Untod State*
fiwiromnantal FrancOo*
AthanaOA 30613
eft and 0<
Water Quality
Assessment:
A Screening
Procedure for Toxic and
Conventional Pollutants in
Surface and Ground
Water—Part I
(Revised—1985)
-------
EPA/600/6-85/002a
September 1985
WATER QUALITY ASSESSMENT:
A Screening Procedure for Toxic
and Conventional Pollutants
1n Surface and Ground Water
(Revised 1985)
Part 1
by
W.B. Mills, D.B. Porcella, M.J. Ungs, S.A. Gher1n1, K.V. Summers,
Llngfung Hole, G.L. Rupp, and G.L. Bowie
Tetra Tech, Incorporated
Lafayette, California 94549
and
D.A. Halth
Cornell University
Ithaca. New York 14853
Produced by:
JACA Corporation
Fort Washington, Pennsylvania 19034
Contract No. 68-03-"131
Prepared 1n Cooperation with U.S. EPA's
Center for Water Quality Modeling
Environmental Research Laboratory
Athens, Georgia
Monitoring and Data Support Division
Office of Water Regulations and Standards
Office of Water
Washington, O.C.
Technology Transfer
Center for Environmental Research Information
Cincinnati, Ohio
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 306i3
-------
DISCLAIMER
Mention of trade names or commercial products does not constitute endorse-
ment or recommendation for use by the U.S. Environmental Protection Agency.
-if-
-------
ABSTRACT
New technical developments in the field of water quality assessment and
a reordering of water quality priorities prompted a revision of the first two
editions of this manual. The utility of the revised manual Is enhanced by
th« Inclusion of methods to predict the transport and fate of toxic chemicals
In ground water, and by methods to predict the fate of metals in rivers. In
addition, major revisions were completed on Chapter 2 (organic toxicants).
Chapter 3 (waste loadings), and Chapter 5 (impoundments) that reflect recent
advancements In these fields.
Applying the manual's simple techniques, the user is now capable of
assessing the loading and fate of conventional pollutants (temperature,
biochemical oxygen demand-dissolved oxygen, nutrients, and sediments) and
toxic pollutants (from the U.S. EPA list of priority pollutants) in streams,
Impoundments, estuaries, and ground waters. The techniques are readily
programmed on hand-held calculators or microcomputers. Host of the data
required for using these procedures are contained in the manual.
Because of its size, the manual has been divided Into two parts. Part
I contains the Introduction and chapters on the aquatic fate of toxic organic
substances, waste loading calculations, and the assessment of water quality
parameters in rivers and streams. Part II continues with chapters on the
assessment of Impoundments, estuaries, and ground water and appendices E, H,
I, and J. Appendices D, F, and G are provided on microfiche in the EPA-printed
manual. Appendices A, B, and C, which appeared in the first two editions,
are now out of date and have been deleted.
This report is submitted in fulfillment of Contract No. 68-03-3131 by
JACA Corp. and Tetra Tech, Inc. under the sponsorship of the U.S. Environ-
mental Protection Agency. Work was completed as of May 1985.
-111-
-------
TABLE OF CONTENTS
Chapter Page
PART I
DISCLAIMER 11
ABSTRACT 111
LIST OF FIGURES (Part I) x11
LIST OF TABLES (Part M x1x
ACKNOWLEDGEMENTS xxv11
1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 PURPOSE AND SCOPE 1
1.3 METHODOLOGY APPLICATION 3
1.3.1 Base Maps 3
1.4 LIMITATIONS 3
REFERENCES 4
2 AQUATIC FATE OF TOXIC ORGANIC SUBSTANCES 5
2.1 INTRODUCTION 5
2.1.1 Background 5
2.1.2 Comparison of Conventional and Toxic Pollutants 5
2.1.3 Water Quality Criteria 7
2.1.4 Frequency of Discharge of Toxic Substances from
Industries 7
2.1.5 Physical and Chemical Characteristics of Toxic
Organic Compounds 18
2.1.6 Scope and Organization of Chapter 18
2.2 SCREENING METHODS FOR TOXIC ORGANIC COMPOUNDS 24
2.2.1 Modeling the Fate of Toxic Organlcs 24
2.2.2 Use of Assessment Techniques as Screening Tools 29
2.3 SPECIATION PROCESSES 41
2.3.1 Acid-Base Effects 41
2.3.2 Sorptlon on Suspended Sediments 46
2.4 TRANSPORT PROCESSES 58
2.4.1 Solubility Limits 58
2.4.2 Volatilization 59
-V-
-------
Chapter Page
2.5 TRANSFORMATION PROCESSES 77
2.5.1 Biodegradation 77
2.5.2 Photolysis 95
2.5.3 Hydrolysis 129
REFERENCES 137
3 WASTE LOADING CALCULATIONS 142
3.1 INTRODUCTION 142
3.2 BACKGROUND POLLUTION LOADS 142
3.3 NONPOINT SOURCE MODELS 150
3.4 RURAL RUNOFF LOADS 151
3.4.1 Source Areas 151
3.4.2 Runoff 152
3.4.3 Erosion and Sediment 160
3.4.4 Chemical Loading Functions for Rural Runoff 178
3.5 SALT LOADS IN IRRIGATION RETURN FLOWS 207
3.5.1 Description 207
3.5.2 Estimation of Return Flows 209
3.6 URBAN RUNOFF LOADS 215
3.6.1 Annual Urban Runoff and Combined Sewer Loads 215
3.6.2 Event Loads In Urban Runoff 219
3.7 GROUND WATER WASTE LOADS 231
3.7.1 Characteristics 231
3.7.2 Water Balance 233
3.7.3 Nitrate Loads to Ground Water from Waste Application
Sites 233
3.7.4 Leaching of Organic Chemicals 239
3.8 ATMOSPHERIC WASTE LOADS 245
3.8.1 Dry Deposition 245
3.8.2 Wet Deposition (Precipitation Scavenging) 253
3.9 POINT SOURCE WASTE LOADS 253
3.9.1 Municipal Waste Loads 254
3.9.2 Industrial Waste Loads 262
REFERENCES 273
4 RIVERS AND STREAMS 278
4.1 INTRODUCTION 278
4.1.1 Scope 278
4.1.2 Significance of Problem Areas 279
4.1.3 Applicability to Other Problems 282
4.1.4 Sources of Pollutants 283
4.1.5 Assumptions 283
4.1.6 Data Requirements 286
4.1.7 Selection of Season 287
4.1.8 River Segmentation 289
-vl-
-------
Chapter Page
4.1.9 Mixing Zones 294
4.1.10 Water and Pollutant Balances 296
4.1.11 Hand Held Calculator Programs 306
4.2 CARBONACEOUS AND NITROGENOUS OXYGEN DEMAND 306
4.2.1 Introduction 306
4.2.2 BOD Decay Rate 310
4.2.3 Mass Balance of BOO 313
4.2.4 Typical Solutions ..... 315
4.2.5 Other Simplifying Procedures. . . 317
4.2.6 Interpretation of Results 321
4.3 DISSOLVED OXYGEN 321
4.3.1 Introduction 321
4.3.2 Dissolved Oxygen Mass Balance 323
4.3.3 federation Rate 323
4.3.4 Effect of Dams on Re aeration 330
4.3.5 Dissolved Oxygen Saturation 331
4.3.6 DO-BOD Interactions 333
4.3.7 Dissolved Oxygen Calculations 335
4.3.8 General Dissolved Oxygen Deficit Equation 340
4.3.9 Photosynthesis and Respiration 341
4.3.10 Benthic Demand 345
4.3.11 Simplifying Procedures 1n Dissolved Oxygen
Calculations 347
4.4 TEMPERATURE 354
4.4.1 Introduction 354
4.4.2 Equilibrium Temperature .... 355
4.4.3 Calculation of Equilibrium Temperature 358
4.4.4 Screening of Thermal Discharges 371
4.4.5 Longitudinal Temperature Variation 381
4.4.6 Diurnal Temperature Variation 383
4.4.7 Low Flow and Temperature 383
4.4.8 Interrelationships Between Temperature Prediction
Tools 385
4.5 NUTRIENTS AND EuTROPHICATION POTENTIAL 3«7
4.5.1 Introduction 387
4.5.2 Bas1c.Theory 388
4.5.3 Estimating Instream Nutrient Concentrations 390
4.5.4 Nutrient Accounting System 392
4.6 TOTAL COLIFORM BACTERIA 395
4.6.1 Introduction 395
4.6.2 Mass Balance for Total Coliforms 396
.7 CONSERVATIVE CONSTITUENTS 400
.7.1 Introduction 400
.7.2 Mass Balance for Conservative Constituents 400
.8 SEDIMENTATION 402
.8.1 Introduction. 402
.8.2 Long-Term Sediment Loading from Runoff 405
.8.3 Bed Material Load 405
-------
Chapter Page
4.9 TOXIC SUBSTANCES 417
4.9.1 Methods of Entry of Toxic Pollutants Into Rivers 417
4.9.2 Vertical Distribution of Sorbate Within Rivers 420
4.9.3 Transport and Transformation Expre •ans for Toxicants
In Rivers 426
4.10 METALS 457
4.10.1 Introduction , 457
4.10.2 Water Quality Criteria, Bd:
-------
Chapter
5.7.2 Stratification Ill
5.7.3 Sedimentation 115
5.7.4 Eutroph1cat1on 123
5.7.5 Hypo11mnet1c DO Depletion 128
5.7.6 Toxicants 133
REFERENCES 136
GLOSSARY OF TERMS 139
6 ESTUARIES 142
6.1 INTRODUCTION 142
6.1.1 General 142
6.1.2 EstuaMne Definition 143
6.1.3 Types of Estuaries 143
6.1.4 Pollutant Flow In an Estuary 145
6.1.5 Estuarlne Complexity and Major Forces 149
6.1.6 Methodology Summary 151
6.1.7 Present Water-Quality Assessment 153
6.2 ESTUARINE CLASSIFICATION 155
6.2.1 General 155
6.2.2 Classification Methodology 155
6.2.3 Calculation Procedure 155
6.2.4 Stratification-Circulation Diagram Interpretation .... 157
6.2.5 Flow Ratio Calculation 163
6.3 FLUSHING TIME CALCULATIONS 165
6.3.1 General 165
6.3.2 Procedure 165
6.3.3 Fraction of Fresh Water Method 170
6.3.4 Calculation of Flushing Time by Fraction of Freshwater
Method 171
6.3.5 Branched Estuaries and the Fraction of Freshwater
Method 176
6.3.6 Modified Tidal Prism Method 176
6.4 FAR FIELD APPROACH TO POLLUTANT DISTRIBUTION IN
ESTUARIES 184
6.4.1 Introduction 184
6.4.2 Continuous Flow of Conservative Pollutants 185
6.4.3 Continuous Flow Non-Conservative Pollutants 197
6.4.4 Multiple Waste Load Parameter Analysis 204
6.4.5 D1spers1on-Advect1on Equations for Predicting
Pollutant Distributions 207
6.4.6 PMtchard's Two-Dimensional Box Model for Stratified
Estuaries 216
6.5 POLLUTANT DISTRIBUTION FOLLOWING DISCHARGE FROM A
MARINE OUTFALL 226
6.5.1 Introduction 226
6.5.2 Prediction of Initial Dilution 227
6.5.3 Pollutant Concentration Following Initial Dilution. ... 248
6.5.4 pH Following Initial Dilution 250
6.5.5 Dissolved Oxygen Concentration Following Initial
Dilution 255
-------
Chapter Page
6.5.6 Far Field Dilution and Pollutant Distribution 257
6.5.7 Farfield Dissolved Oxygen Depletion 263
6.6 THERMAL POLLUTION 266
6.6.1 General 266
6.6.2 Approach 267
6.6.3 Application 269
6.7 TURBIDITY 274
6.7.1 Introduction 274
6.7.2 Procedure to Assess Impacts of Uastewater Discharges
on Turbidity or Related Parameters 276
6.8 SEDIMENTATION 282
6.8.1 Introduction 282
6.8.2 Qualitative Description of Sedimentation 282
6.8.3 EstuaMne Sediment Forces and Movement 283
6.8.4 Settling Velocities 287
6.8.5 Null Zone Calculations 291
REFERENCES 295
7 GROUND WATER 300
7.1 OVERVIEW 300
7.1.1 Purpose of Screening Methods 300
7.1.2 Ground Water vs. Surface Water 301
7.1.3 Types of Ground Water Systems Suitable for Screening
Method 302
7.1.4 Pathways for Contamination 303
7.1.5 Approach to Ground Water Contamination Problems 305
7.1.6 Organization of This Chapter. 309
7.2 AQUIFER CHARACTERIZATION 310
7.2.1 Physical Properties of Water 310
7.2.2 Physical Properties of Porous Media 310
7.2.3 Flow Properties of Saturated Porous Media 319
7.2.4 Flow Properties of Unsaturated Porous Media 323
7.2.5 Data Acquisition or Estimation 329
7.3 GROUND WATER FLOW REGIME 345
7.3.1 Approach to Analysis of Ground Water Contamination
Sites 345
7.3.2 Water Levels and Flow Directions 346
7.3.3 Flow Velocities and Travel Times 353
7.4 POLLUTANT TRANSPORT PROCESSES 363
7.4.1 Dispersion and Diffusion 363
7.4.2 Chemical and Biological Processes Affecting Pollutant
Transport 374
7.5 METHODS FOR PREDICTING THE FATE AND TRANSPORT OF
CONVENTIONAL AND TOXIC POLLUTANTS 382
7.5.1 Introduction to Analytical Methods 382
7.5.2 Contaminant Transport to Deep Wells 390
7.5.3 Solute Injection Wells: Radial Flow 396
-x-
-------
Chapter
Page
7.5.4
7.5.5
7.5.6
7.6
7.6.1
7.6.2
7.6.3
Contaminant Release on the Surface with 1-D Vertical
Two-Dimensional Horizontal Flow with a Slug Source. . . .
Two-Dimensional Horizontal Flow with Continuous
INTERPRETATION OF RESULTS .....
Quantifying Uncertainty
Guidelines for Proceeding to More Detailed Analysis . . .
REFERENCES
References Sited
Additional References on Ground Water Sampling
APPENDIX A.
APPENDIX B.
APPENDIX C.
APPENDIX D
APPENDIX E
APPENDIX F
APPENDIX G
APPENDIX H
APPENDIX I
APPENDIX J
IMPOUNDMENT THERMAL PROFILES
MODELING THERMAL STRATIFICATION IN IMPOUNDMENTS
RESERVOIR SEDIMENT DEPOSITION SURVEYS
INITIAL DILUTION TABLES
EQUIVALENTS BY COMMONLY USED UNITS OF MEASUREMENTS. . . .
ADDITIONAL AQUIFER PARAMETERS
MATHEMATICAL FUNCTIONS
403
410
417
423
423
424
429
435
435
444
A-l
B-l
C-l
0-1
E-l
F-l
G-l
H-l
1-1
J-l
-xl-
-------
LIST OF FIGURES
PART I
Figure Page
II-l Environmental Fate of a Toxic Pollutant 6
IIr2 Spedatlon, Transport and Transformation Processes 1n the
Aquatic Environment 25
11-3 Flow System Representations 27
II-4 Isotherms for Adsorption of a Hydrophobic Pollutant on
Sediments 49
11-5 Relationship Between K and Octanol-water Partition
Coefficient ( KQW) of Energy-related Organic Pollutants 52
II-6 Correlation of Aqueous Solubility with Octanol-water Partition
Coefficient 53
II-7 Relationship Between KQC and KQW for Coarse Silt 54
II-8 Range of Validity of Henry's Law 63
II-9 Schematic Representation of Volatilization from Solution Phase
to Liquid Phase 65
11-10 Mlcroblal Transformations of Phenoxy Herbicides 80
11-11 Ultraviolet Absorption Spectrum of Naphthacene .... 98
11-12 Spectral Distribution of Solar Energy Outside the Earth's
Atmosphere and At the Earth's Surface 99
11-13 Solar Radiation 1n the United States 101
11-14 Photochemical Pathways of an Excited Molecule 107
11-15 Direct Photochemical Reactions of 2,4-0 Ester,
Benz(a)anthracene, and Pentachlorophenol 108
11-16 Comparison of Solar Irradiance with the Absorption Spectra
of a Compound Which Does Not Directly Photolyzt, a Compound
Which Does Directly Photolyze, and a Substance Which
Initiates Indirect Photochemical Reactions 110
11-17 Chromophrolc Groups which Absorb Sunlight 114
11-18 Major Processes which Influence Photolysis of Pollutants 1n
Natural Waters 115
11-19 pH Dependence of Hydrolysis Rate Constants 131
III-l Background Concentrations of Nitrate-Nitrogen, BOD, Total
Phosphorus, and Dissolved Solids 144
-------
Figure Page
111-2 Background Levels of pH, Suspended Sediment, Total Conforms.
III-3
III-4
III-5
III-6
III-7
III-8
III-9
111-10
III-ll
111-12
111-13
111-14
111-15
111-16
111-17
111-18
111-19
1 1 1-20
111-21
111-22
111-23
111-24
111-25
111-26
Background Concentrations of Chloride, Iron •»• Manganese,
Relationships between Streamflow Nitrogen Concentrations
Relationships between Streamflow Phosphorus Concentrations
The Nonpolnt Source Loading Process
SCS Curve Number Runoff Equation ,
Mean Annual Row Crop Runoff 1n Inches for Selected Curve
Average Annual Eros1v1ty Indices for Eastern U.S ,
Average Annual Eros1v1ty Indices for Western U.S
Values of "a" for Equation 1 1 1-14 ,
Sediment Delivery Ratio as a Function of Watershed Drainage
Phosphorus (as P20s) 1n the Surface Foot of Soil
Collection of Irrigation Drainage
Mean Annual Precipitation 1n Inches
Conceptual Model of Depression Storage ,
Mean Annual Potential Evapotranspl ration Minus Precipitation
1n Inches
Nitrogen ( NH4-N and NO^-N) 1n Precipitation ,
145
, 146
147
148
. 149
. 151
. 154
, 159
, 162
163
175
182
182
209
210
. 211
217
. 224
. 232
. 234
, 236
243
, 247
. 254
-X111-
-------
Page
111-27
IV-1
IV-2
IV-3
IV-4
IV-5
IV-6
IV-7
IV-8
IV-9
IV-10
IV-11
IV-12
IV-13
IV-14
IV-1 5
IV-16
IV-17
IV-18
IV-1 9
1V-20
IV-21
IV-22
Influent Copper Concentrations to Wastewater Treatment
Plants as a Function of Percent Industrial Flow
Illustration of River Segmentation Procedure on the James
River
Hypothetical River Having a Variety of Pollutant Sources
and Sinks
River Segmentation for BOO Distribution
Pollutant Discharge where Initial Mixing Occurs a Fractional
Distance Across the River
Sketch of Snake River from Helse to Nee ley, Idaho
Example of Flow Rate Information Tabulated 1n U.S. Geological
Survey's Water Data Report
Example Set of User's Instructions for Hand-Held Calculator
Programs
The BOO Curve for Oxidation of Carbonaceous Hatter and Curve
Hypothetical BOD Waste Loadings 1n a River
Variability of Dissolved Oxygen by Season for 22 Major Water-
ways, 1968-72
Reaeratlon Coefficient as a Function of Depth
Reaeratlon Coefficient for Shallow Streams, Owen's Formulation .
Characteristic Dissolved Oxygen Profile Downstream from a
Flow Process of Solution to Dissolved Oxygen Problem 1n
Dally Dissolved Oxygen Variation 1n Two Rivers
Flow Process 1n Reach by Reach Solution to Critical Dissolved
Hypothetical River Used in Example IV-9
262
291
292
293
295
300
302
304
307
310
311
312
313
318
322
325
326
327
335
336
o43
349
353
-x1v-
-------
IV-23 Mechanisms of Heat Transfer Across a Water Surface 356
IV-24 Schematic of Site No. 3 Cooling Lake 357
IV-25 Observed Temperatures, Site No. 3, July 18-July 24> 1965 .... 358
IV-26 Comparison of Computed Equilibrium and Ambient Temperatures
with Observed Mean Diurnal Temperature Variations for Site
No, 38 July 18-July 24, 1966 359
IV-27 Mean Daily Solar Radiation Throughout the U.S. for July
and August 361
IV-28 Mean Dewpoint Temperature Throughout the United States for
July and August 368
IV-29 Mean Daily Wind Speeds ( mph) Throughout the United States
for July and August 369
IV-30 Idealization of a Run-of-the-River Power Plant 372
IV-31 Downstream Temperature Profile for Completely Mixed Stream,
T-E/Tm_E vs. r 382
IV-32 Measured Air and Water Temperatures for the Santa Ana River
near Mentone, California, In June 1979 384
IV-33 Measured Dissolved Oxygen Concentration and Predicted Satur-
ation Concentration for the Santa Ana River near Mentone,
California 1n June 1979 385
IV-34 Flow Duration Curve, Hatchie River at Bolivar, Tenn 386
IV-35 Frequency of Lowest Mean Discharges of Indicated Duration,
Hatchie River at Bolivar, Tenn 387
IV-36 Three River Temperature Profiles 388
IV-37 Total Collfonn Profiles for the Willamette River 397
IV-38 Salinity Distribution 1n a Hypothetical River 401
IV-39 Division Between Wash Load and Bed Material Load 404
IV-40 * and T for DuBoys Relationship as Functions of Median
Size of Bid Sediment 406
IV-41 Hydraulic Rad11 for Different Channel Shapes 409
IV-42 User Instructions for Yang's Sediment Transport Equation .... 412
IV-43 Program Listing and Sample Input/Output for Yang's Sediment
Transport Equation 413
IV-44 Sediment Discharge as a Function of Water Discharge for the
Colorado River at Taylor's Ferry 416
-xv-
-------
Figure Page
IV-45 Sediment Discharge as a Function of Water Discharge for the
Nlobrara River at Cody, Nebraska 416
IV-46 Toxicant Concentrations Following Initiation and Cessation of
Point Source 421
IV-47 Vertical Equilibrium Distribution of Suspended Solids 1n a
River 422
IV-48 Vertical Distribution of Relative Solute Concentration,
K S - 10 424
P a
IV-49 Vertical Distribution of Relative Solute Concentration,
Va " 10° 42A
IV-50 Instrean Transformation Processes Analyzed for Toxicants .... 430
IV-51 Location Hap of Hudson River, New York 437
IV-52 Hypothetical Concentration Distributions of Finitely Soluble
and Infinitely Soluble Toxicants 440
IV-53 Hypothetical Distribution of Toxicant at Various Locations
Following a Spill 443
IV-54 Illustration of Hypothetical Spill Incident 447
IV-55a Chloroform Concentration 1n Water Column for First 60 Hours
Following a Spill 16.3 Miles upstream 453
IV-556 Chloroform Concentration In the Mississippi River at a Location
16.3 Miles Below the August 19. 1973 Spill 454
IV-56 Summary of Screening Procedures for Metals 1n Rivers 459
IV-57 Measured Total and Dissolved Coooer Concentrations 1n Flint
River, Michigan, During August . J81 Survey 470
IV-58 Extent of Priority Pollutant Contamination 1n Chattanooga
Creek Waters 471
IV-59 Comparison of Observed and Predicted Mercury Concentration
Calculated from a Dilution Model for the North Fork Ho1ston
River 474
IV-60 Station Locations on Coal Creek and Slate River, Colorado. ... 476
IV-61 Physical Processes Influencing the Fate of Metals 1n Rivers. . . 483
IV-62 Relationship Between Stream Velocity, Particle Size, and
the Regimes of Sediment Erosion, Transport, and Deposition . . . 486
IV-63 Comparison of Predicted and Observed Total Metal Concentrations
1n Flint River. Michigan (August 1981) 490
IV-64 Total Z1nc, Copper, and Cadmium 1n the Hint River 491
-xv1-
-------
Page
IV-65 Framework for River Model MICHRIV 493
IV-66 Suspended Sol Ids and Total Metal Concentrations 1n the Flint
River, Michigan, (March 1982) 504
IV-67 Suspended Sol Ids and Total Metal Concentrations In Flint River,
Michigan (August 1981) 508
IV-68 Definition Sketch of Idealized Reservoir 511
IV-69 Definition Sketch Used 1n Example IV-23 514
IV-70 Relationship Between Metal Concentration 1n Water Column and
1n Bedded Sediments During a NonequlHbMum Adsorption Period. . 517
IV-71 River System for Example IV-24 521
IV-72 Spedatlon of Metals 1n the Aquatic Environment 526
IV-73 Relative Characterizations of Environments by pe and pH 527
IV-74 Activity Coefficient and Ionic Strength Relatloi.nips for
Typical Ions and Specific Ions 529
IV-75 Ionic Strength Versus Specific Conductivity for Surface Waters . 530
IV-76 Typical Adsorption Curves for Metal Cations and Anions for a
Range of pH and Adsorbent Levels 534
IV-77 Partition Coefficient for Copper 1n Streams 538
IV-78 Periodic Table of the Elements 540
IV-79 pe/pH Stability Field Diagram for Arsenic at 25°C 542
IV-80 Cadmium Spedatlon as a Function of pH 1n the Presence of
1.55 m2/l S102(s), Cdt « lO"6*! 544
IV-81 pe/pH Diagram Showing Stability of Chromium Species for
Crt - IO-SM 545
IV-82 pe/pH Diagram Showing Areas of Dominance of Five Species of
Copper at Equilibrium at 25°C and 1 atm 546
IV-83 Copper Spedatlon 1n the Presence of Inorganic Ugands; and
1n the Presence of Inorganic Ugands and an Adsorbing Surface,
1.55 m2/l S102(s) 547
IV-84 Effect of Humlc Add on Partitioning of Copper 549
IV-85 Lead Spedatlon 1n the Presence of Inorganic Llgands; and
1n the Presence of Inorganic Llgands and a Solid Adsorbing
Surface, 1.55 m2/l S102(s) 550
IV-86 The pe-pH Diagram for Hg, Showing Predominant Species 1n
Solution for Concentrations of Total Hg Greater than 5 ug/1. . . 551
-------
Figure Page
IV-87 Nickel Carbonate and Nickel Hydroxide Solubility Phase Diagram 553
IV-88 Zinc Spec 1 at 1 on 1n the Presence of Inorganic Ugands and an
Adsorbing Surface 554
IV-89 Zinc Solubility: Z1nc Hydroxide; Zinc Carbonate; and Zinc
Silicate 555
IV-90 Water Resources Regions of the United States 557
IV-91 Example Procedure for Superposition of Adsorption 592
-XV111-
-------
LIST OF TABLES
PART I
Table Page
II-l
II-2
II-3
1 1-4
II-5
1 1-6
II-7
II-8
II-9
11-10
11-11
11-12
11-13
11-14
11-15
11-16
11-17
11-18
11-19
11-20
11-21
Proposed Criteria for Toxic Substances Designated to Protect
Aquatic Life
EPA List of 129 Priority Pollutants and the Relative Frequency
of These Materials in Industrial Wastewaters
Most Commonly Discharged Priority Pollutants
Selected Characteristics of Various Aliphatic Hydrocarbons . . .
Various Characteristics of Selected Pesticides
Selected Characteristics of Polychlorlnated Blphenyls and
Selected Characteristics of Monocyclic Aromatic Hydrocarbons . .
Selected Characteristics of Various Polycyclic Aromatic
Hydrocarbons
Expressions for Toxic Pollutant Levels In Various Water Bodies .
Relative Importance of Processes Influencing Aquatic Fate of
Organic Priority Pollutants
Occurrence of Acids and Bases 1n Neutral and Charged Forms as
pKa and pKb Values for Selected Toxic Organic Acids and
Bases and Vaues of pK* for Water
Procedure for Calculating Fraction of a Compound which Is In
Procedure for Calculating Partition Coefficient
Relationship of Dissolved and Sorbed Phase Pollutant Concentra-
tions to Partition Coefficient and Sediment Concentration. . . .
Henry's Law Constant for Selected Hydrocarbons ....
Henry's Law Constants for Selected Compounds
Typical Values of Pollutant Volatilization Rates in Surface
Waters
Comparison of Tabulated and Predicted Values of Diffusion
Coefficients for Selected Pollutants
Volatilization Rates of Several Priority Pollutants 1n
12 Rivers
6
8
16
17
19
20
21
22
23
30
33
44
45
47
55
56
62
69
69
70
73
-xlx-
-------
Table Page
11-22 Procedure for Predicting Volatilization Rate 74
11-23 Relative Volatilization Mass Fluxes of Several Chemicals 1n
Saturated Solutions 77
11-24 Size of Typical Bacterial Populations 1n Natural Waters 84
11-25 Summary of the Characteristics of the Two General Types of
B1odegradat1on: Metabolism and Cometabol 1 six 85
11-26 Potential B1odegradab1l1ty of Organic Pollutants 1n an Aerobic
Environment 88
11-27 B1odegradat1on Rate Constants under Aerobic Conditions 90
11-28 Calculated Solar Radiant Energy Flux to a Horizontal Surface
under a Clear Sky 100
11-29 Calculated Solar Irradiance In a Water Body Just Beneath the
Surface, Annual Mean at 40°N 102
11-30 Contributions to Light Attenuation Coefficient 105
11-31 Disappearance Quantum Yields, *
-------
Table Page
III-6 C Factor Values for Permanent Pasture, Range and Idle Land . . . 169
111-7 C Factor Values for Undisturbed Forest Land 170
UI-8 C Factor Values for Mechanically Prepared Woodland Sites .... 171
III-9 Practice Factors (P) Used 1n Universal Soil Loss Equation. ... 172
111-10 Expected Magnitudes of Single-Storm Eros1v1ty Indices 173
III-ll Heavy Metal Concentrations 1n Surflclal Materials in the United
States 183
111-12 Representative Dissolved Nutrient Concentrations 1n Rural
Runoff 188
II1-13 Mean Bulk Densities and Available Water Capacities 195
II1-14 Organic Carbon Partition Coefficients for Selected Pesticides. . 195
111-15 Octanal-Water Partition Coefficients for Selected Pesticides . . 197
111-16 Mean First Order Decay Coefficients for Selected Pesticides. . . 198
111-17 First Order Pesticide Decay Coefficients for Selected
Pesticides and Soil Conditions 199
111-18 Mean Daylight Hours per Day 213
II1-19 Saturation Vapor Pressure as Function of Temperature 214
111-20 Pollutant Concentration Factors for Annual Loading Functions . . 218
111-21 Runoff Curve Numbers (Antecedent Moisture Condition II) for
Urban Areas 222
II1-22 Urban Sediment (Solids) Accumulation Rates 226
II1-23 Concentrations of Conventional Pollutants in Urban Sediment. . . 228
II1-24 Concentrations of Metal in Urban Sediment 228
111-25 Concentrations of Mercury and Organic Compounds in Urban
Sediment 229
II1-26 Typical Values of Crop Nitrogen Uptake 238
111-27 Mean Soil Properties 24?
111-28 Atmospheric Contributions of Nitrogen and Phosphorus In
Precipitation 246
II1-29 Field-Measured Dry Deposition Velocities 249
111-30 Average Monthly Atmospheric Levels of Four Pesticides
at Storteville, Mississippi 252
-xx1-
-------
Table Page
111-31 Typical Influent Municipal Waste Concentrations 255
111-32 Municipal Hastewater Treatment System Performance 256
111-33 Median and Mean Phosphorus and Nitrogen Concentrations
and Median Loads 1n Wastewater Effluents Following Four
Conventional Treatment Processes 257
111-34
111-35
111-36
111-37
111-38
111-39
Metal Concentrations and Removal Efficiencies in Treatment
Plants at Selected Cities
Influent Variability Analysis at Moccasin Bend Wastewater
Selected Pollutant Mass Percent Removals at Moccasin Bend
1981 Effluent Concentrations from Five Southern California
Occurrence of Priority Pollutants 1n POTW Influents and
259
260
263
264
265
Effluents for Pollutants Detected 1n at Least 10 Percent
of the Samples 266
111-40 Median Percent Removals of Selected Pollutants through POTW
Treatment Processes 269
111-41 Industrial Categories and Frequently Detected Priority
Pollutants by Category 271
IV-1 Reference Level Values of Selected Water Quality Indicators
for U.S. Waterways 279
IV-2 Condition of Eight Major Waterways 280
IV-3 Water Quality Problem Areas Reported by States 282
IV-4 Example River Water Quality Standards 283
IV-5 Water Quality Parameters Commonly Monitored by States 284
IV-6 Annual Phosphorus and Nitrogen Load for Selected Iowa River
Basins 285
IV-7 Major Waterways: Seasonal Flow Analysis. 1968-72 288
IV-8 Water Quality Analyses for River Screening Methodology 289
fV-9 Experimental Measurements of Transverse Mixing 1n Open Channels
with Curves and Irregular Sides 297
IV-10 Suggested Configuration for Water and Nutrient Balance Table . . 299
IV-11 Solution to Snake River Water and Phosphorus Balance Problem . . 305
-------
Table Page
IV-12
IV-13
1V-14
IV-15
IV-16
IV-17
IV-18
IV-19
IV-20
IV-21
IV-22
IV-23
IV-24
IV-25
IV-26
IV-27
IV-28
IV-29
IV- 30
IV-31
IV-32
IV-33
IV-34
IV-35
Municipal Waste Characteristics Before Treatment
Comparison of Predicted and Observed Reaeration Rates on Small
Typical Hydraulic Properties, Patuxent River
Solubility of Oxygen 1n Water
Dissolved Oxygen Saturation Versus Temperature and Altitude. . .
0 /L. Values Versus D./L and k,/K,
CO Q Q Q L.
Vc Versus °o/Lo and ka/kL
Some Average Values of Gross Photosynthetlc Production of
Average Values of Oxygen Uptake Rates of River Bottoms
Compilation of Information In Example IV-8
Critical Travel Time Results
Net Long Wave Atmospheric Radiation, H
an
Saturated Water Vapor Pressure, e , Versus Air Temperature,
T , and Relative Humidity. ....
a
B and C(B) as Functions of Temperature
Summary of Solar-Radiation Data for Mlneola, Brookhaven, and
the Connetquot River Sites
Eutrophl cation Potential as a Function of Nutrient
Regional Stream Nutrient Concentration Predictive Models ....
Total Nitrogen Distribution 1n a River in Response to Point
and Non-Point Source Loading .
Total CoHfonm Analysis
Salinity Distribution in a Hypothetical River
Relationship of Total Suspended Sediment Concentration to
Problem Potential
Sediment Grade Scale
Computing 0/T for Determining the Hydraulic Radius of a
Parabolic Section
308
328
329
332
333
338
339
342
347
350
352
362
363
364
365
374
390
393
395
397
402
404
407
408
-XX111-
-------
Table Page
IV-36 Relationship Bet**en Width to Depth Ratio of a Graded Stream
and the Suspended and Bed Load Discharge 409
IV-37 Characteristics of the Colorado and Nlobrara Rivers 415
IV-38 Methods of Introduction of Toxic Organic Compounds Into
Rivers, and Fate 1n Terms of Volatilization and Sorption .... 418
IV-39 Mass of Contaminated Sediments and Equivalent Water Depth as
a Function of Depth of Contamination 436
IV-40 Water-Soluble, High Density (p > 1), Immiscible Chemicals. ... 445
IV-41 Water Quality Criteria for Selected Priority Metals for
Protection of Freshwater Aquatic Life 460
IV-42 Typical Concentrations of Metals in Several Soils and in the
Earth's Crust 462
IV-43 Average Concentration of Metals in Various Types of Rock and
Deep Ocean Sediments 463
IV-44 Ranges of Concentrations of Dissolved Minor Elements Measured
at NASQAN Stations During the 1975 Water rear, Summarized by
Water Resources Regions 465
IV-45 Ranges of Total Concentrations of Minor Elements Measured at
NASQAN Stations During the 1975 Water Year, Summarized by
Water Resources Regions 466
IV-46 Summary of Case Studies 468
•
IV-47 Summary of Metal and Suspended Solids Concentrations in
Flint River, Michigan 469
IV-48 Inorganic Priority Pollutants Detected in Chattanooga Creek,
September 1980 472
IV-49 Mercury Concentrations in Water, Suspended Matter, and Bed
Sediments Immediately Upstream and Downstream of Former
Chi oral kali Plant on North Fork Ho Is ton River 473
IV-50 Strews Selected for 1980 U.S. EPA Field Surveys and Metals
Anticipated to be Present in Excess of U.S. EPA Recommended
Aquatic Life Criteria 475
IV-51 Comparison of Mean Total Concentrations of Selected Metals
in the Slate River Versus U.S. EPA Calculated Acute Water
Quality Criteria for Aquatic Life 477
IV-52 Metal Concentrations in Bottom Sediments of Saddle River.
New Jersey, and In Adjacent Soils 478
-xxiv-
-------
Table Page
IV-53
IV-54
IV-55
IV-56
IV-57
IV-58
IV-59
IV-60
IV-61
Average Heavy Metal Concentrations by Particle Size for
Sediments of the Saddle River, New Jersey
Lead Concentrations in Streams Tributary to Cayuga Lake,
New York
Summary of Cadmium, Zinc, and Copp«r 1n Partlculates Carried
Summary of Soluble Cadmium, Z1nc, and Copper 1n Tributary
Streams of Cayuga Lake
Boundary Conditions and Point Sources to Flint River for
August 4-7, 1981
Summary of Screening Procedures for Metals 1n Rivers and Lakes .
Source Data Required for Example IV-24
Summary of Results of Example IV-24
Linear Partition Coefficients for Priority Metals 1n Streams
479
480
481
482
489
499
521
522
536
IV-62 Speciation of Priority Metals Between Dissolved and Adsorbed
Phases as a Function of Suspended Sol Ids Concentrations in
537
IV-63
tV-64
IV-65
IV-66
IV-67
IV-68
IV-69
IV-70
IV-71
IV-72
IV-73
IV-74
IV-75
IV- 76
Summary of Metal Speciation in Oxidizing and Reducing
Environments, Solids Controlling Solubility, and pH-pe
Combinations Conductive to Metal Mobilization
Characteristics of River Waters Chosen for Analysis
Metal Speciation in the Hudson River
Metal Speciation in the Ogeechee River
Metal Speciation In the Ohio River
Metal Speciation 1n the Mississippi River
Metal Speciation 1n the Missouri River
Metal Speciation in the Brazos River
Metal Speciation In the Columbia River
Metal Speciation 1n the Sacramento River
Metal Speciation In Woods Lake Outlet
Metal Speciation In Penobscot River, Maine
541
558
, , 560
562
564
567
569
. . 572
. . 575
. . 577
. . 579
. . 581
. . 584
. . 585
-XXV-
-------
Table Page
IV-77 Me':i Spec1at1on 1n St. Harys River, Florida 587
IV-78 Metal Spec 1 at 1 on in Grand River, South Dakota 588
IV-79 Metal Speclatlon in Pecos River, New Mexico 589
IV-80 Summary of Data Requirements for Screening of Metals 1n Rivers . 599
-xxvl-
-------
ACKNOWLEDGEMENTS
This publication 1s the result of the labors of a number of Individuals
who contributed to both this document and the previous editions. Majo.r
contributors to previous editions are David Dean (waste loadings), Walter
Prick (estuaries), Kendall Haven (estuaries), Robert Hudson (organic toxicants),
and Stanley Zlson (lakes). JACA Corporation designed a new, condensed format
and prepared all text and artwork for this third edition.
In addition, all of the Individuals 1n the U.S. EPA who supported this
work, especially Mr. Tom Barnwell, Ms. Carol Grove, Mr. Bill Vocke, Dr. James
Falco, Mr. Orvllle Macomber, and Mr. Robert Ambrose, must be thanked for
their Input, consideration, and patience.
-xxv11-
-------
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
In 1977, the United States Environmental Protection Agency published Mater
Quality Assessment: A Screening Method for Mondesignated 208 Areas (Z1son, e_t a]_.,
1977). This document was Intended as a simplified methodology that Mater quality
planners in nondeslgnated 208 areas could use to perform preliminary assessments of
surface water quality. The methods addressed both point and nonpolnt sources of
pollutants Including nutrients, sediments, dissolved oxygen deficits, temperature,
salinity, and conforms 1n rivers, lakes, and estuaries. The methodology was applied
to the Sandusky River 1n northern Ohio, to the Ware, Patuxent, and Chester Rivers 1n
Virginia and Maryland, and to the Occoquan Reservoir 1n Virginia. Test results were
favorable (Dean et_ _aj_., 1981). and some urban pollutants 1n streams, lakes and estuaries.
In 1982 the screening methods were revised and updated to Include toxic organic
chemicals 1n surface waters (Mills Q £l_., 1982). The methods were demonstrated for
a formaldehyde spill 1n the Russian River, California (Mills and Porcella, 1983), and
for synfuel contamination of rivers (Mills and Porcella, 1984).
1.2 PURPOSE AND SCOPE
Due to Increased emphasis on contaminant transport 1n ground waters and on
contamination by heavy metals In all natural waters, the screening methods have been
expanded to address these Issues. This report contains a simplified methodology which
can be used by planners or engineers to perform preliminary assessments of toxic and
conventional pollutants 1n surface and ground waters. Conventional pollutants Include
suspended sediments, nitrogen, phosphorus, conform bacteria, BOD, temperature, and
dissolved oxygen deficits. The 129 EPA priority pollutants are Included 1n the sections
on toxic chemicals. Much data are supplied by figures and tables 1n the text and
appendices. An additional source of data for many rate constants used 1n this manual
1s Bowie et_^i-. 1985. All the algorithms are Intended to be used on desk-top calcu-
lators, or on microcomputers. Many of the environmental chemistry, ground water, and
river algorithms have been put on microcomputer (Mills •££!... 1985).
Where Instructive, Introductory material has preceded the actual presentation of
water quality assessment methodologies. This 1s aone to orient the planner toward
pertinent background material, as well as to clearly state limitations of the method-
ologies due to assumptions and simplifications. Further, example calculations are
Included to Illustrate the Ideas being presented. These examples are designed
to unify the theory that has preceded 1t, as well as 1n some cases to Introduce
new but related Ideas.
-1-
-------
The units most commonly used in this report are those that historically
appear in the literature. Often, the units are not metric. Consequently an
English-metric-conversion appendix is included at the end of this report.
Many equations are presented with both English and metric constants.
The report is divided into six major chapters (two through seven). A brief
description of the content of each chapter Is presented in the following paragraphs:
• Chapter 2 deals with the environmental chemistry of toxic organic
chemicals. Processes considered Include photolysis, hydrolysis,
volatilization, blodegradation and adsorption. The purpose of the
chapter 1s to provide an understanding of the processes and to provide
procedures for estimating associated rate and equilibrium constants.
• Chapter 3 addresses methods to estimate pollutant loads from nonpoint
and point sources for both toxic and conventional pollutants. Pro-
cedures include load estimation for single events and annual loads from
agricultural, forested, and urban areas.
• In Chapter 4, impacts of point and nonpoint sources of conventional and
toxic pollutants in rivers are addressed. Conventional pollutants
include BOO-00, temperature, coll form bacteria, nutrients, and sediment
transport. Fate of toxic organic chemicals is assessed with consider-
ation being given to the Importance of volatilization, sorption and
first order degradation. Metals are also assessed, and emphasis is
given to nine priority metals. MINEQL 1s used to predict aqueous
solubility and sped at ion of the metals in natural waters around the
country. Methods are also presented to handle large spills of toxic
chemicals having density the same as or different from the receiving
waters.
• Chapter 5 contains methods for assessing water quality in Impoundments.
The topics covered are sediment accumulation, thermal stratification,
BOD-DO Interactions, eutrophication, and fate of toxic materials. The
physical/chemical processes governing the fate of toxicants as well as
biological uptake and bioconcentration are considered.
• In Chapter 6, methods are presented for estuary classification, flushing
time prediction, and transport of conservative and non-conservative
pollutants and dissolved oxygen in we11-mixed estuaries. For stratified
estuaries, Prltchard's box model 1s used to determine the distribution
of conservative materials. Additionally, methods are presented to
calculate Initial dilution from a waste water discharge and pollution
distribution at the completion of and subsequent to Initial dilution.
• Chapter 7 presents the methodology necessary to predict the transport
and fate of ground water contamination from typical sources. Sets of
-2-
-------
tables are provided to give representative values and methods of measure-
ment for the required ground Mater hydrology and transport parameters.
In addition, five analytical models are presented with worked out
examples to show how contaminant sources such as solute Injection wells,
leaky ponds, landfills, and spills can be handled.
1.3 METHODOLOGY APPLICATION
For each category 1n the methodology, the six conceptual steps shown below can
be followed to screen a river basin:
t Obtain necessary tools and data to make calculations
t Identify problems that are obvious from Inspection of the data
base
• Determine the state variables which will be screened
• Apply procedures and compare where possible to observed data
• Consider consequences of errors
• Reevaluate and make recommendations for further analyses or remedial
actions.
The techniques 1n the screening procedure are designed to Interact which
makes them Ideal for use as an analytical tool for river basin surface waters
which may Include rivers, lakes, and/or estuaries. Although the procedures may
Interact, they can be applied Individually and with Identified data sets for
specific case studies.
1.3.1 Base Maps
The first step In the screening process can be to obtain large scale topographic
maps of the study area. These can be used to determine which water bodies are to be
examined and to establish an order of study. Once this has been done, selected small
scale (7 l/2-m1nute or 15-nrinute series) topographic sheets can be obtained. On
these, the planner can locate and mark point source discharges, regions of specific
kinds of land use, population centers, and Industrial complexes. Use of overlays or
push pins may be helpful In preparing these displays.
1.4 LIMITATIONS
The processes which govern the fate of pollutants 1n the environment are com-
plex. Any methodology, particularly one designed for hand calculation or microcomputer
applications, cannot be Inclusive of all of these processes. An attempt has been made
1n each chapter to cover the assumptions under which algorithms are developed. Users
should be aware of the assumptions, potential errors, and limitations of the tools
presented. Mien deficiencies are noted or the methods deemed Inappropriate, the user
should be prepared to use a higher level analytical tool.
-3-
-------
REFERENCES
Bowie, G.L., W.B. Hills, D.B. Porcella, C.L. Campbell, J.R. Pagenkopf, G.L. Rupp, K.M.
Johnson, P.W.H. Chan, S.A. Gherlnl. 1985. Rates, Constants, and Kinetics Fortnu-
latlons 1n Surface Water Quality Modell": (Edition 2). For U.S. Environmental
Protection Agency, Athens, GA.
Dean, J.O., W.B. mils and O.B. Porcella. 1981. A Screening Methodology for Basin
Wide Water Quality Management. Symposium on Unified River Basin Management. R.M.
North, L.B. Dworsky and O.J. Allee, eds. May 4-7, 1980, Gatllnburg, TN.
Mills, W.B., V. Kwong, I. Mok, and M.J. Ungs. 1985. Microcomputer Methods for
Toxicants 1n Ground Waters and Rivers. Proceedings on the 1985 Conference on
Environmental Engineering. American Society of C1v1l Engineers.
Mills, W.B. and O.B. Porcella. 1984. Screening Methods for Synfuel Chemicals 1n
Aquatic Environments. Journal of Environmental Management, Vol. 18, pp 297-307.
Mills, W.B. and O.B. Porcella. 1983. Screening for Organic Toxicants In Aquatic
Environments. Proceedings of the 1983 National Conference on Environmental
Engineering. American Society of C1v1l Engineers.
Mills, W.B., J.D. Oean, D.B. Porcella, S.A.Gherlnl, R.J.M. Hudson, W.E. FHck, G.L.
Rupp, and G.,.. Bowie. 1982. Water Quality Assessment: A Screening Procedure for
Toxic and Conventional Pollutants. Prepared for U.S. Environmental Protection
Agency Center for Water Quality Modeling, Environmental Research Laboratory,
Athens. Georgia and Monitoring and Data Support Division, Office of Water Regula-
tions and Standards, Washington, DC. Volumes I and II. EPA-6QO/6-82-Q04a,b,c.
Z1son, S.W., K. Haven, and W.B. Mills, 1977. Water Quality Assessment: A Screening
Methodology for Nondeslgnated 208 Areas. U.S. Environmental Protection Agency,
Athens, GA. EPA-600/9-77-023.
-4-
-------
CHAPTER 2
AQUATIC FATE OF TOXIC ORGANIC SUBSTANCES
2.1 IMTRODUCTION
2.1.1 Background
Today's technological society generates enormous quantities of chemicals both as
products for consumption and as waste. As the volume and number of chemicals has
Increased, numerous unintended adverse effects of these chemicals have been observed
1n the environment. Because of the potential hazard that exposure to these compounds
poses to biota, the levels of toxic and carcinogenic substances 1n the environment
have become Important criteria for evaluating environmental quality.
The level, or concentration, of a toxic compound 1n the environment depends on
the quantity added to the environment and the processes which Influence Its fate.
"Transport" processes tend to distribute chemicals between the atmospheric, aquatic,
and soil environments depending on the affinity of the compound for each phase.
"Transformation" processes within each phase chemically alter pollutants to forms of
lesser, equivalent, or sometimes greater toxldty. These processes occur at rates
which are specific to each chemical and to each environmental compartment. The sum
of these processes and their Interactions, as Figure II-l Illustrates, determines the
environmental fate and consequent exposure of biota to a toxic pollutant. The fate
of toxic substances 1n the aquatic environment 1s the concern of this chapter. The
algorithms presented 1n this chapter have recently been programmed on microcomputers
(mils et al_., 1985).
2.1.2 Comparison of Conventional and Toxic Pollutants
Toxic substances frequently exhibit properties which are quite different
from the properties of conventional aquatic pollutants. It 1s worthwhile to compare
these differences in order to better appreciate the problem of analyzing Impacts of
toxicants In surface water systems. Table II-l shows some of the more Important
differences.
Typically, one to two dozen pollutants and water quality parameters are
classified as "conventional". Until the past several years, these parameters
(e.g. 800, nutrients) have received most of the attention of water quality
planners. In contrast to the small number of conventional pollutants there are
thousands of toxicants and many more synthetic chemicals art continually being
developed. Potentially, any of these toxicants could enter the environment.
Even though there are relatively few types of conventional pollutants, numerous
sources combine to routinely discharge large quantities. However, because many
surface water bodies have a capacity to assimilate conventional pollutants ( e.g.
BOO) without apparent adverse effects, this practice 1s, within limits, both accept-
able and pragmatic. Toxic substances, on the other hand, can cause adverse effects
-5-
-------
SOURCE
TRANSPORT AND
TRANSFORMATION
CHEMICAL
EXPOSURE
FIGURE II-l
ENVIRONMENTAL FATE OF A Toxic
POLLUTANT (AFTER HAGUE, 1980;
TABLE II-l
BRIEF COMPARISON OF CONVENTIONAL AND TOXIC POLLUTANTS
Conventional
Toxic
One to two dozen pollutants fall into
this category
Often large quantities required to
produce impact (e.g. thousands
Ibs/day)
Concentrations often expressed as
ppm (mg/1)
Often travel in dissolved form
Mean residence time within water
bodies often equal to or less
than the mean residence time of
moving waters
Many biodegrade into harmless
substances
Thousands fall into this category;
many more being synthesized
Small quantities can produce
impact (e.g. few Ibs/day )
Concentrations often expressed as
ppb (ug/1), or in smaller units
May be highly sorted to suspended
and bedded sediments
Can reside in bedded sediments
for years
Many are transformed to chemicals
which are also toxic; others are
resistant to degradation and
bioconcentrate
-6-
-------
even at low discharge rates.
Concentrations of conventional pollutants are most often expressed In units of
ppm (or mg/1). Because of the small quantities of toxicants which are typically
released, concentrations are often expressed 1n the ppb (orMg/l) range, or In even
smaller units. This represents at least a thousandfold difference relative to
concentrations of conventional pollutants. However, because toxic substances present
1n small amounts can adversely Impact the environment, these small concentrations can
not always be Ignored.
Many conventional pollutants are transported in dissolved form. The mean
residence times of dissolved, conservative pollutants 1n a system Is equivalent to
the mean residence time of water in the system, which is:
• The hydraulic detention time for freshwater lakes
• The travel time for freshwater rivers
• The flushing time for estuaries.
Many toxic chemicals strongly sorb to suspended and bedded sediments and consequently
can become a part of the Immobile sediments In the bed. The residence time of such
chemicals can be on the order of years. Therefore, depending on the properties of
the toxicant the period of impact can greatly exceed the period of discharge (e.g., a
PCB spill may occur in a few minutes, but quantities of the PCB may remain in Immobile,
bedded, sediments for years). Consequently the recovery period of a system can be
years.
2.1.3 Mater Quality Criteria
As previously Indicated, toxicants are presented In the environment 1n quanti-
ties which are often measured in the ppb range. Such small concentrations are often
foreign to many workers In the field. When data or model predictions contain concen-
trations In the ppb range, the significance of the toxicant level 1s not always
obvious (I.e., there 1s no "feel" as to whether the concentration 1s large or small).
Proposed criteria for toxic substances can serve as a basis to gauge the significance
of observed or predicted levels. Table 11-2 shows proposed criteria for numerous
toxicants. Since proposed criteria evolve over time the criteria shown 1n the table
are not necessarily the most current. Nevertheless, their function remains: to
provide a comparison with levels observed or predicted 1n real systems. The data in
these tables come from the "Red Book" (U.S. EPA, 1976) and the Federal Register,
March 15, 1979; July 25, 1979; October 1, 1979; and November 28, 1980. Criteria
designed to protect human health, for'levels of toxicants In domestic water supplies,
are available from these same sources as well.
2.1.4 Frequency of Discharge of Toxic Substances from Industries
Numerous organizations, Including the U.S. Department of Transportation and the
U.S. Environmental Protection Agency, continually collect and analyze data on the
-7-
-------
TABLE 11-2
PROPOSED CRITERIA FOR TOXIC SUBSTANLlS DESIGNATED
TO PROTECT AQUATIC LIFE
Acenaphthene
Acroleln
Acrylon1tr1le
Aldr1n/D1eldr1n
Ant1*ony
Arsenic
Asbestos
Benzene
Benzldlne
Berylllw
CadriuM
Carbon Tetrachlorlde
Chlordane
Chlorinated benzenes
Chi orobenzene
1,2,4 - Trlchl orobenzene
1,2,3,5 - Tetrachl orobenzene
1,2,4,5 - Tetrachl orobenzene
Pentachl orobenzene
24 Hour
Average
LD»
•2ic
2600C
0.0019
1600
40C
LD
LD
LD
5.3C
d
620
0.0043
1500"
210"
170"
97h
16"
Freshwater
MaxlMi* "Red Book"
1700*
6fil»
7550*
2.5 0.003
9000
440*
LD
530*
2500
130* 11. -1100
e 0.4-1.2*
4.0-12.09
1400
2.4 0.01
3500"
470"
390"
220"
36"
Saline Water
24 Hour
Average
710C
LD
LD
0.0019
LD
LD
LD
700C
LO
LD
4.5
2000
0.0040
120"
3.4"
2.6"
9.6
1.3h
MaxiMM "Red Book"
970*
55*
LD
0.71 0.003
LD
504>
LO
5100*
LD
LD
59 5.0
4000
0.09 0.004
280"
7.8"
5.9"
26
2.9h
-8-
-------
TABLE ii-2
(Continued)
Chlorinated Ethanes
1,2 - Dichloroethane
1,1,1 - Trichloroethane
1,1.2 - Trichloroethane
1,1.1,2 - Tetrachloroethane
1,1.2,2 - Tetrachloroethane
Pentachl oroethane
Hexachloroethane
Chlorinated taphthalenes
Chlorinated Phenols
4 - Chlorophenol
2,4,6 - Tri chlorophenol
Chloroalkyl Ethers
Chlorofom
2 - Chlorophenol
Chromium (Hexavalent)
Copper
Cyanide
DDT
24 Hour
Average
,,9/1
3900*
5300*
310*
420*
170*
440*
62*
29
45
52
LD
500
60
0.29
5.6
3.5
0.00023
Freshwater
Maximum "Red Book"
8000*
12000*
710*
960*
380*
1000*
140*
67
180
150
LD
1200
180
21 100
1 J
52 5.0
0.41 .001
Saline Water
24 Hour
Average
880*
240*
LD
LD
70*
38
7.0*
2.8
LD
LD
LD
620*
LD
18
4.0
LD
0.0067h
Maximum "Red Book"
2000*
540*
LD
LD
160*
87
16*
6.4
LD
LD
LD
1400*
LD
1260
23 j
LD 5.0
0.021* .001
-9-
-------
01 chl orobenzenes
1,2 - Dlchlorobenzene
1,3 - Dlchlorobenzene
1,4 - Dlchlorobenzene
3,3' - D1chlorobenz1d1ne
01 chl oroethyl enes
1,1 - 01 chl oroethyl ene
1,2 - 01 chl oroethyl ene
2,4 - Dlchlorophenol
01 chl oropropanes and Dlchloropropenes
1,1 - Dlchloropropane
1,2 - Dlchloropropane
1,3 - Dlchloropropane
1,3 - 01 chl oropropene
2,4 - Olnethyl phenol
D1n1trotoluenes
2,3 - Olnltrotoluene
2,4 - Olnltrotoluene
1,2 - D1phenylhydraz1ne
Endosulfan
24 Hour
Average
,9/1
44
310"
190"
LO
0.4
410
920
4800
18
38
12
620
17
0.042
TABLE 11-2
(Continued)
Freshwater
Maximum "Red Book"
99
700"
440"
LO
11600
11600
110
930
2100
11000
250
86
27
1400
38
0.49 0.003
S
24 Hour
Average
15"
22"
15h
LO
224000
224000
LU
LD
400"
79
5.5"
LD
4.4"
LO
LD
LU
aline Water
Nax1«u« "Red Book"
34"
49"
34h
LO
LD
LD
910"
180
14h
LO
10"
LD
LD
LD 0.001
-10-
-------
Endrln
Enthyl benzene
Fluoranthene
Haloethers
4 - bromophenylphenly ether
Halowethanes
Chloromethane
Bromome thane
Dlchloromethane
TrlbroMomethane
Kept ac hi or
Hexachlorobutadiene
Hexachl orocycl ohexane
Llndane
Other Isomers
Hexachl orocycl opentadl ene
I sophorone
Lead
Mercury (total)
24 Hour
Average
»9/l
0.0023
LD
250"
6.2
7000
140
4000"
840*
0.0038
LD
0.080
LD
0.39
2100
k
0.2
TABLE 11-2
(Continued)
Freshwater
Maximum "Red Book"
M9/l ^g/1
0.18 0.004
LD
56U1'
14
16000
320
9000*
0.52 0.001
LD
2.0
LD
7.0
4700
1 m
4.1 0.05
24 Hour
Average
M9/1
0.0023
LD
0.30
LD
3700"
170"
^OQh
180
0.0036
LD
LD
LO
LD
97
25*>
0.10
Saline Water
Maximum "Red Book"
0.037 0.004
LD
0.69
LD
8400*
380"
4400*
420
0.053 0.001
LO
0.16
LO
LD
220
ees5
3.7 0.10
-11-
-------
TABLE 1 1 -2
{Continued}
Naphthalene
Nickel
N1 trobenzene
Nit rop he no is
2 - NUrophenol
4 - Nitropheno)
2,4 - Dlnltrophenol
2.4 - 01 nltro-6-Mthyl phenol
2.4,6 - Trlnltrophenol
N-N1 trosod 1 phenyl Ml ne
Pentachlorophenol
Phenol
Phthalate esters
Polychlorlnated blphenyls
Polynuclear aromatic hydrocarbons
Selenlm
Silver
2.3,7,8 - Tetrachlorodlbenzo-p-dloxln
Tetrachloroethene
24 Hour
Average
M9/1
LD
n
480
2700*
240*
79"
57h
1500*1
LD
6.2
600
LO
0.014
LD
35
0.0090
LO
310
Freshwater
MaxlMM "Red Book"
«q/l «'
3400^
LD
14
3400
LO 3.0
2.06 0.001
LD
260 p
1.9 p
LO
700
Saline Water
24 Hour
Average
«9/l
LD
7.1
53
LD
53
37"
LD
15C&
LD
3.7
LD
LD
0.030
LO
54
0.26
79
HaxIniM "Red Book"
0Q/1 -g/1
LD
140 p
120
LD
120
84"
LO
340^
LD
8.5
LD
LD
10* 0.001
LD
410 p
2.3 p
180
-12-
-------
Thalllua
Toluene
Toxaphene
Trichloroethene
Vinyl chloride
Z1nc
24 Hour
Average
*9/l
LO
2300h
0.013
1500
LO
47
TABLE 11-2
(Continued)
Freshwater
Maximum "Red Book"
*g/i *g/i
LD
5200h
1.6 0.005
3400
LD
q P
24 Hour
Average
-9/1
LD
100
LD
LD
LD
58
Saline Water
NaxlMM "Red Book*
• 9/1 »g/l
LD
230
0.07 O.OOb
LD
LD
170
Source: The criteria In this Table are fro* the following sources:
• "Red Booka (U.S. EPA 1976)
• Federal Register on these dates:
March 15. 1979 - July 25. 1979 - October 1. 1979 - November 28. 1980
aLD denotes lack of data.
bAcute toxlclty level.
cChrooic toxlclty level.
dlhe value In »g/l should not exceed exp [1.05 In (hardness) -8.53] where hardness Is expressed in *g/l as
CaC03.
eThe value 1n Mg/l should not exceed exp [1.05 In (hardness) -3.73] where hardness 1s expressed In mg/l
as CaC03.
f0.4 mq - 1.2 «g/l for cladocerans and salmonld fishes.
94.0 mg - 12.0 mg/1 for other, less sensitive aquatic life.
nValues derived using procedures other than the guideline.
-13-
-------
TABLE 11-2
(Continued)
*The value In Mg/l should not exceed exp [0.94 In (hardness) -1.23 ]. Mhere hardness Is expressed In mg/1
as CaCOj.
JFor freshwater and marine aquatic life. 0.1 times a 96 hr LC5Q as determined through nonaerated
bloassay using a sensitive aquatic resident species.
kThe value In *g/l should not exceed exp [2.35 In (hardness)-9.48] where hardness 1s expressed In mg/1 as
CaC03.
'The value In *g/l should not exceed exp £1.22 In (hardness)-0.47] where hardness Is expressed 1n mg/l as
CaC03.
•0.01 tines the 96 hour LCso value, using the receiving or comparable water as the diluent and soluble lead
measurements (using an 0.45 micron filter) for sensitive freshwater resident species.
"The value 1n *ig/l should not exceed exp [0.76 In (hardness) +1.06] where hardness 1s expressed In mg/1
as CaC03.
°The value 1n *g/l should not exceed exp [0.76 In (hardness) +4.02] where hardness 1s expressed In mg/1
as CaC03.
PFor marine and/or fresh water aquatic life. 0.01 of the 96 hour LC$Q as determined through bioassay
using a sensitive resident species.
-------
discharge of toxtc substances. Table 11-3 summarizes the results of a study reported
by Keith and Tel Hard (1979) which shows the frequency of detection of the 129
priority pollutants in industrial wastewaters. A total of 32 industrial categories
were analyzed for organics and 28 for metals. The number of samples ranged from 2532
to 2988. Table 11-4 summarizes the most commonly discharged priority pollutants.
Table 111-53, shown in the next chapter, provides a breakdown by industry of the
occurrence of priority pollutants in industrial effluent.
It is common in this country for numerous Industrial plants to release their
effluents into a single water body. Because of this situation a question that natur-
ally arises is: Based on the number and type of industries located on the water
body, what kinds of toxic chemicals are likely to be discharged there? If the
industrial categories of each plant are known, the probability that a particular
pollutant is discharged from at least one of the plants is:
j • 1. M (II-l)
where
fjj » relative frequency of discharge of pollutant type j from plant type
i, expressed as a percent
PJ « probability that pollutant type j is discharged from at least
one of the n plants located on the water body
M - number of toxic substances being analyzed.
If the industrial categories of the plants are not known, then the probability
that a particular pollutant is discharged can be estimated using Table 11-3 together
with the following formula:
where
9j - percent of samples containing pollutant j
PJ • probability that pollutant j is detected in at least one of the n
discharges.
Equation II-l is obviously the more accurate of the two formulae, because 1t Is
based on a knowledge of the types of Industries which discharge. Although the
above equations provide Information on the likelihood that different chemicals
ar discharged Into the environment, and thus can be used to prioritize Investi-
gative efforts, they do not predict quantities of pollutants which are discharged.
Chapter III can be used to generate that type of Information.
-15-
-------
TABLE 11-3
EVA LIST OF 129 PRIORI"Y POLLUTANTS AND THE RELATIVE FREQUENCY OF
THESE MATERIALS IN INDUSTRIAL WASTEWA'ERS
(After Keith a«* Tell-lard, 1979)
J^Ui1
1.2
1.7
2t.l
2*. 3
11.7
7.7
S.O
t.S
10.2
1.4
7.7
l.t
4.2
0.4
l.S
40.2
Ni**«r »f
IndultruK
Cilt;3M«i
Purgtibli
S Acrelft*
10 Acrjrlonl trill
2S Itflitn*
21 Telvtn*
24 Ctn/ICtmtnt
14 CtrMfl tttrtcn1er1d«
10 Chlprobtnitflf
It 1.2-OicnlorctlMnt
2S l.l.l-TncMorsitnin*
I l.l-D1cMore«tft«nt
17 l.l-0>cMorottnyltnt
12 1,1.2-TncMcrotinint
13 1 .l.Z.2-"«trjcMorcttn«n«
2 Chtoroctntn*
1 ?-lMor«ftnjl vinyl ttntr
21 Chlerofern
Piretflt HucOtr •<
S«.T9ltt* Ctttyorttt
Orginici
j 1
1.0
1 1.2-OtcMero»ro0«n«
S 1.3-OUnloro;rop«M
J«. 2S HttAylint entertdt
1.
0.
t H*tfiyl chlerld*
1 H«tn») ftrofliM
1. 12 IromferB
4. 17 OlcKlaroDr&rcnttStn*
f. 11 TrtcMarofluOrc^tmini
0.
4 OUMerodlfluorantthint
2. IS Ct)orod)brc.oo-«t'U/i«
10. It TttricMorettHyltnt
10. 21 Trlcnlorctliyltnt
0. 2 Vinyl cnlcrld*
1.1 11 1.2-tr«nt-DlcMprattn/1l bcnt/1 pntntUu
A<1d txtrtctlblt
2S Phenol
11 2-N1tropn«nol
4-Nltrepixno)
2.4-Olnitraaninel
4,1-01 nltro-o-crtf a)
I Ptnticfcloropntntl
PKtICtd
•-EndotulfiM
l-(ndOtvlfin
Cndeiul'*" iwl'ttt
•-DC
*-!«
4-lHC
Aldr<«
OtfUrU
4.4--OOC
4, 4 --000
4. 4*. DOT
(ndrf H
(Mrtn ildthyd*
{.7 11 riuartnt
7.2 12 Duortnthtnt
S.I
1 Chryttn*
7.1 14 tyrtnt
._ , ,, ( Pntn»nthrt«t
10. < 1* |Antnrie«n«
2.3
l.t
j g
1.2
O.I
0.2
O.I
0.1
0
0.2
1.1
O.I
0.1
1.2
0.1
0.1
1.4
fratnlc toaooundi
I.I 1
Itnioltlantxrictnt
Itnzo ( b ) t 1 uprtntntn*
Bcniofk )f luorAnt^f nc
ItfltoitlPyrtnt
Indtnot 1 .2 ,3-c .d}pyr«nt
01bcntpU.h)intAriceM
l*flia(t.».1 )pt r/ltn«
4-Chloropncnjrl pr>tnyl ttrxr
3.3'-Oicn)0roMn;td>n«
Itnttdtn*
bit(2-Chlerett*iyl )cth«r
1 .2-0)p^tn]rlh)raralln•
h«i*cAlorocjrlclo9tnt«dicnt
M-N1 troiodtpntnjr 1
-------
TABLE II-3 (continued)
•i iMultrttl
Saoplts Citcgorit*
Hgtai!
11. 20 Antinonjr
It. It Aritnic
M. II ••rrlllui
ill 21 Chromiw
SI. 2( Co»t*r
43. 27 Lia<
J3.4 It ToUl C7«n<«n HMHIfl
i Ikt p«rc«nt tf IM*ltt rMrtltfttl tM MMMr »f ttatt thit CM»»niH
tftt tout «t «' 11 Avfutt !»'*. Nu««rt •' t<»ltl r»g»* fr«a IS!
* • :at*l if II tiKuitrul c«ttc>ri» t'1 i.uiitftrnt Mrt in«t/t*i
TABLE II
MOST COMMONLY DISCHARGED
Pollutant
Non-He taTs
B1s (2-Ethylhexyl) Phthalate
Chloroform
Methyl ene Chloride
Total Cyanides
Toluene
Benzene
Phenol
DI-n-Butyl Phthalate
Ethyl benzene
Naphthalene
Phenanthrene and Anthracene
Metals
Copper
21 nc
Chromium
Lead
Nickel
FvrcMt IhMbtr »f
•f l»««»irui.
S^cplti Cattgorles
U.S 20
M.7 27
11. t 21
22. » 2»
11.2 11
M.» 2«
Not •»«1\»bU
Mot Jv«iUb)«
•at f*un« In ill iirfln ti Ml
I t» int irttk th* ixrtft *•>•«
f«r er{«nict «•< II Itr rttlll
-4
PRIORITY POLLUTANTS
Percent of
Samples
41.9
40.2
34.2
33.4
29.3
29.1
29.1
18.9
16.7
10.6
10.6
55.5
54.6
53.7
43.8
34.7
Mtreury
Ntcfctl
Stltntun
Stlvtr
2IMC
Mbtttat ((^brvui)
Tetil pn«fMlt
CD it •*• ««*)/it« itr t itn.
Percent of
Industries
91
88
78
59
88
78
78
72
75
56
50
100
100
100
96
%
-17-
-------
2.1.5 Physical and Chemical Characteristics of Toxic Organic Compounds
The most Intensively investigated toxic pollutants, as a group, are the priority
pollutants. Because of the greater availability of data on priority pollutants from
such sources as Callahan et^ al_. (1979), 0111 Ing e£ aK (1975) and Mackay and Lefnonen
(1975), data are presented for organic priority pollutants In the following categories:
• Halogenated Aliphatic Hydrocarbons (Table 11-5)
• Pesticides (Table 11-6)
• Polychlorlnated Blphenyls (Table IJ-7)
• Monocycllc Aromatic Hydrocarbons (Table 11-8)
• Polycycllc Aromatic Hydrocarbons (Table II-9).
The properties of the pollutants tabulated in Tables II-5 through II-9 are:
• Vapor pressure, Torr (1 Torr • 1 mm-Hg)
• Solubility
• Octanol-water partition coefficient (KQW)
• Volatilization half-life
• Qualitative statement of the Importance of sorptlon.
Specific information is included in the tables for volatilization and sorptlon
because of the demonstrated Importance of these processes In governing the fate of
many pollutants. In particular, for the approximately 103 organic priority pollutants:
• Sorptlon processes are Important for 60
t Sorptlon Is not Important for 28
• It is not certain 1f sorptlon 1s Important for the remaining 15
• Volatilization Is Important for 52
t Volatilization is not important for 44
t It is uncertain 1f volatilization 1s Important for the remaining 7.
The volatilization half-lives presented In the tables were typically measured
under a specific set of laboratory conditions, and consequently are shorter than in
most natural systems. Other useful properties such as molecular weight and specific
gravity are available 1n standard references such as Perry and Chllton (1973).
2.1.6 Scope and Organization of Chapter
The complexity of the transport and transformation processes which Influence the
fate of toxicants require additional analytical tools beyond those required for
conventional pollutants. This chapter develops these analytical tools 1n a general
way that is applicable to rivers, lakes, and estuaries. Individual chapters on the
various surface water types refine these tools further and provide a framework within
which to use them. When used together, the various chapters 1n this document should
help the user to both understand and quantitatively represent the processes Influenc-
ing the aquatic fate of a pollutant.
This chapter presents both a general overview of the screening approach for
-18-
-------
TABLE 11-5
SELECTED CHARACTERISTICS OF VARIOUS ALIPHATIC HYDROCARBONS
Malogenjted *i ipnitlc Vapor
Hydrocarbons
CMorometnane
Dicilorowe thane
TricMoromethane (chloroform)
letracMoromethane
(carbon tetrechlorloe)
CMoroet**"*
1 ,1-OichVoroethane
1,2-OtcMoroethene
1,1.1-Tnchloroethane
1.1.2-TrlcMoroethane
1 .1 ,2.2- Tetracnloroethane
weiacnloroetnane
Chloroetxene
(vinyl chloride)
l.l-OKMoroethene
1,2-trans-O'cMoroethene
frichloroethene
TetracMoroethene
1,2-Otchloropropane
1,3-OicMoropropene
MeiacMorobutadiene
MeiacMorocyciooentadiene 0.
nronawiiune
Bro«odichloro«e thane
01 bromoehlorome thane
Tnbromouwthane
Dtchlorodt'luoromethene
TrlcMorofluoro«» thane
Pressure (Torr)
at 20*C
3700
362
ISO
90
1000
180
61
96
19
S
0.4
2660
591
200
57.9
14
42
25
0.15
081 at 2S*C
1420
SO
IS
10
430C
6«7
a Stirring in an open container of depth
Solubility
64SO-72SO "9/1
at 20*C
13000-20000 -9/1
at 2S*C
8200 Kg/I at 20*C
78S «9/l at 20*C
S74Q «9/l at 20'C
5500 M9/) at 20*C
8690 119/1 at 20-C
440-4400 119/1 at 20*C
4SOO 119/1 at 20*C
2900 *9/l at 20*C
SO «9/l at 22*C
60 «|/l at 10*C
400 -9/1 at 20*C
600 "9/1 at 20*C
1100 «g/ 1 at WC
1SO-200 «9/l
2700 119/1
2700 "9/1
2
0.8 B9/1
900«9/1
-
-
JOOO-9/'
280-9/1
1100 «9/l
65 M> at 200 RPM (D111
***
8
20
93
400
)S
60
30
ISO
ISO
360
2200
4
30
30
200
760
190
95
5500
10*
10
7$
120
200
US
3400
ing et^ tl_. .
VolatlHtation
Half-Life
27 -mutes*
21 -InvtCS*
21 minutes*
29 -Inutes*
21 ..n«t«'
22 -Inutes*
29 minutes'
20 minutes'
21 minutes*
56 minutes*
45 minutes*
26 minutes'
2Z -inutes*
22 minutes*
21 .mutes*
26 minutes*
-------
TABLE II-6
VARIOUS CHARACTERISTICS OF SELECTED PESTICIDES
Pesticide
Ac role In
Aldrtft
Chlordane
000
DOC
DOT
DleldrU
CiMJosul f M
EMlrU
HeptacMor
Heptachlor Cpoilde
Heiachlorocyclotexane
Llndane
Isophorone
TCOO
Toxaphene
Vapor Pressure (lorr)
220 at 20*C
330 at 30*C
2.3«10'5 it 20«C
««HT6 at 2S*C
1»10*5 at 25*C
10.2-18.9KlO-7 «t WC
1.2-6. 5* JO'6 at 20'C
l.SuKT^at 20'C
l.«»10*7 at 25*C
l.&ilO'7 to
2.»*10-7 at 20*C
l«W5 at 25*C
2x10' 7
3*10'4
-
io*5-io-^
10^-10'd
0.38
-
0.2-0.4
Solubility
20.81 at 20*C
17-180 ppb at 2S'C
O.OS6-1.8S pp«
20-100 ppb at 2S*C
1.2-140 ppb at 20*C
2-85 ppb
184-200 ppb at 2S*C
100-2CO ppb at 20*C
220 ppb at 25'C
56-180 ppb at 25*C
200-350 ppb at 25*C
0.70-21.3 Pfm at 2S*C
5-12 pp* at 2S*C
12000 w*
0.2 ppb
0.7-3. pp*
*o*
0.8
M10
600
106
5»105
I04-106
-
4»I03
4xl05
-
-
104
5*103
50
-
2000
Volitil i/ation
lUlf-l. ife
Uncertain
FtM hours to
few days
Several weeks
1 day to 1 Month
1 to 10 hours
4 hours -1 Meek
Ftx hours to
few days
11 days-1 year
-
-
-
-
100-200 days
Probably great
-
-
Sorptlon
Important?
No
Yes
Probably
Tes
Yes
res
Probably
res
Uncertain
Probably
Probably
Probably
Probably
No
res
res
•Conditions described in C«ll
-------
TABLE 11-7
SELECTED CHARACTERISTICS OF POLYCHLORINATED BIPHENYLS AND RELATED COMPOUNDS
VoUtlllutlon
PCIs tntf Ml* ted
Conpounds
Aroclor 1016
Aroclor 1221
Aroclor 1232
Aroclor 1242
Aroclor 1248
Arocter 1254
Aroclor 1260
Percent
CMorlM
41
21
32
42
48
54
60
OensUv
1.33
1.15
1.24
1.35
1.41
1.50
1.58
Vapor Pressure
•t 25 C-(Torr)
4xlO~4
6.7x10°
4x10-3
4»10'4
4.9xlO'4
7.7X10'5
4xlO'5
Solubility
•9/1
0.42
15.
1.45
0.1-0.3
0.054
0.01-0.06
0.0027
*ow
2xlO*-3xl05
600- 104
1.5xl03-3xl04
I04-4xl05
MO6
xlO*
>106
Hilf-Uvei
In
laborttor/
(hrs)*
9.9
-
-
12.1
9.5
10.3
10.2
loss in .
N«tur«l Systems
3.6t After 24 hours
4.2S ifter 24 hours
-
-
-
-
34S-67S After 12 weeks
2-cMoroMpfctht 1 •*•
0.017
6.47
*At M*C In 1 a3 of Mttr, 1 • 4Mp (ttocKay M* Lttnontn, 1975).
bCondUlons described In CalUhan tt_ «i- (1979).
-21-
-------
TABLE 11 -»
SELECTED CHARACTERISTICS OF MONOCYCLIC AROMATIC HYDROCARBONS
Monocyclte AroMttcs Vapor Pressure (Torr) Solubility
Benzene
Chlorooetuene
1 ,2-OicMorooenxene
Heiachlorobenzerte
Ethylbeniene
Toluene
2,4-Otnttrotoluene
2. 6-OlnUro toluene
Pentachloro phenol
2-Hltrophenol
4-NUrophenol
2.4-Otnltrophcnol
4.6-0*nitro-o-cresol
'fttctar and ieinonen (197!
coefficients of ?0 c«/hr
95. at 25'C
MO at 20*C
1.5 it 25*C
10-5 ,t 20«c
7
29 *f25'C
0.001 at S9*C
low
0.0001
1.0 at 49*C
2.2 at 146*C
-
and 3000 c«/hr fur
1800 M9/I at 25*C
^500 mq/}
145 «g/l
•WO yg/l
152 *9/l
535 «g/l
270 «9/l at 22*C
i300 «9/l
14 »g/l
2100 «9/l at 20*C
16000 «g/l at 2S*C
5600 «g/1
, <•;) on water deplti of 1 l ion
liiH'Orlinl '
Uncertain
Probably
Probably
Yes
Probably
Probably
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-22-
-------
TABLE 11 -9
SELECTED CHARACTERISTICS OF VARIOUS POLYCYCLIC AROMATIC HYDROCARBONS
Polycyclic AroMtics
AceMphthene
Acenaphthylene
Huorene
Nipkthilene
Anthracene
Fluor tntheiM
Pfcenanthrene
Benzo(a)anthracene
Benzft[b]flueranthene
8enzo[kJfltMraathene
Chrysene
Pyrene
Benzo[ gh1]perylene
8enzo[i]pyrene
Dlbenzo[i ]inthricene
1 ndeno[ 1 . 2 . 3-cd ]pyrene
Vipor Pressure (Torr)
10-3-10-2 at 20'C
10-3. nr2 «t 20'C
10-3.10-2 .t 20«C
.0492
2*10'* at 20'C
10-* to 10'4 at 20'C
t.BilO-4 at 20'C
5xlO*9 at 20'C
11"" to 10'* at 20'C
9.6x10'" at 20'C
10-11 to 10'* it 20'C
6. 9* 10' 7 at 20'C
MO'10
5x10-9
•vlO' 10
MO' 10
Solubility
3.4 *9/l it 25'C
3.93 «g/l
1.9.9/1
32. mg/\
0,05-0.07 «9/l it 25'C
0.26 119/1 at 25'C
1.0-1.3 1*9/1 at 25'C
0.01 mg/\ at 25'C
-
-
0.002 *9/1 at 25'C
0.14 *9/l at 25'C
0.00026 mg/\ it 25'C
0.0038 «g/l it 25'C
0.0005 «9/l it 25*1
-
"ov,
21.000
12.000
15.000
2.300
28.000
340.000
29.000
4xl05
4x10*
7»10*
4xlOs
2x10^
10 7
10*
10*
5xl07
Volil'iluat ion
Iwportinl?
Less thin sorptton
Less thin sorption
Less thin sorptton
Less thin sorption
Probibly
Probibly Not
Probibiy Not
No
Probibly Not
Probibly Not
Probibly Not
Probibly Not
Probibly Not
Probibly Not
Probibly Not
Probibly Not
Wplion
Invariant?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-23-
-------
toxicants and a detailed description of the processes Included in the screening
methodology. The various topics are organized as follows:
Screening methods for toxic organic substances
Speciation processes
1) Acid-base effects
2) Sorptlon
Transport processes
1) Solubility limits
2) Volatilization
Transformation processes
1) B1odegradat1on
2) Photolysis
3) Hydrolysis.
Lyman €*£]_. (1982) and Mabey et^ al_. (1984) provide additional 1nfo"-iation that can
be used to evaluate the importance of these processes.
2.2 SCREENING METHODS FOR TOXIC ORGANIC COMPOUNDS
2.2.1 Modeling the Fate of Toxic Organlcs
The goal of this screening methodology for toxic pollutants is to help the user
Identify surface water oodles where toxicants could reach hazardous levels. Multiple
approaches for Identifying pollution problems are possible, e.g. extensive field
measurements, statistical correlations of discharges and pollutants detected in
rivers, computer simulation models, etc. The approach taken here is to present
simple methods for assessing the fate of toxicants.
The application of any method necessitates the use of judgment on the part of
those applying 1t. In almost every case, the user must estimate many of the methods'
Input parameters on the basis of limited data. Consequently, even the projections of
detailed computer models such as EXAMS (Burns, et_ al_., 1981) and PEST (Park, e£ aj_.,
1980) are only as good as the accuracy of the assumptions made by their developers
and users. Thus, the goal of the materials presented herein 1s twofold: to present
simple methods and to provide the background necessary to make knowledgeable judgments.
Predicting aquatic fate of pollutants Involves several steps. The steps des-
cribed 1n the remainder of this section Include:
• Determination of Fate-Influencing Processes
• Delineation of Environmental Compartments
• Representation of Hydrologlc Flow
• Mathematical Representation of Speciation Processes
• Mathematical Representation of Transport and Transformation Processes
• Determination of Pollutant Load and Mode of Entry into the Aquatic
Environment.
-24-
-------
IN CLOW
volatilization
V
H
HydfOlyttt
A«t»orp»KX>-D«t
-------
Kdvection. Hydraulic flows transport pollutants which are dissolved or
sorted on suspended sediments into and out of particular aquatic
habitats.
Volatilization. Organic pollutants may enter the atmosphere from
a water body, thereby reducing aquatic concentrations.
Sedimentation. Deposition of suspended sediments containing sorted
pollutants, as well as direct sorption onto or desorption from bottom
sediments can alter pollutant concentrations.
Transformation Processes
Blodegradation. Microbial organisms metabolize pollutants, altering
their toxicity in the process.
Photolysis. The absorption of sunlight by pollutants causes chemical
reactions which affect their toxlcity.
Hydrolysis. The reaction of a compound with water frequently produces
smaller, simpler organic products.
Reduction-Oxidation. Reactions of organic pollutants and metals
which involve the subtraction or addition of electrons strongly
influence their environmental properties. For organics, nearly
all significant redox reactions are microbially mediated.
Bioaccumulation
Bioconcentration. Uptake of toxic pollutants into biota via passive
means, e.g. absorption through fish gills.
Biomagnification. Uptake of toxicants into biota via consumption
of contaminated food.
Once the pertinent processes have been identified, the physical compartments ti-
the environment between which the transport processes act must be delineated. For
most water bodies, compartments representing the atmosphere, bottom sediments, and
one or more water elements are sufficient. These methods are capable of representing
transport of pollutants between the atmosphere and a water body. But rather than
calculating atmospheric concentrations of a pollutant, these methods generally assume
them to be close to zero unless available data Indicate otherwise. Bottom sediments,
however, frequently accumulate high levels of organic pollutants. Because of the
difficulty of modeling the behavior of toxicants in sediments, usually assumptions
which approximate only the removal or addition of a pollutant to the water column are
made. These approximations are presented in the Individual chapters on each water
body.
The next step In assessing the aquatic fate of toxic pollutants 1s to represent
the advectlon or flow of water. Figure II-3 illustrates a representation of rivers
as a segregated flow system and lake layers as completely mixed flow systems.
Although these models are simple, they serve as adequate first-approximations of real
-26-
-------
COMPLETELY MIXED FLO*
NATURAL SYSTEM:
LAKE
IDEALIZATION:
MIXED FLOW
SEGREGATED FLOW
NATURAL SYSTEM:
RIVER
IDEALIZATIONS:
PLUG FLOW
FLOW WITH
AXIAL DISPERSION
FIGURE 11-3 FLOW SYSTEM REPRESENTATIONS
systems. Refinements and limitations of these flow system models are considered in
the individual chapters on rivers, lakes, and estuaries.
The transport and transformation processes responsible for the removal of a
pollutant from the water column are considered next. First-order rate expressions
adequately represent all of the processes considered here. The first-order decay of
-27-
-------
a pollutant by a process 1s represented as follows:
Rate of Pollutant Removal « ki • Cj (II-3)
where
k^ » first-order rate constant for process 1
CT • total concentration of pollutant.
The rate constant for a process is specific to both the chemical it acts upon
and the local environment in which 1t acts.
When all the first-order processes acts independently, the total rate of pollut-
ant removal 1s:
Total Rate of Removal « ky • CT (II-*)
where
*T " kvm + kS + kB + kp + kH (11-5)
and
kvm " Soec1fic mixed-body volatilization rate constant
k, • specific rate constant for removal to bottom sediment
kg • specific rate constant for biodegradation
kp • specific rate constant for photolysis
kH « specific rate constant for hydrolysis.
The addltlvity of processes which are first-order with respect to pollutant concentra-
tion is particularly convenient for analysis.
Many of the decay processes are Influenced by the chemical state of the toxicant.
For example, sorbed pollutants cannot volatilize. Mathematical representations of
equilibria between two species of a chemical can be reduced to the following type of
equation. This type of equation serves well at the low solute concentrations en-
countered in waste waters and natural waters:
Ci • Mj Cj (II-6)
where
C, • concentration of form 1
K,., • equilibrium constant
C, « concentration of form j.
It 1s also convenient to know the fraction of the total pollutant concentration
which is In a given state:
«4 - £i (H-7)
CT
-28-
-------
where
CT - c + cs
C., « concentration In state 1
C - total dissolved phase pollutant concentration
Cj * total sorbed phase pollutant concentration.
To complete the assessment of the aquatic fate of a pollutant the mode of entry
Into the aquatic environment must be considered. Many pollutants enter In dissolved
or sorbed form from a point source. In this case, a simple mixing computation 1s
sufficient to determine the Initial concentration of a pollutant in the water body.
Other cases Include spills, non-point sources, and desorptlon from sediments.
Chapter 4 presents methods for dealing with these cases.
The user may now reckon the concentration of a pollutant 1n a given water body.
The equations which yield the desired results are specific to each surface water type
and are developed 1n the Individual chapters on lakes, rivers, and estuaries. An
equation representative of those 1n each chapter 1s presented 1n Table 11-10. The
Individual chapters go Into greater detail about factors Influencing rate processes
and Interactions with other Important phenomena In each water body (See Sections 4.9,
5.6, 6.4.3, and 6.4.5).
2.2.2 Use of Assessment Techniques as Screening Tools
2.2.2.1 Making Conservative Assumptions
With the computational methods presented In this document, the user could
produce a relatively complete analysis of the aquatic fate of a pollutant. The goal
of this screening method, however, 1s to determ1ne--w1th a minimum of effort—whether
toxicants are likely to reach problem levels In surface water bodies for either
existing or projected loading rates. The user can minimize the effort expended In
screening a pollutant by starting with a simple approach which Incorporates conserva-
tive assumptions about the fate of a pollutant. Conservative assumptions are designed
to yield higher calculated environmental concentrations than probably exist 1n the
real system. If these higher concentrations are below the water quality criterion
under consideration, a violation of the standard Is unlikely. If the Initial predic-
tions are higher than the standard, the user may successively refine the approach
until It becomes apparent that either the standard will be met or that a more detailed
study Is necessary.
Three levels of refinement 1n assessing the aquatic fate of a pollutant are
considered here. In order of Increasing complexity, they are:
1) Treating the pollutant as a conservative substance
2) Considering transport and spedatlon processes
3) Considering transformation, transport, and spedatlon processes.
Each approach has advantages and limitations which the user should consider. By
-29-
-------
TABLE 11-10
EXPRESSIONS FOR TOXIC POLLUTANT LEVELS
IN VARIOUS WATER BODIES
Water Body
Expression for Steady-State
Pollutant Concentration
Ri vers
(Chapter IV)
Impoundments
(Chapter V)
CQ exp
(IV-115)
where x « distance downstream
U » river velocity
C - total dissolved phase concentration
C -C1n/(l
(V-47)
where T « hydraulic residence time
C « total dissolved and sediment phase
concentration
Estuaries
(VI-33)
r.
1
-kt
(VI-34)
where C. - concentration in segment i
f. • fraction of fresh water in segment i
r, - segment i exchange ratio
t - time expressed 1n tidal cycles
following this sequence of refinements, the user should be able to eliminate cases
where water quality problems are unlikely with a minimum of time and effort.
2.2.2.2 Treating the Pollutant as a Conservative Substance
The simplest approach to estimating the concentration of a toxic pollutant Is to
assume it behaves conservatively (I.e. does not undergo reaction):
kT-0
Unless an Internal source of the pollutant exists, this approach will yield the
highest possible pollutant levels since pollutant decay and removal processes are
neglected. The obvious advantage of this approach 1s that it requires no chemical or
environmental data to evaluate rate and equilibrium constants. The only data needed
are pollutant loads and hydrologlcal parameters. Its major drawback 1s that it
neglects the possibility of a compound accumulating in another environmental compart-
-30-
-------
merit, especially bedded sediments. This could result 1n the underestimation of the
duration of the exposure of an aquatic habitat to a chemical. Although the duration
of exposure may be underestimated, water column concentrations would not exceed the
upper limits predicted by this approach at any time during the exposure period.
The fate of conservative pollutants In rivers, Impoundments, and estuaries 1s
discussed In Sections 4.1.9, 5.6.1, and 6.4.
2.2.2.3 Considering Transport and Sped at 1 on Processes
This refinement Incorporates those processes which Influence pollutant transport
out of the aquatic environment but neglects those processes which chemically alter
the compound. Transport processes strongly depend upon chemical speciation, which
therefore must be included. The rate constant for first-order pollutant attenuation
In -"is approach is:
where
k.
« specific mixed body volatilization rate constant.
k. « specific rate constant for removal to bottom sediment
_
vm
This approach requires more Information on the properties of the toxicant and the
environment than when the pollutant 1s assumed to behave conservatively, but the
necessary data are much more readily available than those required to characterize
transformation processes. Nearly all the chemical data necessary to characterize
acid-base equilibria, sediment sorptlon, solubility limitations, and volatilization
for the organic priority pollutants are presented 1n tables In Sections 2.1.5,
2.3.1, and 2.4.2. The necessary environmental data can usually be obtained or
estimated with a minimal amount of effort. Because of the demonstrated Importance of
transport processes and the relative simplicity of assessing them, this Is a good
Intermediate step between the simplest and most complicated approaches.
Transport and speciation processes are applied specifically to rivers. Impound-
ments, and estuaries 1n Sections 4.9, 5.6, 6.4.3, and 6.4.5.
2.2.2.4 Considering Transformation, Transport, and Speciation Processes
The most complex model which the user can employ using these screening methods
Includes consideration of transformation, transport, and speciation processes. With
this approach, the rate constant for first-order attenuation of a pollutant 1s:
-31-
-------
where
kn • specific rate constant for blodegradatlon
D
kp « specific rate constant for photolysis
k^ « specific rate constant for hydrolysis.
The inclusion of the degradative processes (i.e. biodegradation, photoly-
sis, and hydrolysis), considerably increases the chemical and environmental data
required to model a compound's fate. Rather than accurately determining all the
constants for speciation, transport, and transformation, the user should first
ascertain which processes are the most significant for a compound. As a first step
the user should obtain data on the properties of the chemical which influence its
aquatic fate from this document or other sources. From compound specific aata, it is
usually possible to eliminate some processes from consideration. For organic priority
pollutants, consulting the ratings of the relative Importance of aquatic processes
for the fate of each compound, Table 11-11, may aid the user in eliminating unimport-
ant processes. Once the most significant processes have been identified, the user
should collect the environmental data necessary to determine site specific constants.
These site specific constants are then applied in the appropriate equation for each
water body type to obtain the best estimate of the actual pollutant concentrations in
the environment that these methods are capable of making. (See Sections 4.9. 5.6,
6.4.3, and 6.4.5).
Frequently, kinetic and equilibrium constants will depend on the values of
parameters which the user must estimate (e.g., pH). In such cases, assuming conserv-
ative values 1s the best policy. However, calculations using a range of values may
Identify processes for which a more careful determination of the key environmental
and chemical parameters 1s warranted.
Example II-l is an overall example for this chapter. It demonstrates the
Initial steps a user would take In applying these methods to assess the fate of a
particular organic pollutant. The example follows the three level analysis described
above and also draws upon some of the procedures for specific environmental processes
which are developed later in this chapter. This example can serve as a guide to
evaluating the importance of the various fate Influencing processes for a particular
pollutant.
-32-
-------
TABLE 11-11
RELATIVE IMPORTANCE OF PROCESSES INFLUENCING
AQUATIC FATE OF ORGANIC PRIORITY POLLUTANTS (After Callahan et aj_., 1979)
Compound Process
PESTICIDES
Acrolein
Aldrin
Chlordane
DDD
DDE
DDT
Dleldrin
Endosulfan
Endrin and
Heptachlor
Heptachlor
S §
*•> <->
c •£ "S
0 — *•
.;: ~ 01
** ** Ol
Q. <0 IB
U "~ O
o o •*•
10 >• CO
_ + +
+ + ?
+ + ?
+ + .
•f +
•f +
•f +
and Endosulfan Sulfate + + *
Endrln Aldehyde ? ? ?
+ +
Epoxide + - ?
u
at e
u o
Q. j
**
1 •
•f +
+ . +
? •*• -
•f . +
? ++ +
? - *
Hexachlorocyclohexane (a.8.6 Isomers) + ? + ._-
-Hexachlorocyclohexane (Llndane) +.+..-
Isophorone
TCDD
Toxaphene
?
•f
+ + +
*
? - *
•f
PCBs and RELATED COMPOUNDS
Polychlorlnated Biphenyls
2-Chloronaphthalene
HALOGENATED ALIPHATIC HYDROCARBONS
Chloromethane (methyl chloride)
Dlchloromethane (methylene chloride)
Tr1chloron»ethane (chloroform)
Tetrachloromethane (carbon tetrachlorlde)
Chloroethane (ethyl chloride)
1,1-Dichloroethane (ethylldene chloride)
1,2-Dichloroethane (ethylene dlchlorlde)
1,1,1-Trlchloroethane (methyl chloroform)
1,1,2-Trlchloroethane
1,1,2,2-Tetrach1oroethane
Hexachloroethane
Chloroethene (vinyl chloride)
l,l-D1chloroethene (vinyl1dene chloride)
l.2-trans-D1chloroethene
Trlchloroethene
Tetrachloroethene (perchloroethylene)
l,2-D1chloropropane
l,3-D1chloropropene
Hexachlorobutadlene
Hexachlorocyclopentadlene
Bromomethane (ntethyl bromide)
_
.
.
?
_
.
.
_
•>
I
?
?
+
?
.
_
+
•»•
+
+
•»•
+
•f
•f
+
+
?
.
•f
+
+
_
?
?
.
?
?
?
.
.
.
?
_
?
?
+
.
.
_
.
.
.
.
.
.
-
?
.
-
.
.
•f
+
+
•f
+
-33-
-------
TABLE 11-11 (continued)
Compound Process
c c
O 0
4)
a CO
Bromodichloromethane ? ? ?
Dibromochloromethane ? * ?
Tribromomethane (bromoform) ? * ?
Dichlorodif luoromethane ? +
Trichlorof luoromethane ? * -
HALOGENATED ETHERS
Bis(choromethyl) ether ?
Bis(2-chloroethyl) ether +
Bis(2-chloroisopropyl) ether *
2-Chloroethyl vinyl ether + ?
4-Chlorophenyl phenyl ether + 7 ?
4-Bromophenyl phenyl ether + 7 ?
Bis(2-chloroethoxy) methane - - ?
MONOCYCLIC ARQMATICS
Benzene + *
Chlorobenzene * +
1,2-Oichlorobenzene (jj-dichlorobenzene) + + .
1,3-Dichlorobenzene (jrj-dichlorobenzene) + + ?
1,4-Oichlorobenzene (£-dichlorobenzene) + + .
1,2,4-Trichlorobenzene + *
Hexachlorobenzene +. - -
Ethylbenzene 7+7
Nitrobenzene +
Toluene +• + 7
2,4-Oinitrotoluene +
2,6-Dinitrotoluene *
Phenol + +
2-Chlorophenol - - 7
2,4-Dichlorophenol . . +*
2,4,6-Trichlorophenol ? - ?
Pentachlorophenol + - +
2-Nitrophenol ...
4-Nitrophenol +
2,4-Oinitrophenol •*•
2,4-Oimethyl phenol (2,4-xylenol) 7
p-chloro-m-cresol 7
4,6-Oinitro-o-cresol *
PHTHALATE ESTERS
Dimethyl phthalate + - *
Diethyl phthalate * - *
01-n-butyl phthalate + - *
01-n-octyl phthalate + - *
Bis(2-ethylhexyl) phthalate * - *
Butyl benzy phthalate + - +
t;
w ®
5 ^j
— •— 3
* ^ i
>. >i 3
— — u
° ° «
f, ^H -^
0. I| CO
7 - +
? - +
? - •*•
? - ?
?
++
?
?
•»•
4- . -f
•f . +
+ 7
.
+
? ? *
? - *
? - +•
.
.
•»
.
* - ?
+ ? ?
-
+•
.
7
K
. ^U
++
++0
•f
•»»*
•M- 7 7
+
+
•f
•4-
•f
4
-34-
-------
TABLE 11-11 (continued)
Compound Process
4^
J
C
0
•*•
19
0
••—
4-1
a.
a
i/)
r^
.^
^j
lO
*•
o
3»
C
0
•^
•O
U
O1
OJ
o
m
w
^.
o
i
*^f
fH
t .
o
<—
a.
^
o
<-»
1/1
£
^
O
L.
^
^1
z
*v
i
u
4Q
0
a
POLYCYCLIC AROMATIC HYDROCARBONS
Acenaphthenec
Acenaphthylene
Fluorene
Naphthalene
Anthracene
Fluoranthene^
Phenanthrene
Benzo(a)anthracene
Benzo(b)fluoranthene
Benzo(k)fluoranthenec
Chrysene
PyreneC
Benzo(ghi)perylene
Benzo(a)pyrene
Oibenzo(a,h)anthracene
Indeno(l,2,3-cd)pyrene
NITROSAMINES AND MISC. COMPOUNDS
Dlmethylnltrosamlrte
Diphenylnitrosamine
Di-n-propyl nitrosamlne
Benzidine
3,3'-Dichlorobenzidine
l,2-01phenylhydrazine (Hydrazobenzene)
Acrylonltrile
•f
•f
+
•f
•f
•f
•f
*
4-
•f
•f
•f
•f
+•
•f
+•*•
•f
•f
•f
Key to Symbols:
•M- Predominate fate determining process - Not likely to be an Important process
+ Could be an important fate process ? Importance of process uncertain or not
known
Notes
a Blodegradation is the only process known to transform polychlorinated biphenyls
under environmental conditions, and only the lighter compounds are measurably
biodegraded. There is experimental evidence that the heavier polychlorinated
biphenyls (five chlorine atoms or more per molecule) can be photolyzed by
ultraviolet light, but there are no data to indicate that this process is operative
in the environment.
b B%sed on information for 4-n1trophenol.
c Based on information for PAH's as a group. Little or no information for these
compounds exists.
-35-
-------
EXAMPLE II-l
Pentachlorophenol in the Aurum Mirth Watershed I
i
Pentachlorophenol enters the Aurum Mirth River from a continuous point j
source. The river is the sole tributary to Lake Castile. After mixing at j
the point of entry, the concentration of pentachlorophenol in the river 1s
20 «g/l. The travel time from the point of contamination with pentachloro- ;
phenol to Lake Castile is about 6 days. The mean hydraulic residence time I
in Lake Castile is 10 days. I
Use the screening methods to determine which chemical and environmental |
parameters are of the greatest importance for predicting the fate of penta- j
chlorophenol in the watershed's surface waters.
1) TREATING PENTACHLOROPHENOL AS A CONSERVATIVE SUBSTANCE j
The first step in the screening method is to assess the fate of penta- |
chlorophenol treating it as a conservative substance. Sections 4.1.9, 5.6.1, and j
6.4 discuss the fate of conservative pollutants in rivers, lakes, and estuaries.
In this case, we assume no further dilution of the pentachlorophenol occurs in
either the lake or the river. Consequently, the conservative pollutant approach |
I predicts a mean concentration in the river and lake of 20 ug/1.
i
| Table 11-2 lists a proposed water quality standard for pentachlorophenol.
j The 24 hour mean concentration must be less than 6.2 ug/1. Since 20 tfg/l exceeds
this standard, a second level assessment is in order.
Prior to applying the next two levels of analysis it is worthwhile to
; check Table 11-11 for the relative importance of the different transformation and
| transport processes. Table 11-11 summarizes the influence of the aquatic processes ,
I on pentachlorophenol as follows: ]
| t Sorption - Important process i
j • Volatilization - Not an Important process |
j • B1 ©degradation - Important process |
t Direct Photolysis - Important process j
• Hydrolysis - Not an Important process :
j • Bloaccunulatlon - Important process.
I It will be Instructive to compare these statements to the results of ;
I the screening methodology. j
I 2) CONSIDERING TRANSPORT AND SPECIATION PROCESSES !
! To analyze transport and sped at1 on processes, first examine each process for .
' Its potential influence on the fate of pentachlorophenol. ;
I I
-36-
-------
Spedatlon Processes
Acid-Base Effects (Section 2.3.1). The chemical and environmental parameters
governing acid-base effects are:
• Chemical Parameters:
pKa or P1^ ' Ac1d or oase equilibrium constants
• Environmental Parameters:
pH - Hydrogen 1on concentrations.
The pK of pentachlorophenol 1s 4.74, as shown In Table 11-13. According
to Table 11-12, at least 90 percent of the pentachlorophenol will be 1n the
anlonlc state at pH's greater than 5.74. As long as the pH 1n the Aurum Mirth
River and Lake Castile remain above 5.74, the properties of pentachlorophenol as
measured for neutral waters will remain unaffected. But, because pH's below 5.74
could significantly alter the behavior of the compound, 1t 1s Important to deter-
mine actual surface water pH values.
Sorptlon (Section 2.3.2) The key environmental and chemical parameters which
Influence sorptlon are:
• Chemical parameters:
K - Octanol-water coefficient
ow
S^ - solubility 1n water
• Environmental Properties:
Suspended sediment concentration
Organic carbon content of the suspended sediment.
Table 11-8 lists the solubility and octanol-water coefficient of
pentachlorophenol as:
S - 14 mg/1
Kow ' l°5
Assuming an organic carbon content of 2 percent for the suspended sediments,
calculate K using Equations 11-18 and 11-16:
Kp - (.02) (.63) (105) • 1300
According to Table 11-16, greater than 10 percent of the pentachlorophenol
will be 1n the sorbed state at suspended sediment concentrations exceeding
100 mg/1. The relatively strong sorptlon of pentachlorophenol dictates that
the suspended sediment concentration In the Aurum Mirth River and the sediment
trapping efficiency of Lake Castile be Investigated further. Sorptlon of !
pentachlorophenol potentially affects both Us speclatlon and Its transport j
rates. I
i
Transport Processes j
Solubility Limitations (Section 2.4.1). The most Important chemical I
-37-
-------
and environmental factors which influence solubility of a compound are:
• Chemical Parameters:
S.. - Aqueous equilibrium solubility
• Environmental Parameters:
T - Temperature
Salinity.
Table 11-8 lists the solubility limit for pentachlorophenol as 14 mg/1
(14000 wg/ij. At no point in the Aurum Hirth watershed should the solubility of
pentachlorophenol restrict the ability of the aqueous phase to transport it,
Volatilization (Section 2.4,2). The most significant chemical and environ-
mental properties which influence volatilization are:
• Chemical Parameters:
*u • Henry's Law Constant
H
». Environmental Parameters: j
k - P.eaeration constant '
V - Wind speed j
Z - Mixed depth of water body. j
It is possible to estimate the Henry's Law Constant for pentachlorophenol j
from its vapor pressure and aqueous solubility using Equation 11-32. However, it
is simpler to rule out volatilization as a significant transport process on the ;
basis of the volatilization half-life of 100 days given 1n Table 11-8. Because \
laboratory volatilization half-lives -.-e shorter than the true environmental j
values, it 1s safe to assume the environmental half-life will be much greater than |
100 days. Given a total system mean hydraulic residence time of only 16 days |
(6 * 10), volatilization can be safely neglected. j
Summary
Acid-base equilibria and sorptlon significantly Influence the transport j
and sped at ion of pentachlorophenol in the aquatic environment. Acid-base effects
do not influence the near-neutral volatilization and photolysis rate constants
presented in this document as long as pH's remain above 5.7. Sorptlon 1s a '
potentially Important sped ation process. Consequently, the pH values and suspended I
sediment concentrations should be determined 1n order to accurately evaluate these |
processes. |
The strong tendency of pentachlorophenol to sorb on sediments may result In j
sedimentation serving as a significant removal process In Lake Castile. The
absence of net sediment deposition in the river Implies that transport processes
do not reduce pentachlorophenol concentrations In the Aurum Mirth. Thus, the j
second level analysis predicts a total concentration of 20«g/l of pentachlorophenol I
In the Aurum Mirth River with lower levels possible In the lake. Because the
-38-
-------
predicted river concentrations exceed the standard, the third level model 1s
necessary.
3) CONSIDERING TRANSFORMATION, TRANSPORT, AND SPECIATION PROCESSES
To consider transformation, transport, and speculation processes, the trans-
formation processes which were neglected in the level two analysis must be examined
for their potential importance in influencing the rate of pentachlorophenol
degradation.
Transformation Processes
Blodegradation (Section 2.5.1). The key chemical and environmental variables
which influence biodegradatlon are:
Chemical Parameters:
Metabolic Pathway (growth or co-metabolism)
kg - Blodegradation rate constant
Environmental Parameters:
Bacteria) population size
State of adaptation
Inorganic nutrient concentrations - Phosphorus
| Dissolved oxygen
j Temperature
Pollutant concentration.
; According to Table 11-26, pentachlorophenol 1s potentially biodegradable,
) although adaptation may be slow. The reported specific rate constant values, 0.1
I to 1.0 per day, In Table 11-27 are in the same range as the 0.05 to 0.5 per day
| values suggested in Table 11-26. Although both rate constants were determined
under laboratory rather than environmental conditions, they do indicate that
pentachlorophenol can degrade very rapidly.
Table 11-27 also indicates that pentachlorophenol 1s used by bacteria as a
, growth substrate. Thus, the time required for adaptation is of primary concern.
j The most important environmental factors for determining whether microorganisms 1n '
I the Aurum Mirth watershed will adapt to degrade pentachlorophenol are previous
| exposure, time, and the actual concentrations of pentachlorophenol 1n the surface !
| waters (too low— no enzyme Induction; toe high— may have toxic effect on mlcroblota) ,|
j Photolysis (Section 2.5.2). The key chemical and environmental characterls- I
• tics Influencing the rate of photolysis are: |
• Chemical Properties: I
kdo " Near-surfac* rat« constant
«(x) - Light absorption coefficient of pollutant
* - Quantum yield
-39-
-------
0 Environmental Properties:
I - Solar radiant flux
Z - Mixed depth of water body
K - Diffuse light attenuation coefficient
a) Z$(J - Seech 1 disc depth
b) C - Suspended sediment concentration
C.Q. - Dissolved organic carbon concentration
C - Chlorophyll pigment concentration.
According to Table 11-32, the near-surface photolysis rate constant for
ptntachlorophenol 1s .46/day. The size of the rate constant Implies that photolysis
would be an Important factor 1f the water bodies are not too deep or too turbid.
Thus, U 1s Important to gather Information on the water depths, and to estimate
the light attenuation coefficients and the solar radiant flux 1n the Aurum Mirth
watershed.
Hydrolysis (Section 2.5.3). The Important parameters Influencing the
rate of hydrolysis are:
Chemical Parameters:
ka* St* kb " Ac1d> neutral • and °*se catalyzed hydrolysis
rate constants
Environmental Properties:
pH - Concentration of hydrogen 1on 1n the water bodies.
Table 11-40 gives add and base hydrolysis rate constants for pentachlorophenol
of 1.1 x 10 and 3.3 liter mole" day. The neutral rate constant 1s
S.8 x 10"3 per day. The same table lists a half life of 100 days at pH • 7.
Because the add catalyzed rate constant 1s large, significantly higher rates
could occur at lower pH's. Using Equation 11-85, the rate constant for pH « 5
1s:
ku - 1.1 x 10* (10"5) + 5.8 x 10"3 * 3.3 (10~9)
.1
« .23 day l
At this lower pH, degradation by abiotic hydrolysis would be very rapid. Thus,
determining the pH 1n the Aurum Mirth River and Lake Castile Is very Important.
Summary
The consideration given to transformation, transport, and spedatlon processes
Indicates the following processes are of potential Importance to the fate of
pentachlorophenol 1n the Aurum Mirth watershed:
e Acid-base effects
e Sorptlon
e B1odegradat1on
e Photolysis
e Hydrolysis.
-40-
-------
Since the three transformation processes are potentially Important, there 1s
a good possibility that the Initial pentacnlorophenol concentration of 20*ig/l
will be reduced below the 6.2 ng/1 standard. Therefore further analysis as
presented in the specific water body sections is warranted.
The results of this example agree with the summary of rate processes given In
Table 11-11 except for the case of hydrolysis. This demonstrates that the process
summary table can serve as a useful guide but should be supplemented with actual
data whenever possible.
END OF EXAMPLE II-l
2.3 SPECIATION PROCESSES
2.3.1 Acid-Base Effects
The fate of toxic organlcs which are either acids or bases can be strongly
affected by the concentration of hydrogen Ions in a water body. It Is therefore
necessary to have a means for estimating this Influence. This section will first
present a brief review of acid-base equilibria and then will give a technique for
quantifying the influence of hydrogen ion concentration on the behavior of toxicants.
2.3.1.1 Acid-Base Equilibria
Acids by definition donate hydrogen Ions, H*, to solution. Bases, by
definition, accept hydrogen ions from solution. 2-N1tropheno1, one of the 129
priority pollutants, Is an add and donates hydrogen ions as shown by the following
reaction:
OH
2-nitropnenol 2-n1trophenolate + hydrogen Ion
(HP) (P-) (H«)
Acid-base reactions are extremely fast and can be represented by equilibrium
expressions. For the above reaction the expression would be:
-41-
-------
i_L,K (n-io)
[HP] *
where
[H*] « concentration of hydrogen ions, moles/liter
[P~] • concentration of nltrophenolate ions, moles/liter
[HP] - concentration of undissociated nitrophenol, moles/liter
[K ] « an equilibrium constant for acid dissociation (also called
o
an acidity constant).
The extent to which any acid will donate hydrogen ions to the solution depends
on how many hydrogen ions are in solution (the concentration of hydrogen ions) and on
the strength of the acid.
The concentration of free hydrogen ions in natural waters can range from about
-4 -10
10 to 10 moles per liter. Hydrogen ion concentrations are normally
expressed in pH units. In dilute solutions, such as natural waters, pH is defined as
the negative logarithm of the molar hydrogen ion concentration (pH » •^09iQ [H 3).
For the above two concentrations the pH values are 4 and 10.
The strength of an acid is quantified by the equilibrium constant, K .
u
For very strong acids (those which most readily donate hydrogen 1or»s) the value of
this constant is greater than unity. Included in this group are strong acids such as
hydrochloric and nitric add. Toxic organic acids, though, are generally weak acids
-3 -9
and have K values between 10 and 10 . K, values are typically expressed in terms
a a
of negative base ten logarithms. When this approach is used the equilibrium constants
are called "pKa" (pKa - -log1Q Ka).
When the pH of a solution is the same as the pK, value of an add (I.e., pH
d
« pKJ, 50 percent of the add will have donated Its hydrogen ions to the solution
Q
and will exist as a charged anlonlc species. For pH values greater than the pK
Q
value by one or more units, the acid will have donated essentially all of its hydrogen
ions to the solution and will exist in the anlonlc form (I.e., P").
Tne extent to which any base will extract hydrogen Ions from solution depends
upon the concentration of hydrogen ions In solution (pH) and on the strength of the
base. The strength of a base is quantified by an equilibrium constant, K. . For
very strong bases (those that most readily extract hydrogen Ions from solution) the
value of K. is of the order of 1. Toxic organic bases are generally weak and have
Kb values between 10 and 10" . In a manner similar to adds, *b is typically ex-
pressed in terms of negative base ten logarithms and 1s called "pK," (pKfa •
Water itself can behave as a weak acid or a weak base:
-42-
-------
H_0 5=» H* + OH" (acidic behavior)
H20 * H*5=* H.o" (basic behavior)
Note that [H*]-[OH~] « K
where [OH~] - the concentration of hydroxide ion, moles/1
Kw * io'14, at 20°C
pKw a 14, at 20°C.
When the pH of a solution equals the value (pK - pKfe) of a base, 50
percent of the base has accepted hydrogen Ions and will exist as a charged
cationic species. For pH values greater than one unit above the value of
(pK - pO, essentially all of the base will exist 1n electrically neutral
form (e.g. NH_). For pH values less than the value of (p*w - pKb) by 1
or more units, the base will essentially exist in the electrically charged cationic
form (e.g., NH*).
Table 11-12 summarizes the behavior described above for acids and bases.
Values for pK and pK. for selected toxic organic acids and bases and values
°f P*w are given in Table 11-13. Additional pK, values can be found In
W A
Donigian et_ al_. (1983).
Since toxic organics almost always exist in very low concentrations and are at
best only weak acids or weak bases, they will have little influence, if any, on the
pH values of the water. The hydrogen Ion concentration of the water will, however,
determine whether acids or bases exist in neutral or Ionic forms.
Values of pH for natural waters can be obtained from the USGS, the U.S. EPA, and
state and local agencies. Waters with low alkalinities (e.g., _<_ 50 mg/1 as CaC03,
or 1 mill1equivalent/11ter) are quite susceptible to changes in pK due to natural
processes such as photosynthesis and respiration and even to relatively small additions
of strong acid or base. Selection of representative pH values for such waters will
require more data than for systems with higher alkalinities where less change in pH
can be anticipated.
2.3.1.2 Quantifying the Influence of pH on Toxicant Volatilization
Only electrically neutral species are directly volatile. Volatilization rate
expressions must therefore use as the concentration of toxicant only that fraction
which is electrically neutral (non-Ionic). The fraction of an acid or base which Is
In the non-Ionic form can be determined by use of the expressions given below:
For organic acids:
-43-
-------
TABLE 11-12
OCCURRENCE OF ACIDS AND BASES IN NEUTRAL AND CHARGED
FORMS AS A FUNCTION OF pH, pK , AND pK.
d D
Adds
Bases
Definition: Hydrogen ion donors
Definition: Hydrogen ion acceptors
Example:
HN03 —t* H *
General Reaction:
Example:
NH. + H* -^
General Reaction-.
NH,
HP — •»•
•kU
PK+3
O
"*'
pK -2
PV3
H + P"
Sped at 1 on:
Fraction in
Neutral Form
0.001
0.01
0.09
0.5
0.91
0.99
0.999
Fraction in
Ionic Form
0.999
0.99
0.91
0.5
0.09
0.01
0.001
8 * H — •»- BH
Speciation :
Fraction in
pH Neutral Form
PVPV3 0<999
P^V2 °'99
pK -pK.+l 0.91
pK -pK 0.5
pK -pK.-l 0.09
pK^-pl^-2 0.01
PVPV3 °'001
Fraction in
Ionic Form
0.001
0.01
0.09
0.5
0.91
0.99
0.999
For organic bases:
B° "
where
(H-12)
aAo " tht dec1ma1 fraction of the organic add which 1s In the elec-
trically neutral (noo-ionic) form
"Bo " th* decimal fraction of the organic base which is in the elec-
trically neutral (non-1onlc) form
A • the total dissolved concentrations of the toxic organic add (e.g.,
HP+P~), also called the analytical concentration of A
B • the total dissolved concentration of the toxic organic base (e.g.,
BH* + B). also called the analytical concentration of B.
The rate expressions then become in gent-al form:
-44-
-------
TABLE 11-13
pKa AND pKfa VALUES FOR SELECTED TOXIC ORGANIC
ACIDS AND BASES AND VALUES OF pK FOR WATER
Acids
Phenol
2-Chlorophenol
2,4-Dlchlorophenol
2,4,6-Trichlorophenol
Pentachlorophenol
2-N1trophenol
4-N1tropheno1
2,4-D1nitrophenol
2,4-D1methylpheno1
4,6-01nitro-o-creso1
Bases
10.0
8.52
.85
.99
7.
5.
4.74
7.21
7.15
Senzidlne
4.09
10.6
4.35
9.34, 10.43
Seawater
14.63 at 5"C
14.53 at 10°C
14.35 at 15°C
14.17 at 20°C
14.00 at 25°C
13.82 at 30°C
14.03 at 5°C
13.81 at 10°C
13.60 at 15°C
13.40 at 20°C
13.20 at 25°C
13.00 at 30°C
Notes:
4 All pKa values from Callahan ej aj (1979).
b All pKb values from Weast and Astle (1980).
c pK^ values from Stumm and Morgan (1981) and from Dlckson and R1ley (1979).
-45-
-------
and
R « >
-------
TABLE 11-14
PROCEDURE FOR CALCULATING FRACTION OF A COMPOUND
WHICH IS IN THE NEUTRAL (NON-CHARGED) FORM
1. Decimal fraction of a compound which is In the
neutra[ (non-charged) form"
F.T
For Organic Bases % • - « - : - r (2)
Bo B *
2. Procedure
a] Find the pH value of the water, pH •
a) For an organic add, use Table 11-13 to find the
p«A value of the organic acid, pK. « _ .
c) For an organic base, use Table 11-13 to find the
pK value of the organic base,
d) Also use Table 11-13 to find the pKL, value for water,
K -
3. Substitute: For organic acids substitute pH and
p*A into equation 1. 0. « .
For organic bases substitute
pH, pic, and pKy into equation 2. o •
Note: 10° • l (any number to the zero power equals 1)
4. For approximations of the decimal fraction of a compound
which 1s 1n the neutral form use Table 11-12.
fates of sorbates and solutes can be significantly different. Sorbates are trans*
ported along with sediments, and can be deposited in river or lake beds to remain
Indefinitely. Sorbates are in many ways protected from transformation processes
which would otherwise affect the solute. For example:
-47-
-------
• Microblal degradation rates can be reduced. Steen et^ al_. (1978)
performed tests which showed that sorptlon of toxicants to suspended
sediments renders some compounds unavailable for biodegradatlon 1n the
adsorbed state.
• Volatilization is diminished. Since volatilization of a chemical
occurs from the dissolved phase, the sorbate Is not directly available
for volatilization. Rather, the sorbate first desorbs before 1t
volatilizes. Example 11-4 Mill show the significant influence of
sorption on volatilization.
• Direct photolysis of pollutants adsorbed on suspended particles is
inhibited in some cases. Further, suspended solids deposited on
the bed of a river, lake, or estuary, receive very little radiation
for photolytlc reactions.
The net interaction between the surface of a solid and sorbate can result from a
variety of forces, including coulomblc attraction. Van der Waals forces, orientation
energy, induction forces, hydrogen bonding, and chemical forces (Reinbold et al.,
1979). In the case of many organic compounds, the solute-solvent Interaction is
often weak so that even a weak sorbate-sorbent attraction can result fn sorptlon.
This type of sorptlon is referred to as hydrophoblc sorptlon because of the Importance
of the weak solute-solvent attraction. Hydrophoblc sorptlon will be the topic of
much of the following discussion, but 1t 1s preceded by brief discussions of equi-
librium Isotherms and sorption kinetics.
2.3.2.2 Adsorption Isotherms
Adsorption Isotherms describe the relationship between the amount of chemical
sorted and the equilibrium solution concentration. The most commonly used Isotherms
are:
• Langmuir Adsorption Isotherm. This equation was originally developed to
describe adsorption of a gas to a solid surface, but has been used to
describe solid-liquid sorptlon.
• Freundllch Adsorption Isotherm. This empirical equation 1s based on
surface-free energy and monolayer capacity.
• Linear Adsorption Isotherm. This equation assumes that there 1s a
linear relationship between the concentrations of solute and sorbate at
equilibrium. It 1s valid for dilute solutions.
Figure II-* shows example comparisons between the three Isotherms, and Includes
the equations which describe each isotherm. The quantity X 1s the amount of sorbed
chemical per mass of sediment, and CM 1s the amount of dissolved chemical per
-48-
-------
I
c
I
91
I
5000 r-
I
4500 •-
4000 —
3500 r
3COO
Linear Isotherm
Freundlich Isotherm
X -kf-Cw1'"
o<
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Cw (ug dissolved chemical/? solution)
FIGURE 11-4 ISOTHERMS FOR ADSORPTION OF A HYDROPHOBIC POLLUTANT
ON SEDIMENTS
volume of solution. The remaining variables are unknown parameters required to
define the relationship between X and Cw. The linear Isotherm has one unknown
parameter (K ), while both the Freundlich and Langmulr Isotherms have two unknown
parameters (kf,n and m,b, respectively).
For the purposes of this document, analyses will mostly deal with dilute
aqueous solution In the range where the linear Isotherm 1s generally valid. This
approach has the advantage of requiring that one unknown parameter (K ) be
evaluated, rather than two, and of being easier to manipulate mathematically.
Section 2.3.2.4 will present methods of predicting the unknown parameter K .
2.3.2.3 Kinetics of Adsorption
Sorptlon of organic pollutants 1s often treated as a process which achieves
rapid equilibrium so that expressions of kinetics are not needed. The equilibrium
approach will be used in the remaining chapters of this document. However, a brief
-49-
-------
Introduction will be given of sorptlon kinetics.
Studies of sorptlon kinetics are apparently few, with the result that parameters
required in rate expressions are 111 defined and applicable only under a specific set
of conditions. Under these constraints, klretlcs expressions become less attractive
unless the user can determine values of the rate constants which apply to the specific
system being Investigated.
Most typically, kinetics expressions for sorptlon and desorptlon are chosen to
be first order. Specifically:
expresses the kinetic expression for the solute and
S|£--kdx (IMS)
for the sorbate. The concentrations X and Cw are not necessarily equilibrium
concentrations. In these two equations, the rate constant for adsorption 1s k.
o
and for desorptlon is k^. When the rates of adsorption and desorptlon are equal,
Equations 11-14 and 11-15 can be equated, with the result that X • K C ,
where Kp . ka/kd<
Karickhoff (1979) Investigated the sorptlon and desorption of organic pollutants
and found that a very rapid component of adsorption preceded a much slower component
of adsorption, and that first order kinetics were obeyed during each of the two
periods. For the fast process, the time constant was found to range from 4 to 30 per
hour, while for the slow process the time constant ranged from 0.06 to 1.5 per hour.
Approximately half of the sorptlve equilibrium was realized within minutes, while the
slower component required days or weeks to complete. The slower second period was
visualized as diffusive transfer to sorptlon sites that were Inaccessible directly to
the bulk water. Thus, equilibrium conditions are more likely to be rapidly attained
when the number of easily accessible surface sites exceeds the amount of available
sorbate, e.g. when suspended sediment concentrations are high.
2.3.2.4 Partition Coefficients for Organic Chemicals Obeying Linear Isotherms
The single unknown parameter, K , which relates the sorbate and solute
for linear isotherms Is called th« partition coefficient. A number of studies
have been completed which develop empirical relationships for partition coeffic-
ients 1n natural sediments. Several of these studies will be summarized here.
Theoretically based methods of estimating partition coefficients exist, such as
a thermodynamic approach described 1n Pavlou (1979); however, these will not
be discussed here.
-50-
-------
Karlckhoff §1 il- (1979) examined the sorptlon of aromatic hydrocarbons and
chlorinated hydrocarbons on natural sediments. They found It convenient to relate
the partition coefficient directly to organic carbon content of the sediments as
follows:
Kp - KocxQC (11-16)
where
Knr » partition coefficient expressed on an organic carbon basis
xoc " mass ^ract^on °' organic carbon In sediment.
These workers were able to expand this relationship to segregate the Influence
of particle size as follows:
where
f • mass fraction of fine sediments (d < 50 urn]
x « organic carbon content of coarse sediment fraction
x » organic carbon content of fine sediment fraction.
KaMckhoff et al . (1979) were able to relate K._ to the octanol-water
~ " -- Ut
partition coefficient and to the water solubility by the following relationships:
where
KQw • octanol-water partition coefficient (concentration of chemical in
octanol divided by concentration of chemical In water, at equilibrium)
and
KQC - -0.54 log Sw + 0.44 (11-19)
where
SM - water solubility of sorbate, expressed as a mole fraction.
The water solubilities of the compounds examined ranged from 1 ppb to 1000 ppm.
Hassett et al . (1980) found a similar relationship between K and K
— — •— oc ow
for organic energy-related pollutants. Figure 1 1-5 shows the relationship. Data from
KaMckhoff et_ aj_. are Included 1n the plot for comparison.
Prior to the work of Karlckhoff et^^K, Chlou et^ aK (1977) Investigated
the relationship between octanol-water partitioning and aqueous solubilities for a
wide variety of chemicals Including aliphatic and aromatic hydrocarbons, aromatic
-51-
-------
7
6
5
*84
^Jt
^5
— 3
2
1234567
log KOH
REFERENCE: HASSETT £i AL. (1980)
FIGURE 11-5 RELATIONSHIP BETWEEN KQC AND OCTANOL-WATER PARTITION
COEFFICIENT («ow) OF ENERGY-RELATED ORGANIC POLLUTANTS
adds, organochloMne and organophosphate pesticides, and polychlorlnated blphenyls.
Their results, shown 1n Figure II-6, cover more than eight orders of magnitude 1n
solubility and six orders of magnitude 1n the octanol-water partition coefficient.
The regression equation based on this figure 1s:
log
« 5.00 - 0.670 log
(11-20)
where
solubility, tntimol/1.
Bowman and Sans (1983) report additional K versus S^ relationships. Leo et
ow w —
al. (1971) have tabulated K values for thousands of organlcs. Subsequent to
their work 1n 1971, they have determined K. values for many additional com-
ow
pounds.
Brown and Flagg (1981) have extended the work of KaHckhoff it.il. *** developing
an empirical relationship between K and K for nine chloro-s-tr1az1nt and
Ow OC
d1n1troanH1ne compounds. They plotted their results, along with those of Karlckhoff
•t •!•. >s shown 1n Figure II-7. The combined data set produces the following
correlation:
-52-
-------
c.0'
3 '0s
I"'
10
r* ro1 io-
(0 10* IO* 10* 10* »0*
Solubility in Woltr
REFERENCE: CHIOU EI AL, (1977)
FIGURE 11-6 CORRELATION OF AQUEOUS SOLUBILITY
WITH OCTANOL-WATER PARTITION COEFFICIENT
log K • 0.937 log K - 0.006
oc
(H-21)
The linear correlation between K and K for the compounds studied by Brown
UC OW
and Flagg has a larger factor of uncertainty than those studied by Karlckhoff
et al. Other relationships between K and K for specific groups of
"™ ^"~ OC OW
compounds are reported 1n Karlckhoff (1984).
The previous paragraphs have shown how the partition coefficient K can b«
predicted for organic hydrophoblc compounds which obey a linear Isotherm relationship.
First, K 1s predicted based on either water solubility or the octanol-water
partition coefficient. Tables II-5 through 11-9 shown earlier contain K values
for a number of compounds. Then based on an estimate of organic carbon fraction 1n
the fine and coarse sediments, K can be estimated from Equation 11-17. This
procedure Is summarized 1n Table 11-15.
2.3.2.5 Solute and Sorbate Fractions
The relative amount of pollutant sorbed and dissolved depends on both the
suspended sediment concentration and the partition coefficient, and at equilibrium 1s
given by:
(n-22)
4; •
-53-
-------
AND FLAGG (1981)
KARICKHOFF ET AL, (1979)
0 Q50 100 !JO 200 Z.SO JOO J.SO «.00 4.50 5.00 5.50 5.00 630
LOG Kow
Note: The actual error bands for this figure are probably
greater than indicated here due to error in the
measurement of K .
ow
where
FIGURE 11-7
RELATIONSHIP BETWEEN KQC AND Kow FOR COARSE SILT
total dissolved phase concentration
C.*CS
xs
partition coefficient
suspended sediment concentration, on a part per part basis
mass of sorbed pollutant per mass of suspended sediment.
Equation 11-22 can be Illustrated more vividly by tabulating ranges of K
and S values. Table 11-16 shows this Information. Partition coefficients and
suspended sediment concentrations range from 10° to 10. For the lowest
value of the partition coefficient nearly all of the pollutant 1s present in the
dissolved form, regardless of the suspended sediment concentration. Also, for low
suspended sediment concentrations, nearly all of the pollutant is dissolved, unless
the partition coefficient is extremely large. When relatively high partition
coefficients and sediment concentrations occur simultaneously, then most of the
-54-
-------
TABLE 11-15
PROCEDURE FOR CALCULATING PARTITION COEFFICIENT
1. Partition Coefficient
P oc
2. Procedure
3.
Xs * f xf 1
oc oc J
(1)
a. Find KOW (octanol-water partition coefficient)
c.
(1) Use Tables II-5 through II-9
for priority pollutants. K
OR, 1f the value 1s not tabulated
(2) Estimate K^ by:
log KW . 5.00 - 0.670 log Sw •
where S » solubility, t*mole/l
. * , x 10
molecular weight
Use Tables II-5 through II-9 to find Sw (mg/1)
b. Find K
OC'
*oc • °-63 Kow
(2)
Estimate:
(1) f (mass fraction of silt or clay) • _ , (0
-------
TABLE 11-16
RELATIONSHIP OF DISSOLVED AND SOR8ED PHASE POLLUTANT
CONCENTRATIONS TO PARTITION COEFFICIENT AND
SEDIMENT CONCENTRATION
*p
10°
101
102
103
A
104
S (ppm)
1
10
100
1000
10000
1
10
100
1000
10000
1
10
100
1000
10000
1
10
100
1000
10000
1
10
100
1000
10000
VCT
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
0.9
1.0
1.0
1.0
0.9
0.5
1.0
1.0
0.9
0.5
0.1
1.0
0.9
0.5
0.1
0.0
1
-------
EXAMPLE II-3
Determine the fraction of benzo(a)pyrene that 1s dissolved 1n a system
containing 300 ppm suspended solids. The suspended sediments are 70 percent
fines (d < 5" Mm) and the weight fraction of organic carbon is 10 percent of
the fines ana 5 percent of the sand fraction.
From Table 11-9, the solubility of benzo(a)pyrene is 0.0038 mg/1, and
the octanol-water partition coefficient 1s 10 . If, for the moment, the
octanol-water partition coefficient is Ignored, Equation 11-20 can be used
to predict K based on i
be converted to mole/1:
to predict KQW based on solubility. The solubility of 0.0038 mg/1 must
(0.0038 rng/1) (HT3 g/ng)
- 0.015 umole/1
From Equation 11-20, the predicted octanol-water partition coefficient 1s:
log K - 5.00 - 0.670 log (.015)
« 6.22
so K~ " 106'22, which 1s acceptably close to the tabulated value of 10 .
Using the tabulated K K Is computed from Equation 11-18:
ow oc
K • 0.63xl06
oc
• 630,000
From Equation 11-17, the partition coefficient becomes:
Kp « 630,000 [0.2 (1-.7) (.05) + 0.7 (.10)]
> 46,000
The suspended sediment concentration for the system 1s 300 ppm, or 300-10"
parts per part. Using Equation 11-22, the fraction of benzo(a)-pyrene which 1s
dissolved Is:
j UT 1 + 300 • 10"° • 46,000
• 0.067 or about 7 percent
. Consequently, most of the benzo(a)pyrene 1s present as sorbate.
I
I END OF EXAMPLE II-3
-57-
-------
2.4 TRANSPORT PROCESSES
2.4.1 Solubility Limits
The concentration of a compound in a natural water, and therefore the rate of
transport by that water, can be limited by Its equilibrium solubility. The aqueous
solubility of organic compounds ranges widely:
Aqueous Solubility at 25°C
(mass which will dissolve in i liter of water)
Compound (in milligrams)
Sucrose 2,000,000
Benzene 2,000
Toxaphene 2
Chrysene 0.002
Non-polar compounds have limited solubilities In polar solvents such as water.
The solubility of toxic organic compounds 1s generally much lower than for Inorganic
salts. Equilibrium solubilities for toxic organic compounds are given in Tables 11-5
through 11-9. Solubility increases with temperature for most organic compounds,
typically by a factor of about 3 from 0°C to 30°C.
Drganics are generally less soluble 1n sea water than in fresh water as can be
seen in the tabulations below (Rossi and Thomas, 1981):
Solubility at 25°C
Distilled Water Sea Water
Compound (mg/1) (mg/1)
Toluene S07 419
Acenapthene 2.41 1.84
Pyrene 0.13 0.09
In the absence of colloids or micelles, the maximum amount of a toxic organic
substance which can be held in the water column under equilibrium conditions 1s just
the aqueous equilibrium solubility $w, plus the equilibrium amount of solute
sorted on suspended matter:
CT - Sw + fs (Sw) (11-23)
where
CT * total amount of compound which can be held 1n a natural
1 .1
water at equilibrium conditions, i>g liter
Sw - equilibrium aqueous solubility, *g liter"1
f (S ) • equilibrium amount of sorbate on suspended matter; a
function of S . f 1s the sorptlon Isotherm function.
-53-
-------
If a linear sorption isotherm 1s used, as is commonly the case for trace constituents
(see Section 2.3.2), the above expression becomes:
CT 1 Sw (1 * Kp S) (11-24)
where
K « linear partition coefficient (see Section 2.3.2.4), liter Kg"1
S » the "concentration" of suspended matter (sorbent), Kg liter
The inequality results in the above equation because at high solute concentrations
linear Isotherms overpredlct the amount of solute so r bed. The use of linear sorption
Isotherms (a common practice for trace constituents) 1s adequate at pollutant concen-
trations which are equal to, or less than, one half of the equilibrium solubility.
When linear sorption isotherms are used, e.g. those with the simple partition coeffic-
ient approach (K ) presented in Section 2.3.2, one must then check to insure that
the aqueous pollutant concentration 1s less than or equal to one-half of its equi-
1 Ibrium solubi 1 ity.
2.4.2 Volatilization
2.4.2.1 Introduction
Volatilization is defined as the transfer of matter from the dissolved to the
gasec.i phase. A considerable number of toxic substances volatilize in the natural
environment. Volatilization rates depend on the properties of the toxicant and on
the characteristics of the water body. If a toxicant 1s "highly volatile", then
obviously volatilization Is an important process affecting the fate of the toxicant.
However, even for toxicants which are considerably less volatile, volatilization
cannot always be Ignored. This 1s because the fate of a toxicant Is governed by a
variety of processes. If volatilization proceeds as fast as other competing mechanisms,
even though all the rates might be slow, then volatilization will Influence the fate
of the toxicant.
Methods will be provided 1n this section to predict the volatilization rate for
toxic organic substances, which volatilize according to the following relationship:
. v (c - ) . -k; (C - ) (II.25)
where
C « concentration of toxicant 1n dissolved phase (concentration of solute)
KV « volatilization rate constant in units of length/time
KY » volatilization rate constant 1n mixed water body In units of
time"1
-59-
-------
Z • mixed depth of water body
P « partial pressure of toxicant in atmosphere above the water body being
investigated
KH • Henry's Law constant.
For many applications the partial pressure of the compound in the atmosphere is zero,
so that Equation 11-25 simplifies to:
-i£ » -k' C (H-26)
3* V
*n alternate form of Equation 11-26 is in terms of the total pollutant concentra-
tion, CT, and the site specific volatilization rate constant, kym:
1TT ' ^ CT (II-2?)
where
k a
k , .v * (11-28)
vm Z
where
aM « fraction of toxicant present in dissolved phase.
The following sections will illustrate how to predict the volatilization rate for
toxicants of either low or high volatility. But first, a brief discussion of Henry's
Law is required.
2.4.2.2 Henry's Law
Henry's Law is an expression which relates the concentration of a chemical
dissolved in the aqueous phase to the concentration (or pressure) of the chemical in
the gaseous phase when the two phases are at equilibrium with each other. One common
method of expressing Henry's Law is:
P - ^ (11-29)
where
P • equilibrium partial pressure of pollutant in atmosphere above the
water, atm
C • equilibrium concentration of pollutant in the water, mole/m
3
Ky • Henry's Law constant, atm m /mole.
Henry's Law in this form is valid for pollutants present in concentrations up to 0.02
expressed as a mole fraction. For compounds with molecular weights greater than 50
g/mole, a mole fraction of 0.02 represents a concentration of at least 55,000 mg/1.
-60-
-------
Typically toxic pollutants 1n the environment are present at levels far below this
concentration.
Table 11-17 contains values of Henry's Law constants for a number of selected
hydrocarbons. In the table, Henry's Law constant 1s expressed 1n units of atm
m /mole. However, In the literature Henry's Law constant can be defined In
numerous ways. A second, widely used method of defining Henry's Law constant 1s:
< « .£ (H-30)
where
C • molar concentration In air, mole/m
«• « alternate form of Henry's Law constant, dlmenslonless.
n
Equations 11-29 and 11-30 are related as follows:
where
T. « temperature of water, °K.
Ru - universal gas constant.
This relationship 1s based on the Ideal gas law. Equation 11-31 1s useful because of
the frequent necessity to convert literature data from one set of units to another.
Henry's Law constant can be estimated for slightly soluble compounds
(mole fraction <_ 0.02) by the following expression:
P x MW
w
where
P » saturation vapor pressure of pure compound 1n Torr
MM • molecular weight
S • solubility In water 1n ppm.
Figure II-8 Illustrates the limits of Henry's Law for an acetone-water mixture.
Henry's Law 1s obeyed by acetone 1n region 8 (mole fraction of acetone <0.1) and by
water In region A (mole fraction of acetone >0.95). Notice that the generally
accepted limit of validity of Henry's Law (mole fraction £0.02) corresponds to
concentrations of 34,000 mg/1 to 227,000 mg/1 for compounds with molecular weights
between 30 to 200. Thus Henry's Law is likely to be applicable In nearly all cases
of concern In the natural environment. For pollutants which happen to be largely
soluble, however, care must be taken to calculate Henry's law by some method other
than Equation 11-32.
-61-
-------
TABLE 11-17
HENRY'S LAW CONSTANT FOR SELECTED HYDROCARBONS
Oltftnt
Ac*tyl*n«t
EtMn* (f)
Propcnc If)
1-Buttn* (9)
l-P«nt»«t (I)
l-*«i*nt (t)
2-H«ptr«c ropyn« If)
l-Sutyn« (f)
CyclMtktntt
Alk*n»i
C/c ) opt nttft* (i)
Cycloheatn* (t)
Mflliy)cyClPO«fiUnt (t)
^ttn/tcyclph*x*M (t)
PrQpylcyclop«nt«n* (t)
Isobulint (f)
IsOPtnurw (t)
2-N*tH;lp«nt*M (t)
2-»tetftytH«iu«« (t)
2.2-DiB*thyl(wntarM (l)
3-Mttr.ylhtpt4n« (i)
2.2.4-:ri»iftHytp««t«n« (*)
PolycMpnnttM
Aroclor 1242
Aroclor 1748
Aroclor 12S4
Aroclor 12(0
• TlttM in MtlMttO' VCllMt
Si
ConttMt
0.214
0.232
0.2M
0.398
0.412
0.418
0.905
0.0110
0.0194
0.18?
O.I9C
0.3*7
0.42*
0.893
1.24
1.3*4
1.73
.42
.IS
.71
.04
.93*
3.5 1 10°
7 . 1 1 10*'
b«M4 on k, •
^ (CW.T,*
20.
20.
20.
20.
20.
20.
20.
19.8
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
15. »
18.9
18.9
19. t
AroMtiCt
8*«"«"*
Htthiiw (t)*
Ethan* (r)
PropaM (t)
•-BuUnt (f)
n-ftnttM (t)
n-Ht>M4 (t)
»-HtpUM (t)
•-OcUnt (t)
•-Mono* (t)
D«CMM (t)
Dootcint (t)
T«tr«d«can« (t)
,„,,-.
DOT
Al«rtn
EndHn
HepucMor
CMor(UM
TOMPtltM
20 oa/hr tnd k • 3000 o»/hr.
S;
tltni^ * s LMt
Co«tt*nt
5.49»1(TJ
i.MilO*}
8.73«10-3
S.27»10*3
1.4S»10*J
4.2S«10'4
*.3*«10'4
2.28J110-4
2.3SJUO"4
l.tSUO"''
1.4talO-*
O.MS
0.499
0.707
0.947
1.28
1.8S
2.07
3.22
3.29
4.13
7.12
1.14
-9UO-6
.o»io-'
.4*10-*
.»*io-'
.S.10-3
uio-5
0.1
k, (c^M>)*
19.4
19. S
19. f
19.4
19.8
14. i
lt.0
n.f
11.9
18.2
9.1
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
20.
1.9
0.01
0.02
0.80
0.0*
18.
4.8
19.8
-62-
-------
Consider acetone-water mixture
o o* CM o-e 0-8 K)
Mole fraction of acetone
Henry's Low Is obeyed:
by acetone when mole fraction of acetone £0.1 (Region B)
by water when mole fraction of acetone >0,95 (Region A)
General range of validity: mole fraction <0.02
MW Concentration when mole fraction
0.02
30
75
100
200
3UOOO mq/1
85000 mg/1
113000 mg/1
227000 mg/1
FIGURE II-8 RANGE OF VALIDITY OF HENRY'S LAW
-63-
-------
Tables II-5 through II-9 presented vapor pressure and solubility data for the
organic priority pollutants, which can be used to predict Henry's Law constant.
Although Equation 11-32 is not valid for highly soluble chemicals, generally the
toxicants of interest nere are only slightly soluble, so that the expression is
adequate.. The dimensionless form of Henry's Law constant is expressible as:
16.04 P x MW
All variables have been previously defined.
EXAMPLE 11-4
Henry's Law Constant for Chloroform
Calculate Henry's Law constant 1n the two forms expressed by Equations
11-32 and 11-33. Chloroform (also called trlchloromethane or CHCl^) has
the following properties:
Vapor pressure » 150 Torr (from Table 1 1-5)
Solubility • 8200 ppm at 20°C (from Table II-5)
Molecular weight » 12 (carbon)
1 (hydrogen)
3 x 35.5 (chlorine)
Sum - 119
From Equation 11-32:
150x119 -i
H 760x8200
From Equation 11-33, at 20«C (293°K):
16.04x150x119
8200x293
j Henry's constant, expressed as K^, had been found experimentally to be
• 0.12, the same as predicted here.
I ----- ENO OF EXAMPLE II-4
2.4.2.3 Two Film Theory of Volatilization
When a chemical volatilizes from water, the process can be visualized as a mass
transfer occurring over several distinct steps. Figure 11-9 presents a schematic
-64-
-------
Toxicant concentration
Vapor phase
U-^
Direction of movement
•. '• • Gas dim '.'. •' . • • '' '
FIGURE 11-9
SCHEMATIC REPRESENTATION OF VOLATILIZATION
FROM SOLUTION PHASE TO LIQUID PHASE
representation of the process. The concentration of the chemical 1s C In the bulk
liquid solution. As the chemical moves upward 1n the bulk solution It moves through
a thin "liquid film" where a concentration gradient develops because the transfer
rate Is limited by diffusion. The dissolved chemical then volatilizes and passes
through a thin "gas film", where again transfer may be limited, before reaching the
bulk vapor phase.
At the Interface between the gas and liquid films the concentrations 1n the
liquid (C^} and In the gas (Pc1, expressed as partial pressure) are assumed
to be In equilibrium and to obey Henry's Law:
rc1
(11-34)
-65-
-------
In the absence of net accumulation at the interface the mass flux from one phase must
equal the mass flux from the other, or:
where
F • flux of chemical 1n z direction
k , • mass transfer coefficient 1n the gas phase across "gas film"
k^ • mass transfer coefficient 1n the liquid phase across "liquid
film"
PC, Pc1, C, C^ are defined 1n Figure 11-9.
Since it Is not convenient to measure the partial pressure and concentration at the
Interface, 1t 1s worthwhile to develop expressions for bulk transfer coefficients,
given by:
- {n-36)
where
k • overall volatilization rate defined for the gaseous phase
ky1 • overall volatilization rate defined for the liquid phase
S • saturation concentration of chemical 1n equilibrium with PC
P' * partial pressure in equilibrium with C.
Combining Henry's Law equilibrium expressions with Equations ..-35 and 11-36 the
overall volatilization rates become:
J ___ L.i + J_
k R T k,, k .
vg u 11 gi
(11-37)
and
1
kvl
(11-38)
Of the two expressions, normally Equation 11-38 1s more useful for the purposes
of this document because the pollutants being analyzed are 1n the aqueous phase. To
simplify terminology Equation 11-38 will be rewritten as:
. . R T
-L._L + -JL_ (II-39a)
or
(II-39b)
-66-
-------
where the second subscripts to each variable have been dropped. The voKtll 1zat1on
rate, ky, is the same as shown earlier 1n Equation 11-25 and depends on k ,
Kj^, and ki .
There are two special cases of Equation 11-39, depending on the value of Henry's
Law constant. They are:
. for large K (liquid-phase limited)
Ck , for small 1C (gas-phase limited)
nu n
(II-40a)
(II-40b)
10 maice Equation 11-40 usable, "large" and "small" values of K^ have to be
defined. For cases when the liquid phase 1s limiting the transfer rate, a large
fraction, R, of the total resistance exists in the liquid phase, or:
(11-41)
Similarly when the gas phase is limiting:
Ml l l
R r "R r*rr
\kv/ \kl KHkg
(11-42)
Equations 11-41 and 11-42 can be rearranged to express Henry's Law constant explicitly:
k R
_I , for liquid-phase limited
kg 1-R
if
_L ll£ , for gas-phase limited
(II-43a)
(II-43b)
At this point values for R, k1, and k must be specified. "Typical" values
of k and k1 for surface waters are In the range of 20 cm/nr and 3,000 cm/nr,
respectively. For R values of 0.83, 0.90, and 0.95, the phase limiting values of
Henry's Law constants, converted to units of at* m /mole using Equation 11-31,
are as follows:
Henry's Constant (atm-m'/mole)
R
0.83
0.90
0.95
Liquid-phase Limited
7.8 x 10'*
1.4 x 10-'
3 x 10-*
Gas-phase Limited
3.3 x 10'.s
1.8 x I0's
8.4 x 10'*
-67-
-------
Hence, for Henry's Law constants larger than about 1.0 x 10 atm m /mole
most of the resistance to volatilization lies in the liquid phase, and for Henry's
Law constants less than about 1.0 x 10 atm m /mole, most of the resistance
lies in the gas phase. When either of the two phases controls the volatilization
rate, then the simplified Equation 11-40 can be used in lieu of Equation 11-39. The
data in the tables presented earlier can be used to predict lenry's Law constant and
then to decide whether the gas or liquid phase limits volatilization.
Based on the two-film model there are two methods which can be used to estimate
volatilization ratas. One approach is considerably more simple than the other. The
simpler approach is based on the following reasoning. Using "typical" values of
k^ and k , ky can be estimated based solely on KH as the independent variable,
where K. 75 allowed to vary over its potential range of values. As Table 11-18
shows, KH can vary by at least seven orders of magnitude. Based on this variabil-
ity of Henry's Law constant, Table 11-19 presents the associated volatilization
rates. As Henry's Law constant increases, the volatilization rate approaches
20 cm/hr, the liquid phase limiting rate. As Henry's Law constant decreases, so does
the volatilization rate, with the lower limit being zero.
The second method of predicting ky is based on finding k and k^ individually,
rather than assuming typical values. The gas-phase transfer rate can be found based
on the evaporation rate of water as outlined in Mills (1981). Mills showed that:
k '• 700 V (11-44)
where
k- » gas transfer rate for water vapor, cm/hr
V -wind speed, m/sec.
This expression was derived from an empirical relationship shown in Linsley et^ aK,
(1979) for the evaporation of water. Liss (1973) conducted measurements in an
experimental basin and found that:
ic' « 1000 V (11-45)
where the units are the same In Equation 11-44. Considering that the approaches used
to develop Equations 11-44 and 11-45 are different, their agreement 1s good. Still
other relationships exist between k' and V (e.g. Rathbun and Tai, 1983).
The values of k and k- are related by penetration theory (Bird et
al. 1960) as follows:
kg
-68-
-------
TABLE 11-18
HENRY'S LAW CONSTANTS FOR SELECTED COMPOUNDS
Compound
Henry's Law Constant (atm-m3/mo1e)
Vinyl Chloride
Carbon Tetrachloride
Toluene
Aroclor 1254
Flourene
DDT
Dieldrin
3.7
2 x 10'2
6.7 x 10'3
2.8 x NT3
2.4 x 10'4
3.9 x 10'5
2.0 x 10'7
TABLE 11-19
TYPICAL VALUES OF POLLUTANT VOLATILIZATION RATES
IN SURFACE WATERS
^(atm-mS/mole)
10°
10-1
10-2
10-3
io-4
10-5
io-6
10-7
I K^dlmenslonless)
41.6
4.2
4.2 x 10-1
4.2 x 10-2
4.2 x 10-3
4.2 x IO-4
4.2 x 10-5
4.2 x 10-6
kv(cm/hr)*
20.
20.
19.7
17.3
7.7
1.2
0.1
0.01
kv( I/day)**
4.8
4.8
4.7
4.2
1.8
0.3
0.02
0.002
Liquid-film
limited
t
1
Gas-film
limited
*Us1ng kq - 3000 cm/hr
k* « 20 cm/hr.
**For water depth » l m.
-69-
-------
where
D « diffusion coefficient of pollutant In air
a
0 « diffusion coefficient of water vapor 1n air.
wv
Diffusion coefficient data can be found in such references as Perry and Chilton
(1973), or estimated using the W1lke-Chang method, also In Perry and Chilton. If an
analytical method is used to estimate diffusion coefficients, note that it is easier
to predict the ratio of two diffusion-coefficients than to predict each coefficient
individually because some of the required Information cancels out of the ratio, and
consequently is not needed at all.
In many cases it 1s acceptable to approximate the ratio of diffusion coefficients
as follows:
wv
(11-47)
where
MM • molecular weight of pollutant.
Table 11-20 Illustrates the difference between calculating the diffusion coefficient
ratio by using tabulated data from Perry and Chilton and by using Equation 11-47.
The percent differences between the ratios range from 1 to 27 percent and average 15
percent. This agreement 1s acceptable for screening purposes. Combining Equations
11-46, 11-44, and 11-47, the final expression for k (1n units of cra/hr) 1s:
TABLE 11-20
COMPARISON OF TABULATED AND PREDICTED VALUES OF DIFFUSION
COEFFICIENTS FOR SELECTED POLLUTANTS
Pollutant
Diffusion Coefficient Perry A
Perry CMHon
Molecular ft Chilton Predicted
Height (c«2/sec) (cm2/$ec)
Predicted
Percent
Chlorobenzene
Toluene
Chloroform
Naphthalene
Anthracene
Benzene
113
92
119
128
178
78
0.075
0.076
0.091
0.051
0.042
0.077
0.088
0.097
0.086
0.083
0.070
0.106
.58
.59
.64
.48
.44
.59
.63
.66
.63
.61
.56
.69
9
12
1
27
27
17
-70-
-------
kg • 700 [£) V (H-48)
This expression 1s valid for rivers, lakes, and estuaries.
The liquid phase transfer coefficient k^ can be predicted based on the
reaeratlon rate, *a, for the system. The relationship proposed by Smith gt aK
(1981) 1s:
' 3 \n
"l
k' , 0.5 < n < 1 (11-49)
where
DW • diffusion coefficient of pollutant 1n water
DO • diffusion coefficient of dissolved oxygen 1n water
2
ka' » surface transfer rate of dissolved oxygen, expressed 1n the
same units as k^.
In other chapters of this report, the federation rate Is presented as k.,
0
defined as:
ka-ka'/z (n
where
Z • nrixed depth of water body.
For rivers the mixed depth Is the total depth, while for estuaries the mixed depth Is
the total depth only 1f the estuary 1s well mixed*. Otherwise, it 1s the depth to the
pycnocllne. Similarly for lakes, the mixed depth can be less than the total depth,
and can be chosen to be the depth of the epIHmnlon.
The exponent n varies as a function of the theoretical approach used to develop
Equation 11-49. If film theory Is used. I.e., the film Is considered to be a laminar
sublayer, then n « 1. If penetration or surface renewal theory 1s used, n * 0.5.
Using experimental approaches, researchers have found n to vary from 0.5 to t.O.
Since the movement of water In natural water bodies 1s generally turbulent, the
parameter n can be chosen to be 0.5.
Perry and Chllton (1973) provide data and methods to predict the diffusion
coefficient of a pollutant 1n water. The Othner-Thakor relationship, described
In Smith et,al_. (1981) can also be used. As an approximate approach, by using
the square root of the molecular weights the following expression results:
A recent study (Rathbun and Tal, 1981) used a tracer technique to predict the
volatilization rates of four priority pollutants from 12 different rivers. That
-71-
-------
study provides an opportunity to compare, even 1f only to a limited degree, some
of the methods presented here against field results. Table 11-21 briefly summarizes
the results of Rathbun and Tal (1981). As shown by the values of Henry's Law constant
for the four pollutants, each pollutant 1s liquid phase United, since all Henry's
Law constants exceed 1.0 x 10 atm • /mole. The study results were unable
to predict differences 1n volatilization rates for the four pollutants, and found
that the best predictive expression was:
ky • 0.655 k;
Based on Equation 11-51 the screening Methods predict:
kv" 0.7|£ to 0.8 k^
where the range reflects the variability 1n Molecular weight among the four pollutants.
If the default value of 20 cm/hr, suggested earlier 1n this section were used as
a rough estimate of the volatilization rate for liquid phase limited pollutants, this
value would fall within the observed range of 1.5 to 24 cm/hr. It appears that the
screening methods presented here generate acceptable estimates of volatilization
rates.
Table 11-22 summarizes the two methods presented In the manual for calculating
the volatilization rate constant ky. The first approach Is more simplified and
1s based on typical values of k and k,. In the second approach, k and
kj are calculated rather than assumed.
2.4.2.4 Volatilization Half-Life
Numerous researchers have 1n the past calculated the volatilization half-life of
toxicants under controlled laboratory conditions. The result of some of this work
was shown earlier 1n Tables 11-5 through 11-9. Typically, researchers have used the
following expression to calculate the half-life:
0.693Z
V—T~ (1I"
v
where
*l/2 " n*1f"H'* (t1*e required for the concentration of the contami-
nant to decrease by half).
It 1s Important to understand that the volatilization half-life of a toxicant
varies according to the environmental conditions. Under controlled laboratory
-72-
-------
TABLE 11-21
VOLATILIZATION RATES OF SEVERAL PRIORITY POLLUTANTS IN 12 RIVERS3
Pollutant
Benzene
Chloroform
Methyl ene Chloride
Toluene
Study results showed:
Henry's Constant
(atm-m'/mole)
5.5 x 10'1
2.9 x 10' '
2.7 x 10~J
6.7 x 10-J
k - 0.655 k'
Molecular Weight
M9
78
85
92
Range of values for 12 rivers: 1.5 to 24 on/hr
Screening method predicts: k » 0.7 k' to 0.8 k
~ • - Y o
aRathbun, R.E. and D.Y. Tai. 1981. Techniques for Determining
the Volatilization Coefficients of Priority Pollutants in Streams,
Water Research. Volume 15, pp. 243-250.
conditions, where the depth of water 1s extremely small, t. ,- can be extremely
small. If the water depth Increases by 100 fold, for example, so does t^.g.
The volatilization half-life 1s affected by suspended sol Ids 1n the system.
When suspended sol Ids are present. Equation 11-52 should be modified to:
z 4 MT c
r - (I+SJCJ d1'5
Ky r
where
S • suspended solids concentration
K • partition coefficient.
The partition coefficient 1s the ratio of the sorbed pollutant concentration to the
dissolved phase concentration. A method to predict K was discussed earlier 1n
Section 2.3.2. Since the toxicant which sorbs to the sediments Is not directly
available for volatilization, the total flux of volatilizing particles decreases.
The following example Illustrates now sorptlon can Influence the half-life.
-73-
-------
TABLE 11-22
PROCEDURE FOR PREDICTING VOLATILIZATION RATE
1. limit: Htwy-i it, Comunt (Twit II-H)
2. rrocM^rt; Utt T«»lo Il-l»
J. »MiiU: ^ («/*•)•
To eoiw«rt U wltt *f Mr Mr
*t (Mr My) • k, (oW»r) • $g •
Mttft
II:
1. I "But: Htwy'i L*i CoMtdt (^. (W « «J/««1«) •
I.
(•Mftttan rit« (», o^V, or
»«ttr U*mr«t«rt (T, °t) •
•«ttr Mitli (1. aittrtl •
(It* i
t.i •
k. MM: k, (€»/<«•) . 700 (Jj) »
(Mr H,, • TOO «
(t)
(1*1
V
^ x—v
(S) '.-
Mf):
-« 1- "I ('*i
-74-
-------
------------------- EXAMPLE II-5 ----- - - j
The following data for hexachlorobenzene were obtained from Table II-8:
! Solubility » 20 *«g/l !
j Vapor pressure • 10"5 Torr at 20°C \
I Kw »106. I
| Under the conditions reported In the work of Mack ay and Lelnonen (1975): |
j L - 1 m
8 cm/hr • 8 x 10 m/hr.
Hence:
i 0.693 x 1 n , j
! Vex io-> ' 8'7 hours !
I I
I Note that the half-life Is small even though the vapor pressure Is only 10" |
| Torr. The results Indicate that the vapor pressure 1s, by Itself, not necessarily j
j a good Indicator of the Importance of volatilization. j
Now, consider the following conditions which might be encountered 1n a
river:
' k (reaeratlon rate) • 0.5/day |
* -
Suspended sediment concentration • 550 ppm I
K • 5 x 104 I
j Depth -1m. |
, i
I The expression of volatilization half-life modified to account for the presence of j
I the suspended solids 1s: I
i
• From Equation 11-51, the liquid-phase transfer rate for hexachlorobenzene Is: '
! /32V !
i ki " 75T x 0.5 x 1 » 0.29 m/day » 0.01 m/hr • 1 cm/hr i
I I \ &O3 / I
, \ / i
I I
I *
J Henry's Law constant can be estimated based on Equation 11-32. Using the data !
I presented earlier: I
28S 1.9 x 10"" atm-mVmole
H 760 x .02
or
K'H « 7.8 x 10"', dlmensionless
-75-
-------
I Using a default value of 3,000 cm/hr for k , the volatilization rate 1s:
I ky = 1 cm/hr
i
I The half-life becomes:
"""
A comparison of half-lives shows that:
t • 8.7 hours under laboratory conditions
t • 75 days under Instream conditions.
This example Illustrates that half-lives are not always extrapolatable from
one type of system to another due to the combined difference 1n sorpflon effects
and volatilization rates.
•- END OF EXAMPLE 11-5 -•
2.4.2.5 Flux of Volatilizing °o11utants
The preceding sections have provided techniques for predicting volatilization
rates of pollutants. Obviously, If the volatilization rate of one pollutant exceeds
that of a second pollutant, then the first pollutant 1s more volatile than the
second. However, this criterion alone does not determine whether volatilization Is
Important 1n a specific situation. The volatilization flux 1s the rate at which mass
1s transferred to the gaseous phase from the liquid phase and 1s given by the follow-
ing expression:
Flux - kv c - — JI-54)
• kyC, when P • 0 (11-55)
where
C • concentration of pollutant 1n water as solute
P * partial pressure of pollutant 1n atmosphere.
Hence both the volatilization rate and the dissolved phase concentration have to be
considered jointly to predict the flux being volatilized, "able 11-23 Illustrates
-76-
-------
TABLE 11-23
RELATIVE VOLATILIZATION MASS FLUXES OF SEVERAL CHEMICALS IN SATURATED SOLUTIONS
Henry's Law
Constant
Chemical (atm-m3/mo1e)
Carbon Tetrachloride
ODT
Oieldrin
Phenanthrene
2.3 x
3.9 x
2.0 x
1.5 x
ID'2
io-5
io-4
io-3
Volatilization
Rate Constant Solubility
(cm/hr) (ppm) K^ Flux Ratio
20.
3.9
0.02
9.6
785 400 1
.002-. 085 10'-106 5xlO'-2xl06
0.2 - 4 x IO6
1.0 29,000 2 x IO3
aThis Is the ratio of volatilization flux of a saturated solution of carbon
tetrachloride to the volatilization of the specified chemical.
these principals for several chemicals. The volatilization rates for these pollutants
range from a high of 20 cm/hr for carbon tetrachloride to a low of 0.02 cm/hr for
dleldrln. Anthracene has a volatilization rate constant of 18 cm/hr, 90 percent as
high as the volatile carbon tetrachloride. However, the solubility of anthracene In
water Is much lower (0.06 ppm versus 785 ppm). Hence If each of these two chemicals
were to volatilize from saturated solutions, the flux of carbon tetrachloride would
be 15,000 times as great. The same type of comparison can be made for DOT and carbon
tetrachloride. The volatilization rate constant for DOT 1s relatively high (about 20
percent that of carbon tetrachloride), but the solubility 1s so low that the ratio of
volatilization flux would be about 100,000:1.
These comparisons have not considered the relative differences In sorption
characteristics of the pollutants. Since only the solute volatilizes, the volatiliza-
tion flux of a pollutant which 1s mostly sorted to suspended material 1s lower than
1n the absence of suspended material, all other factors remaining the same. Tables
II-5 through 11-9 show the octanol-water partition coefficient, which provides a
measure of relative Importance of sorption for the four pollutants. Because both DDT
and anthracene have higher octanol-water partition coefficients than does carbon
tetrachloride, the ratio of Utilization of mass fluxes Is likely to be even
greater than calculated above for natural systems containing suspended material.
2.5 TRANSFORMATION PROCESSES
2.5.1 Blodegradatlon
-77-
-------
2.5.1.1 Introduction
Microorganisms are ubiquitous in the aquatic environment. Microbes are also
very active chemically due to their ability to supply energy for reactions through
normal metabolic processes and to catalyze reactions through enzymatic activity.
Chemical reactions which proceed very slowly or not at all in the absence of biota
occur at rates of up to eleven orders of magnitude faster In the presence of biological
enzymes. Some of the reactions catalyzed by microorganisms transform or degrade
organic pollutants. Frequently, microbial degradation, or blodegradation. is the
most important, if not the only process which can decompose an organic pollutant in
the aquatic environment.
Although microbial communities catalyze countless reactions, many of them fall
into a few classes of Important reactions. Oxidative reactions make up one very
important class of biochemical reactions. The hydroxylation of aromatic compounds,
such as benzene, 1s an example of an oxidative reaction which generates polar com-
pounds from non-polar ones:
OH
Enzyme
Benzene
Catechol
An extremely Important oxidative reaction unique to microbial organisms is aromatic
ring fission:
CHO
Microbes also catalyze reductive reactions. A notorious example is the
dehydrochlorination of DOT to produce DOE:
ci
Eniv
DOT
-78-
-------
2.5.1.2.1 Metabolism of Growth Substances
Heterotrophlc bacteria degrade certain organic compounds to provide the energy
and carbon required for their growth. Many toxic substances function as growth
substrates for bacteria In a manner similar to naturally occurring organic compounds.
These growth substrates are identifiable by their ability to serve as the sole carbon
source for a bacterial culture. The metabolic transformation of these growth sub-
strates generally results in relatively complete degradation or mineralization, thus
detoxifying toxic growth substrates. The detoxifying effect and relatively rapid
rates of growth metabolism Imply that potential growth substrates pose a lesser
threat to the environment than compounds which cannot be used in this way (Tiedje,
1980).
Before the utilization of a compound can begin, the microbial community must
adapt Itself to the chemical. Investigations of biodegradation of a compound to
which the biota have not been recently exposed, both 1n the field (Spain e£ aK 1980)
and 1n the laboratory (Shamat and Haier, 1980) have shown the existence of a lag time
(lag phase) of 2 to 50 days before the microbial community acclimates. Since the
degradation of a growth substrate Is relatively rapid once a microbial population has
adapted to it, Tiedje (1980) has suggested that the primary concern in assessing
biodegradation of such substances should be the conditions and time period required
for adaptation or acclimation.
The lag time depends on several biological and environmental constraints. The
primary constraint is the development of a sufficiently large bacterial population
which is capable of utilizing the pollutant as a growth substrate. Frequently,
specific organisms with specific enzymes are required to metabolize a pollutant. The
processes of species selection and enzyme induction by which a microbial community
adapts itself to a pollutant require time. The adaptation time 1s Influenced both by
prior exposure of the community to a pollutant and the initial numbers of suitable
species. Spain e_t_ £k (1980) have demonstrated that prior exposure to a compound
reduces or eliminates the adaptation period. Thus, lag times 1n pristine environ-
ments should be much longer than in locations which have been chronically exposed to
a compound. In addition. Ward and Brock (1976) have shown that lag time preceding
the onset of petroleum degradation depends on the initial size of the bacterial
population. Water with larger microbial communities should require relatively
shorter times to develop a viable population of degraders. High microbial biomass
levels are associated with higher BOD, concentrations.
The presence of more easily degraded carbon sources may delay the adapta-
tion of a microbial community to the metabolism of a pollutant. Ward and Brock
(1976) found that microorganisms in lake water metabolized added glucose completely
before degrading hydrocarbons. This diauxic pattern may result in longer lag times.
A final factor which Influences lag time Is rhe concentration of the pollutant
-81-
-------
In the water. There may be concentration thresholds below which adaptation does not
take place. (For example, no adaptation for metabolism of 4-n1trophenal occurred at
concentrations below about 40 xg/1 (Spain et^ al_, 1981). Too high a pollutant concen-
tration, on the other hand, may be toxic to the microbes (Tabak et^ aU, 1981). The
user should be aware of these possibilities when extremely low or high concentrations
are Involved.
Once the microblal community has adapted to the organic pollutant, 1t 1s of
Interest to know the rate at which blodegradatlon occurs. Kinetic expressions for
compounds used as a growth substrate can be relatively complicated since both the
substrate and bacterial concentrations change with time. The Honod equation has been
used to describe the degradation rate of a compound which serves as a sole carbon
source:
d£
dt
dJL
dt
"max
Y
B
(11-56)
where
C • pollutant concentration
B • bacterial concentration
f • blomass produced per unit C consumed
•'max * maximum specific growth rate
Ks • half-saturation constant.
Frequently, the Honod equation 1s reduced to a second-order blodegradatlon
expression by assuming C «K . 1n which case:
- • k
dt 82
B
(H-57)
where
second-order blodegradatlon rate constant
"max
Although Monod kinetics accurately describe some laboratory results, they are
Inapplicable 1n the environment due to the presence of other carbon sources. As a
simple alternative, first order kinetics are frequently applied:
(11-58)
where
» first-order blodegradatlon rate constant.
-82-
-------
Enzymes can catalyze otherwise slow hydrolytic reactions as well:
s s
;CHo)_P_s-cHcooc2Hs — fcsuas - fc (cHo)— P— S-
32__-2s — - fc 3z
CHCOOC.,HS
N*l«th
-------
MICROBIAL TRANSFORMATIONS OF TOXIC CHEMICALS
(Potential Toxin)
O(CH2)3COOH
(Less "oxic Substance)
OH
Cl
OCH2CH2OSO3H
f-CI
Cl
(Potential *oxin)
* H0 + Cl'
Source: Alexander (1980)
FIGURE 11-10 MICROBIAL TRANSFORMATIONS OF PHENOXY HERBICIDES
2.5.1.2 Rates of B1odegradat1on 1n the Environment
The rate at which a compound blodegrades in the aquatic environment depends on
Its role In mlcroblal metabolism. Some organic pollutants serve as food sources
which provide energy and carbon for growth and cell maintenance when metaboi.zed by a
microorganism. In other cases, microorganisms transform the pollutant, but are
unable to derive energy for growth from the reaction. These two metabolic patterns,
growth metabolism and cometabollsm, exhibit distinct characteristics and rates of
degradation. Because of the Important differences between these two types of blodegra-
datlon, they are treated separately 1n the following discussion.
-80-
-------
This first-order expression 1s analogous to the equation commonly used for the
decay of 300 (see Chapter 4). Larson (1981) has shown that first-order kinetics
which include a lag phase (lag time) represent the degradation of growth substrates
reasonably well at initial bacterial concentration of 10 cells/ml or less, a
condition which is usually met 1n tht environment.
2.5.1.2.2 Cometabolism
Microorganisms also degrade compounds which they c?.-iot use as a nutrient
or growth substrate through cometabolism. Cometabolism is thought to occur when
enzymes of low specificity alter a compound to form products which the other enzymes
in the organism cannot utilize. The metabolites formed in the process are structurally
similar to their parent molecules and frequently retain their toxlcity. In some
cases, the product of cometabolism can be used as nutrients by other organisms, but
often these intermediate products accumulate (Alexander, 1980).
The kinetics of microbial cometabolism differ significantly from that of
growth metabolism. Often no lag occurs before cometabolism begins. The degradation
rates, though, are generally slower than the fully adapted rates of growth metabolism
(Tledje, 1980}. Since cometabolism does not provide the microbes with any energy, it
has no effect on the population size. The rate of cometabolism, however, is directly
proportional to the size of the microbial population. Paris et_ aj_. (1981) showed
that a second-order rate law described microbially catalyzed hydrolytlc reactions:
-dT-kB2-B-C (I1'59)
Since the bacterial population, B, Is independent of the rate of cometabolism,
It is possible to reduce Equation 11-59 to a first-order law by making the following
substitution:
S • "H • • (U-60)
In order to use literature values of the second-order blodegradation rate
constant in Equation 11-60, It Is necessary to make an estimate of the size of the
bacterial population. Since different techniques of bacterial enumeration can yield
results which vary over several orders of magnitude, it 1s Important to use estimates
of B based on the same method used to calculate k.^. Table 11-24 lists bacterial
densities which are typical of lakes and rivers. Obviously, large uncertainties 1n
environmental rates of cometabolism exist due to the wide range of possible bacterial
densities. Generally, the user should make conservative assumptions unless other
data, e.g., a high BOD, Indicate larger bacterial densities.
-83-
-------
TABLE II-24
SIZE OF TYPICAL BACTERIAL POPULATIONS IN NATURAL WATERS
Water Body Type
01 i go trophic Lake
Mesotrophic Lake
Eu trophic Lake
EutropMc Reservoir
Dys trophic Lake
Lake Surficial Sediments
40 Surface Waters
Stream Sediments
Rur River (winter)
Bacterial Numbers (cells/ml)
50- 300
450- 1,400
2000-12.000
1000-58,000
400- 2,300
Q in
8x10* - 5xl0iu cells/g dry wt
500-lxlO6
107-108 cells/g
3xl04
Ref.
a
a
a
a
a
a
b
c
d
aWetzel (1975). Enumeration techniques unclear
Paris et il. (1981). Bacterial enumeration using plate counts.
°Herbes I Schwa 11 (1978). Bacterial enumeration using plate counts.
Larson et £l_. (1981). Bacterial enumeration using plate counts.
2.5.1.2.3 Summary
Table 11-25 suMMrlzes some of the major differences between growth metabolism
and cometabol1sm. Although the exceptions to the generalizations about each process
are numerous and some compounds can undergo both processes, the distinction between
the metabolic processes can serve a useful function 1n a screening method. The
generalizations about each process suggest the following approaches when the user has
some knowledge of a compound's metabolic pathway:
Cometabol1sm
a) Find a second-order rate constant and estimate b1amass density.
Apply Equations 11-59, 60.
b) When a) 1s not possible, assume cometabol1sm 1s negligible. I.e.,
k- 0.
-84-
-------
TABLE 11-25
SUMMARY OF THE CHARACTERISTICS OF THE TWO GENERAL TYPES OF
BIODEGRADATION: METABOLISM AND COMETABOLISH (After Tiedje. 1980)
Topics
Metabolism lor Growth
Cometabo li'-m'
Distinguishing characteristics
Degradation rates
Behavior at low pollutant
concentrations
Acclination
Relation of degradation kinetics
to total biomass. e.g. decay
rate - k_- • B • C
Extrapolation
If feet of added carbon
Organism will qrow en i.ibtlliut' as solo f.
source. Generally ultimate degradation.
High rates.
Possible anomalous behavior due to threshold
for eiuyme induction.
Major effect- lay m*y be quite variable or
lengthy due to low initial density of
degraders. and perhaps starvation stale
of organises in natural sample.
Likely not valid, use first-order kinetics.
General: expect eventual degradation in nature.
Quantitative: difficult to be precise because of
growth kinetics and acclimation effects, but
My not be important problem, because of
generally fast rates.
Diauxic pattern — More easily metabolized
substrates are used first.
Oru,* <>n subst.inre as sole C source.
Accumulation of intermediate products likely.
Generally slow rates.
No anomalous behavior, rales are first order in
pollutant concentration.
Often no effect; rarely causes induction, may
increase tolerance to toxic chemical.
May be valid since activity of interest is often
proportional to general biomass.
Measure kinetic parameters accurately: because
of the generally slower rates, extrapolations
will be made over longer tines, and thus measured
parameters need to be accurate. Also environ-
mental influence factors, e.g. temperature. pH,
play a more Important role.
Generally effect is proportional to microbial
population unless specific carbon source happens
to induce or inhibit activity of interest.
•Alteration of a substrate, for purposes other than growth, e.g. for detoxification.
-85-
-------
Growth Metabolism
a) Find a first- or second-order rate constant.
b) Estimate a range of lag times. For chronically exposed water
bodies, assume that no lag time (t, ) occurs. For water bodies
not recently exposed (within 200 days), proceed as follows:
1. Estimate lag time using available Information. If no Information
1s available use a range of 2-20 days.
2. Assume adaptation occurs as follows:
Rivers - At travel times < t. , kn • 0
L 0
- At travel times >. t,, ko >* 0
Lakes - For well mixed lakes, first determine C at time »
t, , Ct due to all processes except
blodegradatlon. Then using Ct as C0 solve
for Ct with a modified time, t^, (t^ • t -
t,}. (Use equations 1n Section 5.6.1)
- For stratified lake use only the volume through
which the Inflow passes (e.g., the hypo11mn1on
volume) 1n calculating the hydraulic residence time
(TM). Then proceed as above.
Estuaries - Consider all processes except blodegradatlon
through that downstream segment for which \, as
measured from the Injection point, becomes greater
than tL. Thereafter Include blodegradatlon.
When no data on which metabolic pathway a compound follows are available, the
user should apply any available kinetic Information and allow for the possibility of
a lag phase prior to the onset of degradation.
2.5.1.3 Chemical Properties Influencing Blodegradatlon
The chemical properties of a compound determine whether microbes can potentially
utilize It as a growth substrate or not. Compounds which serve as bacterial growth
substrates usually decay more rapidly than those which microbes cometabollze. Thus,
significant differences 1n the aquatic fate of pollutants can arise depending on
which degradation process takes place.
Unfortunately, 1t 1s not possible at this time to predict whether a toxic
compound 1s a potential source of energy and carbon solely on the basis of Us
chemical structure. Rather, the b1od«gradab1l1ty of a compound 1s usually Investi-
gated 1n laboratory tests (Gilbert and Lee, 1980). Compounds which are growth
substrates should be able to serve as sole carbon sources for a mlcroblal community.
Compounds which cometabollze should degrade only In the presence of another carbon
-86-
-------
source. A systematic study of the metabolic pathways of the priority pollutants Is
desperately needed.
Table 11-26 contains the results of a preliminary degradation test on the
organic priority pollutants (Tabak et_ a_l_., 1981). Because the experimental conditions
were so favorable for blodegradatlon, the tests serve as a good Indicator of a
compound's potential blodegradabHlty. Since the pollutants were not the sole carbon
sources, no conclusions can be reached about their metabolic pathways. Some Informa-
tion on the rates of adaptation and decay, through, can be extracted from the results.
The adaptation summary results may be used as follows:
• Rapid Adaptation (D) - Use a range of adaptation times from zero days
upward depending upon conditions described above
• Gradual Adaptation (A) - Use a range of adaptation times from 7 days to
more than 20 depending upon the conditions described above.
The rate summary results represent estimates of the blodegradatlon rate constants
assuming the compounds decay according to first-order kinetics. General values
presented at the bottom of the table are gross estimates and should only be used 1f
no better data 1s available. The rate constants should represent an upper limit for
blodegradatlon rates by adapted populations observed 1n the environment.
Table 11-27 contains literature values of blodegradatlon rate constants.
Where possible, the likely metabolic pattern has been Indicated. Some of these
constants were measured under environmentally relevant conditions. In general,
rate constants should be compared with those 1n Table 11-27 before use.
2.5.1.4 Environmental Influences on Blodegradatlon Rates
Environmental conditions strongly Influence the metabolic activity of a nrfcroblal
population. The environment affects the types of metabolic reactions microbes are
able to carry out, the availability of nutrients for these reactions, and the rates
at which these reactions occur. The environmental variables which are responsible
for these effects are discussed 1n the following sections.
2.5.1.4.1 Temperature
In general, a molecule must have an energy greater than a threshold or activation
level 1n order for 1t to react chemically. Since Increasing the temperature Increases
the number of molecules which have this minimum energy, both blotlc and abiotic
reactions generally proceed more rapidly at higher temperatures. However, because
enzymes catalyze most biochemical reactions and mlcroblal populations can adapt to
changes 1n ambient temperatures, the temperature dependence of nrtcroblally mediated
reactions 1s complicated.
-87-
-------
TABLE II-26
POTENTIAL 8IODEGRADABILITY OF ORGANIC POLLUTANTS
IN AN AEROBIC ENVIRONMENT
(After Tabak et al., 1981)
T.it CMMM4
A*tK«ttM
Inarr
TII« COTMM
*tixvr
^^—•*i*
Ult
!««•«« rr
A^H^X«&
H»tUI«tt
Al«r<.
ottiin*
CM.r«M*
DOT ».»'
(W ».»'
000 •.»'
lM*i»lf*«-ll(M
tit**t»l 'M-MU
CMMvlfM t»MlU
H...OU
Kt-1221
KI-12S
KI-1242
ChlorMtKM**
1 ,l-Otckl*rw«MM
1.2-Otckl*r««U4M
1.1.1-Trlelil.f^tM..
1.1.2-THcM.rMtMM
1.1.2 .2-Ttlr«cftUrwtk*M
WuckUrMtMM
Nttiijrl*** cklsrKt
lr«»<»l*rmtMM
C«rM« Utr.t*l.n«i
CM>rtftr<
OU«l*r**rWM»tft«M
Irwcftn
C>Hr««>riaat«IM
f n c» Urtfl wrMttMM
•,,-C.c.Un-a,.) HMT
2-CkUrMt*yl «1«yl «t*»r
4^»l»f»«1*»r' *tk*v
I
»
I
N
•
II
*
II
«
*
0
0
II
»
1
1
c
1
0
0
0
0
A
i.
k
1
N
1
0
I
0
0
0
0
0
0
0
a
0
tCI'l MH
0
2
2
0
H4lO«M(t« 1
1
t
1
1
0
2
2
2
2
1
I
1
0
0
"I'fl
I
I
0
CMH*
-t.l.cM.r
Mt»t«cM.r tpo.t*.
N.uchlonxyclok.1**.
N*i*CHl or«cjr< 1 ohiiMt
l-IMC-Mtl
HtucM or»cytl »Mi*M
«-lMC-«*ltl
HtiKkUrvtyC 1 oMtu*t
iiJUiifT" °'"»r»tthrlt««-c1l
1.2-OtcKlDrotthylt«<-triM
THcM.n>,th,lw.
Tttr«ehl*nwt»rl*M
CM»r»»r«M««l
1.2-0«cM*r«RriMm
CkUr^r<*rltMi
1 ,i-0*tl*loro^f^»rlnn
Htuc* 1 *r*. 1 , }-»vt««l«*«
Ck1er«(t •
•MIM tttan
«.*rwM«»Mi>r1 MMr
l«|.(l^kl*fMtk.iy) MtMM
•1|.<2-ckUr«lM*f»»r1) itk*»
*
N
II
H
K
II
II
D
,
N
N
0
A
1
1
A
A
*
A
0
0
II
•
D
0
0
0
0
a
a
0
2
0
0
0
2
2
1
1
1
1
1
1
J
2
0
0
2
-88-
-------
TABLE 11-26 (Continued)
T«t Compound
Nni»««
ChloroMflun*
IJ-O.cMorootnitn.
1 . J-Olc" 1 orvbdJtnt
1,4-OUhlorobo'iltno
l.J .4-TrlchlorOMnjmt
H.ml
2-CMoro pAtnol
2,4-Dtcnioro P^tnol
2 ,4 ,6-Tnchloro pftenol
HM.chloro ph»ol
2.4 OMtftyltMMl
AdllCttlM Ittt
*MC>«MC A^.
0 2
0 2
T |
T 1
: 1
T 1
nxnol U Co*pou
D 2
0 2
0 2
0 2
A 1
9 2
Tilt Coaoound S^» l«tt
0
2
2
2
1
1
2
2
2
2
0
Pfttlulllt Clttrt
Oinvtnyl pnthlUtt
OUthyl pficn«l<((
01-fl-butyl pnt"*l*lf
Mgthilt"*
/*tn.ptr,...r
Actntpntnyltnt
Afltnr«c»"«
flM.mi.r**
• ItrOM—ti
»ropyl«ni«t
0 2
D 2
0 2
^olycrcl le Aroattu "i
0 2
0 2
0 2
A 1
0 2
Nltrote Antnti «nd H1iciH<
• 0
I1l-(2**thyl htiyl] pAtnllitt A
Dt-n.octyl pntMlttd A
lutyl ftMlyl pntntlitt D
.drocJrBO"!
n,or,~. A
n>«)rmh«« A-
l.2-l*«lMtnr>ctn« H
Pyrtim D«
CHrystno A*
»•«,> Co^ountt
Su»,..tut<^ M.<^
"pNrooo
1 2-01pn«nylhvdrii1ni T
1
1
2
I
2
0
2
1
1
H-ltltroiOdlplOTylMtOf D 2
Ittvltl Of '•••• U «1
conditions, *nt tttt *
dur4tio« • 28 3«y».
Kfy to r*ft S^«iry
II Sot l.)n'fKjntl
D Sia.ni f i c«nt dtor
0* S«*t it 0 *ic»pt
A Slant'ic^nt dtof
A* S«"t H A «ic»pt
1 Slow e«9r4tf«tion
C V*^y llow t}»<)r«d
I Si9»i'iC4i( 3»qr
1*1 to l«tf Su«Mry
(fry crudt «»tl"4tn
0 do ii»>M ftccnt
1 . OS d< y * < h .
2 k, > 5 ««y''.
(1111) ntlnf lunch vr«s d 2t djyt
.44 1 ion .ith ^r.dMl tdJOtttlon fpl1o«*d »r «««<*pti
of flrit-ordtr blodtf rcdit lo» rttt contt<«tl ««y M
dtfr«d«Cioi< rttt
< .i d«y . u>* .01 d«y*
utt .5 d«y
on (toilclty).
•••> froa tht I*for«
-------
TABLE 11-27
BIODEGRADATION RATE CONSTANTS UNDER AEROBIC CONDITIONS
Mtt ConitMt felt Contunt
CtWeu~I (B| ctH"' 4*»''l (l/4*y)
2.4-0 lutoirttnyl mtr 1.2>10"S3 l.JilO"2"
*l«tmor> I.U10"*1 1.1.10'"1
CMIarprootiMi ».2>!0"IO<3 *.2ilO"7<1
Furt4«n J.4.10"3 2.4ilO'5U
StF.S'fl* 2.4ilO"8 2.4S10"5*4
>oljrcMorti»«lt4 1 1 phtnj 1 1
iroclor 1221 - .«'*
Aroelor ;01* - .2'
iroclor 12*2 - .!5IZ
»rocl0r !254 - .l'
4-Chloropmoyl pncnyl tthtr • Oil- OU
•owe cue *rxiMttc»
Nttrot>*nltn« • .7<2
2-Ch'oroto.^, 4.5.10-80 i.talO'*
»h»no)'c ConpOu'>4>
«^x>] - 4.'2
».(2
2-CMor»fl*>ttiol - 1.'*
.3
2.4-DlchlaropMfMl - . S(
.l(*
(2
1."
2.4.0lWthylpMHOl - 1."
2.4-DtnltrbpftfflOl - .2
2.4,4-f-'»itro9»««ol • 0
FMh«l«U Ctttrt
DlMttiyl l.hlO"* .12"
01-tthyl 7.7ilO"* 7.7IIO*4*1
0<-£-outyl 7. 0«10*7 7.0x10
OI-«-octyl 7.4UO"9 7.4I10"6*1
01-(2-»th,lH,,,l l.OklO"10 l.telO'7
I.taW^4
lntjrl Mflljrl - >.M
H«ir.|.ift
S3
t.JilO2
l.lilfl6
3 « 104
3 s 1C4
.1
3.S
45
7.
43-(3
.2
1.
1.U103
.2
.1
.7
2.3
1.4
«
7
.7
.7
3.S
,,
t.fellP
l.OklO3
t.lilO4
i.klO*
a
<2
T
Nf«n«t» MM i» •
TMMfttur* troirth
(•C) Sufttr.t.? Uptriaxiul Con*ltioni
20 ' Mturtl jr'«Ct atttr UnOltt
20 '•« Mturll turfKt «*t*r »*»0lt«
20 ^ Mtural tuvf«ct ««ttr sonelti
> t i
•
' * ACCltMtt* Ktlv4tM lluOVt
7 ' ACCltMtt* •Ctl«4t« t>u«1*
teeh^tM sctivstrt Hudi*
7 7 AccliMtM KttoitM iluOft
7 ' II. «r m»ttr; Log • 5-13 4«yl
7 7 tctUtttd tlu«|t
20 >tl M«ptM «ti«it« tlu4;t. COO 4«
7 7 Mturil iurfKt utter twwlt
20 >tt MtotM «cti»«t*o tlu«t«. COO 9«<
' 7 PolluUO MMT Mttr
20 '•» M*»tM «ctt«it*4 ilud««
1 'Sod umntto*
20 >tt MwtM icttottM ily««t. COO 4K
2$ 7 Mturtl ltt M**tM; Nutritot Iretti
20 Tt* MMtM 4ct<.itM llu«t«
20 Ttt MwtM KtUltM tlv«f*
20 M ActlMtM ll«*tt
? 7 »
T T 7
77 »
? 7 7
T 7 ?
T T I1*tr Mt*r
T 7 »l¥«r Mt«r
•tf
t
t
i
t
»
c
c
c
c
c
c
:»j 4
t
:ij 4
c
4
C
:•> 4
c
f
f
4
4
4
1
,
f
t
f
C
C
-90-
-------
TABLE 11-27 (Continued)
*8? 't
»att C-wstant *antnrtnf J.I«IO"6 3. blO'3"
r0 CoxooviiX
I Rfff V^VA( f1 i/»ww
n«) 1 <• L t f f Tfwcf* CM^C G^Qot^
144/1) CO Sufrtir«tf * lipv^tinfntit CcntfUons
4.0 1? TCI Cof>t**'njt*d *tre*" ted^**f«tt
1.7.103 12 > rr.tl.n. ,.r,»- ,,di-^ll
;.8.10: 12 ttt Cant«ffin«tra >tr»«. ttO'-<«<*otl
M»i02 » 7 T
V
h
h
h
fl
c
h
h
li
k
c
IJ rirtt-O'df rttf conttjnt CC'-Out« ull"9 Cgu*tl»n 11-40
-------
It 1s common practice to represent the temperature dependence of blodegradatlon
using the following empirical formula:
k (T) • k (T ) • 3 (T~V (11-61)
B B o B
where
icB{T) • specific blodegradatlon rate constant at temperature - T
kg(T ) • specific blodegradatlon rate constant at temperature - T
T • ambient temperature, °C
T « reference temperature, °C
e» • temperature coefficient for blodegradatlon.
The results of Larson et^ aj_.' (1981/'and Ward and Brock (1976) show that the
rates of n1tr1lotr1acetate and hydrocarbon blodegradatlon Increased approximately
two-fold over a ten degree temperature range (9g » 1.072). Either this value or
the standard value of 1.047 for BOD decay 1s adequate for screening purposes.
2.5.1.4.2 Nutrient Limitation
Microbes require nutrient such as nitrogen and phosphorus 1n order to metabolize
an organic substrates. Several researchers have suggested that Inorganic nutrient
limitation 1s a significant factor influencing blodegradatlon rates in the aquatic
environment (Ward and Brock, 1976; Roubel and Atlas, 1978; Herbes and Schwa 11,
1978). Ward and Brock (1976) found a high correlation between hydrocarbon degradation
rates and phosphorous concentrations in natural waters. The data fit a saturation
relationship of the Michael 1s-Menten type:
•°277 ' c« in ti\
k (C ) « k (C *) • 2_ (11-62)
B P B p 1 •»• .0277 • C
where
*0(C ) • specific blodegradatlon rate constant at dissolved
D p
inorganic phosphorus concentration, C
C • dissolved Inorganic phosphorus concentration, *g/1
ko(c«*) " non-nutrient limited blodegradatlon rate constant.
D p
This relationship should serve as a good Indicator of possible phosphorus
limitation of b1odegradat1c« in the environment. Generally surface waters downstream
of domestic sewage treatment plants are not limited in either nitrogen or phosphorus.
Equation 11-62 should be applied only when other nutrients such as carbon and nitrogen
are not limiting.
-92-
-------
2.5.1.4.3 Sorption of Substrates
Many organic pollutants adsorb strongly on sediments, {See Section 2.3.2.
The difference in the physical and chemical environments between sorbed and dissolved
pollutants is likely to influence their availability to microbial organisms. Baughman
et_ £j_. (1980) showed that the dissolved fraction of tne compounds studied was avail-
able to biota for degradation whlls the sorbed fraction was not. In such cases, trse
rate of disappearance of the pollutant is:
dCT
dT = kB • Cw " % • kB ' CT (11-63)
where
CM * the pollutant concentration in the aqueous phase
aw • the decimal fraction of the total analytical pollutant concentra-
tion wnich 1s in the aqueous phase (a « 1 - fraction sorbed).
It is well known, however, that bacteria grow very readily on surfaces and that
increasing available surface area in the form of clays and sediments can increase
rates of microbial metabolism. If specific information regarding the effects of
sorption on the rates of biodegradatlon are not available for a compound, it is best
to assume that sorption does not change this rate.
2.5.1.4.4 Solubility
Wodzinski and Bertalini (1972) have shown that in the dissolved state, napnthalene
and biphenyl were degradable while in the pure crystalline state they were not.
Thus, sparingly soluble compounds could degrade slowly for this reason alone. The
extent to which this phenomenon applies to other biodegradatlon reactions has not
been established. The user may assume that only dissolved chemicals are degraded.
2.5.1.4.5 £H
The hydrogen ion concentration also Influences rates of biodegradatlon. Each
bacterial species has a pH range for which it is best suited. Thus, at different pH
values, different species may exist, or a given species may metabolize the pollutant
at a different rate. Hambrlck e£ al_. (1980) found that the mineralization rate of
naphthalene in oxidizing sediments varied in the proportions 1:6:5 at pH 5. 6.5, and
8. The same study found that the mineralization rates of octadccane varied in the
proportions 4:5:7 at the same three pH's. Until more general rules for predicting pH
effects are available, the user should assume biodegradatlon rates are Independent of
pH in the pH range 5-9 and decrease outside this range.
2.5.1.4.6 Anoxlc Conditions
As the concentration of dissolved oxygen in natural water is depleted, metabolic
pathways shift. When the dissolved oxygen concentration drops to about 1 mg/1, the
rate of biodegradatlon becomes dependent on oxygen concentration In addition to
-93-
-------
substrate concentration and the rate of degradation starts to decrease. At a dis-
solved oxygen concentration of about 0.5 to 1.0 mg/1 nitrate begins to substitute for
molecular oxygen as an oxldant.
When oxygen 1s depleted, anaerobic metabolism prevails with Us generally
lower energy yields and growth rates. Host organic substances are blodegraded
more slowly under anaerobic conditions. Rate constants derived for oxygenated
systems are no longer appropriate; their use may overpredlct the amount of
degradation.
Exceptions do exist to the rule of slower degradation under anoxlc conditions.
Reactions such as dehydrochlorlnations and reductive dechloMnations lead to much
higher degradation rates for many chlorinated hydrocarbons. Example compounds
Include Undane, heptachlor, pentachlorophenol, and some one and two carbon
chlorinated alkanes.
EXAMPLE II-6 1
j
BiodegradablHty of Naphthalene j
Evaluate the blodegradablHty of naphthalene discharged into the Lepldoptera
River by a point source just upstream from Northvilie's sewage treatment plant.
Assume the following water quality parameters at the upstream discharge: j
Temperature « 10*C I
Suspended sediment « 10 mg/1 I
Inorganic phosphorus • 5 Mg/1 j
Dissolved oxygen • 5 mg/1. j
First, check the potential blodegradabil1ty of naphthalene 1n Table 11-26. j
The table indicates that naphthalene degrades rapidly, k« • .5 day , and
that bacteria adapt quickly to 1t. !
Next, examine Table 11-27 for further Information on naphthalene's blodegrada- {
bllUy. Naphthalene Is a potential growth substrate. In addition, the data 1n I
this table concur with the rapid degradation rates suggested by Table 11-26. In |
sediment, which had been previously exposed to naphthalene, a blodegradation rate i
i '
constant of 0.14 day was measured. As one would expect for a growth sub- j
j i '
strate, degradation rates are much lower, e.g., k. < 4 x 10 day , in sites
not previously exposed to naphthalene.
Since naphthalene 1s a growth substrate, estimating the adaptation time
1n the Lepldoptera River 1s a primary Issue. Because the point source continuously
discharges naphthalene Into the Lepldoptera River, it Is safe to assume that the
bacterial populations have adapted.
In a complete analysis, the user would check whether the oxygen 1s depleted
-94-
-------
from the river. If so, degradation could be neglected until dissolved oxygen
levels exceed 1.0 mg/1 again.
Sorption by suspended sediment could potentially reduce the rate at which
naphthalene biodegrades. Table II-9 gives a K for naphthalene of 2,300.
Using Equations 11-16 and 11-18 and assuming a suspended sediment organic carbon
content of 2 percent, the partition coefficient is:
Kp - (.02) (.63) (2,300)
« 29
At the suspended sediment levels in the Lepidoptera River 10 mg/1. Table 11-16
shows that sorptlon will not significantly reduce water rolumn concentrations of
naphthalene. Although phosphorus levels are low, assume carbon is the growth- |
limiting substrate. |
Finally, the degradation rate is adjusted to the river water temperature j
using Equation 11-61: '
*B - 0.14 • 1.072(1£M2) j
» 0.12 day"1 j
END OF EXAMPLE 11-6 , •
2.5.2 Photolysis
2.5.2.1 Introduction
The sun provides the aquatic environment with a large supply of energy. Substances
which absorb sunlight transform much of Its radiant energy Into thermal energy. But,
molecules which absorb sunlight In the ultraviolet and visible portion of the spectrum
may gain sufficient energy to Initiate a chemical reaction. Plants use very specific
photochemical reactions to provide energy for the synthesis of sugar from carbon
dioxide. In other photochemical reactions, the absorption of light leads to the
decomposition of a molecule. The latter type of reaction, known as photolysis,
strongly influences the fate of certain pollutants In the aquatic environment.
F jtolysls Is truly a pollutant decay process since It Irreversibly alters the
'•-acting molecule. However, the products of the photochemical decomposition of a
toxic compound may still be toxic. For example, Irradiated 2,4-D esters form 2,4-D
add, a priority pollutant, 1n aerated waters {Zepp et_ aj_., 1975). Upon Irradiation,
DDT reacts to form DDE, which persists in the environment longer thai? DDT (Tinsley,
1979). Thus, even though the methods In this section assume that pollutants Irrevers-
ibly decay through photolysis, the planner should remember that the decomposition of
a pollutant does not Imply the detoxification of the environment.
The rate at which a pollutant photolyzes depends on numerous chemical and
environmental factors. The light absorption properties and reactivity of a compound,
-95-
-------
the light transmission characteristics of natural waters, and the Intensity of solar
radiation are some of the most important factors Influencing environmental photolysis.
These factors will be covered by the following discussion. Understanding these
factors facilitates the computation of rate constants and the identification of
pollutants likely to photolyze - the final two topics of this section.
2.5.2.2 Factors Influencing Photolysis in the Aquatic Environment
2.5.2.2.1 Photochemical Reactions
All chemical reactions which occur at finite rates require the reacting molecule
to gain sufficient energy to become "activated" or form a reactive Intermediate. In
dark or thermal reactions, the thermal energy of the environment supplies the activa-
tion energy. In photochemical reactions, the absorption of light provides the
activation energy.
The "activated" molecules In photochemical reactions differ in Important
respects from those of thermal reactions. Thermally activated molecules usually
remain in the normal or "ground" electronic energy state, whereas photochemically
activated molecules exist in higher, "excited" electronic states. Because of the
excess energy and the alteration of the chemical bonds of photoactivated molecules,
the range of potential reaction products is much greater than that for thermally
activated molecules.
The mechanism by which photoactivated molecules form and react is divided
into three steps: 1) the absorption of light to produce an electronically excited
molecule, 2) the "primary photochemical processes" which transform or de-excite the
excited molecule, and 3) the secondary or "dark" thermal reactions which the Inter-
mediates produced in step 2 undergo (Turro, 1978).
The mechanism of photochemical reactions provides a convenient structure for
a discussion of the factors which influence photolysis in the aquatic environment.
Environmental factors affecting the absorption of light, step 1, will be considered
first. Then, the factors influencing the fate of molecules which become excited by
the absorption of light, steps 2 and 3, are discussed.
2.5.2.2.2 Light Absorption
"Only that light which is absorbed by a system can produce
chemical changes (Grotthaus-Oraper Law)." ,
'-lasstone, 1946)
As this "first law of photochemistry" implies, it 1s necessary to know the rate
at which reacting molecules absorb light in order to determine the rate of a photo-
-96-
-------
chemical reaction 1n the environment. The following factors which influence light
absorption In the aquatic environment are discussed here: 1) molecular absorption of
light, 2) solar radiation, and 3) light attenuation in natural waters.
2.5.2.2.2.1 Molecular Absorption of Light
3oth light and molecules have quantized energies. Light interacts with matter
as quanta with energies inversely proportional to their wavelengths. A molecule has
quantized internal energy states associated with the configuration of its electrons
and the rotation and vibration of its chemical bonds. Since a molecule can absorb
light only as a whole photon, light absorption is possible only if the energy of the
photon corresponds to the energy change of an allowed transition between tne molecule's
Internal energy states. Consequently, the probability of a photon being absorbed
varies strongly with wavelength of the ^ight 1n a way that is unique to every chemi-
cal species.
To initiate a chemical reaction, the absorbed light must be sufficiently
energetic to cause a change in the absorbing molecule's electronic structure.
Generally, radiation with wavelengths in the ultraviolet-visible range, or shorter,
has sufficient energy to Initiate photochemical reactions while radiation witn
wavelengths in the infrared range, or longer, does not. Thus, the ultraviolet-visible
light absorption properties of a chemical are of primary interest in photochemistry.
Photochemical reactions in the aquatic environment depend on the rate at which
molecules 1n aqueous solution absorb light. According to Beer's Law, the rate of
light absorption by a single compound (Ift) in a cross-section of solution with
Infinitesimal thickness (Az) is proportional to the concentration of the light
absorbing specie (C), I.e.,
I (z) - I(z) • 2.3 • •: • C • iz (11-64)
a
where
I(z) • Intensity of the light at a depth z in the solution
e • base 10 molar extinction coefficient.
•e reflects the probability of the light being absorbed by the dissolved molecules and
therefore varies with the wavelength of the Incident light as shown In Figure 11-11.
Absorption spectra, such as shown here, contain Information necessary to compute the
rate at which pollutants absorb radiation available in the environment.
2.5.2.2.2.2 Solar Radiation
The only radiant energy available for absorption by pollutants In the aquatic
environment comes from the sun. The sun emits radiation of nearly constant Intensity
-97-
-------
10*
10*
I
182
200
250
300
(nm)
400
500
Sourct: U.V. At Us of Orq«n1c Compounds.
FIGURE 11-11 ULTRAVIOLET ABSORPTION SPECTRUM OF NAPHTHACENE
and spectral distribution. But, gasts and particles 1n the earth's atmosphere alter
the Incoming solar radiation through scattering and absorption. Scattering of the
direct solar bean creates the diffuse or sky radiation visible at the earth's surface.
Absorption of both diffuse and direct radiation reduces the Intensity of solar
radiation reaching the earth. Since the strength of absorption and scattering
depends strongly on the wavelength of the light Involved, the Interaction of sunlight
with the atmosphere alters the spectral distribution of solar radiation as well, as
Figure 11-12 shows.
The composition of the earth's atmosphere and the geometrical relationship of
the sun and earth change over time causing the solar radiation Incident upon the
earth's surface to vary as well. A comparison of the total solar 1rrad1ance under
clear skies at various times, seasons, and latitudes (Table 11-28) to the extra-
-98-
-------
Key.
a) Spectral distribution of sun's
radiation at edge of outer
atmosphere
b) Spectral distribution of sun's
radiation at earth's surface
3000
Wavelength
-------
TABLE 11-28
CALCULATED SOLAR RAOIAMT ENERGY FLUX TO A HORIZONTAL SURFACE UNDER A CLEAR SKY
(langleys/day)
Latitude
30°N
40°N
50°N
Time
Of Day
Mean1
Mid-Day*
Mean
Mid-Day
Mean
Mid-Day
Season
Spring
680
2100
650
1900
590
1700
Summer
750
2200
740
2100
710
1900
Fall
530
1700
440
1400
330
1000
Winter
440
1400
320
1000
190
650
Annual
Mean
600
1900
540
1600
460
1300
Mean values represent calculated seasonal means under a clear sky. These
should represent upper limits for solar radiant energy at sea level.
Reference: Weast and Astle (1980).
Mid-Day values represent mid-day flux extended over a 24-hour period. These
assume an atmospheric turbidity of 0, precipitable water content of 2 on,
and an atmospheric ozone content of .34 on NTP. Reference: Robinson (1966).
that the fraction of the solar energy In the ultraviolet region decreases with
Increased attenuation of light by the atmosphere. The fraction of the energy which
is visible remains relatively constant. For the purpose of this document, it is
sufficiently accurate to assume that the reduction in UV-visible radiation is propor-
tional to the reduction in the total flux.
2.5.2.2.2.3 Light Attenuation in Natural Waters
Just as the earth's atmosphere reduces the intensity of solar radiation reaching
the earth's surface, natural waters reduce the intensity of radiation available for
absorption by aquatic pollutants. The first process which reduces the availability
of light in the water column is reflection. In most cases, the surface of the water
reflects less than 10 percent of solar radiation (Zepp and Cline, 1977). Reflection
also alters the solar spectrum slightly. A calculated spectral distribution of solar
radiation, expressed in photons, immediately below the surface of a water body 1s
presented in Table 11-29.
-100-
-------
BAN DAILY SOLAR RADIATION (Langleys), ANNUAL
FIGURE 11-13 SOLAR RADIATION IN THE UNITED STATES
Ref: US Dept. Com. (1968)
-101-
-------
TABLE 11-29
CALCULATED SOLAR IRRADIANCE IN A WATER BODY OUST BENEATH
THE SURFACE, ANNUAL MEAN AT 40°N
Wavelength^
(nm)
300
310
320
330
340
350
360
370
380
390
400
410
420
430
440
450
460
470
480
490
500
510
520
550
600
650
700
750
800
Photon Spectral
W(X)C
(101* photons cm2 sec'ren1)
.00303
.0388
.113
.181
.211
.226
.241
.268
.294
.366
.526
.692
.712
.688
.814
.917
.927
.959
.983
.930
.949
.962
1.00
1.04
1.07
1.08
1.07
1.03
.988
Irrad1ancea
W'(X)d
(10 photons cm2 sec1)
.0303
.388
1.13
1.81
2.11
2.26
2.41
2.68
2.94
3.66
5.26
6.92
7.12
6.88
8.14
9.17
9.27
9.59
9.83
9.30
9.49
9.62
10.0
52.0
53.5
54.0
53.6
51.5
49.4
Estimated reference solar flux, I • 540 lang leys/day. 0Q • 1.0
^Centric wavelength of waveband X nm in width,
for 300 . 550, X«50 nm
cMean irradiance over wavelength Interval of width X.
^Integrated irradlanc* over wavelength Interval of width X.
Reference: Burns et al. (1981).
-102-
-------
As solar radiation penetrates deeper into natural waters, it is absorbed
and scattered by participates, dissolved substances, and water itself. Measure-
ments of lignt attenuation in natural waters have been based on the decrease of
solar irradiance, which includes both collimated and scattered light. Lambert's
Law expresses the decrease in the irradiance, I(z), i.e., the total flux incident
upon an element of surface divided by its area, with depth z, as follows:
• K • I(z) (11-65)
az
where
K - diffuse light attenuation coefficient.
The diffuse attenuation coefficient can be expressed as a sum of terms account-
ing for absorption, a, and backward scattering of light, s. (Smith and Tyler,
1976):
K • Da + sfe (11-66)
where
D » radiance distribution function.
Usually, s. is small compared to the absorption term. The absorption term
constitutes part of the beam attenuation coefficient, a, which can be measured
in a spectrophotometer:
a • a + s + s (11-67)
b T
where
sf • the forward scattering coefficient of the solution.
The inclusion of the distribution function, D, In Equation (11-66) accounts for
the difference In mean light pathlength of collimated and diffuse light. Perfectly
diffuse light has a mean path through an element of water which 1s twice as long as
that of a beam of light. The distribution function, generally Increases asymptotic-
ally with depth due to the Increasing fraction of the total light which Is scattered.
In water bodies where scattering can be Ignored, D has a value of 1.2. Miller and
Zepp (1979) reported that the mean value of D for six sediment laden waters was 1.6.
The diffuse light attenuation coefficient of natural waters differs greatly due
to variations In the types and amounts of particles and dissolved substances In the
water. Miller and Zepp (1979). Zepp and Schlotzhauer (1981), and Smith and Baker
(1978) have Investigated the contributions of suspended sediments, dissolved organic
carbon, and chlorophyll pigments to the light attenuation coefficient, by using
Equation (11-66) to integrate the results of these investigations, and assuming
-103-
-------
backscattering to be negligible. Burns et_ al_. (1981) derived the following expres-
sion to estimate the diffuse light attenuation coefficient:
chl a) * (a • DOC) + (a • SS) (11-68)
DOC ss J
where
aw « absorptivity of water
a, » absorptivity of chlorophyll-a pigment
o *"
chl a_ • concentration of chlorophyl l-a_ pigment
aOOC " absorptivity of dissolved organic carbon
DOC - concentration of dissolved organic carbon
a • absorptivity of suspended sediments
SS » concentration of suspended sediments.
Each absorptivity term varies with the wavelength of light, as shown in Table 11-30.
1•ffuse light attenuation coefficients can also be estimated using turbidity
Indicators such as Secchi disc depth. Empirical studies have shown that the diffuse
light attenuation coefficient 1s inversely proportional to the Secchi disc depth,
Zsd:
K » -*- (11-69)
*sd
The proportionality constant. R, has a value between 1.44 and 1.7 for visible
light, I.e. 400-800 nrn. In the middle ultraviolet portion of the spectrum, i.e. near
312 nm, R has a value of 9.15 (Zepp, 1980).
2.5.2.2.3 Fate of Excited Molecules
"Each molecule taking part in a chemical reaction which 1s a
direct result of the absorption of light takes up one quantum of
radiation (Stark-Einstein Law)." (Glasstone, 1946)
According to this "second law of photochemistry", the extent to which a photo-
chemical reaction progresses depends on the number of quanta of light absorbed. Each
absorbed photon produces an electronically excited molecule which can undergo numerous
processes. Including reaction. Factors which Influence the fraction of excited
molecules which undergo reaction, called the quantum yield, comes first 1n the
following discussion of the fate of excited molecules. Then, the two major classes
of enviromental photolysis reactions, direct and sensitized, are discussed.
2.5.2.2.3.1 The Quantum Yield
Although all photochemical reactions are Initiated by the absorption of a
photon, not every absorbed photon Induces a chemical reaction. Besides chemical
-104-
-------
TABLE 11-30
CONTRIBUTIONS TO LIGHT ATTENUATION COEFFICIENT
Waveband
Center
(nm)
300
310
320
330
340
350
360
370
380
390
400
410
420
430
440
450
460
470
480
490
500
510
520
550
600
650
700
750
800
a a
w
(nrl)
.141
.105
.0844
.0678
.0561
.0463
.0379
.0300
.0220
.0191
.0171
.0162
.0153
.0144
.0145
.0145
.0156
.0156
.0176
.0196
.0257
.0357
.?477
.0638
.244
.349
.650
2.47
2.07
a b
a
69.*
67.*
63.*
61 *
58.*
55.
55.
51.
46.
42.
41.
39.
38.
35.
32.
31.
28.
26.
24.
22.
20.
18.
16.
10.
6.
8.
3.
2.
0.
aooc
] [(mg/1)-1 m-1] [(
6.25
5.41
4.68
4.05
3.50
3.03
2.62
2.26
1.96
1.69
1.47
1.27
1.10
0.949
0.821
0.710
0.614
0.531
0.460
0.398
0.344
0.297
0.257
0.167
0.081
.
.
.
-
'".1
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
.35
^Source: Smith and Baker (1981)
Source: Smith and Baker (1978) Calculated using aa « K?/0,
0 • 1.2 *
^Source: Zepp and Schlotzhauer (1981)
aSource: Miller and Zepp (1979). Calculated using ass « KS/D.
*
Denotes extrapolated values.
-105-
-------
reactions, possible processes which excited molecules may undergo Include the reemls-
sion of light through fluorescence and phosphorescence, the Internal conversion of
the photons' energy Into heat, and the excitation of other molecules, as shown 1n
Figure 11-14. The fraction of absorbed photons which cause the desired reactlon(s)
Is termed the quantum yield, *
moles of a given species formed or destroyed
0 = (11-70)
moles of photons absorbed by the system
The quantum yields for photochemical reactions 1n the solution phase exhibit two
properties which greatly simplify their use:
• The quantum yield 1s less than or equal to one
• The quantum yield 1s Independent of the wavelength of the absorbed
photons.
Although exceptions to these rules exist, they are rare for photochemical
reactions 1n the aquatic environment.
Environmental conditions Influence photolysis quantum yields. Molecular oxygen
acts as a quenching agent (see Figure 11-14) in some photochemical reactions, reduc-
ing the quantum yields (Wolfe «1 «J_., 1978). In other cases, 1t has no effect or may
even be a reactant. In any case, rate constant and quantum yield measurements should
be performed In water with oxygen concentrations representative of environmental
conditions.
Suspended sediments also Influence rates of photolysis. Not only do suspended
sediments Increase light attenuation, but they change the reactivity of compounds
sorted on them (Miller and Zepp. 1979). Sorptlon may either Increase or decrease a
compound's reactivity depending on the reaction 1t undergoes. This effect, however,
1s of secondary Importance in comparison to the Increase 1n light attenuation by the
suspended sediments (Burns et^al_., 1981). Thus, the effects of sorptlon will be
neglected.
Chemical sped at1 on also affects rates of photolysis. Different forms of an
organic acid or base may have different quantum yields, as well as absorpt1v1t1es,
causing the apparent photolysis rate of the compound to vary with pH. The possibility
of this should be kept In mind when the pK of a photolyzlng compound Is 7 + 2.
Except where stated otherwise, data contained herein may be assumed Independent of pH
over the range of values observed 1n natural waters.
Photochemically Initiated reactions may show a temperature effect depending upon
the actual mechanisms Involved. General methods for predicting this effect have yet
to be developed. Users of this screening manual should assume thermal effects on
photolysis to be negligible.
Quantum yields vary over several orders of magnitude depending on the nature of
the molecule which absorbs light and the reactions 1t undergoes. The two major
-106-
-------
A, +hMt
Chemical raaction
A. + Q'
Chemical reaction
AO - ground itaie of rtactant molecule
A* - eicittd tlate
Q8 - ground ttatt of quenching molecule
Q* - excited tute
FIGURE iI-14 PHOTOCHEMICAL PATHWAYS OF AN EXCITED
MOLECULE, EXCITED MOLECULES DO NOT
ALWAYS CHEMICALLY REACT,
classes of photochemical reactions of interest in the aquatic environment are direct
and sensitized photolysis. A closer examination of each reaction type follows.
2.5.2.2.3.2 Direct Photolysis
Direct photolysis occurs when the reacting molecule Itself directly absorbs
light. The excited molecule can undergo various types of reactions. Including
fragmentation, reduction, oxidation, hydrolysis, acid-base reaction, addition,
substitution, Isomerization, polymerization, etc. Figure 11-15 shows examples of the
reactions undergone by three toxic substances which directly photolyze.
The quantum yield for the direct photolysis, *,, of a compound is a constant
defined as follows:
* - ~/r (II-71)
d dt/ 'ad
where
C » concentration of the compound
IJd • rate at which the compound absorbs light.
Table 11-31 lists several disappearance quantum yields for direct photolysis of
-107-
-------
OCHyCOR
Cl
Sunlight/
OCH?COR
Cl
fc)
+ R - C.
*OH
Cl
FIGURE 11-15 DIRECT PHOTOCHEMICAL REACTIONS OF (A) 2,4-D ESTER,
(B) BENZ(A)ANTHRACENE, AND (c) PENTACHLOROPHENOL,
aquatic pollutants.
By comparing molecular absorption spectra with the spectral distribution
of sunlight, 1t 1s possible to determine whether or not a compound may directly
photolyze. Benzene, as shown In Figure II-16a, does not directly photolyze because
1t does not absorb light above 275 nrn. Naphthacene, shown 1n Figure II-16b, does
directly photolyze because of Its strong absorptivity 1n the sunlight region of the
spectrum. Humlc adds. Figure II-16c, by virtue of their absorption of sunlight may
Initiate Indirect, or sensitized, photochemical reactions.
•108-
-------
TABLE 11-31
DISAPPEARANCE QUANTUM YIELDS, ca FOR DIRECT PHOTOLYSIS
Compound
Polycyclic Aromatic Hydrocarbons
Naphthalene
1-Methyl naphthalene
2-Methylnaphthalene
Phenanthrene
Anthracene
9-Methyl anthracene
9,10-Dlmethylanthracene
Pyrene
Fluoranthrene
Chrysene
Naphthacene
Benz(a)anthracene
Benz(a)pyrene
2,4-D Esters
Butoxyethyl ester
Methyl ester
Carbaryl
N-Nitrosoatrazine
Trlfluralin
OMDE
aZepp and Schlotzhauer (1979)
bZepp et ah (1975)
;^ Reference
.015
.018
.0053
.010
.0030
.0075
.0040
.0021
(313 nm) .00012
(366 nm) .000002
.0028
.013
.0033
.00089
.056
.031
.0055
.30
.0020
.30
Slolfe et aj.. (1978)
dZepp and CUne (1977)
a
a
a
a
a
a
a
a
a
a
a
a
a
a
b
b
c
d
d
d
-109-
-------
IMMMONI
Sunlight /
Sp«ctPU« /'
j 1
«ClO
FIGURE 11-16
COMPARISON OF SOLAR IRRADIANCE
WITH THE ABSORPTION SPECTRA OF
(A> A COMPOUND WHICH DOES NOT
DIRECTLY PHOTOLYZE, (B) A
COMPOUND WHICH DOES DIRECTLY
PHOTOLYZE, AND
-------
2.5.2.2.3.3 Sensitized Photolysis
Sunlight can cause the degradation of aquatic pollutants by means other than
direct photolysis. A light-absorbing molecule can transfer Its excess energy to an
acceptor molecule causing the acceptor to react as 1f It had absorbed the radiant
energy directly. This reaction mechanism, known as photosensitizatlon, contributes
to the degradation of aquatic pollutants when suitable light absorbing substances, or
pnotosensitizers, are present. 2,5-D1methyl furan 1s an example of a compound which
degrades by sensitized photolysis. It does not react when exposed to sunlight in
distilled water but degrades rapidly in waters containing natural hunlc acids (Zepp
et_a]_. 1981a).
Numerous substances, Including humic acids, titanium dioxide, and synthetic
organic compounds, can sensitize photochemical reactions. But, most potential
sensitizers occur at such low environmental concentrations that they have negligible
effects on photolysis rates. Humic acids, the naturally occurring by-products of
plant matter decay, frequently attain concentrations of 1-10 rag as carbon per liter
in natural systems. Humic adds strongly absorb sunlight with wavelengths shorter
than 500 nm, as the absorption coefficients for dissolved organic carbon,
in Table 11-27, indicate.
The quantum yield for photosensitized reactions, * , 1s defined 1n a
manner similar to the quantum yield for direct photolysis:
* .:*£/,
s dt/ «s
where
C « concentration of the pollutant
I • rate of light absorption by the sensitizing molecule.
as
The quantum yield for sensitized photolysis, however, Is not constant but depends on
the pollutant concentration, such that:
where
Q » a constant.
This 1s due to the fact that the probability of the sensitized molecule donat-
ing its energy to a pollutant molecule Is proportional to the concentration of
the pollutant molecule. Published values of Q are very rare. Zepp et a_K (1981b)
report a Q of 19 (mol/1)" for the photosensitized oxidation of 2,5-dimethylfuran.
-Ill-
-------
2.5.2.2.4 Preliminary Screening of Direct Photolysis
As the preceding discussion Indicates, a number of environmental parameters
influence pnotolysls. The following sections show that the procedure for calculating
the photolysis rate can be quite Involved. Therefore, a preliminary screening which
attempts to determine whether photolysis rates are likely to be significant or
Insignificant (without actually calculating the rate Itself) 1s useful.
If i« 0 (I.e. 1f the molecule does not absorb solar radiation) for 290 ~730 nm, then direct photolysis 1s probably unimportant. Refer-
ences which contain \* values (1n addition to Table 11-32 of this document) Include
Lyman et_ aj_. (1982), FMedel and Orchln (1951), Hershenson (1966) and Kamlet (1960).
The Kamlet reference 1s a series of 20 volumes from 1960 to present and contains \*
values for many thousands of organlcs.
It should be recognized that small 9,, or small « .„ are not good 1nd1-
U TOO A
cators of tne Importance of photolysis. For example, consider the tabulations
below:
*d (benzo[a]pyrene) » 0.00089 at x- 313 nm
but
" 13,000; X • 347 nm
• 24,000; X - 364 nm
«max * 29,000; X- 384 nm
and
"do (near sur'»c« photolysis rate) » 17./day
The quantum yield for benzo[a]pyrene Is small (0.00089) considering that quantum
yields can be as high as 1.0. However, the near surface photolysis rate (I.e. the
photolysis rate 1n a very thin layer of clear water) 1s 17./day, a very large rate.
This result Is caused by the high extinction coefficients for benzo[a]pyrene, and It
1s evident that photolysis can be Important for this compound.
Now consider the case of small «
nH X
For naphthalene :
'max " 25° at x"
but
*d • 0.015
and
"do ' °'2/da*
-112-
-------
For the small c^^ (250), the near surface photolysis rate is 0.2/day. While
this is not an extremely large rate, it may also not be negligible either, depending on
the particular environmental condition.
Certain categories or groups of chemicals are likely to be poor absorbers of
sunlight. A number of these groups are shown below:
Group _ Examples _
alcohols R-OH: ethyl alcohol
ethers R-O-R': diphenyl ether
amines R-NH-lprlmary ) : methyl ami ne
nltrlles R-CN: hydrogen cyanide
(cyanides)
For these groups, photolysis 1s likely to be unimportant. Other groups, however, do
tend to absorb sunlight. Figure 11-17 shows a number of these groups.
A final preliminary screening Is to compare an estimated upper limit photolysis
rate (e.g., using*. » 1) against other first-order rates which have already
been calculated. If these rates are high enough, the photolysis rate, even under
optimal light-absorbing conditions, may be relatively small and therefore negligible.
For example, an upper limit photolysis rate which is calculated to be 20 percent as
large as a hydrolysis rate is relatively Insignificant.
2.5.2.3 Computing Environmental Photolysis Rates
The overall rate at which a pollutant photolyzes in the aquatic environment is
the sum of the rates of direct and sensitized photochemical reactions. At the low
pollutant concentrations observed in the environment, the rates of both direct and
sensitized photolysis are proportional to the concentration of the pollutant. Thus,
photolysis follows a first-order rate law:
. -kp • C (11-74)
dt p
where
kp * overall photolysis rate constant, day*
• kd ' 's
k^ • direct photolysis rate constant, day
k$ • sensitized photolysis rate constant, day" .
Due to the complexity of the units for the parameters in the photolysis section, it
is essential that the user employ the specified units in each equation. All resulting
first-order photolysis rate constants have units of day .
-113-
-------
r«
Oroop (nm)
*C-O Ulctehydt. ktiont)
X-s
-N-N-
-NO,
rY^l
^sX*X^
f^N^^V^
kAJ^
O-o
;c-c-c-o
1 I
295
460
347
278
311
270
360
440
300
330
10
WMfc
18
10
250
MOO
6000
20
1000
20
SOURCE: CALVERT AND PITTS
FIGURE 11-17 CHROMOPHROIC GROUPS WHICH ABSORB SUNLIGHT
The determination of rate constants for direct and sensitized photolysis
1s the subject of the remainder of this section. Section 2.5.2.3.1 Includes a
derivation of the equations for k and k . Sections 2.S.2.3.2 and 2.5.2.3.3
describe how to calculate these constants on the basis of near surface rate constants
or molecular absorption spectra.
2.5.2.3.1 Derivation of Rate Constant Equations
2.5.2.3.1.1 Direct Photolysis
Figure 11-18 shows the major processes which Influence direct photolysis of
pollutants 1n natural waters and Indicates data requirements. This figure can be
translated Into mathematics as follows:
Light absorption within a small wavelength band AX:
Light absorption In a water body of depth Z;
-114-
-------
PROCESS
DATA
LIGHT ABSORPTION BY POLLUTANTS:
MUST BE IN ULTRAVIOLET-VISIBLE
SPECTRUM FOR PHOTOLYSIS TO OCCUR
EXTINCTION
COEFFICIENT,C
LIGHT ATTENUATION IN NATURAL
WATERS: LIGHT REMAINING
AVAILABLE FOR PHOTOLYSIS
DECREASES WITH OE"TH
DIFFUSE
ATTENUATION
INEFFICIENT, K
LIGHT UTILIZED FOR PHOTOLYSIS:
ONLY A FRACTION OF THE AVAILABLE
LIGHT is USEC FOR PHOTOCHEMICAL
REACTIONS
I^REACTION
QUANTUM YIELD,
•o
PREDICTION OF DIRECT
PHOTOLYSIS RATE: K-
FlGURE 11-13
MAJOR PROCESSES WHICH INFLUENCE PHOTOLYSIS
OF POLLUTANTS IN NATURAL WATERS
-115-
-------
Photolysis rate for wavelength band
a e,w,
X A A
The equation for direct photolysis becomes:
y1 y i-e'*'z ( (11-75)
where °
Z • mixed depth of water body, m
\, • 700 nm
V » 300 nm
j » conversion factor « 1.43 x 10 mole-cm -sec-1 -day
« • base 10 molar extinction coefficient of pollutant, 1 mol" cm"
C • concentration of pollutant, mol/1
0 • radiance distribution function
M • photon irradiance near the surface, photons cm" sec" nm"
K • diffuse light attenuation coefficient of the water, m"1
Equation 11-75 can be written In summation notation as:
700 l-e""2
d " A-290 X X KZ (II~
Equation (11-75) incorporates the assumption that C, K, and 0 are independent of
depth.
2.5.2.3.1.2 Sensitized Photolysis
The rate at which a compound decays through sensitized photolysis 1s propor-
tional to the rate at which sensitizing molecules absorb light. The rate at which
sensitizers absorb light 1n the aquatic environment Is:
where
j'as (X) ' C$ (z}'°(^ *w(x) '« " dxdz
I, • rate of light absorption by sensitizers, elnstein 1" day"
as
a • base e absorotion coefficient of the sensitizer, e.g.,
1 m
C • concentration of sensitizer, e.g., mg-DOC/1.
-116-
-------
The rate constant for sensitized photolysis of a compound, k , is then:
k
Al -K
/ 1 - *
a • * •
V 7
A
0
(II-78a)
Equation il-78a includes the assumptions that C , K, and D are independent
of depth and that Q is independent of wavelength.
In terms of summation notation, this equation becomes:
^ .
J-Q$'0-C$- Z as • W • l~*ml (II-7Sb)
2.5.2.3.2 Use of Near Surface Rate Constants
Experimental data for direct photolysis are generally reported as near surface
rate constants, as in Table 11-32. Near the surface of a water body (K-z £0.2), the
mean irradiance is approximately equal to the surface irradiance. This fact permits
Equation 11-75 to be simplified to the following expression which defines the near
surface rate constant, k :
,'•1
kdo ' 2'3 ' *d ' °o
where
k. « near-surface direct photolysis rate constant, day"
D • radiance distribution near the surface (approximate value •
1.2).
According to Equation (11-79), the near surface rate constant is Independent of
the properties of the water It 1s measured In, except for the small variation
in 0 . Thus, when the difference In solar irradiance between the experimental
and environmental conditions is accounted for, the user can apply a near surface rate
constant to other bodies of water using the following expression:
-117-
-------
TABLE 11-32
NEAR-SURFACE DIRECT PHOTOLYSIS RATE CONSTANTS
CJTOura"
•:',;, c'.ic Arctic -yd'ocarcon
Naphthalene
1-Metnylnapntnalent
C-f'«tny Intpntnalene
An-.nracere
9-v«tn>:antnr«cer.e
?. 1 3- jimetny; anthracene
Pyrene
f luOranthren*
Cr-rysene
Nacn'iacene
Serioi a,pyre«e
Ben^aianthracent
Caroarate 'esticidts
CarOaryl
Sropharr
Chlororopna*
dutnyl ester
di.n- Butyl ester
d'-"-octyl ester
di-, 2-etnyih«,yl , ester
2,--; Esters
ButOiyethyl ester
methyl ester
HtucMorocydoMiitMimt
Pentachlorophenol (anion)
3,3' -dichlorob«nzidint
N-nitrosoatrtzine
Triflural in
DHOEi l.;-B'SlP-««tnylpn«nyl 1)-
Notes
1
*30
.23
.?6
.31
2.3
22 ;
130.:
48.3
24. C
79
3.3
490.3
31.0
28.0
.32
<.OC3
<.006
5.1C"3
S.1C"3
S.1C'3
5.1C'3
050
.030
94.
46
670.
300
30.
17
'o-"
210C
21CC
210C
2100
21CC
2100
210C
21CC
2 IOC
2100
2100
2100
2 ICO
2100
740
740
600
600
60C
600
60C
420
420
540
600
2000
1800
1800
2200
"«
1 Parenthetic conwnts a'ter nan* of compound Indicate »nen tne for*
Of th« coapound undergoing photolysis IS something other than th*
neutral fonr
2 Estimated Solar rlu« • usually nign estiiutes to give conservative
photolysis rates.
j Wavelength o' Ria>i«wi sunlight absorotion
• Indicates the i«*«irx*i o' tne absorption spectrv ", used
j
•>! Inn,
310
312
320
3i3
36C
380
400
330
-
320
440
380
340
313
-
-
-
.
J18'
,ef
i
t
a
a
a
a
i
a
a
a
a
a
a
b
c
c
d
d
d
d
d
e
e
f
f
280- 3 JO* f
.
-
f er»nces :
a ) Z»pp
B) 2*pp,
d) uolfe
e) Zepp
'! CalU
9! >PP
9
9
9
and Sc>
(1978)
et ai
«i 11
*t a_T
nan e_i
and C! '
(1978)
iwao:
:i979!
aj_ 1979
.n, ;1977
-118-
-------
700 , .-K2
o y. e,3.
so:
\-T90_ " (II-80D)
"•
-. PI l-e'K(K*)Z (II-SOc)
°oro
k - k • i- • J • ^-?' (II-80d)
Kd Kdo T D K(,;*)Z
where
I • total solar radiation (1 angleys/day)
IQ * total solar radiation under conditions at which kd was
measured (1 angleys/day)
\* * wavelength of maximu- light absorption, i.e. wavelength where the
product «(\)'W(\) is greatest.
This approximate express'"- is valid if the following assumptions are suffici-
ently accurate: 1) the solar irradiance at a wavelength is a constant fraction of
the total solar irradiance (Park e£ a_l_. , 1980) and 2) the light attenuation coeffic-
ient, K, 1s constant over the range of wavelength that the compound absorbs solar
radiation at nigh rates (Burns et^ a_l_. , 1981).
Although it is possible to derive a similar expression for sensitized photolysis,
variation in the absorptivity and reactivity of natural humic substances make extra-
polations based on the concentration of dissolved organic carbon subject to large
errors. An approach taken by Zepp (1980) was to correlate the sensitized photolysis
rate constant with the absorbance of a solution at 366nm. Such an empirical relation-
ship was found for 2, 5-
-------
shown 1n a step-wise fashion In Table 11-33. Note that the effects of water depth
and light attenuation can be estimated based on water-body characteristics from Table
11-34. Thus the method essentially consists of multiplying several numbers together
to find k..
d
If kd(j, which Is required to use the method outlined in Table 11-33, 1s not
directly obtainable, 1t can be calculated from Table 11-35 and then used in con-
junction with the near-surface approach described above. One advantage of using the
near-surface approach (in addition to Its simplicity) 1s that the photolysis rate In
different classes of natural waters can be readily evaluated using Table 11-33, once
k. has been calculated a single time.
In some cases, the near-surface approach may not be applicable. Equations
II-80a through II-80c show some of the simplifications required to develop a near-
surface approach. Photolysis rates for chemicals which have multiple * values
max
within the wavelength range 290 nm < x.<700 nm should not be calculated using the
near-surface approach. Rather, the direct approach outlined In Table 11-37 should be
used 1n conjunction with the procedure shown in Table 11-36.
Very little emphasis is given here on rates of indirect photolysis because
little data are available on indirect photolysis rates. Table 11-38 summarizes the
pertinent work of Zepp (1977). Zepp found that the near-surface half-life for
Indirect photolysis for several chemicals 1n Okefenokee Swamp waters was very short:
from 0.02 hr to 7 hrs. The near-surface rate constant translates to between 2.4/day
to 830./day. However, on a depth-averaged basis, four of the five photolysis rates
are below 0.06/day. Only for pentacene Is the depth-averaged photolysis rate
high (5.8/day). Thus, the same factor (humic material) that is responsible for the
high near-surface rate constants, is also responsible for the small depth-averaged
values because much of the sunlight 1s rapidly absorbed near the water surface. For
this reason, and because of the lack of data. Indirect photolysis 1s ignored in these
assessment procedures.
-120-
-------
T/WLE 11-33
SUMMARY OF NEAR SURFACE APPROACH
1. Predictive Equation:
2. Find:
k
do
- See Table 11-32, or
- See Table 8-12, p. 8-38, lyman jet .al.., where half-Hves are
given:
do
(1.2-1.6)
(1.2)
I « (500-700 langleys/day)
o
X*
IQ - (500-2100 langleys/day; see Table 11-29)
- Table 11-32
- Table 8-5, p. 8-14, of'Lyman et _aj,
- FHedel and OrcMn, 1951.
- Hershenson, 1966.
- Kanlet (ed.), 1960.
3. Knowing A* and Depth of Water body, Z, Find
K(X*)Z
Table 11-34 shows some typical values of this expression.
4. Find k, using equation shown In step 1.
5. Suppose kd(J Is not known from experimental studies. It can be
calculated fro* the procedure shown 1n Table 11-35.
-121-
-------
TABLE 11-34
-K(X*)Z
RANGE OF
1-e
Depth of Water (m)
Mnm) Water Type*
300 A
B
C
D
340 A
8
C
D
380 A
B
C
D
420 A
B
C
0
460 A
B
C
0
500 A
B
C
0
a
Water Type chla
A 0.0
B 0.001
C 0.01
0 0.1
1
0.9
0.5
0.1
0.03
0.9
0.7
0.2
0.04
1.0
0.8
0.2
0.05
1.0
0.8
0.3
0.06
1.0
0.8
0.3
0.07
1.0
0.8
0.3
0.07
(rnq/1)
(Lake Tahoe)
(eutropMc)
2
0.8
0.4
0.06
0.01
0.9
0.5
0.08
0.02
1.0
0.6
0.1
0.02
1.0
0.6
0.1
0.03
1.0
0.7
0.2
0.3
1.0
0.7
0.2
0.4
(highly eutropMc)
3
0.8
0.2
0.04
0.009
0.9
0.4
0.06
0.01
1.0
0.5
0.07
0.02
1.0
0.5
0.09
0.02
1.0
0.6
0.1
0.02
1.0
0.6
0.1
0.02
DOC (mq/1
0.0
0.1
0.5
2.0
5
0.6
0.14
0.03
0.005
0.8
0.2
0.03
0.007
0.9
0.3
0.04
0.009
1.0
0.4
0.05
0.01
1.0
0.4
0.06
0.01
0.9
0.4
0.07
0.01
*
10
0.4
0.07
0.01
0.003
0.7
0.1
0.02
0.004
0.9
0.2
0.02
0.005
0.9
0.2
0.03
0.006
0.9
0.2
0.03
0.007
0.9
0.2
0.03
0.007
SS (mq/1)
0.0
0.5
5.0
20.0
-122-
-------
TABLE 11-35
DIRECT PREDICTION OF NEAR SURFACE RATE
Predictive Equation:
700
k,n ' 2.3 J*J>
do
-------
TABLE 11-36
PROCEDURE FOR CALCULATING DIRECT PHOTOLYSIS RATE CONSTANT
FOR A REFERENCE LIGHT INTENSITY OF 540 LANGLEYS/DAY
.tm-t/t*
.•I'M*4
.IM-M14
.M.-"
.«*••***
.iii-M**
I.M-M*4
I.M-M*4
t.ll-M14
I.M-M*4
I.W-M*4
MKI14
LII-M*4
.11 V
M-i-M1'
M.I-M*4
M.i-M*4
M.I-M*1
i
*
1_
' • ' 10 II 12
» . (iTii . •&,,.., &'» . J8h^ £!, . ,!&,„ ti'U • tr''i
.Ml M. in *M
.1* W. t.M iM
.MM M. •• «.„
.M* M. IM AH
.MM M. I.M i.M
.•>» M. IM tM
.MM M. >» ai(
.MM M. 1 • (.n
.«• «. I.M (H
.•N «. 1 «* IH
.•M M. 'ill §M
.•U M. 1 M ^H
.•M M. I.M (M
.•M m. im n,
.•M M. IM ,„
••» » ••«« I.N
flM M. 1 ftM
.MM M. I-MI
.MM M. I.Nf r-
.MM M. in* ^M
.MM M IMI ^M
Mi 1
•*" «.»
13 14 IS
^
U • **M * f
i 1
1. UMrfMi »,
•*"
^"h^VH*
. ,w»*r
i. Mr • IIMI MMKW M«r
M. 1. • Ml limnKHn
•i " %' U * ' ***
MlMrf I'l
II). ll-lt. • III l
-------
TABLE 11-37
DIRECT PREDICTION OF DEPTH -i'/ERAGED PHOTOLYSIS RATE
• Predictive Equation:
A-700 , -KZ
k, - 2.3 j*J) £ eiHi±:£ AA
0 ° A-290 * A KZ
• Data Required:
!• 'd
2. D
3. e^ versus A
4. Wx versus X
5. Hater body characteristics: depth, chla, DOC, SS
• Approach: Use Table 11-36
a) Enter A versus c In column 5 and appropriate rows, and
calculate cWAA 1n column 6.
b) Enter D (column 7), chU (column 9), DOC (Column 10), and
SS (column 11) 1n appropriate rows.
c) Calculate K (column 12) for appropriate rows.
d) Knowing water depth Z, calculate appropriate values for
column 13.
e) Transfer column 6 entries to column 14.
f) Multiply column 13 entries by column 14 entries and
record In column 15.
g) Sum column 15 entries and use Equation 1 on RHS of sheet
to find kd.
-125-
-------
TABLE 11-38
ESTIMATED HALF-LIVES FOR INDIRECT PHOTOLYSIS OF
ORGAHICS IN OKEFENOKEE SWAMP WATER*
Organic
Naphthacene
Pentacene
H1st1d1ne
Tryptophan
Methlonlne
Half-Ldfe
(hr)15
7
0.02
2
2
3
Near-Surface
Rate Constant
U/dav)D
2.4
830.
8.3
8.3
5.5
Depth-Averaged
Rate Constant
fl/day)r
0.01
5.8
0.06
0.06
0.04
*Zepp, R.G. et_ al., 1977. Singlet Oxygen 1n Natural Waters.
Nature, Vol. 267.
Near-Surface (1 cm) rate constant
C0epth-averaged rate constant. Assume humlc materials
12 «9/l, depth • 3m.
EXAHPLE 11-7
Computation of Photolysis Rate Constants
Compute the mean annual photolysis rate constant for the p«st1dde carbaryl
1n a hypothetical river near Fresno. California. Use both the evaluation of
Integral and near surface rate constant methods described above. Assume the
following physical and chemical parameters apply to the river:
Mean depth • 2 m
Suspended sediments • 10 mg/1
Hu«1c add • 2 mg-DOC/1
Chi orophyl1 a » 0 mg/1
Zepp (1978) reported a quantum yield, *., of .0060 and the follow-
1ng absorpt1v1t1es, «, for carbaryl:
-126-
-------
Wavelength (nm) Absorptivity (M'm")
30U 918
310 356
320 101
330 11
A. Near Surface Rate Constant Method
Table 11-32 contains the following information regarding carbaryl :
kd « .32 day"1
IQ • 2100 langleys/day
X* « 313 nm.
According to Figure 11-13, the mean annual solar irradiance at Fresno,
California is 450 langleys/day.
Assume that the radiance distribution function under reference, D ,
and environmental, D, conditions have values of 1.2 and 1.6 respectively.
To calculate the light attenuation coefficient at the wavelength of maximum
light absorption, 313 nm, we use Equation 11-68 and the data in Table 11-30, at
310 nm:
K - 1.6 (.105 * 67 - 0 + 5.41 • 2 + .35 • 10)
* 23.1m"1
When the water absorbs nearly all of the incident radiation, I.e.,
kZ _>_ 3, the following approximation is valid:
1 - KZ , 1
This approximation can be applied to Equation 11-76 and Equation II-8CW. It both
simplifies the calculations and eliminates the dependence of the rate constant on
the radiance distribution function, D, In cases where the light attenuation
coefficient Is calculated from D, as in this example. In such a case, the user's
choice of a value of D does not affect the result.
Using this approximation In Equation (II-80d), the mean photolysis rate
constant 1s computed to be:
L . 450 . 1.6 . 1
• 2.0 x 10'3 day'1
This example demonstrates the significant difference, 100 fold in this
case, which may exist between near surface and mean photolysis rate constants.
The strong attenuation of light by the river water was the primary cause of
the reduction In rates.
-127-
-------
I B. Evaluation of Integrals
I The absorption data for carbaryl Indicate that we need to concern ourselves
| only with light of wavelength 300-330nm 1n order to determine a mean rate constant.
j First, we assume that D has the same value as above, -1.6. Then, we compute
j the light attenuation coefficients using Equation 11-68 and the data In Table
j 11-36.
! • ••[•••(•„• 3 for all wavelengths of Interest, use the approximation discussed
1n part A.
X c W x 10'14 c.H,
(nm) (M^cm"1) (photons/cm2/s) (K-Z) TT
300 918 .0303 51.6 .539 x 10 14
310 356 .388 46.2 2.99 x 10 14
320 101 1.13 41.4 2.76 x 10 14
330 11 1.81 37.4 .532 x 10 14
£ « 6.82 x 10 14
1
Given that the quantum yield Is .006, the mean photolysis rate constant
can be computed using Equation 11-82 and the above Information:
k., • 2.3 • 1.43 x 10"16 • — • .0060 • 1.6 • 6.82 x 1014 j
d 540 !
• 1.8 x 10"3 day"1 '
I The small difference between the rate constants calculated 1n parts A [
j and B 1s due to the difference 1n the reference solar Intensities. The assumption |
j made here that the spectral distribution of solar energy 1s Independent of Intensity!
-128-
-------
1s only approximately true. Consequently, the greater the discrepancy between the
reference and local solar Intensities, the greater the error 1n rate constants
that can be expected. When the local exceeds the reference Intensity, the actual
rate constant Is probably higher than the calculated value. When the reference
exceeds the local Intensity, the actual rate constant Is probably lower than
cal culated.
END OF EXAMPLE 11-7
-J
2.5.3 Hydrolysis
Some toxic compounds can be altered by direct reaction with water. The chemical
reaction of a compound with water 1s called hydrolysis. Typically 1n hydrolysis
reactions hydroxide replaces another chemical group.
An example hydrolysis reaction for a toxic organic compound 1s given below:
Carbaryl
H20
Water,
a-Naphthanol
Ho NCH:
CO-
Methyl ami ne +• Carbon
Dioxide
Generalized hydrolytic reactions of organic compounds are presented 1n Table
11-39.
Hydrolysis reactions alter the reacting molecules but do not always produce less
noxious products. For example the more toxic 2,4-0 add Is produced from the hydrolysis
of certain 2,4-0 esters. Alternatively the hydrolysis of carbaryl (shown above)
produces less toxic products, I.e. a-naphthanol and methyl amine.
Hydrolysis products may be more or less volatile than the original compound.
Hydrolysis products which Ionize may have essentially zero volatility depending upon
pH. Hydrolysis products are generally more readily blodegraded than the parent
compounds, although there are some exceptions.
Hydrolysis reactions are commonly catalyzed by hydrogen or hydroxide Ions. This
produces the strong pH dependence often observed for hydrolysis reactions. Examples
of this dependency are shown 1n Figure 11-19, where the logarithms of reaction rate
constants (KH) are plotted versus pH. The hydrolysis rate of carbaryl can be
seen to Increase logarithmically with pH. The rate at pH • 8 1s ten times that at pH
• 7 and 100 times that at pH - 6. The hydrolysis rate of parathi on 1s high at low pH
-129-
-------
TABLE 11-39
GENERALIZED HYDROLYTIC REACTIONS OF ORGANIC COMPOUNDS
SsiCTAr.T REACTION CONDITIONS
CAaaoxv_;c ACID ESTERS ACIDIC, NEUTRAL,
n BASIC
/,
R-C'c-R'
AMIDES ACIDIC, BASIC
^°
R~C
i
i
H
CARBAMATES ACIDIC, BASIC
H
i
Rir
n.
C-O-R'
n
0
CRGANOPHOSPHATES BASIC (AciDic,
('AND DERIVATES) NEUTRAL)
0
RO-P -or
l
OR
HALOGENATED ALKANES NEUTRAL, BASIC
R
/C~~X
R'
R"
PRODUCTS
CARBOXYLIC ACID * ALCOHOL
. Q
sS
R-C^ * R'OH
CARBOXYLIC ACID * AMINE
//Q H\
R-cf * ,11
N0ll / NR'
AMINE * ALCOHOL * CARBON DIOXIDE
R-n/H R'0" ^
\
H
PHOSPHATE Di ESTER * ALCOHOL
0
RO — P — OH ROH
i
OR
ALCOHOL * HAL IDE ION
R
R1 — C— OH X
1
R"
SOURCE: l.J. TiusLEY, CHEMICAL CONCEPTS IN POLLUTANT BEHAVIOR, J. WILEY, Nex YORK (1979).
-130-
-------
O Parsthion
0 Carbaryl
O Chloromethant
2.4-0 (2-butoxyettiyl
wttr)
FIGURE 11-19 PH DEPENDENCE OF HYDROLYSIS RATE CONSTANTS
-131-
-------
values, reaches a minimum at pH * 6, and then Increases with Increasing pH. The
hydrolysis rate of chloromethane shows minimal dependence on pH over the range
presented.
Adsorption can aiso Influence hydrolysis rates. Adsorption of an organic
molecule protects It from acid or base catalyzed hydrolysis (Wolfe, 1981). The
amount of adsorption can be predicted using the principles presented 1n Section
2.3.2.
M1crob1a1ly mediated hydrolysis reactions are responsible for the breakdown of
many complex molecules, Including natural polymers such as cellulose. Microorganisms
catalyze hydrolysis reactions 1n the process of using organic compounds as energy
and/or carbon sources. In come tab ol 1 SHI microbes may hydrolyze toxi - organic com-
pounds to hasten their removal from cell protoplasm. N1crob1al1y me_.ated processes
are covered under the general heading of blodegradatlon 1n Section 2.5.1. Here only
abiotic hydrolysis 1s treated.
Abiotic hydrolysis reactions are represented by rate expressions which are first
order 1n the concentration of the compound being hydrolyzed:
R • !£- -k C (11-82)
3t H T
where
R - th«
-1
R • the rate of hydrolysis, mole liter" sec" or *g liter"
sec
specific hydrolysis rate constant, sec"
Cy • the dissolved plus sorbed phase concentration of compound C
mole liter"1 or *g liter"1.
In the literature KH Is typically defined as:
* » k * k [H*> k [OH~] (11-83)
nna b
In this document the specific hydrolysis rate constant, KH, 1s defined
to Include the effects of adsorption:
k - k * a /k [H*> k [OH*]) 1 (H-84)
H L n w \ a b /J
where
"
<*H • the decimal fraction of the total amount of compound C which Is
the neutral hydrolysis rate constant, sec
the decimal fraction of the total amount c
dissolved (Calculation procedures In Section 2.3.2)
the add catalyzed hydrolysis rate constant, liter mole"
sec"1
-132-
-------
[H*] » the molar concentration of hydrogen Ion, mole liter"
([H*] * lo"pH)
kb • the base catalyzed hydrolysis rate constant, liter mole"
sec"1
[OH" J • the concentration o' hydroxide 1on, mole liter"
[OH"] - lO^-'V » 10(PH"14).
Equation 11-84 1s a convenient definition of *H because specific rate constants
which act on the dissolved and total concentrations do not have to be used separately.
Values for the three rate constants kn, kflf kfa for selected compounds
are presented in Table 11-40. Additional values can be found in the literature (e.g.
Mabey and Mill, 1978). The three constants can also be determined by simple labora-
tory tests.
Water body pH values must be obtained for hydrolysis reactions which are pH
dependent (i.e. those for which ka ^ 0 and/or k. ^ 0). It should be
noted that 1n poorly buffered waters (alkalinity £50 mg/1 as CaCOO, pH values
may change by 1-2 units daily due to natural processes alone. In these cases either
additional data must be gathered to characterize the system's pH regime or conserva-
tively low values of kH must be used. Table 11-41 summarizes the procedures.
EXAMPLE 11-8
1 A biodegradation rate constant, kg for the fungicide Captan has been j
given as 0.5 per day. Compare this with the abiotic hydrolysis rate constant,
! kH, at pH « 8.4, a temperature of 25°C, and with 90 percent of the compound
| adsorbed on suspended matter. Values for ka, k&, and kn can be found !
I in Table 11-31. |
j ka - 0 I
j kb - 4.9 x 107 day'1 j
i "n ' !•
( -fa (k [H+] + k [OH~]j] + k
H L* \ « b /Jn
thus
[OH'] i 10PH-14 - la8'4'14 - lO-5'6 - 2.51 x
H -[(1.0-0.9).(4.9 x 107 x 2.5 x lo"6)] * 1.6
"12.3* 1.6 - 13.9 day"1
-133-
-------
TABLE 11-40
HYDROLYSIS RATE PARAMETERS AND ESTIMATED ENVIRONMENTAL
HYDROLYSIS RATES
Njrfroljr
{n«atu)fo«
HtetiCKtor ?
C«rh«rrl
»ro*M
CMorfropfcta
2.4-0(2-l*t*>jr«tk/l ««ttr) 1.7
2.*-0(N*tk)rl ttur)
•iritnion 1.3»UT*
PhOMWt T
D«U for T
NtlitMon 1
Ct»tM
•trlllnt 3.4
"""""""
CMorwvtk***
CMorottlM**
OlcMertMtk***
rrtcklorMtt-M
1 ra*ot \ e k 1 c rovthwt
0 1 k rocock 1 e r«B tkww
TrlkrMMMtkM
H,,.cMorMnl«MHU41«M
M«lo««««ti« Conn
H;(ch)*r«M,tkjrl) ttMr
2-Ckl.rwtkrl «1i«yl ttktr 3.U10*
Mthtdtt (tttrt
0«*tk)r1 «t*r 1.
Otltkyl ttUr 1.
Oi-^-kwtyl t»ttr 1.
0>-«-« liter 1.
*1 T*
H»tickl|ra>l>»Ml 1.1,10
••Ut
*'• Mf<*tl> rtt* MrMMvr iwt it*** M4 n«t tttta
*•' M«*tit nr» «r «try ixll r«t« ttrtttttr
Uiiumi «t «i. (1*71)
wifi «t Jf.Tnii)
i*** jrff' (\m\
r.r* g iT. UMOt
mMy**«4 Mill (l»7t)
« Milf* (t ll- (1HO)
III lati Nriaturt K>«r»lyi<«
kB(tey ) kt(l'«"ltey"') k^ltey" )
3.J,10S 3.S,10"Z
T » .7
4.1,WS 4.3,10'2
.M 1.1,10**
J
1.7 1.7,10''
2.I.101 .2*
l.S,10* .11
J.I, 10° 2.44,10J J.»,10"J
? ? 2.3
? ? 1.2
T ' l.iilO'2
l.l 4.»,107 S.I
l.t . I.I
t.lilO0 31. 2.»>10'1
J.»,10"* 12. 3.1J10'1
1.I.10'1 - 1.1,10'*
2.1*10 1.1*10* 2.li)0*
j
1.0 1.0*10
1.4.101 1.4.10'1
It. t.filO**
It. J.fclO"4
4.U10** - 4.1*10**
1.1*10' - l.iilO1
•
3.U10'1
l.0,lfl3 I.OmlO*4
l.lilO1 l.tiU**
*.1,10* ».1,10"S
1.4,10J 1.4,10'*
l.t I.I*U'7
t.tuio'1 3.1 i.iiir1
•til t>M tet4 1* i if ii tail
Wttll
MtM (»*•')
V (ten)
21
1.
11.
1.1, 107
tf
4.0,10*
2.7
4.1
1.1,10*
.30
.M
11.
.13
.10
2.7110*
3.4,10*
20.
31.
2.1,10s
ft
1.3,10*
S.0,10*
1.0,10S
2.5.101
14.
4.5.10**
.
1.1*10
1.2II01
3.7.101
7.I.101
4.l,103
7.2*10*
1.0,10*
Mf.
t0^'
27
XI
27
27
27
21
21
T
20
20
20
27
21
n
21
25
25
25
25
25
25
25
25
20
25
30
M
30
JO
*
1
..f
f
f
f
,
f
f
f
f
f
*
*
*
f
f
t
•
t
-134-
-------
TABLE 11-41
PROCEDURE FOR CALCULATING HYDROLYSIS RATE CONSTANT
1. Hydrolysis Rate Constant
' [kn + - (kaCH+] *
2. Procedure
a) Find the hydrolysis rate parameters. Use Table 11-40.
1 liters liters
k • day, k « mole day, k. • mole day
b) Does the compound soro? (Table 11-11, Column 1)
If it does, find, <* , the fraction of the total amount of
* compound which is not sorbed
* r
"r 1 *pa
If it does not sorb set
Section
c) If the hydrolysis 1s acid catalyzed (a k vajue exists)
determine the hydrogen ion cencentration; [H ].
CO - io'pH - 10' —
d) If the hydrolysis is base catalyzed (a k. value exists)
determine the hydroxide ion concentration, [OH"].
[OH"] -10 ^ • 10"( - " - J • 10" -
Note: pKy • 14.2 for freshwater at 20aC
• 13.4 for seawater at 20*C
(More precise values for pic. >re givtn
in Table 11-13)
3. Substitute kn, aw, ka, [H*]. kb> [OH~] into equation (1) above.
-135-
-------
I Comparing kH to k«: I
, n t> i
• Ha . IL1 . 27.8 :
I kfi O.S I
I Comparison of kH with kg for the above situation shows that the abiotic I
j hydrolysis rate 1s about 28 tines faster than the blodegradatlon rate. Blodegra- j
j datlon could be neglected here with minimal effect on the results. j
* '
I ENQ QF EXAMPL£ n_8 1
-136-
-------
REFERENCES
Alexander, M. 1980. Biodegradation of Toxic Chemicals in Water and Soil. In:
Dynamics, Exposure, and Hazard Assessment of Toxic Chemicals, ed. R. Haque.
Ann Arbor Science, Ann Arbor, MI.
Baughman, G.L., D.F. Paris, and W.C. Steen. 198U. Quantitative Expression of
Biodegradation Rate. In: Biotransformation and Fate of Chemicals in the
Environment, ed. A.M. Maki, K.L. Dickson, and J. Cairns, Jr. American Soc.
Microbiol., Washington, DC. pp. 105-111.
Bird, R.B., W.E. Stewart, and E.N. Ligntfoot. 1960. Transport Phenomena. John
Wiley and Sons, New York.
Bowman, B.T. and W.W. Sans. 1983. Determination of Octanol-Water Partitioning
Coefficients of 61 Organophosphorus and Carbamate Insecticides and Their
Relationship to Respective Water Solubility Values. Jour. Envir. Sci. Health.
818(6), pp. 667-683.
Brown, D.S., and E.W. Flagg. 1981. Journal Environmental Quality 10(3):382-386.
Burns, I.A., D.M. Cllne, and R.R. Lassiter. 1981. Exposure Analysis Modeling
System (EXAMS): User Manual and System Documentation. Draft. Environmental
Research Laboratory, U.S. Environmental Protection Agency, Athens, GA. 443
pp.
Callahan, M.A., M.w. Slimak, N.w. (iable, I.P. May, C.F. Fowler, J.R. Freed, P.
Jennings, R.L. Durfee, F.C. Whitmore, B. Maestri, W.R. Mabey, B.R. Holt, and C.
Gould. 1979. Water-Related Environmental Fate of 129 Priority Pollutants,
Volumes I and II. Prepared for EPA by Versar, Inc., Springfield, VA. NTIS
PB80-204373.
Calvert, J.G. and J.N. Pitts. 1966. Photochemistry. John Wiley A Sons. New York.
Chiou, C.T., V.H. Freed, D.W. Schmeddlng, and R.L. Kohnert. 1977. Partition Coeffic-
ient and B1oaccumulat1on of Selected Organic Compounds. Environ. Sci. Tech.
11:475-478.
Dickson, A.G., and J.P. Rlley. 1979. The Estimation of Acid Dissociation Constants
In Seawater Media from Potent1ometr1c Titrations with Strong Base. 1. The
Ionic Product of Water -Kw. Mar. Chem. 7:89-99.
Dllllng. W.L., N.B. Terfertlller, and G.J. Kallos. 1975. Evaporation Rates and
Reactivities of Methylene Chloride. Chloroform, 1,1,1-TMchloroethane,
TMchloroethylene, Tetrachl oroethylene and Other Chlorinated Compounds 1n
Dilute Aqueous Solutions. Environ. Sci. Tech. 9:833-838.
Donigian, A.S., T.Y.R. Lo, and E.W. Shanahan. 1983. Rapid Assessment of Potential
Ground-Water Contamination Under Emergency Response Conditions. EPA Report.
Friedel, R.A. and M. Orchin. 1951. Ultraviolet Spectra of Aromatic Compounds.
John Wiley i Sons, New York.
Gilbert, P.A, and C.M. Lee. 1980. Biodegradation Tests: Use and Value. In:
BiotransformatIon and Fate of Chemicals In the Aquatic Environment, ed. A.W.
Mak1, K.L. Dickson and J. Cairns, Jr. American Society for Microbiology,
Washington, D.C. pp. 34-45.
Glasstone S. 1946. The Elements of Physical Chemistry. 0. Van Nostrand, New York.
-137-
-------
HambMck, G.A., R.D. >Laune and W.H. Patrick, Jr. 1980. Effect of Estuarine
Sediment pH and -
-------
Miller, G.C. and R.G. Zepp. 1979. Effects of Suspended Sediments on Photolysis
Rates of Dissolved Pollutants. Water Research 13:453-459.
Mills, W.B., V. Kwong, L. Mok, M.J. Ungs. 1985. Microcomputer Methods for Toxicants
in Groundwaters and Rivers. Proceedings of National Conference on Environmental
Engineering. Boston, MA.
Mills, W.B. 1981. Workshop Notes: Screening Analysis of Toxic and Conventional
Pollutants in Rivers. Washington, D.C.
Moon, P. 1940. Proposed Standard Solar-Radiation Curves for Engineering Use. JFI
230 (1379-23).
Paris, D.F., W.C. Steen, J.T. Barnett, and E.H. Bates. 1980. Kinetics of Degradation
of Xenobiotics by Microorganisms. ACS, Division of Environmental Chemistry
20(2):55-56.
Paris, D.F., W.C. Steen, G.L. Baugnman, and J.T. Barnett, Jr. 1981. Second-order
Model to Predict Microbial Degradation of Organic Compounds 1n Natural Waters.
Applied and Environmental Microbiology 41(3):603-609.
Park, R.A., C.I. Connolly, J.R. Albanese, L.S. Clesceri, G.W. Heltzman. H.H. Herorandson,
8.H. Indyke, J.R. Loehe, S. Ross. D.O. Sharma, and U.W. Shuster. 1980. Modeling
Transport and Behavior of Pesticides and Other Toxic Organic Materials in
Aquatic Environments (PEST). Report No. 7. Center for Ecological Modeling,
Renselaer Polytechnic Institute, Troy, NY. 163 pp.
Pavlou, S.P. 1980. Thermodynamlc Aspects of Equilibrium Sorptlon of Persistent
Organic Molecules at the Sediment-Seawater Interface: A Framework for Predicting
Distributions in the Aquatic Environment. In: Contaminants and Sediments,
Volume 2, ed. R.A. Baker. Ann Arbor Science, Ann Arbor, MI. pp. 323-332.
Perry, R.H., and C.H. Chi Hon. 1973. Chemical Engineers Handbook. McGraw-Hill,
New York.
Pitter, P. 1976. Determination of Biological Degradability of Organic Substances.
Water Research 10:231-235.
Rathbun, E.R. and D. Y. Tal. 1983. Gas F11m Coefficients for Streams. Journal of
Environmental Engineering Division, ASCE. Volume 109, No. 5. pp. 1111-1127.
Rathbun, R.E., and D.Y. Tal. 1981. Technique for Determining the Volatiliza-
tion Coefficients of Priority Pollutants In Streams. Water Research 15(2).
Reinbold, K.A., J.J. Hassett, J.C. Means, and W.L. Banwart. 1979. Adsorption
of Energy-Related Organic Pollutants: A Literature Review. Environmental
Research Laboratory, U.S. Environmental Protection Agency, Athens, Georgia.
178 pp. EPA-600/3-79-086.
Robinson, N., ed. 1966. Solar Radiation. Elsevier Publishing, Amsterdam, London,
and New York. 347 pp.
Rossi, S.S., and H.H. Thomas. 1981. Solubility Behavior of Three Aromatic Hydrocarbons
in Distilled Water and Natural Seawater. Environmental Science and Technology
15(6):715-716.
Roubal , G., and R.M. Atlas. 1978. Distribution of Hydrocarbon-Utll 1z1ng M1croorgan-
isms and Hydrocarbon Biodegradation Potentials in Alaskan Continental Shelf
Areas. Applied and Environmental Microbiology 35(5):897-905.
Sadtler Research Laboratories. Sadtler Standard Spectra: Ultraviolet Spectra.
-139-
-------
Schnltzer, M. 1971. In: Soil Biochemistry, Volume 2. Marcel DeKker, New York.
Schnoor. J.L. 1981. Assessment of the Exposure, Fate and Persistence of Toxic
Organic Chemicals to Aquatic Ecosystems 1n Stream and Lake Env1 ror-ents. Part I.
Lecture notes. Water Duality Assessment of Toxics and Conventional Pollutants in
Lakes and Streams, a workshop sponsored by U.S. Environmental Protection Agency,
June 23-25, 1981 In Ar''igton, Virginia. C1v1l and Environmental Engineering,
Energy Engineering Oiv ion. University of Iowa, Iowa City, IA. 31 pp.
Shamat, N.A.. and W.J. Maier. 1980. Kinetics of Biodegradatlon of Chlorinated
Organics. Journal Water Pollution Control Federation 52(8):2158-2166.
Singer, S.J., and G.L. Nicholson. 1972. Science 175:723.
Smith, J.H., D.C. Bomberger, Jr., and D.L. Haynes. 1981. Volatilization Rates of
Intermediate and Low Volatility Chemicals from Water. Chemosphere 19:281-289.
Smith, R.C.. and K.S. Baker. 1978. Optical Classification of Natural Waters,
Limnology and Oceanography 32(2):260-267.
Smith, R.C., and K.S. Baker. 1981. Optical Properties of the Clearest Natural
Haters (200-800 nm). Applied Optics 20(2):177-184.
Smith, R.C., and J.E. Tyler. 1976. Transmission of Solar Radiation into Natural
Waters, Photochemical and Photoblologlcal Reviews, Volume 1, ed. K.C. Smith.
Plenum Press, New York. pp. 117-155.
Spain, J.C., P.M. Prltchard, and A.W. Bourquln. 1980. Effect of Adaptation on
Biodegradation Rates in Sediment/Water Cores from Estuarlne and Freshwater
Environments. Applied and Environmental Microbiology 40(4):726-734.
Steen, W.C.. O.F. Paris, and G.L. Baughman. 1980. Effect of Sediment Sorption
on Microbial Degradation of Toxic Substances. In: Contaminants and Sediments.
Volume 1, ed. R.A. Baker. Ann Arbor Science, Ann Arbor, MI. pp. 477-482.
Stumm, W., and J.J. Morgan. 1981. Aquatic Chemistry, 2nd edition. John Wiley
and Sons, New York. 780 pp.
Tabak. H.H., S.A. Quave. C.I. Mashni, and E.F. Barth. 1981. Biodegradabil Ity
Studies with Organic Priority Pollutant Compounds. Journal Water Pollution
Control Federation 53(10):1503-1518.
Tiedje, J.M. 1980. Fate of Chemicals in the Aquatic Environment: Case Studies,
81 otransformation and Fate of Chemicals in the Aquatic Environment, ed. A.W.
Mak1, K.L. Oickson, and J. Cairns, Jr. American Society for Microbiology,
Washington, D.C. pp. 114-119.
Tlnsley. I.J. 1979. Chemical Concepts in Pollutant Behavior. A Wiley - Inter-
science Publication. John Wiley and Sons, New York. 265 pp.
Turro, N.J. 1978. Modern Molecular Photochemistry. Benjamin/Cunnings Publishing,
Menlo Park. CA. 628 pp.
U.S. Department of Commerce. 1968. Climatic Atlas of the United States. U.S.
Department of Commerce, Environmental Sciences Services Administration, Environ-
mental Data Service, Washington, D.C.
U.S. Environmental Protection Agency. 1976. Quality Criteria for Water.
U.V. Atlas of Organic Compounds, Volumes 1-4. 1966-1971. Collaboration of Photo-
electric Sptctromttry Group, London, and Institut Fur Spektrochemie und Angewandte
Spektroskople, Dortmund. Plenum Press, New York.
-140-
-------
Ward, O.M., and T.D. Brock. 1976. Environmental Factors Influencing the Rate of
Hydrocarbon Oxidation in Temperate Lakes, Applied and Environmental Microbiology
31(5):764-772.
Weast, R.C., and M.J. Astle, ed. 198U. CRC Handbook of Chemistry and Physics.
CRC Press, Boca Raton, FL.
Wetzel , R.G. 1975. Limnology. U.B. Saunders, Philadelphia, PA. 743pp.
Wodzinski, R.S., and 0. Bertollni. 1972. Physical State in Which Naphthalene
and Bibenzyl Are Utilized by Bacteria. Applied Microbiology 23(6):1077-10bl.
Wolfe, N.L., R.G. Zepp, and O.F. Paris. 1978. Carbaryl, Propham, and Chloro-
propham: A Comparison of the Rates of Hydrolysis and Photolysis with the
Rate of Biolysis. Water Research 12:565-571.
Wolfe, N.L. 1980. Determining the Role of Hydrolysis In the Fate of Organics
in Natural Waters. In: Dynamics, Exposure, and Hazard Assessment of Toxic
Chemicals, ed. R. Hague. Ann Arbor Science, Ann Arbor, MI.
Wolfe, N.L. 1981. Personal Communication.
Zepp, R.G. 1977. Singlet Oxygen in Natural Waters. Nature, Volume 67.
Zepp, R.G. 1978. Quantum fields for Reaction of Pollutants in Dilute Aqueous
Solution. Environmental Science and Technology 12(3):327-329.
Zepp, R.G. 1980. Assessing the Photochemistry of Organic Pollutants in Aquatic
Enviroments. In: Dynamics, Exposure, and Hazard Assessment of Toxic Chemicals,
ed. R. Haque. Ann Arbor Science, Ann Arbor, MI. pp. 69-110.
Zepp. R.G., G.L. Baughman, and P.F. Schl otzhauer. 1981a. Comparison of Photochemical
Behavior of Various Humic Substances in Water: I. Sunlight Induced Reactions of
Aquatic Pollutants Photosensitized by Humid Substances. Chemosphere 10:109-117.
Zepp, R.G., G.L. Baughman, and P.F. Schlotzhauer. 1981b. Comparison of Photochemical
Behavior of Various Humic Substances in Water: II. Photosensitized Reactions.
Chemosphere 10:119-126.
Zepp, R.G. and D.M. Cline. 1977. Rates of Direct Photolysis In Aquatic Environment.
Environ. Sci. Tech. 11:359-366.
Zepp, R.G., and P.F. Schlotzhauer. 1979. Photoreactlvlty of Selected Aromatic
Hydrocarbons in Water. In: Polynuclear Aromatic Hydrocarbons, ed. P.W. Jones
and P. Leber. Ann Arbor Science, Ann Arbor, MI. pp. 141-158.
Zepp, R.G., and P.F. Schlotzhauer. 1981. Comparison of Photochemical Behavior
of Various Humic Substances in Water: III. Spectroscopic Properties of
Humic Substances, Chemosphere 10(5):479-486.
Zepp, R.G., N.L. Wolfe, J.A. Gordon, and G.L. Baughman. 1975. Dynamics of 2,4-0
Esters 1n Surface Waters. Environmental Science and Technology 9(13):1144-1150.
-141-
-------
CHAPTER 3
WASTE LOADING CALCULATIONS
3.1 INTRODUCTION
Receiving water bodies are subject to waste loads from point sources, nonpolnt
sources and atmospheric deposition. Point sources are identifiable discrete dis-
charges from municipal, institutional Jid industrial waste water collection and
treatment systems. Nonpoint sources (also known as diffuse or distributed sources)
are associated with land drainage which enters a water body through dispersed and
often poorly.defined pathways. Atmospheric waste loads are chemicals and paniculate
matter which settle from the atmosphere or are scavenged by precipitation. These
distinctions are not absolute. For example, municipal waste water may be applied to
the land and become nonpolnt source pollution in runoff and percolation. Similarly,
chemicals in precipitation may become a portion of a nonpolnt source runoff load.
This chapter describes computational methods or "loading functions" for esti-
mating waste loads to both surface waters and aquifers. These methods share several
attributes:
• Required computations are relatively straightforward.
• Necessary data for the functions are generally available. Much of these
data are provided in this chapter.
• Notwithstanding computational ease and data availability, use of the
functions is not trivial. Considerable Information regarding the
physical characteristics of the study area must often be compiled.
• The accuracy of loading functions Is not high. In general, the best
results are obtained when input parameters are based on local pollutant
data, such as chemical concentrations in sediment, runoff and wastewater.
The loading functions presented 1n this chapter are appropriate for water
quality screening studies in which the approximate magnitudes of waste loads are
needed. In situations requiring higher precision, waste loads must be based on
monitoring programs and detailed process modeling.
Tie chapter places major emphasis on nonpolnt sources. Point source loads
can often be obtained from available water quality monitoring data. Atmospheric
loads are also best determined by monitoring; reliable computational methods are
not available to handle such major problems as acid rain. By contrast, monitoring of
nonpoint sources 1s often infeaslble and as a result, a number of procedures have
been developed and tested for calculation of nonpolnt source loads.
3.2 BACKGROUND POLLUTION LOADS
Background water quality "represents the chemical and biological composition
of surface waters which would result from natural causes and factors" (Novotny and
-142-
-------
Chester*, 1981). A comparable definition could be given for groundwater. The
concept of background water quality or pollution is somewhat artificial. Few, if
any, water bodies in the United States remain unaffected by human activity. For
example, synthetic organic compounds are routinely found in streams and lakes far
from any obvious source. In spite of this ambiguity, estimation of background
loads is a useful component of water quality planning. These loads represent
a baseline or minimum level of water pollution wnich cannot be eliminated by local or
area-wide water quality management.
Background pollution levels can be measured by water quality sampling of surface
waters in upstream portions of watersheds which are free of human activity and in
aquifers in undeveloped areas. In the absence of such local data, very crude esti-
mates can be determined from the Information given in Figures III-l, 2, and 3. These
figures show mean surface water concentrations of selected water quality parameters
obtained from the U.S. Geological Survey's Hydrologic Benchmark Network (McElroy e£
al., 1976). the concentrations are based on water quality samples from 57 monitoring
locations considered free of human disturbance. More accurate concentration data for
nutrients are available from the U.S. National Eutrophicatlon Survey (Omernik, 1977).
Nitrogen and phosphorus concentrations 1n streamflow are grouped according to land
use and location 1n Figures III-4 and 5. Concentrations for the "90% Forest" category
can be assumed to represent background concentrations.
Annual mass background loads to surface waters are obtained by multiplying
concentrations by streamflow values. Average annual streamflow values for the United
States are shown in Figure III-6. Obviously, when local streamflow data are available
they are preferable to the regional values given in Figure III-6.
EXAMPLE III-l
Background Loading Estimates
Determine the annual background loads of BOO and total phosphorus from
a 50 km2 watershed In northern Illinois.
Solution:
From Figure Ill-l(b), background BOD concentration Is 3.0 mg/1 1n northern
Illinois. Total phosphorus concentrations can be determined from the National
Eutrophicatlon Survey data In Figure III-5. Northern Illinois Is 1n the eastern
area shown 1n Figure 111-5, and the total phosphorus concentration for the 90
percent Forest category 1s 0.011 mg/1. Average annual streamflow for the area Is
10 in (Figure III-6) or 0.254 m.
-143-
-------
(A)
(B)
(C)
(D)
FIGURE II1-1 BACKGROUND CONCENTRATION (MG/L) OF (A) NITRATE-NITROGEN, (B) BOD,
(C) TOTAL PHOSPHORUS AND (D) DISSOLVED SOLIDS (MC£LROY EI AL, 1976)
-144-
-------
'I
(A)
FIGURE III-2 BACKGROUND LEVELS OF (A) pH, (B) SUSPENDED SEDIMENT (MG/L>
(C) TOTAI COLIFORMS (MPN/100 ML) AND (U) SULFATE (MctLKOY EJ AL, 1976)
-145-
-------
(A)
(B)
(0
(D)
FIGURE II1-3 BACKGROUND CONCENTRATIONS OF (A) CHLORIDE (MG/L), (B) IRON + MANAGNESE (UG/L),
(C) TOTAL HEAVY METALS (UG/L) AND (D) ARSENIC + COPPER + LEAD + ZINC (UG/L)
(McELROY EI AL, 1976)
-146-
-------
Foreil
£75%
For««l
>SO%
Forest
£50*
Agriculture
>7S%
Agriculture
>90%
Agriculture
4S
I*
40
land Us* by Region
VI.
Mean Tola! Nitrogen and Mean
Inorganic Nitrogen Stream Concentration*
Del* lt»m 730 '
wo(««iK«di
ill*
) t •»•
1741
3005
.^•.'.•.1 I.V3I
1.t31
7.344
in»i§«nic nil>af*n
-------
Forott
>7S%
Forort
>50%
Forott
> 50%
Agriculture
Agriculture
Agriculture
land U«e by Region
VI.
M«on Total fhotpharu* and Moon
OrthopKotphoru< Stroom Concontrotjon*
Mi*
Milligram* p*r Liter
FIGURE 111-5 RELATIONSHIPS BETWEEN STREAHFLOW PHOSPHORUS CONCENTRATIONS AND LAND USE FROM
THE NATIONAL EUTROPHICATION SURVEY (OMERNIK, 1977)
-148-
-------
FIGURE 111-6 AVERAGE ANNUAL STREAMFLOH IN INCHES (!IN
(LANGBEIN EJ AL, 1949)
- 2.54CM )
-149-
-------
I Noting that:
( 1 mg/1 « 0.001 fcn/<«3
I 1 *m2 . I06m2
j annual runoff is
I 0.254 (5
I Background loads are
I BOD:
I Phosphorus: 0.011(0.001)(12.7) 106 - 140
i
'— ——— — END OF EXAMPLE III-l
0.254 (50) 106« 12.7 106m3.
BOD: 3 (0.001)(12.7) 106 • 38,100 kg/yr
3.3 NONPOINT SOURCE MODE.
The nonpolnt source loading process 1s Illustrated 1n Figure III-7. Precipi-
tation, in the form of rain or snowmelt. comes 1n contact with a "waste" product
located on the land surface or within the soil. Portions of the waste are trans-
ported In runoff and percolation to streams and groundwater aquifers. Nonpolnt
source wastes are any potential pollutant which comes 1n contact with drainage.
Examples Include chemicals 1n urban dust and road Utter, agricultural fertilizers,
pesticides and animal manures, road de-1dng salts, sanitary landfill wastes, eroded
soil, mining slag piles, septic tank effluents, lawn chemicals and toxic wastes 1n
lagoons and land disposal facilities.
Nonpolnt source pollution 1s associated with random hydrologlc events. Combined
with the dispersed nature of drainage patterns, this randomness produces waste loads
-nich are difficult to monitor, and hence most loading estimates are obtained from
mathematical models. The foundations of all nonpolnt source models. Including the
loading functions discussed herein, are equations to predict water movement, especi-
ally runoff and percolation. These equations are supplemented by methods to calcu-
late sediment movement, and together the two components describe nonpolnt source
transport, since pollutants are either dissolved in a water flux or attached to
sediment. The third model component 1s a procedure to estimate the dissolved and
solid-phase (sediment-attached) concentrations of the pollutant. In the loading
functions, these concentrations are obtained empirically or derived from simple mass
balances. In more analytical, and hence complex models, concentrations are obtained
from mechanistic descriptions of chemical and biological processes.
Both average annual and single event loads can be estimated by nonpolnt source
loading functions. The former are useful when the effects of pollution are determined
by long-term mass loads to a water body. Groundwater quality problems are often of
this type. Also, several simple lake eutrophlcatlon models require annual phosphorus
loads as Input. Conversely, major storm events exert the most significant Impacts on
streams and rivers, and estimates of single event nonpolnt source loads are necessary.
-150-
-------
PRECIPITATION
GROUNDWATER
GROUNDWATER
DISCHARGE
FIGURE 1 1 1-7 THE NONPOINT SOURCE LOADING PROCESS
The most comprehensive estimates of nonpolnt source loads are obtained from
continuous simulation models such as HSPF, STORM, SUMH and CREAMS which have been
developed under sponsorship of the U.S. Environmental Protection Agency, Army Corps
of Engineers and Department of Agriculture. Since these models require computer
facilities and extensive data structures, they are beyond the scope of this manual.
Nevertheless, the simulation models are based on the same computational concepts
presented In this chapter, particularly those used for single event loading functions,
Succeeding sections of this chapter present loading functions for rural runoff,
Irrigation return flows, urban runoff and groundwater.
3.4 RURAL RUNOFF LOADS
Nonpolnt source waste loads to surface waters In rural areas include runoff
from cropland (including pasturs and range), forests, barnyards and feed lots, waste
land application and storage facilities, construction sites and mining operations.
Cropland and forest runoff are emphasized in this section, since these nonpolnt
sources are widespread, and their associated loading functions have been most ex-
tensively developed. Runoff loads from the other sources can in principle be esti-
mated by procedures similar to the loading functions used for cropland and forest,
but data are often lacking to implement the calculations.
3.4.1 Source Areas
Nonpolnt source waste loads in runoff can be estimated for several different
spatial scales. The most fundamental unit of analysis is a source area, which is a
land area wit* sufficiently homogeneous soil and pollutant characteristics so that
-151-
-------
runoff loads can be considered uniform. A farmer's field Is often considered a
single source area and associated runoff loads are sometimes referred to as "edge-of-
Meld" loads. Larger scales of analysis consist of aggregations of source areas or
watersheds. Waste loads are transported from source areas by rivulets, ditches,
streams and other drainage paths to eventually exit the watershed 1n streamflow.
During this transit, portions of the wastes may be removed from the water flux
by settling, adsorption, filtering or biochemical processes. The total watershed
waste load 1n streamflow consists of these attenuated runoff sources plus waste loads
from groundwater discharge.
Pollutants 1n runoff may be 1n dissolved and solid-phase forms, with the latter
consisting of participate material, or pollutants that are attached to sediment. The
general loading function forms are:
Dissolved
pollutant
waste load
Runoff water
volume
Dissolved
pollutant
concentration
(III-D
Solid-phase m Sediment Solid-phase pol-
pollutant " flux x lutant concentration
wasteload (concentration in
sediment)
(Hl-2)
Sections 3,4.2. 3.4.3 and 3.4.4 describe methods for computing runoff volumes,
sediment flux and pollutant concentrations, respectively.
3.4.2 Runoff
3.4.2.1 SCS Curve Number Equation
The U.S. Soil Conservation Service's curve number equation (CNE) 1s a standard
procedure for estimating storm runoff (Mockus, 1972; Ogrosky and Mockus, 1964). The
equation 1s:
Q • (P-0.2S)2/(P+0.8S) for P _> 0.2S (III-3)
where
Q • runoff (cm)
P • precipitation (rainfall * snowmelt, cm)
S • water retention parameter (cm).
The 0.2S 1s an Initial precipitation abstraction, and hence 1f P < 0.2S,
Q 1s assumed to be zero.
The retention parameter S 1s computed from dlmenslonless curve numbers (CN)
which are functions of soils, cover, management and antecedent moisture:
-152-
-------
S - (2540/CN) - 25.4 (III-4)
The general form of the equation is shown 1n Figure 111-8.
Although the CNE 1s most frequently applied to rainfall runoff, it may be used
for snowmelt conditions. SnoMnelt water can be estimated by the degree-day equation:
M » 0.45T (III-5)
where
M » snowmelt water (cm)
T » mean air temperature (°C).
If T<0, M«0. Also, M must not exceed the water content of the accumulated snowpack.
The degree-day factor (0.45) 1s an average value (Stewart £t a]_., 1976) and should Of
replaced by a location-specific value when available.
Since dally weather data are used for Equations III-3 and III-5. calculated
runoff is the total runoff for a specific day.
3.4.2.2 Curve Number Selection
Curve numbers describe the hydrologic condition of land surface at the
time of a precipitation event. The combined effects of soils, management and cover
are shown 1n Table III-l for "average" antecedent moisture conditions. Most soils in
the United States have been classified In one of four hydrologic groups. Listings
are available in Mockus (1972) Ogrosky and Hockus (1964) and Soil Conservation
Service (1975). The qualifiers "good," "fair" or "poor" in Table III-l indicate the
extent to which cover and soil management conditions will minimize runoff. For
example, continuous growth of a corn silage on the same site every year will deplete
soil organic matter and encourage runoff. Conversely, corn grain In a rotation with
hay or under no-till conditions will minimize runoff. Similarly, clear-cutting
of woods accompanied by extensive disturbance of the soil surface by log skidding is
a "poor" management practice.
The "woods" category In Table III-l may be used for vegetated forest areas.
Runoff for roads, logging trails and landings should be based on curve numbers for
the "roads and right-of-way" category. Those curve numbers are also appropriate
for construction sites.
The fourth, and most Important factor in curve number selection is the wetness
of the soil. If precipitation falls on soil that has been Inundated by previous
storms, Infiltration Is much less and runoff Is much greater than would be the case
for dry soil. Three different antecedent moisture conditions are specified for the
CNE: I (dry), II (average), and III (wet). Antecedent moisture is approximated by
the five-day antecedent precipitation, which Is the total precipitation (rain -f
-153-
-------
HYDROLOGY: SOLUTION OF RUNOFF EQUATION
PI 0 lo H incfet*
Q«0 lo 6 inch«t
4 » « 7
RAINFALL (P) IN INCHES
, Vktoi;
«tMl raMll
TMtelM« (toll,
|* Ira* ilva
Mi
mm. ooM0*«tMN mva
IS KWI
FIGURE I]1-8 SCS CURVE NUMBER RUNOFF EQUATION din - 2.54CM)
-154-
-------
TABLE III-l
RUNOFF CURVE NUMBERS FOR AVERAGE ANTECEDENT MOISTURE CONDITIONS
(Mockus, 1972)
Lar; ,se
or -;jver
Fl'.'.C*
Rcw crcos
Sr-a 1 1 grjin
Close-seeded
leg-res :r
ro:j; • :i
meadow
Past-re or
ran;e
Measow
woe as
Far-steads
Roads and
(hara sjr'ace,
•So-l V-.;
A
~reat.~ent Hydro ICOtC
Stra;:it row
St'iTpt row Peor
Strji;-t row Gocc
Cc^tr-red Poor
r»^»~ ra* Gc'""
Terriced Poor
'er'ace: Good
St'j'cnt row Poor
S:r>-:.-t row Good
Cc"tc-red Poor
r;r;;v,--ed GcOd
Terraced Poor
Terrjces Gooc
Strai;-t row Poor
Strjig-: row Good
Co"t:.red Poor
Cor:;jr»a Gocc
Ter'jced Poor
Ter'jceo Gooc
Poor
Fair
Good
Cort:-red Poor
Cert;. red Fair
Cor,:;, red Good
Good
Poor
Fair
Good
—
--
Oe<
Lowest Rjnoff Potential: Includes
A
77
72
67
70
65
66
62
65
63
63
61
61
59
66
53
64
55
63
51
66
49
39
47
25
6
30
45
36
25
59
74
if'sti;"
deep sanos
^rc,c=:
8
86
81
78
79
75
74
71
76
75
74
73
72
70
77
72
75
69
73
67
79
69
61
67
59
35
58
66
60
55
74
84
xi tn ve
C
?:
S3
35
a:
82
3C
73
31
33
32
a:
79
78
85
3;
83
76
ec
76
36
79
74
81
75
70
71
77
73
70
82
90
ry 1 it:le silt
0
;-
91
3?
£i
3-
82
81
88
37
Ss
s:
s:
89
85
85
S3
S3
3C
3?
3-
ec
SS
63
79
75
S3
79
77
86
92
and :'j/
also deep, rapidly permeable loess.
Moderately Low Runoff Potential: Mostly sandy soils less sees '."jn -V. tri
loess less ceep or less aggregated than «. out tne grcus is a «r-c'^ 145 a:;v
averagc infiltration after tnorougn wetting.
ely mgn Punof Potentijl: Cor-or^ej shall;- soils
rao^e ciJy jnc col'Dus. '.noLin less :njn :nose sf »
has telflw-avcrjge inf 1 1 fat:cn after oresJl.rj tion.
H'g-e?'. Puno" foten:-ai; Inc'.ces -ost'y ;;a>s of 115" s^I'1'-: ;er cs*1
tne 5':^o alio 'nc'.Jes s:r» sruiiow soils -i;n oeir!/ im^e-'-Bei^'e Sjt"r
near ;r.c sur'jce
-155-
-------
snowmelt) 1n the five days preceding a storm. Approximate limits for the three
antecedent moisture conditions are given In Table III-2. Different limits are
specified for growing and dormant season since evapotransplration dries the soil much
more rapidly during the growing season. In absence of more specific Information, the
growing season may be assumed to consist of months for which average air temperature
1s 10*C or above. Antecedent precipitation Is an Inadequate criterion during snowjnelt,
however, and for such events condition III 1s always assumed (Ha1th and Tubbs,
1981).
The curve numbers for condition II, or CN2 are given In Table III-l. The curve
numbers for the other two conditions, I and III respectively, can be obtained from
the equations given by Hawkins (1978):
CN1 • CH2/(2.334-0.01334CN2) (III-6)
CM3 - CN2/(0.4036 + 0.0059CH2) (IH-7)
TABLE 111-2
ANTECEDENT MOISTURE LIMITS FOR CURVE NUMBER SELECTION
(Ogrosky and Mockus, 1964)
5-Day Antecedent
Antecedent Precipitation
Moisture Condition (cm)
Dormant Growing
Season* Season
I <1.3 <3.6
II 1.3-2.8 3.6-5.3
III >2.8 >5.3
* During snowmelt, condition III 1s always assumed
regardless of antecedent precipitation.
-156-
-------
EXAMPLE 111-2 • —
Cropland Runoff
A three-day rainstorm falls on a 30-ha soybean field during early August.
The crop is continuously grown (no rotation) In straight rows. The soil 1s in
hydrologic group B. Pie relevant prec'oitation 1s as follows:
Date August 123456789
Rain (cm) 0 0 0 0 0 3.8 5.1 0.3 0
Determine the runoff from this stonm.
Solution:
The crop 1s a row crop planted 1n straight rows and In poor hydrologic
condition. From Table III-l, the curve number for condition 2 Is CN2 • 81 for
soil group B. Solving Equations III-6 and III-7 for CN1 and CN3, we have
CN1 - 64.6 and CN3 - 91.9.
The three-day storm begins on August 6. On that day, 5-day antecedent
precipitation 1s 0; hence the soil Is In the driest antecedent moisture condition.
Thus:
CN • CHI - 64.6
and from Equation 111-4:
S » (2540/64.6) - 25.4
- 13.9 cm.
Since precipitation exceeds initial abstraction, 0.2S « 2.78 cm, runoff occurs as
predicted by Equation III-3:
Q - (3.8-2.78)2/(3.8 + 0.8(13.9))
« 0.07 cm.
On August 7, 5-day antecedent precipitation is 3.8 cm, which during the
growing season corresponds to CN - CN2 - 81 (Table 111-2). Thus:
S • (2540/81) - 25.4
» 5.96 cm.
Rain exceeds 0.2S • 1.19 cm, and
Q - (5.1 - 1.19)2/(5.1 + 0.8(5.96))
- 1.55 cm.
On the final day, 5-day antecedent precipitation 1s 3.8 + 5.1 • 8.9 cm,
CN • CN3 • 91.9 and S - 2.24 cm. Since the 0.3 cm of rain dots not exceed
the Initial abstraction of 0.2 (2.24) « 0.45 cm, no runoff occurs.
-157-
-------
I The storm summary 1s as follows:
I
Day
8/6
8/7
8/6
Total
Rainfall
(cm)
3.8
5.1
0.3
9.2
Runoff
(cm)
0.07
1.55
0
1.62
The 1.62 cm of runoff over the 30-ha field can be converted to runoff
3 2 I
volume (m ) by noting that 1 ha • 10,000 • , and hence 1 cm on 1 ha • 0.01
(10,000) • 100 m3. This runoff volume Is 1.62(30){100) • 4660 m3. (
This example Illustrates three Important characteristics of runoff: |
e Runoff 1s a nonlinear function of precipitation; I.e., runoff 1s not I
a constant portion of precipitation. |
• Runoff 1s generally a small fraction of precipitation, particularly j
during the growing season. '
• Runoff 1s dramatically dependent on antecedent moisture conditions.
I END OP EXAMPLE III-2
3.4.2.3 Annual Runoff
The CNE 1s only applicable to Individual storm events, and this 1s a limitation
in nonpoint source studies for which annual taste loads are required. In such cases
annual runoff estimates are necessary. The only My to produce such estimates 1s to
use Equation 111.3 to calculate runoff for each storm 1n a year, and sum the
resulting values for the year. If an average annual runoff Is needed, the process
must be repeated for each of a number of years. The repeated use of Equation 111.3
for all storms 1n a multi-year period 1s not difficult (see for example Ha1th and
Tubbs, 1981), but 1t 1s a continuous simulation modeling process that can only
be Implemented on a computer.
Average annual runoff for row crops has been calculated by Stewart jet ^K (1976)
for the eastern united States. A simulation model based on the CNE was run using
10-25 years of dally weather data from 52 locations. The simulation runs were based
on straight row com 1n good hydrologlc condition on the four different soil groups.
Fallow or bare soil conditions were assumed during the spring. Results of the
simulations are shown 1n Figure III-9. The four soil groups correspond to CM2 • 67,
78, 85 and 89. These runoff values should generally be appropriate for any row
crop. Runoff for situations with curve numbers falling between any two curve numbers
can be determined by linear Interpolation.
-158-
-------
(A)
(B)
(C)
(D)
FIGURE 111-9 MEAN ANNUAL Row CROP RUNOFF IN INCHES
FOR SELECTED CURVE NUMBERS, A; CN2-67,
B: CN2-78; C: CN2-85; D: CN2-89, (1 IN
- 2.54CM) (STEWART A, 1976)
-159-
-------
3.4.2.4 Watershed Runoff
Runoff from a source area such as a farmer's field or logging road 1s given by
Equation I[1-3. Runoff from an entire watershed 1s the sum of runoff from all
source areas within the watershed. If we define:
(L • runoff from source area k (cm)
AK » area of source area k (ha)
AT - total watershed area (ha)
a^ » fraction of watershed covered by source area k • A^/AT
then watershed runoff Q(cm) 1s:
0 • Z \ Qk (III-8)
Watershed runoff volume V(nr) 1s:
V - 100 Z \ Qk
- 100 ATZ a. QL (IH-9)
k *
Equation 111-8 or 111-9 require computation of runoff Qfc from each
source area. An alternative and simpler procedure 1s to determine a weighted average
curve number:
CN - z \ CN. (111-10)
k * K
and compute watershed runoff directly from Equations 111-3 and 111-4. In Equation
111-10 CN. 1s the curve number for source area k.
The second procedure (average curve number) generally produces slightly lower
watershed runoff estimates than Equation 111-8 due to the nonlinear nature of the
CNE. In any case, note that watershed runoff 1s only one component of streamflow.
Additional components Include groundwater discharge and point sources.
3.4.3 Erosion and Sediment
Erosion 1s the rwoval of soil particles by wind and water, and sediment Is the
partlculate matter which 1s carried and eventually deposited by wind and water. Our
concern here 1s with water pollution, and the prediction of sediment loads or yields
1n streamflow. Upstream erosion of soil surfaces and stream channels 1s the source
of streamflow sediment yields. However, watershed sediment yield, as measured 1n
streamflow at the outlet of the watershed, 1s generally substantially less than the
-160-
-------
total upstream erosion since much of the transported sediment has been deposited
or filtered from the water. Near a sediment source, nearly all eroded soil becomes a
sediment mass flux. For example the sediment yield 1n runoff from a corn field is
approximately equal to the eroded soil mass from the field. However, as the runoff
travels from the field In drainage ditches and stream channels, portions of the
sediment are removed, until only a fraction remains to exit the watershed.
Erosion of the land surface by sheet and rill erosion is the major source of
solid-phase pollutants In surface waters, and most of this section is accordingly
devoted to prediction of this sediment source. Although channel erosion may also be
a significant component of sediment yield, 1t Is not generally considered a pollution
hazard and will not be considered In the following discussion.
3.4.3.1 The Universal Soil Loss Equation
The Universal Soil Loss Equation (USLE) 1s an empirical equation which was
developed to predict average annual soil loss by sheet and rill erosion from source
areas (Wischmeier and Smith, 1978). The equation, which was obtained by statistical
analyses of over 10,000 plot-years of erosion field research data Is:
X - 1.29 E(K)(ls)C(P) (III-ll)
where
X • soil loss (t/ha; 1 t « 1 tonne - 1000 kg - 2205 1b)
E • rainfall/runoff eroslvlty Index (10 m-tonne-cm/ha-hr)
K » soil credibility (t/ha per unit of E)
Is « topographic factor
C - cover/management factor
P > supporting practice factor.
The three factors Is, C. P are dimensionless. The 1.29 1s a conversion constant to
obtain metric units.
The USLE Is an Important component of loading functions for runoff waste loads
because Its parameters have been evaluated for a wide range of conditions and many
Important pollutants are transported on eroded soil. For example, most organochloHne
pesticides are very strongly adsorbed to soil particles. Procedures for determining
the USLE parameters are presented In the following subsections.
3.4.3.1.1 Rainfall/Runoff Eroslvlty
The eroslvlty term E 1s related to rainfall intensity. Average annual values
for the united States have been computed by U1schme1er and Smith (1978) and are given
in Figures 111-10 and 11. The values of E 1n these figures are In English units
(102 ft-tons-1n/ac-hr) and can be converted to the metric units of Equation
III-ll by multiplying by 1.735; I.e. E (metric) - 1.735 E (English, Figures 111-10,
-161-
-------
FIGURE 111-10 AVERAGE ANNUAL EROSIVITY INDICES (ENGLISH UNITS)
FOR EASTERN U.S. (WISCHMEIER AND SMITH, 1978)
-16?-
-------
W.H. WischiMicr. SEA. 1976
FIGURE Ill-ll AVERAGE ANNUAL EROSIVITY INDICES (ENGLISH UNITS)
FOR WESTERN U,S, (WISCHMEIER AND SMITH, 1978)
-163-
-------
11). For example the erosivlty for northern Maine Is E » 1.735 (75) « 130.
It can be seen from Figure 111-10 that the intense rainstorms of the Southeast
produce the M -*>est levels of erosivity in the United States. In contrast, erosivity
in much of the western mountain region (Figure 111-11) is less than 10 percent of the
southeast values.
3.4.3.1.2 Soil Erodlblllty
Typical values of K are given in Table III-3 as a function of soil texture and
organic matter content, values for specific soils are available from local Soil and
Hater Conservation Districts and state offices of the Soil Conservation Service.
3.4.3.1.3 Topographic Factor
The topographic factor Is, 1s related to the angle of slope o and slope length x
(m) by:
Is - (0.045x)b (65.41 sin2« * 4.56 sin 9 + 0.065) (111-12)
The slope angle 8 is obtained from percent slope, s by:
9 « tan'l(s/100) UII-13)
in
For example, a slope of s • 8 percent has a slope angle of 6 • 4.6°. The exponent
Equation 111-12 1s given by b - 0.5 for s > 5, b - 0.4 for 3.5 <_ s <_ 4.5, b « 0.3
for 1 <_ s <_ 3, and b « 0.2 for s £ 1 (Wischmeier and Smith, 1978).
Research data support Equation 111-12 for x <_ 100 m and s <_ 18. although In
practice H is often applied beyond these limits.
3.4.3.1.4 Cover/Management Factor
The covtr/managment factor C describes the protection of the soil surface by
plant canopy, crop residues, mulches, etc. The maximum C value 1s 1.0, corresponding
to no protection. Cropland C values change dramatically during the year 1n response
to planting operation*, "jp growth and harvest. Although C values have been de-
termined for each of these stages (H1schme1er and Smith, 1978). generalized annual
values such as those given 1n Table 111-4 are more suitable for loading functions.
Hischmeler and Smith (1978) have also developed C factors for construction
sites; pasture, range and idle land; undisturbed forests; and mechanically prepared
woodland sites. These C values are given in Tables 111-5 through III-8. Note that
cover factors are so small for undisturbed forest and pasture or range with good
ground cover that these erosion sources can generally be neglected 1n water quality
studies.
-164-
-------
TABLE 111-3
SOIL ERODIBILITY, K
(Stewart et al, 1975)
Texture
Sand
Fine sand
Very fine sand
Loamy sand
Loamy fine sand
Loamy very fine sand
Sandy loam
Fine sandy loam
Very fine sandy loam
Loam
S11t loam
Silt
Sandy clay loam
C1 ay 1 oam
Sllty clay loam
Sandy clay
Sllty clay
Clay
0.5X
0.05
0.16
0.42
0.12
0.24
0.44
0.27
0.35
0.47
0.38
0.48
0.60
0.27
0.28
0.37
0.14
0.25
Organic Matter
2%
0.03
0.14
0.36
0.10
0.20
0.38
0.24
0.30
0.41
0.34
0.42
0.52
0.25
0.25
0.32
0.13
0.23
0.13-0.29
4%
0.02
0.14
0.28
0.08
0.16
0.30
0.19
0.24
0.33
0.29
0.33
0.42
0.21
0.21
0.26
0.12
0.19
-165-
-------
TABLE 111-4
GENERALIZED VALUES OF THE COVER AND MANAGEMENT FACTOR, C,
IN THE 37 STATES EAST OF THE ROCKY MOUNTAINS (Stewart et al , 1975)
nt Qup. rotation, and rruru|emenl ^
no
Bate viluv tominuout faltow, (illcd up and down slope
CORN
1 C. RdR. fall TP. conv (I)
2 C. RdR. ipruif TP. conv ( 1 1
3 C. RdL fall TP. conv ill
4 C. RdR. »i iecdm(. tprinf TP. conv ( 1 1
5 C. Rd L. tundmf . tpnnf TP. conv ( 1 1
6 C. fall trued ttalkt. iprutf TP. com ( 1 )
7 Cltitojc >•» < Rd L. fall TP> 1 2 )
8 C. RdL fall <.h«cl spnn* disk. 40- JOT re 1 1 )
9 Cltibpel. * »x teeding. no-till pi in t-k * III
MI ClRJL>-»(RdL spring TPn2)
1 1 C. (all th/ed -k vkhcat. 9O-70 . r,. il)
21 C-CC W-M-M. no-nil |«I 2d A .ird C(6(
22 C^-M. RdL TPfor C. duk lor » (3)
23 CO* M-M. RdL no-till pi 2d C(5)
24 C-* MM KdL. TPfor C. di»k for Vk 14)
25 l-»-M-M-M. RdL TPfor C. duk tor W(5)
26 C. no-nil pi 01 ^-k vxt. 95-80-4 rt (I )
COTTON4
27 Col. ionv (Vhrtlvrn PUiruM 1 i
28 Col. conv (SoulhHI)
MbALMDW
29 Crau A Lcfumr mix
30 Alfalfa leipodcu or Set leu
3 1 Swvci c luver
SORGHUM. ORAIN (WcMctn PUinv/
32 RdL *pr«f TP. ionv (I)
33 No-ti)l pi m threaded 70-5Ovb rr
Prod urn
H,fl,
C
1 00
0 54
50
42
40
38
35
31
24
20
2U
19
17
16
14
12
1 1
087
076
IfeS
062
061
055
051
U39
032
017
0.42
34
0 INM
02(1
025
043
1 1
ivn> level
Mod
value
IU..
062
$v
52
49
48
44
35
3d
24
2S
>
23
24
2'J
1 '
1 X
14
13
II
14
1 1
095
094
1)74
U6I
05 ^
0 49
40
001
0 53
IK
-166-
-------
TABLE 111-4 (Continued)
ProduLIivid level
Crop, rotation, and management "
no High MoO
4
SOYBt*-NS
34
35
36
3*
VUlbAT
38
39
40
41
4:
43
44
45
46
4"
48
49
B. RdL spring TP. ion* 1 1 )
C-B. TP annuall> . conv I 2(
B. no-iiU pi
C-«. no-till pi. fall silted C sulks 1 2)
W-l . f»ll TP-iller V* (2)
W-l . stubble mukh. 500 Ihs u C)
U-h. nubble mulch. 1000lb»rc Ol
Sptin| U RdL. Sepl TP. .onv ils A S DakHl)
Winter *. RdL. Auf TP. coin (kjnM ( 1 )
Spring U . >iubbk mulch. 750 Ibs re 1 1 )
Spring v.. and is noi t complete list of cropping systemt CK potential practice*. Values of C differ
tilth tjinfill pattern tnd pbniuif dates. These {cneralized values show approximately the relative crouon-redutBif efftcliveneo of
various crop systems, but locanorutly derived C vaJues should be uMd for conKrvation pianninf at the field level. Tables of local
•ilucs axe available from the Soil Conservation Service
' Hjfh level it exemplified by k>n|-icrm yield averages pealer than ?S bu. corn or 3 tons frasv^nd-kfume hay.oi col ton manafe-
meni that regularly provide^ .orn M • grass A legume hay
c-k • chemiully lulled pi - ptanl
conv - conventiorul W -wheat
col -cotton we - winter cover
Ibs re • pounds of crop residue per acre renaming on surface after MW crop seeding
% re • percentage of soil surface covered by residue mukh
-------
TABLE 111-5
C FACTOR VALUES FOR CONSTRUCTION SITES
(W1sctme1er and Smith, 1978)1
is,"
Nan*
Straw or Way,
ti*d dawn by
ancharing and
tacking
•a>pm«nt3
Oa.
CrwiHad it***.
U ta m in
Oa.
Wood chip*
Da.
Oa.
Mulch
r*«f p*r acr*
0
1.0
1.0
1.3
1 J
2.0
2.0
2.0
2.0
2.0
2.0
2.0
135
135
135
135
240
240
240
7
7
12
12
12
25
25
25
25
land
SI***
'•«•<»
all
1-5
6-10
1-3
6-10
1-5
6-10
11-15
16-20
21-25
26-33
34-50
<16
16-20
21-33
34-50
<21
21-33
34-30
<16
16-20
<16
16-20
21-33
<16
16-20
21-33
34-50
Factor
C
1.0
0.20
.20
.12
.12
.06
.06
07
.11
.14
.17
.20
.05
.05
.05
.05
.02
.02
.02
.M
.0*
.05
.05
.05
.02
.02
.02
.02
limit2
*••«
—
200
100
300
130
400
200
150
100
73
30
35
200
150
100
75
300
200
150
75
50
150
100
75
200
150
100
75
fr*«a •*
Oi»»Up»d ky an ialaragancy work*
apaliiatUn rota or
Ida ipoaftodi
Wfcon thfc Co* u ascoorfod.
ilurttning a* HM
•wlcfc b M« a«cnar*d ta KM Mil, C
i!o*o* of laih n«»4ng ^ «•!««
040 iht»ld ba takan al d*i»al« tfca wWvo* gi«M in tfci*
-168-
-------
TABLE 111-6
C FACTOR VALUES FOR PERMANENT PASTURE. RANGE AND IDLE LAND
(W1schme1er and Smith, 1978)1
V«geletive coneoy
Tyi»e e,«d t»n* -•
height' e,». .
No appreciable
canopy
Toll weeds or 25
short brush
with overage
drop foil height 50
•f 20 in
75
Apprecioble brwth 25
or bushes, with
overage drop foil
height of 6tt ft 50
Cover tnot centocfs
Percent
Type*
C
W
C
W
C
W
G
W
C
W
C
W
0
0.45
.45
.36
.34
.24
M
.17
.17
.40
.40
44
.34
30
0.20
.24
.17
.20
.13
.16
.10
.12
.It
M
.16
.19
40
0.10
.15
.09
.13
.07
.11
.04
.09
.09
.14
.01
.13
Ik* toil lurfoCC
oreund
40
0.042
.091
.031
.013
.035
.076
.032
.061
.040
.M7
.031
.012
cover
M
0.013
.043
.013
.041
.012
.039
.011
.031
.013
.042
.012
.041
95+
0.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
.003
.011
75 C .21 .14 .M .036 .012 .003
W .21 .17 .17 .071 .040 .011
Trees, but no 25 G .42 .19 .10 .041 .013 .003
opprvtiobl* low W .42 .23 .14 .019 .042 .011
bruih. Average
drop fall height 50 G .39 .11 .09 .040 .013 .003
•f 13 It W .39 .21 .14 .017 .042 .011
75 C J* .17 .09 .039 .012 .003
W Jo JO .13 .M4 .041 .011
' Til* thtod C *«lvet •UU*M thof the vegetation and mvkch ore
randomly dntribwtod over the entire ore*.
' Canopy height it mooMtrad a* the overoge loll height of water
drop* fading froM the canopy to the grovnd. Canopy effect ii in-
versely proportional lo drop foil height and U negligible If foil
height eiceedi 33 ft.
^Portion of total-area tvrfoce that would be hidden front view by
canopy in a vortical projection (a bird't-eye view).
*G: cover at lurfoce h grott. graulike plants, decoying coav
poctod dwff. or litter at toast 2 in deep.
W: cover at surface is mottly broadleof herbaceous plants (a*
weeds with little lateroUoel network near the surface) or
undecayed residues or bath.
-169-
-------
TABLE 111-7
C FACTOR VALUES FOR UNDISTURBED FOREST LAND
(Wischmeler and Smith, 1978)
Percent of Area
Covered by Canopy
of Trees and
Undergrowth
100-75
70-45
40-20
Percent of Area
Covered by
Duff (litter) at
least 5 on deep
100-90
85-75
70-40
Factor
C
0.0001-0.001
0.002-0.004
0.003-0.009
3.4.3.1.5 Supporting Practice Factor
The supporting practice factor P measures the effect of traditional soil conser-
vation practices on cropland erosion. Values of the P factor are given 1n Table
II1-9. Note that two different types of practice factors apply to terracing. For
example, for a doucle terrace (n-2) on a 6 percent slope, P • 0.5/Y7- 0.35. The
value Indicates the amount of erosion from the soil surface. However, approximately
80 percent of the eroded soil 1s trapped 1n the terraces channel and does not leave
the source area. Hence, for purposes of estimating nonpolnt source loads, the
practice factor Is 0.2(0.35) • 0.07.
3.4.3.2 Single Event Erosion Estimates
Although the USLE was developed for average annual erosion estimates, nonpolnt
source studies often require waste loads for specific storm events. When this 1s the
case, the eroslvlty term E 1n Equation 111-11 must be determined for the storms 1n
question. Three different methods may be used to obtain these eros1v1t1es.
Method l; Direct computation from rainfall Intensities.
The most analytical approach Involves the use of rainfall Intensity data di-
rectly to compute storm kinetic energy and maximum Intensities. This procedure, as
described In Wischmeler and Smith (1978) Is generally too cumbersome for screening
studies.
Method 2: Design storms.
Wischmeler and Smith (1978) have analyzed rainfall data throughout the United
States to determine frequencies of E values. The results are given 1n Table 111-10.
and may be used to determine the soil erosion associated with storms of various
-170-
-------
TABLE 111-8
C FACTOR VALUES FOR MECHANICALLY PREPARED WOODLAND SlUS
(H1scl»e1er and Smith, 1978)
Site
Diihod, roVed.
or bedded4
lur«ed*
Mulch
cover'
No««
10
20
40
60
•0
Mo.e
10
20
40
60
10
Oruia (hopped' None
10
20
40
60
10
loit
f«celtent
NC WC
053
.33
.34
.17
.11
.05
.35
.31
.If
.14
.Ot
.04
.16
.15
.12
.Of
.06
.03
0.30
.15
.13
.11
.01
.04
.10
.10
.10
.Of
.06
.04
.07
.07
.04
.06
.05
.03
tancUMo*? and weed «e»«>3
Good
NC
0.72
.46
.34
.33
.15
.07
.36
.34
.If
.14
.Of
.05
.17
.16
.12
.Of
.06
.03
WC
0.27
.20
.17
.14
.11
.06
.10
.10
.10
.Of
.07
.04
.07
.07
.06
.06
.05
.03
Fair *oor
NC
0.15
.54
.40
.27
.11
.Of
.31
.36
.21
.15
.10
.05
.20
.17
.14
.10
.07
.03
WC
0.32
.24
.20
.17
.14
01
.12
.11
.11
.Of
.01
.04
.Of
.01
.07
.06
.05
.03
NC
O.f4
60
.44
.10
.30
.10
.45
.36
.27
.17
.11
.06
.2f
.23
.11
.11
.07
.04
WC
0.36
.36
.22
.If
.15
.Of
.17
.16
.14
.11
.0*
.05
.11
.10
.Of
.07
.05
.04
* Porconlogo ol turtmtf covered by roilduo U co«locl wild the,
loll.
* f*t»ttxt toN condJllo* — Highly liable toll oggiogoUi U lop-
i*ll with («•»« lr»o rood and IllUf mliod U.
Good — Moderately ttobU toll ao,gregolei U lop toil or highly
trable ugycegatei (» tubieJI (laptofl removed during roVUgt, oftly
lf«ei of Illler mliod U.
fair — HtftMy umlobU toll *ggr*gotet U toptoil or nodoiateiy
liable oggrogoloi IN iub«olt, no lilUr mlicd1 U.
foor — No (optod, blgMy •rodlbU toll ofgrogatti l» >vb«o'l, *•
Illltf Mliitd U.
3
NC— No li*
WC — 75 p*«*nt cover of gran and wetdi having on ovcrogo
drop loll VotgM of 3D In. for lnt*rm*dlol* p«ri**l-
ag*i of tovor, lAUrpolali b*lw«t« <«lum«i.
4 Modify lti» tiiltd C valwoi 01 tollowi lo account for •••cli or
iwrloct rowgko«>» and oglngi
Hut year ofur 1r*olM*nli mwillply Ililtd C volv«i by 0.40 lor
rough lurfoco (d*pr*iiioni >o l»(i by 0.4] for Mod*rol*ly
rough/ ood by O.K> IW tmoorh (d*pr*i>io»> < J In).
for | |o 4 yoori ofl»r lr«atm*nli Multiply H«Ud (acton by 0.7.
for 4-f- !• I y*«rii MO l«alo 6.
More Itian I y««fli uio lobU 7.
for foil ] ycorti ut« C volu«i •« lliUd.
f«r 1-f to I yoan alter Irealmenli u>« labl* o.
More ifco* I y*ori after treotmenli UM l«blo 7.
-171-
-------
TABLE 111-9
PRACTICE FACTORS (P) USED IN UNIVERSAL SOIL LOSS EQUATION
{Stewart et «]_, 1975)
Practice
L»nd ilop« (percent)
1.1-2
2.1-7
7.1-12
12.1-18
(Factor P)
K.I-24
Con louring (Pc>
Contout stria cropping (P^l
R.R-M-M1
R.W-M-M
R-R-W-M
R-W
R-O
Contout listing or ridge planting
(Pel)
Contour terracing (P,)
No support practice
0.60
0.30
0.30
0.45
0.32
0.60
0.30
0.23
0.23
0.38
044
0.30
0.30 0.25
30.6/>/T 0.5/VrT
1.0 1.0
0.60
0.30
0.30
0.45
0.32
0.60
0.30
0.6/>/T
1.0
0.80
0.40
0.40
0.60
0.70
0.80
0.40
0
1.0
0.90
0.45
0.45
0.68
0.90
0.90
045
1.0
I R • rowcrop, w • fall-seeded gram. O • spring-seeded grain. M * meadow. The crops are grown in rotation and co arranged on
the field thai towcrop strips are always separated by a meadow or winter-grain strip.
Thrsc P| values estini»te the. amount of soil eroded to the terrace rnannels and are used for conservation planning. For prediction
iff. f i*l*4 «j»r4 •••«••* I thai 9. IM lit«* •*•» fmut* it*t u**J Kw ft ^
ie field thai towcrop uript are always separated by a meadow or wmirr-jram strip.
2 Thrse Pt values estim»ie the amount of soil eroded to the terrace rnannels and are i
of off-field sediment, the P( values are multiplied by 0.2.
3n • number of approaimitdy equaJ-length intervals into which (he field slope is divided by the terraces. Tillage operations must
be parallel to the terraces.
return periods. Note that the English units E values given 1n Table 111-10 must be
multiplied by 1.735 to obtain the metric E used 1n Equation III-ll.
Method 3; Eros1v1t1es from dally rainfall data.
Richardson et aU (1983) developed a regression equation for eroslvlty based on
dally rainfall data. Converting their results to the units of E 1n Equation III-ll,
the expected values of E for a dally rainfall R (cm) Is:
E • 6.46a R
1.81
(III-U)
The coefficient "a" varies with location and season. R1 chardson «rt £l_. (1983)
determined cool season (October-March) and warm season (April.September) coefficients
for the locations shown fn Figure 111-12.
-172-
-------
TABLE 111-10
EXPECTED MAGNITUDES Of SINGLE-STORM EROSIVITY INDICES (ENGLISH UNITS)
(Wiscltneier and Smith. 1978}
10
10
t»4
H««*«
•<
II»MW,
MICK
T»m
O«
f7
49
49
41
99
II
11
a
17
II
73
II
17
94
41
fl
n
0
if
77
14
74
14
If
14
49
17
91
U
II
49
11
in
U
4f
44
n
11
11
14
II
II
13
47
17
t!4
m
114
a
to
ti
n
in
7t
49
4f
14
10
If
O
10
99
41
V
44
47
40
41
49
4f
110 140
111 in
111 141
101 199
til 1*
M 17
101 197
14 4*
a M
u tr
40 M
U 113
JO 44
73 »4
U >0»
100 04
144 301
TOO U3
fl
74
1M
709
ft
101
77
tl
If
14
71
M
41
41
7t
41
41
44
47
fl
74
Ul
14
191
in
777
190
191
101
101
114
4f
»4
71
14
71
M
77
II
Tf
14
114
101
ltd
170
1f4
177
147
711
101
149
41
94
111
177
193
7*
173
777
794
901
114
lit
119
144
9JI
147
in
17f
130
141
17
117
U
M
III
111
11
109
101
140
tTf
lit
C«tf 4>rvllU
37
47
39
II
14
U
41
n
11
Mabwi
C«rfb«M
II
4f
fl
47
17
JI
It
49
It
101
U
74
O
74
n
O
171
1U
47
f7
111
114
77
49
100
I5»
114
tit
tie
tit
u
Ml
OwkH*
FMWCM
(•*••• Or .
104
11
14
U
It
41
17
If
U
11
If
14
11
17
U
4t
14
4*
tt
9
a
M
11
43
ir
41
4
7
„
34
14
11
10
It
If
n
M
77
77
If
77
01
11
11
11
It
u
14
U
JI
"
fl
(4
"
M
49
u
U
11
47
1
11
14
11
37
M
O
It
714
Tf
7t
4(
40
U
43
41
n
4J
94
14
13
jf
JI
tl
40
171
U
111
77
41
If
fl
70
14
14
11
14
74
14
If
74
4J
770
171
14
44
St
lOf
17
41
41
14
43
M
n
ji
41
101
»0
111
toj
114
n
7f
117
111
17
104
N
*•
41
n
47
7t
P4
41
330
141
44
M
tl
199
n
»
to
41
It
49
n
41
n
ITT
109
174
170
141
107
n
in
140
\V)
114
14
M
fl
111
n
ff
114
77
-173-
-------
TABLE 111-10 (Continued)
>•
20
h«>*MI
l«
»»
i*
2*
I*
II
14
U
U
13
IJ
n
it
37
13
»f
30
U
37
33
30
37
27
31
U
44
44
47
14
47
17
I*
30
23
30
23
27
44
4
21
14
}4
a
31
u
Jl
77
17
37
II
14
U
U
40
4S
30
M
70
O
4*
34
U
3f
33
3f
33
n
u
T*
II
It
30
34
34
It
M
33
3i
too
74
110
13*
VI
U
40
O
U
M
77
44
43
l«7
00
MJ
O7
MO
13
U
M
U
4J
U
Ul
43
f7
MI
14
44
47
47
*
4»
u
«
Jt
72
131
»3
137
147
77
S3
J»
71
41
77
100
a
9
Ul
n
134
U
4J
a
•»
117
134
131
II
14
JO
41
43
4J
17
144
10
!«•
3O*
103
41
»4
143
70
74
T7*
1U
M3
30»
114
10
J4
Jl
tl
47
II
43
114
c*r
CMMi
1*
it
4t
33
If
14
n
34
U
O
Jl
27
Jl
7i
44
4
o
17
33
77
J»
47
U
33
11
44
S
3
33
U
30
U
44
3*
If
3*
4
f
104
n
at
37
3t
so
41
It
m
7*
B
«7
»
nr
3»
3J
103
(3
71
«3
41
33
31
4J
43
33
31
7»
31
34
47
3J «•
t44
104
u
t*
34
3*
71
44
7»
7T
M
114
in
114
13*
IM
S
A
l»
t2J
139
II*
41
44
IM
us
104
n
JO
a
T3
n
240
its
in
is
n
41
U
11'
122
o **
44
30
W
41
«•
70
7
U
»
31
112
14*
Ju
144
144
144
I*
as
77
4*
144
Ul
141
144
104
U
1O3
41
I
Jl
4»
41
I3J
77
41
f3
11
27
IM
1JO
»!•
112
171
313
1(3
24
ur
Itt
U
t»4
1*3
204
I«
M
103
134
73
11
40
f»
74
144
tJ
74
14
14
-174-
-------
COOL SEASON (OCT.-MAR.)
WARM SEASON (APR,-SEPT,)
FIGURE 111-12 VALUES OF "A" FOR EQUATION 11
(RICHARDSON £j_ AL, 1983)
-175-
-------
When cropland erosion estimates are made for single storm events, the cover/
management factor should 1n principle be selected for the crop stage corresponding to
the time of year 1n which the storm occurs. The procedures for estimating seasonal
C values as described 1n Wlschmeler and Smith (1978) require crop development In-
formation which 1s usually not available 1n screening studies, and hence the annual C
values given In Table III-4 are often used for single event estimates.
r EXAMPLE III-3 1
I I
j Soil Erosion Computations j
i i
Compare annual soil erosion values in central Michigan and southern Louisiana •
for a com field with the following characteristics: !
; • Soil: silt loam, 41 organic matter [
I e Slope: 61, 100 m length I
I • Moderate productivity, residues left, fall turn-plowed I
| conventional management I
j For both locations, determine annual soil erosion with no conservation practices j
and with contouring. •
Solution; j
' Soil erosion 1s determined by Equation 111-11: j
| X - 1.29 E (K) (Is) C (P) j
j From Figure 111-10, ero$1v1t1es (1n English units) for the two locations are j
approximately 100 (Michigan) and 500 (Louisiana). Multiplying by the metric
! conversion 1.735, we have E » 174 and 868. Other parameters are:
j K - 0.33 (Table III-3) !
I C • 0.52 (Table III-4, line 3). I
| From Table III-9, P • 0.5 for contouring and 1.0 with no practices. I
j The 6 percent slope corresponds to e « tan'^O.Oe) • 3.43° (Equation |
j 111-13) and the Is factor from Equation 111-12 Is: j
• Is « [0.045(100)]°'5 (65.41 $1n2 3.43 + 4.56 sin 3.43 + 0.065) j
' • 1.21.
! Thus 1.29 (K)(ls)C(P) • 0.268 without contouring and 0.134 with contouring. !
I Soil erosion for the two locations and practices:
j
j No practice Contouring
j Michigan 46.6 t/ha 23.3 t/ha
; Louisiana 232.6 t/ha 116.3 t/ha
— END OF EXAMPLE 111-3
-176-
-------
3.4.3.3 Watershed Sediment Yield
3.4.3.3.1 Annual Yields
Watershed sediment yield due to surface erosion 1s:
Y - sd r Xk Ak (111-15)
where
Y • annual sediment yield (tonnes/yr)
X. « erosion from source area k as given by Equation III-ll (t/ha)
A. « area of source area k (ha)
S. » watershed sediment delivery ratio.
The sediment delivery ratio Srf Is a factor which accounts for the attenuation
of sediment through deposition and filtering as It travels from source areas to the
watershed outlet. Although a number of different relationships have been proposed
for Sd, the simple function of watershed drainage area given In Figure 111-13
remains the most generally accepted procedure.
3.4.3.3.2 Seasonal Yields
Equation 111-15 1s appropriate for annual sediment yields and should not be
used to determine event or seasonal watershed sediment yields. Large watershed
sediment yields often do not coincide with major erosion periods. For example, 1n
the eastern United States, most soil erosion 1s caused by late spring and summer
Intense rainstorms, but most sediment discharge occurs during late winter and early
spring runoff. The reason for this 1s that runoff during erosive periods 1s often
Insufficient to transport eroded soil far from a source area. Subsequent large
events "flush" portions of the accumulated sediment from the watershed drainage
network.
Although general procedures are not available for estimating seasonal sediment
2
yields, the following approach produced satisfactory results for an 850 km
watershed In upstate New York (Halth et ±L, 1984).
Sediment yield 1n month m, Y (tonnes), 1s assumed to be proportional to
12
0~ where 0 1s the watershed runoff (cm) during month m. The annual
sediment yield Y, as given 1n Equation 111-15, 1s likewise proportional to QT,
where
12 . .
QT - S 0*'* (111-16)
-177-
-------
1000
DRAINAGE AREA (km*)
FIGURE 111-13 SEDIMENT DELIVERY RATIO As A FUNCTION OF
WATERSHED DRAINAGE AREA (VANONI, 1975)
Thus:
or
Y/Y - 0*"
OT m
.2
(111-17)
Equation 111-17 was used to estimate monthly sediment yields over a 25-month
period for the 850 tan2 West Branch Delaware River Basin 1n upstate New York.
Comparisons with measured sediment yields Indicated that the estimated mean monthly
sediment yield was within 12 percent of the observed value. Based on correlations
between monthly estimated and observed sediments yields, Equation 111-17 explained 92
percent of the observed monthly variations (Halth et _£]... 1984).
3.4.4 Chemical Loading Functions for Rural Runoff
As suggested 1n Section 3.4.1, loading functions for rural runoff are equations
that multiply dissolved and solid-phase pollutant concentrations by volume or mass
fluxes of runoff water or sediment, respectively (Equations III-l and III-2).
Procedures for calculating runoff and sediment yield were described 1n Sections 3.4.2
and 3.4.3. It now remains to outline procedures for determining pollutant concen-
trations 1n runoff and sediment.
-178-
-------
The principal pollutants 1n rural runoff arc nutrients (nitrogen and phosphorus),
heavy metals and synthetic organic pesticides. Although most of these chemicals have
both solid and dissolved phases it is convenient to divide them Into three categories,
based on their main transport phase in runoff:
• Solid phase; chemicals which are strongly associated with sediment.
• Dissolved phase; chemicals which are dissolved in runoff.
• Distributed phase; significant chemical quantities are transported in
both solid-phase and dissolved forms.
Loading functions for the first two c. .egorles are straightforward; empirical esti-
mates are used for the chemical concentrations. Runoff of distributed-phase chemi-
cals is more difficult to model since dissolved and solid-phase concentrations are
influenced by adsorption equilibrium phenomena.
Solid-phase chemicals include organic nitrogen, particulate phosphorus and heavy
metals. The assignment of metals to this category is arbitrary, since dissolved
forms are often present under acidic conditions. However, it 1s assumed here that
the primary sources of metals in rural runoff are metal-based pesticides which are
tightly bound to soil particles (Weber, 1975).
The dissolved chemical group includes only inorganic nitrogen and soluble
phosphorus. Inorganic nitrogen in drainage is mostly nitrate-nitrogen, and this Ion
does not adsorb to soil particles. Phosphorus 1s a special case. Most phosphorus in
runoff 1s solid-phase, but dissolved phosphorus 1s directly available to plants and
algae and hence cannot be neglected in eutrophlcation studies. The loading functions
for solid-phase and dissolved phosphorus are operational means of describing complex
soil chemistry. There Is a continuous set of reactions that relate fixed, adsorbed
and soluble phosphorus forms. Although it 1s possible to model this behavior (Donigian
and Crawford, 1976; Knisel, 1980; Tubbs and Haith, 1981), such models are neither
simple nor especially accurate.
Distrlbuted-phase chemicals include most organic pesticides. Models for runoff
of these chemicals are considerably more complex than the solid-phase and dissolved
chemical loading functions. Indeed, the term "loading function" Is used advisedly,
since models of these adsorbed chemicals are comparable to the continuous simulation
models discussed in Section 3.3.
3.4.4.1 Loading Functions for Solid-Phase Chemicals (Organic Nitrogen, Participate
Phosphorus, Heavy Metals)
The loading function for solid-phase chemicals in runoff from a source
area is:
LS • 0.001 Cs X (111-18)
-179-
-------
where
LS • solid-phase chemical load In runoff (kg/ha)
Cs • concentration of chemical 1n eroded soil (sediment) (mg/kg)
X « soil loss (t/ha).
The "0.001" in Equation 111-18 1s a dimensional conversion constant. Soil loss Is
given by the Universal Soil Loss Equation (Equation 111-11) on either an annual or
single event basis. In determining a source area's contribution to watershed chemical
loading. LS must be modified by a sediment delivery ratio (Section 3.4.4.1.2).
Equation 111-18 1s often considered to be an estimate of total chemical load
rather than just the solid-phase portion. The assumption 1s essentially correct for
heavy metals since they are tightly bound to soil particles. Moreover since most
soil nitrogen 1s In the solid-phase organic form and most soil phosphorus 1s partlc-
ulate, solid-phase nutrient loads will generally be a very large portion of total
loads.
3.4.4.1.1 Solid-Phase Chemical Concentrations
The concentration Cs Is :?st determined by direct measurement. Samples
may be taken of sediment depositions 1n fields and drainage ditches. These samples
are subsequently analyzed for total concentrations of heavy metals, organic nitrogen
or participate phosphorus In the sediment. Stream flow suspended solids samples 1n
rural watersheds free of point sources and urban drainage may also be used. When
sediment sampling Is 1nfeas1ble. procedures described In the following subsections
may be used to obtain approximate concentration estimates.
3.4.4.1.1.1 Organic Nitrogen and Partlculate Phosphorus
Nitrogen and phosphorus concentrations In eroded soil are generally larger than
comparable concentrations 1n uneroded or 1n situ soil. This 1s due to the selective
nature of the erosion process. Lighter organic matter and clay particles are more
readily eroded than heavier sand and silt. Since nutrients tend to be associated
with these light particles, sediment Is "enriched* with nutrients compared to the
soil from which 1t originates. A sediment nutrient concentration can thus be related
to the comparable concentration 1n soil by an enrichment ratio;
Cs • en C1 (111-19)
where
en • nutrient enrichment ratio
C1 • nutrient concentration 1n |ni situ soil (mg/kg).
Soil nutrient concentrations are sometimes available from soil surveys or
extension specialists. Nitrogen concentrations may be Inferred from soil organic
•180-
-------
matter percentages by assuming that organic matter is 5 percent nitrogen (Brady,
1974). Thus, for nitrogen C1 a 0.05(X OM/100)106 - 500 (X OM), where % OH is
percent organic matter in the soil.
Very rough estimates of soil nutrient concentrations can be obtained from the
general maps shown in Figures 111-14 and 15. Figure 111-15 indicates soil content of
P-Or which is 44 percent phosphorus. To use Figures 111-14 and 15, we note
that IS • 10,000 mg/kg, and hence for
Nitrogen: C1 « (% N)104
Phosphorus: C1 » 0.44 (X P^JlO4.
Although these nutrient concentrations are for total nitrogen and phosphorus,
they may be used for organic nitrogen and particulate phosphorus since these nutrient
forms are so dominant In the soil.
Nutrient enrichment ratios are 1n principle event-specific, since they are
related to the degree of erosion which occurs during a storm. With very small
storms, only the finest soil particles are eroded, and the enrichment ratio is high.
Conversely, large storms erode all soil particles, and the enrichment ratio approaches
one. Based on analyses of many field studies of nutrient transport, Menzel (1980)
suggested the relationship:
en . 7.39/Sed0'2 (111-20)
in which Sed is the sediment discharge (kg/ha) during the storm event. Equation
111-20 gives values of en ranging from 2.94 at Sed * 100 kg/ha to 1.35 at
Sed - 5000 kg/ha.
Equation 111-20 can be used directly for single storm loading estimates by
letting Sed • 1000 X, since the units of soil loss X are tonnes/ha. The equation 1s
not suitable for annual loads. For these loads, a midrange value of en • 2.0 Is
appropriate (Ha 1th and Tubbs, 1981). In summary:
| 2.0 for annual loads ,...
en • , (111-21)
I 7.397(1000 H) for single event loads
For very large soil losses (X > 22 t/ha), Equation 111-21 will give en < 1.0 for an
event. When this occurs, tn should be stt equal to 1.0.
3.4.4.1.1.2 Heavy Metals
The U.S. Geological Survey has analyzed soil samples from 863 sites in
the United States for heavy metals. The results, as summarized by McElroy ££ a]_.
(1976), are given in Table III-ll. These concentrations may be used directly as Cs
In Equation 111-18 since 1 ppm • 1 mg/kg, and It may be assumed that no metals
enrichment of sediment occurs (McElroy et aK, 1976).
-181-
-------
NITROGEN
*rcint N
l*Mffici*nt Dot*
in** aos
0.05-0.09
^O.K>-OI»
(JJ 0.20 and 0**
FIGURE 111-14 NITROGEN IN SURFACE FOOT OF SOIL (PARKER, EI AL., 1946)
FIGURE 111-15 PHOSPHORUS (AS P205> IN THE SURFACE FOOT OF SOIL
(NOTE: P205 Is 44Z PHOSPHORUS) (PARKER LL AL, 1946)
-182-
-------
TABLE 111-11
HEAVY METAL CONCENTRATIONS IN SURFICIAL MATERIALS IN THE UNITED STATES
(McElroy et al, 1976)
Element
Arsenic
Barium
Cadmium
Cerium
Chromium
Cobalt
Copper
Iron
Gallium
Germanium
Cold
Hafnium
Indium
Lanthanum
Lead
Manganese.
Molybdenum
Neodymlum
Nickel
Niobium
Palladium
Platinum
Rhenium
Scandium
Strontium
Tantalum
Tellurium
Thallium
Thorium
Titanium
Uranium
Vanadium
Ytterbium
Yttrium
Zinc
Zirconium
Arithmetic
Average
(PP-.)
• •
554
--
86
53
10
25
25,000
19
--
--
--
--
41
20
560
< 3
45
20
13
--
--
--
10
240
--
--
--
--
3,000
--
76
4
29
54
240
•nalysli
Range
(ppm)
< 1,000
15-5,000
< 20
< 150-300
1-1,500
< 3-70
< 1-300
100-100,000
< 5-70
< 10
< 20
< 100
< 10
< 30-200
< 10-700
< 1-7,000
< 3-7
< 70-300
< 5-700
< 10-100
< \
< 1<>
< 30
< 5-50
< 5-3,000
< 200
< 2,000
< 50
< 200
300-15,000
< 500
< 7-500
< 1-50
< 10-200
< 25-2,000
<: 10-2.000
Conterminous
U.S.
(ppm)
^ m
430
--
75
37
7
IB
18,000
14
—
--
--
--
34
16
340
--
39
14
12
--
--
--
8
120
--
--
--
--
2,500
--
56
3
24
44
200
Geometric means
West of 97th
meridian
(ppm)
— —
560
--
74
38
8
21
20,000
18
--
--
--
--
35
18
389
--
36
16
11
--
--
--
9
210
--
--
--
--
2.100
--
66
3
25
51
170
East of 97th
meridian
(ppm)
— —
300
--
78
36
7
14
15,000
10
--
--
--
--
33
14
285
--
44
13
13
--
--
--
7
51
--
--
--
--
3.000
--
46
3
23
36
250
Total
30,099
21,991
23,858
19,263
Note: "--" Indicates all analyses shoved element to be below detectable limits.
-183-
-------
3.4.4.1.2 Watershed Loads of Solid.Phase Chemicals
The annual watershed load of a solid-phase chemical 1n rural runoff Is the sun
of the attenuated runoff loads from all source areas 1n the watershed. Since these
chemicals travel with sediment, attenuation (I.e., transport loss) 1s described by
the sediment delivery ratio, S^:
WLS « Sd r LSfc /^ (111-22)
' °-001 Sd = Csk Xk Ak
where
WLS - annual watershed solid-phase chemical load In rural runoff (kg/yr)
IS. • solid-phase chemical load 1n runoff from source area k (kg/ha)
A. • area of source area k (ha)
Cs. • solid-phase chemical concentration 1n eroded soil (sediment) from
source k (mg/kg)
X. • soil loss from source area k (t/ha).
Single event loads cannot be estimated by Equation 111-22 due to the sediment
transport variations discussed In Section 3.4.3.3.2. However, seasonal loads may be
calculated by assuming them to be proportional to seasonal sediment yields. From
Equation 111-17, we know that the ratio of monthly watershed sediment yield Yffl
to annual yield Y Is:
VY " ^'2/QT (HI-17)
where Q^ 1s watershed runoff 1n month m(cm) and QT 1s given by Equation 111-16.
Thus If WLS 1s the annual chemical load given by Equation 111-22, then WLS .
IH
the load (kg) 1n month m, 1s:
WLSm "
-------
Thus when soil chemical concentrations are uniform, monthly chemical loads can be
obtained directly from monthly sediment loads.
EXAMPLE III-4
I
Watershed Sediment and Phosphorus Loads '
I
The West Branch Delaware River Is an 85,000 ha watershed in south-central New |
York that drains into Cannonsvllle Reservoir. Soil erosion Is a major phosphorus j
source to the reservoir. Major land uses contributing erosion are as follows: j
Land Use Area (ha) Mean K(ls)CP !
Corn 3,430 0.214 \
Hay 13,085 0.012 1
Pasture 5,093 0.016
Inactive Agricultural 3,681 0.017
Logging Roads 20 0.217 j
Determine: j
a) Average annual sediment yield (tonnes/yr)
b) Average annual solid-phase phosphorus input to the reservoir (kg/yr)
j
Solution: j
a) Average annual sediment yield is given by Equations 111-15 and 111-11:
Y - S, z X. A. (111-15) I
u i K K '
I
Xk « 1.29 E (PC) (Is) C (P) (III-ll) (
i
There are five different source areas, each with their associated K (Is) C j
(P) values. Rainfall/runoff erosivity is approximately 125 (Figure 111-10).
Converting to metric units:
I
E - 1.735(125) » 217. •
Soil erosion x. , from each source area 1s:
I
• Corn: 1.29(217) 0.214 - 59.9 t/ha '
• Hay: 1.29(217) 0.012 • 3.4 t/ha I
• Pasture: 4.5 t/ha I
• Inactive Agriculture: 4.8 t/ha j
e Logging Roads: 60.7 t/ha [
2 '
The sediment delivery ratio S, is approximately 0.065 for an 850 km
-185-
-------
I watershed (Figure 111-13). Sediment yield is: I
I Y - 0.065 [59.9(3430) + 3.4(13,085) * 4.5(5,093) I
I * 4.8(3681) + 60.7(20)] j
j • 18,960 tonnes/yr j
j b) Phosphorus load 1s:
LS • 0.001 S,S Cs. X. A (111-22) !
I u . K K r- -
J Since no other Information is available, phosphorus concentrations are obtained I
I from Figure 111-15. New York soils are 0.10 - 0.19 oercer- ,0 . using a mid- |
| range value of 0.15 percent and recalling that P-O, is 44 .ercent phosphorus, j
j we obtain a soil phosphorus level of: '
I C1 - 0.44(0.15) 104 j
| « 660 mg/kg j
j Using an enrichment ratio of 2.0 (Equation 111-21), Cs - 2.0 (660) - .320 j
j mg/kg.
we must assume that Cs Is the same for all source areas and hence:
I % I
j LS - 0.001 Cs Sd Z Xk Ak
j • 0.001 Cs Y j
- 0.001(1320)(18.960) - 25.000 kg/yr. •
! END OF EXAMPLE III-4 -I
3.4.4.2 Loading Functions for Dissolved Chemicals (Inorganic Nitrogen and Soluble
Phosphorus)
The loading function for dissolved nutrients in runoff from a source area
Is:
LD - 0.1 Cd Q (111-25)
where
LD - dissolved chemical load 1n runoff (kg/ha)
Cd • concentration of dissolved chemical in runoff (mg/1)
Q • runoff from source area (cm).
The "0.1" in Equation 111-25 1s a dimensional conversion constant. For event
loads, Q 1s given by Equation III-3. The loading function may also be used for
annual loads provided annual runoff values such as those shown 1n Figure 111-9 are
available.
-186-
-------
3.4.4.2.1 Dissolved Nutrient Concentrations
Concentrations of dissolved nutrients in runoff vary with soil cover. Representa-
tive concentrations are given in Table 111-12. Concentrations for fallow, corn,
small grains, hay and pasture are flow-weighted average concentrations measured in
runoff over several years- from large field sites 1n South Dakota (Dornbush, et al.,
1974). Forest concentrations are the National Eutrophication Survey values for
inorganic nitrogen and orthophosphorus given 1n Figures III-4 and 5 for 90% forested
watersheds.
In the northern U.S., cropland which has manure left on the soil surface,
particularly during snowmelt, is likely to have significantly higher dissolved
nutrient concentrations in runoff than unmanured cropland. The concentrations for
manured fields given in Table 111-12 should be used for snowmelt runoff from fields
which have received winter applications of manure.
Although the representative concentrations given in Table 111-12 should be
replaced by local data whenever possible, such data are unavailable 1n most water
quality screening studies. However, since the concentrations in Table 111-12 are
comparable to other values reported 1n the literature (see for example Baker, 1980),
it is unlikely that use of the representative concentrations would produce large
errors in loading esfmates.
EXAMPLE III-5
Single Event Runoff, Sediment and Nitrogen Load
During the growing season a 7.0 cm rainstorm falls on the Louisiana cornfield
described in Example III-3. The field has an area of 10 ha and Is planted in
straight rows. The soil Is in hydrologic Group B and 1s in poor hydrologic
condition. This storm was preceded by 5.5 cm of rain four days previously.
From Example III-3, the soil has an organic matter content of 4 percent and the
USLE parameters for this field are:
K « 0.33, Is • 1.21, C « 0.52 and P « 1.0
Determine:
a) Storm runoff (cm)
b) Soil loss (tonnes)
c) Solid-phase and dissolved nitrogen in runoff (kg).
Solution:
a) Runoff Is given by the Curve Number Equation (Equation III-3). The
curve number for straight row, poor hydrologic condition, soil B is CN2
• 81 (Table III-l). According to Table III-2, the preceding 5.5 cm of
-187-
-------
TABLE II1-12
REPRESENTATIVE DISSOLVED NUTRIENT CONCENTRATIONS IN RURAL RUNOFF
Soil Cover
Fallow*
Corn4
Small Grains*
May*
Pasture*
Inactive Agriculture1*
Eastern U.S.
HI
-------
where in this case E 1s the event eroslvity given by Equation 111-14: i
E - 6.46 a R1'81 |
The nearest location for the "a" value is State College, Mississippi |
(Figure 111-12), which has a warm season value of a • 0.51. Erosivity j
is thus:
E - 6.46(0.51)(7)1<81 • 112 j
Soil loss Is:
I
X - 1.29(112)0.33(1.21)0.52(1) j
- 30 t/ha
Over 10 ha, the loss Is 30(10) « 300 tonnes.
c) Solid-phase nitrogen loss (kg/ha) is: j
I
LS • 0.001 CsX (111-18) j
i
where :
Cs - en C1 (111-19) !
and )
i
en - 7.39/(1000X)°'2 (111-21) I
j
As described In Section 3.4.4.1.1.1, soil nitrogen concentration C1 j
(mg/kg) can be estimated by assuming that organic matter 1s 5 percent :
nitrogen. The field's 4 percent organic matter gives a nitrogen concen-
tration of:
fi I
C1 • 0.05(0.04)10° • 2000 mg/kg •
The enrichment ratio for the storm 1s:
en » 7.39/[1000(30)]°*2 • 0.94 j
Since this Is less than 1.0, we set en - 1.0, and the solid-phase
nitrogen concentration 1n sediment is: ;
Cs • 1.0 C1 • 2000 mg/kg
The solid-phase nitrogen load Is:
LS • 0.001(2000)(30) • 60 kg/ha
or 600 kg for the 10 ha field. !
The dissolved nitrogen load Is: I
-189-
-------
I LD - 0.1 CdQ (111-25) I
j I
j where from Table 111-12, Cd « 2.9 mg/1 for corn. He/ice: j
| LD » 0.1(2.9)(4.9) - 1.4 kg/ha \
j or 14 kg for the 10-ha field. j
i i
END OF EXAMPLE III-5 •
3.4.4.2.2 Watershed Loads of Dissolved Chemicals
Since all runoff from watershed source areas 1s transported to the watershed
outlet (see Section 3.4.2.4), 1t 1s assumed that dissolved nutrient loads are-not
attenuated. Watershed load 1s thus the sum of the source area loads:
WLD « 0.1 Z Cdk Qy \ (111-26)
where
WLD » annual or event watershed dissolved chemical load 1n rural runoff
(kg)
Cd. • dissolved chemical concentration 1n runoff from source area
k (mg/1)
Q. - runoff from source area k (cm)
A)( • area of source area k (ha).
3.4.4.3 Loading Functions for Distributed Phase Chemicals (Pesticides)
Runoff of pesticides can be described by the same general loading functions used
for nutrients and metals (Equations 111-18 and 111-25). However, the estimation
of dissolved and solid-phase concentrations 1s more difficult for pesticides. All
pesticides are adsorbed to some extent by soil particles, and hence dissolved and
solid-phase concentrations cannot be determined Independently. Also, these concen-
trations are dynamic, since pesticides are decomposed or decayed by photochemical,
chemical, and microbiological process. Decay rates are often sufficiently high that
most of a pesticide will have decomposed within several weeks of application. A
final complicating factor Is the large number of pesticide compounds currently 1n
use, each with Its own properties and characteristic behavior 1n the soil.
It follows that pesticide concentrations In runoff cannot be estimated by simple
empirical methods, since they depend on the relative timing of applications and storm
events, and the specific adsorption and degradation properties of the pesticide.
However relatively simple equations can be used to describe the adsorption and decay
phenomena, and calculations can be made for each storm event following a pesticide
application. The following subsections describe such a model and also provide model
-190-
-------
parameters for a large number of pesticides. The model estimates pesticide load
runoff events from a source area; i.e., a small catchment with uniform soil,
hydrologic and chemical characteristics. Methods are not available to aggregate
these source area loads Into total watershed load.
3.4.4.3.1 Pesticide Runoff Model
The pesticide runoff model developed by Haith (1980) is based on a pesticide
mass balance of the surface centimeter of soil. On day t after a pesticide applica-
tion PQ (g/ha) to the surface soi, layer, the pesticide content is:
pt " Po "Pf-1^ *APt (HI-27)
where
P « pesticide in surface centimeter on day t (g/ha)
' .1
k » pesticide decay rate (day )
AP « additional pesticide application (1f any) on day t (g/ha).
Equation 111-27 is a standard exponential or first-order decay model.
If a previous storm and/or pesticide application was made on some day T prior
to day t, then:
Pt - PT exp [k$(t-T)] +&Pt (111-28)
where
PT • pesticide content after storm event or application on day f (g/ha).
Total pesticide P.. is divided into adsorbed (solid-phase) and dissolved forms
based on a linear adsorption equilibrium relationship.
Pt - At + Dt (111-29)
and
«t • KDdt (111-30)
where
Aj • adsorbed (solid-phase) pesticide 1n surface centimeter on
day t (g/ha)
D, - dissolved pesticide In surface centimeter on day t (g/ha)
at • adsorbed pesticide concentration on soil particles (mg/kg)
d( • dissolved pesticide concentration In solid water (mg/1)
KO « pesticide partition or distribution coefficient (I/kg).
If a rainfall or snowmelt event sufficient to fill the surface layer's volumetric
-191-
-------
available Mater capacity w (cm/cm) occurs on day t. then D,. Is given by 100 *
*• 3
dt and A( Is lOOb a( where b Is the surface soil bulk density (g/cm ).
Substituting these relationships Into Equations II 1-29 and 111-30 produces:
\ - [1/(1 + w/K0b)]Pt (111-31)
and
(1 11-32)
If runoff occurs on day t, portions of \ and 0( will be removed by water
and sediment movement. The solid-phase loss Is the product of adsorbed concentration
and soil loss. Since a( • /L/100b, we have
PXt » (At/100b)Xt (IIJ-33)
where
P)L • solid-phase pesticide in runoff on day t (g/ha)
)L • soil loss (sediment) in runoff on day t (t/ha).
Dissolved pesticide losses are distributed into runoff, percolation and a residual
which remains in the surface layer after a storm. These components are assumed
proportional to the distribution of rainfall Rt (cm) plus snowmelt M, (cm)
into runoff, percolation, and available soil water. Considering only events for
which R + Mt > w, runoff loss of dissolved pesticide Is:
PQt • COt/(Rt * Mt)] Ot (1 11-34)
where
PQj « dissolved pesticide 1n runoff on day t (g/ha)
Q • runoff on day t (cm).
Assuming that the surface layer Is dry prior to the event, percolation loss of
dissolved pesticide from the layer Is [(^ + Mt - (^ - wj/ff^ * Mt)]Dt, and
dissolved pesticide remaining in the soil after the event 1s [w/(Rt * Mt)] Ot<
Pesticide remaining In the surface layer 1$:
Pt* • Pt - P*t - U-»/(\ + \)1 Ot (1 11-35)
Equations 111-33 and 111-34 are the basic loading functions for solid-phase
and dissolved pesticide In runoff. For the solid-phase loads, X^ In Equation
111-33 1s the eroded soil from the source area as given by the Universal Soil Loss
Equation, (Equation III-ll). The remainder of Equation lil-33, /lOO b, 1s
-192-
-------
the pe.stldde concentration in eroded soil or sediment. In the dissolved pesticide
loading function. Equation 111-34, Q is runoff from the source area determined
by the Curve Number Equation (Equation III-3), and \/(\ * \) is the dissolved
pesticide in runoff. These loading functions are of the same form as the solid-phase
and dissolved chemical loading functions of Sections 3.4.4.1 and 3.4.4.2.
3.4.4.3.2 Computational Steps
The pesticide runoff model is implemented by a set of sequential computations:
1. The day of initial pesticide application is designated t • 0 and P
is set equal to the application to the surface centimeter (g/ha).
2. On each day t « 1,2,... following application, a check is made to see if
an "event" occurs. An event is either (i) a new pesticide application
or (ii) a precipitation (rain •»• melt) amount exceeding the soil's
available water capacity. If no event occurs, the computations proceed
to the next day. If there is an event, the current pesticide content of
the soil is determined by Equation 111-28.
3. If Rt * Mt > w, then pesticide leaching will occur, and the
following steps are required:
a. Dissolved pesticide Ot is obtained from Equation 111-32.
b. Runoff Qt is computed by Equation 111-3. If Qt » 0,
go to step e.
c. Dissolved pesticide runoff PQt is determined from Equation 111-34.
d. Adsorbed (solid-phase) pesticide runoff PX( is obtained from
Equation 111-33 with soil loss Xt given by Equation III-ll
and adsorbed pesticide A given by Equation 111-31.
e. Soil pesticide level is updated to P* by Equation 111-35.
Note that Equation 111-35 may predict substantial pesticide losses
in percolation even if no runoff occurs and hence P)L and
P(L are both zero.
These computational steps are repeated for subsequent days following a storm until
the surface pesticide level P becomes negligible. Often the combined effects of
decay and leaching will remove virtually all pesticide from the surface layer within
several weeks of application.
3.4.4.3.3. Data for the Pesticide Runoff Model
Four types of data are required for pesticide runoff calculations: daily
weather records, Universal Soil Loss Equation parameters and runoff curve numbers,
soil properties and pesticide parameters. The first two categories have been dis-
cussed In previous sections. The soil properties needed are available water capacity
(w) and bulk density (b). These parameters are often given In county soil surveys.
-193-
-------
Representative values of w and b for several soil textures are given 1n Table 111-13.
These data are mean bulk densities for 207 soils and mean available water capacities
for 154 soils reported by Baes and Sharp (1983).
Pesticide application rates, timing, and mode of application cannot be general-
ized. This Information can only be obtained from local or regional pest control
specialists. Mode of application refers to surface applied versus soil Incorporated
pesticide. The model describes pesticide behavior Into the surface centimeter of
soil and hence the application P or &P are the chemical additions to that
surface layer. For example, 1f 3000 g/ha of pesticide 1s applied to the soil and
Incorporated to a depth of 5 cm (2 1n.), the application rate for the surface layer
Is 3000/5 * 600 g/ha {assuming complete mixing In the soil). Conversely, If the
pesticide 1s left on the soil surface, the entire 3000 g/ha Is contained In the
surface centimeter.
3.4.4.3.3.1 Pesticide Partition Coefficients
Pesticide adsorption 1s generally considered to be related to soil organic
matter. A genera! relationship given by Rao and Davidson (1980) 1s:
K0 • HOC (%OC/100) (I 11-36)
where
Kg. • pesticide partition coefficient for organic carbon
tOC • organic carbon of the soil, measured as a S .
Table 111-14 lists Kg* values which have been summarized by Rao and Davidson
(1982) from a number of studies. The table entries are means and coefficients of
variation (standard deviation/mean, :s a percent). The mean values can be used to
estimate a partition coefficient for any soil. For example, the KQ value for
atrazlne 1n a soil rfth 2 percent organic carbon 1s:
KJJ - 163 (2/100) • 3.26
Soil organic matter percentage, 10M, 1s often more readily available than IOC.
In such cases, IOC may be estimated as 59 percent of organic matter (Brady, 1974):
%OC • 0.59 (%OH) (111-37)
When KQJ values are unavailable, they may be Indirectly measured by the
octanol-water partition coefficient Kgy. Rao and Davidson (1980) derived
the regression equation:
log Kgg • 1.029 log K^ - 0.18
or
-194-
-------
TABLE 111-13
MEAN BULK DENSITIES AND AVAILABLE WATER CAPACITIES
(Bass and Sharp, 1983)
Soil Type
Silt Loam
Cl ay and Cl ay Loam
Sandy Loam
Loam
Bulk
Density
b (g/cm3)
1.33
1.30
1.50
1.42
Available
Water
Capacity
w (cm/cm)
0.22
O.U
0.14
0.19
TABLE 111-14
ORGANIC CARBON PARTITION COEFFICIENTS FOR SELECTED PESTICIDES
(Rao and Davidson, 1982)
P««cicl
-------
KQC » 0.66 K^j029 (111-38)
Values of K^ for selected pesticides are given 1n Table 111-15.
3.4.4.3.3.3 Pesticide Decay Rates
Pesticide decomposition In the soil Is related to moisture, temperature and pH.
Unfortunately, the only Information usually available 1s a simple pesticide half-life,
which 1s the mean number of days required for 50% of the original pesticide to
decompose In the soil. Decay rate k$ can be obtained from half-life using
Equation 111-27 (with A?t • o). Since at t « half-life, Pt • 0.5 PQ:
Pt - 0.5 P0 - P0 exp (-k$t)
and half-life 1s given by:
-ln(0.5)/k$
or
k$ • 0.69/Half-l1fe (days) (111-39)
Mean decay coefficients from Rao and Davidson (1982) are given In Table 111-16
for 32 pesticides. Three different rates are given for many of these chemicals.
When available, the "field" coefficient should bt used, since It 1n principle most
closely corresponds to actual runoff conditions. The starred {*) lab rates are the
second choice, since they also measure decomposition of the original compound. The
remaining lab rates attempt to describe the complete decay of the pesticide and Its
decomposition products. These "total decay" rates may be used If a very conservative
runoff estimate Is described, but the nature, toxldty and fate of most Intermediate
pesticide decomposition products are so poorly understood that 1t 1s probably mis-
leading to model them with a simple first-order decay rate.
The mean decay coefficients given 1n Table 111-16 are supplemented by the
specific k values given 1n Table 111-17. The latter were summarized from a
large number of decay studies by Nash (1980). Since the coefficients 1n Table 111-17
are often unique to specific soil types, pH and organic matter contents, they are
perhaps less useful In screening studies than the mean values 1n Table 111-16.
However, many commonly-used pesticides are not listed 1n Table 111-16, and In such
cases the data In Table 111-17 may be the best available Information.
-196-
-------
TABLE III-15
OCTANOL-HATER PARTITION COEFFICIENTS FOR SELECTED PESTICIDES
(Rao and Davidson, 1982)
r««ticid«
A. INSECTICIDES
ALDICAXB
ALTOS ID
CARBARYL
CHLORDANE
CHLORPYRIFOS
CHLORPYRiros
CHLbRTTRIFOS
cHLowYitiros, METHYL
CHLORPYlliros, METHYL
ODD
OPE
DOE P.P
DOT
DOT p,p
DOVP
OIALirOR
DIAZZNOM
DICKLOmmllOM
DICIFOL
DIEL0RZM
DINOSEB
EMDRIN
mOXYCHLOR
nuinonzoM
KB
UPTACKLOR
LETTOrOS
LEPTOPHOS
LDOAKE
KAMTVXON
KRBOKTL
KCTBOXrOLOR
Krnm.
PARATBIOH
rERKETHRUf
S.OOOOOE400
1. 76000E-H)2
6. 510OOE+O2
2.07000E402
2.10«OOE-K)3
2.0S900E-H)3
6.60000E-KX
1.28825E-HD5
1.97000E-K»3
2.M170E-KM
1.15000E-KJ5
7.34450E-KK
4.89779E-KI5
3.70000E+O5
1.
4.
1.
1.
3.
4.
1.
1.
1.
2.
1.
7.
FMSALONE
raostET
9SOOOE4O2
69780E-KM
03200E-KJ3
M038E-H15
46100E-K33
93000E+03
9«OOOE-K)2
61900E-H)3
HOOOE-H33
29900E4O3
MOOOC+06
WMOE-H33
4.12200E-H33
2.04174E-M)*
«.43000E-K)2
2.JOMOE402
7.7«OOOC402
1.20000C401
2.050001403
1.20000E-K>3
2.07MOE+03
*. 435001403
7.5JOOOI402
•.23000E-HU
1.W530E-KM
t. 760001402
P«»tlcld«
PROPOXUR
ROWEL
TERBUFOS
TOXAPHENE
B. HERBICIDES
ALACHLOR
ATRAZINE
ATRAZINE
BXPENOX
BROHACIL
CMLORAKBEN
CBLOROPROPHAM
DALAPON
DALAPON, NA SALT
DICAKBA
DICKLOBEMIL
DZURON
HOKUXON
KSMA
NITIOrEM
PARAQUAT .2110.
PICLORAM
PROPACHLOR
PROPANIL
SDUZIME
TEMACIL
TtirtURALZV
2.4-0
2.4-D
2.4-D
2.4. 5-T
2.4.5-T. B0m ISTZI
2.4,5-T ocm urn
C. rONCICXDKS
BEMOHTl
CAPTAB
rcr
K
ow
2 . 80000E-K)!
7.M580E'HX
1.67000E-H)2
1 . 69SOOE+03
4 . 34000E402
2.I2000E402
2.26000E402
1.740OOE402
1.04000E402
1.30000E401
1.16000E+03
5.70000E400
l.OOOOOE+00
3.00000E400
7 . 87000E402
6.50OOOE402
1.33000E402
I.OOOOOE-04
1.24SOOE4Q3
l.OOOOOE+00
2.00000E400
4.10000E401
1.06000E402
>B0000E401
.800001401
.15000E403
.16000X402
.4300OI402
.46000C402
.000001400
.4OOOOI404
.OMOOC402
2.640001402
3.300001401
1.429001404
-197-
-------
TABLE II1-16
MEAN FIRST ORDER DECAY COEFFICIENTS FOR SELECTED PESTICIDES
(Rao and Davidson. 1982)
Ratt Coeff. (day'1)
P«*Clcl4«
Mean
ZCV
A. HERBICIDES
2.4-D
Lib.*
Ub.
Field
0.066
O.OS1
3.6
74.2
23.3
S3. 3
Pesticide
Race Coeff. (day'1)
Hean
xcv
1. INSECTICIDES
PARATHIOff
Ub.*
Field
HETHTL FAIATHION Ub.*
2.4,5-T
ATRAZ1NE
SDtAZIME
TRIFLUJALW
Ub
Ub
UOHACIL
TIXIACIL
LINUXOM
OIUKOM
DICAM3A
PICUJHAN
DAUtfON
TCA
CLYFHOSATE
PARAQUAT
Ub.
Ub.*
Ub.*
Ub.
Field
Ub.*
Field
Ub.*
.* (anaerobic)
. (chain)
Fitld
Ub.*
Ub.
Field
Ub.«
Ub.
Field
Ub.*
Field
Ub.
Field
Ub.*
Ub. (ring)
Ub. (chain)
Field
Ub.*
Ub.
Field
Ub.*
Ub.*
Field
Ub.*
Ub.
Ub.*
Field
0.029
0.035
0.019
0.0001
0.042
0.014
0.022
0.008
0.025
0.0013
0.02
0.0077
0.0024
0.0034
0.013
0.00*5
0.006
0.0096
0.0034
-
0.0031
0.022
0.0022
0.0044
0.093
0.0073
0.0008
0.033
0.047
0.039
0.073
0.1
0.0006
0.0016
0.00013
31.7
82.9
47.4
70.4
33.3
71.4
93.3
63.5
.
.
63.0
49.4
116.2
100.0
33.3
124.0
55.0
19.8
41.2
-
38.1
80.2
-
-
16.1
38.9
111.3
51.5
-
103.4
121.0
93.0
-
DIAZ UWM
FOMF05
HAIATBIOK
PflORATC
CAUonnun
Uk.*
CARJARYL
Field
Ub.*
Ub.
Ub.*
Ub.*
Ub.*
Field
Ub.*
Ub.
(anaerobic)
Field
Ub.*
Ub. (Chain)
DOT
Ub.*
ALDRIM and
DZEL0R0T
CKDUN
Field
Ub.*
(anaerobic)
Ub.*
Field
Ub.*
(anaerobic)
Field (aerobic)
Field
pvt ftwft ftfT
urtMatot
LWSAJtt
tab.
C. FPMC1CIOP
PC?
Ub.
CATTAII
(anaerobic)
Field
Ub.*
Field
Ub.*
(anaerobic)
Ub.*
(anaerobic)
Field
Field
0.029
0.057
0.16
0.046
0.023
0.022
0.012
1.4
0.0084
0.01
0.047
0.0013
0.026
0.016
0.037
0.0063
0.10
0.00013
0.0035
0.013
0.0023
0.03
0.0015
0.0053
0.0024
0.011
0.0046
0.0026
0.0046
0.02
0.07
0.05
0.231
48.3
101.8
-
-
108.7
-
-
71.4
•
30.0
87.2
-
30.0
87.5
36.
101.
79.
130.
82.
-
100.0
53.3
-
-
104.2
119.6
-
60.0
44.3
-
•Theae racea are baaed on ch« dlaappearaace of »olv«nt- extr actable parent
incubation condition*. ualea« atated otharviae.
-198-
under aerobic
-------
TABLE 111-17
FIRST ORDER PESTICIDE DECAY COEFFICIENTS FOR
SELECTED PESTICIDES AND SOIL CONDITIONS
(Nash, 1984)
Ptstlcl*
In*
•AS 34IOF-
r
-tottlitf Soil
$0(1
Al MM or
Mrttratt
Anonle
t.4-0
-taff
taff
2.4-0 iMKtyl
Mttr • vim.
2.4-0 Itooctyl toff
ostor • «1w.
2.4-0 IMKtyl
Mtor •
•1
I
•It
•11
(1)
WW1C10M
A«wi(t Flnvoti
Afontt
NOMICIOCS
(kf/M)
O.OiO
.14M
.0023
.07M
44
44
14
2.0
14 MM
U Jvfy
3D July
.OH4
.0*1*
4013
V«r1e«s
4.S
44
44 ton*
34 n «»4140
4S
4
4W2
4217
items
24 «r«t
34 Forttt
34 ftrwt
24 ftrcn
34 Uofcocftory 30*C
4 4407
4 4W7
4 .1733
>47M
4 .13M
47M
4 .1733
>47M
IS 4*4«
34 Ub«nt«ry 10*C
34
34
34
34
34
II
124
12.1
U.I
12.1
12.1
4731
4417
UOOl
49S1
4SSS
jaw
•199-
-------
TABLE III-17 (Continued)
't*t!cfd«
50"
T»Pt
vV
Crop
«oo1lc«tlon
rate
2.4-0 (sooetyl
tstor • aalne.
01ehlerproe«—
Olcftlorprep
•-OuocMU
•OuacMU
si
OlcMorprop —Cross Ttabers 1
el
el
e
1
»1e
s1l
ill
»U
fs
OtnUrwIne
Oturon— Norfolk
OluroA————*0»cttur
CFTC--
Crrc-
•FlvcfclortMn
riucMortUn
IsoorooaHfl-—
Isooropa) In——
eisiM
Isooropallii"' -— Ocfclay
IsoorttoaUfi
K«rowtl1«tt -cl
*••* • 1C
U
11 Huron-
LlmroiH
L1 Huron ——
Li
•Is
-cl
-0>S
Natr1fe«t1«
BO*
at*
el
cl
-•1
-si
•Ural to
•1traU»
• .4
7.5
7.0
1.7
7.2
4.7
4.7
•.3
C.J
7.0
7.0
7.0
7.0
7.0
4.1
«.$
4.7
4.7
4.7
4.7
«.J
i.3
7.S
7.0
(X)
NEUICIKS
3.2 Loboritory
12.S
0.0257
3.3 Forest
2.8 firtss
3.8 Forest
4.0 laboratory
4.S Laboratory
S.I Various
l.C
2.1
2.»
.«
2.2
1.1
0 Carrots
0 larley
SorfHw
.J
.1
.1
-5.2
k$.2
.45
.IS .
:
«
1
l.M
3.3»
1.12
J
t$ •
3 4 4
3 4 ;
1 7 j
34 ]
2.24
4.48
2.24
4.41
.0578
.0864
.0(93
.0193
.0193
.0064
.0072
.0220
.,.0248
'.0070
L/.0045
.0023-
...003*
S/.0054
.0040
.0304
.0214
.0275
.0057-
.0282
.0118
S/.0104-
.0231
,.0047
'.0280
• .0039
'.00(1
1771
^..1070
/.02W
.0231
.0248
.00211
.0040
.0075
.0073
.005*
.00f2>
2.1
2.f
2.1
t.t
.1
.f
4.0
4.S
Laboratory
Laboratory
.09M-
.OOM
1.12 .0110
1.12 .0079
2.24 .0090
2.24 .0024
.Si .0155
1.12 ,..00»1
2S.OOS4-
.0083
.0144.
.0058
.81 .0394
.15 .0394
. ..0025
.
I^.OOSC
-ZOO-
-------
TABLE 111-17 (Continued!
f«st1c1d»
son
Typa
pH
Crop or Application
condition* ratt
HERBICIDES
Ptbulata fctglna e
•tbulata -Utyburn 1
Mclora* Scot 1. oxbowt
Victor*. Various
rlclora* Nova Scotia cl
rlclora*-—--—So»trt«t tl
Mcloraa- farln el
Mclora*- Chandler f si
r1clora»————Qitstor 1
Mclora^———-- ChMttr 1
f 1 c 1 oram- Varl out
Plcloraia—-~~—OuacMta el
Ptclora* -OuacMta cl
Mclora*——Cross Tlattrs 1
rrofluralln
Profluraltn
•oMtrynt-—-~—~s1
Propazl *»••••' tl
Propazlnt
to tl
cl
S1 !»«»--
SI 1 vox
$11vo*
SlMzInt
Slauzliw
SlMZlM
SlMZlnt
TobvthlurM Various
91 «)Ck C «
|,tniistert
T«b«th1urw»~ rtllutttrt
Tobuthlu
Ttbuthl
HoMton Hack
Mic Ptllvsttrt
Houston Hack
Ittlc Ntlusttrt
Mawtto* Hack
Trial 1at».
Trlallata-
Trial latt
Trial! at>
Mglna c
Coarto si
artt si
Owachlta si
TH a II ata—--Me/bum
Trial lata-
•2
2
2
2
2
.S-T-
,5-
,$-7 Cross Tlafctrt 1
,5.T-~~~-Faii«ii el
,5-T-—••OMHidltr fit
7.5
7.0
4.8
6.3
S.5
5.8
5.8
Various
7.0
4.8
(.5
7
7
4.1
I.S
7.0
4.0
4.5
2.9
1.9
1.7
1.9
1.9
3.3
2.8
3.8
2.0
1.0
2.0
3.3
Z.8
3.8
2
2
1.0
2.0
2.0
Laboratory
Laboratory
Various
Pillow
Orchard grass
Orchard grass
Orchard grass
Orchard grass
Forest
Crass
Fortst
Lcttuct
Fortst
Grass
For*st
Mont
Kont
Crow*
Una r rn nr .if
NOncropPtQ
Com
In siirfac*
runoff water.
In turfact
••lltts.
.85
.65
4.8
4.48
2.24
2.24
2.24
4.48
.05
.6
.(
.6
6
6
6
3 4
3 4
.025
2.24
2.24
a 0396
.0396
.0025
If .00772
.0044
.0050
.0354
.0258
.0268
.0269
I/. 004
I/. 0019
.0044
. .0028
4/.0047
U .0051
.0238
.0108
.0056
. .0061-
i/.otss
.0330
.0495
.0462
I/ .0074
.0083
.0116
.0062
.0539
A^9
.062
.0187
.002*
.0060
.0427
In turfact . ,.
broadcast spray:'*
7.S
7.0
7^
7.0
6.$
7.1
6.3
S.S
4U>
4.S
2.0
2.0
4.*2
3 *3
2.8
3.8
1.9
1.7
In otlltts
In surface
band pellets.
•roadcast Iff
soil stray.
Laboratory
Laboratory
•arley
Ih^hA
HOUV
HOMO
MOM
Fortst
Crass
Fortst
Orchard grass
Orchard grass
2.24
2*4
.*^
2.24
.CS
.65
1.7
3.4
2^
2.2
.6
.6
.4
2.24
2.24
.0201
.0517
.0624
.0069
.0090
.0110
.0144
.0067
.0088
.0053
.0289
.0330
.0330
.0508
.0495
-201-
-------
TABLE 111-17 (Continued)
Son
9*
Crw «r
2.4.S-T CMttw 1
2.4.S-T Ossster !
Trlfl«r«1t*
TrtflwiU* feed il
Tr1Mw*1t»—-UH MM
7rlflnr«)l» Ory Mil
rrtflwraltn -Oe*l«r *<1
Trtrmr»H«u——«c*Ujr til
Tr1flw«U»~——Orttty tfl
Trtflwatln——tlwrfttjltf ft
efUU f»
ttf IM C
AUIcar*
A»«le«r»
AUicir*
A1«r<*
A1«rln
NMUO* cl
— MMitto* el
MM«to
-------
TABLE 111-17 (Continued)
?*it!c!S*
Soil
pH
ON
Crop or
conditions
r«t*
msecncioes
AxtaptioiMthyl Vlnoy 1
l*rw1ck si Vofttablts
•roaopftot—-------Coopos It*
Caroaryl
Cartarjrl Udalpwr cl 7.1 l.( Various
Carfear/1 Jot»n«r si l.( .2* Various
Carftofuran Takt sll 8.S
CCA-12223;- stl 4.8 1.0
C6A-1222321 Sll t.5 2.2
Cklordano—— Itmlck si
Qi lordano———COBCOS Ut
CDlorfonvlMfHOS
OlaztnoA———-Ca«p«tUt
OlulnoA Sultan sll ( 7 3.1 25*C
~Carr<«fto« sll Olsktf Fallow
O.OCT4
.CIO!
.04U
.0605
S.O
S.O
7.4 IHC
7.4 INC
7.4 IHC
7.4 WC
15.0
15.0
10.0
2.0
OloloVln—......Ia*j«r1al sc
01tlO>1n Noltvtllt fsl
Oltldrln -Coo««s1tt
7.*
4.«
*.$
»,p'-OOT
f.t'-OOT 4nM s»l
>.»'-OOT -C*torjvt11« «Kk
• ,p*-OOT—»—— •--Narlttta si
O.»'-OOT r«i fsi
i.p'-OOT Nl«| sll
p.p* -DOT Muck
P.P'-OOT—......COMMICt Sll
1.0
.S
1.0
2.0
1.1
.1
74. S
2.0
.1
J.I
40.0
4.S
4.5
20.0
20.0
.0021
.0140
Tof 1»
.0098
7 of If
1^.0006
-------
TABLE 111-17 (Continued)
Ptsttctt*
ten
ON
Crop or
COMUtwi*
r«tt
(tj
».
»
»
.OOT C«r
•-OOT
•-OOT
•-OOT- C«rr1iift«M t<1
rrlMto*
H1w« id
(11
01 tk*
mstcTicioes
FtllM
•.»'-OOT fantkk (t
OfattttotU——-Cooposltt
0l«i frtntm* *)
mi«UH«i....... OtolMM c
N«1«U(
MrtkWttttw.
•«1
itte
(kf/h«)
4.
4.
11.
4.
11.
11.
17 OOT
1.)
1/0
/0.0024
27 .0044
.0001
.0011
.00»
if .00014
.0007
l/.OOOM
.OMO
.1M4
.0142
.0014
.0012
.0001
.001$
.0014
4.«
«.S
t.s
?.t
;.•
1.0
2.0
1.0
.$
ZtytU
FtllM
10
10
to
11 .«
11.2
11.2
1.12
.0022
.0032
.002S
.OS7I
.1155
.OISi
.0021
.0025
.0021
I/ .0004
.OOM
.0022
.0024
.0017
.0011
.0014
11.2
11.2
11.2
11.2
11.2
.0147
.02M
.0074
.02U
.02*4
PiriUtw
^•rttJilc*.
in
7
1.1
J.I
4.7
1.0
2.0
1.0
1.0
.4
2.441*
1.24*1
.4152
1.M32
l.MM
.04M
.010*
.049*
S.C
u
10
.0033
.2207
.2931
.024*
4/.OS4
I/ .0044
-204-
-------
TABLE III-17 (Continued)
Pestlcio*
OK
Crop or Application
condition* ratt
ParatMon Joon*r si
Parathlon———Macho til
Parathlon-"•—-•-Cfim* c
ParatMon Nad*ra tl
•ParatMon---—~lav«tfl tl
tarathlon Santa Lucia til
Parath Ion ft 1
Parathlon———•• tic I
Parathlon —e
Parathlon tl
Phmthottt———~ -f 11
Phcnthoat*————c
PlMnthoatc————-t 1
Phoratt-——Sacramento wck
Phoratt——-Sacravtnto peat
Phoratt——-S«cr«wnto ptat
PHorUt Takt til
Phorat*——-— StcraMflto t
Phoratt———Stcrcanito c
Zlnophos Sultan til
Zlnopnot Sultan til
Zlnepho* Sultan til
Zlnophos Sultan til
Zlnopnot Sultan tl)
Zlnopnot
8.<
8.5
S.S
8.1
Zlnophot
Dlcltloftntlilon—Co^otltt
Tr Icfcloraut*—CoMpot Itt
I/
I/
1' r
If '
if
If Ottthyl
Mtttr.
IKSCCTICIOCS
.21
(*!/"•)
10
T of 8
0.0727
7 of 7
.
.1306
0.8
2.1
2.3
1.8
.8
2.1
2.3
1.8
I'.'llSO
.out
If'JSSi
«*<«
.2*14
.2MS
!0141
3.1
3.1
3.1
3.1
13
13
13
10
13
13
.0040
*/.0043
l^oosi
1^.0078
A*W
15*C
.01M
KMTICIOCS
.OOH
.0133
.0208
.0075
.0031
.0050
coMCMtrati fomlatlon.
p*»tphorotl»1o«U.
-205-
-------
EXAMPLE III-6
Pesticide Runoff
Two pesticides, carbofuran and atrazlne, have been applied to a cornfield at
planting time. Carbofuran 1s an Insecticide used to control corn rootworm and
atrazlne 1s a herbicide for need control. Three days after each pesticide has
been applied at 4000 g/ha, a 4.5 cm storm occurs which produces 0.2 cm of runoff
and 0.6 t/ha of sediment. The soil has an organic natter content of 31, bulk
density of b * 1.3 g/cm and available water capacity of w - 0.2. Determine
the runoff losses of each pesticide.
Solution:
Partition coefficients KO are determined from K^. values in Table 111-14
(Atrazlne, K^. - 163; Carbofuran, K^. • 29.4):
I
Kg « KQC (IOC/100) (111-36) !
I
where I
*OC • 0.59 10M (Equation 111-37), or %OC • 0.59(3) -1.77 j
Kp • 163(.0177) « 2.89 (Atrazine) j
Up • 29.4 (0.0177) » 0.52 (Carbofuran) j
Decay coefficients k{ (field values) are given In Table 111-16:
k$ • 0.042 (Atrazlne) !
ks • 0.016 (Carbofuran) j
Total adsorbed and dissolved pesticide 1n the surface centimeter are given I
by Equations 111-27, 111-31, and III-32. Assuming the pesticide Is left on j
the soil surface, Initial levels for both pesticides are P » 4000 g/ha. For j
day t « 3: j
i
Atrazlne: j
P3 - 4000 exp [-0.042(3)3 • 3526 g/ha
w/ty - 0.2/2.89(1.3) • 0.0532 !
A3 • [1/(1 * 0.0532)] 3526 - 3348 g/ha \
03 • Cl/(l * 1/0.0532)] 3526 - 178 g/ha I
Similarly, for Carbofuran: |
P3 • 3813 g/ha j
A3 • 2942 g/ha j
D3 « 871 g/ha
-206-
-------
Solid-phase and dissolved losses are given by:
(A3/100b)X3
where
PQ3 • CQ3/(R3 * M3)]03
X3 « 0.6 t/ha
Q3 « 0.2 cm
R3 « 4.5 cm
M3, snowmelt, is obviously zero.
Atrazine:
Carbofuran:
• [3348/1.3(100)]0.6 - 15.5 g/ha
• (0.2/4.5)178 « 7.9 g/ha
PX3 » [2942/1.3(100)] 0.6 « 13.6 g/ha
PQ3 - (0.2/4.5) 871 - 38.7 g/ha
In Summary:
(111-33} |
i
(111-34) j
Losses in
Runoff (g/ha)
Solid-phase
Dissolved
Total
Atrazine
15.5
7.9
23.4
Carbofuran
13.6
38.7
52.3
END OF
111-6-
3.5 SALT LOADS IN IRRIGATION RETURN FLOWS
3.5.1 Description
Pollution of surface waters by salty Irrigation drainage water 1s a problem 1n
many arid regions. As shown In Figure 111-16, water may be diverted from a river to
water crops in an Irrigation district. Portions of the diverted water are lost from
the diversion canal through seepage and evaporation, and most of the remaining water
1s applied to crops 1n the Irrigation district. Much of this applied water Is
consumed by plant evapotransplration (ET) and the excess passes through the soil to
be collected by tile drainage and returned to the river. This drainage water
has a much higher salt concentration than the Irrigated water. As the water moves
through the soil, it retains Its salt mass, but due to ET, the water volume 1s
dlmlnl shed.
-207-
-------
Return flow salinity can be computed by assuming a steady-state condition 1n
wh1ch:
Salts applied in irrigation » Salts removed in drainage
v -sR
or
SQI/R (111-40)
where
s - irrigation water salinity (mg/1)
s m return flow salinity (mg/1)
I * irrigation application (m /day)
R « return flow (m /day).
Salt concentration or salinity is measured either as dissolved solids (mg/1) or
electrical conductivity («mno/cm or mmho/cm). In the Western U.S., an average
conversion factor is 1000 * mho/cm « 640 mg/1. Water fluxes, such as I and R refer to
total water movement over the irrigation season and can be measured in length or
volume units. For example, if I is given in centimeters, it is converted to cubic
meters by l(nr) • I{cm) 100 A, where A - irrigated area (ha).
When the irrigation diversion is taken from a river, as in Figure 111-16,
s is the salinity of the river water. The return flow salinity given by Equa-
tion 111-40 obviously exceeds s since R •< I. The river salinity after the
return flow is:
s (0 - D) + sR
»;• n O.D + B (III-4l)
where
s' « river salinity after return flow (mg/1)
o 3
0 « river flow prtor to diversion (m /day)
0 • Irrigation diversion (m /day).
Since s' > s , the river 1s saltier for the next downstream user. As
o o
successive Irrigation districts withdraw and return water, the river becomes pro-
gressively saltier until It 1s no longer suitable for municipal or agricultural
use.
Variations of the salinity problem Include pimping of Irrigation water from
aquifers and unsteady-state or transient leaching of soil salts. In the former case,
s is the aquifer salinity. The salty drainage flow might be discharged to
surface waters as in Figure 111-16 or allowed to percolate through the soil, thus
producing saltier groundwater. Transient salt leaching often occurs when soils are
initially irrigated or reclaimed. Until a steady-state situation 1s reached, the
-208-
-------
RIVER
DIVE*
LOSSES
(SEEPAGE,
EVAPORATION)
SION
IRRIGATION
RETURN
IRRIGATION
DRAINAGE
FLOW
EVAPOTRANSPIRATION
FIGURE 111-16 COMPONENTS OF AN IRRIGATION SYSTEM
salt load 1n return flow may exceed the salts applied 1n irrigation.
3.5.2 Estimation of Return Flows
Equations 111-40 and 111-41 may be used directly when return flow volumes R
are known. However, accurate return flow measurements are often unavailable and
Indirect estimates are necessary. A general procedure for computing return flows Is
shown In Figure 111-17.
Design factors for Irrigation systems Include irrigation efficiencies, diversions,
leaching fractions and ET. Hater losses 1n the diversion system are Indicated by a
delivery efficiency, E.:
I
EdD
(111-42)
To prevent salt buildup 1n soil which would Injure plants. Irrigation applications
must exceed crop water needs so that applied salts way be washed fro* the soil In
drainage. The leaching fraction 1s the fraction of Irrigation application which 1s
used to control salinity, or the ratio of drainage to Irrigation. As shown 1n Figure
111-17:
LF - (I - E)/I
-209-
(111-43)
-------
RIVER
DIVERSION
RETURN]
LOSS
EVAPOT
IRRIGATION •
OilVERSION , DELIVERY
EFFICIENCY
RANS,
DRAINAGE "
IRRIGATION - EVAPOTRANS,
WATER TABLED.. _^
FLOW
TILE DRAIN
FIGURE 111-17 COLLECTION OF IRRIGATION DRAINAGE
Irrigation leaching fraction
.3,
where
LF -
E • crop ET (mj/day)
Since return flow (R) consists of the drainage water collected In tile drains,
ft « I - E and LF - R/I - R/EdD. Thus:
(LF)EdD
{111-44)
and rearranging Equation 111-40:
S0/LF
(111-45)
If irrigation diversion 0, delivery efficiency E. and leaching fraction
LF are known, return flow volume and salinity can be estimated by Equations 111-44
and 111-45. If LF 1s unknown. It can be determined from Equation 111-43, pro-
vided E, crop ET, 1s available. Since E depends on crop mixture and local weather
conditions, It Is best obtained form jcal irrigation specialists. In the absence of
such data, E may be estimated from potential ET. Potential ET, or PE, 1s a maximum
ET which occurs when the soil Is covered with a dense cover such as alfalfa and water
1s not limiting. Thus potential ET 1s a function of the atmosphere's ability to
absorb water. Actual ET Is generally less than PE, but by letting E • PE. we obtain
a conservative overestimate of return flow salinity.
Potential ET can be determined from pan evaporation data or empirical equations.
Figure 111-18 shows average annual pan evaporation for the U.S. Potential ET Is
-210-
-------
FIGURE 111-18 HEAN ANNUAL PAN EVAPORATION IN INCHES UIN = 2,Slew) (KOHLER EI AL, 1959)
-211-
-------
approximately 701 of pan evaporation. To use the data from Figure 111-18. we must
assume that all annual PE occurs 1n the growing season. Growing season PE may also
6e estimated from Hamon's (1961) equation:
PE • (0.021 H2p)/(T + 273) (111-46)
where
P£ • potential ET (cm/day)
H * mean number of daylight hours per day during period of Interest
T » mean air temperature during period of Interest (°C)
p » saturation water vapor pressure at temperature T (millibars).
Values of H and p are given in Tables 111-18 and 19. The "period of interest" for
irrigation studies is the irrigation season.
EXAW>LF ni-7
Irrigation Return Flows
A 3000 ha irrigation district diverts an average of 350,000 m /day of
water from a river 1n the Irrigation season. During this time, the mean river
flow 1s 1,000,000 m /day. The delivery system 1s 80 percent efficient
and the district operates at an average leaching fraction of 0.3. The river
water salinity is 200 mg/1.
Determine:
a) Return flow volume and salinity
b) River salinity downstream of the return flow.
Solution:
Data for the problem:
D - 350,000 m3/day
Q • 1,000,000 m3/day
so - 200 mg/1
Ed - 0.8
LF • 0.3
a) From equation 111-44, return flow 1s:
R - 0.3(9.8)(350,000)
• 84,000 m3/day
with salinity given by Equation 111-45:
S « 200/0.3 » 667 mg/1
-212-
-------
TABLE 111-18
MEAN DAYLIGHT HOURS PER DAY
Latitude
North Jan Feb Mar Apr Hay Jun Jul Aug Sep Oct Nov Dec
46 8.7 10.0 11.7 13.4 14.9 15.7 15.3 14.0 12.3 10.6 9.1 8.3
46 8.9 10.2 11.7 13.3 14.7 15.4 15.0 13.8 12.3 10.7 9.3 8.5
44 9.2 10.3 11.7 13.2 14.5 15.2 14.8 13.7 12.3 10.8 9.5 8.8
42 9.3 10.4 11.7 13.1 14.3 15.0 14.6 13.6 12.3 10.9 9.7 9.0
40 9.5 10.5 11.8 13.0 14.1 14.7 14.4 13.6 12.2 11.0 9.8 9.2
38 9.7 10.6 11.8 13.0 14.0 14.5 14.3 13.4 12.2 11.0 10.0 9.4
36 9.9 10.7 11.8 12.9 13.8 14.3 14.1 13.3 12.2 11.1 10.1 9.6
34 10.0 10.8 11.8 12.8 13.7 14.2 14.0 13.2 12.2 11.2 10.2 9.8
32 10.2 10.9 11.8 12.8 13.6 14.0 13.8 13.3 12.2 11.2 10.4 10.0
30 10.3 11.0 11.8 12.7 13.5 13.9 13.7 13.0 12.2 11.3 10.5 10.1
28 10.5 11.1 11.8 12.7 13.4 13.7 13.5 13.0 12.1 11.3 10.6 10.3
26 10.6 11.I 11.8 12.6 13.2 13.6 13.4 12.9 12.1 11.4 10.7 10.4
24 10.7 11.2 11.9 12.6 13.1 13.4 13.3 12.8 12.1 11.4 10.9 10.6
-213-
-------
TABLE 111-19
SATURATION VAPOR PRESSURE AS FUNCTION OF TEMPERATURE
(Jensen, 1973)
Temperature
<°C)
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
Saturation Water Vapor Pressure
(millibars)
6.1
7.1
8.1
9.4
10.7
12.3
14.0
16.0
18.2
20.6
23.4
26.4
29.8
33.6
37.8
42.4
47.5
-214-
-------
b) Downstream salinity is computed by Equation 111-41;
200(1,000,000 - 350,000} + 677(84,000}
s
o 1.000,000 - 350,000 * 84,000
• 255 mg/1
•- END OF EXAMPLE III-7
3.6 URBAN RUNOFF LOADS
Nonpoint source pollution from urban runoff differs in several ways from its
rural counterpart. Runoff rates are usually much higher in urban areas due to the
distribution of impervious surfaces (pavements, roofs, etc.). Urban runoff is
collected in separate storm sewers or combined sewers. The later collect both runoff
and sanitary wastewater. During a large runoff event, storm flow may exceed sanitary
flows by one or more orders of magnitude. To avoid flooding from surcharged combined
sewers, combined sewer "overflows" are discharged directly to receiving waters.
These overflows are highly polluting since they contain runoff pollutants, raw
sanitary sewage, and scoured wastewater solids which were previously deposited in the
sewers.
Urban runoff quality is influenced by human activities; Important determinants
are land uses and population density. Land uses may be considered the "source areas"
in an urban watershed; tne total runoff load Is the sum of runoff loads from each
land use.
Sections 3.6.1 and 3.6.2 describe equations for determining annual and event
pollutant loads. The annual loading functions are highly empirical, but provide
estimates of pollutant loads from both separate storm sewers and combined sewer over-
flows. Conversely, the event loading functions are more analytical, but describe
only runoff (I.e., separate storm sewer load).
Urban runoff and combined sewer overflow data are summarized by Huber, et al.
(1979) and E. C. Jordan Co. (1984). Additional references on urban runoff computa-
tions include Novotny and Chesters (1980) and Klbler (1982).
3.6.1 Annual Urban Runoff and Combined Sewer Loads
General urban loading functions have been proposed by Heaney, et^ a_l_. (1977), and
Heaney and Huber (1979) of the form:
L'- (IIN47)
-215-
-------
where
L, • annual load of pollutant due to runoff from land use k (kg/ha)
afc » pollutant concentration factor (kg/ha-cm)
F. - population density function
vfc - street cleaning factor
P - annual precipitation fern).
Total pollutant load from the urban area is:
L-2L)cAk (111-48)
where
L « annual pollutant load 20 days, no street cleaning effects are apparent and V -1.0.
Because most pollution load in.combined sewers is due to raw wastewater and sewer
scour, street cleaning will not significantly reduce loads, and Y. • 1.0 for
combined sewers areas.
-216-
-------
FIGURE 111-19 MEAN ANNUAL PRECIPITATION IN INCHES (!IN = 2.5i4cM) (OILMAN, 1964)
-217-
-------
TABLE II1-20
POLLUTANT CONCENTRATION FACTORS FOR ANNUAL LOADING
FUNCTIONS (HEANEY AND HU8ER, 1979)
Pollutant (kg/ha-cm)
Land Use
Separate Sewers
Residential
Commercial
Industrial
Other Developed
Combined Sewers
Residential
Commercial
Industrial
Other Developed
BOD5
0.35
1.41
0.53
0.05
1.45
5.83
2.21
0.21
SS
7.2
9.8
12.9
1.2
29.7
40.6
53.0
4.9
VS
4.2
6.2
6.3
1.2
17.2
25.6
26.2
4.8
P04
0.015
0.033
0.031
0.004
0.061
0.138
0.291
0.018
N
0.058
0.131
0.122
0.027
0.239
0.539
0.504
0.066
•- EXAMPLE 111-8
Estimation of Annual Urban Pollutant Loads
Consider a city of 4000 hectares of which 20 percent Is commercial, 10
percent Industrial, 65 percent residential and 5 percent 1s In other developed
areas. The residential population density 1s 25 persons/ha. Most of the dty
separate sewers but approximately 30 percent of the residential area still has
combined sewers. The streets are swept every five days In the commercial and
Industrial areas and are not swept 1n the residential areas. The mean annual
precipitation 1s 105 cm. Determine the average annual loads of nitrogen and
phosphate.
has
-218-
-------
I Solution:
| The land use areas are:
Commercial : BOOha
Industrial: 400ha
i Residential: 780ha, combined
j 1820ha, separate
Other: 200ha
Loads from each land use are given by Equation 111-47, with F from Equation
' 111-49. The street cleaning factor 1s:
' V • 5/20 » 0.25
I
I in commercial/Industrial areas and Y « 1.0 In all other areas. The population
I function for residential areas is:
! Fk • 0.142 * 0.134(25)°'54
I - 0.904
I Loading calculations are summarized in the following table.
1 0)c (kg/ha-cm) Lk (kg/ha)
I Land Use Ffc Yk N P04 N PO^
Residential
combined 0.904 1.0 0.239 0.061 22.69 5.79
separate 0.904 1.0 0.058 0.015 5.51 1.42
Commercial 1.0 0.25 0.131 0.033 3.44 0.87
Industrial 1.0 0.25 0.122 0.031 3.20 0.81
Other 0.142 1.0 0.027 0.004 0.40 0.06
Total annual loads are obtained by multiplying each load Lfc by Its respective
area as In Equation 111-48.
Nitrogen:
780(22.69) * 1820(5.51) * 800(3.44) + 400(3.20) * 200(0.40)
- 31,800 kg/yr
Phosphate:
780(5.79) * 1820(1.42) * 800(0.87) * 400(0.81) * 200(0.06)
« 8100 (cg/yr
Over half the pollution load comes from the 780-ha combined sewer residential
area.
' END OF EXAMPLE III-0
3.6.2 Event Loads in urban Runoff
Event loading functions for urban runoff are based on general procedures proposed
by Any e£ £]_. (1974), many of which were Incorporated 1n the U.S. Army Corps of
-219-
-------
Engineers urban runoff model STORM (Hydrologlc Engineering Center, 1977). The basic
loading function 1s similar to that used for solid-phase rural runoff loads (Equation
111-18). Sediment (also referred to as "dirt and dust" or simply "solids") 1n runoff
1s multiplied by a pollutant concentration:
L • 10"6 C Y (111-51)
where
I • pollutant load In urban runoff (kg/ha)
Y • sediment washed off the urban area during a runoff event (kg/ha)
C « pollutant concentration 1n sediment (ppm: ng/g, or mg/kg).
Although Equation 111-51 1s often used for both dissolved and solid-phase pollutants,
we would expect It to be more accurate for the latter.
Sediment washoff 1s limited by the total sediment which has accumulated on land
surfaces:
Y - W X (111-52)
where
X • accumulated sediment at the time of the storm (kg/ha)
W - fraction of X which washes off during the storm.
The washoff function is derived by assuming that washoff rate
-------
where
Q » total storm runoff (cm).
The washoff coefficient 1s determined by assuming 901 of accumulated sediment
will be washed off with 1.27cm (0.51n) of runoff (Amy et_ aj_., 1974). Hence
0.1 X(0) - X(0) exp [- 1.27u] or u - 1.8 cm"1. The fraction of sediment washed off Is:
m X(0)- X(h)
X(0)
• 1 - exp(-1.8Q) (111-56)
and Equation 111-51 can be written:
I • 10'6 [1 - exp(-l.SO)] C X (111-57)
When this loading function 1s applied to an area with multiple land uses, either
loads are weighted from each area:
L-£akLk (111-58)
or weighted average concentrations and sediment accumulations are used:
CX - PgakXk][|akCk] (111-59)
where
ak • fraction of total area In land use k
Lfc • pollutant load from land use It (kg/ha) as given by Equation 111-57
Xfc » accumulated sediment on land use k (kg/ha)
C)( • pollutant concentration In sediment on land use k (rug/kg).
3.6.2.1 Runoff
Two alternative procedures are used In STOW to compute storm runoff. The first
Is the U.S. Soil Conservation Service's Curve Number Equation (Equation III-3) as
described In Section 3.4.2. Appropriate urban curve numbers for average antecedent
moisture conditions (CN2) are given In Table 111-21.
The second option Is based on runoff coefficients and depression storage:
Q • CR(P . OS) (111-60)
-221-
-------
TABLE 111-21
RUNOFF CURVE NUMBERS (ANTECEDENT MOISTURE CONDITION II)
FOR URBAN AREAS (SOIL CONSERVATION SERVICE. 1975)
Hydro!ogle Soil Group
Land Use Description KBCD"
Open spaces, lawns, parks, golf courses, cmeteHes, etc.
Good condition: grass cover on 751 or more of the area 39 61 74 80
Fair condition: grass cover on 50% to 75% of the area 49 69 79 84
Commercial and business area (85% Impervious) 89 92 94 95
Industrial districts (72% Impervious) 81 88 91 93
Residential :
Average lot size Average % Impervious
1/8 acre or less 65 77 85 90 92
1/4 acre 38 61 75 83 87
1/3 acre 30 57 72 81 86
1/2 acre 25 54 70 80 85
1 acre 20 51 68 79 84
Paved parking lots, roofs, driveways, etc. 98 98 98 98
Streets and roads:
Paved with curbs and storm sewtrs 98 98 98 98
Gravel 76 85 89 91
Dirt 72 82 87 89
-222-
-------
where
P - storm precipitation (rainfall + snownelt, cm)
DS - depression storage (cm)
CR » runoff coefficient.
Equation 111-60 (which applies only for P > DS) suggests that precipitation must
satisfy the available depression storage on plant surfaces and in mud puddles, pot
holes, etc., before runoff will occur.
A conceptual view of this runoff process is shown in Figure 111-20. Depression
storage DS is at a maximum value DS* when the land surface is completely dry, and the
depression shown in Figure 111-20 is empty. However, previous events may have
partially filled depressions so that as in the figure, only a portion of DS* remains
to be filled.
Depressions are assumed to be emptied by evaporation, and a general mass balance
is:
DSt * Et - Pt (I H-61)
0 <_ DS £ DS* (111-62)
for
where
DS » depression storage on day t (on)
FV » precipitation on day t (rain + snowmelt, cm)
£t « evaporation on day t (cm)
^«
DS * maximum depression storage (cm).
Evaporation may be assumed equal to potential evapotransplration and determined as in
Section 3.5.2.
The depression storage computation (Equations 111-61,62) is a procedure for
describing antecedent moisture conditions. *en the Curve Number Equation is used,
antecedent moisture is a function of 5-day antecedent precipitation. In Equation
111-60, the water In storage on the land surface 1s the indicator of antecedent
moisture.
Both maximum depression storage DS and the runoff coefficient CR are
functions of the urban area's Impervious surfaces:
CR - cr1 I * cr (1-1) (111-63)
DS* - ds1 1 * dsp (1-1) (111-64)
-223-
-------
PRECIPITATION
P
DEPRESSION STORAGE
FIGURE 111-20 CONCEPTUAL MODEL OF DEPRESSION STORAGE
where
I
cr
1
cr.
d$1 • dsc
Default runoff coefficients used 1n STORM are cr. « 0.90 and cr,
' P
fraction of the urban area which Is Impervious
runoff coefficients for Impervious and pervious areas
maximum depression storage (on) for Impervious and pervious
areas.
0.15 (Hydrologlc
Engineering Center 1977). Typical depression storage coefficients are ds^ • 0.15cm
and ds • 0.60cn (Aron, 1982; Novotny and Chesters, 1980). These values may be used
when more specific local data are unavailable.
Impervious fractions are best estimated directly from aerial photographs or
land-use maps. When these are not available, regression equations based on population
density are sometimes used. The equation given by Htaney and Huber (1979) can be
approximated by:
I • 0.069 PO
0.48
(111-65)
where
PO
population density (persons/ha).
3.6.2.2 Sediment
Sediment and pollutant accumulation 1n urban areas 1s a complex process which
depends on dally deposition from the atmosphere and other sources, removal by street
-224-
-------
cleaning and washoff by runoff. In order to estimate C and X in Equation 111-57, we
must begin by determining the sediment or solids accumulation. This rate may be
measured by monitoring of storm sewer suspended solids data. When these data cannot
be obtained, average values from previous urban monitoring programs must be used.
Urban sediment data are often normalized with respect to the length of street
curbing. This is because most of the dirt and dust which constitutes urban sediment
collects In street gutters. Dally sediment buildup is:
x » 2 Cl (111-66)
where
x • daily sediment buildup (kg/ha-day)
2 - sediment accumulation rate (kg/km of curb per day)
Cl » curb length density (km/ha).
Curb length may be estimated as twice the total street lengths, and Cl is
obtained by dividing curb length by area. Alternatively, the regression equation
given by the American Public Works Association (1974) may be used (converted to
metric units):
Cl » 0.31 - 0.27(0.93)PD (111-67)
Urban sediment accumulation rates from several sources are given in Table
111-22. The rates given by Amy e£ £L, (1974) and Sartor and Boyd (1972) are mean
values based on data from a number of urban areas. The STORM rates are suggested
default values for that model. Although the Sartor and Boyd (1972) rates are larger
than the other two sets, they are generally comparable with the Amy Q t]_., (1974)
data. The Sartor and Boyd rates are recommended for use in Equation 111-66 because
they are conservative and consistent.
Sediment will accumulate at a dally rate x until the streets are cleaned or a
runoff event occurs. The dally sediment mass balance Is:
X.., - )L * x - v . s. (111-68)
where
X» • accumulated sediment at beginning of day t (kg/ha)
v • sediment removed In runoff on day t (kg/ha)
S( • sediment removed by street cleaning on day t (kg/ha).
If a runoff event occurs on day t, then from Equations 111-52 and 56:
-225-
-------
TABLE 111-22
URBAN SEDIMENT (SOLIDS) ACCUMULATION RATES
Land Use
Any et Sartor ft
aM!974) Boyd (1972)* STORM5
(all 1n kg/curbs-1 km-day)
Residential 42
Single-family
residential
Multi-family
residential
Commercial 21
Industrial
Light Industry 110
Heavy Industry 57
Parks
Open space 3.4
48*
66*
69*
127*
10
34
49
68
22
•Recommended values
*C1ted In Novotny and Chesters (1980)
bHydrolog1c Engineering Center (1977)
Cl -
where
0^ • runoff on day t (cm).
Conversely, If the streets are cleaned on day t:
(111-69)
(II1-70)
where
e » street cleaning efficiency (fraction removed by cleaning).
It Is assumed that streets are not cleaned on the same day that a runoff event
occurs.
Sediment accumulations and removal are Illustrated 1n the following example.
-226-
-------
--- EXAMPLE III-9 ----- - ------- - - - -----
Urban Sediment Accumulation and Removal
A stonm occurs on May 31 which removes all sediment from an urban area.
Subsequent storms occur on June 9 and June 15 which produce 0.5cm and l.lcm
of runoff, respectively. On June 6, the streets are cleaned with an efficiency
e « 0.4. The daily sediment buildup is x - 80 kg/ha. How much sediment 1s con-
tained in the runoff from the June 15 storm?
Solution:
Letting May 31 be day t - 0, the next event is the cleaning on day 6
(June 6). Accumulated sediment Is X, • 6(80) • 480 kg/ha.
o
Cleaning removes:
S& - 0.4(480) » 192 kg/ha
and on June 7, remaining sediment Is:
X7 • X6 - S6 * x
- 480 - 192 * 80 - 368 kg/ha.
For the June 9 runoff event, X~ • 368 * 2(80) » 528 kg/ha. Sediment
washoff from Equation 111-69 Is:
Y9 • [1 - exp(-1.8(0.5))] 528
- 313 kg/ha.
On the following day:
*10 * X9 ' Y9 * » j
» 528 - 313 * 80
« 295 kg/ha. !
On June 15, X15 • 295 + 5(80) • 695 kg/ha, and sediment washoff In the }
1.1 cm of runoff is: I
1.8(l.l))] 695
599 kg/ha
END OF EXAMPLE 111-9
3.6.2.3 Pollutant Concentrations
Pollutant concentrations in sediment can be obtained from sampling of sediment
accumulations 1n street gutters or sampling of storm sewer flows. General values for
conventional pollutants are given In Table 111-23. Concentrations of metals and
organic compounds are given 1n Tables 111-24 and 25.
-227-
-------
TABLE II1-23
CONCENTRATIONS OF CONVENTIONAL POLLUTANTS
IN URBAN SEDIMENT (SARTOR AND BOYD, 1972,
CITED IN NOVOTNY AND CNESTERS, I960)
Land Use
Residential Commercial Industrial
Pollutant (ntgAg) (mg/kg) (mg/kg)
B005
COD
KJeldahl Nitrogen
NUr ate- Nitrogen
Phosphate-Phosphorus
9,200 8,300
20,800 19,400
1,700 1,100
SO 500
900 800
7,500
35,700
1,400
60
1,200
TABLE 1 1 1-24
Cd
Cr
Cu
Fe
Pb
Mn
N1
Sr
Zn
CONCENTRATIONS OF
(AMY,
Residential Commercial
(mg/k) (mg/k)
3.0 4.2
192 225
93 133
20,600 23.300
1.430 3.440
392 397
28 46
21 18
350 520
METAL IN URBAN SEDIMENT
et al_, 1974)
Industrial
Light Heavy
(nig/kg)
4.0 3.9
288 278
128 107
21.800 28.600
2.780 1.160
490 570
41 37
27 23
368 317
Weighted Mean
(mg/kg)
3.4
211
104
22.000
1.810
418
35
21
370
-228-
-------
TABLE 111-25
CONCENTRATIONS OF MERCURY AND ORGANIC COMPOUNDS
IN URBAN SEDIMENT {AMY, et al, 1974)
Pollutant
Hg
Endrin
Dleldrin
PCB
Methoxychlor
DDT
Undane
Methyl Parathlon
DDD
Concentration
(mgAg)
0.083
0.0002
0.028
0.770
0.500
0.076
0.0029
0.002
0.082
3.6.2.4 Loading Computations
The basic loading function for pollutants from urban runoff events (Equation
111-57) Is deceptively simple. Storm runoff and sediment accumulation, which are
required by the loading function, depend on dynamic processes and are not easily
computed. If the Curve Number Equation (Equation 111-3) 1s used for runoff, curve
numbers must be selected based on antecedent precipitation. Conversely, the runoff
coefficient/depress Ion storage runoff equation (Equation 111-60) requires the dally
moisture calculations Indicated by Equations 111-61 and 62. Sediment accumulation Is
determined using Equations 111-66, 68, 69, and 70.
Event-based urban runoff loading computations are too complex to be routinely
done by hand. Although the following example demonstrates that hand calculations are
possible, loading estimates are most efficiently done by computer. Indeed, the
equations described 1n this section are the basis of the STORM computer model of
urban runoff waste loads.
-229-
-------
. EXAMPLE 111-10
I
j Lead In Urban Runoff From a Storm Event
i
Estimate the washoff of lead from a 200-ha urban area during a 2-cm rain.
! storm. The area has a population density of 25 persons/ha and Is 601 residential
[ and 401 commercial. The previous storm 20 days ago washed the area clean.
I Streets Mere cleaned 9 days ago with an efficiency of 551. Dally evaporation rate
| during the 20-day period was 0.2 cm/day.
I Solution:
| Since this 1s a multi-land use area, we will use weighted loads as 1n
j Equations 111-59 and 57. Equation 111-60 will be used to compute runoff:
I Q - CR(P - OS)
| To obtain runoff and depression storage coefficients from Equations 111-63 and
j 64, the Impervious fraction I must be calculated from Equation 111-65:
j I - 0.069 PD°'48
j « 0.069(25)°'48 - 0.32
Using the typical coefficients for Impervious and pervious areas given 1n Section
3.6.2.1:
CR • 0.90(0.32) * 0.15(0.68) - 0.39
! OS* - 0.15(0.32) * 0.60(0.68) • 0.46cm
I Since maximum depression storage 1s 0.46cm, and dally evaporation 1s 0.2 cm/day,
I depressions will dry within three days. Therefore, on the day of the storm
| DS • 0.46cm, and runoff 1s:
i
I Q • 0.39(2-0.46) • 0.60cm
j from Equation 111-57:
i
! L • 10'6 [1 - exp(-1.8(0.60))] CX
I • 0.66(10)"6 CX
k
| Thus 661 of the accumulated lead (CX) 1s wished off by the storm.
j Dally sediment accumulation rates can be obtained from Table 111-22.
j Assuming that the residential area 1s divided equally between single-family and
: multi-family residences, rates are (48*66)/2 • 57 kg/km-day for the residential
• area (601) and 69 kg/km-day for the commercial portion (401). The weighted
! average Is:
z • 0.60(57) + 0.40(69) • 61.8 kg/k»-d»y
j Curb length density from Equation 111-67 1s:
i Cl - 0.31 - 0.27(0.93)25 • 0.266 km/ha
-230-
-------
and daily loading 1s:
x » 0.266(61.8) • 16.4 kg/ha.
On day 11, when streets are cleaned, X « 11(16.4) « 180.4 kg/ha. Cleaning
removes 551, leaving 81.2 icg/ha. On the storm day:
X - 81.2 * 9(16.4) - 229 kg/ha.
Lead concentrations, from Table 111-24, are 1430 mg/kg and 3440 mg/kg
for residential and commercial areas, respectively, producing a weighted average
°f:
C - 0.60(1430) «• 0.40(3440) « 2234 mg/kg.
Substituting these values of X and C in the loading function produces
the lead load in runoff:
L « 0.66(10)"6 2234(229) « 0.34 kg/ha
or, over the 200-ha area:
200(0.34) - 68 kg.
END OF EXAMPLE 111-10
3.7 GROUNDWATER WASTE LOADS
3.7.1 Characteristics
Groundwater pollution Is of major concern because 1t endangers water supplies.
Organic chemicals, nuclear wastes, nitrates and other compounds may leach from such
sources as waste land application sites, storage lagoons, landfills, croplands.
lawns, gardens and construction sites. The general characteristics of the problem
are shown in Figure 111-21. The figure shows a "waste" which has been burled beneath
the soil surface. This waste could be contaminants such as PCBs In a landfill,
septic tank drainage, fertilizers, pesticides, or toxic compounds In abandoned waste
dumps. In other situations the wastes may be on the soil surface or contained In a
storage lagoon. Chemicals are leached from wastes by percolation, and this leacnate
moves through the unsaturated soil zone to an underlying aquifer or saturated zone.
Groundwater pollution Is often much more difficult to manage than pollution of
surface waters. Since the water supply Is beneath the soil surface, pollution
effects are seldom visible. When contamination 1s detected In samples from monitor-
ing wells or water systems, 1t 1s usually too late to eliminate the pollution source.
Chemical movement through the unsaturated zone Is relatively slow In the absence of
fractures or other Irregularities which channelize flows. In many soils, pollutants
may move less than a meter per year. A chemical which 1s detected In a well may have
begun Its transit from an abandoned waste dump 20 years ago. Even If the dump 1s
subsequently excavated, a 20-year supply of the chemical remains In the groundwater
-231-
-------
PRECIPITATION
IRRIGATION
EVAPOTRANSPIRATION
WASTE
^^••^ ~*^——M»"«=^ • •^•^^»~'^- — ^
UNSATURATED ZONE
PERCOLATION
(LEACHATE)
WATER 'TABLE'
AQUIFER (SATURATED ZONE)
FIGURE 111-21 POLLUTANT TRANSPORT To AN AQUIFER
transport "pipeline". Compared to surface waters, the "flushing time" of aquifers 1s
very long.
A further :omp1icat1on 1s the conservative nature of many pollutants 1n aquifers.
Aquifers lack much of the self-purifying or assimilative capacity of surface waters.
During transport through the aerated unsaturated zone, chemicals may be removed from
leachate by plant uptake, volatilization, biochemical decay and adsorption. However,
these removal mechanisms are often greatly reduced or eliminated once a chemical
reaches the saturated zone.
Groundwater pollution problems are complex, and they are often analyzed by
computer models based on the differential equations describing water and solute
movement through porous media (Bachmat et_ al_., 1980). These models are well beyond
the scope of this screening manual. The discussion In this section Is limited to
simple procedures to estimate pollution loads to the saturated zone. Pollutant
movement 1n the aquifer 1s not considered and steady-state, uniform one-dimensional
flow 1s assumed. Since the time scale of groundwater pollution 1s measured 1n years,
the loading estimates are annual values.
Succeeding subsections discuss water balances, nitrate loads from land applica-
tion sites and leaching of organic chemicals.
-232-
-------
3.7.2 Water Balance
Little downward movement of a chemical Is possible in the absence of percolation.
Although some movement due to diffusion Is possible, convection and dispersion
associated with a water flux are the major transport mechanisms in the unsaturated
zone. Based on the processes shown in Figure 111-21, percolation is given by;
0 - P * I - E (111-71)
where
Q » annual percolation (cm)
I » annual irrigation (cm)
P - annual precipitation (cm)
E « annual evapotransplratlon (cm).
Equation 111-71 applies to a waste source In or on the soil surface which is not
contained within an Impermeable layer or storage lagoon. In the latter cases,
percolation 1s equal to seepage or leakage through the layer or lagoon bottom.
Mean annual potential evapotransplratlon minus precipitation 1s shown in Figure
111-22. For a vigorous plant cover, ET is approximately equal to potential ET and
the values 1n Figure 111-22. converted to centimeters, can be used in Equation 111-71
to provide a simple screening device for groundwater pollution. In the absence of
Irrigation, negative values of E-P (I.e. P > E and hence Q > 0) Identify areas of
potential groundwater pollution. Conversely, nonlrrigated areas with positive E-P,
and hence negllble percolation, are less likely to have contaminated groundwater.
These conclusions apply only when a vigorous plant cover is maintained on the
waste site to maximize ET. A denuded or fallow site will produce little ET and
maximize opportunities for percolation.
3.7.3 Nitrate Loads to Groundwater From Haste Application Sites
Municipal sewage and sewage sludges are often applied to land. Land application
may thus eliminate a major surface-water pollution source, but It may also create a
groundwater pollution problem. A major concern Is the leaching of Inorganic nitrogen.
In the form of nitrate, from the wastes and subsequent transport to the saturated
zone. Nitrate Is extremely mobile In soils, and since It Is toxic to Infants and
livestock, It Is often considered the most critical pollutant from land application
systems.
This subsection presents a simple nitrate loading calculation procedure adapted
from Ha 1th (1983). The procedure estimates nitrate concentrations as nitrogen In
percolation from the root zone of a land application site.
-233-
-------
0-5
FIGURE 111-22 MEAN ANNUAL POTENTIAL EVAPOTRANSPIRATION MINUS PRECIPITATION IN INCHES
(liN = 2,5<4CM) (POUND ET_ AL, 1976)
-234-
-------
3.7.3.1 Model Description
Components of the model are shown in Figure 1 1 1-23. An annual application of
nitrogen in waste is divided into organic and inorganic forms. Inorganic nitrogen is
subject to volatilization losses, and the remainder is considered available for plant
or crop uptake and leaching. Waste organic nitrogen consists of two components, a
labile or readily mineral izable fraction which is available for plants and leaching
during the first year following application, and a stabilized fraction which miner-
alizes at rates comparable to other forms of soil organic nitrogen. The available
nitrogen supply thus consists of sludge inorganic nitrogen, rapidly mineralized
sludge organic nitrogen and slowly mineralized so*1 and sludge organic nitrogen.
Since inorganic nitrogen in the soil is rapidly oxidized to nitrate, 1t is assumed
that all available nitrogen is nitrate.
Annual mass balances for soil organic nitrogen and available nitrogen are:
(111-72)
At - mOt + (l-v)lOOO N(l-F)Xt + alOOO
- mOt + 1000 N [(l-v)(l-F) + aF] Xt (1 11-73)
where
0. - soil organic nitrogen (including stabilized waste organic nitrogen)
at beginning of year t (kg/ha)
XT • waste application of dry solids In year t (t/ha)
m « annual mineralization rate for soil nitrogen
a » fraction of waste organic nitrogen mineralized during year of
appl Icatlon
N • nitrogen fraction of solids
F • organic fraction of waste sol Ids
Aj. » available nitrogen (nitrate-nitrogen) In year t (kg/ha)
v • fraction of waste inorganic nitrogen which 1s volatilized.
Nitrogen loss by leaching 1s the difference between available nitrogen and
crop uptake:
4 • At - Cnt (1 11-74)
where
Lt » nitrate-nitrogen leachate 1n year t (kg/ha)
Cn » crop nitrogen uptake 1n year t (kg/ha).
Since there are no additional removal mechanisms for nitrate once It passes
-235-
-------
TOTAL APPLIED NITROGEN
ORGANIC
NITROGEN
RATUD
MINIERAVIZATION
SOIL ORGANIC \MINERALIZATION
NITROGEN
VOLATILIZATION
INORGANIC NITROGEN
ROP INTAKE
AVAILABLE NITROGEN
LEACHING »
INPUTS-CROP UPTAKE
FIGURE 111-23 NITROGEN DYNAMICS AT A LAND APPLICATION SITE
be tow the root zone, L Is also the rtU rate-nitrogen waste load to the saturated
zone, although 1f the water table Is well below the soil surface, the load may not
reach the aquifer for several years.
3.7.3.2 Steady-State Loading Function
The loading calculation given by Equation 111-74 1s complicated somewhat by
the need for sequential computations for soil organic nitrogen by Equation 111-72.
However, after many years of waste application at an average rate X (t/ha):
BX
1"") *
-«) + BX/m
(111-75)
-236-
-------
where
B » (l-a)lOOO N F, and 0Q is the initial soil organic nitrogen level.
The steady-state organic nitrogen level ff is BX/m or:
TJ - (l-a)lOOO N F x/m (111-76)
Substituting "0" into Equations 111-73 and 74 produces the steady-state
function:
L - 1000NX [1 - v(l-F)]-Cn (111-77)
where
L • annual steady-state nitrate-nitrogen load to groundwater (kg/ha)
X » average annual solids application rate (t/ha)
N » nitrogen fraction of solids
F » organic fraction of waste nitrogen
v « fraction of waste inorganic nitrogen which is volatilized
Cn m average crop nitrogen uptake.
3.7.3.3 Loading Function Data
Typical values for crop nitrogen uptake are given in Table 111-26. Volatiliza-
tion rates (v) are based on the ammonium content of the waste and the method of
application. If the waste is sprayed or spread on the soil surface, all ammonia can
be assumed to volatilize. For example, if 701 of the inorganic nitrogen in the waste
is in the ammonium form, then v » 0.70. Conversely, when wastes are injected or
otherwise directly incorporated 1n the soil, there is little opportunity for
volatilization and v « 0.
Waste properties (X, N, F) will depend on the specific waste and the operation
of the disposal site.
•-EXAMPLE III-ll
Nitrate-Nitrogen Load from a Sludge Land Application Site !
I
Determine the steady-state loading of nitrate-nitrogen from a land applica- I
tion site for sewage sludge in central Florida. The sludge 1s spread on fescue at |
an annual rate of lOt/ha. The sludge solids are 51 nitrogen and 701 of the j
nitrogen is organic. The Inorganic nitrogen 1s 90% ammonia nitrogen. Also
estimate the average nitrate-nitrogen concentration in percolation entering the
saturated zone.
-237-
-------
TABLE 111-26
TYPICAL VALUES OF CROP NITROGEN
UPTAKE (POWELL, 1976)
Crop Annual Nitrogen Uptake (kg/ha)
Forage Crops
Coastal Bermuda Grass 540-670
Reed Canary Grass 250-400
Fescue 300
Alfalfa 160-250
Sweet Clover 180
Red Clover 90-140
Lespedeza Hay 150
Field Crops
Com 170
Soybeans 100-110
Potatoes 220
Cotton 70-110
Wheat 60-90
Sugar Beets 80
Barley 70
Oats 60
Forest
Young Deciduous {£ 5 yrs) 110
Young Evergreen (^ 5 yrs) 70
Median and Nature Deciduous 30-60
Median and Mature Evergreen 20-30
-238-
-------
Solution:
Equation 111-76 is used to determine steady-state loading:
L - 1000NX [1 - v(l-F)]-Cn
where X « 10, N « 0.05, F » 0.7. Also, since the inorganic nitrogen is 901
ammonia and the sludge is spread on the soil surface, v » 0.9. Crop uptake Cn is
300 kg/ha from Table 111-26.
L - 1000(0.05)10 [1-0.9(0.3)] -300
* 65 kg/ha
To determine the nitrate-nitrogen concentration, percolation 0 must be
estimated. From Figure 111-22, E-P « -5in « -12.7cm for central Florida.
Neglecting water in the sludge. Equation 111-71 indicates percolation
0 - P-E - 12.7cm
Since 1cm over 1 ha is 100 m , total percolation is 1270 m3, and the
nitrate-nitrogen concentration 1s
65/1270 « 0.051 kg/m3 - 51 mg/1
which greatly e..eeds the drinking water standard of 10 mg/1.
—-—END OF EXAMPLE III-ll
3.7.4 Leaching of Organic Chemicals
The potential for groundwater pollution from an organic chemical 1s determined
by adsorption and degradation processes. Organic chemicals are partially adsorbed by
soil particles, and movement of a chemical is retarded or slowed compared to the
movement of the percolation water. Degradation of organic compounds by biochemical
processes and volatilization In the unsaturated zone will reduce the quantity of the
chemical so that only a fraction of the original compound will remain to enter an
aquifer. If the chemical Is strongly adsorbed and rapidly degraded, and the water
table 1s well below the soil surface, there Is minimal chance of groundwater contami-
nation. Conversely, pollution 1s favored by any of the following conditions: weak
adsorption, slow degradation, or high water table.
3.7.4.1 Adsorption
Simple procedures for modeling movement of adsorbed chemicals are based on the
concept of a retardation factor, R (Freeze and Cherry, 1979) which is defined as:
R - u/u$ (111-78)
-239-
-------
where
u » mean water velocity (cm/yr)
u « mean chemical (solute) velocity (cm/yr).
Hartley and Graham-Bryce (1980) have shown that R 1s equivalent to the ratio of
total to dissolved chemical. Consider a soil element with volume one cm3 containing
an organic chemical which Is both dissolved 1n soil water and adsorbed to soil
particles. Total chemical 1n the element 1s:
C • fd + ba (111-79)
where
C • total chemical (».g/cm3)
d « concentration of chemical 1n the soil water (eg/on )
f • soil water content (cm /cm )
a - concentration of chemical on soil particles Ug/g)
b « soil buU density (g/cm3).
If we assume a linear equilibrium adsorption relationship:
a • KDd (111-80)
where
K_ • adsorption partition or distribution coefficient (cm /g)
then the ratio of total to dissolved chemical 1s (fd + bK_d) /fd, or:
R - 1 + (bKp/f) (111-81)
The retardation factor Is thus a function of a chemical property {Kj and
two soil properties (b and f). For flow 1n the unsaturated zone, the moisture
content f Is generally assumed to be field capacity. Typical field capacities and
bulk densities are given 1n Table 111-27. The partition coefficient K_ may bt
estimated from the octanol-water partition coefficient K using Equations 111-38
and 111-36 as explained 1n Section 3.4.4.3.3.1. Values of KQW for many organic
compounds are given In Chapter 2 of this manual.
The retardation factor provides a general Indication of a chemical's mobility 1n
the soil. For nonadsorbed Ions such as chloride and nitrate, R approaches unity and
the chemical moves at approximately the same velocity as the percolation. For
strongly adsorbed chemicals, R 1s much larger than one and movement through the soil
1s slow compared to the percolation velocity (u « u).
The retardation factor also 1s used to estimate the distance which a chemical
moves in t years. Thus, Z/X • ut/u t • R, or:
X • Z/R (II1-82)
-240-
-------
where
2 = water displacement during time t (cm)
X « chemical displacement during time t (cm).
Assuming plug flow, annual water displacement (cm/yr) due to percolation is:
Z - J/w (1 11-83)
where
Q « annual percolation (cm)
w • available water capacity (cm).
Available water capacity is used in Equation 1 11-83 rather than field capacity or
porosity since unsaturated soils drain to field capacity during percolation, and soil
water held below wilting point does not participate in the flow process. Mean values
of w are given in Table 111-13 or may be computed from Table 111-27 as w • field
capacity - wilting point.
Equations 1 1 1-83. 82 and 81 can be combined to estimate the mean annual downward
movement of an organic chemical:
(111-84)
1 +
TABLE 111-27
MEAN SOIL PROPERTIES (BAES AND SHARP, 1983)
Soil Type
Silt loam
Clay and clay loam
Sandy loam
Loam
Bulk
Density
(g/cm3)
1.33
1.30
1.50
1.42
Field
Capacity
on3/cm3
0.35
0.36
0.22
0.32
Wilting
Point
on3/cm3
0.13
0.22
0.08
0.13
Porosity
cm3/on3
0.49
0.51
0.43
0.46
-241-
-------
Due to dispersion, portions of the chemical will be displaced greater or lesser
distances than X. If a chemical 1s Initially at the soil surface, the location of
Its center mass after percolation Q 1s given by X (see Figure 1 11-24).
The time required for the chemical center of mass to reach the aquifer, and
hence the mean travel time of the chemical through the unsaturated zone Is:
T - 100H/X (1 11-85)
where
T • mean time for a chemical to reach the water table (yr)
H • depth to the water table (m) .
3.7.4.2 Degradation
In the absence of chemical decomposition, even strongly adsorbed chemicals will
eventually reach aquifers. The degree of groundwater pollution by an organic chemical
1s very much Influenced by degradation or decay rates. Degradation of organic
compounds 1s discussed 1n detail 1n Chapter 2 of this manual. A first order process
1s generally assumed such that:
C(t) • C(0) exp(-kst) (II 1-86)
where
C(t) • chemical 1n the soil at time t (g/ha)
k - decay rate (yr"1).
Equation 1 1 1-86 may be used to estimate the chemical mass entering the saturated
zone. From Equation 1 1 1-85, the average travel time to the water table Is T and
hence the chemical entering the saturated zone Is:
C(T) - C(0) exp(-ksT) (1 11-87)
where
C(T) - chemical mass entering the water table T years after leaching begins
C(0) • Initial chemical mass at the soil surface (g/ha).
Equation 1 1 1-87 Is only approximate because due to dispersion, portions of the
chemical will require more or less time than T to reach the aquifer. Moreover, decay
rates (k ) are uncertain for most chemicals. Although representative values are
-242-
-------
SOIL SURFACE
DISPLACED CHEMICAL
BAND
CHEMICAL CONCENTRATION
FIGURE 111-24 DOWNWARD MOVEMENT OF A CHEMICAL IN SOIL
given In Chapter 2, most reported rates were measured in waste treatment systems and
surface waters. Few data are available for estimation of decay rates 1n the subsoil.
3.7.4.3 Groundwater Loads of Organic Chemicals
Equations 111-84, 85 and 87 may be used to estimate organic chemical loads to
aquifers. Due to the limitations of the equations (linear adsorption, first order
decay, dispersion, uncertain rates, homogeneous porous media), the calculated loads
should only be considered "order-of-magnltude" estimates.
• •EXAMPLE 111-12
Napthalene Leaching from a Haste Storage Site
50,000 g/ha of napthalene Is leaching from an abandoned waste disposal
site. The site Is on a sandy loam with ft organic matter. Water table depth 1s
1.5m. Mean annual percolation Is 40cm. Based on the Information In Chapter 2,
napthalene has an octanol-water partition coefficient of K • 2300 and a
ow
-243-
-------
I half-life of 1700 days.
| How much napthalene will reach the aquifer and what will be the resulting
! napthalene concentration at the water table surface?
| Solution:
j Equations 1 1 1-36, 37 and 38 must be used to estimate the partition
coefficient
K - K; (XOC/100) (I II- 36) .
!
XOC • 0.59 (XOM) (111-37) I
I
i n?<)
Koc " °'66 Kow (I II- 38) |
The organic carbon partition coefficient is:
KQC « 0.66(2, 300) < - 1900
'.OC - 0.59(1) - 0.59
KD « 1900(0.59/100) - 11.2
Bulk density (b), field capacity (f) and available water capacity (w) may be
estimated from the data in Table 111-27 for sandy loams:
b - 1.5 g/cm3
f • 0.22 cm3/cm3
w • 0.22-0.08 » 0.14cm3/cm3.
Annual napthalene movement is given by Equation 111-84:
| Average time to reach the water table Is:
i
T • 100 H/X (1 11-85)
T - 100(1.5)73.7 - 40.5 yr .
To use Equation 111-87 to calculate the napthalene remaining after 40.5 years, we
must first determine the decay rate ks. From Equation 1 1 1-86, when t « half-life
» 1700/365 - 4.66 yr, C(t) • 0.5C(0). Hence:
0.5 • exp(-4.66kj
S
-244-
-------
or
*s - -ln(0.5)/4.66 - 0.149
Using Equation 111-87:
C(T) - 50,000 exp [-0.149(40.5)]
- 120 g/ha
Thus approximately 120 g/ha of the original 50,000 g/ha will eventually
leach into the aquifer. The center of mass of the napthalene will reach the
aquifer in a littje over 40 years.
To determine the napthalene concentration in water at the aquifer surface, w«
must first divide the 120 g/ha into dissolved and adsorbed components. The
retardation factor R is the ratio of total to dissolved chemical. Equation
III-81 gives:
R - 1 + bKD/f
- I * 1.5(11.2)/0.22 • 77
The dissolved napthalene mass is 120/R:
120/77 - 1.56 g/ha
Assuming this rjss is dissolved in one year's percolation flow, 40cm « 4000m /ha,
the concentration is 1.56/4000 « 0.00039 g/m3 « 0.39^g/l.
END OF EXAMPLE 111-12
3.8 ATMOSPHERIC WASTE LOADS
Atmospheric waste loads are direct mass inputs of pollutants from the atmosphere
to surface wate -. These loads occur as a result of both dry deposition and scaveng-
ing by precipitation. For the purposes of water quality screening studies, atmospheric
loads are often considered constant, and are best determined by monitoring. The sum
of atmospheric and background waste load (see Section 3.3), generally constitutes the
minimum pollution Input to a surface water body.
Regional data are available for a limited number of pollutants. Figure 111-25
and Table 111-28 Indicate atmospheric nutrient loads for regions in the U.S.
3.8.1 Dry Deposition
Pollutants occur In the atmosphere as 1) particulates; 2) gases; or 3) dissolved
1n water vapor. Cautreels and Van Cauwenberghe (1978) give distribution coefficients
between the gas and particulate phases for 55 aliphatic hydrocarbons, polycyclic
aromatic hydrocarbons, phthalic add esters, fatty acid esters, aromatic acids and
basic compounds.
Both particulates and gases may settle out onto receptor surfaces. For particles
-245-
-------
TABLE 111-26
ATMOSPHERIC CONTRIBUTIONS OF NITROGEN AND PHOSPHORUS IN PRECIPITATION
N Contribution in Kq/ha/yr
N03-N»HH4-N Total N
Northeastern U.S.
Southeastern U.S.
Midwestern U.S.
West/Southwestern U.S.
United States
LOW
5.7
1.5
0.2
1.7
-
lilalL
12.1
12.3
20.9
5.7
-
Low
5.7
-
1.7
9.0
-
lit ah
12.1
-
20.9
14.6
-
P Contribution in K
-------
.3kg/ho/yr
l.0k«/ho/yr
I Okg/ho/»r ISkg/tio/yr 20 kg/ho/"
lOkg/ho/yf
FIGURE HI-25 NITROGEN (NHq-N AND NOj-N) IN PRECIPITATION, (PERSONAL COMMUNICATION
WITH MRI, J.H. CRAVENS, REGIONAL FORESTER, U.S.D.A-FS EASTERN REGION, 1974)
-247-
-------
< 0.3 *n in diameter, the major process 1s Brownian diffusion. For diameters 0.5 to
5 un inert ial impaction-i nterceptlon governs and for diameters > 5 Mm, gravitational
settling 's aoninant. For gravitational settling. Stokes' Law may be used to
srec'ct :n« settling velocity. Since Stokes' Law is applicable only to quiescent
media, it should give an upper bound for V (--e deposition velocity). It 1s
stated as.
g (ad)* (•-»,)
.111-88)
18
where
Vd » settling velocity (cm/sec)
a « conversion factor (10" )
g « acceleration of gravity, 981.46 (cm/sec )
» - v ,co$lty of air, 0.000177 (g/cm-sec) at 10°C
a • particle density, -2 (g/cm )
f, - density of air, 0. 31243 (g/cm3) at 10'C
a
: » particle diameter (microns).
For particles < 5 Mm 1n diameter Stokes Law Is not applicable and experimental
values for the deposition velocity should be used. Eisenrelch et_ £]_. (1981) suggest
values of V « 0.1 to 0.5 cm/sec for trace organlcs. Some experimental values
are shown in Table 111-29.
Once the settling velocity is known, the following procedure can be used to
predict the dry deposition loadings:
where
L « V. C A f
d p
(1 11-89)
I • load of the pollutant delivered to the receptor surface as dry
deposition (mass/sec)
Vd • particle settling velocity (m/s«c)
C • concentration of atmospheric partlcul ates (mass/m )
A • projected receptor area (m )
f • fraction (by mass) of the pollutant In the partlculates.
Normally, smaller size particles are more chemically and physically reactive
than larger partlculates, and therefore pollutants will be associated with these
smaller particles. Obviously the particle size to which pollutants are adsorbed
affects their atmospheric residence tine and, hence, loadings. According to Heff
(1979), most polycycllc aromatic hydrocarbons are associated with partlculates in the
1 to 2 micron range. Cautreeii and Cauwenberghe (1978) have shown that aerosol
-248-
-------
TABLE 111-29
FIELD-MEASURED DRY DEPOSITION VELOCITIES
(CT/S)
Sur'aca
PCS
PCS
PCS, 00r
(gas ?has«;
PCS, OCT
PCS
PCS
1015)
PCS
0.5
0.3-3
0.19
1.0
0.14
0.04
Mineral-oi*-caars:
p'ates
Estimated
Gl/cera'-coated
plates
Glycerin-water.
Al pans
0.43
r:«: Eisenreicn c: al.. 1981
-249-
-------
polycyclic aromatic hydrocarbons are associated with particles of median diameter
from 0.7 to l.^nm. In addition, they give the concentrations of 50 trace organic
compounds associated with different size particles. Higher weight polycyclic aromatic
hydrocarbons, aUanes, ana carboxylic acids had significant mass fractions associated
with >1 um diameter particles.
— EXAMPLE 111-13
I Dry Atmospheric Deposition of Pollutants
! Adsorbed to Participates
Estimate the maximum daily loading of pyrene to a watershed having an area of
I 6 2
. 10 m overlain by an air mass having a mean daily participate concentration
I 3 4
I of 50 «g/m . The average pyrene content of the participates is 1.0 x 10
I Mg-pyrene/»»g. Assume a deposition velocity of 0.1 cm/sec.
Solution:
Compute the daily dry deposited load of pyrene, using Equation 111-89:
I
L • V . C A F
| d P
i , o.ooi -2- 5M- io6"2 i.o * io-4«9 pyrene 8640°scc
j sec m u9 day
- 4.32 x 105 Kg/day
I END OF EXAMPLE 111-13
Gas phase pollutants may also be deposited directly to the watershed surface.
In this case the loading equation is:
L • V. C A (111-90)
d
where
L - dry deposited load (mass/sec)
Vd • gas deposition velocity (m/sec)
A • receptor area (« )
C • ambient concentration of the gas phase pollutant (mass/* ).
-250-
-------
'—EXAMPLE 111-14-
Dry Atmospheric Deposition of Gaseous Pollutants
Estimate the annual deposition of toxapnene to a 1 na area at Stoneville, MS
during 1974. >e mean monthly atmospheric concentrations are snown In Taole
II1-30. Assume an averjse deposition velocity of 0.2 cm/sec for tie entire year.
Solution:
Month
n
1
2
3
4
5
6
7
8
9
10
11
12
Vd
(m/sec)
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
.002
L •
Cn
(ng/m
10.
9.
19.
27.
44.
38.
175.
903.
524.
114.
32.
12.
12
T- u r »
- vd Ln A
3)
9
7
1
7
3
6
0
6
6
8
9
6
(m2)
io4
io4
io4
IO4
io4
io4
io4
IO4
IO4
IO4
io4
IO4
"n
(sec)
31
28
31
30
31
30
31
31
30
31
30
31
X
X
X
X
X
X
X
X
X
X
X
X
86400
86400
86400
86400
86400
86400
86400
86400
86400
86400
86400
86400
5
4
1
1
2
2
9
4
2
6
1
6
1
i
1
1
1
1
L '
(ng) 1
.84
.69
.02
.43
.37
.00
.37
.84
.72
.15
.71
.75
.01
X
X
X
X
X
X
X
X
X
X
X
X
,
io8 j
IO8
IO9 !
IO9 !
io9 I
IO9 1
IO10 |
IO10 i
109
!°08 !
IO11 '
ng/year I
i
or
1
101.4 g/ye«r j
-251-
-------
1
i
i
i
i
i
i
i
i
i
i
i
i
i
i
1
i
i
:
'
i
•
i
i
i
i
i
i
i
i
i
i
IADLL U1-JU
AVERAGE MOHTHLY ATMOSPHERIC LEVELS OF
FOUR PESTICIDES AT STONEVILLE. MISSISSIPPI
in"
J««u4ry 1.
r«eru«ry 1. 1
N4rcn 2.1
Aenl 3.L
H«y 1.0
Jun« 0.9
July 5.2
August 10. 1
Wff'focr f 3
Oc:oe«r 4.0
No*t«A«r O.C
0*Ct«a«r 0.0
A»tr«9t 3.2
p»"
Itrnitry 0.0
Ftttrutry 0.0
M«rcn 0.0
April 0.0
M«y 0.0
Jun* 1.4
July 41.4
Aufutt 21k. 9
SOCftPMr 111.7
Octooer 1.4
K0«««*«r 0.0
0*c*ra«r 0.0
A.«rii;t 32.1
Sou ret Artflur tt 1^117
. i
Cndrin '««• )
1173 1174
3.1 0.2
0.1 9.2
0.7 0.4
0.7 3.5
1.2 0.7
3.8 0.7
0.7 9.3
5.0 27.2
1.4 11. 1
5.0 4.3
1.1 1.0
0.2 0.5
2.3 5.3
,
>•») *irt'Mon Inam' )
0.0 1.0
0.0 0.3
0.0 0.3
0.0 0.4
0.0 0.4
22.8 0.9
4.5 40.1
1Z1.3 341.1
711.1 1*7.9
17.1 Z.O
0.0 0.0
0.1 0.0
10.4 44.3
0
ior>
0.0
11.0
4«.3
47.4
3J.4
*4.2
400.7
1540.0
<27.f
17.1
9.3
0.0
2SI.4
10.1
12.4
tt.4
34.1
11. 1
11.2
117.3
515.3
371.1
37.4
14.3
4.3
11.5
rii.ii ..
To,,0*.*. («o,-'>
1Q71
0.3
0.0
14.8
10.8
W.3
101.9
41.1
248.8
O2. S
141.:
0.0
9.9
82.3
i
Total DOT (««• >
3.)
4.8
U.I
11.4
11.4
41.5
1.4
25 4
24.4
11.9
11.9
2.4
14.0
tan
10.3
1.?
19.1
27.7
4* 3
38.5
175.3
903.
at.
114.
32.
12.
159 5
3.3
3.6
7.4
7.7
It t
12.8
24.3
37.9
19.4
5.1
3.3
2.1
11.9
1
1
i
i
1
i
1
1
I
I
1
1
i
i
i
i
i
i
i
j
|
i
i
i
i
i
i
i
i
i
i
-252-
-------
3.8.2 Met Deposition (Precipitation Scavenging)
Precipitation falling through the atmosphere tends to scavenge participates and
absorb gases so that it contains a variety of substances. Because of the volume of
precipitation which generally occurs, it may constitute a significant pollutant
loading. Load calculation for wet deposition is given by:
L • 10 C P A (111-91)
where
L • load of the pollutant delivered to the receptor as wet deposition
(mass/sec)
C • concentration of the pollutant in precipitation (mass/liter)
P • precipitation rate (cm/sec)
A » projected receptor area (m ).
3.9 POINT SOURCE WASTE LOADS
The purpose of this section is to help users estimate waste loadings of toxic
and conventional pollutants from municipal and industrial point sources. Removal
efficiencies and discharge concentrations are both provided.
When possible site-specific data should be used in lieu of the guidance presented
here. Since permitted dischargers are required to routinely .nonitor their discharges,
often the point source data required are available.
When only a few measurements of effluent quality are available, those data may not
be representative of long-term averages. Long-term averages are typically required for
most of the steady-state analyses contained In this document. Figure 111-26, for
example, shows influent cadmium loading to the Kokomo, Indiana, wastewater treatment
plant (Yost e£ aj_., 1981). Cadmium loading appears to exhibit a weekly cycle, with
loadings the lowest on Sundays. For this case, seven-day averages would be appropriate
for preliminary analyses.
When using the data presented in the following sections, users should keep in
mind the variability of removal efficiencies and influent and effluent pollutant
concentrations between point sources. The following factors all contribute to
effluent quality variability:
t Geographic location
• Climate
• Mixture of Influent sources (Industrial/domestic)
• Size of community
• Design flow rate versus actual flow rate.
-253-
-------
o
•o
C7>
c
•5
o
o
6
•o
o
20 ••
15 ••
10 ••
(S)» Sundoy
20 I 30l |40 I 50 I 60
ssssssssss
Ooy» 8-2 8-6 8-13 8-20 8'27 9'3 9-10 9'I7 9-24 KM
FIGURE 111-26 INFLUENT CADHIUM LOADING To PLANT DURING STUDY
(FROM YOST £i AL, 1981)
3.9.1 Municipal Haste Loads
Table 111*31 summarizes typical Influtnt concentrations of conventional pollutants
for wastewater treatment plants. Concentration ranges are shown for strong, medium,
and weak wastewater. Table 111-32 summarizes typical removal efficiencies of common
conventional pollutants fro* a variety of wastewater treatment plant types. Scheme
number 0 in the table denotes the raw wastewater characteristics. The table shows
both percent removal and effluent concentrations based on the characteristics of the
raw wastewater chosen. The removal efficiencies can be used for the range of concen-
trations shown previously In Table 111-31, assuming the plant Is operating within
design conditions.
Table 111-33 summarizes effluent phosphorus and nitrogen concentrations for 662
primary treatment plants, trickling filters, activated sludge plants, and stabili-
zation ponds. The data were collected as part of the National Eutrophlcatlon Survey
Initiated by the U.S. Environmental Protection Ageno 1n 1972 (Gakstatler et al_.,
1978). The user can cross-compare effluent nutrient levels predicted based on joint
use of Tables 111-31 and 111-32 against the values 1n Table 111-33 to help get a typical
range of values. Table 111-33 also shows per capita flow rates, per capita total
phosphorus loads, and per capita total nitrogen loads for each treatment type. These
-254-
-------
TABLE II1-31
Tv?ICAL INFLUENT MUNICIPAL WASTE CONCENTRATIONS
Constituent
Concentration
Strong Medium**
Solids, total
Dissolved, total
Fixed
Volatile
Suspended, total
Fixed
Volatile
Settlable solids, (ml/liter)
Biochemical oxygen demand, 5-day, 20° (BOO, -20°)
Total organic carbon (TOC)
Chemical oxygen demand (COO)
Nitrogen, (total as N)
Organic
Free ammonia
Nitrites
Nitrates
Phosphorus (total as P)
Organic
Inorganic
Chlorides*
Alkalinity (as CaCO,)*
Grease
1,200
850
525
325
350
75
275
20
400
290
1,000
35
35
50
0
0
15
5
10
100
200
150
720
500
300
200
220
55
165
10
220
160
500
40
15
25
0
0
8
3
5
50
100
100
350
250
145
105
100
20
80
5
110
80
250
20
8
12
0
0
f
H
1
3
30
50
50
•Values should b« increased by amount In carriage water.
"In tne absence of other data use medium strength data for planning purposes.
Source: Me tea If and Eddy. 1979
-255-
-------
TABLE 111-32
MUNICIPAL WASTEWATER TREATMENT SYSTEM PERFORMANCE
Influent: see Scheme Number 0 for assumed characteristics.
Effluent Concentrations (mg/1), (X Total Removal Efficiencies*)
Scheme Number
wast* water
1
2
3
4
5
6
7
BOD,
200(01)
COD
500(01}
55
200(0%)
130(35t) 375(25%) 100(251)
40(80%) 125(75X)
25(88%) 100(80%)
18(91%) 70(86%)
18(91%) 70(86%)
13(94%) 60(88%)
2(99%) 15(971)
PT, (mgP/T) ft,., [mgN/TT
10(0%)
00(25%)
30(85%)
12(94%)
7(96%)
7(96%)
1(99.5%)
1(99.5%)
9(10%)
7.5(25%)
7(30%)
1(90%)
1(90%)
1(90%)
1(90%)
40(0%)
32(20%)
26(35%)
24(40%)
22(45%)
4(90%)
3(92%)
2(95%)
•Efficiencies for wastewater treatment are for the approximate concentration
range, as measured by BOD,, of 100 £ BOO, £ 400, (mg/1).
••Scheme No. Process
No treatment
Primary
Primary, plus Activated Sludge (Secondary Treatment)
Primary, Activated Sludge, plus Polishing Filter (High Efficiency
or Super Secondary)
Primary, Activated Sludge, Polishing Filter, plus Phosphorus
Removal and Recarbonatlon
Primary, Activated Sludge, Polishing Filter, Phosphorus Removal,
plus Nitrogen Stripping and Recarbonatlon
Primary, Activated Sludge, Polishing Filter, Phosphorus Removal,
Nitrogen Stripping Recarbonatlon, plus Pressure Filtration
Primary, Activated Sludge, Polishing Filter, Phosphorus Removal.
Nitrogen Stripping Recarbonatlon, Pressure Filtration, plus
Activated Carbon Adsorption
Source: Neta Systems. 1973
-256-
-------
TABLE II1-33
MEDIAN AND MEAN PHOSPHORUS AND NITROGEN CONCENTRATIONS AND
MEDIAN LOADS IN WASTEWATER EFFLUENTS FOLLOWING FOUR
CONVENTIONAL TREATMENT PROCESSES (Gakstatter et al ., 1978)
Treatment Type
Primary
Number of Sampl ed
Total Population
Ortho-P Cone.
(mg/1)
Total -P Cone.
(mg/1 )
Total -P Load
(kg/cap-y)
Total -N Load
(kg/cap-y)
Inorganlc-N Cone .
("9/1 )
Total -N Cone.
(mg/1)
Total -N Load
TN:TP Ratio
Per Capita Flow
(I/cap -d)
Plants
Served
Median
Mean
Median
Mean
Median
Median
Median
Mean
Median
Mean
Median
Median
Median
1,
3.5
4.0
6.6
7.7
1.1
6.4
8.3
22.4
23.8
4.2
473
086
•f
T
•»•
T
+
3.7
+
7
*
+
+
3.4
+
55
.784
0.29*
0.62
0.66
1.19
0.10
1.00
1.40
1.30
3.48
0.40
72
Trickl ing
Filter
3,
5.1
5.4
6.9
7.2
1.2
7.1
8.2
16.4
17.9
2.9
439
459,
+ 0
± °
«• 0
1 °
± °
2.9
* 0
± °
+ 0
*. 1
1 °
2.4
+ i
244
893
.21
.38
.28
.50
.05
.38
.60
.54
.23
.17
<»
Activated
SI udge
4,
4.6
5.3
5.8
6.8
1.0
6.5
8.4
13.6
15.8
2.2
394
244
357,138
* 0.24
+_ 0.40
+ 0.29
1 0.51
±0.06
2.4
+ 0.45
£ 0.69
* 0.62
*. 1.16
_* 0.15
2.4
± 26
Stao
3.9
4.8
5.2
6.6
0.9
1.3
5.5
11.5
17.1
2.0
378
il i zation
Pond
119
270,287
* 0.34
*_ 0.62
* 0.45
* 0.81
+_ 0.10
2.0
* 0.29
* 1.95
* 0.84
* 3.59
*. 0.26
2.2
1 38
•Value * 1 standard error.
-257-
-------
can oe used to generate loadings based on population served. The typical per capita
flow ranges Between 378 and 473
-------
TABU 111-34
VEARLY AVERAGE PHOSPHORUS REMOVAL PERFORMANCE (Barlh and Stensel, I9H1)
Plant
Angola. N.Y.
Ely. Minn.
Roanoke, Va.
Rochester, N.V.
Gladstone. Mich.
Grand Haven,
Mich.
Blue Plains. D.C.
Lima. Ohio
Marl borough, Mass.
Design
Capacity
3.1
1.0
35
20
1.0
3.2
330
18.5
5.5
Flowsheet
Extended aeration—solids
con tact- -tertiary filter
Primary — rock trickling
filter--solids contact--
tertiary filter
Primary --2-stage activated
s!udge--nitrif 1 cat ion- -
floccul at ion- -tertiary
filter
Primary --activated sludge
Primary—rotating biological
contactor
Primary --activated sludge
(domestic A tannery)
Primary--2-stage activated
sludge- -nitrification
Primary --activated sludge--
nitrification towers
Primary--2-stage activated
sludge- -nitrification
Chemical &
Addition Point
FeCU * polymer to
solids contact
Alum * polymer
before secondary
clarif ier
Pickle liquor to
1st stg. aeration
and alum/polyner
before floccul a-
tion basin
Alum before final
clarifier
Alum prior to RBC
Pickle liquor be-
fore primary
FeCI 3 to secondary
FeCl3 » polymer
prior to primaries
Alum or FeS04 to
Ist-stg. aeration
Performance
Intl.
Effl.
Infl.
Effl.
Infl.
Effl.
Infl.
Effl.
Infl.
Effl.
Infl.
Efl.
Infl.
Effl.
Infl.
Effl.
Infl.
Effl.
130
2
180
15
220
2
186
16
129
12
389
16
140
28
157
5
159
3
264
2
123
13
340
1
165
9
118
16
432
19
135
28
126
9
306
8
Phosphorus
, UH^/ 1 Kemoval
Total P Efficiency
6.8 87%
I). 9
3.8 841
0.6
11.9 981
0.2
6.3 87%
0.8
3.5 74%
0.9
5.0 88%
0.6
b.2 711
1.8
5.1 841
0.8
6.8 91%
0.6
-259-
-------
TABLE 111-35
METAL CONCENTRATIONS AND KEMOVAL EFFICIENCIES
IN TREATMENT PLANTS AT SELECTED CITIES
City
Anderson,
Indiana
Buffalo.
New York
Dayton,
Ohio
Grand Rapids,
Michigan
Muddy Creek.
Ohio
Muncle.
Indl ana
Treatment
Received
Secondary
Treatment
Secondary
Treatment
Trickling
Filters
Secondary
Treatment
Conventional
Activated
Sludge
Secondary
Treatment
Influent, i*g/l
Effluent. **g/l
Reaoval Efficiencies. I
Influent, »g/l
Effluent. 1*9/1
Reaoval Efficiencies. I
Influent. 1*9/1
Effluent. »g/l
Reaoval Efficiencies. I
Influent. »g/l
Effluent. 1*9/1
Reaoval Efficiencies, i
Influent, ng/l
Effluent. Kg/I
Reaoval Efficiencies, I
Influent. »g/l
Effluent. 1*9/1
Reaoval Efficiencies. %
at
9.5
3.9
59
18
11.2
37.7
27
16
40.7
.
-
-
8
62.5
3
.
-
-
Cr
1180
142
88
208
78.6
62.2
_
-
-
400
136-325
19-66
-
-
-
240
53
78
Cu
2820
395
86
137
53.4
61.0
«
-
-
500
21S-435
13-57
.
-
-
260
83
68
Nt
2790
885
41
50
44.5
11.0
.
-
-
500
295-410
18-41
-
-
-
140
140
0
In ft
1500
375
75
337
704
41.3
.
-
-
1200
588-780
35-51
-
-
-
1150
345
70
Pb
160
4U
75
99
?5.9
73.8
.
-
-
-
-
-
-
-
-
9JU
167
82
-260-
-------
TABLE 111-35
(Continued)
City
Pittsburgh,
Pennsylvania
Uahtawa.
H*w«tt
Winnipeg,
Han.
Burlington.
Ontario
Average of 6 Cities
near Kansas City
Survey of 20 Plants
tn Ontario
Treatment
Received
Secondary
Treatment
Step
Aeration
Pure
Oxygen
Conventional
Activated
Sludge
.
.
Influent, ng/l
Effluent. »g/l
Removal Efficiencies,
Influent, «g/l
Effluent, ng/l
Removal Efficiencies,
Influent, »g/l
Effluent, cg/l
Removal Efficiencies.
Influent, *>g/l
Effluent. »g/l
Removal Efficiencies,
Influent, og/l
Effluent, eg/I
Re«oval Efficiencies.
Influent, »g/l
Effluent, *g/l
Removal Efficiencies.
Cd
21
J
I 67
5-65
2-21
I 59
.
-
I
6
1
I HO
20.2
-
I 16
20
-
ft 19
Cr
95
31
6/
12-18
8-12
32
166
53
68
29U
61
79
220
-
3;
971)
-
62
Cu
127
56
56
62-90
16-23
74
210
48
77
JIO
84
73
146
-
49
300
-
54
Ni
78
70
10
60-70
35-41
42
32
32
0
330
277
16
.
-
-
no
-
42
Zn
648
227
65
200-320
53-93
71
329
66
00
2400
552
77
733
-
47
ll?0
-
56
Fe Pb
119
23
81
1000- I 1HU 40- m
15U-I77 11-19
85 /3
117
60
49
1540 ?»0
416 16
73 93
210
-
4S
65HO 170
-
69 51
-261-
-------
r', \ ' '.
* *. •'
3.00 10. X 40.30 W. 00
: INDUSTRIAL BY
W.OO
100.00
FIGURE 111-27 INFLUENT COPPER CONCENTRATIONS To WASTEWATER TREATMENT
PLANTS As A FUNCTION UF PERCENT INDUSTRIAL FLOW
Table 111-40 summarizes removal efficiencies of a number of the pollutants as a
function of different types of treatment. There Is a significant increase in per-
centage removal between primary treatment plants and secondary activated sludge
treatment plants for each of the pollutants in the table.
3.9.2 Industrial Waste Loads
Compared to municipal discharges, effluent levels from industrial sources are
less easily predictable because of the variety of categories and treatment processes
used. Table 111-41 shows 35 major industrial categories and frequently detected
priority pollutants associated with the categories. Keith and Tel Hard (1979) have
estimated the frequency of occurrence of the priority pollutants 1n Industrial
wastewater. Their results were shown previously In Table 11-3. If Industrial wastes
are thought to contribute a significant percentage of pollutants to the water body
being analyzed, the user should try to obtain more specific data on the industries
present. Local agencies can provide effluent data for the Industries 1n question.
However, the industries may not be required to monitor for the specific pollutant(s)
of concern. The Effluent Guidelines Division of the U.S. EPA can also provide
guidelines for specific categories of pollutants. They have developed extensive
documentation for each major industrial category. The "treatability manual" (USEPA,
1982 . b, c, d) is one such source that contains data related to approximately 200
pollutants associated with industrial processes. The manual contains the following
information:
-262-
-------
TABLE 111-36
;NF: ,ENT LIABILITY ANALYSIS AT MOCCASIN BEND WASTEWATER TREATMENT PLANT
3. -,-.-*-
. : . i : . 1 j j
3C icr. lor : benzene
,4-3ii-lsracer.zen«
Nap.-.thiiene
3is. :-£:!-.>• Ihexyi;
Pnthalate
3:-^-3_zvl ?s.tw.a'»te
?her.d.-.-.-.r«r.e
"e:ils
rhrc.r. :.a
Copper
Cyar. :c*
-eriury -g. 1)
N ' 1 C < « 1
Silver
Zir.c
Carver.: :or.al
3CC£
TSS"
1 Inf'.uent variability i
30
w
'-* t
13
:o
• T
i
23
88
1^1
25
52
201
5
17
2
5
4 1
12
5
^
225
77
83
303
73
5
332
303
232
inalvs
-~av Srud%-
Stancard
,'._j l'~"
\ .
-9
1 £
JO
1
18
86
325
51
87
155
7
22
6
8
11
15
14
8
2
527
25
84
270
76
2
164
115
93
is conducted on on
S-.x-: •-.-
lean
i -
4 "•
_•}
T
20
40
378
10
81
—a
2
100
1
i*
55
U
^
6
3
226
123
4747
333
98
21
486
435
327
oritv toxic ooi
:;.z-
-;.*?;*::.
_, c
3
— ?
" 3
i 7
• 7
30
236
^ ^
52
:c9
T
45
i
3
-5
7
•>
3
«
160
:»
loo-.
815
37
7
132
112
95
lutar.ts Qt:tc:td
50 ptrctnt of th« tia* or fr«at«r in addition to l«ad and cacr.iua for comb.r.«d
36-day period.
2 Cutl.er values were removed from data base.
-263-
-------
TABLE II I-37
SELECTED POLLUTANT MASS PERCENT REMOVALS AT
MOCCASIN BEND WASTEWATER TREATMENT PLANT
Percent Ree»val
Pollutant
1
Primary
Treatment
Secondary
Overall
Treatment Treatnent
Volatiles
Benzene
1,1,1 -Tr ichlorethane
Chlorofora
1 ,2-Trans-Dichloroethylene
Etbylbeozene
Methyl en* Chloride
Tetrachlorethylene
Toluene
Trichloro«thylane
Acids
Phenol
2 ,4-Dichlorophenol
Base/Neutrals
1 . 2 , 4 -Tr ich 1 o robexuene
1 , 3-Dichlorob«ai«a«
Naphtha l«n«
Bii(2-Ethlyh«jtyl) Phthalac*
Di-S-Butyl Phthalat*
Di«thyl Phthalat*
?h«rvanthr«a«
Conv«nt iona 1 /Non-Convent local
BOOS
TSS"
25
0
21
0
12
21
0
17
IS
7
13
1
0
0
16
25
10
42
12
14
0
0
0
40
0
0
10
30
95
75
15
69
100
49
83
70
78
80
56
100
89
47
88
86
78
92
46
79
30
88
92
77
6
36
36
86
82
56
79
80
11
69
100
49
86
75
80
82
56
100
87
55
91
87
87
91
47
82
40
88
91
57
44
0
0
88
87
1 Priority toxic pollutants listed w«r« detected ia the influent wastewater
50 percent of the tiSM or greater (with the exception lead and cadBiuoi which
were detected 46 percent of the tis»e).
2 Percent r
alone.
val based on MSS reaoval in activated sludge treatment units
-264-
-------
TABLE 111-38
1981 EFFLUENT CONCENTRATIONS FROM FIVE SOUTHERN
CALIFORNIA WASTEWATER TREATMENT PLANTS
Hvoerlon
Flow MGD
General Constituents
(mg/1)
Suspended Sol Ids
Settled Sol Ids
BOO
011 * Grease
NH3-N
Organ1c-N
Total -P
MBAS
CN
Pnenols
Turbidity
Toxlcity (T.U.)
Trace Metals (*g/l)
Ag
As
Cd
Cr
Cu
Hg
Nl
Pb
Se
Zn
Chlorinated Hydro-
carbons (**g/1)
DOT
PCB
TICH
•except as noted
••Total solids
JWPCP
364
167
0.3
202
23.3
39.3
14.0
9.2
5.37
0.08
2.85
79
4.2
8.0
5.0
16.0
11.0
154.0
1.8
148.0
0.0
29.0
500.0
0.84
0.54
1.61
5 mile
369
77
0.9
169
22
16.1
7.3
6.9
4.12
0.08
0.06
63
0.81
25.0
12.0
17.0
54.0
200.0
0.7
108.0
50.0
1.0
217.0
0.050
0.76
0.94
7 mile
(Sludge)
4.72
7,100**
353
.
266
214
.
0.442
0.37
-
-
739.0
183.0
892.0
3.340.0
9,320.0
36.0
2,400.0
2.000.0
44.0
11.800.0
0.58
3.05
4.68
Orange
County
212
119
1.1
151
21.1
25.7
-
-
.
0.04
0.09
79
1.0
13.0
3.0
26.0
82.0
248.0
0.4
69.0
74.0
.
220.0
0.02
1.55
1.56
Point
Loma
130
114
0.95
161
29.3
27.7
-
-
4.38
0.013
0.073
53
1.3
13.0
5.0
8.0
43.0
133.0
0.8
7.5
136.0
.
190.0
0.084
0.665
0.816
Oxnard
17.7
56.9
<0.1
114
12.2
17.0
5.09
.
.
0.001
0.10
44
2.1
3.0
20.0
3.0
0.1
93.0
0.05
6.0
11.0
-
91.0
Not de-
tected
<0.033
<0.033
-265-
-------
TABLE 111-39
OCCURRENCE OF PRIORITY POLLlfTANTS IN POTW
INF..-NTS AND EFFLUENTS FOR POLLUTANTS DETECTED
IN AT LEAST 10 PERCENT OF THE SAMPLES (BURNS AND ROE, 1982)
Parameter
Z1nc
Cyanide
Copper
Toluene
Chromium
Tetrachloroethylene
Methyl ene chloride
b1s( 2-Chloroethoxy)methane
Chi o reform
TMchloroethylene
1 ,1,1 -Trie hi oroethane
Ethyl benzene
Nickel
Phenol
Silver
Mercury
Di-n-butyl phthalate
Lead
1 ,2-trans-D1ch1oroethy1ene
Benzene
Butyl benzyl phthalate
Cadmi urn
01 ethyl phthalate
Napthalene
1, 1-01 chl oroethane
Pent ac hi orophenol
Number of
Sampl es
Analyzed
282
284
282
288
282
288
288
287
288
288
288
288
282
288
282
282
287
282
288
288
287
282
287
287
288
287
Number of
Times
Detected
282
283
281
276
268
273
266
265
263
260
244
231
224
220
208
196
185
176
179
175
165
157
151
142
89
84
INFLUENT
Percent of
Sampl es
Where
Detected
100
100
100
96
95
95
92
92
91
90
85
80
79
79
71
70
64
62
62
61
57
56
53
49
31
29
Units
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ng/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/l
Minimum
Value
Detected
22
3
7
1
8
1
1
2
1
1
1
1
5
1
2
200
1
16
1
1
2
1
1
1
1
1
Maximum
^al.e
9250
7580
2300
13000
2380
5700
49000
670
430
1300
30000
730
5970
1400
320
4000
140
2540
200
1560
560
1800
42
150
24
640
-266-
-------
TABLE 1 1 1-39
(Continued)
Parameter
>-8HC
1,1-Dichloroetnylene
1 ,2-01 chl orobenzene
Phenanthrene
Anthracene
1 ,4-Dichl orobenzene
Arsenic
1 ,2-Dichl oroethane
Antimony
Chl orobenzene
Dimethyl pnthalate
Methyl chloride
1 ,2,4-Trichl orobenzene
2, 4-Dimethyl phenol
Parameter
Cyanide
Zinc
Copper
Methyl ere chloride
Chromi urn
bis(2-Ethylhexyl) phthal ate
Number of
Samples
Analyzed
288
288
287
287
287
287
282
288
282
288
287
288
287
288
Number of
Samples
Analyzed
276
289
289
302
289
302
Number of
Times
Detected
75
74
67
57
52
49
43
42
39
36
33
33
28
28
Number of
Times
Detected
268
272
263
260
247
254
INFLUENT
Percent of
Sam pi es
Where
Detected
26
26
23
20
18
17
15
15
14
13
11
11
10
10
EFFLUENT
Percent of
Samples
Where
Detected
97
94
91
86
85
84
Units
ng/1
ug/l
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/1
ug/l
ug/1
Units
ug/1
ug/1
ug/l
ug/1
ug/l
ug/l
Minimum
Value
Detected
20
1
1
1
1
2
2
1
1
1
1
1
3
1
Minimum
Value
Detected
2
18
3
1
2
1
Maximum
Value
3900
243
440
93
93
200
80
76000
192
1500
110
1900
4300
55
Maximum
Value
2140
3150
255
62000
759
370
-267-
-------
TABLE 1 1 1-39
(Continued)
EFFLUENT
Number of
Samples
Parameter Analyzed
Chi oroform
Tetrachloroethylene
Nickel
Toluene
01-n-butyl phthalate
1,1,1-Trichloroethane
TMchloroethylene
v-BHC
Mercury
Phenol
Cadmi urn
Silver
Ethyl benzene
Benzene
Lead
Pentachl orophenol
01 chl o rob romone thane
01 ethyl phthalate
1 , 2-t rans-01 chl oroethyl ent
Antimony
Arsenic
Butyl benzyl phthalate
Selenium
1 ,l-01ch1oroethylene
302
302
289
302
302
302
302
303
288
302
289
289
302
302
289
301
302
301
302
289
289
302
289
302
Number of
Times
Detected
247
239
216
160
1S8
157
137
99
86
87
81
73
73
69
61
63
47
39
39
37
35
34
29
29
Percent of
Samples
Where
Detected Units
82
79
75
53
52
52
45
33
31
29
28
25
24
23
21
21
16
13
13
13
12
11
10
10
ug/l
ug/l
ug/1
ug/l
ug/l
ug/l
ug/l
"9/1
ng/i
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
ug/l
Minimum
Value
Detected
1
1
7
1
1
1
1
10
200
1
2
1
1
1
20
1
1
1
1
1
1
1
1
1
Maximum
Value
87
1200
679
1100
97
3500
230
1400
1200
89
82
30
49
72
217
440
6
7
17
69
72
34
150
11
-268-
-------
TABLE 111-40
MEDIAN PERCENT REMOVALS OF SELECTED POLLUTANTS
THROUGH POTM TREATMENT PROCESSES (BURNS AND ROE, 1982)
Parameter
600
Total suspended sol Ids
Cadalua
Chroalui
Copoer
Cyanide
Lead
Mercury
Nickel
Silver
Zinc
Beniene
bls(2-Ethylhexyl)phthatate
Butyl bcniyl phthalate
Chloroform
Dt-n-butyl phthalate
Otethyl phthalate
Ethyl beftiene
Primary
(12)
(12)
(6)
(12)
(12)
(12)
(1)
(8)
(9)
(«)
(12)
(8)
(12)
M)
(ID
O)
(1)
02)
19
4S
IS
27
22
27
67
10
14
20
27
25
0
62
14
36
56
13
Secondary
Activated
Stud**
(22)
(22)
(*)
(22)
(22)
(22)
(2)
(8)
(IS)
(5)
(22)
(10)
(8)
(2)
(20)
(«)
(2)
(10)
90
90
as
84
84
(2
82
76
34
83
81
77
62
94
62
68
91
90
Secondary
Trickling
Filter
(S)
(S)
(1)
(3)
(S)
(S)
(1)
(2)
(2)
(2)
(5)
(3)
(S)
(1)
(SI
(5)
(0)
(*)
77
78
II
48
49
S7
20
56
47
S&
43
80
24
70
7S
SO
-
90
Secondary
0? Activated
S)ud4e
(3)
(3)
(2)
(3)
(3)
(3)
(1)
(3)
(2)
(2)
(3)
(2)
(3)
(3)
(3)
(1)
(0)
(3)
91
84
83
76
92
80
97
83
IB
80
ttl
87
64
84
SO
98
-
86
Secondary
(totaling
Biological
Contactors
(1) 92
(1) S8
(0) -
(0) -
(I) 97
(1) 96
(0) -
(0) -
(0) -
(0) -
(U 81
(0) -
(1) «6
(0) -
(0) -
(0) -
<0) -
(0) -
Secondary
Aerated
lagoon
(1)
(1)
ID
(1)
(1)
(1)
(1)
ID
ID
ID
(»>
(0)
(I)
ID
(0)
(1)
(0)
(1)
80
77
44
49
21
7
0
0
14
0
SI
-
23
93
-
SO
-
83
Parallel
Activated Sludye/trh
filter Plants
Activated
Sludne Side
«)
(4)
(2)
(4)
(4)
(4)
(0)
(2)
(4)
(2)
(4)
(I)
(4)
(1)
(1)
(1)
(0)
(3)
92
94
91
75
89
66
-
91
34
79
82
92
B7
80
75
97
-
97
Trkl
Flltei
(4)
(4)
(2)
(4)
(4)
(4).
(0)
(2)
(4)
(2)
(4)
(»
(4)
(1)
(4)
(3)
(0)
(4)
:kliny
illny
r Side
tt?
91
84
63
75
6tt
-
49
0
83
73
92
72
93
69
SO
-
89
Tertiary
IB)
(»)
(3)
-------
TABLE 111-40
(Continued)
Parameter
Secondary
Secondary Secondary Secondary Rotating
Activated Trickling 0? Activated Biological
•rleary Sludge* Miter Sludge Contactors
Parallel
Activated Sludge/trickllm)
Secondary filter flints
Aerated "HeTFvaTidTrickling
lagoon Sludge Side Filler Side
!*_r-Ll*ri
Hethyiene chloride
Napthalene
Phenol
Tetrachloroethylane
Toluene
Trlchloroethylene
l.l.i-Trfcliloroethane
t , 2 -t rans -01 d» loroethy 1 ene
(12) 0
(4) 44
(It) 8
(12) 4
(12) 0
(12) 20
(10) 40
(9) 36
(M)
(6)
(15)
(20)
(21)
(20)
(17)
(19)
48
92
89
82
93
90
88
80
(S)
(0)
(0)
(S)
(S)
(S)
(S)
(4)
76
-
-
82
88
96
92
97
(3) 34
«0)
(3) 99*
(3) 75
(3) 99«
(2) 67
(3) 80
(2) 85
(0)
(0)
<»)
(U
(I)
0)
(0)
(0)
.
-
99»
50
99»
67
-
-
(1)
(0)
ID
(I)
(»
(1)
(1)
(0
96
-
50
91
89
97
91
88
(4)
(0)
(3)
(4)
(4)
(4)
(3)
(0)
52
-
89
76
97*
97
99*
-
(4)
(U)
(3)
(4)
(4)
(4)
(3)
(0)
„
-
94
b?
93
95
91
(«)
<4)
(b)
(8)
(«)
(/)
(/)
(3)
78
M
95
94
90
9/
%
W
*POTN 8. prcdoMlMntly (96 percent) «ctlv«ted sludge. MS Included In the «ctlvtted sludge pUntj.
Note: Nu»ber tn ( ) Is niMber of plants with c«lcul*tcd reao««ls.
Only pltnts with «ver«9« Influent concent rat Ions gretter than three tl»es the oast frequent detection Mail of e«ch pollultnt are tn-
cluded In re*o««l calculations.
-270-
-------
TABLE 111-41
INDUSTRIAL CATEGORIES AND FREQUENTLY DETECTED
PRIORITY POLLUTANTS BY CATEGORY
ill
*!
in
!!£
15
r
-'2
III
ill
H
i
i;a
IT
M
=ii!l£
• * * i'C
_ ^ ..if.Si
l*lrw»l*ri«t
i i
I.I.I
i.r
1.}
{.<
•if*
MCMMlW*
*t*tyl
T T
ii^
Oi-»-«ctrl (HtMl«l«
TT
T J I
'•(•
C •«•>••
ll
-371-
-------
-------
REFERENCES
Alley. W.M. 1981. Estimation of Impervious-Area Washoff Parameters. Water
Resources Research 17(4): 1161-1166.
American Public Works Association. 1974. Nationwide Characterization.
Impacts and Critical Evaluation of Stormwater Discharges. Non-stwered
Urban Runoff and Combined Stwtred Overflows. Monthly Progress Report
to the U.S. Environmental Protection Agency.
Amy. G.. R. P1tt, R. Singh. H.I. Bradford, and M.B. LaGraff. 1974. Water
Quality Management Planning for Urban Runoff. U.S. Environmental
Protection Agency. Washington. D.C.. EPA 440/9-75-004. NTIS PB 241 689/AS.
Aron, G. 1982. Rainfall Abstractions. In: O.F. Klbler (ed.) Urban Stormwater
Hydrology. Water Resources Monograph 7. American Geophysical Union.
Washington, O.C.
Arthur, R.D., J.O. Cain, and B.F. Barrentlne. 1976. Atmospheric Levels of
Pesticides In the Mississippi Delta. Bulletin of Contamination and
Toxicology 15(2):129-134.
Bachmat. Y.. J. Bredehoeft, B. Andrews, D. Holtz. and S. Sebastian. 1980.
Groundwater Management: The Use of Numerical Models. Water Resources
Monograph 5. American Geophysical Union. Washington, D.C.
Baes. C.F. Ill, and R.O. Sharp. 1983. A Proposal for Estimation of Soil
Leaching and Leaching Constants for Use In Assessment Models. J.
Environmental Quality. 12(1): 17-28.
Baker, J.L., 1980. Agricultural Areas as Nonpolnt Sources of Pollution. In:
M.R. Overcash and J.M. Davidson (ed.) Environmental Impact of Nonpolnt
Source Pollution. Ann Arbor Science. Ann Arbor, Michigan pp. 275-303.
Barth, E.F. and H.D. Stensel. 1981. International Nutrient Control for
Municipal Effluents. Journal Water Pollution Control Federation. Vol
53, No. 12. pp. 1691-1701.
Bave. J.L., P. Dan1en, and V.P. Kukreja. 1978. Airborne <11-Butyl and dl-
(2-Ethylhexyl) phthlate at three New York City Air Sampling Stations.
International Journal of Environmental Analytical Chemistry 5:189-194.
Brady. N.C.. 1974. The Nature and Properties of Soil HacMUUn, New York.
Burns and Roe. 1982. Fate of Priority Pollutants In Publicly Owned Treatment
Works. Vol 1 I II. EPA 440/1-82-303.
Cautreels, W.. and K. Van Cauwenberghe. 1978. Experiments on the Distribution
of Organic Pollutants Between Airborne Partlculate Matter and the Corres-
ponding Gas Phase. Atmospheric Environment 12:1133-1141.
Oonlglan, A.S., Jr., and N.H. Crawford. 1976. Modeling Nonpolnt Pollution
from the Land Surface. U.S. Environmental Protection Agency. Athens,
GA. EPA-600/3-76-083.
Oornbush, J.N.. J.R. Anderson, and L.L. Hames. 1974. Quantification of
Pollutants In Agricultural Runoff. U.S. Environmental Protection
Agency. Washington, O.C. EPA-600/3-76-083.
-273-
-------
Elsenrelch, S.J., B.B. Looney, and J.D. Thornton. 1981. Airborne Organic
Contaminants 1n tht Great Lakes Ecosystem. Environmental Science ami
Technology 15(l):30-38.
Freeze. R.A. and J.A. Cherry. 1979. Groundwater. Prentice-Hall, Englewood
CUffs. N.J.
Gakstatter, J.H., M.O. All urn. S.E. Domlnquez, M.R. Crouse. 1978. A Survey
of Phosphorus and Nitrogen Levels In Treated Municipal Uastewater.
Journal of Water Pollution Control Federation. Vol. SO. No. 4.
Glam, C.S., E. Atlas. H.S. Chan, and G.S. Neff. 1980. Phthalate Esters.
PCB and DOT Residues In the Gulf of Mexico Atmosphere. Atmospheric
Environment 14:65-69.
Gilbertson, C.B., F.A. Norstadt, A.C. Mithers. R.F. Holt, A.P. Barnett, T.M.
McCalla. C.A. Onstadt, and R.A. Young. 1979. Animal Waste Utilization
on Cropland and Pasture. U.S. Environmental Protection Agency. Washing-
ton, O.C. EPA-600/2-79-059.
Gllman. C.S.. 1964. Rainfall. In: V.T. Chow (ed). Handbook of Applied
Hydrology. McGraw-Hill, New York. Chapter 9.
Ha 1th, O.A. and L.J. Tubbs. 1981. Watershed Loading Functions for Nonpolnt
Sources. J. Environmental Engineering 01v., ASCE. 106(EE1): 121-137.
Halth. D.A. 1983. Planning Model for Land Application of Sewage Sludge. J.
Environmental Engineering 109(1):66-81.
Halth, O.A.. L.L. Shoemaker, R.L. Ooneker, L.O. Oelwlche. 1984. Agricultural
Nonpolnt Source Control of Phosphorus and Sediment - A Watershed Evalua-
tion, Vol. IV. U.S. Environmental Protection Agency. Ada, OK.
Hamon, W.R.. 1961. Estimating Potential Evapotransplration. J. Hydraulics
D1v.. ASCE. 87(HY3):107-120.
Hartley, G.S. and I.J. Graham-Bryce. 1980. Physical Principles of Pesticide
Behavior. Academic Press, New York.
Hawkins, R.H., 1978. Runoff Curve Numbers with Varying Site Moisture. J.
Irrigation and Drainage 01v., Proc. of the ASCE 104(IR4):389-398.
Heaney. J.P., Huber. W.C., Medina, M.A., Jr.. Murphy, M.P., Nix. S.J. and
S.M. Hasan. 1977. Nationwide Evaluation of Combined Sewer Overflows and
Urban Stormweter Impacts. U.S. Environmental Protection Agency,
Cincinnati, OK. EPA-600/2-77-064.
Heaney, J.P. and W.C. Huber, 1979. Nationwide Cost of Wet-Weather Pollution
Control. J. Water Pollution Control Federation. 51(8):2043-2053.
Huber, W.C., J.P. Heeney, K.J. Stoolenyak, and O.A. Aggldls, 1979. Urban
«aInfa 11-Runnoff-Quality Data Base. U.S. Environmental Protection
Agency. Cincinnati. OH. EPA-600/8-79-004,
Hydrologlc Engineering Center. 1977. Storage, Treatment, Overflow, Runoff
Model "STORM." U.S. Army Corps of Engineers. David, CA. 723-S8-L7520.
Jensen, M.E. (ed.), 1973. Consumptive Use of water and Irrigation Water
Requirements. American Society of Civil Engineers, New York.
-274-
-------
E.G. Jordan Company. 1982. Fate of Priority Pollutants In Publicly Owntd
Treatment Works - 30 Day Study. EPA 440/1-82-302.
E.G. Jordan Company. 1984. Combined Sewer Overflow Toxic Pollutant Study.
EPA-440/1-84-304.
Klbler, D.F. (ed.) 1982. Urban Stormwater Hydrology. Water Resources
Monograph 7. American Geophysical Union. Washington, O.C.
wilsel, W.B. (ed.) 1980. CREAKS: A Field-Scale Model for Chemicals, Runoff
and Erosion from Agricultural Management Systems. Conservation Report
No. 26. .5. Oept. of Agriculture, Washington, D.C.
Kohler, M.A., T.J. Nordenson, and O.R. Baker, 1959. Evaporation Hips for the
United States. U.S. Weather Bureau. Washington. O.C. Tech. Paper 37.
Langbeln. W.B., 1949. Annual Runoff In the United States. U.S. Geological
Survey, Washington, O.C. (Circular 52).
McElroy, A.O.. S.r. Qtlu. J.U. Ntbgtn, A. Aletl. and F.W. Bennett. 1976.
Loading Functions for Assessment of Wattr Pollution from Nonpolnt Sources.
U.S. Environmental Protection Agency, Washington, O.C. EPA-600/2-76-151.
Menzel, R.G. 1980. Enrichment Ratios for water Quality Modeling. Chapter
12. In: CREAMS: A Field Scale Model for Chemicals, Runoff, and
Erosion from Agricultural Management Systems, W.G. Knlsel. ed. Conserva-
tion Research Report No. 26, U.S. Oept. of Agriculture, p. 640.
Mtta Systems, Inc. 1973. Effluent Changes. Prepared for the U.S. Environmental
Protection Agency, Contract No. 68-01-0566.
Metcalf and Eddy, Inc. 1979. Uastewater Engineering: Treatment, Disposal,
Reuse. McGraw-Hill, New York.
Mlncar, R.A.. R.O. Ball. R.L. Church. 1981. Data Base for Influent Heavy
Metals In Publicly Owned Treatment Works. EPA-600/2-81-220.
Mockus, J. 1972. Estimation of Direct Runoff from Storm Rainfall. In:
National Engineering Handbook. Sec. 4, Hydrology. U.S. Soil Conservation
Service, Washington. D.C.
Murphy, B.O., W.C. Johnson, and E.C. Schlatter. 1980. Simulated Deposition
Rates for SO on a Southeastern U.S. Landscape. Agricultural Meteorology
21:179-103.
Nash, R.G. 1980. Dissipation of Pesticides from Soils. In: CREAMS: A
Field Scale Model for Chemicals, Runoff, and Erosion from Agricultural
Management Systems. U.S. Department of Agriculture. Conservation
Research Report No. 26. W.G. Knlsel, ed. p. 640.
Neff, J.M. 1979. Polycyllc Aromatic Hydrocarbons In the Aquatic Environment:
Sources, Fates and Biological Effects. Applied Science Publishers, Ltd..
London.
Neptune. 0. 1980. Priority Pollutant Frequency Listing Tabulations and
Descriptive Statistics. Internal Report to R.B. Schaffer. Director
Effluent Guidelines Division. U.S. Environmental Protection Agency,
November 4, 1980.
-275-
-------
Novotny. V. and 6. Chester*. 1981. Handbook of Nonpolnt Pollution. Van
Nostrand Relnhold, New York.
Ogrosky. H.O.. and V. Mockus. 1964. Hydrology of Agricultural Lands. In:
Handbook of Applltd Hydrology. V.T. Chow. td. McGraw-Hill, Ntw York.
Chapter 21.
Qmernlk, J.M. 1977. Nonpolnt Source-Stream Nutrient Level Relationships:
A Nationwide Study. U.S. Environmental Protection Agency, Corvalll1;.
Oregon. EPA-600/3-77-105.
Parker, C.A.. et al. 1946. Fertilizers and Line In the United States.
USD* Misc. Pub. No. 586.
Pound, C.E., CHtts, R.M.. and O.A. Grlffes. 1976. Land Treatment of
Municipal Uastewater Effluents. Design Factors - I. U.S. Environmental
Protection Agency, Washington, O.C. EPA-625/4-76-JIO.
Powell, G.M. 1976. Land Treatment of Municipal Uastewater Effluents. Design
Factors II. U.S. Environmental Protection Agency. Washington, D.C.
Rao, P.S.C., and J.M. Davidson. 1982. Retention and Transformation of
Selected Pesticides and Phosphorus In Soil-Hater Systems: A Critical
Review. U.S. Environmental Protection Agency, Athens, GA.
EPA 600/3-82-060.
Richardson, C.U.. G.R. Foster, and D.A. Wright. 1983. Estimation of Erosion
Index from Dally Rainfall Amount. Transactions of the ASAE 26(1):
153-156.
Startor. J.D. and G.B. Boyd. 1972. Water Pollution Aspects of Street Surface
Contaminants. U.S. Environmental Protection Agency, Washington, D.C.
EPA-R1-72-081.
Soil Conservation Service. 197S. Urban Hydrology for Small Watersheds. U.S.
Dipt, of Agriculture, Washington, O.C.
Stewart, B.A., D.A. Uoolhlser, W.H. Wlschmeler. J.H. Caro, and M.H. Frere.
197S. Control of Water Pollution from Croplands, Vol. I. U.S. Environ-
mental Protection Agency, Washington, D.C. EPA-600/2-75-026a.
Tubbs. L.J. and D.A. Halth, 1981. Simulation Model for Agricultural Nonpolnt
Source Pollution. J. Water Pollution Control Federation 53(9):
1425-1433.
U.S. Environmental Protection Agency. 1982. Trtatability Data. Vol. 1.
EPA-600/2-82-OOU.
U.S. Environmental Protection Agency. 1982. Industrial Descriptions. Vol.
2. EPA-600/2-82-001b.
U.S. Environmental Protection Agency. 1982. Technology for Control/Removal of
Pollutants. Vol. III. EPA-600/2-82-OOU.
U.S. Environmental Protection Agency. 1982. Cost Estimates to Implement
Treatment. Vol. IV. EPA-600/2-82-OOU.
Vanonl, ..A. (ed.) 1975. Sedimentation Engineering. American Society
of Civil Engineers. New York.
-278-
-------
Weber J.B., 1975. Agricultural Chemicals and Their Importance-as a Nonpoint
Source of Water Pollution. In: P.M. Ashton and R.C. Underwood (ed.)
Nonpoint Sources of Water Pollution. Virginia Water Resources Research
Center. Blacksburg, VA.
Welner. W.C.. H.E. McGulre. and A.F. Gasperlnso. 1976. A Review of Land
Use Nutrient Loading Rate Relationships. Battelle Northwest Laboratories.
R1 chland. WA.
W1schMler. W.H. and D.D. Smith. 1978. Predicting Rainfall Erosion Losses -
A Guide to Conservation Planning. Agriculture Handbook No. 537. U.S.
Dept. of Aglculture, Washington, DC.
Yost. K.J., R.F. Wukasch, T.G. Adams, B. Mlchalczyk. 1981. Heavy Metal
Sources and Flows 1n a Municipal Sewage System: Literature Survey and
Field Investigation of the Kokomo, Indiana, Sewage System.
EPA-600/2-81-224.
-277-
-------
CHAPTER 4
RIVERS AND STREAMS
4.1 INTRODUCTION
The purpose of this chapter Is to present simplified tools which c*n be
used to predict responses of rivers and strews to the Impact of pollutants. The
introductory sections to the chapter should be read prior to solving any problems 1n
order to become familiar with the topics that will be covered and the limitations of
the formulations presented.
Rivers throughout this country are subject to a wide spectrum of geological,
biological, cllmatolog leal, and anthropogenic Impacts which produce a variety of
water quality problems. Approaches which provide guidance to the solution of these
problems, especially ones restricted to hand calculations, must be limited In scope.
The following guidelines have been used In selecting topics to be considered in this
chapter: 1. widely occurring problems, 2. those amenable to hand calculations, and
3. those for which planners can obtain sufficient data.
4.1.1 Sco
The major problem areas to be considered are:
e Carbonaceous (CBOO) and nitrogenous (NBOO) biochemical oxygen demand
e Dissolved oxygen
e Temperature (with a discussion of low flow)
e Nutrients and eutrophteat Ion potential
e CoHform organisms
e Conservative constituents
e Sedimentation and suspended solids
e Toxic substances.
Beginning 1n 1974, the U.S. Environmental Protection Agency has for several
years published the National Hater Quality Inventory which Is a compilation of
current water quality conditions and recent trends 1n the nation's rivers and lakes.
Several of the tables 1n that report series are relevant to this document and are
included here. Table IV-1 Illustrates reference water quality levels used to de-flne
acceptable pollutant limits In U.S. waterways. Table IV-2 shows water quality
conditions in eight major waterway* In the united States, while Table IV-3 summarizes
the most widely observed water quality problems 1n the U.S. These tables will be
cited throughout this chapter.
Local water quality standards, when they exist, are preferable to the general
guidelines provided in Table IV-1. Table IV-4 shows exwple standards for dissolved
oxygen and water temperature for the states of Virginia and Maryland. Parts of the
-278-
-------
TABLE IV-1
REFERENCE LEVEL VALUES OF SELECTED HATER QUALITY
INDICATORS FOR U.S. WATERWAYS (U.S. EPA, 1976}
Parameter Reference Level
Ammonia <_ 0.02 mg/1 as unionized ammonia
~ (for freshwater aquatic life)
Color <_ 75 platinum-cobalt units (for
water supply)
Dissolved Oxygen I5-0 ffl9/1 (to maintain fish
~ populations)
Dissolved Solids <_ 250 mg/1 (for water supply)
Fecal Conforms log mean <_ 200 ptr ml over 30 days
and 90 pefctnt <. 400 ptr ml (for
bathing waters)~
Nltrate-N <_ 10 mg/1 (water supply)
pH between 6.5 and 9.0 (for freshwater
aquatic life)
Phenols <_ 1 ug/1 (for water supply)
Suspended Sol ids and shall not reduce the depth of the
Turbidity compensation point by more than
10 percent (aquatic life)
Total Dissolved Gases < 110 percent saturation (aquatic
life)
standards are significantly different from the reference levels In Table IV-1. For
example the dally average dissolved oxygen standard for natural trout water for the
state of Virginia Is 7.0 mg/1, while S.O mg/1 Is recommended for the protection of
aquatic lift (Table IV-1). Thus, whtn local standards exist, they should be used In
lieu of general reference levels.
4.1.2 Significance of Problem Areas
Oxygen depletion Is ofttn the rtsult of excessive CBOO and N600 loadings par-
ticularly In combination with high temperature and low flow conditions. Increased
nutrient loadings to streams which product elevated ambient conctntratIons can post
substantial potential for tutropMcatlon. The nutrient problem 1s currently ont of
the most widespread areas of conctrn regarding river water quality. The health
-279-
-------
River
TABLE IV-2
CONDITION OF EIGHT MAJOR WATERWAYS (EPA.1974)
Har-Mful
SubsUnces
Physical
Modification
Eutrophicatton
Potential
Mississippi
Missouri
Onto
Detroit are*
rivers
ColuMbta
Snake
UillaMtte
Trace wtals
present in Middle
river
High*, increasing
iron and
Manganese
Cyanide present
but dtMtshtng
Severe gas super
saturation; SOM radio-
activity In lover river
Severe gas super-
saturation, signif-
cant pesticides
Significant sulfite
waste liquor fro*
pulp and paper wastes
High* turbidity and
solids below
Missouri River
High* suspended solids.
turbidity in Middle and
lower river
High* suspended solids
in lower river. sone
iMproveMents
Suspended solids
iMproving. local
teMperature effects
froM discharges
Occasional high*
temperatures
Turbidity froM
natural erosion,
agricultural practices.
reservoir flushing
High* turbidity at
high flow, high
tenperature in suMner
High*, increasing
nutrients but no
algae
High*, increasing
nutrients but no
algae
High* nutrients but
no algae
Snail Increase in
nutrients but no
algae
Nutrients discharged
to Lake Erie
decreasing
High* nutrients but
no algae, except for
sliMe growths in
lower river
Nuisance alga)
blooMS each
si
High* level of
nutrients but
not excessive algae
•280-
-------
TABlt IV-2 (continued)
River
Mississippi
Missouri
Ohio
Tennessee
Detroit area
rivers
Columbia
Snake
UillaMtte
Salinity, Acidity,
and Alkalinity
High* salinity, acidity
below Major
tributaries
High* dissolved salts
in aiddle and lower
river
Low* alkalinity
especially in upper
river
Oxygen
Depletion
Health Hazards and
Aesthetic Deqraualion
Acids and chloride low,*
{•proving despite
large discharges
Approaches ideal
for fresh waters
High* dissolved
solids fro* irrigation
in Middle river
Low* dissolved Mineral
salts, improved pH
Oxygen-demanding
loads from large
cities evident
High* organic loads
from feedlots,
improved near cities
Occasional low*
dissolved oxygen near
Cincinnati and Pittsburgh
Low* BOO and
decreasing COO in
reservoirs
Low* dissolved oxygen
only at Mouths of
area tributaries
Dissolved oxygen
close to saturation
Dissolved oxygen
close to saturation
iMproved dissolved
oxygen, no standards
violations
Commercial fishing
eliminated in lower
river by phenols,
bacteria near cities
High* bacteria and
viruses present in wet
and dry periods
High* bacteria especially
in high population
areas
High* bacteria in small
areas near cities, low
radionuclides
Phenols decreasing,
bacteria unchanged-
to-higher
Very low* bacteria
High* bacteria
below population
centers
High* bacteria, but
improving
*High (or low) relative to other rivers, or relative to other sections of river, or to
national reference levels. Does not necessarily imply standards violations or
dangerous condition.
•281-
-------
TABLE IV-3
WATER QUALITY PROBLEM AREAS REPORTED 8Y STATES*
•JUMBcR RE PORT! i.G PROBLEMS/TOTAL (EPA, 1975)
deoletv.-
Eutropm-
cation
potential
Health
hazards
Sal inity,
acidity.
alkal inity
- ' 1 i r. '. : ; ,
' 'i - * 3
11/13
11/13
3/13
South
5/9
6/9
8/9
6/9
Great
Lakes
6/6
6/6
5/6
2/6
Central
6/3
8/8
8/8
6/8
Southwest
4/4
2/4
3/4
4/4
«est Islands
6/6 4/6
6/6 ./6
5/6 5/6
4/6 2/6
"otal
46/52
43/52
45/52
27/52
Physical 7/13 3/9 3/6 8/8 3/4 6/6 5/6 35/52
codification
Harmful 6/13 6/9 5/6 4/8 4/4 2/6 3/6 30/52
s^astances
• Localized or statewide problems discussed by the States in their reports.
hazards category in Table IV-3 lists elevated co11 form levels as a problem of par-
ticular concern in northeastern and Great Lakes States. Salinity has been identified
as a major problem In the central and southwestern states.
Because of their Importance, each of the problem areas described will be addressed
in this chapter. As shown in Table IV-5, many states routinely measure the parameters
associated with these problems. The total number of states responding to the survey
was 47. Because of the routine surveys conducted, data are commonly available for
performing hand calculations. NBOO, though not directly measured, can be found from
measurements of organic and ammonia nitrogen. Chloride concentration measurements
can be directly converted to salinity.
4.1.3 Applicability to Other Problems
The tools which are presented In this chapter are designed to address specific
water quality problems. However, a number of the tools, which are based on the law
of mass conservation, can be directly applied to other problems with little or no
modification. In the case of temperature prediction, an energy balance 1s used
(wi--ch is analogous to a mass balance).
•282-
-------
TABLE IV-4
EXAMPLE RIVER WATER QUALITY STANDARDS
Class
Virginia
III
IV
V
VI
Mary! and
I
III
IV
Description
Coastal and Piedmont
Mountainous
Put and Take Trout Waters
Natural Trout Waters
Water Contact, Recreation
Natural Trout Waters
Recreational Trout Waters
Dissolved
Minimum
4.0
4.0
5.0
6.0
4.0*
5.0*
5.0*
Oxygen
Average
5.0
5.0
6.0
7.0
5.0*
6.0*
5.0*
Temperature, *F
TM TMAX
5 90
5 87
5 70
5 68
90**
68**
75**
*These values apply except where lower values occur naturally.
**These apply outside the mixing zone. If natural temperature of receiving
water is greater than the standard, then that becomes the standard.
The degree of commonality of source and sinks of a particular pollutant (e.g.,
a nutrient) or water quality Indicator (e.g., dissolved oxygen) is responsible for
the similarities and differences among the specific equations. For example, CBOO and
NBOO produce a similar general effect (oxygen depletion), generally have similar
sources and sinks, and for purposes of this study are assumed to follow first order
decay kinetics. Conforms, also assumed to decay by first order kinetics, are
handled by the mass-balance approach. Conservative substances are different from
BOO and conforms In that they do not decay. Finally, there are some Instances where
a more subjective analysis 1s Indicated and neither a mass nor energy balance 1s
presented.
Once the similarities among water quality parameters are understood, handling
two seemingly different problems can often be accomplished In a straightforward and
similar fashion. For example, the distribution of toxic substances that are either
conservative or follow a first order decay may be evaluated using techniques described
for conservative substances and conforms, respectively.
4.1.4 Sources of Pollutants
Pollutant loadings originate from three general sources: point, nonpolnt, and
natural. Each of these can constitute a major hurdle In meeting the 1983 goals of
•283-
-------
TABLE IV-5
WATER QUALITY PARAMETERS
COMMONLY MONITORED BY STATES* (EPA.1975)
Number
Parameter of States
Flow 47
Dissolved oxygen 47
Conform bacteria 45
Nitrogen (any form) 39
Phosphorus (any form) 35
pH 35
BOO/COO/TOC 27
Water temperature 29
Turbidity 26
Solids (any type) 27
Metals (any type) 17
Chlorides 19
Alkalinity IS
Conductivity 16
Color 11
Sulfate 14
*0n1y parameters listed by at least 10 States and specified as being
part of each State's monitoring program are Included.
flshablc and swlmmable waters. Specifically, point sources (30 states), nonpolnt
sources (37 states), and natural conditions (21 states) are all major contributors to
water quality problems (EPA. 1975).
It Is imperative that the capacity to assess Impacts of nonpolnt sources be a
part of the hand calculation methodology for rivers. Table IY-6 Illustrates the
Importance of nonpolnt source nutrient loading for selected rivers In Iowa. Up to 96
percent of the annual phosphorus load and up to 99 percent of the total nitrogen load
are from nonpolnt sources. Admittedly, quantification of nonpolnt source loads Is
often difficult. Nevertheless, simplified nonpolnt source terms will be Included in
some of the mass-balance formulations. The methodology supplied In Chapter III can
be used to estimate the nonpolnt source loading rates.
4.1.5 Assumptions
In deriving the mass-balance equations, a number of asswptlons were made.
Users should be aware of each assumption so that the tools are not misapplied. The
-284-
-------
TABLE IV-6
ANNUAL PHOSPHORUS AND NITROGEN LOAD FOR SELECTED IOWA RIVER BASINS {EPA,1975)
River
PHOSPHORUS
Floyd
Little Sioux
Char i ton
Des Moines
Iowa
Cedar
NITROGEN
Floyd
Little Sioux
Chariton
Oes Moines
Iowa
Cedar
•Orthophosphate
Total
(Ibs/year)
720,207
1 ,851 .632
879,916
5,621,007
1,723,975*
5,099,507
1,705,984
9,609,556
1,585.427
41.334,897
2,075.830
6.804,881
Point Sources
(Ibs/year)
29,807
129,088
48,203
586,015
103,445*
1,526.775
65.171
85.308
24,795
695,235
91.287
1,552,334
Percent of
Nonpoint Sources Total from
(Ibs/year) Nonooint Sources
690,400
1,722,544
831,713
5,034,992
1,620,530*
3,572,732
1,640,813
9.522.248
1.560,632
40.639,662
1,984,543
5,252,547
95.9
93.3
9C.5
S9.6
94.0
70.1
96.2
99.1
98.4
98.3
'95.6
77.2
most important assumptions are:
• The system is at steady-state.
e Dispersion is small compared to advection (I.e., plug flow is assumed).
e The river system is vertically and laterally mixed.
e When pollutants decay, the rates are first order.
The steady-state assumption Mans that conditions are not changing with time,
but only as a function of distance along the river. The time scale or steady-state
generally should be on the order of a week or longer. For example, the summer low
flow period generally represents a steady-state situation. However, storm events, and
the dynamic responses of a river to them, must be considered a transient phenomenon.
Dispersion effects can usually be neglected when pollutant Input into a river is
continuous. Under these conditions the plug flow assumption is reasonable because
the net dispersive transport 1s small. However, when a slug of pollutant Is dis-
charged instantaneously, dispersive transport Is Important since high concentration
gradients exist around the centrold of the discharged pollutant.
The fully-mixed assumption presupposes that concentration gradients exist only
In the direction of flow (longitudinal direction) and not In either the vertical or
285-
-------
lateral direction. Tht final major assumption 1s that all decay rates can be approxi-
mated by first order kinetics. This means that the decay rate of a substance 1s
proportional to the amount present. First order decay 1s traditionally used 1n CBOO
computations, and occasionally in nitrogen oxidation. The oxidation of inorganic
nitrogen actually prcr«eds in stages from ammonia-N to nitr<».«-N to nltrate-N.
However, for purposes of this report, the first order decay rate is acceptable for
N800 and conforms, as well as CBOO. Before applying first order decay to other
substances, however, care should be taken to determine the validity of the assumption.
For a few of the analyses which follow, several of the aforementioned assumptions
are relaxed. In the discussion of mixing zones. Section 4.1.9, partial mixing is
discussed for wide rivers. In the discussion on toxicants. Section 4.9, the spill
analysis requires that an unsteady- state situation be analyzed where the effects of
dispersion are Included.
4.1.6 Data Requirements
Required in the analysis of most water quality problems are one or more
types of data. For example, strew velocity (U), volumetric flow rate (Q), and
stream temperatures (T) are commonly needed. Decay rates, specific to the particular
problem at hand, are also required.
The U.S. Environmental Protection Agency has published two documents (Bowie et
al_.. 1985 and Zison et. aj... 1978) Intended to provide water quality modelers with a
comprehensive source of Information on rate constants and coefficients. The document
provides extensive Information on both biological and water quality parameters commonly
used in surface water modeling. The contents of the document will be useful to tha
users of this document who are often faced with limited information on process rates
for the water bodies being analyzed.
Stream velocity 1s the most basic hydraulic parameter needed for the analyses
presented in this chapter. Ideally, the appropriate stream velocity 1s the travel
time of neutrally buoyant particles over the reach being Investigated divided by the
distance traveled. Note that this concept of velocity is different from the velocity
determined by:
As defined by Equation IV-1, this concept of velocity exists only at the point in the
river where the cress* sectional area is A. If the point of measurement 1s not
typical of the reach being Investigated, then neither will the velocity be typical.
Consequently, should the user predict stream velocity based on cross- sectional area,
a loci'.ion typical of the Hver reach being Investigated should be chosen.
-286-
-------
An alternate method of predicting stream velocity, which does not depend on
either flowrate 0 or cross-sectional area A is Manning's Equation. A complete des-
cription of the use of this approach is given in many texts on surface water hydraul-
ics, one cf the best being Chow (1959).
According to Manning's Equation stream velocity under uniform flow conditions is
expressible as:
where
n * Manning's n
S - slope, ft/ft
RH « hydraulic rad* .$, ft.
Manning's n is the roughness of the stream bed, and can be predicted as outlined in
Chow (1959). Barnes (1967) provides roughness data for 90 streams In the United
States, and includes cross-sectional areas and photographs of the streams investi-
gated. The slope can be estimated using topographic maps to predict elevation
changes between two locations and then overlying a string over the stream path to
predict distance. The hydraulic radius (which is the ratio of the cross-sectional
area to wetted perimeter) can be estimated in terns of depth when the stream width is
much greater than the depth. Specifically:
{depth, if channel cross-section is rectangular
2/3 maximum depth, if channel cross-section 1s parabolic
4.1.7 Selection of Season
It is reasonable to expect that a particular water quality problem may be
more severe at one time of the year than another. Table IV-7 shows that pollutant
levels can depend on season (summer or winter) and flow rate (high flow or low flow).
Dissolved oxygen problems, for example, are clearly associated with summer, low flow
conditions. Consequently, for any particular pollution problem, users should strive
to perform the analysis under critical conditions. Where planning Is performed with
consideration of the aggravated situation and where proper abatement action 1s taken,
it 1s likely that pollution concentrations will be below problem levels during other
times of the year. If a problem In fact exists, then 1t 1s under these conditions
that it will be most pronounced.
In the following sections, hand calculation methods for each problem area are
described with Illustrative examples. Table IV-8 provides a summary of the material
presented.
-287-
-------
TABLE IV-7
MAJOR WATERWAYS: SEASONAL AND ROW ANALYSIS. 1968-72 (EPA, 1974)
Parameters
Suspended solids
Turbidity
Color
AWMMIta
Nitrite
Nitratefas N)
Nitratejas NO,)
Nitrite plus nitrate
Or9«nic nitrogen
lot*I Kjeldahl nitrogen
loul phosphorus
ToUl phosphate
Dissolved phosphate
Dissolved solids(IOS°C)
Dissolved solids(>80°C)
Chlorides
Sulfates
Alkalinity
ph
Dissolved oxygen
BOOc
COO (.02SN)
Total colifonM(NFD)*
Total coliforas(Hfl)*
Total colifonHJNPN)*
fecal coliform(HF)*
fee*I coliforasJHPN)*
Phenols
Odor
Winter.
High flow
9
13
II
14
3
I?
8
2
3
3
10
8
6
4
3
4
S
6
IS
0
II
6
4
8
4
6
4
S
4
> Minter. Summer. Dominant
Low Flow Low Flow High Flow
(number of reaches exceeding reference levels)**
S 0 4
4
0
I
3
1
4
3
3
6
5
3
3
3
7
8
IS
13
I?
4
19
6
S
10
6
2
6
0
0
0
7
S
8
6
2
0
0
S
S
4
6
6
10
S
10
6
0
8
3
2
2
3
3
1
I
0
3
3
2
1
0
3
2
0
S
0
4
9
I
2
S
4
3
4
0
0
0
High flow
High flow
High flow
Cold weather
Low flow
Cold weather
Cold weather
Inconclusive
Ham weather
War* weather
Cold weather
Cold weather
Cold weather
Low flow
Low flow
Low flow
Marti weather. low flow
Low flow
Cold weather, high flow
Ham weather
Cold weather
Cold weather
Mam weather
High flow. warn weather
Inconclusive
Inconclusive
Cold weather
Inconclusive
Inconclusive
•Membrane filter delayed. membrane filter immediate, aost probable motor. ne*t>rane filter
••Reference levels are available in Table IV-I. Thirty reaches were analy/ed during each season.
•288-
-------
TABLE IV-8
•JATER QUALITY ANALYSES FOR RIVER SCREENING METHODOLOGY
•aur Quality Constituent
CoavuUtlOMl rretedurts
dn lnc)»d«d
iMttr tMPtriturt
Caroonactous and nitrogenous
blocnMical oxygtn dtaand
OmolvM Oxygen
tquillbriuB ttBptraturt
•txlng ttaptraturt
teapertturt prof tit for point sources
100 prof 11ti for point tourcts
100 prof Hit »(th benthlc sourcts added
100 prof Mis Kith both benthtc and nonpolnt
sourcts added
OOO-N800-00 profi It for point tourcts
00 profiles Mltn pnotosyntnettc Myftfl
production «nd BtntMc upukt «ddtd
critic*l dtssolvtd oxygtn conditions
•istt 9 nutntnt
nutritnt profDtS for point sourcts
nutrltnt prof11ts for nonpolnt sourctt
nitrogtn/phosphorus r»tlos for
9rontri llalutton
nonpolnt sourct lotdlng ritti by
Und ust typt
CoHfom
coll fora profllts for point sourcts
col I for* profllts for nonpolnt sourcts
btd 1o*d
susptndtd lo*d
toUl load
• dtciy rttts
•tdlan btd ptrtlclt tilts for
nuatrous
critic*! shotr strtss
itdlatnt transport propensity factor
approiiMtt Md loM/wsptnd«d lo«d
rtlatlonshtp
Toaicants
UilCMt profllts for point and
nonpolnt sourcts
MSS fix* »oUtH1»td, idvtcttd. and
transforatd
spill analysis of loo and high donsttjr
toxicants
tlM rtqulrtd to d«sort toxicant fro*
btddad sodlMnts
- vapor prtssurt. solublltty.xunol-
•attr portion cotffldtnt for
priority polluUnts
• Honry's L*n Constantr
4.1.8 River Segmentation
Although the tools presented in this chapter are of a simplified nature they can
be used to analyze complex river systems (I.e., those which have a number of differed
point and nonpolnt sources of pollution, tributaries and withdrawals). Analysis of
these systems Is accomplished by dividing the river Into segments. The basic tenet
which must be followed 1s simply this: Segments are created so that one of the
analytical tools presented in this chapter can be used to predict the pollutant
-289-
-------
concentration profile within the segment.
Analyses of river systems normally begin at a segment where the boundary
conditions are known, and proceed sequentially downstream. Thus the results found
for one segment are used as the upstream boundary condition for the next segment.
Based on the tools in this chapter, the following rules should be followed when
segmenting:
1. Point sources of pollutants enter the river just above the upstream
boundary of a segment. Tributaries are treated as point sources.
2. Nonpoint sources enter a river throughout the length of a segment.
3. Pollutant concentrations at the upstream end of segments are obtained £>y
mixing the pollutant concentration in the river with the contribution of
the point source at that location (if one exists). The location where
mixing occurs 1s called a mixing r-ne.
4. Generally constant hydraulic variables (e.g., depth and velocity)
are used throughout a segment. If there is a gradual change in the
hydraulic variables over distance, an average value can be used. If
there is an abrupt change in the variable, such as a velocity chanae
caused by a significant deepenirg of the channel, then a new segment can
be created at this boundary.
5. Decay rates, reaeratlon rates, and other rate coefficients remain
constant within a segment. If rate coefficients are known to change
significantly from one location to another in a river, then different
segments should be created. This rule is consistent with rule (*),
•since rate coefficients are often functions of hydraulic variables.
EXAMPLE IV-1
Figure IV-la snows a stretch of the James River, located in Virginia. On
the stretch of the river shown, there 1s 4 tributary (Falling Creek), a sewage
treatment plant (STP), and a nonpolnt source of runoff (agricultural). Segment
the river between locations A and B in order to determine the profile of a
pollutant which 1s discharged from each of the three sources.
First, it should be noted that often there Is more than one way to segment
the river to successfully solve the problem. The most obvious method will be
Illustrated here. Figure IV-lb shows the proposed solution. There are two mixing
zones - the first around the treatment plant and the second around the tributary
which Is treated as a point source. The first egment Is located from below the
first mixing zone to above the second mixing zone, and has a nonpolnt source
discharging throughout the length of the segment, consistent with rule (2). The
second segment Is located below the second mixing zone and continues downstream to
-290-
-------
JAMES
A
I
' 1
) I
, /
r
> i
' i
i i
i i
• '
AGRICULTURAL 1(jNOfr
I B
(a) River Segment Being Analyzed
MIXING ZONE
JAMES RIVER
T
A' '
" — 1 — — \ — 1 — _
• i
1 1
1'
1
1 i
<
1
1
AGRICULTURAL RUNOFF
B
(b) Proposed Segmentation Scheme
FIGURE IV-1 ILLUSTRATION OF RIVER SEGMENTATION
PROCEDURE ON THE JAMES RIVER.
I location B, wnich is the end of the nonpoint source. If Falling Creek had not |
: been present, a single segment and a slnqle mixing zone would have been sufficient j
< to analyze the problem. '
L END OF EXAMPLE IV-1 '
-291-
-------
A second, more comprehensive example will illustrate a number of points about
segmentat'on «ct covered in the previous example. One of these points is that the
seamentat'3" scheme used can vary depending on the pollutant being analyzed.
EXAMPLE IV-2
1
Segment the river shown in Figure .tf-2 beginning at location A and continuing
to location B in order to determine th« instream BOD distribution. How would the
segmentation differ when predicting the dissolved oxygen profile?
Both point and nonpoint sources discharge to the river in Figure IV-2.
Several flows are diverted, and the river width changes over parts of the reach
being investigated. Each of the rules stated earlier will be utilized to segment
the river system. Figure IV-3 snows one solution to the problem. Depending on the
distances between the various sources of pollutants, which are not given in the
SMALL 0»M
n
AGRICULTURE
CONTI
(COW Tl
ATTACHfO
ALOAC
•CSCMVOI*
DIVtHTfO
FIGURE IV-2
HYPOTHETICAL RIVER HAVING A VARIETY OF
POLLUTANT SOURCES AND SINKS,
-292-
-------
(COWT)
TTTT
oivearto
FIGURE IV-3 RIVER SEGMENTATION FOR BOD DISTRIBUTION,
problem, it might be possible to combine some of the segments. The reservoir is
assumed to be analyzed using the methods 1n Chapter 5, and so 1s not segmented.
Mixing zones are Included around the four point sources: the food processing
plant, the tributary, the sewage treatment plant, and the pulp mill. In segments
9 and 11 there appear to be a number of point sources and diversions. Strictly
speaking, segments 9 and 11 do not follow the rules presented earlier, which
require mixing zones around each point source. However, the point sources and
sinks within segments 9 and 11 are assumed to represent equivalent nonpolnt
sources, which act over the length of each segment. This approach can obviously
simplify the analysis of complex river systems by decreasing the number of segments
analyzed. However, the analysis 1s more approximate because the nonpolnt source
1s assumed to be uniformly distributed throughout the segment. Example IV-5
presented later shows a specific application of the concept of an equivalent
nonpolnt source.
-293-
-------
> segment 2 the presence of the small dam is assumed not to influence
*.*e 303 arc* Me, so that its presence does not require a mixing zone. However if
:-5>c'ved oxygen profile were being calculated, segment 2 would be divided
. '»c segments, with a mixing zone around the dam. This division is required
Decajse the Dissolved oxygen concentration can rapidly change [almost instantane-
ously) as the water flows over the dam. The dissolved oxygen concentration just
below the dam should be used as the upstream boundary conditions for the next
segment. The specific information required to accomplish this is discussed later
in Section 4.3.
I a second difference in segmenting for dissolved oxygen occurs in Segment 8.
: The presence of the attached algae is assumed not to influence the 800 profile,
but the algae are internal sources of oxygen. Thus segment 8 would be subdivided
at -.-e upstream location of the attached algal growth.
I
I END OF EXAMPLE IV-2
4.L.9 fixing Zones
A mixing zone, as used in this document, 1s nothing more than a short reach of
a river where a point source and river water mix. It is often assumed, for both
simple and more complex approaches (e.g., QUAL-II computer model), that mixing is
instantaneous and complete across the entire width of the channel. With several
exceptions, such an approach is used in this document.
Assuming complete mixing, the concentration of a pollutant in a river after
IP i x i ng is:
c . CuQu * Cwqw (IV-3a)
. CuQu * M/5.38 dV-3b)
Qw * Qu
where
C « concentration of pollutant in river following mixing, mg/1
C • concentration 1n point source, mg/1
C • concentration 1n river above point source, mg/1
u 3
0 « discharge rate of point source, ft /sec
3
0 » flow rate of river above point of discharge, ft /sec
W « pollutant mass emission rate, Ibs/day.
The concentration C is the pollutant level 1n the mixing zones shown in the earlier
Figures IV-1 and IV-3. These concentrations become the upstream boundary conditions
for the adjacent downstream segment.
-294-
-------
1 J 1 I _L L t 1 IJ L 1 I I I I 1 I I I I I I I I I I I I I I I I I I I I I I I I I I 1
CONTAMINATED
I I I I I I I I l\l I I I I I I I I I I I I I I I I I 1 1 I I I ( J I I I I I I I I I I I I
FIGURE
POINT
[SOURCE,
POLLUTANT DISCHARGE WHERE INITIAL MIXING
OCCURS A FRACTIONAL DISTANCE ACROSS THE RIVER,
Although it is convenient to assume that complete mixing occurs, this assumption
may be inaccurate for wide rivers, depending on the characteristics of the point
source outfall and diffuser. Figure IV-4 illustrates such a case. The river is wide
enough so that the wastewater is initially mixed with only a fraction of the total
river flow. As the po)lutant-riverwater mixture is transported downstream mixing
continues until the pollutant is completely mixed across the channel.
The initial pollutant concentration at the point of discharge is:
C «
Q * - o
* W gu
where
Y
"w
fractional distance across river where Initial mixing occurs.
All other variables have been previously defined.
The significance of Incomplete Initial mixing 1s that pollutant concentrations
can be initially much higher than 1f complete mixing occurs. For example. If the
upstream contribution of the pollutant 1s negligible (C • 0) and If the fraction
of river flow which initially mixes is far greater than the wastewater flow (1 0 »0 ),
W
then:
C ' 7 'cm
(IV-5)
-295-
-------
C « concentration of ocllutant if there is incomplete initial mixing
Zr • concentration of pollutant if there is complete initial mixing.
If Y/W * C.I, tnen the pollutant concentration following incomplete mixing is 10
times higher than if complete mixing were to occur.
The distance l to complete mixing (see Figure IV-4) can be estimated
(as an upper limit) by the following expression:
Lc • W'V J (IV-6)
where
L • distance below point source where complete mixing occurs
W » width of river
J • river velocity
e. » lateral diffusion coefficient.
Values of the lateral diffusion coefficient can be estimated from the data given in
Table IV-9. Also, the foUowino predictive formula can be used:
^0.1-0.2, for a straight rectangular flume
I
• < 0.25, for irrigation channels (IV-7)
^ 0.4-0.8, many natural channels
where
D * mean depth of flow
u* • friction velocity • ^gOS
S • slope of channel.
The actual distance L Is probably less than that calculated from Equation
IV-6 because of secondary mixing, river curvature, and initial momentum of the
discharge. It is also sensitive to river width.
4.1.10 Mater and Pollutant Balances
Many river systems are hydrologically complex. Flow patterns are Influenced by
tributaries, nonpoint sources of runoff, flow withdrawals, as well as point sources
of pollution. If the planner Intends to perform water quality analyses on a basin-wide
scale, U is probably prudent that a water budget be first completed. A water budget
is a statement that:
g| - £ Inflows -£ Outflows - 0, for steady-state (IV-8)
-296-
-------
TABLE IV-9
EXPERIMENTAL MEASUREMENTS OF TRANSVERSE MIXING IN
OPEN CHANNELS WITH CURVES AND IRREGULAR SIDES
C^nne' "if -CO'l Melr Cnejr 'r jns*t:'Se
I'jnnel •« 3 ; _• ;:e"':if": _^'x
• "'"•el ;ec~ft'y . »i , m; .'m'ii i"», .'•'sec ;."•
-«»;;; <;::;s;;"
LJOorltor/
-400rit3ry nootl
3' :n« IJsse' 'wei
[Jtt*1 ''•*'
^4ck*n2ie ^Tver
'rom C0r! S""SJO"
"••"«"-"9 -'M - ' I.TS co;* ::2 :*
Smooth sia«» md i . 3.097 o.ll - • : }f-: 19
Bottom; 3.15 n
boti sues
Smootn siaes «na 2.2 3.397 3.1] - . ; 3-: -
sot tor. 2.5 f
long grotns on
so tn siaes
Groins on s i a«i 1.22 0.9 3.13 :.3C78 - : -5-: '•
Gro'ns on sides 69.5 4.0 D.9* I.C75 • - 51
ind jentle cjrviturt
leneri'V ttruft i2«o 6.7 1.77 3.152 0.67 3 4
er 3eiCi -neludtl one 210-270 4 5.4 3.08 1.1 34
a' 90' jna cn« 130'
wer, Itntt/ -^«nder.n? 350 3.73-1 74 0.29-3.58 0.033-0.051 : 52-: i-
29 i" rejCI S*1o» river *ijh uO >.o
»Si.f "mt
from: Fischer, H.B., E.J. List, R.C.r. Kob, J. Imberger, and N.H. Brooks. 1979.
Mixing in Inland and Coastal waters. Academic Press, New York.
where
S » storage In the river channel.
For the steady-state situations, which are examined here, the water budget simply
states that inflows to the system equal outflows from the system. A water budget
thus provides a method of determining whether the major flow contributions have been
accurately assessed or not. If a large Imbalance in the water budget exists, accurate
evaluation of the major sources of pollutant might be difficult to achieve. An
accurate water balance helps to minimize the possibility of Inaccurate assessment of
pollutant concentration. It does not eliminate the possibility.
Once a water balance has been completed, then a pollutant balance of a conserva-
tive pollutant can be developed based on the following relationship:
Flux • 53 n°* •«* steady-state (IV-9)
out
-297-
-------
where the fluxes are the rates of entry and loss of the pollutant into and
out of the system, respectively. One of the following two expressions can
be used to predict the mass loading rates:
M • 5.38 C Q (IV-10)
where
M « mass loading rate, Ibs/day
C « concentration, mg/1
Q « flow rate, ft3/sec
and
M • 86.4 C Q (IV-11)
where
M » mass loading rate, kgs/day
Q • flow rate, m /sec.
When nutrient and water balances are developed, the following considerations should
De kept in mind:
• In most instances it is probably not possible to develop water or
nutrient balances where inflows and outflows balance to within less than
10 percent of each other.
0 The system's upstream boundary must be included in the balance as a
source and the downstream boundary as a loss.
• All sources and losses should be mutually exclusive of each other.
• Choose system boundaries to coincide with locations of gaging stations
when possible.
• Try to use comparable periods of record of data. This will help to
minimize the impacts of trends which could be present in one record but
not in another.
• It is typically easier to develop water and mass balances on an annual
basis, although balances can be developed for each season of the year.
However, if the system 1s not at steady state, Inflows and outflows
should not balance.
Table IV-10 shows a suggested method of tabulating the results of water and
pollutant balances. Total nitrogen (TN) and total phosphorus (TP) are the pollutants.
All flow rates and loading rates are tabulated individually. Once total loading
rates have been tabulated, the percent contribution from each source can be determined.
Percent contributions help to determine the relative Importances of each source as a
contributor to pollution, and can provide a met nod to prioritize pollution abatement
efforts.
-298-
-------
TABLE IV-10
SUGGESTED CONFIGURATION FOR WATER AND NUTRIENT BALANCE TABLE
SOURCES
- UPSTREAM
- TRIBUTARIES
- IRRIGATION
RETURNS
- MUNICIPAL
- INDUSTRIAL
i
i
i
TOTAL
LOSSES
- DOWNSTREAM
- DIVERSIONS
TOTAL
SOURCES-LOSSES ,nA
X 1UU
LOSSES
FLOW RATE
LOADING RATE
TN I
TP I
-299-
-------
EXAMPLE IV-3
Figure IV-5 shows a hypothetical river which has three tributaries, a
nonpomt source of runoff, and two diversions. Develop a water balance for
this system. The known flow rates are:
Identification Number
1
2
3
4
5
6
7
8
Flow rate (cfs)
2000
4000
1200
200
800
1000
2000
6000
FIGURE IV-5 ILLUSTRATION OF WATER BALANCE
-300-
-------
I The flowrates at locations 1,2,3, and 5 are assumed to comprise the inflow rates
I to the system. The total inflows are:
i
I
j Identification Number Inflows
j 1 2000 cfs
2 4000 cfs
! 3 1200 cfs
! S 800 cfs
I Total 8000 cfs
| The inflow from gage 4 is not needed because gage 5 is located further downstream
j on the same tributary. The outflows consist of diversions 6 and 7 and the down-
j stream outflow past gage 8:
i Identification Number Outflows
! 6 1000 cfs
I 7 2000 cfs
j 8 6000 cfs
j 9000 cfs
j The inflows and outflows differ by 1000 cfs. There are several reasons
for the Imbalance. One, the flow rate past each gage is not measured perfectly,
but differs by some degree from the actual flow rate. Two, the gage at location 5
j does not catch all of the nonpoint source runoff, so there is an additional inflow
I to the system which has not be quantified. Three, depending on the size of the
| reservoir, direct precipitation and evaporation might be significant.
I END OF EXAMPLE IV-3
The following example Illustrates both a water and nutrient balance, and is
based on work performed by Tetra Tech on the Snake River in Idaho (Mills 1979).
- EXAMPLE IV-4
Develop annual water and phosphorus balances for water year 1976 for the j
Snjice River from Heise, Idaho, to below American Falls Reservoir, a distance of •
150 miles. A sketch 1s shown in Figure IV-6. Estimate the phosphorus retention
coefficient for American Falls Reservoir. The retention coefficient is defined as:
m Flux Input - Flux Output
p Flux Input
-301-
-------
Henrys Fork
near RexDurg
Blackfoot River
near Blackfoot
( mciuoe bypass canal)
Portneul River at RocateHo
American Falls Reservoir
Snake River at Neeley
RM713
FIGURE IV-6 SKETCH OF SNAKE RIVER FROM HEISE TO NEELEY, IDAHO,
The required data are shown below:
Surface area of American Falls Reservoir • 56,600 acres
Evaporation rate In this part of United States • 33 Inches/year
Precipitation -11 Inches/ytar
Ground water Inflow Into Snake River: 500 cfs
Ground water Inflow Into American Falls Reservoir: 2,100 cfs.
The total phosphorus concentrations were generated during the study of Mills
(1979) and are provided here:
-302-
-------
Source mg/1
In rainwater 0.03
Snake River near Helse 0.05
Henrys For.. 0.11
Blackfoot River 0.26
Portneuf River 0.68
Groundwater inflow 0.23
Snake River near Neeley 0.08
The surface inflow rates are gaged by the U.S. Geological Survey and are
reported in the U.S. Geological Survey Water Data Report for Idaho (1976). An
example of how the information is tabulated in these reports is shown in Figure
1V-7. From an entry in the Table, the mean flow rate for water year 1976 1$ 8549
cfs at USGS 30307500, near Heise. Rather than showing the remaining tabulations
from the USGS report the flow rates from water year 1976 will simply be tabulated,
as contained in the report.
Source Flow rate
Blackfoot River 453 cfs
Henrys Fork 3,235 cfs
Portneuf River 412 cfs
USGS 13059500 (diversion) 2.333,700 ac-ft/yr
USGS 13069000 (diversion) 800,900 ac-ft/yr
Based on this information the water and total phosphorus balances are calculated
and shown in Table IV-11. The flow rates are all converted to units of cfs. This
requires converting the precipitation, evaporation, and diversions to these units.
A precipitation rate of 11 Inches per year Is equivalent to 71 cfs:
11 t 12 x 56600 x 43560 * 366 » 24 » 3600 - 71 cfs
The diverted flow in ac-ft/yr 1s converted to cfs as shown:
USGS 13059500: 2333700 x 43560 « 366 » 24 » 3600 - 3214
The percent difference between Inflow rates and outflow rates 1s 4 percent.
Based on these flow rates, and the concentrations of total phosphorus
presented earlier, the sources and losses of total phosphorus can be tabulated.
For example, the mass flux of total phosphorus flowing past Helse can be calculated
using Equation IV-10:
M « 5.38 x 8549 x 0.05 • 2300 Ibs/day
Continuing in this manner, the sources and losses are as tabulated in Table
IV-11. The large imbalance 1s caused by retention at American Falls Reservoir.
The phosphorus loading to the reservoir Is:
9589 - 865 - 415 - 8309 Ibs/day
-303-
-------
'39' 33" . in
.259 •:
"*. 2*
, * 11 E . Sonntvillt C0u"t». HyarolOflU UnH
;mil "*iO uO»trt«» '<•«•
38 6 ««, jolt-tic 'n*a -««'yt * an . »na 1C Kilt Ml 5 ',1.186.3 uO.
-i-3t.
it.at. 7.;-: «t 2.368
S»' Stcona, «lttr '*«r
"tin iiluts
1975 :a Stotwo*' 1976
Jul
Sw
2 52
i * •
;
6 .-
9"
3 •?
3 43
i: i:
11
12 «-
13 li
14 I'
15 2i
16 11
1 * 11
;3 11
1? 11
2: i:
21 :3
i^ 3"J*
23 236
21 333
25 391
25 393
336
I'. 396
23 291
3: 35:
3 '. 333
-3U ;-:l
»* J r ; ' "
"4« "1.
"i" 391
ic- : •. '233;;
Mvi^ 532
ic--":f2::5C
j?"
191!
3933
3930
3933
3933
3913
3833
3790
3813
3813
36SC
3480
3323
3213
32::
329C
32:3
331:
31::
1523
33':
3373
3353
3333
3190
3390
3150
3520
••
137360
3573
3933
3123
212900
3S43
5 228530
J55C
3530
2560
3560
1710
3760
3760
3760
3780
3730
3780
3710
1720
3710
3723
3690
3630
368C
3673
3680
3680
3700
3700
3730
3690
3700
3671
3e7Q
3700
3730
111373
3689
3780
3550
226900
3708
228000
3723
3730
3750
3800
3800
3790
3780
3440
3590
3900
3910
3920
1900
3900
4060
4080
4090
4080
4040
4040
4Q40
4040
»060
4090
4090
4090
4100
4090
4100
4100
122610
3955
4100
3720
243200
1270
201100
4110
1143
4130
4130
4540
4773
4640
4713
44iO
4280
4273
4230
1943
1710
3750
3740
1673
3950
3953
3963
4000
4013
4013
3980
1990
1980
4000
4013
..
••
119480
4123
4773
3740
217300
3081
177230
4000
4800
5990
6150
6100
6120
6070
6123
6050
7890
8800
8800
8890
8550
8920
9443
978C
9723
9680
9820
9823
9933
10130
10100
10100
10000
9990
10400
10600
10500
259810
8380
loeoo
4000
515300
3054
187300
10900
11130
9160
9430
10400
117CO
12800
1*000
15400
16000
16130
16130
160CO
18000
16000
16200
163CO
16130
16600
17230
17300
17400
17400
174QO
17200
17130
17700
IS1CO
18230
--
450S20
15027
18233
9330
894200
6005
357300
1 8400
18400
186CC
18900
132::
18130
182C3
18130
1 82 CO
13800
18430
18233
1890:
19500
19400
19300
2040C
224;:
21800
21800
21800
21800
24200
24000
240CO
21900
240CO
222QC
2040:
197GO
637003
20545
242::
18130
1233000
25380
1591400
1900C
19130
laooc
14800
13800
10730
1070C
uooo
122CO
121BC
1210C
12030
11900
11BCC
1190C
11900
12000
1190C
11900
11900
11100
10700
1 01 00
9880
9400
9370
9140
9310
9110
--
363410
12113
19100
9110
720800
25000
1487100
9780
13000
10900
10500
13200
10200
10500
11500
12100
11900
11700
12100
12700
13500
11700
11600
11700
13600
11600
11600
11500
13500
11500
11500
13500
11500
11500
13500
13500
11500
333790
12438
13700
9780
764800
11610
817100
11200
12600
12000
10200
9880
9390
8640
8410
8260
8060
7670
7460
7420
7450
7440
7390
7440
7140
6950
6750
6680
6810
6250
5950
5810
5710
5690
5680
5690
5640
244770
7895
11200
5640
485500
5490
399100
5650
5960
6480
6940
7140
7140
7153
6990
6900
6920
7000
6960
6150
5800
5700
5720
5500
5290
5110
5290
5170
4910
4840
4840
4810
4820
4680
4580
4570
-•
176110
5870
7150
4570
349100
4677
278100
7933 •«•• 21700 "m 2940 *c-ft 5744000 •*««' 8015 *c-'t 5802600 '
| .f " .i'6 'otil 3129210 "MX 8549 Nt> 24200 "m 1120 Ac-Ft 6207000 NMX 8595 *c-Ft 6219600 |
. * *0;.it*a '0' t(or«q« in Jtcksox Likt
-------
I
TABLE IV-11
SOLUTION TO SNAKE RIVER WATER AND PHOSPHORUS BALANCE PROBLEM
Sources
Snake River at Heise
Blackfoot River
Henrys Fork
Portneuf River
Ground water inflow into
Snake River
Ground water inflow into
American Falls Reservoir
Precipitation on American
Falls Reservoir
^^ Sources
Losses
USGS 13059500
USGS 13069000
Snake River at Nee ley
Evaporation
2~i Losses
( Losses
Flow
Rate (cfs)
8,549
453
3,235
412
500
2,100
15,320
Flow
Rate (cfs)
3,214.
1.103
11,360.
215.
15,892.
' '
TP Loading (Ibs/day)
2,300.
1,915.
634.
1,510.
619.
2,600.
11.
9,589.
TP Loading (Ibs/day)
865
415
4,890
-
6,170
Since the phosphorus leaving the reservoir is 4890 Ibs/day, the retention coeffi-
' cient is:
8309 - 4690
8309
.41
American Falls Reservoir retains a significant quantity of the phosphorus which
enters the reservoir and consequently tends to keep phosphorus levels in the Snake
River below the dam depressed compared with what they would otherwise be.
I
I END OF EXAMPLE IV-4 '
-305-
-------
4.1.11 Hand held Calculator Programs
It nas become apparent that, after applying the river screening techniques
conta'r-eo 'n the original manual (Zison Q _a_l_., 1977) to real systems, a substantial
savings of Dcth time and effort could be realized by programming the major computa-
tional sequences. To this end, these algorithms have been programmed on the Tesas
Instrument Tl-59 calculator ana are available upon request in a document prepared by
Tetra Tech (Mills H .ill. 1979)*. To date the algorithms contained in Mills et al.
(1979) preti'Ct:
• Equilibrium temperature
• Longitudinal instream temperature distribution
• Mixing temperatures
• BOO profiles for point and nonpoint sources
• Reaeration rates
• Dissolved oxygen profiles
• waste assimilative capacity and critical dissolved oxygen levels
• Coliform profiles for point and nonpoint sources
• Bed material sediment transport.
For each program contained in tre document the following information is provided
for the user:
• A detailed set of user instructions
• A program listing
• A sample input/output sequence.
An example set of user instructions is shown in Figure IV-8. The first 6 steps
are for data entry and the seventh is for calculation of the required information.
4.2 CARBONACEOUS AND NITROGENOUS OXYGEN DEMAND
4.2.1 Introduction
Many wastes discharged Into waterways contain biologically oxidizable materials
that exert an oxygen demand on waterway resources. This biochemical oxygen demand
(BOO) can be subdivided into carbonaceous (CBOO) and nitrogenous (NBOD) components.
Table IV-12 illustrates typical concentrations of NBOD and CBOD in untreated municipal
waste.
CBOO represents the amount of oxygen required by bacteria to stabilize organic
matter under aerobic conditions. The reaction can be approximated by:
* Attention: W.B. Mills, Tetra Tech, Inc.
3746 Mt. Diablo Blvd., Suite 300 Lafayette, California 94549
-306-
-------
'.fit :r-»:cj' :-ssa!.»a 3«y$e" se'ic't 30w"i;re*n 'r;- <
-> :•' re".:•;:(• :: j!s: :j-:.li:e» the five; ti«« tc :rie cr-:-:il
' -.-« -.^ave' -.-.-» '.^rri ;.: ;c ;e "eg«:w«. :n«n tne :n:-:ii a«--ci
ijSEP IVSTBUC
V:s »"--£:.»{
: . Inter ;-c;r,r - ste ! -sting '9::».ing
:"*$e ii-it'jc-.ions
• • •' ' • i23
-.: :»,;
i : -tj
- •--.»- 3" ^-ce-.-j:^ •-. ivu- ;u»t
:?•(!.. s:,-:e 3' »: :-si3-. La .^g/;.]
,-1: :; . sc-ice a' ;;i'.::or. :, -j/t;
6 :-:»•• s:'f«r :f-je'«ijrt. T t'C;
:<.c.,i:» 4 no ditsliy:
« . ' / «I tin* to critic*] fl»fieit.
I
• cri-.ic*! dt'UU, 0. (ag/l)
TIONS
I t'.'t"
prc;rim
^
^•Q
°0
T
0
R/S
.,$
R/S
R/S
R/S
R/S
R/S
•°£3
i
CiS'v.*'-
' 0
i
g
Q
C
0
C
*,
k<
lc
°c
FIGURE IV-8 EXAMPLE SET OF USER'S INSTRUCTIONS
FOR HAND HELD CALCULATOR PROGRAMS
This reaction assures that the available organic matter Is completely oxidized.
Bacteria, however, might not be able to completely oxidize all of the available
organic matter. Equation IV-13 does Illustrate that oxidation of the nitrogen
is not included as part of CBOO. The reduced nitrogen 1s oxidized to nitrate
in a two step process as
-307-
-------
IV-12
••H/,:C:?AL WASTE CHARACTERISTICS
Approx
Average Daily Flow
Sol ids
"otai
Total volatile
Total Dissolved
'otal Susoenaed
.clatile Suspended
Settleaole
BOD
Carbonaceous '5 aa/'
gal /cap/day
mg/1
mg/1
sig/1
Tig/ 1
mg/1
rng/1
mg/1
Caooracecus { j! f.-3te: mg/1
N- trocenous*
Si trogen
Total
Organic
Ammonia
Ni tri te » Nitrate
Phospnate
Total
Ortno
Poly
Total
Fecal
•Ultimate, Nitrogenous
mg/1
mg/ 1 N
mg/1 N
mg/l N
mg/1 N
mg/1 P04
mg/1 P04
mg/J i»04
mill ion org./100 ml
mi 1 1 ion org./lOO ml
oxygen demand, exclusive
125
800
400
500
300
130
150
180
220
220
50
20
28
2
20
'0
10
30
4
of C800.
Normal
Ranae
100-200
450-' 2:C
250-3C3
300-3CC
ICO-iCC
3G-200
-
1CC-450
'20-580
-
15--GO
5-35
10-60
0-6
10-50
5-25
5-25
2-50
0.3-17
-308-
-------
2NH3 + 302 nitrite-forming ^ 2N02" + 2H* «• 2H20 (IV-13)
bacteria
2N02' «• 02 «• 2H+ nitrate-forming ^ 2NOj- „ 2H+ (IyM4)
bacteria
Based on Equations IV-13 and IV-14 the NBOO is:
' r i r ^ \ r "*
NBOD « 4.57; I Org-Nj + INH/ - N ! )+ 1.14lN02" - N j (IV-15)
Typically the nitrite concentration is negligible so that:
NBOD - 4.57 (TON) (IV-16)
where TON represents total oxidizable nitrogen, the sum of organic and ammonia
nitrogen. A typical value of TON from Table IV-12 is 20 * 28 « 48 mg-N/1, which
corresponds to an NBOO of 220 mg/1.
Typically in the bottle determination of CBOD and NBOO, the carbonaceous
demand precedes the nitrogenous demand by 5 to 10 days, as shown in Figure IV-9.
This had led workers to believe that nitrification can be ignored in river environ-
ments below a source of pollution up to a distance corresponding to a travel time of
five to ten days. Such an assumption might be invalid for several reasons. Given
that there are numerous sources of pollution along many rivers a viable population of
nitrifying bacteria may already be present within the water column. Second, nitrifers
can grow attached to the bottom substrate. Consequently, significant numbers can
exist just below the discharge location and nitrification can proceed Immediately.
Nitrification by attached bacteria 1s more likely to be of significance In relatively
shallow, wide rivers, which have stable bottom substrate (Mills, 1976).
CBOO is a commonly measured characteristic of waste water. The CBOD used 1n the
formulations presented below 1s the ultimate CBOO. Often CBOO is expressed as CBODj,
the oxygen utilized in a 5 day test. The relationship between ultimate (CBOOL)
and 5-day CBOO can be approximated by:
CBOD 5
CBOOL ' (T68-
This relationship assumes a decay rate of 0.23/day, and may be different for effluents
from advanced wastewater treatment plants.
The mass balance equation used 1n the CBOO analysis 1s exactly analogous to the
NBOO equation. The first order decay rate assumption for NBOO stabilization Is
necessary to maintain this analogy, and is sufficient for hand calculations.
-309-
-------
c . j « I : I >« e <« » JJ ii 2* ?t »
'•"<» 3»»»
FIGURE IV-9 THE BOD CURVE, (A) CURVE FOR OXIDATION OF
CARBONACEOUS MATTER, (B) CURVE SHOWING
INFLUENCE OF NITRIFICATION,
Nitrification (the process by which ammonia is oxidized to nitrite, and nitrite
to nitrate) is pH dependent with an optimum range of 8.0 to 8.5 (Wild, 1971). If the
pH of tn< river ij below 7.0, nitrification is not Hkely to be important.
4.2.2 BOD Decay Rate
The decay rate for CBOO will be denoted by kL and for NBOD by kN.
Typical values of both k. and k... Me between 0.1 and 0.6/day, with 0.3/day
L ~
being typical, k, values can, however, exceed the range given here. Values of 1
to 3/day have b*en computed for shallow streams (Thomann, 1972). A figure to be
presented shortly will show how k. depends on depth. The following discussion
will be directed toward k, , but in general will also apply to k^.
The disappearance of BOO from a river is a reflection of both settling and
biochemical oxidation, as shown In Figure IV-10. Biochemical oxidation can consist
of '^stream oxidation (k.L) as well as absorption by attached organisms (k4L). The
total oxidation rate then, is k., where:
The total loss rate k is:
where k.. reflects settling losses.
-310-
-------
ri-OW
I nit ream
d«oxygtnation
L-ULTIMATE BOO
Absorption by
attached organisms
FIGURE IV-IO
MECHANISMS OF BOD REMOVAL
FROM RIVERS
Settling of BOO 's generally more prevalent just below a sewage discharoe where
the discharged material may contain a large suspended fraction. As this material is
transported downstream the settling component becomes less important and the reaction
rate k. approaches the oxidation rate kd. In this chapter, the settling component
will not be explicitly considered. Neglecting settling will tend to cause estimated
instream 800 levels t; be somewhat higher than they actually might be along certain
portions of a river. It should be noted that if instream BOD data are used to
determine k. (one such method will be explained in Figure IV-12) then the
effect of settling is automatically included in k, .
F aure lv-11 illustrates the dependence of kL on river depth. The highest
deoxyqenation rates occur m shallow streams with stable, rocky beds, reflecting
the significance of attached biological organisms. Bowie et al. (1985) contains observed
and predicted values of k. for various natural streams.
The decay coefficients k. and kN are both temperature dependent and
this dependence can be estimated by:
1.047(T-'°>
(IV-17)
where
'20
or kN at 20°C
kT « k, or kN at T°C
T • water temperature, PC.
Numerous methods for computing k. from observed data are available (Nemerow,
1974). One method entails the use of a semi-log plot. The stretch of river contain-
•ig the data to be plotted must have a constant stream area and flow rate, and the
BOO loading must be from a point source located at a position that will be called
x « 0. Plotting the log of BOO concentration versus distance generally produces a
-311-
-------
100
10
'005
Stobte, Rocky B*d
• Moderate Treatment
03
DEPTH (FT)
Unstable, Sandy Channel
•Highly Treated Effluent
with Nitrification
100
1000
FIGURE IV-11
•DEOXYGENATION COEFFICIENT AS A FUNCTION OF
DEPTH, (AFTER HYDROSCIENCE, 1971)
straight line with slope of -k./U. An example 1s shown 1n Figure IV-12. Either
CBOD, or C800L can be plotted as the ordlnate. The slope should be converted
from base 10 logarithms as given in the semi-log plot to base e logarithms as needed
in the formulations used in this chapter. The conversion is made by multiplying the
value for log base 10 by 2.303.
Wright and McDonnell (1979) have more recently developed an expression for
instream 800 decay rate based on the flow rate of the river. The exp"?sslon is:
ld(aiV)
1f Q>80° cfs
1f Q<800 cfs
(IV-18a)
(IV-18b)
-312-
-------
10.0
O
O
10
4Mil««/Doy
-Slop* x U
2.9(flfi)
0.16/Doy
8
.?
I DISTANCE (MILES)
INPUT
16
24
32
36
FIGURE 1V-12 EXAMPLE OF COMPUTATION OF KI FROM STREAM
DATA (FROM HYDROSCIENCE, 19/1)
This expression is particularly attractive because the only hydraulic variable
required is flow rate. Other predictive techniques and rate data from rivers
around the country are contained 1n Zlson e_£ _aK (1978).
4.2.3 Mass Balance of BOD
The general mass-balance equation for BOO In rivers 1s:
fe
- kL L
(JV-19J
-313-
-------
* CBOD (ultimate) remaining to be oxidized, mg/1
Q » volumetric flow rate, ft /sec
A » cross-sectional area, ft
i-r « concentration of CBOD entering through an incremental sideflow
(distributed source), ma/1
Lrd • mass flux of CBOD entering, with no associated flow, mq/l/sec
x • stream distance
— « 0 indicates that steady-state conditions are being assumed and
thus no accumulation of material takes place at any point within the
reach.
The NBOO equation is completely analogous in form to Equation IV-19:
ll - 0 --ifcW) -V "V '**"rd
where
N » the NBOO.
Nrd represents purely a mass flux of nitrogenous material, while Nr hr[)/A
is a source of NBOO entering the river reach through an incremental sideflow.
Thus, in cases where a known distributed source of BOD significantly contributes
to a river reach under study, and the distributed flow (flow associated with a
distributed source) can be neglected, N can be used in lieu of Nr (|^)/A-
Nrd can be estimated by determining the mass M of BOD entering a volume of
river water V in time T. N . is given by:
M
Nrd "^
For any particular reach of a river under investigation the stream cross-
sectional area can be expressed by:
AQ * AAx (IV-21)
where
AQ • stream cross-sectional area at upstream end of the reach
Af • stream cross-sectional area at downstream end of reach
x • distance downstream from beginning of reach
x. • length of reach.
-314-
-------
The cross-sectional area need not be measured directly, but can be computed from:
A . 0
The cross-sect iona) area change can reflect a chanae in stream velocity,
perhaps due tc a bed slope increase or decrease. The length of the reach under
investigation, x , is measured in river miles along the river's centerline.
If use of a constant stream area is assumed, then A. • 0 and A » AQ throughout
the reach.
4.2.4 Typical Solutions
Case 1: The only sour^> of CBOD occurs as a point source at x « 0. The
CBOO distribution is then expressed by:
where
,. . it
UQ • stream velocity at x » 0
LQ « ultimate BOO at the upstream end of the reach
L • ultimate BOO at a distance x downstream
The other terms have previously been defined.
The initial CBOO, LQ, must reflect both CBOO upstream of the reach as well
as that contributed by the point source in question. It is given by:
LA * W/5.38 (IV.?3)
where
w * mass rate of discharge of CBOO, Ib/day
Qu « upstream river flow, cfs
QW « waste flow rate, cfs
LU « upstream CBOO concentration, mg/1.
Case 2: For a point source of CBOO at x • 0 and a distributed mass influx
of CBOO (with no associated flow) entering the river throughout the reach, the
solution 1s:
(IV-24)
-315-
-------
where
Lrfl « -nass rate of CBOO entering the reach per unit volume of river
*ater, nig/I/day.
Case 3: A distributed flow enters the river carrying CBOD and a point source of
CBOO exists at x • 0. The flow rate Q at a distance x 1s:
Q ' °
* ' Q0 * V
where
'0 \
The BOD distribution is given by (the river cross-sectional area is assumed constant
throughout the reach):
where
Lr » concentration of CBOO entering the river in the distributed
flow, mg/1.
Case 3 can also be used to establish the effect a purely diluting inflow (I.e.
Lr» 0) would have on the CBOO distribution.
Case 4: For a point source at x • 0, a distributed source with associated
inflow, and a mass flux with no associated flow (constant river cross-sectional
area), the solution is:
L ,^, t -. j ... - i, -ix-i i (IV-26)
uo
where
k, A * £n
E. • -i-5 1 . as in Case 3.
-31S-
-------
4.2.5 Other Simplifying Procedures
The formulations represented by Equations IV-22 through lv-26 offer a range of
options for examining BOO distribution in rivers. However, there are additional
methods of estimating instream concentrations and determining whether or not signifi-
cant BOD levels exist. Perhaps the simplest method is assuming that BOO does not
decay. An upper limit of the instream concentration at ao> joint can then be deter-
mined by incorporating all known sources, and using the methods presented in Section
4.7. If the computed instream concentrations are below a threshold pollution level,
then there is no need to apply Equations IV-22 through IV-26 because the inclusion of
a decay rate will only lower the concentrations.
It may also be feasible, as a first estimate, to combine the CBOD and NBOD
equations into one, and use that equation to estimate the distribution of the
total oxygen-demanding material. To do this, all source terms must include both CBOO
and NflOO. One decay coefficient is used for both CBOO and NBOD decay. The larger
decay coefficient of the two shoL'1 be used since that will produce the larger oxygen
deficit.
In deciding which of Equations IV-22 through [V-26 to use for any analysis,
the purpose of the analysis as well as data availability should be considered.
If the main purpose is to estimate differences in stream concentrations caused
by various levels of abatement at a sewage treatment plant, the diffuse sources
of BOO need not be considered. The resulting concentration difference can be
expressed as:
„ e*P \-r- /A~* * A. 5-1 | {IV-27»)
AL
/Q \Ei
(!V-27b)
where
» the change in BOO concentration due to a change, ALQt in the
initial concentration.
Equation IV-27A should be used for a Case 1 or Case 2 situation, and Equation IV-27B
for Case 3 or Case 4. If an estimate of the absolute level of BOO 1s desired,
however, then the appropriate expression Including the nonpolnt sources should be
utilized. It should be noted that If the diffuse sources of BOO are large then the
improvement of instream BOO concentrations by point source control will be relatively
minor. In that case the planner should focus on nonpolnt source control.
-317-
-------
EXAMPLE 1V-5
Estimating BOD Distribution in a River
Suppose the user wants to calculate the 800 distribution in the river
Shown below in Figure IV-13. There are nine point sources contributing BOD
iMixing
; Zone
d-4'
BOD-lmg/l
Q«300cf$
Q«aOOcf*
BOD»lmg/l
FIGURE IV-13
Mixing I
Zone ;
i "N n rn m
HYPOTHETICAL BOD WASTE LOADINGS IN A RIVER
in the stretch of river under consideration. The ninth source is assumed to
be a tributary, and contributes substantially more flow than the other eight.
Begin by dividing the river Into reaches. The first reach (I) should include the
first 75 miles in which there is one point source of BOD at the upstream end
(source (1)). Equation IV-22 1s applicable to that reach. Now, there are several
choices available regarding the division of the river between sources (2) and (8).
One choice is to divide the 50 miles into mini-reaches similar to Reach I, and
reapply Equation IV-22 seven more times. A second alternative is to group adjacent
point sources into fewer and larger sources, thereby requiring fewer applications
of Equation IV-22. A third alternative is to assume that sources (2) through (8)
comprise one continuous distributed source, the total pollutant loading of this
equivalent source being equal to the sum of the individual loads. For this
representation to be valid the sources should be both evenly spread spatially and
be discharging comparable loads. The third alternative wi11 be examined here,
and reach II will consist of the 50 miles following Reach I. Equation IV-25 will
be used to analyze Reach II. Reach III, then, will begin just downstream from the
tributary (source (9)).
-318-
-------
For Reach I, Equation IV-22 is first solved. Suppose the following charac-
teristics of waste source (1) are known:
0 » 20 MGD • I-55 (2°) cfs
* 31 cfs
w ' 5000 lo. B005/day
Reca! ' t^at:
L Q * rt/5.38
u u
U° " Qu *°W
w must be in ID. BOD ultimate/day:
w . 5000
.68
* 7353 Ib. BODL/day
then
. (1) (300) * 7353/5.38
° 300 * 31
« 5.0 mo/1
The decay coefficient is estimated from Figure IV-11 as 0.4/day. No correc-
tion will be made for temperature. Equation IV-22 can now be expressed as
(for constant cross-sectional area):
L » 5 exp
/ -±<
\(1.1)(24)(
1(3600)
where x is the downstream distance in feet. Note "ie correction needed to
convert the decay coefficient from units of I/day to I/sec.
The results of the above equation for selected distances downstream can
be expressed as follows:
X
(miles)
0
30
60
75
L(mq/l)
5.0
2.6
1.3
0.9
I
I For Reach II, sources (2) through (8) are assumed to contribute the following
| loading:
I BOO • 8000 Ib/day
j 0-120 MGO
• 186 cfs
The flow distribution, Q, 1n Reach II, 1s then:
Q-Q0+^£x
.
-319-
-------
I where x is in miles (from 0 to 50). Lr, the average BOD. concentra
| tion in the incoming flow is:
8000 lb/day
r 120 MGO 8.34 lb/day
! « 8.0 mg/1
! If the average depth in Reach I! is assumed to be 5 feet, then:
k. • .3/day
I L
J Final ly, E. is computed:
£,
0.3)(30
(50K5280)
Then, using (. from the 75 mile point of Reach I as L :
35
u,n / H n A / TI i \
1- * ~r~
2.5
/ ^11 1
3.2 - 2.:
In tabulated form:
x (ml) Q (cfs) L (mq/1)
0 331 0.9
20 O5 1.8
40 480 2.3
50 517 2.5
Note that the BOO concentration 1s Increasing within this reach.
For reach III, only enough Information 1s given to conpute the Initial
I concentration, utilizing weighted values for the 800 at the end of reach !.
I and that entering through the tributary (source (9)).
I
I L '
I Q
I p EXAMPLE IV-5
-320-
-------
4.2.6 Interpretation of -esults
The most frequent use of 800 data in river water quality analyses involves
their relationship with the dissolved oxygen balance. This relationship will
be discussed more fully in Section 4.3. At this point it is sufficient to say
that t is necessary to predict the BOO distribution in a river in order to compute
dissolved oxygen concentrations.
When a river receives a heavy load of organic matter, the normal processes
of self purification result in a series of zones of decreasingly severe conditions
succeeding one another downstream. Each zone contains characteristic animals and
plants (Nemerow, 1974). A saproblcity system (saprobicity is a measure of biode-
gradable organic matter) has been developed that relates BOO concentrations in
streams to the degree of pollution there. Correlations have been found, for example,
among BOD concentrations, coliform bacteria, and dissolved oxygen in rivers (Sladecek,
1965). Sladecek (1969) has assigned 5-day BOO values of 5 mg/1 to mildly polluted
conditions and 10 mg/1 to substantial pollution.
Sources of drinking water are subject to restraints on the maximum allow-
able BOD that can be contained in raw water and still quality as a drinking water
source. Further, the degree of treatment of the raw water is dependent on the
concentrations of certain constituents, such as BOD. One reference (HEC, 1975) has
stated that water having a 5-day BOO over 4 mg/1, in combination with high levels of
other constituents, represents a poor source of domestic water supply.
As discussed above, BOO in a river can come from a number of sources, both
point and nonpoint. Although BOO reduction from point source might be easier
to accomplish than from nonpoint sources, there is no guarantee that BOO levels
will be substantially lowered.
4.3 DISSOLVED OXYGEN
4.3.1 Introduction
Historically, dissolved oxygen has been and continues to be the single most
frequently used indicator of water quality in streams and rivers. Figure IV-14
shows the seasonal variability of dissolved oxygen in 22 major waterways throughout
the country (EPA, 1974) from 1968 to 1972. Invariably the levels observed from June
to October are lower than those observed In January to March. This Is due primarily
to the influence of temperature on the dissolved oxygen levels. Due to the effect of
temperature, summer 1s the most critical season In terms of organic pollutant assimi-
lation In rivers.
The dissolved oxygen calculations presented below range In complexity from
a simple CBOO-DO relationship to a more general dissolved oxygen mass balance
Including CBOO, NBOO, photosynthesis, respiration, and benthic demands. It should be
-321
-------
Reach
Hudson
Delaware
Susquehanna
Potomac
Alabama
Upper Ohio
Middle Ohio
Lower Ohio
Upper Tennessee
Lower Tennessee
Upper Missouri
Middle Missouri
Lower Missouri
Upper Mississippi
Mississippi nr Min
Middle Mississippi
Lo*er Mississippi
Upper Arkansas
Lower Arkansas
Upper Red
Lower Red
Brazos
Rio Grande
Upper Colorado
Lower Colorado
Sacramento
Columbia
Snake
Willamette
Yukon
Boston Harbor
Chicago Area-Tributaries
Chicago Area-Lake Michlg
Detroit Area-Tributaries
Detroit Area-Rivers
FIGURE IV-
Number of O.C
Stations
19
17
21
IS
'»
3
\
i
i
i
ipollslj
12
i
w
s
j
4
1
!
;
S
14
?
»
11
.1
S
7
•1es 3
:Mgan 'j
Mes J
•?
900 SEASONAL Grtattr
•""han
» ITS 350 5.25 700 E75 1050 12.50 400
#,^^_^_
— *-
— «t
«_
— 4
lA^Mi IftMi
UMK^ ^^^^^1 IVtfl
KCY: ^^-~Ptrc«itil«
*->_- AAA^ ^^
MVQII OOTH^^^
^^•ri^l^AttA
^wwmw
^^
*,
._»_.
-^
* — ,._
•^H
«
^ ^ ^ ^ ^
#^^^_
* ._„ —
•^^
*_
T *
^^_^^^_^^
* »-.
•1^—
«*
»
VARIABILITY OF DISSOLVED OXYGEN BY SEASON FOR
22 MAJOR WATERWAYS, 1968-72 (EPA, 1974)
-322-
-------
stressed, however, that the results calculated from any of the relationships provide
esimates only since each procedure incorporates various assumptions that might not be
fully met. For example, waste loading inflows are assumed to remain constant in
quality arc quantity over time. In reality loadings probably vary over time.
Furthermore tne choice of system parameters involves a certain degree of judgment.
However, for any given situation, the planner can establish an envelope of possible
outcomes by different realistic choices of system parameters.
4.3.2 Dissolved Oxygen Mass-Balance
«
The general dissolved oxygen mass-balance equation that will be utilized
here is given by:
- k,> - V * ka (Cs-C) ' Sb * P-R (IV'28)
where the new symbols introduced are:
C « dissolved oxygen concentration, mg/1
ka * reaeration coefficient, I/day
C$ « saturation value of dissolved oxygen, mg/1
$b » benthic oxygen demand, mg/I/day
P • rate of oxygen production due to photosynthesis, nig/I/day
R • rate of oxygen consumption due to algal respiration, mg/l/day.
Stated in words. Equation IV-24 expresses the following relationship:
At steady state, the rate of addition of dissolved oxygen to a river due to reaeration
and photosynthesis equals the depletion rate caused by the net advectlve flow,
carbonaceous oxidation, nitrogenous oxidation, benthic demands, and algal respiration.
Commonly, the dissolved oxygen mass-balance equation 1s expressed In terms of the
deficit, 0, which is the difference between the saturation and actual concentrations.
4.3.3 Reaeration Rate
The atmosphere acts as the major source for replenishing the dissolved oxygen
resources of rivers. Reaeration tends to equilibrate the dissolved oxygen concentra-
tion in a river with its saturation value. Most commonly, the dissolved oxygen
concentration is below saturation and there 1s a net Influx of oxygen Into the river
from the atmosphere. On occasion, due to the production of dissolved oxygen by
algae, rivers or streams can become supersaturated, in which case there 1s a net loss
of oxygen to the atmosphere.
A number of expressions for the reaeration coefficient, k have been
-323-
-------
developed. Several are presented here. O'Connor's formulation (Thomann, 1972)
states that:
U)17*
at 20* c (IV-29)
where
\ - oxygen diffusivity • 0.000081 ft2/hr at 20'C
H * stream depth in ft
U « stream velocity in ft/sec.
Expressed in English units:
12.9 o . (IV-30)
a
The above formula was verified on streams and rivers ranging in average depth
from 1 foot to 30 feet with velocities ranging from 0.5 to 1.6 fps. Its use should
be limited to streams where the reaeration coefficient 1$ less than 12/day. Figure
lv-15 illustrates how k^ changes with depth and velocity according to this
relationship.
For shallow (0.4 - 2.4 feet), fast moving streams, the following expres-
sion developed by Owens (Thomann, 1972) is preferable, as the experimental work
to develop this expression was done almost exclusively on shadow streams:
,,0.67
k.-zi-eVs *tzo'c (IV"31)
H1'85
where U is in ft/sec and H Is In feet. A graphical representation of Equation
IV-31 is shown in Figure IV-16.
Covar (1976) snowed that there were certain combinations of river depths
and velocities where a formula developed by Churchill (Churchill et jiK, 1962)
is more accurate than either the O'Connor or Owens formulations. The Churchill
expression Is:
kt • 11.6U0'969 H'1-673 per day at 20*C (1V-32)
The regions of validity, and the predicted values, for the three formulations
are shown In Figure IV-17.
Recent studies have suggested that the Owens expression overestimates the
reaeration rate for particularly shallow streams (e.g., less than a foot in depth).
Under these circumstances the Tslvoglou-Mallace method (Tslvoglou and Wallace, 1978)
-324-
-------
too
10
o
b
PJ
5
o
t-
REAERATION COEFFICIt
l§
: \
-
-
03
\^
\\\
\*\
^\\
N
i i i i i i i i
10
Rapid Turbulent
1.0 -2.0 FPS
Moderate
0.5 -1.0 FPS
Slow Stagnant
O.I -0.5 FPS
V
i i i i i i i i
100
100 0
DEPTH (FT)
FIGURE IV-15 REAERATION COEFFICIENT AS A FUNCTION OF DEPTH
(FROM HYDROSCIENCE, 1971)
is more accurate. The expression 1$:
<
1^
7776. US, 9 25'C, Q < 10 cfs
4665.6 US, «5*C, 10 < Q < 3000 cfs
2592. US, 9 25*C, 0 > 3000 cfs
(IV-33a)
(IV-336)
(IV-33c)
where
S • stream slope, ft/ft.
Table IV-13 compares predictions of Ts1voglou-Wallace with observed values for
several small streams 1n Wisconsin. The agreement 1s good.
-325-
-------
4O
V-3trtom Vtlpcity (ft/Me)
O.I
DEPTH (FT.)
V-4.0
40
FIGURE IV-16 PBAERATION COEFFICIENT FOR SHALLOW STREAMS,
OWEN'S FORMULATION
EXAMPLE IV-6
Prediction of Reacratlon Rates
In September, 1969. a study was conducted to determine the reaeratlon
rate coefficients on the Patuxent River In Maryland during the low flow period.
The study was carried out on a seven mile stretch of the river below Laurel,
Maryland. The stream was divided Into seven segments, and the reaeratlon rate
determined for each segment. A portion of the results are shown in the Table
IV-14. Using the hydraulic data In the table predict the reaeratlon rates using
the methods of Tslvoglou-Wallace and of Covar.
Since the method of calculating the reaeratlon for each reach 1s the same, an
example calculation will be shown for the first reach only. Based on a velocity
of 0.39 ft/sec and a slope of 0.0013 ft/ft, the Tslvoglou-Wallace method predicts
a reaeratlon rate of:
-326-
-------
C.
I
2 34 681
Velocity (ft./sec.)
3456
FIGURE IV-17
REAERATION RATE VERSUS DEPTH
AND VELOCITY (FROM COVAR, 1976),
ka » 7776 x 0.39 x 0.0013
- 3.9/day at 25*C
Equation IV-33a is used since Q < 10 cfs.
Using Figure IV-17 and a river depth of 0.8 feet reveals that the Owens
formula is applicable. Applying Equation IV-31 shows that:
* 21'6
0.39
067
0.8
I 15
• 17.4/day at 20°C
-327-
-------
TABLE IV-13
C OH PAR I SON OF PREDICTED AND OBSERVED
REAERATION RATES ON SHALL STREAMS IN WISCONSIN*
Stream
Black Earth Creek
Mud Cretk tributary
Oodge Branch
Isabel le Creek
Madison effluent channel
Mill Creek
Honey Creek
West Branch Sugar River
Koshkonong Creek
Badger Mill Creek
Observed KS
(I/day at 25*C)
8.46
10.7
33.1
14.
2.06
3.31
18.4
42. 5
6.09
7.98
•Grant, R.S., 1976. Reaeratlon-Coeffldent Measurements
Streams 1* Wisconsin Using Radioactive Tracers... with
the Energy -Dissipation Model. U.S. Geological Survey.
Investigations. 76-96.
Predicted kft Using
Tslvoglou's Method
(I/day at 25°C)
7.8
4.2
34.6
-
4.1
2.2
27.4
36.4
4.8
9.1
of 10 Small
a Section on
Water Resources
The results for all the reaches are tabulated below.
REAERATION RATE (I/day)
Observed TslvooJou-Wal lace Owens
Reach (2S'C) (25*C) (20'C)
1-2 3.9 3.9 17.4
2-3 2.7 1.9 7.8
3-4 3.3 3.8 10.7
4-5 3.5 2.9 9.0
5-6 2.4 1.5 7.2
6-7 4.8 2.2 11.0
The predictions using the Tslvoglou-Wallace method are good for all reaches.
while Owens' method predicts values two to three times too large, and provides
evidence that Owens' method probably should not be applied to extremely shallow
rivers.
-328-
-------
I
t
I
j TABLE IV-14
TYPICAL HYDRAULIC PROPLUTIES
! PATIMNT RIVLR (SCPUMIER, 1%'J)
I .. _
' Mi'jiTal ion U.i lc (l/«l.iy)
|
Flow Length Velocity Depth Slojie Observed (sivoi|lou-Vlj| l,u i i.uv.ir
I Reach cfs fj ft/sec ft li/li (25"C) (?'.>"() (<'')()
m
° 1-2 9.8 5,400 0.39 0.80 .0013 3.9
2 2-3 9.8 4.200 0.22 1.00 .0011 ? .7
£ 3-4 9.8 7,200 0.35 1.00 .0014 3.3
^ 4-5 19.5 8,400 0.35 1.10 .0018 3.5
! 5-6 19.5 6,600 0.25 1.10 .0013 2.4
I
j 6-7 19.5 4,800 0.37 1.00 .0013 4.H
j
j
I
-329-
-------
Temperature changes affect the reaeration rate, and the relationship can
be approximated by:
" (IV'34)
where
v*3)T « tne reaeration coefficient at T °C.
In addition to temperature, substantial suspended sediment concentrations can
appreciably alter the reaeration rate in streams (Alonso £t _a_K , 1975). As an
approximation, ka decreases by 9 percent per 1,000 ppm increase in suspended
sediment up to a 4,000 ppm load. Beyond t* dissolved oxygen deficit below dam, mg/1
T » temperature, °C
H « height through which the water falls, ft
a * 1.25 in clear to slightly polluted water; 1.00 in polluted water
b • 1.00 for weir with free fall; 1.3 for step weirs or cascades.
An alternate equation developed from data on the Mohawk River and Barge Canal 1n
New York State (Mastropietro, 1968) 1s as follows:
Da - Ob « 0.037H Da (IV-36)
Equation IV-36 is valid for dams up to fifteen feet high and for temperatures
in the range of 20* to 25*C.
In handling the problem of a dam, a new reach can be started just below the dam.
-330-
-------
^3 car te calculated as the value that occurs at the end of the upstream reach.
Tne -e« ce*"':". 2fct v»nicn will Dec"* the deficit at the beginning of the next
"eicn. ; :»!:./»*.e3 using either of the above two formulas.
-.3.5 :-s?:'«r: r«.vger. Saturation
~** ••»'.:? it •ri<:i at-icsphenc reaeration occurs depends not cnly en k . but
c
a'so :- -."e :"'*'5'-e-ce between the saturation concentration C and the actual
c:-ncer*.r at 'on C. """he saturation value of dissolved oxygen is a function of tempera-
ture, sa:i">ty, and oarometric pressure. The effect of salinity becomes important m
est^ar-i^e systems, ana tc a 'esser degree in rivers where nign irrigation return 'lo
ca" 'eac tc substantia1 salinity values. Tab'e IV-15 depicts the relationship
Between cxygen saturation and chlorinity. The expression relating salinity and
;n i or-' m ty cmcert rat i en 'S:
SiHnity (°/ ) • 0.03 » 0.001805 chlorinity fmg 1) (IV-37)
°'oc * parts per thousand.
Tne temperature dependence (at zero salinity) can be expressed as:
: » 14.65 - 0.41022T * 0.00791T2 - 0.00007774T3 (IV-38)
where T ~s 'r 'C. "his relationship is also found in Table IV-15 for zero
cn'r^-ae concentration.
ic pressure affects C as follows:
/Pfa - P \
CS * Cs \ 760-P
\
(IV-39)
wnere
-s « saturation value at sea level, at the temperature of th« water,
mg/ 1
C ' « corrected value at the alt'tude of the river, mg/1
". • barometric pressure at altitude, mm Hg
w
Pv » saturation vapor pressure of water at the river temperature, mm
Ho
E « elevation, feet.
Table lv-16 illustrates the variability of dissolved oxygen saturation with altitude
and temperature. The significant effect of altitude is apparent and should not be
neglected. For example, at a temperature of 20*C, the saturation value decreases
-331-
-------
TABLE IV-15
SOLUS
ILITY OF OXYGEN IN WATER (STANDARD
Chloride Concentration
Tern.
i n
°C
0
1
2
3
4
5
6
7
3
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
3
14.6
14.2
13.8
13.5
13.1
12.8
12.5
12.2
11.9
11.6
11.3
11.1
10.8
10.6
10.4
10.2
10.0
9.7
9.5
9.4
9.2
9.0
8.8
8.7
8.5
8.4
8.2
8.1
7.9
7.8
7.6
7.5
7.4
7.3
7.2
7.1
5.000
13.8
13.4
13.1
12.7
12.4
12.1
11.8
11.5
11.2
11.0
10.7
10.5
10.3
10.1
9.9
9.7
9.5
9.3
9.1
8.9
8.7
8.6
8.4
8.3
8.1
8.0
7.8
7.7
7.5
7.4
7.3
10,000
Dissolved
13.0
12.6
12.3
12.0
11.7
11.4
11.1
10.9
10.6
10.4
10.1
9.9
9.7
9.5
9.3
9.1
9.0
3.8
8.6
8.5
8.3
8.1
8.0
7.9
7.7
7.6
7.4
7.3
7.1
7.0
6.9
METHODS ,
1971)
in Water - mg/1
15,000
Oxygen - mg/1
12.1
11.8
11.5
11.2
11.0
10.7
10.5
10.2
10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.5
8.3
8.2
8.0
7.9
7.7
7.6
7.4
7.3
7.2
7.0
6.9
6.8
6.6
6.5
20,000
11.3
11.0
10.8
10.5
10.3
10.0
9.8
9.6
9.4
9.2
9.0
8.8
8.6
8.5
8.3
8.1
8.0
7.8
7.7
7.6
7.4
7.3
7.1
7.0
6.9
6.7
6.6
6.5
6.4
6.3
6.1
Difference
— per 100 mg
Chloride
0.317
0.016
0.015
0.015
0.014
0.014
0.014
0.013
0.013
0.012
3.012
0.011
0.011
0.011
0.010
0.010
0.010
0.010
0.009
0.009
0.009
0.009
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
0.008
-332-
-------
TABLE IV-16
DISSOLVED OXYGEN SATURATION
VERSUS TEMPERATURE AND ALTITUDE
-«-n»ra-.j« ALTITUDE (ft)
' - «- \
0
5
10
15
20
25
30
35
0
14.6
12.3
11.3
10.2
9.2
8.4
7.6
7.1
2,000
13.6
11.9
10.5
9.5
8.5
7.8
7.1
6.6
4,000
12.5
11.0
9.7
8.8
7.9
7.2
6.5
6.1
6,000
11.5
10.1
8.9
3.0
7.2
6.6
6.0
5.6
8.000
10.5
9.2
3.1
7.3
6.6
6.0
3.4
5.1
from 9.2 fig/I to 7.2 mg/1 as the altitude increases from sea level to 6000 feet,
approximate elevation of Lake Tahoe and the Truckee River in California and Nevada
4.3.6 DO-BOD Interactions
A widely used dissolved oxygen predictive equation 1s the Streeter-Phelps
relationship which predicts the dissolved oxygen concentration downstream from
a point source of BOD. Assuming a constant river cross-sectional area, the dis-
solved oxygen deficit (C -C) can be expressed as:
where
k • reaeratlon coefficient, I/day
a
DQ « initial deficit (at x « 0), mg/1
D » deficit at x, mg/1
LQ • initial BOO (at x « 0), mg/1
k. » BOD decay coefficient, I/day.
L and D are found by proportioning BOO and DO deficit concentrations just upstream
-333-
-------
of the waste discharge with the influx from the discharge itself. As presented earl-
ier in the BOO section, I is given by:
. 8 * LM Qu
0
where
w * discharge rate of BOO, Ib/day
L * concentration of BOO in the river upstream of the
waste discharge, mg/1
Q « river flow rate upstream of discharge, cfs
0 « flow rate of waste discharge, cfs
Q*0 • flow rate of river in the reach under consideration, cfs.
W in Equation IV-41 should be expressed 1n terms of ultimate BOO, and not 5-day
BOO.
The initial deficit is found from:
- C . C*Qw * Cu°u . Dw°w * DuQu (IV.42)
0
o s n > n 0 •»• 0
w xu w u
where
C • concentration of dissolved oxygen 1n the waste, mg/1
C • concentration of dissolved oxygen upstream of th« waste discharge,
mg/1
0 • dissolved oxygen deficit in waste, mq/1
0 • dissolved oxygen deficit upstream, mg/1.
In cases where information 1s lacklno, 0 can normally be assumed to be in the
range 1-2 mg/1.
If NBOO 1s to be considered as well as CBOO, Equation IV-40 can be modified as
fo I lows :
0 • 0Q exp
<'»•">
If the decay coefficient of NBOO 1s approximately equal to that of CBOO, Equation
IV-40 can be utilized instead of the more complicated Equation IV-43. In this case,
L fn Equation IV-40 Is replaced by the sun of L and N .
-------
4.3.7 Dissolved Oxygen Calculations
Calculation of dissolved oxygen in rivers can proceed as shown in Figure IV-19.
Tr«e planne-- "eeds to estimate the waste loading scheme *or the prototype, whether it
be 'cr i 21 ygir projecticn or for current conditions. The river system can then be
aivided into 'eaches and by repeated use of Equation IV-40, dissolved oxygen calcula-
tions can be performed 'or each reach, starting from a known boundary condition and
proceeding downstream. All data and calculations should be succinctly and clearly
recorded to minimize errors.
The dissolved oxygen profile downstream from a waste discharge characteristic-
ally has a shape shown in Figure IV-18. If the reach is j'ong enough, the dissolved
oxygen deficit will increase to some maximum value, 0 , at a distance x (termed tne
critical distance). 0 is called the critical deficit. Within any reach there
will always be a minimum dissolved oxygen value that occurs, but it may not be the
critical deficit, which is defined as the minimum point on a dissolved oxygen sag.
The difference between the minimum and critical values should be kept in mind. AS
one exampl- ~f the difference between the values, a reach may have a dissolved oxygen
profile where concentrations are monotonically decreasing throughout the reach. The
minimum DO wi11 then occur at the downstream end of the reach, but this will NOT be
the critical DO value, since DO is still decreasing in the downstream direction.
The travel time to the critical deficit is given by:
;iV-44)
-------
projected Haste loading
CHterl* Met for
Hand Calculations
Divide i-iver Into reaches
scenario !source/sink distribution)
Determine temperature independent
parameters for each reach:
u, Q, d, A (as needed)
Determine reaction rates at 2(r C:
V V V se
for each reach
[ Incorporate temperature corrections |
Determine C for each reach
J
Begin reachX
I by-reach
\Calculat1ons J
Calculate conditions at i»o
(upstream end of present reech)
Use Computer
Model
Perform and record
desired calculations
FIGURE IV-19 FLOW PROCESS OF SOLUTION TO DISSOLVED OXYGEN
PROBLEM IN RIVERS
•336-
-------
The distance downstream can be computed by knowing the trave1 time and flow ve'ocity:
U • tc (IV-45)
'he critical se''cit can be found from:
.V^ H . =3
-------
TABLE IV-17
DC/L0 VALUES VERSUS 00/LQ AND ka
30
:: '4
•A n
•A 12
J» »4
' j i 35
.'2 37
•t 11
•t | 9:
1 91
?0 93
.'2 i 95
24 9t
2* M
:i 94
sc • ;i
32 ' 33
34 ;4
3t T6
31 • :•
4C ' *9
D 42
o
-w 44 : • '2
L ' •
o
41 5
SO : 17
6 1 ' 25
33
S : • 41
9 50
: si
' M
' 2 ' -5
• 3 '13
' 1 92
'5 2 30
i t 2 34
' ' 2 '7
< 1 2 26
' 9 2 34
2 3 2 43
2 2 52
. 3
to
tl
62
63
65
61
67
M
49
T '
'2
• I
•6
••
•8
M
ii
12
14
15
14
U
M
90
92
94
• 36
'3
20
2?
35
' 43
• so
SI
' 4t
i 75
1 >3
• 91
2 30
2 'A
I "
•: it
51 44
52 »S
5: 16
54 17
55 41
SI 44
57 SO
51 S-
54 52
SC 53
62 54
63 45
64 56
65 57
M 51
67 54
61 60
'C 41
62
'2 63
'3 65
•4 66
'6 47
61
•1 44
M '6
91 12
M If
' 35 97
' '2 ' 34
' 20 ' '3
'28 21
'34 '30
U ' 40
•3
•2
. ,,
• 10
90
; 9
19
40
40
41
42
13
44
45
15
41
47
41
44
SO
3!
52
53
54
55
Si
57
51
60
tl
12
63
tl
't
U
92
• x
• -o
35
M
31
37
31
34
40
40
41
42
43
44
41
47
41
44
SO
51
52
53
54
55
51
57
51
IS
72
tl
40
3
32
33
33
34
35
t
31
3'
31
34
31
41
42
43
44
IS
It
47
41
II
50
51
S3
54
55
U
70
5
3C
K
31
31
32
33
34
34
35
36
37
3*
31
34
40
tl
12
43
44
IS
46
47
44
SO
51
53
to
21
21
24
24
30
3^
31
31
33
33
34
35
36
3'
31
34
40
41
42
13
44
IS
17
II
If
51
k
21
26
2'
27
28
24
24
3C
3'
3'
32
33
34
35
34
37
31
34
40
41
42
44
45
47
41
50
L
2
24
25
25
2t
26
2'
It
29
24
30
K
31
32
33
34
35
36
)7
31
40
4:
13
44
It
2 3
23
23
21
21
25
26
.'6
.'»
<'
28
24
30
31
3:
32
31
35
36
37
39
40
42
; 5 I ' 29 3 ' ! 3 3 « 3 ' ! j . i 3 i : - - •
22 21 2C '9 •!'•••« 5 • - -
22 2' 25 '9 1 S ' 4 4 S :
23 22 2' 2C » ' 1 " '• i
2) II 2' 2: "9 1 S - • « • •
24 2: 2' ;• <: 93 -. - • 4 • ;
24 23 22 2' 2C I". J ' ' • - •
.'5 24 23 22 r- 2: i ">
25 24 23 22 2' i' :: 9 9 ; :
26 25 21 23 22 ." '.' ::••••
2' U 25 21 23 22 .' ' 2' " '.'. <
28 21 25 21 21 .'3 12 12 '.' '.' .'.
29 21 2' 2t 26 25 .'S 21 .'1
30 24 28 27 •-• 26 .'6
31 3C 29 24 28 28
32 31 31 K 30
34 33 32 32
35 U 34
36 J4
31
22 I 2 M 2 26
5 '
43 5S 57 54 61 63 tS «7 64 71 73 7J
7 ? I l I ) IS 17 14
43 95
Q
0
L"
0
0 00
3 32
3 34
3 31
3 31
3 10
3 12
3 U
0 It
0 II
;]
13
U
It
'5
IS
it
'1
17
11
'3
'3
13
11
14
'S
15
l|
17
II
'2
:3
•3
U
14
14
IS
II
17
II
12
•2
1
13
13
14
14
IS
II
,,
1}
12
13
13
14
It
15
II
11
1!
II
U
13
13
14
15
II
11
11
11
II
12
13
It
II
11
II
11
11
U
U
13
11
11
11
11
U
II
12
13
1
10
11
H
11
H
12
13
11
10
'0
M
11
11
12
13
11
10
10
10
II
It
12
12
10
10
:o
11
,,
11
12
10
10
10
10
11
: I
12
Of
1C
1C
10
U
11
12
Of
Of
10
10
'0
11
12
Of
Of
Of
10
10
11
'2
Of
Of
Of
10
10
11
Of
Of
Of
Of
to
11
Of
9f
Of
Of
10
10
M
3f
04
34
'0
10
31
31
34
34
34
10
31 X
3i :i ;i
31 :a -.'.
34 :9 .;
39 :9 :»
•o : :
-338-
-------
TABLE IV-18
katr VERSUS D0/L0 AND k,/kL
..-.:. : i J 3
:; :i •> ~.i n -; :s •» • 22
: :•, •• -p j; ;i :i 9
- :? ;: s- so r x x i
:« :< ;: s< •» *9 M : M • •]
M :i iJ ii '1 M »i • :3 1 39
•; ' :* 44 94 'i Si M ' JO ' 3t
: ' N 4j jj '5 34 « M ' 33
u , :i ;a S3 '4 32 89 »S ' X
6 ; ;a 4- Si '2 3. 4' »2 97
3 i ;: 4- s " •> « M 93
:: M 4t s: •: " :3 J' 9 ; •: i, M 62
: : a i 12 ii sa
- ; .; j; ,j ,5
;. : :; 4 : 4 ;e ta •• 51
: « :; :; 9 J 5i li :. -
.-*.;;; ; ; 5- 53 u 43
;a :: ;» a ; :3 s: >t 19
; :. : -9 ' :' 48 43 35
: : •• :: * 42 j- 2) '1
: :« ;; ;i j
29
' 21
• :2
• il 1 ii i M t 72 ' 'i
' 42 l 47 l 50 '54 t !7 : 6' i M 6*
' 17 t 41 -1 44 1.47 1 SO 1 51 i 1! M7
l 21 '21 IK l 12 ' H ' Ji l It ' 37
• 20 '22 -23 ' 2» 12! '21 '21 ' 2i
l 13 ' i! : 11 l It l it ' '1 ' '« ' H
' 37 l 37 ' C7 1 07 1 01 1 34 ' 32 M
1 00 ' 00 M 97 »i 92 » 14
91 92 K )7 |] 7| 'J M
H »1 «C H 70 U U **
78 74 i ' 71
•19 ' i> il i! M
•31 '14 11 '31 ' 11
21 ' 24 :3 ,'2 20
• i • • 39 : • :4 30
H J2 M J3 "
7» M it 5' 41
59 49 )» :s :9
V> it >
Ji
', " ^
: :2
: *
: 5t
: M
: '3
3 '2
3 '*
0 't
3 18
2 :i
' )2
' M
it
' 53
' 37
' '1
97
70
M
; -i
. J
: :e
»4
J2
i9
' 54
' H
' 'i
92
62
22
it S "
9 5 5
i 31 2 :'
r 99
il U
. ,3 ...
' M ' 54
' JS 1 14
• 13 1 13
»' »x
53 42
35
! 9 i
? -4 2 It
2 31
(? '• n
• *2 i 73
' 54 : 14
1 33 1 Jl
l 07 1 03
H t7
29 ><
i ] 61
2 19 2 21
2 « 2 07
l 90 1 92
1 73 • 74
' 13 1 11
1 2» 1 27
99 94
Ctt ^«
t 7 (9
2.24 2 24
2 Of 2 11
i 91 > M
1 74 17!
l 52 l H
1 24 1 22
U 12
3* 21
2 21
2 U
l N
1 71
1 10
' 19
7!
0*
V
' 3
2 X
2 1!
i 97
'. 71
1 41
1 11
67
kL
71 77 T •
'3 ' ' ' J
2 12 2 11 t 17
2 It 2 11 2 20
' 91 1 H t.OO
1.7! • :! 17!
1 41 1 41 14!
Ill 1 07 1 01
il 47 li
2 »
2 21
2 01
1 7!
1 41
97
21
2 41
2 21
2 01
1 7!
1 41
9t
01
2 4]
2 24
2 02
1 7!
1 N
11
2 U 2 44 2 4| 2 M
2 24 2 27 2 21 2 X
2 01 2 31 2 54 2 31
1 74 1 74 1 71 17]
1 14 1 M 1 11 ' 21
71 70 12 SI
2 12 2 11 2 55
2 31 2 12 2 13
20! 2 M 2 0<
• :2 i *i • ':
• 24 '21 ' ''
40 2t :i
-339-
-------
amount of BOO that can be discharged into a river without causing the minimum dissolved
oxygen level to fall below a specified value. In constructing Tables IV-17 and IV-1B
extra detail was incorporated for DO/LO values between 0.0 and 0.5. This 1s
necessary because most practical problems fall within this range.
The following steps show how to use Table IV-17.
I. Find the reaeration rate (ka) and the BOO decay rate (kL)
for the river being investigated.
2. Find the BOO concentration in the river just below the point of mixing
(«-o).
3. Find the dissolved oxygen deficit at this location (D0 » C$ - C).
4. Compute ka/kL and D0/L0.
5. Using the ratios ka/kL and OO/LO, find 0C/L0 where Dc 1s the critical
deficit.
6. Finally, calculate DC . (0C/L0) L0, and C^,, • Cs - Oc.
To use Table IV-18 complete these steps:
1.-4. Repeat steps 1 through 3 above.
5. Using the ratios ka/kL and 0Q/L0, find k,tc.
6. Calculate tc - (katc)/ka.
4.3.8 General Dissolved Oxygen Deficit Equation
The most general dissolved oxygen mass-balance formulation to be presented
in this chapter is as follows:
«•>)]
"V-49)
where
P • oxygen production rate due to photosynthesis, mg/l/day
R • oxygen utilization rate due to respiration, mg/l/day
S. » bent hie demand of oxyoen, mg/l/day.
The distance function f(x) expresses the cross-sectional area relationship throughout
-340-
-------
the reach. The area can increase or decrease linearly or remain constant. The
general form of the relationship is:
MO • AQ, . ;.A <2,2 , IA - A, -Ao
XL
where
Af « area at x * XL
A « area at x « 0
XL • length of reach.
For a reach of constant cross-sectional area, AA« 0.
In developing Equation IV-49 the following relationship for C800 was used
(as originally presented in the BOO section):
f(x) J - ^ (IV-22)
I KL
An analogous expression for NBOO was also used.
In Equation IV-49, the distributed sources and sinks (P, R, Sg, Lrd, NJ
are all mass fluxes, and no volumetric flow rate is associated with any of these
sources and sinks of dissolved oxygen.
4.3.9 Photosynthesis and Respiration
The difficulty of accurately assessing the impact of photosynthesis and respira-
tion on the dissolved oxygen resources of streams is not readily apparent from the
single terms appearing in Equation IV-49. Of concern are both free floating and
attached algae, as well as aquatic plants. The extent to which algae impact the
dissolved oxygen resources of a river is dependent on many factors, such as turbidity,
which on decrease light transmlttance through the water column. Additionally, the
photosynthetic rate constantly changes in response to variations in sunlight intensity
and is not truly constant as implied by Equation IV-49. Hence if algal activity is
known to be a significant factor affecting the dissolved oxygen balance, the use of a
computer model is recommended In order to accurately assess such influences. For
example, in the Truckee River in California and Nevada, the diurnal variation of
dissolved oxygen has exhibited a range of from 150 percent saturation during the
daylight hours to 50 percent saturation at night due to algal photosynthesis and
respiration, respectively. At the most, hand calculations can give estimates
of net dissolved oxygen production rates that then can be compared to the other
source/sink terms in Equation IV-28. From this comparison the significance of
each can be estimated.
-341-
-------
TABLE IV-19
• LUES OF G30SS =-OTOSY:,Th£TIC PRODUCTION OF
•TE;R --:::A:;:,, 1972 --,c THOVAS AND O'CC'imi, 1966)
Gross -roojction Average -»iii
'
c-.ee Sr.er - Setter: 9
= :'.ed a'gae
T:ca' Creet - D-ator- Blo^r 6
(5Z-:C9.10° diatoms/1 )
3-7
:-..=~-sn Ri.e- est-ary - 0.5-2.0
Seattle, /.
'.ejse 3:ver Syster - 0.3-2.4
'.C'rf Carol ma
3ive«- l.ei 3.2-17.6 6.7-15.4
'Orf. Carolina Streams 9.8 21.5
Laboratory Streams 3.4-4.0 2.4-2.9
Table IV-19 presents some observed values of photosynthetlc oxygen production
••ates. AS shown in the table, dissolved oxygen production is expressed in units of
rate per unit area (gm/m -day). To convert to units of concentration per unit time,
the algal production rate must be divided by river depth:
P « I (IV-50)
where
? • production rate of dissolved oxygen, gm/m2-day
H • average river depth, meters
P - production rate of dissolved oxygen, mq/l-day.
P can now be directly compared to other terms In Equation IV-28.
By using a regression equation developed by Erdmann (1979a, 19796), the produc-
tion rate of dissolved oxygen, P, can be determined directly if the diurnal variation
of dissolved oxygen is known. When water temperature 1s fairly constant throughout
the day, the photosynthetlc oxyoen production rate becomes:
P • 24DO (IV-51)
-342-
-------
s
o
01
CO
2
12
11
O
CURVE A •'
• _ ••"^
/
WYMAN CREEK. CAUF
AUGUST 6.1962
AVERAGE O1 MG/L
/"-•
\. •
'-*•'
X
V
-•''
-_.—-"—- ^
CURVE 8
RIVER IVELENGLANO
MAY 31. 1959
AVERAGE HD4 MG/L
1
I
O6OO
O9CO
12OOI
15OO
18OO
HOURS
21OO
24OO
COCO O6CO
FIGURE IV-20 DAILY DISSOLVED OXYGEN VARIATION IN Two RIVERS.
where
ADO * difference between the dally maximum dissolved oxygen concentraf
and the dally m1n1mun dissolved oxygen concentration, mg/1.
Since Equation IV-51 Is based on regression analysis, the units are not consistent.
The importance of a constant water temperature 1s illustrated by Figure IV-20.
This figure shows the hourly variation of dissolved oxygen over a 24 hour period for
Wyman Creek in California and for the Ivel River in England. Both exhibit large
diurnal dissolved oxygen variations, although the reasons differ. In Curve A (Ivel
River), the dissolved oxygen level gradually increases from 0600 hr to 1800 hr, and
then decreases over the next 12 hours. The cause of the changing dissolved oxygen
levels is a net photosynthetlc oxygen production during the daylight hours, and a net
consumption during evening and night. Curve B 1s almost a mirror image of curve A
since the minimum dissolved oxygen levels occur during daylight hours and the maximun
during nighttime. The variations exhibited by curve B are principally caused by a
changing water temperature. During the day this creek absorbs considerable solar
-343-
-------
radiation causing th« water temperature to rise and the dissolved oxygen saturation
level to decrease. At night the creek cools off and the dissolved oxygen saturation
level increases. Curve B then is free from the Influence of photosynthetlc effects,
so it would be erroneous to apply Equation IV-51. Erdmann (1979a, 1797b) and Kelly
e_t jj_L (1975) provide more sophisticated methods to predict P when both photosynthetlc
and temperature effects occur concurrently. Example IV-7 illustrates the utility of
Equation IV-51.
r EXAMPLE IV-7 •
j i
1 Prediction of Photosynthetlc Oxygen Production Rate I
i
i On Mechuns River near Charlottesvl He, Virginia,
• collected the following data:
| Time of Day
1 (hours after midnight)
I 0.0
! 0.5
1 l-°
! 1.5
I 2.0
2.5
1 3.0
j 3.5
1 4.0
i 4-5
! 5.0
1 5.5
6.0
1 6.5
i 7.0
! 7.5
I S.O
' 8.5
1 9.0
; 9.5
1 10.0
j 10.5
! 11.0
1 11.5
12.0
1 12.5
i 13.0
1 13.5
1 14.0
! 14.5
| 15.0
; 15.5
1 16.0
I 16.5
1 17.0
Stream
Temperature. *C
23.3
23.3
23.4
23.4
23.5
23.5
23.5
23.5
23.4
23.4
23.3
23.2
23.1
23.0
22.9
22.8
22.7
22.7
22.7
22.7
22.8
23.0
23.2
23.5
23.6
24.3
24.8
25.3
25.5
25.5
25.9
26.1
26.1
26.1
26.1
i
Kelly .et *±. (1975) j
i
Dissolved !
Oxygen (mg/l) |
7.6 I
7.6 !
7.6 |
7.5 !
7.4 1
7.2 ;
7.3 1
7.3 |
7.3 !
7.3 |
7.3 ;
7.3 1
7.3 I
7.3 !
7.4 |
7.4 !
7.5 |
7.6 ;
7.7 1
7.8 I
8.0 !
8.1 |
8.4 '
8.5 1
8.7 ;
8.9 1
9.0 j
9.1 !
9.2 |
9.3
9.2 1
9.2 ;
9.2 1
9.1 I
9.0 '
-344-
-------
Time of Day Stream Dissolved
(hours after midnight) Temperature, *C Oxygen (mg/l)
17.5 25.8 8.9
18.0 25.8 8.8
18.5 25.5 8.6
19:0 25.3 8.5
19.5 25.1 8.3
20.0 24.8 8.2
20.5 24.5 8.0
21.0 24.2 8.0
21.5 24.0 7.9
22.0 23.8 7.6
22.5 23.7 7.7
23.0 23.6 7.7
23.5 23.6 7.6
24.0 23.5 7.5
Using a sophisticated analysis, Kelly et_ _a_L found the daily mean photosyn-
thetic oxygen production to be 4.40 mg/1. Using the data shown above and Equation
IV-51 estimate the daily photosynthetic oxygen production, P (mg/l/day).
The minimum dissolved oxygen is 7.2 mg/l, which occurs at 0230. The maximum
dissolved oxygen is 9.3 mg/1 which occurs at 1430. Hence:
P - 2AOO - 2(9.3-7.2) - 4.2 mg/l/day
This compares very well with the value found by KelJy e_£ jj_. using a more
sophisticated analysis, even though the stream temperature varies by a few
degrees during the day. Probably one reason for the good agreement is that
the maximum and minimum values occur about 12 hours apart, which the method
assumes they do.
END OF EXAMPLE IV-7
Values of photosynthetic respiration vary widely, ranging from 0.5 gm/m /day
to greater than 20 gm/nr/day. One suggested relationship between respiration and
chlorophyll ^ is given as (Thomann, 1972):
R(mg/l/day) - 0.024 (chlorophyll a_) (ug/1) (IV-52)
where
1 ng/1 • 10"3 mg/1.
Chlorophyll ^ concentration is most commonly expressed in terms of ng/1.
4.3.10 Benthic Demand
In addition to oxygen utilization by respiration of attached algae, benthic
-345-
-------
deposits of organic material and attached bacterial growth can utilize dissolved
oxygen. Table IV-20 illustrates some uptake rates. As with photosynthesis, the
uptake rates are expressed in gm/m2-day. To use these values in Equations IV-28 or
IV-49, division by stream depth (in meters) is necessary. Temperature effects can be
approximated by:
~'20 (IV-53)
The areal extent of significant oxygen demanding benthic materials is often
limited to the region just below the outfall vicinity. Although the oxygen demand may
be great over a short distance, it may be insignificant over larger distances. The
response of rivers to areally limited benthic deposits is generally to move the
critical deficit upstream, but not to lower its value significantly.
Bowie et al. (1985) contains significantly more data and further discussion
of benthic oxygen demand in rivers. Additionally Butts and Evans (1978) conducted
extensive studies of sediment oxygen demand on 20 streams in Illinois. They found
that benthic oxygen demand could be predicted as:
TB - 0.15T + 0.30S + 0.11 logN - 0.56 (IV-54)
where
Tg • benthic oxygen demand, g/m -day
T • water temperature, *C
0. » depth of sediment, inches
2
N » number of macroinvertebrates per m .
They found that N typically ranged from 10,000 to 1,000,000. Within this range
the sum of the last two terms is between ^0.1, and is negligible compared to the
first two terms. Under these conditions Equation IV-54 simplifies to:
0.15T + 0.30S (IV-55)
The depths of sediment found during the study of Butts and Evans (1978) ranged from 1
to 17 inches. Consequently Equation IV-55 is applicable to streams which have fairly
significant benthic oxygen demands. For cleaner streams Equation IV-55 probably
overestimates the benthic oxygen demand.
-346-
-------
TABLE IV-20
AVERAGE VALUES OF OXYGEN UPTAKE RATES OF
RIVER BOTTOMS (AFTER THOMANN, 1972)
Uptake (gms 02/m -day)
9 20°C
Bottom Type and Location
Range
Approximate
Average
Sphaerotilus - (10 gm dry wt/M?)
Municipal Sewage Sludge - 2-10.0
Outfall vicinity
Municipal Sewage Sludge - 1-2
"Aged" Downstream of Outfall
7
4
1.5
Cellulosic Fiber Sludge
Estuarine mud
Sandy bottom
Mineral soils
4-10
1-2
0.2-1.0
0.05-0.1
7
1.5
0.5
0.07
4.3.11 Simplifying Procedures in Dissolved Oxygen Calculations
Using Equation IV-49 might be untenable for several reasons, such as lack
of available data, or because of the vol'jtninous calculations required to apply
it to a large number of reaches. Several suogestions are offered here that should
simplify analysis of dissolved oxygen problems.
Since the general scope of this section 1s to facilitate the determination
of existing or potential problem areas, the analysis should proceed from the simple
to the more complicated approach. It may be adequate to analyze the dissolved oxygen
response to the most severe loadings first, neglecting those of secondary importance.
If such an analysis clearly indicates dissolved oxygen problems, then the inclusion
of any other pollutant discharges would only reinforce that conclusion. More rigorous
procedures (e.g., a computer model) could then be employed to perform a detailed
analysis.
Suppose the improvement of dissolved oxygen levels due to decreased loading from
a point source is of interest. This is a common situation since it relates to the
design of waste loading abatement schemes. Such improvement can be estimated by:
•347-
-------
exp
(1V-56)
where
ALQ • the change in the Initial BOO, ma/1
AD » change In deficit in response toALQ.
Equation IV-56 was formulated from Equation IV-49 assuming that LQ and DQ are
the only changes of significance.
Many rivers have a large number of point sources. Although this 1s not necessar-
ily a complicating factor, a detailed analysis might be too time consuming for hand
calculations. There are several possible alternatives to deal with this situation in
order to reduce the number of reaches to be analyzed. The first, already mentioned,
is to consider only the significant pollutant sources. Second, as was Illustrated in
Example IV-5, a number of uniformly distributed point sources can be considered as a
single distributed source. Third, combining several adjacent point sources is also
possible, 1f the length of the reach under consideration Is.long relative to the
distance of separation between the point sources. Analogously, a distributed source
can be approximated as a point source, contributing the same waste loading and
located at the center of the distributed source.
It may be that the planner wants only to determine the critical dissolved
oxygen concentration in each of a series of reaches. In this case no more than
two values of dissolved oxygen per reach need be calculated. Figure IV-21 shows the
solution process to be followed.
One final note on dissolved oxygen evaluations should be made here. It may be
that if the planner is interested primarily in locating dissolved oxygen problems, he
need not perform any computations. This is especially likely where dissolved oxygen
data are available at various locations on the river. Plotting dissolved oxygen time
trends may reveal when, as well as where, annual dissolved oxygen minima occur.
EXAMPLE IV-8
' Determining River Assimilative Capacity from ;
I Tables IV-17 and IV-16 j
j j
j Suppose the user wants to determine waste assimilative capacity (MAC) for a j
j river reach that has the following characteristics: j
-348-
-------
f
Go to next
reach
Find D
at x
D - Dc
at
X « 0
Determine k and k,
for each reach
/ Begin reach |
I calculations!
\
Find Do,L0
(at x - o)
Find tc. xc * Utc
FIGURE IV-21 FLOW PROCESS IN REACH BY REACH SOLUTION TO
CRITICAL DISSOLVED OXYGEN VALUES
-349-
-------
I Critical dissolved oxygen concentration • 5.0 mg/l (-user establishes this) I
j Initial deficit • 1.0 mg/l j
j Average velocity • 0.5 fps j
j Average depth » 4 feet :
Chloride concentration • 0
Temperature range » 10*C to 35*C !
! First, ka and kL need to be found. From Figure IV-17, ka (20*) • 0.8/day, (
I and from Figure IV-11, kL « 0.4/day. At any other temperature then, kfl and I
| k^ can be found from the temperature relationships previously developed:
\ • (kL)2Q 1.047 T'20 (IV-17) !
Using Table IV-15 the dissolved oxygen saturation concentration within the tempera- I
ture range of Interest can be found. This Information can then be then compiled |
Into Table IV-21 shown below. ;
TABLE IV-21
COMPILATION OF INFORMATION IN EXAMPLE IV-8
T
(°C)
10
15
20
25
30
35
s
(mg/l)
11.3
10.2
9.2
8.4
7.6
7.1
Cc
(mg/l)
5.0
5.0
5.0
5.0
5.0
5.0
(mg/l)
6.3
5.2
4.2
3.4
2.6
2.1
VDc
0.16
0.19
0.24
0.29
0.38
0.48
k /k.
a L
2.5
2.2
2.0
1.8
1.6
1.4
Using the values of DQ/DC and ka/kL, LQ can be found, which 1n
this case Is the WAC.
Procedure
1. Table IV-21 Is entered at the appropriate *a/kL column. This is
2.5 at 10*C.
2. Next, the entry within the ka/kL column in Table IV-17 is fountf
-350-
-------
such that:
Do/Lo - °o . 0.16
Since the left-most column of Table IV-17 1s DQ/L0 and the entries are
D /L , the ratio of these values 1s calculated until that ratio equals 0.16.
CO n nc
For example, try DO/LQ » 0.05. Then DC/LQ « 0.23 and ^§ » 0.22 > 0.16; too
big.
Try 0 /LQ » 0.04. Then DC/LQ « 0.23 and • .17; close enough.
D.
. , ,
Then _£ . .23, or LQ , L_i . 27.4 mg/1
0
I The results are tabulated below for the temperature range 10*C to 35*C.
I
j T('C) WAC (mg/1) DO/LQ
! 10 27.4 0.04
I 15 20.0 0.05
I 20 15.0 0.07
j 25 11.3 0.09
j 30 7.6 0.13
35 5.4 0.19
. LQ is directly related to the loading rate of BOD, as expressed earlier
j in Equation IV-41:
1 WAC - (L ) - LuQ" * "crU1caW5.38
I ° critical QU + Qw
j From equation IV-41 the critical waste loading W can be found. If desired,
this procedure can be repeated for different river flow rates, and WAC and
• wcritical found for tne various flows. To do this, different average depths
, and velocities will be needed. Generally this analysis 1s most applicable to
j minimum flow conditions, as this 1s the most critical situation, but higher flows
I may be of interest to assess the benefits of flow augmentation decisions. Novotny
| and Krenke) (1975) have used a 20 year, 3-day low flow In analyzing the Holston
j River in Tennessee. For further discussion of low flow calculations refer to
j Section 4.4.6.
In interpreting the results of this example the user should be looking
, more at trends rather than particular results. For example, notice how the
J WAC decreases with increasing temperature. For every 10* increase the WAC
is approximately halved. A similar relationship between WAC and flow rate
could also be determined.
Finally, using Table IV-18, the travel time tc can be determined to
-351-
-------
the point of critical deficit. The appropriate DO/LO and ka/kL values ar*
used to find tc. Table IV-22 illustrates these results.
TABLE IV-22
CRITICAL TRAVEL TIME RESULTS
T(°C)
10
15
20
25
30
35
VkL
2.5
2.2
2.0
1.8
1.6
1.4
VLo
0.04
.05
.07
.09
.13
.19
t.
1
1
1
1
1
0
ka
.4
.3
.2
.13
.0
.9
ka
.•63
.71
.8
.9
1.0
1.1
todays)
2.2
1.8
1.5
1.2
1.0
0.8
END OF EXAMPLE IV-8
I
I EXAMPLE IV-9
I
Critical Deficit Calculations for Multiple Reaches
Suppose the critical deficit in each of the three reaches of the river
illustrated in Figure IV-22 is to be determined. The conditions upstream of
the first discharge are:
T » 27'C Depth » 5.0 feet
Q « 600 cfs D » 1 ma/1
U - 0.4 fps Lu « 2 mg/1
Using these data, along with the solution process outlined in Figure IV-21,
the following procedure can be used:
1. Determine ka, !CL for each reach. For this example it will be
assumed that the average depth, velocity, and temperature remain relatively
constant over the three reaches, so that k and k|_ are also the same.
kd (20) » 0.5, (from Figure IV-17)
kL (20) • 0.35, (from Figure IV-11)
Using the temperature correction:
ka (27) » 0.60, (from Equation IV-34)
k (27) - 0.43, (from Equation IV-17)
-352-
-------
1
! 1
1
1
1
j Ou=lmg/l
• ._>_.^^_— -
Qu=600cf» i
1
i
j
.Mixing ^Mixing . 1
Zone / Zone ^s j
' O (§)(§)'
i I 1
i 1
! ^ !
™^~ • • • • • i • "~^^ )
ii 1 1 •
i
/ I2MI. ^/ 4MI. \ 1
IN /|N A, \
! B.O.D.L=40mg/l B.QD.L=50mg/l B.O.DL=20mg/l |
1 Q'SOMGO Q»60MGD Q = lOMGD |
• FIGURE IV-22 HYPOTHETICAL RIVER USED IN EXAMPLE IV-9
I The saturation dissolved oxygen concentration at 27*C and 0% salinity is (from I
j Table IV-15) 8.1
i
2. For the
|
mg/ 1 . |
1
first reach, calculate L and D : !
00
, . (2M60Q) + (40) (50) (1.55) :
1 Lo
i
600 * (50)(1.55) !
I • 6.35 mg/1 j
i
j For lack of
better information about the dissolved oxygen characteris- I
j tics of the waste, it can be assumed that 0 « D * 1 mg/1. The location |
of the critical deficit can now be calculated using Table IV-18, or Equation j
! IV-45. In this ex«nple Table IV-18 will be used. To use that table, the follow- •
j ing ratios are needed:
I
' Do/Ln m
i 0 0
! and
I
ka/kL m
1 a L
1
1/6.35 - 0.16 j
.
1
0.60/0.48 - 1.3 j
i i
. From Table IV-18, k t - .92 or ;
I a c 1
i
1
i
1
te - .92/0.6 • 1.53 days j
, . (0.4) (1.53) (3600) (24) . , j
c 5280 '
1 Since xc < 12, the critical deficit actually exists, and is located 10 miles j
| downstream. From Table 1V-17 0 can be found by entering it with the same 1
j ratios used in Table IV-18. The result is: j
j r£ * .38 — Of - 2_£ mg/1 j
1 0
•— i
-353-
-------
I 3. Before the critical conditions In reach 2 can be calculated, the con-
| ditions at the upstream end of that reach must be established. The conditions at
j the downstream end of reach 1 are:
j D - 2.3 mg/1, from Equation IV-40
L » 2.6 mg/1 from Equation IV-42
The conditions at the upstream end of reach 2 are thus:
L . (2.6) (677) + (60) (1.55) . /}
0 677+93 *
0 « 2.3 can be used for lack of better information on the dissolved oxygen
concentration in the effluent to reach 2. For use in Table IV-18, it is found
that:
VLo • '28
so
kat - .76
tc • .76/0.6 « 1.3 days
xc « 8.3 miles
Since reach 2 is only 4.0 miles long, the critical deficit is not reached.
Instead the maximum deficit will occur at the downstream end of reach 2, where
D - 3.3 mg/1 (Equation IV-40)
L « 6.22 mg/1 (Equation IV-22)
4. For the beginning of reach 3, LQ and 0Q must be found:
L - (20)(10)(1.55) * (770.51(6.22) . fi 5 l}
0 770.5 + (101(1.55)
For D , it can be assumed that C « 5.0 mg/1. From Equation IV-41, then:
0-81 (8.1 - 3.3) (770.5) * (5.0)(10)(1.55) . 3 - „
0 ' 770.5 + 15.5
The calculations of critical conditions can now be made for this reach, as
for the previous two.
1 END OF EXAMPLE IV-9
4.4 TEMPERATURE
4.4.1 Introduction
The biota comprising an established aquatic ecosystem generally respond negatively
to significant abnormal temperature fluctuations. Anthropogenic modifications of
rivers and streams can alter the thermal regime, most often by elevating the maximum
and mean water temperatures. Repercussions of elevated temperatures are manifested
-354-
-------
through a shift in the ecological balance and 1n the water quality of rivers. For
example, there is a progression in the predominance of algal species from diatoms to
green algae to blue-green algae as water temperature increases through a specific
range. Thermal discharges can increase the ambient temperature enough to alter the
predominant species to the undesirable blue-green algae. Increased metabolic activity
of aquatic organisms, such as fish, also accompanies elevated temperature. If the
increase is high enough, the results can be lethal. Much data are available today
(e.g., Committee on Water Quality Criteria, 1972) which specify lethal threshold
temperatures for aquatic organisms.
Water quality may be adversely affected through decreased solubility of dissolved
oxygen and increased biochemical reaction rates. Adequate dissolved oxygen levels,
particularly at elevated temperatures, are critical because of the increased metabolic
activity. Yet, as previously discussed the saturation concentration of dissolved
oxygen diminishes with rising temperature. Worse still, is the concurrent low flow
condition which is associated, in many parts of the country, with the warm summer
months. For example, in a study of 30 river reaches in the U.S. (EPA, 1974), 20 had
lower flows in the summer months than in the winter. This situation further reduces
assimilative capacity and usually results in the most critical dissolved oxygen
levels over the year.
*an can alter the thermal regime of rivers by removing trees, changing the flow
regime, and by increasing thermal discharges. Diversions of water from a river can
reduce the water depth, and increase the mean and diurnal fluctuation of stream
temperature.
In Long Island, modification of the natural environment of streams has increased
average stream temperatures during the summertime by as much as 9 to 14*F (Pluhcwsi,
1968). Concurrent temperature differences of as much as 14 to 18*F between sites on
the sane stream were observed on days of high solar radiation. A principal factor
involved in these occurrences was the removal of vegetation along the banks of the
streams, permitting significantly greater penetration of solar radiation. Other
contributing factors cited by Pluhowski Included increased stormwater runoff, a
reduction in the amount of groundwater Inflow, and the introduction of ponds and
lakes.
4.4.2 Equilibrium Temperature
If a body of water at a given Initial temperature is exposed to a set of con-
stant meteorological conditions, It'will tend to approach some other temperature
asymptotically. It may warm by gaining heat or cool by losing heat. Theoretically,
after a long period of time the temperature will become constant and the net heat
transfer will be zero. This final temperature has been called the eauilibrium
temperature, E. At equilibrium, the heat gained by absorbing solar radiation and
-355-
-------
» Shortwave solar radiation (400-2800 BTU ft"2 day"1)
Hfl • Long wave atmospheric radiation (2400-3200 BTU ft"2 day"1)
Hbr-Long wave back radiation (2400-3600 BTU ft"2 day"1)
*
H « Evaporative heat loss (2000-8000 BTU ft"2 day"1)
"2
"1
Conductive heat loss or gain (-320-+400 BTU ft" day )
- Reflected solar (40-200 BTU ft"2 day"1)
- Atmospheric reflection (70-120 BTU ft"2 day"1)
i i
NET RATE AT WHICH HEAT CROSSES WATER SURFACE
Ha " Hsr " Har)
(Hbr ~ H
c + He)JBTU ft"2 day"'
Absorbed Radiation (Ho) Temperature Dependent Terms
Independent of Water Temperature
"
- W(es - ea)
FIGURE IV-23
MECHANISMS OF HEAT TRANSFER ACROSS A
WATER SURFACE (PARKER AND KRENKEL, 1969)
long-wave radiation from the atmosphere will exactly balance the heat lost by back
radiation, evaporation, and conduction.
These heat fluxes are Illustrated in Figure IV-23 which also shows typical ranges
for the fluxes. Some of these terms (H$, HJf Hjr, HJr) are independent of water
temperature, while the remainder (Hj,r»Hc»He) arc dependent uP°n w«ter temperature.
At equilibrium then, Hn (net transfer) equals zero, or:
HS * Hsr
Ha " V " Hbr
-H
(IV-57)
In actuality, the water temperature rarely equals the equilibrium tempera-
ture because the equilibrium temperature Itself is constantly chanolna with the
local meteorological conditions. The equilibrium temperature will rise during
-356-
-------
the day when solar radiation 1s greatest, and fall to a minimum at night when
solar radiation 1s absent.
A daily average equilibrium temperature may be computed using a number of
factors Including dally average values of radiation, temperature, wind speed,
and vapor pressure. The daily average value will reach a maximum in midsummer
and a minimum in midwinter. Since the actual water temperature always tends to
approach, but does not reach the equilibrium temperature, it will usually be
less than equilibrium 1n the spring when temperatures are rising, and greater
than equilibrium in the fall when temperatures are dropping. During a 24 hour
period, the equilibrium temperature usually rises above the actual water temperature
during the day and falls below the water temperature at night, forcing the water
temperature to follow a diurnal cycle.
The amplitude of the actual diurnal water temperature cycle is generally
dampened significantly In comparison to the amplitude of the equilibrium temperature
cycle due to the large heat capacity of water. A thermal discharge into a water body
will usually increase the actual daily amplitude because of the water temperature
dependent terms In Equation IV-57. This situation is illustrated in the following
example (Edinger, et al., 1968). Figure IV-24 illustrates a flow through a cooling
pond into which a thermal effluent is discharged (at Station B).
Sto H.
Sto.G.
Sto E
Sto
Sto. C
FIGURE IV-24
SCHEMATIC OF SITE No, 3
COOLING LAKE (FROM EDINGER,
ET AL,, 1968)
Temperature observations were recorded at Stations 8 through H at four-hour periods
for one week. The findings are depicted in Figure IV-25.
-357-
-------
120
UJ
7/18 7/19 ' 7/20
DAY (4 HOUR PERIODS)
7/21
7/22 7/23
7/24
FIGURE IV-25
OBSERVED TEMPERATURES, SITE No, 3,
JULY 18 - JULY 24, 1965 (EDINGER,
ET AL,, 1968)
The highest temperatures and largest diurnal temperature variables are recorded at
Station B. The peak temperature at Station B occurs just after noon, corresponding
to the peak loading from the plant. At Station C the peak temperature is at 1800
hours, Indicating the lag in flow time from Stations B to C. The peak temperatures
at the remaining stations are more Influenced by meteorological conditions, and less
by the thermal discharge. The relationship of the observed temperatures to the
equilibrium temperature over a 24-hour period is shown in Figure IV-26. Note the
amplitude of the equilibrium temperature E (33*F amplitude). The average equilibrium
temperature, T, is approximately 91 *F. A progression from Station B to Station
H indicates that the daily water temperature tends to approach the average equilibrium
temperature.
Stations G and H, and the ambient temperature TN, all reflect the predominating
influence of meteorological conditions. When the ambient water temperature is above
the instantaneous equilibrium temperature E, it tends to decrease, and when the
temperature is below E, it tends to Increase. In the early morning and late evening
hours, when E is low, the water temperature decreases at these stations. During
midday when E is higher, however, the temperatures at these stations increase.
4.4.3 Calculation of Equilibrium Temperature
Studies (Edinger and Geyer, 1965) have shown that the equilibrium temperature of
a well mixed body of water can be estimated by:
HR - 1801
K - 15.7 fe - C(B) * 0.26
K (.26+3) L a
'•J
(IV-58)
-358-
-------
Tn (Ambient Temp.)
E (Equilibrium)
8 12 16
TIME OF DAY (MRS.)
24
FIGURE IV-26
COMPARISON OF COMPUTED EQUILIBRIUM AND
AMBIENT TEMPERATURES V/ITH OBSERVED MEAN
DIURNAL TEMPERATURE VARIATIONS FOR SITE
No. 3, JULY 18-JuLY 24, 1966 (EDINGER,
ET AL,, 1968)
where
E » equilibrium teperature, *F
K » thermal exchange coefficient, BTU/ft /day/'F
HD « net incoming short (H.J and lonq (H ) wave radiation
K A s n o n
BTU/ftVday
T - air temperature, *F
O
ea « water vapor pressure of ambient air at air temperature, mmHg
B « proportionality coefficient, mmHg/*F
C(B) > value dependent on B, mmHq.
The thermal exchange coefficient K is expressible as:
K - 15.7 + (0.26 + B) f(u) I
::v-59)
where
f(u) « a function of wind speed.
Different relationships for f(u) have been developed. For purposes of hand ca'cula-
tions, the following relationship will be used:
f(u) « 11.4u
(IV-60)
-359-
-------
where
u • the dally average wind speed in mph.
To calculate E using Equation IV-58 an Iterative procedure is needed, since K,
B, and C(B) depend on E. The following steps outline a solution procedure.
1. Data needed to start the procedure Include T , relative humidity,
a
wind speed, and net shortwave solar radiation. Figure IV-27 illustrates
daily average solar radiation reaching the continental United States for
the months July and August. It is during these months that stream
temperatures usually reach their annual maxima. These values do not
account for the albedo of water (the percent of incoming solar radiation
that is reflected), but since this is small, it can be ignored. Because
of the variability caused by topography, vegetative cover, and other
factors, local sources of information should be used when possible for
solar radiation values.
2. Calculate H. - H + H (BTU/ft2/day). If Figure IV-27 is utilized
K Sn an _
for Hsn, convert from langleys/day to BTU/ft /day by multiplying by 3.7.
H can be estimated from Table IV-23 by knowing the air temperature
and the cloud cover fraction (0.1 to 1.0).
3. Determine e from Table IV-24 by entering with T and relative
humidity.
4. Choose an initial value for E. The air temperature T can be the
first guess.
5. Enter Table IV-25 for B and C(B) at E (*F).
6. Knowing u, f(u), and B, calculate K from Equation IV-59.
7. From Equation IV-58 make the next estimate of E (E ) by evaluating
the right hand side of that equation (call this result F(E)).
8. The next estimate of E is Enew - 0.3E + 0.7 F(E).
(Note: this choice of E brings aoout a more rapid convergence to
the answer than would use of E alone).
9. If IEnew-Ei <1'F. thenEMtui, - Enew.
If |E,._ - El > 1*F, return to step 5 with E__, and repeat the procedure
new
until the convergence criterion is met, namely, E,c»uai *
Instantaneous, daily, weekly, or even longer term average equilibrium temperature,
T, can be calculated by using mean meteorological conditions over the period of
interest and following the solution procedure just outlined. Calculating the daily
average T under the most crucial annual meteorological conditions (usually occurring
in July or August) yields the highest temperature about which that water body tends
to naturally oscillate. The repercussions of man's activities in terms of altering
T can thus be estimated and analyzed for potential impact.
-360-
-------
JULY
CANADA
I
MEXICO
AUGUST
,490
o
NOTE: To convtrt L«ngltys/diy to BTU/ftVd«y, multiply by 3.7.
FIGURE IV-27 MEAN DAILY SOLAR RADIATION (LANGLEYS) THROUGHOUT
THE U,S, FOR JULY AND AUGUST (U,S, DEPARTMENT
OF COMMERCED 1968)
-361-
-------
TABLE IV-23
NET LONG WAVE ATMOSPHERIC RADIATION, H
an
Cloud
Cover
.1
.2
.3
.4
5
6
.7
8
.9
1.0
Tempera-
ture
35
65
35
65
35
65
35
65
35
65
35
65
35
65
35
65
35
65
35
66
"an
(BTU/Sq.
Ft/Day)
1685
2400
1694
2412
1708
2432
1728
2461
1754
2497
1785
2542
1822
2595
1865
2656
1914
2725
1968
?803
Tempera-
ture
40
70
40
70
40
70
40
70
40
70
40
70
40
70
40
70
40
70
40
70
Han
(BTU/Sq.
Ft/Day)
1790
2540
1799
2553
1814
2575
1335
2605
1863
2644
1896
2691
1936
2747
1981
2812
2033
28H5
2091
2967
Tempera-
ture
45
75
45
75
45
75
45
75
45
75
45
75
45
75
45
75
45
75
45
75
Han
(BTU/Sq.
Ft/Day)
1900
2688
1910
2701
1926
2724
1949
2756
1978
2797
2013
2847
2055
2907
2103
2975
2158
3053
2220
3139
Tempera-
ture
50
80
50
80
50
80
50
80
50
80
50
80
50
80
60
80
50
80
50
80
"an
(BTU/Sq.
Ft/Day)
2016
2842
2026
2857
2043
2881
2067
2914
2098
2958
2136
3011
2180
3074
2232
3146
2290
3228
2365
3320
Tempera -
lure
55
85
55
85
55
85
55
85
55
85
55
85
55
85
55
86
5S
85
55
85
H
an
(BTU/Sq.
Ft/Day)
2138
3004
2149
3019
2167
3045
2192
3080
2225
3126
2265
3182
2312
3249
2366
3325
2428
3412
2497
3509
Tempera-
ture
60
90
60
90
60
90
60
90
60
90
60
90
60
90
60
90
60
90
60
90
H
an
(BTU/Sq.
Ft/Day)
2266
3173
2277
3190
2296
3216
2323
3254
2358
3303
2400
3362
2450
3432
2508
3513
2573
«604
2646
3707
-362-
-------
TABLE IV-24
SATURATED HATER VAPOR PRESSURE. e$, VERSUS AIR TEMPERATURE, Tfl,
AND RELATIVE HUMIDITY
'a
(°F>
35
40
45
50
55
60
65
70
75
80
85
90
95
100
V
(nmHg)
5.2
6.3
7.6
9.1
11.0
13.1
15.6
18.6
22.0
26.0
30.5
35.8
41.8
48.7
RELATIVE
0.1
0.5
0.6
0.8
0.9
1.1
1.3
1.6
1.9
2.2
2.6
3.1
3.6
4.2
4.9
0.2
1.0
1.3
1.5
1.8
2.2
2.6
3.1
3.7
4.4
5.2
6.1
7.2
8.4
9.7
0.3
1.6
1.9
2.3
2.7
3.3
3.9
4.7
5.6
6.6
7.8
9.2
10.7
12.5
14.6
0.4
2.1
2.5
3.0
3.6
4.4
5.2
6.2
7.4
8.8
10.4
12.2
14.3
16.7
19.5
0.5
2.6
3.2
3.8
4.6
5.5
6.6
7.8
9.3
11.0
13.0
15.3
17.9
20.9
24.4
H U M 1
0.6
3.1
3.8
4.6
5.5
6.6
7.9
9.4
11.2
13.2
15.6
18.3
21.5
25.1
29.2
1 D I T Y
0.7
3.6
4.4
5.3
6.4
7.7
9.2
10.9
13.0
15.4
18.2
21.4
25.1
29.3
34.1
0.8
4.2
5.0
6.1
7.3
8.8
10.5
12.5
14.9
17.6
20.8
24.4
28.6
33.4
39.0
0.9
4.7
5.7
6.8
8.2
9.9
11.8
14.0
16.7
19.8
23.4
27.5
32.2
37.6
43.8
1.0
5.2
6.3
7.6
9.1
11.0
13.1
15.6
18.6
22.0
26.0
30.5
35.8
41.8
48.7
-363-
-------
TABLE IV-25
B AND C(B) AS FUNCTIONS OF TEMPERATURE
Temperature
(°F)
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
B
(mmHq/°F)
.286
.296
.306
.317
.328
.340
.352
.365
.378
.391
.405
.419
.433
.448
.464
.479
.496
.512
.529
.547
.564
.583
.601
.620
.640
C(B)
(mmHq)
-5.5
-4.5
-4.1
-4.2
-4.6
-5.4
-6.3
-7.5
-8.7
-10.0
-11.2
-12.5
-13.6
-14.7
-15.8
-16.7
-17.6
-18.3
-19.0
-19.6
-20.1
-20.7
-21.2
-21.7
-22.3
Temperature
(°F)
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
B n
(mmHq/°F)
.660
.680
.701
.722
.743
.765
.787
.810
.333
.857
.881
.905
.930
.955
.980
1.006
1.033
1.060
1.087
1.114
1.142
1.171
1.200
1.229
1.259
1.289
C(B)
(mmHq)
-22.9
-23.6
-24.4
-25.4
-26.5
-27.8
-29.3
-31.0
-33.0
-35.1
-37.6
-40.3
-43.2
-46.4
-49.7
-53.3
-57.1
-61.0
-64.9
-68.9
-72.9
-76.7
-80.4
-83.8
-86.8
-89.3
EXAMPLE IV-10
Calculation of Equilibrium Temperature
On Long Island, New York, studies done by Pluhowskl (1968) have Indicated
that shading of streams by a natural vegetative canopy can drastically affect the
shortwave solar radiation reaching those streams. The results of some of his
findings are presented 1n Table IV-26. In the simmer, when leaves are on the
trees, the actual solar radiation reaching the Connetauot River can be as low as
29X of that reaching unobstructed sites at nearby Mlneola or Brookhaven.
Suppose the user 1s Interested 1n predictinc how the removal of the riparian
-364-
-------
TABLE IV-26
SUMMARV OF SOLAR-RADIATION DATA
FOR MINEOLA, BROOKHAVEN, AND THE CONNETQUOT RIVFR SITES
Mean-Dally Solar Radiation in Langleys:
for the Indicated Periods
Solar
Site
(1)
1
2
3
1
2
3
1
2
3
1
2
3
1
Notes :
Dates
(2)
Jan. 30, 31, 1967
Jan. 28, 29, 1967
Jan. 25, 26, 1967
Apr. 21-23, 1967
Apr. 16-18, 1968
Apr. 19, 20, 1967
Apr. 24-26, 1967
June 9-11, 1967
June 7, 8. 1967
June 12-14, 1967
Aug. 26-28, 1967
Aug. 22-24, 1967
Aug. 29, 30, 1967
Nov. 28. 29, 1967
Mineola
(3)
235
148
135
466
452
436
408
600
664
527
275
277
504
204
Brookhaven
(4)
244
130
135
464
502
386
411
599
671
523
260
328
484
Connetquot
River
Estimated
(5)
240
137
135
465
502
429
410
599
669
525
266
308
492
204
Connetquot
River
Observed
(6)
148
96
104
343
389
384
401
254
531
443
78
162
338
86
Ratio=
Connetquot River
Observed
Connetquot
River
Unobstructed
(7)
0.62
.70
.77
.74
.77
.90
.98
.42
.79
.84
.29
.53
.69
.42
Solar site 1 is typically heavily forested, solar site 2 is moderately to heavily forested,
and solar site 3 is moderately forested.
Radiation data in column 5 are estimated unobstructed horizon values for Connetquot River
based on data from Mineola and Brookhaven (cols. 3,4).
-365-
-------
vegetative cover might effect T. Consider the period 22-24 August, 1967, when
the Connetquot River received 162 langleys/day of a possible 308 langleys/day of
shortwave solar radiation. Representative meteorological conditions at this time
were:
Ta . 65*F
u » 2 mph
Cloud cover fraction « 0.5
Relative humidity - SOX
The steps in solving for T are as follows: I
I 1. Data have been gathered, as previously listed. |
j 2. H$n » 162 (3.7) « 600 BTU/ft2/day. This value assumes that the vege- j
j tative canopy blocks 47X of the solar radiation. From Table IV-23, j
j Hin is (.5 cloud cover at 65*F) 2497 BTU/ft2/day. Thus,
I ?
HB - 2497 + 600 « 3097 BTU/ftVday.
I I
3. At SOX relative humidity and an air temperature of 65*F, e » J
! 12.5 mmHg from Table IV-24. J
I 4. As an initial guess of E, assume E, « 65"F, the air temperature. I
j 5. From Table IV-25, B - .56, C(B) - -20.1 j
j 6. K » 15.7 + (.26 + .56) (11.4) (2) » 34.4 j
I 7 c/c i . -0.05(65)2 A 3098 - 1B01 A 34.4 - 15.7 j
; * 1' 34.4 337334.4(.Z6 * .56) |
I X [12.5 * 20.1 * .26(65)] - -.6.1 + 37.7 + 33.0 - 64.6 I
I 8. E2 - .3(65) + .7(64.6) - 64.7 \
j 9. Since lEj-Ejl < 1'F I ' 64.7'F j
j Mow suppose the user wants to findT for no reduction in H$n due to shading. j
I Steps 1 through 9 again are repeated, using H « 308(3.7) » I
. j *" '
| 1140 BTU/ft /day, with otherwise the same meteorological conditions. Without de- I
j tailing the calculations here, it is found that T « 74.7*. a 10"F Increase. |
j It is evident then that altering the solar radiation penetrating to the j
j stream can significantly change T. Even more severe cases of repression of short- j
< wave radiation (as noted by the 71X reduction on 26-28 August, 1967, Table IV-26)
! are possible, exemplifying the large differences which may be observed.
! I
I ENO op EXAMPLE IV-10 J
The approach Illustrated in Example IV-10 for predicting equilibrium temperature
is obviously time consuming, and has been programmed for hand held calculators in
Mills et .a_L (1979) . A simplified approach is also available for predicting equi-
librium temperature (Brady ^ _aj_., 1969) and is described below. The predictions are
usually within 3*F or less of those found by the more complicated approach.
-366-
-------
The data required for the simpler approach are:
T^, dewpoint temperature (*F)
U, mean daily wind speed (mph)
H net incoming shortwave radiation (Btu/ft /day).
Short wave solar radiation data were previously shown in Figure IV-27. The Climatic
Atlas (U.S. Department of Commerce, 1968) contains compilations of dewpoint tempera-
ture and windspeed. Figures IV-28 and IV-29 show these data for the months of July
and August. Figures IV-27 through IV-29 provide the user with all the data needed to
predict equilibrium temperature using the approach of Brady et al.
To find the equilibrium temperature the following equations are applied
sequentially:
COMPUTE ONCE >• F(U) - 70 * 0.7 U2 (IV-61)
_ T - ^E, + TDW 2 (IV-62)
B - 0.255 - 0.0085T * Q.00020«T2 (iv-63)
K - 15.7 * (fi * .26 ) F(U) (IV-64)
Eui " TD * Hs ' K (IV-65)
The wind speed function f(U) is found once from Equation IV-61. The dewpoint tem-
perature (T.) is a convenient starting choice as an initial guess of the equilib-
rium temperature. T can then be calculated from Equation IV-62; B from Equation
IV-63; K from Equation IV-64; and finally a new equilibrium temperature (Ei+t) from
Equation IV-65. If E^ and E^ differ by more than 1*F, return to Equation IV-62 with
E-+j and repeat the procedure until convergence is attained (usually within 2 or 3
cycles).
ITERATE OVER
THESE EQUATIONS
EXAMPLE IV-11
Equilibrium Temperature Using Simplified Approach
Determine the average daily surface water equilibrium temperature for Little
Rock, Arkansas during the month of August. Based on Figures IV-27 through IV-29
the following data are found:
Td - 68'F
U - 7 mph
HJn - (525M3.7) - 1943 Btu/ft2/day
-367-
-------
JULY
55
65
75-
AUGUST
70
75-^75
FIGURE IV-23 MEAN DEWPOINT TEMPERATURE (°F) THROUGHOUT THE
UNITED STATES FOR JULY AND AUGUST (U,S, DEPART-
MENT OF COMMERCE, 1968)
-368-
-------
JULY
FIGURE IV-29 MEAN DAILY WIND SPEEDS (MPH) THROUGHOUT THE
UNITED STATES FOR JULY AND AUGUST (U.S. DEPARTMENT
OF COMMERCE, 1968)
-369-
-------
Assume as a first guess that E » T » 68*F I
then: j
I f(U) • 70 * .7 (7)2 • 104. I
1 T - {Td + Td)/2 - 68* I
j B • .62 j
j K - 15.7 * (.62 + .26) (104) « 107. j
• E • 68 + 1943/107 - 86*F j
. For the second Iteration:
I I
j T » (86 + 68)/2 « 77
! B - 0.81 !
! K - 127 I
I E « 83.3'F I
• *
| At the end of a third iteration E « 83.7*F, so convergence has been attained by I
i i
| three iterations. |
j As a comparison, the equilibrium temperature will also be calculated using j
• the longer approach. The required data are: '
\ Ta «80'F i
j Td '68*F i
' U - 7.
j Hsn - 1943 -
I sky cover • 0.5 (from climatic atlas) \
| A summary of the procedure is: I
| 1. Han - 2958 !
I H^ • 1943 + 2958 « 4901 I
j 2. Since Td » 68*. e « 17.4 \
j 3. Choose E » Tfl - 80*F j
j 4. B .881 j
C(B) - -37.6 j
! 5. f(U) - 70 + 0.7 (7)2 » 104
! K • 15.7 + (0.26 + .881) (104) • 134
| 6. F(E) • 79.3 !
j 7. E • .3(80) * .7 (79.3) - 80*F, after one pass. I
j Since the starting guess of 80*F is virtually Identical with the calculated value |
j at step 7, a second iteration is not required. The two procedures predict equi- j
j librium temperatures which differ by about 4*F. j
, i
! END OF EXAMPLE IV-11 1
-370-
-------
To estimate the effects of shad 1 no, the incoming solar radiation should be
calculated first assuming no shading, but otherwise using existing meteorological
conditions for the time of the year of interest. The effects of shading should be
superimposed upon this result ss a percent reduction. The following (Pluhowski,
1968) can serve as guidelines in estimating solar radiation reduction:
• 0-25 percent reduction: shading generally restricted to early morning
and late afternoon.
t 25-50 percent reduction: some sunshine penetration in morning and
evening. Considerable sunshine between 1000 and 1400 hours.
• 50-75 percent reduction: very little sunshine penetration in morning or
late afternoon. Scn^e sunshine between 1000 and 1400 hours.
• Greater than 75 percent reduction: very little penetration even at noon.
4.4.4 Screening of Thermal Discharges
4.4.4.1 Introduction
This section presents a set of procedures which can be used to determine whether
the thermal discharge at a proposed power plant site or the discharge from the
expansion of. an existing site is likely to violate thermal standards. Procedures are
presented to test for contravention of the following types of standards:
t The AT Criterion: The increase In temperature of water passing through
the condenser must not exceed a specified maximum.
• The Maximum Discharge Temperature Criterion: The temperature of the
heated effluent must not exceed a specified maximum.
• The Thermal Block Criterion: The cross-sectional area of a river
occupied by temperatures greater than a specified value must not exceed
a specified percentage of the total area.
0 The Surface Area Criterion; The surface area covered by isotherms
exceeding a specified temperature increment (above ambient) must not
exceed a specified maximum.
Actual values associated with the above standards vary by political jurisdiction.
Accordingly, regulations must be consulted.
The thermal discharge screening procedures are designed to address the following
questions:
t Is the power plant, as proposed, acceptable at the candidate location?
• What is the largest power plant that can be placed at the candidate
location? Eguivalently, can an existing power plant at the candidate
location be expanded?
The methods do not analyze interactions among multiple powerplznts on the same
river. Such an analysis can be rather more complex. A report by Tetra Tech (1978)
-371-
-------
Intake Channel ^_J i | 1 '* ' \ j. Outlet
Channel
PLAN VIEW
FIGURE IV-30 IDEALIZATION OF A RUN-OF-THE-RIVER
POWER PLANT
addresses that question.
The methods developed to evaluate in stream thermal criteria use heat balance
equations assuming a steady-state, well mixed system at low flow. The power plants
are assumed to employ once through cooling, as shown 1n Figure IV-30.
The selection of well mixed conditions appears to be justified. Studies by
Stefan and Gulliver (1978) on the Mississippi and Missouri Rivers have dealt with the
lateral mixing of thermal plumes which were released at the shoreline and were not
Initially well mixed across the river. The Investigators found that over a short
distance, thermal losses were negligible and that the well-mixed Isotherm (the
Isotherm that would result were the plume Initially well-mixed laterally and ver-
tically) eventually extended across nearly the entire width of the river, albeit at
some distance downstream. This Indicates that 1f the thermal block criterion 1s not
met for the well mixed case, 1t Is not likely to be met for the shoreline discharge
either. A similar conclusion can be reached regarding the surface area constraint.
Thus, at this level of analysis, it Is not necessary to consider the consequence of
Incomplete lateral or vertical mixing adjacent to the shoreline discharge.
One simplification which can be used at the option of the user for the surface
area calculation should be mentioned. Surface water that 1s undisturbed by anthro-
pogenic Influences (in a thermal sense) approaches the equilibrium temperature. This
temperature 1s dictated by natural meteorological conditions. Surface water tempera-
ture in rivers, especially during steady low-flow periods, can be near equilibrium.
In calculating the surface area occupied by Isotherms exceeding a specified tempera-
ture, it 1s necessary to know the equilibrium temperature. However, since the
procedure for calculating equilibrium temperature 1s fairly complicated, considerable
savings 1n computational effort can be obtained by assuming the ambient water
1s at Us equilibrium temperature.
Some circumstances, in addition to anthropogenic Influences, tend to produce
ambient temperatures different from eauilibrlum. For example:
-372-
-------
• Locally, large Quantities of groundwater may discharge into the river
• Hypo limn ionic releases from large reservoirs may occur nearby
• Snow melt may supply a substantial amount of inflow.
As a result of the first two influences, the stream water temperature may be
lower than equilibrium since the source of the water comprising the stream flow has
been shielded from the heating effect of solar radiation. Snow melt, although not
likely to influence the river's thermal regime during the late summer, can be important
through spring and into early summer in areas where high-mountain snowpack exists
over most, or all, of the year.
The screening procedure that follows assumes the river water, once it has been
heated by the thermal plume, is above equilibrium. This means that the water tempera-
ture will then decrease in the downstream direction, which is generally, but not
always, true.
Table IV-27 shows the data needed to apply the thermal screening methods. The
symbols are defined in the table and suggested default values are given for variables
where appropriate. The variables are introduced in the table in the order they occur
1n the screening procedure.
4.4.4.2 Evaluating the Thermal Block Criterion
The initial temperature elevation that results when the thermal plume becomes
well mixed with the river water is given as:
(IV-66)
!c ^ . 1 3.414 x 10* (IV_67)
^
where
AT^ • temperature elevation of the initially well mixed isotherm
CF)
Q • flowrate of cooling water (m3/s)
e " r ' '
T « temperature of heated effluent (*F)
T • temperature of river water upstream of power plant (*F).
All other terms are defined in Table IV-27.
To f ind AT^, Equation IV-67 is solved. If AT^ is less than the thermal block temper-
ature increment (ATtb), the thermal block criterion is not contravened. Otherwise,
it is.
-373-
-------
TABLE IV-27
DATA NEEDED FOR THERMAL DISCHARGE SCREENING
Variable
Term Definition
Default Value
MWe
P
CP
ATtb
AT,
maxl
'max 2
Temperature of heated effluent (*F)
Maximum legal allowable tempera-
ture of heated effluent (*F)
The lesser of ATMX, and
AT i»f\ m*xl
ATmax2 ( F)
The maximum allowable flow rate
through the cooling system
new fossil fuel
plants:38
nuclear plants:32
new fossil fuel
p1ants:48
nuclear plants:6S
7(>10
1000
2.2
5
20
The isotherm defining the boundary
of the surface area for which legal
Units have been established (*F)
Mean velocity of the river
(m/s)
Mean hydraulic depth of river 1n
reach under consideration («)
Equilibrium temperature (*F)
Surface thermal transfer coeffi-
cient (Btu/d • »F • n»2)
.25Qp
-374-
-------
TABLE IV-27 (continued)
Variable
Term Definition
Default Value
sa
Tra
Relative
humidity
sn
an
Surface area of river down to AT
isotherm (mO
Legal maximum surface area limit
which can be covered by the AT
and greater Isotherms (m2)
Average surface width of river
down to AT Isotherm (m)
River temperature just above where
a tributary joins the mainstem
CFJ
Temperature of tributary (°F)
Flow rate of tributary (m3/s)
Air Temperature (°F)
Wind speed at 7 r.eters above
surface (m/s)
Net shortwave solar radiation
(Btu/m2 • d)
Net long v.ave solar radiation
(Btu/m2 . d)
4.4.4.3 Acceptability of the Temperature Rise Across the Condenser
and of the Temperature of the Heated Effluent
Whether these criteria are met or not depends on a number of factors, such as
the cooling water flow rate. Since the cooling water flow rate can be designed to be
within a specified range, it is determined here whether a feasible range exists such
that the two above mentioned criteria are met.
The minimum acceptable flow rate such that both temperature criteria do not
exceed their standards is as follows:
-------
As an example of how ^Tmaxm^m is chosen, suppose the following conditions exist:
Maximum legal temperature rise across the condenser « 20*F
Maximum legal temperature of the heated effluent • 86*F
Ambient river temperature » 74*F.
From these conditions, ^Tmax2 (tne allowable temperature Increase across the condenser
such that the temperature of the effluent does not^ exceed the legal maximum) » 86*F -
74*F » 12*F. So Tmaxm1n » minimum (20*F, 12*F) - 12*F. 12'F must be chosen, then,
as the maximal temperature rise across the condenser.
Once Equation IV-68 has been solved, the ratio of cooling water to river flow
should be checked so that the value 1s within acceptable limits. Equation IV-66 can
be rewritten as:
Since AT has been calculated from Equation IV-67 and AT has been calculated as
wm
AT . , the flow rate fraction can be calculated from Equation IV-69. If this
maxmifn
fraction exceeds a certain percent (e.o., 25 percent or some user defined value),
then the cooling water flow rate is too large to be acceptable. If the flow rate
fraction is not excessive, the actual flow rate can be chosen so that:
where
(Q ) » maximum allowable cooling water flow rate (m/s)
4.4.4.4 Evaluating the Surface Area Constraint
The evaluation of this criterion may require the user to perform considerably
more calculations than for any of the other prescreenlng criteria. The two major
complicating factors that are encountered are: 1. determining the river equilibrium
temperature, and 2. evaluating the effects of tributaries.
If 1t Is the case that AT does not exceed AT the surface area criterion
MH ) 4
will not be contravened and no calculations have to be performed.' If AT_ exceeds
AT , the criterion might be exceeded. In this case It 1s necessary to determine
the distance from the location of the thermal discharge to the downstream location
of the AT Isotherm. This distance 1s given by:
-pC Vd
-376-
-------
where
Tsa ' *Tsa * Tr
Twm - *m * Tr
Section 4.4.3 discusses procedures for predicting K and E. Once K and E are found,
x can be determined from Equation IV-71. If one or more tributaries exist with
the distance x,,, then xc, should be recalculated as discussed in Section 4.4.4.5.
s a s a
The surface area Included within this reach is:
A - xe, • W (IV-72)
So
where
A » surface area of the river from the point of thermal
discharge to xcl (m )
S o
W » average river width in this reach (m).
If A < A then the surface area criterion is not contravened. Otherwise,
s a
1t is.
4.4.4.5 Evaluating the Effects of a Tributary 1n Mitigating Temperature Within
a Thermal Plume
Tributaries, when they join a river subjected to the Influences of a thermal
plume, generally act to reduce the elevated river temperature. They may therefore
prevent the surface area constraint from being exceeded when It otherwise would.
Equation IV-71 assumes no tributaries exist throughout the reach defined
by x . If It 1s found that x > xt (xt 1s defined below under Equation
IV-73) then 1t 1s necessary to examine the Impact of the tributary flow on the
surface area constraint. This 1s done by computing the water temperature (°F)
just above the location where the tributary joins the mainstream using the following
equation:
Tr, ' (Twm-E)«*p(PCpVd'*24. 360o) * E (IV'73)
where
Tra " r1ver temperature just upstream of tributary (°F)
x^ • distance from power plant discharge to tributary (m).
After the river has mixed with the tributary the new river temperature (°F) 1s
given by:
. TraQr * TtQt (IV-74)
new Q+ Qt
-377-
-------
where
T, • temperature of the tributary (°F)
1 3
Qt • flow rate of tributary (m /s).
If:
newl*Tsa + Tra
then this location marks the downstream location of the AT Isotherm and
the surface area A can be calculated using the distance x as the distance
down to the tributary, xt- Otherwise the AT isotherm 1s located further
downstream. In this case Equation IV-71 1s reappHed (first making appropriate
adjustments to V and d) where the Initial temperature Is (Tr)new (which was
T in Equation IV-71) and the final temperature 1s still T . The distance x,, is
*nn sd sa
determined by adding this additional distance to xt.
4.4.4.6 Determining Whether the Thermal Block or the Surface Area Constraint
Is the More Limiting
One of these two constraints may cause a greater limitation on power plant
size than the other. If AT. < AT,, the thermal block constraint will
tb sa
be more limiting, and there is no need to continue with the analysis in this part.
If, however, ATtb > AT$a, the surface area constraint may be more limiting.
To determine 1f It is, find AT^ (call 1* AT ) using the following equation:
'^sa
where
Tsa ' E exp £Tvd . 24 0600 I ' Tr (IV-76)
Tsa " *Tsa * Tr
Sa_ (IV-78)
U
If a tributary exists in the reach delineated by x,., recompute x,, as outlined
sd sa
1n Section 4.4.4.5.
If AT < AT... , the surface area constraint is more restrictive, so
WSJlid tD ~
set AT » AT . Otherwise set AT^. « AT... .
fNn WITiS a Win tO
-378-
-------
4.4.4.7 Determining the Maximum Plant Capacity
The maximum power plant capacity can be determined based upon the maximum
well mixed temperature elevation and the river flow rate. It is given by:
414 x 1C6
e
« -^ • DC 'AT Q • ^5— (I)
C p "" r 3.4.4 x 106
By using Equation IV-80 and the maximum allowable AT , the maximum capacity
can be found.
4.4.4.8 Readjusting the Maximum Cooling Water Flow Rate
If the minimum acceptable flow rate 1s greater than the maximum allowable,
the power plant size must be reduced. To do this, set:
where
Q0 « actual cooling water flow rate (m /s)
« maximum allowable cooling water flow rate (m /s).
(Q )
T - AT . P max (IV-82)
wm u maxmin Q
ATmaxmin " AT calculated earlier.
p
AT is recalculated by:
wm
where
(Note: the surface area and thermal block constraints are still met and need
net be recomputed.)
p EXAMPLE IV-12
I
Estimating AT Across a Power Plant Heat Exchange 'Jr.it
I Suppose the user wants to determine AT for the Hartford E'ectric Light
I Company's South Meadow Steam Electric Power Plant (a fossil fuel plant) located on
| the Connecticut River. Data available are (Jones £! _a_K , 1975):
i
I Capacity 217 WM
| Cooling water flow rate 341 ft3/sec
-379-
-------
where
YC - 62.4 BTU/ft3/8F
QQ - flow rate, ft3/sec.
Substituting the appropriate values Into the above equation, 1t 1s found that
(using the known thermal loading to the cooling water):
j Equation IV-83 Is not feasible to use when the thermal loading rate to the
j cooling water Is unknown. As an alternative approach, the following expres-
• slon can be employed:
j AT . .1 . !i Mye . _1_ . 3-4U3|0™* (IV~84)
I where
| e « percent of total energy produced that 1s transmitted as
j electricity. For new fossil fuel plants: 38 percent; for
j nuclear plants: 32 percent
e « percent of total energy produced that Is dissipated through
cooling water. For new fossil fuel plants: 48 percent; for
' nuclear plants: 68 percent
-380-
I Waste heat discharged to cooling water . . . 422 HW
| Since the waste heat being dissipated through the cooling water 1s known, AT
j can be calculated directly using that value in conjunction with the known flow
j rate. Assume, however, that the waste heat being discharged 1s not known. It can
' be estimated from the plant capacity as follows. First, assume the plant effid- j
• ency is 33 percent. The rate at which fuel Is burned when at capacity is then:
j — - 658 MW |
.33
! If 10 percent of the total energy is lost up the stacks, then approximately
! 58 percent is dissipated through the cooling water, or: J
! 658 (.58) « 382 MW !
J Compared with the known 422 MW of heat discharged to the cooling water, the j
I above calculation would underestimate AT. I
I AT 1s calculated by: (
i i
i AT « tnerTial loadinc rate to cooling water 1n megawatts x j
-------
I MWe « capacity of power plant In megawatts electric.
j Equation IV-84 predicts that AT 1s:
i
! 1 58 -,, 1 3.414 106 ,, cor
| 34T ' 37 *217 6T4 1600— ' 17'5 F
' AT 1s only about 1°F less than predicted by Equation IV-83.
I
I END OF EXAMPLE IV-12
4.4.5 Longitudinal Temperature Variation
If the temperature at a particular location 1n a river 1s known, the steady-state
temperature distribution downstream from that point can be estimated by:
T - E /-.061 • Kx i (IV_
- E CAK V oCpUd
where
T « temperature at x - 0, *F
T « stream temperature at a distance x, where x is measured in miles
E » equilibrium temperature, *F
K « thermal transfer coefficient, BTU/ft2/day/*F
U - stream velocity, ft/sec
d « stream depth, feet
P - water density, lb/ft3
Cp » heat capacity of water, BTU/lb/*F (PCp - 62.4 BTU/ft3/*F).
An important fact is revealed upon inspection of Equation IV-85. Suppose
that a thermal discharge heats the ambient water to a temperature T , but
T is less than the instantaneous equilibrium temperature E. In that instance
the stream temperature will continue to rise exponentially downstream, approaching
E. The rate at which T approaches E is dependent on the thermal transfer coeffic-
ient, as well as stream velocity and depth. Equation IV-66 is graphically illus-
trated in Figure IV-31.
f EXANPLE IV-13
i
j Use of Figure IV-31
I ?
Suppose an average dally thermal transfer coefficient, K, of 200 BTU/ft /day
; has been calculated. The river of interest has an initial temperature "excess"
I (i.e., T -E > 0). How far downstream will that excess be 50 percent of the
-381-
-------
FIGURE IV-31 DOWNSTREAM TEMPERATURE PROFILE FOR COMPLETELY
MIXED STREAM, T-E/TM-E vs, r (FROM EDINGER,
1965) "
I
original? Other stream data:
U - .5 fps
d « 4 feet
62.4 BTU/ft3/*F
I pl
-------
x « 0
rcCdU (0.68) (62.4) (4) ( .5){24)(3600)
200
3.6 x 104 *"eet - 6.9 miles
The associated travel time is T » 3'6 jl 10 x - hr - 20.4 hours
'
END of EXAMPLE IV-13 --------------------- '
4.4.6 Diurnal Temperature Variation
Although it is beyond the scope of this report to analyze diurnal stream temper-
ature variations, a few brief statements should be made. Diurnal stream temperature
variations on Long Island, New York, were mentioned in Section 4.4.1. Documentation of
large diurnal temperature variations is not limited to New York. For example, studies
in Oregon (Brown, 1969), Hawaii (Hathaway, 1978) and California have revealed that
solar radiation entering shallow streams and rivers produces a significant difference
between maximum and minimum daily temperatures. Figure IV-32 shows one such example on
the Santa Ana River near Mentone, California. The water temperature varied by 17°F
over a period of 24 hours. One significant effect of the temperature variation is its
effect on dissolved oxygen levels. Figure IV-33 shows the measured dissolved oxygen
concentrations and predicted saturation levels over the same time period at the same
location on the Santa Ana River. The dissolved oxygen concentrations ranged from a
high of 9.2 mg/1 to a low of 8.0 mg/1. The variations were caused predominantly by the
temperature changes. This illustrates several points:
t Temperature data concomitant with dissolved oxygen data might be
needed to properly interpret the cause of dissolved oxygen variations in
shallow rivers receiving large amounts of solar radiation
0 Removing riparian vegetation around shallow rivers tends to increase the
daily maximum temperature and decrease the daily minimum temperature
• Impacts on the dissolved oxygen levels and indigenous biota can be
significant.
4.4.7 Low Flow and Temperature
Evidence has previously been cited in this chapter to show that in many parts of
the country high temperature conditions are concomitant with low flow. The planner
needs to be able to quantify better the nebulous term "low flow" to fruitfully use
this concept as a planning tool. For example, suppose a decision is made based on
the low flow condition of this year. What are the chances that this low flow will be
-383-
-------
C
UJ
KEY
^^^^v
Air Ttrnperaturt
Wattr Temptraturt
0240
6/20
TIME OF DAY (Military Timt)
0640
1040
FIGURE IV-32
MEASURED AIR AND WATER TEMPERATURES FOR
THE SANTA ANA RIVER NEAR MENTONE, CALIFORNIA,
IN JUNE 1979,
exceeded in the future? If they are high, then any decision (e.g. at particular
level of waste abatement at a sewage treatment plant) based on the observed conditions
could have unexpected deleterious results at a future time. It is paramount then, to
predict how often flow will fall below a specified rate.
Two measures or Indices of low flow that have be«n found useful are flow
duration and low-flow frequency. Although 1t Is beyond the scope of this report to
explain 1n detail how to develop these measures, examples of each will be presented
that explain their utility. The majority of the material 1n this section 1s from
Cragwall (1966) who provides a discussion on low flow, and cites additional references.
Many texts on engineering hydrology (e.g., Unsley et. ±K, 1958) also discuss low
flow. Figure IV-34 shows a flow duration curve for the Hatchle River at Bolivar,
Tennessee. The vertical axis 1s the dally discharge and the horizontal 1s the
percent of time a flow 1s equaled or exceeded. For example, 95 percent of the time
from 1930-58 the flow exceeded 177 cfs. It can also be assumed that this flow (177
cfs) will probably be exceeded 95 percent of the time 1n other years. Thus this
concept offers one means by which to quantify "low flow".
-384-
-------
10.0
9.8
9.6
9.4
9.2
1040
6/19
KEY
ObscrvKJ DO
Saturation, C,
1440
1840
2240
0240
6/20
TIME OF DAY (Military Timt)
0640
1040
FIGURE IV-33
MEASURED DISSOLVED OXYGEN CONCENTRATION
AND PREDICTED SATURATION CONCENTRATION FOR
THE SANTA ANA RIVER NEAR MENTONE, CALIFORNIA,
IN JUNE 1979.
A second concept Is the low flow frequency curve, Illustrated In Figure IV-35.
This depicts the relationship between discharge and recurrence Interval of different
duration flows. For example the 7 day mean flow of 100 cfs can be expected to occur
once each 19 years. Stated another way, since probability 1s the reciprocal of
recurrence Interval, 1n any one year there 1s about a 5 percent probability that a
seven day mean flow of less than 100 cfs will occur. A commonly used flow for
analyses 1s the 7 day mean flow at a recurrence Interval of 10 years, or 7Q10.
4.4.8 Interrelationships Between Temperature Prediction Tools
The three major temperature prediction tools presented 1n Section 4.4 are:
0 Water temperature alterations caused by a power plant
• Equilibrium temperature
• Longitudinal river temperature profile.
-385-
-------
IQOOO
1.000
u
UJ
or
5
>,
PERCENT OF TIME INDICATED DISCHARGE
WAS EQUALLED OR EXCEEDED
FIGURE IV-34
FLOW DURATION CURVE. HATCHIE RIVER AT
BOLIVAR, TENN, (FROM CRAGWALL, 1966)
Figure IV-36 shows three river temperature profiles which Illustrate how these
tools can be used jointly. Curve A represents a temperature profile of a river
where a power plant 1s located a distance 0 below some reference point. The tempera-
ture on the river above the power plant Is T. which Is slightly below the equi-
librium temperature. Due to the thermal discharge from the power plant, the river's
temperature 1s Increased to T4> above the equilibrium temperature. Below the
mixing zone area, the water temperature gradually decreases toward equilibrium, as
the excess heat 1s dissipated Into the atmosphere.
Curve B Illustrates the temperature profile of a river whose water comes
predominantly from the hypo11mn1on of a reservoir. While In the reservoir the
water 1s Insolated from the solar radiation, so the temperature 1s below the equi-
librium temperature. As the water 1s withdrawn from the reservoir and begins to flow
downstream, Us temperature Increases due to solar radiation and atmospheric heating.
The temperature tends to approach the same equilibrium temperature (the two rivers
are assumed to be 1n the same geographic area).
Curve C shows the temperature profile of river B which now has a power plant,
-386-
-------
10000
RECURRENCE INTERVAL (YEARS)
FIGURE IV-35
FREQUENCY OF LOWEST MEAN DISCHARGES OF
INDICATED DURATION, HATCHIE RIVER AT
BOLIVAR, TENN, (FROM CRAGWALL, 1966)
similar to the one on river A, discharging Into It. If the flow rates of the two
rivers are the same, so 1s the Initial temperature Increase (I.e., T. - T. -
T^ - T_). However, the temperature of the river continues to Increase,
1n contrast to profile A, because T- 1s less than E. This Illustrates an
unusual, but entirely possible, situation where river temperature continues to
Increase below a thermal discharge.
4.5 NUTRIENTS AND EUTROPHICATION POTENTIAL
4.5.1 Introduction
Within the past decade the elements most often responsible for accelerat-
ing eutrophlcatlon - nitrogen and phosphorus - have shown generally Increasing
levels 1n rivers (EPA, 1974). Median concentrations Increased 1n the period from
1968 to 1972 over the period from 1963 to 1967 1n 82 percent of the reaches sampled
for total phosphorus, 74 percent for nitrate, and 56 percent for total phosphate.
These Increasing concentrations afford more favorable conditions for eutrophlca-
tlon, although many rivers with high nutrient levels do not have algal blooms. Algal
-387-
-------
T«
I
s
A (Power plant prosont)
E
B (R«l*««« from hypoHnwnon)
DI8TANCI
FIGURE IV-36 THREE RIVER TEMPERATURE PROFILES
growth can be Inhibited 1n numerous ways. For example, turbidity can decrease light
transmlttance through water and effectively stop growth. Decreasing turbidity could,
however, have a deleterious side effect of promoting excessive algal growth, unless
stream nutrient levels are concurrently decreased. High water velocity can also
prevent algae from reaching bloom proportions before they are carried out of the
river system. The eutrophlcatlon problem, then, 1s transferred to the water body Into
which the river empties.
4.5.2 Basic Theory
Stumm and Morgan (1970) have proposed a representation for the sto1ch1ometry of
algal growth:
106C0
16N03" + HP042" * 122H20 * 18H*(+ trace
elements; energy)
1C H 0 ' N P I + 138 0
1 106 263 110 16 1' 2
algal protoplasm
(IV-86)
-388-
-------
where
P • photosyntehsls
R - respiration.
Observe that in the algal protoplasm the ratio of C:N:P i.s:
C:N:P • 106:16:1, by atomic ratios (IV-87)
C:N:P « 41:7:1, by weight ratios (IV-88)
From the above two equations it can be inferred that only small amounts of
phosphorus are needed to support algal growth in relation to the amounts of carbon
and nitrogen required. If phosphorus 1s not present in the amount required for algal
growth then algal production will be curtailed, regardless of how much of the other
nutrients is available. Phosphorus 1s then termed growth limiting. It 1s possible
for other elements, particularly nitrogen, and occasionally carbon or trace metals,
to be growth limiting as well (Stumm and Stumm-Zo)linger, 1972).
Nitrogen uptake by algae is generally 1n the nitrate form if nitrate is available.
However, different types of fresh water algae can utilize either organic nitrogen or
inorganic nitrogen in the form of ammonia, depending on which is available (Stumm and
Stumm-Zollinger, 1972). Algae typically require phosphorus in an inorganic form,
usually as orthophosphate ion (Kormondy, 1969).
Some Indication of whether nitrogen or phosphorus is growth limiting may be made
by determining the weight ratio of the appropriate forms of nitrogen and phosphorus
found in a river, and comparing that with the stoichiometric ratio required for
growth. This gives an Idea regarding the nutrient on which control efforts should
focus. Specifically, let:
p LIN]_ (IV-89)
[OP04-P]
where
[TN] « concentration of total nitogren in river, mg-N/1
[OPO^-P] - concentration of orthophosphate, mg-P/1.
If R>10, phosphorus 1s more likely to limit than N.
If R<5, nitrogen 1s more likely limiting than P.
If 5
-------
Both Lehman, et al. (1975) and Lund (1965) provide specific algal data as well as
further discussions.
The following table (Table IV-28) shows an approximate relationship between
total nitrogen and total phosphorus concentrations and the potential algal biomass
that can result. Both nitrogen and phosphorus must be present In the amounts shown
for the resultant growth to occur.
TABLE IV-28
EUTROPHICATION POTENTIAL AS A
FUNCTION OF NUTRIENT CONCENTRATIONS
p
(rcg-P/1 )
0.013
0.13
1.3
N
(mg-N/1 )
0.092
0.92
9.2
Dry Algal Cells
(mg/1)
1.45
14.5
145.0
Significance
Problem threshold
Problem likely to exist
Severe problems possible
4.5.3 Estimating Instream Nutrient Concentrations
Because of the transformations that occur among the different nltogren and
phosphorus compounds it is not possible to conveniently track any particular form of
nitrogen or phosphorus through a stretch of river. However, if total nitrogen and
total phosphorus can be considered conservative, a mass balance approach can be
easily formulated for these constituents. In reality this assumption may not be met
for a variety of reasons.
For example, algae utilize nutrients, die, and settle to the bottom. Although
there is a recycling of algal cell-bound nutrients, the settling rate may surpass the
rate of recycling. Assuming total nitrogen and total phosphorus to be conservative
should give an estimate of the upper limit of the Instream concentrations of these
nutrients.
The instream concentration of total nitorqen (TN) or total phosphorus (TP)
resulting from a point discharge is (formulas w11 I be presented for TN only; those
for TP are exactly analogous):
TN ™uQu * TNwQw (IV-90.)
TN° Q« * a*
TNuQu * Wp/5.33 (IV-90b)
—
-390-
-------
where
TN « Instream TN upstream of discharge, mg-N/1
TN « concentration of TN in point discharge, mg-N/1
Q • flow 1n river upstream of point discharge, cfs
Q^ » flow rate of point discharge, cfs
TN « resulting Instream TN concentration, mg-N/1
w « loading rate of point source, Ib/day.
Tfie expression for TNQ 1s given by either Equation IV-91A or IV-91B.
The
appropriate form to use will depend on the form of the available data.
To determine the Instream concentration of total nitrogen due to a distributed
discharge, use:
TN
TN
(TNr - TNQ)
(IV-91a)
or
TN
TNoQo ,
wx
5.33 Q
(IV-91b)
where
TN
TN
o
x
Q
QO
AQ
TN entering with the distributed flow, mg-N/1
Instream TN at x « 0, mg-N/1
distance downstream from the point source discharge
stream flow rate at x, cfs
stream flow rate at x « 0, cfs
Incremental flow increase per unit distance, cfs/mile
w • mass flux of TN entering the stream through the distributed source,
1b/day/m1le.
The choice of whether to use Equation IV-91a of IV-91b depends on the available
data. Based on the approach detailed 1n Chapter III, the mass flux of nutrient
entering the stream (1n units of Ib/day/mlle) can be generated. When this approach
1s used, then Equation !V-91b 1s applicable.
To use Equation IV-91a the concentration of pollutant from the nonpolnt source
has to be known. This can be accomplished using the approach of Omernlk (1977).
Nonpoint source nitrogen and phosphorus concentrations are predicted as fractions of
land use type or based on color coded maps if land use categories are not known. The
data used to predict nitrogen and phosphorus concentrations were generated in a
National Eutrophication Survey (NES) program wherein a nationwide network of 928
nonpoint-source watersheds were monitored. This method accounts for only the nonpoint
source contribution. Consequently, if point source exist within the watershed, their
contributions must be Included as well in order to accurately predict instream
concentrations.
-391-
-------
Table IV-29 summarizes the predictive formulas developed by Omernlk for total
phosphorus, orthophosphorus, total nitrogen, and Inorganic nitrogen. The formulas
are regionalized by eastern, central, and western United States. Agricultural, urban,
and forested lands comprise the Independent variables 1n the formulas.
Qnernllc's analysis of the NES data Indicates that:
e Streams draining agricultural watersheds had considerably higher
nutrient concentrations than those draining forested watersheds.
e Nutrient concentrations were generally directly proportional to the
percent of the land 1n agriculture and Inversely proportional to the
percent of land 1n forest.
t Mean concentrations of total phosphorus and total nitrogen were nearly
nine times greater 1n streams draining agricultural lands than 1n
streams draining forested lands.
• Mean phosphorus concentrations In streams draining forested watersheds
1n the west were generally twice as high as those 1n the east.
t Total and Inorganic nitrogen 1n streams draining agricultural watersheds
were considerably higher 1n the heart of the corn belt than elsewhere.
As an alternative to the equations shown In Table IV-29, Omernik provides
three colored maps of nonpolnt source related concentrations of nutrients in streams.
They can be used where detailed Information necessary for more accurate prediction 1s
unavallable.
4.5.4 Nutrient Accounting System
It may be desirable to determine the Impact of each nutrient source on the
total Instream concentration in order to distinguish among the major sources.
An accounting procedure utilizing Equations IV-90 and IV-91 can be developed to
do this. The following steps outline the procedure.
1. Segment River. Divide the river Into major segments. These segment
divisions may reflect waste loading distributions or another convenient
division scheme chosen at the discretion of the planner. The segments
are not necessarily the same as the reaches that have previously been
discussed (see Section 4.1). The delineation of reaches as described
earlier 1s based upon lengths of river having uniform hydraulic conditions.
Segments, as used here, are purely a convenient subdivision of the river.
2. Quantify and Locate Sources of Nutrients. The quantification of point,
nonpolnt, and natural sources on the ma1nstern and tributaries should be
accomplished using the best available data. Tabulation can be performed
for each different season to reflect the discharge pattern characteristic
of each season. The quantification should Include total nitrogen and
total phosphorus. Tabulate data in terms of average dally input (Ib/day).
-392-
-------
TABLE IV-29
REGIONAL STREAM NUTRIENT CONCENTRATION PREDICTIVE MODELS
Nutrient Fom Model, Correlation Coefficient and Multiplicative Standard
'Region Error
Totil pnesonprtit
Eatt Log1(J (PCONC) * -1.8364 • 0.00971 (X agric « X urb)
r « 0.74. f « 1.85
Central loglfl (PCONC) «-1.5697 » 0.00811 (X agric » X urb) -0.002312 (X for)
r « 0.70. f « 2.05
West Log1Q (PCONC) =-1.1504 • 0.00460 (XagHc « Xurb) -0.00632 (X for)
r » 0.70. f « 1.91
Orthoohosohorus
East Log10 (OPCONC) « -2.2219 • 0.00934 (X agric » X urb)
r - 0.73, f « 1.86
Ctntra) Loglfl (OPCONC) « -2.0815 » O.OOE68 (X agric • X urb)
r « 0.63. f « 2.05
We*t Log,0 (OPC3NC) =• -1.5513 • 0.00510 (X agric « X urb) -0.00476 (X for)
r « 0.64. f « 1.91
Total nitrogen
Ea*t Log]0 (NCCHC) » -0.08557 • 0.00716 (X agric * X urb) -0.00227 (X for)
r « 0.85. f « 1.51
Central Log)(J (NCONC) « -0.01609 « 0. OC399 (X agric » X urb) -0.00306 (X for)
r - 0.77. f - I.SO
West Log)Q (NCCNC) - -0.03665 • 0 00«25 (X agric • X urb) -0.00376 (X for)
r * 0.61. f » 1.75
Inorganic nitrogen
East Log)0 (1KCONC) '-0.3479 « 0.00858 (X agric • X urb) -0.00584 (X for)
r « 0.84. f « 1.93
Central Log]0 (INCCNC) « -0.5219 • 0.00*82 (X agric « X urb) -0.00572 (X for)
r « 0.71.f « 2 06
West log)0 (INCSXC) » -0.6339 • 0.00789 (X agric « X urb) -0.00657 (X for)
r - 0.65. f » 2.45
From: Omernik (1977)
-393-
-------
Characterize the location of the nutrient sources by river mile. For
nonpolnt sources characterize by river mile at both the beginning
and end of the source.
3. Perform Mass-Balance. Sum the known sources to determine the total
nutrient loading to each segment. Then make the following comparisons:
a. Compare the total loading with the nutrient Input from the malnstem
at the upstream end of the segment. This direct comparison permits
an assessment of the collective Impact of the nutrient sources
entering a segment and the upstream contribution of the malnstem.
b. Perform an intersource comparison to ascertain the relative Impact
of each nutrient source. Express the results for each source as a
percent of the total loading.
When a tributary has a high percent contribution steps 1 through 3 can be
repeated for the tributary Itself to track the sources of the nutrients.
Apply Equations IV-90 and IV-91 to each reach within the segment to determine
the Instream nutrient concentration throughout the segment. Once this Is done that
step can be repeated for the next reach.
By applying this analysis one can determine the relative Impact of any discharge,
determined jointly by the flux of the nutrient and the discharge location. Section
4.1.10 provided a detailed example problem which Illustrates the procedure. A brief
example also follows.
I EXAMPLE IV-14
I
i Computing Total Nitrogen Distribution
I I
This example Illustrates the use of Equations IV-90b and IV-916 In calculating j
the total nitrogen distribution in a river. Suppose the user has been able to
' estimate the point and nonpolnt loading of total nitrogen in a river as shown In
I Table IV-30.
i
| If these loading rates are estimated over a year, then the flow rates used
j should also be average annual flows. To compute the concentration at mile
; 0, Equation IV-90b can be used:
' 0.25 mg-N/1
| T75T
i where the following conversions were used:
I 1 MGD • 1.55 cfs
j 1 mg/1 « 8.34 Ib/MG
j To determine the concentration at milepoint 9.99, use Equation IV-91b:
-394-
-------
TABLE IV-30
TOTAL NITROGEN DISTRIBUTION IN A RIVER IN
RESPONSE TO POINT AND NON-POINT SOURCE LOADING
Reach
Number
River
Mile-
Point
TN TN Q TN Concen-
Added* Cumulative Cumulative tration
(Ibs/day) (Ibs/day) (cfs) (mg-N/1)
1
2
3
4
0
9.99
10.0
14.99
15.0
20.99
21.0
26.0
400 L
500 D
0
700 D
800 L
650 D
0
900 D
400
900
900
1,600
2,400
3,050
3,050
3,950
300
400
400
600
700
900
900
1,000
0.25
0.42
0.42
0.50
0.64
0.62
0.62
0.73
* "L" indicates a localized or point source. "D" indicates a d-i
or non-point source whose range of input is over the entire reach.
TN - (0.25)
300
500
T755
0.42 mg-N/1
Note that wx in Equation IV-91 is the 500 Ib/day shown in Table IV-30. By
reapplying these two basic equations for each reach the user can work downstream
through the four reaches. Also note that the total nitrogen concentration has
decreased slightly through reach 3, even though more TN has been added. This is
because the incoming flow has served to lower the concentration by dilution.
•— END OF EXAMPLE IV-14 '
4.6 TOTAL COLIFORM BACTERIA
4.6.1 Introduction
Total coHform bacteria are considered an indicator of the presence of pathogenic
organisms, and as such relate to the potential for public health proolems. Allowable
levels of total coliform bacteria In rivers vary from state to state and according to
the water use description characterizing the particular river segment. For example,
in Montana (Montana State Dept. of Health and Environmental Sciences, 1973) the raw
-395-
-------
water supply may not have more than an average of 50 MPN/100 ml* total conforms 1f
It 1s to be used as a potable water supply following simple disinfection. In water
suitable for bathing, swimming and recreation, as well as growth and marginal propa-
gation of salmonld fishes, an average of 1,000 MPN/100 ml 1s allowable.
Concentrations of total conforms vary with the season of the year. Often
the heaviest loadings occur during the summer months, but this Impact 1s somewhat
offset due to the more rapid die-off at higher temperatures and more Intense solar
radiation. In the Willamette River (Figure IV-37), for example, the highest counts
of 1971-72 were actually observed from November through May (EPA, 1974).
Treated municipal sewage comprises a major source of conform pollution.
Urban stormwater runoff can also be significant, especially through combined sewer
outflows. Rural storm water runoff transports significant fecal contamination from
livestock pastures, poultry and pig feeding pens, and feedlots. Wildlife both within
refuges and In the wilds can contribute as well. For guidance in the interpretation
of preliminary coll form analyses. Table IV-31 can be used.
4.6.2 Mass Balance for Total Conforms
The mass balance equations applicable to total col 1 form organisms are exactly
analogous to Equations IV-18, IV-21, and IV-23A and IV-23B, since first order decay
is used for both. For purposes of hand computations, the following decay coefficient
Is acceptable:
ktc - 1.0 + 0.02 (T-20) (IV-92)
where
ktc • decay coefficient for total conforms, I/day
T » water temperature, °C.
Those equations with the widest applicability are listed below. For a point source
of conforms:
TC * TC exp
Q
(IV-93)
*MPN means "Most Probable Number". Conform organisms are not counted Individually.
but their densities are statistically determined and the results stated as MPN/100
ml.
-396-
-------
SEASONAL RIVER PROFILES
WILLAMETTE RIVER
Total Coliform*
LEGEND:
1
0
o
w
&
•»
E
t.
O
"o
O
"5
100,000
10,000
i nno
1 ,WWW
100
10
1
w u n. i w vv* i . i 7 ' &
_. .,._ . •• %>
is%
\ .1 y\ /V/1 OREGON
A/\/ / \ / STANDARD?
\A 1 ^~*^ ^ A
^1 /^ \
~ V '
1 1 1 I 1 1 1 I I ]
0 20 40 60 80 IOO 120 140 160 180 200
River Miles
__ 100,000 p
E
o
2 10,000
E
o
1,000
100
10
NOV. TO MAY
'1972
OREGON \
STANDARD -^
0 20 4O 60 80 100 120 140 160 180 20O
I River Miles
FIGURE IV-37 TOTAL COLIFORM PROFILES FOR THE WILLAMETTE RIVER (EPA, 1974)
TABLE IV-3I
TOTAL COLIFORM ANALYSIS (EPA, 1976)
If the Calculated
Concentration is:
Probability of
a Coliform Problem
Less than 100/100 ml
Less than 1,000/100 ml
More than 1,000/100 ml
More than 10,000/100 ml
Improbable
Possible
Probable
Highly Probable
-397-
-------
For both point and distributed sources of conforms:
-Q2) (IV-94)
For a change in coHform concentration due to a point source modification:
TCr t L T
-------
I x » 10 mi les
j ku • 1.0/day at 20*C
j First the computations will be performed assuming no distributed flow. Equation
: IV-95A is then applicable. Computing the expondent j x (at a flow distance
• of 10 miles):
! (1.0) (10) (5280)
I Jtcx - (24) (3600) (1) " °'611
so
-TC
• exp (-.611) • 0.54
or
I
0.54 £
(1.0) (500)
Etc " (24) (3600) (0.0057)
» 2.02
Then
2.02
iTC /SOON2'02 . „
rrr; = (m) ' °-39
or
iTC « 0.39 JTC0
I For ATCQ • 1,000 MPN/100 nml, ATC « -390 MPN/100 ml.
| Note that this decrease 1s 150 MPN/100 ml less than If no distributed flow
j existed.
j To determine the absolute total conform level, simply add to the original
• level the resulting change caused by the waste loading modification.
END OF EXAMPLE IV-15
-399-
j For example if ATCQ - -1,000 MPN/100 ml then ATC « -540 MPN/100 ml (negative j
j ATC indicates that the colifonm level has decreased from what it previously j
was)> i
Now suppose the distributed flow of 300 cfs is included in the computa-
tion. Then: !
f
^ 2 1
A • Q /U« ' 500/1 - 500 ft* !
° ° I
300 ? !
AQ • Tb75280J" • °-0057 ft /sec I
j
i
I
-------
4.7 CONSERVATIVE CONSTITUENTS
4.7.1 Introduction
Conservative constituents are those which are not reactive and remain either 1n
solution or 1n suspension. They are advected through the water column at the velocity
of the river with no loss of mass. The analysis of nutrients, already discussed 1n
this report, was performed assuming they acted conservatively. Other substances,
such as salinity, can also be considered as conservative. Chapter 3 contains Informa-
tion on salinity 1n Irrigation return flow for many rivers with salinity problems.
4.7.2 Mass Balance for Conservative Constituents
Two simple mass balance equations are sufficient for analyzing conservative
constituents. The first relates the Instream concentration due to a point source
loading:
S - dV-96)
where
S - resulting pollutant concentration, mg/1
S « upstream concentration, mg/1
Q « upstream flow rate, cfs
0 » point source flow rate, cfs
U • loading rate of pollutants, Ib/day.
When a distributed flow 1s present along some length of the river, then the distribu-
tion of the conservative pollutant Is given by:
S Q
S - -2-2. * —21— (IV-97)
Q 5.38 Q
where
w » distributed loading rate, Ib/day/mi
x » distance downstream, miles
S « initial concentration (at x » 0), mg/1.
Srt in Equation IV-97 is identical with S in Equation IV-96.
-400-
-------
EXAMPLE IV-16
Calculating Salinity Distribution in a River
Salinity problems are receiving Increased attention In the western United
States, particularly relating to the economic Issues in the Colorado River Basin
and international compacts with Mexico. In the Colorado River high salinity
.levels in the lower reaches adversely affect nearly twelve million people and
approximately one million acres of fertile irrigated farmland (Bessler and Maletic,
1975). The salinity now averages approximately 865 mg/1 at Imperial Dam and is
projected to be 1,160 mg/1 or more by the year 2000, unless firm control actions
are taken.
Consider the river shown in Figure IV-38, Predict the salinity distribution
based on the inflows and withdrawals shown. Assume the data are averaged over a
period of a year. These data, along with the salinity concentrations at different
river mileposts are shown in Table IV-32.
To calculate S (salinity at milepoint 100) use Equation IV-96:
<- m 0.500 + (2xl06) (1.55/8.34)
• 186 mg/1
At milepost 199.9, Equation IV-97 is appropriate and S is given by:
,. „ (186) (2000) (4xl06) (1.55/8.34)
507JC5000
• 223 mg/1
Q = 3000cfs Q=5000cf, Qs2500cfs
Q«500cft!
Q = l500cfi * Q=3000cft
W=2x!06|b/doy Q,,ooOcf, W = 8xlo6|b/doy
100 200 300 400 500 600 700 750
RIVER MILES
i
FIGURE IV-38 SALINITY DISTRIBUTION IN A HYPOTHETICAL RIVER I
•
-401-
-------
TABLE IV-32
SALINITY DISTRIBUTION IN A HYPOTHETICAL RIVER
Reach
Number
1
2
3
4
5
6
7
8
9
10
River
Mile
Point
0
99.9
100
199.9
200
279.9
280
359.9
360
449.9
450
499.9
500
524.9
525
599.9
600
649.9
650
750
Salinity
Added*
(Ibs/day)
0
0 ,
2x1 0*
4x1 05
0
0 a
-1.2xl06
0
o ft
25x10°
0
0 ,
8 106
0 6
-7.9x10°
0 ..
-4.7x10°
0
0 ft
20x1 0°
L
0
L
0
L
L
L
0
Salinity
Cumulative
(Ibs/day)
0
0 c
2x1 0*
6x10°
6x1 of
6x1 05 ,
4.8x10°
4.8x10?
4.8x10°
29.8x10°
29.8x10°.
29.8x10°
37.8X106;
37.8xlOb
29.9x10?.
29.9x10°
25.2x10°
25.2x10°
25.2x10°
45.2x10°
Q
Cumulative
(cfs)
500
500
2000
5000
5000
5000
4000
4000
4000
9000
9000
9000
12000
12000
9500
9500
8000
8000
8000
10000
Salinity
Concentration
(mg/1 )
0
0
186
223
223
223
223
223
223
615
615
615
585
585
585
585
585
585
585
840
*'L' indicates a localized or point source at the milepoint shown in
the same row.
'D' indicates a diffuse or non-point source ending at the milepoint
shown in the same row and beginning at the milepoint in the above row.
At milepoint 280, 1,000 cfs of flow leaves the mainstem (perhaps for irrigation •
purposes). The concentration of salinity in this flow is the same as that in the
mainstem. So the mass rate of withdrawal is: !
W - '^^ (223 x 1000) j
« -1.2 x 106 Ib/day j
A negative sign is used to signify a withdrawal. Completing the remainder j
of the table is solely a matter of reapplying these basic concepts. ;
END OF EXAMPLE IV-16
4.8 SEDIMENTATION
4.8.1 Introduction
One of the more difficult classes of hydraulic engineering problems associated
with rivers involves the erosion, transportation, and deposition of sediment. Sedi-
-402-
-------
mentation is important economically, particularly relating to.filling of reservoirs
and harbors, and to maintaining channel navigability and stability. Table !V-2,
located in Section 4.1, documents some suspended solids problems encountered in eight
major U.S. waterways.
The sediment load carried in a river can be divided Into two components: the
bed material load and the wash load. The bed material load is composed of those
solid particles represented in the bed. The transport of this material Is accomp-
lished both along the bed (bed load) and suspended within the water (suspended load).
Although there is no sharp demarcation delineating bed load from suspended load, many
researchers have developed individual expressions for each transport component. The
total bed material load is the sum of the bed load and the suspended load. Other
researchers have developed a unified theory from which the total bed material load
can be predicted from a single expression.
The wash load is usually produced through land erosion, rather than channel
scour. Wash load is composed of grain sizes finer than found 1n the bed material.
It readily remains in suspension and 1s washed out of the river without being depos-
ited. A definite relationship between the hydraulic properties of a river and the
wash load capacity apparently does not exist, making it difficult to advance an
analytical method for washload prediction (Graf, 1971). Not all the credible mater-
ial entering a stream 1s transported as wash load, but a large portion may become
part of the bed material and be transported as bed material load.
Figure IV-39 provides a graphical Illustration of the difference between wash
load and bed material load. For a particular flow condition 1n a particular river,
the river has the capacity to transport a certain quantity of sediment (q ) which
generally decreases as particle size Increases. At some large particle size the
river cannot exert enough force to transport particles of that size or larger. This
situation would occur at some point to the right of point 0 on curve COO. This same
river might be supplied with sediment at a rate AOB, which is unrelated to transport
capacity.
To the left of point 0 the river 1s transporting all the material of that size
range being supplied to 1t. Sediment having diameters less than d* are classified as
wash load, because the amount being transported Is supply limited, and not transport
limited. To the right of point 0, supply exceeds transport capacity. The amount
given by the curve 00 1s transported, and the difference in OB and 00 Is deposited in
the stream bed. The methods to be presented in the following sections are generally
concerned with predicting curve 00 (I.e. the bed material load), although Section
4.8.2 does provide a brief description of how to estimate long-term sediment supply
rates.
As a guide in evaluating whether a river 1s carrying a significant quantity
of suspended sediment, Table IV-33 can be consulted. 100 rug/1 is the delinea-
-403-
-------
FOR A PARTICULAR
FLOW CONDITION IN
A PARTICULAR STREAM
o
>
l
<
o
2
to
ID
Q
M
cr
tu
i
d SIZE OF SEDIMENT PARTICLE
FIGURE IV-39 DIVISION BETWEEN WASH LOAD AND BED MATERIAL
LOAD (FROM: COLORADO STATE UNIVERSITY, 1979)
TABLE IV-33
RELATIONSHIP OF TOTAL SUSPENDED SEDIMENT CONCENTRATION
TO PROBLEM POTENTIAL (AFTER EPA, 1976)
If Calculated
Concentration is:
Probability of
a Problem
Less than 10 mg/1
Less than 100 mg/1
More than 100 mg/1
Improbable
Potential
Probable
-404-
-------
tlon between a potential and probable problem. In a table previously Introduced
(Table IV-1), a reference level of 80 mg/1 was set for protection of aquatic life.
4.8.2 Long-Term Sediment Loading from Runoff
The procedures outlined in Chapter 3 wi11 permit an assessment of the sediment
loading to a river on a long-term basis. When using those procedures care should be
taken to incorporate the entire drainage area of the watershed. As an estimate, the
loading can be assumed conservative (i.e. all sediment that comes into the river will
be washed out of the river over an extended time period). Under that assumption the
procedure outlined in Section 4.7 can be utilized for an estimate of average yearly
suspended so-lids concentrations at locations throughout the river system. This
result should be interpreted as an indicator of the impact of the runoff on sediment
loads within a river and not as actual suspended solids concentrations. Not all of
the incoming sediment will be transported as suspended load since a large fraction
can be transported as bed load. The transport process is generally of an intermittent
nature with higher concentrations occurring during periods of high flow.
Care should be taken not to apply the conservative assumption at points on
a river where that assumption is clearly violated, such as at reservoirs which
can be efficient sediment traps. An example for the computation of sediment loading
to rivers has been considered in Chapter 3.
4.8.3 Bed Material Load
As previously mentioned, the estimation of bed material transport poses a
difficult problem, and is an area where there is no consensus regarding the best
predictive relationship to use. Numerous bed material load relationships (Task
Committee on Preparation of Sedimentation Manual, 1971) have been developed over the
past century, some requiring considerably more input data than others. In this
report the DuBoys relationship (Task Committee on Preparation of Sedimentation
Manual, 1971) will be used in part because of its simplicity. The relationship,
which is restricted to uniform flow in alluvial channels, 1s:
(IV-98)
where
gK » bed load, Ib/sec/ft of width of river
T
4/ « coefficient depending on grain size, ftj/lb/sec
T0 " vRH S> bed shear stress«
Y « specific weight or water, Ib/ft
R., « river hydraulic radius, ft
-405-
-------
S » slope of stream, ft/ft
2
TC « critical shear stress, Ib/ft .
The values of 4> and r can be expressed as functions of the median size (by
weight) of the bed sediment (^Q). These relationships are expressed graphically
1n Figure IV-40. To aid In determining d5Q Table IV-34 is presented to show the
size range of sediment and each associated class name. If the class name of the
predominant sediment type comprising a stream bed 1s known, then the sediment size
(in mm) can be estimated.
200
MEDIAN SIZE OF BED SEDIMENT, d«o
(MM)
FIGURE IV-40
* AND TC FOR DuBOYS RELATIONSHIP AS FUNCTIONS OF MEDIAN
SIZE OF BED SEDIMENT (TASK COMMITTEE ON PREPARATION c=
SEDIMENTATION MANUAL, 1971)
Once d5g is estimated, then 4* and T can easily be evaluated, leaving
only T to compute. A summary of hydraulic rad11 (the ratio of cross-sectional
area to wetted perimeter) for different channel geometries is shown in Figure 1V-41.
For very wide, shallow channels, the hydraulic radius approximately equals the depth
of flow. Many river cross-sections can be approximated by a parabolic section. To
calculate "c" in the relationship for hydraulic radius of a parabolic section, refer
to Table IV-35.
If the bed slope 1s unknown it can be estimated by using a topographic map
and finding contour lines approximately five hundred feet above and below tne
point on the river where the measurement is to be made. Dividing this elevation
difference by the horizontal distance over which the difference is measured, produces
the slope.
-406-
-------
TABLE IV-34
SEDIMENT GRADE SCALE (TASK COMMITTEE ON PREPARATION
OF SEDIMENTATION MANUAL, 1971)
Class Nane
very large DOulce-'
uarge ro'jlae-s
*^6C i jf" -C u ' 06 '" S
i" <3 • ~ C ^ 1 C6 '" :
-arse c scales
S"a'. ' ccocles
,'ery coarje crave'.
Coarse gravel
"ecnur cra.e'
r-ne gra.e'
;ery '">e grave1
ver> coarse sana
Coarse sand
Meavji! sand
rmd sand
Very *i>ie sand
Coarse silt
Vec'ur silt
Fire S' 1 t
Very fine Silt
Coa-se :'ay
Medium clay
Fine clay
Very fine day
= .:.
Mi 1 1 imeters
4096-2048
2048-102i
•024-512
i'2-25£
246-123
126-64
64-32
32-16
16-8
5-4
4-2
2-1 2.000-1.000
1-1/2 1.000-0.500
1 '2-1/4 0.500-0.250
',4-1,8 0.250-0.125
••'3-1/16 0.125-0.062
1 16-1/32 0.062-0.031
1.32-1/64 C. 031-0. 016
' 64-1.. 123 0.016-0.003
',128-1/256 0.008-0.004
: 256-' 5:2 3 004-0. 0020
1/512-1/024 0. 0020-0. 0010
1/1024-1/2048 0.0010-0.0005
i -22.48-1/4096 0. 0005-0. OCC24
Rdn(|p A-.-c....ai* Sieve Mesn
.,.ۥ' "ngs Per !ncn
^nned States
Microns IncneS fyler Standard
160-5C
8C-4C
••
-------
TABLE IV-35
COMPUTING D/T FOR DETERMINING THE HYDRAULIC RADIUS OF
A PARABOLIC SECTION (FROM KING, 1954)
*
D
T
.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
*?•
.00
.667
.650
.607
.554
.500
.451
.408
.370
.338
.311
c
.01
.667
.646
.602
.546
.495
.446
.404
.367
.335
.308
.02
.666
.643
.597
.543
.490
.442
.400
.364
.333
.306
.03
.665
.639
.592
.537
.485
.437
.396
.360
.330
.303
.04
.664
.635
.586
.532
.480
.433
.392
.357
.327
.301
.05
.662
.631
.581
.526
.475
.428
.388
.354
.324
.298
.06
.660
.626
.575
.521
.470
.424
.385
.351
.321
.296
.07
.658
.622
.570
.516
.465
.420
.381
.348
.319
.294
.08
.656
.617
.564
.510
.460
.416
.377
.344
.316
.291
.09
.653
.612
.559
.505
.455
.412
.374
.341
.313
.289
Adequate methods that are within the scope of this report and which would
provide a straightforward estimation of suspended sediment discharge presently
do not exist. Most relationships require a known reference level concentration
at some depth within the river to predict the concentration at another depth (Morris
and Wiggert, 1972). To determine the suspended sediment load, then, a summation of
contributions at each depth must be made. Since these formulas apply to one grain
size this procedure should be repeated for all grain sizes present. Einstein (Graf,
1971) has developed a method for computing suspended sediment discharge that does not
require knowledge of a reference concentration, but it is an advanced approach. For
this report the contribution of the suspended load will be estimated from the bed
material load by the relationship given in Table IV-36. The relationship in Table
IV-36 is valid for graded channels (by graded is meant that the slope is stable over
time, being neither steepened nor flattened by flow or other influence). Once the
width to depth ratio for the stream in question is determined, the suspended load can
then be approximated after first computing the bed material load, and then using
Table IV-36.
Once the suspended load discharge is estimated the average concentrations
at a section can be computed by:
1.6 x 104 (!V-99a)
-408-
-------
CHANNEL SUDPE
HYDRAULIC RADIUS
2X? D
. x - D/b, z - e/D
1 + 2 x 'l + *2
Trapezoidal
T :>J
~7F
D
Rectangular
bD
b + 20
Triangular
zO
z » e/D
Parabolic
„ (for c, see
c° Table IV-35)
FIGURE IV-41 HYDRAULIC RADII FOR DIFFERENT CHANNEL SHAPES (FROM KING, 1954)
TABLE IV-36
RELATIONSHIP BETWEEN WIDTH TO DEPTH RATIO OF A GRADED STREAM AND
THE SUSPENDED AND BED LOAD DISCHARGE (AFTER FENWICK, 1969)
Suspended Bed Load % of Width-
Load 5 of Total Total Bed Depth
Bed Material Load Material Load Ratio
85-100
65-85
30-65
0-15
15-35
35-70
7
7-25
25
-409-
-------
or
9ss 4
C . -ii 1.6 x 10* (IV-99b)
ss q
where
C__ • average suspended solids concentration, mg/1
GSS » suspended solids discharge, Ib/sec
Q - flow rate, cfs
g.. • suspended solids discharge per unit width, Ib/sec/ft
q « flow rate per unit width, cfs/ft.
The procedures discussed In this section can be summarized as follows:
1. Determine the bed load discharge gb (Ib/sec/ft) using Equation IV-98.
The required Input data are channel slope, hydraulic radius {see Figure
IV-41), and the median sediment size, dcQ. Once d™ has been estimated
the unknown parameters r and 4> can be found from Figure IV-40.
2. Multiply gb by the river width to find the total bed load discharge.
3. Determine tne width/depth ratio.
4. Use Table IV-36 to determine the suspended load.
5. To determine the suspended sediment concentration use Equation IV-99.
6. Compare the suspended sediment concentration against the data in Table
IV-33 to find out 1f a problem potentially exists.
7. The total bed material load Is sum of the total bed load (step 2) and
the total suspended load (step 4).
The user may be primarily concerned with the total bed material load rather than
either bed load or suspended load Individually. Total bed material load can be
directly calculated using a number of predictive formulas. The method of Yang (1976)
based on unit stream power is presented here. Yang's method has been verified for the
following parameter ranges:
Median bed size: from 0.16 mm to 1.0 mm
Channel depth: from 0.2 ft to 49.9 ft
Water temperature: from 0°C to 29.4°C
Stream velocity: front 1.23 fps to 7.82 fps
Flow rate: from 2.7 cfs to 470,000 cfs
Slope: from 0.0000428 to 0.00188
Total sediment con-
centration (excluding
wash load): from 2.8 ppm to 2,440 ppm.
The Input data are the same as for the DuBoy's method, with the addition of
water temperature. The predictive formula, however, 1s considerably more complicated.
-410-
-------
so the method has been programmed on a hand held calculator and the program 1s
Included. The predictive expression 1s:
tail") U
log Ct • 5.435 - 0.286 log ^ - 0.457 log -f-
+ (1.799 - 0.409 log £ - 0.314 log ^) log (£ - ^)
where
C » total sediment concentration 1n parts per million by weight
D • median sieve diameter
S • water surface slope or energy slope
U^ » shear velocity
U « average water velocity
U « critical average water velocity at Incipient motion
v « kinematic viscosity
w » terminal fall velocity.
The term cr can be calculated as:
w
°-66 when 1.2 < ^ < 70
(IV-100)
) - 0.06
and
^- 2.05 when 70 <. ^ HV-102)
Figure IV-42 shows the required user Instructions to execute the program
on a TI-59. Figure IV-43 contains the program listing and a sample Input/output.
This program was written by Colorado State University (1979).
EXAMPLE IV-17
Estimation of Bed Material Load
Table IY-37 shows characteristics of the Colorado River at Taylor's Ferry,
California, and of the Nlobrara River near Cody, Nebraska. Suppose one desires to
calculate the bed load for the Colorado River at this location for flow ranges of
8-35 cfs/ft. The following data will be used:
d50 * 0.33 mm
V - 62.4 lb/ft3, at 60°F
S - 0.000217 ft/ft
-411-
-------
TITLE.
.PAGE.
.OF.
PROGRAMMED DATE.
Partitioning (Op 1T) I <• 6. Q 6. Ql Ubmy Module
Pnntar Optional Card« 1
PROGRAM DESCRIPTION
Program: Yang's Sediment Transport Equation
USER INSTRUCTIONS
STEP! PROCEDURE
1
2
3
4
S
6
7
Enter kinematic viscosity, v(-^-)
Enter slope SQ (ft/ft)
Enter median sediment diameter, d$ (ft)
Enter flow velocity, U (-j~ )
Enter flow depth, Y (ft)
Compute sediment concentration (ppm)
To Input new data, repeat steps 1
through 6.
ENTER
V
*0
ds
U
Y
PRESS
A
B
C
0
E
2nd
A'
DISPLAY
V
so
«.
U
Y
Ct
FIGURE IV-42 USER INSTRUCTIONS FOR YANG'S SEDIMENT TRANSPORT EQUATION.
Using Figure IV-40 one finds:
4- - 64
TC « 0.019
All that remains is the computation of the hydraulic radius. Since the width
Is much greater than the depth, assume Ru * D:
n
R . /"« ft at q • 8 cfs/ft
H \ 12 ft at q - 35 cfs/ft.
Using Equation IV-98 1t is found that the bed'load 1s:
. ^0.12 Ib/sec/ft at q - 8cfs/ft
b \ 1.5 Ib/sec/ft at q « 35 cfs/ft.
The actual bed material load observed at Taylor's Ferry has been compared with the
OuBoys prediction for a range of flow rates (Task Committee on Preparation of
Sedimentation Manual, 1971). This relationship is shown in Figure IV-44 (The
-412-
-------
Proerta
000
001
002
003
004
005
006
007
003
009
0 1 0
Oil
012
0 1 3
014
015
0 1 6
91-
013
01.9
020
021
022
023
024
025
026
' J
031
052
033
034
055
036
057
053
059
060
061
062
063
064
065
066
067
063
069
070
071
072
073
074
075
076
077
073
079
030
031
032
n$3
03*
035
036
037
033
039
090
091
092
093
094
095
096
097
093
099
100
34
73
06
65
43
06
34
53
43
02
34
42
00
92
76
11
*^
06
22
52
92
76
12
42
01
32
76
13
42
02
92
76
14
*2
03
•a-?
76
15
*2
04
92
76
16
71
77
53
53
03
02
rx
6
X
RCL
06
i.
RCL
02
STD
00
RTN
LBL
A
STD
06
!NV
EE
RTN
LBL
B
STD
01
RTN
LBL
C
STD
02
RTH
LBL
D
STD
03
RTH
LBL
E
STD
0*
RTN
LBL
ft'
SER
GE
C
(
3
2
.
101
102
103
10*
105
106
107
103
109
110
111
112
113
114
115
116
117
113
119
12f>
121
122
123
1 2*
125
126
127
123
129
1 30
131
132
133
13*
135
136
137
133
139
1*0
1*1
1*2
143
14-1
1*5
1*6
147
143
1 *9
150
02
65
*3
04
65
43
01
34
34
42
03
65
*3
02
55
43
06
54
42
07
32
07
00
32
77
39
53
53
02
92
05
55
53
43
07
2*
75
93
00
fir.
54
54
35
93
06
06
54
*2
03
61
2
X
RCL
04
X
RCL
01
j
rx
STD
05
X
RCL
02
•f
RCL
06
>
STD
07
X4* T
7
0
V«* * ^
GE
11
*
•'
2
.
5
-
<
RCL
07
LDG
-
.
0
if,
;
>
f
.
6
6
>
STD
03
GT2
151
152
153
134
153
156
137
158
15?
160
161
162
163
164
165
166
167
163
169
170
171
172
173
17*
175
176
177
173
179
ISO
131
132
133
134
135
136
137
133
139
190
191
192
193
19*
193
196
197
1*5
19 i
200
70
76
39
02
93
00
05
*2
08
76
70
53
52
05
93
0*
02.
05
75
93
'•'•i
03
06
65
52
*3
00
65
42
02
cr
*3
06
V •
•ii-
— ^i
09
75
93
0*
05
07
65
^ w
43
05
55
43
00
54
Pr!D
LBL
fl
^
.
fi
5
STD
03
LBL
Pfili
(
<
5
.
4
3
5
-
.
2
3
6
X
f
RCL
00
f.
RCL
02
-
PCL
06
>
LGG
STC
09
-
.
4
c;
^
X
V
RCL
0 5
r
RCL
;"i ;"*,
i
FIGURE IV-U3
PROGRAM LISTING AND SAMPLE INPUT/OUTPUT FOR
YANG'S SEDIMENT TRANSPORT EQUATION
-413-
-------
fregraa Listing (continued): Stmplt Input:
v • .0000111
SQ » .0017
d$ • .000623
U • 2.89
Y » 0.51
201
202
203
204
205
206
20"
203
^Q'S
210
211
212
213
214
215
216
217
213
21?
220
•^ •*. -~.
• • *
223
224
225
'^ •"* to
^ £
23
42
10
54
35
53
53
01
93
07
09
09
75
93
04
00
09
65
-3
09
'r -
* . •!
03
01
04
65
43
LOG
STD
10
>
•*•
<
<
1
.
7
o
•S
.
t
4
0
c.
X
RCL
09
"
9
2
1
•T
X
FCL
223
229
230
231
232
M! m- »'
23 A
235
236
237
-i : i
2:9
«i~'J
2^1
2-i£
2 4 3
"' * "*
H5
246
r'l
"r""'?
• -T
250
251
2*2
253
254
10
54
6-
53
43
03
65
43
0 1
55
43
00
75
43
OS
65
43
01
?j,
23
•• **
e <
JT
X
(
RCL
03
X
RCL
01
+
RCL
00
-
RCL
03
X
RCL
01
j
LCG
'
}
INV
LOG
flDV
FRT
RTN
2117.066395
FIGURE IV-43 (CONTINUED)
j DuBoys curve 1n Figure IV-44 does not quite match the calculations 1n this example
' because slightly different data were used). Observe that the DuBoys relationship
• over-predicts the bed material load for nearly all flow ranges. This pattern 1s
! repeated for the Nlobrara River (Figure IV-45). This suggests that the bed
{ material load estimated by the DuBoys relationship will 1n general exceed the
I actual bed material load. This 1s further substantiated by other work (Stall et^
| a1., 1958). The more accurate predictions of bed material load occur under high
j flow conditions, which 1s generally when the prediction of bed material load 1s
j most Important.
: To estimate the suspended load contribution first calculate the width-
depth ratio:
U/D - / 88 at q - 8 i
I 29 at q « 35
cfs/ft
cfs/ft.
-414-
-------
TABLE IV-37
CHARACTERISTICS OF THE COLORADO AND NIOBRARA RIVERS
(TASK COMMITTEE ON PREPARATION OF SEDIMENTATION MANUAL, 1971)
Data
Depth range, ft
Range in q, in cubic feet per
second per foot of width
Mean width, in feet
Slope, in feet per foot
Minimum value
Maximum value
Value used in calculations
Water temperature, in degrees
Fahrenheit
Minimum value
Maximum value
Value used in calculations
Geometric mean*sediment size,
in mill imeters
d-,, in mill imeters
d,^, in mil 1 imeters
d,,.* in mil 1 imeters
dgo> in millimeters
Mean size, d . in millimeters
m
*The geometric mean of a set of values
geometric mean of the values 1, 2, 3,
Compare with arithmetic mean of 2.5.
Stream
Colorado Niobrara
River River
(Taylor's Ferry) (Cody, Neb.)
4-12 0.7-1.3
8-35 1.7-5
350 110
0.000147 0.00116
0.000333 0.00126
0.000217 0.00129
48 33
81 86
60 60
0.320 0.283
0.287 0.233
0.330 0.277
0.378 0.335
0.530 0.530
0.396 0.342
xn 1s! n xj n. Thus the
and 4 is (1x2x3x4)1/4 - 2.213.
-415-
-------
COLORADO RIVER
AT TAYLORS FERRY
FIGURE
8 0 80-
-v 0.60-
£ 040-
UJ
g 0.20-
S n in
eo 0.08—
o 006-
^ 0.04-
UJ
? 002-
o
Lul
Duboyr/
/
e
x^
e e
% e
*— Observed
2 4 6 810 20 406080100
WATER DISCHARGE (cfs/ft.)
I
i
SEDIMENT DISCHARGE AS A FUNCTION OF WATER DISCHARGE j
FOR THE COLORADO RIVER AT TAYLOR'S FERRY (TASK j
COMMITTEE ON PREPARATION OF
SEDIMENTATION MANUAL, 1971) !
NIOBRARA RIVER
j FIGURE IV-U5
4 6 810
WATER DISCHARGE (cfs/ft)
SEDIMENT DISCHARGE AS A FUNCTION OF HATER DISCHARGE
FOR THE NIOBRARA RIVER AT CODY, NEBRASKA (TASK
COMMITTEE ON PREPARATION OF SEDIMENTATION
MANUAL, 1971)
-416-
-------
I In both cases W/D > 25. Referring to Table IV-36, the suspended load should
| be between 30 and 65 percent of the bed material load. Assume 1t 1s on the
| lower end of the scale, about 401. Then the suspended load 1s:
j m C 0.08 Ib/sec/ft at q « 8 cfs/ft
' ss \ 1.0 Ib/sec/ft at q • 35 cfs/ft
i or
i C m /160 mg/l at q « 8 cfs/ft
! ss \440 mg/l at q - 35 cfs/ft
I from Equation IV-99. These concentrations Indicate that suspended sediment
I concentrations are excessively high throughout the range of flows normally
i
| encountered at Taylor's Ferry. Data on suspended sediment concentrations have
j been gathered at Taylor's Ferry (U.S. Bureau of Reclamation, 1958). The averages
j of 30 measurements taken there are as follows:
Q - 7350 cfs (or q « 21 cfs/ft)
! Cee - 132 mg/l
I
I Observed range of suspended sediment concentration: 40-277 mg/l.
I The method of Yang predicts total concentrations of 40 to 80 mg/l, which is
| within but toward the lower end of the observated data. The method of DuBoy's
| predicts concentration between 160 and 440 mg/l which is toward, and beyond, the
j upper end of observation. These results illustrate the possible variability of
predictions between different approaches, and are not necessarily atypical.
I END OF EXAMPLE IV-17
4.9 TOXIC SUBSTANCES
4.9.1 Methods of Entry of Toxic Pollutants into Rivers
Although Chapter 3 discussed both point and nonpoint sources of pollutants,
the major pollutant source categories are summarized in Table IV-38 to indicate
how these scenarios differentially govern a pollutant's fate. For simplicity,
fate is analyzed in terms of volatilization and sorption since these processes
are Important for a wide number of toxic organic chemicals. These processes
govern whether a pollutant remains in the water column and whether the pollutant
is transported as solute or sorbate. If the effects of these processes are known,
even If only qualitatively, then the Influence of processes such as photolysis and
biodegradation can be better predicted. For example, 1f a pollutant is sorbed to
suspended and bedded sediments, 1t is more protected from photolytic reactions than
when 1t 1s dissolved 1n the water column.
A common mode of pollutant entry is by a continuous discharge, either from
a municipal or industrial source. As mixing of the effluent and river water occurs,
-417-
-------
TABLE IV-38
METHODS OF INTRODUCTION OF TOXIC ORGANIC COMPOUNDS INTO RIVERS,
AND FATE IN TERMS OF VOLATILIZATION AND SORPTION
Pathway
Fate
Continuous input
Cessation of continuous
input
solute transported and volatilized
sorbate transported with suspended solids
and with bed load
sorbed to immobile sediments
buried by sorption and net deposition
desorbed from immobile sediments
solute transported and volatilized
resorbed to suspended sediments
contaminated sediments resuspended
portion remains buried
Washoff from land
application
transport of a major portion of pollutant
may be governed by first large storm event
transported as solute and sorbate
settles and accumulates on bed
buried
subsequently resuspended
Accidental releases
(e.g. spills)
Leaching
- If s.g. >1, pollutant settles on streambed
- Volatilization may be unimportant
--reentrained back into stream and sorbed
on suspended solids
—pollutant can be slowly transported along
bottom
--diffused into bedded sediments
- If s.g. <1, pollutant tends to remain on
surface and be transported at speed of
surface current
--volatilization can be important
--gradually dissolved and sorbed
--dispersion attenuates peak concentrations
—wind speed and direction influential
- slow movement (years) of solute from dump
or disposal site to stream
- continues for years after cleanup of
dump
-418-
-------
partitioning begins. The sorbate is transported with the suspended sediments, and
can interact with the bed load and immobile bedded sediments. Depending on the rate
of exchange of the sorbate with the bedded sediments and on the net sediment deposition
rate, some of the sorbate can gradually become buried in the bedded sediments.
If a continuous input ceases, the water column initially tends to clean Itself
of the pollutant as uncontaminated upstream water replaces contaminated river water
downstream from the former source of pollution. However, pollutant from the contami-
nated bottom sediments can desorb back Into the water column at low concentrations
and the river bed becomes an Internal source of pollutant. The desorption period can
last a long period of time, depending on the amount of pollutant contained in the
bottom sediments. Section 4.9.3.4 discusses this phenomenon in detail.
Periodic nonpoint sources, such as washoff after an agricultural application, is
another pathway of pollutant entry Into rivers. The mass of pollutant transported
tends to be governed by the timing of the first storm event following application
together with the degradation and volatilization processes operative during the
Interim period.
Accidental releases of pollutants, even through infrequent events, can be
important. Exceptionally high concentrations of pollutants can result from spills
and the total mass supplied almost Instantaneously can be the equivalent of a con-
tinuous release lasting for many days. For example, 1n 1973 a chloroform spill on
the Mississippi River resulted 1n about 800,000 kg (1,750,000 Ibs) of chloroform
being released over a period of several hours (Thibodeaux, 1977). Based on the
background concentration of chloroform in the river (5 ppb), the release was equiv-
alent to a continuous supply of chloroform released at background rates for a period
of 300 days.
Many chemicals 1n their pure or nearly pure form have specific gravities sig-
nificantly different from unity. Because of this, and their often limited solubility
in water, it is a mistake to believe that all spilled pollutants travel witn the
speed of the river, have Infinite dissolution capability, and disperse accordingly.
High density pollutants can sink to the river bed and become slowly reentrained back
Into the water column while simultaneously diffusing and sorbing into the bedded
sediments. Depending on the rate of dissolution of the spilled pollutant, as well as
the significance of the sorptlon and diffusion processes, the spilled pollutant may
remain 1n the riverine system for either an extended or brief period of time.
In contrast to high density pollutants, pollutants with specific gravities
less than unity tend to at least partially remain on or near the water's surface
while undergoing dissolution. For these pollutants, volatilization and photolysis
can be extremely Important. As the pollutant 1s dissolved in the water coljmn and
moves downstream, dispersion becomes Important 1n attenuating the peak concentration.
Pollutants which leach from a surface or subsurface disposal site may eventually
-419-
-------
reach a river. Although the mass Input rate may be low, the source can be continuous
and last for years, even after cleanup of the site.
The sequence of Instream events following the Initiation and then the cessation
of point sources of toxicants further Illustrates the role that sorptlon plays 1n
governing fate of sorbates. Figure IV-46 Illustrates the two situations. Figure
!V-46a shows the pollutant distributions below a point source at two distinct times
(t^ and t2 where t2 > t^) following Initiation of the point source.
As the toxicant Is discharged the water column concentration (the sum of the dissolved
and sorbed phases) abruptly Increases at the mixing location. As the pollutant
travels downstream, the sorbate tends to partially desorb onto the formerly uncon-
tamlnated bottom sediments. Additionally there may be a net exchange between the
bedded sediments and water column sediments, even If there 1s no net deposition. As
a result of these processes, the water column concentration tends to decrease 1n the
downstream direction. It may take a period of time greater than t, for the
effects of the discharge to reach a distance D*. Depending on the distance, and on
the rate of accumulation of the toxicant In the bottom sediments, as well as on other
factors, the time required for the water column concentration to be noticeably
elevated at D* could greatly exceed the travel time of the river over the distance.
After the discharge of the toxicant has continued for a period of time, the net
exchange with the bedded sediment may diminish, so that the toxicant concentration
becomes constant over some distance both In the water column and 1n the sediments.
This situation 1s Illustrated by the solid curve 1n Figure IV-466. Suppose at this
time the Input of the pollutant ceases. The water column concentration just below
the point source tends to abruptly approach zero. As this happens, desorptlon of the
toxicant from the bedded sediment can occur, tending to replenish pollutant levels 1n
the water column, but to a lower level. Gradually, the pollutant can be desorbed
from the bedded sediments at a given location so that the bottom sediments are
naturally cleansed, from the upstream to the downstream direction. This process can
take many years and low levels of pollutant 1n the water column can be detected
throughout this period. More discussion of this phenomenon Is provided later 1n
Section 4.9.3 and Example IV-18. Host of the pathways for river contamination pre-
sented 1n Table IV-38 have been programmed on microcomputers (Hills et. aj_., 1985).
4.9.2 Vertical Distribution of Sorbate within Rivers
Even though most of the analytical tools presented later 1n Section 4.9.3
assume that, for simplicity, suspended solIds concentrations are uniformly distributed
throughout the water column, 1n reality this 1s not true. The vertical distribution
of solids depends both on particle and river characteristics. Heavier particles
(those with the greater settling velocities) are transported closer to the stream
bottom while the lighter particles are more uniformly distributed. This observation
1s significant because pollutants which sorb to the particles also exhibit a non-
-420-
-------
I
Concentration at time t)
after discharge begins
Concentration at time t2 t,
after discharge begins
SI RT
POLLUTANT
INPUT
ANT
FIGURE IV-46
DISTANCE DOWNSTREAM
D*
Key:
2
<5
»-
POLLUT
j
/
k
r BED^ — ^x.
/ ^ WATER COLUMN ^y^x^
I/ \
1
DISTANCE DOWNSTREAM
-------
INCREASED SETTLING
VELOCITY
0.001
RELATIVE SEDIMENT CONCENTRATION S/Sa
FIGURE IV-47 VERTICAL EQUILIBRIUM DISTRIBUTION OF
SUSPENDED SOLIDS IN A RIVER
Figure IV-47 shows the vertical distribution of suspended solids 1n an equilibrium
condition. The parameter shown In the figure 1s defined:
where
V
•Tff
V • settling velocity of suspended solids
(IV-103)
s
x
U*
9
R
von (Carman's constant (~0.4)
shear velocity - (g RH S)0*5,
H
ft/sec
acceleration due to gravity, 32.2 ft/sec
hydraulic radius of river, ft
S • slope, d1mens1onless.
Very small values of z represent clay-sized particles, while larger values represent
first silt, and then sand. Figure IV-47 Illustrates that clay particles tend to be
uniformly distributed vertically (50 percent 1n the top half of the water column).
About 75 percent of silt and over 95 percent of the sand particles (typically) reside
In the bottom half of the water column. This suggests that 1n rivers where the
suspended sediments are silt and sand, the sorbed pollutant distribution will be
-422-
-------
vertically skewed. If the suspended material 1s predominantly clay the sorbed
pollutant distribution will be uniform. Since pollutants tend to sorb to sand to a
lesser degree than to silt and clay, the vertical distribution of sorbed pollutant
will not be as skewed as the suspended sediment distribution.
Figures IV-48 through IV-49 show the fraction of pollutant present as solute
(C/Ct) versus relative depth for families of z values and ICSa values. Sa 1s
the suspended sediment concentration a small distance above the bottom. For K Sa
values less than 0.1, the sorbate concentration 1s generally negligible compared to
the solute concentration regardless of the depth or the nature of the suspended
material. For larger K Sa values, the sorbate level can be Important, depending
of the nature of the suspended material. For extremely large K Sa values, the sorbate
concentration will greatly exceed the solute concentration, at least near the river
bed.
Based on the hydraulic characteristics of the river, characteristics of the
material being transported 1n suspension, and the partition coefficient of the
pollutant, predictions can be made of the pollutant's distribution 1n the water
column. To use Figures IV-48 and IV-49 requires knowledge of Sa, the suspended
solids concentration at a distance n - a above the bottom (where typically a « 0.05,
or 5 percent of the river's depth). The equilibrium expression for suspended sedi-
ments, which is found 1n numerous sediment transport texts (e.g. Graf, 1971) can be
rearranged to express Sa as:
where
n - relative depth above bottom.
To use this equation the suspended solids concentration must be known at one depth In
the water column. Typically, a depth averaged suspended solids concentration might
be readily available. Under these circumstances Sa can be estimated as:
Sa •—\ , ' x a ' (IV-105)
dn
where
S • depth average suspended sediment concentration.
The denominator of Equation IV-105 can be integrated numerically by one of many
available solution techniques (e.g. see Carnahan et^ jj_., 1969). For the case
-423-
-------
0.9-
0.8-
0.7- •
X 0.6 —
*-
a.
o °5"
> 0.4- •
< 0.3--
uj
* 0.2--
0.1- •
0.05-
S, • 10.
0.001 0.01 01 1.0
RELATIVE POLLUTANT CONCENTRATION. 7°-
^T
FIGURE IV-^8
VERTICAL DISTRIBUTION OF RELATIVE SOLUTE
CONCENTRATION, KpSA = 10,
z
&
$
5
0.9-
0,8-
07.
0.6-
0.5-
0.4-
0.3-
0.2
0.1-1
O.OS
KpSa*100
0.001 0-01 0.10
RELATIVE POLLUTANT CONCENTRATION C/CT
FIGURE IV-49 VERTICAL DISTRIBUTION OF RELATIVE SOLUTE
CONCENTRATION, KpSA = 100.
-424-
-------
when a - 0.05 the relationship between Sa and S 1s given below:
2 « 0 » Sa « S
2 - 0.2—»-Sa • 1.8 S
z « 0.6—»-Sa * 4.4 S
z - l.C—^-Sa - 8.2 S (IV-106)
z « 2.0—»-Sa - 17 S
z • 5.C—»-Sa ' 20 S
Based on a knowledge of S, Sa can be estimated from Equation IV-106, and in turn can
be used In Figures IV-48 and IV-49.
Typically there 1s a segregation of particle sizes found in suspension compared
with these found 1n the bed load, and In the Immobile bed materials. Based on these
differences, the following can be hypothesized:
Xs > Xbl > X1m
where
X * sorbed pollutant concentration on suspended materials, mass
pollutant/mass sediment
Xbl * S01"'*6^ pollutant concentration on bed load, mass pollutant/
mass sediment
X, - sorbed pollutant concentration on Immovable sediment, mass
pollutant/mass sediment.
Investigations carried out by Miles (1976) appear to support this relationship.
Miles collected Insecticide residues on stream sediments and In the water column.
Results of the DOT analysis of B1g Creek, Norfolk County, Ontario, 1973 (DOT was
banned In 1970) are as follows:
Concentration of DOT on Sediments
(mass of pollutants/mass of sediments)
Suspended sediments 110 ppb • X
Bed load 76 ppb - X^
Immovable bed 26 ppb • X.
Miles (1976) also found that DOT transported In the dissolved phase ranged from
10 to 92 percent of the total transported In the water column. This finding 1s
consistent with the results 1n Table 11-14 which shows that the percent of pollutant
transported In the dissolved phase can be high even for pollutants such as DDT as
long as the suspended solids concentration 1s not extremely high.
Contaminant data collected In bedded sediments can be very Illuminating.
Although in a screening approach 1t Is not anticipated the user will go to the
field to collect sediment core samples, some data might be available. Depending
-425-
-------
on the quantity of data available the following types of Information might be
determlnable:
• The spatial extent of contaminated sediments, and pollutant concentra-
tions In the sediments
• The depth of contaminated sediment
• The quantity of toxicant contained In the sediment
• A time history of pollution levels to determine whether they are
Increasing or decreasing
• The probable sources of the pollutant, based on the location and
quantity of contaminated sediments.
Although extensive sampling Is required to accurately determine all of the above
Items, such programs have been successfully accomplished. For example, an extensive
sediment sampling program was conducted 1n the Hudson River In New York to determine
the sources of PCBs In the contaminated sediments, and the degree of contamination
(Turk, 1979).
4.9.3 Transport and Transformation Expressions for Toxicants 1n Rivers
The tools presented in this section can be used to predict instream concentra-
tions of toxicants for a variety of different situations. Specifically, the following
scenarios are addressed:
t Mixing zone analysis
• Continuous point source discharges
• Continuous nonpoint source discharges
• Oesorption from bedded sediments
• Spills and Instantaneous release of soluble chemicals, and
• Spills of high density chemicals which sink to the river bed.
In contrast to many conventional organic pollutants which degrade into innocuous
substances, many toxicants are transformed to other chemicals which can be as harmful
or more harmful than the original. Consequently, when toxicants are continuously
discharged into a river, in addition to predicting the concentration profile, it is
useful to also determine:
• The pollutant's advection rate past a specified location
• The pollutant's volatilization rate over a specified reach
• The pollutant's rate of transformation to other species over a specified
reach.
The toxicant's fate is thus segregated Into the processes of advection, volatilization,
and transformation.
In the following three sections on mixing zones, point sources, and nonpoint
sources, the user will find there are different methods of approaching the problems.
One way to simplify the analysis is to first assume toxicants act conservatively.
-426-
-------
The user can then perform a first level analysis to find out whether criteria are
violated. If they are not, then a detailed analysis Is really not required if the
objective is to determine criteria compliance. If violations are predicted, a more
detailed analysis of these "hot spots" can be performed by considering the various
processes affecting the toxicant In the river. This approach requires more work, but
by judiciously applying the tools available, the analyses can be expedited.
4.9.3.1 Mixing Zone Expressions
Section IV-4.1.9 presented earlier delineated one- and two-dimensional mixing
zone expressions for conventional pollutants. The one-dimensional expressions need
to be extended in order to differentiate between solute and sorbate. To do this, the
following expressions for pollutant concentration and the suspended solids concentra-
tions are needed:
(IV-107)
Cut Qu
to
wt
(IV-108)
where
V Cut
wt
concentration of suspended solids and concentration
of sum of solute and sorbate in the river above the location
of mixing, respectively
concentration of suspended solids and concentration
of sum of solute and sorbate in the wastewater, respectively
concentration of suspended solids and concentration of sum of
solute and sorbate in the river following mixing, respectively
The dissolved phase concentration, C, of the pollutant at the completion of mixing is
given by:
S, C
to
C -
to
(IV-1C9)
where C. and S are found from the two previous expressions.
The concentration of the solute following mixing depends on characteristics of
the waste source, the river's flow rate, and the suspended solids concentration
in the river and waste source. The solute concentration might also change after
mixing with a tributary of very high suspended solids concentration (high S },
even if 1t contains no additional pollutant (C
wt
0).
Equation IV-108 1s particularly useful because it predicts the total instream
-427-
-------
concentration of toxicant following Initial mixing. This Is often the critical test
In establishing whether or not water quality standards are violated by a point
source.
In cases where Initial mixing Is Incomplete (that Is the waste 1s Initially
diluted with a fraction of the total river flow), the two-dimensional mixing equation
shown earlier as Equation IV-4w1ll more accurately predict C.Q. Then Equation
IV-109 can be used to find the solute concentration.
When there are numerous discharges of the same toxicant, analysis becomes
more complicated. The most straightforward method of handling this situation
1s to sequentially apply Equation IV-108 to the series of discharges to find the
concentration as a function of distance downstream. If the solute concentration is
needed, then sequential application of Equations IV-108 and IV-109 1s required.
The analysis of multiple point sources can be simplified in one of two ways.
One, the sources can be transformed to an equivalent nonpoint source by assuming that
the toxicant input 1s uniformly distributed between the series of point sources. This
approach is discussed in Section 4.9.3.3. Two, a series of closely grouped point
sources can be handled as an equivalent point source. The equivalent point source
has a flow rate equal to the sum of the flow rates from the individual plants, or:
Qw • 2- Qw1 (iv-no)
where
Q , » flow rate from ith treatment plant
n - number of treatment plants being grouped.
The total pollutant load can be expressed in one of two ways. If the concentra-
tions in the wastewater are known then the total loading is:
Cw Q» ' Cw1
where
If the mass emission rates are known instead then:
C . « concentration of toxicant in effluent of 1th plant.
"^ 1-1
where
M. « mass emission rate of toxicant from 1th plant is Ibs/day.
The conversion factor 5.38 converts mass emission rate in Ibs/day to flow units
1n cfs and concentration units 1n mg/1 (ppm).
The grouping procedure described above has been applied by the U.S. Environmental
-428-
-------
Protection Agency (1981) to a case study in Indiana to evaluate the economic impact
of toxicant standards. Numbers of point sources were grouped together using a
procedure called cluster analysis. The cluster analysis added the loadings of major
and minor industrial dischargers within a ten-mile radius of each other. Ten clusters
were identified and few violations occurred within them once the best available
technology was attained.
For certain applications the object of using a mixing zone equation is to
directly find the maximum allowable concentration in the discharge so that the
receiving water criteria are not violated. Under these circumstances Equation
IV-108 can be rewritten as:
C (Q. + Q - C Q
(Quc
where
(C fL, " maximum allowable concentration of the toxicant
Wt nlaX
in the waste discharge so that the water quality criterion
is met under critical conditions
C. « water quality criterion for the toxicant
Quc » critical river flow rate (e.g., 7Q10).
Equation IV-113b is applicable when the concentration of the toxicant is zero upstream
of the discharge point.
4.9.3.2 Point Source Discharges
For point sources of toxicants, the pollutant interactions depicted in Figure
IV-50 are simulated. While transformation of toxicants is general ly more complex
than this, in many instances these interactions are sufficient to analyze the in-
stream processes affecting not only point source discharges but also nonpoint source
discharges, and instantaneous releases of soluble pollutants. Figure IV-50 reveals
that:
• The solute only is assumed to volatilize.
• First order transformation processes degrade only the solute.
• Adsorption and desorption are assumed to occur at rates much faster than
other processes.
• No interactions with the bottom sediments occur (this is analyzed
in later sections).
-429-
-------
I//H///////////II///UIIII/IIIIIIIIIIIII1IIIIIIIIIIHIIIIIIIIIIIIIIIIII1
FIGURE IV-50 INSTREAM TRANSFORMATION PROCESSES
ANALYZED FOR TOXICANTS,
Based on these interactions, the concentration profile below a point source of
toxicant is expressible as:
.
where
C * concentration of dissolved phase of the toxicant at a distance
x below the point source
CQ « concentration of the dissolved phase of the toxicant at x «
0 (after the point source discharge has mixed with the river water)
D • water depth
£k^ • individual first order decay rates which are transforming
the toxicant (other than volatilization)
P » partial pressure of the toxicant In the atmosphere above the river.
The remaining variables have previously been defined.
Typically the partial pressure 1s zero, so that Equation IV-114 simplifies to:
C - CQ exp
"k
(IV-115)
-430-
-------
The initial dissolved phase concentration 1s given by:
Ctn (IV-116)
where
C was defined by Equation IV-108.
The total pollutant concentration, C , at any location 1s:
Ct - C (1 + KpS) (IV-117)
The sorbed phase concentration expressed as mass per unit volume of water 1s:
and the sorbed phase concentration expressed as mass per unit mass of sediment is:
X - KpC (IV-119)
The most direct application of Equation IV-114 cr IV-115, plus Equations
IV-117 through IV-119 is to find the Instream concentration as a function of distance
below the point source. There are, however, other uses of the expressions. Consider
Equation IV-115, for example. The ratio C/C can be directly calculated as a
function of distance. Thus the fractional dissolved phase concentration can be
calculated without ever knowing the initial concentration C . This approach has
the advantage of requiring less data. Similarly, the fractional concentration can be
calculated for any specified distance, such as the end of a reach. Or, the distance
x can be found so that the fractional concentration is some specified number, which
may relate to an acceptable level of toxicant. The length of river subjected to
unacceptable levels can then be found.
The user might additionally want to know the distribution of pollutant fluxes in
terms of advection (M^), volatilization (M ), and transformation (1^). Expressions
for these are presented for the case of P « 0. These formulae allow the user to pre-
dict the fluxes associated with the point source discharge where volatilization is
not altered by a background concentration In the atmosphere. Under these conditions:
M - M, + M + M. (IV-120)
d V t
Equation IV-120 states that the rate of entry of the toxicant into the river (M)
equals the rate of advecticn of that toxicant past some location x , plus the
-431-
-------
rate of volatilization across the water surface between the discharge location and
some other specified location plus the rate of transformation of the toxicant to
other substances within the water column between the same two locations. By knowing
expressions for each of M , H , and I"L the user knows the major processes
controlling the toxicant's fate within any reach of river.
The mass flux advected past a location x$ 1s given by:
Cs (IV-121)
where the concentration C$ 1s evaluated at x • x$. The volatilization mass flux 1s
given by:
Mv ' Ac "v C0
u (i + K s)
/ k; + £k \
f - U (1 * Kpl) *sj
(IV-122)
where
AC - cross-sectional area of river
All other terms have previously been defined.
In some cases the user might have an estimate of the average dissolved phase concen-
tration, C, within the reach under consideration. Under these circumstances the
volatilization flux Is simply:
M, ' At k r (IV-123)
where
surface area of the reach under Investigation.
The transformation mass flux Is expressible as:
U (1 + K S)
1-exp
(IV-124)
Since the sum of Equations IV-121, IV-122, and IV-124 equals the mass emission rate
of the toxicant, Equation IV-120 can be used to double check the fluxes calculated.
4.9.3.3 Nonpolnt Source Discharge
This section parallels the previous section on point source discharges by
presenting expressions for the steady-state concentration profile, and for mass
fluxes. In addition to applying this methodology to a nonpolnt source, another and
possibly more useful application 1s to express a series of point sources as an
-432-
-------
equivalent nonpolnt source. The equivalent nonpolnt source discharge rate Is simply
the sum of the discharge rates of the pollutant from all the point sources. This
approach 1s not as accurate as analyzing each point source Individually but 1s much
faster depending on the number of point sources. For example, suppose a river
segment has ten separate point sources located within 50 miles of each other. The
most rigorous analysis Mould consist of considering each point source Individually,
where mixing zone and point source equations are applied sequentially ten times each.
This obviously 1s a great deal of work for a hand calculation approach. By consid-
ering these point sources as a single equivalent nonpolnt source, a single equation
application 1s sufficient to analyze the problem. Example IV-5 shown earlier in the
BOO section Illustrates this procedure.
The solute concentration in a river resulting from a steady nonpoint source of
toxicant is:
/ \ /n a. »w \ L,
(IV-125 )
where
k2
k3
1 + KS
P m
A m
C
« k, + k1 + '
2 v '
- total concentration of toxicant in nonpoint source
'tn
Qf * river flow rate at end of nonpoint source
Q « river flow rate at beginning of nonpoint source
x. • length of ncnpoint source.
Equations IV-117 through IV-119 can be used to find Ct, C , and X, respectively.
In a manner similar to point source discharges. Equation IV-120 which expresses
the mass balance between toxicant inflow rate to the river and loss rate by advection,
and transformation, is valid. The appropriate expressions are (when P » 0):
1 (Q0+mx)C + (Qp-Hnx) Ck S at x - x$
solute sorbate
transport transport
(IV-126)
-433-
-------
for the advcctlve flux. For the volatilization flux:
For the transformation flux:
ftt " -Ik,
£ *S + Zk1AC (C0 * M i **
\ *3/ m icj-kj
As a first cut analysis, the user might want to assume that the toxicants act
conservatively. If criteria are not violated under these circumstances, then
criteria will not be violated if decay or transformation processes are included.
4.9.3.4 Desorption of Toxicant from a River Bed
Because many toxicants are transported as sorbate rather than as solute, a
significant fraction of the pollutant which enters a riverine system can ultimately
be deposited in the bedded sediments. If the toxicant is resistant to degradation
processes it can remain 1n the sediments for extended periods of time. During this
time, the toxicant can slowly be desorbed back into the water column or scoured into
suspension.
Figure IV-46 shown earlier Illustrated an idealization of the process of
desorption of a toxicant from bedded sediments. The process can be described as
follows. Supposed the average concentration of the pollutant in the bedded sediment
is X when the analysis bealns (called t « 0). The concentration X, at any later
time is estimated from mass balance considerations as:
o • for x * ¥nr
8 P (IV-129)
. otherwise
where
XQ • concentration of pollutant 1n bed at some time t « 0
M$ • mass of contaminated sediment per unit area of river bed, g/cm2
U » stream velocity, cm/sec
6 • equivalent depth of water In sediment M cm
K
p » partition coefficient.
Equation IV-129 reveals that desorption can be Interpreted as a frontal phenom-
enon where desorption Is completed at one location before progressing downstream.
Based on this interpretation, an effective removal velocity of the front ^s:
U « O- (IV-130)
-434-
-------
The time T required to desorb the toxicant over any specified distance is:
where
x, « length of contaminated river segment.
During the period of desorption the average concentration in the water column is:
Xn6
-2- for x> Uet (IV-132)
P
0 , otherwise (IV-133)
To use Equations IV-129 through IV-133, estimates for X M , and 6 are
required. If both the mass of contaminated sediment per unit area of river bed
(M.) and the mass of toxicant in the sediments are known, then X can be deter-
mined. Conversely, if both X and the total mass of toxicant in the sediments
are known, then M^ can be calculated.
In lieu of having data on M and 6, these quantities can be estimated based
on the depth of contaminated sediments by using Table IV-39. In addition to the
depth, the percent solids by weight must be estimated. This parameter generally
increases with depths and can be chosen as 50 percent, unless better data are avai-
lable. The data in Table IV-39 were derived from the following two equations:
M °C
Ms ' .. / .. Tn7TF\ (IV-134)
and
(IV-135)
where
2
MS « mass of contaminated sediment, g/m
6 « equivalent water depth, mm
S » specific gravity of solids
D « depth of contamination, mm.
In cases where the depth of contamination exceeds 100 mm the equations can be
used in lieu of Table IV-39.
The Hudson River in New York State provides an illustration of an extreme
-435-
-------
TABLE IV-39
MASS OF CONTAMINATED SEDIMENTS AND EQUIVALENT WATER
DEPTH AS A FUNCTION OF DEPTH OF CONTAMINATION
Depth (mm)
1
5
10
20
50
100
Percent Solids by Weight
20
50
80
20
50
80
20
50
80
20
50
80
20
50
80
20
50
80
Ms (g/cm2)
0.02
0.06
0.11
0.11
0.30
0.55
0.23
0.60
1.1
0.45
1.2
2.2
1.1
3.0
5.5
2.3
6.0
11.0
6(1.)
0.9
0.6
0.3
4.5
3.0
1.4
9.1
6.0
2.7
18.
12.
5.5
45.
30.
14.
91.
60.
27.
case of PCB contamination (Turk, 1980). Between 1951 and 1977 PCSs were discharged
from point sources near Fort Edward and Hudson Falls, about 80 km (50 mi) above
Albany, New York. Figure IV-51 shows the general vicinity.
During this time period the mass emission rate of PCBs decreased from 15 kg/day
(33 Ibs/day) to less than 1 g/day (0.002 Ibs/day). PCB concentration in the bottom
materials range from about 200 ng/g near Fort Edward to about 4 ng/g near Waterford,
about 70 km (43 mi) downstream. In 1975 the New York State Department of Environmental
Conservation began a study to determine the source of contamination. At that time
they estimated that the total mass of PCBs in the bottom sediments was 225,000 kg
(500,000 Ibs).
It has been found that PCBs are being naturally desorbed from the river bed
under moderate and low flow conditions. The estimated transport rates are:
At Glen Falls « 0.0 kg/day (above discharge)
At Schuylersville « 4.0 kg/day
At Stillwater - 5.0 kg/day
At Waterford « 4.0 kg/day (70 km downstream).
It is interesting to note that these transport rates are approximately 30 percent as
-436-
-------
Glens
Hudson
Falls
N
0 $ 10 IS KILOMETERS
FIGURE IV-51 LOCATION MAP OF HUDSON RIVER, NEW YORK,
high as the original point source mass emission rates. At a desorption rate of about
4 kg/day, the river between Glen Falls and Waterford would be rid of PCBs in about
150 years.
Turk (1980) found that flood events transport large quantities of PCBs, although
this transport mechanism is only operative periodically. Turk estimated that due to
the combined removal rates of PCBs during high flow periods (by scour) and during low
flow periods (by desorption), the residence time of PCBs above Waterford would be
about one century.
EXAMPLE IV-18
For discharges of 600 m /sec or less, it has been found that the Hudson River bed
provides 4 kg/day of PC8s to the water column at locations between Schuylersvi1le
and Waterford, New York. Determine the PCB concentration in the water column at
the following two flow rates:
-437-
-------
a. 600 iir/sec
b. 50 nr/sec.
Compare these concentrations to the freshwater criterion of 0.001 jig/1 promulgated
in the "Red Book".
Since the mass emission rate and river flow rate are known, Equation IV-11
can be rearranged to yield the total Instream concentration:
C» '
, 1.5 x 600 V 80 / . 160 «« - 16 00
/I00-80> Jo on
1.5 "
where J
M • mass loading, kg/day
C « concentration of pollutant, ppm
3 i
Q « flow rate, m /sec. ;
I For the problem at hand: I
| M « 4 kg/day j
j Q • 50 and 600 m3/sec. j
3
j For Q » 600 fir/sec: j
i CT - -soxm ' °-08 x 10~3 ppm i
| « 0.08 ng/1, or 80 times the Red Book criterion. |
| For Q « 50 nr/sec: |
I CT " 86.44x 50 " °'9 x 10"3 ppm j
I « O.g Kg/U or gOO times the criterion. I
As a second part to the problem estimate the time required to remove the PCBs
in the sediment by desorption (ignoring scour), assuming the desorptlon rate of 4 ;
I kg/day is not known. Base the calculations on Table 1V-39 or Equations 1V-130 and \
j IV-131. Use the following data: ]
I Depth of contaminated sediment • 600 mm j
| River velocity » 1 fps I
j Partition coefficient : 103 to 104 j
j Because the depth of contamination exceeds the maximum value tabulated in Table i
• IV-39, Equations IV-134 and IV-135 are used Instead. Assuming S - 1.5 and P « 80: '
i i
i Hs , ,6°° ,nA..M * M 9/0.7
-438-
-------
The effective transport velocity 1s:
ue" erriS*m -25 x 10 u for "p
' and
Ue ' 6TTTO-5 - -25 x 10-3 U for Kp - 103
I The time required for desorption over the 70 km (43 mi) reach is:
j
| T - JVlO^°x l Sec - 290 year for Kp - 104
I
and
T « 29 years for K - 10
| Probably the biggest unknown in this problem is K . Based on a range of
34 ^
| K from 10 to 10 , the time of desorption ranges from 29 to 290 years,
j within the range predicted from observed desorption rates.
L..
END OF EXAMPLE IV-18
4.9.3.5 Instantaneous Releases of Low Density Toxicants
Many toxicants have specific gravities less than or equal to unity. Should a
toxicant less dense than water be spilled in its pure form, the toxicant can ride
atop the water body for a period of time, while (perhaps) being rapidly volatilized
and photolyzed as it becomes entrained and dissolved in the river.
Analysis of releases of low density pollutants Is complicated and, 1n many
cases, beyond the scope of hand calculation analyses. Often spills of toxicants
occur over a part of the river, so the resultant movement Is three-dimensional
because the toxicant spreads laterally, longitudinally, and vertically due to
turbulence and advectlon. Buoyant spreading and mixing can further complicate
the dispersal process.
Toxicant spills can occur 1n numerous ways. In one Instance the toxicant
may be discharged directly onto the surface of the river, and depending on the
rate of mixing with ambient water a significant portion could volatilize directly
from the pure phase. On the other hand submerged spills may result in the chemical
becoming mixed with river water before 1t reaches the water's surface. Under these
circumstances volatilization fluxes will not be as great.
When a chemical is spilled In pure form, the time required for the chemical
-439-
-------
It Is worthwhile to calculate the volume of water required for a mass M of
spilled chemical to be diluted to Us solubility Unit. This can provide a rough
Idea as to whether mixing 1s likely to be "Instantaneous" or not. Suppose that a
mass M of spilled chemical has a solubility C . The volume of water needed to be
mixed with the pure chemical so that the solubility 11m1t 1s achieved 1s:
V0 - M * 10 (IV-138)
where
M » mass of sp11 1 , kg
C • solubility, mg/1
5 3
V - volume of water, m
o
The concentration profile resulting from an Instantaneous spill (and assuming
concentrations at or below the solubility limit are rapidly attained) 1s expressed
by:
where
C • dissolved phase concentration
k1 + r-k,
V
M « total mass released
The remaining variables have been previously defined.
In most Instances the user would like to predict the maximum concentrations remaining
1n the river for different elapsed times following the spill, given by the peaks In
Figure IV-53. Under such conditions, and assuming P • 0, Equation IV-139 simplifies
to:
exp (-kl) {IV-140)
The various components of the mass balance at time t follow (for P • 0).
Mass of dissolved pollutants MQ (t • t$):
M0 (t • t$) ' M
-442-
-------
25
20
CD
a.
a
o>
'10
\
h \
\25Miles
\
\
XTSMiles
1 ,
\
""*"-• -^ .125 Miles
1 ,A~;-
200 Miles
"A;—
250 Miles
TV
50 100 150
Hours from Beginning ol Spill
200
250
FIGURE IV-53 HYPOTHETICAL DISTRIBUTION OF TOXICANT AT VARIOUS
LOCATIONS FOLLOWING A SPILL
Mass of sorbed pollutants M (t « t ):
KpS MD exp (-kfits)
Mass of pollutant which was volatilized My (t « t$)
Mv (t - ts) - ^v [1.exp(-kets)]
(IV-142)
(IV-143)
Mass of pollutant which has decayed MQk (t • t$):
Dk
- [l - exp(-kets)J (IV-144)
Equations IV-140 through IV-144 allow the user to assess the fate of the pollutant
for any desired time t$ following the spill.
A direct extension of the instantaneous pollutant release in a plane is the
volumetric release, where the pollutant is effectively released within some Initial
-443-
-------
^L [e
2V0 L
volume of water. For this case, the dissolved phase concentration Is:
| erf t^^-} -erf (*4^M| (exp(-k t)) (IV-145)
\v40t / \v4Dt /I \ /
where
L • length of zone of Initial contamination
erf « the error function
All other variables have been previously defined.
The location of the maximum concentration for any time t$ after release 1s
approximately given by:
x - Ut$ + L/2 (IV-146)
4.9.3.6 Spill Analysis of High Density Toxicants
Spills of hazardous chemicals have been of concern for quite a number of years,
and Interest will increase as the quantity and variety of toxicants transported
increase. In past years the primary emphasis has been on analysis and containment of
oil spills. This has probably been for a number of reasons:
• Large quantities of oil are transported, and are therefore subject
to more frequent spills.
• The environmental consequences of an oil spill can be severe and
visually offensive.
• Oil floats, so oil spills are easy to detect and monitor.
In contrast to oil, many hazardous chemicals have specific gravities greater
than one, so that in their pure form, they tend to sink in water. Table IV-40 lists
some such chemicals. Chlorine, although it may be transported under pressure as a
liquid, is a gas under atmospheric conditions. Even so, if a liquid chlorine barge
were involved in an accident on a river some of the chlorine could become dissolved
in the water since the solubility of chlorine in water 1s 50,000 mg/1, although most
would probably gasify and form a toxic cloud.
The chemicals shown in Table IV-40 are generally either slightly soluble
(10 to 10,000 ppm) or soluble (10,000 to 1,000,000 ppm). In any case the solubility
levels generally exceed or greatly exceed proposed water quality criteria. Thus if a
mass of chemical were spilled into a river, H 1s to be expected that concentrations
near the chemical's solubility limit could be detected In the immediate vicinity of
the spill. As the chemical is dissolved and travels downstream, it could eventually
become mixed over the channel cross-section and expose all organisms living within
the water column (and perhaps those living in the bedded sediments as well) to its
effects. With increasing distance the concentrations of the toxicant will decrease
to reflect the additional mixing afforded by the flow of the entire river, plus
-444-
-------
TABLE IV-40
WATER-SOLUBLE, HIGH DENSITY (p>l), IMMISCIBLE CHEMICALS
Species
Acetic acid
Acetic anhydride
Acetophenone
Aniline
Benzaldehyde
Benzyl alcohol
Bromine
Carbon disulfide
Carbon tetrachloride
Chlorine (liquid)b
Chloroform
Chloroptnalene
Oichloroethane
Ethyl bromide
Cthylene bromide
Furfural
Glycerol
Hydrogen peroxide
Mercury0
Naphthalene
Nitrobenzene
Phenol
Phenylhydrazine
Phosphorus trichloride
Trichloroethane
N-Propyltro.Tide
Ouinol ine
Tetrachlcroethane
water"
• In air. water, and its
b Under pressure.
C Mercury *n
Solubility
In water Interfaclal Tension
(mg/1) Air Water
50.000 68.030.
500.000
5.550
34.000 44.0
1.000 40.04 15.5120.
46.000 39.02Q. <-7522.!
41.700 41.520.
2.200 - *8-362Q.
500 - 4520.
50.000
5.000 27.142Q. 32.82(f
40.742Q.
9.000 23.435.
10.600 - 31.220.
4.300 - 36.5420.
83.100 43.520.
63.418.
50.000
.0005 470 3752Q.
30 28.8J27.
1900 «3.92Q.
67.000 40.92Q.
-
50.000
10 22^,.
2.500
60.000 45.020.
3.000 36.322 5.
N.A. 73.05)g. N.A.
Te-.perature is *C.
reference.
(dynes/en)*
Vapor
2>.B20.
32.720.
39-820.
42-V
-
;. 39-020.
41.520.
-
26.952Q.
18-42(T
-
-
-
24.1520.
38.3720.
43.520.
7'-lll.2'
28.e127.
43.920.
40.020.
46.12Q.
29.120.
-
19.6520.
-
-
72
-445-
-------
dispersion, degradation, and volatilization processes.
A technique 1s presented here to estimate the concentration which can exist in
the water column and the duration of the elevated levels following a spill. In
particular tools are presented to predict:
• The concentration of toxicant In the water column at the downstream
end of the spll 1
• The concentration of the toxicant after 1t has become completely
mixed with the entire river
• The time required to dissolve the spilled toxicant
• The amount of toxicant remaining sorbed to the bottom sediments and in
the pore water following dissolution.
It 1s, of course, more accurate but more costly to measure concentrations directly
rather than predicting them. However, since the toxicant 1s "somewhere" on the river
bottom, and might not be Immobile, detecting the location of the toxicant will take
time. By estimating the dissolution time of the spill, 1t can be determined if it 1s
feasible to even set up and carry out a sampling program.
The tools delineated above are useful not only to analyze spills which have
occurred, but also for answering hypothetical questions which relate to the consequence
of spills based on river traffic, sizes of containers, kinds of toxicants being
transported, and characteristics of the rivers. Based on this Information the user
can evaluate possible "spill scenarios" to predict Impacts before they occur. Such
Information would be useful to formulate post-spill responses. In situations where a
spill of a toxicant would produce extreme consequences, provisions could be made to
mitigate the consequences before they occur.
4.9.3.6.1 Description of Spill Process
Spills which contaminate rivers can be the result of a variety of accidents:
leaking barges, broken pipelines, highway accidents, and clandestine dumping.
The scope here 1s limited to those situations where the toxicant has been deposited
on the bottom of the river. This situation Is most likely to result from an accident
on or under the water's surface. Figure IV-54 conceptualizes what night happen when
a barge carrying a high density pollutant ruptures.
Depending on the volume of contaminant, the size of the hole, among other
factors, the toxicant might Issue from the barge as a continuous jet. However,
because the volumetric flow rate of the jet 1s probably small, and perhaps even
Intermittent, the toxicant probably breaks up Into drops of various sizes as it
falls through the water column. Some of the finest drops might never reach the
stream bed, but rather be transported in suspension within the water column, and
gradually dissolve. The majority of the toxicant may settle on the river bed and
form drops, globs, or pools (using the terminology of Thibodeaux, 1979). The drop
-446-
-------
Water column
velocity profile
River bottom.
sand dune-
crest and valleys, -»-
Large drops
5feU«fe#'ww^//»i;V/^/^x/vJ/^/W>^^
^eiope of zone of contamination'
Droplet Droplet-glob Pool
zone zone zone
FIGURE IV-54 ILLUSTRATION OF HYPOTHETICAL SPILL INCIDENT
(FROM THIBODEAUX, 1979),
size depends on the intrafacial tension and density differences between the toxicant
and the water (Hu and Klntner, 1955). Pools tend to form in the valleys of sand
waves, and occur when large drops or globs coalese. Thibodeaux (1980) provides
techniques to estimate the residence time of drops, globs, and pools. For the
simplified analyses here the spill Is assijmed to be 1n the shape of a continuous
pool.
4.9.3.6.2 Fate of Pollutant Following Settling
Once the toxicant has settled on the river bed Its fate 1s governed by numerous
processes. Depending on the texture of the bottom materials (e.g. sands, cobbles,
boulders), the density of the toxicant, and Its Interfacial tension, the toxicant
could settle in deep depressions, and dissolution would be slowed.
Many pollutants have large partition coefficients so that sorption to bottom
sediments is significant. The characteristics of the sediments affect the partition
coefficient, but in many cases sorption can compete with dissolution as a major
process controlling the pollutant's fate. Although transformation processes other
than sorption and dissolution are operative the moment the toxicant enters the water,
they are not considered here.
In September 1974 an electrical transformer being loaded onto a barge fell
Into the Duwamish Waterway in the State of Washington (Thibodeaux, 1980). 250
gallons of Aroclor 1242, a PCB mixture of specific gravity 1.4, were spilled into the
river. Divers observed that pools of free PCB on the bottom moved back and fortn
with the tide. Pools of PCBs were removed from the bottom using suction dredges, and
a second stage operation involved a high solids dredge. Probably due to its low
solubility (0.2 ppb) and high sorption characteristics, much of the PCB was recovered
(from 210 to 240 gallons).
-447-
-------
4.9.3.6.3 Predictive Tools
It 1s hypothesized that a toxicant spill contaminates an area of width W
and length L$, where the length 1s measured 1n the flow direction. The toxicant
which reaches the river bed 1s assumed to be highly concentrated, and Its dissolution
1s controlled by a thin layer Immediately above where molecular diffusion limits the
vertical flux of the pollutant. Above this layer the toxicant 1s rapidly entrained
Into the river. There are several expressions available to determine the thickness
of the diffusion layer (e.g. Novotny, 1969 and Mills, 1976). The expression developed
by Mills will be used here, because the required Information 1s easier to attain
while the two approaches appear to give comparable results. The expression Is:
11.6 • 1.49 v Rh '/6
2 - (IV-147)
Un
where
6. • thickness of diffusive sublayer
v - dynamic viscosity of water
Rh « hydraulic radius of the river
U » river velocity
n • Manning's coefficient.
Just downstream from the spill zone, but before complete mixing with the river,
the concentration of the toxicant 1n the water column 1s:
C (IV-148)
L o \ «dH U / S
where
C • (C -C ) exp
LL uo V exp
C - background concentration of chemical
C • solubility of chemical In water
0 » diffusion coefficient of chemical In water
H • water depth
U - river velocity.
The concentration at the location of complete mixing Is:
. -*} (IV-149)
~WM "L * "0 \" W /
where
W$ - spill width
W - river width.
-448-
-------
The time T. required to dissolve the chemical Is:
Td ' T-J-TJ&7-- (IV-150)
where
MQ • total amount of pollutant which is dissolved («n amount less
than or equal to the amount spllled).
As the spilled toxicant dissolves In the flowing river water, it concur-
rently diffuses Into the Immobile bedded sediments, where a portion 1s sorbed
onto the sediments. Consequently, some residual toxicant will remain in the bottom
sediments following the initial dissolution phase. The residual will then slowly
diffuse and desorb back out into the river, although diffusion deeper into the
sediments can also occur because of the concentration gradient. The time required
for the residual toxicant to naturally desorb and diffuse back into the water column
can greatly exceed the original period of dissolution.
The quantity of toxicant which resides In the sediments following the initial
dissolution period can be predicted as follows. It is assumed that the dissolution
and downward diffusion/sorption proceed independently until all the spilled toxicant
has been removed. The time t can be found such that this statement is true. From a
practical standpoint, the user can simply determine the time required for complete
dissolution, and then find the total mass which would have diffused/sorbed into the
bottom sediments during this period. Since this approach accounts for more toxicant
than was originally present, the time period should be decreased by the fractional
amount of toxicant created. If the amount of excess toxicant is no more than IS
percent of the total amount spilled, then a time adjustment is not required.
Based on the processes of sorption and diffusion the vertical profile of
dissolved chemical in the river bed at time t following the appearance of the
toxicant on the bottom Is given by:
__» . i -_* / * _\
(IV-151)
(v4Tt)
where
C • concentration of dissolved chemical 1n the pore water, in units
of mass of dissolved chemical per unit volume of pore water
Cfa • background concentration of chemical in pore water
C$ » solubility of chemical In water
z • vertical distance, measured downward from the sediment-water interface
D « o
P e
-449-
-------
0 » effective molcular diffusion coefficient
p « density of sediments
K » partition coefficient
n - porosity of porous medium.
From Equation IV-151, the total mass of pollutant found In the sediments
at time t 1s:
/(C * Cs)
(IV-152a)
(IV-152b)
w J \ 'I
where
A « spill area
C$ » concentration of pollutant sorbed to sediments, per unit volume
of pore water.
Ce can be related to C by:
/ i _ v
(IV-153)
Combining Equations IV-151, IV-152 and IV-153 the total mass 1n the sediment
1s:
/ * _ V -
(IV-154)
°'563C l * pk
. EXAMPLE IV-19
The following Is an excerpt from Chemical Engineering Volume 80, September 3,
j 1973. as reported In Thlbodeaux (1979).
I "Approximately 1.75 x 10 Ibs of chloroform were released
| from a barge that sank near Baton Rouge. Louisiana, and the
chemical began flowing down the Mississippi River toward the
Gulf of Mexico. Although state health officials did not push
the panic button, noting that they did not anticipate too much
trouble from the accident, the U.S. Coast Guard warned downriver
communities to keep a close surveillance on their water supply
systems, particularly 1f Intakes were close to the river bottom
(chloroform Is heavier than water)."
Based on the low flow conditions and the time history of the chloroform ccncentra-
-450-
-------
tion much of the chloroform (of specific gravity 1.5) was Initially deposited on
the river bed. Determine the fate of the chloroform during the first few days
following the spill. The following processes are considered:
t Dissolution Into the main body of the water
t Diffusion and sorptlon Into the bottom sediments
t Volatilization Into the atmosphere
t Sorptlon to suspended sediments.
Since chloroform 1s highly volatile and does not have a strong tendency
to sorb to solids, volatilization Is an important process controlling Us fate,
while sorption Is not. The following analysis substantiates this statement.
The data pertinent to the spill are (Thibodeaux, 1979; Neely e± ^aK, 1976):
River flow rate - 7590 m3/sec (268,000 cfs)
Width of river » 1220 m » 4000 ft
River velocity * 56.3 cm/sec « 1.85 ft/sec
Water depth » 11 m « 36.3 ft
Diffusion coefficient of chloroform in water « 1x10 cm /sec
Length of spill zone « 180 m « 590 ft
Background chloroform concentration * 5 ppb.
Using a Manning's n of 0.03, the diffusion layer thickness is:
) The average concentration of chloroform in the water just below the spill zone
I during the period of dissolution is:
j C. = (5 x 10-3 - 8200) expf -1- x 10"5 x 180 ] + 820Q
! L \2.8 x 1C'2 x 11. x 56.3/
I
« 850 ppb
• In order to estimate the time required to dissolve the chloroform the average
; width of the spill zone is reauired. The width is estimated to be 256 ft (78 m)
j (Thibodeaux, 1981).
I Based on these data the dissolution ti.ne is:
I
j T , 0.9 x 1.75 x 106 ,n .
! Td 5~38~T~.8TO x 1.85 x 156 x 36.2 2° dayS
I
, The factor 0.9 is used in the above expression because about 10 percent of
j the spill dissolved before ever reaching the bottom (Neely _et^ _a_l_., 1976).
I The amount of chloroform which diffused and sorbed into the sediments
-4E1-
-------
during this time period (20 days) will be estimated. The porosity of the sandy
bottom is approximately 0.35, and the partition coefficient is assumed to be 1.0.
This is a realistic value based on KQw « 93 (see Table II-5). The total mass
contained in the sediments after 20 days is:
• .35 (,80 x 78) (M.K x 1 x $) K^£$^^
x ID'2-3 x (5-4.437) =6000 kg
6000 kg is less than 2 percent of the total mass which reaches the bottom
(715,000 kg). Based on this result, it is not likely that the dissolution period
is markedly affected by diffusion of the chloroform into the bottom sediments.
Because of the vertical concentration gradient that has been established in the
sediment profile, some of the chloroform will temporarily continue to diffuse
downward after the dissolution period. Hence concentrations in the water column
due to desorption ;f the chloroform and upward diffusion back into the water
column are not likely to be high compared to those observed djr'ng the initial
dissolution period.
Following the chloroform spill, chloroform concentrations we measured
at several locations in the Mississippi River below the spill. Figure IV-55a
shows the time history of the chloroform concentration at a location 16.3 miles
below the spill for the first 60 hours following the spill. A more compressed
time scale is shown in Figure IV-55b and illustrates how the concentrations varied
for 20 days following the spill. The peak concentration passes very rapidly (on
the order of 1 day) and the maximum observed concentration is aoout 365 ppb. At
this location, the chloroform is approximately well-mixed with the river at this
point (Neely et_ aj_., 1976).
Based on Figure IV-55b the total amount of chloroform passing the location
can be estimated as follows:
Mass » /CQdt » Q f Cdt
•/CQdt - Q/"
j The right-most integral is six.ply the area under the concentration-time curve in
• Figure IV-55b. Without showing the calculations, the total -nass of chloroform
(above background) which passes the location 16.3 miles below trie so'll is about
; 300,000 kg. Since the total amount of chloroform spilled was about 300,OCO kg,
I more than half of the chloroform was unaccounted for. It is unlikely, as earlier
I calculations showed, that diffusion and sorptlon into the bottom seaiment
I significant. Volatilizal'on could be important and will be c^ scussed shc
-452-
-------
350
300
o
a 250
c
o
£ 200
4)
U
§
O
n 150
-J
.O
O
100
50
•Actual Data
10
20 30 40
Elapsed Time. Hours
50
60
FIGURE IV-55A
CHLOROFORM CONCENTRATION IN WATER COLUMN
FOR FIRST 60 HOURS FOLLOWING A SPILL
16,3 MILES UPSTREAM,
The observed results shown 1n Figure IV-55a are compared against those
predicted in this example. A concentration of 850 ppb was predicted just below
the spill site; the maximum shown in Figure IV-55a is 365 ppb. It is expected,
for several reasons, that the concentrations 16.3 miles below the spill site will
be less than at the spill site. First it is probable that additional dilution
occurred as the chloroform was transported to the sampling site. An estimate of
the dilution can be attained by multiplying the river width by the spill width,
or:
4000
15
ne ^ell-mixed concentration becomes:
> 60 ppb
| Comparing this to Figure i'/-55a, 1t Is noted that this value approximates the
I average concentration following an elapsed time of about 20 hours, but misses the
j peak during the first 20 hours. There may be a numoer of factors responsible for
-453-
-------
400
I300
5
&
M
I
.1 200
n
"E
E
5
I
100
FIGURE IV-55B
' r ' i • i
• Field data
• ••
CHLOROFORM CONCENTRATION IN THE
MISSISSIPPI RIVER AT A LOCATION 15.3
MILES BELOW THE AUGUST 19, 1973 SPILL.
this behavior, and one o* the most Important will be examined here. During tne
spill of chloroform, It was estimated that about 10 percent, or 80,000 kg were
transported downstream directly without ever reaching the river bottom. The
travel time to the sampling site 1s:
TTO ' » »«"•
Figure IV-55a shows that this coincides with the arrival of the peak at mile
16.3. The peak concentration can be estimated using Equation IY-140 presented
earlier. The diffusion coefficient 1s approximately 210 m /sec (McQulvey
et^ aj_., 1976) for the lower Mississippi River. The predicted peak In concentration
at mile 16.3 1s:
80000 x 10'
2 x 4000 x 36.3 x (.3048)2 \V210'3600-13
520 ppb
-454-
-------
This concentration is somewhat higher than the maximum 365 ppb observed, but j
this is to be expected since Equation IV-140 assumes the mass is input instantan- I
eously, while in reality about 8 hours elapsed. Further if the concentration due |
to the dissolved portion of the spill 1s calculated at 20 hours, a concentration j
of 15 ppb 1s obtained. This illustrates that the mass due to initial dissolution j
has almost passed the sampling location, and the remaining contribution to the
elevated concentrations measured 1s due largely to dissolution of chloroform which
has settled on the river bottom. It appears that there are two basic phenomena j
which account for the measured concentration-time profile: an initial period of I
dissolution of chloroform (less than 1 day) before it settles to the bottom, and a |
subsequent period (10 to 15 days) of dissolution of settled chloroform. j
The absence of an adequate mass balance between the amount of chloroform j
which entered the river as a result of the spill and the amount which passed a
location 16.3 mi below the spill has not been addressed. Volatilization losses
could be one reason for the imbalance. .
Equation IV-123 can be used to estimate the volatilization losses. Since the J
chloroform was initially deposited on the bottom of the river, during a portion of I
the travel distance it was not in contact with the atmosphere, and so volati 1 ization |
could not occur. The approximate travel time for vertical mixing to occur is j
(Fischer et aj.- • 1979> : i
"•V !
where I
H » water depth |
e « vertical diffusivity. |
Choosing an e value of 50 cnr/sec, based on Fischer £t^ _ah (1979) and a j
depth of 11 m, the travel time required to effect vertical mixing is: j
t . 0-4 (HOP)2 hr . 2 7 hrs '
1 50-3600 nr '•' nrs j
Based on a velocity of 1.85 ft/sec, the travel distance Is about 3.3 miles. I
Hence the pollutant is in contact with the atmosphere for about 13 miles. |
Since only the dissolved phase of chloroform volatilizes, the fraction j
of the total chloroform as solute will be estimated using Equation IV-109: '
The partition coefficient K was estimated as 1.0. The sediment concentra-
tion is about 400 ppm. Hence:
1 + 1 x JOO + 10-
-455-
-------
I Thus, essentially all the chloroform is dissolved and is available for volatiliza- I
j tlon. |
j Henry's Law constant for chloroform can be found based on the data in j
• Table II-5: j
Vapor pressure « 150 Torr
j Solubility in water » 8200 ppm
I Molecular weight « 118. (
| Henry's Law constant is: I
1 ISO x 118 . 3 ]Q_3 atm-rn3 '|
j 760 x 8200 mole
From Table 11-15 a typical volatilization rate 1s about 17 cm/hr.
j The average chloroform concentrations for the 13 miles above the data ;
I collection point are: I
j 200 ppb for 1 day I
| 40 ppb for the next 9 days |
j 10 ppb for the next 9 days. j
The total amount of chloroform volatilized Is (using Equation IV-109): j
j Z kv C1 Ac At j
- 0.17 x 24 x 1200 x 21 x 103(200 + 40 x 9 + 10 x 9 -5 x 19)x 103 j
j • 5.8 x 107 - 58000 kg j
j Hence, all of the unaccounted for chloroform (about 480,000 kg) could not have |
j volatilized within 13 miles. j
Over 50 percent of the chloroform still remains unaccounted for. It Is j
. possible that other transformation processes were operative. The environmental •
I fate of chloroform In terms of photolysis, hydrolysis, oxidation, and biological
I degradation was reviewed In Callahan e£ _aK, 1979. It was concluded that these !
I processes are of minor Importance compared to volatilization and so are probably j
| not significant here. I
j It 1s possible that the samples of chloroform shown in Figure IV-55b were not |
j cross-sectional averages. The chloroform concentration could have been weighted j
> toward the stream bottom or toward one side. A dye study performed by McQulvey j
! (1976) on the lower Mississippi River showed that 50 miles were required before
j complete mixing was attained, while the sampling was conducted 16.3 miles below
I the spill. Even though chloroform does not sorb strongly, there is a possibility '
| that the suspended solids and bed load concentration near the bottom of the river I
j were high enough to cause substantial sorptlon. Based on the evidence there Is a j
j distinct possibility that some of the "missing" chloroform was actually advected j
; past the sampling locations without being detected. j
I OF £XAMPLE Iv.19
-456-
-------
4.10 METALS
4.10.1 Introduction
4.10.1.1 Background
In addition to organic chemicals, metals comprise a second major category of
toxic contaminants which are discharged Into rivers. Metals differ from toxic
organics 1n a number of ways, and these differences influence the approach used to
predict their fate. One difference is that metals are naturally occurring elements
and their fate can be detailed individually since the number of different elements is
relatively small. In contrast, an individualized approach is not always feasible for
the thousands of organic toxicants. However, basic properties of many organic
chemicals have been tabulated or are derivable which can be used to predict their
fate.
Two, organic chemicals are occasionally spilled into rivers because many of the
chemicals are transported in large volumes. Metals, on the other hand, most often
enter rivers frcn continuous sources. Consequently, methods to handle spills, wnile
being an integral part of the screening procedures presented for organic toxicants in
the previous section, are not emphasized here.
Three, metals are naturally occurring and are cycled througnout the environment
by biogeochemical processes. Consequently it is not appropriate to arbitrarily
ignore background concentrations of metals, an approach reasonable for synthetic
organic toxicants. Background sources of metals can produce concentrations which, in
certain instances, approacn water quality standards.
Four, the fate of many metals is predominantly controlled by transport processes
since they generally do not degrade, volatilize, or photolyze as do many organic
toxicants (altnougn there are exceptions). However, metals do speciate into many
different forms in tne aquatic environment, and the species may differ in toxicity
and behavior.
4.10.1.2 Organization
Screening metMccs presented in Section 4.10.3 can be used to predict the fate of
metals. These tools assume that metals are distributee between two basic phases:
dissolved and adsorbed. Linear partitioning is used to represent equilibrium adsorp-
tion and thus to quantitatively relate the two phases.
In Section 4.10.4, a detailed analysis of the speciation of arsenic, cadmium,
chromium, copper, lead, mercury, nickel, silver, and zinc (As, Cd, Cr, Cu, Pb, Hg,
Ni, Ag, and Zn, respectively) is presented. The major processes affecting speciation
are delineated, and the equilibrium moael MiNEQL is used to predict the speciaticn of
above solutes for 14 cifferent rivers and an acidified lake m tne United States.
-457-
-------
Simulated metal concentrations vary from background to well above the 1984 U.S. EPA
water quality criteria.
While the tools presented in Section 4.10.3 can be used independently of metal
species distribution predicted by MINEQL, the two approaches can be coupled together
to estimate species concentrations at different locations throughout a river. The
steps required to accomplish this are described at the end of Section 4.10.4.
Because of the potential importance of background contributions of metals,
methods are presented in Section 4.10.2 to address this problem. Since background
sources can be significant, this contribution should not be arbitrarily dismissed.
Numerous case studies of metals in rivers In the eastern and western United
States are also reviewed. The reviews may help the user to understand how metals
respond to different aquatic conditions and to establish concentration ranges which
have been documented in past studies.
Finally, in Section 4.10.5, guidance is provided for a limited field sampling
program and river/stream reconnaissance. A primary reason for suggesting a low-level
data collection program is a concession to the difficulty of predicting metal concen-
trations in rivers. Although users are not required to perform a field study before
doing the screening analyses, in some instances they may decide a limited field study
is appropriate.
The data requirements for the screening methods are summarized in Table IV-80 of
Section 4.1C.5. Because degradation or removal rates are not required for the
screening analyses, the data requirements are somewhat more modest than for organic
toxicants. The more important data are flow rates, loading rates, background levels,
and partition coefficients. Section 4.10.5 provides more discussion on the relative
Importance of the data requirements. A summary of the screening methods for metals
Is sho«n 1n Figure IV-56. An application and summary of the methods has recently been
published (Mills and Mok, 1985). Also, many of the algorithms presented in Section
4.10 have been programmed for microcomputers (Mills, et_ £l_., 1985).
4.10.2 Water Quality Criteria, Background Concentrations, and Case Studies
4.10.2.1 Witer Quality Criteria
Table IV-41 summarizes the most current U.S. EPA criteria (Federal Register
July 2?, 1985 »nd November 28, 1980) for the protection of freshwater aquatic life
for arsenic, cadmium, chromium, copper, lead, mercury, nickel, silver, and zinc. The
1984 criteria pertain to arsenic, cadmium, chromium, copper, lead, and mercury. The
1980 criteria pertain to nickel, silver, and zinc. Many of the criteria depend on
water hardness. Examples are shewn in the table for hardnesses of 50, 100, and 200
mg/1 as CaCOj. At the bottom of the table, expressions relating hardness to trie
water quality criteria are shown. Note that the water quality criteria are expressed
a-s total dissolved netal.
-453-
-------
Review Case Studfts
(Section 4.10.2.3)
Decide if United field
Reconnaissance and/or Sampling
Program 1s Appropriate
(Section 4.10.S)
Identify and Quantify Background
Contributions (Section 4.10.2.2)
and Primary Anthropogenic
Sources (Chapter 3)
and Suwarue Data Needed for Analysts
(Table iv-73)
c
Start
Consider More detailed
Study or Implementation
of Renedlal Action
Oocu*ent Problen Areas
Apply Screening Methods (Section 4.10.3)
e Dilution Only
e Dilution Plus Scour of Sediments
e Dilution Plus Deposition of Sediments
e Presence of Lakes
e Desorptlon From Bedded Sediments
Determine Concentrations of
Metals Over Time and Distance
(Section 4.10.3)
Are As. Cd. Cr, Co, Hg, N1
Pb, Ag, or In
Being Analyied
Review General Description
of Fate (Section 4.10.4.1)
Review Appropriate
Metal Fate Discussion
(Section 4.1C.4.2)
Estimate Metal Speclatlon
(Section 4.10.4.3)
Superpose Adsorption Isotherm
Estimate Levels of
Most To»lc Soecies
FIGURE IV-56 SUMMARY OF SCREENING PROCEDURES FOR METALS IN RIVERS
-459-
-------
TABLE IV-41
WATER QUALITY CRITERIA FOR SELECTED PRIORITY METALS FOR
PROTECTION OF FRESHWATER AQUATIC LIFE
(1980 and 1985 U.S. EPA Criteria)
Total Dissolved Metal8
4 day Average Concentration
Not to Be Exceeded More
Than Once Every 3 Years'* »c
1 hour Average Concentration
Not to Be Exceeded More
Than Once Every 3 Yearsb«c
Arsenic
(trlvalent Inorganic)
Cadmium
Chromium (hexavalent)
Chromium (trlvalent)
Copper
Lead
Me rcu ry
Nickel
Silver
Z1nc
190
0.66, 1.1, 2C
11
120, 210, 370C
6.5, 12, 2ic
1.3, 3.2, 7.7C
0.012
56, 96, 160C
(30 day average)
47
(30 day average)
360
1.8, 3.9, 8.6C
16
980, 1700, 3100C
9.2, 18, 34C
34, 83, 200C
2.4
1100, 1800, 3100C
(Instantaneous maximum)
1.2, 4.1, 13C
(Instantaneous maximum)
180, 320, 570C
(Instantaneous maximum)
'The total dissolved metal 1s defined to be "add soluble". No approved methods are
presently available. The total recoverable method 1s recommended.
bThe water quality criteria ( g/1) are related to water hardness (mg/1 as CaCOj) as:
Arsenic: independent of hardness
r.Hm-i ,m . e*P (0.7852 (In (hardness)) -3.49), 4-day average
caamium gxp ( M28 (ln (hardness)) -3.828), 1-hour average
Chromium (VI): Independent of hardness
rh..««< - /TIM . exP (0.819 (In (hardness)) + 1.56), maximum, 4-day average
Chromium (nij Mp (Q>819 (1|) (hardncss)) <. 3.688), 1-hour average
r «... . exP (0.8548 (In (hardness)) -1.465), 4-day average
Lopper exp (0.9422 (In (hardness)) -1.464), 1-hour average
, H m exp (1.266 (In (hardness)) -4.661), 4-day average
™ exp (1.266 (In (hardness)) -1.416), 1-hour average
Mercury: Independent of hardness
Nirk»i « exP (O-76 0° (hardness)) +1.06), 30-day average
c exp (0.76 (In (hardness)) +4.02), maximum at any time
Silver * exp (1.72 (In (hardness)) -6.52), maximum at any time
Z1nc » exp (0.83 (In (hardness)) +1.95), maximum at any time
cThe three water quality criteria are examples for total hardness levels of 50, 100,
and 200 mg/1 as CaCOa.
-460-
-------
4.10.2.2 Background Levels of Metals
4.10.2.2.1 Introduction
In contrast to most organic toxicants which are not initially present In the
environment, metals occur naturally and cycle by blogeochemical processes throughout
the environment. Consequently, of the metals that may be present In a stream or
river, a small fraction, a moderate fraction, or nearly all might be from natural
sources.
When trace metal concentrations in streams are analyzed to see whether water
quality standards are violated, and whether wasteload allocation schemes are required,
a knowledge of background sources should be included as a part of the analysis.
Background sources can be defined to include both natural sources and sources produced
by man which are transported across watershed boundaries (e.g. dry deposition of
metal-enriched ash). Background sources can also be thought of as sources which are
not readily controllable, and thus contributions from these sources are likely to be
present regardless of the remedial action chosen.
In this section, coverage of background sources is limited to weathering from
rocks and riparian soils. Typical values of metal concentrations are provided.
However, metals are not uniformly distributed throughout the environment but can be
locally enrlchea in natural deposits. Should a river intersect a mineral deposit, the
levels of metals in the stream from this source can be high. Contributions of
background sources can be quantified by sampling upstream of the locations of major
anthropogenic Influence.
4.10.2.2.2 Stream Contributions From Rocks and Soils
Tables IV-42 and IV-43 summarize data which show typical concentrations of
metals and Inorganics in soils and rocks. The soil samples from New Jersey and New
York in Table IV-42 are generally similar to average concentrations in the earth's
crust. However, deviations can occur locally, so these numbers should be used with
caution. Soil Conservation Service soil surveys might provide data on levels of
metals 1n local soils. Chapter 3 also provides additional data.
Concentrations of metals 1n streams from the background sources can be estimated
from the following formula:
Cb » X-S-10'3 (IV-155)
where
X • metals concentration in soils, M-g/g
S • background Instream suspended solids concentration, mg/1
£. « total, metal concentration 1n the stream due to the soil and rock particles
b
in suspension and may include a dissolved component, ng/1.
As an example, suppose a stream has a background suspended solids level of 40 mg/1.
Based on a typical :inc concentration of 80 *g/g in soils,
-461-
-------
TABLE IV-42
TYPICAL CONCENTRATIONS OF JCTALS IN SEVERAL SOILS
AND IN THE EARTH'S CRUST
(Values 1n jig/g)
Metal
Ag
As
Cd
Cr
Cu
Hg
N1
Pb
Zn
Soils In
New Jersey*
—
—
--
9.3
40.5
--
11.9
86.8
96.3
Soils 1n Upstate
New York6
—
—
0.2
—
21.6
—
--
7.9
79.9
Average 1n
Earth's Crustc
0.5
5.0
0.15
10. -100.
4. -55.
0.005. -1.0
80.
15.
50.
"Kubota et aK (1974).
^Hber and Hunter (1979).
°Weast (1977).
Cb • 40-80-10"3 « 3 ng/1 of zinc
This 1s less than 10 percent of the U.S. EPA criteria level of 47 ng/1 (Table IV-41),
However, for some of the other metals (e.g. copper), typical contributions from
background sources can approach the 30-day criteria.
For a number of the metals (Cr, Cu, N1 , Pb, and Zn), background levels of about
1 ng/'l are common. For Ag, Cd, and Hg, background levels are probably closer to 0.1
, or even less.
4.10.2.3 Case Studies of Metals In Rivers
4.10.2.3.1 Introduction
This section provides a sampling of case studies of metals 1n rivers. Case
studies help to reveal Important master variables which control the fate of metals
-462-
-------
TABLE IV-43
AVERAGE CONCENTRATION OF METALS IN VARIOUS TYPES OF ROCK
AND DEEP OCEAN SEDIMENTS
(Values in jig/g)
Plutonic
Granitic
Chromium
Manganese
Iron
Cobalt
Nickel
Copper
Zinc
Chromium
Manganese
Iron
Cobalt
Nickel
Copper
Zinc
Ultramafic
1.6 xlO3
1.62xl03
9.4xl04
150
Z.OxlO3
10
50
Shale
90
950
4.72xl04
19
68
45
95
Basaltic
170
1.5 xlO3
8.65xl04
48
130
87
105
Sedimentary Rock
Sandstone
35
100
9.8xl03
0.3
2.0
1
16
Plagioclase
22
540
2.64xl04
7
15
30
60
Carbonate
11
l.lxlO3
3.8xl03
0.1
20
4
20
Orthoclase
4.1
370
1.42xl04
1.0
4.5
10
39
Deep Ocean
Carbonate
11
l.OxlO3
9.0xl03
7
30
30
35
Syenite
30
850
3.67x10*
1
4
5
132
Sediments
Clay
90
6.7xl03
6.5xl04
74
225
250
165
From: Rubin, 1976.
-463-
-------
Since there are potentially many processes which Influence the behavior of metals In
aquatic systems (see Section 4.10.4.1), elimination of processes of secondary Im-
portance Is beneficial In screening analyses.
Some of the questions which users are likely to pose during a fate analysis
are:
• Is downstream transport of metals Important? That 1s, do metals move
downstream 1n the water column significant distances below their points
of entry or are they rapidly deposited and/or adsorbed 1n the bedded
sediments so that water column concentrations are rapidly depleted?
• Are the metals In the water column present In adsorbed or dissolved
form? Dissolved species are likely to be more toxic and can be trans-
ported further than the particulate form of the metal.
• What Is the relationship between water column concentrations and con-
centrations In the bedded sediments? Metal concentrations In bedded
sediments are often found to far exceed those 1n the water column.
• What metals are typically present 1n rivers 1n the highest concentrations?
• What is the effect of a reservoir (or large backwater region) on the
metal concentrations further downstream?
• Is metal desorption from bottom sediments likely to occur as a result of
decreased water column concentrations? Desorption Is a natural cleansing
mechanism, but may also take a significant period of time (e.g. 1 to 5
years for several stream miles). During the period of desorption, a
low level of metal is maintained in the water column.
A review of case studies often provides insights Into resolving these questions,
and others which arise during the course of a study. Methods to address each of
these questions are presented in Section 4.10.3, and general qualitative answers to
these questions are provided i-i Section 4.10.3.3.
Before discussing Individual case studies, the U.S. Geological Survey's NASQAN
network is briefly mentioned. Through the USGS's National Stream Quality Accounting
Network (NASQAN), water quality samples are collected at approximately 345 stations
throughout the United States (3r1ggs and Ficke, 1977). Among the quality parameters
measured are arsenic, cadmium, chromium, copper, lead, mercury, selenium, and zinc.
The data contained in Briggs and Ficke (1977) are summarized in Tables IV-44 and
IV-45.
Note that the upper limits of the measured concentrations for cadmium, ::irani urn,
copper, lead, and zinc are, at times, close to U.S. EPA criteria for Instantaneous
maximum levels (see Table IV-41). The upper levels measured for mercury occasionally
exceed the suggested criteria of 4.1 ng/1 (instantaneous maximum). These results
suggest that cadrrium, chromlun, copper, lead, zinc, and mercury often require careful
investigation on a 3ite-by-site basis.
-464-
-------
TABLE 1V-44
RANGES OF CONCENTRATIONS OF DISSOLVED MINOR ELEMENTS MEASURED AT NASQAN STATIONS DURING THE 1975 WATER YEAR,
SUMMARIZED BY WATER RESOURCES REGIONS (Briggs and Ficke, 1977)a
Range of Concentrations (/
-------
TABLE 1V-45
HANWS OF T01AL CONCFNTRAT10NS OF MINOR FLEMENTS MEASURED AT NASQAN STATIONS DURING THE 1975 WATER YEAR,
SUMMARIZED BY WATER RESOURCES REGIONS (Briygs and Ficke, 1977)a
Range of Concentrations (fg/1)
Region
Nunber and N.une
01 New England
02 Mid-Atlantic
03 South Atlantic-Gulf
04 Great Lakes
OS Ohio
06 Tennessee
07 Upper Mississippi
08 Lower Mississippi
09 Souris-Red-Rainy
10 Missouri Basin
11 Arkansas-White-Re
-------
4.10.2.3.2 Case Studies
The following case studies illustrate a range of metal concentrations which are
present in rivers and streams in the United States. The case studies are summarized
in Table IV-46. A number of different source types are represented.
4.10.2.3.2.1 Flint River. Michigan
The Flint River study in Michigan (Delos et _al., 1983) provides a detailed
account of the fate of zinc, cadmium, and copper in the river. Results of the study
are used in Section 4.10.3 to compare against predictions made by the analytical
screening procedures in this document.
During the Flint River study, zinc, cadmium, and copper were analyzed in a 60 km
(37 mile) stretcn of the river. Data were collected in August 1981, December 1981,
and March 1982. The watershed is both agricultural and urban. Two wastewater
treatment plants provide the main sources of metals within the study reach (in
addition to the flux of metal across the upstream boundary).
Table IV-47 summarizes the reported average metal concentrations and average
suspended solids levels. The metal concentrations shown in the table are the range
of averages at the locations sampled (typically 5 to 9 stations in the 60 km reach).
Zinc and cadmium levels are generally below the criteria levels of 47 ng/1 and 2.0
Kg/1, respectively, and copper is near its criterion level.
In most cases, the levels of metals in the water column do not decrease substanti-
ally with distance downstream. Figure IV-57 illustrates the total and dissolved
copper levels during the August 1981 survey. Wastewater discharges are present at km
41 and km 71. Only minor sources are present between these locations.
4.10.2.3.2.2 Chattanooga Creek, Tennessee
Chattanooga Creek is tributary to tne Tennessee River and is 42 km (20 miles) in
length. The basin is significantly industrialized and contains 13 permitted indus-
trial sources, as well as agricultural and domestic discharges. Past studies indi-
cate that the creek is degraded by both conventional ana toxic pollutants. The
September 1980 study of Milligan et_ a_l_. (1981) report tnat the creek is contaminated
with organic and inorganic toxicants. Their findings related to metals are summarized
here.
Figure IV-58 shows the 12 sampling locations selected in tne lower 15 km of the
creek. The priority metals detected in the water column and in the sediments are
summarized in Table IV-48. The metal concentrations are generally indicative of
contaminated conditions. Mercury levels in the water column (0.3 to 0.9 ug/1) are
above the 1984 U.S. EPA criteria for the protection of aquatic life (0.2 ^g/1 for
chronic toxicity). Chromium and zinc levels are near their criteria limits. Levels
in the water column appear to be fairly constant ove<- distance. As noted b> M-ni
-467-
-------
Table IV-46
SUMMARY OF CASE STUDIES
Location
Flint River, MI
Chattanooga Creek, TN
North Fork Holston
River, VA
Slate River, CO
Saddle River. NJ
Cayuga Watershed, NY
*
Acute criteria exceeded.
Source of Metal
wastewater
Industrial
chloralkall
plant
mine drainage
urban
rural
Metals
Zn, Cd, Cu
Cr, Hg, Zn, As,
tig
As, Cd, Cr, Cu,
Pb, N1, Ag, Zn
Pb, Zn, Cu, Ml.
Pb, Cd, Zn, Cu
Concentrations EPA Chronic Downstream
Measured In Criteria Transport
Water? Bed? Exceeded? Important?
Yes No No Yes
Yes Yes Hg, Zn Yes
Yes Yes Hg Yes
Yes* No As,* Cd,* Cu* Yes
No Yes -- not documented
Yes No No not documented
-463-
-------
TABLE IV-47
SUMMARY OF METAL AND SUSPENDED SOLIDS CONCENTRATIONS IN FLINT RIVER, MICHIGAN
Suspended
Study Period Solids (Bg/l)
August 1981
December 1981
(Run 1)
December 1981
(Run 2)
Harch 1982
4.
a.
6.
16.
-13.
-15.
-22.
-24.
Zinc (Mg/l)
Total Particulate
8
5
6
7
.-24. 6. -20.
.-14. 2. -11.
.-30. 2. -22.
.-17. 4. -8.
Cad*lui (pg/1)
Dissolved
4. -12.
4. -14.
2. -8.
4. -6.
Total
0.05-0.16
0.05-0.15
0.02-0.13
0.02-0.06
Parttculate
0.04-0.1
0.02-0.08
0.01-0.08
0.01-0.02
Dissolved
0.02-0.12
0.00-0.15
0.00-0.08
0.00-0.02
Copper (jig/1)
Total
2.5-8.
2. -5.
2. -15.
2.2-5.2
Particulate
1.-4.
1.-2.
1.-7.
0.5-1.5
Dissolved
2. -4.
2. -4.
1.-3.
2. -2. 5
-•I'D?-
-------
KEY
w -
1 8 "
ec*
«::
0 -
i
10 -
1 8 -
f
DISSOLVED COR
0 M * «
l 1 l 1
_ MEAN. PLUS OR MINUS
4 ONE STANDARD
1 DEVIATION
FLOW DIRECTION
II ^i
H it,
1 1 i { ' }
) 5 15 25 35 45 55 65 75
RIVER KILOMETERS
(»)TOTAL COPPER. /ig/l
FLOW DIRECTION
TT I IT "IT
M I I t f I J i
II 1}
15 25 35 45
RIVER KILOMETERS
(b)DISSOLVED COPPER, pg/l
55
65
75
FIGURE IV-57 MEASURED TOTAL AND DISSOLVED COPPER CONCENTRATIONS
IN FLINT RIVER. MICHIGAN, DURING AUGUST 1981 SURVEY,
et_ aK (1981), the levels of metals 1n the sediments are from 2 to 50 times nigner
than levels measured In the Tennessee River sediments, irfHch suggest that tre source
of metal contamination In Chattanooga Creek 1s local.
4.10.2.3.2.3 North Fork Holston River. Virginia
Wastes from an inactive chloralkall plant closed in 1972 and located on the
North Fork Holston River continue to contaminate both the water column and bottom
-470-
-------
M-t
-8S-1
NUMBER OF
POIXUMNT3 OCTfCTEO N \MKTCR
30-4
SS» - SAMPUNC STO
8CAU
0
FIGURE IV-5S
EXTENT OP PRIORITY POLLUTANT CONTAMINATION IN CHATTANOOGA
CREEK WATERS.
seainents of the river (Turner and Lindberg, 1978). The river is a fast-flowing
mountain stream with a coarse, rocky substrate in rrany areas, but with silt ano clay
substrates in backwater regions.
Two large settling ponds at the plant site drain into the river and provide the
source of contamination. Upstream of tne ponds the levels of mercury are low. Below
the plant, tne levels increase significantly, as shown in Table IV-49. upstream of
the plant, the mercury in the water column averages O.C08Mg/l, while downstream, the
average is 0.15 ng/1, a 20-fold Increase. Approximately a third of the mercury below
the discharge 1s 1n dissolved form.
Plots of total mercury in the water column versus distance below the waste
discharge were developed by Turner and Lindberg for low and high flow rates. They
are shewn in Figure IV-59. They plotted predicted levels of mercury versus distance,
assuming that the mercury behaves conservatively 1n the water column. At high flow,
the mercury appears to be conservative while at low flow rates, seme loss of mercury
-471-
-------
TABLE IV-48
INORGANIC PRIORITY POLLUTANTS DETECTED IN CHATTANOOGA CREEK, SEPTEMBER 1980
Station Number and Chattanooga Creek Mile
1
dMpouftd 9.1
ChrootuB. total
Mercury, total 0.3
Zinc, total 19.
2
6.6
0.3
31.
3
Pond
(6.3)
0.3
29.
4
5.2
142.
0.3
22.
5 6
UruiMMd
Tributary
(4.5) (4.5)
Water Colum
0.8 0.9
55. 140.
7A
(4.15)
Sables (i
0.5
24.
78
(4.15)
>g/l)
0.4
23.
8 9
Dobbs
Brook
(2.2) 2.1
0.9 0.9
52. 30.
10
0.6
0.4
38.
11
UnnaMd
Tributary
(0.3)
0.5
40.
12
0.1
0.3
43.
Sediment Simple* (iig/g)
Arsenic, total
leryllliM. total
CtdaluB. total
ChroBtuB. total
Copper, total
Lead, total
Mercury, total
Nickel, total
SelenluM. total
Stiver, total
Zinc, total
0.2
0.295
..
21.
2.4
7.04
0.85
10.3
-
-
25.
4.1
0.56
.
20.
8.6
26.
0.98
23.
-
-
45.
3.0
0.48
.
33.
6.7
21.
0.49
12.
-
-
29.
13
0.5
1.7
98.
140.
66.
240.
18.
-
1.0
230.
13.0
0.6
1.7
110.
27.4
37.
64.4
7.0
-
0.6
83.0
8.5
0.7
-
76.
11.7
38.
3.5
11.0
2.5
-
62.
2.0
0.7
-
24.0
7.4
10.
0.01
10.
-
-
37.
2.4
0.7
-
37.
8.2
16.
0.9
11.
-
-
46.
2.
0.5
1.2
35.
32.9
232.
1.8
16.
-
0.6
234.
4.
0.5
0.4
59.
28.6
106.
2.3
21.
-
0.4
154.
1.7
0.3
1.9
25.0
33.0
140.
0.24
13.0
-
1.7
380.
1.2
0.4
4.0
12.0
48.0
250.
1.8
20.1
-
1.6
1.100.
1.2
0.8
2.4
26.0
33.0
150.
0.88
14.5
.
1.7
340.
-472-
-------
TABLE IV-49
MERCURY CONCENTRATIONS IN WATER. SUSPENDED HATTER, AND BED SEDIMENTS
IMMEDIATELY UPSTREAM AND DOWNSTREAM OF FORMER CHLORALKALI PLANT
ON NORTH FORK HOLSTON RIVER
Statistic
Water Column
Total Ha Dissolved Hq
Suspended Bottom
Particulate Hg Sediment Hga
Utg/g)
Mean
Standard Deviation
Number Samples
Mean
Standard Deviation
Number Samples
*S1lt-clay fraction
0.008
0.004
10.
0.15
0.05
11.
only.
0.001
—
9.
0.05
0.02
11.
Upstream
0.41
0.17
7.
Downstream
7.6
3.8
10.
0.13
0.03
7.
19.3
1.2
3.
from the water column 1s evident. In both cases, however, mercury Is transported far
downstream (120 km) 1n significant concentrations.
Further down the river, at km 155 (not shown 1n the figure), 1s a large Impound-
ment - Cherokee Lake. Much of the suspended sediments settle In this lake and take
the adsorbed mercury with tnem. Mercury In the surflclal sediments ranges from about
0.47 ng/g to 2.4 »$/g. These levels are expected based on the levels of mercury
found In the suspended matter 1n the North Fork Holston River (Table IV-49).
4.10.2.3.2.4 Slate River. Colorado
Slate River, Colorado, 1s one of a number of rivers and creeks (see Table IV-50)
Investigated 1n a cooperative effort by U.S. EPA's Environmental Monitoring Systems
Laboratory, Las Vegas, Nevada. The purpose of the Investigations was to study
degradation and recovery of biological communities 1n streams where the toxic metal
concentrations exceed the U.S. EPA's 1930 recommended acute criteria for aquatic
life. The Slate River study Is summarized here as an example (Janlk et. al_., 1982).
Figure IV-60 shows the station locations on the Slate River and Its tributary.
Coal Creek, where drainage from the Keystone Mine enters the creek. Locations
-473-
-------
^006-
0.02-
OJ6-
aw-j
aw H
0.02-
-20 20 60 100
RIVER KILOMETERS
(•)HIGH FLOW
140
1 i ' \ • r
-20 20 60 100
RIVER KILOMETERS
(b) LOW FLOW
140
REFERENCE;
FIGURE IV-59
TURNER AND LINDBERG, 1978.
COMPARISON OF OBSERVED AND PREDICTED MERCURY CONCENTRATION
CALCULATED FROM A DILUTION MODEL FOR THE MORTH FORK
HOLSTON RIVER,
sampled on the Slate River Include a control station (034), two stations 1n the
Impact zone downstream of Coal Creek (035, 036) and two stations 1n the recovery zone
(037, 038).
Table IV-51 shows average concentrations at each station and the water quality
criteria. The criteria are exceeded for arsenic, cadmium, copper, silver, and zinc.
There 1s generally some decrease 1n the level of total metals from the Impact zone to
the recovery zone, although statistical tests reported by Janlk. et_ aj_., Indicate
that analytical variation or field replicate variation may be an Important reason for
the difference. Even so, water quality criteria are exceeded In the recovery zone as
well as 1n the Impact zone.
Janlk et_ aj_. (1962) also Indicate that a large percentage (generally 75 to 100
percent) of the metals are transported In the dissolved fraction. While suspended
solid levels are not reported, these results do. In a general sense, appear to be
contradictory to the findings of other Investigations.
-474.
-------
TABLE IV-50
STREAMS SELECTED FOR I960 U.S. EPA FIELD SURVEYS AND METALS
ANTICIPATED TO BE PRESENT IN EXCESS OF U.S. EPA RECOMMENDED
AQUATIC LIFE CRITERIA
Major Pollution Source
Stream
Metal(s)
Prickly Pear Creek, Montana
Silver Bow Creek, Montana
Slate River, Colorado
Tar Creek, Oklahoma
Red River, New Mexico
Copper, Z1nc, Cadmium
Copper, Cadmium, Zinc
Copper, Z1nc, Silver, Cadmium
Zinc, Cadmium, Silver, Lead
Copper, Cadmium
Industrial
Leon Creek, Texas
Little M1$s1ss1newa River, Indiana
Chromium, Nickel
Lead, Chromium
Pub!1c-0wned Treatment works(POTW)
Bird Creek, Oklahoma
Cedar Creek, Georgia
Maple Creek, South Carolina
Irwln Creek, North Carolina
Blackstone River, Massachusetts
Mill River, Ohio
Cayadutta Creek, New York
White River, Indiana
References: Janlk et j»K (1982).
Arsenic, Selenium
Chromium, Silver
Chromium
Chromium, Zinc, Nickel, Lead
Cadmium, Lead
Nickel
Chromium, Cadmium
Copper
4.10.2.3.2.5 Saddle River. New Jersey
The Saddle River near Lodi, New Jersey was Investigated to determine the Impact
of urbanization on the levels of heavy metals 1n the bottom sediments of the river
(Wllber and Hunter, 1979). The study area encompasses a distance of about 13 (cm (8
ml). The lower 8 km (5 ml) are dominated by nonpoint sources of runoff from the city
of Lod1. Industries and municipalities do not discharge directly into this section
of the river. Further upstream, however, two wastewater treatment plants discharge
their effluent.
The average heavy metal concentrations In the sediments of the river are srown
In Table IV-52. Ninety-six sediment samples from 18 cores were taken. The priority
metals analyzed are lead, zinc, copper, nickel, chromium, and cadmium. The tabula-
tions Indicate a general enrichment of each of the priority metals in the lower
-.175-
-------
KHJOMfTEM
REFERENCE: JANIK ET AL.< 1982
FIGURE IV-60 STATION LOCATIONS ON COAL CREEK AND SLATE RIVER, COLORADO,
urbanized Saddle River. Average enrichment factors (concentrations 1n the lower
river divided by concentrations 1n the upper river) are 6.7 for Pb, 3.5 for Zn, 3.1
for copper. 2.8 for nickel, 5.1 for chromium, and 5.2 for cadmium. The results
appear to Indicate that the urban nonpolnt sources have Increased concentrations of
metals 1n the river's sediments.
The heavy metal concentrations were subdivided by bedded sediment particle size.
The results are shown 1n Table IV-53 for sizes ranging from coarse sand to clay.
Generally the concentrations Increase with decreasing particle size. However, on a
total mass basis, most of the metals are associated with the larger particles because
the silt-clay fraction comprises only 1 percent of the solids by weight.
4.10.2.3.2.6 Cavuga Lake Basin. New York
The water of 12 streams tributary to Cayuga Lake, New York were sampled for
the priority metals lead, cadmium, zinc, and copper (Kubota et_a_1_., 1974). A number
of the streams flow predominantly through rural countryside and others flow through
the City of Ithaca. Sample collection focused on periods of high and low streamflow
from March through August 1970.
-476-
-------
TABLE IV-51
COMPARISON OF MEAN TOTAL CONCENTRATIONS OF SELECTED METALS (pg/1)
IN THE SLATE RIVER VERSUS U.S. EPA CALCULATED^CUTE WATER QUALITY
CRITERIA FOR AQUATIC LIFE
Stations
Control
034
Hardness (mg/1) 55.
Total Arsenic (Detection Limit
Actual (X) 658.9
1980 Criterion 440.
Total Cadmium (Detection Limit
Actual (X) ND*
1980 Criterion 2.
Total Chromium (Detection Limit
Actual (X) 9.2
1980 Criterion** 21.
Total Copper (Detection Limit •
Actual (X) 11.0
1980 Criterion 13.
Impact
035
61.
« 110.0)
1069.7
440.
« 7.5)
13.2
2.
• 5.0)
9.8
21.
11.0)
38.8
14.
Total Lead (Detection Limit • 120.0)
Actual (X) ND ND
1980 Criterion 83.
Total- Nickel (Detection Limit *
Actual (X) 46.5
1980 Criterion 1174.
Total Silver (Detection Limit «
Actual (X) 12.4
1980 Criterion 1.
Total Z1nc (Detection L1*1t • 9
Actual (X) 55.8
1980 Criterion 196.
95.
9.0)
95.4
1270.
12.0)
17.7
2.
.0)
1068.3
214.
036
68.
936. S
440.
10.2
2.
7.7
21.
24.0
15.
ND
107.
72.9
1374.
ND
2.
1005.2
233.
Recovery
037 038
71.
776.6
440.
8.1
2.
7.6
21.
16.6
16.
ND
113.
43.8
1418.
ND
2.
744.5
241.
75.
617.6
440.
9.6
2.
12.4
21.
15.6
17.
ND
122.
45.2
1465.
NO
2.
430.4
254.
ND » Nondetectable.
**
Criteria are for hexavalent chromium.
4 ^7
--»/ 7-
-------
TABLE IV-52
METAL CONCENTRATIONS IN BOTTOM SEDIMENTS OF SADDLE RIVER,
NEU JERSEY. AND IN ADJACENT SOILS
Metal Concentration (pg/g)
River
Mile
Pb
Zn
Cu
N1
Upper
16.6
8.2
38.6
12.6
84.2
66.4
28.9 6
20.5 6
.5
.4
Cr
Cd
Mn
Fe
Saddle River
6
3
.5
.6
0.4
0.4
197.4
111.0
8439
5956
Lower Saddle River
5.6
1.3
0.5
163.5
152.4
200.0
247.6
275.1
269.8
60.3 17
61.5 15
104.8 22
.5
.2
.3
24
17
34
.6
.8
.9
1.7
1.6
2.9
200.2
185.2
164.0
12872
11092
14565
N/A
86.8
96.3
Adjacent Soils 1n Watershed
40.5 11.9 9.3 not Measured 145.0 12300
Data of Hllber and Hunter (1979).
Table IV-54 suMMarlzes the levels of dissolved and participate lead 1n the Mater
coluwi. The concentrations of soluble lead 1n the rural streams do not differ
appreciably fro* concentrations 1n the stream flowing through urbanized areas.
Participate and dissolved levels of cadxiuM, zinc, and copper also do not reflect an
Impact fro« urbanization (Tables IV-55 and IV-56). The observed levels of trace
elemnts 1n these streams appear to reflect predominantly natural background sources.
4.10.2.3.2.7 Additional Studies
Numerous other studies of Metals in rivers can be found throughout the litera-
ture. Of the various priority Metals, Mercury appears to be the most widely studied.
So** of the regaining literature on Metals 1n rivers 1s briefly suMMarized here.
Mercury distribution 1n the Ottawa River, Canada, has been studied and reported
by a nuMber of researchers, Including Ranaraoorthy and Rust, 1976; Kudo «t.al_., 1977;
-478-
-------
TABLE IV-53
AVERAGE HEAVY METAL CONCENTRATIONS BY PARTICLE SIZE
FOR SEDIMENTS X>F THE SADDLE RIVER, NEW JERSEY
(ug/g)
Particle Size (M)
420-1000
(coarse sand)
250-420
(medium sand)
125-250
(fine sand)
63-125
(very fine sand)
5.8-63
(siU)
0.15-5.8
(fine to coarse clay)
0.01-0.15
(very fine clay)
NO * Noruletectibi*
River Mile
16.6
8.2
5.6
0.5
16.6
8.2
5.6
0.5
16.6
8.2
5.6
0.5
16.6
8.2
5.6
0.5
16.6
8.2
5.6
0.5
16.6
8.2
5.6
0.5
16.6
8.2
5.6
0.5
Pb
15
9
310
482
23
13
16
90
45
18
11
91
126
349
113
173
360
1127
259
582
860
3073
816
1940
1894
13372
1476
2747
Zn
22
42
388
413
30
28
63
119
48
34
35
135
125
440
155
251
420
3298
389
661
917
3365
1320
2348
2159
21279
4715
4680
Cu
11
7
206
252
11
8
10
44
14
12
6
29
81
3180
31
44
735
1222
151
258
1017
12221
417
1042
2272
84302
1145
1364
N1
4
4
20
28
4
4
6
12
4
5
4
11
14
169
11
21
60
202
23
46
72
559
99
189
ND
2907
488
444
Cr
5
3
29
46
5
3
S
15
6
5
4
12
18
34
15
27
41
127
33
143
201
321
126
563
530
1337
610
852
Cd
ND*
0.2
2.0
4.0
ND
0.3
0.5
1.0
0.1
0.5
0.3
0.9
1.4
3.4
1.3
1.3
5.2
14.5
7.9
6.9
7.2
27.9
30.6
26.9
37.9
290.7
120.0
34.0
Kudo e_t_a]_., 197^; and In tne Proceedings of the International Conference on Transport
of Persistent Chemicals In Aquatic Ecosystems, 1974. Much of the research an mercury
In rivers deals wlcn adsorption and desorptlon between the bedded sediments and the
water column.
Jenne (1972) summarizes concentrations of mercury in rivers throughout the
United States. •Tne U.S. Geological Survey provides a collation of papers on
-479-
-------
TABLE IV-54
LEAD CONCENTRATIONS IN STREAMS TRIBUTARY TO CAYUGA LAKE, NEW YORK
Sample Source
Primarily rural
Canoga
Great Gully
Little Creek
Sheldrake
Taughannock
Salmon
Inlet
Buttermilk
Urbanized
Trunansburgc
Six Mile
Cascadllla
Fall Creek
Soluble
No. Samples
With
Detectable
Amounts
4/8
6/8
4/7
5/8
5/8
5/9
8/9
3/8
4/8
6/9
5/9
7/9
M9/1
Meanb
1.17
0.62
0.57
0.42
0.74
2.99
0.66
0.40
1.11
0.73
0.50
0.93
Maximum
2.67
1.33
1.00
0.67
1.00
16.1
1.33
0.67
1.67
1.33
1.00
2.67
Participate
No. Samples
With
Detectable
Amounts
5/8
8/8
6/7
7/8
8/8
8/9
8/9
6/8
7/8
8/9
9/9
7/9
Fraction,
Mean
1.37
1.38
0.66
1.39
1.57
0.91
1.89
1.45
3.94
3.14
3.88
2.91
W/l
Maximum
2.06
6.17
1.85
2.62
4.01
2.62
6.17
3.09
7.41
8.23
6.99
8.33
Source: Kubota et aU, 1974.
'Samples with detectable amounts/total number of samples.
Means are given for detectable amounts.
cSampl1ng site located below sewage treatment plant.
mercury (1970) and lead (1976) In the environment. The U.S. Geological Survey (1970)
also has summarized data on selected trace elements (arsenic, cadmium, hexavalent
chromium, lead, zinc, and mercury) 1n surface waters 1n the United States.
Finally, the U.S. Environmental Protection Agency has published a series of
documents that review the environmental effects of pollutants. Among the pollutants
reviewed are chromium (Towill et.a]_., 1978), lead (Bell et_ al_.. 1978), and cadmium
(Mammons et.al_., 1978).
4.10.3 Analytical Models for Fate Prediction of Metals 1n Rivers
4.10.3.1 Introduction
Figure IV-61 Illustrates a number of Important processes which Influence the
fate of metals 1n rivers. Consider an example where effluent from the pond 1n the
-480-
-------
TABLE IV-55
SUMMARY OF CADMIUM, ZINC, AND COPPER IN PARTICULATE3 CARRIED
BY TRIBUTARY STREAMS OF CAYUGA LAKE
Cadmium, ng/1
Stream
Primarily rural
Canoga
Great Gully
Little Creek
Sheldrake
Taughannock
Salmon
inlet
Buttermilk
Urbanized
Trumansburg
Six Mile
Cascadllla
Fall Creek
No. Samples
With
Detectable
Amounts
5/8
6/8
1/7
7/8
7/8
6/9
4/9
5/8
5/8
4/9
6/9
5/9
Meanb
0.09
0.06
iO.05
0.09
0.11
0.10
0.13
0.10
0.09
0.21
0.10
0.44
Z1nc, j/g/1
No. Samples
With
Detectable
Amounts
8/8
8/8
6/7
8/8
8/8
9/9
8/9
7/8
8/8
8/8
8/8
8/9
Mean
6.40
10.28
2.91
5.48
6.95
3.94
10.71
8.96
9.45
10.05
14.67
14.29
Copper, Acg/1
No. Samples
WltJ)
Detectable
Amount
8/8
8/8
7/7
8/8
8/8
9/9
9/9
8/8
8/8
9/9
9/9
9/9
Mean
1.69
1.35
1.72
1.11
1.46
1.37
3.37
1.64
1.30
5.92
2.43
2.89
Source: Kubota et _§].., 1974.
Samples with detectable amounts/total number of samples.
Means are given for detectable amounts.
cSamp!1ng site located below sewage treatment plant.
figure overflows Into the river. The main objective of predictive analyses for
metals 1s normally to find their concentration distributions with distance, and
possibly with time (I.e., to find Cj. C2, Cj, and C4 as depicted 1n the figure).
Once metals enter a river, they begin to adsorb to particles suspended 1n the water
column and to particles 1n the river bed. Eventually, the bed can become contami-
nated with metals at depths below the sediment-water Interface ranging rrom a few
millimeters to many centimeters. If the flow rate 1n the river were to Increase
enough, the shear force exerted by the moving water on the bed would be sufficient
to scour metal-contaminated solids back Into the water column. In zones where
velocity 1s significantly diminished, as In a reservoir, the metal-contaminated
sediments can settle out of the water column, and establish a metal-contaminated
.081-
-------
TABLE IV-56
SUMMARY OF SOLUBLE CADMIUM, ZINC. AND COPPER IN
TRIBUTARY STREAMS OF CAYUGA LAKE
Stream
Cadmium,
No. Samples
with
Detectable
Amounts*
M9/1
Meanb
Zinc,
No. Samples
with
Detectable
Amounts
M9/1
Mean
Copper, i.
No. Samples
with
Detectable
Amounts
ig/1
Mean
Primarily rural
Canoga 6/8 0.25
Great Gully 5/8 0.07
Little Creek 3/7 0.20
Sheldrake 6/8 0.10
Taughannock 4/8 0.28
Salmon 8/9 0.10
Inlet 6/9 0.28
Buttermilk 7/8 1.10
Urbanized
Trumansburg 6/8 0.07
Six H11e 6/9 0.25
Cascadllla 2/9 0.29
Fall Creek 7/9 0.17
8/8
8/8
7/7
8/8
8/8
9/9
9/9
8/8
8/8
9/9
9/9
9/9
7.97
1.88
2.24
1.61
1.17
2.27
2.71
0.83
3.20
1.57
1.40
3.51
8/8
8/8
7/7
8/8
8/8
9/9
9/9
8/8
8/8
8/9
9/9
8/9
0.79
0.40
0.32
0.53
0.53
0.51
0.39
0.54
0.77
0.88
1.70
0.75
Source: Kubota £t a].., 1974.
^Samples with detectable amounts/total number of samples.
Means are given for detectable amounts.
cSampl1ng site located below sewage treatment plant.
layer on the bottom. In the thin layer of contaminated sediments along the bottom,
metal concentrations can be hundreds to thousands of times higher on a unit-volume
basis than In water column.
Tributaries provide dilution water which can rather abruptly decrease metal
concentrations. Also partitioning between the dissolved and sorted phases can be
shifted If the suspended solid concentrations or other water quality parameters are
altered.
Suppose that the pond overflow 1n Figure IV-61 Is eliminated after a period of
discharge of many years. During the period of the discharge the bottom sediment on
the river has probaoly accumulated metals. Once the metal concentrations 1n the
water column are lowered due to elimination of the pond overflow, the metal in the
bed tends to desorb back Into the water column, a process which may continue (depend-
.482-
-------
FIGURE IV-61 PHYSICAL PROCESSES INFLUENCING THE FATE OF METALS IN RIVERS
Ing on the rate of desorptlon) for years. Thus, the recovery period of the meta)-
contamlnated river may take considerably longer than anticipated from the point
source elimination.
The tools presented In this section can be used to address the cases described
above and are Hotted to steady-state analyses, with the exception of the method
which predicts adsorption and desorptlon of metals on bottom sediments. The methods
treat metals as pollutants with two phases: an adsorbed phase and a dissolved phase.
Each approach 1s summarized below.
• Dilution Approach. The change In metal concentration 1n a river 1s
assumed due to loading from point and nonpoint sources, and dilution
with background water.
e Dilution Plus Scour or Deposition of Metal-Contaminated Sediments.
Exchange of metal-contaminated sediments between the water column and
river bed can alter the concentration 1n the water column.
-433-
-------
• Influence of Small Lakes. Small lakes or backwater regions are often
present on river systems, and potentially could be a sink of adsorbed
Mtals which settle along with suspended solIds In these quiescent
reglons.
e Desorptlon from (or Adsorption to) Bedded Sediments. Dissolved metal In
the water column can be adsorbed to bedded sediments If a nonequ111br1um
condition exists between the bed and the water column. Similarly,
desorptlon of part1culate metal from bedded sediments may occur 1f metal
concentrations are reduced 1n the water column (for example, by waste
load allocation).
e Concentration Factors In Bedded Sediments. Concentrations of metals 1n
many bedded sediments are often significantly higher than levels 1n the
water column.
While some of the equations presented 1n the following sections may appear
complicated, the equations are no more sophisticated than the more familiar BOD-DO
analyses presented earlier 1n the chapter. Even the data requirements are generally
less comprehensive than for dissolved oxygen analyses. However, since the methods
are less familiar, they may require some study before they are fully understood.
4.10.3.2 Dilution Approach
Using the dilution approach, total metal concentration (partlculate plus dis-
solved) 1s simulated as a conservative pollutant. The dissolved component Is estimated
from the total concentration using linear partitioning:
C • ^ (IV-.156)
1 * KpS-10"6
where
C • dissolved phase metal concentration, »g/l
Cj » total metal concentration, ng/1
S • suspended solids concentration, mg/1
Kp * partition coefficient, cm /gm (or I/kg).
Partition coefficients are summarized later In Section 4.10.4.1.
Under the appropriate conditions the dilution approach appears to be useful for
predicting metal concentrations throughout a river. Before the method 1s discussed,
the major assumptions Inherent In the procedure are reviewed. Decay or other loss
processes (e.g.* volatilization) are not considered. For metals this Is generally a
good assumption for the range of environmental conditions likely to be encountered 1n
rivers. Even though the species distribution can change with distance (1n response
to a pH change, for example), total metal typically Is not degraded. A second
Important assumption made In the dilution approach Is that the metal 1n the water
column does not Interact with the river bed, either 1n the part1!culate form or 1n the
-484-
-------
dissolved form. This situation 1s generally true when:
• The suspended solids In the river remain fairly constant with distance.
If scour or deposition 1s significant theft a net Influx or loss of
sol Ids and Mtals may occur.
e The sources of metals to the river are fairly constant over time. If
major changes 1n the discharge of metals occur, this can create a
driving force for adsorption to (or desorptlon from) the bed, which then
acts as an fnternal source or sink.
Field data of suspended solids can be reviewed to determine whether significant
losses or gains of solids occur within the study reach. Alternatively, a predictive
method, such as Figure IV-62, can be used. Based on the mean Mver velocity, the
figure shows when deposition, transport, and erosion of solid particles 1s likely to
occur. Note that the velocity when erosion occurs 1s significantly higher than the
sedimentation velocity, except for particle sizes larger than sand (which are not of
concern for metals adsorption). This means that as the stream velocity first drops
below the velocity required to erode a certain size particle, the particle 1s not
deposited, but continues to be transported unless the velocity further decreases
below the sedimentation velocity.
As the figure shows, sedimentation of clays and small silts Is not likely to
occur In free flowing rivers, but can occur 1n relatively small reservoirs on the
river with detention times exceeding a few days. Under such conditions the net
velocity can be on the order of 0.1 cm/sec, or less, and the effects of settling of
parti culates may be Important.
While sedimentation of clays and small silts 1s not likely In most free flowing
rivers, scour of these same sized particles Is more probable. Clay 1s likely to be
scoured at velocities near 3 fps (100 cm/sec), and silts between 1 and 3 fps (30 to
100 cm/sec), depending on their size. Consequently, during high flow conditions when
the water 1s moving rapidly, bottom scour of silts and clays, and perhaps of sand 1s
possible. If the scoured sediments are contaminated with metals then the total
metal being transported will Increase over distance (assuming for the moment that
further dilution 1s negligible). Based on Figure IV-62, a fairly large envelope of
stream velocities exists such that the clay and silt fractions of solids (those which
adsorb most of the metals) are transported In suspension with the stream water.
Dilution models are useful for both point and nonpolnt sources. While dilution
models have been presented elsewhere 1n this document they are summarized here for
ease of reference. For point sources, the concentration In a stream following
mixing, Cyf 1s:
^u u * * *
*
u w
-185-
-------
XXX)
BOO -
300 -
200 -
WO -
50 -
30 -
20 -
10 -
5-
3 -
2 -
1 -
0.5-
0.3-
0.2-
0.1
SEDIMENTATION
I I I I
MOM
c»eio
i
!5
Note:
30cm/MC
888 | ggg
CLAY SILT SAND
PARTICLE DIAMETER, mm
FIGURE W-62 RELATIONSHIP BETWEEN STREAM VELOCITY, PARTICLE SIZE, AND
THE REGIMES OF SEDIMENT EROSION, TRANSPORT, AND
DEPOSITION (FROM GRAF, 1971),
« concentration of total metal In the river above the point source,
where
Qy -flow rate 1n the river above the point source. m /$ or cfs
CJM » concentration of total metal 1n the point source, ng/1
QI, "flew rate of tne point source, n /s or cfs.
According to the dilution model, the petal's concentration does not change with
distance downstream unless there are additional Inflows as loadings of metals.
When CTu 1$ negligible, Equation IV-157a can be rewritten as:
(IV-157b)
wnere
dilution attained after mixing.
-486-
-------
The nonpolnt source representation can be written In one of two forms:
or
* 'Tb
-------
j , — -., —EXAMPLE IV-20 ' 1
i i
j Tht Flint Rlvtr study dtscrlbtd tarlltr (Section 4.10.2.3,2.1) provldts an j
« opportunity to test tht dilution approach undtr a varltty of hydrologlc conditions. :
[ To Implement tht dilution approach, tht data required art rlvtr and wasttwattr
| flow rates, and associated metal concentrations. Tht data used art summarized In '
I Table IV-ST. Two wasttwattr treatment plants art tht largest sources of metals 1n I
j tht study reach. Together with tht upstream contributions from the river, these j
} three sources art assumed to comprise tht total metal Input to tht system (the j
I minor sources shown In Tablt IV-57 art neglected). j
Tht Flint Hestewattr Trtatmtnt Plant discharges at km 70.7, which 1$ about 1,2 >
! km below tht boundary at Mill Street. After mixing, tht levels of total zinc,
I cadmium, and copper 1n the river are:
i I
I Zinc; :
I i
! x% . 2.66 x 7.7 * 1.68 x SS ,
j ^ 2.66 * 1.68
! Cadmium.'
. 2.66 x 0.067 + 1.68 x 0.16 .
0.10
2.66 + 1.68
' *
! Copper;
2.66 r 2.9 » 1.68x8.3 . 5
-------
TABLE IV-57 ,
BOUNDARY CONDITIONS AND POINT SOURCES TO FLINT RIVER j
FOR AUGUST 4-7, 1981 j
Discharge Concentration J
Discharge Total Total Total
Source Flow Suspended Sol Ids Zinc Cadmium Copper f
(«3s) (mg//) (kg/d) (ng/t)
! Upstream Boundary 2.66 13.5 3100. 7.7 0.067 2.9 !
| M1U Road (km 71.9) I
* i
I Flint WWTP 1.68 4.1 600. 55. 0.16 8.3 t
j (km 70.7) j
i Flint Fly Ash 0.04 39.5 150. 63. 1.32 80. j
' Ponds (km 70.0) '
I I
; Brent Run 0.15 5.9 77. 3.8 0.11 3.8 ;
I (km 41.6) {
! Ragnone WWTP 0.69 58.7 3500. 84. 0.54 28.5 !
| (km 41.1) |
! P1ne Run 0.06 7.0 33.7 5.0 0.04 3.8 j
(km 29.7) j
; Silver Creek 0.085 6.8 50.0 5.0 0.04 3.8 j
! (km 25.2) !
I I
| I
Modified from: Delos et ,jl. (1983) j
• •
I I
. >
I Copper; j
i i
I c , 5.0 x 4.34..+.CL69.X.23..5. . 3., ^, j
T 4.34*0.69
t •
} Neglecting the minor sources below the Ragnone WWTP, the profiles of total !
I zinc, cadmium, and copper are shown in figure IV-63. Also shown 1n the figure are {
j observed data (mean and one standard deviation) and predictions from the MICHRIV I
j model as reported by Oelos «t_ al_. (1983). M1CHKIV is a computer model wnich j
j analyzes metals in greater detail than th> screening procedures, and therefore I
: requires more data.
Tne dilution model generally predicts values witnin 25 to 50 percent of
1 the means of tne ooserved va'-jes, and also within 25 :.> M percent of the MICHRIV '
-489-
-------
MfAN4STAMOAM>
MVMTIOM OF OMXA
"i«r—;
H
Hff>t OMfCTlON
ott»
MVffl MLOMCTM*
(hi TOTAL COFMft «*/!
ttu it
an
«M
•M
now oMtcnoN
WVfN KUOMfTfM
FIGURE IV-63
COMPARISON OF PREDICTED AND OBSERVED
TOTAL METAL CONCENTRATIONS IN FLINT RIVER,
MICHIGAN (AUGUST 1981). (AVERAGE FLOW -
2,66 M3/SEC (94 CFS) AT KM 71,9)
I
nodtl predictions. Figure IV-64 shows that the dilution model also 1s applicable
under other flow regimes In the Flint River: December when the flow rate was
about 26.4 m3/sec (930 cfs), and Harch when the flow rate was 93.4 m3/sec (3300cfs),
For both the December and March surveys, there do not appear to be significant
differences between predictions from the dilution model and from the MICHRIY model.
I
-490-
-------
n«
O
ni
i
no
o
i"
MfOMLZMC.OCCCMKIt MM
MTOTAl 21NC. MMCM MM
• •
* •
i * "
' •-
1
L , *•
. , t-n
1 • f • 1 • •
I ..
!••
t •
1 4
flOWOWECnON
\ I
* H
1 1 1 1 • • f
MVfM KHOMCIEW
(wrouL copftn. OKXMKH MI
to)1OTAl COfffM. MANCH
«v
(
1 1
•
1
1
1
now on
i
ECTK3N
. T
1
--- DILUTION MOOU
- MICtMVMOOCL
§ UCAHCCMMMND
T MVIMION Of DMA
MVEM MLOMTTim
MITOTAl CAOMMJM.
MMEM H&OMETBI*
HlTOIAL CAOMUM. MANCN Wt>
FIGURE IV-6^1 TOTAL ZINC, COPPER, AND CADMIUM IN THE FLINT RIVER
-491-
-------
While dilution modeling can produce quite acceptable results under a variety of
conditions, the user should have access to tools which can be used when processes 1n
addition to dilution are Important. The following section addresses some of these
situations.
4.10.3.3 Settling and Resuspenslon of Adsorbed Metals 1n Rivers
This section begins with a brief discussion of the recently completed MICHRIV
model (Oelos ej^al_., 1983). This model's framework Is shown 1n Figure IV-65. The
most Interesting feature of the model 1s that 1t attempts to handle the exchange of
contaminants between the water column and the bed. Resuspenslon and deposition of
contaminated sediments redistributes adsorbed contaminants to and from the bed.
Also, diffusion can be a driving force for dissolved phase Interaction between the
sediment and water column. For purposes of Illustration, the MICHRIV model 1s
simplified here, but the essence of the model (exchange of metal between the water
column and bed) 1s retained.
The model 1s simplified based on these two assumptions:
e K , • K.2 » 0; that 1s, there Is no degradation or decay of metals,
and
e K . « X 2; that 1s, the partition coefficient 1n the bedded sediments
and 1n the water column are the si
The first assumption Is quite reasonable since most metals do not decay or otherwise
degrade (an exception Is elemental mercury which can volatilize).
Regarding the second assumption, there Is reason to suspect that the sol Ids
partition coefficients for suspended and bedded sediments can differ since the
characteristics of solids In the bed can differ from those suspended 1n the water
column. However, because of the range of uncertainty Inherent 1n partition coeffic-
ient prediction, there 1s no reason to consider differences between K . and K ~ for
these screening analyses.
Using these two assumptions, the model formulations from Oelos et.al_. (1983) are
simplified as follows. The simplification procedure 1s shown 1n detail so the user
can clearly see how the two assumptions are used. The final results of these simpli-
fications are shown later as Equations IV-172 through IV-175, and show how the metal
concentrations In the water column and bed are related.
From Oelos «t_al_. tn* relationship between the total concentration of metal 1n
the water column (CT1) and 1n the bed (CT2) 1s:
fa -- **
<"rs + »d> V + Ktfd2 * Kd2 fd2 H2
-492-
-------
LOtDU*,)
WMItft
ACTIVf Hj
SEDMENT
FIGURE IV-65
TOANMOHT
ASSUMED ZEflO
FRAMEWORK FOR RIVER MODEL MICHRIV (REDRAWN
FROM DELOS ET AL,> 1983)
where
*pl* *p2
fdl, fd2
rs
P4rt1culate fraction of metal 1n water column and 1n bed,
respectively
dissolved fraction of metal 1n water column and 1n bed,
respectively
settling velocity, w/day
resuspenslon velocity, m/day
diffusion coefficient, in/day
decay rate In sediment, I/day
depth of active sediment, m
solids concentration fn water column and 1n bed. respectively,
From Equation IV-160:
14£
'dl
CT2
dl CT1
Cfd2 *s fpl
dl3
dl
KLfd2
Kd2fd2
-------
since
——• M • (w
*2 "$ vwr$
Now
so
or
4 (IV-161)
f .-i!k- (IV-162)
*2 p2
«nd
• 1 +
"2 X
. K f (IV-163)
PZ aZ
or
f
• IL. (IV-164)
d2"2
f - V
SlMllarly
' K , f^ (IV-165)
dl
-494-
-------
Substituting Equations 1V-164 and IV-166 Into Equation IV-161 produces:
Cdl "s "l Kp2 * *l- * K
-------
rl <"rs +
Using the assumptions made before (K . « K 2 and Kdl •
s1npl1f1es to:
« 0), r/r « 1. Thus
^.Vp^.ipir ^ i.
Hj mj Hj Hj L $ «! J
To summarize, under the simplifications made here, the MICHRIV model equations
become:
exp
.ia/. a, U
H! \ $ •! rs/u
(IV-172)
1 + K
pl-1
(IV-173)
Cd2 * Cdl
(IV-163b)
or
and
d2
. cT1
'*
(IV-174a)
(IV-1746)
(IV-175)
(IV-176)
-496-
-------
where
Xl»*2 " MSS of pollutant adsorbed per mass of sediment In the water column
and 1n the bed, respectively, ng/g
PS • density of solids 1n sediment* gm/cm (e.g. for sand 2.54 gm/cm)
n • porosity of sediment (volume fraction occupied by Mater).
The most notable results obtained In the above analyses are that the dissolved metal
concentration in the water column and 1n the bedded sediments are the same (Equation
IV-169b), and so are the participate metal concentrations, expressed per unit weight
of sediment (Equation IV-174t>). However, on a unit volume basis, the total metal
concentration In the sediment far exceeds the concentration 1n the water column
(Equation IV-175).
Typically, first order decay rates are positive numbers, which Indicate that
pollutants decrease In concentration with distance. However, the kj term 1n Equation
IV-171b can either be positive or negative. For example, If significant scour of
partlculate metal from the bottom 1s occurring, then KT < 0 and the total metal
concentration can Increase downstream.
While 1t Is possible that metal concentrations can Increase In the water column
due to scour (e.g., see Figure IV-64 which shows total zinc and copper 1n the Flint
River during March 1982), at steady-state cundttlons this should not happen when the
only source of loading 1s a single source located at x « 0. Rather than use Equation
IV-172 to simulate the effects of scour on water column concentration, one of two
other alternatives has been selected. One approach 1s to retain the unsteady-state
nature of the transient scour situation. While this Introduces more complexity, 1t
shows that elevated metal concentrations In the water column caused by scour are
due to a previous discharge or hydrologic condition when metals had contaminated the
bed, and not due to the current steady discharge conditions.
The two unsteady equations relating the total metal concentrations 1n the bed
and In the water column are (using the previous notation):
fll . *s fol ^ *rs fp2 ^2 KL fdl CTI *L f d?
and
0C_, L* t r t* f r v t f Y t r
~T 1 W_ ' _,^ **T1 "•• * ' ^«V*T*5 "t * ^1 ^»T1 *S ' A*) ^T'i
+ u —-=•* 5 pi Tl + rs P2"2 _ T. dl "1 + JL d2 "2 (JV-178)
-497-
-------
While these equations can be solved exactly and used to predict the total Instream
Mtal concentration due to a scour condition, they are not practical for screening
analyses. The primary emphasis here 1s to predict longitudinal pollutant distribution
when scour 1s Mich nore Important than deposition or diffusion. The approach Is to
spedfy (rather than calculate) the concentration In the bed and to assume 1t remains
constant over the period of analysis. Thus the screening tools which are presented on
the following pages are fundamentally different from the previous MICHRIV equations,
such as Equation IV-172. Table IV-58 summarizes the screening equations and defines
the variables used 1n each equation.
H! u
C_(0) dV-179)
where
C2i • concentration of total metal 1n the bedded sediments ,ng/l (a direct
measurement of this value 1s preferable)
Cf}(0) • concentration of total metal In the water column at an upstream
boundary , ng/1 .
While Equation IV-179 represents steady-state conditions, 1t 1s valid only as long as
the sediments being scoured have a total metal concentration of C.j. Once the
contaminated sediments have been scoured, then the Instream metal concentration 1s
expected to return to 0^(0). The period of validity, T, of the equation can be
approximated by:
H2
T--*- (IV-180)
"rs
where
Hg • depth of contaminated sediment, m
wr$ • resuspenslon velocity, m/day
Typically, Equation IV-179 1s expected to be used during high flow conditions,
perhaps for a seasonal analysis. For an application of this type, the period of
validity of the equation should be on the order of one to two months. Using repre-
sentative data (H? « 5 cm and wr$ • 2 x 10 m/day) for an example,
T - ""« - 250 days.
^
5 x
2 x 10~Vday
For tne example conditions, Equation IV-179 1s applicable for seasonal analysis.
When settling of solids Is Insignificant, the resuspenslon velocity, w can !>e
estimated as:
uH, ASS
-498-
-------
TABLE IV-58
SUmARY OF SCREENING PROCEDURES FOR METALS IN RIVERS AND LAKES
Caution
Use
Data Requirements
IV-157*
Dilution Analysis
This equation tt used to calculate the
concentration tt total Mtal In • river
•ftor • point source discharge mixes
with river water.
CT.
^u
flow rite In river •boy*
point sourc*
ftoM rite of point source
concentration of total
Mtal In point source
concentration of total
Mtal In river above the
point source
faueents
This equation Is anst applicable when exchange of
suspended solids and Mtals with bad Is negligible.
See Figure IV-*7: HAM wan Mater velocity Is In
•transportation* regime, this condition Is approxt-
•ately true. Also, the equation can be used as a
first approximation regardless of exchange with bed.
1V-156 Once the total concentration versus
distance Is found from Equation I
the aaaunt dissolved can be calculate*
using this equation.
S • suspended solids concen-
tration
Kp • partition coefficient
C. • totel Mtal concentration
This equation Is used to find total dissolved octal at
locations In a river once total netal concentration
at these sane locations has been calculated.
Dilution and Scour of Metal-Contaminated StdVatnts
IV-179 This equation Is used to predict the
total netal concentration In a rlvar
when •etal-contanlMted sedtoents are
resuspended Into the water colon*.
"21
H,
u
rs
• concentration of total
Mtal In bedded sedt-
Mnts
« concentration of total
Mtal In the water
column at an upstream
boundary
• water depth
• strew velocity
• fraction of Mtal 1n bed
which Is In partIcolate
fora H)
• resuspenston velocity
(see Equation IV-181)
The equation does not keep track of the depth of contam-
inated sedtottnts. It assuaes this depth Is not exceeded
during the period of scour. Equation IV-180 can be used
to estlMte the period of validity of the equation.
Figure IV-66 Illustrates the Importance of scour.
-499-
-------
TABLE IV-58 (continued)
Coiiatlon
Bate *.eoMlre»enti
Consents
Dilution and Scour of Metal-Contaminated Sedlaents (continued)
IV-itt This equation Is iiMd to calculate the
teU) o»tal concentration In • rltnw
when awtal-contoBlMted sedlawjnts are
resuspendod Into tin water colum.
Tbtt tt M alternate for* of Equation
IV-17t. See COM«OtS.
ASS • change In suspended
concentration over the
distance where the total
octal concentration Is to
be calculated
K, • partition coefficient
Sp • average suspended solids
pc^vfom steMtQr mj^u o toy ic
period
Cfj • concentration of total natal
In uator colon during the
previous steady hydrologlc
period
This equation can be used In lieu of Equation IV-179
when the concentration of total octal In the bed.
C* !• IS UHkHOHM•
Dilution and Oeposltton of Metal-Contaalnated Sedle»nts
IV-18Sd Tbtt equation Is used to predict the
concentration of total aetal In a rlner
tdM Mtal-contamlMtod solids art
settltaf.
tf0 • concentration of total
•eta! at an upstream
boundary
Up - partition coefficient
$5(0) - suspended solids concentra-
tion at upstrtw boundary
Mj • depth of Mater
u • velocity of water
•f • settling velocity (see
Equation IV-184)
Figure IV-67 Illustrates the taportance of deposition.
IV-lMa This equation Is a stapllf Ication of
Equation IV-185d. See CoeMnts.
SS(0) - see above
SS(«) • suspended solids concen-
tration at a distance *
below upstrean boundary.
where total octal concen-
tration Is to be calculated
This equation Is used when the octal
to the suspended solids (I.e.. when
Is highly adsorbed
SS I<)-»»1).
-500-
-------
TABLE IV-58 (continued)
taut ton
Use
Data Kequlrenents
Dilution end Deposition of Hetal-Conta»inated Sedie»nts (continual
IV-lB*b This equation Is also a stejriiftcaUoo of
Equation Itf-USd. See Comets.
cTo - «t
Kp - Ml
SS(0) - Mt
w( - tee
M1 - u<
U - See
This equation Is applicable when the enow* of
Mhtch Is adsorbed Is SMll. NhM this Is Inn.
Is relatively unimportant, as Eolation l«-18(c shows.
and the vetai tends to act conservatively in the water
co turn.
IV-lUc This e*atlo« fradtcts the concentration
of total «tal leavlnt • lake, hased on
the suseended solids concentrations
entering and leavlnf the late.
IV-1M This equation shews that the dissolved
phase «etal concentration is not affected
hy the settllnt adsorved avtat.
Settling of Mttals Intakes
c) • concentration of total
netal coning Into the lake
S(,S° • concentration of suspended
solids entering and leaving
the lake, respectively
• partUton coefficient
This equation considers • Median-size suspended solid,
and does not differentiate hy size fraction.
IV-19M This equation predicts the concentration
of total otttal leavlRf a lake, eased on
concentrations of impanded solids hy
size fraction.
- see above
- concentration of suspended
solids In class J entering
and leaving the lake.
respectively (see Eepatlon-
WO)
• partition coefficient of
•vtals associated Mlth sed-
taent site J
Size fractions of sand, slits, and clays entering a lake
are required. Settling velocity of solids in each group
are predicted using Equation IV-191.
-501-
-------
TABLE IV-58 (continued)
Equation
UM
Data llequtreMnts
COMMtS
Settling of Metals in lakes (continued)
Nils equation Is tfce sane *t
I »-!*&». eicept tkt partition
coef/lcleirt* am related to specific
surface art*.
Sane as Equation IV-ItSb, e«capt
specific surface areas (tt'«) art
Typical values of specific surf
t(«i are 10 mt/t for sand, «0
tf/g for clay.
areas far tkis
for silt, and 200
Adsorption
Detorptlpo of the Metal Mt»te» Mater CO>M» and led
IV-IM Thlt equation ts ««ed t« predict Mter
col«M concantraUtt* of aetal due to
adsorption or desorptlon wftk ked.
•
*b
- Initial total e»tal concen-
tration In Mater CO)MM
(at tH»)
• upstreaB boundary concen-
tration (at iH»
• MM of contevtnated
sedtamt* per Mlt area
(M* Takle IV-N)
• Mter deptn
• streaa velocity
• partition coefficient
• vertical dtffvtlon coeffi-
cient
• void fraction of wdar-
lytiNj sedtaent
• ovlk density
This equation differs fron all of UM etove In tkat It
Is an unsteady-state equation (I.e., ttae variability Is
considered). This equation ts Intended to provide only
a crude approximation of the taportance of adsorption
or desorptlon fron tta Md, because SON of the required
data win likely be difficult to estimate. Equation
IV-200 ts a slepllftcatlen of Equation IV-1M MM tfce
•tied layer Is Mall. Equation IV-201 Is also a stMlt-
f teat ion useful uMn the boundary concentration is
Mfltolble.
-502-
-------
The tenn .iSS/Ax 1s the change 1n suspended solids concentration over distance
V10 . r + C-.0 (IV-W2)
If C21 1n Equation IV-179 1s not available. Equation IV-179 can be
expressed 1n the alternate form as:
C-.(0)
p Sp ' 10'6 lp W
where
concentration of total metal 1n the water column during
the previous steady period (previous to the period of sustained
scour)
S - suspended solids concentration during previous steady period,
rog/1 .
The purpose of using the previous steady-state period (a non-scour period) 1s that
the concentration 1n the bedded sediments during the scour period 1s to a large
degree dictated by the previous conditions.
Equation IV- 182 shows that the product
ASS K'lO'6
1 * Kp V10"6
1s a helpful Indicator of the Importance of scour. If
ASS K -lO'6
1 + Kp Sp-10
then the total metal concentration during the scour period (at a distance downstream
where the suspended solids have Increased by ^SS) will exceed the total metal
concentration during the previous steady-state period. If this fraction 1s less than
unity then the background concentration (Cy^(O)) Is important In determining whether
scour produces higher concentrations than the previous period.
1
1
| The Flint River study provides a
j Figure IV-66a shows the suspended sol
' March 1982 survey and Indicates a net
velocity Is:
I w _ 1 • 2 • (25 - 121
rs 0.2 • 30
• 103> !05 "
source of data to simul
Ids concentration 1n the
scour condition exists.
• 1.4 • 10"4 m/day
1
ate a scour condition. 1
i
river during the |
The resuspension j
•
1
i
\
-503-
-------
X MCAMAffttWOMIID
f MVWnONOUMIA
• •
w w
• •
MJUnOM MOOft
— SUflSMJP
JMMN4III
ocvunoN
now
• »
•
4
•
now DMtcnoN
I I I
» 4* N
WVOHULOMCTtM
MiaiM.xmc.Mn
FIGURE IV-66 SUSPENDED SOLIDS AND TOTAL METAL CONCENTRATIONS IN THE
FLINT RIVER, MICHIGAN (MARCH 1982).
-504-
-------
The data for velocity (1 m/sec), depth (2 m) and bedded sediment concentration
(0.2 kg/1) are from Delos et_a_K (1983). Note that Figure IV-62 shown earlier
suggests that scour may be Important at a stream velocity of 1 m/sec.
The fraction f _ 1s probably very close to unity for each of the three j
metals zinc, copper, and cadmium. To use Equation IV-179, the concentration
1n the bed, C21, and the upstream concentrations CT1(0) are needed. The CT1(0) !
values are the same as those calculated 1n preparing the dilution analyses for j
Figures !V-64(d) through (f). I
t
The C21 values can be estimated looking at the historical data for the |
previous steady period (December), and by calculation based on total metal j
concentration as found by the dilution model (Figure IV-64a, b, and c). For
the present analysis, C^ is based on the participate levels 1n the water column
(during December 1981); !
C *C -^C 1
P2 ^ pi .
The levels are shown below: j
Cp1>^9/1 Cp2.ng/l I
Z1nc 6. 12 x 10 !
Copper 1. 2 x 10 }
Cadmium 0.03 0.06 x 10 I
These numbers are based on m^ - 0.00001 kg/1 (for December 1981) and »2 • 0.2 |
kg/1. Substituting the required Information Into Equation IV-179, the results are j
shown plotted In Figure 1V-666, c, and d. Equation IV-156 1s applied twice, since j
there are two point sources In the reach. j
The Increase 1n concentration due to scour over the 70 km region for each metal
, M-g/1, due to scour over 70 km
Z1nc 6.8
Copper 1.1
Cadmium 0.04
For example, tne Incremental zinc concentration 1s:
*-< f«9 C-, 1.4 • 10"4 • 1 ' 12 ' 10*4 ,
rs PZ Z1 x • • 70 • 103 • 6.8 MS/1
2 ' 1 ' 86400
I
• END OF EXAMPLE IV-21
-505-
-------
During periods when settling rather than scour 1s the predominant mechanism
affecting suspended solid concentrations 1n t *ater column, the suspended solids
concentrations, SS(x), change over distance:
SS(x) . SS(0) exp
/-*s*\
\H1« /
(IV-183)
The effective settling velocity can be found directly from this equation:
(IV-184)
When metal-contaminated solids settle out of the water column, the process can be
modeled by the following ordinary differential equation:
*T '*«C*
(IV-185a)
where C 1s the paniculate metal concentration, ng/1 .
C$ can be related to CT
.
°T
K SS-10"
P - _
(1+KpSS-lO"6)
(IV-185b)
Upon substitution of C$ from Equation IV-185b and SS from Equation IV-
183 Into Equation IV-185a, and by specifying the boundary condition for CT as:
Cj • CTo at x » 0
H 1s possible to solve Equation IV-185a as a function of distance to get:
(IV-185C)
exp
r /
ln
SS(0)-10'6 + 6^1"
(IV-135d)
In (KpSS(0)-10-6 + 1) -^
When the metal is highly sorbed to the sediment (I.e., K SS»10~° »1), this equation
simplifies to:
SS(0)
SS(x)
(IV-186a1
-506-
-------
If settling were to continue Indefinitely downstream, then the metal would eventually
be depleted from the water column. The above equation also shows that the decrease
1n metal concentration 1s directly proportional to decrease 1n solids concentration.
By monitoring solids the decrease 1n metals can be directly estimated as long as
*pss<
If the fraction of metal which 1s adsorbed 1s small, Equation IV-185 simplifies
to
-w_x
KpSS{0)-10-
/ "V \ I
-* (e~^T - 1 j
(IV-186b)
Eventually, as the solids are depleted from the water column, the total concentration
approaches
Under these conditions (K SS(0)-10~6<1), the total loss from the water column by
settling Is not likely to be large. For example, 1f K SS(0)-10"6 « 0.1, then
CT - 0.9 CT
•- EXAMPLE IV-22
I During the August 1981 Flint River survey, the suspended solids profile
| (Figure IV-67a) Indicates that a net deposition of suspended solids was occurring
j between km 40 and km 70. Based on Equation IV-184, the approximate deposition
j rate between km 70 and km 40 1s:
' / \
| . 0.5 x °i2 In OS 1 x 86400 • 0.26 m/day
| s 30 x 103 \4/
i
! where the depth (0.5 m) and velocity (0.2 m/sec) are taken from Delos et al.
j (1983).
j Average partition coefficients are also obtained from estimates 1n Delos et_
j a]_. (1983) and are:
i
! Metal K (cm3/gro)
! 21 nc 0.2 x 106
I Copper 0.06 x 106
I Cadmium 0.1 x 106
-507-
-------
MBTII Vl 1 W
1:
5 *'
at-
1-
1:
•.
7SW£
MJWM
4-.-,- "
• • 1 * •
• • • a » «
WVIII MUNCIIM
(OMKNOfO SOUOta*
T - j
It 1 IT
MVM MLOMTieW
FIGURE IV-67 SUSPENDED
MICHIGAN
71.9.)
•cnoN
ri^
» •
/I
^\
i
V
i * '
| 4-
• .
.... ouinuN MODO.
i SffaT'
nowoMKnoN
T T "* r 1 ^*
" ' ^^
• n o»»a«4»m«ii
WVBI MLOMfTBW
nowOMtcno
N
i
m» -
1 t» -
2 *»•
fIDMf OMBCTION
t 1 1 . T
T
{ | tpqj-flj
>•*»•«•• H
MVOI MtOMEIBV
SOLIDS AND TOTAL METAL CONCENTRATIONS IN FLINT RIVER,
(AUGUST 1981). (AVERAGE FLOW - 2.66HVSEC (94 CFS) AT KM
-508-
-------
Based on these partition coefficients, the product K.SS • 10"6 1s between 0.6 to
2.0 for the metals. Hence, Equation IV-185d Is probably more appHcabJe than
Equations IV-186a, b, or c. The equation Is used to predict the concentration at
km 40 based on the boundary concentration calculated 1n the dilution example
(Example IV-20) and the remaining data shown earlier. The calculation 1s shown In
detail for zinc:
line:
26 exp
In |0.2 x 10 x 10 x 10" + exp
( &
\.S x .
r* 4 exD ( r?6x 30000
2 x 86400,
- In (0.2 x 106 x 10 x 10'6 + 1) - *26 * 3000°
.5 x .2 x 86400
- 26 (0.6) • 16
Copper:
Cuy » 5 (.8)
Cadmium;
Cdy » 0.1 (.7) • 0.07 /ig/1
The results are plotted 1n Figure IV-67b, c, and d. The predicted values agree
more closely with the observed values than do the predictions from the dilution
analyses.
END OF EXAMPLE IV-22
4.10.3.4 Settling of Metals In Small Impoundments on Rivers
The preceding section presented models which could be used to predict the
effects of either settling or scour of solids on trace metal concentrations In
rivers. While settling of metal contaminated sol Ids certainly can occur 1n rivers
during low flow conditions, most settling 1s likely to occur 1n reservoirs or lakes
located on rivers. Investigations frequently reveal that total metal concentrations
1n river systems, when averaged over a long period of time, decrease with distance.
This Indicates there 1s a net loss of metal from the water column, and that perhaps
settling Is a primary reason. In an extreme approach, some researchers have Ignored
downstream transport of :neta1s altogether, outside of a small mixing zone, and have
hypothesized that metals are rapidly depleted from the water column either by settling
-509-
-------
or adsorption to bedded sediments. However, this approach contradicts the results
presented earlier which show that metals, even ones which adsorb strongly such as
nercury, can be transported downstream.
Solids that are suspended 1n the water column do tend to settle because of
gravitational forces, but also tend to remain 1n suspension because of turbulence.
Evidence shows that suspended solids levels can remain fairly constant over long
distances. In reservoirs which are more quiescent than rivers, the turbulent eddies
diminish, and often gravitational settling becomes Important. Thus for these screen-
Ing analyses, reservoirs are modeled as sinks of metals.
Based on the Idealized reservoir and nomenclature shown 1n Figure IV-68, mass
balances for solids and metals are developed. The solids mass balance 1s:
(IV-187)
f^'i)
where
T • hydraulic detention time, days
S1, S° « concentration of suspended solids 1n Inflow and outflow,
respectively, mg/1
H » mean depth of reservoir, m
w$ • settling velocity, m/day.
The total metal concentration leaving the reservoir at steady state can
be approximated as:
4
Tw S° K
(IY-188a)
1 +—
H
(lV-188b)
(S1 - S°) K -10'6
1 + E 7-
l * S° K -lO'6
P
d (i + s° K-i
-------
O^r^rT
^$
FIGURE IV-68 DEFINITION SKETCH OF IDEALIZED RESERVOIR
The dissolved metal concentration 1s:
S1-10'6)
KpS1-lO'6)
KpS1'lO"6
(IV-189)
If the particle sizes of suspended sediments 1n the Inflowing and outflowing
waters are significantly different, then the partition coefficient can also be quite
different 1n the two waters. This 1s primarily because the smaller particles have
greater surface area available for adsorption. The following analysis shows how to
approximately account for these differences.
For the solids balance, each particle size (e.g., fine silt) can be accounted
for Individually:
$?-
(IV-190)
where the subscript j denotes particle class.
If the Inflowing and outflowing concentrations of particles can be estimated by
class size then the settling velocities can be calculated as:
(IV-191)
This approach assumes the Internally generated solIds 1n class J are of negligibly
small amount.
The linear adsorption Isotherm for particle class j 1s:
X, - K . C (IV-192)
-511-
-------
where
Xj • mass of Metal adsorbed per mass of sediment 1n jth particle class
K j » partition coefficient for jth particle size
then
Csj " KPJ C° S5'10"6 (IV-193)
or
rO . vrO - f°^v e°.in~^
Cs 1Csj C 1KpJ SJ 10 ' (IV-194)
The concentration of metal In the outflow becomes:
(IV-195a)
(lv.195b)
. C1 (1 + IK s^UT6) (IV-195c)
and
cS
C° JL . . c1 . (IV-189b)
Equation IV-189b shows that the dissolved fraction of the metal Is not affected, by
the reservoir so that 1f most of the metal 1s dissolved, then the reservoir 1s not an
effective sink for the metal .
To handle multiple partition coefficients 1n a manner amenable to s1e4:<1f1ed
calculation. It Is hypothesized that partition coefficients are Inversely proportional
to specific surface area, or
(IV-196)
SA
p
-512-
-------
where
r denotes a reference particle group (e.g., clay)
o
SA • specific surface, m /g-
The tabulations below show ranges and typical values for specific areas for sands,
silts, and clays.
Solid Range of Specific Areas (m2/g) Typical Value (ra2/g)
Sand 1-50 10
Silt 50 - 100 60
Clay 100 - 800 200
Ra1 et^aK (1983) provide a good summary of specific surface areas (and other adsorp-
tion related parameters) for a variety of solid surfaces, Including metal hydroxides.
Using the relationship embodied 1n Equation IV-196, C becomes:
fi
pr10"
ef.e|
Tpr-10"*
^~
(lv-197)
EXAMPLE IV-23
Consider a river with two reservoirs located on 1t (Figure IV-69). The
detention time and depth of each reservoir are:
Reservolr Detention Time (days) Depth (m)
Haley 3 3
Dell 20 6
Suppose the suspended solids entering the first reservoir from upstream are:
Settling Specific j
Percent Concentration Velocity Surface Area ;
Category by Weight (mg/1) (cm/sec) (m2/q) J
Sand 50 100 0.1 10 j
S1lt 25 50 5 x 10'2 60 i
Clay 25 50 3 x 10"4 200 j
The total zinc concentration in the river upstream of Haley Reservoir 1s assumed
to be 150 Kg/1. Find the total zinc concentration 1n the water column below Haley
Reservoir and below Dell Reservoir assuming that the loss from the water column 1s \
by sedimentation only. I
« 3 •
Assume that a typical partition coefficient for zinc on clays 1s 10 cm ,'gm. |
-513-
-------
HALEY
RESERVOIR
DELL
RESERVOIR
FIGURE IV-69 DEFINITION SKETCH USED IN EXAMPLE IV-23
The specific surface areas of the solIds are typical values based on the data
presented earlier. The settling velocities are based on Stokes Law, as described
1n Chapter 5.
From Equation IV-190, the concentrations of the suspended materials leaving
Haley Reservoir are:
100
sand 3 (.001) 86400 + l
• 1.1 mg/1
50
'•lit 3.5. 10-4
86400 ••• 1
• 1.1 mg/1
'clay
50
3 • 3
36400 + 1
40 mg/1
Nearly all the silt and sand are deposited 1n the reservoir, but only 20 percent
of the clay. The metal concentration 1n the outflow fro* Haley Reservoir 1s:
Zn • 150
104"*/200 (10 x 1.1 * 60 x 1.1 + 200 x 40)
10*^/200 (10 x 100 + 60 x 50 + 200 x 50)
• 150 (.83) • 120^9/1 .
Considering Dell Reservoir, 1t 1s apparent at a glance that the quantity of
sand and silt leaving the reservoir 1s negligible. The concentration of clay
leaving the reservoir Is:
-514-
-------
*2 • 15 mg/1 . ;
20 ' V 10 86400 + 1 !
•
The total metal 1n the outflow of Dell Reservoir 1s: '
,„«-« '•
1 + -%vr- 200*15 I
Zn • 120 j^ 2= j
10 (10 x 1.1 + 60 x 1.1 + 200 x 40)
• 120 (.82) • 100/ig/l . I
It 1s somewhat surprising that the zinc remains 1n such high concentrations |
after passing through two reservoirs. However, consider that the dissolved I
zinc upstream of the first reservoir Is: i
•
Zn 150 I
dissolved T74-5
(100 x 10 + 50 +60 + 50 + 200)
« 88 M9/1 . !
Hence, most of the zinc remaining 1n the river downstream of Dell Reservoir 1s j
dissolved. :
END OF EXAMPLE IV-23
4.10.3.5 Adsorption and Desorptlon of Metal Between the Mater Column and Bedded
Sediments
The analyses presented 1n the previous sections are steady-state analyses, and
Ignore diffusive transport between the water column and bed. This Is justified 1f the
degradation rate of metal 1s negligible and the partition coefficients 1n the water
column and the bed are the same. From these assumptions, the dissolved phase concen-
trations of metal are found to be the same In the water column and 1n the bed.
Consequently, there 1s no net adsorption or desorption between the bedded and sus-
pended materials.
However, situations do occur when the steady-state assumption Is not valid. For
example, consider a new discharge which begins operation on a previously uncontaml-
nated river reach. As the contaminated water moves downstream, the metal 1n the
water column Is In contact with uncontamlnated bedded sediment, and some of the
dissolved metal can adsorb onto and diffuse Into the bedded sediment. During this
-515-
-------
process, the concentration In the water column decreases with distance. Figure IV-46
shown previously In this chapter Illustrates the converse situation which occurs when
an Influx of metal 1s reduced so that the metal 1n the bedded sediments 1s no longer
1n equilibrium with the metal 1n the water column. Then, desorptlon tends to
occur.
An approach 1s shown below that can address these situations, and can be used to
answer questions such as:
e How long 1s required for river sediments to be cleansed of excessive
metal concentrations by the process of natural desorptlon?
e If a new source of metal contamination begins discharge, how long 1s
required before the Impact of the elevated metal concentrations 1s fully
felt downstream?
The following approach considers adsorption and desorptfon as the mechanisms of
transporting metal between the water column and bed. The effects of resuspenslon and
deposition are not considered mainly because the time frame of Interest here Is
likely to be months to years. Resuspenslon and deposition rates fluctuate consider-
ably over extended time periods, and tend to negate each other.
Figure IV-70 Illustrates the relationship between the dissolved metal concentra-
tion 1n the water column and 1n the bedded sediments during a period when conditions
are not 1n equilibrium. Near the sediment-water Interface, there 1s likely to exist
a relatively thin layer where the dissolved phase concentration Is approximately the
same as In the water column. Below this mixed layer, vertical diffusion 1n the
sediment controls movement of the dissolved metal. By developing and simultaneously
solving mass balance equations for the metal 1n the water column, In the mixed
layer, and 1n the diffusion layer, the dissolved phase concentration 1n the water
column Is found to be:
r f 6 <*• x) * C0» x f (IV-198b)
where
CQ » Initial concentration 1n the water column (at t • 0), Kg/1
Cj, • constant background concentration (at x • 0), ng/1
t • time, days
x » distance below location where C • C^ m
n \ Kn'10"6
B « - + =—* -*-• days/ m
u uH (1 + KpS-KT6)
u • stream velocity, m/day
M$ * mass of contaminated sediments, per unit area, In thin mixed
layer, g/m2
-516-
-------
U
OF
METAL
CONCENTRATION
WATER COLUMN
(H)
.•.;>..,.;.f MOVEMENT. ...•
MIXED LAYER
DIFFUSION LAYER
FIGURE IV-70
RELATIONSHIP BETWEEN METAL CONCENTRATION IN
WATER COLUMN AND IN BEDDED SEDIMENTS DURING
A NONEQUILIBRIUM ADSORPTION PERIOD
fl - water depth, m
K • partition coefficient, I/kg
S « suspended sediment concentration, mg/1
6(t,x) • an unspecified function of time and distance, unities*.
Equation IV-198b above Indicates that the boundary condition (C • Cb) propagates
downstream at a speed 1/8 which, depending on the magnitude of M$KD. can be sig-
nificantly slower than the stream velocity. The quantity 1/B 1s a useful parameter
because 1t can be used to estimate the time required for the effects of a change 1n
pollutant loading to be propagated downstream.
Equation IV-198 can be used to estimate the residual concentration In a river
when the boundary concentration Is set equal to zero after a period when metals have
been accumulating 1n the bed (I.e., when C^ • 0 and CQ > 0). Conversely, the same
equation can be used to predict the adjustment In stream metal concentration following
start-up of a discharge where formerly there was none (I.e., when C. > 0, C • 0).
To perform these calculations, the function G(t,x) 1s required. Using an
approximation:
(Cb - C0) erfc
2uH
- x
* C.
(IV-199)
-517-
-------
where
Dn • vertical diffusion coefficient, m2/day
6 » void fraction
p -bulk density, g/m3
Kp » partition coefficient, I/kg.
This 1s the equivalent of Equation IV-198a, which 1s valid for large tines following
a change 1n mass loading to a river (e.g., one year or greater). The major difficulty
1n using Equation IV-199 1s finding appropriate Input data for two of the Important
unknowns: D (or Dffl) and M$. When K S 1$ significantly less than unity
and the depth of the mixed layer 1s small. Equation IV-199 can be further simplified
to
Suppose the temporarily changing concentration due to metal desorptlon from bedded
sediments 1s desired. Set Cfa • 0, so
* (IV-201)
In this case, when the mixed layer 1s of negligible thickness, substituting typical
values of 0, u, H Into Equation IV-201 shows that C«CQ. That 1s, desorptlon of
metal back Into the water column produces metal concentrations which are significantly
below CQ, so the levels are not likely to be significant enough to be of concern. On
the other hand, under these conditions the concentrations In the bed are likely to
remain elevated for a long period of time, and could adversely Impact the benthic
community. In summary, 1t appears that unless there exists a mixed layer of sedi-
ments which can exchange metal with the water column at a rate substantially higher
than by diffusion processes alone, desorptlon of metals from the bed back Into the
water column 1s likely to produce concentrations 1n the water column of secondary
Importance. Scour of the contaminated sediments 1s probably more responsible for
relntrodudng the metal back Into the water column at rates where noticeably elevated
concentrations may occur.
4.10.3.6
4.10.3.6.1 Overall Approach
The tools presented 1n the previous sections can be used to evaluate the Influ-
-516-
-------
ence of three hydrologlcal conditions (all steady-state) on metal concentrations 1n
rivers. The hydrologic conditions can be defined In tentt of the suspended solids
levels 1n rivers:
e a period of net settling
• a period of net scour
• a period of equilibrium (scour and sedimentation balance out each other,
or each 1s negligible).
In order to use these concepts within the screening framework presented here, the
annual cycle of events In a river 1s viewed as a series of steady-state conditions
(each of which nay be of several months' duration) separated by unsteady transition
periods. The term "steady-state" 1s used cautiously here because each of the pseudo-
steady periods may have elements of unsteadiness. For example, consider a high flow
condition where metal-contaminated sol Ids are continually being scoured from the
bottom. Scouring of contaminated sediments will continue only as long as they are
present, I.e., until the depth of contamination 1s exceeded. After that happens, the
solids being scoured no longer contribute metal to the water column. This Illus-
trates that a river's steady-state periods may really have some dependency on a
previous steady period. To properly use the steady-state approach 1n the screening
manual requires the user to carefully define the pseudo-steady periods, and select
the data appropriate for each period.
To begin the analysis, a dilution approach Is recommended. In the case of either
single or multiple sources. This Implies that multiple sources Interact with each
other, and downstream concentrations depend on both total mass loading and total
dilution. A dilution analysis does not require either suspended solids concentra-
tions or partition coefficients, unless the fraction of dissolved metal Is required.
Large reservoirs 1n the river system may require that settling of metal contaminated
sediments within the reservoir be calculated, especially 1f most of the metal 1s
being transported in partlculate form.
As a next step, the user may wish to perform a scour analysis to see If metal
concentrations become elevated in the water column during scour even though the flow
rate 1s probably higher (thus, more dilution water 1s available). In such a case,
the user might want to perform an anlysls of each hydrologlc condition throughout the
annual cycle since each condition 1s not truly Independent of the other.
4.10.3.6.2 Uncertainty
Analysis of uncertainty for these screening methods 1s limited to a perturbation
analysis: perturb (or change) the value of a variable within the range of uncertainty
and see how the results change. If significant changes occur, the results are
sensitive to that variable. At that time, some data could be collected to remedy the
situation (see Section 4.10.5). Typically, for analyses discussed in this document,
-519-
-------
the variables which require more accurate quantification are background and point
sources, flow rates, and partition coefficients.
A particular use of uncertainty analyses Is to address the question: Is there a
significant difference In the metal concentration 1n the water column when the
dilution approach 1s used compared to when settling and/or scour occur? For example,
a user may have to decide whether concentration levels predicted to be 25 percent
higher during a scour period, compared to an equilibrium period when net scour and
settling are zero, are really different. By perturbing Important variables within
the range of realism, their questions can be addressed.
EXAMPLE IV-24 .
I I
• i
| For the river shown 1n Figure IV-71, three point sources and three background |
| sources contribute copper to the river system. Calculate the total copper concen- |
j tratlon at locations PI, P2, P3, P4, and PS for the following two conditions. j
I Condition lisa moderate to low flow period where the solIds within the water '
column appear to be 1n equilibrium over distance. Perform a dilution analysis
! during this period. Condition 2 1s a high flow period which follows Condition 1.
I Data collected reveal that solIds are being scoured along the mainstem during j
I Condition 2 as follows: I
j e ASS • 50 mg/1, from point source 1 to PI |
j e ASS • 25 mg/1, from just below tributary B to P2 j
e ASS - 10 mg/1, from just below tributary C to P4. j
. Determine which condition (the steady-state or scour condition) produces higher j
! Instream metal concentrations. Table IV-59 summarizes the required source data.
j The dilution analysis (Condition 1} proceeds by applying the mixing equation !
I (Equation IV-156) at each location, working from upstream to downstream: |
! C. Q. + C, Q, 5 • 50 + 500 • 5 •
I C . - -*-* i-i 50
pi QA + Q! 50+5
•
I
1 CB2 - 50 • 5S * 5 • 10 - 43 Mg/1
j p£ 55 + 10
i
I
i Cr Q • + C- Q- 5-20+75-2
i '""^T—5 u
i
! c . 11 • 22 +43 • 65 . 35
I P4 22+65
-520-
-------
B
FIGURE IV-71 RIVER SYSTEM FOR EXAMPLE IV-24
TABLE IV-59
SOURCE DATA REQUIRED FOR EXAMPLE IV-24
Condition 1
Source
A
B
C
1
2
3
Q(cfs)
50
10
20
5
2
10
Tota1-Cu(/ig/1)
5
5
5
500
75
400
Condition 2
Q(cfs)
500
100
200
5
2
10
Total-Cu(/ig/l)
5
5
5
500
75
400
r . 35 • 67 + 400 • 10 . 7,
I, _ • —————— m /^
P5 87 * 10
The results are summarized in Table IV-60.
For condition 2, the Incremental total metal concentration 1n each of the
three reaches of the ma instem Is, based on Equation IV-182:
-6 t
where
ASS-Kp-Css-10
K « partition coefficient for copper (-10 I/kg, assumed for
this example)
-521-
-------
1
i
•
i
i
I Location
1
P3
i PPS
I ....._,
TABLE IV -60
SUMMARY OF RESULTS OF EXAMPLE 1V-24
Condition 1 Condition 2
Q(cfs) Total-Cu(0g/l) Q(cfs) Total -Cu(jig/l)
55 50 505 24
65 43 605 28
22 11 202 6
87 35 807 25
97 73 817 30
i
1
i
• CSS * steady-state concentration of dissolved copper in the
j water column during Condition 1 .
1 For Css, use an average value over the river:
; 50 + 43 + 35 f . 43f .,
1
i
j
i
j
j
i
i
j
i
i
j
i
i
i
*
i
i
! 3 d d» ^ !
j 1
1 f m * . .*>/•» .rr._4«n CC m CA imn/1 A, if inn •
rd
Condi t1(
| The Incremental
j scour are:
i
j Reach 1
i
I
j Reach 2
i
1
i
1
i Reach 3
i
I To each of tries
| added as shown
I example, below
i c " 5 '
1 + 10* • 50 • 10"6
jn 1.
copper concentration in each of the three reaches caused by
: 50 - 104 • | • 43 • 10"6 « 14 jtg/1
: 25 ' 104 • | • 43 • 10"6 • 7 /xg/1
: 10 • 104 • | • 43 • 10"6 - 3 ng/1
e results, the concentration at the upstream end of each reac.n is
in Equation IV-182. This 1s done by dilution analysis. For
point source 1:
500 + 500 • 5 . 1Q -,,
505
*
i
i
i
j
i
i
j
j
i
i
i
i
i
i
i
i
-522-
-------
At location PI: C - 10 + 14 » 24
The results at the remaining locations are shown in Table IV-b3.
The total copper levels in the river during the high flow are only 50 percent
as high as during the lower flow condition. Apparently, the effects of dilution
caused by the higher flow are more important than the scouring effect of the
copper contaminated sediments. The fact that the factor ASS • 1C- 10~6 is
I less than unity further indicates that the importance of scour is mitigated by the
I moderate size (<1) of this product.
i
I
I END OF EXAMPLE IV-24
4.10.3.6.3 Answers to Questions
In Section 4.10.2.3 (Selected Case Studies), a number of questions were posed
which pertain to the the fate of metals In rivers. Based on the predictive tools
just presented, these questions are answered here in a qualitative sense.
• Is downstream transport of metals important? Metals can be transported
far downstream from their place of input. In some cases, dilution
models appear adequate to predict the total metal concentration in the
water column. Dilution models are appropriate when scouring of ratal
contaminated solids or settling of adsorbed metals are of secondary
concern .
• Are metals which are present in the water column present entirely in
adsorbed form or is there a significant dissolved fraction? Depending
on the quantity and characteristics of the solids in the water column
and the partition coefficient of the metals, the metal can be present in
predominantly adsorbed form (>9W) or predominantly in dissolved form
(>90X). Wren most of the metal is dissolved then downstream transport
of the metal will be important. However, if most of the metal is
adsorbed to suspended solids, downstream transport can sti 11 be important
unless significant settling of the suspended matter occurs. .Note that
while solids tend to settle by gravitational forces, t*.ey are also xept
in suspension by turbulent eddies which are generally present in
free flowing rivers. Consequently, solids (and the adsorbed ratals} can
travel a long distance before settling.
• What is the relationship between the wate- column concentrations ard
concentrations in the bedded sediments? At steady-state conditions, the
following approximation appears to be valid:
Xw • Xb
-523-
-------
where
Cw, Cb • dissolved phase concentration 1n water column and bed, respectively
*w* *b * mass °^ meta^ adsorbed per mass of sediment in water column and
bed, respectively.
At first glance, these relationships appear to imply that there is no difference
in water column and bedded metal concentration. However, this is not true. Based
on the above, the following can be concluded:
C «C ,.
sw sb
where
Cjw, C^ « total concentration in water column and in bed, on a unit volume
basis, respectively
C , C$. » adsorbed phase concentration in water column and in bed, on a
unit volume basis, respectively.
On a unit volume basis, then, the metal concentration in the bed (within the contami-
nated layer, which might be quite thin) is likely to be significantly higher than
in the water column. The primary reason for this is that the bed acts as a concen-
trator for metals that are settling. For example if solids that were suspended in a
1 m deep river settle onto a thin 1 mm layer at the sediment-water interface, then the
concentration factor for solids and metals is 1000. Thus, a sorbed phase concentration
of 10 pg/1 in the water column becomes 10000 ug/1 or 10 mg/1 in tne contaminated bed.
• What is the effect of reservoirs on metal concentrations downstream? If
most of the metal that enters the reservoir 1s adsorbed, then the
reservoir can act as a sink for the metal, depending on the hydraulic
detention time and depth of the reservoir. If the detention time of the
reservoir is relatively short and if the reservoir is shallow, only a
small percentage of the total surface area in suspension might settle so
that much of the adsorbed metal can still pass through the reservoir.
Also, the dissolved fraction of the metal does not settle, so that
unless the adsorbed fraction is great and most of it settles, tnen much
of the metal that enters the reservoir can move downstream.
• What is the effect of metal desorption from river beds? Metal desorption
from oedded sediments back into the water column appears to occur but at
slow rates. If external sources of metals were suddenly cut back, then
the equilibrium which may have existed between the bed and water
column concentrations is disrupted. Under these conditions there is net
desorption of metal into the water column, which tends to provide an
internal source until an equilibrium is reestablished again. Complete
desorption of metals from bedded sediments may taxe years, effectively
slowing the recovery of the river in response to point source reduction.
-524-
-------
These concentrations may be low enough to be of secondary concern to
organisms in the water column, but may be hign enough to be of primary
concern to the benthlc community.
4.10.4 Speciation of Metals and Equilibrium Modeling
4.10.4.1 Introduction
While the analyses in the preceding sections have proceeded under the assumption
that the fate of metals can be determined based on analysis of a single dissolved
species and a single adsorbed species, metals do not obey such a simplified pattern
of Dehavior. Figure IV-72 illustrates a more general picture of behavior. Dissolved
metal might be present as trie free ion, or as complexes with inorganic or organic
ligands. The adsorbed metal can be adsorbed to a variety of surface types and
appears to obey different adsorption isotherms. If concentrations of metals become
tpo high, precipitates may form and settle from the solution, or remain suspended in
solution if particle size remains small enough. This section introduces the processes
affecting the speciation of metals. A generic discussion of processes precedes a
metal-by-metal discussion of rine of the priority metals. The generic discussion
emphasizes fate in fresnwater environments. It does not detail all processes, Dut
rather those which are often of greatest importance. This discussion should be
balanced against the screening procedures found earlier in this document. This
second point of view can help the user to see more clearly the simplification and
assumptions made in the screening procedures and to interpret results of the screening
analysis more intelligently.
4.10.4.2 Major Processes Affecting Speciation
This discussion emphasizes the predominant fate processes likely to be of
importance for metals discharged to fresnwater rivers and streams jnder oxic con-
ditions. This scenario, while not completely general, does encompass many commonly
occurring situations and is consistent with the scope of this section. Figure 1V-73
shows the general range of pH-pe values considered here, as well as the range which
can be found in
-------
PRECIPITATES
SOLUBLE COMPLEXES
WITH ORGANIC LIGANDS
SOLUBLE COMPLEXES
WITH INORGANIC
LIGANDS
ADSORBED SPECIES
• ADSORPTtON/COPRECIPITATION ON
HYDROUS IRON/MANGANESE OXIDES
• ION EXCHANGE
• ADSORPTION TO CLAYS, SILICATES.
OTHER MINERALS
• ADSORPTION TO ORGANIC SOLIDS
FIGURE IV-72 SPECIATION OF METALS IN THE AQUATIC ENVIRONMENT
• Adsorption
• Oxidation-reduction
• Dilution.
Several other processes of minor importance are also specifically addressed.
Before beginning the process-by-process discussion, some cf the importa.-t
"master variables" (terminology often used by Stumm and Morgan, 1970} that control
the fate of the metals are reviewed. These master variables are pH, pe, icnic
strength, and water temperature.
The solution pH (« -log 1 H*], where !H*|is hydrogen ion activity * hydrogen ion
concentration) is a measure of t.'ie hydrogen ion concentration. Typically, in *resn
water, pH « 7 denotes neutral conditions, while pH values exceeding 7 denote basic
conditions, and pH less than 7 denote acidic conditions. Ocean waters nave a rela-
tively constant pS of 3.3-3.3, due to their buffering capacity. The pH of ra-,nwater
containing only atmospheric carbon dioxide is typically 5.6-5.7. However, t>r^" otr.sr
acidic inputs are significant, rainwater pH can be lowerea to 4.2 or so (acic r^-.-t >.
The ?H of river water is more variable than that of the ocean, anc ixjst of;*p -a^ges
from 5.2 to 8.0. Solution pH can strongly -;nflueric9 tne speciav;n of meta';s -.e.
ana re^ce their rate and tox;c;ty.
their fern, sucr. as Cu~* or CjCri
-526-
-------
20
IS
-15
FIGURE IV-73 RELATIVE CHARACTERIZATIONS OF
ENVIRONMENTS BY PE AND pH
A related concept is pe (« -log |e~), where le'lis the electron activity).
The pe describes the oxidation-reduction status of a water body, and is influential
in dictating species likely to be present in water bodies. Together with pH, pe can
be used to create equilibrium phase diagrams.
Another measure of oxidation-reduction often used is the electrochemical redox
potential, EH. EH and pe are related as follows:
EH (in volts) - 0.059 pe, at 25°C
The dissolved oxygen level can also be related to pe. Based on the oxygen-water
redox reaction, this relationship is:
where
pe « 1/4 log PQ + 20.75 -pH
Q
» partial pressure of dissolved oxygen, atmospheres.
For cissolved oxygen levels corresponding to saturation (PQ * Q.2.!, pe » 13.6
-527-
-------
at pH » 7. For dissolved oxygen levels of about 0.1 mg/1, pe * 13.1 at pH » 7.
Hence, in most surface water systems, even in the presence of a small amount of
dissolved oxygen, the pe values are typically in the range of 12 to 13.6. Only in
anoxic systems (or under nonequilibrium conditions) are pe values negative. While
the concept of redox equilibrium is a useful one, the user is cautioned that many
natural systems are not likely to be in redox equilibrium.
Ionic strength is a measure of the concentration of ionic species in a solution.
In very dilute solutions, with low concentrations of ions, the ions behave indepen-
dently of each other. However, as the concentrations of ions increase, electrostatic
interactions between the ions also increase. Consequently under these conditions,
activities rather than concentrations are used in chemical equilibrium equations.
Activities are defined as:
1C} « y [C] UV-202)
where
|C| * activity of species C
[C] = concentration of species C
' * activity coefficient.
For ideal or very cilute solutions, V= 1 and activity equals concentration. Figure
IV-74 illustrates now activity coefficients change with ionic strength and charge.
An ionic strength of 0.5M corresponds to seawater and an ionic strength of O.OOOOM
corresponds to distilled water. Unless ionic strength is extremely low, activity
coefficients are not expected to equal unity, especially for divalent and trivalent
ions.
For the analyses here, ionic strength corrections are automatically accounted
for in the equilibrium model MINEQL. The jser simply has to specify the "typical"
water aopropriate for the conditions being analyzed (see Section 4.10.4.3).
Ionic strengtr (I) can se estimated from either total dissolved solics (TDS) or
specific conductivity, two commonly available parameters. The appropriate relationship
between TOS and ionic strength is:
I • 2.5 • 10'5 • TOS (IV-202)
where
IDS « total dissolved solids, mg/1.
The relationship oetween specific conductivity (^1*10) ana ionic strength is shown in
Figure IV-75. The relationships between ionic strength and TDS and between ionic
streigt^ and cono-jct i vi ty are ^alid for ionic strengths less than 0.1, wnicn is more
than acequate fo>- 'resh water systems. TDS levels for nest fresh «ater in tms
country a^e 'ess :r>an 12uOmg'l. CorresDonaing to Equation (IJ-IU2) aoove, tne ionic
st reig*.n. i s :
-523-
-------
.1 .2 .3 .4
IONIC STRENGTH
(b)
.5
0.10-
0.01-
I
LU
K aooi
o
0.0001 -
0.00001
(14)
-^CUNTtLBERG
APPROXIMATION
1 I ' I ' I ' i ' 1 ' I ' 1 ' i ' I '
10 03 0.8 0.7 0.6 0.5 0.4 0.3 02 0.1 0
ACTlVfTY COEFFICIENT
(b)
REFERENCE: MOREL AND SCHI=F, 1980, AND SNOEYINK AND JENKINS, 1980
FIGURE
ACTIVITY COEFFICIENT AND IONIC STRENGTH RELATIONSHIPS
FCP T-/p!CiL IONS fi) AND S°ECI~IC ICNS (B),
-5Z3-
-------
i
0.1
0.05-
0.01—
o.ooi-
o.oooi
10
i i
100 1000
SPECIFIC CONDUCTIVITY >imho/cm
10000
FIGURE IV-75
IONIC STRENGTH VERSUS SPECIFIC CONDUCTIVITY
FOR SURFACE WATERS (FROM LIND, 1970)
i - 2.5 • itr • 1200 « 0.03
At this Ionic strength, activity coefficients are likely to be about 0.8 to 0.9 for
monovalent ions, 0.5 to 0.6 for divalent ions, and 0.1 to 0.3 for trivalent ions (see
Figure IV-74b).
Water temperature influences virtually every aspect of the chemistry of metals,
including solubility and equilibrium constants, and reaction kinetics. Temperature
corrections can be used 1n MINEQL.
4.10.4.2.1 Precipitation and Dissolution
Metals are subject to solubility limitations in natural waters. Should the
solubility product (defined below) be exceeded, then that metal tends to precic'tate.
By precipitating, the metal forms a solid phase (say lead hydroxide, Pb(CH)2 •'s))
which might remain suspended 1n the water column if the solid phase particles are
small enough ana do not grow and if water turbulence is sufficient to keep then in
suspension. However, should the solid grow to sufficient size, or be transported to
3 slowly noving cacxwater region, then sedimentation of the sol'd is likely.
-530-
-------
The solubility product for a metal cation (a cation 1s a positively charged ion)
with an anion (a negatively charged ion) is represented as:
MaBb(s) ^TaMb+ + bBa" (IV-204)
where
M,BK(S) = tne solid phase species
a D
n* r the positively charged metal cation (with charge b+)
Ba~ = the anion (with charge a-).
The equilibrium constant for this reaction is defined as:
(IV-ZOSl)
, |MbT }BaT (IV-205D)
where
{M 3,(s)} x activity of solid phase, defined as unity by convention
|M } * activity of metal cation
| Ba } - activi ty of anion
* * the equilibrium constant, called the solubility product.
The relationship between activity and concentration can be invoked so that
Equation IV-205b can be expressed in terms of concentrations (and activity coeffici-
ents) rather than in terms of activities. This is desirable because concentration
prediction, rather than activity prediction, 1s the objective of the analyses.
Invoking the definition of activity coefficient as the ratio of activity to concen-
tration, Equation IV-2050 can be rewritten as:
(Iv-206a1
where
>Mi>o • activity coefficients for the metal cation ana tne anion, respectively
[^r ] » concentration of metal cation
_3a~] * .concentration of anion.
When 'i ,.,••*!, and >.*! , Equation W-206b can be written in the simpler, more faciliar,
form:
-531-
-------
tr
8
3
100
80-
60-
30 -
20 -
INCREASING ADSORBENT
pH
(a)
§
cc
100
80 -
60 -
40 -
20-
0
INCREASING ADSORBENT
i
PH
(b)
FIGURE IV-76
TYPICAL ADSORPTION CURVES FOR METAL CATIONS (A)
AND AN IONS (B) FOR A RANGE OF PH AND ADSORBENT
LEVELS,
linear and nonlinear equilibrium isotherms or other mechanistic approaches for
predicting adsorption (e.g. electric double-layer theory). For this screening
approach, the linear equilibrium adsorption isotherm is used. Linear partition
coefficients are presented from the literature.
HydroQual, as aocumented in Delos et_ aj_. (1983), has calculated linear partition
coefficients for rivers and laices for various metals. They analyzed approximately
20,000 records from data bases such as STORE! in order to arrive at their predictions.
-534-
-------
They found the partition coefficients to be dependent on suspended solids concentra-
tions, but to be independent of pH. Table IV-61 summarizes their results. Under
most conditions, the expressions in the table predict that the partition coefficients
are likely to exceed 10 I/kg, and in some cases, exceed 10 I/kg.
Based on linear partitioning, the fraction of metal dissolved is given by:
^ " wyM-io-* (IV-208)
where
r— * fraction dissolved
CT
K » linear partition coefficient, I/kg
SS = suspended solids, mg/1.
The results of this equation are shown in Table IV-62 for each of the metals in Table
IV-61 for a range of suspended solids from 1 mg/1 to 1000 mg/1. In most instances,
the percent of the metal which is adsorbed exceeds 50 percent. This is in contrast
to many toxic organics which often have smaller partition coefficients and, therefore,
more of the toxicant is transported in the dissolved state (see, for example, Table
11-14).
While the results in Tables IY-61 and IV-62 provide useful information, the
possible error associated with the predicted partition coefficients is quite large.
Figure IV-77 illustrates this for copper. The envelope of values encompasses an
order of magnitude.
Rai et_ aK (1983) have summarized adsorption data for numerous priority metals.
The summary includes K values for linear partitioning as well as data for other
types of isotherms. As a result of reviewing the work of Rai et al. (1983), and
other literature sources it appears that there is generally no consistency as to
"expected" values of K for a particular as metal in the natural environment.
Some researchers report relatively small partition coefficients for metals while
others, such as Oelos et_ a_l_. (1983), report significantly larger values. This
uncertainty (or variaoility) emphasizes that site-speci fie K values should be
used if possible and that better methods for predicting the importance of partitioning
are probably warranted. Based on the fact that metals speciate and each species
exhibits different adsorption tendencies, it is not difficult to see why a "K
approach" 1s limited in predictive ability. However, the alternative approaches are
not well supported either and site-specific data can 5e quite beneficial, regardless
of the approach.
4.10.4.2.3 Oxidation-Reduction
Oxidation-reduction reactions are conceptually analogous to acid-base reactions,
exceot t.iey are s'g.v'icantl./ s.o*er. Oxicants and reductants are aefined as electron
-535-
-------
10'
i
MEAN & STANDARD
DEVIATION OF DATA
RELATIONSHIP USED
TO PRODUCE
PARAMETERS IN
TABLE IV-58
^ ' ' " 'I ^ T-r^.fir,
10 100
SUSPENDED SOLIDS.
1000
REFERENCE: DELOS ET AL,, 1983
FIGURE IV-77 PARTITION COEFFICIENT FOR COPPER IN STREAMS
oxygen 75 oresent. The model MINEQL has been run for conditions corresponding to
oxidizing environments.
4.10.4.2.4 Dilution
When wastewater is discharged into a river, the metal speciation of the wastewater-
river water mixture can be significantly altered compared with metal speciation in
the wastewater. This is because the master variables of pH, pe, ionic strength, and
temperature in tne wastewater-river mixture can be quite different from those of the
wastewater alone.
Examoles of processes likely to be important during nixing are:
Solubility changes due to significant changes in species concentrations
and redox conditions (say from anoxic conditions wnere sulfides can
control solubility to oxic conditions and where carbonates or hydroxides
can control solubility*.
-538-
-------
• pH changes can Influence the fraction of species adsorbed and the
relative distribution of the dissolved species.
4.10.4.2.5 Processes of Secondary Importance
Other processes that can influence the fate of a few of the priority metals are
volatilization, photolysis, and biodegradation. Each of these processes is discussed
in detail in Chapter II of this document as they relate to organic contaminants.
Volatilization is the physical transfer of metal from the water column into the
atmosphere. Elemental mercury can volatilize and so possibly can arsenic. Photolysis
is the chemical process of degradation through absorption of solar energy and may
influence the fate of cyanide and mercury. Biodegradation, or the microbial degra-
dation of metal complexes, can influence a number of priority metals. In this
section, the processes of volatilization, photolysis, and biodegradation are given
minor attention. While photolysis and biodegradation may influence metal speciation,
the metal itself can still be present in the water column, although as a complex
which may have different toxicity than before.
4.10.4.3 Metal-by-Metal Discussion
This section summarizes the fate of nine priority metals in oxidizing surface
water environments. Figure IV-78 shows a periodic table of the elements and' illus-
trates the placement of each of the priority metals discussed (see the circled
elements). The common oxidation states of each metal are shown above and to the left
of the symbol for each metal. Table IV-63 summarizes the discussion that is to
follow. The table shows the metals affected by oxidation-reduction, the primary
species which are likely to be present in each environment, the solids controlling
solubility, and pH-pe combinations that are conducive to mobilizing the metals
(e.g., Increasing solubility).
4.10.4.3.1 Arsenic
A source of arsenic in the natural environment in the United States is coal-fuel
power plants, which emit approximately 3000 tons of arsenic per year (Nelson, 1977).
Arsenic trloxlde, ASjO-^s), is formed and is often the compound which first reaches
surface waters. Concentrations of arsenic in surface waters of the United States
range from 5 to 340 ppb, with a mean value of 64 ppb (Kopp, 1969). lr contrast,
average arsenic concentrations In ocean waters are about 2 ppb.
Figure IV-79 is a pe-pH diagram for arsenic, without the influence of organic
material, showing its behavior for total arsenic ranging from 1Q~6 to 10" ° M (0.1 to 100
ppm). In oxidizing environments likely to exist In most surface waters (i.e. pe
-539-
-------
u
• • 1
H
• • 3
U
tMl
• ' II
H*
nt>
• ' it
K
»•*
•'17
"»
c»
•'17
fr
imi
•41
• • 4
B*
t.*.t
•l«2
*•
«4»
.120
C«
4*«
•11*
V
'M
**
•>M
*•
IM*
MS
21
Sc
44M
3t
Y
»7
LI
3* t
M
Ac
Ol'l
AtlMIH)
iv*.
Jt
T.
• 'M
40
II
72
HI
1 4*
104
Ku'
t 4§W*"J»
V*
23
V
M*4
41
Nb
73
Tl
105
HI*
U
CM
14*11
to
Th
rot
VW
••24
"©
UM
42
UO
74
W
MM
M
•.
n*t
tl
ft
nn
4f M
VMJ
«'f»
•'U«
»4»4
43
Tc
7i
»M f
to
1*4 |4
t2
U
m*
•4M
^•i^W
•»M
'%,
UM
44
"«
7t
O.
tl
t3
Np
....
-¥M-
Co
MU
4S
flh
77
In
U
Sm
IMI
t4
P«
ii4n
©
M.I
• 41
Fd
71
*****
•Mf Tt»>
U
itIM
t5
A«
u.»
I*
"©
Utk
"47
^
7t
Aw
mt»
MM4M 1
H
t»l
M
Cm
•W»)
M
©
MM
•'41
t^
;;M
•
llMWMKl
IS
Tb
t7
ft*
«.'.
MA
•> S
•
i**i
•>I3
M
MM
•»31
Q.
Hit
•> I4»>
;}i»
p
>»t»
•'33
>J©
»«•!
• >J,
Sb
I
:5fc
M
.
..,»
100
F/»
.n»
VIA
t
0
> ..tM
:!'«
s
»UM
••34
$»
•'MM
:!"
r.
* * (4
••o
It
101
M4
1 r»t»
J5j
? '
At
70
102
M«
"»"
2
H*
10
H*
Mil
I*
A<
MM
M
Mr
UM
}4
*»
*•
Hn
71
103
Lr
tun
•Oun• c«Md KM no«prM«ni
MM! HMMMMV *>• |«M|I»« nun** tar
lo *• Ml o< ffw
FIGURE IV-78 PERIODIC TABLE OF THE ELEMENTS (FROM SNOEYINK
AND JENKINS, 1980)
values exceeding 8 or so), the pentavalent arsenic species predominate, with
2
and HAsO'" being the most likely anions. Only at relatively low pe values (around 0
or less) at trie pH range lively to exist in natural waters will the trivalent form be
stable. As evidenced from the pe-pH diagram, arsenic(V) is a tr-protic acid, and its
behavior is quite similar to that of phospnoric acid.
Arsenic(V) forms a series of salts with alkaline-earth metals (magnesium,
calcium, strontium, and barium) and with a number of the heavy metals (such as
nickel, copper, zinc, cadmium, and lead) which are quite insoluole. For example,
oversaturation with respect to Pb,(AsOA)->(s) at ar, As(V) concentration of 10" M can
-5 5
occur when lead concentrations exceed 1C M at pH * 7. However, 10 M of e-.ther lead
or arsenic is high and far exceeds the 1980 U.S. EPA's water quality criteria, and
the simultaneous occurrence of both is unlikely. Whi'e thermccyamics favcr oxida-
-540-
-------
TABLE IV- 63
SUMMARY OF METAL SPECIATION IN OXIDIZING AND REDUCING ENVIRONMENTS, SOLIDS CONTROLLING
SOLUBILITY, MO pH-pe COMBINATIONS CONDUCIVE TO METAL MOBILIZATION
Unaffected by
Clement OxtdtUon-ftedwctton
Ai
Cd C«l**.CdSOj.CdCO°
Cr
Cu
Pb Ph**,PbCoS' N>(CO,)?~
OxtdUlnq Environment
ny^.H*^-
HCrOj"
Cu2*.CuSOj,Cu(OH)^
Reducing Environment
HjAtO^.HjAsOj
Cr3*.Crf2*.CrOH2*,Cr(OH)*.Cr(OM)'
Cu*.CoCI2,Cu(OH)^
Controlling Solid*
Fe»,04.A,2S3
CdC03.Cd3(P04)2.|C.,C.Ii»(CO;,)£~ in(OH)2.ZnC03.ZnS104.Znf*204 low pH
-541-
-------
024681 12 V*
-15
FIGURE IV-79 PE/PH STABILITY FIELD DIAGRAM FOR ARSENIC AT 25 C
tlon of As(III) to As(V) in oxic surface waters, the kinetics of the oxidation
process are thought to be such that As(III) can remain 1n this reduced form for
relatively long periods of time; e.g. order of months (Tallman and Shaikh, 1980). As
indicated earlier, arsenic from combustion can enter surface waters as As^O-^s).
This compound tnen reacts with water to form arsenious add, H^AsOj which is in the
*5 oxidation state (see Figure IV-79). Depending on the rate of the oxidation reaction,
the more toxic trivalent arsenic might remain present 1n the water.
Both As(IIi) and As(V) species are expected to adsorb onto surfaces, such as
oxide surfaces and clays. The As(V) species have greater adsorptivs tendency than do
the As(III) species. Adsorption generally decreases with Increasing pH, indicating
that adsorption is more likely to be important in acidic waters.
4.10.4.3.2 Catini urn
In natural fresh water, cadmium can be present in extremely low concentrations
(e.g. less than 0.01 Mg/1). Sources of cadmium include both industrial effluents
(pigments, plastics, alloys, and electroplating) and municipal effluents industrial
sources may account for up to 90 percent of the cadmium released domestically
(Environmental Science and Tecnnology, 1971). Cadmium is extremely toxic to fish,
and effects on trie growth rate nave been observed at concentrations between 5 and 10
-542-
-------
(Gardiner, 1974). The suggested U.S. EPA criteria for protection of aquatic life
are quite low also, varying from 0.01 to 0.05 ng/1 for waters having hardnesses of 50
and 200 mg/1 as CaCOj, respectively.
Cadmium speciation is similar to that of zinc, as might be expected, since they
are located in the same group in the periodic table and in adjacent periods 4 and 5
(see Figure IV-78). In oxic surface waters, cadmium is present in the- 2+ oxidation
state, so that redox reactions are not important for this element.
Cadmium can form complexes with hydroxide, carbonate, chloride, sulfate, and
humic materials. Complexes with humic materials can be important when sufficient
organic matter is present.
In fresh water, the concentrations of cadmium are usually far below the maximum
permitted by its solubility products. Solubility is probably controlled by the
carbonate, which should limit the soluble Cd(II) to between 0.1 to 1.0 mg/1. In the
pH range of natural waters, hydroxide solubility is about an order of magnitude
greater than this.
Figure IV-80 shows the speciation of cadmium in freshwater as a function of pH
in the presence of an adsorbate Si02(s). The total cadmium present is 10 M (0.1
mg/1). Even at these high concentrations, cadmium does not begin to precipitate
until the pH exceeds about 6.9. The uncomplexed cadmium is the dominant species
below this pH.
Adsorption of cadmium appears to follow a linear isotherm at total cadmium
concentrations of about 5.0 ng/1 or less (liardiner, 1974). Concentrations of cadmium
in natural waters are generally less than this, except in cases of extreme pollution.
The proportion of cadmium adsorbed in the water column can be significant.
Gardiner (1974) found that between 30 and 46 percent of cadmium in the water column
was sorbed at total cadmium levels between 2.1 and 5.5ng/l, and at a suspended
solids level of 34 mg/1.
According to the work of Gardiner, levels of EDTA, a strong organic complexing
agent, below about 30 ^g/1 did not significantly affect cadmium speciation. At
higher levels (i.e. 300 v;g/l), the percent adsorbed was decreased by about half.
4.10.4.3.3 Chromiuo
-8
Bacxgrouna chromium levels in many natural waters are approximately 10 M i,0.5
ppb). Chromium levels in wastewater range up to 10" M (500 ppb), whicn is near tne
solubility for chromium at neutral pH, where Cr(OH)3(s) can control solubility (for
anoxic condi tions ).
Figure IV-81 is a pe-pH diagram showing the stability of chromium species at a
total chromium concentration of 10 M (The effects of organic species are not included
but can ae important.) At the ?e values normally encountered in river waters tacove
5), tne free Cr * icn is expected to be present only in very acicic water (pH<-i}.
-543-
-------
6.2 6.4
PH
-log MOLAR
CONSTITUENT
Cl
NH3
Br
F
S04
PCO
CONCENTRATION
3.65
5.5
6.62
5.5
3.9
3.5
REFERENCE: VUCETA AND MORGAN, 1978
FIGURE IV-80 CADMIUM SPECIATION AS A FUNCTION OF pH IN THE PRESENCE
OF 1,55 M2/L Si02(s), Cot = 1CT5M,
However, as the pH increases, at low pe, important soluble Cr(III) species are likely
to be CrOHZ* ana CKOH)^ . The most important Cr(VI) species are HCrO^ and Cr04", anc
they are likely to be present at pH values between 6.2 and 8 at high pe values.
These anionic forms of chromium are fairly soluble and are relatively mobile in
surface waters.
The presence of other metals can control the concentration of Cr(VI). Such
metals include barium, calcium, strontium, copper, and lead. Lead concentrations as
low as 10" M (10 ppb) can produce chromate precipitation if concentrations of
chromium (VI) exceed 10 M at pH » 7.
While Cr(VI), in the presence of Fe(III) and dissolved sulfides, can be readily
reduced to Cr(III), these compounds are not likely to be present in oxic surface
waters. Cr(III) on the other hand can be oxidized by dissolved oxygen, but rather
slowly. Tne fate of chromium depends or, its oxidation state: Cr'III', is likely to
-544-
-------
-20
FIGURE IV-81 PE/PH DIAGRAM SHOWING STABILITY OF
CHROMIUM SPECIES FOR CRT = 10" M,
be sorbed and found in bottom sediments, and Cr(VI) is more likely to be carried in
the water column.
4.10.4.3.4 Copper
Elemental copper is a soft metal with art atomic weight of 63.5 and a density of
3
8.9 g/cm . It is a ubiquitous element in rocks and minerals and usually occurs as
sulfides and oxides. Its concentrations in natural rocks typically vary from 4 ppm
to 55 ppm, and natural background levels are often between 1 to 10 ~g/l in rivers.
Industrial sources of copper include smelting and refining industries, copper-wire
mills, and iron- and steel-producing Industries.
The 2+ valence state is stable in oxic environments, while copper complexes with
1+ valences exist in reducing environments (Figure IV-82). For oxic waters, pe is
typically near 10, and the equilibrium valence state is 2" regardless of pH.
Several different researchers have investigated tne equilibrium speciation of
copper, both in the presence and in the absence of organic complexing agents. Figure
IV-83a snows the predictions of _ong and Angino (1977) in r'resh water in the presence
of the ligands OH', CT, CO^", SC^~, and HCO^. The results snow thai either the free
-545-
-------
13.5
REFERENCE: HEM (1975),
FIGURE IV-82
PE/PH DIAGRAM SHOWING AREAS OF DOMINANCE OF FIVE
SPECIES OF COPPER AT EQUILIBRIUM AT 25°C AND 1 ATM,
copper predominates (at pH values below about 6.5) or CuJOH)^ predominates at higner
pH values.
Vuceta and Morgan (1978) investigated the speciation of 10 M of total copper
also in the presence of inorganic ligands, plus an adsorbing surface expressed as SiC2
(s). Their results are snown in Figure IV-836, and indicate that for pH>6.5, ttie
adsorbed form of copper predominates. The free ion is probably present in significant
quantities below pH 7 and is the predominant species below pH 6.5. Copper solubility
in most fresh water appears to be controlled by malacnite (Cu^OHKCCU) ratner than
the hydroxide (Stiff, 1971). However, precipitation of malachite is a slow process.
Organic ligands can complex copper and increase its solubility in water. Vuceta
and Morgan (1978) found that when EDTA was added as an organic ligand to their
equilibrium model, the adsorbed and organically coirplexed copper were the aredominant
species, when at least 10" * M EDTA was present. At higher amounts of organic
-546-
-------
3J5 4.5 5.5 65 7.5 83 9.5 10.5 115
CONSTITUENT
Cl
SO,
Ca
Mg
-loo MOLAR
CONCENTRATION
3.60
3.0
3.4
3.8
6.2 6.4 66 6.8 7.0 7.2 7.4 7.8 7.8 8.0
-log MOLAR
CONCENTRATION
3.65
5.5
6.62
5.5
3.9
3.5
REFERENCE:
FIGURE IV-83
(A) LONG AND ANGINO, 1977
(B) VUCETA AND MORGAN, 1978
COPPER SPECIATION (A) IN THE PRESENCE OF INORGANIC
LlGANDSj (B) IN THE PRESENCE OF INORGANIC LlGANDS
AND AN ADSORBING SURFACE, 1,55 M /i_ SiO,(s).
-547-
-------
complexing agents, a greater quantity of adsorbing surface is required for the sorbed
phase to be of significance. Gupta and Harrison's (1982) investigations produced
similar results. They found that the addition of humlc materials reduced the
adsorption coefficient, K , in a dilute system of kaolin and copper in water. The
influence of the humic acid was evident at concentrations below 1 mg/1 (Figure
IV-84).
4.10.4.3.5 Lead
Lead is a constituent of hundreds of natural minerals, and its average abundance
in the earth's crust is 15 ppm. Natural background levels of lead in inland waters
typically average 1 M.g/1 or less. However, areas impacted by man, such as from lead
mining, can have concentrations on the order of 50 ng/1. Lead is used in metal
products (with storage batteries being a primary use), pigments, gasoline antiknock
additives, and other mi seellaneous uses.
While lead exists in three oxidation states (0, 2 , and 4 ), the 2* oxidation
state predominates in natural surface waters. Long and Angino (1977) evaluated the
equilibrium speciation of lead in a freshwater environment containing only the
? 2
inorganic liganas OH , Cl , CO^ , S0£ , and HCOj. Figure IV-85a shows their results.
The Pb * ion is the predominant specie at pH s7, while at 75pH«9, PbCO° is the
major specie.
Vuceta and Morgan (1978) also used an equilibrium model approach for lead
speciation in fresh water in the presence of inorganic ligands plus an adsorbing
2+
surface. Figure IV-85b shows their results. Again, Pb predominates below pH * 7,
and PbCO? predominates at higher pH. For the amount of adsorbing surface used in the
J 2
analysis (1.55 m /I SiO-(s)), the concentration of adsorbed lead was approximately an
order of magnitude below the dissolved phase concentration. Small additions of iron
and manganese oxioes to the model data base, wnich provide further adsorption
surfaces, did not appreciably change the results.
Lead can precipitate as a number of compounds including PbS04(s), PbCQ^U),
Pb(OH)2(s), PbS(s), and Pb3(P04)2. The PbCOjfs) can control solubility in natural
waters. Near pH » 8, the lead solubility is probably between 30 to 100 ^g/1, and
rapidly increases at lower pH values. In soft waters at low pH values, lead solubility
can oe quite high so that using such a water type for drinking may be a health hazard
if lead pipes comprise the water distribution system.
Lead readily forms complexes with organic liganas, which tend to increase the
amount of lead which can be dissolved in water. In Vuceta and Morgan's equilibrium
modeling results (1978), they found that when a strong complexing agent suc.h. as EDTA
exceeded about 10 *^ M, more of the lead was complexed than was not. The total lead
present was 10" M
-548-
-------
•000
7000.
^ 6000
u
£. sooo J
4000
30OO-
3000
8 EXPERIMENT*!.
POINTS
234 S * 7 •
HUMC AClDppm
K>
REFERENCE: GUPTA AND HARRISON, 1982
FIGURE IV-84
EFFECT OF HUMIC ACID ON PARTITIONING OF COPPER,
(NOTE. THE HUMIC ACID WAS OBTAINED FROM THE
ALDRIDGE CHEMICAL COMPANY; NA-SALT, WATER
SOLUBLE,)
Lead is readily adsorbed to numerous solid surfaces, including organic matter,
clay, silica, and iron and manganese oxides. Lead adsorption is pH dependent and the
adsorption edge occurs at lower pH than that of either copper or zinc.
4.10.4.3.5 Mercury
Elemental mercury is a silver-white metal and is a liquid at room temperature.
It has a specific gravity of 13.5 and a vapor pressure of 0.0012 torr. It is used in
the electrolytic preparation of cnlorine and caustic soda, in electrical apparatus
such as mercury battery cells, in control instruments such as thermometers, in
laboratory applications, and in other Industrial applications. Concentrations of
mercury 1n natural rocks range from about 5 to 1000 ppb, with 80 ppb being a typical
value. Typical background mercury levels in natural surface waters average 0.01 to
Mercury can exist in the natural environment in one of three oxidation states:
0 (the metallic form), 1* (mercurous), or 2+ (mercuric). Figure IV-86 illustrates
for the predominant inorganic species present in water under equilibrium conditions
(Gavis and Ferguses, 1972).
For typical ?e-pH values in surface waters, eitner the chlorice or hydroxide
-549-
-------
T*I' I*T
4J U U U
PH
as KXS its
CONSTITUENT
Cl
SO,
c«
Mg
-log MOLAR
CONCENTRATION
3.66
3.0
3.4
3.8
6u2 6.4 66 6J 7.0 7.2 7.4 78 7J
CONSTITUENT
•tog MOLAR
CONCENTRATION
3.65
5.S
6.62
5.5
3.0
3.5
ao
PH
(b)
REFERENCES:
(A) LONG AND ANGINO (1977)
(fi) VUCETA AND MORGAN (1978)
FIGURE IV-85
LEAD SPECIATION (A) IN THE PRESENCE OF INORGANIC
LlGANDS; (B) IN THE PRESENCE OF INORGANIC LlGANDS
AND A SOLID ADSORBING SURFACE (1,55 M2/L Si02(s)),
-550-
-------
FIGURE IV-86 PE/PH DIAGRAM FOR HG, SHOWING PREDOMINANT SPECIES
IN SOLUTION FOR CONCENTRATIONS OF TOTAL HG
GREATER THAN 5
species predominate for the conditions shown. The solubilities of these compounds
are great enough that precipitation of mercury In oxidizing environments Is usually
not a concern.
Mercury possesses an affinity for sulfhydryl groups (-SH) and can form organic
sulfhydryl complexes. Mercury also forms compounds with alkyl groups (e.g. dimethyl
mercury). The methyl mercury 1on (C^Hg*) can be discharged from Industrial effluents
and can oe synthesized from Inorganic mercury by bacteria which reside 1n sediments.
Mercury strongly adsorbs to a variety of solids, Including organlcs, clays,
metal oxides, and sand. Halide Ions (e.g. Cl", Br',1 ") appear to suppress
mercury adsorption.
Based on elemental mercury solubility of 19.2 ng/1 at 50°C and 81.3 wg/1 at 30"C
and a vapor pressure of 0.0012 torr, the calculated Henry's Law constant lies between
-2-33
10 to 10" atm*m /mole. Elemental mercury 1s relatively volatile. Both organic
and inorganic mercury compounds exhibit volatility.
4.10.4.3.7 NUkel
Nickel 1s present in the earth's crust at an average concentration of approxi-
mately 30 ppm. Background levels In surface waters are 1 ng/1 or less. Although
nickel forms compounds with valences of 0, 1*, 2+, 3+, and 4*, the Important valence
state is 2+.
-551-
-------
Nickel can precipitate as the hydroxide and carbonate, as studied by Patterson
et_ al_. (1977). Figure IV-87 shows the solubility limits for nickel carbonate and
-1 2
nickel hydroxide 1n the presence of 10 >fc M total inorganic carbon (TIC). While this
amount of total carbon is significantly greater than found 1n natural rivers, the
figure does Illustrate that neither nickel carbonate nor the hydroxide is likely to
limit solubility 1n the natural environment except at high nickel concentrations.
Nickel can be adsorbed by a variety of substances, including iron and manganese
oxides and organics. However, nickel is thought to be relatively mobile in the
aquatic environment, especially in comparison with other metals. The work of Vuceta
and Morgan (1978) appear to substantiate this. They evaluated the chemical speciation
and adsorption of 10 M (19 ng/1) of total nickel in the presence of inorganic
2 2
ligands which included OH , CO^ , and S0£ and a solid surface expressed as Si02(s)
(the equivalent of 310 mg/1). They found that at pH * 7, practically all of the Ni
(18ug/l) was present on the free divalent cation and that only a small amount was
adsorbed. Adding small amounts of Fe(OH)3(s) and Mn02(s) had little influence on the
amount of nickel adsorbed; the free ion again was the predominant species.
Complexation with organic ligands can be very important for nickel, and tends to
further increase the mobility of this meta1. . Vuceta and Morgan (1978) found that 10
M (90 ng/1 ) of EDTA added to natural water at pH » 7 complexed about 50 percent of
the nickel, with the remainder present as the free ion.
4.10.4.3.8 Silver
Elemental silver is a white ductile metal having an atomic weight of 107.9. It
is primarily used in electroplating, as a conductor, in alloys, paints, jewelry,
silverware, and mirror production. Background levels of silver are low in the
aquatic environment, ranging from about 0.09 ng/1 to 0.6 Kg/1 (Kharkov et_ aj_., 1968).
In the earth's crust, silver typically occurs in concentrations of about 0.1 ppm.
Silver 1s quite toxic to bacteria, invertebrates, and fish. Chronic to XT city to
freshwater aquatic life may occur at concentrations as low as 0.12 ng/1 . Of the
heavy metals only mercury is considered more toxic.
Silver can have valence states of 0, If, 2+, and 3+. The 0 and I* valence
states are the most prevalent in the aquatic environment.
Solubility controls in the aquatic environment are probably not exerted oy
either the oxide or carbonates. However, silver halides are quite insoluble and can
control solubility. Hem (1970) states that chloride concentrations as low as 10" M
(35 mg/1) can limit solubility to below about 10 n-g/l .
Silver adsorbs to a variety of surfaces, inching ferric hydroxides, clay
minerals, and organics. Also, adsorption :o manganese dioxide can ie significant
(Kharkov et al ., 1968).
-552-
-------
0 2 4 6 I 10 12
pH
REFERENCE: PATTERSON EJ AL, (1977),
FIGURE IV-37 NICKEL CARBONATE AND NICKEL HYDROXIDE
SOLUBILITY PHASE DIAGRAM (TIC =• 10"1<2M),
4.10.4.3.9 Z1nc
The chemistry of zinc 1s similar to that of cadmium, which is found directly
below it in the periodic table. Zinc, however, is more abundant than cadmium; and is
an essential element In trace quantities for building tissues. In oxic aqueous
systems, zinc exists in the 2+ oxidation state.
Typical concentrations of zinc in soils range from 10 to 300 pom, with the
average being approximately 50 ppm (Bowen, 1966). The median concentration of Zn in
surface waters of the United States is approximately 20 ug/1 (Durum et^aU, 1971).
Zinc Is used as an oxide pigment in rubber and paint, in agricultural fertilizers
and sprays, in the textile industry, and battery production. The major metallurgical
uses are in the galvanizing of metal and production of crass and other alloys.
Figure IV-88 from Vuceta and Morgan (1978) shows zinc speciation as a function
of pH, assuming the total zinc 1s 10" M (7 ng/1), and in the presence of an adsorbing
surface, expressed as Si02(s). Throughout the pH range shown (6.2 to 8.0), the free
metal ion predominates. The next most predominate species are sulfate and carbonate,
even more prevalent than the adsorbed zinc.
Models of zinc speciation based on Inorganic ligands are altered in the presence
of organic matter, which appear to increase soluble zinc. The results of Vuceta and
Morgan (1978) suggest that organic complexation is important at pri values below
approximately 6 and at ligand concentrations exceeding 100 ^g/1.
Zinc 1s normally undersaturated in natural waters. 3otential solubility controls
-553-
-------
4.10.4.4 Equilibrium Modeling Analysis
4.10.4.4.1 Introduction
The analysis in Section 4.10.3 treats metals as pollutants which sped ate Into
either adsorbed or dissolved form. Based on this approach, the distribution of
metals 1n the water column or in the bedded sediments can be estimated using a
relatively modest amount of data, particularly with respect to the chemical charac-
teristics of the water which transports the metals. In contrast to this simplified
picture of metal behavior, the previous section showed that metals can form a large
number of compounds, soluble and insoluble, with organic and inorganic ligands and
can become adsorbed to organic or inorganic sol Ids.
Although approaches that consider metal speciation require more Information to
Implement, they can also address questions that the analyses in Section 4.10.3 can
not. For example:
• How do the chemical properties of the river water affect metal speciation?
• What are likely to be the predominant species of metal present?
• When is precipitation likely to occur?
t Under what conditions are the more toxic species likely to be present?
Equilibrium models provide a key for answering these questions since they can calculate
the species' distribution of metals for a specific set of receiving water conditions.
The models assume that rate-limited processes are so fast that the species quickly
come to a state of equilibrium. While equilibrium models themselves are not transport
models, they can be combined with transport models. That equilibrium models ratner
than rate models have been developed by researchers indicates that knowledge is still
quite limited regarding the fate of metals In aquatic systems and that rate models
do not appear to be feasible at present.
Based on the foregoing, the approach selected here is to choose an equilibrium
moael and to apply 1t to a variety of water types found throughout the United States.
The results are tabulated for easy reference.
4.10.4.4.2 Choice of Typical Waters
Typical waters chosen for the purposes of this report are based on the United
States Geological Survey's National Water Quality Network (NASQAN; Brlggs and Ficke,
1977). That report 1s based on the 1975 water year and is their most recent report.
Figure IV-90 shows the USGS's water resources regions within the United States.
Fifteen stations were selected within these regions based on regional ^presen-
tation, population, and interrelationships of the chemical constituents in the
rivers. They are also shown in Figure IV-90.
The characteristics of the waters at the selected stations are shown in Table
IV-64. The standard deviation as well as mean values are given for water temperature,
-556-
-------
Region numbers and
01 New England t?
02 Mid Atlantic 13
03 South Atlontlc-GwIf U
04 Great loket IS
OS Ohio '*
04 Tennessee 17
07 Upper Mississippi 18
08 lower Mississippi 19
09 Souris-fted-Rolny 20
10 Missouri 0asin 21
11 Arkonsos-White-Red
Texas-Gull
Rio Grande
Upper Colorado
Lower Colorado
Great Bosin
Pacilic Northwest
California
Alaska
Hawaii
Caribbean
Locations
Selecte-J
FIGURE IV-90 VATER RESOURCES REGIONS OF THE UNITED STATES
suspended solids, and pH. Tne range, ratner than the mean, is shown for total
organic carbon.
For comparison against the standard waters, a low pH water at tne outlet of
Woods Lake in the Adirondack Mountains 1n New York State has been added. The pH of
the water averages 4.7, the alkalinity Is -10 neq/1, and the water 1s very soft
(hardness » 6 mg/1 as CaCO,). In such a water, metals might tend to dissolve, and
due to the low hardness, criteria levels would be quite low for the metals whose
standards relate to hardness.
4.10.4.i.3 Equilibrium Model Chosen
A variety of equilibrium models capable of predicting metal speclation nave oeen
develooed 1n the past-decade. Summaries of tne performance ana caoaci1ities of f.e
-557-
-------
TABLE IV-64
CHARACTERISTICS OF RIVER WATERS CHOSEN FOR ANALYSIS
li
1 I »
' > *l
1 4 *
4 t *
J
]
! i
•*•• MI/MM
p«I». MMHM
.!«<». MUM*
>H MIMM
1 f M«<>«*4 MM MM
1 M MtlMrl MMMM
1 II 1.
1 14 0
r
it.n.
MJ>.
M.I*.
II. i*.
I/.HI.
U.ill.
5
P i
n.i. •
I4..I).
11..*. »
S
a
c s
5 I
i. *t.
M.
1. Ml.
11.144. II. Ml.
II. .11
IM.
IIM.illM. t». 14*.
,
•>
1
in.
M.
Ml.
ru.
tit.
n*.
*M Ml MM* n.H. IB.llM. «l. MB. 10*.
lt«rM» MUMM
t II Ul«»l. I4UMM
1* II t
II 1 »
It 1 f
M>M*« II44MU
M* Itt*. It
1.0ml M«MIM
ll.il.
M.it.
U.it
II.H.
».!«.
NM.II44*.
Dill. »
100. ilM. *
1.
!.•}. (
II 1 ll. Mrjn W11MM M.lt. i..l.
14 W «*•** MRMM
IS It f
MM M4*I1M
ll.iM.
U.H.
Mil .IM*.
IM.
1. M.
1. If.
M.
1 M.
M.
IlM.
U >ll. )|. MM.
MM.
IM.
II*.
It.
M.
41.
"1
a
p
s i
W
5
11
s
I S
t. -i». i.jn.i i*.
t.-ll. i.in.i r.
t.-it. i.t
4.-I. 1.1
l.-tl. I.+
4.- II. l.»
t.-ll. ».•
t. l.k
M.t 41.
*.* 11.
i*.J «.
•O.t II.
*.! II.
i».I M.
/.-ll. ?.*>*.l II.
l.-l. I.OW.4 M.
t. 4.7M.I I.
)..».» t.t.
M.-ll. t.l
M.t l.t
ll.* 1.)
MM. f.-f*. I.IX.I W.
IIU*.
I.-I4. I.M.I 4tl.
1
1
ll
0
).
1.
U.
1.
If.
II.
II.
11.
I.
t.
I.I
1.4
I.I
I*.
III.
1
I i
1* I
z z
t. 1
4. 1
M. 1
M. I
4. 1
H. t
41. 4
M. 4
I. 1
1. 1
•V'
XlMlrM IfW
•I/I
I.M
I.I
t.*l
*.«
I.U
•.1
0.1
•••1
I.M
I.U
*.« 1. 1 I.I.
1.1 1.4 *.l
1.4 1.4 It
lit. M
Mil. 11
I. II
I.It
jf
M.
II.
Ml.
III.
14*.
M*.
IM.
114.
M.
41.
1.
n.
u.
m.
MM.
f1
•.1
».*!
•.I
*.t
•.I
I.I
•.4
*.?
1.4
t.M
r.*
.Mi
ji
*.*!
•.M
* (1 *A
S S
I
i/te
>tJ*|M ***l*t.
II. U. 1.
M. 1
• •
M. •
4.
4 *.l
U I.*
I.M *.M I.I t.t 1.*
•.M
1.4
I.M
••"
M*. M*
in. in§
IM.
MI*.
Source: lr<«9* «nd FUU. Itl/.
'Nr«n tone tUnd«rd dolitlon.
-558-
-------
models can be found 1n Nordstrom et. al_., 1979. The model selected for this project
Is MINEQL,
MINEQL (Westall et.aU. 197fi) nas «volved over the years and is based on an
earlier equilibrium model called REDEQL. The version of MINEQL used in this
chapter is called MINEQL+STANFORD, in recognition of modifications done at Stanford
University.
MINEQL uses the equilibrium constant approach (in contrast to the direct.minimi-
zation of the G1bbs free energy function) to attain equilibrium composition of
species. The model is quite versatile and allows the user numerous options. For
example, redox reactions can be considered or not at the discretion of the user.
Adsorption can be modeled using the electrical double layer theory, which considers
the Interaction of charged ions in solution and at a solid surface. Precipitation
can be considered at the discretion of the user, or suppressed as desired.
4.10.4.4.4 Results
Tables IV-65 through IV-79 summarize the predictions of MINEQL for the 15
natural waters selected. For each metal, a range of concentrations is analyzed
beginning at typical background levels and continuing for concentrations well above
the water quality criteria. Concentrations are expressed as -log molar and ng/1.
Organic ligands have not been Included in this analyses. Organics are not
included because major uncertainties exist as to the effects of organics on metals
(particularly how to quantify the effects) and not because they are unimportant.
In selecting surface waters for analysis, the primary variables that should be
considered by the user include pH, hardness, bicarbonate (an approximate measure of
alkalinity), and specific conductivity. By cross-comparing results from several
similar rivers, sensitivity to parameters can be estimated.
While Tables IV-65 through IV-79 are straightforward in their use, a number of
features of the tables may not be readily apparent, and are listed here.
1. The concentrations of numerous metals are elevated enough in model
simulations so that they precipitate in some rivers. For example, in the
Hudson River, 81 percent of the lead is predicted to precipitate as
Pb(OH)2(s) when the total lead 1s 1000 ng/1 (see Table IV-65). The
solubility of lead as limited by this process would be 0.19 (1000) - 190 ng/1.
2. If the metals are present in low enough concentrations so that precipi-
tation does not occur, the percent metal speciation Is largely independent
of total metal concentration. For example, Table IV-65 shows that
speciation of zinc 1n the Hudson River at concentrations below 1000
can be predicted by knowing that approximately 72 percent is In ,
15 percent is 2nCO°, 8 percent is ZnHCOj, and 2 percent is
ZnOH*. Thus, the user has the flexibility of ignoring precipitates
-559-
-------
TABLE 1V-65
METAL SPECIATION IN THE HUDSON RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and
Species
Arsenic
Cadmlum
Chromlum(III)
(without redox)
8.9-5.4 (0.1-540 ng/1)
9.5-6.2 (0.1-70
5.0 (520
5.6 (130 Hg/l)
6.0 (40 H9/1)
8.7-6.6 (0.1-4 ng/1)
741 HAsO*-
4
26% H 2*301
95X Cd2*
2% CdSOO
4
21 CdCl +
96% Cr(OH)3(s)*
3% Cr(OH)*
2% Cr(OH)'
4
81% Cr(OH)3(s)*
11% Cr(OH)*
7S Cr(OH)-
4
23% Cr(OH)3(s)*
47% Cr(OH)*
30X Cr(OH)-
61% Cr(OH)*
2
38% Cr(OH)'
Chromlum(VI)
8.7-5.0 (0.1-520 ng/1)
93%
Copper
4.8 (1000 jig/I)
5.4 (250fi9/U
7% HCrO-
4
95% Cu2(OH)2C03{s)*
3% Cu(OH)0
2
1% CuCQO
81% Cu2(OH)2C03(s}*
12% Cu(OH)°
6% CuCOO
1% Cu2+
-560-
-------
TABLE IV-65 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMf and ng/1
Species
Copper
(Continued)
Lead
Mercury
Nickel
Silver
Z1nc
6.0 (60 ng/1)
8.5-6.6 (0.2-16 ng/1)
5.3 (1000 jig/1)
5.8 (316 ng/1)
8.3-6.3 (0.4-100 ng/1)
12-9 (0.0002-0.2 fig/1)
7.8-4.8 (1-1000 ng/1)
8.8-6.2 (0.1-70 ng/1)
7.3-4.'8 (2-1000
Predicted solubility limitations.
49% Cu(OH)°
24% CuCO°
21% Cu2(OH)2C03(s)*
4S
52* Cu(OH)0
31% CuCOO
61
11 Cu(OH)4
811 Pb(OH)2(s)*
16% PbCO°
11 Pb(OH)*
52X PbCOO
41X Pb(OH)2(s)*
4% Pb(OH)*
3%
88X PbCO°
7% Pb(OH)4
4%
84%
15% HgCl OHO
94% N1 2+
4% N10H+
2% N1 S0°
4
65% Ag*
33% AgCl°
72%
15% ZnCCP
8% ZnHCO*
2% ZnOH*
-561-
-------
TABLE IV-66
METAL SPECIATION IN THE OGEECHEE RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Arsenic
Cadmium
Chromlun(III)
(without redox)
Chromlum(VI)
Copper
8.9-5.4 (0.1-540
9.5-6.2 (0.1-70
5.0 (520 ng/1)
5.5 (160
8.7-6.0 (0.1-50
4.8 (1000 ng/1)
5.4 (250 ng/1)
8.5-6.5 (0.2-60
54%
46% HAsO2'
4
98%
1% CdCl*
91% Cr(OH)3(s)*
9% Cr(OH)*
2
61% Cr(OH)3(s)*
35% Cr(OH)+
2
2% Cr{OH)-
1% Cr(OH)2+
90% Cr(OH)*
2
6% Cr(OH)'
4
77%
23% HCrO-
4
93%
3% Cu(OH)Q
2
2% Cu2*
1% CuCOO
3
72%
11% Cu(OH)°
10% Cu2*
6% CuCOP
40% Cu(OH)°
36% Cu2*
22%
3% CuOH
-562-
-------
TABLE IV-66 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and fig/1
Species
Lead
Mercury
Nickel
Silver
Zinc
5.3 (1000ng/l)
5.8 (316 jig/I)
8.3-6.3 (0.4-100 (ig/1)
12-9 (0.0002-0.2 jig/1)
7.8-4.8 (1-1000
8.8-6.2 (0.1-70
7.3-4.8 (2-1000
72% PbC03(s)*
16% PbCCP
8% Pb2+
4% PbOH*
52% PbCO°
23%
13% PbOH*
11% PbC03(s)*
58% PbCO°
26%
15% PbOH*
77% Hg(OH)0
23% HgClOHO
98% N1 2+
1% N10H*
79% Ag*
21% AgCl°
92% Zn2*
4% ZoHCO*
2% ZnCO0
Predicted solubility limitations.
-563-
-------
TABLE IV-67
METAL SPECIATION IN THE MUSKEGON RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Arsenic
8.9-5.4 (0.1-540 ug/1)
941 HAs02-
4
6% H2AsO~
Cadmlurn
6.2 (70 »ig/l)
Chromlum(III)
(without redox)
Chromlun(VI)
Copper
9.5-6.8 (0.1-20
5.0 (520 ug/1)
5.6 (130 jig/1)
8.7-6.3 (0.1-20 »ig/l)
8.7-5.0 (0.1-520
4.8 (1000
5.4 (250 ng/1)
50%
46% CdC03(s)*
21 CdCl*
IS CdSOO
4
IS CdCO°
91% Cd2"*"
4% CdCI *
2% CdSOO
4
2% CdCO°
91% Cr(OH)3(s)*
7% Cr(OH)-
4
62% Cr(OH)3(s)*
36% Cr(OH)'
4
2% Cr(OH)*
94% Cr(OH)-
4
6% Cr(OH)*
99% Cr02-
4
1% HCrO-
4
95% Cu2(OH)2C03(s)*
41 Cu(OH)°
2
It CuCOO
8C% Cu2(OH)2C03(s)*
16% Cu(OH)°
-564-
-------
TABLE IV-67 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and p.g/1
Species
Copper
(Continued)
Lead
Mercury
Nickel
6.0 (60
8.5-6.6 (0.2-16 jig/1)
5.3 (1000 ng/1)
5.8 (316 Kg/1)
6.3 (100 ng/1)
8.3-6.9 (0.4-15 tig/1)
12-9 (0.0002-0.2
4.8 (1-1000 jig/1)
5.3 (300 rt/D
5.8 (90 jig/1)
7.8-6.3 (1-15 ng/1)
-565-
65% Cu(OH)°
2
181 CuCOO
3 .
161 Cu2(OH)2C03(s)1
77% Cu(OH)°
21% CuCOO
92% Pb(OH)2(s)*
71 PbCO°
75% Pb(OH)2(s)*
23% PbCOO
72% PbCOO
22% Pb(OH)2(s)*
3% PbOH+
3% Pb(C03)2-
93% PbCOO
4% Pb(OH)*
3% Pb(C03)2-
94% Hg(OH)°
6% HgCIO^
93% N1(OH)2(s)*
6% Ml2*
1% N10H+
77% N1(OH)2(S}*
18% N1 2*
4% N10H*
58% N12+
28% N1(OH)2(S}*
12% N10H*
1% N1S0°
4
80% N1 2*
17% N10H+
2% NiSQO
4
-------
TABLE IV-67 (Continued)
fetal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
SI 1ver
Z1nc
6.2 (70
8,8-6,7 (0,1-15 iig/1)
4.8 (1000
7.3-5.3 (2-320 >ig/l)
41% Ag +
10% AgCl°
AgCl(s)
2% AgCl -
50% Ag+
48t AgCl o
3%AgCl-
581 ZnC03(s)
211
10% Zn(C03)2-
7X
2% ZnHCO*
49% ZnCO°
23% Zn(C03}2-
17%
6% ZnHCO*
3% Zn(OH)°
2% ZnOH*
*Pred1cted solubility limitations.
-566-
-------
TABLE 1V-68
METAL SPECIATION IN THE OHIO RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Arsenic
Cadmium
Chromium(III)
(without redox)
Chromlum(VI)
Copper
8.9-5.4 (0.1-540 vig/1)
9.5-6.2 (0.1-70 jig/1)
5.0 (520 jig/1)
5.5 (160 »ig/l)
6.0 (50 jig/1)
6.5-8.7 (0.1-8 ng/1)
8.7-5.0 (0.1-520 ng/1)
4.8 (1000 ng/1)
66% HAsOZ-
4
341 H2As 04
93%
5S CdSOO
4
3% CdCl*
94% Cr(OH)3(s)*
4% Cr(OH)+
1% Cr(OH)-
4
76% Cr(OH)3(s)*
18% Cr(OH)*
5% Cr(OH)-
4
75% Cr(OH)+
19% Cr(OH)-
4
4% Cr(OH)3(s)*
2% CrOH*
79% Cr(OH)0
2
20% Cr(OH)-
4
2% CrOH*
90% CrO?"
4
10% HCrO-
4
95% Cu2(OH)2C03{s)1
2% CuCOO
3
2% Cu(OH)0
2
-567-
-------
TABLE IV-68 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and
Species
Copper
(Continued)
5.3 (300 ng/1)
Lead
Mercury
Nickel
Silver
5.8 (100 ng/1)
8.5-6.5 (0.2-15 ug/1)
5.3 (1000 Kg/l)
5.8 (316
6.3-8.3 (0.4-100
12-9 (0.0002-0.2
7.8-4.8 (1-1000 ng/1)
8.8-6.2 (0.1-70 ng/1)
811 Cu2(OH)2C03(s)
81 CuCOO
81 Cu(OH)°
21 Cu2+
351 CuCOO
345 Cu(OH)Q
21% Cu2(OH)2C03(s)*
9X Cu2*
44% CuCO°
43% Cu(OH)0
2
11%
81S PbC03(s)*
161 PbCOP
52X
Alt PbC03(s)*
31 Pb2*
31 PbOH+
881 PbCCP
61
61 Pb OH *
721 Hg(OH)0
2
271 HgClOHO
11 HgCl°
931 N1 2+
51 N1SO°
4
21 N10H*
931 Ag*
381 AgCl°
11 AgCIO
-568-
-------
TABLE IV-68 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and
Species
Z1nc
7.3-4.8 (2-1000 ng/1)
Predicted solubility limitations.
721
11% ZnCCP
10% ZnHCO*
41 ZnSO°
4
TABLE IV-69
METAL SPECIATION IN THE MISSISSIPPI RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and n-9/1
Species
Arsenic
Cadnri urn
Chromlum(III)
(without redox)
8.9-5.4 (0.1-540 ng/1)
6.2 (70 ^g/1)
9.5-6.7 (0.1-20 ng/1)
5.0 (520 fxg/1)
5.5 (160 ng/1)
6.3-8.7 (0.1-20
90% HAsO2'
4
10% H2AsO"
91%
6% CdC03(s)*
IS CdCOO
97% Cd2*
It CdCC°
94% Cr(OH)3(s}*
6% Cr(OH)-
4
74% Cr(OH)3(s)*
23% Cr(OH)'
4
4% Cr(OH)*
86% Cr(OH)-
4
14% Cr(OH)*
2
1% HgCiOHO
-569-
-------
TABLE IV-69 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Chromlum(YI)
Copper
Lead
8.7-5.0 (0.1-520
4.8 (1000
5.4 {250 jig/1)
6.0 (60 M9/D
8.5-6.6 (0.2-16 ng/1)
5.3 (1000 ng/1)
5.8 (316 jig/1)
8.3-6.4 (0.4-100
98% CrO2'
4
2% HCrO-
4
95% Cu2(OH)2C03(s)*
3% Cu(OH)°
IX CuCOO
3
81% Cu2(OH)2C03(s)*
14% Cu(OH)°
2
5% CuCO°
56% Cu(OH)0
221 Cu2(OH)2C03(s)*
2U CuCOO
71X Cu(OH)0
2
27X CuCO°
87X Pb(OH)2(s)*
12% PbCO°
3
60% Pb(OH)2(s)*
37% PbCO°
94% PbCOO
4% PbOH+
"
2% PbCO
j
Mercury
12-9 (0.0002-0.2
99%
-570-
-------
TABLE IV-69 (Continued)
Metal
Tested Ranye of Total Metal
Concentrations as pMj and >ig/l
Species
Nickel
S11ver
Zinc
4.8 (1000 ng/1)
5.3 (320 jig/1)
7.8-5.8 (1-40 ng/1)
8.8-6.2 (0.1-70 ng/1)
4.8 (1000 (ig/1)
7.3-5.3 (2-320 ng/1)
83% IH(OH)2(s)*
15* N1 2+
2% N10H+
47% M(OH)2(s)*
46% N1 2+
6% N10H*
87% N12+
12% N10H+
91% Ag*
9% AgCl°
55% ZnC03(s)*
21% ZnCOO
3
14% Zn2*
5% Zn(C03)2-
4% ZnHCO*
3
46% ZnCOO
3
30% Zn2+
12% Zn(C03)2-
2% ZnOH*
'Predicted solubility limitations.
-571-
-------
TABLE IV-70
METAL SPECIATION IN THE MISSOURI RIVER
Metal
Tested Range of Total Metal
Concentrations as pMy and ng/1
Species
Arsenic
Cadnrf um
Chromlun(III)
{without oxidation-
reduction)
:hromium(Vi;
Copper
8.9-5.4 (0.1-540 >ig/l)
6.2 (70 ng/1)
9.5-6.8 (0.1-20 ng/1)
5.0 (520 ng/1)
5.6 (100 >ig/l)
8.7-6.3 (0.1-20 ng/1)
8.7-5.0 (0.1-520 ng/1)
4.8 (1000
93% HAsfl2-
4
7»
66t
21% CdC03(s)*
10% CdSOO
4
2% CdCl*
83% Cd2+
12% CdSOO
4
3% CdCl*
1% CdCQO
3
92% Cr(OH)3(s)*
7% Cr(OH)-
4
67% Cr(OH)3(s)*
30% Cr(OH)'
4
3% Cr(OH)+
91% Cr(OH)-
4
9% Cr(OH)*
2
77% CrO2-
4
1% HCrO-
4
95% Cu2(OH)2C03(s)*
3% Cu(OH)0
1%
5.4 (250 >xg/l)
6.0 (60 ng/1)
81%
13% Cu (OH)0
5% CuCQO
55% Cu(OH)°
22% Cu2(OH)2C03(s)
21% CuCO°
-572-
-------
TABLE IV-70 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Copper
(Continued)
Lead
Mercury
Nickel
Silver
8.5-6.6 (0.2-16 ng/1)
5.3 (1000
5.8 (316 ng/1)
8.3-6.3 (0.4-100 jig/1)
12-9 (0.0002-0.2 H-g/1)
4.8 (1-1000 ng/1)
5.3 (300 ng/1)
7.8-5.8 (1-90 jig/1)
6.2 (70ng/l)
8.8-6.8 (0.1-20 Kg/1)
72* Cu(OH}0
2
30% CuCO°
II Cu(C03)2-
891 Pb(QH)2(s)*
10% PbCQO
66% Pb(OH)2(s)*
31X
IX Pb(C03)2-
1% Pb(OH)*
93% PbCOO
3
3% Pb(CO)2-
3% Pb(OH)*
93% Hg(OH)0
2
7% HgCIOHO
86% N1(OH)2(s)*
10% N12+
2% N1SO°
4
2% N10H+
56% N1(OH)2(S)*
33% N12*
5% N1SO°
4
5% N10H*
77% N12+
12% N10H*
NiSO°
4
53% Ag2*
40% AgClQ
5% AgCl(s)*
55X
42% AgCIO
1% AgSOj
-573-
-------
TABLE IV-70 (Continued)
fetal
Tested Range of Total Metal
Concentrations as pMj and jig/1
Species
21 nc
4.8 (1000
7.3-5.3 (2-320 jig/1)
53% ZnC03(s)*
21% ZnCOO
3
10% Zn2+
10% Zn(C03)2'
2% ZnSo<>
4
3% ZnHCO*
44% ZnCOO
21% Zn2+
21% Zn(C03)2-
7% ZnHCO*
4% ZnSOO
4
2% ZnOH*
2% Zn(OH)o
2
*Pred1cted solubility limitations.
•574-
-------
TABLE IV-71
METAL SPECIATION IN THE BRAZOS RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and
Species
Arsenic
Cadmium
Chronrium(III)
(no redox)
Chrom1um(Vi;
Copper
8.9-5.4 (0.1-540
9.5-6.2 (0.1-70 ng/1)
5.0 (520
5.6 (130
8.7-6.3 (0.1-20 Kg/1)
8.7-5.0 (0.1-520
4.8 (1000 ug/1)
5.3 (300ng/l)
6.0 (60
57% HAsO2-
4
43% H2AsO'
815 Cd2*
15% CdCl*
4X CdSO°
4
921 Cr(OH)3(s)*
7t Cr(OH)+
2
66% Cr(OH)3(s)*
30* Cr(OH)>
3t Cr(OH)-
4
88% Cr(OH)+
9% Cr(OH)-
4
4% CrOH2+
88% CrO2'
4
12% HCrO-
4
95% Cu2(OH)2C03(s)*
4% CuCOO
1% Cu(OH)°
2
77% Cu2(OH)2C03(s)*
15% CuCOO
5% Cu(OH)0
2
3% Cu2+
601 CuCO°
29% Cu(OH)°
2
14K
-575-
-------
TABLE IV-71
(Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and
Sped es
Copper
(Continued)
Lead
8.5-6.6 (0.2-16 ng/1)
Mercury
Nickel
Silver
Z1nc
5.3 (1000 ng/1)
5.8 (316 ng/1)
8.3-6.3 (0.4-100
12-9 (0.0002-0.2 ng/1)
7.8-4.8 (1-1000 ng/1)
6.2 (70 rig/1)
8.8-6.8 (0.1-15
7.3-4.8 (2-1000 ng/1)
Predicted solubility limitations.
ea cuco°
3
2tt Cu(OH)o
2
1SK
sa Pbco3(s}*
161 PbCO°
3
53 PbCO°
43 PbC03(s)*
a Pb2+
a PbOH*
901 PbCO°
3
a Pb2+
5 PbOH*
601 HgClOHO
291
191 Hg(OH)°
a N1SO°
4
tt N10H*
41% AgCl(s)*
401 AgCl°
101 AgCl-
2
91 Ag+
671
17% AgCT
2
16% Ag*
66% Zn2*
17% ZnHCO*
3
11% ZnCOO
3
* ZnSQO
4
-576-
-------
TABLE IV-72
METAL SPECIATION IN THE COLUMBIA RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and u.g/1
Species
Arsenic
Cadmium
Chromlum(IIT)
(no redox)
8.9-5.4 (0.1-540 ng/1)
9.5-6.2 (0.1-70 |ig/l)
5.0 (520 ng/1)
82% HAsO2'
4
5.6 (130ng/l)
6.3 (20 ng/1)
Chromlum(VI)
8.7-6.8 (0.1-5 ng/1)
8.7-5.0 (0.1-520 ng/1)
18%
97% Cd2+
2% CdSO°
4
96% Cr(OH)3(s)*
3% Cr(OH)-
4
2% Cr(OH)*
82% Cr(OH)3(s)*
11% Cr(OH)-
4
7% Cr(OH)*
47% Cr(OH)-
4
30% Cr(OH)+
24% Cr(OH)3(s)*
61% Cr(OH)-
4
39% Cr(OH)-1-
95%
Copper
4.8 (1000 ng/1)
5.4 (ZSOng/l)
4% HCrO-
4
95%
3% Cu(OH)°
2
1% CuCOO
81%
14% Cu(OH)0
5% CuCOO
3
-577-
-------
TABLE IV-72 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pNj and ng/1
Species
Copper
(Continued)
Lead
Mercury
Nickel
Silver
Zinc
6.0
8.5-6.6 (0.2-16 Mg/1)
5.3 (1000 ng/1)
5.8 (316^9/1)
8.3-6.3 (0.4-100 M9/1)
12-9 (0.0002-0.2 Hg/1)
4.8 (1000
7.8-5.3 (1-250 ng/1)
8.8-6.2 (0.1-70 ug/1)
4.8 (1000^0/1)
57% Cu(OH)0
2
211 CuCOO
3
201 Cu(OH)2C03(s)*
21 Cu2+
711 Cu(OH)o
2
201 CuCOO
31 Cu 2+
881 Pb(OH)2(s}*
111 PbCO°
611 Pb(OH)2(s)*
351 PbCOO
31 PbOH+
901 PbCOO
71 PbOH*
31 Pb2+
971 Hg(OH)0
Z
31 HgClOHO
571 N1 2+
391 N1(OH)2(s)*
41 N10H*
921 N1 2+
61 N10H+
21 N1SOO
4
861 Ag2+
131 AgCl°
551 Zn2+
211 ZnCO°
*
121 ZnC03(s)
71 ZnHCO*
21 ZnOH*
578-
-------
TABLE IV-72 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pHy and
Species
Z1nc
(Continued)
7.3-5.3 (2-200 Kg/1)
62%
24% ZnCO°
8% ZnHCO*
21 ZnOH*
1% ZnCO°
IX ZnSO°
4
1% Zn(OH)°
"Predicted solubility limitations.
TABLE IV-73
METAL SPECIATION IN THE SACRAMENTO RIVER
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Arsenic
8.9-5.4 (0.1-540 ng/1)
53% HAs02-
Cadmlurn
Chromlum(III)
(no redox)
9.5-6.2 (0.1-70 ng/1)
5.0 (520 ng/1)
5.6 (130 Kg/1)
8.7-6.3 (0.1-20 ng/1)
47%
98%
92% Cr(OH)3(s)*
7% Cr(OH)+
2
68% Cr(OH)3($)*
28% Cr(OH)+
3% Cr(OH)-
88% Cr(OH)+
9% Cr(OH)-
3% CrOH2+
-579-
-------
TABLE IV-73 (Continued)
Metal
Tested Range of Total Metal
Concentratfons as pMy and ng/1
Species
Chromlun(VI)
Copper
Lead
8.7-5.0 (0.1-520 ng/1)
4.8 (1000 Kg/1)
5.4 (250 Kg/1)
6.0 (60 Kg/1)
8.5-6.6 (0.2-16 yg/1)
5.3 (1000 Kg/1)
5.8 (316 Kg/1)
8.3-6.4 (0.4-100 ug/1)
821 Cr02-
4
18% HCrO-
4
951 Cu2(OH)2C03(s)*
21 CuCQO
2% Cu(OH)0
2
78%
9% CuCO°
81 Cu(OH)0
2
5%
37t CuCOO
321 Cu(OH)0
2
19%
101 Cu2(OH)2C03(s)*
411 CuCOO
36% Cu(OH)0
21%
2% CuOH*
79% PbC03(s)*
16% PbCOO
2% Pb2*
2 PbOH*
52% PbCO°
35% PbC03(s)*
8% Pb2+
5% PbOH*
80% PbCOO
3
12% P
8% PbOH*
-580-
-------
TABLE IV-73 (Continued)
Metal
Mercury
Nickel
Silver
Z1nc
Tested Range of Total Metal
Concentrations as pMj and n-g/1
12-9 (0.0002-0.2 Mg/1)
7.8-4.8 (1-1000 ng/1)
8.8-6.2 (0.1-70 ng/1)
7.3-4.8 (2-1000 ^g/1)
Species
84% Hg(OH)0
2
10% HgClOHO
971 N12+
2% N10H+
83% Ag2+
17% AgdO
83% Zn2+
9% ZnHCO*
3
6% ZnCOO
3
* Predicted solubility limitations.
TABLE IV-74
METAL SPECIATION IN THE COLORADO RIVER
Metal
Tested Range of Total Metal
Concentrations as pMy and Kg/1
Species
Arsenic
Cadmlurn
8.9-5.4 (0.1-540 i-g/1)
6.2 (70Mg/l)
8.7-6.8 (0.1-20ng/l)
95% HAsO2-
5%
69%
18% CdSOO
4
9% CdCl*
3% CdC03{s)*
71%
18% CdSOO
9% CdCio
-581-
-------
TABLE IV-74 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and Kg/1
Species
Chromlun(III)
(no redox)
Chromium(VI)
Copper
Lead
5.0 (520 Kg/1)
5.6 (130
8.7-6.3 (0.1-20
4.8 (1000 Kg/1)
5.4 (250
6.0 (60
8.5-6.6 (0.2-16ng/l)
5.3 (1000 Kg/1)
5.8 (316K9/1)
6.3 (100K9/1)
8.3-6.8 (0.4-60 Kg/1)
901 Cr(OH)3(s)*
91 Cr(OH)-
4
60S Cr(OH)3(s)*
381 Cr(OH)'
4
21 Cr(OH)0
2
94% Cr(OH)'
4
61 Cr(OH)0
991 CrOj-
95% Cu2(OH)2C03(s)*
4X Cu2*
791 Cu2(OH)2C03(s)*
171 Cu(OH)<>
2
41 CuCOO
701 Cu(OH)0
2
171 CuCOO
121 Cu2(OH)2C03(s)*
791 Cu(OH)o
2
191 CuCOO
31
951 Pb(OH)2(s)*
41 PbCOo
3
851 Pb(OH)2(s)*
141 Pt)CO°
511 PtJ(OH)2(s)*
441 PbCOO
3
31 PbOH*
911 PbCOO
61 PbOH*
-582-
-------
TABLE IV-74 (Continued)
fetal
Tested Range of Total Metal
Concentrations as pMy and g/1
Species
Mercury
Nickel
Silver
Z1nc
12-9 (0.0002-0.2
4.8 (1000 iig/1)
5.3 (300ng/l)
7.8-5.8 (1-40 ng/1)
6.2 (70ng/l)
8.8-6.8 (0.2-15 ng/1)
4.8 (1000 ng/1)
81% Hg(OH)o
2
19% HgClOHO
90% N1(OH)2(s)*
71 N1 2+
2X N1SOO
4
68% N1(OH)2(s}*
221 Ni 2+
6% N1SCP
4
4X N10H+
69% N12+
18% N1SOO
4
13% N10H+
40% AgC1(s)*
40% AgC1°
13% Ag2*
8% AgCl -
66% AgCl0
21% Ag2*
13% AgCT
50% ZnC03(s)*
21% ZnCQO
10% Zn(C03)2-
10% Zn2*
3% ZnSOO
4
2% ZnHCO*
-583-
-------
TABLE IV-74 (Continued)
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Z1nc
(Continued)
7.3-5.3 (2-250 fig/1)
42% ZnCQO
21% Zn2*
21% Zn(C03)2-
6% ZnSO°
4
5% ZnHCO+
3% Zn(OH)+
2
2% ZnOH*
Predicted solubility limitations.
TABLE IV-75
METAL SPECIATION IN WOODS LAKE OUTLET
Metal
Tested Range of Total Metal
Concentrations as pMj and ng/1
Species
Arsenic
Cadmium
Chromlum(III)
(without redox)
Chromlum(VI)
Copper
8.9-5.4 (0.1-540 ^g/1
9.5-6.2 (0.1-70 pg/1)
8.7-5.0 (520
8.7-5.0 (0.1-520 ng/1)
8.5-4.8 (0.2-1000 ng/1)
99%
99%
1% CdSOO
4
73% CrOH2+
12% CrSO*
4
11% Cr(OH)*
2
-584-
3%
66% CrOH2*
11% CrSO*
4
11% HCrO-
4
10% Cr(OH)+
3%
99%
1% CuSCO
4
-------
TABLE IV-75 (Continued)
fetal
Tested Range of Total Metal
Concentrations as pMj and Kg/1
Species
Lead
Mercury
Nickel
Silver
Z1nc
8.3-5.3 (1-1000 Hg/1)
12-9 (0.0002-0.2 ng/1)
7.8-4.8 (1-1000 Kg/1)
8.8-6.2 (0.1-70 Kg/1)
7.3-4.8 (2-1000
97%
1% PbSOO
4
60% HgClOHO
22% Hg(OH)0
18% HgCl°
99% N1 2+
1% N1SOO
4
98% Ag+
2% AgClo
99%
1% ZnSOo
3
TABLE IV-76
METAL SPECIATION IN PENOBSCOT RIVER, MAINE
Metal
Concentration Range
Species
Arsenic
Cadnrf urn
0.3 - 1000
0.008 - 80 Mg/1
Chromium III solubility - 95 Kg/1
(without redox)
<95 Kg/1
Chromium VI 0.1 - 500 ug/1
7tt
301 HAsO2"
4
98.1% Cd2+
1.3% CdSO°
4
Cr(OH)3(s) 1s solubility control
91% Cr(OH)+
2
8% Cr(OH)2-»
tt Cr(OH}-
4
Cr02-
4
37%
-585-
-------
TABLE IV-76 (Continued)
Metal
Concentration Range
Species
Copper
Lead
Mercury
Nickel
Silver
21 nc
solubility • 130 Hg/1
<130
solubility - 510 K9/1
<510 K9/1
0.0002 - 0.2
1. - 1000
0.03 - 100
1. - 1000 Kg/1
Cu2(OH)2C03(s) 1s solubility control
0)2+
181 Cu(OH)°
2
IX CuCO°
2
Pb(OH)2(s) 1s solubility control
44t
4S PbCQO
PbOH*
73X Hg(OH)o
2
2ft HgClOHO
98S N12*
IX N1SO°
Ag+
141 AgCl°
9»
ZnMCO*
3
-586-
-------
TABLE IV-77
METAL SPECIATION IN ST. MARYS RIVER, FLORIDA
Metal
Concentration range
Species
Arsenic
Cadmium
Chromium III
(without redox)
Chromium VI
Copper
Lead
0.3-1000
0.008-80
0.1-500 Mg/l
99*
0.1-500
0.1-1000
0.1-1000 M9/1
IX HAsO^
97X Cd2+
2X CdCl*
69X CrOH2
25X
4X
2'
Cr
3+
92X
5X
CrOH
2+
2+
99X Cu
96X Pb
2*
2X PbSO
Mercury
Nickel
Silver
21nc
0.0002-0.2 M9/1
0. 1-1000 M9/1
0.1-70 M9/1
0.1-1000 M9/1
71X HgCl2
23X HgCIOH0
IX Hg(OH)°
99X N12+
74X Ag+
26X AgCl°
97X Zn2+
IX ZnHCO*
-587-
-------
TABLE IV-78
METAL SPECIATION IK GRAND RIVER, SOUTH DAKOTA
Metal
Concentration Range
Species
Arsenic
Cadmium
Chromium III
Chromium VI
Copper
Lead
Mercury
Nickel
1-1000 ug/1
solubility • 10 H9/1
<10
solublllty - 240
<240
1-500 ug/1
solubility » 70
<70 Kg/1
solubility « 35
<35 ug/1
0.002-0.2 r*g/l
solubility - 30
<30 ug/1
991 HAsO2'
B H^sO;
CdC03(s) 1s solubility control
57%
2tt
13 CdCl*
9J CdCO°
3 Cd(OH)*
Cr(OH)3(s)
991 Cr(OH)-
4
lOOt Cr02-
4
Cu2(OH)2C03(s) 1s solubility control
88X Cu(OH)0
Id CuCO°
» Cu(C03}2-
Pb(OH)2(s) 1s solubility control
56Bi PbCO°
3
44i PB(C03)2-
93 Hg(OH)0
8X HgClOHO
N1(OH)2(s) 1s solubility control
4S N12+
3» N10H+
15%
N1{OH)°
-588-
-------
TABLE IV-78 (Continued)
Metal
Concentration Range
Species
Silver
Z1nc
solubility » 60
<60 K/1
1-1000
AgCl(s) 1s solubility control
64% AgCl°
24% AgCl-
111 Ag+
84% Zn(C03)2-
13% ZnCO°
2% Zn(OH)°
TABLE IV-79
METAL SPECIATION IN PECOS RIVER, NEW MEXICO
Metal
Concentration Range
Species
Arsenic
Cadmium
Chromium VI
Copper
1-1000 Kg/1
0.1-80
Chromium III solubility « 85 Kg/1
<85 Kg/1
1-500 Kg/1
solubility • 70
<70 Kg/1
98ft HAsQ2-
a H2HsO-
59% CdCl*
17%
17% CdClo
2
651 CdSOO
4
Cr(OH)3(s) 1s solubility control
98X Cr(OH)-
a Cr(OH)*
2
99% Cr02-
4
Cu2(OH)2C03(s) 1s solubility control
90% Cu(OH)o
2
9% CuCOO
3
-589-
-------
TABLE IV-79 (Continued)
Metal
Concentration Range
Species
Lead
Mercury
Nickel
Silver
Z1nc
solubility - 8 H9/1
<8
0.002-0.2 ng/1
solubility - 90
<90 Kg/1
0.1-70 ng/1
Solubility • 800 ng/1
<800 ng/1
Pb(OH)2(s) 1s solubility control
83t
91 Pb(C03)2-
41 PbOH*
37% HgClOff
24* HgCl°
18% HgCl-
1» HgCl2-
4
« Hg(OH)0
N1(OH)2(s) 1s solubility control
60K N12*
2X N1SOO
IB N10H*
ft N1C1*
6» AgCl-
2
1B AgCl2-
ZnC03(s) 1s solubility control
26S ZnCO°
24» Zn(C03)2-
231
Id ZnSO°
* Zn(OH)o
2
4t
Z ZnOH*
-590-
-------
and being able to estimate metal sped at ion at these higher concentrations
exactly the same way as for lower concentrations.
The tables do not consider the influence of adsorption. While MINEQL
can simulate adsorption using electric double-layer theory, this option
was not utilized. However, adsorption can be superimposed on the results
in the table as follows. First consider the case without precipitation,
the case most likely to be of concern for these screening analyses. The
information required to account for adsorption 1s the partition coefficient,
K , and suspended solids concentration, S, so that the dissolved fraction
can be calculated:
C 1
ps-io-6
where
Cy « total metal in water column
C - total dissolved phase concentration.
The dissolved species can be approximated using the same percent distri-
butions present when no adsorption occurs, except the percents in the
tables become the percent of total dissolved metal, not total metal.
As an example, consider the results from MINEQL shown below for a river
when pH » 8.
With Adsorption
Species Without Adsorption (Area«6.9 m2/!)
Cu-Adsorbed O.Ot 98.1%
Cu(OH)° 95.6X 1.8% (95.6)
CuCO° 2.1% « 1% (2.1)
Cu2* 1.1% « 1% (1.1)
CuOH* 0.6% « 1% (0.6)
CuSOj 0.5% « 1% (0.5)
Without adsorption, 95.6% of the total copper Is present as Cu(OH)°
2
When 6.9 m /I of adsorbing surface 1s added for the conditions simulated,
about 98% of the copper adsorbs, leaving only a dissolved. However,
the percentage distributions of the dissolved species (the percents are
shown in parentheses) are the same percent distributions without adsorption
(e.g. 95.6 percent of the dissolved copper is Cu(OH)°). Figure IV-S1
provides a mathematical justification for the procedure suggested
above.
Now consider adsorotion at metal concentrations where precipitation 1s
predicted to occur. The sketch below will help to explain the species
shift when adsorption occurs.
-591-
-------
Consider I *me» with 3 dissolved attt'l specie*
H (frit Ion)
H, (1st Mttal-llgand complex)
ML2 (2nd Mta1-I1oand coapltx)
where H » Ptj + «-2 • total dissolved wtii • B^
*,
N-4— L->
«*L2-^-»
the eoulHbrli
"
"•i =^*l-V*Li
ML2 =>ML2 • 02-M-L2
t* spedatlon fraction for free wtal ion is
1
f
,. ^1 ' L1T ^1L2T
1 Ufl.H * T^CH
(typ
T
Determine fro*
local data or use
Table lV-«2
constant for each
-these are the fractional
spades In tables IV-65
through IV-79
Suatary:
To account for adsorption, use Tables IV-45 through IV-79
directly but consider the percent species to be dissolved
etal ratner than total «et«1.
FIGURE IV-91 EXAMPLE PROCEDURE FOP SUPERPOSITION OF ADSORPTION
-592-
-------
PRECIPITATED
ADSORB
SPECIES
ORBED
DIRECTION OF
SPECIES SHIR-
AS an adsorbing species 1s added to a water where dissolved and
precipitated species are In equilibrium, some of the dissolved
species become adsorbed. In turn, precipitates begin to re-dissolve
to satisfy solubility conditions. As long as an excess of precipi-
tates are present, the concentrations of total dissolved species will
not change with or without adsorption. However, when all of the
precipitated solids re-dissolve, then the concentration of dissolved
species will decrease compared to the case with no adsorption.
The tabulations below Illustrate an example from MINEQl for a
total copper concentration of 5-10 M (318ng/l).
Species
Cu-Adsorbed
Cu(OH) (s)
Cu(OH)°
No Adsorption
0.0%
58.3ft
39.9%
Adsorption
2,
Adsorption
.2,
(Area-69 nT/1)
98.7X
0.0%
1.2%
(Area-0.69 irT/1)
15.4%
42.9%
39.9%
fc 2
When a relatively small amount of adsorbing species 1s added (0.69 ur/1),
15.4% of the copper adsorbs, and the total precipitated copper decreases
from 58.3* to 42.9%. However, the total dissolved copper remains
constant at 39.9%. When significantly more surface area is added, more
copper adsorbs until eventually the dissolved copper 1n the water column
is below solubility, and all of the precipitated copper redissolves.
Based on the above Illustration, the following procedure is suggested to
account for the possibility of simultaneous precipitation and adsorption.
Using the total concentration CT of Interest from the appropriate taole,
find
1+K.
If C is less than CSQI , the solubility of the metal which can be found
as described previously in step (1), then all of the precipitate is
re-dissolved and '-n]y the adsorbed and dissolved species exist. Thus,
-593-
-------
both C and C (-C-,-C) are known, and the species of C can be found as
described In step (3).
If C> C . , the dissolved metal concentration becomes C . , and the
total dissolved plus adsorbed concentration, Cj, 1s:
°T ' csoi dV10"6'
I
where CT 1s less than CT. The precipitated metal 1s Cj - Csoi. {1 + Kps . 10"6)
Again, the species distribution of CSQI can be found using step (3).
The following tabulations summarize the two cases:
Amount dissolved
Amount adsorbed
Amount precipitated
Amount adsorbed K'"6 KpSC-10"6
As mentioned previously, the results 1n the tables are valid for oxidizing
conditions, with one exception. Chromium (III), which Is thermodynamical ly stable
under reducing conditions, was allowed to exist by not Involving redox reactions.
The oxidation of chromium (III) to chromium (VI) 1s thought to be very slow, so 1f
chromium enters a river as the 1 11+ 1on, It may persist. However, chrom1um( III) is
relatively Insoluble In most waters (from 30 to 50 ng/1 ) and is also less toxic than
chromium(YI) so that chrom1um( III) 1s likely to be of secondary Importance.
Throughout the range of metal concentrations examined, some of the metals did
not precipitate at all. Those metals are: arsenic, chromium(VI) , and mercury. The
remaining metals precipitate under at least some of the conditions, and the precipi-
tates are:
• Cd: CdC03(s)
• Cr(III): Cr(OH)3(s)
• Cu: Cu2«3H)2C03(s)
• Pb: Pb(OH)2($) or PbC03(s)
• N1: N1(OH)2(s)
t Ag: AgCI(s)2
• Zn: ZnC03(s)
The only natural water where no metal precipitation occurred was at Woods La
-------
of judgment because of the supersaturation possibility and because of uncertainty 1n
solubility products. For example, the solubility products used by MINEQL for Pb(OH)(s)
and Nl(OH)-(s) appear to be on the lower end of an acceptable scale which spans 3
to 4 orders of magnitude for the solubility product (Slllen, 1966), I.e. the solu-
bilities are taken to be lower rather than higher. Hence, predicted hydroxide
solubility limitations for lead and nickel, which occur 1n a number of the surface
waters chosen, should be Interpreted with this 1n mind.
While the mathematical tools presented in Section 4.10.3 can be used independently
of metal species distribution, the results from MINEQL can also be used 1n conjunction
with the mathematical tools. The approach 1s as follows:
1. Using the transport-fate expressions in Section 4.10.3, find the temporal
and spatial distributions of the metal (s) of Interest. Record C- (total
concentration), C (the dissolved concentration), and C/Cj (the fraction
dissolved) at each location of Interest.
2. Select an appropriate natural water from Tables IV-65 through IV-79, and
enter the table using the total metal concentration found from step (1)
at each location of Interest in the water body (1f three locations are
of interest, for example, the procedure outlined here is repeated three
times).
3. Since the effects of adsorption are not directly included in the tables,
the procedure outlined earlier should be used to account for adsorption
and to find the percent distribution and amount of each dissolved
species. Note that C/Cy values (see step (1)) do not have to be recal-
culated for the earlier procedure.
4. In all likelihood, the percent distribution of the dissolved species
will not change significantly within a given river, although the total
dissolved concentration can change. (An exception is an acid-mine-drainag?
situation, where pH can change significantly over distance.) When this
is the case, the first three steps have to be completed only one time.
The percentages found are then multiplied by C at each location to
find the species distribution at each location.
EXAMPLE IV-25
This example Illustrates some differences 1n lead spedation likely to
exist between two of the river waters shown earlier in Table IV-64: the Ogeecnee
River in Georgia and the Colorado River in Colorado. Some of the water quality
characteristics of the rivers are summarized below.
-595-
-------
Species
58% P
26% Pb
I River p_H Suspended Solids, mg/1 Total Hardness
j Ogeechee 6.9 14 21.
j Colorado 8.1 2450 374.
.
I The water 1n the Colorado 1s quite hard while the Ogeechee River water 1s very
| soft. Consequently, hardness-related water quality criteria are different for
j the two rivers. For example, the 24-hour average criteria levels for cadmium i
j and lead are 20 and 870 times higher 1n the Colorado than 1n the Ogeechee, j
respectively. •
, Lead solubility in the Ogeechee Is controlled by the carbonate (see Table
! IV-66) while in the Colorado, the hydroxide controls (see Table IV-74). The j
I lead solubility in the Ogeechee appears to be about 220 ng/1 . This is found from I
i i
the prediction by MINEQL that 11 percent of the total lead of 250^9/1 is present |
as PbC03(s). The remaining 89%, or 220 ng/1, is dissolved. j
In the Colorado River, the Pb(OH)2(s) limits the total soluble lead to j
appproximately 50 ng/1 (49 percent of 100 ng/1 1s soluble). Note that 1n the •
absence of precipitation, the percent distribution of lead 1s Independent of
the total lead in the water column. As shown in Tables IV-66 and IV-74, the
predominant species of dissolved lead are:
58% PbCO° j
2% I
15% PbOH
Colorado 91% PbCO? !
6% PbOH I
Now, consider adsorption of lead to the suspended matter in the rivers. !
Based on the suspended solids levels and typical partition coefficients, it |
is assumed that 70 percent of the lead 1s adsorbed in the Ogeechee River and j
99 percent in the Colorado River (see Table IV-62). Because such high fractions j
of the metals acsorb, it is unlikely that precipitates form even at high con-
centrations of metal, based on the procedure described earlier in this section.
As an example, consider a total lead concentration of 60 ng/1 in the Colorado .'
River. In the aosence of adsorption, precipitation of Pb(OH)2(s) is predicted. )
However, if 99 percent of the 60 ^g/1 is adsorbed, then only 1 percent or 0.6 ng/1 I
of the lead is dissolved 1n tne water column. This is considerably below the j
50 Kg/1 solubility of lead, so that precipitation does not occur. According to j
Taole IV-74, nearly all (91 percent) of the 0.6 ^g/1 dissolved lead is present as
PbCO°, !
3 I
END OF EXAMPLE IV-25 J
-596-
-------
4.10.5 Execution of Limited Field Reconnaissance and Sampling Program
4.10.5.1 Introduction
Since a screening level analysis 1s Intended to make use of a minimal amount
of existing data to estimate where severe water quality problems are likely to occur
and where problems are unlikely to occur, the suggestion that a limited field re-
connaissance and sampling program be conducted appears Incongruous with the intent of
the approach. However, a limited field reconnaissance and sampling program may be
useful on a screening level for a number of reasons.
Perhaps the most Important 1s the complexity of the problem of predicting fate
of metals. Researchers are still far from developing a well accepted unified theory
which can be used to predict the fate of metals in the natural aquatic environment.
A review of the literature quickly reveals the divergency of views that now exist,
particularly in the area of quantification. A good example is the partitioning of
metals onto solids. Ignoring the fact that a variety of approaches exist related to
adsorption, and considering only the linear Isotherm adsorption approach used in this
chapter, documented results show that the adsorption coefficient, K is quite
variable for a given metal under the conditions encountered in natural rivers. While
in groundwater systems this variability may not be as important because of the large
amount of solids surface area for the metal to adsorb (i.e., well over 99 percent of
the metal is likely to be adsorbed for K >10 I/kg), in surface water the available
solid surface area is typically only about 0.01 to 0.02 percent as great as in
groundwater systems. With these much smaller surface areas, the percent metal that
is dissolved can vary considerably.
For example, at a suspended solids level of 100 mg/1, a metal is 91 percent
dissolved when K * 10 I/kg and 9 percent dissolved when K « 10 I/kg. In the
first instance, mucn of the metal is transported downstream and is influenced to a
small degree by solids settling or scour. Under these conditions, multiple waste
sources on a river interact with each other to produce gradually elevating levels of
metals over distance (unless dilution is important).
On the other hand, when only 9 percent of the metal Is dissolved, then settling
zones in the river tend to remove the metal from the water column, and downstream
sources can act independently of each other. Thus, the partition coefficient
becomes an important parameter to quantify.
The best way of interpreting K for metals is to consider the coefficient as a
parameter (not necessarily of solid scientific validity) that relates concentrations
of solid and particulate metals and is likely to be highly variable from one set of
conditions to anotr.er. Because of its importance, local determination of K is
recommended.
A second reason for local sampling is tne importance and variability of natural
metal sources. In some cases, natural sources of metals may be largely or entirely
-b97-
-------
responsible for exceeding standards. Thus, removing a large percentage of point
source contributions may have a negligible Impact on levels of metals.
Before undertaking a field reconnaissance and sampling program, the following
questions should be answered:
1. Have all potential sources of Information been accessed? Delos et al.
(1983) summarize likely sources.
2. Have the available data been summarized, categorized, and compared
against the Information needs required to conduct a screening analysis?
Table IV-80 shows the kinds of Information required to carry out the
screening analyses presented in this document. While supporting Informa-
tion may be useful for other purposes envisioned by the user, they are
not likely to be critical for the purposes of these screening procedures.
3. Have preliminary screening analyses been carried out to see whether the
missing, or poorly quantified data are important? The proposed screening
analyses should be performed first with "best judgment" data to verify
that the missing Information do make a significant difference. If not,
there is no reason, within the scope of the objectives of this chapter,
to collect that kind of data.
4. Have the objectives of the field reconnaissance/sampling program been
defined and have these activities been planned thoroughly? Before
setting foot into the field, all the reasons for doing so should have
been carefully thought out. Pre-planning will save time and money, and
more likely produce the intended results. Particularly, personnel
requirements and assignments and equipment needs (including backup
equipment) should be carefully addressed.
4.10.5.2 Field Reconnaissance
A field reconnaissance is a firsthand survey of the system under investigation.
A reconnaissance can be used to verify, if only in a qualitative sense, some of the
data which are being used for the screening analysis. In particular, this kind of
data can consist of:
• Identification and locations of point sources
• Locations of appropriate upstream and downstream boundaries
• Estimation of water depths 1n different reaches of the river
• Estimation of surface widths in different reaches of the river
• Identification of free-flowing reaches, backwater areas, and locations
and sizes of lakes or reservoirs
§ Visual (photographic) documentation of the system.
-598-
-------
TABLE IV-80
SUMMARY OF DATA REQUIREMENTS FOR SCREENING OF METALS IN RIVERS
Data
Httnedology Hntrt
Data Art Uttd*
Hydraulic Pit!
1. *1»tr$:
• «1»tr flM ritt. Q
• Crott-itctional arta, *
• Wattr dtpth. H
• »tKh ItngtM, i
• Wattrthtd trtis by
tributary dralfltgt, A
• Strtaa »tloc1ty, U
2. LMt»
* Hydraulic rflidtnct t1«t. T
* Ktan atpth. N
Source data
1. lackgrovnd
* Nttal concentrations, CgT
• loundary flow rilis, Qv
t Boundary tutptndtd wlldt. S
t Silt. day friction o' tutptndtd
to Hot
* locitloni
2. »otit Sourctt
• locinont
• Flw rttt, Q,
* Mttll conetntrttlon. CT_
wMdl, S
0, *. S. L
0. *, S
0, R, S. L, AO
X, S. AD
0
«. S, AO
»t< 0«ti
t Otpth of
« 'orojlt/ of t*d1a«(its, n
* Dtm'ty of »oHai m s«41«tntt
(t.;., 2.7 for tmd), f>.
t Kttjl conetfttritlon m
bt« Sort 09 prolonged
tcaur ptrtod, C..
• Partition catff'dtiit, K
p
• Stttlln? ytloelty, «
tauUlartga
ttoi> Of
* Nvftwil
• To til organic carbon
* Of*- ««Jor cattor.s and anlont
t $g|ptnd«d tolldl
• ConductN)*.;
D, It. S, t.
0, *, S. L
0, t, S, L. AO
0. *, S, L
D, «, S, L
0, (. S. L
D. K, S, L
0. II. S, L. AO
All
S. L
An accurate tttlMtlon of flow ratt U vtry important btcauit of
Itj dilution capability.
Tht avtraft »ittr dtpth Is volifit dlvldtd by turfact arta.
Uattrthtd tuoartat can bt ustd to tttlaatt 'lo» ratti.
Tht rvqulrtd vtloclty li dlttanct divldtd by tr«v«i tint. It can
bt approilaattd by Q/A only ««tn A 11 rtprt(tntati«t af tnt
rtach 6*tro
•Knout
Ont Important rtaion for dtttr*1n!ng tutptndtd tolldt
canctntratlont It to dtttnlrM tht dlttolvtd conctntratlon, C.
of Mtilt. baitd on CT. S, and Up. Movtvtr, *f C it tnowi
along *1th Cr and $, utt thU information to find *p rathtr
than ullng nttraturt
for tht tcrttntng analytlt, tht tftpth of contaa*na!1on U sot:
uMful during a otrlod of prolongtd tcour >htn Mtal (» btmg
Input Into tht w«ttr colum fron tht btd.
Tnt partition cotfflcttnt It a *try 1a«ortant ptranttr-. Local
determination It prtftrablt.
ThU paraMttr it 4*rl«rd battd on equation IV-1B4.
Thlt partMttr 11 atrivcd fitted on Cquat1»n IV-lll.
Tatlt IY-64 tu*>tritit tht cht»lcil characttrltt'ct chott* to
characunzt 14 riv»rt and t latt throm)l>out tht Un'ua Stattt.
Hattr wallty crtttrla for aany attilt art ktywt to narsnttt. and
incrtatt *1th incrtttlng hardnttt (i«« Tablt '.1~t\i.
*0 - illation
* • Dilution and rtiutptntlon
S - illutlo* and Mttllng
L - lant on rl*«r
AO - aaurptlon/dctorptlon
I -
-599-
-------
One of the primary purposes of the reconnaissance, 1n addition to verifying data
used in the screening analyses, 1s to gain a feel for the Importance of settling and
scour of solids, as a function of reach, throughout the study area. If there are
extensive settling zones where a high percentage of the solids settle, and 1f the
partition coefficients of the metals under Investigation are high, then much of the
metals are likely to settle also. On the other hand, 1f the suspended solids are
transported downstream with relatively little settling or 1f K S-10"6 1s small
(e.g.,
-------
generated. While it Is not possible to always decide beforehand what are the most
Important parameters for a particular situation, some preliminary calculations with
the screening tools will be useful to reveal what appears most important. In many
cases, the magnitude of the partition coefficient is likely to play an important
role. The partition coefficient is not measured directly, but is calculated from
measured values of adsorbed metal per unit suspended solids (X) and dissolved metal
(C); I.e., Kp - X/C.
If resources permit, a comprehensive survey where samples are collected at the
system boundaries and at important sources at a single point in time 1s generally
very useful for predictive purposes. If the survey 1s performed during steady-state
conditions, all samples do not have to be collected 1n a truly simultaneous fashion.
The principal- advantage of a comprehensive survey over repeated sampling at one or
two stations is that the Information collected can be used to more accurately recon-
struct overall cause and effect mechanisms, understand better system responses and
thus more reliably predict concentrations throughout the system.
-601-
-------
REFERENCES
Alonso, C.V., Jr. MeHenry, and J.-C.S. Hong. 1975. The Influence of Suspended
Sediment on the Reaeratlon of Uniform Streams. Water Research 9:695-700.
American Public Health Association. 1973. Standard Methods for the Examination of
Water and Waste Water, 13th ed. American Public Health Assn., Washington, D.C.
Bansal, M.K. 1975. Deoxygenatlon 1n Natural Streams, Water Resources Bulletin
11(3):491-504.
Barnes, H.H. 1967. Roughness Characteristics of Natural Channels. Geological
Survey Water-Supply Paper 1849.
Barrett, M.J., A.L. Gameson, and C.G. Ogden. 1960. Aeration Studies of Four W1er
Systems. Water and Water Engineering. London.
Bell. M.A., R.A. Ewlng, G.A. Lutz. V.L. Hoioman, B. Paris, H.H. Krause. 1978. Reviews
of the Environmental Effects of Pollutants: VII. Lead. Battelle-Columbus Labora-
tories for U.S. Environmental Protection Agency. EPA-600/1-78-029.
Bessler, M.B., and J.T. Maletlc. 1975. Salinity Control and Federal Water Quality
Act. American Society of Civil Engineers, Journal of the Hydraulics Division.
101{HY5):581-594.
Bowie, G.L., W.B. Mills, O.B. Porcella, C.L. Campbell, J.R. Pogenkopf, G.L. Rupp, K.M.
Johnson, P.W.H. Chan, and S.A. Gher1n1. 1985. Rates, Constants and Kinetic
Formulations in Surface Water Quality Modeling (Ed. 2). For U.S. Environmental
Protection Agency.
Brady, O.K., W.L. Graves, and J.C. Geyer. 1969. Surface Heat Exchange at Power Plant
Cooling Lakes. Edison Electric Institute.
BMggs, J.C. and J.F. F1cke. 1977. Quality of Rivers of the United States, 1975 Water
Year - Based on the National Stream Quality Accounting Network (NASQAN). USGS
Open-File Report 78-200.
Brown, G.W. 1969. Predicting Temperature of Small Streams. Water Resources Research.
5(1).
Butts, T.A., and R.L. Evans. 1978. Sediment Oxygen Research Studies of Selected
Northeastern Illinois Streams. Office of Water Research and Technology.
Callahan, M.A. et al. 1979. Water Related Environmental Fate of 129 Priority Pollut-
ants. EPAT$Jc74-79-Q29.
Carnahan, B.. H.A. Luther, and J.O. Wilkes. 1969. Applied Numerical Methods. John
Wiley & Sons, New York.
Chadderton, R.A. et al. 1981. Analysis of Waste Load Allocation Procedures. Water
Resources BuTTeTTn. 17:760.
Chow, V.T. 1959. Open Channel Hydraulics. McGraw-Hill, New York.
Committee on Water Quality Criteria, National Academy of Sciences and National Academy
of Engineer-ing. 1973. Water Quality Criteria 1972. U.S. Environmental Protec-
tion Agency, Washington, D.C. EPA-R3-73-033.
-602-
-------
Covar, A.P. 1976. Selecting the Proper Reaeratlon Coefficient for Use In Water
Quality Models. Presented at the U.S. Environmental Protection Agency Conference
on Environmental Simulation and Modeling, April 19-22.
Cragwall, J.S. 1966. Low-Flow Analysis of Streamflow Data. 5th Annual Sanitary and
Water Resources Engineering Conference. Nashville, TN.
Delos, C.G., W.L. Richardson, J.V. DePinto, D.W. Rogers, K. Rygwelskl, R. WetMngton,
R.B. Amblose, J. P. St. John. 1983. Technical Guidance Manual for Performing
Waste Load Allocations. Book II: Streams and Rivers. Chapter 3: Toxic Sub-
stances, For the U.S. Environmental Protection Agency. Draft.
DHscoll, E.D., J.L. Mandnl, and P.A. Mangarella. 1981. Technical Guidance Mahual
for Performing Waste Load Allocations. Book II: Streams and Rivers. Chapter 1:
BUD/DO and Ammonia Tox1c1ty. For U.S. Environmental Protection Agency.
Durum, W.H., J.D. Hem, and S.G. Heldel. 1971. Reconnaissance of Selected Minor
Elements In Surface Waters of the United States. Geological Survey Circular 643.
Edlnger, J.E. 1965. Heat Exchange 1n the Environment. Johns Hopkins University,
Baltimore, MD. p. 43.
Edlnger, J.E., O.K. Brady, and J.C. Graves. 1968. The Variations of Water Temperature
Due to Electric Cooling Operations. Journal of Water Pollution Control Federation.
40(9):1637-1639.
Edlnger, J.E., and J.C. Geyer. 1965. Heat Exchange In the Environment. Edison
Electric Institute. Publication 65-902.
Environmental Science and Technology. 1971. Metals Focus Shift to Cadmium. 5(9).
Erdmann, J.B. 1979a. Systematic Diurnal Curve Analysis. Journal Water Pollution
Control Federation. 51(1).
Erdmann, .J.B. 1979b. Simplified Diurnal Curve Analysis. Journal Environmental
Engineering Division, ASCE, 105(EE6).
Fenwlck, G.B. 1969. State of Knowledge of Channel Stabilization 1n Major Alluvial
Rivers. U.S. Army Corps of Engineers, Technical Report .No. 7.
Fischer, H.8., E.J. List, R.C.Y. Koh, J. Imberger, N.H. Brooks. 1979. Mixing 1n
Inland and Coastal Waters. Academic Press, p. 483.
Forstner, U. and G.T.W. WUtmann. 1979. Metal Pollution 1n the Aquatic Environment.
Sprlnger-Verlag.
Gameson, A.L., K.G. Vandyke, and C.G. Oger. 1958. The Effect of Temperature on
Aeration at W1ers. Water and Water Engineering. London.
Gardiner, J. 1974. The Chemistry of Cadmium 1n Natural Water-II. The Adsorption
of Cadmium on River Muds and Naturally Occurring Solids. Water Research, Vol. 8,
pp. 157-164.
Gavis, J. and J.F. Ferguson. 1972. The Cycling of Mercury Through the Environment.
Water Research. Vol. 6.
Graf, W.H. 1971. Hydraulics of Sediment Transport. McGraw-Hill, New York.
Gupta, G.C. and F.L. Harrison. 1982. Effect of Humlc Add on Copper Adsorption
By Kaolin. Water, A1r, and Soil Pollution. Vol. 17.
-603-
-------
Mammons, A.S., J.E. Huff, H.M. Braunsteln, J.S. Drury, C.R. Shrlner, E.B. lewis, B.L.
Whitfield, I.E. Towlll. 1978. Reviews of the Environmental Effects of Pollutants:
IV. Cadmium. Oak Ridge National Laboratory for U.S. Environmental Protection
Agency. EPA-600/1-78-026.
Hem, J.D. 1970. Study and Interpretation of the Chemical Characteristics of Natural
Waters. U.S. Geological Survey Water Supply Paper 1473.
Hem, J.D. 1975. Discussion of "Role of Hydrous Metal Oxide in the Transport of Heavy
Metals in the Environment" by G.F. Lee. In Heavy Metals In the Aquatic Environ-
ment. P.A. Krenkel (ed.).
Hu, S., and R.C. Kintner. 1955. The Fall of Single Liquid Drops Through Water. Amer.
Inst. Chem. Eng. J. 1(1) :42.
Hydrologic Engineering Center, Corps of Engineers. 1975. Water Quality Modeling of
Rivers and Reservoirs. U.S. Army Corps of Engineers.
Hydroscience Inc. 1971. Simplified Mathematical Modeling of Water Quality. U.S.
Environmental Protection Agency, Washington, D.C.
Janik, J.J., S.M.S. Melancon, L.S. Blakey. 1982. Site Specific Water Quality Assess-
ment: State River, Colorado. For U.S. Environmental Protection Agency, Las
Vegas. EPA-600/X-82-027.
Jenne, E.A. 1972. Mercury in Water of the United States. U.S. Geological Survey Open
File Report.
Jones, H.G.M., H. Bronheim, and P.F. Palmedo. 1975. Electricity Generation and Oil
Refining. Mesa New York Bight Atlas. Monograph No. 25. New York Sea Grant
Institute, Albany, NY.
Karmondy, E.J. 1969. Concepts of Ecology. Prentice-Hall, Englewood Cliffs, NJ.
Kelly, M.G., G.M. Hornberger, B.J. Cosby. 1975. A Method for Monitoring Eutrophication
1n Rivers. University of Virginia.
Kharkor, D.P., K.K. Turekian, and K.K. Bertine. 1968. Stream Supply of Dissolved
Silver, Molybdenum, Antimony, Selenium, Chromium, Cobalt, Rubidium, and Cesium to
the Oceans. Geochem. Cosmochim. Acta. Vol. 32.
King, H.M. 1954. Handbook of Hydraulics. McGraw-Hill, New York.
Kopp, w.F. 1969. The Occurrence of Trace Elements in Water. In D.D. Hemphi'l (ed.),
Proc. 3rd Annual Conference on Trace Substances in Environmental Health.
Krenkel, P.A., and F.L. Parker. 1969. Biological Aspects of Thermal Pollution.
Vanderbilt University Press.
Kubota, J., E.L. Mills, and R.T. Oglesby. 1974. Lead, Cd, Zn, Cu, and Co in Streams
and Lake Waters of Cayuga Lake Basin, New York. Environmental Science and Tech-
nology. 8( 3), March.
Kudo, A., D.C. Mortimer, J.S. Hart. 1975. Factors Influencing Desorption of Mercury
from Bed Sediments. Canadian Journal of Earth Sciences, Vol. 112.
Kudo, A., R.D. Townsend, and D.R. Miller. 1977. Prediction of Mercury Distribution in
River Sediments. Journal of the Environmental Engineering Division, ASCE. Vol.
103, No. EE4. Altmann. 1980.
-604-
-------
Leckie, J.O., M.M. Benjamin, K. Hayes, G. Kaufman, S. Altman. 1980. Adsorption/
Copredpltatlon of Trace Elements from Water with Iron Oxyhydroxlde. Prepared for
Electric Power Research Institute. CS-1513.
Lehman, J.T., 0.6. Butkln, and G.E. Likens. 1975. The Assumptions and Rationales of a
Computer Model of Phytoplankton Population Dynamics Limnology and Oceanography,
20(3): 343-362.
Lind, C.J. 1970. U.S. Geological Survey Professional Paper 700D, pp. 0272-280.
Linsley, R.K., M.A. Kohler, and J.H. Paulhus. 1958. Hydrology for Engineers. McGraw-
Hill, New York.
Long, D.T. and E.E. Angino. 1977. Chemical Speciation of Cd, Cu , Pb, and Zn in Mixed
Freshwater,, Seawater, and Brine Solutions. Geochemica et Cosmochimlca Acta.
Vol. 41.
Levering, T.G. Lead in the Environment. 1976. Geological Survey Professional Paper
951.
Lund, J.W.G. 1965. The Ecology of Freshwater Phytoplankton. Biol. Rev. 40:231-293.
Mastropietro, M.A. 1968. Effects of Dam Reaeration on Waste Assimilation Capacities
of the Mohawk River. Proceedings of the 23rd Industrial Waste Conference. Purdue
University.
McElroy, A.D. , S.Y. Chiu, J.W. Nebjen, A. Aleti, and F.W. Bennett. 1976. Loading
Functions for Assessment of Water Pollution from Nonpoint Sources. U.S. Environ-
mental Protection Aqencv. Washlnoton. O.C. EPA-600/3-76-151.
McQuivey, P.S., and T.N. Keefer. 1976. Dispersion-Mississippi River Below Baton
Rouge, LA. Journal of Hydraulics Division, ASCE 102(HY10).
Miles, J.R.W. 1976., Insecticide Residues on Stream Sediments 1n Ontario, Canada.
Pesticide Monitoring Journal 10(3).
Milligan, J.O., K.F. Nelson, and I.E. Wallace. 1981. Chattanooga Creek: The Occur-
rence and Distribution of Toxic Pollutants - September 1980. Tennessee Valley
Authority. PB81-241119.
Mills, W.B. 1976. A Computational Model That Simulates Biofilm Nitrification in
Streams. Engineer's Thesis. Stanford University.
Mills, W.B. 1979. A Nutrient Analysis of the Snake River and Its Tributaries within
the State of Idaho. Tetra Tech Report TC-3923. Prepared for Idaho Department of
Health and Welfare.
Mills, W.B., V.H. Colber, and J.D. Dean. 1979. Hand-Held Calculator Programs for
Analysis of River Quality Interactions. Supplemental Volume to: Water Quality
Assessment, a Screening method for Nondesignated 208 Areas.
Mills, W.B. 1981. Workshop on Screening Methods for Conventional and Toxic Pollutants
1n Rivers, unpublished notes.
i
Mills, W.3., V. Kwong, L. Mok, M.J. Ungs. 1985. Microcomputer Methods for Toxicants
in Ground Waters and Rivers. Proceedings of 1985 Conference on Environmental
Engineering, ASCE. Boston, MA.
Mills, W.B. and L. *><. 1985. Simplified Methods to Predict the Fate of Metals
In Rivers. Proceedings of 1985 Conference on Environmental Engineering, ASCE.
Boston, MA.
-605-
-------
Montana State Oept. of Health and Environmental Sciences. 1973. Water Quality
Standards. MAC 16-2.14{10)-$14480.
Morel, F.M.M. and S.L. Schlff. 1980. Geochemistry of Municipal Waste in Coastal
Waters. Ralph Parsons Laboratory, Massachusetts Institute of Technology.
Neely, W.B., G.E. Blau, and T. Alfrey, Jr. 1976. Mathematical Models Predict Concen-
tration-Time Profiles Resulting from Chemical Spill 1n a River. Environmental
Science and Technology 10(l):72-76.
Nemerow, N.L. 1974. Scientific Stream Pollution Analysis. Scripta Book Co., Wash-
ington, D.C.
Nordstrom, O.K., L.N. Plummer, T.M.L. Wlgley, T.J. Wolery, J.W. Ball, E.A. Jenne, R.L.
Bassett, D.A. Crerar, T.M. Florence, B. Fritz, M. Hoffman, G.R. Holdren, Jr., G.M.
Lafon, S.V. Mattlgod, R.E. McDuff, F. Morel, M.M. Reddy, G. Sposlto, J. Thrallkill.
1979. Comparison of Computerized Chemical Models for Equilibrium Calculations 1n
Aqueous Systems. In Chemical Modeling 1n Aqueous Systems. ACS Symposium Series
93.
Novotny, V. 1969. Boundary Layer Effects on the Course of the Self-Purification of
Small Streams. In: Adv. Water Pollution Res. ed. S. H. Jenkins, Pergamon. pp.
39-50.
Novotny, V., and P.A. Krenkel. 1975. A Waste Assimilative Capacity Model for a
Shallow, Turbulent Stream. Water Research 9:233-241.
Odum, H.T., W. McConnel, and W. Abbott. 1958. The Chlorophylla of Communities.
Institute of Marine Science. V:65-69.
Omernlk, J.M. 1977. Nonpoint Source--Streams Nutrient Level Relationships: A Nation-
wide Study. CorvalUs Environmental Research Laboratory. EPA-600/3-77-105.
Parker, F.L., and P.A. Krenkel. 1969. Engineering Aspects of Thermal Pollution.
Vanderbilt University Press.
Patterson, J.W., H.E. Allen, J.J. Scala. 1977. Carbonate Precipitation for Heavy
Metal Pollutants. J. Water Pollution Control Federation. 49(12).
Pluhowski, E.J. 1970. Urbanization and Its Effect on Stream Temperature. U.S.
Geological Survey Professional Paper 627-0. pp. 1-109.
Proceedings of the International Conference on Transport of Persistent Chemicals on
Aquatic Ecosystems. 1974.
Ra1, 0., J.M. Zachara, A.P. Schwab, R.L. Schmidt, O.C. Girvln, and J.E. Rogers. 1983.
Attenuation 3ates, Coefficients, and Constants 1n Leachate Migration: A Critical
Review. Battelle Pacific Northwest Laboratories. Electric Power Research Insti-
tute RP-2485-3.
Ramamoorthy, S., and B.R. Rust. 1976. Mercury Sorption and Desorptlon Characteristics
of Some Ottawa River Sediments. Canadian Journal of Earth Sciences. Vol. 13.
Schumm, S.A. 1960. The Shape of Alluvial Channels In Relation to Sediment Type.
Professional paper 352-8. U.S. Geological Survey, Washington, D.C.
Slllen, L.G. 1964. Stability Constants of Metal-Ion Complexes. Special Publication
No. 17.
Sladecek, V. 1965. The Future of the Saproblty System. Hydrobilogia 25:518-537.
-606-
-------
Sladecek, V. 1969. The Measures of Saproblty. Verh. Int. Ver. Limnol. 17:546-559.
Snoeyink, V.L. and D. Jenkins. 1980. Water Chemistry. John Wiley & Sons.
Stall, J.B., N.L. Rupanl, and P.K. Kandaswamy. 1958. Sediment Transport 1n Money
Creek. American Society of Civil Engineers, Journal of the Hydraulics Division
84
-------
Turner, R.R. and S.E. Lindberg. 1978. Behavior and Transport of Mercury 1n River-
Reservoir System Downstream of Inactive Chi oral kali Plant. Environmental Science
and Technology. 12(8).
U.S. Bureau of Reclamation, Interim Report. 1958. Total Sediment Transport Program,
Lower Colorado River Basin. USBR. Denver, CO.
U.S. Department of Commerce. 1968. Climatic Atlas of the United States. U.S. Dept.
of Commerce, Environmental Sciences Services Administration, Environmental Data
Service, Washington, D.C.
U.S. Environmental Protection Agency. 1975. National Water Quality Inventory. Report
to Congress. EPA-440/9-75-014.
U.S. Environmental Protection Agency. 1976. The Influence of Land Use on Stream
Nutrient Levels. Ecological Research Series. EPA-600/13-76-014.
U.S. Environmental Protection Agency. 1976. Quality Criteria for Water.
U.S. Environmental Protection Agency. 1980. Ambient Water Quality Criteria for
Nickel.
U.S. Environmental Protection Agency. 1980. Ambient Water Quality Criteria for
Silver.
U.S. Environmental Protection Agency. 1980. Ambient Water Quality Criteria for Zinc.
U.S. Environmental Protection Agency. 1981. The Economic Impact of Promulgating Toxic
Standards for Ind1ana--A Case Study.
U.S. Environmental Protection Agency. 1985. Federal Register. No. 145, July 29, p
30784.
U.S. Geological Survey. 1970. Mercury in the Environment. Professional Paper 713.
U.S. Geological Survey. 1976. Lead in the Environment. Professional Paper 957.
Vuceta, J. and J.J. Morgan. 1978. Chemical Modeling of Trace Metals in Fresh Waters:
Role of Complexation and Adsorption. Environmental Science and Technology.
12(12).
Weast, R.C. (ed.) 1977. Handbook of Chemistry and Physics, 58th Edition. CRC Press,
Cleveland, Ohio. 2398 pp.
Westall, J.C., J.L. Zachary, and F. Morel. 1976. MINEQL: A Computer Program for the
Calculation of Chemical Equilibrium Composition of Aqueous Systems. Technical
Note No. 18, Massachusetts Institute of Technology.
Wllber, W.G., and J.V. Hunter. 1979. The Impact of Urbanization on the Distribution
of Heavy Metals in Bottom Sediments of the Saddle River. Water Resources Bulletin.
15(3), June.
Wild, H.E., C.N. Sawyer, and T.C. McMahon. 1971. Factors Affecting Nitrification
Kinetics. Journal of the Water Pollution Control Federation 43:1845.
Wright, R.M., and A.J. McDonnell. 1979. In-Stream Deoxygenation Rate Prediction.
Journal Environmental Engineering Division, ASCE. 105(EE2).
Yang, C.T. 1976. Minimum Unit Stream Power and Fluvial Hydraulics. Journal o' the
Hydraulics Division, ASCE 102(HY7).
-608-
-------
Zison, S.W., K.F. Haven, and W.B. Mills. 1977. Water Quality Assessment: A Screening
Method for Mondesignated 208 Areas. EPA-600/3-77-023.
Zison, S.W., y.B. Mills, D. Delmer, and C.W. Chen. 1978. Rates, Constants, and
Kinetics Formulations 1n Surface Water Quality Modeling. EPA-600/3-78-105.
-609-
U . S . G3
-------
|