Analyses must also identify situations in which threshold criteria are exceeded in the baseline case but met under a regulatory option. Failing to account for other source contributions has an uncertain effect on estimated benefits. For example, if non sample MP& M facilities are major contributors to aggregate pollutant discharges to a receiving stream, then the analysis will likely understate the extent of aquatic habitat improvements that may be accomplished by reduced MP& M pollutant discharges. Conversely, if the total MP& M contribution to the aggregate pollutant discharges to a receiving stream is not significant, then reducing MP& M discharges may reduce but not eliminate AWQC exceedances, and the benefits of the MP & M regulation can be overstated. The net effect of the following are unknown:

  excluding other sources understates the number and extent of baseline exceedances;

  excluding non sample MP& M facilities understates the reduction in MP& M pollutant discharges due to the rule; and   the number of cases in which estimated baseline exceedances are eliminated may be either over or understated,

depending on the contribution of pollutants from non MP& M sources.

b. Water body modeling EPA made four major assumptions concerning all water body modeling, and two major assumptions specific to stream modeling. These assumptions are summarized below:

  Complete mixing of POTW discharge flow occurs immediately. This mixing results in the calculation of an " average" concentration, even though the actual concentration may vary across the width and depth of the water body.

  Pollutant loads to the receiving water body are continuous and representative of long term facility operations. This assumption may overestimate long term risks to human health and aquatic life, but may underestimate potential short term effects.

  In the absence of data from EPA's Permit Compliance System ( PCS) on specific individual POTW flow,

POTW daily flow rates were set equal to the simple arithmetic mean flow among minor POTWs reporting flows in PCS. The arithmetic mean for minor POTWs was used because all POTWs receiving discharges from the sample MP& M facilities for which flow data are not available in the PCS database are classified as minor dischargers in the PCS database.

  EPA used 1Q10 and 7Q10 receiving stream flow rates to estimate aquatic life impacts, and harmonic mean flow rates to estimate human health impacts, when modeling stream reaches. EPA estimated 1Q10 low flows by using the results of a regression analysis conducted for OPPT of 1Q10 and 7Q10 flows from representative U. S. rivers and streams ( Versar, 1992). EPA estimated harmonic mean flows from the mean and 7Q10 flows as recommended in the Technical Support Document for Water Quality based Toxics Control ( U. S. EPA, 1991). These flows may not be the same as those used by specific states to assess impacts.

  Where data on stream flow parameters were not available, EPA set mean and 7Q10 flow values equal to the corresponding mean values associated with reaches located upstream and downstream of the sample reach.

I 27 MP& M EEBA: Appendices Appendix I: Environmental Assessment c. Exposure analyses MP& M exposure assessment in freshwater locations uses two sets of human health based AWQC:

  AW QC for the protection of human health from the consumption of organisms and drinking water, and   AWQC for the protection of human health from consumption of organisms only.

MP& M exposure assessments in marine locations use AWQC for the protection of human health from the consumption of organisms only, because saltwater is not used for drinking water supply.

d. Extrapolation from sample set to national level Although the sample set should represent a national group of facilities discharging to waterways and POTWs, effluent from an individual sample facility may have a different potential environmental impact than effluent from the facilities it is assumed to represent. For example, a facility that discharges to a stream with a very low flow may be similar to the facilities it represents in all aspects except available dilution in the receiving stream. The sample frame used in the MP& M analysis was not designed to take receiving water body characteristics into account. Using sample weights to extrapolate environmental impacts may either under or overstate estimated impacts.

I. 3 DATA SOURCES The following three sections describe the various data sources used to evaluate water quality and POTW impacts.

I. 3.1 FacilitySpecific Data Section I. 2.1 provides detailed information on sample size and distribution, and on receiving waterways. The names,

locations, and the flow data for the POTWs to which the MP& M facilities discharge were obtained from the MP& M facility surveys and EPA's PCS database. EPA took alternative measures to obtain a complete set of receiving POTWs if these sources did not yield information for a given facility. EPA used latitude/ longitude coordinates ( if available) to locate those POTW s that have not been assigned a reach number in PCS. EPA identified the nearest POTW in the case of facilities for which the POTW receiving the plant discharge could not be positively identified. EPA based its identification of the closest linear distance on the latitude/ longitude coordinates of the MP& M facility or the city in which it was located. EPA then identified the corresponding reach in PCS, and obtained POTW flow from the Needs Survey or PCS.

EPA identified reaches to which direct MP& M facilities discharge by identifying the receiving reach in PCS or by identifying the nearest reach. EPA based its identification of the closest linear distance on the MP& M facility s latitude/ longitude coordinates.

I. 3.2 Water bodySpecific Data a. Streams and rivers EPA used 1Q10, 7Q10, and mean flow data for the 521 streams and rivers. EPA obtained 7Q10 and mean flow data from the W. E. Gates study data or from measured stream flow data, both of which are contained in EPA's GAGE file. The W. E. Gates study contains calculated average and low flow statistics based on the best available flow data and on drainage areas for reaches throughout the United States. The GAGE file also includes average and low flow statistics based on measured data from United States Geological Survey ( USGS) gaging stations. In the absence of data on stream flow parameters, EPA set 7Q10 and mean flow values equal to the corresponding median values associated with the sample reaches. EPA used the results of a regression analysis conducted for OPPT of 1Q10 and 7Q10 flows from representative U. S. rivers and streams ( Versar, 1992) to estimate 1Q10 flows. EPA estimated harmonic mean flows from the mean and 7Q10 flows as recommended in the Technical Support Document for Water Quality based Toxics Control ( U. S. EPA, 1991).

I 28 MP& M EEBA: Appendices Appendix I: Environmental Assessment b. Lakes EPA used data on hydraulic residence time ( i. e., the amount of time water remains in a lake) to analyze small lakes, and CDFs ( which describe dilution in a portion of a lake) to analyze the Great lakes. 5 The sample MP& M facilities discharged directly to one lake reach and indirectly to 23 lake reaches: 15 to small lakes, 3 to sections of Lake Erie, 5 to sections of Lake Michigan, and 1 to a section of Lake Ontario. EPA calculated the average hydraulic residence time for small lakes based on lake surface and drainage areas. EPA obtained data on lake surface and drainage area from the U. S. Army Corps of Engineers, Major Dams: Map Layer Description File ( USCE, 1999). CDFs were readily available for Lake Michigan, but not for the three sample reaches on Lake Erie. EPA arithmetically averaged the seven chronic CDFs available for reaches discharging to Lake Erie ( 1, 1, 4, 4, 10, 10, 4) ( U. S. EPA, 1992a, p. A 4) for the three reaches being modeled.

c. Estuaries and bays Sixty two bays and estuaries receive discharges from sample MP& M facilities. Data necessary to support water quality modeling were not available for eight of the 62 bays/ estuaries. A dilution model predicted pollutant concentrations in the chronic and acute mixing zones, based on site specific CDFs ( U. S. EPA, 1992a and Versar, 1994), to estimate the pollutant concentrations in 28 of these complex water bodies.

Both acute and chronic CDFs were available for 20 of the 62 bays/ estuaries. EPA estimated acute and chronic CDFs for New York bays/ estuaries by arithmetically averaging available values for nearby New Jersey sites discharging to the Arthur Kill ( acute: 1.5, 4.0, 5.0; chronic: 5; 20; 10) and Upper New York Bay ( acute: 8.0; chronic: 22.9). Acute and chronic CDFs for Buzzards Bay in Massachusetts were estimated by arithmetically averaging values for nearby Massachusetts and Rhode Island sites discharging to the Atlantic Ocean.

EPA could not identify or approximate chronic CDFs for the remaining 13 sample reaches. Acute CDFs are available for 46 of the 62 bays/ estuaries. EPA extrapolated acute CDFs for two bays/ estuaries in Florida by using CDFs for another Florida bay. Likewise, EPA extrapolated acute CDFs for four bays/ estuaries in California by using CDFs for another California bay.

EPA obtained DCP values for five of the 13 sample bays/ estuaries for which CDFs were not available from the Development of Mixing Zone Dilution Factors report ( Versar, 1994). EPA then used a dilution model that predicts pollutant concentrations in the estuarine environment using a site specific DCP value.

I. 3.3 Information Used to Evaluate POTW Operations Since many MP& M facilities considered in the alternative options are indirect dischargers, the Agency consulted with POTW s as they would have had to implement the rule. EPA consulted with POTWs individually and through the Association of Municipal Sewerage Agencies ( AMSA). In addition, EPA consulted with pretreatment coordinators and State and local regulators.

EPA used removal efficiency rates, inhibition values, and sewage sludge regulatory levels to evaluate POTW operations.

EPA obtained POTW removal efficiency rates from several sources. The Agency developed rates from POTW removal data and pilot plant studies or used removals for a similar pollutant when data were not available. Use of the selected removal rates assumes that the evaluated POTWs are well operated and have at least secondary treatment in place ( U. S. EPA, 2000).

EPA obtained inhibition values from the Guidance Manual for Preventing Interference at POTWs ( U. S. EPA, 1987a) and from CERCLA Site Discharges to POTWs: Guidance Manual ( U. S. EPA, 1990). EPA used the most conservative values for activated sludge ( i. e., the lowest influent concentrations that would cause inhibition). The Agency used a value based on compound type ( e. g., aromatics) for pollutants with no specific inhibition value.

EPA obtained sewage sludge regulatory levels from the Federal Register 40 CFR Part 257 et al., Standards for the Use or Disposal of Sewage Sludge; Final Rules ( February 19, 1993) and from the Federal Register 59( 38): 9095 9099 ( February 25,

1994) and 60( 206): 54,764 54,770 ( October 25, 1995) for eight metals regulated in sewage sludge. EPA used pollutant limits established for the final use or disposal of sewage sludge when the sewage sludge is applied to agricultural and non agricultural land or is applied to a dedicated surface disposal site.

5 Small lakes are defined as any non Great lakes, including reservoirs.

I 29 MP& M EEBA: Appendices Finally, EPA obtained sludge partition factors from the Report to Congress on the Discharge of Hazardous Wastes to Publicly Owned Treatment Works ( Domestic Sewage Study) ( U. S. EPA, 1986).

Table I. 5 lists POTW treatment removal efficiency rates, inhibition values, sewage sludge partition factors, and sewage sludge regulatory levels used in the evaluation of POTW operations. Appendix I: Environmental Assessment Table I. 5:

CAS Number Pollutant Name POTW Inhibition Level Value (   g/ l) POTW Sludge Partition Factor Sludge Criteria Value ( mg/ kg) POTW Removal Efficiency Rate ( Percentage)

51285 Dinitrophenol, 2,4 1000 0.10000000149 77.51 57125 Cyanide 5000 1 70.44 59507 Parachlorometacresol 5000 0.07900000364 63 62533 Aniline 1000 0.1 93.41 62759 Nitrosodimethylamine, N 0.1 77.51 65850 Benzoic acid 10000 0.1 80.5 67641 Acetone 120000 0.1 83.75 67663 Trichloromethane 500000 0.015 68122 Dimethylformamide, N, N 1000 0.1 87 75003 Chloroethane 0.0075 77.51 75092 Dichloromethane 150000 0.1395 54.28 75150 Carbon disulfide 50000 0.0075 84 75354 Dichloroethene, 1,1 150000 77.51 75694 Trichlorofluoromethane 700 77.32 78591 Isophorone 120000 0.079 77.51 78831 Isobutyl alcohol 1000000 0.1 28 78933 Methyl ethyl ketone 120000 0.1 96.6 79016 Trichloroethene 20000 0.0578 77.51 80626 Methyl methacrylate 120000 99.96 83329 Acenaphthene 500000 0.366 98.29 84742 Di n butyl phthalate 10000 0.216 84.66 85018 Phenanthrene 500000 0.366 94.89 85687 Butyl benzyl phthalate 10000 0.452 81.65 86306 Nitrosodiphenylamine, N 90.11 86737 Fluorene 500000 0.366 69.85 88755 Nitrophenol, 2 50000 26.83 91203 Naphthalene 500000 0.275 94.69 91576 Methylnaphthalene, 2 5000 0.079 28 92524 Biphenyl 5000 0.366 96.28 95476 Xylene, o 5000 0.149 77.32 95487 Cresol, o 90000 0.079 52.5 98555 Terpineol, alpha 1000000 0.1 94.4 98862 Acetophenone 120000 0.1 95.34 99876 Cymene, p 5000 0.0075 99.79 100027 Nitrophenol, 4 50000 0.1 77.51 100414 Ethylbenzene 200000 0.06 93.79 100425 Styrene 500000 0.149 93.65 100516 Benzyl alcohol 1000000 0.1 78 100754 Nitrosopiperidine, N 1000 77.32 101848 Diphenyl Ether 1000 77.32 105679 Dimethylphenol, 2,4 40000 0.079 77.51 POTWRelated Data for 132 MP& M Pollutants I 30 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 5:

CAS Number Pollutant Name POTW Inhibition Level Value (   g/ l) POTW Sludge Partition Factor Sludge Criteria Value ( mg/ kg) POTW Removal Efficiency Rate ( Percentage)

106445 Cresol, p 90000 0.079 71.67 107028 Acrolein 50 0.10000000149 77.51 108101 Methyl isobutyl ketone 120000 0.1 87.87 108372 Bromo 3 chlorobenzene, 1 100 77.32 108383 Xylene, m 5000 0.149 95.07 108883 Toluene 200000 0.278 96.18 108907 Chlorobenzene 140000 0.154 96.37 108952 Phenol 90000 0.146 95.25 110861 Pyridine 1000 0.1 95.4 112403 Dodecane, n ( a)

112958 Eicosane, n ( a)

117817 Bis( 2 ethylhexyl) phthalate 10000 0.728 59.78 117840 Di n octyl phthalate 10000 0.075 68.43 120127 Anthracene 500000 0.55 77.51 122394 Diphenylamine 1000 0.08 77.32 123911 Dioxane, 1,4 120000 0.1 45.8 124185 Decane, n 9 127184 Tetrachloroethene 20000 0.034 84.61 129000 Pyrene 500000 0.366 83.9 131113 Dimethyl phthalate 0.1 77.51 132650 Dibenzothiophene 5000 0.366 84.68 137304 Ziram \ Cymate 50 142621 Hexanoic acid 10000 84 206440 Fluoranthene 5000 0.366 42.46 544763 Hexadecane, n ( a)

591786 Hexanone, 2 120000 77.32 593453 Octadecane, n ( a)

606202 Dinitrotoluene, 2,6 5000 0.1 77.51 629594 Tetradecane, n ( a)

629970 Docosane, n 88 630013 Hexacosane, n ( b)

630024 Octacosane, n ( b)

638686 Triacontane, n ( b)

646311 Tetracosane, n ( b)

694804 Bromo 2 chlorobenzene, 1 100 77.32 832699 Methylphenanthrene, 1 5000 0.366 84.55 1576676 Dimethylphenanthrene, 3,6 500000 0.366 84.55 1730376 Methylfluorene, 1 500000 0.366 84.55 2027170 Isopropylnaphthalene, 2 500000 0.1 77.32 7429905 Aluminum 1 91.36 7439896 Iron 5000 1 81.99 7439921 Lead 100 1 300 77.45 7439954 Magnesium 1000000 1 14.14 7439965 Manganese 10000 1 35.51 7439976 Mercury 100 1 17 71.66 7439987 Molybdenum 1 18.93 7440020 Nickel 5000 1 420 51.44 POTWRelated Data for 132 MP& M Pollutants I 31 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 5:

CAS Number Pollutant Name POTW Inhibition Level Value (   g/ l) POTW Sludge Partition Factor Sludge Criteria Value ( mg/ kg) POTW Removal Efficiency Rate ( Percentage)

7440224 Silver 30 1 88.28 7440235 Sodium 3500000 1 2.69 7440280 Thallium 1 71.66 7440315 Tin 9000 1 42 7440326 Titanium 1 91.82 7440360 Antimony 1 66.78 7440382 Arsenic 40 1 41 65.77 7440393 Barium 1 15.98 7440417 Beryllium 1 71.66 7440428 Boron 1000 1 30.42 7440439 Cadmium 500 1 39 90.05 7440473 Chromium 1000 1 80.33 7440484 Cobalt 1 6.11 7440508 Copper 1000 1 1500 84.2 7440575 Gold 1 32.52 7440622 Vanadium 20000 1 9.51 7440655 Yttrium 1 32.52 7440666 Zinc 5000 1 2800 79.14 7440702 Calcium 2500000 1 8.54 7664417 Ammonia as N 480000 38.94 7782492 Selenium 1 100 34.33 14265442 Phosphate 57.41 14808798 Sulfate 84.61 16887006 Chloride 57.41 16984488 Fluoride 61.35 18496258 Sulfide 25000 57.41 18540299 Chromium hexavalent 1000 1 57.41 20324338 Tripropyleneglycolmethylether 120000 52.4 136777612 Xylene, o & p( c) 5000 0.149 36832 179601231 Xylene, m & p ( c)

C003 BOD 5 day ( carbonaceous) 89.12 C004 Chemical Oxygen Demand ( COD) 81.3 C009 Total Suspended Solids ( TSS)

C010 Total Dissolved Solids ( TDS)

C012 Total Organic Carbon ( TOC) 70.28 C020 Total Recoverable Phenolics 57.41 C021 Total Kjeldahl Nitrogen 57.41 C025 Amenable Cyanide 57.41 C036 Oil and Grease ( as Hem) 86.08 C037 Total Petroleum Hydrocarbons ( as Sgt hem)

C042 Weak acid Dissociable Cyanide Phosphorus ( as PO4)

Oil and Grease POTWRelated Data for 132 MP& M Pollutants Sources: U. S. EPA ( 1985), U. S. EPA ( 1987), U. S. EPA ( 1990).

I 32 MP& M EEBA: Appendices Appendix I: Environmental Assessment In the absence of data on POTW flow rates, EPA set the POTW flow rate equal to the arithmetic mean flow among minor POTWs in the PCS database, using the following steps:

1. Calculate arithmetic mean flow among minor POTWs in the PCS database. The estimated arithmetic mean flow for minor POTWs in the PCS database is one million gallons per day ( MGD).

2. Set POTW flow rate equal to the relevant arithmetic mean flow. For all POTWS with missing flow data, EPA set their flow rates equal to the arithmetic mean flow rate for minor POTWs in the PCS database, one MGD.

I. 4 RESULTS EPA assessed the environmental impacts of MP& M dischargers on water bodies and POTWs under the baseline conditions and those corresponding to four regulatory options: the Final Option, Proposed/ NODA Option, and two 433 Upgrade Options on the basis of sample facility data. The Agency extrapolated the findings from the sample facility analyses to the national level using facility sample weights, as described in Appendix G.

MP& M facilities nationwide currently discharge an estimated 53 million pounds of pollutants per year to publicly owned treatment works ( POTWs) and approximately 6.2 million pounds of pollutants directly to surface waters. MP& M facility effluents contain 42 priority or toxic pollutants, 81 nonconventional pollutants, and three conventional pollutants ( BOD, TSS,

and O& G).

EPA estimates that the final rule will lead to a modest reduction in pollutant discharges to the waters of the U. S. As shown by Table I. 6, the regulation will reduce discharges of pollutants with acute and chronic effects on aquatic life by 8,959 and 12,270 pounds per year, respectively. The final rule does not regulate indirect dischargers and thus will not reduce pollutant loads received by POTWs.

EPA estimates that the Proposed/ NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would remove 3,299, 91, and 110 thousand pounds per year of eight sewage sludge contaminants, respectively. In addition, the Proposed/ NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would result in 30,226, 133, and 551 thousand pounds per year reduction in 86 pollutants causing inhibition of POTW operations.

The Proposed/ NODA Option would reduce discharges of pollutants with acute and chronic effects on aquatic life by 97 and 117 million pounds per year, respectively. The Directs + 413 to 433 Upgrade Option and the Directs + All to 433 Upgrade Option would reduce discharges of 132 and 353 thousand pounds of pollutants with acute effects on aquatic life, and 136 and 576 thousand pounds of pollutants with chronic effects on aquatic life, respectively.

I 33 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 6: & M Facility Discharges ( National Basis) a Category POTW Impacts Receiving Stream Impacts: Aquatic Life Toxicity Activated Sludge Inhibition Biosolids Contaminants HAP Acute Chronic Selected Option # of Pollutants N/ A N/ A N/ A 106 113 Baseline ( 1,000 lbs/ yr) N/ A N/ A N/ A 868 1,154 Post Compliance ( 1,000 lbs/ yr) N/ A N/ A N/ A 859 1,142 Proposed/ NODA Option # of Pollutants 85 8 35 105 112 Baseline ( 1,000 lbs/ yr) 39,594 3,589 408 141,522 187,742 Post Compliance ( 1,000 lbs/ yr) 9,369 290 189 44,827 70,428 Directs + 413 to 433 Upgrade # of Pollutants 86 8 35 106 113 Baseline ( 1,000 lbs/ yr) 1,085 253 3 868 1,154 Post Compliance ( 1,000 lbs/ yr) 952 161 3 935 1,018 Directs + All to 433 Upgrade # of Pollutants 86 8 35 106 113 Baseline ( 1,000 lbs/ yr) 1,085 253 3 868 1,154 Post Compliance ( 1,000 lbs/ yr) 534 143 3 514 578 MP a Excludes loadings from facilities projected to close in the baseline. See Chapter 5.

Source: U. S. EPA analysis.

I. 4.1 Human Health Impacts Under this human health benefit category EPA assessed the reduced occurrence of pollutant concentrations that are estimated to exceed human health based AWQC. This analysis provides an alternative measure of the expected reduction in risk to human health. Table I. 7 presents information on baseline and post compliance exceedances of human health AWQC criteria for all the regulatory options.

EPA estimates that in stream concentrations of four pollutants ( i. e., arsenic, iron, manganese, and n nitrosodimethylamine)

will exceed human health criteria for consumption of water and organisms in 78 receiving reaches nationwide as the result of baseline MP& M pollutant discharges. EPA estimates that there are human health AWQC exceedances caused by n nitrosodimethylamine ( NDM A). EPA did not consider NDM A pollutant reductions in its benefits analyses because of the low number of detected values for that pollutant. EPA estimates that the final rule will not eliminate the occurrence of concentrations in excess of human health criteria for consumption of water and organisms and for consumption of organisms on any of the reaches on which baseline discharges are estimated to cause concentrations in excess of AWQC values.

The Proposed/ NODA Option would eliminate instances of in stream pollutant concentrations exceeding AWQC limits for consumption of water and organisms and consumption of organisms only in 63 and 68 reaches, respectively, nationwide. The Directs + 413 to 433 Upgrade O ption would not eliminate any instances of in stream pollutant concentrations exceeding AWQ C limits for consumption of water and organisms and consumption of organisms only. The Directs + All to 433 Upgrade Option would not eliminate any occurrences of pollutant concentrations in excess of AWQC values for consumption of water and organisms, but would eliminate instances of pollutant concentrations in excess of AWQC values for consumption of organisms only in 21 reaches nationwide. As noted above the Agency did not estimate reductions in NDM A loadings under the post compliance scenario due to data limitations.

I 34 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 7: mmary of Estimated AWQC Exceedances for Protection of Human Health ( National Basis)

Category Human Health Water and Organisms Human Health Organisms Only Streams ( No.) Pollutants ( No.) Total Exceedances Streams ( No.) Pollutants ( No.) Total Exceedances Selected Option: Traditional Extrapolation Baseline 78 4 121 21 1 21 Post Compliance 78 4 121 21 1 21 Selected Option: Post Stratification Extrapolation Baseline 112 4 154 21 1 21 Post Compliance 112 4 154 21 1 21 Proposed/ NODA Option Baseline 5,852 26 7,085 197 12 335 Post Compliance 5,789 21 6,667 128 9 212 Directs + 413 to 433 Upgrade Baseline 78 4 121 21 1 21 Post Compliance 78 4 121 21 1 21 Directs + All to 433 Upgrade Baseline 78 4 121 21 1 21 Post Compliance 78 2 78 0 Su 0 0 Source: U. S. EPA analysis.

I 35 MP& M EEBA: Appendices Table I. 8 summarizes pollutants estimated to exceed human health based AWQC criteria for consumption of water and organisms under the baseline and post compliance conditions. Appendix I: Environmental Assessment Table I. 8: Summary of Pollutants Estimated to Exceed Human HealthBased AWQC Criteria for Consumption of Water and Organisms ( National Basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade Basea PCb Base PC Base PC Base PC Base PC Aniline 0 0 0 0 20 17 0 0 0 0 Antimony 0 0 0 0 0 0 0 0 0 0 Arsenic 45 45 45 45 772 557 45 45 45 45 Bis( 2 ethylhexyl) phthalate 0 0 0 0 85 43 0 0 0 0 Cadmium 0 0 0 0 0 0 0 0 0 0 Chloroethane 0 0 0 0 17 14 0 0 0 0 Copper 0 0 0 0 16 0 0 0 0 0 Cresol, p 0 0 0 0 9 9 0 0 0 0 Dibenzofuran 0 0 0 0 12 9 0 0 0 0 Dichloroethene, 1,1 0 0 0 0 97 81 0 0 0 0 Dichloromethane 0 0 0 0 17 17 0 0 0 0 Dinitrophenol, 2,4 0 0 0 0 9 9 0 0 0 0 Dinitrotoluene, 2,6 0 0 0 0 9 9 0 0 0 0 Dioxane, 1,4 0 0 0 0 17 17 0 0 0 0 Fluoranthene 0 0 0 0 9 9 0 0 0 0 Iron 21 21 21 21 28 0 21 21 21 0 Isophorone 0 0 0 0 9 9 0 0 0 0 Manganese 21 21 21 21 54 0 21 21 21 0 Mercury 0 0 0 0 0 0 0 0 0 0 Naphthalene 0 0 0 0 9 9 0 0 0 0 Nickel 0 0 0 0 16 0 0 0 0 0 Nitrophenol, 4 0 0 0 0 9 9 0 0 0 0 Nitrosodimethylamine, N 32 32 67 67 5,789 5,789 32 32 32 32 Nitrosodiphenylamine, N 0 0 0 0 17 17 0 0 0 0 Pyrene 0 0 0 0 9 9 0 0 0 0 Pyridine 0 0 0 0 12 9 0 0 0 0 Thallium 0 0 0 0 16 0 0 0 0 0 Trichloroethene 0 0 0 0 21 17 0 0 0 0 Trichloromethane 0 0 0 0 12 12 0 0 0 0 Total Exceedances 121 121 154 154 7,085 6,667 121 121 121 77 a Base = Baseline discharge level b PC = Post Compliance discharge level Source: U. S. EPA analysis.

I 36 MP& M EEBA: Appendices Table I. 9 summarizes pollutants estimated to exceed human health based AWQC criteria for consumption of organisms only under the baseline and post compliance conditions. Appendix I: Environmental Assessment Table I. 9: Summary of Pollutants Estimated to Exceed Human HealthBased AWQC Criteria for Consumption of Organisms Only ( National Basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade Basea PCb Base PC Base PC Base PC Base PC Aniline 0 0 0 0 12 9 0 0 0 0 Antimony 0 0 0 0 0 0 0 0 0 0 Arsenic 0 0 154 111 0 0 Bis( 2 ethylhexyl) phthalate 0 0 0 0 24 12 0 0 0 0 Cadmium 0 0 0 0 0 0 0 0 0 0 Chloroethane 0 0 0 0 0 0 0 0 0 0 Copper 0 0 0 0 16 0 0 0 0 0 Cresol, p 0 0 0 0 0 0 0 0 0 0 Dibenzofuran 0 0 0 0 12 9 0 0 0 0 Dichloroethene, 1,1 0 0 0 0 17 17 0 0 0 0 Dichloromethane 0 0 0 0 0 0 0 0 0 0 Dinitrophenol, 2,4 0 0 0 0 0 0 0 0 0 0 Dinitrotoluene, 2,6 0 0 0 0 0 0 0 0 0 0 Dioxane, 1,4 0 0 0 0 0 0 0 0 0 0 Fluoranthene 0 0 0 0 9 9 0 0 0 0 Iron 0 0 0 0 0 0 0 0 0 0 Isophorone 0 0 0 0 0 0 0 0 0 0 Manganese 21 21 21 21 32 0 21 21 21 0 Mercury 0 0 0 0 0 0 Naphthalene 0 0 0 0 0 0 0 0 0 0 Nickel 0 0 0 0 16 0 0 0 0 0 Nitrophenol, 4 0 0 0 0 0 0 0 0 0 0 Nitrosodimethylamine, N 0 0 0 0 27 27 0 0 0 0 Nitrosodiphenylamine, N 0 0 0 0 9 9 0 0 0 0 Pyrene 0 0 0 0 9 9 0 0 0 0 Pyridine 0 0 0 0 0 0 0 0 0 0 Thallium 0 0 0 0 0 0 0 0 0 0 Trichloroethene 0 0 0 0 0 0 0 0 0 0 Trichloromethane 0 0 0 0 0 0 0 0 0 0 Total Exceedances 21 21 21 21 335 212 21 21 21 0 0 0 0 0 0 0 0 a Base = Baseline discharge level b PC = Post Compliance discharge level Source: U. S. EPA analysis.

I. 4.2 Aquatic Life Effects EPA evaluated the effects of MP& M facility discharges on aquatic habitats and ecosystem functioning under the baseline conditions and the post compliance scenarios corresponding to the four regulatory alternatives considered for the MP& M regulation. This analysis compared the estimated baseline and post compliance in stream concentrations of MP& M pollutants with AWQC for aquatic species. As noted in the preceding sections, aquatic life AWQCs addressed in this analysis set the upper limit on pollutant concentrations assumed to be protective of aquatic life.

I 37 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 10 presents the number of MP& M discharge reaches on which pollutant concentrations are estimated to exceed chronic and acute exposure criteria for protection of aquatic life. EPA estimated that, as the result of baseline MP& M pollutant discharges, in stream concentrations exceed acute exposure criteria for aquatic species in 18 and 15 receiving reaches nationwide based on the traditional extrapolation and post stratification extrapolation, respectively. In addition,

baseline in stream concentrations in 353 and 350 receiving reaches exceed chronic AWQC for protection of aquatic life based on the traditional extrapolation and post stratification extrapolation, respectively.

Table I. 10: mmary of Estimated AWQC Exceedances for Protection of Aquatic Life ( National Basis)

Category Acute Aquatic Life Chronic Aquatic Life Streams ( No.) Pollutants ( No.) Total Exceedances Streams ( No.) Pollutants ( No.) Total Exceedances Selected Option: Traditional Extrapolation Baseline 18 4 35 353 9 423 Post Compliance 9 1 9 344 5 362 Selected Option: Post Stratification Extrapolation Baseline 15 4 26 350 9 402 Post Compliance 9 1 9 344 5 362 Proposed/ NODA Option Baseline 330 17 631 928 47 2,582 Post Compliance 86 12 254 539 39 1,369 Directs + 413 to 433 Upgrade Baseline 18 4 35 353 9 423 Post Compliance 0 0 0 53 3 53 Directs + All to 433 Upgrade Baseline 18 4 35 353 9 423 Post Compliance 0 0 0 32 2 32 Su Source: U. S. EPA analysis.

Based on the traditional extrapolation, EPA estimates that the final option will eliminate concentrations in excess of acute and chronic criteria in nine reaches. Likewise, EPA estimates that the final option will eliminate concentrations in excess of acute and chronic criteria in six reaches based on the post stratification extrapolation.

The Proposed/ NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option would eliminate exceedances of chronic AWQC values on 389, 300, and 321 reaches, respectively. These options would also eliminate in stream pollutant concentrations in excess of acute AWQC value on 244, 18, and 18 reaches under the Proposed/ NODA Option, Directs + 413 to 433 Upgrade Option, and Directs + All to 433 Upgrade Option, respectively.

Table I. 11 presents the number MP& M reaches on which pollutant concentrations are estimated to exceed chronic AWQC for protection of aquatic life by pollutant.

I 38 MP& M EEBA: AppendicesAppendix I: Environmental Assessment I 39 Table I. 11: Aquatic Life ( National Basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade BaseaPCbBasePCBasePCBasePCBasePC Acenaphthene00 Acrolein0443300 Aluminum0321200 Ammonia as N051000 Aniline0454200 Anthracene0642900 Biphenyl0900 Butyl benzyl phthalate0900 Cadmium970210 Carbon disulfide038340 Chromium046120 Cobalt012120 Copper93446990 Cyanide00 Di n butyl phthalate00 Di n octyl phthalate012120 Dibenzofuran000021120 Dibenzothiophene015120 Dimethylphenanthrene, 3,6 024210 Dinitrophenol, 2,4 00 Dinitrotoluene, 2,6 021210 Diphenyl Ether021210 Fluoranthene030240 Fluorene027210 Fluoride0541300 Iron012000 Isopropylnaphthalene, 2 0151200 Lead02448300 Magnesium0121200 Manganese032000 Methylfluorene, 1 0151200 Methylnaphthalene, 2 0121200 Methylphenanthrene, 1 0151200 Molybdenum01033900 Naphthalene0900 Nickel01631600 Phenanthrene0242100 Phenol0900 Pyrene0212100 Selenium0785000 Silver91661310 Styrene00 Sulfide029328300 Summary of Pollutants Estimated to Exceed Chronic AWQC for Protection of 00099000 00000 00000 00000 00000 00000 000900 000000 060909 000000 000000 000000 99990 00003000 00099000 000000 000 000000 000000 00099000 000000 000000 000000 000000 00000 00000 00000 00000 00000 00000 00000 00000 00000 00000 000900 00000 00000 000000 00000 00000 060909 00009000 00000 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 11: Aquatic Life ( National Basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade Basea PCb Base PC Base PC Base PC Base PC Tin 0 83 21 0 0 Titanium 0 6 0 0 Vanadium 0 157 142 0 0 Zinc 9 85 33 0 Total Exceedances 35 9 26 9 2,582 1,369 35 0 35 0 Summary of Pollutants Estimated to Exceed Chronic AWQC for Protection of 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 9 0 9 a Base = Baseline discharge level b PC = Post Compliance discharge level Source: U. S. EPA analysis.

Table I. 12 presents the number MP& M reaches on which pollutant concentrations are estimated to exceed acute AWQC for protection of aquatic life by pollutant.

Table I. 12: mmary of Pollutants Estimated to Exceed Aquatic Life Based Acute AWQC ( National Basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade Basea PCb Base PC Base PC Base PC Base PC Acenaphthene 0 0 Acrolein 0 33 26 0 Aluminum 9 10 0 Ammonia as N 0 0 0 0 Aniline 0 9 0 0 Anthracene 0 64 29 0 0 Biphenyl 0 9 0 0 Butyl benzyl phthalate 0 0 0 0 Cadmium 9 0 Carbon disulfide 0 0 Chromium 0 0 Cobalt 0 0 Copper 276 267 273 267 241 69 276 9 276 9 Cyanide 0 0 Di n butyl phthalate 0 0 Di n octyl phthalate 0 0 Dibenzofuran 0 0 Dibenzothiophene 0 0 Dimethylphenanthrene, 3,6 0 0 Dinitrophenol, 2,4 0 0 Dinitrotoluene, 2,6 0 0 Diphenyl Ether 0 0 Fluoranthene 0 21 21 0 Fluorene 0 21 21 0 Fluoride 0 0 Iron 0 0 Isopropylnaphthalene, 2 0 0 Su 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 9 0 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 9 6 9 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I 40 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 12: mmary of Pollutants Estimated to Exceed Aquatic Life Based Acute AWQC ( National Basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade Basea PCb Base PC Base PC Base PC Base PC Lead 18 9 15 18 0 18 0 Magnesium 21 21 21 21 0 0 21 21 21 0 Manganese 9 0 Methylfluorene, 1 0 0 Methylnaphthalene, 2 0 0 Methylphenanthrene, 1 0 0 0 Molybdenum 0 0 Naphthalene 0 0 Nickel 9 23 0 Phenanthrene 0 0 Phenol 0 0 Pyrene 0 0 Selenium 0 12 12 0 Silver 64 56 61 56 60 12 64 23 64 23 Styrene 0 0 Sulfide 0 0 Tin 0 0 Titanium 0 0 Vanadium 0 0 Zinc 9 85 33 0 Total Exceedances 423 362 402 362 631 254 423 53 423 32 Su 6 9 9 9 0 9 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 9 9 0 9 0 0 0 0 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 9 0 9 a Base = Baseline discharge level b PC = Post Compliance discharge level.

Source: U. S. EPA analysis.

I. 4.3 POTW Effects EPA evaluated the effects of indirect MP& M dischargers on POTW operations for the final and alternative options. 788 sample MP& M facilities discharge 132 pollutants to 572 POTWs. Of these, EPA evaluated 89 pollutants for potential inhibition of POTW operations and eight pollutants for potential sludge contamination. The 788 indirect sample MP& M facilities discharge 52.8 million pounds per year of priority and nonconventional pollutants to the receiving POTW s. The final MP& M rule does not regulate indirect dischargers and thus will not reduce the baseline MP& M loadings to receiving POTWs.

a. Biological inhibition EPA estimated inhibition of POTW operations by comparing predicted POTW influent concentrations to available inhibition levels for 89 pollutants. EPA s analysis shows that 51 POTW s had influent concentrations that exceed the biological inhibition values for one of the four following pollutants silver, cadmium, chromium and copper under the baseline conditions corresponding to the Final Option and the 433 Upgrade Options ( see Table I. 13). Both of the 433 Upgrade Options would eliminate influent concentrations in excess of POTW inhibition criteria at 21 POTWs. Under the baseline conditions corresponding to the Proposed/ NODA Option, 293 POTWs had influent concentrations in excess of the biological inhibition criteria. The Proposed/ NODA Option would eliminate influent concentrations in excess of the biological inhibition criteria at 156 POTWs.

I 41 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 13: National Summary of Projected Inhibition and Sludge Contamination Problems Category Biolo gical Inh ibition (# of P OT W s) Sludg e Co ntam ination (# of PO TW s)

POTWs ( No.) Pollutants ( No.) Total Exceedances POTWs ( No.) Pollutants ( No.) Total Exceedances Selected Option: Traditional Extrapolation Baseline 51 4 139 1,020 7 2,702 Post Compliance 51 4 139 1,020 7 2,702 Selected Option: Post Stratification Extrapolation Baseline 51 4 139 1,020 7 2,702 Post Compliance 51 4 139 1,020 7 2,702 Proposed/ NODA Option Baseline 293 12 885 5,328 8 14,493 Post Compliance 137 8 410 5,259 8 14,321 Directs + 413 to 433 Upgrade Baseline 51 4 139 1,020 7 2,702 Post Compliance 30 4 115 1,005 7 2,626 Directs + All to 433 Upgrade Baseline 5 4 139 1,020 7 2,702 Post Compliance 30 4 115 1,005 7 2,562 Source: U. S. EPA analysis.

I 42 MP& M EEBA: Appendices Table I. 14 presents MP& M pollutants that are estimated to upset POTW operations and contaminate sewage sludge. Appendix I: Environmental Assessment Table I. 14: Summary of Pollutants Estimated to Impact POTW Operations ( National basis)

Pollutant Selected Option:

Traditional Extrapolation Selected Option:

Post Stratification Extrapolation Proposed/ NODA Option Directs + 413 to 433 Upgrade Directs + All to 433 Upgrade Basea PCb Base PC Base PC Base PC Base PC Biological Inhibition (# of POTWs)

Acrolein 0 0 0 0 77 65 0 0 0 0 Arsenic 0 0 75 65 0 0 Benzoic acid 0 0 0 0 68 0 0 0 0 0 Bromo 2 chlorobenzene, 1 0 0 0 0 48 48 0 0 0 0 Bromo 3 chlorobenzene, 1 0 0 0 0 48 48 0 0 0 0 Chromium 30 30 30 30 81 7 30 27 30 27 Copper 27 27 27 27 142 0 27 27 27 27 Iron 0 0 0 0 65 32 0 0 0 0 Lead 39 39 39 39 150 81 39 30 39 30 Nickel 0 0 0 0 50 0 0 0 0 0 Silver 42 42 42 42 65 65 42 30 42 30 Zinc 0 0 0 0 16 0 0 0 0 0 Total Exceedances 139 139 139 139 885 410 139 115 139 115 Sludge Contamination (# of POTWs)

Lead 234 234 234 234 2,829 2,790 234 234 234 234 Mercury 0 0 0 0 118 118 0 0 0 0 Nickel 763 763 763 763 2,371 2,325 763 751 763 687 Arsenic 84 84 84 84 1,686 1,683 84 84 84 84 Cadmium 754 754 754 754 1,877 1,871 754 739 754 739 Copper 534 534 534 534 1,874 1,835 534 500 534 500 Zinc 224 224 224 224 2,132 2,132 224 209 224 209 Selenium 109 109 109 109 1,567 1,567 109 109 109 109 0 0 0 0 a Base = Baseline discharge level b PC = Post Compliance discharge level.

Source: U. S. EPA analysis.

b. Sewage sludge EPA estimated that baseline concentrations of seven metals at the national level fail to meet Land Application High limits for sludge disposal at 1,020 POTWs under the final regulatory alternatives. These concentrations were compared with the relevant metals concentration limits for the following sewage sludge management options: Land Application High ( Concentration Limits), Land Application Low ( Ceiling Limits), and Surface Disposal.

The Agency estimates that the final regulation will not eliminate metal concentrations in excess of sludge contamination criteria at any of these 1,020 POTW s, since indirect dischargers are exempted from the final rule. EPA estimated that 15 POTWs would be able to upgrade their sewage sludge disposal practices by meeting Land Application High sludge concentration limits under the 433 Upgrade Options. Under the Proposed/ NODA Option, 69 POTWs would be able to upgrade their sewage sludge disposal practices to Land Application High.

I 43 MP& M EEBA: Appendices Appendix I: Environmental Assessment GLOSSARY action levels: the existence of a contaminant concentration in the environment high enough to warrant implementation of drinking water treatment technology.

acute toxicity ( AT): the ability of a substance to cause severe biological harm or death soon after a single exposure or dose. Also, any poisonous effect resulting from a single short term exposure to a toxic substance ( See also: chronic toxicity).

( http:// www. epa. gov/ OCEPAterms/ aterms. html)

adsorption: removal of a pollutant from air or water by collecting the pollutant on the surface of a solid material; an advanced method of treating waste in which activated carbon removes organic matter from wastewater.

( http:// www. epa. gov/ OCEPAterms/ aterms. html)

adsorption coefficient ( Koc): represents the ratio of the target chemical adsorbed per unit weight of organic carbon in the soil or sediment to the concentration of that same chemical in solution at equilibrium.

alkalinity: the capacity of bases to neutralize acids ( e. g., adding lime to lakes to decrease acidity).

( http:// www. epa. gov/ OCEPAterms/ aterms. html)

ambient water quality criteria ( AWQC): levels of water quality expected to render a body of water suitable for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,

swimming, farming, fish production, or industrial processes. ( http:// www. epa. gov/ OCEPAterms/ aterms. html)

atm/ m3 mole: atmosphere per cubic meter mole ( see also: mole).

benthic: relating to the bottom of a body of water; living on, or near, the bottom of a water body.

( http:// www. ucmp. berkeley. edu/ glossary/ gloss5ecol. html)

bioconcentration factor ( BCF): indicator of the potential for a chemical dissolved in the water column to be taken up by aquatic biota across external surface membranes, usually gills.

BIODEG: a web based biodegradation database developed by Syracuse Research Corporation.

( http:// esc. syrres. com/ efdb/ biodgsum. htm)

biodegradation: a process whereby organic molecules are broken down by microbial metabolism.

biodegradation half life: represents the number of days a compound takes to be degraded to half of its starting concentration under prescribed laboratory conditions.

biological oxygen demand ( BOD): the amount of dissolved oxygen consumed by microorganisms as they decompose organic material in an aquatic environment.

cancer potency slope factors ( SFs): a plausible upper bound estimate of the probability of a response per unit intake of a chemical over a lifetime. The slope factor is used to estimate an upper bound probability of an individual developing cancer as a result of a lifetime of exposure to a particular level of a potential carcinogen.

carcinogens: chemicals that EPA believes can cause or have the potential to cause tumors or cancers in humans, either directly or indirectly.

CHEMFATE: a web based chemical fate database developed by Syracuse Research Corporation.

( http:// esc. syrres. com/ efdb/ Chemfate. htm)

chronic toxicity ( CT): the capacity of a substance to cause long term toxic or poisonous health effects in humans, animals,

fish, and other organisms ( see also: acute toxicity). ( http:// www. epa. gov/ OCEPAterms/ cterms. html)

critical dilution factors ( CDFs): express the relationship between a point source loading and the resulting concentration at the edge of the mixing zone. Typically, this is expressed as a ratio of parts receiving water to one part effluent.

I 44 MP& M EEBA: Appendices Appendix I: Environmental Assessment dissolved concentration potentials ( DCPs): represents the concentration of a nonreactive dissolved substance under well mixed, steady state conditions given an annual load of 10,000 tons.

dry metric tons ( DMT): dry measure is a system of units for measuring dry commodities. 1 DMT= 1,000 kilogram.

EC1: the concentration at which one percent of the test organisms show a significant sub lethal response.

EC5: the concentration at which five percent of the test organisms show a significant sub lethal response.

Environmental Research Laboratory Duluth fathead minnow database: a database developed by EPA's Mid Continent Ecology Division ( MED) which provides data on the acute toxicity of hundreds of industrial organic compounds to the fathead minnow. ( http:// www. eoa. gov/ med/ databases/ fathead_ minnow. html)

GAGE: a U. S. Geological Survey stream flow database. The database contains stream flow data and drainage area measurement from all U. S. Geological Survey flow gages.

hazardous air pollutant ( HAP): air pollutants that are not covered by ambient air quality standards but which, as defined in the Clean Air Act, may present a threat of adverse human health effects or adverse environmental effects ( e. g., beryllium,

mercury, ethylbenzene, chloroethane, and doxane). ( http:// www. epa. gov/ OCEPAterms/ hterms. html)

Health Effects Assessment Summary Tables ( HEAST): a comprehensive listing of provisional human health risk assessment data relative to oral and inhalation routes for chemicals of interest to EPA. Unlike data in IRIS, HEAST entries have received insufficient review to be recognized as high quality, Agency wide consensus information ( U. S. EPA. 1997.

Health Effects Assessment Table; FY 1997 Update. EPA 540 R 97 036).

Henry's Law ( H): chemical law stating that the amount of a gas that dissolves in a liquid is proportional to the partial pressure of the gas over the liquid, provided no chemical reaction takes place between the liquid and the gas. The law is named after William Henry ( 1774 1836), the English chemist who first reported the relationship. ( www. infoplease. com)

human health based water quality criteria ( WQC): levels of water quality expected to render a body of water suitable for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking, swimming, farming, fish production, or industrial processes. ( http:// www. epa. gov/ OCEPAterms/ wterms. html)

Integrated Risk Information System ( IRIS): IRIS is an electronic database with information on human health effects of various chemicals. IRIS provides consistent information on chemical substances for use in risk assessments, decision making,

and regulatory activities.

LC50 ( Lethal Concentration): a standard measure of toxicity that tells how much of a substance is needed to kill half of a group of experimental organisms in a given time ( see also: LD 50). ( http:// www. epa. gov/ OCEPAterms/ lterms. html)

LD50 ( Lethal Dose): the dose of a toxicant or microbe that will kill 50 percent of the test organisms within a designated period. The lower the LD 50, the more toxic the compound.

l/ kg: liter per kilogram Lowest Observed Effect Concentration ( LOEC): the lowest level of pollutant concentration that causes statistically and biologically significant differences in test samples as compared to other samples subjected to no stressor.

( http:// www. epa. gov/ OCEPAterms)

Maximum Allowable Toxicant Concentration ( MATC): for a given ecological effects test, the range ( or geometric mean) between the No Observable Adverse Effect Level and the Lowest Observable Adverse Effects Level.

( http:// www. epa. gov/ OCEPAterms/ mterms. html)

maximum contaminant levels ( MCLs): the maximum permissible level of a contaminant in water delivered to any user of a public system. MCLs are enforceable standards. ( http:// www. epa. gov/ OCEPAterms/ mterms. html)

mg/ kg: milligram per kilogram I 45 MP& M EEBA: Appendices Appendix I: Environmental Assessment   g/ l: microgram per liter mole: the amount of substance that contains Avogardo s number of atoms, molecules or other elementary units.

National Estuarine Inventory ( NEI): The National Estuarine Inventory is a series of inter related activities that define,

characterize, and assess the nation's estuarine systems. NEI data are compiled in a systematic and consistent manner that enables the nation's estuaries to be compared and assessed according to their environmental quality, economic values, and resource uses. A principal feature of the NEI is the determination of the physical dimensions and hydrologic features of estuarine systems of the United States which are primary determinants of estuarine processes and ultimately affect the ecology of a system.

National Oceanic and Atmospheric Administration ( NOAA): organization within the Bureau of Commerce that conducts research and gathers data about the global oceans, atmosphere, space, and sun.

No Observed Effect Concentration ( NOEC): exposure level at which there are no statistically or biologically significant differences in the frequency or severity of any effect in the exposed or control populations.

( http:// www. epa. gov/ OCEPAterms/ nterms. html)

oil and grease ( O& G): organic substances that may include hydrocarbons, fats, oils, waxes, and high molecular fatty acids. Oil and grease may produce sludge solids that are difficult to process. ( http:// www. epa. gov/ owmitnet/ reg. htm)

organic carbon ( OC): carbon in compounds derived from living organisms.

partition factor: a chemical specific value representing the fraction of the load expected to partition to sewage sludge during wastewater treatment.

Permit Compliance System ( PCS): a computerized database of information on water discharge permits, designed to support the National Pollutant Discharge Elimination System ( NPDES).

( http:// www. epa. gov/ ceisweb1/ ceishome/ ceisdocs/ pcs/ pcs exec. htm)

pH: an expression of the intensity of the basic or acid condition of a liquid; natural waters usually have a pH between 6.5 and 8.5. ( http:// www. epa. gov/ OCEPAterms/ pterms. html)

pollutants of concern ( POCs): the 150 contaminants identified by EPA as being of potential concern for this rule and which are currently being discharged by MP& M facilities.

Premanufacture Notices ( PMN): a notice, required by Section 5 of TSCA, that must be submitted to EPA by anyone who plans to manufacture or import a new chemical substance for a non exempt commercial distribution. The notice must be submitted at least 90 days prior to the manufacture or import of the chemical.

( http:// www. epa. gov/ oppt/ newchems/ index. htm)

priority pollutant ( PP): 126 individual chemicals that EPA routinely analyzes when assessing contaminated surface water,

sediment, groundwater, or soil samples. These chemicals are also known as toxic pollutants.

quantitative structure activity relationship ( QSAR): an expert system that uses a large database of measured physicochemical properties, such as melting point, vapor pressure, and water solubility, to estimate the fate and effect of a specific chemical based on its molecular structure. ( http:// www. epa. gov/ med/ databases/ aster. html)

reference doses ( RfDs): RfDs represent chemical concentrations  expressed in mg of pollutant/ kg body weight/ day

which, if not exceeded, are expected to protect an exposed population, including sensitive groups such as young children or pregnant women.

Secondary Maximum Contaminant Levels ( SMCLs): non enforceable water treatment levels applying to public water systems and specifying the maximum contamination levels that, in the judgment of EPA, are required to protect the public welfare. These treatment levels apply to any contaminants that may adversely affect the odor or appearance of such water and consequently may cause people served by the system to discontinue its use.

I 46 MP& M EEBA: Appendices Appendix I: Environmental Assessment suspended solids: small particles of solid pollutants that float on the surface of, or are suspended in, sewage or other liquids. They resist removal by conventional means.

Superfund Chemical Data Matrix ( SCDM): a source for factor values and benchmark values applied when evaluating potential National Priorities List ( NPL) sites using the Hazard Ranking System ( HRS).

( http:// www. epa. gov/ superfund/ resources/ scdm/ index. htm)

systemic toxicants: chemicals that EPA believes can cause significant non carcinogenic health effects when present in the human body above chemical specific toxicity thresholds.

total Kjeldahl nitrogen ( TKN): TKN is defined as the total of organic and ammonia nitrate. It is determined in the same manner as organic nitrogen, except that the ammonia is not driven off before the digestion step.

total organic carbon ( TOC): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by tests for " total suspended non filterable solids" ( see also: suspended solids).

total petroleum hydrocarbons ( TPH): a general measure of the amount of crude oil or petroleum product present in an environmental media ( e. g. soil, water, or sediments). While it provides a measure of the overall concentration of petroleum hydrocarbons present, TPH does not distinguish between different types of petroleum hydrocarbons.

total suspended particles ( TSP): method of monitoring airborne particulate matter by total weight.

( http:// www. epa. gov/ OCEPAterms/ tterms. html)

total suspended solids ( TSS): a measure of the suspended solids in wastewater, effluent, or water bodies, determined by tests for " total suspended non filterable solids" ( see also: suspended solids).

United States Geological Survey ( USGS): a governmental organization that provides reliable scientific information to:

describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological,

energy, and mineral resources; and enhance and protect our quality of life. ( www. noaa. gov)

volatilization: a process whereby chemicals dissolved in water escape into the air.

I 47 MP& M EEBA: Appendices Appendix I: Environmental Assessment ACRONYMS AQUIRE: AQUatic Information REtrieval System ASTER: ASsessment Tools for the Evaluation of Risk AT: acute toxicity AWQC: ambient water quality criteria BCF: bioconcentration factor BOD: biological oxygen demand CDF: critical dilution factor CT: chronic toxicity DCP: dissolved concentration potential DMT: dry metric tons H: Henry's Law HAP: hazardous air pollutant HEAST: Health Effects Assessment Summary Tables IRIS: Integrated Risk Information System Koc: adsorption coefficient LOEC: Lowest Observed Effect Concentration MATC: Maximum Allowable Toxicant Concentration MCL: maximum contaminant level NEI: National Estuarine Inventory NOAA: National Oceanic and Atmospheric Administration NOEC: No Observed Effect Concentration O& G: oil and grease OC: organic carbon PCS: Permit Compliance System PMN: Premanufacture Notices POC: pollutant of concern PP: priority pollutant QSAR: quantitative structure activity relationship RBC: EPA s Region III Risk Based Concentration Table RfD: reference dose SCDM: Superfund Chemical Data Matrix SF: cancer potency slope factor SMCL: Secondary Maximum Contaminant Level TKN: total Kjeldahl nitrogen TOC: total organic carbon TPH: total petroleum hydrocarbons TSP: total suspended particulates TSS: total suspended solids USGS: United States Geological Survey WQC: water quality criteria I 48 MP& M EEBA: Appendices Appendix I: Environmental Assessment REFERENCES Arthur D. Little. 1983. Evaluation of waterborne exposure pathways to paragraph 4( c) pollutants. Draft Report, April 28.

Also: Paragraph 4( c) list of detected chemicals.

Arthur D. Little. 1986. Bioaccumulation Study.

Birge, W. J. et al. 1979. Aquatic toxicity tests on inorganic elements occurring in oil shale. Oil Shale Symposium

Sampling, Analysis and Quality Assurance, March. EPA 600/ 9 80 022.

Clay, D. R. 1986. Office of Toxic Substances, U. S. Environmental Protection Agency. Memorandum to J. M. Conlon,

OWRS, U. S. Environmental Protection Agency.

Holdway, D. A. and J. B. Spraque. 1979.   Chronic toxicity of vanadium to flagfish.  Water Research 13: 905 910.

Howard, P. H., ed. 1991. Handbook of Environmental Degradation Rates. Chelsea. MI: Lewis Publishers, Inc.

ICF, Inc. 1985. Superfund Public Health Evaluation Manual  Draft.

Leblanc, G. A. 1980.   Acute toxicity of priority pollutants to water flea ( Daphnia magna).  Bull Environmental Contamination Toxicology 24: 684 691.

Lyman, W. J.; W. F. Reehl, and D. H. Rosenblatt. 1981. Handbook of Chemical Property Estimation Methods

Environmental Behavior of Organic Compounds. New York, NY: McGraw Hill, Inc. ( reported or estimated using methods outlined).

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Syracuse Research Corporation.

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U. S. Environmental Protection Agency ( U. S. EPA). 1972. " Blue Book , NAS NAE ( Water Quality Criteria  1972).

Washington, DC: U. S. EPA. EPA R3 73 033. If the code @ AA is provided with this reference, the value is an estimate based on application factors described in the " Blue Book."

U. S. Environmental Protection Agency ( U. S. EPA). 1976.   Red Book  ( Quality Criteria For Water). Washington, DC: U. S.

EPA.

U. S. Environmental Protection Agency ( U. S. EPA). 1980. Ambient water quality criteria documents. Washington, DC:

Office of Water, U. S. EPA. EPA 440/ 5 80 Series. Also refers to any update of criteria documents ( EPA 440/ 5 85 and EPA 440/ 5 87 Series) or any Federal Register notices of proposed criteria or criteria corrections. The most recent National Recommended Water Quality Criteria used in this report were published in the Federal Register on December 10, 1998.

U. S. Environmental Protection Agency ( U. S. EPA). 1984. Summary of current oral Acceptable Daily Intakes ( ADIs) for systemic toxicants. Cincinnati, Ohio: Environmental Criteria and Assessment Office, U. S. EPA, May.

U. S. Environmental Protection Agency ( U. S. EPA). 1985. Report to Congress on the Discharge of Hazardous Wastes to Publicly Owned Treatment Works ( Domestic Sewage Study). Office of Water Regulations and Standards. Washington, DC:

U. S. EPA.

U. S. Environmental Protection Agency ( U. S. EPA). 1987. Guidance Manual for Preventing Interference at POTWs.

Washington, DC: U. S. EPA U. S. Environmental Protection Agency ( U. S. EPA). 1990. CERCLA Site Discharges to POTWs: Guidance Manual.

Washington, DC: Office of Emergency and Remedial Response EPA/ 540/ G 90/ 005.

I 49 MP& M EEBA: Appendices Appendix I: Environmental Assessment U. S. Environmental Protection Agency ( U. S. EPA). 1997. Health Effects Assessment Summary Tables ( HEAST).

Washington, DC: Office of Research and Development and Office of Emergency and Remedial Response. U. S. EPA.

U. S. Environmental Protection Agency ( U. S. EPA). 1998. Risk Based Concentration Table, October. Philadelphia, PA:

Region III, U. S. EPA.

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U. S. EPA.

U. S. Environmental Protection Agency ( U. S. EPA). 1998/ 99a. Aquatic Toxicity Information Retrieval ( AQUIRE) Data Base. Duluth, MN: Environmental Research Laboratory, U. S. Environmental Protection Agency. 1998 Database retrieval.

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Versar. 1992. Upgrade of Flow Statistics Used to Estimate Surface Water Chemical Concentrations for Aquatic and Human Exposure Assessment. Prepared for the Office of Pollution Prevention and Toxics, U. S. EPA.

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Zhang, R. and Zhang, S. 1982.   Toxicity of Fluorides to Fishes.  C. A. Selects  Environm Pollut 24, 97: 176354k.

I 50 MP& M EEBA: Appendices Appendix J: Spacial Distribution of MP& M Facilities and Recreational User Populations Appendix J: Spacial Distribution of MP& M Facilities and Recreational User Populations INTRODUCTION This appendix compares the national distribution of all MP& M facilities by state and the national distribution of recreational participants by state ( see Table J. 1 and Figure J. 1).

EPA based the distribution of MP& M facilities by state on Census data on total numbers of facilities in the SICs that make up the MP& M industries, not just water dischargers. This comparison assumes that the state distribution of water discharging MP& M facilities is the same as the overall distribution of MP& M facilities. APPENDIX CONTENTS Table J. 1 Distribution of MP& M Facilities and Participants of Water Based Recreation by State ..... J 2 Figure J. 1 Cumulative Distribution of Facilities and Participants ................. ... J 4 EPA based the distribution of recreational participants by state and by type of recreation activity on information provided by the National Demand Study data. This comparison suggests that the reaches that benefit from the final rule are also those where a very large percentage of all recreational participants reside and recreate.

J 1 MP& M EEBA: AppendicesAppendix J: Spacial Distribution of MP& M Facilities and Recreational User Populations J 2 Table J. 1: Distribution of MP& M Facilities and Participants of Water based Recreation by State State Percent of State Population Participating by Activity Average # of Per Person Trips per Season by Activity Total State Pop. ( 1990)

( Millions) Potential ( Extrapolated) # Participants Based on State Population Nat.  l # of MP& M Facilities State % of National Facilities Cum. ST % of Facilities Cumulative Percent Distribution of Participants by State BoatViewFishSwimBoatViewFishSwimBoatViewFishSwimBoatViewFishSwim CA11.7% 36.9% 13.6% 20.1% 5.4147.111.729.83,490,51310,992,8494,057,1545,983,73668,35911.9% 11.9% 10.3% 19.5% 9.4% 13.8%

TX12.8% 16.4% 18.9% 14.5% 7.2510.66.517.02,171,7912,792,3033,205,9782,456,19238,1766.6% 18.5% 16.7% 24.5% 16.8% 19.4%

NY12.4% 25.6% 11.2% 20.5% <IP_ADDRESS>.718.02,231,3744,602,2092,022,1833,695,71436,3296.3% 24.8% 23.3% 32.6% 21.5% 27.9%

FL18.7% 32.6% 20.5% 24.2% 10.117.917.115.412.92,423,4184,221,4382,657,9433,126,99130,1985.2% 30.0% 30.5% 40.1% 27.7% 35.1%

IL11.8% 17.2% 14.6% 9.4% 9.6913.75.711.41,349,1051,962,3351,667,9851,079,28428,3434.9% 34.9% 34.5% 43.6% 31.5% 37.6%

OH11.5% 15.8% 14.2% 14.0% <IP_ADDRESS>0.81,251,5901,718,8511,535,2841,518,59626,4604.6% 39.5% 38.2% 46.6% 35.1% 41.1%

PA10.5% 14.4% 15.2% 13.7% 9.47.410.9811.91,249,0141,713,3911,809,4691,633,32626,2374.6% 44.1% 41.9% 49.7% 39.3% 44.8%

MI16.0% 24.8% 18.4% 20.8% 8.69.4128.59.31,484,6652,307,6871,710,5931,936,52023,6624.1% 48.2% 46.3% 53.8% 43.2% 49.3%

NJ15.9% 32.3% 15.9% 23.9% <IP_ADDRESS>.37.71,225,2462,495,0461,225,2461,849,00819,8053.4% 51.6% 49.9% 58.2% 46.1% 53.5%

NC8.8% 17.9% 16.5% 13.5% <IP_ADDRESS>.46.6586,3171,188,9201,091,201895,76215,1582.6% 54.3% 51.6% 60.3% 48.6% 55.6%

IN14.3% 15.0% 20.3% 16.3% 7.79<IP_ADDRESS>94,663831,6241,127,312905,54614,6562.5% 56.8% 54.0% 61.8% 51.2% 57.7%

MA15.7% 30.9% 15.7% 28.9% 8.711.6<IP_ADDRESS>2,3321,860,501942,3321,739,68913,9152.4% 59.2% 56.8% 65.1% 53.4% 61.7%

WI15.7% 22.1% 18.1% 19.7% <IP_ADDRESS>.9768,9401,079,788883,463965,26613,8452.4% 61.6% 59.0% 67.0% 55.4% 63.9%

GA11.5% 13.9% 16.6% 11.5% <IP_ADDRESS>.46.5746,819903,1291,076,808746,81913,7472.4% 64.0% 61.2% 68.6% 57.9% 65.6%

MO13.0% 12.6% 18.8% 15.2% 5.24585.1665,035646,562960,606775,87413,3952.3% 66.3% 63.2% 69.8% 60.1% 67.4%

VA13.4% 17.0% 16.2% 13.4% <IP_ADDRESS>.2827,1021,049,7831,002,066827,10212,8292.2% 68.6% 65.7% 71.6% 62.5% 69.3%

WA25.0% 39.2% 18.8% 25.9% 5.811.7<IP_ADDRESS>,216,6731,907,623916,2601,261,73511,9912.1% 70.6% 69.3% 75.0% 64.6% 72.2%

MN17.6% 19.6% 19.6% 17.6% 5.416.5<IP_ADDRESS>67,875857,162857,162767,87511,2722.0% 72.6% 71.5% 76.5% 66.6% 73.9%

TN17.9% 13.5% 22.6% 14.5% 7.53.7<IP_ADDRESS>73,280659,0791,103,957708,51010,8081.9% 74.5% 74.1% 77.7% 69.1% 75.6%

MD14.8% 18.7% 17.1% 12.1% 8.8<IP_ADDRESS>.8706,988893,037818,617576,7538,9931.6% 76.0% 76.2% 79.3% 71.0% 76.9%

AL14.7% 11.9% 20.6% 13.8% <IP_ADDRESS>0.64.0593,114481,905834,066556,0448,8251.5% 77.6% 77.9% 80.1% 72.9% 78.2%

CT16.4% 37.1% 14.5% 27.0% <IP_ADDRESS>2.33.3537,5161,219,747475,495888,9698,5931.5% 79.1% 79.5% 82.3% 74.0% 80.2%

LA16.4% 15.3% 27.0% 13.8% 43.4<IP_ADDRESS>92,165647,5091,138,723580,5258,5001.5% 80.5% 81.6% 83.4% 76.7% 81.5%

CO6.6% 13.2% 25.9% 11.3% 17.214.8<IP_ADDRESS>17,554435,109854,678372,9508,2311.4% 82.0% 82.2% 84.2% 78.7% 82.4%

OR20.3% 37.8% 24.9% 23.0% <IP_ADDRESS>.42.8576,3231,074,057707,306654,9137,9781.4% 83.3% 83.9% 86.1% 80.3% 83.9%

KY11.9% 12.3% 22.4% 10.0% 6.539.417.53.7437,524454,352824,564370,2127,8221.4% 84.7% 85.2% 86.9% 82.2% 84.8%

AZ7.3% 11.2% 11.8% 10.7% 7.2<IP_ADDRESS>67,685411,823432,415391,2327,7991.4% 86.1% 86.0% 87.7% 83.2% 85.7%

IA13.5% 16.4% 18.7% 13.5% 54.4<IP_ADDRESS>73,482454,673519,627373,4827,6611.3% 87.4% 87.1% 88.5% 84.4% 86.5%

OK11.2% 12.6% 25.2% 14.0% 4.93.4<IP_ADDRESS>1,954395,948791,896439,9426,9721.2% 88.6% 88.2% 89.2% 86.2% 87.5% MP& M EEBA: AppendicesAppendix J: Spacial Distribution of MP& M Facilities and Recreational User Populations Table J. 1: Distribution of MP& M Facilities and Participants of Water based Recreation by State State Percent of State Population Participating by Activity Average # of Per Person Trips per Season by Activity Total State Pop. ( 1990)

( Millions) Potential ( Extrapolated) # Participants Based on State Population Nat.  l # of MP& M Facilities State % of National Facilities Cum. ST % of Facilities Cumulative Percent Distribution of Participants by State BoatViewFishSwimBoatViewFishSwimBoatViewFishSwimBoatViewFishSwim J 3 SC13.8% 19.9% 26.0% 15.5% 9.88.5<IP_ADDRESS>81,589693,488905,387539,3806,9071.2% 89.8% 89.6% 90.4% 88.3% 88.8%

KS6.7% 17.0% 18.5% 13.3% 17.69<IP_ADDRESS>65,172422,105458,810330,3436,3701.1% 90.9% 90.1% 91.1% 89.4% 89.5%

AR14.1% 12.5% 28.1% 18.0% 4.6<IP_ADDRESS>.4330,571293,841661,141422,3965,8251.0% 91.9% 91.1% 91.7% 90.9% 90.5%

MS13.6% 12.1% 23.6% 15.7% 6.324.217.412.92.6349,222312,462606,544404,3635,1650.9% 92.8% 92.1% 92.2% 92.3% 91.4%

NE10.7% 15.5% 10.7% 15.5% <IP_ADDRESS>.91.6169,113244,274169,113244,2744,4240.8% 93.6% 92.6% 92.7% 92.7% 92.0%

UT8.1% 17.1% 13.5% 12.6% <IP_ADDRESS>.81.7139,691294,902232,818217,2963,6330.6% 94.2% 93.0% 93.2% 93.3% 92.5%

WV9.5% 10.3% 18.3% 15.9% 6.64.6<IP_ADDRESS>70,807185,041327,381284,6793,4420.6% 94.8% 93.5% 93.5% 94.0% 93.1%

RI15.8% 40.4% 19.3% 36.8% <IP_ADDRESS>1.0158,442404,907193,651369,6973,1060.5% 95.3% 94.0% 94.2% 94.5% 94.0%

ME22.2% 44.4% 27.8% 37.5% 7.65.710.510.31.2272,873545,746341,091460,4732,9800.5% 95.9% 94.8% 95.2% 95.3% 95.1%

NH18.8% 31.2% 14.1% 34.4% 3.314.9<IP_ADDRESS>7,985346,641155,989381,3052,9600.5% 96.4% 95.4% 95.8% 95.6% 95.9%

NM6.7% 8.6% 12.4% 9.5% <IP_ADDRESS>.81.5101,005129,863187,580144,2922,9270.5% 96.9% 95.7% 96.0% 96.1% 96.3%

ID24.1% 25.3% 20.5% 20.5% 5.84.3<IP_ADDRESS>2,590254,720206,202206,2022,5720.4% 97.3% 96.4% 96.5% 96.5% 96.7%

NV17.3% 21.3% 13.3% 12.0% 4.87.3<IP_ADDRESS>8,318256,391160,244144,2202,4060.4% 97.7% 97.0% 96.9% 96.9% 97.1%

MT14.5% 20.0% 34.5% 29.1% 7.815.6<IP_ADDRESS>16,228159,813276,041232,4552,2040.4% 98.1% 97.4% 97.2% 97.5% 97.6%

SD16.7% 21.4% 16.7% 21.4% 2.31.8670.7116,001149,144116,001149,1442,0490.4% 98.5% 97.7% 97.5% 97.8% 97.9%

ND15.0% 15.0% 25.0% 15.0% 3.734.511.50.695,82095,820159,70095,8201,7490.3% 98.8% 98.0% 97.7% 98.2% 98.2%

HI16.4% 58.2% 18.2% 47.3% 6.733.96.615.51.1181,347644,788201,496523,8901,6770.3% 99.1% 98.5% 98.8% 98.7% 99.4%

VT20.6% 17.6% 8.8% 20.6% <IP_ADDRESS>0.40.6115,86299,31049,655115,8621,4880.3% 99.3% 98.9% 99.0% 98.8% 99.6%

DE15.7% 41.2% 15.7% 13.7% 6.41<IP_ADDRESS>04,497274,305104,49791,4351,3790.2% 99.6% 99.2% 99.5% 99.0% 99.8%

WY19.4% 16.1% 48.4% 6.5% <IP_ADDRESS>.587,79173,159219,47829,2641,3090.2% 99.8% 99.4% 99.6% 99.5% 99.9%

AK34.5% 41.4% 37.9% 6.9% <IP_ADDRESS>0.6189,670227,604208,63737,9341,1560.2% 100.0% 100.0% 100.0% 100.0% 100.0%

Source: mation on total MP& M facilitiesby state is from Census data; information on where recreating people live is from NDS data.

Infor MP& M EEBA: Appendices Appendix J: Spacial Distribution of MP& M Facilities and Recreational User Populations Figure J. 1: Cumulative Distribution of Facilities and Participants a The numbers refer to states in the order they appear in the above table. Therefore, 1 is California, 2 is Texas, 3 is New York, etc.

Sources: Information on total MP& M facilities by state is from Census data; information on where recreating people live is from NDS data.

J 4 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer INTRODUCTION EPA identified eight surface water evaluation studies that quantified the effects of water quality improvements on various water based recreational activities. As noted in Chapter 15 of this report, the Agency selected these studies based on technical criteria for evaluating study transferability ( Desvousges et al., 1987; Desvousges et al.,

1992; and B oyle and Bergstrom, 1992), including the following:

  The environmental change valued at the study site must be the same as the environmental quality change caused by the rule ( e. g., changes in toxic contamination vs. changes in nutrient concentrations);

  The populations affected at the study site and at the policy site must be the same ( e. g.,

recreational users vs nonusers);

  The assignment of property rights at both the study and policy sites must lead to the same Appendix K: Selecting WTP Values for Benefits Transfer APPENDIX CONTENTS K. 1 Desvousges et al., 1987.

for Water Quality Improvements: A Contingent Valuation Study for the Monongahela River ...... K 2 K. 2 Farber and Griner, 2000. ng Watershed Quality Improvements Using Conjoint Analysis ........... K 3 K. 3 Jakus et al., 1997. portfish Consumption Advisories Affect Reservoir Anglers  Site Choice? . . K 5 K. 4 Lant and Roberts, 1990. reenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure ................. ............. K 6 K. 5 Audrey Lyke, 1993. rete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case Study ................. ................ K 7 K. 6 Montgomery and Needelman, 1997. re Effects of Toxic Contamination in Freshwater Fish . . K 8 K. 7 Phaneuf et al., 1998. Water Quality Improvements Using Revealed Preference Methods When Corner Solutions are Present  ............. K 8 Glossary ................. ................. .... K 10 Acronyms ................. ................. ... K 11 References ................. ................. .. K 12 Option Price Estimates Valui Do S G Disc The Welfa   Valuing theoretically appropriate welfare measure ( e. g., willingness to pay ( WTP) vs. willingness to accept compensation); and   The candidate studies should be based on defensible research methods. Six of the eight studies are published in peer reviewed journals. One study, Tudor et al. ( 2002), was presented at the annual American Agricultural Economic Association and the Northeastern Resource and Environmental Economic meetings. 1 The eighth study, Lyke ( 1993),

is an unpublished Ph. D. dissertation.

In addition to the above criteria, the Agency considered authors' recommendations regarding the robustness and theoretical soundness of various estimates in selecting point estimates for benefits transfer.

The rest of this appendix presents welfare estimates from seven studies used in estimating recreational benefits from the final regulation and provides EPA s reasons for selecting specific values from each study. The study by Tudor et al. ( 2002) is discussed in detail in Chapter 21. All welfare estimates from that study are eligible for use in benefits transfer, because the study is based on the policy scenarios specific to the MP& M regulation.

1 Preliminary results of this study were presented at the annual American Agricultural Economic Association meeting ( L. Tudor et al., 1999a) and at the annual Northeastern Agricultural and Resource Economic Association meeting ( L. Tudor et al., 1999b). EPA subjected this study to a formal peer review by experts in the natural resource valuation field. The peer review concluded that EPA had done a competent job, especially given the available data. This study can be found in Chapter 21. The peer review report is in the docket for the rule.

K 1 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer K. 1 DESVOUSGES ET AL., 1987. OPTION PRICE ESTIMATES FOR WATER QUALITY IMPROVEMENTS: A CONTINGENT VALUATION STUDY FOR THE MONONGAHELA RIVER This study used findings from a contingent valuation ( CV) survey to estimate WTP for improved recreational fishing from enhanced water quality in the Pennsylvania portion of the Monongahela River. In a hypothetical market, each survey respondent was asked to provide an option price for different water quality changes, such as " raising the water quality from suitable for boating ( hereafter,   boatable  water) to a level where gamefish would survive ( hereafter,   fishable  water)." Table K. 1 lists water quality changes evaluated in the study and the corresponding WTP estimates. The following discussion provides justification for selecting the point estimates EPA used in the benefits transfer analysis in Chapter 15.

Table K. 1: Changes in the Resource Value from a Specified Water Quality Improvement from Desvousges et al.

Water Quality Change Valued Adjusted to 2001$ b Original Estimates ( 1981$)

User Nonuser Combined User Nonuser Combined Iterative Bidding: $ 25 starting point Unsuitable to Boatable $ 53.4 $ 57.8 $ 56.4 $ 27.4 $ 29.7 $ 29.0 Boatable to Fishablea $ 36.8 $ 28.3 $ 30.9 $ 18.9 $ 14.5 $ 15.9 Fishable to Swimmable $ 23.0 $ 14.0 $ 16.9 $ 11.8 $ 7.2 $ 8.7 Boatable to Swimmable $ 62.5 $ 42.2 $ 48.9 $ 32.1 $ 21.7 $ 25.1 Unsuitable to Swimmable $ 115.9 $ 100.0 $ 105.3 $ 59.5 $ 51.4 $ 54.1 Iterative Bidding: $ 125 starting point Unsuitable to Boatable $ 184.4 $ 75.6 $ 111.7 $ 94.7 $ 38.8 $ 57.4 Boatable to Fishable $ 113.1 $ 51.2 $ 71.9 $ 58.1 $ 26.3 $ 36.9 Fishable to Swimmable $ 64.4 $ 22.5 $ 36.6 $ 33.1 $ 11.6 $ 18.8 Boatable to Swimmable $ 194.1 $ 78.9 $ 117.3 $ 99.7 $ 40.5 $ 60.2 Unsuitable to Swimmable $ 378.5 $ 154.2 $ 229.0 $ 194.4 $ 79.2 $ 117.6 Direct Question: no payment card Boatable to Unsuitable $ 88.2 $ 27.6 $ 47.7 $ 45.3 $ 14.2 $ 24.5 Boatable to Fishable $ 60.9 $ 21.0 $ 34.2 $ 31.3 $ 10.8 $ 17.6 Fishable to Swimmable $ 39.3 $ 16.6 $ 24.1 $ 20.2 $ 8.5 $ 12.4 Boatable to Swimmable $ 103.0 $ 39.5 $ 60.7 $ 52.9 $ 20.3 $ 31.2 Unsuitable to Swimmable $ 191.2 $ 67.2 $ 108.4 $ 98.2 $ 34.5 $ 55.7 Direct Question: payment card Boatable to Unsuitable $ 91.1 $ 103.2 $ 99.3 $ 46.8 $ 53.0 $ 51.0 Boatable to Fishable $ 88.2 $ 42.6 $ 57.1 $ 45.3 $ 21.9 $ 29.3 Fishable to Swimmable $ 44.5 $ 15.0 $ 24.3 $ 22.9 $ 7.7 $ 12.5 Boatable to Swimmable $ 138.6 $ 58.3 $ 83.6 $ 71.2 $ 29.9 $ 42.9 Unsuitable to Swimmable $ 229.6 $ 161.3 $ 182.8 $ 117.9 $ 82.8 $ 93.9 ( 1987)

Location: Pennsylvania portion of the Monongahela River Estimating Approach: CV Survey Population : Recreational Users and Nonusers a The value selected for benefits transfer is given in bold.

b WTP values from the original study are adjusted to 2001$ based on the Consumer Price Index ( CPI).

Source: Desvousges et al., 1987.

EPA judged that only one value from this study met the requirements for the quality of research methods and was compatible with the environmental changes and population characteristics considered in the analysis of recreational benefits from the MP& M rule. EPA selected this value for the following reasons:

K 2 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer   Environmental quality change. The Desvousges et al. ( 1987) study derived WTP values for five different changes in water quality, as shown in Table K. 1 above. EPA judged that only one of these improvements, from   boatable  to   fishable,  is compatible with the changes in water quality expected under the MP& M rule. Streams unsuitable for recreational activities such as boating are likely to be affected by multiple environmental stressors from many sources, including many that are not related to MP& M discharges ( e. g., severe oxygen depletion.) In these cases, it is reasonable to assume that changes in concentrations of MP& M pollutants would reduce or eliminate one of the stressors on the reach, but would be unlikely to change the designation of the reach.

The analysis in Chapter 15 assumes that reaches with ambient water quality criteria ( AWQC) exceedances under the baseline conditions are boatable and likely to support rough fishing, but may not be clean enough to support gamefishing. AWQC are set at a level below which pollutant concentrations are not expected to cause significant harm to human health or aquatic life. Exposure to pollutant concentrations above the AW QC levels are expected to have a harmful effect. Therefore, by definition, water with pollutant levels that exceed criteria set to protect human health or aquatic life are not suitable waters for sensitive aquatic species or ideal as a sources of fish for consumption.

Removing AWQC exceedances is therefore comparable to shifting water quality from " boatable" to " fishable." The Agency did not use the boatable to swimmable designation because a more limited number of reaches are suitable for swimming nationally due to reasons not related to MP& M discharges ( e. g., amenities, pathogens). Determining national level locations affected by MP& M pollutants that are suitable for swimming required more resources than were available for the national analysis.

  Research methods. The authors used four different payment vehicles in their CV study. For the recreational benefits analysis, EPA decided to use the WTP estimates derived from the   iterative bidding  ( IB) payment vehicle, because it is universally preferred to the   direct question/ open ended  format for eliciting option price bids.

Survey respondents in the direct question format are asked to state the most that they would be willing to pay for the program or policy. This format confronts respondents with an unfamiliar choice. Studies that use this approach usually have high non response rates.

Respondents in the IB format are asked whether they would be willing to pay a given amount. If the answer is yes,

then this amount is raised in pre set increments until the respondent says that he or she will not pay the last amount given. If the answer is no, then the amount is decreased until the respondent indicates WTP the stated amount.

Some studies found that the respondent s final WTP amount depends on the initial amount offered. This problem is referred to in economic literature as starting point bias. The Agency selected the WTP estimates derived using the $ 25 starting point IB process to avoid upward starting point bias. Table K. 1 shows that the selected estimates are the most conservative among all the payment vehicles used.

  Population characteristics. The user population considered in this study matches the user population characteristics considered in EPA s analysis ( i. e., recreational anglers, boaters, and wildlife viewers).

K. 2 FARBER AND GRINER, 2000. VALUING WATERSHED QUALITY IMPROVEMENTS USING CONJOINT ANALYSIS Farber and Griner ( 2000) used a CV study to estimate changes in water resource values to users from various improvements in Pennsylvania s water quality. The study defines water quality as   polluted,    moderately polluted,  and   unpolluted

based on a water quality scale developed by EPA Region III.   Polluted  streams are unable to support aquatic life,

  moderately polluted  streams are somewhat unable to support aquatic life, and   unpolluted  streams adequately support aquatic life. Farber and Griner developed WTP estimates for water quality improvements for the following three water quality changes:

  from   moderately polluted  to   unpolluted,

  from   severely polluted  to   moderately polluted,  and K 3 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer   from   severely polluted  to   unpolluted.

The authors used six different model variations to estimate the WTP for the three improvements scenarios for various population groups ( e. g., users, nonusers, and a mix of users and nonusers). Table K. 2 presents the estimated WTP values.

The following discussion provides EPA s reasons for selecting point estimates for the use in benefits transfer.

Table K. 2: Estimate WTP for Specified Water Quality Improvements from Farber and Griner ( 2001$)

Water Quality Change Valued Binary Choice Model Intensity of Preference Model User Nonuser Combine User Nonuser Combine Basic Moderately Polluted to Unpolluted $ 49 .7 $ 6.3 $ 40 .4 $ 56 .2 $ 14 .0 $ 54 .2 Severely Polluted to Moderately Polluted $ 66 .9 $ 5.8 $ 55 .6 $ 73 .8 $ 51 .4 $ 70 .9 Severely Polluted to Unpolluted $ 11 7.3 $ 44 .9 $ 95 .7 $ 12 9.6 $ 57 .7 $ 11 6.8 Interactive Moderately Polluted to Unpolluted $ 48 .2 $ 3.2 $ 38 .0 $ 56 .9 $ 13 .3 $ 54 .6 Severely Polluted to Moderately Polluted $ 65 .2 $ 1.5 $ 52 .7 $ 75 .1 $ 50 .6 $ 71 .9 Severely Polluted to Unpolluted $ 11 5.5 $ 41 .3 $ 92 .9 $ 13 3.1 $ 57 .6 $ 11 9.5 Fixed Effects Moderately Polluted to Unpolluted a $ 24 .5 $ 16 .4 $ 28 .3 $ 41 .8 $ 5.5 $ 41 .0 Severely Polluted to Moderately Polluted $ 42 .4 $ 10 .6 $ 38 .2 $ 63 .4 $ 30 .3 $ 59 .0 Severely Polluted to Unpolluted $ 86 .6 $ 48 .4 $ 80 .4 $ 11 0.5 $ 31 .0 $ 98 .6 Location: Lower Allegheny Watershed in Western Pennsylvania Estimating Approach: Conjoint Analysis Survey Population: Recreational users and nonusers a Values selected for the use in benefits transfer are given in bold.

b WTP values from the original study are adjusted to 2001$ based on CPI.

Source: Farber and Griner, 2000.

The Agency selected only two values from this study based on their compatibility with the environmental changes and population characteristics considered in both the original study and the analysis of recreational benefits from the MP& M rule.

The following discussion summarizes EPA s reasons used in the selection process:

  Environmental quality change. EPA judged that only one water quality improvement scenario change from   moderately polluted  to   unpolluted  is compatible with the environmental quality change expected from the final regulation AWQC are set at a level below which pollutant concentrations have not been demonstrated to cause significant harm to human health or aquatic life. Exposure to pollutant concentrations above the AWQ C levels are expected to have a harmful effect. Therefore, by definition, water with pollutant levels that exceed criteria set to protect human health or aquatic life are polluted waters.

EPA chose the case where the policy variable changed from moderately polluted to unpolluted because this is likely to be the most frequently occurring scenario for reaches with MP& M discharges. Streams unable to support any aquatic life ( i. e.,   severely polluted ) are likely to be affected by numerous environmental stressors, in addition to MP& M discharges. Eliminating MP& M related AWQC exceedences would eliminate or reduce one of the stressors,

but is unlikely to change the quality of the water from severely polluted to unpolluted. It is more realistic to assume that most streams affected by MP& M facility discharges are moderately polluted, i. e., these streams support some aquatic life; but sensitive species are adversely affected by MP& M pollutants exceeding AWQC values protective of aquatic life. Removing all AWQC exceedances would make such streams unpolluted.

  Research methods. EPA considered only two of the six versions of the benefits transfer model based on the authors  recommendations. The authors appear to prefer the   fixed effects  versions of both the binary choice K 4 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer ( BC) and intensity of preference ( IP) models. Specifically, they note that " A likelihood ratio test, with degrees of freedom being the number of individuals in the estimating sample, can be used to test the superiority of the fixed effects model. Such a test shows the fixed effects model to be a statistical improvement over either the basic or interactive models" ( see Table K. 2). In addition, they state that, " the purpose of estimating a fixed effects model was to account for the possibility that some respondents may approve of all changes, regardless of price and quality. If this behavior existed in the sample, not controlling for it would result in overestimates of marginal valuations for each type of quality change. This expectation is supported by the fact that the fixed effects valuation estimates are lower than the others."

  Population characteristics. The user population considered in this study matches the user population characteristics considered in EPA s analysis ( i. e., recreational anglers, boaters, and wildlife viewers).

K. 3 JAKUS ET AL., 1997. DO SPORTFISH CONSUMPTION ADVISORIES AFFECT RESERVOIR ANGLERS SITE CHOICE?

Jakus et al. ( 1997) used a repeated discrete choice travel cost ( TC) model to examine the impacts of fish consumption advisories ( FCA) in eastern and middle Tennessee. The estimated consumer surplus from recreational fishing in middle and east Tennessee is $ 26.02 and $ 52.57 per angler per day, respectively, under the baseline water quality conditions. The estimated welfare gain from removing FCAs is $ 2.04 and $ 3.16 per angler per day , respectively. Table K. 3 summarizes the study s estimates.

Table K. 3: Consumer Surplus from Recreational Fishing from Jakus et al. ( 1997) a Water Quality Change Valued Consumer Surplus Adjusted to 2001$ Consumer Surplus ($ 1997)

Site Choice Model  multinomial logit Average surplus per trip in middle TN ( baseline water quality $ 26.02 $ 23.60 Benefit per trip from removing all advisories in middle TN $ 2.04 $ 1.85 Average surplus per trip in East TN ( baseline water quality conditions) $ 52.57 $ 47.67 Benefit per trip from removing all advisories in east TN $ 3.16 $ 2.86 Benefit per trip from removing Watts Bar advisory $ 1.75 $ 1.59 Repeated Discrete Choice Model  repeated nested logit model Seasonal benefit from removing all advisories in middle TN $ 24.22 $ 21.96 Seasonal benefit from removing all advisories in east TN $ 52.27 $ 47.40 Seasonal benefit from removing Watts Bar advisory $ 30.43 $ 27.60 Location: Tennessee Estimating Approach: TC Survey Population: Tennessee residents; anglers and non anglers a Values selected for the use in benefits transfer are given in bold.

b WTP values from the original study are adjusted to 2001$ based on CPI.

Source: Jakus et al, 1997.

EPA selected two values from this study for use in benefits transfer, based on their compatibility with the environmental quality change and population characteristics at both the original study and policy sites, for the following reason:

  Environmental quality change. FCAs are usually triggered by the presence of toxic pollutants in fish tissue. EPA expects the final regulation to reduce discharges of toxic pollutants, including those linked to FCAs ( e. g., mercury and lead). The Agency therefore assumed that the removal of FCAs is compatible with water quality improvements expected from the final regulation.

K 5 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer The recreational benefits analysis uses consumer surplus estimates for both regions studied by the authors, because MP& M facilities are located in these regions as well as throughout heavily populated regions of the U. S. EPA did not include the value corresponding to the Watts Bar lake in the benefits transfer analysis because this lake is included in the set of fishing areas for east Tennessee.

K. 4 LANT AND ROBERTS, 1990. GREENBELTS IN THE CORNBELT: RIPARIAN WETLANDS, INTRINSIC VALUES, AND MARKET FAILURE Lant and Roberts ( 1990) used a CV study to estimate the recreational and nonuse benefits of improved water quality in selected Iowa and Illinois river basins. River quality was defined by means of an interval scale of   poor,    fair,    good,  and   excellent.  The authors defined the four water quality intervals as follows:

    poor  water quality is inadequate to support any recreation activity,

    fair  water quality is adequate for boating and rough fishing,

    good  water quality is adequate for gamefishing, and     excellent  is adequate to support swimming and exceptional fishing.

Table K. 4 summarizes WTP values for specified water quality improvements from this study.

Table K. 4: WTP Values for a Specified Water Quality Improvement from Lant and Roberts ( 1990)

Water Quality Change Valued Adjusted to 2001$ Original Study Values 1987$

Use Value Nonuse Value Use Value Nonuse Value Poor to fair $ 47.5 $ 58.6 $ 30.50 $ 37.61 Fair to good a $ 57.8 $ 73.5 $ 37.10 $ 47.16 Good to excellent $ 64.7 $ 67.3 $ 41.51 $ 43.22 Location: Selected Iowa and Illinois river basins Estimating Approach: CV Survey Population: Recreational users and nonusers a The values given in bold were selected for the use in benefits transfer.

b WTP values from the original study are adjusted to 2001$ based on CPI.

Source: Lant and Roberts, 1990.

The Agency judged that only one value from this study is compatible with the environmental changes and population characteristics considered in the analysis of recreational benefits from the MP& M rule, for the following reasons:

  Environmental quality change. The Agency judged that only one of the three possible water quality changes considered in this study   fair  to   good  was compatible with the water quality change expected under the MP& M rule. EPA assumed in its analysis of recreational benefits expected from the MP& M rule that reaches with AWQC exceedances under the baseline conditions may support rough fishing, but may not be clean enough to support more sensitive species such as those desired for game fishing. Removing AWQC exceedances will shift water quality from   fair  to   good.

  Population characteristics. The user population considered in this study matches the population characteristics considered in EPA s analysis ( i. e., recreational anglers, boaters, and wildlife viewers).

K 6 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer K. 5 AUDREY LYKE, 1993. DISCRETE CHOICE MODELS TO VALUE CHANGES IN ENVIRONMENTAL QUALITY: A GREAT LAKES CASE STUDY Lyke s ( 1993) study of the Wisconsin Great Lakes open water sport fishery showed that anglers may place a significantly higher value on a contaminant free fishery than on one with some level of contamination. Lyke estimated the value of the fishery to Great Lakes trout and salmon anglers if it was improved enough to be " completely free of contaminants that may threaten human health.  The author also estimated various policy scenarios that affect the value of recreational fishing in the Wisconsin Great Lakes, including reducing the daily bag limit for lake trout and restoring naturally reproducing populations of lake trout. Table K. 5 presents welfare estimates from this study.

Table K. 5: WTP Estimates for Specified Water Quality Improvements from Lyke ( 1993) a Water Quality Change Valued Adjusted to 2001$ b Original Study Value Value of WI Fishing Change in Value Value of WI Fishing Change in Value CV  linear logit model 1990 fishing conditions remain the same as 1989 $ 95,062,744 $ 66,600,000 WI daily bag limit for lake trout reduced to one a day $ 43,962,951 ($ 51,099,793 $ 30,800,000 ($ 35,800,000 Great Lakes fish are free of pollutants affecting human health $ 105,625,27 $ 10,562,527 $ 74,000,000 $ 7,400,000 Restoring naturally reproducing populations of lake trout $ 17,271,159 $ 17,271,159 $ 12,100,000 $ 12,100,000 WI inland fishing conditions remain the same as 1989 $ 964,330,17 $ 675,600,00 Restoring naturally reproducing populations of lake trout in WI waters of Great Lakes ( inland anglers only) $ 0 $ 0 $ 0 $ 0 CV  constant elasticity of substitution model ( mean)

1990 fishing conditions remain the same as 1989 $ 118,899,79 $ 83,300,000 Great Lakes fish are free of pollutants affecting human health $ 156,011,38 $ 37,111,581 $ 109,300,00 $ 26,000,000 CV  constant elasticity of substitution model ( median)

1990 fishing conditions remain the same as 1989 $ 26,834,528 $ 18,800,000 Great Lakes fish are free of pollutants that affect human health $ 40,537,266 $ 13,702,738 $ 28,400,000 $ 9,600,000 Location: Wisconsin Estimating Approach: TC and CV Survey Population: Wisconsin Great Lakes and inland anglers a The values selected for the use in benefits transfer are given in bold.

b WTP values from the original study are adjusted to 2001$ based on CPI.

Source: Lyke, 1993.

EPA selected two WTP values from this study for use in benefits transfer for the following reasons:

  Environmental quality change. EPA judged that only one policy scenario Great Lakes fish that are free from contaminants harmful to human health is compatible with water quality improvements associated with removal of all AWQ C exceedances. Other scenarios, such as reducing daily bag limit for lake trout to one per day and restoring naturally reproducing populations of lake trout, are irrelevant to the MP& M regulation. The Agency used estimates from the   1990 fishing conditions remain the same as 1989 conditions" scenario as an estimate of the baseline value of recreational fishing in Wisconsin.

  Research methods. The Agency did not consider estimates from the TC model because the author noted that   the nested logit travel cost model results seem too high.

K 7 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer K. 6 MONTGOMERY AND NEEDELMAN, 1997. THE WELFARE EFFECTS OF TOXIC CONTAMINATION IN FRESHWATER FISH Montgomery and Needelman ( 1997) estimated benefits from removing   toxic  contamination from lakes and ponds in New York State. They used a binary variable as their primary water quality measure, which indicates whether the New York Department of Environmental Conservation considers water quality in a given lake to be impaired by toxic pollutants. Their model controls for major causes of impairments other than   toxic  pollutants, to separate the effects of various pollution problems that affect the fishing experience. Table K. 6 lists environmental quality changes considered in the study and the WTP values corresponding to a specified water quality change.

Table K. 6: Welfare Estimates from Montgomery and Needelman ( 1997)

Water Quality Change Valued Compensating Variation per Capita per Season ( 2001$) b Compensating Variation per Capita per Season ( 1989$)

Eliminate toxic contamination in all lakes a $ 90.28 $ 63.25 All toxic lakes are closed to fishing $ 124.31 $ 87.09 Raise pH in acidic lakes ( none are threatened or impaired) $ 19.73 $ 13.82 Close all acidic lakes to fishing $ 21.20 $ 14.85 Eliminate toxic contamination and raise pH in acidic lakes $ 113.39 $ 79.44 Location: New York State Estimating Approach: TC  Repeated discrete choice model Survey Population: New York State residents; anglers and non anglers a The values selected for the use in benefits transfer are given in bold.

b WTP values from the original study are adjusted to 2001$ based on CPI.

Source: Montgomery and Needelman, 1997.

The Agency selected only one value from this study for use in the benefits transfer based on its compatibility with environmental quality changes at both the original study and the MP& M sites, for the following reason:

  Environmental quality change. Only one of the five policy scenarios considered eliminate toxic contamination in all lakes is directly compatible with the potential changes brought about by the MP& M rule. The MP& M rule is unlikely to significantly affect the acidity in lakes and streams affected by MP& M discharges. The last three policy scenarios in Table K. 6 involve changes in pH levels, and are therefore not included in the benefits transfer.

The Agency also did not consider the estimate from the second scenario in Table K. 6 closing all toxic lakes to fishing in benefits transfer, because it does not consider water quality improvement per se.

K. 7 PHANEUF ET AL., 1998. VALUING WATER QUALITY IMPROVEMENTS USING REVEALED PREFERENCE METHODS WHEN CORNER SOLUTIONS ARE PRESENT Phaneuf et al. ( 1998) studied angling in Wisconsin Great Lakes. They estimated changes in recreational fishing values resulting from a 20 percent reduction of toxin levels in lake trout flesh. The study uses a TC model to value water quality improvements when corner solutions are present in the data. Corner solutions arise when consumers visit only a subset of the available recreation sites, setting their demand to zero for the remaining sites. Phaneuf et al. found that improved industrial and municipal waste management results in general water quality improvement. Table K. 7 presents findings from this study based on two policy scenarios and four different model specifications.

K 8 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer Table K. 7: Welfare Estimates from Phaneuf et al. ( 1998)

Water Quality Change Valued Adjusted to 2001$ a Study Values ( 1989$)

RNL RPRN KT System RNL RPRN KT System 20% reduction in toxins $ 41.62 $ 12.53 $ 166.21 $ 15.69 $ 29.16 $ 8.78 $ 116.45 $ 10.99 Loss of South Lake Michigan $ 232.19 $ 140.37 $ 12,119 $ 441.36 $ 162.67 $ 98.34 $ 849.09 $ 309.21 Location: Wisconsin Great Lakes Estimating Approach: TC models, including: RNL: Repeated Nested Logit model;

RPRNL: Random Parameters Repeated Nested Logit model; KT: Kuhn Tucker model; and System: Systems of Demands model Survey Population: Wisconsin anglers; Great Lakes and inland anglers a WTP values from the original study are adjusted to 2001$ based on CPI.

Source: Phaneuf et al, 1998.

The Agency selected only one value for use in benefits transfer for the following reasons:

  Environmental quality change. Only one policy scenario evaluated in this study a 20 percent reduction in the toxin levels in fish tissue is compatible with the water quality changes expected from the MP& M regulation ( i. e.,

removal of aquatic life based AWQC exceedances. The second scenario loss of South Lake Michigan fishing sites is irrelevant to the final regulation.

  Research methods. Phaneuf et al. estimated four different models and provided WTP estimates based on each of them. The authors indicated, however, that " the KT model comes closest to matching the ideal theoretical model"

( see authors conclusions, page 1030). Other models either rely on more restrictive assumptions or require additional research. The Agency chose the value from the KT model based on the authors  recommendation, which is one of the selection criteria for values used in benefits transfer.

K 9 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer GLOSSARY ambient water quality criteria ( AWQC): Levels of water quality expected to render a body of water suitable for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,

swimming, farming, fish production, or industrial processes. ( http:// www. epa. gov/ OCEPAterms/ aterms. html)

binary choice ( BC): offers respondents to a contingent valuation survey specific dollars and cents choices, for example,

  Would you be willing to pay between $ 10 and $ 20 per year to improve visibility at the Grand Canyon?

conjoint analysis: " any decompositional method that estimates the structure of consumer's preferences given his or her overall evaluations of a set of alternatives that are prespecified in terms of levels of different attributes. Price typically is included as an attribute." ( Green and Srinivasan, 1990).

contingent valuation ( CV): a method used to determine a value for a particular event, where people are asked what they are willing to pay for a benefit and/ or are willing to receive in compensation for tolerating a cost. Personal valuations for increases or decreases in the quantity of some good are obtained contingent upon a hypothetical market. The aim is to elicit valuations or bids that are close to what would be revealed if an actual market existed.

( http:// www. damagevaluation. com/ glossary. htm)

corner solutions: a corner solution arises when a consumer who has a choice of two goods, x1 and x2, chooses to consume no x1 at the utility maximum.

direct question/ open ended ( OE): in the OE approach, respondents are asked the most they would be willing to pay for the program or policy. This approach has a virtue of not providing any hints about what might be a reasonable value. This approach, however, confronts respondents with an unfamiliar choice ( i. e., placing a price on environmental commodities).

Studies that use the OE approach have high item non response rates.

fish consumption advisory ( FCA): an official notification to the public about specific areas where fish tissue samples have been found to be contaminated by toxic chemicals which exceed FDA action limits or other accepted guidelines.

Advisories may be species specific or community wide.

intensity of preference ( IP): an experimental design that allows individuals to state an intensity of preferences for or against the alternative to the status quo. For example, the individual designates they would " probably yes" or " definitely yes"

prefer the alternative to the status quo.

iterative bidding ( IB): with IB, respondents are asked whether they would be WTP a given amount. If the answer is yes,

this amount is raised in pre set increments until the respondent says that he or she will not pay the last amount given. If the answer is no, then the amount is decreased until the respondent indicates a willingness to pay the stated amount.

starting point bias: when survey interviewers suggest a first bid this can influence the respondent s answer and cause the respondent to agree too readily with bids in the vicinity of the initial bid. ( http:// www. damagevaluation. com/ glossary. htm)

travel cost ( TC): method to determine the value of an event by evaluating expenditures of participants. Travel costs are used as a proxy for price in deriving demand curves for a recreation site. ( http:// www. damagevaluation. com/ glossary. htm)

willingness to pay ( WTP): maximum amount of money one would be willing to pay or give up to buy some good.

( http:// www. damagevaluation. com/ glossary. htm)

K 10 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer ACRONYMS AWQC: ambient water quality criteria BC: binary choice CV: contingent valuation FCA: fish consumption advisory IB: iterative bidding

IP: intensity of preference TC: travel cost WTP: willingness to pay K 11 MP& M EEBA: Appendices Appendix K: Selecting WTP Values for Benefits Transfer REFERENCES Boyle, K. J. and J. C Bergstrom. 1992.   Benefit Transfer Studies: Myths, Pragmatism and Idealism.  Water Resources Research, Vol. 28, No. 3, March: 657 663.

Desvousges, W. H. et al. 1987.   Option Price Estimates for Water Quality Improvements: A Contingent Valuation Study for the Monongahela River.  Journal of Environmental Economics and Management, 14: 248 267.

Desvousges, W. H. et al. 1992.   Benefit Transfer: Conceptual Problems in Estimating Water Quality Benefits Using Existing Studies.  Water Resources Research, Vol. 28, No. 3, March: 675 683.

Farber, S. and B. Griner. 2000. Valuing Watershed Quality Improvements Using Conjoint Analysis. University of Pittsburgh, PA.

Jakus, P. M., M. Downing, M. S. Bevelhimer, and J. M. Fly. 1997.   Do Sportfish Consumption Advisories Affect Reservoir Anglers  Site Choice?  Agricultural and Resource Economic Review, 26( 2).

Lant, C. L. and R. S. Roberts. 1990.   Greenbelts in the Cornbelt: Riparian Wetlands, Intrinsic Values, and Market Failure.

Environment and Planning A, Vol. 22: 1375 1388.

Lyke, A. J. 1993. Discrete Choice Models to Value Changes in Environmental Quality: A Great Lakes Case Study. PhD dissertation, Madison, WI: University of Wisconsin, Department of Agricultural Economics.

Montgomery, M. and M. Needelman. 1997.   The Welfare Effects of Toxic Contamination in Freshwater Fish.  Land Economics 73( 2): 211 223.

Phaneuf, D. J., C. L. Kling, and J. A. Herriges. 1998.   Valuing Water Quality Improvements Using Revealed Preference Methods When Corner Solutions Are Present.  American Journal of Agricultural Economics, 80: 1025 1031.

Tudor, L., E. Besedin, M. Fisher, S. Smith, and L. Snyder. 1999. What Pollutants Matter for Consumers of Water Based Recreation? Presented at the annual meeting of the Northeastern Agricultural and Resource Economics Association.

Morgantown, WV. June 1999.

Tudor, L., E. Besedin, M. Fisher, and S. Smith. 1999. Economic Analysis of Environmental Regulations: Application of the RUM model to Ecological Benefits Assessment for MP& M Effluent Guideline Limitations. Presented at the annual meeting of American Agricultural Economics Association. Nashville, TN. August 1999.

K 12 MP& M EEBA: Appendices Appendix L: Parameters Used in the IEUBK Model Appendix L: Parameters Used in the IEUBK Model INTRODUCTION APPENDIX CONTENTS Table B. 1: Description of Parameters Used In the IEUBK Lead Model This appendix contains a comprehensive list of model parameters that are used in the IEUBK model for lead in children.

The remainder of this appendix is a reproduction of Appendix B: Description of Parameters In the IEUBK Lead Model, taken from the Technical Support Document for the Integrated Exposure Uptake Biokinetic Model for Lead in Children ( v0.99d)

( December 1994).

L 1 APPENDIX B: DESCRIPTION OF PARAMETERS IN THE IEUBK LEAD MODEL TABLE B 1. DESCRIPTION OF PARAMETERS IN THE IEUBK LEAD MODEL PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED ABSD Total absorption for dust at low saturation 0.3 0 84 E Based on US EPA ( 1989a). unitless U 1c,

U 2 ABSF Total absorption for food at low saturation 0.5 0 84 E Based on US EPA ( 1989a). unitless U 1a, U 2 ABSO Total absorption for other ingested lead at low saturation 0.0 0 84 E Based on the default condition that there is no other source of lead ingestion in the household. unitless U 1d, U 2 ABSS Total absorption for soil at low saturation 0.3 0 84 E Based on US EPA ( 1989a). unitless U 1e, U 2 ABSW Total absorption for water at low saturation 0.5 0 84 E Based on US EPA ( 1989a). unitless U 1b, U 2 air_ absorb( t) Net percentage absorption of air lead 32 32 32 32 32 32 32 0 11 12 23 24 35 36 47 48 59 60 71 72 84 E Deposition efficiencies of airborne lead particles were estimated by U S EPA ( 1989a). A respiratory deposition/ absorption rate of 25% to 45% is reported for young children living in non point source areas while a rate of 42% is calculated for those living near point sources. An intermediate value of 32% was chosen. % U 4 air_ concentration( t) Outdoor air lead concentration 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0 11 12 23 24 35 36 47 48 59 60 71 72 84 E Based on the lower end of the range 0.1  0.3 µ g Pb/ m3 that is reported for outdoor air lead concentration in U. S. cities without lead point sources ( US EPA 1989) µ g/ m3 E 1,2,11 NOTE: I = interior parameter, E = Exterior, user selectable parameter B 2  PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED AVF, AVW, AVD, AVO,

AVS Bioavailability 1 0 84 I Parameter added for later flexibility in describing the absorption process;

has no effect in current algorithm. unitless U 1a U 1e AVINTAKE Available intake U 2 0 84 I The amount of Pb that is available for intake µ g U 1a, b, c, d, e can_ fruit( t) Lead intake from canned fruit when fruit is consumed only in canned form 1.811 1.063 1.058 0.999 0.940 0.969 1.027 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). µ g/ day E 5d can_ veg( t) Lead intake from canned vegetables when vegetable is consumed only in canned form 0.074 0.252 0.284 0.295 0.307 0.291 0.261 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). µ g/ day E 5b contrib_ percent Ratio of indoor dust lead concentration to soil lead concentration 0.70 0 84 E Analysis of soil and dust data from 1983 East Helena study ( US EPA, 1989) g/ g per g/ g E 11 CONRBC Maximum lead concentration capacity of red blood cells 1200 0 84 I Based on Marcus ( 1983) reanalysis of infant baboon data from Mallon ( 1983). See Marcus ( 1985a) for assessment of form of relationship and estimates from data on human adults [ data from deSilva ( 1981a, b), Manton and Malloy ( 1983), and Manton and Cook ( 1984)] and infant and juvenile baboons ( Mallon, 1983). µ g/ dL B 2.5 constant_ soil_ conc( t) Soil lead concentration 200 200 200 200 200 200 200 0 11 12 23 24 35 36 47 48 59 60 71 72 84 E Air Quality Criteria Document for Lead. ( US EPA, 1986) µ g/ g E 8 NOTE: I = interior parameter, E = Exterior, user selectable parameter B 3 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED constant_ water_ conc Water lead concentration 4.0 0 84 E Based on analysis of data from the American Water Works Service Co.

( Marcus, 1989) µ g/ L E 6a CRBONEBL( t) Ratio of lead concentration ( µ g/ kg) in bone to blood lead concentration ( µ g/ L) B 4c 0 84 I Data in Barry ( 1981) were used.

Bone lead concentration was calculated as an arithmetic average of the concentrations in the rib, tibia, and calvaria. The blood lead concentrations were taken directly from the study.

Concentrations in each of the following eight age groups were considered:

stillbirths, 0 12 days, 1 11 mos, 1 5 yrs, 6 9 yrs, 11 16 yrs, adult ( men), and adult ( women). Ages 0 and 40 yrs were assumed for stillbirths and adults,

respectively. L/ kg B 1h CRKIDBL( t) Ratio of lead concentration ( µ g/ kg) in kidney to blood lead concentration ( µ gL) B 4a 0 84 I Data in Barry ( 1981) were used.

Lead concentrations in kidney ( combined values for cortex and medulla)

and blood were taken directly from the study.

Concentrations in each of the following eight age groups were considered:

stillbirths, 0 12 days, 1 11 mos, 1 5 yrs, 6 9 yrs, 11 16 yrs, adult ( men), and adult ( women). Ages 0 and 40 yrs were assumed for stillbirths and adults,

respectively. L/ kg B 2h CRLIVBL( t) Ratio of lead concentration ( µ g/ kg) in liver to blood lead concentration ( µ g/ l) B 4b 0 84 I Data in Barry ( 1981) were used.

Lead concentrations in liver and blood were taken directly from the study.

Concentrations in each of the following eight age groups were considered:

stillbirths, 0 12 days, 1 11 mos, 1 5 yrs, 6 9 yrs, 11 16 yrs, adult ( men), and adult ( women). Ages 0 and 40 yrs were assumed for stillbirths and adults,

respectively. L/ kg B 2e, 2f NOTE: I = interior parameter, E = Exterior, user selectable parameter B 4 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED CROTHBL( t) Ratio of lead concentration ( µ g/ kg) in other soft tissue to blood lead concentration ( µ g/ L) B 4d 0 84 I Data in Barry ( 1981) were used.

Lead concentration ratio for soft tissues was calculated as a weighted arithmetic average of concentration ratios for muscle ( 53.8%), fat ( 24.0%),

skin ( 9.4%), dense connective tissue ( 4.4%), brain ( 2.7%), GI tract ( 2.3%),

lung ( 1.9%), heart ( 0.7%), spleen ( 0.3%), pancreas ( 0.2%), and aorta ( 0.2%), where the weights applied are given in parentheses. The weight associated with each soft tissue component was equal to the weight of the component ( kg) divided by weight of all soft tissues ( kg). These weights were estimated from Schroeder and Tipton ( 1968) and are assumed to apply in the range 0 84 months of age.

Concentrations in each of the following eight age groups were considered:

stillbirths, 0 12 days, 1 11 mos, 1 5 yrs, 6 9 yrs, 11 16 yrs, adult ( men), and adult ( women). Ages 0 and 40 yrs were assumed for stillbirths and adults,

respectively. L/ kg B 2n, 2o DAYCARE( t) Dust lead intake at daycare E 12c 0 84 I Simple combination of the total amount of dust ingested daily, fraction of total dust ingested as daycare dust, and dust lead concentration at daycare. µ g/ day E 9d DaycareConc Dust lead concentration at daycare 200 0 84 E Based on the assumption that default daycare dust concentrations are the same as default residence dust concentrations. µ g/ g E 12c DaycareFraction Fraction of total dust ingested daily as daycare dust 0 0 84 E Based on the default assumption that the child does not attend daycare. unitless E 9.5,12c diet_ intake( t) User specified diet lead intake 5.53 5.78 6.49 6.24 6.01 6.34 7.00 0 11 12 23 24 35 36 47 48 59 60 71 72 84 E Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). µ g/ day E 4a DietTotal( t) Total Dietary Intake E 4b 0.84 I Summation of all dietary sources; same as INDIET( t) µ g/ day E 4b DustTotal( t) Daily amount of dust ingested E 10 0 84 I Simple combination of total amount soil and dust ingested daily and fraction of this combined ingestion that is dust alone. g/ day E 9c, 12a

12e NOTE: I = interior parameter, E = Exterior, user selectable parameter B 5 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED EXAIR( t) Air lead intake E 3 0 84 I Simple combination of average air lead concentration and ventilation rate. µ g/ day U 4 f_ fruit( t) Lead intake from fresh fruit if no home grown fruit is consumed 0.039 0.196 0.175 0.175 0.179 0.203 0.251 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). µ g/ day E 5e f_ veg( t) Lead intake from fresh vegetables if no home grown vegetables are consumed 0.148 0.269 0.475 0.466 0.456 0.492 0.563 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). µ g/ day E 5c FirstDrawConc First Draw water lead concentration 4.0 0 84 E Based on analysis of data from the American Water Works Service Co.

( Marcus, 1989) µ g/ L E 6b FirstDrawFraction Fraction of total water consumed daily as first draw 0.5 0 84 E In the absence of appropriate data, a conservative value corresponding to consumption largely after four fours stagnation time was used, e. g. early morning or late afternoon. unitless E 6b, 7 FountainConc Fountain water lead concentration 10 0 84 E Default assumption is that the drinking fountain has a lead lined reservoir,

but that consumption is not always first draw. Therefore, a value was selected from the range of 5 25 g/ L. µ g/ L E 6b FountainFraction Fraction of total water consumed daily from fountains 0.15 0 84 E A default value was based on 4 6 trips to the water fountain at 40 50 ml per trip. none E 6b, 7 fruit_ all( t) Daily amount of all frults consumed 38.481 169.000 63.166 61.672 61.848 67.907 80.024 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). g/ day E 5f NOTE: I = interior parameter, E = Exterior, user selectable parameter B 6 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED HomeFlushedConc Home flushed water lead concentration 1.0 0 84 E Based on analysis of data from the American Water Works Service Co.

( Marcus, 1989) µ g/ L E 6b HCT0 Hematocrit at birth 0.45 0 I Data from Silve et al. ( 1987); also Spector ( 1956) and Altman and Ditmer ( 1973) decimal percent B 7b, d InCanFruit( t) Lead intake from canned fruit E 5d 0 84 I Simple combination of the fraction of non home grown fruits consumed daily, and lead intake from canned fruits when fruits are consumed only in canned form. µ g/ day E 4b InCanVeg( t) Lead intake from canned vegetables E 5b 0 84 I Simple combination of the fraction of vegetables consumed daily as non

home grown, and lead intake from canned vegetables when vegetables are consumed only in canned form. µ g/ day E 4b INDIET( t) Diet lead intake E 4a or E 4b 0 84 I Two options are provided.

Default option  Considers composite diet lead intake.

Alternate option  Combines lead intake from several individual components of diet. µ g/ day U 1a, U 2 IndoorConc( t) Indoor air lead concentration E 1 0 84 I Algebraic expression of relationship µ g/ m3 E 2 indoorpercent Ratio of indoor dust lead concentration to corresponding outdoor concentration 30 0 84 E Based on homes near lead point sources. The default value is reported in OAQPS ( USEPA 1989, pp A 1) and is estimated by Cohen and Cohen ( 1980). % E 1 INDUST( t) Household dust lead intake E 9a or E 9c 0 84 I Two options are provided.

Default option  Assumes that all dust lead exposure is from the household.

Alternate option  Considers dust lead exposure from several alternative sources as well. µ g/ day U 1 c, U 2 NOTE: I = interior parameter, E = Exterior, user selectable parameter B 7 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED INDUSTA( t) Lead intake from alternate dust sources E 9b or E 9d 0 84 I Two options are provided.

Default option  Assumes that lead intake from alternate sources is zero.

Alternate option  Combines lead intake from several alternate sources. µ g/ day U 1.5c, U 2 InFish( t) Lead intake from fish E 5h 0 84 I Simple combination of total meat consumed daily, fraction of meat consumed as fish, and lead concentration in fish. µ g/ day E 4b InFrFruit( t) Lead intake from non home grown fresh fruits E 5e 0 84 I Simple combination of the fraction of fruits consumed daily as non home grown and lead intake from fresh fruits. µ g/ day E 4b InFrVeg( t) Lead intake from non home grown fresh vegetables E 5c 0 84 I Simple combination of the fraction of vegetables consumed daily as non

home grown and lead intake from fresh vegetables. µ g/ day E 4b InGame( t) Lead intake from game animal meat E 5i 0 84 I Simple combination of total meat consumed daily, fraction of meat consumed as game animal meat, and lead concentration in game animal meat. µ g/ day E 4b InHomeFruit( t) Lead intake from home grown fruits E 5f 0 84 I Simple combination of total amount of fruit consumed daily, fraction of fruit consumed as home grown, and lead concentration in home grown fruit. µ g/ day E 4b InHomeVeg( t) Lead intake from home grown vegetables E 5g 0 84 I Simple combination of total amount of vegetable consumed daily, fraction of vegetables consumed as home grown, and lead concentration in home grown vegetables. µ g/ day E 4b InMeat( t) Lead intake from non game and non fish meat E 5a 0 84 I Simple combination of total amount of meat consumed daily, fraction of meat consumed as non game and non fish meat, and lead concentration in non game and non fish meat. µ g/ day E 4b InOtherDiet( t) Combined lead intake from dairy food, juice, nuts,

beverage, pasta,

bread, sauce,

candy, infant and formula food 3.578 3.506 3.990 3.765 3.545 3.784 4.215 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Sum of the amounts of lead ingested in food items not substituted by the calculation of exposure to lead in home grown fruits and vegetables, wild game or fish. Pb concentration from data provided to EPA by FDA ( US EPA ( 1986). Quantity consumed from Pennington ( 1983). µ g/ day E 4b, E 4c NOTE: I = interior parameter, E = Exterior, user selectable parameter B 8 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED INOTHER( t) Combined other sources of ingested lead,

such as paint chips, ethnic medicines, etc. 0 0 84 E Assumes no other sources of ingested lead g/ day U 1d, U 2 INSOIL( t) Soil lead intake E 8 0 84 I Simple combination of total amount of soil and dust ingested daily, fraction of this combined ingestion that is soil alone, and lead concentration in soil. µ g/ day U 1e, U 2 INWATER( t) Water lead intake E 6a or E 6b 0 84 I Two options are provided.

Default option  Simple combination of water consumed daily and a constant water lead concentration.

Alternate option  Water lead concentration depends on contribution from several individual sources of water. µ g/ day U 1b, U 2 MCORT( t) Mass of lead in cortical bone B 7e and B 9f 0 and 0 84 I 0 months  Simple combination of an assumed bone to blood lead concentration ratio, blood lead concentration, and weight of cortical bone.

Basis for value of bone to blood lead concentration ratio was human autopsy data ( Barry, 1981).

0 84 months  Application of the Backward Euler solution algorithm to the system of differential equations ( B 6a B 6i in Table A 3).

Both cases above assume that the cortical bone to blood lead concentration ratio is equal to the bone ( composite) to blood lead concentration ratio. µ g B 6b, 6i, 6.5b,

6.5i, 8a, 9f meat_ all( t) Daily amount of meat ( including fish and game)

consumed 29.551 87.477 95.700 101.570 107.441 111.948 120.961 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). g/ day E 5h NOTE: I = interior parameter, E = Exterior, user selectable parameter B 9 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED meat( t) Lead intake from meat if no game meat or fish is consumed 0.226 0.630 0.811 0.871 0.931 1.008 1.161 0 11 12 23 24 35 36 47 48 59 60 71 72 84 I Pb concentration from data provided to EPA by FDA ( US EPA ( 1986).

Quantity consumed from Pennington ( 1983). µ g / day E 5a MKIDNEY( t) Mass of lead in kidney B 7f and B 9c 0 and 0 84 I 0 months  Simple combination of an assumed kidney to blood lead concentration ratio, blood lead concentration, and weight of kidney. Basis for the value of the kidney to blood lead concentration ratio was human autopsy data ( Barry, 1981).

0 84 months  Application of the Backward Euler solution algorithm to the system of differential equations ( B 6a B 6i in Table A 3). µ g B

6b, 6f, 6.5b, 6.

5f, 8d, 9c MLIVER( t) Mass of lead in liver B 7g and B 9b 0 and 0 84 I 0 months  Simple combination of an assumed liver to blood lead concentration ratio, blood lead concentration, and weight of the liver. Basis for the value of the liver to blood lead concentration ratio was human autopsy data ( Barry, 1981).

0 84 months  Application of the Backward Euler solution algorithm to the system of differential equations ( B 6a B 6i in Table A 3). µ g B

6b, 6e, 6.5b, 6.

5e, 8d, 9b MOTHER( t) Mass of lead in soft tissues B 7h and B 9d 0 and 0 84 I 0 months  Simple combination of an assumed soft tissue to blood lead concentration ratio, blood lead concentration, and weight of the soft tissues at birth. Basis for the value of soft tissue to blood lead concentration ratio was human autopsy data ( Barry et al., 1981), using total lead and total weight of other tissue.

0 84 months  Application of the Backward Euler solution algorithm to the system of differential equations ( B 6a B 6i in Table A 3). µ g B

6b, 6g, 6.5b, 6.

5g, 8d, 9d MPLASM( t) Mass of lead in plasma pool B 7d and B 9g 0 and 0 84 I 0 months  Simple combination of the mass of lead in blood and red blood cells.

0 84 months  Based on the assumption that the lead concentration in plasma ECF is equal to the lead concentration in the plasma. µ g B 10a NOTE: I = interior parameter, E = Exterior, user selectable parameter B 10 PARAMETER NAME DESCRIPTION DEFAUL T VALUE OR EQN.

NO. AGE RANGE ( mo) I or E BASIS FOR VALUES/ EQUATIONS UNITS EQUATION WHERE USED MPLECF( t) Mass of lead in plasma extra

cellular fluid ( plasma ECF) B 7b and B 8a 0 and 0 84 I 0 months  Based on two assumptions.

( 1) masses of lead in plasma ECF and red blood cells are in kinetic quasi

equilibrium, and ( 2) lead concentration in the plasma ECF is equal to lead concentration in the plasma.

0 84 months  Application of the Backward Euler solution algorithm to the system of differential equations ( B 6a B 6i in Table A 3). µ g B 6a, 6c

6i, 6.5a,

6.5c

6.5i, 8a, 9a 9g MRBC( t) Mass of lead in red blood cells B 7c and B 9a 0 and 0 84 I 0 months  Based on the assumption that the masses of lead in plasma

ECF and red blood cells are in kinetic quasi equilibrium.

0 84 months  Application of the Backward Euler solution algorithm to the system of differential equations ( B 6a B 6i in Table A 3). µ g B

6a, 6d, 6.5a, 6.

5d, 8d, 9a, 10a MTRAB( t) Mass of lead in trabecular bone B 7i and B 9e 0 and 0 84 I 0 months  Simple combination of an assumed bone to blood lead concentration ratio, blood lead concentration, and weight of trabecular bone. Basis for the value of bone to blood lead concentration ratio was human autopsy data ( Barry, 1981).