reduced risk of non-cancer health hazards.
b The estimated benefits of the Proposed/NODA Option are not directly comparable to the final option alternatives. The total number
of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final rule and the two
upgrade options. After deciding in July 2002 not to consider the NO DA option as the basis for the final rule, EPA performed no more
analysis on the NODA option, including not updating facility counts and related analyses for the change in subcategory and discharge
status classifications.
Source: U.S. EPA analysis.
For each discharge reach, EPA selected the maximum of the target organ-specific hazard index values calculated for a given
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
discharge reach to characterize the potential for adverse non-cancer health affects from exposure to MP&M pollutants among
exposed individuals. The results of EPA's analysis suggest that His for individuals in the exposed populations may decrease
after facilities comply with the final rule (see Table 13.5 for detail). Increases in the percentage of exposed populations that
would be exposed to no risk of non-cancer adverse human health effects due to the MP&M discharges occur in both the fish
and drinking water analyses. The shift to lower hazard indices should be considered in conjunction with the finding that the
hazard indices for incremental exposures to pollutants discharged by MP&M facilities (for which reference doses are
available) are less than one in the baseline analysis for the entire population associated with sample facilities. Whether the
incremental shifts in hazard indices are significant in reducing absolute risks of non-cancer adverse human health effects is
uncertain and will depend on the magnitude of contaminant exposures for a given population from risk sources not accounted
for in this analysis.
Table 13.5 shows that the Proposed/NODA Option and the 433 Upgrade Options would result in similar shifts in the exposed
populations from higher to low hazard index values. All of these three alternative regulatory options would increase the
population with a zero incremental risk of non-cancer health effects from exposure to MP&M pollutants.
Although EPA was unable to associate an economic value with changes in the number of individuals exposed to pollutant
levels likely to result in non-cancer health effects, the reductions in health risk indicated by this benefit measure further
indicate that the final regulation can be expected to yield human health benefits.
13.2.4 Human Health AWQC Results
The final human health benefit category is the reduced occurrence of pollutant concentrations that are estimated to exceed
human health-based AWQC. This analysis provides an alternative measure of the expected reduction in risk to human health.
EPA estimates that in-stream concentrations of 4 pollutants (i.e., arsenic, iron, manganese, and n-nitrosodimethylamine) will
exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of
baseline MP&M pollutant discharges. EPA estimates that there are human health AWQC exceedances caused by
n-nitrosodimethylamine (NDMA). EPA did not consider NDMA pollutant reductions in its benefits analyses because of low
number of detected values for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of
concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms
on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values.
EPA's analysis of the 433 Upgrade Options yields similar results. However, the Directs +A11 to 433 option would reduce the
number of pollutants causing in-stream concentrations to exceed the human health-based AWQC values from 4 to 2 (i.e.,
exceedances from iron and manganese are eliminated). As shown in Table 13.6, the Proposed/NODA Option would not result
in a significant reduction in the number of reaches that are estimated to exceed human health-based AWQC for consumption
of water and organisms under the baseline discharge level. The Proposed/NODA option, however, eliminates human health-
based AWQC for consumption of organisms only on 69 (35 percent) of the 197 reaches, in which in-stream pollutant
concentrations exceeded the relevant criteria in the baseline. The Agency points out that analytic results corresponding to the
Proposed/NODA Option are not directly comparable to the analytic results corresponding to the final rule alternatives due to
the inconsistent baseline conditions (see Chapter 5 of this report for detail).
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MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
Table 13.6: MP&M Discharge Reaches with Pollutant Concentrations Exceeding Human
Health -Based AWQC Limits and Reductions Achieved"
Category
Baseline
Post-Compliance
Percent Reduction
Fi
Baseline
Post-Compliance
Percent Reduction
Baseline
Post-Compliance
Percent Reduction
Baseline
Post-Compliance
Percent Reduction
Baseline
Post-Compliance
Percent Reduction
Human Health Water and
Organisms
'
Number of
Reaches
Final Option: T
78
78
0.0%
nal Option: Post-
112
112
0.0%
Propose
5,852
5,789
1.1%
413 to 43
78
78
0.0%
Directs + All t
78
78
0.0%
Number of
Pollutants
raditional Extrapo
4
4
Stratification Ext
4
4
J/NODA Option"
26
21
3 Upgrade Option
4
4
o 433 Upgrade Op
4
2
Human Health Organisms Only
r
Number of
Reaches
ation
21
21
0.0%
rapolation
21
21
0.0%
197
128
34.6%
21
21
0.0%
ion
21
0
100.0%
Number of
Pollutants
1
1
1
1
12
9
1
1
1
0
a Regulatory alternatives are based on the Traditional Extrapolation.
b The estimated benefits of the Proposed/NODA Option are not directly comparable to the final option
alternatives. The total number of facilities reported for the Proposed/NODA Option analysis differs from the
facility count reported for the final rule and the two upgrade options. After deciding in July 2002 not to consider
the NODA option as the basis for the final rule, EPA performed no more analysis on the NO DA option, including
not updating facility counts and related analyses for the change in subcategory and discharge status classifications.
Source: U.S. EPA analysis.
13.3 LIMITATIONS AND UNCERTAINTIES
This section discusses limitations and uncertainties in the human health benefits analysis. The analysis does not include all
possible human health benefits, and therefore does not provide a comprehensive estimate of the total human health benefits
associated with the final rule. Quantification of changes in human health risk described in this chapter are not possible for all
pollutants whose discharges will be reduced by the final regulation. Due to current research limitations, cancer potency
factors, reference doses, and AWQC are not available for 6 metals, 27 organics, 8 nonconventional pollutants, and 3
conventional pollutants. The methodologies used also involve significant simplifications and uncertainties, as described
below. Whether these simplifications and uncertainties, taken together, are likely to lead to an understatement or
overstatement of the estimated economic values for the human health benefits that were analyzed is not known.
13.3.1 Sample Design <& Analysis of Benefits by Location of Occurrence
The MP&M industries are estimated to include over 43,867 facilities nationwide that generate wastewater while processing
metal parts, metal products, and machinery. Many of these facilities are quite small and, individually, discharge relatively
small quantities of pollutants. Most individual facilities are not likely to have a significant adverse impact on human health at
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
any one MP&M reach. The industry discharges a significant quantity of pollutants in the aggregate, however, because of the
large number of facilities. Thus, the combined effect of discharges from several facilities at a given reach may well result in
appreciable risks to human health. Multiple dischargers affecting a single reach were found to be common, based on the
sample facility data.
The sample of MP&M facilities on which this analysis is based (910 facilities) represents only approximately 2 percent of
MP&M facilities nationwide. This sample was based on basic industry characteristics rather than geographic location. As a
result, the sample does not accurately reflect the likelihood of co-occurrence of MP&M facilities on a reach and, therefore,
the contribution to in-waterway pollutant concentrations made by multiple facilities. For example, the sample may include
three MP&M facilities, all discharging to the same reach. In reality, however, five MP&M facilities might discharge to this
reach.
The omission of co-occurrence of discharges from additional facilities does not create a problem in the analysis of
incremental cancer risk, because each facility's contribution to total risk can be estimated separately and is assumed be
linearly additive. The cancer effects associated with individual facility discharges can be summed over facilities to estimate
occurrence of cancer events in the total population. Therefore, the application of sample weights in the cancer analysis
accounts for pollutant contributions from facilities co-occurring on MP&M reaches that are not present in the sample of
facilities.
This omission does present a problem, however, when analyzing changes in hazard indices and changes in in-waterway
pollutant concentrations relative to human health-based AWQC for reaches to which more than one facility discharge. For
these reaches, changes in hazard indices and in-waterway pollutant concentrations from reduced pollutant discharges should
account for the total discharge of pollutants over the several facilities whose discharges may affect the reach. When facilities
whose discharges to the reach have unequal sample weights, however, results from the sample facility analysis cannot be
extrapolated to the population simply by multiplying estimated benefit values by the sum of the sample weights of the
individual facilities. See Appendix G for an explanation of the sample weighting methodology devised to partially address
this problem.
While this weighting methodology does recognize the contributions of facilities with different sample facility weights to
aggregate results, it still does not account for the contributions made by co-occurring facilities not included in the sample.
The omission of the frequency of true multiple discharger effects on aggregate instream concentrations and pollutant
exposures understate the benefits.
13.3.2 In-Waterway Concentrations of MP<&M Pollutants
Human health benefits are based on the estimated changes in in-waterway concentrations of MP&M pollutants. A variety of
factors affect in-waterway concentrations, including flow rates under average and low flow conditions, flow depth, chemistry
of the waterway, mixing processes, longitudinal dispersion, flow geometry, suspension of solids, and reaction rates. This
analysis takes into account only site-specific variations in flow rates and flow depth. Standard values are used for other inputs
to the water quality model, due to lack of data on the reaches affected by sample facility discharges. These standard values
may not be accurate for all the sample facility reaches. In addition, the flow characteristics of the sample facility reaches may
not be representative of the national distribution of those characteristics. Extrapolating the sample facility benefits to
national results based on sample facility weights may therefore introduce distortions. The net effect of these assumptions and
extrapolations on the aggregate benefits estimates is uncertain.
13.3.3 Joint Effects of Pollutants
The analyses of human health benefits ignore the potential for joint effects of more than one pollutant. Each pollutant is dealt
with in isolation; the individually estimated effects are then added together. As such, the analyses do not account for the
possibility that several pollutants may combine to yield more or less adverse effects to human health than indicated by the
simple sum of the individual effects. The impact of this limitation on the results of this analysis is unknown.
13.3.4 Background Concentrations of MP<&M Pollutants
Background concentrations of MP&M pollutants are not considered in the benefits analysis. Rather, the analysis assumes that
MP&M facilities are the only source of each of the regulated pollutants in the waterway. Background contributions, either
from other upstream sources or contaminated sediments from previous discharges, are not considered. Even if discharges of
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
these contaminants are reduced or eliminated, sediment contamination and subsequent accumulation of the regulated
pollutants in aquatic organisms may continue for years.
Excluding background contributions to in-waterway pollutant concentrations affects the results for non-cancer risk and
changes in human health-based AWQC exceedances. In the non-cancer risk analysis, hazard indices are likely to be
systematically biased downwards by the omission of exposures to these chemicals from other water-related and non-water-
related sources.12 The net result is understated absolute risks of non-cancer health hazards. Similarly, reductions in human
health-based AWQC exceedances calculated for a given MP&M reach are likely to be systematically biased downwards. The
analysis is therefore likely to understate the frequency with which in-waterway pollutant concentrations move from values
exceeding pollutant specific AWQC to values less than pollutant specific AWQC as a result of the regulation.
13.3.5 downstream Effects
The analysis of cancer effects from drinking water consumption considered exposures from intakes downstream of the
MP&M discharges. EPA, however, did not evaluate cancer risk to recreational and subsistence fishermen fishing downstream
reaches, because of resource constraints. In addition, due to differential weighting of sample facility results, it was not
possible to evaluate hazard indices indicating non-cancer health hazards or human health-based AWQC excursions in
downstream reaches. By omitting these downstream effects, this analysis potentially understates baseline risks that would be
reduced by the final option:
»• cancer cases (from fish consumption),
•• populations exposed to non-cancer risks, and
»• waterways with pollutant concentrations exceeding human health-based AWQC.
13.3.6 Exposed Fishing Population
Estimating the exposed fishing populations for specific MP&M reaches requires statistics on county fishing licences. EPA
collects these data for every state where the MP&M facilities are located where the state collects these data at the county
level. Where fishing license data were not available at the county level, EPA estimated the exposed fishing population based
on state fishing license statistics and census data. This approach is likely to understate actual fishing populations. As noted in
Section 13.1.1, the 1996 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation found that 34 percent of
the anglers (16 years of age and older) did not have licenses (U.S. Department of the Interior, 1996). In addition, data
limitations hamper the estimate of the number of anglers who actually fish a given MP&M reach. Estimating the number of
anglers fishing MP&M reaches based on the ratio of MP&M reach length to the total number of MP&M reach miles in the
county recognizes the effect of the quantity of competing fishing opportunities on the likelihood of fishing a given reach, but
it does not account for the differential quality of fishing opportunities. If water quality in substitute sites is distinctly worse or
better, the estimates of the exposed populations are likely to be overstated or understated.
In addition, the number of subsistence anglers was assumed to equal 5 percent of the recreational fishing population. The
magnitude of subsistence fishing in the United States or in individual states is not known. As a result, this estimate may
understate or overstate the actual number of subsistence anglers.
Finally, to account for the effect of a fish advisory on fishing activity, and therefore on the exposed fishing population, EPA
reduced the fishing population at an MP&M reach under a fish advisory by 20 percent. This could either overestimate or
underestimate the risk associated with consumption of contaminated fish, because (1) anglers who change locations may
simply be switching to other locations where advisories are in place and therefore maintain or increase their current risk, and
(2) anglers who continue to fish contaminated waters may change their consumption and preparation habits to reduce the risks
from the contaminated fish.
12 Ideally, the analysis would include not only background concentration and exposure effects from water-related exposures but
would also account for exposures to chemicals by other routes including, air exposures including dust inhalation, and food contamination.
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MPAM EEBA Part III: Benefits Chapter 13: Human Health Benefits
13.3.7 Treatment of Cancer Latency
Cancer latency refers to the time between the initial event that leads to cancer (e.g., chemical damage to DNA) and the onset
of cancer. Ideally, cancer would be detected at a very early stage, when very few cells are involved. In reality, cancer latency
is a very complex issue, and the time to detection varies considerably.
> Latency is related to health, age, immune status, genetics and other characteristics of the individual.
> Latency is also related to the specific carcinogen, the route of exposure, the type of cancer, the technology used for
cancer detection, and numerous other factors.
»• Environmentally induced cancers may not follow a typical progression pattern; their latency may be unusually
shortened.
> Cancers may begin long before they are detected. The exact progress and time of recognition/detection of cancer
cannot be predicted, because of the numerous factors involved.
»• Variations in timing of cancer detection are partially attributable to the type of cancer involved, the individuals
affected, and differences in the medical technology used.
> The fundamental issue is when the damage related to cancer actually begins in an individual and when the continued
cell damage stops. Damage to the individual begins when cancer is induced. Once cellular changes begin, the
immune system and other body resources are diverted to limiting the carcinogenic process and organ system damage
is occurring.
EPA assumed that benefits of avoiding cancer begin to accrue when the initial events leading to cancer cease, even though the
benefits may not be clinically measurable until some point in the future. In making this assumption, the Agency considered
two factors:
*• uncertainty as to how and when exposure changes translate into reduced cancer risk, and
*• economic uncertainty associated with the value of avoiding cancer and the timing at which a value of cancer
avoidance is recognized.
The monetary valuation of mortality risk from cancer in EPA benefit-cost analyses is based on the VSL. This is derived from
a number of revealed-preference studies that estimate the value of avoided premature mortality. The estimates correspond to
the value of unforeseen instant death with no significant period of morbidity. The value of an avoided cancer case used in this
analysis may therefore be understated, and ultimately the estimated value of the human health benefit of the final regulation
may be understated.
13.3.8 Treatment of Cessation Lag
In August 2001, EPA's Science Advisory Board (SAB) recommended that EPA should not assume that a reduction in cancer
cases immediately follows a reduction in exposure (U.S.EPA, 2001). The SAB explained that, in fact, there is a lag between
the time when exposures are reduced and the time when a reduction in risk occurs, and that "...if the lag between reduction in
exposure and reduction in risk is long, there will be fewer cancer fatalities avoided in years immediately following the policy
than if the lag were shorter." However, the Agency points out the published studies that attempted to address cessation lag
found that after cessation of exposure, cancer risk begins to decline quickly (U.S. EPA, 2001).
The analysis of cancer benefits presented did not account for a cessation lag because the relevant information was not
available for all but one (arsenic) MP&M pollutants. Not accounting for cessation lag results in an upper bound estimate of
cancer-related benefits (U.S. EPA, 2001).
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
13.3.9 Use of Mean Individual Exposure Parameters
EPA used mean individual exposure parameters and not the distribution of exposure parameters to estimate hazard indices,
cancer risk, and adverse human health effects associated with exposure to lead for the populations affected by MP&M
discharges. Because individuals associated with high-end exposure parameter estimates would have higher health risks, EPA's
approach is likely to result in underestimation of human health risk reduction from the final MP&M regulation.
13.3.10 Cancer Potency Factors
EPA's estimates of cancer cases were calculated using cancer potency factors that are upper bound estimates of cancer
potency, potentially leading to overestimation of cancer risk.
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MP&M EEBA Part III: Benefits Chapter 13: Human Health Benefits
GLOSSARY
ambient water quality criteria (AWQC): AWQC present scientific data and guidance of the environmental effects of
pollutants which can be useful to derive regulatory requirements based on considerations of water quality impacts; these
criteria are not rules and do not have regulatory impact (U.S. EPA. 1986. Quality Criteria for Water 1986. U.S.
Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC. EPA 440/5-86-001).
marine reach: a specific length of marine coastline.
MP&M reach: a reach to which an MP&M facility discharges.
no observed adverse effect level (NOAEL): exposure level at which there are no statistically or biologically significant
differences in the frequency or severity of any effect in the exposed or control populations.
reach: a specific length of river, lake shoreline, or marine coastline.
reference dose (RfD): an estimate of the maximum daily ingestion in that is likely to be without an appreciable risk of
deleterious effects during a lifetime.
value of a statistical life saved (VSL): a monetary value of fatalities. A statistical life is saved when the mortality rate of
a group of people is reduced sufficiently that one less person will die than would otherwise be the case. One must distinguish
between statistical and actual lives. An actual life is saved when the identity of the beneficiary is known before the lifesaving
expenditure is made.
waterway: streams, lakes, bays, and estuaries.
willingness-to-pay (WTP): maximum amount of money one would give up to buy some good.
(http://www.damagevaluation.com/glossary.htm)
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MPAM EEBA Part III: Benefits
Chapter 13: Human Health Benefits
ACRONYMS
AWQC: ambient water quality criteria
NOAEL: no observed adverse effect level
RfD: reference dose
RSEI: Risk Screening Environmental Indicator Model
SDWIS: Safe Drinking Water Information System
VSL: value of a statistical life saved
WTP: willingness-to-pay
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MPAM EEBA Part III: Benefits Chapter 13: Human Health Benefits
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Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1991. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 4th
ed. New York, NY: McGraw-Hill Inc.
Amdur, M. O., J. Doul, and C. D. Klaassen, eds. 1996. Cassarett and Doul's: Toxicology, the Basic Science of Poisons. 5th
ed. New York, NY: McGraw-Hill Inc.
Helton, T., R. Roundy, and N. Weinstein. 1986. "Urban Fishermen: Managing the Risks of Toxic Exposure." Environment
Vol. 28. No. 9, November.
Connelly, N. and B. Knuth. 1993. Great Lakes Fish Consumption Health Advisories: Angler Response to Advisories and
Evaluation of Communication Techniques, Human Dimensions Research Unit, Dept. of Natural Resources, NY State College
of Agriculture and Life Sciences, Cornell University, HDRU Series No 93-3, February.
Connelly, N., B. Knuth, and C. Bisogni. 1992. Effects of the Health Advisory and Advisory Changes on Fishing Habits and
Fish Consumption in New York Sport Fisheries, Human Dimensions Research Unit, Dept. of Natural Resources, NY State
College of Agriculture and Life Sciences, Cornell University, HDRU Series No 92-9, September.
Knuth, B. and C. Velicer. 1990. Receiver-Centered Risk Communication for Sportfisheries: Lessons from New York
Licensed Anglers. Paper presented at the American Fisheries Society Annual Meeting, Pittsburgh, Penn, August.
Silverman, W. 1990. Michigan's Sport Fish Consumption Advisory: A Study in Risk Communication. Thesis submitted in
partial fulfillment of the requirements for the degree of Master of Science (Natural Resources) at the University of Michigan,
May.
U.S. Department of Commerce, Bureau of the Census. 1997. http://www.census.gov.
U.S. Department of the Interior. 1993. 1991 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, DOI,
March.
U.S. Department of the Interior, U.S. Fish and Wildlife Service and U.S. Department of Commerce, Bureau of the Census.
1996. 1996 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, DOI.
U.S. Environmental Protection Agency. (U.S. EPA). 1980. Ambient water quality criteria documents. Washington, DC:
Office of Water, U.S. EPA. EPA 440/5-80 Series. Also refers to any update of criteria documents (EPA 440/85 and EPA
440/5-87 Series) or any Federal Register notices of proposed criteria or criteria corrections.
U.S. Environmental Protection Agency. (U.S. EPA). 1997a. Health Effects Assessment Summary Tables (HEAST). Office of
Research and Development and Office of Emergency and Remedial Response, Washington, DC: U.S. EPA. OERR 9200/6-
303 (92-1).
U.S. Environmental Protection Agency. (U.S. EPA). 1997b. The Benefits and Costs of the Clean Air Act, 1970 to 1990.
Washington, DC: Office of Air and Radiation, U.S. EPA. EPA 410-R-97-002, October.
U.S. Environmental Protection Agency. (U.S. EPA). 1997c. Exposure Factors Handbook. Volumes I, II, and III.
Washington, DC: National Center for Environmental Assessment, Office of Research and Development. EPA-600-P-95-
002Fa,b,c. August.
U.S. Environmental Protection Agency. (U.S. EPA). 1998. Ambient Water Quality Criteria Derivation Methodology:
HumanHealth. Technical Support Document. EPA-822-B-98-005. July.
U.S. Environmental Protection Agency. (U.S. EPA). 1998/99. Integrated Risk Information System (IRIS) Retrieval.
Washington, DC: U.S. EPA.
U.S. Environmental Protection Agency. (U.S. EPA). 1999a. National Listing of Fish and Wildlife Consumption Advisories.
Washington, DC: Office of Water, U.S. EPA.
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MPAM EEBA Part III: Benefits Chapter 13: Human Health Benefits
U.S. Environmental Protection Agency. (U.S. EPA). 1999b. Risk-Screening Environmental Indicators Model: Version 1.0.
Washington, DC: Office of Pollution Prevention and Toxics, U.S. EPA. July 6.
http://www.epa.gov/opptintr/env_ind/index.html.
U.S. Environmental Protection Agency. (U.S. EPA). 2000a. Methodology for Deriving Ambient Water Quality Criteria for
the Protection of Human Health. EPA 822-B-00-004. October.
U.S. Environmental Protection Agency. (U.S. EPA). 2000b. Estimated Per Capita Water Inge stion in the United States.
EPA-822-R-00-008. April.
U.S. Environmental Protection Agency. (U.S. EPA). 2000c. Guidelines for Preparing Economic Analyses. Washington,
DC: EPA 240-R-00-003. September.
U.S. Environmental Protection Agency. (U.S. EPA). 2001. Arsenic Rule Benefits Analysis: An SAB Review. Washington,
DC: EPA-SAB-EC-01-008. August.
West, P., R. Marans, F. Larkin, and M. Fly. 1989. Michigan Sport Anglers Fish Consumption Survey: A Report to the
Michigan Toxic Substances Control Commission, University of Michigan School of Natural Resources, Natural Resources
Sociology Research Lab, Technical Report #1, May.
Wexler, P., ed. 1998. Encyclopedia of Toxicology, Vol. 1-3.
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MP&M EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Chapter 14: Lead-Related Benefits
INTRODUCTION
The human health benefits analysis presented in the previous
chapter examined both cancer and non-cancer health
risks from exposure to MP&M pollutants. EPA performed a
separate analysis of benefits from reduced exposure to lead.
The analysis of health effects from exposure to lead is based
on dose-response functions tied to specific health
endpoints to which monetary values can be applied. In this
way it differs from the analysis of non-cancer health risk from
exposure to other MP&M pollutants. This analysis assessed
benefits of reduced lead exposure from consumption of
contaminated fish tissue to three population groups: (1)
preschool age children, (2) pregnant women, and (3) adult
men and women. These lead-related benefits were estimated
for the final MP&M regulation, the 433 Upgrade Options, and
the Proposed/NODA option.
EPA estimated benefits to preschool children based on a
dose-response relationship for intelligence quotient (IQ)
decrements. The Agency calculated monetary values for
avoided neurological and cognitive damages based on the
impact of an additional IQ point on an individual's future
earnings and the cost of compensatory education for children
with learning disabilities. EPA also assessed the incidence of
neonatal mortality due to changes in maternal blood lead
(PbB) levels during pregnancy based on willingness-to-
pay (WTP) values for avoiding death. EPA estimated that
the final regulation will not yield any benefits to children from
reduced expo sure to lead.
CHAPTER CONTENTS
14.1 Overview of Lead-Related Health Effects 14-2
14.1.1 Children Under Age One 14-3
14.1.2 Children Between the Ages of One and
Seven 14-3
14.1.3 Adults 14-4
14.2 Health Benefits to Children 14-4
14.2.1 PbB Distribution of Exposed Children .... 14-5
14.2.2 Relationship Between PbB Levels
andlQ 14-12
14.2.3 Value of Children's Intelligence 14-12
14.2.4 Value of Additional Educational
Resources 14-14
14.2.5 Changes in Neonatal Mortality 14-17
14.3 Adult Health Benefits 14-17
14.3.1 Estimating Changes in Adult PbB
Distribution Levels 14-20
14.3.2 Male Health Benefits 14-22
14.3.3 Female Health Benefits 14-26
14.4 Lead-Related Benefit Results 14-28
14.4.1 Preschool Age Children Lead-Related
Benefit Results 14-28
14.4.2 Adult Lead-Related Benefit Results 14-29
14.5 Limitations and Uncertainties 14-31
14.5.1 Excluding Older Children 14-31
14.5.2 Compensatory Education Costs 14-32
14.5.3 Dose-Response Relationships 14-32
14.5.4 Absorption Function for Ingested
Lead in Fish Tissue 14-32
14.5.5 Economic Valuation 14-33
Glossary 14-35
Acronyms 14-38
References 14-39
The health effects in adults that EPA was able to quantify all
relate to lead's effect on blood pressure (BP). Quantified
health effects include incidence of hypertension in adult men, initial non-fatal coronary heart disease (CHD), non-fatal
strokes (cerebrovascular accidents (CBA) and atherothrombotic brain infarctions (Bl)}, and premature mortality.
EPA used cost of illness (COD estimates (i.e., medical costs and lost work time) to estimate monetary values of reduced
incidence of hypertension, initial CHD, and strokes. EPA used COI estimates to estimate monetary values for reduced
incidence of hypertension, initial CHD, and strokes. This analysis uses the $6.5 million estimate of the value of a statistical
life saved recommended in the Guidelines for Preparing Economic Analysis (EPA, 2000a) to estimate monetary value of
reduced incidence of premature mortality. EPA estimated that the final rule will achieve no lead-related health benefits
among adults.
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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
14.1 OVERVIEW OF LEAD-RELATED HEALTH EFFECTS
The MP&M regulation will reduce lead exposure by reducing the amount of lead discharged to water bodies from MP&M
facilities, thereby reducing health and ecological risks. This section provides a brief summary of the human health effects
from exposure to lead. Data for this analysis are taken from the Agency for Toxic Substance and Disease Registry's
(A TSDR) Draft Toxicological Profile for Lead (1997) unless otherwise noted. The discussion provided in this section is
qualitative and was not used to generate risk estimates.
Lead and lead compounds are toxic and pose threats to human health and well being. The health effects of very high levels of
PbB include convulsions, coma, and death from lead toxicity. These effects have been understood for many years. The
effects of lower doses of lead are not fully understood, however, and continue to be the subject of intensive scientific
investigation (CDC,1991b).
Lead accumulates in the body and is stored in various organ systems. While high level exposures are of immediate concern
due to acute toxicity, exposure to small amounts can accumulate over time to harmful levels. Accumulated lead is very
persistent, with a half-life in bone of approximately 27 years.1 Known or strongly suspected health effects include kidney,
stomach, and respiratory cancer, nervous system disorders, hypertension, anemia and blood disorders, gastrointestinal
disorders, renal damage, and other effects (ATSDR, 1997; CARB, 1996). Increased mortality from these effects has been
observed in studies (ATSDR, 1997).
Many lead-associated adverse health effects are both chronic in nature and relatively common. These effects include but are
not limited to hypertension, coronary artery disease, and impaired cognitive function. Specific cases of these conditions are
difficult to link to lead exposure because the same adverse health effects or endpoints can arise from a variety of causes.
Despite numerous studies conducted by EPA and other institutions, dose-response functions are available only for a handful
of health endpoints associated with elevated PbB levels. The available research does not always allow complete economic
evaluation, even for quantifiable health effects.
Lead is harmful to any exposed individual, and the effects of lead on children are of particular concern. Children's rapid
development rate makes them more susceptible to neurobehavioral deficits resulting from lead exposure. U.S. EPA
identifies three sensitive populations: children under age one, children between the ages one and seven, and adult men and
women (U.S. EPA, 1990). New research suggests that children older than seven may also be a hypersensitive population.
Recent research on brain development among 10- to 18-year-old children shows unanticipated and substantial growth in brain
development, mainly in the early teenage years (Giedd et al., 1999). This analysis does not, however, include this group due
to data limitations. Table 14.1 summarizes the quantifiable health effects on children under seven and adult men and women,
along with other important, non-quantified, known health effects on these populations.
1 A half-life of 27 years means that it takes 27 years for the levels measured in bone to decrease by 50 percent.
2 In a pioneering study, Schwartz et al. quantified a number of health benefits that would result from reducing the lead content of
gasoline (U.S. EPA, 1985). EPA extended this work by analyzing lead in drinking water (U.S. EPA, 1986a) and by funding the study of
lead in the air (U.S. EPA, 1987).
14-2
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Table 14.1: Quantified and Unqualified Health Effects of Lead
Population Group
Quantified Health Effect
Unqualified Health Effect
Children ages 0-7
Neonatal mortality due to decreased gestational
age and low birth weight caused by maternal
exposure to lead
Nervous system effects in children younger than 7
years - IQ decrements, cases of IQ less than
70, PbB levels greater than
Fetal effects from maternal exposure (including
diminished IQ and reduced birth weight)
Low IQ (70
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
14.1.3 Adults
EPA has classified lead as a probable human carcinogen (Group 2b) based on animal toxicological evidence (IRIS, 2002a;
see file titled Lead and Compounds (inorganic)). Lead also has been strongly suggested as the causative agent in numerous
studies of kidney, stomach, and respiratory cancer in humans. The cancers observed in human studies are usually lethal. A
cancer potency factor for lead has not been published by U.S. EPA, however, due to uncertainties associated with human
studies. The California Environmental Protection Agency (CEPA) has also classified lead as a carcinogen and estimated a
cancer potency factor of 8.5 x 10"3 per mg/kg/day for exposure to lead and lead compounds (California Air Resource Board
[CARB], 1996).3 Reduced cancer risk associated with reduced exposure to lead can be estimated based on cancer cases
avoided (see Section 13.2.1). The Agency did not incorporate cancer effects from exposure to lead in the final rule analysis
because these effects appeared very small compared to other adverse health effects from exposure to lead (e.g., neurological
damages to children).
Elevated PbB has been linked to elevated BP in adults, especially in men aged 40 to 59 (Pirkle etal., 1985). Elevated BP,
itself a health hazard, is also a risk factor for heart attack, stroke (Shurtleff, 1974; McGee and Gordon, 1976; Pooling Project
Research Group [P_PRG], 1978), and premature death. Since heart disease and its related diseases are the primary cause of
death in the United States, avoiding their exacerbation by minimizing lead exposure can be assumed to have considerable
benefits for the affected population. Although elevated BP in women results in the same effects as for men, the general
relationships between BP and these health effects differ somewhat across gender (Shurtleff, 1974).
Other known or strongly suspected health endpoints include nervous system disorders in adults, anemia and blood disorders,
gastrointestinal disorders, and renal damage (Reels et al., 1976; Factor-Litvak et al., 1993; 1998; and 1999). Finally, data
suggest that lead is genotoxic and may cause chromosomal damage in humans leading to birth defects (Anwar, 1994;
Apostoli et al., 2000; Sallmen et al, 2000). Lead may also cause other adverse reproductive effects in women, including
increased miscarriage and stillbirth (U.S. EPA 1990). A study of National Health and Nutrition Examination Surveys
(NHANES) II data by Silbergeld et al. suggests that accumulated lead is stored in women's bone tissues and is mobilized
back into the blood during the bone demineralization associated with pregnancy, lactation, and osteoporosis (Silbergeld et al.,
1988). Many of these effects cannot be quantified due to a lack of information on the dose-effect relationship.
14.2 HEALTH BENEFITS TO CHILDREN
The following analysis assesses benefits to children from reduced lead exposure, via reduced consumption of contaminated
fish tissue.4 This analysis uses PbB concentrations as a biomarker of lead exposure.5 EPA estimated PbB levels in the
population of exposed children to obtain both baseline and post-compliance readings. Changes in those readings yielded
estimated benefits from reduced lead exposure in the form of avoided damages. Avoided neurological and cognitive damages
are expressed as changes in overall IQ levels, including reduced incidence of extremely low IQ scores (<70, or two standard
deviations below the mean), and reduced incidence of PbB levels above 20 fig/dL. The neurological and cognitive damages
avoided are then quantified using the value of compensatory education that an individual would otherwise need, and the
impact on that individual's future earnings. This analysis does not quantify additional benefit categories, such as the costs of
PbB screening and medical treatment. The reduced loss in IQ points, reduced cases of IQ levels below 70 points, and reduced
special education costs associated with various PbB levels are likely to be the largest benefit categories. This analysis does
not estimate the cost of group homes and other special care facilities.
The analysis of health benefits to children involves the following steps:
»• estimate the baseline and post-compliance lead discharges from MP&M facilities;
3 The cancer potency factors for lead acetate and lead subacetate are 28x10 ' and 3.8xl02, respectively.
4 This analysis does not consider the beneficial effects due to reduced drinking water exposure. EPA has issued drinking water
criteria for lead. This analysis assumes drinking water treatment has already reduced lead content below threshold levels.
5 PbB concentration is the most common measure of body-lead burden. Other measures of body-lead burden include lead in bones,
teeth, and hair.
14-4
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
»• estimate lead concentrations in receiving water bodies before and after final effluent guidelines based on lead
discharge estimates, effluent flow, characteristics of the receiving POTWs, and characteristics of receiving water
bodies;
> estimate the baseline and post-compliance dietary lead intake of children via fish consumption;
> estimate PbB levels of exposed children before and after the final regulation, based on in-stream lead concentrations,
bioconcentration factors, and fish consumption rates for children;
*• assess changes in health impacts to children from reduced lead exposure, including changes in IQ loss, changes in
incidence of IQ<70, and changes in neonatal mortality;
»• estimate monetary benefits resulting from reduced adverse health impacts to children; and
»• estimate benefits from changes in neonatal mortality from reduced maternal exposure to lead.
Figure 14.1 depicts the above steps.
The following sections summarize the relevant dose-response relationships for children, and discuss data sources used for the
dose-response relationships. Each section also includes the methods used to value the changes in health effects based upon
dose-response relationships.
14.2.1 PbB Distribution of Exposed Children
This section describes the estimation of changes in PbB distribution of exposed children.
a. Estimating lead concentrations in the receiving water bodies
Estimating health risks associated with lead exposure from fish consumption requires calculating in-waterway lead
concentrations. The method and formulas for this calculation were identical to those described for the analysis of cancer
effects for the fish consumption pathway (see Chapter 1 3 on Human Health Benefits and the Environmental Assessment in
Appendix I for details.)6
b. Estimating PbB levels in exposed children
This analysis considers children that are born today and live in recreational and subsistence fishermen households. The
analysis considers a continuous exposure pattern for children from birth through the seventh birthday. Exposure, health
effects, and benefits are calculated separately for children living in recreational and subsistence fishing households. This
analysis relies on EPA's Integrated Exposure, Uptake, and Biokinetics (IEUBK) Model for Lead in Children (IEUBK
version 0.99d, March 8, 1994).
»»» Description of th e IEUBK m ode I
The IEUBK model uses exposure, uptake, and biokinetic response information to estimate the PbB level distribution for a
population of children receiving similar exposures. The estimated distribution may be used to predict the probability of
elevated PbB levels in children exposed to a specific combination of environmental-lead levels. The model addresses four
components of environmental risk assessment:
> the multimedia nature of exposure to lead;
*• the differential bioavailability of various sources of lead;
»• the pharmacokinetics of internal distribution of lead to bone, blood, and other tissues; and
»• inter-individual variability in PbB levels.
The water quality model used for the Ohio case study is discussed in Appendix H.
14-5
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Figure 14.1 Assessing Benefits to Children from Reduced Lead Discharges from MP&M Facilities
B ase line lead dis charges from MP&M
facilities
Post-compliance lead discharges
from MP&M facilities
Baseline ambient water
quality conditions
Post-compliare e
ambient water quality conditions
B ase line dietary lead intake
via fis h c orEiimption
Post-compliance dietary lead intake
via fish consumption
Use lEUBKto estimate change in
children's blood kad distribution
from baseline to post-compliance
Change in IQ dis tribution from
baseline to post-compliance
Change in incidence
of b lood-lead levels
>2Q
Avoided IQ loss
Change in incidence
ofIQ<70
Avoidedbss in
lifetime earnings
Avoided costs
of c ompens atory
educ ation
Monetary benefits from reduced
exposure of children to lead
Reduced
neonatal mortality
Value of life
Source: U.S. EPA analysis.
14-6
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
The model uses estimated or measured lead concentrations in fish tissues and other media, such as soil, dust, air, and water to
estimate a continuous exposure pattern for children from birth through the seventh birthday (U.S. EPA, 1995). The model
then estimates a distribution of PbB levels for a population of children receiving similar exposures by predicting its
geometric mean (GM). The inter-individual and biological variability in PbB levels of children exposed to similar
environmental lead levels is represented by the geometric standard deviation (GSD). This analysis uses an empirical
estimate of the variability in PbB concentrations, a GSD of 1.6, estimated from residential community PbB studies (U.S. EPA,
1995). This estimate is applied for predictions of the national distribution of PbB concentrations.
The model has three distinct functional components that work together in a series:
*• exposure,
*• uptake, and
»• biokinetics response.
Each model component is a set of complex equations and parameters. The Technical Support Document (U.S. EPA, 1995)
provides the scientific basis of the parameters and equations used in the model, while the Guidance Manual (U.S. EPA, 1994)
includes a detailed description of the exposure pathways, absorption mechanism, biokinetic compartments, and associated
comparted transfers of lead.
»»» Inputs to the IEUBK m odel
The IEUBK model uses three sets of parameters:
*• exposure parameters estimate the amount of environmental lead taken into the body, through breathing or ingestion;
*• uptake parameters estimate the amount of lead absorbed from environmental sources;
> biokinetic parameters characterize the transfer of lead between compartments of the body (e.g., between blood and
bone) and elimination of lead from the body.
The IEUBK model allows the user to input values for most exposure and uptake parameters. The biokinetic parameter values
cannot be altered. When exposure and uptake values are not specified, the IEUBK model provides default values. Table 14.2
summarizes the key parameter values used in this analysis and indicates whether a value is an IEUBK default value or has
been specified by EPA.7
1. Exposure parameters include exposure rates and exposure concentrations:
*• Exposure rates: Children in recreational fishing households are assumed to consume 6.03 grams offish per day.
Children living in subsistence households are assumed to consume 30.33 grams of fish per day. These fish
consumption rates are based on uncooked fish weights. The fish consumption rate for children in recreational fishing
households is calculated as a weighted average based on West et al. (U.S. EPA, 1997a) for children ages 1-5 (5.63
grams of fish per day) and children ages 6-10 (7.94 grams of fish per day). For children of subsistence fishing
households, the fish consumption rate is calculated as a weighted average based on Columbia River Intertribal Fish
Commission (CRITFC, 1994) estimates for children under age 5 (19.6 grams offish per day) and the Continuing
Survey of Foods by Individuals (U.S. EPA, 2002b) for children ages 3-5 (40.31 grams offish per day) and ages 6-10
(61.49 grams of fish per day).
*• Exposure concentrations: EPA used estimated in-stream concentrations of lead to calculate lead concentration of the
fish consumption exposure pathway. The Agency used 1996 monitoring data (U.S. EPA, 1996b) on lead
concentrations in air and the Housing and Urban Development National Survey (HUD, 1995) for data on lead
concentrations in dust and soil to characterize lead exposure concentrations for these exposure pathways.8 This
7 A complete list of IEUBK default parameters is presented in Appendix L.
8 EPA found that the typical PbB level distribution predicted in the IEUBK Model for Lead in Children based on the default values
for air, dust, soil, and drinking water lead concentrations did not correspond to the most recent national population PbB distribution
(NHANES III, Phase 2, 1994). Therefore, the Agency used more recent data to characterize the background exposure to environmental
14-7
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
analysis uses median concentration values for these three pathways as inputs to the IEUBK to characterize
background exposure to environmental lead. EPA used the IEUBK default value for lead concentration in drinking
water that takes into account contributions of lead from plumbing. Because of past use of lead in plumbing, lead
concentrations in tap water are likely to be above the current water quality standard for lead in drinking water.
2. Uptake of ingested lead: Lead bioavailability varies across the chemical forms in which lead can exist. Many factors
complicate the estimation of bioavailability, including nutritional status and timing of meals relative to lead intake. The
Agency used the default media-specific bioavailabilities in the IEUBK model for this analysis.
3. Biokinetic parameters: The data on which these parameter values are based originate from a variety of sources, including
available clinical data (U.S. EPA, 1995). These parameters cannot be changed by the user.
lead. Median values from recent monitoring data allowed the Agency to match the lEUBK-predicted PbB distribution to the NHANES-
derived distribution.
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Table 14.2: Selected List of Parameters Used in the IEUBK Model
Variable
Value
IEUBK
Default
Data Source
Exposure Rates
Fish:
Recreational
6.03 g/day
Fish: Subsistence ! 30.33 g/day
Fresh Fruit
Fresh Vegetables
Meat (Including
fish and game)
j
I Air (Time spent
! outdoors)
No ! The fish consumption rate for children in recreational fishing households is calculated as a
! weighted average based on West et al. (U.S. EPA, 1997a) for children ages 1-5 (5.63
! g/day) and children ages 6-10 (7.94 g/day). The fish consumption rate for children in
j subsistence fishing households is calculated as a weighted average based on Columbia
No ! River Intertribal Fish Commission (CRITFC, 1994) estimates for children under age 5
| (19.6 g/day) and the Continuing Survey of Foods by Individuals (U.S. EPA, 2002b) for
! children ages 3-5 (40.31 g/day) and ages 6-10 (61.49 g/day).
38.481 g/day 0-11 months
169.000 g/day 12-23 months
63.166 g/day 24-35 months
61 .672 g/day 36-47 months
61.848 g/day 48-59 months
67.907 g/day 60-71 months
80.024 g/day 72-84 months
56. 84 g/day 0-11 months
106.50 g/day 12-23 months
155.75 g/day 24-35 months
157.34 g/day 36-47 months
158.93 g/day 48-59 months
172.50 g/day 60-71 months
199.65 g/day 72-84 months
29.551 g/day 0-11 months
87.477 g/day 12-23 months
95.700 g/day 24-35 months
101 .570 g/day 36-47 months
107.441 g/day 48-59 months
1 1 1 .948 g/day 60-71 months
120.961 g/day 72-84 months
Yes
Yes
Yes
Values taken from Pennington, J. A T. (1983) Revision of the total diet study food list and
diets. Journal of American Dietetic Association 82(2): 166-173
Values taken from Pennington, J. A. T. (1983) Revision of the total diet study food list and
diets. Journal of American Dietetic Association 82(2): 166-173
Values taken from Pennington, J. A. T. (1983) Revision of the total diet study food list and
diets. Journal of American Dietetic Association 82(2): 166-173
1 hrs/day 0-11 months
2 hrs/day 12-23 months
3 hrs/day 24-35 months
4 hrs/day 36-47 months
4 hrs/day 48-59 months
4 hrs/day 60-71 months
4 hrs/day 72-84 months
Yes ! Based on values reported in (1) U.S. Environmental Protection Agency (U.S. EPA),
! Review of the National Ambient Air Quality Standards for Lead: Assessment of Scientific
! and Technical Information. OAQPS Staff Paper, Air Quality Management Division,
| Research Triangle Park, NC (EPA 1989c), and (2) Report of the Clean Air Scientific
\ Advisory Committee on Its Review of the OAQPS Lead Staff Paper.
| EPA-SAB-CASAC-90-002 (EPA 1990a)
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Table 14.2: Selected List of Parameters Used in the IEUBK Model
i i i
! IEUBK i
Variable Value i _ _ . i Data Source
j Default j
Exposure
Concentrations
Food Lead Intake
i i i
Water (Daily j 0.20 L/day 0-11 months j Yes j Exposure Factors Handbook. U.S. EPA Office of Health and Environmental Assessment,
amount of water j 0.50 L/day 12-23 months j Washington, DC. EPA/600/8-89/043 (1989b)
consumed) j 0.52 L/day 24-35 months
| 0.53 L/day 36-47 months
I 0.55 L/day 48-59 months
| 0.58 L/day 60-71 months
1 0.59 L/day 72-84 months
Soil (Combined
soil and dust
consumption)
Fish Tissue
Outdoor Air
Indoor Air
Water
Soil
Dust
Fresh Fruit
0.085 g/dayO-llmonths
0.135 g/day 12-23 months
0.135 g/day 24-35 months
0.135 g/day 36-47 months
0.100 g/day 48-59 months
0.090 g/day 60-71 months
0.085 g/day 72-84 months
site-specific
0.03 [ig/m3
30% of Outdoor Air
4.0 [ig/L
61.78jig/g
187.1 Ijig/g
0.039 ng/dayO-11 months
0.196 ng/day 12-23 months
0.175 ng/day 24-35 months
0.175 ng/day 36-47 months
0.179 n g/day 48-59 months
0.203 ng/day 60-71 months
0.251 ^g/day 72-84 months
Yes
No
No
Yes
Yes
No
No
Yes
Based on value reported in Review of the National Ambient Air Quality Standards for
Lead: Assessment of Scientific and Technical Information. OAQPS Staff Paper, Air
Quality Management Division, Research Triangle Park, NC (1989c)
Estimated based on predicted lead concentration in receiving reaches and bioconcentration
factor for lead (49 L/Kg)
Median value for 1996 from EPA's AIRS (Aerometric Information Retrieval System) air
monitoring data (U.S. EPA, 1996b)
Based on value reported in Review of the National Ambient Air Quality Standards for
Lead: Assessment of Scientific and Technical Information. OAQPS Staff Paper, Air
Quality Management Division, Research Triangle Park, NC (1989c)
Analysis of data from American Water Works Service Co. in Marcus, A.H. (1989)
Distribution of lead in tap water. Parts I and n. Report to the U.S. EPA Office of Drinking
Water/Office of Toxic Substances, from Battelle Memorial Institute under Contract 68-D8-
0115.
Median values from the Housing and Urban Development National Survey (U.S.
Department of Housing and Urban Development, 1995)
Based on data provided by FDA in Air Quality Criteria for Lead Vol I-IV. U.S. EPA
Environmental Criteria and Assessment Office, Research Triangle Park, NC. EPA
600/8-83-028a-d(1986b)
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Table 14.2: Selected List of Parameters Used in the IEUBK Model
Variable
Value
IEUBK
Default
Data Source
Fresh Vegetables j 0.148 ^g/day 0-11 months
| 0.269 ng/day 12-23 months
| 0.475 ng/day 24-35 months
| 0.466 ng/day 36-47 months
0.456 ng/day 48-59 months
0.492 ng/day 60-71 months
0.563 ng/day 72-84 months
Yes ! Based on data provided by FDA in Air Quality Criteria for Lead Vol I-IV. U.S. EPA
I Environmental Criteria and Assessment Office, Research Triangle Park, NC. EPA
| 600/8-83-028a-d(1986b)
Meat (No fish or
game meat)
Other Foods (No
fish or game
meat)
0.226 ng/dayO-11 months
0.630 ng/day 12-23 months
0.811 ng/day 24-35 months
0.871 ng/day 36-47 months
0.931 ng/day 48-59 months
1.008 ng/day 60-71 months
1.161 ng/day 72-84 months
3.578 ng/dayO-11 months
3.506 ng/day 12-23 months
3.990 ng/day 24-35 months
3.765 ng/day 36-47 months
3.545 ng/day 48-59 months
3.784 ng/day 60-71 months
4.215 ng/day 72-84 months
Yes
Yes
Based on data provided by FDA in Air Quality Criteria for Lead Vol I-IV. U.S. EPA
Environmental Criteria and Assessment Office, Research Triangle Park, NC. EPA
600/8-83-028a-d(1986b)
Based on data provided by FDA in Air Quality Criteria for Lead Vol I-IV. U.S. EPA
Environmental Criteria and Assessment Office, Research Triangle Park, NC. EPA
600/8-83-028a-d(1986b)
Lead Absorption
Factor
Food
Air
Water
Soil
Dust
0.5
32%
0.5
0.3
0.3
Yes
Yes
Yes
Yes
Yes
Based on values reported in the Review of the National Ambient Air Quality Standards for
Lead: Exposure Analysis Methodology and Validation; Report No. EPA-450/2-89/011;
U.S. EPA Office of Air Quality Planning and Standards, Research Triangle Park, NC
(1989d)
Biokinetic Parameters
IEUBK default values and equations were used for all biokinetic parameters (these cannot be changed by the user). The complete list of
IEUBK biokinetic parameters is listed in Appendix L and in the Technical Support Document: Parameters and Equations Used in the
IEUBK Model for Lead in Children. U.S. EPA, EPA540-R-94-040, (1995)
Age Fish Introduced in Infant Diet
9 months
N/A
Literature on dietary guidelines for children from various childcare organizations,
including the National Network for Child Care
Source: U.S. EPA analysis.
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MP&M EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
c. Estimating changes in the PbB level in exposed children from reduced MP<&M discharges
EPA used the IEUBK model in this analysis to estimate the effect of lead-contaminated fish consumption on children's PbB
concentrations. The Agency first calculated lead concentration in fish tissue corresponding to each reach affected by MP&M
discharges to provide inputs to the IEUBK model. The model uses the specified fish tissue concentrations in conjunction with
fish ingestion rates and bioavailability factors to determine the dose of lead absorbed by the body. This dose is then used to
predict the GM PbB concentration for children associated with each reach affected by lead discharges from the MP&M
facilities.
EPA used the IEUBK model to predict the baseline and post-compliance PbB distributions for children that consume fish
from reaches affected by lead discharges from MP&M facilities. The difference between the estimated baseline and post-
compliance PbB distribution is the basis for the analysis of benefits to children from the MP&M regulation.
14.2.2 Relationship Between PbB Levels and IQ
A dose-response relationship between PbB and IQ decrements determined by Schwartz (1994) suggests that a decrease of
0.25 IQ points can be expected for every 1 Hg/dL increase in PbB (Schwartz, 1994). The p-value (< 0.0001) indicates that
this relationship is highly significant.
EPA multiplied the 0.25 IQ points lost per Hg/dL increase in PbB by the average increase in PbB level for children and by the
number of exposed children to obtain the total change in number of IQ points for the population. The average PbB level
modeled in this analysis is a GM, not the arithmetic mean used by Schwartz (1993). To adjust for this difference, equation
14.1 uses a ratio between the arithmetic mean and the GM of a log norm ally-distributed random variable. The ratio
between the expected value (mean) of the distribution and the GM is 1.117 for the assumed GSD of children's PbB levels
(1.6).
The total avoided loss of IQ points for each group is estimated as:
(AVOIDED LOSS of IQ POINTS)k= *GMk*.25x(Pop,) II (14.1)
where:
(Pop)k = the number of children (up to age seven) in anglers' families in the vicinity of a given MP&M reach; and
GMk = the GM of the PbB distribution in the population of children.
As shown in equation 14.1, the population of children up to age seven is divided by seven to avoid double-counting. The
IEUBK model calculates the GM of the PbB distribution in the population of children born today, assuming a continuous
exposure pattern for children from birth through the seventh birthday. Assuming that children are evenly distributed by age,
this division adjusts this equation to apply only to children age 0-1. Dividing by seven undercounts overall benefits. Children
from age 1 to 7 are not accounted for in the base year of the analysis, although they are presumably affected by the lead
exposure, because the IEUBK model assumes a continuous exposure pattern for children from birth through the seventh
birthday.
14.2.3 Value of Children's Intelligence
Available economic research provides little empirical data on society's overall WTP to avoid a decrease in an infant's IQ.
This analysis uses research that monetizes a subset of effects associated with decreased IQ. These effects represent only some
components of society's WTP to avoid IQ decreases, and underestimate society's WTP when employed alone. For the
purpose of this analysis, these effects are the only ones available at this time to approximate the WTP to avoid IQ decrements.
Recent studies provide concrete evidence of long-term effects from childhood lead exposure (Schwartz, 1994). This analysis
assumes a permanent loss of IQ points based on PbB levels estimated for children up to age seven, and considers two
consequences of this IQ decrement:
*• the decreased present value of the infant's expected lifetime earnings, and
> the increased educational resources expended for an infant who becomes mentally handicapped or needs
compensatory education as a consequence of lead exposure.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
a. Estimating the effect of IQ on earnings
Reduced IQ has direct and indirect effects on earnings. This analysis models the overall impact from a one-point reduction in
IQ as the sum of these direct and indirect effects on lifetime earnings. EPA used the most recent estimates of the effects of IQ
on earnings based on Salkever (1995).9 Salkever provided updated estimates of the direct and indirect effects of IQ loss on
earnings, using the most recent available data set, the National Longitudinal Survey of Youth (NLSY). Salkever used
regression analysis techniques to estimate direct and indirect effects of IQ on earnings. Three different relationships are
estimated separately for male and female respondents:
•• a least-squares regression of highest grade on IQ test scores;
> a probit regression of a 0-1 indicator of positive earnings on highest grade and IQ test scores;
> a least-squares regression, for persons with positive earnings, of the logarithm of earnings on highest grade and IQ
test scores.
Other variables were included in each regression to control for effects of family background (parents' education and income),
the age of the respondent, ethnic group, and residence location (urban U.S., non-urban U.S., south versus non-south).
Based on the regression results, Salkever estimated the effects of IQ on earnings as the sum of direct and indirect effects:
•• The direct effect is the sum of effects of IQ test scores on employment and earnings for employed persons, holding
the years of schooling constant.
> The indirect effect is the sum of effects of IQ test scores on years of schooling attained, and the subsequent effect of
years of schooling on the probability of employment and on earnings for employed persons.
The analysis found that percentage effects of lead exposure are greater for females than for males. The total estimated effect
of the loss of an IQ point on earnings, based on the Salkever study, is an earnings reduction of 1.93 percent for men and 3.22
percent for women. The total effect of the loss of an IQ point on earnings also includes non-IQ effects on schooling (e.g.,
behavioral problems).
b. Valuing foregone earnings
EPA monetized IQ loss effects by combining the percent earnings loss estimate with an estimate of the present value of
expected lifetime earnings. EPA used the 1992 data on money income for the U.S. population (U.S. Department of
Commerce, 1993) to calculate the mean present value of lifetime earnings of a person born today. The data included earnings
for employed persons and employment rates as a function of educational attainment, age, and gender. The following
assumptions were used to calculate the mean present value of lifetime earnings of a person born today:
•• The distribution of earnings for employed persons and labor force participation rates remains constant over time.
•• A person earns income from age 18 through age 67.
»• Real wages grow one percent per year.
•• Future earnings are discounted at a three percent annual rate.
The money income data (U.S. Department of Commerce, 1993) form the best available basis for projecting lifetime earnings,
but involve some uncertainties. Labor force participation rates of women, the elderly, and other groups will likely continue to
change. Currently, men tend to earn more than women due to higher wage rates and higher labor force participation.
Expected lifetime earnings increase with education for both men and women. Real earnings of women will probably continue
to rise relative to real earnings of men. Educational attainment has risen over time and may continue to rise. Unpredictable
9 EPA did not incorporate earlier studies of the effects of IQ on earnings in this analysis because the Salkever study is more complete
in capturing the various pathways through which IQ affects earnings, such as the indirect effect of IQ on earnings via its effect on
educational attainment. Also, other studies are much older. The IQ/earning effect is likely to be higher during the high tech boom in the
last decade.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
fluctuations in the economy's growth rate will probably affect labor force participation rates and real wage growth for all
groups. Medical advances that increase life expectancy will probably increase lifetime earnings.
Although earnings data alone form an incomplete measure of an individual's value to society, this analysis does not account
for those individuals who do not participate in the labor force at all throughout their working years and whose productive
services are not measured by wage rates. The largest group in this population are those who remain at home doing housework
and child rearing. Volunteer work also contributes significantly to social welfare, and volunteerism rates tend to increase with
educational attainment and income. Assuming that the opportunity cost of non-wage-compensated work equals the
average wage earned by persons of the same sex, age, and education, the average lifetime earnings estimates would be
significantly higher. Recalculating the tables using full employment rates for all age, sex, and education groups would
provide higher lifetime earnings estimates. To be conservative, this analysis considered only the value of lost wages and does
not include the opportunity cost of non-wage-compensated work.
The adjusted value of expected lifetime earnings equals the present value for an individual entering the labor force at age 18
and working until age 67. Given a three percent social discount rate, the other assumptions mentioned, and current survival
probabilities, the present value of lifetime earnings of a person born today in the U.S. would be $448,957 (2001 $).10
c. Valuing costs of education
The increase in lifetime earnings from additional education equals the gross return on education. The cost of education is
subtracted from the gross return to obtain the net increase in earnings from additional education. The cost of education has
two components: the direct cost of the education, and the opportunity cost of lost income during the education. The
marginal cost of education used in this analysis was assumed to be $8,898 (2001$) per year. This figure was derived from
the U.S. Department of Education's reported ($6,961) average per-student annual expenditure (current plus capital
expenditures) in public primary and secondary schools in 1995-96 (U.S. Department of Education, 1998)." EPA adjusted
this value to 2001 dollars based on CPI for education.
Salkever's study found the estimated effect of IQ on educational attainment to be 0.1007 years per IQ point. The estimated
cost of an additional 0.1007 years of education per IQ point is $896 (i.e., 0.1007 x $8,898). This marginal cost was
discounted to the time the exposure and damage is modeled to occur (age zero) because this cost is incurred after the
completion of formal education. The average level of educational attainment in the population over age 25 is 12.9 years (U.S.
Department of Education, 1993). The marginal educational cost was therefore assumed to occur at age 19, resulting in a
discounted present value cost of $511 (2001$).
The other component of the cost of education is the opportunity cost of lost income while in school. Income loss is frequently
cited as a major factor in the decision to terminate education, and must be subtracted from the gross returns to education. An
estimate of the lost income was derived assuming that people in school are employed part-time but that people out of school
are employed full-time. The opportunity cost of lost income is the difference between full-time and part-time earnings. The
value of lost income associated with being in school an additional 0.1007 years is $746 (2001$) discounted to age zero.
d. Estimating the total effect of IQ on earnings
Combining the value of lifetime earnings ($448,957) with the estimate of percent wage loss per IQ point yielded $10,675 per
IQ point. Subtracting the education and opportunity costs reduced this value to $9,419 per IQ point (2001$).
14.2.4 Value of Additional Educational Resources
Children with IQs less than 70 and whose PbB is greater than 20 Hg/dL will require additional educational resources including
an educational program tailored to the mentally handicapped. Some children whose PbB is greater than 20 Hg/dL will need
additional instruction while attending school later in life. The following sections describe approaches used to quantify the
number of children with IQs less than 70 and to estimate increased educational costs resulting from lead exposure.
10 Assuming a seven percent social discount rate, the present value of lifetime earnings of a person born today in the U.S. would be
$101,247 (2001$). Appendix M presents a sensitivity analysis with respect to the value of an IQ point.
11 In comparison, the average annual cost of tuition, fees, room, and board for a four-year public undergraduate institution was $8,655
(2001$) for the year 2000-2001 (U.S. Department of Education, 2001).
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
a. Children with IQs less than 70
»»» Quantifying the number of children with IQs less than 70
Increases in the mean PbB levels of children results in an increased incidence of children with very low IQ scores. IQ scores
are normalized to have a mean of 100 and a standard deviation of 15. An IQ score of 70 is two standard deviations below the
mean, and is generally regarded as the point below which children require significant special compensatory education tailored
to the mentally handicapped.
The relationship presented here for estimating changes in the incidence of IQs less than 70 used the most current IQ point
decrement function provided by Schwartz (1993). It assumed that, for a baseline children's PbB distribution (defined by GM
and GSD), the population also has a normalized IQ point distribution with a mean of 100 and a standard deviation of 15. The
proportion of the population expected to have IQs less than 70 was determined from the standard normal distribution
function for this baseline condition:
P(/g<70)= $(z) (14.2)
where:
P(IQ <70) = probability of IQ scores less than 70
z = standard normal variate (i.e., the number of standard deviations); computed for an IQ score of 70, with
mean IQ score of 100 and standard deviation of 15 as:
70- 100 _
Z= — = -2 (14.3)
(z) = standard normal distribution function;
2
e 2 du (14.4)
The integral in the standard normal distribution function does not have a closed form solution. Values for (z) are usually
obtained using software with basic statistical functions or from tables typically provided in statistics texts. The solution for
<&(z) where z = -2 is 0.02275. That is, for the normalized IQ score distribution with a mean of 100 and standard deviation of
15, approximately 2.3 percent of children are expected to have IQ scores below 70.
EPA made two key assumptions to relate changes in the proportion of children with IQ scores below 70 to changes in
population mean PbB levels:
1. The mean IQ score will change as a result of changes in the mean PbB level as:
(14'5)
A Mean IQ = -0.25 x A Mean PbB
where:
A Mean IQ = the change in the mean IQ score between the baseline and post-compliance scenarios, and
A Mean PbB = the change in the mean PbB level between the two scenarios.
This relationship relies on Schwartz' estimate (1993) of a decrease of 0.25 IQ points for each Hg/dL increase in PbB.
The mean PbB level referred to here is the arithmetic mean (or expected value) for the distribution, obtained as described
previously from the GM and GSD.
2. The standard deviation for the IQ distribution is 15 for both the baseline and the post-compliance scenario.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
Using these assumptions, EPA determined the change in the probability of children having IQ less than 70 for a given change
in mean PbB from:
AP(IQ<10) = 3>(zBl) - 0(zft) = 0(zfl;) - 0.02275 (14 6)
where:
*(ZBI) = baseline standard normal distribution function, and
<&(zpc) = post-compliance standard normal distribution function.
_ 70- (100+ Q.25xbMeanPbB) (14.7)
15
EPA then converted a given change in the mean PbB level between the baseline and post-compliance scenarios into a measure
of IQ. The procedure above yielded an estimate of the percent of the population with IQs less than 70. EPA multiplied this
percent by the population of exposed children to estimate the increased incidence of children with low IQs. As in the IQ point
loss equation, EPA applied the results of this function to children age 0-7 and divided by seven to avoid double counting.
(See discussion under equation 14.1.)
This procedure quantified only the change in the number of children who pass below the 70 point IQ threshold. EPA
quantified other changes in children's IQ using the IQ point loss function (Equation 14.1) described previously. Treating
these two endpoints additively does not result in double counting, because the value associated with the IQ point loss function
is the change in individual lifetime earnings, while the value associated with IQs less than 70 is the increased educational
costs for the individual, as discussed below.
»»» Valuing educational costs
EPA estimated the number of avoided cases of children with IQs less than 70. Compensatory education expenses will no
longer be incurred for these cases. Kakalik et al. (1 98 1), using data from a study prepared for the Department of Education's
Office of Special Education Programs, estimated part-time special education costs for children who remained in regular
classrooms at $3,064 extra per child per year in 1978. Adjusting for changes in the GDP price defla tor yielded an estimate
of $6,959 per child in 2001 dollars. EPA used the incremental estimate of the cost of part-time special education to estimate
the annual cost per child needing special education as a result of lead impacts on mental development. EPA assumed that
compensatory education begins at age seven and continues through age 18 (grades one through twelve). Discounting future
expenses at a rate of three percent yielded an expected present value cost of approximately $58,012 per child (2001 $). This
discounting underestimates the cost because Kakalik et al. measured the increased cost to educate children attending regular
school rather than a special education program. The costs of attending a special education program are likely to be much
higher than those associated with regular schooling. In addition, some compensatory education programs begin earlier than at
age seven. For example, some states, such as Connecticut and Rhode Island, offer Head Start programs to disadvantaged
children beginning at age three.
b. Children with PbB levels greater than 20 [Jg/dL
»»» Quantifying the number of children with PbB levels greater than 20 \ig/dL
EPA obtained the percentage of children with PbB levels greater than 20 Hg/dL directly from the estimated distribution of
PbB levels for a given location (IEUBK). EPA then multiplied this percentage by the number of exposed children in the
vicinity of a given MP&M reach to estimate the number of children with PbB levels greater than 20
*»* Estimating and valuing compensatory education for children with PbB levels greater than 20 \ig/dL
EPA assumed that 20 percent of the children with PbB levels greater than 20 Hg/dL would require and receive compensatory
education for three years. After this time, no further educational expenditures are incurred by those children. These
12 See Section 13.1.1 for detail on estimating the affected population. The percentage of children in the affected population is
estimated based on the Census data.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
assumptions are conservative. Many studies show adverse cognitive effects of PbB levels at 15 ng/dL (CDC, 1991b). Some
studies of the persistence of cognitive effects indicate that the effects often last longer than three years.
The Kakalik et al. (1981) estimate of part-time special education costs for children who remained in regular classrooms can
be used to estimate the cost of compensatory education for children suffering low-level cognitive damage. As indicated
above, the part-time special education cost per child is $6,959 per year in 2001 dollars. The Agency assumes that
compensatory education starts at age 7 and continues for 3 years. Discounting future costs at a rate of 3 percent annually to
account for the age at which costs are incurred (i.e., age 7 through 9) yields a present value estimate of $16,485 in 2001
dollars.
14.2.5 Changes in Neonatal Mortality
a. Quantifying the relationship between maternal PbB levels and neonatal mortality
U.S. EPA (1990) cites a number of studies linking fetal exposure to lead (via in utero exposure from maternal lead intake) to
several adverse health effects. These effects include decreased gestational age (i.e., premature birth), reduced birth weight,
late fetal death, and increases in infant mortality.
The CDC (CDC, 1991a) developed a method to estimate changes in infant mortality due to changes in maternal PbB levels
during pregnancy. The analysis linked the following two relationships:
*• gestational age as a function of maternal PbB (Dietrich et al., 1987), and
> infant mortality as a function of gestational age. This is performed using data from the Linked Birth and Infant
Death Record Project from the National Center for Health Statistics (CDC, 1991a).
Combining the two relationships provided a decreased risk of infant mortality of 10"4 (or 0.0001) for each 1 Hg/dL decrease in
maternal PbB level during pregnancy. EPA used this relationship for its analysis of maternal PbB levels and neonatal
mortality.
b. Valuing changes in neonatal mortality
This analysis used the estimated WTP for avoiding a mortality event to estimate the monetary benefit associated with
reducing risks of neonatal mortality. This analysis uses the $6.5 million (2001$) estimate of the value of a statistical life
saved recommended in the Guidelines for Preparing Economic Analysis (EPA, 2000a). For detail on valuing reduced
mortality risks see Section 13.2.1.
14.3 ADULT HEALTH BENEFITS
Lead exposure has been shown to have adverse effects on the health of adults as well as children. The quantified adult health
effects included in the benefits analysis all relate to lead's effects on BP.13 The estimated relationships between these health
effects and lead exposure differ between men and women. Quantified health effects include increased incidence of
hypertension (estimated for males only), initial CHD, strokes (initial CB A and BI), and premature mortality. This analysis
does not include other health effects associated with elevated BP, and other adult health effects of lead including
neurobehavioral and possible cancer effects.
13 Citing laboratory studies with rodents, U.S. EPA (1990) also presents evidence of the genotoxicity and/or carcinogenicity of lead
compounds. The animal lexicological evidence suggests that human cancer effects are possible, but dose-response relationships are not
currently available.
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MP&M EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Estimating adult health benefits from reduced exposure to lead requires analytic steps similar to those used in estimating
children's health benefits. These steps are:
»• estimate in-stream lead concentrations in the reaches affected by MP&M discharges;
»• estimate baseline and post-compliance adult dietary lead intake via fish consumption. The analysis of adult health
benefits from reduced exposure to lead via contaminated fish uses the results from water quality modeling efforts
described in Appendix I;
»• estimate changes in the PbB level distribution in the affected adult population;
»• estimate changes in health status in the affected population of adult men, and the monetary value of health benefits
from reduced lead discharges from MP&M facilities; and
> estimate changes in health status in the affected population of adult women, and the monetary value of health
benefits from reduced lead discharges from MP&M facilities.
Figure 14.2 depicts the above steps. Table 14.3 summarizes per-case costs of lead-related illnesses.
Illness
Hypertension3
CHDa'b
Stroke3
Low Birth
Weighf
Death ~ Any
Illness'
Gender
Male
Female
Male
Female
Male
Female
Female
Male
Female
Table 14.3
Cost per
Case (2001$)
$1,141
$1,141
$76,347
$76,347
$335,135
$251,351
$89,503
$6.5 Million
$6.5 Million
^^ ^,
: Pen-Case Costs of Lead-Related Illnesses
Cost Description
The cost estimates were derived by taking Krupnick et al.'s (1989) average annual
Care.
The costs were estimated (Wittels et al., 1990) for three CHDs (acute myocardial
infarction, uncomplicated angina pectoris, and unstable angina pectoris) for 5
years post-diagnosis using a three percent discount rate. The probability of medical
service was multiplied by the estimated price of the service and the average cost for
the three CHD types. Since the effect of elevated PbB on CHD incidence rates is
beyond the scope of this analysis, weighting factors were not used to account for the
different probabilities of contracting the three types of CHD. Value adjusted to 2001$
using the CPI for Medical Care.
The cost estimates (Taylor et al., 1996) represent the expected lifetime cost of a stroke
for males and females age 45-74, including the present discounted value of the stream
of medical expenditures and the stream of lost earnings. Note that the study used a
five percent discount rate. EPA did not adjust this value to reflect a 3 percent
discount rate used elsewhere in this analysis. Values adjusted to 2001 $ using the CPI
for Medical Care.
The cost estimate was extrapolated from direct costs for LBW taken from Lewitt et al.,
using a three percent discount rate (Lewitt et al., 1995). The value includes medical,
special education, and grade repetition costs. Value adjusted to 2001$ using the CPI
for Medical Care.
Value taken from U.S. EPA's Guidelines for Preparing Economic Analysis (2000a).
The value is the central estimate recommended in the document based on a range of
estimates available from studies measuring the value of a statistical life. Value
adjusted to 2001$ using the CPI for All Items.
" Costs were taken from U.S. EPA, 1997b.
b Extends methodology in U.S. EPA, 1997b to discount medical costs over a 5 year period.
c Note that this analysis does not estimate occurrence of low birth weight cases, due to data limitations. Cost was taken from U.S.
EPA, 1999.
d Value taken from U.S. EPA, 2000a.
Source: U.S. EPA analysis.; U.S. EPA 1997b; U.S. EPA 1999, U.S. EPA 2000a.
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MPcxM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Figure 14.2 Assessing Benefits to Adults from Reduced Lead Discharges from MP&M Facilities
Bise Me le id discharge s from MP&M
facflitfas
Post-c amp lianc e Is id discharges
from MP&M facilities
Baseline ambientwater
quality c conditions
Po st-c omplianc t
ambient water quality c onditions
Baseline distary lea.d intalse
Post-compUaruce dietary Is ad intake
via fish c on enjEnji tion
Estimats d base line b lood le id
distribution iiexposedadutts
Estinate d p ost-c ompliuu: e b ise liie b lood
Is id distribution in txp os ed adults
Baseline health sffects to adults
Post-c omp Uani: sheaKheffsctstoaduls
Changes in advers s he alh effects to aduls from base line to p ost-c omp liance
Hypertension
(mile srispecfisd
age ranges)
Non-f atal c oronary
lis art disease (CHD)
(males and females)
Hon-fatal stroke s
(males and females)
Be due e d mortally
Avoids d iHne ss costs
Vahis of lires
Monstary bsnsf its from rs due s d adult s xpo sure to Is ad
Source: U.S. EPA analysis.
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
14.3.1 Estimating Changes in Adult PbB distribution Levels
a. Estimating values of PbB concentrations in exposed adults
EPA adapted the methodology described in the Interim Approach to Assessing Risks Associated with Adult Exposure to Lead
in Soil (hereafter, Interim Guidance) to estimate changes in the distribution of PbB levels in exposed adults from reduced
MP&M discharges (U.S. EPA, 1996a). The methodology presented in the Interim Guidance used a simplified representation
of lead biokinetics to predict quasi-steady state PbB concentrations among adults who have relatively steady patterns of
exposures to lead. This methodology is recommended by the Technical Review Workgroup (TRW) to assess the effects
of ingesting lead-contaminated soil on PbB levels of women of childbearing age, to derive risk-based remediation goals
(RBRG) protective of the developing fetus in exposed adult women.14 The Interim Guidance describes the basic algorithms
to be used in the analysis and provides a set of default parameters that can be used in cases where site-specific data are not
available. The TRW points out that this methodology is an interim approach recommended for use pending further
development and evaluation of integrated exposure biokinetic models for adults.
The dose-response relationship recommended in the Interim Guidance for exposures to lead-contaminated soil can be
modified to analyze PbB levels in recreational and subsistence anglers exposed to lead-contaminated fish tissue. In both
cases, the exposure pathways involve ingestion. The Interim Guidance differs from this analysis mainly in the medium
containing lead (soil versus fish tissue). Substituting ingestion of lead in fish for ingestion of lead in soil yields the following
equation:
adult, central
= PbB
8)
'S)
adult.O
AT
where:
PbB adult central = central tendency estimate of PbB concentrations (ng/dL) in adults exposed to lead in fish at a
concentration of PbW;
PbB adlllt 0 = typical PbB concentration (fig/dL) in adults in the absence of exposures via fish consumption;
PbW = in-stream lead concentrations (fig/L);
BCF = bioconcentration factor of lead in fish tissue (L/kg);
INF = average daily fish consumption (g/day);
AFF = absolute gastrointestinal absorption fraction for ingested lead in fish tissue (dimensionless);
BKSF = biokinetic slope factor relating (quasi-steady state) increases in typical adult PbB concentrations to
average daily lead uptake (fig/dL PbB increase per mg/day lead uptake);
EF = exposure frequency for ingestion of contaminated fish (days of exposure during the averaging period);
may be taken as days per year for continuing, long-term exposure;
CF = conversion factor (10 3 kg/g); and
AT = averaging time, the total period during which fish consumption may occur; 365 days/year for
continuing long-term exposure.
Equation 14.8 is recommended for females aged 17 to 45 (U.S. EPA, 1996a). Studies of adult males, however, provided
many of the parameters used in the Interim Guidance. For example, the biokinetic slope factor (BKSF) relating increase in
typical adult blood concentrations to average daily lead uptake was developed on data reported by Pocock et al (1983). These
data characterize the relationship between tap water lead concentrations and blood lead concentrations for a sample of adult
males.15 Thus, EPA judged that this model can be applicable to all adults. Table 14.4 summarizes values for the model
parameters.
14 EPA's TRW for lead began considering methodologies to evaluate nonresidential adult exposure to lead in 1 994. A TRW
committee on adult lead risk assessment formed in January 1996 to develop a generic methodology that could be adapted for use in site-
specific assessments of adult health risks.
15 For detail, see p. A- 10, Recommendations of the Technical Review Workgroup for Lead to Assessing Risks Associated with Adult
Exposure to Lead in Soil (U.S. EPA, 1996a).
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
Table 14.4: Summary of Parameter Values for Estimating PbB Levels in Adults
Parameter
PbBadlllt>0
BKSF
INF
EF
BCF
AFF
Unit
Hg/dL
|ig/dL per
M- g/day
g/day
day/yr
L/kg
dimen-
sionless
Value
4.55-3.45
0.4
'
17.5
142.4
365
49
0.03
Comment "
Male adult PbB levels based on NHANES III Phase 2 (U.S. EPA, 1991-
1994). Female adult PbB levels based on NHANES III Phase 2 (U.S.
EPA, 1996a).
Based on analysis of Pocock et al. (1983) and Sherlock et al. (1984) data.
Daily fish consumption; lower value (on left) for recreational anglers and
higher value (on right) for subsistence anglers. Fish consumption rates for
adults are taken from the Methodology for Deriving Ambient Water Quality
Criteria for the Protection of Human Health (EPA, 2000b). Both these
rates, 142.4 g/day for adult subsistence anglers and 17.5g/day for adult
recreational anglers, are used for the specific sub -population that they
represent. EPA was not able to break these rates down by gender or age
group for use in this analysis.
Days per year for continual long-term exposure.
Bioconcentration factor of lead in fish tissue.
Absolute gastrointestinal absorption fraction for ingested lead in fish
tissue. Based on Maddaloni (1998).
a For detailed information on the sources of the parameters and uncertainties associated with their use, see U.S. EPA, 1996a.
Source: U.S. EPA analysis.
*»* Typical adult PbB concentrations at baseline
Previous research suggests males have a higher background PbB level (U.S. EPA, 1996a). This analysis uses population-
specific typical concentrations to account for differences in background lead exposure between genders and between two
socioeconomic subgroups considered in the analysis (i.e., recreational and subsistence fishermen). EPA used data for adult
males and females from NHANES III to characterize the baseline distribution of PbB concentrations in the relevant sub-
populations for each MP&M reach and affected population (NHANESIII, 1991-1994). The baseline PbB distribution
scenario reflects site-specific population characteristics because baseline PbB levels differ across ethnic, income, and urban
status groups.
*»* Bio availability of lead from fish tissue
To identify lead bioavailability in fish tissue, EPA reviewed lead absorption data from various materials reported in the lead
toxicity summary document: Draft Toxicological Profile for Lead (ATSDR, 1997). EPA also reviewed Measurement of
Soil-Borne Lead Bioavailability in Human Adults, and Its Application to Biokinetic Modeling (Maddaloni, 1998) and
consulted with the study author (March, 2000). Numerous studies have found that lead ingested with food is absorbed at a
significantly lower rate than lead ingested after fasting. The Interim Approach reports this dynamic and notes that "the
bioavailability of ingested soluble lead in adults varies from less than 10 percent when ingested with a meal to between 60 and
80 percent when ingested after a fast" (U.S. EPA, 1996a). TRW uses a 20 percent lead bioavailability factor for soil. This
factor is based on lead consumption interspersed with and between meals throughout the day, and is therefore likely to
overestimate PbB levels in adults exposed to lead-contaminated fish. In the absence of data on lead incorporated into food,
however, EPA considered this to be the most appropriate data to use in estimating absorption.
In the most recent study reviewed for this analysis (Maddaloni, 1998), non-fasted subjects showed a mean percent absorption
of 2.52 with a range of 0.2 to 5.2 percent and a confidence value of 0.66. The male and female study subjects had normal
clinical chemistry parameters and were between 21 and 40 years of age. The study used soil as the dosing vehicle. Other
studies have used water as the dosing vehicle, but soil is considered to be more similar to fish consumption.
EPA selected an absorption value of 3 percent for lead ingested in fish tissue, based on Maddaloni's results. The value of 3
percent provides a reasonable estimate for most adults. This analysis does not address individuals who may have higher lead
absorption, or are at elevated risk due to lead exposure. These individuals include pregnant women, who have higher calcium
requirements (and are therefore more likely to be calcium-deficient), people with poor nutritional status (including iron and
calcium deficiencies), and individuals with other metabolic disorders. By evaluating subsistence and recreational anglers at
proposal and for final rule options with lead benefits, the analysis is already focusing on sub-populations at higher risk than
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
the average population. To maintain an approach that represents likely exposures, intakes, and risks, EPA chose not to
consider individuals at unusually high risk within an already-high risk sub-population.
14.3.2 Male Health Benefits
This section describes the health effects of reduced lead exposure that this analysis has quantified for men; the next section
presents a similar discussion for women.
a. Hypertension
»»» Quantifying the relationship between PbB levels and hypertension
Studies have linked elevated PbB to elevated BP in adult males, especially men aged 40 to 59 (Pirkle et al., 1985). Further
studies have demonstrated a dose-response relationship for hypertension (defined as diastolic BP above 90 mm Hg for this
model) in males aged 20 to 74 (Schwartz, 1988). This relationship is:
kPr(HYP) = - (14.9)
V ' 1 2.144 - .793* (In PhS^ .. 2.744 - 0.793* (In PbBJ ^ '
L ' Q i < Q
where:
APr(HYP) = the change in the probability of hypertension,
e = base of the natural logarithm (2.76)
PbB! = PbB level in the baseline scenario, and
PbB2 = PbB level in the post-compliance scenario.
»»» Valuing reductions in hypertension
The best measure of the social costs of hypertension, society's WTP to avoid the condition, cannot be quantified without basic
research that is well beyond the scope of this project. Ideally, the measure would include all the medical costs associated with
treating hypertension, the individual's WTP to avoid the worry that hypertension could lead to a stroke or CHD, and the
individual's WTP to avoid the behavioral changes required to reduce the probability that hypertension leads to a stroke or
CHD.
This analysis used recent research results to quantify two benefit category components: medical costs and lost work time.
Krupnick and Cropper (1989) estimated the medical costs of hypertension, using data from the National Medical Care
Expenditure Survey. Medical costs include expenditures for physician care, drugs, and hospitalization. In addition,
hypertensives have more bed disability days and work-loss days than non-hypertensives of comparable age and sex. Krupnick
and Cropper estimated the increase in work-loss days at 0.8 per year. Valuing this estimate at the estimated mean daily wage
rate and adjusting the costs to 2001 dollars yields an estimate of the annual cost of each case of hypertension of $1,141.
The benefits estimate in this analysis likely underestimates the true social benefit of avoiding a case of hypertension for
several reasons:
* It does not include a measure of the value of pain, suffering, and stress associated with hypertension.
* It does not value the direct costs (out-of-pocket expenses) of diet and behavior modification (e.g., salt-free diets,
etc.). These costs, which are typical for severe modifications, are likely to be significant.
*• This analysis does not address the loss of satisfaction associated with the diet and behavior modifications.
*• This analysis does not include the value of avoiding side effects associated with the medication for hypertension,
which include drowsiness, nausea, vomiting, anemia, impotence, cancer, and depression.
> The analysis does not include the effects of the disease on family members.
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MPAM EEBA Part III: Benefits
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b. Changes in CHD
*»* Quantifying the relationship between PbB and BP
EPA quantified the effect of changes in PbB levels on changes in BP to predict the probability of both hypertension and other
cardiovascular illnesses, such as CHD, strokes, and premature mortality. Several cardiovascular illnesses include PbB as a
risk factor (Shurtleff, 1974; McGee and Gordon, 1976; PPRG, 1978). Based on the results of a meta-analysis of several
studies, Schwartz (1992) estimated a relationship between a change in BP associated with a decrease in PbB from 10 Hg/dL to
5 fig/dL. The following equation uses the coefficient reported by Schwartz to relate BP to PbB for men:
PbB,
— -
PbB
(14 10)
where:
ADBPmen = the change in men's diastolic BP expected from a change in PbB;
PbB! = PbB level in the baseline scenario (in fig/dL); and
PbB2 = PbB level in the post-compliance scenario (in fig/dL).
EPA used this PbB to BP relationship to estimate the incidence of initial CHD, strokes (BI and initial CBA), and premature
mortality in men.
*»* Quantifying the relationship between BP and CHD
This analysis used estimated BP changes to predict the increased probability of initial CHD and stroke occurrence (U.S. EPA,
1987). Increased BP also increases the probability of CHD and stroke recurrence, but EPA did not quantify these
relationships in this analysis. An equation with different coefficients for each of three age groups can predict first-time CHD
events in men. A 1978 study by the PPRG supplied information for men between ages 40 and 59. PPRG used a
multivariate model (controlling for smoking and serum cholesterol) relating the probability of CHD to BP. The model used
data from five different epidemiological studies. The equation for the change in 10-year probability of a first-time occurrence
of CHD related to an increase in BP is:
&Pr(CHDM „) =
W-W
1 + e
4996 _ 0.030365* DSP,
4.996 - 0.030365 * DBF,
where:
APr(CH D40_59)
DBP[
DBP2
the change in 10-year probability of an occurrence of a CHD event for men between ages 40 and
59;
mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for subsistence and recreational fishermen aged 40 to 59 is 81.8 and 80.0, respectively; and
mean diastolic BP in the post-compliance scenario.
Information presented in Shurtleff (1 974) helped define the relationship between BP and first-time CHD in older men. This
study used data from the Framingham Study (McGee and Gordon, 1976) to estimate univariate relationships between BP and
a variety of health effects, by sex and for three age ranges: 45 to 54, 55 to 64, and 65 to 74 years. The study performed single
composite analyses for ages 45 to 74 for each sex. For every equation, t-statistics on the BP variable are significant at the
99th percent confidence interval. EPA predicted first-time CHD related to an increase in BP for men aged 60 to 64 from the
following equation:
where:
APr(CHD60_64)
DBP(
DBP2
1 + e
4995 _ 0.030365* DSP,
4.996 - 0.030365 * DSP,
the change in 2-year probability of occurrence of a CHD event for men aged 60 to 64;
mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for subsistence and recreational fishermen aged 60 to 64 is 79.5 and 77.8, respectively; and
mean diastolic BP in the post-compliance scenario.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
The following equation uses data from Shurtleff (1974) to predict the probability of first-time CHD related to an increase in
BP for men aged 65 to 74:
65 ~
1 4.90723 - 0.02031 *DBPl 1 4.90723 - 0.0203 1*DBP2
where:
APr(CHD65.74) = the change in 2-year probability of occurrence of a CHD event for men aged 65 to 74;
DBP( = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for subsistence and recreational fishermen aged 65 to 74 is 79.5 and 76.4, respectively; and
DBP2 = mean diastolic BP in the post-compliance scenario.
EPA used the above equations to estimate the number of CHD events avoided in a given year due to water quality
improvements from reduced MP&M lead discharges. The resulting CHD incidence estimates include both fatal and non-fatal
events. Only the non-fatal CHD events are considered here because mortality benefits are estimated independently in this
analysis (see Section 14.3.2.d, below). Shurtleff (1974) reported that two-thirds of all CHD events were non-fatal. This
factor was therefore applied to the estimate of avoided CHD events due to reductions in PbB and BP for each age category.
»»» Valuing reductions in CHD events
EPA first estimated the number of CHD events avoided each year by multiplying the number of exposed recreational and
subsistence anglers in the relevant age group by the change in annual probability of a CHD event. Changes in annual
probability of CHD events for different age groups are calculated by dividing the change in probability over ten- and two-year
periods by the relevant number of years.
EPA then used the central tendency estimate of the COI associated with pollution-related CHD to estimate the benefits of
avoiding an initial CHD event. The cost estimates (Wittels et al., 1990) represent the weighted medical costs of three separate
CHDs (acute myocardial infarction, uncomplicated angina pectoris, unstable angina pectoris), experienced within five years
of diagnosis. EPA estimated the costs by multiplying the probability of a medical test or treatment (within five years of the
initial CHD event) by the estimated price of the test or treatment.16 The estimated cost for acute myocardial infarction was
then reduced by 23%, which represents the proportion of cases that go unrecognized by the patient and therefore do not result
in any medical costs (based on Hartunian et al., 1981). EPA used a three percent discount rate to calculate the present value of
these costs. EPA then calculated the final cost estimate by taking the simple average of the three CHD types. The central
tendency estimate of the COI associated with a case of pollution-related CHD is about $76,347 (2001$).
This estimate likely underestimates the full COI because it does not include lost earnings. It likely underestimates total WTP
to avoid CHD to an even greater extent because it does not include WTP to avoid the pain and suffering associated with the
CHD event.
This analysis combined the value of reducing CHD events with the value of reducing hypertension, even though these
conditions often occur together. The two values represent different costs associated with the conditions. The valuation for
hypertension includes hypertension-associated work day loss and medical costs. CHD valuation is based on the medical costs
for treatment associated with the CHD itself. EPA estimated these two values separately and added them together.
c. Changes in initial C&A and initial BI
»»» Quantifying the relationship between BP and first-time stroke
Strokes include two types of health events: initial CBA and initial BI. The risk of CBA has been quantified for the male
population between 45 and 74 years old (Shurtleff, 1974). For initial CBA, the equation is:
16 EPA obtained costs from Appendix G oftheBeneflts and Costs of the Clean Air Act: 1970 to 1990, prepared for U.S. Congress by
U.S. EPA, Office of Air and Radiation and Office of Policy, Planning, and Evaluation, 1997b.
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
1
1
,
1 + e
8.58889 - 0.04066* DSP,
8.58889 - 0.04066* DBF,
where:
APr(CBAmen) =
DBP( =
DBP2 =
the change in 2-year probability of CBA in men;
mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP for
subsistence and recreational fishermen aged 45 to 74 is 81.1 and 78.8, respectively; and
mean diastolic BP in the post-compliance scenario.
For initial BI, the equation is (Pirkle et al., 1985):
(14.15)
t
1 +
9.9516 - 0.04840*
..
1 +
9.9516 - 0.04840 * DBF,
where:
APr(BImen)
DBP(
DBP2
the change in 2-year probability of brain infarction in men;
mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP for
subsistence and recreational fishermen aged 45 to 74 is 81.1 and 78.8, respectively; and
mean diastolic BP in the post-compliance scenario.
Similarly to CHD events, this analysis estimates only non-fatal strokes to avoid double-counting with premature mortality.
Shurtleff reported that 70 percent of strokes were non-fatal. EPA applied this factor to the estimates of both CBA and BI to
ensure that the estimate of avoided CBA and BI events included only non-fatal events (Shurtleff, 1974).
»»» Valuing reductions in strokes
Similarly to CHD events, EPA first calculates the number of avoided strokes per year and then uses the estimated lifetime cost
of a stroke to value reductions in strokes. Taylor et al. estimated the lifetime cost of stroke, including the present value (in
1990 dollars) of the stream of medical expenditures and the present discounted value of the stream of lost earnings, using a
five percent discount rate (Taylor et al., 1996). The estimated expected lifetime cost of a non-fatal stroke for males aged 45
to 74 is 335,135 (2001$). 17
d. Changes in premature mortality
*»* Quantifying the relationship between BP and premature mortality
It is well established that elevated BP increases the probability of premature death. There are, however, several underlying
conditions that cause elevated BP (e.g., cholesterol level). U.S. EPA (1987) used population mean values for serum
cholesterol and smoking to reduce results from a 12-year follow-up of men aged 40 to 54 in the Framingham Study (McGee
and Gordon, 1976) to an equation with one explanatory variable (DBP):
4) =
4'
(14.16)
1 + e
5315g _ o.03516*£>BP,
..
1 + e
5.3158 - 0.03516*Z>BP,
where:
APr(MORT40_54) =
DBP( =
DBP =
the change in 12-year probability of death formen aged 40 to 54;
mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for subsistence and recreational fishermen aged 40 to 54 is 81.9 and 79.9, respectively; and
mean diastolic BP in the post-compliance scenario.
17 EPA obtained cost from Appendix G of the Benefits and Costs of the Clean Air Act: 1970 to 1990, prepared for U.S. Congress by
U.S. EPA, Office of Air and Radiation and Office of Policy, Planning, and Evaluation, 1997b.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
This analysis used information from Shurtleff (1 974) to estimate the probability of premature death in men older than 54
years. The present analysis estimates a two-year probability based on the Shurtleff study's two-year follow-up period. EPA
predicted mortality for men aged 55 to 64 years old using the following equation:
4.89528 - 0.01866* DSP, t 4.89528 - 0.01866*£>BP,
1 + e i 1 + 6 2
where:
APr(MORT55.64) = the change in two-year probability of death in men aged 55 to 64;
DBP( = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for subsistence and recreational fishermen aged 55 to 64 is 80.6 and 79.0, respectively; and
DBP2 = mean diastolic BP in the post-compliance scenario.
Using data from Shurtleff (1974), EPA predicted premature mortality for men aged 65 to 74 using the following equation:
&Pr(MORT65 _4) =
v 65-74' . - ., t
1 + e ' 1 +
where:
APr(MORT65.74) = the change in two-year probability of death in men aged 65 to 74;
DBP( = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for subsistence and recreational fishermen aged 65 to 74 is 79.5 and 76.4, respectively; and
DBP2 = mean diastolic BP in the post-compliance scenario.
»»» Valuing reductions in premature mortality
Similarly to health outcomes discussed in the preceding sections, EPA first estimated changes in annual probability of
premature mortality for men in different age groups. The Agency then calculated avoided premature death cases by
multiplying the estimated change in annual probability of premature mortality by the relevant population. This analysis uses
the $6.5 million (2001$) estimate of the value of a statistical life saved recommended in the Guidelines for Preparing
Econom ic Analysis (EPA, 2000a). This value is based on WTP to avoid the risk of death.
The values of avoiding CHD, BA, and BI events are all based on COI estimates associated with a non-fatal health event. On
the other hand, the value of the change in premature mortality is based on the value of avoiding a health event that does end in
death. Thus, these two endpoints are additive.
14.3.3 Female Health Benefits
Recently expanded analysis of data from NHANES II by Schwartz indicates a significant association between PbB and BP in
women (Schwartz, 1990). Another study, by Rabinowitz et al. (1987), found a small but demonstrable association between
maternal PbB, pregnancy hypertension, and BP at time of delivery.
a. Relationship between BP and PbB
Although women are at risk for lead-induced hypertension, no dose-response function for hypertension in women is available
at this time. Therefore, the Agency did not quantify changes in risk for lead-induced hypertension in women for this analysis.
This analysis used an adjusted dose-response function for a change in BP associated with a decrease in PbB in men (Equation
14.10) to estimate lead-induced changes in blood pressure in women. Equation 14.19 is used to provide input values for the
analyses discussed in the following sections.
A review often published studies examined the effect of lead exposure on the BP of women, relative to the effect on men
(Schwartz, 1992). All of the reviewed studies included data for men; some included data for women. Schwartz used a
concordance procedure that combined data from each study to predict the decrease in diastolic BP associated with a decrease
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
from 10 Hg/dL to 5 Hg/dL PbB (Schwartz, 1992). The results suggest that when PbB is decreased, women experience aBP
change that is 60 percent of the change seen in men. Equation (14.10) can be rewritten for women as:
PbBl
(14.19)
,„ = (0.6 x 1.4) x
where:
ADBPwomen = the change in women's diastolic BP expected from a change in PbB;
PbB( = PbB level in the baseline scenario; and
PbB2 = PbB level in the post-compliance scenario.
b. Changes in CHD
*»* Quantifying the relationship between BP and CHD
Elevated BP in women results in the same effects as for men (CHD, two types of stroke, and premature death). However, the
general relationships between BP and these health effects are not identical to the dose-response functions estimated for men.
All relationships presented here have been estimated for women aged 45 to 74 years old using information from Shurtleff
(1974). EPA estimated first-time CHD related to an increase in BP in women using the following equation:
APr(CHD ) = - (14-20)
v women-' 69401 _ 0.03072*DBP, , 6.9401 - 0.03072*DBP2
1 + 6 ' 1 + 6 2
where:
APr(CHDwomen) = change in 2-year probability of occurrence of CHD event for women aged 45-74;
DBP! = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP
for women in subsistence and recreational households aged 45 to 74 is 76.5 and 74.8, respectively;
and
DBP2 = mean diastolic BP in the post-compliance scenario.
EPA estimated non-fatal CHD events by assuming that two-thirds of all estimated CHD events are not fatal (Shurtleff, 1974).
»»» Valuing reductions in CHD events
The Agency first calculated the number of avoided CHD events for women using Equation 14.20. EPA assumed that values
of reducing CHD events for women equal those calculated for men (above): $76,347 (2001$) per CHD event.
c. Changes in BI and initial CBA
*»* Quantifying the relationship between BP and first-time stroke
EPA predicted the relationship between BP and initial CBA for women using the following equation:
APr(CBA ) = - (14.21)
v women ' 9.07737 - 0.04287 * DBP, , 9.07737 - 0.04287 * DBP2
1 + 6 1 + 6
where:
APr(CBAwomen) = change in two-year probability of cerebrovascular accident in women aged 45 to 74;
DBP[ = mean diastolic BP in the baseline scenario; and
DBP2 = mean diastolic BP in the post-compliance scenario.
The following equation illustrates the relationship between BI and initial BI in women:
\ = 1 _ 1 (14.22)
women' 10.6716 - 0.0544* DBP, , 10.6716 - 0.0544*£>BP,
1 + e ' 1 + e 2
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
where:
APr(BIwomen) = change in 2-year probability of brain infarction in women aged 45 to 74;
DBP( = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic BP for
women in subsistence and recreational households aged 45 to 74 is 76.5 and 74.8, respectively; and
DBP2 = mean diastolic BP in the post-compliance scenario.
EPA multiplied the predicted incidences of avoided BI and CBA by 70 percent to estimate only non-fatal strokes (Shurtleff,
1974).
»»» Valuing reductions in strokes
EPA calculated the value of avoiding an initial CBA or an initial BI for women in the same way as for men (see above). EPA
predicted lead-related stroke for women in the United States between the ages of 45 and 74, of whom 38.2 percent are aged
45 to 54 and the remaining 61.8 percent are aged 55-74. Using the gender- and age-specific values in Taylor et al. (1996),
EPA estimated the average value of avoiding a stroke among women aged 45 to 74 to be about $25 1,351 (2001 $).
d. Changes in premature mortality
»»» Quantifying the relationship between BP and premature mortality
The following equation estimates the risk of premature mortality in women (Shurtleff, 1974):
APr(MORT ) = - (14 23)
v women' 5.40374 - 0.0\5U*DBP, , 5.40374 - 0.0\5U*DBP.,
1 + e ' 1 + e 2
where:
APr(MORTwomen) = the change in two-year probability of death for women aged 45 to 74;
DBP( = mean diastolic BP in the baseline scenario; based on the Phase 2 NHANES III, mean diastolic
BP for women in subsistence and recreational households aged 45 to 74 is 76.5 and 74.8,
respectively; and
DBP2 = mean diastolic BP in the post-compliance scenario.
»»» Valuing reductions in premature mortality
EPA predicted changes in lead-related premature mortality for women in the same way as for men (see above). EPA assumed
the value of reducing premature mortality in women to be equal to that estimated for all premature mortality, $6.5 million
(2001$) per incident (see Section 13.2.1).
14.4 LEAD-RELATED BENEFIT RESULTS
This section describes the estimated benefits of reduced lead exposure from consumption of fish in three populations: (1)
preschool age children, (2) pregnant women, and (3) adult men and women. Benefit estimates for pregnant women appear
with those for preschool age children, because the beneficiaries in this category are children under the age of one who suffer
in utero fetal lead exposure from maternal lead intake during pregnancy. EPA estimated that the final regulation will yield no
benefits to children or adults from reduced exposure to lead. Alternative regulatory options considered by EPA were
estimated to yield benefits from reduced exposure to lead. The following discussion reviews the estimated benefits from these
alternative options.
14.4.1 Preschool Age Children Lead-Related Benefit Results
EPA analyzed the monetary value of health benefits to children from reduced lead exposure in four categories:
•• reduced neo-natal mortality,
•• avoided IQ loss,
•• reduced incidence of IQ below 70, and
*• reduced incidence of PbB levels above 20 fig/dL.
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
From this analysis, EPA estimated that the final rule will yield no lead-related benefits to children.
Other regulatory options considered by EPA were found to yield lead-related benefits to children. Table 14.5 summarizes
lead-related benefits estimated for the 433 Upgrade Options. EPA estimated that the Directs + 413 to 433 Upgrade Option
and the Directs + All to 433 Upgrade Option would reduce 0.15 and 0.17 cases of neonatal mortality, and avoid the loss of 32
and 36 IQ points, respectively. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would
result in $1.3 and $1.5 million (2001$) in annual lead-related benefits for children, respectively.
Table 14.5: National Annual Benefits from Reduced Lead in Children (2001$) — 433 Upgrade Options"
Category
Neonatal mortality
Avoided IQ Loss
Reduced IQ < 70
Reduced PbB > 20 ng/L
Total Benefits
Directs + 413 to 433 Upgrade
Reduced Cases
or IQ Points
0.15
31.99
0.11
0.00
Benefit Value
(2001$)
$995,630
$301,323
$6,637
$0
$1,303,590
Directs + All to 433 Upgrade
Reduced Cases
or IQ Points
0.17
36.19
0.13
0.00
Benefit Value
(2001$)
$1,109,294
$340,845
$7,501
$0
$1,457,640
a Based on the Traditional Extrapolation.
Source: U.S. EPA analysis.
Table 14.6 summarizes lead-related benefits estimated for the Proposed/NODA Option. EPA estimated that the
Proposed/NODA Option would reduce 1.60 cases of neonatal mortality and avoid the loss of 1,078 IQ points. Annual lead-
related benefits for children equal $20.8 million (2001$) under the Proposed/NODA Option, which substantially exceeds
estimated lead-related benefits for children under the two 433 Upgrade Options.
Table 14.6: National Annual Benefits from Reduced Lead in Children (2001$) — Proposed/NODA Option"
Category
Neonatal mortality
Avoided IQ Loss
Reduced IQ < 70
Reduced PbB > 20 ng/L
Total Benefits
Reduced Cases or IQ Points
1.60
1,078.38
3.72
0.00
Benefit Value (2001$)
$10,417,781
$10,157,286
$216,007
$0
$20,791,073
a Based on the Traditional Extrapolation.
Source: U.S. EPA analysis.
The results from the estimated lead-related benefits for children are conservative, because this analysis omits other lead-
related impacts, such as the cost of group homes and other special care facilities. Table 14.1 presents other omitted benefits
categories. Section 14.5 discusses uncertainty and limitations inherent in this analysis.
14.4.2 Adult Lead-Related Benefit Results
As discussed previously, EPA quantified only the lead-related health effects in adults that relate to lead's effect on BP. These
health effects include increased incidence of hypertension, initial non-fatal CHD, non-fatal stokes (CBA and BI), and
premature mortality. EPA used COI estimates (i.e., medical costs and lost work time) to estimate monetary values for
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
reduced incidence of hypertension, initial CHD, and strokes. EPA based monetary values for changes in risk of premature
mortality on estimates of the value of a statistical life saved. The results are conservative estimates, because this analysis does
not include other health effects associated with elevated BP or with lead. Other effects of lead in adults can include nervous
system disorders, anemia, and possible cancer effects.
From this analysis, EPA estimated that the final rule will yield no lead-related health benefits to adults.
Other regulatory options considered by EPA were found to yield lead-related benefits to adults. Table 14.7 summarizes lead-
related benefits estimated for the 433 Upgrade Options. EPA estimated that the Directs + 413 to 433 Upgrade Option and the
Directs + All to 433 Upgrade Option respectively would reduce hypertension among males by 53 and 60 cases annually. Both
the 433 Upgrade Options would also reduce the annual incidence of premature mortality among men and women by
approximately 0.1 cases. EPA estimated annual lead-related benefits for adults under the Directs + 413 to 433 Upgrade
Option at $0.70 million (2001$) and under the Directs + All to 433 Upgrade Option at $0.79 million (2001 $).
Table 14.7: National Adult Lead Annual Benefits (2001$) — 433 Upgrade Options" "
Category
Men
Women
Hypertension
CHD
CBA
BI
Mortality
CHD
CBA
BI
Mortality
Total Benefits
Directs + 413 to 433 Upgrade
Reduced Cases
53.47
0.05
0.02
0.01
0.07
0.02
0.01
0.01
0.02
Mean Value of
Benefits
$61,004
$4,155
$5,698
$3,226
$474,735
$1,662
$2,417
$1,487
$150,190
$704,574
Directs + All to 433 Upgrade
Reduced Cases
59.58
0.06
0.02
0.01
0.08
0.02
0.01
0.01
0.03
Mean Value of
Benefits
$67,982
$4,631
$6,350
$3,596
$529,125
$1,853
$2,694
$1,658
$167,417
$785,304
a Based on the Traditional Extrapolation.
b National Level Exposed Population:
(1) Directs + 413 to 433 Upgrade
Hypertension: 139,745 men ages 20 to 74;
CHD, CBA, BI, and mortality: 56,564 men and 62,666 women ages 45-74.
(2) Directs + 413 + 50% LL Upgrade
Hypertension: 139,745 men ages 20 to 74;
CHD, CBA, BI, and mortality: 56,564 men and 62,666 women ages 45-74.
Source: U.S. EPA analysis.
Table 14-8 summarizes lead-related benefits estimated for the Proposed/NODA Option. EPA estimated that this option
would reduce hypertension among males by approximately 545 cases and the incidence of premature mortality among men
and women by 0.96 cases annually. Lead-related benefits for adults under the Proposed/NODA Option would be $7.05
million annually, which substantially exceeds estimated benefits under the two 433 Upgrade Options.
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Chapter 14: Lead-Related Benefits
Table 14.8: National Adult Lead Annual Benefits (2001$) -
Proposed/NODA Option" "
Category
Men
Women
Hypertension
CHD
CBA
BI
Mortality
CHD
CBA
BI
Mortality
Total Benefits
Reduced Cases
545.25
0.54
0.17
0.10
0.73
0.22
0.10
0.06
0.23
Mean Value of Benefits
$622,126
$41,564
$56,907
$32,197
$4,750,132
$16,472
$23,928
$14,714
$1,489,984
$7,048,025
a Based on the Traditional Extrapolation.
b National Level Exposed Population:
Hypertension: 539,142 men ages 20 to 74;
CHD, CBA, BI, and mortality: 218,226 men and 241,768 women ages 45-74.
Source: U.S. EPA analysis.
14.5 LIMITATIONS AND UNCERTAINTIES
This section discusses limitations and uncertainties in the lead-related benefits analysis. Developing dose-response functions
depends on relating lead exposure to PbB levels, then evaluating PbB levels in relation to specific health outcomes.
Quantitative dose-response functions for most health effects associated with lead exposure currently do not exist. For this
reason, the analysis does not provide a comprehensive estimate of health benefits from reduced lead discharges from MP&M
facilities.
Table 14.1 summarizes quantified and non-quantified health effects. Economic research does not always yield a complete
evaluation, even for those effects that can be quantified. This uncertainly is likely to bias the estimate of lead-related benefits
of the MP&M regulation downward. The analysis methodologies used here also involve significant simplifications and
uncertainties. Section 13.3 discusses similar limitations and uncertainties associated with the assessment of risk associated
with non-lead-related human health hazards and the possible direction of bias associated with sample design and benefits
analysis by:
•• occurrence location,
»• estimated in-waterway concentrations of MP&M pollutants, and
»• estimated exposed fishing population.
The next five sections discuss other omissions, biases, and uncertainties in the lead-benefit analysis. Table 14.9 provides a
summary of this discussion.
14.5.1 Excluding Older Children
Recent research on brain development among 10- to 18-year-old children shows unanticipated and substantial growth in brain
development, mainly in the early teenage years (Giedd et al., 1999). This growth appears to be a second "burst" of cell
development in some brain areas, in addition to the previously recognized period of rapid growth during early childhood.
One of lead's fundamental effects is to disrupt the protective coating (myelin) on nerve cells. This disruption can lead to
permanent impairment if it occurs during development. New research suggests that older children may be a hypersensitive
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
sub-population, along with children aged 0 to 7. Excluding this sub-population from the analysis may significantly
underestimate benefits from reduced lead discharges.
14.5.2 Compensatory Education Costs
This analysis assumes that compensatory education is required only for children with IQs less than 70, and that part-time
special education costs are assumed to be incurred only from grades 1 through 12 (Section 14.2.4). This assumption
underestimates compensatory education costs for the following reasons:
•• Children with IQ scores between 70 and 85 will likely be assigned to special education or "slow" classes that will
likely be smaller than regular classes and require more teacher attention. Children in this IQ range may frequently
require more than 12 years to graduate and are more likely to drop out of school. Such children therefore require
additional education costs.
*• Compensatory education may begin before grade one. Some states (e.g., Connecticut) offer compensatory education
programs for disadvantaged children beginning at age three.
This analysis is based on a study that measured the increased cost to educate children with low IQs attending a regular school,
not a special education program (Kakalik et al., 1981). The cost to attend a special education program is generally much
higher than that for regular schooling.
Some overlap may exist between estimates of the avoided costs of compensatory education due to reduced incidence of
children with IQ below 70 and PbB levels above 20 ng/dL because children with PbB levels may also have low IQ scores.
Estimating the magnitude of this overlap is, however, not feasible due to data paucity. In addition, the estimated avoided cost
of compensatory education due to reduced incidence of children with PbB levels above 20 ng/dL is negligible compared to
other benefits from reduced exposure to lead. Thus, this overlap does not introduce a significant bias in the estimate of total
benefits from reduced exposure to lead to children.
14.5.3 Dose-Response Relationships
The dose-response functions described for each health outcome considered above generally quantify the adverse health effects
expected from increased lead exposure. For children, these effects are defined in terms of changes in PbB. For adults, these
effects are estimated in terms of changes in BP, which are in turn related to changes in PbB levels. Uncertainty is inherent in
the dose-response functions, which are typically expressed in terms of the standard deviations of the dose-response
coefficients used in the analysis. Any uncertainty affecting the dose-response coefficients will also indirectly affect the
accuracy of this analysis.
14.5.4 Absorption Function for Ingested Lead in Fish Tissue
Numerous research groups have evaluated lead absorption under a variety of conditions. ATSDR reports a range of three
percent to 45 percent in the studies they present, which consider lead intake with and without food (ATSDR, 1997).
Absorption appears to be affected by total lead intake, with some studies showing a higher absorption proportion with higher
doses. Animal studies show a saturation effect, which modifies absorption.
Lead's chemical form also determines its absorption rate. For example, lead sulfide has approximately 10 percent of the
bioavailability of lead acetate (ATSDR, 1997). Particle size and solubility are also important absorption factors. EPA could
not obtain data to describe lead's precise chemical form, particle size, and other physical parameters in fish tissue, which
would allow more refined absorption estimates. These characteristics vary because MP&M facilities produce lead using
different processes and release it in different forms.
An individual's nutritional status also affects lead absorption rates. People who are malnourished, particularly with respect to
calcium and iron, have high absorption rates (ATSDR, 1997). EPA assumed that anglers were not malnourished, and made
no adjustment for their nutritional status. See the section on lead absorption in Maddaloni (1998) for a discussion of factors
influencing absorption. In the absence of data on lead incorporated into food, EPA considered data from studies of lead
absorption during meals to be the most appropriate data to use in estimating absorption.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
14.5.5 Economic Valuation
This analysis used IQ differentials to represent cognitive damage to children resulting from lead exposure. The economic
analysis relates IQ level to annual earnings, which serve as the basis for valuing benefits from reduced lead exposure. IQ
differentials are used rather than WTP, the preferred measure to use, because WTP values to avoid cognitive damage are not
available. This analysis likely underestimates the value of an IQ point because special education and lost wages form only a
portion of the costs associated with lost cognitive functioning. A simple IQ change analysis does not capture all the ways in
which a child, family, and society are affected by the effects of lead-induced cognitive damage.
Dollar values associated with most of the adult health and welfare endpoints represent only some components of society's
WTP to avoid these health effects. EPA used COI estimates to value reductions in CHD events, strokes, and hypertension.
These values are likely to be downward-biased because the value of pain and suffering avoided is not included. Employed
alone, these monetized effects will underestimate society's WTP.
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Table 14.S
Omissions/Biases/
Uncertainties
Excluding older children
Compensatory education
costs
Dose-response relationship
Absorption factor for lead in
fish tissue
Economic valuation
Overall impact
: Key Omissions, E
Directional
Impact on
Benefits
Estimates
downward
uncertain
uncertain
uncertain
downward
downward
Siases, and Uncertainties in the Lead-Benefit Analysis
Comments
New research suggests that older children may be a hypersensitive sub-
population, as children aged 0 to 7 are now considered. Excluding this sub-
population from the analysis may significantly underestimate benefits from
reduced lead discharges.
Assuming that compensatory education is required only for children with IQs
less than 70 and that part-time special education costs are incurred from grades 1
through 12 underestimates the special education costs because:
^ Children with IQ scores between 70 and 85 will likely be assigned to special
education or "slow" classes, requiring more teacher attention, and taking
longer to graduate or dropping out altogether.
^ Compensatory education may begin before grade one.
^ The cost to attend a special education program is generally much higher
than that for regular schooling.
A potential overlap exists between estimates of the avoided costs of
compensatory education due to reduced incidence of children with IQ below 70
and PbB levels above 20 g/dL because children with PbB levels may also have
low IQ scores. This overlap may introduce an upward bias in the estimate of the
lead-related benefits to children. This bias is, however, negligible due to the
magnitude of the avoided compensatory education cost estimates.
Uncertainty is inherent in the dose-response functions (expressed in changes in
PbB for children, changes in BP for adults). Any uncertainty affecting the dose-
response coefficients will also indirectly affect the accuracy of this analysis.
Absorption rate appears to be affected by:
^ total lead intake, with some studies showing a higher absorption proportion
with higher doses;
^ lead's chemical form. Because MP&M facilities produce lead using
different processes and release it in different forms, EPA could not obtain
data to describe lead's precise chemical form, particle size, and other
physical parameters in fish tissue, which would allow more refined
absorption estimates;
^ an individual's nutritional status; and
^ time of lead ingestion. In the absence of data on lead incorporated into
food, EPA considered data from studies of lead absorption during meals to
be the most appropriate data to use in estimating absorption.
The values associated with cognitive damage to children and adult health effects
are likely to be downward-biased. For children, a simple IQ change analysis
does not capture all effects of lead-induced IQ loss on a child, family, and
society. The valuation of adults' health effects from lead exposure do not
include the value of avoided pain and suffering. Employed alone, these
monetized effects will underestimate society's WTP.
Source: U.S. EPA analysis.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
GLOSSARY
absolute gastrointestinal absorption fraction: the fraction of lead in food ingested daily that is absorbed from the
gastrointestinal tract.
acute toxicity: the ability of a substance to cause severe biological harm or death soon after a single exposure or dose.
Also, any poisonous effect resulting from a single short-term exposure to a toxic substance.
(http://www.epa.gov/OCEPAterms/aterms.html)
angina pectoris: a syndrome characterized by paroxysmal, constricting pain below the sternum, most easily precipitated
by exertion or excitement and caused by ischemia of the heart muscle, usually due to a coronary artery disease, as
arteriosclerosis, (www.infoplease.com)
arithmetic mean: the mean obtained by adding several quantities together and dividing the sum by the number of
quantities, (www.infoplease.com)
atherothrombotic brain infarctions (Bl): scientific name for a stroke.
bioavailability: degree of ability to be absorbed and ready to interact in organism metabolism.
(http://www.epa.gov/OCEPAterms/bterms.html)
biokinetics: the study of movements of or within organisms, (www.infoplease.com)
biomarker: a physical, functional, or biochemical indicator of a certain process or event. It is commonly used to measure
the progress of a disease, the effects of treatment, or the status of a condition.
blood lead (PbB): concentration level of lead in blood stream; usually expressed in fig/dL.
blood pressure: the pressure of the blood against the inner walls of the blood vessels, varying in different parts of the body
during different phases of contraction of the heart and under different conditions of health, exertion, etc.
(www.infoplease.com)
central tendency estimate: major trend in group of data.
cerebrovascular accident (CBA): stroke.
coronary heart disease (CHD): disorder that restricts blood supply to the heart; occurs when coronary arteries become
narrowed or clogged due to the build up of cholesterol and fat on the inside walls and are unable supply enough blood to the
heart.
diastolic: pertaining to or produced by diastole, or (of blood pressure) indicating the arterial pressure during the interval
between heartbeats, (www.infoplease.com)
discounting: degree to which future dollars are discounted relative to current dollars. Economic analysis generally
assumes that a given unit of benefit or cost matters more if it is experienced now than if it occurs in the future. The present is
more important due to impatience, uncertainty, and the productivity of capital. This analysis uses a three percent discount rate
to discount future benefits, (http://www.damagevaluation.com/glossary)
dose response: shifts in lexicological responses of an individual (such as alterations in severity) or populations (such as
alterations in incidence) that are related to changes in the dose of any given substance.
dose-response assessment: 1. Estimating the potency of a chemical. 2. hi exposure assessment, the process of
determining the relationship between the dose of a stressor and a specific biological response. 3. Evaluating the quantitative
relationship between dose and toxicological responses.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
dose-response curve: graphical representation of the relationship between the dose of a stressor and the biological
response thereto.
dose-response functions: see dose-response relationship.
dose-response relationship: the quantitative relationship between the amount of exposure to a substance and the extent
of toxic injury or disease produced. (http://www.epa.gov/OCEPAterms/dterms.html)
encephalopathy: any brain disease, (www.infoplease.com)
GDP price deflator: measure of the percentage increase in the average price of products in GDP over a certain base year
published by the Commerce Department, (http://www.damagevaluation.com/glossary.htm)
genotoxic: may cause chromosomal damage in humans leading to birth defects.
geometric mean (GM): for a set of nnumbers {xls x2, x3, ..., xn} it is the n-th root of their product: (x! * x2* x3 ... xn) 1/n.
geometric standard deviation (GSD): a measure of the inter-individual variability in blood lead concentrations in a
population whose members are exposed to the same environmental lead levels. For a lognormal distribution, GSD is the
exponential of the standard deviation of the associated normal distribution.
half-life: time required for a living tissue, organ, or organism to eliminate one-half of a substance which has been introduced
into it.
health endpoints: an observable or measurable biological event or chemical concentration (e.g., metabolite concentration
in a target tissue) used as an index of an effect of a chemical exposure.
heme synthesis: creation of heme; an iron compound of protoporphyrin which constitutes the pigment portion or protein-
free part of the hemoglobin molecule and is responsible for its oxygen-carrying properties.
Integrated Exposure, Uptake, and Biokinetics (IEUBK): the IEUBK model is an exposure-response model that uses
children's environmental lead exposure to estimate risk of elevated blood lead (typically> 10 fig/dL) through estimation of
lead body burdens in mass balance framework.
least-squares regression: a tool of regression analysis that computes a best-fit line to represent the relationship between
two (or more) variables based on the principle that the squared deviations of the observed points from that line are minimized
(see also: regression analysis).
lognormal distribution: a distribution of a random variable for which the logarithm of the variable has a normal
distribution, (www.infoplease.com)
lognormally-distributed random variable: same as lognormal distribution.
marginal cost: the increase in total costs as one more unit is produced, (http://www.damagevaluation.com/glossary.htm)
multivariate: (of a combined distribution) having more than one variate or variable, (www.infoplease.com)
nephropathy: any kidney disease, (www.infoplease.com)
neurobehavioral deficits: neurologic effects as assessed by observation of behavior. These effects may include
behavioral and attentional difficulties, delayed mental development, lack of motor and perceptual skills, and hyperactivity.
neurobehavioral function: see neurobehavioral deficits.
non-cancer health risks: include systemic effects, reproductive toxicity, and developmental toxicity.
normal distribution: a random variable X is normally distributed if its density is given by f x (x) = f (x; \i, a), where \i and
a are the mean and the variance of the distribution.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
opportunity cost: the highest-valued sacrifice needed to get a good or service.
(http://www.damagevaluation.com/glossary.htm)
p-value: the probability of obtaining a given outcome due to chance alone. For example, a study result with a significance
level of p<0.05 implies that 5 times out of 100 the result could have occurred by chance.
(http://www.teleport.com/~celinec/glossary.htm)
pharmacokinetics: the study of the way drugs move through the body after they are swallowed or injected.
(http://www.epa.gov/OCEPAterms/pterms.html)
probability distribution: a distribution of all possible values of a random variable together with an indication of their
probabilities, (www.infoplease.com)
probit regression: a regression model, where the dependent variable is set up as a 0-1 dummy variable and regressed on
the explanatory variables. The predicted value of the dependent variable could be interpreted as the probability that a certain
event will take place (e.g., an individual will buy a car, visit a particular location, or get a specific disease).
quasi-steady state: almost not changing state.
regression analysis: a procedure for determining a relationship between a dependent variable, such as predicted success
in college, and an independent variable, such as a score on a scholastic aptitude test, for a given population. The relationship
is expressed as an equation for a line, (www.infoplease.com)
risk-based remediation goals (RBRG): target human health and environmental risk levels to be achieved via remedial
actions at Superfund sites.
Technical Review Workgroup (TRW): a workgroup formed in 1994 to evaluate methodologies for adult lead risk
assessment.
|J£)[/L: microgram per liter
|jg/£/L: microgram per decaliter
willingness-to-pay (WTP): maximum amount of money one would give up to buy some good.
(http://www.damagevaluation.com/glossary.htm)
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MPAM EEBA Part III: Benefits
Chapter 14: Lead-Related Benefits
ACRONYMS
A TSDR: Agency for Toxic Substances and Disease Registry
Bl: atherothrombotic brain infarction
BP: blood pressure
CARB: California Air Resources Board
CBA: cerebrovascular accidents
CDC: Centers for Disease Control
CEP A: California Environmental Protection Agency
CHD: coronary heart disease
CO I: cost of illness
GM: geometric mean
GSD: geometric standard deviation
IEUBK: Integrated Exposure, Uptake, and Biokinetics
NHANES: National Health and Nutrition Examination Surveys
NLSY: National Longitudinal Survey of Youth
PbB: blood lead
PPRG: Pooling Project Research Group
RBRG: risk-based remediation goals
TRW: Technical Review Workgroup
WTP: willingness-to-pay
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
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Commun. Health 37: 1-7.
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Relative Weight and ECG Abnormalities to Incidence of Major Coronary Events: Final Report of the Pooling Project."
Journal of Chronic Disease. Vol. 31.
Rabinowitz, M., D. Bellinger, A. Leviton, H. Needleman, and S. Schoenbaum. 1987. "Pregnancy Hypertension, Blood
Pressure During Labor, and Blood Lead Levels." Hypertension 10(4): October.
Reels, Harry et al. 1976. "Impact of Air Pollution by Lead on the Heme Biosynthetic Pathway in School-Age Children."
Archives of Environmental Health. November/December: 310-316.
Sallmen, M., M. L. Lindbohm, A. Anttila, H. Taskinen, and K. Hamminki. 2000. "Time to Pregnancy among the Wives of
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Salkever, D. S. 1995. "Updated Estimates of Earnings Benefits from Reduced Exposure of Children to Environmental
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Schwartz, J. 1988. "The Relationship Between Blood Lead and Blood Pressure in the NHANES II Survey." Environmental
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Perspectives, in press.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
Schwartz,!. 1992. "Chapter 13: Lead, Blood Pressure and Cardiovascular Disease." In: H. L. Needleman, ed., Human
Lead Exposure. CRC Press.
Schwartz, J. 1993. "Beyond LOEL's, p Values, and Vote Counting: Methods for Looking at the Shapes and Strengths of
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Richards and T. S. Wilson. 1984. "Reduction in Exposure to Lead from Drinking Water and Its Effect on Blood Lead
Concentrations." Human Toxicology 3: 183-392.
Shurtleff, D. 1974. "Some Characteristics Related to the Incidence of Cardiovascular Disease and Death." The Framingham
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Silbergeld, E. K., J. Schwartz, and K. Mahaffey. 1 988. "Lead and Osteoporosis: Mobilization of Lead from Bone in
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United States." Stroke 27(9): 1459-1466.
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Educational Research and Improvement, U.S. Department of Education.
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MPAM EEBA Part III: Benefits Chapter 14: Lead-Related Benefits
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the IEUBK Model for Lead in Children." EPA 540-R-94-040.
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EPA-821-C-02-003. August.
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Ward N., R. Watson, and D. Bryce-Smith. 1987. "Placental Element Levels in Relation to Fetal Development for
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Chapter 15: Recreational Benefits
CHAPTER CONTENTS
15.1 Ecological Improvements from the
MP&M Regulation 15-3
15.1.1 Overview of Ecological Improvements 15-3
15.1.2 Quantification of Ecological Improvements .. 15-3
15.1.3 Benefiting Reaches 15-4
15.1.4 Geographic Characteristics of MP&M Reachesl5-6
15.2 Valuing Economic Recreational Benefits 15-6
15.2.1 Transferring Values from Surface Water
Valuation Studies 15-6
15.2.2 Recreational Fishing 15-9
15.2.3 Wildlife Viewing 15-13
15.2.4 Recreational Boating 15-17
15.2.5 Nonuse Benefits 15-20
15.3 Summary of Recreational Benefits 15-20
15.4 Limitations and Uncertainties Associated with
Estimating Recreational Benefits 15-22
Glossary 15-26
Acronyms 15-28
References 15-29
INTRODUCTION
The final Metal Product and Machinery (MP&M)
regulation is expected to provide ecological benefits through
improvements in the habitats or ecosystems (aquatic and
terrestrial) that are affected by the MP&M industry
discharges. Society is expected to value such ecological
improvements by a number of mechanisms, including
increased frequency and value of use of the improved habitat
for recreational activities. In addition, individuals may also
value the protection of habitats and species that are adversely
affected by effluent dischargers even when they do not use or
anticipate future use of the affected waterways for
recreational or other purposes.
This chapter presents EPA's analysis of ecological benefits
from reduced effluent discharges to the nation's waterways
as a result of the final MP&M regulation, the 433 Upgrade
Options, and the Proposed/NODA option. EPA assessed
ecological benefits in terms of reduced occurrence of
pollutant concentrations in excess of AWQC protective of
aquatic life and human health. For this analysis, EPA estimated the in-waterway pollutant concentrations of MP&M facility
discharges for the baseline and the final rule and identified those reaches in which MP&M facility discharges would cause one
or more pollutant concentrations to exceed ambient water quality criteria (A WQC) for aquatic species and human
health.1'2 The change in the number of reaches with concentrations in excess of AWQC from the baseline to post-compliance
scenarios provides a quantitative measure of the improvement in aquatic species habitat expected to result from the final
regulation.
As discussed in Chapter 12, EPA performed all benefits analysis on a basis of the sample facility data. The Agency then
extrapolated findings from the sample facility analyses to the national level using two alternative extrapolation methods: (1)
traditional extrapolation and (2) post-stratification extrapolation. EPA also used the differential extrapolation technique in
addition to both traditional and post-stratification approaches when a sample reach was estimated to receive discharges from
multiple facilities. Appendix G provides detailed information on the extrapolation approaches used in this analysis.
Reducing concentrations of MP&M pollutants to below AWQC limits for protection of aquatic species and human health will
generate benefits to users of water resources for recreation, including anglers, boaters, and viewers. These benefits include:
»• increased value of the recreational trip or day, and
»• increased number of days that consumers of water-based recreation choose to visit the cleaner waterways.
1 For this analysis, a reach is a length of river, shoreline, or coastline on which a pollutant discharge may be expected to have a
relatively uniform effect on concentrations. The typical length of a reach in this analysis was five to ten kilometers, although some were
considerably longer.
2 AWQC set limits on pollutant concentrations that are assumed to be protective of aquatic life. Pollutant concentrations that exceed
AWQC can harm organisms that live in or consume water. MP&M pollutants can also harm other organisms that consume these
organisms. These organisms at risk include humans who may recreate in contaminated waters or consume aquatic organisms living in
them.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
EPA estimated national annual recreational use benefits for three water-based recreation activities (i.e., recreational fishing,
boating, and viewing) and nonuse benefits, but did not estimate national swimming
estimated the following recreational use benefits of the final MP&M rule (2001$):
boating, and viewing) and nonuse benefits, but did not estimate national swimming benefits due to data limitations.3 EPA
•• recreational fishing benefits range from $287,220 to $923,988 and from $187,123 to $601,976, based on the
traditional and post-stratification extrapolation, respectively;
»• near-water recreation (viewing) benefits range from $185,172 to $334,315 and from $120,639 to $217,805, based on
the traditional and post-stratification extrapolation, respectively; and
•• boating benefits range from $114,111 to $316,078 and from $74,343 to $205,924, based on the traditional and post-
stratification extrapolation, respectively.
EPA also estimated nonuse benefits from improved water quality in the nation's surface water resulting from the final rule.
Empirical estimates from surface water valuation studies indicate that nonuse values for water resources may be substantial
because people who do not use or expect to use affected waterways for recreational or other purposes may still value
protecting habitats and species impacted by effluent discharges (Harpman, et al., 1993; Fisher and Raucher, 1984; Brown,
1993). The Agency estimated that nonuse benefits will range from $293,252 to $787,190 and from $191,053 to $512,852,
based on the traditional and post-stratification extrapolation, respectively.
EPA calculated the total value of enhanced water-based recreation opportunities by summing over the three recreation
categories and nonuser value. Since recreational trips corresponding to fishing, boating, and wildlife viewing considered in
this analysis are stochastically independent (i.e., only the primary activity is counted on each trip occasion), benefits from
improved recreational opportunities corresponding to these activities are additive. The total annual recreational benefit based
on the traditional extrapolation is estimated at $879,755 to $2,361,570 (2001$), with a midpoint estimate of $1,499,756
(2001$). Likewise, total annual recreational benefit based on the post-stratification extrapolation is estimated at $573,158 to
$1,538,557 (2001$), with a midpoint estimate of $977,087 (2001$).
The analysis of recreational benefits presented in this chapter uses the National Demand Study (NDS) data to estimate the
number of participants in wildlife viewing and boating in the counties affected by MP&M discharges.4 To estimate the
number of recreational fishermen, EPA used fishing license data. The NDS survey asked respondents to report the number of
recreational trips taken annually for the primary purpose of boating and wildlife viewing. The Agency used these data to
estimate the number of participants and the number of recreational trips taken annually by state and activity type.
Appendix N summarizes this information.
EPA chose to use fish license data rather than the NDS data to estimate the number of recreational anglers fishing the MP&M
reaches because these data are often available at the county level and therefore provide location-specific information.
Although the use of the NDS and fish license data yields similar estimates of the number of recreational anglers at the state
level (see Chapter 21) fish license data are likely to be more accurate at the county level. The use of the fish license data in
the recreational fishing benefit analysis also provides consistency with other parts of the benefits analysis (see Chapters 13
and 14 for detail).
Benefit categories examined in this chapter are different from and generally do not overlap with benefits associated with
reduced risk to human health discussed in Chapter 13. Nevertheless, there is some likelihood that the valuation of ecological
benefits based on enhanced recreational fishing overlaps to a degree with the valuation of human health benefits from reduced
cancer risk via fish consumption.
3 Fewer water bodies are designated for primary contact recreation, such as swimming, than for secondary contact recreation, such as
boating and fishing. Assessing recreational swimming benefits requires first obtaining information on designated uses of the sample
MP&M reaches from the 305(b) database. This analysis was not feasible due to resource and time constraints.
4 Additional information on the NDS survey can be found in Chapter 21.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
15.1 ECOLOGICAL IMPROVEMENTS FROM THE MP<&M REGULATION
15.1.1 Overview of Ecological Improvements
Many MP&M pollutants can adversely affect the survival, growth, and reproduction of aquatic organisms. Such effects are
ecologically significant when they affect the size, structure, or function of populations:
»• MP&M pollutants can affect population size by reducing prey, and by affecting development or reproduction in
sensitive life stages of target species;
> MP&M pollutants can alter population structure by impairing sensitive age groups or affecting the development or
maturation rates of target species; and
> MP&M pollutants can impact population function by decreasing genetic diversity and changing interactions among
different populations in the affected areas.
MP&M pollutants may also contaminate fish tissue and therefore decrease the value of fishery resources. Thus, the final
MP&M regulation may generate a broad range of ecological effects by reducing MP&M pollutant discharges. Ecological
effects associated with reductions in MP&M discharges may include:
*• recovery of populations of aquatic species that are particularly sensitive to MP&M pollutants;
*• decreases in noxious algae, which affect the taste and odor of the receiving waters;
»• increases in the concentrations of dissolved oxygen (DO) in the water column;
> improvements in the natural assimilative capacity of the affected waterways;
*• decreases in fish tissue contamination; and
> terrestrial life benefits.
Improvements in aquatic species habitat are expected to improve the quality and value of water-based recreation and nonuse
values of the affected resources. Recent studies valuing recreational fishing showed that the value of water resources for
recreational fishing increases as the level of toxic contamination in fish tissue decreases (Lyke, 1993; Phaneuf et al., 1998;
and Jakus et al., 1997). Thus, knowing that the water is cleaner and does not contain any or contains fewer pollutants that
harm humans and aquatic life, increases individuals' enjoyment of their recreational experience. The value of a recreational
fishery also increases from increased number, size, diversity, and health of recreational fish species.
Participants in other water-based recreation, such as boating and wildlife viewing, will also benefit from improved abundance
and diversity of aquatic and terrestrial species. For example, wildlife viewers may benefit from improved abundance of
piscivorous birds (e.g., osprey and cormorants) whose population is likely to increase due to an increase in the forage fish
populations. Boaters may benefit from enhanced opportunities for companion activities, such as fishing and wildlife viewing
(e.g., piscivorous birds) and from improved water clarity and smell. Reducing conventional pollutant loadings will also
improve visual aesthetics, thereby enhancing all water-based recreation experiences.
15.1.2 Quantification of Ecological Improvements
EPA evaluated potential impacts to aquatic life from the final MP&M regulation by estimating in-waterway concentrations of
pollutants discharged by MP&M facilities and comparing those concentrations within AWQC limits for protection of aquatic
species. Pollutant concentrations in excess of AWQC limits indicate a significant detriment to the aquatic species habitat.
EPA expects that eliminating these exceedances as the result of the MP&M regulation will significantly improve aquatic
species habitat and thus provide a quantitative measure of ecological benefit for this regulatory analysis.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
For this analysis, EPA estimated in-waterway concentrations for all MP&M pollutants for which AWQC limits are available.
Of the 132 MP&M pollutants of concern, AWQC values are available for 114 pollutants.5 Table 1.3 in Appendix I lists the
pollutants evaluated in this analysis and their acute and chronic aquatic life AWQC. The acute value is the maximum
allowable one-hour average concentration at any time at which aquatic life can survive. The chronic value is the average
concentration of a toxic pollutant over a four-day period at which aquatic life is not unacceptably affected. The endpoints of
concern are one or more sub-lethal responses, such as changes in reproduction or growth in the affected organisms. The
chronic levels should not be exceeded more than once every three years.
EPA used the mixing and dilution methods outlined in Appendix I to estimate the in-waterway concentrations resulting from
MP&M facility discharges. Acute and chronic exposure concentrations for each pollutant are calculated on the basis of 7Q10
and 1Q10 stream flow rates, where 7Q10 is the lowest consecutive seven-day average flow with a recurrence interval often
years, and 1Q10 is the lowest one-day average flow with a recurrence interval often years. For reaches to which more than
one sample MP&M facility discharge, EPA summed the discharge values by pollutant for all known sample facilities
discharging to the reach.
EPA first identified the MP&M discharge reaches in which MP&M discharges alone caused one or more pollutant
concentrations to exceed AWQC limits for aquatic species under the baseline discharge level. If concentrations of all MP&M
pollutants exceeding the limits in the baseline fell below AWQC limits as a result of the final rule, then aquatic species habitat
conditions on that discharge reach would likely improve significantly as a result of the final regulation. The final regulation
would result in partial aquatic habitat improvements if concentrations of some, but not all, MP&M pollutants fell below their
AWQC limits. Although not explicitly accounted for in this analysis, species habitat conditions are likely to improve
whenever in-waterway concentrations are reduced, regardless of whether or not they fall to levels below aquatic AWQC.
EPA's analysis based on the traditional extrapolation method indicates that pollutant concentrations at current industry
discharge levels exceed acute exposure criteria for protection of aquatic species on 1 8 receiving reaches, and exceed chronic
exposure criteria for protection of aquatic species on 353 receiving reaches.6 EPA estimates that the final rule would
eliminate concentrations in excess of the acute aquatic life exposure criteria on nine reaches, and would eliminate
concentrations in excess of the chronic aquatic life exposure criteria on nine reaches.
Similarly, EPA's analysis based on the post-stratification extrapolation method indicates that baseline pollutant concentrations
at current industry discharge levels exceed acute exposure criteria for protection of aquatic species on 15 reaches, and exceed
chronic exposure criteria for protection of aquatic species on 350 reaches. EPA estimates that the final rule would eliminate
concentrations in excess of the acute aquatic life exposure criteria on six reaches, and would eliminate concentrations in
excess of the chronic aquatic life exposure criteria on six reaches. Table 15.1 summarizes these results.
15.1.3 Benefiting Reaches
As a first step in estimating the monetary value of improvements in the aquatic habitats affected by MP&M discharges from
the final MP&M rule, EPA identified reaches that are likely to experience significant water quality improvements from
reduced MP&M discharges due to the final MP&M rule (hereafter, benefiting reaches). A reach is considered to benefit
from the MP&M rule if at least one AWQC exceedance is eliminated due to reduced MP&M discharges. This approach
differs from some past approaches where EPA took credit for pollution reductions only in cases where all AWQC
exceedances are eliminated. EPA believes that the latter approach significantly underestimates benefits from reduced
pollutant discharges.
This analysis combines two AWQC calculation procedures:
»• analysis of in-waterway concentrations relative to human health AWQC limits described in Chapter 13,7 and
5 Facilities in the Oily Wastes subcategory discharge 122 of the 132 POCs evaluated. See Chapter 12 for detail.
6 This analysis used baseline pollutant loads for direct and indirect dischargers belonging to all subcategories considered for
regulation.
7 Although EPA estimated the value of reduced cancer risk from consumption of contaminated fish tissue, the Agency was unable to
estimate the value of reduced systemic risk from consumption offish caught in the reaches affected by MP&M discharges (see Chapter 13).
The recreational benefits analysis presented in the following sections assumes that some of the value of reduced systemic health risk is
implicitly captured in the increased value of water resources fromreduced occurrence of human health-based AWQC exceedances. For
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
»• analysis of in-waterway concentrations relative to aquatic life AWQC limits described in the preceding section of
this chapter.
Table 15.1 summarizes the number of reaches with estimated baseline concentrations that exceed AWQC limits for either
human health or aquatic species, and the number of those reaches where the regulation is estimated to eliminate or reduce
exceedances. Based on the traditional extrapolation, the combined analysis over all AWQC limit categories (i.e., acute and
chronic aquatic life and human health) indicates that MP&M pollutant concentrations would exceed at least one AWQC limit
on 395 reaches as the result of baseline MP&M discharges. The expected discharge reductions from the final rule eliminate
exceedances on nine of these discharge reaches, leaving 386 reaches with concentrations of one or more pollutants that
exceed AWQC limits.
Likewise, based on the post-stratification extrapolation, the combined analysis indicates that MP&M pollutant concentrations
would exceed at least one AWQC limit on 426 reaches as the result of baseline MP&M discharges. The expected discharge
reductions from the final rule eliminate exceedances on six of these discharge reaches, leaving 420 reaches with
concentrations of one or more pollutants that exceed AWQC limits.
EPA assigned full benefits in situations where the rule eliminates all AWQC exceedances and partial benefits where the rule
eliminates one or more, but not all, AWQC exceedances. EPA calculates partial benefits as the ratio of the AWQC
exceedances removed by reducing MP&M discharges to the total number of AWQC exceedances caused by MP&M facilities
in the baseline. For example, if the MP&M rule removes seven out of a total ten baseline AWQC exceedances on a benefiting
reach, the Agency attributes a 70 percent benefit to the MP&M regulation, where 100 percent would represent an "AWQC
exceedance-free" level.
Table 15.1: Estimated MP&M Discharge Reaches with MP&M Pollutant Concentrations in Excess of AWQC
Limits for Protection of Aquatic Species or Human Health
Regulatory
Status
Baseline
Final Option
Baseline
Final Option
Number of Reaches with Concentrations
Exceeding AWQC Limits
AWQC Limits for
Aquatic Species
Acute
18
9
15
9
Chronic
353
344
350
344
AWQC Limits for
Human Health
H20 and
Organisms
Selected O
78
78
Selected Optio
112
112
Organisms
Only
ition: Traditk
21
21
n: Post-Stratii
21
21
Total Number of
Reaches with
Concentrations
Exceeding
AWQC Limits
mal Extrapolation
395
386
'ication Extrapolatioi
426
420
Number of Benefiting Reaches
All AWQC
Exceedances
Eliminated
N/A
9
i
N/A
6
Reaches with
Some
Exceedances
Eliminated
N/A
0
N/A
0
Note: In the baseline, the total number of reaches with concentrations exceeding AWQC limits does not equal the sum of the numbers in
the separate analysis categories because some reaches were estimated to have concentrations in excess of AWQC limits for more than one
analysis category.
Source: U.S. EPA analysis
Surface water valuation studies show that benefits from partial improvements are likely to be considerable. For example,
Carson and Mitchell (1993) found that almost nine out often individuals indicated that "halfway" improvements are worth the
same as a complete improvement in water quality. The remaining one out often individuals were willing to pay a reduced
amount for partial improvements in water quality.
example, some studies showed that anglers place a much higher value on fishery resources that are safe for consumption (Lyke, 1993 and
Phaneuf, 1997).
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
The effects of partially removing AWQC exceedances, however, are difficult to generalize. The overall improvement in
surface water quality from reduced toxic loadings will depend on the amount and duration of exceedances, together with the
kinds of chemical(s) that are removed from the mixture by regulatory action. AWQC are developed on a
chemical-by-chemical basis; they are not designed to assess the toxicity of multiple chemicals. In most cases, the toxicities of
chemicals in a mixture are considered additive (i.e., the total toxicity is the sum of the toxicities of the individual chemicals).
Total toxicity decreases by the amount of a chemical removed from the mixture. Benefits to sensitive aquatic species (i.e.,
amphibians, fish, benthic invertebrates, zooplankton) could occur if the concentration of one chemical fell below its AWQC
even when two or more other chemicals still were at or exceeding their respective AWQC. The reason is that the total toxic
pressure in the receiving water decreases so that a smaller fraction of the most sensitive species remain affected. For
example, consider a case in which three chemicals exceeding their chronic AWQC adversely affect 7 percent of all aquatic
species in a receiving water. If certain species are particularly sensitive to one of the three chemicals, then eliminating the
AWQC exceedance for this chemical would lower the percentage of sensitive species being adversely affected.
15.1.4 Geographic Characteristics of MP<&M Reaches
EPA cannot identify all of the specific reaches affected by MP&M facilities that reduce discharges under the final rule
because location is known only for the facilities included in the random stratified sample. EPA assumes that facilities
represented by the sample facility have the same environmental and geographic characteristics that affect benefits from the
final rule. These characteristics include water body type and physical characteristics (e.g., stream flow conditions),
populations residing near the water body, and the number of potential recreational users affected.
The analysis of the sample reach locations indicates that sample MP&M reaches tend to be located in heavily populated areas.
For example, approximately 35 percent of sample reaches receiving discharges from sample MP&M direct dischargers are
located adjacent to counties with populations of at least 500 thousand residents. These reaches have a greater number of
potential recreational users than do reaches in less populated areas.
15.2 VALUING ECONOMIC RECREATIONAL BENEFITS
The final MP&M rule will improve aquatic habitats by reducing concentrations of priority (i.e., toxic), nonconventional,
and conventional pollutants in water. In turn, these improvements will enhance the quality and value of water-based
recreation, such as fishing, wildlife viewing, camping, waterfowl hunting, and boating. The Agency used the estimated
increase in the monetary value of recreational opportunities for fishing, boating, and wildlife viewing as a partial measure of
the economic benefit to society from the improvements to aquatic species habitat expected to result from the final MP&M
regulation. The Agency also estimated nonuse benefits from improvements in aquatic habitats and ecosystems that are
affected by the MP&M industry discharges.
This analysis uses a benefits transfer approach to monetize changes in water resource recreational values for reaches
affected by MP&M discharges.8 This approach builds upon an analysis of applicable surface water valuation literature to
estimate the total WTP value (including both use and nonuse values) for improvements in surface water quality.
15.2.1 Transferring Values from Surface Water Valuation Studies
EPA identified several surface water evaluation studies that quantified the effects of water quality improvements on various
water-based recreational activities. The Agency used the following technical criteria for evaluating study transferability
(Boyle and Bergstrom, 1990):
•• The environmental change valued at the study site must be the same as the environmental quality change caused by
the rule (e.g., changes in toxic contamination vs changes in turbidity);
*• The populations affected at the study site and at the policy site must be the same (e.g., recreational users vs
nonusers);
8 Benefits transfer involves the application of value estimates, functions, and/or models developed in one context to address a similar
resource valuation question in another context.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
> The assignment of property rights at both sites must lead to the same theoretically appropriate welfare measure (e.g.,
willingness-to-pay vs willingness to accept compensation).
In addition to the above criteria, the Agency considered authors' recommendations regarding robustness and theoretical
soundness of various estimates.
Existing studies are unlikely to meet all of the above criteria. Boyle and Bergstrom (1990) reported that most researchers will
likely encounter problems with at least one criterion. This analysis is no exception. The major limitation in performing the
national analysis is the comparability of the water quality changes considered in the original studies with the water quality
changes considered in this analysis. These comparisons are discussed below.
The Agency used eight of the most comparable studies and calculated the changes in recreation values resulting from water
quality improvements (as a percentage of the baseline value) implied by those studies. EPA took a simple mean of upper- and
lower-bound estimates from these studies to derive a range of percentage changes in the water resource values due to water
quality improvements. The studies used for benefits transfer in the MP&M regulatory analysis included Lyke (1993), Jakus et
al. (1997), Montgomery and Needelman (1997), Phaneuf et al. (1998), Desvousges et al. (1987), Lant and Roberts (1990),
Farber and Griner (2000), and Tudor et al. (2002). Appendix K presents WTP values for various water quality improvements
and summarizes EPA's reasoning for selecting specific WTP estimates for benefits transfer. Each of the eight studies and the
WTP values selected for benefits transfer are discussed briefly below.
Lyke's (1 993) study of the Wisconsin Great Lakes open water sport fishery showed that anglers may place a significantly
higher value on a contaminant-free fishery than on one with some level of contamination. Lyke estimated the value of the
fishery to Great Lakes trout and salmon anglers if it were improved enough to be "completely free of contaminants that may
threaten human health," and found that this value would add between 11 and 31 percent of the fishery's current value.
Jakus et al. (1997) used a repeated discrete choice travel cost (TC) model to examine the impacts of sport-fishing
consumption advisories in eastern Tennessee. The model controlled for anglers' knowledge of advisories, the type of angler
(i.e., fish consumption vs. catch and release), and catch rate. The estimated welfare gain (as a percentage of baseline) from
cleaning up six reservoirs and removing these advisories ranges from six to 8 percent. These estimates are below Lyke's
estimated 11 to 3 1 percent range, due to the difference in methodology used. The TC method captures use values only, while
the combined TC and stated preferences method used in Lyke captures both the use and nonuse components of the resource
value to users. Differences in the fisheries and user populations may also affect the estimated percentage changes in the
resource value.
Montgomery and Needelman (1997) estimated benefits from removing "toxic" contamination from lakes and ponds in New
York State. They used a binary variable as their primary water quality measure, which indicates whether the New York
Department of Environmental Conservation considers water quality in a given lake to be impaired by toxic pollutants. The
model controls for major causes of impairments other than "toxic" pollutants to separate the effects of various pollution
problems that affect the fishing experience. The estimates from Montgomery and Needelman imply that removing "toxic"
impairments in all New York lakes and ponds would increase recreational fishing value by 13.7 percent.
Phaneuf et al. (1998) studied angling in the Wisconsin Great Lakes. They estimated changes in recreational fishing values
resulting from a 20 percent reduction of toxin levels in lake trout flesh. The study uses a TC model to value water quality
improvements when corner solutions are present in the data. Corner solutions arise when consumers visit only a subset of the
available recreation sites, setting their demand to zero for the remaining sites. Phaneuf et al. found that improved industrial
and municipal waste management results in general water quality improvement. This improvement leads in turn to a 20
percent decrease in fish tissue toxin levels, yielding a welfare gain of $166.21 (2001$) per angler per year.9 This estimate
implies that recreational fishing values would increase by approximately 27.5 to 34.3 percent from reduced toxin levels. This
analysis estimates use values only.
Desvousges et al. (1987) used findings from a contingent valuation (CV) survey to estimate WTP for improved
recreational fishing from enhanced water quality in the Pennsylvania portion of the Monongahela River. In a hypothetical
market, each survey respondent was asked to provide an option price for different water quality changes, including "raising
9 The study used the 1989 survey data on recreational angling in Wisconsin's Great Lakes. Therefore, this analysis assumes that all
estimates in the original study are in 1989 dollars.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
the water quality from suitable for boating (hereafter, "beatable" water) to a level where gamefish would survive (hereafter,
"fishable" water)."
In applying Desvousges et al. for the MP&M analysis, EPA assumed that reaches with AWQC exceedences under the
baseline conditions are likely to support rough fishing but may not be clean enough to support gamefishing. Removing
AWQC exceedences is therefore comparable to shifting water quality from "boatable"to "fishable." This is a relatively
conservative assumption. Desvousges et al. found that improving water quality from "beatable" to "fishable" would yield a
5.9 to 7.9 percent increase in water resource value to recreational anglers.
Lant and Roberts (1990) used a CV study to estimate the recreational and nonuse benefits of improved water quality in
selected Iowa and Illinois river basins. River quality was defined by means of an interval scale of "poor," "fair," "good," and
"excellent." The authors defined "fair" water quality as adequate for boating and rough fishing and "good" water quality as
adequate for gamefishing.
For the MP&M analysis, EPA assumes that eliminating AWQC exceedences is roughly equivalent to shifting water quality
from "fair" to "good." The estimates from this study imply an increase of 9.7 to 13.1 percent in recreational fishing value
from improving water quality from "fair" to "good."
Farber and Griner (2000) used a CV study to estimate changes in water resource values to users from various improvements
in water quality in Pennsylvania. The study defines water quality as "polluted," "moderately polluted," and "unpolluted"
based on a water quality scale developed by EPA Region III: "Polluted" streams are unable to support aquatic life;
"moderately polluted" streams are somewhat unable to support aquatic life; and "unpolluted" streams adequately support
aquatic life. Streams unable to support aquatic life (i.e., "polluted") are likely to be affected by environmental stressors
unrelated to MP&M discharges, such as acidity or severe oxygen depletion.
The MP&M analysis assumes that most streams affected by MP&M facility discharges are moderately polluted; i.e., these
streams support aquatic life, but sensitive species may be adversely affected by MP&M pollutants that exceed AWQC values
protective of aquatic life. Removing all AWQC exceedences would make such streams unpolluted. The estimates from this
study imply that improving water quality from "moderately polluted" to "unpolluted' would yield an increase in recreation
fishery value ranging from 3.9 to 9 percent.
Tudor et al. (2002) used a TC model to estimate changes in water resource recreation values resulting from eliminating
MP&M pollutant concentrations in excess of AWQC limits at recreation sites in Ohio.10 The study involves four recreation
activities -- fishing, boating, near-water recreation, and swimming -- and covers most recreationally-important water bodies in
all Ohio counties. The study considers two types of water quality effects from MP&M pollutants on consumers' decisions to
visit a particular water body:
(1) visible or otherwise perceivable effects (e.g., turbidity and odor); and
(2) "toxic" effects that are not directly perceivable by consumers.
Because priority and nonconventional pollutants at high enough concentrations may adversely affect aquatic species, "toxic"
effects may be indirectly observable via species abundance and diversity. The study uses a dummy variable to account for
effects of "toxic" MP&M pollutants, identifying recreation sites at which estimated concentrations of one ormore MP&M
pollutants exceed AWQC for protection of aquatic life. The study estimated that eliminating AWQC exceedances and
reducing TKN concentrations would yield per trip benefits of $1.34, $1.78, $.60, and $0.33 (2001$) from improved fishing,
boating, wildlife viewing, and swimming opportunities, respectively. The estimated changes in the recreational use value of
Ohio water resources, are 0.77, 1.67, and 0.77 percent for fishing, boating, and wildlife viewing, respectively. This analysis
estimates use values only.
With the exception of the Tudor et al. (2002) study, the types of water quality changes assessed in these studies are only
roughly comparable to those studied in the MP&M analysis. Whereas the analysis of the final MP&M regulation and Tudor
et al. (2002) assessed the impact of eliminating AWQC exceedances, the other studies used other measures of water quality
improvement. EPA addressed the differences in measurement between the other studies and the MP&M analysis by linking
10 Preliminary results of this study were presented at the annual American Agricultural Economic Association meeting (Tudor et al.,
1999a) and at the annual Northeastern Agricultural and Resource Economic Association Meeting (Tudor et al., 1999b). EPA subjected this
study to a formal peer review by experts in the natural resource valuation field. The peer review concluded that EPA had done a competent
job, especially given the available data. This study can be found in Chapter 21. The peer review report is in the docket for the rule.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
water quality changes expected from the MP&M regulation to the type of water quality changes assessed in the other studies.
EPA assumed that eliminating AWQC exceedances is roughly comparable to the following discrete water quality changes:11
»• "achieving a contaminant free fishery;"
•• reducing the level of toxins in fish tissue;
»• removing fish consumption advisories (FCA); and
> improving water quality from "beatable" to "fishable," from "fair" to "good," and from "moderately polluted" to
"unpolluted."
The MP&M analysis uses the estimates derived from the eight surface water evaluation studies described above to calculate a
range of national WTP values. The following sections present the methodology and relevant values used to estimate the value
of improved fishing, wildlife viewing, and boating opportunities resulting from the MP&M regulation.
15.2.2 Recreational Fishing
The MP&M rule will improve the recreational angling experience by reducing concentrations of priority, nonconventional,
and conventional contaminants in water. EPA estimated the benefits of these reductions by estimating:
•• the number of recreational fishing days on benefiting reaches;
»• the baseline fishery value of each benefiting reach; and
»• changes in recreational fishery value, using values from the available surface water valuation studies.
a. Number of recreational fishing days
EPA calculated the annual number of person-days of recreational fishing for each benefiting reach using a two-step approach:
*»* Participating population
The geographic area from which anglers would travel to fish a reach is assumed to include only those counties that abut a
given reach. As noted in Chapter 13, this assumption is based on the finding in the 1991 National Survey of Fishing, Hunting,
and Wildlife-Associated Recreation that 65 percent of anglers travel less than 50 miles to fish (U.S. Department of the
Interior, 1993). NDS data showed that recreational anglers travel from 20 to 66 miles to their destination, with an average
one-way travel distance of 30 miles.12'13
EPA estimated the population participating in recreational fishing using the number of licensed fishermen in counties
bordering MP&M discharge reaches using the following steps:
*• assume that fishing activity among these anglers is distributed evenly among all reach miles within those counties;
*• compute the length of the MP&M reach as a percentage of total reach miles within corresponding counties;
> multiply the estimated ratio by the total fishing population in counties abutting the reach to estimate the number of
anglers who may fish an MP&M reach; and
11 Section 15.1.3 discusses a method used for estimating partial water quality improvements.
12 See Chapter 21 for detail on the NDS data.
13 These estimates exclude outliers.
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
*• reduce the number of anglers by 20 percent in reaches where MP&M and other pollutants have required a fish
consumption advisory. This reduction is an estimate of angler response to the presence of a fish consumption
advisory.14
*»* Average number of fishing days
Anglers generally participate in recreational fishing several times a year. The U.S. Fish and Wildlife Service (FWS)
provides estimates of the average number of fishing days per angler in each state. The FWS estimates range from 10.5 days
per angler in Arizona to 21.1 days per angler in Alabama for freshwater fishing, andV.3 days per angler in Louisiana to 18.7
days per angler in Virginia for saltwater fishing.15
EPA calculated the total number of angler days by multiplying the number of recreational anglers for each benefiting reach by
the average number of fishing days for the reach (based on the state in which the reach is located).
b. Baseline fishery value
The net value of a recreational fishing day is the total value of the fishing day exclusive of any fishing-related costs (e.g.,
license fees, travel costs, bait, tackle, charter boats, etc.) incurred by the angler.
EPA used two recreational fishing valuation studies (Bergstrom and Cordell (1991) and Walsh et al. (1992)) to calculate the
net economic value per recreational fishing day under the baseline conditions. Both studies used a meta-analysis of
recreational fishery valuation studies to estimate per-day values of the three types of recreational fishing: warmwater,
coldwater, and anadromous. Based on the two studies, EPA developed an average per-day value for each type of recreational
fishing. This analysis uses low and high average benefit values for fishing days of $28.11 and $60.43 (2001$) to estimate a
range of the baseline fishery values.
Table 15.2: Baseline Values of Fishing
Fishery Type
Warmwater
Coldwater
Anadromous
Per-day Value (2001$)"
Bergstrom and Cordell
(1991)"
$19.52
$27.77
$36.73
Walsh et al.
(1992)'
$36.70
$47.71
$84.15
Range of above
Average Per-day Value
(2001$)
$28.11
$37.74
$60.43
$28.11 -$60.43
a Original study values were adjusted to 2001 dollars based on the relative change in CPI from 1987 to 2001.
b Study location: various U.S. locations. Estimating approach: meta-analysis of TC studies.
° Study location: various U.S. locations. Estimating approach: meta-analysis of CV and TC studies.
Source: U.S. EPA analysis
EPA calculated the total baseline value for each fishery located on a benefiting reach by multiplying the estimated net value of
a recreational fishing day by the total number of fishing days calculated in subsection (a) above. Applying facility weights
and summing over all benefiting reaches provides a total baseline recreational fishing value for MP&M reaches expected to
benefit from the elimination of pollutant concentrations in excess of AWQC limits.
14 See Belton et al. (1986), Knuth and Velicer (1990), Silverman (1990), West (1989), Connelly et al. (1992), and Connelly and
Knuth (1993) for more information on angler response to fish advisories.
15 These averages reflect participation levels in the 48 contiguous states. No sample facility is located in Hawaii or Alaska.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
c. Changes in recreational fishery value
Expected benefits from the final MP&M regulation include an increase in the quality of an angler's recreational opportunities
and/or the number of days an angler chooses to fish each season.
EPA assumes that the expected welfare gain for recreational anglers is a function of changes in the overall quality of all
recreational opportunities available to each angler. Recreational anglers residing in the counties abutting MP&M reaches will
therefore benefit from improved recreational opportunities whether or not they actually visit an MP&M reach.
EPA used the eight studies discussed above to calculate the changes in recreation values from water quality improvements (as
a percentage of baseline) implied by those studies. Table 15.3 compiles information on the baseline values, values of changes
in water quality, and percentage changes in values reported or implied by these studies.
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.3: Studies Estimating Changes in Value of a Recreational Fishery
Study
Lyke(1993)
Jakus et al.
(1997)
Montgomery
and
Needelman
(1997)
Phaneuf at al.
(1998)
Desvousges
etal. (1987)
Lant and
Roberts
(1990)
Farber and
Griner (2000)
Tudor
etal. (2002) g
Type of Water Quality
Change Valued
Fish tissue is completely
free of toxic
contaminants that may
threaten human health
Lifting FCAs
Elimination of toxic
impairment
20% reduction of toxic
contamination in trout
flesh
L J
Improvement from
"beatable" to "fishable"
Improvement from
"fair" to "good"
j
Improvement from
"moderately polluted"
to "unpolluted"
h j
Elimination of AWQC
exceedances
Baseline Value of
Recreational
Angling (2001$)
$95.0-$119.0
million per year a
$26.0-$52.6
per trip
$656.6 per angler
per year b
$484.5 -$605 .8 per
angler per year a
h
$28. 11- $37. 73 per
h .HIP.:
$28. 11- $37. 73 per
trip"
h
$28.11- $37.73 per
trip"
h
$173. 34 per trip
Value of Water
Quality Change
(2001$)
$10.5-$37.1
million per year a
$2.0-$3.2
per trip
$90.3 per
angler per year
$166. 2 per angler
per year
h
$2.21 per trip"
$3. 67 per trip e
h
$1.49-$2.55per
tripf
h
$1.34 per trip
Average percentage change in recreational fishery value (based on above studies)11
Value of Change
as % of Baseline
ll%-31%a
6.0% - 8.0%
13.7%
27.5% - 34.3%
5.9% - 7.9%
9.7% -13.1%
3.9% - 9.0%
0.77%
9.8% -14.7 %
Type of Benefits
Included
Use and nonuse values
for recreational
anglers
Use values for
recreational anglers
Use values for
recreational anglers
Use values for
recreational anglers
L
Recreational and
nonuse values to users
Recreational and
nonuse values to users
L
Recreational use
values to users and
nonusers
L
Use values for
recreational anglers
Recreational and
nonuse values to users
a The baseline fishery value for the study site location is based on the baseline fishery value reported in Lyke (1993). The study used
data from two mail surveys conducted in 1989 at the University of Wisconsin-Madison. These surveys were originally used by Lyke
(1993).
b Based on the average value for a coldwater fishing day of $37.74 (see Table 15.2), multiplied by the average number of freshwater
(non-Great Lakes) angling days per year in New York State (17.4 days, USFWS, 1996).
° Range based on the range of values for a fishing day used in this analysis (see Table 15.2);
d Based on the value of water quality improvement of $36.79 per year (updated from 1987 dollars reported in Desvousges et al., 1987).
divided by the average number of freshwater angling days per year in Pennsylvania (16.6 days, USFWS, 1996).
° Based on the value of water quality improvement of $57.81 per year (updated from 1990 dollars reported in Lant and Roberts)
divided by the average number of freshwater angling days per year in Iowa and Illinois (16.6 and 15.5 days, USFWS, 1996).
f Based on the values of water quality improvements ranging from $24.55 to $41.93 per year reported in Farber and Griner (2000),
divided by the average number of freshwater angling days per year in Pennsylvania (16.6 days, USFWS, 1996).
g See Chapter 21 of this report for detail. The baseline value of recreational fishery is based on the estimated mean value of water
resources for recreational anglers reported by Tudor et al. (2002). The estimated median value of recreational fishing is $175.48. These
values were derived from a September 23, 2002 analysis.
h EPA took a simple mean of lower- and upper-bound estimates from the eight studies to calculate a range of percentage changes in the
recreational fishery value from improved water quality conditions. When only one value is available from the study (i.e., Tudor et al.,
2002), EPA used this value in calculating both the lower- and upper-bound estimates.
Source: U.S. EPA analysis
EPA used the percentage change in the fishery value implied by the eight studies to estimate increased recreational fishing
values for all MP&M reaches in which the regulation eliminates AWQC exceedances of one or more MP&M pollutants. That
is, the Agency estimated benefits for all MP&M discharge reaches where at least one AWQC exceedance is eliminated due to
reduced MP&M discharges. As noted above, EPA took a simple mean of lower- and upper-bound estimates from the eight
studies described above to calculate a range of percentage changes in the recreational fishery value from reduced MP&M
discharges. These studies yielded estimates of increased value ranging from 9.8 to 14.7 percent. Multiplying these
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
percentages by the baseline value of fisheries located on benefiting reaches yielded a range of benefits from eliminating
pollutant concentrations in excess of AWQC limits.
Table 15.4 below summarizes the results of EPA's recreational fishing benefits analysis.
Table 15.4: Summary of Recreational Fishing Benefits (2001$)
Low
Estimate
High
Estimate
Low
Estimate
High
Estimate
Number of
Benefiting
Reaches
9
9
6
6
Participating
Population
(millions)
0.98
0.98
1.08
1.08
Average
Number of
Fishing Days
Selected Op
17.3
17.3
Selected Option
17.2
17.2
Total Angler
Days
(millions)
tion: Tradition
16.98
16.98
: Post-Stratific
18.61
18.61
Baseline
Fishery
Value/ Rec.
Day
al Extrapolatio
$28.11
$60.43
ation Extrapol
*toc 1 1
4>2o. 1 1
$60.43
Baseline
Fishery Value
($ millions)
n
$477
$1,026
ation
$523
$1,125
% Change in
Fishery Value
9.8%
14.7%
9.8%
14.7%
MP&M
Benefits
$287,220
$923,988
$187,123
$601,976
Source: U.S. EPA analysis
15.2.3 Wildlife Viewing
EPA expects that water quality improvements from the MP&M regulation will decrease the uptake of pollutants through
aquatic food chains. These changes are expected to increase the health and reproductive success of sensitive wildlife species
that feed on fish and other aquatic organisms. In particular, Piscivorous (i.e., fish-eating) bird species such as the osprey
(Pandion haliaetus), bald eagle (Haliaeetus leucocephalus), great blue heron (Ardeidae herodias), mergansers (Merginae
sp.), and cormorants (Phalacrocorax sp.) will benefit from increased numbers, size, and health of forage fish. Increased
food and lower pollutant levels in fish flesh will improve reproduction in these birds, leading to healthier and larger bird
populations. Reducing conventional pollutant loadings will also improve visual aesthetics, thereby enhancing wildlife
viewing and other near-water-based recreation experiences, such as photography, camping, picnicking, and waterfowl hunting
(hereafter, this discussion refers to all of these activities as "wildlife viewing").
As with the recreational fishing analysis, EPA assumes that the expected welfare gain for consumers of viewing activities is a
function of changes in the overall quality of all recreational opportunities available to each consumer. Consumers of water-
based recreation residing in the counties abutting MP&M reaches are therefore likely to benefit from improved recreational
opportunities whether or not they actually visit an MP&M reach.
EPA estimated wildlife viewing benefits using an approach similar to that used in estimating recreational fishing benefits.
EPA estimated:
*• the number of wildlife viewing days on benefiting reaches;
> the baseline value of wildlife viewing for each benefiting reach; and
> changes in wildlife viewing value, using values from the available surface water valuation studies.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
a. Number of wildlife viewing days
EPA calculated the annual number of person-days of wildlife viewing for each benefiting reach using a two-step approach:
*»* Participating population
The analysis of the NDS data showed that participants in viewing activities travel from 16 to 117 miles to their destination,
with an average one-way travel distance of 34 miles.16 EPA therefore assumes that improvements in recreational
opportunities will benefit only recreational users residing within the counties abutting MP&M reaches. EPA estimated the
population participating in viewing activities using the number of water-based recreation consumers residing in the counties
traversed by benefiting reaches using the following steps:
> estimate resident populations in the counties traversed by the benefiting reaches using Census data;
*• calculate the number of wildlife viewing participants based on the percent of the population engaged in wildlife
viewing activities;
> estimate the percentage of individuals that participate in wildlife viewing in each state using NDS data. The total
state population participating in wildlife viewing ranges from 8.6 percent in New Mexico to 44.4 percent in Maine;
and
*• adjust the number of wildlife viewing participants within the affected county based on the ratio of the affected reach
length to the number of total reach miles in the affected county to calculate the population potentially benefiting from
the rule.17'18
*»* Average number of viewing days
Recreators generally participate in wildlife viewing several times a year. The Agency used NDS data on the number of
wildlife viewing trips to estimate the average number of user days in each state. The NDS data show that the number of
wildlife viewing trips in the 48 states range from 1.8 days per user in South Dakota to 24.2 days per user in Mississippi.19
EPA multiplied the number of wildlife viewing consumers by estimates of the average number of days per user in each state to
estimate the annual number of user days for each benefiting MP&M reach.
b. Baseline value of wildlife viewing
EPA estimated the baseline value of wildlife viewing for the benefiting reaches based on the estimated annual person-days
calculated in subsection (a) above and the estimated value per person-day of wildlife viewing.
EPA used two recreational activity valuation studies (Bergstrom and Cordell (1991) and Walsh et al. (1992)) to calculate the
net economic values per wildlife viewing day. These studies estimate net benefit values for four recreational activities:
wildlife viewing, waterfowl hunting, camping, and picnicking. Based on the two studies, EPA developed an average per-day
value for three of the four activities.20 EPA's MP&M benefits analysis uses the lowest average benefit value, $22.73, for the
low estimate of wildlife viewing benefits and the highest average value, $28.73, for the high estimate. Table 15.5 presents
information on the relevant values reported in these studies.
Using facility sample weights and summing over all benefiting reaches provides the total baseline value of wildlife viewing
for MP&M reaches that EPA expects to benefit by eliminating pollutant concentrations in excess of AWQC limits.
16 These estimates exclude outliers.
17 Information in EPA's Reach File 1 indicates that the ratio of affected reach length to the total number of reach miles within a
county ranges from 0.02 to 0.39.
18 This analysis assumes that recreation activities among residents of the counties affected by MP&M discharges are distributed
evenly across all reach miles within those counties.
19 See Chapter 21 for details on the NDS data.
20 EPA excluded the per-day value of waterfowl hunting ($55.53) from the activities included in this analysis, because this activity is
limited to designated hunting areas only.
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.5: Baseline Values of Wildlife Viewing
Recreational Activity
Camping
Picnicking
Near-water Activities
Per-day Value (2001$)"
r
Bergstrom and Cordell
(1991)"
$27.10
$18.46
$20.07
Walsh et al.
(1992)c
$30.38
$27.00
$34.59
Range of above
Average Per-day Value
(2001$)
$28.73
$22.73
$27.33
$22.73 - $28.73
a Original study values were adjusted to 2001 dollars based on the relative change in CPI from 1987 to 2001.
b Study location: various U.S. locations. Estimating approach: meta-analysis of TC studies.
° Study location: various U.S. locations. Estimating approach: meta-analysis of contingent valuation (CV) and TC studies.
Source: U.S. EPA analysis
c. Changes in wildlife viewing value
EPA selected a subset of the candidate benefits transfer studies discussed in Section 15.2.1 to estimate changes in water
resource value to wildlife viewers due to the MP&M rule. The four selected studies include Tudor et al. (2002), Desvousges
et al. (1987), Lant and Roberts (1990), and Farber and Griner (2000)21. Table 15.6 compiles information on the baseline
values of wildlife viewing, values of changes in water quality, and percentage change in values reported or implied by these
studies.
21 The remaining four studies value changes in the value recreational fishing only.
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.6: Studies Estimating Changes in Value of Wildlife Viewing
Study
Desvousges
etal. (1987)
Lant and
Roberts
(1990)
Farber and
Griner
(2000)
Tudor
et al. (2002)
Water Quality
Change Valued
Improvement from
"beatable" to
"fishable"
Improvement from
"fair" to "good"
Improvement from
"moderately polluted"
to "unpolluted"
Elimination of AWQC
exceedances
Baseline Value of
Wildlife Viewing
(2001$)
$22.8 - $28.7
per trip a
fc
*t99 8 t^S 7
4>ZZ.o - 4>Zo. /
per trip a
fc
*t99 8 498 7
4>ZZ.o - 4>Zo. /
per trip a
h
$77.99 per trip '
Value of Water
Quality Change
(2001$)
$5. 00 per trip b
h ^
$8. 60 per trip c
h ^
$3.33 -$5.69 per
trip"
h 4
$0.60 per trip
Average percentage change (based on the above studies) f
Value of Change
as % of Baseline
17.4% - 22.0%
h
29.9% - 37.8%
h
11. 6% -25.0%
h
0.77%
,
Type of Benefits
Included
Recreational and
nonuse values to users
Recreational and
nonuse values to users
Recreational and
nonuse values to users
Recreational use
values to users
14. 9% -21. 3%
a Based on the range of median values for a near-water recreation day (updated to 2001 dollars) reported in Walsh et al. (1992) and
Bergstrom and Cordell (1991) (seeTablelS.5).
b Based on the value of water quality improvement of $36.79 per person per year (updated from 1987 dollars reported in Desvousges et
al.) divided by the average number of near-water recreation days per year in Pennsylvania (7.37 days, NDS, 1993).
° Based on the value of water quality improvement of $57.79 per year (updated from 1990 dollars) reported in Lant and Roberts
divided by the average number of near-water recreation days per year in Iowa and Illinois (9.58 and 5.04 days, NDS, 1993).
d Based on the value of water quality improvements ranging from $24.55 to $41.93 per person per year reported in Farber and Griner
(2000) divided by the average number of near-water recreation days per year in Pennsylvania (7.37 days, NDS, 1993).
° The baseline value of viewing is based on the estimated mean value of water resources for wildlife viewers reported by Tudor et al.
(2002). The estimated median value of recreational fishing is $82.77. These values were derived from a September 23, 2002 analysis.
f EPA took a simple mean of lower- and upper-bound estimates from the four studies to calculate a range of percentage changes in the
wildlife viewing value from improved water quality conditions. When only one value is available from the study (i.e., Tudor et al.,
2002), EPA used this value in calculating both the lower- and upper-bound estimates.
Source: U.S. EPA analysis
This analysis uses the change of 14.9 percent for the low benefits estimate and 21.3 percent for the high benefits estimate to
calculate benefits from reduced MP&M facility discharges to users of water-based recreation. These values represent the
average of the low and high values, respectively, estimated in the four studies.
Table 15.7 below summarizes the results of EPA's wildlife viewing benefits analysis.
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.7: Summary of Wildlife Viewing Benefits (2001$)
Low
Estimate
High
Estimate
Low
Estimate
High
Estimate
Number of
Benefiting
Reaches
9
9
6
6
Participating
Population
(millions)
3.12
3.12
S
3.17
3.17
Ave.
Number of
Viewing
Days
Selected Opt
7.5
7.5
elected Option:
7.5
7.5
Total
Viewing
Days
(millions)
on: Traditiona
23.52
23.52
Post-Stratifies
23.91
23.91
Baseline
Value/ Rec.
Day
1 Extrapolatior
$22.73
$28.73
ition Extrapola
$22.73
$28.73
Total
Baseline
Value
($ millions)
i
$535
$676
tion
$544
$687
% Change
in Value
14.9%
21.3%
14.9%
21.3%
Benefit
from
MP&M
$185,172
$334,315
$120,639
$217,805
Source: U.S. EPA analysis.
15.2.4 Recreational Boating
Improvements in water quality from the final MP&M rule may enhance recreational boating by (1) providing more
opportunities for companion activities (e.g., fishing and wildlife viewing) and (2) improving visual aesthetics. EPA assumes
that the expected welfare gain for boaters is a function of changes in the overall quality of all recreational opportunities
available to each boater on a given day.
This analysis estimates recreational boating benefits the same way as recreational fishing and wildlife viewing benefits. The
analysis estimates:
*• the number of recreational boating days on benefiting reaches,
> the baseline value of boating for each benefiting reach, and
> changes in recreational boating value.
a. Number of recreational boating days
EPA calculated the annual number of recreational boating days for each benefiting reach using two steps:
*»* Participating population
The analysis of the NDS data showed that boaters travel from 10 to 108 miles to their destination, with an average one-way
travel distance of 32 miles.22 This analysis therefore considers only boaters residing in the counties abutting MP&M reaches.
EPA estimated the number of boaters residing in the counties traversed by benefiting reaches by combining information from
Census data and NDS data on the proportion of individuals participating in boating in each state. The percent of the total
state population in the 48 states participating in boating ranges from 8.0 percent in Colorado to 28.7 percent in Washington.
EPA further adjusted the number of boaters likely to use MP&M reaches within the affected county based on the ratio of the
affected reach length to the number of total reach miles in the affected county.23
22 These estimates exclude outliers.
23 See section 13.1.1 for detail.
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
*»* Average number of boating days
People using benefiting reaches for boating generally participate in this activity several times per year. The NDS data show
the number of boating trips in the 48 states ranging from 3.2 days per user in New Hampshire to 14.6 days per user in
Colorado.
EPA estimated the annual number of user days for recreational boating activities by multiplying the number of boaters by the
average number of boating days peruser in each state.
b. Baseline value of boating
EPA estimated the baseline value of boating on benefiting reaches using the estimated annual person-days of boating per
reach and estimated values per person-day of various types of boating. EPA calculated a range of net economic values per
recreation day of boating based on studies by Bergstrom and Cordell (1991) and Walsh et al. (1992). Mean net benefit values
for motorized and non-motorized boating are $37.30 to $59.26 in 2001 dollars. Table 15.8 compiles information on the
relevant values reported in these studies.
Table 15.8: Baseline Values of a Boating Day
Recreational Activity
Motorized
Non-motorized
Per-day Value (2001$)"
Bergstrom and Cordell
(1991)"
$25.43
$42.67
Walsh et al. (1992)'
$49.18
$75.85
Boating (any type)
Average Per-day Value
(2001$)
$37.30
$59.26
$37.30 - $59.26
a Original study values were adjusted to 2001 dollars based on the relative change in CPI from 1987 to 2001.
b Study location: various U.S. locations. Estimating approach: meta-analysis of TC studies.
° Study location: various U.S. locations. Estimating approach: meta-analysis of CV and TC studies.
Source: U.S. EPA analysis
Weighting by facility sample weights and summing over all benefiting reaches provides a total baseline value of boating for
MP&M reaches expected to benefit by eliminating pollutant concentrations in excess of AWQC limits.
c. Changes in recreational boating values
The Agency used the same four studies discussed in Section 15.2.3 to calculate the change in per-day boating value as a result
of water quality improvements. EPA expressed this change as a percentage of the baseline value. Table 15.9 compiles
information on the baseline values of boating, values of changes in water quality, and percentage change in boating values
reported or implied by these studies.
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.9: Studies Estimating Changes in Value of Recreational Boating
Study
Desvousges
etal. (1987)
Lant and
Roberts
(1990)
Farber and
Griner (2000)
Tudor et al.
(2002)
Water Quality
Change Valued
Improvement from
"beatable" to
"fishable"
L
Improvement from
"fair" to "good"
L
Improvement from
"moderately
polluted" to
"unpolluted"
Elimination of
AWQC exceedances
Baseline Value of
Boating (2001$)
$37. 30 -$59.26
per trip a
h
$37.30 - $59.26
per trip a
h
$37.30 - $59.26
per trip a
$106. 60 per trip6
Value of Water
Quality Change
(2001$)
$3. 92 per trip b
h ^
$7.91 per
trip"
h 4
$2.62 - $4.48 per
trip"
$1.78prtrip
Average percentage change (based on the above studies) f
Value of Change as
% of Baseline
6.6% -10.5%
h
13. 3% -2 1.2%
h
4.4%-12.0%
1.67%
,
Type of Benefits
Included
Recreational and
nonuse values to
users
Recreational use
values to users and
nonusers
Recreational and
nonuse values to
users
Recreational values
for users
6.5% -11. 4%
a Based on the average value for a boating day (updated to 2001 dollars) reported in Walsh et al. (1992) and Bergstrom and Cordell
(1991).
b Based on the value of water quality improvement of $36.79 per person per year (updated from 1987 dollars) reported in Desvousges
et al. divided by the average number of boating days per year in Pennsylvania (9.37 days, NDS, 1993).
° Based on the value of water quality improvement of $57.79 per person per year (updated from 1990 dollars) reported in Lant and
Roberts divided by the average number of boating days per year in Iowa and Illinois (9.58 and 5.04 days, NDS, 1993).
d Based on the value of water quality improvements ranging from $24.55 to $41.93 per person per year reported in Farber and Griner
(2000) divided by the average number of boating days per year in Pennsylvania (9.37 days, NDS, 1993).
° The baseline value of boating is based on the estimated mean value of water resources for boaters reported by Tudor et al. (2002).
The estimated median value of recreational boating is $112.55. These values were derived from a September 23, 2002 analysis.
f EPA took a simple mean of lower- and upper-bound estimates from the four studies described to calculate a range of percentage
changes in the recreational boating value from improved water quality. When only one value is available from the study (i.e., Tudor et
al., 2002), EPA used this value in calculating both the lower- and upper-bound estimates.
Source: U.S. EPA analysis
This analysis uses the change of 6.5 percent for the low benefits estimate and 11.4 percent for the high benefits estimate to
calculate benefits to boaters from reduced MP&M facility discharges. These values represent the average of the low and high
values, respectively, estimated in the four studies.
Table 15.10 summarizes the results of EPA's recreational boating benefits analysis.
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.10: Summary of Recreational Boating Benefits (2001$)
Low
Estimate
High
Estimate
Low
Estimate
High
Estimate
Number of
Benefiting
Reaches
9
9
6
6
Participating
Population
(millions)
2.53
2.53
S
2.57
2.57
Ave.
Number of
Boating
Days
Selected Opl
8.3
8.3
elected Option
8/1
.4
8/1
.4
Total
Boating
Days
(millions)
ion: Tradition
21.06
21.06
: Post-Stratific
21.47
21.47
Baseline
Value/ Rec.
Day
il Extrapolatioi
$37.30
$59.26
ation Extrapok
$37.30
$59.26
Total
Baseline
Value
($ millions)
i
$786
$1,249
ition
$801
$1,272
% Change
in Value
6.5%
11.4%
6.5%
11.4%
MP&M
Benefits
$114,111
$316,078
$74,343
$205,924
Source: U.S. EPA analysis.
15.2.5 Nonuse Benefits
EPA estimated changes in nonuse values for this analysis because nonuse value is a sizeable portion of the total value of water
resources. Individuals who never visit or otherwise use a natural resource may still be affected by changes in its status or
quality. Empirical estimates indicate that such "nonuse values" maybe substantial for some resources (Harpman et al., 1993;
Fisher and Raucher, 1984; Brown, 1993). Most studies have found that nonuse values exceed use values. Brown reviewed
31 CV studies in which both use and nonuse values were estimated, and calculated the ratio of nonuse values to use values
(Brown, 1993). The goal of Brown's study was to assess consistency of ratios of use to nonuse value and to develop a basis
for obtaining a rough estimate of nonuse value, and therefore total values, for the many studies that measured only use values.
His 31 estimated ratios range from 0.1 to 10, with the median ratio of 1.92. The ratios of nonuse to use values reported by
Brown for the studies that valued environmental improvements in water resources range for users of those resources from
0.85 to 2.56. The estimated average ratio is 1.57. That is, for every dollar of annual use-benefit value to users of the subject
environmental resource, the annual nonuse value to resource users for the subject environmental resource is $1.57.
Carson and Mitchell suggested that nonuse benefits account for 19 to 39 percent of total WTP values for water quality
improvements depending on the definition of nonuse values (Carson and Mitchell, 1993). The ratio of nonuse to use value
ranges from one-fourth to two-thirds based on the Carson and Mitchell study (1993). Fisher and Raucher (1984) found that
nonuse benefits comprise one-half of recreational use benefits.
EPA used findings from the Fisher and Raucher (1984) study in which nonuse values are estimated to be equal to 50 percent
of use values to estimate nonuse benefits from the final MP&M regulation. The method has long been used by EPA as a
pragmatic alternative to omitting nonuse values entirely. EPA acknowledges that this method is crude and nonuse values
estimated by the 50 percent of use value approach are quite low given the applicable literature discussed above.
The Agency estimates that nonuse benefits from the final MP&M rule will range from $293,252 to $787,190 and from
$191,053 to $512,852, based on the traditional and post-stratification extrapolation, respectively.
15.3 SUMMARY OF RECREATIONAL BENEFITS
EPA assumes that eliminating concentrations of MP&M pollutants in excess of AWQC limits will achieve water quality
protective of aquatic life and human health. This improved water quality then generates benefits for both users and nonusers
of water-based recreation. These benefits can be seen as an increase in the value of each day spent on or near the waterway,
as well as an increase in the number of days spent on or near the waterway. EPA estimated the monetary value of improved
water-based recreational opportunity for the 9 discharge reaches based on the traditional extrapolation (6 reaches based on the
post-stratification extrapolation) for which concentrations in excess of AWQC limits would be eliminated.
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
EPA first estimated the number of recreational days on benefiting reaches for each water-based activity. The Agency then
calculated the baseline value of these activities and then calculated the percentage changes in this value stemming from water
quality improvements.
EPA calculated partial benefits for reaches with reduced numbers of AWQC exceedances by adjusting the percentage increase
in the recreational value of these reaches. EPA made these adjustments based on the ratio of the number of AWQC
exceedances eliminated post-compliance to the number of AWQC exceedances occurring at baseline.
Table 15.11 summarizes benefit estimates by recreational category for the final rule based on the traditional and post-
stratification extrapolation methods. The activities considered in this analysis are stochastically independent; EPA calculated
the total value of enhanced water-based recreation opportunities by summing over the three recreation categories. EPA also
estimated the changes in nonuse value resulting from reduced MP&M discharges based on the ratio of use to nonuse values
implied by the Fisher and Raucher study (Fisher and Raucher, 1984). Based on the traditional extrapolation, the estimated
increase in nonuse value ranges from $0.29 to $0.79 million (2001$), with a midpoint value of $0.50 million (2001$). The
resulting increased value of recreational activities to consumers (users and nonusers) of water-based recreation ranges from an
estimated $0.59 to $1.57 million (2001$) annually. The estimated mean value of recreational benefits is $1.00 million
(2001$) annually. Likewise, based on the post-stratification extrapolation, the estimated increase in nonuse value ranges from
$0.19 to $0.51 million (2001$), with a midpoint value of $0.33 million (2001$). The resulting increased value of recreational
activities to consumers (users and nonusers) of water-based recreation ranges from an estimated $0.38 to $1.03 million
(2001$) annually. The estimated mean value of recreational benefits is $0.65 million (2001$) annually.
Tables 15.12 and 15.13 summarize benefit estimates for the 433 Upgrade Options and Proposed/NODA Option, respectively.
Recreational use and nonuse benefits are almost 200 times higher under the two 433 Upgrade Options, and over 430 times
higher under the Proposed/NODA Option.
Table 15.11: Estimated Recreational Benefits from Reduced MP&M Discharges (Thousands, 2001$)
Recreational Activity
Fishing
Boating
Viewing and near-water activities
Total Recreational Use Benefits
Nonuse Benefits (Vz of the
Recreational Use Benefits)
Total Recreational Benefits
:
Traditional Extrapolation I Post-Stratification Extrapolation
Low Value
$287
$114
$185
$587
$293
$880
Midpoint
Value
$537
$203
$260
$1,000
$500
$1,500
High Value
$924
$316
$334
$1,574
$787
$2,362
Low Value
$187
$74
$121
$382
$191
$573
Midpoint
Value
$350
$132
$169
$651
$326
$977
High Value
$602
$206
$218
$1,026
$513
$1,539
Source: U.S. EPA analysis.
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.12: Estimated Recreational Benefits from Reduced MP&M Discharges (Thousands, 2001$)°
Recreational Activity
Fishing
Boating
Viewing and near-water activities
Total Recreational Use Benefits
Nonuse Benefits (1A of the
Recreational Use Benefits)
Total Recreational Benefits
:
Directs + 413 to 433 Upgrade Directs +A11 to 433 Upgrade
Low Value
$28,713
$36,511
$56,584
$121,808
$60,904
$182,712
Midpoint
Value
$53,703
$64,854
$79,434
$197,990
$98,995
$296,986
High Value
$92,369
$101,134
$102,158
$295,661
$147,831
$443,492
Low Value
$29,052
$36,652
$56,657
$122,360
$61,180
$183,541
Midpoint
Value
$54,337
$65,103
$79,536
$198,976
$99,488
$298,464
High Value
$93,460
$101,523
$102,290
$297,272
$148,636
$445,908
a Based on the Traditional Extrapolation.
Source: U.S. EPA analysis.
Table 15.13: Estimated Recreational Benefits from Reduced MP&M Discharges (Thousands, 2001$)°
Recreational Activity
Fishing
Boating
Viewing and near-water activities
Total Recreational Use Benefits
Nonuse Benefits (1A of the
Recreational Use Benefits)
Total Recreational Benefits
Proposed/NODA Optionb
Low Value
$53,897
$75,847
$140,623
$270,366
$135,183
$405,550
Midpoint Value
$100,805
$134,724
$197,410
$432,939
$216,469
$649,408
High Value
$173,386
$210,089
$253,884
$637,360
$318,680
$956,040
a Based on the Traditional Extrapolation.
b The estimated recreational benefits of the Proposed/NODA Option are not directly comparable to the final option alternatives. The
total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final rule and
the two upgrade options. After deciding in July 2002 not to consider the NODA option as the basis for the final rule, EPA performed
no more analysis on the NODA option, including not updating facility counts and related analyses for the change in subcategory and
discharge status classifications.
Source: U.S. EPA analysis.
15.4 LIMITATIONS AND UNCERTAINTIES ASSOCIATED WITH ESTIMATING
RECREATIONAL BENEFITS
EPA assessed recreational benefits in terms of reduced occurrence of pollutant concentrations exceeding acute and chronic
toxic effect levels for aquatic species. EPA also attached a monetary value to ecological improvements expected to result
from the MP&M regulation, in the form of the increased value of three water-based recreation activities recreational fishing,
wildlife viewing, and boating plus the increase in nonuse value. The estimated increase in value detailed in this chapter
constitutes only a partial measure of the value to society of improving aquatic habitats and aquatic life. This benefits analysis
is limited because it ignores improvements to recreational activities other than fishing, boating, and wildlife viewing (e.g.,
swimming), as well as non-recreational benefits, such as increased assimilative capacity and improvements in the taste and
odor of the affected waters.
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
The methodologies used to assess ecological benefits also involved significant simplifications and uncertainties, whose
combined effect on the estimated benefits is not known. Estimated economic values may be under- or overestimated. Some
of these simplifications and uncertainties also apply to the human health benefits analysis, and have been discussed at length
in the previous chapter, including those associated with:
•• developing the sample of MP&M facilities analyzed in the EEBA,
> estimating in-waterway concentrations of MP&M pollutants,
*• considering background concentrations of MP&M pollutants, and
*• considering downstream effects.
Table 15.14 summarizes the additional elements of uncertainty that are specific to the recreational benefits analysis.
Table 15.14: Key Omissions, Biases, and Uncertainties in the Analysis
for Improved Recreational and Nonuse Benefits
Assumption/Limitation
Direction of Impact on Benefit Estimates
Scope of Recreational Benefits Analysis
Only the receiving reach itself is
estimated to provide benefits.
Water quality in reaches downstream of the reaches affected by MP&M discharges may also
improve, generating additional benefits to society. Excluding these benefits from the analysis
biases benefits estimates downward.
Only recreational users living in the
counties abutting MP&M reaches are
assumed to benefit from water quality
improvements due to the MP&M
rule.
The analysis underestimates the total value of benefits from the MP&M regulation because it
does not account for people's WTP for water quality improvements to distant water bodies.
For example, economic values for improving nationally-significant water bodies (e.g., Great
Lakes, Chesapeake Bay, Long Island Sound) are likely to be substantial at a regional level or
even nationwide.
The analysis of recreational fishing
ignores effects that occur in
secondary industries.
The analysis of recreational benefits ignores potential economic effects on tourism industries
stemming from improved recreational opportunities. Improved recreational fishing may have a
positive effect on industries supplying bait, tackle, charter boats, etc. An increase in consumer
demand for boating may have positive effects on industries such as boat construction, sales,
rentals, boating equipment, marinas, racing activities, etc. Improvements in wildlife viewing
and near-water recreation opportunities may benefit industries involved in providing other
recreational opportunities, such as tours, books, binoculars, etc.
The analysis of recreational benefits
ignores changes in the value of
water-based recreational activities
other than fishing, wildlife viewing,
and boating (e.g., swimming or
waterskiing).
The estimate of recreational benefits is incomplete because it includes only a subset of
recreational activities (i.e., fishing, wildlife viewing, and boating) for which society may value
improved aquatic habitat. It ignores changes in value for other water-based recreational
activities, such as swimming or waterskiing. In addition, the analysis did not consider other
changes in the affected reaches, such as improved taste and odor.
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.1
Assumption/Limitation
Extrapolating from sample facility
results to national results is based on
the sample facility weights
Congestion Externalities
The waters assessed by local-level
studies are not necessarily nationally
representative.
Types of water quality changes
expected from the MP&M rule may
differ from the water quality changes
considered in the original studies.
Compatibility of time periods
considered in the original studies and
in the analysis of MP&M costs and
benefits.
Converting annual WTP values to
per-trip values
4: Key Omissions, Biases, and Uncertainties in the Analysis
for Improved Recreational and Nonuse Benefits
Direction of Impact on Benefit Estimates
j(?)
I This extrapolation technique is not ideal and introduces uncertainty into the analysis. Facility
1 sample weights are based on facility size and type of industry. These weights do not
! necessarily account for the frequency benefit pathway characteristics in the MP&M facility
I universe. Therefore benefit estimates may suffer from uncertainties associated with the
I extrapolation method. For example, a sample facility may have a significant impact on benefit
1 estimates if it is more likely to be located in a densely populated area, such as a facility located
1 in Cleveland, Ohio, or a facility discharging in Long Island Sound, than the facilities it
I represents. The opposite may also be true.
:
:
1 To improve accuracy of the national benefit estimates, EPA used an alternative extrapolation
! method (i.e., post-stratification extrapolation). This method relies on adjusted sample facilities
! weights that account for the distribution of benefit pathway characteristics, including water
1 body type and population size, in the MP&M facility universe. Appendix G summarizes this
1 extrapolation approach.
1 (+)
I Recreational benefits associated with water quality improvements can be eroded by congestion
1 if policies greatly increase the number of participants. This can be particularly problematic
1 when policies affect geographically scattered sites, so that there is considerable switching to
1 the improved site from substitute sites. Congestion may be a lesser problem for national
I regulations that might affect the total number of recreation days and the overall value of
! recreational opportunities, but are less likely to have a large effect on industrial sites relative to
1 its substitutes.
Benefits Transfer
(?)
The studies selected came from the Midwest and the Northeast. As a result, the resources
valued, as well as respondent preferences, may not be representative of the rest of the country.
(?)
The types of water quality changes expected from the MP&M regulation are only roughly
comparable with the majority of water quality changes considered in the original studies
(Tudor et al. is the only exception). Due to the paucity of available studies, the Agency made
simplifying assumptions to "map" the water quality changes valued in the original studies onto
those expected from the rule. Although these assumptions are likely to increase uncertainty
associated with recreational benefits estimates, the direction of bias is not known.
(+)
Most studies considered in the benefits transfer analysis did not specify payment periods. The
scenario in the Farber and Griner (2000) paper asked for payments for the next five years.
This scenario implies that five years of pollution control will result in permanent water quality
changes. The analysis of the MP&M regulation assumes that pollution control continues over
15 years and that water quality improvements depend on continued operation of the water
pollution controls. EPA therefore chose the annual WTP values presented in the paper, as
opposed to the total value paid over five years, annualized over the 1 5 years considered in the
cost analysis. This assumption may result in an overestimation of the regulation's benefit.
The magnitude of this error is unlikely to be significant because this study is used in
combination with other surface water valuation studies.
Baseline Value of Fishery
!(+)
1 EPA converted annual WTP values reported in the three CV studies used in this analysis to
1 per-trip values by dividing seasonal welfare gain per user reported in each CV study by the
! average number of fishing, boating, or viewing days in a given state. This calculation implies
I that every individual participates in only one activity, which may not be the case. This
1 implication may result in an overestimation of the per-trip welfare gain, and, consequently, an
! overestimation of total recreational benefits from the final rule.
..1
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MP&M EEBA Part III: Benefits
Chapter 15: Recreational Benefits
Table 15.
Assumption/Limitation
This analysis estimates the baseline
value of the fisheries at locations
across the country using a range of
values for all types of fisheries.
The total number of recreational
person-days in the counties abutting
MP&M reaches is evenly distributed
across all reach miles in these
counties.
Nonuse values are estimated as one-
half of recreational use benefits.
Overall Impact on Benefits
Estimates
14: Key Omissions, Biases, and Uncertainties in the Analysis
for Improved Recreational and Nonuse Benefits
Direction of Impact on Benefit Estimates
(?)
Site-specific fisheries may have higher or lower baseline values, and thus, higher or lower
benefits from reduced MP&M discharges.
(+)
This method for estimating the number of recreational users potentially affected by water
quality improvements from the final regulation accounts for the quantity but not quality of
potential recreational opportunities available to recreational users. There may be important
substitute sites in or outside the counties abutting MP&M reaches. Ignoring recreationally
important substitute sites may result in overestimation of benefits from the final regulation.
Ideally the analysis would consider recreational importance of both sites affected by MP&M
discharges and substitute sites.
Nonuse Values
\ (?)
! It is unknown what bias estimating nonuse values based on recreational use values has on
! benefits.
| (?)
+ Potential overestimate.
? Uncertain impact.
- Potential underestimate.
Source: U.S. EPA analysis.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
GLOSSARY
1Q10: the lowest one-day average flow with a recurrence interval often years.
7Q10: the lowest consecutive seven-day average flow with a recurrence interval often years.
ambient water quality criteria (AWQC): published and periodically updated by the EPA under the Clean Water Act.
The criteria reflect the latest scientific knowledge on the effects of specific pollutants on public health and welfare, aquatic
life, and recreation. The criteria do not reflect consideration of economic impacts or the technological feasibility of reducing
chemical concentrations in ambient water. The criteria serve as guides to states, territories, and authorized tribes in
developing water quality standards and ultimately provide a basis for controlling discharges or releases of pollutants into our
nation's waterways. AWQC are developed for two exposure pathways: ingestion of the pollutant via contaminated aquatic
organisms only, and ingestion of the pollutant via both water and contaminated aquatic organisms.
benefiting reaches: reaches where the MP&M rule is expected to eliminate existing AWQC exceedences. These
receiving waters are likely to experience significant water quality improvements as a result of the reduced MP&M discharges.
A reach is considered to benefit if at least one AWQC exceedance is eliminated due to reduced MP&M discharges.
benefits transfer: involves the application of value estimates, functions, and/or models developed in one context to
address a similar resource valuation question in another context. Often a meta-analysis is undertaken where benefits estimates
based on existing studies are used to develop new estimates which are applicable to the scenario under consideration. This
process accounts for relevant differences in study characteristics, such as the quality of environmental resource, the
environmental change considered, and the user population being investigated.
contingent valuation (CV): directly asks people what they are willing to pay for a benefit and/or willing to receive in
compensation for tolerating a cost through a survey or questionnaire. Personal valuations for increases or decreases in the
quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit valuations or bids that are
close to what would be revealed if an actual market existed.
conventional pollutants: biological oxygen demand (BOD), total suspended solids (TSS), oil and grease (O&G), pH, and
anything else the Administrator defines as a conventional pollutant.
dissolved oxygen (DO): oxygen freely available in water, vital to fish and other aquatic life and for the prevention of
odors. DO levels are considered a most important indicator of a water body's ability to support desirable aquatic life.
Secondary and advanced waste treatment are generally designed to ensure adequate DO in waste-receiving waters.
(http://www.epa.gov/OCEPAterms/dterms.html)
Metal Products and Machinery (MP&M): industry includes facilities that manufacture, rebuild, and maintain metal parts,
products, or machines.
National Demand Study (NDS): U.S. EPA and the National Forest Service conducted the National Demand Survey for
Water-Based Recreation in 1993. The survey collected data on demographic characteristics and water-based recreation
behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over.
nonconventional pollutant: catch-all category that includes everything that is not classified as a priority pollutant or a
conventional pollutant.
piscivorous: feeding preferably on fish.
priority pollutant (PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
sediment, groundwater, or soil samples.
toxic pollutants: EPA's Office of Water narrowly defines a toxic pollutant as one of 126 priority pollutants. This
definition is not completely synonymous with pollutants that have a "toxic" effect. Many nonconventional pollutants may
also be hazardous to aquatic life and human health.
"toxic" pollutant: any pollutant that has an adverse effect on aquatic life or human health.
15-26
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
travel cost (TC) model: derives values by evaluating expenditures of recreators. Travel costs are used as a proxy for price
in deriving demand curves for the recreation site, (http://www.damagevaluation.com/glossary.htm)
U.S. Fish and Wildlife Service (FWS): the principal federal agency responsible for conserving, protecting, and
enhancing fish, wildlife, and plants and their habitats for the continuing benefit of the American people.
(http://www.fws.gov/r9extaff/pafaq/fwsfaq.html)
willingness-to-pay (WTP): maximum amount of money one would give to buy some good.
(http://www.damagevaluation.com/glossary.htm)
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MPAM EEBA Part III: Benefits
Chapter 15: Recreational Benefits
ACRONYMS
1Q10: the lowest 1-day average flow with a recurrence interval of 10 years
7Q10: the lowest 7-day average flow with a recurrence interval of 10 years
AWQC: ambient water quality criteria
CV: contingent valuation
DO: dissolved oxygen
FWS: U.S. Fish and Wildlife Service
MP&M: Metal Products and Machinery
NDS: National Demand Study
7"C; travel cost
WTP: willingness-to-pay
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
REFERENCES
Helton, T., R. Roundy and N. Weinstein. 1986. Urban Fishermen: Managing the Risks of Toxic Exposure. Environment,
Vol. 28. No. 9, November.
Brown, T. 1993. "Measuring Nonuse Value: A Comparison of Recent Contingent Valuation Studies." In: Bergstrom, J. C.,
Benefits and Costs Transfer in Natural Resource Planning. Sixth Interim Report. Athens, GA: University of Georgia,
Department of Agriculture and Applied Economics.
Bergstrom, J. C. and K. Boyle. 1990. "Using Benefits Transfer to Value Groundwater Benefits: Conceptual Issues."
Presented at the joint AAEA/AERE meeting in Orlando, FL. August 1993.
Bergstrom, J. C. and H. K. Cordell. 1991. "An analysis of the Demand for and Value of Outdoor Recreation in the United
States." Journal of Leisure Research, Vol. 23, No. 1: 67-86.
Carson, R. T., and R. C. Mitchell. 1993. "The Value of Clean Water: The Public's Willingness to Pay for Beatable,
Fishable, and Swimmable Quality Water." Water Resources Research 29(7): 2445-2454.
Connelly, N. and B. Knuth. 1993. Great Lakes Fish Consumption Health Advisories: Angler Response to Advisories and
Evaluation of Communication Techniques. Human Dimensions Research Unit, Dept. of Natural Resources, NY State College
of Agriculture and Life Sciences, Cornell University, HDRU Series No 93-3, February.
Connelly, N., B. Knuth, and C. Bisogni. 1992. Effects of the Health Advisory and Advisory Changes on Fishing Habits and
Fish Consumption in New York Sport Fisheries. Human Dimensions Research Unit, Dept. of Natural Resources, NY State
College of Agriculture and Life Sciences, Cornell University, HDRU Series No 92-9, September.
Desvousges, W. H. et al. 1987. "Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study for
the Monongahela River." Journal of Environmental Economics and Management, 14: 248-267.
Farber, S. and B. Griner. 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis. University of
Pittsburgh, PA.
Fisher, A. and R. Raucher. 1984. "Intrinsic Benefits of Improved Water Quality: Conceptual and Empirical Perspectives."
In: Advances in Applied Microeconomics, Vol. 3, V.K. Smith, editor, JAI Press.
Harpman, D. A., M. P. Welsh, and R.C. Bishop. 1993. "Nonuse Economic Value: Emerging Policy Analysis Tool." Rivers,
Vol. No.4.
Jakus, P. M., M. Downing, M. S. Bevelhimer, and J. M. Fly. 1997. "Do Sportfish Consumption Advisories Affect Reservoir
Anglers' Site Choice?" Agricultural and Resource Economic Review, 26(2).
Knuth, B. and C. Velicer. 1990. Receiver-Centered Risk Communication for Sportfisheries: Lessons from New York
Licensed Anglers. Paper presented at the American Fisheries Society Annual Meeting, Pittsburgh, Penn, August.
Lant, C. L. and R. S. Roberts. 1990. "Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure."
Environment and Planning A, Vol. 22: 1375-1388.
Lyke, A. J. 1993. "Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case Study." PhD
dissertation. Madison, WI: University of Wisconsin, Department of Agricultural Economics.
Montgomery, M. and M. Needelman. 1997. "The Welfare Effects of Toxic Contamination in Freshwater Fish." Land
Economics 73(2): 211-223.
Phaneuf, D. J., C. L. Kling, and J. A. Herriges. 1998. "Valuing Water quality Improvements Using Revealed Preference
Methods When Corner Solutions are Present." American Journal of Agricultural Economics 80: 1025-1031.
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MP&M EEBA Part III: Benefits Chapter 15: Recreational Benefits
Silverman, W. 1990. Michigan 's Sport Fish Consumption Advisory: A Study in Risk Communication. Thesis submitted in
partial fulfillment of the requirements for the degree of Master of Science (Natural Resources) at the University of Michigan,
May.
Tudor, L., E. Besedin, M. Fisher, and S. Smith. 1999a. "Economic Analysis of Environmental Regulations: Application of
the Random Utility Model to Recreational Benefit Assessment for the MP&M Effluent Guideline." Presented at the annual
American Agricultural Economics Association meeting, Nashville TN.
Tudor, L., E. Besedin, S. Smith, and L. Snyder. 1999b. "What Pollutants Matter for Consumers of Water-Based
Recreation?" Presented at the annual Northeastern Agricultural and Resource Economics Association meeting, Morgantown,
WV, June.
Tudor, L., et al. 2002. Economic Analysis of Environmental Regulations: Using a Random Utility Model to Assess
Recreational Benefits for Effluent Guidelines. Final Report. Washington, DC: U.S. EPA. January.
U.S. Department of Commerce, Bureau of the Census. 1 996. Projections of Household by Type 1 995-2010 (series 1).
http://www.census.gov/population/projections/nation/hh-fam/tableln.txt.
U.S. Department of the Interior. 1993. 1991 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, DOI,
March.
U.S. Environmental Protection Agency (U.S. EPA). 1986. Quality Criteria for Water. EPA 440/5-86-001.
U.S. Fish and Wildlife Service (USFWS). 1996. 1996 National Survey of Fishing, Hunting, and Wildlife-Associated
Recreation. U.S. Fish and Wildlife Service and Bureau of the Census.
Walsh, R. G., D. M. Johnson, and J. R. McKean. 1992. "Benefit transfer of Outdoor Recreation Demand Studies, 1968-
1988." Water Resource Research, Vol.28, No. 3: 707-713.
West, P., R. Marans, F. Larkin, and M. Fly. 1989. Michigan Span Anglers Fish Consumption Survey: A Report to the
Michigan Toxic Substances Control Commission, University of Michigan School of Natural Resources, Natural Resources
Sociology Research Lab, Technical Report #1, May.
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MP&M EEBA Part III: Benefits
Chapter 16: POTW Benefits
Chapter 16: POTW Benefits
INTRODUCTION
The final rule only regulates direct dischargers. Therefore,
the selected option does not affect POTW operations. For the
alternative policy options that consider both direct and
indirect dischargers, EPA evaluated two categories of
productivity benefits for publicly-owned treatment
works (POTWs):
* reduced interference with the operations of
POTWs, and
*• reduced contamination of sewage sludge (i.e.,
biosolids) at POTWs that receive discharges from
MP&M facilities.
CHAPTER CONTENTS
16.1 Reduced Interference with POTW Operations ... 16-2
16.2 Assessing Benefits from Reduced Sludge
Contamination 16-2
16.2.1 Data Sources 16-2
16.2.2 Sludge Generation, Treatment, and Disposal
Practices 16-4
16.2.3 Overview of Improved Sludge
Quality Benefits 16-7
16.2A Sludge Use/Disposal Costs and Practices .. 16-8
16.2.5 Quantifying Sludge Benefits 16-10
16.3 Estimated Savings in Sludge Use/Disposal Costs 16-15
16.4 Methodology Limitations 16-16
Glossary 16-18
Acronyms 16-19
References 16-20
Interference with POTW processes occurs when high levels
of toxics, such as metals or cyanide, kill bacteria required for
wastewater treatment processes. The removal of these pollutants would eliminate the need for extra labor and materials to
maintain POTW operations.
Toxic priority and nonconventional pollutants may also pass through a POTW and contaminate sludge generated during
primary and secondary wastewater treatment.1 POTW treatment of wastewater with reduced pollutant concentrations
translates into cleaner sludge, which can be disposed of using less expensive and more environmentally benign methods. In
some cases, cleaner sludge may have agricultural applications, which would generate additional resource conservation
benefits.
Some MP&M pollutants that pass through a POTW and contaminate sludge are not currently subject to sewage sludge
pollutant concentration limits. The alternative policy options would reduce concentrations of these pollutants in sewage
sludge as well, which may translate into reduced environmental and human health risks. EPA did not estimate the reduced
risk attributable to the reduction of these pollutants.
Wastewater from MP&M facilities also contains hazardous air pollutants (HAPs). These pollutants may represent
unacceptable health risks to POTW workers if released into the air at high enough concentrations during the wastewater
treatment cycle. This reduction in pollutants may translate into health benefits to POTW workers and those living near
POTWs.
The remaining sections of this chapter present methodology for estimating benefits to the receiving POTWs from reducing
pollutants in the wastewater of indirect MP&M dischargers. As noted above, the final option does not affect POTW
operations since it regulates direct dischargers only. For the alternative options that consider both direct and indirect
dischargers, EPA evaluated two benefits measures associated with MP&M pollutants: (1) the reduction in pollutant
interference at POTWs; and (2) pass-through of pollutants into the sludge, which limits options for POTW disposal of sewage
sludge.
The term sewage sludge, also called biosolids, is often shortened to sludge throughout this chapter for simplicity.
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MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits
16.1 REDUCED INTERFERENCE WITH POTW OPERATIONS
High levels of some MP&M pollutants (such as metals, chlorobenzene, polyaromatic hydrocarbons, and oil and grease) can
kill bacteria that are required for the wastewater treatment process (U.S. EPA, 1987). POTWs affected by such "inhibition
problems" may incur extra labor and materials costs to maintain system operations. As a partial measure of the economic
benefits resulting from the alternative regulatory options, EPA estimated the extent to which reduced MP&M discharges
would decrease pollutant concentrations to below POTW pollutant inhibition values, using the following steps:
»• estimate the baseline and post-compliance influent concentrations for each POTW receiving discharges from
MP&M facilities, based on annual pollutant loadings from the MP&M facility, the number of POTW operating days
per year, and the gross volume of influent;
*• compare baseline and post-compliance influent concentrations with available inhibition levels (see Table 1.5 in
Appendix I); and
»• estimate the change in the number of POTWs in which influent concentrations of MP&M pollutants exceed POTW
inhibition values.
Adverse effects on POTW operations, including inhibition of microbial degradation, are likely when influent
concentrations of one or more pollutants exceed an inhibition value. EPA estimated influent concentrations in excess of
POTW inhibition values for the sample facilities for the baseline and the alternative regulatory options. Results of this
analysis are presented in Appendix I of this report. Eliminating the exceedances will result in operating cost savings to
POTWs. EPA has not estimated a monetary value for this benefit, however, due to data limitations.
The final rule only regulates direct dischargers. Therefore, the selected option does not affect POTW operation. For the
alternative policy options that consider both direct and indirect dischargers, EPA estimated that 51 POTWs had influent
concentrations in excess of biological inhibition values for one or more pollutants under the baseline conditions
corresponding to the 433 Upgrade Options. This represents 0.3% of the over 1 6,000 POTWs operating nationwide. (Table
1.12 in Appendix I provides detailed information on pollutants exceeding POTW inhibition criteria.) Both upgrade options
would eliminate exceedances of POTW inhibition criteria in 21 POTWs.
EPA's analysis finds that influent concentrations in 293 POTWs exceed biological inhibition values for one or more
pollutants under the Proposed/NODA Option. The Proposed/NODA Option would eliminate inhibition criteria exceedances
in 156 of the affected POTW.2
POTWs may impose local limits to prevent inhibitions. If local limits are in place, the estimated reduction in potential
inhibition problems at the affected POTWs may be overstated. In this case, however, the estimated social cost of the MP&M
regulation is also overstated.
16.2 ASSESSING BENEFITS FROM REDUCED SLUDGE CONTAMINATION
16.2.1 bata Sources
The analysis of POTW benefits from improved sludge quality draws on several data sources. The §308 POTW Surveys
provide most of the required information. EPA collected information from 147 POTWs representing a 98 percent response
rate to the 150 surveys that were mailed. EPA also used the §308 survey of MP&M facilities. The two data collection efforts
were not designed to provide a match between the MP&M sample facilities and the POTWs to which they discharge. EPA
obtained a significant amount of information from the POTW Surveys, but had substantially less information on the POTWs
that receive discharges from the MP&M facilities. To address this data limitation, EPA used the POTW Survey data to infer
2 The total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final
rule and the upgrade options (Directs + 413 to 433 Upgrade Option, Directs + All to 433 Upgrade Option). After deciding in July 2002 not
to consider the NODA option as the basis for the final rule, EPA did not perform any more analyses on the NO DA option - including not
updating facility counts and related analyses for the change in subcategory and discharge status classifications.
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MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits
information on the key factors that are likely to influence choice of sewage sludge use and disposal practices for the POTWs
receiving discharges from the MP&M facilities.
The POTW Survey contains three sections. Section 1 provides general information on POTW location and size. Section 2
provides data on the cost of administering pre-treatment programs (see Appendix F). Section 3 contains data on the cost of
treating and disposing of sewage sludge and provides new and more consistent data for analyzing the effect of reduced
pollutant loadings on sewage sludge management costs.
The POTW Survey asked for the following information:
•• current sludge disposal practices;
•• sludge disposal costs for one or more disposal methods;
»• reasons for not using a less expensive disposal method;
•• number of MP&M facilities discharging to the POTW, by flow size (less than 1 million gal/year; 1-6.25 million
gal/year; greater than 6.25 million gal/year);
> total metal loadings discharged to the POTW from all sources; and
*• percentage of total metal loadings attributable to MP&M facilities.
The POTW Survey was intended to address data limitations encountered in the Phase 1 analysis, particularly the inadequacy
of information about POTWs that receive discharges from the MP&M sample facilities. The only information available for
the Phase I analysis was POTW geographic location, influent volume, and the metals content of the discharge received from
the sampled MP&M facilities. Discharges to the POTW by non-sampled MP&M facilities and by non-MP&M facilities were
not known. These discharges may significantly affect sewage sludge quality, however, resulting in a discrepancy between
predicted and actual pollutant concentrations in sewage sludge and the corresponding disposal practices. In addition, lack of
information on the factors that may influence a POTW's decisions about sludge management practices introduced additional
uncertainty in the analysis.
EPA used the POTW Survey to calculate the following parameters:
»• baseline percentage of the total metal loadings to POTWs by POTW flow category attributable to MP&M facilities;
•• post-compliance loading reductions for non-sampled MP&M facilities discharging to the receiving POTWs;
•• costs of sewage sludge disposal practices; and
*• percentage of qualifying sludge that is not beneficially used for any of the following reasons: lack of land; lower cost
alternative; inability to meet vector or pathogen requirements; poor weather; stricter state standards; and other
reasons.
EPA also used the data provided by the Association of Metropolitan Sewerage Agencies (AMSA) to refine its analysis of
POTW benefits for the final rule. AMSA provided EPA with comments on the proposed MP&M rule and supplemented these
comments with a spreadsheet database (AMSA, 2000). The database contains data from an AMSA formulated survey and
covers responses from 176 POTWs, representing 66 pretreatment programs. The AMSA survey was conducted to verify data
from EPA's survey of POTWs and therefore included similar, although fewer, variables compared to EPA's survey.
EPA used the results of the AMSA survey to supplement information from the MP&M POTW Survey on percentage of metal
loadings contributed by MP&M facilities and the number of MP&M facilities served by POTWs. Based on the results of the
joint analysis of the EPA and AMSA surveys, EPA revised the following elements of the POTW benefits methodology: (1)
the number of MP&M facilities served by small, medium, and large POTWs, (2) percentage of metal loadings contributed by
MP&M facilities, and (3) percentage of qualifying sludge that is not land-applied.
Finally, EPA used other data sources in this analysis, including Handbook for Estimating Sludge Management Costs (EPA,
1985) and Regulatory Impact Analysis of the Part 503 Sludge Regulation (EPA, 1993b).
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
16.2.2 Sludge Generation, Treatment, and Disposal Practices
a. Sludge generation
POTW s generally treat wastewater from industrial indirect dischargers along with domestic wastewater. Sludge results from
primary, secondary, and advanced wastewater treatment. The extent and type of wastewater treatment determine the chemical
and physical character of the sludge. Sludge maybe conditioned, thickened, stabilized, and dewatered to reduce its volume.
Sludge contains five classes of components: organic matter, pathogens, nutrients, inorganic chemicals, and organic chemicals.
The mix and levels of these components ultimately determine the human health and environmental impact of sludge
use/disposal, and so may also dictate the most appropriate uses and disposal practices (EPA, 1993b).
Organic matter (the primary constituent of sludge) comes from human waste, kitchen waste, and stormwater runoff. Organic
and inorganic chemicals in sludge come from industrial processes that discharge to municipal sewers. The concentration of
inorganic pollutants in sludge, including metals, depends upon the volume and type of industrial wastes discharged to the
POTW, as well as the extent and character of stormwater runoff.
b. Sludge use/disposal practices
After treatment, sludge can be used in the following ways:
»• Land Application: Spraying or spreading on the land surface, injection below the surface, or incorporation into the
soil, for soil conditioning or fertilization of crops or vegetation. Agricultural lands (pasture, range land, crops),
forest lands (silviculture), and drastically disturbed lands (land reclamation sites) may all receive sludge;
•• Bagged Application: Collection of sludge in containers for application to land (i.e., distribution and marketing);
•• Surface Disposal: Disposal on land specifically set aside for this use, including surface impoundments (also called
lagoons), sludge monofills (i.e., sludge-only landfills), and dedicated sites (i.e., land on which sludge is spread solely
for final disposal);
•• Co-disposal: Disposal in a municipal solid waste landfill (MSWL) or hazardous waste landfill; and
»• Incineration: Combustion of organic and inorganic matter at high temperatures in an enclosed device.
Land application and bagged application are beneficial uses of sludge. Both methods can be categorized as being "high" or
"low," depending on pollutant concentrations in sewage sludge. "High" applications meet stringent limits on the total
concentration of a given pollutant at a given application site. "High" sludge is exempt from meeting pollutant loading rate
limits and certain record-keeping requirements. "Low" applications meet less stringent "ceiling" limits for pollutants. Ceiling
limits govern whether a sewage sludge can be applied to land at all. "Low" applications require more record-keeping because
POTWs must track total (cumulative) loadings applied to each given site, in addition to tracking the concentration of sludge
applied at any given time.
Many POTWs use more than one use/disposal practice, which helps to maintain flexibility and avoid the capacity limitations
of a single practice. The practice chosen depends on several factors, including:
•• cost to prepare sludge for use/disposal;
•• pollutant concentrations;
»• market demand for sludge;
•• cost to transport sludge to use/disposal sites;
»• availability of suitable sites for land application, landfilling, or surface disposal;
»• weather and other local conditions;
»• allowance of a safety factor to account for unplanned or unforseen conditions;
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MPAM EEBA Part III: Benefits
Chapter 16: POTW Benefits
»• state environmental regulations; and
•• public acceptance (EPA, 1993b).
The choice of use/disposal method is restricted by the quality of the sludge generated by the POTW. Sludge for beneficial
uses must meet more stringent standards for pollutant concentrations than sludge used or disposed of in other ways. Similarly,
sludge that is surface-disposed in an unlined unit generally must meet more stringent standards than sludge surface-disposed
in a lined unit, disposed in an MSWL, or incinerated. Sludge disposed in a MSWL must meet more stringent standards than
incinerated sludge.
Table 16.1 summarizes sludge use/disposal methods according to the number and percent of dry metric tons (DMT), based on
information provided in Section 3 of the §308 POTW Survey. The information presented in this table takes into account data
provided by AMSA on POTW characteristics such as POTW flow and the total amount of sludge generated by each POTW.
Because the AMSA data was collected five years after the EPA POTW Survey was administered and it does not correspond
to the base year of the analysis (1996), EPA did not use AMSA data to adjust the allocation of sludge to each use/disposal
method category.
Table 16.1: Sludge Use/Disposal (1996) by POTWs Discharging > 2 Million Gallons/Day"
Use/Disposal Sub-Class
Total Beneficial Use
Land Application- High
Bag Application-High
Land Application-Low
Bagged Application-Low
Total Surface Disposal
Surface Disposal: Unlined Unit
Surface Disposal: Lined Unit
Co-Disposal: Municipal Landfill
Incineration
Unknown: Other
All
Thousand DMT
2,641.2
1,017.4
339.9
1,283.9
0
528.2
347.2
181.0
1,768.8
1,129.9
543.2
6,611.2
Percent of DMT
39.9%
15.4%
5.1%
19.4%
0%
8.0%
5.3%
2.7%
26.8%
17.1%
8.2%
100.0%
a The §308 POTW Survey did not collect information from POTWs discharging < 2 million gallons per day.
Source: U.S. EPA, POTW Survey and AMSA Survey (2000) on Proposed MP&M Effluent Guidelines.
As Table 16.1 shows, 39.9 percent of total sludge tons reported by respondents is used beneficially (land application and
bagged application). Co-disposal in a municipal landfill is the second most frequently used disposal method, accounting for
26.8 percent of all sludge disposed in the U.S. Surface disposal in unlined and lined units, incineration, and "other" disposal
methods account for 5.3 percent, 2.7 percent, 17.1 percent, and 8.2 percent of all sludge tons, respectively. No sludge was
sent to a hazardous waste landfill by the POTW Survey respondents.
c. Pollutant limits and disposal options
Section 405 (d) of the Clean Water Act, as amended, requires EPA to specify acceptable management practices and numerical
limits for certain pollutants in sludge. The Agency published Standards for the Use/Disposal of Sludge (40 CFR Part 503,
February 1993) to protect public health and the environment from reasonably anticipated adverse effects of pollutants in
sludge (U.S. EPA, 1993a). The standards include general requirements, pollutant limits, management practices, operational
standards, monitoring frequency, record-keeping, and reporting for the final use and disposal of sludge in four circumstances:
* sludge co-disposed with household waste in a MSWL;
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MP&M EEBA Part III: Benefits
Chapter 16: POTW Benefits
•• sludge land-applied for beneficial purposes (including bagged sludge);
•• sludge disposed on land or on surface disposal sites; and
> incinerated sludge.
With the exception of MSWLs, the standards for each practice include numerical limits on sludge pollutant concentrations.
Part 503 sets limits on pollutant concentrations for land application at two levels:
»• Land Application-Low limits, which govern whether sludge can be applied to land at all; and
»• more stringent Land Application-High limits which define, in part, sludge that is exempt from meeting certain
record-keeping requirements.
For sludge meeting only the Land Application-Low limits, Part 503 contains pollutant loading rate limits. These determine
the amount of sludge and associated pollutant content that may be applied to a particular site.
EPA did not establish pollutant-specific, numerical criteria for toxic pollutants of concern in the sludge disposed in MSWLs,
because the design standards applicable to MSWLs are considered adequate to protect human health and the environment.
Also, MSWL sludge is co-disposed with household waste, making precise numerical criteria infeasible. The Solid Waste
Disposal Facility Criteria (40 CFR Part 258, Federal Register 50978, October 9, 1991) specify that POTWs using an MSWL
must ensure that their sewage is non-hazardous and passes the Paint Filter Liquid Test.
The pollutant limits for sludge land application, surface disposal, and incineration constrain a POTW's choice of sludge
use/disposal practice. Table 16.2 presents numerical limits for the three sludge use/disposal practices for eight MP&M
pollutants. The land application pollutant limits place restrictions on concentrations of metals in sludge; the surface disposal
criteria cover a subset of the metals regulated for land application. The MP&M effluent limitations guideline covers five
metals and causes incidental removal of the remaining three metals regulated under the Part 503 sludge regulation. The
alternative policy options would improve the quality of sewage sludge generated by POTWs receiving discharges from
MP&M facilities and, as a result, would increase sludge use/disposal options for the affected POTWs.
Table 16.2: Sludge Use/Disposal Pollutant Limits
Pollutant
Arsenic
Cadmium
Copper
Lead
Mercury
Nickel
Selenium
Zinc
Application Limits
Low Limits (Low)
(mg/kg dry weight)
75
85
4,300
840
57
420
100
7,500
High Limits (High)
(mg/kg dry weight)
41
39
1,500
300
17
420
100
2,800
Surface Disposal Limits
(mg/kg dry weight)"
73
420
MP&M Pollutants of
Concern
•
•
•
•
•
•
•
•
a Pollutant limits for active sludge unit whose boundary is greater than 150 meters from the surface disposal site property line.
Source: Standards for the Use or Disposal of Sludge; Final Rules. 40 CFR Part 257 et al. Federal Register February 19,
1993a.
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
d. Reasons for not land-applying qualifying sludge
POTW characteristics including location, state regulations, and community concerns also affect use/disposal methods for
sludge. The POTW Survey provided information on the percentage of sludge that qualified for beneficial use but was not
beneficially used. Survey data indicate that 57 percent of qualifying sludge was not land-applied, for the following reasons:
»• land application is more expensive than another method;
»• land is not available for sludge application;
•• the cumulative pollutant loads at the land application site used had been exceeded;
•• the vector or pathogen requirements to land apply could not be met at an acceptable cost; and
»• inclement weather, concern over liability, stakeholder complaints, stricter state standards, desire to diversify
practices, or technical problems.
Of the 57 percent of sludge that was not land-applied, only 11 percent of qualifying sludge was otherwise beneficially used
(i.e., sold in bags). Therefore, only 50 percent of the total qualifying sludge is beneficially used.3 In addition, POTW Survey
data indicate that, on average, 7.5 percent of all sludge that qualifies for surface disposal is not surface disposed.
16.2.3 Overview of Improved Sludge Quality Benefits
This section discusses potential economic productivity benefits resulting from cleaner sludge, describes the methodology used
to estimate benefits to POTWs directly affected by the regulation, and presents the results of the analysis.
EPA expected that the alternative regulatory options would reduce MP&M facility discharges of eight metals with Part 503
limits. The influent pollutant reductions to the receiving POTWs translate into sludge with reduced pollutant concentrations,
allowing the sludge to meet the criteria for lower-cost use/disposal methods. The reduction in pollutants will then provide
many POTWs with greater flexibility in the disposal of their sludge, and for some the opportunity to use less expensive
methods of sludge use/disposal. In some cases, wastewater treatment systems may be able to use the cleaner sludge in
agricultural applications, generating additional agricultural productivity benefits. Numerous benefits will result from reduced
contamination of sludge, including the following:
> POTWs may have less expensive options for use/disposal of sludge. Methods involving stricter criteria are generally
less expensive than the alternatives. In particular, land application usually costs substantially less than incineration
or landfilling. As a result, under the alternative policy options sludge from some POTWs may meet more stringent
criteria for less expensive use/disposal methods.
•• Some sludge currently meeting only Land Application-Low concentration limits and pollutant loading rate limits
would meet the more stringent Land Application-High concentration limits. Users applying sludge meeting Land
Application-High pollutant limits would be exempt from meeting pollutant loading rate limits. They would have
fewer record-keeping requirements than users of sludge meeting only Land Application-Low concentration and
loading rate limits.
»• By land-applying sludge, POTWs may avoid costly siting negotiations for more contentious sewage sludge use or
disposal practices, such as incineration.
»• POTW sludge provides supplemental nitrogen, which enhances soil productivity when land-applied. Sludge applied
to agricultural land, golf courses, sod farms, forests, or residential gardens is a valuable source of nitrogen fertilizer.
> Non-point source nitrogen contamination of water may be reduced if sludge is used as a substitute for chemical
fertilizers on agricultural land. Compared to nitrogen in most chemical fertilizers, nitrogen in sludge is relatively
insoluble in water. The release of nitrogen from sludge occurs largely through continuous microbial activity,
resulting in greater plant uptake and less nitrogen runoff than from conventional chemical fertilizers.
3 Percent of Qualifying Sludge Beneficially Used = (100% - 57%) +[(57% x 11%)/100%]=50%
16-7
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MPAM EEBA Part III: Benefits
Chapter 16: POTW Benefits
•• The organic matter in land-applied sludge can improve crop yields by increasing the ability of soil to retain water.
•• Reduced concentrations of sludge pollutants not currently regulated may reduce human health and environmental
risks. Human health risks from exposure to these unregulated sludge pollutants may occur from particulate
inhalation, dermal exposure, ingestion of food grown in sludge-amended soils, ingestion of surface water containing
sludge runoff, ingestion of fish from surface water containing sludge runoff, or ingestion of contaminated ground
water.
> Land application of sludge satisfies an apparent public preference for this practice of sludge disposal, apart from
considerations of costs and risk.
This analysis assumes that POTWs will choose the least expensive sludge use/disposal practice for which their sludge meets
pollutant limits. POTWs with sludge pollutant concentrations exceeding the Land Application-High, Land Application-Low,
or surface disposal pollutant limits in the baseline may be able to reduce sludge use/disposal costs after MP&M facilities have
complied with the effluent limitations considered under alternative regulatory options.
As public entities, POTWs are not forced by the market to act as profit-maximizing or cost-minimizing agents, but rather are
assumed to optimize their jurisdictional welfare function. POTWs take factors other than cost into consideration when
determining their sludge use/disposal methods. These factors may include the desire to be perceived by the public as using
sludge in an environmentally friendly way, or the desire to enhance relationships with clients by providing no-cost or low-cost
fertilizer. Greater flexibility in disposal practices may therefore provide benefits beyond cost savings.
16.2.4 Sludge Use/Disposal Costs and Practices
This section summarizes the estimated cost differences of various use and disposal methods, based on the POTW Survey.
Alternative sludge use/disposal practices costs vary considerably among POTWs, based on several factors, the most important
being the availability of local agricultural land or land suitable for surface disposal of sludge. Table 16.3 lists and ranks the
use/disposal methods from least expensive to most expensive, according to the average qualitative ranking of each method in
the POTW Survey.
Table 16.3: National Estimate of Qualitative Ranking of
Use/Disposal Methods
Mean Rankings
Least Expensive
$
$
$
$
Most Expensive
Land Application- High
Land Application-Low
MSWL
Bagged Application- High
Surface Disposal in Unlined Unit
Bagged Application-Low
Surface Disposal in Lined Unit
Incineration
Hazardous Waste Landfill
Source: U.S. EPA, §308 POTW Survey.
Land Application-Low and Land Application-High were ranked as the two cheapest sewage sludge disposal options,
supporting the assumption that beneficial use of sludge offers cost savings. The third least expensive option co-disposal in
an MSWL costs less on average than either bagging sludge or surface disposing in an unlined unit.
16-&
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MPAM EEBA Part III: Benefits
Chapter 16: POTW Benefits
EPA used the POTW Survey data as the primary source for estimating an average difference in costs among certain
combinations of use/disposal practices (e.g., the cost savings achieved by switching from incineration to land application).
Table 16.4 compares the cost savings realized by switching to sludge land application and surface disposal practices from less
stringently regulated sludge use/disposal practices. While on average the estimates provided in Table 1 6.4 are expected to
hold, the cost savings will vary for individual POTWs. POTWs whose sludge qualifies for beneficial use post-compliance but
did not qualify for such use in the baseline may achieve cost savings in some, but not all, circumstances. For example, a
POTW may not achieve cost savings from agricultural application due to sludge transportation costs or because there are less
expensive alternatives for that particular facility. Switching from sewage sludge co-disposal in a MSWL to surface disposal
offers no savings to a POTW.
Table 16.4: Cost Savings for Shifts in Sludge Use/Disposal Practices (2001$/DMT)
Switch From
Incineration
Surface Disposal on
Lined Unit
Surface Disposal on
Unlined Unit
Co-disposal:
MSWL
Land Application-
Low
Switch To:
Land
Application"
(High)
$103.82
$126.39
$6.44
$100.44
$0.54-1.09
Land
Application"
(Low)
$103.82
$126.39
$6.44
$100.44
Sold in a Bag for
Land Application
$95.91
$71.89
$0.59
$69.96
Surface Disposal
on Unlined Unit
$103.08
No Saving
Surface Disposal
on Lined Unit
No Saving
No Saving
a EPA assumes that the costs of land application to forests, public contact sites, and reclaimed land are similar to the costs of
agricultural application.
Source: U.S. EPA analysis of the §308 POTW Survey data.
The cost section of the POTW Survey did not distinguish between low and high land application or low and high bagged
application. Therefore, costs provided in the survey reflect the cost of both methods. To estimate the cost savings of
avoiding these requirements by meeting Land Application-High limits, EPA used the compliance requirements for meeting
Land Application-Low limits for bulk sludge (U.S. EPA, 1997). These cost savings provide a partial measure of the monetary
benefit of improved sludge quality.
EPA estimates that the incremental record-keeping associated with the cumulative Land Application-Low limits requires two
to four hours per application. Materials costs for meeting these requirements should be negligible. EPA estimated the record-
keeping costs avoided from upgrading sludge quality from Land Application-Low to Land Application-High standards, using
the following assumptions:
> a 40-acre site is a typical site size for land application (approximately 16 hectares) (US EPA, 1997);
> the typical application rate for land application is 7 DMT per hectare per application (US EPA, 1997); and
•• labor at POTWs costs an average of $30.42 per hour (2001$), based on the §308 POTW Survey.4
Based on these assumptions, EPA estimated that $0.54 to $1.09 would be saved per DMT of sludge upgraded from Land
Application-Low to Land Application-High.5
4 See Appendix F for detail.
5 Savings per DMT are calculated by dividing the estimated labor cost per application ($30.42 per Hour * Hours per Application) by
the total amount of sludge disposed of per one application (16 Hectares * 7 DMT per hectare).
16-9
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MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits
16.2.5 Quantifying Sludge Benefits
EPA estimated the number of POTWs receiving MP&M discharges and the associated quantity of sludge that would not meet
Land Application-High pollutant limits, Land Application-Low pollutant limits, or surface disposal pollutant limits under both
the baseline and regulatory options. EPA then assumed that, as a result of compliance with the MP&M effluent limitations
guideline, a POTW meeting all pollutant limits for a less costly sludge use/disposal method would benefit from the reduced
cost of that particular method. EPA estimated the reduction in sludge use/disposal costs using the steps described below:
1. Estimate total industrial baseline and post-compliance loadings of Part 503 regulated metals for each POTW with
MP&M sample facility discharges;
2. Calculate the baseline and post-compliance sludge pollutant concentrations for all MP&M wastewater discharged to
the POTW;
3. Compare POTW sludge pollutant concentrations with sludge pollutant limits for surface disposal and land
application;
4. Estimate baseline and post-compliance sludge use/disposal practices based on the estimated pollutant concentrations
in sewage sludge;
5. Identify POTWs that upgrade their sewage sludge disposal practices under the alternative policy options; calculate
the economic POTW benefits by multiplying the cost savings for the shift in practices by the quantity of newly
qualified sludge; adjust the estimate of benefits for the percentage of POTWs that cannot land apply sewage sludge
due to transportation costs or other reasons, such as cold temperature; and
6. Estimate national benefits using MP&M sample facility weights.
a. Step 1: Estimate total industrial baseline and post-compliance loadings of Part 503
regulated metals
EPA estimated the quantities of Part 503 metals discharged to POTWs receiving wastewater from MP&M sample facilities
and facilities operating in other metal discharging industries.6 EPA used POTW Survey data to estimate the total metal
loadings and percent of total loadings discharged to POTWs by MP&M facilities.
The POTW Survey provides the following information:
•• number of known MP&M facilities discharging to the POTW,
»• total loadings of each regulated metal received by the POTW, and
•• percent of the total metal loadings attributable to MP&M industries.
6 EPA did not include metals from residential wastewater due to lack of data. The effect on the analysis of omitting residential metal
loadings is not known.
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MP&M EEBA Part III: Benefits
Chapter 16: POTW Benefits
Table 16.5 summarizes this information by POTW flow volume.
Table 16.5: MP&M Contribution
to Total Industrial Loadings Received by POTWs
MP&M Contribution
MP&M facilities
small (<1 MG/year)
medium (1-6.25 MG/year)
large (>6.25 MG/year)
Chemicals
Arsenic
Cadmium
Copper
Lead
Mercury
Nickel
Selenium
Zinc
POTW size (million gallons per day)
2-10
Average nunu
32.8
2.5
1.2
MP&Mperci
7.4
16.1
18.9
13.8
7.9
25.1
7.2
20.2
11-50
>er ofMP&Mfacih
72.1
8.0
2.7
mtage of total loadi
14.0
23.4
21.6
19.8
20.8
24.4
8.5
16.0
>50
ties per POTW
147.7
24.5
10.4
ngs by weight
7.0
12.8
10.9
10.3
6.0
15.8
3.3
8.2
Source: U.S. EPA, §308 POTW Survey.
EPA estimated total baseline metal loadings from all MP&M sources, as follows:
where:
PLMt
LMP
sman>kjl
AvgNumSm
SampleSm
mediLim,k,i
SampleSm
SampleMed
SampleLg
(16.1)
AvgNumMed
= baseline loadings of pollutant k to POTW; from all MP&M sources (fig/year);
= loadings of pollutant k from small (< 1 MG/year) sample MP&M facilities, discharging to POTW i
(fig/year);
= the average number of small MP&M facilities discharging to POTW z; EPA estimated the average
number of MP&M facilities of a given size (small, medium, large) that discharge to POTWs in
given flow categories, based on the §308 POTW Survey (see Table 16.5);7'8
= number of MP&M small (< 1 MG/year) sample facilities discharging to POTW;
= loadings of pollutant k from medium (1-6.25 MG/year) sample MP&M facilities, discharging to
POTWs (fig/year);
= the average number of medium MP&M facilities discharging to POTW i (based on the POTW
flow category (see Table 16.5));
7 EPA classified MP&M facilities as small, medium, and large flow in the POTW Survey, based on their discharge volume.
8 This analysis considers the following POTW flow categories: (1) from 2 MG/day tolO MG/day; (2) from 11 to 50 MG/day; and
(3) greater than 50 MG/day.
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MP&M EEBA Part III: Benefits
Chapter 16: POTW Benefits
SampleMed = number of MP&M medium (1-6.25 MG/year) sample facilities discharging to POTW z;
LMPlalgeki = loadings of pollutant k from large (>6.25 MG/year) sample MP&M facilities discharging to POTW
z (fig/year);
AvgNumLg = the average number of large MP&M facilities discharging to POTW z (based on the POTW flow
category (see Table 16.5)); and
SampleLg = number of MP&M large (>6.25 MG/year) sample facilities discharging to POTW z.
EPA estimated total baseline metal loadings from all industrial sources using data from the POTW Survey, as follows:
PL, =
PLMk. • 100%
(16.2)
where:
PLMkl
100%
%MPk
total baseline loadings of pollutant k from all industrial sources to POTW z (fig/year),
baseline loadings of pollutant k to POTW z from all MP&M sources (fig/year),
the total reported POTW transfers of pollutant k from all industrial sources, and
the percentage of total reported POTW transfers of pollutant k from MP&M facilities in a given POTW
flow category (see Table 16.5).
Post-compliance pollutant loadings to POTWs are calculated by subtracting the reduction in MP&M loadings due to the
regulation from the estimated total baseline loadings.
b. Step 2: Calculate baseline and post-compliance sludge quality
First, for each metal with limits under the Part 503 regulation, EPA calculated POTW influent concentrations based on the
pollutant loading and POTW flow rates, as follows:
PL,
where :
OD,
IC,=
k'' FL x OD
POTW influent concentration of pollutant k (fig/liter) for POTW z;
total loading of pollutant k to POTW z (fig/year) ;
= POTW z flow (liters/day); and
= POTW z operation days (365 days/year).
(16.3)
Second, EPA calculated sludge pollutant concentrations for each pollutant:
TREk x ppk x SG
(16.4)
where:
PCki = concentration of pollutant k in POTW z sludge (mg/kg or ppm),
ICki = POTW z influent concentration of pollutant k (fig/liter or ppb),
TREt = treatment removal efficiency for pollutant k (unitless),
PFk = sludge partition factor for pollutant k (unitless), and
SG = sludge generation factor ((L-mg)/(fig-kg) or ppm/ppb).
The partition factor represents the fraction of the pollutant load expected to partition to sludge during wastewater treatment.
This factor is chemical-specific. EPA uses a sludge generation factor of 5.96 (mg of chemical/kg sludge)/( g chemical/L of
wastewater). The value of 5.96 is based on the "normal quantity of sludge produced" by a POTW with primary
sedimentation/activated sludge/digestion/dewatering as reported in Wastewater Engineering (Metcalf & Eddy, 1972). The
estimated sludge generation factor indicates that concentration in sludge is 5.96 ppb dry weight for every 1 ppb of pollutant
removed and partitioned to sludge.
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
c. Step 3: Compare sludge pollutant concentrations at each POTW with limits for surface
disposal and land application
EPA next compared sludge baseline and post-compliance pollutant concentrations to pollutant limits for land application and
surface disposal using the following formula:
where :
SEp = sludge exceeds concentration limits for disposal or use practice, p;
PCt = sludge pollutant, k, concentration; and
CRkp = sludge pollutant, k, criterion for disposal or use practice,/).
If any sludge pollutant concentration at a POTW exceeds the pollutant limit for a sludge use/disposal practice in the baseline
(i.e., PC/CR >1), then EPA assumed that the POTW cannot use that sludge use/disposal practice. If, as a result of compliance
with the MP&M regulation, a POTW meets all pollutant limits for a sludge use/disposal practice (i.e., PC/CR < 1), that
POTW is assumed to benefit from an increase in sludge use/disposal options.
d. Step 4: Estimate baseline sludge use/disposal practices at POTWs that can meet land
application or surface disposal pollutant limits post -compliance
Benefits from changes in sludge use/disposal practices depend on the baseline practices employed. EPA assumes that
POTWs choose the least expensive sludge use/disposal practice for which their sludge meets pollutant limits. POTWs with
sludge qualifying for land application in the baseline are assumed to dispose of their sludge by land application; likewise,
POTWs with sludge meeting surface disposal pollutant limits (but not land application pollutant limits) are assumed to
dispose of their sludge on surface disposal sites.
EPA assumed that the mix of surface disposal practices employed by POTWs in the baseline (e.g., surface disposal in a lined
unit and surface disposal in an unlined unit) matches that of national surface disposal practices as calculated from the POTW
Survey (see Table 16.1).
POTW Survey data indicate that 25 percent of total sludge meeting Land Application-High standards is sold in bags and 75
percent is land-applied. None of the sludge meeting Land Application-Low standards is sold in bags. Each POTW meeting
Land Application-High standards in the post-compliance scenario is assumed to sell 25 percent of its sludge in bags and to
land-apply the remainder.
The POTW Survey shows that 34 percent of total surface disposed sludge is disposed of in lined units and 66 percent in
unlined units. This mix of surface disposal practices may not match the actual sludge disposal surface practices of any
individual POTW. In aggregate, however, the assumed surface disposal practices are consistent with actual POTW sludge
surface disposal practices. Survey data also showed that, on average, 7.5 percent of all sludge that qualifies for surface
disposal was not surface disposed.
POTWs generating sludge exceeding land application and surface disposal pollutant limits in the baseline are assumed to
either incinerate sludge or place sludge in a MSWL. The survey indicates that 39 percent of sludge not land-applied or
deposited in surface disposal sites is incinerated and 61 percent is placed in MWSLs. Each POTW exceeding surface
disposal and land application limits in the baseline is assumed to incinerate 39 percent of its sludge and co-dispose of the
remainder. Again, this mix of sludge use/disposal practices may not match the actual sludge disposal practices of any single
POTW; in aggregate, however, the assumed distribution corresponds to actual practices.
Using the sludge disposal cost differentials from Table 16.4, EPA estimated savings for shifts into land application and
surface disposal from the assumed mix of baseline use/disposal practices (see Table 16.6). As previously discussed, EPA
assumed that 50 percent of sludge could not be used beneficially (land-applied or sold in bags) and disposed less expensively
through agricultural application of sludge due to transportation costs, land availability, or weather constraints. The Agency
did not estimate benefits for this percentage of the sludge newly qualified for land application.
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MPAM EEBA Part III: Benefits
Chapter 16: POTW Benefits
e. Step 5: Calculate economic benefits for POTWs receiving wastewater from sample MPAAA
facilities
Table 16.6 shows the cost savings for shifts from composite baseline sludge use/disposal practices to land application or
surface disposal. Reductions in sludge use/disposal costs are calculated for each POTW receiving wastewater from an
MP&M facility, using the following formula:
2200
x CD
(16.6)
where:
SCR,
FLt
S
CD,
estimated sludge use/disposal cost reductions resulting from the regulation for POTW i (2001$);
POTW i wastewater flow (million gallons/year);
sludge to wastewater ratio, assumed to be 1,127 Ibs. (dry weight) per million gallons of water (Ibs./million
gallons) and divided by 2,200 to convert pounds to metric tons; and
estimated cost differential between least costly composite baseline use/disposal method for which POTW i
qualifies and least costly use/disposal method for which POTW i qualifies post-compliance (2001$/DMT).
Table 16.6: Cost Savings from Shifts in Sludge Use/Disposal Practices from
Composite Baseline Disposal Practices (2001$/DMT)
Baseline POTW Mix of Sludge
Use/Disposal Practices
Meets Land Application-Low
pollutant limits, but not Land
Application-High limits
Meets surface disposal pollutant
limits, but not Land Application-
Low limits
Assumed disposal mix:
34% lined unit
66% unlined unit
Does not meet land application
pollutant limits or surface disposal
pollutant limits
Assumed disposal mix:
39% incineration,
61% co-disposal
Post-Compliance POTW Sludge Use/disposal Practice
Agricultural
Application-High (75%
of sludge meeting Land
Application-High
pollutant limits)
$0.54-1.09
L
$126.39
$6.44
$103.82
$100.44
Bagged Sludge
(25% of sludge
meeting Land
Application-High
pollutant limits)
N/A"
L
$71.89
$0.59
$95.91
$69.96
Agricultural
Application-
Low
N/A
L
$126.39
$6.44
$103.82
$100.44
Surface Disposal"
(Meet surface pollutant
limits; do not meet land
application pollutant
limits)
N/A
L
N.A.
$0-$103.08
N/A
a Surface disposal includes monofills, surface impoundments, and dedicated sites.
b Not applicable (i.e., there is no cost savings).
Source: U.S. EPA, §308 POTW Survey.
EPA assumed that only 50 percent of the sludge qualified for land application is beneficially used (i.e., land-applied or sold in
bags). The remaining 50 percent of the sludge newly qualified for land application will be disposed of by other methods;
therefore, EPA assumed that no cost savings will be associated with 50 percent of the sludge qualified for land application.
To ensure that these benefits are not overstated, this analysis includes an adjustment to the estimate of national sludge
use/disposal cost benefits for POTWs that may be located at some distance from agricultural sites. This adjustment does not
apply to benefits from shifts into surface disposal.
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MP&M EEBA Part III: Benefits Chapter 16: POTW Benefits
f. Step 6: Estimate national sludge benefits
EPA scaled the sludge use/disposal cost reductions to the national level as follows:
NSCR = Ti(FWi x SCR) (16'7)
/= i
where:
NSCR = national estimated sludge use/disposal cost reductions resulting from the regulation (2001$);
n = number of POTWs estimated to shift into meeting surface disposal or land application pollutant limits as a
result of MP&M effluent limitations;
= facility sample weights for facility or facilities discharging to POTW i', and
= estimated sludge use/disposal cost reductions resulting from the regulation for POTW i (2001$).
16.3 ESTIMATED SAVINGS IN SLUDGE USE/DISPOSAL COSTS
Of the POTWs receiving discharge wastewater from MP&M facilities, 1,020 POTWs exceed the Land Application-High
pollutant limits and 856 exceed the Land Application-Low pollutant limits at baseline discharge levels under the alternative
options considered for the final rule. This represents approximately 6 percent of the over 16,000 operating POTWs
nationwide. The number of POTWs exceeding Land Application-High and Land Application-Low pollutant limits under the
Proposed/NODA Option at baseline conditions is equal to 5,328 and 3,728, respectively.9
The final rule only regulates direct dischargers and, as a result, sewage sludge quality will not be affected by the selected
option. EPA, however, did estimate savings in sludge disposal costs for the alternative options which consider both direct and
indirect dischargers. EPA used the estimated sludge use/disposal cost differentials presented in Table 16.6 to calculate cost
savings for the POTWs expected to upgrade their sludge disposal practices under alternative policy options. These results are
presented in Table 16.7 below. The benefits are estimated at $11,319 to $22,539 (2001$) annually for both upgrade options.
The Proposed/NODA Option would result in more substantial cost savings (i.e., $22.8 million (2001$)) to POTWs. However,
the Proposed/NODA Option is not directly comparable to the two upgrade options due to inconsistent baselines.
9 The total number of facilities reported for the Proposed/NODA Option analysis differs from the facility count reported for the final
rule and the upgrade options (Directs + 413 to 433 Upgrade Option, Directs + All to 433 Upgrade Option). After deciding in July 2002 not
to consider the NODA option as the basis for the final rule, EPA did not perform any more analyses on the NO DA option - including not
updating facility counts and related analyses for the change in subcategory and discharge status classifications.
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MP&M EEBA Part III: Benefits
Chapter 16: POTW Benefits
Table 16.7: National Estimate of Cost Savings from Shifts in Sludge Use/Disposal
Under the Alternative Policy Options"
Shift
Directs
Upgrade from minimum Land Application-Low limits to
Land Application-High pollutant limits
Upgrade from not meeting land application or surface
disposal limits to Land Application-High pollutant limits
Upgrade from not meeting land application or surface
disposal limits to Land Application-Low pollutant limits
Total
Direct
Upgrade from minimum Land Application-Low limits to
Land Application-High pollutant limits
Upgrade from not meeting land application or surface
disposal limits to Land Application-High pollutant limits
Upgrade from not meeting land application or surface
disposal limits to Land Application-Low pollutant limits
Total
Pro]
Upgrade from minimum Land Application-Low limits to
Land Application-High pollutant limits
Upgrade from not meeting land application or surface
disposal limits to Land Application-High pollutant limits
Upgrade from not meeting land application or surface
disposal limits to Land Application-Low pollutant limits
Total
Category /Number
ofPOTWs
+ 413 to 433 Upgrade
15
0
0
15
s + All to 433 Upgrade
15
0
0
15
)osed/NODA Option
45
24
25
93
Associated Sludge
Quantity (DMT/Year)
16,548
0
0
16,548
16,548
0
0
16,548
88,389
140,460
316,565
545,414
Estimated Benefits
(2001$)
$11,319 to $22,539
$0
$0
$11,319 to $22,539
$11,319 to $22,539
$0
$0
$11,319 to $22,539
$60,458 to $120,386
$6,725,273
$16,009,889
$22,795,620 to
$22,855,548
a Based on the Traditional Extrapolation.
Source: U.S. EPA analysis.
16.4 Methodology Limitations
EPA used the POTW Survey to develop estimates of the cost-saving differentials for the various sludge use/disposal
practices. Sludge use/disposal costs vary by POTW. The POTWs affected by the MP&M regulation may face costs that
differ from those estimated. As a result, the analysis may over- or under-estimate the cost differentials.
POTW Survey data were also used to estimate metal loadings to POTWs in the baseline analysis. There are two major
limitations associated with this approach:
*• The baseline metal loadings from individual MP&M facilities of interest may differ from this estimate. The effect of
using the §308 survey data to characterize the POTWs that receive MP&M discharges is therefore not known.
»• The total share of metals coming from MP&M facilities is likely to be underestimated because lower flow MP&M
facilities are not always known by the POTW. During the pretest of the MP&M POTW questionnaire, POTWs told
EPA that they were not aware of many of the lower flow facilities that were discharging to them. The POTW would
have to use the phone book in order to find and permit these facilities. EPA consequently considered exempting low
flow facilities in the general metals and only oily wastes indirect discharge categories under some of the alternative
regulatory options.
16-16
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
This analysis assumes that the mix of disposal practices estimated for a specific POTW may not match the actual sludge
disposal practices used by that POTW. We know that the mix in the aggregate, as confirmed by the POTW Survey, is correct.
The practices used in this analysis are therefore consistent with actual POTW sludge surface disposal practices. Because
accurate assumptions for specific POTWs could not be made, the analysis may over- or underestimate the cost differentials.
EPA quantified, but did not monetize economic benefits from reducing interference with POTW operations for the alternative
regulatory options. EPA did not estimate cost reductions that occur at POTWs with sludge inhibition problems caused by
MP&M discharges. These omissions thereby underestimate the benefits of the regulation.
16-17
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
GLOSSARY
hazardous air pollutants (HAPs): air pollutants that are not covered by ambient air quality standards but which, as
defined in the Clean Air Act, may present a threat of adverse human health effects or adverse environmental effects. Such
pollutants include asbestos, beryllium, mercury, benzene, coke oven emissions, radionuclides, and vinyl chloride. MP&M
pollutants include but are not limited to: chlorobenzene, dioxin,l,4-isophorone, and pyrene.
(http://www.epa.gov/OCEPAterms/hterms.html)
hazardous waste landfill: an excavated or engineered site where hazardous waste is deposited and covered.
(http://www.epa.gov/OCEPAterms/hterms.html)
influent concentrations: measure of a pollutant's concentration in wastewater being received by a POTW for treatment
(see also: pollutant inhibition values).
interference: the obstruction of a routine treatment process of POTWs that is caused by the presence of high levels of
toxics, such as metals and cyanide in wastewater discharges. These toxic pollutants kill bacteria used for microbial
degradation during wastewater treatment (see: microbial degradation).
microbial degradation: the breakdown of organic molecules via biochemical reactions occurring in living
microorganisms such as bacteria, algae, diatoms, plankton, and fungi. POTWs make use of microbial degradation for
wastewater treatment purposes. This process is inhibited by the presence of toxics such as metals and cyanide because these
pollutants kill microorganisms.
municipal solid waste landfill (MSWL): common garbage or trash generated by industries, businesses, institutions, and
homes. Also known as municipal solid waste. (http://www.epa.gov/OCEPAterms/mterms.html)
pathogens: microorganisms (e.g., bacteria, viruses, or parasites) that can cause disease in humans, animals and plants.
(http://www.epa.gov/OCEPAterms/pterms.html)
pollutant inhibition values: determined threshold concentration for a pollutant, which when exceeded by the pollutant's
influent concentration in wastewater received for treatment will have adverse effects on POTW operations, such as inhibition
of microbial degradation (see: microbial degradation).
publicly-owned treatment works (POTWs): a treatment works as defined by Section 212 of the Act, which is owned by
a state or municipality. This definition includes any devices or systems used in the storage, treatment, recycling, and
reclamation of municipal sewage or industrial wastes of a liquid nature.
(http://www.epa.gov/owm/permits/pretreat/final99.pdf)
silviculture: management of forest land for timber. (http://www.epa.gov/OCEPAterms/sterms.html)
vector: 1. An organism, often an insect or rodent, that carries disease. 2. Plasmids, viruses, or bacteria used to transport
genes into a host cell. A gene is placed in the vector; the vector then "infects" the bacterium.
(http://www.epa.gov/OCEPAterms/vterms.html)
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
ACRONYMS
DMT: dry metric tons
HAPs: hazardous air pollutants
MS WL: municipal solid waste landfill
POTWs: publicly-owned treatment works
16-19
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MPAM EEBA Part III: Benefits Chapter 16: POTW Benefits
REFERENCES
Association of Metropolitan Sewage Agencies (AMSA). 2000. Survey on Proposed MP&M Effluent Guidelines.
Metcalf and Eddy. 1972. Wastewater Engineering. New York, NY: McGraw-Hill, Inc.
Solid Waste Disposal Facility Criteria. 40 CFR Part 258, Federal Register 50978, October 9, 1991)
Standards for the Use/Disposal of Sludge. 40 CFR Part 503, February 1 993.
U.S. Environmental Protection Agency (U.S. EPA). 1985. Handbook for Estimating Sludge Management Costs
U.S. Environmental Protection Agency (U.S. EPA). 1987. Guidance for Preventing Interference with POTW Operations.
U.S. Environmental Protection Agency (U.S. EPA). 1993a. Standards for the Use and Disposal of Sludge; Final Rules. 40
CFR Part 257 et al., Federal Register, February 19.
U.S. Environmental Protection Agency (U.S. EPA). 1993b. Regulatory Impact Analysis of the Part 503 Sludge Regulation.
Final. Office of Water. EPA 821-12-93-006. March.
U.S. Environmental Protection Agency (U.S. EPA). 1997. Economic Assessment for Proposed Pretreatment Standards for
Existing and New Sources for the Industrial Laundry Point Source Category. Office of Water. EPA 821-R-97-008 (pp 10-51
- 10-54).
16-20
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MP&M EEBA Part III: Benefits
Chapter 17: Environmental Justice A Protection of Children
Chapter 17: Environmental Justice <&
Protection of Children
INTRODUCTION
Executive Order 12898 requires that, to the greatest extent
practicable and permitted by law, each federal agency must
make achieving environmental justice part of its mission.
Therefore, EPA examined whether the final regulation will
promote environmental justice in areas affected by MP&M
discharges.
CHAPTER CONTENTS
17.1 Demographic Characteristics of Populations Living
in the Counties Near MP&M Facilities 17-1
17.2 Protection of Children from Environmental Health
and Safety Risks 17-3
Glossary 17-4
Reference 17-5
EPA concludes that discharges from MP&M facilities regulated
under the final rule do not have a disproportional environmental impact on minority populations, based on the demographic
characteristics of the populations residing in the counties affected by MP&M discharges.
The final rule is not subject to Executive Order 13045, "Protection of Children from Environmental Health Risks and Safety
Risks" (62 FR 1 9885, April 23, 1 997), because it is based on technology performance and not on health or safety risks.
However, EPA analyzed the reduction of children's health impacts associated with the MP&M regulation, and determined that
reductions in the baseline lead exposure are minimal.
The following section assesses whether MP&M discharges have a disproportionally high impact on minority populations.
17.1 bEMoeRAPHic CHARACTERISTICS OF POPULATIONS LIVING IN THE COUNTIES
NEAR MP<&M FACILITIES
EPA assessed whether adverse environmental, human health, or economic effects associated with MP&M facility discharges
are more likely to affect minorities and low-income populations. This analysis uses data on the race, national origin, and
income level of populations residing in counties traversed by reaches receiving discharges from the 32 sample MP&M
facilities considered in the final rule analysis. The 32 sample facilities are located in 46 counties in 12 states. The MP&M
survey was designed to provide a representative coverage of various types of MP&M facilities, but not of their geographical
location. EPA is therefore able to analyze only the location characteristics of the sample facilities, and not all 43,901 MP&M
dischargers.1
EPA compared demographic data from the 1990 Population Census for the counties traversed by sample MP&M reaches
with the corresponding state level indicators (U.S. Census Bureau, 1990). EPA considered several demographic
characteristics to assess the environmental justice of the final regulation, including the relative proportions of African
Americans, Native Americans, and Asian or Pacific Islanders, median income, the proportion of the population below the
poverty level, unemployment percentage, and the proportion of the population that are children. Table 17.1 presents the
results of this analysis, which show that the demographic characteristics of MP&M counties generally reflect state averages.
EPA calculated median income for the group of counties in each state receiving MP&M discharges as a weighted average of
each county's median household income. County's populations are used as weights in this analysis. EPA calculated this
summary variable in place of the true median household income for MP&M counties because appropriate census data are not
1 This estimate of MP&M facilities includes baseline closures.
2 Average median income in MP&M counties =
Z, Median Income (i) x Number of Households (i)/Z Number of Households (i), where (i) is a sample MP&M county.
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MP&M EEBA Part III: Benefits
Chapter 17: Environmental Justice A Protection of Children
available. The Agency notes that comparing this weighted average median income to the state-level median income may
introduce uncertainty in the analysis.
Income data, as well as other characteristics examined to determine whether minority and/or low-income populations are
subject to disproportionally high environmental impacts, show that the socioeconomic characteristics of populations residing
in counties affected by MP&M discharges reflect corresponding state averages. Based on these findings, EPA expects that
environmental benefits resulting from the MP&M rule will not accrue to populations disproportionally based on race or
national origin and therefore will promote environmental justice.
Table 17.1: County Level Comparison of Demographic Data: Counties with Sample MP&M Facilities Versus
Entire State
State
California
MP&M Only
Entire State
Indiana
MP&M Only
Entire State
Kentucky
MP&M Only
Entire State
Maryland
MP&M Only
Entire State
Mississippi
MP&M Only
Entire State
Missouri
MP&M Only
Entire State
New York
MP&M Only
Entire State
North Carolina
MP&M Only
Entire State
Ohio
MP&M Only
Entire State
Oklahoma
MP&M Only
Entire State
Counties
3
58
3
93
1
120
1
24
3
82
1
115
2
63
3
100
2
89
4
77
%
White
58.64%
69.07%
95.38%
90.59%
98.44%
92.06%
94.61%
71.03%
56.88%
63.46%
99.45%
87.68%
92.64%
74.47%
88.47%
75.60%
89.17%
87.81%
82.68%
82.26%
%
African-
American
11.82%
7.39%
3.76%
7.75%
0.54%
7.11%
4.47%
24.87%
42.46%
35.59%
0.04%
10.69%
4.90%
15.90%
10.71%
21.96%
9.69%
10.62%
8.48%
7.38%
% Native
Am.,
Eskimo,
or Aleut
0.53%
0.84%
0.23%
0.26%
0.12%
0.19%
0.35%
0.30%
0.09%
0.34%
0.44%
0.44%
0.34%
0.33%
0.23%
1.25%
0.24%
0.21%
6.99%
8.03%
% Asian
or Pacific
Islander
11.20%
9.57%
0.41%
0.66%
0.74%
0.47%
0.34%
2.88%
0.47%
0.49%
0.04%
0.77%
0.78%
3.83%
0.33%
0.76%
0.74%
0.82%
1.06%
1.04%
Median
Income
$36,100
$35,798
$24,785
$28,797
$34,485
$22,534
$36,019
$39,386
$26,342
$20,136
$17,594
$26,362
$25,864
$32,965
$26,189
$26,647
$28,527
$28,706
$26,456
$23,577
o/
/o
Below
Poverty
Level
13.98%
12.51%
14.31%
10.68%
7.40%
19.03%
7.50%
8.27%
19.31%
25.21%
18.87%
13.34%
12.13%
13.03%
10.75%
12.97%
11.70%
12.54%
13.63%
16.71%
%
Unemployed
7.04%
6.65%
7.42%
5.74%
3.64%
7.37%
4.56%
4.30%
6.93%
8.43%
4.00%
6.16%
10.57%
6.88%
3.94%
4.79%
6.82%
6.60%
5.86%
6.87%
%
Children
25.83%
26.01%
23.38%
26.29%
29.38%
25.93%
26.86%
24.31%
28.00%
29.04%
24.21%
25.71%
28.09%
23.66%
24.10%
24.27%
24.76%
25.85%
26.20%
26.60%
77-2
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MP&M EEBA Part III: Benefits
Chapter 17: Environmental Justice A Protection of Children
Table 17.1: County Level Comparison of Demographic Data: Counties with Sample MP&M Facilities Versus
Entire State
State
Pennsylvania
MP&M Only
Entire State
Washington
MP&M Only
Entire State
Counties
22
68
1
40
%
White
92.89%
88.57%
84.94%
88.64%
%
African-
American
6.12%
9.15%
4.97%
3.03%
% Native
Am.,
Eskimo,
or Aleut
0.12%
0.13%
1.18%
1.71%
% Asian
or Pacific
Islander
0.64%
1.14%
7.90%
4.34%
Median
Income
$27,851
$29,069
$36,179
$31,183
%
Below
Poverty
Level
11.56%
11.13%
7.96%
10.92%
o/
70
Unemployed
6.46%
5.97%
4.15%
5.72%
%
Children
22.88%
23.54%
22.56%
25.86%
Source: U.S. EPA analysis of 1990 Census Data (U.S. Bureau of Census 1990).
17.2 PROTECTION OF CHILDREN FROM ENVIRONMENTAL HEALTH AND SAFETY RISKS
EPA assessed whether the final regulation will benefit children, including reducing health risk from exposure to MP&M
pollutants from consumption of contaminated fish tissue and drinking water and improving recreational opportunities. EPA
was able to quantify only one category of benefits specific to children: avoided health damages to pre-school age children
from reduced exposure to lead. This analysis considered several measures of children's health benefits associated with lead
exposure for children up to age six. Avoided neurological and cognitive damages were expressed as changes in three metrics:
(1) overall IQ levels; (2) the incidence of low IQ scores (<70); and (3) the incidence of blood lead levels above 20 mg/dL.
EPA also assessed changes in the incidence of neonatal mortality from reduced maternal exposure to lead. EPA's
methodology for assessing lead-related benefits to children is presented in Chapter 14 of this report.
The Ohio case study analysis showed that the final rule is expected to yield $422,000 (2001$) in annual benefits to children in
the state of Ohio from reduced neurological and cognitive damages and reduced incidence of neonatal mortality. On the other
hand, the national-level analysis shows that benefits to children from reduced lead discharges are negligible nationwide. As
noted in Chapter 18, different findings from these two analyses are likely to be due to insufficient data and a more simplistic
approach used in the national-level analysis.
Children over age seven are also likely to benefit from reduced neurological and cognitive damages from reduced exposure to
lead. Giedd et al. (1999) studied brain development among 10 to 18 year-old children and found substantial growth in brain
development, mainly in the early teenage years. This research suggests that older children may be hypersensitive to lead
exposure, as are children aged 0 to 7.
Additional benefits to children from reduced exposure to lead not quantified in this analysis may include prevention of the
following adverse health effects: slowed or delayed growth, delinquent and anti-social behavior, metabolic effects, impaired
heme synthesis, anemia, impaired hearing, and cancer (see Chapter 14 of this report for details).
17-3
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MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children
GLOSSARY
MP&M reach: a reach to which an MP&M facility discharges.
-------
MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children
REFERENCES
U.S Bureau of Census. 1990. 1990 Census of Population Data, http://www.census.gov/.
Giedd, J.N., L. Blumenthal, N.O. Jeffries, F.X. Castellanes, Hong Liu, A. Zijdenbos, Tomas Paus, Alan C. Evans, and J.
Rapoport. 1999. "Brain Development During Childhood and Adolescence: A Longitudinal MRI Study." Nature
Neuroscience.VoL 2, No. 10. October: 861-863.
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MP&M EEBA Part III: Benefits Chapter 17: Environmental Justice & Protection of Children
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison
Chapter 18: MPAM Benefit / Cost
Comparison
INTRODUCTION
CHAPTER CONTENTS
The preceding Chapters 12 through 16 provided quantitative
and qualitative assessments of the expected benefits to
society from reduced MP&M effluent discharges under the
final regulation. Chapter 11 assessed the regulation's
expected social costs. This chapter sums the estimated
values for the benefit categories that EPA was able to
monetize, and compares the aggregate benefits estimate with the estimate of social costs.
18.1 Estimating National Level Benefits and Costs .... 18-1
18.2 Social Costs 18-2
18.3 Benefits 18-2
18.4 Comparing Monetized Benefits and Costs 18-2
18.1 ESTIMATING NATIONAL LEVEL BENEFITS AND COSTS
EPA traditionally estimates national level costs and benefits by extrapolating analytic results from sample facilities to the
national level using sample facility weights. EPA's traditional sampling approach relies on information about the economic
and technical characteristics of the regulated community. Although important for understanding the technical requirements
and costs of a regulation, this sampling approach does not incorporate information that could significantly affect the
occurrence and distribution of regulatory benefits, such as characteristics of the receiving water body and the size of
population that may benefit from reduced pollutant discharges. As a result, the traditional sampling approach may yield
benefit estimates that are less accurate than those that could be obtained by using a sampling framework that accounts for such
benefit-receptor characteristics.
EPA recognizes that using a traditional extrapolation method to estimate national-level benefits may lead to a large degree of
uncertainty in benefits estimates. Therefore, in addition to the traditional extrapolation method used in the proposed rule,
EPA also estimated national-level benefits for the final rule using an alternative extrapolation method.1
Under this method, EPA used an alternative set of sampling weights, based on a post-sampling stratification method, to
calculate alternative national estimates of benefits. EPA adjusted the original sample weights using two variables that are
likely to affect the occurrence and size of benefits associated with reduced discharges from sample MP&M facilities:
receiving water body type and size, and the size of the population residing in the vicinity of the sample facility. The Agency
used a commonly used post-stratification method calling "raking" to adjust original sample weights to reflect these benefit
pathway characteristics. EPA used data from three data sources - EPA's Permit Compliance System database (PCS), EPA's
Reach File 1, and Census Data - to develop the adjusted weights. Because of data limitations, EPA restricted the
re-weighting effort only to direct dischargers and excluded indirect dischargers that are not considered in the final MP&M
rule. EPA therefore performed this alternative analysis for only the selected option. Appendix G details the post-sampling
stratification method used to adjust the original sample weights.
EPA uses the post-stratification extrapolation benefit estimates to validate general conclusions that the Agency draws from its
main analysis based on the traditional extrapolation method.
1 EPA also conducted a sensitivity analysis of national benefits for the final MP&M regulation by extrapolating the results of the
Ohio case study to the national level. The results of this analysis are presented in Appendix G.
18-1
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison
18.2 SOCIAL COSTS
As discussed in Chapter 11, EPA estimated the cost to society from compliance with the final regulation. The components of
social costs include the resource cost of compliance (e.g., labor, equipment, material, and other economic resources needed to
comply with the rule), costs to governments administering the regulation, and the social costs of unemployment resulting from
facility closures. EPA estimated that the final rule will cause no unemployment and thus impose no unemployment-related
costs to society. EPA also estimated that governments will incur no additional costs from administering the regulation. EPA
estimated the final rule's annual cost to society at $13.82 million (2001$). This value is based only on the estimated resource
cost of compliance.
18.3 BENEFITS
EPA developed a partial monetary estimate of the final rule's expected benefits based on three benefit categories: human
health, water-based recreation (including nonuse value), and economic productivity benefits (avoided sewage sludge disposal
costs). The Agency estimated the total monetized benefits by summing the monetary values reported in the preceding
chapters across all categories of benefits. As noted in Chapter 12, these benefits estimates are incomplete because they omit
numerous mechanisms by which society is likely to benefit from reduced effluent discharges from the MP&M industry.
Examples of benefit categories not reflected in these monetized estimates include:
> non-lead and non-cancer related health benefits,
> improved aesthetic quality of waters near discharge outfalls,
*• benefits from improved wildlife habitat, including habitat for threatened or endangered species,
*• tourism benefits, and
*• reduced costs of drinking water treatment.
The Agency estimated the total national benefits based on three extrapolation approaches. Table 18.1 summarizes the
monetary value of benefits to society from the final rule. Traditional extrapolation yields total benefit values of $0.88 to
$2.36 million (2001$) annually, with a midpoint estimate of $1.45 million (2001$). Benefits estimates based on the post-
stratification extrapolation method range from $0.57 to $1.54 million (2001 $), with a midpoint estimate of $0.98 million.
The ranges of national benefit estimates from the two extrapolation methods substantially overlap, with each method
confirming the value estimated by the other method. This finding provides confidence in the reasonableness of the estimates
from the separate extrapolation methods, given the limitations of data and coverage of benefit categories underlying the
analysis for both methods.
18.4 COMPARING MONETIZED BENEFITS AND COSTS
EPA cannot perform a complete cost-benefit comparison because not all of the benefits resulting from the final regulatory
option can be valued in dollar terms. As reported in Table 18.1, combining the national estimates of benefits and costs yields
the following value of net monetizable benefits under the traditional and post-stratification extrapolation methods:
*• Under the traditional extrapolation technique, the estimated net monetizable benefits range from negative $11.5
million to negative $12.9 million annually (2001$). Comparing the midpoint estimate of social costs with the
midpoint estimate of monetized benefits results in a net benefit of negative $12.3 million (2001$).
*• The post-stratification extrapolation method, which does not affect the estimated costs of the rule, results in total net
monetizable benefits ranging from negative $12.3 to negative $13.3 million (2001$), with a midpoint estimate of
negative $12.8 million (2001$).
18-2
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 18: MP&M Benefit / Cost Comparison
The lack of a comprehensive benefits valuation limits the assessment of the relationship between costs and benefits of the
final rule. EPA believes that the benefits of regulation, even in the low-estimate case (post-stratification extrapolation), would
be comparable to the social costs if all of the benefits of regulation could be quantified and monetized.
18-3
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 18: MP&M Benefit / Cost Comparison
Table 18.1: Comparison of National Annual Monetizable Benefits to Social
Benefit and Cost Categories
: :
Low
Costs: Final Rule
Mid |
(2001$)
High
Final Option — Traditional Extrapolation
Benefit Categories
Reduced cancer risk from fish consumption
Reduced cancer risk from water consumption
Reduced risk from exposure to lead
Enhanced water-based recreation
Nonuse benefits
Avoided sewage sludge disposal costs
Total Monetized Benefits"
$90
$0
$0
$586,503
$293,252
N/A
$879,845
$90
$0
$0
$999,838
$499,919
N/A
$1,499,846
$90
$0
$0
$1,574,380
$787,190
N/A
$2,361,660
Cost Categories
Resource costs of compliance
Costs of administering the final regulation
Social costs of unemployment
Total Monetized Costs
$13,824,563
$0
$0
$13,824,563
$13,824,563
$0
$0
$13,824,563
$13,824,563
$0
$0
$13,824,563
Net Monetized Benefits (Benefits Minus Costs)"
Final Option
Benefit Categories
Reduced cancer risk from fish consumption
Reduced cancer risk from water consumption
Reduced risk from exposure to lead
Enhanced water-based recreation
Nonuse benefits
Avoided sewage sludge disposal costs
Total Monetized Benefits"
($12,944,718)
— Post-Stratification Extrapolation
$134
$0
$0
$382,105
$191,053
N/A
$573,292
($12,324,717)
$134
$0
$0
$651,392
$325,696
N/A
$977,221
($11,462,903)
$134
$0
$0
$1,025,705
$512,852
N/A
$1,538,691
Cost Categories
Resource costs of compliance
Costs of administering the final regulation
Social costs of unemployment
Total Monetized Costs
$13,824,563
$0
$0
$13,824,563
$13,824,563
$0
$0
$13,824,563
$13,824,563
$0
$0
$13,824,563
Net Monetized Benefits (Benefits Minus Costs)b
($13,251,271)
($12,847,342)
($12,285,872)
* EPA did not estimate low and high benefits estimates for reduced cancer risk or lead exposure because it used a single estimate for the
value of a statistical life (VSL) to estimate mortality benefits in these categories. EPA calculated low and high estimates of total monetized
benefits by adding midpoint benefits estimates for cancer risk and lead exposure to respective low and high estimates of recreation and
nonuse benefits.
b EPA's estimate of social cost is based only on the estimated resource cost of compliance and was calculated as only a single value instead
of a range. Low, mid, and high net benefit values were calculated by subtracting the total monetized cost estimate from low, mid, and high
estimates of total monetized benefits.
Source: U.S. EPA analysis.
18-4
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
Chapter 19: Social Costs and
Benefits of Regulatory Alternatives
INTRODUCTION
EPA considered three regulatory options as alternatives to the
selected MP&M rule. These options (the Proposed/NODA
Option, Directs + 413 to 433 Upgrade Option, Directs + All
to 433 Upgrade Option) are described in Chapter 4. EPA
estimated the social costs and benefits of these three options,
using the same methods applied in the analyses of the final
rule. This chapter summarizes the results of these benefit and
cost analyses. The total number of facilities reported for the
Proposed/NODA Option (Option II) analysis differs from the
facility count reported for the final rule and Options III and
IV. After deciding in July 2002 not to consider the NODA
option as the basis for the final rule, EPA performed no more
analysis on the NODA option, including not updating facility
counts and related analyses for the change in subcategory and
discharge status classifications.
CHAPTER CONTENTS
19.1 Estimated Social Costs 19-1
19.1.1 Compliance Costs for MP&M Facilities ..19-1
19.1.2 Government Administrative Costs 19-2
19.1.3 Cost ofUnemployment 19-2
19.1.4 Total Social Costs 19-3
19.2 Estimated Benefits 19-4
19.2.1 Human Health Benefits 19-4
19.2.2 Recreational Benefits 19-5
19.2.3 Avoided Sewage Sludge Disposal or
Use Costs 19-6
19.2.4 Total Monetized Benefits 19-7
19.3 Comparison of Estimated Benefits and Costs .... 19-7
Glossary 19-10
Acronym 19-11
19.1 ESTIMATED SOCIAL COSTS
EPA estimated social costs for the final rule and alternative options in Chapter 11: Social Costs. This section provides a
summary of those results.
19.1.1 Compliance Costs for MP<&M Facilities
Table 19.1 presents the estimated resource value of compliance costs by discharge status under the final option and alternative
regulatory options. These compliance costs are not adjusted for the effect of taxes or pass-through of compliance costs to
customers, and therefore represent the social value of resources used for compliance. EPA annualized compliance costs using
a 7 percent discount rate over a 15-year analysis period. A more detailed description as well as the results presented by
subcategory can be found in Chapter 11: Social Costs. The total resource compliance costs of the final rule are equal to
$13.8 million (2001$). The total annualized compliance costs under the Proposed/NODA Option are $1,620.3 million, or
117 times the final rule's compliance costs. The total annualized compliance costs under the Directs + 413 to 433 Upgrade
Option are $96.8 million, or 7 times the final rule's costs. The total annualized compliance costs under the Directs + All to
433 Upgrade Option are $138.2 million, or 10 times the final rule's costs.
Table 19.1: Resource Value of Compliance Costs under Different Options (millions, 2001$)
Option Indirect Direct Total
Option I: Selected Option (Directs Only)
Option II: Proposed/NODA Option
Option III: Directs + 413 to 433 Upgrade Option
Option IV: Directs + All to 433 Upgrade Option
$0.0
$1,111.4
$83.0
$124.4
$13.8
$508.9
$13.8
$13.8
$13.8
$1,620.3
$96.8
$138.2
Source: U.S. EPA analysis.
19-1
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
19.1.2 Government Administrative Costs
The final rule excludes all indirect dischargers from coverage. EPA therefore expects no POTW administrative costs for the
final rule. Under the alternative options, which include indirect dischargers, EPA expects no increase in permitting costs for
facilities that already hold a permit in the baseline. However, governments will incur additional permitting costs for
unpermitted facilities (under the Proposed/NODA option only) and to accelerate repermitting for some indirect dischargers
that currently hold permits. The alternative regulatory options may also cause some administrative costs to decrease. For
example, control authorities will no longer have to repermit facilities that are estimated to close as a result of the MP&M rule.
EPA estimates that each of the three alternative options considered would result in reduced POTW regulatory costs. These
cost savings result from regulatory closures (i.e., facilities that currently hold a permit and would have required repermitting
in the baseline, but that will no longer require repermitting under the regulatory options). The cost savings as a result of
regulatory closures outweigh the additional costs of issuing new permits (under the Proposed/NODA option only) and
repermitting on an accelerated, three-year schedule.
Table 19.2 below presents the estimated permitting costs to governments of administering the final rule and alternative
options. Chapter 7: Government and Community Impact Analysis describes the methodology used to estimate these
administrative costs. Estimated annualized cost savings to POTWs for the three alternative regulatory options range between
$0.05 and $1.0 million under the Proposed/NODA option, and between $0.03 and $0.2 million under the Directs + 413 to 433
Upgrade Option and the Directs + All to 433 Upgrade Option (all costs in (2001$).
Table 19.2: Annualized Government Administrative Costs by Regulatory Option
(2001$)
Option
I: Selected Option
II: Proposed/NODA Option
III: Directs + 413 to 433 Upgrade
IV: Directs + 433 to All Upgrade
Low
n/a
(46,000)
(26,000)
(26,000)
Medium
n/a
(198,000)
(56,000)
(55,000)
High
n/a
(1,027,000)
(218,000)
(213,000)
Source: U.S. EPA analysis.
19.1.3 Cost of Unemployment
The loss of jobs associated with any facility closures would represent a social cost of the regulation. However, from its
facility impact analysis, EPA estimates that no facilities will close as a result of the final rule. Accordingly, EPA estimates a
zero cost of unemployment for the final regulation.
Table 19.3 presents the estimated social costs of unemployment for the alternative regulatory options, for which EPA
estimated closures. These estimates include the estimated willingness-to-pay to avoid cases of involuntary unemployment,
and the cost of administering the unemployment compensation system for unemployed workers. EPA annualized costs using
a 7 percent discount rate over a 15-year analysis period.
The Agency based lower-bound estimates of the number of net job losses expected from compliance. Net job losses are
estimated at 26,060 jobs under the Proposed/NODA Option, 7,319 under the 413 Upgrade Option, and 7,011 under the Local
Limits Option. The gross estimate for lost employment, which does not consider increased employment from compliance
activities and thus provides a conservative upper-bound of potential unemployment effects, is 32,729 jobs under the
Proposed/NODA Option and 7,874 under both 433 Upgrade Options. From these estimates for lost employment, social costs
of unemployment under the Proposed/NODA Option range from $344 million to $454 million (2001$). Social costs of
unemployment under the 433 Upgrade Options range from $83 million to $109 million (2001$).
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
Table 19.3: Social Costs of Unemployment for Final Rule and Alternative Options
(millions, 2001$)
Unemployment/
Cost Category
Net Unemployment
(FTE-years)a
Gross Unemployment
(FTE-years)a
Annualized Cost of
Unemployment
Annualized
Administrative Cost
Total Social Cost of
Unemployment
Option I:
Selected Option
(Directs Only)
n/a
n/a
n/a
n/a
n/a
Option II:
Proposed/NODA Option
Low | Mid | High
26,060
32,729
$344.16 $399.22 $454.29
$0.44 $0.44 $0.44
$344.60 $399.66 $454.73
Option III:
Directs + 413 to 433
Upgrade Option
Low | Mid | High
7,319
7,874
$82.80 $96.05 $109.30
$0.11 $0.11 $0.11
$82.91 $96.16 $109.40
Option IV:
Directs + All to 433
Upgrade Option
Low | Mid | High
7,011
7,874
$82.80 $96.05 $109.30
$0.11 $0.11 $0.11
$82.91 $96.16 $109.40
a Number of FTE positions multiplied by the duration of employment/unemployment. EPA assumed that workers losing jobs due to
regulatory closures would be unemployed for one year. The timing and duration of employment gains due to compliance expenditures
differ for employment associated with manufacturing and installing equipment (in the first year) and operating and maintaining
equipment (all 15 years of the analysis period).
Source: U.S. EPA analysis.
19.1.4 Total Social Costs
EPA estimated that the final rule will not result in social costs of unemployment and that governments will not incur
additional costs in administering the regulation. EPA estimates the total social cost of the final rule at $13.8 million (2001 $).
This cost results entirely from the estimated resource costs of compliance.
For the Proposed/NODA Option, EPA estimated social costs to range from $1.96 billion to $2.07 billion (2001$) annually
based on the cost estimates presented above. The midpoint estimate, $2.02 billion is almost 150 times greater than the final
rule's social cost. This increase results from the more stringent technology requirements for most subcategories under the
Proposed/NODA Option compared to those under the final rule. In addition, this alternative option includes additional
subcategories not covered by the regulation.
For the Directs + 413 to 433 Upgrade Option, EPA estimated social costs to range from $180 million to $206 million (2001$)
annually. The midpoint estimate, $193 million, is 14 times greater than the final rule's social cost. This increase results from
requiring facilities currently regulated under the Electroplating regulations (40 CFR 41 3) to comply with the Metal Finishing
regulations (40 CFR 433).
For the Directs + All to 433 Upgrade Option, EPA estimated social costs to range from $221 million to $247 million (2001$)
annually. The midpoint estimate, $234 million, is 17 times greater than the final rule's social cost. This increase results from
requiring facilities currently regulated by local limits or general pretreatment standards to meet with the Metal Finishing
regulations (40 CFR 433).
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives
19.2 ESTIMATED BENEFITS
EPA estimated the benefits for the alternative options based on the methodologies described in Chapters 12 through 16.
19.2.1 Human Health Benefits
EPA used the methodology described in Chapter 13 to assess human health benefits from reduced incidence of cancer from
consumption of contaminated fish tissue and drinking water under the three alternative options.
EPA estimated that the final rule, as well as both upgrade options, would reduce incidence of cancer from consumption of
contaminated fish by 1.4E-5 cancer cases per year. The Proposed/NODA Option would eliminate an estimated 0.57 cancer
cases per year from the baseline level. The estimated monetary value of reduced incidence of cancer from consumption of
contaminated fish is $3.68 million under the Proposed/NODA Option, $90 (2001 $) under the final rule and Directs + 413 to
433 Upgrade Option, and $169 (2001 $) under the Directs + All to 433 Upgrade Option.
Under the final rule, as well as both upgrade options, EPA expects no reductions in cancer cases from consumption of
contaminated drinking water. Under the Proposed/NODA Option, 0.001 fewer cancer cases are expected annually from the
baseline level. Estimated annual monetary benefits resulting from fewer cancer cases caused by the consumption of
contaminated drinking water are $6,536 (2001$) for the Proposed/NODA Option.
EPA used the methodology described in Chapter 14 to assess benefits to children and adults from reduced exposure to lead
under the alternative options. EPA estimated that the final rule will yield no lead-related benefits to children from reduced
consumption of contaminated fish. Annual lead-related benefits for children of $20.8 million (2001 $) are expected for the
Proposed/NODA Option. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would
result in $1.3 and $1.5 million (2001$) in lead-related benefits for children, respectively.
EPA estimated that the Proposed/NODA Option would reduce neonatal mortality by 1.60 cases, and avoid an estimated loss
of 1,078 IQ points. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would reduce
cases of neonatal mortality by 0.15 and 0.17, and avoid the loss of 32 and 36 IQ points, respectively. EPA estimated lead-
related benefits for adults at $7.0 million under the Proposed/NODA Option, and approximately $0.7 million (2001$) for both
upgrade options. Combined lead-related benefits for children and adults total $27.8 million for the Proposed/NODA Option,
and between $2.0 and $2.2 million (2001$) for both upgrade options. Table 19.4 summarizes all health-related benefits.
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
Table 19.4: Annual Human Health Benefits for the Alternative Options (2001$)
Regulatory Option
T, j j/^ T,- i Reduced Cancer Risk
Reduced Cancer Risk
, _. , „ . . from Water Lead-Related Benefits
from Fish Consumption :
Consumption
: : : : :
: : : : :
ill i :
Number ! ,_ Number ! ,„
„„ Monetary „_ Monetary ...
of Cancer : ., , J : of Cancer : ,, , Children Adult
„ Value „ Value
Cases Cases
: : : : :
ill i :
Total
Monetized
Human
Health
Benefits
Proposed/NODA Option
Baseline
Proposed/NODA Option
Baseline
Selected Option
Directs + 413 to 433
Upgrade
Directs + 41 3 + 50% LL
Upgrade
0.920
0.353
0.033
0.033
0.033
0.033
$3,684,973
Fin
$90
$90
$169
3.117
3.116
al Option Al
5.3E-07
5.3E-07
5.3E-07
5.3E-07
$6,536
;ernatives
$0
$0
$0
$20,791,073
$0
$1,303,590
$1,457,640
$7,048,025
$0
$704,574
$785,304
$31,530,607
$90
$2,008,254
$2,243,113
Source: U.S. EPA analysis.
19.2.2 Recreational Benefits
EPA used the methodology described in Chapter 15 to assess improvements in recreational benefits under the alternative
options. The Agency found that the final option will reduce the occurrence of pollutant concentrations in excess of ambient
water quality criteria (AWQC) limits by 2 percent (9 of 395 baseline occurrences) (see Table 19.5). EPA found that the
Proposed/NODA Option would reduce pollutant concentrations in excess of AWQC limits by 2.6 percent (154 of 5,999
baseline occurrences), while both upgrade options would reduce such occurrences by 72 percent (285 of 395 baseline
occurrences) from the baseline level.
EPA estimated the range of recreational value increases (including both use and nonuse value) for these reaches resulting
from habitat improvements for each option. EPA expects recreational value of improved reaches to increase by $0.9 million
to $2.4 million annually under the final rule, $406 million to $956 million annually under the Proposed/NODA Option,
$182.7 million to $443.5 million under the Directs + 413 to 433 Upgrade Option, and by $183.5 million to $445.9 million
under the Directs + All to 433 Upgrade Option (2001$) (see Table 19.7). The midpoint estimates of combined annual
recreational and nonuse benefits under these options are $1.5 million, $649 million, $297.0 million, and $298.5 million
(2001$). The midpoint estimates of recreational and nonuse benefits are approximately 200 times greater under the upgrade
options than under the final rule.
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
Table 19.5: Number of MP&M Discharge Reaches with MP&M Pollutant Concentrations Exceeding AWQC Limits
Regulatory Status
Baseline
Proposed/NODA
Option
Baseline
Selected Option
Directs + 413 to 433
Upgrade
Directs + 41 3 + 50% LL
Upgrade
Number of Reaches with Concentrations Exceeding
AWQC Acute
Exposure Limits for
Aquatic Species
330
86
18
9
0
0
AWQC Chronic
Exposure Limits for
Aquatic Species
Proposed/NODA Option
928
539
Final Option Alternatives
353
344
53
31
AWQC Limits for
Human Health
5,865
5,803
78
78
78
78
Number of Reaches
with Concentrations
Exceeding AWQC
Limits"
5,999
5,845
395
386
109
109
a All reaches exceeding aquatic acute exposure limits also exceed chronic exposure limits. In order not to double count the number of
reaches expected to benefit from the regulation, the total number of reaches exceeding AWQC limits is the sum of the number of
reaches that exceed human health criteria and the number exceeding aquatic chronic criteria, which do not also exceed AWQC limits for
human health.
Source: U.S. EPA analysis.
19.2.3 Avoided Sewage Sludge Disposal or Use Costs
The final rule will not regulate indirect dischargers and therefore will not reduce metals discharges to POTW s or the number
of POTWs that exceed land application standards for sewage sludge disposal. However, reduced metals discharges to
POTWs resulting from the Proposed/NODA Option would enable 48 additional POTWs to dispose of sewage sludge by land
application, resulting in $22.8 million (2001$) incest savings (see Table 19.6). The Directs + 413 to 433 Upgrade Option
and the Directs + All to 433 Upgrade Option would not reduce the number of POTWs that exceed land application standards.
However, under both upgrade options 15 POTWs would be able to improve their sludge quality from meeting the land
application low standard to meeting the land application high standard, resulting in approximately $16,929 (2001$) in cost
savings to POTWs.
Table 19.6: Cost Savings from Land Application
Regulatory Option
Baseline
Proposed/NODA Option
Baseline
Selected Option
Directs + 413 to 433 Upgrade
Directs + All to 433
# of POTWs Exceeding Land Application
(High) Standards
Proposed/NODA Option
5,328
5,259
Final Option Alternatives
856
856
856
856
Cost Savings from Upgrading Sewage
Sludge Disposal Methods (2001$)
$22,825,584
$0
$16,929
$16,929
Source: U.S. EPA analysis.
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives
19.2.4 Total Monetized Benefits
EPA estimates total monetized benefits under the final option ranging from $879,845 to $2,361,660 (2001$), with a midpoint
estimate of $1,499,846. Total monetized benefits for the Proposed/NODA Option range from $460 million to $1,010 million,
with a midpoint estimate of $704 million (2001$). Total monetized benefits estimates for the Directs + 413 to 433 Upgrade
Option and the Directs + All to 433 Upgrade Option are similar, with respective ranges of $185 million to $446 million, and
$186 million to $448 million (2001 $). Midpoint estimates of total monetized benefits for these options are $299 million and
$301 million (2001$), respectively. Midpoint estimates for monetized benefits for the upgrade options are approximately 200
percent higher than the midpoint estimate of benefits for the final rule.
19.3 COMPARISON OF ESTIMATED BENEFITS AND COSTS
Combining the estimates of social benefits and social costs under the final option yields net monetized benefits ranging from
negative $11.5 million to negative $12.9 million (2001$), with a midpoint estimate of negative $12.3 million (see Table 19.7).
Under the Proposed/NODA Option, net monetized benefits range from negative $1,505 million to negative $1,064 million
(2001$) per year, with a midpoint estimate of negative $1,316 million. Annual net monetized benefits under the Directs +
413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option range from $5 million to $240 million, and negative
$35 million to positive $201 million (2001 $) per year, respectively. Midpoint estimates of net benefits for these options are
$106 million and $66 million (2001$), respectively (see Table 19.7). As discussed in Chapter 12, the benefits assessment of
regulatory options is necessarily incomplete due to the omission of numerous mechanisms by which society is likely to benefit
from reduced effluent discharges.
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
Table 19.7: Comparison of Social Benefits and Costs of Alternative Options (2001$)
i i i
Benefit and Cost Categories'1 Low Mid High
Selected Option
Benefit Categories
Reduced Cancer Risk from Fish Consumption
Reduced Cancer Risk from Water Consumption
Reduced Risk from Lead Exposure
Enhanced Water-Based Recreation
Nonuse Benefits
Avoided Sewage Sludge Disposal Costs
Total Monetized Benefits"
Cost Categories
Resource Costs of Compliance
Administration Costs to POTWs
Social Costs of Unemployment
Total Monetized Costs
Net Monetized Benefits (Benefits Minus Costs)b
$90
$0
$0
$586,503
$293,252
N/A
$879,845
$13,824,563
$0
$0
$13,824,563
($12,944,718)
$90
$0
$0
$999,838
$499,919
N/A
$1,499,846
$13,824,563
$0
$0
$13,824,563
($12,324,717)
$90
$0
$0
$1,574,380
$787,190
N/A
$2,361,660
$13,824,563
$0
$0
$13,824,563
($11,462,903)
Proposed/NODA Option
Benefit Categories
Reduced Cancer Risk from Fish Consumption
Reduced Cancer Risk from Water Consumption
Reduced Risk from Lead Exposure
Enhanced Water-Based Recreation
Nonuse Benefits
Avoided Sewage Sludge Disposal Costs
Total Monetized Benefits"
Cost Categories
Resource Costs of Compliance
Administration Costs to POTWs
Social Costs of Unemployment
Total Monetized Costs
Net Monetized Benefits (Benefits Minus Costs)"
$3,684,973
$6,536
$27,839,098
$270,366,433
$135,183,216
$22,795,620
$459,875,876
$1,620,252,136
($46,000)
$344,597,370
$1,964,803,507
($1,504,927,631)
$3,684,973
$6,536
$27,839,098
$432,938,869
$216,469,435
$22,825,584
$703,764,495
$1,620,252,136
($198,000)
$399,662,865
$2,019,717,002
($1,315,952,507)
$3,684,973
$6,536
$27,839,098
$637,360,014
$318,680,007
$22,855,548
$1,010,426,176
$1,620,252,136
($1,027,000)
$454,728,360
$2,073,953,497
($1,063,527,321)
19-&
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MP&M EEBA Part IV: Comparison of Costs and Benefits
Chapter 19: Social Costs and Benefits of Regulatory Alternatives
Table 19.7: Comparison of Social Benefits and Costs of Alternative Options (2001$)
Benefit and Cost Categories'1
Low
Mid
High
Directs + 413 to 433 Upgrade
Benefit Categories
Reduced Cancer Risk from Fish Consumption
Reduced Cancer Risk from Water Consumption
Reduced Risk from Lead Exposure
Enhanced Water-Based Recreation
Nonuse Benefits
Avoided Sewage Sludge Disposal Costs
Total Monetized Benefits"
$0
$2,008,254
$121,808,075
$60,904,038
$11,319
$184,731,776
$0
$2,008,254
$197,990,383
$98,995,192
$16,929
$299,010,848
$90
$2,008,254
$295,661,071
$147,830,535
$22,539
$445,522,489
Cost Categories
Resource Costs of Compliance
Administration Costs to POTWs
Social Costs of Unemployment
$96,779,134
$96,779,134
($26,000)
($56,000)
82,907,075
6,155,345
Total Monetized Costs
$179,660,209
$192,878,479
$96,779,134
($218,000)
$109,403,616
$205,964,750
Net Monetized Benefits (Benefits Minus Costs)"
$5,071,567
$106,132,369
$239,557,739
Directs + All to 433 Upgrade
Benefit Categories
Reduced Cancer Risk from Fish Consumption
Reduced Cancer Risk from Water Consumption
Reduced Risk from Lead Exposure
Enhanced Water-Based Recreation
Nonuse Benefits
Avoided Sewage Sludge Disposal Costs
$169 I
$169 I
$2,243,113 I
$2,243,113 I
$122,360,444 j
$198,976,248 j
$61,180,222 I
$99,488,124 !
$11,319 |
$16,929 |
Total Monetized Benefits" I
$185,795,267 |
$300,724,583 |
$169
$2,243,113
$297,272,287
$148,636,143
$22,539
$448,174,251
Cost Categories
Resource Costs of Compliance
Administration Costs to POTWs
Social Costs of Unemployment
$138,237,664 !
$138,237,664 !
($26,000) |
($55,000) |
2,907,075 I
$96,155,345 !
Total Monetized Costs j $221,118,739 j
$234,338,009 j
$138,237,664
($213,000)
$109,403,616
$247,428,280
Net Monetized Benefits (Benefits Minus Costs)"
($35,323,472) j
$66,386,574 j
$200,745,971
a EPA did not estimate low and high benefits estimates for reduced cancer risk or lead exposure because a single estimate for the value of
a statistical life (VSL) was used to estimate mortality benefits in these categories. EPA calculated low and high estimates of total monetized
benefits by adding midpoint benefits estimates for cancer risk and lead exposure to respective low and high estimates of recreation and
nonuse benefits.
b EPA's estimate of social costs for the final regulation is based only on the estimated resource costs of compliance and is a single value
instead of a range. EPA calculated low, mid, and high net benefit values by subtracting the total monetized cost estimate from low, mid,
and high estimates of total monetized benefits.
° EPA calculated the low net benefit value by subtracting the high value of costs from the low value of benefits, and calculated the high net
benefit value by subtracting the low value of costs from the high value of benefits. The mid net benefit value is the mean value of benefits
less the midpoint of costs.
d Category values may not sum to reported totals due to rounding of individual estimates for presentation purposes.
Source: U.S. EPA analysis.
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives
GLOSSARY
ambient water quality criteria (AWQC): published and periodically updated by the EPA under the Clean Water Act.
The criteria reflect the latest scientific knowledge on the effects of specific pollutants on public health and welfare, aquatic
life, and recreation. The criteria do not reflect consideration of economic impacts or the technological feasibility of reducing
chemical concentrations in ambient water. The criteria serve as guides to states, territories, and authorized tribes in
developing water quality standards and ultimately provide a basis for controlling discharges or releases of pollutants into our
nation's waterways. AWQC are developed for two exposure pathways: ingestion of the pollutant via contaminated aquatic
organisms only, and ingestion of the pollutant via both water and contaminated aquatic organisms.
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives
ACRONYM
AWQC: ambient water quality criteria
19-11
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MP&M EEBA Part IV: Comparison of Costs and Benefits Chapter 19: Social Costs and Benefits of Regulatory Alternatives
THIS PAGE INTENTIONALLY LEFT BLANK
19-12
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MP&M EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Chapter 20: Baseline Conditions in
Ohio
INTRODUCTION
Section IV of this EEBA focuses on the state of Ohio as a
case study of the MP&M regulation's expected benefits and
costs. Ohio has a diverse water resource base, a relatively
large number of MP&M industry facilities, and a more
extensive water quality ecological database than many other
states. EPA gathered extensive data on MP&M facilities and
on Ohio's baseline water quality conditions and water-based
recreation activities to support the case study analysis.
These data characterize current water quality conditions,
water quality changes expected from the regulation, and the
expected welfare changes from water quality improvements
at water bodies affected by MP&M discharges.
The case study analysis supplements the national-level
analysis performed for the MP&M regulation in two
important ways. First, the case study used improved data
and methods to determine MP&M pollutant discharges from
both MP&M facilities and other sources, hi particular, EPA
administered 1,600 screener questionnaires in the state of
Ohio to augment information on Ohio MP&M facilities. The
Agency also used information from the sampled MP&M
facilities to assign discharge characteristics to non-sampled
MP&M facilities1. Second, the analysis used an original
travel cost study to value four recreational uses of water
resources affected by the regulation: swimming, fishing,
boating, and near-water activities. The added detail provides
a more complete and reliable analysis of water quality
changes from reduced MP&M discharges. The case study
analysis therefore provides more complete estimates of changes in human welfare resulting from reduced health risk,
enhanced recreational opportunities, and improved economic productivity.
The statewide case study of recreational benefits from the MP&M regulation combines water quality modeling with a
random utility model (RUM) to assess how changes in water quality from the regulation will affect consumer valuation of
water resources. The study addresses a wide range of pollutant types and effects, including water quality measures not often
addressed in past recreational benefits studies. The estimated model supports a more complete analysis of recreational
benefits from reductions in nutrients and "toxic" pollutants.2
CHAPTER CONTENTS
20.1 Overview of Ohio's Geography, Population,
and Economy 20-2
20.2 Profile of MP&M Facilities in Ohio 20-3
20.3 Ohio's Water Resources 20-6
20.3.1 Aquatic Life Use 20-8
20.3.2 Water Recreation in Ohio 20-11
20.3.3 Commercial Fishing in Ohio 20-12
20.3.4 Surface Water Withdrawals 20-12
20.4 Surface Water Quality in Ohio 20-12
20.4.1 Use Attainment in Streams and Rivers
in Ohio 20-13
20.4.2 Lake Erie and Other Lakes Use
Attainment 20-13
20.4.3 Causes and Sources of Use
Non-Attainment in Ohio 20-14
20.5 Effects of Water Quality Impairments on Water
Resource Services 20-15
20.5.1 Effect of Water Quality Impairment on Life
Support for Animals and Plants 20-15
20.5.2 Effect of Water Quality Impairment on
Recreational Services 20-17
20.6 Presence and Distribution of Endangered and
Threatened Species in Ohio 20-18
20.6.1 E&T Fish 20-19
20.6.2 E&T Mollusks 20-19
20.6.3 Other Aquatic E&T Species 20-20
Glossary 20-24
Acronyms 20-27
References . ... 20-28
1 Appendix H provides a detailed discussion on the approach used to estimate discharge characteristics for non-sampled MP&M
facilities.
2 The term "toxic" used here refers to the 126 priority or toxic pollutants specifically defined as such by EPA, as well as
nonconventional pollutants that have a toxic effect on human health or aquatic organisms.
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
This and the next two chapters present the Ohio case study. This chapter provides background information on the state of
Ohio, the following chapter presents the results from the recreational benefits analysis, and the last chapter summarizes social
costs and benefits of the final regulation for the state of Ohio.
20.1 OVERVIEW OF OHIO'S GEOGRAPHY, POPULATION, AND ECONOMY
Table 20.1 summarizes general information on Ohio. Ohio is large, heavily-industrialized, and densely-populated. The state
covers a total surface area of 44,828 sq. mi. (106,607 sq. km.), of which water represents 3,875 sq. mi. (10,036 sq. km.).
About 90 percent of the water surface area consists of Lake Erie; the remainder includes inland waters, such as lakes,
reservoirs, and rivers (including the Ohio River). The state housed 11,353,140 people in 2000. The three largest
metropolitan areas are located on Lake Erie (Toledo and Cleveland) and the Ohio River (Cincinnati).
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MP&M EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Table 20.1: Facts about the State of Ohio
Geography
"V
Location Midwest United States, northeast part:
south of Lake Erie
east of Indiana
north of the Ohio River
Total land area 40,953 sq. mi. (106,607 sq. km.)
Of the 26,451,000 acres of terrestrial surface area in Ohio:
97 percent is non-federal land (National Resources Inventory (NRI))
3,558,000 acres, representing 13.5 percent of the total area of Ohio, are developed
The remaining non-federal lands are rural land, classified mostly as crop land, forest, and pasture
lands. (USDA, 1992a)
Total water surface area 3,875 sq. mi. (10,036 sq. km.)
Approximately 90 percent is represented by Lake Erie, and 10 percent are inland waters including
rivers, lakes, and reservoirs."
Total area 44,828 sq. mi. (116,104 sq. km.)
Demographics
Population 11,353,140 in 2000, approximately4 percent oftotal U.S. population (U.S. Census Bureau)
Population increase: 4.7 percent from 1990 to 2000, compared to a 13.1 percent increase in the U.S.
population overall.
Most densely populated part of the state: northeastern Ohio, both urban and rural areas.
Largest cities: Cleveland, Cincinnati, and Toledo.
Economics
Ohio Midwest U.S.
••" '
Per capita income (1996$) $23,537 $24,166 $24,231
Rank in per capital income in
the U.S.: 21
••+ i- >
Percent of population below 11.5% N/A 13.8%
the poverty level (1995
Current Population Survey
data, DOC 1996)
TI
Ohio per capita income increased by 16 percent from 1986 to 1996.
Income growth is consistent with other midwestern states and is 2 percent greater than overall U.S. per
capita income growth.
Gross State Product (GSP) $303,569,000,000 (1996$), representing 4 percent of Gross Domestic Product (GDP) for the U.S. in
1996.
* !
Percent increase in GSP/GDP Ohio GSP U.S. GDP
from 1986 to 1996 (in > *
adjusted 1996$) 25% 29%
" Total water surface areas are estimated by the USDA' s National Resources Inventory (NRI) (USDA 1992b).
(http://www.ftw.nrcs.usda.gov/nri_data.html)
Source: U.S. EPA analysis.
20.2 PROFILE OF MP<&M FACILITIES IN OHIO
EPA selected Ohio as the case study state because MP&M industries account for a large share of the state's economy (see
Table 20.2). Data from the 1997 Economic Censuses show that industries containing MP&M facilities employ 19.8 percent
of Ohio's total industrial workers and produce 21.2 percent of industrial worker output by value. MP&M industries also
account for 22.1 percent of payroll payments, indicating that jobs in MP&M industries are more highly paid than industrial
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MP&M EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
jobs on average in Ohio. The discussion below explains the sources and methodology EPA used, and then presents detailed
results and caveats.
Table 20.2: MP&M Share of Industrial Output and Employment in Ohio, 1997
MP&M
Total
MP&M Share
Total Employment
827,507
4,087,393
19.8%
Payroll
$23,233,857,000
$112,777,104,000
22.1%
Value of Output
$132,117,226,000
$677,978,137,000
21.2%
Source: Department of Commerce 1992 Economic Censuses.
EPA obtained employment, payroll, and output data from the 1997 Economic Census CD-ROM, drawing from the eight
economic censuses in Table 20.3. Employment and payroll numbers include all employees (i.e., production plus non-
production workers). The measure of output differs according to the source, but in each case the output measures shown in
Table 20.2 correspond conceptually to total revenue. EPA extracted the EMPLOYEE, PAYROLL, and VALUE fields for
each 4-digit SIC industry in the MP&M category and for the entire state of Ohio. Industries include both in-scope and out-of-
scope facilities.
Table 20.3: The Economic
Source
Census of Retail Trade
Census of Wholesale Trade
Census of Service Industries'1
Census of Transportation,
Communications, and Utilities
Financial, Insurance, and Real Estate
Industries
Census of Manufacturers
Census of Mineral Industries
Census of Construction Industries
Censuses
Measure of
Output
Value of sales
Value of sales
Value of receipts
Value of revenue
Value of receipts
Value of shipments
Value of shipments
Value of
construction work
a Includes both taxable and non-taxable establishments.
Source: Department of Commerce 1997 Economic Censuses.
The MP&M industries include facilities to which the MP&M rule may not apply. For example, MP&M industries include
non-dischargers, but census data do not distinguish between in-scope and out-of-scope facilities. In addition, EPA
substantially revised the scope of the final regulation by excluding from the final regulation all indirect dischargers and direct
dischargers in all subcategories except for Oily Wastes. Definition of MP&M subcategories is provided in Section 4.1 of this
report. The final rule applies to an estimated 172 direct discharging facilities in Ohio.
Also, the analysis examines only the industrial sectors for which the Department of Commerce compiles statistics in the
Economic Censuses. Published industrial employment and output measures often exclude military and other government
personnel and farm output and employment, whether those exclusions are noted or not. The analysis excludes $4.7 billion in
value of agricultural products sold in 1997 by farms in Ohio, according to the U.S. Department of Agriculture's 1997 Census
of Agriculture. The Ohio analysis also excludes the government sector, which employed approximately 760,000 people in
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
Ohio in 1 997, according to the U.S. Bureau of Labor Statistics.3 These exclusions are normal when economists compare the
size of industrial groups.
If total employment in Ohio includes the government sector, then MP&M industries account for only 16.7 percent, rather than
19.8, percent of employment. If total industrial manufacturing and non-manufacturing output in Ohio includes the agricultural
sector, then MP&M industries account for only 21.0, rather than 21.1, percent of output. This said, data from the Bureau of
Labor Statistics and USDA are not completely consistent with the Economic Census data.
EPA augmented information on MP&M facilities available from published data sources and the Section 308 survey by
oversampling the state of Ohio with 1,600 screeners. The Agency used information from the Section 308 survey and the
1,600 screeners to characterize discharges from MP&M facilities in Ohio and to assess the economic impact of the final
regulation at the state level. Figure 20.1 depicts locations of the Ohio facilities included in the case study analysis.
The map of facility locations shows that the additional information from 1,600 screeners enabled EPA to perform the benefits
assessment with a greater level of detail than is possible at the national level. The added detail results in a more complete and
reliable analysis of changes in human welfare resulting from reduced health risk and improved recreational opportunities.
U.S. Bureau of the Census, Statistical Abstract of the United States, 1993, Washington, D.C., 1993.
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Figure 20.1: Location of Sample MP<&M Facilities in Ohio
Source: U.S. EPA analysis.
20.3 OHIO'S WATER RESOURCES
The benefits of enhanced water quality stem directly from enhancing water quality and/or quantity of services provided by
water resources. To aid in understanding the analysis of benefits from the final rule in Ohio, this section summarizes
environmental services provided by Ohio's water resources.
Ohio is a water-rich state:
> 24,000+ miles of named and designated rivers and streams;
> 451-mile border on the Ohio River;
> 200,000 acres among 450 lakes, ponds, rivers, and reservoirs; and
> 230+ miles of Lake Erie shoreline.
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
These water resources provide three broad categories of services: in-stream, withdrawal, and existence services.
Water resources provide in-stream services prior to the withdrawal of water from the water body. Major in-stream services
include life support for animals and plants, water-based recreation, commercial fishing and navigation, water storage, and
aesthetics. Withdrawal services include uses of water resources after the water is withdrawn from the water body. These uses
include drinking water supply, irrigation, production and processing services, and sanitary services. Existence services are
not linked to current uses of water bodies, and arise from knowing that species diversity or the natural beauty of a given water
body is preserved.
The Ohio Environmental Protection Agency (Ohio EPA) assesses surface waters in their Ohio Water Resource Inventory
(O WRI) report based on water resource services provided by the assessed water body. The main focus of this assessment is
on beneficial uses associated with Ohio's water resources, including aquatic life use, recreation, and public water supply.
Table 20.4 shows how Ohio surface waters fall into these use designations.
Table 20.4: Summary of Designated Life Uses for Ohio Surface Waters (1996)
Use Designation
Total
Aquatic Life Usea
Exceptional Warmwater Habitat (EWH)
Warmwater Habitat (WWH)
Other
Recreation
Primary Contact (PCR)b
Secondary Contact (SCR)
Public Water Supply
Stream/River
(Miles)"
43,917
24,067
3,217
18,318
2,532
224,96
1,188
Lakes /
Reservoir
(Acres)"
200,000
193,903
193,903
200,000
118,801
Lake Erie
(Shore
Miles)"
236
236
236
236
a Total river/stream miles are based on Ohio EPA estimates. U.S. EPA estimates 61,532 total river
miles and 29,113 total perennial miles based on RF3, which includes many smaller undesignated
streams.
b Note that some water bodies have more than one designated use (e.g., aquatic life and primary
recreation).
Source: Ohio EPA, OWR1, 1996.
The aquatic life use category is further subdivided into seven categories. The most widely-applied aquatic use designation in
Ohio is Warmwater Habitat (WWH). accounting for 18,318 (76 percent) stream and river miles (Ohio EPA, OWRI, 1996).
The second most widely applied designation is Exceptional Warmwater Habitat (EWH). accounting for 3, 217 stream
and river miles (13 percent), 236 Lake Erie shore miles (100 percent), and 193,903 acres of inland lakes (100 percent). Other
aquatic life categories include:
- Modified Warmwater Habitat (MWH).
•• Limited Resource Waters (LRW).
- Limited Warmwater Habitat(LWH).
•• Seasonal Salmonid Habitat (SSH). and
- Coldwater Habitat (CWH).
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Recreational uses are subdivided into Primary Contact Recreation (PCR) and Secondary Contact Recreation
(SCR):
> Primary Contact Recreation (PCR) rivers and streams deep enough for full human body immersion activities,
such as swimming.
•• Secondary Contact Recreation (SCR) only deep enough to permit wading and incidental contact, such as boating.
Approximately half of the designated stream miles, all inland lakes, and all of the Lake Erie shore miles are designated for
PCR (see Table 20.4). In addition, three percent of the designated stream miles (1,188 miles) are suitable for SCR.
The following sections detail each category of water resource use.
20.3.1 Aquatic Life Use
The Ohio water resources support hundreds of aquatic species and plants. Ohio water resources are also home to a number of
endangered and threatened species. Suitable stream and lake habitat are essential for both resident and transient animal
populations, including imperiled aquatic species. Habitats include specific biotic components (e.g., assemblages of plant and
animal species) and physical (e.g., dissolved oxygen (DO) content and temperature range) components. Water quality
impairments associated with siltation, excess nutrients, or low DO can adversely affect habitats that support important
activities, such as reproduction, foraging, migration, and overwintering.
The following sections briefly introduce water-dependent biological resources in Ohio. Water quality effects on life support
for animals and plants are discussed in Section 20.5
a. Ohio fish species
Fish are found throughout Ohio in almost every inland surface water body and Lake Erie. Many fish species serve important
recreational or commercial functions, while others are important forage for birds, other fish, and land-based species.
Ecosystem well-being therefore depends on the health offish and other aquatic species populations. The Ohio EPA monitors
biological data, especially those on sensitive aquatic species, to determine the aquatic life use attainment of surface waters.
The state gives high priority to healthy aquatic ecosystem maintenance.
Ohio's rivers and lakes offer a variety of man-made and natural habitats that offer excellent fishing opportunities for
numerous gamefish species. The state of Ohio spends significant resources on fishery management, trout stocking, and
recreational area maintenance to enhance these fish populations. Table 20.5 below provides brief summaries of the habitat
and diet of major recreational and commercial fish species in Ohio (Ohio DNR, 1999).
Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio
Fish
Bass
Bullhead
Burbot
Carp
Catfish
(channel,
flathead)
Native or
introduced?
Most native bass
(e.g., largemouth,
smallmouth,
spotted, and sock)
Native
Native
Introduced
j
Native
L 4
Habitat
Ponds, lakes, rivers, and streams in
every county; Lake Erie
Throughout Ohio; concentrations in
northern and west central Ohio
Lakes and rivers; prefer deep waters, but
move inshore to spawn
Warm lakes, streams, and ponds with
abundant organic matter, in every
county
i
Throughout Ohio's rivers and lakes;
tolerate a wide range of conditions
L
Spawning
season
Mid- April to
mid-June
Mid-May to
June
Winter
Late April to
June
L
When waters
reach 70° F in
temperature
Diet
Frogs, crayfish, insects, and other
fish
Insect larvae, crayfish, snails,
dead animals
Minnows and the young of other
fish species
Insect larvae, mollusks, fish,
crustaceans
Bottom feeders with a diet of
insect larvae, mollusks, and fish
both dead and alive
L
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio
Fish
Crappie,
white
Crappie,
black
Drum
Lamprey
Muskellunge
(Muskie)
Perch, white
Perch, yellow
Pike
Salmon
(chinook and
coho)
Sauger
Saugeye
(cross
between
sauger and
walleye)
Sucker, white
Sunfish
Native or
introduced?
j
Native
Native
Introduced
Native
Native
Introduced
j
Native
Introduced
Native
Bluegill,
pumpkinseed,
green, warmouth,
and longear
sunfish are native;
redear sunfish are
introduced
Habitat
Larger ponds, reservoirs, and rivers,
including near-shore habitats of Lake
Erie, in most areas of Ohio
L
Same general habitat as white crappie,
slightly less widely distributed
Lake Erie; drums support a commercial
fishery
„
Lake Erie and tributaries; Ohio River
and larger tributaries
Historically found in Lake Erie bays and
tributaries and streams of Ohio River
drainage; now also found in several
impoundments
Lake Erie and tributaries
Lakes, impoundments, ponds, slow-
moving rivers
,
Historically abundant in Lake Erie and
tributaries; today distributed in a small
portion of Lake Erie, Sandusky Bay,
Maumee Bay, and their tributary streams
in marshes, bays, and pools with
abundant vegetation
Stocked in Lake Erie for both
recreational and commercial fishing
purposes
L
Lake Erie and its tributaries; Ohio River
Stocked into many Ohio impoundments
Every county; Lake Erie
Rivers, streams, and lakes throughout
Ohio, and Lake Erie
Spawning
season
May and June
L
May and June
Spring into
late summer
April and early
May, when
temperatures
reach low- to
mid-50s
April and May
April and May
As ice breaks
in late
February and
early March
Pike is a
popular ice-
fishing species
L
Spring, when
water
temperatures
reach high 40s
April to May
Between May
and August
Diet
Insects and small fish
Insects and small fish
Mollusks, crayfish, fish, insects
Some species parasitize other fish
by attaching themselves to a
larger host's flank and feeding on
its flesh
Suckers, gizzard shard, and other
soft-rayed fish
Insects, crustaceans, other fish
Mostly fish, but are opportunistic
feeders; will occasionally eat
frogs, muskrats, small ducks
Insects, crayfish, other small fish
during low light (dawn and dusk)
Bottom feeders, consuming
various plant and animal species
Adults feed mostly on smaller
fish, insects, crustaceans
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Table 20.5: Recreationally or Commercially Valuable Fish Species in Ohio
Fish
Trout
Walleye
Whitefish
Native or
introduced?
Lake and brook
trout are native;
rainbow and
brown trout are
introduced and
maintained by
stocking
Native
j
Native
Habitat
Lake trout populations are stocked in
Pennsylvania and New York and are not
highly prevalent in Ohio and Lake Erie
waters; Brook trout are stocked in
several locations throughout Ohio
Historically found in Lake Erie, but has
been stocked in the Ohio River and
reservoirs throughout the state
Shallow bays of Lake Erie's western
basin
Spawning
season
April
L
Diet
Shiners, gizzard shad, alewives,
rainbow smelt
fc
Bottom feeders with a diet of
mollusks and insect larvae
Source: U.S. EPA analysis.
b. Other species dependent on aquatic resources
Resident and migratory bird species make extensive use of Ohio waters. Areas along the banks or shorelines of rivers,
streams, lakes, ponds, and reservoirs provide high quality nesting areas; the waters themselves are an abundant source of food.
Ohio waters also serve as important staging areas for birds migrating to or from points north or south. Wading or aquatic
birds are generally unaffected by water quality impairments directly. They are affected indirectly, however, through feeding
on fish or invertebrates whose populations may be affected by point and non-point pollution sources. The regulations aimed
at protecting aquatic species will therefore benefit wading and aquatic bird species indirectly. More than 130 aquatic bird
species rely on Lake Erie and its tributaries. Many species are also found near inland surface waters. Major classifications of
birds in Ohio include (Ohio DNR, 1999):
> Waterfowl, residing year-round in Ohio waters, especially Lake Erie. Large groups of migrating and breeding birds
are also found elsewhere in the state. More than 30 species are associated with the Great Lakes area alone. All
species depend on fish and crustaceans or aquatic plants for feeding. Waterfowl include loons, grebes, swans, ducks
and geese. The trumpeter swan is of particular interest to Ohio, which became one of several states involved in
efforts to restore these birds to the Midwest beginning in 1996 (Ohio DNR, 1999).
*• Wading birds, including bitterns, herons, and egrets. These species both reside in Ohio waters and use them as
breeding grounds. They use "stand-and-wait" methods to catch fish or other aquatic organisms in shallow waters.
Many wading birds, such as the great egret, black-crowned night heron, and American bittern, frequent Lake Erie
and surrounding areas.
> Marsh birds, including rails, moorhens, coots, and gallinules. They may feed on insects, crustaceans, mollusks,
frogs, invertebrates, and small fish. These bird populations suffer from excessive development and habitat
destruction. Ohio surface waters, especially those around Lake Erie, can serve as important breeding grounds for
these and other bird species.
*• Shore birds, including 42 species of plovers, sandpipers, gulls, and terns, in the Lake Erie and other Ohio areas.
Many of them feed on aquatic organisms from Lake Erie.
*• Raptors, including the bald eagle and osprey. These birds of prey rely on fishing for a large part of their diet. Bald
eagles are also a nationally-listed threatened species.
*• The belted kingfisher, which relies on fish in Ohio waters as a main source of food.
Ohio's biological resources also includes reptiles. Several species of lizards, snakes, and turtles depend on aquatic habitats
for food and breeding. These reptiles include:
> Lizards - The five-fined skink, reported in areas along Lake Erie, can be found throughout Ohio.
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
»• Snakes - The eastern fox snake, Eastern massasasuga, eastern ribbon snake, copperbelly water snake, Lake Erie
water snake, and northern water snake feed within aquatic habitats.
*• Turtles - The midland smooth softshell turtle and eastern spiny softshell turtle, found in the Ohio River and
tributaries, are among Ohio turtles requiring aquatic habitats.
20.3.2 Water Recreation in Ohio
EPA used the 1994 Survey of National Demand for Water-based Recreation (NDS) (U.S. EPA, 1994) to
characterize recreational uses of Ohio's water resources. The 1994 survey collected data on demographic characteristics and
water-based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over.
Respondents reported on water-based recreation trips taken within the previous 12 months, including the primary purpose of
their trips (e.g., fishing, boating, swimming, and viewing), total number of trips, trip length, distance to the recreation site(s),
and number of participants. EPA estimated recreational water use in Ohio by taking the following steps:
»• estimate the percentage of survey respondents that visited Ohio, by state;
•• apply this percentage to the total number of state residents aged 16 and over, to yield the total number of participants
from each state;
> estimate the total number of recreation trips during the 12-month period for in-state and out-of-state participants;
> estimate the total number of recreation trips for out-of-state participants by multiplying an average number of trips
per Ohio water body visitor by the total number of participants from each state;
»• estimate the average number of annual trips per out-of-state visitor based on the number of times the respondents
visited the site of their last recreational trip (i.e., Ohio water body).4 EPA assumed that Ohio residents whose last
recreation trip was in-state used Ohio water bodies for all of their recreation trips during the 12-month period; and
> estimate the total number of in-state trips, summing the weighted number of recreation trips over all Ohio
respondents.
EPA found that:
»• An estimated one million individuals made about 6.3 million boating trips to Ohio waters in 1993. In-state residents
made 90 percent of the boating trips.
•• Approximately one million people visited Ohio water bodies for recreational fishing.5 These visitors accounted for
about 15.6 million fishing trips to the area. Recreational fishermen from Ohio were the most frequent users of the
state water resources, representing approximately 97 percent of all visitors.
*• Approximately 972,000 and 896,000 visitors used the Ohio water bodies for near-water viewing and swimming,
respectively, in 1993. These visitors account for approximately 9.4 and 7.8 million viewing and swimming trips to
the area. Ohio residents account for 89 percent of viewers and 93 percent of swimmers.
»• Most out-of-state recreational users came from the states surrounding Ohio, such as Indiana, Michigan, and
Pennsylvania.
4 NDS collected information only on the last site visited. Its numbers do not reflect people whose last visit was to a different area, but
who may have also visited an Ohio water body on a previous trip during the year. See Section 21.3 for detail on the NDS data.
5 EPA compared the estimated number of participants with total fishing licenses issued by Ohio in 1996. Ohio issued a total of
895,770 licenses for resident and nonresident fishing. The NDS data therefore provide relatively accurate information on participation
rates.
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20.3.3 Commercial Fishing in Ohio
Commercial fishing is a minor activity in Lake Erie: 12 license holders share a total of 19 licenses (LECBA 2003).
Commercial catch data compiled by the Great Lakes Fishery Commission are summarized in Table 20.6 (Baldwin et al.
2002).
Table 20.6: Commercial Catches for Ohio
Lake Erie Waters (1990)
Fish
Yellow perch
Carp
White perch
Sheep shead
White bass
ChannelcCatfish
Quillback
Buffalo
Bullheads
Suckers
Goldfish
Gizzard shad
Lake whitefish
Rock bass
Catch (1990 Ibs)
1,559,000
1,190,000
786,000
640,000
392,000
365,000
134,000
132,000
59,000
41,000
31,000
19,000
10,000
1000
Source: Baldwin et al. (2002)
Yellow perch represents about half of the dockside value for the entire commercial fishery in the Ohio waters of Lake Erie.
The value of this fishery ranged from $1.3 million to $2.5 million between 1993 and 1998. Overfishing and pollution have
decreased the yellow perch population throughout Lake Erie dramatically over the past 30+ years. Annual catches averaged
around 20 million pounds during the 1960s and 70s. The Lake Erie Committee set the 1998 lakewide total allowable
catch (TAC) quota for this species at 7.44 million pounds. The yellow perch fishery rebounded somewhat over the past
couple of years, due to strong annual recruitment, strict commercial catch restrictions, and a strict creel limit of 30 fish per
day for the sport angler (LECBA 2003).
20.3.4 Surface Water Withdrawals
Water resources provide a wide range of services upon being withdrawn (removed) from the water body. Once used, water
can be returned to its original sources, returned to another water body, or consumed (e.g., for human drinking water). Water
withdrawals from surface water averaged 9,615 mgd in 1995 (USGS 1995). The majority of this water is used in power
generation, accounting for 85 percent of all surface water withdrawals. Public water supply accounts for ten percent of all
withdrawals. Industrial and commercial water use account for one and four percent of the total, respectively. Water quality
and quantity impairments can have substantial impacts on the key withdrawal services that water provides to a wide range of
economic entities.
20.4 SURFACE WATER QUALITY IN OHIO
This section describes current water quality conditions in Ohio and the effects of water quality impairments on beneficial uses
of Ohio's water resources. Ohio EPA assessed designated use attainment in approximately 42 percent of Ohio streams and
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rivers; approximately 64 percent of lakes, ponds, and reservoirs; and all of the Lake Erie shoreline (Ohio EPA, OWRI, 1996).
The OWRI report summarizes the results of this assessment. This report provides information on designated use support by
water type and use designation, identifies major pollutant/stressors that affect the quality of surface water bodies and prevent
designated use attainment, and lists major sources of impairment. The following three sections summarize findings from the
199 6 OWRI report.
20.4.1 Use Attainment in Streams and Rivers in Ohio
Most water bodies are designated for several uses and more than one use can be impaired at a time. The most commonly
occurring sole impairment in fresh water bodies is to aquatic life support. The Ohio EPA used an ecosystem approach that
relies on various tools to determine aquatic life use attainment. Water chemistry, physical and habitat assessment, and direct
sampling of biota all contribute to determine whether a water body meets an attainment status. Field data yield biological
indices that eventually determine a final attainment score.
Ohio EPA assessed 6,560 perennial river miles for aquatic life use attainment. Of the 6,560 river miles assessed for aquatic
life use:
•• 38.5 percent (2,536 miles) are in full attainment (i.e., all water quality indicators meet criteria for specific water
bodies);
•• 10.8 percent (708 miles) are in full attainment, but are threatened by pollution and other sources;
•• 23.3 percent (1,528 miles ) are in partial attainment (i.e., one of two, or two water quality indicators do not meet
criteria); and
*• 21A percent (1,797 miles) are in non-attainment (i.e., no criteria are met or the river experiences a severe toxic
impact).
Fecal coliform bacteria counts determine recreational use attainment. Such counts are less stringent for Secondary Contact
Recreation than for Primary Contact Recreation. Ohio EPA has assessed 2,402 river miles for recreation use since 1988
(Ohio EPA, OWRI, 1996). Of the 2,402 river miles assessed for recreation use:
*• 57 percent (1,370.3 miles) of the sampled rivers and streams are in full attainment (i.e., a water body meets all
chemical criteria for recreational use and human contact);
*• 19.7 percent (474.1 miles) are in partial attainment (i.e., a water body only partially meets human contact criteria);
and
*• 23.2 percent (557.4 miles) are in non-attainment (i.e., a water body fails to meet human contact criteria).
20.4.2 Lake Erie and Other Lakes Use Attainment
Lake Erie, which has a history of pollution problems, currently has fish consumption advisories for carp and channel catfish
(Ohio DNR, 1999). Ohio EPA assesses Lake Erie as having partial use attainment for aquatic life and fish consumption, and
full attainment for recreation.6 Ohio EPA used parameters specified by the Ohio EPA Lake Condition Index (LCI) to
develop use attainment for other lakes. Only approximately two percent of all lakes are in full use attainment for aquatic life,
recreation, and fish consumption. Approximately 82, 50, and 53 percent are in full attainment for aquatic life, recreation, and
fish consumption, respectively, but are threatened by pollution for these categories. High percentages of lake acres are in
partial attainment for recreation (38.8 percent) and public supply (43.8 percent) use designations. Table 20.7 shows use
attainment for Lake Erie and other lakes, ponds, and reservoirs.
Further methodologies to better assess use attainment in Lake Erie are still under development by the Ohio EPA.
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Chapter 20: Baseline Conditions in Ohio
Table 20.7: Use Attainment Summary for Lake Erie one Other Lakes
Use Category
Lake Erie (Unit: Shore M
Aquatic Life (EWH)
Recreation
Fish Consumption
Lakes, Ponds, & Reservoi
Aquatic Life (EWH)
Recreation (PCR)
Public Water Supply
% of Total
Units Assessed
%
lies)"
100
100
100
rs (Unit: Acres)
64.7
64.4
64.1
Full Attainment
Units
231
1,651
1,392
1,301
%
98
2.2
1.8
1.7
Full Attainment,
Threatened
Units
63,174
38,499
40,846
%
82.2
50.3
53.6
Partial
Attainment
Units
236
5
236
10,686
29,793
33,365
%
100
2
100
13.9
38.9
43.8
Non-Attainment
Units
1,302
6,582
673
%
1.7
9.0
0.9
" Assessments are based on unit of measure presented in parentheses.
Source: Ohio EPA, OWR11996.
20.4.3 Causes and Sources of Use Non-Attainment in Ohio
Ohio EPA assessed the causes and sources of impairment to Ohio surface waters and examined trends in major causes and
sources from previous assessment cycles. The following discussion summarizes findings from the 1996 OWRI report (Ohio
EPA, 1996).
a. Causes
Causes are the agents responsible for damage and threats to aquatic life. The major causes of impairment in Ohio surface
waters include:
> organic enrichment/low DO,
> habitat modifications,
> siltation,
> flow alteration,
> nutrients, and
> metals.
Ohio EPA examined trends in these major causes from previous assessment cycles through 1996. They found that point
source-related causes declined, while non-point sources became major contributors. Ohio EPA concluded that this trend
"reflects the relative effectiveness of the programs to control point sources compared to general lack of measures to control
many [non-point sources]" (Ohio EPA, OWRI, 1996).
Organic enrichment, which alters DO levels and affects aquatic communities, is the main cause of impairment in Ohio's rivers
and streams. Inadequate wastewater treatment from municipal and industrial point sources account for most of this
impairment. Metals are a major cause of impairment to approximately 226 river miles, a moderate cause of impairment to
179 river miles, and a minor cause of impairment or threat to 165 river miles.
Nutrients, resulting mostly from agricultural non-point sources, are the main cause of impairment in lakes. Metals are a major
cause for impairment in approximately 250 acres of Ohio's lakes, ponds, and reservoirs, and form the main cause of
impairment in Lake Erie, the major water resource in Ohio (90 percent of the surface water volume). Highly developed areas
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bordering the lake contribute urban runoff, along with discharges from industrial and municipal sources. Other causes of
impairment in Lake Erie include priority organics, DO, and nutrients.7
b. Sources
Sources are the origins of the agents responsible for damage and threats to water resources. The major sources of impairment
to Ohio surface waters include:
> municipal and industrial discharges,
*• hydromodification,
*• agricultural runoff,
»• urban runoff, and
»• mining.
Point source-caused impairment has declined over time, while that from non-point sources, such as agricultural and urban
runoff, has increased. Point sources remain a major source of impairment in almost 900 miles, or 25 percent, of Ohio's
affected rivers and streams. Point sources are the major source of impairment for Lake Erie. They form a major source of
impairment for 24 shore miles, and a moderate source of impairment for an additional 281 shore miles of Lake Erie. In
addition, point sources adversely affect 1,678 lake acres.
Non-point sources related to agricultural and urban runoff form the major source of impairment for some 9,000 acres, or two-
thirds of Ohio's lakes, ponds, and reservoirs. In addition, 46 Lake Erie shore miles list non-point sources as their major
impairment source.
20.5 EFFECTS OF WATER QUALITY IMPAIRMENTS ON WATER RESOURCE SERVICES
Water resource services are negatively affected by pollutants that impair the aquatic ecosystems. Certain pollutants can
adversely affect aquatic species directly by increasing species morbidity and/or impairing reproductive success, or indirectly
by adversely altering food chain interactions. These direct and indirect impacts can change quantity and type of fish and other
species in the aquatic ecosystem. In the worst case scenario, an impaired ecosystem no longer supports any aquatic life. High
pathogen counts or excessive eutrophication in water bodies that are suitable for swimming may force swimmers to go
elsewhere or forego swimming altogether. Any aesthetic degradation decreases the value of each individual's recreational
experience. In severe cases, the affected water bodies become unsuitable for recreation. Water quality impairments also
increase the cost of treating water to meet drinking water standards.
This section details the effects of water quality impairments on in-stream services provided by Ohio's water resources.
20.5.1 Effect of Water Quality Impairment on Life Support for Animals and Plants
Deficiencies in water quantity and quality can impair the health of aquatic ecosystems. In worst case scenarios, the ecosystem
may no longer support aquatic life at all. The major causes of water quality impairment in Ohio include high biological
oxygen demand (BOD) from organic enrichment, habitat and flow alterations, nutrients, siltation and turbidity,
metals, pH, ammonia, and priority organics. Habitat, flow alterations, and thermal discharges are unrelated to MP&M
effluents and are not discussed here. MP&M effluents contribute to the remaining major causes of water quality impairment,
with the ecological effects outlined below.
a. BOD/COD
BOD and chemical oxygen demand (COD) are two methods to determine the oxygen requirements of pollutants in
wastewater. Low oxygen level is the primary cause of impairment in Ohio's rivers and streams and a major source of
7 Major, moderate, and minor impacts refer to the high, moderate, and slight magnitude codes specified by the U.S. EPA for the
30 l(b) report.
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
impairments in Ohio's lakes. When bacteria decompose excess organic matter, they consume DO in surface waters. Oxygen
is needed to chemically (abiotically) oxidize the pollutants present in wastewater. When too much oxygen is needed to
oxidize pollutants, hypoxic (oxygen deficient) or anoxic (oxygen depleted) conditions result. Sources of high oxygen demand
include effluents from municipal treatment plants and certain industries, and runoff from feedlots or farms. Another source is
eutrophication caused by excessive nutrient input. The nutrients stimulate algal blooms. Bacteria consume the algae when
they die, decreasing DO in the water column. DO is a critical variable for fish and invertebrate survival. If oxygen
concentrations drop below a minimum level, organisms suffocate and either move out or die (EPA, 1986). This effect can
drastically reduce the amount of useable aquatic habitat.
b. Nutrients
Nutrients are the leading causes of impairment in Ohio lakes and comprise one of the major causes of impairment in rivers,
streams, and Lake Erie. The overabundance of nitrogen and phosphorus is one of the most documented forms of aquatic
ecosystem pollution. Although both compounds are essential nutrients for phytoplankton (free-floating algae) and periphyton
(attached algae), which form the base of the aquatic food web, too much nutrient input overstimulates primary productivity
and results in eutrophication. The impact of these compounds has contributed significantly to water quality decline in the
United States (EPA, 1992). Phosphorus is a limiting nutrient in most freshwater systems (Wetzel, 1 983), whereas nitrogen is
typically limited in estuarine and marine systems.
In freshwater, excess phosphate (PO4) has been linked to eutrophication and nuisance growth of algae and aquatic weeds
(Wetzel, 1983), even though direct toxicity to fish and other aquatic species is not a major concern. DO in the water column
decreases, however, when algae and other aquatic plants die off, and certain toxins may be produced, both of which can
contribute to fish kills.
c. Siltation and turbidity
Siltation and turbidity are the third leading causes of impairments in Ohio rivers and lakes, except Lake Erie. Siltation is the
most important factor in surface water degradation in the U.S. (EPA, 1992). Major sources include urban and stormwater
runoff, mining and logging activities, and runoff from plowed fields (EPA, 1992). All these inputs create cloudy water with
increased turbidity and decreased visibility and light penetration. High primary productivity by phytoplankton following
excessive nutrient input can also increase turbidity. Excess suspended matter decreases the amount of light penetrating the
water column, which can reduce primary productivity. This turbidity can eliminate or displace fish species requiring clear
water to live, feed, or reproduce.
d. Metals
Metals are the leading cause of impairment in Lake Erie and comprise one of the major causes of impairment in inland lakes
and rivers. Metals are naturally-occurring inorganic constituents of the earth's crust. Priority pollutant metals commonly
found in the aquatic environment include antimony, arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium,
silver, thallium and zinc (EPA, 1998a). These compounds enter the aquatic environment via urban stormwater runoff,
industrial and municipal effluents, and atmospheric deposition. As a group, metals can be highly toxic: water quality
criteria (WQC) for acute toxicity range from around 1,100 |Jgf// (chromium VI in saltwater) to around 1 fig/1 (mercury in
freshwater); WQC for chronic toxicity range from 120 fig/1 (zinc in freshwater) to <1.0 fig/1 (mercury in salt- and freshwater)
and are therefore an order of magnitude lower (EPA, 1998a).
Once metals reach the aquatic environment, they tend to associate with organic and inorganic particulates in the water
column. Sediments become long-term sinks for metals, which accumulate in the bottom. Metals can enter the food chain
when ingested by benthic invertebrates or other burrowing organisms. Most metals have bioconcentration factors
(BCFs) ranging from 100 to 10,000 and can therefore bioaccumulate in aquatic organisms. A few, including selenium, lead,
and mercury, may reach hazardous levels in fish or wildlife receptors and result in avian developmental or neurological
abnormalities.
e. Organic chemicals
Priority organics are the second most frequent cause of impairment in Lake Erie and comprise one of the major causes of
impairment in rivers and streams. Thousands of different compounds exist as organic chemicals, including petroleum
hydrocarbons and myriad industrial chemicals. They enter the aquatic environments via municipal and industrial effluents,
stormwater runoff, contaminated groundwater, atmospheric deposition, illegal dumping, or accidental releases. Aquatic
toxicities vary by orders of magnitude depending on the compound. Factors influencing toxicity and long-term ecological
effects include water solubility, volatility, biodegradation potential, and bioaccumulation potential.
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
Excessive amounts of organic chemicals degrade surface water quality by causing acute or (more typically) chronic toxicity.
This toxicity impairs growth, development, and/or reproductive success in fish and aquatic invertebrates. Persistent and low
water-soluble organic chemicals accumulate in sediments and are taken up into local aquatic food chains. They can reach
dangerous concentrations in fish and avian receptors, resulting in reproductive failures or other avian health effects.
f. pH
Approximately 180 river miles are pH-impaired in Ohio. pH is a measure of acidity. Acid reaches surface waters via
atmospheric deposition ("acid rain"), industrial effluents, and leachates from mine overburdens or spoils. Acidity by itself
is a key variable shaping aquatic communities: it is a toxicant in its own right but also controls metal solubility, and the
toxicity of several metals and ammonia.
Aquatic species vary widely in their sensitivity to pH: the most sensitive vertebrate and invertebrate species die off when
average pH ranges between 6.0 and 6.5. Most fish species are eliminated when pH reaches 5.0. Only a few can survive at pH
4.5 (U.S. EPA, 1999). Macro invertebrates exhibit the same pattern, except that hardy species can survive down to a pH of
about 3.5.
g. Ammonia
Large amounts of ammonia enter lakes and rivers via wastewater treatment plants and industrial effluents, atmospheric
deposition, and non-point source surface runoff. Approximately 150 river miles in Ohio are ammonia-impaired. This
compound, unique among regulated pollutants, is also produced naturally inside fish as a metabolic waste product. Excess
ammonia usually diffuses rapidly out of the blood stream and into the surrounding water via the gills. High concentrations of
external un-ionized ammonia (NH3) reduce or reverse this diffusive gradient and allow ammonia to build up to toxic levels
inside the organism (EPA, 1998c).
Ammonia in surface water exists in two major forms: un-ionized ammonia (NH3), which is highly toxic to fish or
invertebrates, and ammonium ion (NH4+), which is much less toxic. Which form prevails depends mainly upon the pH level;
temperature and ionic composition play a smaller role. EPA calculated a WQC that becomes more severe with decreasing
acidity. For example, the acute criteria for surface waters containing salmonids equals 36.7 mg/1 at pH=6.0 but only 2.14
mg/1 at pH=8.5. For surface waters without salmon, the acute criteria for the same pH equal 55.0 mg/1 and 3.2 mg/1,
respectively (EPA, 1998c).
20.5.2 Effect of Water Quality Impairment on Recreational Services
Healthy surface waters are essential to support a diversity of recreational uses, including viewing and other near-water
activities. Industrial or other human activities impair surface water quality. Certain metals and chlorinated compounds can
bioaccumulate in aquatic food chains and reach unhealthy levels in carnivorous fish or shellfish. Health advisories to limit or
avoid their consumption may result. High concentrations of toxic compounds can also lead to human contact advisories. The
release of untreated or poorly treated sewage can cause high levels of pathogenic bacteria in water and result in swimming
advisories or beach closures. All of these actions limit the full use of surface waters and can have significant local economic
impacts.
a. Fish consumption advisories
In 1997, the Ohio Department of Health (ODH) issued a statewide fish consumption advisory to protect women of
childbearing age and children six years or younger against mercury's neurological and developmental effects. The advisory,
which applies only to these two population groups, recommended that these women and children eat no more than one meal
per week of any fish caught in Ohio waters. The advisory covers all state waters because most of the mercury measured in
fish tissues originates from region-wide fossil fuel combustion processes. The mercury reaches surface waters via
atmospheric deposition on the surrounding landscape (Ohio DNR, 1999).
Since 1983, the ODH has developed numerous water body-specific fish consumption advisories for approximately 174 water
body segments (rivers and lakes) and Lake Erie. These water bodies represent a relatively small fraction of Ohio's 5,000
discrete water body segments, as determined by Ohio EPA. The contaminants of greatest concern include polychlorinated
biphenyls (PCBs), mercury, polycyclic aromatic hydrocarbons (PAHs), lead, organometallics, Mirex, phthalate
esters, Chlordane, and hexachlorobenzene. Of these, four mercury, PAHs, lead, and phthalates are included on the
MP&M list of pollutants of concern (POCs). As a group, these contaminants are generally characterized as lipophilic
(i.e., fat loving), resistant to biological degradation or cellular metabolism, and toxic. Once they reach surface water, they
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
concentrate in sediments and bioaccumulate or biomagnify through aquatic food chains. These compounds can linger for
decades in aquatic systems.
The kind of sports or recreational fish species affected by the consumption advisories varies by water body segment. More
than 23 different species are covered by advisories, including walleye, common carp, sauger, saugeye, white crappie,
freshwater drum, and various species of bass, perch, catfish, salmon, trout, suckers, and sunfish. Restrictions vary depending
on the pollutant, the fish species concerned, and the concentrations measured in edible tissues. The ODH developed
maximum recommended rates offish consumption that include outright consumption bans, one meal every two months, one
meal a month, or one meal a week. The same water body segments can commonly have different advisories for different fish
species (Ohio DNR, 1999).
b. Contact advisories
The ODH also issued human contact advisories for nine water body segments in Ohio located on the Black River, Little
Scioto River, Mahoning River, the middle fork of the Little Beaver Creek, and the Ottawa River. Swimming or wading is
prohibited due to the presence of high levels of PAHs, PCBs, Mirex, phthalate esters, and/or Chlordane. Of these, PAHs and
phthalates are included on the list of MP&M POCs. Fish consumption advisories also cover all of these segments (Ohio
DNR, 1999).
c. Beach closures
Beach closures typically occur during the summer months when high levels of fecal coliform bacteria or other disease-causing
organisms (e.g., Escherichia coli) proliferate in surface waters. Such waters can become contaminated from several sources,
including: agricultural runoff, sewer overflows, boating wastes, and poor hygienic practices by some bathers. Excessive
levels of indicator pathogens in surface waters can indicate a serious threat to human health and may cause health departments
to post warnings, restrict access, or forbid swimming altogether. The MP&M regulation is not expected to reduce beach
closures during summer months.
Numerous public bathing beaches dot Ohio's 262-mile shoreline along Lake Erie. The ODH has developed a composite
metric based on E. coli counts in surface waters at 11 selected beaches along Ohio's north coast. The metric tracks the
average number of days that swimming advisories are posted at the 11 beaches for a 15 week period beginning around
Memorial Day and continuing through Labor Day. The most recent data available show that the 11 beaches were under
advisement an average of 21 days during the summer months (minimum of 0 days and maximum of 49 days) in 1996.
The ODH developed a 4-tiered scale to score and track the average number of days that the 11 public beaches are under
advisement from one year to the next. Between 1990 and 1996, the average (based on a five-year running average) number of
beach advisories scored in the "fair" category consistently, meaning that the beaches were under advisement between 20 and
30 days in the summer (State of Ohio, 1998).
Ohio's lakes, ponds, and reservoirs (excluding Lake Erie) yielded no quantitative data on beach closures. The 1996 Ohio
Water Resource Inventory of Public Lakes, Ponds and Reservoirs provides a breakdown of the portion of Ohio's 446 public
lakes that are threatened or impaired as a result of high levels of fecal coliform bacteria.
20.6 PRESENCE AND DISTRIBUTION OF ENDANGERED AND THREATENED SPECIES IN
OHIO
Many factors can affect the survival of endangered and threatened (E&T) species. Some factors are species-specific;
others result from one or more anthropogenic stressors. Inherent vulnerability factors include narrow geographic distribution,
slow reproductive rates, or requirements for large areas. Major anthropogenic stressors include intentional taking (e.g.,
fishing), incidental taking, physically altering habitat (e.g., converting wetlands into agricultural land), water pollution, and
introducing alien species. A single stressor or a set of stressors can contribute to a species' decline or extinction. Previous
studies reported that more than 40 percent of endangered aquatic species were affected by five or more environmental
stressors, and only seven percent of federally-listed species had a single threat to their survival. Although stressors seldom act
alone, water pollution is one of the major hazards to E&T aquatic species, cited as responsible for the decline of 19 (54
percent) out of 35 E&T fish species in Ohio (Ohio DNR, 1998).
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
The following sections provide an overview of E&T species found in Ohio, their distribution, and the major hazards
threatening their survival. Species discussed below include those listed under both the federal Endangered Species Act
(50 CFR Part 17) and the Ohio Department of Natural Resources' (DNR) Division of Natural Areas and Preserves. The
MP&M regulation concentrates on water-related benefits; these sections therefore describe only those species associated with
aquatic environments.8 The DNR list includes 90 E&T species with a total of 1,227 observations throughout Ohio.
"Observations" refers to locations where species were observed; most species have multiple observations. This analysis
includes observations spanning the years 1980 to 1988.
20.6.1 EAT Fish
E&T fish inhabit almost every major water body in Ohio, including Lake Erie and the Ohio River and its tributaries. The
Ohio DNR lists 35 total state-listed E&T fish species, of which 13 are threatened and 22 endangered. The list includes only
one federally-listed species, the scioto mad torn.
Of the total E&T fish, approximately 12 species use Lake Erie as a possible habitat and nine use the Ohio River. Most of the
species listed live in riverine habitats. Approximately 28 species were identified in a river system in Ohio, including the
Ohio, Scioto, Muskingham, Miami, Walhondig, and Maumee River systems. MP&M facilities are found on all these major
river systems.
The DNR lists 384 observations of E&T fish in Ohio, of which 240 observations of 30 different species have been reported
since 1980. Figure 20.2 maps the observations of E&T fish in Ohio and shows the extent to which these observations were
reported in the state. Multiple observations can occur for a single species. In southern Ohio, most observations come from
the Muskingham and Scioto River systems and the Ohio River. Most observations in northern Ohio came from Lake Erie
tributaries or the lake itself.
In addition to water pollution, cited above as major hazard to E&T aquatic species, other major hazards to E&T fish include
siltation and impoundments. Approximately two-thirds of E&T fish species are threatened by siltation, and 17 percent are
threatened by impoundments or dams. MP&M regulations can improve affected ecosystems or habitats by reducing
discharges from MP&M facilities. These improvements can then help reduce siltation and restore some of the E&T fish
populations.
Many obscure E&T fish species have a pure existence value. Some E&T species, like brook trout and lake sturgeon, have
high potential for consumptive uses. Restoring their populations and those of other commercial and recreational fish species
may enhance recreational fishing opportunities. Table 20.8 lists E&T fish in Ohio, their habitat locations, and the cause for
their E&T listing. The table lists species alphabetically by scientific name.
20.6.2 EAT Mollusks
Mo Husks yield the largest number of reported observations of aquatic E&T species in Ohio, representing 48 percent of the
total 1,227 observations. The Ohio DNR lists 29 E&T mo Husk species, four threatened and 25 endangered. Of these, five
mollusk species are on the federal endangered species list: catspaw, clubshell, fanshell, white catspaw, and pink mucket.
Ohio's E&T mollusks concentrate in five major areas: Lake Erie and the Grand River tributary, Scioto River and Big Arby
tributary, Muskingham River, Little Miami River, and the Ohio River. E&T mollusk populations reside mostly along the
mainstems of large rivers and in Lake Erie, but are also found in the St. Joseph, Sandusky, and Cuyahoga Rivers.
8 "Aquatic species" were identified by the Ohio Department of Natural Resources, Division of Natural Areas and Preserves. These
species include any species that are "closely associated with aquatic habitats through their breeding or feeding requirements."
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Figure 20.2: E&T Fish Observances in Ohio"
(1980-1997)
Lake Erie f1
^f
\'
r i
\
•V'V;r\'sr-*'!" ^? Vx, \-1O xS ^-^- r*v^
\^'-f *$v-~- >) ^V; -.,. >J \ ^ -r/S { T
"f ' ;/ ^•\//^-^x^i....,,cXvW'v' ., * .f v •,;/'-. ,_j.,^f i .'"'i>t ':^r\l>j>-j; V-~
,,.;—. -, k-.S;'\^.S.5^1 "•• i.sV^-''"^^--5^^' "^'"'"•!^'"~t:x~~- ""^
"i-^> V/-f ^\M^,^^S^P^;
. //^A^^jfe^^ w^ £^^^l??r
Ohio
a Each $ represents an observance.
Source: U.S. EPA analysis.
20.6.3 Other Aquatic EAT Species
Improved water quality resulting from the MP&M regulation may also benefit other aquatic E&T species. Unlike fish and
mollusks, whose primary habitat is a surface water body at all times, these species may use surface water-related habitats only
for breeding or feeding. Improved water quality may benefit these populations indirectly by enhancing the quality and
quantity of aquatic biological resources.
Other aquatic-associated E&T species of Ohio include:
* Birds ten state-listed species, one threatened and nine endangered, include one federally-listed threatened species,
the bald eagle. The state-listed species include: American and least bitterns, common and black terns, yellow- and
black-Crowned night-herons, king rail, osprey, and snowy egret. These species are observed mostly along the Lake
Erie coast. The bald eagle is observed mostly in Ohio's northeast corner.
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
*• Amphibians three state-listed endangered species: blue-spotted salamander, observed in the very northwest
section of the state along small streams and near the Maumee River; eastern spadefoot, found near the Ohio and
Muskingham Rivers; and eastern hellbender, observed along the Muskingham and Scioto River systems and
tributaries of the Ohio River.
*• Reptiles two species: the copperbelly water snake, a state-listed endangered and federally-listed threatened
species found in lakes and ponds in the northwest corner of Ohio; and the Lake Erie water snake, state-listed as
threatened and a proposed threatened species for the federal list, found only along the edges of the Lake Erie islands.
> Mammals the river otter is state-listed as endangered. Sparse observations of the animal come from various small
creeks and lakes in the eastern part of Ohio.
•• Crustaceans the state-listed endangered Sloan's crayfish has been observed in several small tributaries of the
Great Miami River system.
*• Insects nine state-listed species, one threatened and eight endangered, are reported throughout the state.
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Table 20.8: Endangered and Threatened Fish Species of Ohio
Common Name
Lake sturgeon
Longnose sucker
Rosyside dace
Cisco
Blue sucker
Lake chub sucker
Bluebreast darter
Spotted darter
Tippecanoe darter
Tonguetied minnow
Western banded
killifish
Goldeye
Mississippi silvery
minnow
Ohio lamprey
Northern brook
lamprey
Mountain brook
lamprey
Silver lamprey
Blue catfish
Spotted gar
Scientific Name
Acipenserfulvescens
Catostomus catostomus
Clinostomus
funduloides
Coregonus artedi
Cycleptus elongatus
Erimyzon sucetta
Etheostoma camurum
Etheostoma maculatum
Etheostoma tippecanoe
Exoglossum laurae
Fundulus diaphanus
menona
Hiodon alosoides
Hybognathus nuchalis
Ichthyomyzon bdellium
Ichthyomyzonfossor
Ichthyomyzon greeleyi
Ichthyomyzon unicuspis
Ictalurus furcatus
Lepisosteus oculatus
Number of
Observations
3
1
53
1
2
28
19
8
11
16
9
16
1
4
25
6
40
1
1
Last
Observed
1979
1950
1997
1976
1985
1994
1995
1992
1994
1996
1994
1989
1983
1992
1992
1993
1993
1987
1978
Federal
Status
State
Status
E
E
T
E
E
T
T
E
T
T
E
E
E
E
E
E
T
E
E
Habitat
Lake Erie, spawning in larger rivers such
as Maumee and Auglaize
Lake Erie
Small, upland streams of Teays and
Little Scioto River systems
Lake Erie
Ohio River and lower reaches of large
tributaries
Lakes (not Erie) and larger streams
Scioto and Muskingham River systems,
large streams
h
Large streams of Muskingham and
Scioto systems
Muskingham and Scioto River systems
Great Miami River system
Lake Erie and larger tributaries
Ohio River and lower reaches of large
tributaries
Ohio River and tributaries
Ohio River and lower reaches of large
tributaries
Small streams, tributaries of Grand and
Scioto rivers
Mahoning River and tributaries
Lake Erie and larger tributaries
Scioto River
Lake Erie
Causes for Listing
Pollution and dams
Pollution creating low
oxygen levels
Runoff and siltation
Pollution and overfishing
Pollution, dams, increased
turbidity and siltation
Increased turbidity and
siltation
Pollution and siltation
Pollution and siltation
Undetermined, likely
pollution and siltation
Siltation
Pollution
Siltation
Pollution and siltation
Pollution, siltation, and dams
Pollution, siltation, and dams
Pollution, siltation, and dams
Siltation and dredging
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
Table 20.8: Endangered and Threatened Fish Species of Ohio
Common Name
Shortnose gar
Speckled chub
Greater redhorse
Popeye shiner
Bigeye shiner
Bigmouth shiner
Blackchin shiner
Blacknose shiner
Mountain madtom
Northern madtom
Scioto madtom
Pugnose minnow
Channel darter
Scientific Name
Lepisosteus
platostomus
Macrhybopsis
aestivalis
Moxostoma
valenciennesi
Notropis ariommus
Notropis hoops
Notropis dorsalis
Notropis heterodon
Notropis heterolepis
Noturus eleutherus
Noturus stigmosus
Noturus trautmani
Opsopoeodus emiliae
Percina copelandi
Number of
Observations
9
1
12
4
22
16
2
7
11
10
1
6
18
Last
Observed
1981
1990
1989
1993
1995
1994
1983
1983
1991
1989
1957
1982
1991
Federal
Status
E
State
Status
E
E
T
E
T
T
E
E
E
E
E
E
T
Habitat
Scioto River and tributaries
Ohio and Muskingham rivers, large
rivers
Maumee river system, large streams
Extirpated from Ohio, creeks and small
rivers of Maumee system
Great Miami River and Ohio River
systems, upland streams
Black and Rocky River systems, brooks
and small streams
Lake Erie and other lakes
Lake Erie and other lakes
Ohio River tributaries, larger streams and
rivers
Muskingham, Little Miami, Walhondig
Rivers
Big Darby Creek, tributary of Scioto
Lakes, canals, streams, and Lake Erie
Lake Erie and Ohio River
Causes for Listing
Pollution and siltation
Pollution and siltation
Pollution and siltation
Siltation
Siltation and impoundments
Competition with silver
minnow
Increased turbidity and
siltation
Siltation
Pollution and siltation
Pollution and siltation
Increased turbidity and
siltation
Siltation
River darter 1 Percina shumardi 8 ! 1989 ! T 1 Lake Erie and larger tributaries of Ohio ! Pollution and siltation
: .
: : : : : : P i vpr :
: AviVCi
Paddlefish 1 Polyodon spathula 11 1996 T 1 Ohio River tributaries, larger streams and ! Pollution and siltation
1 rivers
Brook trout 1 Salvelinus fontinalis 1 1997 T 1 Tributaries of Lake Erie ! Habitat destruction -
! timbering and non- native
.
i species
Source: Division of Natural Areas and Preserves, Ohio Department of Natural Resources, Natural Heritage Program 1998.
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GLOSSARY
ammonia: a compound of nitrogen and hydrogen (NH3). It is a colorless, pungent gas.
biological oxygen demand (BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose
organic material in polluted water.
bioconcentration factors (BCFs): indicators of the potential for chemicals dissolved in the water column to be taken up
by aquatic biota across external surface membranes, usually gills.
biotic: pertaining to the characteristics of a naturally occurring assemblage of plants and animals that live in the same
environment and are mutually sustaining and interdependent.
chemical oxygen demand (COD): the amount of oxygen consumed in the complete chemical oxidation of matter, both
organic and inorganic, present in polluted water.
Coldwater Habitat (CWH): a designation assigned to a water body based on the potential aquatic assemblage.
dissolved oxygen (DO): oxygen freely available in water, vital to fish and other aquatic life and for the prevention of
odors. DO levels are considered a most important indicator of a water body's ability to support desirable aquatic life.
Secondary and advanced waste treatment are generally designed to ensure adequate DO in waste-receiving waters.
(http://www.epa.gov/OCEPAterms/dterms.html)
endangered and threatened (E&T): animals, birds, fish, plants, or other living organisms threatened with extinction by
anthropogenic (i.e., man-caused) or other natural changes in their environment. The Endangered Species Act contains
requirements for declaring a species endangered.
Endangered Species Act: federal legislation enacted in 1973 that protects animals, birds, fish, plants, or other living
organisms threatened with extinction by anthropogenic or other natural changes in their environment. For a species to be
protected under this act it must be "listed" as either an "endangered" or "threatened" species.
eutrophication: process by which bodies of water receive increased amounts of dissolved nutrients, such as nitrogen and
phosphorus, that encourage excessive plant growth and result in oxygen depletion.
Exceptional Warmwater Habitat (EWH): the aquatic life use designed to protect aquatic communities of exceptional
diversity and biotic integrity. Such communities typically have a high species richness; often include strong populations of
rare, endangered, threatened, and declining species; and/or are exceptional sport fisheries.
existence services: services that are not linked to current uses of water bodies. They arise from the knowledge that
species diversity or the natural beauty of a given water body is being preserved.
in-stream services: water use taking place within the stream channel for purposes such as life support for animals and
plants, water-based recreation, hydroelectric power generation, navigation, commercial fishing, water storage, and aesthetics.
Limited Resource Waters (LRW): an aquatic life use assigned to streams with very limited aquatic life potential, usually
restricted to highly acidic mine drainage streams, or highly modified small streams (<3 sq. mi. drainage area) in urban or
agricultural areas with little or no water during the summer months.
Limited Warmwater Habitat (LWH): see limited resource waters.
metals: inorganic compounds, generally non-volatile (with the notable exception of mercury), that cannot be broken down
by biodegradation processes. They are of particular concern due to their prevalence in MP&M effluents. Metals can
accumulate in biological tissues, sequester into sewage sludge in POTWs, and contaminate soils and sediments when released
into the environment. Some metals are quite toxic even when present at relatively low levels.
|J£)[//: micrograms per liter.
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Modified Warmwater Habitat (MWH): aquatic life use assigned to streams that have irretrievable, extensive, man-
induced modifications that preclude attainment of the Warmwater Habitat use, but which harbor the semblance of an aquatic
community. Such waters are characterized by poor chemical quality (low and fluctuating dissolved oxygen), degraded habitat
conditions (siltation, habitat simplification), and species that are tolerant of these effects.
nonconventional pollutants: a catch-all category that includes everything not classified as either apriority or
conventional pollutant.
nutrients: any substance, assimilated by living things, that promotes growth. The term is generally applied to nitrogen and
phosphorus in wastewater, but is also applied to other essential and trace elements.
(http://www.epa.gov/OCEPAterms/nterms.html)
Ohio EPA Lake Condition Index (LCI): an ecologically-based index that aggregates results across ten ecological
metrics.
Ohio Water Resource Inventory (OWRI): a biennial report to U.S. EPA and Congress required by Section 305(b) of the
Clean Water Act. The report is composed of four major sections: (1) inland rivers and streams, wetlands, Lake Erie, and water
program description; (2) fish tissue contaminants; (3) inland lakes, ponds, and reservoirs; and (4) groundwater.
overburdens: rock and soil cleared away before mining.
(http://www.epa.gov/OCEPAterms/oterms.html)
pH: an expression of the intensity of the basic or acid condition of a liquid. Natural waters usually have a pH between 6.5
and 8.5. (http://www.epa.gov/OCEPAterms/pterms.html)
pollutants of concern (POCs): the 131 contaminants identified by EPA as being of potential concern for this rule and
that are currently being discharged by MP&M facilities. EPA used fate and toxicity data, in conjunction with various
modeling techniques, to identify these pollutants and assess their potential environmental impacts on receiving water bodies
and POTWs. MP&M pollutants of concern include 43 priority pollutants, 3 conventional pollutants, and 86 nonconventional
pollutants.
polychlorinated biphenyls (PCBs): a group of toxic, persistent chemicals that are mixtures of chlorinated biphenyl
compounds having various percentages of chlorine. PCBs are industrial chemicals formerly used in electrical transformers
and capacitors for insulating purposes, and in gas pipeline systems as a lubricant.
polycyclic aromatic hydrocarbons (PAHs): a class of organic compounds with a fused-ring aromatic structure. PAHs
result from incomplete combustion of organic carbon (including wood), municipal solid waste, and fossil fuels, as well as
from natural or anthropogenic introduction of uncombusted coal and oil. PAHs include benzo(a)pyrene, fluoranthene, and
pyrene.
Primary Contact Recreation (PCR): water recreation activities requiring full human body immersion, such as
swimming, diving, water skiing, and surfing.
priority organics: prority pollutants that are organic chemicals.
priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
sediment, groundwater, or soil samples.
random utility model (RUM): a model of consumer behavior. The model contains observable determinants of consumer
behavior and a random element.
Secondary Contact Recreation (SCR): water recreation activities requiring some direct contact with water but where
swallowing of water is unlikely, such as paddling, wading, and boating.
siltation: deposition of finely divided soil and rock particles on the bottom of stream and river beds and in reservoirs.
Survey of National Demand for Water-based Recreation (NDS): a U.S. EPA survey of recreational behavior. The
1993 survey collected data on socioeconomic characteristics and water-based recreation behavior using a nationwide stratified
random sample of 13,059 individuals aged 16 and over, (http://www.epa.gov/opei)
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MP&M EEBA Part V: Ohio Case Study Chapter 20: Baseline Conditions in Ohio
total allowable catch (TA C): amount offish permitted to be removed under a fishery management regime in which the
total catch allowed of a certain species for a fishing season has been fixed in advance.
"toxic" pollutants: refers to the 126 priority or toxic pollutants specifically defined as such by EPA, as well as
nonconventional pollutants that have a toxic effect on human health or aquatic organisms.
turbidity: cloudy condition in water that interferes with the passage of light through the water column. It is caused by the
presence of suspended silt or organic matter in the water body.
un-ionized: neutral form of an ionizable compound. With reference to ammonia, it is the neutral form of ammonia-nitrogen
in water, usually occurring as NH4OH. Un-ionized ammonia is the principal form of ammonia that is toxic to aquatic life. The
relative proportion of un-ionized to ionized ammonia (NH4+) is controlled by water temperature and pH.
Warm water Habitat (WWH): a designation assigned to a water body based on the potential aquatic assemblage.
water quality criteria (WQC): specific levels of water quality that, if reached, are expected to render a body of water
suitable for certain designated uses.
withdrawal services: services associate with water removed from the ground or diverted from a surface-water source for
uses such as drinking water supply, irrigation, production and processing services, and sanitary services.
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MPAM EEBA Part V: Ohio Case Study
Chapter 20: Baseline Conditions in Ohio
ACRONYMS
BCFs: bioconcentration factors
BOD: biological oxygen demand
COD: chemical oxygen demand
CWH: Cold water Habitat
DO: dissolved oxygen
E&T: endangered and threatened
EWH: Exceptional Warmwater Habitat
LRW: Limited Resource Waters
LWH: Limited Warmwater Habitat
MWH: Modified Warmwater Habitat
ODH: Ohio Department of Health
DNR: Ohio Department of Natural Resources
LCI: Ohio EPA Lake Condition Index
OWRI: Ohio Water Resource Inventory
POCs: pollutants of concern
PCBs: polychlorinated biphenyls
PAHs: polycyclic aromatic hydrocarbons
PCR: Primary Contact Recreation
RUM: random utility model
SSH: Seasonal Salmonid Habitat
SCR: Secondary Contact Recreation
NDS: Survey of National Demand for Water-based Recreation
TAC: total allowable catch
WWH: Warmwater Habitat
WQC: water quality criteria
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REFERENCES
Baldwin, N. A., R. W. Saalfeld, M. R. Dochoda, H. J. Buettner, and R.L. Eshenroder. August 2002. Commercial Fish
Production in the Great Lakes 1867-2000. http://www.glfc.org/databases/commercial/commerc.asp
Lake Erie Charter Boat Association (LECBA). 2003. www.lecba.org.
Ohio Department of Natural Resources, Ohio Division of Wildlife. 1999. Fish Consumption Advisories.
http://www.dnr.state.oh.us/odnr/wildlife/index.html.
Ohio Department of Natural Resources, Division of Natural Areas and Preserves. 1998. Database File of Aquatic and
Associated Aquatic Endangered & Threatened Animals.
Ohio Environmental Protection Agency. 1998. State of the Lake Report (www.epa.ohio.gov/oleo/leqi/leqi.html)
Ohio Environmental Protection Agency. 1996. Ohio Water Resource Inventory. Volume 1: Summary, Status , and Trends
and 3: Ohio's Public Lakes, Ponds, and Reservoirs, www.chagrin.epa.state.oh.us/document_index.
U.S. Department of Agriculture (USDA). 1992a. Agricultural Waste Management Field Handbook. National Engineering
Handbook Series, Part 651. 210-AWMFH, 4/92.
U.S. Department of Agriculture. 1992b. National Resources Inventory. National Resources Conservation Services.
http://www.ftw.nrcs.usda.gov/nri_data.html.
U.S. Department of Commerce, Bureau of the Census. 1992. Census of Manufactures, Census of Transportation, Census of
Wholesale Trade, Census of Retail Trade, Census of Service Industries.
U.S. Department of Commerce, Bureau of the Census. 1999. Ohio Population, Demographic, and Housing Statistics.
http://www.census.gov/cgi-bin/datamap/state739.
United States Geological Survey (USGS). 1995. Water Use in the United States, http://water.usgs.gov/watuse.
U.S. Environmental Protection Agency (U.S. EPA). 1986. Ambient Water Quality Criteria for Dissolved Oxygen. EPA
440/5-86-003.
U.S. Environmental Protection Agency (U.S. EPA). 1992. Managing Non-point Source Pollution: Final Report to
Congress. EPA-506/9-90.
U.S. Environmental Protection Agency (U.S. EPA). 1994. National Demand for Water-Based Recreation Survey.
Washington, D.C.: Office of Policy Evaluation and Information.
U.S. Environmental Protection Agency (U.S. EPA). 1998a. National Recommended Water Quality Criteria; Notice;
Republication. 63(237:68354-68364).
U.S. Environmental Protection Agency (U.S. EPA). 1998b. Condition of the Mid-Atlantic Estuaries. EPA 600-R-98-147.
U.S. Environmental Protection Agency (U.S. EPA). 1998c. 1988 Update of Ambient Water Quality Criteria for Ammonia.
EPA822-R-98-008.
U.S. Environmental Protection Agency (U.S. EPA). 1999. Progress Report on the EPA Acid Rain Program. U.S. EPA
Office of Air and radiation. EPA 430-R-99-011.
Wetzel, R.G. 1983. Limnology, 2nd ed. Saunders College Publishing.
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MP&M EEBA Part V: Ohio Case Study
Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
Chapter 21: Modeling Recreational
Benefits in Ohio with a RUM Model
INTRODUCTION
The recreational benefits analysis outlined in this chapter
focuses on Ohio as a case study of the MP&M regulation's
expected benefits. EPA combined water quality modeling
and a random utility model of consumer behavior (RUM)
to assess how changes in water quality from the MP&M
regulation will affect consumer valuation of water resources
for recreational uses. The RUM analysis provides a
framework for estimating the effect of ambient water quality
and other site characteristics on the total number of trips
taken for different water-based recreation activities and the
allocation of these trips among particular sites.
The Agency used this case study to address limitations
inherent in the benefits transfer method used in the analysis
of recreational benefits at the national level (see Chapter 15
for detail). The RUM model assesses water quality
characteristics directly affected by the MP&M regulation,
such as presence of ambient water quality criteria
(A WQC) exceedances and nonconventional nutrient Total
Kjeldahl Nitrogen (TKN) concentrations and their effect
on recreation behavior. The direct link between the water
quality measures included in the RUM model and the water
quality measures affected by the regulation, as well as the
site specific nature of the analysis reduce uncertainty in
benefit estimates. In general, RUM models are well-regarded
in the economic literature and when these models are
appropriately applied, the results are thought to be quite
reliable.
CHAPTER CONTENTS
21.1 Methodology
21.1.1 Overview
21.1.2 Modeling the Site Choice Decision
21.1.3 Modeling Trip Participation
21.1.4 Calculating Welfare Changes from Water
Quality Improvements
21.1.5 Extrapolating Results to the State Level . .
21.2 Data
21.2.1 The Ohio Data
21.2.2 Estimating the Price of Visits to Sites . . .
21.2.3 Site Characteristics
21.3 Site Choice Model Estimates
21.3.1 Fishing Model
21.3.2 Boating Model
21.3.3 Swimming Model
21.3.4 Viewing (Near-water Activity) Model ...
21.4 Trip Participation Model
21.5 Estimating Benefits from Reduced MP&M
Discharges in Ohio
21.5.1 Benefiting Reaches in Ohio
21.5.2 Estimating Recreational Benefits in Ohio
21.6 Limitations and Uncertainty
21.6.1 One-State Approach
21.6.2 Including One-Day Trips Only
21.6.3 Nonuse Benefits
21.6 A Potential Sources of Survey Bias
Glossary
Acronyms
References .
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Benefits transfer results are subject to uncertainty because water quality changes evaluated in available recreation demand
studies are only roughly comparable with water quality measures considered in regulatory development. This case study
analysis improves upon previous recreation demand studies that focused mainly on directly observable water quality effects,
e.g., designated use support (i.e., whether a water body supports fishing), the presence of fish advisories, an oil sheen, or
eutrophication. The Ohio case study includes unobservable water quality effects as well. The MP&M regulation affects a
broad range of pollutants, many of which are toxic to human and aquatic life but are not directly observable (i.e., priority and
nonconventional pollutants. These unobservable toxic pollutants degrade aquatic habitats, decrease the size and
abundance offish and other aquatic species, increase fish deformities, and change watershed species composition. Water
quality changes (i.e., changes in toxic pollutant concentrations) affect consumers' water resource valuation for recreation,
even if consumers are unaware of changes in ambient pollutant concentrations.
This study allows for a more complete estimate of recreational benefits from reduced discharges of MP&M pollutants. In
addition to estimates of recreational benefits from reduced frequency of AWQC exceedances, the Ohio case study evaluated
changes in the water resource values from reduced discharges of TKN. The analysis also values additional recreational uses
not addressed in the national analysis, such as swimming.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
The study used data from the National Demand Survey for Water-Based Recreation (NDS), conducted by U.S. EPA
and the National Forest Service, to examine the effects of in-stream pollutant concentrations on consumer decisions to visit a
particular water body (U.S. EPA, 1994).
21.1 METHODOLOGY
21.1.1 Overview
The Ohio study combines direct simulation and inferential analyses to assess how changes in water quality will affect
consumers' valuation of water resources.
The direct simulation analysis component estimates baseline and post-compliance water quality at recreation sites actually
visited by the surveyed consumers and all other sites within the consumers' choice set, visited or not.
The inferential analysis component, a RUM analysis of consumer behavior, estimates the effect of ambient water quality and
other site characteristics on the total number of trips taken for different water-based recreation activities and the allocation of
these trips among particular recreational sites. The RUM analysis is a travel cost model (TCM), in which the cost to travel
to a particular recreational site represents the "price" of a visit.
The main advantage of the RUM model is inclusion of the effect of substitute sites on site values. For any particular site,
assuming that it is not totally unique in nature, the availability of substitutes makes the value for that site lower than it would
be without available substitutes.
EPA modeled two consumer decisions:
»• how many water-based recreational trips to take during the recreational season (the trip participation model); and
*• conditional on the first decision, which recreation site to choose (the site choice model).
The econometric estimation proceeded in two steps, each corresponding to the above decisions. The Agency estimated these
decisions in reverse order (i.e., EPA modeled the second decision, site choice, first).
> Modeling the Site Choice Decision. Assuming that a consumer decides to take a water-based recreation trip, EPA
estimated the likelihood that the consumer will choose a particular site as a function of site characteristics, the price
paid per site visit, and household income. A consumer weighs the attributes for various "choice set" sites against the
travel costs to each site. These travel costs include both the cost of operating a vehicle and the opportunity costs of
time spent traveling. The consumer then weighs the value given to the site's attributes against the cost of getting to
the site when making a site selection. The site choice model estimates how recreational users value access to specific
sites, and estimates per trip economic values for changes in water quality at recreational sites in the study area.
EPA estimated the site choice model using a two-level nested multinomial loci it (NMNL) model, which groups
sites with similar characteristics. The nested logit model assumes that individuals first choose the group of sites and
then a site within that group. This study assumes that individuals first choose a water body type (Lake Erie, rivers, or
small lakes) and then a specific site. EPA used the estimated site-choice model coefficients to estimate the value to
the consumer of being able to choose among Ohio recreation sites on a given day. This measure is referred to as the
"inclusive value."
•• Modeling Trip Frequency. The site choice models estimated in the previous step treat the total number of
recreational trips taken each season as exogenous to the site selection. The Agency estimated the expected
number of trips taken during the recreation season using a Negative Binomial Poisson model (Hausman et al.,
1995; Feather et al., 1995; and Creel and Loomis, 1992), which treats trip frequency as a pre-season decision
regarding total participation in a given recreation activity.
EPA estimated the total number of trips during the recreation season as a function of the expected maximum
utility (inclusive value) from recreational activity participation on a trip, and socioeconomic characteristics affecting
demand for recreation trips (e.g., number of children in the household). The coefficient of the individual's expected
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maximum utility of taking a trip) provided a means of estimating the seasonal welfare effect of water quality
improvements, because changes in water quality change the value of available recreation sites.
Estimating the site choice and total trip participation models jointly is theoretically possible, but computational requirements
make an integrated utility-theoretic model infeasible. EPA estimated separate site choice and trip frequency models for the
four recreational activities: boating, swimming, fishing, and near-water recreation (e.g., viewing wildlife).1
The Agency used estimated coefficients of the indirect utility function with estimated changes in water quality to calculate
per-trip changes in consumer welfare from improved water quality at recreation sites within each consumer choice set. Trip
frequency per season increases if site water quality changes are substantial. A sample consumer's expected seasonal welfare
gain is therefore a function of both welfare gain per trip and the estimated change in number of trips per season.
Combining the trip frequency model's prediction of trips under the baseline and post-compliance and the site choice model's
corresponding per-trip welfare measure yields the total seasonal welfare measure.
EPA calculated each individual's seasonal welfare gain for each recreation activity from post-compliance water quality
changes, and then used Census population data to aggregate the estimated welfare change to the state level. The sum of
estimated welfare changes over the four recreation activities yielded estimates of total welfare gain.
To analyze water quality improvement benefits in the RUM framework, EPA used available discharge, ambient concentration,
and other relevant data to measure baseline and post-compliance water quality at the impact sites. Appendix H provides
detail on water quality modeling used in this analysis.
21.1.2 Modeling the Site Choice Decision
EPA used the RUM framework to estimate the probability of a consumer visiting a recreation site. This framework is based
on the assumption that a consumer derives utility from the recreational activity at each recreation site. Each visit decision
involves choosing one site and excluding others.
The consumer's decision involves comparing each site and choosing the site that produces the maximum utility. An observer
cannot measure all potential determinants of consumer utility, so the indirect utility function will have a non-random element
(V) and a random error term (£), such that the actual determinants of consumer utility V = V + £. The probability (7ijn) that
sitey will be visited by an individual n is defined as:
71 =Pr(V. +£ >V +gj (2L1>
jn > jn yn sn ^snr
where:
Vjn + 5jn = utility of visiting sitey, and
Vsn+ ?Sn = utility of visiting a substitute site.
Estimating the model requires specifying the functional form of the indirect utility function, V, in which site choice is
modeled as a function of site characteristics and the "price" to visit particular sites. For example, a set of conditional utility
functions (one for each site alternativey in the choice set) can be determined as follows:
Jn' 'Jn
where:
Vjn = the utility realized from a conventional budget-constrained, utility maximization model conditional on choice of
sitey by consumer n;
PM = marginal utility of income;
Mjn = the income of individual n available to visit sitey;
1 The Agency also attempted a model structure that allows for interaction among the choice of recreational activities. In this model, a
person first chooses a recreational activity and then chooses a site. This model did not perform very well because less than ten percent of
recreational users included in the dataset participate in all four activities.
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Pjn = a composite measure of travel and time costs for consumer n on site alternative j;
P = a vector of coefficients representing the marginal utility of a specified site characteristic to be estimated along
with PM (e.g., size of the water body, presence of boating ramps); and
Xjn = a vector of site characteristics for site alternativey as perceived by consumer n. These characteristics include the
actual monitored and/or modeled water quality parameters that are hypothesized to be determinants of consumer
valuation of water-based recreation resources, and that may also be affected by the MP&M regulation.
The magnitude of the coefficients in Equation 21.2 reflects the relative importance of site characteristics when consumers
decide which site to visit. The coefficients (P) of water quality characteristics of recreation sites are expected to be positive;
that is, all else being equal, consumers of water-based recreation would prefer "cleaner" recreation sites. The coefficient on
travel cost is expected to be negative, i.e., consumers prefer lower travel costs.
To estimate the site choice probabilities, EPA specified and estimated a nested multinomial logit model (NMNL) for fishing,
boating, and swimming activities. The nested structure explicitly groups similar alternatives, which allows for a richer pattern
of substitution among alternative sites. The NMNL is based on the assumption that an individual chooses first between
groups of alternatives and then, within the chosen group, between individual alternatives. For this analysis, EPA grouped all
recreational sites in Ohio by water body type based on site similarities. EPA tested various alternative site groupings, but the
models presented here were most successful at explaining the probability of selecting a site. The best model used the
following activity-specific site groupings:
»• Fishing model:
*• Group 1: Lake Erie sites;
•• Group 2: river sites;
*• Group 3: small lakes and reservoirs;
»• Boating model:
*• Group 1: Lake Erie sites;
•• Group 2: inland sites, including rivers, small lakes, and reservoirs;
•• Swimming model:
*• Group 1: Lake Erie sites;
•• Group 2: inland sites, including rivers, small lakes, and reservoirs;
•• Viewing model: EPA used a non-nested model in which an individual compares all sites and chooses the one
offering the highest utility level for each trip occasion.
First, the Agency attempted to estimate a nested model based on the three water body types lakes, rivers, and Lake Erie
for all four recreational activities included in the analysis. This structure, however, performed well only for fishing. A
two-nested model that included inland and Lake Erie sites seemed to perform better for the boating and swimming models.
None of the nested structures performed well for participants in near-water/wildlife viewing activities.
This finding is not surprising because sites are grouped based on their similarities within a given nest. It is reasonable to
assume that inland lakes, rivers, and Lake Erie sites are dissimilar from an angler's point of view, because each of the three
water body types is likely to support different fish species. Lake sites may therefore not be close substitutes for rivers sites.
For other activities, differences in fishery resources across water body types are unlikely to be important. Water body size
and the presence of recreational amenities are likely to play a more important role than differences in fish species and the type
of aquatic habitat. Lake and river sites may therefore be regarded as substitutes for each other by boaters and swimmers.
Lake Erie, on the other hand, is a unique water resource that differs from inland water bodies because of its physical
characteristics (e.g., size and water temperature); river and lake sites are therefore not likely to be considered substitutes for
Lake Erie sites. Finally, participants in near-water recreation use water resources indirectly and are therefore more likely to
regard recreational sites located on different water body types as close substitutes to each other. For this reason, the viewing
model is a simple logit model without a nested structure.
2 Three of the four models (fishing, boating, and swimming) passed specification tests for appropriateness of a nested structure (see
Section 21.3 for detail). Test results showed that only two site groups are appropriate for the boating and swimming models inland sites
(rivers, small lakes, and reservoirs) and Lake Erie sites in Ohio. The fourth activity, wildlife viewing, did not pass specification tests for a
nested structure and was estimated as a flat multinomial logit (MNL) model.
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The models assume that an individual first decides to visit a specific water body grouping (hereafter, region), then decides
which site within that group to visit. An individual probability of visiting sitey, given the choice of region R, is a simple
multinomial logit. If the random terms $nj for individual n at sitey are independently and identically distributed and have an
extreme value Weilbull distribution, then n jn takes the form (McFadden, 1981):
v=—v (2L3)
Ee f
ye/-
where:
71 jn|r = probability of selecting sitey in region r;
y
S '" = the consumer's utility from visiting sitey;
r = regions -- "Lake Erie," "rivers," etc. as specified above for a given activity; and
ZY
g |n = the sum of the consumer's utility at each sitey for all sites in the opportunity set for region r.
i*r
Estimated parameters of the indirect utility function are then used to estimate the inclusive value. For consumer n, the
inclusive value measures the overall quality of recreational opportunities for each water-based activity and represents the
expected maximum utility of taking a trip. Note that, although EPA used a random draw from the opportunity set for the
purpose of estimating the model parameters, the Agency calculated the inclusive value (i.e., the expected maximum utility)
using all recreation sites in the consumer's opportunity set in a given region.
The inclusive value is calculated as the log of the denominator in Equation 21.2 (McFadden, 1981).
(2L4)
where:
Ir = inclusive value for sites associated with region R;
/>"
= individual n's utility from visiting sitey; and
W = a vector of baseline water quality characteristics.
The probability of choosing a particular region is:
*
Kr=—R (21.5)
/•=!
where:
71 r = probability of selecting region r;
Ir = the inclusive values for a given region;
yr = the coefficient on the inclusive value for a given region; and
r = activity-specific regions (e.g., "Lake Erie," "rivers," and "small lakes" for fishing).
To estimate the model described by Equations 21.2 and 21.5, EPA used a standard statistical software package, LIMDEP.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
21.1.3 Modeling Trip Participation
After modeling the site choice decision, the next step modeled the determinants of the number of water-based recreation trips
a consumer takes during a season. To link the quality of available recreation sites with consumer demand for recreation trips,
EPA modeled the number of recreation trips taken during the recreation season as a function of the inclusive value estimated
in the previous step and socioeconomic characteristics affecting demand for recreation activities. The dependent variable, the
number of recreation trips taken by an individual during the recreation season, is an integer value greater than or equal to
zero. To account for the non-negative property of the dependent variable, EPA used count data models based on probability
densities that have the non-negative integers as their domain.
One of the simplest count data models is a Poisson estimation process, which is commonly used with count data, such
as number of recreation trips taken during the recreation season. Inherent in the model specification is the assumption that
each observation of a number of trips is drawn from a Poisson distribution. Such a distribution favors a large number of
observations with small values (e.g., two trips, four trips) or zeros, resulting in its being skewed toward the lower end. Due to
the nature of the observed number of trips, it is quite reasonable to assume that the underlying distribution can be
characterized as a Poisson distribution. Figure 21.1 shows the number of recreation trips taken per year and the number of
respondents who reported taking that number of trips.
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Figure 21.1: Number of Trips Per Year By Activity Type
Number of Trips Per Year: Fishing
» 12
1357
22 24 26 28 30 32 34 I 36 I 38 I 40 I 42 44
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
Number of Trips
20
18
16
14
12
10
8
Number of Trips Per Year: Swimming
i I 20 I :
M 40 F.
2 I 4 I 6 I 8 10 I 12 I 14 I 16 I 18 I 20 I 22 I 24 I 26 I 28 I 30 I 32 I 34 I 36 I 38 I 40 I 42 I 44
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
Number of Trips
Number of Trips Per Year: Viewing
Number of Trips Per Year: Boating
60
55
50
j 45
CD
> 40
| 35
& 30
25
20
15
10
0.
I
•5
M 20 22 I 24 I 26 I 26 30 32 I 34 I 36 I 38 40 4
2 I 4 I 6 I 8 10 I 12 I 14 I 16 I 18 I 20 I 22 I 24 I 26 I 28 I 30 I 32 I 34 I 36 I 38 I 40 I 42 I 44
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
2 I 4 I 6 I 8 10 I 12 I 14 I 16 I 18 I 20 I 22 I 24 I 26 I 28 I 30 I 32 I 34 I 36 I 38 I 40 I 42 I 44
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
Number of Trips
Number of Trips
Source: U.S. EPA analysis ofNDS data (U.S. EPA, 1994)
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Estimating the Poisson model is similar to estimating a nonlinear regression. The single parameter of the Poisson distribution
is A, which is both the mean and variance of yn. The probability that the actual number of trips taken is equal to the estimated
number of trips is estimated as follows (Green, 1993):
Prob(Yn=yn)=
•'«•
where:
Yn = the actual number of trips taken by an individual in the sample;
yn = the estimated number of trips taken by an individual in the sample;
n = 1, 2,..., N, the number of individuals in the sample; and
An = P'X, expected number of trips for an individual in the sample, where X is a vector of variables affecting the
demand for recreational trips (e.g., inclusive values and socioeconomic characteristics) and (3 is the vector of
estimated coefficients.
From Equation 21.6, the expected number of water-based recreation trips per recreation activity season taken by an individual
is given by:
E\yn\xn]=Var\yn\Xn]=e»'x» (2L7)
where:
E[ynxn] = the expected number of trips, yn, given xn;
Var[ynxn] = the variance of the number trips, yn, given xn;
P = a vector of coefficients on x; and
x = a matrix of socioeconomic variables and inclusive values.
An empirical drawback of the Poisson model is that the variance of the number of trips taken must be equal to the mean
number of trips, and this equality is not always supported by actual data. In particular, the NDS survey data exhibit
overdispersion, a condition where variance exceeds the mean. The estimated variance-to-mean ratios of the number of
trips in the NDS data sample are 31, 27.9, 35.6, and 10.5 for fishing, swimming, viewing, and boating trips, respectively.
Overdispersion is therefore present in the data set.
To address the problem of overdispersion, EPA used the negative binomial regression model, an extension of the
Poisson regression model, which allows the variance of the number of trips to differ from the mean. In the negative binomial
model, A is respecified so that (Green, 1993):
where the error term (e) has a gamma distribution, E[exp(£j)] is equal to 1.0, and the variance of e is a.
The resulting probability distribution is:
e expiejA,. m 9)
Prob[Y= yn\e]= *^JL (21^
where:
yn = 0,1,2... number of trips taken by individual n in the sample;
n = 1,2,..., N number of individuals in the sample; and
An = expected number of trips for an individual in the sample.
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Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
Integrating e from Equation 21.9 produces the unconditional distribution of yn. The negative binomial model has an
additional parameter, a, which is the overdispersion parameter, such that:
Var\yn]=E\yn](\+aE\yn])
The overdispersion rate is then given by the following equation:
(21.10)
Var\yn}
(21.11)
EPA used the negative binomial model to predict the seasonal number of recreation trips for each recreation activity based on
the inclusive value, individual socioeconomic characteristics, and the overdispersion parameter, a. If the inclusive value has
the anticipated positive sign, then increases in the inclusive value stemming from improved ambient water quality at
recreation sites will lead to an increase in the number of trips. The combined MNL model site choice and count data trip
participation models allowed the Agency to account for changes in per-trip welfare values, and for increased trip participation
in response to improved ambient water quality at recreation sites.
21.1.4 Calculating Welfare Changes from Water Quality Improvements
EPA estimated the welfare change associated with water quality improvements from the baseline to post-compliance
conditions as a compensating variation (CV), which equates the expected value of realized utility under the baseline and
post-compliance conditions. The expected seasonal change in welfare attributed to the quality improvements for an individual
n in the sample consists of two components:
*• per trip welfare gain, and
*• increased number of trips under the post-compliance water quality condition.
The Agency first calculated the welfare gain from water quality improvement for each consumer on a given day by using a
CV measure for consumer n (Kling and Thompson, 1996):
In
CV -
R Jr
£ (^ eF>(r })
r= 1 ]= 1
-In
R ^ 1
£ (£ eV]n(~W))
r= 1 j= 1
« 0
(21.12)
where:
CVn = the compensating variation for individual n at sitey on a given day;
r = "Lake Erie," "inland," etc.
j = l,...Jr represents a set of alternative sites for a given recreational activity in region r;
In
= the inclusive value index (I);
W ° = a vector of information describing baseline water quality;
W1 = a vector of information describing post-compliance water quality; and
PM = the implicit coefficient on income that influences recreation behavior.
In deriving Equation 21.12, EPA assumed that the marginal utility of income, PM, is constant across alternatives (as well as
across quality changes). If this assumption does not apply, the derivation of Eq. 21.12 is more complicated (Hausman et al.,
1995).
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EPA then estimated the low and high values of the seasonal welfare gain for individual n in the sample as follows: 3
= (71 - 7°) x 7° (21 13)
low, n _ n
(I I ) x 7 (21.U)
"high, n _ n ( '
V\L
where:
Wlow n = lower bound estimate of the seasonal welfare gain for individual n;
Whi^i n = upper bound estimate of the seasonal welfare gain for individual n;
I1 = the post-policy inclusive value;
Y1 = the estimated number of trips after water quality improvement;
1° = the baseline inclusive value;
Y° = the estimated number of trips in the baseline; and
PT = the implicit coefficient on income that influences recreation behavior.
These estimates are per individual in the population for those individuals meeting qualifications for inclusion in the NDS
response set (i.e., respondents whose home state is Ohio and respondents from the neighboring states whose last trip was to
Ohio's sites).4 EPA extrapolated the estimates of value per individual to the Ohio state level based on Census data (U.S.
Bureau of the Census, 2000). The following section details the extrapolation method used in the analysis.
21.1.5 Extrapolating Results to the State Level
EPA used a simplified extrapolation technique to estimate the state-level benefits. EPA first estimated the number of
participants in fishing, swimming, boating, and wildlife viewing in Ohio, based on the estimated percentage of the NDS
survey respondents residing in Ohio who participate in a given activity and the state adult population. The 2000 Census data
provide information on the number of Ohio residents aged 16 and older. EPA then multiplied the estimated average seasonal
welfare gain per participant in a given recreational activity by the corresponding number of recreational users. The total
welfare gain to the users of water-based recreation in Ohio is the sum of fishing, swimming, boating, and wildlife viewing
benefits.
21.2 &ATA
This section describes the data and supporting analyses required to implement the RUM analysis. The following general
categories of data and supporting analyses are required:
> information on the consumers of water-based recreation responding to the NDS in Ohio;
> recreation sites identified for the water quality and RUM analyses, including the sites visited by consumers of water-
based recreational activity and supplemental sites in their choice sets;
> estimated price of visiting the sites. The "visit price" is estimated as a function of travel distance (and travel time)
between each consumer's hometown and each site in the choice set; and
»• information on site characteristics likely to be important determinants of consumer behavior. Of particular
importance to this analysis are the water quality and related characteristics of sites in the choice set, and how those
characteristics may be expected to change as a result of regulation.
3 EPA selected this approach for calculating seasonal welfare gain per individual based on Dr. Parsons' recommendation (G.R.
Parsons, 1999).
4 Section 21.2.1 provides a detailed description of the data sample used in the analysis.
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The following sections discuss each category of data and/or supporting analysis below.
21.2.1 The Ohio &ata
EPA obtained information on survey respondent socioeconomic characteristics and recreation behavior, including last trip
profile and the annual number of trips associated with each water-based activity, from the NDS (U.S. EPA, 1994). The 1994
survey collected data on demographic characteristics and water-based recreation behavior using a nationwide stratified
random sample of 13,059 individuals aged 16 and over. Respondents reported on water-based recreation trips taken within
the past 12 months, including the primary purpose of their trips (e.g., fishing, boating, swimming, and viewing), total number
of trips, trip length, distance to the recreation site(s), and number of participants. Where fishing was the primary purpose of a
trip, respondents were also asked to state the number of fish caught. Table 21.1 shows the number of trips taken per year by
primary recreation activity, as reported in the NDS.
EPA selected case study observations for Ohio residents who took trips within or outside of the state. Trips to Ohio
recreation sites by residents of neighboring states were also included in the site choice models, but not in the trip participation
models.5 All four activity models included single-day trips only. EPA included only activity participants with valid
hometown ZIP codes, whose destination site was uniquely identified. The Agency used data on both Ohio participants and
Ohio non-participants to estimate total seasonal trips, but included only Ohio participants and several residents of nearby
states in the site choice models. Although they could not be used in the site choice model, participant observations from Ohio
with missing location information were used to analyze the number of trips. Tables 21.1 and 21.2 list valid observations by
activity, residence, and model type. Figure 21.2 illustrates the distribution of the sample observations in relation to the
location of MP&M facilities affected by the rule in Ohio.
Table 21.1: Classification of Sample Observations for Estimation of the Site Choice Models
Participants (Total)
Fishing
Swimming
Viewing
Boating
Total
Ohio
Residents
609
122
147
231
109
Ohio Residents
with Last Trip
In-State
408
103
100
126
79
Valid Ohio
Residents with
Last Trip In-
State
237
66
58
64
49
Valid Ohio
Residents with
Last Trip
Outside State
35
9
14
2
10
Valid
Nonresidents
with Last
Trip in Ohio
11
0
2
7
2
Valid for
Site Choice
Model
297
84
76
73
64
Source: U.S. EPA analysis.
5 These additional observations total 11 across the four activities and thus represent only a small fraction of total observations.
Including only Ohio respondents in the trip participation models underestimates the benefits associated with water quality improvements,
because the welfare gains to recreators from neighboring states are ignored.
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Table 21.2: Classification of Sample Observations for Estimation of the Trip Participation Models
Ohio Residents
Non-Participants
Participants (Total)
Fishing
Swimming
Viewing
Boating
Total Observations
Total
300
609
122
147
231
109
909
Residents with Last
Trip In-State
408
103
100
126
79
408
Residents with Last
Trip Outside State
34
4
9
7
14
34
Valid for Trip
Participation Model
291
322
84
78
75
85
613
Source: U.S. EPA analysis.
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Figure 21.2: Location of MP&M Facilities in Relation to the Visited Sites
Facilities Included
in the MP&M Rule
Ohio MP&M Facilities and NDS Visited Sites
Visited Sites by Type
(ft/1 Viewing "*• Fishing
•»••. Swimming x* Boating
Rfl Reach
State Boundary
Source: U.S. EPA analysis.
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21.2.2 Estimating the Price of Visits to Sites
EPA estimated trip "price" for each consumer of water-based recreation as the sum of travel costs plus the opportunity cost of
time, following the procedure described in Haab et al. (2000). Based on Parsons and Kealy (1992), this study assumed that
time spent "on-site" is constant across sites and can be ignored in the price calculation.
To estimate consumers' travel costs, EPA first used ZipFip software to calculate the one-way distance to each site for each
participant.6 The average estimated one-way distance to the site visited is 37.56 miles. EPA then multiplied round-trip
distance by average motor vehicle cost per mile ($0.29, 1993 dollars).7'8 The model adds the opportunity cost of travel time,
measured in terms of wages lost, to the travel cost for those who would have lost income by taking the recreation trip. For
these consumers the dummy variable LOSEINC equals one. Travel times equal the round-trip distance divided by a travel
speed of 40 mph and multiplied by the individual's hourly wage as calculated below.
The travel cost variable in the model was calculated as follows:
Visit Price = \ Round Trip Distance x $.29 + Round Trip Dlstance x (Wage) If LOSEINC =1 m 15)
40 mph ^ ' *
Round Trip Distance x $.29 If LOSEINC = 0
Individuals not losing income (e.g., individuals taking vacation or a weekend trip or individuals whose work schedule is not
flexible) do not face lost wages as a result of the trip and inclusion of the opportunity cost of time would be inappropriate.
These consumers still have an opportunity cost for their travel time, which could otherwise be spent doing something else,
like fishing. In other words, a shorter distance traveled allows for a longer time spent at the recreation site. For these
consumers, the analysis included an additional round-trip travel time variable calculated as:
Travel Time = I Round Trip Distance/40 If LOSEINC = 0
(21.16)
0 If LOSEINC =1
The average one-way estimated travel time to the visited site is 56.34 minutes.9
21.2.3 Site Characteristics
EPA identified 1,954 recreation sites on 1,631 reaches in the universal opportunity set. Of these, 580 observations are known
recreational sites (e.g. parks); 1,366 observations are Reach File 1 (RF1) reaches without a known recreational site; and
eight observations are neither located in RF1 nor identified as known recreation sites but were visited by an NDS respondent.
6 The program was created by Daniel Hellerstein and is available through the USDA at
http://usda.maunlib.cornell.edu/datasets/general/93014.
7 Note that all expenditures are in 1993 dollars because the NDS trip choices and the associated expenditure occurred in 1993.
8 The estimate of motor vehicle cost per mile was based on estimates compiled by the Insurance Information Institute.
9 The average travel time to the visited site was fairly uniform across the activities. Average one-way time to the visited site was 51.38
minutes, 71.64 minutes, 43.76 minutes, and 58.57 minutes for fishing, boating, swimming, and viewing, respectively.
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Each consumer choice set theoretically includes hundreds of substitutable recreation sites in Ohio and in the neighboring
states. To prevent the recreation site analysis from becoming unmanageable, EPA analyzed a sample of recreation sites for
each consumer observation. The Agency then created a randomly-drawn reduced choice set for each recreational activity as
follows:10
> Fishing. The reduced choice set consists of 20 Lake Erie sites, 20 river sites, and 20 small lakes/reservoirs. Thus, a
total individual choice set consists of 60 alternatives (including the chosen site);
»• Boating. The reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites (including rivers and
lakes/reservoirs). A total individual choice set consists of 40 alternative sites (including the chosen site);
> Swimming. Similar to boating, the reduced choice set consists of 20 Lake Erie sites and 20 inland recreation sites
(including rivers and lakes/reservoirs). A total individual choice set consists of 40 alternative sites (including the
chosen site);
> Wildlife Viewing. The reduced choice set consists of 40 sites, including Lake Erie, river, and small lake/reservoir
sites.
Each participant choice set, by definition, includes the site actually visited by the respondent. For each consumer, EPA drew
the additional sites from a geographic area defined by a distance constraint (and the water body types listed above). The
Agency used a 120-mile distance limit for inland recreation sites (Ohio rivers, small lakes, or reservoirs). All Lake Erie sites
are eligible for inclusion in the choice sets for all models. EPA assumed that consumers of water-based recreation would be
willing to travel farther to visit Lake Erie sites, because this water resource presents unique recreational opportunities.11 EPA
used the resulting aggregate choice set of sites for all individuals participating in a given recreation activity to model
consumer decisions regarding trip allocation across recreation sites.
The Agency used two classes of characteristics to estimate site choice:
*• those unaffected by the MP&M regulation, but likely to determine valuation of water-based recreational resources;
and
•• those affected by the regulation and hypothesized to be significant in explaining recreation behavior and resource
valuation.
Regulation-independent site characteristics include water body type and size, location characteristics, and the presence of site
amenities (e.g., boat ramps, swimming beaches, picnic areas). Regulation-dependent site characteristics include regulation-
affected water quality variables.
a. Regulation-independent site characteristics
Site characteristics that are likely to be important determinants of consumer valuation of water-based recreational resources
but that are independent of the MP&M regulation include general site descriptors. These descriptors include the type and size
of the water body and location characteristics, and the presence of site amenities. EPA obtained data on regulation-
independent site characteristics from two main sources, RF1 and the Ohio Department of Natural Resources (ODNR).
RF1 provided water body type (i.e., lake, river, or reservoir) and physical dimension (i.e., length, width, and depth). The
dummy variables, LAKE ERIE, RIVER, and LAKE characterize water body types. If a site is located on Lake Erie, LAKE
ERIE takes the value of 1; 0 otherwise. If a site is located on river, RIVER takes the value of 1; 0 otherwise. Finally, if a site
is located on a small lake or reservoir, LAKE takes the value 1; 0 otherwise. Water body size was determined by the length of
the reach segment in miles for rivers and Lake Erie sites. For small lakes and reservoirs, the appropriate water body size is the
water body area in acres. The site choice models use the logarithm of water body size as a measure of site importance,
10 McFadden (1981) has shown that estimating a model using random draws can give unbiased estimates of the model with the full set
of alternatives.
11 Travel distance from respondent's hometown to the Lake Erie sites did not exceed 250 miles.
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because people are more likely to be aware of large water bodies.12 Water body size data for sites not located in RF1 came
from the ODNR.
ODNR, supplemented by the Ohio Atlas and Gazetteer, provided data on recreational amenities and site setting (e.g.,
presence/absence of boat ramps, swimming beaches, or picnic areas; public accessibility; and size of land available for
recreation). EPA used land available for recreation, LN(LAND), (e.g., acreage of state park, fishing, hunting, and other
recreation areas) to approximate site setting and attractiveness. Dummy variables represent the presence of three recreational
amenities: BEACH is a swimming beach; RAMP is a boating ramp; and PARK indicates a park.
b. Regulation-dependent site characteristics
Selecting regulation-dependent site characteristic variables that are both policy-relevant and significant in explaining
recreation behavior proved challenging. MP&M facilities discharge many pollutants, most of them unlikely to have visible
indicators of degraded water quality (e.g., odor, reduced turbidity, etc). EPA hypothesized that pollutant loadings can,
nonetheless, reduce the likelihood of selecting a recreation site. Reduced pollutant discharges improve water quality and
aquatic habitat, thereby increasing fish populations and enhancing the recreational fishing experience, hi addition, in-stream
nutrient concentrations are good predictors of eutrophication, which causes aesthetic losses and may thus affect the utility of a
water resource for all four recreational uses.
The connection between the policy variables (i.e., the change in concentrations of MP&M pollutants) and the effects
perceived by consumers (e.g., increased catch rate, increased size offish, greater diversity of species, or improved aesthetic
qualities of the water body) are not modeled directly, but are captured implicitly in the differential valuation of water
resources as reflected in the RUM analyses.
EPA considered two types of pollutant effects in defining water quality variables for model inclusion:
*• visible or otherwise directly perceivable effects (e.g., water turbidity); and
*• unobservable toxic effects likely to impact aquatic habitat and species adversely.
The Agency accounted for directly observable effects using the ambient concentrations of nutrients (e.g., TKN) as an
explanatory variable.
Rather than include the concentrations of all toxic pollutants separately, EPA constructed a variable to reflect the adverse
impact potential of toxic pollutants on aquatic habitat. EPA identified recreation sites at which estimated concentrations of
one or more MP&M pollutants exceeds AWQC limits for aquatic life protection, to assess the likely adverse impacts on
aquatic organisms. A dummy variable, AWQC_EX, takes the value of 1 if in-stream concentrations of at least one MP&M
pollutant exceed AWQC limits for aquatic life protection, 0 otherwise. This approach accounts for the fact that adverse
effects on aquatic habitat are not likely to occur below a certain threshold level.
c. Biological factors
Numerous biological parameters (e.g., abundance of sport fish) that are a function of the availability and quality of suitable
habitat for breeding and feeding are also likely to affect recreation behavior. To account for biological parameters affecting
the demand for water-based recreation, EPA used relative fish abundance (Biomass) obtained from the Ohio Water
Resource Inventory (OWRI) database (OH EPA, 1996). Relative fish abundance is measured as the total fish weight (in
kg) per 300 meters. Because this variable reflects presence of both tolerant and intolerant fish species, it is less correlated
with the two regulation-dependent water quality variables (i.e., TKN and AWQC) included in the analysis compared to the
index of well-being (IWB2) used in the proposed rule analysis.
Chemical properties of the waters (e.g., pollutant concentrations) are likely to affect the diversity and abundance of the fishery
resources. Biological parameters may also be affected by numerous anthropogenic stressors unrelated to water quality, such
as over-fishing, physical alteration of habitat, invasion of exotic species, etc. Although EPA used the baseline values of
relative fish abundance to estimate the site choice models, the Agency did not estimate changes in biological parameters
caused by the regulation analysis due to data limitations and the challenges posed by modeling population impacts of a broad
spectrum of pollutants at hundreds of recreation sites.
12 EPA uses the logarithm of water body size because it expects the effect of water body size on utility to diminish as that size
increases.
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d. Presence of fish advisories
Another important factor that may affect a recreational consumer's decision to visit a particular site is presence of fish
consumption or contact advisories (FCAs). EPA obtained information on fish consumption advisories and contact
advisories at reaches in Ohio from the ODNR (Ohio DNR, 1999). Fish consumption advisories and contact advisories were
listed by the name of the stream or river with the consumption advisory. An advisory that applied to only part of the river
included the names of cities, towns, or highways to identify the stretch of the reach for which the advisory was relevant. The
name of the river and the other geographic identifiers were used to assign reach numbers from RF1 to the consumption
advisories. EPA created a dummy variable for each type of advisory (i.e., fish advisories and contact advisories). The
variable takes the value of 1 if the relevant advisories are present; 0 otherwise.
21.3 SITE CHOICE MODEL ESTIMATES
EPA estimated four separate models of recreational demand: fishing, boating, swimming, and viewing. The Agency classified
trips by the primary activity listed by the respondent. All four activity models cover single-day trips. EPA estimated the site
choice model using the site actually visited and randomly-drawn sites from the choice set for each recreation activity as
described in Section 21.2-3 above.
EPA estimated activity models for five alternative choice sets (i.e., five random draws from the universal choice set),
producing five sets of estimated coefficients. Mean estimates from the five alternative draws represent EPA's best estimate of
actual coefficient values. Table 21.3 lists the variables used as arguments in the utility function and presents the mean
estimation results for the four models. In estimating site choice models for fishing, boating, swimming, and viewing, the
Agency restricted the coefficient on travel cost to be equal across all four models to ensure a constant marginal utility of
income across all four activities.
The following sections provide a short description of the results of the site choice model corresponding to each recreation
activity.
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Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
Table 21.3: Site Choice Model Estimation Results
(Mean parameter estimates from five random draws)"
Variable
TRCOST b
TIME"
RAMP"
LN(LAND) '
PARKf
BEACH g
LN(SIZE) h
Biomass '
TKNJ
AWQCk
ERIE
Inland
RIVER
LAKE
Adj.R2
All
Lake Erie
River
Lake
Lake Erie
River
^^^ ^^,
Activity
r
Fishing
-0.044 (-22.704)
-1.474 (-7.482)
0.878 (7.509)
N/A
N/A
N/A
N/A
0.908 (6.639)
0.171(1.993)
0.050 (-0.348)
N/A
0.068 (2.328)
-0.584 (-3.763)
-0.573 (-3.698)
I
0.811(9.895)
N/A
0.591 (6.945)
0.429 (2.629)
0.467
Boating
-0.044 (-22.704)
-0.362 (-4.27)
N/A
N/A
N/A
N/A
0.502 (5.777)
N/A
N/A
N/A
-0.130 (-1.777)
0.017 (0.4432)
-1.187 (-6. 863)
-0.172 (-1.179)
nclusive Values
0.296 (6.098)
0.088 (2.525)
N/A
N/A
0.280
Swimming
-0.044 (-22.704)
-0.436 (-7.007)
N/A
0.058(2.431)
0.753 (3.79)
0.491 (2.96)
-0.273 (-6.083)
N/A
N/A
N/A
N/A
N/A
-0.660 (-4.631)
N/A
0.730 (7.466)
0.275 (6.302)
N/A
N/A
0.408
Viewing
-0.044 (-22.704)
-0.719 (-12.647)
N/A
0.162 (7.471)
0.787 (4.638)
N/A
N/A
0.665 (10.474)
-0.261 (-4.937)
-0.429 (-4.329)
N/A
N/A
-0.711 (-4.401)
N/A
N/A
N/A
N/A
N/A
a EPA performed this analysis based on five alternative draws to assess sensitivity of the estimated coefficients with
respect to random draws.
b Travel Cost is calculated as 0.29 * round-trip distance.
° Travel Time is (round-trip distance / 40)*Wage.
d 1 if a boating ramp is present, and 0 otherwise.
° Log of the number of land acres.
f 1 if the site is a park, and 0 otherwise.
g 1 if a swimming beach is present, and 0 otherwise.
h Log of the size of the water body. For rivers and Lake Erie, this is the log of the reach segment length or Lake Erie
shore segment in miles. For lakes, this is log of the lake circumference.
1 Biomass is measured as the total fish weight (in kg) per 300 meters.
J In-stream concentrations of TKN (mg/1).
k 1 for any reach if in-stream concentrations of at least one MP&M pollutant exceeds the AWQC limits for protection of
aquatic life, and 0 otherwise.
Note: T-statistic for test that the estimated coefficient equals 0 is given in parentheses beside the coefficient estimates.
N/A indicates that the variable was not included in the estimation for this activity.
Source: U.S. EPA analysis.
21.3.1 Fishing Model
The estimated fishing model includes travel cost (TRCOST), time (TIME) spent traveling, and site characteristics. The
Agency included the following site characteristics in the fishing model: boat ramp (RAMP), water body size (LN(SIZE)),
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relative fish abundance (Biomass), TKN concentrations, and presence of AWQC exceedances. Table 21.3 shows that most
coefficients have the expected sign and are significantly different from zero at the 95th percentile. Travel cost and travel time
have a negative effect on the probability of selecting a site, indicating that anglers prefer to visit sites closer to their homes
(other things being equal).
Anglers who fish from a boat are likely to view the presence of a boat ramp as an important factor that may affect their site
choice. However, the presence of a boat ramp is unlikely to be important for anglers who fish from shore. Thus, the Agency
used an interaction variable (RAMP x USE_BOAT) such that the ramp variable was turned on only if the angler reported
using a boat on his last fishing trip. A positive sign on the boat ramp indicates that anglers owning a boat are more likely to
choose sites with a boat ramp.
The water body size has a different effect on the probability of selecting a site in the Lake Erie, river, and small lake/reservoir
groups. The larger the river or the Lake Erie shore segment, the more likely that anglers visited the reach. The size of inland
lakes and reservoirs does not have a significant effect on the probability of visiting the site.
The Agency used the square root of the fish weight per 300 meters as a measure offish abundance (Biomass). The probability
of a river site visit increases as the relative fish abundance at the site increases. However, inclusion of this variable in the
Lake Erie nest was not significant, which indicates that relative fish abundance does not have a significant effect on choosing
a Lake Erie site. This finding is counterintuitive and is likely to be due to the lack of variation in the relative fish abundance
variable for the Lake Erie sites. This variable was excluded from the Lake Erie nest in the final model presented here. Data on
relative fish abundance were not available for lakes.
Finally, higher ambient concentrations of TKN, which indicate potential eutrophication problems, and presence of AWQC
exceedances negatively affect the probability of site selection. In other words, anglers prefer cleaner sites, all else being
equal.
Estimated inclusive values on Lake Erie sites, rivers, and small lakes lie within a unit interval [0,1] and are significantly
different from 0, indicating that the nested choice structure is appropriate.13
EPA found other variables, tested as explanatory variables, to be insignificant, including the presence of FCAs. It might be
expected, a priori, that the presence of an FCA decreases a site's likelihood as a fishing choice. In fact, the existence of
FCAs did not significantly affect a site's probability of being chosen; 59 percent of the sites actually chosen by NDS
respondents had an FCA in place. Creel surveys provided by ODNR indicated that, on average, anglers released 70 percent
of their catch (ODNR, 1997). This finding suggests that recreational anglers are aware of FCAs, and catch but do not
consume fish in the affected areas.
21.3.2 Boating Model
The estimated boating model includes travel cost (TRCOST), time (TIME) spent traveling, and site characteristics. The
Agency included the following site characteristics in the boating model: water body size (LN(SIZE)), relative fish abundance
(Biomass), TKN concentrations, and presence of AWQC exceedances. Table 21.3 shows that most coefficients have the
expected sign and are significantly different from zero at the 95th percentile.
Travel cost and travel time have a negative effect on the probability of selecting a site, indicating that boaters prefer to visit
sites closer to their homes (other things being equal). However, the magnitude of the travel time coefficient indicates that
boaters are willing to travel farther than participants in other recreational activities. This is not surprising, since motor-
boating and sailing are restricted to the sites where these activities are allowed. The positive coefficient on the water body
size variable (LN(SIZE)) indicates that the larger the water body the more likely the boaters visited it.
The coefficients on water quality variables (TKN and AWQC) are negative, indicating that boaters prefer to visit cleaner
sites. The Biomass coefficient is positive, but insignificant for inland sites, and negative for Lake Erie sites. The negative
coefficient on this variable is likely to be due to the fact that 88 percent of the sample trips used in this model were
motorboating trips. Motorboating itself is likely to be a significant environmental stressor for biological communities due to
noise and turbidity associated with this activity. Thus, lower fish abundance at popular boating sites may indicate that
intensive motorboating may adversely affect species abundance. As was the case with the fishing model, the estimated
Inclusive values equal to 1 cause the model to collapse to a flat multinomial logit.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
inclusive value is significantly different from zero and lies within a unit interval [0.1], supporting the nested model
framework.
21.3.3 Swimming Model
EPA included the travel cost and time variables (TRCOST, TIME), physical characteristics of the site, and ambient TKN
concentrations in the swimming model. This model also includes the presence of recreational amenities that are likely to be
important to swimmers: presence of a beach, the designation of the site as a park, and the natural log of the land acres. All
estimated coefficients have the expected sign and are significantly different from zero at the 95th percentile.
Price, travel time, the presence of a park with a beach, and the size of the land area around the site all increase the probability
of a particular site being chosen for swimming. Swimmers are less likely to visit large sites (referring to the size of the water
body) or sites with visible water quality effects as indicated by higher in-stream concentrations of TKN. As for the fishing
and boating models, the estimated inclusive value is significantly different from zero and lies within a unit interval [0,1]
supporting the nested model framework.
Again, some variables expected to be significant, such the presence of contact advisories, are not. This variable's
insignificance probably stems from its scarcity. Of 1,954 sites included in the universal opportunity set, contact advisories are
in place for only 13. (None of the sites actually visited had contact advisories in place.) The probability that a chosen site has
contact advisories in place is very small, because individual choice sets are randomly selected.
The fish Biomass variable representing biological characteristics of a water body also did not have a significant influence on
consumer decisions to visit a particular site and was dropped from the model. This outcome is not surprising, since abundant
aquatic life may, in fact, interfere with swimming activities.
21.3.4 Viewing (Near-water Activity) Model
EPA included the travel cost and time variables (TRCOST, TIME), physical site characteristics, and ambient TKN
concentrations in the viewing model. In addition, the Agency included the natural log of the land acres and the designation
of the site as a park. All estimated coefficients have the expected sign and are significantly different from zero at the 95th
percentile.
The probability of choosing a site for near-water activities is most significantly related to visit price, travel time, land size,
and in-stream concentrations of TKN. Similarly to the fishing model, the water body size has a different effect on the
probability of selecting a site in the Lake Erie, river, and small lake/reservoir groups. The larger the Lake Erie shore segment,
the more likely that viewers visited the site. The negative coefficients on river and inland lake size indicate that consumers
prefer smaller inland water bodies for near-water and wildlife viewing activities.
21.4 TRIP PARTICIPATION MODEL
EPA estimated the determinants of individual choice concerning how many trips to take during a recreation season with a
separate model for each of the four activities. These participation models rely on socioeconomic data, and on estimates of
individual utility (the inclusive value) derived from the site choice models. Variables of importance include age, ethnicity,
gender, education, and the presence of young or older children in the household. Whether or not the individual owns a boat is
particularly important in boating participation, and is included in the model for that activity only. Variable definitions for the
trip participation model are:
> IVBASE: inclusive value, estimated using the coefficients obtained from the site choice models;
»• STRIPS: number of trips taken by the individual;
»• AGE: individual's age. If the individual did not report age, their age is set to the sample mean;
> MALE: equals 1 if the individual is a male, 0 otherwise;
»• NOHS: equals 1 if the individual did not complete high school, 0 otherwise;
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*• COLLEGE: equals 1 if the individual completed college, 0 otherwise;
»• AFAM: equals 1 if the individual is African American, 0 otherwise;
> YNGKIDS: equals 1 if there are kids 6 years or younger, 0 otherwise;
»• OLDKIDS: equals 1 if there are kids 7 years or older, 0 otherwise;
*• OWNBT: equals 1 if individual owns a boat, 0 otherwise;
•• Constant: a constant term representing each individual's utility associated with not taking a trip; and
> a (alpha): overdispersion parameter estimated by the Negative Binomial Model.
Table 21.4 presents explanatory variables and a mean value for each.
Table 21.4: Mean Values for Explanatory Variables Used in the Participation Models
i : i i :
Variables 1 Non-Participant ! Boating Fishing Swimming Viewing
(Mean) j (N=291) | (N=85) j (N=84) j (N=78) | (N=75)
STRIPS
AGE
MALE
NOHS
COLLEGE
AFAM
YNGKIDS
OLDKIDS
OWNBT
0.00
43.99
0.33
0.17
0.15
0.11
0.18
0.38
N/A
7.71
39.06
0.49
0.09
0.32
0.02
0.26
0.48
0.53
10.07
38.53
0.65
0.14
0.20
0.05
0.24
0.58
N/A
9.46
34.76
0.47
0.13
0.32
0.03
0.24
0.56
N/A
9.59
36.91
0.47
0.13
0.35
0.12
0.27
0.48
N/A
Source: U.S. EPA analysis.
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Table 21.5 presents the results for the participation models of the four recreation activities.
Table 21.5: Trip Participation Negative Binomial Model Estimates
Variables/ Statistics
IVBASE
AGE
MALE
NOHS
COLLEGE
AFAM
YNGKIDS
OLDKIDS
OWNBT
Constant
Alpha a
Boating
0.12
(Hi.)
-0.07
(-4.73)
1.23
(2.75)
1.29
(2.37)
-0.19
(-0.29)
-3.74
(-1.81)
1.51
(2.96)
-1.67
(-3.58)
3.82
(5.26)
0.20
(0.11)
5.77
(5.85)
Fishing
0.82
(2.86 )
-0.04
( -2.06)
2.22
( 3.25)
-1.09
(-1.56)
-0.40
(-0.721 )
-1.44
(-1.53)
-0.95
(-1.26)
1.11
(2.78)
N/A
-5.74
(-3.01)
9.03
(7.16)
Swimming
0.72
(157)
-0.06
( -2.24)
1.15
(1.52)
-0.92
( -0.96)
0.53
(0.71)
-4.07
( -2.68)
0.35
(0.42 )
0.4
(0.65)
N/A
-0.1
( -0.06)
8.92
( 6.78)
Viewing
0.47
( 3.66)
-0.05
( -2.77)
0.91
( 2.00)
0.1
(0.17)
1.22
(2.05)
-1.16
(-1.34)
-0.17
( -0.38)
0.8
(1.81)
N/A
-1.98
(-1.6)
8.17
(6.03)
Note: T-statistic for test that coefficient equals 0 is given in parentheses below the coefficient estimates.
N/A indicates that the variable was not included in the estimation for this activity.
Source: U.S. EPA analysis.
Parameter estimates of the inclusive value index (IVBASE) in the swimming, fishing, and viewing models are positive and
differ significantly from zero at the 95th percentile, indicating that water quality improvements have a positive effect on the
number of trips taken during a recreation season.
The estimated coefficient on IVBASE in the boating model, while positive, was not statistically significant. Taking a boating
trip often requires more preparation (e.g., taking a boat to the water body) than taking other trips. Therefore, although water
quality improvements increase the value of a boating day, factors other than water quality are likely to have a stronger impact
on the number of boating trips per season.
The AGE variable is negative and significant for all four recreation activities: younger people are likely to take more
recreation trips. Ethnicity and gender (the AFAM and MALE variables) also have a significant impact on whether an
individual participates in water-based recreation. African Americans living in Ohio are less likely to participate in any of the
four recreation activities than representatives of other ethnic groups. Males are more likely than females to participate in any
of the recreation activities.
Education also influences trip frequency significantly. People who did not complete high school (NOHS=1) tend to take
fewer fishing or swimming trips. Those with a college degree (COLLEGE=1) are more likely to participate in swimming and
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viewing. Respondents who attended college are less likely, however, to participate in fishing and boating than those who
completed only a high school education. For the boating model, the COLLEGE variable is not significantly different from
zero.
The presence of older children (OLDKIDS) in the household is associated with greater participation in swimming, viewing
(near-water recreation), and fishing activities, but is not a significant determinant in decisions to participate in boating.
Younger children in the household (YNGKIDS) tends to lead to greater participation in boating and swimming, but leads to
fewer fishing or viewing trips.
21.5 ESTIMATING BENEFITS FROM REDUCED MP<&M &ISCHARSES IN OHIO
21.5.1 Benefiting Reaches in Ohio
EPA identified reaches where it expects the MP&M rule to eliminate or reduce the number of existing AWQC exceedances
(hereafter, benefiting reaches). The Agency first identified the reaches in which baseline discharges from industrial sources,
including both MP&M and non-MP&M facilities, caused one or more pollutant concentrations to exceed AWQC limits for
aquatic species. A reach is considered to benefit from the MP&M rule if at least one AWQC exceedance is eliminated due to
reduced MP&M discharges. Although the method for identifying benefiting reaches is similar to the method used in the
national analysis (see Chapter 15 for detail), there are three notable differences:
> Unlike the national analysis, the Ohio case study incorporates information on all industrial and municipal point
source discharges and non-point sources to assess in-stream concentrations of toxic and nonconventional pollutants
in the baseline and post-compliance. Appendix H provides information on the data sources and methods used to
assess ambient water quality conditions in Ohio.
*• The water quality model used in this analysis estimates ambient pollutant concentrations in the reaches receiving
discharges from MP&M facilities and reaches below the initial discharge reach. Appendix H provides detail on the
water quality model used in this analysis.
»• The analysis of recreational benefits accounts for changes in TKN concentrations.
EPA's analysis indicates that pollutant concentrations at the baseline discharge levels from all industrial sources (including all
MP&M facilities) exceed acute exposure criteria for aquatic life on 15 reaches, and exceed chronic exposure criteria for
protection of aquatic species on 21 reaches. EPA estimates that reducing pollutant discharges from oily waste facilities
directly discharging to the receiving streams would not eliminate all concentrations in excess of the acute aquatic life
exposure criteria or the chronic exposure criteria on any reach under the final rule; it would reduce the number of acute and
chronic exceedances on one reach.
In addition, baseline pollutant concentrations exceed human health-based AWQC for consumption of water and organisms on
three reaches and exceed AQWC for consumption of organisms only on two reaches. EPA estimates that reducing pollutant
discharges from oily waste facilities directly discharging to the receiving streams would reduce the number of pollutants
exceeding the human health-based AWQC on one reach under the final rule; it would not eliminate all human health-based
AQWC exceedances on any reach in Ohio. Table 21.6 summarizes these results. In addition, the final regulation is
estimated to reduce in-stream concentrations of TKN in the affected reaches. The estimated average reductions are 0.54
percent in lakes and 0.45 percent in rivers and streams.
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Table 21.6.: Estimated MP&M Discharge Reaches with MP&M Pollutant Concentrations in Excess of AWQC
Limits for the Oily Wastes Subcategory for Protection of Aquatic Species or Human Health
Regulatory Status
Baseline
Final Regulation
Number of Reaches with
Concentrations
Exceeding AWQC
Limits for Human
Health
H20 and
Organisms
•j
T
Org.
Only
2
2
Number of Reaches
with Concentrations
Exceeding AWQC
Limits for Aquatic
Species
Acute
15
15
Chronic
21
21
Number of Benefiting Reaches
'
All AWQC
Exceedances
Eliminated
0
Reaches with Some AWQC
Exceedances Eliminated
1
Source: U.S. EPA analysis.
21.5.2 Estimating Recreational Benefits in Ohio
To estimate peoples' willingness-to-pay for water quality improvements, the Agency first calculated per-person seasonal
welfare gain corresponding to the final regulation. Table 21.7 presents, for each recreation activity, the compensating
variation per trip (the median value over all individuals in the sample) associated with the reduced MP&M discharges.
Because the trip choices and the associated expenditures occurred in 1993, the welfare gain was calculated in 1993 dollars
and then adjusted to 2001 dollars based on the Consumer Price Index (CPI).
The model indicates that the reductions in MP&M discharges from the final regulation result in a modest increase in per-trip
values for three of the four recreation activities (fishing, viewing, and swimming). There is no welfare gain to boaters from
improved water quality under the post-compliance scenario.14 Table 21.7 provides the mean estimates of welfare gain per
recreational user in Ohio.
Table 21.7: Welfare Sain per Recreational User in Ohio (2001$)
Fishing
Boating
Viewing
Swimming
Per Trip Welfare
Gain
$0.02
$0.00
$0.01
$0.01
Average Number of Trips
per Person per Year
13.6
6.22
9.26
8.72
Mean Seasonal
Welfare Gain
$0.17
$0.00
$0.11
$0.01
Source: U.S. EPA analysis.
Table 21.7 also reports seasonal compensating variation per individual. The reported seasonal welfare gain includes both the
increase in the utility from better water quality at the available recreation sites receiving MP&M discharges and the increase
in utility from greater recreational trip participation.
As noted above, the Ohio case study evaluated changes in the water resource values from both reduced discharges of TKN
and reduced frequency of AWQC exceedances. Changes in TKN concentration in the Ohio water bodies resulting from
reduced MP&M discharges from the Oily Wastes subcategory account for approximately 96 percent of the monetary value of
benefits resulting from the final rule.
14 The choice set of recreational sites available to boaters was restricted to the sites where motorboating and sailing is permitted
because the majority of Ohio boaters included in this analysis used either motor or sail boats. Water quality improvements at the sites
where boating is not allowed does not result in welfare gain to boaters.
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Both the per-trip and seasonal welfare estimates are much lower than values reported in the existing studies of water-based
recreation. This is not surprising, since the water quality changes expected from the final rule are very modest.
To calculate state-level recreational benefits from the final rule, EPA first calculated seasonal welfare gain from water quality
improvements per individual in the sample. The Agency then multiplied the average welfare gain per individual by the
corresponding number of participants in a given activity (see Section 21.1.5 above for detail). The resulting product is the
annual benefit from the final MP&M rule to consumers of a given water-based recreation activity in Ohio. Table 21.8
summarizes state-level results.
Table 21.8: Estimated Recreational and Nonuse Benefits from Reduced MP&M Discharges
from the Oily Wastes Subcategory in Ohio
Activity
Fishing
Boating
Viewing
Swimming
Total Recreational Use Benefit
Nonuse Benefits
Total Recreational Benefits (Use
+ Nonuse)
Percentage of Ohio Residents
Participating in Single-Day
Trips (fromNDS)
10.2%
7.7%
9.1%
9.1%
Number of
Participants Aged
16 and older8
892,283
676,026
798,220
798,220
Total Annual
Welfare Gain to
Recreational
Users in Ohio
$153,102
$0
$88,047
$9,783
$250,933
$125,466
$375,859
a EPA estimated the number of participants in each recreation activity by multiplying the percent of NDS
survey respondents from Ohio participating in a single day trip for each activity by the total adult population
aged 16 an older (8,790,969). This analysis uses the 2000 Census data to estimate current population in Ohio.
Source: U.S. EPA analysis.
Under the final regulation, the extrapolation from the sample to the adult population in Ohio yields mean annual benefits
estimates of $153,102, $9,783, $88,047, and $0 (2001$) for fishing, swimming, viewing, and boating, respectively. The total
mean recreational use benefit is $250,932 (2001$). The Agency used the same approach as in the national analysis to
estimate nonuse benefits (see Section 15.2.3, Nonuse Benefits, for detail). EPA estimated nonuse benefits as one-half of
recreational use benefits for low, mid, and high estimates, respectively. The estimated mean nonuse benefit is $125,466
(2001$).
21.6 LIMITATIONS AND UNCERTAINTY
21.6.1 One-State Approach
Some benefits are likely to be missed by a state-level case study. For example, residents from neighboring states undoubtedly
recreate in Ohio waters, and residents of Ohio undoubtedly recreate in neighboring states. A state-by-state approach that
restricts its analysis to recreation activities within the state misses these categories of benefits.15 This omission is likely to be
more significant for unique locations of high quality (e.g., Lake Erie), where participants travel significant distances, and for
sites very close to state boundaries.
15 Note that EPA used a few observation on visitors from neighboring states to estimate site choice models. The analysis does not
include these observations in calculating state-level benefits from water quality improvements.
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21.6.2 Including One-bay Trips Only
Use of day-trips only tends to understate recreational benefits for swimming, fishing, boating, and viewing, since recreation as
part of multi-day trips is excluded. Inclusion of multi-day trips, however, can be problematic. Multi-day trips are frequently
multi-activity trips. An individual might travel a substantial distance, participate in several recreation activities and go
shopping and sightseeing, all as part of one trip. Recreational benefits from improved recreational opportunities for the
primary activity are overstated if all travel costs are treated as though they are associated with the one recreational activity of
interest. The total benefits per trip from water quality improvements are not overstated, however, if individuals participated
only in several water-based activities.
21.6.3 Nonuse Benefits
Estimating nonuse benefits using the 50 percent rule is less precise than using a more sophisticated benefits transfer approach.
However, limiting the benefits of water quality improvements only to recreational benefits would significantly underestimate
the benefits of the rule. The effects of using the simpler approach, e.g. either overestimation or underestimation of benefits, is
unknown. Other benefits include aesthetic benefits for residents living near water bodies, habitat values for a variety of
species (in addition to recreational fish), and nonuse values. To correct for this limitation of using only a travel cost model,
EPA quantified nonuse values in proportion to recreation values. This approach provides only a rough approximation of the
value of water resources to nonusers. For example, some natural resources have high use values but small or negligible
nonuse values (e.g., cows), while other species have very high nonuse values but small or negligible use values (e.g., blue
whales).
21.6 A Potential Sources of Survey Bias
The survey results could suffer from bias, such as recall bias (e.g., Westat, 1989), nonresponse bias, and sampling effects.
a. Recall bias
Recall bias can occur when respondents are asked the number of days in which they recreated over the previous season, such
as in the NDS survey. Some researchers believe that recall bias tends to lead to an overstatement of the number of recreation
days, particularly for more avid participants. Avid participants tend to overstate the number of recreation days, since they
count days in a "typical" week and then multiply them by the number of weeks in the recreation season.16 They often neglect
to consider days missed due to bad weather, illness, travel, or when fulfilling "atypical" obligations. Some studies also found
that the more salient the activity, the more "optimistic" the respondent tends to be in estimating number of recreation days.
Individuals also have a tendency to overstate the number of days they participate in activities that they enjoy and value.
Taken together, these sources of recall bias may result in an overstatement of the actual number of recreation days.
b. Nonresponse bias
A problem with sampling bias may arise when extrapolating sample means to population means. This could happen, for
example, when avid recreation participants are more likely to respond to a survey than those who are not interested in the
forms of recreation, are unable to participate, assume that the survey is not meant for them, or consider the survey not worth
their time.
c. Sampling effects
Recreational demand studies frequently face two types of observations that do not fit general recreation patterns:
non-participants and avid participants:
Non-participants are those individuals who would not participate in the recreation activity under any conditions. This analysis
assumes that an individual is a non-participant in a particular activity if he or she did not participate in that activity at any site.
This assumption tends to understate benefits, since some individuals may not have participated during the sampling period
simply by chance, or because price/quality conditions were unfavorable during the sampling period.
Avid participants can also be problematic because they claim to participate in an activity an inordinate number of times. This
16 Westat (1989) uses ten or more activity-days per year as an indicator of an "avid" user.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
reported level of activity is sometimes correct, but often overstated, perhaps due to recall bias (see Westat, 1989). Even
where the reports are correct, these observations tend to be overly influential. EPA dropped observations of participants who
reported more than 100 trips per year when estimating trip participation models, to correct for potential bias caused by these
observations.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
GLOSSARY
ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its
designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,
swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html)
compensating variation (CV): the amount of money a person would need to pay or receive in orderto leave that person
as well off as they were before a change.
consumer choice set: the set of alternatives (e.g., alternative recreation sites) from which a consumer may choose.
exogenous: external to the inner workings of a system or model; variables are exogenous to the extent that they are "given"
and not the result of the operation of the system or anything going on in the model itself.
expected maximum utility: see "inclusive value."
fish biomass (Biomass): measure of biological factors in the water body represented by the total fish weight in
kilograms per 300 meters.
fish consumption advisories (FCAs): an official notification to the public about specific areas where fish tissue
samples have been found to be contaminated by toxic chemicals which exceed FDA action limits or other accepted guidelines.
Advisories may be species specific or community wide.
inclusive value: the value to the consumer of being able to choose among X alternatives (e.g., among a number of
recreational sites) on a given trip occasion.
indirect utility function: gives the maximum value of utility for any given prices and money income. The indirect utility
function is obtained when the quantity of goods that maximizes consumer utility subject to a budget constraint are substituted
into a utility function.
inferential analyses: based on interpretation.
multinomial logit (MNL): a utility maximization model. In this model, an individual is assumed to have preferences
defined over a set of alternatives (e.g., recreation sites). The choice model takes the form of comparing utilities from different
alternatives and choosing the one that produces the maximum utility. In this framework, observed data consist of attributes of
the choices (e.g., available recreational amenities at different sites) and the choice actually made. Usually no characteristics of
the individuals are observed beyond their actual choice.
National Demand Survey for Water-Based Recreation (NDS): a U.S. EPA survey of recreational behavior. The
1994 survey collected data on socioeconomic characteristics and water-based recreation behavior using a nationwide stratified
random sample of 13,059 individuals aged 16 and over, (http://www.epa.gov/opei)
negative binomial regression model: an extension of the Poisson regression model that allows the variance of the
process to differ from the mean (see also Poisson distribution and Poisson estimation process).
Negative Binomial Poisson model: (see negative binomial regression model).
nested multinomial logit model (NMNL): an extension of MNL (see above). In this model, an individual is assumed to
choose among different groups of alternatives first (i.e., Great Lakes or inland recreation sites) and then to choose specific
alternatives (e.g., a particular river reach, lake, or Great Lakes site) in the choice set for each group.
nonconventional pollutants: a catch-all category that includes all pollutants that are not classified as priority pollutants
or conventional pollutants.
Ohio Water Resource Inventory (OWRI): a biennial report to U.S. EPA and Congress required by Section 305(b) of the
Clean Water Act. The report is composed of four major sections: (1) inland rivers and streams, wetlands, Lake Erie, and water
program description; (2) fish tissue contaminants; (3) inland lakes, ponds, and reservoirs; and (4) groundwater.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
overdispersion: condition for a distribution where the variance exceeds the mean. It usually signifies a nonrandom
dispersion, for example the case where a small minority of the population is responsible for the majority of recreational trips
taken.
Poisson distribution: a random variable X is defined to have a Poisson distribution if the probability density of X is given
by fx(X)= f^ (X;A) = e"1 Ax / x! for x = 0,1,2.., and 0 otherwise. In this model, A is both the mean and variance of X.
Poisson estimation process: used to model discrete random variables. Typically, a Poisson random variable is a count
of the number of events that occur in a certain time interval or spatial area, for example, the number of recreational trips taken
during a recreational season.
priority pollutants: 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
sediment, groundwater, or soil samples.
random utility model (RUM): a model of consumer behavior. The model contains observable determinants of consumer
behavior and a random element.
Reach File 1 (RF1): a database of approximately 700,000 miles of streams and open waters in the conterminous United
States. The database contains information on stream flow, time travel velocity, reach length, width, depth, and other stream
attributes.
site choice model: used to determine which recreational site is chosen by the consumer. EPA estimated the likelihood that
the consumer will choose a particular site as a function of site characteristics, the price paid per site visit, and household
income.
Total Kjeldahl Nitrogen (TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as
organic nitrogen, except that the ammonia is not driven off before the digestion step.
travel cost model (TCM): method to determine the value of an event by evaluating expenditures by participants. Travel
costs are used as a proxy for price in deriving demand curves for recreation sites.
(http://www.damagevaluation.com/glossary.htm)
total seasonal welfare: see "welfare effect."
trip participation model: used to estimate the number of water-based recreational trips taken during the recreation season.
EPA estimated the total number of trips during the recreation season as a function of the expected maximum utility (inclusive
value) from recreational activity participation on a trip and socioeconomic characteristics affecting demand for recreation
trips (e.g., number of children in the household).
utility-theoretic: consistent with the behavioral postulate that individuals act to maximize their welfare (utility) that
underlines the structure of models of consumer behavior.
welfare effect: gain or loss of welfare to the group of individuals (e.g., fishermen) as a whole.
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
ACRONYMS
AWQC: ambient water quality criteria
CV: compensating variation
FCAs: fish consumption advisories
IWB2: index of well-being
LIMDEP: Limited Dependent Variable
MNL: multinomial logit
NDS: National Demand Survey for Water-Based Recreation
NMNL: nested multinomial logit model
ODNR: Ohio Department of Natural Resources
OWRI: Ohio Water Resource Inventory
RUM: random utility model
RF1: Reach File 1
TKN: Total Kjeldahl Nitrogen
TCM: travel cost model
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MP&M EEBA Part V: Ohio Case Study Chapter 21: Modeling Recreational Benefits in Ohio with a RUM Model
REFERENCES
Bockstael, N. E., I. E. Strand, and W. M. Hanemann. 1987. Time and the Recreation Demand Model. American Journal of
Agricultural Economics 69: 213-32.
Creel, M. and J. Loomis. 1992. "Recreation Value of Water to Wetlands in the San Joaquin Valley: Linked Multinomial Logit
and Count Data Trip Frequency Models." Water Resource Research. Vol 28. No 10, October: 2597-2606.
Feather, Peter M., Daniel Hellerstein, and Theodore Tomasi. 1995. "A Discrete-Count Model of Recreation Demand."
Journal of Environmental Economics and Management, 29:214-227.
Green, W. H. 1993. Econometric Analysis. New York, NY: Macmillan Publishing Company.
Hausman, J., G. Leonard, and D. McFadden. 1995. "A Utility-Consistent, Combined Discrete Choice and Count Data
Model: Assessing Recreational Use Losses Due to Natural Resource Damage." /. of Public Economics No 56 pp. 1-30.
Jones, C. A. and Y. D. Sung. 1993. Valuation of Environmental Quality at Michigan Recreational Fishing Sites:
Methodological Issues and Policy Application. Final Report. EPA Contract No. CR-816247-01-2. September.
Kling, C. L. and C. J. Thomson. 1996. "The Implication of Model Specification for Welfare Estimation in Nested Logit
Models." American Journal of Agricultural Economics Association, No. 78, February: 103-114.
McFadden, D. 1981. "Econometric Models of Probabilistic Choice." In: C. F. Manski and D. L. McFadden, eds., Structural
Analysis of Discrete Data. Cambridge, MA: MIT Press.
Ohio Atlas and Gazetteer, The. 1995. Freeport, ME: Delorme.
Ohio Department of Natural Resources, Division of Wildlife. Creel Survey Summaries from 1992 to 1997.
Ohio Department of Natural Resources, Division of Wildlife. 1999. Fish Consumption Advisories.
Ohio Environmental Protection Agency. 1996. Ohio Waste Resource Inventory Volume 1: Summary Status, and Trends; and
Volume 3: Ohio Public Lakes, Ponds, and Reservoirs, www.Chagrin.EPA.State.OH.US/document_index.
Parsons, G. and M. J. Kealy. 1992. "Randomly Drawn Opportunity Sets in a Random Utility Model of Lake Recreation."
Land Economics 68 No. 4: 418-33.
Parsons, G. R. 1999. Comments on Assessing the Recreational Benefits of the MP&M Regulation: A State-level Case Study
Based on the Random Utility Model Approach. Memo to Abt Associates Inc., August.
U.S. Department of Commerce, Bureau of the Census. 2000. http://www.state.oh.us/odhs/octf/stats/gjcs/ohio.pdf.
U.S. Environmental Protection Agency (U.S. EPA). 1994. National Demand for Water-Based Recreation Survey.
Washington, D.C.: Office of Policy Evaluation and Information.
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MP&M EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
Chapter 22: MPAM Benefit-Cost
Analysis in Ohio
INTRODUCTION
This chapter presents estimated benefits and costs of the final
MP&M regulation in Ohio. The preceding chapter
summarized the methodology and results of the case study of
the expected recreational benefits from water quality
improvements in Ohio. This chapter first presents estimates
of the remaining three benefit categories, including:
•• reduced human health risk from expo sure to
carcinogens and systemic health toxicants,
> changes in health risk from exposure to lead for
adults and children, and
•• publicly-owned treatment works (POTW)
benefits.1
CHAPTER CONTENTS
22.1 Benefits of the Final Regulation 22-1
22.1.1 Human Health Benefits (Other than Lead) . 22-2
22.1.2 Lead-Related Benefits 22-3
22.1.3 Economic Productivity Benefits 22-4
22.1.4 Total Monetized Benefits 22-4
22.2 Social Costs of the Final Regulation 22-5
22.2.1 Baseline and Post-Compliance Closures .. 22-5
22.2.2 Compliance Costs for MP&M Facilities .. 22-6
22.2.3 Total Social Costs 22-7
22.3 Comparison of Monetized Benefits and Costs
in Ohio 22-7
Glossary 22-8
Acronyms 22-9
The chapter then presents the social costs of the final regulation for the state of Ohio and compares the aggregate benefits and
social costs estimates. From this analysis, EPA estimates that the final regulation will have net monetizable benefits in Ohio
of $868 thousand annually (2001$).
EPA estimated MP&M costs and benefits in Ohio using methodologies similar to those used for the national-level analysis but
with greater detail and coverage of information. In addition to the RUM study of recreational benefits discussed in the
previous chapter, other analytical improvements included the following:
*• the use of more detailed data on MP&M facilities. EPA oversampled the state of Ohio with 1,600 screeners to
obtain information on co-occurrence of MP&M discharges;
•• the use of data on non-MP&M discharges to estimate current baseline conditions in the state; and
•• the use of a first-order decay model to estimate in-stream concentrations in the Ohio water bodies. This model
allows the assessment of the environmental effects of MP&M discharges on the reaches receiving MP&M discharges
and downstream reaches.
Appendix H describes the water quality model used in this analysis and the approach and data sources used to estimate total
pollutant loadings from all industrial and municipal sources to Ohio's water bodies. The Agency believes that the added level
of detail results in more robust benefit-cost estimates.
22.1 BENEFITS OF THE FINAL REGULATION
EPA estimates that MP&M facilities in all subcategories in Ohio discharge approximately 127.6 million pounds of pollutants
per year to POTWs, and approximately 83.6 million pounds of pollutants directly to surface water. EPA estimates that the
final regulation will reduce direct discharges by approximately 0.5 million pounds of pollutants annually.
The final rule regulates only direct dischargers. Therefore, the selected option does not affect POTW operations.
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MPAM EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
22.1.1 Human Health Benefits (Other than Lead)
EPA estimates total monetized human health benefits from the final regulation of $14,504 (2001$). Chapter 13 details the
methodologies used to estimate human health benefits from reduced exposure to carcinogens and systemic health toxicants
other than lead.
a. Reduced incidence of cancer cases from consumption of contaminated fish and drinking
water
Table 22.1 shows the number of cancer cases avoided by the final regulation for both the drinking water and fish consumption
pathways. EPA estimates that improved water quality resulting from the final regulation will reduce the incidence of cancer
cases via the drinking water and fish consumption pathways from 0.11 cases in the baseline to 0.10 cases under the final
regulation, with a total annual value of $14,504. Essentially all of the cancer avoidance occurs via the fish consumption
pathway, which yields annual cancer avoidance benefits of $14,503. Monetized cancer avoidance benefits from reduced
drinking water contamination are negligible.
Table 22.1: Estimated Annual Benefits from Avoided
Cancer Cases from Fish and Drinking Water Consumption
| Cancer Cases j Benefits (2001$)
Baseline*
Drinking Water
Fish Consumption
Total
Drinking Water
Fish Consumption
Total
0.1026421
0.00331
0.11
Final Regulation
0.1026420
0.00108
0.10
negligible*
$14,503
$14,504
a The baseline includes baseline loadings from dischargers in all
subcategories.
b Monetized cancer avoidance benefits from reduced drinking
water contamination are approximately $1.
Source: U.S. EPA analysis.
b. Systemic health effects
EPA's analysis of in-waterway pollutant concentrations indicates that baseline hazard ratios, for both the fish consumption
and drinking water pathways, for the population associated with sample facilities only, are less than one on all reaches but
one. For those reaches with a baseline hazard ratio of less than one, EPA's analysis finds shifts in populations from higher
(but less than 1.0) to lower hazard ratio value between the baseline and post-compliance cases. For the single reach with a
baseline hazard ratio greater than one, the hazard ratio declined but did not fall below one.
c. Reduced frequency of human health-based AWQC exceedances in Ohio's water bodies
Baseline in-waterway concentrations of MP&M pollutants exceed human health-based ambient water quality criteria
(A WQ C) limits for consumption of water or organisms in three reaches. Two reaches exceeded human health-based AWQC
for consumption of organisms only. EPA estimates that the final regulation will not eliminate these exceedences of human
health AWQC on any reach but will reduce the number of exceedences on one reach.
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MPAM EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
22.1.2 Lead-Related Benefits
Total monetized lead-related benefits in Ohio for children and adults under the final regulation are $422,113 (2001$).
Chapter 14 of this report describes the methodology used to estimate these benefits.
a. Estimated benefits to Ohio's children
Table 22.2 presents lead-related benefits from the final regulation for preschool age children and pregnant women in Ohio.
EPA estimates that the final regulation will reduce neonatal mortality by 0.024 cases annually, with an annual monetary value
of $162,094 (2001$).
EPA estimates that the final regulation will avoid the loss of an estimated 26.96 IQ points among preschool children in Ohio,
with an annual value of $253,934 (2001$). The annual avoided costs of compensatory education from reduced incidence of
children with IQ below 70 and blood lead levels above 20 Hg/dL amount to approximately $6,085. In total, the final
regulation results in lead-related benefits for Ohio children of $422,113 annually (2001$).
Table 22.2: Ohio Child Lead Annual Benefits (2001$): Final
Regulation
Category
Neonatal mortality
Avoided IQ loss
Reduced IQ < 70
Reduced PbB > 20 ng/dL
Total Benefits
Reduced Cases
or IQ Points
0.024
26.96
0.09
0.04
Monetary Value of
Benefits
$162,094
$253,934
$5,345
$740
$422,113
Source: U.S. EPA analysis.
b. Adult benefits
Table 22.3 presents benefit estimates for reduced lead-related health effects in adults. These health effects include increased
incidence of hypertension, initial non-fatal coronary heart disease (CHD), non-fatal stokes (cerebrovascular
accidents [CBAJ and brain infarction [BID, and premature mortality. The final regulation would reduce hypertension in
Ohio by an estimated 9.4 cases annually among males, with annual benefits of approximately $10,670 (2001$). Reducing the
incidence of initial CHD, strokes, and premature mortality among adult males and females in Ohio would result in estimated
benefits of $963, $2,115, and $103,645, respectively. Overall, adult lead-related benefits total $117,393. This analysis does
not include other lead-related health effects from elevated blood pressure (BP) or from effects such as nervous system
disorders, anemia, and possible cancer effects.
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MP&M EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
Table 22.3: Ohio Adult Lead Benefits (2001$):
Final Regulation
Category
Men
Women
Hypertension
CHD
CBA
BI
Mortality
CHD
CBA
BI
Mortality
Total Benefits
Final Regulation
Reduced Cases
8.697
0.011
0.005
0.003
0.015
0.003
0.002
0.001
0.004
Monetary Value of Benefits
$10,670
$693
$947
$535
$79,178
$270
$392
$241
$24,467
$117,393
Source: U.S. EPA analysis.
22.1.3 Economic Productivity Benefits
The selected option does not affect POTW operations because the final rule regulates only direct dischargers. For the
alternative policy options that consider both direct and indirect dischargers, EPA evaluated two categories of productivity
benefits for POTWs:
>• reduced interference with the operations of POTWs, and
•• reduced contamination of sewage sludge (i.e., biosolids) at POTWs that receive discharges from MP&M facilities.
Chapter 16 presents the methodology for evaluating POTW benefits. EPA's analysis found that the alternative policy options
did not yield POTW productivity benefits in Ohio.
22.1.4 Total Monetized Benefits
Summing the monetary values over all benefit categories (Chapters 21 and 22) yields total monetized benefits in Ohio of
$930,408 (2001 $) annually for the final regulation (see Table 22.4). As noted in Chapter 12, this benefit estimate is
necessarily incomplete because it omits some mechanisms by which society is likely to benefit from reduced effluent
discharges from the MP&M industry. Examples of benefit categories excluded from this estimate include: non-lead-related,
non-cancer health benefits; improved aesthetic value of waters near discharge outfalls; benefits from improved habitat for
wildlife, including threatened or endangered species; tourism benefits; and reduced costs for drinking water treatment.
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MPAM EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
Table 22.4: Estimated Annual Benefits in Ohio from Reduced MP&M Discharges under the Final
Regulation 2001$)
Benefit Category
1 . Reduced Cancer Risk:
Fish Consumption
Water Consumption"
2. Reduced Risk from Exposure to Lead:
Children
Adults
3. Enhanced Water-Based Recreation
4. Nonuse benefits
5. Avoided Sewage Sludge Disposal Costs
Total Monetized Benefits
Low
$14,503
n/a
$422,113
$117,393
$250,932
$125,466
$0
$930,408
Mid
$14,503
n/a
$422,113
$117,393
$250,932
$125,466
$0
$930,408
High
$14,503
n/a
$422,113
$117,393
$250,932
$125,466
$0
$930,408
a The monetized cancer avoidance benefits from reduced drinking water contamination are negligible.
Source: U.S. EPA analysis.
22.2 SOCIAL COSTS OF THE FINAL REGULATION
22.2.1 Baseline and Post-Compliance Closures
The methodology used to assess baseline and post-compliance closures differed from the methodology used for the national
analysis presented in Chapter 5. The screener data collected for Ohio facilities did not provide financial data to perform an
after-tax cash flow or net present value test. EPA therefore used data from the national analysis to estimate the percentage of
facilities that close in the baseline and post-compliance. EPA assumed that the frequency of Ohio facility closures would be
the same as that found in the national analysis for facilities with the same discharge status, subcategory, and flow category.
For example, 2 percent of Oily Wastes facilities discharging less than one million gallons per year close in the baseline in the
national analysis, and this same percentage is assumed for Ohio screener direct dischargers in that flow size category.
Table 22.5 summarizes the numbers of facilities in Ohio closing or excluded from the final regulation by discharge status. All
indirect dischargers operating post-regulation are excluded from requirements by subcategory exclusions. Of the 198 direct
dischargers operating post-regulation, 85 (or 43 percent) are excluded from requirements by subcategory exclusions. A total
of 113 direct discharging facilities in the Oily Wastes subcategory are therefore subject to requirements under the final
regulation.
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MP&M EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
Table 22.5: Regulatory Impacts for Ohio MP&M Facilities by Discharge Type
Number of MP&M facilities operating in the baseline
Number of MP&M facilities with subcategory exclusions
Number of MP&M facilities operating in the baseline estimated subject to
regulatory requirements
Number of regulatory closures
Percent of MP&M facilities operating in the baseline and subject to
regulatory requirements that are regulatory closures
Indirect
1,682
1,682
0
0
0.0%
Direct
198
85
113
0
0.0%
Total
1,880
1,767
113
0
0.0%
Source: U.S. EPA analysis.
22.2.2 Compliance Costs for MP<&M Facilities
The calculation of annualized compliance costs in Ohio uses the methodology presented in Chapter 11. These compliance
costs are not adjusted for the effect of taxes or for recovery of costs through price increases, and therefore represent the social
value of resources used for compliance. EPA annualized compliance costs using a social discount rate of seven percent over
an estimated 1 5-year useful life of compliance equipment.
In calculating compliance costs for Ohio facilities, EPA combined the compliance cost estimates developed for the "detailed
questionnaire" Ohio facilities included the national analysis with compliance costs estimated for the additional "screener
questionnaire" facilities included in the Ohio analysis. The Agency estimated compliance costs for each Ohio screener
facility and then calculated an annualized compliance cost by subcategory, flow range, and discharge status for the Ohio
facilities. These costs included facilities that might be assessed as baseline closures and thus would overstate expected
compliance costs to the extent that some facilities are expected to close and not incur compliance costs. Because EPA
estimated closures among Ohio screener facilities based on the closure rates from the national analysis, it was not possible to
identify specific Ohio screener facilities as baseline or post-regulation closures and to remove their compliance costs from the
total compliance cost estimates on a facility-specific basis. Instead, EPA reduced the total compliance costs, by facility
category, by the estimated fraction of facilities assessed as baseline closures from the national analysis. EPA added these
costs for the "screener questionnaire" facilities to the estimated compliance costs for the "detailed questionnaire" facilities to
calculate total compliance costs for Ohio MP&M facilities.
Table 22.6 reports the estimated resource value of compliance costs by discharge status and subcategory. The total estimated
annualized compliance costs are $62 thousand.
Table 22.6: Resource Value <
Subcategory
General Metals
MF Job Shop
Non Chromium Anodizer
Oily Wastes
Printed Wiring Boards
Railroad Line Maintenance
Steel Forming & Finishing
Total
>f Compliance C
Indirect
$0
$0
$0
$0
$0
$0
$0
$0
osts in Ohio
Direct
$0
$0
$0
$62,232
$0
$0
$0
$62,232
(2001$)
Total
$0
$0
$0
$62,232
$0
$0
$0
$62,232
Source: U.S. EPA analysis.
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MPAM EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
22.2.3 Total Social Costs
As discussed in Chapter 11, the regulation's social costs include the resource cost of compliance (e.g., labor, equipment,
material, and other economic resources needed to comply with the rule), costs to governments administering the regulation,
and the social costs associated with unemployment resulting from facility closure. EPA estimated that the final rule will not
result in social costs of unemployment and that governments will not incur additional costs in administering the regulation.
Accordingly, as shown in Table 22.7, EPA's estimate of the final rule's social costs in Ohio is the same as that reported for
the resource cost of compliance, $62 thousand (2001 $) annually.
Table 22.7: Annual Social Costs for the
(2001$, costs annualized
Component of Social Costs
Resource value of compliance costs
Government administrative costs
Social cost of unemployment
Total Social Cost
Final Regulation in Ohio
at 7%)
Final Rule
$62,232.0
$0.0
$0.0
$62,232.0
Source: U.S. EPA analysis.
22.3 COMPARISON OF MONETIZED BENEFITS AND COSTS IN OHIO
EPA cannot perform a complete cost-benefit comparison because not all of the benefits resulting from the final rule can be
valued in dollar terms. As reported above, for Ohio, EPA estimated the final rule's social cost at $62 thousand annually
(2001$) and estimated monetizable benefits of $930 thousand annually (2001$). Subtracting the social costs from social
benefits yields a net monetizable benefit of $868 thousand annually (2001$).
In contrast to the national estimates of costs and benefits for the final regulation, the Ohio case study shows substantial net
positive benefits even for the lower-bound benefits estimate. This difference results mainly from the more complete
assessment of benefits from reduced MP&M pollutant discharges and more detailed water quality modeling. In addition to
estimating recreational benefits from reduced frequency of AWQC exceedences, the Ohio case study estimated changes in
water resource values from reduced discharges of TKN. Changes in TKN concentration in Ohio water bodies account for
approximately 96 percent of the monetary value of recreational and nonuse benefits from the final rule. EPA also included an
additional recreational benefit category in the Ohio analysis: swimming. Although the estimated per-trip welfare gain to
swimmers is less than the gain for participants in other water-based recreational activities, this benefit category accounts for a
sizable portion of the state-level benefits. Other factors that affect the Ohio benefit-cost comparison include: the presence of
unique water resources such as Lake Erie; use of a more sophisticated water quality model, which estimates water quality
changes in reaches downstream from the discharge reach; and a more accurate account of baseline water quality conditions.
The presence of unique water resources, such as Lake Erie, and other numerous recreational opportunities (e.g., inland lakes,
rivers, and reservoirs), suggest that the estimated benefits for Ohio are likely to be higher than the average of benefits for
other states.
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MPAM EEBA Part V: Ohio Case Study Chapter 22: MPAM Benefit-Cost Analysis in Ohio
GLOSSARY
ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its
designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,
swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html)
blood pressure (BP): the pressure of the blood on the walls of the arteries.
brain infarction (Bl): stroke.
cerebrovascular accidents (CBA): stroke.
coronary heart disease (CHD): disorder that restricts blood supply to the heart; occurs when coronary arteries become
narrowed or clogged due to the build up of cholesterol and fat on the inside walls and are unable supply enough blood to the
heart.
interference: the obstruction of a routine treatment process of POTWs that is caused by the presence of high levels of
toxics, such as metals and cyanide in wastewater discharges. These toxic pollutants kill bacteria used for microbial
degradation during wastewater treatment (see: microbial degradation).
publicly-owned treatment works (POTW): a treatment works as defined by Section 212 of the Act, which is owned by
a state or municipality. This definition includes any devices or systems used in the storage, treatment, recycling, and
reclamation of municipal sewage or industrial wastes of a liquid nature.
(http://www.epa.gov/owm/permits/pretreat/final99.pdf)
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MPAM EEBA Part V: Ohio Case Study
Chapter 22: MP&M Benefit-Cost Analysis in Ohio
ACRONYMS
AWQC: ambient water quality criteria
Bl: brain infarction
BP: blood pressure
CBA: cerebrovascular accidents
CHD: coronary heart disease
POTW: publicly-owned treatment works
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MP&M EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Appendix A: Detailed Economic
Impact Analysis Information
INTRODUCTION
This appendix provides information to support the economic
analyses of MP&M industries evaluated for the final rule and
presented in Chapter 3 through Chapter 11 of the EEBA.
The first section below provides the SIC and NAICS codes
that define the MP&M industrial sectors. The second section
presents information on the annual turnover of
establishments ("births" and "deaths") in the industrial
sectors. The third section provides a description of the
MP&M surveys that supported the economic impact and
benefits analyses presented in the EEBA (see Section 3 of
the TDD).
A.I MP<&M SIC AND NAICS CODES
CHAPTER CONTENTS
A.I MP&M SIC and NAICS Codes A-l
A.I.I SIC Codes by Sector A-l
A. 1.2 Bridge Between NAICS and SIC codes A-7
A.2 Annual Establishment "Births" and "Deaths" in
MP&M Industries A-26
A.3 Description of MP&M Surveys A-28
A.3.1 Screener Surveys A-28
A.3.2 Ohio Screener Surveys A-28
A.3.3 Detailed MP&M Industry Surveys A-28
A.3.4 Iron and Steel Survey A-29
A.3.5 Municipality Survey A-29
A.3.6 Federal Facility Survey A-29
A.3.7 POTW Survey A-29
References A-31
Standard Industrial Classification (SIC) codes and North
American Industrial Classification System (NAICS) codes are hierarchical systems that allow for detailed classification of
industries using numerical codes. This section lists and describes the SIC codes that make up the MP&M industry sectors. It
also describes the process by which data organized by NAICS code was converted to SIC code format.
A. 1.1 SIC Codes by Sector
Table A.I lists and describes the 4-digit SIC codes that make up the MP&M industry sectors. These codes were used until
recently to define industries for reporting of Federal Census data, and are the framework for the part of the industry profile
(Chapter 3) based on publicly available material.
SIC Code
3761
3764
3769
3721
3724
3728
4581
3713
3715
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
1 Standard Industrial Classification Groups
Aerospace
Guided Missiles and Space Vehicles
Guided Missile and Space Vehicle Propulsion
Other Space Vehicle and Missile Parts
Aircraft
Aircraft
Aircraft Engines and Engine Parts
Aircraft Parts and Auxiliary Equipment
Airports, Flying Fields, Airport Terminal Services
Bus And Truck
Truck and Bus Bodies
Truck Trailers
A-l
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
SIC Code
4111
4119
4131
4141
4142
4173
4212
4213
4214
4215
4231
3661
3663
3669
3671
3675
3677
3678
3679
3699
2796
3398
3412
3421
3423
3425
3429
3433
3441
3443
3444
3446
3448
3449
3451
3452
3462
3466
3469
3492
3493
3494
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
Standard Industrial Classification Groups
Local And Suburban Transit
Local Passenger Transit, N.E.C.
Intercity And Rural Bus Transportation
Local Bus Charter Service
Bus Charter Service, Except Local
Bus Terminal And Service Facilities
Local Trucking without Storage
Trucking, Except Local
Local Trucking with Storage
Courier Services, Except by Air
Trucking Terminal Facilities
Electronic Equipment
Telephone and Telegraph Apparatus
Radio and Television Broadcast and Communications Equipment
Communications Equipment, N.E.C.
Electron Tubes
Electronic Capacitors
Electronic Coils and Transformers
Connectors for Electronic Applications
Electronic Components, N.E.C.
Electrical Machinery, Equipment, And Supplies, N.E.C.
Hardware
Platemaking and Related Services
Metal Heat Treating
Metal Shipping Barrels, Drums, Kegs, Pails
Cutlery
Hand And Edge Tools, Except Machine Tools and Handsaws
Hand Saws and Saw Blades
Hardware, N.E.C.
Heating Equipment, Except Electric and Warm Air Furnace
Fabricated Structural Metal
Fabricated Plate Work (Boiler Shops)
Sheet Metal Work
Architectural and Ornamental Metal Work
Prefabricated Metal Buildings And Components
Miscellaneous Metal Work
Screw Machine Products
Bolts, Nuts, Screws, Rivets, and Washers
Iron and Steel Forgings
Crowns and Closures
Metal Stamping, N.E.C.
Fluid Power Valves and Hose Fittings
Steel Springs
Valves And Pipe Fittings, Except Brass
A-2
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
SIC Code
Standard Industrial Classification Groups
3495 ! Wire Springs
j
3496 I Miscellaneous Fabricated Wire Products
j
3498 ! Fabricated Pipe and Fabricated Pipe Fitting
r
3499 ! Fabricated Metal Products, N.E.C.
j
3541 ! Machine Tools, Metal Cutting Types
j
3542 ! Machine Tools, Metal Forming Types
j
3544 ! Special Dies and Tools, Die Sets, Jigs and Fixtures, and Industrial Molds
j
3545 ! Machine Tool Access and Measuring Devices
j
3546 ! Power Driven Hand Tools
j
3965 ! Fasteners, Buttons, Needles, Pins
Household Equipment
2514 Metal Household Furniture
2522 Office Furniture, Except Wood
2531 Public Building and Related Furniture
2542 Partitions and Fixtures, Except Wood
2591 Drapery Hardware and Window Blinds/shades
2599 Furniture and Fixtures, N.E.C.
3431 Metal Sanitary Ware
3432 Plumbing Fittings and Brass Goods
3442 Metal Doors, Sash, and Trim
3631 Household Cooking Equipment
3632 Household Refrigerators and Home and Farm and Freezers
3633 Household Laundry Equipment
3634 Electric Housewares and Fans
3635 Household Vacuum Cleaners
3639 Household Appliances, N.E.C.
3641 Electric Lamps
3643 Current-carrying Wiring Devices
3644 Noncurrent-carrying Wiring Devices
3645 Residential Electrical Lighting Fixtures
3646 Commercial, Industrial, and Institutional
3648 Lighting Equipment, N.E.C.
3651 Radio/television Sets Except Communication Types
7623 Refrigeration and Air-conditioning Service and Repair Shops
Instruments
3812 I Search, Detection, Navigation, Guidance, Aeronautical, Nautical Systems and Instruments
3821 I Laboratory Apparatus and Furniture
3822 I Automatic Environmental Controls
F
3823 I Process Control Instruments
F
3824 ! Fluid Meters and Counting Devices
3825 ! Instruments to Measure Electricity
3826 ! Laboratory Analytical Instruments
3827 I Optical Instruments and Lenses
3829 I Measuring and Controlling Devices, N.E.C.
A-3
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
SIC Code
3841
3842
3843
3844
3845
3851
7629
3315
3316
3317
3471
3479
3523
3524
3531
3532
3536
3537
3795
3465
3592
3647
3694
3711
3714
3716
3751
3792
3799
4121
5013
5511
5521
5561
5571
5599
7514
7515
7519
7532
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
Standard Industrial Classification Groups
Surgical and Medical Instruments and Apparatus
Orthopedic, Prosthetic and Surgical Suppl.
Dental Equipment and Supplies
X-ray Apparatus and Tubes
Electromedical Equipment
Ophthalmic Goods
Electric Repair Shop
Iron and Steel
Steel Wiredrawing and Steel Nails and Spikes
Cold-Rolled Steel Sheet, Strip, and Bars
Steel Pipe and Tubes
Job Shop
Plating and Polishing
Metal Coating and Allied Services
Mobile Industrial Equipment
Farm Machinery and Equipment
Garden Tractors and Lawn and Garden Equipment
Construction Machinery and Equipment
Mining Machinery and Equipment, Except Oil Field
Hoists, Industrial Cranes and Monorails
Industrial Trucks, Tractors, Trailers
Tanks and Tank Components
Motor Vehicle
Automotive Stampings
Carburetors, Piston Rings, Valves
Vehicular Lighting Equipment
Electrical Equipment for Motor Vehicles
Motor Vehicle and Automobile Bodies
Motor Vehicle Parts and Accessories
Mobile Homes
Motorcycles
Travel Trailers and Campers
Miscellaneous Transportation Equipment
Taxicabs
Motor Vehicle Supplies and New Parts
Motor Vehicle Dealers (New and Used)
Motor Vehicle Dealers (Used Only)
Recreational Vehicle Dealers
Motorcycle Dealers
Automotive Dealers, N.E.C.
Passenger Car Rental
Passenger Car Lease
Utility Trailer and Recreational Vehicle Rental
Top, Body, and Upholstery Repair and Paint Shops
A-4
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
SIC Code
Standard Industrial Classification Groups
7533 I Auto Exhaust Systems
j
7537 I Auto Transmission Repair
j
7538 ! General Automotive Repair
r
7539 ! Auto Repair Shop, N.E.C.
j
7549 ! Auto Services, Except Repair and Carwashes
Office Machine
3571 Electronic Computers
3572 Typewriters
3575 Computer Terminals
3577 Computer Peripheral Equipment, N.E.C.
3578 Calculating, Accounting Machines Except Computers
3579 Office Machines, N.E.C.
7378 Computer Maintenance and Repairs
7379 Computer Related Services, N.E.C.
Ordnance
3482 Small Arms Ammunition
3483 Ammunition, Except for Small Arms
3484 Small Arms
3489 Ordnance and Accessories, N.E.C.
fc
Miscellaneous Metal Products
3497 Metal Foil and Leaf
3861 Photographic Equipment and Supplies
3931 Musical Instruments
3944 Games, Toys, Children's Vehicles
3949 Sporting and Athletic Goods, N.E.C.
3951 Pens and Mechanical Pencils
3953 Marking Devices
3993 Signs and Advertising Displays
3995 Burial Caskets
3999 Manufacturing Industries, N.E.C.
7692 Welding Repair
7699 Repair Shop, Related Service
Precious Metals and Jewelry
3873 Watches, Clocks, and Watchcases
3911 Jewelry, Precious Metal
3914 Silverware, Plated Ware and Stainless
3915 Jewelers' Materials and Lapidary Work
3961 Costume Jewelry
7631 Watch, Clock, Jewelry Repair
Printed Circuit Boards
3672 Printed Circuit Boards
Railroad
3743 ! Railcars, Railway Systems
4011 ! Railroad Transportation
A-5
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
SIC Code
4013
3731
3732
4412
4424
4432
4449
4481
4482
4489
4491
4492
4493
4499
3511
3519
3533
3534
3535
3543
3547
3548
3549
3552
3553
3554
3555
3556
3559
3561
3562
3563
3564
3565
3566
3567
3568
3569
3581
3582
3585
3586
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
Standard Industrial Classification Groups
Railroad Transportation
Ships and Boats
Ship Building and Repairing
Boat Building and Repairing
Deep Sea Foreign Transportation
Deep Sea Domestic Transportation
Freight Transportation Great Lakes
Water Transportation of Freight, N.E.C.
Deep Sea Passenger Transportation
Ferries
Water Passenger Transportation, N.E.C.
Marine Cargo Handling
Towing and Tugboat Service
Marinas
Water Transportation Services, N.E.C.
Stationary Industrial Equipment
Steam, Gas, Hydraulic Turbines, Generating Units
Internal Combustion Engines, N.E.C.
Oil Field Machinery and Equipment
Elevators and Moving Stairways
Conveyors and Conveying Equipment
Industrial Patterns
Rolling Mill Machinery and Equipment
Electric and Gas Welding and Soldering
Metal Working Machinery, N.E.C.
Textile Machinery
Woodworking Machinery
Paper Industries Machinery
Printing Trades Machinery and Equipment
Food Products Machinery
Special Industry Machinery, N.E.C.
Pumps and Pumping Equipment
Ball and Roller Bearings
Air and Gas Compressors
Blowers and Exhaust and Ventilation Fans
Industrial Patterns
Speed Changers, High Speed Drivers and Gears
Industrial Process Furnaces and Ovens
Mechanical Power Transmission Equipment, N.E.C.
General Industrial Machinery, N.E.C.
Automatic Merchandising Machines
Commercial Laundry Equipment
Refrigeration and Air and Heating Equipment
Measuring and Dispensing Pumps
A-6
-------
MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information
SIC Code
3589
3593
3594
3596
3599
3612
3613
3621
3629
7353
7359
Table A.I: MP&M Sectors and SIC Codes Evaluated for the Final Rule"
Standard Industrial Classification Groups
Service Industry Machines, N.E.C.
Fluid Power Cylinders and Actuators
Fluid Power Pumps and Motors
Scales and Balances, Except Laboratory
Machinery, Except Electrical, N.E.C.
Transformers
Switchgear and Switchboard Apparatus
Motors and Generators
Electric Industrial Apparatus, N.E.C.
Heavy Construction Equip Rental, Leasing
Equipment Rental, Leasing, N.E.C.
a EPA evaluated options for these industrial sectors but did not regulate them all under the final rule.
N.E.C. = Not Elsewhere Classified
Source: Executive Office of the President, Office of Management and Budget, Standard Industrial Classification Manual
1987.
A.1.2 Bridge Between NAICS and SIC codes
In 1997, the Census Bureau switched from using SIC codes to using NAICS codes. NAICS codes allow for greater
comparability with the International Standard Industrial Classification System (ISIC), which is developed and maintained by
the United Nations. NAICS codes also better reflect the structure of today's economy, including the growth of the service
sectors and new technologies, than do the decades-old SIC codes. Because EPA chose to create regulatory subgroups for the
MP&M industries based on aggregated four-digit SIC codes, it was necessary for EPA to convert some data based on NAICS
codes into SIC code format.
The SIC-NAICS conversion is not always straightforward because NAICS and SIC codes often don't map on a one-to-one
basis. Specific industries that were grouped together in one SIC code sometimes map to several NAICS codes, and
sometimes several SIC codes were aggregated together in one NAICS code.
To address this conversion problem, EPA created a "bridge" that converts the NAICS classification structure to the SIC
structure using share values computed from Economic Census data. This bridge is based on data from the 1997 Census,
which reported the share of number of establishments and value of output that each SIC code that contributed to each NAICS
code, and vice versa.
The first step in creating the bridge was to obtain a table that listed the value of shipments (VOS) that each NAICS code
contributed to each SIC code. Since the total VOS for each NAICS code was known, EPA computed share values for each
NAICS, which were equal to the percent of total VOS in that NAICS code that was classified in a certain SIC code. The
equation is:
Share of NAICSX going to SICy = (VOS that NAICSX contributed to SICy) / (total VOS for NAICSX) (A-l)
Using these share values, EPA converted data classified by NAICS to SIC format, simply by multiplying VOS for each
NAICS by its share value, for each SIC, and then summing up the totals for each SIC. For example, if NAICS codes 333121,
332456, and 332457 all contributed a portion of their output to SIC 3322, then:
VOS for SIC3322 = (share of NAICS333121 going to SIC3322) * (VOS for NAICS333121)
+ (share of NAICS332456 going to SIC3322) * (VOS for NAICS332456) (A-2)
+ (share of NAICS332457 going to SIC3322) * (VOS for NAICS332457)
A-7
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Occasionally it was not possible to compute share values because the Census Bureau withheld some 1997 VOS data because
of disclosure issues1. In those cases, EPA estimated 1997 VOS based on 1992 Census data and then used those estimates to
compute share values. First, EPA calculated the average VOS per establishment in 1992 for each relevant SIC code:
VOS per establishment for SICy =
(VOS for SICy in 1992) / (number of establishments for SICy in 1992)
(A-3)
EPA then multiplied this average VOS per establishment for a certain SIC by the number of establishments that each NAICS
contributed to that SIC in 1997:
Estimated VOS that NAICS, contributed to SICV in 1997 =
A y
(VOS per establishment for SICy) * (number of establishments NAICSX contributed to SICy in 1997)
(A-4)
EPA used this estimated VOS to compute an estimated share value.
To gain a rough measure of how accurately the NAICS codes could be broken into sectors, EPA calculated, by sector: (1) the
percentage of NAICS codes that matched "one-to-one" with an SIC code, (2) the percentage that did not match one-to-one but
were contained in a single sector, and (3) the percentage that didn't match one to one and were contained in multiple sectors
(Figure A.I, Table A.2).
Figure A.I: Percentage of VOS 1997 to 1999 Attributable to One-to-One NAICS-SIC Match,
Not One-to-One but in the Same Sector, and Not One-to-One but in Different Sectors
1
1
n f.
Q, U. 6
JS 0.4 •
§02-
n -
.
2
1
3
i
4
n
h
567
i
8
n
p.
9 10
Sector
T
jj
11
-
U
12 13
-
t
14 15 16 17 18 19
D one-to-onermtch Bnot one-to-one, same sector D not one-to-one, different sectors
Sectors: 1 Hardware; 2 Aircraft; 3 Electronic Equipment; 4 Stationary Industrial Equipment; 5 Ordnance; 6 Aerospace; 7 Mobile
Industrial Equipment; 8 Instruments; 9 Precious Metals and Jewelry; 10 Ships and Boats; 11 Household Equipment; 12 Railroad; 13
Motor Vehicle; 14 Bus and Truck; 15 Office Machine; 16 Printed Circuit Boards; 17 Job Shop; 18 Miscellaneous Metal Products; 19
Iron and Steel
Source: Department of Commerce, Bureau of the Census, Manufacturing Industry Series; U.S. EPA analysis.
1 The Bureau of the Census does not release any data that could reveal data about a specific firm. In cases when a NAICS or SIC
code is so specific that it includes only a few firms, information about VOS is not released. However, the number of establishments in a
specific industry is not considered private information.
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 2: Percentage of Input One-to-One, Not One -to-One but in the Same Sector,
and Not One-to-One and in Different Sectors
i
:
Sector :
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
VOS I Employment j
VOS 1 Employment j
VOS ! Employment
One-to-One One-to-One Same Sector Same Sector j Different Sectors j Different Sectors
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
62.5% |
100.0% |
46.7% |
63.3% |
100.0% |
100.0% |
91.8% |
30.4% |
10.2% |
100.0% |
67.5% |
0.0% |
85.3% |
39.1% |
73.1% |
100.0% |
99.9% |
83.1% |
98.1% |
62.8% |
100.0% |
47.6% |
62.0% |
100.0% |
100.0% |
91.8% |
29.4% |
8.4% |
100.0% |
66.2% |
0.0% |
84.2% |
40.7% |
73.5% |
100.0% |
99.9% |
82.1%|
97.9% |
64.3% |
100.0% |
47.2% |
68.1% |
100.0% |
100.0% |
88.1% |
30.2% |
8.3% |
100.0% |
60.6% |
0.0% |
69.5% |
42.8% |
59.9% |
100.0% |
99.9% |
76.5% |
95.3% |
64.9% |
100.0% |
47.3% |
68.3% |
100.0% |
100.0% |
88.0% |
29.3% |
8.7% |
100.0% |
60.0% |
0.0% |
68.0% |
43.4% |
58.9% |
100.0% |
99.9% |
76.1% |
95.3% |
YEAR: 1997
18.2% |
0.0% |
47.2% |
3.9% |
0.0% |
0.0% |
5.5% |
14.4% |
0.0% |
0.0% |
6.3% |
0.0% |
1.1% |
0.0% |
26.4% |
0.0% |
0.0% |
12.2% |
0.0% |
YEAR: 1998
17.9% |
0.0% |
46.0% |
3.8% |
0.0% |
0.0% |
5.5% |
15.1% |
0.0% |
0.0% |
6.9% |
0.0% |
1.3% |
0.0% |
26.0% |
0.0% |
0.0% |
12.9% |
0.0% |
16.5% |
0.0% |
43.2% |
4.4% |
0.0% |
0.0% |
7.8% |
14.4% |
0.0% |
0.0% |
4.5% |
0.0% |
3.1% |
0.0% |
38.6% |
0.0% |
0.0% |
17.8% |
0.0% |
16.3% |
0.0% |
42.7% |
4.4% |
0.0% |
0.0% |
8.0% |
14.7% |
0.0% |
0.0% |
4.8% |
0.0% |
3.4% |
0.0% |
39.7% |
19.3% |
0.0% |
6.2% |
32.8% |
0.0% |
0.0% |
2.7% |
55.2% |
89.8% |
0.0% |
26.3% |
100.0% |
13.6% |
60.9% |
0.5% |
0.0% |
0.1% |
4.6% |
1.9% |
19.3% |
0.0% |
6.4% |
34.2% |
0.0% |
0.0% |
2.7% |
55.5% |
91.6% |
0.0% |
26.9% |
100.0% |
14.6% |
59.3% |
0.5% |
19.2%
0.0%
9.7%
27.6%
0.0%
0.0%
4.1%
55.4%
91.7%
0.0%
34.9%
100.0%
27.4%
57.2%
1.5%
0.0%
0.1%
5.7%
4.7%
18.8%
0.0%
10.0%
27.3%
0.0%
0.0%
4.1%
55.9%
91.3%
0.0%
35.2%
100.0%
28.6%
56.6%
1.4%
0.0% | 0.0% | 0.0%
0.0% | 0.1% | 0.1%
18.2% | 4.9% | 5.8%
0.0% | 2.1% | 4.7%
A-9
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 2: Percentage of Input One-to-One, Not One -to-One but in the Same Sector,
and Not One-to-One and in Different Sectors
:
:
Sector '
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
VOS ! Employment j
VOS 1 Employment j
VOS ! Employment
One-to-One One-to-One Same Sector Same Sector j Different Sectors I Different Sectors
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
62.3% |
100.0% |
48.7% |
61.6% |
100.0% |
100.0% |
89.6% |
29.9% |
7.1% |
100.0% |
65.5% |
0.0% |
84.6% |
40.5% |
75.6% |
100.0% |
99.9% |
82.0% |
97.7% |
64.3% |
100.0% |
47.9% |
67.8% |
100.0% |
100.0% |
87.0% |
29.8% |
7.5% |
100.0% |
57.8% |
0.0% |
68.3% |
45.8% |
56.6% |
100.0% |
99.9% |
76.8% |
95.0% |
YEAR: 1999
18.3% |
0.0% |
45.4% |
3.6% |
0.0% |
0.0% |
7.4% |
15.2% |
0.0% |
0.0% |
7.7% |
0.0% |
1.3% |
0.0% |
23.8% |
0.0% |
0.0% |
13.0% |
0.0% |
16.4% |
0.0% |
42.3% |
4.3% |
0.0% |
0.0% |
8.8% |
15.2% |
0.0% |
0.0% |
5.4% |
0.0% |
3.9% |
0.0% |
41.9% |
0.0% |
0.0% |
17.1% |
0.0% |
19.4% |
0.0% |
5.9% |
34.7% |
0.0% |
0.0% |
3.0% |
54.9% |
92.9% |
0.0% |
26.9% |
100.0% |
14.1% |
59.5% |
0.6% |
0.0% |
0.1% |
5.0% |
2.3% |
19.3%
0.0%
9.8%
27.9%
0.0%
0.0%
4.2%
55.1%
92.5%
0.0%
36.8%
100.0%
27.9%
54.2%
1.6%
0.0%
0.1%
6.1%
5.0%
Sectors: 1 Hardware; 2 Aircraft; 3 Electronic Equipment; 4 Stationary Industrial Equipment; 5 Ordnance; 6 Aerospace; 7 Mobile
Industrial Equipment; 8 Instruments; 9 Precious Metals andJewelry; 10 Ships and Boats; 11 Household Equipment; 12 Railroad; 13
Motor Vehicle; 14 Bus and Truck; 15 Office Machine; 16 Printed Circuit Boards; 17 Job Shop; 18 Miscellaneous Metal Products; 19
Iron and Steel
Source: Department of Commerce, Bureau of the Census, Manufacturing Industry Series; U.S. EPA analysis.
Table A.3 presents the data that was used to calculate the relationship between NAICS and SIC codes. The table lists the
MP&M sector to which each SIC code belongs, gives a short description of each SIC, and lists NAICS codes that encompass
similar industries. The table also lists the number of establishments, the value of shipments, and the number of employees
that are contributed to each SIC by each NAICS, as well as the share values, i.e. the portion of its total value of shipments that
a given NAICS code contributes to a given SIC code.
A-10
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC
3761
3764
3769
3721
3724
3728
4581
3713
3715
4111
4119
4131
4141
4142
4173
SIC Industry
Guided Missiles and Space
Vehicles
Guided Missile and Space
Vehicle Propulsion
Other Space Vehicle and
Missile Parts
Aircraft
Aircraft Engines and Engine
Parts
Aircraft Parts and Auxiliary
Equipment
Airports, Flying Fields,
Airport Terminal Services
Truck and Bus Bodies
Truck Trailers
Local And Suburban Transit
Local Passenger Transit,
N.E.C.
I
:
N^ICS | 1997 NAICS Industry
Code •
1
Aerospace
--,.,. I Guided Missile and Space Vehicle
336414 !,, - .
i Manufacturing
1 Guided Missile and Space Vehicle Propulsion
336415 | Unit and Propulsion Unit Parts
i Manufacturing
1 Other Guided Missile and Space Vehicle
336419 I Parts and Auxiliary Equipment
I Manufacturing
h i .„.
Aircraft
336411 1 Aircraft Manufacturing
1 Aircraft Engine and Engine Parts
33o412 ': , r r
i Manufacturing
1 Other Aircraft Parts and Auxiliary Equipment
33o413 ': , r r
i Manufacturing
4881 1 1 | Air Traffic Control
4881 19 | Other Airport Operations
j Other Support Activities for Air
4ooiyU : _ _, .
j Transportation
561720 | Janitorial Services
Bus & Truck
33621 1 ! Motor Vehicle Body Manufacturing
336212 ! Truck Trailer Manufacturing
4851 1 1 ! Mixed Mode Transit Systems
.„-.-,, ! Bus and Other Motor Vehicle Transit
485113-,, ^
I Systems
j All Other Transit and Ground Passenger
4oj>yyy • _ ,
j Transportation
485320 j Limousine Service
485410 j School and Employee Bus Transportation
485991 I Special Needs Transportation
j All Other Transit and Ground Passenger
4oj>yyy • _ ,
j Transportation
4871 10 ! Scenic and Sightseeing Transportation, Land
621910 | Ambulance Services
Intercity And Rural Bus /lon-miy* i. j r> i n T _* *•
_ . 485210 • Interurban and Rural Bus Transportation
Transportation
Local Bus Charter Service 485510 j Charter Bus Industry
Bus Charter Service, Except ..occmim. -* n TJ ^
T , 485510 i Charter Bus Industry
Local
Bus Terminal And Service 422400 ^ ^tner Support Activities for Road
Facilities 1 Transportation
Number of
Establishments
22
28
49
204
369
1,138
114
1,699
2,400
127
715
390
28
542
534
3,234
158
1,789
232
307
3,275
407
482
1,049
26
Sales,
Shipments
or Receipts
14,791,466
3,239,033
898,758
56,273,651
22,617,284
20,073,061
43,450
3,243,149
5,859,631
203,918
8,719,326
5,507,768
51,567
1,152,525
601,988
1,873,924
158,947
1,141,413
67,395
462,186
4,443,174
1,147,432
459,953
1,308,246
15,253
Share
Value
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
99.8%
100.0%
1.0%
96.2%
100.0%
100.0%
100.0%
89.9%
100.0%
3.6%
100.0%
10.1%
82.9%
88.4%
100.0%
26.0%
74.0%
3.9%
A-ll
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC
4212
4213
4214
4215
4231
3661
3663
3669
3671
3675
3677
3678
SIC Industry
Local Trucking without
Storage
Trucking, Except Local
Local Trucking with Storage
Courier Services, Except by
Air
Trucking Terminal Facilities
Telephone and Telegraph
Apparatus
Radio and Television
Broadcast and Comm Eq
Communications Eq, N.E.C.
Electron Tubes
Electronic Capacitors
Electronic Coils and
Transformers
Connectors for Electronic
Applications
1
:
N^ICS | 1997 NAICS Industry
Code •
:
4841 10 ! General Freight Trucking, Local
484210 | Used Household and Office Goods Moving
A oA^n '* Specialized Freight (except Used Goods)
484220 | - . -
: Trucking, Local
562111 | Solid Waste Collection
5621 12 1 Hazardous Waste Collection
5621 19 | Other Waste Collection
* General Freight Trucking, Long-Distance,
4841/1 : _ . . .
| Truckload
.„..,, 1 General Freight Trucking, Long-Distance,
4841/z i T _, _ . . .
| Less Than Truckload
484210 1 Used Household and Office Goods Moving
1 Specialized Freight (except Used Goods)
484/30 i _ . . T _ .
| Trucking, Long-Distance
4841 10 1 General Freight Trucking, Local
484210 1 Used Household and Office Goods Moving
1 Specialized Freight (except Used Goods)
484//0 • . . .
i Trucking, Local
492110 | Couriers
492210 1 Local Messengers and Local Delivery
1 Other Support Activities for Road
455490 • .
i Transportation
Electronic Equipment
334210 1 Telephone Apparatus Manufacturing
-1-1^^1^ i Electronic Coil, Transformer, and Other
334416 -T , T., I
\ Inductor Manufacturing
1 Printed Circuit Assembly (Electronic
334415 E . i i \ TI ,r r
| Assembly) Manufacturing
1 Radio and Television Broadcasting and
334220 1 Wireless Communications Equipment
! Manufacturing
I Other Communications Equipment
1 Manufacturing
33441 1 ! Electron Tube Manufacturing
334414 ! Electronic Capacitor Manufacturing
-1-1^^1^ I Electronic Coil, Transformer, and Other
334416 I ' .
| Inductor Manufacturing
I
334417 I Electronic Connector Manufacturing
1
Number of
Establishments
14,545
3,259
34,935
7,083
414
827
23,111
6,210
3,555
14,439
915
2,286
543
2,362
5,384
14
598
7
20
1,091
497
159
129
426
347
Sales,
Shipments
or Receipts
11,108,345
1,198,983
18,932,851
18,211,495
1,095,553
837,625
51,142,148
25,010,091
9,111,477
20,500,392
1,164,931
2,273,241
782,939
19,289,602
3,519,100
12,989
38,300,044
8,904
1,364,671
37,042,241
4,233,288
3,858,499
2,482,163
1,512,232
5,598,906
Share
Value
90.5%
9.5%
96.0%
100.0%
100.0%
100.0%
100.0%
100.0%
72.4%
100.0%
9.5%
18.1%
4.0%
53.1%
100.0%
3.3%
100.0%
0.6%
5.2%
94.2%
100.0%
100.0%
100.0%
97.9%
100.0%
A-12
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC
3679
3699
2796
3398
3412
3421
3423
3425
SIC Industry
Electronic Components
N.E.C.
Electronic Mach.,
Equipment, & Suppl. N.E.C.
Platemaking and Related
Services
Metal Heat Treating
Metal Shipping Barrels,
Drums, Kegs, Pails
Cutlery
Hand & Edge Tools, Except
Mach. Tools, Saws
Hand Saws and Saw Blades
I
:
N^ICS | 1997 NAICS Industry
Code •
:
! Radio and Television Broadcasting and
334220 I Wireless Communications Equipment
1 Manufacturing
-1-1^^10 '' Printed Circuit Assembly (Electronic
33441s : . . . , , , ,, , .
i Assembly) Manufacturing
334419 1 Other Electronic Component Manufacturing
...... 1 Other Motor Vehicle Electrical and
! Electronic Equipment Manufacturing
332212 1 Hand and Edge Tool Manufacturing
1 Printing Machinery and Equipment
333293 5 , r .
\ Manufacturing
333314 1 Optical Instrument and Lens Manufacturing
. . . . . n 1 Other Commercial and Service Industry
333319 i, , , . ., .
| Machinery Manufacturing
...... \ Machine Tool (Metal Cutting Types)
3335 12 5 , T .
\ Manufacturing
333618 1 Other Engine Equipment Manufacturing
1 Welding and Soldering Equipment
1 Manufacturing
1 Electromedical and Electrotherapeutic
3345 1U E . , r r
| Apparatus Manufacturing
1 Search, Detection, Navigation, Guidance,
33451 1 1 Aeronautical, and Nautical System and
I Instrument Manufacturing
1 1 /i ci £ i Analytical Laboratory Instrument
3345 16 E - r r
i Manufacturing
I Other Measuring and Controlling Device
3345 19 E A , ,* ^ .
| Manufacturing
335129 I Other Lighting Equipment Manufacturing
j All Other Miscellaneous Electrical
1 Equipment and Component Manufacturing
Hardware
:
323122 I Prepress Services
I
33281 1 | Metal Heat Treating
:
332439 1 Other Metal Container Manufacturing
: °
:
I Cutlery and Flatware (except Precious)
33221 1 5 A , r- ^_ •
j Manufacturing
:
332212 j Hand and Edge Tool Manufacturing
1
332213 j Saw Blade and Handsaw Manufecturing
Number of
Establishments
126
695
1,851
253
4
5
5
57
8
2
6
11
7
10
5
4
567
1,276
808
151
164
1,069
176
Sales,
Shipments
or Receipts
2,265,873
24,704,154
10,547,090
1,420,996
140,811
0
7,320
934,728
151,363
0
11,101
52,855
77,832
36,473
6,174
859
4,051,267
2,663,020
3,485,459
1,310,595
2,198,365
5,677,903
1,452,540
Share
Value
5.8%
94.8%
100.0%
8.4%
2.1%
0.9%b
0.2%
10.0%
2.8%
0.7%b
0.2%
0.5%
0.2%
0.5%
0.1%
0.0%
58.8%
53.2%
100.0%
57.8%
99.6%
86.0%
100.0%
A-13
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
I
3429 I Hardware N.E.C.
j Heatg. Equip. Except Elec. &
j Warm Air Frnc.
3441 ! Fabricated Structural Metal
j
| Fabricated Plate Work
! (Boiler Shops)
3444 j Sheet Metal Work
I Architectural and Ornamental
| Metal Work
j Prefabricated Metal
j Buildings & Components
I
3449 ! Miscellaneous Metal Work
j
345 1 j Screw Machine Products
j Bolts, Nuts, Screws, Rivets,
j and Washers
3462 j Iron and Steel Forgings
3466 j Crowns and Closures
3469 j Metal Stamping N.E.C.
j Fluid Power Valves and Hose
j Fittings
3493 | Steel Springs
3494 I Valves & piPe Fittings,
! Except Brass
3495 j Wire Springs
I
:
N^ICS | 1997 NAICS Industry
Code •
i
332439 ! Other Metal Container Manufacturing
332510 1 Hardware Manufacturing
,,-m Q i Other Metal Valve and Pipe Fitting
I Manufacturing
1 Heating Equipment (except Warm Air
! Furnaces) Manufacturing
332312 1 Fabricated Structural Metal Manufacturing
3323 1 3 | Plate Work Manufacturing
T T-I * 1 n * Power Boiler and Heat Exchanger
332410 5 , r ~
| Manufacturing
332420 I Metal Tank (Heavy Gauge) Manufacturing
I Air-Conditioning and Warm Air Heating
333415 1 Equipment and Commercial and Industrial
I Refrigeration Equipment Manufacturing
332322 | Sheet Metal Work Manufacturing
332439 1 Other Metal Container Manufacturing
,,,,,, ! Ornamental and Architectural Metal Work
332323 •
1 Manufacturing
j Prefabricated Metal Building and Component
332.3 11:-.- r- ^_ •
j Manufacturing
3321 14 | Custom Roll Forming
332312 j Fabricated Structural Metal Manufacturing
332321 j Metal Window and Door Manufacturing
-.I/-*™'-, i Ornamental and Architectural Metal Work
332.32.3 •
I Manufacturing
332721 I Precision Turned Product Manufacturing
--,„,, I Bolt, Nut, Screw, Rivet, and Washer
332722: - .
| Manufacturing
3321 1 1 | Iron and Steel Forging
3321 15 I Crown and Closure Manufacturing
3321 16 | Metal Stamping
332214 | Kitchen Utensil, Pot, and Pan Manufacturing
i Fluid Power Valve and Hose Fitting
1 Manufacturing
33261 1 I Spring (Heavy Gauge) Manufacturing
| Other Metal Valve and Pipe Fitting
! Manufacturing
j All Other Miscellaneous Fabricated Metal
j Product Manufacturing
332612 | Spring (Light Gauge) Manufacturing
334518 ! Watch, Clock, and Part Manufacturing
Number of
Establishments
117
952
16
453
2,900
1,035
472
614
9
4,479
126
1,744
604
401
152
33
6
2,745
1,040
421
67
2,166
77
424
129
222
23
394
2
Sales,
Shipments
or Receipts
402,378
10,359,952
0
3,387,391
14,200,270
2,806,913
3,849,100
4,764,118
43,264
15,957,992
275,440
3,536,413
4,199,550
3,074,662
2,166,021
364,564
91,939
8,326,077
8,134,661
4,924,426
969,982
12,041,638
1,369,914
6,602,909
761,711
2,753,397
73,983
2,481,151
0
Share
Value
17.7%
96.0%
3.9%"
91.1%
86.8%
100.0%
100.0%
100.0%
0.2%
100.0%
12.1%
88.2%
100.0%
100.0%
13.2%
3.6%
2.3%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
94.4%
0.7%
100.0%
2.5%b
A-14
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
! Miscellaneous Fabricated
I Wire Products
, .„„ ! Fabricated Pipe and
I Fabricated Pipe Fitting
I
I
I
-i A nn *: Fabricated Metal Products
3499
JN.E.C.
|
|
! Machine Tools, Metal
I Cutting Types
,, c . , ! Machine Tools, Metal
3542 •
! Forming Types
| Special Dies & Tools, Die
I Sets, Jigs, Etc.
. . . . \ Machine Tool Access &
3545 :,. . _ .
1 Measuring Devices
3546 j Power Driven Hand Tools
I Fasteners, Buttons, Needles,
I Pins
2514 j Metal Household Furniture
j Office Furniture, Except
jWood
j Public Buildng & Relatd
! Furniture
|
1 Partitions & Fixtures, Exc
JWood
j Drapery Hardware and
j Window Blinds/Shades
j Furniture and Fixtures,
JN.E.C.
1
:
N^ICS | 1997 NAICS Industry
Code •
:
,,,,.„ ! Other Fabricated Wire Product
332615 :, , ,, , .
i Manufacturing
1 Fabricated Pipe and Pipe Fitting
I Manufacturing
3321 17 ! Powder Metallurgy Part Manufacturing
332439 ! Other Metal Container Manufacturing
332510 1 Hardware Manufacturing
I Other Metal Valve and Pipe Fitting
! Manufacturing
I All Other Miscellaneous Fabricated Metal
I Product Manufacturing
--_-..- I Showcase, Partition, Shelving, and Locker
33 /21 J 5 , , p
E Manufacturing
339914 1 Costume Jewelry and Novelty Manufacturing
---£...,, I Machine Tool (Metal Cutting Types)
333 J 12 5 , f p
• Manulactunng
1 Machine Tool (Metal Forming Types)
333 j 13 E ,. , p
• Manulactunng
333511 I Industrial Mold Manufacturing
j Special Die and Tool, Die Set, Jig, and
333 j 14 E _. ,. , p
• Fixture Manufacturing
332212 I Hand and Edge Tool Manufacturing
T T T c T c j Cutting Tool and Machine Tool Accessory
333 J 1 J : A , p ^ .
j Manufacturing
333991 I Power-Driven Handtool Manufacturing
j Fastener, Button, Needle, and Pin
1 Manufacturing
Household Equipment
337124 j Metal Household Furniture Manufacturing
j Office Furniture (except Wood)
33 7214 • ,, p _, .
j Manufacturing
j Motor Vehicle Seating and Interior Trim
33o3oU E A x „ .
j Manufacturing
337127 I Institutional Furniture Manufecturing
339942 j Lead Pencil and Art Good Manufacturing
--__1 _ j Showcase, Partition, Shelving, and Locker
33 1 L\_J \ , , „ .
j Manulactunng
:
337920 | Blind and Shade Manufacturing
1
337127 j Institutional Furniture Manufacturing
j Surgical Appliance and Supplies
33y 113j,, p
E Manufacturing
Number of
Establishments
1,253
856
128
98
58
7
2,592
78
82
393
225
2,529
4,746
185
1,920
217
249
420
359
184
267
17
926
488
727
16
Sales,
Shipments
or Receipts
4,587,656
4,024,999
1,317,301
273,541
435,815
0
7,558,137
123,057
49,953
5,183,521
2,255,011
5,116,635
8,244,855
714,277
5,347,173
3,609,779
0
2,422,853
8,230,935
6,060,320
1,697,870
110,985
5,249,474
2,393,564
2,305,770
645,688
Share
Value
87.3%
100.0%
100.0%
12.1%
4.0%
1.7%b
71.9%
1.5%
3.9%
97.2%
100.0%
100.0%
100.0%
10.8%
100.0%
100.0%
99.2%b
100.0%
100.0%
57.1%
41.9%
9.0%
65.6%
100.0%
57.0%
4.2%
A-15
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
3431 | Metal Sanitary Ware
j Plumbing Fittings and Brass
j Goods
3442 | Metal Doors, Sash, and Trim
! Household Cooking
I Equipment
! Household Refrig. & Home
j & Farm & Freezers
! Household Laundry
j Equipment
I Electric Housewares and
j Fans
3635 j Household Vacuum Cleaners
I Household Appliances
JN.E.C.
3641 j Electric Lamps
j Current-Carrying Wiring
j Devices
j Concurrent-Carrying Wiring
j Devices
- , „ , ! Residential Electrical
3645 • .
! Lighting Fixtures
! Commercial, Industrial, and
! Institutional
3648 | Lighting Equipment N.E.C.
! Radio/Television Sets Except
! Commun. Types
7623 ! Refrig, air condition
i
:
! Search, Del, Nav, Ggnc,
! Aero, Naut Sys/Inst
! Laboratory Apparatus and
35/1 !
• Furniture
N^ICS ! 1997 NAICS Industry
Code •
i-nr>r>o '' Enameled Iron and Metal Sanitary Ware
33299s s , , p
| Manufacturing
,,,„. , ! Plumbing Fixture Fitting and Trim
I Manufacturing
1 All Other Miscellaneous Fabricated Metal
I Product Manufacturing
332321 1 Metal Window and Door Manufacturing
! Household Cooking Appliance
I Manufacturing
,,-,,, ! Household Refrigerator and Home Freezer
335222 : , , p
| Manufacturing
1 Household Laundry Equipment
! Manufacturing
1 Heating Equipment (except Warm Air
! Furnaces) Manufacturing
...... \ Electric Housewares and Household Fan
335211 :
! Manufacturing
335212 1 Household Vacuum Cleaner Manufacturing
1 All Other Industrial Machinery
333298 5 , T p
\ Manufacturing
1 Other Major Household Appliance
33522o ': , r p
i Manufacturing
3351 10 1 Electric Lamp Bulb and Part Manufacturing
1 Current-Carrying Wiring Device
33 593 1 : , , p
i Manufacturing
1 Noncurrent-Carrying Wiring Device
33 5932 ': , r p
i Manufacturing
1 Residential Electric Lighting Fixture
335 121 S - - p
i Manufacturing
,,-.,, 1 Commercial, Industrial, and Institutional
335 122 •
1 Electric Lighting Fixture Manufacturing
335129 1 Other Lighting Equipment Manufacturing
:
334310 1 Audio and Video Equipment Manufacturing
:
1 Commercial and Industrial Machinery and
811310 1 Equipment (except Automotive and
! Electronic) Repair and Maintenance
, .„
811412 ! Appliance Repair and Maintenance
Instruments
j Search, Detection, Navigation, Guidance,
33451 1 1 Aeronautical, and Nautical System and
1 Instrument Manufacturing
,
I Laboratory Apparatus and Furniture
3391 11:,, P , •
• Manufacturing
Number of
Establishments
88
116
5
1,384
84
27
17
16
138
34
4
36
82
519
219
497
356
327
554
2,343
1,671
680
385
Sales,
Shipments
or Receipts
1,575,505
3,590,128
118,059
9,876,049
3,543,231
4,887,364
3,723,375
329,270
3,488,251
2,399,206
0
3,300,662
3,306,009
5,877,522
4,451,186
2,177,355
4,047,437
3,054,806
8,454,194
1,890,237
789,622
32,497,776
2,471,153
Share
Value
100.0%
100.0%
1.1%
96.4%
100.0%
100.0%
100.0%
8.9%
100.0%
100.0%
0.2%b
100.0%
100.0%
100.0%
100.0%
96.6%
100.0%
100.0%
100.0%
10.8%
19.9%
99.8%
100.0%
A-16
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
1 Automatic Environmental
35/2 : .
; Controls
3823 I Process Control Instruments
! Fluid Meters and Counting
I Devices
! Instruments to Measure
35/J I . . .
; Electricity
- 0, . ! Laboratory Analytical
3o2o i T
i Instrumeats
j Optical Instruments and
j Lenses
I Measuring and Controlling
3*2y | Devices N.E.C.
! Surgical & Medical
! Instruments & Apparatus
j
! Orthopedic, Prosthetic &
! Surgical Suppl.
|
j Dental Equipment and
j Supplies
3844 j X-Ray Apparatus and Tubes
3845 j Electromedical Equipment
3851 | Ophthalmic Goods
|
|
7629 1 Electric repair shop
|
|
1
I
N^ICS | 1997 NAICS Industry
Code •
:
! Automatic Environmental Control
334512 1 Manufacturing for Residential, Commercial,
1 and Appliance Use
1 Instruments and Related Products
334513 I Manufacturing for Measuring, Displaying,
i and Controlling Industrial Process Variables
.„.
1 Totalizing Fluid Meter and Counting Device
! Manufacturing
--...,, I Electronic Coil, Transformer, and Other
334416 -T , '
| Inductor Manufacturing
,,.-.. 1 Instrument Manufacturing for Measuring and
! Testing Electricity and Electrical Signals
--.,.,, I Analytical Laboratory Instrument
334516 !,, - .
i Manufacturing
:
:
333314 1 Optical Instrument and Lens Manufacturing
:
1 Other Measuring and Controlling Device
3345 19 i , T p
\ Manufacturing
,,,,„-,-,, 1 Surgical and Medical Instrument
3391 12 ': , r p
i Manufacturing
, , n . . , 1 Surgical and Medical Instrument
3391 [2 ': , r p
i Manufacturing
322121 | Paper (except Newsprint) Mills
322291 1 Sanitary Paper Product Manufacturing
1 Electromedical and Electrotherapeutic
334j 1U E . , r p
| Apparatus Manufacturing
1 Surgical Appliance and Supplies
3391 13 E - r p
i Manufacturing
TTQI i 4 1 Dental Equipment and Supplies
1 Manufacturing
334517 1 Irradiation Apparatus Manufacturing
-, -, ,r 1 r, I Electromedical and Electrotherapeutic
334510 • . ^ . . p ^ . ^
| Apparatus Manufacturing
3391 15 | Ophthalmic Goods Manufacturing
0 j Computer and Office Machine Repair and
ol 1212 • , , . _,
j Maintenance
j Communication Equipment Repair and
ol 1213 • , , . _,
j Maintenance
„ I Other Electronic and Precision Equipment
I Repair and Maintenance
j Home and Garden Equipment Repair and
ol!411 •-.- .
j Maintenance
811412 I Appliance Repair and Maintenance
Number of
Establishments
317
1,002
222
17
826
664
495
853
6
1,598
2
16
74
1,636
877
155
460
575
1,538
201
2,033
579
4,327
Sales,
Shipments
or Receipts
2,935,692
7,890,923
3,765,769
24,303
13,852,897
7,157,038
3,174,652
5,114,547
62,148
18,450,024
0
651,398
807,427
14,743,779
2,699,867
3,942,256
10,567,566
3,607,813
913,258
231,458
2,509,452
185,507
3,125,853
Share
Value
100.0%
100.0%
100.0%
1.6%
100.0%
99.5%
99.8%
99.9%
0.3%
99.7%
1.4%b
6.7%
7.1%
95.8%
100.0%
100.0%
92.5%
100.0%
10.7%
14.4%
86.1%
18.5%
78.6%
A-17
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC
3315
3316
3317
3471
3479
3523
3524
3531
3532
3536
3537
3795
SIC Industry
Steel Wiredrawing and Steel
Nails and Spikes
Cold-Rolled Steel Sheet,
Strip, and Bars
Steel Pipe and Tubes
Plating and Polishing
Metal Coating & Allied
Services
Farm Machinery and
Equipment
Garden Tractors & Lawn &
Garden Equipment
Constr Mach and Eq
Mining Mach. & Equip.,
Except Oil Field
Hoists, Industrial Cranes &
Monorails
Industrial Trucks, Tractors,
Trailers
Tanks and Tank Components
I
:
N^ICS | 1997 NAICS Industry
Code •
1
Iron and Steel
33 1222 1 Steel Wire Drawing
- n , , , 0 1 Other Fabricated Wire Product
332618 j „ .
: Manufacturing
:
331221 | Rolled Steel Shape Manufacturing
1
- - ., « -, n i Iron and Steel Pipe and Tube Manufacturing
33 121U 5 „ ~ , , „ ,
j from Purchased Steel
Job Shop
1 Electroplating, Plating, Polishing, Anodizing,
332813 5 1^1
i and Coloring
1 Metal Coating, Engraving (except Jewelry
332812 1 and Silverware), and Allied Services to
I Manufacturers
33991 1 1 Jewelry (except Costume) Manufacturing
339914 1 Costume Jewelry and Novelty Manufacturing
339912 1 Silverware and Hollowware Manufacturing
Mobile Industrial Equipment
332212 1 Hand and Edge Tool Manufacturing
,,,,,, ! Ornamental and Architectural Metal Work
332323 ;. - .
| Manufacturing
j Farm Machinery and Equipment
333 111:-.- r- ^_ •
\ Manufacturing
j Conveyor and Conveying Equipment
333922 • , r ,, ^_ .
\ Manufacturing
332212 j Hand and Edge Tool Manufacturing
- - - I Lawn and Garden Tractor and Home Lawn
1 and Garden Equipment Manufacturing
333120 j Construction Machinery Manufacturing
a a a n^a i Overhead Traveling Crane, Hoist, and
1 Monorail System Manufacturing
336510 | Railroad Rolling Stock Manufacturing
j Mining Machinery and Equipment
333 131-,, ,, _, .
j Manufacturing
a a a n^a i Overhead Traveling Crane, Hoist, and
1 Monorail System Manufacturing
332439 ! Other Metal Container Manufacturing
ii/ir.r.r. i All Other Miscellaneous Fabricated Metal
332999 •„ , .,, - . .
| Product Manufacturing
j Industrial Truck, Tractor, Trailer, and Stacker
I Machinery Manufacturing
--,„„- ! Military Armored Vehicle, Tank, and Tank
336992;,, t,T c L. •
• Component Manufacturing
Number of
Establishments
273
31
186
235
3,404
2,156
22
16
12
1
140
1,339
28
3
145
785
87
25
292
220
4
19
461
37
Sales,
Shipments
or Receipts
4,920,798
370,492
6,343,466
7,565,377
5,979,405
8,460,896
5,798
2,257
6,296
0
380,152
15,921,455
33,377
0
7,454,511
21,965,455
1,805,198
346,760
2,710,923
1,340,561
6,775
27,488
5,538,326
0
Share
Value
100.0%
7.0%
100.0%
100.0%
100.0%
100.0%
0.1%
0.2%
0.7%
0.1%b
9.5%
100.0%
0.5%
0.3%b
100.0%
100.0%
57.4%
4.2%
100.0%
42.6%
0.3%
0.3%
100.0%
86.0%b
A-18
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
3465 ! Automotive Stampings
! Carburetors, Piston Rings,
I Valves
! Vehicular Lighting
I Equipment
! Electrical Equipment for
! Motor Vehicles
I
! Motor Vehicle and
I Automobile Bodies
I
I
|
! Motor Vehicle Parts and
! Accessories
|
I
|
3716 | Mobile Homes
3751 j Motorcycles
3792 j Travel Trailers and Campers
I
j Miscellaneous Transportation
J 177 • _ .
i Equipment
4121 j Taxicabs
j Motor Vehicle Supplies and
| New Parts
I Motor Vehicle Dealers (New
1 and Used)
j Motor Vehicle Dealers (Used
jOnly)
5561 j Recreational Vehicle Dealers
I
:
N^ICS | 1997 NAICS Industry
Code •
1
Motor Vehicle
336370 ! Motor Vehicle Metal Stamping
- n ,- , , 1 Carburetor, Piston, Piston Ring, and Valve
336311 j „ .
: Manufacturing
:
:
336321 {Vehicular Lighting Equipment Manufacturing
1
I Other Motor Vehicle Electrical and
j Electronic Equipment Manufacturing
336111 I Automobile Manufacturing
T T £ ., ., « 1 Light Truck and Utility Vehicle
33ol 12 E ,. , „
• Manufacturing
336120 I Heavy Duty Truck Manufacturing
33621 1 I Motor Vehicle Body Manufacturing
I Military Armored Vehicle, Tank, and Tank
1 Component Manufacturing
33621 1 j Motor Vehicle Body Manufacturing
j Gasoline Engine and Engine Parts
3363 \L \ , x ,, .
\ Manufactunng
j Other Motor Vehicle Electrical and
1 Electronic Equipment Manufacturing
j Motor Vehicle Steering and Suspension
1 Components (except Spring) Manufacturing
336340 | Motor Vehicle Brake System Manufacturing
336350 j Motor Vehicle Transmission and Power Train
I Parts Manufacturing
336399 | All Other Motor Vehicle Parts Manufacturing
336213 I Motor Home Manufacturing
I Motorcycle, Bicycle, and Parts
336991 • , ~ (-. .
| Manufacturing
336214 j Travel Trailer and Camper Manufacturing
332212 j Hand and Edge Tool Manufacturing
336214 j Travel Trailer and Camper Manufacturing
j All Other Transportation Equipment
j Manufacturing
485310 | Taxi Service
! Motor Vehicle Supplies and New Parts
42 1 120 5 TTn * *
• Wholesalers
441310 I Automotive Parts and Accessories Stores
:
44 1 1 1 0 j New Car Dealers
:
:
44 11 20 | Used Car Dealers
:
:
441210 I Recreational Vehicle Dealers
Number of
Establishments
810
141
106
569
194
112
84
76
6
23
881
193
212
269
523
1,508
88
385
315
1
498
378
3,184
12,620
16,253
25,897
23,340
3,014
Sales,
Shipments
or Receipts
23,668,110
2,755,311
3,282,824
9,074,335
95,385,563
110,400,169
14,490,344
82,633
0
265,552
25,974,369
6,446,681
10,750,312
10,033,288
33,288,093
34,193,298
3,943,709
0
3,076,049
0
1,485,367
4,557,989
1,280,597
83,214,728
22,093,428
518,971,824
34,680,468
10,069,749
Share
Value
100.0%
100.0%
100.0%
53.6%
100.0%
100.0%
100.0%
0.9%
14.0%b
2.9%
100.0%
38.1%
100.0%
100.0%
100.0%
99.6%
100.0%
99.0%b
67.4%
0.1%b
32.6%
100.0%
100.0%
100.0%
51.2%
100.0%
100.0%
100.0%
A-19
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
5571 ! Motorcycle Dealers
5599 | Automotive Dealers, N.E.C.
7514! Passenger Car Rental
7515 ! Pas senger Car Lease
j Utility Trailer and
I Recreational Vehicle Rental
j Top, Body, and Upholstery
j Repair and Paint Shops
7533 j Auto Exhaust Systems
7537 j Auto Transmission Repair
7538 j Gen Automotive Repair
7539 | Auto Repair Shop, N.E.C.
I
! Auto Services, Except Repair
1 and Carwashes
3571 j Electronic Computers
3572 j Typewriters
3575 j Computer Terminals
j Computer Peripheral Eq
| N.E.C.
j Calculating, Accounting
j Machines Except Computers
i
3579 I Office Machines, N.E.C.
|
1 Computer Maintenance and
/3 /8 i _
1 Repairs
I Computer Related Services,
j N.E.C.
3482 | Small Arms Ammunition
! Ammunition, Except for
! Small Arms
3484 | Small Arms
1
I
N^ICS | 1997 NAICS Industry
Code •
:
441221 ! Motorcycle Dealers
441229 | All Other Motor Vehicle Dealers
532111 1 Passenger Car Rental
532112 1 Passenger Car Leasing
_-_1 _„ I Truck, Utility Trailer, and RV (Recreational
I Vehicle) Rental and Leasing
1 Automotive Body, Paint, and Interior Repair
ol 1 121 5 , , r •
j and Maintenance
811112 1 Automotive Exhaust System Repair
811113 1 Automotive Transmission Repair
811111 1 General Automotive Repair
0 1 Other Automotive Mechanical and Electrical
olllloi— . , , r •
E Repair and Maintenance
488410 | Motor Vehicle Towing
j Automotive Oil Change and Lubrication
oi i iy i E „,
• Shops
0 1 All Other Automotive Repair and
oil lyo : , , .
j Maintenance
Office Machine
3341 1 1 1 Electronic Computer Manufacturing
3341 12 ! Computer Storage Device Manufacturing
3341 13 ! Computer Terminal Manufacturing
I Other Computer Peripheral Equipment
3341 19 E , , ,* ^ .
| Manufacturing
333313 j Office Machinery Manufacturing
j Other Computer Peripheral Equipment
3341 iy 5 , , r- ^_ •
\ Manufacturing
333313 j Office Machinery Manufacturing
334518 | Watch, Clock, and Part Manufacturing
339942 | Lead Pencil and Art Good Manufacturing
0 j Computer and Office Machine Repair and
ol 1212 • , ,. . ,
j Maintenance
334611 I Software Reproducing
541512 i Computer Systems Design Services
54 1 5 1 9 | Other Computer Related Services
Ordnance
332992 I Small Arms Ammunition Manufacturing
! Ammunition (except Small Arms)
! Manufacturing
332994 | Small Arms Manufacturing
Number of
Establishments
3,635
1,678
4,367
879
360
35,569
5,251
6,768
77,751
9,674
5,893
7,413
1,646
563
211
142
1,006
35
61
134
16
13
6,087
124
20,233
8,405
113
53
198
Sales,
Shipments
or Receipts
7,369,260
2,517,267
14,783,704
3,800,424
256,119
17,755,296
1,985,377
2,431,584
25,598,455
3,494,643
2,295,188
2,787,318
798,626
66,331,909
13,907,367
1,483,460
25,130,308
144,380
1,870,426
3,047,549
0
257,020
7,565,169
1,258,435
15,942,861
4,339,989
938,818
1,497,045
1,251,792
Share
Value
100.0%
100.0%
100.0%
100.0%
2.5%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
73.5%
100.0%
100.0%
100.0%
93.1%
4.5%
6.9%
95.5%
19.6%b
20.8%
89.0%
100.0%
31.1%
100.0%
100.0%
100.0%
100.0%
A-20
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
! Ordnance and Accessories,
JN.E.C.
3497 | Metal Foil and Leaf
! Photographic Equipment &
35ol I ..
; Supplies
3931 I Musical Instruments
j Games, Toys, Children's
j Vehicles
I Sporting and Athletic Goods,
JN.E.C.
3951 j Pens and Mechanical Pencils
3953 j Marking Devices
j Signs and Advertising
3773 \ ,-^ . ,
\ Displays
3995 | Burial Caskets
|
j
|
! Manufacturing Industries,
JN.E.C.
|
|
j
7692 | Welding Repair
:
:
N^ICS | 1997 NAICS Industry
Code •
i
,,,,,-JQQ^ 1 Other Ordnance and Accessories
j Manufacturing
Miscellaneous Metal Products
------ I Laminated Aluminum Foil Manufacturing for
j Flexible Packaging Uses
I All Other Miscellaneous Fabricated Metal
I Product Manufacturing
09^009 1 Photographic Film, Paper, Plate, and
j Chemical Manufacturing
0 0 0 0 1 c I Photographic and Photocopying Equipment
3333 1 J 5 , T „
E Manufacturing
339992 1 Musical Instrument Manufacturing
I Motorcycle, Bicycle, and Parts
1 Manufacturing
--„„-_ j Game, Toy, and Children's Vehicle
337732. •
\ Manufacturing
:
339920 1 Sporting and Athletic Goods Manufacturing
I
339941 I Pen and Mechanical Pencil Manufacturing
339943 I Marking Device Manufacturing
:
339950 | Sign Manufacturing
1
339995 | Burial Casket Manufacturing
-, 1 ,inr.r. \ All Other Miscellaneous Textile Product
314999 •,,...
I Mills
316110 j Leather and Hide Tanning and Finishing
j All Other Miscellaneous Chemical Product
1 and Preparation Manufacturing
326199 | All Other Plastics Product Manufacturing
332212 ! Hand and Edge Tool Manufacturing
-,-,™nr. I All Other Miscellaneous Fabricated Metal
332999 = , . - , ,, . .
| Product Manufacturing
j Residential Electric Lighting Fixture
33 J 121 • ,, ,, _, .
j Manufactunng
337127 ! Institutional Furniture Manufacturing
339999 | All Other Miscellaneous Manufacturing
I Other Personal and Household Goods Repair
1 and Maintenance
Number of
Establishments
70
43
64
311
428
576
4
785
2,571
112
634
5,709
177
52
26
9
140
7
185
53
5
2,284
4,840
Sales,
Shipments
or Receipts
1,750,485
1,546,143
1,711,600
12,895,637
8,410,124
1,356,651
0
4,534,497
10,591,160
1,590,770
643,007
7,910,809
1,271,184
173,353
24,625
80,624
319,241
0
285,362
69,864
28,296
7,183,815
1,640,808
Share
Value
100.0%
100.0%
16.3%
100.0%
100.0%
100.0%
1.0%b
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
2.8%
0.7%
0.6%
0.5%
0.6%b
2.7%
3.1%
0.7%
85.4%
36.8%
A-21
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC
7699
3873
3911
3914
3915
3961
7631
3672
3743
3731
3732
4412
4424
SIC Industry
Repair Shop, Related Service
Watches, Clocks, and
Watchcases
Jewelry, Precious Metal
Silverware, Plated Ware &
Stainless
Jewelers' Materials &
Lapidary Work
Costume Jewelry
Watch, Clock, Jewelry Repair
Printed Circuit Boards
Railcars, Railway Systems
Ship Building and Repairing
Boat Building and Repairing
Deep Sea Foreign
Transportation
Deep Sea Domestic
Transportation
1
:
N^ICS | 1997 NAICS Industry
Code •
:
^oo-i r>n iOther Support Activities for Water
455390 | .
: Transportation
561 622 | Locksmiths
561790 | Other Services to Buildings and Dwellings
562991 | Septic Tank and Related Services
0 1 Computer and Office Machine Repair and
ol 1212 5 , , .
j Maintenance
0 1 Other Electronic and Precision Equipment
oi i2iy s _. . i » r •
E Repair and Maintenance
1 Commercial and Industrial Machinery and
811310 j Equipment (except Automotive and
j Electronic) Repair and Maintenance
1 Home and Garden Equipment Repair and
ol 141 1 5 , r .
\ Maintenance
811412 1 Appliance Repair and Maintenance
81 1430 1 Footwear and Leather Goods Repair
I Other Personal and Household Goods Repair
j and Maintenance
:
334518 | Watch, Clock, and Part Manufacturing
1
Precious Metals and Jewelry
33991 1 1 Jewelry (except Costume) Manufacturing
j Cutlery and Flatware (except Precious)
33221 1 5 , , r
• Manufacturing
339912 I Silverware and Hollowware Manufacturing
I Jewelers' Material and Lapidary Work
1 Manufacturing
339914 I Costume Jewelry and Novelty Manufacturing
j Other Personal and Household Goods Repair
oi i4yu E i n ^ •
j and Maintenance
Printed Circuit Boards
334412 ! Bare Printed Circuit Board Manufacturing
Railroad
336510 | Railroad Rolling Stock Manufacturing
Ships and Boats
33661 1 ! Ship Building and Repairing
336612 | Boat Building
I Other Personal and Household Goods Repair
1 and Maintenance
I
483 1 1 1 j Deep Sea Freight Transportation
I
j Coastal and Great Lakes Freight
4o3 1 13 • _ .
• Transportation
Number of
Establishments
12
3,799
1,254
2,538
104
838
16,404
3,032
181
82
3,946
128
2,272
11
151
394
826
1,716
1,401
207
700
1,043
1,739
487
292
Sales,
Shipments
or Receipts
4,737
1,081,317
0
0
23,844
404,627
13,600,413
816,008
59,338
18,294
1,362,271
718,191
5,416,836
8,032
899,684
919,066
1,223,475
345,774
9,787,576
7,916,635
10,571,810
5,622,040
821,273
11,570,718
3,114,639
Share
Value
0.7%
100.0%
22.4%"
81.8%b
0.3%
13.9%
77.7%
81.5%
1.5%
7.0%
30.6%
77.9%
99.9%
0.4%
99.3%
100.0%
95.9%
7.8%
100.0%
95.8%
100.0%
100.0%
18.4%
100.0%
66.6%
A-22
-------
MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
! Freight Transportation Great
••3^1 \ -r -1
; Lakes
444Q ^ Water Transportation of
I Freight, N.E.C.
j Deep Sea Passenger
j Transportation
4482 | Ferries
4489 ! Water Passen8er
1 Transportation, N.E.C.
4491 | Marine Cargo Handling
4492 | Towing & Tugboat Service
j
4493 | Marinas
j
! Water Transporation
| Services, N.E.C.
j Steam, Gas, Hydraulic
j Turbines, Generator Units
j Internal Combustion Engines,
JN.E.C.
! Oil Field Machinery and
! Equipment
j Elevators and Moving
j Stairways
j Conveyors and Conveying
j Equipment
3543 | Industrial Patterns
j Rolling Mill Machinery and
j Equipment
j Electric and Gas Welding and
j Soldering
j Metal Working Machinery,
1 N.E.C.
1
:
N^ICS | 1997 NAICS Industry
Code •
:
.„,-.-., ! Coastal and Great Lakes Freight
4531 13 :„ , ,.
i Transportation
:
:
48321 1 ! Inland Water Freight Transportation
:
4831 12 1 Deep Sea Passenger Transportation
1 Coastal and Great Lakes Passenger
! Transportation
1 Coastal and Great Lakes Passenger
! Transportation
483212 1 Inland Water Passenger Transportation
483212 1 Inland Water Passenger Transportation
487210 1 Scenic and Sightseeing Transportation, Water
488310 | Port and Harbor Operations
488320 | Marine Cargo Handling
1 Coastal and Great Lakes Freight
4831 13 i_ .
i Transportation
48321 1 1 Inland Water Freight Transportation
488330 1 Navigational Services to Shipping
713930 | Marinas
488330 1 Navigational Services to Shipping
1 Other Support Activities for Water
455390 • .
i Transportation
1 Commercial Air, Rail, and Water
53241 1 1 Transportation Equipment Rental and
1 Leasing
h i .„.
Stationary Industrial Equipment
...... 1 Turbine and Turbine Generator Set Units
333611 : -
i Manufacturing
333618 1 Other Engine Equipment Manufacturing
336399 | All Other Motor Vehicle Parts Manufacturing
! Oil and Gas Field Machinery and Equipment
333 132 • , f ., .
| Manufacturing
I
333921 I Elevator and Moving Stairway Manufacturing
1
a a an-} 9 1 Conveyor and Conveying Equipment
1 Manufacturing
332997 | Industrial Pattern Manufacturing
Q Q Q c 1 . j Rolling Mill Machinery and Equipment
333 J 16 • A .- „ .
j Manufacturing
j Welding and Soldering Equipment
jjjyyZ. • , J „ ^_ .
\ Manufacturing
I Other Metalworking Machinery
333 J lo • A n „ .
• Manufacturing
Number of
Establishments
32
222
80
64
61
76
154
654
168
623
292
161
361
4,217
504
640
126
86
297
7
563
196
871
673
100
244
474
Sales,
Shipments
or Receipts
519,863
2,821,121
3,908,143
89,597
92,493
121,992
171,135
861,001
889,125
4,456,243
1,043,440
566,027
1,014,026
2,541,481
499,176
444,499
454,392
5,783,057
0
123,954
6,240,079
1,607,066
6,346,525
623,927
700,084
4,433,877
3,463,811
Share
Value
11.1%
83.3%
100.0%
49.2%
50.8%
41.6%
58.4%
76.3%
100.0%
100.0%
22.3%
16.7%
67.0%
100.0%
33.0%
67.7%
7.1%
100.0%
99.3%b
0.4%
100.0%
100.0%
99.5%
100.0%
100.0%
99.8%
100.0%
A-23
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MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC | SIC Industry
3552 ! Textile Machinery
3553 j Woodworking Machinery
3554 ! Paper Industries Machinery
! Printing Trades Machinery
! and Equipment
3556 ! Food Products Mach
|
j Special Industry Machinery,
JN.E.C.
|
j Pumps and Pumping
j Equipment
3562 | Ball and Roller Bearings
3563 j Air and Gas Compressors
j Blowers and Exhaust and
j Ventilation Fans
3565 j Industrial Patterns
j Speed Changers, High Speed
j Drivers & Gears
, . ,„ ! Industrial Process Furnaces
3567 • , _
! and Ovens
! Mechanical Power
3568 ! Transmission Equipment,
JN.E.C.
j General Industrial
j Machinery, N.E.C.
j Automatic Merchandising
3 JO 1 i , r , .
\ Machines
j Commercial Laundry
3 Jo2 i .
j Equipment
j Refrigeration & Air and
j Heating Equipment
j Measuring and Dispensing
j JoO j ~
i Pumps
I Service Industry Machines,
3589 IN.E.C.
1
:
N^ICS | 1997 NAICS Industry
Code •
:
333292 1 Textile Machinery Manufacturing
,,,,.„ ! Sawmill and Woodworking Machinery
I Manufacturing
333291 1 Paper Industry Machinery Manufacturing
1 Printing Machinery and Equipment
333293 5 , T ~
\ Manufacturing
333294 1 Food Product Machinery Manufacturing
,,,,,„ 1 Plastics and Rubber Industry Machinery
333220 i , T ~
\ Manufacturing
333295 1 Semiconductor Machinery Manufacturing
1 All Other Industrial Machinery
33329o ': , r r
i Manufacturing
...... \ Other Commercial and Service Industry
333319 •, , . . , , ,. .
| Machinery Manuiactunng
i Pump and Pumping Equipment
33391 1 E , x j* . •
| Manufacturing
332991 | Ball and Roller Bearing Manufacturing
333912 j Air and Gas Compressor Manufacturing
333411 j Air Purification Equipment Manufacturing
^ ^ ^ . 1 _ j Industrial and Commercial Fan and Blower
333412 :
1 Manufacturing
333993 j Packaging Machinery Manufacturing
j Speed Changer, Industrial High-Speed Drive,
1 and Gear Manufacturing
j Industrial Process Furnace and Oven
1 Manufacturing
:
- ^ ^ , , ^ 1 Mechanical Power Transmission Equipment
333613 :
1 Manufacturing
I
j All Other Miscellaneous General Purpose
1 Machinery Manufacturing
:
33331 1 I Automatic Vending Machine Manufacturing
I
TOOT 1 Commercial Laundry, Drycleaning, and
1 Pressing Machine Manufacturing
I Air-Conditioning and Warm Air Heating
333415 1 Equipment and Commercial and Industrial
1 Refrigeration Equipment Manufacturing
i Motor Vehicle Air-Conditioning
336391 • , , (-. .
| Manuiactunng
-J-J-JQ-, a ! Measuring and Dispensing Pump
3337 1 .3 • , , ,-.
E Manufactunng
33331 0 ^ Otner Commercial and Service Industry
I Machinery Manufacturing
Number of
Establishments
478
327
366
546
597
455
257
1,677
78
489
185
314
370
204
689
268
404
299
1,257
121
68
792
60
71
1,165
Sales,
Shipments
or Receipts
1,779,034
1,321,752
3,438,235
0
2,877,841
3,584,992
11,158,627
0
644,019
6,826,043
6,120,940
5,633,008
2,174,729
1,901,196
4,858,270
2,402,392
2,871,475
3,301,091
7,991,746
1,325,960
604,966
22,846,865
5,626,596
1,316,899
7,596,253
Share
Value
100.0%
100.0%
100.0%
99.1%"
100.0%
100.0%
100.0%
99.8%b
6.9%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
87.5%
100.0%
100.0%
99.8%
100.0%
100.0%
81.3%
A-24
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MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 3: Relationships between SIC and NAICS Codes Based on 1997 Economic Census for MP&M
Industries Evaluated for the Final Rule"
(thousands, 1997$)
SIC
3593
3594
3596
3599
3612
3613
3621
3629
7353
7359
SIC Industry
Fluid Power Cylinders and
Actuators
Fluid Power Pumps and
Motors
Scales and Balances, except
Laboratory
Machinery, Except Electrical,
N.E.C.
Transformers
Switchgear and Switchboard
Apparatus
Motors and Generators
Electric Industrial Apparatus,
N.E.C.
Heavy Construction Equip
Rental, Leasing
Equip Rental, Leasing,
N.E.C.
1
:
N^ICS | 1997 NAICS Industry
Code •
:
1 Fluid Power Cylinder and Actuator
! Manufacturing
:
333996 ! Fluid Power Pump and Motor Manufacturing
:
---„„_ I Scale and Balance (except Laboratory)
I Manufacturing
332710 | Machine Shops
1 All Other Miscellaneous Fabricated Metal
I Product Manufacturing
. . . . . n 1 Other Commercial and Service Industry
333319 i, , , . ., .
| Machinery Manufacturing
-,-,-,nr.r. * All Other Miscellaneous General Purpose
333999 :
! Machinery Manufacturing
1 Power, Distribution, and Specialty
! Transformer Manufacturing
1 Switchgear and Switchboard Apparatus
3353 13 5 , r ~
\ Manufacturing
335312 1 Motor and Generator Manufacturing
-,-,mr.r. * All Other Miscellaneous Electrical
335999 :
I Equipment and Component Manufacturing
234990 | All Other Heavy Construction
1 Construction, Mining, and Forestry
532412 ! Machinery and Equipment Rental and
| Leasing
532210 1 Consumer Electronics and Appliances Rental
532299 | All Other Consumer Goods Rental
532310 | General Rental Centers
1 Commercial Air, Rail, and Water
53241 1 ! Transportation Equipment Rental and
| Leasing
1 Construction, Mining, and Forestry
532412 ! Machinery and Equipment Rental and
I Leasing
„.
1 Office Machinery and Equipment Rental and
! Leasing
1 Other Commercial and Industrial Machinery
1 and Equipment Rental and Leasing
562991 1 Septic Tank and Related Services
Number of
Establishments
320
170
122
23,619
132
50
836
318
583
528
413
2,295
3,286
3,011
3,133
6,509
498
671
400
3,408
563
Sales,
Shipments
or Receipts
3,528,906
2,712,058
682,940
27,143,131
506,611
172,536
1,146,348
4,716,162
7,609,164
11,788,281
2,838,366
2,734,732
5,339,163
1,790,890
2,133,450
3,910,618
0
1,555,089
436,178
6,775,140
0
Share
Value
100.0%
100.0%
100.0%
100.0%
4.8%
1.8%
12.5%
100.0%
100.0%
96.3%
41.2%
8.7%
77.4%
100.0%
99.1%
100.0%
74.3%b
22.6%
7.1%
69.7%
18.2%b
a EPA evaluated options for these industrial sectors but did not regulate them all under the final rule.
b Share values were calculated using estimated value of shipments data.
N.E.C. = Not Elsewhere Classified
Source: Department of Commerce, Bureauofthe Census, 1997 Economic Census, Bridge Between NAICS and SIC; and EPA analysis.
A-25
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MP&M EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
A. 2 ANNUAL ESTABLISHMENT "BIRTHS" AND "DEATHS" IN MP<&M INDUSTRIES
EVALUATED FOR THE FINAL RULE
EPA used the Statistics of U.S. Businesses (SUSB) dynamic data to estimate the rate at which MP&M facilities evaluated for
the final rule enter and leave the industry each year. The SUSB dynamic data report numbers of facilities starting up, closing,
expanding employment and contracting employment each year from 1989 through 1997 (the latest currently available.)
Table A.4 shows the average number of facilities (establishments) operating at the beginning of each year for the period 1989
through 1997, the number of facility "births" and "deaths", and the average "birth rate" and "death rate" for each of the major
3-digit manufacturing SIC codes that include MP&M 4-digit SIC codes evaluated for the final rule. This table shows that,
over the period 1989-1997, annual closure rates ranged from 6 to over 12 percent in the different industries, with an overall
average of almost 8 percent.
Table A.4: Annual Births and Deaths for MP&M Establishments Evaluated for the Final Rule by 3 Digit SIC
Codes (1989-1997)
:
:
I
SIC | SIC Description
i
:
:
:
j Metal Cans And Shipping
I Containers
3420 | Cutlery, Handtools, And Hardware
:
I Plumbing And Heating, Except
jQj\J ! -1_,1 .
1 Electric
3440 | Fabricated Structural Metal Products
3450 | Screw Machine Products, Bolts, Etc.
3460 ! Metal Forgings And Stamping
3470 | Metal Services, N.E.C.
3480 | Ordnance & Accessories, N.E.C.
3490 | Misc. Fabricated Metal Products
3510 ! Engines And Turbines
3520 ! Farm And Garden Machinery
-,-„ ! Construction And Related
3530 ' , .
1 Machinery
3540 ! Metalworking Machinery
3550 ! Special Industry Machinery
3560 ! General Industrial Machinery
3570 ! Computer And Office Equipment
'• Refrigeration And Service
3D50 : . .
1 Machinery
3590 | Industrial Machinery, N.E.C.
3610 ! Electric Distribution Equipment
3620 ! Electrical Industrial Apparatus
3630 ! Household Appliances
Average #
Establishments at the
Beginning of the Year
464
2,294
687
12,268
2,436
3,812
5,028
390
7,084
346
1,711
3,165
11,072
4,427
3,961
2,025
2,104
21,972
764
2,024
461
Average
Establishment
Births
22
143
45
853
84
199
341
39
606
26
133
217
672
307
243
262
154
1,996
53
117
44
Average
Establishment
Deaths
35
139
53
908
111
226
340
40
531
24
129
230
660
317
225
246
165
1,659
51
130
41
% Births
4.7%
6.2%
6.6%
7.0%
3.4%
5.2%
6.8%
10.0%
8.6%
7.5%
7.8%
6.9%
6.1%
6.9%
6.1%
12.9%
7.3%
9.1%
6.9%
5.8%
9.5%
% Deaths
7.5%
6.1%
7.8%
7.4%
4.6%
5.9%
6.8%
10.2%
7.5%
6.8%
7.5%
7.3%
6.0%
7.1%
5.7%
12.1%
7.9%
7.5%
6.6%
6.4%
8.9%
2 The data are disaggregated only to the 3-digit SIC level, and EPA therefore was unable to calculate closure rates for the specific
4-digit SICs that comprise the MP&M industries evaluated for the final rule. The analysis does not include 3-digit SICs that may include
large numbers of non-metal products producers, for example SIC 241 (furniture, both wood and metal.)
A-26
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MPAM EEBA: Appendices
Appendix A: Detailed Economic Impact Analysis Information
Table A. 4: Annual Births and Deaths for MP&M Establishments Evaluated for the Final Rule by 3 Digit SIC
Codes (1989-1997)
i
:
:
SIC | SIC Description
i
:
i
! Electric Lighting And Wiring
! Equipment
3650 ! Household Audio & Video Equip
3660 ! Communications Equipment
. „„ ! Electronic Components And
3670 :
; Accessories
! Misc. Electrical Equipment &
1 Supplies
3710 ! Motor Vehicles And Equipment
3720 | Aircraft And Parts
! Ship And Boat Building And
3 730 i _
1 Repairing
3740 ! Railroad Equipment
3750 i Motorcycles, Bicycles, & Parts
! Guided Missiles, Space Vehicles,
3 760 i _
I Parts
! Miscellaneous Transportation
1 Equipment
3810 ! Search & Navigation Equipment
3820 i Measuring And Controlling Devices
3840 i Medical Instruments And Supplies
3850 | Ophthalmic Goods
3860 i Photographic Equip & Supplies
i Watches, Clocks, Watchcases &
30 (j \ n
\ Parts
I Jewelry, Silverware, And Plated
j Ware
3930 j Musical Instruments
3940 j Toys And Sporting Goods
3950 | Pens, Pencils, Office, & Art Supplies
3960 ! Costume Jewelry And Notions
3990 ! Miscellaneous Manufactures
TOTAL |
Average #
Establishments at the
Beginning of the Year
1,905
766
1,794
6,068
1,890
4,477
1,633
2,669
189
256
127
962
758
4,209
3,770
536
784
159
2,606
434
2,843
975
1,010
7,338
136,653
Average
Establishment
Births
123
96
169
614
136
387
122
343
15
38
7
106
34
275
334
40
71
12
246
46
384
62
105
784
11,103
Average
Establishment
Deaths
143
87
159
522
157
372
127
339
15
25
11
109
60
295
289
48
72
20
275
35
345
70
128
740
10,698
% Births
6.5%
12.5%
9.4%
10.1%
7.2%
8.6%
7.5%
12.9%
7.9%
14.8%
5.5%
11.0%
4.5%
6.5%
8.9%
7.5%
9.1%
7.5%
9.4%
10.6%
13.5%
6.4%
10.4%
10.7%
8.1%
% Deaths
7.5%
11.4%
8.9%
8.6%
8.3%
8.3%
7.8%
12.7%
7.7%
9.7%
8.4%
11.3%
7.9%
7.0%
7.7%
8.9%
9.1%
12.7%
10.6%
8.0%
12.1%
7.2%
12.7%
10.1%
7.8%
N.E.C. = Not Elsewhere Classified
Source: Small Business Administration, Statistics of U.S. Businesses.
A-27
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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information
A.3 DESCRIPTION OF MP<&M SURVEYS
EPA used two screener and seven detailed questionnaires (surveys) issued between 1989 and 1996 to collect financial and
technical data from a sample of facilities that were evaluated for regulation under the final MP&M rule (see Section 3 of the
TDD). The responses to these surveys provided the basic financial and economic information used in the facility and firm
impact analyses. In addition, the POTW Survey provided information on facility permitting costs associated with regulatory
options considered by EPA. The various surveys are described below as they relate to the financial and economic analyses.
The MP&M rulemaking docket provides copies of the survey instruments and detailed information on the conduct of the
surveys.
A.3.1 Screener Surveys
In 1990, EPA distributed 8,342 screener surveys to sites believed to be engaged in the original seven Phase I MP&M sectors.
In 1996, EPA distributed 5,325 screener surveys to sites believed to be engaged in the eleven Phase II MP&M sectors. The
screener surveys helped EPA to identify sites to receive the more detailed follow-up surveys and to make a preliminary
assessment of the MP&M industry evaluated for the final rule. EPA identified the SIC codes applicable to the respective
MP&M sectors evaluated for the final rule and randomly selected names and addresses in those SICs to receive the screener
surveys based on Dun & Bradstreet databases.
A.3.2 Ohio Screener Surveys
EPA also sent the 1996 screener survey to 1,600 randomly selected sites in Ohio to support the Ohio case study.
A.3.3 Detailed MP<&M Industry Surveys
Based on responses to the screener surveys, EPA sent a more detailed survey to a selected group of water-using MP&M
facilities evaluated for the final rule. EPA collected financial and technical data from sample facilities in two phases.
Based on responses to the 1990 screener, EPA sent the Phase I detailed survey to a select group of water-using facilities. The
Agency designed this survey to collect detailed technical and financial information. EPA selected 1,020 detailed survey
recipients from water-discharging screener respondents, water-using screener respondents that did not discharge process
water, and a non-randomly selected group of known water-discharging facilities that did not receive the screener.
EPA used information from the first two groups of survey recipients to develop pollutant loadings and reductions and to
develop compliance cost estimates. Because EPA did not randomly select the third group of recipients, EPA did not use the
data to develop national estimates.
To reduce burden on survey recipients for Phase II of the data collection effort, EPA developed two similar detailed surveys.
Based on the development of the 1995 MP&M proposal, EPA chose to collect more detailed information from sites with
annual process wastewater discharges greater than one million gallons per year (1 MGY). EPA sent the "long" detailed
survey to all 353 1996 screener respondents evaluated for the final rule who indicated they discharged one million or more
gallons of process wastewater annually and performed MP&M operations. The Agency sent the "short" detailed survey to
101 randomly selected 1996 screener respondents evaluated for the final rule who indicated they discharged less than one
million gallons of process wastewater annually and performed MP&M operations.
The detailed survey responses provide financial, economic, and employment information about the site or the company
owning the facility. In addition, the 1996 long detailed questionnaire included a section that requested supplemental
information on other facilities owned by the company. EPA included this voluntary section to measure the impact of the final
MP&M effluent guidelines on companies with multiple facilities that discharge process wastewater. This section requested
the same information collected in the 1996 MP&M screener survey. Responses to questions in this section provided
information on the size, industrial sector, revenue, unit operations, and water usage of the company's other facilities.
The 1996 short survey included the identical general facility and economic information collected in the long detailed survey,
with one exception. Short survey recipients were not asked to provide information on the liquidation value of their plant.
A-28
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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information
A.3.4 Iron and Steel Survey
EPA also developed a detailed survey, under a separate rulemaking effort, to collect detailed information from facilities
covered by the Iron and Steel Manufacturing effluent guidelines (40 CFR Part 420). Following field sampling of iron and
steel sites and review of the completed industry surveys, EPA decided at proposal that some iron and steel operations would
be more appropriately covered by the MP&M rule because they were more like MP&M operations. EPA relied on the Iron &
Steel survey for financial and economic information on 47 iron and steel facilities. Commenters on the proposed rule stated
that these operations and resulting wastewaters are comparable to those at facilities subject to the Iron and Steel
Manufacturing effluent guidelines and that these discharges should remain subject to Part 420 rather than the final MP&M
rule. Also at NOD A, EPA considered including in the Steel Forming and Finishing subcategory wastewater discharges
resulting from continuous electroplating of flat steel products (e.g., strip, sheet, and plate). EPA also relied on the Iron &
Steel survey for financial and economic information on these 24 iron and steel facilities. EPA re-examined its database for
facilities that perform continuous steel electroplating, and found that, contrary to its initial finding, continuous electroplaters
do not perform operations similar to other facilities in this subcategory (i.e., steel forming and finishing facilities performing
cold forming on steel wire, rod, bar, pipe, and tube). Thus, EPA included continuous electroplaters performing electroplating
and coating operations in the General Metals subcategory for analyses supporting the final rule. As described in Section VI of
the preamble to the final rule, EPA is not revising limitations or standards for any of these facilities. Such facilities will
continue to be regulated by the General Pretreatment Standards (Part 403), local limits, permit limits, and Iron & Steel
effluent limitations guidelines (Part 420), as applicable.
A.3.5 Municipality Survey
EPA distributed surveys in 1996 to city and county facilities that might operate facilities engaged in MP&M operations
evaluated for the final rule. The Agency designed this survey to measure the rule's impact on municipalities and other
government entities that perform maintenance and rebuilding operations on MP&M products (e.g., bus and truck,
automobiles). The Agency sent the municipality survey to 150 city and county facilities randomly selected from the
Municipality Year Book-1995 based on population and geographic location. EPA allocated sixty percent of the sample to
municipalities and 40 percent to counties. The 60/40 distribution was approximately proportional to their aggregate
populations in the frame. EPA divided the municipality sample and the county sample into three size groupings as measured
by population. The surveys collected information on costs of service and on the financial and economic characteristics of the
governments operating these facilities.
A.3.6 Federal Facility Survey
EPA designed this survey to assess the impact of the MP&M effluent limitations guidelines and standards on federal agencies
that operate MP&M facilities. EPA distributed the survey to federal agencies likely to perform industrial operations on metal
products or machines. The Agency requested that the representatives of the seven chosen federal agencies voluntarily
distribute copies of the survey to sites they believed performed MP&M operations. The information collected in the 1996
federal survey was identical to the long survey. After engineering review and coding, EPA entered data from 44 federal
surveys into the database. Because EPA did not randomly select the survey recipients, data from these questionnaires were
not used to develop national estimates.
A.3.7 POTW Survey
EPA distributed the Publicly-Owned Treatment Works (POTW) survey in November 1997. The Agency designed this survey
to estimate possible costs and burden that POTWs might incur in administering MP&M permits or other control instruments
and to estimate benefits from implementation of the options considered for the final rule. The Agency sent the POTW Survey
to 150 POTWs with flow rates greater than 0.50 million gallons per day. EPA randomly selected the recipients from the 1992
Needs Survey Review, Update, and Query System Database (RUQus), and divided the POTW sample into two strata by daily
flow rates: 0.50 to 2.50 million gallons, and 2.50 million gallons or more.
In addition to the total volume of wastewater treated at the site, the POTW Survey requested the number of industrial permits
written, the cost to write the permits, the permitting fee structure, the percentage of industrial dischargers covered by National
Categorical Standards (i.e., effluent guidelines), and the percentage of permits requiring specific administrative activities.
EPA used this information to estimate administrative burden and costs. In addition, EPA requested information on the use or
disposal of sewage sludge generated by the POTW. The Agency only required POTWs that received discharges from an
MP&M facility to complete those questions. The POTW Survey requested the following sewage sludge information: amount
A-29
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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information
generated, use or disposal method, metal levels, use or disposal costs, and the percentage of metal loadings from MP&M
facilities. The Agency used this information to assess the potential changes in sludge handling resulting from the MP&M rule
and to estimate economic benefits of these options to the POTW.
A-30
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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information
REFERENCES
U.S. Department of Commerce. 2000. U.S. Bureau of the Census. The Bridge Between NAICS and SIC Report. March.
http://www.census.gov/epcd/www/naicensu.html
U.S. Department of Commerce. 2001. U.S. Bureau of the Census. Manufacturing Industry Series: Industry Stats on NAICS
Basis with Distribution Among 1987 SIC-Based Industries. ECON97S Report Series CD-Rom.
Small Business Administration. Statistics of U.S. Businesses, http://www.sba.gov/advo/stats/iat data.html
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MP&M EEBA: Appendices Appendix A: Detailed Economic Impact Analysis Information
THIS PAGE INTENTIONALLY LEFT BLANK
A-32
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MP&M EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Appendix B: Cost Pass-Through
Analysis
INTRODUCTION
This appendix presents the methodology and results from
the analysis of cost pass-through (CPT) potential for
19 MP&M sectors.1 This analysis consists of two parts:
1. an econometric analysis of the historical
relationship of output prices to changes in
input costs, and
2. an analysis of market structure
characteristics.
These two analyses together provide a numerical estimate
of how much of compliance-related cost increases a sector
can be expected to pass on to its consumers.
The rest of this appendix is organized into the following
six sections:
»• B.I: Rationale for developing sector-specific
CPT coefficients as opposed to firm-specific
CPT coefficients;
•• B.2: Econometric analysis of CPT potential,
based on the historical changes in output
prices relative to changes in input costs;
APPENDIX CONTENTS
B.I The Choice of Sector-Specific CPT Coefficients B-l
B.2 Econometric Analysis B-2
B.2.1 Framework B-3
B.2.2 Data Used to Estimate the Regression
Equation B-4
B.2.3 Regression Results B-6
B.3 Market Structure Analysis B-9
B.3.1 Measures Descriptions B-9
B.3.2 Results B-13
B.4 Validation of Econometrically-Estimated CPT
Coefficients B-16
B.4.1 Other Metal Products B-17
B.4.2 Job Shops B-17
B.4.3 Motor Vehicle B-18
B.4.4 Aircraft B-18
B.4.5 Mobile Industrial Equipment B-18
B.4.6 Aerospace B-18
B.5 Adjusting Estimates of Compliance CPT Potential B-18
Attachment B.A: Selected Review of CPT Literature B-20
B.A.1 Ashenfelter et al. (1998), "Identifying the
Firm-Specific Cost Pass-Through Rate." B-20
B.A.2 Exchange Rate Pass-Through B-20
B.A.3 Tax Pass-Through B-20
B.A.4 Studies Cited B-20
Acronyms B-22
»• B.3: Analysis of the market structure factors expected to affect cost recovery;
•• B.4: Validation of econometric estimates of the CPT coefficients;
•• B.5: Adjustment of estimated CPT coefficients to reflect the portion of an MP&M sector that will incur
compliance costs; and
> B.6: Attachment: Findings from a review of the CPT literature.
B.l THE CHOICE OF SECTOR-SPECIFIC CPT COEFFICIENTS
EPA believes the use of sector-specific CPT coefficients instead of firm-specific CPT coefficients in the impact analysis is an
appropriate and practical way of analyzing compliance CPT. The sector-wide rate provides an estimate of the change in each
facility's output prices as a function of the regulation-induced increase in its production costs, assuming that the same cost
increase is experienced by all establishments competing with the facilities in question. For MP&M sectors in which a large
fraction of establishments will be affected by the regulation, it is reasonable to assume that the MP&M compliance cost acts
1 The analysis of cost pass-through potential presented here refines in several places the methodology developed for the Phase I
MP&M analysis. These refinements are highlighted at the appropriate stages of the discussion that follows.
B-l
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
like an industry-wide cost shock. As noted below in section five, EPA applies an additional adjustment to the estimated CPT
rate to reflect the fraction of total sector output that is estimated to incur regulation-induced production cost increases.
In contrast to the concept of a sector-specific CPT adjustment, a firm-specific CPT rate relates a change in the prices charged
by a specific firm to a change in its production costs, assuming no change in the production cost for rival producers of that
product. Not surprisingly, previous studies have found that the CPT rate for changes on an individual firm's costs differs
from the rate at which a firm would pass through cost changes that are common to all, or a substantial fraction of, firms in an
industry (e.g., Ashenfelter et al., 1998). It is true, however, that firms in an industry will have differing CPT among each
other to some extent for reasons such as, differentiated products (e.g., products of different firms are not commodities and are
not perfectly substitutable); imperfectly competitive markets (e.g., markets in which individual firms possess different degrees
of market power); and segmented markets (e.g., geographically segmented markets). In the presence of such imperfections,
individual firms will very likely respond differently in their ability to pass on cost increases in higher output prices even when
the production cost increase applies to all, or a substantial fraction, of an industry's production. Nonetheless, estimating the
CPT ability of individual firms or sub-sector groups of firms within the MP&M sectors would require a detailed analysis of
market segments and substitutability of MP&M products. While this effort may be theoretically possible, it would be highly
expensive and an overall daunting challenge given the breadth of the MP&M industry sectors.
Therefore, this analysis of CPT potential in the MP&M industry is undertaken at the sector-specific level under the
assumption of perfect competition in these sectors -- including product homogeneity (i.e., products produced by one firm are
perfect substitutes for products produced by other firms), and homogeneity of production technology and cost across firms
(i.e., pricing is at marginal cost).2 Under these conditions, the price response to a general industry-wide change in production
costs is likely to be industry-wide and similar across all firms.
B.2 ECONOMETRIC ANALYSIS
EPA performed an econometric analysis of input costs and output prices to estimate historical CPT elasticities for 18 of the 19
Phase I and Phase II MP&M Sectors. EPA could not estimate historical CPT coefficients for Aerospace due to data
limitations. These elasticities indicate the changes in output prices by sector that have occurred historically in relation to
changes in the cost of production inputs. Two factors determine the share of a cost increase that a facility can pass through to
its customers: the elasticity of demand and the elasticity of supply in the facility's market. Both factors are difficult to
measure accurately; among other reasons, observed changes in price are due to simultaneous changes in demand and supply.
In view of this difficulty, this pass-through analysis does not decompose cost pass-through into the separate effects stemming
from elasticity of demand and elasticity of supply.
An additional analytic challenge involves joint consideration of quantity and price effects. Specifically, the amount of cost
increase that a firm may recover through a revenue increase may generally be decomposed into a change in price and a change
in quantity sold. In most markets, increased prices (in response to increased costs) translate into reduced quantity of sales.
The interaction of supply and demand elasticities determines whether or not total revenue increases.
For practical reasons, this analysis focused on the change in equilibrium price due to a change in input costs and furthere
assumes that the sale quantities of businesses complying with the regulation do not change. The analysis determined changes
in market quantities from closures rather than by estimating output changes in non-closing facilities. The analysis assumed
that the quantity of shipments or sales does not vary with the increase in fixed and average costs unless the facility closes.
The following grounds support this restriction:
> The cost model for the individual facility reflects a constant marginal cost relationship. The change in
quantity of output at a facility is a function of the change in equilibrium price and the marginal cost relationship
at the facility. For instance, in the case in which marginal cost increases with output, an upward shift in the
marginal cost relationship due to compliance costs will generally cause a facility to reduce its production
quantity. The extent of changes in production quantity will vary across facilities based on the shift in marginal
cost and the rate at which marginal cost changes with production. Engineering analysis of facilities provides no
information, however, about any change in the marginal cost relationship for a given facility, providing only
lump-sum costs. In lieu of this information, the analysis uses constant marginal costs, which in turn means that
2 These assumptions likely approximate the real world for those MP&M sectors that consist of a large number of small, highly
competitive firms such as Job Shops or Printed Wiring Boards.
5-2
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
profit-seeking facilities will tend not to change their output quantities in response to added costs resulting from
regulation. As a result, the only quantity-related decision that can be meaningfully analyzed at the facility level
is whether to terminate production completely.
*• An estimate of quantity response would be based on the aggregate industry response and would not be
logically applicable to the facility-level analysis. An analysis can estimate quantity elasticity response to
changes in input costs, but this value would represent the aggregate quantity response in the particular MP&M
sector. The aggregate response encompasses a diversity of responses across facilities: a few facilities may
eliminate production entirely while others may reduce, keep the same, or even increase output. Applying the
aggregate quantity response to individual facilities while simultaneously allowing for terminated production
would exaggerate the likely facility-level quantity response and the likelihood of facility closures. The current
analysis simulates the aggregate response from a micro-analytic perspective: exiting facilities that found
compliance to be an uneconomic proposition affect the industry-wide quantity response.
B.2.1 Framework
The analysis measured the sensitivity of equilibrium prices to changes in input costs. The "cost elasticity of price," denoted
-Ep, measured the percentage change in output price per percent change in unit input costs. EPA estimated the cost elasticity
of price by regressing annual output price indices on annual input price indices. The methodology's direct estimation
measured actual changes in output price with respect to changes in input costs. This practice took into account the full range
of possible mechanisms by which input costs affect output prices, including technical changes, substitution, non-competitive
pricing mechanisms, imperfect information phenomena, and any other shifts or irregularities in the supply and demand
functions.
The 19 MP&M industry sectors encompass 224 industrial 4-digit SIC codes. EPA, however, could estimate the cost elasticity
of price based on historical data for only 170 manufacturing SIC codes. EPA could not estimate the cost elasticity of price for
Aerospace and non-manufacturing industries due to data limitations, but assigned a CPT coefficient to the aerospace sector
based on the market structure analysis (see Section 2 for details, below).4 EPA assumed zero CPT for non-manufacturing
industries because these industries tend to be very competitive.
For each MP&M sector, EPA estimated a relationship for the k = 1 to 10 yearly observations (from 1987 to 1996) by
least-squares linear regression, as follows:
where:
Poutk = price index for the bundle of goods produced by the MP&M sector, year k;
Ep = elasticity of output price with respect to input costs for a given MP&M sector;
Pjnk-i = price index of inputs (labor and non-labor) to a given sector, year k-l;
b = elasticity of output price with respect to employment costs;
e = error term; and
ln(x) = natural log of x
Specifying the key regression variables as logarithms permitted EPA to estimate the elasticities of output prices with respect
to the independent variables directly. That is:
3 The elasticity measure also applies to revenue because quantity of production is assumed constant.
4 Output Price Index data for the Aerospace sector were unavailable. EPA attempted to use proxy data for missile manufacturing,, a
component of the defense sector, to estimate a CPT coefficient for the Aerospace sector. This analysis did not produce meaningful results.
The missile manufacturing industry witnes sed a sharp decline in producer prices during the 1987-1996 time period, therefore yielding a
negative CPT coefficient for the Aerospace sector. Since the Aerospace sector and the missile manufacturing industry are sufficiently
different from each other, EPA decided not to use the estimated CPT coefficient and instead derive a coefficient for the Aerospace sector
based solely on the market structure analysis.
B-3
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
(B-2)
which is the elasticity of output price with respect to input cost changes in the previous year.
EPA's use of the logarithmic transformations also eliminated any linear trend over time; in effect, the individual yearly
observations become cross-sectional variables. The model therefore required no specific time-series structure.
EPA considered additional independent variables that might aid in explaining output price changes. For example, EPA
included some measures of aggregate income, but these measures did not contribute significantly to the estimated
relationships.
The coefficients Ef from this regression are the estimated cost-elasticities of price for each MP&M sector. The estimated
coefficients address the question: over the period of analysis, by how much did output prices change as input costs increased?
The value of Ef for each sector, linked with other information on market structure, yielded a composite measure of cost
pass-through potential by MP&M sector. As discussed below, EPA used the results of the market structure analysis to
validate the estimated values of Ef, which represent the expected CPT potential for the different MP&M sectors. The
validated Ep values are the CPT coefficients ultimately assigned to sectors for the economic/financial impact analysis.
B.2.2 bata Used to Estimate the Regression Equation
Estimating Ef required a measure of the change over time in input costs and a measure of the change in output price for each
MP&M sector. EPA lagged output prices by a year because the market takes time to respond to price changes (i.e., input
prices from 1988 would predict output prices in 1989). For example, exchange rate pass-through studies found the lags
associate with price pass-through can extend from 5 to 8 quarters (J. Menon, 1995). EPA used data on changes of annual
output price indices from 1987 to 1996 and input price indices from 1986 to 1995. The final data set contains ten years of
data for each of the 18 industrial sectors of concern. The analysis estimated the relationship between change in output price
index (dependent variable) and change in input cost index. The input cost index (independent variables) combines a wide
range of non-labor cost values, including energy, with employment cost values.
a. Dependent variable
The dependent variable is the output price index. The Producer Price Index (PPI), an appropriate measure of output price,
measures changes in the price that the producer receives at the plant gate and is therefore the relevant price for the producer's
production decisions. MP&M products are often intermediate goods whose market prices are producer prices. EPA
estimated the dependent variable as the weighted average of PPIs for the goods produced by the industries in each sector.
EPA calculated the output price index for the sectors as follows:
N
p
out, k
(B-3)
N
where:
Poutk = average output price index value for a given MP&M sector in year k;
qik = value of shipments for SIC industry z, year k; and
PPIik = Producer Price Index for the output of SIC industry z, year k.
EPA used the following information to fill in data gaps for all output prices when the PPI series had missing data:
> Information at the 3-digit SIC code level if data were unavailable at the 4-digit SIC code level;
*• The percentage change in price at the 3-digit level, applied to the 4-digit level to calculate missing values, if data
at the 4-digit level were available for several years; and
B-4
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
*• A best-fit line to extrapolate data for years with missing data when at least five years worth of data were
available.
b. Independent variables
The independent variable is the input cost index. The input cost index averages the producer price index values for
commodity inputs to the sector in question, weighted by the share of each input to sector output. The weighted average
calculation involves two steps: (1) estimating input cost indices at the 4-digit SIC level and (2) developing the input cost
index at the MP&M sector level. These steps are discussed in detail below.
*»* Estimating Input Cost Indices at the 4-digit SIC level
EPA first identified the composition of production inputs required to produce output from a given industry by obtaining direct
requirement coefficients from the 1992 Benchmark Input-Output Tables of the United States? The direct requirement
coefficients are defined as follows: for each dollar of output from industry z, the direct requirements coefficient rj indicates
the value of input y required to achieve one dollar of output from industry z. The sum of all requirements coefficients rj for
industry z equals one. Note that the direct requirements coefficients from the input -output table include information on the
purchase of capital goods. Changes in the cost of capital goods are therefore reflected in the PPI series for the associated
industries. Because only one set of direct requirements coefficients were available for and are used in the analysis, this
analysis assumes that the input mix remains constant over the ten-year period considered in the analysis.
EPA then used yearly PPI values and the Employment Cost Index (EC I) from the Bureau of Labor Statistics to estimate
changes in the labor and non-labor components of production cost over time. The Agency used ECI for private manufacturers
to estimate changes in labor cost for all sectors except for aircraft manufacturing, for which a sector-specific ECI is available.
EPA calculated the input cost index for a 4-digit SIC group as a weighted average of prices for (a) all non-labor inputs for
which the PPI series data were available and (b) labor input. The percentage of inputs accounted for in our regression model
ranges from 39 percent to 100 percent, with an average of 66 percent.
To summarize, EPA calculated the input cost index as follows. For each 4-digit SIC industry, z, that uses non-labor inputs, j,
the average input price for the year k is:
p _
-*j I. ~~
f'
X
pplj
k~*
-r, >
-------
MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
N
,,
p = 1 _ (B-5)
in, k N
where:
Pink = average input price index value for a given MP&M sector in year k;
Pik = input price index value for SIC industry z, year k ; and
qik = value of shipments for SIC industry z, year k.
B.2.3 Regression Results
Table B.I below gives the estimated parameter values (corrected for autocorrelation) and t-statistics for each of the sectors.
Most of the estimated parameters have the expected sign and are statistically significant at 95th percentile. The estimated
parameters show that 16 of the 18 MP&M sectors have been able to increase prices, at the margin, between 42 percent and
121 percent for every one percent increase in non-labor input costs. The estimated input cost coefficients are negative for
two industrial sectors: Printed Circuit Boards and Office Machines. This finding suggest that additional market factors such
strong domestic and global competition drive output prices down.
Figure B.I below depicts output price and input cost trends from 1987 to 1996 for these two industries. It shows that in both
sectors, output prices decreased faster than input costs. This difference indicates that significant competition in these sectors
drives output prices down, undoubtedly through rapid technology innovation. An inverse relationship between labor cost and
output prices also indicates presence of strong competition in these two sectors. Based on these findings, it is reasonable to
assume that the printed circuit board and office machine sectors have zero CPT ability.
B-6
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Table B.I: CRT Regression Results By Sector
MP&M Sector
Aerospace
Aircraft
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shop
Mobile Industrial Equipment
Motor Vehicle
Office Machines
Ordnance
Other Metal Products
Precious Metals & Jewelry
Printed Circuit Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
Regression Coefficients (t-statistics in parenthesis)
'
Phase 1 Proposed Rule
(1982 to 1991)
'
Non-Labor
Input Costs
.774
(12.73)
.924
(37.22)
.930
(30.91)
.899
(25.28)
.889
(27.02)
.921
(43.03)
.923
(46.44)
N/A
N/A
.901
(23.94)
.898
(27.85)
.920
(35.05)
.907
(29.05)
N/A
.938
(24.82)
n/a
h .911
(30.52)
.970
(34.68)
.909
(28.09)
Labor Input
Costs
.001
(4.21)
.003
(3.32)
.003
(2.46)
.005
(3.46)
.005
(3.68)
.003
(4.16)
.003
(4.34)
N/A
N/A
.004
(2.68)
.004
(3.36)
.004
(3.52)
.004
(3.18)
N/A
:6o2
(1.68)
n/a
.004
(3.23)
:6o'i
(0.93)
"6'6'4
(3.06)
Phase 2 Model
(1987 to 1996)
' ^
Intercept
N/A
-0.9280
(-1.45)
0.629
(1.00)
2.79
(4.06)
1.06
(1.80)
1.69
(2.91)
1.06
(1.79)
1.12
(1.57)
1.97
(3.33)
0.546
(0.92)
0.833
(1.03)
~5
(17.2)
1.89
(3.63)
L71
(3.04)
i".69
(2.47)
6.23
(9.07)
0.548
(0.914)
0.817
(1.53)
'0.973'
(1.78)
Total Input Costs
(Labor+Non-Labor)
N/A
1.20
(8.90)
0.864
(6.52)
0.395
(2.72)
0.772
(6.22)
0.636
(5.22)
0.771
(6.18)
0.767
(5.14)
0.575
(4.61)
0.884
(7.05)
0.820
(4.76)
1933
(-15.6)
0.591
(5.41)
0.631
(5.34)
0.640
(4.42)
-0.337
(-2.31)
0.881
(6.98)
0.823
(7.32)
b".791
(6.88)
N/A = Not available from the Phase I analysis.
Source: U.S. EPA analysis
B-7
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Table B.2: Output Prices and Unit Input Cost Trends in the Printed Circuit Board and Office Machine Sectors
JJW •
t.TJ •
tJO •
3.7J •
o'
...t.. Kniid U^ i^lipi Gnu
& jt ,,1? ^ ^ ^ ^ ^ ^ ^ j^
3.7J'
'-^^^C^*-^ * •'-*••*...» ^j^.i-Q^-r.^
p £• ^ ^ ^ ^ ^ q* ^ ^ ^
Source: EPA Analysis.
Table B.I also presents Phase 1 results for comparison. Note the following differences in the Phase 1 and Phase 2 analyses:
1. Time period:
> Phase 1 analysis covers 1982 to 1991;
•• Phase 2 analysis covers 1987 to 1996.
2. Explanatory variables:
> Phase 1 analysis included non-labor and labor cost variables separately. The model has no intercept term. Note
that EPA then used only the non-labor input cost coefficient to estimate a CPT potential for a given sector;
> Phase 2 analysis combines labor and non-labor input costs because compliance costs are associated with both.
The intercept term captures additional market trends (e.g., increased import penetration) not reflected in the
input cost indices.
3. Industrial sectors:
> Phase 1 analysis included 15 industrial sectors. It excluded iron and steel, job shops, other metal products, and
printed circuit boards industries;
»• Phase 2 analysis includes 18 of the 19 industrial sectors and excludes the aerospace industry. The Phase 1
analysis included aerospace, but EPA used proxies from the aircraft industries to estimate output price indices
for the aerospace-related 4-digit SICs. EPA now estimates the CPT potential for this sector based on the market
structure analysis alone.
EPA assigned MP&M sectors to low, average, and high CPT categories based on the natural breaks in the estimated
parameter values. The estimated parameter values exhibit two distinct breaks in their distribution, between Precious Metals
and Jewelry (65.89 percent) and Hardware (78.17 percent) and between Motor Vehicle (82.45 percent) and Railroad (88.49
percent). EPA added the Aerospace sector to the high CPT category based on results from the market structure analysis.
Table B.3 summarizes results from this analysis.
B-&
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MP&M EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Table B.3:
Low CPT
Office Machine
Printed Circuit Boards
Electronic Equipment
Job Shop
Ordnance
Other Metal Products
Household Equipment
Precious Metals & Jewelry
Classification of MP&M Sectors by
Average CPT
Hardware
Instruments
Iron & Steel
Stationary Industrial Equipment
Ships & Boats
Motor Vehicle
CPT Ability
High CPT
Railroad
Mobile Industrial Equipment
Bus & Truck
Aircraft
Aerospace3
a Aerospace assigned to High category based on results from the market structure analysis (discussed in the next section).
Source: U.S. EPA analysis
B.3 MARKET STRUCTURE ANALYSIS
The second part of the analysis of cost pass-through potential is based on an analysis of the current market structure of the
MP&M industry sectors. Information on the competitive structure and market characteristics of an industry provide insight
into the likely ranges of supply and demand elasticities and the sensitivity of output prices to input costs. For example, when
input costs increase, the profit-maximizing firm attempts to maintain its profits by increasing output prices accordingly. The
amount of the cost increase that the firm can pass on as higher prices depends on the relative market power of the firm and its
customers. The market structure analysis described in this section attempts to measure the relative market power enjoyed by
firms in each MP&M sector and provides ordinal rankings used to validate the CPT coefficients estimated by the
econometric analysis. The analysis represents the current market structure and CPT ability of firms in the MP&M sectors and
in no way attempts to forecast the future market structure of these sectors.
B.3.1 Measures descriptions
The following discussion describes five indicators of market power used to assess cost pass-through potential for the 19
MP&M sectors. Only manufacturing firms have been considered; non-manufacturing firms have been excluded due to data
limitations. As noted above, EPA assigned zero CPT ability to non-manufacturing firms. The five indicators of market
power analyzed are : the eight-firm concentration ratio, import competition, export competition, long term growth, and
competition barriers. Each of these factors are discussed in detail below.
a. Concentration
The extent of concentration among a group of market participants is an important determinant of that group's market power.
A group of many small firms typically has less market power than a group of a few large firms, because the latter are in a
more advantageous position to collude with each other. All else being equal, highly-concentrated industries are therefore
expected to pass-through a higher proportion of the compliance costs that will result from this regulation.6
This analysis uses the eight-firm concentration ratio, which measures the percentage of the value of shipments concentrated in
the top eight firms in each four-digit SIC category, as an indicator of market concentration. The analysis estimates sector
concentration ratios as the weighted averages of component industry concentration ratios, weighted by SIC value of
shipments.7 An increase in the sector concentration ratio makes firms in an industry better able to pass on larger portions of
their input cost increases without adversely affecting quantities sold to a significant extent.
6 A substantial body of empirical research exists that has addressed the relationship between industry concentration and market
power. Eg., see Waldman & Jensen, 1997.
7 The eight-firm concentration ratio and value of shipments data used are for the year 1992.
B-9
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
This analysis is potentially limited by the necessity to aggregate component industries into sectors. The accuracy of any
analysis to characterize market power originating from industry concentration depends to a great extent on defining the
relevant market. A well-defined market requires including all competitors and excluding all non-competitors. Defining the
relevant market too narrowly overstates market power, while defining the market too broadly would underestimate it.
Aggregating concentration ratios for the four-digit SIC categories into a sector concentration ratio results in a sector average
that may overstate market power for some portions of the sector and understate market power for other portions. This
analysis would likely estimate concentration ratios for markets that in general are too broadly-defined.8 Even so, the sectoral
concentration ratios estimated should provide meaningful information that will assist in determining relative market power for
each sector, because firms producing similar or related products are still classified within the same sector and each sector
produces a distinctly different family of products (e.g., motor vehicles, aircrafts, ships and boats).
Another important determinant of the relevant market is its geographical extent. Given the nature of the MP&M industry,
however, this factor is not important because it pertains more to industries dealing with perishable commodities and those
with high transportation costs.
b. Import competition
Theory suggests that imports as a percent of domestic sales are negatively associated with market power because competition
from foreign firms limits domestic firms' ability to exercise such power. Firms belonging to sectors in which imports make
up a relatively large proportion of domestic sales will therefore be at a relative disadvantage in their ability to pass-through
costs compared to firms belonging to sectors with lower levels of import penetration, a measure of import competition.
Import penetration, the ratio of imports in a sector to the total value of domestic consumption in that sector, is particularly
significant because foreign producers will not incur costs as a result of this regulation.
In the market structure analysis, higher import penetration generally means that firms are exposed to greater competition from
foreign producers and will thus possess less market power to increase prices in response to regulation-induced increases in
production costs. The Census Bureau provides import data at the four-digit SIC level. EPA estimated sector import
penetration ratios as the ratio of the sum of component industry imports divided by the sum of component industry value of
domestic consumption9.
c. Export competition
The MP&M regulation will not increase the production costs of foreign producers with whom domestic firms must compete in
export markets. As a result, sectors that rely to a greater extent on export sales will have less latitude in increasing prices to
recover cost increases resulting from regulation-induced increases in production costs. They will therefore have a lower CPT
potential, all else being equal.
This analysis uses export dependence, defined as the percentage of shipments from a sector that is exported, to measure the
degree to which a sector is exposed to competitive pressures abroad in export sales. EPA used export data at the four-digit
SIC level and derived sector export dependence ratios: the sum of component industry exports divided by the sum of
component industry value of shipments.
That domestic producers export a substantial share of their product does not necessarily imply that they are subject to greater
competitive pressures abroad compared to what they face in domestic markets. Such would be the case in sectors where U.S.
producers are the dominant suppliers worldwide. To account for this possibility, EPA analyzed in more detail those sectors
showing high export dependence to see if domestic firms in those sectors appear to dominate the world market.10 Based on
information presented in the profile of MP&M industry profile, EPA determined that firms in all four of these sectors (i.e.,
precious metals and jewelry, ordnance, office machine, and aircraft) operate in highly competitive international markets. The
conventional theory that higher export dependence results in relatively lower market power is therefore assumed to hold true
for all MP&M sectors.
8 The four-digit SIC category, while not a perfect delineation, is most often used by industrial organization economists in their studies
because, among publicly available data sources, these industries appear to correspond most closely to economic markets (Waldman &
Jensen, 1997).
9 Census data on imports, exports, and value of shipments for the year 1996 were used for estimating this and the next market
structure indicator.
10 EPA considered sectors with export dependence exceeding 30 percent for this part of the analysis.
B-10
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
A substantial body of literature studies the link between environmental regulation and competitiveness in international trade.
Overall, little empirical evidence seems to support the hypothesis that environmental regulations have had a significant
adverse effects on the international competitiveness of domestic firms (Jaffe et al., 1995). Nonetheless, export dependence as
an important independent factor in assessing the validity of the estimated CPT coefficients. If historical changes in input
costs have affected both domestic and foreign firms more or less uniformly, then the econometrically estimated Ep would not
address situations in which only domestic firms face higher costs. Determining the exact extent to which changes in input
costs have affected both domestic and foreign producers uniformly is beyond the scope of this analysis. Such changes,
however, can affect a significant proportion of cost changes related to the non-environmental aspect of inputs, such as those
for energy, imported raw materials, and imported manufactured inputs.
Given the above, European and other developed countries have also implemented strict environmental regulations comparable
to U.S. regulations; even changes in environmental costs have therefore often been relatively uniform across domestic and
foreign firms. This uniformity may account for the fact that past studies do not show substantial impacts of U.S.
environmental regulation on the balance of trade.
Because this regulation will affect only domestic firms, and the analysis assumes that no similar regulatory response is
expected in foreign countries at least in the short term, domestic firms will face relatively higher production costs compared to
their international competitors as a result of regulation. To study the impact of this regulation on the change in MP&M
industry competitiveness in international markets, the market structure analysis must therefore include measures that assess
the effect of each sector's dependence on export markets on its ability to pass through costs.
d. Long-term industry growth
An industry's competitiveness and the ability of firms to engage in price competition are likely to differ between declining
and growing industries. Most studies have found that recent growth in revenue is positively related to profitability (Waldman
& Jensen, 1997), which suggests a greater ability to recover costs fully.
Based on Census Bureau data, EPA estimated the average growth rate in the value of shipments between 1988 and 1996 for
each sector, with the value of shipments for each component industry also serving as the weights for deriving average sector
growth rates. EPA expects firms in sectors with higher growth rates to be better positioned to pass through compliance costs
rather than being forced to absorb such cost increases in order to retain market share and revenues.
e. Competition barriers
Barriers to entry and exit help a concentrated industry exert market power by deterring potential competitors from entering the
market. Without these barriers, a firm that tries to pass through compliance costs by raising its prices risks losing its market
share to new firms that see an opportunity to compete at higher prices.
> Entry barriers are the fixed costs of beginning business in an industry. Entry barriers include high capital
costs, brand name reputations that require a large advertising expense to overcome, a long learning curve, and
any other factors that make the costs for new entrants higher than the costs of existing firms.
»• Exit barriers are the fixed costs that cannot be salvaged upon leaving the industry. They are sometimes called
sunk costs and are measured as the difference between the replacement value of a facility's capital and its
liquidation value. Exit barriers include factors that make it difficult for a firm to liquidate its assets, such as
specialized machinery that cannot be sold or converted to alternative uses, brand names that cannot transfer well
to other products, or substantial shutdown liabilities that would offset the value of assets in liquidation. The
capital valuations are typically needed to measure exit barriers.
An analysis measuring entry and exit barriers can avoid problems of data availability by identifying directly the presence of
above-normal profits that such barriers would permit. This analysis uses a sector's risk-normalized return on assets
(ROA) as an indicator of profit rates and the likely presence of entry and exit barriers. A popular measure used by managers
for measuring firm performance, the ROA is used an indicator of firm profitability. This analysis estimates an ROA before
interest payments and taxes to compare firms with different capital structures. Using the pre-tax ROA results in the adding
back of the interest tax shield and permits comparing ROAs among firms assumed to be entirely equity-financed. The
analysis measures firm riskiness by the Asset Beta, which is the firms' Equity Beta (i.e., measure of the firm's riskiness as an
investment relative to the market for equity investments as a whole), adjusted to remove their financing decision from the beta
calculation. With this adjustment, the analysis can compare firms with different capital structures because the Asset Beta
represents the beta of common stock had the firm been entirely equity-financed.
B-ll
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
The Capital Asset Pricing Model (CAPM) states that the expected risk premium on an investment (return earned over and
above the risk-free rate) reflect investment's riskiness relative to the market (beta). The Treynor Ratio, a commonly used
performance measure that uses betas as a measure of risk, embodies this principle of the CAPM:
Treynor Ratio = (Return from Investment - Risk Free Interest Rate) / (Beta of Investment)
For this analysis, however, the Treynor Ratio, or any other performance measure requiring estimation of the risk premium on
an investment, could not be used. More than 60 percent of the firms in the analysis had five year, pre-tax ROAs that were
lower than the risk-free interest rate of 5.21 percent (return on the three-month U.S. Treasury Bill for the five-year period
1996-2000). The analysis using the Treynor Ratio yielded results that did not permit a meaningful comparison of risk-
normalized ROAs among sectors. This analysis therefore used a modified form of the Treynor Ratio that adjusts the total
return and not just the risk premium by the riskiness of an investment. Applying this modification, the analysis estimated the
risk-normalized ROAs as follows:
Risk-Normalized ROA = ROA / Asset Beta
The analysis estimated risk-normalized ROAs for sectors using firm level data as opposed to data at the 4-digit SIC level, and
identified firms belonging to each MP&M sector using a two step process:
> First, EPA assigned facilities (and their parent firms) responding to the MP&M facilities survey to the sector
from which they received the largest portion of their revenues.
•• Second, EPA identified additional facilities belonging to each sector using a financial information Web site
(marketguide.com), which provides a classification of publicly-traded firms by the 4-digit SIC code of their
largest business segment based on revenues.
EPA estimated ROA and Beta values for a five-year time period, and estimated sector risk-normalized ROAs by weighting
each firm's risk-normalized ROA by its market capitalization.11
The use of the risk-normalized ROA measure only assigns MP&M sectors relative rankings and does not imply that they face
high or low barriers to competition in absolute terms. The analysis assumes that higher risk-adjusted profits in general
indicate potential entry and exit barriers and above average market power.
1' EPA further studied the business activities of firms belonging in the MP&M facilities survey that were identified as conglomerates
or found to own multiple facilities belonging to more than one MP&M sector, and of firms in the broader sample having a market
capitalization exceeding $25 billion. This additional step ensured that the market capitalization weight used in the analysis represented only
the fraction of revenues that the firm receives from its business activities in the MP&M sector(s) of interest.
B-12
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MP&M EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
B.3.2 Results
EPA used these five indicators to assign each sector a cost pass-through score. Higher numerical values indicate greater CPT
potential for some indicators (e.g., industry concentration) and lesser CPT potential for others (e.g., import competition).
Table B.4 summarizes the specific ranking definitions for each indicator.
Table 6.4: Summary of Ranking Rules for Assessing Relative
Pass -Through Potential Based on Market Structure Considerations"
8-Firm Concentration Ratio
Ratio of Imports to Shipments
Ratio of Exports to Shipments
Average Growth Rate of Shipments
Risk-Normalized Pre-Tax Return on Assets
Variable Indicates Greater Pass-
Through Potential (High Rank)
Greater than median
Lesser than median
Lesser than median
Greater than median
Greater than median
Variable Indicates Lesser Pass-
Through Potential (Low Rank)
Lesser than median
Greater than median
Greater than median
Lesser than median
Lesser than median
a All assessments of pass-through potential are relative among the 19 MP&M Sectors.
Source: U.S. EPA analysis.
For each of the five indicators, EPA ranked sectors from 1 to 19, with 1 assigned to the sector assessed to have the lowest
CPT potential and 19 assigned to the sector assessed to have the highest CPT potential.12 Based on this scoring system, the
possible score for a sector when all five of its ranks are summed ranges from 5 to 95. Table B.5 presents a summary of the
results for the market structure analysis.
12 This ranking scale differs from the scale used to assign scores in the market structure analysis undertaken for the Phase I MP&M
analysis. In the Phase I analysis, depending on the variable under consideration, a sector received a value of+1 if it indicated a greater
CPT potential relative to the median and a value of -1 if it indicated a lesser CPT potential relative to the median . The sector at the
median received a value of 0. The use of the median value as the threshold for determining relatively higher or lower (+1 or -1) market
power was somewhat arbitrary, especially for values closely centered around the median. The new scale, since it considers individual
sector ranks, is superior because it explicitly recognizes that extreme values are more likely to be indicative of high or low market power,
and accordingly assigns them a higher or lower score. For example, the old scale would assign a sector with industry concentration just
above the median (e.g., other metal products) the same score of+1 as a very highly-concentrated industry, such as aerospace. The new
scale, however, recognizes the difference in industry concentration between the two sectors and therefore assigns the first sector a rank
close to 10 and aerospace a rank close to 19.
B-13
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Table B.5: Results of the Market Structure Analysis"
i
:
:
:
Overall j
_ , Sector
Rank j
i
:
:
j Precious Metals and
I Jewelry
„
j Printed Circuit
j Boards
3 j Ordnance
j Household
j Equipment
4 j Office Machine
! Electronic
1 Equipment
7 | Aircraft
8 j Iron and Steel
| Other Metal
! Products
! Stationary
10 ! Industrial
1 Equipment
1 1 ! Hardware
1 2 j Instruments
j Mobile Industrial
j Equipment
14 j Ships and Boats
15 | Job Shop
15 | Motor Vehicle
17 j Aerospace
17 | Bus & Truck
19 j Railroad
8-flrm
Concentration
Ratio
,
Value
35.0
35.0
76.90
54.22
61.38
47.27
85.3
41.87
54.27
41.16
24.52
44.2
58.56
58.20
19.26
77.30
92.29
42.51
71.00
Rank
4
3
16
10
14
9
18
6
11
5
2
8
13
12
1
17
19
7
15
Import
Penetration
(%)
Value
77.36
21.99
18.92
33.18
51.85
24.55
22.74
4.54
32.40
17.71
14.31
15.33
21.42
6.49
0.00
27.56
0.75
2.86
15.16
Rank
1
8
10
3
2
6
7
16
4
11
j
14
12
9
15
19
5
18
17
13
Export
Dependence
(%)
Value
49.85
17.07
50.17
17.02
43.41
24.04
46.43
1.32
17.57
23.64
11.37
23.07
29.62
6.48
0.00
15.74
0.75
3.04
10.26
Rank
2
10
1
11
4
6
3
17
9
7
13
5
15
19
12
18
16
14
Avg. Annual
Growth Rate
(%)
Value
-1.9
1.5
-7.3
1.5
3.1
5.1
-1.7
0.4
1.1
3.7
2.1
1 C
1 .0
2.8
-1.5
3.1
2.6
-7.6
4.8
7.6
Rank
3
2
9
15
18
4
6
7
16
11
10
13
5
14
12
1
17
19
Risk-
Normalized
ROA (%)
Value
14.43
7.50
12.30
12.02
9.58
7.21
16.15
11.38
26.60
16.78
17.18
19.64
18.13
16.11
13.44
18.10
13.19
12.31
14.62
Rank
10
2
6
5
3
1
13
4
19
14
15
18
17
12
9
16
8
7
11
Aggregate
Score
20
31
35
38
38
40
45
49
50
53
55
56
57
59
62
62
64
64
72
a Shaded values are the medians for each market structure indicator.
Source: U.S. EPA analysis
This rank scoring system has some important limitations:
1. This grading scale implicitly assigns equal weights to each of the five market structure indicators. Clearly, the impact
of each of these five indicators on market power will vary from sector to sector, and some indicators are likely to
dominate others within each sector.
2. Although the ranking scale distinguishes between sectors with extreme values and those that are close to the median,
it does not permit an accurate judgement about how significant a particular value may be in determining market
power. For each indicator, sectors are simply ranked from 1 to 19 based on the lowest to highest market power
potential. The change in market power expected as one moves from sector 1 to sector 5 is not likely to be equal,
however, to the change in market power expected as one moves from sector 6 to sector 10.
In general, the market structure analysis revealed that a discernable gap exists in the estimated parameters around rank 4/5 and
around rank 14/15 for most indicators (see Table B .6). For each indicator, two small groups, each containing about four to
B-14
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
five sectors, therefore seem to have relatively low and high market power. A much larger group of about nine to ten sectors
exhibit average market power.
Table B.6: Distribution of Estimated Parameters for Market Structure Variables
Rank
-i
*-\
T
4
»
6
rj
9
10"
11
12
13
14
15a
16
17
18
19
8-firm
Concentration
Ratio
19.26
24.52
35.00
35.07
41.16
41.87
42.51
44.22
47.27
54.22
54.27
58.20
58.56
61.38
71.00
76.90
77.30
85.32
92.29
Import
Penetration
77.36%
51.85%
33.18%
32.40%
27.56%
24.55%
22.74%
21.99%
21.42%
18.92%
17.71%
15.33%
15.16%
14.31%
6.49%
4.54%
2.86%
0.75%
0.00%
Export
Dependence
50.17%
49.85%
46.43%
43.41%
29.62%
24.04%
23.64%
23.07%
17.57%
17.07%
17.02%
15.74%
11.37%
10.26%
6.48%
3.04%
1.32%
0.75%
0.00%
Average Annual
Growth Rate
-7.6%
-7.3%
-1.9%
-1.7%
-1.5%
0.4%
1.1%
1.5%
1.5%
1.8%
2.1%
2.6%
2.8%
3.1%
3.1%
3.7%
4.8%
5.1%
7.6%
Risk-Normalized
ROA
7.21
7.50
9.58
11.38
12.02
12.30
12.31
13.19
13.44
14.43
14.62
16.11
16.15
16.78
17.18
18.10
18.13
19.64
26.60
a Highlighted rows mark the natural gaps in the various indicators.
Source: U.S. EPA analysis
The aggregate market structure scores for all sectors range from a low of 19 to a high of 71. Apart from the lowest score
(precious metals and jewelry) and the highest score (railroad), all the other scores are uniformly distributed with no clear
breaks in their distribution that can be used for classifying sectors by their CPT potential (see Table B.5). EPA therefore used
an alternative classification system for the market structure analysis. Based on the average aggregate score of 50 (average
rank of 10), EPA assigned sectors with an aggregate score of 40 or below (average rank of 8 or less) to the low CPT category,
and assigned sectors with an aggregate score of 60 or above (average rank of 12 or more) to the high CPT category. EPA
assigned sectors with aggregate scores between these cutoffs to the average CPT category. Table B.7 shows the
categorization of all 19 sectors by their CPT potential based on this classification system. In total, EPA classified six, eight,
and five sectors in the low, average, and high CPT categories, respectively. The classification cutoffs, though somewhat
arbitrary, result in a sector classification similar to the trends witnessed for most individual indicators, such that about five
sectors are classified in the low and high CPT categories and the remaining sectors are classified as having average CPT
potential.
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Table B.7:
Low CPT
Precious Metals & Jewelry
Printed Circuit Boards
Ordnance
Household Equipment
Office Machine
Electronic Equipment
Classification of MP&M Sectors by
Average CPT
Aircraft
Iron & Steel
Other Metal Products
Stationary Industrial Equipment
Hardware
Instruments
Mobile Industrial Equipment
Ships & Boats
CPT Ability
High CPT
Job Shop
Motor Vehicle
Aerospace
Bus & Truck
Railroad
Source: U.S. EPA analysis
Although recognizing the limitations of the ranking scale, EPA believes that it is useful for presenting the results succinctly
and provides a basis for validating the estimated CPT coefficients. Analyzing the relative importance of each indicator for
each of the sectors is beyond the scope of this analysis.
B.4 VALIDATION OF ECONOMETRIC ALLY- ESTIMATED CPT COEFFICIENTS
The econometric analysis provides a quantitative assessment of what the cost pass-through ability of each sector appears to
be. The market structure analysis yields a judgment of what the pass-through ability of each sector ought to be. In this
section the two analyses are brought together, with the results of the market structure analysis used to validate the CPT
coefficients estimated by the econometric analysis.
Table B.8 shows a comparison of each sector's CPT classification based on the econometric analysis and the market structure
analysis. The two analyses classify 13 of the 19 sectors in the same CPT category. For these sectors, the market structure
analysis appears to validate the CPT coefficient derived using the econometric analysis. No econometric estimate is available
for one sector (aerospace); for this sector, EPA used only the market structure analysis. For the remaining five sectors,
however, the two analyses assign sectors to different CPT categories. EPA undertook a more detailed analysis of these
sectors' market structure to validate their CPT coefficient. Specifically, EPA examined the following two factors affecting
firm's market power in a given industrial sector:
•• Whether any (i.e., one or more) of the five structural indicators may be extremely important or irrelevant for a
particular sector, and therefore whether its effect on market power is being under-weighted or over-weighted,
respectively.
•• Whether other factors affecting market power for these sectors have not been included in the market structure
analysis, but which possibly have substantial effects on market power/CPT ability in particular sectors.
The discussion below summarizes EPA's review and conclusions for each of these six sectors.
B-16
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
Table B.8: Comparison of Sectoral Classification Based on Econometric and Market
Structure Analysis
Sector Econometric Analysis Market Structure Analysis
CPT Categorization Matches
Electronic Equipment
Household Equipment
Office Machine
Ordnance
Precious Metals and Jewelry
Printed Circuit Boards
Hardware
Instruments
Iron and Steel
Ships and Boats
Stationary Industrial Equipment
Bus & Truck
Railroad
C
Other Metal Products
Job Shop
Motor Vehicle
Aircraft
Mobile Industrial Equipment
Aerospace
Low
Low
Low
Low
Low
Low
Average
Average
Average
Average
Average
High
High
PT Categorization Does Not Match
Low
Low
Average
High
High
CPT Comparison Not Possible
N/A
Low
Low
Low
Low
Low
Low
Average
Average
Average
Average
Average
High
High
Average
High
High
Average
Average
High
Source: U.S. EPA analysis.
B.4.1 Other Metal Products
This sector is assigned to the low category by the econometric analysis and the average category by the market structure
analysis. EPA believes that the estimated CPT coefficient for this sector is accurate and that the market structure score for
this sector is somewhat misleading because of the exceptionally high risk-normalized ROA derived for it. A priori, there
appears to be no reason why firms in this sector should be able to earn significantly higher returns than in other sectors, and
the high risk-normalized ROA estimated is likely an artifact of the small sample of firms for which financial data were
available to estimate risk-normalized returns for this sector. The other four indicators of market power suggest below-average
CPT for this sector, which agrees with the CPT coefficient estimated from the econometric analysis.
B.4.2 Job Shops
EPA assigned this sector to the low category by the econometric analysis and the high category by the market structure
analysis. EPA believes that the market structure analysis may be misleading due to the high CPT ranks assigned to the Import
Penetration and Export Dependence indicators of market power for this sector. These two indicators of market power are not
relevant for this sector, however, because the sector is not trade-oriented. EPA expects the level of domestic competition
among job shops to be the single most important factor that determines market power and the ability of firms to pass through
costs in the sector. The Job Shop sector has the lowest concentration ratio among all the sectors, suggesting that the sector is
characterized by a substantial number small firms (see Table 3.8 in the MP&M Industry Profile) that are most likely engaged
B-17
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
in intense competition among each other. The estimated, low, CPT coefficient for this sector therefore appears to be
appropriate.
B.4.3 Motor Vehicle
This sector is assigned to the average category by the regression analysis and the high category by the market structure
analysis. EPA believes that this sector is characterized by average cost pass-through potential due to the extremely
competitive nature of the motor vehicle industry both domestically and in international markets. In recent years, in a bid to
remain or become more competitive, the trend in this industry has been towards the continual consolidation of firms into
globalized manufacturers. In fact, motor vehicle manufacturers are no longer constrained within national boundaries, as
mergers and joint ventures include some of the largest firms from different countries. In addition, manufacturers have
increasingly standardized the design of motor vehicles and their parts, changes that have resulted in much less product
differentiation (but greater product quality) among manufacturers. The increasing intensity of global competition and the
move towards decreasing product differentiation are likely to limit the ability of domestic producers to pass-through
significant portions of their cost increases associated with this regulation. Therefore, the finding of an average cost pass-
through coefficient appears to be justified.
B.4.4 Aircraft
This sector is assigned to the high category by the econometric analysis and the average category by the market structure
analysis. Based on the unique nature of the global aircraft industry, EPA believes that the estimated CPT coefficient for this
sector is appropriate. Not only is the industry concentrated domestically (concentration ratio of 85.3), but this is also true of
the global aircraft manufacturing industry. In recent years, the industry has witnessed substantial restructuring through
mergers and consolidation, both nationally and internationally (see section 3.2.2 in the MP&M Industry Profile). The highly
concentrated nature of the industry, combined with the sizeable share of the domestic market that is controlled by domestic
aircraft manufacturers, suggests that firms in this sector have the ability to pass through a significant portion of their cost
increases.
B.4.5 Mobile Industrial Equipment
EPA assigned this sector to the high category by the econometric analysis and the average category by the market structure
analysis. EPA believes that this sector is more appropriately characterized by average CPT because the sector has witnessed
certain trends in recent years that suggest that firms in this sector do not have a high ability to pass through cost increases.
Specifically, growth rates in the construction and the farm and machinery equipment industries started to level off or even
declined in recent years after a sustained period of growth (see section 3.2.10 in the MP&M Industry Profile). These
declining trends are not fully represented in the regression analysis because the last year of analysis is 1996. EPA therefore
revised the CPT coefficient for this sector to equal the average CPT value for all sectors classified in the average category
based on the regression analysis.
B.4.6 Aerospace
Since the market structure analysis categorizes the Aerospace sector in the high CPT category, EPA estimated the CPT
coefficient for this sector as the average CPT value for all sectors classified in the high category based on the regression
analysis (excluding Mobile Industrial Equipment whose CPT coefficient was revised based on the market structure analysis).
B.5 ADJUSTING ESTIMATES OF COMPLIANCE CPT POTENTIAL
The CPT values estimated above reflect sector level CPT potential. The methodology must consider that ability to pass on
cost increases through price increases will differ at the industry level versus the facility level. Cost increases that affect all
facilities in an industry are more likely to be recovered through industry-wide price increases, whereas cases where only a few
facilities in an industry incur cost increases are less likely to result in price increases. This analysis must therefore take into
account the proportion of an industry that will experience cost increases when applying industry-level cost pass-through
coefficients.
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MP&M EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
For the final MP&M rule, EPA will use the method used in the Phase I analysis where EPA adjusted the industry-level cost
pass-through coefficient downward in proportion to the percentage of sector output bearing compliance cost. The ratio of the
revenues in water-discharging facilities affected by the rule divided by total revenues in the MP&M sector provided a
measure of the fraction of production in the MP&M sector likely to be affected by cost increase. That is, a cost pass-through
percentage of 90 percent would be reduced to 72 percent if 80 percent of the sector output was subject to the regulation (.80 x
.90 = .72). EPA applied this adjusted pass-through percentage to the percentage cost increase experienced by the regulated
facilities only (i.e., sum of compliance costs divided by the sum of baseline costs for the facilities subject to the rule). Table
B.9 presents the adjusted CPT coefficients estimated for each sector.
Table B.9: Adjuste
Sector
Aerospace"
Aircraft"
Bus & Truck
Electronic Equipment
Hardware
Household Equipment
Instruments
Iron and Steel
Job Shop
Mobile Industrial Equipment"
Motor Vehicle
Office Machines'1
Ordnance
Other Metal Products
Precious Metals & Jewelry
Printed Circuit Boards
Railroad
Ships and Boats
Stationary Industrial Equipment
d Estimates of Compliance C
Unadjusted Cost Pass-
Through Potential
0.98
1.20
0.86
0.39
0.77
0.64
0.77
0.77
0.57
0.79
0.82
(9.33)
0.59
0.63
0.64
(0.34)
0.88
0.82
0.79
ost Pass-Through Potential
Estimated Fraction of
Sector's Revenue Subject to
Regulation (%)
100.00
100.00
100.00
100.00
33.50
100.00
100.00
100.00
43.70
100.00
44.10
34.50
100.00
100.00
42.90
53.60
100.00
100.00
32.20
by MP&M Sector
Adjusted Cost Pass-Through
Potential
1.00
1.00
0.96
0.42
0.26
0.64
0.77
0.77
0.25
0.79
0.36
0.00
0.59
0.63
0.27
0.00
0.88
0.82
0.25
a CPT coefficient for the Aerospace sector estimated based on the market structure analysis.
b For the Aircraft sector, the cost-pass through potential is capped at 100%.
° CPT coefficient for the Mobile Industrial Equipment sector revised based on the market structure analysis.
d For the Office Machine and Printed Circuit Boards sectors, the cost-pass through coefficients are set to zero based on both the
estimated negative regression coefficient and the results of the market structure analysis.
Source: U.S. EPA analysis
B-19
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
ATTACHMENT B.A: SELECTED REVIEW OF CRT LITERATURE
To support the CPT analysis, EPA undertook a selected review of previous CPT analyses. The two most studied areas in the
literature deal with exchange rate pass-through and tax pass-through. Unfortunately, neither of these study types is useful in
assessing the reliability of the MP&M CPT results. Sections B.A.2 and B.A.3 provide a brief summary of this studies. One
study (Ashenfelter et al,1998) estimates the pass-through rate for cost changes faced by an individual firm and compares it
with passes-through of cost changes common to all firms in an industry. This appears to be the most relevant to the analysis
of compliance costs pass through. Section B.A.I provides a brief summary of findings from this study.
B.A.I Ashenfelter et ol. (1998), "Identifying the Firm-Specific Cost Pass-Through
Rate."
As noted above, Ashenfelter et al. (1998) examines the pass-through rate for cost changes faced by only an individual firm
(Staples, an office superstore chain), and distinguishes that rate from the rate at which a firm passes through cost changes
common to all firms in an industry. Based on their analysis, they find the combined firm-specific and industry-wide pass-
through rate (i.e., with no distinction between cost changes specific to the individual firm and those applicable to the entire
industry) to be 57 percent. Conversely, the pass-through rate estimated for only firm-specific cost changes is about 15 percent
and the pass-through rate for only industry-wide cost changes is close to 85 percent. The finding of a high CPT rate for
industry-wide cost changes lends supportto EPA's finding of similarly high historical CPT rates formany of the MP&M
sectors.
B.A. 2 Exchange Rate Pass-Through
The exchange rate pass-through literature examines the response of local currency import prices to variation in the exchange
rate between exporting and importing countries. Based on seven studies covering the period 1970 to the mid-1980s, Menon
(1995) finds that the estimated aggregate pass-through of exchange rate changes to import prices ranges from a low of 48.7
percent to a high of 91 percent. The mean value for pass-through for the sample of studies he considered is 69.9 percent. In
contrast, Feinberg (1989) considers the impacts of exchange rate movements on U.S. domestic prices and finds an average
pass-through of 16 percent in real terms. The pass-through is close to complete for industries that are heavily reliant on
imported inputs and producing goods highly substitutable for imports. Pass-through rates are much lower for capital-intensive
and concentrated industries and those protected by barriers to entry. The exchange rate pass-through scenario, however, is
not comparable to the nature of compliance cost changes expected under the MP&M regulation and the resultant pass-through
responses from domestic producers because the studies focus primarily on the impact of exchange rate changes on prices of
imported goods and not on prices of domestically produced goods. Feinberg's study appears to be more relevant, but he does
not present pass-through rates for individual industries, and does not explain why pass-through rates are much lower for
capital-intensive and concentrated industries and those protected by barriers to entry.
B.A. 3 Tax Pass-Through
The literature on tax pass-through examines the impact of excise tax changes on prices. Of the several studies that addressed
the issue of tax pass-through, the majority report pass-through rates slightly in excess of a 100 percent (Ashenfelter et al.,
1998). This literature is not entirely relevant to the CPT scenario being analyzed for this rule because most of these studies
analyze changes in excise tax rates in the cigarette industry. In addition, excise tax changes on final goods do not affect
manufacturing costs, and they have a uniform impact on the entire industry. Excise taxes do affect domestic producers,
however, by altering final demand and therefore revenues received.
B.A.4 Studies Cited
Ashenfelter, Orley, et al. (1998), "Identifying the Firm-Specific Cost Pass-Through Rate," FTC Working Paper No. 217,
January.
Feinberg, Robert M (1989), "The Effects of Foreign Exchange Movements on U.S. Domestic Prices,"The Review of
Economics and Statistics.
Jaffe, A. etal., (1995), "Environmental Regulation and the Competitiveness of U.S. Manufacturing: What does the Evidence
Tell Us?" Journal of Economic Literature, XXXIII (March): 132-63.
B-20
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MP&M EEBA: Appendices Appendix B: Cost Pass-Through Analysis
Menon, Jayant (1995), "Exchange Rate Pass-Through," Journal of Economic Surveys, 9(2).
Waldman, Don E. and Elizabeth J. Jensen (1997), Industrial Organization: Theory and Practice. Addison-Wesley.
B-21
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MPAM EEBA: Appendices
Appendix B: Cost Pass-Through Analysis
ACRONYMS
CAPM: Capital Asset Pricing Model
CPT: cost pass-through
EC I: Employment Cost Index
PPI: Producer Price Index
ROA: risk-normalized return on assets
5-22
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MPAM EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector
Appendix C: Summary of Moderate
Impact Threshold Values by Sector
INTRODUCTION
APPENDIX CONTENTS
C.I Developing Threshold Values for Pre-Tax Return on Assets
(PTRA) C-l
C.2 Developing Threshold Values for Interest Coverage Ratio
(ICR) C-2
C.3 Summary of Results C-4
References C-5
Facilities subject to moderate impacts from the rule are
expected to experience financial stress short of closure.
This analysis uses two financial indicators: (1) Pre-Tax
Return on Assets (PTRA) and (2) Interest Coverage Ratio
(ICR). These threshold values were compared to pre- and
post-compliance PTRA and ICR values for sample
facilities to determine if facilities choosing to remain in
business after promulgation of effluent guidelines would
experience moderate impacts on their ability to attract and finance new capital. The remainder of this appendix describes the
sources and methodology used to derive sector-specific moderate impact threshold values.
EPA calculated the thresholds using income and financial structure information by 4-digit SIC code from the Risk
Management Association (RMA) Annual Statement Studies for eight years 1994-2001 (RMA, 2001; RMA 1998). This
source provides quartile values derived from statements of commercial bank borrowers and loan applicants for firms having
less than $250 million in total assets. These criteria may introduce bias, since firms with particularly poor financial
statements might be less likely to apply to banks for loans, and some types of firms may be more likely to use bank financing
than others. However, the RMA data offers the advantage of being available by 4-digit SIC codes and for quartile ranges.
RMA did not provide data for all 4-digit SIC codes associated with an MP&M sector. Out of 174 manufacturing SIC codes
and 50 non-manufacturing SIC codes, 52 manufacturing SIC codes (30 percent) and 13 non-manufacturing SIC codes (26
percent), had no years of data available. RMA did not compile data for any SIC codes in two manufacturing sectors,
Ordnance and Aerospace and one non-manufacturing sector, Precious Metals and Jewelry. When data were not available for
any SIC codes within the sector, EPA calculated an average manufacturing or non-manufacturing threshold to use as a proxy.
The 4-digit SIC code data were consolidated into weighted sector averages, weighted by 1997 value of shipments from the
Economic Censuses (U.S. DOC, 1997). For each sector and impact measure, a separate threshold was calculated for
manufacturing and non-manufacturing SIC codes. The use of the RMA data for calculating the threshold values for pre-tax
return on assets and interest coverage ratio is outlined below.
c.l ENVELOPING THRESHOLD VALUES FOR PRE-TAX RETURN ON ASSETS (PTRA)
Pre-tax return on total assets measures the effectiveness of management in employing the resources available to it. A low
ratio may indicate that a borrower would have difficulty financing treatment investments and continuing to attract investment.
The following data from Risk Management Association Annual Statement Studies were used to calculate PTRA:
»• % Profit Before Taxes I Total Assets Ratio of profit before taxes divided by total assets and multiplied by
100 for the lowest quartile of values in each 4-digit SIC code.
»• Operating Profit Gross profit minus operating expenses.
•• Profit Before Taxes Operating profit minus all other expenses (net).
RMA provides a measure of pre-tax return on assets that approximates the measure that EPA defined for the moderate impact
analysis. As defined by RMA, this measure is the ratio of pre-tax income to assets, designated ROARMA:
ROARMA = Pre-Tax Income (EBT) / ASSETS25th
C-l
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MPAM EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector
However, as defined by EPA for its analysis, the numerator of the PTRA measure requires the use of earnings before interest
and taxes (EBIT) instead of pre-tax income (EBT). Defined as EBIT, the PTRA numerator will capture all return from assets,
whether going to debt or equity. To derive a pre-tax, total return value, EPA adjusted RMA's measure of PTRA using the
median percentage values of EBIT and EBT available from RMA. This adjustment yields the PTRA measure that EPA used
in the moderate impact analysis, designated ROAMP&M:
ROAMP&M = ROARMA * EBIT /EBT
Negative values are included in the weighted-sector PTRA averages but a different method is used to adjust the ROA values
reported in RMA to the value used in the moderate impact analysis. Specifically, using only those observations (i.e., 4-digit
SIC code and year combinations) with positive values for % Profit Before Taxes / Total Assets, Operating Profit, and Profit
Before Taxes, EPA calculated an adjustment factor by subtracting the difference between ROAMP&M and ROARMA as follows:
ROAMP&M-ROARMA = adjustment factor.
Those values were consolidated into sector-specific adjustment factors, weighted by 1997 value of shipments from the
Economic Censuses (U.S. DOC, 1997). Each negative PTRA observation from RMA was adjusted by its sector specific
adjustment factor to approximate the measure used in the moderate impact analysis:
ROARMA + sector-specific adjustment factor = ROAMP&M
The sector-specific adjustment factors average 0.47 for manufacturing sectors and range from 0.13 for the Office Machines
sector to 0.60 for the Aircraft and Motor Vehicle sectors. The sector-specific adjustment factors average 0.22 for non-
manufacturing sectors and range from 0.15 for the Motor Vehicle sector to 0.74 for the Railroad sector.
C.2 &EVELOPINS THRESHOLD VALUES FOR INTEREST COVERAGE RATIO (ICR)
Interest coverage ratio is a measure of a firm's ability to meet current interest payments and, on a pro-forma basis, to meet the
additional interest payments under a new loan. A high ratio may indicate that a borrower would have little difficulty in
meeting the interest obligations of a loan. This ratio also serves as an indicator of a firm's capacity to take on additional debt.
The following data from Risk Management Association Annual Statement Studies were used to calculate ICR:
> EBIT/Interest25th Ratio of earnings (profit) before annual interest expense and taxes
(EBIT) divided by annual interest expense for the lowest quartile of
values in each 4-digit SIC code.
*• % Depr., Dep., Amort./Salesmed Median ratio of annual depreciation, amortization and depletion
expenses divided by net sales and multiplied by 100.
> Operating Profit Gross profit minus operating expenses.
RMA provides a measure of interest coverage that approximates the measure that EPA defined for the moderate impact
analysis. As defined by RMA, this measure is the ratio of earnings before interest and taxes to interest, designated ICRRMA:
ICRRMA=EBIT/INTEREST25th
However, as defined by EPA for its analysis, the numerator of the ICR measure requires the use of earnings before interest,
taxes, depreciation, and amortization (EBITDA) instead of earnings before interest and taxes (EBIT). Defined this way, the
ICR numerator will include all operating cash flow that could be used for interest payments. To derive the desired ICR value
(designated ICRMP&M), EPA adjusted the RMA value as outlined below:
ICRMP&M = EBITDA / INTEREST
Therefore, ICRMP&M = ICRRMA * (EBIT + DA) / EBIT
or ICRMP&M = ICRRMA * {1+ [(DA / SALES) / (EBIT / SALES)]}
C-2
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MPAM EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector
For consistency of calculation, EPA used the median values available from RMA for the adjusting both the numerator
(DA / SALES) and denominator (EBIT / SALES) terms.1
EPA used the same method as described above to adjust the negative ICR values reported in RMA to the value used in the
moderate impact analysis. Including only those observations with positive values for EBIT/Interest, % Depr., Dep.,
Amort./Sales, and Operating Profit, an adjustment factor was calculated by subtracting the difference between ICRMP&M and
ICRRMA as follows:
ICRMP&M-ICRRMA = adjustment factor.
A sector-specific adjustment factor was calculated for ICR values similar to the PTRA. Each negative ICR observation from
RMA was adjusted by its sector specific adjustment factor to approximate the measure used in the moderate impact analysis:
ICRRMA + sector-specific adjustment factor = ICRMP&M
The sector-specific adjustment factors average 0.59 for manufacturing sectors and range from 0.28 for the Precious Metals
and Jewelry sector to 0.79 for the Printed Circuit Board sector. The sector-specific adjustment factors average 0.50 for non-
manufacturing sectors and range from 0.24 for the Office Machines sector to 1.85 for the Aircraft sector.
1 Numerator (% Depr., Dep., Amort./Sales) is available for quartile values; denominator (Operating Profit) only for
median values.
C-3
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MPAM EEBA: Appendices
Appendix C: Summary of Moderate Impact Threshold Values by Sector
C.3 SUMMARY OF RESULTS
Table C.I shows the resulting threshold values for PTRA and ICR by sector. The PTRA values for manufacturers range from
zero percent for the Office Machine sector to 2.8 percent for the Aircraft and Household Equipment sectors and for the non-
manufacturers the values range from 0.3 percent for the Office Machine sector to 3.1 percent for the Railroad sector. The
ICR values for manufacturers range from 1.4 for the Office Machine and Railroad sectors to 2.3 for the Hardware, Household
Equipment, and Printed Circuit Board sectors and for the non-manufacturers the values range from 1.2 for the Office Machine
sector to 2.9 for the Aircraft sector.
In assessing moderate impacts, EPA used the non-manufacturing threshold for facilities that reported 100 percent of their
revenues came from rebuilding and maintenance; otherwise, EPA used the manufacturing threshold.
Table C.I: Summary of Moderate Impact Thresholds by Sector
Sector
Hardware15
Aircraft
Electronic Equipment*
Stationary Industrial Equipment
Ordnance"
Aerospace"
Mobile Industrial15
Instrument
Precious and Non-Preciousa
Ships and Boats
Household Equipment
Railroad"
Motor Vehicle
Bus and Truck
Office Machine
Printed Circuit Board"
Job Shop"
Other Metal Products
Iron and Steel
Unknown Sector"
Pre-Tax Return on Assets (PTRA)
,„ „ . . Non-
Manufacturing • .
Manufacturing
2.6%
2.8%
2.1%
2.1%
2.2%
2.2%
2.6%
2.2%
1.8%
1.7%
2.8%
1.1%
2.4%
2.3%
0.0%
2.5%
2.3%
1.0%
2.4%
1.6%
0.4%
1.6%
2.5%
1.6%
1.6%
1.6%
2.0%
1.6%
1.0%
2.6%
3.1%
1.5%
1.7%
0.3%
1.6%
1.6%
1.7%
N/A
2.2% 1.6%
1
Interest Coverage Ratio (ICR)
,„ „ . . Non-
Manufacturing • .
Manufacturing
2.3
2.2
'
2.2
2.1
2.1
2.1
2.1
2.1
1.7
1.6
2.3
1.4
2.0
2.0
1.4
2.3
2.2
1.6
2.2
zi
1.9
2.9
1.9
2.8
1.9
1.9
1.9
2.0
1.9
2.0
2.0
2.7
1.7
2.8
1.2
1.9
1.9
1.8
N/A
1-9
a When data were not available for any SIC codes within the sector, EPA calculated an average manufacturing or non-
manufacturing threshold to use as a proxy.
" There are no non-manufacturing SIC codes in several sectors, but in these sectors there are some facilities who reported
that all of their revenue came from rebuilding and maintenance. In these cases, EPA used the average non-manufacturing
thresholds in that sector as a proxy for the non-manufacturing threshold.
Source: RMA, 2001; RMA, 1998; U.S. Economics Census, 1997; U.S. EPA Analysis, 2002.
C-4
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MPAM EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector
REFERENCES
U.S. Department of Commerce. 1997. Bureau of the Census. Census of Manufacturers, Census of Transportation, Census of Wholesale
Trade, Census of Retail Trade, Census of Service Industries.
Risk Management Association (RMA). 1997-1998. Annual Statement Studies.
Risk Management Association (RMA). 2000-2001. Annual Statement Studies.
C-5
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MPAM EEBA: Appendices Appendix C: Summary of Moderate Impact Threshold Values by Sector
THIS PAGE INTENTIONALLY LEFT BLANK
C-6
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Appendix D: Estimating Capital
Outlays for MP&M Discounted Cash
Flow Analyses
INTRODUCTION
APPENDIX CONTENTS
The economic impact analysis for the Metal Products &
Machinery Industry (MP&M) final regulation involved
calculation of the business value of sample facilities on the
basis of a discounted cash flow (DCF) analysis of
operating cash flow as reported in facility questionnaires.
Business value is calculated on a pre- and post-compliance
basis and the change in this value serves as an important
factor in estimating regulatory impacts in terms of
potential facility closures. For proposal, the business
D.I Analytic Concepts Underlying Analysis of
Capital Outlays D-2
D.2 Specifying Variables for the Analysis D-4
D.3 Selecting the Regression Analysis Dataset D-7
D.4 Specification of Models to be Tested D-8
D.4.1 Linear Model Specification D-9
D.4.2 Log-Linear Model Specification D-10
D.4.3 Sensitivity Analysis D-12
D.5 Model Validation D-12
Attachment D.A: Bibliography of Literature Reviewed
for this Analysis D-17
nt D.B: Historical Variables Contained in the Value
Line Investment Survey Dataset D-18
value calculation was based only on cash flow from
.... . . . - . . Attachment D.B: Historical Variables Contained in the Value
operations and did not recognize cash outlays for capital
acquisition as a component of cash flow. EPA Office of
Water (OW) previously identified that the omission of
capital acquisition cash outlays from the DCF analysis
may lead to overstatement of the business value of sample facilities and, as a consequence, understatement of regulatory
impacts in terms of estimated facility closures.
In response to this omission, the Office of Management and Budget suggested the adoption of depreciation as a surrogate for
cash outlays for capital replacement and additions. However, for several reasons EPA believes depreciation is a poor
surrogate. First, depreciation is meant to capture the consumption/use of previously acquired assets, not the cost of replacing,
or adding to, the existing capital base. Therefore, depreciation is fundamentally the wrong concept to use as a surrogate for
capital outlays for capital replacement and additions. Second, depreciation is estimated based on the historical asset cost,
which may understate or overstate the real replacement cost of assets. Third, both book and tax depreciation schedules
generally understate the assets' useful life. Thus, reported depreciation will overstate real depreciation value for recently
acquired assets that are still in the depreciable asset base, and conversely, understate the real depreciation value of assets that
have expired from the depreciable asset base but still remain in valuable use. Finally, depreciation does not capture the
important variations in capital outlays that result from differences in revenue growth and financial performance among firms.
Businesses with real growth in revenues will need to expand both their fixed and working capital assets to support business
growth, and all else being equal, growing businesses will have higher ongoing outlays for fixed and working capital assets.
Similarly, the ability of businesses to renew and expand their asset base depends on the financial productivity of the deployed
capital as indicated by measures such as return on assets or return on invested capital. As a result, businesses with "strong"
asset productivity will attract capital for renewal and expansion of their asset base, while businesses with "weak" asset
productivity will have difficulty attracting the capital for renewal and expansion of the business' asset base. All else being
equal, businesses with strong asset productivity will have higher ongoing outlays for capital assets; businesses with weak asset
productivity will have lower ongoing outlays for capital assets.
As an approach to addressing the omission of capital acquisition cash outlays from the DCF analysis, EPA undertook to
estimate a regression model of capital outlays using capital expenditure and relevant explanatory financial and business
environment information for public-reporting firms in the MP&M industry sectors. The estimated model was then used to
estimate capital outlays for facilities in the MP&M sample dataset. The estimated capital outlay values were used in the DCF
analyses to calculate business value of sample facilities and estimate regulatory impacts in terms of facility closures.
D-l
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
This appendix reports the results of this effort, including: an overview of the analytic concepts underlying the analysis of
capital outlays; specific variables included in the regression analysis; summary of data selection and preparation; general
specification of regression models to be tested; and the findings from the regression analyses.
b. 1 ANALYTIC CONCEPTS UNDERLYING ANALYSIS OF CAPITAL OUTLAYS
On the basis of general economic and financial concepts of investment behavior, EPA began its analysis by outlining a
framework relating the level of a firm's capital outlays to explanatory factors that:
»• can be observed for public-reporting firms either as firm-specific information or general business environment
information and thus be included in a regression analysis; and
»• for firm-specific information, are also available from the MP&M sample facility dataset.
To aid in identifying the explanatory concepts and variables that might be used in the analysis and as well in specifying the
models for analysis, EPA reviewed recent studies of the determinants of capital outlays. EPA's review of this literature
generally confirmed the overall approach in seeking to estimate capital outlays and helped to identify additional specific
variables that other analysts found to contribute important information in the analysis of capital outlays (e.g., the decision to
test capacity utilization as an explanatory variable, see below, resulted from the literature review). Articles reviewed are
listed in Attachment D.A to this appendix.
Table D.I beginning below and continuing the following two pages summarizes the conceptual relationships between a firm's
capital outlays and explanatory factors that EPA sought to capture in this analysis. In the table, EPA outlines the concept of
influence on capital outlays, the general explanatory variable(s) that EPA identified to capture the concept in a regression
analysis, and the hypothesized mathematical relationship (sign of estimated coefficients) between the concept and capital
outlays. Table D.2 identifies the specific variables included in the analysis, including any needed manipulations and the
correspondence of the variables to MP&M survey information.
Table D.I: Summary of Factors Influencing Capital Outlays
Explanatory Factor/Concept To Be
Captured in Analysis
Translation of Concept to Explanatory Variable(s)
Expected
Relationship
Availability of attractive opportunities for
additional capital investment. A firm's
owners, or management acting on behalf of
owners, should expend cash for capital
outlays only to the extent that the expected
return on the capital outlays whether for
replacement of, or additions to, existing
capital stock are sufficient to compensate
providers of capital for the expected return
on alternative, competing investment
opportunities, taking into account the risk of
investment opportunities.
Historical Return On Assets of establishment as a indicator of
investment opportunities and management effectiveness, and, hence,
of desirability to expand capital stock and ability to attract capital
investment. Use of a historical variable implicitly assumes past
performance is indicative of future expectations.
Positive
Business growth and outlook as a
determinant of need for capital expansion
and attractiveness of investment
opportunities. All else equal, a firm is more
likely to have attractive investment
opportunities and need to expand its capital
base if the business is growing and the
outlook for business performance is
favorable.
Revenue Growth, from the prior time period(s) to the present,
provides a historical measure of business growth and is a potential
indicator of need for capital expansion. Use of a historical variable
implicitly assumes past performance is indicative of future
expectations.
Positive
Clearly, the theoretical preference is for a forward-looking indicator
of business growth and need for capital expansion. Options EPA
identified include Index of Leading Indicators and current Capacity
Utilization, by industry. Higher current Capacity Utilization may
presage need for capital expansion.
Positive
D-2
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Table D.I: Summary of Factors Influencing Capital Outlays
Explanatory Factor/Concept To Be
Captured in Analysis
Translation of Concept to Explanatory Variable(s)
Expected
Relationship
Importance in capital in business
production. All else equal, the more capital
intensive the production activities of a
business, the greater will be the need for
capital outlay to replenish, and add to, the
existing capital stock. More capital intensive
businesses will spend more in capital outlays
to sustain a given level of revenue over time.
The Capital Intensity of production as measured by the production
capital required to produce a dollar of revenue provides an indicator
of the level of capital outlay needed to sustain and grow production.
As an alternative to a firm-specific concept such as Capital Intensity
of production, differences in business characteristics might be
captured by an Industry Classification variable.
Positive
Life of capital equipment in the business.
All else equal, the shorter the useful life of
the capital equipment in a business, the
greater will be the need for capital outlay to
replenish, and add to, the existing capital
stock.
No information is available on the actual useful life of capital
equipment by business or industry classification. However, the
Capital Turnover Rate, as calculated by the ratio of book
depreciation to net capital assets, provides an indicator of the rate at
which capital is depleted, according to book accounting principles:
the higher the turnover rate, the shorter the life of the capital
equipment. However, the measure is imperfect for reasons of both
the inaccuracies of book reporting as a measure of useful life, and as
well the confounding effects of growth in the asset base due to
business expansion which will tend to lower the indicated turnover
rate, all else equal, without a real reduction in life of capital equipment.
As above, an alternative to a firm-specific concept, differences in
business characteristics might be captured by an Industry
Classification variable.
Positive,
generally, but
with
recognition of
the potential
for counter-
trend effects
The cost of financial capital. The cost at
which capital both debt and equity is
made available to a firm will determine
which investment opportunities can be
expected to generate sufficient return to
warrant use of the financial capital for
equipment purchases. All else equal, the
higher the cost of financial capital, the fewer
the investment/capital outlay opportunities
that would be expected to be profitable and
the lower the level of outlays for replacement
of, or additions to, capital stock.
Preferably, measures of cost-of-capital would be developed
separately for debt and equity.
The Cost of Debt Capital, as measured by an appropriate benchmark
interest rate, provides an indication of the terms of debt availability
and how those terms are changing over time. Preferably, the debt
cost/terms would reflect the credit condition of the firm, which could
be based on a credit safety rating (e.g., S&P Debt Rating). While
such information would be available for public firms, EPA judged
that developing a comparable concept for MP&M sample facilities
would not be possible within the scope of this analysis.
Negative
The cost of equity capital is more problematic than the cost of debt
capital since it is not directly observable for either public-reporting
firms or, in particular, private firms in the MP&M dataset.
However, a readily available surrogate such as Market-to-Book
Ratio provides insight into the terms at which capital markets are
providing equity capital to public-reporting firms: the higher the
Market-to-Book Ratio, the more favorable the terms of equity
availability. Although such information would not be available for
private firms in the MP&M sample, EPA judged that it would be
possible to develop a industry-level value for use with the MP&M
facility analysis.
Negative
D-3
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Table D.I: Summary of Factors Influencing Capital Outlays
Explanatory Factor/Concept To Be
Captured in Analysis
Translation of Concept to Explanatory Variable(s)
Expected
Relationship
The price of capital equipment. The price
of capital equipment in particular, how
capital equipment prices are changing over
time will influence the expected return
from capital outlays. All else equal, when
capital equipment prices are increasing, the
expected return from incremental capital
outlays will decline and vice versa
However, although the generally expected
effect of higher capital equipment prices is to
remove certain investment opportunities
from consideration, the potential effect on
total capital outlay may be mixed. If
expected returns are such that the demand to
invest in capital projects is relatively
inelastic, the effect of higher prices for
capital equipment may be to raise, instead of
lower, the total capital outlay for a firm.
Index provides an indicator of the change in capital equipment
prices.
Negative,
generally, but
with
recognition of
the potential
for counter-
trend effects
Source: U.S. EPA analysis.
b.2 SPECIFYING VARIABLES FOR THE ANALYSIS
Working from the general concepts of explanatory variables outlined above, EPA defined the specific explanatory variables to
be included in the analysis. A key requirement of the regression analysis is that the firm-specific explanatory variables
included in the regression analysis later be able to be used as the basis for estimating capital expenditures for facilities in the
MP&M dataset. As a result, in defining the firm-specific variables, it was necessary to ensure that the definition of variables
selected for the regression analysis using data on public-reporting firms be consistent with the data items available for
facilities in the MP&M dataset.
Also, EPA's selection of firm-specific variables was further constrained by an earlier decision to use the Value Line
Investment Survey (VL) as the source of firm-specific information for the regression analysis. The decision to use VL as the
source of firm-specific data for the analysis was driven by several considerations:
»• Considerably lower price than alternatives. VL data were available at a price of $95 for a one-time data
purchase; the price for other data sources such as Bloomberg and Standard & Poor's ranged from $7,000 to
$11,000.
> Reasonable breadth of public-reporting firm coverage. The VL dataset includes 7,500 firms.
> Reasonable breadth of temporal coverage. VL provides data for the most recent 10 years i.e., 1991-2000.
Although ideally EPA would have preferred a longer time series to include more years not in the "boom"
investment period of the mid- to late-1990s.
> Timeliness of access. The VL data are provided as a standard package and thus could be available within a
week of ordering while other data sources (e.g., Bloomberg) would have required more time because data would
have provided as a custom purchase.
> Reasonable coverage of concepts/data needed for analysis. The VL data includes a wide range of financial data
that are applicable to the analysis (VL provides 37 data items over the 10 reporting years; see Attachment DB).
However, because of the pre-packaged nature of the VL data, it was not possible to customize any data items to
support more precise definition of variables in the analysis. In particular, EPA found that certain balance sheet
items were not reported to the level of specificity preferred for the analysis. Overall, though, EPA expects the
consequence of using more aggregate, less-refined concepts should be minor.
D-4
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
The decision to use VL data for the analysis constrained, in some instances, EPA's choice of variables for the analysis.
Table D.2 reports the specific definitions of variables included in the analysis (both the dependent variable and explanatory
variables), including any needed manipulations, the data source, the MP&M estimation analysis equivalent (either the
corresponding variable(s) in the MP&M questionnaire or other source outside the questionnaire), and any issues in variable
definition.
Table D.2: Variables For Capital Expenditure Modeling Analysis
Variables for Regression Analysis
t *
Variable Source
Dependent Variable
Gross ! Value Line
expenditures j
on fixed
assets:
CAPEX,
includes
outlays to
replace, and
add to,
existing
capital stock
Calculation
Obtained from VL as Capital
Spending per Share.
CAPEX calculated by
multiplying by Average
Shares Outstanding.
MP&M Analysis
Equivalent
None: to be estimated
based on estimated
coefficients.
Comment / Issue
This value and all other dollar values
in the regression analysis were deflated
to 1996 (base year for MP&M
regulatory analysis) using 2-digit SIC
PPI values.
Explanatory Variables
Firm-Specific Variables
On Assets:
ROA
Value Line
ROA = Operating Income /
Total Assets. Both
Operating Income, defined
as Revenue less Operating
Expenses (CoGS+SG&A),
and Total Assets were
obtained directly from VL.
From Survey: Revenue
less Total Operating
Expenses (Material &
Product Costs +
Production Labor +
Cost of Contract
Work + Fixed
Overhead + R&D +
Other Costs &
Expenses)
Would have preferred a post -tax
concept in numerator and a deployed
production capital concept in
denominator. However, VL provides
no tax value per se and would require
calculation of tax using an estimated tax
rate, which could introduce error. Also
neither VL nor MP&M survey data
provide sufficient information to get at
deployed production capital.
D-5
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Table D.2: Variables For Capital Expenditure Modeling Analysis
Variables for Regression Analysis
'
Variable
Revenue
Growth:
RVGR
Revenue:
REV
Capital
Turnover
Rate: CAPT
Capital
Intensity:
CAPI
,
Market-to-
Book Ratio:
MV/B
Source
Value Line
Value Line
Value Line
Value Line
Value Line
L
Calculation
Primary formulation tested
for linear models was
percentage change in revenue
over two years prior to
current year; RVGR = (REVt
-REVt2)/REVt2 VL
provides 10 years of financial
statement values 1991-
2000, including Revenue by
year.
For log-linear models, the
growth concept was dropped
and REV was used as the
explanatory variable (see
below and also see later
discussion under model
specification).
In the linear models, REV
included as a scale variable
together with REVGR, as
outlined above. For log-
linear models, retained only
REV as the explanatory
variable. The simple variable,
REV, captures the percent
change/growth concept in the
log-linear formulation.
CAPT = Depreciation /
Total Assets. Depreciation
and Total Assets directly
available from VL.
CAPI = Total Assets /
Revenue. Total Assets and
Revenue directly available
fromVL
MV/B = average market price
of common equity (Price)
divided by book value of
common equity (Book Value
per Share). Price and Book
Value per Share directly
available from VL.
i
MP&M Analysis
Equivalent
No equivalent needed.
Analysis proposed to set
this value to zero in
estimating capital outlay
values for MP&M
facilities. The use of a
zero growth value is
consistent with
estimating the
replacement capital
expenditures in a no-
growth steady-state.
From Survey: Revenue
From Survey:
Depreciation / Total
Assets
From Survey:
Total Assets / Revenue
B
Use average of MV/B
for firms by MP&M
industry group in
regression analysis
dataset; calculated at
time of MP&M industry
survey.
,
Comment / Issue
Using a revenue growth term in the
analysis defined over the prior two
years requires three years of revenue
data (e.g., current year plus trailing two
years) and effectively eliminates two
observation years from the analysis
(1991 and 1992). Given that these data
years occurred at the end of a recession
period and before the mid- to late-90s
economic boom period, EPA was very
concerned about the potential loss of
these years from the analysis dataset.
In the end, the use of a log-linear model
eliminated the need to construct the
lagged difference variables and thus
mooted the concern over loss of early
year observations. The use of the log-
linear model, however, also eliminated
the potential to set the growth term to
zero in estimating baseline capital
outlays for MP&M facilities.
Using REV only and not REVGR in
the log-linear model restored the two
data years at the beginning of the
analysis period (1991 and 1992) to the
analysis dataset. EPA believes
including data for the first two
observation period years is important
for the generality of the analysis.
Also tested Total Assets as a scale
variable, which provided good, but not
as strong, an explanation, as REV.
Would have preferred denominator of
net fixed assets instead of total assets.
However, VL provides detailed balance
sheet information for only the four most
recent years. Not possible to separate
current assets and intangibles from total
assets.
As above, would have preferred net
fixed assets instead of total assets, but
needed data are not available from VL
for the full analysis period.
B
Ultimately found MV/B highly
correlated with other, more important
explanatory variables, which makes
sense, given that equity terms would be
derived from more fundamental factors,
such as ROA. Omitting MV/B from
the analysis eliminated the need to
define an approach to use this variable
with MP&M survey data.
D-6
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Table D.2: Variables For Capital Expenditure Modeling Analysis
Variables for Regression Analysis
'
Variable
General Busine
Interest on
10-year, A-
rated
industrial
debt:
DEBTCST
Index of
Leading
Indicators:
ILI
Capacity
Utilization
by Industry:
CAPUTIL
Producer
Price Index
series for
capital
equipment:
CAPPRC
Source
ss Environment
Bloomberg
Financial
Services
Conference
Board
Federal
Reserve
Board
(Dallas
Federal
Reserve)
Bureau of
Labor
Statistics
Calculation
Variables
DEBTCST = annual average
of rates for each data year
Monthly index series
available from Conference Boa:
For linear models, ILI =
percent change from
beginning to end of current
year.
For log-linear models, ILI =
geometric mean of current
year values.
Monthly index series
available from Federal
Reserve.
For linear models,
CAPUTIL = percent change
in annual average values from
prior year to current year.
For log-linear models,
CAPUTIL = current year
average value.
Annual average values
available from BLS.
For linear models,
CAPPRC = percent change
from prior year to current
year.
For log-linear models,
CAPPRC = current year
average value as reported by
BLS.
MP&M Analysis
Equivalent
Use average of
DEBTCST rates at time
of MP&M industry
survey.
For linear formulation,
d.use average of year-to-
year percent change in
ILI at time of MP&M
industry survey.
For log-linear
formulation, use average
of ILI values at time of
MP&M industry survey.
For linear formulation,
use average of year-to-
year percent change in
CAPUTIL at time of
MP&M industry survey.
For log-linear
formulation, use average
of CAPUTIL values at
time of MP&M industry
survey.
For linear formulation,
use average of year-to-
year percent change in
CAPPRC at time of
MP&M industry survey.
For log-linear
formulation, use average
of CAPPRC values at
time of MP&M industry
survey.
Comment / Issue
10-year maturity, industry debt selected
as reasonable benchmark for industry
debt costs. 10 years became "standard"
maturity for industrial debt during
1990s.
BLS reports PPI series for capital
equipment based on "consumption
bundles" defined for manufacturing and
non-manufacturing industries. For this
analysis, EPA used the PPI series based
on the manufacturing industry bundle.
Source: U.S. EPA analysis.
b.3 SELECTING THE REGRESSION ANALYSIS &ATASET
In addition to specifying the variables to be used in the regression analysis, EPA also needed to select the public firm dataset
on which the analysis would be performed.
As noted above, EPA used the Value Line Investment Survey as the source for public firm data. VL includes over 7,500
publicly traded firms and identifies firms' principal business both by a broad industry classification (e.g., Electrical
Equipment, Machinery) and by an SIC code assignment. In most instances, the SIC codes assignment is only at the 2-digit
level. To build the public firm dataset corresponding to the MP&M industry sectors, EPA initially selected all firms included
in the following 2-digit SIC code families:
D-7
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MPAM EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
> 2500: Furniture and fixtures,
»• 3300: Primary metal industries,
> 3400: Fabricated metal products,
»• 3500: Industrial machinery and equipment,
»• 3600: Electrical and electronic equipment,
»• 3700: Transportation equipment, and
> 3800: Instruments and related products.
From manual inspection, EPA deleted firms in four-digit SIC code 3579, which, in the VL classification, was comprised only
of software manufacturers. In addition, in SIC code group 3300, EPA included firms only in the ferrous metal processing
sectors: SIC codes 3311, 3312,3315, 3316,3317, and 3398.'
As a result of this selection, EPA developed an initial dataset of 1,015 firms. On inspection, EPA found that a substantial
number of firms did not have data for the full 10 years of the analysis period. The general reason for the omission of some
years of data is that the firms did not become publicly listed in their current operating structure whether through an initial
public offering, spin-off, divestiture of business assets, or other significant corporate restructuring that renders earlier year
data inconsistent with more recent data until after the beginning of the 10-year data period. As a result, the omission of
observation years for a firm always starts at the beginning of the data analysis period. This systematic front-end truncation of
firm observations in the dataset could be expected to bias the analysis in favor of the capital expenditure behavior nearer the
end of the 1 990s decade. To avoid this problem, EPA removed all firm observations that have fewer than 10 years of data.
As a result, the dataset used in the analysis has a total of 3,900 yearly data observations and represents 390 firms.
Table D.3 presents the number of firms by industry classifications.
Table D.3: Number of Firms
SIC Industry Classification
2500: Furniture and fixtures
3300: Primary metal industries
3400: Fabricated metal products
3500: Industrial machinery and equipment
3600: Electrical and electronic equipment
3700: Transportation equipment
3800: Instruments and related products
by Industry Classifications
Number of Firms
13
27
24
119
101
65
41
b.4 SPECIFICATION OF MODELS TO BE TESTED
On the basis of the variables listed above and their hypothesized relationship to capital outlays, EPA specified a time-series,
cross sectional model to be tested in the regression analysis. EPA's dataset consisted of 390 cross sections observed at 10
years (1991 through 2000). The general structure of this model was as follows:
CAPEXU =/(ROAu, REV,(, CAPT,.(, CAPI,.(, DEBTCST,,, CAPPRC,, CAPUTIL/()
1 These 4-digit SIC codes include all MP&M sectors in SIC 2-digit code 33plus 4-digit SIC code 3311, to capture information for
the steel manufacturing industry.
2 When VL adds a firm to its dataset, it fills in the public-reported data history for the firm for the lesser of 10 years or the length of
time that the firm has been publicly listed and thus subject to SEC public reporting requirements.
D-&
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Where:
CAPEX,, = capital expenditures of firm z, in time period t;1
t = year (year = 1991, . . . ,2000);
z = firmz(z'=l, . . . , 390);
j = industry classification y
ROA,, = return on total assets for firm z in year t;
REVi( = revenue ($ millions) for firm z in year t;
CAPT,, = capital turnover rate for firm z in year t;
CAPI,, = capital intensity for firm z in year t;
DEBTCST, = financial cost of capital in year t;
CAPPRC, = price of capital goods in year t;
CAPUTILy, = the Federal Reserve Board's Index of Capacity utilization for a given industry y in year t.
EPA tested both linear and log-linear model specifications. Both models fit quit well, achieving overall correlation (R ) in the
upper 80 percent/low 90 percent range. However, the pattern of coefficient significance was better in the log-linear model. In
addition, the log-linear model offered advantages in terms of retention of early time period observations and variable
specification, as discussed below. Therefore, EPA selected a log-linear specification as the final model. The following
paragraphs briefly discuss testing of both linear and log-linear forms of the model. Parameter estimates are presented for the
final log-linear model only because this specification appeared to be superior to a linear model.
b.4.1 Linear Model Specification
EPA first tested linear models of CAPEX as a function of the proposed explanatory variables. In testing linear models of
CAP EX, EPA tested a number of structural modifications within the overall hypothesized framework of explanatory
variables. These included:
> Testing the influence of industry classification on the estimation of the coefficients for certain of the explanatory
variables: e.g., using the product of an industry classification dummy variable and CAPPRC to test whether
certain industries in particular, "high-tech" vs. "traditional" industries responded differently to change in
price of capital equipment over time.
> Testing contemporary vs. lagged specification of certain explanatory variables: e.g., using prior, instead of
current, period revenue, REV, as an explanatory variable.
> Testing scale-normalized specification of the dependent variable: e.g., using CAPEX/REV as the dependent
variable instead of simple CAPEX.
»• Testing flexible functional forms that included quadratic terms.
»• Testing additional explanatory variables including the index of 10 leading economic indicators (ILI) and market-
to-book ratio (MV/B).
EPA also tested the data for autocorrelation, heteroscedasticity, and multicollinearity.
Cross-sectional, time-series datasets typically exhibit both autocorrelation and group-wise heteroscedasticity characteristics.
Autocorrelation is frequently present in economic time series data as the data display a "memory" with the variation not being
independent from one period to the next. Heteroscedasticity usually occurs in cross-sectional data where the scale of the
dependent variable and the explanatory power of the model vary across observations. Not surprisingly, the dataset used in
this analysis had both characteristics.
The collinearity diagnostic showed that several independent variables are collinear. In particular, EPA found that the index of
leading economic indicators (ILI) and the price of capital equipment (CAPPRC) variables are highly correlated. EPA further
found that the market-to-book ratio variable (MV/B) was highly correlated with both capital turnover (CAPT) and return-on-
assets (ROA) variables. To address the multicollinearity issue, EPA substituted capacity utilization (CAPUTIL) for the index
of leading economic indicators (ILI) and dropped the market-to-book ratio (MV/B) variable in the final model.
All dollar values were deflated to 1996 (base year for MP&M regulatory analysis) using 2-digit SIC PPI values.
D-9
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
b.4.2 Log-Linear Model Specification
The main advantage of the log-linear model is that it incorporates directly the concept of percent change in the explanatory
variables. Specifying the key regression variables as logarithms permitted us to estimate directly as the coefficients of the
model, the elasticities of capital expenditures with respect to firm financial characteristics and general business environment
factors, hi addition, by eliminating the need to use percent change variables, EPA was able to avoid losing early year
observations in the analysis dataset. Finally, the logarithmic transformations helped to reduce outlier effects in the model.
EPA specified a log-linear model, as follows:
ln(CAPEX,.() = a + S[p, ln(Xi()] + S[Yy ln(Y()] + e
Where:
CAPEXi( = capital expenditures of firm z, year t;
P^ = elasticity of capital expenditures with respect to firm characteristic X;
X,-(, = a vector of financial characteristics of firm z, year t;
Yy = elasticity of capital expenditures with respect to economic indicator Y;
Y, = a vector of economic indicators, year t; for CAPUTIL, Y is also differentiated by industry
classification
e = an error term; and
\n(x) = natural log of x
Based on this model, the elasticity of capital expenditures with respect to an explanatory variable, for example, return on
assets is calculated as follows:
d(CAPEX)jCAPEX
d\n.(ROA) d (ROA) I ROA
Because the log-linear specification incorporates directly the concept of percent change in the explanatory variables, EPA
dropped the "change" specification variables i.e., revenue growth (REVGR), year-to-year change in the Index of Leading
Indicators (ILIGR), and year-to-year change in the Capital Equipment Price Index (CAPPRC) from the analysis. For these
variables, EPA used the logarithm of the simple, unadjusted values in the log-linear specification.
One disadvantage of the specified log-linear model is that the logarithmic transformation is not feasible for negative and zero
values. This means that negative and zero values require linear transformation to be included in the analysis. The following
variables in the sample required transformation:
> CAP EX: four firms in the sample reported zero capital expenditures at least in one time period. EPA set these
expenditures to $1.
»• REVENUE: one firm reported negative revenue (-$1,018) in one time period. Because this is likely due to
accounting adjustments from prior period reporting, EPA set the firm's revenue in the current time period to $1.
»• ROA: the values for return on assets in the public firm sample range from -1.1 to 0.6. Approximately 25 percent
of the firms in the dataset reported negative ROAs in at least one year. To address this issue while reducing
potential effects of data transformation on the modeling results, EPA used the following data transformation
approach:
D EPA excluded 12 firms with any annual ROA values below the 99th percentile of the ROA distribution (i.e.,
ROA < -0.31).
D EPA used an additive data transformation to ensure that remaining negative ROA values were positive in
the logarithm transformation. The additive transformation was performed by adding 0.31 to all ROA
values.
D-10
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MP&M EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
The analysis tested several specifications of a log-linear model, including models with slope dummies for different industrial
sectors and models with the intercept suppressed. The model presented below was most successful at explaining firms'
investment behavior.
EPA estimated the specified model using the generalized least squares procedure. This procedure involves the following two
steps:
»• First, EPA estimated the model using simple OLS, ignoring autocorrelation for the purpose of obtaining a
consistent estimator of the autocorrelation coefficient (p);
•• Second, EPA used the generalized least squares procedure, where the analysis is applied to transformed data.
The resulting autocorrelation adjustment is as follows:
zi,t = zi,t - Pzi,,-i
where Za is either dependent or independent variables.
EPA was unable to correct the estimated model for group-wise heteroscedasticity due to computational difficulties. The
statistical software used in the analysis (LIMDEP) failed to correct the covariance matrix due to the very large number of
groups (i.e., 390 firms) included in the dataset. Application of other techniques to correct for group-wise heteroscedasticity
was not feasible due to time constraints. The estimated coefficients remain unbiased; however, they are not minimum
variance estimators.
Table D.4 presents model results. The model has a fairly good fit, with adjusted R2 of 0.89. All coefficients have the
expected sign and all but two (constant and capital price) are significantly different from zero at the 95th percentile.
Table D.4: Time Series, Cross-Sectional Model
Results
Variable
Constant
Ln(ROA)
Ln(REV)
Ln(CAPT)
Ln(CAPI)
Ln(DEBTCST)
Ln(CAPPRC)
Ln(CAPUTIL)
Autocorrelation Coeffi
r
Coefficient
-2.077
0.618
1.025
0.6
0.976
-0.205
-0.478
0.904
cient
0.413
t-Statistics
-0.97
9.353
113.867
20.285
27.342
-2.653
-0.939
3.176
27.842
The empirical results show that the output variable (REV) is a dominant determinant of firms' investment spending. A
positive coefficient on this variable means that larger firms invest more, all else equal, which is clearly a simple expected
result. Very important for the MP&M analysis, as expected, firms with higher financial performance and better investment
opportunities (ROA) invest more, all else equal: for each one percent increase in ROA, a firm is expected to increase its
capital outlays by 0.62 percent. Other firm-specific characteristics were also found important and will aid in differentiating
the expected capital outlay for MP&M facilities according to firm-specific characteristics. Firms that require more capital to
produce a given level of business activity (i.e., firms that have high capital intensity, CAPI) tend to invest more: a one percent
increase in capital intensity leads to a 0.98 increase in capital spending. Higher capital turnover/shorter capital life (CAPT)
also has a positive effect on investment decisions: a one percent increase in capital turnover rate translates to a 0.60 percent in
capital outlays.
The model also shows that current business environment conditions play an important role in firms' decision to invest. The
D-ll
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
most influential factor is capacity utilization in manufacturing facilities. A one percent increase in the Federal Reserve Index
of Capacity Utilization for the relevant industrial sector (CAPUTIL) leads to a 0.90 percent increase in capital investment.
Negative signs on the debt cost (DEBTCST) and capital price (CAPPRC) variables match expectations, indicating that less
costly credit and falling (either relatively or absolutely) capital equipment prices are likely to have a positive effect on firms'
capital expenditures. That these systematic variables are significant in the regression analysis means that EPA will be able to
control for economy- and industry-wide conditions in estimating capital outlays for MP&M facilities.
b.4.3 Sensitivity Analysis
To examine the degree to which the estimated model was affected by transformation of ROA values and inclusion/exclusion
of firms with the lowest ROA values, EPA ran two additional models. First, EPA estimated a model based on a subset of data
that includes only firms with positive ROA values. Second, EPA estimated a model based on a complete dataset that includes
the 12 firms with the lowest ROA values. Although all three models produced compatible results, the first model shows some
notable differences in the estimated coefficients compared to the model presented in the preceding section. EPA found that
when firms with the lowest negative ROAs are excluded from the analysis:
»• The magnitude of the ROA effect on capital expenditures decreases;
> The magnitude of the debt cost effect on capital expenditures decreases slightly;
*• The coefficient on the capital price term becomes significant.
These differences can be expected since firms with negative ROAs are weak performers and therefore are less likely to have
large capital outlays. Not surprisingly, general economic indicators that affect firms' decisions to invest can be less or more
important if a firm's financial performance/asset productivity is weak. For financially weaker firms, the financial cost of
capital is a more important factor compared to firms that are strong financially. This finding indicates a strong "threshold of
adequate financial performance" effect: capital outlays fall off severely at the lowest financial performance levels but the
marginal effect of financial performance becomes more moderate as asset productivity moves into a more acceptable i.e.,
positive return range. Price of capital goods appears to be an insignificant factor in firms' decision to invest when weak
firms are included in the analysis. At first, this finding seems to be counterintuitive: previous studies of investment behavior
found a strong capital price effect on firms' decision to invest in high tech equipment. However, because financially weak
firms are less likely to invest in general, it is reasonable to assume that they will not respond as strongly to changes in capital
equipment prices. Thus, their investment decisions were relatively less affected by falling high-tech equipment prices in the
last decade.
b.5 MODEL VALIDATION
To validate the results of the regression analysis, EPA used the estimated regression equation to calculate capital expenditures
and then compared the resulting estimate of capital expenditures with actual data. EPA used two methods to validate its
results:
»• EPA used median values for explanatory variable from the Value Line data as input to estimate capital
expenditures and then compared the estimated value to the median reported capital expenditures, and
> EPA used MP&M survey data to estimate capital expenditures and then compared the estimated values to
depreciation reported in the survey.
First, EPA estimated capital expenditures for a hypothetical firm based on the median values of the four dependent variables
from the Value Line data and the relevant values of the three economic indicators. The estimated capital expenditures for this
hypothetical firm are $10.9 million. EPA then compared this estimate to the median value of capital expenditures from the
Value Line data. The median capital expenditure value in the dataset is $11.3 million, which provides a very close match to
the estimated value. This is not surprising since the same dataset was used to estimate the regression model and to calculate
the median values used in this analysis.
EPA also used MP&M survey data to confirm that the estimated capital expenditures seem reasonable. Because the MP&M
survey does not provide information on capital expenditures, EPA compared the capital expenditure estimates to the
D-12
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
depreciation values reported in the survey. Depreciation had been proposed as a possible surrogate for cash outlays for
capital replacements and additions. However, depreciation does not capture important variations in capital outlays that result
from differences in firms' financial performance.
For this analysis, EPA chose a representative facility from each of the nineteen MP&M sectors for model validation. The
selected facility for each sector corresponds as closely as possible to the hypothetical median facility in the sector based on
the distribution of facility revenues and facility return on assets. For each of the nineteen facilities, EPA estimated capital
expenditures using the estimated regression equation and facility financial data. Table D.5 shows the estimated regression
coefficients, financial averages for the nineteen MP&M sectors, estimated facility capital expenditures, reported facility
depreciation, and the comparison of capital expenditures and depreciation.
As shown in Table D.5, the estimated model provides reasonable estimates of capital expenditures. A facility's size, as
indicated by revenue, is a principal determinant of the general range of value for capital expenditures, all else equal (i.e.,
greater revenues correspond to greater predicted capital expenditures). However, the size of capital expenditures relative to
the depreciation allowance depends substantially on a facility's return on assets. Facilities with lower return on assets tend to
invest less than indicated by depreciation while facilities with higher return on assets tend to invest more than depreciation.
This finding is consistent with the expectation that businesses with higher financial performance will have relatively more
attractive investment opportunities and are more likely to attract the capital to undertake those investments. To highlight this
relationship between capital expenditure, depreciation allowance, and a facility's return on assets, EPA presents graphs for the
Hardware, Iron & Steel, Job Shops, and Printed Circuit Board sectors that plot MP&M survey facilities in these sectors along
with linear trend lines for each sector's depreciation and capital expenditures with respect to return on assets.4
For presentation purposes, some outlier facilities were excluded from the graphs.
D-13
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MPAM EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Table D.5: Estimation of Capital Outlays for MP&M Sample Facilities: Median Facilities Selected
and ROA Percentiles
Sectors
Coefficient
Intercept
(-2.077)
Aerospace
Aircraft
BUS&
Truck
Electronic
Equipment
Hardware
Household
Equipment
Instruments
Iron &
Steel
Job Shop
Mobile
Industrial
Equipment
Motor
Vehicle
Office
Machine
Ordnance
Other
Metal
Products
Precious
Metals &
Jewelry
Pre-Tax j
Return j
:
on
:
Assets
(ROA) !
i
:
0.62 1
:
:
:
^ £
0.02 1
0.05 1
j
0.06 1
i
:
0.05 1
i
0.03 1
j
0.05 1
i
0.15J
j
0.12J
i
0.03 1
:
:
0.07 1
:
:
:
L i
j
0.1 1
i
o.i 1
i
0.05 1
:
:
0.08 1
i
:
:
L i
:
:
0.04 1
i
:
:
: : :
: : :
1 i i
I Capital | | Cost
„ E _, r i Capital i _
Revenue • Turnover ; T . i of
• n . \ Intensity i „ ,.
• Rate i Debt
: : :
: : :
: : :
: : :
1 i i
1 i i
1.03J 0.60 1 0.98J (0.21)
5 i i
: : :
: : :
£ j, £
90.66J 0.02 1 1.29J 7.11
18.39J 0.06 1 0.54J 9.8
1 i i
58.09J 0.03 1 0.25 1 7.11
: : :
: : :
1 i i
36.85J 0.12J 0.4 1 7.11
: : :
: : :
11.99J 0.06 1 0.61 1 9.8
1 i i
18J 0.05 1 0.8J 7.11
: : :
: : :
62.47 1 0.04 1 0.47 1 7.11
1 i i
23.17J 0.06 1 0.47 1 6.4
: : :
: : :
2J 0.07 1 0.26 1 7.11
: : :
1 i i
37.6 1 0.03 1 0.63 1 9.8
: : :
: : :
: : :
i , i i
1 i i
104.44 1 0.06 1 0.46 1 7.11
: : :
: : :
: : :
28.95J 0.06 1 0.43 1 7.11
: : :
: : :
27.08 1 0.04 1 0.65 1 9.8
: : :
1 i i
27.78J 0.17J 0.44 1 7.11
1 i i
: : :
: : :
i , i i
: : :
1 i i
13.5J 0.03 1 0.62 1 7.11
1 i i
: : :
: : :
:
:
:
Price of j
Capital |
Goods j
i
:
:
i
:
(0.48)|
i
:
L I
135.4 1
115.87J
j
135.4 1
i
:
135.4 1
i
115. 87 1
j
135.4 1
i
135.4 1
j
136.9 1
i
135.4 1
:
:
115. 87 1
:
:
:
L I
:
135.4 1
i
:
135.4 1
:
:
115.87J
:
:
135.4J
i
:
:
L I
:
:
135.4J
i
:
:
: :
: :
: :
^ . 1 Estimated !
Capacity • „ .. ,
TTA.f. J \ Capital
Utilization • _ r _.
j Expenditures j
: i
: :
: :
: i
: :
0.90 1
: :
: :
: :
J, j,
73.67J 2,113,741 1
80.01 1 440,385 1
j i
73.69J 471.199J
: i
j i
86.37 1 1, 100,627 1
: i
81.93J 311,085]
j i
84.24 1 624,804 1
: :
: :
77.21 1 1,195,144]
j i
90.82 1 617,740 1
: :
: :
81.92J 25,146]
: i
: :
79.45 1 670,447 1
: :
: :
: :
i , i
: :
81.24J 2,473,215J
: :
: :
j i
85.02J 661,715 1
: :
: :
79.77 1 674,446 1
: i
: :
80.01 1 1, 100,691 1
: i
: :
: :
i , i
: :
: :
77.21 1 224,438 1
: i
: :
: :
Depreciation
1,821,434
558,478
503,124
1,730,023
403,535
745,476
1,139,873
613,834
37,250
586,609
2,810,386
748,972
770,051
2,034,831
226,708
by Revenue
! Difference
1 between
! Depreciation
1 and Capital
1 Expenditures
i
:
:
:
:
:
:
:
:
j.
-0.14
0.27
j
0.07
i
:
0.57
i
0.3
j
0.19
i
-0.05
j
-0.01
i
0.48
:
:
-0.13
:
:
:
i
:
0.14
i
:
0.13
:
:
0.14
:
:
0.85
i
:
:
i
:
:
0.01
i
:
:
For facilities that responded to the Phase 1 survey, EPA calculated a 3-year average of the non-facility specific information over the
years in which survey data were collected (1987-1989). Likewise, for facilities that responded to the Phase 2 survey, EPA
calculated a 3-year average of the non-facility specific information for the years 1994-1996. Since the Iron and Steel sector was
surveyed in 1997, EPA calculated a 3-year average of the non-facility specific information for the years 1995-1997.
Source: U.S. EPA analysis
D-14
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MPAM EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Figure D.I: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in
the Hardware Sector
E
1
i
EZ 3
!*
*
a
a
a
s
f
B
-a
n 17
*
D.1D
DIE •
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4D -DIH -D^ -D.1D Dm D.1D DJ3] Dm D.tD 0£H a jU
+ DEpre^lcri • Csplb
F*turn pn AGGptc
Source: U.S. EPA analysis.
Figure D.2: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in
the
El
E
<
a
i
n
a
B
= =
T; s
i*
s
ii
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0
-a
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+
nnfi .
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+ DIN .
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• UU1 . •
nm
m -am -a.m am
^ DEprE^lcri • C=plU
+ +
a.ia Dm am a.to as] DSJ
R?ijm pn Ac w tc
Source: U.S. EPA analysis.
D-15
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MPAM EEBA: Appendices
Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
Figure D.3: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in
the Job Shop Sector
-U.4U -LJJU -U.JJ -U.1U
DUD D.1Q D.3D
Re turn an A GCP tc
a 3D D.H.D DfD D.SD
Dcprccbllon • Capital Oultav? Lhear (DEprEclalcriJ — — LhEar (Caplbl Oulay;J
Source: U.S. EPA analysis.
Figure D.4: Comparison of Estimated Capital Outlays to Reported Depreciation for MP&M Survey Facilities in
the Printed Circuit Board Sector
i I
S
fl
-D JO -D
AD D ±0
Source: U.S. EPA analysis.
D-16
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
ATTACHMENT b.A: BIBLIOGRAPHY OF LITERATURE REVIEWED FOR THIS ANALYSIS
As noted above, EPA relied on previous studies of investment behavior to select critical determinants of firms' capital
expenditures. Empirical results from these studies suggest that investment is most sensitive to quantity variables (output or
sales), return-over-cost, and capital utilization (R. Chirinko). Empirical results from more recent studies further found that
increasing depreciation rates and capital equipment prices were of first-order importance in the equipment investment
behavior in the 1990 (T. Tevlin, K. Whelan). Specifically, declining prices of micro-processor based equipment played a
crucial role in the investment boom in the 1990.
Chirinko, Robert S. 1993. "Business Fixed Investment Spending: A Critical Survey of Modeling Strategies, Empirical
Results and Policy Implications. "Journal of Economic Literature 31, no. 4: 1875-1911.
Goolsbee, Austan. 1997. "The Business Cycle, Financial Performance, and the Retirement of Capital Goods." University of
Chicago, Graduate School of Business Working Paper.
Greenspan, Alan. 2001. "Economic Developments." Remarks before the Economic Club of New York, New York, May 24.
Kiyotaki, Nobuhiro and Kenneth D. West. 1996. "Business Fixed Investment And The Recent Business Cycle In Japan."
National Bureau of Economic Research Working Paper 5546.
McCarthy, Jonathan. 2001. "Equipment Expenditures since 1995: The Boom and the Bust." Current Issues In Economics
And Finance 7, no. 9: 1-6.
Opler, Tim and Lee Pinkowitz, Rene Stulz and Rohan Williamson. 1997. "The Determinants and Implications of Corporate
Cash Holdings." Working paper, Ohio State University College of Business.
Tevlin, Stacey and Karl Whelan. 2000. "Explaining the Investment Boom of the 1990s." Board of Governors of the Federal
Reserve System Finance and Economics Discussion Paper no. 2000-11
Uchitelle, Louis. 2001. "Wary Spending by Companies Cools Economy." New York Times, May 14, p. Al.
D-17
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MP&M EEBA: Appendices Appendix D: Estimating Capital Outlays for MP&M Discounted Cash Flow Analyses
ATTACHMENT b.B: HISTORICAL VARIABLES CONTAINED IN THE VALUE LINE
INVESTMENT SURVEY &ATASET
All variables are provided for 10 years (except where a firm has been publicly reported for less than 10 years):
> Price of Common Stock
•• Revenues
*• Operating Income
•• Operating Margin
> Net Profit Margin
•• Depreciation
*• Working Capital
> Cash Flow per share
•• Dividends Declared per share
*• Capital Spending per share
•• Revenues per share
> Average Annual Price-Earnings Ratio
»• Relative Price-Earnings Ratio
> Average Annual Dividend
•• Return Total Capital
*• Return Shareholders Equity
>• Retained To Common Equity
•• All Dividends To Net Worth
> Employees
•• Net Profit
> Income Tax Rate
»• Earnings Before Extras
> Earnings per share
•• Long Term Debt
> Total Loans
»• Total Assets
> Preferred Dividends
»• Common Dividends
> Book Value
>• Book Value per share
*• Shareholder Equity
»• Preferred Equity
> Common Shares Outstanding
»• Average Shares Outstanding
•• Beta
*• Alpha
•• Standard Deviation
D-18
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MPAM EEBA: Appendices
Appendix £: Calculation of Capital Cost Components
Appendix E: Calculation of Capital
Cost Components
INTRODUCTION
E.l CALCULATION OF ONE-TIME
CAPITAL COST COMPONENTS
APPENDIX CONTENTS
E.l Calculation of One-Time Capital Cost Estimates E-l
EPA used the engineering estimates of total one-time capital costs to calculate the purchase cost paid to manufacturers of
compliance equipment, and the costs of shipping, installation, insurance, engineering, and consultants. Two components of
capital costs were used to estimate job gains due to compliance requirements: (1) the estimated direct capital equipment cost
and (2) the labor cost of installation. Table E.l shows the cost components that comprise the total capital costs attributed to
the regulation.
Table E.l: Components of Total Installed Capital Costs (millions, 2001$, before tax)"
Cost Component
(a) Total installed direct capital costs
(b) Direct capital equipment cost
(c) Shipping (20% of a)
(d) Labor cost of installation (7% off)
(e) Indirect costs: insurance, engineering &
consultants (47.6% of a)
(f) Total installed capital costs
Option I:
Selected Option
$4,407,590
$3,070,680
$881,518
$455,392
$2,098,013
$6,505,602
Option II:
Proposed/
NODA Option
$802,051,833
$558,773,471
$160,410,367
$82,867,995
$381,776,672
$1,183,828,505
Option III:
413 to 433
Upgrade Option
$95,552,532
$66,569,538
$19,110,506
$9,872,488
$45,483,005
$141,035,538
Option IV:
All to 433 Upgrade
Option
$148,434,303
$103,411,210
$29,686,861
$15,336,232
$70,654,728
$219,089,032
a Excludes costs for baseline and regulatory closures.
Source: U.S. EPA analysis.
The components of total capital costs for the final rule in Table E.l are discussed below in reverse order of the table
presentation.
*• Total installed capital costs: EPA estimated the total one-time capital cost for each facility expected to comply with
the regulation.1 Compliance costs are discussed in more detail in Chapter 5: Facility-Level Impact Analysis of this
EEBA. The national estimate of capital costs for the regulation is $6.5 million ($2001).2
1 See the Technical Development Document for a description of the methods used to estimate capital costs.
2 The $6.5 million is the sum of one-time outlays for purchasing and installing the capital equipment needed to comply with the final
rule. This expense is not the annual equivalent of that capital investment. The capital outlay is annualized in the economic impact analysis
over a 15-year period. The resulting value, which is part of the total annual cost of compliance, is $0.7 million.
E-l
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MP&M EEBA: Appendices Appendix £: Calculation of Capital Cost Components
*• Indirect Costs: MP&M project engineers estimate that indirect costs such as insurance, engineering, and consulting
are 47.6% of installed direct capital cost. EPA calculated the total direct and indirect cost using the total capital cost.
The national estimate of indirect costs for the regulation is $2.1 million.
*• Total Installed Direct Capital Costs: The direct capital costs include the cost of compliance equipment, shipping,
and the labor cost of installation. The national estimate of direct costs for the regulation is $4.4 million. MP&M
project engineers estimate that shipping costs might be as much as 20 percent of the total installed direct capital cost.
The estimated one-time shipping cost is $0.9 million for the final regulatory option. Installation labor costs are
estimated by the engineers to be seven percent of the total installed capital costs. The estimated one-time cost of
installation labor is $0.5 million for the final regulatory option. Therefore, the direct capital equipment cost is $3.1
million, the remainder of the total installed direct capital cost when the cost of shipping and installation are
subtracted out.
E-2
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MP&M EEBA: Appendices Appendix F: Administrative Costs
Appendix F: Administrative Costs
INTRODUCTION
Effluent guidelines and limitations are implemented by Federal, State, and local government entities through the NPDES
permit program (for direct dischargers) and the General Pretreatment Regulations (for indirect dischargers). A new effluent
guideline rule may require that facilities: (1) be permitted for
the first time; (2) be issued a different form of permit, if they
already have a permit in the baseline; and (3) be repermitted
sooner than would otherwise be required. In these cases, the
permitting authority will incur additional costs to implement
the effluent guideline rule.
This appendix provides information on the unit costs of these
permitting activities and describes the calculation of
government permitting costs for the final MP&M rule and
regulatory alternatives. EPA expects no additional costs for f3'1 Permit Application and Issuance F-4
APPENDIX CONTENTS
F.I Effluent Guidelines Permitting Requirements F-l
F.I.I NPDES Basic Industrial Permit Program F-l
F.I.2 Pretreatment Program F-2
F.2 POTW Administrative Cost Methodology F-2
F.2.1 Data Sources F-2
F.2.2 Overview of Methodology F-3
F.3 Unit Costs of Permitting Activities F-4
F.3.2 Inspection F-7
F.3.3 Monitoring F-7
F.3.4 Enforcement F-9
F.3.5 Repermitting F-10
F.4 POTW Administrative Costs by Option F-10
Appendix F Exhibits F-12
References F-25
permitting direct dischargers under the final rule. Costs for
issuing permits for indirect dischargers are based on
information reported by publicly-owned treatment works
(POTWs) in the Metal Products and Machinery (MP&M)
POTW Survey. EPA also used the data provided in the
Association of Metropolitan Sewerage Agencies (AMSA)
survey to supplement information from the MP&M POTW
Survey. EPA evaluated POTW administrative costs for
pretreatment options for the final rule. As discussed in
Section VI of the preamble to the final rule, EPA is not establishing any pretreatment standards in the final rule.
The remainder of this appendix is organized as follows: Section F.I provides an overview of permitting requirements under
the NPDES Permit Program and the General Pretreatment Regulations. Section F.2 describes the MP&M POTW Survey and
the methods used to develop annualized cost estimates for permitting indirect dischargers. Section F.3 presents the estimates
of unit costs by permitting activity for indirect dischargers. The final Section F.4 lists the steps involved in applying these
unit costs to calculate administrative costs for regulatory options evaluated by EPA for the final rule.
F.l EFFLUENT GUIDELINES PERMITTING REQUIREMENTS
Any facility that directly discharges wastewater to surface water is required to have a permit issued under the National
Pollution Discharge Elimination System (NPDES) permit program. Facilities that discharge indirectly through a POTW are
regulated by the General Pretreatment Regulations for Existing and New Sources of Pollution (40 CFR Part 403). The major
portion of government administrative costs associated with implementing an effluent guidelines rule are the costs of managing
the NPDES and Pretreatment programs.
F.I.I NP&ES Basic Industrial Permit Program
Best Practical Technology (BPT), Best Control Technology (BCT), and New Source Performance Standards (NSPS) for
effluent limitations guidelines are implemented through the NPDES industrial permit program. However, EPA does not
expect the administrative costs associated with the NPDES industrial permit program to increase as a result of the final rule.
The Clean Water Act prohibits discharge of any pollutant to a water of the U.S. except as permitted by a NPDES permit.
Therefore, every facility that discharges wastewater directly to surface water must hold a permit specifying the mass of
pollutants that can be discharged to waterways. The final rule will affect the terms of the permits but is unlikely to increase
the administrative costs associated with permitting.
The final rule may decrease the administrative burden of NPDES permits. The TDD and rulemaking record for the final rule
F-l
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MP&M EEBA: Appendices Appendix F: Administrative Costs
provide valuable information to permitting authorities that may reduce the research required to develop Best Professional
Judgment (BP J) permits.1 Further, establishing discharge standards may reduce time spent by permitting authorities
establishing limits and the frequency of evidentiary hearings. The promulgation of limitations may also enable EPA and the
authorized States to cover more facilities under general permits. General permits are single permits covering a common class
of dischargers in a specified geographic area.
F.I.2 Pretreatment Program
The General Pretreatment Regulations (40 CFR Part 403) establish procedures, responsibilities, and requirements for EPA,
States, local governments, and industry to control pollutant discharges to POTWs. Under the Pretreatment Regulations,
POTWs or approved States implement categorical pretreatment standards for existing sources (PSES) and new sources
(PSNS).
Discharges from an MP&M facility2 to a POTW may already be permitted in the baseline.3 For example, industrial users
subject to another Categorical Pretreatment Standard would have a discharge permit. Other significant industrial users (SIU)
that are typically permitted by POTWs include industrial users that:
•• discharge an average of 25,000 gallons per day or more of process wastewater to a POTW,
> contribute a process waste stream that makes up five percent or more of the average dry weather hydraulic or organic
capacity of the POTW treatment plant, or
»• have a reasonable potential for adversely affecting the POTW 's operation or for violating any pretreatment standard.
As discussed in Section VI of the preamble to the final rule, EPA did not establish or revise any pretreatment standards in the
final rule. Consequently, there are no POTW administrative costs associated with the final rule. Under the options evaluated
for the final rule, which include options for setting pretreatment standards, EPA expects no increase in permitting costs for
indirect dischargers that already hold a permit in the baseline. However, governments will incur additional permitting costs
for unpermitted facilities (under the NOD A/Proposal option only) and to accelerate repermitting for some indirect dischargers
that currently hold permits. The remainder of this appendix estimates these cost increases. As with direct industrial
dischargers, promulgation of the MP&M rule may cause some administrative costs to decrease. For example, control
authorities will no longer have to repermit facilities that are estimated to close as a result of some of the options EPA
evaluated for the final rule. These cost savings are reflected in estimates of total government administrative costs associated
with the regulatory options considered for the final rule.
F.2 POTW ADMINISTRATIVE COST METHODOLOGY
F.2.1 bata Sources
EPA collected information from POTWs to support development of the MP&M effluent guideline (see Section 3 of the
TDD). Of 150 surveys mailed, EPA received responses to 147, for a 98 percent response rate. The POTW Survey asked
respondents to provide information on administrative permitting costs for indirect dischargers, sewage sludge use and disposal
costs and practices, and general information (including number of permitted users and number of known MP&M dischargers).
The administrative cost information included the number of hours required to complete specific permitting and repermitting,
1 Permits issued to facilities not covered by effluent guidelines or water quality-based standards are developed based on BPJ (see
NPDES' permit writers manual).
2 MP&M facilities are defined on the basis of three considerations: (1) they produce metal parts, products, or machines for use in one
of the 19 industry sectors evaluated for coverage in the MP&M point source category; (2) they use operations in one of the eight regulatory
subcategories evaluated for coverage in the MP&M point source category; and (3) they discharge process wastewater, either directly or
indirectly, to surface waters. In this document, the term "MP&M facilities" refers to all facilities meeting the above definition, regardless
of whether a facility's industrial sector, subcategory, or discharger category is covered by the final regulation.
3 Under the General Pretreatment Program, a facility's discharges may be controlled through a "permit, order or similar means". For
simplicity, this document refers to the control mechanism as a permit.
F-2
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MP&M EEBA: Appendices Appendix F: Administrative Costs
inspection, monitoring, and enforcement activities. Respondents were also asked to provide an average labor cost for all staff
involved in permitting activities. EPA used the survey responses on administrative costs to estimate a range of costs incurred
by POTWs to permit a single MP&M facility.
The Association of Metropolitan Sewerage Agencies (AMSA) also provided data on administrative costs to EPA (see Section
3 of the TDD). EPA used the data provided in the AMSA survey to verify and, in some cases, supplement its own analyses of
POTW administrative costs for regulatory options evaluated for the final rule. AMSA provided EPA with comments on the
proposed MP&M rule and supplemented these comments with a spreadsheet database. The database contains data from an
AMSA formulated survey and covers responses from 176 POTWs, representing 66 pretreatment programs. The AMSA
survey was conducted to verify data from EPA's survey of POTWs and therefore included similar, although fewer, variables
compared to EPA's survey. Elements EPA verified using the AMSA survey include: (1) the estimated number of indirect
dischargers; and (2) the unit costs of certain permitting activities, including permit implementation, sampling, and sample
analysis. Elements EPA added to its analysis using the AMSA data include: (1) screening costs for POTWs that do not
currently operate under a pretreatment program; and (2) oversight costs associated with implementing the MP&M regulation.
F.2.2 Overview of Methodology
EPA estimated the annualized costs of permitting indirect dischargers under the different regulatory options evaluated for the
final rule using the following steps:
»• Determine the number and characteristics of indirect dischargers that will be permitted under each
regulatory option evaluated for the final rule. Only the NODA option includes costs for permitting an MP&M
facility for the first time. The final rule does not cover indirect dischargers while the other regulatory options only
regulate those indirect dischargers that already hold permits in the baseline. For the NODA option, EPA determined
how many new permits would be issued. The NODA option requires only concentration-based permits, no mass-
based permits.
> Use the data from the POTW Survey to determine a high, middle, and low hourly burden for permitting a
single facility. EPA defined the low and high estimates of hours such that 90% of the POTW responses fell above
the low value and 90% of responses fell below the high value. The median value is used to define the middle hourly
burden.
»• Use the data from the POTW Survey to determine the average frequency of performing certain
administrative functions. For administrative functions that are not performed at all facilities, survey data were used
to calculate the portion of facilities requiring these functions. For example, the survey data show that on average
38.5% of facilities submit a non-compliance report.
»• Multiply the per-facility burden estimate by the average hourly wage. EPA determined a high, middle, and low
dollar cost of administering the rule for a single facility by multiplying the per-facility hour burden by the average
hourly wage. The POTW Survey reported an average hourly labor rate of $39.33 ($2001) for staff involved in
permitting. This is a fully-loaded cost, including salaries and fringe benefits.
*• Calculate the annualized cost of administering the rule. The number of facilities, hourly burden estimate,
frequency estimates, and hourly wage estimates are all combined to determine the total cost of administering the rule.
The type of administrative activities required varies over time and the total administrative cost is calculated over a 15
year time period. EPA calculated the present value of total costs using a seven percent discount rate, and then
annualized the present value using the same seven percent discount rate.
F.3 UNIT COSTS OF PERMITTING ACTIVITIES
This section presents unit costs for the following permitting activities:
»• Permit application and issuance: developing and issuing concentration-based permits at previously unpermitted
facilities; providing technical guidance; and conducting public and evidentiary hearings;
•• Inspection: inspecting facilities both for the initial permit development and to assess subsequent compliance;
F-3
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MP&M EEBA: Appendices Appendix F: Administrative Costs
»• Monitoring: sampling and analyzing permittee's effluent; reviewing and recording permittee's compliance self-
monitoring reports; receiving, processing, and acting on a permittee's non-compliance reports; and reviewing a
permittee's compliance schedule report for permittees in compliance and permittees not in compliance;
> Enforcement: issuing administrative orders and administrative fines; and
*• Repermitting.
EPA believes that theses functions constitute the bulk of the required administrative activities. To these costs, EPA added a
provision for managerial oversight of 25 percent.4 There are other relatively minor or infrequent administrative functions
(e.g., providing technical guidance to permittees in years other than the first year of the permit, or repermitting a facility in
significant non-compliance), but their costs are likely to be insignificant compared to the estimated costs for the five major
categories outlined above. EPA also added a cost for identifying facilities to be permitted for POTWs that do not currently
operate under a Pretreatment Program. EPA estimates this cost to be approximately $0.8 million. This cost only applies to
the NODA/Proposal Option since facilities subject to the upgrade options already hold permits.
For each major administrative function, this section provides below: (1) a description of the activities involved, (2) the
estimated percentage of facilities that require the administrative function; (3) the frequency with which the function is
performed, and (4) high, middle, and low estimates of per facility hours and costs. All costs are presented in year 2001
dollars.
F.3.1 Permit Application and Issuance
Before issuing a wastewater discharge permit to a facility, the permit authority typically inspects the facility, monitors the
facility's wastewater, and completes pollutant limits calculations and permit paperwork. This section discusses the costs of
completing limits calculations and paperwork; subsequent sections address inspection and monitoring costs. This section also
discusses the costs of technical assistance that the control authority may provide facilities to facilitate compliance with new
limits. Finally, this section includes the costs of public and evidentiary hearings that may be required for some permits.
a. Issue a concentration-based permit at a previously unpermitted facility
To issue a concentration-based permit, permit authorities first review permit applications for completeness. If an application
is incomplete, the authorities notify the applicant and request the missing information. Completed applications are assigned to
permit writers, who review the applications in more detail as they develop permit conditions. The effort required to complete
these activities depends, in part, on the extent to which the permit authority has automated the permitting process.
EPA assumed that one-third of facilities will be permitted in each of the three years following the rule's effective date because
compliance is mandated within three years of the date the standard is effective (40 CFR Section 403.6). EPA further assumed
that facilities are repermitted in five year cycles. (The administrative costs of repermitting are discussed separately below.)
The actual number of facilities that are permitted each year is likely to differ somewhat from EPA's simplifying assumption.
These minor differences in permit timing are not expected to significantly change the estimated administrative costs.
4 The 25 percent oversight cost provision is based on comments and data received from the Association of Metropolitan Sewerage
Agencies (AMSA).
F-4
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
Table F.I: Administrative Activity: Develop and issue a concentration- based permit at a previously
unpermitted facility
Percent of facilities for which
activity is required
100% of unpermitted MP&M facilities
(applicable to NODA/Proposal option only)
Frequency
of activity
One time
Typical costs (2001$)
r
Low
4.0 hours;
$122
Median
10.0 hours;
$304
High
40.0 hours;
$1,217
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
b. Issue a mass-based permit for a previously unpermitted facility5
The same administrative activities required to issue a concentration-based permit are also required for a mass-based permit.
In addition, for mass-based permits issued under the MP&M rule, the permit writer must determine whether the facility
practices pollution prevention and water conservation methods equivalent to those specified as the basis for BPT. If so, the
permitting authority must determine the facility's historical flow rate. If not, the authority must derive a mass-based limit
based on other factors such as production rates. When a facility matches BPT water conservation practices and provides
historic flow data, development of a mass-based permit is a relatively straight-forward process. However, the task will be
more challenging at a facility practicing only limited water conservation, particularly if the facility has multiple production
units and generates integrated process and sanitary wastewaters.
Table F.2: Administrative Activity: Develop and issue a mass-based permit at a previously
unpermitted facility
Percent of facilities for which
activity is required
100% of MP&M facilities being issued a new mass-based
permit
(estimates used for the proposed rule)
Frequency
of activity
One time
Typical costs (2001$)
Low
4.0 hours;
$122
Median
13.0 hours;
$396
High
40.0 hours;
$1,217
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
c. Issue a mass-based permit for a facility with a concentration-based permit6
Some of the activities described above for issuing a mass-based permit will be simplified in cases where the facility already
holds a concentration-based permit. For example, much of the basic information required in the permitting application will
already be in the permitting authorities' records. However, the potentially labor-intensive task of determining the flow basis
for the permit remains.
5 None of the regulatory options considered for the final rule require issuance of mass-based permits for previously unpermitted
facilities. However, since these costs were developed for the proposed rule, they are presented in this appendix even though they are not
used in the administrative costs estimates.
6 None of the regulatory options considered for the final rule require conversion of a concentration-based to a mass-based permit.
However, since these costs were developed for the proposed rule, they are presented in this appendk even though they are not used in the
administrative costs estimates.
F-5
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
Table F.3: Administrative Activity: Develop and issue a mass-based permit at a facility holding a
concentration-based permit
Percent of facilities for which
activity is required
100% of MP&M facilities with permit conversion
(estimates used for the proposed rule)
Frequency
of activity
One time
Typical costs (2001$)
f
Low
2.0 hours;
$61
Median
8.0 hours;
$243
High
20.0 hours;
$608
Source: U.S. EPA analysis ofPOTWSurvey responses; U.S. Department of Labor, Bureau of Labor Statistics.
d. Provide technical guidance to a permittee
Technical guidance is frequently provided by permit authorities to permittees concurrent with the issuance of a new permit.
There are no legal requirements that a permit authority provide a permittee with technical guidance. However, such guidance
is generally in the interest of all parties as it can expedite the permitting process, accelerate the permittee's compliance, and
reduce the compliance burden. The extent of technical guidance provided varies dramatically among permit authorities. In
some cases, a permit authority may hold a one-day workshop to provide information on a new pretreatment standard to
facilities. In other cases, a permit authority may meet extensively with individual permittees to educate them regarding their
responsibilities under pretreatment standards. The range of technical guidance appears to depend on whether the permittee
already has a wastewater permit, whether the permittee is part of a multi-facility company, the resources of the permit
authority, and the extent to which the permit authority has written or standardized guidance available for dissemination.
EPA assumed that permit authorities provide technical guidance to all facilities being issued a new mass-based or
concentration-based permit under the MP&M pretreatment standards. Costs for technical guidance were estimated separately
for facilities receiving a concentration-based permit and facilities receiving a mass-based permit. EPA assumed that technical
guidance is provided in the year the initial permit is issued.
Table F.4: Administrative Activity: Provide technical guidance to permittee on permit compliance
Percent of facilities for which
activity is required
100% of MP&M facilities being issued a new concentration-
based permit
(applicable to NODA/Proposal option only)
100% of MP&M facilities being issued a new mass-based
permit
(estimates used for the proposed rule)
Frequency
of activity
One time
L J
One time
Typical costs (2001$)
f
Low
1.5 hours;
$46
L
2.0 hours;
$61
Median
4.0 hours;
$122
L J
4.0 hours;
$122
High
12.0 hours;
$365
L
12.0 hours;
$365
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
e. Conduct a public or evidentiary hearing on a proposed permit
Federal regulations provide for a period during which the public may submit written comments on a proposed permit for
direct dischargers and/or request that a public hearing be held. Permitting authorities for indirect dischargers may have the
same requirements. Thus, proposed permits for indirect dischargers may be subject to public comments and hearings.
Pretreatment public hearings are typically conducted at a scheduled local government (e.g., City Council) meeting. The
meetings may require substantial preparation.
Federal regulations also provide for evidentiary hearings following final permit determination for direct dischargers. Again,
permitting authorities for indirect dischargers may have these requirements as well. Thus, final permit determinations for
indirect dischargers may be subject to evidentiary hearings.
Data from the POTW Survey indicated that a public or evidentiary hearing would be required for 3.6% of indirect dischargers
being issued a new mass-based or concentration-based permit, on average.
F-6
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
Table F.5: Administrative Activity: Conduct a public or evidentiary hearing
Percent of facilities for which
activity is required
3 .2% of MP&M facilities being issued a new mass-based or
concentration-based permit
(applicable to NODA/Proposal option only)
Frequency
of activity
One time
Typical costs (2001$)
Low
2.0 hours;
$61
Median
8.0 hours;
$243
High
40.0 hours;
$1,217
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
F.3.2 Inspection
Permit authorities may choose to integrate their inspection and monitoring work force or to administer these functions
separately. This discussion covers inspections only; monitoring is discussed below. Inspections are performed both to assess
conditions for initial permitting and to evaluate compliance with permit requirements. Inspections involve record reviews,
visual observations, and evaluations of the treatment facilities, effluents, receiving waters, etc. EPA assumed that the initial
inspection would occur in the same year a new permit is issued, and that all permitted facilities would be inspected annually to
assess compliance.
Table F.6: Administrative Activity: Inspect facility for permit development
Percent of facilities for which
activity is required
1 00% of MP&M facilities being issued a new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
One Time
Typical costs (2001$)
r
Low
2.2 hours;
$66
Median
5.0 hours;
$152
High
12.0 hours;
$365
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
Table F.7: Administrative Activity: Inspect facility for compliance assessment
Percent of facilities for which
activity is required
1 00% of MP&M facilities being issued a new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
Annual
Typical costs (2001$)
r
Low
2.0 hours;
$61
Median
3.3 hours;
$101
High
10.0 hours;
$304
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
F.3.3 Monitoring
Permitting authorities monitor facilities both to gather data needed for permit development and to assess compliance with
permit conditions. Monitoring includes sampling and analysis of the permittee's effluent, review of the permittee's
compliance self-monitoring reports, receipt of non-compliance reports, and review of compliance schedule reports. These
activities are discussed below.
a. Sample and analyze permittee's effluent
As noted above, inspection and monitoring staff may be integrated or distinct. The costs of inspection were presented above.
Federal regulations require that the permit authority "randomly sample and analyze the effluent from industrial users ...
independent of information supplied by industrial users" (40 CFR Part 403.8). The permit authority obtains samples required
by the permit and performs chemical analyses. The results are used to verify the accuracy of the permittee's self-monitoring
program and reports, determine the quantity and quality of effluents, develop permits, and provide evidence for enforcement
proceedings where appropriate.
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
EPA estimated sampling costs for all facilities issued a new permit under the MP&M rule, and assumed annual monitoring.
Although EPA requires only annual effluent sampling, some localities sample more frequently. EPA encourages this practice.
Table F.8: Administrative Activity: Sample and analyze permittee's effluent
Percent of facilities for which
activity is required
100% of MP&M facilities being issued a new
permit
(applicable to NODA/Proposal option only)
Frequency
of activity
Annual
Typical costs (2001$)
Low
1.0 hour;
$30
Median
3.0 hours;
$91
High
17.7 hours;
$537
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
b. Review and record permittee's compliance self-monitoring reports
40 CFR Part 403.12 specifies that: "Any Industrial User subject to a categorical pretreatment standard ... shall submit to the
Control authority during the months of June and December ... a report indicating the nature and concentration of pollutants in
the effluent which are limited by such categorical pretreatment standards." The permit authority briefly reviews these
submissions and may enter the information into a computerized system and/or file the data.
EPA estimated the costs of handling annual self-monitoring reports for all facilities being issued a new permit under the
MP&M rule.
Table F.9: Administrative Activity: Review and enter data from permittee's compliance self-
monitoring reports
Percent of facilities for which
activity is required
100% of MP&M facilities being issued a new
permit
(applicable to NODA/Proposal option only)
Frequency
of activity
2 reports per year
Typical costs (2001$)
f
Low
0.5 hours;
$15
Median
1.0 hour;
$30
High
4.0 hours;
$122
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
c. Receive, process, and act on a permittee's non-compliance report
Generally, when a permittee violates a permit condition, it must submit a non-compliance report to the permit authority.
Permittees report both unanticipated bypasses or upsets and violations of maximum daily discharge limits. The permit
authority receives and processes both verbal and written non-compliance reports. In some cases, immediate action by the
permit authority is required to mitigate the problem.
Data from the POTW Survey indicate that 38.5 percent of all facilities submit at least one non-compliance report annually.
Of facilities that submit at least one non-compliance report, the median number of reports filed per year is five reports.
Table F.10: Administrative Activity: Receive, process and act on a permittee's non-compliance reports
Percent of facilities for which
activity is required
38.5% of all indirect dischargers receiving a new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
5 times per year
Typical costs (2001$)
Low
1 .0 hour;
$30
Median
2.0 hours;
$61
High
6.0 hours;
$183
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
d. Review a permittee's compliance schedule report
Permittees submit reports to permit authorities that state whether compliance schedule milestones contained in their permits
have been met. If the facility is in compliance, the permit authority reviews and files the report.
Data from the POTW Survey indicate that approximately 17% of all facilities are issued compliance milestones. Of these
facilities, 94% meet the milestones. Facilities submit an average of two compliance milestone reports per year. The cost of
handling the report depends on whether the facility is in compliance with the schedule.
Table F.ll: Administrative Activity: Review a compliance schedule report
Percent of facilities for which
activity is required
Meeting milestones: 16.0% of all facilities issued a new permit
- 94% of the 17% who have compliance milestones
(applicable to NODA/Proposal option only)
Not meeting milestones: 1% of all facilities issued a new
permit - 6% of the 17% who have compliance milestones
(applicable to NODA/Proposal option only
Frequency
of activity
2 reports per
year
2 reports per
year
Typical costs (2001$)
Low
0.5 hours;
$15
1.0 hours;
$30
Median
1 .0 hour;
$30
2.0 hours;
$61
High
2.7 hours;
$81
6.0 hours;
$183
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
F.3.4 Enforcement
When a permitting authority identifies a permit violation, the authority determines and implements an appropriate
enforcement action. Considerations when determining enforcement response include (1) the severity of the permit violation,
(2) the degree of economic benefit obtained by the permittee through the violation, (3) previous enforcement actions taken
against the violator, (4) the deterrent effect of the response on similarly situated permittees, and (5) considerations of fairness
and equity. EPA estimated administrative costs for two levels of enforcement actions: (1) less severe actions such as issuing
an administrative order, and (2) more severe activities such as levying an administrative fine.
EPA estimated that, annually, seven percent of facilities issued a new permit under the MP&M rule will require a minor
enforcement action, such as issuing an administrative compliance order, hi addition, EPA estimated that seven percent of
facilities receiving a new permit will require more severe enforcement actions such as a fine or penalty.
Table F.12: Administrative Activity: Minor enforcement action e.g., issue an administrative order
Percent of facilities for which
activity is required
7% of MP&M facilities being issued a new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
Annual
Typical costs (2001$)
r
Low
1 .0 hour;
$30
Median
3. 7 hours;
$112
High
12.0 hours;
$365
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
Table F.13: Administrative Activity: Minor enforcement action, e.g., impose an administrative fine
Percent of facilities for which
activity is required
7% of MP&M facilities being issued a new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
Annual
Typical costs (2001$)
r
Low
1 .0 hour;
$30
Median
5.0 hours;
$152
High
24.0 hours;
$730
Source: U.S. EPA analysis of POTW Survey responses; U.S. Department of Labor, Bureau of Labor Statistics.
F-9
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
F. 3.5 Repermitting
The duration of permits cannot exceed five years. Renewing a permit for a facility in compliance with an existing permit is
expected to be a relatively straightforward task. The data submitted in the permit application generally require few changes,
although pollutant limits may need to be recalculated in some cases. The labor required for repermitting depends, in part, on
the extent to which the permit authority has automated the paperwork.
Table F.14: Administrative Activity: Repermit
Percent of facilities for which
activity is required
100% of MP&M facilities being issued a new permit
(applicable to NODA/Proposal option only)
Frequency
of activity
every 5 years
Typical costs (2001$)
f
Low
1.0 hour;
$30
Median
4.0 hours;
$122
High
20.0 hours;
$608
Source: U.S. EPA analysis ofPOTWSurvey responses; U.S. Department of Labor, Bureau of Labor Statistics.
In addition to repermitting MP&M facilities being issues a new permit, EPA also considered two other types of cost: (1) the
costs associated with repermitting facilities that already hold a permit in the baseline sooner than would otherwise be
required; and (2) cost savings associated with no longer having to permit facilities that already hold a permit in the baseline
but that are estimated to close as a result of the rule. Both cost components are reflected in the POTW administrative costs
presented in the next section.
F.4 POTW ADMINISTRATIVE COSTS BY OPTION
Exhibits F.I through F.7 at the end of this appendix present the calculation of POTW permitting costs for the final rule and
the three regulatory alternatives considered by EPA.
Exhibit F.I provides an overview of the permitting activities, the estimated percentage of facilities that require the
administrative function, the frequency with the function is performed, and per facility hours and costs for each function.
Exhibit F.2 contains the per facility hour burden and other assumptions described above for each of the three types of
permitting (new concentration-based permit, new mass-based permit, and converting a concentration-based to a mass-based
permit.)
Exhibits F.3 through F.5 show hours by type of permit for the low, medium, and high estimate of per-facility burden,
respectively. These exhibits also summarize costs and dollars by year and permit type.
Exhibit F.6 presents the number of facilities requiring different types of permitting, for each of the regulatory options. The
exhibit shows the total number of facilities that will be subject to requirements, the baseline permit status of those facilities,
and the number of facilities by expected post-compliance permit status. These estimates are based on facility survey
information about baseline permit status and the results of the facility impact analysis described in Chapter 5 of the EEBA.
The exhibit also shows the number of currently-permitted facilities that are projected to close as a result of the rule, and which
will therefore no longer require re-permitting.
The final Exhibit F.7 shows the resulting calculation of POTW administrative hours and costs by year for each regulatory
option. This exhibit also shows the present value of these costs, the annualized cost, and the maximum hours and costs
incurred in any one year, for each option. These calculations reflect the incremental number of facilities requiring different
types of permitting, inspection, monitoring, enforcement and repermitting in each year multiplied by the unit hours and cost
per facility for those activities. All facilities are assumed to receive a permit under the final rule within the three-year
compliance period. Some facilities with existing permits are repermitted sooner than they otherwise would be on the normal
five-year permitting cycle. The cost analyses calculates incremental costs by subtracting the costs of repermitting these
facilities on a five-year schedule from the costs of repermitting all such facilities within three years. EPA assumes that the
required initial permitting activities will be equally divided over the three-year period. The analysis also calculates the net
F-10
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MP&M EEBA: Appendices Appendix F: Administrative Costs
change in the number of facilities requiring permitting by subtracting the number of facilities that close due to the rule from
the number of facilities that will require new permits under each regulatory option.
More detailed information on these cost calculations is provided in the docket for the final rule.
F-ll
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MPAM EEBA: Appendices
Appendix F: Administrative Costs
APPENDIX F EXHIBITS
Exhibit F.I: Government Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs
Exhibit F.2: Per-Facility Hours and Assumptions
Exhibit F.3: Low Estimate of Hours and Costs per Facility
Exhibit F.4: Medium Estimate of Hours and Costs per Facility
Exhibit F.5: High Estimate of Hours and Costs per Facility
Exhibit F.6: Number of Facilities Requiring Additional Permitting
Exhibit F.7: POTW Administrative Costs by Option
F-12
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MP&M EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.I: Government Administrative Activities for Indirect Dischargers: Per Facility Hours and Costs
.... . ... Percent of facilities for which
Administrative Activity ... . ,
activity is required
Develop and issue a concentration- ,„„„/,, . .......
, , . . . • 100% of unpermitted facilities
based permit at a previously :, .. ,. f XT^T^A/TI i ^ i \
...... : (applicable to NODA/Proposal option only)
unpermitted facility
_. . ,. , , . 1 100% of MP&M facilities being issued a
Develop and issue a mass-based permit • . ,
. , ,, .,. : new mass-based permit
at a previously unpermitted facility : , , - . , . .
1 (estimates used for the proposed rule)
Develop and issue a mass-based permit 1 100% of MP&M facilities with permit
at a facility holding a concentration- ! conversion
based permit j (estimates used for the proposed rule)
I 100% of MP&M facilities being issued a
! new concentration-based permit
Provide technical guidance to a j (applicable to NODA/Proposal option only)
permittee on permit compliance j 1 00% of MP&M facilities being issued a
! new mass-based permit
! (estimates used for the proposed rule)
I 3.2% of MP&M facilities being issued a
„ , ... . , . . I new mass-based or concentration-based
Conduct a public or evidentiary hearing •
! permit
j (applicable to NODA/Proposal option only)
j 100% of MP&M facilities being issued a
Inspect facility for permit development j new permit
! (applicable to NODA/Proposal option only)
T . ... . .. 1 100% of MP&M facilities being issued a
Inspect facility for compliance
: new permit
assessment :, ,. ,. XT/^T^A/TI i 11
! (applicable to NODA/Proposal option only)
j 100% of MP&M facilities being issued a
Sample and analyze permittee's effluent: new permit
! (applicable to NODA/Proposal option only)
_ . , > j- • 1 100% of MP&M facilities being issued a
Review and enter data from permittee s : .
i- if v • _t -new permit
compliance self-monitoring reports :, .. ,. XT/^T^A/TI i 11
(applicable to NODA/Proposal option only)
_ . , 1 38.5% of all indirect dischargers receiving a
Receive, process and act on a
, . . • new permit
permittee s non-compliance reports :, .. ,. XT/^T^A/TI i 11
! (applicable to NODA/Proposal option only)
1 Meeting milestones: 16.0% of all facilities
! issued a new permit - 94% of the 17% who
! have compliance milestones
1 (applicable to NODA/Proposal option only)
1 Not meeting milestones: 1% of all facilities
! issued a new permit - 6% of the 17% who
! have compliance milestones
1 (applicable to NODA/Proposal option only)
, ,. ,, . . 1 7% of MP&M facilities being issued a new
Minor enforcement action e.g., issue an j
administrative order K .. ,, XT^T^A/TI i i \
! (applicable to NODA/Proposal option only)
, ,. . . . \ 7% of MP&M facilities being issued a new
Minor enforcement action, e.g., impose j
an administrative fine \, .. ... ^.-.T^./r. , , ,.
! (applicable to NODA/Proposal option only)
| 100% of MP&M facilities being issued a
Repermit j new permit
! (applicable to NODA/Proposal option only)
Frequency
of activity
One time
One time
One time
One time
One time
One time
One Time
Annual
Annual
2 reports
per year
5 times per
year
2 reports
per year
2 reports
per year
Annual
Annual
Every 5
years
Typical hours and costs
Low
4.0 hours;
$122
4.0 hours;
$122
2.0 hours;
$61
1.5 hour;
$46
2.0 hours;
$61
2.0 hours;
$61
2.2 hours;
$66
2.0 hours;
$61
1.0 hour;
$30
0.5 hours;
$15
1.0 hour;
$30
0.5 hours;
$15
1.0 hours;
$30
1.0 hour;
$30
1.0 hour;
$30
1.0 hour;
$30
Median
10.0
hours;
$304
13.0
hours;
$396
8.0 hours;
$243
4.0 hours;
$122
4.0 hours;
$122
8.0 hours;
$243
5.0 hours;
$152
3.3 hours;
$101
3.0 hours;
$91
1 .0 hour;
$30
2.0 hours;
$61
1 .0 hour;
$30
2.0 hours;
$61
3.7 hours;
$112
5.0 hours;
$152
4.0 hours;
$122
High
40.0
hours;
$1,217
40.0
hours;
$1,217
20.0
hours;
$608 year
12.0
hours;
$365
12.0
hours;
$365
40.0
hours;
$1,217
12.0
hours;
$365
10.0
hours;
$304
17.7
hours;
$537
4.0 hours;
$122
6.0 hours;
$183
2.7 hours;
$81
6.0 hours;
$183
12.0
hours;
$365
24.0
hours;
$730
20.0
hours;
$608
Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor Statistics
(Sept. 2002 rate, adjusted to $2001).
F-13
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MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.2: Pen-Facility Hours and Assumptions
i i
Activity i Low j Medium! High j%Facilj x/yr i Notes
New concentration-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary
hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring
reports
process & act on non-compliance
reports
review compliance schedule report -
in compliance with schedule
review compliance schedule report -
not in compliance with schedule
minor enforcement action (e.g., admin
order)
minor enforcement action (e.g., admin
fine)
repermit
New mass-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary
hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring
reports
process & act on non-compliance
reports
review compliance schedule report -
in compliance with schedule
review compliance schedule report -
not in compliance with schedule
minor enforcement action (e.g., admin
order)
minor enforcement action (e.g., admin
fine)
repermit
4.0
1.5
2.0
2.2
2.0
1.0
0.5
1.0
0.5
1.0
1.0
1.0
1.0
4.0
2.0
2.0
2.2
2.0
1.0
0.5
1.0
0.5
1.0
1.0
1.0
1.0
10.0
4.0
8.0
5.0
3.3
3.0
1.0
2.0
1.0
2.0
3.7
5.0
4.0
13.0
4.0
8.0
5.0
3.3
3.0
1.0
2.0
1.0
2.0
3.7
5.0
4.0
40.0
12.0
40.0
12.0
10.0
17.7
4.0
6.0
2.7
6.0
12.0
24.0
20.0
40.0
12.0
40.0
12.0
10.0
17.7
4.0
6.0
2.7
6.0
12.0
24.0
20.0
100.0%| 1 lone-time
100.0%! 1 lone-time
3.2%! 1 1 one-time, 3.2% of facilities
100.0%! 1 lone-time
100.0%! ll annual
100.0%! ll annual
100.0%| 2J2x/year
38.5%! 51 5x/year, 38.5% of facilities
. \ \ 2x/yr, 17% of facilities with compliance
1 1 milestones, of which 94% in compliance
„„, 1 ,1 2x/yr, 17% of facilities with compliance
1 1 milestones, of which 6% not in compliance
7.0%! 1 1 annual, 7% of facilities
7.0%! 1 1 annual, 7% of facilities
100.0%; 1 ; every three years
100.0%! 1 lone-time
100.0%! 1 lone-time
3.2%! 1 1 one-time, 3.2% of facilities
100.0%| l! one-time
100.0%! ll annual
100.0%! ll annual
100.0%! 2! 2x/year
38.5%! 5! 5x/year, 38.5% of facilities
. \ \ 2x/yr, 17% of facilities with compliance
; milestones, of which 94% in compliance
„„, 1 ,; 2x/yr, 17% of facilities with compliance
; milestones, of which 6% not in compliance
7.0%! 1 1 annual, 7% of facilities
7.0%! 1 1 annual, 7% of facilities
100.0%i 1 i every three years
Converting concentration-based to mass-based
develop and issue permit 2.0J 8.0J 20.0J 100.0%j ij one-time
provide technical guidance O.OJ O.OJ O.OJ 0.0%! OJ N/A
F-14
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MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.2: Pen-Facility Hours and Assumptions
Activity
conduct public or evidentiary
hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring
reports
process & act on non-compliance
reports
review compliance schedule report -
in compliance with schedule
review compliance schedule report -
not in compliance with schedule
minor enforcement action (e.g., admin
order)
minor enforcement action (e.g., admin
fine)
repermit
Low
0.0
0.0
2.0
1.0
0.5
1.0
0.5
1.0
1.0
1.0
1.0
Medium
0.0
0.0
3.3
3.0
1.0
2.0
1.0
2.0
3.7
5.0
4.0
High
0.0
0.0
10.0
17.7
4.0
6.0
2.7
6.0
12.0
24.0
20.0
% Facil
0.0%
0.0%
100.0%
100.0%
100.0%
38.5%
16.0%
1.0%
7.0%
7.0%
100.0%
|
x/yr i Notes
:
OJN/A
:
:
OJN/A
j
1 j annual
:
1 j annual
:
:
2 j 2x/year
i
:
:
5! 5x/year, 38.5% of facilities
i
:
j 2x/yr, 17% of facilities with compliance
j milestones, of which 94% in compliance
:
j 2x/yr, 17% of facilities with compliance
j milestones, of which 6% not in compliance
:
:
1 j annual, 7% of facilities
i
:
:
:
1 j annual, 7% of facilities
i
:
:
1 j every three years
Discount rate: 7%
Average hourly rate: $30.42 ($2001)
Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor Statistics
(Sept. 2002 rate, adjusted to $2001).
F-15
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.3: Low Estimate of Hours and Costs per Facility
(average considering frequency of activity and percent of facilities requiring activity)
: : :
..... I T ... . ,, Annual Repermit
Activity : Initial Year : , . . , : Ji
• (non-permitting year) • Year
New concentration-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
New mass-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
Upgrading from concentration-based to mass-based
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
4
2
0
2
2
1
1
2
0
0
0
0
14
$425
4
2
0
2
2
1
1
2
0
0
0
0
14
$440
2
0
0
0
2
1
2
1
1
2
0
0
0
0
6
$190
2
1
1
2
0
0
0
0
6
$190
2
1
2
1
1
2
0
0
0
0
1
7
$220
2
i
i
2
0
0
0
0
1
7
$220
2
1
F-16
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.3: Low Estimate of Hours and Costs per Facility
(average considering frequency of activity and percent of facilities requiring activity)
Activity
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
Initial Year
1
2
0
0
0
0
8
$251
Annual
(non-permitting year)
1
2
0
0
0
0
6
$190
Repermit
Year
1
2
0
0
0
0
1
7
$220
Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor
Statistics (Sept. 2002 rate, adjusted to $2001).
F-17
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.4: Medium Estimate of Hours and Costs per Facility
(average considering frequency of activity and percent of facilities requiring activity)
: : :
..... I T ... . ,, Annual ! Repermit
Activity : Initial Year : , . . , : Ji
• (non-permitting year) : Year
New concentration-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
New mass-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
10
4
0
5
3
3
2
4
0
0
0
0
32
$986
13
4
0
5
3
3
2
4
0
0
0
0
35
$1,077
3
3
2
4
0
0
0
0
13
$400
3
3
2
4
0
0
0
0
13
$400
3
3
2
4
0
0
0
0
4
17
$522
3
3
2
4
0
0
0
0
4
17
$522
Upgrading from concentration-based to mass-based
develop and issue permit 8
provide technical guidance 0
conduct public or evidentiary hearings 0
inspection for permit development 0
inspection for compliance assessment 333
F-18
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.4: Medium Estimate of Hours and Costs per Facility
(average considering frequency of activity and percent of facilities requiring activity)
Activity
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
Initial Year
3
2
4
0
0
0
0
21
$643
Annual
(non-permitting year)
3
2
4
0
0
0
0
13
$400
Repermit
Year
3
2
4
0
0
0
0
4
17
$522
Source: Estimates of hours by activity from the 1996 MP&M POT W Survey. Average hourly rate from Bureau of Labor
Statistics (Sept. 2002 rate, adjusted to $2001).
F-19
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.5: High Estimate of Hours and Costs per Facility
(average considering frequency of activity and percent of facilities requiring activity)
: : :
..... i T ... . ,, Annual I Repermit
Activity : Initial Year , . . , : Ji
• (non-permitting year) • Year
New concentration-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
New mass-based permit
develop and issue permit
provide technical guidance
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
40
12
1
12
10
18
8
12
1
0
1
2
116
$3,529
40
12
1
12
10
18
8
12
1
0
1
2
116
$3,529
10
18
8
12
1
0
1
2
51
$1,543
10
18
8
12
1
0
1
2
51
$1,543
10
18
8
12
1
0
1
2
20
71
$2,151
10
18
8
12
1
0
1
2
20
71
$2,151
Upgrading from concentration-based to mass-based
develop and issue permit 20
provide technical guidance 0
conduct public or evidentiary hearings
inspection for permit development
inspection for compliance assessment
r\ '• '•
o
,-,
o
T T
10 10 10
F-20
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.5: High Estimate of Hours and Costs per Facility
(average considering frequency of activity and percent of facilities requiring activity)
Activity
sample and analyze effluent
review & record self-monitoring reports
process & act on non-compliance reports
review compliance schedule report - in compliance with schedule
review compliance schedule report - not in compliance with schedule
minor enforcement action (e.g., admin order)
minor enforcement action (e.g., admin fine)
repermit
Total Hours by Year
Total Dollars by Year
Initial Year
18
8
12
1
0
1
2
71
$2,151
Annual
(non-permitting year)
18
8
12
1
0
1
2
51
$1,543
Repermit
Year
18
8
12
1
0
1
2
20
71
$2,151
Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of Labor
Statistics (Sept. 2002 rate, adjusted to $2001).
F-21
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.6: Number of Facilities Requiring Additional Permitting
Option II: NODA/Proposal Option
Number of facilities operating post-regulation requiring a permit
Of facilities operating post-regulation:
existing concentration-based
existing mass-based
no permit in baseline
concentration based to be converted to mass-based
new concentration-based
new mass-based
Number of currently permitted facilities closing (no longer requiring a permit)
Of facilities closing due to the rule:
existing concentration-based
existing mass-based
Option III: Directs + 413 to 433 Upgrade
Number of facilities operating post-regulation requiring a permit
Of facilities operating post-regulation:
existing concentration-based
existing mass-based
no permit in baseline
concentration based to be converted to mass-based
new concentration-based
new mass-based
Number of currently permitted facilities closing (no longer requiring a permit)
Of facilities closing due to the rule:
existing concentration-based
existing mass-based
Option IV: Directs + 413+50%LL Upgrade
Number of facilities operating post-regulation requiring a permit
Of facilities operating post-regulation:
existing concentration-based
existing mass-based
no permit in baseline
concentration based to be converted to mass-based
new concentration-based
new mass-based
Number of currently permitted facilities closing (no longer requiring a permit)
Of facilities closing due to the rule:
existing concentration-based
existing mass-based
3,687
692
2,892
103
0
722
209
513
954
184
770
0
0
0
0
120
0
120
1,414
515
899
0
0
0
0
120
C
12C
Source: U.S. EPA analysis.
F-22
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.7: POTW Administrative Costs by Option (@ 7% discount rate)
Option II: NODA/Proposal Option
Year Relative to Promulgation of Rule
T f *
! i! 2
Total Hours
High 32,561 ! -15,017
Medium 33,603 j -4,289
Low 33,638! -2,472
Total Costs
High $990,604! $-456,868
Medium ! $1,022,297; $-130,480
Low ! $1,023,378! $-75,221
High ! Medium
NPV I $-9,357,000! $-1,802,000
Annualized Cost ! $-1, 027,000 ! $-198,000
Max One Year Hours I 32,561 I 33,603
Max One Year Costs ! $991,000; $1,022,000
3j 4
-28,095! -60,763
-7.680J -14,480
-4,083! -5,908
$-854,738! $-1,848,612
$-233,655! $-440,526
$-124,220! $-179,746
Low
$-422,000;
$-46,000!
33,638!
$1,023,000!
5J 6
-60,763 j -30,038
-14,480j -8,335
-5,908j -4,372
$-1,848,612 j $-913,859
$-440,526= $-253,575
$-179,746j $-133,008
:
:
:
i
:
:
:
:
7| 8
-30,038j -30,038
-8,335j -8,335
-4,372j -4,372
$-913,8591 $-913,859
$-253,575= $-253,575
$-133,008j $-133,008
:
:
:
i
:
:
:
:
9J 10
-60,763 j -60,763
-14,480j -14,480
-5,908j -5,908
$-1,848,612 j $-1,848,612
$-440,526= $-440,526
$-179,746j $-179,746
:
:
:
i
:
:
:
:
11 1 12
-30,038j -30,038
-8,335j -8,335
-4,372j -4,372
$-913,8591 $-913,859
$-253,575= $-253,575
$-133,008j $-133,008
:
:
:
i
:
:
:
:
13 1 14
-30,038j -60,763
-8,335j -14,480
-4,372j -5,908
$-913,8591 $-1,848,612
$-253,575= $-440,526
$-133,008j $-179,746
:
:
:
i
:
:
:
:
15
-60,763
-14,480
-5,908
$-1,848,612
$-440,526
$-179,746
Option III: Directs + 413 to 433 Upgrade
Year Relative to Promulgation of Rule
ll 2
: :
Total Hours
High 33 j -2,513
....„ £ j.
Medium -144; -805
Low -185 j -498
Total Costs
High $1,000! $-76,451
....„ £ j.
Medium j $-4,394; $-24,479
Low $-5,616J $-15,154
'• High '• Medium
£ j.
NPV j $-l,982,000| $-509,000
Annualized Cost $-218,OOOJ $-56,000
Max One Year Hours j 33 j -144
Max One Year Costs i $1,000: $-4,000
j. i
3j 4
-5,059! -13,011
-1,465| -3,055
-812J -1,209
$-153,901! $-395,845
$-44,563! $-92,952
$-24,692J $-36,789
Low !
$-238,000!
$-26,OOOJ
-185!
$-6,OOOJ
5j 6
-13,01lj -5,059
-3,055J -1,465
-1,209J -812
$-395,845j $-153,901
$-92,952J $-44,563
$-36,789J $-24,692
i
:
:
:
:
i
:
7j 8
-5,059j -5,059
-l,465j -1,465
-812J -812
$-153,901j $-153,901
$-44,563J $-44,563
$-24,692J $-24,692
i
:
:
:
:
i
:
9j 10
-13,01lj -13,011
-3,055i -3,055
-1,209J -1,209
$-395,845j $-395,845
$-92,952J $-92,952
$-36,789J $-36,789
i
:
:
:
:
i
:
11 1 12
-5,059j -5,059
-l,465j -1,465
-812J -812
$-153,901j $-153,901
$-44,563J $-44,563
$-24,692J $-24,692
i
:
:
:
:
i
:
13 1 14
-5,059j -13,011
-1,4655 -3,055
-812J -1,209
$-153,901j $-395,845
$-44,563J $-92,952
$-24,692J $-36,789
i
:
:
:
:
i
:
15
-13,011
-3,055
-1,209
$-395,845
$-92,952
$-36,789
F-23
-------
MPAM EEBA: Appendices
Appendix F: Administrative Costs
Exhibit F.7: POTW Administrative Costs by Option (@ 7% discount rate)
Option IV: Directs + 413+50 %LL Upgrade
Total Hours
High
Medium
Low
Total Costs
High
Medium
Low
NPV
Annualized Cost
Max One Year Hours
Max One Year Costs
Year Relative to Promulgation of Rule
1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 j 10 j 11 j 12 j 13 j 14 j 15
1,566 I -980 j -3,525 j -15,311 j -15,311 j -3,525 j -3,525 j -3,525 j -15,311 j -15,311 j -3,525 j -3,525 j -3,525 j -15,311 j -15,311
162
-108
$47,645
$4,935
$-3,283
High
$-1,940,000
$-213,000
1,566
$48,000
-498
-421
$-29,805
$-15,150
$-12,822
Medium
$-501,000
$-55,000
162
$5,000
-1,158
-735
$-107,256
$-35,234
$-22,360
Low
$-236,000
$-26,000
-108
$-3,000
-3,515
-1,324
$-465,813
$-106,945
$-40,288
-3,515
-1,324
$-465,813
$-106,945
$-40,288
-1,158
-735
$-107,256
$-35,234
$-22,360
-1,158
-735
$-107,256
$-35,234
$-22,360
-1,158
-735
$-107,256
$-35,234
$-22,360
-3,515
-1,324
$-465,813
$-106,945
$-40,288
-3,515
-1,324
$-465,813
$-106,945
$-40,288
-1,158
-735
$-107,256
$-35,234
$-22,360
-1,158
-735
$-107,256
$-35,234
$-22,360
-1,158
-735
$-107,256
$-35,234
$-22,360
-3,515
-1,324
$-465,813
$-106,945
$-40,288
-3,515
-1,324
$-465,813
$-106,945
$-40,288
Source: Estimates of hours by activity from the 1996 MP&M POTW Survey. Average hourly rate from Bureau of'Labor Statistics (Sept. 2002 rate, adjusted to $2 001).
F-24
-------
MP&M EEBA: Appendices Appendix F: Administrative Costs
REFERENCES
Association of Metropolitan Sewage Agencies (AMSA). 2000. Survey on Proposed MP&M Effluent Guidelines.
U.S. Department of Labor, Bureau of Labor Statistics. Average Hourly Rate.
U.S. Environmental Protection Agency. 1996. MP&M POTW Survey.
F-25
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MP&M EEBA: Appendices Appendix F: Administrative Costs
THIS PAGE INTENTIONALLY LEFT BLANK
F-26
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MP&M EEBA: Appendices Appendix &'• Extrapolation Methods
Appendix £: Extrapolation Methods
INTRODUCTION „
APPENDIX CONTENTS
__ . . . . ...,..,. , G.I Using Raking to Adjust MP&M Facility Sample
EPA estimates both cost and benefits of environmental
regulations based on a random stratified sample of MP&M
facilities.1 EPA then estimates national level costs and
benefits by extrapolating analytic results from sample
facilities to the national level using statistically determined
sample facility weights.
Sample facility weights used in the benefit cost analysis of
environmental regulations are based on detailed
.„. „ .„. ,••,• , G.3.2 Level of Recreational Activities on Reaches
questionnaire stratification. Stratification means dividing the .ff . ,, •t.rnot.i!T\- i, /- 1 A
H 6 Affected by MP&M Discharges G-14
Weights G-2
G.I.I Data Sources G-2
G.I.2 Raking Adjustment G-3
G.2 Methodology for Developing Sample-Weighted
Estimates for Sites with More Than One
MP&M Facility G-7
G.3 Methodology for Extrapolation of Ohio Case
Study Results to the National Level G-13
G.3.1 Change in Pollutant Loads G-14
G.3.3 Differences in Household Income G-14
G.4 Results G-15
Glossary G-17
population of facilities into a number of non-overlapping
sub-populations (strata). These strata consist of facilities that
are homogeneous with respect to facility size (i.e., number of
employees or revenue) or engineering characteristics such as
wastewater flow because this information was not available at
the time the sample frame was developed. The sample
weights for facilities in the sample are based on the total population in each category and probabilities of selection in each
stratum.
EPA traditionally uses a standard linear weighting technique (hereafter, traditional extrapolation) to estimate national
compliance costs, changes in pollutant removals, and national-level benefits of environmental regulations. However, using
sample weights that are based only on facility-specific (e.g., engineering) characteristics and various non-facility factors can
lead to a conditional bias in the estimation of national-level benefits. In particular, this approach omits consideration of
important non-facility factors that influence the occurrence and size of benefits.
Non-facility factors that are likely to affect the occurrence and size of benefits from reduced sample facility discharges and
that are not reflected in the standard stratification and sample-weighting approach include the receiving water body type and
size and the size of the population residing in the vicinity of a sample facility. Furthermore, co-occurrences of facilities
discharging to the same reach may also affect the occurrence of benefits. Many of the environmental assessment and benefits
analyses include comparisons of the estimated baseline and post-compliance pollutant concentrations (e.g., sludge
concentrations or in-waterway concentrations) with the relevant threshold values. Because the effect of aggregate discharges
from several facilities is likely to be different from the sum of effects from these facilities considered independently, it is also
important to account for the likelihood of joint discharges of MP&M facilities to the same reach.
The Agency used two approaches to address omission of these important non-facility factors (i.e., water body type and size,
affected population, and co-occurrence of MP&M discharges) in designing the MP&M facilities sample. First, EPA adjusted
sampling weights through post-strati fication using two variables receiving water body type and size and the size of the
population residing in the vicinity of the sample facility. Section G.I presents the method of doing this adjustment. Second,
EPA used a differential sample weighting technique in developing national estimates of environmental effects and
benefits. This method accounts for the presence of more than one facility with different sample weights discharging directly or
indirectly (through a POTW) to reaches affected by multiple MP&M dischargers. Section G.2 of this appendix describes the
differential sample weighting technique.
EPA used both the traditional extrapolation-based weights and the sample weights adjusted through post-stratification
(hereafter, post-stratification extrapolation) to analyze the final MP&M rule's benefits. The benefit estimates based on the
post-stratification extrapolation weights are used to validate general conclusions that EPA draws from its main analysis based
on the traditional extrapolation method. In addition to developing national benefit estimates based on both traditional and
A census of all MP&M facilities was not performed due to the large size of the MP&M industry.
G-l
-------
MP&M EEBA: Appendices Appendix &'• Extrapolation Methods
post-stratification extrapolation weights, EPA developed a third estimate of national benefits based on the Ohio case study
results. Section G.3 of this appendix discusses this method in detail. The Agency recognizes that the extrapolation method
used for the Ohio case study results is not rigorous. Therefore, this method is used to supplement the main results.
&.1 USING RAKINS TO ADJUST MP<&M FACILITY SAMPLE WEIGHTS
Omitting information that affects the occurrence and size of benefits from the original sample frame's design may lead to
conditional bias in MP&M rule benefit estimates. To address this problem, EPA used a post-stratification weight-adjustment
method called raking to account for two additional variables that were not accounted for in the original sample design and
that may affect benefit occurrence:
*• physical characteristics of the receiving water body (including type and size); and
»• size of the population residing in the vicinity of the sample facility.
&. 1.1 bata Sources
EPA first classified the universe of MP&M facilities into different poststrata. The Agency relied on three data sources to
identify discharge reach characteristics and the population size in the vicinity of the discharge reach:
1. EPA's Permit Compliance System database (PCS) indicated water bodies to which MP&M facilities discharge;
2. EPA's Reach File 1 (RF1) provided additional information on the receiving water bodies, including water body type,
flow characteristics, and counties abutting these water bodies; and
3. Census data provided information on county populations.
The PCS database provides information on facilities covered by NPDES permits. The database covers only those facilities
that discharge directly to surface or ground water. No information is available on the location of MP&M facilities that
discharge to surface water indirectly or via POTWs. EPA therefore limited post-stratification to direct discharging facilities.
The Agency used the resulting adjusted sample weights to estimate national-level benefits for only the final regulatory option,
which covers only direct discharging facilities. Chapters 13 through 19 of this report present benefit estimates in various
benefit categories considered in this analysis.
The extent of improvement in estimation accuracy depends on the reliability of the information used for post-stratification.
Accordingly, it was necessary to understand and account for PCS database limitations in implementing a post-stratification
approach. The PCS database is designed to provide information on a facility's SIC codes, facility flow, and receiving reach
characteristics. These characteristics include water body name and type, stream ID, and stream flow. Although these data can
be used to classify facilities in the identified poststrata, these fields are not always populated in the database. To fill missing
data, EPA combined data from PCS with supplementary analyses and information from RF1, using the following framework:
*• PCS provided a stream ID and information on the water body type and flow characteristics. EPA obtained stream
characteristics from PCS and used the stream ID to obtain information on counties abutting the reach from RF1;
•• PCS provided a stream ID, but not the water body type and flow characteristics. EPA used the stream ID to obtain
information on water body type, flow characteristics, and counties abutting the reach from RF1;
*• PCS provided water body name and type, but not stream ID and flow characteristics. EPA first used facility lat/long
data to assign the PCS facility to the nearest reach that matches the water body name provided in PCS. The Agency
then used the identified stream ID from RF1 to obtain information on water body type, flow characteristics, and
counties abutting the reach from RF1;
2 See Chapter 21 for a detailed discussion the Ohio case study.
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MP&M EEBA: Appendices
Appendix &'• Extrapolation Methods
*• PCS provided no receiving stream information on the, but facility lat/long data were available. EPA first used these
data to assign the PCS facility to the nearest reach. The Agency then used the identified stream ID to obtain
information on water body type, flow characteristics, and counties abutting the reach;
*• PCS provided neither information on the receiving stream nor facility lat/long data. EPA assumed the distribution of
the receiving water body characteristics, including the size of the population residing in the counties abutting the
receiving reaches, to be similar to the distribution of these characteristics across facilities with known characteristics.
PCS identifies 4,290 direct discharging facilities with MP&M SIC codes that had active NPDES permits 1997. Of these,
EPA classified 3,242 facilities into the poststrata considered in this analysis. Because the total number of PCS facilities with
MP&M SIC codes differs from the sum of sampling weights of direct dischargers considered in the final regulation, the
Agency assumed that the sum of the sampling weights provides the correct estimate of the MP&M facility universe. Thus, the
count of facilities in the benefits analysis matches the number of MP&M facilities. This analysis yielded an adjustment factor
of 2,832/ 3,242= 0.87 Table G.I lists facility counts from PCS data, adjusted to equal the sample frame total.
Table 6.1: Facility Counts from PCS Data
(Adjusted to Equal the Sample Frame Total)
First Variable: Water Body Type and
Size
-, . , , „ PCS Facilities
Variable Category _
& J Count
Bay-Lakes Combined 288
Small Streams 543
Medium Streams 1514
Large Streams 487
Total 2,832
Second Variable:
Variable Category
Pop < 100,000
100,000
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MP&M EEBA: Appendices
Appendix &'• Extrapolation Methods
Water body type was one of the two post-stratification variables used for raking. EPA originally used six categories of this
variable: Bay/Ocean, Great Lakes, Lakes, Lakes, Small Streams, Medium Streams, and Large Streams. However, the number
of MP&M sample facilities in Bay/Ocean, Great Lake, and Lake categories was too small for some categories to implement
raking. Therefore, EPA combined categories in which the number of facilities in the sample was either zero or too small to
create four categories:
> Bay-Lakes Combined (includes, Bays, Oceans, Great Lakes and Lakes);
> Small Streams;
•• Medium Streams; and
*• Large Streams.
Table G.2 shows the number of sampled facilities in each category of water body type, the sum of the sampling weights of the
sampled facilities, and the known number of facilities in the population in that category. Comparing the sum of the MP&M
facilities sampling weights and the PCS-based count of facilities for each category of water body type shows that Bay-Lake
Combined and Small Streams are under-represented in the MP&M sample frame while Medium and Large Streams are over-
represented.
Table G.2: Facility Distribution by Water Body Type
Number of Facilities
in the MP&M
Sample Frame
Bay-Combined
Small Streams
Medium Streams
Large Streams
Total
MP&M Sample Frame PCS Facilities
Ratio of
Number of | Sum of the | Number of | Number PCS to
Facilities in the j Sampling ! Facilities in the ! Sample-
Sample Weights Population Weighted
Facilities
7
7
43
25
82
38.7
231.3
1,439.4
1,122.6
2,832.0
288
543
1514
487
2,832.0
7.44
2.35
1.05
0.43
1.00
Source: PCS data.
Table G.3 shows the six population categories created in the EPA analysis. Comparing the sum of the MP&M facilities'
sampling weights and the PCS-based count of facilities corresponding to each category of water body type shows that
facilities from the population size category of less than 100,000, greater than 4,000,000, and greater than 2,000,000 but less
than 4,000,000 are over-represented in the sample. Conversely, facilities in the population categories from 100,000 to
500,000 and from 500,000 to 1,000,000 are under-represented.
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MPAM EEBA: Appendices
Appendix &'• Extrapolation Methods
Table 6.3: Distribution of Facilities by Population Size
| MP&M Sample Frame
Population Number of
1 Facilities in the
Sample
Pop< 100,000 18
:
100,0004,000,000 2
Total | 82
Sum of the
Sampling
Weights
1,303.0
1,171.8
136.3
121.6
61.8
37.6
2,832.0
PCS Facilities
Number of
Facilities in
the
Population
934
1,155
403
276
47
17
2,832.0
Ratio of
Sample-
Weighted
to PCS
Facilities
1.40
1.01
0.34
0.44
1.31
2.21
1.00
Source: PCS data.
Raking is an iterative process in which adjusted sample weights are synthetically generated to match known characteristics of
the population along single stratification dimensions and, as a result, should reflect the population characteristics within multi-
dimensional stratification cells. The iterative process works as follows. First, EPA multiplied the sampling weight of each
facility in each category of water body type by the ratio of the total number of facilities in the population to the sum of the
sampling weights in that category. For example, using the numbers in Table G.2, EPA multiplied the sampling weights of all
sampled facilities in the Bay-Combined category by the ratio 288/38.7 = 7.44. The sum of the adjusted weights, 38.72x
7.44=288.08, is the known population total. Similarly, EPA multiplied all the sampling weights of facilities in the Large
Streams category by the ratio 487/1122.6 = 0.43, to yield 1,122.6x0.43= 482.7 as the sum of the adjusted weights. EPA
performed the same calculations for the other categories of water body type.
These calculations match the sum of the sampling weights to the known control totals for the single stratification dimension of
water body type. At this first step, however, it is very unlikely that the resulting sums will agree with the known number of
facilities within categories of the second stratification dimension, population size category. Table G.4 shows the sum of the
adjusted sampling weights and the PCS population totals by population sizes after Iteration 1.
Table 6.4: Sum of the Sampling Weight by Population Category
after Iteration 1
Population
< 100,000
100,0004,000,000
Total
Sum of the
Adjusted
Sampling Weights
1,542.49
728.62
133.42
294.18
58.31
74.99
2,832.01
Number of Facilities
in the Population
(PCS Based)
934
1,155
403
276
47
17
2,832
Source: U.S. EPA analysis.
To correct for this inconsistency, EPA multiplied each weight by the ratio of the known total to the sum of the adjusted
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MP&M EEBA: Appendices Appendix &'• Extrapolation Methods
weights for each facility in each population size category. For example, the Agency multiplied each facility in the first
population category by the ratio 934/1542.49. Now, the resulting sum of the adjusted weights agrees with the category totals
for the population category, but differs from the category totals for water body type.
EPA therefore repeated this process of sequentially adjusting sample weights one dimension at a time until the sum of the
adjusted sampling weights simultaneously agreed with the total population counts of facilities for both water body type and
population size categories. After seven iterations, the sum of the sampling weights agreed with PCS-based counts for both
variables except for a difference of less than one.
Tables G.5 and G.6 show the sum of the sampling weights before and after this iterative process in each cell. Obtaining the
estimated numbers in each cell of Table G.6 by aggregating the final raked sampling weights may yield better estimates of the
cell populations than summing the original sampling weights in Table G.5.
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MP&M EEBA: Appendices
Appendix &'• Extrapolation Methods
Table 6.5: Estimated Number of MP&M Facilities in each Poststratum before Raking
Population Size
Pop< 100,000
100,0004,000,000
Total
Water Body Type
Bay-
Combination
0
11
1
27
0
0
39
Small
Streams
151
9
25
19
0
27
232
Medium
Streams
1,114
208
31
25
51
10
1,439
Large
Streams
38
944
79
51
11
0
1,122
Total
1,303
1,172
136
122
62
37
2,832
Source: PCS data.
Table 6.6: Estimated Number of MP&M Facilities in Each Poststratum after Raking
Population
Pop< 100,0000
100,0004,000,0000
Total
Water Body Type
Bay-
Combination
0
112
16
161
0
0
289
Small
Streams
204
50
210
64
0
14
542
Medium
Streams
726
575
126
39
45
3
1,514
Large
Streams
4
418
51
13
2
0
488
Total
934
1155
403
277
47
17
2,833
Source: U.S. EPA analysis
Tables G.5 and G.6 show that sampling weights increase for small stream facilities in the population <100,000 category, while
sampling weights decrease for medium and large stream facilities in the same population category, due to their over-
representation in the sample.
G.2 METHODOLOGY FOR &EVELOPINS SAMPLE-WEIGHTED ESTIMATES FOR SITES
WITH MORE THAN ONE MP<&M FACILITY
The MP&M analysis is based on a random stratified sample of MP&M facilities intended to provide detailed information
about specific facility characteristics and to provide national estimates with these characteristics. They are not reach-specific
sample weights designed to estimate the national occurrence of reaches associated with a specific characteristic of MP&M
discharges. For example, the sum of MP&M sample facility weights discharging to one reach is an accurate estimate of the
number of national facilities similar to the sample facilities, but is not a valid national estimate of all potential MP&M
discharges to that reach or the number of reaches similar to that reach. Accordingly, to use the sample weights to estimate
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MP&M EEBA: Appendices Appendix &'• Extrapolation Methods
the number of similar facilities on similar reaches nationwide requires some adjustments to the standard sample-weight based
extrapolation process.
It may not be valid to assume that the co-location of sample facilities is similar to the co-location characteristics of all MP&M
facilities. This point is illustrated by the case in which two sample facilities with different weights discharge to the same
reach. Assume that one of these two sample facilities has a sample weight of five and the other has a sample weight of 200.
The sample weights indicate that there are four additional facilities in the U.S. that are economically and technically similar to
the facility with the weight of five. It is also correct to estimate that the other four facilities will discharge the same volume of
the same pollutants as the other four facilities. Now let us assume that there are 199 other facilities nationwide similar to the
facility with the weight of 200. The more numerous facilities represented by the facility with a weight of 200 could only
rarely be co-located with one of the four facilities represented by the sample facility with a weight of five.
EPA developed a method that accounts for joint occurrence on reaches of facilities with different statistical weights to
estimate the number of reaches affected by MP&M facilities nationwide. EPA created a series of new discharge variables (a
discharge event) for each reach affected by MP&M sample facilities, and assigned weights for the discharge events that
provide a national estimate of pollutant discharges across all reaches. The sample discharge events (flows and pollutant
loadings) are calculated based on the sum of the flows and pollutant loadings for subsets of the MP&M sample facilities that
discharge to that reach. The weights for the discharge events are developed from the facility weights for those subsets of
facilities. The calculation includes direct MP&M facility discharges and indirect discharges from POTWs (for options that
include them) after considering pollutant removals from POTW treatment.
The number of discharge events on a sample reach equals the number of unique sample weights for the facilities on the reach.
EPA calculated a sample weight for each discharge event based on the sample weights of the facilities contributing loadings
and flows to the event. Table G.7 illustrates discharge event calculations and corresponding sample weights. Steps for
calculating the relevant parameters for discharge events on reaches affected by multiple discharges are as follows:
> Rank pollutant loadings (or discharge flows) in ascending order of facility sample weight for each pollutant of
concern discharged by one or more of those facilities.
•• Generate the first discharge event loadings (or flows) as the total loadings (or flows) from all sample facilities on the
reach. Assign the smallest sample weight to the first discharge event (Wt( in Table G.7) among the facilities
discharging to the reach. A smaller sample weight relative to the others means that this facility represents no other
population facilities that could occur jointly with the other facilities. The weight of the first facility is therefore
considered as "used up," and that facility's loadings (or flows) are not included in subsequent discharge events
defined for the reach.
*• Generate subsequent discharge events by removing the loadings (or flows) of facilities with the smallest sample
weight from a running sum of loadings (or flows) of all facilities in the ranking. The weight assigned to each
subsequent event is the remaining unused weight of the facility with the smallest weight among the facilities
remaining in the particular discharge event. Calculate this weight as the difference between the weight of the next
facility in the ranking and the weight of the previous facility (Wt2-Wt!).
EPA avoids double counting indirect dischargers by including the discharge flow of any given POTW into a reach only once
in any given discharge event, even when multiple sample facilities discharge indirectly into one POTW.
This methodology generates a set of discharge events (loadings or flows) for each pollutant discharged to the reach. The
following steps illustrate application of the differential weighting technique to estimating the national number of reaches on
which ambient water quality criteria (AWQC) are exceeded:
•• assign a weight to each discharge event based on the weights of the facilities discharging to the reach;
•• combine the effluent flow with the stream flow of the reach;
•• divide the pollutant loading into the stream flow to determine the pollutant concentration caused by the event;
•• compare pollutant concentration to AWQC values to determine whether the concentration exceeds those values;
•• identify an estimated AWQC "exceedance" if the concentration is greater than a criterion; and
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MP&M EEBA: Appendices
Appendix &'• Extrapolation Methods
give the AWQC exceedance event the weight of the discharge event, to establish national estimates of the number of
reaches on which an AWQC is exceeded.
Table 6.7: Construction of Discharge Events for Any Pollutant Discharged to Any Reach
Event Number
Loadings and Flows Assigned to
^ _ &
Event
„, . , . . . , . „
Weight Assigned to Event
& &
One
i-1
JV-J
Wt
Two
N-2
N-l
N
LoadN_2
LoadN
LoadN4 + LoadN
Flow*, + FlowN
LoadN+FlowN
WtN-2 " WtN-3
Notes: N sample facilities discharge to the reach and are ranked in ascending order of sample weight and indexed by i (1 =
facility with smallest weight, N = facility with largest weight); Load, = Loading from facility i; Flow, = Flow from facility i or
the POTW associated with facility i; Wt, = Sample weight of facility i; and a POTW's flow is included only once per event,
even if multiple facilities in that event discharged through that POTW, to avoid over-counting the POTW's flow.
Source: U.S. EPA analysis.
This weighting method is a relatively simplistic approach to a complex analytic issue, and does not provide a precise estimate
of the national distribution of in-stream MP&M pollutant concentrations that reflects the true co-location characteristics of
MP&M facilities. A statistically-valid estimate of that distribution is not possible given the design of the Section 308 surveys.
However, the differential weighting technique does correct for the significant overstatement of benefits that would result from
using a simple weighting approach to estimate national reach characteristics. The Agency believes that this method is a
reasonable approach to addressing this issue, given time and resource constraints. Approaches that are both more
sophisticated and more expensive might not yield significantly different aggregate findings.
Figure G.I provides a graphical example of a hypothetical reach to which three known sample facilities discharge. Table G.8
provides a numeric example of this calculation for a hypothetical reach to which three known sample facilities discharge.
G-9
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MPAM EEBA: Appendices
Appendix &'• Extrapolation Methods
Figure G.la: Estimating MP&M Pollutant Loadings to Receiving Streams When Using a Random Sample of MP&M
Facilities
IT t>l> Itm:
Result:
S dli tun:
Information en the 0 c currenc e t>f Joint Discharge!
£eo graphic Discharge Location of Non Simple F acifrtief is Unknown
Unl ere ftim ation of B as din e M I & M Disthirpts iiul M I <5: Ji' t ontrilnitioiL to I'roljltni
None Known at ttiis Time
M P5: M h imp Ifi f-iu ilitifi (
Simp li WBiftt ,=1
Pi.:..1 TIL;EIh C L&mkil K
PiolTUb^ C Lamkil Tf
IT P^ M iio ii-h UIL p IA f-ic ilitLci h
Simple WKiylitl = !impli Wui^tri = !impb
If Only Sample Facility Discharges Are Considered
Facility 1,
Chemical X
Facility 1,
Chemical V
In-streim concentrition (K) = 30 g/1,
whkh is grsitsr thin AW QC (K ) = 20 g/1
Number of Exceedence Events = 5
In-streim c one entntiori (V) = +0 g/1,
whkh is less than AW QC (Y)= 50 g/1.
Number of EKceedence Errents= 0
If All M.PKM Discharges Are Considered (Chemical Y)
In-streim concsntritions (Y) = 70 g/1,
which is greitsrthin AW C! C (Y) = f 0 g/1.
ofActualExceeience Eventf =1
1 + 2,3,4
of E ftim ate I Exceelence Eventf = 0
Un I er eitint ation of Event* =1
Source: U.S. EPA analysis.
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MPAM EEBA: Appendices
Appendix &'• Extrapolation Methods
Figure G.lb: Estimating MP&M Pollutant Loadings to Receiving Streams When Using a Random Sample of MP&M
Facilities
Facilities With Different Weights Located on the Same Reaches
Solution: Use of Differential Weighting M ethod
For this illufixation, assume that concentrations are different at each discharge i oint, tut facility
and ftre am flow ar e th e f am e.
IXPftlX (impli ficilitiif
S imj In W a ifL11 =1 (i. liicliij-s t D ffl
SimpliWoiflit! = I, 1 i»LILIIfl 20 J/1
? imi k W D iftt, =2, 1 ij i Lii ?! 1 (i %fl
FT* -w, = FT* -Wi = Flo vn = li)00 L/liy
ii-h -UIL p k f.iu ilit>r
Differential Weighting Method
+ 2 + 3
1 + 2
In-stream coneentrition = 70 g/1 > AWQCr= 50 g/1
-Hum t er of Benefit Events = min {SW1 5W2,SW3}= 2
In-stream coneentritions = 60g/l > AW (3(^=50 g/1
of Benefit Events = min{SWl, SW2}=4
In-strsam c one entritions = 4-Og/l >
' Hum ter of E e nef it En ents = 0
T o tal N until er o f Benefit Events = 6
Predicted Events = 6
g/1
Note: The situation may be further complicated by actually having a non-sampled MP&M facility on the same reach. The differential
weighting method does not address this issue.
Source: U.S. EPA analysis.
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MPAM EEBA: Appendices
Appendix &'• Extrapolation Methods
Figure G.lc: Estimating MP&M Pollutant Loadings to Receiving Streams When Excluding Background
Concentrations
Pro]) lem 3:
Results:
Omitting Discharges from Nou-MP& M Facilities
Uncertainty, May Underestimate or Overestimate Benefits
MPftM sample facilities
Sample Weight ,= 15
Hon-MPfiM facilities
Case I: Underestimation of Benefits When all Discharges arc Considered
If Only Sample Facility Discharges Are Considered:
B if ehrie
left
In-stream concentration (X ) = 30 g/1 < AW Q C(X ) = 40 g/1
Number of Else line Exceedence Events = 0.
In-stream concentration (H ) = 15 g/lAWQC(K)=40 g/1
of Exceedence Eirenti =15
1 + 2,3,4
1 + 2,3,4
In-streim concentrations (X ) = 35 g/1 er ofB eKefitLveitts = 15
Case 2: O veres timation of Benefits When all Discharges are Considered
If Only Sample Facility Discharges Are Considered
B if dine
left
In-stream concentration (H ) = 30 g/1 > AW QC(H) = 20 g/1
"Humter of E aseline EKC eedenc e Events = 15.
In-stream concentration (X )= 15 g/l< AWQ C(K )= 20 gA
..Numter of Postt ompliance Exceeds nee Events = 0
ber of Benefit Events =11
If N ell III P ft M BifchiTf ef Are t in* id tit d
Bit diii e
left
C 1'Jlll'llULll
1 + 2,3,4
2,3,4
In-stream concentrations (H) = 95grt >AW Q C(H )=20 g/1
Hum ter of Exce edenc e Events =15
In-stre am cone entrations (H)= 80g/l>AWC!C(H)=20g/l
umber of Ex ce edenc e Events = 15
be r of Ben efitE ve K ts = 0
Source: U.S. EPA analysis.
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MPAM EEBA: Appendices
Appendix &'• Extrapolation Methods
Table 6.8: Example of Differential
Facility
j
Weight
|
Sample Weighting Technique
Pollutant A
| Ibs/yr
Raw data:
I
2
3
Total
Reach flow (gal/year):
10
3
1
14
5
2
12
19
Calculating flow and pollutant loadings for the reach
1. Rank facilities in ascending order of weights
3
2
1
1
3
10
2. Calculate flow and pollutant loadings for discharge event 1 with
Facility
3
2
1
Event 1
3. Eliminate the facility with
weight = 2 (3-1)
2
1
Event 2
4. Eliminate the facility with
with weight = 7 (10-3)
1
Event 3
Pollutant A
Ibs/yr
12
2
5
19
12
2
5
weight = 1
Flow
gal/year
10,000,000
4,000,000
2,000,000
16,000,000
Flow
gal/year
2,000,000
4,000,000
10,000,000
16,000,000
100,000,000
10,000,000
4,000,000
2,000,000
Remaining Weight
0
2
9
the lowest weight and calculate flow and pollutant loadings for discharge event 2 with
2
5
7
4,000,000 0
2,000,000 7
6,000,000
the next lowest weight and calculate and pollutant loadings for discharge event 3
5
5
2,000,000 0
2,000,000
5. Estimate national in-stream concentrations based on the flows, loadings, and weights for each discharge event and
the reach flow
_. , Pollutant A Facility Stream
Discharge T ,. ™ ™
_ Loading Flow Flow
Ibs/yr gal/year gal/year
1 19
2 7
Total Affected
Reaches:
16,000,000 100,000,000
6,000,000 100,000,000
2,000,000 100,000,000
Total In-stream
Flow Concentration Weight
gal/year ppb
116,000,000 0.0955 1
106,000,000
102,000,000
0.0385 2
0.0286 * 7
10
Source: U.S. EPA analysis.
G.3 METHODOLOGY FOR EXTRAPOLATION OF OHIO CASE STUDY RESULTS TO THE
NATIONAL LEVEL
EPA extrapolated the Ohio case study results to the national level based on three key factors that affect the occurrence and
magnitude of benefits:
*• the estimated change in MP&M pollutant loadings, which reflects the potential for improvements in surface water
quality;
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MP&M EEBA: Appendices Appendix &'• Extrapolation Methods
> the level of recreational activities on the reaches affected by MP&M discharges. Recreational level reflects the
degree to which potentially affected water resources are likely to be in demand by local residents; and
> the average household income level, which affects the willingness-to-pay (WTP) for water quality improvements.
&.3.1 Change in Pollutant Loads
The first step in applying this alternative extrapolation method was to develop a measure of benefits per pound of pollutant
removed for each category of benefits. EPA developed this measure by simply dividing the state-level benefit estimates by
the total number of pounds of pollutant removed by the regulation in the state of Ohio ($ per pound of pollutant removed).
EPA developed three different measures to better represent the relationship between pollutants and benefit categories:
> Cancer health benefits: EPA divided cancer benefits from the Ohio case study by total carcinogen pounds removed
in Ohio to estimate cancer health benefit per pound of carcinogen load removed;
•• Lead health benefits: EPA divided lead health benefits from the Ohio case study by total lead pounds removed in
Ohio to estimate lead health benefit per pound of lead load removed; and
> Recreational benefits: EPA divided recreational benefits from the Ohio case study by total pounds of pollutants
removed (i.e., all pollutants except for total dissolved solids and biological oxygen demand) in Ohio to estimate
recreational benefit per pound of pollutant load removed.
All of these values are readily available from the Ohio case study. EPA extrapolated the state-level benefits for each of these
benefit categories to the national level. First, the Agency multiplied the three estimated benefit per pound of pollutant values
for Ohio by the total number of pounds of pollutant removed in each of the three pollutant categories at the national level.
Then, EPA summed across the three benefit categories to obtain an initial estimate for total benefits at the national level.
G.3.2 Level of Recreational Activities on Reaches Affected by MP<&M Discharges
The second step was to adjust for differences between Ohio and the nation in the level of recreational activity on reaches
affected by MP&M discharges. The level of recreational activity reflects the degree to which water resources likely to be
affected by MP&M discharges are in demand by local residents. EPA accounted for differences between Ohio and the nation
in recreational intensity because the total user value of water quality improvements is a function of the number of users
associated with a particular reach. For this adjustment factor, EPA used the ratio of the number of recreational user days per
reach mile at the national level to the number of recreational user days per reach mile in Ohio. Due to data limitations
preventing identification of all reaches affected by MP&M discharges, this analysis used total recreational user days and reach
miles nationally and in Ohio, rather than only for those reaches affected by MP&M discharges. EPA used the National
Demand Study (NDS) to estimate the number of user days for each recreation activity. Appendix N of this report provides the
relevant data by state and recreation activity. To estimate the number of recreational user days, EPA summed the activity-
specific values over the four activities considered in this analysis (i.e., recreational fishing, boating, swimming, and wildlife
viewing). EPA's Reach File 1 provided information on the total number of reach miles in Ohio and in the 48 contiguous
states. The Agency then calculated the number of user days per reach mile in the state of Ohio and in the nation by simply
dividing the total number of user days by the total number of reach miles in the corresponding region. EPA then calculated
the adjustment factor as follows:
D „• r A „• -,u AI? Average Number of Recreational User Days per Reach Mile in the U.S.
Recreational Activity AF = - - - - - - — — -
Average Number of Recreational User Days per Reach Mile in Ohio
(G.I)
= 2,306 =
4,148
£.3.3 Differences in Household Income
In the third step, EPA adjusted the extrapolated benefits based on the expectation that the WTP for water quality
improvements will vary with household income level for different parts of the country. The adjustment factor used is the ratio
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MP&M EEBA: Appendices Appendix &: Extrapolation Methods
of the average household income of the nation to the average household income of Ohio. This adjustment factor assumes that
households around the country are willing to pay the same proportion of their incomes for water quality improvements,
although the absolute value of this dollar amount will vary due to regional differences in average household income. The
average household income of the nation is estimated as a weighted average, with the median household income for each state
weighted by the proportion of MP&M facilities located in that state. The U.S. Census Bureau's Current Population Surveys
(March 1999, 2000, and 2001) provide the basis for data on the median household income by state for the year 2000. 3 The
1 992 Economic Census provides information on total MP&M facilities by state.4
T AT^ (Weighted) Median Household Income in the U.S.
Income AF = ± - ^ - '- -
Median Household Income in Ohio
(G.2)
$43,894
G.A RESULTS
Table G.9 presents national benefits based on the extrapolation of Ohio case study results. Based on this approach, the
monetary value of benefits from reduced MP&M discharges is $2.5 million (2001$) for the final option. This estimate is 60%
higher compared to the benefit estimate based on the traditional extrapolation methodology (i.e., $1.5 million (2001$)). As
noted in the prior discussion, this difference is likely to be due to the more rigorous approach used for the Ohio case study.
The national-level analysis of human health benefits finds negligible health benefits from the final rule, hi contrast, the Ohio-
based extrapolation of human health benefits yields $10,860 and $295,202 (2001$) in human health benefits at the national
level from reduced incidences of cancer cases and adverse health impacts from lead exposure, respectively. As shown in
Table G.9, the estimated human health benefits to Ohio residents exceed the national-level benefits based on this
extrapolation method. This finding is due to the fact that the estimated pollutant removals for lead and carcinogens in Ohio
exceed those at the national level. As discussed in Appendix H, EPA administered 1,600 screener questionnaires to augment
information on Ohio's MP&M facilities. The Agency used information from the sampled MP&M facilities to estimate
discharge characteristics of non-sampled MP&M characteristics (see Appendix H for detail on estimating sample facility
discharges in Ohio). As a result, the MP&M facilities included in the case study analysis represent a significant portion of the
MP&M facility universe in Ohio. In contrast, the sample facilities used at the national-level analysis represent only 2 percent
of the MP&M facility universe. Thus, analytic findings from the national-level analysis may have a larger than desired degree
of uncertainty due to a very small sample size.
3 Source: http://www.census.gov/hhes/income/incomeOO/statemhi.html
4 Appendix J presents information on distribution of MP&M facilities by state.
G-15
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MPAM EEBA: Appendices
Appendix &'• Extrapolation Methods
Table 6.9: Extrapolation of Ohio Case Study Results to the National Level (2001$)
Category
Pounds removal of carcinogens
Total cancer benefits
Total cancer benefits per pound removal of carcinogens
Pounds removal of Lead
Total lead benefits
Total lead benefits per pound removal of lead
Pounds removal of total pollutants
Total recreational benefits
Total recreational benefits per pound removal of total pollutants
Nonuse benefits (1A of total recreational benefits)
Total benefits prior to application of adjustment factors
Reach miles
Annual recreation days (millions)
Annual recreation days per reach mile
Recreational activity adjustment factor
Total benefits prior to application of income adjustment factor
Average household income
Income Adjustment factor
Total benefits
Ohio
52.45
$31,895.42
$608.11
217.06
$540,549.14
$2,490.32
483,258.02
$250,932.62
$0.52
$125,466.31
$948,843.49
11,927
49
4,148
$43,894
Nation
17.86
$10,860.86
118.54
$295,202.69
5,412,810.88
$2,810,612.05
$1,405,306.03
$4,521,981.63
713,702
1,646
2,306
0.5559
$2,513,907.82
$42,909
0.9776
$2,457,494.66
Source: U.S. EPA analysis.
G-16
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MP&M EEBA: Appendices Appendix &'• Extrapolation Methods
GLOSSARY
ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its
designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,
swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html)
differential sample weighting technique: weighting method for all threshold value-based analyses, such as the lead-
related benefits analysis.
reach: a specific length of river, lake, or marine shoreline
standard linear weighting technique: weighting method used where the effects being considered (e.g., compliance
costs) are linearly additive over facilities.
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G-18
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MP&M EEBA: Appendices
Appendix H: Fate and Transport Model for DW and Ohio Analyses
Appendix H: Fate and Transport
Model for DW and Ohio Analyses
INTRODUCTION
For the drinking water (DW) and Ohio analyses, EPA
used a simplified fate and transport model to quantify the
fate and transport of MP&M pollutant releases to surface
waters. This model estimates pollutant concentrations at
the initial point of discharge and below the initial
discharge reach.
The national MP&M analysis considered pollutant
concentrations only at the point of discharge (see
Appendix 1.2.2). The drinking water and Ohio analyses
account for the in-stream concentrations of pollutants at
the initial point of discharge and in reaches downstream
from the initial discharge reach.
This appendix describes the equations characterizing the
model, its underlying assumptions, and the data sources
used in model estimation. EPA combined the equations
defining the model with geographic information (reach
flow, velocity, length, etc.) to estimate pollutant
concentrations at the initial point of discharge and below the initial discharge reach.
APPENDIX CONTENTS
H.I Model Equations H-l
H.2 Model Assumptions H-3
H.2.1 Steady Flow Conditions Exist within the
Stream or River Reach H-3
H.2.2 Longitudinal Dispersion of the Pollutant is
Negligible H-3
H.2.3 Flow Geometry, Suspension of Solids, and
Reaction Rates Are Constant within a
River Reach H-4
H.3 Hydrologic Linkages H-4
H.4 Associating Risk with Exposed Populations H-4
H.5 Data Sources H-4
H.5.1 Pollutant Loading Data Used in the
Drinking Water Risk Analysis H-4
H.5.2 Pollutant Loading Data Used in the Ohio Case
Study Analysis H-4
Glossary H-8
Acronyms H-9
References H-10
The estimation of pollutant concentrations below the initial discharge reach includes several factors that reduce the in-stream
pollutant concentrations with the passage of time. These factors include: volatilization, sedimentation, and chemical
decay from hydrolysis and microbial degradation. EPA adjusted concentrations for changes in stream flow volume in
downstream reaches. The discussion below outlines the main assumptions of this analysis. Although more advanced models
are available that account for time-variable flow, sediment transport, channel geometry changes within a reach, and detailed
simulation of all in-stream processes, these models will not necessarily produce more accurate results without sufficient data
to support the input parameters. Estimates of the additional input parameters required by these models are subject to a high
degree of uncertainty when applied on a national scale, and gathering such data is beyond the scope of this study.
EPA has previously applied the approach used in this analysis. For example, the first-order contaminant degradation
relationship described below in Equation H.I is currently being used by the Office of Pollution Prevention and Toxics for
exposure analysis in the Risk Screening Environmental Indicator (RSEI) model (U.S. EPA, 1999).
H. l MODEL EQUATIONS
The total pollutant concentration in the water columns for each reach included in the analysis is calculated by the following
equation expressed in generic terms of mass (M), length (L), and time (T):
(H.I)
H-l
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
where:
CT = total toxicant concentration in the water column (M/L3),
WT = mass input rate of toxicant (M/T),
Q = river flow (L3/T),
VT = overall net loss rate of chemical (L/T),
H = flow depth (L),
x = distance downstream from the point of release (L), and
U = flow velocity (L/T).
In reaches where more than one facility discharges or where pollutant loadings occur from upstream reaches, the mass input
rate (WT) represents a combined input rate from all relevant industrial facilities affecting the reach. The relevant industrial
facilities in the drinking water risk analysis are all MP&M sample facilities (see Chapter 13). The relevant industrial facilities
in the Ohio case study analysis include:1
> all sample MP&M facilities,
> non-sample MP&M facilities, and
> non-MP&M facilities.
The overall net loss rate of chemical (VT) is given by:
(H.2)
where:
VT = overall net loss rate of chemical (L/T),
VTd = dissolved chemical loss rate (L/T),
VTs = loss of chemical due to sediment interaction (L/T),
kj = volatilization transfer coefficient (L/T),
Kd = dissolved chemical decay rate (hydrolysis and microbial degradation) (1/T),
H = flow depth (L),
fd = dissolved fraction of toxicant (unitless),
vn = net loss of solids (L/T), and
fp = particulate fraction of toxicant (unitless).
The dissolved and particulate fractions of the pollutant,/;, and fp , respectively, are estimated by:
and
f = _ - _ (H.3)
" i - V
K s
f = P (H.4)
' i * V
1 See Chapter 22 for detail.
H-2
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
where :
p
Kp = partition coefficient [L3/M], and
S = suspended solids [M/L3].
The dissolved concentration of metals and most other pollutants in the water column is generally considered a more accurate
expression than the total concentrations of the toxic or bioavailable fraction. For this reason, EPA modified Equation (H.I) to
express the pollutant concentrations in terms of dissolved concentration. The dissolved fraction of a pollutant is estimated as:
(H.5)
Substituting Equation (H.I) for CTyields the dissolved pollutant concentration for downstream distance x from the discharge
reach:
WT
V ft
Q
/ \
(1 + */)" (1 + Kfi"
1 + K/
xfiV
1^
(H.6)
H.2 MODEL ASSUMPTIONS
The following three principal assumptions underlie Equation H.5:
H.2.1 Steady Flow Conditions Exist within the Stream or River Reach
This assumption is necessary due to this study's broad geographical coverage. This assumption significantly reduces the
computational effort and input parameter requirements and still produces a good first-order fate and transport model of
pollutants in surface waters.
The pollutant concentration is completely mixed, both laterally (across the stream) and vertically (with depth) within each
reach. The approach involves a two-dimensional model in which the concentration is uniform over the entire cross-section of
the stream reach but varies with the distance of the reach. EPA assumed that the contaminant completely mixes at the point of
release. This assumption will likely underestimate the concentration of a contaminant release in areas where mixing is
incomplete (e.g., shore-hugging plume) and overestimate concentrations in areas beyond the point showing incomplete mixing
(e.g., in areas beyond a shore-hugging plume).
H.2.2 Longitudinal Dispersion of the Pollutant is Negligible
The model does not account for mixing outside the plane of discharge along the river reach, although it predicts variation in
pollutant concentrations over distance due to both pollutant fate and decay and the differing hydrology of downstream
reaches. In natural streams, longitudinal velocity gradients due to channel irregularities can cause mixing, thereby decreasing
the peak concentrations as the contaminant moves downstream from the point of release. Under steady-state situations,
however, the longitudinal dispersion of the pollutant is assumed to be negligible.
The solution of the dispersion equation approximates a first-order decay function such as the one shown in Equations H.I and
H.5 under steady flow conditions and complete lateral and vertical mixing.
H-3
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
H.2.3 Flow Geometry, Suspension of Solids, and Reaction Rates Are Constant within
a River Reach
EPA assumes the data that describe a river reach and that are calculated for a reach to be constant for the full extent of the
reach.
H.3 HYDROLOSIC LINKAGES
EPA modeled pollutant concentrations for a distance of 500 km downstream from the discharge point in the drinking water
risk analysis. In the Ohio case study analysis, EPA used the lesser of 500 km or the distance to the Ohio border from the
initial discharge point to identify reaches potentially affected by pollutant discharges from the discharge point. The Agency
obtained information on the hydrologic linkages between reaches from the RSEI Model (U.S. EPA, 1999). The data file in
RSEI provided flow (mean flow, 7Q10) and velocity (mean, low) data for each reach.
EPA used the process equations listed above to estimate both the initial pollutant concentrations at the beginning of each
reach and the changes in concentrations as pollutants traveled to the end of the reach. The concentration at the end of each
reach served as the value for the beginning of the next reach.
H.4 ASSOCIATING RISK WITH EXPOSED POPULATIONS
The number of individuals served by each drinking water intake is an output of the fate and transport model described in this
appendix. If a drinking water intake exists on the initial reach or any downstream reach, then the model calculates the in-
stream pollutant concentration at that intake. Data on the population served by the intake is saved with the concentration for
further analysis (see Chapter 13 for a discussion of the cancer risk assessment).
H.5 &ATA SOURCES
Data sources used for the fate and transport model are discussed briefly in the section below, by categories of information.
H.5.1 Pollutant Loading bata Used in the Drinking Water Risk Analysis
EPA estimated annual pollutant loadings (kg/yr) for the direct and indirect sample MP&M facilities analyzed under the
various regulatory options. The Agency first adjusted pollutant loadings for indirect dischargers to reflect POTW treatment,
and then divided annual pollutant loadings by the number of days in one year (365) to establish daily pollutant loadings.
H.5.2 Pollutant Loading bata Used in the Ohio Case Study Analysis
EPA estimated pollutant discharges from both MP&M and significant non-MP&M sources at the reaches included in the Ohio
case study analysis. Consumer perception and valuation of enhanced water-based recreational opportunities depend on the
absolute level of pollutant contamination at recreation sites, and on the change in contamination from the baseline to the
post-compliance cases. For this reason, capturing the effect of concurrent discharges from all MP&M and other pollutant
sources is particularly important for the recreational benefits analysis.
EPA used the Office of Water's BASINS software package to identify all possible point source dischargers contributing to
ambient pollutant concentrations at a given reach. BASINS is a GIS-based system that serves as a database management
system for water quality monitoring, point-source pollutant discharge, and various geo-technical data. Several sources
provide information on point source discharges to BASINS, including the Permit Compliance System (PCS) and Toxic
Release Inventory (TRI) databases. Version 2.0 includes data reported through 1996. Preprogrammed queries in BASINS
2 EPA is not establishing pretreatment standards for indirect dischargers under the final rule.
H-4
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
generate information on various point source discharge variables at either the state or watershed level. BASINS data on point
source dischargers include:
»• location information on major industrial dischargers, including PCS facilities and facilities reporting under TRI;
•• SIC codes;
*• flow volume; and
*• discharge characteristics for up to 50 pollutants or parameters for PCS facilities.
The following sections describe steps used to characterize both MP&M and non-MP&M discharges in Ohio.
a. Characterize MP&M facility discharges
EPA used different approaches to assign discharge characteristics to MP&M facilities in Ohio, based on the level of
information available for each facility. The Agency divided all MP&M facilities into three groups, based on the level of
information provided by different sources:
*»* Facilities covered by the detailed Phase 1 and 2 questionnaire (hereafter, sampled MP&M facilities)
The detailed surveys contain data on:
*• discharge status;
*• discharge volume;
*• industrial processes used;
*• pollution prevention activities;
*• employment, revenue, and costs.
EPA engineers estimated loadings of 126 MP&M pollutants using information on facilities' processes and pollution
prevention acth
characteristics.
prevention activities.3 All MP&M facilities in this group therefore have extensive data on their location, size, and discharge
*»* Facilities covered by the detailed Iron and Steel questionnaire (hereafter sampled I&S facilities)
The detailed I&S survey contained data similar to the detailed MP&M survey. EPA engineers used data on I&S facilities'
processes and pollution prevention activities to estimate pollutant loadings from these facilities.
*»* Facilities covered by the Phase 2 screener questionnaire or that were covered by the Phase 1 mini-DCP (hereafter,
MP&M screener facilities).
The screener surveys contain significantly fewer data on MP&M facilities. The data collected from the screener survey
recipients include:
> facility location, which can be used to assign the facilities to receiving waterways or receiving POTWs;
•• SIC codes;
*• discharge status (i.e., whether the facility discharges process wastewater and the approximate amount);
*• employment and revenue data;
*• whether the facility is engaged in manufacturing, maintenance or repairing activities; and
3 There are 132 pollutants of concern. EPA engineers estimated pollutant loadings for only the pollutants for which EPA is
considering calculating pollutant removals at each option. For example, pollutant loadings are not provided for sodium, calcium, and TDS.
H-5
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
*• data on MP&M unit operations (including type of MP&M unit operations performed at the site, and whether process
wastewater is discharged as a result of each operation).
The project engineers used these data to estimate pollutant loadings for these facilities. Loading estimates for the screener
facilities, which are based on less comprehensive information, involve greater uncertainty.
*»* Facilities that respond to neither the screener nor detailed questionnaires (hereafter referred to as non-sampled
MP&M facilities)
To address the problem of omitted discharge information on non-sampled MP&M facilities, EPA used information from the
1600 screener MP&M facilities and a random draw approach to assign the relevant characteristics for non-sampled MP&M
facilities. Each screener facility represents n non-sampled facilities, where n is determined by the screener facility sample
weight. All non-sampled facilities are smaller indirect dischargers because all direct MP&M facility dischargers and large
indirect discharging facilities in Ohio are covered by the long, short, or screener questionnaire.
The exact location of non-sampled facilities is unknown. All non-sampled facilities discharge to one of the Ohio POTWs
because they are indirect dischargers. The Agency assigned n facilities represented by each screener facility to the receiving
POTWs by drawing a random sample of ft POTWs from the universe of POTWs in Ohio.4 The Agency assigned screener
facility characteristics (i.e., pollutant loadings) to all n facilities represented by the screener facility.
EPA used a random draw procedure for all observations from the screener survey that have a sample weight greater than one.
b. Characterize non-MP&M point source discharges
EPA used preprogrammed queries in BASINS to obtain information on all non-MP&M point source discharges in Ohio.
BASINS data on non-MP&M point source dischargers include:
> location,
•• SIC codes,
*• flow volume, and
*• discharge characteristics for up to 50 pollutants or parameters for PCS facilities.
The Agency assigned discharge characteristics to all non-MP&M industrial direct discharges based on the information
provided in BASINS. POTW effluent may contain pollutants from both MP&M and non-MP&M discharges. The Agency
combined information from BASINS with loading estimates provided by the project engineers to estimate total pollutant
loadings from a given POTW. This analysis used the following assumptions to estimate total POTW pollutant loadings under
the baseline discharge levels:
*• If a POTW was not estimated to receive discharges from the MP&M facilities, then the analysis used POTW
loadings reported in BASINS.
*• If a pollutant or a parameter was not reported in BASINS, then the analysis used aggregate loadings from all MP&M
facilities discharging to a given POTW to calculate total POTW loadings of a given pollutant.
*• If a POTW was estimated to receives discharges from MP&M facilities and a given pollutant was reported in
BASINS, then the analysis used the greater of the aggregate loadings from all MP&M facilities or POTW loadings
reported.
EPA estimated post-compliance pollutant loadings from each POTW by subtracting the estimated reduction in the MP&M
facility loadings for a given pollutant from its total baseline loadings for a given POTW.
c. Characterize non-point source discharges
The water quality analysis in Ohio used empirical data on Total Kjeldahl Nitrogen (TKN) concentrations to characterize
the baseline water quality conditions. Empirical data on in-stream concentrations captured TKN contribution from both point
4 The Agency was unable to validate random assignments because POTWs do not know all of their MP&M dischargers.
H-6
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
and non-point sources under baseline conditions. EPA estimated changes in TKN concentrations resulting from the final rule
by using the estimated pollutant loading reductions from MP&M sources and the water quality model described above. The
Agency assumed that the non-point source contribution of toxic pollutants found in MP&M effluent to ambient concentrations
of these pollutants in Ohio's streams and lakes is negligible.
H-7
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
GLOSSARY
BASINS: a software package that serves as a database management system for water quality monitoring, point source
pollutant discharge, and various geo-technical data, and also provides an analytic platform for modeling in-stream pollutant
concentrations over an entire watershed based on multiple sources of pollutants within the watershed.
(http://www.epa.gov.OST/BASINS)
hydrolysis: the decomposition of organic compounds by interaction with water. ( http://www.epa.gov/OCEPAterms)
metals: inorganic compounds, generally nonvolatile, and which cannot be broken down by bio degradation processes. They
are a particular concern because of their prevalence in MP&M effluents. Metals can accumulate in biological tissues,
sequester into sewage sludge in POTWs, and contaminate soils and sediments when released to the environment. Some
metals are quite toxic even when present at relatively low levels.
microbial degradation: a process whereby organic molecules are broken down by microbial metabolism.
Permit Compliance System (PCS): a computerized database of information on water discharge permits, designed to
support the National Pollutant Discharge Elimination System (NPDES).
(http://www.epa.gov/ceiswebl/ceishome/ceisdocs/pcs/pcs-exec.htm)
MP&M reach: a reach to which an MP&M facility discharges.
sedimentation: letting solids settle out of wastewater by gravity. ( http://www.epa.gov/OCEPAterms)
Total Kjeldahl Nitrogen (TKN): the total of organic and ammonia nitrogen. TKN is determined in the same manner as
organic nitrogen, except that the ammonia is not driven off before the digestion step.
Toxic Release Inventory (TRI): database of toxic releases in the United States compiled from SARA Title III Section 313
reports. ( http://www.epa.gov/OCEPAterms)
volatilization: a process whereby chemicals dissolved in water escape into the air.
(http://www.epa.gov/OCEPAterms)
H-t
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
ACRONYMS
PCS: Permit Compliance System
RSEI: Risk Screening Environmental Indicator model
TKN: Total Kjeldahl Nitrogen
TRI: Toxic Release Inventory
H-9
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MP&M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses
REFERENCES
U.S. Environmental Protection Agency (U.S. EPA). 1999. Risk-Screening Environmental Indicators Model: Version 1.0,
July 6, Washington, DC: Office of Pollution Prevention and Toxics. http://www.epa.gov/opptintr/env_ind/index.html.
H-10
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MP&M EEBA: Appendices
Appendix I: Environmental Assessment
Appendix I: Environmental
Assessment
INTRODUCTION
This Environmental Assessment estimates the environmental
impact of MP&M discharges on water bodies and POTWs
under both current conditions and those corresponding to
four regulatory options: the Final Option, Proposed/NODA
Option, Directs + 413 to 433 Upgrade Option, and Directs +
All to 433 Upgrade Option.1 EPA estimates four types of
environmental impacts:
•• the occurrence of pollutant concentrations in excess
of EPA ambient water quality criteria
(A WQ C) for protection of human health in
waterways (e.g., streams, lakes, bays, and estuaries)
receiving discharges from MP&M facilities;
•• the occurrence of pollutant concentrations in excess
of AWQC for protection of aquatic species in
waterways receiving discharges from MP&M
facilities;
*• the occurrence of POTW inhibition problems
resulting from MP&M facilities' discharges; and
»• barriers to POTWs' use of preferred sewage sludge
management or disposal methods (i.e., beneficial
land application or surface disposal), due to metals
discharges from MP&M facilities.
APPENDIX CONTENTS
I.I MP&M Pollutant Characterization 1-4
1.1.1 Identifying MP&M Pollutants 1-4
I.I .2 Physical-Chemical Characteristics and Toxicity
Data of MP&M Pollutants 1-9
I.I .3 Grouping MP&M Pollutants Based on Risk to
Aquatic Receptors 1-21
1.1.4 Assumptions and Limitations 1-23
1.2. Methodology 1-23
1.2.1 Sample Set Data Analysis and National
Extrapolation 1-23
1.2.2 Water Quality Modeling 1-23
1.2.3 Impact of Indirect Discharging Facilities on
POTW Operations 1-25
1.2.4 Assumptions and Limitations 1-27
1.3 Data Sources 1-28
1.3.1 Facility-Specific Data 1-28
1.3.2 Water Body-Specific Data 1-28
1.3.3 Information Used to Evaluate POTW Operations . . 1-29
1.4 Results 1-33
1.4.1 Human Health Impacts 1-34
1.4.2 Aquatic Life Effects 1-37
1.4.3 POTW Effects 1-41
Glossary 1-44
Acronyms 1-48
References . 1-49
EPA also estimated changes in human health risk from reduced exposure to MP&M pollutants via consumption of
contaminated fish and drinking water. Chapters 13 and 14 of this EEBA present both the methodology used to estimate
human health impacts from exposure to MP&M pollutants and the results of this analysis.
EPA assessed potential environmental impacts of MP&M discharges on the receiving water bodies and POTWs by using
pollutant fate and toxicity data in conjunction with various modeling techniques. EPA quantified the releases of 132
pollutants of concern under the final and alternative regulatory options. EPA then evaluated potential site-specific aquatic
life and human health impacts resulting from the baseline and post-regulation pollutant releases. EPA compared projected
water concentrations for each pollutant to either (a) EPA water quality criteria, or (b) toxic effect levels (i.e., lowest reported
1 The results of the Proposed/NODA Option are not directly comparable to the final option alternatives. The total number of facilities
reported for the Proposed/NODA Option analysis differs from the facility count reported for the final rule and the two upgrade options.
After deciding in July 2002 not to consider the NODA option as the basis for the final rule, EPA performed no more analysis on the NODA
option, including not updating facility counts and related analyses for the change in subcategory and discharge status classifications.
2 EPA originally identified 150 MP&M POCs. Of these 150 POCs, the Agency estimated loadings for 132 pollutants for the phase 2
proposal and NODA. The benefits analysis presented in earlier chapters is based on 132 pollutants for which loadings are available. The
final regulation covers only the Oily Wastes subcategory and benefit reductions were estimated for 122 pollutants.
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
or estimated toxic concentration that causes a problem) in the absence of water quality criteria for a pollutant. Figure I.I
depicts steps used in the environmental assessment. The following sections detail these analytic steps.
Figure I.la: MP&M Environmental Impact Assessment
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1-2
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MPcxM EEBA: Appendices
Appendix I: Environmental Assessment
Figure I.lb: MP&M Environmental Impact Assessment (Continued)
CDiEictiiiiPOIW ^\ f
/vijujftd HP&H 1 (
F-iL,ili1y L*-I'liiLf* J V^
Pollutant Specific
Inf dim ition
CakuktePOC Concentration
it Hum onic M ein & C empire to
Chronic Criteria for
Hum in Health & Aquatic Life
C alculate P 0 C C one entration
it 7 Q 10 Flow & Compile to
Acute Criteria for
Hum in Health ft Aquitk L ife
Identify Streims
With Exceedince
Problem s
Cikulats PO C
C one entiition in
Fish Tissue Using
Eio concentration
Fictor (E CF)
Cikulite PO C
C one entrition in
Drinking W iter
Cikukte Cancer Risk
(Tissue C oncentrition K
C onsumption E it* x
Cincer Potency Slope
Fictor)
C akukte System k
Heilth Risk (Tissue
C one entrition x
C onsumption R it* x
Hon-cineer Reference
Dose
C ilculite HReereitionil
ft Subsistence Anglers
Using ImpictedStreims
ft H Fishing D iys
Compare with
Fish Advisories
Estim ite tt C me er C ises
(Risk x Exposed
Population)
Estimate Population it
Risk from System ic
Heilth Hizards
Cakulate Risk
(DW C oncentrationx
C onsumption R ite x
CancerPotencyElope
Factor)
Estim ate H C anc er Cases
(Risk x Exposed
Population)
Calculate Systemic Risk
(DW Concentrations
C onsumption R ite x
Non-cancer Reference
Dose {RfD})
Estimate Population it
Risk from System ic
Heilth Huards
Source: U.S. EPA analysis.
1-3
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
The remainder of this appendix is organized as follows. Section I.I provides information on the pollutants found in MP&M
discharges. Section 1.2 describes the methodology used to estimate environmental impacts, including extrapolation of sample
sets to the national level and estimates of water quality impacts. Section 1.3 describes data sources for both MP&M facilities
and POTWs. Section 1.4 presents the environmental assessment results.
I.I MP<&M POLLUTANT CHARACTERIZATION
The extent of human and ecological exposure and risk from environmental releases of toxic chemicals depends on
chemical-specific properties, the mechanism and media of release, and site-specific environmental conditions.
Chemical-specific properties include toxic effects on living organisms, and the fate of chemicals in the environment. EPA
estimated the fate of MP&M pollutants based on their propensity to volatilize, adsorb onto sediments, bioconcentrate, and
biodegrade. EPA characterized the fate and toxicity of MP&M pollutants in three steps:
•• identifying pollutants of concern (POCs) in MP&M discharges,
•• compiling physical-chemical and toxicity data for those pollutants, and
•• grouping pollutants based on their characteristics.
The pollutant-specific fate and toxicity data were used in various portions of the quantitative benefits assessment. In addition,
EPA summarized the distribution of MP&M pollutants based on their fate and toxicity properties using the groupings
developed in the third step. This summary is presented in Chapter 12.
1.1.1 Identifying MP<&M Pol lutants
EPA sampled MP&M facilities nationwide to assess the concentrations of pollutants in MP&M effluents. The Agency
collected samples of raw wastewater from MP&M facilities and applied standard water analysis protocols to identify and
quantify the pollutant levels in each sample. EPA used these analytical data, along with selection criteria, to identify 132
contaminants of potential concern. MP&M POCs include 43 priority pollutants (PP), 3 conventional pollutants, and 86
nonconventional pollutants.
EPA then evaluated the potential environmental fate of these pollutants and their toxicity to humans and aquatic receptors.
EPA was able to assess the potential fate and toxicity of 11 8 of these pollutants, including 43 priority pollutants (33 priority
organics, nine priority metals and one inorganic) and 75 nonconventional pollutants (50 nonconventional organics, 18
nonconventional metals, and seven nonconventional inorganics). Table I.I presents the potential fate and toxicity, based on
known characteristics of each chemical, of 132 pollutants of concern. Potential fate and toxicity data are not available for
four conventional, 2 nonconventional, and eight bulk nonconventional pollutants (also listed in Table I.I) associated with
adverse water quality impacts, as described in Section 12.1.3 of this report.
1-4
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1. 1 : Potential Fate and Toxicity of Pollutants of Concern
Toxicity to Toxicity to
Type" | Pollutant j CAS j Aquatic Life j Aquatic Life j Volatility j Adsorption j BCFb j Biodeg' j RfD" j SFe j DWC "g | HAP" j PP1
1 I I (Freshwater) 1 (Saltwater) II 1 1 1 1 1 1 1
1 1 1 Acute 1 Chronic! Acute 1 Chronic!
O Acenaphthene
O i Acetone
O Acetophenone
O j.Acrolein
O j. Aniline
O ^Anthracene
O i Benzoic acid
O : Benzyl alcohol
O : Biphenyl
O Bis(2-ethylhexvl) phthalate
O i Bromo-2-chlorobenzenej 1-
O iBromo-3-chlorobenzenej 1-
O iButvd benzyl phthalate
O Carbon disulfide
O Chlorobenzene
O : Chloroethane
O : Cresol o-
O i Cresol p
O i Cyanide
O : Cymene, j>
O :Decanef n-
O i Dibenzothiophene
•
O : Dichloro methane
O : Dimethyl phthalate
O JDimethylformamide, N,N-
O i Dimethylphenanthrene, 3,6-
O JDimethylphenol, 2,4-
O IDi-n-butyl phthalate
O •Dinitrophenol.,2J4-
O iDinitrotoluene, 2,6-
O I Di-n-octyl phthalate
O JDioxane, 1,4-
O Diphenylamine
O JDiphenyl ether
O IDocosane, n-
O IDodecane, n-
O i Eicosane, n-
O i Ethylbenzene
O • Fluoranthene
O -Fluorene
O -Hexacosane, n-
O -Hexadecane, n-
O -Hexanoic acid
O -Hexanone, 2-
83329
67641
98862
f 1.Q7028....
62533
f 120127
65850
100516
92524
117817
694804
108372
85687
75150
108907
75003
95487
106445
h 57125
99876
124185
132650
75354
75092
131113
68122
1576676
105679
84742
51285
606202
117840
123911
.122394
101848
629970
112403
112958
100414
206440
86737
630013
h 544763
142621
591786
Moderate
Low
h Low
f High
f High
Low
Low
Moderate
Unknown
h Low
Low
Moderate
Low
Low
Low
Low
Low
h High
Low
Low
Moderate
Low
Low
Low
Low
Moderate
Low
Moderate
Low
Low
Moderate
Low
Low
Moderate
Low
Low
Low
Low
r High
Moderate
Low
f Low
Low
Low
Low
Low
f Low
High
, High
, High
Low
Low
Low
Unknown
f Low
Low
Low
High
Low
Low
Low
Low
h High
Low
Low
Low
Low
Low
Low
Low
Moderate
Low
Low
Low
Moderate
Moderate
Low
Low
h Low
Low
Low
Low
Low
H!&
Hj&
Low
h Low
Low
Low
Moderate
Low
Unknown
f High
f High
Unknown
Low
Low
Unknown
Unknown
Unknown
Moderate
Unknown
Low
Unknown
Low
Unknown
h High
Low
Low
Unknown
Low
Low
Low
Unknown
Unknown
Unknown
Moderate
Low
Unknown
Unknown
Unknown
Unknown
h Low
Low
Low
Low
Moderate
H!&
Moderate
Low
f Low
Unknown
Unknown
Low
Low
Unknown
f High
f Moderate
Unknown
Low
Low
Unknown
Unknown
Unknown
Low
High
Low
Unknown
Low
Unknown
h High
Low
Low
Unknown
Low
Low
Low
Unknown
Unknown
Unknown
High,
Low
Unknown
Unknown
Unknown
Unknown
Unknown
Low
Low
Low
Moderate
Moderate
Moderate
Low
f Low
Unknown
Unknown
Moderate
Moderate
h Low
r M.Qdgate
f Low
f Moderate
Low
Low
Moderate
Nonvolatile
Moderate
Moderate
Low
High
h Hi&
High
Low
Low
Unknown
Hi&h
Unknown
Moderate
h High
r High
Nonvolatile
Nonvolatile
Low
Low
Low
Low
Low
Low
Low
Low
Moderate
Unknown
Unknown
Unknown
h High
Moderate
Moderate
Unknown
Unknown
Moderate
Moderate
Moderate
.Lp.w.
h Low
....NpnadsorDtive...
f Low
HMl
Low
Nonadsorptive
Moderate
High ,
Moderate
Moderate
High
Low
Low
Low
Low
Low
Low
Moderate
High.
, High _
Low
Low
Low
Nonadsorptive
High
Low
Moderate
Moderate
Low
Moderate
Low
Moderate
Moderate
High
High
High
Low
, Sigh
Moderate
Unknown
High
Low
Low
Moderate
Insignificant
Low
.....Moderate...
f Low
......Moderate...
Low
Insignificant
Moderate
Moderate
Moderate
Moderate
Moderate
Low
Low
Low
Low
Low
Insignificant
Hi£h
High
High
Low
Insignificant
Low
Insignificant
h High
Moderate
Moderate
Insignificant
Low
r High
Insignificant
Moderate
Moderate
r High
High
High
Low
r High
Low
Unknown
h High
Low
Low
Low,
Moderate
Moderate
Low
f Moderate
f Resistant
Moderate
Moderate
Moderate
Moderate
Low
Low
Moderate
Unknown
Low
Low
Moderate
High.
Moderate
Low
Moderate
Unknown
Resistant
Low
Moderate
Moderate
Moderate
Moderate
Moderate
Resistant
Resistant
Low
Resistant
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Resistant
Low
Moderate
Moderate
Moderate
Moderate
/„
j/
/
, /„
/
/
/
s.
J/
/
/
/
/
/
2
2
s
s
I
s
s
s
s
s
2
s
s
s
s
,
/
^
,
/
,
/
/
,
/
,
,
111:::::::.
M
M
M
M
M
/
/
/
^
*
,
/
/
/
/
;:::::::::z::::::::;
,
/
/
/
;:::::::::z::::::::;
,
/
/
/
,
/
,
,
/
/
:::::::::z:::::
,
/
/
/
::::::::z:::::
/
/
/
,
/
/
/
/
/
,
/
/
/
,
O j Isobutyl alcohol 78831 Low Low Low Low Moderate Low j Insignificant j Moderate /
1-5
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1. 1 : Potential Fate and Toxicity of Pollutants of Concern
Toxicity to Toxicity to
Type" | Pollutant j CAS j Aquatic Life j Aquatic Life j Volatility j Adsorption j BCFb j Biodeg' j RfD" j SFe j DWC "g j HAP" j PP1
1 1 I (Freshwater) 1 (Saltwater) II 1 1 1 1 1 1 1
1 I I Acute 1 Chronic! Acute 1 Chronic!
O i Isophorone
O ^.s.oDronvlnanhthalene.^.-.
O i Methyl ethyl ketone
O : Methyl isobutyl ketone
0 i.MethYlm.etiiacr.Ylate
0 tMettrYmuorene,.!-
O i Methytoaphthalene, 2-
O i Methylphenanthrenej 1-
O Naphthalene
O i Nitrophenol 2-
O i Nitrophenol, 4-
O Nitrosodimethvlamine^ N-
O : Nitrosodiphenylaminej N-
O ^Nitrosopiperidine, N-
O i Octacosane^ n-
O iOctadecane^n-
O Parachlorometacresol
O Phenanthrene
O ! Phenol
O IPyrene
O : Pyridine
O i Styrene
O i Terpineol, alpha-
O Tetrachlor oethene
O : Tetraco sane n-
O i Tetradec ane, n-
O i Toluene
O ITriacontane^ n-
O ITrichloroethene
O Trichlorofluoromethane
O i Trichloromethane
O Tripropyleneglycolmethylether
O IXylene, m-
o jXjjene^ !£.&.£*
0 iXyfene, o-
O IXylene, o- &]>*
O :Ziram\ Cymate
M iAluminum
M iAntimony
M IBarium
M -Beryllium
M Cadmium
M iCalcium
M • Chromium
M • Chromium hexavalent
78591
.....2027.J.7.Q....
.7.8933
r 108101
8062.6
173.0.376....
91576
832699
91203
88755
100027
62759
86306
f 100754
630024
593453
59507
85018
108952
129000
110861
100425
98555
127184
646311
629594
108883
638686
79016
75694
67663
20324338
108383
179601231
h 95476
136777612
137304
7429905
7440360
7440393
7440417
7440439
7440702
7440473
18540299
Low
r....M.°.493te...
LP.H.
, Low
P...Mfi.4£&<:...
Low
Moderate
Low
Low
Low
Low
Low
r Low
Low
Low
Low
Moderate
h Low
Moderate
Low
Low
Low
Low
Low
, Low
f Low
Low
Low
Low
Low
Low
Low
Low
f Low
Low
High
Moderate
Low
Low
Moderate
High
Unknown
Moderate
High
Low
r....M.°.493te...
Low.
, Low.
, Low.
Low
Moderate
Low
Low
Low
Low
Low
r Low
Low
Low
Low
Moderate
h Low
Moderate
Low
Low
Low
Low
Low
, Low
f Low
Low
Low
Low
Low
Low
Low
Low
f Low
Low
High
Moderate
Low
Low
High
High
h Low
Moderate
Moderate
.Low.
f... Unknown...
Low
^..Unknown...
^..Unknown...
Moderate
Unknown
.Low.
.Low.
Low
Low
Low
f Unknown
Low
Low
....Unknown^
Moderate
h Low
Unknown
Unknown
Low
Unknown
Low
Low
, Low
h Low
Low
Low
Unknown
Low
Unknown
Low
Low
h Low
Low
Low
Unknown
Low
Unknown
Unknown
High
Unknown
Low
Low
.Low.
f... Unknown...
Low
,.... Unknown...
,.... Unknown...
Moderate
Unknown
.Low.
.Low.
Low
Low
Low
f Unknown
Low
Low
Unknown
Moderate
h Low
Unknown
Unknown
Low
Unknown
Low
Low
, Low
h Low
Low
Low
Unknown
Low
Unknown
Low
Low
h Low
Low
Low
Unknown
Low
Unknown
Unknown
High
Unknown
Moderate
Moderate
Low
f Moderate
.MP.dgate
f Moderate
f .Mfi&rate
f .Mfi&rate
Moderate
Low
Moderate
Low
Nonvolat ile
Nonvolat ile
Low
f Nonvolatile
Unknown
Unknown
Low
Moderate
Low
Moderate
Low
High
Moderate
High
Unknown
Unknown
High
Unknown
High
High
h High
Nonvolatile
High
High
High
High
Nonvolat ile
Nonvolat ile
Nonvolat ile
Nonvolat ile
Nonvolat ile
Nonvolat ile
Nonvolat ile
Nonvolat ile
Nonvolat ile
Low
, High ,
....Nonadsorp.tive....
Low
,. Low
High
Moderate
r High
Low
Low
Low
Low
Moderate
.....Non^offitiye....
Unknown
High.
Low.
High
Low
High
Nonadsorptive
Low
Low
Low
High
High.
Low
Unknown
Low
Low
Low
Low
Low
Low
Low
Low
Nonadsorptive
High
h High
High
High
High
High
High
High
Insignificant
.High,
...Mfflifif.™!..
...Mffliflf.ffit..
, Low
.High
High
.High
Los
.Low.
Moderate
Insignificant
Moderate
..JSSSMfifJffi'...
Unknown
r High
Moderate
Moderate
Insignificant
High
Insignificant
Low
Low
Low
High
High
Low
Unknown
Low
Low
Insignificant
Insignificant
Moderate
Moderate
Moderate
Moderate
Insignificant
Moderate
Insignificant
Unknown
Low
Moderate
Unknown
Low
Low
Low
r Unknown.
f Moderate
r Moderate
r Low
f Unknown
Unknown
Moderate
Low
Moderate
Resistant
Low
f Resistant
Moderate
Moderate
Low ,
Resistant
High
Resistant
Moderate
Low
Moderate
Resistant
Moderate
Moderate
Moderate
Moderate
Resistant
Resistant
Resistant
Moderate
Low
Low
Low
Low
Resistant
Resistant
Resistant
Resistant
Resistant
Resistant
Resistant
Resistant
Resistant
s.
/
/
s.
/
/..
/
/...
I
s
s
s
s
2
s
s
s
s
2
2
s
s
s
s
s
s
2
s
s
^
,
/
/
,
/
,
/
/
,
,
M
M
M
M
THM
M
M-
M
M
SM
M
M
M
M-
M
M
^
/
/
/..
^
/.
/
:::z::
s
s
:::::::z:::::
/
/
/
::::z::::
, .».
/
,
::::::::z::::
/
/
/
/..
:::±:
/
/
:::::::z::::
/
/
,
/
,
M jCobalt I 7440484 I Low I Moderate I Unknown I Moderate I Nonvolatile I High j Unknown j Resistant
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1. 1 : Potential Fate and Toxicity of Pollutants of Concern
Toxicity to Toxicity to
Type" | Pollutant j CAS j Aquatic Life j Aquatic Life j Volatility j Adsorption j BCFb j Biodeg' j RfD" j SFe j DWC "g | HAP" j PP1
1 I I (Freshwater) 1 (Saltwater) II 1 1 1 1 1 1 1
1 1 1 Acute 1 Chronic! Acute 1 Chronic!
M i Copper
M [COM
M jJroB.
M {Lead
M j.M3H?.?B!!ffi
M j.Miffli.ffl?.1!?.
M i Mercury
M Molybdenum
M 1 Nickel
M i Selenium
M ! Siher
M : Sodium
M : Thallium
M {Tin
M i Titanium
M : Vanadium
M : Yttrium
M !zinc
OI i Ammonia as N
OI : Arsenic
OI I Boron
OI : Chloride
OI : Fluoride
OI I Phosohate
OI I Sulfate
OI ! Sulflde
OI : Phosphorus (as PO4)
CP I BOD 5-day (carbonaceous)
CP : Oil and Grease
CP : Oil and Grease (as Hem)
CP : Total Suspended Solids (TSS)
BNCP I Amenable Cyanide
BNCP I Chemfcal Oxygen Demand (COD)
BNCP ! Total Disserved SolBs (TDS)
BNCP ! Total KjeHahl Nitrogen
BNCP I Total Organic Carbon (TOC)
7440508
7.4.405.7.5....
7.13?.8%....
7439921
7439954...
7439965
7439976
7439987
7440020
7782492
7440224
7440235
7440280
7.4403.1.5....
7440326
7440622
7440655
7440666
7664417
7440382
7440428
16887006
16984488
14265442
14808798
18496258
C-003
C-036
C-009
C-025
C-004
C-0.10
h C-021
C-012
High.
....U.IJJSBP.HB...
....Unknown...
r High
^..IMBSHB...
h High
Unknown
Moderate
High
h High
Low
Low
r... Unknown. ..
Unknown
Low
Unknown
Moderate
h Low
Moderate
Unknown
Low
Low
Unknown
Unknown
Unknown
Unknown
High.
....U.IJJSBP.HB...
LP.H.
r High
Low
h High
Moderate
Moderate
High
h High
Low
Moderate
^...M&te.t.?...
Low
High
Unknown
Low
h Low
Low
Moderate
Low
Low
Unknown
Low
High
Unknown
High.
....U.IJJSBP.HB...
Low
r...M.o.4.erate...
^..y.^feiSiYH...
^..IMBP.;?!!...
h High
Unknown
High
Moderate
h High
Unknown
Low
r... Unknown. ..
Unknown
Unknown
Unknown
High
h Low
r High
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
High.
....U.IJJSBP.HB...
Low.
r High
^..y.^feiSiYH...
,..M8&f&!£...
r High
Unknown
High
Moderate
h High
Unknown
Low
r... Unknown...
Unknown
Unknown
Unknown
Moderate
h Low
Moderate
Unknown
Unknown
Unknown
Unknown
Unknown
High
Unknown
Nonvolat ile
.....Npnvolat.ile....
.....Nonvolatile....
r....N.pnvpJat.ile_.
,.....Np.n.Y.0.!at.ile....
^...Nonvolatile....
h High.
Nonvolatile
Nonvolatile
Nonvolatile
Nonvolatile
Nonvolatile
Nonvolatile
,.....Np.n.Y.0!atile....
Nonvolatile
Nonvolatile
Nonvolatile
Nonvolatile
Moderate
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
High
High
High
High
High
High
h High
f High
Low
High ,
High
High
High
High
h High
f High
High ,
High ,
Nonadsorptive
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Moderate
.....Unknown...
.....Unknown...
Low
High
r.....y.nknown...
h High
Unknown
Low
Insignificant
Insignificant
Unknown
Moderate
r....y.nknown...
Unknown
Unknown
Unknown
Low
Unknown
Low
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Resistant
, Resistant
Resistant
,. Resistant
r Resistant.
Resistant
Resistant
Resistant
Resistant
Resistant
Resistant
Resistant
r Resistant.
Resistant
Resistant
Resistant
Resistant
Moderate
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
/
/.
/;
/
../
../
•/
/
/;
/
/
2
/
/
/
,
,
,
/
,
,
rt
SM
TT
SM
M
-M
-M
SM
M
Ilii;;;;;;;;
M
SM
M
SM
,
,
,
,
,
,
/
h L
/.
/
,
/
,
,
1-7
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1. 1 : Potential Fate and Toxicity of Pollutants of Concern
Type" | Pollutant
|
j Total Petroleum Hydrocarbons
BNCP : (as Set -hem)
...BMGE....|.lH!ai.Kffisy.SK!!!s..?.iSBH!!!S
BNCP | Weak-acid Dissocial* Cyanide
CAS
C;037
, C;020
C-042
Toxicity to
Aquatic Life
(Freshwater)
:
Acute Chronic
:
:
:
:
:
Toxicity to
Aquatic Life
(Saltwater)
:
Acute Chronic
:
:
:
:
:
Volatility
Adsorption
BCFb
Biodegc
RfD"
SFe
DWC "8
HAP"
ppi
Table Notes:
Unless indicated otherwise, all metals are assumed to be nonvolatile, to have high adsorption,
and to be resistant to biodegradation.
a Type
O
M
OI
CP
BNCP
b BCF
° Biodeg
d RfD
Organic
Metal
Other Inorganic
Conventional Pollutant
Bulk Nonconventional Pollutant
Bioconcentration Factor
Biodegradation Potential
Reference Dose
SF = Slope Factor
DWC = Drinking Water Criteria
Drinking Water Criteria Codes
M
SM
THM
TT
h HAP
' PP
Maximum Contaminant Level (MCL) established for health-based effect
Secondary Maximum Contaminant Level (SMCL) established for taste or
aesthetic effect
MCL established for trihalomethanes
Treatment technology action level established
Hazardous Air Pollutant
Priority Pollutant
Source: U.S. EPA analysis.
1-8
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
1.1.2 Physical-Chemical Characteristics and Toxicity bata of MP<&M Pollutants
Pollutants present in MP&M effluents can have significant effects on human health and aquatic receptors. EPA used various
data sources to evaluate both pollutant-specific fate and toxicity and potential human health effects, including:
•• reference doses (RfDs),
>• cancer potency slope factors (SFs).
>• human health-based water quality criteria (WQC}.
•• maximum contaminant levels (MCLs) for drinking water protection and other drinking water related criteria,
and
•• hazardous air pollutant (HAP) and priority pollutant (PP) lists.
To evaluate potential fate and effects in aquatic environments, the Agency relied on:
»• measures of acute and chronic toxicity to aquatic species,
•• bioconcentration factors for aquatic species,
•• Henry's Law (H) constants (to estimate volatility),
*• adsorption coefficients (to estimate association with bottom sediments), and
*• biodegradation half-lives (to estimate the removal of chemicals via microbial metabolism).
The data sources used in the assessment include:
»• EPA ambient WQC documents and updates;
> EPA's Assessment Tools for the Evaluation of Risk (ASTER):
•• the AQUatic Information REtrieval System (AQUIRE) and the Environmental Research Laboratory-Duluth
fathead minnow database;
>• EPA's Integrated Risk Information System (IRIS);
•• EPA's Health Effects Assessment Summary Tables (HEAST);
•• EPA's 1991 and 1993 Superfund Chemical Data Matrix (SCDM);
*• Syracuse Research Corporation's CHEMFATE and BIODEG databases; and
> EPA and other government reports, scientific literature, and other primary and secondary data sources.
EPA also obtained information on chemicals for which the sources listed above did not provide physical-chemical properties
and/or toxicity data, to ensure that the assessment be as comprehensive as possible. To the extent possible, EPA estimated
values for the chemicals using the quantitative structure-activity relationship (QSAR) model incorporated in ASTER.
The Agency also used published linear regression correlation equations to determine some physical-chemical properties.
a. Human health effects
EPA used various data sources to determine pollutant-specific toxicity to human health. EPA obtained RfDs and SFs from
IRIS, HEAST, and EPA's Region II Risk-Based Concentration (RBC) table. EPA developed drinking water criteria and
human health-based AWQC values for two exposure routes: (1) ingesting the pollutant via contaminated aquatic organisms
only (carcinogens and non-carcinogens), and (2) ingesting the pollutant via both water and contaminated aquatic organisms
1-9
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
(non-carcinogens only). Table 1.2 summarizes pollutant toxicity data pertaining to human health. In addition to fate and
toxicity data, Table I.I also includes HAP and PP lists. Short descriptions and definitions for each of the measures of human
health effects are provided below.
Table 1.2: Human Health Data for 132 MP&M Pollutants of Concern
CAS Number
51285
57125
59507
62533
62759
65850
67641
67663
68122
75003
75092
75150
75354
75694
78591
78831
78933
79016
80626
83329
84742
85018
85687
86306
86737
88755
91203
91576
92524
95476
95487
98555
98862
99876
100027
100414
100425
100516
100754
101848
105679
106445
107028
|
Pollutant Name
|
! Dinitrophenol, 2,4-
! Cyanide
! Parachlorometacresol
! Aniline
jNitrosodimethylamine, N-
! Benzoic acid
! Acetone
! Trichloromethane
! Dimethylformamide, N,N-
! Chloroethane
! Dichloromethane
! Carbon disulfide
! Dichloroethene, 1,1-
! Trichlorofluoromethane
! Isophorone
! Isobutyl alcohol
! Methyl ethyl ketone
! Trichloroethene
! Methyl methacrylate
! Acenaphthene
! Di-n-butyl phthalate
! Phenanthrene
! Butyl benzyl phthalate
jNitrosodiphenylamine, N-
! Fluorene
! Nitrophenol, 2-
! Naphthalene
! Methylnaphthalene, 2-
! Biphenyl
jXylene, o-
! Cresol, o-
! Terpineol, alpha-
! Acetophenone
! Cymene, p-
! Nitrophenol, 4-
! Ethylbenzene
! Styrene
! Benzyl alcohol
jNitrosopiperidine, N-
! Diphenyl Ether
! Dimethylphenol, 2,4-
! Cresol, p-
! Acrolein
Human Health
Ingesting
Water and I
Organisms
(US/1) |
70!
700!
56000!
5.8!
0.00069!
130000!
3500!
5.7!
3500!
12!
4.7!
3400!
0.057!
9100!
36!
10000!
21000!
3.1!
48000!
1200!
2700!
AWQC Values I
Ingesting
Organisms j Slope Factor j Reference Dose j
Only
(US/1) ! (mg/kg/day)
14000!
220000!
270000!
95! 0.0057
8.1! 51
2900000!
2800000!
470! 0.0061
220000000!
520! 0.0029
1600! 0.0075
94000!
3.2! 0.6
66000!
2600! 0.00095
1500000!
6500000!
92! 0.011
2300000!
2700!
12000!
3000!
5!
720!
5200!
16! 0.0049
1500!
680!
75!
720!
42000!
1700!
21000!
84!
1200!
100000!
30000!
3400!
98000!
220!
3100!
6700!
10000!
1100!
29000!
160000!
810000!
540!
170!
410!
2300!
3100!
1000!
(mg/kg/day) !
Drinking
Water
Criteria
(US/1)
0.002!
0.02!
200
2!
4!
0.1!
0.01!
100
0.1!
0.4!
0.06!
5
0.1!
0.009!
7
0.3!
0.2!
0.3!
0.6!
0.006!
5
1.4!
0.06!
0.1!
0.2!
0.04!
0.02!
0.02!
0.05!
2! 10000
0.05!
0.1!
0.008!
0.1!
0.2!
0.3!
0.02!
0.005!
0.02!
700
100
I-10
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.2: Human Health Data for 132 MP&M Pollutants of Concern
i
:
i
:
:
:
CAS Numberj Pollutant Name
i
:
:
108101 ! Methyl isobutyl ketone
108372 IBromo-3-chlorobenzene, 1-
108383 IXylene, m-
108883 ! Toluene
108907 IChlorobenzene
108952 ! Phenol
110861 IPyridine
112403 jDodecane, n- (a)
112958 jEicosane, n- (a)
117817 ! Bis(2-ethylhexyl) phthalate
1 1 7840 I Di-n-octyl phthalate
120127 ! Anthracene
122394 I Diphenylamine
123911 jDioxane, 1,4-
124185 ;Decane,n-
127184 1 Tetrachloroethene
129000 IPyrene
131113 1 Dimethyl phthalate
132650 ; Dibenzothiophene
1 37304 1 Ziram \ Cymate
142621 ; Hexanoic acid
206440 1 Fluoranthene
544763 ; Hexadecane, n- (a)
591786 jHexanone, 2-
593453 ; Octadecane, n- (a)
606202 jDinitrotoluene, 2,6-
629594 ; Tetradecane, n- (a)
629970 ! Docosane, n-
630013 ; Hexacosane, n- (b)
630024 ! Octacosane, n- (b)
638686 ; Triacontane, n- (b)
6463 1 1 ; Tetracosane, n- (b)
694804 ; Bromo-2-chlorobenzene, 1-
832699 ! Methylphenanthrene, 1-
1576676 ; Dimethylphenanthrene, 3,6-
1730376 ! Methylfluorene, 1-
2027170 j Isopropylnaphthalene, 2-
7429905 ! Aluminum
7439896 jlron
7439921 ILead
7439954 ! Magnesium
7439965 ! Manganese
7439976 j Mercury
7439987 ! Molybdenum
7440020 ! Nickel
7440224 ! Silver
7440235 ! Sodium
7440280 ! Thallium
Human Health AWQC Values 1
i i i i
Ingesting j Ingesting j j Drinking
Water and Organisms Slope Factor j Reference Dose 1 Water
Organisms Only Criteria
(ns/l)
2800
42000
6800
680
21000
35
1.8
37
4100
470
3.2
320
230
310000
700
300
1400
34
20000
300
50
0.05
610
170
1.8
(ns/D
360000
100000
200000
21000
4600000
5400
5.9
39
6800
1000
2400
3500
290
2900000
220000000
370
65000
900
47000
100
0.051
4600
110000
6.5
(mg/kg/day)
0.014
0.011
0.052
(mg/kg/day)
0.08
2
0.2
0.02
0.6
0.001
0.02
0.02
0.3
0.025
0.01
0.03
0.02
0.04
0.04
0.001
1
0.3
0.14
0.005
0.02
0.005
0.00007
(us/1)
10000
1000
100
6
5
50
300
15
50
2
100
2
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.2: Human Health Data for 132 MP&M Pollutants of Concern
i
:
i
:
:
:
CAS Numberj Pollutant Name
i
:
:
7440315 jTin
7440326 ! Titanium
7440360 jAntimony
7440382 jArsenic
7440393 I Barium
7440417 ! Beryllium
7440428 I Boron
7440439 ! Cadmium
7440473 I Chromium
7440484 ! Cobalt
7440508 ! Copper
7440575 jGold
7440622 I Vanadium
7440655 ! Yttrium
7440666 ; Zinc
7440702 1 Calcium
7664417 ; Ammonia as N
7782492 1 Selenium
14265442 1 Phosphate
14808798 ISulfate
16887006 ; Chloride
16984488 1 Fluoride
18496258 jSulfide
18540299 ! Chromium hexavalent
; Tripropyleneglycolmethyl
20324338 | ether
136777612 jXylene, o- & p- (c)
179601231 jXylene, m- & p- (c)
COOS ! BOD 5 -day (carbonaceous)
; Chemical Oxygen Demand
C004 | (COD)
C009 ! Total Suspended Solids (TSS)
CO 1 0 ; Total Dissolved Solids (TDS)
CO 1 2 ! Total Organic Carbon (TOC)
C020 ; Total Recoverable Phenolics
C02 1 ! Total Kj eldahl Nitrogen
C025 ; Amenable Cyanide
C036 ! Oil And Grease (as Hem)
; Total Petroleum
C037 1 Hydrocarbons (as Sgt-hem)
; Weak-acid Dissociable
C042 1 Cyanide
! Phosphorus (as PO4)
; Oil and Grease
Human Health AWQC Values 1
i i i i
Ingesting j Ingesting j j Drinking
Water and Organisms Slope Factor j Reference Dose 1 Water
Organisms Only Criteria
(ns/l)
14
0.02
1000
66
14
50000
650
9100
170
100
100
42000
42000
(ns/D
4300
0.16
1100
84
1000000
1200
69000
11000
10000
2000
100000
100000
(mg/kg/day)
1.5
(mg/kg/day)
0.6
4
0.0004
0.0003
0.07
0.002
0.09
0.0005
1.5
0.06
0.04
0.007
0.3
0.005
0.06
0.003
2
2
(us/1)
6
50
2000
4
5
100
1300
5000
50
250000
250000
4000
100
10000
10000
Sources: U.S. EPA (1980), U.S. EPA (1984), U.S. EPA (1997), U.S. EPA (1998), U.S. EPA (1998/99), Worthing (1987).
1-12
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
*»* Systemic toxicants
Systemic toxicants are chemicals that EPA believes can cause significant non-carcinogenic health effects when present in
the human body above chemical-specific toxicity thresholds. These effects may result from acute or chronic chemical
exposures, and include:
*• systemic health effects (i.e., loss of one or more neurological, respiratory, reproductive, immunological, or
circulatory functions);
> organ-specific toxicity (e.g., liver and kidney effects);
*• developmental toxicity (e.g., reduced weight in newborns or loss of IQ); and
> lethality.
EPA typically relies on animal toxicity data to develop RfDs for systemic toxicants that can enter the human body via
ingestion. These values represent chemical concentrations expressed in mg of pollutant/kg body weight/day. Certain exposed
populations are considered to be protected if these chemical concentrations are not exceeded. These populations include
sensitive groups, such as young children or pregnant women. EPA included all available RfD data for the MP&M pollutants
of concern (POCs) in the analysis.
»»» Carcinogens
Carcinogens are chemicals that EPA believes can cause or have the potential to cause cellular damage, which can lead to
tumors or cancers in humans, either directly or indirectly. Unlike systemic toxicants, most carcinogens are not believed to
have a toxicity threshold. Any amount of a carcinogen therefore has the potential to result in a cancer event, even though such
a probability can be very small at low concentrations. The Agency has developed SFs, using animal or epidemiological data,
that express the probability that a chemical will induce tumor or cancer development. EPA included all available SF data for
the MP&M POCs in the analysis.
*»* Drinking water criteria
EPA developed human health-based drinking water criteria to assess the health hazards associated with the presence of certain
toxic chemicals in drinking water. The criteria are usually presented as MCLs. MCLs for non-carcinogens represent
chemical-specific concentrations (expressed in |Jgf//) that are not expected to result in adverse health effects in exposed
populations if not exceeded in drinking water. MCLs for carcinogens represent chemical-specific concentrations (expressed
in Hg/1) that are expected to result in less than one additional cancer case per million lifetime exposures if not exceeded in
drinking water. The Agency also investigated additional drinking water criteria, including:
> Secondary Maximum Contaminant Levels (SMCLs) established for taste or aesthetic effects,
»• MCLs established specifically for trihalomethanes, and
> action levels developed on the basis of treatment technology.
EPA included all the available primary and secondary drinking water criteria for the MP&M POCs in the analysis.
*»* Pollutant uptake via water and/or organisms
EPA has developed WQC for numerous priority toxic pollutants to protect the health of humans who consume water and
organisms or only organisms obtained from aquatic habitats contaminated by those PPs. The criteria, expressed in Hg/1,
represent concentrations in surface waters that will cause adverse health effects in humans when exceeded. EPA obtained all
available human health WQC for the MP&M POCs and included them in the analysis.
»»» Priority pollutants (PPs)
Priority pollutants are 126 individual chemicals, defined by the Agency as toxic, that EPA routinely analyzes when assessing
contaminated surface water, sediment, groundwater, or soil samples. These chemicals are of particular concern to the Agency
because of their high toxicity or persistence in the environment. EPA identified all MP&M PPs and included them in the
analysis.
1-13
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
*»* Hazardous air pollutants (HAPs)
HAPs are compounds that EPA believes may represent an unacceptable risk to human health if present in the air. HAPs,
expressed in fig/m3, can be of particular concern to POTW workers if released into the air at high enough concentrations
during the wastewater treatment cycle. EPA identified all HAPs among the MP&M POCs analyzed.
b. Aquatic receptor effects
The potential impact of chemicals on aquatic receptors can be assessed qualitatively based on five effect and fate parameters:
*• aquatic toxicity (acute and chronic),
*• bioconcentration,
> volatilization,
*• adsorption, and
*• biodegradation.
Site-specific risks require a measure of exposure and cannot be quantified using this approach. Chemicals can be classified
and ranked in terms of their impacts on aquatic receptors, however, by using the five parameters discussed below. Table 1.3
summarizes the measured or estimated values of these parameters for the MP&M POCs. Each effect and fate parameter is
described below.
Biological oxygen demand (BOD), oil and grease (O&G). pH, and total suspended solids (TSS): These
fate/effect parameters are relevant only for specific chemicals. These parameters are not available for the conventional
pollutants or bulk nonconventional pollutants, such as total petroleum hydrocarbons (TPH). alkalinity, total organic
carbon (TO C), or total Kjeldahl nitrogen (TKN). Most of these pollutants are responsible for significant environmental
impacts, however. Section 12.2.4 outlines these impacts in greater detail.
*»* Aquatic toxicity data
The Agency addressed two general classes of aquatic toxicity:
»• Acute toxicity (A T) assesses the impacts of a pollutant after a relatively short exposure duration, typically 48 and
96 hours for invertebrates and fish, respectively. The endpoint of concern is mortality, reported as the LC50. This
value represents the concentration lethal to 50 percent of the test organisms for the duration of the exposure.
»• Chronic toxicity (CT) assesses the impact of a pollutant after a longer exposure duration, typically from one week
to several months. The endpoints of concern are one or more sub-lethal responses, such as changes in reproduction
or growth in the affected organisms. The results are reported in various ways, including EC1 or ECS (i.e., the
concentration at which one percent or five percent of the test organisms show a significant sub-lethal response),
NOEC (No Observed Effect Concentration), LOEC (Lowest Observed Effect Concentration), or
MATC (Maximum Allowable Toxicant Concentration).
*»* Bioconcentration factor (BCF) data
The bioconcentration factor (BCF, measured in I/kg) is a good indicator of the potential for a chemical dissolved in the
water column to be taken up by aquatic biota across external surface membranes, usually fish gills. The BCF is defined as
follows:
BCF = equilibrium chemical concentration in target organism (mo/ko, wet weight)
mean chemical concentration in surrounding water (\ig/L) '
EPA analyzes POCs with elevated BCF values because these pollutants can bio concentrate in aquatic organisms and transfer
up the food chain if they are not metabolized and excreted. This transfer can result in significant exposures to predators
(including humans) consuming contaminated fish or shellfish.
1-14
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
Although the bioaccumulation factor (BAF) is a better measure of the potential for a chemical dissolved in the water column
to be taken up by aquatic biota, field measured BAFs are not yet available. EPA recognizes that using bioconcentration
factors will underestimate the risk to aquatic organisms.
*»* Volatilization data
Volatilization is a process whereby chemicals dissolved in water escape into the air. Chemicals with higher volatilization
potential are typically of less concern to aquatic receptors because they tend to be removed quickly from the water column.
These volatile pollutants are a concern to human health when inhaled. For aquatic receptors, however, POCs with higher
volatilization potential present lower hazards.
EPA used the air/water partitioning coefficient H to estimate a chemical's volatilization potential. H represents the ratio of a
chemical's aqueous phase concentration to its equilibrium partial pressure in the gas phase (at 25°C); units are typically
expressed as aim.m3/mole. Metals do not have measurable partial pressures (with some notable exceptions, including
several organic mercury compounds), and are therefore considered to be nonvolatile unless otherwise indicated.
*»* Adsorption data
Adsorption is a process whereby chemicals associate preferentially with the organic carbon (OC) found in soils and
sediments. Highly adsorptive compounds tend to accumulate in sludge or sediments. Such chemicals are also more likely to
be taken up by benthic invertebrates and to affect local food chains. Both accumulation in sediment and the effect on local
food chains make these chemicals more likely to impact higher predators, including humans.
EPA used the adsorption coefficient £Koc|to assess the potential of organic MP&M POCs to associate with organic
carbon. Koc represents the ratio of the target chemical adsorbed per unit weight of organic carbon in the soil or sediment to
the concentration of that same chemical in solution at equilibrium. Metals in the aquatic environment typically end up in the
sediment phase but do not bind to the organic carbon (except for nickel). The Agency assumed that all metals show a high
affinity for sludge and sediments independent of their negligible Koc values.
*»* Biodegradation data
Biodegradation is a process whereby organic molecules are broken down by microbial metabolism. Biodegradation
represents an important removal process: compounds that are readily biodegraded generally represent lower intrinsic hazards
because they can be eliminated rapidly. These compounds are therefore less likely to create long-term toxicity problems or to
accumulate in sludge or sediments and organisms. Chemicals that biodegrade slowly or not at all can accumulate and linger
for longer periods of time in sludge or sediments, and represent a higher hazard to aquatic receptors.
EPA used biodegradation half-life to estimate the potential for an organic chemical to biodegrade in the aquatic
environment. Biodegradation half-life represents the number of days a compound takes to be degraded to half of its starting
concentration under prescribed laboratory conditions. Metals do not biodegrade.
Table 1.3 summarizes pollutant toxicity data pertaining to aquatic life.
1-15
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.3: Aquatic Life Toxicity
CAS
Number
51285
57125
59507
62533
62759
65850
67641
67663
68122
75003
75092
75150
75354
75694
78591
78831
78933
79016
80626
83329
84742
85018
85687
86306
86737
88755
91203
91576
92524
95476
95487
98555
Pollutant Name
1 Dinitrophenol, 2,4-
I Cyanide
I Parachlorometacresol
I Aniline
1 Nitrosodimethylamine, N-
I Benzoic acid
I Acetone
I Trichloromethane
1 Dimethylformamide, N,N-
I Chloroethane
1 Dichloromethane
I Carbon disulfide
1 Dichloroethene, 1,1-
! Trichlorofluoromethane
I Isophorone
1 Isobutyl alcohol
1 Methyl ethyl ketone
! Trichloroethene
1 Methyl methacrylate
! Acenaphthene
1 Di-n-butyl phthalate
! Phenanthrene
I Butyl benzyl phthalate
! Nitrosodiphenylamine, N-
1 Fluorene
! Nitrophenol, 2-
; Naphthalene
! Methylnaphthalene, 2-
1 Biphenyl
I Xylene, o-
1 Cresol, o-
1 Terpineol, alpha-
: :
: :
: :
Freshwater Aquatic Life
i i
: :
Data for 132
MP&M Pollutants of Concern
Bio i „ , i Adsorption i Bio
Saltwater Aquatic Life ! concentration 1 „ . . ! Coefficient 1 degradation
_ . Constant °
Factor (K ) Half-Life
Acute Value 1 Chronic Value j Acute Value j Chronic Value j Value j
Gigfl) | Gig/0 | Gigfl) | Gig/0 | (i/kg) |
1160J
22i
4050!
250J
2800001
180000J
62100001
13300J
71000001
65614J
3300001
2100J
116001
17387J
120000!
949000J
32200001
40700J
191000!
580J
850!
180J
820!
5800J
212!
160000J
i6oq
1133J
360!
3820!
14000!
12742J
790!
5.2!
1300!
4|
4000!
17178J
1866000!
6300J
710000!
21069J
82500!
2|
5114!
6412J
11000!
4000J
233550!
14850J
19100!
208J
500!
19!
260!
1000J
sj
3451J
370!
417!
230!
1332!
2251!
4879J
1500!
H
29400J
4300000!
:
:
5640000!
19610J
:
:
256000!
:
:
:
224000!
:
:
12900!
600000J
1287000!
14000J
970J
450!
noj
510!
3300000J
1000!
32000J
1200!
600J
4600!
60001
10200!
:
:
:
940!
H
2940J
430000!
10000!
1961J
2560!
2!
22400!
1290!
60000J
128700!
2000J
710J
3.4!
ni
400!
33000J
lOOJ
16000J
120!
eoj
460!
600J
1020!
!
1.51!
H
79!
19.9J
0.026!
15i
0.39!
3.75J
0.005!
7.2!
0.91!
11.5J
5.6!
49!
4.38!
2.2!
1!
10.6J
6.6!
242!
89!
48S
414!
136!
30!
13.5;
10.5!
256S
436!
20S
18!
4S
Value (atm/ !
m3-mole) !
0.000000443!
:
:
0.0000025!
0.0000019!
0.000000263!
0.00000154!
0.00004!
0.00367!
0.000000018!
0.00882!
0.00219!
0.0303!
0.0261!
0.097!
0.00000576!
0.00001 18!
0.00006!
o.oios!
0.00034!
0.00009!
0.00000181!
0.00002!
0.00000126!
o.ooooos!
0.00006!
0.00000947!
0.00048!
0.00052!
0.0003!
0.005191
0.0000012
0.0000544!
,, , Value
Value I
1 (days)
2386!
45i
604!
54!
12!
182!
18!
40!
6.1!
37.6J
28!
89!
343!
93i
25!
61.7!
5.2!
104!
22!
3890!
6310!
issoq
17000!
1200!
2830!
114!
871!
ssoq
i4oq
1291
103!
5891
263
16
100
26
180
16
7
180
16
28
28
180
360
28
7.2
7
360
28
102
23
200
7
34
60
28
20
20
7
28
7
15
1-16
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
CAS
Number
98862
99876
100027
100414
100425
100516
100754
101848
105679
106445
107028
108101
108372
108383
108883
108907
108952
110861
112403
112958
117817
117840
120127
122394
123911
124185
127184
129000
131113
132650
137304
142621
Table 1.3: Aquatic
Pollutant Name
jAcetophenone
jCymene, p-
jNitrophenol, 4-
I Ethylbenzene
! Styrene
I Benzyl alcohol
jNitrosopiperidine, N-
I Diphenyl Ether
I Dimethylphenol, 2,4-
I Cresol, p-
I Acrolein
1 Methyl isobutyl ketone
IBromo-3-chlorobenzene, 1-
I Xylene, m-
I Toluene
I Chlorobenzene
! Phenol
I Pyridine
I Dodecane, n- (a)
I Eicosane, n- (a)
1 Bis(2-ethylhexyl) phthalate
I Di-n-octyl phthalate
1 Anthracene
I Diphenylamine
IDioxane, 1,4-
! Decane, n- a
; Tetrachloroethene
! Pyrene
1 Dimethyl phthalate
! Dibenzothiophene
1 Ziram \ Cymate
Life Toxic ity
:
:
:
Freshwater Aquatic Life
i
:
Data for 132
MP&M Pollutants of Concern
Bio
Saltwater Aquatic Life I concentration 1
Factor
Acute Value j Chronic Value j Acute Value j Chronic Value
Gigfl) | Gig/0 ! Gigfl) | Gig/0
162000!
6500J
7680!
9090J
4020!
10000J
1019538!
4000J
2120!
7500!
14!
505000J
17841
16000J
55001
2370J
42001
93800J
180001
18000J
690!
2.781
3790J
98500001
18000J
4990!
591J
330001
420[
8!
| Hexanoic acid 320000J
31094!
237!
1300!
4600J
402!
1000J
282592!
:
:
:
1970!
2570J
5.8!
50445J
6821
3900J
looo!
2100J
200!
25000J
13001
1300J
69!
2.21
734J
14573001
1300J
510!
6l|
1700!
122J
1.8!
15170J
4400!
7170!
430!
9100!
isooo!
:
:
:
:
:
55!
812000!
12000!
6300!
10500!
5800!
:
:
500000!
sooooo!
:
:
40!
:
:
:
sooooo!
10200!
:
:
:
58000!
:
:
5200!
:
:
:
440
1900
43
910
1500
240
5.5
81200
1200
5000
1050
2410
50000
50000
16
50000
450
5800
520
Value !
(I/kg) |
111
770!
79!
37.5|
13.5!
4!
h j.
930|
94!
17.6!
215!
2.4|
190!
208|
10.7!
10.3J
1.4!
2i
14500!
100000!
130!
5460!
478!
269|
0.4!
8800|
30.6!
1110J
36!
1100!
0.001!
16!
:
:
Henry's Law j
Constant I
i
£
Value (atm/ !
m3-mole)
0.00001!
0.01 1]
0.000000000415!
0.00788J
0.00283!
0.000000743J
0.000000275!
0.000448J
0.000000951!
O.OOOOOlj
0.00012!
0.00014J
0.00078!
0.00718J
0.00664!
0.00377J
0.000000333!
0.00000888J
:
:
:
0.0000001!
0.000000445J
0.00007!
0.000000496J
0.0000048!
:
:
0.0184!
0.00001 1|
0.000000105!
0.00002J
0.0000225J
Adsorption i
Coefficient 1
(Koc) |
Value !
45!
4000J
236!
250J
920!
6.1|
9!
7800J
18!
49!
5!
19!
1500!
190J
95!
275!
30.2!
I
95000!
30000000!
87420!
2390!
16000!
1910J
17!
58200!
363!
62700!
40!
noooj
0.4!
38]
Bio
degradation
Half-Life
Value
(days)
16
100
7
10
28
16
180
15
7
0.667
28
7
100
28
22
150
3.5
7
17
17
23
28
460
20
180
17
360
1900
7
12
1-17
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
CAS
Number
206440
544763
591786
593453
606202
629594
629970
630013
630024
638686
646311
694804
832699
1576676
1730376
2027170
7429905
7439896
7439921
7439954
7439965
7439976
7439987
7440020
7440224
7440235
7440280
7440315
7440326
7440360
7440382
7440393
Table 1.3: Aquatic
Pollutant Name
! Fluoranthene
I Hexadecane, n- (a)
jHexanone, 2-
! Octadecane, n- (a)
I Dinitrotoluene, 2,6-
I Tetradecane, n- (a)
! Docosane, n- b
I Hexacosane, n- (b)
! Octacosane, n- (b)
I Triacontane, n- (b)
I Tetracosane, n- (b)
I Bromo-2-chlorobenzene, 1-
1 Methylphenanthrene, 1-
1 Dimethylphenanthrene, 3,6-
1 Methylfluorene, 1-
I Isopropylnaphthalene, 2-
1 Aluminum
[Iron
jLead
I Magnesium
1 Manganese
1 Mercury
1 Molybdenum
| Nickel
I Silver
I Sodium
1 Thallium
JTin
1 Titanium
! Antimony
1 Arsenic
Life Toxic ity
:
:
:
Freshwater Aquatic Life
i
:
Data for 132
MP&M Pollutants of Concern
Bio
Saltwater Aquatic Life ! concentration !
Factor
Acute Value j Chronic Value j Acute Value j Chronic Value
Gigfl) | Gig/0 | Gigfl) | Gig/0
45!
18000J
428000!
18000J
18500!
18000J
530000!
530000J
530000!
530000J
530000!
2942J
5551
543!
627!
540J
7501
:
:
651
64700J
L4i
470J
3.41
1640000J
14001
:
:
:
3500!
3401
| Barium 410000J
7.1!
1300J
38868!
1300J
60!
1300J
68000!
68000J
68000!
68000J
68000!
1196J
541
21[
115!
78|
87!
1000!
2.51
6470!
3881
0.77!
27.81
52!
0.341
1020000!
401
18.6!
19l!
1600!
1501
2813!
40!
500000!
500000!
500000!
500000!
500000!
500000!
500000!
500000!
:
:
:
:
:
:
:
:
33000!
210!
:
:
:
1.8!
74!
1.9!
:
:
21301
:
:
:
4800!
69!
:
:
:
16
50000
50000
50000
50000
50000
50000
50000
50000
3300
8.1
10
0.94
8.2
0.19
213
2900
Value j
(I/kg) |
1150!
32300J
6.6J
10100J
12!
19500J
100000!
:
:
:
:
:
loooooi
240J
47901
33000J
33001
3200J
23l!
:
:
49!
85215J
5500!
47|
0.51
:
:
lie!
:
:
:
i
36 441
: :
: :
: :
:
:
Henry's Law j
Constant I
i
£
Value (atm/ !
m3-mole)
0.0000161!
:
:
0.000113!
:
:
:
0.000000747!
:
:
:
:
:
:
:
0.0006!
0.00000781
0.0000053!
0.000081
0.00063!
:
:
:
:
:
0.018!
:
:
:
:
:
:
:
:
:
:
:
:
:
Adsorption I
Coefficient j
(Koc) |
Value !
41700!
207000!
12!
66900!
100!
126000!
noooooooi
:
:
:
:
:
420000000!
1500!
360001
330000!
330001
33000!
:
:
:
:
:
30000!
300!
:
:
:
:
:
:
:
:
:
:
Bio
degradation
Half-Life
Value
(days)
440
17
16
17
180
17
17
17
17
17
17
100
20
1-18
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.3: Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern
S~l A C<
., , Pollutant Name
Number j
7440417 JBerylHum
7440428 | Boron
7440439 ! Cadmium
7440473 | Chromium
7440484 | Cobalt
7440508 | Copper
7440575 1 Gold
7440622 | Vanadium
7440655 | Yttrium
7440666 jZinc
7440702 | Calcium
76644 1 7 | Ammonia as N
7782492 ! Selenium
14265442 j Phosphate
14808798 ISulfate
16887006 | Chloride
16984488 ! Fluoride
18496258 jSulfide
1 8540299 | Chromium hexavalent
20324338 j Tripropyleneglycolmethylether
136777612 IXylene, o- & p- °
179601231 jXylene, m- & p- c
COOS | BOD 5 -day (carbonaceous)
C004 | Chemical Oxygen Demand (COD)
C009 | Total Suspended Solids (TSS)
CO 1 0 | Total Dissolved Solids (TDS)
CO 1 2 | Total Organic Carbon (TOC)
C020 ! Total Recoverable Phenolics
C02 1 | Total Kj eldahl Nitrogen
C025 ! Amenable Cyanide
C036 | Oil and Grease (as Hem)
Bio i „ , i Adsorption I Bio
Freshwater Aquatic Life Saltwater Aquatic Life ! concentration 1 „ . . ! Coefficient 1 degradation
_ . Constant °
Factor • (K ) • Half-Life
, .j. f. i. i OC i.
Acute Value 1 Chronic Value i Acute Value i Chronic Value
Gigfl) | Gig/0 | Gigfl) | Gig/0
130! 5.3!
: Q i /-: :
I 3L6L I
4.3! 2.2! 42! 9.3
570J 74J 1100J 50
1620! 49! 10
13! 9[ 4.8! 3.1
11200J 9J
120J 120J 90J 81
200000!
13300J 3060J 3800J 570
12.831 51 2901 71
: : :
: : :
1000000!
860000J 230000J
16001 1601
: <->: : <->
: 2: : 2
161 Ill HOOJ 50
2484600| 683870|
26001 12051 60001 600
2600| 1205J 6000| 600
: : :
: : :
: : :
: : :
: : :
: : :
: : :
: : :
: : :
: : :
Value j Value (atm/
(I/kg) j m3-mole)
19!
:
:
64!
16J
360!
:
:
:
47!
0.0000161
4.8!
:
:
:
:
:
:
:
16!
0.2J 0.0000000001
2081 0.0076
208| 0.0076
:
:
:
:
:
:
:
:
:
:
Value
3.1
46
260
260
Value
(days)
16
16
28
28
1-19
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.3: Aquatic Life Toxicity Data for 132 MP&M Pollutants of Concern
S~l A C<
., , Pollutant Name
Number j
I Total Petroleum Hydrocarbons (as
C037 iSgt-hem)
C042 | Weak-acid Dissociable Cyanide
! Phosphorus (as PO4)
I Oil and Grease
Bio i „ , i Adsorption I Bio
Freshwater Aquatic Life Saltwater Aquatic Life ! concentration 1 „ . . ! Coefficient 1 degradation
_ . Constant °
Factor • (K ) • Half-Life
, i i L i OC' i.
Acute Value
(Hg/l)
Chronic Value
Oil/2
Acute Value
(Hg/l)
Chronic Value
(Hg/l)
Value
(I/kg)
Value (atm/
m3-mole)
Value
Value
(days)
a Aquatic toxicity data for n-decane are reported based on structural similarity
b Aquatic toxicity data for n-docosane are reported based on structural similarity
° Values for the most stringent isomer (p-Xylene) are assumed
Sources: Arthur D. Little (1983), Arthur D. Little (1986), Birge et al. (1979), Clay (1986), Holdway and Spraque (1979), ICF, Inc. (1985), Leblanc (1980), Lyman et al. (1981), U.S.
Atomic Energy Commission (1973), U.S. EPA (1972), U.S. EPA (1976), U.S. EPA (1980), U.S. EPA (1993), U.S. EPA (1998/99a), U.S. EPA (1998/99b), Zhang and Zhang (1982).
1-20
-------
MP&M EEBA: Appendices
Appendix I: Environmental Assessment
1.1.3 Grouping MP<&M Pollutants Based on Risk to Aquatic Receptors
The impact assessment for aquatic receptors looks at the six individual fate and effects parameters for each MP&M POC,
including acute and chronic aquatic toxicities, bioconcentration factors, Henry's Law constants, adsorption coefficients, and
biodegradation half-lives. EPA grouped POCs with similar attributes, and assigned qualitative descriptors of potential
environmental behavior and impact to each group. This grouping was used to describe the range of MP&M pollutant
characteristics in Chapter 12. The grouping described below focuses specifically on aquatic environments and their biological
receptors; it does not cover the human health toxicity data discussed in the previous section.
Table 1.4 provides a summary of the categorization scheme for the six fate and effects parameters.
Table 1.4: Summary of Categorization Scheme For Six Fate and Effects Parameters
Parameter
Acute Toxicity (AT)
Chronic Toxicity (CT)
Bioconcentration Factor (BCF)
Henry's Law Constant (H)
Adsorption Coefficient (KoC)
Biodegradation Half-Life (t1/2)
High Hazard
AT<100ng/l
CT < 10ng/l
BCF > 500
H>103
Koc> 10,000
t1/2<7d
Moderate Hazard
100 < AT< l,000ng/l
10 < CT < 100ng/l
50 < BCF < 500
10 5 < H < 10 3
1,000s Koc< 10,000
7 d < t1/2 < 28 d
Low Hazard
AT>l,OOOng/l
CT>100ng/l
5 < BCF < 50
3.0x10' < H < 10s
10 < Koc< 1,000
28d180d
Source: U.S. EPA analysis.
a. Acute and chronic aquatic toxicity
EPA used the available AT data to group chemicals according to their relative short-term effects on aquatic organisms, using
the following categories:
AT< 100ng/l
lOOjig/l < AT < l,000ng/l
AT> 1,000 fig/1
High acute toxicity
Moderate acute toxicity
Low acute toxicity
These categories reflect the fact that acute toxicity decreases when higher concentrations of a pollutant are required to induce
short-term mortality in the test organisms. EPA's Office of Pollution Prevention and Toxics (OPPT) uses this categorization
as guidance to assess data submitted in Premanufacture Notices (PMN) (EPA, 1996).
EPA used the available CT data to group chemicals according to their relative long-term effects on aquatic organisms, based
on the following categories:
High chronic toxicity
Moderate chronic toxicity
Low chronic toxicity
CT<
lOjig/1 < CT < lOOjig/l
CT> 100 fig/1
These categories assume that CT occurs at a concentration averaging one tenth of that responsible for acute toxicity. They
also reflect the fact that chronic toxicity decreases when higher concentrations of a pollutant are required to induce longer-
term lethal or sub-lethal responses in the test organisms.
1-21
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
b. Bioconcentration factor (BCF)
EPA used the available BCF data to group chemicals according to their potential to bioconcentrate in aquatic organisms,
based on the following categories:
> BCF > 500 High potential to bioconcentrate
»• 50 BCF<5 No significant potential to bioconcentrate
These categories reflect the fact that decreased BCF reduces the intrinsic hazard of a chemical to aquatic receptors, because
the chemical is less likely to accumulate in biological tissues.
c. Volatilization potential
EPA used available H data to group organic chemicals according to their potential to volatilize from water into air, based on
the following categories:
•• H > 10"3 High potential to volatilize
»• 10"5< H < 10"3 Moderate potential to volatilize
•• 3.0*10"7< H < 10"5 Low potential to volatilize
•• H<3.0*10"7 No potential to volatilize
Increased volatility decreases a chemical's hazard to aquatic receptors because the chemical is more likely to quickly move
from the receiving water into the atmosphere. (The opposite is true for human health; hazard to human health increases with
increased volatility because a volatile chemical is more available for intake by inhalation.)
d. Adsorption potential
EPA used the available Koc to group the organic POCs according to their potential to adsorb to sediments, based on the
following categories:
* Koc>10,000 High potential for adsorption
•• 1,000 < Koc < 10,000 Moderate potential for adsorption
»• 10 < Koc< 1,000 Low potential for adsorption
*• Koc < 10 No significant adsorption
A lower adsorption potential indicates a lower potential for a chemical to be a hazard to aquatic receptors. The lower the
adsorption potential the less likely a chemical is to accumulate in sediments or to affect benthic invertebrates and to be taken
up into local food chains.
e. Biodegradation potential
EPA used biodegradation half-lives to group organic POCs according to their potential to biodegrade, based on the following
categories:
> *i/2 < 7 d Rapid rate of biodegradation
* 7 d < t1/2 < 28 d Moderate rate of biodegradation
> 28 d 180 d Resistant to biodegradation
A faster rate of biodegradation by microbial metabolism decreases an organic chemical's hazard to aquatic receptors. The
more rapid the rate of biodegradation, the more quickly a chemical will be removed from the aquatic environment. Most
metals occur as inorganic compounds (notable exceptions include organic forms of certain metals, such as mercury, lead, or
selenium), and are not removed by biodegradation. EPA assumes that all metals are resistant to biodegradation for the
purposes of this assessment.
1-22
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
1.1.4 Assumptions and Limitations
The following are the major assumptions and limitations associated with the data compilation and categorization used in the
MP&M analysis:
*• Some data are estimated, and subject to uncertainty;
> Data are unavailable for some chemicals and parameters;
> The POCs considered in this study do not include all the constituents that may be present in MP&M pollutants;
> Data derived from laboratory tests may not accurately reflect conditions in the field; and
> Available aquatic toxicity and bio concentration test data may not represent the most sensitive species.
1.2 METHODOLOGY
1.2.1 Sample Set bata Analysis and National Extrapolation
This analysis uses discharge information from 862 sample MP&M facilities (excluding two sample facilities in Puerto Rico)
that discharge directly or indirectly to 607 receiving waterways (521 rivers/streams, 62 bays/estuaries, and 24 lakes). The
in-stream water quality analysis excluded eight of the 62 marine reaches due to data limitations. EPA performed
environmental assessment on a basis of the sample facility data. The Agency then extrapolated findings from the sample
facility analyses to the national level using two alternative extrapolation methods: (1) traditional extrapolation and (2) post-
stratification extrapolation. EPA also used the differential extrapolation technique in addition to both traditional and post-
stratification approaches when a sample reach was estimated to receive discharges from multiple facilities. Appendix G
provides detailed information on the extrapolation approaches used in this analysis. Based on the extrapolation methods used
in this analysis, EPA estimates that approximately 43,901 MP&M facilities discharge to between 29,500 and 40,000 water
bodies nationwide.3
EPA evaluated the national-level environmental impacts of reducing pollutant discharges from MP&M facilities to the
nation's water bodies for the final rule. EPA considered only pollutant loadings from MP&M facilities to particular water
bodies in the national analysis. With one exception, EPA did not take background loadings from other sources into account.
For the analysis of sewage sludge quantity, EPA was able to use information from the Phase 2 Section 308 survey of POTWs
to estimate total metal loadings from all sources to a POTW of a given size (i.e., small, medium, and large). The Agency
based this estimate on survey estimates of the average number of small, medium, and large MP&M facilities discharging to a
POTW in each size category and the percent contribution of total metal loadings discharged from MP&M facilities.
1.2.2 Water Quality Modeling
EPA used four different equations to model the impacts of MP&M discharges on receiving waterways. EPA used a simple
stream dilution model for MP&M facilities that discharge into streams or rivers. This model does not account for fate
processes other than complete immediate mixing.4 EPA derived the facility-specific data (i.e., pollutant loading and facility
flow) used in this equation from sources described in Sections 3.1 and 5.2 of this report.
The Agency used one of three receiving stream flow conditions (the lowest one-day average flow with a recurrence interval of
10 years (1Q10), the lowest consecutive seven-day average flow with a recurrence interval of 10 years (7Q10), and the
harmonic mean flow), depending on the criterion or toxic effect level being considered.
3 These estimates include facilities that were assessed to be baseline closures by the MP&M economic analysis.
4 EPA used an exponential decay model to estimate pollutant concentrations for the analysis of cancer risk from drinking water
consumption for streams. This model is discussed in detail in Appendix G.
1-23
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
The 1Q10 and 7Q10 flows are used in comparisons of in-stream concentrations with acute and chronic aquatic life criteria or
toxic effect levels, respectively, as recommended in the Technical Support Document for Water Quality-based Toxics Control
(U.S. EPA, 1991).
The harmonic mean flow, defined as the inverse mean of reciprocal daily arithmetic mean flow values, is used in comparisons
of in-stream concentrations with human health criteria or toxic effect levels based on lifetime exposure. EPA recommends the
long-term harmonic mean flow as the design flow for assessing potential long-term human health impacts. Harmonic mean
flow is preferable to arithmetic mean flow because in-stream pollutant concentration is a function of, and inversely
proportional to, the stream flow downstream of the discharge.
The event frequency represents the number of times an exposure event occurs during a specified time period. EPA set the
event frequency equal to the facility operating days to assess impacts on aquatic life. The calculated in-stream concentration
is thus the average concentration on days the facility is discharging wastewater. EPA set the event frequency at 365 days to
assess long-term human health impacts. The calculated in-stream concentration is thus the average concentration on all days
of the year. This frequency leads to a lower calculated concentration because of the additional dilution from days when the
facility is not operating, but it is consistent with the conservative assumption that the target population is present to consume
drinking water every day and contaminated fish throughout an entire lifetime. The following equation calculates in-stream
concentration for streams and rivers:
c,. =
(OD • FF) + (EF • SF)
(1.2)
where:
Cis
L
OD =
FF =
EF =
SF =
in-stream pollutant concentration
facility pollutant loading (fig/yr); for indirect dischargers, L = L in-jjj.ectfacility
treatment removal efficiency (unities s);
facility or POTW operating days (days/yr);
MP&M facility flow (L/day); for indirect dischargers, FF = POTW flow (L/day);
event frequency (days/yr); and
receiving stream flow (L/day).
(1-TMT), where TMT is POTW
EPA used the following simple steady-state model for facilities that discharge into lakes other than the Great lakes. This
model takes into account pollutant degradation and the hydraulic residence time of the lake:
where:
T
k
"lake
C
(1 + Tw • K)
(1.3)
steady-state lake concentration of pollutant
steady-state inflow concentration of pollutant
mean hydraulic residence time (yr),
first-order pollutant decay rate (yr-1), and
where:
V
Q
r =
w Q
(1.4)
lake volume (m3), and
mean total inflow rate (m3/yr).
1-24
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
EPA used alternative means to predict pollutant concentrations suitable for comparison with ambient criteria or toxic effect
levels for facilities discharging to hydrologically complex waters, such as bays and estuaries. Where possible, EPA employed
site-specific critical dilution factors (CDFs} to predict the concentration at the edge of a mixing zone. Where CDFs were
not available, EPA used available estuarine dissolved concentration potentials (DCPs).
EPA obtained site-specific CDFs from a survey of states and regions conducted by EPA's Office of Pollution Prevention and
Toxics (Mixing Zone Dilution Factors for New Chemical Exposure Assessments, U.S. EPA, 1992a). The dilution model for
estimating estuary concentrations by using a CDF is presented below:
c.. =
EF • FF • CDF
(1.5)
where:
C es = estuary pollutant concentration
L = facility pollutant loading (fig/yr); for indirect dischargers, L = L j^ea facility * (1-TMT), where TMT is POTW
treatment removal efficiency (unitless);
EF = event frequency (days/yr);
FF = facility flow (L/day); for indirect dischargers, FF = POTW flow (L/day); and
CDF = critical dilution factor (unitless).
EPA used acute CDFs to evaluate acute aquatic life effects and chronic CDFs to evaluate chronic aquatic life or adverse
human health effects. EPA assumed that the drinking water intake and fishing location are at the edge of the chronic mixing
zone. EPA set the event frequency equal to the facility operating days for comparison with aquatic life criteria or toxic effect
levels, and equal to 365 days for comparison with human health criteria or toxic effect levels.
The National Oceanic and Atmospheric Administration (NO A A) has developed DCPs to predict pollutant
concentrations in various salinity zones for each estuary in NOAA's National Estuarine Inventory (NED. A DCP
represents the concentration of a nonreactive dissolved substance under well-mixed, steady-state conditions given an annual
load of 10,000 tons. DCPs account for the effects of flushing by considering the freshwater inflow rate, and dilution by
considering the total estuarine volume. DCPs reflect the predicted estuary-wide response, and may therefore not be indicative
of concentrations at the edge of much smaller mixing zones. The dilution model used for estimating pollutant concentrations
using DCPs is presented below:
C = L ' DCP (L6)
EL • CF
where:
C es = estuary pollutant concentration (fig/L);
L = facility pollutant loading (kg/yr); for indirect dischargers,
L = L indirect facility *(1-TMT), where TMT is POTW treatment removal efficiency (unitless);
DCP = dissolved concentration potential (fig/L);
BL = benchmark load (10,000 tons/yr); and
CF = conversion factor (907.2 kg/ton).
EPA determined potential water quality impacts by comparing projected waterway pollutant concentrations to EPA water
quality criteria or toxic effect levels for the protection of aquatic life and human health. EPA determined water quality
exceedances by dividing the projected waterway pollutant concentration by the EPA water quality criteria or toxic effect
levels for the protection of aquatic life and human health. A value greater than one indicates an exceedance.
1.2.3 Impact of Indirect Discharging Facilities on POTW Operations
a. Analysis of biological inhibition
Inhibition of POTW operations occurs when high levels of toxics, such as metals or cyanide, kill the bacteria required for the
wastewater treatment process. EPA analyzed inhibition of POTW operations by comparing calculated POTW influent
1-25
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
concentrations with available inhibition levels. Exceedances are indicated by a value greater than one. POTW influent
concentrations are estimated as:
c . = L
.
pl OD • PF
where:
Cpi = POTW influent concentration (fig/L),
L = facility pollutant loading (fig/yr),
OD = facility operating days (days/yr), and
PF = POTW flow (L/day).
b. Analysis of sludge disposal practices
EPA also analyzed the effects of MP&M discharges on POTW operations by comparing the estimated concentrations of
metals in sewage sludge with the published metals concentration limits for preferable sewage sludge disposal or use practices.
In particular, EPA examined:
*• whether MP&M baseline discharges would prevent POTWs from being able to meet the metals concentration limits
required for more favorable and lower-cost sewage sludge use/disposal practices (i.e., beneficial land application and
surface disposal); and
*• whether limitations on the selection of management practices would be removed under the final rule.
EPA estimated the sewage sludge concentrations of eight metals for sample facilities under baseline and post-regulatory
option discharge levels. EPA compared these concentrations with the relevant metals concentration limits for three sewage
sludge management options: Land Application-High (Concentration Limits), Land Application-Low (Ceiling Limits), and
Surface Disposal. Metal concentrations in sewage sludge are estimated as:
c = L • TMT • PART • SGF (1.8)
sp OD • PF
where:
C sp = sewage sludge pollutant concentration (mg/kg),
L = facility pollutant loading (fig/yr),
TMT = POTW treatment removal efficiency (unitless),
PART = pollutant-specific sludge partition factor (unitless),
SGF = sludge generation factor (mg/kg per Hg/L),
OD = POTW operating days (days/yr), and
PF = POTW flow (L/day).
EPA derived the facility- specific data to evaluate POTW operations from the sources described in Sections 3.1 and 5.2. EPA
examined multiple MP&M facilities discharging to the same POTW by summing the individual loadings before calculating
the POTW influent and sewage sludge concentrations.
The partition factor is a chemical-specific value representing the fraction of the load expected to partition to sewage sludge
during wastewater treatment. For this analysis, EPA used a sludge generation factor of 5.96 mg/kg per Hg/L. This factor
indicated that the resulting concentration in sewage sludge is 5.96 mg/kg dry weight for every 1 Hg/L of pollutant removed
from wastewater and partitioned to sewage sludge.
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
1.2.4 Assumptions and Limitations
The following discussion focuses on major assumptions and limitations associated with these in-stream water quality analyses.
a. Other source contributions
EPA did not account for "other source contributions" of MP&M pollutants to estimate in-stream concentrations of these
pollutants. Accounting for the discharges from other sources is important because assessing benefits from reduced
exceedance of AWQC limits depends on comparing concentrations of pollutants from all sources with applicable thresholds.
Analyses must also identify situations in which threshold criteria are exceeded in the baseline case but met under a regulatory
option. Failing to account for other source contributions has an uncertain effect on estimated benefits. For example, if
non-sample MP&M facilities are major contributors to aggregate pollutant discharges to a receiving stream, then the analysis
will likely understate the extent of aquatic habitat improvements that may be accomplished by reduced MP&M pollutant
discharges. Conversely, if the total MP&M contribution to the aggregate pollutant discharges to a receiving stream is not
significant, then reducing MP&M discharges may reduce but not eliminate AWQC exceedances, and the benefits of the
MP&M regulation can be overstated. The net effect of the following are unknown:
*• excluding other sources understates the number and extent of baseline exceedances;
*• excluding non-sample MP&M facilities understates the reduction in MP&M pollutant discharges due to the rule; and
*• the number of cases in which estimated baseline exceedances are eliminated may be either over- or understated,
depending on the contribution of pollutants from non-MP&M sources.
b. Water body modeling
EPA made four major assumptions concerning all water body modeling, and two major assumptions specific to stream
modeling. These assumptions are summarized below:
•• Complete mixing of POTW discharge flow occurs immediately. This mixing results in the calculation of an
"average" concentration, even though the actual concentration may vary across the width and depth of the water
body.
•• Pollutant loads to the receiving water body are continuous and representative of long-term facility operations. This
assumption may overestimate long-term risks to human health and aquatic life, but may underestimate potential
short-term effects.
•• In the absence of data from EPA's Permit Compliance System (PCS) on specific individual POTW flow,
POTW daily flow rates were set equal to the simple arithmetic mean flow among minor POTWs reporting flows in
PCS. The arithmetic mean for minor POTWs was used because all POTWs receiving discharges from the sample
MP&M facilities for which flow data are not available in the PCS database are classified as minor dischargers in the
PCS database.
»• EPA used 1Q10 and 7Q10 receiving stream flow rates to estimate aquatic life impacts, and harmonic mean flow rates
to estimate human health impacts, when modeling stream reaches. EPA estimated 1Q10 low flows by using the
results of a regression analysis conducted for OPPT of 1Q10 and 7Q10 flows from representative U.S. rivers and
streams (Versar, 1992). EPA estimated harmonic mean flows from the mean and 7Q10 flows as recommended in the
Technical Support Document for Water Quality-based Toxics Control (U.S. EPA, 1991). These flows may not be
the same as those used by specific states to assess impacts.
*• Where data on stream flow parameters were not available, EPA set mean and 7Q10 flow values equal to the
corresponding mean values associated with reaches located upstream and downstream of the sample reach.
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
c. Exposure analyses
MP&M exposure assessment in freshwater locations uses two sets of human health-based AWQC:
•• AWQC for the protection of human health from the consumption of organisms and drinking water, and
•• AWQC for the protection of human health from consumption of organisms only.
MP&M exposure assessments in marine locations use AWQC for the protection of human health from the consumption of
organisms only, because saltwater is not used for drinking water supply.
d. Extrapolation from sample set to national level
Although the sample set should represent a national group of facilities discharging to waterways and POTWs, effluent from an
individual sample facility may have a different potential environmental impact than effluent from the facilities it is assumed to
represent. For example, a facility that discharges to a stream with a very low flow may be similar to the facilities it represents
in all aspects except available dilution in the receiving stream. The sample frame used in the MP&M analysis was not
designed to take receiving water body characteristics into account. Using sample weights to extrapolate environmental
impacts may either under- or overstate estimated impacts.
1.3 &ATA SOURCES
The following three sections describe the various data sources used to evaluate water quality and POTW impacts.
1.3.1 Facility-Specific bata
Section 1.2.1 provides detailed information on sample size and distribution, and on receiving waterways. The names,
locations, and the flow data for the POTWs to which the MP&M facilities discharge were obtained from the MP&M facility
surveys and EPA's PCS database. EPA took alternative measures to obtain a complete set of receiving POTWs if these
sources did not yield information for a given facility. EPA used latitude/longitude coordinates (if available) to locate those
POTW s that have not been assigned a reach number in PCS. EPA identified the nearest POTW in the case of facilities for
which the POTW receiving the plant discharge could not be positively identified. EPA based its identification of the closest
linear distance on the latitude/longitude coordinates of the MP&M facility or the city in which it was located. EPA then
identified the corresponding reach in PCS, and obtained POTW flow from the Needs Survey or PCS.
EPA identified reaches to which direct MP&M facilities discharge by identifying the receiving reach in PCS or by identifying
the nearest reach. EPA based its identification of the closest linear distance on the MP&M facility's latitude/longitude
coordinates.
1.3.2 Water body-Specific bata
a. Streams and rivers
EPA used 1Q10, 7Q10, and mean flow data for the 521 streams and rivers. EPA obtained 7Q10 and mean flow data from the
W.E. Gates study data or from measured stream flow data, both of which are contained in EPA's GAGE file. The W.E. Gates
study contains calculated average and low flow statistics based on the best available flow data and on drainage areas for
reaches throughout the United States. The GAGE file also includes average and low flow statistics based on measured data
from United States Geological Survey (USGS) gaging stations. In the absence of data on stream flow parameters, EPA
set 7Q10 and mean flow values equal to the corresponding median values associated with the sample reaches. EPA used the
results of a regression analysis conducted for OPPT of 1Q10 and 7Q10 flows from representative U.S. rivers and streams
(Versar, 1992) to estimate 1Q10 flows. EPA estimated harmonic mean flows from the mean and 7Q10 flows as
recommended in the Technical Support Document for Water Quality-based Toxics Control (U.S. EPA, 1991).
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
b. Lakes
EPA used data on hydraulic residence time (i.e., the amount of time water remains in a lake) to analyze small lakes, and CDFs
(which describe dilution in a portion of a lake) to analyze the Great lakes.5
The sample MP&M facilities discharged directly to one lake reach and indirectly to 23 lake reaches: 15 to small lakes, 3 to
sections of Lake Erie, 5 to sections of Lake Michigan, and 1 to a section of Lake Ontario. EPA calculated the average
hydraulic residence time for small lakes based on lake surface and drainage areas. EPA obtained data on lake surface and
drainage area from the U.S. Army Corps of Engineers, Major Dams: Map Layer Description File (USCE, 1999). CDFs were
readily available for Lake Michigan, but not for the three sample reaches on Lake Erie. EPA arithmetically averaged the
seven chronic CDFs available for reaches discharging to Lake Erie (1, 1, 4, 4, 10, 10, 4) (U.S. EPA, 1992a, p. A-4) for the
three reaches being modeled.
c. Estuaries and bays
Sixty-two bays and estuaries receive discharges from sample MP&M facilities. Data necessary to support water quality
modeling were not available for eight of the 62 bays/estuaries. A dilution model predicted pollutant concentrations in the
chronic and acute mixing zones, based on site-specific CDFs (U.S. EPA, 1992a and Versar, 1994), to estimate the pollutant
concentrations in 28 of these complex water bodies.
Both acute and chronic CDFs were available for 20 of the 62 bays/estuaries. EPA estimated acute and chronic CDFs for New
York bays/estuaries by arithmetically averaging available values for nearby New Jersey sites discharging to the Arthur Kill
(acute: 1.5,4.0, 5.0; chronic: 5; 20; 10) and Upper New York Bay (acute: 8.0; chronic: 22.9). Acute and chronic CDFs for
Buzzards Bay in Massachusetts were estimated by arithmetically averaging values for nearby Massachusetts and Rhode Island
sites discharging to the Atlantic Ocean.
EPA could not identify or approximate chronic CDFs for the remaining 13 sample reaches. Acute CDFs are available for 46
of the 62 bays/estuaries. EPA extrapolated acute CDFs for two bays/estuaries in Florida by using CDFs for another Florida
bay. Likewise, EPA extrapolated acute CDFs for four bays/estuaries in California by using CDFs for another California bay.
EPA obtained DCP values for five of the 13 sample bays/estuaries for which CDFs were not available from the Development
of Mixing Zone Dilution Factors report (Versar, 1994). EPA then used a dilution model that predicts pollutant concentrations
in the estuarine environment using a site-specific DCP value.
1.3.3 Information Used to Evaluate POTW Operations
Since many MP&M facilities considered in the alternative options are indirect dischargers, the Agency consulted with
POTWs as they would have had to implement the rule. EPA consulted with POTWs individually and through the Association
of Municipal Sewerage Agencies (AMSA). In addition, EPA consulted with pretreatment coordinators and State and local
regulators.
EPA used removal efficiency rates, inhibition values, and sewage sludge regulatory levels to evaluate POTW operations.
EPA obtained POTW removal efficiency rates from several sources. The Agency developed rates from POTW removal data
and pilot-plant studies or used removals for a similar pollutant when data were not available. Use of the selected removal
rates assumes that the evaluated POTWs are well-operated and have at least secondary treatment in place (U.S. EPA, 2000).
EPA obtained inhibition values from the Guidance Manual for Preventing Interference at POTWs (U.S. EPA, 1987a) and
from CERCLA Site Discharges to POTWs: Guidance Manual (U.S. EPA, 1990). EPA used the most conservative values for
activated sludge (i.e., the lowest influent concentrations that would cause inhibition). The Agency used a value based on
compound type (e.g., aromatics) for pollutants with no specific inhibition value.
EPA obtained sewage sludge regulatory levels from the Federal Register 40 CFR Part 257 et al., Standards for the Use or
Disposal of Sewage Sludge; Final Rules (February 19, 1993) and from the Federal Register 59(38):9095-9099 (February 25,
1994) and 60(206):54,764-54,770 (October 25, 1995) for eight metals regulated in sewage sludge. EPA used pollutant limits
established for the final use or disposal of sewage sludge when the sewage sludge is applied to agricultural and
non-agricultural land or is applied to a dedicated surface disposal site.
Small lakes are defined as any non-Great lakes, including reservoirs.
1-29
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MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Finally, EPA obtained sludge partition factors from the Report to Congress on the Discharge of Hazardous Wastes to
Publicly-Owned Treatment Works (Domestic Sewage Study) (U.S. EPA, 1986).
Table 1.5 lists POTW treatment removal efficiency rates, inhibition values, sewage sludge partition factors, and sewage sludge
regulatory levels used in the evaluation of POTW operations.
CAS
Number
51285
57125
59507
62533
62759
65850
67641
67663
68122
75003
75092
75150
75354
75694
78591
78831
78933
79016
80626
83329
84742
85018
85687
86306
86737
88755
91203
91576
92524
95476
95487
98555
98862
99876
100027
100414
100425
100516
100754
101848
105679
Table 1.5:
•
Pollutant Name
! Dinitrophenol, 2,4-
! Cyanide
! Parachlorometacresol
! Aniline
!Nitrosodimethylamine, N-
! Benzoic acid
! Acetone
! Trichloromethane
! Dimethylformamide, N,N-
! Chloroethane
! Dichloromethane
! Carbon disulfide
! Dichloroethene, 1,1-
! Trichlorofluoromethane
! Isophorone
! Isobutyl alcohol
! Methyl ethyl ketone
! Trichloroethene
! Methyl methacrylate
! Acenaphthene
! Di-n-butyl phthalate
! Phenanthrene
! Butyl benzyl phthalate
! Nitrosodiphenylamine, N-
! Fluorene
! Nitrophenol, 2-
! Naphthalene
! Methylnaphthalene, 2-
! Biphenyl
! Xylene, o-
! Cresol, o-
! Terpineol, alpha-
! Acetophenone
! Cymene, p-
! Nitrophenol, 4-
! Ethylbenzene
! Styrene
! Benzyl alcohol
! Nitrosopiperidine, N-
! Diphenyl Ether
! Dimethylphenol, 2,4-
POTW-Related Data for 132 MP&M Pollutants
i
i
i
L.
POTW
Inhibition Level
Value
1000J
5000!
5000!
1000!
10000!
120000!
500000!
iooo!
150000J
50000J
150000!
700!
120000J
1000000J
120000J
20000!
120000J
500000J
10000J
500000!
10000J
500000!
50000!
500000!
5000!
5000!
5000!
90000!
1000000!
120000!
5000!
50000!
200000!
500000!
1000000!
iooo!
iooo!
40000!
i.
i
POTW Sludge
Partition Factor i
i
j
0.10000000149J
i!
0.07900000364!
0.1!
o.i!
0.1!
0.1!
0.015!
o.i!
0.0075!
0.1395J
0.0075J
0.079J
o.i!
o.i!
0.0578!
0.366J
0.216J
0.366!
0.452J
0.366!
0.275!
0.079!
0.366!
0.149!
0.079!
oil
aif
0.0075!
o.i!
0.06!
0.149!
o.i!
0.079!
i..
i
Sludge Criteria j
Value
(mg/kg)
i
L.
POTW Removal
Efficiency Rate
(Percentage)
77.51
70.44
63
93.41
77.51
80.5
83.75
87
77.51
54.28
84
77.51
77.32
77.51
28
96.6
77.51
99.96
98.29
84.66
94.89
81.65
90.11
69.85
26.83
94.69
28
96.28
77.32
52.5
94.4
95.34
99.79
77.51
93.79
93.65
78
77.32
77.32
77.51
1-30
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MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.5: POTW-Related Data for 132 MP&M Pollutants
., , Pollutant Name
Number i
106445 jCresol, p-
107028 JAcrolein
108101 | Methyl isobutyl ketone
108372 jBromo-3-chlorobenzene, 1-
108383 jXylene, m-
108883 | Toluene
108907 jChlorobenzene
108952 | Phenol
110861 jPyridine
1 1 2403 | Dodecane, n- (a)
112958 JEicosane, n- (a)
117817 | Bis(2-ethylhexyl) phthalate
1 1 7840 | Di-n-octyl phthalate
120127 | Anthracene
122394 JDiphenylamine
123911 JDioxane, 1,4-
124185 JDecane,n-
127184 JTetrachloroethene
129000 jPyrene
131113 | Dimethyl phthalate
132650 ! Dibenzothiophene
1 37304 | Ziram \ Cymate
1 4262 1 ! Hexanoic acid
206440 | Fluoranthene
544763 ! Hexadecane, n- (a)
591786 JHexanone, 2-
593453 jOctadecane, n- (a)
606202 | Dinitrotoluene, 2,6-
629594 | Tetradecane, n- (a)
629970 JDocosane, n-
630013 | Hexacosane, n- (b)
630024 | Octacosane, n- (b)
638686 ! Triacontane, n- (b)
6463 1 1 ! Tetracosane, n- (b)
694804 | Bromo-2-chlorobenzene, 1 -
832699 | Methylphenanthrene, 1-
1576676 ! Dimethylphenanthrene, 3,6-
1730376 | Methylfluorene, 1-
2027 1 70 | Isopropylnaphthalene, 2-
7429905 | Aluminum
7439896 jlron
7439921 JLead
7439954 j Magnesium
7439965 j Manganese
7439976 j Mercury
7439987 j Molybdenum
7440020 1 Nickel
POTW
Inhibition Level
Value
(US/1)
90000
50
120000
100
5000
200000
140000
90000
1000
10000
10000
500000
1000
120000
20000
500000
5000
50
10000
5000
120000
5000
100
5000
500000
500000
500000
5000
100
1000000
10000
100
POTW Sludge
Partition Factor
0.079
0.10000000149
0.1
0.149
0.278
0.154
0.146
0.1
0.728
0.075
0.55
0.08
0.1
0.034
0.366
0.1
0.366
0.366
0.1
0.366
0.366
0.366
0.1
1
1
1
1
1
1
1
5000! 1
Sludge Criteria
Value
(mg/kg)
300
17
420
POTW Removal
Efficiency Rate
(Percentage)
71.67
77.51
87.87
77.32
95.07
96.18
96.37
95.25
95.4
59.78
68.43
77.51
77.32
45.8
9
84.61
83.9
77.51
84.68
84
42.46
77.32
77.51
88
77.32
84.55
84.55
84.55
77.32
91.36
81.99
77.45
14.14
35.51
71.66
18.93
51.44
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MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.5: POTW-Related Data for 132 MP&M Pollutants
., , Pollutant Name
Number i
7440224 | Silver
7440235 | Sodium
7440280 | Thallium
7440315 JTin
7440326 | Titanium
7440360 JAntimony
7440382 JArsenic
7440393 | Barium
7440417 | Beryllium
7440428 | Boron
7440439 | Cadmium
7440473 | Chromium
7440484 | Cobalt
7440508 | Copper
7440575 JGold
7440622 | Vanadium
7440655 JYttrium
7440666 jZinc
7440702 | Calcium
76644 1 7 | Ammonia as N
7782492 j Selenium
14265442 j Phosphate
14808798 jSulfate
16887006 | Chloride
16984488 j Fluoride
18496258 jSulfide
18540299 | Chromium hexavalent
20324338 j Tripropyleneglycolmethylether
136777612 jXylene, o- & p- (c)
179601231 JXylene, m- & p- (c)
COOS JBOD 5-day (carbonaceous)
C004 | Chemical Oxygen Demand (COD)
C009 | Total Suspended Solids (TSS)
CO 1 0 | Total Dissolved Solids (TDS)
CO 1 2 | Total Organic Carbon (TOC)
C020 ! Total Recoverable Phenolics
C02 1 | Total Kj eldahl Nitrogen
C025 | Amenable Cyanide
C036 | Oil and Grease (as Hem)
! Total Petroleum Hydrocarbons (as
C037 jSgt-hem)
C042 ! Weak-acid Dissociable Cyanide
! Phosphorus (as PO4)
! Oil and Grease
POTW
Inhibition Level
Value
(^g/1)
30
3500000
9000
40
1000
500
1000
1000
20000
5000
2500000
480000
25000
1000
120000
5000
POTW Sludge
Partition Factor
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0.149
Sludge Criteria
Value
(mg/kg)
41
39
1500
2800
100
POTW Removal
Efficiency Rate
(Percentage)
88.28
2.69
71.66
42
91.82
66.78
65.77
15.98
71.66
30.42
90.05
80.33
6.11
84.2
32.52
9.51
32.52
79.14
8.54
38.94
34.33
57.41
84.61
57.41
61.35
57.41
57.41
52.4
36832
89.12
81.3
70.28
57.41
57.41
57.41
86.08
Sources: U.S. EPA (1985), U.S. EPA (1987), U.S. EPA (1990).
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
In the absence of data on POTW flow rates, EPA set the POTW flow rate equal to the arithmetic mean flow among minor
POTWs in the PCS database, using the following steps:
1. Calculate arithmetic mean flow among minor POTWs in the PCS database. The estimated arithmetic mean flow for
minor POTWs in the PCS database is one million gallons per day (MGD).
2. Set POTW flow rate equal to the relevant arithmetic mean flow. For all POTWS with missing flow data, EPA set
their flow rates equal to the arithmetic mean flow rate for minor POTWs in the PCS database, one MGD.
1.4 RESULTS
EPA assessed the environmental impacts of MP&M dischargers on water bodies and POTWs under the baseline conditions
and those corresponding to four regulatory options: the Final Option, Proposed/NODA Option, and two 433 Upgrade Options
on the basis of sample facility data. The Agency extrapolated the findings from the sample facility analyses to the national
level using facility sample weights, as described in Appendix G.
MP&M facilities nationwide currently discharge an estimated 53 million pounds of pollutants per year to publicly-owned
treatment works (POTWs) and approximately 6.2 million pounds of pollutants directly to surface waters. MP&M facility
effluents contain 42 priority or toxic pollutants, 81 nonconventional pollutants, and three conventional pollutants (BOD, TSS,
and O&G).
EPA estimates that the final rule will lead to a modest reduction in pollutant discharges to the waters of the U.S. As shown by
Table 1.6, the regulation will reduce discharges of pollutants with acute and chronic effects on aquatic life by 8,959 and
12,270 pounds per year, respectively. The final rule does not regulate indirect dischargers and thus will not reduce pollutant
loads received by POTWs.
EPA estimates that the Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade
Option would remove 3,299, 91, and 110 thousand pounds per year of eight sewage sludge contaminants, respectively. In
addition, the Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would
result in 30,226, 133, and 551 thousand pounds per year reduction in 86 pollutants causing inhibition of POTW operations.
The Proposed/NODA Option would reduce discharges of pollutants with acute and chronic effects on aquatic life by 97 and
117 million pounds per year, respectively. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade
Option would reduce discharges of 132 and 353 thousand pounds of pollutants with acute effects on aquatic life, and 136 and
576 thousand pounds of pollutants with chronic effects on aquatic life, respectively.
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MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.6: MP&M Facility Discharges (National Basis)0
Category
# of Pollutants
Base line (1, 000 Ibs/yr)
Post-Compliance (1,000 Ibs/yr)
# of Pollutants
Baseline (1,000 Ibs/yr)
Post-Compliance (1,000 Ibs/yr)
# of Pollutants
Base line (1,000 Ibs/yr)
Post-Compliance (1,000 Ibs/yr)
# of Pollutants
Baseline (1,000 Ibs/yr)
Post-Compliance (1,000 Ibs/yr)
POTW Impacts
Activated Sludge
Inhibition
N/A
N/A
N/A
85
39,594
9,369
86
1,085
952
86
1,085
534
Biosolids
Contaminants
Selected Opti
N/A
N/A
N/A
Proposed/NODA (
8
3,589
290
Directs + 41 3 to 433
8
253
161
Directs + All to 433
8
253
143
HAP
an
N/A
N/A
N/A
Dption
35
408
189
Upgrade
35
3
3
Upgrade
35
3
3
Receiving Stream Impacts: Aquatic
Life Toxicity
Acute
106
868
859
105
141,522
44,827
106
868
935
106
868
514
Chronic
113
1,154
1,142
112
187,742
70,428
113
1,154
1,018
113
1,154
578
Excludes loadings from facilities projected to close in the baseline. See Chapter 5.
Source: U.S. EPA analysis.
1.4.1 Human Health Impacts
Under this human health benefit category EPA assessed the reduced occurrence of pollutant concentrations that are estimated
to exceed human health-based AWQC. This analysis provides an alternative measure of the expected reduction in risk to
human health. Table 1.7 presents information on baseline and post-compliance exceedances of human health AWQC criteria
for all the regulatory options.
EPA estimates that in-stream concentrations of four pollutants (i.e., arsenic, iron, manganese, and n-nitrosodimethylamine)
will exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of
baseline MP&M pollutant discharges. EPA estimates that there are human health AWQC exceedances caused by
n-nitrosodimethylamine (NDMA). EPA did not consider NDMA pollutant reductions in its benefits analyses because of the
low number of detected values for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of
concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms
on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values.
The Proposed/NODA Option would eliminate instances of in-stream pollutant concentrations exceeding AWQC limits for
consumption of water and organisms and consumption of organisms only in 63 and 68 reaches, respectively, nationwide. The
Directs + 413 to 433 Upgrade Option would not eliminate any instances of in-stream pollutant concentrations exceeding
AWQC limits for consumption of water and organisms and consumption of organisms only. The Directs + All to 433
Upgrade Option would not eliminate any occurrences of pollutant concentrations in excess of AWQC values for consumption
of water and organisms, but would eliminate instances of pollutant concentrations in excess of AWQC values for consumption
of organisms only in 21 reaches nationwide. As noted above the Agency did not estimate reductions in NDMA loadings
under the post-compliance scenario due to data limitations.
1-34
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.7: Summary of Estimated AWQC Exceedances for Protection of Human Health (National Basis)
Category
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Human Health Water and Organisms
Streams (No.)
78
78
S
112
112
5,852
5,789
78
78
78
78
j
Pollutants (No.) \ „ T°]al
Exceedances
Selected Option: Traditional Extr
4J 121
:
4! 121
:
sleeted Option: Post-Stratification E
4J 154
:
4! 154
:
Proposed/NODA Option
26 1 7,085
: '
21 1 6,667
Directs + 413 to 433 Upgra
4J 121
:
4! 121
:
Directs + All to 433 Upgra
4J 121
:
2J 78
Human Health Organisms Only
Streams (No.)
apolation
21
21
xtrapolation
21
21
197
128
de
21
21
ie
21
0
Pollutants (No.)
1
1
1
1
12
9
1
1
1
0
Total
Exceedances
21
21
21
21
335
212
21
21
21
0
Source: U.S. EPA analys
1-35
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.8 summarizes pollutants estimated to exceed human health-based AWQC criteria for consumption of water and
organisms under the baseline and post-compliance conditions.
Table 1.8: Summary of Pollutants Estimated to Exceed Human Health-Based AWQC Criteria for Consumption of
Water and Organisms (National Basis)
Pollutant
Aniline
Antimony
Arsenic
Bis(2-ethylhexyl) phthalate
Cadmium
Chloroethane
Copper
Cresol, p-
Dibenzofuran
Dichloroethene, 1,1-
Dichloromethane
Dinitrophenol, 2,4-
Dinitrotoluene, 2,6-
Dioxane, 1,4-
Fluoranthene
Iron
Isophorone
Manganese
Mercury
Naphthalene
Nickel
Nitrophenol, 4-
Nitrosodimethylamine, N-
Nitrosodiphenylamine, N-
Pyrene
Pyridine
Thallium
Trichloroethene
Trichloromethane
Total Exceedances
Selected Option: j Selected Option: j
Traditional j Post-Stratification j
Extrapolation Extrapolation
Base" |
oj
o]
45|
oj
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
o!
21!
o!
o!
o!
o!
32|
o!
o!
o!
o!
o!
o!
1211
PC"
oj
°!
45!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
o!
21!
o!
o!
o!
o!
32!
o!
o!
o!
o!
o!
o!
1211
Base
oj
o]
45|
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
o!
21!
o!
o!
o!
o!
67!
o!
o!
o!
oj
oj
oj
154!
PC !
oj
oj
45!
oj
oj
oj
oj
oj
oj
oj
0!
0!
0!
0!
0!
21!
0!
21!
0!
0!
0!
0!
67!
0!
0!
0!
0!
0!
0!
154!
j
Proposed/NODA j
Option
:
Base
20!
o!
772 !
85!
o!
iv!
16!
9!
12!
97!
iv!
9!
9!
n!
9!
28!
9!
54!
o!
9!
16!
9!
5,7891
17!
9!
12!
16!
21!
12!
7,0851
PC
17 !
o!
557!
43!
o!
14!
o!
9!
9!
81 !
n!
9!
9!
n!
9!
o!
9!
o!
o!
9!
o!
9!
5,7891
17!
9!
9!
o!
17!
12!
6,6671
i
Directs + 413 to j
433 Upgrade
i
Base
oj
0!
45!
0!
0!
0!
0!
0!
0!
0!
0!
0!
0!
0!
0!
21!
0!
21!
0!
0!
0!
0!
32!
0!
0!
0!
0!
0!
0!
121!
PC
oj
o]
45|
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
o!
21!
o!
o!
o!
o!
32!
o!
o!
o!
oj
o]
o]
121!
Directs + All to
433 Upgrade
Base
o
o
45
0
0
0
0
0
o
o
o
o
o
o
o
21
0
21
0
o
o
o
32
o
o
o
o
o
o
121
PC
0
0
45
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
32
0
0
c
c
c
c
T,
a Base = Baseline discharge level
b PC = Post-Compliance discharge level
Source: U.S. EPA analysis.
1-36
-------
MP&M EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.9 summarizes pollutants estimated to exceed human health-based AWQC criteria for consumption of organisms only
under the baseline and post-compliance conditions.
Table 1.9: Summary of Pollutants Estimated to Exceed Human Health -Based AWQC Criteria for Consumption of
Organisms Only (National Basis)
Pollutant
Aniline
Antimony
Arsenic
Bis(2-ethylhexyl) phthalate
Cadmium
Chloroethane
Copper
Cresol, p-
Dibenzofuran
Dichloroethene, 1,1-
Dichloromethane
Dinitrophenol, 2,4-
Dinitrotoluene, 2,6-
Dioxane, 1,4-
Fluoranthene
Iron
Isophorone
Manganese
Mercury
Naphthalene
Nickel
Nitrophenol, 4-
Nitrosodimethylamine, N-
Nitrosodiphenylamine, N-
Pyrene
Pyridine
Thallium
Trichloroethene
Trichloromethane
Total Exceedances
Selected Option: j Selected Option: j
Traditional I Post-Stratification I
Extrapolation 1 Extrapolation j
Base" |
oj
o]
o]
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
oj
Oj
Oj
Oj
Oj
Oj
Oj
Oj
Oj
Oj
Oj
211
PC"
oj
o
o]
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
o!
, j...
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
211
Base
oj
o]
o]
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
oj
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
21!
PC !
oj
oj
0 j
oj
oj
oj
oj
oj
oj
0|
0|
0|
0|
0|
0|
0|
0|
21!
0!
i..
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
21!
j
Proposed/NODA |
Option
:
Base
12 |
o]
1541
24|
o!
o!
16|
o!
12!
iv!
o!
o!
o!
o!
9!
o!
o!
32!
o!
, i....
0|
161
0|
271
9j
9j
0|
0|
0|
0|
3351
PC
9|
o!
in!
12!
o!
o!
o!
o!
9!
iv!
o!
o!
o!
o!
9!
o!
o!
o!
o!
, j..
0|
0|
0|
271
9|
9|
0|
0|
0|
0|
2121
i
Directs + 413 to j
433 Upgrade
i
Base
oj
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
21!
0!
i...
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
21!
PC
oj
o]
o]
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
o!
21!
oj
0|
0|
0|
0|
0|
0|
0|
0|
0|
0|
21!
Directs + All to
433 Upgrade
Base
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
o
21
0
0
0
0
0
0
0
0
0
0
0
21
PC
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
c
c
c
c
a Base = Baseline discharge level
b PC = Post-Compliance discharge level
Source: U.S. EPA analysis.
1.4.2 Aquatic Life Effects
EPA evaluated the effects of MP&M facility discharges on aquatic habitats and ecosystem functioning under the baseline
conditions and the post-compliance scenarios corresponding to the four regulatory alternatives considered for the MP&M
regulation. This analysis compared the estimated baseline and post-compliance in-stream concentrations of MP&M pollutants
with AWQC for aquatic species. As noted in the preceding sections, aquatic life AWQCs addressed in this analysis set the
upper limit on pollutant concentrations assumed to be protective of aquatic life.
1-37
-------
MP&M EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.10 presents the number of MP&M discharge reaches on which pollutant concentrations are estimated to exceed
chronic and acute exposure criteria for protection of aquatic life. EPA estimated that, as the result of baseline MP&M
pollutant discharges, in-stream concentrations exceed acute exposure criteria for aquatic species in 18 and 15 receiving
reaches nationwide based on the traditional extrapolation and post-stratification extrapolation, respectively. In addition,
baseline in-stream concentrations in 353 and 350 receiving reaches exceed chronic AWQC for protection of aquatic life based
on the traditional extrapolation and post-stratification extrapolation, respectively.
Table 1. 10: Summary of Estimated AWQC Exceedances for Protection of Aquatic Life (National Basis)
Category
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Acute Aquatic Life
f
Streams (No.)
18
Q
S
15
Q
330
86
18
0
18
0
Pollutants
(No.)
Selected Option
4
1
elected Option: PC
4
1
Propos
17
12
Directs +
4
0
Directs •+
4
0
Total
Exceedances
Traditional Extr
35
9
st-Stratification E
26
9
ed/NODA Option
631
254
413 to 433 Upgra
35
0
All to 433 Upgra
35
0
Chronic Aquatic Life
r
Streams (No.)
apolation
353
344
xtrapolation
350
344
928
539
de
353
53
ie
353
32
Pollutants
(No.)
9
5
9
5
47
39
9
3
9
2
Total
Exceedances
423
362
402
362
2,582
1,369
423
53
423
32
Source: U.S. EPA analysis.
Based on the traditional extrapolation, EPA estimates that the final option will eliminate concentrations in excess of acute and
chronic criteria in nine reaches. Likewise, EPA estimates that the final option will eliminate concentrations in excess of acute
and chronic criteria in six reaches based on the post-stratification extrapolation.
The Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would
eliminate exceedances of chronic AWQC values on 389, 300, and 321 reaches, respectively. These options would also
eliminate in-stream pollutant concentrations in excess of acute AWQC value on 244, 18, and 18 reaches under the
Proposed/NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option, respectively.
Table I.I 1 presents the number MP&M reaches on which pollutant concentrations are estimated to exceed chronic AWQC for
protection of aquatic life by pollutant.
1-38
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1. 11: Summary of Pollutants Estimated to Exceed Chronic AWQC for Protection of Aquatic Life
(National Basis)
Pollutant
Acenaphthene
Acrolein
Aluminum
Ammonia as N
Aniline
Anthracene
Biphenyl
Butyl benzyl phthalate
Cadmium
Carbon disulfide
Chromium
Cobalt
Copper
Cyanide
Di-n-butyl phthalate
Di-n-octyl phthalate
Dibenzofuran
Dibenzothiophene
Dimethylphenanthrene, 3,6-
Dinitrophenol, 2,4-
Dinitrotoluene, 2,6-
Diphenyl Ether
Fluoranthene
Fluorene
Fluoride
Iron
Isopropylnaphthalene, 2-
Lead
Magnesium
Manganese
Methylfluorene, 1-
Methylnaphthalene, 2-
Methylphenanthrene, 1-
Molybdenum
Naphthalene
Nickel
Phenanthrene
Phenol
Pyrene
Selenium
Silver
Styrene
Sulfide
Selected Option: I Selected Option: I ^n^nm IT,- *. , A-,*, ± n- ^ , m^
_,.., :_ _ .JT . : Proposed/NODA : Directs + 413 to : Directs + All to
Traditional ; Post-Stratiflcation ; r _, ... TT ; ... TT
„ . ... • „ . ... Option • 433 Upgrade • 433 Upgrade
Extrapolation : Extrapolation I
Base" |
0!
oj
o!
oj
o!
oj
o!
o|
9|
oj
o!
o|
9|
0!
0!
0!
0!
0!
0!
0!
0!
0!
0!
0!
oj
o!
oj
o!
oj
o!
oj
o!
oj
o!
oj
o!
oj
o!
o|
o!
9!"'
6f"
•?•••
o|
PC" Base
01 0!
o! o|
ol ol
o! oi
ol ol
oi ol
ol ol
oi oi
ol el
oi oi
ol ol
oi ol
9\ 9\
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
oi o|
ol o|
oi oi
oi oi
oi oj
oi oi
oi oj
oi oi
oi oj
oi oi
oi oj
oi oi
oi oj
o| o|
oi oj
oi oj
6t ef"
6t 6t-
? •?•••
oi oj
PC Base
Oi 9!
Oj 44j
Ol 32|
0| 51|
Ol 45|
0| 64|
Ol 9|
Oj 9|
0| 70|
0| 38J
0| 46|
0| 12J
9i 344!
01 3!
01 9!
01 12!
01 21!
01 15!
01 24!
01 9!
01 21!
01 21!
01 30!
01 27!
Oj 54j
ol 12!
Oj 15j
Oi 244!
Oj 12j
Ol 32!
Oj 15j
n! 12!
\J. l^i
Oj 15j
Oi 103!
Oj 9j
Oi 163!
Oj 24|
Ol 9!
Ol 21?
: :
Oj 78|
OJ 166?'"
6j 9]'"
Oi 293!'"
: :
PC Base
9! 0!
33j Oj
12| o!
oi oj
42| o!
29i Oj
9| 0|
oi oj
2l| 9l
34i Oj
12! o!
12! Oj
69| 9l
01 0!
9! 0!
121 0!
121 0!
121 0!
211 0!
91 0!
211 0!
211 0!
241 0!
211 0!
13! Oj
o| o|
12! Oj
83| 0|
121 Oj
o| oj
121 Oj
12| OJ
121 Oj
39| 0|
91 Oj
16| Oj
21| 0?
0| Oj
2i| oi
50i OJ
m[ 9J'"
6[ b]'"
283J 0?'"
PC Base
oi oj
Oj Oj
ol o!
oj oj
ol o!
oj oj
ol o!
oj oj
Ol 9l
oj o!
ol o!
oj oj
Ol 9l
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
01 0!
Oj Oj
ol o!
Oj Oj
ol o|
Oj Oj
ol o|
Oj Oj
ol o|
Oj Oj
ol o|
Oj Oj
0| Oj
oi oj
: :
ol oj
oi oj
: :
ol oj
6? f
0! Oj'"
? •?•••
01 0!
: :
PC
c
0
0
0
0
0
0
0
0
0
0
0
0
c
c
c
c
c
c
c
c
c
c
c
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
c
c
0
1-39
-------
MP&M EEBA: Appendices
Appendix I: Environmental Assessment
Table 1. 11
: Summary of Pollutants Estimated to Exceed Chronic AWQC for Protection of Aquatic Life
(National Basis)
Selected Option: j Selected Option: j
Traditional I Post-Stratification I
Pollutant Extrapolation j Extrapolation j
Tin
Titanium
Vanadium
Zinc
Total Exceedances
Base" |
oj
ol
oj
9!
35|
PCb |
oj
Base
0!
ol ol
oi ol
ol
9|
6|
26!
PC
oj
j
Proposed/NODA |
Option
i
Base
83|
Ol 6J
OJ 157|
ol
9|
85!
2,582!
PC
21|
j
Directs + 413 to j
433 Upgrade
1
Base
0!
oi oi
142| ol
33|
1,369|
9|
35!
PC
oj
Directs + All to
433 Upgrade
Base
oj
ol ol
o| o|
ol
ol
9|
35|
PC
0
0
0
0
0
a Base = Baseline discharge level
b PC = Post-Compliance discharge level
Source: U.S. EPA analysis.
Table 1.12 presents the number MP&M reaches on which pollutant concentrations are estimated to exceed acute AWQC for
protection of aquatic life by pollutant.
Table 1.12: Summary of Pollutants Estimated to Exceed Aquatic Life Based Acute AWQC (National Basis)
i Selected Option: i Selected Option: i _ _mvT«~vT»» * n- ± , A-II ^ * T»- ^,.11^
„ ,. . , i,, o ./ . i Proposed/NODA i Directs + 413 to i Directs + All to
Traditional ! Post-Stratification j r _, ... TT ... TT
Pollutant i T^ ^ i .L- i T^ ^ i *• Option • 433 Upgrade • 433 Upgrade
riiiiiiiiiiii Extrapolation Extrapolation r r& r&
1 Base" i
Acenaphthene 0!
Acrolein 0!
Aluminum 9!
Ammonia as N 0!
Aniline 0!
Anthracene 0!
Biphenyl OJ
Butyl benzyl phthalate 0!
Cadmium 91
Carbon disulfide 0!
Chromium 0!
t *...
Cobalt 0!
r *•••
Copper 276!
Cyanide 0!
••••• r *•••
Di-n-butyl phthalate 0;
Di-n-octyl phthalate 0;
Dibenzofuran 0!
t £...
Dibenzothiophene 0!
t £...
Dimethylphenanthrene, 3,6- 0!
Dinitrophenol, 2,4- 0!
•-• •••••• i i-
Dinitrotoluene, 2,6- Oi
i i-
Diphenyl Ether Oi
•••••- - i i-
Fluoranthene Oi
i *...
Fluorene Oi
i *...
Fluoride Oi
i *...
Iron Oi
t i-
[sopropylnaphthalene, 2- 0!
PC" Base
o| oj
o| oj
°l 6j
o| oj
0! 0!
o| oj
Oi Oj
o| oj
ol el
01 0!
01 0!
!• 4""
01 0!
£ £...
2671 273!
!• 4""
01 0!
£ £...
01 Oj
Oi Oj
6| 0!
£ £...
01 0!
|. ^...
01 Oj
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£ £...
Oi 0!
|. ^...
Oi 0!
£ £...
Oi 0!
# 4""
OE Oi
# 4""
OE Oi
# 4""
OE Oi
£ 4"..
Oi 0!
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PC Base
o| oj
0| 33j
o| ioj
ol oj
Oi 9!
Oi 64j
Oj 9j
ol oj
ol 9!
01 0!
01 7!
01 0!
? *•••
2671 241!
!• 4""
01 0!
01 0;
Oi Oi
01 0!
01 0!
01 0;
Oi 9!
Oi Oi
Oi Oi
Oi 21!
01 21!
01 0!
01 0!
Ol 0!
i i...
PC Base
o| oj
26| Oj
°l 9j
o| oj
01 0!
29| OJ
01 0!
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!• 4""
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691 276!
!• 4""
01 0!
£ 4"..
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|. 4""
01 0!
£ 4"..
01 0;
9i 0!
£ 4"..
Oi 0!
£ 4"..
Oi 0!
£ 4"..
21! Oi
211 0!
Oi Oi
e *...
Oi Oi
£ 4"..
Oi 0!
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PC Base
o| oj
o| oj
0| 9j
ol oj
oi oi
ol oj
oj oj
ol oj
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01 0!
01 0!
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!• 4""
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6| 0;
01 0!
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oi oi
oi oi
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01 0!
oi oi
i i...
PC
c
c
c
0
0
0
0
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c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
1-40
-------
MP&M EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.12: Summary of Pollutants Estimated to Exceed Aquatic Life Based
Pollutant
Lead
Vlagnesium
Vlanganese
VIethylfluorene, 1-
Methylnaphthalene, 2-
Vlethylphenanthrene, 1-
Vlolybdenum
STaphthalene
Nickel
Phenanthrene
Phenol
Pyrene
Selenium
Silver
Styrene
Sulfide
Tin
ritanium
Vanadium
Zinc
Total Exceedances
Selected Option: j Selected Option: j
Traditional j Post-Stratification j
Extrapolation 1 Extrapolation 1
Base" |
1.1.
21!
9!
o!
o!
o!
o!
o!
9!
o!
o!
o!
o!
64J
o!
o!
o!
o!
o!
9!
423!
PCb |
Base
?! 15j.
21! 21!
o!
o!
o!
o!
o!
o!
9!
o!
o!
o!
o!
56!
o!
o!
o!
o!
o!
o!
362!
6\
o!
o!
o!
o!
o!
9!
o!
o!
o!
o!
61!
o!
o!
o!
o!
o!
6\
402!
PC !
I
Proposed/NODA j
Option
:
Base
?! 9}
21! oj
o!
o!
o!
o!
o!
o!
9!
o!
o!
o!
o!
56!
o!
o!
o!
o!
o!
o!
362!
oj
0!
0!
0!
0!
0!
23|
9!
0!
0!
12!
60!
0!
0!
0!
0!
0!
85!
631!
PC !
Acute AWQC (National Basis)
:
Directs + 413 to j
433 Upgrade
:
Base
6| 1.1.
0! 21!
o!
o!
o!
o!
o!
o!
o!
9!
o!
o!
12!
12!
o!
o!
o!
o!
o!
33!
254!
9!
0!
0!
0!
0!
0!
9!
0!
0!
0!
0!
64!
0!
0!
0!
0!
0!
9!
423!
PC !
Directs + All to
433 Upgrade
Base
P] 1.8}
21| 21!
o!
o!
o!
o!
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o!
o!
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o!
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23!
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o!
o!
53!
?!
0!
0!
0!
0!
0!
?!
0!
0!
0!
0!
64!
0!
0!
0!
0!
0!
?!
423!
PC
0
0
0
0
0
0
0
0
0
0
0
0
0
23
0
0
0
0
0
0
32
a Base = Baseline discharge level
b PC = Post-Compliance discharge level.
Source: U.S. EPA analysis.
1.4.3 POTW Effects
EPA evaluated the effects of indirect MP&M dischargers on POTW operations for the final and alternative options. 788
sample MP&M facilities discharge 132 pollutants to 572 POTWs. Of these, EPA evaluated 89 pollutants for potential
inhibition of POTW operations and eight pollutants for potential sludge contamination. The 788 indirect sample MP&M
facilities discharge 52.8 million pounds per year of priority and nonconventional pollutants to the receiving POTWs. The final
MP&M rule does not regulate indirect dischargers and thus will not reduce the baseline MP&M loadings to receiving
POTWs.
a. Biological inhibition
EPA estimated inhibition of POTW operations by comparing predicted POTW influent concentrations to available inhibition
levels for 89 pollutants. EPA's analysis shows that 51 POTWs had influent concentrations that exceed the biological
inhibition values for one of the four following pollutants silver, cadmium, chromium and copper under the baseline
conditions corresponding to the Final Option and the 433 Upgrade Options (see Table 1.13). Both of the 433 Upgrade Options
would eliminate influent concentrations in excess of POTW inhibition criteria at 21 POTWs. Under the baseline conditions
corresponding to the Proposed/NODA Option, 293 POTWs had influent concentrations in excess of the biological inhibition
criteria. The Proposed/NODA Option would eliminate influent concentrations in excess of the biological inhibition criteria at
156 POTWs.
1-41
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.13: National Summary of Projected Inhibition and Sludge Contamination Problems
Category
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Baseline
Post-Compliance
Biological Inhibition (# of POTWs) Sludge Contamination (# of POTWs)
POTWs (No.)
51
51
S
51
51
293
137
51
30
5
30
Pollutants
(No.)
Selected Option
4
4
elected Option: PC
4
4
Propos
12
8
Directs +
4
4
Directs •+
4
4
Total
Exceedances
Traditional Extr
139
139
st-Stratification E
139
139
ed/NODA Option
885
410
413 to 433 Upgra
139
115
All to 433 Upgra
139
115
POTWs (No.)
apolation
1,020
1,020
xtrapolation
1,020
1,020
5,328
5,259
de
1,020
1,005
ie
1,020
1,005
Pollutants
(No.)
7
7
7
7
8
8
7
7
7
7
Total
Exceedances
2,702
2,702
2,702
2,702
14,493
14,321
2,702
2,626
2,702
2,562
Source: U.S. EPA analysis.
1-42
-------
MPAM EEBA: Appendices
Appendix I: Environmental Assessment
Table 1.14 presents MP&M pollutants that are estimated to upset POTW operations and contaminate sewage sludge.
Table 1.14: Summary of Pollutants
Pollutant
Estimated to Impact POTW Operations (National basis)
Selected Option: i Selected Option: j _ _mvT«~vT»» !T»- ^ , A-II ± *ii\ T»- ^ , m* A~>~>
„ ,. . , =„ „ .... ' Proposed/NODA i Directs + 413 to 433: Directs + All to 433
Traditional • Post-Stratification • _ . TT , TT ,
_ . , .. • „ . ... Option Upgrade Upgrade
Extrapolation Extrapolation
Base" i
PC" Base !
PC
Biological Inhibition (#
Acrolein
Arsenic
Benzoic acid
Bromo-2-chlorobenzene,
Bromo-3-chlorobenzene,
Chromium
Copper
Iron
Lead
Nickel
Silver
Zinc
Total Exceedances
Lead
Mercury
Nickel
Arsenic
Cadmium
Copper
Zinc
Selenium
1-
1-
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0!
0!
o!
o!
so!
27!
o!
39!
o!
42!
o!
139!
234!
o!
763!
84!
754!
534!
224!
109!
o|_
oj
o!
o!
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30!
27!
o!
39!
o!
42!
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139!
Sludge
234!
0!
763!
84!
754!
534!
224!
109!
°l
oj
o!
o!
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30!
27!
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39!
o!
42!
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139!
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27J
o|
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o|
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139J
Base PC Base |
of POTWs)
zzi
75j
68!
48|
48|
8l|
142!
65!
150J
so!
65!
16!
885!
65I
65|
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48|
48|
7j
oj
32|
8li
oj
65!
oj
410!
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30!
27!
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39!
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42!
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139!
PC Base |
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27!
27!
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0!
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o!
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so!
27!
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39!
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42!
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139!
PC
0
0
c
0
0
27
27
0
30
0
30
0
115
Contamination (# of POTWs)
234!
o|
763!
84!
754!
534!
224!
109!
234J
o|
763J
84J
754J
534J
224J
109!
2,829! 2,790|
us!
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1,686! 1
1,877! 1
1,874! 1
2,132! 2
1,567! 1
118!
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,835!
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234!
0!
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739!
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234
0
687
84
739
500
209
109
a Base = Baseline discharge level
b PC = Post-Compliance discharge level.
Source: U.S. EPA analysis.
b. Sewage sludge
EPA estimated that baseline concentrations of seven metals at the national level fail to meet Land Application-High limits for
sludge disposal at 1,020 POTWs under the final regulatory alternatives. These concentrations were compared with the
relevant metals concentration limits for the following sewage sludge management options: Land Application-High
(Concentration Limits), Land Application-Low (Ceiling Limits), and Surface Disposal.
The Agency estimates that the final regulation will not eliminate metal concentrations in excess of sludge contamination
criteria at any of these 1,020 POTWs, since indirect dischargers are exempted from the final rule. EPA estimated that 15
POTWs would be able to upgrade their sewage sludge disposal practices by meeting Land Application-High sludge
concentration limits under the 433 Upgrade Options. Under the Proposed/NODA Option, 69 POTWs would be able to
upgrade their sewage sludge disposal practices to Land Application-High.
1-43
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
GLOSSARY
action levels: the existence of a contaminant concentration in the environment high enough to warrant implementation of
drinking water treatment technology.
acute toxicity (AT): the ability of a substance to cause severe biological harm or death soon after a single exposure or
dose. Also, any poisonous effect resulting from a single short-term exposure to a toxic substance (See also: chronic toxicity).
(http://www.epa.gov/OCEPAterms/aterms.html)
adsorption: removal of a pollutant from air or water by collecting the pollutant on the surface of a solid material; an
advanced method of treating waste in which activated carbon removes organic matter from wastewater.
(http://www.epa.gov/OCEPAterms/aterms.html)
adsorption coefficient (K0J: represents the ratio of the target chemical adsorbed per unit weight of organic carbon in the
soil or sediment to the concentration of that same chemical in solution at equilibrium.
alkalinity: the capacity of bases to neutralize acids (e.g., adding lime to lakes to decrease acidity).
(http://www.epa.gov/OCEPAterms/aterms.html)
ambient water quality criteria (AWQC): levels of water quality expected to render a body of water suitable for its
designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,
swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html)
atm/m -mole: atmosphere per cubic meter mole (see also: mole).
benthic: relating to the bottom of a body of water; living on, or near, the bottom of a water body.
(http://www.ucmp.berkeley.edu/glossary/gloss5ecol.html)
bioconcentration factor (BCF): indicator of the potential for a chemical dissolved in the water column to be taken up by
aquatic biota across external surface membranes, usually gills.
BIODEG: a web-based biodegradation database developed by Syracuse Research Corporation.
(http://esc.sy ires, com/efdb/b iodgsum.htm)
biodegradation: a process whereby organic molecules are broken down by microbial metabolism.
biodegradation half-life: represents the number of days a compound takes to be degraded to half of its starting
concentration under prescribed laboratory conditions.
biological oxygen demand (BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose
organic material in an aquatic environment.
cancer potency slope factors (SFs): a plausible upper-bound estimate of the probability of a response per unit intake of
a chemical over a lifetime. The slope factor is used to estimate an upper-bound probability of an individual developing
cancer as a result of a lifetime of exposure to a particular level of a potential carcinogen.
carcinogens: chemicals that EPA believes can cause or have the potential to cause tumors or cancers in humans, either
directly or indirectly.
CHEMFA TE: a web-based chemical fate database developed by Syracuse Research Corporation.
(http://esc.syrres.com/efdb/Chemfate.htm)
chronic toxicity (CT): the capacity of a substance to cause long-term toxic or poisonous health effects in humans, animals,
fish, and other organisms (see also: acute toxicity). (http://www.epa.gov/OCEPAterms/cterms.html)
critical dilution factors (CDFs): express the relationship between a point source loading and the resulting concentration
at the edge of the mixing zone. Typically, this is expressed as a ratio of parts receiving water to one part effluent.
1-44
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
dissolved concentration potentials (DCPs): represents the concentration of a nonreactive dissolved substance under
well-mixed, steady-state conditions given an annual load of 10,000 tons.
dry metric tons (DMT): dry measure is a system of units for measuring dry commodities. 1 DMT=1,000 kilogram.
EC1: the concentration at which one percent of the test organisms show a significant sub-lethal response.
ECS: the concentration at which five percent of the test organisms show a significant sub-lethal response.
Environmental Research Laboratory-Duluth fathead minnow database: a database developed by EPA's
Mid-Continent Ecology Division (MED) which provides data on the acute toxicity of hundreds of industrial organic
compounds to the fathead minnow. (http://www.eoa.gov/med/databases/fathead_minnow.html)
GAGE: a U.S. Geological Survey stream flow database. The database contains stream flow data and drainage area
measurement from all U.S. Geological Survey flow gages.
hazardous air pollutant (HA P): air pollutants that are not covered by ambient air quality standards but which, as defined
in the Clean Air Act, may present a threat of adverse human health effects or adverse environmental effects (e.g., beryllium,
mercury, ethylbenzene, chloroethane, and doxane). (http://www.epa.gov/OCEPAterms/hterms.html)
Health Effects Assessment Summary Tables (HEAST): a comprehensive listing of provisional human health risk
assessment data relative to oral and inhalation routes for chemicals of interest to EPA. Unlike data in IRIS, HEAST entries
have received insufficient review to be recognized as high quality, Agency-wide consensus information (U.S. EPA. 1997.
Health Effects Assessment Table; FY 1997 Update. EPA-540-R-97-036).
Henry's Law (H): chemical law stating that the amount of a gas that dissolves in a liquid is proportional to the partial
pressure of the gas over the liquid, provided no chemical reaction takes place between the liquid and the gas. The law is
named after William Henry (1774 1836), the English chemist who first reported the relationship, (www.infoplease.com)
human health-based water quality criteria (WQC): levels of water quality expected to render a body of water suitable
for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for
drinking, swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/wterms.html)
Integrated Risk Information System (IRIS): IRIS is an electronic database with information on human health effects of
various chemicals. IRIS provides consistent information on chemical substances for use in risk assessments, decision-making,
and regulatory activities.
LC50 (Lethal Concentration): a standard measure of toxicity that tells how much of a substance is needed to kill half of a
group of experimental organisms in a given time (see also: LD 50). (http://www.epa.gov/OCEPAterms/lterms.html)
LD50 (Lethal Dose): the dose of a toxicant or microbe that will kill 50 percent of the test organisms within a designated
period. The lower the LD 50, the more toxic the compound.
I/kg: liter per kilogram
Lowest Observed Effect Concentration (LOEC): the lowest level of pollutant concentration that causes statistically
and biologically significant differences in test samples as compared to other samples subjected to no stressor.
(http://www.epa.gov/OCEPAterms)
Maximum Allowable Toxicant Concentration (MATC): for a given ecological effects test, the range (or geometric
mean) between the No Observable Adverse Effect Level and the Lowest Observable Adverse Effects Level.
(http://www.epa.gov/OCEPAterms/mterms.html)
maxim um con taminan t levels (MCLs): the maximum permissible level of a contaminant in water delivered to any user
of a public system. MCLs are enforceable standards. (http://www.epa.gov/OCEPAterms/mterms.html)
mg/kg: milligram per kilogram
1-45
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
microgram per liter
mole: the amount of substance that contains Avogardo's number of atoms, molecules or other elementary units.
National Estuarine Inventory (NEI): The National Estuarine Inventory is a series of inter-related activities that define,
characterize, and assess the nation's estuarine systems. NEI data are compiled in a systematic and consistent manner that
enables the nation's estuaries to be compared and assessed according to their environmental quality, economic values, and
resource uses. A principal feature of the NEI is the determination of the physical dimensions and hydrologic features of
estuarine systems of the United States which are primary determinants of estuarine processes and ultimately affect the ecology
of a system.
National Oceanic and Atmospheric Administration (NOAA): organization within the Bureau of Commerce that
conducts research and gathers data about the global oceans, atmosphere, space, and sun.
Wo Observed Effect Concentration (NOEC): exposure level at which there are no statistically or biologically significant
differences in the frequency or severity of any effect in the exposed or control populations.
(http://www.epa.gov/OCEPAterms/nterms.html)
oil and grease (O&G): organic substances that may include hydrocarbons, fats, oils, waxes, and high-molecular fatty
acids. Oil and grease may produce sludge solids that are difficult to process, (http://www.epa.gov/owmitnet/reg.htm)
organic carbon (OC): carbon in compounds derived from living organisms.
partition factor: a chemical-specific value representing the fraction of the load expected to partition to sewage sludge
during wastewater treatment.
Permit Compliance System (PCS): a computerized database of information on water discharge permits, designed to
support the National Pollutant Discharge Elimination System (NPDES).
(http://www.epa.gov/ceiswebl/ceishome/ceisdocs/pcs/pcs-exec.htm)
pH: an expression of the intensity of the basic or acid condition of a liquid; natural waters usually have a pH between 6.5 and
8.5. (http://www.epa.gov/OCEPAterms/pterms.html)
pollutants of concern (POCs): the 150 contaminants identified by EPA as being of potential concern for this rule and
which are currently being discharged by MP&M facilities.
Pre manufacture Notices (PMN): a notice, required by Section 5 of TSCA, that must be submitted to EPA by anyone
who plans to manufacture or import a new chemical substance for a non-exempt commercial distribution. The notice must be
submitted at least 90 days prior to the manufacture or import of the chemical.
(http://www.epa.gov/oppt/newchems/index.htm)
priority pollutant (PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,
sediment, groundwater, or soil samples. These chemicals are also known as toxic pollutants.
quantitative structure-activity relationship (QSAR): an expert system that uses a large database of measured
physic ochemical properties, such as melting point, vapor pressure, and water solubility, to estimate the fate and effect of a
specific chemical based on its molecular structure, (http://www.epa.gov/med/databases/aster.html)
reference doses (RfDs): RfDs represent chemical concentrations - expressed in mg of pollutant/kg body weight/day -
which, if not exceeded, are expected to protect an exposed population, including sensitive groups such as young children or
pregnant women.
Secondary Maximum Contaminant Levels (SMCLs): non-enforceable water treatment levels applying to public
water systems and specifying the maximum contamination levels that, in the judgment of EPA, are required to protect the
public welfare. These treatment levels apply to any contaminants that may adversely affect the odor or appearance of such
water and consequently may cause people served by the system to discontinue its use.
1-46
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
suspended solids: small particles of solid pollutants that float on the surface of, or are suspended in, sewage or other
liquids. They resist removal by conventional means.
Superfund Chemical Data Matrix (SCDM): a source for factor values and benchmark values applied when evaluating
potential National Priorities List (NPL) sites using the Hazard Ranking System (HRS).
(http://www.epa.gov/superfund/resources/scdm/index.htm)
systemic toxicants: chemicals that EPA believes can cause significant non-carcinogenic health effects when present in the
human body above chemical-specific toxicity thresholds.
total Kjeldahl nitrogen (TKN): TKN is defined as the total of organic and ammonia nitrate. It is determined in the same
manner as organic nitrogen, except that the ammonia is not driven off before the digestion step.
total organic carbon (TOC): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by
tests for "total suspended non-filterable solids" (see also: suspended solids).
total petroleum hydrocarbons (TPH): a general measure of the amount of crude oil or petroleum product present in an
environmental media (e.g. soil, water, or sediments). While it provides a measure of the overall concentration of petroleum
hydrocarbons present, TPH does not distinguish between different types of petroleum hydrocarbons.
total suspended particles (TSP): method of monitoring airborne particulate matter by total weight.
(http://www.epa.gov/OCEPAterms/tterms.html)
total suspended solids (TSS): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by
tests for "total suspended non-filterable solids" (see also: suspended solids).
United States Geological Survey (USGS): a governmental organization that provides reliable scientific information to:
describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological,
energy, and mineral resources; and enhance and protect our quality of life, (www.noaa.gov)
volatilization: a process whereby chemicals dissolved in water escape into the air.
1-47
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MPAM EEBA: Appendices
Appendix I: Environmental Assessment
ACRONYMS
AQUIRE: AQUatic Information REtrieval System
ASTER: Assessment Tools for the Evaluation of Risk
AT: acute toxicity
AWQC: ambient water quality criteria
BCF: bioconcentration factor
BOD: biological oxygen demand
CDF: critical dilution factor
CT: chronic toxicity
DCP: dissolved concentration potential
DMT: dry metric tons
H: Henry's Law
HAP: hazardous air pollutant
HE A S T: Health Effects Assessment Summary Tables
IRIS: Integrated Risk Information System
Koci adsorption coefficient
LOEC: Lowest Observed Effect Concentration
MA TC: Maximum Allowable Toxicant Concentration
MCL: maximum contaminant level
NEI: National Estuarine Inventory
NO A A: National Oceanic and Atmospheric Administration
NOEC: No Observed Effect Concentration
O&G: oil and grease
OC: organic carbon
PCS: Permit Compliance System
PMN: Premanufacture Notices
POC: pollutant of concern
PP: priority pollutant
QSAR: quantitative structure-activity relationship
RBC: EPA's Region III Risk-Based Concentration Table
RfD: reference dose
SCDM: Superfund Chemical Data Matrix
SF: cancer potency slope factor
SMCL: Secondary Maximum Contaminant Level
TKN: total Kjeldahl nitrogen
TOC: total organic carbon
TPH: total petroleum hydrocarbons
TSP: total suspended particulates
TSS: total suspended solids
USGS: United States Geological Survey
WQC: water quality criteria
1-48
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MP&M EEBA: Appendices Appendix I: Environmental Assessment
REFERENCES
Arthur D. Little. 1983. Evaluation ofwaterborne exposure pathways to paragraph 4 (c) pollutants. Draft Report, April 28.
Also: Paragraph 4(c) list of detected chemicals.
Arthur D. Little. 1986. Bioaccumulation Study.
Birge, W. J. et al. 1979. Aquatic toxicity tests on inorganic elements occurring in oil shale. Oil Shale Symposium -
Sampling, Analysis and Quality Assurance, March. EPA-600/9-80-022.
Clay, D. R. 1986. Office of Toxic Substances, U.S. Environmental Protection Agency. Memorandum to J.M. Conlon,
OWRS, U.S. Environmental Protection Agency.
Holdway, D. A. and J. B. Spraque. 1979. "Chronic toxicity of vanadium to flagfish." Water Research 13:905-910.
Howard, P. H., ed. 1991. Handbook of Environmental Degradation Rates. Chelsea. MI: Lewis Publishers, Inc.
ICF, Inc. 1985. Superfund Public Health Evaluation Manual - Draft.
Leblanc, G. A. 1980. "Acute toxicity of priority pollutants to water flea (Daphnia magna)." Bull Environmental
Contamination Toxicology 24: 684-691.
Lyman, W. J.; W. F. Reehl, and D. H. Rosenblatt. 1981. Handbook of Chemical Property Estimation Methods -
Environmental Behavior of Organic Compounds. New York, NY: McGraw-Hill, Inc. (reported or estimated using methods
outlined).
Syracuse Research Corporation. 1997. CHEMFATE Datafile within the Environmental Fate Database. Syracuse, NY:
Syracuse Research Corporation.
U.S. Atomic Energy Commission. 1973. Toxicity of power plant chemicals to aquatic life. Washington, DC: U.S. Atomic
Energy Commission.
U.S. Environmental Protection Agency (U.S. EPA). 1972. "Blue Book", NAS-NAE (Water Quality Criteria - 1972).
Washington, DC: U.S. EPA. EPA-R3-73-033. If the code @ AA is provided with this reference, the value is an estimate
based on application factors described in the "Blue Book."
U.S. Environmental Protection Agency (U.S. EPA). 1976. "Red Book" (Quality Criteria For Water). Washington, DC: U.S.
EPA.
U.S. Environmental Protection Agency (U.S. EPA). 1980. Ambient water quality criteria documents. Washington, DC:
Office of Water, U.S. EPA. EPA 440/5-80 Series. Also refers to any update of criteria documents (EPA 440/5-85 and EPA
440/5-87 Series) or any Federal Register notices of proposed criteria or criteria corrections. The most recent National
Recommended Water Quality Criteria used in this report were published in the Federal Register on December 10, 1998.
U.S. Environmental Protection Agency (U.S. EPA). 1984. Summary of current oral Acceptable Daily Intakes (ADIs) for
systemic toxicants. Cincinnati, Ohio: Environmental Criteria and Assessment Office, U.S. EPA, May.
U.S. Environmental Protection Agency (U.S. EPA). 1985. Report to Congress on the Discharge of Hazardous Wastes to
Publicly-Owned Treatment Works (Domestic Sewage Study). Office of Water Regulations and Standards. Washington, DC:
U.S. EPA.
U.S. Environmental Protection Agency (U.S. EPA). 1987. Guidance Manual for Preventing Interference atPOTWs.
Washington, DC: U.S. EPA
U.S. Environmental Protection Agency (U.S. EPA). 1990. CERCLA Site Discharges to POTWs: Guidance Manual.
Washington, DC: Office of Emergency and Remedial Response EPA/540/G-90/005.
1-49
-------
MP&M EEBA: Appendices Appendix I: Environmental Assessment
U.S. Environmental Protection Agency (U.S. EPA). 1997. Health Effects Assessment Summary Tables (HEAST).
Washington, DC: Office of Research and Development and Office of Emergency and Remedial Response. U.S. EPA.
U.S. Environmental Protection Agency (U.S. EPA). 1998. Risk-Based Concentration Table, October. Philadelphia, PA:
Region III, U.S. EPA.
U.S. Environmental Protection Agency (U.S. EPA). 1998/99. QSAR. Duluth, MN: Environmental Research Laboratory,
U.S. EPA.
U.S. Environmental Protection Agency (U.S. EPA). 1998/99a. Aquatic Toxicity Information Retrieval (AQUIRE) Data
Base. Duluth, MN: Environmental Research Laboratory, U.S. Environmental Protection Agency. 1998 Database retrieval.
U.S. Environmental Protection Agency (U.S. EPA). 1998/99b. Assessment Tolls for Evaluation of Risk (ASTER) Data
Base. Duluth, MN: Environmental Research Laboratory, U.S. Environmental Protection Agency. 1998 Database retrieval.
U.S. Environmental Protection Agency (U.S. EPA). 1998/99c. Integrated Risk Information System (IRIS) Retrieval.
Washington, DC: U.S. EPA. 1998 Database retrieval.
U.S. Environmental Protection Agency (U.S. EPA). 2000. Development Document for the Proposed Effluent Limitations
Guidelines and Standards for the Metal Products and Machinery Point Source Category. Washington, DC: U.S. EPA.
Versar. 1992. Upgrade of Flow Statistics Used to Estimate Surface Water Chemical Concentrations for Aquatic and Human
Exposure Assessment. Prepared for the Office of Pollution Prevention and Toxics, U.S. EPA.
Versar. 1994. Development of Mixing Zone Dilution Factors. Preliminary Draft, Progress Report. Prepared for U.S. EPA,
Office of Pollution Prevention and Toxics, Economics, Exposure, and Technology Division. October 27. EPA Contract No.
68-D3-0013.
Worthing, C. R. 1987. The Pesticide Manual - a world compendium, Eighth Edition. Surry, UK: The British Crop Protection
Council. ISBN 0-948404-42-6.
Zhang, R. and Zhang, S. 1982. "Toxicity of Fluorides to Fishes." C.A. Selects - Environm Pollut24, 97:176354k.
1-50
-------
MP&M EEBA: Appendices
Appendix J: Special Distribution of MP&M Facilities and Recreational User Populations
Appendix J: Special Distribution of
AAPAM Facilities and Recreational
User Populations
INTRODUCTION
This appendix compares the national distribution of all
MP&M facilities by state and the national distribution of
recreational participants by state (see Table J.I and Figure
J.I).
APPENDIX CONTENTS
Table J.I Distribution of MP&M Facilities and Participants
of Water-Based Recreation by State J-2
Figure J.I Cumulative Distribution of Facilities
and Participants J-4
EPA based the distribution of MP&M facilities by state on
Census data on total numbers of facilities in the SICs that make up the MP&M industries, not just water dischargers. This
comparison assumes that the state distribution of water-discharging MP&M facilities is the same as the overall distribution of
MP&M facilities.
EPA based the distribution of recreational participants by state and by type of recreation activity on information provided by
the National Demand Study data. This comparison suggests that the reaches that benefit from the final rule are also those
where a very large percentage of all recreational participants reside and recreate.
J-l
-------
MPAM EEBA: Appendices
Appendix J: Special Distribution of MP&M Facilities and Recreational User Populations
Table J.I: Distribution of MP&M Facilities and Participants of Water-based Recreation by State
! Percent of State Population
o, , ! Participating by Activity
1 Boat : View
CA ! 11.7%! 36.9%
XX I 12.8%| 16.4%
NY I 12.4%| 25.6%
FL I 18.7%| 32.6%
: :
TT : i i oo/ : i 7 907
ll_j • ll.o/o: 1 / .Z /o
OH I 11.5%| 15.8%
PA I 10.5%| 14.4%
MI I 16.0%| 24.8%
NJ I 15.9%l 32.3%
NC I 8.8% I 17.9%
IN | 14.3%| 15.0%
MA | 15.7%| 30.9%
WI | 15.7%| 22.1%
GA | 11.5%| 13.9%
MO | 13.0%| 12.6%
VA | 13.4%| 17.0%
WA | 25.0%| 39.2%
MN | 17.6%| 19.6%
TN | 17.9%| 13.5%
MD | 14.8%| 18.7%
AL | 14.7%| 11.9%
CT | 16.4%| 37.1%
LA | 16.4%| 15.3%
CO | 6.6% | 13.2%
OR | 20.3%| 37.8%
KY | 11.9%| 12.3%
AZ | 7.3% | 11.2%
IA | 13.5%| 16.4%
OK I 11.2%! 12.6%
i
Fish i Swim
13.6%! 20.1%
18.9%l 14.5%
11.2%| 20.5%
20.5%| 24.2%
14.6%| 9.4%
14.2%| 14.0%
15.2%| 13.7%
18.4%| 20.8%
15.9%l 23.9%
16.5%| 13.5%
20.3%| 16.3%
15.7%| 28.9%
18.1%| 19.7%
16.6%| 11.5%
18.8%| 15.2%
16.2%| 13.4%
18.8%| 25.9%
19.6%| 17.6%
22.6% | 14.5%
17.1%| 12.1%
20.6%| 13.8%
14.5%| 27.0%
27.0%| 13.8%
25.9% I 11.3%
24.9% I 23.0%
22.4% | 10.0%
11.8%| 10.7%
18.7% | 13.5%
25.2%! 14.0%
i
: : :
Average # of Per-Person ! .
Trips per Season by 1 Total State! Potent'al (Extrapolated) # Participants ;Nat ,, # o£
Activity !Pop.(1990)! Based on State Population I MP&M
Boat i View
5.4 ! 14
7.2 I 5
7.9 1 5.7
10.1 I 17.9
9.6 I 9
8 I 8.2
9.4 I 7.4
8.6 I 9.4
10.9 1 6.4
7.7 I 5.2
7.7 | 9
8.7 | 11.6
10 | 6.1
11.4 | 4.1
5.2 | 4
9 I 4.2
5.8 | 11.7
5.4 | 16.5
7.5 | 3.7
8.8 | 12.1
7.5 | 9.2
7.7 | 6.8
4 | 3.4
17.2 | 14.8
8.8 | 7.2
6.5 | 3
7.2 | 8
5 | 4.4
4.9 ! 3.4
: (Millions) :
Fish 1 Swim 1 Boat
7.1 ! 11.7 ! 29.8 ! 3,490,513
10.6 I 6.5 I 17.0 I 2,171,791
9.2 I 8.7 I 18.0 I 2,231,374
17.1 I 15.4 I 12.9 1 2,423,418
13.7 I 5.7 I 11.4 I 1,349,105
13.1 I 8.8 I 10.8 I 1,251,590
10.9 1 8 11.9 1 1,249,014
12 I 8.5 I 9.3 I 1,484,665
6.3 I 7.3 I 7.7 I 1,225,246
13.6 I 7.4 I 6.6 I 586,317
11.8J 5.5 | 5.5 | 794,663
14.3 | 9.5 | 6.0 | 942,332
11.5J 6.2 | 4.9 I 768,940
10.3 | 7.4 | 6.5 | 746,819
5 | 8 | 5.1 | 665,035
8.4 | 6.1 | 6.2 | 827,102
18.2 ! 5.8 ! 4.9 ! 1,216,673
j. £ 4...............
11.5J 6.8 | 4.4 | 767,875
15.1 | 6.7 | 4.9 I 873,280
13.2 | 8.4 | 4.8 | 706,988
18.6 | 10.6 | 4.0 | 593,114
7.7 | 12.3 | 3.3 | 537,516
13.4 | 4.4 | 4.2 | 692,165
13.1 | 5.2 | 3.3 | 217,554
13.2 | 7.4 | 2.8 | 576,323
9.4 | 17.5 | 3.7 | 437,524
8.3 | 5.7 | 3.7 | 267,685
13.8 | 2.7 | 2.8 | 373,482
14.6! 4.2 ! 3.1 ! 351,954
i i
! Facilities
View i Fish j Swim j
10,992,849 ! 4,057,154 1 5,983,736 ! 68,359
2,792,303 I 3,205,978 I 2,456,192 I 38,176
4,602,209 1 2,022,183 I 3,695,714 I 36,329
4,221,438 I 2,657,943 I 3,126,991 1 30,198
1,962,335 I 1,667,985 I 1,079,284 I 28,343
1,718,851 I 1,535,284 I 1,518,596 I 26,460
1,713,391 1 1,809,469 1 1,633,326 I 26,237
2,307,687 I 1,710,593 I 1,936,520 I 23,662
2,495,046 I 1,225,246 I 1,849,008 I 19,805
1,188,920 I 1,091,201 I 895,762 I 15,158
831,624 ! 1,127,312 1 905,546 ! 14,656
> i I i
1,860,501 ! 942,332 1 1,739,689 ! 13,915
j. j. ;. j.
1,079,788 ! 883,463 1 965,266 ! 13,845
> i I i
903,129 ! 1,076,808 1 746,819 ! 13,747
> i I i
646,562 ! 960,606 1 775,874 ! 13,395
j. j. ;. j.
1,049,783 ! 1,002,066 1 827,102 ! 12,829
> i I i
1,907,623 ! 916,260 1 1,261,735 ! 11,991
j. j. ;. j.
857,162 ! 857,162 1 767,875 ! 11,272
j. j. ;. j.
659,079 ! 1,103,957 1 708,510 ! 10,808
> i I i
893,037 ! 818,617 1 576,753 ! 8,993
j. j. ............. j.........................i........
481,905 ! 834,066 1 556,044 ! 8,825
j. j. ............. j..........................i........
1,219,747 ! 475,495 1 888,969 ! 8,593
> i I i
647,509 ! 1,138,723 1 580,525 ! 8,500
> i I i
435,109 ! 854,678 1 372,950 ! 8,231
j. j. ............. j..........................i........
1,074,057 ! 707,306 1 654,913 ! 7,978
> i I i
454,352 ! 824,564 1 370,212 ! 7,822
j. j. ............. j..........................i........
411,823 ! 432,415 1 391,232 ! 7,799
j. j. ............. j..........................i........
454,673 | 519,627 | 373,482 j 7,661
395,948 ! 791,896 I 439,942 ! 6,972
i
State % of
National
Facilities
11.9%
6.6%
6.3%
5.2%
4.9%
4.6%
4.6%
4.1%
3.4%
2.6%
2.5%
2.4%
2.4%
2.4%
2.3%
2.2%
2.1%
2.0%
1.9%
1.6%
1.5%
1.5%
1.5%
1.4%
1.4%
1.4%
1.4%
1.3%
1.2%
:
:
.^ „,.,! Cumulative Percent Distribution
Cum. SI :
„, , i of Participants by State
Facilities • T
1 Boat 1 View
11.9% ! 10.3%! 19.5%
18.5% I 16.7%| 24.5%
24.8% I 23.3% I 32.6%
30.0% I 30.5%| 40.1%
34.9% 1 34.5% I 43.6%
39.5% I 38.2% I 46.6%
44.1% I 41.9%l 49.7%
48.2% I 46.3% I 53.8%
51.6% I 49.9%l 58.2%
54.3% I 51.6%| 60.3%
56.8% | 54.0%| 61.8%
59.2% | 56.8%| 65.1%
61.6% | 59.0%| 67.0%
64.0% | 61.2%| 68.6%
66.3% | 63.2% | 69.8%
68.6% | 65.7% | 71.6%
70.6% | 69.3% | 75.0%
72.6% | 71.5%| 76.5%
74.5% | 74.1%| 77.7%
76.0% | 76.2% | 79.3%
77.6% | 77.9% I 80.1%
79.1% I 79.5% | 82.3%
80.5% | 81.6%| 83.4%
82.0% | 82.2% | 84.2%
83.3% | 83.9%| 86.1%
84.7% | 85.2% | 86.9%
86.1% | 86.0%| 87.7%
87.4% | 87.1%| 88.5%
88.6% I 88.2%! 89.2%
i i
Fish 1 Swim
9.4% | 13.8%
16.8% | 19.4%
21.5% | 27.9%
27.7% | 35.1%
31.5% | 37.6%
35.1% | 41.1%
39.3% | 44.8%
43.2% | 49.3%
46.1% | 53.5%
48.6% | 55.6%
51.2% | 57.7%
53.4% | 61.7%
55.4% | 63.9%
57.9% | 65.6%
60.1% | 67.4%
62.5% | 69.3%
64.6% | 72.2%
66.6% | 73.9%
69.1% | 75.6%
71.0% | 76.9%
72.9% | 78.2%
74.0% ! 80.2%
i.
76.7% | 81.5%
78.7% | 82.4%
80.3% | 83.9%
82.2% | 84.8%
83.2% ! 85.7%
i
84.4% | 86.5%
86.2% I 87.5%
i
J-2
-------
MP&M EEBA: Appendices
Appendix J: Special Distribution of MP&M Facilities and Recreational User Populations
Table J.I: Distribution of MP&M Facilities and Participants of Water-based Recreation by State
State
SC
KS
AR
MS
NE
UT
WV
RI
ME
NH
NM
ID
NV
MX
SD
ND
HI
VT
DE
WY
AK
Percent of State Population
Participating by Activity
' T
Boat ! View
13. 8% | 19.9%
6.7% | 17.0%
14.1% | 12.5%
13.6% | 12.1%
10.7% | 15.5%
8.1% | 17.1%
9.5% | 10.3%
15. 8% | 40.4%
22.2% | 44.4%
18.8%j 31.2%
6.7% | 8.6%
24.1%| 25.3%
17.3%j 21.3%
14.5% | 20.0%
16.7% | 21.4%
15.0% | 15.0%
16.4% | 58.2%
20.6% | 17.6%
15.7%j 41.2%
19.4% | 16.1%
34.5% | 41.4%
f
Fish ! Swim
|
26.0%| 15.5%
18.5% | 13.3%
28.1%| 18.0%
23.6% | 15.7%
10.7% | 15.5%
13. 5% | 12.6%
18.3% | 15.9%
19.3% | 36.8%
27.8% | 37.5%
14.1%| 34.4%
12.4% | 9.5%
20.5% | 20.5%
13.3% | 12.0%
34.5% | 29.1%
16.7% | 21.4%
25.0%| 15.0%
18.2% | 47.3%
8.8% | 20.6%
15.7% | 13.7%
48.4% | 6.5%
37.9% I 6.9%
Average # of Per-Person ! _ , , . . ,_, , , , _ „ _ , . .
. „ : _, , , „ , , Potential (Extrapolated) # Participants
Trips per Season by Total State „ . c. . „ , a
Activity jPop. (1990)j Based on State Population
' ;
Boat ! View
|
9.8 | 8.5
17.6 | 9
4.6 | 10.2
6.3 | 24.2
3.9 | 2.1
6.6 | 3.5
6.6 | 4.6
6.9 | 4.6
7.6 | 5.7
3.3 | 14.9
3.7 | 5.6
5.8 | 4.3
4.8 | 7.3
7.8 | 15.6
2.3 | 1.8
3.7 | 3
6.7 | 33.9
7.1 | 5.5
6.4 | 11
6.3 | 4.6
5.4 1 7.1
] (Millions) f
Fish ! Swim ! ! Boat
i i i
16.2 | 7.5 | 3.5 | 481,589
12.9 | 6.2 | 2.5 | 165,172
13.3 | 7.3 | 2.4 | 330,571
17.4 | 12.9 | 2.6 | 349,222
13.9 | 3.9 | 1.6 | 169,113
3.6 | 6.8 | 1.7 | 139,691
17.2 | 6.7 | 1.8 | 170,807
8.3 | 7 1.0 | 158,442
10.5 | 10.3 | 1.2 | 272,873
13.2 | 15.7 | 1.1 | 207,985
9.8 | 3.8 | 1.5 | 101,005
13.4 | 9.5 | 1.0 | 242,590
15.4 | 6.3 | 1.2 | 208,318
20.7 | 8.3 | 0.8 | 116,228
6 | 7 0.7 | 116,001
4.5 | 11.5 | 0.6 95,820
6.6 | 15.5 | 1.1 | 181,347
8.7 | 10.4 | 0.6 | 115,862
11.5 | 6.9 | 0.7 | 104,497
8.1 | 8 0.5 87,791
17.4 | 2 0.6 | 189,670
1 !
View ! Fish ! Swim
I j
693,488 | 905,387 j 539,380
422,105 | 458,810 j 330,343
293,841 | 661,141 j 422,396
312,462 | 606,544 j 404,363
244,274 | 169,113 j 244,274
294,902 | 232,818 j 217,296
185,041 327,381 j 284,679
404,907 | 193,651 j 369,697
545,746 | 341,091 j 460,473
346,641 | 155,989 j 381,305
129,863 | 187,580 j 144,292
254,720 | 206,202 j 206,202
256,391 160,244 j 144,220
159,813 | 276,041 j 232,455
149,144 | 116,001 | 149,144
95,820 159,700 j 95,820
644,788 | 201,496 j 523,890
99,310 49,655 115,862
274,305 | 104,497 j 91,435
73,159 219,478 j 29,264
227,604 208,637 j 37,934
Nat.'l # of
MP&M
Facilities
6,907
6,370
5,825
5,165
4,424
3,633
3,442
3,106
2,980
2,960
2,927
2,572
2,406
2,204
2,049
1,749
1,677
1,488
1,379
1,309
1,156
State % of
National
Facilities
1 9°/
1 .Z /o
1.1%
1.0%
0.9%
0.8%
0.6%
0.6%
0.5%
0.5%
0.5%
0.5%
0.4%
0.4%
0.4%
0.4%
0.3%
0.3%
0.3%
0.2%
0.2%
0.2%
i
C m ST^ Cumulative Percent Distribution
o/ „<• '' of Participants by State
/O OI !
T^ ..... :
Facilities r r
! Boat ! View
i i
89.8% | 89.6% | 90.4%
90.9% J90.1%j 91.1%
91.9% | 91.1%| 91.7%
92.8% | 92.1%| 92.2%
93.6% | 92.6% | 92.7%
94.2% | 93.0%| 93.2%
94.8% | 93. 5% | 93.5%
95.3% | 94.0%| 94.2%
95.9% | 94.8% | 95.2%
96.4% | 95.4% | 95.8%
96.9% J95.7%j 96.0%
97.3% J96.4%j 96.5%
97.7% J97.0%j 96.9%
98.1% J97.4%j 97.2%
98.5% J97.7%j 97.5%
98.8% J98.0%j 97.7%
99.1% J98.5%j 98.8%
99.3% J98.9%j 99.0%
99.6% J99.2%j 99.5%
99.8% J99.4%j 99.6%
100.0% 1 100.0%| 100.0%
!
Fish ! Swim
j
88.3% 1 88.8%
f
89.4% ! 89.5%
f
90.9% ! 90.5%
f
92.3% I 91.4%
f
92.7% I 92.0%
f
93.3% I 92.5%
f
94.0% I 93.1%
f
94.5% I 94.0%
f
95.3% I 95.1%
f
95.6% I 95.9%
f
96.1% 1 96.3%
f
96.5% 1 96.7%
f
96.9% ! 97.1%
f
97.5% 1 97.6%
f
97.8% 1 97.9%
f
98.2% 1 98.2%
f
98.7% 1 99.4%
f
98.8% 1 99.6%
f
99.0% 1 99.8%
f
99.5% 1 99.9%
f
100.0%! 100.0%
Source: Information on total MP&M facilities by state is from Census data; information on where recreating people live is from NDSdata.
J-3
-------
MPAM EEBA: Appendices
Appendix J: Special Distribution of MP&M Facilities and Recreational User Populations
Figure J.I: Cumulative Distribution of Facilities and Participants
1 20.0% -,
1 00.0%
80.0%
60.0%
40.0%
20.0%
0.0%
nLnh-cn^nini^tnT-coini^cnr-niD^-cn^tninf-cn
Number of states3
Facilities
— Boating
- - - -Viewing
Fishing
— — Swim m ing
" The numbers refer to states in the order they appear in the above table. Therefore, 1 is California, 2 is Texas, 3 is New York, etc.
Sources: Information on total MP&Mfacilities by state is from Census data; information on where recreating people live is from NDSdata.
J-4
-------
MPAM EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
Appendix K: Selecting WTP Values
for Benefits Transfer
INTRODUCTION
EPA identified eight surface water evaluation studies that
quantified the effects of water quality improvements on
various water-based recreational activities. As noted in
Chapter 15 of this report, the Agency selected these
studies based on technical criteria for evaluating study
transferability (Desvousges et al., 1987; Desvousges et al.,
1992; and Boyle and Bergstrom, 1992), including the
following:
*• The environmental change valued at the study
site must be the same as the environmental
quality change caused by the rule (e.g., changes
in toxic contamination vs. changes in nutrient
concentrations);
*• The populations affected at the study site and at
the policy site must be the same (e.g.,
recreational users vs nonusers);
APPENDIX CONTENTS
K.I Desvousges et al., 1987. Option Price Estimates
for Water Quality Improvements: A Contingent
Valuation Study for the Monongahela River K-2
K.2 Farber and Griner, 2000. Valuing Watershed Quality
Improvements Using Conjoint Analysis K-3
K.3 JakusetaL, 1997. Do Sportfish Consumption
Advisories Affect Reservoir Anglers'Site Choice? .. K-5
K.4 Lant and Roberts, 1990. Greenbelts in the Cornbelt:
Riparian Wetlands, Intrinsic Values, and
Market Failure K-6
K.5 Audrey Lyke, 1993. Discrete Choice Models to Value
Changes in Environmental Quality: A Great Lakes
Case Study K-7
K.6 Montgomery and Needelman, 1997. The Welfare
Effects of Toxic Contamination in Freshwater Fish . . K-8
K.7 PhaneufetaL, 1998. "Valuing Water Quality
Improvements Using Revealed Preference Methods
When Corner Solutions are Present" K-8
Glossary K-10
Acronyms K-l 1
References K-12
The assignment of property rights at both the
study and policy sites must lead to the same
theoretically-appropriate welfare measure (e.g., willingness-to-pay
compensation); and
| vs. willingness-to-accept
*• The candidate studies should be based on defensible research methods. Six of the eight studies are published in peer
reviewed journals. One study, Tudor et al. (2002), was presented at the annual American Agricultural Economic
Association and the Northeastern Resource and Environmental Economic meetings.1 The eighth study, Lyke (1993),
is an unpublished Ph.D. dissertation.
In addition to the above criteria, the Agency considered authors' recommendations regarding the robustness and theoretical
soundness of various estimates in selecting point estimates for benefits transfer.
The rest of this appendix presents welfare estimates from seven studies used in estimating recreational benefits from the final
regulation and provides EPA's reasons for selecting specific values from each study. The study by Tudor et al. (2002) is
discussed in detail in Chapter 21. All welfare estimates from that study are eligible for use in benefits transfer, because the
study is based on the policy scenarios specific to the MP&M regulation.
1 Preliminary results of this study were presented at the annual American Agricultural Economic Association meeting (L. Tudor et al.,
1999a) and at the annual Northeastern Agricultural and Resource Economic Association meeting (L. Tudor et al., 1999b). EPA subjected
this study to a formal peer review by experts in the natural resource valuation field. The peer review concluded that EPA had done a
competent job, especially given the available data. This study can be found in Chapter 21. The peer review report is in the docket for the
rule.
K-l
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MPAM EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
K.I bESVOUSSES ET AL., 1987. OPTION PRICE ESTIMATES FOR WATER QUALITY
IMPROVEMENTS: A CONTINGENT VALUATION STUDY FOR THE MONONSAHELA RIVER
This study used findings from a contingent valuation (CV) survey to estimate WTP for improved recreational fishing from
enhanced water quality in the Pennsylvania portion of the Monongahela River. In a hypothetical market, each survey
respondent was asked to provide an option price for different water quality changes, such as "raising the water quality from
suitable for boating (hereafter, 'beatable' water) to a level where gamefish would survive (hereafter, 'fishable' water)." Table
K.I lists water quality changes evaluated in the study and the corresponding WTP estimates. The following discussion
provides justification for selecting the point estimates EPA used in the benefits transfer analysis in Chapter 15.
Table K.I: Changes in the Resource Value from a Specified Water Quality Improvement from
Desvousges et al. (1987)
Water Quality Change Valued
Unsuitable to Beatable
Beatable to Fishablea
Fishable to Swimmable
Beatable to Swimmable
Unsuitable to Swimmable
Unsuitable to Beatable
Beatable to Fishable
Fishable to Swimmable
Beatable to Swimmable
Unsuitable to Swimmable
Beatable to Unsuitable
Beatable to Fishable
Fishable to Swimmable
Beatable to Swimmable
Unsuitable to Swimmable
Beatable to Unsuitable
Beatable to Fishable
Fishable to Swimmable
Beatable to Swimmable
Unsuitable to Swimmable
Adjusted to 2001$"
User
Iterative Si
$53.4
$36.8
$23.0
$62.5
$115.9
Iterative Sia
$184.4
$11.3,1.
$64,i.
$194.1
$378.5
Direct Qm
$88.2
$60.9
$3?,3.
$103.0
$191.2
Direct Q
$?.1,1...
m9
.L
$4.4,5...
$138.6
$229.6
Nonuser
Ming; $25 sta
$57.8
ccoo -i
3)28.3
$14.0
h $42,2...
$100.0
Wing: $125 st
$7.5,6...
$5.1,2...
$22,5
$78.9
$154.2
jstion: nopqyi
$27,6.
$2.1,0...
$16.6
$3.?,5...
$67.2
uestion: paym
$103.2
$4.2,6...
$1.5.1)...
$5.8,3...
$161.3
Combined
rting point
$56.4
$30.9
$16.9
$48.9
$105.3
irting point
$111.7
$71.9
$36.6
$117.3
$229.0
•nent card
$47.7
$34.2
$24.1
$60.7
$108.4
ent card
$99.3
$57.1
$24.3
$83.6
$182.8
Original Estimates (1981$)
User
$27.4
$18.9
$11.8
h $3.2,1...
$5?,5..
$94.7
$5.8,1...
$3.3,1...
$99.7
$194.4
$4.5,3...
$3.1,3.
$20.2
$52.9
$98.2
$46.8
$4.5,3..
$22.9
$7.1.:!.
$117.9
Nonuser
$29.7
$14.5
$7.2
$21.7
$51.4
$38.8
$26,3...
$1.1,6...
$40,5...
$79.2
$1.4,2...
$10.8
$0 c
S.9.:-?....
$20.3..
$34.5
', $53,0..
$2.!,?....
$L2_
$29,9...
$82.8
Combined
$29.0
$15.9
$8.7
$25.1
$54.1
$57.4
$36.9
$18.8
$60.2
$117.6
$24.5
$17.6
$12.4
$31.2
$55.7
$51,0.
$29.3..
$12.5
$4.2,9.
$93.9
Location: Pennsylvania portion of the Monongahela River
Estimating Approach: CV
Survey Population : Recreational Users and Nonusers
a The value selected for benefits transfer is given in bold.
b WTP values from the original study are adjusted to 2001$ based on the Consumer Price Index (CPI).
Source: Desvousges et al., 1987.
EPA judged that only one value from this study met the requirements for the quality of research methods and was compatible
with the environmental changes and population characteristics considered in the analysis of recreational benefits from the
MP&M rule. EPA selected this value for the following reasons:
K-2
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MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer
> Environmental quality change. The Desvousges et al. (1987) study derived WTP values for five different changes
in water quality, as shown in Table K.I above. EPA judged that only one of these improvements, from "beatable" to
"fishable," is compatible with the changes in water quality expected under the MP&M rule. Streams unsuitable for
recreational activities such as boating are likely to be affected by multiple environmental stressors from many
sources, including many that are not related to MP&M discharges (e.g., severe oxygen depletion.) In these cases, it
is reasonable to assume that changes in concentrations of MP&M pollutants would reduce or eliminate one of the
stressors on the reach, but would be unlikely to change the designation of the reach.
The analysis in Chapter 15 assumes that reaches with ambien t water quality criteria (A WQC) exceedances
under the baseline conditions are beatable and likely to support rough fishing, but may not be clean enough to
support gamefishing. AWQC are set at a level below which pollutant concentrations are not expected to cause
significant harm to human health or aquatic life. Exposure to pollutant concentrations above the AWQC levels are
expected to have a harmful effect. Therefore, by definition, water with pollutant levels that exceed criteria set to
protect human health or aquatic life are not suitable waters for sensitive aquatic species or ideal as a sources of fish
for consumption.
Removing AWQC exceedances is therefore comparable to shifting water quality from "boatable"to "fishable." The
Agency did not use the beatable to swimmable designation because a more limited number of reaches are suitable for
swimming nationally due to reasons not related to MP&M discharges (e.g., amenities, pathogens). Determining
national level locations affected by MP&M pollutants that are suitable for swimming required more resources than
were available for the national analysis.
»• Research methods. The authors used four different payment vehicles in their CV study. For the recreational
benefits analysis, EPA decided to use the WTP estimates derived from the "iterative bidding" (IB) payment
vehicle, because it is universally preferred to the "direct question/open-ended" format for eliciting option price
bids.
Survey respondents in the direct question format are asked to state the most that they would be willing to pay for the
program or policy. This format confronts respondents with an unfamiliar choice. Studies that use this approach
usually have high non-response rates.
Respondents in the IB format are asked whether they would be willing to pay a given amount. If the answer is yes,
then this amount is raised in pre-set increments until the respondent says that he or she will not pay the last amount
given. If the answer is no, then the amount is decreased until the respondent indicates WTP the stated amount.
Some studies found that the respondent's final WTP amount depends on the initial amount offered. This problem is
referred to in economic literature as starting point bias. The Agency selected the WTP estimates derived using the
$25 starting point IB process to avoid upward starting point bias. Table K.I shows that the selected estimates are the
most conservative among all the payment vehicles used.
•• Population characteristics. The user population considered in this study matches the user population characteristics
considered in EPA's analysis (i.e., recreational anglers, boaters, and wildlife viewers).
K.2 FARBER AND 6RINER, 2000. VALUING WATERSHED QUALITY IMPROVEMENTS
USING CONJOINT ANALYSIS
Farber and Griner (2000) used a CV study to estimate changes in water resource values to users from various improvements
in Pennsylvania's water quality. The study defines water quality as "polluted," "moderately polluted," and "unpolluted"
based on a water quality scale developed by EPA Region III. "Polluted" streams are unable to support aquatic life,
"moderately polluted" streams are somewhat unable to support aquatic life, and "unpolluted" streams adequately support
aquatic life. Farber and Griner developed WTP estimates for water quality improvements for the following three water
quality changes:
»• from "moderately polluted" to "unpolluted,"
»• from "severely polluted" to "moderately polluted," and
K-3
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MP&M EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
> from "severely polluted" to "unpolluted."
The authors used six different model variations to estimate the WTP for the three improvements scenarios for various
population groups (e.g., users, nonusers, and a mix of users and nonusers). Table K.2 presents the estimated WTP values.
The following discussion provides EPA's reasons for selecting point estimates for the use in benefits transfer.
Table K.2: Estimate WTP for Specified Water Quality Improvements from Farber and Griner (2001$)
Water Quality Change Valued
Moderately Polluted to Unpolluted
Severely Polluted to Moderately Polluted
Severely Polluted to Unpolluted
Moderately Polluted to Unpolluted
Severely Polluted to Moderately Polluted
Severely Polluted to Unpolluted
Moderately Polluted to Unpolluted a
Severely Polluted to Moderately Polluted
Severely Polluted to Unpolluted
Binary Choice Model
User
j
$49.7
$66.9
$117.3
Int
$48.2
$65.2
$115.5
Fixe
$24.5
$42.4
$86 6
Nonuser
Basic
$6.3
$5.8
$44.9
eractive
$3.2
$1.5
$41.3
d Effects
$16.4
$10.6
$48 4
Combine
$40.4
$55.6
$95.7
$38.0
$52.7
$92.9
$28.3
$38.2
$80.4
Intensity of Preference Model
User
$56.2
$73.8
$129.6
$56.9
$75.1
$133.1
$41.8
$63.4
$110.5
Nonuser
$14.0
$51.4
$57.7
$13.3
$50.6
$57.6
$5.5
$30.3
$31 0
Combine
$54.2
$70.9
$116.8
$54.6
$71.9
$119.5
$41.0
$59.0
$9^6
Location: Lower Allegheny Watershed in Western Pennsylvania
Estimating Approach: Conjoint Analysis
Survey Population: Recreational users and nonusers
a Values selected for the use in benefits transfer are given in bold.
b WTP values from the original study are adjusted to 2001$ based on CPI.
Source: Farber and Griner, 2000.
The Agency selected only two values from this study based on their compatibility with the environmental changes and
population characteristics considered in both the original study and the analysis of recreational benefits from the MP&M rule.
The following discussion summarizes EPA's reasons used in the selection process:
> Environmental quality change. EPA judged that only one water quality improvement scenario change from
"moderately polluted" to "unpolluted" is compatible with the environmental quality change expected from the
final regulation
AWQC are set at a level below which pollutant concentrations have not been demonstrated to cause significant harm
to human health or aquatic life. Exposure to pollutant concentrations above the AWQC levels are expected to have a
harmful effect. Therefore, by definition, water with pollutant levels that exceed criteria set to protect human health
or aquatic life are polluted waters.
EPA chose the case where the policy variable changed from moderately polluted to unpolluted because this is likely
to be the most frequently occurring scenario for reaches with MP&M discharges. Streams unable to support any
aquatic life (i.e., "severely polluted") are likely to be affected by numerous environmental stressors, in addition to
MP&M discharges. Eliminating MP&M-related AWQC exceedences would eliminate or reduce one of the stressors,
but is unlikely to change the quality of the water from severely polluted to unpolluted. It is more realistic to assume
that most streams affected by MP&M facility discharges are moderately polluted, i.e., these streams support some
aquatic life; but sensitive species are adversely affected by MP&M pollutants exceeding AWQC values protective of
aquatic life. Removing all AWQC exceedances would make such streams unpolluted.
> Research methods. EPA considered only two of the six versions of the benefits transfer model based on the
authors' recommendations. The authors appear to prefer the "fixed effects" versions of both the binary choice
K-4
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MPAM EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
CBC) and intensity of preference (IP) models. Specifically, they note that "A likelihood ratio test, with degrees
of freedom being the number of individuals in the estimating sample, can be used to test the superiority of the fixed
effects model. Such a test shows the fixed effects model to be a statistical improvement over either the basic or
interactive models" (see Table K.2). In addition, they state that, "the purpose of estimating a fixed effects model was
to account for the possibility that some respondents may approve of all changes, regardless of price and quality. If
this behavior existed in the sample, not controlling for it would result in overestimates of marginal valuations for
each type of quality change. This expectation is supported by the fact that the fixed effects valuation estimates are
lower than the others."
Population characteristics. The user population considered in this study matches the user population
characteristics considered in EPA's analysis (i.e., recreational anglers, boaters, and wildlife viewers).
K.3 JAKUS ET AL., 1997. &O SPORTFISH CONSUMPTION ADVISORIES AFFECT
RESERVOIR ANGLERS' SITE CHOICE?
Jakus et al. (1997) used a repeated discrete choice travel cost (TO model to examine the impacts of fish consumption
advisories (FCA) in eastern and middle Tennessee. The estimated consumer surplus from recreational fishing in middle
and east Tennessee is $26.02 and $52.57 per angler per day, respectively, under the baseline water quality conditions. The
estimated welfare gain from removing FCAs is $2.04 and $3.16 per angler per day , respectively. Table K.3 summarizes the
study's estimates.
Table K.. 3 '.Consumer Surplus from Recreational Fishing from Jakus et al.
„. ... Consumer Surplus
Water Quality Change Valued ... . ,. „„„.,.,,
J & 1 Adjusted to 2001$
Site Choice Model — multinomial loeit
Average surplus per trip in middle TN (baseline water quality $26.02
Benefit per trip from removing all advisories in middle TN $2.04
Average surplus per trip in East TN (baseline water quality conditions) j $52.57
Benefit per trip from removing all advisories in east TN $3.16
Benefitper trip from removing Watts Bar advisory $1.75
Repeated Discrete Choice Model — repeated nested logit model
Seasonal benefit from removing all advisories in middle TN $24.22
Seasonal benefit from removing all advisories in east TN $52.27
Seasonal benefit from removing Watts Bar advisory ^$^^^3
(1997)°
Consumer Surplus
($1997)
$23.60
$1.85
$47.67
$2.86
$1.59
$21.96
$47.40
$27.60
Location: Tennessee
Estimating Approach: TC
Survey Population: Tennessee residents; anglers and non-anglers
a Values selected for the use in benefits transfer are given in bold.
b WTP values from the original study are adjusted to 2001$ based on CPI.
Source: Jakus et al, 1997.
EPA selected two values from this study for use in benefits transfer, based on their compatibility with the environmental
quality change and population characteristics at both the original study and policy sites, for the following reason:
> Environmental quality change. FCAs are usually triggered by the presence of toxic pollutants in fish tissue. EPA
expects the final regulation to reduce discharges of toxic pollutants, including those linked to FCAs (e.g., mercury
and lead). The Agency therefore assumed that the removal of FCAs is compatible with water quality improvements
expected from the final regulation.
K-5
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MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer
The recreational benefits analysis uses consumer surplus estimates for both regions studied by the authors, because
MP&M facilities are located in these regions as well as throughout heavily populated regions of the U.S. EPA did
not include the value corresponding to the Watts Bar lake in the benefits transfer analysis because this lake is
included in the set of fishing areas for east Tennessee.
K.4 LANT AND ROBERTS, 1990. SREENBELTS IN THE CORNBELT: RIPARIAN
WETLANDS, INTRINSIC VALUES, AND MARKET FAILURE
Lant and Roberts (1990) used a CV study to estimate the recreational and nonuse benefits of improved water quality in
selected Iowa and Illinois river basins. River quality was defined by means of an interval scale of "poor," "fair," "good," and
"excellent." The authors defined the four water quality intervals as follows:
•• "poor" water quality is inadequate to support any recreation activity,
> "fair" water quality is adequate for boating and rough fishing,
*• "good" water quality is adequate for gamefishing, and
*• "excellent" is adequate to support swimming and exceptional fishing.
Table K.4 summarizes WTP values for specified water quality improvements from this study.
Table K.4: WTP Values for a Specified Water Quality Improvement from Lant and Roberts (1990)
Water Quality Change Valued
Poor to fair
Fair to gooda
Good to excellent
Adjusted to 2001$
:
Use Value Nonuse Value
:
$47. 5J $58.6
:
$57.8J $73.5
$64.7J $67.3
Original Study Values 1987$
:
Use Value 1 Nonuse Value
:
$30.50J $37.61
:
$37.10J $47.16
$41.5l| $43.22
Location: Selected Iowa and Illinois river basins
Estimating Approach: CV
Survey Population: Recreational users and nonusers
a The values given in bold were selected for the use in benefits transfer.
b WTP values from the original study are adjusted to 2001 $ based on CPI.
Source: Lant and Roberts, 1990.
The Agency judged that only one value from this study is compatible with the environmental changes and population
characteristics considered in the analysis of recreational benefits from the MP&M rule, for the following reasons:
> Environmental quality change. The Agency judged that only one of the three possible water quality changes
considered in this study "fair" to "good" was compatible with the water quality change expected under the
MP&M rule. EPA assumed in its analysis of recreational benefits expected from the MP&M rule that reaches with
AWQC exceedances under the baseline conditions may support rough fishing, but may not be clean enough to
support more sensitive species such as those desired for game fishing. Removing AWQC exceedances will shift
water quality from "fair" to "good."
*• Population characteristics. The user population considered in this study matches the population characteristics
considered in EPA's analysis (i.e., recreational anglers, boaters, and wildlife viewers).
K-6
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MPAM EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
K.5 AUDREY LYKE, 1993. DISCRETE CHOICE MODELS TO VALUE CHANGES IN
ENVIRONMENTAL QUALITY: A GREAT LAKES CASE STUDY
Lyke's (1 993) study of the Wisconsin Great Lakes open water sport fishery showed that anglers may place a significantly
higher value on a contaminant-free fishery than on one with some level of contamination. Lyke estimated the value of the
fishery to Great Lakes trout and salmon anglers if it was improved enough to be "completely free of contaminants that may
threaten human health." The author also estimated various policy scenarios that affect the value of recreational fishing in the
Wisconsin Great Lakes, including reducing the daily bag limit for lake trout and restoring naturally reproducing populations
of lake trout. Table K.5 presents welfare estimates from this study.
Table K.5: WTP Estimates for Specified Water Quality Improvements from Lyke (1993)°
Water Quality Change Valued
CV- linei
1990 fishing conditions remain the same as 1989
WI daily bag limit for lake trout reduced to one a day
Great Lakes fish are free of pollutants affecting human health
Restoring naturally reproducing populations of lake trout
WI inland fishing conditions remain the same as 1989
Restoring naturally reproducing populations of lake trout in WI
waters of Great Lakes (inland anglers only)
CV — constant elasticity <
1990 fishing conditions remain the same as 1989
Great Lakes fish are free of pollutants affecting human health
CV— constant elasticity 0,
1990 fishing conditions remain the same as 1989
Great Lakes fish are free of pollutants that affect human health
Adjusted to 2001$"
Value of WI
Fishing
ir logit model
$95,062,744
$43,962,951
$105,625,27
$17,271,159
$964,330,17
$0
if substitution m
$118,899,79
$156,011,38
f substitution mo
$26,834,528
$40,537,266
Change in
Value
($51,099,793
$10,562,527
$17,271,159
$0
odel (mean)
$37,111,581
del (median)
$13,702,738
Original Study Value
Value of WI
Fishing
$66,600,000
$30,800,000
$74,000,000
$12,100,000
$675,600,00
$0
$83,300,000
$109,300,00
$18,800,000
$28,400,000
Change in
Value
($35,800,000
$7,400?000
$12,100,000
$0
$26,000,000
$9,600,000
Location: Wisconsin
Estimating Approach: TC and CV
Survey Population: Wisconsin Great Lakes and inland anglers
a The values selected for the use in benefits transfer are given in bold.
b WTP values from the original study are adjusted to 2001$ based on CPI.
Source: Lyke, 1993.
EPA selected two WTP values from this study for use in benefits transfer for the following reasons:
> Environmental quality change. EPA judged that only one policy scenario Great Lakes fish that are free from
contaminants harmful to human health is compatible with water quality improvements associated with removal of
all AWQC exceedances. Other scenarios, such as reducing daily bag limit for lake trout to one per day and restoring
naturally reproducing populations of lake trout, are irrelevant to the MP&M regulation. The Agency used estimates
from the "1990 fishing conditions remain the same as 1989 conditions" scenario as an estimate of the baseline value
of recreational fishing in Wisconsin.
»• Research methods. The Agency did not consider estimates from the TC model because the author noted that "the
nested logit travel cost model results seem too high."
K-7
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MP&M EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
K.6 MONTGOMERY AND NEEDELMAN, 1997. THE WELFARE EFFECTS OF TOXIC
CONTAMINATION IN FRESHWATER FISH
Montgomery and Needelman (1997) estimated benefits from removing "toxic" contamination from lakes and ponds in New
York State. They used a binary variable as their primary water quality measure, which indicates whether the New York
Department of Environmental Conservation considers water quality in a given lake to be impaired by toxic pollutants. Their
model controls for major causes of impairments other than "toxic" pollutants, to separate the effects of various pollution
problems that affect the fishing experience. Table K.6 lists environmental quality changes considered in the study and the
WTP values corresponding to a specified water quality change.
Table K.6: Welfare Estimates from Montgomery and Needelman (1997)
Water Quality Change Valued
Eliminate toxic contamination in all lakes a
All toxic lakes are closed to fishing
Raise pH in acidic lakes (none are threatened or impaired)
Close all acidic lakes to fishing
Eliminate toxic contamination and raise pH in acidic lakes
Compensating Variation
per Capita per Season
(2001$)"
$90.28
$124.31
$19.73
$21.20
$113.39
Compensating Variation
per Capita per Season
(1989$)
$63.25
$87.09
$13.82
$14.85
$79.44
Location: New York State
Estimating Approach: TC — Repeated discrete choice model
Survey Population: New York State residents; anglers and non-anglers
a The values selected for the use in benefits transfer are given in bold.
b WTP values from the original study are adjusted to 2001$ based on CPI.
Source: Montgomery and Needelman, 1997.
The Agency selected only one value from this study for use in the benefits transfer based on its compatibility with
environmental quality changes at both the original study and the MP&M sites, for the following reason:
> Environmental quality change. Only one of the five policy scenarios considered eliminate toxic contamination
in all lakes is directly compatible with the potential changes brought about by the MP&M rule. The MP&M rule
is unlikely to significantly affect the acidity in lakes and streams affected by MP&M discharges. The last three
policy scenarios in Table K.6 involve changes in pH levels, and are therefore not included in the benefits transfer.
The Agency also did not consider the estimate from the second scenario in Table K.6 closing all toxic lakes to
fishing in benefits transfer, because it does not consider water quality improvement per se.
K.7 PHANEUF ET AL., 1998. VALUING WATER QUALITY IMPROVEMENTS USINS
REVEALED PREFERENCE METHODS WHEN CORNER SOLUTIONS ARE PRESENT
Phaneuf et al. (1998) studied angling in Wisconsin Great Lakes. They estimated changes in recreational fishing values
resulting from a 20 percent reduction of toxin levels in lake trout flesh. The study uses a TC model to value water quality
improvements when corner solutions are present in the data. Corner solutions arise when consumers visit only a subset of
the available recreation sites, setting their demand to zero for the remaining sites. Phaneuf et al. found that improved
industrial and municipal waste management results in general water quality improvement. Table K.7 presents findings from
this study based on two policy scenarios and four different model specifications.
K-&
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MPAM EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
Table
Water Quality
Valued
Change
20% reduction in toxins
Loss of South Lake
Michigan
:
I
K.7: Welfare Estimates from
RNL
$41.62
$232.19
Adjusted to 2001$"
RPRN KT
r $12.53 r $166.21 r
\ $140.37 I $12,119 I
Phaneuf
I...
System
$15.69
$441.36 |
et al.
(1998)
Study Values (1989$)
RNL
$29.16
$162.67
RPRN
$8.78
$98.34
KT
, $116.45 ,
1 $849.09 I
System
$10.99
$309.21
Location:
Estimating Approach:
Survey Population:
Wisconsin Great Lakes
TC models, including:
RNL: Repeated Nested Logit model;
RPRNL: Random Parameters Repeated Nested Logit model;
KT: Kuhn-Tucker model; and
System: Systems of Demands model
Wisconsin anglers; Great Lakes and inland anglers
a WTP values from the original study are adjusted to 2001$ based on CPI.
Source: Phaneuf etal, 1998.
The Agency selected only one value for use in benefits transfer for the following reasons:
> Environmental quality change. Only one policy scenario evaluated in this study a 20 percent reduction in the
toxin levels in fish tissue is compatible with the water quality changes expected from the MP&M regulation (i.e.,
removal of aquatic life-based AWQC exceedances. The second scenario loss of South Lake Michigan fishing
sites is irrelevant to the final regulation.
»• Research methods. Phaneuf et al. estimated four different models and provided WTP estimates based on each of
them. The authors indicated, however, that "the KT model comes closest to matching the ideal theoretical model"
(see authors conclusions, page 1030). Other models either rely on more restrictive assumptions or require additional
research. The Agency chose the value from the KT model based on the authors' recommendation, which is one of
the selection criteria for values used in benefits transfer.
K-9
-------
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer
GLOSSARY
ambient water quality criteria (AWQC): Levels of water quality expected to render a body of water suitable for its
designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,
swimming, farming, fish production, or industrial processes. (http://www.epa.gov/OCEPAterms/aterms.html)
binary choice (BC): offers respondents to a contingent valuation survey specific dollars and cents choices, for example,
"Would you be willing to pay between $10 and $20 per year to improve visibility at the Grand Canyon?"
conjoint analysis: "any decompositional method that estimates the structure of consumer's preferences given his or her
overall evaluations of a set of alternatives that are prespecified in terms of levels of different attributes. Price typically is
included as an attribute." (Green and Srinivasan, 1990).
contingent valuation (CV): a method used to determine a value for a particular event, where people are asked what they
are willing to pay for a benefit and/or are willing to receive in compensation for tolerating a cost. Personal valuations for
increases or decreases in the quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit
valuations or bids that are close to what would be revealed if an actual market existed.
(http://www.damagevaluation.com/glossary.htm)
corner solutions: a corner solution arises when a consumer who has a choice of two goods, xl and x2, chooses to consume
no X( at the utility maximum.
direct question/open-ended (OE): in the OE approach, respondents are asked the most they would be willing to pay for
the program or policy. This approach has a virtue of not providing any hints about what might be a reasonable value. This
approach, however, confronts respondents with an unfamiliar choice (i.e., placing a price on environmental commodities).
Studies that use the OE approach have high item non-response rates.
fish consumption advisory (FCA): an official notification to the public about specific areas where fish tissue samples
have been found to be contaminated by toxic chemicals which exceed FDA action limits or other accepted guidelines.
Advisories may be species specific or community wide.
intensity of preference (IP): an experimental design that allows individuals to state an intensity of preferences for or
against the alternative to the status quo. For example, the individual designates they would "probably yes" or "definitely yes"
prefer the alternative to the status quo.
iterative bidding (IB): with IB, respondents are asked whether they would be WTP a given amount. If the answer is yes,
this amount is raised in pre-set increments until the respondent says that he or she will not pay the last amount given. If the
answer is no, then the amount is decreased until the respondent indicates a willingness-to-pay the stated amount.
starting point bias: when survey interviewers suggest a first bid this can influence the respondent's answer and cause the
respondent to agree too readily with bids in the vicinity of the initial bid. (http://www.damagevaluation.com/glossary.htm)
travel cost (TC): method to determine the value of an event by evaluating expenditures of participants. Travel costs are
used as a proxy for price in deriving demand curves for a recreation site, (http://www.damagevaluation.com/glossary.htm)
willingness-to-pay (WTP): maximum amount of money one would be willing to pay or give up to buy some good.
(http://www.damagevaluation.com/glossary.htm)
K-10
-------
MPAM EEBA: Appendices
Appendix K: Selecting WTP Values for Benefits Transfer
ACRONYMS
AWQC: ambient water quality criteria
BC: binary choice
CV: contingent valuation
FCA: fish consumption advisory
IB: iterative bidding"
IP: intensity of preference
7"C; travel cost
WTP: willingness-to-pay
K-ll
-------
MP&M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer
REFERENCES
Boyle, K. J. and J.C Bergstrom. 1992. "Benefit Transfer Studies: Myths, Pragmatism and Idealism. " Water Resources
Research, Vol. 28, No. 3, March: 657-663.
Desvousges, W. H. et al. 1987. "Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study for
the Monongahela River." Journal of Environmental Economics and Management, 14: 248-267.
Desvousges, W. H. et al. 1992. "Benefit Transfer: Conceptual Problems in Estimating Water Quality Benefits Using
Existing Studies." Water Resources Research, Vol. 28, No. 3, March: 675-683.
Farber, S. andB. Griner. 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis. University of
Pittsburgh, PA.
Jakus, P.M., M. Downing, M.S. Bevelhimer, and J.M. Fly. 1997. "Do Sportfish Consumption Advisories Affect Reservoir
Anglers' Site Choice?" Agricultural and Resource Economic Review, 26(2).
Lant, C. L. and R.S. Roberts. 1990. "Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure."
Environment and Planning A, Vol. 22: 1375-1388.
Lyke, A.J. 1993. Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case Study. PhD
dissertation, Madison, WI: University of Wisconsin, Department of Agricultural Economics.
Montgomery, M. and M. Needelman. 1997. "The Welfare Effects of Toxic Contamination in Freshwater Fish." Land
Economics 73(2): 211-223.
Phaneuf, D. J., C. L. Kling, and JA. Herriges. 1998. "Valuing Water Quality Improvements Using Revealed Preference
Methods When Corner Solutions Are Present." American Journal of Agricultural Economics, 80: 1025-1031.
Tudor, L., E. Besedin, M. Fisher, S. Smith, and L. Snyder. 1999. What Pollutants Matter for Consumers of Water-Based
Recreation? Presented at the annual meeting of the Northeastern Agricultural and Resource Economics Association.
Morgantown, WV. June 1999.
Tudor, L., E. Besedin, M. Fisher, and S. Smith. 1 999. Economic Analysis of Environmental Regulations: Application of the
RUM model to Ecological Benefits Assessment for MP&M Effluent Guideline Limitations. Presented at the annual meeting of
American Agricultural Economics Association. Nashville, TN. August 1999.
K-12
-------
MP&M EEBA: Appendices Appendix L: Parameters Used in the IEUBK Model
Appendix L: Parameters Used in the
IEUBK Model
INTRODUCTION
APPENDIX CONTENTS
This appendix contains a comprehensive list of model
., . , . ., TT,TTT1T^ , , ,, , , • Lead Model
parameters that are used in the IEUBK model for lead in
Table B. 1: Description of Parameters Used In the IEUBK
children.
The remainder of this appendix is a reproduction of Appendix B: Description of Parameters In the IEUBK Lead Model, taken
from the Technical Support Document for the Integrated Exposure Uptake Biokinetic Model for Lead in Children (v0.99d)
(December 1994).
L-l
-------
APPENDIX B: DESCRIPTION OF PARAMETERS
IN THE IEUBK LEAD MODEL
-------
TABLE B-1. DESCRIPTION OF PARAMETERS IN THE IEUBK LEAD MODEL
PARAMETER NAME
ABSD
ABSF
ABSO
ABSS
ABSW
air_absorb(t)
air_concentration(t)
DESCRIPTION
Total absorption
for dust at low
saturation
Total absorption
for food at low
saturation
Total absorption
for other ingested
lead at low
saturation
Total absorption
for soil at low
saturation
Total absorption
for water at low
saturation
Net percentage
absorption of air
lead
Outdoor air lead
concentration
DEFAUL
T VALUE
OR
EON.
NO.
0.3
0.5
0.0
0.3
0.5
32
32
32
32
32
32
32
0.1
0.1
0.1
0.1
0.1
0.1
0.1
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
I
or
E
b
E
E
E
E
E
E
BASIS FOR VALUES/EQUATIONS
BasedonUSEPA(1989a).
Based on US EPA (1989a).
Based on the default condition that there is no other source of lead
ingestion in the household.
Based on US EPA (1989a).
Based on US EPA (1989a).
Deposition efficiencies of airborne lead particles were estimated by U S
EPA (1989a). A respiratory deposition/absorption rate of 25% to 45% is
reported for young children living in non-point source areas while a rate of
42% is calculated for those living near point sources. An intermediate value
of 32% was chosen.
Based on the lower end of the range 0.1 - 0.3 ug Pb/m3 that is reported for
outdoor air lead concentration in U.S. cities without lead point sources (US
EPA 1989)
UNITS
unitless
unitless
unitless
unitless
unitless
%
ug/m3
EQUATION
WHERE
USED
U 1c
U 2
U-1a,U-2
U-1d,U-2
U-1e,U-2
U-1b,U-2
U-4
E-1,2,11
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-2
-------
PARAMETER NAME
AVF, AVW, AVD, AVO,
AVS
AVINTAKE
can_fruit(t)
can_veg(t)
contrib percent
CONRBC
constant_soil_conc(t)
DESCRIPTION
Bioavailability
Available intake
Lead intake from
canned fruit when
fruit is consumed
only in canned
form
Lead intake from
canned vegetables
when vegetable is
consumed only in
canned form
Ratio of indoor
concentration to
soil lead
concentration
Maximum lead
concentration
capacity of red
concentration
DEFAUL
T VALUE
OR
EON.
NO.
1
U-2
1.811
1.063
1.058
0.999
0.940
0.969
1.027
0.074
0.252
0.284
0.295
0.307
0.291
0.261
0.70
1200
200
200
200
200
200
200
200
AGE
RANGE
(mo)
0-84
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-84
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
I
or
E
I
I
I
I
E
I
E
BASIS FOR VALUES/EQUATIONS
Parameter added for later flexibility in describing the absorption process;
has no effect in current algorithm.
The amount of Pb that is available for intake
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
Analysis of soil and dust data from 1983 East Helena study (US EPA, 1989)
Based on Marcus (1983) reanalysis of infant baboon data from Mallon
(1983). See Marcus (1985a) for assessment of form of relationship and
estimates from data on human adults [data from deSilva (1981a,b), Manton
and Malloy (1983), and Manton and Cook (1984)] and infant and juvenile
baboons (Mallon, 1983).
Air Quality Criteria Document for Lead. (US EPA, 1986)
UNITS
unitless
ug
ug/day
ug/day
g/g per
g/g
ug/dL
ug/g
EQUATION
WHERE
USED
U-1a-U-1e
U-1a,b,c,d,e
E-5d
E-5b
E-11
B-2.5
E-8
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-3
-------
PARAMETER NAME
constant_water_conc
CRBONEBL(t)
CRKIDBL(t)
CRLIVBL(t)
DESCRIPTION
Water lead
concentration
Ratio of lead
concentration
(|jg/kg) in bone to
blood lead
concentration
Cnn/l 1
IH»"-J
Ratio of lead
concentration
(|jg/kg) in kidney
to blood lead
concentration
(ugL)
Ratio of lead
concentration
(|jg/kg) in liver to
blood lead
concentration
Cnn/h
vny'M
DEFAUL
T VALUE
OR
EON.
NO.
4.0
B-4c
B-4a
B-4b
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
1
or
E
E
1
1
1
BASIS FOR VALUES/EQUATIONS
Based on analysis of data from the American Water Works Service Co.
(Marcus, 1989)
Data in Barry (1981) were used.
Bone lead concentration was calculated as an arithmetic average of the
concentrations in the rib, tibia, and calvaria. The blood lead concentrations
were taken directly from the study.
Concentrations in each of the following eight age groups were considered:
stillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), and
adult (women). Ages 0 and 40 yrs were assumed for stillbirths and adults,
respectively.
Data in Barry (1981) were used.
Lead concentrations in kidney (combined values for cortex and medulla)
and blood were taken directly from the study.
Concentrations in each of the following eight age groups were considered:
stillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), and
adult (women). Ages 0 and 40 yrs were assumed for stillbirths and adults,
respectively.
Data in Barry (1981) were used.
Lead concentrations in liver and blood were taken directly from the study.
Concentrations in each of the following eight age groups were considered:
stillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), and
adult (women). Ages 0 and 40 yrs were assumed for stillbirths and adults,
respectively.
UNITS
ug/L
L/kg
L/kg
L/kg
EQUATION
WHERE
USED
E-6a
B-1h
B-2h
B-2e,2f
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-4
-------
PARAMETER NAME
CROTHBL(t)
DAYCARE(t)
DaycareConc
DaycareFraction
dietjntake(t)
DietTotal(t)
DustTotal(t)
DESCRIPTION
Ratio of lead
concentration
(|jg/kg) in other
soft tissue to blood
lead concentration
(ug/L)
Dust lead intake at
daycare
Dust lead
concentration at
daycare
Fraction of total
dust ingested daily
as daycare dust
User-specified diet
lead intake
Total Dietary
Intake
Daily amount of
dust ingested
DEFAUL
T VALUE
OR
EON.
NO.
B-4d
E-12c
200
0
5.53
5.78
6 49
6.24
6.01
6.34
7.00
E-4b
E-10
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-11
12-23
24 35
36-47
48-59
60-71
72-84
0.84
0-84
I
or
E
I
I
E
E
E
I
I
BASIS FOR VALUES/EQUATIONS
Data in Barry (1981) were used.
Lead concentration ratio for soft tissues was calculated as a weighted
arithmetic average of concentration ratios for muscle (53.8%), fat (24.0%),
skin (9.4%), dense connective tissue (4.4%), brain (2.7%), Gl tract (2.3%),
lung (1.9%), heart (0.7%), spleen (0.3%), pancreas (0.2%), and aorta
(0.2%), where the weights applied are given in parentheses. The weight
associated with each soft tissue component was equal to the weight of the
component (kg) divided by weight of all soft tissues (kg). These weights
were estimated from Schroeder and Tipton (1968) and are assumed to
apply in the range 0-84 months of age.
Concentrations in each of the following eight age groups were considered:
stillbirths, 0-12 days, 1-11 mos, 1-5 yrs, 6-9 yrs, 11-16 yrs, adult (men), and
adult (women). Ages 0 and 40 yrs were assumed for stillbirths and adults,
respectively.
Simple combination of the total amount of dust ingested daily, fraction of
total dust ingested as daycare dust, and dust lead concentration at daycare.
Based on the assumption that default daycare dust concentrations are the
same as default residence dust concentrations.
Based on the default assumption that the child does not attend daycare.
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
Summation of all dietary sources; same as INDIET(t)
Simple combination of total amount soil and dust ingested daily and fraction
of this combined ingestion that is dust alone.
UNITS
L/kg
ug/day
ug/g
unitless
ug/day
ug/day
g/day
EQUATION
WHERE
USED
B-2n,2o
E-9d
E-12c
E-9.5,12c
E-4a
E-4b
E-9c,12a-
12e
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-5
-------
PARAMETER NAME
EXAIR(t)
f_fruit(t)
f_veg(t)
FirstDrawConc
FirstDrawFraction
FountainConc
FountainFraction
fruit_all(t)
DESCRIPTION
Air lead intake
Lead intake from
fresh fruit if no
home-grown fruit
is consumed
Lead intake from
fresh vegetables if
no home-grown
vegetables are
consumed
First Draw water
lead concentration
Fraction of total
water consumed
daily as first draw
Fountain water
lead concentration
Fraction of total
water consumed
daily from
fountains
Daily amount of all
fruits consumed
DEFAUL
T VALUE
OR
EON.
NO.
E-3
0.039
0 196
0.175
0 179
0.203
0.251
0.148
0.269
0.475
0.466
0.456
0.492
0.563
4.0
0.5
10
0.15
38.481
169.000
63 166
61.672
61.848
67.907
80.024
AGE
RANGE
(mo)
0-84
0-11
12 23
36-47
48-59
60-71
72-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-84
0-84
0-84
0-84
0-11
12-23
24 35
36-47
48-59
60-71
72-84
I
or
E
I
I
I
E
E
E
E
I
BASIS FOR VALUES/EQUATIONS
Simple combination of average air lead concentration and ventilation rate.
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
Based on analysis of data from the American Water Works Service Co.
(Marcus, 1989)
In the absence of appropriate data, a conservative value corresponding to
consumption largely after four fours stagnation time was used, e.g. early
morning or late afternoon.
Default assumption is that the drinking fountain has a lead-lined reservoir,
but that consumption is not always first draw. Therefore, a value was
selected from the range of 5-25 g/L.
A default value was based on 4-6 trips to the water fountain at 40-50 ml per
trip.
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
UNITS
ug/day
ug/day
ug/day
ug/L
unitless
ug/L
none
g/day
EQUATION
WHERE
USED
U-4
E-5e
E-5c
E-6b
E-6b,7
E-6b
E-6b,7
E-5f
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-6
-------
PARAMETER NAME
HomeFlushedConc
HCTO
InCanFruit(t)
InCanVeg(t)
INDIET(t)
IndoorConc(t)
indoorpercent
INDUST(t)
DESCRIPTION
Home flushed
water lead
concentration
Hematocrit at birth
Lead intake from
canned fruit
Lead intake from
canned vegetables
Diet lead intake
Indoor air lead
concentration
Ratio of indoor
dust lead
concentration to
corresponding
outdoor
concentration
Household dust
lead intake
DEFAUL
T VALUE
OR
EON.
NO.
1.0
0.45
E-5d
E-5b
E-4a
or
E-4b
E-1
30
E-9a
or
E-9c
AGE
RANGE
(mo)
0-84
0
0-84
0-84
0-84
0-84
0-84
0-84
I
or
E
E
I
I
I
I
I
E
I
BASIS FOR VALUES/EQUATIONS
Based on analysis of data from the American Water Works Service Co.
(Marcus, 1989)
Data from Silve et al. (1987); also Spector (1956) and Altman and Ditmer
(1973)
Simple combination of the fraction of non-home grown fruits consumed
daily, and lead intake from canned fruits when fruits are consumed only in
canned form.
Simple combination of the fraction of vegetables consumed daily as non-
home grown, and lead intake from canned vegetables when vegetables are
consumed only in canned form.
Two options are provided.
Default option - Considers composite diet lead intake.
Alternate option - Combines lead intake from several individual components
of diet.
Algebraic expression of relationship
Based on homes near lead point sources. The default value is reported in
OAQPS (USEPA 1989, pp A-1) and is estimated by Cohen and Cohen
(1980).
Two options are provided.
Default option - Assumes that all dust lead exposure is from the household.
Alternate option - Considers dust lead exposure from several alternative
sources as well.
UNITS
ug/L
decimal
percent
ug/day
ug/day
ug/day
ug/m3
%
ug/day
EQUATION
WHERE
USED
E-6b
B-7b,d
E-4b
E-4b
U-1a, U-2
E-2
E-1
U-1-c, U-2
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-7
-------
PARAMETER NAME
INDUSTA(t)
InFish(t)
InFrFruit(t)
InFrVeg(t)
InGame(t)
InHomeFruit(t)
InHomeVeg(t)
InMeat(t)
InOtherDiet(t)
DESCRIPTION
Lead intake from
alternate dust
sources
Lead intake from
fish
non-home grown
fresh fruits
non-home grown
fresh vegetables
Lead intake from
game animal meat
Lead intake from
home grown fruits
Lead intake from
home grown
vegetables
Lead intake from
non-game and
non-fish meat
Combined lead
intake from dairy
food, juice, nuts,
beverage, pasta,
bread, sauce,
candy, infant and
formula food
DEFAUL
T VALUE
OR
EON.
NO.
E-9b
or
E-9d
E-5h
E-5e
E-5c
E-5i
E-5f
E-5g
E-5a
3.578
3.506
3.990
3.765
3.545
3.784
4.215
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
I
or
E
I
I
I
I
I
I
I
I
I
BASIS FOR VALUES/EQUATIONS
Two options are provided.
Default option - Assumes that lead intake from alternate sources is zero.
Alternate option - Combines lead intake from several alternate sources.
Simple combination of total meat consumed daily, fraction of meat
consumed as fish, and lead concentration in fish.
Simple combination of the fraction of fruits consumed daily as non-home
grown and lead intake from fresh fruits.
Simple combination of the fraction of vegetables consumed daily as non-
home grown and lead intake from fresh vegetables.
Simple combination of total meat consumed daily, fraction of meat
consumed as game animal meat, and lead concentration in game animal
meat.
Simple combination of total amount of fruit consumed daily, fraction of fruit
consumed as home grown, and lead concentration in home grown fruit.
Simple combination of total amount of vegetable consumed daily, fraction of
vegetables consumed as home grown, and lead concentration in home
grown vegetables.
Simple combination of total amount of meat consumed daily, fraction of
meat consumed as non-game and non-fish meat, and lead concentration in
non-game and non-fish meat.
Sum of the amounts of lead ingested in food items not substituted by the
calculation of exposure to lead in home grown fruits and vegetables, wild
game or fish. Pb concentration from data provided to EPA by FDA (US EPA
(1986). Quantity consumed from Pennington (1983).
UNITS
ug/day
ug/day
ug/day
ug/day
ug/day
ug/day
ug/day
ug/day
ug/day
EQUATION
WHERE
USED
U-1.5c, U-2
E-4b
E-4b
E-4b
E-4b
E-4b
E-4b
E-4b
E-4b, E-4c
NOTE: I = interior parameter, E = Exterior, user selectable parameter
-------
PARAMETER NAME
INOTHER(t)
INSOIL(t)
INWATER(t)
MCORT(t)
meat_all(t)
DESCRIPTION
Combined other
sources of
ingested lead,
such as paint
chips, ethnic
medicines, etc.
Soil lead intake
Water lead intake
Mass of lead in
cortical bone
Daily amount of
meat (including
fish and game)
consumed
DEFAUL
T VALUE
OR
EON.
NO.
0
E-8
E-6a
or
E-6b
B-7e
and
B-9f
29.551
87.477
95.700
101.570
107.441
1 1 1 .948
120.961
AGE
RANGE
(mo)
0-84
0-84
0-84
0
and
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
I
or
E
E
I
I
I
I
BASIS FOR VALUES/EQUATIONS
Assumes no other sources of ingested lead
Simple combination of total amount of soil and dust ingested daily, fraction
of this combined ingestion that is soil alone, and lead concentration in soil.
Two options are provided.
Default option - Simple combination of water consumed daily and a
constant water lead concentration.
Alternate option - Water lead concentration depends on contribution from
several individual sources of water.
0 months - Simple combination of an assumed bone to blood lead
concentration ratio, blood lead concentration, and weight of cortical bone.
Basis for value of bone to blood lead concentration ratio was human
autopsy data (Barry, 1981).
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
Both cases above assume that the cortical bone to blood lead concentration
ratio is equal to the bone (composite) to blood lead concentration ratio.
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
UNITS
g/day
u g/day
u g/day
ug
g/day
EQUATION
WHERE
USED
U-1d, U-2
U-1e,U-2
U-1b, U-2
B-6b,6i,6.5b,
6.5i,8a,9f
E-5h
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-9
-------
PARAMETER NAME
meat(t)
MKIDNEY(t)
MLIVER(t)
MOTHER(t)
MPLASM(t)
DESCRIPTION
Lead intake from
meat if no game
meat or fish is
consumed
Mass of lead in
kidnev
Mass of lead in
liver
Mass of lead in
soft tissues
Mass of lead in
plasma pool
DEFAUL
T VALUE
OR
EON.
NO.
0.226
0.630
0.81 1
0.871
0.931
1.008
1.161
B-7f
and
B-9c
B-7g
and
B-9b
B-7h
and
B-9d
B-7d
and
B-9g
AGE
RANGE
(mo)
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0
and
0-84
0
and
0-84
o
and
0-84
o
and
0-84
I
or
E
I
I
I
I
I
BASIS FOR VALUES/EQUATIONS
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
0 months - Simple combination of an assumed kidney to blood lead
concentration ratio, blood lead concentration, and weight of kidney. Basis
for the value of the kidney to blood lead concentration ratio was human
autopsy data (Barry, 1981).
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
0 months - Simple combination of an assumed liver to blood lead
concentration ratio, blood lead concentration, and weight of the liver. Basis
for the value of the liver to blood lead concentration ratio was human
autopsy data (Barry, 1981).
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
0 months - Simple combination of an assumed soft tissue to blood lead
concentration ratio, blood lead concentration, and weight of the soft tissues
at birth. Basis for the value of soft tissue to blood lead concentration ratio
was human autopsy data (Barry et al., 1981), using total lead and total
weight of other tissue.
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
0 months - Simple combination of the mass of lead in blood and red blood
cells
0-84 months - Based on the assumption that the lead concentration in
plasma-ECF is equal to the lead concentration in the plasma.
UNITS
ug /day
ug
ug
ug
ug
EQUATION
WHERE
USED
E-5a
B-
6b,6f,6.5b,6.
5f,8d,9c
B-
6b,6e,6.5b,6.
5e,8d,9b
B-
6b,6g,6.5b,6.
5g,8d,9d
B-10a
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-10
-------
PARAMETER NAME
MPLECF(t)
MRBC(t)
MTRAB(t)
multiply_factor
OCCUP(t)
OccupConc
DESCRIPTION
Mass of lead in
plasma-extra-
cellular fluid
(plasma-ECF)
Mass of lead in red
blood cells
Mass of lead in
trabecular bone
Ratio of indoor
dust lead
concentration to
air lead
concentration
Dust lead intake
from secondary
occupation
Secondary
occupational dust
lead concentration
DEFAUL
T VALUE
OR
EON.
NO.
B-7b
and
B-8a
B-7c
and
B-9a
B-7i
and
B-9e
100
E-12a
1200
AGE
RANGE
(mo)
0
and
0-84
0
and
0-84
o
and
0-84
0-84
0-84
0-84
I
or
E
I
I
I
E
I
E
BASIS FOR VALUES/EQUATIONS
0 months - Based on two assumptions.
(1) masses of lead in plasma-ECF and red blood cells are in kinetic quasi-
equilibrium, and
(2) lead concentration in the plasma-ECF is equal to lead concentration in
the plasma.
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
0 months - Based on the assumption that the masses of lead in plasma-
ECF and red blood cells are in kinetic quasi-equilibrium.
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
0 months - Simple combination of an assumed bone to blood lead
concentration ratio, blood lead concentration, and weight of trabecular
bone. Basis for the value of bone to blood lead concentration ratio was
human autopsy data (Barry, 1981).
0-84 months - Application of the Backward Euler solution algorithm to the
system of differential equations (B-6a-B-6i in Table A-3).
Both cases above assume that trabecular bone to blood lead concentration
ratio is equal to bone (composite) to blood lead concentration ratio.
Analyses of the 1983 East Helena study in (USEPA 1989, Appendix B-8)
suggest about 267 ug/g increment of lead in dust for each ug /m3. lead in
air. A much smaller factor of 100 ug/g PbD per ug/m3 is assumed for non-
smelter community exposure.
Simple combination of amount of dust ingested, fraction of the total dust
ingested as secondary occupational dust, and lead concentration in
secondary occupational dust
Air Quality Criteria Document for Lead. (US EPA, 1986)
UNITS
ug
ug
ug
UG Id
My 'y
per
uci/rn^
ug/day
ug/g
EQUATION
WHERE
USED
B-6a,6c-
6i,6.5a,
6.5c-
6.5i,8a,9a-9g
B-
6a,6d,6.5a,6.
5d,8d,9a,10a
B-
6b,6h,6.5b,6.
5h,8d,9e
E-11
E-9d
E-12a
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-11
-------
PARAMETER NAME
OccupFraction
PAINT(t)
PaintConc
PAF
PaintFraction
PBBLDMAT
PBBLDO
PBBLOODEND(t)
RATBLPL
DESCRIPTION
Fraction of total
dust ingested as
secondary
occupation dust
Dust lead intake
from lead based
home paint
Leadconcentration
in housedust
containing lead
based paint
Fraction of total
absorption as
passive absorption
at low dose
Fraction of total
dust ingested that
results from lead
based home paint
Maternal blood
lead concentration
Lead concen
(ration in blood
Lead concen
(ration in blood
Ratio of lead mass
in blood to lead
mass in plasma-
ECF
DEFAUL
T VALUE
OR
EON.
NO.
0
E-12e
1200
0.20
0
2.5
B-7a
B-10a
100
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
adult
0
0-84
0-84
I
or
E
E
I
E
E
E
E
I
I
I
BASIS FOR VALUES/EQUATIONS
The default condition is that there is no adult in the residence who works at
a lead-related job.
Simple combination of amount of dust ingested daily, fraction of the total
dust ingested as lead-based home paint, and lead concentration in lead-
based home paint.
Air Quality Criteria Document for Lead. (US EPA, 1986)
Based on in vitro everted rat intestine data (Aungst and Fung, 1981),
reanalyses (Marcus, 1994) of infant baboon data (Mallon, 1983) and infant
duplicate diet study (Sherlock and Quinn, 1986)
The default is that there is no lead-based paint in the home.
Based in part on Midvale 1989 study. The default value of 2.5 g/dLhas
little influence of the early post natal exposure of the child.
Based on 85% of maternal blood lead concentration (US EPA 1989)
Simple combination of the blood lead concentrations determined in each
iteration in the solution algorithm between the previous month and that
month.
Based on the lower end of the 50-500 range for the red cell/plasma lead
concentration ratio recommended in Diamond and O'Flaherty (1992a).
UNITS
unitless
ug/day
ug/g
unitless
unitless
ug/dL
ug/dL
ug/dL
unitless
EQUATION
WHERE
USED
E-9.5,12a
E-9d
E-12e
U-1athru U-
1f
E-12e
B-7a
B-7b, 7c, 7e-
7i
B-10c
B-2b-
2d,2g,2i,2k,2
m
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-12
-------
PARAMETER NAME
RATFECUR
RATOUTFEC
SATINTAKE(t)
SATINTAKE24
SCHOOL(t)
SchoolConc
SchoolFraction
SECHOME(t)
SecHomeConc
DESCRIPTION
Ratio of
endogenous fecal
lead elimination
rate to urinary lead
elimination rate
Ratio of
elimination rate via
soft tissues to
endogenous fecal
lead elimination
rate
Half saturation
absorbable lead
intake
Half saturation
absorbable lead
intake for a 24
month old
Dust lead intake
from school
Dust lead
concentration at
school
Fraction of total
dust ingested daily
as school dust
Dust lead intake at
secondary home
Secondary home
dust lead
concentration
DEFAUL
T VALUE
OR
EON.
NO.
0.75
0.75
U-3
100
E-12b
200
0
E-12d
200
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
I
or
E
I
I
I
E
I
E
E
I
E
BASIS FOR VALUES/EQUATIONS
Assume child ratio is larger than the adult ratio; values derived from a
reanalysis of data from Ziegler et al. (1978) and Rabinowitz and Wetherill
(1973).
Within the range of values derived from a reanalysis of data from Ziegler et
al. (1978) and Rabinowitz and Wetherill (1973).
Assumed proportional to the weight of body . The coefficient of
proportionality is assumed to depend on the estimate of the parameter for a
24 month old and the corresponding body weight.
Extrapolated from reanalysis of human infant data (Sherlock and Quinn,
1986) and infant baboon data (Mallon, 1983)
Simple combination of amount of dust ingested daily, the fraction of total
dust ingested daily as school dust, and lead concentration in dust at school
By default, this dust lead concentration is set to the same as the residential
dust lead concentration.
Based on the default assumption that children are not in school.
Simple combination of amount of dust ingested daily, fraction of dust
ingested daily as secondary home dust, and lead concentration in dust at
the secondary home.
Based on the assumption that dust lead concentration in a secondary home
is the same as the default dust lead concentration in the primary home.
UNITS
unitless
unitless
ug/day
ug/day
ug/day
ug/g
unitless
ug/day
ug/g
EQUATION
WHERE
USED
B-1f
B-lg
U-1athru U-
1e
U-3
E-9d
E-12b
E-9c,E-
9.5,12b
E-9d
E-12d
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-13
-------
PARAMETER NAME
SecHomeFraction
soiljndoor(t)
soil_ingested(t)
TBLBONE(t)
TBLFEC(t)
DESCRIPTION
Fraction of total
dust ingested daily
as secondary
home dust
Indoor household
dust lead
concentration
Soil and dust
(combined)
consumption
Lead transfer time
from blood to bone
Lead transfer time
from blood to
feces
DEFAUL
T VALUE
OR
EON.
NO.
0
E-11
0.085
0.135
0.135
0.135
0.100
0.090
0.085
1
and
B-1e
B-1f
AGE
RANGE
(mo)
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
24
and
0-84
0-84
I
or
E
E
I
E
I
I
BASIS FOR VALUES/EQUATIONS
Based on the default assumption that the child does not spend a significant
amount of time in a secondary home.
Under alternate dust sources model, based on assumption that both soil
and outdoor air contribute to indoor dust lead.
Based on values reported in OAQPS report (USEPA 1989, pp. A-16). The
values reported were estimated for children, ages 12-48 mos, by several
authors such as Binder et al. (1986) and Clausing et al. (1987). Sedman
(1987) extrapolated these estimates to those for children, ages 0-84 mos.
24 months - Initialization is keyed to the two year old child, based in part on
information from Heard and Chamberlain, (1982) for adults, and O'Flaherty
(1992). Once the concentration ratios are fixed, the exact value of this
parameter, within a wide range of possible values, has little effect on the
blood lead value.
0-84 months - Assumed proportional body surface area. The coefficient of
proportionality is assumed to depend on an estimate of the parameter for a
24 month old and the corresponding body surface area. Also, it is
assumed that body surface area varies as 1/3 power of the weight of body
based on Mordent! (1986).
Simple combination of an assumed ratio of urinary lead elimination rate to
endogenous fecal lead elimination rate, and lead transfer time from blood to
urine (See RATFECUR).
The ratio of of elimination rates was estimated for adults using Chamberlain
et al. (1978), and Chamberlain (1985) and is assumed to apply to ages 0-84
months.
UNITS
unitless
ug/g
g/day
days
days
EQUATION
WHERE
USED
E-9b,12d
E-9c
E-8-9a,10
B-1h,2i,2k
B-1g,2e,2f
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-14
-------
PARAMETER NAME
TBLKID(t)
TBLLIV(t)
TBLOTH(t)
TBLOUT(t)
DESCRIPTION
Lead transfer time
from blood to
kidney
Lead transfer time
from blood to liver
Lead transfer time
from blood to other
soft tissue
Lead transfer time
from blood to
elimination pool
via soft tissue
DEFAUL
T VALUE
OR
EON.
NO.
10
and
B-1d
10
and
B-1b
10
and
B-1c
B-1g
AGE
RANGE
(mo)
24
and
0-84
24
and
0-84
24
and
0-84
0-84
I
or
E
I
I
I
I
BASIS FOR VALUES/EQUATIONS
24 months - Initialization is keyed to the two year old child, based in part
on information from Heard and Chamberlain, (1982) for adults, and
O'Flaherty (1992). Once the concentration ratios are fixed, the exact value
of this parameter, within a wide range of possible values, has little effect on
the blood lead value.
0-84 months - Assumed proportional body surface area. The coefficient of
proportionality is assumed to depend on an estimate of the parameter for a
24 month old and the corresponding body surface area. Also, it is
assumed that body surface area varies as 1/3 power of the weight of body
based on (Mordenti, 1986).
24 months - Initialization is keyed to the two year old child, based in part on
information from Heard and Chamberlain, (1982) for adults, and O'Flaherty
(1992). Once the concentration ratios are fixed, the exact value of this
parameter, within a wide range of possible values, has little effect on the
blood lead value.
0-84 months - Assumed proportional body surface area. The coefficient of
proportionality is assumed to depend on an estimate of the parameter for a
24 month old and the corresponding body surface area. Also, it is
assumed that body surface area varies as 1/3 power of the weight of body
based on (Mordenti, 1986).
24 months - Initialization is keyed to the two year old child, based in part
on information from Heard and Chamberlain, (1982) for adults, and
O'Flaherty (1992). Once the concentration ratios are fixed, the exact value
of this parameter, within a wide range of possible values, has little effect on
the blood lead value.
0-84 months - Assumed proportional body surface area. The coefficient of
proportionality is assumed to depend on an estimate of the parameter for a
24 month old and the corresponding body surface area. Also, it is assumed
that body surface area varies as 1/3 power of the weight of body based on
(Mordenti, 1986).
Simple combination of an assumed ratio of elimintion rate via soft tissues
to endogenous fecal lead elimination rate, times the lead transfer time from
blood to feces (See RATOUTFEC).
UNITS
days
days
days
days
EQUATION
WHERE
USED
B-2g,2h
B-2d,2e
B-2m,2n
B-2n,2o
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-15
-------
PARAMETER NAME
TBLUR(t)
TBONEBL(t)
TCORTPL(t)
time_out(t)
DESCRIPTION
Lead transfer time
from blood to urine
Lead transfer time
from bone to blood
Lead transfer time
from cortical bone
to plasma-ECF
Time spent
outdoors
DEFAUL
T VALUE
OR
EON.
NO.
20
and
B-1a
B-1h
B-2I
1
2
3
4
4
4
4
AGE
RANGE
(mo)
24
and
0-84
0-84
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
I
or
E
I
I
I
E
BASIS FOR VALUES/EQUATIONS
24 months - Assumed proportional to body surface area. The coefficient of
proportionality is assumed to depend on an adult estimate for the parameter
and the corresponding body surface area. The adult estimate of 39 days
was obtained using Araki et al (1986a, 1986b, 1987), Assenato et al
(1986), Campbell et al (1981), Carton et al (1987), Chamberlain et al.
(1978), Folashade et al (1991), Heard and Chamberlain (1981), He et al
(1988), Kawaii et al (1983), Kehoe (1961), Koster et al (1989), Manton and
Malloy (1983), Rabinowitz and Wetherill (1973), Rabinowitz et al (1976),
and Yokoyama et al (1985).
0-84 months - Assumed proportional body surface area. The coefficient of
proportionality is assumed to depend on an estimate of the parameter for a
24 month old and the corresponding body surface area.
Both cases above assume that (a) body surface area varies as 1/3 power of
weight of body based on (Mordenti, 1986) and (b) respectively, 70 kg and
12.3 kg are standard adult and 2 year old body weights based on Spector
(1956).
Since glomerular filtration rate (GFR) is proportional to body surface area
for ages 24 months based on (Weil, 1955), surface area scaling is
equivalent to scaling by GFR for ages 24 months.
Based on the assumption that masses of lead in bone and blood are in
kinetic quasi-equilibrium.
Based on the assumption that the cortical and trabecular bone pools have
similar lead kineticsfor children younger than 84 months.
Values are reported in the OAQPS staff report (USEPA 1989, pp. A-2) and
the TSD (USEPA 1990a). The values have been derived from a literature
review (Pope, 1985).
UNITS
days
days
days
hrs/day
EQUATION
WHERE
USED
B-1f,2c
B-2J.2I
B-6b,6i,6.5b,
6.5i,8d,9f
E-2
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-16
-------
PARAMETER NAME
TimeStep
TKIDPL(t)
TLIVFEC(t)
TLIVPL(t)
TOTHOUT(t)
TOTHPL(t)
TPLCORT(t)
DESCRIPTION
Length of time-
step in solution
algorithm
from kidney to
plasma-ECF
Lead transfer time
from liver to feces
from liver to
plasma-ECF
Lead transfer time
from soft tissues to
elimination pool
Lead transfer time
from soft tissues to
plasma-ECF
Lead transfer time
from plasma-ECF
DEFAUL
T VALUE
OR
EON.
NO.
1/6
B-2h
B 2f
B-2e
B-2o
B-2n
B-2k
AGE
RANGE
(mo)
0-84
0-84
0 84
0-84
0-84
0-84
0-84
I
or
E
E
I
I
I
I
I
I
BASIS FOR VALUES/EQUATIONS
This user-selectable parameter is available mainly for adjusting the model
run time to the speed of the computer. Newer, faster computers can run
the model at the shortest TimeStep (15 min) in less than one minute. The
default value, 4 hours, is based on a tradeoff between numerical accuracy
of results and computer run-time. Except in the case of extreme exposure
scenarios, there is no difference in the numerical accuracy at any user
selectable value for TimeStep.
Based on the assumption that the lead transfer time from kidney to blood is
equal to the lead transfer time from kidney to plasma-ECF.
Based on the assumption that the masses of lead in liver and blood are in
kinetic quasi-equilibrium.
Based on the assumption that the lead transfer time from liver to blood is
equal to the lead transfer time from liver to plasma-ECF.
Based on the assumption that the masses of lead in soft tissues and blood
are in kinetic quasi-equilibrium.
Based on the assumption that the lead transfer time from soft tissues to
blood is equal to the lead transfer time from soft tissues to plasma-ECF.
Based on the following assumptions:
The rate at which lead leaves the plasma-ECF to reach the bone is
proportional to the rate which lead leaves the blood to reach the same pool.
The cortical and trabecular bone pools have similar lead kinetics for
children younger than 84 months.
The cortical bone is 80% of the weight of bone based on Leggett et al.
(1982).
UNITS
day
days
days
days
days
days
EQUATION
WHERE
USED
B-6.5a,6.5d-
6.5i,7b,7c,
8a,d,9a-
9f,10a-10b
B
6b,6f,6.5b,6.
5f,8d,9c
B-6e,6.5e,
8c,d,9b
B-
6b,6e,6.5b,6.
5e,8c,d,
9b
B-6g,6.5g,
8c,d,9h
B-
6c,6g,6.5c,6.
5g,8c,d,
9h
6.5i,8b,c,9f
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-17
-------
PARAMETER NAME
TPLKID(t)
TPLLIV(t)
TPLOTH(t)
TPLRBC
TPLRBC2(t)
TPLTRAB(t)
TPLUR(t)
DESCRIPTION
Lead transfer time
from plasma-ECF
to kidney
Lead transfer time
from plasma-ECF
to liver
Lead transfer time
from plasma-ECF
to soft tissues
Lead transfer
time from plasma-
ECF to red blood
cells
Lead transfer
time from plasma-
ECF to red blood
cells constrained
by the maximum
capacity of red
blood cell lead
concentration
Lead transfer time
from plasma-ECF
to trabecular bone
Lead transfer time
from plasma-ECF
to urine
DEFAUL
T VALUE
OR
EON.
NO.
B-2g
B-2d
B-2m
0.1
B-2.5
B-2i
B-2c
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-84
0-84
I
or
E
I
I
I
I
I
I
I
BASIS FOR VALUES/EQUATIONS
Based on the assumption that the rate at which lead leaves the plasma-
ECF to reach the kidney is proportional to the rate at which lead leaves the
blood to reach the same pool.
Based on the assumption that the rate at which lead leaves the plasma-
ECF to reach the liver is proportional to the rate at which lead leaves the
blood to reach the same pool.
Based on the assumption that the rate at which lead leaves the plasma-
ECF to reach the soft tissues is proportional to the rate which lead leaves
the blood to reach the same pool.
Initialization value of 0.1 was assigned as plausible nominal value reflecting
best professional judgement on appropriate time scale for composite
process of transfer of lead through the red blood cell membrane to lead
binding components.
Simple combination of the lead transfer time from plasma-ECF to red blood
cells, and the ratio of red blood cell lead concentration to the corresponding
maximum concentration. Based on Marcus (1985a) and reanalysis of infant
baboon data.
Based on the following assumptions:
The rate at which lead leaves the plasma-ECF to reach the bone is
proportional to the rate which lead leaves the blood to reach the same pool.
The cortical and trabecular bone pools have similar lead kinetics.
The trabecular bone is 20% of the weight of bone based on Leggett et al.
(1982).
Based on the assumption that the rate at which lead leaves the plasma-
extra-cellular fluid to reach the urine pool is proportional to the rate at which
lead leaves the blood to reach the same pool.
UNITS
days
days
days
days
days
days
days
EQUATION
WHERE
USED
B-
6c,6f,6.5c,6.
5f,8b,c,9c
B-
6c,6e,6.5c,6.
5e,8b,c,
9b
B-
6c,6g,6.5c,6.
5g,8b,c,
9d
B-2b,2.5,7b,
7c
B-
6a,6d,6.5a,6.
5d,8b,9a
B-
6c,6h,6.5c,6.
5h,8b,c,
9e
B-6c,6.5c,8a
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-18
-------
PARAMETER NAME
TRBCPL
TTRABPL(t)
TWA(t)
UPAIR(t)
UPDIET(t)
UPDUST(t)
UPDUSTA(t)
UPGUT(t)
UPOTHER(t)
UPSOIL(t)
UPTAKE(t)
UPWATER(t)
DESCRIPTION
Lead transfer time
from red blood
cells to plasma-
ECF
Lead transfer time
from trabecular
bone to plasma-
extra-cellular fluid
Time weighted
average air lead
concentration
Air lead uptake
Diet lead uptake
Dust lead uptake
Dust lead uptake
rate from alternate
sources
Total gut uptake
Uptake of other
ingested lead
Soil lead uptake
Total lead uptake
Water lead uptake
DEFAUL
T VALUE
OR
EON.
NO.
B-2b
B-2J
E-2
U-4
U-1a
U-1c
U-1.Sc
U-1f
U-1d
U-1e
U-5
U-1b
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
I
or
E
I
I
I
I
I
I
I
I
I
I
I
I
BASIS FOR VALUES/EQUATIONS
Based on the assumption that the transfer time out of RBC is similar at all
ages, since mean red cell value is similar.
Based on the assumption that the cortical and trabecular bone pools have
similar lead kinetics for children younger than 84 months.
Simple combination of outdoor and indoor air lead concentrations and the
number of hours spent outdoors.
Simple combination of media-specific lead intake and the corresponding net
absorption coefficient.
Simple combination of media-specific lead intake and the corresponding net
absorption coefficient.
Simple combination of media-specific lead intake and the corresponding net
absorption coefficient.
Simple combination of media-specific lead intake and the corresponding net
absorption coefficient.
Sum of all gastrointestinal uptake.
Assumes no other gut lead intake
Simple combination of media-specific lead intake and the corresponding net
absorption coefficient.
Simple combination of the media-specific daily lead uptake rates,
translated to a monthly rate.
Simple combination of media-specific lead intake and the corresponding net
absorption coefficient.
UNITS
days
days
ug/m3
ug/day
ug/day
ug/day
ug/day
ug/day
ug/day
ug/day
ug/mo
ug/day
EQUATION
WHERE
USED
B-
6b,6d,6.5b,6.
5d,7b,7c,
8c,d,9a
B-
6b,6h,6.5b,6.
5h,8c,d,
9e
E-3
U-5
U-1f
U-1f
U-1f
U-5
U-1f
U-1f
B-6a,6.5a,8a
U-1f
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-19
-------
PARAMETER NAME
UserFishConc
userFishFraction
UserFruitConc
userFruitFraction
UserGameConc
userGameFraction
UserVegConc
userVegFraction
DESCRIPTION
Lead
concentration in
fish
Fraction of total
meat consumed as
fish
Lead
concentration in
home grown fruits
Fraction of total
fruits consumed as
home grown fruits
Lead
concentration in
game animal meat
Fraction of total
meat consumed as
game animal meat
excluding fish
Lead
concentration in
home grown
vegetables
Fraction of total
vegetables
consumed as
home grown
vegetables
DEFAUL
T VALUE
OR
EON.
NO.
0
0
0
0
0
0
0
0
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
1
or
E
E
E
E
E
E
E
E
E
BASIS FOR VALUES/EQUATIONS
Based on the assumption that only commercially available fish are
consumed.
Based on the assumption that only commercially available fish are
consumed.
Based on the assumption that only commercially available fruits are
consumed.
Based on the assumption that only commercially available fruits are
consumed.
Based on the assumption that only commercially available meat is
consumed.
Based on the assumption that only commercially available meat is
consumed.
Based on the assumption that only commercially available vegetables are
consumed.
Based on the assumption that only commercially available vegetables are
consumed.
UNITS
ug/g
unitless
ug/g
unitless
ug/g
unitless
ug/g
unitless
EQUATION
WHERE
USED
E-5h
E-5a,5h
E-5f
E-5d,5e,5f
E-5i
E-5a,5i
E-5g
E-5b,5c,5g
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-20
-------
PARAMETER NAME
veg_all(t)
vent_rate(t)
VOLBLOOD(t)
VOLECF(t)
VOLPLASM(t)
VOLRBC(t)
water_consumption(t)
DESCRIPTION
Daily amount of all
vegetables
consumed
Ventilation rate
Volume of blood
Volume of extra-
cellular fluid (ECF)
Volume of plasma
Volume of red
blood cells
Daily amount of
water consumed
DEFAUL
T VALUE
OR
EON.
NO.
56.84
106.50
155.75
157.34
158.93
172.50
199.65
2
3
5
5
5
7
7
B-5a
B-5d
B-5c
B-5b
0.20
0.50
0.52
0.53
0.55
0.58
0.59
AGE
RANGE
(mo)
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
0-84
0-84
0-84
0-84
0-11
12-23
24-35
36-47
48-59
60-71
72-84
I
or
E
I
E
I
I
I
I
E
BASIS FOR VALUES/EQUATIONS
Pb concentration from data provided to EPA by FDA (US EPA (1986).
Quantity consumed from Pennington (1983).
Values are reported in the OAQPS report (USEPA 1989, pp. A-3) and the
TSD (USEPA 1990a). These estimates are based on body size in
combination with smoothed data from Phalen et al., (1985).
Statistical fitting of data from Silve et al (1987); also Spector (1956) and
Altman and Ditmer (1973)
The volume of extracellular fluid that exchanges rapidly with plasma is
estimated 73% of the blood volume based on Rabinowitz (1976). This
additional volume of distribution is assumed to be the volume the extra-
cellular fluid pool, which is the difference between the volume of the
distribution and the blood volume.
Statistical fit to VOLBLOOD(t) - VOLRBC(t)
Statistical fit to hematocrit x blood volume
Exposure Factors Handbook (US EPA, 1989b)
UNITS
g/day
m3/day
ug/dL
dl_
dl_
dl_
L/day
EQUATION
WHERE
USED
E-5g
E-3
B-
1h,2e,2f,2h,2
n,2o,5d,
5e,5m,10a
B-9g
B-7b,7c,9g
B-2.5
E-6a,6b
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-21
-------
PARAMETER NAME
weight_soil
WTBLOOD(t)
WTBODY(t)
WTBONE(t)
WTCORT(t)
WTECF(t)
WTKIDNEY(t)
WTLIVER(t)
WTOTHER(t)
DESCRIPTION
Percentage of total
soil and dust
ingestion that is
soil
Weight of blood
Weight of body
Weight of bone
Weight of cortical
bone
Weight of extra-
cellular fluid (ECF)
Weight of kidney
Weight of liver
Weight of soft
tissues
DEFAUL
T VALUE
OR
EON.
NO.
45
B-5m
B-5f
B-5g
B-5i
B-5e
B-5J
B-5k
B-51
AGE
RANGE
(mo)
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
0-84
1
or
E
E
1
1
1
1
1
1
1
1
BASIS FOR VALUES/EQUATIONS
Guidance Manual, Section 2.3 (US EPA, 1994)
Based on an blood density of 1.056 kg/I (Spector 1956).
Statistical fitting of data from Silve et al. (1987); also Spector (1956) and
Altman and Ditmer (1973). Also, body weight of 24 month old is assumed
to be 12.3 kg (Spector 1956).
12-84 months - Based on child skeletal ash data in Harley and Kneip
(1984) and the following assumptions.
WTBONE = (WTBONEADULT / WTSKEL_ASHADULT) * WTSKEL_ASH
where
WTBONEADULT=10kg
WTSKEL_ASHADULT = 2.91 kg
0-1 2 months- Assumed to be 11% of the weight of the body. The ratio of
weight of bone to weight of body (11%) is based on the 12-month estimate
for WTBONE from the above equation, and an estimate for WTBODY at the
same age.
Assumed to be 80% of the weight of the bone based on Leggettet al.
(1982).
Based on an assumed ECF density approximately the same as water, of
1.0kg/L.
Statistical fitting of data from Silve et al. (1987); also Spector (1956) and
Altman and Ditmer (1973). Also, body weight of 24 month old is assumed
to be 12.3 kg (Spector 1956).
Statistical fitting of data from Silve et al. (1987); also Spector (1956) and
Altman and Ditmer (1973). Also, body weight of 24 month old is assumed
to be 12.3 kg (Spector 1956).
Simple combination of the weight of body and the weights of kidney, liver,
bone, blood and extra-cellular fluid.
UNITS
%
kg
kg
kg
kg
kg
kg
kg
kg
EQUATION
WHERE
USED
E-8,10
B-5I
B-1a-
1e,5f,5g,5l
B-5h,5i
B-1h,5l,7e
B-5I
B-5j,5l,7f
B-2e,2f,5l,7g
B-2n,2o,7h
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-22
-------
PARAMETER NAME
WTTRAB(t)
DESCRIPTION
Weight of
trabecular bone
DEFAUL
T VALUE
OR
EON.
NO.
B-5h
AGE
RANGE
(mo)
0-84
1
or
E
1
BASIS FOR VALUES/EQUATIONS
Assumed to be 20% of the weight of the bone based on Leggett et al.
(1982).
UNITS
kg
EQUATION
WHERE
USED
B-1h, 5l,7i
NOTE: I = interior parameter, E = Exterior, user selectable parameter
B-23
-------
MP&M EEBA: Appendices Appendix M: Sensitivity Analysis of Lead-Related Benefits
Appendix M: Sensitivity Analysis of
Lead-Related Benefits
INTRODUCTION
APPENDIX CONTENTS
The methodology for estimating lead-related benefits for MJ Values for Quantified Lead-Related Health Effects M-l
., , ,„„ ,, ,| .. ... , . „, . , . T .. M.2 Lead-Related Benefit Results M-2
the MP&M regulation is discussed in Chapter 14. In its ,. - , _ . . . „, ., , T , „ , ,
s v M.2.1 Preschool Age Children Lead-Related
main analysis, EPA uses a three percent discount rate to „ r-^ ** ~
M.2.2 Adult Lead-Related Benefits M-3
value benefits associated with reductions in exposure to
lead. OMB, however, frequently recommends the use of a
seven percent discount rate in benefit-cost analyses for
government regulations. This appendix therefore presents a
sensitivity analysis of the results for lead-related benefits estimated using a seven percent discount rate and compares them
with estimated lead-related benefits in the main (three percent) analysis. Because EPA found that the final rule will not yield
any lead-related health benefits to either children or adults, the analysis in this appendix is limited only to the two Upgrade
Options considered as alternatives to the final rule, and the Proposed/NODA Option.
M.l VALUES FOR QUANTIFIED LEAD-RELATED HEALTH EFFECTS
Table M.I below compares per-case values for lead-related health effects estimated using a three percent discount rate and a
seven percent discount rate. Values for some health effect categories do not change for the following two reasons:
•• Discounting is not used in estimating a specific value. For example, the cost of treating hypertension used in this
analysis is the estimate of annual medical costs and lost work time associated with this condition.
*• The original study did not provide sufficient information for estimating the cost of illness value based on a seven
percent discount rate. Taylor et al. (1996) used a five percent discount rate to estimate the expected lifetime cost of
a stroke. The authors do not provide sufficient information to recalculate the value based on a different discount
rate. Therefore, EPA did not revise this value in the main analysis to reflect discounting at a three percent rate.
M-l
-------
MPAM EEBA: Appendices
Appendix M: Sensitivity Analysis of Lead-Related Benefits
Table M.I: Comparison of Pen-Case Values
for Lead-Related Health Effects (2001 $)
Health Category
Lead-Related Health
Effects ft
Value of an IQ point [A-(B+C)J
(A) Wage loss per IQ point
(B) Cost of additional education per IQ point
(C) Opportunity cost of lost income while in school
Additional education cost for children with IQ < 70
Additional education cost for children with PbB > 20 ug/dL
Value of preventing neonatal mortality
Lead-Related Health Effects i
Hypertension (male & female)15
CHD (male & female)
Stroke (male)"
Stroke (female)"
Mortality (male & female)a
Value/Cost @ 3%
Discount Rate
r Children
$9,419
$10,675
$511
$746
$58,012
$16,485
$6,500,000
or Adults
$1,141
$76,347
$335,135
$251,351
$6,500,000
Value/Cost @ 7%
Discount Rate
$1,817
$2,427
$247
$363
$36,831
$12,169
$6,500,000
$1,141
$74,115
$335,135
$251,351
$6,500,000
a Value of a Statistical Life (VSL) is taken from U.S. EPA's Guidelines for Preparing Economic Analyses. The recommended value was
not adjusted in the main analysis.
b Annual cost of treatment. No discounting is required.
c Values based on Taylor et al. (1996) which uses a five percent discount rate to estimate the expected lifetime cost of a stroke. EPA
used this value in the main analysis presented in Chapter 14 of this report.
Source: U.S. EPA analysis.
M.2 LEAD-RELATED BENEFIT RESULTS
This section presents lead-related benefits of the alternative regulatory options - the 433 Upgrade Options and the
Proposed/NODA Option - based on a seven percent discount rate.
AA.2.1 Preschool Age Children Lead-Related Benefits
Table M.2 summarizes lead-related benefits for children estimated for the 433 Upgrade Options based on a three percent and
a seven percent discount rate. As shown in Table M.2, using a seven percent discount rate results in a 19 percent reduction in
the total monetary value of lead-related benefits for preschool children compared to the value of benefits estimated based on a
three percent discount rate. Changes in the monetary values associated with individual benefit categories range from zero
percent (neonatal mortality) to 81 percent (avoided IQ loss).
M-2
-------
MPAM EEBA: Appendices
Appendix M: Sensitivity Analysis of Lead-Related Benefits
Table M.2: Comparison of the Monetary Value of Lead-Related Benefits to Children (2001$) Based
on Alterantive Discount Rates - 433 Upgrade Options
Category
Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade
Reduced
Cases or
IQ Points
Neonatal mortality 0.15
Avoided IQ Loss 31.99
Reduced IQ< 70 0.11
Reduced PbB > 20 ng/LJ 0.00
Total Benefits |
Mean Benefit Value (2001$)
%
3% DR 7% DR Change
$995,630
$301,323
$6,637
$0
$1,305,590
$995,630 | 0%
$58, 128 | 81%
$4,21 3 | 37%
$0 | 0%
$1,057,970 | 19%
Reduced
Cases
orlQ
Points
t 0.17
36.19
f 0.13
0.00
Mean Benefit Value (2001$)
1 %
3%DR 7%DR 1 Change
$1, 109,294 | $1,109,294 | 0%
$340,845 | $65,752 j 81%
$7,501 | $4,762 | 37%
$0 | $0 | 0%
$1,457,640 I $1,179,808 I 19%
Source: U.S. EPA analysis.
Table M.3 summarizes lead-related benefits for children estimated for the Proposed/NODA Option based on a three percent
and a seven percent discount rate. As shown in Table M.3, using a seven percent discount rate results in a 40 percent
reduction in the total monetary value of lead-related benefits for preschool children compared to the value of benefits
estimated based on a three percent discount rate. Changes in the monetary values associated with individual benefit
categories range from zero percent (neonatal mortality) to 81 percent (avoided IQ loss).
Table M.3: Comparison of the Monetary Value of Lead-Related Benefits to Children (2001$)
Based on Alterantive Discount Rates - Proposed/NODA Option
Category
Category
Neonatal Mortality
Avoided IQ Loss
Reduced IQ < 70
Reduced PbB > 20 ng/L
Total Benefits
Reduced Cases or IQ Points
1.60_
1,078.38
3.72
0.00
Benefit Value (2001$)
3%DR | 7%DR | % Change
$10,417,781
$10,157,286
$216,007
$0
$20,791,073
$10,417,781
$1,959,421
$137,140
$0
$12,514,342
0%
81%
37%
0%
40%
Source: U.S. EPA analysis.
M.2.2 Adult Lead-Related Benefits
Table M.4 presents lead-related benefits for adults for the 433 Upgrade Options based on a three percent and a seven percent
discount rate. Under both 433 Upgrade Options the difference between the total monetary value of benefits to adults estimated
based on a three percent and a seven percent discount rate is negligible (less than 0.1 percent). The reduction in total benefits
is marginal between the two discount rate scenarios because the monetary value of only one lead-related benefit category for
adults (i.e., CHD) is affected by the discount rate.
M-3
-------
MPAM EEBA: Appendices
Appendix M: Sensitivity Analysis of Lead-Related Benefits
Table M.4: Comparison
Category
Men ! Hypertension
ICHD
ICBA
[m
| Mortality
Women ICHD
ICBA
[BI
| Mortality
Total Benefits
r
of the Monetary Value of Lead-Related Benefits to Adults (2001$) Based on
Alterantive Discount Rates - 433 Upgrade Options
Directs + 413 to 433 Upgrade
V
:
:
:
Reduced Cases!
53.47J
0.05J
0.02J
O.Olj
0.07J
0.02J
O.Olj
O.Olj
0.02J
:
:
^^^^
Mean Value of Benefits j
3% DR 1
$61,004J
$4,155J
$5,698J
$3,226J
$474,735J
$1,662J
$2,417J
$1,487J
$150,190J
$704,574!
7% DR !
$61,004J
$4,033J
$5,698J
$3,226J
$474,735J
$1,614J
$2,417|
$1,487J
$150,190|
$704,404J
Directs + All to 433 Upgrade
V
:
:
:
Reduced Cases!
59.58J
0.06J
0.02J
O.Olj
0.08J
0.02J
O.Olj
O.Olj
0.03J
:
:
^^^^^
Mean Value of Benefits
3% DR !
$67,982J
$4,63lj
$6,350J
$3,596J
$529,125J
$1,853]
$2,694J
$1,658]
$167,417J
$785,304!
7% DR
$67,982
$4,495
$6,350
$3,596
$529,125
$1,799
$2,694
$1,658
$167,417
$785,115
Source: U.S. EPA analysis.
Table M.5 summarizes lead-related benefits for adults for the Proposed/NODA Option based on a three percent and a seven
percent discount rate. For this option, the estimated total monetary values of benefits drop from $7,048,025 under the three
percent discount rate to $7,046,328 under the seven percent discount rate (i.e., a decrease of less than 0.1 percent). This
marginal difference in the total value of benefits based the three percent and the seven percent discount rate is due to the fact
that only one benefit category (i.e., CHD) is affected by the discount rate.
Table M.5: Comparison of the Monetary Value of Lead-Related Benefits to Adults (2001$) Based on
Alterantive Discount Rates - Proposed/NODA Option
Category
Men
Women
Hypertension
CHD
CBA
BI
Mortality
CHD
CBA
BI
Mortality
Total Benefits
Reduced Cases
545.25
0.54
0.17
0.10
0.73
0.22
0.10
0.06
0.23
Mean Value of Benefits
3% DR
$622,126
$41,564
$56,907
$32,197
$4,750,132
$16,472
$23,928
$14,714
$1,489,984
$7,048,025
7% DR
$622,126
$40,349
$56,907
$32,197
$4,750,132
$15,991
$23,928
$14,714
$1,489,984
$7,046,328
Source: U.S. EPA analysis.
M-4
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-Based Recreation Survey
Appendix N: Analysis of the National
Demand for Water-Based Recreation
Survey
INTRODUCTION
This appendix presents EPA's analysis of the National
Demand for Water-based Recreation Survey (NDS). The
objective of this analysis is to determine the number of
people who participate in water-based recreation and their
total number of recreation trips, characterize participation
and number of trips taken by water body type, and provide
more detailed information on specific recreation activities
(e.g., fish species targeted on fishing trips) and
expenditures associated with various activities.
APPENDIX CONTENTS
N.I Background Information N-l
N.2 DataAnalysis N-2
N.3 Participation in Water-Based Recreation by Activity Type N-2
N.4 Allocation of Trips by Water Body Type N-l 1
N.5 One-Way Travel Distance N-16
N.6 Individual Expenditures per Trip N-l9
N.7 Distribution of Direct Costs for Single-day Trips N-22
N.8 Profile of Boating Trips N-27
N.9 Profile of Fishing Trips N-30
N. l BACKGROUND INFORMATION
U.S. EPA cooperated with the National Forest Service and several other federal agencies and interested groups to collect data
on the outdoor recreation activities of Americans. The 1993 NDS collected data on demographic characteristics and water-
based recreation behavior using a nationwide stratified random sample of 13,059 individuals aged 16 and over. Respondents
reported on water-based recreation trips taken within the past 12 months, including the primary purpose of their trips (i.e.,
fishing, boating, swimming, and viewing), and number of trips, trip length, distance to the recreation site(s), number of
participants, their trip expenditures, and detailed trip allocation information on the last trip taken for each recreation type. For
example, respondents reported:
*• where fishing was the primary purpose of a trip, the number of fish caught and the species targeted (i.e., coldwater,
warmwater, anadromous, or marine);
> the type of water body (e.g., lake, river, ocean, wetland); and
•• where boating was the primary purpose of trip, the type of boating (i.e., motorboating, sailing, canoeing, rowing,
rafting, and other floating).
EPA used NDS data to characterize water-based recreation activities nationwide, including:
•• percent of state population participating in water-based recreation by recreation activity and trip length (i.e., single-
day vs. multiple-day trips);
»• average number of water-based recreation trips per person by recreation activity and trip length;
> allocation of single- or multiple-day trips among different water body types by recreation type;
»• mean one-way distance traveled to the site visited on last trip;
> total expenditures per person for last single-day or multiple-day trip;
*• distribution of total expenditures among various expenditure categories for single- and multiple-day trips (e.g.,
lodging, boat rental, and entrance fee);
> allocation of fishing trips by target species; and
»• allocation of boating trips by boating type.
N-l
-------
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-Based Recreation Survey
N.2 &ATA ANALYSIS
The NDS used a random digit dialed population-based sample (aged 16 and over) of the nation. For simple random sampling,
estimates of the sample mean and total are consistent estimates of the population mean and total. EPA therefore treats
sample-based estimates as being representative of the population-based estimates. For example, the percent of survey
respondents participating in a given water-based recreation activity is theoretically consistent with the percent of the state
population (aged 16 and over) that participates in that activity. The estimated percentages can be applied to the state
population (aged 16 and over) to derive the number of participants in various water-based recreation activities in each state.
The survey database cannot be used to characterize subsistence fishing because subsistence fishermen's behavior differs
significantly from recreational fishermen's behavior. In addition, this population subgroup is likely to be under-represented in
the survey database due to various factors. First, subsistence fishermen constitute a relatively small portion of the total
fisherman population. They also tend to have a lower education level. Some of them may lack long-distance telephone
services and/or have language barriers. These factors are likely to result in inadequate representation of this subgroup in the
survey data.
N.3 PARTICIPATION IN WATER-BASED RECREATION BY ACTIVITY TYPE
This analysis estimates the percent and the number of state residents who participated in water-based recreation by activity
type and trip length (i.e., single-day vs. multiple-day). Participants in each activity in a given state include state residents who
took at least one single-day and/or multiple-day trip for each respective activity during the previous 12 months. Because
some participants took both single-day and multiple-day trips, the percent and the number of state residents participating in all
trips does not equal the sum of the single-day plus multiple-day percentages or number of participants. The analysis also
estimates the average number of recreation trips per person per year, by recreational activity, trip length (single-day vs.
multiple-day trips), and state of residence. Tables N.I, N.2, N.3, and N.4 characterize participation in boating, fishing,
swimming, and viewing, respectively.
1. Estimating the percent of state population participating in each of the four water-based recreation activities. The
total percent of state residents participating in each activity equals the total number of respondents who took at least
one single-day and/or multiple-day trip divided by the state's sample size. Similarly, the percent participating in
single-day or multiple-day trips for each respective activity equals the respective number of sample respondents who
took either single-day or multiple-day trips, respectively, divided by the state's sample size.
2. Estimating the number of state residents participating in each of the four water-based recreation activities. EPA
calculated the total number of participants in each state by multiplying the percent of sample respondents who took
at least one single-day and/or multiple-day trip by each state's actual population 1 6 years of age and older. Similarly,
the total number of participants in single-day or multiple-day trips for each respective activity equals the respective
percent of sample respondents who took either single-day or multiple-day trips, respectively, times the state's
population 1 6 years of age and older.
3. Estimating the average number of trips per person per year. EPA estimated the average number of recreation trips
per person per year by dividing the total number of trips taken for each activity by state residents by the total number
of participants in this activity. Similarly, dividing the number of single-day trips or multiple-day trips by the
respective number of participants provided the average number of single-day and multiple-day trips per person,
respectively. Tables N.I -N.4 also show the mean trip length for the last multiple-day trip.
For comparison purposes, Tables N.2 and N.4 also present estimates of the total percent and the number of state residents
participating in recreational fishing and wildlife viewing based on the U.S. Fish and Wildlife Service's (USFWS) 1996
National Survey of Fishing Hunting and Wildlife Associated Recreation. The table shows that the two surveys yield similar
results. NDS estimates, however, are slightly higher than USFWS estimates for some states. NDS fishing and viewing
participation estimates are higher for 47 and 30 states, respectively. This discrepancy may be due to the difference in the year
when the respective surveys were conducted.
N-2
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.I: Participation in Boating
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
[A
[D
[L
[N
KS
KY
LA
MA
MD
ME
MI
MN
vio
VIS
VIT
•vfC
"TO
"ffi
MH
•vfj
: :
: :
: :
: :
| |
State Pop. ! „ . i Sample
16 and Up 1 ^T6 i Weight
; Size ; &
457J28J 29J 15,784
3,451/86! 218! 15,833
2,072/221 128J 16,192
3,907,526! 178! 21,952
25,599,275! 1,313! 19,497
3,322,455! 212! 15,672
2,651,452! 159! 16,676
468,575! 35! 13,388
610,269! 51! 11,966
12,741,821! 662! 19,247
6,250,708J 373J 16,758
949,184! 55! 17,258
2,281,002! 171! 13,339
969,166! 83! 11,677
9,530,327J 466J 20,451
4,682,392! 300! 15,608
2,058,489! 135! 15,248
3,161,283! 219! 14,435
3,394,854J 189J 17,962
5,008,007! 249! 20,112
4,085,342! 257: 15,896
1,010,2731 721 14,032
7,628,170J 576J 13,243
3,782,817! 2451 15,440
4,331,937! 277: 15,639
2,160,1651 1401 15,430
701,423 1 55 1 12,753
6,291,1821 407! 15,457
502,176! 40! 12,554
1,314,9741 841 15,654
960,593 | 64 1 15,009
6,545,471! 347! 18,863
I :
Total Participation in Boating j Participation in Single-Day Trips ! Participation in Multiple-Day Trips
Percent
Population
48%
18%
20%
12%
20%
13%
20%
11%
20%
23%
18%
20%
19%
30%
18%
21%
13%
16%
20%
23%
19%
33%
24%
24%
22%
18%
22%
14%
28%
20%
23%
18%
| Avg# |
; Trips ; Days ; _ . ; ., , ,
„ _ . ; ; ; Percent • Number of
# People ; per ; per ; ;
: _ '• ~r : Population : People
! Person ; Year ;
; per Year ; ; ;
219J09J 5.1J 5.7J 45%! 205,978
621,285! 7.9! 10.6! 16%! 552,254
414,524J 6.4J 24. ll 16%l 331,620
468,903! 7.3! 10.8! 9%! 351,677
5,119,855! 5.3! 11.3! 14%! 3,583,898
431,919! 10.6! 14.0! 8%l 265,796
530,290J 8.7J 18. 3J 18%! 477,261
51,543! 2.0 1 2.7 1 9%l 42,172
122,054! 10.6! 13.3! 18%! 109,848
2,930,619! 10.3! 16.6! 20%! 2,548,364
1,125,12?! 10. 9J 19.0J 15%| 937,606
189,837! 7.6 1 9.5 1 18%! 170,853
433,390! 6.6! 13.31 17%! 387,770
290,750! 5.8 1 8.1 ! 25%! 242,292
1,715,459! 8.3J 15.9! 13%! 1,238,943
983,302! 9.3 1 18.3 1 15% | 702,359
267,604! 13.9! 27.1! 9%! 185,264
505,805! 6.2 1 9.0 ! 13% | 410,967
678,971 | 4.2 1 6.2 1 18% | 611,074
1,151,842! 11.8! 11.7! 18%! 901,441
776,215! 9.11 18.2! 17%! 694,508
333,3901 7.1 | 18.2 1 26%! 262,671
1,830,761 | 9.3! 17.4 1 19% | 1,449,352
907,8761 5.9 1 8.8 1 20%! 756,563
953,026! 6.0! 11.4! 16%! 693,110
388,8301 10.0 ! 14.9 1 16% | 345,626
154,313 | 5.8! 7.9! 15% 1 105,213
880,7651 7.5 | 12.1 ! 12% | 754,942
140,609! 4.2! 13.8! 18%! 90,392
262,9951 5.2 | 13.3 | 13% | 170,947
220,936 | 3.7 | 8.4 | 22% | 211,330
1, 178,185 1 10.1 1 12.2 1 17%! 1,112,730
Avg#of |
Trips per ! Percent
Person per! Population
Year ;
5.1! 14%
7.1 1 6%
5.0! 7%
7.3 1 6%
5.1! 11%
14.6 1 7%
7.9 1 7%
2.3! 3%
10.8! 8%
9.8 1 5%
10.4J 9%
6.6 1 2%
4.7! 5%
5.6! 8%
9.0 1 8%
7.5 ! 8%
14.2! 9%
6.4 ! 5%
4.1 | 5%
8.1 ! 8%
8.9 ! 6%
6.7 1 13%
9.0 1 11%
5.9 ! 7%
5.3 ! 12%
7.1 ! 6%
7.8 ! 7%
7.2 ! 6%
3.9! 13%
3.8 | 10%
3.2 ! 5%
10.5 I 3%
! Avg # of
Number of! Trips per
People ! Person per
; Year
64,082 1 1.2
207,095! 5.5
145,084J 6.9
234,452! 3.3
2,815,920! 3.3
232,572! 2.6
h 185,602! 5.2
14,057! 1.0
48,822! 2.2
637,091! 7.0
h 562,564! 5.2
18,984! 18.0
114,050! 8.3
77,533! 4.0
762,426 1 4.9
374,591! 9.3
185,264! 6.6
158,064! 2.6
169,743 1 2.0
400,641 ! 4.2
245,121! 4.1
131,3351 4.7
839,099 1 5.0
264,797! 4.1
519,832! 3.5
129,6101 10.3
49,100 1 1.8
377,471 ! 4.0
65,283 ! 3.8
131,497! 5.8
48,030 | 3.3
196,364 I 2.7
Mean Trip
Length
(days)
2.5
2.3
8.3
3.2
4.2
3.5
6.2
3.0
4.0
5.3
4.0
2.0
4.1
3.2
4.3
3.6
3.8
4.7
5.0
3.7
7.9
7.0
4'5
3.3
3.9
2.5
4.7
3.5
6.4
3.8
7.3
5.1
N-3
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.I:
:
:
:
:
|
State
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY
: :
: :
: :
: :
| |
State Pop.! CND^ i
1 , , Tf ; Sample ;
16 and Up; „.
Size
L37A134|
.1=537,896!
14,797,284!
8,789,530!
2,665,966!
2,673,283!
9,693,987!
827,474!
3,115,130!
577,391!
4,445,987!
15,618,097!
1,598,531!
..5,529,436!
479,265!
..4,552,631!
4,156,609!
..1,455,370!
381,882!
105]
75!
774!
650!
143!
217!
742!
57!
181!
42!
296!
657!
111!
389!
34}
324!
299!
126!
31!
:
:
:
:
Participation in Boating
I :
Total Participation in Boating j Participation in Single-Day Trips !
Participation in Multiple-Day Trips
Samnle i i T, . i „ i Avg # of ! ! Avg # of i .„ _ .
jampic . . Xrips : Days : : : : : : : Mean Trip
Weiffht i Percent ; , ! ; ; Percent ; Number of; Trips per ; Percent ! Number of; Trips per ; T
TTClglll : ; # people : per : per : . . : _ , „ ' „ , A. ' „ , : T, ' Length
; Population ; ; _ ; ,, ; Population; People ; Person per; Population; People ; Person per;
; r ; Person : Year : r : ,r ; ; „ : (days)
; „ Year Year v J '
• • • per Year ; ; ; ; ; ; ; ;
13,0491
20:505!
19,1181
13,522!
18,643!
..12,319]
13,065.1
14,517!
17,211!
..13,74JJ
1 5,020 1
23,772!
14,401!
14,214!
14,096J
14,051!
13,902!
11,551!
12,319!
19%!
23%!
18%J2
17%! 1
20%!
26%!
15%!l
16%!
19%!
26%!
23%ll
18%J2
20%!
19%!l
24% I
35%! 1
22%!
13%!
26%!
260.,325l
.35323.6!
,663^51 ll
,494.,220!
533.,193!
695.,054!
,454.,098l
.132,396!
591,875!
150., 122!
,022,577J
,811,257!
319,706!
,050,593!
11 5,024 1
,593^1!
914,454!
189,198!
99,289!
3.8J
10. l!
6.5!
7.0!
4.5!
8.4 1
9.2!
8.0!
9.0!
6.5 1
7.9!
8.2!
4.5 1
9.9!
7.1!
6.0!
10.3!
5.1!
5.5!
8.9!
18.4]
9.2!
10.6]
8.3 1
12.2!
16. ll
N/A!
16.5!
24.1!
10.3]
14.7]
9.6!
17.4]
12.4]
10.4]
14.7!
8.3]
6.2!
io%l
21%!.
13%l
13%!
13%!
22%!
12%l
16%!
15%!
21%!
20%l
15%!
11%!
15%!
21%|
29%!
19%!
io%!
23%!
137,0.13!
322:958!
l:923:647l
1,142,639!
346:576!
588;122!
1S163:278J
132;396!
467,270!
121;252!
889;197J
2,342,715!
175,838!
829,415!
100,646 1
1,320,263!
789,756!
145,537!
87,833!
3.5J
6.8!
7.5!
7.5!
4.9!
8.9!
9.1!
6.9!
8.9!
4.8!
7.7!
7.4!
5.6!
8.6!
7.1!
5.7!
10.0]
6.6!
^//T
n%!
9%j
5%!
7%!
8%!
9%!
5%!
2%!
7%!
io%!
6% 1
7%!
13%!
6%!
9%!
14%!
7%!
2%!
6%j
150,715!
138,411!
739,864!
615,267!
213,277!
240,595!
484,699!
16,549!
218,059!
57,739!
266,759!
1,093,267!
207,809!
331,766!
43,134!
637,368!
290,963!
29,107!
22,913!
3.4!
8.9]
3.0!
3.3]
3.2!
2.7]
5.6!
10.0]
4.5 !
7.0!
4.5!
5.7]
2.3 j
8.1!
2.3!
3.4]
4.5!
2.3]
2.0!
3.6
3.5
4.5
3.6
3.9
4.9
4.7
N/A
5.8
7.5
3.1
3.9
4.4
4.2
7.0
4.2
4.2
9.0
2.5
N/A - Not Available
Source: NDS.
N-4
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
: :
: :
: :
: :
: :
: :
: :
: :
Pop. 16 !
State and Up j
i i
: :
: :
: :
: :
AK 457,728 1
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
3,451,586!
2,072,622 1
3,907,526!
25, 599,275 1
3,322,455!
2,651,452J
468,575!
610,269J
12,741,821 1
6,250,708 1
949,184!
2,281,002J
969,166!
9,530,327J
4,682,392!
2,058,489J
3,161,283!
3,394,854J
5,008,007!
4,085, 342J
1,010,273!
7,628,170J
3,782,817!
4,331, 937J
2,160,165!
701,423J
6,291,182!
:
:
:
:
:
:
NDS
Sample 1
Size
i
:
:
29 1
218!
128J
178!
1,313J
212!
159J
35!
5l|
662!
373 1
55!
17l|
83!
466 1
300 !
135J
219!
189J
249!
257J
72!
576J
245!
277 1
140!
55|
407!
1
Sample j
Weight |
15,784 1
15,833!
16,192J
21,952!
1 9,497 1
15,672!
16,676J
13,388!
11,966J
19,247!
16,758J
17,258!
13,339J
11,677!
20,45 1|
15,608!
15,248J
14,435!
17,962J
20,112!
15,896J
14,032!
13,243J
15,440!
15,639J
15,430!
12,753J
15,457!
:
Percent i
pop. !
(NDS- |
based) !
59% |
25%!
37% |
20%!
22% |
38%!
18%|
N/A!
20% |
25%!
23% |
20%!
24% |
40%!
20% |
26%!
28% |
30%!
34% |
22%!
21%!
31%!
27% |
34%!
26% I
27%!
42% |
25%!
Table N.2: Participation
Total Participation in Fishing
Percent 1 1 Avg # of 1
i i i
Pop. 1 Number j Trips per j
(USFW | of Peoplej Person j
S-based) ! ! per Year j
41%l 270,060J 13.8J
21%
26%
14%
12%
23%
14%
N/A
19%
18%
18%
14%
23%
32%
18%
19%
19%
23%
26%
12%
15%
21%
20%
31%
23%
21%
24%
20%
862,896! 16.8!
766,870 ! 11.9!
781,505! 8.1 1
5,631,840J 6.5J
1,262,533! 12.0J
477,261 ! 6.9J
N/A! N/A!
122,054J 10.3J
3,185,455! 15.3!
1,437,663! 8.8J
189,837! 6.1J
547,440J 11.9J
387,666! 12.3!
1,906,065! 13.7J
1,217,422! n.oj
576,377 1 11.3J
948,385! 9.0!
1,154,250J 15.0J
1,101,762! 15.3!
857,922J 12.2J
313,185! 9.9!
2,059,606! 10.5J
1,286,158! 10-3!
1,126,304J 5.8J
583,245! 17.0J
294,598! 19.5J
1,572,796! 11.2!
in Recreational Fishing
1
Days !
per 1
Year
18.3 1
19.5 1
16.2!
16.5 1
13. ll
19.5 !
8.0!
N/A!
16.0 1
17.4!
12.6 !
6.2!
16.7!
19.4!
27.4!
14.6 !
18.8 1
13.9!
18.9!
24.0!
15.7J
11.2 1
16.4!
18.7!
10.5J
19.3!
26.2!
15.1!
Single-Day Trips
| Avg # of |
o/ T, ! M n i ! Trips per j
% Pop. : # People : _
! Person ;
! ! per Year !
55%l 251.750J 12.8J
21%! 724,833! 18.5!
31%| 642,513J 12.5J
15%! 586,129! 7.0 1
16%l 4,095, 884J 6.6J
30%! 996,736! 12.2!
16%| 424,232J 7.1 1
N/A! N/A! N/A!
18%l 109,848J 10.8J
21%! 2,675,782! 16.6!
19%| 1,187,635J 9.8J
18%! 170,853! 6.6!
19%l 433,390J 13.4J
33%! 319,825! 10. 8!
16%| 1,524,852J 14.2J
23%! 1,076,950! 11. ij
21%l 432,283J 12.2J
25%! 790,321! 9.2!
29%| 984,508J 15.3J
18%! 901,441! 13.3!
18%l 735,362J 12.9J
28%! 282,876! 10.5!
20% | 1,525,634J 11.3J
24%! 907,876! 10.9!
20%| 866,387J 5.5J
25%! 540,041! 16.8!
36%| 252,512! 19.7J
20%! 1,258,236! 12.5!
:
:
:
:
%pop. !
i
:
:
:
24% I
6%|
12% |
11%!
10%l
17%!
4%|
N/A!
4%i
5%|
8%|
2%|
7% 1
20%!
7%|
8%|
13%l
12%!
8%|
6%|
6% 1
6%|
10% j
19%!
9%l
7%|
20%!
12%!
Multiple-Day Trips
| Avg # of |
! Trips per!
# People : _
• Person :
! per Year !
109,855J 4.3J
207,095! 5.0 1
248,715! 4.1 1
429,828! 5.4 1
2,559,928J 3.8J
564,817! 5.1J
106,058! 2.1!
N/A! N/A!
24,411 1 3.0J
637,091! 4.7 1
500,057! 2.9 1
18,984! 1.0!
159,670J 3.8J
193,833! 6.0!
667,123! 7.8!
374,591! 3.8!
267,604J 4.8J
379,354! 3.9!
271,588! 7.8!
300,480! 14.1 1
245,121 1 2.9J
60,616! 2.0!
762,817! 5.0J
718,735! 4.5!
389,874J 4.3J
151,212! 5.8!
140,285! 4.9!
754,942! 3.2!
Mean
trip
length
(days)
3.7
3.2
4.3
3.8
4.8
4.3
3.6
N/A
10.5
3.7
4.6
3.0
5.4
3.5
5.9
4.0
4.3
4.0
3.2
3.4
5.7
4.5
4.3
4.4
4.2
2.5
4.0
3.4
TV-5
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.2: Participation in Recreational Fishing
State
: : : : :
ill i i
Total Participation in Fishing Single-Day Trips Multiple-Day Trips
ill i i
Pop. 16 ! NDS ! Sample 1 Percent 1 Percent 1 | Avg # of | j Avg # of j j Avg # of j Mean
and Up !SrPle! Weight ! ^ j JJP- j Number j Trips per j j j j Trips perj j j Trips perj trip
Size (NDS- ! (USFW j of People j Person j _T ! Person j ! Person ; length
! ! ! based) j S-based) j j per Year j j j j per Year j j j per Year j (days)
ND 1 502,176J 40 1 12,554 1 33% 1 24% 1 165.718J 4.5 1 6.3 1 30%l 150,653J 3.9J 10%l 50.218J 3.0J 3.0
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
1,314,974! 84! 15,654! 20%
960,593 1 64 1 15,009 j 16%
6,545,471! 347! 18,863! 19%
1,370,134J 105 1 13,0491 22%
1,537,896! 75! 20,505! 21%
14,797,284J 774J 19,118J 15%
8,789,530! 650 ! 13,522! 19%
2,665, 966 1 143 1 18,643 1 32%
2,673,283! 217! 12,319! 36%
9,693,987 1 742 j 13,065 j 21%
827,474! 57! 14,517 1 21%
3,115,130J 181 1 17,211 1 29%
577,391! 42! 13,747! 26%
4,445,987 1 296 j 15,020 j 26%
15,618,097! 657! 23,772! 29%
1, 598,531 1 111! 14,401 1 23%
5,529,436! 389! 14,214! 26%
479,265 1 34 1 14,096 j 9%
4,552,631! 324! 14,051 j 27%
4,156,609J 299J 13.902J 29%
1,455,370! 126! 11,551 j 25%
19%! 262,995! 11.9J 24.8! 14%
18%| 153,695J 14.9J 23. 6J 14%
13%! 1,243,639! 5.9J 7.0 1 16%
19%l 301,4291 8.7 1 12.3J 16%
17%! 322,958! 11.9J 15.2! 17%
11%J2,219,593J 8.8 1 16.2J 12%
13%! 1,670,011 1 14.6! 23.2! 16%
31%l 853,109J 13.0J 12.9J 28%
21%! 962,382! 11.4J 15.2! 29%
15%J2,035,737J 10.8J 16.1 1 17%
14%! 173,770! 7.8! 7.5 1 19%
24% 1 903,388J 16. ll 20.4 1 27%
31%! 150,122! 8.2 1 13.7! 24%
17%| 1,155,957] 15. lj 19.0J 24%
18%! 4,529,248! 10.2! 16.6! 23%
21%l 367,662J 5.6J 17.9J 15%
18%! 1,437,653! 8.2! 13.0J 19%
19%] 43,134] 12.0J 8.7] 9%
22%! 1,229,210! 14.9! 21.3! 22%
25%l 1,205,417J 10.4J 18.4J 22%
18%! 363,842! 17.0J 22.4! 22%
184,096! 13.2
134,483 1 13.2
1, 047,275 1 6.1
219,22ll 8.5
261,442! 13.5
1,775,674J 9.1
1,406,325 1 14.0
746,470 1 13.4
775,252! 11.8
1,647,978J 11.1
157,220! 8.3
841,085 1 15.6
138,574! 7.7
1,067,037J 14.8
3,592,162! 10.1
239,780J 4.1
1,050,593 1 8.7
43,134J 8.7
1,001,579! 16.1
914454J n.i
320,181! 16.3
12%
8%
4%
12%
5%
5%
7%
1%
13%
8%
2%
7%
12%
6%
13%
11%
10%
3%
12%
14%
8%
157,797! 4.3 1 6.0
76,847 1 6.0 1 4.0
261,819! 3.1! 2.8
164,416J 4.2 1 2.7
76,895! 3.8 1 4.8
739,864 1 4.4 1 6.0
615,267! 7.6 1 4.1
26,660 1 4.1 1 9.0
347,527! 4.6 1 3.5
775,519J 5.0 1 3.7
16,549! 2.0 1 N/A
218,059J 6.8 1 3.6
69,287! 2.6 1 5.5
266J59J 5.2J 4.4
2,030,353! 5.3! 3.6
175,838J 5.9J 5.4
552,944! 4.3! 4.0
14,378J 10.0J N/A
546,316! 4.6! 4.0
581.925J 4.5 1 4.6
116,430! 6.9! 3.7
WY | 381,882 j 31 1 12,319J 58%| 31%| 221.492J 12.9J 46.0J 52% | 198,579J 8.2J 32% | 122,202J 10.0J 7.0
N/A - Not Available
Source: U.S. Fish and Wildlife Service's (USFWS) 1996 National Survey of Fishing Hunting and Wildlife Associated Recreation.
N-6
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.3: Participation in Recreational Swimming
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
[D
[L
[N
KS
KY
LA
MA
MD
ME
MI
MN
MO
VIS
VtT
sic
STO
:
:
:
i
State Pop. |
16 and Up i
i
:
:
:
:
:
:
457,7281
3,451,586|
2,072,6221
3,907,526J
25,599,275!
3,322,455J
2,651,452!
468,575J
610,269!
12,741,82l|
6,250,708!
949,184J
2,281,002!
969,166|
9,530,327!
4,682,392!
2,058,489!
3,161,283|
3,394,854!
5,008,007!
4,085,342!
l,010,273j
7,628,170!
3,782,817!
4,331,937!
2,160,165|
701,423!
6,291,182!
502,176!
STE 1,314,974|
! !
NDS ! „ ,
_ , • Sample :
Sample „, ...
„. Weight
Size &
29! 15,784!
218J 15,833J
1281 16,192!
178J 21,952J
1,313! 19,497!
212J 15,672J
1591 16,676!
35J 13,388J
51! 11,966!
662J 1 9,247 1
373! 16,758!
55J 17,258|
171! 13,339!
83J 1 1,677 1
466! 20,451!
3001 15,608!
135! 15,248!
219J 14,435 1
189! 17,962!
2491 20,112!
257! 15,896!
72 1 14,032 |
576! 13,243!
2451 15,440!
277! 15,639!
140J 15,430 1
55! 12,753!
4071 15,457!
40! 12,554!
84 1 15,654 |
Total Participation in Swimming I
| | Avg# |
D * ^VT u J TriPs i Days i
Percent • Number of:
T, T, i Per : Per :
Pop. People „ \ .*
! Person | Year |
: : :
! ! per Year ! j
7%| 32,041! 2.0! N/A!
23%| 793,865| 7.7J 11.5J
23%! 476,703! 8.5! 20.1!
19%| 742,430J 4.5J 7.6J
29%! 7,423,790! 9.5! 13.0!
17%| 564,817J 4.0J 6.4J
41%! 1,087,095! ll.OJ 19.8!
17%| 79,658J 2.5J 6.4J
22%! 134,259! 5.3! 8.5!
33%| 4,204,801 1 13. 3J 17.9J
29%! 1,812,705! 5.3! 11.0!
58% | 550,527J 19.2J 27. 6J
18%! 410,580! 2.4 1 4.0!
25% | 242,292 1 8.9J ll.lj
21%! 2,001,369! 4.0! 8.0!
22%! 1,030,126! 5.01 8.9!
•S- i- i- *•
19%! 391,113! 5.5! 9.3!
17% | 537,418| ll.OJ 13.2J
24%! 814,765! 4.3! 9.3!
41%! 2,053,283! 9.41 17.8!
27%! 1,103,042! 5.6! 11.4!
46% | 464,726 | 14.5 j 29.7 j
30%! 2,288,451! 8.5! 16.3!
24%! 907,876! 5.41 6.5!
22%! 953,026! 6.4! 10.9!
21% | 453,635 | 10.5 | 12.8 j
40%! 280,569! 6.9! 10.1!
23% ! 1,446,972! 5.71 10.8!
25%! 125,544! 8.0! N/A !
19% | 249,845 | 3.5 j 10.7 j
Participation in Single-Day . .
Participation in Multiple-Day Trips
T : ^ T T ^
i i i i i i
| Avg # of | | Avg # of |
Percent j Number of j Trips per j Percent j Number of j Trips per j
Pop. ! People ! Person per j Pop. j People j Person per j
Year Year
i i i i i i
7%! 32,041! 2.0! 3%
17%| 586,770| 9.3J 7%
21%! 435,251! 6.8! 9%
13%| 507,978J 5.3J 7%
22%! 5,631,840! ll.OJ 9%
12%| 398,695J 5.0J 6%
33%! 874,979! 11.3! 18%
9%| 42,172J 2.0J 9%
14%! 85,438! 6.9! 6%
26% | 3, 312,873 1 14. 9J 8%
15%! 937,606! 6.6! 13%
56% | 531, 543 1 16.5 1 24%
13%! 296,530! 2.7! 4%
23% | 222,908 1 8.8J 8%
12% | 1,143,639! 5.4 j 8%
17% | 796,007 1 5.4 1 8%
14% | 288,188! 6.0! 7%
11%| 347,741 | 15.7J 5%
16%! 543,177! 4.7! 11%
34%! 1,702,722! 8.91 14%
14% | 571,948! 7.8! 12%
40% | 404,109 | 12. 8 | 15%
24%! 1,830,761! 8.6! 10%
18%! 680,907! 6.61 5%
16%! 693,110! 7.7! 9%
17% | 367,228 1 11.9J 6%
33%! 231,470! 7.6! 16%
15%! 943,677! 7.01 10%
15% | 75,326! 11.5! 13%
13,732! 1.0!
241,61l| 3.3J
186,536! 5.9!
273,527J 1.9J
2,303,935! 3.1!
199,347J 1.7J
477,261! 4.4!
42,172J 3.3J
36,616! 2.7!
1,019,346J 5.2J
812,592! 3.6!
227,8041 8.3!
h * +
91,240! 1.3!
77,533 1 3.1J
762,426! 2.2!
374,591! 2.3!
hi •!•
144,094! 3.0!
158,064 1 1.8 |
373,434! 2.8!
701,1211 6.1!
490,241! 3.2!
151, 541 | 9.9 |
762,817! 4.7!
189,1411 2.2!
389,874! 2.5!
129,610 | 2.5 |
112,228! 2.0!
629,1181 2.4!
65,283 ! 2.4 !
15% | 197,246 | 3.9J 6% | 78,898 1 1.6 1
Mean Trip
length
(days)
N/A
4.6
6.0
5.6
4.9
4.8
5.5
3.0
5.8
4.9
4.8
3.4
6.8
3.0
5.9
5.5
4.4
5.5
4.9
5.0
5.1
5.8
6.0
3.4
5.1
4.4
4.8
6.0
N/A
15.0
N-7
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.3: Participation in Recreational Swimming
: :
: :
: :
i »
State "feI"H S*
16 and Up :
|
i
:
:
:
:
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY
960,593!
6,545,47l|
1,370,134]
1,537,896]
14,797,284!
8,789,530J
2,665,966!
2,673,283J
9,693,987!
827,474J
3,115,130]
577,39l|
4,445,987!
15,618,097!
1,598,531!
5,529,436J
479,265!
4,552,63l|
4,156,609!
1,455,370!
381,882!
I j
Total Participation in Swimming I
Participation in Single-Day . .
Participation in Multiple-Day Trips
JT*C : : * _ ii :
^1 Sample j Avg # j | Avg # of 1 1 Avg # of 1
™PC Weight Percent 1 Number of! TnpS 1 DayS 1 Percent 1 Number of 1 Trips per 1 Percent 1 Number of 1 Trips per 1 Mfan ^Ip
Size : : : : per : per : : : : : : : length
| Pop. | People | P(T | year j Pop. | People j Person per j Pop. j People j Person per j
! Year Year l y '
j j j j per Y ear j j j j j j j j
64! 15,009!
347J 18,863 1
105! 13,049!
75J 20,505 1
774! 19,118!
650J 13.522J
143! 18,643!
217J 12.319J
742! 13,065!
57J 14.517J
18l! 17,211!
42J 1 3,747 1
2961 15,020!
657J 23J72J
111! 14,401 1
389J 14.214J
34! 14,096!
324J 14,051 1
2991 13,902!
1261 11,551!
31! 12,319!
42%! 403,449!
39%| 2,552,734J
15%! 205,520!
19%| 292,200J
33%! 4,883,104!
23%| 2,021, 592J
28%! 746,470!
34%| 908,916J
28%! 2,714,316!
40%| 330,990 1
22%! 685,329!
24% | 138,574J
23%! 1,022,577!
24% | 3,748,343 1
20%! 319,706!
28%| 1,548,242J
26%! 124,609!
35%| 1,593,421 1
27%! 1,122,284!
25%| 363,842|
6%| 22,913!
15.8! 56.5!
6.2J 12.7J
2.7! 4.2 1
6.3! 13.2!
7.6! 15.0!
7.3J 15.6J
3.4! 5.7!
6.7J 12.7J
5.7! 10.4!
6.9J N/AJ
6.0! 9.4!
7.3! 9.2!
5.8! 9.7!
5.1J 7.7J
5.9! 10.4!
4.9! H.l!
12.3! 19.6!
5.4J 10.7J
5.5! 8.9!
6.51 12.7!
s.o! N/A!
38%! 365,025!
28%| 1,832,732J
10%! 137,013!
12%| 184,548J
25%! 3,699,321!
15%| 1,318,430J
16%! 426,555!
27% | 721J86J
17%! 1,647,978!
37%| 306,165J
17%! 529,572!
24% | 138,574J
17%! 755,818!
16%| 2,498,896J
15%! 239,780!
17%| 940,004J
24%! 115,024!
28%| 1,274,737J
22%! 914,454!
18%| 261,967|
6%| 22,913!
14.5! 16%!
6.9J 16%|
3.8! 5%|
6.3J 9%|
8.1! 11%!
8.7J 6%|
4.1! 8%|
7.1J 12%|
7.5! 12%!
7.0J 11%|
6.9! 5%|
7.2J 7%|
6.7! 8%|
6.0J 9%|
6.2! 10%!
5.5J 13%|
11.6! 6%!
5.7J 14%|
6.1! 7%!
6.4J 9%|
8.0! 3%!
153,695! 7.2 1
1, 047,275 1 3.1J
68,507! 1.4!
138,41l| 4.6J
1,627,701! 4.5!
527,372J 4.7J
213,277! 2.9!
320,794J 3.2J
1,163,278! 2.7 1
91,022J 2.5J
155,756! 3.0!
40,417J l.OJ
355,679! 2.5!
1,405,629J 2.3J
159,853! 2.5!
718,827J 3.2J
28,756! 8.5!
637,368J 2.4J
290,963! 2.1!
130,983! 5.l!
11,456! 1.0!
15.8
6.1
3.7
4.2
6.0
8.1
4.1
6.4
5.1
N/A
6.0
7.0
5.4
4.3
4.5
5.2
4.5
6.3
7.0
4.4
N/A
N/A - Not Available
Source: NDS.
N-8
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
_, , Pop. 16 !„ , S
State jTT Sample •
• and Up • _. • 1
Size
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MX
NC
ND
NE
NH
|
Table
N.4: Participation in
Wildlife Viewing (Near-Water Recreation)
Total Participation in Near- Water Recreation Single-Day Trips
|
Multiple-Day Trips
Sample ! Percent j Percent j j Avg # of ! Avg # of ! ! Avg # of !
height j Pop. Pop. ! Number of! Trips per y ! Percent ! Number of! Trips per ! Percent j Number of! Trips per j Mean trip
| (1VDS- | (USFWS- | People j Person per J*er | Pop. j People j Person perj Pop. j People j Person perj length
j based) 1 based) j j Year j j j Year ! ! ! Year !
! 457,728! 29! 15,784!
j. j. j. j.
i 3,451,586! 218! 15,833!
j. j. ^ j.
i 2,072,622! 128! 16,192!
j. ^ j. j.
i 3,907,526! 178! 21,952!
j. ^ j. j.
! 25,599,275! 1,313! 19,497!
j. ^ j. j.
! 3,322,455! 212! 15,672!
j. ^ j. j.
i 2,651,452! 159! 16,676!
j. ^ ^ j.
468,575! 35! 13,388!
j. ^ j. j.
610,269! 5l! 11,966!
j. ^ j. j.
! 12,741,821! 662! 19,247!
j. ^ j. j.
i 6,250,708! 373! 16,758!
j. ^ ^ j.
949,184! 55! 17,258!
j. ^ i i
i 2,281,002!
j. j.
969,166!
1711
i
831
13,339!
„ J,
11,677J
t Y
\ 9,530,32?! 466! 20,451!
j. j. ^ j.
i 4,682,392! 300! 15,608!
j. j. ^ j.
i 2,058,489! 135! 15,248!
j. ^ j. j.
i 3,161,283! 219! 14,435!
j. ^ j. j.
i 3,394,854! 189! 17,962!
j. ^ ^ j.
i 5,008,00?! 249! 20,112!
j. j. ^ j.
i 4,085,342! 25?! 15,896!
j. j. ^ j.
1,010,273! 72! 14,032!
j. ^ j. j.
i 7,628,170! 576! 13,243!
j. ^ ^ j.
i 3,782,81?! 245! 15,440!
j. j. ^ j.
i 4,331,93?! 27?! 15,639!
j. j. ^ j.
i 2,160,165! 140! 15,430!
j. ^ j. j.
701,423! 55! 12,753!
j. j. ^ j.
i 6,291,182! 40?! 15,457!
j. ^ j. j.
502,176! 40! 12,554!
j. j. ^ j.
1,314,974! 84! 15,654!
j. ^ j. j.
960,593! 64! 15,009!
i i i i
48%!
i,
36%!
L
28 Ay
L
25 Ay
L
ciO/
M%
L
25 Ay
i,
60%!
L
ciO/
M%
L
57 Ay
i,
44%!
i,
36%!
i,
64%!
L
32%!
i,
43%!
i,
31%|
i,
31%|
i,
33%!
L
28 Ay
i,
34%!
i,
50%!
i,
46%!
i,
54%!
i,
44%!
i,
33%!
L
32%!
1.
29%!
1.
33%!
1.
45%!
1.
25%!
1.
25%!
1.
42%!
i
50%! 219,709! 7.2 8.4! 41%! 187,668! 7.1!
j. ^ h |. j. ^ j.
30%! l,242,57l! 4.4 8.2! 12%! 414,190! 9.2!
j. t L L t t L
34%|
^
580,334J
976,882J
6.4 15.0! 13%! 269,44l! 10.2!
h 1. j. ^ j.
4.7 8.7! 11%! 429,828! 8.0!
L L t t L
25%! 13,055,630! 11.2 14.3! 37%! 9,471,732! 14.0!
j. ^ h |. ^ ^ j.
42%! 830,614! 8.6 11.7! 13%! 431,919! 14.8!
j. t L L t t L
j....
N/AJ
l,590,87l|
238,973J
5.3 8.8! 38%! 1,007,552! 6.7!
h 1. ^ j. j.
3.9 30.7! 23%! 107,772! 2.4!
L L t t L
34%! 347,853! 9.9 16.6! 41%! 250,21o! 11.0!
j. ^ h |. j.....r t i
25%! 5,606,40l! 14.2 18.2! 32%!
^ j. h |. j.....
29%! 2,250,255! 3.1 9.4! 14%!
^ \ t t ^
4,077,383J
875,099J
17.9!
L
4.1!
14%! 607,478! 30.3 30.7! 56%! 531,543! 33.9!
j. ^ h |. j. ^ j.
38%! 729,92l! 2.9 7.1! 16%! 364,960! 4.4!
j. j. h |. j. j. j.
40%! 416,74l! 3.2 7.0! 24%! 232,600! 4.2!
^ j. h |. ^ j. j.
35%! 2,954,40l! 5.9 10.6! 17%! 1,620,156! 9.0!
^ ^ h |. j. j. j.
35%! 1,451,542! 5.4 11.2! 15%! 702,359! 9.0!
^ t L L t t i
32%|
32%|
679,30l|
885,159J
5.8 12.8! 17%! 349,943! 9.0!
h 1. j. ^ j.
2.4 9.1! 12%! 379,354! 3.0!
L L t t L
27%! 1,154,250! 3.2 8.5! 15%! 509,228! 3.4!
^ ^ h |. j. ^ j.
35%! 2,504,004! 9.8 21.0! 31%! 1,552,482! 11.5!
j. t L L t :. t i
34%|
46%|
36%|
38%|
1,879,257J
545,547J
3,356,395J
1,248,330J
6.3 12.7! 18%! 735,362! 12.1!
h 1. j. ^ j.
5.4 6.6! 44%! 444,520! 5.7!
h 1. ^ ^ j.
6.3 10.3! 24%! l,830,76l! 9.4!
h 1. j. ^ j.
10.5 15.2! 19%! 718,735! 16.5!
L L t t L
40%! 1,386,220! 2.7 8.1! 13%! 563,152! 4.0!
j. t L L t :. t i
23%|
47%|
626,448J
231,470!
t
11.3 15.2! 12%! 259,220! 24.2!
h 1. ^ j. j.
10.1 12.9! 20%! 140,285! 15.6!
L L t t L
35%! 2,831,032! 4.1 11.5! 18%! 1,132,413! 5.2!
j. j. h |. ^ ^ j.
23%! 125,544! 2.6 3.4! 15%! 75,326! 3.0!
j. ^ h |. j. ^ j.
35%! 328,744! 1.8 5.9! 14%! 184,096! 2.1!
j. j. h |. j. j. j.
44%! 403,449! 12.2 21.5! 31%! 297,784! 14.9!
i i L i i i L
7%! 32,04l!
:. :.
O/10/! Q o o T o 1 :
24yo 828,381
|. j.
16%! 331,620!
1. ^
13%! 507,978!
1. ^
16%! 4,095,884!
:. :.
10%! 332,2461
1. ^
20%! 530,290!
1. ^
31%! 145,258!
1. j.
24%! 146,465!
1. j.
13%! 1,656,43?!
1. j.
21%! 1,312,649!
:. :.
9%! 85,4271
1. j.
15%! 342,150!
1. j.
23%! 222,908!
1. ^
13%! 1,238,943!
1. j.
14%! 655,535!
:. :.
1/1 0/! OQOloo
14yo: 288,188
|. j.
15%! 474,192!
1. j.
19%! 645,022!
1. ^
22%! 1,101,762!
1. j.
29%! 1,184,749!
1. j.
11%! 111,130!
1. j.
16%! 1,220,50?!
1. j.
14%! 529,594!
1. j.
17%! 736,429!
:. :.
14%! 302,4231
:. :.
i co/ '. in^oi^'
lj/o: 1UJ,ZU
1. j.
29%! 1,824,443!
:. :.
5%! 25,1091
1. j.
8%! 105,198!
1. ^
9%! 86,453!
i i
8.0J 2.0
1.9J 4.0
3.3J 5.5
2.1J 4.7
3.2J 4.2
1.9J 5.4
3.1J 4.4
4.6J 10.5
4.7J 4.5
3.7J 4.7
2.6J 5.2
1.8J 4.0
1.2J 9.0
1.5J 5.6
2.2J 6.1
2.4J 6.3
2.5J 7.7
2.0J 7.3
3.1J 4.0
5.9J 5.3
2.4J 5.2
3.5J 2.8
2.7J 5.2
2.4J 5.5
2.1J 5.8
1.8J 5.7
1.2J 6.0
3.2J 4.5
4.0J 2.0
1.7J 8.6
5.2! 9.5
i
N-9
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
1
State |
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
XX
UT
VA
VT
WA
WI
WV
WY
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
I I I
Table
N.4: Participation in Wildlife Viewing (Near -Water Recreation)
:
Total Participation in Near-Water Recreation Single-Day Trips
Multiple-Day Trips
Pop. 16 | NDS Sample j Percent j Percent j j Avg # of j j Avg # of j j Avg # of j
and Up ! *amPle ! Weight ! Pop. Pop. j Number of! Trips per ! Percent ! Number of! Trips per ! Percent ! Number of! Trips per ! Mean trip
| Slze | | (NDS- | (USFWS- | People j Person per j ^er Pop. j People j Person per j Pop. j People j Person per j length
I | based) j based) j j Year j ear j Year j j Year j
6,545,47l| 347J 1 8,863 1
1,370,134J 105J 13,049J
1,537,896J 75J 20,505J
14,797,284J 774J 19,118J
8,789,530J 650J 13,522J
2,665,966J 143J 1 8,643 1
2,673,283J 217J 12,319J
9,693,987J 742J 13,065J
827,474J 57J 14,517J
3,115,130J 181J 17,21l|
577,39l| 42J 13,747J
4,445,987J 296J 15,020J
15,618,097J 657J 23,772J
l,598,53l| lllj 14,40l|
5,529,436J 389J 14,214J
479,265J 34J 1 4,096 1
4,552,63l| 324J 14,05l|
4,156,609J 299J 13,902J
1,455,370J 126J ll,55l|
381,882J 3l| 12,319J
54%|
29% |
35%|
45%|
36%|
34%|
59%|
39%|
56%|
45%|
29% |
33%|
47% |
58%|
38%|
27% |
29% |
26%| 3,534,554J 5.5J
29%| 397,339J 2.6J
21%! 538,264! 6.2!
23%| 6,658,778J 4.3 j
33%| 3,164,23l| 4.7J
35%| 906,428J 1.9J
42%| 1,577,237J 6.4J
37%| 3,780,655J 3.9J
32%| 463,385J 4.0J
29% | 1,401,808J 5.3J
30%| 167,443J 2.1 1
37%| 1,822,855J 2.1 j
25%| 5,153,972J 3.6J
30%| 495,545J 2.4J
37%| 2,267,069J 3.4J
48%| 225,255J 5.6J
39%| 2,640,526J 9.2J
42%| l,579,51l| 4.6J
31%! 392,950! 4.3!
39%| 110,746J 3.1J
11.8 32%| 2,094,55l| 6.3J
7.8 9%! 123,312! 5.6!
,. ^ ; j. j.
11.0 21%! 322,958! 7.2!
,. j. ; j. j.
9.9 25%| 3,699,32l| 5.7J
11.1 16%! 1,406,325! 8.2!
^ j. ;. ; ^ j.
5.3 12%! 319,916! 3.4!
,. j. ; ^ j.
12.4 38%| 1,015,848J 7.2J
9.4 14%! 1,357,158! 7.4!
,. ^ ;. ; ^ j.
9.2 40%| 330,990J 4.6J
10.9 20%| 623,026J 8.3 j
7.9 21%! 121,252! 1.8!
,. ^ ; ^ j.
6.1 13%! 577,978! 3.?!
,. ^ ; ^ j.
7.6 16%! 2,498,896! 5.o!
:. ;. ; :. t
4.6 17%! 271,750! 3.5!
:. ; :. t
11.4 17%! 940,004! 4.2!
^ ^ ; ^ j.
9.6 18%! 86,268! 5.s!
^ ^ ; ^ j.
13.4 40%| 1,821,052J 11.6J
8.8 22%| 914,454J 6.1 j
16.1 10%! 145,537! 4.6!
,. ^ ; ^ j.
4.5 16%| 61,10l| 4.6J
23%|
23%|
33%|
24% |
25%|
5%
25%|
16% |
25%|
32% j
29% |
13%]
1,505,458J 3.7J 5.0
246,624J 1.4J 6.9
353,716J 2.6J 3.8
2,663,51 1| 2.2J 7.5
l,670,01l| 1.9J 7.3
453,214J 1.5J 5.4
882,183J 3.3J 4.3
2,326,557J 1.9J 5.7
74,473 1 4.6 1 8.0
778,782J 2.8J 4.7
28,870J 4.5 1 8.5
1,111,497J 1.4J 5.7
2,498,896J 2.2J 4.8
175,838J 1.2J 5.9
1,382,359J 2.7J 5.7
153,365J 2.5J 4.3
1,320,263 1 2.6 1 4.1
665,057J 2.3 1 5.4
247,413J 3.9J 5.8
49,645J 1.2J 3.5
N/A - Not Available
Source: U.S. Fish and Wildlife Service's (USFWS) 1996 National Survey of Fishing Hunting and Wildlife Associated Recreation.
N-10
-------
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey
N.4 ALLOCATION OF TRIPS BY WATER BODY TYPE
This analysis assesses the allocation of trips by water body type, recreation activity, and state of residence. EPA determined
the number of trips taken to each water body type based on the water body type visited on the last single- or multiple-day trip
for each recreation activity. Dividing the total number of trips taken in a state to a given water body type for a given activity
by the total number of trips taken for that activity in the state provided estimates of the percent taken to the various water
body types. The NDS distinguishes four general water body types:
> Lakes:
— lakes,
— ponds, and
— reservoirs;
•• Streams:
— rivers,
— streams, and
— canals;
•• Oceans:
— oceans,
— bays, and
— sounds; and
•• Other:
— wetlands, and
— unknown water body types.
Note that respondents in several states apparently provided inaccurate information. For example, Montana residents are
unlikely to take single-day trips to the ocean. The data indicate, however, that five, six, and eleven percent of participants
reported that they took single-day fishing, swimming, and viewing trips to the ocean, respectively. This inconsistency may
arise due to the following two factors:
•• respondents traveled to other states for multi-purpose multiple-day trips and participated in the given activity on only
one day per trip; and
•• response errors (e.g., some respondents identified water body types incorrectly).
Tables N.5 and N.6 show allocation of single- and multiple-day trips by water body type for boating, fishing, swimming, and
viewing.
N-ll
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.5: Allocation of Single- Day Trips by Water Body Type
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
Boating (%) Fishing (%) Swimming (%) Viewing (%)
Lake ! Stream ! Ocean" ! Other* ! Lake ! Stream ! Ocean ! Other
30%| 20%| 50%| 0%j 9%| 45%j 45%j 0%
50%! 44%! 6%| 0%! 56%! 29%! 16%! 0%
78%l 22%l 0%l 0%l 78%l 22% 1 0%l 0%
ioo%! o%! o%! o%! 76%! 19%! 5%| o%
38%| 8%| 51%| 2%| 43%| 16%| 40% | 1%
79%! 21%! 0%! 0%! 65%! 33%! 2%| 0%
38%l 27%l 35%l 0%l 35%l 22% 1 43%l 0%
o%! 33%! 67%! o%! N/A! N/A! N/A! N/A
25%| 0%| 75%| 0%| 25%| 38%| 38%| 0%
15%! 27%! 56%! 1%! 24%! 24%! 52%! 1%
79%l 12%l 9%l 0%l 60%l 21%l 18%l 2%
22%! o%! 78%! o%! io%! o%! 90%! o%
43%| 52%| 0%| 4%| 59%| 38%| 0%| 3%
65%! 35%! 0%! 0%! 47%! 53%! 0%! 0%
55%l 40%l 5%l 0%l 74%l 22% 1 4%l 0%
88%! 12%! 0%! 0%! 82%! 13%! 5%| 0%
100%| 0%| 0%| 0%| 96%| 4%| 0%| 0%
77%! 23%! 0%! 0%! 73%! 24%! 2%| 0%
45%l 45%l 10%l 0%l 39%l 43%l 14%l 4%
21%! 36%! 44%! 0%! 49%! 21%! 31%! 0%
13%| 34%| 53%| 0%| 30%| 32%| 39%| 0%
63%! 19%! 19%! 0%! 65%! 15%! 20%! 0%
80%| 14%| 5%| 0%| 73%| 20%| 8% I 0%
77%! 23%! o%! o%! 90%! io%! o%! o%
53%| 42%| 3%| 3%| 73%| 21%| 4%| 2%
47%! 42%! 11%! 0%! 76%! 15%! 9%! 0%
75%| 25%| 0%| 0%| 42%| 53%| 5%| 0%
61%! 19%! 19%! 0%! 52%! 24%! 24%! 0%
100%| 0%| 0%| 0%| 80%| 20%| 0%| 0%
89%! n%! o%! o%! ioo%! o%! o%! o%
Lake
100%
57%
78%
63%
28%
83%
33%
67%
0%
13%
67%
0%
70%
59%
93%
92%
94%
64%
38%
33%
23%
67%
92%
84%
52%
50%
63%
36%
83%
77%
Stream ; Ocean ; Other ; Lake ; Stream ; Ocean ; Other
0%| 0%| 0%| 18%| 18%| 64%| 0%
13%! 30%! 0%! 25%! 15%! 55%! 5%
17%l 0%l 4%l 38%l 38%l 23%l 0%
32%! 5%| 0%! 50%! 11%! 33%! 6%
9%| 61%| 2%| 11%| 2%| 86%| 1%
4%| 8%| 4%| 65%! 15%! 19%! 0%
5%l 60%l 2%l 20%l 9%l 69%l 2%
o%! 33%! o%! o%! 50%! so%! o%
0%| 100%| 0%| 18%| 6%| 76%| 0%
9%! 79%! 0%! 4%| 5%| 90%! 1%
7%l 23%l 2%l 53%l 2%l 44% 1 0%
o%! ioo%! o%! o%! o%! 93%! 7%
17%| 9%| 4%| 60%| 28%| 8%| 4%
35%! 6%| 0%! 44%! 44%! 6%! 6%
707 5 no/ : no/ : 7^o/ : 1 1 o/ i no/ i /io/
/ /o : U /o : U /o : / O /o : 1 1 /o : y /o : Q /o
4%| 2%| 2%| 78%! 15%! 5%| 2%
6%| 0%| 0%| 71%| 10%| 14%| 5%
18%! 18%! 0%! 58%! 25%! 13%! 4%
27%l 27%l 8%l 32%l 16%l 48%l 4%
4%| 63%! 0%! 15%! 8%| 76%! 2%
19%| 58%| 0%| 10%| 18%| 70%| 3%
4%| 30%! 0%! 19%! 6%! 74%! 0%
3%| 5%| 0%| 81%| 8%| 8%| 3%
7%| 7%! 2%| 95%! 5%| 0%! 0%
36%| 7%| 5%| 60%| 30%| 7%| 3%
36%! 14%! 0%! 38%! 13%! 50%! 0%
31%| 6%| 0%| 78%| 11%| 11%| 0%
15%! 42%! 7%! 22%! 14%! 63%! 2%
17%| 0%| 0%| 100%| 0%| 0%| 0%
23%! 0%! 0%! 64%! 27%! 9%! 0%
N-12
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.5: Allocation of Single- Day Trips by Water Body Type
State
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
Boating (%) Fishing (%) Swimming (%) Viewing (%)
Lake j Stream j Ocean" j Other* j Lake j Stream j Ocean j Other
58% j 25%| 17%| 0%| 44%| 44% j 11%| 0%
24%! 11%! 65%! 0%! 31%! 13%! 55%! 2%
43%l 43%l 14%l 0%l 38%l 62% 1 0%l 0%
92%! 0%! 8%| 0%! 60%! 40%! 0%! 0%
47% | 18%| 35%| 0%| 53%| 21%| 26%| 0%
83%! 11%! 5%! 1%! 84%! 13%! 2%! 1%
88%l 13%l 0%l 0%l 94%l 3%l 3%l 0%
41%! 36%! 23%! 0%! 31%! 56%! 13%! 0%
46%| 32%| 19%| 3%| 54%| 27% | 18%| 2%
11%! 22%! 67%! 0%! 36%! 18%! 45%! 0%
64% 1 20%l 12%l 4%l 66%l 13%l 19%! 2%
ioo%! o%! o%! o%! 57%! 43%! o%! o%
75%| 17%| 8%| 0%| 63%| 34%| 3%| 0%
74%! 8%| 18%! 0%! 64%! 13%! 23%! 0%
78%! 0%l 22%l 0%l 87%l 13%l 0%! 0%
31%! 35%! 35%! 0%! 27%! 38%! 35%! 0%
100%| 0%| 0%| 0%| 67%| 33%| 0%| 0%
38%! 27%! 33%! 1%! 36%! 30%! 34%! 0%
66%! 30%l 4%l 0%l 78%l 20%l 2%l 0%
83%! 8%| 8%| 0%! 43%! 57%! 0%! 0%
Lake
55%
20%
50%
100%
43%
86%
80%
50%
53%
29%
68%
89%
72%
62%
64%
23%
71%
63%
80%
55%
Stream ; Ocean ; Other ; Lake ; Stream ; Ocean ; Other
0%| 41%| 5%| 25%| 0%| 75%| 0%
2%! 77%! 0%! 9%| 4%| 86%! 1%
30%l 20%l 0%l 25%l 38%! 38%! 0%
o%! o%! o%! 85%! o%! 8%! 8%
7%| 49%| 1%| 40%| 9%| 50%| 1%
4%| 7%| 3%! 71%! 9%| 18%! 2%
15%l 0%l 5%l 87%l 7%l 7%l 0%
26%! 22%! 2%! 11%! 13%! 77%! 0%
19%| 26%| 2%| 37%| 10%| 51%| 2%
5%! 67%! 0%! 4%| 0%! 96%! 0%
4%l 29%l 0%l 31%l 7%l 62%l 0%
n%! o%! o%! 75%! 13%! 13%! o%
23%| 5%| 0%| 48%| 18%| 33%| 0%
16%! 20%! 2%! 41%! 10%! 48%! 1%
36%l 0%l 0%l 89%! 6%l 6%l 0%
17%! 58%! 2%! 16%! 13%! 70%! 2%
14%| 14%| 0%| 80%| 0%| 20%| 0%
25%! 12%! 0%! 21%! 11%! 67%! 1%
10%! 5%! 5%! 73%! 13%l 7%l 7%
35%! 10%! 0%! 55%! 18%! 27%! 0%
WY 83%l 0%j 17%l 0%l 73%! 27% 1 0%l 0%l 100%l 0%l 0%l 0%l 100%l 0%l 0%l 0%
" Note that respondents in several states apparently provided inaccurate information because some states at great distances from the ocean report individuals taking single-
day trips to the ocean.
b Other includes wetlands and unknown water body types.
N/A - Not Available
Source: NDS.
N-13
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.6: Allocation
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
Boating (%)
Lake ! Stream 1 Ocean 1 Other" 1
75%!
43%!
71%!
63%!
49% |
85%!
33%!
ioo%|
n%!
46%!
78%!
80%!
58%!
68%!
100%|
78%!
57%!
42%!
i
63%!
76%!
63%!
75%!
50%!
25%!
52%!
1
o%!
23%|
8% 1
33% |
1
25%!
1
20%!
23%|
16%!
1
22%!
1
13%!
1
33%!
25%|
19%!
25% |
43%!
25%!
25% |
0%!
33% |
100%!
1
43%!
27% |
50%!
1
o%!
1
o%!
32%!
55%|
13%!
1
17%!
25%|
19%!
1
14% 1
So/ :
/o :
8%|
1
21%!
50%!
1
o%!
1
o%!
29%|
16%!
I
1
13%!
25%!
1
o%!
25%|
of Multiple- Day Trips by
Fishing (%)
Water Body Type
Swimming (%)
Lake j Stream 1 Ocean 1 Other 1 Lake ! Stream 1 Ocean 1 Other 1
33% 1
40%!
45% |
73%!
58% |
48%!
60% |
N/A 1
50% j
21%!
24% |
78%|
56%!
70%|
69%!
100%|
71%!
53%!
44%|
50%!
65%|
83%!
52%|
60%!
50%|
i
o%!
36%!
13%!
17%!
36%!
N/A!
j
i
ii%!
22% |
38%!
i
18%!
33%!
50%!
29%!
0%!
50%!
67%!
40%!
7%!
n%!
12%!
40%!
N/A!
50% |
36%!
40%!
ioo%!
o%|
o%!
13%!
0%|
o%!
20%!
iT%|
o%!
5%|
20%!
72%!
o%! o%!
20%! 19%!
7%! 7%!
13%| 19%|
4%! 33%!
0%[ 14%[
N/A! o%!
o%| o%|
32%! 9%!
24% | 15%|
0%| 14%|
6%! 75%!
no/ i T^o/i
/o: Jj/o:
13%! 35%!
0%| 63%|
12%! 25%!
23%| 11%|
27%! 7%|
11%| 15%|
o%! 50%!
20%! 50%!
9%! 50%!
14%| 30%|
20%! 14%!
0%| 50%|
:
o%!
7% !
12% |
o%!
o%|
2%|
1
25%!
1
19%!
1
o%!
1
o%!
0%|
62%!
67% |
50%!
1
25%!
73% |
67%!
100%|
52%!
62% |
83%!
57%|
0%!
25%|
24%!
25%|
38%!
79%|
63%!
69%|
17%!
22%|
25%|
57%!
79%!
1
19%!
36%!
29% |
42%!
33%!
1
38%!
1 O /O
17%!
29%|
0%!
35%|
41%!
1
19%!
30%!
I
1
33%!
22%|
44%!
35%|
29%!
33%|
16%!
Viewing (%)
Lake ! Stream j Ocean j Other
33% 1
7% !
30%|
7% !
1
30%!
10%|
8% !
1
7%|
8%|
1
50%!
23%|
17%!
30%|
5%|
16%!
1
25%!
40%|
44%!
20%|
8%|
33%|
0%| 33% |
0%! 72%!
0%| 43% |
11%! 52%!
6%| 52% |
4%! 41%!
0%j 60% |
no/ • T^O/ ;
U /o : / J /o :
: :
oo/ : Qio/ '•
o /o : o j /o :
3%! 53%!
2%| 70%!
0%! 40%!
7%| 52%|
11%! 28%!
7%! 36%!
4%| 57%!
9%| 39%|
0%! 78%!
5%| 67%!
3%| 52%!
: :
So/ : H i o/ :
/o: / 1 /o:
: :
no/: O^O/i
U /o: L J /O:
2%| 22%|
2%| 30%!
5%| 42%|
4%! 67%!
11%! 33%!
O O/ : O O O/ 1
Z/o: oZ /o:
33%
21%
26%
30%
29%
26%
30%
17%
8%
36%
20%
60%
21%
11%
34%
23%
22%
16%
21%
29%
15%
50%
36%
23%
32%
21%
22°X
13°X
N-14
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.6: Allocation of Multiple-Day Trips by Water Body Type
State
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
Boating (%) Fishing (%) Swimming (%) Viewing (%)
Lake ! Stream 1 Ocean 1 Other"
100%| 0%j 0%| 0%
75% 1 25%! 0%! 0%
0%| 33% | 67% | 0%
0%j 0%| 67%! 33%
85% | 8%| 0%| 8%
75%! 0%! 25%! 0%
41%| 17%| 22% | 20%
68%! 18%| 9%| 6%
62% | 8%| 8%| 23%
38%! 31%! 15%! 15%
42% | 9%| 36%| 12%
o%! o%! o%! 0%
56%| 0%| 33%| 11%
75%! 0%! 0%! 25%
50%| 14%| 21%| 14%
53%! 6%| 26%! 15%
85%! 0%! 0%! 15%
: : :
35%! 15%! 45%! 5%
100%| 0%j 0%| 0%
27%! 27%! 36%! 9%
78%! 11%! 11%! 0%
: : :
20%! 20%! 0%! 60%
Lake ! Stream 1 Ocean 1 Other
100%| 0%! 0%! 0%
75%! 13%! 13%! 0%
o%| o%! ioo%| 0%
18%! 18%! 18%! 45%
70% | 20% | 10%| 0%
33%! 17%! 17%! 33%
50% | 17%! 20%! 13%
70%! 13%! 7%| 10%
50% | 10%| 10%| 30%
42%! 33%! 8%| 17%
53%| 16%! 22%! 9%
0%! 0%! 0%! 100%
17%| 17%| 50%| 17%
25%! 75%! 0%! 0%
33%| 33%! H%! 22%
53%! 10%! 25%! 12%
70%! 10%! 0%! 20%
: : :
24%! 19%! 43%! 14%
o%| o%! o%! 0%
29%! 32%! 25%! 14%
73%! 15%! 3%! 9%
: : :
25%! 50%! 0%! 25%
Lake
25%
33%
40%
4%
33%
20%
22%
14%
30%
9%
17%
0%
0%
100%
15%
16%
50%
12%
50%
29%
43%
9%
Stream 1 Ocean 1 Other 1 Lake ! Stream 1 Ocean 1 Other
0%| 0%| 75% | 25% | 0%! 0%! 75%
0%! 33%! 33%! 40%! 10% | 30%! 20%
0%| 60% | 0%| 14%| 0%! 71%! 14%
0%! 83%! 13%! 9%| 3%| 58%! 30%
17%| 0%| 50% | 27% | 0%| 50% | 23%
20%! 40%! 20%! 31%! 0%! 62%! 8%
0%| 62% | 16%| 12%| 5%| 55%! 28%
5%| 59%! 21%! 20%! 2%| 59%! 19%
10%| 25%| 35%| 21%| 6%| 38%| 35%
17%! 30%! 43%! 8%! 5%| 72%! 16%
3%| 64% | 16%| 12%| 4%| 65%! 20%
o%! o%! ioo%! n%! n%! 33%! 44%
8%| 67% | 25%| 2%| 2%| 81%| 15%
0%! 0%! 0%! 25%! 0%! 25%! 50%
4%| 65%| 15%| 11%| 1%! 71%! 16%
16%! 42%! 26%! 17%! 2%| 57%! 24%
25%! 0%! 25%! 25%! 0%! 38%! 38%
: : : : : :
2%! 71%! 16%! 4%! 3%! 78%! 15%
0%| 50%| 0%| 55%| 0%! 45%! 0%
19%! 39%! 13%! 12%! 9%| 68%! 11%
9%! 30%! 17%! 34%! 4%! 38%! 25%
: : : : : :
9%! 55%! 27%! 9%! 9%! 70%! 13%
WY 100%| 0%j 0%j 0%j 100%| 0%| 0%| 0%| 0%| 0%| 0%| 0%| 50%j 0%| 0%| 50%
a Other includes wetlands and unknown water body types.
N/A - Not Available
Source: NDS.
N-15
-------
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey
N.5 ONE-WAY TRAVEL DISTANCE
This analysis estimates the average one-way distance to sites by trip duration (i.e., single day versus multi-day trips), trip
length, recreation activity, and state of residence. EPA estimated the mean one-way distance traveled based on the distance
reported for the last single- or multiple-day trip for each activity. As shown in Table N.7, some respondents indicated
traveling to the ocean across long distances on single-day trips. These values are likely to be due to the following two factors:
•• respondents traveled long distances for multi-purpose multiple-day trips and participated in the given activity on only
one day on the trip; and
*• response errors.
EPA estimated the average travel distance traveled after dropping outliers because these outliers may provide undue influence
on sample means.
N-16
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.7: Average One-Way Distance
AK .
AL
AR
AZ
CA .
CO
CT
DC
DE .
FL
GA
HI
IA
ID
IL
IN
KS *
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
Boating I
4ii
31!
52|
541
32i
41!
so!
461
36|
21!
34|
37!
eo!
35!
52!
47!
42[
55!
3d
22
3
44!
3l!
45!
42
11!
43!
42
3
7CJ
21
49
7d
108
Miles to Single-Day Site
Fishing I Swimming
47|
29!
38!
45!
40!
56!
4l!
N/AJ
32i
23!
52!
13!
25i
48!
34!
29!
221
32!
27!
25!
4a;
24!
33!
55!
4q
27!
172
491
55!
29!
24
3l|
sq
46
32I
35!
19!
44!
26|
15!
36!
417!
50!
20!
42!
14!
26|
101!
so!
50!
52i
46!
39!
30!
51
30!
25!
16!
39j
36!
31!
45!
35;
71|
28
4l|
711
43
|
Viewing I
39i
53!
222!
117!
31i
69!
49!
85!
189!
24!
46!
13!
49|
54!
29!
64!
68]
106!
53!
29!
38[
23!
32!
16!
71!
39!
102
61
ii
56
34
4l|
252
53
Boating I
76i
93!
215!
205!
233|
372!
168!
loooi
1,625!
317!
199!
3!
314!
228!
255!
295!
1511
151!
132!
136!
581]
436!
192!
354!
195
72
5881
153!
154
125
186=
483
16l!
48
Miles to Multiple-Day Site
Fishing I Swimming
193!
218!
246!
323!
316!
260!
161]
N/Aj
1.700J
381!
283!
80!
321J
146!
368!
378!
177[
143!
76!
154!
1991
148!
2491
185!
265!
122
154!
182
13(1
603
248;
179
19l!
401!
N/AJ
230!
282!
413!
272!
548!
194!
165!
85I
154!
261J
32!
228!
100!
213!
368!
272!
391!
244!
144!
2001
152
234!
132
20CJ
203!
352
264!
45;
40(J
108;
227j
201
254!
Viewing
43
214
394
383
22(
89'
33(
68?
24?
23'
33(
4<
1,35'
50'
70'
si:
861
69'
24?
39?
26:
3
38'
55
62?
48:
50(
26
12(
15
71
47(
l,31f
56?
N-17
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.7: Average One-Way Distance
NY
OH
OK
OR
PA .
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY |
Boating I
32i
63!
62|
31!
40J
ioi
is!
431
29!
401
43!
37!
4ii
23!
34!
86!
69!
Miles to Single-Day Site
Fishing I Swimming
26|
38!
so!
36!
38i
26!
33!
35!
271
38!
66!
30!
20!
20:
&
so!
30!
46!
25I
30!
46!
33!
36I
18!
40!
19!
24|
38!
44!
39!
33|
20!
so!
95!
32=
|
Viewing I
37i
45!
47!
51!
57i
26!
eo!
46!
84!
65!
68!
69!
50i
41!
33!
158!
471
Boating I
202!
265!
189!
398!
296!
713!
352!
6ii
190!
235!
407!
71
154!
289!
338!
73!
Miles to Multiple-Day Site
Fishing I Swimming
195!
262!
244!
200!
228!
132!
143!
888!
187!
122!
159!
:
:
198!
303!
278!
561
194!
498!
232!
97!
210!
200!
400!
493!
261!
207!
256!
78I
205!
104!
328!
:
:
Viewing
692
778
542
143
391
433
25C
74C
481
442
59*
30:
33^
27^
54?
42<
23(
a Based on one observation only.
N/A - Not Available
Source: NDS.
N-18
-------
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey
N.6 INDIVIDUAL EXPENDITURES PER TRIP
This analysis estimates the mean total expenditures per person by trip length, recreation activity, and state of residence. Total
expenditures for single-day boating, fishing, and viewing trips consist of transportation, entrance fee, and boat rental. Total
expenditures for multiple-day boating, fishing, and viewing trips include expenses for transportation, entrance fees, boat
rental, and lodging. Transportation includes expenses for plane, train, bus, or ship only, and do not reflect costs associated
with operating a car. Expenditures on single-day and multiple-day swimming trips do not include boat rental. Expenditures
on single-day and multiple-day trips for all activities do not include bait, tackle, recreational clothing and equipment (e.g.,
photographic supply and binoculars), boat ownership, or food. Results of the analysis are presented below in Table N.8.
N-19
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.8: Individual Expenditures per Trip
State
:
:
:
:
:
:
Average Expenditures per Person on Single-day Trips
(1993$ per trip)
:
:
:
:
:
:
Average Expenditures per Person on Multiple-day Trips
(1993$ per trip)
I Boating I Fishing I Swimming I Viewing 1 Boating I Fishing 1 Swimming I Viewing
AK
AL
AR
AZ
CA
CO
CT
DC'
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
:
:
:
$16!
$14 j
$18!
$16J
$531
$49 1
$19!
$17J
$61
$22 1
$19!
$22 1
$81
$21 1
$37!
$18|
$19!
$18|
$501
$19|
$49!
$2|
$26!
$10|
$26!
$14|
$10!
$15J
$24!
$5|
$22!
$ll|
$12!
N/AJ
$18!
$22 1
$17!
$7|
$2!
$o|
$9!
$io|
$3!
$2J
$14!
$13|
$51!
$2|
$7!
$6|
$11!
$26|
$0!
$2J
$1!
$7|
$5!
$3|
$35!
$2J
$2!
$2J
$8!
$01
$1!
$l|
$2!
$3|
$2!
$35|
$1!
$18|
$2!
$l|
$2!
$2|
$2!
$l|
$12!
$5J
$1!
$3|
$3!
$18J
$7!
$3J
$2!
$4J
$28!
(tri :
$0:
$2!
$l|
$1!
$14|
$2!
$18J
$1!
$3|
$36!
$2|
$4!
$l|
$9!
$l|
$66!
$23 1
$59!
$41 1
$454!
$320 1
$387!
$2,000 1
$43!
$376J
$147!
$no|
$119!
$54|
$342!
$299J
$89!
$340J
$186!
$89:
$116!
$329J
$227!
$198|
$164!
$52|
$98!
$421 1
$48!
$84 1
$220!
$235 1
$114!
N/AJ
$63!
$852J
$275!
(tri :
$0:
$662!
$29 1
$333!
$32l|
$175!
$180J
$58!
$197J
$178!
$44|
$125!
$160|
$122!
$169|
$0!
$153J
$361!
$184J
$455!
$248 1
$330!
$200 1
$325!
$234J
$279!
$118J
$340!
$63 1
$241!
$175J
$178!
$117J
$251!
$309J
$300!
$143|
$379!
$99J
$278!
$18l|
$70
$261
$399
$126
$328
$325
$505
$354
$120
$375
$249
$75
$488
$118
$495
$661
$518
$298
$245
$274
$288
$22
$255
$261
$352
$329
N-20
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.8: Individual Expenditures per Trip
:
:
:
State
Average Expenditures per Person on Single-day Trips
(1993$ per trip)
Average Expenditures per Person on Multiple-day Trips
(1993$ per trip)
I Boating I Fishing I Swimming I Viewing 1 Boating 1 Fishing I Swimming 1 Viewing
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY
$8!
$13J
$14!
$3J
$161
$32 1
$151
$25 1
$251
$26 1
$11!
$15J
$231
$23 1
$27!
$5J
$261
$152|
$251
$io|
$61
$llj
$10!
$46|
$5!
$23!
$26 1
$2!
$4|
$7!
$44 1
$3!
$l|
$29!
$15|
$221
$5|
$2l!
$16|
$12!
$6J
$41
$12|
$2!
$23|
$l!
$19|
$5!
$2|
$5!
$l!
$24 1
$oi
$85 1
$3!
$13J
$221
$l|
$51
$8J
$2!
$22 1
$19!
$3|
$2!
$o|
$o!
$2|
$4!
$l|
$61
$13|
$3!
$14|
$o!
$oi
$3|
$oi
$2|
$o!
$7J
$321
$4J
$81
$22 1
$3!
$l|
$9!
$2|
$7!
$2J
$141
$3|
$131
$243J
$2!
$lj
$8!
$3|
$22!
$25!
$165J
$53!
$24 1
$127!
$360 1
$73!
$104J
$242!
$403 1
$173!
$429 1
$275!
$o|
$576!
$248J
$458!
$15l|
$164!
$175|
$100!
$266J
$425!
$250|
$85!
$95!
$132J
$3!
$310J
$0!
$168J
$78!
$25 1
$76!
$239 1
$314!
$5l|
$310!
$o|
$201!
$54J
$49!
$138J
$10!
$116J
$0!
$170J
$135!
$275|
$17!
$542!
$393 |
$0!
$150J
$955!
$631 1
$41!
$554J
$298!
$560 1
$137!
$543 1
$275!
$o|
$731!
$io|
$329!
$324J
$117!
$317J
$22!
$217|
$468!
$209|
$0!
$86
$227
$0
$237
$342
$414
$218
$116
$459
$465
$268
$248
$399
$240
$265
$715
$315
$349
$419
$319
$372
$165
$308
$356
$114
a Average boating expenditures in Washington, D.C. are based on a single observation.
N/A - Not Available
Source: NDS.
N-21
-------
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey
N.7 DISTRIBUTION OF DIRECT COSTS FOR SINGLE-DAY TRIPS
This analysis estimates the percent of total expenditures for single-day and multiple-day trips spent on each component of
total expenditures. Total expenditures for single-day boating, fishing, and viewing trips consist of:
»• transportation,
> entrance fee, and
»• boat rental.
Lodging is not included in single-day expenditures. Swimming trip expenditures do not include boat rental. Transportation
includes expenses for:
*• plane,
»• train,
•• bus, or
*• ship only
and do not include automobile travel costs.
EPA determined the percent of total expenditures for each category by dividing the total amount spent on each category by
the total expenditures in a state for a given activity.
Tables N.9 and N.10 present results for single- and multiple-day trips, respectively.
N-22
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
Boating
(% of total expenditures)
lf !
Transb
o%!
g%!
o%i.
0%:
45%;
g%!
o%i.
0%:
0%;
0%!
o%i.
62%:
0%;
0%!.
4%!
0%;
0%;
0%!.
p%!
0%;
31%;
0%;
0%;.
0%;
0%;
0%;
0%;
0%;
o%;
0%!
0%;.
15%;
o%;
0%!
23%;
Table
i
Enter Fee j Boat Rental j
3% I
12%!
8% I
6% !
13%!
9%!
9%!
35%!
30%!
10%!
7% I
0%j
3%l
5% !
13%!
2% !
24%l
2% !
68%!
43%!
17%]
23%!
8%!
17%!
25%!
36%!
8%!
23%!
30%!
0%!
48%!
10%!
8%!
19%!
29%!
97%!
88%!
92%!
94%!
42%!
91%!
91%!
65%!
70%!
90%!
93%!
38%!
97%!
95%!
82%l
98%!
76%|
98%!
32%!
57%!
52%!
77%!
92%!
83%!
75%!
64%!
92%!
77%!
70%!
100%!
52%!
74%!
92%!
81%!
48%!
N.9: Distribution of Direct
Fishing
(% of total expenditures)
V
Trans
0%j
4%J_
36%j_
0%!
15%!
57%!..
0%j_
N/A!
0%!
1%J_
o%i..
0%j
0%!
0%!
0%!..
0%j
0%!
0%!
0%!
4%!
0%!
0%!
0%!
0%!
0%!
0%!
96%!
0%!
0%!
0%!
0%!
8%!
0%!
0%!
5%!
Costs for
L.
Enter Fee j Boat Rental
5% I
28%!
41%!
25%!
21%!
17%!
3%j...
N/A!
52%!
12%!
29%!
18%!
0%|
0%!
13%!
24%!
51%!
27%!
46%!
28%!
2%!
0%!
10%!
65%!
20%!
44%!
4%!
12%!
32%!
0%!
61%!
48%!
49%!
67%!
36%!
95% !
68%;
23%;
75%;
65%;
25%;
97%;
N/A;
48%;
87%;
71%;
82%;
100%;
100%;
87%;
76%;
49%;
73%;
54%;
68%!
98%;
100%!
90%;
35%:
80%;
56%!
o%;
88%:
68%;
100%:
39%;
44%!
5i%;
33%:
58%;
Single- Day Trips
Swimming"
(% of total expenditures) j
Trans Enter Fee
N/A
0%
0%
0%
37%
0%
0%
0%
0%
0%
66%
N/A
0%
0%
0%
0%
0%
96%
0%
88%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
26%
91%
0%
44%
N/A;
100%;
100%;
100%;
63%;
100%;
100%;
100%;
100%;
100%;
34%!
N/A!
100%;
100%;
100%;
100%;
100%;
4%!
100%;
i2%r
100%;
100%!
100%;
100%:
100%;
100%:
100%;
100%:
100%;
100%:
100%;
74%;
9%;
100%!
56%;
(%
Trans
0%!
0%!
0%!
83%!
50%!
84%!
0%!
0%!
0%i
0%j
83%l
N/A!
0%|
0%!
6%j
53%|
0%|
82%!
0%!
0%!
17% I
0%!
0%!
0%!
1%!
0%!
N/A!
40%!
N/A!
0%!
0%!
35%!
98%!
0%!
5%!
Viewing
of total expenditures)
Enter Fee j Boat Rental
27%!
61%!
100%!
17%!
34%!
16%!
100%!
73%!
17%!
26%!
11%!
N/A!
63%|
100%!
80%!
41%!
55%|
9% !
100%!
78%!
82%!
94%!
65%!
20%!
92%!
100%!
N/A!
41%!
N/A!
5%!
100%!
47%!
2%!
32%!
76%!
73%
39%
0%
0%
16%
0%
0%
27%
83%
74%
5%
N/A
37%
0%
13%
6%
45%
9%
0%
22%
1%
6%
35%
80%
7%
0%
N/A
19%
N/A
95%
0%
18%
0%
68%
19%
N-23
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.9: Distribution of Direct Costs for Single-Day Trips
State
:
:
:
:
:
:
;.
Boating
(% of total expenditures)
Fishing Swimming"
(% of total expenditures) j (% of total expenditures) j
Viewing
(% of total expenditures)
| Trans" j Enter Fee j Boat Rental Trans j Enter Fee j Boat Rental Trans Enter Fee j Trans Enter Fee j Boat Rental
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY
:
28%;
0%:
0%;
0%:
0%;
0%:
0%;
26%:
0%;
0%:
9%;
0%:
0%;
0%:
0%;
0%:
6% I
27%!
17%]
ii%!
o%!
66%!
.!?%.[...
0%!
2%i
42%!...
25%l
8% !
18%!
!?%[...,
33%.l
47% 1
66%!
73%i
83%!
89%!
100%!
34%!
81%!
73%!
98%!
59%!
66%!
92%!
82%!
81%!
67%!
53%!
7%j
58%!
0%l
0%!
0%!
o%i
0%l
0%!
0%!
0%J_
o%i
0%j
45%!
1%J_
o%i
0%!
14%!
.?%!
25%l
22%!
1%!
io%i
39%:
22%!
20%!
60%!
25%i_
0%!
16%!
20%!
o%]_
53%!
79% !
33%:
75% i
78%:
99%;
90%:
61%;
78%:
80%;
40%:
75%;
100%:
39%;
79%:
100%;
48%:
0%;
0%i...
0%]
94%:
0%;
0%l...
0%;
0%:
0%;
P.%i...
0%;
0%:
0%;
2%l...
78%;...
N/AI
100%;
100%:
100%;
6%:
100%;
100%:
100%;
100%:
100%;
100%:
100%;
100%:
100%;
98%:
22%;
N/AI
43%!
0%!
0%!
49%!
0%!
95%!
0%!
98%!
0%!
0%!
20%!
0%!
17%!
48%!
88%!
0%!
11%!
o%i
87%i
4.1%!
100%!
2%!...
100%!
2%!
33%!
84%!...
§0%l
100%!
56%!
36%!...
6%l
16%!
46%
100%
13%
10%
0%
3%
0%
0%
67%
16%
0%
0%
28%
16%
6%
84%
" Swimming expenditures do not include boat rental.
b Transportation expenses include expenditures on plane, train, bus, or ship taken on the trip only and do not reflect travel costs.
N/A - Not Available
Source: U.S. EPA analysis.
N-24
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.10: Distribution of Direct Costs for Multiple-Day
|
State J"
AK |
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
Boating
(% of total expenditures)
|
Fishing
(% of total expenditures^
|
Trips
Swimming"
(% of total expenditures) ! (%
Viewing
of total expenditures)
„ „ | Enter j Lodg- j Boat j j Enter j . j Boat j j Enter j . j | Enter | . | Boat
Trans : _ . f : _ , : Trans : _ : Lodging : : Trans _ i Lodging : Trans : _ : Lodging :
! Fee mgc | Rental j j Fee ! ! Rental j j Fee ! ! ! Fee ! Rental
0%|
b'%]
28%:
b'%]
22%!
ioo%!
b'%]
io%!
14%!
o%!
0%|
o%!
18%!
8%!
0%|
o%!
0%!
o%!
0%|
o%!
QO/ :
17%!
b%]
o%!
0%:
12%!
b%]
o%!
0%:
34%!
b%]
o%!
2%|
o%!
l°/\
5%!
6%|
7%!
b'%!
i%!
21%]
l%!
O /oi
2%|
3%!
ao/
J /O
o%j
72%!
3%|
l%!
0%:
0%]
6%!
13%:
7%|
3%!
15%|
20%!
51%]
55%!
53%l
77%!
16%!
b'%]
77%!
71%!
51%!
91%!
63%l
92%!
41%!
58%!
30%]
12%!
81%!
38%!
56%]
22%!
58%!
78%!
74%]
65%!
100%!
55%!
16%]
66%!
b'%!
60%!
52%]
37%!
84%|
80%!
48%|
40%!
13%]
16%!
63%!
b%!
23%l
18%!
28%!
9%j
16%]
7%!
35%!
31%!
68%|
85%!
16%!
61%!
44%|
6%j
26%!
5%j
23%]
34%!
b%j
25%!
84%]
28%!
87%!
41%]
60%!
0%|
0%!
12%|
20%!
N/A!
0%|
0%!
o /oi
N/A!
0%|
0%!
14%!
7%!
0%|
21%!
0%|
0%!
0%|
0%!
b'%1
18%!
b'%!
30%!
N/A!
o%|
0%!
3%|
o%!
io%!
16%]
o%!
N/A!
b'%]
2%!
N/A!
b'%]
8%!
l'%]
3%!
31%!
14%!
b%|
15%!
6%|
0%!
b'%1
0%|
6%!
N/A!
37%!
5%|
o%!
77%]
37%!
73%]
65%!
58%:
69%!
95%!
N/A!
100%:
16%!
69%!
N/A!
64%:
78%!
79%!
81%!
74%]
48%!
62%!
78%!
98%]
80%!
83%!
73%!
64%]
69%!
92%:
69%!
100%|
64%!
N/A!
25%!
83%]
100%!
20%|
63%!
20%]
24%!
14%]
io%!
N/A!
b'%!
81%!
19%!
N/A!
36%:
15%!
25%]
27%!
2%|
6%!
27%!
30%]
31%!
o /oi
b%]
o%!
N/A!
39%!
12%]
o%!
N/Aj
4%!
b'%!
31%!
23%l
12%!
17%!
b'%1
8%|
b'%1
18%!
b'%1
33%!
17%!
24%]
b'%1
19%!
12%]
b'%1
17%!
b'%1
30%!
b'%1
N/A!
b%!
20%!
0%|
45%!
N/A]
b'%1
b%!
b'%1
2%|
2%!
b'%1
2%!
b'%1
3%!
57%!
b'%1
o%!
b'%1
b'%1
4%!
b'%]
o%!
b'%1
N/A]
o%!
b'%1
18%]
2%!
N/AJ
96%!
100%|
69%!
76%l
87%!
83%!
100%!
92%l
98%!
99%!
100%!
82%l
97%!
63%!
26%!
76%!
100%!
91%!
81%!
88%!
96%!
77%!
99%!
70%|
100%!
95%!
92%!
N/A!
100%!
91%!
79%!
82%|
53%!
0%|
b'%1
29%!
20%:
23%!
11%!
22%!
b'%]
7%!
26%:
20%!
21%!
20%!
16%]
14%!
1 o/:
1 /o:
21%!
13%]
o%!
33%!
44%!
24%|
o%!
b'%1
N/A]
60%!
b'%1
21%!
17%]
7%!
0%|
o%!
o /oi
11%:
1%!
1%:
5%!
1%1
14%!
37%!
b%]
o%!
3%T
5%!
2%|
6%!
3%T
o%!
8%|
2%!
N/A]
o%!
0%:
1%|
1%!
71%|
99%!
84%]
69%!
63%l
72%!
88%!
42%!
99%!
88%!
77%!
100%!
71%|
65%!
74%!
42%!
82%]
86%!
94%!
73%!
83%'j
94%!
63%!
54%!
67'%'!
97%!
95%!
93%!
N/A!
40%!
100%!
76%!
82%]
92%!
29%
1%
0%
1%
5%
3%
0%
30%
0%
0%
5%
0%
1%
1%
3%
1%
1%
0%
2%
0%
2%
0%
2%
2%
1%
1%
0%
0%
N/A
0%
0%
1%
1%
1%
N-25
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.10: Distribution of Direct Costs for Multiple-Day
|
State J'"
NY |
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY i
Boating
(% of total expenditures)
|
Fishing
(% of total expenditures^
|
Trips
Swimming"
(% of total expenditures) !
Viewing
(% of total expenditures)
„ „ | Enter | Lodg- j Boat j | Enter j . | Boat j | Enter | . j | Enter | . j Boat
Trans : _ . f : _ , : Trans : _ i Lodging : : Trans _ : Lodging : Trans : _ : Lodging :
! Fee mgc | Rental | j Fee ! _ j Rental j j Fee ! _ _J ! Fee ! Rental
20%| 13%|
8%| 2%|
16%! 0%!
4%! l%!
37%! 4%!
N/A! N/A!
1 no/ 1 no/ 1
1 U 70! U 70!
o%! o%!
6%! 5%!
54%! 3%!
o%! so%!
io%! 4%!
o%! o%!
58%|
49%!
62%]
43%!
36%|
N/A!
24%!
96%!
53%|
56%!
66%!
30%!
50%!
46%!
35%!
80%!
71%!
8%|
41%!
22%|
52%!
23%l
N/A!
67%!
4%!
46%l
33%!
34%!
14%!
0%]
40%!
52%!
20%!
29%l
0%|
5%!
i%]
N/A!
o%]
6%!
N/A!
57%!
26%!
0%!
0%|
5%|
i'%]
81%]
2%!
i%!
N/A!
io%!
12%!
o%!
5%!
60%!
io%!
N/A]
o%!
21%!
3%!
4%!
75%!
79%!
87%!
61%!
N/A!
87%!
69%!
38%!
70%!
40%!
60%!
N/A]
18%!
48%!
97%!
96%!
20%]
15%!
11%]
37%!
N/A!
18%!
63%!
19%!
31%!
N/A!
25%!
O /oi
0%!
9%|
6%!
18%]
41%!
19%!
N/A!
12%]
21%!
0%]
23%!
13%!
o%!
N/A!
i%|
24%!
1%|
N/A!
o%]
8%!
0%]
4%!
N/A!
90%!
70%!
78%j
59%!
81%!
N/A!
ioo%!
ioo%!
88%i
72%!
99%!
97%!
100%!
73%!
87%!
ioo%!
N/A!
16%| 0%|
17%! o%!
30%| 1%|
5%! 3%!
9%| 0%|
20%! o%!
o 1 o/ : 1 o/ :
21/o: l/o:
28%! 0%!
7%| 0%|
20%! i%!
34%] 0%]
eo%! o%!
24%! 1%!
i o o/ : 1 o/ :
1 o /o: 1 /o:
o%! 13%!
0%! 10%!
82%|
79%!
68%]
91%!
89%!
79%!
76%!
72%!
91%]
76%!
66%!
90%!
40%!
73%!
81%!
87%!
90%!
1%
4%
2%
1%
1%
0%
1%
0%
2%
3%
0%
1%
0%
1%
0%
0%
0%
a Swimming expenditures do not include boat rental.
b Transportation expenses include expenditures on plane, train, bus, or ship taken on the trip only and do not reflect travel costs.
° Total expenses for multiple-day trips include lodging, while total expenditures for single-day trips do not.
N/A - Not Available
Source: NDS; U.S. EPA analysis.
N-26
-------
MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey
N.8 PROFILE OF BOATING TRIPS
This analysis provides a profile of sample boater characteristics by state of residence. Table N.I 1 shows distribution of
boaters by type of boating in which they participated on their last trip and the source of the boat used on their most recent
boating trip.
Boating types include:
> motorboating;
»• sailing;
•• white water kayaking and canoeing;
> other kayaking or canoeing;
»• rowing, rafting, tubing, or floating;
*• wind surfing; and
»• other.
Boat sources include:
•• boaters who used their own boat, including those who indicated using either their own boat or one belonging to
someone in their immediate family on their last trip;
»• boat renters, including those who either rented or chartered a boat on their last trip; and
> other, including respondents who did not indicate either using their own boat or renting a boat.
Dividing the number of respondents who participated in each boating type on their last trip by the total sample of boaters
provided an estimate of the percent participating in each type.
N-27
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.ll: Profile of Boating
Ststc !•
Total Number of Source Boat Used on Last Trip"
Boaters (Percent of Boaters)
NDS
! Sample !
...AK..,.
AL
AR
...AZ....,
...CA..,
CO
CT
...DC..,.
..P.E..,.
FL
GA
...HI...,
...IA...v
ID
IL
...M...,
...KS...,
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
...N.C....,.
ND
NE
NH
"11"
NM
NV
NY
14i
39!
25!
21!....
269!
27!
32!
4]....
Ipi
152!
69!
11!....
321
25!
86=
62!....
18i
35!
38!
58!....
491
24!
141!
59]....
601
25!
12!
57]
1.1]
n!
15!
64!
20!
n!
137!
| j
Sample
„. . Own
Weighted
220,972! 36%!
617,486: 51%!
404,809! 48%!
461,000: 57%!
5^244,634! 31%!
423,143! 44%!
533,626! 41%!
53,5511 0%!....
119,661! 60%!
2.,925,614! 31%!
1 .,156,297! 30%!
189,837! 9%!
426,854! 25%!
291,917! 56%i
L,758,816! 34%!
967,694! 35%!
274,465! 44%!
505,228! 54%!
682,563! 47%!
1 .,166,524! 28%!
778,917! 20%!
336,758! 21%!
L,867,312! 30%!
910,964! 39%!
938:326! 38%!
385,744! 36%!
153:038! 67%!
881,075! 30%!
138,098! 55%!
266,126! 18%!
225,139! 13%!
1,207,234! 17%!
260,978! 55%!
348,590! 41%!
2,619,1581 21%!
Trips
Type of Boating on Last Tripb
(Percent of Boaters)
! White !
: Other :
Rent ! Other ! Motor ! Sail ! Water I " , ! Row ! Raft ! Wind Surf! Other
! K-aA,at ! Kayak !
: : : : Ivayak : : : : :
36%! 29%!
21%! 28%!
20%! 32%!
14%! 29%!
23%! 46%!
22%! 33%!
34%! 25%!
50%! 50%!
10%! 30%!
32%! 37%!
32%! 38%!
36%! 55%!
31%! 44%!
12%! 32%!
28%! 38%!
26%! 39%!
22%! 33%!
17%! 29%!
18%! 34%!
45%! 28%!
47%! 33%!
46%! 33%!
43%! 26%!
29%! 32%!
28%! 33%!
32%! 32%!
0%! 33%!
30%! 40%!
27%! 18%!
12%! 71%!
53%! 33%!
53%! 30%!
10%! 35%!
12%! 47%!
50%; 28%!
71%! 0%!
79%! 0%!
88%! 0%!
67%! 14%!
66%! 14%!
70%! 4%|
50%! 22%!
50%! 50%!
80%! 10%!
74%! 8%|
77%! 9%!
36%! 36%!
81%! 3%!
72%! 4%|
66%! 5%!
84%! 10%!
83%! 0%!
89%! 0%!
87%! 3%!
53%! 19%!
65%! 10%!
63%! 8%|
76%! 9%!
83%! 2%|
75%! 3%!
80%! 12%!
42%! 0%!
70%! 9%!
82%! 0%!
88%! 0%!
80%! 7%!
70%! 13%!
65%! 5%!
82%! 0%j
64%; 11%!
21%! 7%j
8%! 0%!
o%! o%!
0%! 10%!
2%! 1%!
4%| 4%|
6%! 9%!
0%! 0%!
io%! o%!
3%! 5%!
7%! o%!
9%! 9%!
9%! 3%!
0%! 4%|
2%! 6%!
2%| 0%!
0%! 6%!
0%! 3%!
3%! 0%!
9%! 12%!
4%! 0%!
8%| 13%!
4%! 3%!
2%| 3%!
5%! 12%!
0%! 8%j
17%! 17%!
7%! 2%!
o%! o%!
12%! 0%!
13%! o%!
0%! 5%j
o%! 15%!
6%! 0%!
1%; 6%!
o%! o%!
0%! 3%!
o%! 4%!
10%! 0%!
1%! 4%!
4%| 7%|
o%! 6%!
0%! 0%!
o%! o%!
1%! 1%!
o%! 3%!
0%! 0%!
o%! o%!
4%| 16%!
o%! 6%!
0%! 0%!
n%! o%!
0%! 3%!
3%! o%!
0%! 0%!
2%! 8%!
0%! 0%!
l%! l%!
0%! 0%!
o%! o%!
0%! 0%!
0%! 8%j
2%! 5%!
o%! o%!
0%! 0%!
o%! o%!
2%| 0%!
o%! io%!
6%! 0%!
4%! 2%!
o%! 0%
0%! 10°X
0%! 8°X
0%! 0°X
o%! IPX
4%! 4°X
0%! 6°X
0%! 0°X
o%! o°x
0%! 7°X
0%! 4°X
0%! 9°X
0%! 3°X
0%! 0°X
0%! 15°X
0%! 5°X
0%! 0°X
0%! 6°X
0%! 5°X
0%! 7°X
2%! 8°X
0%! 8°X
1%! 6°X
0%! 10°X
0%! 5°X
0%! 0°X
0%! 17°X
0%! 5°X
9%! 9°X
0%! 0°X
0%! 0°X
0%! 11°X
0%! 5°X
6%! 0°X
0%; 12°X
N-28
-------
MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.ll: Profile of Boating Trips
State I-
:
1
.OH..].
OK !
OR !
PA |
...RI....I.
SC !
SD !
TN !
...TX..I.
UT !
VA !
VT !
WA i
WI !
WV I
WY !
Total Number of Source Boat Used on Last Trip"
Boaters (Percent of Boaters)
NDS i
Sample
109]
2?=..
57!
111!..
34!
11!
67]..
m.
22=..
72!
8!..
65!
17!
8!
Sample
„. . , , ! Own !
Weighted
1,473,937! 30%!
540,650! 21%!
702,199; 49%!
1,450,179! 28%!
130,654! 33%!
585,163! 53%!
151,221; 18%!
1,006,355! 45%!
2,805,077! 41%!
316,826! 59%!
1,023,443; 36%!
112,768! 50%!
1,601,852! 37%!
903,611! 42%!
196,359! 47%!
98.550! 38%!
:
Rent ! Other
i
:
37%! 33%!
34%! 45%!
21%; 30%!
40%! 32%!
56%: 11%!
21%! 26%!
36%; 45%!
25%! 30%!
20%: 39%!
9%! 32%!
43%; 21%!
50%! 0%!
26%: 37%!
38%! 20%!
6%: 47%!
0%l 63%!
Motor | Sail i
7^0/i 70/i
7.5..4 .7/94..
66%! 7%!
70%; 9%!
7.2%i 6%!..
33%: 44%!
71%! 3%!
82%; 0%!
84%! 6%|
80%: 5%i
•+ •*••
86%! 0%|
57%; 22%!
50%! 13%!
70%: 11%!
68%! 8%!
59%! 0%!
63%l 0%!
Type of Boating on Last Tripb
(Percent of Boaters)
White
Other
Water " ,
Kayak Kayak
6%; 3%i...
7%: 3%:
0%; 4%!
0%: 11%!
\J /O; y /O;
907 : no/ :
/oj U /o;
1%: 3%!
0%! 0%:
1%; 8%=
13%! 13%!
m m...
5%! 15%!
6%: 6%!
13%! 13%!
Row i Raft i
no/i 70/E
-°-4 --4
0%! 0%!
2%; 9%!
2%! 1%!
no/ : n°/ :
/O- U /O-
r T
U /Oj U /O;
no/ : no/ :
U /oj U /o=
2%; 2M
U /O; U /O;
no/ : 1 O/ :
U /oj 1 /o=
0%! 13%!
QO/; 40/i
3.4 14
2%! 3%!
no/ 1 no/ :
U /oj U /oj
0%! 13%!
:
Wind Surf i Other
i
:
0%: 6°A
3%! 14°X
0%; 7°X
1%! 14°X
o%; m
0%! 12°X
0%; 9°X
o%; .§2<
0%! 14°X
0%; 10°X
o%; 6^
0%! 0%
0%: 29°X
0%! 0%
a Own includes those who used their own boat or one belonging to someone in their immediate family.
Rent includes those who rented or chartered a boat.
Other includes those who did not indicate either using own boat or renting a boat.
b Kayak includes kayak or canoe; raft includes rafting, tubing, or floating; other includes other or type not indicated.
N/A - Not Available
Source: U.S. EPA analysis; NDS.
N-29
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MP&M EEBA: Appendices Appendix N: Analysis of the National Demand for Water-based Recreation Survey
N.9 PROFILE OF FISHING TRIPS
This analysis provides a profile of fishing trips, including angling success rate, average catch, and type of fisheries targeted on
the last trip by state of residence. The success rate equals the total number of fishermen who report catching at least one fish
on their last trip divided by the total number of fishermen in each state. The average catch equals the total fish caught by all
fishermen divided by the total number of fishermen in the state. Average catch therefore includes those who did not indicate
catching any fish. Similarly, the percent of fishermen who fished from a boat equals the total number of fishermen who
reported fishing from a boat on their last trip, divided by the total number of fishermen. Finally, the percent of fishermen who
participated in each type of fishing equals the total number of fishermen who reported fishing in either cold, warm, salt,
anadromous, or other water divided by the total number of fishermen. Other includes both those who indicated other and
missing values. Results of the analysis are presented below in Table N.I 2.
N-30
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MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.
I Sample 1 Fish Catch on Last Trip3
\\7aiirlitarl L
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
Number of |Average
Fishermen | ofFisn
268,3231...
870,8131
761,0411
790,286!
5,556,583!
1,253,757!
466,922!
N/A!
119,661!
3,156,584!
1,457,940!
189,837!
546,907!
385,331!
1,942,878!
1,201,814!
579,427!
952,715!
1,149,580!
1,086,074!
842,502!
308,695!
2,052,719!
1,281,526!
1,141,630!
586,331!
293,322 [
1,592,117!
163,207!
266, 126!
150,093!
1,244,960!
300,125!
12: Profile of Fishing
Number ! Success Rate
Caught ! (% of fishermen ) j
91...
7!
7!
4!
5!
4!
3!
N/A!
6!
5!
6!
21!
7!
5!
6!
6!
5!
8!
4!
5!
37"
6!
sT"
4!
sT"
3!
TbT"
4!
97"
2!
sT"
3"!
....65%1.
67%!
85%!
67%!
73%!
65%!
71%!
N/A!
70%!
70%!
74%!
91%!
76%!
73%!
74%!
74%!
74%!
76%!
80%!
72%!
77%!
68%!
75%!
70%!
74%!
82%!
78%!
77%!
69%!
88%!
40%!
73%!
61%!
:
Fished from a !
:
Boat on Last I
Trip
(% of
fishermen) j
i
65%;
71%!
62%;
39%;
47%;
16%;
50%;
N/A;
50%;
57%;
47%;
36%;
37%;
21%;
44%;
45%;
39%;
39%;
56%;
43%;
62%;
55%;
6i%;
63%;
37%;
55%;
22%;
42%;
46%;
4i%[
60%;
45%;
22%;
Trips
Type of Water Fished on
Last Trip"
Cold Warm Salt lAnadromous!
(%of (%of (%of (%of
fishermen) 1 fishermen) 1 fishermen) i fishermen) 1
41%
22%
36%
44%
47%
79%
29%
N/A
20%
14%
32%
0%
51%
85%
38%
39%
21%
26%
19%
39%
38%
64%
54%
53%
51%
24%
87%"
27%"
54%
41%
50"%
18%
74%"
h .Q%!..
45%;
60%;
47%;
14%;
13%;
21%;
N/A;
20%;
23%;
33%;
18%;
41%;
o%;
45%;
47%;
74%;
65%T
47%;
19%!"
17%;
i8%!"
28%;
34"%!"
36%!
58%!"
9%T
22%T"
46%T
53%!"
30%;
2i%!"
17%!
53%!
20%!
o%!
3%!
28%!
5%!
43%!
N/A!
60%!
50%!
24%!
82%!
0%j
0%!
4%!
4%!
0%j
2%!
25%!
30%!
38%!
18%!
3%l
0%j
3%!
13%!
4%!
42%!
6%!
0%!
10%!
48%!
4%!
6%i
2%i
0%|
3%!
5% I
6%'j
7% I
N/A!
0%!
4%J
0%!
0%!
0%!
9%!
2%!
1%!
0%!
3%T
2%!
2%!
0%!
0%!
7%j
2%T
3%!
b'%!
b'%!
4%T
b%!
b%!
ib%!
2%T
b%!
Other
(% of
fishermen)
0%
11%
4%
3%
6%
4%
0%
N/A
0%
9%
10%
0%
7%
6%
11%
9%
5%
5%
8%
11%
8%
0%
9%
11%
8%
5%
0%
5%
0%
6%
0%
11%
4%
N-31
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MPAM EEBA: Appendices
Appendix N: Analysis of the National Demand for Water-based Recreation Survey
Table N.12: Profile of Fishing Trips
State
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
wv
WY
: : :
:
Sample ^ Fish Catch on Last Trip" ^ Fished from a j Type of Water pished on Last Trip"
Weighted 1 I BoatonLast i
Number of » ».T i_ c. T. ^ Cold Warm Salt lAnadromous! Other
iiumuei ui Average Number : Success Rate : (% Of ! ,0/ , ! (0/ , ! ,0/ , ! ,0/ , ! (0/ ,
Fishermen • nf Fich Taimht : f »/ nf fUhprmpn V r i. -i '/0° '/0° ' /0 ° '/0° '/0°
: oi risn L^augnt :i /o oi iisnermen ) : fishermen) :*-i ^:^-i ^:^-i ^:^-i \ • r i \
\ ! ! 11S11C1 me"; : fishermen) | fishermen) j fishermen): fishermen) j fishermen)
328.,084| 4| 75%! 13%! 63%
2,236,799! 4| 80%! 49%! 46%
1,649.,727! 5! 72%! 48%! 41%
857,583 ! 6| 70%! 33%! 26%
960,904! 3! 51%! 40%! 62%
2,064,218! 4! 61%! 44%! 51%
174,205! 5! 50%! 50%! 33%
912,165! 8! 81%! 60%! 32%
151,221! 7! 64%! 45%! 73%
1,141,537! 4! 76%! 46%! 36%
4,564,193! 5! 66%! 55%! 23%
360,030! 3! 56%! 16%! 84%
1,421,449! 7! 73%! 46%! 28%
42,288! 5! 100%! 33%! 33%
1,250,568! 2! 56%! 60%! 49%
1,209,448! 9! 69%! 59%! 57%
358,067! 6! 58%! 16%! 68%
221,738! 4! 72%! 28%; 89%
19%! 6%i 0%
19%! 26%! 2%
45%! 4%! 3%
57%! 9%! 0%
8% I 13%! 12%
25%! 16%! 3%
17%! 33%! 0%
34%! 25%! 6%
27%! 0%! 0%
51%! 5% ! 3%
45%! 24%! 1%
8% ! 0%! 0%
21%! 39%! 3%
67%; 0%! 0%
8%! 22%! 15%
34%; 0%! 1%
26%; 0%! 0%
6%! 0%! 0%
13%
7%
7%
9%
6%
5%
17%
4%
0%
5%
7%
8%
9%
0%
6%
7%
6%
6%
a Missing values for fish catch were included as zero in both the mean and the median.
b Other includes both those that indicated other and missing values.
N/A - Not Available
Source: NDS; U.S. EPA analysis.
N-32
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