INTERIM REPORT

                         ON

          LOADING FUNCTIONS FOR ASSESSMENT

                         OF

        WATER POLLUTION FROM NONPOINT SOURCES
                         By
                    A. D. McElroy
                     S. Y. Chiu
                    J. W. Nebgen
                      A. Aleti
                    F. W. Bennett

             Midwest Research Institute
             Kansas City, Missouri 64110
                 Project #68-01-2293
               Program Element #1HB617
                   Project Officer

                Paul R. Heitzenrater
Agriculture and Nonpoint Sources Management Division
         Office of Research and Development
        U.S. Environmental Protection Agency
               Washington, D.C. 20460
           ENVIRONMENTAL PROTECTION AGENCY
         OFFICE OF RESEARCH AND DEVELOPMENT
               WASHINGTON, D.C. 20460

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                                  NOTICE
This document has been reviewed by the Office of Research and Development, U. S.
Environmental Protection Agency, and approved for publication as  an INTERIM
report. Approval does not signify that the contents necessarily reflect the views and
policies of the Environmental Protection Agency nor does mention of  commercial
products constitute  endorsement or recommendation for  use. This  report is being
circulated for comment on its technical accuracy and policy implications. Following
receipt of these comments, it is planned to make appropriate changes  and publish a
final report. Publication of this interim report is, therefore, on a limited basis.

This INTERIM report is intended to provide state-of-the-art information on methods to
assess the load  of  pollutants on watercourses from nonpoint sources.  The  "user"
should be aware that some of the technical aspects may be changed  in the final
report. Nevertheless, this interim report does outline  the type of methods  that can be
used to generate the assessments and specifies the data which are required.

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                                CONTENTS
                                                                    age
1.0  Introduction 	    1

2.0  Guidelines for Use of the Handbook	    5

     2.1  Introduction  	    5
     2.2  Terminology, Symbols and Formulas 	    6
     2.3  Procedure for Use of the Handbook	    6
     2.4  Summary of Loading Functions  	    7

          2.4.1  Sediment from Sheet and Rill Erosion	    7
          2.4.2  Nutrients and Organic Matter 	   10
          2.4.3  Pesticides	   14
          2.4.4  Salinity in Irrigation Return Flow	   15
          2.4.5  Acid Mine Drainage	   17
          2,4.6  Heavy Metals and Radiation 	   19
          2.4.7  Urban and Related Sources	   21
          2.4.8  Livestock in Confinement	   23
          2.4.9  Terrestrial Disposal 	   23
          2.4.10 Background Emissions of Pollutants 	   24

     2.5  Limitations and Accuracies  	   25

3.0  Sediment from Soil Erosion	   29

     3.1  Introduction	   29
     3.2  Sediment Loading from Surface Erosion 	   30

          3.2.1  Overview	   30
          3.2.2  Sediment Loading Function for Surface Erosion   .   35
          3.2.3  Procedure for Use of the Sediment Loading
                   Function	   37
          3.2.4  Example of Assessing Sediment Loading from
                   Surface Erosion  	   39
                                    iii

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                           CONTENTS (continued)
                                                                     Page

          3.2.5  Determination of Source Characteristic Factors .  .    43
          3.2.6  Source Characteristic Factors for Predicting
                   Maximum and Minimum Sediment Loading 	    71
          3.2.7  Source Areal Data	    74

     3.3  Sediment Loadings from Other Sources:  Gullies,
            Streambanks, and Mass Soil Movement	    82

          3.3.1  Overview	    82
          3.3.2  Methods for Quantifying Sediment Loading from
                   Gullies, Streambanks, and Mass Soil Movement .  .    85

     References	    86

4.0  Nutrients and Organic Matter 	    90

     4.1  Introduction	    90
     4.2  Nitrogen	    91

          4.2.1  Introduction	    91
          4.2.2  Precipitation	    92
          4.2.3  Nitrogen Loading Function  	    94
          4.2.4  Evaluation of Parameters in the Nitrogen Loading
                   Function	    96

     4.3  Phosphorus	102

          4.3.1  Introduction	102
          4.3.2  Phosphorus Loading Function   	   104
          4.3.3  Evaluation of Parameters in Phosphorus Loading
                   Function	104

     4.4  Organic Matter	107

          4.4.1  Organic Matter Loading Function   	   107
          4.4.2  Evaluation of Parameters in the Organic Matter
                   Loading Function 	   107
                                    iv

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                          CONTENTS (continued)
                                                                     Page
     4.5  Accuracy of Loading Functions 	  107
     4.6  Example of Loading Computation  	  108

          4.6.1  Nitrogen Loading 	  109
          4.6.2  Phosphorus Loading 	  110
          4.6.3  Organic Matter Loading 	  110

     References	112

5.0  Pesticides	  114

     5.1  Introduction	114
     5.2  Pesticide Loading Functions 	  116

          5.2.1  Case 1:  Insoluble Pesticides, Average Soil
                   Concentrations Known 	  116
          5.2.2  Case 2:  Water Insoluble Pesticides, Current
                   Area-Specific Data Available 	  117
          5.2.3  Case 3:  Water Soluble and Water Insoluble
                   Pesticides, Stream to Source Approach  	  118

     5.3  General Information 	  119

          5.3.1  Pesticide Solubility 	  119
          5.3.2  Pesticide Persistence  	  119

     5.4  Load Calculation:  Examples	120
     5.5  Limitations in Use	120

     References	123
     Bibliography 	  124

6.0  Salinity in Irrigation Return Flow	125

     6.1  Introduction	125
     6.2  Option I:  Source to Stream Approach	126
                                    v

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                          CONTENTS  (continued)
          6.2.1  Load Estimation Equation and Information Needs .  .   126
          6.2.2  Load Calculation - Irrigation Return Flow  ....   127

     6.3  Option II:   Stream to Source Approach 	   129

          6.3.1  Loading Equation and Information Needs 	   129
          6.3.2  Option II Load Calculation	131

     6.4  Option III:  Loading Values for Salinity Loads in
            Irrigation Return Flow  	   133
     6.5  Estimated Range of Accuracy 	   133

     References	139

7.0  Acid Mine Drainage	140

     7.1  Introduction	140
     7.2  Option I:  Source to Stream Approach  	   141

          7.2.1  Loading Function and Information Needs 	   141
          7.2.2  Constants  Ka  and  K^  in Option I Loading
                   Function	142
          7.2.3  Load Index Factors for Option I Loading Function .   144
          7.2.4  Background Alkalinity Term for Option I Loading
                   Function	146
          7.2.5  Procedure for Using Option I Loading Function  .  .   146
          7.2.6  Examples of Option I Loading Function Utilization.   148

     7.3  Option II:  Stream to Source Approach 	   151

          7.3.1  Loading Function and Information Needs 	   151
          7.3.2  Procedure for Using Option II Mine Drainage
                   Loading Function 	   152
          7.3.3  Example of Option II Loading Function for Mine
                   Drainage	   154

     7.4  Estimated Range of Accuracy	154
     References	158
                                    vi

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                          CONTENTS (continued)


                                                                     Page

8.0  Heavy Metals and Radioactivity 	  159

     8.1  Introduction	159
     8.2  Option I:  Source to Stream Approach  	  161

          8.2.1  Information Requirements for Loading Value
                   Equation	161
          8.2.2  Procedure for Using Option I Loading Value
                   Equation	161
          8.2.3  Example of Option I Source to Stream Approach  .  .  163

     8.3  Option II:  Stream to Source Approach	165

          8.3.1  Loading Value Equations and Information Needs  .  .  165
          8.3.2  Estimation of Heavy Metal and Radioactivity
                   Emissions from Background  	  166
          8.3.3  Procedure for Using Option II Loading Value
                   Equations	175
          8.3.4  Example of Option II Stream to Source Approach .  .  175

     8.4  Expected Accuracy of Methods  	  177
     8.5  Heavy Metals Attached to Sediment 	  179

          8.5.1  Loading Function 	  179
          8.5.2  Information Needs  	  180
          8.5.3  Relationship between Heavy Metals in Soils and in
                   Surface Waters 	  180
          8.5.4  Reliability of the Procedure	181

     Reference	185

9.0  Urban and Related Sources	186

     9.1  Pollutants from Urban Runoff  	  186

          9.1.1  Loading Functions  	  187
          9.1.2  Procedure for Loading Calculations 	  190
          9.1.3  Street Length and Land Use Data for Urban Areas   .  193
          9.1.4  Example	194
          9.1.5  Techniques for Assessing Urban Runoff Pollution
                   Characteristics  	  198
                                   vii

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                           CONTENTS (continued)
      9.2  Pollutants from Motor Vehicular Traffic on Roadways .  .  .   199

           9.2.1  Sources of Roadway Traffic Data	202
           9.2.2  Example	202

      9.3  Street and Highway Deicing Salts  	   202

           9.3.1  Loading Functions  	   202
           9.3.2  Sources of Required Data	204

      References	205

10.0  Livestock in Confinement	206

      10.1  Introduction	206
      10.2  Loading Function for Livestock Operations  	   207
      10.3  Feedlot Runoff Evaluation  	   208

            10.3.1  Factors in Runoff Estimation 	   208
            10.3.2  Precipitation Data Analysis  	   209
            10.3.3  Estimation of Runoff from Feedlots 	   211

      10.4  Pollutant Concentration in Feedlot Runoff  	   222
      10.5  Pollutant Delivery Ratio, FLd  	   225
      10.6  Feedlot Area, A	225
      10.7  Methods for Developing Feedlot Statistics  	   227
      10.8  Accuracy of Prediction	232
      10.9  Procedure for Computing Pollutant Loading  	   232
      10.10 Example	233
      References	234

11.0  Terrestrial Disposal 	   236

      11.1  Introduction	236
      11.2  Loading Function for Landfills 	   238
      11.3  Procedure for Computing Landfill Pollutant Loadings  .  .   241
                                    viii

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                           CONTENTS (continued)
                                                                      Page
            11.3.1  Landfill Characteristics including Number, Size,
                      Location,  Age, and Surface Area	241
            11.3.2  Percolation and Leachate Data  	  242
            11.3.3  Pollutant Concentration Data 	  242
            11.3.4  Leachate Delivery Ratio  	  242

      11.4  Accuracy of Predicted Loads  	  243
      11.5  Example	244
      References	246

12.0  Background Pollutant Load Estimation Procedures  	  247

      12.1  Introduction	247
      12.2  Stream to Source Methods	247

            12.2.1  Options Available  	  247
            12.2.2  Information Needs for Background Loading Value
                      Equations	248
            12.2.3  Loading Value Equations and Definition of
                      Conversion Factors 	  249
            12.2.4  Estimation of Background Pollutant
                      Concentrations 	  251
            12.2.5  Procedure for Using Loading Value Eqs. (12-1) or
                      (12-2)	251
            12.2.6  Examples of Using Loading Value Equations  . .  .  254
            12.2.7  Estimated Ranges of Accuracy for Stream to
                      Source Options for Background Pollutant Loads.  255

      12.3  Source to Stream Option	259

            12.3.1  Description of Source to Stream Option 	  259
            12.3.2  Estimated Ranges of Accuracy for the Stream to
                      Source (USLE-Sediment) Option for Background
                      Pollutant Loads  	  262

      12.4  Iso-Pollutant Maps for Estimating Background Pollutant
              Loads	262
      References	286
      Glossary	287
      Symbols	293

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                           CONTENTS (concluded)
Appendix A - Monthly Distribution of Rainfall Erosivity Factor R .  .   298

Appendix B - Methods for Predicting Soil Erodibility Index K . .  .  .   316

Appendix C - Topographic Factor LS for Irregular Slopes  	   324

Appendix D - K •  LS Indexes for Land Resource Areas East of the
               Continental Divide  	   328

Appendix E - Estimated Soil Losses from Selected Cropping Systems in
               Areas West of the Continental Divide (From 1972 SCS
               Survey)	347

Appendix F - Reproduction of "Pesticide Residue Levels in Soils,
               FY1969--National Soils Monitoring Program 	   389

Appendix G - Pesticide Properties:  Persistence, Solubility,
               Leachability, Runoff  	   425

Appendix H - Statistics of Deicing Salt Application on Highway
               and Tollways in the United States	439
                                     x

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                                 FIGURES

No.

3-1    Flow Diagram for Calculating Sediment Loading From
         Surface Erosion 	         38

3-2    Mean Annual Values of Erosion Index (in English units)
         for the Eastern United States	         44

3-3    Mean Annual Values of Erosion Index (in English units)
         for Hawaii	         46

3-4    Soil Moisture - Soil Temperature Regimes of the
         Western United States 	         48

3-5    Relationships Between Annual Average Rainfall Erosivity
         Index and the 2-Year, 6-hr Rainfall Depth for Three
         Rainfall Types in the Western United States 	         49

3-6    Storm Distribution Regions in the Western United
         States	         50

3-7    Slope Effect Chart Applicable to Areas A-l in
         Washington, Oregon, and Idaho and all of A-3	         53

3-8    Slope—Effect Chart for Areas Where Figure 3-7 is Not
         Applicable	         54

3-9    Slope Effect Chart for Irregular Slopes 	         56

3-10   Sediment Delivery Ratio for Relatively Homogeneous
         Basins	         66
                                    XI

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                           FIGURES (continued)


No.

3-11   Projected Variation of Soil Erosion for Lands with
         Constant Cover Factor, in Parts of Michigan, Missouri,
         Illinois, Indiana, and Ohio	       73

3-12   Projected Variation of Soil Erosion on Continuous Corn
         Lands in Central Indiana	       75

4-1    Percent Nitrogen (N) in Surface Foot of Soil	       98

4-2    Soil Nitrogen vs Humidity Factor and Temperature	      100

4-3    Nomograph for Humidity Factor,  H	      101

4-4    Nitrogen (NH^-N and N03~N) in Precipitation 	      103

5-5    Phosphorus Content in the Top 1 ft of Soil	      105

7-1    Background Alkalinity Concentrations (ppm CaCO^)  ....      147

7-2    Background Sulfate Concentrations (ppm) 	      153

8-1    Background Total Heavy Metals (ppb) 	      168

8-2    Background Iron + Manganese (ppb)	      169

8-3    Background Arsenic + Copper + Lead + Zinc (ppb)	      170

8-4    Background Miscellaneous Heavy Metals (ppb) 	      171

8-5    Background Radioactivity (picocuries/liter) 	      172

8-6    Background Alpha Radioactivity (picocuries/liter) ....      173

8-7    Background Beta Radioactivity (picocuries/liter)	      174

9-1   Climate Zone  for the  Cities from which  Data Are  Available
        and Used  in the URS Study	      192

9-2   Correlation Between Population Density  and Curb  Length
        Density	      195
                                    xii

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                          FIGURES (concluded)
No.
11-1   Leachate Flow Through Path Through Zones Where Attenua-
         tion May be Effected	     237

11-2   Average Annual Percolation 	     240

12-1   The National Hydrologic Benchmark Network   	     266

12-2   Background Suspended Sediment (ppm)  	     267

12-3   Background Nitrate Concentrations (ppm as N) 	     268

12-4   Background Total Phosphorus Concentrations  (ppm as P)  .  .     269

12-5   Background BOD Concentrations (ppm)  	     270

12-6   Background Total Coliform Count (MPN/100 ml) 	     271

12-7   Background Conductivity (micromhos)  	     272

12-8   Background pH (standard units) 	     273

12-9   Background Total Dissolved Solids (ppm)  	     274

12-10  Background Alkalinity (ppm CaO^)	     275

12-11  Background Hardness (ppm as CaC03)	     276

12-12  Background Chloride Concentrations (ppm) 	     277

12-13  Background Sulfate Concentrations (ppm)  	     278

12-14  Background Total Heavy Metal Concentrations (ppb)  ....     279

12-15  Background Iron + Manganese (ppb)	     280

12-16  Background Arsenic + Copper + Lead + Zinc (ppb)	     281

12-17  Background Miscellaneous Heavy Metals (ppb)  	     282

12-18  Background Total Radioactivity (picocuries/liter)  ....     283

12-19  Background Alpha Radioactivity (picocuries/liter)  ....     284

12-20  Background Beta Radioactivity (picocuries/liter) 	     285

                                   xiii

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                                 TABLES
No.                                                                   Page

2-1     Source - Pollutant Matrix	 .     8

3-1     Some Reported Quantitative Effect of Man's Activities on
          Surface Erosion	    32

3-2     Applicability of Rr and RS Factors in the Areas West of the

3-3
3-4
3-5
3-6
3-7
3-8

3-9

Relative Protection of Ground Cover Against Erosion 	
"C" Values for Permanent Pasture, Rangeland, and Idle Land .
"C" Factors for Woodland 	
"C" Factors for Construction Sites 	
"P" Values for Erosion Control Practices on Croplands. . . .
Typical Values of Drainage Density .............

Summary of Applicability of Characteristic Factors 	
47
59
61
62
63
64
68

69
3-10    Estimated Range of Accuracy of Sediment Loads from Surface
          Erosion.	   71

3-11    Land Use and Treatment Needs Categories of the Conservation
          Needs Inventory	„	   78
                                   xiv

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                           TABLES (Continued)

No.

4-1     Nutrient and Sediment Losses	   96

4-2     Effect of Clear-Cutting and Fertilization on Nutrient
          Output in Douglas Fir Forests	   97

4-3     Probable Range of Loading Values for Nutrients and Organic
          Matter	   108

4-4     Sediment Yield in Example ..... 	   109

4-5     Available Nitrogen Loading,  Y(NA)E , in Example	   110

4-6     Available Phosphorus Loading,  Y(PA)E , in Example	   110

4-7     Organic Matter Loadings in Example	   Ill

5-1     Estimates of Accuracy for Pesticides	   122

6-1     Comparison of Salinity Loads Obtained with Option I Load
          Estimation Equation with Reported Salinity Loads in
          the Grand Valley, Colorado	   128

6-2     Comparison of Salinity Loads Estimated by Option II Methods
          with Those Reported by EPA	132

6-3     Salt Yields from Irrigation in Green River Subbasin ....   134

6-4     Salt Yields from Irrigation in Upper Colorado Main Stem
          Subbasin	134

6-5     Salt Yields from Irrigation in San Juan River Subbasin.  .  .   135

6-6     Salt Yields from Irrigation in Lower Colorado River Basin  .   135

6-7     Salt Yields from Irrigation for Selected Areas in
          California	136
                                   xv

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                         TABLES (Continued)

No.                                                                  Page

6-8      Estimated Range of Accuracy for Option I (Source to
           Stream) Procedure for Estimating Salinity from
           Irrigation Return Flow	136

6-9      Estimated Range of Accuracy for Option II (Stream to
           Source) Procedure for Estimating Salinity from
           Irrigation Return Flow	137

7-1      Values of  Ka  and  K^  for Acid Mine Drainage Option I
           Loading Function	143

7-2      Load Index Values for Active and Inactive Surface and
           Underground Mines 	 143

7-3      Example of Determination of Load Index Values 	 145

7-4      Conversion Factors  a  to be Used for Option II Mine
           Drainage Loading Function 	 152

7-5      Estimated Mine Drainage Emissions from Tioga and Janiata
           River Basins Using Option II Loading Function 	  155

7-6      Estimated Range of Loads for Option I (Source to Stream)
           Acid Mine Drainage Loading Functions	156

7-7      Estimated Range of Loads for Option II (Stream to Source)
           Acid Mine Drainage Loading Functions	157

8-1      Conversion Factors  a  to be Used for Option I Loading
           Value Equations	*  162

8-2      Nonpoint Heavy Metal Emissions Estimates from Some Inactive
           Mines in the Coeur d'Alene Valley, Idaho, Using Option
           I Methods	164

8-3      Conversion Factors "a" to be Used for Option II Loading
           Value Equation	167

8-4      Heavy Metal  Pollutant Emissions  from Several Streams  in
           Clear Creek County, Colorado	176
                                   xvi

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                            TABLES (continued)
 No.                                                                     Page

 8-5      Expected Accuracy of Option I (Source to Stream) Method
           for Heavy Metals	    178

 8-6      Expected Accuracy of Option II (Stream to Source) Method
           for Heavy Metals	    178

 8-7      Heavy Metal Concentrations in Surficial Materials in the
           United States 	    182

 8-8      Expected Accuracy of Heavy Metal Loads Delivered with
           Sediment	    184

 9-1      Solid Loading Rates and Compositions — Nationwide Means and
           Substitutions of the Nationwide Means at 8070 Confidence
           Level	    188

 9-2      Mean Concentrations of Mercury and Chlorinated Hydrocarbons
           in Street Dirt from Nine U.S. Cities	    189

 9-3      Equivalent Curb-Length per Unit Area of Street Surface,
           Arranged by Land Use Types	    196

 9-4      General Land Consumption Rates for Various Land Uses  . .  .    196

 9-5      Deposition Rates of Traffic-Related Materials 	    200

10-1      Stations for Which Local Climatological Data are Issued,
           as of 1 January 1974	    210

10-2      Hourly Precipitation  	    212

10-3      Local Climatological Data	    213

10-4      Daily Precipitation 	    214

10-5      Seasonal Rainfall Limits for Various Antecedent Moisture
           Conditions	    216

10-6      Runoff (in Inches) from Feedlot Sufaces for Various
           Antecedent Moisture Conditions  	    216
                                    xvii

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                            TABLES (continued)


No.                                                                     Page

10-7     Daily Precipitation Data (Inches) for Kansas City,
10-8
10-9
10-10
10-11
10-12

10-13

11-1
11-2

11-3
12-1
Estimated Runoff (Inches) for Kansas City, Missouri - 1974.
Runoff and Rainfall Relationships on Beef Cattle Feedlots .

Runoff Characteristics from Cattle Feedlots in Kansas . . .
Number of Cattle Feedlot and Fed Cattle Marketed--in Small
Lots, by States (1974) 	
Estimated Range of Accuracy for Predicting Pollutant Loads
from Feedlots ..... 	

Estimated Range of Predicted Loads for Various Pollutants

Pollutant Loading Rates in Example 	 ...
Conversion Factors "a" to be Used for Options I and III
£• .L.V
220
221
223
224

226

232
239

244
245

           Loading Value Equation:  Flow as Direct Runoff, Q(R)   .  .    250

12-2     Conversion Factors "a" to be Used for Options II and IV
           Loading Value Equation:  Flow as Streamflow, Q(str) .  .  .    252

12-3     Listing of Background Isopollutant Maps 	    253

12-4     Expected Accuracy of Background Pollutant Loads Calculated
           Using Stream to Source Methods	    256

12-5     Expected Accuracy of Background Pollutant Loads Calculated
           Using Stream to Source Methods	    257

12-6     Expected Accuracy of Background Pollutant Loads Calculated
           Using Stream to Source Methods	    258

12-7     "C" Values for Permanent Pasture, Rangeland, and Idle Land.    260

                                   xviii

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                             TABLES (Concluded)

No.                                                                  Page

12-8      "C" Factors for Woodland.	   261

12-9      Expected Accuracy of Background Pollutant Loads Cal-
            culated Using the Source to Stream (USLE-Sediment
            Option	   262

12-10     Location of Hydrologic Benchmark Stations .  .  ......   264
                                       xix

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                               SECTION 1.0

                              INTRODUCTION

The rates and magnitudes of discharges of pollutants from nonpoint sources
do not relate simply to source characteristics or source-related param-
eters.  Evaluation of the severity of this problem is hampered by the lack
of tools to quantify pollutant loads, and scanty and imprecise data on the
interrelationships between control measures and pollutant loads are a de-
terrent to formulation of control or regulatory strategies.  This User's
Handbook is the result of a program which had as one objective the develop-
ment of nonpoint pollution loading functions for significant sources and
significant pollutants.

The Handbook has two basic functions.  First, it presents loading functions
together with the methodologies for their use.  Second, it presents some of
the needed data, provides references to other sources of data, and suggests
approaches for generation of data when available data are inadequate.  A
corollary function consists of assessments of the adequacies of functions
and their supporting inventories of data, and an assessment as well of the
extent to which pollutants and nonpoint sources are adequately covered.

A loading function, as the term is used here, is a mathematical expression
which one uses to calculate the emission of a pollutant from a nonpoint
source and discharge of the pollutant into surface waterways.  For pur-
poses of this Handbook, a substance becomes a. pollutant only when it is
deposited in surface waters.  For example, the movement of sediment and
nutrients from a corn field to the edge of the field does not qualify as
pollutant discharge, even though the transport process may be an impor-
tant part of the overall pollutant emission mechanism.

A source is a land area devoted reasonably exclusively to a specific use,
which therefore can be treated as a unit with respect to land use practices
and potential for pollutant discharges.  A cornfield, a field of soybeans,
a highway under construction, a mine, a forest, and a landfill are sources.
Similarly, a group of cornfields is considered to be a source, if practices
from field to field are sufficiently uniform that an average set of data
adequately describes the entire acreage.

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A load is defined as the quantity of pollutant discharged to surface
waters from the source per unit of time:   load = kilogram BOD per hectare
per day, etc.  The loading function is the expression or equation which
permits calculation of the load.  The function has provisions for calcu-
lation of a load on a per hectare basis (or other suitable unit dimension),
and total source load is calculated by multiplying the unit load by source
size.  Finally, all sources within an area of interest, such as a watershed
or river basin, can be summed up to obtain the load of a particular pollu-
tant discharged to the surface waters from all identified sources.

A tremendous variety and quantity of data are necessary for product^ e
use of the loading functions.  A small fraction of that body of data is
included in this Handbook, primarily in the appendices.  The user is re-
ferred to other sources of data ranging from systematic compilations to
the knowledge and judgment of local people.  These sources are delineated
in succeeding sections as specific data needs arise for individual load-
ing functions.

Essentially three categories of data are needed.  One category is the in-
formation which describes the areal characteristics of a source:  its lo-
cation within a county, basin, state; its sizes, perhaps its dimensions;
and its basic land use, i.e., row crops,  construction of residences, solid
waste disposal, and strip mining.

A second category of data is that which is characteristic of a source or
area, independently of land use.  This category includes data which de-
scribe agricultural productivity, water permeability, erodibility, and
similar properties of soils; topographic features of the land; rainfall
and runoff; and stream miles, locations,  and stream densities.

The third category is the data which are needed to describe how a source
is used.  Examples are tillage methods and conservation practices, crop-
ping patterns, quantities and schedule of pesticide use, irrigation flows,
and population densities.

It may perhaps be construed from the above discussion that the loading
functions are straightforward expressions or equation, matched by pre-
cise, well-documented data, and that calculations can be made by routine
procedures with perhaps little discriminatory inputs of judgment by the
user.  This seldom is the case.  A substantial fraction of the presenta-
tions of the  following sections is devoted to procedural descriptions
which should  assist the user in using his or other local judgments and
inputs, and instruct the user in the limits of applicability of the
functions.

-------
Emphasis is given to loading functions or estimating procedures which are
generally useful from the standpoint of the depth, quality, and quantity
of available data or information.   For this reason the functions are in
the main relatively simple and basic concepts, as opposed to theoreti-
cally oriented descriptions of physical, chemical, mechanical, and bio-
logical processes.   Indeed, where  necessary and appropriate, estimates
and the rule of thumb approach are preferred to a more rigid theoretical
function which suffers from the lack of key data.

The loading functions cover the following sources and pollutants:

Sources

     Agriculture:  cropland, pasture and rangeland, irrigated land, wood-
                     land, and feedlots

     Silviculture:   growing stock, logging, road building

     Construction:   urban development and highway construction

     Mining:  surface mining and underground mines

     Terrestrial disposal:  landfill and dumps

     Utility maintenance:  highways and streets, and deicing

     Urban runoff

     Precipitation

Pollutants

     Nutrients:  nitrogen and phosphorus

     Sediment

     Biodegradable  organics

     Pesticides

     Salinity

     Radioactivity

-------
     Mine drainage

     Metals

     Microorganisms

Methods have been developed to estimate the quantities of pollutants ex-
pected in the absence of man's influence.  These pollutant loadings are
designated "natural background."

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                             SECTION 2.0

                 GUIDELINES FOR USE OF THE HANDBOOK

2.1  INTRODUCTION

This handbook has been developed for use with data or information on
record and accessible to the user.  Some exceptions occur; that is,
certain loading functions assume the capability on the part of the
user to procure data by field and laboratory analysis or other on-site
data procurement methods.  Field sampling and analysis is suggested
when data on record are inadequate, perhaps not in existence at all.

The handbook user should obtain and use the best data he can find.
The best data usually are those which have been measured or developed
locally.  Such data can supplement the data or data sources recom-
mended throughout the handbook, or it can be used in a complementary
fashion, i.e., to help arrive at a range of values appropriate for
the specific user area.

The user is encouraged to use functions which are more area specific
than those presented in the handbook, to use research models if he is
so inclined, and to adapt suggested methodologies so that they directly
represent his area.

The information regarding loading functions and their use is presented
in Sections 3.0 through 12.0 and in Appendices A through H.  The texts
of Sections 3.0 through 12.0 are devoted chiefly to descriptions of the
functions themselves, of the terms within the functions, and of proce-
dures for use of the loading functions.  These sections contain certain
tables and figures which either present data needed in the loading func-
tions or which describe procedures for use of the loading functions.
Lengthy compilations of data are for the most part presented in the
appendices.  In addition, the appendices contain certain types of back-
ground information and presentations of specific procedures which are
essential but which do not fit conveniently in the discussions in Sec-
tions 3 through 12.0.

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The following subsections present information on terminology, symbols,
formulas, and procedures for use of the handbook.  The loading functions
themselves are presented together with definitions of terms in tabular
form.  The material presented in the remaining parts of this section
should be consulted by the user to help him define his specific prob-
lem and to guide him to those parts of the handbook which he will use
in calculating the emissions of nonpoint pollutants from his sources.

2.2  TERMINOLOGY, SYMBOLS AND FORMULAS

The terminology and symbols conform with some exceptions to standard
symbols and terms.  A broad range of subject matter is covered, and
the symbols normally used in one area or discipline overlap or are in
conflict with those of another.  These conflicts were sometimes re-
solved; other times the best course was to keep the old and familiar
terminology.

Equations and formulas for the most part avoid the abbreviated notation
and symbolism typical of engineering or physical science equations, in
favor of more cumbersome but more readily interpreted terms.

The term  Y  universally denoted pollutant load for a source.  The usual
symbols for basic parameters have been used almost without exception:  Q
for volume rate of water flow,  runoff or streamflow;  P  for precipitation;
C  for concentration, etc.   S  uniformly denotes sediments.  The majority
of the terms and symbols used in the handbook are defined in the summary
of loading functions presented in Section 2.4, and in the "Glossary" and
"Symbols" given in the last part of the handbook.  Miscellaneous symbols
are defined as they occur throughout the text of Sections 3.0 through 12.0
and the appendices.

The general format of the equations or loading functions is shown in
Section 2.4.  Note that multiplication of one term by another is to
be performed only if the two terms are separated by the dot (•) symbol.
The parenthesis is not used to denote multiplication; it has been re-
served for the function of separating and defining terms or symbols.
Thus, C(HM)ap denotes "background concentration of heavy metal," and
Y(RAD) "load of radiation."

2.3  PROCEDURE FOR USE OF THE HANDBOOK

Several basic steps are involved in estimation of pollutant loads.
These are:

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1.  Establish the boundaries of the area under consideration,  which will
usually be a political or physical entity:   an urban area,  a watershed,
a minor basin, a state, etc.  Define the general character of the area:
agriculture, silviculture, mining, urban, etc.

2.  Identify nonpoint sources in the area,  in appropriate detail.  Iden-
tify pollutants to be evaluated for each source.  The source-pollutant
matrix, Table 2-1, will assist in defining sources and pollutants.

3.  Identify loading function options:  Section 2.4 and Sections 3.0
through 12.0.

4.  Identify data needs, determine availability of data for possible op-
tions; assess quality and depth of coverage of available data:  Sections
3.0 through 12.0 and Appendices A through H.

5.  Select loading functions which best match the problem with quality
and depth of data.

6.  Procure necessary data for all sources/pollutants.

7.  Calculate pollutant loads (see Sections 3.0 through 12.0) for indi-
vidual sources, and sum to obtain total loads.

2.4  SUMMARY OF LOADING FUNCTIONS

In this section approaches to calculation of pollutants are summarized.
Limitations to their use are presented as well, in summary fashion.  Pol-
lutants and sources which must be treated by approximate methods which
require much discretion in use are delineated.

The summary cites no references.  These are cited in Sections 3.0 through
12.0 and the appendices.  References to tables, figures, and equations in
Sections 3.0 through 12.0 are provided to facilitate location of methods
and procedures, together with detailed discussion of dimensional units.

2.4.1  Sediment From Sheet and Rill Erosion

The basic tool for estimation of sediment from sheet and rill erosion is
the Universal Soil Loss Equation (USLE).  The loading function based on
the USLE is:

-------
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-------
                       Y(S),., - A-(R-K-L-S-C-P-Sd)                   (3-1)
                           III

where    Y(S)E = sediment loading

             A = source area

             R = the rainfall factor; Figures 3-2 and 3-3, or methods
                   presented in Section 3.2

             K = the soil erodibility factor; USDA  K  factor lists,
                   Appendices B, D, and E

             L = the slope length factor; Figures 3-7, 3-8, and 3-9,
                   Appendices C, D, and E

             S = the slope gradient factor; Figures 3-7, 3-8, and 3-9,
                   Appendices C, D, and E

             C = the cover factor; USDA  C  factor lists,  Tables 3-3  to
                   3-6

             P = the practice factor, Table 3-7

             Sd = sediment delivery ratio, Eq.  (3-2), Figure 3-10

  The USLE was  developed primarily for agriculture, and  has been used
  chiefly east  of the  Rocky  Mountains.  The  factors are  best defined for
  these areas of  use,  and methods  for use  in silviculture,  construction,
  mining, and other sources  outside agriculture are not  well developed.
  For the latter  sources the USLE  can serve  as the basis for estimations
  by personnel  skilled  in soil science and hydrology.  The USLE  is  quite
  useful in areas outside agriculture for  estimating  the probable  impact
  of control measures  (dikes,  vegetation, slope modification), even  though
  it may be inaccurate  for estimation  of absolute values  for sediment
  yields.

  2.4.1.1  Streatnbank  and gully  erosion  (see Section  3.0) -

  Streambank and  gully erosion are not treated by the USLE, and  landslides
  are similarly not treated.   Calculation  of sediment yields from  these
  sources requires  examination of  experience and  data in the area  of in-
  terest, by local  personnel.

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 2.4.1.2   Sediment  from  urban  runoff  -

 Methods  for  estimating  sediment  in urban  runoff  are  presented  in  Section
 9.0 (see 2.4.7).   The basic method involves  the  use  of values  for various
 urban areas  developed by analysis of sediment loads  in many urban areas.

 2.4.1.3   Sediment from  feedlots  (see Section 10.0) -

 The Universal Soil Loss Equation is  not used for feedlots.   Measured data
 on feed lot runoff are  used as the basis for prediction.  Feed lot loading
 functions are synopsized in Section  2.4.8.

 2.4.2  Nutrients and Organic  Matter  (see Section 4.0)

 The principal method of estimating nutrient and  organic matter loads con-
 sist of  first calculating sediment yields, and multiplying sediment yields
 by factors which denote concentrations of these  substances in  the soil and
 enrichment in the erosion process.

 2.4.2.1   Nitrogen -

 Yields of total nitrogen (NT, all forms of nitrogen) are estimated by mul-
 tiplying sediment yields by concentrations of total nitrogen in soil and
 by an enrichment factor.  In addition to sediment-carried nitrogen, nitro-
 gen carried in rainfall is included  in the loading function.  Available
 nitrogen is the sum of precipitation-borne nitrogen and a fraction of the
 sediment-borne nitrogen.
                       Y(NT)E = a-Y(S)E.Cs(NT).rN                    (4-1)
                        Y(NT) = Y(NT)P + YCNK
                                     Jtj       JLL

                        Y(NA) = Y(NT)E-fN + Y(N)pr                   (4-4)

where     Y(S)E ~ sediment load

         Y(NT)E = total nitrogen from erosion

         Y(N)pr = nitrogen from rainfall, discharged to streams

             NT = sum of nitrogen of all chemical forms

              A = area of source

             rN = enrichment factor
                                   10

-------
             NA =  available  (mineralized)  nitrogen

             % =  ratio of NA to  NT  in sediment

              a =  dimensional constant

              b =  attenuation factor

            Npr =  rate of deposition of nitrogen from the atmosphere in
                    precipitation
                = concentration of nitrogen in soil

          Q(OR) = overland runoff

           Q(P) = total precipitation

"Available nitrogen" is the forms of nitrogen which are readily available
for plant nutrition:  nitrate, ammonia, and simple amines.  Essentially
all of the nitrogen in rainfall is in the available form.

The loading functions for nitrogen based on the USLE and presented in
Section 4.0 do not apply to nitrogen from certain specific sources.

Nitrogen from terrestrial disposal operations, presented in Section 11.0
(see 2.4.9) is estimated by procedures for estimating leachate volumes,
pollutant concentrations in leachates, and delivery ratios for leachates.

Nitrogen from feedlots, presented in Section 10.0 (see 2.4.8) is esti-
mated from runoff volumes and a range of observed nitrogen concentrations,

Nitrogen in urban runoff, presented in Section 9.0 (see 2.4.7) is esti-
mated from data on urban runoff characteristics.

The loading functions for nitrogen do not encompass soluble nitrogen
forms, principally nitrate, which are leached into subsurface waters
and eventually reach surface waters in groundwater or drainage flows.
Methods for treating such situations via a generalized function are
not available, and local experience, data and expertise must be relied
upon.

The loading functions for nitrogen based on sediment as a carrier become
increasingly inadequate as sediment yields diminish.  This inadequacy
will be most evident in situations where erosion is minimal and mineral-
ized nitrogen is abundant.  A newly harvested forest temporarily devoid
                                  11

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of growing timber may have a temporary excess of mineralized nitrogen
which is susceptible to both leaching and transport overland in sedi-
ments and in solution.  Nitrogen emissions from terraced fields will
be higher proportionately in mineralized nitrogen than will emissions
from fields with less control of runoff and erosion.

2.4.2.2  Phosphorus -

Functions for phosphorus are presented in:

     Section 4.0, Nutrients and Organic Matter;
     Section 9.0, Urban Runoff; and
     Section 10.0, Livestock in Confinement.

Refer to Sections 2.4.7 and 2.4.8 for a summary of functions for phos-
phorus in urban runoff and feedlot runoff.  Functions for sediment-borne
nutrients from other sources are presented below.

Phosphorus is carried almost entirely on sediment.  In situations where
erosion can be predicted, the loading function for phosphorus can be ex-
pressed as the product of sediment yield times phosphorus concentration
in sediment.  The concentration of phosphorus is taken to be the concen-
tration in the eroding soil times an enrichment  factor.

The  load of available phosphorus is calculated by multiplying the load
of total phosphorus by the ratio of available phosphorus to total phos-
phorus .
                     Y(PT) = a-Y(S)E-Cs(PT).rp                      (4-8)

                     Y(PA) = Y(PT)-fp                               (4-9)

where    Y(PT) = yield of total phosphorus

             a = dimensional constant

        Cg(PT) = concentration of phosphorus in soil

             T-
             P = enrichment factor

         Y(PA) = yield of available phosphorus

             P = ratio of available phosphorus to  total phosphorus
                                   12

-------
2.4.2.3  Organic matter -

Functions for organic matter (BOD) are presented in:

     Section 4.0, Nutrients and Organic Matter;
     Section 9.0, Urban Runoff (see Section 2.4.7); and
     Section 10.0, Livestock in Confinement (see Section 2.4,8).

Essentially all nonpoint emissions of organic matter are sediment borne.
Organic matter loading functions are expressed as a function of sediment
yields, with the exception of feedlot runoff, where a range of concen-
trations in runoff is used to calculate loads.  For other nonurban sedi-
ment and for urban sediments, the yield of organic matter is a product
of sediment yield and organic matter concentration in sediment; an en-
richment factor is needed to account for preferential erosion of organic-
rich sediments in nonurban sources.


                     Y(OM)E = a-CS(OM)-Y(S)E'rOM                  (4-12)

where   Y(OM)E = organic matter loading

         Y(S)E = total sediment loading from surface erosion (see Eq.
                   (3-D)

            OM = enrichment factor

             a = dimensional constant

        Cg(OM) = organic matter content of soil

Loads of organic matter calculated by procedures in the handbook cover
major known sources,  except for special cases which are not amenable to
treatment by generalized functions.  Some of these are:

     Direct or wind-blown deposition in streams of leaves from forests.

     Capture of hay and other vegetative debris by floodwaters.

     Irresponsible dumping of livestock wastes or other organic wastes
       in sites susceptible to washout or erosion (including improper
       field spreading of manure).
                                   13

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2.4.3  Pesticides

Loading functions for pesticides are among the least satisfactory of
those presented in the handbook.

In one option (Case I Method, Section 5.0) national historical data
on concentrations of pesticides in soils are the basis for estimation.
These concentrations are multiplied by sediment yields to calculate
pesticide loads.  The method is restricted to insoluble pesticides.
A major drawback of the approach is its insensitivity to peak loads
which may occur during the pesticide use season at times of high run-
off.  The method also suffers (presently) from a relative scarcity of
data on soil concentrations.  It is useful primarily for large areas
(states, major basins) where average yields may be adequate, and small
watershed specific loads are not required.

2.4.3.1  Insoluble pesticides, Case I and Case II methods -
                    Y(HIF) = Y(S)E-CS(HIF)-10~6                   (5-1)

where   Y(HIF) = total pesticide loading for source

         Y(S)E = sediment loading, Eq. (3-1)

       Cs(HIF) = concentration of pesticide in soil

The adequacy and applicability of the above method may be increased sub-
stantially through inputs of local/regional data and experience on pesti-
cide usage, levels in soils, rainfall and runoff patterns in relation to
pesticide use, and data on persistencies (life times) of pesticides in
the use environment.  The Case II method for insoluble pesticides is
based on losses in sediments, as in the Case I method, with liberal in-
puts of local data.

2.4.3.2  Soluble plus insoluble pesticides (Case III method) -

A Case III method is presented, in Section 5.0, for both soluble and in-
soluble pesticides, which requires that local data be obtained on runoff
and on pesticide concentrations in runoff.  If historical data of this
type are available it may be used for predictive purposes.  If none are
available, data accumulated in a sampling program in the area or region
of concern will serve as the basis for development of a predictive capa-
ability.
                                   14

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                          Y(HIF) = EQjCi'a                         (5-2)
                                   i

where             Q = runoff volumes

                  C = concentration in runoff

                  i = storm event

                  a = dimensional constant

2.4.4  Salinity in Irrigation Return Flow

Three optional methods are presented in Section 6.0 for estimating salin-
ity in irrigation return flow.  Each of the options is  valid in principle
but  has drawbacks due to one or more of the following reasons:  (a)  data
inputs are not readily accessible; (b) the quality of existing data inputs
varies widely; and (c) a good deal of insight about specific cases is re-
quired on the part of the user.   At the present time, a good bit of effort
is underway to develop mathematical models for salinity in irrigation re-
turn flow.  It is reasonable to expect that outputs from these models will
yield loading functions which will supercede the three methods presented
in this handbook.

The three methods are:

     Option I;  Calculation of Water and Salt Balances About the Irrigation
       Site


               Y(TDS)iRF = a-A-C(TDS)GW-[IRR + Pr - CU]            (6-1)

where     Y(TDS)iRp = salinity load in irrigation return flow

                  A = irrigated  area

                IRR = irrigation water added to crop root annually

                 Pr = annual precipitation

                 CU = annual water consumptive use
                                  15

-------
                   = concentration of total dissolved solids in ground-
                       water contributing to subsurface return

                 a = conversion factor

     Option II;  Salt Balances in Streamflow

    Y(TDS)IRp = a.[Q(Str)B.C(TDS)B - Q(Str)A'C (TDS)A] - Y(TDS)BG - Y(TDS)pT   (6-4]

where     Y(TDS)Tr>T, = salinity load in irrigation return flow
                IKr

           Y(TDS)Bg = salinity load contribution of background

           Y(TDS)pT = salinity load contribution of point sources

            Q(Str)g = streamflow below irrigated areas

            Q(Str)A = streamflow above irrigated areas

            C(TDS)g = total dissolved solids concentration below irrigated
                        area

            C(TDS)A = total dissolved solids concentration above irrigated
                        area

                  a = conversion constant

     Option III;  Estimation From Historical Data

Loads from several irrigated areas have been quantitatively assessed over
a period of years.  These data, synopsized in Tables 6-3 through 6-7, and
other data available to the user, can serve as guidelines for current es-
timations of salinity in irrigation return flows.

Option I requires specific information concerning how much water is de-
livered, how much is used consumptively, and the concentration of salt
in groundwater where applied irrigation water may be lost by deep perco-
lation.  Uncertainty in any of these input data will affect the accuracy
of the procedure.  Option I is most realistic in cases where the ground-
water dissolved solids are several times higher than dissolved solids in
applied irrigation water, and is recommended for use in those areas meet-
ing such a specification.  Furthermore, the procedure is not recommended
for sprinkler irrigation systems where evaporative losses in the delivered
water may be excessive.


                                   16

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Option II has been the method traditionally used to estimate salinity
from irrigation return flow.  Basically, this method consists of mea-
suring salinity loads in streams above and below irrigated areas.  Dif-
ferences in salinity are thus attributed to irrigation.  The principle
uncertainty in the Option II method lies in the contribution of back-
ground to the salinity load.  Definition of background salinity will re-
quire knowledge and insight about the particular area being considered.

Option III--loading values--is perhaps the most reliable method.  How-
ever, loading values must have been determined for the area of interest
or for like areas in order  for the method to be useful.  Such values are
not available in many irrigated areas.  The measurement of salinity loads
requires extensive monitoring and analysis, which are beyond the resources
of many irrigation projects.  As mathematical models are developed for
predicting salinity from irrigation return flow, it is likely that their
outputs can be used to obtain valid estimates of salinity loading values
from specific areas.

2.4.5  Acid Mine Drainage

Two options are presented for acid mine drainage emissions to surface
waters — source to stream approach, and stream to source approach.  The
loading functions are discussed in Section 7.0 of the handbook.

Source to stream approach - The source to stream loading function has
been developed based upon statistical analysis of acid mine drainage
data in the Monongahela River Basin.  The loading function describes
the potential acid formation from a "typical" mine, and allows for the
neutralization of part of the acid by background alkalinity.  Defini-
tion of the typical mine was established by regression analysis.  The
source to stream loading function is:
     Y(AMD)  =  N[Ka-(IAU +  IIV +  1^ +  IIS)  - Kb-Q(R).C(Alk)BG]    (7-1)

where       Y(AMD) = acid mine drainage load

                 N = total number of sources which are potential emitters
                       of mine drainage

IAU, IAS, Ijy,  IIS = load index values, Table 7-2

            ^a> Kb = constants determined from regression analysis,
                       Table 7-1
                                  17

-------
              Q(R) = flow as annual average runoff

          C(Alk)BQ = concentration of background alkalinity,  Figure 7-1

This loading function depends upon knowledge of the numbers of mines
in various categories (underground and surface, active and inactive)
and upon the neutralization potential of background.  It should be ap-
plicable to all mines associated with pyritic wastes whether they be
coal or metal ore mines.  There is a good deal of uncertainty in its
application to metal mines, since the function was developed for the
Appalachian coal regions of the United States, and becomes less ac-
curate as one moves westward into coal areas of the midwest and west.

Stream to source approach - The stream to source loading function for
acid mine drainage is based upon comparison of sulfate loadings in
streams to sulfate contributions from background and from point sources.
The rationale for this approach lies in the fact that sulfate is a prin-
cipal product of acid mine drainage.  The loading function is:


           Y(AMD) = a-A-Q(R)[c(S04) - C(S04)BG - C(SC>4)pT]       (7-8)

      or   Y(AMD) = a-Q(Str)[c(S04> - C(SC>4)BG - 0(804)^]

where       Y(AMD) = acid mine drainage

                 A = area containing mine drainage sources

              Q(R) = flow as annual average runoff

            Q(Str) = flow as streamflow

            0(804) = sulfate concentration in surface waters

          C(S04)BQ = sulfate concentration in surface waters attribu-
                       table to background, Figure 7-2

          C(SO,)pT = sulfate concentration from point sources

                 a = dimensional constant

The stream to source approach does not allow  for neutralization of acid
mine drainage between the point where it is formed and the point where
it is discharged.  Thus, high values may be estimated in some cases.
                                  18

-------
The main uncertainty in the function is contribution of sulfate from
background and point sources.  Knowledge of the area under considera-
tion  is essential for accuracy.  If the function is used in primarily
rural areas, the point source sulfate contribution term can be ignored.

2.4.6  Heavy Metals and Radiation (see Sections 8.0, 9.0 and 11.0)

Nonpoint sources of heavy metals and radiation include sediment, abandoned
mine sites, chat piles, tailings piles, urban runoff, and landfill.

Methods for estimation of emissions of heavy metals from urban runoff
and landfill are presented in Sections 9.0 and 11.0 (see 2.4.7 and
2.4.9), respectively.  Methods for estimation of loads from mines and
mining refuse are presented in Section 8.0.  The latter methods are
summarized below.

In general, methods for calculating loads of heavy metals and radio-
activity are relatively crude.  Their principal usefulness likely is
limited to pinpointing of problem areas, so that needs for analysis
in greater depth can be determined.

One option for estimation of loads assumes that data on individual
sources are available, and can be summed to a total load (Option I
below).  A second option assumes no source data are available and
historical data (or handbook user-generated data) on streamflow or
runoff and on concentrations of the pollutants in the flows are com-
pared with information on background levels of the pollutants (Option
II below).

The special case of  heavy metals associated with sediment emissions is
considered in Section 8.5.

     Option I;  Summation of Loads From Individual Sources
                   Y(HM, RAD) = a°EQn'C(HM, RAD)n       (8-1) and (8-2)
                                  n

where    Y(HM, RAD) = heavy metal (HM) load or radioactivity (RAD) load

        C(HM, RAD)n = heavy metal or radioactivity concentration emitted
                        from the  nt"  source

                 Qn = flow from the  ntn  source

                  a = conversion factor, Table 8-1
                                   19

-------
       Option II:  Estimation From Data on Runoff or Streamflow


            Y(HM, RAD) = a.A.Q(R)-[C(HM, BAD) - C(HM, RAD)BG] (8-3) and  (8-5)

      or    Y(HM, RAD = a.Q(Str)[c(HM, RAD) - C(HM, RAD)BG]   (8-4) and  (8-6)

  where    Y(HM, RAD) = heavy metal (HM) load or radioactivity (RAD) load

           C(HM, RAD) = concentration of heavy metal or radioactivity : n
                          runoff or streams

         C(HM, RAD)gp = heavy metal or radioactivity concentration emitted
                          from background, Figures 8-1 through 8-7

                    A = area containing nonpoint sources

                 Q(R) = flow as average annual runoff

               Q(Str) = flow as Streamflow

                    a = conversion constant, Table 8-3


     Heavy Metals Attached to Sediment

The special case of heavy metals in the soil matrix carried into surface
waters with sediment is treated by the following method.  The method is
discussed in detail in Section 8.5.

                    Y(HM)S = a.Cs(HM)-Y(S)E                         (8-7)

where   Y(HM)  = yield of heavy metals in sediment
             O

        C (HM) = concentration of heavy metals in the eroded soil
         O

         Y(S),., = sediment yield as defined by Eq. (3-1).
             hi

             a = conversion factor
                                    20

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2.4.7  Urban and Related Sources (see Section 9.0^

An extensive amount of data have been assembled and evaluated in several
recent studies.  These data comprise loading values for pollutants in ur-
ban and highway runoff.  Pollutants documented include solids (sediment),
BOD, COD, phosphorus, nitrate, ammonia, coliforms, organic nitrogen, and
heavy metals.

The loading functions are summarized below.  The data on which the func-
tions are based have been analyzed for standard error, which usually is
relatively low (< + 50%)„  Discretion must be exercised in extrapolations
to urban areas for which no data exist.

2.4.7.1  Urban runoff -

     Solids
                          Y(S)U = L(S).Lst                        (9-D
                                  21

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where    Y(S)  = solid loading from urban nonpoint sources

           L(S) = solid loading rate, Table 9-1

           Lst = street curb-length

     Other pollutants


                        Y(i)u = a.Y(S)u-C(i)u                     (9-2)

where    Y(i)  = loading of pollutant  i  from urban nonpoint sources

             a = dimensional constant

         Y(S)  = urban solid loading

         C(i)n = concentration of pollutant  i  in solids, Tables 9-1, and
                   9-2

2.4.7.2  Road Traffic -


                        Y(i)tr = Y(i)-LH-TD-AX                    (9-4)

where   Y(i)tr = loading of pollutant  i  from road traffic

           Y(i) = deposition rate of pollutant  i  , Table  9-5

            LH = length of highway

            TD = traffic density

            AX = average number of axles per vehicle

 2.4.7.3   Street and highway deicing salts -


                           Y(DI) = a-b-DI                         (9-5)

where     Y(DI) = salt  loading

             a = dimensional constant

             b = attenuation factor

            DI = amount of deicer applied, Appendix H

                                  22

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2.4.8  Livestock in Confinement (see Section 10.0)

Data on ranges of concentrations of pollutants in feed lot runoff have
been combined with methods for estimating runoff quantities from feed-
lots.  The overall methodology is crude, and differences between actual
and estimated loads may be great.  The precision of the estimates is
also dependent on the accuracy of information on feed lot locations,
areas, and sizes.  Feedlots with runoff control are excluded from the
nonpoint category.

Pollutants covered are sediment, BOD, phosphorus, nitrogen, and coliforms,
                      Y(i)FL = a.Q(FL).C(i)FL.FLd.A              (10-1)

where   ^(^-)pL = l°ading fate of pollutant  i  from a livestock facility

             a = a constant

         Q(FL) = direct runoff from feedlots

        C(i)pT = concentration of pollutant  i  in runoff, Tables 10-1
                   and 10-2

           FLj = delivery ratio, feedlots

             A = area of livestock facility

2.4.9  Terrestrial Disposal

Leachates from wastes disposed on land vary widely in quantity and com-
position, and delivery of leachate-contained pollutants to surface waters
may range from 0 to 100%.  The loading function for these pollutants is
thus very crude, and reasonably accurate results depend greatly on the
availability of site-specific information.  The handbook presents synop-
ses of data on pollutant concentrations in leachates, and suggests a gen-
eral methodology which should be adapted to local or regional needs.

The loading function requires knowledge either of percolating or leach-
ate rates, information on pollutant concentration, and knowledge of site
characteristics which permit estimation of a delivery ratio.
                    Y(i)LF = a-C(i)LF.Q(LF)-LFd-A                (11-1)
                                  23

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where   ^(^LF = l°a(3ing rate of pollutant  i

             a = dimensional factor

        C(i);yr = concentration of pollutant  i  in leachate at site,
                   Table 11-1

         Q(LF) = landfill flow rate (Figure 11-2 gives percolation rates)

           LFj = leachate delivery ratio

             A = area of landfill

2.4.10  Background Emissions of Pollutants (see Section 12.0)

Stream to source approach - The background, or "natural" rates of pollu-
tant emissions is a sensitive, controversial area.  One approach to def-
inition and estimation of background loads is based on the National Hy-
drologic Benchmark Network.  Iso-pollutant maps for various pollutants
have been developed from the Network data.  These may be used to deduce
probable "natural" in-stream pollutant concentrations, or to estimate
delivered loads.


                      Y(i)BG = a.A-Q(R)-C(i)BG                   (12-1)

                 or   Y(i)BG = a.Q(Str)oC(i)BG                   (12-2)

where   Y(i)gG = load of background constituent  i

             a = conversion factor, Tables 12-1 and 12-2

             A = watershed area

          Q(R) = flow as average annual runoff

        Q(Str) = flow as streamflow

        C(i)gQ = estimated concentration  of background constituent  i  ,
                   Figures 12-1 through 12-18, Tables 13-1 and  13-2, and
                   Figures 13-1 and 13-2

The  iso-pollutant maps will not adequately represent many  local, site-
specific, problem areas.  Background concentrations and loads should in
such cases be deduced from local, site-specific data.
                                   24

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Source to stream approach - Where a "natural" condition can be defined,
it is in principal possible to calculate background loadings via use
of loading functions for a specific pollutant.  In Section 12.3 is pre-
sented a method for calculating natural nonpoint emissions of sediment.
The method may be extended to phosphorus, total nitrogen, BOD, and heavy
metals.  It consists of calculation of sediment yields  from a natural  site,
namely,  land with a vegetative cover typical  of that which existed before
man changed that condition.

          Y(S)T,_ = Y(S)  from Eq. (3-1) for natural conditions
              BG       E

         Y(NT)BG = Cs(NT)BG-Y(S)BG-Va

         Y(PT)BG= Cs(PT)BG-Y(S)BG-rp-a

          rN, rp = enrichment ratios

          CS(NT) = concentration of nitrogen in soil

          C (PT) = concentration of phosphorus in soil

               a = dimensional constant


2.5  LIMITATIONS AND ACCURACIES

The estimation of nonpoint pollution is an approximate science, in its
present stage of development.  In some instance the term science is not
appropriate.  The loading functions presented in this handbook should
be adopted and used with this understanding.  In not one case does a
function cover all possible variables and all possible situations.  Par-
ticularly lacking is the capability to follow a dynamic, hour by hour
event  or a  day by day situation, and develop an integrated load
curve which reflects changes with time.  In nearly all cases scien-
tific methods will permit reasonably accurate measurement of gross pro-
cesses in a dynamic event, e.g.,  a rainstorm with its accompanying over-
land runoff and transport of pollutants.   It is not the purpose of this
handbook, however, to present the detail  of measurement methodologies,
for the objective of the work has been to provide a methodology which
will permit estimation of nonpoint pollutant emissions with a minimum
of field measurement and will be dependent primarily  on existing data
and information.

The lack of scientifically derived expressions (or even valid empirical
relationships) has led to the development of estimating procedures
based on averaged data.  In only one case--soil loss—has the accumu-
lated data  been developed into a  currently useful load equation based

                                   25

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on parameters which represent the physical,  phenomena involved  in
generation and transport of the pollutant.   The Universal Soil Loss
Equation (USLE) represents long term average on an annual basis.   It
can be used to predict an assumed single storm event or a series  of
storm events, but factor values for single events or for seasonal events
are not as complete and available as "annual average" factors.   If one
is interested in extremes over a period of years, i.e., the equivalent
of 7 d.ay-10 year low flow, factor values are essentially nonexistent.

The program which generated this handbook has "piggy-backed" the  USLE
to formulate loading functions for nitrogen, phosphorus, organic  mat-
ter, and certain pesticides under certain conditions.  These functions
thus are based on the well established USLE factors and on an extensive
body of data relating to the specific pollutants.  Again, the pollutants
are better treated in average terras than for the nonaverage situation.

The above discussion illustrates a key point regarding the accuracies
and limits of usefulness of the loading functions presented in this
handbook.  The technology is usually adequate to reasonably good  for
predicting averaged pollutant loads.  The spread or range of values
which make up that average is likely to be high, however, aid an esti-
mated accuracy which includes the probable actual extreme loads about
the calculated average will therefore be much worse than the accuracy
in predicting the average.  The estimates of accuracies presented in
Sections 3.0  to  12.0 for the most part are our estimates of the capa-
bility of the loading function to predict an average load, whether it
be an "annual average" or a "30 day-maximum average."  The user should
recognize that any specific real year may be quite atypical with regard
to rainfull quantities, intensities, runoff, vegetative cover and other
factors, and that the actual load may be well outside the specified
accuracies.

It should be emphasized that sufficient data for statistical analyses
are simply not available for statistically valid estimation of accura-
cies.  The reported accuracies are generally best  estimates based on
characteristics of the function, its required data base, and the re-
ported/observed ranges of loads of various pollutants.

Worthy of special mention is the fact that good, area-specific input
data will give much better results than nonspecific or haphazardly
selected data.

Some nonpoint sources are not amenable to treatment by loading func-
tions, for one or more of several reasons:  (1) the source may be so
irregular in occurrence that it can only be described by local personnel;
                                  26

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(2) data on loads may be lacking; and (3) the source itself cannot be
described in terms which can be translated into rates of pollutant
emission.  A list of sources and pollutants which fall in this category
follows:

Roadside erosion
Gully erosion
Landslide, creep
Streambank erosion
Improper manure spreading or dumping
Bacteria from nonurban areas, excepting feedlots
Direct deposition of vegetation in surface waters:  leaf fall, wind
  blown organic matter
Floodwater transport of floodplain debris
Floodwater scouring of floodplains
Salt leakage from oil fields
Drainage-borne pollutants

  Forests
  Wetlands
  Agricultural lands

Nutrients in irrigation return flow
Groundwater contamination with nitrates, metals, bacteria, pesticides
Direct deposition of fertilizers and pesticides in surface waters
Improper disposal of construction and demolition debris
Nonregulated, unauthorized dumping of domestic and industrial wastes

The loading functions and associated guidelines presented in the handbook
vary considerably in sophistication, overall adequacy, demands for data
collection, and requirements for local judgments, technical skills, and
other resources.  Nothing really constructive can be gained by ranking
them by order of adequacy or by other yardsticks, and the limitations
of each have been pointed out throughout the test.  It is appropriate
to point out a situation or two which currently present difficult chal-
lenges .

Perhaps the greatest void consists of the lack of a capability to sys-
tematically describe the movement of pollutants through the earth, from
surface soil into the root zone, to storage in soil and subsoil moisture,
into near surface and deep aquifers, and movement from thence to the
surface as drainage and baseflow in streams.  The transport of pollu-
tants via these processes is little understood.  Nitrate movement via
subsurface routes is inadequately dealt with by current technology,
and is essentially excluded from the loading functions.  Metals,
                                  27

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salts, bacteria, and soluble organic materials are treated generally
with marginally adequate procedures; landfill leachate movement is a
case in point.  Treatment of irrigation return flow, and its load of
salts, nutrients, etc., is a particularly difficult problem; this
problem is better described than are like problems simply because it
has been extensively and capably studied and monitored.
                                 28

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                              SECTION 3.0

                     SEDIMENT FROM SOIL EROSION

3.1  INTRODUCTION

The sediment produced by erosion of sloping lands,  gullies,  and  strearabanks,
and transported to surface water is generally recognized  as  the  greatest
single pollutant from nonpoint sources.  Sediment reduces water  quality and
often degrades deposition areas.  Sediment occupies space needed for water
storage in reservoirs, lakes, and ponds; restricts  streams and  drainageways;
alters aquatic life and reduces the recreational and consumptive use value
of water through turbidity.  More importantly, sediment,  particularly that
produced from eroded topsoil, also carries other water pollutants such as
nitrogen, phosphorus, organic matter, pesticides and pathogens.

Erosion of soil by water can take a variety of forms.   Sheet erosion is the
uniform removal of a thin layer of soil, normally by the  impact  of falling
raindrops.  Channel erosion exists as rill erosion, gully erosion, and
streambank erosion, caused by detachment and transportation  of  sediment by
flowing streams (channels) of water.  Rill erosion is  the result of soil
removal by small concentrations of surface water, such as that  often found
between the rows of cultivated crops planted up and down  slopes.  Channels
formed in rill erosion are small enough to be smoothed completely by cul-
t iva t io n me thod s.

Gully erosion, similar to rill erosion, is also caused by temporary con-
centration of surface runoff.  However, erosion by  gullying  cuts, by defi-
nition, deeply enough into soil/subsoil that channels  so  formed  cannot be
smoothed completely by ordinary tillage tools.

Streambank erosion refers to carrying off of the soil  material  on the
sides of a permanent streambed, including those with intermittent flow,
by the energy of moving water.
                                    29

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Sediments are also produced from mass soil movement,  which is  the downslope
movement of a portion of land surface under the effect of gravity.   Such
movements may take the form of landslide,  mudflow,  or downward creep of an
entire hillside.

This section presents methods for assessing sediment  loading from various
sources.  Sheet erosion and rill erosion are treated  together  as  surface
erosion in Section 3.2; the remaining are  presented in Section 3.3.

3.2  SEDIMENT LOADING FROM SURFACE EROSION

3.2.1  Overview

In general, the most important contributor of sediment nationwide is sur-
face erosion.  Erosion agents, including water, wind, and rain splash,  work
continuously to break down the earth's surface to produce sediment from
cropland, forests, pastures, construction sites, mining sites, road  rights-
of-way, etc.

The basic mechanisms of soil erosion by water consist of:  (a) soil  detach-
ment by raindrops; (b) transport by rainfall; (c) detachment by runoff; and
(d) transport by runoff.—   The damage caused by raindrops hitting the  soil
at a high velocity is the first step in the erosion process.  Raindrops
shatter the soil granules and clods, reducing them to smaller  particles and
thereby reducing the infiltration capacity of soil.  The force of the rain-
drops also carries the splashed soil, resulting in movement of soil  down-
slope.

When the rate of rainfall exceeds the rate of infiltration, depressions on
the surface fill and overflow, causing runoff.  Runoff water breaks  sus-
pended soil particles into smaller sizes,  which helps to keep them in sus-
pension.

3.2.1.1  Factors affecting surface erosion -

Factors which have been considered the most significant in affecting erosion
of topsoil consist of:

1.  Rainfall characteristics,

2.  Soil properties,

3.  Slope factors,
                                    30

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4.  Land cover conditions, and

5.  Conservation practices.

Rainfall characteristics define the ability of the rain to splash and erode
soil.  Rainfall energy is determined by drop size, velocity, and intensity
characteristics of rainfall.

Soil properties affect both detachment and transport processes.  Detachment
is related to soil stability, basically the size, shape, composition, and
strength of soil aggregates and clods.  Transport is influenced by perme-
ability of soil to water, which determines infiltration capabilities and
drainage characteristics; by porosity, which affects storage and movement
of water; and by soil surface roughness, which creates a potential for tem-
porary detention of water.

Slope factors define the transport portion of the erosion process.  Slope
gradient and slope length influence the flow and velocity of runoff.

Land cover conditions affect detachment and transportation of soil.  Land
cover by plants and their residues provides protection from impact of rain-
drops.  Vegetation protects the ground from excessive evaporation, keeps
the soil moist, and thus makes the soil aggregates less susceptible Lo de-
tachment.  In addition, residues and stems of plants furnish resistance to
overland flow, slowing down runoff velocity and reducing erosion.

Conservation practices concern modification of the soil factor or the slope
factor, or both, as they affect the erosion sequence.  Practices for ero-
sion control are designed to do one or more of the following:  (a) dissipate
raindrop impact forces; (b) reduce quantity of runoff; (c) reduce runoff
velocity; and (d) manipulate soils to enhance the resistance to erosion.

3.2.1.2  Effect of man's activities on surface erosion -

Man alters surface erosion primarily by changing cover and altering the
hydraulic system through which the water and sediment are transported.

Activities which impact surface erosion can be categorized into four classes:
cropping practices, silvicultural activities, mining activities, and con-
struction activities.  Depending on the initial status of land and the na-
ture of activity, a wide range of impact can be expected.  Table 3-1 lists
some reported values of the magnitude of the impact.

Surface erosion from croplands - Cropping practices change the soil cover
so that it favors one type of plant and discourages the growth of others.
The practices expose the soil and leave it loose and liable to erosion.

                                    31

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           Table 3-1.  SOME REPORTED QUANTITATIVE EFFECT OF
                   MAN'S ACTIVITIES ON SURFACE EROSION
Initial status

  Forestland


  Grassland


  Forestland


  Forestland


  Forestland


  Forestland

  Row crop

  Pastureland

  Forestland
   Type of
 disturbance

   Planting
  row crops

   Planting
  row crops

   Building
logging roads

 Woodcutting
 and skidding

     Fire
    Mining

 Construction

 Construction

 Construction
    Magnitude of
    impact by the
specific disturbance::*/

     100-1,000
      20-100


       220


       1.6


       7-1,500


      1,000

        10

       200

      2,000
    Reference

    Brown (2)


    Brown (2)


   Megahan (3)


   Megahan (3)


   Ralston and
     Hatchell (4)

Collier et al.  (5)

   USDA/SCS  (6)

   USDA/SCS  (6)

   USDA/SCS  (6)
a_/  Relative magnitude of surface erosion from disturbed surface,  assuming
      "1" for the initial status.  The first row of the table, for example,
      indicates that transforming a forestland into row crops will increase
      surface erosion 100 to 1,000 times.
                                    32

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Soil erosion can be affected by cropping practices  such as  tillage,  irriga-
tion, planting, fertilization, and residue disposition.

Tillage detaches soil and promotes oxidation of organic matter in soils.
These processes decrease aggregation and reduce the infiltration capacity.
Plowing creates a plow pan.  Agricultural machinery compresses the soil,
reducing large-pore space and, consequently, its infiltration capacity.
All this results in higher runoff and erosion rates.

Crop planting varies in its effect on erosion, depending on the species,
the stand density, the distance between the rows, and the direction of the
rows with respect to the slope.  The denser and the more nearly on the
contour the planting is made, the less erosion will result.

Fertilization helps to ensure stands, causes faster and heavier growth,  and
is consequently a help in protecting the soil and in creating beneficial
residues.  Manure can serve both as a fertilizer and a ground cover.

Crop residues help to protect soil from detachment by rainfall and runoff.
They also contribute to making up organic matter in soils and therefore
increase soil stability against water erosion.

Surface erosion from forest lands - Forestland generally can be character-
ized by:  (a) a vegetative canopy above the ground surface; (b) a layer  of
decayed and undecayed plant remains on the surface; and (c) a system of
living and dead roots within the soil body.  These conditions insulate the
soil against the impact of rain, obstruct overland flow, and retard move-
ment of soil by water action.  These conditions reduce erosion and sediment
production to a minimum.

Major causes of erosion on forest lands include:

1.  Damage to cover from cutting, logging, and reforestation activities,
and construction of roads and fire lanes.

2.  Damage to cover because of fire, grazing, and recreational activity.

3.  Damage on land reverting to forest cover from other land use, such as
strip mines, and on which adequate cover conditions have not developed.

Surface erosion from  pasturelands - The dense cover of grasses, legumes,
and other low growing plants is generally effective in protecting the soil
from erosion by rainfall and runoff.  Consequently, the amount of erosion
from a well-managed pasture is small.
                                   33

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Overgrazing is the major cause of accelerated erosion on pasturelands.   The
grazing animals may eat the forage down to the ground, lessening the effec-
tiveness of plants in intercepting the raindrops.   Open spots on pasturelands
can erode as rapidly as cultivated fields.

Surface erosion from construction sites and mining sites - Construction and
mining activities involve extensive earth-moving operations.  In these  di-
verse earth-moving activities, the natural protective ground cover is dis-
turbed; compacted soils are dislodged and redistributed; highly erosive
soils from the deeper horizons are exposed to the elements; shallower,
smoother terrain is recontoured to steeper slopes; and runoff is often in-
creased and accelerated.

Sediment production from construction sites differs from that caused by
other types of nonpoint sources in that it is generally of limited duration.
Agricultural operations continue to produce sediment-containing runoff year
after year, while intensive sediment yields from a construction project
typically last from a few weeks to a few years, during which time the areas
of exposed soils may be well stabilized by vegetation, chemical application,
or other control measures, either permanent or temporary.

3.2.1.3  Sediment delivery -

Sediment loadings to surface waters are dependent on erosion processes  at
the sediment sources and on the transport of eroded material to the recep-
tor water.  Only a part of the material eroded from upland areas in a water-
shed is carried to streams or lakes.  Varying proportions of the eroded
materials are deposited at the base of slopes, in swales, or on flood plains.

The portion of sediment delivered from the erosion source to the receptor
water is expressed by the delivery ratio.

Factors affecting sediment delivery ratio - Many factors influence the sedi-
ment delivery ratio.  Variations in delivery ratio may be dependent on some
or all of the following factors and others not identified.  The reader is
referred to References 7 and 8 for more detailed discussion of the subject.

     Proximity of sediment sources to the receptor water—e.g., channel-
     type erosion produces sediment that is immediately available to the
     stream transport system, and therefore has a high delivery ratio.
     Materials derived from surface erosion, however, often move only short
     distances and may lodge  in areas remote from the stream, and therefore
     have a low delivery ratio.

     Size and density of sediment sources--when the amount of sediment
     available for transport  exceeds the  capability of the runoff transport
     system, deposition occurs and the sediment delivery ratio is decreased.

                                    34

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     Characteristics of transport system—runoff resulting from rainfall
     and snowmelt is the chief agent for transporting eroded material.
     The ability to transport sediment is dependent on the velocity and
     volume of water discharge.

     Texture of eroded materjal--in general,  delivery ratio is  higher for
     silt or clay soils than for coarse textured soils.

     Availability of deposition areas—deposition of eroded material mostly
     occurs at the foot of upland slopes, along the edges  of valleys and in
     valley flats.

     Relief and length of watershed slopes—the relief ratio of a watershed
     has been found to be a significant factor influencing the  sediment-
     delivery ratio.  The relief ratio is defined as the ratio  between  the
     relief of watershed between the minimum and maximum elevation, and the
     maximum length of watershed.

3.2.2  Sediment Loading Function for Surface  Erosion

Sediment loading is defined in this handbook  as the quantity of soil mate-
rial that is eroded and transported into the  watercourse.   Sediment loading
is dependent on (a) on-site erosion, and (b)  delivery, or  the ability of
runoff to carry the eroded material into the  receptor water.

The sediment loading function is based on concepts of the  mechanisms of
gross erosion and sediment delivery.  The Universal Soil Loss Equation—'
(USLE) is chosen to predict the on-site surface (including sheet and rill)
erosion, for the following reasons:

1.  This equation is applicable to a wide variety of land  uses  and climatic
conditions.

I.  It predicts erosion rates by storm event  and season, in addition to
innual averages.

5.  An extensive nationwide collection of data has been made for factors
Included in the equation.

?he sediment loading function has the form:
                     n
             Y(S)E = E  [Ai.(R.K.L.S.C-P-Sd)i]                      (3-1)
                                    35

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where  Y(S)  = sediment loading from surface erosion,  tons/year
           Hi

           n = number of subareas in the area

Source areal factor:

          A.- - acreage of subarea i, acres

Source characteristic factors:

           R = the rainfall factor, expressing the erosion potential *. -
                 average annual rainfall in the locality, is a summation
                 of the individual storm products of the kinetic energy
                 of rainfall, in hundreds of foot-tons per acre, and the
                 maximum 30-min rainfall intensity, in inches per hour,
                 for all significant storms, on an average annual basis

           K = the soil-erodibility factor, commonly expressed in tons
                 per acre per R unit

           L = the slope-length factor, dimensionless ratio

           S = the slope-steepness factor, dimensionless ratio

           C = the cover factor, dimensionless ratio

           P = the erosion control practice factor, dimensionless ratio

          S, = the sediment delivery ratio, dimensionless

The R factor in the above equation can be expressed in metric units
[(hundreds of meter-metric tons/ha-cm) times (maximum 30-min intensity,
cm/hr)] by multiplying  the English R values by 1.735.  The factor for
direct conversion of K  to metric-tons per hectare per metric R unit is
1.292.12/

Equation  (3-1) can be used to predict sediment loading resulting from
sheet and rill erosion  from noncroplands as well as croplands.  It does
not predict sediment contributions from gully erosion, streambank erosion,
or mass soil movement.

In Sections 3.2.3 and 3.2.4 below, procedures and an example will be pre-
sented for estimating sediment  loadings based on the above described  load-
ing function.  Section  3.2.5  presents data and data sources of source char-
acteristic factors.  Methodology  for predicting minimum  and maximum erosion
rates is  presented  in Section 3.2.6.  Section 3.2.7 presents data sources
for source areal  factors.

                                    36

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3.2.3  Procedure for Use of the Sediment Loading Function

The following procedure is to be used to calculate sediment loading from a
designated area based on the loading function in Eq. (3-1).  The terminology
applies to agricultural lands, but the procedure is applicable to other non-
point sources.  This procedure is shown as a flow diagram in Figure 3-1.

Estimation of surface erosion should be made for each land-use type.  For
a land-use type, if 90% or more of the area is made up of one soil type,
one may calculate soil loss for the land use based on that soil type.  If
there is less than 90% of one soil type, one should calculate soil loss for
each soil type that makes up at least 10%, of the land use, and then obtain
a weighted average for the entire land-use area.—'

Obtain basic land data -

Total area

Land use acres in the area

     Cropland,
     Pastureland,
     Woodland, etc.

Soil characteristic information including soil name, soil texture, etc.,
for each land use.

Information about canopy and ground cover condition for each land use.

Topographic information, such as slope gradient and slope length of the
land.

Information about the type and extent of conservation practices.

Determine factor values -

Determine R:  Use the appropriate isoerodent map (see Figure 3-2 and 3-3),
or procedures described in the Section 3.2.5.1 for the western United States.

Obtain K:  Obtain the K values of the named soils from published lists of
SCS, or determine K values on  nomographs  (Figures B-l and B-2 in Appendix B)
from soil properties.

Determine LS:  Refer to Figures 3-7 or 3-8 for uniform slopes, or Figure 3-9
for irregular slopes.
                                     37

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Obtain C:  Refer to the appropriate table for the crop or ground cover con-
dition for C value in Section 3.2.5.4.

Obtain P:  Refer to Table 3-7.

Determine sediment delivery ratio, Sj:  Obtain from local sources or from
Figure 3-10 by using drainage density and soil texture for homogeneous
watersheds.

Calculations -

Multiply R, K. LS, C, and P, and S^ to obtain sediment loadings for crop-
land, pasture, and woodland in annual yields per unit area of source.

Multiply loading rates by source sizes (total hectares or acres) for crop-
land, pastureland, and woodland to obtain total loading per source.

Sum source loadings calculated in the item above to obtain total loading
from land uses (total loading in the watershed will require summation of
other sources within the watershed).

3.2.4  Example of Assessing Sediment Loading from Surface Erosion

Assume a watershed area of 830 acres in Parke County, Indiana (west central).
Compute sediment loading from the watershed from sheet and rill erosion in
terms of average daily loading, maximum daily loading during a 30-consecutive-
day period, and minimum during a 30-consecutive-day period.

Basic information -
                                                                            •
Land use types:

     Cropland
     Pasture
     Woodland

Delivery ratio:  6070

Land information:

     Cropland - 180 acres

          Continuous corn
          Conventional tillage, average yield ~ 40 to 45 bu
          Cornstalks are left after harvest
          Contour strip-cropped

                                    39

-------
          Soil - Fayette silt loam
          Slope - 6%
          Slope length - 250 ft

     Pasture - 220 acres

          No appreciable canopy
          Cover at surface - grass and grass like plants
          Percent of surface or ground cover - 80%
          Soil - Fayette silt loam
          Slope - 6%
          Slope length - 200 ft

     Woodland - 430 acres

          Medium stocked
          Percent of area covered by tree canopy - 50%
          Percent of area covered by litter - 80%
          Undergrowth - managed
          Soil - Bates silt loam
          Slope - 12%
          Slope length - 150 ft

Maximum and minimum rates - The ratios between 30-day maximum and average
daily rates, and 30-day minimum and average daily rates for continuous corn,
pasture, and woodland for this area are evaluated in Section 3.2.6.  They
are:

Continuous corn:  Ratio--30-day maximum/average daily = 3.2
                  Ratio--30-day minimum/average daily = 0.25

Pasture and woodland:  Ratio—30-day maximum/average daily = 2.5
                       Ratio--30-day minimum/average daily = 0.25

Calculations of loading per acre -

Cropland:

     R = 200 (Figure 3-2)

     K = 0.37 (USDA-SCS)

     LS = 1.08 (Figure 3-8)

     C = 0.49 (Section 3.2.5.4)
                                    40

-------
     P = 0.25 (Table 3-7)




     Sd = 0.60




Calculate average annual loading per acre.




Y(S)     n = 200 x 0.37 x 1.08 x 0.49 x 0.25 x 0.6
 x  annual
           = 5.87 tons/acre/year




Calculate average daily loading per acre.




Y(S)avg_ daily = 5.87 tons/acre/year -f- 365 days


               = 0.016 tons/acre/day = 32 Ib/acre/day




Calculate maximum loading per acre during a 30-consecutive-day period,





Y(Sho-day max = °-016 tons/acre/day x 3.2

               = 0.052 tons/acre/day = 104 Ib/acre/day




Calculate minimum loading per acre during a 30-consecutive-day period,





Y(S)    , v   .  = 0.016 tons/acre/day x 0.25


               = 0.004 tons/acre/day = 8 Ib/acre/day




Pasture:




     R = 200




     K = 0.37




     LS = 0.95




     C = 0.013 (Table 3-4)




     P = 1.0




     Sd = 0.60




Y(S)       = 200 x 0.37 x 0.95 x 0.013 x 1.0 x 0.6
    annual

           = 0.548 tons/acre/year = 1,100 Ib/acre/year





        . daily = °-548 tons/acre/year -f 365 days

               = 0.0015 tons/acre/day = 3 Ib/acre/day
                                    41

-------
Y(S)30-day max = °-0015 tons/acre/day x 2.5

               = 0.0038 tons/acre/day = 7.6 Ib/acre/day




Y(S)on A    •   = 0.0015 tons/acre/day x 0.25
 x 'jU-day mm                      J

               - 0.0004 tons/acre/day = 0.8 Ib/acre/day




Woodland:




     R = 200




     K = 0.32




     LS = 2.75




     C = 0.003 (Table 3-5)




     P = 1.0




     Sd = 0.60





Y(S)annual = 2o° x °-32 x 2-75 x °-003 x 1-° x °-60

           = 0.3168 tons/acre/year




Y(s)avg. daily = °-3168 tons/acre/year •=- 365 days

               = 0.0009 tons/acre/day = 1.8 Ib/acre/day




Y(S),n  ,       = 0.0009 tons/acre/day x 2.5
    30-day max                      J
               = 0.0022 tons/acre/day = 4.4 Ib/acre/day




Y(S)-A  ,    .  = 0.0009 tons/acre/day x 0.25
    30-day mm
               = 0.0002 tons/acre/day = 0.4 Ib/acre/day




Calculations of gross loading -




Average daily:




     Cropland - 180 acres x 0.016 tons/acre/day =  2.88 tons/day




     Pasture - 220 acres x 0.0015 tons/acre/day =  0.33 tons/day




     Woodland -430 acres x 0.0009 tons/acre/day =  0.39 tons/day





          Total                  Y(s)avg.  total =  3'60 tons/daY
                                     42

-------
30-day maximum:

     Cropland - 180 acres x 0.052 tons/acre/day = 9.36 tons/day

     Pasture - 220 acres x 0.0038 tons/acre/day = 0.84 tons/day

     Woodland -430 acres x 0.0022 tons/acre/day = 0.95 tons/day

          Total            Y(S)30-max total    = U-15 tons/day

30-day minimum:

     Cropland - 180 acres x 0.004 tons/acre/day =0.72 tons/day

     Pasture - 220 acres x 0.0004 tons/acre/day = 0.09 tons/day

     Woodland -430 acres x 0.0022 tons/acre/day = 0.09 tons/day

          Total            Y(S)30.day rain total = 0.90 tons/day

3.2.5  Determination of Source Characteristic Factors

3.2.5.1  The rainfall factor (R) -

R is a factor expressing the erosion potential of precipitation in a locality.
It is also called index of erosivity, erosion index, etc.  It is the summa-
tion of the individual storm products of the kinetic energy of rainfall (de-
noted by E), and the maximum 30-min rainfall intensity (denoted by I) for
all significant storms within the period under consideration.  The product
El reflects the combined potential of raindrop impact and runoff turbulence
to transport dislodged soil particles from the site.?./

Average annual R for the eastern United States - Values of average annual
rainfall-erosivity index, R, for the 37-state area east of the Rocky
Mountains have been determined and plotted in the map reproduced in Figure
3-2.2/  On this map, the lines joining points with the same erosion index
value are called isoerodents.  Points lying between the indicated iso-
erodents may be approximated by linear interpolation.

Values of R factors in the mountainous areas west of the 104th meridian were
not included in Figure 3-2 because of the sporadic rainfall pattern in the
mountains.  Values of the erosion index at specific areas can be computed
from local recording rain gage records with the help of a rainfall-energy
table and the computation procedure presented by Wischmeier and Smith.—
                                    43

-------
  106° 104° 102° 100° 98° 96°  94° 92°  90° 88° 86° 84° 82°  80°  78°  76°  74°  72°  70°  68°   66°
                                                                                         48'
                                                             82°   80°    78°   76
104°  102°   100°  98° '  96° '   94°   92°   90
    Figure 3-2.   Mean annual values of  erosion  index  (in English units)
                     for  the eastern United States^-'
                                          44

-------
Average annual R for Hawaii - Isoerodents for most islands of Hawaii have
been developed and are shown in Figure 3-3. 1!/

Methods for developing annual R values for the western United States -
Methods are described in Conservation Agronomy Technical Note No. 32, USDA/
SCS, Portland, Oregon, September 1974.il/

The index of R for some portions of this region is the combined effect of
rainfall and snowmelt, designated by R  and R , respectively.  The R  fac-
tor is important in Areas A-l, B-l, and C (refer to Table 3-2 and Figure
3-4).  The R  factor is important in all areas.

Annual R  factor:  The annual R  factor is obtained by using as base the
2-year, 6-hr rainfall (2-6 rainfall).  Relationships  between Rr and 2-6
rainfall vary to conform to specific local climatic characteristics.  These
relationships are designated as Type I, Type IA, and  Type II, and are shown
in Figure 3-5.  Specific areas applicable to these curves are shown in
Figure 3-6.  Type I curve is for the central valley and coastal mountains
and valleys of southern California.  Type IA curve applies to the coastal
side of the Cascades in Oregon and Washington, the coastal side of the
Sierra Nevada Mountains in northern California, and the coastal regions of
Alaska.  Type II curve applies to the remainder of the region.  For 2-6
rainfall data, refer to Technical Paper No. 40, U.S.  Department of Commerce,
Weather Bureau, Washington, D.C., May 1961, or other  suitable rainfall fre-
quency analysis reports.

Annual RS factor:  To obtain the annual R  factor for a given location,
obtain the average annual total precipitation by snowfall (in inches of
water depth) and multiply it by the constant 1.5 to give annual R .
                                                                 s

Sources of snowfall data:  The 1941 Yearbook of Agriculture, USDA,
Washington, D.C.; "Climates of the States ," Water Information Center, Inc.,
Port Washington, New York, 1974; data resulting from the Western Federal-
State-Private Cooperative Snow Surveys, coordinated by SCS/USDA, Portland,
Oregon; or other equally suitable precipitation records.

Data on snow density is necessary to convert depth of snow to depth of
meltwater.  Snow at the time of fall may have a density as low as 0.01 and
as high as 0.15 g/ml.  The average snow density for the United States is
taken to be 0.10.1^-'   If snowfall is recorded as inches of precipitation,
no conversion is required.
Annual R factor:  The annual R factor for the western United States is the
summation of effect of rainfall, R^ ,  and snow
                                  X.
significant, values of R and Rr are the same.
summation of effect of rainfall, R^ ,  and snowmelt, R0 .   Where R0 is not
                                  X.                 o          S
                                    45

-------
 CO
 CO

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        Table 3-2.   APPLICABILITY OF Rr AND Rs FACTORS  IN THE  AREAS
                      WEST OF THE ROCKY MOUNTAINS-i^/
     Areas
(see Figure 3-3)

      A-l
      A-2
      A-3
      A-4
      B-l
    Typical  locations

Washington,  Idaho, Nevada,
California,  western Utah

Cascades, Sierra, Tetons of
Idaho, Wasatch Mountains

West of Cascades, San Joaquin
Valley, west of Sierras

Areas  of  southern California,
east of Santa Annas, southern
Nevada, intermountain Nevada,
Salt Lake area, Utah.
Western Montana, Colorado,
eastern Utah, high elevations
of Arizona
Rr

XS./


X
X
                                                    X
RS

X

 b/
      B-2
Great plains area of eastern      X
Montana, Wyoming, Colorado
(includes gently sloping
mesas and uplands at lower
elevations of Monticello,
Utah area)

Rainfall during summer is         X
high; high elevations
  a/  X needed.
  b/  - Not needed.
                                    47

-------
                                                      SCALE
                                                O   50  100 150 200 MILES
Figure 3-4.   Soil moisture - soil  temperature regimes
                of the western United StatesJL^/
                            48

-------
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                                                                                X
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                                                                                      .d
                                                                                      4J
                                                                                      X
                                                                                      CU
                                                                                      TJ
                                                                                      C
                                                                                      •H
 >
•H
 CO
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 P
 QJ
                                                                                         d
                                                                                         •H

                                                                                         W
                                                                                         (1)
                                                                                         0,
                                                                                         >,
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        LEGEND - Storm Distribution
        I     | TYPE IA
              TYPE I
              TYPE I
                                                                          13 /
Figure  3-6.   Storm  distribution  regions in  the  western United States—
                                       50

-------
Example for computing Rr, R ,  and R:  Assume that Minidoka County, Idaho,
                           s
is the area under consideration.  The 2-6 rainfall for this area is 0.75 in.
Using the Type II curve of Figure 3-5, which applies to this area, the Rr
factor is 12.  The annual average snowfall for this area is 36 in., which
is equivalent to 3.6 in. of precipitation.  The annual R  factor, therefore,
                                                        S
is 3.6 x 1.5 = 5.4. The annual R factor is obtained by adding Rr and Rg, or
12 + 5.4 = 17.4, rounding to 20.

Monthly distribution of R factor - The monthly distribution of the erosion
index for the 37 states east of the Rocky Mountains has been reported in
USDA-ARS Agriculture Handbook No. 282.-'   The erosion index distribution
curves are reproduced and shown in Appendix A.  Average monthly erosion in-
dex values are expressed as percentages of average annual values and plotted
cumulatively against time.

The monthly distribution of erosion index for the islands of Hawaii also has
been developed.—   These curves are shown in Appendix A.

For the areas west of the Rockies in the continental United States, the
monthly distribution of erosion index R is the summation of R  and Rs.
Where Rg values are not needed, the R and Rr curves are the same.

As of June 1974, the monthly R distribution curves for portions of the area
had been made available.^-'  The reader should contact the state Soil
Conservation Service for such information.  Procedures suggested by SCS for
computing and plotting monthly R distribution curves for the western United
States are described in Appendix A.

3.2.5.2  The soil-erodibility factor (K) -

K factor is a quantitative measure of the rate at which a soil will erode,
expressed as the soil loss (tons) per acre per unit of R, for a plot with
9% slope, 72.6 ft long, under continuous cultivated fallow.

K factors for topsoils, as well as subsoils, for most soil series have been
developed.  Values of K for soils studied thus far vary from 0.12 to 0.70
tons/acre/unit R.

The K values for named soils at different locations of the nation can be
obtained from the regional or state offices of the Soil Conservation Service.

K values of soils can be predicted from soil properties.  In Appendix B of
this handbook, two nomographs are presented from which K values may be de-
termined for topsoils and subsoils when the governing soil properties are
known.
                                    51

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3.2.5.3  The topographic factor (LS) -

Soil loss is affected by both length (L) and steepness of slope (S).   These
factors affect the capability of runoff to detach and transport soil  material,

The slope length factor is the ratio of soil loss from a specific length of
slope to that length(72.6 ft) specified for the K factor in the equation.
Slope length is defined as the distance from the point of origin of overland
flow to either of the following, whichever is limiting, for the major part
of the area under consideration:  the point where the slope decrease::  -> the
extent that deposition begins; or the point where runoff enters a well-
defined channel that may be part of a drainage network, or a constructed
channel that may be part of a drainage network, or a constructed channel
such as a terrace or diversion.  Slope length can be determined accurately
by on-site inspection of a field, or by measurements from aerial photographs,
or topographic maps.  When theland is terraced, the terrace spacing should
be used.  All slope lengths are compared to a slope length of 72.6 ft, which
has a factor value of 1.

The slope gradient or percent slope factor is the ratio of soil loss  from
a specific percent slope to that slope (9%) specified for the K value in
the ULSE.  A 9% slope has a factor value of 1.  Slope data may be obtained
from topographic maps, engineering or land level surveys, and other sources.
A widely used method is to estimate slope from soil survey maps in which
the soils have been mapped by slope range.

The slope length (L) and slope gradient (S) are combined in the USLE into
a single dimensionless topographic factor, LS, which can be evaluated using
a slope-effect chart.

Slope-effect charts for uniform slopes - The slope-effect chart in Figure
3-7 is designed for the following areas shown in Figure 3-4:  A-l in
Washington, Oregon, and Idaho; and all of A-3.i^/

For the remainder of the U.S., the slope-effect chart, Figure 3-8, is to
be used.ll/

Slope-effect charts in Figure 3-7 and 3-8 can be used when uniform slopes
are assumed.  The following steps are to be used for obtaining LS values
from these charts:

1.  Enter the chart on the horizontal axis with the appropriate value of
slope  length.

2.  Follow the vertical line  for  that slope  length to where it intersects
the curve for the appropriate percent slope.

                                    52

-------
                              Slope  Length, Meters
          20    30  40   60  80100  150200  300 400  600  800
    40.0



    20.0



    10.0


     6.0

     4.0
 £   2.0
 o
     1.0


     0.6

     0.4



     0.2



     0.1
                                       (Slope%)
-~ 60
   50
"Z- 45
-* 40
*~ 35
*~ 30
^ 25
   20
+~ 18
^ 16
*~ 14
-- 12
.- 10
                                                 .- 6
                                                           ,^ 4
                                                 .— 2
                                                 .-0.5
70   100       200       400  600    1000

                     Slope Length,  Feet
                                                      2000
Figure 3-7.  Slope effect chart applicable to Areas A-l in Washington,
               Oregon, and Idaho and all of A-3^?-a->'
£/  See Figure 3-4.
b_/  Dashed lines are extensions of LS formulae beyond values tested in
      studies.
                                53

-------
20.0
10.0
 8.0
 6.0
 4.0
 3.0
 2.0
 1.0
 0.8
 0.6
 0.4
 0.3
 0.2
 0.1 l«^
      3.5    6.0    10
                 Slope Length,  Meters
                20       40    60     100
200
400   600
    10
20       40    60     100      200      400   600   1000     2000
                   Slope Length,  Feet
     Figure  3-8.   Slope—effect  chart for areas where Figure 3-7 is not
    _a/   The  dashed lines  represent  estimates for slope dimensions beyond
           the  range of  lengths  and  steepnesses for which data are available.
                                        54

-------
3.  Read across the point of intersection to the vertical axis.   The number
on the vertical axis is the LS value.

Slope-effect charts for irregular slopes - An irregular slope should be
divided into a series of segments such that the slope gradient within each
segment can be treated as uniform.  The slope segments need not be of
equal length.  The total soil loss from the entire slope is calculated
based on the effective LS value for the entire length of the irregular
slope.

A family of curves shown in Figure 3-9 was designed to facilitate the de-
termination of the LS factor for the irregular slopes ranging from 2 to
20%.  The quantity plotted on the vertical scale is designated by the sym-
bol U.  Slope lengths, designated by \, are plotted on the horizontal scale.

Assume an irregular slope with n segments illustrated as follows:
where     X^ = distance from the top of the entire slope (the point at which
                 overland flow begins) to the lower end of the jth segment

        X- i = length of entire slope above segment j

          Xg = overall slope length

          Sj = the slope gradient of segment j,  in percent

The steps taken for calculating LS for irregular slopes using Figure 3-8 are:

1.  Enter on the horizontal axis with the value  of X-_i (the slope length
above segment j).

2.  Move vertically to the curve with the appropriate percent slope for
segment j.

                                    55

-------
   3
10001
 900
 800

 700
 600

 500

 400


 300
 200
.28 4   5   6  7  8  9 10
   X (METERS)
20     30   40  50 60 708090100
200 250
           I  F  I  I
 100
  90
  80
  70

  60

  50

  40


  30
  20
  10
                             Steepness of
                             Slope Segment:   20°/c
                                                                       i  i  i i   i
   10
           20     30    40  50  60 708090100
                                     X(FEET)
                    200    300   4   5678
                                                                157
    Figure 3-9.   Slope  effect  chart  for irregular  slopes—
                                      56

-------
3.  Read on the vertical scale the value of Uj-.

4.  Enter the figure with the value of X. (the  distance  from the  top  of
the entire slope to the lower end of the jth segment), repeat Steps  1 through
3 to obtain the value of U^ ..

5.  Subtract U-• fromU,..

6.  Repeat Steps 1 through 5  for each of the slope segments.

7.  Sum n values of Uo • - U,  ., divide the sum by  Xe (the overall  slope
length).  The result is the effective LS value  for the entire length  of  the
irregular slope.

Examples of the use of the above procedure to calculate  LS factors  for ir-
regular slopes are given in Appendix C of this  handbook.

The percentage of the total sediment yield that comes  from each of  the n
segments can be obtained through a similar procedure.  The relative sediment
contribution of segment j, assuming constant soil erodibility for the entire
slope, is given by:

                              U2i - Uli
                           .
For constructed slopes or mined slopes that cut into successive soil hori-
zons, the soil erodibility K may vary considerably from upper to lower parts
of a slope.  When variations in slope gradient are associated with varia-
tions in soil erodibility along an irregular slope, K and U2 - U^ must be
combined as follows to estimate the relative sediment contribution of seg-
ment j.
K<
                                '  (U2j  '
                          n
                         jiiKJ
3.2.5.4  The cover management factor (C) -

In the ULSE, the factor C represents the ratio of soil quantity eroded from
land that is cropped or treated under a specified condition to that which
                                   57

-------
is eroded from clean-tilled fallow under identical slope and rainfall condi-
tions.  C ranges in value from near zero for excellent sod or &. well-
developed forest to 1.0 for continuous fallow,  construction areas,  or other
extensively disturbed soil.

Factor C for croplands - In order to evaluate the cover management  factor
for crops, five crop stage periods have been selected for relative  uniformity
of cover and residue effects within each period.   These five periods are
defined as follows:2/

Period F:  Rough fallow - Turn plowing to seeding.

Period 1:  Seedbed - Seeding to 1 month thereafter.

Period 2:  Establishment - From 1 to 2 months after seeding.  (Exception:
             for fall-seeded grain, Period 2 includes the winter period and
             extends to 30 April in the North and 1 April in the South, with
             intermediate latitudes interpolated.)

Period 3:  Growing crop - From Period 2 to crop harvest.

Period 4:  Harvest, residue or stubble - From crop harvest to turn  plow or
             new seedbed.  (When meadow is established in small grain,
             Period 4 ends 2 months after grain harvest.  Thereafter, it is
             classified as established meadow.)

The average cover factor C for the entire year or years of crop rotation is
computed by crop stages.  Input for calculation of C includes average plant-
ing and harvesting dates, productivity, disposition of crop residues, tillage,
and monthly distribution of the erosion index R.   The C value for each of
these time periods is weighted according to the percentage of annual rainfall
factor occurring in that period.  The summation of these RC products for the
entire year or years of crop rotation is then converted to a mean annual C.

Values of factor C for croplands are highly variable with rainfall  pattern,
planting dates, type of vegetative cover, seeding method, soil tillage, dis-
position of residues, and general management level.  Ranges of C value for
several types of vegetation and ground cover are listed in Table 3-3, in
order of decreasing protection against erosion (increasing C value  from
near zero to 1).

The reader is advised to consult with state conservation agronomists of SCS
for appropriate C values for crops in the local area.  The reader is also
referred to USDA-ARS Agriculture Handbook No. 282—  for a listing of approxi-
mated C values for various crops at each crop stage, as well as a working
table for derivation of average C value for periods of crop rotation.

                                    58

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    Table 3-3.  RELATIVE PROTECTION OF GROUND COVER AGAINST EROSION
                   (In order of increasing C factor)
  Land-use groups
Permanent vegetation
Established meadows
Small grains
Large-seeded legumes
Row crops
Fallow
      Examples

Protected woodland
Prairie
Permanent pasture
Sodded orchard
Permanent meadow

Alfalfa
Clover
Fescue

Rye
Wheat
Barley
Oats

Soybeans
Cowpeas
Peanuts
Field peas

Cotton
Potatoes
Tobacco
Vegetables
Corn
Sorghum

Summer fallow
Period between plowing and
  growth of crop
Range of "C" values
    0.0001-0.45
    0.004-0.3
    0.07-0.5
    0.1-0.65
    0.1-0.70
    1.0
                                   59

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Factor C for pasture, range and idle land - C values typical of permanent
pasture, range, and idle lands, with varying cover and canopy conditions,
are given in Table 3-4.  These values were developed by Wischmeier.—'

Factor C for woodland - Wischmeier—  also estimated factor C for some
woodland situations.  Data are presented in Table 3-5.

Factor C for urban and road areas, construction and mining sites - On these
areas and sites, the factor C represents the effect of land cover or treat-
ment that may be used to protect soil from being eroded„  Table 3-6—'
lists values of the factor C for various soil covers and treatments.
3.2.5.5  The practice factor (P) -
The factor P accounts for control practices that reduce the erosion poten-
tial of runoff by their influence on drainage patterns, runoff concentration,
and runoff velocity.

For croplands, control practices refer to contour tillage, cross-slope farm-
ing, and contour strip-cropping.  The practice value P is the ratio of soil
loss from a specified conservation practice to the soil loss occurring with
up- and downhill tillage, when other conditions remain constant.  Table
3-7—'  shows P values for up and downhill farming, cross -slope farming with-
out strips, contour farming, cross-slope farming with strips, and contour
strip-cropping .

Terracing is also an effective practice to reduce soil erosion.  The quan-
titative effect of terracing is accounted for in the slope length factor,
since the horizontal terrace interval becomes the slope length, after the
terraces are constructed.

3.2.5.6  Sediment delivery ratio
The sediment-delivery ratio, in this handbook, is defined as the fraction
of the gross erosion which is delivered to a stream.  The classical method
for determining an average delivery ratio is by comparing the magnitude of
the sediment yield at a given point in a watershed (generally at a reservoir
or a stream sediment measuring station), and the total amount of erosion.
The quantities of gross erosion from sloping uplands are computed by erosion
prediction equation for surface erosion, and estimated by various procedures
for gullies, stream channels, and other sources (see Section 3.3 of this
handbook).  The sediment yield at a given downstream point is obtained
through direct measurements.
                                    60

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        Table 3-4.  "C" VALUES FOR PERMANENT PASTURE, RANGELAND, AND IDLE LAND
                                                                               16.a/

Vegetal canopy
Type and height
of raised canopy—'
Canopy
cover—'
(7°)
Type!/
Cover that contacts the surface
Percent ground cover
0 20 40 60 80 95-100
    Column no.
No appreciable canopy
Canopy of tall weeds
  or short brush
  (0.5 m fall height)
Appreciable brush
  or bushes
  (2 m fall height)
Trees but no appreci-
  able low brush
  (4 m fall height)
25

50

75


25

50

75


25

50

75
G
W

G
W
G
W
G
W

G
W
G
W
G
W

G
W
G
W
G
W
0.45
0.45
0.20
0.24
0.10
0.15
0.042
0.090
0.013
0.043
0.003
0.011
0.36
0.36
0.26
0.26
0.17
0.17
0.17
0.20
0.13
0.16
0.10
0.12
0.09
0.13
0.07
0.11
0.06
0.09
0.038
0.082
0.035
0.075
0.031
0.067
0.012
0.041
0.012
0.039
0.011
0.038
0,003
0.011
0.003
0.011
0.003
0.011
0.40
0.40
0.34
0.34
0.28
0.28
0.18
0.22
0.16
0.19
0.14
0.17
0.09
0.14
0.085
0.13
0.08
0.12
0.040
0.085
0.038
0.081
0.036
0.077
0.013
0.042
0.012
0.041
0.012
0.040
0.003
0.011
0.003
0.011
0.003
0.011
0.42
0.42
0.39
0.39
0.36
0.36
0.19
0.23
0.18
0.21
0.17
0.20
0.10
0.14
0.09
0.14
0.09
0.13
0.041
0.087
0.040
0.085
0.039
0.083
0.013
0.042
0.013
0.042
0.012
0.041
0.003
0.011
0.003
0.011
0.003
0.011
aj  All values shown assume:  (1) random distribution of mulch or vegetation, and
      (2) mulch of appreciable depth where it exists.
b/  Average fall height of waterdrops from canopy to soil surface:  m = meters.
_c/  Portion of total-area surface that would be hidden from view by canopy in
      a vertical projection (a bird's-eye view).
_d/  G:  Cover at surface is grass, grasslike plants, decaying compacted duff,
      or litter at least 5 cm (2 in.) deep.
    W:  Cover at surface is mostly broadleaf herbaceous plants (as weeds) with
      little lateral-root network near the surface and/or undecayed residue.
                                            61

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                  Table 3-5.   "C" FACTORS FOR WOODLAND-iH
16/



Stand condition
Well stocked
Medium stocked

Poorly stocked

Tree canopy
percent of
area^-'
100-75
70-40

35-20
Forest
litter
percent of
area*/
100-90
85-75

70-40


c/
Undergrowth—'
Managed"-'
Unmanaged—'
Managed
Unmanaged
Managed
Unmanaged


"C" factor
0.001
0.003-0.011
0.002-0.004
0.01-0.04
0.003-0.009
0.02-0.09-/
a_l  When tree canopy is less than 2070, the area will be considered as
      grassland or cropland for estimating soil loss.
b_/  Forest litter is assumed to be at least 2-in. deep over the percent
      ground surface area covered.
_c/  Undergrowth is defined as shrubs, weeds, grasses, vines, etc., on
      the surface area not protected by forest litter.  Usually found
      under canopy openings.
_d/  Managed - grazing and fires are controlled.
    Unmanaged - stands that are overgrazed or subjected to repeated
      burning.
e_t  For unmanaged woodland with litter cover of less than 757o, C values
      should be derived by taking 0.7 of the appropriate values in
      Table 3-4.  The factor of 0.7 adjusts for the much higher soil
      organic matter on permanent woodland.
                                     62

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             Table 3-6.   "C" FACTORS FOR CONSTRUCTION SITESiL
                                                           17/
             Type of cover

          None (fallow)

          Temporary seedings
            First 60 days
            After 60 days

          Permanent seedings
            First 60 days
            After 60 days
            After 1 year

          Sod (laid immediately)
C value
 1.00
 0.40
 0.05
 0.40
 0.05
 0.01

 0.01
                            Rate of application
                  In metric tons
    Maximum allowable
      slope length
Mulch
Hay or straw



Stone or gravel



Chemical mulches
First 90 days
After 90 days
Woodchips





per hectare
1/2
1
1-1/2
2
14
55
120
220



2
4
6
11
18
23
In tons per acre
1/2
1
1-1/2
2
15
60
135
240

£/
£/
2
4
7
12
20
25
C value
0.34
0.20
0.10
0.05
0.80
0.20
0.10
0.05

0.50
1.00
0.80
0.30
0.20
0.10
0.06
0.05
(ft)
20
30
40
50
15
80
175
200

50
50
25
50
75
10°
150
200
(m)
6
9
12
15
5
24
53
61

15
15
8
15
23
30
46
61
£/  As recommended by manufacturer.
                                     63

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   Table 3-7.   "P" VALUES FOR EROSION CONTROL PRACTICES ON CROPLANDS—/



Slope_
Up- and
down-
hill
Cross-slope
farming
without strips

Contour
fa rming
Cross-slope
farming with
strips

Contour
strip -cropping
 2.0-7      1.0          0.75          0.50        0.37            0.25
 7.1-12     1.0          0.80          0.60        0.45            0.30
12.1-18     1.0          0.90          0.80        0.60            0.40
18.1-24     1.0          0.95          0.90        0.67            0.45
Measurements of sediment accumulations in reservoirs and sediment-load
records in streams show wide variations in sediment yields from watersheds.
Estimates show that as little as 57o and as much as 10070 of the materials
eroded in some watersheds may be delivered to a downstream point.  Estimates
of the delivery ratio for some specific watersheds can be obtained from the
Soil Conservation Service, USDA.

Many delivery-ratio analysis studies were aimed  at  finding  measurable
influencing factors that can be related to sediment-delivery ratio.  A popu-
lar means of developing such information is by statistical analysis using
the sediment-delivery ratio as the dependent variable and measurable water-
shed factors as the independent, or controlling variables.  As pointed out
in Section 3.2.1.3 of this handbook, many physical and hydrological factors
of watersheds may influence sediment delivery ratios.  Some are more pro-
nounced in their effect than others.  Some lend themselves to quantitative
expression whereas others do not.  To this date, however, the science of
sedimentology has not progressed to the state where the relative influence
of each of the individual physical and hydrological facrors has been evalu-
ated, and their relative influence on the delivery ratio of sediment has not
been determined to the degree of accuracy desired.  Nevertheless,  empirical
relationships for delivery ratios have been proposed and are presented below.
Estimates of sediment loading can be made through the use of these rela-
tionships, but such estimates should be tempered with judgment and consider-
ation of other influencing factors which are not included in the quantita-
tive expressions.

Sediment delivery ratio for construction sites - The MITRE Corporation
reportedi?./ that the sediment delivery ratio for construction sites can be
approximated by a function of the overland distance between the  construc-
tion site and the receptor water.
                                   64

-------
The format of the sediment delivery ratio proposed by MITRE for con-
struction sites has the following forms:
                             Sd = D-°-22                         (3-2)

where        D = overland distance between the erosion site and the
                   receptor water, in ft
The above equation was empirically derived from available data.  The
data base for the derivations has values of  D  from 0 to 800 ft.  MITRE
suggests that this function should be subjected to further testing, par-
ticularly in areas of the Midwest and Central U.S. from which no data were
obtained and used for deriving the above equation.

Sediment delivery ratio for other intensely distrubed sites - For mining
sites, or for  forestland areas such as logging roads, fire lanes, sedi-
ment delivery ratio relationships have not yet been established due to
lack of systematically measured data.  It is suggested, however, that the
delivery ratio developed by MITRE and expressed in Eq. (3-2) be used as
the first approximation for these sites.  This needs to be validated when
appropriate data become available.

Sediment delivery ratio from relatively homogeneous basins - Sediment de-
livery ratios have been evaluated in many areas of the country, particu-
larly the eastern half of the United States.  The delivery ratio usually
depicts a general trend in basins that are relatively homogeneous with
respect to soils, land cover, climate, and topography.  The Soil Conser-
vation Service*-"' has reported an analysis of data from stream and res-
ervoir sediment surveys from widely scattered areas.

This analysis shows that sediment delivery ratios vary inversely with
"drainage basin size".  It also indicates the effect of soil texture of
upland soil on the sediment delivery ratio.
The delivery ratio relationships reported by SCs'  were utilized by the
MRI study group in developing delivery ratios for sediment loading to
watercourses.  The result is shown in Figure 3-10.  The horizontal scale
of the figure is the reciprocal of drainage density which is defined as
the ratio of total channel-segment lengths (accumulated for all orders
within a basin) to the basin area.  The reciprocal of drainage density
may be thought of as an expression of the closeness of spacing of chan-
nels, or the average distance for soil particles to travel from erosion
site to the receptor water.
                                  65

-------
 tn

 0)
Q

 ft)
 O)
                                                                                                          r-  O
                                                                                                                                           (U
                                                                                                                                           4-1
                                                                                                                                           3
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                                                                       66

-------
The delivery ratio relationship shown in Figure 3-10 also takes into
account the effect of soil texture.  For example, if soil texture of
upland soil is essentially silt or clay, the sediment delivery ratio
will be higher than when the soil texture is coarse.

The delivery ratio relationships in Figure 3-10 need to be further vali-
dated by acquisition of new data.  They also need to be improved in the
future to include other factors relative to deposition mechanisms.

The following steps are to be used to obtain delivery ratio (S
-------
          Table 3-8.   TYPICAL VALUES  OF DRAINAGE DENSITY
      Location

Appalachian Plateau
  Province

Central and eastern
  United States

Dry Areas of the Rocky
  Mountain Region

The Rocky Mountain Region
  (except the above)

Coastal ranges of
  southern California
Badlands in South Dakota
Badlands in New Jersey
                               Drainage  density
                                 -1
 km
1.9-2.5
  5-10
 31-62
  5-10
 12-25
mile
                -1
3.0-4.0
 50-100
8.0-16
 20-40
125-250    200-400
183-510    310-820
  Reference
Smith  (20)
8.0-16.0    Strahler (23)
Melton (24)


Melton (24)


Smith (20)
Melton (24)
Maxwell (25)

Smith (26)

Schumn (21).
                                 68

-------
































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3.2.5.7  Summary of applicabilities of source characteristic  factors  -

The preceding paragraphs indicate that assessment of sediment loadings from
surface erosion requires quantitative information of soil credibility, rain-
fall and snowmelt erosivity, topography,  vegetative cover,  conservation
practices, and sediment delivery ratio.   Applicability of each factor var-
ies with specific location of the site and also with type of  land  distur-
bance.  Table 3-9 gives a total summary of variations in application  of
those factors.

3.2.5.8  Limitations of the loading function -

The USLE predicts soil losses from sheet and rill erosion.   It does not
predict sediment from gullies,  streambank erosion, landslides, road ditches,
irrigation, or from wind erosion.

The USLE was developed primarily for croplands, and has been  chiefly  based
upon experimental plot data from the areas east of the Rocky Mountains.
The loading function therefore  is best defined for these areas of use.
For croplands in the western United States and sources outside agriculture
such as silviculture, construction, and mining, the factors are not well
developed.

Specific limitations of factors include:

R:  Research is needed to determine the effective R values more accurately
in both the east and west of the continental United States.

L and S:  The relationships on which the slope effect charts  are based
were derived from data taken on slopes not exceeding 20% and  length not
exceeding 400 ft.  How far these dimensions can be exceeded before those
relationships change has not been determined.

C:  More work is needed to improve definitions of cover factor, particu-
larly for areas outside agriculture, such as undisturbed forest, harvested
forests, logging roads, mining sites, rangeland, and construction sites.

P:  The reported values of the  practice factor have been limited to crop-
land.  Definition of practice factor values is needed for various conser-
vation practices on silviculture, mining, construction and other areas
outside agriculture.

S^:  The science of sedimentology has not progressed to the state where
the sediment-delivery ratio can be predicted to the degree of accuracy de-
sired.  In addition, for the benefit of pollution analysis, delivery  ratios
should be developed for prediction of sediment loadings reaching the
"receptor waters" rather than "reservoirs."

                                     70

-------
The loading function in Eq. (3-1) and supporting data in tables and figures
were designed to predict longtime average loadings for specific conditions.
Sediment loading for a specific year may be substantially greater or smaller
than the annual averages because of differences in number, size, and timing
of erosive rainstorms, and in other weather parameters.  The reader is re-
ferred to Table 11 of USDA Agriculture Handbook 282—' for a listing of 50,
20, and 5% probability values of R factor at 181 key locations in the area
east of the Rocky Mountains.  These may be used for further characteriza-
tion of soil-loss hazards.

Due to the uncertainties embedded in factor values, it is advisable that
sediment loading computed by Eq. (3-1) be accepted as reasonable estimates
rather than as absolute data.  Table 3-10 lists the best estimate of the
range of accuracy for Eq. (3-1) and available supporting data.  The range
figures pertain to annual average.  For a specific year, the range may be
much larger than those given.
       Table 3-10.  ESTIMATED RANGE OF ACCURACY OF SEDIMENT LOADS
                         FROM SURFACE EROSION
  Predicted loading                     Estimated range of accuracy
   (MT/ha/year)                         	(MT/ha/year)	

         0.1                                    0.001 ~ 1.0
         1                                        0.1-5
        10                                          5 ~ 15
       100                                         50 ~ 150
     1,000                                        500 ~ 1,500
3.2.6  Source Characteristic Factors for Predicting Maximum and Minimum
          Sediment Loading

The  loading function in Eq. (3-1) can be used to predict sediment loading
other than annual averages.  Variations of the loading rate are embedded
in rainfall factor R and cover factor C.  The evaluation procedure is il-
lustrated in the following examples.

Example  1:  Variations caused by rainfall factor alone - The rainfall
erosion  index R varies within a year, as shown in percent erosion index
curves in Appendix A.  For lands where cover factor is relatively constant,
such as  woodland and grassland, temporal distribution of rainfall factor
R governs temporal variations in erosion.
                                    71

-------
Figure 3-11 shows an example of monthly distribution of percentages of
annual R values.  This distribution curve is for parts of Michigan, Missouri,
Illinois, Indiana, and Ohio based on Curve 16 in Figure A-2d.   The following
steps are required for evaluating monthly variation of R values.

1.  Read the percent of annual erosion index, at the predetermined time in-
terval, on the appropriate erosion index distribution curve (for  this spe-
cific example, Curve 16 on Figure A-2d in Appendix A).

2.  For each time interval, subtract the reading of the first  date from
that of the last date.

3.  Results of Step 2 are the percents of annual index that are to be ex-
pected within the particular periods.  Use these data for plotting distri-
bution curve.  The percent average daily is 0.274, which is obtained by
dividing 100 (percent) by 365 (days in a year).

The curve in Figure 3-11 indicates that, if other factors hold constant,
soil erosion in this area would have its maximum from 20 June  to  20 July,
and minimum from late December to late January.

One estimates that, based on the R distribution in Figure 3-11, the maxi-
mum daily loading rate during a 30-consecutive-day period for  woodland and
grassland in this particular area is approximately 2.5 times that of aver-
age daily loading rate for 1 year; the minimum daily rate during  a 30-
consecutive-day period is approximately one-fourth of the average daily
rate.

Example 2:  Variations caused by the combined effects of rainfall factor
and cover factor - For croplands, where soils are tilled and surface con-
ditions change drastically from one crop stage to another, evaluation of
erosion variation should include both the R factor and C factor.

Required steps to achieve such evaluations are:

1.  Determine average dates of each crop stages.

2.  Determine C factor values for each crop stage from such information
as productivity, disposition of crop residues and tillage.

3.  Obtain monthly distribution of R.

4.  Multiply C factor values by the R value of the corresponding period.

Variations of RC products are the temporal variations of sediment loading.
                                    72

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    0.8 r-
    0.7
 x
 o
 Q)
 Q_
    0.6
    0.5
 CD

J  0.4
 u.
 D
oo
O
 c
 
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In this example,  temporal variation of surface erosion rate for continuous
cornland in central Indiana was calculated.   Again,  the erosion index dis-
tribution Curve No. 16 on Figure A-2d was used.   Assumptions were conven-
tional tillage, a yield average of 40 to 59  bu of corn per acre, and corn-
stalks left on the field after harvesting.  The dates, C values, and per-
cent of erosion index for five-crop stages,  and RC products are:
                                          Percent R
  Crop stage,                Cover
   starting-                factor,
  ending date                 CS.'

Turn plowing, 5/1-5/19       0.55
Seeding, 5/20-6/19           0.70
Establishment, 6/20-7/19     0.58
Growing crop, 7/20-10/9      0.32
Harvest and                  0.50
  stubble, 10/10-4/30

             Total
Reading^-/
  13.8
  19.5
  36.0
  57.3
  91.0
Percent
in the
period

  5.7
 16.5
 21.3
 33.7
 22.8
             100
   RC
product

  3.14
 11.55
 12.35
 10.78
 11.40
             49.22
a_/  Reference source:  USDA-Agricultural Research Service Handbook No.
      282,27 Table 2.
b/  Reading from Figure A-2d (Curve 16) for starting date.
The annual C factor is estimated at 0.49.  Temporal variation of surface
erosion rate, in terms of percent of annual total, is shown in Figure 3-12.
It is seen that the maximum erosion from this continuous cornland would
occur in mid-June through mid-July, nearly identical to the period of
maximum erosion with constant soil cover (Figure 3-11).  The 30-day max-
imum is approximately 3.2 times average daily, which is higher than the
previous (constant C factor) case due to the magnifying effect caused
by the overlapping of a high R period with a high C period.  Figure
3-12 also shows that minimum erosion would occur during the winter sea-
son; the 30 day minimum is one-fourth of the average daily load.

3.2.7  Source Areal Data

Information and data of considerable variety are needed to assess sediment
loading by surface erosion from various sources.  Pertinent source charac-
teristic data including soil erodibility, rainfall erosivity, slope length,
slope gradient, vegetative cover, conservation practices, and delivery
ratio, have been presented in the previous sections.  This section presents
sources of data relevant to acreages of land use and land disturbance.
                                    74

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   1.0 r
                                           — 30-Day Maximum

                                                Cropstage

                                            F - Turn  Plowing
                                           Cl - Seeding
                                           C2 - Establishment
                                           C3 - Growing Crop
                                           C4 - Harvest and Stubble
      1/1   2/1  3/1  4/1   5/1  6/1  7/1  8/1   9/1  10/1  11/1 12/1  1/1

                             Date  Month/Day

Figure 3-12.  Projected variation of soil erosion on  continuous corn
                lands in central Indiana^'
Source:  Midwest Research Institute.
                                    75

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The following are sources of areal data which are pertinent to assessing
sediment loadings from various nonpoint sources.

Land use -

     "Conservation Needs Inventory" - Soil Conservation Service
     "Census of Agriculture" - Bureau of Census
     State cropland and livestock reports - State Agriculture Department
     Forest survey reports - Forest Service
     Range survey reports - Soil Conservation Service, Forest Service
     Forest cutting and fire reports - Forest Service, State Foresters
     "Watershed Conservation and Development Field Data" - Bureau of Land
       Management

Housing construction -

     Statistical Yearbook - U.S. Department of Housing and Urban
       Development
     County and City Data Book - U.S. Bureau of Census
     "U.S. Census of Population and Housing" - U.S. Bureau of Census
     "Housing Authorized by Building Permits and Public Contracts" - U.S.
       Bureau of Census
     "Construction Report" - U.S. Bureau of Census

Mining activities -

     Mineral Yearbook - U.S. Bureau of Mines
     Mining permits - State

Highways and roads -

     U.S. Federal Highway Administration
     State Highway Department

The following data sources are particularly pertinent to assessment of
surface erosion for large areas.

Data for agricultural lands — the Conservation Needs Inventory (CNI) - The
CNI is one of the major sources of data for agricultural land in the
United States.  The first inventory was made in  1958  to 1960 and updated
in  1967.  The objective of the inventory was to  develop current, detailed
data on land use and conservation treatment needs on  rural  land and to  ob-
tain data on watershed project needs on both privately and  publicly owned
land in the U.S.  The inventory includes all acreage  except urban and
built-up areas and land owned by the federal government, other than crop-
land operated under lease or permit.

                                    76

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Inventoried lands are compiled by county in terms  of land  use,  land capa-
bility class and subclass,'1- and conservation treatment needs  as shown in
Table 3-11.  The seven major rural land use categories are subdivided into
18 secondary land use classifications and current  (1967)  conservation
treatment needs.  Each group is inventoried according to  land capability
classes and subclasses.

It is important to note that not all land was classified  or inventoried
in the CNI.  For the noninventoried land (including federal noncropland,
urban buildup, and small water bodies), there has  been thus far no  infor-
mation concerning use of land by capability.  For  most regions  the  propor-
tion of total land in the noninventory group is  not significant.  However,
in the western states the proportion of this group may be  very  high.

Copies of state inventories may be obtained from the State Conservation
Needs Inventory Committee, and/or University Agricultural  Extension Service,
Magnetic tapes of the inventory are available from the Statistical  Labora-
tory, Iowa State University, Ames, Iowa.

The U.S.  Soil Conservation Service in 1972 solicited soil  scientists  in
the United States for the soil data relevant to  surface erosion,  in format
compatible with the format of CNI.  Data are reported by Land Resources
       (LRA) and by land capability class and subclass. For  all LRAs east
*  Land Capability Classification is one of a number of interpretive group-
      ing of  soil survey maps made primarily for agricultural purposes.
          In this classification, the arable soils are grouped according
      to their potentialities and limitations for sustained production of
      the common cultivated crops that do not require specialized site con-
      ditioning or site treatment.  Nonarable soils (soils not suitable for
      long-time sustained use for cultivated crops) are grouped according
      to their potentialities and limitations for the production and per-
      manent vegetation and according to their risks of soil damage if
      mismanaged.
          The capability classification provides three major categories:
      (a) capability unit; (b) capability subclass; and (c) capability
      class.  The reader is advised to consult with State Conservation
      Needs Inventory for detailed descriptions of classifications.
** Land Resource Areas (LRA), as delineated by the Soil Conservation
      Service, U.S.  Department of Agriculture, are broad, geographic areas
      having  similar patterns of soil (including slope and erosion), climate,
      water resources, land use, and type of farming.  Delineation and de-
      scription of LRAs are available in USDA-SCS, Agriculture Handbook
      No. 296, "Land Resource Areas of the United States," December 1965,
      and USDA-ERA series on "The Look of Our Land--An Airphoto Atlas of
      the Rural United States."
                                    77

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           Table 3-11.
  LAND USE AND TREATMENT NEEDS CATEGORIES OF THE
       CONSERVATION NEEDS INVENTORY
   Primary use
  classification
   Secondary use
  classification
  Treatment classification
Cropland in tillage
  rotation
Corn and sorghum
Other row crops
Close-grown crops
Summer fallow
Rotation hay and pasture
Hayland
Conservation use only
                      Idle
Other cropland
Orchards, vineyards and
  bush fruit
Open land formerly
  cropped
Pastureland
Rangeland
Treatment adequate
Treatment needed--nonirrigated
  Residue and annual cover
  Sod in rotation
  Contouring
  Strip-cropping or terracing
    diversion
  Permanent cover
  Drainage
Treatment needed — irrigated
  Cultural and management
    practices
  Improved system
  Water management

Treatment adequate

Treatment not adequate
                           Treatment adequate
                           Treatment unfeasible
                           Needs change in land use
                           Protection only
                           Improvement only
                           Improvement and brush control
                           Reestablishment of vegetative
                             cover
                           Reestablishment and brush
                             control

                           Treatment adequate
                           Treatment unfeasible
                           Needs change in land use
                           Protection only
                           Improvement only
                           Improvement and brush control
                           Reestablishment of vegetative
                             cover
                           Reestablishment and brush
                             control
                                        78

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                             Table 3-11.(Concluded)
   Primary use
  classification

Forestland
Forestland grazed
   Secondary use
  classification

Commercial
Other land
Noncommercial


Commercial

Noncommercial
On farms
Not on farms
  Treatment classification

Treatment adequate
Noncommercial — stand establish-
  ment and reinforcement
Commercial--stand establishment
  and reinforcement
Commercial--timberstand improve-
  ment

Treatment adequate
Forage improvement
Reduction or elimination of
  grazing

Treatment adequate
Treatment not adequate
                                       79

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of the continental divide,  information solicited includes  name of dominant
soil, dominant slope length,  dominant slope percent,  and K factor.  These
data were reported in Data Form 1.

For LRAs west of the continental divide, where K factors had not been de-
veloped before the survey,  information solicited includes  dominant soil
name, dominant slope length,  slope  percent, and estimated  soil losses
(tons/acre/year) from selected cropping systems.  Data were solicited in
Form 1W.

For convenience of use, the MRI study group has combined factors in Form 1
and calculated K-LS indexes for various land capability classes and sub-
classes for LRAs in the areas east  of the continental divide.  Values of
the K'LS index, and questionnaire returns in Form 1W (for  LRAs west of
continental divide) are presented in the Appendices D and  E, respectively,
of this handbook.  These data can be used together with land-use data in
the State Conservation Needs Inventory for assessing gross erosion from
agricultural lands in large areas.

Data for commercial forests - The most recent data on state and national
levels are presented in "The Outlook for Timber in the United States,"
U.S. Department of Agriculture, Forest Service, Forest Resource Report No.
20, October 1973.  This is a report on the nation's timber supply and
demand situation and outlook, related primarily to the commercial timber-
lands in the U.S. that are suitable for production of timber crops.  This
report provides statistical data, as of 1970, on the current area and con-
dition of the nation's forestland,  inventories of standing timber, and
timber growth and removals by individual states.  Information is also in-
cluded on recent trends in forestland and timber resources, trends in util-
ization of the nation's forest for timber and other purposes, and trends
in consumption of wood products.  This report represents the latest in a
series of similar timber appraisals prepared by the Forest Service in the
past.

If more local detail data are needed, they likely can be provided by the
forest and range experiment stations.  An important timber resources in-
ventory on a local  level available from the forest and range experiment
stations is "Forest Statistics"  (or "The Timber Resources").  The recent
publications present inventories of timber resources on the state and
county  levels.  The forest resource data and the accompanying discussions
of  forest area, volume, growth,  and cut are useful for planners.

Despite the availability of considerable information on the United States
timber  inventory, there are important gaps in information necessary to
assess  pollutant  loadings  from forested areas.  There is far more informa-
tion available  today concerning  standing timber volume on forestland than
                                     80

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there is concerning soil and topographic characteristics,  the acreage of
forest harvested, method of harvest, mileage of roads built and maintained,
percent canopy and ground cover situation,  and current soil and water con-
servation practices.  One possible method of obtaining such information is
through personal contact with local knowledgeable persons.   The following
are individuals who may be able to supply such needed data:

     U.S. Forest Service

          Resource management staff officers
          District rangers
          Forest supervisors
          Regional foresters

     U.S. Bureau of Land Management

          State director
          District manager

     State and local agencies

          State foresters
          County foresters

     Private forest industries

Data for mining and construction activities^ - The extent of construction
and mining activities in a given locale can be estimated directly from
sources such as building permits, construction reports, and mining permits.
Similar data also can be obtained from some other sources,  such as census
data for housing units, highways, roads, utility transmission lines,  etc.,
in which data are assembled periodically.  Data gathered in different years
can be translated into average annual acreages of land being disturbed by
construction activities.

For example, the census in County and City Data Book, U.S. Department of
Commerce, Bureau of the Census, includes the total number of housing units
between 1967 and 1972.  Also given are the number of units  in single family
units and the number in multiple units.  From these figures the average
annual number of new single and multiple dwelling units can be determined.
With actual data or an approximation of acreage per housing unit, one may
estimate the average annual acres of land used for new housing.
                                     81

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Construction activities for a given site are generally of limited duration,
and so is sediment production.   MRI economists  estimated  the  average  dura-
tion of construction to be:

     6 months for residential buildings,
     11 months for nonresidential buildings, and
     18 months for nonbuilding construction.

3.3  SEDIMENT LOADINGS FROM OTHER SOURCES:   GULLIES,  STREAMBANKS, AND MASS
     SOIL MOVEMENT

3.3.1  Overview

3.3.1.1  Gully erosion -

Gully erosion is caused by temporary concentration of runoff  during and
immediately after rainfall.  Sediment production from gullies is accom-
plished by scouring on the bottom or sides  by running water,  by slides of
materials into gullies from the side, and by erosion over the well-defined
headscarp.

Gully erosion is common to most regions in the United States.  Expansion
of gully development is most vividly apparent in arid and semi-arid areas
such as southwestern U.S.  where climatic changes are easily expressed in
network changes, and also in those areas where the influence  of man has
been substantial or rapid, or both.

Gullies usually are found on slopes greater than 5 degrees.  Gullies are
especially active during the rainy season,  and are particularly well-
developed on the margins of uplands composed of highly friable sandstones.

Development of gullies is associated with improper land use and severe
climatic events.  The effect of land use on gully development is connected
with modification of land cover and soil conditions, and subsequent changes
in runoff patterns.  Gullies have developed following the removal of trees
on the lower part of the sides of glacial troughs, and following compaction
of ground, change in topsoil, and changes in infiltration characteristics.
The impact of land use on gully development is most striking when original
plant cover on steep slopes is removed and runoff occurs with little im-
pediment.

Climatic fluctuations also may cause gully development.  Climatic fluctua-
tion may cause disappearance of vegetation cover, and lead to vivid gully-
ing activities.
                                    82

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Sediment production from gully development has been described for some
regions in the U.S.?ff»29/  The quantity, though often large,  is usually
less than that produced by surface erosion.  However, economic losses from
dissection of uplands, damage to roads and drainage structures, and deposi-
tion of relatively infertile overwash on flood plains are disproportionately
large.

The prediction of gully growth has thus far received little attention, al-
though some studies have developed empirical prediction procedures for
specific localities.

3.3.1.2  Streambank erosion -

In the streambank erosion process, energy from streamflow, ice, and floating
debris, and the force of gravity are applied to the streambank and stream-
bed.  If the energy is greater than the resistance of soil particles form-
ing the channel, erosion results.  Brown?.' suggests that in most forest and
range country and in areas with less than 51 cm (20 in.) of precipitation
annually, channel-type erosion (including gully, streambank,  etc.) generally
produces the greater part of the sediment.  Where a watershed is primarily
agricultural and has more than 5.1 cm (20 in.) of precipitation, a major
part of the sediment production is generally from sheet erosion.  Gottschalk—'
suggests that streambank erosion is dominant in the semiarid and arid areas
of the United States and in the mountainous areas of the Central and South
Pacific Coast regions.  Andersoni!/ estimated sediment yields from the North
Coast watersheds of California, and the Williamette Basin of western Oregon,
and concluded that sediment contribution from streambank erosion in that
part of the country is greater than from other sources combined.

In 1969, the Corps of Engineers, in conjunction with Soil Conservation Ser-
vice personnel, completed the "National Assessment of Stream Bank Erosion."—'
All districts in the nation provided information on the amounts of stream-
bank erosion in their areas.  Stream density by land resource area was used
to determine total stream miles and bank miles.  Estimates were then made
on how many of these banks erosion was negligible, moderate, and serious.
Damages were determined at the site where erosion occurred and where the
ensuing sediment was deposited.  Cost of treatment was calculated for both
moderate and serious cases.

A report on the nationwide assessment was issued by the Corps in October
1969.  Regional inventory reports are available from appropriate district
offices.
                                     83

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3.3.1.3  Mass soil movement -

Mass soil movement is the downs lope movement of a portion of the land sur-
face under the effect of gravity.   Such movements may take the form of
landslide, mudflow, or downward creep of an entire hillside, and contribute
to sediment loadings to surface waters.  In many areas this source of supply
is unimportant.  However, mass soil movement may constitute the dominant
process of erosion in areas with exceptionally steep slopes, high rainfall,
or low-strength soil, such as that of mountainous areas of western North
America, as well as of southern California.  In such areas, soil may  ^-main
in place as the result of a delicate balance between forces tending tc
cause downslope movement and various forces tending to resist it.  Any dis-
turbance may upset this delicate balance and result in initiation or accel-
eration of mass soil movement.

Landslide is influenced by the slope of the land, composition of soil, and
                                   oo /
water content of the soil.  Dyrness—'  indicated that stony soils from
basalt and andesite were 14 to 37 times more stable than those from tuffs
and breccias, which are volcanic parent materials, and normally weather
rapidly to silts and clays.  Silts and clays can retain large quantities
of water.  The water adds to the soil burden and reduces its strength,
thus promoting landslides.  In Oregon, landslides normally occur near peak
stream flow from winter storm runoff when the water content of soil is at
the maximum.

Man's activities may play an important role in initiation and acceleration
of mass soil movements.  In a review of mass erosion research in the
western United States, Swanston—' made the following statements about the
effect of disrupting activities of man on mass soil movements:

    "Road building stands out at the present time as the most damaging
     activity.  Soil failures relating to this activity are the result
     primarily of slope loading from road fill and sidecasting, inade-
     quate provision for slope drainage, and of bank cutting.

     Fire, natural and man-caused, is a second major contributor to
     accelerated soil-mass movement in some areas.  This relates largely
     to the destruction of the natural mechanical support of soils, often
     abetted by surface denudation of the soil mantle, opening it to the
     effects of surface erosion.

     Logging affects slope stability mainly through destruction of pro-
     tective surface vegetation, obstruction of main drainage channels
     by logging debris, and the progressive loss of mechanical support
     on the slopes as anchoring root systems decay."
                                     84

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Very little work has been done to establish quantitative cause and effect
relationships between mass soil movements and causative factors, including
natural characteristics and man's activities in watersheds.

3.3.2  Methods for Quantifying Sediment Loading from Gullies, Streambanks,
         and Mass Soil Movement

The cause/effect interrelationships of gully erosion, streambank erosion,
and mass soil movement have yet to be put into proper perspective.  Methods
are therefore not available for any given locality and any set of existing
or assumed conditions for accurately predicting contributions of sediment
loading from these sources.  The discussion and general facts presented in
the preceding paragraphs will serve as guidelines for estimation of channel
erosion and mass soil movement.  These guidelines generally apply to two
options, presented below, for estimating gully and streambank erosion and
mass soil movement at the local/regional level.  These options may be used
separately or in combination.

3.3.2.1  Estimation from historical local data and research results -

The local history of gully erosion, streambank erosion, and mass soil move-
ment can be obtained by local interview and from existing research results.
Research results are available in engineering surveys and basin and project
reports.  Public agencies which have these results include:  Department of
Army--Corps of Engineers; Department of the Interior—Bureau of Land
Management, Bureau of Mines, Fish and Wildlife Service, and National Park
Service; Department of Agriculture--Forest Service and Soil Conservation
Service; state departments of water resources; public works authorities;
and planning commissions.

3.3.2.2  Estimation from historical topographic data -

Quantification of sediment production from gullies, streambanks, and mass
soil movement also can be made through use of aerial photographs.  A large
area of the United States was photographed from the air about 35 years ago.
Many areas have been rephotographed periodically.  These aerial photographs
provide valuable tools to determine the boundaries and lateral movement of
channels during various periods of time and are used extensively in water-
shed investigations whenever available.  The following agencies and organi-
zations have aerial photographs of parts of the United States:  Department
of the Interior—Geological Survey, Topographic Division; Department of
Agriculture—Agriculture Stabilization and Conservation Service, Soil Con-
servation Service, and Forest Service; Department of Commerce--Coast and
Geodetic Survey;  Department of the Air Force; National Aeronautics and
Space Administration;  various state agencies; and commercial aerial survey
and mapping firms.

                                    85

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                             REFERENCES
 1.  Meyer, L. D.,  and W. H. Wischmeier, "Mathematical Simulation of the
      Process of Soil Erosion by Water," paper presented at the 1968 Winter
      Meeting of the American Society of Agricultural Engineers, Chicago,
      Illinois, 10-13 December 1968.

 2.  Brown, C. B.,  "Effect of Land Use and  Treatment on Pollution,"
      Proceedings  of the National Conference on Water Pollution,  PHS,
      Department pf Health, Education and  Welfare,  Washington,  D.C.  (1960).

 3.  Megahan, W. F. , "Logging, Erosion, Sedimentation—Are They Dirty Words,"
      J. Forestry. 7£(7) (1972).

 4.  Ralston, C. W., and G. E. Hatchell, "Effects of Prescribed Burning on
      Physical Properties  of Soil," in Proceedings, Prescribed Burning
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 5.  Collier, C. R., et al., "Influence of Strip Mining on the Hydrologic
      Environment of Beaver Creek Basin, Kentucky, 1955-1959," USGS Pro-
      fessional Paper 427-B (1964).

 6.  USDA Soil Conservation Service, "Controlling Erosion on Construction
      Sites," Agriculture  Information Bulletin 347 (1970).

 7.  USDA  Soil  Conservation Service, National Engineering Handbook,  Sec-
      tion  3,  "Sedimentation," Washington, D.C., April 1971.

 8.  USDA  Agriculture Research Service, "Present and Prospective Technology
      for Predicting Sediment Yield and Sources," Proceedings  of the
      Sediment=Yield Workshop, USDA Sedimentation Laboratory,  Oxford,
      Mississippi, 28-30 November 1972.

 9.  Wischmeier, W. H.,  and D. D.  Smith, "Predicting  Rainfall—Erosion
      Losses from Cropland East  of the  Rocky Mountains,"  Agriculture
      Handbook 282, U.S.  Department  of  Agriculture,  Agriculture  Research
      Service,  May  1965.

10.   Wischmeier, W.  H.,  "Upland  Erosion Control," in Environment Impact
       on Rivers,  p.  15-1 to 15-26,  H. W.  Shen (ed.), Fort Collins,
       Colorado (1972).

11.  U.S.  Department of Agriculture, Soils Technical  Note No. 3, Soil
      Conservation Service, Honolulu, Hawaii, May  1974.
                                   86

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12.  Wischmeler, W. H. ,  and D. D. Smith, "Rainfall Energy and Its
       Relationship to Soil Loss," Transaction, 39:285-291, American
       Geophysical Union (1958) .

13.  U.S. Department of Agriculture Conservation Agronomy Technical
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14-  Garstka,  W.  U.,  "Snow and  Snow  Survey,"  in Handbook  of Applied Hydrology.
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15.  Porter,  G.  R., and  W.  H. Wischmeier,  "Evaluating  Irregular  Slopes  for
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       Agricultural Engineers,  Paper No. 73-227,  St. Joseph,  Michigan (1973).

16.  Wischmeier,  W. H.,  "Estimating  the  Cover and Management  Factor for
       Undisturbed Areas,"  presented at  USDA  Sediment  Yield Workshop,
       Oxford, Mississippi  (1972).

17-  Water Resources  Administration, "Technical Guide  to  Erosion and
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       Resources, Annapolis, Maryland, September  1973.

18.  U.S. Environmental  Protection Agency, "Effect  of  Hydrologic Modifi-
       cations on Water  Quality," report draft by the  MITRE Corporation,
       October 1974.

19.  U.S. Department  of  Agriculture, Engineering  Technical Note  No. 16,
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20.  Smith,  K. G., "Standards for Grading  Texture of Erosional Topography,"
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21*  Schumm,  S.  A., "The Evolution of  Drainage Systems and Slopes in Bad-
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22.  Strahler, A. N.,  "Quantitative  Geomorphology of Drainage Basin and
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       4-76,  V.  T. Chow  (ed.),  McGraw-Hill, Inc., New  York, New  York (1964).

9 Q
"•  Strahler, A. N.,  "Hypsometric  (Area-Altitude)  Analysis of Erosional
       Topography," Geo. Soc. Amer.  Bull.. 63:1117-1142 (1952).
                                   87

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24.  Melton,  M.  A.,  "An Analysis  of  the  Relations  Among  Elements  of  Climate,
       Surface Properties  and Geomorphology,"  Project  No.  NR  389-042,
       Technical Report No.  11, Columbia University, Department of Geology,
       New  York  (1957).

25.  Maxwell, J. C., "Quantitative Geomorphology of the San Dimas Experi-
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       York  (1960).

26.  Smith, K.  G., "Erosional Processes and Landforms in Badlands National
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27.  Carlston, C. W.,  and  W.  B. Langbein, "Rapid Approximation of Drainage
       Density:   Line Intersection Method," U.S.  Geological Survey,  Water
       Resource  Division,  Bulletin 11 (1960).

28.  Glymph,  L.  M.,  "Relation of  Sedimentation to  Accelerated Erosion in
       the Missouri River  Basin," Soil Conservation Service,  Technical
       Paper No. 102 (1951).

29.  Leopold, L. B.,  W.  W. Emmett, and R. M.  Myrick,  "Channel and Hillslope
       Process  in a Semiarid Area in New Mexico,"  UiS. Geological Survey,
       Paper No.  102  (1966).

30.  Gottschalk, L.  C.,  "Effect of Watershed Protection Measures  on Reduc-
       tion of  Erosion and Sediment Damages in the United States," Int.
       Assoc. Sci. Hyd.  Pub. ,  59.:426-427  (1962).

31.  Anderson,  H. W., "Relative Contribution of Sediment  from  Source Areas
       and Transport Processes,"  in Proceedings of a Symposium on Forest
       Land Uses and Stream Environment, Oregon State  University, pp.  55-
       63, August 1972.

32.  U.S. Army  Corps of Engineers, "A Study of Streambank Erosion in  the
       United States," submitted  to Committee  on Public Works, House  of
       Representatives, October  1969  (available from  the U.S. Government
       Printing Office, Washington, D.C.).

33.  Dyrness, C. T., "Mass Soil Movements in  the H. J. Andrews Experimental
       Forest," USDA Forest Service Research Paper PNW-42,  Pacific Northwest
       Forest and Range Experimental Station,  Portland,  Oregon, 12 pages
       (1967).
                                   88

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34.  Swanston,  D.  N.,  "Principal Mass  Movement  Processes  Influenced  by
       Logging, Road  Building,  and  Fire," in  Proceedings  of  a  Symposium
       Forest Land Uses  and  Stream  Environment,  Oregon  State University,
       Corvallis,  Oregon,  19-21 October  1970.
                                    89

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                               SECTION 4.0

                      NUTRIENTS AND ORGANIC MATTER

4.1  INTRODUCTION

Nitrogen and phosphorus are the primary nutrients which are important in
agricultural and silvicultural practices. The effect of these nutrients on
receiving waters is the increased potential for algal blooms — especially in
lakes and reservoirs--thus interfering with many beneficial uses of these
waters.  Of the two nutrient elements, phosphorus has received greater em-
phasis because of the available technology to control phosphorus discharges
from municipal and industrial sources.  Nitrogen is also important as a
rate-limiting nutrient for algal growth in some surface waters; however,
the nitrogen pathways in plant nutrition are relatively more complex than
those of phosphorus.  Technology for controlling nitrogen emissions from
point sources is not sufficiently advanced to economically justify its
adaptation to nonpoint pollutant emissions.

The magnitude of losses of these two nutrient elements from different
source activities can, in principle, be calculated by making nutrient bud-
gets of all source inputs and outputs, and specifically determining out-
puts to surface waters.  Methods for estimation of quantities involved in
the several parts of a nutrient budget are not well enough developed for
use in nutrient loading functions.  In addition, the quantities of nu-
trients that actually reach a stream from a given source are subject to
variation depending upon the nature of the intervening terrain.  The pre-
diction of nutrient losses from various land uses can in part be accomp-
lished by loading functions which describe the changes of  nutrient con-
tent in the soil in response to various external variables such as cultural
practices, fertilizers, and climatic differences, and which account for
soil losses by erosion.

Organic matter from cropland and pastureland carries oxygen-consuming ma-
terials that can degrade the quality of receiving waters by stripping its
oxygen content, and carries potentially pathogenic microorganisms from
livestock wastes and other rural runoff.  A loading function for organic
                                   90

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matter has been developed based on the organic matter content of soil
and sediment yield.

These assumptions are more nearly correct for nitrogen when erosion is
moderate to extensive, and are less correct when erosion is slight or
when surface runoff is negligible.  In the latter cases dissolved forms
of nitrogen are the principle nitrogen pollutants.  These are transported
either to subsurface waters or directly to surface waters in runoff.
Functions which describe either of the latter phenomena are not yet avail-
able, and the approach to estimating dissolved forms of nitrogen accord-
ingly involves a combination of local or regional experience supplemented
by measurements of soluble nitrogen forms in runoff and baseflow.

Nutrient and organic matter loading functions presented in this section
are accordingly based on the sediment loading function developed in Sec-
tion 3.0 entitled "Sediment Loading Functions."  It is assumed that the
nutrients and organic matter are carried through surface runoff and that
most of these are removed with sediment.

Because the currently available data applicable to the entire U.S. may
not reflect the local conditions, it is suggested that local data when-
ever available be used in preference to the general data presented in
this section.

4.2   NITROGEN

4.2.1  Introduction

Soil nitrogen is derived from several sources which include geologic
weathering, microbial reactions, precipitation, and chemical fixation.
Addition of chemical fertilizers and organic residues to soil constitutes
man's effort to increase or supplement nitrogen forms which can be read-
ily utilized by plants.  Although the cultivated soils contain a large
reservoir of total nitrogen in the plowed layer—about 2 to 4 tons/acre--
available nitrogen is usually quite small—a few pounds per acre.  The
significance of this available nitrogen to water pollution is great, how-
ever.  As much as 95% of total nitrogen in the soil is organically bound
and is not readily released in solutions for plant growth.  The ammonium
ion in soil which is tightly bound to clay or other anionic molecules in
soil is also not readily available for plant growth.  Nitrate which is
not held by soil particles can be readily transported through the soil
profile to below the root zone in the absence of an actively growing crop
and can eventually join the groundwater pool.  The time of migration of
groundwater nitrogen to surface waters can extend to several decades
                                   91

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depending upon groundwater hydrology relative to surface water hydrology.
Significant nitrogen losses to the air occur through volatilization and
denitrification processes.

4.2.2  Precipitation

Precipitation contains significant quantities of numerous substances,
including nitrogen and phosphorus .ii^'  That precipitation which falls
on surface waters carries with it a load which becomes a part of the
total pollutant load.  The direct contribution via precipitation is neg-
ligible for surface streams, and may be substantial for lakes or still-
standing waters—as much as 5070 of the total nutrient input.—'  Contri-
butions of precipitation-borne nutrients to surface waters via overland
runoff will vary in proportion to both precipitation and runoff.  The
simplest approach is to assume that overland runoff carries with it,
without loss to the soil, the nitrogen and phosphorus load which it con-
tained when it reached the earth.  Overland runoff is seldom very direct
except in high intensity/high quantity storm events or in certain types
of snowmelt, and rainfall entrained nutrients will in most runoff events
be exposed to mineral and organic matter in the soils.  Phosphorus and
nitrogen should be somewhat attenuated by exposure to the soil.
That fraction of precipitation-borne phosphorus carried in precipitation
which does not discharge to streams via overland runoff becomes a part
of the inventory of phosphorus in the soil, and becomes relatively im-
mobile in the surface layers of soil.  The surface-sorbed phosphorus be-
comes a nonpoint pollutant when it is discharged to streams on eroded
sediment.

That fraction of precipitation-borne nitrogen which is not immediately
carried off in overland runoff also enters the soil compartment where it
continues its participation in the complex nitrogen cycle:  some stays
in the root zone, and may be completely utilized by plant life; some
moves below the root zone, and thus becomes involved in a very ill-
defined physical-chemical-biological-hydrologic system; some of that
which stays in the root zone is a candidate for transport, later, to
surface streams in overland runoff.

Since only a small fraction of precipitation incident on land enters
surface waters by overland runoff, the great majority of precipitation-
borne phosphorus and nitrogen is deposited on the land and becomes a part
of its continually changing inventory of nutrients.  The present discus-
sion is concerned with estimation of the fractions of the precipitation-
borne nutrients transported directly, via overland runoff, to surface
waters.  An analysis of "national average" data is instructive.


                                   92

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Annual average precipitation is 76 cm (30 in.)-  Annual average runoff
via all processes is 25 cm (10 in.)-  The fraction of runoff occurring
by the overland varies widely; for purposes of discussion 20% of total
runoff will suffice.  Average annual overland runoff is thus 5 cm (2
in.), or about 7% of precipitation.

Reported deposition rates of nitrogen and phosphorus in rainfall range
from about 5 to 10 kg/ha/year (4.4 to 8.9 Ib/acre/year) for nitrogen,
and reportedly average 0.05 to 0.06 kg/ha/year (0.045 to 0.055  lb/acre/
year) for phosphorus.J^r'

Seven percent of the precipitation-borne phosphorus and nitrogen might
thus be carried directly to surface waters, if no absorption on soil is
assumed.  Nonattenuated yield rates, for stream deposition, national
average basis, would accordingly be 0.35 to 0.7 kg/ha (0.31 to 0.62-
lb/acre) of nitrogen, and 0.0035 to 0.004 kg/ha (0.0031 to 0.0036 Ib/
acre) of phosphorus.  If one assumes that phosphorus is 5070 attenuated
and nitrogen 25% attenuated, the net yields become 0.28 to 0.53 kg/ha
(0.25 to 0.47 lb/acre) of nitrogen, and 0.0018 to 0.002 kg/ha (0.0016
to 0.0018 lb/acre) of phosphorus.

If one translates the above data into in-stream concentrations  (assum-
ing no in-stream transformations), the results are 0.11 to 0.21 ppm
nitrogen, and 0.7 to 0.8 ppb of phosphorus.  Comparison of these con-
centrations with the national benchmark station data summarized in
Figures 12-3 and 12-4 reveals the perhaps fortuituous comparison that
nitrogen concentrations estimated from precipitation are the same as
what appears to be an average for nationally observed concentrations
in locations relatively unaffected by man.  The above estimated concen-
trations for phosphorus are lower than benchmark station concentrations
(0.7 to 0.8 ppb vs 10 to 200 ppb of total phosphorus).  This compari-
son indicates that the load of precipitation-borne phosphorus is a small
fraction of the phosphorus nonpoint contribution to surface streams, but
that nitrogen contributions are a significant part of the in-stream bur-
den of available forms of nitrogen (particularly nitrate).

A comparison of nutrient contribution  from precipitation with that from
croplands reveals that, on a national basis, the eroded soil from crop-
lands yields about 20 kg/ha/year (18 Ib/acre/year) of total nitrogen..1'
Assuming a 77» value for the available fraction in total nitrogen, the
load of available nitrogen from cropland becomes 1.42 kg/ha/year (1.26
Ib/acre/year).  This value compares with 0.28 to 0.53 kg/ha/year (0.25
to 0.47 Ib/acre/year) of available nitrogen in precipitation.  Since the
                                  93

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cropland nitrogen loading function does not account for precipitation
loads, the total contribution to a stream should include both these
sources.  The total load of "available" nitrogen thus is about 1.8 kg/
ha/year (1.6 Ib/acre/year), on a national average basis, from cropland.

Although available nitrogen is extremely significant in the enrichment
of stream nutrition, the role of the remainder of the total nitrogen
carried on eroded sediment is also substantial.  Since streams are dy-
namic in nature, there is a continuous mineralization of soil nitrogen
by the microorganisms in the bottom sediment which is supplied with
oxygen from both stream reaeration processes and photosynthetic pro-
cesses.  Thus, the delayed release of available nitrogen to the aquatic
systems can be as significant as the available nitrogen in precipi-
tation and eroded soil.  For example, in-stream nitrogen burdens averaged
over the Missouri River basin translate to an average yield of about 3
lb/acre/year_' of nitrate-nitrogen, which is two to three times the de-
livered rate from nonpoint sources and precipitation.

Nitrogen loading from precipitation should be added to that from surface
erosion processes to obtain the total load.  Since the load for phosphorus
from precipitation is small, the phosphorus loading function does not in-
clude the contribution from precipitation.

4.2-3  Nitrogen Loading Function

While the complex interactions in soil, air, water, and plants are rea-
sonably well understood, methods for quantifying movements within the
system are still in the research stage.  Methods which are suitable for
general use oversimplify the problem, must be used with discretion, and
may be quite inadequate in certain cases.  In particular, it is not
presently possible to describe leaching processes for soluble forms of
nitrogen.  The nitrogen loading function is made up of two sources:   (a)
erosion; and  (b) precipitation.  Total nitrogen loading is obtained by
adding the yields from both sources.  The loading functions exclude
leaching losses, and predict the amount of total nitrogen that is re-
leased to surface waters by runoff and erosion.  The nitrogen in precip-
itation is mostly in available form.

Nitrogen loading function for erosion loss is:
                      Y(NT)E = a'Y(S)E-Cs(NT)-rN                    (4-1)
                                   94

-------
where    Y(NT)g = total nitrogen loading from erosion, kg/year (Ib/year)

              a = dimensional constant (10 metric, 20 English)

         CS(NT) = total nitrogen concentration in soil, g/100 g

          Y(S)E = sediment loading from surface erosion, MT/year (tons/
                    year)

             r^j = nitrogen enrichment ratio

Available nitrogen can be obtained by using a fraction  f^  which is the
ratio of available  N  to total  N  in sediment.  Thus, the available
nitrogen in sediment is
                            Y(NA)E = Y(NT)E'fN-                    (4-2)


Nitrogen loading function for precipitation is

                                    Q(OR)
                         Y(N)p  = AO - .Npr-b                   (4-3)
                             ^      Q(Pr)

where   Y(N)pr  = stream nitrogen load from precipitation, kg/year
                    (Ib/year)

              A = area, ha (acres)

          Q(OR) = overland flow from precipitation, cm/year (in/year)

          Q(Pr) = total amount of precipitation, cm/year (in/year)

            Npr = nitrogen load in precipitation, kg/ha/year (lb/acre/
                    year)

              b = attenuation factor

Almost all of  Y(N)pr   will be in the available form so that the total
available nitrogen from both erosion and precipitation may be obtained
by adding Eqs. (4-2) and (4-3).  Thus,


                       Y(NA) = Y(NT)E'fN  + Y(N)pr                 (4-4)
                                  95

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4.2.4  Evaluation of Parameters in the Nitrogen Loading Function

The value of  Y(S)g  can be evaluated from the sediment loading function
presented in Section 3.0 "Sediment Loading Functions."  The value of the
enrichment ratio  r^  is variable according to the soil texture and cul-
tural treatment.  VietsJ/ presented the values of  r^  using data from
small experimental plots (see Table 4-1).  Hagin and Amberger,.?-' as well
as Stoltenberg and White ,Z' have proposed an  rN  value of 2.0.  Massey
et a.1.§.'  estimated the value of  r^  as 2.7.  Because of wide variations
in the properties of erodible soil, a single value of  rN  is not prob-
able; the values reported range from 2.0 to 4.0, and a value in this
range should be selected for a specific location unless local data are
available.
               Table 4-1.  NUTRIENT AND SEDIMENT LOSSES^
                        5/
        Source
  Total loss (kg/ha)
Soil        N
Enrichment
 ratio,  r
 N         P
Check
Rye winter cover crop
Manure  (45 MT/ha)
Rye and manure  (45 MT/ha)
29,100
13 , 160
18,390
8,130
74.5
38.9
52.8
21.5
75.8
37.7
44.3
19.6
3.88
4.08
4.28
3.35
1.59
1.56
1.47
1.47
 Nutrient  losses  from  forest  soils are  typically very  low.   Kilmer^/
 cited  several  authors to  show that  nutrient  losses  from forestlands  are
 insignificant.   Clear-cutting and burning  of forest areas  appear to  be
 the most  important practice  involved in accelerated release of nutrients.
 Table  4-2 shows  that  clear-cutting  and nitrogen fertilization accelerated
 nitrogen  and phosphorus  losses to a slight extent.
                                   96

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   Table 4-2.  EFFECT OF CLEAR-CUTTING AND FERTILIZATION ON NUTRIENT
                     OUTPUT IN DOUGLAS FIR FORESTS^/
                                            N                   P
               Treatment                 (kg/ha)             (kg/ha)

   Control                                 0.21                0.01
   Clear-cut                               0.39                0.05
   Fertilized  (200 Ib/acre) urea           0.28                0.03
   Ammonium sulfate                        0.43                0.07

    a/   Source:   Cole  and  Gessel  (1965)  cited by  Kilmer.2'
Nitrogen losses by leaching are also negligible from actively growing
grassland.  However, losses from legume grass mixtures can be high.
Lysimeter studies by Low and Armitage (page 7 of Ref. 9) showed that
clover produced about 10 times as much N loss in drainage as that in
actively growing grass; however, the loss was 100 times as much when
the clover crop died.

Runoff losses of nitrogen from grass sod plots ranged from 27° of applied
nitrogen when soil moisture was 12.5%, to 14% at 25.8% moisture.—/
Timmons et al.ii'  determined N and P losses in runoff solution and sed-
iment in Minnesota.  Their results indicate that leaching losses from
a hay rotation could contribute to substantial N and P losses in solu-
tion.

The value of  CS(NT)  in the plowed layer of soil is variable from location
to location and from time to time.  Estimates of native soil nitrogen
in the U.S. indicate a range between 0.02 and 0,^%.—'  Parker et al.
published a map in 1946 showing the nitrogen content in the top 1-ft
layer in the U.S.M'  (see Figure 4-1).   Since 1946 the nitrogen content
of the cropland soil has most probably decreased due to cultivation.
However, fertilizer inputs to cropland have offset part of the depleted
nitrogen.

Precipitation also contributes to the soil nitrogen.  Atmospheric nitro-
gen extracted by soil microbes becomes incorporated into soil organic
matter;  animal manures, crop residues,  and other wastes contribute sig-
nificant amounts of nitrogen to the soil.  Jenny—'  expressed the nitro-
gen content of the soil in terms of temperature, T,  and a humidity fac-
tor,  H.   Jenny's equation is:
                                  97

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                    CS(NT) = 0.55 e-0-08T (1 _ e-0.005H}           (4_5)


                         H =       "(4-6)
where         P = precipitation, mm/year

         CS(NT) = concentration of soil nitrogen, g/100 g

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                                      - 2360/(273+T)]
The solution of Eq. (4-5) is shown graphically in Figure 4-2.  The value
of humidity factor, H, can be determined from Eqs.  (4-6) and (4-7).  A
nomograph solution of H is shown in Figure 4-3.  For given values of
precipitation, relative humidity and temperature, the value of H can be
quickly and accurately established from Figure 4-3.  For example, given
P-L = 500 mm/year (19.7 in/year), RH^ =  60%, and TI = 5°C (41°F), the
value of H factor can be determined as follows:  using a straight-edge
ruler, align P^ and RH^ to intersect on the index line at "a" as shown
on the inset of Figure 4-3.  Align "a" with T^ on the temperature scale
to intersect the H scale.  The result on the H scale is 194.

Data in Figure 4-1 may be used as a check on current data.  Equations
(4-6) and (4-7) may be used to calculate nitrogen content of soil more
precisely if necessary data are available for using these equations.  If
local data are considered to be more reliable than those presented herein,
local data may be preferentially used.

The fraction of available nitrogen to total nitrogen in soil,  f>r  is
variable, depending upon many factors such as soil characteristics, degree
of mineralization, and organic matter content.  The most important forms
of available nitrogen are NH/+, N0o~, and certain simple organic compounds
containing free amide or amino groups.  Nitrate is only a minor source of
available nitrogen in soil.
   Modified  from Gladstone, S., Elements of Physical Chemistry, D. Van
     Nostrand Company, Inc., New York, New York  (1946).
                                   99

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0.01
             100
200
  300        400
H, HUMIDITY FACTOR
                                                       500
                                          600
                                                                            700
 Figure 4-2.   Soil nitrogen vs humidity  factor and temperature
                                 100

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                                                       101

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The available nitrogen in soil rarely exceeds 10% of total nitrogen.
Data from Lopez and Galvezlz'  suggest that about 8% of total nitrogen
in soil is available in mineralized form for plant growth.

The values of  Q(OR)  and  Q(Pr)   may be obtained from local data sources.
The value of  Q(Pr)  (annual average precipitation) is usually obtained
from the weather bureau statistics for the area.   The value of overland
runoff can be roughly estimated from stream flow data.  A user unfamiliar
with hydrology should consult with qualified personnel in state conser-
vation services, agricultural extension service,  the Corps of Engineers,
or the Agricultural Research Service for assistance in interpretation of
stream flows.  These resources will also have historical information on
overland runoff in relation to precipitation.

Values of  Np    are usually available from measurements made in the
local research stations.  In the absence of actual data, data in Figure
4-4 may be used.

4.3  PHOSPHORUS

4.3.1  Introduction

Phosphorus occurs naturally in soil from weathering of primary phosphorus-
bearing minerals in the parent material.  Additions of plant residues
and fertilizers by man enhances the phosphorus content of the surface
soil layer.

Phosphorus in soils occurs either as organic or inorganic phosphorus.
The relative proportion of the phosphorus in these two categories varies
widely.  Organic phosphorus is generally high in surface soils where or-
ganic matter tends to accumulate.  Inorganic forms are prevalent in sub-
soils.  Soil phosphorus is readily immobilized due to its affinity to
certain minerals.  In strongly acid soils the formation of iron and
aluminum phosphates, and in alkaline soils, the formation of tricalcium
phosphate reduces the availability of soil phosphorus.  Once it is lost
to a stream, the nature of phosphorus existing in sediment or in solu-
tion becomes significant in the nutrition of aquatic microorganisms.

Phosphorus transport from a given site to stream can occur either by ero-
sion or by leaching.  The predominant mode of transport is via soil ero-
sion.  Soil solution usually contains less than 0.1 ug of phosphorus per
milliliter; the leaching losses are thus extremely low even in well-
drained soils.  Exceptions are sands and peats which have little tendency
to react with phosphorus.
                                  102

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                                           103

-------
Phosphorus losses from well managed pastures and forested soils are
usually low.   For example, unfertilized pastures lost about 0.03 kg/ha
of P during a 6-month period,  while addition of 45 kg of P per hectare
resulted in an escape of only 0.04 kg/ha during a similiar period of
time.2/

4.3.2  Phosphorus Loading Function

The loading function for phosphorus is based on the soil erosion mecha-
nism.  The loading function is:
                       Y(PT) = a.Y(s)E-Cs(PT)-rp                   (4-8)

where     Y(PT) = total phosphorus loading, kg/year (Ib/year)

              a = a dimensional constant (10 metric, 20 English)

          Y(S)g = sediment loading, MT/year (tons/year)

         C0(PT) - total phosphorus concentration in soil, g/100 g
          o

             rp - phosphorus enrichment ratio

Available phosphorus may be computed from Eq.  (4-9):


                             Y(PA) = Y(PT)-fp                     (4-9)

where     Y(PA) = yield of available phosphorus, kg/year (Ib/year)

             fp = ratio of available phosphorus to total phosphorus

4.3.3  Evaluation of Parameters in Phosphorus Loading Function

Sediment loading,  Y(S)g , may be obtained from procedures outlined in
Section 3.0 "Sediment Loading Functions."

The value of  Cg(PT) , the total phosphorus content of the soil, is
variable.  For any given location, current and local data are preferred
to generalized values given in this report.  No central repository of
current nationwide data exists.  Parker et al.J^' published data on the
phosphorus content of soil in the top 30 cm (1 ft) for the 48 states, as
shown in Figure 4-5.  Parker's data, although obtained 30 years ago, will
serve as a check on current data.  Soil surveys periodically made by the
                                  104

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105

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Soil Conservation Service contain more recent information on soil phos-
phorus content.  State agricultural extension service personnel can also
provide reasonable estimates of soil phosphorus content in a given area.
These sources should be given priority in determining the phosphorus
content of the soil.

The enrichment ratio,  rp ,  has been the least researched parameter in
the loading function.  As reported in Table 4-1, the reported  rp values
                                Q /
average about 1.5.  Massey et al.—'  obtained an  rp  value of 3.4, and
Stoltenberg and WhiteZ' reported a value of 2.0.  Hagin and Amberger^.'
have used a value of  rp  of 2.5 in their simulation model for nutrient
losses from agricultural sources.  Massey et al.®.'  have developed an
empirical equation to determine  rp :
               log rp = 0.319 + 0.25 (-log X) + 0.098 (-log Y)    (4-10)

where         X = sediment loss, tons/acre-in of runoff

              Y = sediment loss, tons/acre

The determination of available phosphorus in the soil is difficult.  Most
reported data fail to distinguish between soluble phosphorus, adsorbed
or particulate phosphorus, and organic phosphorus in sediment runoff.
Total phosphorus is a somewhat meaningless parameter, since only the
soluble orthophosphate form is readily available for uptake by aquatic
organisms.  Other forms of phosphorus in sediment can, however, act as
a source or sink for subsequent release in available form.

Schuman et al. have reported an empirical relation between sediment phos-
phorus (concentration in ppm,  Cs(PT) ) and soluble phosphorus (concen-
tration in ppm,  Cq(P) ) for Iowa soils.  The relation may be stated as:
                            CQ(P) = a + b-Cs(PT)                  (4-11)

where  a  and  b  are regression coefficients.  The reported values of
a  and  b  are 0.018 and 0.047, respectively.!^/  Equation (4-11) shows
that the ratio of solution phosphorus to sediment phosphorus is just
under 1:20.

Taylor—' suggested that about 107o of the total phosphorus in eroded
soil would be available for aquatic plant growth.
                                   106

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4.4  ORGANIC MATTER

4.4.1  Organic Matter Loading Function

The  loading function is:


                       Y(OM)E = a-Cs(OM)-Y(S)E-rOM                (4-12)

where    Y(OM)E = organic  loading, kg/year  (Ib/year)

              a = a dimensional constant  (10 metric, 20 English)

         CC(OM) = organic matter concentration of soil, g/100 g
           D

           Y(S)E = sediment  loading, MT/year (tons/year)

            rOM = enrichment ratio for organic matter in eroded soil

4.4.2  Evaluation of Parameters in the Organic Matter Loading Function

The value  of  Y(S)E  can be obtained from procedures discussed in
Section 3.0.  The value of  CS(OM)  should be obtained preferably from
current or historical data for a given area, e.g., from the extension
service.  For approximate values,  Cg(OM)  may be taken as equal to
20 x Cs(NT) , where  Cs(NT)  is the total nitrogen concentration in
the  soil.lZ/

The value  of  TQ^ , the enrichment ratio, is more difficult to assess
due to lack of research data.  Values of  rg^  are in the range of 1 to
5.  The enrichment ratio for sandy soils will be high.   Conversely, the
enrichment ratio will be low when the mineral fraction of the soil is
finely divided and highly erodible.  The user should consult with local
soil experts and should use local data when available.

4.5  ACCURACY OF LOADING FUNCTIONS

The accuracy of predicting loads using the loading functions presented
in the preceding sections depends,  to a large extent, on the availability
of reasonably accurate data for evaluating the various  parameters in the
functions.   For example, the nitrogen loading function is composed of
several parameters each of which is in turn a function  of several other
variables.   In addition, several options are available  to the user to
develop the parameter values from his own sources of information which
may alter the prediction accuracy.   However, if the used values reflect
the long-term average rather than a specific year, and  if reasonably
large areas are used such as large watersheds (> 100 sq miles) rather
                                  107

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than individual plots or small watersheds, the expected accuracy can be
reasonably estimated.  Using the reasoning that the error in individual
parameters will tend to cumulate to a larger error, the expected ranges
of predicted values for given "true" or estimated values of load are
presented in Table 4-3.
           Table 4-3.  PROBABLE RANGE OF LOADING VALUES FOR
                     NUTRIENTS AND ORGANIC MATTER
                               Estimated value        Probable range
  Loading function               (kg/ha/year)          (kg/ha/year)

Total N sediment*/                    1                   0.1-10
Total N sediment                     10                     5-20
Total N sediment                     50                    30-75
Total N precipitation*!/               0.3                 0.1-0.6
Total P.£/                             1.0                 0.5-3.0
Total P                               5.0                   2-10
Total P                              10.0                   5-20
Organic matter                       10.0                   5-20
Organic matter                      100                    50-200
a./  Available N in sediment will range from 3 to 87<, of total N.
b/  Available N is equal to total N in precipitation.
£/  Available P in sediment will range from 5 to 10% of total P.
4.6  EXAMPLE OF LOADING COMPUTATION

The watershed given in Section 3.0, entitled "Sediment Loading Func-
tions," for Parke County in Indiana will be used to illustrate the metho-
dology presented in this section for computing the loads.  It is required
to compute available nitrogen, available phosphorus, and organic matter
loading for the given area for the following conditions:

     Average, daily loading;
     Maximum daily loading during a 30 consecutive day period; and
     Minimum daily loading during a 30 consecutive day period.
                                   108

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The following data, plus soil loss data, are required:

     Soil nitrogen content.
     Soil phosphorus content.
     In the absence of reliable soil nitrogen data, soil nitrogen con-
       tent may be calculated from average annual temperature, average
       annual precipitation, average annual relative humidity, and
       saturated vapor pressure at given temperature.

4.6.1  Nitrogen Loading

Using the following data, soil nitrogen content is calculated:

     Average annual temperature = 10°C
     Average annual precipitation = 96.5 cm
     Average annual relative humidity = 70%

Using the nomograph given in Figure 4-3, the value of H factor was de-
termined to be 350.  From Figure 4-2, and using H = 350 and T = 10°C,
the value of  Cs(NT) , the soil nitrogen content was estimated to be
0.204% or 0.204 g/100 g.  Using Eq. (4-1).
                         Y(NT)E = 20-Y(S)E-0.204-2.0              (4-13)

                                = 8.16-Y(S)E

Assuming that 6% of total nitrogen is available,  Y(NA)g = 0.49'Y(S)£.

The values of areal sediment yield as given in the example in Section
3.0, entitled "Sediment Loading Functions," are shown below in Table 4-4.


                 Table 4-4.   SEDIMENT YIELD IN EXAMPLE

Sediment yield (tons/day)
Land use
Cropland
Pasture
Woodland
Daily average
2.88
0.33
0.39
Maximum 30 days
9.36
0.84
0.95
Minimum 30 days
0.72
0.09
0.09
     Total         3.60               11.15                  0.90
                                   109

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The nitrogen loadings are shown in Table 4-5 using the data in Table
4-4 and Eq. (4-13).
     Table 4-5.  AVAILABLE NITROGEN LOADING,  Y(NA)E , IN EXAMPLE

Land use
Cropland
Pasture
Woodland

Daily average
1.41
0.16
0.19
Nitrogen loading (Ib/day)
Maximum 30 days
4.59
0.41
0.47

Minimum 30 days
0.35
0.04
0.04
     Total          1.76                5.47                 0.43
4.6.2  Phosphorus Loading

Assume  CS(PT) = 0.255g/lOOg for the area, 10% of  CS(PT)  is available
phosphorus,  Cg(PA) ; and rp  is 1.5, and using Eq. (4-8);
                     Y(PA)E = 20-Y(S)E-0.255-1.5-0.10              (4-14)

                            = 0.765 Y(S)E

Phosphorus loadings computed from Table 4-2 and Eq.  (4-8) are shown in
Table 4-6.


    Table 4-6.  AVAILABLE PHOSPHORUS LOADING,  Y(PA)s , IN EXAMPLE

Land use
Cropland
Pasture
Woodland

Daily average
2.20
0.25
0.30
Phosphorus loading
Maximum 30 days
7.16
0.64
0.73
(Ib/day)
Minimum 30 days
0.55
0.07
0.07
     Total          2.75                8.53                 0.69

4.6.3   Organic Matter  Loading

Isomg  Eq.  (4-12),  data for   Cg(OM)  ,   Y(S)g  ,   rQM  are  needed.


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Assume that the value of  CS(OM)/CS(NT)  equals 20 and  rQM =2.5,

               Y(OM)E = 20-2.5-Y(S)E'20-Cs(NT)

                      = 1000-CS(NT).Y(S)E

         Using CS(NT) = 0.2%,

               Y(OM)E = 200-Y(S)£                                  (4-15)

The values of organic  loading are computed from Eq.  (4-12) and presented
in Table 4-7.

            Table 4-7.  ORGANIC MATTER LOADINGS IN EXAMPLE


               	Organic matter loading (Ib/day)	
Land use       Daily average      Maximum 30 days      Minimum 30 days

Cropland            576                1,872                 144
Pasture              66                  168                  18
Woodland             78                  190                  18

     Total          720                2,230                 180
                                  111

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                                REFERENCES
 1.   Carroll,  D.,  "Rainwater as a Chemical Agent of Geological Processes -
       A Review,"  GS WS Paper 1535-G,  U.S. Geologic Survey (1962).

 2.   Loehr,  R.  C.,  "Characteristics and Comparative Magnitude of Nonpoint
       Sources," JWPCF, 46(8):1849 (August 1974).

 3.   Phase II  Report, EPA Contract No.  68-01-2293 (Draft submitted in
       November 1975).

 4.   McElroy,  A. D., S. Y. Chiu, and A. Aleti,  "Analysis of Nonpoint
       Source  Pollutants in the Missouri Basin Region," Office of Re-
       search  and  Development,  Environmental Protection Agency, Report
       No. EPA-600/5-75-004 (March 1975).

 5.   Viets,  F.  G.,  Jr., "Fertilizer Use in Relation to Surface and
       Groundwater Pollution,"  In:  Fertilizer Technology and Use (2nd
       ed.), p. 517, Soil Science Society of America, Madison, Wisconsin
       (1971).

 6.   Hagin,  J., and A. Amberger, "Contribution of Fertilizers and Manures
       to the  Nitrogen and Phosphorus Load of Waters.  A Computer Simula-
       tion,"  Technion-Israel Institute of Technology, Haifa, Israel
       (1974).

 7.   Stoltenberg,  N. L., and J. L. White,  "Selective Loss of Plant Nu-
       trients by  Erosion," Soil Science Society of America, Proceedings,
       17:406-410  (1953).

 8.   Massey, H. F.,  M. L. Jackson, and 0.  E. Hays, "Fertility Erosion on
       Two Wisconsin Soils," Agron. J., 45:543-547 (1953).

 9.   Kilmer, V. J.,  "Nutrient Losses Through Leaching and Runoff,"
       Tennessee Valley Authority, Muscle Shoals, Alabama (undated manu-
       script).

10.   Moe, P. G.,  J.  V. Mannering, and C. G. Johnson, "Loss of Fertilizer
       Nitrogen in Surface Runoff Water," Soil Sci. , 104j389-394 (1967).

11.   Timmons,  D.  R., R. F. Holt, and J. J. Latterell, "Leaching of Crop
       Residues as a Source of Nutrients in Surface Runoff Water," Water
       Resources Research, 6:1367-1375 (1970).
                                   112

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12.   Jenny,  H. ,  "A Study on the Influence of Climate Upon the Nitrogen
       and Organic Matter Content of the Soil," Missouri Agr. Exp. Sta.
       Res.  Bui.  152 (1930).

13.   Parker, C.  A. et al., "Fertilizers and Lime in the United States,"
       USDA Misc.  Pub.  No. 586 (1946).

14.   Lopez,  A.  B., and N. L. Galvez, "The Mineralization of the Organic
       Matter of Some Philippine Soils Under Submerged Conditions,"
       Philippine  Agr. ,  4-2:281-291  (1958), cited in Ref. 17.

15.   Schuman, G.  E., R.  G. Spomer, and R. F. Piest, "Phosphorus Losses
       from Four Agricultural Watersheds on Missouri Valley Loess,"
       Soil Science Society of America, Proceedings, 3_7_(2):424 (1970).

16.   Taylor, A.  W., "Phosphorus and Water Pollution," J. Soil and Water
       Conserv. , £2:228-231 (1967).

17.   Buckman, H.  0., and N. C. Brady, The Nature and Properties of Soil,
       7th ed.,  The MacMillan Company,  New York (1969).
                                   113

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                              SECTION 5.0

                              PESTICIDES

5.1  INTRODUCTION

Pesticides dissipate by several mechanisms:  chemical degradation (hy-
drolysis; oxidation); biochemical degradation by soil organisms and
enzymatic systems; volatilization; absorption in plant or animal tissue,
with or without decomposition; leaching into subsurface soils, possibly
into subsurface aquifers; and overland transport in surface runoff and
eroded sediment.  Losses by leaching processes and by overland transport
mechanisms are relevant to contamination of water.  Pesticide loading
functions must relate mechanisms for these processes to quantities de-
posited in surface waters.  The total load of pesticide deposited in
surface waters equals the sum of (a) pesticide transported overland,
and (b) pesticide transported by subsurface processes (leaching, soil
moisture movement, drainage water movement, groundwater discharge to
surface).  Soluble pesticides are subject to leaching into subsurface
soils and waters, solubilize in overland runoff water, and are also
transported overland as sediment-bound material.  Insoluble pesticides
are transported to surface waters primarily by being carried on eroded
sediment.

Data requirements for a precise pesticide loading function are as fol-
lows:

1.  Quantity of pesticide in the source, expressed as some suitable
function of the area, volume, or mass of the source, e.g., concentration
in erodible soil layer; concentration and concentration distribution in
leachable soil profile.  The quantity information should be time spe-
cific, i.e., detail source quantities/concentrations as a function of
time elapsed since application, season, etc.  Since most pesticides de-
grade, rates of degradation are needed to enable calculation of source
quantities as a function of time.
                                   114

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2.  Quantitative data on overland runoff, by month, season, and year.

3.  Quantitative data on sediment transported from the source and deliv-
ered to surface streams.

4.  Quantitative data on percolation; seepage; drainage water inventories
and movement; and groundwater inventories and movement.

5.  Accurate coefficients, rate constants, etc., for desorption--
solubilization--leach transport of pesticides through soil columns, of
numerous possible soil types.

6.  Information on miscellaneous modes of pesticide removal from the
source, such as by volatilization or by removal in harvested vegetative
matter.

Some of the required data is not available or is unknown, and other data
are known or available in varying accuracy and degree of coverage of
source situations.

The approach to estimation of contamination of water by pesticides will
therefore vary in response to a combination of three factors:  (a) degree
of required accuracy; (b) availability of data; and (c) capabilities of
predictive functions.  The greatest impediment is lack of data.  Loading
functions and approaches to estimation of pesticide pollution are pre-
sented, in succeeding sections, for three source conditions.  These are:

Case 1 - Water Insoluble Pesticides:  Average concentrations of pesticide
in soils known.  Pesticide load is calculated as a function of sediment
loads.  Approach most applicable to large areas.  Use limited to annual
average loads.

Case 2 - Water Insoluble Pesticides:  Pesticide use history accurately
known, soil analytical data current and extensive, pesticide properties
(especially rates of disappearance) well known.  Calculate  load as fun-
ction of sediment loss; useful for annual average, 30-day maximum, 30-day
minimum.

Case 3 - Water Soluble and Water Insoluble Pesticides:  Concentrations
in runoff waters known, runoff water flows known (stream source approach).
Calculate loads at watershed discharge points, distribute  load over water-
shed land uses in proportion to known or probable pesticide use.

These approaches or options do not treat pesticides discharged to ground-
water aquifers and subsurface drainage.  The latter can be  treated if
                                   115

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drainage discharge flows are known, together with concentrations of pes-
ticides in the drainage.  Pesticide contamination in groundwater aquifers
is presently a research area.

These approaches do not preclude the use, in special or highly documented
situations, of research models or approaches which are being locally de-
veloped by research scientists.

5.2  PESTICIDE LOADING FUNCTIONS

5.2.1  Case 1;  Insoluble Pesticides, Average Soil Concentrations L jwn

The loading function is:


                     Y(HIF)* = Y(S)E-C(HIF)-1(T6-A                  (5-1)

where    Y(HIF) = pesticide yield for source, kg/day (Ib/day)

          Y(S)E = sediment yield, kg/ha/day (Ib/acre/day)

         C(HIF) = concentration of pesticide in soil (ppm)

              A = size of source, ha (acres)

Sediment yields,  Y(S)E  , for the source are calculated by methods pre-
sented in Section 3.0.

Pesticide concentrations in soils throughout the United States are being
monitored by the Environmental Protection Agency, Office of Pesticide
Programs, in the National Pesticide Monitoring Program (NPMP).  Data re-
positories for this monitoring program are a source of average soil con-
centration data.  Results for 35 pesticides are summarized, for FY 1969
in Pesticides Monitoring Journal, ^(3):194-228, 1972.  (This article is
reproduced in Appendix F.)  The FY 1969 data cover cropland soils in 43
states and noncropland soils in 11 states.

The NPMP FY 1969 study is a source of soil concentration data which may
be used as  C(HIF)  values in Eq. (5-1).  The "range of detected residues"
will serve as input for calculation, with Eq. (5-1), of the range of pes-
ticide loads which may be expected in the area of interest.  Similarly,
the "percent positive sites" indicate whether a particular pesticide is
*  KEF denotes Herbicide, Insecticide, and fungicide.
                                  116

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distributed over much of the area or has limited distribution.  The NPMP
information thus tends to be useful chiefly for estimating possible ex-
tremes in pesticide loads and for estimating total loads from a large
area such as a minor river basin.

It is imperative that the user of this function obtains up-to-date site
or area-specific data on soil concentrations.  Current NPMP data should
be consulted, as should local sources of data, notably universities,
state and local health departments, and environmental agencies.

5.2.2  Case 2;  Water Insoluble Pesticides, Current Area-Specific Data
         Available

Case 2 covers the source with well-documented concentration data obtained
by analysis of samples taken from the source, in combination with pesti-
cide use data and knowledge of the persistence of the pesticide.  If the
source is sampled frequently at well-distributed sampling sites, other
information may be unnecessary.  If the sampling is less complete, in-
formation on application rates and persistency will help deduce concen-
trations.  The basic loading function is the same as for Case 1, i.e.,
Eq. (5-1).  The values used for  C(HIF)  are determined from different
sources than the sources for Case 1.  Guidelines for determining  C(HIF)
follows:

1.  Document beginning of the season residual concentrations, if any, of
pesticides of interest.

2.  Obtain data on application rates and schedules.  Calculate concentra-
tion in surface soils (3 to 5 cm (1 to 2 in,)) of applied pesticide, tak-
ing into account the fraction of the pesticide which reaches the soil
surface, and the depth the pesticide is mixed into the soil.

3.  Add values from Steps 1 and 2 to obtain an initial concentration.

4.  From information on pesticide persistency, estimate fraction of pes-
ticide which remains after appropriate intervals of time:   days for short-
lived pesticides;  months for pesticides with growing season persistency;
and years for long-lived pesticides.

5.  If pesticide is applied more than once per season, repeat Steps 1,
2, 3,  and 4 for each application and estimate concentration throughout
growing season and up to the start of the next growing season.

6.  Calculate sediment loads,  Y(S)g , from sources by procedures pre-
sented in Section 3.0.
                                   117

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Calculate annual average  Y(S)_  if pesticides are relatively persistent
                              Ei
and a reasonable yearly average value can be deduced.   Calculate  Y(HIF)
from Eq. (5-1):

             Y(HIF) average = *(S)E average-C(HIF) average-1Q-6

Calculate  Y(S),.,  by months if pesticide concentrations vary widely through-
out the year.  Calculate  Y(HIF)  annual average,  30-day maximum and 30-day
minimum by calculating monthly loads.

             Y(HIF) monthly = Y(S)E monthlyC(HIF)•10"6

Sum for a year to obtain annual average.  Select 30-day maximum and 30-
day minimum from computed monthly loads.

5.2.3  Case 3;  Water Soluble and Water Insoluble Pesticides, Stream to
         Source Approach

Water soluble pesticides are in part transported overland in surface
runoff and absorbed on sediments; they are also susceptible to migration
downward in the soil column, where they are not subject to overland trans-
port mechanisms.  For lack of a procedure for predicting the ultimate fate
of the fraction which moves downward from the surface, it has been by-
passed in loading function development.  That fraction transported over-
land may be estimated if runoff is measured and analyzed for pesticides.
Specifically, watershed hydrographs for storm events must be determined
by measurement, or calculated from predictive models,!—-'  and concentra-
tions of pesticides determined for water samples collected at various
stages of the hydrograph(s)."  The data so obtained convert to pesticide
loads by multiplying increments of flow by the respective concentration
values:

                     Y(HIF) storm event = SQ^i' a                   (5-2)

where        Qj[ = increment of flow

             Ci = C(HIF) of the ith increment of flow

              a = 10   if dimensions of Q and C are liters and ppm

                          f\                                  ^
              a = 62 x 10   if dimensions of Q and C are feet  and ppm

             Units of Y(HIF):  kilograms (Ib)
*  Base flow (nonstorm event) stream data on flows and concentrations
     will not suffice.  Many pesticides decompose in water and may be-
     come trapped in bottom sediments.  Concentrations under base flow
     conditions do not accurately reflect storm event loads.

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The storm event load can be distributed back to the land by several op-
tions; for example:

     Uniformly over the watershed.
     Nonuniformly to broad categories of sources, e.g., row crops.
     Specifically to identified or suspect sources, in proportion to
       source size.

It will be necessary to sum storm events for the season, perhaps for the
year, to obtain annual loads.  The 30 day maximum loadings fall naturally
out of cumulative storm event loads.

This procedure has several limitations and disadvantages.  Extensive use
will be costly, and limited use will not suffice to adequately describe
large areas.  An appropriate use is as follows:  with selective runoff
measurement and analysis it will be possible to develop the relatively
modest inventory of data and experience needed to estimate pesticide
loads for sensitive areas, e.g., an intensive agricultural area which
depends heavily on herbicides and insecticides, and has a relatively
stable and predictable pattern of use.  Combination of accumulated in-
formation on pesticide use patterns with representative measured con-
centrations and loads of pesticides will more than adequately serve as
a predictive "loading function."  Since many of the persistent pesti-
cides are being phased out, the peak loads which occur in storms which
follow pesticide application are increasingly important.  This basic
approach will, if properly used, deal with this problem adequately.

5.3  GENERAL INFORMATION

5.3.1  Pesticide Solubility

The dividing line between solubility and insolubility is diffuse and is
affected by factors such as the presence of other constituents in the
solution phase, pH, soil acidity, and organic matter in soil.   Solubility
denotes,  for purposes of the handbook, relatively little to moderate re-
sistance to leaching, and insolubility denotes moderate to high resistance
to leaching.  Limited solubility data and indices of leachability are
presented in Appendix G, Table G-2.   A pesticide with a leaching index
of one or two is treated as "insoluble." An index of three or four is
treated as "soluble."

5.3.2  Pesticide Persistence

General information on persistence is presented in Appendix G.  Particu-
larly relevant to load calculation are the data which, though only semi-
quantitative, permit estimation of rates of disappearance in soils.   Resi-
dues, concentrations and percent losses of selected pesticides are compiled
from recent literature and presented in Table G-3.

                                    119

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5.4  LOAD CALCULATION:  EXAMPLES

Case 1 Method

Conditions:  Refer to Section 3.0, entitled "Sediment Loading Function."

     Dieldrin
     Continuous corn
     A = 73 ha
     Y(S)g (30-day maximum) = 117 kg/ha/day
     Y(S)  (annual average) = 36 kg/ha/day
     C(I) range, 0.01 to 0.58 ppm from Appendix F, Table F-3

     Probable minimum load, annual average
     Y(I) = 36-0.01-73 x 10-6 = 26 x 10-6 kg/day

     Probable maximum load, annual average
     Y(I) = 36-0.58-73 x 10-6 = 1,524 x 10~6 kg/day

Case 2 Method

Conditions:  Refer to above example.

     2,4-D
     Application rate:  5 kg/ha
     Application date:  15 June
     Persistence:  4 weeks (Appendix G)
     Residue zero at season start
     Y(S)E = 117 kg/ha/day, for 1-month period, 15 June to 15 July

Calculations

     Initial concentration in erodible soil layer (5 cm), about 5 ppm
     Average concentration, estimated from persistence information
       equals 2 to 3 ppm for 15 June to 15 July period
     Y(H) = 117 x 2.5 x 73 x 10'6 = 0.0214 kg/day
     Y(H) (30-day maximum) = 0.0214 kg/day

5.5  LIMITATIONS IN USE

As stated earlier, pesticide behavior in the environment is both complex
and variable, and the accuracy of estimation reflects these complexities.
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The National Pesticide Monitoring Program, which serves as the basis for
Case I, contains data which generally indicate levels of pesticides in
soils throughout the country, and the frequency at which pesticides are
observed is an indication of the intensity of the use pattern.  The data
and the Case I method should, however, be used only to derive an estimate
of loads over very large areas, and the results should be presented with
two qualifications:  (a) that peak loads for nonpersistent pesticides are
apt to be overlooked by the method; and (b) that pesticides which leach
readily into the soil (and thus may contaminate subsurface waters) will
not be accounted for.  Examination of the range of values reported in the
NPMP system reveals the fact that loads calculated from that data base
may differ substantially from actual loads, especially if one wishes to
apply calculated loads to a specific small area.

The Case II method depends upon area-specific and pesticide-specific data,
and thus will calculate loads considerably closer to actual values than
Case I.  Since the data requirement is fairly extensive, its use is prob-
ably restricted to a small region--several counties perhaps—in which
pesticide use is uniform and other parameters are also relatively uniform.
The Case II method will, with care in use, be somewhat sensitive to peak
loads, i.e., when it rains soon after pesticide application.

The Case III method can be accurate with care in use.  As indicated in
Section 5.2, the approach is probably best used to develop data and ex-
perience at local or regional levels, so that pesticide loads can be
estimated with confidence but not necessarily with a high degree of ac-
curacy.

Estimates of accuracy expected for Cases I through III are presented in
Table 5-1.
                                   121

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           Table 5-1.  ESTIMATES OF ACCURACY FOR PESTICIDES
Annual average
(g/ha/year)
Probable
Estimated range
Storm event
(g/ha/day)
Probable
Estimated range
Case 1 method
  (insoluble pesticide)
1-10
0.001-100
                        Not  applicable
Case 2 method                 1
  (insoluble pesticide)      20

Case 3 method                 1
  (soluble and insoluble     20
    pesticides)
          0.01-10
             5-50

           0.1-5
            10-50
               1
              20

               1
              20
0.1-10
  5-50

0.1-5
 10-50
                                   122

-------
References

1.  Holton, H. N., and N. C. Yokes, "USDA HL-73 Revised Model of  Water-
      shed Hydrology," Plant Physiology Report No.  1,  ARS-USDA (1973).

2.  Crawford, N. H., and R. K. Linsley, "Stanford Watershed Model IV,"
      Stanford University, Stanford, California, Technical Report No.
      39 (1966).

3.  Crawford, N. H., and A. S. Donigian, Jr.,  "Pesticide Transport and
      Runoff Model for Agricultural Lands," Office  of  Research and
      Development, U.S. Environmental Protection Agency, EPA-660/2-74-
      013 (December 1973).

4.  Frere,  M. H.,  C. A. Onstad,  and H.  N.  Holtan, "ACTMO,  an Agricultural
      Chemical Transport Model," ARS-H-3,  ARS-USDA, June 1975.
                                  123

-------
Bibliography

Bailey, G. W., and J. L. White, "Review of Adsorption and Desorption of
  Organic Pesticides by Soil Colloids with Implications Concerning Pes-
  ticide Bioactivity," J. Agr. Food Chem., 12_ (1964).

Edwards, C. A., "insecticide Residues in Soils," Res. Reviews, JL3 (1966).

Frissel, M. J8, "The Adsorption of Organic Compounds, Especially Herbi-
  cides on Clay Minerals," Verslag, Landbouwk, 7j5_ (1961) „

Getzin, L. W., "The Effect of Soils Upon the Efficiency of Systemic In-
  secticides with Special Reference to Thimet," Dissertation Abstract,
  19. (1958).

Hamaker, J« W., Mathematicl Prediction of Cumulative Levels of Pesticides
  in Soils, Advances in Chemistry 60-Organic Pesticides in the Environ-
  ment, American Chemical Society, Washington, D.C«

Kiigemagi, U», "Biological and Chemical Studies on the Decline of Soil
  Insecticides," J. EC on. Entomol., 51^ (1958).

Lichtenstein, Es P., and K. R. Schulz, "Insecticide Residues Colorimetric
  Determinations of Heptachlor in Soils and Some Crops," J. Agr. Food
  Chem., 12.  (1964).

Nauman, K., Einfluss von Pflanzenschutzmittel auf die Bodenmikroflora,
  Hit.  Biol. Bund., anst. Berlin, 9^  (1959).

"Production, Distribution, Use and Environmental Impact Potential of
  Selected Pesticides," Final Report by Midwest Research Institute,
  Kansas City, Missouri and RvR Consultants, Shawnee Mission, Kansas,
  March 1974.

U.S. Department of Agriculture, "Quantities of Pesticides Used by
  Farmers  in 1971," Economic Research Service, Washington, D.C., in
  press (1974).
                                   124

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                              SECTION 6.0

                  SALINITY IN IRRIGATION RETURN FLOW

6.1  INTRODUCTION

The accurate prediction of salinity emissions in irrigation return flows
requires detailed knowledge of the particular system being studied.  Prac-
tice has shown that salinity in irrigation return flows varies widely in
differing regions of the country because of the specific natures of the
soils, underlying geological formations, regional topography, and irriga-
tion practices.  As a result, a simple "loading function" applicable to
all irrigation cases has no validity under present state of the art.  A
discussion of the data needs for irrigation return flow salinity models
pointing out this fact has been prepared by the Environmental Protection
Agency.i'

For purposes of making assessments of salinity from irrigation return
flow, three optional methods are suggested in this section.  The user is
cautioned, however, that the methods are not universally applicable and
hence may yield estimates that are not accurate.   The most accurate pre-
diction method remains long-term monitoring of the particular irrigation
area to quantify actual salinity outputs in irrigation return flow.

The three procedures presented here for estimating salinity in irrigation
return flow are:

Option I - Source to Stream Approach:   The first  option involves the es-
timation of irrigation water percolating into groundwater.  The irriga-
tion water acts as a hydraulic "head"  on the groundwater, which pushes
the groundwater into surface waters as subsurface return.  This approach
is valid for only a few areas of the country when valid relationships
between applied water and return flows exist.   Furthermore, this option
should not be used in cases of spray irrigation where evaporative losses
associated with the applied water are  significant.   This option is most
valid in those cases where the total dissolved solids in groundwater
                                  125

-------
contributing to return flow are very high (ca.  10 times) compared to total
dissolved solids concentrations in applied water.

Option II - Stream to Source Approach:  The second option involves a back-
estimation procedure for salinity discharges in irrigation return flow.
Salinity measurements taken at sampling points above and below irrigated
areas will establish the amount of salt discharged in the area drained
by the stream between the two points.  This salt load, however, includes
that discharged from background, salt springs,  and point sources, as well
as that discharged from irrigation return flow.  This method requires a
good definition of salinity sources other than irrigation return flow,
particularly that of background.  This method is the one which has been
most widely used by others, especially where the total salinity loads are
measured at the discharge points of drainage basins.

Option III - Loading Values for Salinity in Irrigation Return Flow:  A
third method for estimating salinity loads in irrigation return flows is
the use of loading values established for given areas through reduction
of stream monitoring data.   A list of such loading values for areas in
the Colorado River basin are presented.  These values are applicable only
to the particular region and should not be used except where indicated.

6.2  OPTION I:  SOURCE TO STREAM APPROACH

6.2.1  Load Estimation Equation and Information Needs

An equation to estimate salinity in irrigation return flow has been form-
ulated based upon data reported by Skogerboe et al._'  The equation is:
                 Y(TDS)IRF = a-A-C(TDS)GW-[lRR + Pr - Cu]         (6-1)

where Y(TDS)jTvp = salinity load in irrigation return flow, kg/day (lb/
                    day)

              A - area under irrigation, ha (acre)

            IRR = volume of water added to crop root zone annually for
                    irrigation, cm (in.)

             Pr = annual precipitation, cm (in.)

             CU = annual consumptive use of water in growing crops, cm
                    (in.)
                                   126

-------
                = average concentration of total dissolved solids in
                    groundwater contributing to subsurface return, ppm

              a = conversion factor to obtain proper units of load.  If
                    Y is in kg/day, a = 2.7 x 10~4; if Y is in Ib/day,
                    a = 6.2 x 1CT4.

The volume of water applied to the crop root zone,  IRR , can be deter-
mined by subtracting the volume of tailwater from the total water de-
livered to the irrigation site.  This information would be available
from local irrigation districts.

Annual precipitation,  Pr , is available from local weather data.  Aver-
age annual precipitation can be used for purposes of estimating gross
salinity loads.

The  CU factor, consumptive use, can be estimated by standard formulae
such as Jensen-Haise Method or the Blaney-Criddle Method.  The Jensen-
Haise Method for estimating consumptive use is described in detail in
                                                     p /
the Skogerboe et al. report on irrigation scheduling.—'   Information
needed for the Blaney-Criddle consumptive use formula can be found in
Todd's Water Encyclopedia.3/

The key data needed in the irrigation return flow loading function are
the groundwater total dissolved solids concentrations, C(TDS)Q^J.  These
values must represent groundwater which maintains perennial strearaflow.
In general, the quality of water in perched water tables is the proper
information.  For large irrigation areas, one should use an average
groundwater TDS value obtained from several observation wells.


The user is cautioned to avoid using the Option I method for cases involv-
ing sprinkler irrigation methods.   This method does not account for
evaporation losses during application.   If valid information is available
concerning evaporation losses, it should be incorporated into the esti-
mation procedure.   Evaporation basically will cause an increase in the
TDS  of applied water which will show up as increased  TDS  in the ground-
water contributing to return flow.

6.2.2  Load Calculation - Irrigation Return Flow

Load calculation involves three basic steps:

1.  Obtain necessary information for Eqs.  (6-1), (6-2),  or (6-3) from
sources identified above.
                                   127

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2.  Substitute values into appropriate equations (Eqs. (6-1), (6-2), or
(6-3)).

3.  Compute loads.

The Option I loading value equation (Eq. (6-1)) has been used to esti-
mate loads which can be compared directly to those reported by Skogerboe
      I/
Skogerboe.
et al.—'   Data used as inputs to the equation were those measured by
            Data inputs for Eq.  (6-1) are tabulated in Table 6-1, to-
gether with calculated loads.   These are compared with reported loads.
 Table 6-1.  COMPARISON OF SALINITY LOADS OBTAINED WITH OPTION I LOAD
      ESTIMATION EQUATION WITH REPORTED SALINITY LOADS2-/ IN THE
                        GRAND VALLEY, COLORADO
    (Essential information:  a = 6.2 x 10"^; C(TDS)GW = 6,700 ppm)

  Equation
  factors   Plot No. 1  Plot No. 2  Plot No. 3  Plot No. 4  Plot No. 5
A (acre)
IRR (in.)
Pr (in.)
CU (in.)
Calculated
load
(Ib/day)
Reported
load
(Ib/day)
8.5
31.4
1.0
26.9
194


379


                             8.
                            23,
                             4.1
                            19.
                           293
                           344
25.7
42.1
1.2
33.5
1,046
15.0
29.1
2.7
20.7
692
                                       1,291
521
                                                                 10
                                                                 24
                                                                  3.3
                                                                 17.
                                                                484
545
As can be seen from the comparison, the calculated loads compare reason-
ably with reported loads in four out of the five cases.  One reason for
discrepancies between the calculated and reported values may be that the
equation disregards changes in soil moisture storage during the year.
In general, the changes in soil moisture storage which occur during and
between irrigation events should add to zero over an annual period, and
hence would have little effect on annual irrigation return flow volume.
Some irrigation water applied to the crop root zone is retained as soil
moisture, and hence does not show up as either consumptive use or irriga-
tion return flow.  Soil moisture storage is an information input which is
not readily accessible.
                                  128

-------
The Option I loading value equation should be considered only as a first
approximation method for estimating salinity in irrigation return flow.
Its usefulness will depend primarily upon three factors:  (1) the concen-
tration of total dissolved solids in shallow groundwater which is trans-
mitted to surface waters as subsurface return; (2) reliable estimates of
the volume of applied water, tailwater, and return flows; and (3) good
information pertaining to consumptive losses in the complete irrigation
system.  If these data are deemed insufficient, one should estimate sa-
linity in irrigation return by other procedures.

6.3  OPTION II:  STREAM TO SOURCE APPROACH

6.3.1  Loading Equation and Information Needs

A second method for estimating salinity loads from irrigation return
flow involves the stream to source approach.  In this option, salinity
loads in streams are determined above and below areas of irrigation.
Differences in salinity loads represent total salt being discharged by
the area by background and point sources, as well as irrigation return
flow.  Therefore, salt loadings from irrigation return flow are deter-
mined by subtracting out contributions from background and from point
sources.

The Option II loading value equation is:


Y(TDS)1RF = a-[Q(str)B-C(TDS)B - Q(str)A«C(TDS)A] - Y(TDS)BG - Y(TDS)pT   (6-4)

where Y(TDS)jgj. = yield of salinity in irrigation return flow, kg/day
                    (Ib/day)

       Y(TDS)BG = salinity load contribution of background, kg/day (lb/
                    day)

       Y(TDS)p,j, = salinity load contribution of point sources, kg/day
                    (Ib/day)

        Q(str)g = streamflow of surface water be low irrigated areas,
                    liters/sec (cfs)

        Q(str)^ = streamflow of surface waters above irrigated areas,
                    liters/sec (cfs)

        C(TDS)g = concentration of total dissolved solids in stream
                    below irrigated area, ppm
                                   129

-------
        C(TDS)  = concentration of total dissolved solids in stream
                    above irrigated areas,  ppm

              a = conversion constant needed to obtain proper units of
                    load.  If flow units are liters/sec,  a = 0.0864
                    (metric system, kg/day).  If flow units are cfs,
                    a = 5.39 (English system,  Ib/day).

Flow and concentration data obtained above  and below irrigated areas can
be obtained from U.S. Geological Survey records of the region, or in some
cases from local water quality monitoring data.  The use  of these data in
the loading value equation will indicate total salt added to surface waters
between two points.

The salt load from point sources in the area under consideration can be
determined using information supplied by persons responsible for the point
sources.  Point source contributions may be estimated from data contained
in discharge permit applications available  from state and local pollution
control agencies, and from regional Environmental Protection Agency offices.
The total dissolved solids from the individual point sources in the area
are summed to yield total point source contributions.

The most difficult piece of information to  be obtained is quantities of
salt discharged from background.  In many cases, particularly in the arid
and semiarid regions where irrigation is intensive, this  estimation can
only be accomplished by knowledge of the characteristics  of the particular
area.

This estimation relies upon the judicious use of information concerning
background in a particular region.  The use of broad general definitions
of background such as those presented in Section 12.0 of  this handbook is
not recommended for the Option II method for salinity in  irrigation return
flow.  An estimation of background TDS levels may be made using the U.S.
Geological Survey's Hydrologic Investigations Atlas, HA-61, Plate l.^y
This plate contains information concerning  dissolved solids concentration
for surface waters throughout the conterminous United States.  It does
not differentiate between point and nonpoint contributions to salinity,
nor does it account for cumulative effects  of runoff from a wide variety
of sources into stream water.  The use of this map is recommended as a
first approximation of background.

The equation needed to define background total dissolved  solids load can
be formulated in two ways, depending upon the units of flow.  If flow is
measured as annual average runoff, the equation is:
                                   130

-------
                        Y(TDS)BG = a-A-Q(R)-C(TDS)BG              (6-5)

where  Y(TDS)BQ = salinity load from background, kg/day (Ib/day)

              A = area under consideration, ha (acre)

           Q(R) = flow, as annual average runoff, cm (in.)

       C(TDS)BG = concentration of background total dissolved solids as
                    determined by local information, ppm

              a = conversion constant to obtain proper units of load.
                    If load is kg/day, a = 2.7 x 10"^; if load is Ib/day,
                    a = 6.2 x 10-4.

If flow is measured as actual flow in liters per sec (cfs), the equation
for estimating salinity loads in background becomes:


                Y(TDS)BG = a-C(TDS)BG-[Q(str)B - Q(str)A]         (6-6)

where  Q(str)g  and  Q(str)^  are the flows be low and above the irrigated
areas, respectively.  If the load is kg/day, a = 0.0864; if the load is
Ib/day, a = 5.39.  The concentration of total dissolved solids in back-
ground,  C(TDS)gQ , is the same as defined previously.

After proper information has been obtained, it is substituted into the
correct background total dissolved solids equation  (Eqs. (6-5) or (6-6)),
and background total dissolved solids load computed.

6.3.2  Option II Load Calculation

The step-by-step procedure presented below is used  for Option II stream
to source load calculations.

1.  Obtain needed flow and concentration information for points above and
below irrigated areas.  In many cases, information  obtained at the mouth
of a drainage basin containing irrigated agriculture is sufficient, thus
obviating the need for above stream data.

2.  Estimate total salinity loads above and below irrigated areas using
proper flow and concentration data.  The total salinity load from irri-
gated areas, including its nonirrigated land uses,  is determined by sub-
tracting upstream load from downstream load, via Eq. (6-4).
                                   131

-------
3.  Obtain data pertaining to point source contribution and sum indi-
vidual point sources to obtain total point load.

4.  Determine background total dissolved solids load using Eqs. (6-5)
or (6-6) and procedures outlined previously in this section.

5.  Estimate salinity load from irrigation return flow by subtracting
values obtained in Steps 3 and 4 from the value obtained in Step 2.

             Y(TDS)IRF = Step 2 - Step 3 - Step 4

                       = Y(total) - Y(background) - Y(point)

The Option II stream to source approach for estimating salinity loads
in irrigation return flow has been applied to several subbasins of the
Colorado River.  Values generated by the Option II load estimation equa-
tion have been compared with values reported by the Environmental Pro-
tection Agency in Appendix A to their report concerning the "Mineral
Quality Problem in the Colorado River Basin."  Results of the compari-
son are presented in Table 6-2.
   Table 6-2.  COMPARISON OF SALINITY LOADS ESTIMATED BY OPTION II
                METHODS WITH THOSE REPORTED BY EPA§/

Flow at
basin
mouth
Basin (cfs)
C(TDS) at
basin
mouth
(ppm)

C(TDS)BG
estimate
(ppm)
Calculated
load using
Option II
(tons/day)

Reported
load
(tons/day)
Black Forkil/
Gunnison£'
Big Sandy
Whit&§/
  663
3,100
  140
  901
  495
  558
2,190
  472
  200
  200
1,300
  300
  527
2,990
  336
  217
  481
3,100
  200
   20
a/  U.S. Environmental Protection Agency, Regions VIII and IX, "Natural
      and Man-Made Conditions Affecting Mineral Quality," Appendix A of
      EPA Report, The Mineral Quality Problem in the Colorado River
      Basin  (1971).
b_/  Reference a, Figure 20.
£/  Reference a, Figure 34.
d/  Reference a, Figure 18.
e/  Reference a, Figure 25.
                                   132

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From the data in Table 6-2, it is seen that Option II tends to overpre-
dict salinity in irrigation return flow.  The overprediction may be due
to conservative estimates of background contributions, or to emissions
from unknown natural point sources such as salt springs.  The data in
Table 6-2 clearly point out the fact that background, particularly that
in arid or semiarid areas, needs to be carefully considered.  For example,
the high background level in the Big Sandy Creek area is due to water
seepage from saline lake beds in the area.  Such characteristics must be
known if the Option II approach is to yield valid results.

6.4  OPTION III:  LOADING VALUES FOR SALINITY LOADS IN IRRIGATION RETURN
       FLOW

Perhaps the most useful method of estimating salinity loads is through
loading values determined for particular regions.  Lists of such values
are presented in Tables 6-3 through 6-7 for subbasins in the Colorado
River basin, and for irrigated regions in California.

Studies in the Twin Falls area and the Colorado River basin indicate that
the range of values for salt pickup  from irrigated lands is roughly 1.3
to 22 MT/ha/year (0.5 to 8 tons/acre/year)..£'   An average salt pickup rate
might be 5 MT/ha/year (2 tons/acre/year).  On a per day basis, the range
becomes 3 to 50 kg/ha/day (3 to 44 Ib/acre/day),  and the average becomes
12 kg/ha/day (11 Ib/acre/day).

6.5  ESTIMATED RANGE OF ACCURACY

The accuracy of the three optional procedures for estimating salinity
loads from irrigation return flow will be no better than the accuracy of
the input data.  For this particular system, the quality of the input
data is likely to be quite variable.  More often than not, the quality
of input data will be less than that desired by the user.  In addition,
the estimation procedure mechanisms tend to compound errors inherent in
the input data.

With these factors taken into account, ranges of error for Options I and
II have been estimated.  The Option III method--loading values--is the
most accurate method if proper input data are available.  However, its
use requires loading values generated from on-site data, and such data
are most often not available.

Table 6-8 presents the estimated range of error for the Option I (Source
to Stream) procedure.  The error is estimated for several ranges of areas
which emit an average annual load of either 1 or 10 MT/year.
                                   133

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            Table 6-3.  SALT YIELDS FROM IRRIGATION IN GREEN RIVER SUBBASIN3./
                                                       Average salt yield
              Area                        (tons/acre/yr)    (kg/ha/day)    (Ib/acre/day)

Green River above New Fork River                0.1            0.6            0.5
Big Sandy Creek                                 5.6           34.3           30.7
Blacks Fork in Lyman area                       2.4           14.7           13.2
Hams Fork                                       0.3            1.8            1.6
Henry's Fork                                    4.9           30.1           26.9
Yampa River above Steamboat Springs             0.2            1.2            1.1
Yampa River, Steamboat Springs to Craig         0.4            2.5            2.2
Milk Creek                                      1.0            6.1            5.4
Williams Fork River                             0.3            1.8            1.6
Little Sanke above Dixon                        0.3            1.8            1.6
Little Sanke, Dixon to Baggs                    0.5            3.1            2.7
Ashley Creek                                    4.2           25.8           23.0
Duchesne River                                  3.0           18.4           16.4
White River below Meeker                        2.0           12.3           11.0
Price River                                     8.5           52.2           46.6
San Rafael River                                2.9           17.8           15.9
aj  U.S. Environmental Protection Agency,  Regions VIII and IX,  "Natural and Man-
      Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
      Mineral Quality Problem in the Colorado River Basin (1971).
     Table 6-4.  SALT YIELDS FROM IRRIGATION IN UPPER COLORADO MAIN STEM SUBBASIN§/
                                                       Average salt yield
              Area                        (tons/acre/yr)    (kg/ha/day)    (Ib/acre/day)

Main stem above Hot Sulphur Springs             0.3            1.8            1.6
Main stem, Hot Sulphur Springs to               0.9            5.5            4.9
  Kremmling
Muddy Creek Drainage Area                       2.4           14.7           13.2
Brush Creek                                     0.7            4.3            3.8
Roaring Fork River                              3.5           21.5           19.2
Colorado River Valley, Glenwood Springs         2.3           14.1           12.6
  to Silt
Colorado River, Silt to Cameo                   3.5           21.5           19.2
Grand Valley                                    8.0           49.1           43.8
Plateau Creek                                   0.9            5.5            4.9
Gunnison River above Gunnison                   0.3            1.8            1.6
Tomichi Creek above Parlin                      0.3            1.8            1.6
Tomichi Creek, Parlin to mouth                  0.3            1.8            1.6
Uncompahgre above Dallas Creek                  4.5           27.6           24.7
Lower Gunnison                                  6.7           41.1           36.7
Naturita Creek near Norwood                     2.8           17.2           15.3
 aj  U.S. Environmental Protection Agency, Regions VIII and IX, "Natural and Man-
      Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
      Mineral Quality Problem in the Colorado River Basin (1971).

                                         134

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          Table 6-5.  SALT YIELDS FROM IRRIGATION IN SAN JUAN RIVER SUBBASIN^/
                                                       Average salt yield
              Area

Fremont River above Torrey, Utah
Fremont River, Torrey to
  Hanksville, Utah
Muddy Creek above Hanksville, Utah
San Juan above Carracas
Florida, Los Pinos, Animas drainage
Lower Animas Basin
LaPlata River in Colorado
LaPlate River in New Mexico
(tons/acre/yr)
0.4
5.8
3.1
2.7
0.2
3.5
1.4
0.3
(kg/ha/day)
2.5
35.6
19.0
16.6
1.2
21.5
8.6
1.8
(Ib/acre/day)
2.2
31.8
17.0
14.8
1.1
19.2
7.7
1.6
£/  U.S. Environmental Protection Agency, Regions VIII and IX, "Natural and Man-
      Made Conditions Affecting Mineral Quality," Appendix A of EPA Report, The
      Mineral Quality Problem in the Colorado River Basin (1971).
         Table 6-6.  SALT YIELDS FROM IRRIGATION IN LOWER COLORADO RIVER BASIN^-/
                                                       Average salt yield
              Area

Virgin River
Colorado River Indian Reservation
Palo Verde Irrigation District
Below Imperial Dam
  (Gila and Yuma projects)
(tons/acre/yr)
2.3
0.5
2.1
variable
(kg/ha/day)
14.1
3.1
12.9
-
(Ib/acre/day)^
12.6
2.7
11.5
_
a/  U.S. Environmental Protection Agency,  Regions  VIII and IX,  "Natural  and Man-
      Made Conditions Affecting Mineral Quality,"  Appendix A of EPA Report, The
      Mineral Quality Problem in the Colorado River Basin (1971).
                                        135

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          Table 6-7.   SALT YIELDS FROM IRRIGATION FOR SELECTED
                         AREAS IN CALIFORNIA^/
                                       Average salt yield
         Area

North coastal
Central coastal
Sacramento
Delta-Central Sierra
San Joaquin
Tulare
Colorado Desert
(tons/acre/year)
0.353
0.808
0.707
0.974
0.827
0.768
10.9
(kg/ha/day)
2.2
5.0
4.3
6.0
5.1
4.7
67
(lb/ acre /day)
1.9
4.4
3.9
5.3
4.5
4.2
60
sj  California Regional Framework Study Committee for Pacific Southwest
      Inter-Agency Committee, Water Resources Council, "Comprehensive
      Framework Study, California Region, Appendix XV, Water Quality,
      Pollution, and Health Factors," June 1971.
 Table 6-8.  ESTIMATED RANGE  OF ACCURACY FOR OPTION I  (SOURCE  TO STREAM)
     PROCEDURE FOR ESTIMATING SALINITY FROM IRRIGATION RETURN  FLOW

Area
considered
(ha)
< 100

100 - 1,000

1,000 - 10,000

> 100,000

Calculated
load
(MT/ha/year)
1
10
1
10
1
10
1
10
Probable range
of loads
(MT/ha/year)
0.7 - 1.5
8-13
0.5 - 3
6-15
0.3 - 5
4-20
0.1 - 10
2-25
                                   136

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As can be seen from the table, the Option I procedure is deemed most ac-
curate when used for small areas, and when larger loads are calculated.
This aspect of accuracy arises because the Option I function is totally
dependent upon local conditions such as total dissolved solids in ground-
water, water consumptive use variations from crop to crop,  irrigation
water supplied to specific fields, and variation of deep percolation
losses.  If any of these data are extrapolated to larger areas, the vari-
ations in input data become wider, and hence the procedure  becomes less
accurate for large areas.

In principle, error in the Option I method can be minimized for large
areas by summing up the values obtained for small areas. However, it
is questionable whether such a summation would yield calculated values
with any higher accuracy than those obtained using the Option II method,,

Estimated ranges of error for the Option II (Stream to Source) procedure
are given in Table 6-9.  When Option II is used, the most accurate loads
will be calculated when large areas are considered.  The ranges shown in
Table 6-9 assume that background salinity loads have been carefully con-
sidered.  Since these background loads are the most uncertain component
of the procedure, the breadth of the error range is determined by this
uncertainty.
Table 6-9.  ESTIMATED RANGE OF ACCURACY FOR OPTION II (STREAM TO SOURCE)
     PROCEDURE FOR ESTIMATING SALINITY FROM IRRIGATION RETURN FLOW

Area
considered
(ha)
< 1,000

1,000 - 10,000

> 100,000

Calculated
load
(kg/ha/day)
1
10
1
10
1
10
Probable range
of loads
(kg/ha/day)
0.2 - 5
4-30
0.5 - 3
6-20
0.8 - 1.5
8-13
                                   137

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The Option II method is deemed to be less accurate when small areas are
considered.  The decrease in accuracy for small areas is inherent due to
uncertainty in flow measurements as well as uncertainty in background.
In general, small areas are associated with small streams draining the
area.  The amount of variation for small streams is usually quite high
(and more unpredictable) than that of large streams.

No estimate of error has been given for the Option III procedure for
estimating salinity loads from irrigation return flow.  The accuracy
of this option—use of salinity loading values—depends chiefly on the
trouble taken by the user to characterize his region and develop site-
specific information on his loadings.  This option can be the most ac-
curate of the three discussed, provided that the values used are accu-
rate.

The availability of accurate loading values for the Option III approach
is quite limited.  Accurate values can be obtained through long term
monitoring and analysis of irrigated areas, an expensive and time con-
suming operation.  However, various mathematical methods for predicting
salinity in irrigation return flow are being developed.  These models
will tend to describe the complicated relationships between the water
used for irrigation and the land being irrigated which result in salin-
ity emissions.  It may be that at some future time, these models will
be  sufficiently validated  so that their outputs can produce loading
values for use in the Option III procedure.  The user of this handbook
is  encouraged to keep abreast of these modeling projects so that their
output can be used  to obtain accurate estimates of  salinity from irri-
gation return flow.
                                  138

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                               REFERENCES
1.  Hornsby, A. G. ,  "Prediction Modeling for Salinity Control in Irriga-
      tion Return Flow," U.S. Environmental Protection Agency, Report
      No. EPA-R2-73-168, March 1973.

2.  Skogerboe, G. V., W. R. Walker, J. H. Taylor, and R. S. Bennett,
      "Evaluation of Irrigation Scheduling for Salinity Control in Grand
      Valley," Grant No. S-800278, U.S. Environmental Protection Agency,
      Report No. EPA-660/7-74-052, June 1974.

3.  Todd, D. K., The Water Encyclopedia, pp. 101-108, Water Information
      Center, Port Washington, New York (1970).

4.  Rainwater, F. H., "Stream Composition of the Conterminous United
      States," U.S.  Geological Survey, Hydrologic Investigations Atlas,
      HA-61, Washington, D.C. (1962).

5.  Skogerboe, G. V., and J. P.  Law, Jr., "Research Needs for Irrigation
      Return Flow Quality Control," U.S. Environmental Protection Agency,
      Report No. 13030-11/71, November 1971.
                                   139

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                              SECTION 7.0

                          ACID MINE DRAINAGE

7.1  INTRODUCTION

The emission of acid mine drainage arises from land disturbances created
by coal and metals mining activities.  The mine drainage arises because
of atmospheric and hydrologic actions on pyritic materials associated
with the mined materials.  The pyritic materials may be in residues left
behind at the mined-out site, or in residues produced by coal processing
or mineral beneficiation.  If pyrites (or other sulfurous materials) are
not associated with a particular mined product, e.g., quarrying, sand and
gravel operations, etc., then acid mine drainage will not occur.  Thus, the
presence or absence of pyritic materials is the determining factor for
nonpoint emissions of mine drainage.

Mine drainage can arise from active and inactive mines and from under-
ground and surface activities.  In addition, mine drainage can arise from
processing wastes, e.g., tailings piles and gob piles.  In considering
nonpoint emissions from these latter sources, processing wastes disposed
of on the land surface are considered as surface mines.

Basically, regional mine drainage problems arise because of an assemblage
of individual sources in an area.  A procedure for estimating mine drain-
age loads based upon the statistical distribution of individual sources
is presented here as Option I.  The procedure was developed using data
gathered by Environmental Quality Systems, Inc., in a study dealing with
estimation of mine drainage emissions in the Monongahela River Basin,—'
and from data obtained for the Appalachian Regional Commission^' for their
                                   140

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 report concerning mine drainage in Appalachia.—'   This  procedure  is  funda-
 mentally a  source to stream  loading function.  On  the other hand, sulfate
 analysis of surface waters are key indicators of nonpoint emissions  of
 mine drainage,  since sulfate is the end product of atmospheric/hydrologic
 reactions with  pyrite.  Thus, an Option II estimation procedure is pre-
 sented which uses the stream to source approach and is based on sulfate
 concentrations  in surface waters.  A brief description of these two  op-
 tions follow.

 Option I -  Source to Stream Approach:  A loading estimation procedure is
 presented which relates the number of total sources in an area, the  dis-
 tribution of these sources among four categories (active underground,
 active surface, inactive underground, and inactive surface), and neutral-
 ization of  acidic products of pyrite weathering with background alkalinity.
 This approach is particularly useful for heavily mined areas of the  coun-
 try, such as the coal mining regions of Appalachia.  In other areas  where
 mining is less  concentrated, this statistical approach may not be adequate.

 Option II -  Stream to Source Approach:  The second option involves compar-
 ing sulfate  loadings found in surface waters with sulfate loadings ex-
 pected from natural background.  Increases in sulfate loading as surface
 waters move  through an area over the background contribution can be  at-
 tributed to  nonpoint emissions of mine drainage in the area.  This second
 approach should be considered when detailed information about the number
 of sources  is unknown, where mining density is low, or when streamflow
 data are' deemed more appropriate to use.

 7.2  OPTION  I:  SOURCE TO STREAM APPROACH

 7.2.1  Loading Function and Information Needs

 The loading  function for the Option I approach contains three fundamental
 elements:   the number of potential sources of mine drainage; the amount
 of raw acidity formed from the sources;  and the neutralization capacity
 of the background.   The second element—amount of raw drainage formed--
 involves the statistical distribution term to account for the widely var-
 iable source-to-source loads arising from the individual sources.   The
 loading function is:
     Y(AMD) = N[Ka-(IAU + IAS + Ij-u + IIS) - Kb'Q(R)-C(Alk)BG]      (7-1)
where         N = total number of sources which are potential emitters of
                    acid mine drainage
                                   141

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     Statistical Distribution Term

             Ka = constant representing the raw acid load generated by
                    the "typical" site.  A range of values for  Ka  is
                    presented in Table 7-1, and discussed in Section
                    7.2.2.

IA.U> ''-AS' -""IIP
  I-rc           = load index values for the number of Active Underground
                    sources, Active Surface sources, Inactive Underground
                    sources, and Inactive Surface sources.  The load in-
                    dex values are presented in Table 7-2, and discussed
                    in Section 7.2.3.

     Background Neutralization Term

             Kjj = constant representing the neutralization capacity of
                    background alkalinity for raw acid produced at the
                    "typical" site.  A range of values for  K,   is pre-
                    sented in Table 7-1, and discussed in Section 7.2.2.

           Q(R) = flow as annual average runoff in the area, cm/year
                    (in/year)

       C(Alk)BG = concentration of background alkalinity in the area,
                    ppm as CaC03.  C(Alk)RG  can be determined through
                    use of an isoalkalinity map (see Figure 7-1, Section
                    7.2.4).

7.2.2  Constants  Ka  and  Kb  in Option I Loading Function

Description of the acid mine drainage discharge from the "typical" source
was determined by subjecting a number of mine drainage data obtained in
the Monongahela River Basin!' to regression analysis.  These data repre-
sented the acid load discharged at specific sites from about 7,000 poten-
tial sites.  The regression analysis indicated that the distribution of
mine drainage quantities from the 7,000 sources could be well fit (index
of determination = 0.998) to a hyperbolic function dependent upon (a) the
number of sources, (b) the quantity of mine drainage from the largest
source, and (c) the cumulative amount of mine drainage emitted from all
sources.  The regression equation has the form:
                          lim          A'N	 = 1                   (7-2)
                         N—-»»     "
                                   .f^B. + A'N
                                   1=1 i
                                   142

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           Table 7-1.  VALUES OF  Ka  AND  Kb  FOR ACID MINE
                   DRAINAGE OPTION I LOADING FUNCTION
           Units of
             load

Metric      kg/day

English     Ib/day
Value of
   K
   130
   280
Range of
	a

 110-150

 250-320
Value of
   Kb

  0.15

  0.62
Range of
   Kb

0.10-0.20

0.35-0.75
         Table 7-2.   LOAD INDEX VALUES FOR ACTIVE AND INACTIVE
                     SURFACE AND UNDERGROUND MINES
Fraction
of mines
in category
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00

Active
underground
0.00
0.33
0.50
0.60
0.67
0.71
0.75
0.78
0.80
0.82
0.83
0.85
0.86
0.87
0.88
0.88
0.89
0.89
0.90
0.90
0.91
Load
Active
surface
0.00
0.08
0.16
0.22
0.27
0.32
0.36
0.39
0.43
0.45
0.48
0.50
0.53
0.55
0.56
0.58
0.60
0.61
0.63
0.64
0.65
index
Inactive
underground
0.00
0.13
0.23
0.31
0.37
0.42
0.47
0.51
0.54
0.57
0.60
0.62
0.64
0.66
0.67
0.69
0.70
0.71
0.72
0.74
0.75

Inactive
surface
0.00
0.03
0.06
0.08
0.11
0.13
0.15
0.17
0.19
0.21
0.23
0.24
0.26
0.28
0.29
0.31
0.32
0.33
0.35
0.36
0.37
                                  143

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where         A = the quantity of mine drainage from the largest source
            N
            EB- = the cumulative amount of mine drainage from all sources
           i=lx
              N = the number of potential mine drainage sources in the
                    area
               N
The ratio of   £ Bj_  to  N  thus determines the acid load from the "typi-
              1=1
cal" site.  Furthermore, the equation implies that the load will be more
accurate when the number of sources considered becomes very large.

A part of the raw mine drainage generated within the mining area will
have been neutralized by background alkalinity before it is discharged
to surface waters.  From the consideration of the background neutraliz-
ing capacity in the Monongahela River basin, it has been possible to
establish values of  Ka  and  K^  for the loading function (7-1) based
upon the regression analysis represented by Eq. (7-2).  These values are
presented in Table 7-1.

The value  Ka  represents the raw acid generated at the "typical" mine
site as determined by Eq. (7-2).  The value  K^  represents the neutral-
ization of part of the raw acid by background alkalinity in the area
directly affected by the "typical" site.

7.2.3  Load Index Factors for Option I Loading Function

The  Ka  values presented in Table 7-1 have been established based on
data pertaining to the Monongahela River basin.  In order to use them
in other regions of the country, the  Ka  term must be corrected to re-
flect the distribution of potential mine drainage sources.  This correc-
tion is accomplished through the use of "load index factor" determined
in the following manner:

The total number of sources are separated into four components:  number
of active underground  (AU), active surface  (AS), inactive underground
(IU) and inactive surface (IS).  The fraction of each source is deter-
mined for each category by dividing the number of sources in a certain
category by the total number of sources.

After the category fractions have been determined, a load index value is
found in Table 7-2 for each category.  The first column of Table 7-1 in-
dicates the fraction of mine in each category; subsequent columns contain
the load index value for each category.  This procedure is exemplified in
Table 7-3, using a hypothetical situation involving 1,800 mines.
                                   144

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        Table  7-3.   EXAMPLE  OF  DETERMINATION OF  LOAD INDEX VALUES
                            Number  of        Fraction  of
                             sources           sources         Load  index

Active underground              180             0.10              0.50
Active surface                  450             0.25              0.32
Inactive underground            630             0.35              0.51
Inactive surface                540             0.30              0.15

      Total                    1,800             1.00              1.48
After  fractions of mines have been determined  in  each  category,  the  ap-
propriate  load index value  is established  for  each  category  by referring
to  the appropriate column in Table 7-2.  After the  individual  load index
values have been  determined, they are  added  together to  yield  a total load
index value required for the loading function.  The total  is the factor
(IATJ + IAC + ITTJ  + ^TC) ^n  tne  loading  function.

The load index values have  been established  by proportionating the total
load and total number of sources  (as determined by  the regression analy-
sis results of Eq. (7-2)) into  contributions from active underground,
active surface, inactive underground,  and  inactive  surface sources in the
Monongahela River basin.  The bases for the  proportionment were obtained
from data  in the  1969 Appalachian Regional Commission  report concerning
mine drainage..?/  This exercise yielded a  series  of four equations defin-
ing load index values for each  of the  four types  of mine drainage sources.
The equations from which the load index values in Table  7-2  were derived
are:
                                                                     (7-3)
                     AU   0.10 + nAU
                          0-54
                             HAC
                                                                     (7-4)
                          0.34 + nIO                                  -


                    IlS * 1.7o" .IS                                 (7-6)

where  n^y ,  n^s ,  n.^ , and  n-j-g are the fractions of mines in each of
the four categories.
                                   145

-------
                   nAU + nAS + nIU + nIS = 1'°                      (7'>

The total number of sources in an area is determined by study of state
and local historical records.  Basically, what is needed is the number
of active and inactive underground and surface sources.  The total num-
ber of sources need not be an exact count; a reliable estimate is quite
satisfactory for use in the loading function.

Information concerning active sources can be found in the annual Minerals
Yearbook, published by the U.S.  Bureau of Mines.   An alternate source of
information about active sources will be state and local permit programs.
Uncontrolled waste piles associated with active mines should be counted
as active surface mines.

Information about inactive mines may be more difficult to obtain.  Prob-
ably the best source of information on inactive mines will come through
analysis of historical records of the area.  These records should be
available in state archives.

7.2.4  Background Alkalinity Term for Option I Loading Function

The  Kb  values presented in Table 7-1 have also been established from
Monongahela River basin data.  These too must be corrected in order to
reflect changes in the neutralizing capacity of background.  The correc-
tion factors involve alkalinity concentrations in background and average
annual runoff.

Background alkalinity concentrations are determined by locating mining
areas on the iso-alkalinity map (Figure 7-1), estimating concentration,
and using this value in the alkalinity term.  If other values of back-
ground alkalinity concentrations are deemed more appropriate than those
shown on the map, then they should be used instead.  In areas afflicted
with acid mine drainage emissions, one should be cautious about using
"unaffected" stream values of alkalinity.  Although data may have been
generated in areas unaffected by mining activity, unknown sources of
mine drainage may be present which would lower background alkalinity es-
timates.

Average annual runoff can be estimated from standard runoff maps such as
that in the U.S. Geological Survey's National Atlas, Plates 118 and 119.

7.2.5  Procedure for Using Option 1 Loading Function

The procedure for putting together components of the source to stream
loading function to estimate levels is outlined below.
                                   146

-------
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147

-------
1.  Estimate total number of potential mine drainage sources through re-
view of state and local records,  permits, etc., as indicated in Section
7.2.3.

2.  Establish load index values for the following categories:  active
underground, active surface, inactive underground, and inactive surface,
by procedures indicated in Section 7.2.3.

3.  Sum up load index values established in Step 2.

4.  Determine constant  Ka  from Table 7-1.

5.  Multiply results generated in Steps 1, 3, and 4 to obtain value of
statistical distribution term of the loading function.

6.  Determine average annual runoff in area from standard runoff maps,
e.g., U.S. Geological Survey's National Atlas, Plates 118 and 119.

7.  Determine background alkalinity from iso-alkalinity map  (Figure 7-1)
or from other data deemed to reflect background alkalinity concentrations
more adequately.

8.  Determine proper constant  K^  from Table 7-1.

9.  Multiply values yielded by Steps 6, 7, and 8 to obtain background
alkalinity term.

10. Subtract value obtained in Step 9 from that obtained in  Step 5.

11. Multiply value obtained in Step 10 by the total number of mine drain-
age sources established in Step 1.  This final step will yield the load
of acid mine drainage being emitted from the mining region under consid-
eration.

7.2.6  Examples of Option I Loading Function Utilization

The mine drainage loading function has been used to estimate loads emitted
from two basins in Appalachia--West Branch Susquehanna, and Allegheny.
These examples are presented to indicate how the mine drainage loading
function can be used.

7.2.6.1  Case I:  West Branch Susquehanna

Data Source - Federal Water Pollution Control Administration, Ohio Basin
Region, U.S. Department of the Interior, "Stream Pollution by Coal Mine
                                   148

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Drainage in Appalachia,11 Attachment A to Appendix C of the Appalachian
Regional Commission Report, Acid Mine Drainage in Appalachia, Washington,
D.C. (1969).

Step 1.  Number of mine sources N:  4,400
         Number of draining sources:  967

Step 2.  Load index values (Table 7-2):

         Active underground:     19; 27»  = 0.02
         Active surface:         17; 2%  =0.02
         Inactive underground:  630; 65% =0.65
         Inactive surface:      301; 31% = 0.31

              Total:            967 100% = 1.00

         Load indexes:               I^j = 0.07
                                     IAS = 0.02
                                     Ijy = 0.66
                                     IIS = 0.15

Step 3.  Load index summation total:       0.90

Step 4.  Constant  K   from Table 7-1:     280
                    SL

Step 5.  Calculation of statistical distribution term:  280 x 0.9 = 252

Step 6.  Average annual runoff Q(R):       20 in.

Step 7.  Background alkalinity C(Alk)gQ (from Figure 7-1):  10 ppm

Step 8.  Constant  Kb  (from Table 7-1):   0.62

Step 9.  Calculation of background alkalinity term:  0.62 x 20 x 10 = 124

Step 10.  Subtract alkalinity term from statistical distribution term:
           252 - 124 = 128

Step 11.  Compute acid mine drainage load:   4,400 x 128 = 560,000 Ib/day

         Mine drainage (calculated) = 560,000 Ib/day

         Mine drainage (reported)   = 500,000 Ib/day
                                   149

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7.2.6.2  Case II:  Allegheny River Basin (1966)

Data Sources - Appalachian Regional Commission Report, Acid Mine Drainage
in Appalachia (1969); Tybout, R. A., "A Cost Benefit Analysis of Mine
Drainage," paper presented before 2nd Symposium on Coal Mine Drainage
Research, Pittsburgh, Pennsylvania, 14-15 May 1968; and U.S. Bureau of
Mines, Minerals Yearbook, 1966. Washington, D.C. (1967).

Step 1.  Number of mine sources N (Tybout):  6,626

Step 2.  Load index values (Table 7-2):

         Active underground:         228;  3% =0.03
         Active surface:             310;  5% = 0.05
         Inactive underground:     2,350; 36% =0.36
         Inactive surface:         3,738; 56% = 0.56

              Total:               6,626 100% =1.00

         Load indexes:                     IAU = 0.10
                                           IAS = 0.08
                                           IlU = 0.51
                                           IIS = 0.24

Step 3.  Load index summation total:              0.93

Step 4.  Constant  Ka  from Table 7-1:           280

Step 5.  Calculation of statistical distribution term:  280 x 0.93 = 260

Step 6.  Average annual runoff Q:                20 in.

Step 7.  Background alkalinity C(Alk)BG (from Figure 7-1):  10 ppm

Step 8.  Constant  Kb  (from Table 7-3):         0.62

Step 9.  Calculation of background alkalinity term:  0.62 x 20 x 10 = 124

Step 10. Subtract alkalinity term from statistical distribution term:
           260 - 124 = 136

Step 11. Compute acid mine drainage load:  6,626 x 136 = 900,000 Ib/day

         Mine drainage (calculated) = 900,000 Ib/day

         Mine drainage (reported)   = 866,000 Ib/day
                                   150

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7.3  OPTION II:  STREAM TO SOURCE APPROACH

7.3.1  Loading Function and Information Needs

Since acid mine drainage is basically a discharge of sulfuric acid (and
its reaction products), the presence of sulfate in stream water analyses
is often a good indicator of nonpoint emissions from mine drainage
sources.  Thus, a comparison of sulfate levels detected in streams with
that expected from natural background will yield an estimate of nonpoint
emissions of mine drainage.  The loading function can be expressed in
two ways, depending upon the units of flow.
             Y(AMD) = a-A-Q(R)-[C(S04) - C(S04)BQ - C(S04)pT]       (7-8)


             Y(AMD) = a-Q(str)-[C(S04) - C(S04)BQ - C(S04)PT]       (7-9)

where    Y(AMD) = yield of acid mine drainage, kg CaC03/day (Ib CaCO«/day)

              A = area containing mine drainage sources, ha (acre)

   Q(R)j Q(str) = flow; Eq. (7-8) requires flow units  Q(R)  as annual
                    average runoff, in cm/year (in/year).   Equation (7-9)
                    requires flow units  Q(str)  as streamflow in liters/
                    sec (cfs).

         C(SO^) = concentration of sulfate in surface waters, ppm

                = concentration of sulfate in surface waters attributable
                    to background, ppm

                = concentration of sulfate in sources attributable to
                    point sources, ppm

              a = conversion constant for obtaining proper load

The two key elements of this loading function are in the conversion fac-
tor  a  and in the concentration of background sulfate  C(S04)BG.  The
value of  a  to be used in the  loading function is determined by the
units of flow.  A table of  a  values is presented in Table 7-4.  The
values take into account the conversion of sulfate concentrations (in
ppm) to their calcium carbonate equivalents (ppm as CaC03).  This con-
version is necessary in order to obtain load units of kilograms CaCOo
per day (Ib CaC03/day) .
                                   151

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          Table 7-4.   CONVERSION FACTORS  a  TO BE USED FOR
              OPTION II MINE DRAINAGE LOADING FUNCTION

Sulfate
concentration
units
ppm
ppm
ppm
ppm
Units of
flow
Q
cm/year
in/year
liters/sec
cfs
Units of
area
A
ha
acre

Value of
a
2.8 x 1CT4
6.4 x 10"4
0.090
5.61

Units of
Y(AMD)
kg CaC03/day
Ib CaC03/day
kg CaC03/day
Ib CaC03/day
The background levels of sulfate can be estimated through the use of an
iso-sulfate background map presented in Figure 7-2.  The region of in-
terest is identified on the map, and sulfate levels estimated through the
contours.  If more specific data are available which are believed to de-
scribe background sulfate levels more adequately, then these data would
be preferred to the use of Figure 7-2.

Other components of the loading function are obtained through standard
sources.  Sulfate concentration in streams,  C(S04) , and streamflow,
Q(str) , can be obtained from U.S. Geological Survey studies and from
local water quality records.  Annual average runoff can be estimated with
the U.S. Geological Survey Surface Runoff Map, Plates 118 and 119, in the
National Atlas.  Sulfate contributions from point sources,  C(SO^)p-T. ,
can be estimated from data contained in permit applications for point
source discharges.

7.3.2  Procedure for Using Option II Mine Drainage Loading Function

The step-by-step procedure for using the Option II loading function is
outlined below.

1.  Obtain necessary water quality data, streamflow data, and areal data
from U.S. Geological Survey records, local records, or other similar
sources.

2.  From these data establish appropriate values for  A ,  Q(R) , and
C(S04) .

3.  Determine value for background sulfate,  C(SO^)BG , using Figure 7-2,
or from local water quality information thought to be more appropriate.
                                   152

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153

-------
4.  Determine value of conversion constant  a  by means of Table 7-4.

5.  Insert  a ,   A ,   Q(R) ,   6(804)  and  C(S04)BG  values established
in the above steps into proper form of Option II loading function.

6.  Compute mine drainage loads.

7.3.3  Example of Option II Loading Function for Mine Drainage

An example of how the Option II loading function can be used is summar-
ized in Table 7-5.  This table contains results for the Tioga and Juniata
river basins in Appalachia obtained by the Option II loading function.
The Option II estimates are within 80 to 110% of the reported loads.

The loading function works out well in these cases because mine drainages
(and background) are the principal sources of sulfate in the area.  If
the loading function is applied to more highly industralized areas, e.g.,
the Anthracite Region of Appalachia, it tends to overpredict the nonpoint
loads of mine drainage.  In the industrial areas, point source discharges
of sulfate report as nonpoint discharges within the context of the Option
II method.  Therefore, the Option II approach should be used mainly in
rural areas.  If amounts of the point source contributions of sulfate are
known, however,  they can be subtracted from estimates yielded by the Op-
tion II loading function.  This procedure would ameliorate some of the
deficiencies of using the stream to source approach in populated or in-
dustrialized areas.

7.4  ESTIMATED RANGE OF ACCURACY

A series of estimated value ranges for several acid mine drainage loads
calculated using the Option I procedure are presented in Table 7-6.  Two
ranges are presented—one for Appalachia, and one for regions other than
Appalachia.  As can be seen by the ranges, the loading function is more
accurate when applied to coal mining in Appalachia than it is when used
in other parts of the country.

One major source of error in the Option I loading function lies in the
number of mine drainage sources in the area being considered with the
loading function.  If not enough sources are available in an area, it
is likely that their distribution of loads will not meet that of the
"typical" mine from which the loading function was developed.  This prob-
lem will most often be encountered in regions outside of Appalachia where
mining activity density  (number of mines in the area being considered)
is small.
                                   154

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 Table 7-6.  ESTIMATED RANGE OF LOADS FOR OPTION I (SOURCE TO STREAM)
                ACID MINE DRAINAGE LOADING FUNCTIONS
  Number of
mine drainage
   sources

    > 1,000
   100-1,000
      < 100
                   Calculated
                      load
                    (kg/day)

                      1,000
                     10,000
                    100,000
                        500
                      5,000
                     50,000
                        100
                      1,000
                     10,000
                                        Probable range of loads
                                       	(kg/day)	
                                                         Other than
                                                         Appalachia
  Appalachia

   200-5,000
 5,000-20,000
80,000-120,000
     0-3,000
 1,000-20,000
20,000-80,000
     0-1,000
     0-10,000
 1,000-30,000
                                                             sJ
                                                             sJ
                                                            0-5,000
                                                          500-50,000
                                                       10,000-100,000
                                                            0-2,000
                                                            0-20,000
                                                          500-50,000
a/  Areal density of mining activities outside of Appalachia is less
      than 1,000 mines per total area considered.
                                                                    a
Another source of uncertainty lies in the choice of the constants  K
and  KD  in the loading function.  The calculated loads in Table 7-6
assume  Ka = 130  and  K^ - 0.54  as indicated in Table 7-1.  However,
a range of values is provided for both  Ka  and  K^  in Table 7-1 and
the user is at liberty to select values of these constants which best
represent his area.  The larger ranges in areas outside Appalachia as
indicated in Table 7-6 reflect a higher degree of uncertainty in proper
choices of  K
                and
                         in the loading function.
A third source of error in the Option I function is found in the estima-
tion of background neutralization capacity.  The neutralization capacity
of background will vary widely throughout the area in which mine drainage
sources are located.  This variation in background alkalinity, together
with the relatively large areas that need to be considered in order to
have enough sources for the statistical distribution, is probably the
biggest source of error in the loading function.

The estimates in Table 7-6 suggest that more uncertainty is to be ex-
pected with small loads than with large loads.  This greater uncertainty
is due to the subtraction steps in the Option I procedure.  The nearer
                                   156

-------
 in magnitude the  statistical distribution and background alkalinity
 terms, the greater the potential error in the calculated value.  Indeed,
 there may be instances where calculated acid mine drainage emissions
 will be  zero (or  a negative value), when in fact acid mine drainage is
 present.  These occurrences are most probable in areas having high back-
 ground alkalinities such as found in the Midwest.

 The Option II  stream to source approach for acid mine drainage depends
 strictly upon  measured sulfate levels in streams compared to estimated
 sulfate  levels in background.  Estimated ranges of acid mine drainage
 loads are presented in Table 7.7. An area of 1 million hectares  (4,000
 sq miles) has  been used to differentiate between larger and small areas
 in Table 7-7.  The range of loads arising from smaller areas are some-
 what broader on a percentage basis than are the loads from the larger
 areas.   The differences in breadth reflect the fewer number of sources
 in the smaller area, as well as a higher degree of uncertainty in back-
 ground levels.
 Table 7-7.  ESTIMATED RANGE OF LOADS FOR OPTION II  (STREAM TO SOURCE)
                 ACID MINE DRAINAGE LOADING FUNCTIONS
Area containing
mining sources
(ha)
< 1,000,000

> 1,000,000


Calculated
load
(kg /day)
1,000
10,000
1,000
10,000
100,000

Probable range of
loads (kg/day)
200-10,000
5,000-30,000
500-3,000
6,000-20,000
70,000-150,000
In addition to area differences, Table 7-7 also indicates a wider range
for small total loads than for large total loads.  These differences are
due to uncertainties in the difference between total sulfate and back-
ground sulfate, i.e., the  [C(SO,) - C(SO,)  ]  term.  The larger loads
thus reflect a larger "net" sulfate attributable to acid mine drainage.
A larger net value is inherently more accurate than a small net value.
Thus, larger loads calculated by the Option II procedure are more ac-
curate than smaller loads calculated in the same manner.
                                   157

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                              REFERENCES
1.   Environmental Quality Systems,  Inc.,  "Determination of Estimated
      Mean Mine Water Quantity and Quality from Imperfect Data and His-
      torical Records," Report to the Appalachian Regional Commission,
      January 1973.

2.   Appalachian Regional Commission, "Acid Mine Drainage in Appalachia,"
      Washington, D.C. (1969).
                                  158

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                              SECTION 8.0

                    HEAVY METALS AND RADIOACTIVITY

8.1  INTRODUCTION

Two options are presented for estimating nonpoint loads of heavy metals
or radioactivity.  The estimation procedures for both heavy metals and
radioactivity are identical, except that input data for heavy metals re-
quire concentration reported in parts per billion, while input data for
radioactivity require units of picocuries per liter.  The two options
are:

Option I - Source to Stream Approach:  A summation of loads emitted by
known sources in the area under consideration.  These sources represent,
in most cases, emissions from abandoned mining sites and from their as-
sociated processing operations such as tailings piles.  This approach
will be sufficient in those areas where the nonpoint sources have been
identified.  Since all mines do not produce drainage to transport heavy
metals or radioactivity, the contribution of the nondraining mines to
the total load will be zero.  In many other cases, drainage from many
inactive mining sites will be negligible when compared to the few major
sources.  Contribution to the total load from many minor sources may be
insignificant compared to the load from the few major sources.

Option II - Stream to Source Approach:  An estimation of loads obtained
by difference between total load and estimated background load.   This
option should be used where specific sources of heavy metal or radioac-
tivity have not been identified or characterized.  Since most heavy metals
and radioactive nuclides tend to precipitate within a short distance after
their discharge into surface waters, the possibility exists that heavy
metals or radioactive contents of stream water samples do not accurately
reflect the quantity of materials actually delivered to the stream by non-
point sources.  This problem can be overcome by using water quality data
sampled at points known to be in close proximity to the nonpoint sources,
even though precise locations of nonpoint sources are unknown.
                                   159

-------
In addition to the above methods, the special case of heavy metals associ-
ated with sediment loads is discussed in Section 8.5.  The U.S. Geological
Survey has reported results of an extensive sampling and analysis program
in which heavy metal contents of surficial soils in the United States were
determined.!'  This study indicates that heavy metals in sediment consti-
tute a significant nonpoint load in terms of quantity.  However, the impact
of the heavy metal load on surface water quality is much less severe.  The
method described in Section 8.5 "piggy-backs" the heavy metal concentration
of soils onto the sediment loading function (Eq. 3-1, Section 3.2.2) in
order to estimate sediment-borne heavy metal nonpoint loads arising from
the various sources of sediment emissions.
                                   160

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8.2  OPTION I:  SOURCE TO STREAM APPROACH

8.2.1  Information Requirements for Loading Value Equation

The loading value equations for estimating heavy metal or radioactivity
loads emitted by nonpoint sources using the source to stream approach
are:
                           Y(HM) = a £ Qn-C(HM)n                   (8-1)
                                     n

                          Y(RAD) = a S Q -C(RAD)                   (8-2)
                                     n  n       n

where     Y(HM) = yield of heavy metal from a given area, kg/day (Ib/day)

         Y(RAD) = yield of radioactivity from a given area, picocuries/
                    day

         C(HM)n = concentration of heavy metals emitted from ntn source,
                    ug/liter (ppb)

        C(RAD)n = concentration of radioactivity emitted from the ntn
                    source, picocuries/liter

             Qn = flow transporting pollutant from the n*-n source, liters/
                    sec (cfs)

              a = conversion factor needed to obtain proper units of load
                    (see Table 8-1)

Flow  (Q)  and concentration  (C)  information is obtained from data gath-
ered  from recent nonpoint monitoring studies, or from permit application
records.  The nonpoint sources emitting heavy metals or radioactivity are
likely to be abandoned or inactive mining sites, milling and ore beneficia-
tion sites, and associated waste rock dumps and tailings ponds.  If such
data are not available, it may be necessary to use the Option II approach
rather than Option I.

8.2.2  Procedure for Using Option I Loading Value Equation

The step-by-step procedure for using Option I for heavy metals and radio-
activity loads is:
                                   161

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1.  Obtain data for composition and flow of drainage from individual
nonpoint sources.

2.  If heavy metal concentration data are reported in units other than
parts per billion (or ug/liter), convert data to parts per billion units.
Conversion of parts per million to parts per billion is accomplished by:

                          ppb = 1,000 x ppm

If radioactivity units are reported in units other than picocuries per
liter, convert data to proper units.

3.  If flow units in raw data are reported as gallon per minute, gallon
per day, etc., convert flows to liters per second (cfs).

4.  Add concentrations of heavy metals or radioactivities to obtain total
concentrations.  Heavy metals include all metallic constituents except:
sodium, potassium, calcium, magnesium, and aluminum.  The metalloids
arsenic and antimony should be counted as heavy metals.

The total heavy metals may be separated into three subcategories if de-
sired:

     Category A - iron + manganese;
     Category B - arsenic + copper + lead + zinc; and
     Category C - remaining heavy metals.

5.  Multiply flows and concentrations for each individual site.

6.  Add up all computed values obtained in Step 5.

7.  Choose proper conversion constant from Table 8-1 based upon units of
flow,  Q , established in Step 3.

8.  Compute load by multiplying the sum obtained in Step 6 by conversion
constant identified in Step 7.

8.2.3  Example of Option I Source to Stream Approach

The Option I approach has been applied to heavy metals emissions from
abandoned mine sites in the Coeur d'Alene River Valley in Idaho.  The
computations are summarized in Table 8-2.
                                  163

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The information in Table 8-2 points out the major weakness of the Option
I approach—it is not known whether all nonpoint sources are accounted
for.  It is believed, however, that major contributors are known, and
that this sum represents a good "average" for heavy metal contributions
in the region.

8.3  OPTION II:  STREAM TO SOURCE APPROACH

8.3.1  Loading Value Equations and Information Needs

Four loading value equations for estimating heavy metal or radioactivity
loads emitted by nonpoint sources using the stream to source approach
can be used.  These equations are:


     Case 1      Y(HM) = a'A'Q(R)•[C(HM) - C(HM)BG]                (8-3)


     Case 2      Y(HM) = a-Q(str)-[C(HM) - C(HM)BG]                (8-4)


     Case 3     Y(RAD) = a-A-Q(R)•[C(RAD) - C(RAD)BG]              (8-5)


     Case 4     Y(RAD) = a-Q(str)-[C(RAD) - C(RAD)BG]              (8-6)

where     Y(HM) = yield of heavy metal from a given area, kg/day (Ib/day)

         Y(RAD) = yield of radioactivity from a given area, picocuries/
                    day

          C(HM) = concentration of total heavy metals emitted from non-
                    point sources, ppb (ug/liter)

        C(HM)BG = concentration of total heavy metals emitted from back-
                    ground,  ppb (ug/liter)

         C(RAD) = concentration of radioactivity emitted from nonpoint
                    sources,  picocuries/liter

       C(RAD)Rp = concentration of radioactivity emitted from background
                    sources,  picocuries/liter

              A = area containing nonpoint sources, ha (acres)
                                  165

-------
           Q(R) = flow as average annual runoff,  cm (in.)

         Q(str) = flow as streamflow, liters/sec  (cfs)

              a = conversion factor needed to obtain proper units of
                    load (see Table 8-3)

The four cases are differentiated by the type of  flow data available to
the user.  Cases I and 3 require average annual runoff in centimeters
per year (in/year) obtainable from standard runoff maps, e.g., U.S. Geo-
logical Survey's National Atlas, Plates 118 and 119.  Cases 2 and 4 can
use measured streamflow values measured in volume per time unit (i.e.,
liters/sec or cfs).  The values for particular streams are available in
U.S. Geological Survey records, STORET data, and  U.S.  Army Corps of
Engineers streamflow records.

In areas of highly variable flow (such as mountainous regions), the use
of annual average runoff values (Cases 1 and 3) is recommended unless
precise knowledge of streamflow at the sampling site is known.  If an-
nual average runoff is used, it is desirable in some cases to not con-
sider the area  (A)  factor if the areal extent drained above the sam-
pling point is not known accurately.  If so done  for Cases 1 and 3, the
units of load would become kilograms per hectare  per day (Ib/acre/day).

On the other hand, if good streamflow and concentration data are avail-
able to the user, he should use the Cases 2 or 4  loading value equations
to estimate heavy metal or radioactivity emissions.

8.3.2  Estimation of Heavy Metal and Radioactivity Emissions from
         Background

A key part of the Option II approach is the estimation of heavy metals
and radioactivity emissions from background.  A series of maps have been
developed for estimating background concentrations of heavy metals and
radioactivity.  These maps are:

     Figure 8-1.  Background total heavy metals (ppb)
     Figure 8-2.  Background iron + manganese (ppb)
     Figure 8-3.  Background arsenic + copper 4- lead -I- zinc (ppb)
     Figure 8-4.  Background miscellaneous heavy  metals  (ppb)
     Figure 8-5.  Background radioactivity  (picocuries/liter)
     Figure 8-6.  Background alpha radioactivity (picocuries/liter)
     Figure 8-7.  Background beta radioactivity (picocuries/liter)
                                   166

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If other sources are not available, these maps should be used to esti-
mate background concentrations of needed constituents.

8.3.3  Procedure for Using Option II Loading Value Equations

The step-by-step procedure for using the Option II approach for estimat-
ing heavy metal and radioactivity emissions is:

1.  Obtain data for heavy metal and radioactivity concentrations from a
selected number of monitoring stations.  The data should include concen-
tration and flow information.

2.  Obtain data for heavy metal and radioactivity concentrations in back-
ground.  These data may be local data deemed proper by the user, or from
the maps (Figures 8-1 through 8-7) presented earlier.

3.  If flow data are not available with concentration data, estimate flow
as annual average runoff from standard U.S. Geological Survey Runoff Maps.

4.  After proper flow and concentration data have been acquired, choose
the proper case and determine "a" value from Table 8-3.

5.  Sum up heavy metal concentrations to obtain total heavy metals.  The
total heavy metal may be broken into three subtotals:  iron + manganese;
arsenic + copper + lead + zinc; and miscellaneous heavy metals.  The
heavy metal constituents to be considered in the summing process have
been identified in Section 8.2.2, Step No. 4, of this report.

6.  If flow data are limited to average annual runoff, and if good areal
data are not known,  do not consider area (A) factor in loading value equa-
tion.  This aspect will yield results in kilogram per hectare per day
(Ib/acre/day).

7.  After data have been obtained and processed using the above steps,
insert into proper loading value equation and compute loads.

8.3.4  Example of Option II Stream to Source Approach

The Option II approach has been used to estimate heavy metal loading from
nonpoint sources in Clear Creek County, Colorado.  Results of this esti-
mation are presented in Table 8-4.  These computations have used the Case
1 loading value equation, i.e., flow is estimated by average annual run-
off.  In addition, areal data were insufficient to estimate loads.  Thus,
loads are reported as kilogram per hectare per day.
                                   175

-------
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In order to obtain actual loads, drainage areas represented by each
sampling station should be known.  Estimation of the areas might be
accomplished by studying U.S. Geological Survey topographic maps.
Once areas are estimated, they should be multiplied by the proper
loading rates and summed to obtain estimates for the total load
arising from the nonpoint sources.

8.4  EXPECTED ACCURACY OF METHODS

In the case of the Option I approach where individual sources are summed,
the accuracy will depend upon the variability of the emission loads from
each source.  However, as more and more sources are included in the sum-
mation, the more accurate will be the estimate.  This increased accuracy
will arise because of the greater number of points forming the statisti-
cal distribution of sources.  Option I also assumes that the principal
sources of heavy metal and radioactivity emissions are known for the area
under consideration.  Thus, the use of Option I should be restricted to
only those persons with knowledge of the area.  An estimate of the ac-
curacy of Option I methods for heavy metals is presented in Table 8-5
for several calculated loads.  The accuracy for radioactivity would be
expected to fall into the same percentage ranges.

Option II is inherently less accurate than Option I, since it depends
upon a good estimate of background emissions of heavy metals and radio-
activity, and involves a comparison of these components actually found
with the background estimates.  Background heavy metals are particularly
difficult to estimate, since wide variations in concentrations are noted
and emissions are nonuniform throughout the country.  Because specific
sources are not involved, heavy metal and radioactivity loads are con-
sidered to be diffuse and emitted uniformly from all land area considered
when Option II is used.  Thus, use of the Option II method is more accu-
rate with larger areas.  Accuracy ranges are narrowed further when large
loads are emitted rather than small loads, since Option II involves com-
parison of total loads with background loads.   The greater the difference
between total and background levels, the less  will be the error intro-
duced by the subtraction operation.   An estimate of expected accuracy
for heavy metals emissions using Option II is  presented in Table 8-6.
                                   177

-------
    Table 8-5.   EXPECTED ACCURACY OF OPTION I (SOURCE TO STREAM)
                     METHOD FOR HEAVY METALS
Number
of
sources
10

50


100



Calculated
load
(kg/day)
0.1
1
1
5
10
1
5
10
20
Probable range
of loads
(kg /day)
0.01-1.0
0.5-5
0.7-3
2-10
5-15
0.8-2
3-8
7-15
17-25
    Table 8-6.  EXPECTED ACCURACY OF OPTION II (STREAM TO SOURCE)
                     METHOD FOR HEAVY METALS
     Area                   Calculated                Probable range
 containing                    load                      of loads
sources  (ha)               (kg/ha/year)                 (kg/ha/year)

< 1,000,000                      1                        0.2-15
                                10                          3-30

> 1,000,000                      1                        0.4-10
                                10                          5-20
                                   178

-------
8.5  HEAVY METALS ATTACHED TO SEDIMENT

8.5.1  Loading Function

The largest single nonpoint heavy metal load into surface waters  will be
that which is carried by sediment.  The U.S. Geological Survey has under-
taken an in-depth study—  to determine the elemental composition  of sur-
ficial materials in the United States.  Soil samples were collected from
863 sites throughout the 48 conterminous states and analyzed for  44 differ-
ent elements.  Of these 44 elements, 36 are heavy metals as defined earlier,
i.e., metallic or metalloid elements with atomic number greater than 20.
Sediment arising from various sources throughout the country will carry
these elements into surface waters.  Thus, the amount of heavy metal de-
livered with the sediment is directly proportional to the sediment load.

The loading function is:

                    Y(HM)S = a-Cs(HM)-Y(S)E                         (8-7)

where   Y(HM)e = yield of heavy metals in sediment, kg/day (Ib/day)

        C_(HM) = concentration of heavy metals in eroded soil, ppm

         Y(S)  = sediment yield metric tons/year (tons/year)
             E
             a = conversion factor to obtain proper units of load.  If
                   Y(S)E is expressed in metric tons/year, a = 2.74 x 10  ,
                   if Y(S)  is expressed as English tons/year, a  = 5.48 x
                          E
The loading function assumes that there is no enrichment (or loss) of
heavy metals in the eroded soil.  One would not expect to have either en-
richment or loss of heavy metals in the sediment,  since they are tied up
in insoluble forms in the soils.

The loading function (Eq. (8-7)) is apt to yield very large values for
heavy metal emissions.  The metals are an integral part of the soil-
sediment matrix, and most are sparingly soluble in water.  The fraction
of the load which solubilizes in surface waters is usually very small,
and the impact on water quality is thus very much less than one would cal
culate on the basis of a total load discharged to the stream.
                                    179

-------
8.5.2  Information Needs

Two basic pieces of information are needed to estimate emissions  of heavy
metals associated with sediment:  the amount of sediment produced,  Y(S)E;
and the amount of heavy metals in the eroded soils,  Cq(HM).   The  value of
Y(S)  is determined using sediment loading procedures  (USLE  Eq.  (3-1)) de-
scribed in Section 3.0.  The value of Cg(HM) can be  determined from the data
collected by the U.S.  Geological Survey in their report concerning  elemental
composition of surficial materials,—  or from other  data available  locally.

The average concentrations and ranges of the various heavy metals in sur-
ficial materials obtained from the 863 sampling stations are presented in
Table 8-7.  In addition to the arithmetic averages,  the geometric means
(another form of "averaging") have also been included  on a national basis,
as well as an Eastern and Western areal basis.  The  line separating East
from West is the 97th meridian.

In many cases, the elements were found to be in concentrations less than
the detection limits of the analytical methods employed by the USGS.  The
concentrations of these elements are shown in Table  8-7 to be less  than
the detection limits,  and no'average can be presented.  The  metals  which
fall in this category are arsenic, cadmium, germanium, gold, hafnium, in-
dium, platinum, palladium, rhenium, tantalum, tellurium, thallium,  thorium,
and uranium.  Many of these elements are known, from other studies, to be
present in soils.

For those elements which were generally found to be  above detection limits,
the concentration at each sampling site has been plotted and mapped in the
U.S. Geological Survey report.—   Specific heavy metals in specific areas
can be estimated from these maps.  Thus, the USGS report can serve  as a
basic reference for U.S. data concerning heavy metals  in sediment.

8.5.3  Relationship Between Heavy Metals in Soils and  in Surface  Waters

As can be seen in Table 8-7, the predominant heavy metal in  surficial mate-
rial is iron.  The metal having next greatest abundance is titanium.  On
the average, these two elements constitute about 937» of all  heavy metals
in soils.  The remaining 7% is made up of many elements, ranked in the
following order:  manganese, barium, strontium, zirconium, cerium,  vanadium,
zinc, chromium, neodymium, lanthanium, yttrium, copper, lead, nickel, gal-
lium, niobium, cobalt, and scandium.

The high percentage of titanium in the soils is not  reflected in surface
waters.  As has been shown in Figure 8-1 and 8-2, iron and manganese con-
stitute the major portion of heavy metals in surface waters.  Thus, one
concludes that the solubility mechanisms of manganese and titanium in soils

                                   180

-------
differ substantially.  From the surface water quality data, it would also
seem that the zinc, copper and lead constituents of eroded soils are rela-
tively mobile in aqueous systems.

On a total load basis, the heavy metals emitted through the sediment route
are much greater than the load detected in surface waters.  A comparison
of heavy metals loads in natural background indicates that the surface wa-
ter load is approximately 1% of the load coming via the sediment route.
Most of the metals which are detected in the streams consist of iron, man-
ganese, arsenic, copper, lead and zinc, whereas those in the sediment are
primarily iron and titanium.  Thus, the impact of heavy metals on the qual-
ity of surface waters is probably much smaller than the absolute loads of
sediment indicate.  However, the differences in solubility and mobility
mechanisms of individual metal components are important for establishing
impacts of specific species.

8.5.4  Reliability of the Procedure

The reliability of estimating heavy metal loads through the procedure dis-
cussed above is a function of three factors:  (a) the accuracy of the sedi-
ment loads delivered to the stream (Table 3-10); (b) the accuracy of the
heavy metal concentration measurements in the soil; and (c) the variability
of heavy metal concentrations in the eroded soils.  Of these three factors,
the one concerning variability of heavy metal content will be the most un-
certain.  The estimated ranges of values for several heavy metal loads is
presented in Table 8-8.
                                  181

-------












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Table 8-8.  EXPECTED ACCURACY OF HEAVY METAL LOADS
              DELIVERED WITH SEDIMENT

Calculated                          Probable range
   load                                of loads
 (kg/day)                              (kg/day)

    0.1                                0.001-1.5
    1                                  0.05-10
    10                                 1-30
    100                                30-200
                         184

-------
Reference

1.  Shacklette, H.  T.,  J.  C.  Hamilton,  J.  G.  Boernagen,  and J.  M.  Bowles,
      "Elemental Composition of Surficial  Materials in the Conterminous
      United States,"  U.S.  Geological Survey  Proffessional Paper 574-D,
      Washington,  D.C.  (1971).
                                  185

-------
                               SECTION 9.0

                        URBAN AND RELATED SOURCES

This section describes pollutant loading functions for developed urban
areas and related sources.  In the subsections that follow, the sources
and types of pollutants and factors affecting pollutant generation, as
well as loading functions and relevant data, are presented in the fol-
lowing sequence:  urban runoff, Section 9.1; traffic related pollutants>
Section 9.2; and street and highway deicing salts, Section 9.3.

Discussion in this section pertains only to established areas.  Loading
functions for areas under construction are presented in Section 3.0,
"Sediment Loading Functions," of this Handbook.

9.1  POLLUTANTS FROM URBAN RUNOFF

From established urban areas, stormwater may pick up various wastes
ranging from settled dust and ash to debris coming directly from man
himself.  The quantities  of solids from urban nonpoint sources are quite
significant in quantity.  Fly ash and dust from industrial processes
such as steel mills, cement manufacturing, and certain chemical pro-
cesses are known to be profuse.  Dusts from the burning of organic fuels
are a significant factor, and solids in sizable quantities also result
from off-street mud, automotive exhaust, organic debris from tree  leaves
and grass trimmings, and  discarded litter.

In this Handbook, the nonpoint pollutant loading function for urban
areas is formulated from  pollutant loading values obtained in a recent
URS studyi' for the U.S.  Environmental Protection Agency.  In that study
URS reviewed a  large number of published reports, extracted and statis-
tically analyzed data, and presented average solid loading values and
chemical and biological composition of solids.

In analyses of  urban runoff data, URS assumed that only the runoff from
street surfaces contributed to urban nonpoint pollution.  The resulting
                                    186

-------
loading values for solid wastes are given in terms of pounds per curb-
mile per day.  The user should note that these values represent contri-
butions from both street and nonstreet surfaces.

Data developed in the URS study include nationwide means,  as well as a
more detailed breakdown of data into major source categories, of solids
loading rates and pollutant composition of street solids.   Table 9-1
reproduces, from the URS report, data which are divided into 13 subsets
among three major source categories including climate, land use, and
average daily traffic.  These data are different from the  whole set means
which are given in the last column of the table,  at the 80% confidence
level.  Whenever the mean of any parameter (solid loading  rates or com-
position) in any subset differs significantly from the mean of the set
of all data, that number may be substituted for the mean of the set of
all data.  Table 9-1 also gives the percent standard error of the mean
which indicates the degree of confidence that may be placed on the mean.

Table 9-2 presents the means and standard deviations of concentrations
of mercury and several pesticides, which resulted from a set of data
that were characterized as "very small and unreliable."

9.1.1  Loading Functions

The functions which make use of solid accumulation rates and solid com-
position and provide for quantitative assessment  of pollutant loadings
in urban runoff within a specified urban area are given as follows:

     Loading functions for solids
                            Y(S)U = L(S)u-Lst                     (9-1)

where     Y(S)U = daily total solids loading, kg/day (Ib/day)

          L(S)U = daily solids loading rates, kg/curb-km/day (Ib/curb-
                    mile/day)

            L   = street curb-length  (approximately 2.0 x street
             s u
                    length), curb-km  (curb-miles).
                                    187

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     Loading  functions for other pollutants
                          Y(i)u = a-Y(S)u-C(i)u                   (9-2)

where     Y(i)u = daily total loading of pollutant  i  , kg/day  (Ib/day);
                    MPN(x 10) per day for total coliform and  fecal
                    coliform

              a = conversion factor
                = 10~6  (metric and English)

          Y(S)U = daily total loading of solids, kg/day  (Ib/day), cal-
                    culated in Eq. (9-1)

          C(i)  = concentration of pollutant  i  in solids, mg/g; MPN/g
                    for total coliform and fecal coliform

Equations (9-1) and (9-2), along with solid loading values and composi-
tions in Tables 9-1 to 9-2, provide the means to assess daily average
pollutant loadings from urban areas.

It is important to note that pollutant loadings so calculated are street
surface loadings rather than loadings at outfalls to the receiving waters.
The transport of storm runoff in sewers and removal of pollutants in some
treatment systems would reduce pollutant loads to some extent.  Such ef-
fects are not included in loading factors suggested in Tables 9-1 and 9-2.
Furthermore, the methodology presented above does not reflect the effect
of housekeeping practices in the urban area.   Good housekeeping practices
such as cleaning of street solids by  sweeping, and the use of catchment
basins to remove solids and organic matter, will reduce pollutant loads
from streets to receiving waters.U

9.1.2  Procedure for Loading Calculations

Data in Tables 9-1 and 9-2 represent  two options as well as two levels of
accuracy for a user to assess pollutant loadings from a given urban area.
Application of the "subset" data may  result in higher accuracy, but re-
quire more data and more computation  effort,  than if "nationwide means"
are used.

Option I - In this option the user will use nationwide means  presented
in Tables 9-1 and 9-2.  Proceed  as follows:

1.  Determine solid loading rate and  solid  composition from tables.
                                   190

-------
2.  Determine street length (include that of primary and secondary streets
but not driveways, alleys, or parking lots).

3.  Calculate daily solid loading using Eq. (9-1).

4.  Calculate daily loading of other pollutants using Eq. (9-2).

Option II - In this option the user will make use of data presented for
source categories in Table 9-1.  Steps needed for loading calculations
are:

1.  Characterize the study urban area.  When applicable, the entire area
should be divided into individual homogeneous sections with unique char-
acteristics.  Each individual section is then defined as a subarea (e.g.,
residential area).

2.  Determine street length in each subarea.

3.  Enter the Table 9-1 at the line labeled "All Data."

4.  Select a category of climate, land use, or average daily traffic,
which best applies to an area and move upward to the line of data to the
right of the category heading.

5.  Substitute those values available in the row selected for the cor-
responding values in the row labeled "All Data."  In choosing the sub-
stitute loading factors, the following priority sequence of source cate-
gories is suggested:  (a) climate; (b) land use; (c) average daily traf-
fic.   The climatic zones of the U.S. delineated by the URS are shown in
Figure 9-1.  Caution: it is not permissible to use more than one row of
substitutions at a time, i.e., to use a BOD value for land use and COD
for climate in order to form a new row of loading rate and composition
data.  It is both proper and useful, however, to repeat the above process
to obtain several new rows of data to present a range of composition
and loading rates.  Use data from Table 9-2, if desired.

6.  Repeat Steps 4 and 5 for all subareas.

7.  Use Eq. (9-1) to calculate total solid loading in a subarea.

8.  Use solid loading (Step 7), Eq. (9-2) and selected composition data
to calculate total loading of other pollutants in a subarea.

9.  Sum up loadings of subareas to obtain the loading of the entire study
area.
                                    191

-------
[NORTHWEST
              (INSUFFICIENT DATA) (
I j   imJwauWe\^^V^^r --—
 1	4-H  I >  •Ab/TAHJb'r
\ P^TST^
   San J6«  [SOUTHWEST'
   \       . t     T —. ,
                                   SOUTHEAST) ftlant
                                     V   I    I

     Figure 9-1.  Climate zone for the cities from which data are available

                    and used in the URS study—/
                             192

-------
The calculation procedure delineated for Option I and Option II above is
illustrated in Section 9.1.4.

Option III - In this option, the user will make use of site specific
data.

The recent URS study has assembled all presently available data on the
rates of accumulation of solids and on the concentrations of various
pollutant constituents in those solids that collect on street surfaces.
These data are probably adequate for most urban planning operations.
The user, however, may alternatively replace these loading factors by
site specific data to obtain better prediction.

If site specific data are lacking, users are encouraged to conduct samp-
ling and analytical programs of their own.  The data from site specific
tests, if handled properly, may be used in analyzing the area's runoff
problems instead of using values given in this Handbook.  This would be
desirable in most instances, especially in areas or under specific con-
ditions that were not documented in the URS study.

Recommended procedures for conducting site specific tests are given in
Appendix B of the URS Report.I/

With the lack of site specific data, the user may wish to examine the
available published data for source and reliability.  The user is re-
ferred to Appendix A of the URS Report!./ for description of available
data sources, as well as procedures for processing these data.

9.1.3  Street Length and Land Use Data for Urban Areas

Data on street length - Street length data are available from local
public works departments or street departments.  They can also be ob-
tained by measurement on aerial photographs.

Survey statistics for the U.S. indicate that street surfaces occupy on
the average about one-sixth of the urban area.—   The American Public
Works Association^/ recently developed a regression between curb length
of urban area versus population density.  Data from many cities across
the country were used.  The resulting regression equation is:
                                    193

-------
                  CL = 413.11 - (352.66)(0.839)PD                 (9-3)

where        CL = curb length density, ft/acre

             PD = population density, number/acre

The correlation coefficient for the equation is 0.72.  The regression
curve is shown in Figure 9-2.
Curb length can be estimated if street surface acreage is known.
9-3 presents equivalent curb length per unit area of street surface, sug-
gested by URS..I'  However, if actual values are known, it is best to use
known values.

Land use data - The following references provide survey data and analy-
sis results relative to land uses in major urban areas of the U.S.:

Bartholomew, H. , Land Use in American Cities, Harvard University Press,
Cambridge, Massachusetts  (1955).

Niedercorn, J.  H. , and E. F. R. Hearle, "Recent Land-Use Trends in
Forty-Eight Large American Cities," The RAND Corporation, Santa Monica,
California, Memorandum RM-3664-FF, June 1963.

Manuel, A. D. ,  R. H. Gustafson, and R. B. Welch, "Three Land Research
Studies," National Commission on Urban Problems, Research Report No. 12,
Washington, D.C. (1968).

The American Public Works Association?-' estimated land consumption rates
for various land uses in American cities, shown in Table 9-4.  These
rates can be used to estimate acreages in different land uses if the
number of population is known.

9. 1.4  Example

The study area  is a 250-acre urban watershed in Atlanta, Georgia.  The
area is mainly  residential and has 17 curb-miles of primary and second-
ary streets.  Predict the average daily loadings of BOD and lead in run-
off from the entire area.

Option I - Use  nationwide means of solid loading rate and compositions
given in Table  9-1.
                                    194

-------
       GROSS POPULATION DENSITY, POP/HECTARE
600
550
500
450
UJ
< 400
>
LU
UJ
"- 350
t—
g 300
UJ
Q
JE 250
O
Z
^ 200
CQ
D
U 150
100
50
0
i i i i i i i i i i i i
'
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• — _ 	 * _ . .._ 	 * —

/• •
•••/•*
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r :
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I
ii ii I i i i i
/y/
750
700
650
600
550 S
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450 §
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400 ^
350 £
Z
300 Q
i
250 o
Z
LU
200 -1
CO
150 3
100
50
n
             30    40     50    60    70    80
        GROSS  POPULATION DENSITY, POP/ACRE
90   100
Figure 9-2.  Correlation between population density  and
                curb length density.—'
                        195

-------
         Table 9-3.   EQUIVALENT CURB-LENGTH PER UNIT AREA
              OF STREET SURFACE,  ARRANGED BY LAND USE
                               TYPES!/
                        Equivalent curb-km
                          per hectare of
                          street surface
                 Equivalent curb-miles
                      per acre of
                    street surface
Open land
General residential
General commercial
Light industrial
Heavy industrial
All land use types
2.11
2.15
1.63
1.71
1.59
1.83
0.53
0.54
0.41
0.43
0.40
0.46
            Table 9-4.  GENERAL LAND CONSUMPTION RATES
                     FOR VARIOUS LAND USES:4./
Land use
Residential
Commercial
Industrial
Park
Land
< 100,000
Population
0.1049
0.0101
0.0177
0.0146
consumption (acres/capita)
> 100,000
Population
0.0714
0.0084
0.0083
0.0093
> 250,000
Population
0.0585
0.0073
0.0077
0.0078
                                 196

-------
Calculate solid loading -

L(S)U = 156 Ib/curb-mile/day

Y(S)U = 156-17 = 2,652 Ib/day

Calculate BOD and Pb loadings -

C(BOD)U = 19,90(MO~6 Ib/lb solid

C(Pb)u = 1,810-KT6 Ib/lb solid

Y(BOD)U = 2,652-19,900-10-6 = 52.8 Ib/day

Y(Pb)u = 2,652-1,810-10~6 = 4.8 Ib/day

Option II - Use substitutions at 80% confidence level.

Atlanta is in the southeast.  Move upward in Table 9-1 to southeast
climate category.  A loading substitution is available.  Make all avail-
able substitutions into the row labeled "All Data."  The new row has,
among others:

Solid loading rate, L(S)U = 103 Ib/curb-mile/day

BOD concentration, C(BOD)U = 19,900-KT6 Ib/lb solid

Pb concentration, C(Pb)u = 1,370-lQ"6 Ib/lb solid

Calculate solid loading -

Y(S)U = 103'17 = 1,751 Ib/day

Calculate BOD and Pb loadings -

Y(BOD)u = 1,751-19,900-10'6 = 34.8 Ib/day

Y(Pb)u = 1,751-1,370-10"6 = 2.40 Ib/day

Pollutant loadings calculated in Option II are lower than those in Op-
tion I and probably better represent the real situation in Atlanta,
Georgia.
                                    197

-------
9.1.5  Techniques for Assessing Urban Runoff Pollution Characteristics

The material presented in the preceding sections provides states and local
water quality planners with methodologies and data for predicting urban
nonpoint pollutant loadings.  It is not intended to serve as a basis for
characterization of runoff flowing from an urbanized area.  Rather, it
is intended to give a first-cut assessment of nonpoint urban pollutant
loading without extensive data generation.

The water pollution characteristics of urban runoff are related to both
the quantity and quality of runoff.  There are numerous analytical methods
which have been developed for assessing the quality and quantity of run-
off following a rainfall or snowmelt incidence, as a function of time.
Variations of runoff characteristics with respect to time are especially
important if storage, treatment, or other methods of disposal are under
consideration; identification of temporal variations will enable one to
identify and treat the most polluted portion of the runoff.

The presently available analytical methods to assess the water pollution
characteristics of urban runoff consist of several levels of sophistica-
tion.  The most accurate and definitive methods are the most difficult
to utilize.  Simplistic methods are available to allow the user to ob-
tain approximate estimates.

The user is referred to the literature listed below for methods to assess
the pollution characteristics of urban runoff.  The analytical tools
presented in these references range from simple desk calculations  to sophis-
ticated computer techniques.

Amy, G., R. Pitt, R. Singh, W. L. Bradford,  and M. B. LaGraff, Water
  Quality Management Planning for Urban Runoff, U.S. Environmental Pro-
  tection Agency, Washington, D.C. (EPA-440/9-75-004)  (NTIS PB 241 689/
  AS) December 1974.

Brater, E. F., and J. D. Sherrill, Rainfall-Runoff Relations on Urban
  and Rural Areas, a study by the University of Michigan for the U.S.
  Environmental Protection Agency, Cincinnati, Ohio (EPA-670/2-75-046)
  May 1975.

DiGiano, F. A., and P. A. Mangarella.(Ed.), Applications of Stormwater
  Management Models, Short Course Proceedings prepared by the University
  of Massachusetts, for the U.S. Environmental Protection Agency, Cincinnati,
  Ohio  (EPA-670/2-75-065) June 1975.

Metcalf and Eddy, Inc., University of Florida, and Water Resources Engineers,
  Inc., Stormwater Management Model, U.S. Environmental Protection Agency
  (Report  11024 DOC 07/71), 4 Volumes, October 1971.

                                    198

-------
U.S. Corps of Engineers, Urban Runoff: Storage, Treatment and Overflow
  Model "STORM," U.S. Army, Davis, California, Hydrologic Engineering
  Center Computer Program 723-58-L2520, May 1974.

9.2  POLLUTANTS FROM MOTOR VEHICULAR TRAFFIC ON ROADWAYS

Motor vehicular traffic contributes a substantial portion of pollutant
material accumulated on the surface of roadways.  Significant levels of
toxic heavy metals, asbestos, and slowly biodegradable petroleum products
and rubber are deposited directly from motor vehicles.  Contributed by
traffic are also large quantities of particulate materials and nutrients.
All of these constitute a significant source of water pollution.

In a recent study conducted by Biospherics, Inc.,—  for the U.S. Environ-
mental Protection Agency, the deposition rates of traffic related mater-
ials were measured.  The sampling activities of that study were conducted
at different locations of urban roadways in Washington, D.C., with a
principal objective of determining the specific contributions of motor
vehicular traffic to materials deposited on roadways.  During the investi-
gation, efforts were made to isolate pollutant contributions through other
mechanisms unrelated to motor vehicular traffic, such as land use, street
litter, air pollutant fallout, etc.

Traffic-dependent rates of deposition of roadway surface contaminants
determined by Biospherics, Inc., are given in Table 9-5.  These deposition
rates (Kg/axle-km, or Ib/axle-mile) are highly correlated with total traf-
fic at sampling sites and therefore considered to be traffic dependent.
This is not to imply that these materials are directly emitted by motor
vehicles.  To the contrary, some of the traffic related materials may have
origins other than with the motor vehicle itself.

Information developed by Biospherics can be used to estimate, for a speci-
fic section of a roadway, the traffic related pollutant loads using the
function below:

                     Y(i)tr = y(i)tr-LH-TD'AX                     (9-4)

where    Y(i)tr = loading of pollutant  i , kg/day (Ib/day)

         y(i)tr = deposition rate of pollutant  i , kg/axle-km  (lb/
                    axle-km).

             LH = length of highway section, km (mile)

             TD = traffic density, vehicles/day

             AX = average number of axles per vehicle

                                   199

-------








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The following comments are made regarding the data (Table 9-5) developed
by Biospherics, Inc.

1.  These data are deposition rates of traffic related materials on road-
way surfaces.  They do not represent the discharge of pollutant into the
surface waterways.  Correlation has not yet established between the loads
emitting to the streams and the dry weather accumulation on road sur-
face.

2.  These deposition rates, however, may represent, on a high side, the
emission of pollutants from traffic related sources.   It appears that the
loads flushed to receiving water by storm events depend on the surface
deposition and an  attenuation  factor which is influenced by the climatic
characteristics of the specific location, particularly the return fre-
quency of rainfall and runoff events sufficient to flush the surface.

3»  It has been reported!' that a total rainfall of 0.5 in. will remove
907» of road surface particulates.  The storms of following duration and
intensities are considered to produce such a result:

•  0.1 in/hr for 300 min  (5 hr)

•  0.33 in/hr for 90 min  (1-1/2 hr)

*  0.5 in/hr for 60 min (1 hr)

•  1.0 in/hr for 30 min (1/2 hr)

It has also been reported that  total rainfalls of 0.27, 0.15, 0.08 and
0.02 in. will remove 70, 50, 30, and 10% respectively, of road surface
particulates.  The return frequency of storms in various regions of the
United States has been developed in a  study  conducted by the American
Public Works Association  for EPA.—'

4.  A very limited amount of work on highway runoff has been reported
and the loading functions or values which include effects from all pol-
lutant sources on highways are still not available.  With the absence of
available data, the deposition rates established by Biospherics may be
used as a first approximation,  with the following understandings:

a.  Pollutants originating from highways, in addition to traffic related
pollutants, may also come from sources such as atmospheric fallout, litter,
spill, and runoff from adjacent areas.  Influences from these and other
sources are not included  in the given deposition rates.
                                   201

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b.  These deposition rates were measured on urban roadways.  If directly
applied to highway situations, they may result in a higher prediction
than that actually occurred, due to the following reasons: (a) a higher
travel speed on highways than on the urban roadways, and  (b) a lower
frequency of stop-and-go on highways.

9.2.1  Sources of Roadway Traffic Data

Data on mileage of urban and rural roadways and annual vehicle-miles of
travel are generally available at state highway departments.  These data
are presented in reports, such as New Mexico's "Traffic Survey, 1973,"
and Oregon's "Traffic Volume Tables for 1973."  The states also prepare
traffic flow maps showing travel on major routes.

9.2.2  Example

A 100-km  section of a highway has a traffic density of 40,000 cars/day.
Assuming  two axles per vehicle, the following calculations are made to
estimate  loadings of BOD and total phosphate from Eq.  (9-4).

y(BOD)tr  = 1.52 x 10"6 kg/axle-km

Y(BOD)tr  = 1.52 x 10"6 x 100 x 40,000 x 2 = 12.2 kg/day

y(PT)tr = 4.03 x lO'7 kg/axle-km

Y(PT)tr = 4.03 x KT7 x 100 x 40,000 x 2 = 3.2 kg/day

9.3  STREET AND HIGHWAY DEICING SALTS

A set of  loading functions for deicing salts has been developed which
describes (a) average daily loading in a year, (b) average daily load-
ing in the winter season, and  (c) maximum daily  loading in a 30-
consecutive day period.

The 30-day minimum is negligible, since practically all of the deicing
salt enters surface waters or moves to subsurface or groundwater during
the winter and early spring months.

9.3.1  Loading Functions

Loading  function for annual daily average - The  deicing salt  loading
function  for daily average in a year for an area under consideration is
developed from (a) quantity of salt applied per  year and  (b) proportion
of  salt  reaching surface water.
                                   202

-------
           Y(DI)average daily (annual) - a-b-DI/365                (9-5)

where    Y(DI)average daily
           (annual)          = quantity of salt loading to water course,
                                 average over 1 year, kg/day (Ib/day)

                           a = conversion factor,
                             = 1,000 (NT, kg)
                             = 2,000 (tons, Ib)

                           b = attenuation factor, dimensionless

                          Dl = amount of deicer applied in the area,
                                 MT/year (tons/year)

For urban streets, the attenuation factor  "b"  is 1.0, with the assump-
tion that applied salt is completely flushed into the storm sewer system
and into the receiving waters.

For rural areas, the attenuation factor   "b"  has been found to be in
the range of 0.5 to 0.9, due to. losses to subsurface and groundwater.
A value of 0.7 is recommended,  if local values for deicing salt losses
are available, however, they should be used in the loading function in
preference to 0.7.

Loading function for daily average in winter season - This function is
the same as Eq. (9-5) except that the denominator (365 days) is substi-
tuted by the number of days in the winter season.

Loading function for 30-day maximum - This loading function was developed
by evaluating snowfall frequency in an area and salt loading per snow-
fall day.  For the latter, the function has the form:
                  Y(DI)snowfall day = a-b-DI/SD                    (9-6)

where   Y(Dl)snowfall day = sa^-t loading per snowfall day, kg/day  (Ib/
                              day)

                       SD = the number of snowdays, defined as days in
                              which 2.5 cm  (1.0 in.) or more of snow
                              falls

The 30 day maximum load can be determined by estimating the greatest
number of snowdays occurring in a 30-day period (SDor)).  The ratio of
the number of snowdays during the 30 day maximum period to the total
number of snowdays in the winter season will define the percentage of
the annual salt application during the 30-day period.  Thus:
                                  203

-------
                                   SD30
                                   —
                                                                  (9-7)
where      ^30 = t^e tonn^ge of salt applied during the 30 day maximum
                    period

The loading function which describes the maximum daily loading in a 30
consecutive day period, therefore, is:
                     Y30-day maximum = ^-b-DI30)/30          (9-8)

In the northern latitudes, especially in rural areas, the largest frac-
tion of the snowfall and applied salt remains on the ground until the
spring thaw, and hence, the 30 day maximum load is shifted to the spring
months.  The user should rely on local experience to determine the 30
day maximum period.

9.3.2  Sources of Required Data

Number of snowdays (SD) - The number of snowdays in the winter season
can be estimated with climiatic maps in The National Atlas of the United
States, U.S. Department of the Interior, Geological Survey (1970), or
Climatic Atlas of the United States, U.S. Department of Commerce, June
1968, or other equally suitable sources.

Amount of deicing salt applied - Data may be obtained from the follow-
ing agencies:

     Street departments;
     Public work departments;
     Highway departments; and
     Tollway authorities.

Nationwide data on deicing salt application are periodically collected
by and available from the Salt Institute, Alexandria, Virginia.

Appendix H of this Handbook presents available statistics relative to
deicing salts application on highways.  Information includes tonnage
of salt (sodium and calcium chlorides) applied, application rates per
snowday per unit length of single-lane roads, mileage of highways and
tollways treated, and mean annual snowdays.  Application figures were
determined by survey in the late 1960's.
                                  204

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                              REFERENCES
1.  Amy, G., R. Pitt, R.  Singh, W.  L.  Bradford, and M.  B.  LaGraff,
      "Water Quality Management Planning for Urban Runoff," a study
      by the URS Research Company for  the U.S.  Environmental Protection
      Agency, Washington, D.C.  (EPA 440/9-75-004) (NTIS PB 241 689/AS),
      December 1974.

2.  Startor, J. D.,  and G. B.  Boyd, "Water Pollution Aspects of Street
      Surface Contaminants," a study by the URS Research Company for
      the U.S. Environmental Protection Agency, Washington, D.C. (EPA-
      R2-72-081), November 1972.

3.  Manuel, A. D., R. H.  Gustafson, and R. B.  Welch, "Three Land Re-
      search Studies," National Commission on Urban Problems, Report
      No. 12, Washington, D.C.  (1968).

4.  American Public  Works Association, "Nationwide Characterization,
      Impacts and Critical Evaluation of Stormwater Discharges, Non-
      sewered Urban Runoff and Combined Sewered Overflows," Monthly
      Progress Report to  the U.S« Environmental Protection Agency,
      August 1974.

5.  Biospherics, Inc., "Effect of Urban Roadway Use on Runoff Pollution
      Loading Factors," for the U.S. Environmental Protection Agency,
      Final Report (draft), August  1974.

6.  American Public  Works Association, "Nationwide Characterization, Im-
      pacts and Critical  Evaluation of Stormwater Discharges, Nonsewered
      Urban Runoff and Combined Sewer  Outflows, (Final Report Draft),"
      for the U.S. Environmental Protection Agency, Washington, D.C.,
      August 1975.
                                 205

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                             SECTION 10.0

                       LIVESTOCK IN CONFINEMENT

10.1  INTRODUCTION

The loading function for livestock in confinement is applicable only to
feedlots that operate without adequate runoff control facilities.  The
feedlots which come under either the federal NPDES permit program, or
state and local regulations which require runoff control are excluded from
the scope of the handbook.

State and local regulations concerning feedlot runoff control requirements
vary.  Sometimes these requirements may exceed those of NPDES or encompass
smaller lots than the lower limits of NPDES.  Thus, the loading function
includes those feedlots not covered under NPDES, less those which ade-
quately manage/control waste and pollutant runoff in response to local
regulatory requirements or for other causes.  Some of the added exclusions
are:  completely closed confinement hog and poultry lots sized below NPDES
limits; and dairy lots below the NPDES limit, which control both milking
operation wastes and loafing/feeding area wastes.  Turkey and laying hen
operations operated with the confined range management system will be in-
cluded (unless runoff is controlled).  The principal livestock operations
covered in this handbook are thus the smaller beef, dairy, and hog opera-
tions, and poultry operations which involve confined range.

The present (1975) requirements for NPDES permits for animal confinement
facilities were published in the Federal Register dated 3 May 1973.  Ac-
cording to these requirements, the following categories of animal feedlot
facilities are included under the NPDES permit program:

     Slaughter steers and heifers, 1,000 head or more;
     Dairy cattle, 700 head or more;
     Swine over 55 Ib, 2,500 or more;
     Sheep, 10,000 or more;
     Turkeys, 55,000 or more;
     Laying hens and broilers--continuous flow watering, 100,00 or more;
                                 206

-------
     Laying hens and broilers--liquid manure handling system, 30,000 or more;
     Ducks, 5,000 or more; and
     Combination of animals within a facility, 1,000 animal units.

The following multipliers are used to calculate the number of animal units
in lots with more than one type of animal.

     Slaughter steers and heifers - 1.0;
     Dairy cattle - 1.4;
     Swine - 0.4; and
     Sheep - 0.1.

The above enumerated size limits are under review, and the handbook user
should ascertain what current limits are specified before proceeding with
load calculations.

Most livestock operations eventually dispose residual wastes on land--
cropland, pasture, etc.  The land-disposed wastes are nonpoint sources of
pollution, which are covered in Section 4.0 entitled "Nutrients and Organic
Matter."  The loading functions presented in Section 4.0 are satisfactory
for wastes disposed on land by practices which minimize or eliminate runoff
incidents with land-spread manure.  The data base for mismanaged land
spreading .LS not adequate for development and  use of a loading function;
local judgment and estimates will be required.

On-site feedlot wastes are quite variable--by region, by season, by type
of animal, and by lot management practices.  Particularly variable is the
on-site inventory of wastes.  Beef cattle operations typically will develop
a permanent net inventory of wastes over a few centimeters of the lot sur-
face.  Open poultry and hog lots will have a considerably smaller average
inventory of on-site wastes on a per unit area basis.  These variabilities
lead naturally to wide variations in pollutant loads and concentrations
carried off the lots in runoff.  Thus, it has been concluded that average
or typical numbers should not be presented for the convenience of the hand-
book user.  Rather, a range of values will be presented, and the burden of
determining the proper position within the range is shifted to the user.

10.2  LOADING FUNCTION FOR LIVESTOCK OPERATIONS

The loading function is based upon the premise that the size and area of
individual and cumulative feedlots can be determined and located within
the area under assessment, and that the following factors can be adequately
established:  (a) quantities of runoff, Q, from lots, as a function of
appropriate units of time; (b) concentrations, C, of pollutants in the
runoff; and (c) the fraction, FL,, of runoff-contained pollutant delivered
to streams.  The loading function based upon these premises is:

                                  207

-------
                     Y(i)FL = a.Q(R).C(i)FL.FLd.A                   (10-1)


where     Y(i)   = loading rate of pollutant  i  from a livestock facility,
                     kg/day (Ib/day)

            Q(R) = direct runoff, cm/day (in/day)

          C(i)py = concentration of pollutant  i  in runoff,  mg/liter

             FL, = delivery ratio

               A = area of livestock facility, ha (acres)

               a = a dimensional constant (0.1 metric, 0.23 English)

10.3  FEEDLOT RUNOFF EVALUATION

10.3.1  Factors in Runoff Estimation

Runoff volume is dependent upon many factors.  The most important variables
are:  (a) amount and intensity of precipitation; (b) soil moisture condi-
tion; (c) topography including slope and surface cover; and (d) soil charac-
teristics.  Stocking rates (area per animal), which are determined in part
by  local precipitation patterns such as humid and arid condition, may affect
the degree of compaction of the surface and thus the runoff volume.

Precipitation - Precipitation varies in duration and intensity for a given
location, and average precipitation values may lead to errors in calcula-
tion of runoff volumes.  Feedlot surfaces can absorb and store a definite
water volume in any specific period of time, and runoff may not occur
until the volume of rainfall exceeds the absorptive and storage capacity
of  the surface.  Similarly, rainfall intensity has a significant effect
on  the rate of runoff and may affect the runoff volume.

Snow may accumulate on feedlots in cold climates and may not result in run-
off until thaw conditions set in.  Significant runoff may result from snow-
melt in middle and northern latitudes of the U.S.  The volume of snowmelt
may be computed from records of total snowfall.  The water equivalent of
snow in precipitation varies significantly, but  it is generally assumed
that 10 in. (25 cm) of snowfall contains 1  in. (2.5 cm) of water.i'

 Soil moisture  - The amount of runoff is affected by the degree of satura-
tion of soil with water.  A dry soil-manure mixture has greater capacity
to  absorb precipitation and to retain moisture than a wet mixture.  Ante-
cedent precipitation is thus an important factor in determining the soil
                                    208

-------
moisture.  When one rain follows closely after another of equal intensity
and duration, a greater volume of runoff may result from the second rain.

Topography - The slope and surface cover, i.e., whether concrete lot or
dirt lot, may affect the runoff volume.  For feedlot situations, the effect
of slope on runoff volume was not shown to be significant.  The effect of
paving the lot surface, however, was reported to be significant.  Manure
handling practices, including frequency of cleaning the surface, may have
an effect on the amount of runoff.  Even on unsurfaced feedlots, the sur-
face soil-manure mixture is subject to compaction and tends to provide a
sufficient and effective barrier to seepage.  This is especially true in
a continuously operating feedlot.

Soil characteristics - A coarse, sandy soil has a greater infiltration
capacity than a clay soil.  The infiltration capacity for bare soils is
in the range of 0.5 to 1.0 in/hr (1.25 to 2.5 cm/hr) for sandy soils,
0.1 to 0.50 in/hr (0.25 to 1.25 cm/hr) for intermediate soils, and 0.01 to
0.10 in/hr (0.025 to 0.25 cm/hr) for clay and clay loam type soils.  These
rates are for bare soils which are not excessively trampled or excessively
compacted.  The movement of animals within the lot will often create a
soil-manure mixture at the surface, which will reduce the natural infiltra-
tion capacity of the soil and increase the runoff volume.

10.3.2  Precipitation Data Analysis

The single most important characteristic of precipitation is its variability.
Estimation of runoff will be greatly influenced by the quality of precipita-
tion data.  In general, the longer the record, the better is the estimate
of probable precipitation for a given  location.  A 1-year precipitation rec-
ord is not a good indicator of the probable occurrence of precipitation in
the future, but there is no simple way to determine a priori what length
of records will give a reliable estimate of average precipitation in a given
location.

Depending upon the time and resources  available, the local planner should
determine the length of records that must be included in  the analysis.  A
wide variety of precipitation data is  available.  Local climatological data
are issued on a monthly basis by the U.S. Department of Commerce--National
Oceanic and Atmospheric Administration.  Table 10-1 shows the locations by
state for which weather records are issued.  There are three categories of
publications which will help to determine the amount of daily precipitation
for a given  location:
                                    209

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Table 10-].
STATIONS FOR WHICH LOCAL CLIMATOLOGICAL DATA ARE ISSUED,
            AS OF 1 JANUARY 1974


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A1ABAMA
Birmingham
Huntsvi 1 le
Mobile
Montgomery
AIASKA
Anchorage
Annette
Barrow
Bethel
Settles
Big Delta
Cold Bay
Fairbanks
Gulkana
Homer
Juneau
King Salmon
Kociak
Kotzebue
HcGrath
None
St. Paul Iflland
Summit
Talkeetna
Unalakleet
Yakutat
ARIZONA
Flagstaff
Tucson
Wins low
Yuoa

ARKANSAS
Fort Smith
Little Rock

CALIFORNIA
Bakersf ie Id
Bishop
Blue CanyoO
Eureka
Fresno
Long Beach
Los Angeles Airport
Los Angeles
Mt. Shasta
Oakland
Red Bluff
Sacramento
Sand berg
San Diego
San Francisco
Airport
City
Stockton

COLORADO
Alamosa
Colorado Spi ings
Denver
Pueblo
CONNECTICUT
Bridgeport
Hartford
DELAWARE
Wilmington
DISTRICT OF COLUMBIA
Washington'Dulles J nt ' 1


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FLORIDA
Apalachicola
Day ton-i Beach
Jacksonvi lie
Key West
Lakeland
Miami
Orlando
Pensacola
Tampa
West Palm Beach

GEORGIA
Athens
At lanta
Augusta
Columbus
Macon
Rome

HAWAII
Hilo
Honolulu
Kahului
Lihue

IDAHO
Boise
Lewiston

ILLINOIS
Cairo
Chicago
Midway Airport
O'Hare Airport
Moline
Peer la
Rockf ord
Springf ie Id

INDIANA
Evansville
Fort Way lie
I ndianapol is
South Bend
IOWA
Bur lington
D«s Koines
Dubuque
S loux City
Waterloo
KANSAS
Concordia
Dodge City
Topeka
Wichita

KENTUCKY
Lexington
Louisville
LOUISIANA
Alexandr 1.1
Baton Rouge
Lake Ch-u les
New Orleans
Shreveport
MAINE
Caribou
Portland
MAR YI AND
Ba Itimore


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Published if S
MASSACHUSETTS
Boston
Blue Hill Obs.
MICHIGAN
Alpena
Detroit
City Airport
Detroit Metro AP
PI i nt
Grand Rapids
Houghton Lake
Lansing
Marquette
Muskegon
Sault Ste. Marie

MINNESOTA
Duluth
International Falls
Rochester
St. Cloud

MISSISSIPPI
Jackson
Her idian

MISSOURI
Columbia
Kansas City
St. Louis
Springfield

MONTANA
Bil lings
Glasgow
Great Falls
Havre
Helena
Kalispell
Miles City
Missoula

NEBRASKA
Grand Island
Lincoln
Norfolk
Omaha
Scottsbluf f
Valentine
NEVADA
Elko
Ely
Las Vegas
Reno

NEW HAMPSHIRE
Concord
Mt. Washington
NEW JERSEY
Airport
State Marina
Newark

N&1 MEXICO
Albuquerque
Clayton
Roswell
NEW YORK
Albany
K

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or more available per

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NEW YORK (Contd.)
Buffalo
New York
Central Park
J.F. Kennedy I nt ' 1 AP
LaGuardia Fie Id
Rochester
Syracuse

NORTH CAROLINA
Ashevi 1 le
Cape Hatteras
Charlotte
Greensboro
Raleigh
Wilmington

NORTH DAKOTA
Bismarck
Fargo
Williston
OHIO
Akron-Canton
Cincinnati
Abbe Obs.
Airport
Cleveland
Columbus
Dayton
Mansf ic id
Toledo
Youngs town
OKLAHOMA
Oklahoma City
Tulsa

OREGON
Astoria
Burns
Eugene
Meacham
Medford
Pendleton
Portland
Salem
Sexton Summit

PACIFIC ISLANDS
Guam
Johnston
Koror
Kwa jslein
Majuro
Pago Pago
Ponape
Truk (Moen)
Wake
Yap
PENNSYLVANIA
Allentown
Erie
Hamsburg
Philadelphia
Pittsburgh
Airport
City
Scranton
Williams port
RHODE ISLAND
Block Island
Providence
SOUTH CAR-OLINA
Charleston
Airport
City
Columbia
Greenville-
Spartanburg
ons. c. Annual Sou


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SOUTH DAKOTA
Aberdeen
Huron
Rapid City
Sioux Falls
TENNESSEE
Bristol
Chattanooga
Knoxvi lie
N. -v"le
Oa RLJtft



TEXAS
Abilene
Ama r 1 1 1 o
Austin
Brownsvil le
Corpus Christi
Dallas
De 1 Rio
El Paso
Fort Worth
Galveston
Houston
Lubbock
Midland
San Ange lo
San Antonio
Waco
Wichita Falls

UTAH
Mil ford
Salt Lake City
Wend over
VERMONT
Bur 1 ington

VIRGINIA
Lynchburg
Norfolk
Richmond
Roanoke
Wallops Island
WASHINGTON
Olympia
Qui 1 layute Airport
Seattle-Tacoma AP
Seattle Urban Site
Spokane
Stampede Pass
Walla Walla
Yakitna
WEST INDIES
San Juan, P. R.

WEST VIRGINIA
Beckley
Charleston
Elkins
Hunt ington
Parkers burg
WISCONSIN
Green Bay
La Crosse
Madison
Milwaukee
WYOMING
Casper
Cheyenne
Lander
S he r id a n
• ued .

                                 210

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1.  Hourly precipitation data at various stations in each state are re-
ported by month.  Daily summaries are also included.  These data are ex-
tensive, and can be used to determine precipitation amounts for a given
location quite accurately.  Table 10-2 shows typical results for Missouri
in January 1974.

2.  Local climatological data for a given station are summarized by month.
Data are given on a daily basis, and at 3-hr intervals.  An example of the
type and extent of data in this category is shown in Table 10-3 during the
month of January for the weather station located at International Airport
in Kansas City, Missouri.

3.  Climatological data for each state are reported monthly.  These data
include both the official observatory data and data from other private and
public climatological records.  These data are presented on a daily basis.
Typical precipitation data for parts of Missouri during January 1974 are
presented in Table 10-4.

10.3.3  Estimation of Runoff from Feedlots

The quantity of pollutants discharged from a feedlot depends largely upon
the runoff volume and the pollutant concentration in the runoff.  Limited
data on cattle feedlot runoff characteristics in terms of various pollutant
concentrations are presented later in Tables 10-10 and 10-11.

The overall method consists of estimation of probable storm events for the
period of interest, by analysis of historic data, calculation of runoff
from individual storm events, and summation of runoff from all storm events,
The period of interest may be a year, or some fraction of a year--usually
30 days.

Methods for estimating runoff volumes from feedlots may be divided into
two categories:

1.  Soil Conservation Service (SCS) Method; and

2.  Empirical Regression Method.

Both the methods predict runoff volume from a given precipitation event.
The SCS method utilizes the concept of 'Soil cover and hydrologic (infil-
tration) capacity of soil in calculating runoff.  The regression method,
as the name implies, is based on the linear regression of observed rainfall-
runoff relationships for any given location.  Because of the variability
of the observed runoff patterns, and also because the regression coeffi-
cients may not be established adequately for a given location, the regres-
sion method is considered to be less reliable in predicting runoff volumes.

                                   211

-------
Table 10-2.  HOURLY PRECIPITATION
MISSOURI
H0( Rl S \\1Ol MS JL,LV 19 it,
SI ATI()\
JFFFFR5QN C ITY L U

JFFF£R50N BARRACKS 2SW

JEWETT 7 E

KANSAS CITY WSMU AP'




«F«NE,

KIPKSVHLE RADIO KIRX

LAKESinE
-
t,
25

1
31

10
14

2S
3


1

31

15
2?
2-5
A \1 llmuii.'ini!
1 2 t
A ', 6
789

HOI Hs
\ll\l II s
1
IS
2
10
10 11 12
P \l Hour bmlmp
I 2 I
4 r> f,
~ ft P
MONTH! "1 \l \\ \R M \M(H M ^
t
4^
(,
60
12
120
24
180
10 11 12

U ( 1 \H ! A1ION

DATE/TIME DF
FNPING
.1 .2 . ?
. V
AMOUNT
QrtTt/TIME OF
FNDING
AMfUNT
DATE/TIME QF
ENDING

DATE/TIME OF
ENDING
AMOUNT
DATF/TI^E DF

AMOUNT
DATE/TIME OF
ENCJNG
*
AMOUNT
DATE/TIME DF
ENDING
AMOUNT
DATF/TIME QF
AMOUNT
DATE/TIME OF
ENDING

AMOUNT
DATE/TIME OF
ENDING
AMOUNT
ENCING
AMOUNT
DATE/TIME DF
ENDING
AMOUNT
DiTf/TJME OF
.1 .2 .2
. 1
.1
.2
4/3 SCOA +
.1

•
-

,4M,00>

.34
.6
3/11IOOP
. t,
3/10:*t5P

.3
3/a:ooP
.3
S/8 IOOP
-
-

22/11IOOP
4/3'OOi
.2
4/2U5A

-
-

.!,>
14/<.IOOP

.66
3/10IOOP
1.1
3/1! IOOP
.6
3/niooP

.;
3/9IOOP
.4
3/BU'P
-
-
. 1
.66
?2/12IOOP
,5
*/^ IOOA+
.2
4/3I15A*

-
-

.O
UM IOOP
.01
.79
3/11IOOP
1.2
3/12IOOP
.7
3/111 15P*

. 5
3/9IOOP
3/S 1 15P
-
-

23/2 :OOA
.6
*t/* iOOA
.3
4/2 ".5A

-
-
.12
.45

1.12
3/12:oOP
1.3
4/1 :OOA
.8
3/l::i5p.

.6
3/11IOOP
.4
3/6:15P
-
-
.3
1 .00
.1
.6
4/41 OOA
,4
4/3I15A*

-
-
.01
.45
.45
14/4IOOP

1.12
1.3
4/ 1 IOOA
1.2
3/11I15P

.6
3/1 1IOOP
.5
3/9IOOP
-
-

1 .00

4/4 IOOA
4/31 ISA

-
-

.45
14/4 1 JOP
.34
1.12
3/12'OOP
1.3
4/1 IOOA
1.2
3/11M5P
.3 .2
3/111 OOP
,6
3/10'lSP
-
-









.3Z .13 .3*
.3 .8 .1


.1





TOT \L

.6
.1
.1





.13
.45

1.12
.01
1.2
.1


.6




.5
.1
.1
.3
                 212

-------
   V
                           Table  10-3

LOCAL  CLIMATOLOGICAL  DATA
U.S. DEPARTMENT OF COMMERCE
NATIONAL  OCEANIC AND ATMOSPHERIC ADMINISTRATION
ENVIRONMENTAL DATA SERVICE
Kansas  City,  Missouri
Nat  Weather  Service  Met  Obsy
International Airport
January  19 74
              ' 39 17  N
                         Longitude
                                9*.  43
                                          Flevation 'ground1
                                                       1014
                                                               Standard time used
                                                                                     HBAN H03947






&>
&
1
1
2
4
5
6
7
8
10
11
12
14
15
16
17
18
19
20
21
27
23
24
25
26
27
28
29
30
31





Temperature



|

X
S
U 2
0
10
17
ie
17
17
13
12
6
8
39
42
43
43
40
34
i9
37
35
41
48
53
44
36
47
56
60*
44
Sum
~~A^f~
30.6




I & i

c ' ^
S I <
3 4
-13 -7*
0
-1
1
1
-3
2
3
-9
-13*
24
29
33
30
33
31
33
33
30
25
28
30
34
29
28
35
39
26
Sum
A5v|7
17.0
5
B
10
9
7
8
8
-2
-3
32
36
38
37
37
33
36
35
33
33
38
42
39
33
38
46
50*
35

Avg
23,8
i.
Q.
&
S
T
Weather types
on dates (if



~n | 	 r 	 1 3 Thunderstorm
E
0
c
t
t £ tn.
4 ke pellets
oc 5 Hall
« 8. s z

u •* « "3 7 Duststorm
g 2 i £ ,°, « Smols. Hale
*" 1 "^ * *> Nlo»lno snow
I
6 7A 7B 8
-35 -16
-23 i- 5
-20
-18
-
-2
9
1
-19
-19
-;
9
-30
5

9
11

n
10





1
1
6
R
7
5
5
0
4
11



;

4
9
7
1
5

Dep
-4.0
Number of days
Maximu
> 90° t 1
m Temp

0 1 13
Minimurr
.6 32°
24
. j
3
6
- 

Avg
15

Temp
< 0"
7
72 0
60
57
55
0
0
0
56 j 0
58 . 0
57
57
67
68
33
29
27
28
26
32
29
30
32
32
27
23
26
32
27
19
15
	 30_
Total
Dep
119
Total
3153
Dep
67
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Total
-15^
0
Total
0
Dep
0
„
6
1 8

1

46 8
1 6
1
1
1 8
8
1
2 8
2
2 6
2 8
1
1 8
1


1
4 6
1 468





01
ice on
at
06AM

In
Jl 	
4
4
4
4
3
3
3
7
7
7
7
6
0
3
T
T
T
T
T
T
0
0
0
0
T
0
0
0
— N — ' 	 d 	
Precipitation
1 01 inch n 1 1
=• 1 0 inch 1
Thunderstorm
Heavy fog >
	 4
Precipitation
I Snou
Water ice
equiv.,. Mels
In





10 ' 11
.04 .5
.07
0
0
T
0
.12
.03
0
0
0
0
0
0
T
.02
.12
T
.12
0
0
0
.30
.02
.01
0
0
0
Total
Dep
-0.20
.7
0
0
T
0
.9
.4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
T
T
0
0
0
Total


A\g Wind Sunshine
station.

es- i 1 Fastest
sure j -u

Elev •£ c t g "
1
fp
m
mile 1

g

et
£1, Z ? S o. ' Z a- S

12
29
29
29
29
28
29
28
28
29
29
28
28
28
28
28
78
28
26
28
29
29
29
26
26
28
28
28
28


13 1 14 I 15
££ a
16
40 35 2.51 4.6 9
32 07
70
06
92
00
92
98
23
40
99
93
83
82
87
87
66
87
92
09
14
02
60
87
80
60
67
94
Ko


07
06
35
14
02
02
37
12
71
71
21
19
36
04
76
03
34
24
2 1
19
19
33
71
2 1
70
01
r


5.0
2.2
5.1
4.6
2.6
8.4
10.9
7.6
3.2
9.4
10.4
11.7
6.3
7.8
5.5
4.0
3.9
9.7
5.0
4.8
6.3
6.8
5.3
2.8
10.3
14.5
7.6
:hc


6.5 13
4.0
5.3
8.2
6.6
12.4
11.7
7.8
7.3
10.1
10.5
12.1
6.9
8.9
6.5
4.8
6.6
10.5
6.2
5.2
8.6
7.6
6.8
5.0
10.4
14.5
11. n
m o n


Greatest in 24 hours and dates
Precipitation
.301 26
Snow, ice pellets
2.6
Clear 8 Partly cloudy 6 Cloudy 17
9-10
10
12
13
10
20
13
U
13
16
17
19
11
18
!•>
1 1
17
17
10
7
71
19
17
16
16
73
,22
17
t
I
NE
N
NH
E
NH
N
NH
E
S
S
SH
S
N
N
H
NE
NH
SH
SH
SH
S
NH
SH
S
SH
NU
th
Date 30


c


XS
18
7.7
6.7
6.0
7.1
6.7
7.6
1.2
5.5
10.0
7.5
9.1
7.9
8.3
5.6
0.6
0.0
3.8
1.1
0.7
9.0
9.8
9.7
0.3
5.7
9.1
9.5
9.3
8.8
Total
Possible
303.2



„ 3
£ "
u B.
&*s
19
6?
71
64
75
10
8?
12
57
100
77
94
80
85
57
7
0
39
11
7
VI
97
97
3
57
90
92
91
87
*
month
65
Sk> cover
Tenths



S

r. f

20
4
10
1
10
5
8
10
10
1
9
1
10
t
10
10
10
10
10
10
4
0
0
10
10
1
1
3
4
Sum
Avg
6.5

0
fnfi,

•§•§
S 6
21
5
6
2
9
6
6
7
8
1
7
1
6
4
9
10
10
8
10
10
3
0
0
8
9
4
2
1
4
Sum
Avg
>•">
Greatest depth on ground of snow.
ice pellets or ice and date
7 1 14*








Q
22
1
2
4
5
6
7
8
10
U
12
14
15
16
17
18
19
20
21
22
23
24
25
26
n
28
29
30
31





                            HOURLY PRECIPITATION (Water equivalent in inches)
*
Q
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
A M Hour ending at
1

.02







T











T





T



2

.02







T









T

T





T



3

.02







.01









T

.06





.01



4

.01







.01











T





T



5









T











.04





T



k 6









T








T
.01

.01









7









.01








T
.05

.01









8









T








T
.04

T



T






- Below zero temperature or nettatixe departure from
t > 70- at Alaskan station*
+ Also on an earlier date or i ates
X Hea\\ fop restrict. \isibilit\ to V, mile or less
T In the Hourh Free pitatlon table and m columns
9 10. and 11 indicates an amount too small to
measure
The season for depree da\s bftrm* with Juh for heating
DaU in columns 6. 12. 13 14 jnd 15 are ba-ed on 8
9





T



T








T
.02





.04





10





T

T

T









T





.04





11





T

.02

T











T



.04





12







.03

T











T



.06





P M Hour ending at
1







.02
T
















.06





2







.02
T
T















.03





3







.02
T
T








.02






.03





4







T
.04
T








T






T





5







T
• 03
T















T





6







.01
.05
T








T







.01




7







T
.02
T








T







.01




8







T
.01
T








T







T




9







T
.02
T








T







T




10
.01






T
.01









T







T




11
.02







.01









T

T





T




12
.01







.01








T
T

T





T




a
5
1
2
3
4
S
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Subscription Price: Local Climatolog- SUMMARY BY HOURS
ical Data $ 2.00 per year including
annual issue if published, foreign
mailing 75c extra. Single copy: 20c
:or monthly issue; 15c for annual
summary. Make checks payable to De- j
iL IcT"
| | J 0 D
partment of Commerce, NOAA. Send pay- o"5x£
nents and orders to National Climatic ^js'w*"
Center, Federal Building, Asheville, ' *"|
North Carolina 28801. ^~
•
00 4
031 5
Hind directions are tho-e fron'"nhkh the wi d blows J certify that this is an official 06 5
Resultant wind H the \ectoi sum of wind diiectnm* publication of the National Oceanic ! 09 6
and speed, divided In the miml.ei o' obser\.,ti,,n- and Atmospheric Administration, and ! 12 7
F«ure, for d,r«t,™> are ,en. ,,_f rf,,r«-. l,om triw is compiled from records on file at i 15 6
im ri »h , ~, the National Climatic Center, Ashe- '18 6
 20
perati
u.
.0
u "3
5.0
20
19
18
! 2ll 19
29.02! 27
28.97 29
28.97 27
" ,r"l 17 "Vntr". in r-'ol !T ilTfi.''^" rt'Jr'5 ville , North Carolina 28801 . 21 6128.98! 25
26
24J
re I-*
-; — 01 x
b. (Q .H
So r-t E
^ £%.
14 72
14 73
13 74
14 74
16 66
17 62
17 66
22 16 70
Resultant
»•«: -
•o & u
<^
f D
7.4 30
7.9 30
7.8 30
7,6 21
8.3 24
1,1 27
8.8 07
8.6 17
1-mmute .peed* If the / appears lit Col 17 .peed- . ./ ,/ A
Anv errors detected will be corrected and changes in '"i/CW 	 f+- 7 /?**"7
TJJ=
g_ C
W

1.2
1.0





1.1
.6
5:
1.3J

•ummary data will be annotated in the annual summary Director, National Climatic Center USCOMM 	 NOAA 	 ASHEVILLE 475
NOTICE: CLlHATDinr.KnL MDRPAL." BiSED ON THE PERIOD 1941-1970 ARE EFFECTIVE AS FDILOHS
      HEATING DEGREE DAYS - JU1Y 1. 1973
      COOLING DEGREE DAYS< TEMPERATURE* AND PRECIPITATION - JANUARY 1, 1974
                                          213

-------
Table 10-4.  DAILY PRECIPITATION


. . .
A"ITY
BRDOKHELO
BURLINGTON JUNCTION
CAKRDLLTDN
CWIILICOTME
CHHLICQTHE BAOIQ KCHI
CQL.OKA
CONCEPTION
CONCORD1A
FAIRFAX
FAYETTE
FOUNTAIN GROVE Wl
GRANT CITY
HAMILTON 2 W
KANSAS CITY INT MSD A** R
KING C!TY
lEES SUMMIT REEC Hit
LUC (ONE
MARSHALL
MARYVILLE 2 E
MERCER 6 NW
KUAN
ODESSA
OREGON
POLO
PRINCETON. 6 SH
SALISBURY
SPICKARD T W
1 ARK 10
TRENTON
UNIDN.VILLE
WAVERLY
' * •
AUKVASSE
BOWL ING GREEN 2 NE
CENTRAL IA
EDINA
FREEODM
FUL7QN
GEMLD
GREGORY LANDING
HERMANN
KAKOKA
LA BELLE
LOUISIANA
KADJSON
MARTIN5BURG
MEMPHIS
MEXICO
MOBIRLY RADIO KwlX
MONROE CITY
OHENSVILLE
PACIFIC
PALMYRA
PARIS
SAINT CHARLES
SAINT LOUIS WSD AP R
SHELBINA
STIFFENVILLE
UNION,
VANCAL IA
KARRESTOS 1 N
WEBSTER GRO/iS
WEST CENTRAL
PLAINS 03
APPLETDN CITY
BUTLER
CALIFORNIA
CAMOf NTON
CUJT3N CITY
CLIMAX SPRINGS
CLINT3N 3 NW
COLE CAMP 9 SE
(LOCK
HARRlSONVlLLt
jEf FIRSDN CITY l U
LAKESIDE
OSCEOLA
s
£

1.3*
2.oe
1.59
1.83
1 .49
l.B*
1.62
1.5S
1.2*
1.6*
1.83
1 57
1.33
1.6*
1.09
1.75
i~.9(
1.31
l.BD
l.Bl
3.79
1 26
i.54|
1.46
2.94
1.53
2.97
1.31
1.91
l.»T
4.0>
3.1
2.12
2.9
J.7*
2.B
2.9fl
3.6
1.1
1.9
4.0
3.74
4.2
2.7
2.B
3.0
3.3
4.3
l.B
:.B
3.4
4.7
3.9
2.1
2.2
2.3
2.B
* .42
2.6
1.9
1.0
1.2
2.9
3.1
2.7
1.3
2.2
1.9
1.3

. >|3

.10
.01 .03
.13
.02
.20
.06
T .10
.02 T
.!«
.04
.12
.03
T .13
.04 .07
.OB
T
T .03
.10
.04 .03
.20
.02
.OB
T
.20
.03
T .06


T .04
T
T T
T
T
T
T
.02 .03
T
T
T
.0*
T T

.03 .03
02
.08


.01 T
.02

T

4 | 5 6

T

T
.01
T
.10
.04
T
T
.07
t
.08
T T
.03
.10
T
.03
T
.02
.03
T
.07
T



.24
T



T
T
.04
T

T



f




789

.02 .n
T .32
.21 .09
T
.22 .10
.12
.48
.IB
• 27
>01 .23
•16 .06
T .3ft
• 20
.14
-12 .10
.390 .40
•IB .12
T
.010 .15
T .30
.29
.18
.22
.20
.20
.14 T
.14 .11
.20 T
.20 .11
.21 T
T .18
.06 .21
T .45
.16
.02 .30
.08 -
T .13
.10 .30 .05
.02 .23
T .31
T .13
.20 .11
T .13
T .23
.23
•02 .03
•01 .IB
.12
.21 .06
.05
.14
.IB
.21 .51
.23
.19 .62
T .23
.1
.02 .2
T ,0
.1
.1
.20 .1
• 1
.10 .1
T
.03 .!
.?
.1
.02 .1
.10 -
.1
.11 .5

10 11 12

.31 T
.29 .08
.37
.28 .03
.28 T
.22
.13 .03
.47 ,02
.72
.04
.38
.23 .03
.21 T
.37 .02
.03
.08
.30
.200 .70
.06 .02
.10 .10
.13 .08
.61
.03 .33
.21 T
.12
.40 .03
.13 T
.61 T
.22
.19 .08
.11 .07
.69
.68
.36
.26 .03
.36 .01
.20
.2b .11
.27 .30 T
.S3 .03 T
.1* T
.23 .04
.31 .03
.41 .02
.40
.23 .09
.44 .09
.45
.30 -
T .89 T
.62 .02 T
.29 ,06
.60 T
.08 .01
.30 .01
.23 .04
.22 .07
.72
.81 ,rl
.43 .03
.53 T
1.08 T
.46
.69
.36
.5? T
.39 T
.43 T
1.00
.11 T
.50
.30
Day of Month
13 14 IS



T
T
T

T

T
1.53

T
T
T
T
1.40









T






.&e
T


16 17 16



.01 .02
T
T
T
T

T T
.04
.03
.02

T ,0»
T .04
T
T T
T
.83
.32
T
.02
.12
.?0
T


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                  214

-------
However, because of the simple format, the regression method may be easier
to use on a routine basis, especially when adequate experimental data exist.

10.3.3.1  Soil conservation service (SCS) method -

The Soil Conservation Service of the U.S. Department of Agriculture has
developed a method of estimating direct runoff from small agricultural
plots due to single storm events .?_/  The rainfall-runoff relationship given
by SCS is:
                     Q = 
-------
AMC - II:  The average condition.

AMC - III;  Highest runoff potential.  The watershed is practically satu-
rated from antecedent rains.

The AMC for feedlots can be estimated from 5-day antecedent rainfall by the
use of Table 10-5, which given the rainfall limits for "dormant season."
No upper limit is intended for AMC - III  (see Figure 4-9, Ref. 2).
            Table 10-5.  SEASONAL RAINFALL LIMITS FOR VARIOUS
                     ANTECEDENT MOISTURE CONDITIONS
                                             Total 5-day
                                         antecedent rainfall
          AMC group                      (in.)          (cm)

             I                           < 0.5          < 1.3
             II                         0.5-1.1        1.3-2.8
             III                         > 1.1          > 2.8


                                              O £ /
Using feedlot runoff data from various authors^^^-'  and Eqs. (10-2) and (10-3),
the amount of runoff from a given rainfall amount is computed for the
three antecedent moisture conditions, as shown in Table 10-6.
      Table 10-6.  RUNOFF (IN INCHES) FROM FEEDLOT SURFACES FOR
                VARIOUS ANTECEDENT MOISTURE CONDITIONS
                                           Precipitation (in.)
Runoff condition               0.5     1.0     1.5     2.0      2.5     3.0

AMC III
  Surfaced                    0.318   0.792   1.282   1.776   2.272   2.770
  Unsurfaced                  0.258   0.707   1.185   1.673   2.166   2.660

AMC I and II
  Surfaced                    0.138   0.505   0.938   1.398   1.871   2.352
  Unsurfaced                  0.071   0.360   0.741   1.165   1.612   2.072
                                   216

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The following procedure is suggested to calculate runoff from feedlots
using the SCS method:

a.  Determine feedlot area, which includes feeding pens, sick pens, feed
mixing and handling and equipment storage areas, alleys, and other open
areas associated with feedlot management.  In the absence of actual data,
assume total area as 1157o of feeding pen area.

b.  Select the time period for which storm data are required.  While no
simple procedure is available to determine the representative storm periods
for a given location, storm data during the past 3 years may be used to
approximate the most recent trends in precipitation.  Lesser time intervals
may be used if the records show no substantial deviation from expected
norms in precipitation patterns.  Select precipitation data on a daily basis.
Express all precipitation in terms of equivalent water by using the ratio of
one volume of water to 10 volumes of snowfall.

c.  Determine storm rainfall (P) from Step (b) above.  For each location,
the nearest weather station data may be used unless special rain gages were
installed for the location.

d.  Determine the amount of runoff for a given storm using data in Table
10-6. If local data permit an accurate determination of CN values, Eqs.
(10-2) and (10-3) may be applicable for a given location.

e.  Determine runoff (inches or centimeters) by adding runoffs for each of
the storms over a given period of time.  It is useful to determine monthly
values in order to obtain maximum and minimum 30-day runoff volumes.  By
adding the monthly runoff volumes, annual runoff may be obtained.

f.  Calculate total runoff volume by multiplying runoff depth in Step (e)
with area of feedlot determined in Step (a).  The result may be expressed
in volume units (acre-inch to ft-' or hectare-centimeter to m^).

10.3.3.2  Example -

An example computation of runoff for a hypothetical feedlot located near
the climatological station at International Airport, Kansas City, Missouri,
is shown below.

Assume that the feedlot comprises 220 acres (88 ha).  Calculate total run-
off volume from the feedlot using 1974 climatological data for the location.

The daily precipitation data for the station are presented in Table 10-7.
These data may be obtained from any one of the three categories of
                                    217

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                                                                        218

-------
climatological reports discussed earlier (i.e., hourly precipitation data -
Missouri; local climatological data - Kansas City, Missouri;  and climato-
logical data - Missouri).

Using Eqs. (10-2) and (10-3), and substituting a CN value of 91, Eq. (10-2)
becomes:

                         n = (Pr - 0.1978)2                          (1Q_5)
                             (Pr + 0.7912)
Table 10-8 was prepared using data in Table 10-7 and Eq.  (10-5) for each
daily event.

The results in Table 10-8 show that rainfall of less than 0.4 in. produces
no runoff.  Only 37 calculations were involved for the 1974 data.  On an
annual basis, the results in Tables 10-7 and 10-8 show that 36.12 in. pre-
cipitation resulted in a runoff of 14.58 in.  On a 220-acre feedlot, the
annual runoff volume thus amounts to 267.30 acre-ft (32.60 ha-m).  This is
equivalent to an annual .runoff volume of 87 million gallons or 0.326 million
cubic meters.

10.3.3.3  Empirical regression method -

The general empirical relation between rainfall and runoff developed in
literature for feedlots may be expressed as follows:
                         Q = L-Pr - B                                (10-6)


where     Q = runoff, cm (in.)

         Pr = precipitation, cm (in.)

          L = regression coefficient (slope)

          B = regression constant, cm (in.) (intercept)

The regression constant  B  may be regarded as that amount of precipitation
that is stored on the feedlot surface and hence not available as runoff.
The coefficient  L  may be similarly regarded as a fraction of the net avail-
able precipitation (i.e., total precipitation minus total storage and other
seepage losses) that results in surface runoff.  Under dry conditions, the
value of  B  may be higher than that under wet conditions.  The value of  L
may be higher for surfaced lots than for unsurfaced lots.

                                   219

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The reported values of  L  and  B  are based on the least-squares fit of
experimental data to Eq. (10-6) under different climatic and geographic
conditions „  Consequently, significant variations may be found in these
data.

Kreis et al.—' have determined the values of  L  and  B  for a commercial
feedlot in central Texas having an annual precipitation of 37 in. to be 0.5
and 0.124, respectively.  Wells et al.—'  showed similar values for south-
western cattle feedlots.  They obtained  L  and  B  of 0.746 and 0.192 for
surfaced feedlots and 0.345 and 0.309 for unsurfaced lots.

Loehr—'  reviewed literature for feedlot runoff and evaluated the regression
coefficients  L  and  B  in Eq. (10-6) for various conditions.  Loehr"s
results are shown in Table 10-9.
        Table 10-9.  RUNOFF AND RAINFALL RELATIONSHIPS ON BEEF
                           CATTLE FEEDLOTS9-/
                           Minimum rainfall
                           to produce runoff
           JB	            (cm)        (in.)              Conditions
0.945     0.34              1.0         0.4-               Surfaced lot
0.882     0.37              1.0         0.4               Unsurfaced lot
0.53      0.14            1.0-1.3     0.4-0.5             3 to 9% slopes
0.93      0.41              1.2         0.45              1968 runoff
0.45      0.05              1.3         0.5               1969 runoff
0.,49      0.06              1.3         0.5               1970 runoff
0.50      0.12            0.5-6.8     0.2-0.32            1969 to 1970
The following procedure is suggested to determine the runoff volume using
the Regression Method:

a.  Determine feedlot area and precipitation for a given site using the pro-
cedure described in SCS method, Steps (a), (b), and (c).

b.  Determine the regression coefficients for the site conditions in the
area from local experimental data or other reported values applicable to
the area.  Otherwise, assume the following ranges of values:
                                    221

-------
Site condition

Surfaced lot
Surfaced lot
Unsurfaced lot
Unsurfaced lot
Moisture condition        L

       Wet             0.5-0.95
       Dry             0.5-0.95
       Wet             0.5-0.95
       Dry             0.5-0.95
(in.)
0.0-0.2
0.2-0.4
0.0-0.3
0.3-0.5
(cm)
0.0-0.5
0.5-1.0
0.0-0.8
0.8-1.0
c.  Calculate runoff (centimeters or inches) using Eq. (10-6) and data in
Steps (a) and (b) above.

d.  Calculate monthly or annual runoff using the procedure described in SCS
method, Step (e).

e.  Calculate total volume of runoff by multiplying runoff depth (Step (d))
with feedlot area (Step (a)).

10.4  POLLUTANT CONCENTRATION IN FEEDLOT RUNOFF

Some of the reported data on feedlot runoff characteristics are presented
in tabular form in Tables 10-10 and 10-11.  As indicated earlier, the range
in concentrations is wide.  The handbook user has two alternatives.

1.  He may use the range of values given in the tables as guidelines for
selecting concentrations for livestock operations in his area.  If this al-
ternative is selected, he should use values at both the lower and upper
range of the data which appear to represent his area, and estimate a prob-
able range of loads rather than an assumed average load.

2.  He may use data obtained on a current basis in his area.  If this al-
ternative is selected, he should be careful to determine and specify the
local range of values.  This second alternative is preferred over alterna-
tive (1).

Essentially no data exist for concentrations of pollutants in runoff from
hog lots, poultry ranges, and dairy and sheep lots.  In lieu of actual
local data (alternative (2) above), the beef cattle runoff data can be used
as guideline data for other livestock.  Pollutant concentrations in runoff
are relatively insensitive to the quantity of waste exposed to the runoff,
particularly if the lot surface has been in use for extended periods.  It
is not proper to attempt to factor down the concentrations in proportion to
relative rates (hogs versus beef cattle, e.g.) of animal waste deposition
on feedlot surfaces.  If actual data are not available, pollutants in run-
off from lots other than beef cattle feedlots should be assumed to lie
within the ranges reported for beef cattle.
                                    222

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                                   223

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 Table  10-11.  RUNOFF CHARACTERISTICS FROM  CATTLE FEEDLOTS  IN  KANSAS*-!!/
                                Concrete                       Nonpaved

Ammonia-N
                                 1.3-7.0 mg/£                     1.0-3.8  mg/4
  Spring-fall                      20-77 mg/4                       13-45  mg/4
  Summer                          50-139 rag/4                       26-62  rag/A

NH3-N:  Kjeldahl-N, %
  Winter                       0.01-0.05                        0.02-0.6
  Spring-fall                    0.3-0.4                        0.06-0.2
  Summer                         0.1-0.4                          0.1-0.3

Nitrite-N
  October-November               1.0-5.0 rag/4                     1.0-2.3  mg/4
  July-August                    1.0-6.0 rag/4                     1.0-7.0  mg/4

Suspended solids
  July-August
    Moist - 1 in/hr                6,000 mg/jj,                       5,000  mg/jj
    Dry - 0.4 in/hr                3,000 mg/4                       1,500  mg/4
    Dry - 2.5 in/hr                1,400 mg/4                       2,000  mg/4
    Wet - 2.5 in/hr                3,000 mg/4                       3,000  mg/4
    Wet - 0.3 in/hr               12,000 mg/4                      10,500  rag/4
  October -November
    Wet - 1 in/hr                  2,000 mg/4                       1,800
    Wet - 0.5 in/hr                2,500
Bacterial densities (in
millions of organisms per
100 ml), 70% limits
  July-November
    Total coliform                33-348                           22-348
    Fecal coliform                35-240                             8-79
    Fecal streptococci            13-240                             8-79
   Kansas data shown here are typical for Midwestern states.  These values
     tend to increase in the West and decrease in the East.
                                    224

-------
Measurements of pollutant concentrations indicate trends helpful in select-
ing data for load calculation.  These trends are:  (a) runoff from winter
thawing conditions produce greater concentrations of pollutants than that
produced by rainfall under warmer conditions, and (b) runoff from concrete
(surfaced) feedlots contain higher concentrations of COD, BOD, and nitrogen
than that from unsurfaced feedlots.  BOD concentrations in runoff from sur-
faced lots are approximately twice those from unsurfaced feedlots.

10.5  POLLUTANT DELIVERY RATIO, FL,
                                  d

The proportion of on-site-generated pollutants in feedlot runoff delivered
to streams has not been documented.  Delivery ratios have therefore been
developed by the study group in consultation with EPA personnel.  Literature
information on sediment delivery and the system developed and presented in
Section 3.0 are the basis for development of values for FL^.  The following
additional considerations were involved:

1.  The majority of the pollutant load is carried away in the first part of
the runoff hydrograph.

2.  Feedlot solids are fine textured and tend not to settle out of overland
runoff.

3.  Observation has shown that buffer strips have limited value for
permanent retention of runoff-contained sediment.

The delivery ratio is therefore expected to be higher than delivery ratios
for sediment from similarly located cropland.  Recommended delivery ratios
are:

Case I - Feedlot near (within 0.2 km, 0.1 mile) a permanent unobstructed
waterway:  FLd 2 0.9.

Case II - Feedlot located more than 0.2 km (0.1 mile) from stream or un-
obstructed waterway:  FL^ = 0.7 to 0.9.

10.6  FEEDLOT AREA, A

The  A  factor in Eq. (10-1) is determined in effect by multiplying feedlot
populations by stocking rates and proportioning areas among specific lots.
In practice,  A  is determined by approximation from data from various
sources such as:  "Cattle on Feed,"!-!/ state departments of agriculture
and state environmental or health agencies, design manuals, and Special
Census of Agriculture Reports.JA/  Some statistics on beef cattle feedlots
are shown in Table 10-12.
                                    225

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   Table  10-12.
 NUMBER OF  CATTLE FEEDLOT AND FED  CATTLE  MARKETED--
  IN SMALL  LOTS, BY  STATES  (1974)14.ia/
     State

Arizona
California
Colorado
Idaho
Illinois
Indiana
Iowa
Kansas
Michigan
Minnesota
Missouri
Montana
Nebraska
New Mexico
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Dakota
Texas
Washington
Wisconsin

23 States
 Under 1,000  head  feedlot
	capacity	
           Cattle marketed
              (1,000  head)
Lots (No.)

       6
      28
     425
     502
  14,445
  10,477
  31,835
   5,660
   1,667
  10,970
  11,979
     211
  14,510
       7
     880
   8,175
     358
     305
   5,997
   9,123
   1,001
     165
   7,084

 135,810
                     1
                    13
                   131
                    11
                   755
                   336
                 2,710
                   400
                   177
                   795
                   348
                    26
                 1,330
                     1
                    53
                   328
                    36
                    22
                   114
                   407
                    85
                    33
                   149

                 8,261
Lots (No.)

      47
     167
     613
     574
  14,500
  10,500
  32,000
   5,800
   1,700
  11,020
  13,000
     276
  14,970
      48
     900
   8,200
     400
     331
   6,000
   9,200
   1,200
     186
   7,100

 137,732
Total all feedlots
      Cattle marketed
        (1,000 head)
              895
            2,002
            1,892
              344
              850
              361
            3,097
            2,240
              242
              864
              400
              187
            3,355
              355
               84
              386
              566
              126
              123
              585
            3,899
              301
              180

           23,334
a/  Number of feedlots under 1,000 head capacity is number of lots
      operating at end of year.
                                   226

-------
The feed lot area should include area devoted to feed handling and mixing,
sick pens, alleys and equipment storage.  Beef cattle lots are typically
15% larger than the feeding pen area.

The following procedure illustrates how  A  may be calculated.  Data for
Nebraska reported in "Cattle on Feed,"i!/ have been used to estimate aver-
age areas and total area of small feedlots.
     Data reported, for feedlots with < 1,000 head:
          Number of lots
          Cattle marketed annually

     Data assumed:
          Stocking rate

          Turnover rate
     Calculated data:
          Average area/lot
          Total pen area

     Total production area
          (Pen area x 1.15)
                14,510
             1,330,000
          23 m2/animal
        250 ft2/animal
                2/year
    0.1 ha (0.26 acre)
1,525 ha (3,800 acres)

1,754 ha (4,370 acres)
10.7  METHODS FOR DEVELOPING FEEDLOT STATISTICS

Several options are available to the user to evaluate the parameters in
loading function for feedlots.  The following discussion is intended to
facilitate the selection and use of appropriate data and data sources,
especially when the direct use of field data is not possible.

The data on the total number of livestock by county and state are published
in Census of Agriculture statistics, by the U.S. Department of Agriculture.
The census data also show the number of livestock "on feed."  State sum-
maries of livestock on feed, by capacity of feedlots, are published by the
Agricultural Statistics divisions of the State and U.S. Departments of
Agriculture.  A large fraction of the published state data is related to
beef cattle feedlots.  State data sources contain livestock data on a
county basis.

The locations of feedlots within a given region can only be obtained by
reference to state/local statistics.  However, statistical distributions
(locations estimated from partial data) of feedlot sites within a state
will usually be adequate for area-wide estimates of pollution from feedlots,
                                   227

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The distribution data will also help to assess probable distances to given
surface waters, and hence, the amounts of pollutants delivered.

The areas of feedlots may be obtained either from actual inventories of
feedlot data for a region, or estimated by using statistical projections of
sanpled sites for which data exist.  An indirect method of estimating feed-
lot area involves a knowledge of animal type, total number of animals, and
stocking rates (area per animal).  The stocking rate differs for different
livestock types and usually falls within the following ranges:
     Beef cattle
     Dairy cattle
     Swine, breeding
     Swine, growing-finishing
     Sheep
     Turkeys, range
-  100 to 400 ft2 (9 to 36 m2)
    80 to 400 ft2 (7 to 36 m2)
-  100 to 250 ft2 (9 to 23 m2)
-  200 to 1,500 ft2 (18 to 135 m2)
    15 to 100 ft2 (1 to 9 m2)
-  100 to 200 ft2 (9 to 18 m2)
Feedlot surface conditions, climatic conditions, and other factors determine
the actual stocking rate within the above range.

The required data on feedlot numbers, areas, and locations can thus be de-
veloped from several sources of data as indicated by the following cases:

Case 1.  Little or No Local Data Available

     Beef Cattle

          Given Data -

             Number of small lots (< 1,000 head), by state, Census of
               Agriculture statistics.

             Total number  of cattle, by county,  Census of Agriculture
               statistics.

             Turnover rate of 2/year.

          Estimated Data -

             Average  lot size  (small  lots)  equals number of cattle per
               turnover divided by  number of  lots.

             Average  lot area equals  average  size times stocking  rate se-
               lected from range  given above  (100 to 400  ft2/animal,.  10 to
               40 m2/animal).
                                    228

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        Number of lots by county,  i.e.,  number  of  small  lots  per
          county equals number of  small  lots  in state  times  total
          cattle in county divided by total cattle in  state.

        Delivery ratio:  in absence of information on  distance  to
          watercourses, use 0.9.
     Given Data  -

        Hog population,  by  county,  Census  of Agriculture  statistics.

        Sixty  percent  of hogs  in  small  lots (< 2,500 head per  lot).

     •   Stocking rate,  in range of  200  to  1,500 ft2/animal (20 to
          140  m^/animal).

        Delivery ratio:   0.9.

        Turnover rate:   2/year.

     Estimation  -

        Total lot  area, in county equals 0.15  times total county
          population times  stocking rate.   Convert to hectares or acres,

Turkeys

     Given Data -

     •  Total population, by county, Census of Agriculture statistics.

     Assumptions -

        Eighty percent of turkeys on range.

     •  Stocking rates, in the range of 100 to 200 ft2/bird (10 to 20
          m2/bird)

        Delivery ratio:  0.9.

        Turnover rate:   2/year.

     Estimations -

      •  Lot area equals 0.2 times  county  population times  stocking
           rate.  Convert to hectares or acres.
                               229

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     Sheep

          Given Data -

             Total population, by county, Census of Agriculture statistics.

          Assumptions -

             Eighty-five percent in open lots.

          •   Stocking rates range from 15 to 100 ft2/animal (1.5 to 10 m2/
               animal).

          •   Delivery ratio:   0.9.

             Turnover rate:  2/year

          Estimation -

             Lot area, in county equals  0.2 times county population times
               stocking rate.   Convert to hectares  or acres.

Case 2.   Local, Actual Data Available

In an idealized case, perhaps  for a small watershed,  data on  feedlot sizes,
locations, and areas will either be a matter of record,  or can be readily
obtained by questionnaire or other means.  Feedlots covered by NPDES permits
should be subtracted from the  total, and other  lots with runoff control also
deleted.  The remainder will be counted  as nonpoint sources.

          Given Data -

             Number of small lots and livestock population per lot, local
               data.

             Area of each lot, local data.

             Location of each lot from the nearest water course, actual data
               of record.

          Assumptions -

             Delivery ratios:
               0.9 (less than 0.1 mile or 0.2 km from stream).
               0.7 (greater than 0.1 mile or 0.2 km from stream).
                                     230

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Case 3.  Combination of Local Data and Area-Wide Data

Determination of area, location, and livestock population of small feedlots
at the local level (county/state) involves a search of various  data sources--
including an evaluation of unpublished data of record.  State departments of
agriculture—agricultural statistics divisions, animal husbandry divisions
of state agricultural extension services, agricultural economics departments
of land grant universities, state environmental protection agencies,  state
public health departments, county tax assessors' offices, and state revenue
departments are some of the sources of local data.   Because of the variations
in jurisdiction in different state governments, the local planner responsible
for making the assessment of nonpoint source pollution from livestock in con-
finement should ascertain the availability of data  from appropriate sources
within the state.

The county based livestock population data are published by the U.S.  Depart-
ment of Agriculture—Census of Agriculture.  However, areal data for  small
lots are not available directly from the census data.  An example calcula-
tion of the area, delivery ratio, and livestock population in small beef
cattle feedlots from a mix of local and area-wide data is shown below:

          Given Data -

             Number of livestock, county, Census of Agriculture statistics.

             Number of small lots (< 1,000 head) by county, from state
               agricultural extension division.

             Number of cattle in small lots, by county, from state agri-
               cultural extension division.

             Stocking rates — local data from land grant university, agri-
               cultural economics department.

             Turnover rate equals 2/year.

          Assumptions -

             Delivery ratio—for lots less than 0.1 mile (0.2 km) from
               stream:  0.9; for lots more than 0.1 mile (0.2 km):  0.7.

             None of the small lots reported runoff control.

          Estimation -

             Area of all  lots in county equals  number  of small  lots per
               turnover times number of cattle  per small lot times stock-
               ing rate.


                                    231

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10.8  ACCURACY OF PREDICTION

The major uncertainties in the loading function are pollutant concentrations
and delivery ratios.  If reliable data on pollutant concentration, feedlot
areas by source, and precipitation-runoff are obtained for the local condi-
tions, the accuracy of prediction can be reasonably good.  The pollutant
delivery ratio tends to be quite high for existing feedlots located near
streams.  For others, the determination of FL, from local data accurately
will improve the prediction accuracy.  Using average, long-term conditions,
the range of accuracies expected are presented in Table 10-13.
        Table 10-13.  ESTIMATED RANGE OF ACCURACY FOR PREDICTING
                      POLLUTANT LOADS FROM FEEDLOTS
                               Estimated value              Probable range
Pollutant                       (kg/ha/year)                (kg/ha/year)

BOD5                               10,000                   2,000-50,000
N-total                               600                     100- 3,000
N-available                            50                      10-   200
P-total                               250                      50- 1,000
Suspended solids                   10,000                   5,000-25,000
10.9  PROCEDURE FOR COMPUTING POLLUTANT LOADING

The following step-by-step procedure is suggested to compute the potential
pollutant loading from feedlots, based on the discussion of loading function
presented in this section.  It is assumed that the regional boundary is es-
tablished for assessing the loadings.

1.  Determine the number of feedlots.

2.  Determine the number and kind of livestock in each feedlot.

3.  Determine the area  A  of individual and total feedlots using either
actual data or procedures outlined earlier in this section.

4.  Obtain precipitation data  Pr for the time interval required, i.e.,
storm event, 30-day period, year, from local weather stations, or from the
National Climatic Center, U.S. Weather Bureau.

5.  Compute runoff volume  Q  from options presented above.
                                    232

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6.  Determine the range of pollutant concentrations in feed lot runoff either
from local records or from Tables 10-10 and 10-11.

7.  Determine the value of the delivery ratio FL^ from a knowledge of feed-
lot location in relation to the stream or from drainage density in the basin.

8.  Determine load of each pollutant by using Eq. (10-1), Items 3, 5, 6, and
7 above.

9.  Convert results to annual average, daily value, expressed as a range of
loads consistent with ranges of input data on pollutant concentrations.

10.10  EXAMPLE

An open, unsurfaced feedlot in eastern Kansas has an area of about 5 acres
and carries on an average 900 head of cattle at any given time.  The feedlot
is located 1/4 mile from a small creek which eventually discharges into the
Kansas River.  Assuming that the BOD^ concentration of feedlot runoff ranges
from 5,000 to 10,000 mg/liter and a monthly precipitation of 6 in., calculate
the daily load delivered to the creek during the 30-day period.

In the absence of precipitation event data, interpolation of precipitation
and runoff data presented in Table 10-7 and Table 10-8 for the months of
August and October show an average runoff of 2.5 in.

The delivery ratio is estimated to be 0.8 for a silt-clay soil and a drain-
age density of 4 miles/sq mile.

Thus the BOD^ loading for a concentration of 5,000 mg/liter is, from Eq.
(10-10),
                   Y(BOD)F  = 0.23 x 5,000 x 2.5 x 0.8 x 5
                                           30
                            = 380 Ib/day
For BODr of 10,000 mg/liter, the loading is Y(BOD)   = 760  Ib/day.
       S                                          j1 l-i
                                    233

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                              REFERENCES
 1.   Chow, V.  T., Handbook of Applied Hydrology, McGraw-Hill Book Co., New
       York  (1964).

 2.   National  Engineering Handbook:  Section 4 - Hydrology, Soil Conservation
       Service, U.S. Department of Agriculture (1972).

 3.   Bergsrud, F. G., Masters Thesis, Kansas State University, Manhattan,
       Kansas  (1968).

 4.   Miner,  J. R.,  "Water Pollution Potential of Cattle Feedlot Runoff,"
       Ph.D. Thesis, Kansas  State University, Manhattan, Kansas (1967).

 5.   Shuyler,  L. R., D. M. Farmer, R. D. Kreis, and M. E. Hula, "Environment
       Protecting Concepts of Beef Cattle Feedlot Wastes Management,"
       Environmental Protection Agency, Corvallis, Oregon (1973).

 6.   Koelliker, J.  K., H. L. Manges, and R. I. Lipper, "Performance of Feed-
       lot Runoff Control Facilities in Kansas," Paper presented at the 1974
       Annual  Meeting of American Society of Agricultural Engineers, Oklahoma
       State University, Stillwater, Oklahoma, 23-26 June 1974.

 7.   Kreis,  R. D.,  M. R. Scalf, and J. F. McNabb, "Characteristics of Rainfall
       Runoff  from  a Beef Cattle Feedlot," U.S. Environmental Protection
       Agency, Report No. EPA-R2-72-061 (1972).

 8.   Wells,  D. M.,  R. C. Albin, C. W. Grub, E. A. Coleman, and G. F. Meenaghan,
       "Characteristics of Wastes from Southwestern Cattle Feedlots," EPA
       13040 DEM 01/71, U.S. Environmental Protection Agency (1971).

 9.   Loehr,  R. C.,  Agricultural Waste Management, Academic Press (1974).

10.   Gilbertson, C. B., T. M. McCalla, J. R. Ellis, 0. E. Cross, and W. R.
       Woods,  "The  Effect of Animal Density and Surface Slope on Character-
       istics  of Runoff, Solid Wastes and Nitrate Movement on Unpaved Beef
       Feedlots," University of Nebraska Technical Bulletin SB 508, June 1970.

11.   Miner,  J. R.,  L. R. Fina, J. W. Funk, R. I. Lipper, and G. H. Larson,
       "Stormwater  Runoff from Cattle Feedlots," Proceedings National Sym-
       posium  on Animal Waste Management, East Lansing, Michigan (1966).
                                    234

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12.  Smith, S. M., and J. R. Miner, "Stream Pollution from Feedlot Runoff,"
       Proceedings 14th Sanitary Engineering Conference, University of
       Kansas, Lawrence, Kansas (1964).

13.  Crop Reporting Board, "Cattle on Feed," SRS-USDA, January 1975.

14.  U.S. Department of Commerce, "Census of Agriculture 1969 Dairy Cattle,"
       Volume V, Part 8, Special Reports (1973).
                                  235

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                            SECTION 11.0

                        TERRESTRIAL DISPOSAL

11.1  INTRODUCTION

Solid wastes and slurries disposed on landfill sites have a significant
potential to pollute local groundwater aquifers,  and thus to pollute
nearby surface streams.  Water that infiltrates landfill cover soil may
produce leachate, in quantity dependent on precipitation, antecedent
moisture condition of the landfill soil, solid waste composition, and
groundwater hydrology.  The absorptive capacity of the landfill, its
areal extent, and the amount of recharge water available for infiltra-
tion are the key parameters that determine the total volume of leachate.
Open dumps can be expected to produce more leachate than sanitary land-
fills.

Leachates contain significant concentrations of BOD, COD, iron,  chlo-
rides, and nitrates.  Where toxic wastes have been discharged, the
leachates also contain heavy metals and toxic substances.  The charac-
ter of leachate, thus, is highly sensitive to the type of waste in the
land disposal site, the age of the site, and the temperature and mois-
ture content of the fill.

Once a leachate is produced, it may react with soil constituents at
rates depending upon the reactivity of the substances in the leachate.
The concentration and the total quantity of a given pollutant in the
leachate may be attenuated by physical-chemical processes and biologi-
cal processes.  The attenuation may proceed both in saturated and un-
saturated zones of the soil, as shown in Figure 11-1.

The degree of attenuation cannot be predicted with reasonable accuracy.
Soil, especially in the unsaturated zone, is probably most important in
this attenuation.  The leachate is also in effect attenuated by dilution
in groundwaters, and groundwater movement through underground aquifers
results in reactions (chemical reaction, physical absorption-desorption,
                                  236

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                                                                           0)
                                                                           M
                                                                           01
                                                                           to
                                                                           0)
                                                                           C
                                                                           o
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                                                                           60
                                                                               O
                                                                               a)
                                                                           60
                                                                           3   (U

                                                                           2  -°
                                                                          J2   ^,
                                                                           4-1   CD


                                                                           I   I
                                                                           0)   rd
                                                                          4J   3
                                                                               O)
                                                                           o>   n)
                                                                          >-t
                                                                          60
                                                                          •r-l
237

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and biological reactions) which degrade the pollutant, equilibrate it with
geological strata, and possibly transform certain constituents to insoluble
minerals.

11.2  LOADING FUNCTION FOR LANDFILLS

The actual loading rate for a given pollutant cannot be made without knowl-
edge of soil properties, hydrology and landfill characteristics.  It is not
possible, therefore, to predict the extent of pollutant load that is actu-
ally transmitted to a stream with presently available data.  Approximate
at-site leachate emission rates can be determined from the knowledge of
percolation rates and pollutant concentrations expected from landfill sites.
If a site is located close to a surface water course, the at-site emission
rate may be close to the stream loading rate.  If the site is distant from
surface waters, the emissions may be markedly attenuated.

The loading function for a given pollutant is thus given by:


                   Y(i)LF = a.CQ(i)LF-p-LFd.A                       (11-1)
where     Y(i)   = average loading rate of pollutant i, kg/year (Ib/year)
              LF
               p = percolation rate, cm/year (in/year)

         CQ(i)LF = average concentration of pollutant i, in leachate at
                     site, mg/liter

               a = a dimensional factor, 0.1 metric (0.23 English)

             LF, = leachate delivery ratio for landfill

               A = area of landfill, ha (acres)

The delivery ratio, LF , varies in theory from 0 (no delivered pollutant)
to 1.0 (100% delivery).  Values of LF  are a matter of local judgment.

Pollutant concentrations, CQ(I) F, vary with the site characteristics and
vary widely even within a given region.  A range of reported values is
presented in Table 11-1.—   Pollutant concentration is influenced by the
amount of leachate produced.  Leachate volume is in turn influenced by
several factors including surface cover, subsurface lining characteristics
of the landfill, and climatic conditions.  Percolation rate may, in some
cases, be higher or lower than leachate flow rate.  Published average
annual percolation rates for the United States are shown in Figure 11-2—'

                                    238

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Table 11-1.  CHEMICAL CHARACTERISTICS OF LEACHATEsi/

Constituent
BOD5
COD
Organic nitrogen
Nitrate (as N)
Ammonia (as N)
Sulfate
Chloride
Iron (total Fe)
Hardness
Copper
Zinc
Manganese
Lead
Cadmium
Concentration,
80 - 33,
tng/1
100
150 - 71,000
50
0.2 - 1,
0 - 1,
28 - 3,
4.7 - 2,
0 - 2,
0 - 22,
0
0
0.1 -
0.1 -
0.03 -
200
300
000
770
467
820
800
9.9
370
125
2
17
                         239

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240

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The percolation map indicates potential leachate quantities throughout
the country.  Most severe leachate problems are expected east of the
Mississippi and in the Pacific Northwest.  For conditions east of the
Mississippi, where an average of 30 cm (12 in.) of percolation and 80,000
ha (200,000 acres) of landfill surface were assumed,  the net annual amount
of leachate produced has been estimated to be 246 million meters--' (65
billion gallons), or 3,000 m3/ha (325,000 gal/acre) of landfill surface.I/
This amount would be reduced by 507o or more with proper cover and vegetation
on the site.  The percolation map (Figure 11-2) should serve principally
as a guideline for local analysis.  For example, percolation in areas which
experience highly seasonal precipitation will not conform well to data on
the map, and its use would give results in error.  Landfill sites should
therefore be analyzed on a local or an areal basis, and percolation data
developed should take into account engineering practice in the area as well
as climatological and hydrological data specific to the area.  In this re-
gard, it must be emphasized that old sites as well as current sites are to
be included in the analysis.

11.3  PROCEDURE FOR COMPUTING LANDFILL POLLUTANT LOADINGS

In order to compute pollutant loadings from landfill leachates in a region,
the following data are needed:

     Landfill characteristics including number, size, location, age, and
       surface area.
     Percolation and leachate data.
     Pollutant concentration data.
     Leachate delivery ratio.

The availability of specific data will dictate the degree of accuracy one
can achieve in computing the loading rates.  Thus, several options are
open to determine the pollutant loadings in a given region.

11.3.1  Landfill Characteristics Including Number, Size, Location, Age,
          and Surface Area

                        o /
The 1968 National Survey—' published extensive  statistics  on  community
solid waste practices by  region.  Waste Age,!/ made a telephone  survey of
solid waste disposal practices by region,,  These  reports,  while  estimat-
ing the number and area of sites, are too broad  for use  in a  local  situa-
tion.  Local and state health departments should  be consulted  for  specific
site information,,
                                   241

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11.3.2  Percolation and  Leachate  Data

Case I - When the landfill site is not engineered as a sanitary landfill,
i.e0, the surface and bottom are not adequately lined with impervious
material, the leachate flow rate can reasonably be assumed to be equal to
percolation rates typical of the area.  Rates indicated in Figure 11-2
may be adequate, but locally specific data are to be preferred.  When
groundwater recharge occurs during wet conditions or when the groundwater
table is shallow, upwelling may occur; calculation of leachate rates will
be very difficult in such cases and will be the province of the local
engineer.  Monitoring stations will be needed to obtain accurate infor-
mation.

Case II - When the site is engineered to reduce leachate and/or percola-
tion, such as in lined or compacted landfills, considerably smaller
amounts of leachate will leave the site.  Data on local design condi-
tions and monitored parameters should be obtained to determine the
actual rates of leachate production.  Sites with similar physical and
climatological characteristics and waste constituents should provide
reasonably accurate data.

11.3.3  Pollutant Concentration Data

As  shown in Table  11-1, the concentrations of pollutants vary  greatly.
For example, the reported BOD5 concentration  ranges  are 2,000  rag/liter
to  30,000 mg/liter.  The  factors  affecting leachate  composition are
complex.  There  is no simple way  to predict the pollutant concentra-
tion for given  site  conditions.   Monitored or field  data should be
obtained for specific situations.  In general, leachate from a com-
pleted  fill where  no more waste  is being disposed of can be expected
to  decrease with time,

11.3.4  Leachate Delivery Ratio

Most published studies describe the on-site pollution potential,  and not
the  actual load delivered to a stream.  The delivery ratio is  thus a re-
search area.

The  approach to selection of a delivery ratio should be on a site-by-site
basis, with the delivery ratio developed after consideration of the fol-
lowing factors:

1.   Proximity of landfill to surface waters.

2.   Proximity to subsurface aquifers.
                                   242

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3.  Subsurface water quantities, flows, and direction of flow.

4.  Quantity of leachate in proportion to aquifer inventories  and flows.

5.  The attenuating characteristics of soils for the pollutant  of concern.

6.  The age of the site.

Confidence in the delivery ratio (as well as leachate quantities and pollu-
tant concentrations) can be markedly increased by analysis  of  groundwaters
and soils at strategically located sampling spots.

Selection of a delivery ratio is thus the province of local specialists in
hydrology, water and soil chemistry, and landfill design.   The  delivery
ratio should seldom be more than 0.5 save in exceptionally  poorly designed
and managed dumps.  Conversely, a delivery ratio near zero  should be accepted
only after rigorous examination of site characteristics.

11.4  Accuracy of Predicted Loads

The accuracy of prediction depends upon the accuracy of parameters used in
the loading function.  For local situations where small areas  are involved,
the area of landfill can be easily and accurately determined from local
data sources.  Determination of percolation rates for the area  can also be
obtained from experimental data and other reported results  for  similar soil
characteristics and precipitation rates.  The percolation rate  also is de-
pendent upon the engineering design of the landfill site, its  age, and
groundwater characteristics.  Long-term average rates are generally more
precise than short-term, yearly averages.  The delivery ratio  is usually
obtained with less certainty.  The delivery ratio can usually  be estimated
to be near zero for small leachate rates.  At high rates the uncertainty
in the delivery ratio becomes greater.  Concentrations of pollutants in
the leachate are extremely variable and are subject to greater  fluctuation
than other parameters.  Consequently, greater error is introduced in the
prediction even if other parameters are accurately estimated.   The expected
range of pollutant loads is shown in Table 11-2.  The ranges were estimated
on the assumption that some actual site data are available, and that actual
characteristics have been evaluated in estimating load; i.e.,  the range of
values presented in Table 11-1 has not been used in the estimations.
                                   243

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             Table 11-2.   ESTIMATED RANGE OF PREDICTED LOADS  FOR
                VARIOUS POLLUTANTS IN LEACHATES  IN LANDFILLS
                          Estimated value      Range of predicted  values
Pollutant                   (kg/ha/year)        	(kg/ha/year)	

BOD5                          10,000               1,000-100,000
COD                           20,000               2,000-200,000
Nitrogen - Total                 500                  50-5,000
11.5  EXAMPLE

A well engineered sanitary landfill operating during the past 5 years is
located in eastern Kansas where the annual precipitation is 36 in/year.
The site has a total area of 35 acres and is located about 1 mile from a
major river.  The rate of percolation is estimated, from local data on
rainfall plus landfill surface characteristics, to be 1.5 in/year through
the fill material, which is primarily composed of municipal refuse.
Leachate from a test well located on-site was analyzed during high flow
period, with the following results:

BOD5 = 8,000 mg/liter

COD = 12,000 mg/liter

pH  = 6.3

Alkalinity  as CaCOs =  3,620 mg/liter

Chloride as  Cl = 284 mg/liter

NH4-N = 84  mg/liter

Assuming that the  leachate directly enters  the river, calculate  the
pollutant  loadings  for 6005, chlorides,  and nitrogen.

Site data  and engineering  features of  the  landfill were  used  by  local
engineers  to arrive at a delivery ratio  in the range  of  0805  to  0.2.
Assuming a LF
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            Table 11-3.  POLLUTANT LOADING RATES IN EXAMPLE



                                Annual load                Daily load
    Pollutant                    (Ib/year)                  (Ib/day)

BOD5                               9,660                     26.5

Chloride                             343                      0.94

Nitrogen (Nfy-N)                     101                      0.28
                                 245

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                               REFERENCES


1.  Unpublished document,  Leachate  Meeting -  Office of Solid Waste
      Management Programs, Environmental Protection Agency,  Washington,
      B.C.,  August 1974.

2.  Nelsons  L.  B., and R.  E.  Uhland,  "Factors that  Influence Loss of
      Fall Applied Fertilizers  and  Their Probable  Importance in Dif-
      ferent Section of the United  States," Soil Science  Society of
      America,  Proceedings, 19(4)  (1955).
                            -H5&E*

3.  Munich,  A.  J., A. J.  Klee,  and  P.  W. Britton,  "1968 National Survey
      of Community Solid Waste  Practices," Preliminary Data  Analysis,
      USPHS, Cincinnati (1968).

4.  "Exclusive  Waste Age Survey of  the Nation's Disposal  Sites," Waste
      Age. 6(1):17-24, January  1975.
                                    246

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                              SECTION 12.0

             BACKGROUND POLLUTANT LOAD ESTIMATION PROCEDURES

12.1  INTRODUCTION

Nonpoint pollution loads can arise from land which has not been disturbed
by man's activities.  Such loads, referred to as "background" loads,
represent natural nonpoint emissions, and have a significant effect, upon
surface water quality.  In general, a clear-cut distinction between
loads arising from background sources and loads arising from man's land
use practices is virtually impossible to achieve, either philosophically
or technically.   Therefore, one should approach the problem of background
pollutant loads  somewhat warily, but also firmly.  Any estimation of back-
ground pollutant loads will have an unavoidable element of arbitrariness.

This section will present estimation procedure options for background
pollutant emissions.

Two different approaches are discussed-~a stream-to-source approach (Sec-
tion 12.2) and a source to stream approach (Section 12.3), together with a
discussion at the expected accuracy of each method.

12.2  STREAM TO SOURCE METHODS

12.2,1  Options  Available

Four options for estimating nonpoint pollution loads emitted from natural
background have  been developed using the stream to source approach.  These
options, together with their constraints, are:

Option I - A method of estimating general background loads over large
areas.  The method utilizes annual average runoff in the area considered
and iso-pollutant concentration maps developed for this purpose.   The
method yields estimates of pollutant loads on an average annual basis,
reported on a per day basis.
                                   247

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Option II - A second method for estimating general background levels over
a large area.  The method utilizes the iso-pollutant maps as in Option I,
but streamflow carrying the pollutant is used rather than average annual
runoff.  This method can be used to estimate maximum and minimum pollu-
tant loads within a year by utilizing maximum and minimum flows during
the year, and represents the stream to source approach.

Option III - A method for estimating general background  levels on a local
or small watershed scale.  Average annual runoff for the watershed is used.
Background pollutant concentrations deemed appropriate from local water
quality data are used instead of iso-pollutant maps.  This method should
be used when considering local nonpoint problems, and will yield estimates
of annual average background loads reported on a per day basis.  This op-
tion uses the same approach as the first one except that provisions are
made for the user to use his judgment to define natural  background concen-
trations .

Option IV - A method, applicable to localized areas and  to small water-
sheds, for estimating background loads, based on local data and experience.
The method utilizes streamflow data for pollutant transport as in Option
II, and local information deemed appropriate concerning  background pol-
lutant concentrations as in Option III.  This method permits ready estima-
tion of 30 day maximum and minimum loads by considering  flow volumes at
various times of the year.  If a detailed description of natural background
over a large area is desired, the area can be subdivided into local units,
Option IV applied to each of the units, and the loads computed for each sub-
unit summed over the whole area.

12.2.2  Information Needs for Background Loading Value Equations

The following information is needed for use in the background loading
value equations presented below.

Area  (A) from which background pollutants are being emitted.

Flow  (Q) of water in which background pollutants are transported.

Concentration (C) of pollutants arising in the area and  transported by
flow.

Conversion factor  (a) needed to yield proper dimensional units of pol-
lutant loads.
                                   248

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Methods for obtaining this information for each option, together with
descriptions of the loading value equations,  are presented below.

12.2.3  Loading Value Equations and Definition of Conversion Factors

Options I and III - Flow as average annual runoff, in centimeters per
year (in/year).  Average annual runoff can be obtained from standard run-
off maps available from the U.S. Geological Survey (National Atlas,  Plates
118 to 119), Water Information Center's Water Atlas of the United States
(Plate 21), or from local records if available.
                       Y(i)BG = a-A.Q(R)-C(i)BG                  (12-1)

where   Y(i)gG = yield of background constituent  i  in kilograms per day
                   (lb/day)* except where noted in Table 12-1

             a = dimensional constant; see Table 12-1

             A = area under consideration, in hectares (acre)

          Q(R) = flow as average annual runoff, in centimeter per year
                   (in/year)

        C(i)   = estimated concentration of background constituent  i
                   (see Section 12.4, Figures 12-1 through 12-19)

Options II and IV - Flow as streamflow, in liter per second (cfs).  Stream-
flow data may be obtained from U.S. Geological Survey records, STORET data,
U.S. Array Corps of Engineers records, or other records available at the
local level.  Annual average strearaflow should be used when considering
background nonpoint emissions on the annual basis.  When estimating back-
ground emissions at specific times, e.g., 30 day maximum or minimum, the
proper streamflows at those times should be used in the computations.
   If load units of mass per unit area per day are desired, the area
     term is not used.
                                  249

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                       Y(i)BG • a-Q(str)-C(i)BG                  (12-2)

where   Y(i)BQ = yield of background constituent  i , in kilograms per
                   day (Ib/day) except where noted in Table 12-2

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                   12.4, Figures 12-2 through 12-20

12.2.4  Estimation of Background Pollutant Concentrations

Options I and II:  Background Maps for Estimating Concentrations C(i)
for Loading Value Equations - A series of maps have been developed indi-
cating general levels of background pollutant concentrations throughout
the United States.  These maps are based upon data collected at surface
water quality stations comprising the National Hydrologic Bench-Mark Network
established by the U.S. Geological Survey.  These iso-pollutant maps are
presented in Section 12.4 along with a descrption of the National Hydrologic
Bench-Mark Network.  The background pollutants considered are presented in
Table 12-3.

Options III and IV:  Use of Local Information for Estimation of Background
Pollutant Concentrations - If local information is believed to reflect a
better definition of background than the maps presented in Section 12.4,
it should be used in the loading value Eqs. (12-1) and (12-2).

12.2.5  Procedure for Using Loading Value Eqs. (12-1) or (12-2)

1.  Identify pollutant.

2.  Determine area to be considered.

3.  Choose units of volume flow to use in equations, i.e.,  liters per
second (cfs) or centimeters per year (in/year).

4.  Choose method of estimating pollutant concentration, i.e., back-
ground maps or local information.

5.  Decisions at Steps 3 or 4 establish option for estimation.  When
option is established, use Tables 12-1 or 12-2 as appropriate to identify
correct value of "a", the conversion factor to obtain proper units of
load.
                                   251

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        Table  12-3.   LISTING OF  BACKGROUND ISOPOLLUTANT  MAPS
        Constituent

 Suspended  sediment
 Nitrate
 Total  phosphorus

 BOD
 Total  coliform
80154
00630
00650

00310
31501
           STORET
            code
     Figure No.
(see Section 12.4)

        12-2
        12-3
        12-4

        12-5
        12-6
Conductivity
PH

Total dissolved solids
Alkalinity
Hardness
Chloride
Sulfate
00095
00400

00515
00410
00900
00940
00945
        12-7
        12-8

        12-9
        12-10
        12-11
        12-12
        12-13
Total heavy metals
Iron and manganese
Arsenic, copper, lead,
  and zinc
Miscellaneous heavy
  metals

Total radioactivity

Alpha radioactivity
Beta radioactivity
E Heavy metal parameters
01045 + 01055
01002 + 01042 + 01551 +
  01092
E Remaining heavy metals
01515 + 01516 + 03515 +
  03516
01515 + 01516
03515 + 03516
        12-14
        12-15
        12-16

        12-17
        12-18

        12-19
        12-20
                                   253

-------
 6.  Steps 2 through 6 will yield all necessary inputs for loading values
 Eqs. (12-1) and (12-2).

 7.  Compute background loads, Y(i) , for pollutant identified in Step 1.

 12.2.6  Examples of Using Loading Value Equations

 The following are examples of loading value estimations using Option I.
 Options II, III, and IV are used in the identical manner, except for
 input data units.

 1.  Case I;  Background phosphate emisssions from 4,040 ha (10,000 acres)
 of wheat in western North Dakota

 a = 2.7 x 10'4 (6.2 x 10"4).

 Area = 4,040 ha (10,000 acres).

 Phosphate concentration (Figure 12-4) = 0.15 ppm.

 Average annual runoff = 1.3 cm (0.5 in.).

 Load (metric):  0.15 x 1.3 x 0.00027 x 4,040 ha = 0.21 kg/day = 210 g/day.

 Load (English): 0.15 x 0.5 x 0.00062 x 10,000 acres = 0.46 Ib/day.

 2.  Case II:  Background heavy metals from Spokane River Basin above
 Roosevelt Lake (Water Resources Council subbasin 1603)

 a = 2.7 x 10"7 (6.2 x 10'7).

 Area = 6,404 sq mile = 1,670,000 ha (4,100,000 acres).

 Heavy metal concentration (Figure 12-14) = 300 ppb.

 Average annual runoff = 25 cm (10 in.).

 Load (metric):  300 x 25 x 2.7 x 10~7 x 1,670,000 ha = 3.4 x 103 kg/day =
 3.4 MT/day.

Load (English):  300 x 10 x 6.2 x 10'7 x 4,100,000 = 7,600 Ib/day =
 3.8 tons/day.
                                    254

-------
3.  Case III;  Background radioactivity emissions from the Cheyenne
River Basin (Water Resources Council subbasin 1072);

a = 270 (280).

Area = 20,497 sq miles = 5,300,000 ha (13,000,000 acres).

Radioactivity level (Figure 12-18) = 20 picocuries/liter.

Average annual runoff = 2.5 cm (1.0 in.).

Load (metric):  20 x 2.5 x 270 x 5,300,000 = 7.2 x 1010 picocuries/day =
7.2 x 10^ microcuries/day = 0.072 curies/day.

Load (English):  20 x 1.0 x 280 x 13,000,000 = 7.2 x 1010 picocuries/day =
7.2 x 10^ microcuries/day = 0.072 curies/day.

12.2.7  Estimated Ranges of Accuracy for Stream to Source Options for
          Background Pollutant Loads

Typical background pollutant loads have been estimated on an annual basis
for the background pollutants identified in Table 12-3.  As has been
stated earlier, the accuracy of these loads is dependent upon the size of
the area being considered.  Thus, the probable range of values will vary
depending upon the size of the area to which the estimation methods are
applied.

Table 12-4 through 12-6 present typical loads together with probable
ranges of values for the background pollutant loads.  Table 12-4 repre-
sents small areas (less than 10,000 ha) of county or watershed size.
Table 12-5 is concerned with the 10,000 to 10,000,000 ha size, represent-
ing an areal range between county and minor river basin.  Table 12-6
deals with areas greater than 10,000,000 ha representing minor river
basins and larger areas.

As can be seen in the probable ranges of Table 12-4, the use of iso-
pollutant maps for the small areas leads to a fairly broad range of ac-
curacy.  Local data, where available, will lead to much more accurate
loads for areas of county level or smaller.  The user is encouraged to
use local data for small areas, and consider the iso-pollutant maps as
a back-up or reference method.
                                  255

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Background pollutant loads and their probable ranges for areas varying
in size from a county (10,000 ha) to a minor river basin (10,000,000 ha)
are shown in Table 12-5.  In these intermediate sized areas, the differ-
ence in accuracy between the use of the iso-pollutant maps and local data
is judged to be relatively small.  Thus, either method should result in
satisfactory estimation of background loads.  It is recommended, however,
that the user should lean towards the local data if he is considering the
low range of the areal spread indicated in Table 12-5.

For larger areas, the use of iso-pollutant maps will yield satisfactory
results.  The uncertainty represented by Table 12-6 is no greater than
the differences between contours on the iso-pollutant maps.  On the other
hand, if local data are extrapolated to large areas, a significant amount
of error can be introduced into the calculations.  In principle, back-
ground pollutant loadings for large areas could be obtained by summing
many smaller areas for which background loadings have been obtained us-
ing local data.  It is questionable, however, whether this summing pro-
cedure would be any more accurate than the use of Option I and II methods
using the iso-pollutant maps for the large areas.

12.3  SOURCE TO STREAM OPTION

12.3.1  Description of Source to Stream Option

The source to stream approach for estimating pollutant loads from back-
ground involves using the Universal Soil Loss Equation and its associ-
ated delivery ratio factor to estimate soil losses from land having nat-
ural cover.  These "natural" areas include grassland, rangeland, desert,
forest, or woodlands, and areas transitional  between forest-grassland
etc.  A table of cover  C  factors are presented to facilitate the iden-
tification of the proper cover factor to be used.  These  C  values are
presented in Table 12-7 and 12-8.  Background sediment loads should be
estimated using the methods outlined in Section 3.0, together with the
appropriate  C  factor.  Regional vegetative cover patterns needed to
identify specific  C  values in Table 12-7 and 12-8 can be established
using descriptors of natural vegetation such as that presented in the
U.S. Geological Survey's National Atlas, Plates 90 and 91 (potential
natural vegetation).

This method can also be used for estimating pollutant loads transmitted
by sediment attachment, e.g., nitrogen and phosphorus.  In this case,
one would substitute the  Y(S)  value for background into the appropri-
ate loading functions described in Section 4.0.  Heavy metals in the
sediment can be estimated using procedures in Section 8.5.
                                  259

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              Table  12-7.   "C"  VALUES  FOR PERMANENT PASTURE,
                         RANGELAND,  AND IDLE LAND!/
  Vegetal canopy
 Type and height
of raised canopyJi'
   column no.:
No appreciable canopy
Canopy of tall weeds
 or short brush
 (0.5 m fall height)
                       Canopy
                       cover—'
25

50

75
                                Type!/
G
W

G
W
G
W
G
W
                   Cover that contacts the surface
                  	Percent ground cover—'	
 0
 4

0.45
0.45

0.36
0.36
0.26
0.26
0.17
 20
 5_

0.20
0.24

0.17
0.20
0.13
0.16
0.10
 40
 6_

0.10
0.15

0.09
0.13
0.07
0.11
0.06
                                  60
                                  7
                               80
                               8
                                                          0.042 O.On
                                                          0.090 0.043

                                                          0.038 0.012
                                                          0.082 0.041
                                                          0.035 0.012
                                                          0.075 0.039
                                                          0.031 0.011
95-100
  9

0.003
0.011

0.003
0.011
0.003
0.011
0.003
                                        0.17  0.12  0.09  0.067 0.038  0.011
Appreciable brush
 or bushes
 (2 m fall height)
Trees but no appreci-
 able low brush
 (4 m fall height)
25

50

75


25

50

75
G
W
G
W
G
W

G
W
G
W
G
W
                                        0.40  0.18  0.09  0.040 0.013  0.003
                                        0.40  0.22  0.14  0.085 0.042  0.011
                                        0.34  0.16  0.085 0.038 0.012  0.003
                                        0.34  0.19  0.13  0.081 0.041  0.011
                                        0.28  0.14  0.08  0.036 0.012  0.003
                                        0.28  0.17  0.12  0.077 0.040  0.011

                                        0.42  0.19  0.10  0.041 0.013  0.003
                                        0.42  0.23  0.14  0.087 0.042  0.011
                                        0.39  0.18  0.09  0.040 0.013  0.003
                                        0.39  0.21  0.14  0.085 0.042  0.011
                                        0.36  0.17  0.09  0.039 0.012  0.003
                                        0.36  0.20  0.13  0.083 0.041  0.011
a/  All values shown assume:  (1) random distribution of mulch or vegetation,
      and (2) mulch of appreciable depth where it exists.
W  Average fall height of waterdrops from canopy to soil surface:  m = meters.
c_/  Portion of total-area surface that would be hidden from view by canopy in
      a vertical projection (a bird's-eye view).
d/  G:  Cover at surface is grass, grasslike plants, decaying compacted duff,
      or litter at least 5 cm (2 in.) deep.
    W:  Cover at surface is mostly broadleaf herbaceous plants (as weeds)
      with little lateral-root network near the surface, and/or undecayed
      residue.
                                   260

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                  Table  12-8.  "C" FACTORS FOR WOODLAND
                                                       I/
Stand condition

Well stocked


Medium stocked


Poorly stocked
Tree canopy
percent of
  area3-/
 100-75
  70-40
  35-20
                                   Forest
                                   litter
                                 percent of
100-90
 85-75
 70-40
Und ergrowth—'

 Managed—'
 UnmanagedS/

 Managed
 Unmanaged
 Managed
 Unmanaged
"C" factor

0.001
0.003-0.011

0.002-0.004
0.01-0.04

0.003-0.009
Q.02-0.09^/
a/  When tree canopy is less than 20%, the area will be considered as grass-
      land, or cropland for estimating soil loss.  See Table 13-1.
b_/  Forest litter is assumed to be at least 2 in. deep over the percent
      ground surface area covered.
£/  Undergrowth is defined as shrubs, weeds, grasses, vines, etc., on the
      surface area not protected by forest litter.  Usually found under
      canopy openings.
d_/  Managed - grazing and fires are controlled.
    Unmanaged - stands that are overgrazed or subjected to repeated burning.
je/  For unmanaged woodland with litter cover of less than 75%,  C  values
      should be derived by taking 0.7 of the appropriate values in Table 13-1.
      The factor of 0.7 adjusts for the much higher soil organic matter on
      permanent woodland.
                                    261

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12.3.2  Estimated Ranges of Accuracy for the Stream to Source (USLE-
          Sediment) Option for Background Pollutant Loads

Background pollutant loads for several sediment related pollutants are
presented in Table 12-9, together with their probable ranges.  The prob-
able ranges can be translated as a percentage range based on the calcu-
lated load, e.g., the range for a calculated total phosphorus load of
1.5 kg/ha/year would be 0.3 to 15 kg/ha/year.
Table 12-9.  EXPECTED ACCURACY OF BACKGROUND POLLUTANT LOADS CALCULATED
           USING THE SOURCE TO STREAM (USLE-SEDIMENT) OPTION
                              Calculated                Probable range
                                 load                      of loads
   Pollutant                 (kg/ha/year)                (kg/ha/year)

Sediment                         500                      100 - 1,000
Total nitrogen                     3                      0.3-10
Total phosphorus                   0.5                    0.1-5
Organic matter                    50                        5 - 200
BOD                                5                      0.5 - 20
Heavy metals                       5                      0.5-20

12.4  ISO-POLLUTANT MAPS FOR ESTIMATING BACKGROUND POLLUTANT LOADS

The U.S. Geological Survey established the National Hydrologic Bench-
mark Network^/ in order to obtain water quality data for "natural back-
ground."

This network  consists of 27 surface water stations in 37 dates chosen
using the following criteria:

1.  No man-made storage, regulation, or diversion currently exists or
is probable for many years.

2.  Groundwater within the basin will not be affected by pumping from
wells.

3.  Conditions are favorable for accurate measurement of streamflow,
chemical and  physical quality of water, groundwater conditions, and
the various characteristics of weather, principally precipitation.
                                   262

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4.  The probability is small of special natural changes due to such
things as major activities of beavers, overgrazing or overbrowsing by
game animals, or extensive fires.

The approximate locations of the 57 benchmark stations are mapped in
Figure 12-1, and defined more specifically in Table 12-10.

A series of iso-pollutant maps have been developed based upon average
concentration ranges obtained at the stations comprising the National
Hydrologic Benchmark Network.  The maps (Figures 12-2 through 12-20)
are for the pollutants identified in Table 12-3.
                                  263

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         Table 12-10.  LOCATION OF HYDROLOGIC BENCHMARK STATIONS^'
Station
  No.

   1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
02369800
02450250
09508300
07340300
07060710
11475560
11264500
10250600
07083000
09352900
02327100
02212600
02178400
16717000
12416000
13169500
03276700
06897950
07373000
01054200
04001000
05124480
05376000
02479155
06288200
05014500
06775900
10249300
10244950
01466500
09430600
08377900
01362198
03460000
06332515
05064900
03237280
             Station location

Blackwater River near Bradley, Alabama
Sipsey Fork near Grayson, Alabama
Wet Bottom Creek near Childs, Arizona
Cossatot River near Vandervoort, Arkansas
North Sylamore Creek near Fifty Six, Arkansas
Elder Creek near Branscomb, California
Merced River near Yoseraite, California
Wildrose Creek near Wildrose Station, California
HaIfmoon Creek near Malta, Colorado
Vallecito Creek near Bayfield, Colorado
Sopchoppy River near Sopchoppy, Florida
Falling Creek near Juliette, Georgia
Tallulah River near Clayton, Georgia
Honolii Stream near Papaikou, Hawaii
Hayden Creek below North Fork, near Hayden Lake,  Idaho
Wickahoney Creek near Bruneau, Idaho
South Hogan Creek near Dillsboro,  Indiana
Elk Creek near Decatur City, Iowa
Big Creek at Pollock, Louisiana
Wild River at Gilead, Maine
Washington Creek at Windigo, Isle  Royale, Michigan
Kawishiwi River near Ely, Minnisota
North Fork Whitewater River near Elba, Minnisota
Cypress Creek near Janice, Mississippi
Beauvais Creek near St. Xavier, Montana
Swiftcurrent Creek at Many Glacier, Montana
Dismal River near Thedford, Nebraska
South Twin River near Round Mountain, Nevada
Steptoe Creek near Ely, Nevada
McDonalds Branch in Lebanon St. Forest,  New Jersey
Mogollon Creek near Cliff, New Mexico
Rio Mora near Tererro, New Mexico
Esopus Creek at Shandaken, New York
Cataloochee Creek near Cataloochee, North Carolina
Bear  Den Creek near Mandaree, North Dakota
Beaver Creek near Finley, North Dakota
Upper Twin Creek at McGaw, Ohio
                                    264

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                         Table 12-10 (Concluded)
Station
  No.

  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
07311200
07335700
11492200
13331500
01545600
02135300
02197300
06409000
06478540
03604000
03497300
08431700
08103900
10172200
02038850
12447390
12039300
04063700
13018300
06623800
              Station  location

Blue Beaver Creek near Cache, Oklahoma
Kiamichi River near Big Cedar, Oklahoma
Crater Lake near Crater Lake, Oregon
Minam River at Minam, Oregon
Young Womans Creek near Renovo, Pennsylvania
Scape Ore Swamp near Bishipville, South Carolina
Upper Three Runs near New Ellenton, South Carolina
Castle Creek near Hill City, South Dakota
Little Vermillion River near Salem, South Dakota
Buffalo River near Flat Woods, Tennessee
Little River above Townsend, Tennessee
Limpia Creek above Fort Davis, Texas
South Fork Rocky Creek near Briggs, Texas
Red Butte Creek near Salt Lake City, Utah
Holiday Creek near Andersonville, Virginia
Andrews Creek near Mazama, Washington
N.F. Quinault River near Amanda Park, Washington
Popple River near Fence, Wisconsin
Cache Creek near Jackson, Wyoming
Encampment River near Encampment, Wyoming
                                   265

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                              REFERENCES
1.  Wischmeier, W.  H.,  "Estimating the Cover  and  Management  Factor for
      Undisturbed Areas," presented at USDA Sediment  Yield Workshop,
      Oxford, Mississippi (1972).

2.  Biesecker, J. E.,  and D.  K.  Leifste,  "Water Quality of Hydrologic
      Bench-Marks—An  Indicator  of Water  Quality," U.S.  Geological Survey
      Circular 460-E, Washington,  D.C. (1973).
                                   286

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                               GLOSSARY
Active surface mine - A site at which coal (or other mineral associated
     with pyrite) is being actively mined representing a potential source
     of acid mine drainage.

Active underground mine - A coal mine (or metal mine associated with py-
     rite) in active operation.  A potential site for generation of acid
     mine drainage.

Antecedent  moisture condition (AMD) - The degree of wetness of a water-
     shed at the beginning of a storm.

Average daily traffic (ADT) - An average value for the daily vehicular
     traffic on a specific roadway.

Background - A description of pollutant levels arising from natural
     sources, and not because of man's utilization of the land.

Base flow - Stream discharge derived from groundwater sources.  Sometimes
     considered to include flows from regulated lakes or reservoirs.
     Fluctuates much less than storm runoff.

Biochemical oxygen demand (BOD) - The amount of oxygen required by bac-
     teria to stabilize decomposable organic matter under aerobic condi-
     tions.  Usually the test is limited to 5 days, when it is termed
     5-day BOD or BOD5-

Canopy - The cover of leaves and branches formed by the tops or crowns
     of plants.

Chemical oxygen demand (COD) - Total quantity of oxygen required for
     oxidation of organic (carbonaceous) matter to carbon dioxide and
     water using a strong oxidizing agent (dichromate) under acid
     conditions.

Commercial forest - The  forest which is both available and suitable for
     growing continuous  crops of raw logs or other industrial timber
     products,  and is judged capable of growing at least 20 ft^ of timber
     per acre per year.

Conservation Needs Inventory - An inventory,  based upon sampling for
     field surveys, of soil, slope, erosion,  land use, and other factors.
     Needed conservation practices are also recorded.  A given percent
     of an area, generally a county, is sampled.   The data are expanded
     to the entire area.

                                   287

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Consumptive use factor - A factor which measures the amount of water
     transpired and evaporated during irrigation.

Contour farming - Conducting field operations, such as plowing, planting,
     cultivating, and harvesting, on the contour.

Contour stripcropping - Layout of crops in comparatively narrow strips
     in which the farming operations are performed approximately on the
     contour.  Usually strips of grass, close-growing crops, or fallow
     are alternated with those in cultivated crops.

Cover crop - A close-growing crop grown primarily for the purpose of
     protecting and improving soil between periods of regular crop pro-
     duction or between trees and vines in orchards and vineyards.

Cover factor "C" - A factor based on a maximum value of 1.0 that reflects
     the effectiveness of vegetative land cover in controlling erosion.
     The factor is used in the Universal Soil Loss Equation.

Cover, ground - Any vegetation producing a protecting mat on or just
     above the soil surface.  In forestry, low-growing shrubs, vines,
     and herbaceous plants under the trees.

Creep - Slow mass movement of soil and soil material down relatively
     steep slopes primarily under the influence of gravity, but facili-
     tated by saturation with water, strong wind,  and by alternate freez-
     ing and thawing.

Cross-slope fanning - Conducting field operations, such as plowing, plant-
     ing, cultivating, and harvesting across the general slope of the
     field.

Curb length - The distance of single street curb,  or the length of one
     side of a street or other thoroughfare.  Distinguished from street-
     length which normally represents two or more curb length.

Curie - A unit of radioactivity equivalent to 3.7 x 1010 disintegrations
     per second.

Direct runoff - The water that enters the stream channels during a storm
     or soon after.  It may consist of rainfall on the stream surface,
     surface runoff, and seepage of infiltrated water.

Diversion terrace - Diversions, which differ from terraces in that they
     consist of individually designed channels across a hillside.
                                   288

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Drainage area - The area draining into a stream at a given point.

Drainage density - Ratio of the total length of all drainage channels in
     a drainage basin to the area of that basin.

Enrichment ratio - The ratio of concentration of a substance in eroded
     sediment to that in the soil.

Erosion, rill - An erosion process in which numerous small channels only
     several inches deep are formed; occurs mainly on recently culti-
     vated soils.

Erosion, sheet - The removal of a fairly uniform layer of soil from the
     land surface by runoff water.

Field stripcropping - A system of stripcropping in which crops are grown
     in parallel strips laid out across the general slope but which do
     not follow the contour.  Strips of grass or close-growing crops are
     alternated with strips of cultivated crops.

Gob pile - Waste material generated during the processing of coal.  A
     potential source of acid mine drainage.

Heavy metal - A metallic (or metallaoid) element of atomic number greater
     than 20.

Humidity factor - A functional term relating relative humidity, precipi-
     tation,  saturated vapor pressure, and temperature.

Inactive surface mine - An abandoned or unreclaimed surface mining site
     at which acid mine drainage may be generated.

Inactive underground mine - An abandoned or inactive underground mine in
     which acid mine drainage may be generated.

Infiltration - The flow of a liquid into a substance through pores or
     other openings, connoting flow into a soil.

Irrigation return flow - The return to surface waters of water used to
     irrigate agricultural land.  It consists of tailwater, deep percola-
     tion, by-pass water, and canal seepage.

Load index,  I  -  A dimensionless number between 0 and 1.0 reflecting
     the probability of acid mine drainage from one of four types of
     sources:  active underground, active surface,  inactive underground,
     or inactive surface.
                                   289

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Most probable number (MPN) - A statistical indication of the number of
     bacteria present in a given volume (usually 100 ml).

Nitrification - The biological oxidation of ammonium salts to nitrites
     and the further oxidation of nitrites to nitrates.

Nitrogen, available - Usually ammonium, nitrite, and nitrate ions, and
     certain simple amines are available for plant growth.  A small
     fraction of organic or total nitrogen in the soil is available at
     any time.

Nutrient, available - That portion of any element or compound in the soil
     that can be readily absorbed and assimilated by growing plants.

Organic matter (soil) - The organic fraction of the soil that includes
     plant and animal residues at various stages of decomposition, cells
     and tissues of soil organisms, and substances synthesized by the
     soil population.

Organic nitrogen  - "Original" form of nitrogenous nutrients.  Gradually
     converted to ammonia nitrogen and to nitrites and nitrates, if
     aerobic conditions prevail.

Percolation - The downward movement (or flow),  of water through the
     pores of any substance (such as soil).

Phosphorus, available - Inorganic phosphorus which is readily available
     for plant growth.  Only a small fraction of total phosphorus in the
     soil is available at any time.

Practice factor "P" - A factor based on a maximum value of 1.0 that re-
     flects the effectiveness of supporting conservation practices in
     controlling erosion.  The factor is used in the Universal Soil Loss
     Equation.

Pyritic material - Materials containing pyrite  (FeS2).  A generic term
     including other disulfides which can oxidize to form acid mine
     drainage such as arsenopyrite (AsFeS2) or chalcopyrite  (CuFeS2).

Radioactivity, alpha - The spontaneous emission of alpha particles
     (helium nuclei) by a radioactive substance.

Radioactivity, beta - The spontaneous emission of beta particles  (elec-
     trons) by a radioactive substance.
                                   290

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Rainfall factor "R" - A numerical expression of rainfall used in the
     Universal Soil Loss Equation.

Relief - The difference in elevation between the high and low points of
     a land surface.

Relief ratio - The ratio between the relief of watershed and the maximum
     length of watershed.

Rill - A small, intermittent watercourse with steep sides, usually only
     a few inches deep and, hence, no obstacle to tillage operations.

Root zone - The part of the soil that is penetrated or can be penetrated
     by plant roots.

Runoff - That portion of the precipitation on a drainage area that is
     discharged from the area in stream channels.  Types include surface
     runoff, groundwater runoff, or seepage.

Runoff, urban - The flow of waters in urban areas from precipitation or
     thaw incidents from gutters into street inlets or from other con-
     nections into storm or combined-sewer system.

Sediment delivery - The quantity of sediment, measured in dry weight or
     by volume, transported through a stream cross-section in a given
     time.

Sediment delivery ratio - The fraction of the soil eroded from upland
     sources that actually reaches a continuous stream channel or storage
     reservoir.

Slope length factor "L" - A factor used in the Universal Soil Loss Equa-
     tion to reflect relative effect of slope length on soil erosion.
     Slope length is defined as the average distance, in feet, from the
     point of origin of overland flow to whichever of the following
     limiting conditions occurs first:  (a) the point where slope de-
     creases to the extent that deposition begins or (b) the point where
     runoff enters well-defined channels.

Slope steepness factor "S" - A factor used in the Universal Soil Loss
     Equation to represent relative effect of slope gradient on soil
     erosion.  Slope gradient is dafined as the degree of deviation of
     a surface from the horizontal, in percent.
                                   291

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Soil erodibility factor "K" - A factor used in the Universal Soil Loss
     Equation to reflect relative basic erodibility differences of soils.

Soil texture - The relative proportions of the three broad particle size
     classifications: sand, silt, and clay, in a soil mass.

Tailings pile - Residues generated during the beneficiation of metal
     ores.  If material is pyritic, it is a potential source of acid
     mine drainage.

Terrace - An embankment or combination of an embankment and channel con-
     structed across a slope to control erosion by diverting or storing
     surface runoff instead of permitting it to flow uninterrupted down
     the slope.

Topographic factor "LS" - A dimensionless factor used in the Universal
     Soil Loss Equation to represent the combined effects of slope length
     and steepness.

Total dissolved solids - The dissolved salt loading in surface and sub-
     surface waters.  Equivalent to salinity.
                                   292

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                               SYMBOLS
A

AMD

AS; AU

AX

BG

C
  ^  'source

C(Alk)BG

CL

cu

D


DD

DI

DI30

E

fN

fP

FL

FLd

H
 Source  area, ha

 Acid  mine drainage

 Active  surface or underground mine

 Average number of axles  per vehicle

 Background source

 Cover management factor

 Concentration of pollutant  i  in sediment

 Concentration, C of pollutant  i  in source

 Concentration of background alkalinity, mg/llter

 Curb-length density, m/ha

 Annual  consumptive use of water, cm/year

 Overland distance between erosion site and receptor
  water, ft

Drainage density, km"*

Amount of deicer applied in the area, kg/year

 30-Day maximum,  DI

Annual average erosion rate,  MT/ha/year

Ratio of NA:NT in eroded sediment

Ratio of PA:PT in eroded sediment

Small feedlot source

Feedlot delivery ratio

Humidity factor
                                  293

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HIF               Herbicide, Insecticide, Fungicide; any pesticide

HM                Heavy metals

I                 Load index for acid mine drainage

IRF               Irrigation return flow

IRR               Irrigated water added annually to crop root zone,
                    cm/year

IS; IU            Inactive surface or underground mine

K                 Soil erodibility factor

L                 Slope length factor

Lst               Street length, km

L(S)              Daily street solids loading rate, kg/curb-Ian/day

LF; LFd           Landfill, landfill delivery ratio

LH                Length of highway section, km

LS                Topographic factor

X (Lambda)        Slope length,  m

NA                Available (or mineralized) nitrogen

Np                Nitrogen yield rate per unit area from precipitation,
                    kg/ha/year

NT                Sum of nitrogen of all chemical forms

OM                Organic matter

OR                Overland runoff

p                 Percolation rate,  cm/year

P                 Conservation practice factor

Pr                Annual average precipitation, cm/year
                                   294

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PA                Available phosphorus

PD                Population density, number/ha

PT                Total phosphorus; also point source

Q^; Q             Runoff due to a storm event, cm

Q(FL)             Feedlot runoff, cm/year

Q(LF)             Landfill leachate flow rate, cm/year

Q(OR)             Overland runoff, cm/year

Q(P)              Total precipitation flow rate, cm/year

Q(Perc)           Percolation flow rate, cm/year

Q(R)              Direct runoff, cm/year

Q(Str)            Stream flow rate,  liters/sec

Q(t)              Runoff over a period of time, t

R                 Rainfall erosivity factor

Rr                Rainfall erosivity factor due to rainfall

RS                Rainfall erosivity factor due to snowmelt

RAD               Radioactivity

RH                Relative humidity, %

r«                Enrichment ratio for nitrogen (ratio of concentration
                    of nitrogen in sediment to that in soil)

TQJJ               Enrichment ratio for organic matter (ratio of concen-
                    tration of organic matter in sediment to that in soil)

r                 Enrichment ratio for phosphorus (ratio of concentration
                    of phosphorus  in sediment to that in soil)

S                 Slope gradient factor; also sediment
                                   295

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SD

SD30

SVPt

T

TD

TDS

U
Y(AMD)

Y(DI)

Y(HIF)

Y(HM)
Y(i)tr
Y(N)pr

Y(NA)

Y(NT)E

Y(OM)E
Sediment delivery ratio (ratio of the amount of sedi-
  ment delivered to a stream to the amount of on-site
  erosion)

Number of snow days

Thirty-day maximum SD

Saturated vapor pressure at given temperature, mm Hg

Annual average temperature, °C

Traffic density, number of vehicles/day

Total dissolved solids

Composite topographic factor for irregular slopes

Traffic related pollutant  i , kg/axle-km/day

Acid mine drainage loading, kg/year

Deicing salt loading, kg/year

Total pesticide loading, kg/year

Heavy metal loading, kg/year

Loading of pollutant  i  from small feedlots, kg/year

Loading of pollutant  i  from landfills, kg/year

Loading of pollutant  i  from traffic sources, kg/year

Loading of pollutant  i  from urban areas, kg/year

Nitrogen loading from precipitation runoff, kg/year

Available nitrogen loading, kg/year

Total nitrogen loading from erosion, kg/year

Organic matter loading, kg/year
                                  296

-------
Y(PA)




Y(FT)





Y(RAD)





Y(S)E




Y(S)u





Y(TDS)BG





Y(TDS)IRF





Y(TDS)PT






Y<1>BG
Available phosphorus loading, kg/year




Total phosphorus loading, kg/year




Loading of radioactive substances, microcuries/year





Sediment loading from surface erosion, MT/year





Loading of street solids from urban areas, kg/year





Salinity (TDS) load from background, kg/year





Salinity (TDS) load in irrigation return flow, kg/year





Salinity (TDS) load from point sources, kg/year





Yield of pollutant  i  from background, kg/year
                                  297

-------
                    APPENDIX A
MONTHLY DISTRIBUTION OF RAINFALL EROSIVITY FACTOR R

   Distribution Curves for the Eastern United States

   Figure A-l - Key Map for Selection of Distribution
   Curve

   Figure A-2a through A-2i - Distribution Curves

   Distribution Curves for Hawaii (Figures A-3a
   through A-3c)

   Methods for Developing R Distribution Curves for
   the Western United States
                         298

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-------
 METHODS FOR DEVELOPING R DISTRIBUTION CURVES  FOR THE WESTERN UNITED STATES^

 Rs   is  significant in portions  of this area.   Divide the annual  R,. for
 the location by the average annual precipitation to obtain a factor.
 Multiply each month's precipitation by this factor to  obtain monthly  Rr
 values.  Add the prorated monthly  Rg  values to  Rj.  for the months when
 snowmelt occurs, to obtain the  monthly  R values.   Compute the monthly
 accumulative percent.  The following example  is  for Hylton, in Elko County,
Nevada.   The 2-6  rainfall  for  this  area  is  0.9  in.
mined  from the  Type  II  curve on Figure 3-4, is  18.
average  is 12.72  in.  Factor is 18  i  12.72  « 1.42.
             The  annual  Rr deter-
             Annual precipitation
Monthly precipitation  (water depth)  for December  through March  is  4.92 in.
Rs  - 4.92 x  1.5 = 7.38.  This  is prorated, based on  local  judgment to

January 10%   or  0.7
February 20%  or  1.5
March 50%     or  3.7
April 20%     or  1.5
            Precipitation
            (inches water
                   Cumulative
Month
January
February
March
April
May
June
July
August
September
October
November
December
depth)
(2)
1.18
1.14
1.29
1.49
1.48
0.91
0.63
0.52
0.63
1.17
0.97
1.31
                               Rr
 Rs
ill
 R*
ill
                                                                    ill
1.68
1.62
1.83
2.12
2.10
1.29
0.89
0.74
0.89
1.66
1.38
1.86
0.7
1.5
3.7
1.5
-
-
-
-
-
-
-
-
2.38
3.12
5.53
3.62
2.10
1.29
0.89
0.74
0.89
1.66
1.38
1.86
2.38
5.50
11.03
14.65
16.75
18.04
18.93
19.67
20.56
22.22
23.60
25.46
0.093
21.6
43.3
57.5
65.8
70.9
74.4
77.3
80,8
87.3
92.7
100
*  Columns (3) + (4).

If  Conservation Agronomy Technical Note No. 32, U.S. Department of Agri-
      culture, Soil Conservation Service, West Technical Service Center,
      Portland, Oregon, September 1974.
                                   312

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Values in cumulative percent column (7) are the points used in plotting
the monthly  R  distribution curve.

For A-2, A-3, and A-4 Areas Shown in Figure 3-4

R   is not significant in most parts of these areas.  Use the monthly rain-
fall distribution as the  R  distribution.  Simply accumulate monthly pre-
cipitation amounts and divide each by the annual precipitation.  The re-
sults obtained for each month will be the points for plotting the monthly
R  distribution curve.

For B-l and C Areas Shown in Figure 3-4

Rs  in most parts of these areas is significant.

1.  "Multipliers" are used to time average monthly precipitation amounts.
Sum the results of multiplications to obtain the "factored annual precipi-
tation."  Divide the annual  Rr  for the location by the "factored annual
precipitation" to obtain a factor which will be used to convert monthly
precipitation amounts to the monthly  R  values (see the previous section
for A-l area).  Values of multipliers are:

Month(s)                     Multipliers

January, February, March         0.1
April                            1.0
May                              4.0
June, July, August               7.0
September, October               2.0
November, December               0.1

2.  Add the prorated  R   values to the months when the snowmelt occurs to
                       s
obtain the monthly  R  values.  Compute the monthly accumulative percents
which are points used in plotting the monthly  R  distribution curve.  The
following example is for a hypothetical area which has an annual rainfall
factor  Rr  of 25, and a  Rg  factor of 7.5 (4.94 x 1.5 rounded to 7.5).
The 4.94 in. is total precipitation for December, January, February,
and March.  Rg factor is prorated to:

       January                0%         or        0 in.
       February              33.3%       or        2.5 in.
       March                 33.37.       or        2.5 in.
       April                 33.3%       or        2.5 in.
                                 313

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Month
(1)
January
February
March
April
May
June
July
August
September
October
November
December

Precipi-
tation
(in.)
(2)
1.33
1.14
1.35
1.48
1.43
1.00
0.80
0.78
0.85
1.14
0.92
1.12


Multiplier
(3)
0.1
0.1
0.1
1.0
4.0
7.0
7.0
7.0
2.0
2.0
0.1
0.1
Factored
monthly
pptn. (Col 2
x Col. 3)
(4)
0.13
0.11
0.13
1.48
5.72
7.00
5.60
5.46
1.70
2.28
0.09
0.11

Monthly
Rr*
(5)
0.11
0.09
0.11
1.24
4.80
5.78
4.69
4.58
1.43
1.91
0.08
0.09
Total
13.34
              29.81
25.0

Month
(1)
January
February
March
April
May
June
July
August
September
October
November
December
Monthly
Rs
(6)
--
2.5
2.5
2.5
--
—
--
--
--
—
--
--
Monthly R
=Rr + Rs
(7)
0.11
2.59
2.66
3.74
4.80
5,87
4.69
4.58
1.43
1.91
0.08
0.09
Cumulative
R
(8)
0.1
2.7
5.4
9.1
13.9
19.8
24.5
29.0
30.5
32.4
32.4
32.5
%
(9)
--
8
17
28
43
61
75
89
94
99
100
100
Total
    7.5
32.5
   In this example, the calculated factor value is 0.84 (25 -t- 29.81)
     Monthly Rr is obtained by multiplying each "factored monthly
     pptn." with 0.84.
                                 314

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For B-2 Area Shown in Figure 3-3

In this area, no  Rg  values are needed.  Follow the same procedure and
use the same set of multipliers as the preceding section for areas B-l
and C, except that steps for obtaining monthly  Rs  values are not used.
The cumulative  R  and cumulative percent are computed from monthly  R^
(column 5 in the preceding example).
                                   315

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                  APPENDIX B
METHODS FOR PREDICTING SOIL ERODIBILITY INDEX K

   Nomograph for predicting K values of surface
   soils using chemical and physical parameters.

   Nomograph for predicting K values of high clay
   subsoils using chemical mineralogical and
   physical parameters.
                       316

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NOMOGRAPH FOR PREDICTING K VALUES OF SURFACE SOIL
In 1971 Wischmeier et al.—  presented a soil erodibility nomograph
derived from statistical analysis of 55 soil types.  Five soil param-
eters are included in the nomograph to predict erodibility:  percent
silt plus very fine sand; percent sand greater than 0.10 millimeter;
organic matter content; soil structure; and permeability.  Values of the
parameters may be obtained from routine laboratory determinations and
standard soil profile descriptions.

The nomograph is reproduced here as Figure B-l.
                      21
Description of Factors-

Grain size distribution

Grain size distribution has a major influence on a soil's erodibility:
the greater the silt content, the greater the soil's erodibility; the
smaller the sand content, the greater the soil's erodibility.

Particles in the very fine sand classification behave more like silt
than sand.  Therefore, the percentage of very fine sand should be
subtracted from the total percentage of sand and added to the per-
centage of silt.
JY  Wischmeier, W. H., C. B. Johnson, and B. U. Cross, "A Soil Erodi-
      bility Nomograph for Farmland and Construction Sites," J. Soil
      and Water Conservation, 26^:189-193 (1971).
2j  "Technical Guide to Erosion and Sediment Control Design (Draft),"
      Water Resources Administration, Maryland Department of Natural
      Resources, Annapolis, Maryland, September 1973.
                                      317

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Organic matter

The percentage of organic matter was determined, in work by Wischmeier,
et al., by the Walkley-Black method.—   The organic matter content is
approximately 1.72 times the percent carbon.  Soil erodibility decreases
as organic matter content increases.

Soil structure

The soil structure is descriptive of the overall arrangement of the soil
solids.  The four parameter values and their descriptions are as follows:
Parameter
Value	     Descriptions

              Granular - All rounded aggregates may be placed in this
              category.  These rounded complexes usually lie loosely
              and are readily shaken apart.  When wetted, the voids are
              not closed readily by swelling.

  1           Very fine granular - less than 1 mm.

  2           Fine granular - 1 to 2 mm.

  3           Medium granular - 2 to 5 mm.

  3           Coarse granular - 5 to 10 mm.

  4           Blocky - Aggregates have been reduced to blocks,  irregularly
              six-faced, and with their three dimensions more or less
              equal.  In size, the fragments range from a fraction of an
              inch to 3 or 4 in. in thickness.

  4           Platy - Aggregates are arranged in relatively thin plates
              or lenses.

  4           Prismatic - Aggregates or pillars are vertically  oriented,
              with tops plane, level, and clean cut.  They commonly occur
              in subsoils of arid and semi-arid regions.

  4           Columnar - Aggregates or pillars are vertically oriented,
              with rounded tops.  They commonly occur when the  soil pro-
              file is changing and the horizons are degrading.


II  Walkley, A., and I. A. Black, "An Examination of the Degtjareff Method
      for Determining Soil Organic Matter," Soil Sci. ,  37_,  pp.  29-38 (1934)
                                    319

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              Massive - Soil units are very large, irregular, feature-
              less as far as characteristic aggregates are concerned.
Soil permeability

Soil permeability is that property of the soil that enables the soil
to transmit water.  Since different soil horizons vary in permeability,
the relative permeability classes refer to the soil profile as a whole.
The relative permeability classes are as follows:
Class         Permeability rates in in/hour

  1           Rapid                 over 6.0

  2           Moderately rapid    2.0 to 6.0

  3           Moderate            0.6 to 2.0

  4           Moderately slow     0.2 to 0.6

  5           Slow                0.06 to 0.2

  6           Very slow        less than 0.06


Reading the Nomograph

Entry values for all of the nomograph curves, except permeability class,
are for the upper 6 or 7 in. of soil.  For soils in cuts, the entry
values are for the upper 6 or 7 in. of the newly exposed layer.  In
reading the nomograph, interpolate linearly between adjacent curves
when the entry data do not coincide with the plotted curves of percent
sand or percent organic matter.  The percent of coarse fragments may be
significant and is not included in the nomograph.  Therefore, reduce
the value of K read from the nomograph by 10% for soils with stratified
subsoils that include layers of small stones or gravel without a seriously
impeding layer above them.

Enter the left scale of the nomograph with the appropriate percent silt
plus very fine sand, move horizontally to intersect the correct percent-
sand curve (interpolating to the nearest percent), vertically to the
correct organic matter curve, and then horizontally to the right scale
for first approximation of soil erodibility.
                                   320

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For soils having a fine granular structure and moderate permeability,
the value of K can be obtained directly from this scale.  However, if
the soil is other than of fine granular structure, or permeability is
other than moderate, it is necessary to proceed to the second part of
the nomograph, horizontally to intersect the correct structure curve,
vertically downward to the permeability curve, and horizontally to the
soil erodibility index scale.
NOMOGRAPH FOR PREDICTING K VALUES OF HIGH CLAY SUBSOILS
Subsoils are commonly heavier in texture than the surface soils.  In
addition, subsoils likely have aggregating agents that are very much
different from those found in surface soils and the degree of aggre-
gation is known to have a profound influence on erodibility.

From an EPA studyi' conducted at Purdue University, a multiple linear
regression equation and nomograph were developed which can be used to
estimate the erodibility factor, K, of many high clay soils.  Multiple
regression analysis revealed that amorphous iron, aluminum and silicon
hydrous oxides serve as soil stabilizers in subsoils (whereas, organic
matter is the major stabilizer in surface soils).  The nomograph was
developed from the multiple linear regression equation relating the
erodibility factor to the soil texture factor, M, the amount of CDB
(citrate-dithionite-bicarbonate) extractable iron and aluminum oxides,
and the amount of CDB extractable silica oxide.

The equation used to derive the nomograph was:

  K red = 0.32114 + 2.0167 x 10"4 M - 0.14440 (7, Fe203 + 70 A1203)

                                    - 0.83686 (7o Si02)

where Kprecj = Predicted K value of subsoil
      M = Soil texture factor, defined by percent new silt (percent
            new silt + percent new sand).  "New" silt has 2 to 100 pm
            mean diameter.  "New" sand has 100 to 2,000 urn mean diameter.
      % Fe203 = Percent CBD extractable iron oxide of soil.
      7« A1203 = Percent CDB extractable aluminum oxide of soil.
      % Si02 = Percent CDB extractable silica oxide of soil.
I/  Roth, C. B., D. W. Nelson, and M. J. M. Romkens, "Prediction of
      Subsoil Erodibility Using Chemical, Mineralogical, and Physical
      Parameters," for the U.S. Environmental Protection Agency (EPA-660/
      2-74-043), Washington, D.C., June 1974.
                                   321

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The nomograph for estimating the erodibility factor,  K,  of high  clay
subsoils is reproduced in Figure B-2.
                                   322

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s





CM
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s




CO
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s
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\
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00
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4—
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OOOOQOOOOOC
i — CM CO ^ iO vQrN-OOO»C
^
(UJLU I'Q - 200*0) PUDS 9u!d ^aA + J|!S 4ua3J<
00 2000 3000 4000 0 0.2 0.4 0.6 0.8 1.0 1.1
Factor M Metric Tons/Hectare/Metric R Unit
Soil Erodibility Factor, K
ograph for estimating the erodibility factor K of high clay subsoils^'
CM
D "
'd t
Roth, C. B., D. W. Nelson, and M. J. M. Romkens, "Prediction of Subsoil Erodibility Using Chemic
Mineralogical, and Physical Parameters," for the U.S. Environmental Protection Agency (EPA-660
2-74-043), Washington, D.C., June 1974.
323

-------
               APPENDIX C
TOPOCPAPHIC FACTOR LS FOR IRREGULAR SLOPES
                    324

-------
 This appendix presents  examples  of calculating  LS  values  for  irregular
 slopes,  one of convex slope and  the other  concave  slope.
 EXAMPLE 1--CONVEX SLOPE
 The slope is  shown below with  values  of  slope  length  and  slope  percent
 indicated on  each  of  the three segments.
 Segment  I

 Enter  the slope effect chart  in Figure 4-8 at 85'  (X^) on the horizontal
 scale, move upward to the curve for 2% slope, and  read U9 T = 17.
                                                        £• , 1
The upper end of Segment I is at zero length, therefore XQ
U1§1 = 0.
                                                             0 and
U
 2,I
              17
Segment II
X2 = 85' + 60'
X, = 85'
               = 145
Enter the slope effect chart with lengths of 145'  and 85', use the curve
for 57o.  Obtain U0 __ =91 and U, TT =41.  Thus
                 L , 11           i- > II
U2,II - U1,II
                   - 41 = 50.
Segment III
X3 = 85' + 60' + 65' = 210'
X2 = 85' + 60' = 145'
                                  325

-------
Enter the slope chart with lengths of 210' and 145", use 8% curve,
obtain U2 ni = 310 and U^ m = 170.
U
2,in " ui,m
                 310 - 170 = 140
The computation is summarized below.  The effective topographic factor
LM is estimated at 0.99 for the entire slope.
Seg-
ment,
j
(1)
I
Segment
Length,
ft
(2)
85
Segment
Slope,
(3)
2

\ • \ r -1
(4) (5)
85 0

U2,j-
U2,j Ul,j Ul,j
(6) (7) (8)
17 0 17
Segment
LS,
Col(8)-K2)
(9)
0.20
cent
of
Total
Yield*
(10)
8
II
       60
145   85    91    41
50
        65
Entire
Slope  210
                      210   145    310   170   140

                                             207
0.83

2.15

0.99
 24

 68

100
*  Assume constant soil erodibility for the entire slope, computed by
     dividing Col. (8) by 207, i.e., [£(U2>j - UL .)].
EXAMPLE 2--CONCAVE SLOPE
A concave slope consists of three segments with values  of slope length
(feet) and slope gradient (%) shown in the graph below:
Segment I

J^ - 65'
n~ • 0
                                  326

-------
Use curve for 87« in Figure 4-8.  Obtain
U2   = 52
Segment II
X2 = 65' + 60' = 125'
Xl = 65'
Use 57, curve, obtain
U2,II - 68
Ui TT = 27
Segment III

X3 = 65' + 60' + 85' = 210'
X2 = 65' + 60' = 125'

Use 2% curve, obtain


U2,m - 62
ui,m = 32

Computations are summarized in the following table.  The effective LS
value for the entire slope is estimated at 0.59.
ment  Length,  Slope
 j      ft
(
        (2)
         65
II 60
III 85
Entire
Slope 210
5
2


Segment
Slope
%
(3)
8
5
2

!!2'J-
Ul 4
Segment
LS
Col. (6)- Col. (8)4-
Xj
(4)
65
125
210

Xj-l
(5)
0
65
125

U2,j
(6)
52
68
62

U1,J
(7)
0
27
32

Col. (7)
(8)
52
41
30
123
Col. (2)
(9)
0.84
0.68
0.35
0.59
Per-
cent
of
Total
Yield
(10)
42
33
25
100
                                 327

-------
                           APPENDIX D
          K •  LS INDEXES  FOR LAND RESOURCE AREAS EAST OF
                      THE CONTINENTAL DIVIDE*
Calculated from results of 1972 SOS questionnaire  survey.
                                328

-------
K«LS INDEX -- metric tons per hectare per unit  of  erosion index. R
LAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
Vila
VI Iw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
32 33 42 46 50/51
0.14 (0.06)
0.27 (0.12) 0.38 (0.17) 0.41 (0.19) 0.22 (0.10)
0.14 (0.06) 0.14 (0.06) 0.31 (0.14)
0.14 (0.06) 0.19 (0.08)
0.31 (0.14) 0.18 (0.08)
0.49 (0.22) 0.45 (0.20) 0.94 (0.42)
0.09 (0.04) 0.29 (0.13) 0.22 (0.10) 0.27 (0.12)
0.31 (0.14) 0.13 (0.06) 0.26 (0.12) 0.43 (0.19)
0.25 (0.11) 0.18 (0.08)
0.40 (0.18) 0.94 (0.42) 0.11 (0.05) 2.2 (0.99)
0.18 (0.08) 0.23 (0.10)
0.25 (0.11) 0.13 (0.06) 0.18 (0.08)
0.58 (0.26) 0.27 (0.12)

0.04 (0.02) 0.22 (0.10) 0.19 (0.09)


1.19 (0.53) 4.9 (2.2) 3.6 (1.6) 4.5 (2.0)
0.07 (0.03) 5.2 (2.3) 0.18 (0.08) 2.8 (1.2) 4.3 (1.9)
0.25 (0.11) 0.25 (0.11)
0.43 (0.19) 0.49 (0.22) 0.29 (0.13) 0.54 (0.24)
4.3 (1.9) 2.9 (1.3) 0.16 (0.07) 0.63 (0.28)
7.8 (3.5) 1.0 (0.45) 9.3 (2.4)
0.29 (0.13)
.0.74 (0.33)
0.76 (0.34) 15.0 (6.7) 8.1 (3.6)
9.5 (4.2) 22.0 (10.0)



52
0.13 (0.06)
0.74 (0.33)
0.25 (0.11)
0.25 (0.11)
0.22 (0.10)
0.33 (0.15)


0.25 (O.U)
1.0 (0.45)
0.22 (0.10)






3.4 (1.5)
0.22 (0.10)
0.25 (0.11)

9.3 (4.1)
0.19 (0.09)



6.5 (2.9)


                                329

-------
K-LS INDEX  --  metric tons per hectare per unit of  erosion  index, R
LAND
CLASS .ind
SUBCLASS
I
lie
11s
Uw
lie
Hie
Ills
IIIw
IIIc
IVe
TVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VI Iw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
53 54 55 56 57 56
0.19 (0.08) 0.18 (0.08) 0.22 (0.10) 0.13 (0.06)
0.58 (0.26) 0.65 (0.29) 0.22 (0.10) 1.2 (0.10) 0.43 (0.19) 0.25 (0.11)
0.58 (0.26) 0.49 (0.22) 0.38 (0.17) 0.43 (0.19) 0.13 (0.06) 0.13 (0.06)
0.29 (0.13)
0.36 (0.16) 0.43 (0.19) 0.22 (0.10) 0.25 (0.11) 0.13 (0.06) 0.43 (0.19)
1.1 (0.48) 1.1 (0.48) 1.1 (0.48) 0.18 (0.08) 1.2 (0.54) 0.76 (0.34)
0.22 (0.10) 1.1 (0.49) 0.25 (0.11) 0.25 (0.11) 0.11 (0.05) 0.13 (0.06)
0.49 (0.22)
0.11 (0.05)
2.1 (0.93) 0.90 (0.40) 2.1 (0.93) 0.11 (0.05) 3.2 (1.4) 0.99 (0.44)
0.29 (0.13) 0.67 (0.30) 0.38 (0.17) 0.22 (0.10) 0.76 (0.34) 0.34 (0.15)

0.25 (0.11)




1.6 (0.70) 2.4 (1.07) 1.6 (0.70) 0.27 (0.12) 5.4 (2.4) 2.3 (1.0)
1.1 (0.49) 0.13 (0.06) 0.09 (0.04) 0.76 (0.34) 0.22 (0.10)
1.4 (0.62) 0.25 (0.11)
0.43 (0.19)
4.2 (1.89) 0.34 (0.15) 0.34 (0.15) 6.9 (3.1) 7.1 (3.2)
0.58 (0.26) 1.1 (0.51) 1.0 (0.45) 0.22 (0.10) 3.1 (1.4) 0.29 (0.13)


8.6 (3.8)
6.5 (2.9)


                                 330

-------
K-LS INDEX — metric tons per hectare per unit  of erosion index, R
LAND
CLASS and
SUBCLASS
I
lie
Us
llw
lie
Hie
Ills
IIIw
Hie
IVe
IV s
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VIHe
VIIIs
VIIIw
VHIc
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
59 60 61 62 63 64
0.13 (0.06) 0.13 (0.06) 0.18 (0.08) 0.13 (0.06)
0.34 (0.15) 0.13 (0.06) 0.43 (0.19) 0.49 (0.22) 0.22 (0.10)
0.16 (0.07) 0.16 (0.07) 0.25 (0.11) 0.09 (0.04) 0.11 (0.05)

0.13 (0.06) 0.25 (0.11) 0.29 (0.13) 0.14 (0.06) 0.11 (0.05)
1.1 (0.51) 0.72 (0.32) 0.43 (0.19) 0.85 (0.38) 0.99 (0.44) 0.43 (0.19)
0.22 (0.10) 0.43 (0.19) 0.45 (0.20) 0.22 (0.10) 0.13 (0.06)
0.13 (0.06)
0.13 (0.06) 0.29 (0.13) 0.25 (0.11) 0.43 (0.19)
0.99 (0.44) 0.99 (0.44) 0.99 (0.44) 1.6 (0.70) 1.3 (0.59) 1.1 (0.48)
0.85 (0.38) 0.45 (0.20) 0.45 (0.20) 0.20 (0.09) 0.22 (0.10)

0.27 (0.12)


0.13 (0.06)

3.7 (1.7) 4.4 (1.96) 4.6 (2.1) 3.7 (1.6) 3.0 (1.3) 1.2 (0.54)
0.16 (0.07) 0.99 (0.44) 2.8 (1.3) 3.3 (1.5) 2.2 (0.96)
0.13 (0.06)

17.0 (7.7) 110. (49.0) 6.6 (2.9) 6.9 (3.1) 8.8 (3.9) 10.0 (4.5)
8.2 (3.6) 8.8 (3.9) 8.8 (3.9) 16.0 (7.0)


12.0 (5.5)
6.5 (2.9) 13.0 (6.0)


                              331

-------
K'LS INDEX -- metric  tona per hectare per unit of erosion index. R
(ton* per acre per unit of erodon Index. R)
LAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
LAND RESOURCE AREA
65 66 67
0.22 (0.10)
0.09 (0.04) 0.43 (0.19) 0.49 (0.22)
0.13 (0.06)
0.22 (0.10)
0.13 (0.06) 0.22 (0.10) 0.22 (0.10)
0.18 (0.08) 0.27 (0.12) 0.25 (0.11)
0.18 (0.08)
0.16 (0.07)
0.25 (0.11)
0.11 (0.05) 0.81 (0.36) 0.07 (0.03)
0.20 (0.09) 0.34 (0.15)
0.11 (0.05)
0.11 (0.05)


0.11 (0.05)

0.92 (0.41) 1.6 (0.72) 2.2 (0.96)
0.09 (0.04) 0.34 (0.15) 0.11 (0.05)
0.07 (0.03)

3.6 (1.6) 3.1 (1.4) 2.3 (1.0)
3.6 (1.6) 0.60 (0.27) 0.43 (0.19)
0.07 (0.03)

9.4 (4.2)



70 71
0.16 (0.07) 0.13 (0.06)
0.16 (0.07) 0.29 (0.13)
0.18 (0.08) 0.20 (0.09)

0.13 (0.06)
0.31 (0.14) 1.3 (0.56)
0.34 (0.15) 0.2 (0.09)

0.31 (0.14)
0.65 (0.29) 1.4 (0.64)
0.18 (0.08)
0.16 (0.07)
0.31 (0.14)


0.27 (0.12)

0.65 (0.29) 7.6 (3.4)
0.22 (0.10) 0.09 (0.04)
0.18 (0.08)
0.58 (0.26)
12. (5.3) 13. (5.8)
1.5 (0.67) 2.4 (1.1)



0.13 (0.06)


72
0.22 (0.10)
0.36 (0.16)
0.29 (0.13)

0.22 (0.10)
0.35 (0.16)
0.29 (0.13)

0.25 (0.11)
0.58 (0.26)







1.7 (0.77)



2.2 (1.0)
3.7 (1.7)






                              332

-------
K-l.S INDEX —  mclric  tons per hectare per unit of erosion index. R
i AMU
fl.ASS tmd
SUBCLASS
1
1U'
] 1',
llu
II.-
II le
Ills
lllw
Hlc
IVt
TVs
IVv
IVc
Ve
Vs
Vw
Vc
Vie
', Is
VIw
VU
Vile
Vlls
VIIw
Vlic
VIIle
VH1-.
VIlIw
VI lie
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
73 74 75 (Nebr.) 75 (Kans.) 76 77
0.16 (0.07) 0.07 (0.03)
0.4J (0.19) 0.49 (0.2J; 0.34 (0.15) 0.43 (0.19) 0.43 (0.19) 0.07 (0.03)
0.29 (0.13) 0.29 fO.13) 0.29 (0.13) 0.29 (0.13) 0.29 (0.13) 0.09 (0.04)

0.25 (0.11) 0.25 (0.11) 0.16 (0.07) 0.25 (0.11)
0.78 (0.35) 0.58 (0.26) 0.49 (0.22) 0.49 (0.22) 0.43 (0.19)
0.20 (0.09)

0.43 (0.19)
0.78 (0.35) 1.7 (0.74) 1.1 (0.47) 0.56 (0.25) 0.13 (0.06)
0.22 (0.10) 0.22 (0.10) 0.34 (0.15) 0.22 (0.10)

0.43 (0.19)


0.04 (0.02)

1.7 (0.77) 1.5 (0.67) 3.J (1.5) 4.2 (1.9) 1.7 (0.77) 0.18 (0.08)
3.3 (1.5) 0.45 (0.20)
0.07 (0.03)
0.76 (0.34)
13.0 (5.8) 2.2 (1.0) 0.04 (0.02)
4.2 (1.9) 13.0 (5.8) 0.78 (0.3,5.) 0.78 (0.35) 0.56 (0.25)


0.04 (O.Oj)
4.9 (2.2)


                              333

-------
K'LS INDEX -- metric tons per hectare per  unit of erosion Index,  R
LAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IV s
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
Vllle
VIIIs
VlIIw
VlIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
78 79 80 81 62 83
0.18 (0.08) 0.18 (0.08) 0.04 (0.02)
0.43 (0.19) 0.18 (0.08) 0.49 (0.22) 0.29 (0.13) 0.31 (0.14) 0.22 (0.10)
0.29 (0.13) 0.29 (0.13) 0.29 (0.13) 0.07 (0.03) 0.07 (0.03)
0.09 (0.04) 0.22 (0.10) 0.07 (0.03)
0.22 (0.10) 0.25 (0.11) 0.07 (0.03) 0.04 (0.02) 0.07 (0.03)
0.65 (0.29) 0.20 (0.09) 0.49 (0.22) 0.36 (0.16) 0.43 (0.19) 0.76 (0.34)
0.29 (0.13) 0.07 (0.03) 0.09 (0.04)
0.07 (0.03) 0.09 (0.04)
0.31 (0.14) 0.11 (0.05)
0.45 (0.20) 0.67 (0.30) 0.43 (0.19) 0.31 (0.14) 0.25 (0.11)
0.22 (0.10) 0.22 (0.10) 0.38 (0.17) 0.07 (0.03) 0.07 (0.03)
0.31 (0.14) 0.25 (0.11) 0.09 (0.04)
0.07 (0.03) 0.07 (0.03)

0.22 (0.10)
0.22 (0.10) 0.07 (0.03) 0.07 (0.03)

1.7 (0.74) 0.72 (0.32) 1.7 (0.74) 0.36 (0.16) 0.43 (0.19)
1.7 (0.74) 0.72 (0.32) 0.65 (0.29) 0.90 (0.14)
0.04 (0.02) 0.04 (0.02)
0.49 (0.22)
0.83 (0.37) 1.9 (0.87) 0.83 (0.37) 0.34 (0.15)
1.0 (0.45) 0.11 (0.05) 2.9 (1.3) 1.3 (0.68) 0.81 (0.36)


0.54 (0.24)
1.6 (0.72)


                              334

-------
K-LS INDEX --  metric tons per hectare per unit  of erosion Index, R
LAND
CLASS and
SUBCLASS
I
He
us
IIw
He
Hie
Ills
I IIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VHs
Vllw
VIIc
Vllle
VIHs
VIIIw
vine
(tons per acre per unit of erosion index.R)
LAND RESOURCE AREA
84 85 86 87 88 90
0.22 (0.10) 0.16 (0.07)
0.27 (0.12) 0.43 (0.19) 0.22 (0.10) 0.38 (0.17) 0.49 (0.22) 0.67 (0.30)
0.29 (0.13) 0.19 (0.09) 0.25 (0.11)
0.49 (0.22)
0.25 (0.11)
0.38 (0.17) 0.58 (0.26) 0.65 (0.29) 0.67 (0.30) 1.4 (0.63) 1.4 (0.63)
0.22 (0.10) 0.11 (0.05)
0.29 (0.13)
3.7 (1.7)
0.65 (0.29) 1.0 (0.45) I. I (0.54) 0.67 (0.30) 0.31 (0.14) 2.6 (1.2)
0.29 (0.13) 0.45 (0.20) 0.07 (0.03)



0.13 (0.06)
0.22 (0.10)

1.3 (0.58) 1.0 (0.45) 2.2 (0.96) 1.6 (0.73) 5.0 (2.2) 4.6 (2.1)
0.65 (0.29) 0.85 (0.38) 0.8 (0.34) 1.3 (0.59)


0.83 (0.37) 8.0 (3.6) 4.0 (1.8)
2.0 (0.89) 4.1 (1.8) 3.0 (1.4) 1.6 (0.70)



0.29 (0.13)


                             335

-------
LAND
CLASS and
SUBCLASS
1
He
Us
Hw
lie
Ille
Ills
HIw
lllc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vlc
\'IIe
Vlls
VI Iw
VIIc
vui*
vnis
VIIlw
VIIIc
(cons per acre per unit of eroalon Index, R)
LAND RESOURCE AREA
91 92 (Mich.) 92 (Wl«c.) 93 94 95
0.16 (0.07) 0.13 (0.06) 0.13 (0.06)
0.67 (0.30) 0.58 (0.26) 0.67 (0.30) 0.58 (0.26) 0.31 (0.14) 0.67 (0.30)
0.13 (0.06) 0.13 (0.06) 0.13 (0.06) 0.18 (0.08) 0.20 (0.09) 0.16 (0.07)
0.25 (0.11) 0.38 (0.17) 0.16 (0.07) 0.29 (0.13) 0.16 (0.07)

1.4 (0.63) 1.5 (0.65) 1.3 (0.60) 1.2 (0.54) 0.87 (0.39) 1.4 (0.63)
0.27 (0.12) 0.31 (0.14) 0.11 (0.05) 0.27 (O.X2) 0.43 (0.19)
0.16 (0.07) 0.20 (0.09) 0.09 (0.04)

0.85 (0.38) 3.1 (1.4) 3.6 (1.6) 3.2 (1.4) 2.0 (0.90) 3.4 (1.5)
0.11 (0.05) 0.38 (0.17) 0.27 (0.12) 0.07 (0.13) 0.27 (0.12) 0.27 (0.12)
0.07 (0.03) 0.27 (0.12) 0.13 (0.06) 0.07 (0.03) 0.07 (0.03)


0.49 (0.22) 0.85 (0.38)
0.22 (0.10) 0.22 (0.10)

2.2 (0.98) 4.6 (2.1) 7.1 (3.2) 6.0 (2.7) 2.9 (1.3) 2.6 (1.2)
0.65 (0.29) 0.20 (0.09) 0.54 (0.^4) 0.65 (0.29) 0.27 (0.12) 2.5 (1.1)


4.0 (1.8) 7.7 (3.4) 9.6 (4.3) 6.0 (2.7) 5.0 (2,2) 5.4 (2.4)
1.6 (0.70) 1.4 (0.63) 1.7 (0.77) 2.2 (0.99) 0.27 (0.12)






336

-------
K'LS INDEX  --  metric  tons per hectare per unit  of  erosion Index, R
LAND
CLASS and
SUBCLASS
I
lie
IIS
IIw
He
Ille
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIlIc
(Cons per acre per unit of erosion index, R)
LAND RESOURCE AREA
96 97 98 99 100 101
0.16 (0.07) 0.16 (0.07)
0.45 (0.20) 0.43 (0.19) 0.47 (0.21) 0.49 (0.22) 0.31 (0.14) 0.58 (0.26)
0.13 (0.06) 0.13 (0.06) 0.16 (0.07) 0.20 (0.09) 0.16 (0.07) 1.1 (0.49)
O.H (0.05) 0.16 (0.07) 0.22 (0.10)

1.3 (0.59) 0.92 (0.41) 1.3 (0.56) 1.5 (0.69) 1.2 (0.52) 2.1 (0.93)
0.31 (0.14) 0.38 (0.17) 0.31 (0.14) 0.11 (0.05) 0.31 (0.14) 0.20 (0.09)
0.18 (0.08)

1.4 (0.62) 0.65 (0.29) 2.7 (1.2) 3.1 (1.4) 1.6 (0.73)
0.27 (0.12) 0.27 (0.12) 2.5 (1.1) 0.27 (0.12) 0.20 (0.09) 0.65 (0.29)
0.07 (0.03) 0.07 (0.03) 0.34 (0.15)


0.13 (0.06)


2.6 (1.2) 1.7 (0.77) 2.2 (0.96) 2.5 (1.1) 6.2 (2.8) 6.9 (3.1)
0.65 (0.29) 0.58 (0.26) 0.25 (0.11) 0.27 (0.12) 1.6 (0.7) 0.25 (0.11)


4.3 (1.9) 4.1 (1.8) 3.3 (1.5) 4.7 (2.1) 9.3 (4.1) 10.1 (4.5)
1.9 (0.83) 0.45 (0.2) 0.99 (0.44) 0.65 (0.29) 8.8 (3.9) 0.65 (0.29






                            337

-------
K-LS INDEX  -- metric tons per hectare per  unit of erosion index. R
LAND
CLASS and
SUBCLASS
I
lie
IIs
IIw
lie
Ille
Ills
IIIw
lllc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIHc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
102 103 104 105 106 107
0.27 (0.12) 0.22 (0.10) 0.29 (0.13) 0.25 (0.11) 0.16 (0.07)
0.65 (0.29) 0.43 (0.19) 0.49 (0.22) 0.67 (0.30) 0.43 (0.19) 0.36 (0.16)
0.18 (0.08) 0.13 (0.06) 0.13 (0.06) 0.18 (0.08) 0.20 (0.09) 0.22 (0.10)


1.1 (0.51) 1.2 (0.54) 0.94 (0.42) 1.6 (0.70) 1.7 (0.74) 1.3 (0.58)
0.45 (0.20) 0.11 (0.05) 0.27 (0.12) 0.11 (0.05) 0.11 (0.05)
0.20 (0.09) 0.22 (0.10)

1.7 (0.77) 3.7 (1.7) 2.9 (1.3) 3.7 (1.7) 1.9 (0.84) 3.6 (1.6)
0.38 (0.17) 0.27 (0.12) 0.2 (0.09) 0.27 (0.12)
0.04 (0.02)


0.09 (0.04)


3.7 (1.6) 5.1 (2.3) 3.5 (1.6) 3.7 (1.7) 7.8 (3.5) 4.8 (2.2)
0.54 (0.24) 0.76 (0.34) 0.72 (0.32) 0.54 (0.24) 7.8 (3.5) 0.96 (0.43)


4.2 (1.9) 9.9 (4.4) 5.1 (2.3) 5.2 (2.3) 12.9 (5.8) 15.0 (6.9)
2.2 (0.96) 0.76 (0.34) 2.9 (1.3) 17.0 (7.8) 12.9 (5.8) 0.96 (0.43)






                          338

-------
K'LS INDEX —  metric  tons j>er hectare per unit of erosion index,  R
LAND
CLASS and
SUBCLASS
I
lie
Us
Ilw
lie
Hie
ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
Vila
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
(tons per acre per unit of erosion Index, R)
LAND RESOURCE AREA
108 109 110 111 112 113
0.09 (0.04)
0.43 (0.19) 0.43 (0.19) 0.43 (0.19) 0.43 (0.19) 0.36 (0.16) 0.25 (0.11)
0.09 (0.04) 0.29 (0.13)
0.09 (0.04)

1.2 (0.54) 1.4 (0.63) 0.87 (0.39) 1.2 (0.52) 0.92 (0.41) 0.92 (0.41)
0.22 (0.10) 0.04 (0.02)


1.7 (0.77) 1.4 (0.63) 4.1 (1.8) 3.1 (1.4) 0.92 (0.41) 1.5 (0.67)
0.16 (0.07) 0.27 (0.12) 0.34 (0.15)






6.0 (2.7) 2.6 (1.2) 6.9 (3.1) 4.5 (2.0) 2.0 (0.89) 5.8 (2.6)
0.83 (0.37) 0.34 (0.15) 0.49 (0.22) 2.6 (1.2)


1.7 (0.77) 6.0 (2.7) 7.9 (3.5) 13.0 (5.8) 11.7 (5.2)
0.72 (0.32) 6.6 (2.9) 0.72 (0.32) 0.20 (0.09) 0.78 (0.35)






                         339

-------
K-LS INDEX --  metric  tons per hectare per unit of erosion  index. R
LAND
CLASS and
SUBCLASS
I
He
Us
IIw
He
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
114 (Ohio) 114 (III.) US 116 117 118
0.15 (0.07) 0.16 (0.07)
0.43 (0.19) 0.58 (0.26) 0.36 (0.16) 0.43 (0.19) 0.22 (0.10) 0.27 (0.12)
0.34 (0.15) 0.22 (0.10)


1.4 (0.63) 1.1 (0.47) 1.2 (0.54) 0.43 (0.19) 0.76 (0.34) 0.92 (0.41)

0.20 (0.09) 0.20 (0.09)

3.1 (1.4) 2.2 (0.96) 0.72 (0.32) 0.49 (0.22) 1.7 (0.76)
0.83 (0.37) 0.72 (0.32) 0.54 (0.24)






6.0 (2.7) 6.1 (2.7) 2.6 (1.2) 4.3 (1.9) 5.3 (2.4) 3.0 (1,3)
3.1 (1.4) 2.5 (1.1) 1.8 (0.79) 1.8 (0.79) 1.1 (0.48) 1.1 (0.48)


18.0 (7.8) 3.1 (1.4) 10.0 (4.6) 7.6 (3.4) 14.1 (6.3) 9.3 (4.1)
21.0 (9.5) 4.6 (2.1) 6.6 (2.9) 2.7 (1.2) 14.1 (6.3) 9.3 (4.1)






                          340

-------
K-LS INDEX —  metric  tons per hectare per unit of erosion  indext
LAND
CLASS and
SUBCLASS
I
He
Us
IIw
He
Ille
His
IIIw
IIIC
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
VIIIs
VIIIw
VIIIc
(tons per acre per unit of erosion Index, R)
»
LAND RESOURCE AREA
125 126 127 128 (Va.) 128 (Ca.) 129
0.07 (0.03)
0.45 (0.20) 0.72 (0.32) 0.69 (0.31) 0.65 (0.29) 0.29 (0.13) 0.38 (0.17)
0.18 (0.08) 0.18 (0.08)
0.16 (0.07) 0.09 (0.04)

1.2 (0.53) 2.4 (1.1) 2.1 (0.95) 5.0 (2.2) 1.6 (0.70) 0.63 (0.28)
0.31 (0.14) 0.49 (0.22)
0.87 (0.39)

2.9 (1.3) 5.0 (2.2) 5.0 (2..2) 5.2 (2.3) 4.9 (2.2) 1.4 (0.62)
0.45 (0.20) 0.99 (0.44) 0.20 (0.09) 3.2 (1.4) 0.22 (0.10)
0.87 (0.39)


0.11 (0.05)


6.1 (2.7) 10.0 (4.5) 8.2 (3.6) 11.5 (5.2) 7.7 (3.4) 2.6 (1.2)
6.0 (2.7) 2.1 (0.93) 1.2 (0.54) 5.7 (2.5)
1.9 (0.86)

8.9 (4.0) 18.2 (8.1) 10.8 (4.8) 18.2 (8.1) 4.9 (2.2) 2.6 (1.2)
10.5 (4.7) 18.2 (8.1) 13.0 (6.0) 10.8 (4.8) 5.8 (2.6) 4.8 (2.1)






                          341

-------
  LAND
CLASS and
SUBCLASS

    I

   lie

   Us

   IIw

   He

  Ille

  Ills

  IIIw

  IIIc

   IVe

   IVs

   IVw

   IVc

    Ve

    Vs

    Vw

    Vc

   Vie

   Vis

   VIw

   Vic

  Vile

  VIIs

  VIIw

  VIIc

 VHIe

 VIIIs

 VIIlw

 VIlIc
                      K'LS  INDEX  -- metric tons per hectare per unit of erosion Index. R
                                 (tons  per acre per unit of erosion index. R)
    130
                     131
0.18  (0.08)

0.76  (0.34)
0.25  (0.11)
              LAND RESOURCE AREA
                     132
0.25  (0.11)

0.38  (0.17)
1.0   (0.45)
0.22  (0.10)
0.96  (0.43)
3.1   (1.4)
                 1.8   (0.82)
6.5   (2.9)

0.81  (0.36)
4.1   (1.9)
6.5   (2.9)

9.9   (4.4)
8.0   (3.6)

7.0   (3.1)
 133 (N.C.)

0.18  (0.08)

0.49  (0.22)

0.22  (0.10)

0.18  (0.08)




0.76  (0.34)

0.27  (0.12)
                 1.4   (0.62)

                 0.27  (0.12)
 133 (Ala.)

0.13  (0.06)

0.25  (0.11)

0.11  (0.05)
0.56  (0.25)

0.20  (0.09)

0.13  (0.06)



1.1   (0.48)

0.43  (0.19)
                                  4.9    (2.2)

                                  2.2    (0.99)
 133 (La.)




O.//  (0.10)

0.18  (0.08)
0.34  (0.15)

0.27  (0.12)
                                  0.49  (0.22)
                                  1.7    (0.78)      3.09   (1.4)

                                  0.65   (0.29)
                                                                                    0.49  (0.22)
                                               342

-------
K-LS INDEX --  metric  tons per hectare per unit of erosion  index,  ft
LAND
CLASS and
SUBCLASS
I
lie
Us
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
VIIw
VIIc
VHIe
Villa
VIIIw
VIIIc
(Cons per acre per unit of erosion index, R)
LAND RESOURCE AREA
135 136 (N.C.) 136 (Ga.) 137 138 139
0.09 (0.04) 0.13 (0.06)
0.29 (0.13) 0.49 (0.22) 0.65 (0.29) 0.49 (0.22) 0.22 (0.10) 0.58 (0.26)
0.16 (0.07) 0.43 (0.19) 0.40 (0.18) 0.09 (0.04) 0.16 (0.07)
0.16 (0.07) 0.09 (0.04)

0.43 (0.19) 1.14 (0.51) 0.65 (0.29) 0.92 (0.41) 0.25 (0.11) 1.1 (0.48)
0.25 (0.11) 0.07 (0.03) 0.11 (0.05) 0.27 (0.12) 0.20 (0.09)
0.22 (0.10)

0.22 (0.10) 1.9 (0.83) 1.3 (0.58) 1.7 (0.78) 0.45 (0.20) 1.9 (0.83)
0.83 (0.37) 0.16 (0.07) 0.20 (0.09)






1.2 (0.54) 4.1 (1.8) 2.4 (1.1) 3.4 (1.5) 5.1 (2.3)
1.2 (0.53) 1.0 (0.46) 0.83 (0.37)


5.0 (2.2) 6.3 (2.8) 4.9 (2.2) 7.0 (3.1)
4.8 (2.2) 0.4 (0.18) 1.4 (0.61) 9.8 (4.4)






                          343

-------
K-LS INDEX -- metric tons per hectare per unit of erosion  index. R
LAND
CLASS and
SUBCLASS
I
lie
I Is
IIw
lie
Hie
Ills
IIIw
IIIc
IVe
IVs
IVw
IVc
Ve
Vs
Vw
Vc
Vie
Vis
VIw
Vic
Vile
VIIs
Vllw
VIIc
VHIe
VIIls
VIIIw
VlIIc
(tons per acre per unit of erosion index, R)
LAND RESOURCE AREA
140 141 142 143 144 145

0.40 (0.18) 0.43 (0.19) 0.76 (0.34) 0.40 (0.18) 0.31 (0.14) 0.58 (0.26)
0.13 (0.06) 0.16 (0.07) 0.16 (0.07) 0.38 (0.17) 0.22 (0.10) 0.20 (0.09)
0.13 (0.06) 0.16 (0.07) 0.22 (0.10) 0.38 (0.17) 0.38 (0.17) 0.31 (0.14)

1.7 (0.74) 1.1 (0.49) 2.4 (1.1) 0.85 (0.38) 1.2 (0.52) 1.3 (0.58)
O.-iO (0.09) 0.20 (0.09) 0.27 (0.12) 0.27 (0.12) 0.22 (0.10) 0.54 (0.24)
0.54 (0.24) 0.40 (0.18) 0.45 (0.20) 0.11 (0.05) 0.13 (0.06) 0.13 (0.06)

2.9 (1.3) 2.4 (1.1) 5.6 (2.5) 2.2 (1.0) 4.2 (1.9) 0.43 (0.19)
1.4 (0.37) 0.83 (0.37) 0.83 (0.37) 0.72 (0.32) 0.83 (0.37) 0.38 (0.17)
0.22 (0.10) 0.25 (0.11) 0.25 (0.11) 0.45 (0.20)


0.13 (0.06) 0.18 (0.08)


4.3 (1.9) 5.2 (2.3) 6.0 (2.7) 3.4 (1.5) 2.6 (1.2) 1.8 (0.82)
1.9 (0.84) 0.56 (0.25) 0.81 (0.36) 0.85 (0.38) 0.83 (0.37) 0.92 (0.41)
1.6 (0.70)

6.4 (2.8) 5.4 (2.4) 5.4 (2.4) 11.0 (4.7)
6.4 (2.8) 0.18 (0.08) 3.8 (1.7) 0.38 (0.17) 3.2 (1.4) 5.8 (2.6)



5.8 (2.6)


                         344

-------
                      K-LS INDEX — metric tons per hectare per unit of erosion index.  R
                                 (tons per acre per unit of erosion  index.  R)
  LAMP
CLASS and
SUBCLASS

    I

   lie

   Us

   IIw

   lie

  Hie

  Ills

  IIIw

  IIIc

   IVe

   IVs

   IVw

   IVc

    Ve

    Vs

    Vw

    Vc

   Vie

   Vis

   VIw

   Vic

  Vile

  VIIs

  VI Iw

  Vile

 Vine

 vnis

 VIIIw

 VIIIc
                                              LAND RESOURCE AREA
    146
0.56  (0.25)

0.38  (0.17)

0.38  (0.17)

0.25  (0.11)

0.85  (0.38)

0.27  (0.12)

0.38  (0.17)




2.2   (1.0)

0.72  (0.32)

0.25  (0.11)
2.9   (1.3)

2.2   (1.0)
                     147
                                      148
                                                       149
                                                                        150
 0.81  (0.36)

 0.22  (0.10)
 1.9   (0.86)

 0.76  (0.34)
 3.9   (1.7)
 0.38  (0.17)
11.0   (5.0)

 6.0   (2.7)
                16.0  (7.2)

2.8   (1.2)      11.0  (5.1)
0.65  (0.29)
1.6  (0.70)
1.4   (0.61)

0.83  (0.37)
2.7   (1.2)

2.8   (1.3)
                  8.9    (4.0)

                  6.4    (2.9)
1.4   (0.62)

0.22  (0.10)

0.20  (0.09)
4.5   (2.0)
                 6.0   (2.7)

                 0.34  (0.15)
                 0.27  (0.12)
                                                                                         151
0.58  (0.26)

0.27  (0.12)

0.34  (0.15)
1.1   (0.48)     0.38  (0.17)     0.38  (0.17)

0.27  (0.12)     0.16  (0.07)     0.07  (0.03)
                 1.1   (0.48)
                 0.16  (0.07)
7.1   (3.2)
                                           345

-------
                      (C-LS INDEX -- metric  tons  per hectare  per  unit  of  erosion  Index.  R
                                 (tons  per  acre  per unit  of  erosion index,  R)
  LAND
CLASS and
SUBCLASS

    I

    lie

    Us

    IIw

    lie

   Hie

   Ills

   II Iw

   IIIc

    IVe

    IVs

    IVw

    IVc

     Ve

     Vs

     Vw

     Vc

    Vie

    Vis

    VIw

    Vic

   Vile

   VIIs

   VIIw

   VIIc

  Vine

  VIIIs

  VI IIw

  VIIIc
    152
2.2   (0.98)
0.07  (0.03)
0.20  (0.09)
0.04  (0.02)
                                              LAND RESOURCE  ARFA
 153 (S.C.)

0.13  (0.06)

0.43  (0.19)

0.31  (0.14)
 153 (N.C.)

0.18  (0.08)

0.43  (0.19)

0.83  (0.37)

0.18  (0.08)
                                                       154
0.04  (0.02)

0.09  (0.04)




0.13  (0.06)
0.31  (0.14)
1.5   (0.67)

0.38  (0.17)
                                  2.0   (0.89)
                                  0.09   (0.04)
0.31  (0.14)
                                  0.45  (0.20)
                                  1.7   (0.75)
                                                                        155
2.5   (1.1)      1.3   (0.60)     1.5   (0.67)      0.34   (0.15)

0.20  (0.09)     0.27  (0.12)     0.31  (0.14)      0.34   (0.15)      0.27  (0.12)
0.13  (0.06)
                                  0.07  (0.03)
                                           346

-------
                      APPENDIX E
ESTIMATED SOIL LOSSES FROM SELECTED CROPPING SYSTEMS IN
          AREAS WEST OF THE CONTINENTAL DIVIDE
                 From 1972 SCS Survey
                            347

-------
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    -------
                                   APPENDIX F
          REPRODUCTION OF "PESTICIDE RESIDUE LEVELS IN SOILS.  FY1969-
                      NATIONAL SOILS MONITORING PROGRAM*
    *  Published in Pesticides Monitoring Journal,  6(3):194-228,  December 1972.
                                          389
    

    -------
                             PESTICIDES  IN   SOIL
             Pesticide Residue Levels in Soils, FY1969—National Soils Monitoring Program
                                        G. B. Wiersma1, H. Tai2, and P. F. Sand'
                        ABSTRACT
    
    This report is a summary of the FY 1969 results of the Na-
    tional Soils Monitoring Program,  an  integral  part of  the
    National  Pesticide  Monitoring  Program  (NPMP).  Pesti-
    cide residues in cropland soil for 43 Stales and noncropland
    soil for !J States are reported. Tables for each State give
    the number of samples  collected, arithmetic  means and
    ranges of residue levels Selected, and  the percent of sites
    with  detectable residues. In addition, for selected pesticides
    and various States and State groupings, a frequency distri-
    bution of pesticide residues was determined. Use records for
    FY 1969 are  given by the pesticides  used, the percent  of
    sites  treated, the average application rates, and the average
    amounts applied per site.  Comparisons are made between
    residue levels in different land-use areas.
                        Introduction
    
    The National Soils Monitoring Program is an integral
    part  of  the National  Pesticide  Monitoring  Program
    (NPMP),  which was initiated as a result of a recom-
    mendation made by  the  President's  Science  Advisory
    Committee in its report of 1964 entitled "Use of Pesti-
    cides" that  the  appropriate Federal agencies  "develop
    a continuing network to monitor residue levels in  air,
    water, soil, man,  wildlife,  and fish."  The NPMP as
    originally designed was described in  the first issue of
    the  Pesticides Monitoring Journal (1),  and a revised
    description to reflect certain program realignments  and
     1 Pesticides Regulation Division, Office of Pesticide Programs, Environ-
      mental Protection Agency, Washington. D. C. 20460.
     1 Pesticides Regulation Division, Office of Pesticide Programs, Environ-
      mental Protection Agency, Mississippi Te»I Facility, Bay St. Louis.
      Miss. 39520.
     * Plant Protection and Quarantine Programs. Animal and Plant Health
      Inspection Service. U.S. Department of Agriculture, Hyatuvillc, Md.
      20782.
                                                     390
    other changes was published  in the June 1971  issue of
    this Journal (2).
    
    The objectives of the NPMP are to determine levels and
    trends of pesticides  in the various components of the
    environment (2). The establishment of baseline or back-
    ground levels  of pesticide residues through  the  NPMP
    will  provide  a basis for comparison  of subsequently
    identified  pesticide  residue levels in an  environmental
    component.
    
    The  Panel on Pesticides Monitoring  of the Working
    Group on Pesticides (2) listed five bases  for concern to
    be used in evaluating  pesticide  residue levels in the
    various environmental components. They are:
      (1)  any concentration of a pesticide known to be
           potentially harmful;
      (2)  increasing trends;
      (3)  exceeding standards;
      (4)  recognition of adverse effects on humans; and
      (5)  erratic variability (a statistically oriented observa-
           tion that is potentially common to each  stratum
           sampled).
    
    The results of this  study serve to establish a baseline
    of pesticide residues in  cropland  and noncropland soils
    at a particular point in lime (FY 1969). The present data
    and  all future data  will be evaluated using applicable
    criteria included  in  the  five  bases of  concern outlined
    above.
    
             Sampling Procedures and Methods
    In general, sampling techniques  involved in this  study
    were the same as those described by Wiersma,  Sand,
    and Cox (.?).
    
                         PESTICIDES MONITORING JOURNAL
    

    -------
    In FY 1969, cropland soil was sampled in every State
    except Alaska, Hawaii,  Kansas, Minnesota,  Montana,
    Oregon,  and Texas.  Noncropland  was sampled in 11
    States—Arizona,  Georgia,  Idaho, Iowa, Maine, Mary-
    land,  Nebraska,  Virginia,  Washington, West Virginia,
    and Wyoming. Samples collected in FY 1969 included
    both  soil and  mature  crops and/or those  ready  for
    harvest;  however, results of crop analyses  are not pub-
    lished in this report.
    
                   A nalytical Procedures
    ORGANOCHLORINE AND ORGANOPHOSPHOROUS
    COMPOUNDS
    A subsample  of  soil weighing 300  g, wet  weight, was
    placed in a 2-qt fruit jar with  600 ml of  3:1  hexane-
    isopropanol solvent. The jars were sealed  and rotated
    for 4  hours.  After rotation, the soil  was allowed  to
    settle,  and 200  ml of the extract solution  was filtered
    into a 500-ml  separatory funnel. Isorpropanol  was re-
    moved with two washings  of distilled water,  and  the
    remaining solution was  then  filtered through a funnel
    containing glass  wool  and  anhydrous sodium  sulfate
    (Na.,SOj). Further cleanup  was  normally  not required
    before analysis.
    
    Gas-Liquid Chromalograpliy
    
    Analyses  were  performed   on  gas  chromatographs
    equipped with tritium foil electron affinity detectors for
    organochlorine  compounds  and  thermionic  or  flame
    photometric  detectors  for  organophosphorous  com-
    pounds.  A dual-column  system employing  polar and
    nonpolar columns  was utilized  to identify  and  confirm
    pesticides. Instrument parameters  were as follows:
    
      Columns:     Glass, 183 cm long by 6 mm, o d , and 4 mm, j,d ,
                  with one of (he following packings:
                  3% DC-200 on 100/120 mesh Gas Chrom Q or 9%
                  QF-1 on 100/120 mesh Gas Chrom Q
      Carrier gas:   5% methane-argon at a flow rate of 80 ml/min
      Temperatures:  Detector 200' C
                  Injection port 250° C
                  Column QF-I 166° C
                  Column DC-200  I70'-I75° C
    When necessary, confirmation of residues  was made  by
    thin layer chromatography or p-values. The lower  limit
    of detection was 0.01 ppm.  The average recovery rate
    for all pesticides was 100% (with  a ±\Q% error); the
    data  were corrected for recovery and  also adjusted to
    a dry-weight basis by determining  the moisture content
    on a separate portion of each sample using the  oven
    drying method.
    
    ATRAZINE
    After a  4-hour SoxhJet extraction  of a 50-g  subsample
    of soil with 25 ml of water  and 300 ml  of  methanol,
    the sample extract  was transferred to a 1-liter  separatory
    funnel and 200 ml of water added. The sample extract
    was partitioned three times with a portion of  150 ml
    of freon  113 for each partitioning. The freon 113 frac-
    tions  were  combined  and concentrated  to incipient
    dryness.  The  sample  was  then dissolved  in  hexane,
    adjusted  to  a 5-ml  volume, and  injected into a  gas-
    liquid chromatograph.
    
    Gas-Liquid  Chromatography
    
    A thermionic  flame detector  with  rubidium  sulfate
    coating on a helix coil  was used. Instrument parameters
    were as follows:
    
      Column:         Glass, 183 cm lor.g by 6 mm, o.d., and 4 mm,
                     i.d., packed »ith 3K Versamid 900 on 100/120
                     mesh Gaj Chrom  Q
      Carrier gas:       Helium
      Detector fuel gas:  Oxygen (200-300 ml'mm);
                     Hydrogen (20-30 ml min)
      Temperatures:
    Detector 2<0' C
    Injection port 240° C
    Column 2-tO' C
    Confirmation  was  made  using a  DC-200  column  at
    180° C  and  a  Coulson  detector  (reductive mode)  at
    the following temperature settings: pyrolysis tube—850°
    C, transfer line—220C C. and block—220C C.
    
    The minimum detection  limit  was 0.01  ppm,  and re-
    covery was about 100%.
    
    2,4-D
    Analyses were made following  the  procedure developed
    by Woodham et at. (4). The analytical method involved
    a dicthyl ether extraction  of  acidified  soil, an  alkali
    wash to  remove interfering substances,  and an esteri-
    fication procedure  using  109c  boron trichloride  in  2-
    chloroethanol reagent. The  2-chloroethyl  ester of  2,4-D
    was  then analyzed by gas chromatography. The minimum
    detection limit was 0.01 ppm, and  the average reco\ery
    was 85%. Results were corrected for percent recover)'.
    
    ARSENIC
    Arsenic  was determined by atomic absorption  spectro-
    photometry.  The  soil sample  was first  extracted with
    9.6N hydrochloric acid (HCL)  and reduced  to trivalent
    arsenic with  stannous chloride. The trivalent arsenic
    was partitioned from HCL solution to  benzene, then
    further partitioned  into water for the absorption  meas-
    urement.  A  Perkin-Elmer  Model  303 instrument was
    used, and  absorbance  was  measured with  an arsenic
    lamp at  1972 A with argon as an aspirant to an air-
    hydrogen flame. The minimum detection limit  was 0.1
    ppm, and the recovery value for arsenic  averaged  709o.
    Results were corrected for percent  recovery.
    
                           Results
    The data in this report are for soils only (both  crop-
    land and noncropland) and  include results for all  States
     VOL. 6, No. 3, DECEMBER 1972
                                                       391
    

    -------
    sampled in the study. Caution should be exercised when
    interpreting the arithmetic means presented in the tables,
    because pesticide residue data are not normally distrib-
    uted,  and  the  arithmetic  means for pesticide residues
    tend to be greater than the corresponding median. There-
    fore,  they cannot be considered an indication  of the
    central tendency of the data. Information accompanying
    the arithmetic means in this report such as the percent
    occurrence, range of detected residues, and number of
    observations can aid in evaluating the arithmetic mean.
    
    RESIDUES—ALL STATES
    Table  1  presents a summary  of pesticide residues in
    cropland soils for all 43 States sampled. Percent occur-
                      TABLE 1.—Summary of pesticide residues in cropland soil from 43 States—FY 1969
    COMPOUND
    Aldrin
    Arsenic
    Atrazine
    Caibophenolhjon
    Chlordane
    2.4-D
    DCPA (DacthalS)
    o.p'-DDE
    p,p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    DEF
    Diazinon
    Dicofol
    Dieldrin
    Eodosulfan (!)
    Endosulfan (II)
    Endosulfajn sulfate
    Endrin
    Endrio aldehyde
    Endria ketone
    Ethion
    Heptachlor
    Heptachlor epoxide
    Isodrin
    Lindane
    Malathion
    Methoxychlor
    Ethyl paraLhion
    PCNB
    o.p'-TDE
    P.P--TDE
    Toxaphene
    Trifluralin
    N CM HER OF
    SAMPLES
    ANALYZED '
    1,729
    1,726
    199
    66
    1,729
    188
    1,729
    1,729
    1.729
    1,729
    1.729
    1.729
    1,729
    66
    1,729
    1.729
    1.729
    1.729
    1,729
    1.729
    1,729
    1.729
    66
    1,729
    1.729
    1.729
    1,729
    66
    1.729
    66
    1.729
    1.729
    1,729
    1.729
    1.729
    NUMBER OF
    POSITIVE
    SAMPLES
    189
    1.713
    28
    1
    151
    3
    1
    79
    429
    243
    384
    4SI
    1
    2
    9
    480
    5
    9
    II
    39
    1
    9
    1
    68
    139
    11
    15
    2
    1
    7
    1
    49
    265
    73
    60
    PERCENT
    POSITIVE
    SITES5
    10.9
    99.3
    14.1
    1.5
    S.7
    1.6
    O.I
    4.6
    24.8
    14.1
    22.2
    26.1
    O.I
    3.0
    0.5
    27.8
    0.3
    0.5
    0.6
    2.3
    0.1
    0.5
    1.5
    3.9
    8.0
    0.6
    0.9
    3.0
    O.I
    10.6
    0.1
    2.8
    15.1
    4.2
    3.5
    MEAN RESIDUE
    LEVEL
    (PPM)
    0.02
    6.43
    0.01
    <0.01
    0.04
    
    -------
    rence of residues is based on the number of sites with
    residues greater than or equal to the sensitivity limit.
    
    The data for atrazine, 2,4-D, and the organophosphates
    arc not truly comparable with those  determined  for the
    organochlorines or arsenic, because analyses for atrazine
    and 2,4-D were made only  when use records indicated
    that they had been applied—199 and 188 times, respec-
    tively, and  analyses  for  organophosphates  were per-
    formed on only 66 of the 1,729 samples.
    
    Elemental arsenic residues were found most frequently,
    with 99.3%  of the  sites having detectable  residues and
    a mean level of 6.4 ppm. It is probable that most of this
    arsenic  was  from natural  sources,  although agricultural
    sources cannot be ruled out at this time.
    
    The  most widely  distributed organochlorine pesticide
    was dieldnn, with 27.8%  of the sites having detectable
    residues, followed by DDTR residues (a compilation of
    all members of the DDT group) found at 26.1% of the
    sites;  aldrin, found  at 10.9%; and chlordane, found at
    8.7%. DDTR had the highest  mean residue level, with
    0.31 ppm found in cropland soils. With the exception
    of individual members  of the  DDT group, the other
    organochlorines  had  average  residues  ranging  from
    <0.01 to 0.07 ppm.
    
    Based  on the  66  samples   analyzed  for  organophos-
    phates, ethyl parathion was detected  10.6% of the  time,
    with a mean residue level of 0.06 ppm. Malathion and
    diazinon were  each detected 3.0%  of  the time,  with
    mean residue levels of 0.01 and <0.01 ppm, respectively.
    In  the  188  samples  analyzed  for 2,4-D and other
    chlorophenoxy  herbicides, 2,4-D  was the only one de-
    tected;  2.4-D  was found in  1.6%  of  188  samples
    analyzed,  with  a  mean  residue  level  of  <0.01 ppm.
    Atrazine was detected in  14.1%  of the  199 samples
    analyzed,  with  a  mean residue level of 0.01 ppm—the
    highest  mean residue  of the herbicides detected.  Tri-
    fluralin  was detected in 3.5%  of the 1,729 samples, with
    a mean  residue  level of <0.01 ppm.
    
    
    The residues found in  noncropland soils  for  the 11
    States sampled are presented  in  Table 2. The mean
    arsenic  residue  level was 5.0 ppm, occurring  in 98.5%
    of the samples. DDTR was  detected in 16.1% of the
    noncropland  soils at levels ranging  from 0.01  to  0.62
    ppm, with a  mean level  of 0.01  ppm. With the excep-
    tion of  members of the  DDT group,  dieldrin was the
    most widely distributed pesticide, occurring in 4.0% of
    the samples, with residues ranging between 0.01  to  0.09
    ppm and a mean residue  level of <0.01 ppm.
    
    RESIDUES—INDIVIDUAL STATES
    The pesticide  residue  summaries for  cropland by in-
    dividual States  are given  in Table 3. and similar results
    are shown for noncropland  in Table 4.  It  would be
    impractical to  attempt to comment on  the results for
    each  State: therefore,  in order  to facilitate summariz-
    ing the  data.  Figs. 1. 2. and  3 are presented.  These are
    for three  of  the  most- commonly occurring residues—
    arsenic,  DDTR. and dieldrin. Means for each pesiicide
    in each  State were calculated, and distribution of  these
    averages are indicated on the corresponding Figures.
                    TABLE 2.—Summary of pesticide residues in noncropland soil from 11 Slates—FY 1969
    COMPOUND
    Aldrin
    Arsenic
    Chlordane
    o.p'-DDE
    p,p'-DDE
    o.p'-DDT
    P.p'-DDT
    DDTR
    Dicofol
    Dieldrin
    Heptachlor epoxide
    P p'-TDE
    Toxaphenc
    NUMBER OF
    SAMPLES
    ANALYZED '
    199
    198
    199
    199
    199
    199
    199
    199
    199
    199
    199
    199
    199
    NUMBER OF
    PosnivE
    SAMPLES
    ,
    195
    3
    1
    27
    7
    18
    32
    2
    8
    2
    6
    I
    PEPCEVT
    PosrmE
    Sins-
    0.5
    98.5
    1.5
    0.5
    13.6
    3.5
    9.1
    16.1
    1.0
    4.0
    1.0
    3.0
    0.5
    Mt»s RESIDUE
    LEVEL
    (PPM)
    <0.01
    5.01
    <0.01
    
    -------
                                                                                                KEY
    
                                                                                         |      I   No  Sample
    
                                                                                                  01  ppm
    
                                                                                                  .01  ppm to
                                                                                                  03  ppm
    
                                                                                                  03  ppm to
                                                                                                  .06  ppm
    
                                                                                                J*.06  ppm
                                      FIGURE I.—Arsenic residues in cropland soil
    The class  intervals  for  the keys accompanying  each
    Figure were obtained in  the  following  manner: The
    range of residues for the Nation was obtained, and the
    highest value was converted to a logarithm. This value
    was then divided by the number of desired classes. The
    resulting intervals were added to obtain the class bound-
    aries  which, in turn, were converted to  the  untrans-
    formed  dimensions.  Essentially, this took advantage of
    the fact that most residue data are logarithmically distrib-
    uted.
    
    Distribution of arsenic residues across the United States
    is presented in Fig.  1. The highest residue levels were
    found in the New England States  (Connecticut, Maine,
    Massachusetts,   New Hampshire,  Rhode  Island,  and
    Vermont),   Arkansas, Kentucky,  New   York,  North
    Dakota, Ohio, and Pennsylvania; these individual States
    and the New  England  States had  mean  residues of
    arsenic >8.4 ppm. The remaining  residues were distrib-
    uted  primarily  in  the 2.0  to 8.4 ppm  range,  with
    Wyoming and Florida having less  than 2.0 ppm. Those
    States left blank were not sampled.
    
    The distribution of DDT residues  (DDTR) is shown in
    Fig. 2. Once again, the key indicates the range of residues
    for each of the class intervals. A  similar map  for diel-
    drin residues is presented in Fig. 3.
    The mean residue levels, the percent positive  sites, and
    the range of residue  levels  for the  12 States with the
    highest arsenic residues are shown in Table 5.
    
    Residue data for  the five States with the highest DDTR
    residues  are presented in Table 6. Although  Michigan
    had a mean residue of 2.09 ppm and a range of 0.01  to
    78.36 ppm,  only  23.5%  of  the samples had detectable
    residues, indicating that  the residues  were not widely
    distributed. By contrast, Mississippi had a mean residue
    of 2.06 ppm with 89.7% of its sites having detectable
    residues  and a narrower range (0.03  to 13.14 ppm). Al-
    though the range was narrower, pesticide  residues were
    more widely distributed in Mississippi than in Michigan.
    
    The seven States wth the highest dieldrin residues are
    listed  in  Table 7. The highest mean residue level, 0.11
    ppm, was found  in Illinois, with 61.3% of the sites hav-
    ing detectable residues.  In general, the other six States
    tended to have  mean residues approximating one an-
    other, 0.06, 0.07, or 0.08 ppm.
    
    PESTICIDE USE RECORDS
    When soil samples were collected, an attempt was made
    to determine what pesticides had  been used on the sites
    for the  year of sampling. The  summary tables for the
    use recoids  show the percent of times a  pesticide was
                                                       394
                                                                                 PESTICIDES MONITORING JOURNAL
    

    -------
    situations where  there  were  too few  observations to
    calculate a reliable distribution.  Space did not permit
    printing tables showing distribution of pesticide residues
    for percentiles other than the fiftieth.
    
    CROPPING REGIONS ANALYSIS
    The data were grouped by counties into various crop-
    ping regions, and these are shown in Tables 16 and  17.
    The boundaries for the various  cropping areas  were
    based on a major  land-use map  of the  United  States
    compiled  by  F. J.  Marschner  of  the  U.S. Department
    of Agriculture, Bureau of Agricultural Economics, 1950.
    No effort was made to make a land-use division within
    counties. This resulted in a good definition of the larger
    land-use areas such as the corn belt and cotton-growing
    areas. The land in  the United  States was  grouped  into
    several  major  land-use  areas—corn,  cotton, general
    farming, hay, small grain, vegetables, and fruit. In some
    cases, two areas overlapped. Irrigated  land was  deter-
    mined from information obtained  at the time of sample
    collection in this study.
    
    It  is of interest to  make a  few individual comparisons
    between the cropping regions and the  national means.
    For example,  note  that in. the corn region,  aldrin  oc-
    curred 23.5%  of the time (Table 17) with a mean residue
    level  of 0.05  ppm (Table  16).  However, nationally,
    aldrin only occurred  10.9% of the  time with a mean
    level  of 0.02  ppm (Table  1),  an  indication of   the
    heavier  use of aldrin in the corn region. But, in the corn
    region, the mean residue level of DDTR was 0.14 ppm
    which is well below the national mean of 0.31 ppm.
    
    The vegetable and  fruit cropping region  had  the high-
    est level of DDTR, over two times higher than the next
    highest  cropping region and over  six times higher than
    the national mean for DDTR. This might result from a
    high use  of DDT  in various orchard  operations.  The
    next highest residue was found in the  cotton and vege-
    table region,  with  approximately equal  amounts   de-
    tected between them. The rest  of  the amounts  of DDT
    in  the  cotton  and general farming,  general  farming,
    hay and general farming, and irrigated land were simi-
    lar to one another. The two areas  with the least amount
    of DDTR  in the soil  were the corn and small grains
    cropping regions.
    
    The  corn,  vegetable, and vegetable  and fruit cropping
    regions  had the heaviest residues of dieldrin. Residues
    of dieldrin  in  the other cropping  regions  were either
    equal to or below  the mean  residues detected for all
    States (Table 1).
    
    The  cotton cropping region had the highest toxaphene
    residues. The cotton and general  farming and  general
    farming  cropping regions  had residue  levels of about
    half  those detected in the cotton cropping region.
    
    
                     A cknowledgment
    
    It  is not possible to list, by name,  all  the  people  who
    contributed to  this  study;  however, special  mention is
    made of the staff at the  Monitoring Laboratory,  Mis-
    sissippi  Test Facility,  Bay St. Louis, Miss., who proc-
    essed and analyzed the  samples for chemical residues and
    contributed immeasurably  to this  study and of the in-
    spectors  from  the  Animal  Plant  Health  Inspection
    Service  (APHIS) who  collected the samples.  Finally,
    recognition is due Dr.  Edwin Cox,  Biometrical Services
    Staff, USDA, for the sample allocation procedures and
    to Dr. Richard Daum  of the  Animal Plant Health In-
    spection Service, USDA, for the probit analyses.
      See Appendix for chemical name* and compounds discussed in this
      paper.
                      LITERATURE CITED
    (/) Pestic. Monit. L 1967.  1(1): 1-22.
    (2) Pestic. Monit. J. 7977. 5(1):35-71.
    (3) Wiersma, G. B., P. F. Sand, and E. L. Cox. 1971. A
       sampling design to determine pesticide residue levels in
       soils of the conterminous  United States. Pestic. Monit J.
       5(l):63-66.
    (4) Woodham, D.  W.,  W. G. Mitchell, C. D.  Loftis. and
       C. W. Collier. 1971. An  improved gas chromatographic
       method  for the analysis  of  2,4-D free acid in soil. J.
       Agric. Food Chem.  19(1):186-188.
    (5) Daum, R. L.,  1970.  Revision of two  computer programs
       for probit analysis. Bull. Entomol. Soc. Am.  16:10-15.
    VOL. 6, No. 3, DECEMBER 1972
                                                         395
    

    -------
                                                                                                        0  pp-i
                                       FIGURE 2.—DDTR residues in cropland soil
                                                                                                     KEY
    
                                                                                              I      j   No Sample
                                                                                                       2 0 ppm to
                                                                                                       4.1 ppm
    
                                                                                                       4.1 ppm lo
                                                                                                       8 •* ppm
    
                                                                                                      28.4 ppm
                                      FIGURF. 3.—Dii'ldrin residues in cropland soil
    VOL. 6, No. 3, DECEMBER 1972
                                                           396
    

    -------
    used, the  average  application rate expressed in pounds
    per  acre  of the  active ingredients, and the  average
    amount applied per site. The average amount  per site
    was  determined by dividing the total amount of active
    ingredient of a pesticide used by the total number of
    sites surveyed.
    
    Table 8 shows  130 different pesticides reported to have
    been used on cropland in  the year of sampling. Those
    most commonly used  were  atrazine,  captan,  2,4-D,
    malathion, and  methylmercury dicyandiamide.  Technical
    DDT was used on 3.44% of the sites, aldrin on 4.16%
    of the sites,  and dieldrin on 1.19% of the sites.
    
    On noncropland sites 2,4-D, malathion. and mirex were
    reported to  have been used  (Table  9).  However, these
    should not  be  considered  the only  pesticides used on
    noncropland sites. In  genera], records of treatment of
    noncropland sites are less  accurate than those kept for
    cropland.  The   breakdown  of  pesticide  usage by  in-
    dividual  States for cropland  and  noncropland  soils,
    respectively, are shown  in Tables 10 and 11. Of  the
    43 States with  cropland soil  analyzed,  use records for
    4  showed  no  pesticides used  on the sampling sites:
    Nevada (2  sites);  New Hampshire  (2  sites);  Vermont
    (5 sites);  and  Wyoming (17 sites).  Of  the  11 States
    with noncropland soil analyzed.  8 reported no pesticides
    used on the sampling  sites;  Arizona (43 sites); Iowa
    (7 sites); Maine (11 sites); Maryland (3  sites); Virginia
    (14 sites); Washington (11 sites); West Virginia (9 sites);
    and  Wyoming (37  sites).
    
    Because of the  number of States and  pesticides presented
    in Tables 10 and  11, it is  difficult to make all  possible
    comparisons between  the  use  patterns  indicated  and
    the detected residues shown  in  Tables 3  and  4. There-
    fore, comparisons have been restricted to those States
    having the highest  residues  as shown in Figs. 1, 2, and 3
    (arsenic, DDTR, and dieldrin, respectively).
    
    Table  12 compares those States  having  the highest
    arsenic  residues with  the  average amount applied  per
    site  and the percent of sites which  reported  using an
    arsenic  compound. The amount of  arsenic applied  did
    not seem  to be directly related  to the amount detected
    in the soil.  Arkansas,  Kentucky, North  Dakota,  and
    Ohio reportedly used  no  arsenic  compounds,  whereas
    New England,  New York, and Pennsylvania reported
    using sodium arsenite and lead arsenate. The application
    rates were below  the  detected  residue levels, and  the
    percent of times used was below  the percent of times
    residues were detected. It also must be considered  that
    the application rates v.ere  for the active  ingredients of
    sodium arsenite and  lead arsenate, and not for elemental
    arsenic  alone.  A fair  assumption  v,ould  be  that most
    arsenic  residues de:ected  in cropland  soils   probably
    resulted from natural levels of arsenic.
    A similar comparison for the five States with the high-
    est DDTR residues is found in Table 13. It is interesting
    to note that  use records for four of the States listed
    (California, Michigan,  Mississippi, and  South Carolina)
    indicate that the amount applied was less than the mean
    level detected in the  soil. Also, in all five States, the per-
    cent of sites positive  for DDTR was approximately three
    or four times greater than the percent of sites reportedly
    treated with DDT. Unlike arsenic, the residues of DDTR
    could only result from the use of DDT either in the year
    of sampling or in previous years.
    
    Table  14  lists the seven States  with the highest dieldrin
    residues. In most cases, the average amount of aldrin/
    dieldrin applied approximated the mean residue of diel-
    drin detected  in the soil, but  ihe percent of sites re-
    portedly  treated with dieldrin or aldrin  was always con-
    sideably  less   than  the percent of  sites with dieldrin
    residues.  This  wider distribution  of dieldrin  residues,
    when compared to use records for the year of sampling,
    probably  indicates residues from previous years.
    
    PESTICIDE FREQUENCY DISTRIBUTION
    The statistics discussed thus far,  namely  the mean, the
    range, and the percent of sites at which residues were
    detected, do not describe their distribution. To describe
    this distribution, probit analysis was  used.  The residue
    levels were ranked from lowest lo highest, accumulated,
    and the percentages  computed. The residues were trans-
    formed to logarithms, the percentages to probits,  and
    the relationship between the logarithms of the residues
    and  the  probits of  the  accumulated percentages  was
    calculated by regression analysis. The  computer program
    used was that of Daum. (5); the  theory and techniques
    as applied in the cited reference were modified slightly.
    
    The residue levels at  the fiftieth percentile point (median)
    for the individual pesticides in soil for each State along
    with  the upper and  lower  95%  fiducial limits  are
    presented in  Table  15.  For example,  in the  State of
    Alabama,  the  fiftieth  percentile  point  (median)  for
    arsenic was 4.09  ppm. Thus, 50% of the sites  had
    residues  less  than  4.09 ppm. The upper  and the lower
    fiducial limits of the residues establish the 95%  confi-
    dence  interval  about the residue  value for the  fiftieth
    percentile. It  should  be noted  that the mean  for  a
    particular State is not the same as the fiftieth percentile
    point  (median) from  the frequency  distribution.  For
    example, the  mean level of arsenic for Alabama was 6.1
    ppm,  while the frequency distribution indicated  4.09
    ppm for  Ihe fiftieth percentile point. This is an example
    of the fact that residue data are not normally distributed
    and the mean and median arc not identical.
    
    Not all pesticides are shown for all States. A cutoff point
    of six or more pairs of observations was used to eliminate
                                                     397
                         PESTICIDES MONITORING JOURNAL
    

    -------
    TABLE 3.—Pesticide residues in cropland soil from 43 States—FY 1969
    COMPOUND
    NUMBER op
    SAMPLES
    ANALYZED '
    NUMBER OF
    PosmvB
    SAMPLES
    PERCENT
    POSITIVE
    SITES'
    MEAN RESIDUE
    LEVEL
    ("M)
    RANGE OP
    DETECTED RESIDUES
    (I-PM)
                               ALABAMA
    Artenic
    Chlordanc
    o.p'-DDE
    P.P'-DDE
    o,p'-DDT
    p.p'-DDT
    DDTR
    Dieldrin
    Endrin
    Heptacnlor
    HepUchlor epoxide
    Lindane
    o.p'-TDE
    P.P'-TDE
    Toxaphene
    Trifluralin
    23
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    23
    3
    1
    19
    16
    20
    20
    5
    2
    2
    3
    2
    I
    13
    6
    7
    100.0
    13.6
    4.6
    86.4
    72.7
    90.9
    90.9
    22.7
    9.1
    9.1
    13.6
    9.1
    4.6
    59.1
    27.3
    31.8
    6.11
    0.04
    <0.01
    0.17
    0.09
    0.78
    1.13
    0.01
    <0.01
    <0.01
    <0.01
    
    -------
                    TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    
    COMPOUND
    
    NUMBER OP
    SAMPLES
    ANALYZED1
    NUMBER OF
    POSITIVE
    SAMPLES
    PERCENT
    PosmvE
    SITES'
    MEAS RESIDUE
    LEVEL
    (PPM)
    RANGE OF
    DETECTED RESIDLXS
    (PPM)
    CALIFORNIA— Continued
    p,p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    Diazinon
    Dicofol
    Dieldrin
    Endosulfan (I)
    EndosuUan (11)
    Endosulfan sulfatc
    Endrin
    Heptachlor epoxide
    Lindane
    Ethyl parathioa
    o,p'-TDE
    p.p'-TDE
    Toxaphene
    Trifluralin
    65
    65
    65
    65
    17
    65
    65
    65
    65
    65
    65
    65
    65
    17
    65
    65
    65
    65
    55
    32
    48
    55
    1
    6
    20
    1
    5
    5
    9
    8
    2
    1
    13
    40
    10
    7
    84.6
    49.2
    73.9
    84.6
    5.9
    9.2
    30.8
    1.5
    7.7
    7.7
    13.9
    123
    3.1
    5.S
    20.0
    61.5
    15.4
    10.8
    OJ7
    0.08
    0.54
    1.47
    
    -------
    TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    
    COMPOUND
    
    NUMBER OP
    SAMPLES
    ANALYZED >
    NUMBr.K OF
    POSITIVE
    SAMPLES
    PERCENT | MEAN Rts.'aLE
    PosiTivt i LE\EL
    SITES1
    (PPM)
    RANGE OF
    DETECTED RESIOIES
    (PPM)
    FLORIDA — Continued
    Uiazinon
    Dicldrin
    Endrin
    Endrin aldehyde
    Endrin ketone
    Ethion
    Heptachlor
    HeptacMor epoxide
    Ethyl parathion
    o.p'-TDE
    p,p'-TDE
    Toxaphene
    Tnfluralin
    5
    18
    18
    18
    18
    5
    18
    18
    5
    18
    IS
    18
    18
    
    Arsenic
    Chlordanc
    2,4-D
    o.p'-DDH
    p,p'-DDF.
    o.p'-DDT
    p,p'-DDT
    DDTR
    DEF
    Dicldrin
    Endrin
    Heplachlor epoxjde
    PCNB
    o.p'-TDE
    p,p'-TDE
    Toxaphene
    Tnfluralin
    29
    22
    3
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    22
    1
    7
    2
    1
    1
    1
    1
    3
    2
    1
    11
    2
    1
    200
    38.9
    11.1
    56
    5.6
    :oo
    5.6
    16.7
    400
    5 6
    61.1
    11.1
    5.6
    0.03
    0.08
    0.03
    ,p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    Heptachlor epoxide
    o.p'-TDE
    p,p'-TDE
    Trifluralin
    33
    33
    33
    33
    33
    33
    33
    33
    33
    33
    33
    32
    2
    8
    6
    8
    g
    3
    1
    3
    6
    2
    97.0
    6.1
    24.2
    182
    24.2
    242
    9.1
    3.0
    9 1
    18.2
    6.1
    3.22
    <0.01
    0X11
    0.01
    0.04
    0.07
    0.01
    
    -------
                    TABLE 3.—Pesticide residues in cropland soil from
    1 NlJMBtX OF
    COMTOUNO SAMPLES
    ANALYZED J
    
    NUMBIROF PERCENT
    POSITIVE POSITIVE
    SAMPLES Sms-
    I
    MEAN RESIDUE J RA-.OL OF
    L£VEL Dcirrna KIM;K i«.
    irrvi) ! (ffM)
    i
    ILLINOIS— Continued
    Dieldrin
    Heptachlor
    Heptachlor epoxide
    Isodrin
    o.p'-TDE
    p,p'-TDE
    Trifluralin
    142
    142
    142
    142
    142
    142
    142
    87
    31
    36
    2
    1
    5
    2
    61.3
    21.8
    25.4
    1.4
    0.7
    3 5
    1 4
    0 11
    003
    002
    <001
    <0.01
    
    0.06-0.1 4
    001-0.5?
    0.02-0 0«
    002
    003
    0.01
    0.03
    FOWA
    Aldrin
    Arsenic
    Atrazine
    Chlordane
    p,p'-DDE
    o,p'-DDT
    P,p'-DDT
    DDTR
    Dieldrin
    Heptachlor
    Heptachlor epoxide
    Isodrin
    o,p'-TDE
    P,p'-TDE
    Trifluralin
    151
    152
    48
    151
    151
    151
    151
    151
    151
    151
    151
    151
    151
    151
    151
    48
    152
    13
    32
    21
    6
    23
    25
    81
    14
    31
    2
    1
    8
    5
    •»!.«
    100.0
    27.1
    21.2
    139
    40
    15.2
    166
    53.6
    9.3
    20.5
    1J
    0.7
    5J
    3J
    0.04
    7.51
    0.05
    0.13
    0.01
    6-107.i5
    0.01-1.55
    O.V.-63Q
    001-0.18
    0.01-0.05
    0.01-0.34
    0.01-0.60
    0.01-0.42
    0.01-0.97
    0.01-0.33
    0.01-0.02
    0.10
    0.01-0.5O
    0.02-0.08
    KENTUCKY
    Aldrin
    Arsenic
    Chlordane
    o,p'-DDE
    p,p'-DDE
    o,p'-DDT
    P.P'-DDT
    DDTR
    Dieldrin
    Heptachlor
    Hcplachlor epoxide
    Isodrin
    o,p'-TDE
    p,p'-TDE
    31
    31
    31
    31
    31
    31
    31
    31
    31
    31
    31
    31
    31
    31
    8
    31
    4
    1
    5
    3
    6
    6
    17
    n
    1
    2
    1
    4
    25.8
    1000
    12.9
    3.2
    16.1
    9.7
    19.4
    19.4
    54.8
    65
    3.2
    6.5
    3.2
    129
    0.03
    8.41
    0.02
    <0.01
    0.01
    0.02
    0.04
    0.08
    0.06
    <0.01
    <0.01
    <0.01
    
    -------
    TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    
    COMPOUND
    
    NUMBFR OF
    SAMPLES
    ANALYZED '
    NUMBER OF
    POSITIVE
    SAMPLES
    PEBCEVT
    Posrm^
    Srres'
    MEAN RESIDUE
    LEA-EL
    (H-M)
    RASOE op
    DETECTED RtsroL^s
    (PPM)
    LOUISIANA
    Aldrin
    Arsenic
    Chlordanc
    o.p'-DDE
    p.p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    Diddrin
    Endrin
    Endrin ketone
    p,p'-TDE
    Toxaphene
    Thfluralin
    27
    27
    27
    27
    27
    27
    27
    27
    27
    27
    27
    27
    27
    27
    5
    26
    1
    2
    12
    9
    13
    13
    10
    1
    1
    9
    4
    '
    18.5
    963
    3.7
    7.4
    44.4
    33.3
    48.2
    48 .2
    37.0
    3.7
    3.7
    33J
    14.8
    3.7
    <0.01
    2.15
    <0.01
    <0.01
    0.19
    0.10
    0.61
    0.99
    0.02
    <0.0l
    <0.01
    0.08
    0.57
    
    -------
                    TABLE 3.—Pesticide residues in cropland soil from 43 Stales—FY 1969—Continued
    
    COMPOUND
    
    NUMBER op
    SAMPLES
    ANALYZED »
    NUMBER of
    POSITIVE
    SAMPLES
    P«CE«rr
    POSITIVE
    Smj =
    MEAN RESIDUE
    LEVEL
    (PPM)
    RANGE OF
    DrrtcTEO RESIDUES
    (PPM)
    MICHIGAN— Continued
    Dictdrin
    Endosulfan (I)
    Endosulfan sulfate
    Endrin
    p.p'-TDE
    51
    51
    51
    51
    51
    11
    2
    2
    I
    5
    21.6
    3.9
    3.9
    2.0
    9.8
    0.05
    0.01
    0.02
    <0.01
    0.65
    0.01-1.01
    O.OVfl.24
    0.25-0.94
    0.01
    0.02-31.43
    MISSISSIPPI
    Arsenic
    o.p'-DDE
    p,p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    Endrin
    Endrin ketone
    Lindane
    o,p'-TDE
    p.p'-TDE
    Toxaphenc
    Trifluralin
    30
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    30
    9
    26
    22
    26
    26
    10
    1
    1
    2
    2
    20
    14
    6
    100.0
    31.0
    89.7
    75.9
    89.7
    89.7
    34.5
    3.5
    3.5
    69
    6.9
    69.0
    48.3
    20.7
    5.70
    0.01
    OJl
    0.20
    1.36
    2.06
    0.01
    0.01
    <0.01
    <0.01
    0.03
    0.15
    0.78
    0.02
    1.10-16.90
    0.01-0.08
    0.01-1.43
    0.02-1 J5
    0.01-9.28
    0.03-13.14
    0.02-0.10
    0.19
    0.11
    0.01-0.04
    043-0.49
    0.01-0.81
    0.10-8.80
    0.02-0.25
    MISSOURI
    Aldrin
    Arsenic
    Chlordane
    p,p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    Endrin
    Heptachlor
    Heptachlor epoxide
    Isodrin
    Toxaphene
    Trifluralin
    82
    81
    82
    82
    82
    82
    82
    82
    82
    82
    82
    82
    82
    82
    18
    80
    6
    1
    2
    3
    3
    26
    1
    5
    5
    1
    1
    5
    220
    98.8
    7J
    3.7
    2.4
    3.7
    3.7
    31.7
    1.2
    6.1
    6.1
    1.2
    1.2
    6.1
    0.05
    5.99
    0.03
    <0.01
    <0.01
    
    -------
    TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    
    COMPOUND
    
    NUMBER OF
    SAMPLES
    ANALYZED '
    NtrMBER OF
    POSITIVE
    SAMPLES
    PEHCIST
    POSITIVE
    Srrts'
    MEAN RESIPLE
    UVEL
    
    -------
                     TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    
    COMPOUND
    
    NUMBER OF
    SAMPLES
    ANALYZED '
    NUMBER OF
    POSITIVE
    SAMPLES
    PmcE^i
    Posmvt
    SPTES:
    MEAN RESIOL-C 1 R»>cr OF
    LtvtL DETECTED RESISTS
    (MM) (rfM)
    NORTH CAROLINA— Continued
    o.p'-DDE
    p,p'-DDE
    0,p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    Endrin
    Heptachlor
    HeptacMor epoxide
    I&odrin
    Ethyl parathlon
    o,p'-TDE
    p,p'-TDE
    Toxaphene
    Trifluralm
    31
    31
    31
    31
    31
    31
    31
    31
    31
    31
    6
    31
    31
    31
    31
    6
    22
    14
    19
    22
    10
    2
    2
    4
    1
    1
    11
    19
    7
    2
    19.4
    71.0
    45.2
    61.3
    71.0
    32.3
    6.5
    6.5
    12.9
    3.2
    16.7
    35.5
    61.3
    22.6
    6.5
    
    -------
    TABLE 3.—-Pesticide residues in cropland soil from 43 States—FY 1969—Continued
    
    COMPOUND
    
    NUMBER OF
    SAMPLES
    ANALYZED '
    NUMBER op
    POSITIVE
    SAMPLES
    PERCENT
    POSITIVE
    Srres1
    MEAN RESIDUT
    LEVEL
    (PPM)
    R>SGE OF
    DETECTED RESIDU-ES
    (FfM)
    OKLAHOMA— Continued
    Dieldrin
    Heptachlor cpoxide
    P,P'-TDE
    Trifluralin
    
    Arsenic
    Chlordane
    0,p'-DDE
    p,p'-DDE
    o,p'-DDT
    p.p'-DDT
    DDTR
    Dicofol
    Dieldrin
    Endosulfan (II)
    Endosulfan sulfate
    Heptachlor epoxide
    Ethyl parathion
    o,p'-TDn
    p.p'-TDE
    Trifluralin
    64
    64
    64
    64
    2
    1
    2
    1
    3.1
    1.6
    3.1
    1.6
    <0.01
    <0.01
    <0.01
    <0.01
    0.01
    0.01
    0.01-0.02
    0X13
    PENNSYLVANIA
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    29
    J
    29
    29
    29
    29
    6
    1
    9
    5
    8
    11
    1
    10
    I
    1
    4
    1
    4
    7
    2
    100.0
    20.7
    3.5
    31.0
    17.2
    27.6
    37.9
    3.5
    34.5
    3.5
    3 5
    13.8
    333
    13.8
    24.1
    6.9
    10.80
    0.07
    <0.01
    0.07
    0.03
    0.12
    0.27
    0.02
    0.02
    <0.01
    
    -------
                    TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    COMPOUND
    NUMBER OF
    SAMPLES
    ANALYZED '
    NUMBER OF
    POSITIVE
    SAMPLES
    PERCENT
    POSITIVE
    StTES'
    MEAN RESIDI/E
    LEVEL
    (P'M)
    RANGE OF
    DETECTED RESIDUES
    (PPM)
    SOUTH DAKOTA— Continued
    o.p'-DDT
    p.p'-DDT
    DDTR
    Dieldrin
    Heptachlor
    Heptachlor epoxide
    Lindane
    P.P'-TDE
    106
    106
    106
    106
    106
    106
    106
    106
    2
    2
    4
    9
    1
    3
    3
    '
    1 9
    1.9
    3.8
    8.5
    0.9
    2.8
    2.8
    0.9
    <0.01
    <0.01
    <0.01
    0.01
    <0.01
    <0.01
    <0.01
    <0.01
    0.01-0.03
    0.02-0.04
    0.01-0.10
    0.01-OOS
    0.01
    0.01-0.03
    0.01-0 J02
    0.02
    TENNESSEE
    Arsenic
    Chlordane
    p.p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    Endrin
    p,p'-TDE
    Toxaphene
    Trifluraltn
    27
    21
    27
    27
    27
    27
    27
    27
    27
    27
    27
    27
    1
    10
    7
    10
    11
    6
    1
    6
    4
    2
    100.0
    3.7
    37.0
    25.9
    37.0
    40.7
    22.2
    3.7
    22.2
    14.8
    7.4
    8.05
    0.01
    0.02
    0.01
    005
    0.11
    <0.01
    <0.01
    0.03
    0.14
    <0.01
    231-15.63
    0.20
    0.0 1-0.26
    0.01-0.08
    0.01-038
    0.01-0.70
    0.01-0.03
    0.02
    0.02-0.36
    0.13-2.19
    0.04-0.05
    UTAH
    Arsenic
    Chlorrtanc
    p.p'-DDE
    P.p'-DDT
    DDTR
    Dieldrin
    Heptachlor
    Heplachlor epoxide
    12
    12
    12
    12
    12
    12
    12
    12
    11
    4
    2
    1
    2
    2
    2
    3
    91.7
    33.3
    16.7
    83
    16.7
    16.7
    16.7
    25.0
    4.16
    0.04
    
    -------
                      TABLE 3.—Pesticide residues in cropland soil from 43 Slates—FY 1969—Continued
    COMPOUND
    NUMBE> op
    SAMPLES
    ANALYZED »
    NUMBER OF
    POSITIVE
    SAMPLES
    PEHCENT
    POSITIVE
    SITES'
    MtAV RxstDlT
    LEVU.
    (PPM)
    RANGE or
    Drrecno Rtsiovts
    (PPM)
    WASHINGTON
    Aldrin
    Arsenic
    2,4-D
    o,p'-DDE
    p.p'-DDE
    o,p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    o.p'-TDE
    P,P'-TDE
    Toxaphene
    Trifluralin
    45
    45
    6
    45
    45
    45
    45
    45
    45
    45
    45
    45
    45
    2
    45
    1
    2
    10
    6
    10
    11
    8
    1
    3
    |
    '
    4.4
    100.0
    16.7
    4.4
    22.2
    13.3
    22.2
    24.4
    17.8
    2.2
    6.7
    2.2
    2.2
    <0.01
    2.61
    <0.01
    <0.01
    0.17
    0.06
    0.46
    0.72
    0.02
    
    -------
                        TABLE 4.—Pesticide residues in noncropland soil from II Slates—FY 1969
    COMPOUND
    
    NUMBER oh
    SAMPLES
    ANALYZI o '
    NUMBFR OF
    POSITIVE
    SAMPLES
    PEKCCST
    POSITIVE
    Snrs1
    MEAV RESIDUE
    LZVIL
    (PPVI)
    RANGE or
    Drrtciio RCS.:DCES
    
    ARIZONA
    Arsenic
    Chlordane
    p.p'-DDE
    p.p'-DDT
    DDTR
    Dieldrin
    44
    44
    44
    44
    14
    44
    44
    1
    8
    1
    8
    1
    100.0
    2.3
    18.2
    2J
    18.2
    2.3
    6.63
    <0.01
    <0.01
    <0.01
    <0.01
    <0.01
    1J5-30.64
    0.08
    0.01-0.06
    0.03
    0.01-009
    0.03
    GEORGIA
    Arsenic
    p.p'-DDE
    o.p'-DDT
    p.p'-DDT
    DDTR
    Dieldrin
    p,p'-TDE
    19
    10
    10
    in
    10
    10
    10
    18
    6
    2
    5
    7
    1
    1
    94.7
    60.0
    20.0
    500
    70.0
    1 0.0
    10.0
    1.47
    0.02
    <0.01
    0.02
    0.05
    
    -------
                     TABLE 4.—Pesticide residues in noncropland soil from II States—FY 1969—Continued
    
    COMPOUND
    
    NUMBER OF
    SAMPIES
    ANALYZED '
    NUMBER OF
    PosrrivE
    SAMPLES
    PEKCEVT
    PosmvE
    Sms*
    MEAN RESIDUE
    LEVEL
    (PPM)
    RANGE or
    DETECTED Kismets
    (r?M)
    NEBRASKA— Continued
    DDTR
    Dico/ol
    Dieldrin
    Heptachlor epoxide
    19
    19
    19
    19
    3
    2
    2
    '
    I5.g
    10.5
    10.5
    5.3
    <0.01
    0.02
    <0.01
    <0.01
    0.01-O.07
    0.10-0.29
    0.01
    0X11
    VIRGINIA
    Arsenic
    p,p'-DDT
    DDTR
    Dieldrin
    p.p'-TDE
    10
    13
    13
    13
    13
    10
    3
    3
    2
    1
    1000
    23.1
    4.07
    0.01
    23.1 0.01
    15.4
    7.7
    0.01
    <0.01
    0.50-12X2
    0.03-0.07
    0.03-0.09
    0.03-009
    0.02
    WASHINGTON
    Arsenic
    p,p'-DDE
    p,p'-DDT
    DDTR
    21
    21
    21
    21
    21
    3
    2
    3
    100.0
    KJ
    9.5
    1O
    6.94
    <0.01
    <0.01
    <00!
    1.58-54.17
    0.01-0.02
    0.01
    0.01-0.03
    WEST VIRGINIA
    Arsenic
    p,p'-DDE
    p,p'-DDT
    DDTR
    Dieldrin
    p.p'-TDE
    6
    g
    g
    g
    8
    g
    6
    
    
    
    
    
    100.0
    12.5
    J2.5
    12.5
    12.5
    12.5
    5.16
    <0.01
    0.01
    0.01
    0.01
    <0.01
    2.67-1 3 .26
    0.02
    0.05
    O.OS
    0.04
    0.01
    WYOMING
    Arsenic
    Chlordane
    o,p'-DDE
    p.p'-DDE
    o,p'-DDT
    p.p'-DDT
    DDTR
    Dieldrin
    Heptachlor epoxide
    Toxaphene
    37
    37
    37
    37
    37
    37
    37
    37
    37
    37
    16
    
    
    
    
    
    
    
    
    
    973
    2.7
    2.7
    2.7
    2.7
    2.7
    2.7
    2.7
    2.7
    2.7
    2.73
    0.01
    <0.01
    0.01
    <0.01
    <0.01
    0.02
    <0.01
    <0.01
    0.01
    OJ5-I9J3
    0.50
    0.02
    OJ1
    0.05
    0.18
    0.56
    0.02
    0.01
    OJ2
    1 One sample per site.
    * Percent based on number of sites with residues greater than or equal to the sensitivity limits.
                                                             410
                                                                                        PESTICIDES MONITORING JOURNAL
    

    -------
                   TABLE 5.—Arsenic residue data for the 12 States having the highest residue levels—FY 1969
    STATE
    Arkansas
    Kentucky
    New England <
    New York
    North Dakota
    Ohio
    Pennsylvania
    NUMBER OF
    SAMPLES
    ANALYZED
    47
    31
    19
    37
    158
    69
    29
    PERCENT
    POSITIVE
    SITES"
    100.0
    100.0
    100.0
    94.6
    100.0
    100.0
    100.0
    MEAN RESIDUE
    LEVEL
    (PPM)
    9.0
    8.4
    10.2
    9.4
    8.5
    11.2
    10.8
    RANGE op
    DETECTED RESIDUES
    (PPM)
    1.7-28.2
    1.6-12.8
    1.0-14.1
    1.2-4J.9
    1.0-37.5
    1.2-41.5
    3.0-64.9
    1  Percent based on number of sites with residues greater than or equal to the sensitivity limits.
    '  Connecticut, Maine. Massachusetts, New Hampshire, Rhode Island,  and Vermont.
                   TABLE 6.—Pesticide residue data for 5 Slate\ having the highest DDTR residue levels—FY 7969
    STATE
    Alabama
    California
    Michigan
    Mississippi
    South Carolina
    NUMBER OF
    SAMPLES
    ANALYZED
    22
    65
    51
    29
    17
    PERCENT
    POSITIVE
    SITES'
    90.9
    84.6
    23.5
    89.7
    88.2
    MEAN RESIDUE
    LEVEL
    (PPM)
    1.13
    1.47
    2.0»
    2.06
    1.17
    RANGE OF
    Dtiu.ru> Ri smuts
    (PPM)
    0.05-8.08
    0.01-41.81
    0.01-78-36
    0.03-13 J4
    0.01-4.78
    1 Percent based on number of sites with residues greater than or equal to the sensitivity limits.
                    TABLE  7.—Residue data for the 7 States with the  highest dieldrin residue levels—FY  1969
    STATE
    Florida
    Illinois
    Iowa
    Kentucky
    North Carolina
    Virginia/XVest Virginia
    NUMBER OF
    SAMPLES
    ANALYZtD
    18
    142
    151
    31
    31
    27
    PERCENT
    POSITIVE
    SITES'
    38.9
    61.3
    536
    54.8
    32.3
    25.9
    MEAN RESIDUE
    LEVEL
    (PPM)
    0.08
    0.11
    0.06
    0.06
    0.08
    0.07
    RANGE OF
    DETECTED RESIDUES
    (PPM)
    0.01-0.52
    0.01-1. <2
    0.01-042
    0.01-065
    0.01-1.53
    001-1.60
    1 Percent based on number of sites »nh residues greater ilian or equal to the seOMtmty limits.
     VOL. 6, No. 3, DECHMBER 1972
    411
    

    -------
    TABLE 8.—Summary of pesticides used in FY 1969 on cropland for all 43 Stales
    
    
    
                            ALL STATES—1,684 SITES
    COMPOUND
    Aldrin
    Amibcn
    Aramite
    Atrazine
    Azinphosmethyl
    Azodrin
    Bacillus ihuringienus
    Barban
    Beneiin
    Benzene hexachloride
    Bidrin
    Binapacryl
    Bordeaux mixtures
    Cacodylic acid
    Captan
    Carbaryl
    Carbophenothion
    CDAA
    Cercsan L
    Cercsan M
    Ceresan icJ
    Chevron RE-5353
    Chlordane
    Chlorobenzilale
    Chloroneb
    Chloroxuron
    Chromophon
    CIPC
    Copper carbonale
    Copper oxide
    Copper oxychloride sulfale
    Copper-8-quinolmolate
    Copper sulfale
    Coloran
    2.4-D
    2,4-DB
    Dalapon
    DDT technical
    DEF
    Demeton
    Diazinon
    Dicamba
    Dichlone
    Dichloropropanc
    Uichloropropene
    Dichlorprop
    Dicofol
    Dieldrin
    Difolatan
    Dimelan
    Dimcthoate
    Dinitrobulylphcnol
    Dmitrocresol
    Dinocap
    Dioxathion
    
    Diphenamid
    Diquat
    Disulfoton
    PERCENT
    OF
    SITES
    TREATED
    4.16
    2.14
    0.12
    7.66
    0.59
    042
    0.12
    0.12
    0.18
    0.06
    0.24
    0.06
    0.06
    0.06
    11.16
    1.72
    0.18
    0.89
    1.25
    1.48
    1.84
    0.30
    0.12
    0.12
    0.36
    0.30
    0.06
    0.12
    006
    0.18
    0.12
    0.06
    0.36
    0.48
    15.14
    0.89
    0.42
    3.44
    0.59
    0.18
    1.96
    0.30
    0.12
    0.06
    0.36
    0.06
    0.42
    1.19
    0.06
    0.06
    0.12
    0.95
    0.06
    0.12
    0.12
    
    0.24
    0.06
    1.72
    AVERAGE
    APPLI-
    CATION
    RATP,
    (IB/ACRE)
    1.25
    1.07
    2.35
    1.88
    1.70
    2.07
    9.50
    0.17
    1.36
    3.00
    0 18
    2.12
    0.50
    001
    0.12
    364
    1.83
    1.78
    0.01
    0.01
    0.01
    1.72
    3.10
    1.31
    0.05
    1.65
    0.15
    1.50
    0.60
    4.23
    4.68
    0.0 1
    13.53
    0.74
    0.54
    0.48
    2.12
    5.56
    1.66
    0.59
    1.22
    0.39
    2.00
    54.43
    70.07
    2.00
    2.12
    0.17
    0.01
    0.01
    0.75
    3.78
    300
    0.22
    2.60
    
    2.19
    0.83
    1.77
    AVERAGE
    AMOUNT
    APPLIED
    PHR SITE
    (L8/ACRE)
    00522
    0.0229
    0.0028
    0 1442
    0.0101
    0.0086
    0.0113
    0.0002
    0.0024
    0.0018
    00004
    0.0013
    0.0003
    0.0000
    0.0133
    0.0627
    0.0033
    0.0158
    0.0002
    0.0001
    0.0003
    0.0051
    0.0037
    0.0016
    00002
    0.0049
    0.0001
    0.0018
    0.0004
    0.0075
    0.0056
    0.0000
    0.0482
    0.0035
    0.0825
    0.0042
    0.0088
    0.1915
    0.0099
    0.0011
    0.0240
    0.0012
    0.0024
    0.0323
    0.2496
    0.0012
    0.0088
    0.0021
    0.0000
    0.0000
    0.0009
    0.0359
    0.0018
    0.0003
    0 0031
    
    0.0052
    0.0005
    0.0305
    COMPOUND
    PERCENT
    OF
    Srrts
    TREATED
    Dilhane M-;5 j 030
    Diuron
    DSMA
    Endosulfan (1)
    Endnn
    1 1.13
    [ 0.36
    j 0.48
    | 0.48
    EPN j 0.06
    EPTC
    Ethion
    Ethilene dibromide
    Falore
    Ferbam
    Folex
    Heptachlor
    Herbisan
    Hexachlorobenzene
    i 0.36
    0-24
    i O.U
    0.06
    0.06
    0.06
    1.96
    0.06
    0.06
    Lead arcenate ; 0.06
    Lindane I 0.65
    Linuron j 0.77
    Malathion '< 7.54
    i
    Maleic hydrazide '. 0-36
    Maneb ! OJO
    MCPA ; 1.07
    Methox>chlor j -.20
    Methyl demeton ' 0.06
    Meth>lmercur>
    dicyandiamide
    Meth>lmercury nitrite
    Mevinphos
    Mirex
    Monuron
    MSMA
    Nabam
    Naled
    Nitralin
    Nitrate
    Norea
    NPA
    Oxydemetonmethjl
    Ethyl paralhior.
    Methyl parathion
    PCNB
    PCP
    Phenylmercury urea
    Phorate
    Phosphamidon
    Picloram
    PMA
    Polyram
    Prometryn*
    Propanil
    Propazine
    Ramrod
    Ro-N«t
    Roundup
    Randox T
    Silvex
    Sima^ine
    Simetrync
    Sodium arsenite
    Sodium chlorate
    5.46
    0.06
    0-36
    0.24
    0.06
    0.48
    0.24
    OJO
    0.36
    1.13
    0.12
    OJ6
    0.18
    1.84
    3.03
    0.42
    0.06
    0.06
    0.65
    0.12
    0.12
    0.18
    0.06
    0.06
    0.41
    0.06
    IJ7
    0.12
    0.12
    0.12
    0.12
    0.12
    0.06
    0.24
    0.06
    AVERAGE
    APPLI-
    CATION
    RATE
    (LS/AOLE)
    5.82
    0.93
    1.52
    1.11
    2.21
    1.50
    2.65
    2.06
    14.62
    2.00
    9.12
    1.50
    OJ3
    10.00
    0.01
    3.80
    AVUAGE
    AMOLVT
    APPLIED
    PE» Srre
    (U/ACKE)
    0.0173
    0.0105
    0.0014
    0.0053
    0.0105
    0.0009
    0.0094
    0.00*9
    0.0174
    o.oon
    0.0054
    O.fXO?
    OCO65
    0.006C
    00000
    0.0023
    0 03 0.0002
    0.73 ! 0.0056
    0.17
    1X3
    0.0127
    0.0051
    2.14 I O.OOM
    0.33
    O.W
    1^0
    0.01
    0.01
    I.4S
    0.0 1
    1.60
    1.21
    1.78
    1.62
    0.76
    64.58
    0.46
    1.01
    0.40
    1.48
    3.07
    1.59
    1-50
    0.01
    2.17
    0.13
    0.63
    0.06
    10.40
    2.00
    3.96
    2.00
    1.45
    1.88
    0.78
    0.90
    0.63
    2.07
    2.00
    5.25
    6.00
    0.0035
    0.000*
    O.OO">9
    O.W06
    0.0000
    O.OCU3
    0.0000
    0.0010
    0.0058
    0.00^2
    OXXX3
    0.0027
    0.72S6
    0.0006
    0.003 6
    O.OCO7
    0.0272
    0.0929
    0.0066
    OJXX19
    OJKftQ
    OXIK2
    0.0001
    0.0007
    0.0001
    0.0062
    0.0012
    0.0165
    0.0012
    0.0198
    0.0022
    0.0009
    0.0011
    0.0007
    0.0025
    00012
    0.0125
    0.0036
                                                             PESTICIDES MONITORING JOURNAL
                                    412
    

    -------
       TABLE 8.—Summary of pesticides used in FY 1969
            on cropland for nil 43 Slates—Continued
    TABLE 10.—.Summary of pesticides media FY
             on cropland by Stare—Continued
    
    COMPOUND
    Stiobane
    Sulfur
    2,4,5-T
    TCA
    TDC technical
    Tctradifon
    Thiram
    Toxaphene
    Trichlorofon
    Tnfluralin
    Vernolate
    Zineb
    Ziram
    
    PERCEN i
    SITES
    TREATED
    0.12
    0.71
    0.18
    0.06
    0.36
    0.12
    1 07
    1 90
    0.06
    4.33
    0.53
    0.18
    0.06
    
    AVERAGE
    APPLI-
    CATION
    RATE
    (LB/ACRC)
    16.50
    34.00
    0.83
    2.00
    231
    0.50
    0.03
    9.87
    0.80
    0.76
    1.29
    4.90
    0.80
    AVERAGE
    AMOUNT
    APPLIED
    PER SITE
    (LB/ACRE)
    00196
    02423
    0.0015
    0.0012
    00082
    00006
    00003
    0.1876
    00005
    00327
    0.0069
    0.0087
    00005
    
    
    
    TABLE 9. — Summary vf pesticide* used in FY 1969
    on noncropland for all 11 States
    ALL STATES— 195 SITES
    
    
    
    COMPOUND
    
    2,4-D
    Malathion
    Mirex
    
    
    
    SITES
    TREATED
    0.51
    0.51
    0.51
    
    AVERAGE
    APPLI-
    CATION
    RAIT;
    (LB/ACKF.)
    2.00
    0.61
    0.01
    
    AVERAGE
    AMOUN r
    APPLIED
    PIR SITE
    (LB/ACRE)
    00103
    00031
    0.0001
    
    
    TABLE 10. — Summary of pesticides used in
    FY 1969 on cropland by State
    
    
    
    COMPOUND
    
    
    
    SITES
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (LB/ACRE)
    AVERAGE
    AMOUNT
    APPLIED
    PER SITE
    (LB/ACRE)
    ALABAMA— 23 SITES
    
    
    Ajodrin
    Benzene hexachloride
    Captan
    Carharyl
    Cercsaii M
    Copiier sulfatc
    Coloran
    DDT technical
    nr.F
    Disulfoton
    Diuron
    DSMA
    Kndrin
    Fl'N -
    
    4.35
    435
    21 74
    4.35
    435
    8.70
    4.35
    39.13
    435
    8.70
    8 70
    435
    8 70
    435
    
    0.84
    3.00
    004
    0.40
    001
    3608
    1.50
    10.73
    1.50
    0.35
    095
    1 00
    1 20
    1 SO
    
    0.0365
    0 1304
    00083
    00174
    01)004
    3 1374
    0.0652
    42000
    00^52
    00304
    00826
    00415
    0 1043
    OOf.52
    
    COMPOL^D
    AlABAMA-
    PERCENT
    OF
    SITES
    TREATED
    AVEXACE
    APPLI-
    CATION
    RATE
    ILB ACRE)
    AW«»CE
    A V'OL'*»T
    APPLIED
    PE» SITE
    (LB 'ACKE)
    -23 SITES— Continued
    
    n
    Malathion
    Ethyl parathion
    Methyl parathion
    MSMA
    Phorate
    Prometryne
    Thiram
    Toxaphene
    Trifluralin
    Vernolaic
    8.70
    4.35
    52 17
    8.70
    435
    4J5
    4.15
    17J9
    47.83
    8.70
    2.50
    1,00
    3.42
    1.50
    1.00
    2.00
    0.02
    3.45
    0.61
    1.05
    0.217.!
    00435
    1.78^8
    0.1304
    0.0435
    u.oro
    o.c«y>9
    060CO
    0.2913
    00913
    ARIZONA— 9 SITES
    Azodrin
    Captan
    Ceresan L
    De melon
    Dieldrin
    Diuron
    11.11
    11.11
    11.11
    11.11
    11.11
    11 11
    Endosulfan (I) 11.11
    Naled
    Ethyl paraihion
    Methyl paraihi'm
    PCNB
    Phorate
    Strobane
    Toxaphene
    Trifluralin
    
    11.11
    22.22
    44.44
    11 11
    II. 11
    11.11
    22.22
    
    6.25
    0.01
    001
    0.13
    O.Oi
    1.00
    2. no
    0.50
    5 50
    2.75
    1.50
    15.00
    200
    1 12
    
    0.69-U
    0.001 1
    00011
    0.0144
    0.0011
    0.1111
    02222
    00556
    1.2222
    1 2244
    C 1W
    1.6667
    o.:::2
    0.2500
    
    ARKANSAS— 45 SITES
    Aldrin
    Captan
    Ceresan M
    Chloroxuron
    2,4-D
    2,4-DB
    DEF
    Disulfoton
    Diuron
    DSMA
    
    Linuron
    NPA
    
    Methvl paraihion
    Propantl
    2,4.5-T
    Thiram
    Tnfluralm
    
    222
    13.33
    2.22
    6.67
    2.22
    2.22
    2.22
    2.22
    2.22
    4.44
    2.22
    8.89
    6.67
    2.22
    2.22
    4.44
    4.44
    4.44
    15.56
    
    0.25
    0.03
    001
    1.00
    0.05
    1.75
    100
    O.O!
    0.75
    3.00
    12.00
    0.94
    034
    0X4
    POO
    5.50
    0.88
    0.03
    0.79
    
    0.0056
    0.0036
    0.0002
    0.0*67
    0.0011
    0.03 S9
    0.0222
    0 10*6
    0.0002
    0.0167
    0.1333
    02661
    0.0833
    0.0362
    Oj009S
    0.2667
    oj;4^4
    0.0 3 '9
    0.00 1*
    0.1222
    
    CALIFORNIA— 66 SITES
    
    Afdmilc
    Atrjzmc
    3.03
    1.52
    ?J5
    2.50
    0.0712
    00379
    VOL. 6, No. 3, DncLMiirR 1972
                                                        413
    

    -------
    TABLE 10.—Summary of pesticides used in FY 1969 on cropland by State—Continued
    COMPOUND
    PERCENT
    OF
    SITES
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (IB 'ACRt)
    AVERAGE
    AMOUNT
    Al'PLIED
    PFR SITE
    (IB/ACRE)
    CALIFORNIA— 66 SITES— Continued
    Azinphosmethyl
    Bacillus thurmgiensis
    Benefin
    Binapacryl
    Bordeaux mixtures
    Captan
    Carbaryl
    Carbophenothion
    Ceresan red
    Chlordane
    2.4-D
    DDT technical
    Dia7inon
    Dichloropropcne
    Diclilorprop
    Dicofol
    Dimcthoatc
    Dioxjlhion
    Diphcn.imid
    Disnlfoton
    Diuion
    OithjiiB M-45
    Endosulfan (I)
    Ethion
    Malathion
    MCPA
    Mevinphos
    Nabarn
    Naled
    Ethyl parathion
    Methyl parathion
    Propanil
    Simazine
    Simetryne
    Sulfur
    Tetradifon
    Toxaphenc
    Trichlorofon
    Tnfluralin
    
    3.03
    3.03
    1.52
    1.52
    1 52
    1 52
    6.06
    303
    3.03
    1 52
    3.03
    13.64
    606
    4.55
    1.52
    7.58
    1 52
    3.03
    1 52
    303
    1.52
    3.03
    7.58
    3.03
    4.55
    3.03
    9.09
    1.52
    606
    606
    4.55
    3 03
    1.52
    1.52
    7.58
    303
    606
    1.52
    6.06
    
    0.4S
    950
    1.83
    2.12
    050
    230
    10.76
    1.75
    0.01
    500
    063
    281
    099
    8.67
    200
    2.59
    1 00
    ?. 60
    2.62
    400
    0.75
    500
    0.99
    1.38
    1 65
    076
    1 48
    3.50
    1.90
    2.03
    6.10
    3 63
    375
    200
    35.79
    0.50
    9.75
    0.80
    0.88
    
    00145
    02879
    00277
    00321
    00076
    0.0348
    06521
    00530
    0.0003
    00758
    00189
    03844
    00603
    03939
    00303
    0.1962
    0.0152
    0.0788
    0.0397
    0.1212
    0.0114
    0.1515
    0.0750
    0.0417
    0.0750
    00230
    0.1345
    0.0530
    0 1152
    0.1230
    0.2773
    0.1098
    0.0568
    0.0303
    2.7115
    0.0152
    0.5908
    0.0121
    0.0530
    
    
    COLORADO— 60 SITES
    
    Aldrin
    Carbaryl
    Ceresan M
    2,4-D
    2,4-DB
    Endrin
    Malathion
    Ethyl parathion
    Picloram
    PMA
    	 	
    
    3.33
    1.67
    1.67
    10.00
    1.67
    5.00
    1.67
    1.67
    1 67
    1 67
    	
    
    008
    1.00
    0.01
    0.51
    070
    033
    0.60
    0.25
    1.00
    015
    	
    
    0.0027
    00167
    00002
    00508
    0.0117
    0.0167
    00100
    0.0042
    00167
    00025
    	
    CONNECTICUT— 2 SlTf.S
    Atrazinc 5000 2 50
    1 251)0
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    CO.vfPOL.--D
    Pa CENT
    OF
    SITES
    TlEATEO
    AVERAGE
    APPU-
    CAT10N
    RATE
    (U/ACU)
    AVEXAGE
    AMOUNT
    APPLIED
    Pa SITE
    CJ/ACHE)
    DELAWARE— 3 SITES
    Captan
    Lindane
    
    33.33
    33.33
    
    0.04
    0.08
    
    0.0133
    0.0267
    
    FLORIDA— 15 SITES
    
    
    Atrazine
    Azmphos methyl
    Captan
    Carbophenothion
    Chlorobenzjlale
    Copper oxide
    Copper ox>chlonde
    sulfate
    2,4-D
    Dalapon
    DDT technical
    Diazmon
    Dichloropropene
    Dicofol
    Ethion
    Ferbam
    Mirex
    Eth>l parathion
    Methyl paraihion
    Sulfur
    2,4,5-T
    TDE technicaJ
    Toxaphene
    Zineb
    
    
    6.67
    6.67
    t.67
    6.67
    I3J3
    667
    
    6.67
    6.67
    6.67
    6.67
    6.67
    667
    6.67
    I3J3
    
    0.80
    2.50
    7.50
    2.00
    1.31
    7.50
    
    8.00
    1.50
    
    0.0533
    0 1667
    0.5003
    01333
    0.1753
    0.5000
    
    0.5333
    0.1000
    i.-o ; 0.1133
    7.00 0.4667
    2.90 j 0.1933
    194.40
    1.50
    2.75
    6.67 9.12
    6.67 | 0.01
    13o3
    667
    6.6?
    6.67
    667
    667
    13.33
    
    2.85
    10.00
    46.50
    0.75
    10.00
    2.00
    6.55
    
    129600
    0.1000
    OJ667
    0.6080
    0.0007
    0.3 «»
    0.6*67
    3.1000
    00500
    06667
    0.1333
    0?733
    
    
    GEORGIA— 2S SITES
    i
    Atrazine
    A^odrin
    Benefin
    Captan
    Ceresan red
    Copper oxychlond«
    sulfate
    Copper sulfate
    2,4-D
    DDT technical
    Disulfoton
    Folcx
    Malathion
    Maleic h>draiide
    Methoxvchlor
    Mirex
    Ethyl parathion
    Methyl parathion
    PC SB
    Sulfur
    Thiram
    Toxaphenc
    Triflurslm
    
    7.14
    3.57
    7.14
    39.29
    10.7T
    
    3.57
    3.57
    10.71
    21.43
    3J7
    3.57
    21.43
    7.14
    2S.57
    3.57
    3.57
    14.29
    3.57
    7.14
    1071
    n ex
    17.86
    7.14
    
    3.00
    4.00
    1.12
    0.08
    0.01
    
    tJ6
    2.72
    0.50
    14.18
    2.00
    1.50
    0.34
    2.41
    0.02
    0.01
    1.00
    1.84
    1000
    40.20
    0.04
    14.45
    1.25
    
    0.2143
    0.1429
    0.0200
    0.0307
    0.0011
    
    0.0486
    OOS71
    0.0536
    3.0395
    0.0714
    0.0536
    0.0732
    01725
    0.0>M6
    0.0004
    0 0357
    0.:632
    03571
    : 8714
    00.^43
    2.5S04
    00s<)3
                                       414
                                                                 PESTICIDES MONITORING JOURNAL.
    

    -------
                   TABLE  10.—Summary of pesticides used in FY 1969 on cropland by Slate—Continued
    
    COMPOUND
    PERCENT
    OP
    SITES
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    [ (LB/ACRE)
    AVERAGE
    AMOUNT
    APPLILO
    PIR SITE
    (tn/AC»E)
    IDAHO— 33 SITES
    Cjptan
    Ccrcsan M
    Ceresan L
    CIPC
    2,4-D
    2,4-DB
    DDT technical
    Dieldrin
    Diouat
    EPTC
    Htxachlorobcnwne
    Ro-Nect
    Trifluratin
    12.12
    6.06
    15.15
    3.03
    12.12
    303
    606
    3.03
    3 03
    3.03
    3.03
    606
    606
    001
    0.01
    0.01
    2.00
    2.12
    0.50
    0 50
    001
    0.83
    0 38
    001
    1.87
    0 56
    0.0015
    0.0006
    0.0015
    00606
    02576
    00152
    0 0303
    00003
    0.0252
    0.0115
    0.0003
    0.1136
    0.0342
    
    ILLINOIS— 141 SITES
    Aldrin
    Amihcn
    Atraztne
    Captan
    Carbaryl
    CD A A
    Ccrcsan red
    Ceresan L
    Chevron RE-5353
    2,4-D
    2,4-DB
    Diazinon
    Dicldrin
    Heptachlor
    I inuron
    MalaLhion
    Metlioxychlor
    Ramrod
    Roundup
    Silvex
    Thiram
    Trifluralin
    Vernolate
    
    19.15
    7.80
    922
    4965
    0.71
    7 SO
    0.71
    0.71
    1.42
    2057
    2.13
    3.55
    2 13
    9.93
    1.42
    39.72
    10.64
    7.80
    0.71
    0.71
    0.71
    2.84
    0.71
    
    1 52
    091
    2 19
    006
    4 80
    1 51
    001
    006
    2.56
    0.42
    035
    I 86
    0.23
    046
    0 66
    003
    0.01
    1.22
    0.07
    025
    001
    0.97
    037
    
    02914
    0.0707
    0 2023
    00317
    00340
    0.1176
    0.0001
    00004
    00363
    00864
    00075
    0.0659
    00050
    0.0455
    0 0094
    00104
    00011
    00955
    0.0005
    0.0018
    0.0001
    0.0277
    0.0026
    
    INDIANA— 75 SITES
    
    
    AJdrin
    Amibcn
    Atrazinc
    Caplan
    Carbaryl
    CDAA
    Ceri'san L
    2,4-D
    DDT tcchnic.il
    Dieldrin
    Difolat.in
    Hcptaclilor
    Matathion
    Mcihox>chlor
    
    10.67
    5.33
    13.33
    26.67
    1 33
    1.33
    2.67
    10.67
    1 33
    1 33
    1 33
    5.33
    17.33
    400
    
    1.11
    0.85
    1.79
    0.01
    1 60
    1.07
    001
    0.35
    001
    0.01
    001
    032
    0.01
    o.oi
    
    0.1187
    0.0453
    02393
    00027
    0.0213
    0.0143
    0.0003
    00373
    0.0001
    0.0001
    0.0001
    0.0172
    0.0017
    00004
    
    COMPOUND
    PERCENT
    or
    SITES
    THEATED
    AVERAGE
    APPLI-
    CATION
    RATE
    {LB/AC*£)
    AVEFAGE
    AMOUNT
    APPLIED
    PE* SITE
    (LB/ACTE)
    INDIANA— 75 SITES— Continued
    Methylmercury
    dicyandiamide
    Ramrod
    Roundup
    Trifluralin
    Zineb
    
    2.67
    2.67
    1.33
    2.67
    1.33
    
    0.01
    1.40
    1.50
    0.75
    1.60
    
    0.0003
    0.0373
    0.0200
    0.0200
    00213
    IOWA— 151 SITES
    
    Aldnn
    Armben
    Alrazme
    Captan
    Carbaol
    CDAA
    Chevron RE-53^3
    2,4-D
    Diazinon
    Dicamba
    Dieldnn
    Heptachlor
    Lindane
    Ethyl parathion
    Phoratc
    Ramrod
    Randox T
    Thiram
    Trifluralm
    8.61
    8.61
    10.60
    2.65
    066
    1.32
    0 66
    2053
    6.62
    0.66
    0.66
    4.64
    1.32
    0.66
    1.32
    3.97
    0.66
    0.66
    2.65
    0.68
    1.12
    2.00
    0.03
    1.00
    1.50
    1 00
    0.62
    1.19
    0.50
    0.15
    0.35
    0.06
    0.32
    0.95
    2.02
    0.40
    0.06
    0.47
    0.0587
    0.0966
    0.2123
    o.ooos
    00066
    0.0199
    0 CO66
    0.1278
    0.0791
    0.0033
    0.00 10
    00164
    0.0009
    0.00:i
    00126
    05801
    00026
    0.0004
    0.0125
    KENTUCKY— 31 SITES
    
    Aldrin
    Atrazine
    2,4-D
    Dalapon
    DDT technical
    EPTC
    
    9.68
    19.35
    3 23
    3.23
    3.23
    3.23
    
    2.00
    1.33
    0.50
    1.50
    3.00
    1.50
    
    0.1935
    0.2581
    00161
    0.04S4
    0.0968
    0.0484
    
    
    LOUISIANA— 27 SITES
    
    
    Aldrin
    Captan
    Carbaryl
    Ceresan L
    Cotoran
    2,4-D
    Dalapon
    DDT technical
    DEF
    Dimetan
    M.il.uhion
    Mcih\!mcicur>
    dicyandjamtdc
    Meth\lniercur> nilnlc
    MSMA
    Nitr.nc
    
    22.22
    3.70
    3.70
    3.70
    3.70
    11.11
    3.70
    7.41
    3.70
    3.70
    3.70
    
    3.70
    3.70
    3.70
    22.22
    
    0.08
    0.25
    12.00
    O.OI
    1.00
    1.58
    2.00
    23.25
    9.00
    0.01
    1.00
    
    001
    0.01
    1.50
    72.00
    
    00178
    0.0093
    0.4444
    0.0004
    0.0370
    0.1759
    0.0741
    1.7222
    OJ333
    O.OTXM
    0.0370
    
    0.0004
    0(004
    0.0556
    160000
    VOL. 6, No. 3, DECEMBER 1972
                                                    415
    

    -------
    TABLE 10.—Summary of pesticides used in FY 1969 on cropland by Stale—Continued
    COMPOUND
    PERCENT
    or
    SITES
    TREATED
    AvtRA(,t
    APPLI-
    CATION
    RATE
    (LB/ACRE)
    AVLHAGF.
    AMOUNT
    APPLIED
    PER SITE
    (LB/ACRE)
    LOUISIANA— 27 SITES— Continued
    Methjl parathion
    Propaml
    Silvcx
    Strobane
    TCA
    Toxaphene
    Trifluralin
    
    7.41
    11.11
    3.70
    3.70
    3.70
    3.70
    3.70
    
    7.20
    3.17
    1.00
    18.00
    2.00
    75.00
    1.00
    
    0.5333
    0.3519
    0.0370
    0.6667
    0.0741
    2.7778
    0.0370
    
    MAINE— « SITES
    
    
    Dalapon
    Dmitrobutylphenol
    Disulfoton
    Malalhion
    
    Maneb
    Sodium arsemte
    
    
    U.SO
    37.50
    25.00
    12.50
    
    12.50
    25.00
    
    
    4.90
    1.37
    8.50
    1.00
    
    0.70
    8.80
    
    
    0.6125
    0.5I2S
    2.1250
    0.1250
    
    0.0875
    2.2000
    
    MARYLAND— 13 SITES
    
    
    Atraxinc
    Captan
    2,4-D
    Dicldrin
    Lindane
    Malathion
    Methoxychlor
    Thiram
    
    
    30.77
    30.77
    15.38
    769
    15.38
    23.08
    7.69
    7.69
    
    
    1 26
    0,03
    0.54
    0,01
    0,01
    0,01
    0.01
    0.01
    
    
    03885
    0.0100
    00838
    0.0008
    00015
    0.0023
    0.0008
    0.0008
    
    MASSACHUSETTS— 2 SITES
    
    Carbaryl
    Dinitrobut)/phenol
    Disulfoton
    Dilhanc M-45
    Maleic hydrazide
    Oxydcmetonmethyl
    Ethyl paraibion
    5000
    5000
    50.00
    50.00
    50.00
    5000
    50.00
    083
    3.06
    1.50
    12.40
    2.32
    0.25
    0.53
    0.4150
    1.5300
    0.7500
    6.2000
    1.1600
    0.1250
    0.2650
    
    MICHIGAN— 51 SITES
    
    Atrazine
    Azinphosmethyl
    Captan
    CDAA
    Ceresan red
    CIPC
    Chloroxuron
    2,4-D
    DDT technical
    Dmitrobutylphenol
    Diuron
    EPTC
    Herbisan
    Malalhion
    Mctlioxychlor
    
    11.76
    1.96
    1.96
    1.96
    1.96
    1.96
    3.92
    9.80
    1.96
    1.S6
    1.96
    1.96
    1.96
    3.92
    1.96
    1.49
    8.00
    001
    6.00
    0.01
    1.00
    2.63
    0.53
    1 50
    11.25
    2.00
    200
    1000
    0.50
    0.01
    J 	 	 J
    0.1753
    0.1569
    0.0002
    0.1176
    0.0002
    0.0196
    0.1029
    0.0524
    0.0294
    02206
    0.0392
    00392
    0.1961
    0.0198
    0.0002
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    Co.MPOCVD
    PEICESI
    OP
    SITES
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (LI/AOie)
    AVERAGE
    AMOUNT
    APPLIED
    PER SITE
    (L»/AC*E)
    MISSISSIPPI— 29 SITES
    AzinphosmethM
    Azodrin
    Bidrin
    Captan
    Ceresan M
    Ceresan red
    Ceresan L
    Chloroneb
    Co lo ran
    DDT technical
    DEF
    Disulfoton
    Diuron
    DSMA
    Endrin
    
    Linuron
    Malathion
    MethoxychJor
    Mirex
    MSMA
    Norea
    Nilralin
    Methyl parathion
    PCNB
    Sodium chlorate
    Toxaphene
    Trifluralin
    Vernolate
    
    3.45
    6.90
    6.90
    24.14
    3.45
    27.59
    3.45
    17.24
    13.79
    31.03
    20.69
    0.25
    0.76
    0.03
    0.09
    0.01
    0.02
    0.00*6
    0.0524
    0.0024
    0.0110
    00003
    0.0045
    0.01 | 0.0003
    0.06
    0.47
    3.47
    0.0100
    0.0652
    1.0759
    0.82 0.1650
    31.03 0.05
    17.24 | 0.63
    10J4 0.70
    3.45 • 2.00
    I
    3.45 ' 0.42
    6.90 1.40
    3.45
    6.90
    1034
    3.45
    13.79
    4IJ8
    10.34
    3.45
    34.48
    37.93
    3.45
    
    0.01
    0.01
    1.44
    OJ3
    0.99
    2.14
    0.13
    6.00
    7.50
    0.85
    OJO
    
    0.0152
    0.1079
    0.0728
    0.0690
    
    0.0145
    0.0969
    0.0003
    0.0007
    0.14?6
    0.0114
    0.1372
    oes-ii
    0.0131
    0.2069
    2.SS62
    OJ241
    0.0103
    
    MISSOURI— *1 SITES
    Aldrin
    Amiben
    Atrazine
    Bidrin
    Captan
    Ceresan M
    2.4-D
    2.4-DB
    Diazinon
    Dinitrobutylphenol
    Heptachlor
    Linuron
    NPA
    Methyl parathion
    Propazine
    Ramrod
    Trifluralin
    Vernolate
    
    4.94
    4.94
    12.35
    1.23
    103
    1.23
    11.11
    2.47
    1.23
    2.47
    1 21
    |.*J
    2.47
    2.47
    1.23
    1.23
    1.23
    7.41
    2.47
    
    1.65
    1.15
    1.87
    0.10
    0.01
    0.01
    0.77
    035
    0.93
    0.53
    0.19
    0.37
    1.07
    0.50
    2.00
    1.10
    0.91
    1J8
    
    0.0815
    0.056S
    0.2315
    0.0012
    0.0001
    0.0001
    0.0856
    0.0062
    0.0115
    0.0131
    0.0023
    0.0091
    0.0264
    0.0062
    0.0247
    0.0136
    0.0673
    0.0340
    
    NEBRASKA— 103 SITES
    
    Amiben
    Atrazine
    Capian
    Ceresan red
    
    0.97
    4.85
    17.48
    0.97
    
    2.50
    0.82
    0.04
    0.01
    
    O.OMJ
    C.0400
    0.0069
    0.001!
    
                                      416
                                                               PESTICIDES MONITORING JOURNAL
    

    -------
                    TABLE 10.—Summary of pesticides used in FY 1969 on cropland ty Stale—Continued
    COMPOUND
    PF.RCFNI
    OK
    Snt3
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (LB/ACRE)
    AVF.HACE
    AMOUNT
    APPLIED
    PER SITE
    (LB/ACRE)
    NEBRASKA— 103 SITES- Continued
    Cercsan L
    Chevron RE-53S3
    2,4-D
    Diazinon
    Dieldrin
    Disulfoton
    EPTC
    Matathion
    Methoxychlor
    Methylmercury
    dicyandiamide
    Nabam
    Norca
    Eth>l paratlnon
    Phorate
    Ramroc]
    Thirain
    
    0.97
    1.94
    1456
    4.85
    3.88
    0.97
    097
    17.48
    1.94
    
    4.85
    0.97
    0.97
    3.88
    0.97
    0.9V
    4.85
    
    001
    1 25
    0.44
    098
    001
    0.22
    3 00
    0.01
    0.01
    
    001
    0.01
    0.60
    0.50
    0.90
    0.83
    0.03
    
    0.000 1
    00243
    00644
    0.0476
    0.0004
    0.0021
    0 0291
    0.0017
    0.0002
    
    00005
    0.0001
    0.0058
    0.0194
    0.0087
    0.0081
    0.0014
    
    NEW JERSEY— 5 SITES
    
    
    2,4-l>
    Monuron
    Ethyl parathion
    Sulfur
    
    
    40.00
    2000
    20.00
    2000
    
    
    0.31
    1 60
    0.54
    900
    
    
    0.1240
    0.3200
    0.1080
    1.8000
    
    NEW MEXICO— 10 SITES
    
    
    Azodrin
    Carbaryl
    DDT technical
    Diuron
    Ethyl parathion
    Toxapheiii!
    
    
    1000
    1000
    1000
    20.00
    10.00
    10.00
    
    
    1 50
    250
    1.00
    1.12
    2.50
    1.00
    
    
    0.1500
    0.2500
    0.1000
    02250
    0.2500
    0.1000
    
    NEW YORK— 38 SITES
    
    
    Atra/ine
    Aiinphosinclhyl
    Caplan
    Copper sulfale
    2,4-D
    Dalapon
    DDT technical
    Demeton
    Diazinon
    Dichlone
    Dinitrobutylphenol
    Diuron
    Endosulfan (1)
    Lead arscnate
    Malathion
    MCHA
    Mcthoxychlor
    Mcthylmercury
    dicyandiamide
    
    23.68
    5.26
    13.16
    2.63
    7.89
    2.63
    5.26
    2.63
    2.63
    2.63
    5.26
    5.26
    5.26
    2.63
    5.26
    5.26
    263
    
    1053
    
    1.36
    1.15
    0.87
    3.00
    0.21
    2.50
    1.35
    0.04
    1.00
    2.20
    15.22
    2.40
    0.95
    3.80
    0.01
    0.33
    0.01
    
    0.01
    
    0.321J
    0.0605
    0.1150
    0.0789
    0.0166
    0.0658
    0.0711
    0.00 11
    0.0263
    0.0579
    08013
    0.1263
    0.0500
    0.1000
    0.0005
    00176
    0.0003
    
    0.0011
    COMPO<-».D
    PERCENT
    OF
    SITES
    TREATED
    AVEAAGE
    APPLI-
    CATION
    RATE
    (L» 'ACRE)
    ANLRACE
    AMOUNT
    A^fLIEo
    PE« SITE
    (L9 kCRE)
    NEW YORK— 38 SITES— Continued
    Nabam
    Nitrate
    Ox\demetonmsth>l
    Ethvl parathion
    Phosphamidon
    Sodium arser.te
    
    2.63
    7.89
    2.63
    5.26
    2.63
    2.63
    
    2.40
    26.17
    0.15
    0.45
    0.15
    0.90
    
    00632
    2X><58
    00039
    00:39
    00039
    0.0237
    
    NORTH CAROLINA— 29 SITES
    
    
    Aldrm
    Atrazinc
    Carbar>l
    Ceresan red
    Chromophon
    Copper carbonate
    Copper-8-quiix>!ipo;ate
    2,4-D
    2.4-DB
    DDT technical
    Diazinon
    Dicamba
    Dichloropropene
    Dieldrin
    Dinitrobutylphenol
    Diphenamid
    EPTC
    Ethylene dibromide
    Lindane
    Maleic hydr&ade
    Ethyl parathion
    Methyl paratbion
    Phorate
    Sulfur
    TDE technical
    
    Toxaphene
    Trifluralin
    Vernolate
    
    
    6.90
    6..90
    20.69
    3.45
    3.45
    3.45
    3.45
    20.69
    3.45
    13.79
    IS/1*
    3.45
    3.45
    6.90
    3.45
    6.90
    iAj
    1 *t
    3.45
    10.34
    
    1.75
    2.75
    1X3
    0.10
    0.15
    0.60
    0.01
    1.00
    OX'7
    0.70
    1.12
    1.20
    20.00
    1.25
    1.50
    1.07
    *SX>
    
    0.1207
    0.1*97
    0.2966
    0.0034
    0.0052
    0.0207
    1 0.0
    0 01 O.OCO3
    0.47
    6.90 1 0.50
    3.45
    6.90
    3.4$
    10.34
    690
    6.90
    3.45
    6.90
    
    0.04?6
    0.0345
    0.83 ' 0.0246
    1.13 j 0.0783
    11.10
    0.26
    001
    8.65
    0.57
    I.SS
    
    Oj«2«
    0,0272
    00007
    0.5966
    0.0197
    0.1276
    
    
    NORTH DAKOTA— 159 SITES
    Barban
    Capten
    Ceresan M
    Cercsan red
    Ceresan L
    2,4-D
    Dicamba
    Disulfoton
    Endrm
    Heptachlor
    Lindane
    Matathion
    Maneb
    MCPA
    Meth>lmercurv
    dic>andian-,idc
    1J6
    0.63
    0.63
    2JI
    5.03
    42.14
    1.89
    0.63
    0.63
    1.26
    1.89
    0.63
    O.f.3
    5.66
    
    41.51
    0.17
    0.01
    0.01
    0.01
    0.01
    0.40
    o.os
    3.00
    0.25
    0.04
    O.C2
    0.01
    1.50
    0.30
    
    0.01
    0.0021
    0.0001
    0.0001
    0.0003
    O.OO05
    0.1673
    0.0016
    0.0189
    0.0016
    0.0005
    O.OCKW
    0.000 1
    0.0094
    0.0171
    
    0.0042
    VOL. 6, No. 3, DJECKMHER 1972
                                                       417
    

    -------
    TABLE 10.—Summary of pesticides used in FY 1969 on cropland by State—Continued
    COMPOUND
    PERCENT
    OF
    SITES
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (LB/ACRE)
    AVERAGE
    AMOUNT
    APPLIED
    PER SITE
    (LB/ACRE)
    NORTH DAKOTA— 159 SITES— Continued
    Phenylmercury urea
    PMA
    Polyram
    
    0.63
    1.26
    0.63
    
    0.01
    0.0 1
    10.40
    
    0.0001
    00001
    0.0654
    
    OHIO— 66 SITES
    
    
    Aldrin
    Amiben
    Atrazine
    Captan
    Ceiesan M
    Copper sulfate
    2,4-D
    Dalapon
    Diazmon
    Dichlone
    Dictdrin
    Dinocap
    Dhhane M-45
    Linuron
    Malathion
    Ma neb
    Methylmercury
    dicyandiamide
    NPA
    PCP
    Picloram
    Randox T
    Sulfur
    TDE technical
    Trifluralin
    Ziram
    
    
    6.06
    3.03
    I2.U
    12.12
    1.52
    1.52
    19.70
    1.52
    1.52
    1.52
    1.52
    1.52
    1.52
    1.52
    1061
    303
    
    1.52
    1.52
    1 52
    i.52
    1.52
    1.52
    1.52
    1.52
    1 52
    
    
    3.00
    1.75
    1.20
    0.02
    0.01
    1.60
    044
    1.50
    0.50
    1.80
    0.01
    0.01
    030
    075
    0.01
    0.75
    
    005
    2.27
    1.50
    0.2S
    1.40
    25.00
    0.80
    1.00
    0.80
    
    
    0.1818
    0.0530
    0.1455
    0.0021
    0.0002
    0.0242
    0.0859
    0.0227
    0.0076
    0.0273
    00002
    0.0002
    0.0045
    0.0114
    0.001 1
    0.0227
    
    0.0008
    0.0344
    0.0227
    0.0038
    0.0212
    0.3788
    0.0121
    0.0152
    0.0121
    
    OKLAHOMA— 65 SITES
    
    Cacodylic acid
    Captan
    Caibary!
    Cere son M
    Ceresan red
    Chloroneb
    2,4-D
    2,4-DB
    Dieldrin
    Dimethoate
    Dinitrobutylphenol
    Disulfoton
    Falone
    Methylmercury
    dicyandiamidc
    Nitrate
    Ethyl parathion
    Methyl parathion
    PCNB
    Phosphamidon
    Thiram
    Trifluralin
    
    1.54
    4.62
    1.54
    20.00
    12.31
    1.54
    6.15
    4.62
    4.62
    1.54
    1.54
    7.69
    1.54
    
    1.54
    10.77
    3.08
    1231
    1.54
    1.54
    1.54
    462
    
    0.01
    001
    0.30
    0.01
    0.01
    0.01
    0.86
    0.50
    0.01
    0.50
    2.00
    0.58
    200
    
    001
    16.64
    0.75
    0.65
    0.01
    0 12
    0.01
    I.IO
    
    0.0002
    0.0005
    0.0046
    0.0020
    0.0012
    0.0002
    0.053 1
    00231
    0.0005
    0.0077
    0.0308
    0.0445
    0.0308
    
    0.0002
    1.7923
    00231
    0.0800
    0.0002
    0.0018
    0.0002
    0.0508
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    COMPOUND
    PtRCENT
    OF
    Sms
    TREATED
    AVEKAGE
    APPLI-
    CATION
    RATE
    (LB/ACRE)
    A>T*AGE
    AMOLVT
    APPLIED
    PE* SITE
    (LB/ACRE)
    PENNSYLVANIA— 31 SITES
    Atrazinc
    Azinphosmcthvl
    Captan
    Carbarvl
    Chlordane
    Copper sulfate
    2,4-D
    DDT technical
    Dicofol
    Dimtrobut) Iphenol
    Dinurocresol
    Dmocap
    19.35
    3.23
    3.23
    3.23
    3.23
    3.23
    16.13
    9.68
    3.23
    3.23
    3.23
    3.23
    Diuron 3.23
    Lindane
    Linuron
    Maneb
    Methyl demeton
    Nitrate
    Ethjl parathion
    Phorate
    Simazine
    3.23
    3.23
    3.23
    3.23
    3.23
    6.45
    3.23
    3.23
    Sodium arsenue ' 3.23
    Trifluralin
    
    3.23
    
    1.57
    ti.50
    0.01
    0.32
    1.20
    1.70
    0.92
    0.83
    0.42
    0.82
    3.00
    0.44
    OJ2
    0.02
    1. 00
    7.00
    1.50
    100.00
    0.45
    12.50
    040
    2.50
    0.75
    
    03032
    0.0161
    0.0003
    0.0103
    0.03S7
    10548
    0.14S4
    0.0806
    0.01J5
    0.0265
    00968
    0.01 42
    0.0103
    00006
    O.OJ23
    O.:i58
    0.0^84
    3.2258
    0.0:90
    0.4032
    0.0129
    0.0&06
    0.0242
    
    
    RHODE ISLAND— 1 SITE
    
    _
    Carbaryl
    DDT technical
    Disulfoton
    Dithane M-4S
    EPTC
    Ox>demetcnmeth)l
    
    
    100.00
    100.00
    100.00
    100.00
    100.00
    100.00
    
    
    0.80
    2.00
    2.00
    6.40
    5.00
    0.80
    
    
    08000
    2.0000
    20000
    6.4000
    5.0000
    0.8000
    
    
    SOUTH CAROLINA— 17 SITES
    
    
    Azodrin
    Carbaryl
    2,4-D
    DDT technical '
    DEF
    Demeton
    Diuron
    MSMA
    Nabam
    Ethyl parathion
    Meth>l parathion
    Phorate
    TDE technical
    Toxaphene
    Tnfluralin
    
    
    5.88
    17.65
    11.76
    29.41
    5.88
    5.88
    5.88
    5.88
    5.88
    11.76
    11.76
    5.88
    5.88
    17.65
    35.29
    
    
    0.40
    7.19
    0.40
    2.46
    0.20
    1.60
    0.72
    0.45
    1.20
    0.51
    5.10
    0.20
    2.25
    6.17
    0.21
    
    SOUTH DAKOTA— 106 SITES
    Atrazine
    Captan
    CarbanI
    1.89
    10.38
    0.94
    1.40
    0.01
    1.05
    
    0.0235
    1J^82
    0.0471
    0.7229
    0.0118
    0.0941
    0.0424
    00)265
    0.0706
    0.0600
    0.6000
    0.0118
    0.1324
    1.0894
    0.0753
    
    
    0.0264
    0.0010
    0.0099
                                       418
                                                               PESTICIDES MONITORING JOURNAL
    

    -------
                   TABLE 10.—Summary of pesticides used in FY 1969 on cropland fry Stale—Continued
    COMPOUND
    PERCENT
    OF
    SITES
    TREATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (LB/ACRE)
    AVERAGE
    AMOUNT
    APPLIED
    PER SITE
    (LB/ACRE)
    SOUTH DAKOTA— 106 SITES— Continued
    Ceresan M
    2.4-D
    Dalapon
    Dieldrin
    Heptachlor
    Lindanc
    Malathion
    MCPA
    Melhoxychlor
    Methylmercury
    dicyandiamide
    Phorate
    Ramrod
    Thiram
    0.94
    26.42
    0.94
    1.89
    3.77
    0.94
    6.60
    4.72
    3.77
    10.38
    0.94
    0.94
    0.94
    0.01
    0.47
    0.74
    0.01
    0.02
    0.01
    0.01
    0.20
    0.01
    0.01
    0.60
    1.00
    0.01
    0.0001
    0.1230
    0.0070
    0.0002
    0.0007
    0.0001
    0.0007
    0.0095
    0.0004
    0.0010
    0.0057
    0.0094
    0.0001
    TENNESSEE— 28 SITES
    Atrazme
    Bidrin
    Caplan
    Ceresan M
    Ceresan red
    Cotoran
    2,4-DB
    Disulfoton
    Diuron
    Lmuron
    Malaihion
    Methylmercury
    dicyandiamide
    MSMA
    Nitrate
    Nilralin
    PCNB
    Trifluralin
    21.43
    3.57
    10.71
    7.14
    3.57
    7.14
    7.14
    3 57
    7.14
    7.14
    3.57
    3.57
    3.57
    7.14
    3.57
    3.57
    14.29
    1.98
    0.54
    0.01
    0.01
    0.01
    0.78
    0.43
    0.01
    0.06
    0.75
    0.01
    0.01
    0.46
    250.00
    0.15
    0.01
    0.38
    0.4250
    00193
    0.0011
    00007
    0.0004
    0.0557
    •0 0307
    0.0004
    00046
    00536
    00004
    00004
    0.0164
    17.8571
    0.0054
    0.0004
    0.0543
    UTAH— 12 SITES
    Dichloropropene
    Heplachlor
    8.33
    8.33
    18000
    0.34
    15.0000
    0.0283
    VIRGINIA— 20 SITES
    Atra/ine
    Azinphosmethyl
    Carbaryl
    Copper oxide
    2,4-D
    2,4-DB
    DDT technical
    Diazinon
    Dtnitrobutylphenol
    Diphenamid
    Disulfoton
    Ethylcnc dibromidc
    Dichloropropane
    5.00
    5.00
    5.00
    10.00
    10.00
    500
    5.00
    5.00
    5.00
    5.00
    10.00
    5.00
    5.00
    4.00
    2.00
    2.75
    2.60
    1.12
    0.20
    2.00
    0.50
    1.50
    400
    680
    23.24
    54.43
    0.2000
    0.1000
    0.1375
    0.2600
    0.1125
    00100
    0.1000
    0.0150
    0.0750
    0.2000
    06800
    1.1620
    2.7215
    
    
    
    COMPOL^D
    PERCENT
    OF
    SITES
    THEATED
    AVERAGE
    APPLI-
    CATION
    RATE
    (LB/ACXE)
    AVERAGE
    AMOUNT
    AfPLIED
    Pta SITE
    UB'ACRF >
    VIRGINIA— 20 SITES— Continued
    Malathion
    Metboxychlor
    Ethyl parathion
    Phorate
    Sulfur
    Vernolate
    5.00
    5.00
    5.00
    5.00
    5.00
    5.00
    0.95
    1.00
    6.00
    3.00
    57.00
    2.40
    0.0475
    0.0500
    OJOOO
    0.1500
    2MO
    0.1200
    WASHINGTON— 2 SITES
    Ceresan L
    2,4-D
    50.00
    50.00
    WEST VIRGINIA— 5
    Azinphosme th> 1
    Eth>1 parathion
    20.00
    20.00
    0.01
    1.00
    SITES
    0.50
    1.50
    0.0050
    OJOOO
    
    0.1000
    03000
    WISCONSIN— 68 SITES
    Alrazine
    Ceresan red
    2,4-D
    Ramrod
    Trifluralm
    29.41
    1.47
    2.94
    1.47
    1.47
    2.61
    0.01
    0.75
    2.00
    2.00
    0.76
    -------
        TABLE 12.—Comparison of residues detected with use records for 12 Stales with highest arsenic residues, FY  1969
    STATE
    Arkansas
    Kentucky
    New England '
    New York
    North Dakota
    Ohio
    Pennsylvania
    AVERAGE
    AMOUNT Am IEO
    (LB/ACRE)
    •0.13
    PERCENT
    OF Sins
    TREATED
    4.4
    No Arsenic Compound* Used
    •0.88
    •0.12
    10.0
    5.3
    No Arsenic Compounds Used
    No Arsenic Compel
    «o.oa
    inds Used
    3.2
    MEAN RESIDUE
    1 LEVEL
    ' (FPM)
    i
    9.0
    8.4
    ,: 10J
    9-4
    8.5
    | 11.2
    ;
    PEACE vr
    POSITIVE
    Sins'
    100.0
    100.0
    100.0
    94.6
    100.0
    100.0
    1000
    1 Percent based on number of sites with residues greater than or equal to the sensiu'Mi>  limits.
    ' Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont.
    « Calculated for DSMA.
    ' Calculated for sodium arsenite.
    * Calculated for sodium arsenite and  lead arsenate.
        TABLE 13.—Comparison of residues delected with use  records for 5 States with  highest  DDTR  residues. FY 1969
    STATE
    Alabama
    California
    Michigan
    Mississippi
    South Carolina
    AVERAGE
    AMOUNT APPLIED
    (LB/ACRE)
    4.20
    0.38
    0.03
    1.07
    0.72
    PEXCENT
    OF SITES
    TREATED
    39.1
    13.6
    2.0
    31.0
    29.4
    MEAN RESIDUE
    LEVEL
    (PPM)
    1.13
    1.47
    2.09
    2.06
    1.17
    PEHCT.NT
    POSITIVE
    Srrts"
    9-"'.9
    F-i.6
    23.5
    89.7
    8S.2
    1 Percent based on number of sites with residues greater than or equal to the sensitivity limits.
         TABLE 14.—Comparison of residues detected with use records for 7 Stares n-iV/i highest dieldrin residues, FY 1969
    STATE
    Florida
    Illinois
    
    Iowa
    
    Kentucky
    North Carolina
    
    Virginia/West Virginia
    AVERAGE
    AMOUNT APPLIED
    (LB/ACRE)
    0.00
    0.29 aldrin
    0.01 dieldrin
    0.06 aldrin
    it> II.T.IK.
                                                                 420
                                                                                               PESTICIDES MONITORING JOURNAL
    

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     TABLE IS.—Fiftieth percentile value for pesticide residues in cropland soil including the 95% confidence interval by Slate
    PESTicroe
    UPPEH
    LIMIT
    (PPM)
    FIFTIETH
    PERCENTILE
    RESIDUE LEVEL
    (PFM)
    LOWER
    LIMIT
    (PPM)
    ALABAMA
    Arsenic
    p.p'-DDE
    o.p'-DDT
    p,p'-DDT
    DDTR
    p,p'-Tt>E
    
    Arsenic
    p,p'-DDE
    o.p'-DDT
    p,p'-DDT
    DDTR
    Dicldrin
    P,P'-TDE
    Toxaphene
    4.42
    0.10
    0.03
    0.27
    0.48
    0.02
    4.09
    0.07
    0.02
    0.21
    0.38
    0.01
    3.76
    0.04
    0.01
    0.15
    0.29
    0.01
    ARKANSAS
    7.42
    0.02
    0.01
    0.04
    O.Ifr
    0.00
    0.01
    O.IS
    7.26
    0.02
    001
    0.03
    0.09
    0.00
    0.01
    0.04
    7.10
    0.02
    0.00
    0.03
    0.08
    0.00
    0.01
    0.00
    CALIFORNIA
    Arsenic
    o.p'-DDE
    p.p'-DDE
    o.p'-DDT
    P,p'-DDT
    DDTR
    Dieldrin
    o,p'-TDE
    p.p'-TDE
    Toxaphene
    4.02
    000
    0.05
    0.01
    0.04
    0.14
    0.00
    0.00
    0.02
    0.02
    3.92
    0.00
    0.04
    0.01
    0.03
    0.13
    0.00
    0.00
    OOi
    0.01
    3.83
    0.00
    0.04
    0.00
    0.03
    0.12
    0.00
    0.00
    0.01
    0.00
    COLORADO
    Arsenic
    4.26
    4.20
    4.15
    FLORIDA
    Arsenic
    Chlordane
    P.P'-DDE
    o.p'-DDT
    p,p'-DDT
    DDTR
    P.p'-TDE
    0.64
    0.05
    0.03
    0.01
    0.07
    0.10
    0.02
    0.58
    0.03
    0.02
    0.00
    0.05
    0.08
    0.01
    0.53
    0.02
    0.01
    0.00
    0.03
    0.06
    0.00
    GEORGIA
    Aiscnic
    p.p'-DDE
    O.P'-DDT
    p,p'-DDT
    DDTR
    P.p'-TDE
    Toxaphene
    1.96
    0.06
    0.0 1
    0.17
    0.30
    0.01
    0.36
    1.88
    0.05
    0.01
    0.01
    0.23
    0.01
    028
    1.80
    0.04
    0.01
    0.09
    0 17
    0.01
    0.18
    PESTICIDE
    Urpw
    LIMIT
    (PPM)
    FIFTIETH
    Puccvnte
    RCSIDIT LEVEL
    (PPM)
    Lown
    LIMIT
    (PPM)
    IDAHO
    Arsenic
    p,p'-DDT
    DDTR
    2.8J
    0.00
    0.01
    2.53
    0.00
    0.00
    2.21
    o.co
    0.00
    ILLINOIS
    Aldrin
    Arsenic
    Chlordane
    DDTR
    Dieldrin
    Hcplachlor
    Heptachlor cpoxide
    0.03
    6.28
    0.01
    0.00
    0.03
    0.00
    0.00
    0.00
    610
    0.01
    0.00
    0.02
    0.00
    0.00
    0.00
    6.1}
    0.00
    040
    0.02
    0.00
    0.00
    INDIANA
    Aldrin
    Arsenic
    Dieldrin
    0.00
    7.24
    0.00
    0.00
    7.03
    0.00
    IOWA
    Aldrin
    Arsenic
    Alrazine
    Chlordane
    p.p'-DDE
    p,p'-DDT
    DDTR
    Dieldrin
    Heptachlor
    Heplachlor epoxjde
    0.00
    5.86
    0.01
    *O.OI
    0.00
    0.00
    0.00
    0.02
    0.00
    0.00
    0.00
    5.78
    0.00
    0.01
    000
    0.00
    0.00
    0.01
    0.00
    0.00
    0.00
    6.82
    0.00
    
    0.00
    5.71
    0.00
    0.00
    0.00
    0.00
    0.00
    0.01
    C.OO
    0.00
    KENTUCKY
    Atdrin
    Arsenic
    Dieldrin
    0.00
    8.45
    0.01
    0.00
    7.89
    0.01
    0.00
    7JO
    0X0
    LOUISIANA
    Arsenic
    p.p'-DDE
    o.p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    I .SO
    0.01
    0.01
    0.02
    0.02
    0.0)
    1.6S
    0.00
    0.00
    0.01
    0.01
    001
    1-51
    0.00
    0.00
    0.00
    0.01
    0.00
    MICHIGAN
    Arsenic
    p.p'-DDE
    DDTR
    Dicldrin
    4.92
    0.00
    000
    0.00
    3.83
    0.00
    0.00
    0.00
    2.93
    0.00
    0.00
    0.00
    VOL. 6, No. 3, DECEMBER 1972
                                                       421
    

    -------
    TABLE J5.—Fiftieth perccntile value far pesticide residues in cropland soil including the 95% confidence interval
                                              by State—Continued
    PESTICIDE
    UPPER
    LIMIT
    (PPM)
    FIFTIETH
    PERCENHLE
    RESIDUE LEVEL
    (PPM)
    LOWER
    LIMIT
    (PPM)
    MID-ATLANTIC STATES GROUP 1
    Arsenic
    5.87
    5.34
    4.83
    MISSISSIPPI
    Arsenic
    P.//-DDE
    o.p'-DDT
    p.p'-DDT
    DDTR
    P.p'-TDE
    Toxaphene
    4.86
    0.11
    0.06
    0.36
    0.61
    0.03
    0.24
    4.68
    0.09
    0.06
    0.29
    0.55
    0.02
    0.16
    4.51
    0.08
    0.05
    0.24
    0.45
    0.01
    0.09
    	
    MISSOURI
    Aid/in
    Arsenic
    Dieldrin
    0.00
    5.4J
    0.00
    0.00
    5.05
    0.00
    0.00
    4.69
    0.00
    NEBRASKA
    Anenic
    Chlordane
    p,p'-DDE
    DDTR
    Dieldrin
    473
    0.01
    000
    0.00
    0.00
    4.56
    000
    0.00
    0.00
    0.00
    4.40
    0.00
    0.00
    0.00
    0.00
    NEW ENGLAND STATES GROUP *
    Arsenic
    p,p'-DDE
    p.p'-DDT
    DDTR
    p.p'-TDE
    6.39
    0.02
    0.05
    0.04
    0.01
    5.71
    0.01
    0.02
    0.02
    0.01
    5.09
    0.00
    0.00
    0.01
    0.00
    NEW YORK
    Arsenic
    p,p'-DDE
    o.p'-DDT
    p,p'-DDT
    DDTR
    Dieldrin
    p.p'-TDE
    6.34
    0.00
    0.00
    0.01
    0.00
    0.00
    0.00
    6.12
    0.00
    0.00
    0.00
    0.00
    0.00
    0.00
    5.90
    0.00
    0.00
    0.00
    0.00
    0.00
    0.00
    NORTH CAROLINA
    Arsenic
    p.p'-DDE
    o.p'-DDT
    p.p'-DDT
    DDTR
    Dieldrin
    o.p'-TDE
    3.42
    0.03
    0.02
    0.05
    0.18
    0.01
    0.03
    307
    0.03
    0.01
    003
    0.13
    0.00
    0.02
    2.77
    0.02
    0.01
    0.02
    0.08
    0.00
    o.ot
    
    
    PESTICIDE
    UPPER
    Lism
    (PPM)
    FIFTIETH
    PEHCEVTIIE
    RESIDUE Lrvci.
    (WM)
    LOWER
    LIMIT
    (PPM)
    NORTH CAROLINA— Continued
    p.p'-TDE
    Toxaphene
    0.03
    0.16
    0.02
    0.07
    0.01
    0.01
    NORTH DAKOTA
    Arsenic
    P.p'-DDT
    DDTR
    6.82
    0.00
    0.00
    6.79
    0.00
    OHO
    6.75
    0.00
    0.00
    OHIO
    Aldrin
    Arsenic
    DDTR
    Dieldrin
    0.00
    7.85
    0.00
    0.00
    0.00
    7.58
    0.00
    0.00
    0.00
    7J2
    0.00
    0.00
    OKLAHOMA
    Arsenic
    2.19
    2.11
    1.02
    PENNSYLVANIA
    Arsenic
    p.p'-DDE
    p,p'-DDT
    DDTR
    Dieldrin
    p.p'-TDE
    7.22
    0.00
    0.00
    0.00
    0.01
    0.00
    6.65
    0.00
    0.00
    0.00
    0.00
    0.00
    6.15
    0.00
    0.00
    0.00
    CM
    0.00
    SOUTH CAROLINA
    Arsenic
    P.p'-DDE
    o.p'-DDT
    p.p--DDT
    DDTR
    P.p'-TDE
    1.98
    0.08
    0.04
    0.18
    0.3 1
    0.06
    1.82
    0.06
    0.03
    0.13
    0.15
    0.04
    1.68
    0X13
    0.02
    0.08
    0.06
    0.02
    SOUTH DAKOTA
    Arsenic
    Dieldrin
    3«ft
    0.00
    3.76
    0.00
    3.68
    0.00
    TENNESSEE
    Arsenic
    p.p'-DDE
    p.p'-DDT
    DDTR
    7.18
    0.00
    0.01
    0.01
    7.00
    0.00
    0.00
    0.01
    6.81
    0.00
    0.00
    0.00
                                                  422
                                                                                PESTICIDES MONITORING JOURNAL
    

    -------
       TABLE 15.—Fiftieth  perccntilr  value  for pesticide residues in cropland soil including the 9Sc,i confidence interval
                                                    by Stale—Continued
    PESTICIDE
    UPPtR
    LIMIT
    (PPM)
    FIFTIETH
    PFRCENTILE
    RCSIDUC LEVEL
    (PPM)
    LOWER
    LIMIT
    (PPM)
    VIRGINIA AND WEST VIRGINIA
    Arsenic
    p,p'-DDT
    DDTR
    Hcpiachlor epoxlde
    2.90
    0.0 1
    0.02
    0.01
    2.72
    000
    001
    000
    2.56
    0.00
    0.01
    0.00
    WASHINGTON
    Arsenic
    p.p'-DDT
    DDTR
    2.30
    0.00
    0.00
    222
    0.00
    0.00
    2.14
    0.00
    0.00
    WESTERN STATES GROUP'
    Arsenic
    p.p'-DDE
    3.57 3.40
    0.01 0.00
    3.23
    000
    
    
    PESTICIDE
    UPPER
    LIMIT
    (PPM)
    FIFTIETH
    PERCE STILE
    RESIDUE LEVEL
    (PPM)
    Low-En
    LIMIT
    (PPMI
    WESTERN STATES GROUP— Continued
    p.p'-DDT
    DDTR
    0.00
    0.01
    0.00
    0.01
    000
    0.00
    WISCONSIN
    Arsenic
    DDTR
    Dieldrin
    3.33
    0.00
    0.00
    3.14
    0.00
    0.00
    2.97
    0.00
    0.00
    WYOMING
    Arsenic
    0.92
    0.83
    0.78
    1 Includes Delaware, Maryland, and New Jersey.
    • Includes Connecticut, Maine, Massachusetts, New Hampshire. Rhode
    Island, and Vermont.
    * Includes Arizona. Nevada, New Mexico, and Utah.
                  TABLE 16.—Mean pesticide residues in ppm in soil for various  cropping regions, FY 1969
    COMPOUND
    Aldrin
    Arsenic
    Alrazine
    Carbophenolhion
    Chlordane
    2.4-D
    DCPA
    o.p'-DDE
    p.p'-DDE
    o.p'-DDT
    p.p'-DDT
    DDTR
    DEF
    Diazmon
    Dicofol
    Dieldrin
    Endosulfan (I)
    Endosulfan (II)
    Endosulfan sulfate
    Endrin
    Endrin aldehyde
    Endrin ketone
    Ettiion
    Ethyl paralhion
    Heptachlor
    Heptachlor epoxidc
    Isodnn
    Ltndane
    Malathion
    Mcthoxychlor
    PCNB
    o,p'-TDE
    r.p'-TDE
    Toxaphene
    Trifluralin
    CORN
    0.05
    7.44
    002
    
    009
    
    —
    
    -------
        TABLE \1.—Percent of sites with di-tcclablf pesticide residues in ppm in toil for \arious cropping regions. FY 1969
    COMPOUND
    
    Aldrin
    Arsenic
    Alrazine
    Carbophenolhion
    Chlordanc
    CORN
    
    23.5
    100.0
    14.5
    
    14.5
    2.4-D
    DCPA
    o,p'-DDE
    p,p'-DDE
    o,p'-DDT
    />,p'-DDT
    DDTR
    DKF
    Diazinon
    Dicofol
    Dieldrin
    Endosutfan (I)
    Endosulfan (II)
    Emlnsulfan sulfate
    Endcin
    Endrin Aldehyde
    Emliin ketone
    Elhion
    Ethyl parathion
    ConroN
    COTTON
    AND
    Ghl^tRAI.
    FARM IM;
    1
    6.4
    100.0
    
    
    1.8
    
    0.9
    984
    
    
    26
    
    — — _
    0.5
    95
    3.0
    7.7
    10.9
    —
    
    06
    41 8
    0.3
    0.2
    03
    03
    —
    —
    
    
    Htpuchlor 8.6
    Hcptachlor epoxide
    Isodrin
    Lindane
    Maljihion
    Melhoxychlor
    PCNB
    o.p'-TDF.
    p.p'-TDE
    Toxaphene
    Tnfluralin
    13.3
    1 2
    0.3
    
    —
    	 .
    03
    3.3
    0.2
    2,0
    15.6 i 5.2
    69.7 44.8
    51.4 , 250
    66.1 : 43.1
    72.5 i 47.4
    — '
    — —
    !— . —
    14.7
    — i
    GENERAL
    FARM IMC
    
    6.6
    99.4
    
    H«Y AND
    GENERAL
    FARMING
    
    2.1
    99.3
    
    _
    7.2 5.5
    14.3
    
    
    —
    10.8 2.8
    46.4 21.4
    27.1 10.3
    42.2 16.5
    49.4 12.8
    0.6 —
    
    
    — ' 0.7
    25.3 | 15.2
    — i 0.7
    _ 0.7
    —
    7.3
    —
    2.8
    —
    --
    2.6
    —
    0.9
    -
    _ _
    — 1.7
    0.9
    —
    —
    __
    —
    	
    0.9
    47.7
    22.9
    12.8
    3.4
    	
    2.6
    —
    —
    	
    26
    25.9
    12.1
    7.8
    - 1.4
    V6 —
    — —
    — i —
    
    10.0
    —
    1.8 1.4
    7.8
    1.8
    1.2
    
    —
    0.6
    10.8
    355
    10.2
    6.0
    4.1
    	
    0.7
    
    —
    __
    2.1
    .1.7
    
    — *•
    UMCAlED
    USD
    
    6.5
    99.1
    
    —
    11. 1
    
    0.9
    19.4
    58J
    33J
    33.7
    
    —
    125
    5.6
    39.8
    1.8
    5.6
    5.6
    11. 1
    —
    :.s
    6.3
    i:.s
    0.9
    13.0
    	
    l.g
    	
    ~~*
    	
    13.0
    38.0
    12.0
    93
    SMALL
    GRAINS
    
    0.6
    99.4
    83
    
    0.9
    1.7
    —
    —
    5.8
    3.0
    5.8
    6..
    _
    
    —
    7.0
    —
    
    
    09
    —
    —
    
    
    —
    0.9
    —
    0.6
    
    ~" "
    
    _
    1.8
    —
    03
    VE6ETAILC
    
    I.I
    98.9
    
    
    4J
    —
    —
    J.3
    38.3
    23.4
    31.9
    39.4
    _
    
    —
    23.4
    —
    1.1
    1.1
    3.2
    _
    1.1
    
    
    —
    4J
    _L_rl
    3.2
    
    1..
    	
    5J
    27.7
    1.1
    2.1
    VcccitaiE
    AND
    FRLIT
    
    3.0
    93.9
    
    
    21.2
    
    —
    15.1
    60.6
    36.4
    57.6
    63.6
    —
    
    —
    21.2
    —
    —
    —
    6.1
    3.0
    3.0
    
    
    3.0
    12.1
    	
    	
    
    —
    	
    6.1
    45 .<
    6.1
    3.0
    NOTE:  Blank = not analyzed; — = not detected.
                                                           424
    PESTICIDES MONITORING JOURNAL
    

    -------
                      APPENDIX G
    PESTICIDE PROPERTIES:   PERSISTENCE. SOLUBILITY,
                 LEACHABILITY. RUNOFF
                           425
    

    -------
     Organochlorine Insecticides
       Heptachlor, Aldrin, Metabolites
      	i	i	i	i	i	L
          1234
                    Years
                                      Phosphate Insecticides
                                               Diazinon
                                               mmm
                                               Di-Syston
                                               mm
                                               Phorate
                                       Malathion, Parathion
                                      0
         468
              Weeks
    10   12
      Urea, Triazine, and Picloram Herbicides
                                      Benzoic Acid and Amide Herbicides
      Propazine,  Picloram
      BBBB
      Simazine
      Atrazine,  Monuron
      mm
      Diuron
       Linuron, Fenuron
       ••
       Prometryne
        j	i
           ii    i   i    i   i
     0   2   4  6   8  10  12  14  16 18
                    Months
                                       2,3,6-TBA
                                       mmm
                                       Bensulide
                                       Diphenamide
    
                                       Ami ben
    2    4    6    8    10   12
              Months
    Phenoxy, Toluidine,  and Nitrile Herbicides    Carbamate and Aliphatic Acid Herbicides
      Triflurali n
      mmm
      2,4,5-T
      BBBH
      Dichlobenil
     Source:
      23456
           Months
    
    Figure G-l.  Persistence of  individual pesticides in soils.
    
      Kearney, P. C., and C. S.  Helling,  "Reactions of Pesticides in Soils,"
        Residue Reviews, Vol. 25  (1969).
                                          426
    

    -------
    Table G-l.   PERSISTENCE OF PESTICIDES AND THEIR
                DEGRADATION PRODUCTS IN SOIL
    Pesticide and
    Degradation Products
    Organochlorine
    Insecticides:
    Chlordane
    
    
    
    
    
    DDT
    
    
    
    
    
    
    
    
    
    
    Endosulfan
    Application Rate
    10 pg/llter
    Six rates ranging
    0.625 to 20 Ib/acre
    
    Normal rates
    10 Ib/acre/year
    1 to 2 Ib/acre
    20 Ib/acre/year
    1 to 2-1/2 Ib/acre
    10 Ib/acre
    1 Ib/acre
    
    100 ppm
    High rate
    Normal
    10 to 20 Ib/acre
    10 to 20 Ib/acre
    25 Ib/acre
    2 Ib/acre
    Type of Soil
    or Water
    
    Natural river vater
    Loam soil
    Soils
    Normal agricultural
    soils
    Sandy clay soil
    Soils
    Sandy clay soil
    Soils
    Silt loam soil
    Maine forest soil
    Soil
    Sandy loam
    Soil
    Normal agricultural
    soils
    Soil
    Soil
    85 soil types
    Soil
    Persistence Time
    20 to 8 weeks
    14.3 months
    9 to 13 years
    5 years
    4 years
    1 to 6 years
    4 years
    4 to 30 years
    15 years
    30 years
    4 years
    17 years
    3 years
    4 years
    > 4 years
    > 10 years
    8 years
    96 days
    Comments
    85% remains
    50% remains
    257. remains
    25 to OZ remains
    Half life
    5X remains
    Half Ufa
    51 remains
    10.61 reaalns
    Persistence
    22% remains
    397. remains
    361 remains
    25 to n remains
    Persistence
    Persistence
    44% remains
    No detectable
    amounts remain
                          427
    

    -------
    Table G-l.  (Continued)
    Pesticide and
    Degradation Products
    Toxaphene
    
    
    
    Organophosphorus
    Insecticides:
    Carbophenothion
    Diazlnon
    
    
    
    
    
    
    
    
    
    
    
    Dimethoate
    
    
    
    Ethion
    Guthion
    Malathion
    
    Application Rate
    20 Ib/acre/year
    140 ppm
    50 ppm
    100 ppm
    
    
    2 to 4 Ib/acre
    
    
    3 Ib/acre
    
    
    High application
    rates
    Normal
    
    
    2 kg/hectare
    2 to 4 Ib/acre
    3 ug/llter
    
    2 kg/hectare
    4 to 6 Ib/acre
    2 to 6 Ib/acre
    50 Ib/acre
    Normal
    Type of Soil
    or Water
    Sandy clay soil
    Soil
    Sandy loam
    Sandy loam
    
    
    Fine sandy soil
    Different types
    of soils
    Soil
    Soil
    
    Submerged tropical
    soil
    Normal agricultural
    soils
    Sandy loam soil
    Loam soil
    Fine sandy soil
    Silt loam soil,
    sandy loam soils
    Loam soil
    Fine sandy soil
    Fine sandy soil
    Loam soil
    Normal agricultural
    
    Persistence Time
    4 years
    > 6 years
    11 years
    14 years
    
    
    6 to 8 months
    20 weeks
    
    26 weeks
    < 40 days
    
    50 to 70 days
    
    12 weeks
    
    1 month
    10 months
    6 to 8 months
    1 month
    
    2 months
    2 to 3 months
    6 to 8 months
    5 months
    1 week
    
    Comments
    Half life
    Persistence
    50% remains
    45% remains
    
    
    5% remains
    < 8% remains
    
    Persistence
    Very low levels
    remain
    6 to 2% remains
    
    25 to 0% remains
    
    ~ 5% remains
    5% remains
    < 10% remains
    30% remains
    
    25% remains
    < 107. remains
    43 to 23% remains
    64 to 13% remains
    25 to 07. remains
             soils
              428
    

    -------
    Table G-l. (Continued)
    Pesticide and
    Degradation Products
    
    Methyl para th ion
    
    Parathion
    
    
    
    
    
    
    
    
    Paraoxon
    p-Nltrophenol
    
    Aminoparathion
    
    Phorate
    
    
    
    
    
    
    Herbicides:
    Amitrole
    
    
    Application Rate
    5 Ib/acre
    20 mg/kg
    
    31.4 Ib/acre
    31.4 Ib/acre
    1 Ib/acre
    
    
    5 Ib/acre
    Normal
    
    
    20 ppn
    20 ppm
    
    20 ppm
    
    3 Ug/g
    
    10 ppm
    Normal
    
    2 to 8 Ib/acre
    
    
    2 to 10 lb/-acre
    
    Type of Soil
    or Water
    Silt loam soil
    Sand-clayey soil
    
    Sandy loam soil
    Sandy loam soil
    Silty clay loam soil
    
    Soil
    Silt loam soil
    Normal agricultural
    soils
    Sandy loam soil
    Silt loam soil
    Silt loam soil
    
    Silt loam soil
    
    Silt loam soil and
    sandy loam soil
    Sandy loam soil
    Normal agricultural
    soils
    Sandy loam soil
    Fine sandy soil
    
    Moist loam field soil
    Soil
    
    Persistence Time
    8 days
    7 to 11 days
    
    4 years
    16 years
    2 months
    
    5 years
    3 months
    1 veek
    
    4 weeks
    1 day
    16 days
    
    2 days
    
    1 month
    
    68 days
    2 weeks
    
    1 to 2 weeks
    2 months
    
    3 to 5 weeks
    30 days
    
    Comments
    3. IX remains
    Complete
    decomposition
    3Z remains
    0.1X remains
    No detectable
    amounts
    Persistence
    31 remains
    Persistence
    
    < 5X remains
    < 107. remains
    No residues
    detected
    No residues
    detected
    Complete breakdown
    
    50% remains
    25 to OX remains
    
    < 2T, remains
    < 12X remains
    
    Persistence
    207. remains
               429
    

    -------
    Table G-l. (Continued)
    Pesticide and
    Degradation Products Application Rate
    20 pptn
    3 to 18 Ib/acre
    8.9 Ib/acre
    Atrazine 2 to 10 kg/hectare
    1 to 100 pptn
    1 and 2 Ib/acre
    Normal
    2 Ib/acre
    2 to 4 Ib/acre
    2 to 3 Ib/acre
    3 to 8 Ib/acre
    3.2 to 4 Ib/acre
    2,4-D Normal
    0.5 to 3 Ib/acre
    4 Ib/acre
    3.6 Ib/acre
    10 Ib/acre
    1.5 kg/hectare
    Type' of Soil
    or Water
    Soil
    Soil
    Soil
    Loam soil
    Four Hawaiian soils
    Soils
    Normal agricultural
    soils
    Soil
    Soil
    Soil
    Soil
    Soil
    Normal agricultural
    soils
    Moist loam soil
    Peat soils
    Clay loam soil
    Several soil type
    Podsolic soil
    Persistence Time
    7 weeks
    1 to 3 months
    4 to 5 months
    4 months
    34 days
    > 200 days
    10 months
    17 months
    4 to 7 months
    4 to 7 months
    12 months
    4 to 8 months
    1 month
    1 to 4 weeks
    4 to 18 weeks
    2 months
    2 to 14 weeks
    2 to 7 weeks
    Comments
    Persistence
    Residual phyto-
    toxlclty
    Residual phyto-
    toxicity
    32 to 627. remains
    15 to 30% remains
    Persistence
    25 to 0% remains
    Persistence
    Residual phyto-
    toxiclty
    Residual phyto-
    toxlclty
    Residual phyto-
    toxlclty
    Residual phyto-
    toxlcity
    25 to 07. remains
    Persistence
    Persistence
    Persistence
    Persistence
    Complete
                                                       detoxification
                 430
    

    -------
                                         Table  G-l.  (Continued)
    Pesticide and
    Degradation Products
    
    
    
    Application Rate
    Average
    4 to 40 Ib/acre
    5 Ib/acre
    Type of Soil
    or Water
    Soil
    Soils
    Soils
    Persistence Time
    1 month
    1 month
    1 month
    Comments
    Persistence
    Residual phyto-
    toxlcity
    Residual phyto-
    toxicity
     Dacthal
    
     Dalapon
    Diphenamid
    Recommended rates
    
    SO ppm
    
    
    
    
    50 ppm
    
    
    
    Normal
    
    
    5 to 40 Ib/acre
    
    7.4 to 20 Ib/acre
    
    
    20 Ib/acre
    
    
    6 to 8 Ib/acre
    
    
    Recommended rates
    
    Normal
    
    
    3 to 4 Ib/acre
    
    
    3 Ib/acre
    
    
    
    3.75 Ib/acre
    Most soil types
    
    43 different
      California soils
    
    Soils
    
    Different types of
      soils (20 to 27%
      moisture)
     100 days
    
     Range from
       2 to 8 weeks
    
     5 weeks
    
     4 to 5 weeks
                                                       Normal agricultural      8 veeks
                                                         soils
    
                                                       Moist loam field  soil    10  to 60 days
    
                                                       Soils                   1 month
                                                       Soils
                                                       Soils
    Most soil types
    
    Normal agricultural
      soils
    
    Soils
                                                       Soils
                                                       Soils
     3 to 4 months
    
    
     1 to 2 months
    
    
     3 to 6 months
    
     8 months
    
    
     10 to 12 months
    
    
     3 months
    
    
    
    < 3 months
    Average half life
    
    Total dlsapperance
      to 66% remaining
    
    No phytotoxlcity
    
    No residue remains
    25 to 0% remains
    
    
    Persistence
    
    Residual phyto-
      toxlcity
    
    Residual phyto-
      toxicity
    
    Residual phyto-
      toxiclty
    
    Average persistence
    
    25 to OJ. remains
                                                 Residual phyto-
                                                   toxiclty
    
                                                 Residual phyto-
                                                   toxicity
    
                                                 Residual phyto-
                                                   toxiclty
                                                        431
    

    -------
                                     Table G-l.  (Continued)
    Pesticide and
    Degradation Products
    Diuron
    
    
    
    
    
    
    
    
    
    
    DNBP
    
    
    
    
    
    
    
    
    DMOC
    
    MCPA
    
    
    Application Rate
    Normal
    
    1 to 3 Ib/acre
    10 to 40 Ib/acre
    1 to 2 Ib/acre
    
    3.6 to 4 Ib/acre
    1 to 2 Ib/acre
    
    2 Ib/acre
    
    6 to 9 Ib/acre
    
    16 Ib/acre
    8 Ib/acre
    
    12 Ib/acre
    
    0.05 Ib/acre
    
    4 kg/hectare
    SO ppm
    1/2 to 3 Ib/acre
    
    Type of Soil
    or Water
    Normal agricultural
    soils
    Moist loam field soil
    Moist loam field soil
    Clay loam and silt
    loam soils
    Soils
    Soils
    
    Soils
    
    Moist loam field
    soil
    Soil
    Soil
    
    Soil
    
    Soil
    
    Soil
    Soil
    Mosit loam field
    soil
    
    Persistence Time
    8 months
    
    3 to 6 months
    6 to 24 months
    18 to 20 weeks
    
    5 to 7 months
    4 to 8 months
    
    15 months
    
    3 to 5 weeks
    
    4 to > 8 weeks
    6 months
    
    > S months
    
    > 3 months
    
    28 weeks
    7 days
    1 to 4 weeks
    
    
    Comments
    25 to 07. remains
    
    Persistence
    Persistence
    Persistence
    
    Residual phyto-
    toxiclty
    Residual phyto-
    toxicity
    Residual phyto-
    toxlcity
    Persistence
    
    Persistence
    Residual phyto-
    toxicity
    Residual phyto-
    toxicity
    Residual phyto-
    toxlclty
    < 0.01 ppm remains
    Persistence
    Persistence
    
                            Normal
                                                  Normal agricultural
                                                    soil
                                              3 months
    25 to 07. remains
    Pyrazon
    4 ppm
                                                  Soil
                                                                        6  to 7 months
    Almost disappeared
                                                   432
    

    -------
    Table G-l. (Continued)
    Pesticide and
    Degradation Products
    
    Simazine
    
    
    
    
    
    
    
    
    
    
    Sodium arsenite
    Sodium chlorate
    
    Sutau
    TCA
    
    Application Rate
    Recommendation rdtes
    3.6 Ib/acre
    1 to 4 Ib/acre
    10 to 40 Ib/acre
    2 Ib/acre
    
    3 kg/hectare
    Normal
    2 to 5 Ib/acre
    0.45 to 4.5 Ib/acre
    4 Ib/acre
    3.2 to 4.0 Ib/acre
    Recommendation rates
    450 to 1,200 Ib/acre
    300 Ib/acre
    Recommendation rates
    15 Ib/acre
    
    lype of Soil
    or Water
    Soils
    Clay loam soil
    Moist loam field soil
    Moist loam field soil
    Soil
    Soil
    Soil
    Normal agricultural
    soil
    Soil
    Soil
    Soil
    Soil
    Soils
    Moist loam field
    soil
    Soil
    Several soils
    Soil
    Soils
    Persistence Time
    3 to 6 months
    60 days
    3 to 6 months
    6 to 24 months
    17 months
    24 weeks
    11 weeks
    1 year
    12 months
    3 to 7 months
    18 months
    4 to 14 months
    5 years
    6 to 12 months
    > 1 year
    1.5 to 3 weeks
    42 to 64 days
    5 weeks
    Comments
    Average persistenc
    Persistence
    Persistence
    Persistence
    Persistence
    157. activity remair
    Total decompositior
    25 to 0% remains
    Residual phyto-
    toxlcity
    Residual phyto-
    toxlcity
    Residual phyto-
    toxicity
    Residual phyto-
    toxicity
    Phytotoxicity
    Persistence
    Persistence
    Half life
    Persistence
    No phytotoxicity
               433
    

    -------
                                 Table G-l.  (Concluded)
      Pesticide and
    Degradation Products
    Trifluralin
     Other Pesticides:
    
    Captan (fungicide)
    Naban (fungicide)
    
    Ziram (fungicide)
    Application Rate
    40 to 100 Ib/acre
    Normal
    8 to 60 Ib/acre
    12.5 to 67 Ib/acre
    16 to 30 Ib/acre
    1 and 2 Ib/acre
    0.75 Ib/acre
    Normal
    Type of Soil
    or Water
    Moist loam field soil
    Normal agricultural
    soil
    Soils
    Soils
    Sotls
    Soils
    Soils
    Normal agricultural
    Persistence Time
    50 to 90 days
    12 weeks
    1 to 3 months
    7 to 12 months
    4 months
    > 200 days
    10 to 12 months
    6 months
    Comments
    Persistence
    25 to 07. remains
    Residual phyto-
    toxicity
    Residua., phyto-
    toxicity
    Residual phyto-
    toxicity
    Persistence
    10 to 157. remains
    25 to 07. remains
                                              soils
                          Well distributed in
                           soil
    100 pptn
    Fumus sandy soils
    Soil
    Soil
    Soil
    Soil
    3 weeks
    1 to 2 days
    65 days
    > 20 days
    > 35 days
    Half decay value
    Half life
    Persistence
    Persistence
    Persistence
     Source:  "The Effects of Agricultural  Pesticides in the Aquatic  Environment,"
                 Irrigated Croplands, San Joaquin  Valley, Office of Water Programs,
                 Environment, American Chemical Society, Washington,  B.C.
                                              434
    

    -------
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    REFERENCES TO TABLE G-3
    
     1.  U.S. Environmental Protection Agency,  "A Catalog of Research in
           Aquatic Pest Control and Pesticide Residues in Aquatic Environ-
           ments," May 1972.
    
     2.  Axe, J.A., A. C. Mathers, and A. F.  Wiese,  "Disappearance of Atra-
           zine, Propazine, and Trifluralin from Soil and Water," 22nd Annual
           Meeting of the Southern Weed Science Society, Proceedings. 21-23
           January 1969.
    
     3.  Sheets, T. J., W. L. Rieck, and J. F.  Lutz, "Movement of 2,4-D,
           2,4,5-T, and Picloram in Surface Water," Southern Weed Science
           Society, Proceedings (1972).
    
     4.  Caro, J. H., H. P. Freeman, D. E. Glotfelty, B. C. Turner, and
           W. M. Edwards, "Dissipation of Soil-Incorporated Carbofuran in
           the Field," J. Agr. Food Chem.. 2J.(6) : 1010-1015 (1973).
    
     5.  Bailey, G. W., et al., "Herbicide Runoff from Four Coastal Plain Soil
           Types," EPA Report No.  EPA-660/2-74-017,  April 1974.
    
     6.  Ritter, W. F., H. P. Johnson, W. G.  Lovely, and M. Molnau, "Atra-
           zine, Propachlor, and Diazinon Residues on Small Agricultural
           Watersheds.  Runoff Loses, Persistence, and Movement," Environ.
           Sci. Technol., 8(l):38-42 (1974).
    
     7.  Hall, J. K., M. Pawless,  and E. R. Higgins, "Losses of Atrazine in
           Runoff Water and Soil Sediment," J. Environ. Quality, 1(2):172-
           176, April/June 1972.                                 ~"
    
     8.  White, A. W., A. B. Barnett, B. G. Wright, and J. H. Holladay,
           "Atrazine Losses from Fallow Land Caused by Runoff and Erosion,"
           Environ. Sci. Technol.. l(9):740-744 (1967).
    
     9.  Haan, C. T., "Movement of Pesticides by Runoff and Erosion," Tr. ASAE,
           14(3):445, May-June 1971.
    
    10.  Barnett, A. P., E. W. Hauser, A. W. White, and J. H. Holladay, "Loss
           of 2,4-D.  Wash-Off from Cultivated Fallow Land," Weeds, 15:133-
           137  (1967).                                              ~~
    
    11.  Epstein, E., and W. J. Grant, "Chlorinated Insecticides in Runoff Water
           as Affected by Crop Rotation," Soil Sci. Am. Proc., 32(3):423,
           May-June  1968.
                                       438
    

    -------
                       APPENDIX H
    STATISTICS OF PRICING SALT .APPLICATION ON HIGHWAY
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    -------
         Table H-2.   MILEAGE  OF  TREATED  HIGHWAYS AND TOLLWAYS, AND MEAN ANNUAL
                     SNOWDAYS BY FTYTE
           State
    
    Northeastern States
    
      Maine
      New Hampshire
      Vermont
      Massachusetts
      Connecticut
      Rhode Island
      New York
      Pennsylvania
      New Jersey
      Delaware
      Maryland
      Virginia
    
    North-Cental States
    
      Ohio
      West Virginia
      Kentucky
      Indiana
      Illinois
      Michigan
      Wisconsin
      Minnesota
      North Dakota
    
    Southern States
    
      Arkansas
      Tennessee
      North Carolina
      Mississippi
      Alabama
      Georgia
      South Carolina
      Louisiana
      Florida
    Single-Lane
    Kilometers
    Treated
    x 1,000-'
    12.1
    11.3
    7.4
    15.1
    15.1
    8.4^
    59.4
    89.0
    12.9
    1.3
    10.8
    22.2
    173 . lk/
    27.2
    34.9
    25.3
    62.9
    37.8
    40.0
    186. Ok/
    111.8k/
    N.A.
    N.A.
    12.2
    5.3
    0.1
    7.2
    N.A.
    N.A.
    0.0
    Single-Lane
    Miles
    Treated
    x 1,000-/
    7.5
    7.0
    4.6
    9.4
    9.4
    5.2^
    36.9
    55.3
    8.0
    0.8
    6.7
    13.8
    107.6k/
    16.9
    21.7
    15.7
    39.1
    23.5
    25.0
    115.6k/
    69.5k/
    N.A.
    N.A.
    7.6
    3.3
    0.1
    4.5
    N.A.
    N.A.
    0.0
    
    
    Mean Annual
    Snowdays — /
    30
    30
    20
    18
    15
    12
    20
    18
    7
    5
    8
    5
    10
    12
    5
    8
    9
    20
    18
    15
    10
    3
    3
    3
    1
    1
    1
    1
    1
    0
                                           443
    

    -------
    Table  H-2.  (Concluded)
    Single-Lane Single-Lane
    Kilometers Miles
    Treated Treated Mean Annual
    State
    West-Central States
    Iowa
    Missouri
    Kansas
    South Dakota
    Nebraska
    Colorado
    Southwestern States
    Oklahoma
    New Mexico
    Texas
    Western States
    Washington
    Idaho
    Montano
    Oregon
    Wyoming
    California
    Nevada
    Utah
    Arizona
    District of Columbia
    Alaska
    Hawaii
    a/ Source: Hanes, R. E. , L.
    Deicing Salts
    x 1,000 x
    
    21.1
    51.5
    41.7
    96. 9±'
    123.9^
    3.9
    
    N.A.
    11.7
    N.A.
    
    24.6
    16.1
    3.2
    29.8
    20.3
    9.7
    N.A.
    20.4
    N.A.
    1.3
    N.A.
    0.0
    W. Zelazny, and R. E.
    1,000
    
    13.1
    32.0
    25.9
    60.2^'
    77.0^
    2.4
    
    N.A.
    7.3
    N.A.
    
    15.3
    10.0
    2.0
    18.5
    12.6
    6.0
    N.A.
    12.7
    N.A.
    0.8
    N.A.
    0.0
    Blaser, Effects
    Snowdays
    
    10
    7
    7
    10
    10
    20
    
    3
    10
    3
    
    15
    20
    20
    20
    20
    5
    10
    20
    10
    7
    23
    0
    of
    on Water Quality and Biota, Highway Research
    Board, National Cooperative Highway
    91 (1970).
    b_/ MRI estimates.
    c/ Source: U.S. Department
    National Atlas
    N.A. - Not available.
    
    
    Research Program
    
    
    Report
    
    
    of the Interior, Geological Survey, The
    of the United States
    
    (1970).
    
    
    
                    444
    US GOVERNMENT PRINTING OFFICE 1975-657-695/5319  Region No. 5-11
    

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