MP& M EEBA: Appendices Appendix G: Extrapolation Methods INTRODUCTION EPA estimates both cost and benefits of environmental regulations based on a random stratified sample of MP& M facilities. 1 EPA then estimates national level costs and benefits by extrapolating analytic results from sample facilities to the national level using statistically determined sample facility weights.

Sample facility weights used in the benefit cost analysis of environmental regulations are based on detailed questionnaire stratification. Stratification means dividing the population of facilities into a number of non overlapping sub populations ( strata). These strata consist of facilities that are homogeneous with respect to facility size ( i. e., number of employees or revenue) or engineering characteristics such as wastewater flow because this information was not available at the time the sample frame was developed. The sample weights for facilities in the sample are based on the total population in each category and probabilities of selection in each stratum. Appendix G: Extrapolation Methods APPENDIX CONTENTS G. 1 Raking to Adjust MP& M Facility Sample Weights ................. ............. G 2 G. 1.1 ................. ....... G 2 G. 1.2 ing Adjustment ................. .. G 3 G. 2 y for Developing Sample Weighted Estimates for Sites with More Than One MP& M Facility ................. ...... G 7 G. 3 Study Results to the National Level ....... G 13 G. 3.1 e in Pollutant Loads ............. G 14 G. 3.2 ctivities on Reaches Affected by MP& M Discharges .......... G 14 G. 3.3 Differences in Household Income ....... G 14 G. 4 ults ................. ................ G 15 Glossary ................. ................. .. G 17 Using Data Sources Rak Methodolog Methodology for Extrapolation of Ohio Case Chang Level of Recreational A Res EPA traditionally uses a standard linear weighting technique ( hereafter, traditional extrapolation) to estimate national compliance costs, changes in pollutant removals, and national level benefits of environmental regulations. However, using sample weights that are based only on facility specific ( e. g., engineering) characteristics and various non facility factors can lead to a conditional bias in the estimation of national level benefits. In particular, this approach omits consideration of important non facility factors that influence the occurrence and size of benefits.

Non facility factors that are likely to affect the occurrence and size of benefits from reduced sample facility discharges and that are not reflected in the standard stratification and sample weighting approach include the receiving water body type and size and the size of the population residing in the vicinity of a sample facility. Furthermore, co occurrences of facilities discharging to the same reach may also affect the occurrence of benefits. Many of the environmental assessment and benefits analyses include comparisons of the estimated baseline and post compliance pollutant concentrations ( e. g., sludge concentrations or in waterway concentrations) with the relevant threshold values. Because the effect of aggregate discharges from several facilities is likely to be different from the sum of effects from these facilities considered independently, it is also important to account for the likelihood of joint discharges of MP& M facilities to the same reach.

The Agency used two approaches to address omission of these important non facility factors ( i. e., water body type and size,

affected population, and co occurrence of MP& M discharges) in designing the MP& M facilities sample. First, EPA adjusted sampling weights through post stratification using two variables receiving water body type and size and the size of the population residing in the vicinity of the sample facility. Section G. 1 presents the method of doing this adjustment. Second,

EPA used a differential sample weighting technique in developing national estimates of environmental effects and benefits. This method accounts for the presence of more than one facility with different sample weights discharging directly or indirectly ( through a POTW) to reaches affected by multiple MP& M dischargers. Section G. 2 of this appendix describes the differential sample weighting technique.

EPA used both the traditional extrapolation based weights and the sample weights adjusted through post stratification ( hereafter, post stratification extrapolation) to analyze the final MP& M rule s benefits. The benefit estimates based on the post stratification extrapolation weights are used to validate general conclusions that EPA draws from its main analysis based on the traditional extrapolation method. In addition to developing national benefit estimates based on both traditional and 1 A census of all MP& M facilities was not performed due to the large size of the MP& M industry.

G 1 MP& M EEBA: Appendices Appendix G: Extrapolation Methods post stratification extrapolation weights, EPA developed a third estimate of national benefits based on the Ohio case study results. 2 Section G. 3 of this appendix discusses this method in detail. The Agency recognizes that the extrapolation method used for the Ohio case study results is not rigorous. Therefore, this method is used to supplement the main results.

G. 1 USING RAKING TO ADJUST MP& M FACILITY SAMPLE WEIGHTS Omitting information that affects the occurrence and size of benefits from the original sample frame s design may lead to conditional bias in MP& M rule benefit estimates. To address this problem, EPA used a post stratification weight adjustment method called raking to account for two additional variables that were not accounted for in the original sample design and that may affect benefit occurrence:

  physical characteristics of the receiving water body ( including type and size); and   size of the population residing in the vicinity of the sample facility.

G. 1.1 Data Sources EPA first classified the universe of MP& M facilities into different poststrata. The Agency relied on three data sources to identify discharge reach characteristics and the population size in the vicinity of the discharge reach:

1. EPA's Permit Compliance System database ( PCS) indicated water bodies to which MP& M facilities discharge;

2. EPA's Reach File 1 ( RF1) provided additional information on the receiving water bodies, including water body type,

flow characteristics, and counties abutting these water bodies; and 3. Census data provided information on county populations.

The PCS database provides information on facilities covered by NPDES permits. The database covers only those facilities that discharge directly to surface or ground water. No information is available on the location of MP& M facilities that discharge to surface water indirectly or via POTWs. EPA therefore limited post stratification to direct discharging facilities.

The Agency used the resulting adjusted sample weights to estimate national level benefits for only the final regulatory option,

which covers only direct discharging facilities. Chapters 13 through 19 of this report present benefit estimates in various benefit categories considered in this analysis.

The extent of improvement in estimation accuracy depends on the reliability of the information used for post stratification.

Accordingly, it was necessary to understand and account for PCS database limitations in implementing a post stratification approach. The PCS database is designed to provide information on a facility's SIC codes, facility flow, and receiving reach characteristics. These characteristics include water body name and type, stream ID, and stream flow. Although these data can be used to classify facilities in the identified poststrata, these fields are not always populated in the database. To fill missing data, EPA combined data from PCS with supplementary analyses and information from RF1, using the following framework:

  PCS provided a stream ID and information on the water body type and flow characteristics. EPA obtained stream characteristics from PCS and used the stream ID to obtain information on counties abutting the reach from RF1;

  PCS provided a stream ID, but not the water body type and flow characteristics. EPA used the stream ID to obtain information on water body type, flow characteristics, and counties abutting the reach from RF1;

  PCS provided water body name and type, but not stream ID and flow characteristics. EPA first used facility lat/ long data to assign the PCS facility to the nearest reach that matches the water body name provided in PCS. The Agency then used the identified stream ID from RF1 to obtain information on water body type, flow characteristics, and counties abutting the reach from RF1;

2 See Chapter 21 for a detailed discussion the Ohio case study.

G 2 MP& M EEBA: Appendices Appendix G: Extrapolation Methods   PCS provided no receiving stream information on the, but facility lat/ long data were available. EPA first used these data to assign the PCS facility to the nearest reach. The Agency then used the identified stream ID to obtain information on water body type, flow characteristics, and counties abutting the reach;

  PCS provided neither information on the receiving stream nor facility lat/ long data. EPA assumed the distribution of the receiving water body characteristics, including the size of the population residing in the counties abutting the receiving reaches, to be similar to the distribution of these characteristics across facilities with known characteristics.

PCS identifies 4,290 direct discharging facilities with MP& M SIC codes that had active NPDES permits 1997. Of these,

EPA classified 3,242 facilities into the poststrata considered in this analysis. Because the total number of PCS facilities with MP & M SIC codes differs from the sum of sampling weights of direct dischargers considered in the final regulation, the Agency assumed that the sum of the sampling weights provides the correct estimate of the MP& M facility universe. Thus, the count of facilities in the benefits analysis matches the number of MP& M facilities. This analysis yielded an adjustment factor of 2,832/ 3,242= 0.87 Table G. 1 lists facility counts from PCS data, adjusted to equal the sample frame total.

Table G. 1: Facility Counts from PCS Data ( Adjusted to Equal the Sample Frame Total)

First Variable: Water Body Type and Size Second Variable: Population Size Variable Category PCS Facilities Count Variable Category PCS Facilities Count Bay Lakes Combined 288 Pop   100,000 934 Small Streams 543 100,000< Pop   500,000 1155 Medium Streams 1514 500,000< Pop   1,000,000 403 Large Streams 487 1,000,000< Pop   2,000,000 276 2,000,000< Pop   4,000,000 47 Pop< 4,000,000 17 Total 2,832 2,832 Source: PCS data.

G. 1.2 Raking Adjustment Raking is a post stratification method that can be used when multiple variables form the poststrata. If the original sampling weights need to be adjusted using post stratification with two variables, then the analysis must create a set of poststrata resulting from the cross classification of the two post stratification variables. EPA s analysis used the following steps:

1. Combine the variables " water body type" ( four categories), with " population size residing in the vicinity of the sampled facility" ( six categories) to yield 24 poststrata.

2. Classify each sampled facility into one of the 24 poststrata.

3. Determine how many facilities fall into each poststratum.

4. Multiply the sampling weight of a facility in a poststratum by the ratio of the number of facilities in the population in the poststratum to the sum of the sampling weights of all facilities in that stratum. If the number of facilities in the population are known only by each category of the two variables, then the weights can be adjusted through raking.

This section briefly describes the raking procedure.

G 3 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Water body type was one of the two post stratification variables used for raking. EPA originally used six categories of this variable: Bay/ Ocean, Great Lakes, Lakes, Lakes, Small Streams, Medium Streams, and Large Streams. However, the number of MP& M sample facilities in Bay/ Ocean, Great Lake, and Lake categories was too small for some categories to implement raking. Therefore, EPA combined categories in which the number of facilities in the sample was either zero or too small to create four categories:

  Bay Lakes Combined ( includes, Bays, Oceans, Great Lakes and Lakes);

  Small Streams;

  Medium Streams; and   Large Streams.

Table G. 2 shows the number of sampled facilities in each category of water body type, the sum of the sampling weights of the sampled facilities, and the known number of facilities in the population in that category. Comparing the sum of the MP& M facilities sampling weights and the PCS based count of facilities for each category of water body type shows that Bay Lake Combined and Small Streams are under represented in the MP& M sample frame while Medium and Large Streams are over

represented.

Table G. 2: Facility Distribution by Water Body Type Number of Facilities in the MP& M Sample Frame MP& M Sample Frame PCS Facilities Number of Facilities in the Sample Sum of the Sampling Weights Number of Facilities in the Population Ratio of Number PCS to Sample

Weighted Facilities Bay Combined 7 38.7 288 7.44 Small Streams 7 231.3 543 2.35 Medium Streams 43 1,439.4 1514 1.05 Large Streams 25 1,122.6 487 0.43 Total 82 2,832.0 2,832.0 1.00 Source: PCS data.

Table G. 3 shows the six population categories created in the EPA analysis. Comparing the sum of the MP& M facilities

sampling weights and the PCS based count of facilities corresponding to each category of water body type shows that facilities from the population size category of less than 100,000, greater than 4,000,000, and greater than 2,000,000 but less than 4,000,000 are over represented in the sample. Conversely, facilities in the population categories from 100,000 to 500,000 and from 500,000 to 1,000,000 are under represented.

G 4 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Table G. 3: Distribution of Facilities by Population Size Population MP& M Sample Frame PCS Facilities Number of Facilities in the Sample Sum of the Sampling Weights Number of Facilities in the Population Ratio of Sample

Weighted to PCS Facilities Pop   100,000 18 1,30 3.0 934 1.40 100,000< Pop   500,000 35 1,17 1.8 1,155 1.01 500,000< Pop   1,000,000 12 136.3 403 0.34 1,000,000< Pop   2,000,000 12 121.6 276 0.44 2,000,000< Pop   4,000,000 3 61.8 47 1.31 > 4,000,000 2 37.6 17 2.21 Total 82 2,832.0 2,832.0 1.00 Source: PCS data.

Raking is an iterative process in which adjusted sample weights are synthetically generated to match known characteristics of the population along single stratification dimensions and, as a result, should reflect the population characteristics within multi

dimensional stratification cells. The iterative process works as follows. First, EPA multiplied the sampling weight of each facility in each category of water body type by the ratio of the total number of facilities in the population to the sum of the sampling weights in that category. For example, using the numbers in Table G. 2, EPA multiplied the sampling weights of all sampled facilities in the Bay Combined category by the ratio 288/ 38.7 = 7.44. The sum of the adjusted weights, 38.72x 7.44= 288.08, is the known population total. Similarly, EPA multiplied all the sampling weights of facilities in the Large Streams category by the ratio 487/ 1122.6 = 0.43, to yield 1,122.6x0.43= 482.7 as the sum of the adjusted weights. EPA performed the same calculations for the other categories of water body type.

These calculations match the sum of the sampling weights to the known control totals for the single stratification dimension of water body type. At this first step, however, it is very unlikely that the resulting sums will agree with the known number of facilities within categories of the second stratification dimension, population size category. Table G. 4 shows the sum of the adjusted sampling weights and the PCS population totals by population sizes after Iteration 1.

Table G. 4: Sum of the Sampling Weight by Population Category after Iteration 1 Population Sum of the Adjusted Sampling Weights Number of Facilities in the Population ( PCS Based)

  100,000 1,542.49 934 100,000< Pop   500,000 728.62 1,155 500,000< Pop   1,000,000 133.42 403 1,000,000< Pop   2,000,000 294.18 276 2,000,000< Pop   4,000,000 58.31 47 > 4,000,000 74.99 17 Total 2,832.01 2,832 Source: U. S. EPA analysis.

To correct for this inconsistency, EPA multiplied each weight by the ratio of the known total to the sum of the adjusted G 5 MP& M EEBA: Appendices Appendix G: Extrapolation Methods weights for each facility in each population size category. For example, the Agency multiplied each facility in the first population category by the ratio 934/ 1542.49. Now, the resulting sum of the adjusted weights agrees with the category totals for the population category, but differs from the category totals for water body type.

EPA therefore repeated this process of sequentially adjusting sample weights one dimension at a time until the sum of the adjusted sampling weights simultaneously agreed with the total population counts of facilities for both water body type and population size categories. After seven iterations, the sum of the sampling weights agreed with PCS based counts for both variables except for a difference of less than one.

Tables G. 5 and G. 6 show the sum of the sampling weights before and after this iterative process in each cell. Obtaining the estimated numbers in each cell of Table G. 6 by aggregating the final raked sampling weights may yield better estimates of the cell populations than summing the original sampling weights in Table G. 5.

G 6 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Table G. 5: Estimated Number of MP& M Facilities in each Poststratum before Raking Population Size Water Body Type Bay

Combination Small Streams Medium Streams Large Streams Total Pop   100,000 0 151 1,114 38 1,303 100,000< Pop500,000 11 9 208 944 1,172 500,000< Pop   1,000,000 1 25 31 79 136 1,000,000< Pop   2,000,000 27 19 25 51 122 2,000,000< Pop   4,000,000 0 0 51 11 62 > 4,000,000 0 27 10 0 37 Total 39 232 1,439 1,122 2,832 Source: PCS data.

Table G. 6: Estimated Number of MP& M Facilities in Each Poststratum after Raking Population Water Body Type TotalBay

Combination Small Streams Medium Streams Large Streams Pop   100,0000 0 204 726 4 934 100,000< Pop   500,000 112 50 575 418 1155 500,000< Pop   1,000,000 16 210 126 51 403 1,000,000< Pop   2,000,000 161 64 39 13 277 2,000,000< Pop   4,000,0000 0 45 2 47 > 4,000,0000 0 4 3 0 17 Total 289 542 1,514 488 2,833 0 1 Source: U. S. EPA analysis Tables G. 5 and G. 6 show that sampling weights increase for small stream facilities in the population   100,000 category, while sampling weights decrease for medium and large stream facilities in the same population category, due to their over

representation in the sample.

G. 2 METHODOLOGY FOR DEVELOPING SAMPLE WEIGHTED ESTIMATES FOR SITES WITH MORE THAN ONE MP& M FACILITY The MP& M analysis is based on a random stratified sample of MP& M facilities intended to provide detailed information about specific facility characteristics and to provide national estimates with these characteristics. They are not reach specific sample weights designed to estimate the national occurrence of reaches associated with a specific characteristic of MP& M discharges. For example, the sum of MP & M sample facility weights discharging to one reach is an accurate estimate of the number of national facilities similar to the sample facilities, but is not a valid national estimate of all potential MP& M discharges to that reach or the number of reaches similar to that reach. Accordingly, to use the sample weights to estimate G 7 MP& M EEBA: Appendices Appendix G: Extrapolation Methods the number of similar facilities on similar reaches nationwide requires some adjustments to the standard sample weight based extrapolation process.

It may not be valid to assume that the co location of sample facilities is similar to the co location characteristics of all MP& M facilities. This point is illustrated by the case in which two sample facilities with different weights discharge to the same reach. Assume that one of these two sample facilities has a sample weight of five and the other has a sample weight of 200.

The sample weights indicate that there are four additional facilities in the U. S. that are economically and technically similar to the facility with the weight of five. It is also correct to estimate that the other four facilities will discharge the same volume of the same pollutants as the other four facilities. Now let us assume that there are 199 other facilities nationwide similar to the facility with the weight of 200. The more numerous facilities represented by the facility with a weight of 200 could only rarely be co located with one of the four facilities represented by the sample facility with a weight of five.

EPA developed a method that accounts for joint occurrence on reaches of facilities with different statistical weights to estimate the number of reaches affected by MP& M facilities nationwide. EPA created a series of new discharge variables ( a discharge event) for each reach affected by MP& M sample facilities, and assigned weights for the discharge events that provide a national estimate of pollutant discharges across all reaches. The sample discharge events ( flows and pollutant loadings) are calculated based on the sum of the flows and pollutant loadings for subsets of the MP& M sample facilities that discharge to that reach. The weights for the discharge events are developed from the facility weights for those subsets of facilities. The calculation includes direct MP& M facility discharges and indirect discharges from POTW s ( for options that include them) after considering pollutant removals from POTW treatment.

The number of discharge events on a sample reach equals the number of unique sample weights for the facilities on the reach.

EPA calculated a sample weight for each discharge event based on the sample weights of the facilities contributing loadings and flows to the event. Table G. 7 illustrates discharge event calculations and corresponding sample weights. Steps for calculating the relevant parameters for discharge events on reaches affected by multiple discharges are as follows:

  Rank pollutant loadings ( or discharge flows) in ascending order of facility sample weight for each pollutant of concern discharged by one or more of those facilities.

  Generate the first discharge event loadings ( or flows) as the total loadings ( or flows) from all sample facilities on the reach. Assign the smallest sample weight to the first discharge event ( Wt1 in Table G. 7) among the facilities discharging to the reach. A smaller sample weight relative to the others means that this facility represents no other population facilities that could occur jointly with the other facilities. The weight of the first facility is therefore considered as   used up,  and that facility s loadings ( or flows) are not included in subsequent discharge events defined for the reach.

  Generate subsequent discharge events by removing the loadings ( or flows) of facilities with the smallest sample weight from a running sum of loadings ( or flows) of all facilities in the ranking. The weight assigned to each subsequent event is the remaining unused weight of the facility with the smallest weight among the facilities remaining in the particular discharge event. Calculate this weight as the difference between the weight of the next facility in the ranking and the weight of the previous facility ( Wt2  Wt1 ).

EPA avoids double counting indirect dischargers by including the discharge flow of any given POTW into a reach only once in any given discharge event, even when multiple sample facilities discharge indirectly into one POTW.

This methodology generates a set of discharge events ( loadings or flows) for each pollutant discharged to the reach. The following steps illustrate application of the differential weighting technique to estimating the national number of reaches on which ambient water quality criteria ( AWQC) are exceeded:

  assign a weight to each discharge event based on the weights of the facilities discharging to the reach;

  combine the effluent flow with the stream flow of the reach;

  divide the pollutant loading into the stream flow to determine the pollutant concentration caused by the event;

  compare pollutant concentration to AWQC values to determine whether the concentration exceeds those values;

  identify an estimated AWQC   exceedance  if the concentration is greater than a criterion; and G 8 MP& M EEBA: Appendices Appendix G: Extrapolation Methods   give the AWQC exceedance event the weight of the discharge event, to establish national estimates of the number of reaches on which an AWQC is exceeded.

Table G. 7: Construction of Discharge Events for Any Pollutant Discharged to Any Reach Event Number Loadings and Flows Assigned to Event Weight Assigned to Event One Wt1 Two Wt2  Wt1     N  2 LoadN 2 + LoadN 1 + LoadN FlowN 2 + FlowN 1 + FlowN WtN 2  WtN 3 N  1 LoadN 1 + LoadN FlowN 1 + FlowN WtN 1  WtN 2 N LoadN + FlowN WtN  WtN 1   Notes: N sample facilities discharge to the reach and are ranked in ascending order of sample weight and indexed by i ( 1 =

facility with smallest weight, N = facility with largest weight); Loadi = Loading from facility i; Flowi = Flow from facility i or the POTW associated with facility i; Wti = Sample weight of facility i; and a POTW s flow is included only once per event,

even if multiple facilities in that event discharged through that POTW, to avoid over counting the POTW s flow.

Source: U. S. EPA analysis.

This weighting method is a relatively simplistic approach to a complex analytic issue, and does not provide a precise estimate of the national distribution of in stream MP& M pollutant concentrations that reflects the true co location characteristics of MP& M facilities. A statistically valid estimate of that distribution is not possible given the design of the Section 308 surveys.

However, the differential weighting technique does correct for the significant overstatement of benefits that would result from using a simple weighting approach to estimate national reach characteristics. The Agency believes that this method is a reasonable approach to addressing this issue, given time and resource constraints. Approaches that are both more sophisticated and more expensive might not yield significantly different aggregate findings.

Figure G. 1 provides a graphical example of a hypothetical reach to which three known sample facilities discharge. Table G. 8 provides a numeric example of this calculation for a hypothetical reach to which three known sample facilities discharge.

G 9 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Figure G. 1a: Estimating MP& M Pollutant Loadings to Receiving Streams When Using a Random Sample of MP& M Facilities Source: U. S. EPA analysis.

G 10 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Figure G. 1b: Estimating MP& M Pollutant Loadings to Receiving Streams When Using a Random Sample of MP& M Facilities Note: The situation may be further complicated by actually having a non sampled MP& M facility on the same reach. The differential weighting method does not address this issue.

Source: U. S. EPA analysis.

G 11 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Figure G. 1c: Estimating MP& M Pollutant Loadings to Receiving Streams When Excluding Background Concentrations Source: U. S. EPA analysis.

G 12 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Table G. 8: Example of Differential Sample Weighting Technique Facility Weight Pollutant A lbs/ yr Flow gal/ year Raw data:

1 10 5 2,000,000 2 3 2 4,000,000 3 1 12 10,000,000 Total 14 19 16,000,000 Reach flow ( gal/ year): 100,000,000 Calculating flow and pollutant loadings for the reach:

1. facilities in ascending order of weights 3 1 12 10,000,000 2 3 2 4,000,000 1 10 5 2,000,000 2. late flow and pollutant loadings for discharge event 1 with weight = 1 Facility Pollutant A lbs/ yr Flow gal/ year Remaining Weight 3 12 10,000,000 0 2 2 4,000,000 2 1 5 2,000,000 9 Event 1 19 16,000,000 3. ity with the lowest weight and calculate flow and pollutant loadings for discharge event 2 with weight = 2 ( 3 1)

2 2 4,000,000 0 1 5 2,000,000 7 Event 2 7 6,000,000 4. inate the facility with the next lowest weight and calculate and pollutant loadings for discharge event 3 with weight = 7 ( 10 3)

1 5 2,000,000 0 Event 3 5 2,000,000 5. tream concentrations based on the flows, loadings, and weights for each discharge event and the reach flow Discharge Event Pollutant A Loading lbs/ yr Facility Flow gal/ year Stream Flow gal/ year Total Flow gal/ year In stream Concentration ppb Weight 1 19 16,000,000 100,000,000 116,000,000 0.0955 1 2 7 6,000,000 100,000,000 106,000,000 0.0385 2 2,000,000 100,000,000 102,000,000 0.0286 7 Total Affected Reaches: 10 Rank Calcu Eliminate the facil Elim Estimate national in s Source: U. S. EPA analysis.

G. 3 METHODOLOGY FOR EXTRAPOLATION OF OHIO CASE STUDY RESULTS TO THE NATIONAL LEVEL EPA extrapolated the Ohio case study results to the national level based on three key factors that affect the occurrence and magnitude of benefits:

  the estimated change in MP& M pollutant loadings, which reflects the potential for improvements in surface water quality;

G 13 MP& M EEBA: Appendices Appendix G: Extrapolation Methods   the level of recreational activities on the reaches affected by MP& M discharges. Recreational level reflects the degree to which potentially affected water resources are likely to be in demand by local residents; and   the average household income level, which affects the willingness to pay ( WTP) for water quality improvements.

G. 3.1 Change in Pollutant Loads The first step in applying this alternative extrapolation method was to develop a measure of benefits per pound of pollutant removed for each category of benefits. EPA developed this measure by simply dividing the state level benefit estimates by the total number of pounds of pollutant removed by the regulation in the state of Ohio ($ per pound of pollutant removed).

EPA developed three different measures to better represent the relationship between pollutants and benefit categories:

  Cancer health benefits: EPA divided cancer benefits from the Ohio case study by total carcinogen pounds removed in Ohio to estimate cancer health benefit per pound of carcinogen load removed;

  Lead health benefits: EPA divided lead health benefits from the Ohio case study by total lead pounds removed in Ohio to estimate lead health benefit per pound of lead load removed; and   Recreational benefits: EPA divided recreational benefits from the Ohio case study by total pounds of pollutants removed ( i. e., all pollutants except for total dissolved solids and biological oxygen demand) in Ohio to estimate recreational benefit per pound of pollutant load removed.

All of these values are readily available from the Ohio case study. EPA extrapolated the state level benefits for each of these benefit categories to the national level. First, the Agency multiplied the three estimated benefit per pound of pollutant values for Ohio by the total number of pounds of pollutant removed in each of the three pollutant categories at the national level.

Then, EPA summed across the three benefit categories to obtain an initial estimate for total benefits at the national level.

G. 3.2 Level of Recreational Activities on Reaches Affected by MP& M Discharges The second step was to adjust for differences between Ohio and the nation in the level of recreational activity on reaches affected by MP& M discharges. The level of recreational activity reflects the degree to which water resources likely to be affected by MP& M discharges are in demand by local residents. EPA accounted for differences between Ohio and the nation in recreational intensity because the total user value of water quality improvements is a function of the number of users associated with a particular reach. For this adjustment factor, EPA used the ratio of the number of recreational user days per reach mile at the national level to the number of recreational user days per reach mile in Ohio. Due to data limitations preventing identification of all reaches affected by MP& M discharges, this analysis used total recreational user days and reach miles nationally and in Ohio, rather than only for those reaches affected by MP& M discharges. EPA used the National Demand Study ( NDS) to estimate the number of user days for each recreation activity. Appendix N of this report provides the relevant data by state and recreation activity. To estimate the number of recreational user days, EPA summed the activity

specific values over the four activities considered in this analysis ( i. e., recreational fishing, boating, swimming, and wildlife viewing). EPA s Reach File 1 provided information on the total number of reach miles in Ohio and in the 48 contiguous states. The Agency then calculated the number of user days per reach mile in the state of Ohio and in the nation by simply dividing the total number of user days by the total number of reach miles in the corresponding region. EPA then calculated the adjustment factor as follows:

( G. 1)

G. 3.3 Differences in Household Income In the third step, EPA adjusted the extrapolated benefits based on the expectation that the WTP for water quality improvements will vary with household income level for different parts of the country. The adjustment factor used is the ratio G 14 MP& M EEBA: Appendices Appendix G: Extrapolation Methods of the average household income of the nation to the average household income of Ohio. This adjustment factor assumes that households around the country are willing to pay the same proportion of their incomes for water quality improvements,

although the absolute value of this dollar amount will vary due to regional differences in average household income. The average household income of the nation is estimated as a weighted average, with the median household income for each state weighted by the proportion of MP& M facilities located in that state. The U. S. Census Bureau s Current Population Surveys ( March 1999, 2000, and 2001) provide the basis for data on the median household income by state for the year 2000.3 The 4 1992 Economic Census provides information on total MP& M facilities by state.

( G. 2)

G. 4 RESULTS Table G .9 presents national benefits based on the extrapolation of Ohio case study results.

monetary value of benefits from reduced MP& M discharges is $ 2.5 million ( 2001$) for the final option. This estimate is 60%

higher compared to the benefit estimate based on the traditional extrapolation methodology ( i. e., $ 1.5 million ( 2001$)). As noted in the prior disc ussion, this difference is likely to be due to the mor e rigor ous appr oac h used for the O hio ca se stud y.

The national level analysis of human health benefits finds negligible health benefits from the final rule. In contrast, the Ohio

based extrapolation of human health benefits yields $ 10,860 and $ 295,202 ( 2001$) in human health benefits at the national level fro m red uced incidences o f cance r cases and adve rse hea lth impa cts from lead e xpo sure, respec tively. wn in Ta ble G .9, the estimated huma n health bene fits to Oh io resid ents exc eed the natio nal leve l bene fits based on this extrap olation metho d. T his finding is due to the fact tha t the estima ted p ollutant re mov als for lea d and carcin ogens in O hio exceed those at the national level. As discussed in Appendix H, EP A administered 1,600 screener questionnaires to augment inform ation o n Oh io s M P& M facilities. T he Agenc y used inform ation fro m the sa mple d M P& M facilities to estim ate discharge c harac teristics of non sam pled M P& M chara cteristics ( se e Ap pendix H for detail on estimating sa mple facility discharges in Ohio). As a result, the MP & M facilities included in the case study analysis represent a significant portion of the MP & M facility universe in Ohio. , the sample facilities used at the national level analysis represent only 2 percent of the MP & M facility universe. Thus, analytic findings from the national level analysis may have a larger than desired degree of uncertainty due to a very small sample size. Based on this approach, the As sho In contrast 3 Source: http:// www. census. gov/ hhes/ income/ income00/ statemhi. html 4 Appendix J presents information on distribution of MP& M facilities by state.

G 15 MP& M EEBA: Appendices Appendix G: Extrapolation Methods Table G. 9: Extrapolation of Ohio Case Study Results to the National Level ( 2001$)

Category Ohio Nation Pounds removal of carcinogens 52.45 17.86 Total cancer benefits $ 31,895.42 $ 10,860.86 Total cancer benefits per pound removal of carcinogens $ 608.11 Pounds removal of Lead 217.06 118.54 Total lead benefits $ 540,549.14 $ 295,202.69 Total lead benefits per pound removal of lead $ 2,490.32 Pounds removal of total pollutants 483,258.02 5,412,810.88 Total recreational benefits $ 250,932.62 $ 2,810,612.05 Total recreational benefits per pound removal of total pollutants $ 0.52 Nonuse benefits ( ½ of total recreational benefits) $ 125,466.31 $ 1,405,306.03 Total benefits prior to application of adjustment factors $ 948,843.49 $ 4,521,981.63 Reach miles 11,927 713,702 Annual recreation days ( millions) 49 1,646 Annual recreation days per reach mile 4,148 2,306 Recreational activity adjustment factor 0.5559 Total benefits prior to application of income adjustment factor $ 2,513,907.82 Average household income $ 43,894 $ 42,909 Income Adjustment factor 0.9776 Total benefits $ 2,457,494.66 Source: U. S. EPA analysis.

G 16 MP& M EEBA: Appendices Appendix G: Extrapolation Methods GLOSSARY ambient water quality criteria ( AWQC): levels of water quality expected to render a body of water suitable for its designated use. Criteria are based on specific levels of pollutants that would make the water harmful if used for drinking,

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

differential sample weighting technique: weighting method for all threshold value based analyses, such as the lead

related benefits analysis.

reach: a specific length of river, lake, or marine shoreline standard linear weighting technique: weighting method used where the effects being considered ( e. g., compliance costs) are linearly additive over facilities.

G 17 MP& M EEBA: Appendices Appendix G: Extrapolation Methods THIS PAGE INTENTIONALLY LEFT BLANK G 18 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses INTRODUCTION For the drinking water ( DW) and Ohio analyses, EPA used a simplified fate and transport model to quantify the fate and transport of MP& M pollutant releases to surface waters. This model estimates pollutant concentrations at the initial point of discharge and below the initial discharge reach.

The national MP& M analysis considered pollutant concentrations only at the point of discharge ( see Appendix I. 2.2). The drinking water and Ohio analyses account for the in stream concentrations of pollutants at the initial point of discharge and in reaches downstream from the initial discharge reach.

This appendix describes the equations characterizing the model, its underlying assumptions, and the data sources used in model estimation. EPA combined the equations defining the model with geographic information ( reach flow, velocity, length, etc.) to estimate pollutant concentrations at the initial point of discharge and below the initial discharge reach. Appendix H: Fate and Transport Model for DW and Ohio Analyses APPENDIX CONTENTS H. 1 ions ................. ........... H 1 H. 2 ssumptions ................. ......... H 3 H. 2.1 Stream or River Reach ................. ... H 3 H. 2.2 tudinal Dispersion of the Pollutant is Negligible ................. ............. H 3 H. 2.3 eometry, Suspension of Solids, and Reaction Rates Are Constant within a River Reach ................. ........... H 4 H. 3 ................. ........ H 4 H. 4 ociating Risk with Exposed Populations ....... H 4 H. 5 ................. ............... H 4 H. 5.1 g Data Used in the Drinking Water Risk Analysis .............. H 4 H. 5.2 g Data Used in the Ohio Case Study Analysis ................. ......... H 4 Glossary ................. ................. ..... H 8 Acronyms ................. ................. .... H 9 References ................. ................. .. H 10 Model Equat Model A Steady Flow Conditions Exist within the Longi Flow G Hydrologic Linkages Ass Data Sources Pollutant Loadin Pollutant Loadin The estimation of pollutant concentrations below the initial discharge reach includes several factors that reduce the in stream pollutant concentrations with the passage of time. These factors include: volatilization, sedimentation, and chemical decay from hydrolysis and microbial degradation. EPA adjusted concentrations for changes in stream flow volume in downstream reaches. The discussion below outlines the main assumptions of this analysis. Although more advanced models are available that account for time variable flow, sediment transport, channel geometry changes within a reach, and detailed simulation of all in stream processes, these models will not necessarily produce more accurate results without sufficient data to support the input parameters. Estimates of the additional input parameters required by these models are subject to a high degree of uncertainty when applied on a national scale, and gathering such data is beyond the scope of this study.

EPA has previously applied the approach used in this analysis. For example, the first order contaminant degradation relationship described below in Equation H. 1 is currently being used by the Office of Pollution Prevention and Toxics for exposure analysis in the Risk Screening Environmental Indicator ( RSEI) model ( U. S. EPA, 1999).

H. 1 MODEL EQUATIONS The total pollutant concentration in the water columns for each reach included in the analysis is calculated by the following equation expressed in generic terms of mass ( M), length ( L), and time ( T):

( H. 1)

H 1 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses where:

CT = total tox icant co ncentration in th e water colum n ( M / L3),

W T = mass input rate of toxicant ( M/ T),

Q = river flow ( L3/ T),

VT = overall net loss rate of chemical ( L/ T),

H = flow depth ( L),

x = distance downstream from the point of release ( L), and U = flow velocity ( L/ T).

In reaches where more than one facility discharges or where pollutant loadings occur from upstream reaches, the mass input rate ( WT) represents a combined input rate from all relevant industrial facilities affecting the reach. The relevant industrial facilities in the drinking water risk analysis are all MP & M sample facilities ( see Chapter 13). The relevant industrial facilities in the Ohio case study analysis include: 1   all sample M P& M facilities,

  non sample MP& M facilities, and   non M P& M facilities.

Th e ove rall net loss rate of chem ical ( VT) is given b y:

( H. 2)

where:

VT = overall net loss rate of chemical ( L/ T),

VTd = dissolved chemical loss rate ( L/ T),

VTs = loss of chemical due to sediment interaction ( L/ T),

kl = volatilization transfer coefficient ( L/ T),

Kd = dissolved chemical decay rate ( hydrolysis and microbial degradation) ( 1/ T),

H = flow depth ( L),

fd = dissolved fraction of toxicant ( unitless),

v n = net loss of solids ( L/ T), and f p = particulate fraction of toxicant ( unitless).

The disso lved and particulate fractions of the p ollutant, fd, and f p , respectively, are estimated by:

( H. 3)

and ( H. 4)

1 See Chapter 22 for detail.

H 2 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses where :

K p = partition coefficient [ L3/ M], and S = suspended solid s [ M / L3].

The disso lved concentration of metals and m ost other po llutants in the w ater co lumn is genera lly consid ered a mo re acc urate expression than the total concentrations o f the toxic or bio availab le fractio n. this reaso n, EP A modified Eq uation ( H. 1 ) to express the p ollutant concentration s in terms of dissolved c oncentration. he dissolved fraction of a pollutant is estimated as:

( H. 5)

Substituting Equation ( H. 1) for CT yields the dissolved pollutant concentration for downstream distance x from the discharge reach:

( H. 6) For T H. 2 MODEL ASSUMPTIONS The following three principal assumptions underlie Equation H. 5:

H. 2.1 Steady Flow Conditions Exist within the Stream or River Reach This assumption is necessary due to this study s broad geographical coverage. This assumption significantly reduces the computational effort and input parameter requirements and still produces a good first order fate and transport model of pollutants in surface waters.

The pollutant concentration is completely mixed, both laterally ( across the stream) and vertically ( with depth) within each reach. The approach involves a two dimensional model in which the concentration is uniform over the entire cross section of the stream reach but varies with the distance of the reach. EPA assumed that the contaminant completely mixes at the point of release. This assumption will likely underestimate the concentration of a contaminant release in areas where mixing is incomplete ( e. g., shore hugging plume) and overestimate concentrations in areas beyond the point showing incomplete mixing ( e. g., in areas beyond a shore hugging plume).

H. 2.2 Longitudinal Dispersion of the Pollutant is Negligible The model does not account for mixing outside the plane of discharge along the river reach, although it predicts variation in pollutant concentrations over distance due to both pollutant fate and decay and the differing hydrology of downstream reaches. In natural streams, longitudinal velocity gradients due to channel irregularities can cause mixing, thereby decreasing the peak concentrations as the contaminant moves downstream from the point of release. Under steady state situations,

however, the longitudinal dispersion of the pollutant is assumed to be negligible.

The solution of the dispersion equation approximates a first order decay function such as the one shown in Equations H. 1 and H. 5 under steady flow conditions and complete lateral and vertical mixing.

H 3 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses H. 2.3 Flow Geometry, Suspension of Solids, and Reaction Rates Are Constant within a River Reach EPA assumes the data that describe a river reach and that are calculated for a reach to be constant for the full extent of the reach.

H. 3 HYDROLOGIC LINKAGES EPA modeled pollutant concentrations for a distance of 500 km downstream from the discharge point in the drinking water risk analysis. In the Ohio case study analysis, EPA used the lesser of 500 km or the distance to the Ohio border from the initial discharge point to identify reaches potentially affected by pollutant discharges from the discharge point. The Agency obtained information on the hydrologic linkages between reaches from the RSEI Model ( U. S. EPA, 1999). The data file in RSEI provided flow ( mean flow, 7Q10) and velocity ( mean, low) data for each reach.

EPA used the process equations listed above to estimate both the initial pollutant concentrations at the beginning of each reach and the changes in concentrations as pollutants traveled to the end of the reach. The concentration at the end of each reach served as the value for the beginning of the next reach.

H. 4 ASSOCIATING RISK WITH EXPOSED POPULATIONS The number of individuals served by each drinking water intake is an output of the fate and transport model described in this appendix. If a drinking water intake exists on the initial reach or any downstream reach, then the model calculates the in

stream pollutant concentration at that intake. Data on the population served by the intake is saved with the concentration for further analysis ( see Chapter 13 for a discussion of the cancer risk assessment).

H. 5 DATA SOURCES Data sources used for the fate and transport model are discussed briefly in the section below, by categories of information.

H. 5.1 Pollutant Loading Data Used in the Drinking Water Risk Analysis EPA estimated annual pollutant loadings ( kg/ yr) for the direct and indirect sample MP& M facilities analyzed under the various regulatory options. 2 The Agency first adjusted pollutant loadings for indirect dischargers to reflect POTW treatment,

and then divided annual pollutant loadings by the number of days in one year ( 365) to establish daily pollutant loadings.

H. 5.2 Pollutant Loading Data Used in the Ohio Case Study Analysis EPA estimated pollutant discharges from both MP& M and significant non MP& M sources at the reaches included in the Ohio case study analysis. Consumer perception and valuation of enhanced water based recreational opportunities depend on the absolute level of pollutant contamination at recreation sites, and on the change in contamination from the baseline to the post compliance cases. For this reason, capturing the effect of concurrent discharges from all MP& M and other pollutant sources is particularly important for the recreational benefits analysis.

EPA used the Office of Water's BASINS software package to identify all possible point source dischargers contributing to ambient pollutant concentrations at a given reach. BASIN S is a GIS based system that serves as a database management system for water quality monitoring, point source pollutant discharge, and various geo technical data. Several sources provide information on point source discharges to BASINS, including the Permit Compliance System ( PCS) and Toxic Release Inventory ( TRI) databases. Version 2.0 includes data reported through 1996. Preprogrammed queries in BASINS 2 EPA is not establishing pretreatment standards for indirect dischargers under the final rule.

H 4 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses generate information on various point source discharge variables at either the state or watershed level. BASINS data on point source dischargers include:

  location information on major industrial dischargers, including PCS facilities and facilities reporting under TRI;

  SIC codes;

  flow volume; and   discharge characteristics for up to 50 pollutants or parameters for PCS facilities.

The following sections describe steps used to characterize both MP& M and non MP& M discharges in Ohio.

a. Characterize MP& M facility discharges EPA used different approaches to assign discharge characteristics to MP& M facilities in Ohio, based on the level of information available for each facility. The Agency divided all MP& M facilities into three groups, based on the level of information provided by different sources:

  Facilities covered by the detailed Phase 1 and 2 questionnaire ( hereafter, sampled MP& M facilities)

The detailed surveys contain data on:

  discharge status;

  discharge volume;

  industrial processes used;

  pollution prevention activities;

  employment, revenue, and costs.

EPA engineers estimated loadings of 126 MP& M pollutants using information on facilities' processes and pollution prevention activities. 3 All MP& M facilities in this group therefore have extensive data on their location, size, and discharge characteristics.

  Facilities covered by the detailed Iron and Steel questionnaire ( hereafter sampled I& S facilities)

The detailed I& S survey contained data similar to the detailed MP& M survey. EPA engineers used data on I& S facilities'

processes and pollution prevention activities to estimate pollutant loadings from these facilities.

  Facilities covered by the Phase 2 screener questionnaire or that were covered by the Phase 1 mini DCP ( hereafter,

MP& M screener facilities).

The screener surveys contain significantly fewer data on MP& M facilities. The data collected from the screener survey recipients include:

  facility location, which can be used to assign the facilities to receiving waterways or receiving POTWs;

  SIC codes;

  discharge status ( i. e., whether the facility discharges process wastewater and the approximate amount);

  employment and revenue data;

  whether the facility is engaged in manufacturing, maintenance or repairing activities; and 3 There are 132 pollutants of concern. EPA engineers estimated pollutant loadings for only the pollutants for which EPA is considering calculating pollutant removals at each option. For example, pollutant loadings are not provided for sodium, calcium, and TDS.

H 5 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses   data on MP& M unit operations ( including type of MP& M unit operations performed at the site, and whether process wastewater is discharged as a result of each operation).

The project engineers used these data to estimate pollutant loadings for these facilities. Loading estimates for the screener facilities, which are based on less comprehensive information, involve greater uncertainty.

  Facilities that respond to neither the screener nor detailed questionnaires ( hereafter referred to as non sampled MP& M facilities)

To address the problem of omitted discharge information on non sampled MP& M facilities, EPA used information from the 1600 screener MP& M facilities and a random draw approach to assign the relevant characteristics for non sampled MP& M facilities. Each screener facility represents n non sampled facilities, where n is determined by the screener facility sample weight. All non sampled facilities are smaller indirect dischargers because all direct MP& M facility dischargers and large indirect discharging facilities in Ohio are covered by the long, short, or screener questionnaire.

The exact location of non sampled facilities is unknown. All non sampled facilities discharge to one of the Ohio POTWs because they are indirect dischargers. The Agency assigned n facilities represented by each screener facility to the receiving POTW s by drawing a random sample of n POTW s from the universe of POTWs in Ohio. 4 The Agency assigned screener facility characteristics ( i. e., pollutant loadings) to all n facilities represented by the screener facility.

EPA used a random draw procedure for all observations from the screener survey that have a sample weight greater than one.

b. Characterize non MP& M point source discharges EPA used preprogrammed queries in BASINS to obtain information on all non MP& M point source discharges in Ohio.

BASINS data on non MP& M point source dischargers include:

  location,

  SIC codes,

  flow volume, and   discharge characteristics for up to 50 pollutants or parameters for PCS facilities.

The Agency assigned discharge characteristics to all non MP& M industrial direct discharges based on the information provided in BASINS. POTW effluent may contain pollutants from both MP& M and non MP& M discharges. The Agency combined information from BASINS with loading estimates provided by the project engineers to estimate total pollutant loadings from a given POTW . This analysis used the following assumptions to estimate total POTW pollutant loadings under the baseline discharge levels:

  If a POTW was not estimated to receive discharges from the MP& M facilities, then the analysis used POTW loadings reported in BASINS.

  If a pollutant or a parameter was not reported in BASINS, then the analysis used aggregate loadings from all MP& M facilities discharging to a given POTW to calculate total POTW loadings of a given pollutant.

  If a POTW was estimated to receives discharges from MP& M facilities and a given pollutant was reported in BASINS, then the analysis used the greater of the aggregate loadings from all MP& M facilities or POTW loadings reported.

EPA estimated post compliance pollutant loadings from each POTW by subtracting the estimated reduction in the MP& M facility loadings for a given pollutant from its total baseline loadings for a given POTW.

c. Characterize non point source discharges The water quality analysis in Ohio used empirical data on Total Kjeldahl Nitrogen ( TKN) concentrations to characterize the baseline water quality conditions. Empirical data on in stream concentrations captured TKN contribution from both point 4 The Agency was unable to validate random assignments because POTWs do not know all of their MP& M dischargers.

H 6 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses and non point sources under baseline conditions. EPA estimated changes in TKN concentrations resulting from the final rule by using the estimated pollutant loading reductions from MP& M sources and the water quality model described above. The Agency assumed that the non point source contribution of toxic pollutants found in MP& M effluent to ambient concentrations of these pollutants in Ohio s streams and lakes is negligible.

H 7 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses GLOSSARY BASINS: a software package that serves as a database management system for water quality monitoring, point source pollutant discharge, and various geo technical data, and also provides an analytic platform for modeling in stream pollutant concentrations over an entire watershed based on multiple sources of pollutants within the watershed.

( http:// www. epa. gov. OST/ BASINS)

hydrolysis: the decomposition of organic compounds by interaction with water. ( http:// www. epa. gov/ OCEPAterms)

metals: inorganic compounds, generally nonvolatile, and which cannot be broken down by biodegradation processes. They are a particular concern because of their prevalence in MP& M effluents. Metals can accumulate in biological tissues,

sequester into sewage sludge in POTWs, and contaminate soils and sediments when released to the environment. Some metals are quite toxic even when present at relatively low levels.

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

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

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

MP& M reach: a reach to which an MP& M facility discharges.

sedimentation: letting solids settle out of wastewater by gravity. ( http:// www. epa. gov/ OCEPAterms)

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

Toxic Release Inventory ( TRI): database of toxic releases in the United States compiled from SARA Title III Section 313 reports. ( http:// www. epa. gov/ OCEPAterms)

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

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

H 8 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses ACRONYMS PCS: Permit Compliance System RSEI: Risk Screening Environmental Indicator model TKN: Total Kjeldahl Nitrogen TRI: Toxic Release Inventory H 9 MP& M EEBA: Appendices Appendix H: Fate and Transport Model for DW and Ohio Analyses REFERENCES U. S. Environmental Protection Agency ( U. S. EPA). 1999. Risk Screening Environmental Indicators Model: Version 1.0,

July 6, Washington, DC: Office of Pollution Prevention and Toxics. http:// www. epa. gov/ opptintr/ env_ ind/ index. html.

H 10 MP& M EEBA: Appendices Appendix I: Environmental Assessment INTRODUCTION This Environmental Assessment estimates the environmental impact of MP& M discharges on water bodies and POTWs under both current conditions and those corresponding to four regulatory options: the Final Option, Proposed/ NODA Option, Directs + 413 to 433 Upgrade Option, and Directs +

All to 433 Upgrade Option. 1 EPA estimates four types of environmental impacts:

  the occurrence of pollutant concentrations in excess of EPA ambient water quality criteria ( AWQC) for protection of human health in waterways ( e. g., streams, lakes, bays, and estuaries)

receiving discharges from MP& M facilities;

  the occurrence of pollutant concentrations in excess of AWQC for protection of aquatic species in waterways receiving discharges from MP& M facilities;

  the occurrence of POTW inhibition problems resulting from MP& M facilities' discharges; and   barriers to POTW s  use of preferred sewage sludge management or disposal methods ( i. e., beneficial land application or surface disposal), due to metals discharges from MP& M facilities. Appendix I: Environmental Assessment APPENDIX CONTENTS I. 1 racterization ................. . I 4 I. 1.1 ying MP& M Pollutants ................. I 4 I. 1.2 hysical Chemical Characteristics and Toxicity Data of MP& M Pollutants ................. ..... I 9 I. 1.3 MP& M Pollutants Based on Risk to Aquatic Receptors ................. .......... I 21 I. 1.4 umptions and Limitations ................. I 23 I. 2. hodology ................. ................. I 23 I. 2.1 ple Set Data Analysis and National Extrapolation ................. .............. I 23 I. 2.2 r Quality Modeling ................. .... I 23 I. 2.3 ct of Indirect Discharging Facilities on POTW Operations ................. .......... I 25 I. 2.4 umptions and Limitations ................. I 27 I. 3 ................. ................. I 28 I. 3.1 ty Specific Data ................. ...... I 28 I. 3.2 r Body Specific Data ................. .. I 28 I. 3.3 ation Used to Evaluate POTW Operations . . I 29 I. 4 ................. ................. ..... I 33 I. 4.1 n Health Impacts ................. ..... I 34 I. 4.2 ic Life Effects ................. ....... I 37 I. 4.3 Effects ................. ............ I 41 Glossary ................. ................. ....... I 44 Acronyms ................. ................. ...... I 48 References ................. ................. ..... I 49 MP& M Pollutant Cha Identif P Grouping Ass Met Sam Wate Impa Ass Data Sources Facili Wate Inform Results Huma Aquat POTW EPA also estimated changes in human health risk from reduced exposure to MP& M pollutants via consumption of contaminated fish and drinking water. Chapters 13 and 14 of this EEBA present both the methodology used to estimate human health impacts from exposure to MP& M pollutants and the results of this analysis.

EPA assessed potential environmental impacts of MP& M discharges on the receiving water bodies and POTWs by using pollutant fate and toxicity data in conjunction with various modeling techniques. EPA quantified the releases of 132 pollutants of concern under the final and alternative regulatory options. 2 EPA then evaluated potential site specific aquatic life and human health impacts resulting from the baseline and post regulation pollutant releases. EPA compared projected water concentrations for each pollutant to either ( a) EPA water quality criteria, or ( b) toxic effect levels ( i. e., lowest reported 1 The results of the Proposed/ NODA Option are not directly comparable to the final option alternatives. The total number of facilities reported for the Proposed/ NODA Option analysis differs from the facility count reported for the final rule and the two upgrade options.

After deciding in July 2002 not to consider the NODA option as the basis for the final rule, EPA performed no more analysis on the NODA option, including not updating facility counts and related analyses for the change in subcategory and discharge status classifications.

2 EPA originally identified 150 MP& M POCs. Of these 150 POCs, the Agency estimated loadings for 132 pollutants for the phase 2 proposal and NODA. The benefits analysis presented in earlier chapters is based on 132 pollutants for which loadings are available. The final regulation covers only the Oily Wastes subcategory and benefit reductions were estimated for 122 pollutants.

I 1 MP& M EEBA: Appendices or estimated toxic concentration that causes a problem) in the absence of water quality criteria for a pollutant. Figure I. 1 depicts steps used in the environmental assessment. The following sections detail these analytic steps. Appendix I: Environmental Assessment Figure I. 1a: MP& M Environmental Impact Assessment Source: U. S. EPA analysis.

I 2 MP& M EEBA: Appendices Appendix I: Environmental Assessment Figure I. 1b: MP& M Environmental Impact Assessment ( Continued)

Source: U. S. EPA analysis.

I 3 MP& M EEBA: Appendices Appendix I: Environmental Assessment The remainder of this appendix is organized as follows. Section I. 1 provides information on the pollutants found in MP& M discharges. Section I. 2 describes the methodology used to estimate environmental impacts, including extrapolation of sample sets to the national level and estimates of water quality impacts. Section I. 3 describes data sources for both MP& M facilities and POTWs. Section I. 4 presents the environmental assessment results.

I. 1 MP& M POLLUTANT CHARACTERIZATION The extent of human and ecological exposure and risk from environmental releases of toxic chemicals depends on chemical specific properties, the mechanism and media of release, and site specific environmental conditions.

Chemical specific properties include toxic effects on living organisms, and the fate of chemicals in the environment. EPA estimated the fate of MP& M pollutants based on their propensity to volatilize, adsorb onto sediments, bioconcentrate, and biodegrade. EPA characterized the fate and toxicity of MP& M pollutants in three steps:

    identifying pollutants of concern ( POCs) in MP& M discharges,

    compiling physical chemical and toxicity data for those pollutants, and     grouping pollutants based on their characteristics.

The pollutant specific fate and toxicity data were used in various portions of the quantitative benefits assessment. In addition,

EPA summarized the distribution of MP& M pollutants based on their fate and toxicity properties using the groupings developed in the third step. This summary is presented in Chapter 12.

I. 1.1 Identifying MP& M Pollutants EPA sampled MP& M facilities nationwide to assess the concentrations of pollutants in MP& M effluents. The Agency collected samples of raw wastewater from MP& M facilities and applied standard water analysis protocols to identify and quantify the pollutant levels in each sample. EPA used these analytical data, along with selection criteria, to identify 132 contaminants of potential concern. MP& M POCs include 43 priority pollutants ( PP), 3 conventional pollutants, and 86 nonconventional pollutants.

EPA then evaluated the potential environmental fate of these pollutants and their toxicity to humans and aquatic receptors.

EPA was able to assess the potential fate and toxicity of 118 of these pollutants, including 43 priority pollutants ( 33 priority organics, nine priority metals and one inorganic) and 75 nonconventional pollutants ( 50 nonconventional organics, 18 nonconventional metals, and seven nonconventional inorganics). Table I. 1 presents the potential fate and toxicity, based on known characteristics of each chemical, of 132 pollutants of concern. Potential fate and toxicity data are not available for four conventional, 2 nonconventional, and eight bulk nonconventional pollutants ( also listed in Table I. 1) associated with adverse water quality impacts, as described in Section 12.1.3 of this report.

I 4 MP& M EEBA: AppendicesAppendix I: Environmental Assessment I 5 Table I. 1: Potential Fate and Toxicity of Pollutants of Concern TypeaPollutantCAS Toxicity to Aquatic Life ( Freshwater) Toxicity to Aquatic Life ( Saltwater) VolatilityAdsorptionBCFbBiodegcRfDdSFeDWCf/ gHAPhPPi AcuteChronicAcuteChronic OAcenaphthene83329ModerateLowModerateLowModerateModerateModerateLow   OAcetone67641LowLowLowLowModerateLowInsignificantModerate   OAcetophenone98862LowLowUnknownUnknownLowLowLowModerate   OAcrolein107028HighHighHighHighModerateNonadsorptiveModerateLow     OAniline62533ModerateHighLowLowLowLowLowModerate   OAnthracene120127HighHighHighModerateModerateHighModerateResistant   OBenzoic acid65850LowLowUnknownUnknownLowLowLowModerate   OBenzyl alcohol100516LowLowLowLowLowNonadsorptiveInsignificantModerate   OBiphenyl92524ModerateLowLowLowModerateModerateModerateModerate   OBis( 2 ethylhexyl) phthalate117817UnknownUnknownUnknownUnknownNonvolatileHighModerateModerate   M   OBromo 2 chlorobenzene, 1 694804LowLowUnknownUnknownModerateModerateModerateLow OBromo 3 chlorobenzene, 1 108372LowLowUnknownUnknownModerateModerateModerateLow OButyl benzyl phthalate85687ModerateLowModerateLowLowHighModerateModerate   OCarbon disulfide75150LowHighUnknownHighHighLowLowUnknown   OChlorobenzene108907LowLowLowLowHighLowLowLow   M   OChloroethane75003LowLowUnknownUnknownHighLowLowLow       OCresol, o 95487LowLowLowLowLowLowLowModerate   OCresol, p 106445LowLowUnknownUnknownLowLowLowHigh   OCyanide57125HighHighHighHighUnknownLowInsignificantModerate   M   OCymene, p 99876LowLowLowLowHighModerateHighLow ODecane, n 124185LowLowLowLowUnknownHighHighModerate ODibenzothiophene132650ModerateLowUnknownUnknownModerateHighHighUnknown ODichloroethene, 1,1 75354LowLowLowLowHighLowLowResistant   M   ODichloromethane75092LowLowLowLowHighLowInsignificantLow   M   ODimethyl phthalate131113LowLowLowLowNonvolatileLowLowModerate   ODimethylformamide, N, N 68122LowLowUnknownUnknownNonvolatileNonadsorptiveInsignificantModerate   ODimethylphenanthrene, 3,6 1576676ModerateModerateUnknownUnknownLowHighHighModerate ODimethylphenol, 2,4 105679LowLowUnknownUnknownLowLowModerateModerate   ODi n butyl phthalate84742ModerateLowModerateHighLowModerateModerateModerate     ODinitrophenol, 2,4 51285LowLowLowLowLowModerateInsignificantResistant     ODinitrotoluene, 2,6 606202LowModerateUnknownUnknownLowLowLowResistant   ODi n octyl phthalate117840ModerateModerateUnknownUnknownLowModerateHighLow   ODioxane, 1,4 123911LowLowUnknownUnknownLowLowInsignificantResistant   ODiphenylamine122394LowLowUnknownUnknownLowModerateModerateModerate   ODiphenyl ether101848ModerateLowLowUnknownModerateModerateModerateModerate ODocosane, n 629970LowLowLowLowUnknownHighHighModerate ODodecane, n 112403LowLowLowLowUnknownHighHighModerate OEicosane, n 112958LowLowLowLowUnknownHighHighModerate OEthylbenzene100414LowLowModerateModerateHighLowLowModerate   M   OFluoranthene206440HighHighHighModerateModerateHighHighResistant   OFluorene86737ModerateHighModerateModerateModerateModerateLowLow   OHexacosane, n 630013LowLowLowLowUnknownUnknownUnknownModerate OHexadecane, n 544763LowLowLowLowUnknownHighHighModerate OHexanoic acid142621LowLowUnknownUnknownModerateLowLowModerate OHexanone, 2 591786LowLowUnknownUnknownModerateLowLowModerate   OIsobutyl alcohol78831LowLowLowLowModerateLowInsignificantModerate                                                             MP& M EEBA: AppendicesAppendix I: Environmental Assessment Table I. 1: Potential Fate and Toxicity of Pollutants of Concern TypeaPollutantCAS Toxicity to Aquatic Life ( Freshwater) Toxicity to Aquatic Life ( Saltwater) VolatilityAdsorptionBCFbBiodegcRfDdSFeDWCf/ gHAPhPPi AcuteChronicAcuteChronic I 6 OIsophorone78591LowLowLowLowLowLowInsignificantLow       OIsopropylnaphthalene, 2 2027170ModerateModerateUnknownUnknownModerateHighHighUnknown OMethyl ethyl ketone78933LowLowLowLowModerateNonadsorptiveInsignificantModerate   OMethyl isobutyl ketone108101LowLowLowLowModerateLowInsignificantModerate   OMethyl methacrylate80626LowLowUnknownUnknownModerateLowLowLow   OMethylfluorene, 1 1730376ModerateLowUnknownUnknownModerateHighHighUnknown OMethylnaphthalene, 2 91576LowLowModerateModerateModerateModerateHighModerate   OMethylphenanthrene, 1 832699ModerateModerateUnknownUnknownLowHighHighUnknown ONaphthalene91203LowLowLowLowModerateLowLowModerate     ONitrophenol, 2 88755LowLowLowLowLowLowLowLow   ONitrophenol, 4 100027LowLowLowLowNonvolatileLowModerateModerate     ONitrosodimethylamine, N 62759LowLowLowLowNonvolatileLowInsignificantResistant     ONitrosodiphenylamine, N 86306LowLowLowLowLowModerateModerateLow   ONitrosopiperidine, N 100754LowLowUnknownUnknownNonvolatileNonadsorptiveInsignificantResistant OOctacosane, n 630024LowLowLowLowUnknownUnknownUnknownModerate OOctadecane, n 593453LowLowLowLowUnknownHighHighModerate OParachlorometacresol59507LowLowUnknownUnknownLowLowModerateLow   OPhenanthrene85018ModerateModerateModerateModerateModerateHighModerateResistant   OPhenol108952LowLowLowLowLowLowInsignificantHigh     OPyrene129000ModerateModerateUnknownUnknownModerateHighHighResistant   OPyridine110861LowLowUnknownUnknownLowNonadsorptiveInsignificantModerate   OStyrene100425LowLowLowLowHighLowLowLow   M   OTerpineol, alpha 98555LowLowUnknownUnknownModerateLowLowModerate OTetrachloroethene127184LowLowLowLowHighLowLowResistant   M   OTetracosane, n 646311LowLowLowLowUnknownHighHighModerate OTetradecane, n 629594LowLowLowLowUnknownHighHighModerate OToluene108883LowLowLowLowHighLowLowModerate   M   OTriacontane, n 638686LowLowLowLowUnknownUnknownUnknownModerate OTrichloroethene79016LowLowLowLowHighLowLowResistant   M   OTrichlorofluoromethane75694LowLowUnknownUnknownHighLowLowResistant   OTrichloromethane67663LowLowLowLowHighLowInsignificantResistant   THM   OTripropyleneglycolmethylether20324338LowLowUnknownUnknownNonvolatileLowInsignificantModerate OXylene, m 108383LowLowLowLowHighLowModerateLow   M   OXylene, m & p* 179601231LowLowLowLowHighLowModerateLow   M   OXylene, o 95476LowLowLowLowHighLowModerateLow   M   OXylene, o & p* 136777612LowLowLowLowHighLowModerateLow   M   OZiram \ Cymate137304HighHighLowLowNonvolatileNonadsorptiveInsignificantResistant   MAluminum7429905ModerateModerateUnknownUnknownNonvolatileHighModerateResistant   SM MAntimony7440360LowLowLowLowNonvolatileHighInsignificantResistant   M   MBarium7440393LowLowUnknownUnknownNonvolatileHighUnknownResistant   M MBeryllium7440417ModerateHighUnknownUnknownNonvolatileHighLowResistant   M   MCadmium7440439HighHighHighHighNonvolatileHighModerateResistant   M   MCalcium7440702UnknownLowUnknownUnknownNonvolatileHighUnknownResistant MChromium7440473ModerateModerateLowModerateNonvolatileHighLowResistant   M MChromium hexavalent18540299HighModerateLowModerateNonvolatileHighLowResistant   M MCobalt7440484LowModerateUnknownModerateNonvolatileHighUnknownResistant                                       MP& M EEBA: AppendicesAppendix I: Environmental Assessment Table I. 1: Potential Fate and Toxicity of Pollutants of Concern TypeaPollutantCAS Toxicity to Aquatic Life ( Freshwater) Toxicity to Aquatic Life ( Saltwater) VolatilityAdsorptionBCFbBiodegcRfDdSFeDWCf/ gHAPhPPi AcuteChronicAcuteChronic I 7 MCopper7440508HighHighHighHighNonvolatileHighModerateResistant   TT   MGold7440575UnknownUnknownUnknownUnknownNonvolatileHighUnknownResistant MIron7439896UnknownLowLowLowNonvolatileHighUnknownResistant   SM MLead7439921HighHighModerateHighNonvolatileHighLowResistantTT   MMagnesium7439954LowLowUnknownUnknownNonvolatileHighHighResistant MManganese7439965UnknownLowUnknownModerateNonvolatileHighUnknownResistant   SM MMercury7439976HighHighHighHighHighHighHighResistantM   MMolybdenum7439987UnknownModerateUnknownUnknownNonvolatileHighUnknownResistant   MNickel7440020ModerateModerateHighHighNonvolatileLowLowResistant   M   MSelenium7782492HighHighModerateModerateNonvolatileHighInsignificantResistant   M MSilver7440224HighHighHighHighNonvolatileHighInsignificantResistant   SM   MSodium7440235LowLowUnknownUnknownNonvolatileHighUnknownResistant MThallium7440280LowModerateLowLowNonvolatileHighModerateResistant   M   MTin7440315UnknownModerateUnknownUnknownNonvolatileHighUnknownResistant   MTitanium7440326UnknownLowUnknownUnknownNonvolatileHighUnknownResistant   MVanadium7440622LowHighUnknownUnknownNonvolatileHighUnknownResistant   MYttrium7440655UnknownUnknownUnknownUnknownNonvolatileHighUnknownResistant MZinc7440666ModerateLowHighModerateNonvolatileHighLowResistant   SM OIAmmonia as N7664417LowLowLowLowModerateNonadsorptiveUnknownModerate OIArsenic7440382ModerateLowHighModerateUnknownUnknownLowUnknown   M   OIBoron7440428UnknownModerateUnknownUnknownUnknownUnknownUnknownUnknown   OIChloride16887006LowLowUnknownUnknownUnknownUnknownUnknownUnknownSM OIFluoride16984488LowLowUnknownUnknownUnknownUnknownUnknownUnknown   M OIPhosphate14265442UnknownUnknownUnknownUnknownUnknownUnknownUnknownUnknown OISulfate14808798UnknownLowUnknownUnknownUnknownUnknownUnknownUnknownSM OISulfide18496258UnknownHighUnknownHighUnknownUnknownUnknownUnknown OIPhosphorus ( as PO4) UnknownUnknownUnknownUnknownUnknownUnknownUnknownUnknown CPBOD 5 day ( carbonaceous) C 003 CPOil and Grease CPOil and Grease ( as Hem) C 036 CPTotal Suspended Solids ( TSS) C 009 BNCPAmenable CyanideC 025 BNCPChemical Oxygen Demand ( COD) C 004 BNCPTotal Dissolved Solids ( TDS) C 010 BNCPTotal Kjeldahl NitrogenC 021 BNCPTotal Organic Carbon ( TOC) C 012   MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 1: Potential Fate and Toxicity of Pollutants of Concern Typea Pollutant CAS Toxicity to Aquatic Life ( Freshwater) Toxicity to Aquatic Life ( Saltwater) Volatility Adsorption BCFb Biodegc RfDd SFe DWC f/ g HAPh PPi Acute Chronic Acute Chronic BNCP Total Petroleum Hydrocarbons ( as Sgt  hem) C 037 BNCP Total Recoverable Phenolics C 020 BNCP Weak acid Dissociable Cyanide C 042 Table Notes:

Unless indicated otherwise, all metals are assumed to be nonvolatile, to have high adsorption,

and to be resistant to biodegradation.

a Type O = Organic M = Metal OI = Other Inorganic CP = Conventional Pollutant BNCP = Bulk Nonconventional Pollutant b BCF = Bioconcentration Factor Biodeg = Biodegradation Potential d RfD = Reference Dose Source: U. S. EPA analysis. e SF = Slope Factor f DWC = Drinking Water Criteria g Drinking Water Criteria Codes M = Maximum Contaminant Level ( MCL) established for health based effect SM = Secondary Maximum Contaminant Level ( SMCL) established for taste or aesthetic effect THM = MCL established for trihalomethanes TT = Treatment technology action level established h HAP = Hazardous Air Pollutant i PP = Priority Pollutant I 8 c MP& M EEBA: Appendices Appendix I: Environmental Assessment I. 1.2 PhysicalChemical Characteristics and Toxicity Data of MP& M Pollutants Pollutants present in MP& M effluents can have significant effects on human health and aquatic receptors. EPA used various data sources to evaluate both pollutant specific fate and toxicity and potential human health effects, including:

  reference doses ( RfDs ),

  cancer potency slope factors ( SFs),

  human health based water quality criteria ( WQC ),

  maximum contaminant levels ( MCLs) for drinking water protection and other drinking water related criteria,

and   hazardous air pollutant ( HAP) and priority pollutant ( PP) lists.

To evaluate potential fate and effects in aquatic environments, the Agency relied on:

  measures of acute and chronic toxicity to aquatic species,

  bioconcentration factors for aquatic species,

  Henry's Law ( H) constants ( to estimate volatility),

  adsorption coefficients ( to estimate association with bottom sediments), and   biodegradation half lives ( to estimate the removal of chemicals via microbial metabolism).

The data sources used in the assessment include:

  EPA ambient WQC documents and updates;

  EPA's ASsessment Tools for the Evaluation of Risk ( ASTER);

  the AQUatic Information REtrieval System ( AQUIRE) and the Environmental Research Laboratory Duluth fathead minnow database;

  EPA's Integrated Risk Information System ( IRIS);

  EPA's Health Effects Assessment Summary Tables ( HEAST);

  EPA's 1991 and 1993 Superfund Chemical Data Matrix ( SCDM) ;

  Syracuse Research Corporation's CHEMFATE and BIODEG databases; and   EPA and other government reports, scientific literature, and other primary and secondary data sources.

EPA also obtained information on chemicals for which the sources listed above did not provide physical chemical properties and/ or toxicity data, to ensure that the assessment be as comprehensive as possible. To the extent possible, EPA estimated values for the chemicals using the quantitative structure activity relationship ( QSAR) model incorporated in ASTER.

The Agency also used published linear regression correlation equations to determine some physical chemical properties.

a. Human health effects EPA used various data sources to determine pollutant specific toxicity to human health. EPA obtained RfDs and SFs from IRIS, HEAST, and EPA s Region II Risk Based Concentration ( RBC) table. EPA developed drinking water criteria and human health based AWQC values for two exposure routes: ( 1) ingesting the pollutant via contaminated aquatic organisms only ( carcinogens and non carcinogens), and ( 2) ingesting the pollutant via both water and contaminated aquatic organisms I 9 MP& M EEBA: Appendices ( non carcinogens only). Table I. 2 summarizes pollutant toxicity data pertaining to human health. In addition to fate and toxicity data, Table I. 1 also includes HAP and PP lists. Short descriptions and definitions for each of the measures of human health effects are provided below. Appendix I: Environmental Assessment Table I. 2:

CAS Number Pollutant Name Human Health AWQC Values Ingesting Water and Organisms Ingesting Organisms Only Slope Factor Reference Dose Drinking Water Criteria (   g/ l) (   g/ l) ( mg/ kg/ day) ( mg/ kg/ day) (   g/ l)

51285 Dinitrophenol, 2,4 70 14000 0.002 57125 Cyanide 700 220000 0.02 200 59507 Parachlorometacresol 56000 270000 2 62533 Aniline 5.8 95 0.0057 62759 Nitrosodimethylamine, N 0.00069 8.1 51 65850 Benzoic acid 130000 2900000 4 67641 Acetone 3500 2800000 0.1 67663 Trichloromethane 5.7 470 0.0061 0.01 100 68122 Dimethylformamide, N, N 3500 220000000 0.1 75003 Chloroethane 12 520 0.0029 0.4 75092 Dichloromethane 4.7 1600 0.0075 0.06 5 75150 Carbon disulfide 3400 94000 0.1 75354 Dichloroethene, 1,1 0.057 3.2 0.6 0.009 7 75694 Trichlorofluoromethane 9100 66000 0.3 78591 Isophorone 36 2600 0.00095 0.2 78831 Isobutyl alcohol 10000 1500000 0.3 78933 Methyl ethyl ketone 21000 6500000 0.6 79016 Trichloroethene 3.1 92 0.011 0.006 5 80626 Methyl methacrylate 48000 2300000 1.4 83329 Acenaphthene 1200 2700 0.06 84742 Di n butyl phthalate 2700 12000 0.1 85018 Phenanthrene 85687 Butyl benzyl phthalate 3000 5200 0.2 86306 Nitrosodiphenylamine, N 5 16 0.0049 86737 Fluorene 720 1500 0.04 88755 Nitrophenol, 2

91203 Naphthalene 680 21000 0.02 91576 Methylnaphthalene, 2 75 84 0.02 92524 Biphenyl 720 1200 0.05 95476 Xylene, o 42000 100000 2 10000 95487 Cresol, o 1700 30000 0.05 98555 Terpineol, alpha

98862 Acetophenone 3400 98000 0.1 99876 Cymene, p

100027 Nitrophenol, 4 220 1100 0.008 100414 Ethylbenzene 3100 29000 0.1 700 100425 Styrene 6700 160000 0.2 100 100516 Benzyl alcohol 10000 810000 0.3 100754 Nitrosopiperidine, N

101848 Diphenyl Ether 105679 Dimethylphenol, 2,4 540 2300 0.02 106445 Cresol, p 170 3100 0.005 107028 Acrolein 410 1000 0.02 Human Health Data for 132 MP& M Pollutants of Concern I 10 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 2:

CAS Number Pollutant Name Human Health AWQC Values Ingesting Water and Organisms Ingesting Organisms Only Slope Factor Reference Dose Drinking Water Criteria (   g/ l) (   g/ l) ( mg/ kg/ day) ( mg/ kg/ day) (   g/ l)

108101 Methyl isobutyl ketone 2800 360000 0.08 108372 Bromo 3 chlorobenzene, 1

108383 Xylene, m 42000 100000 2 10000 108883 Toluene 6800 200000 0.2 1000 108907 Chlorobenzene 680 21000 0.02 100 108952 Phenol 21000 4600000 0.6 110861 Pyridine 35 5400 0.001 112403 Dodecane, n ( a)

112958 Eicosane, n ( a)

117817 Bis( 2 ethylhexyl) phthalate 1.8 5.9 0.014 0.02 6 117840 Di n octyl phthalate 37 39 0.02 120127 Anthracene 4100 6800 0.3 122394 Diphenylamine 470 1000 0.025 123911 Dioxane, 1,4 3.2 2400 0.011 124185 Decane, n

127184 Tetrachloroethene 320 3500 0.052 0.01 5 129000 Pyrene 230 290 0.03 131113 Dimethyl phthalate 310000 2900000 132650 Dibenzothiophene 137304 Ziram \ Cymate 700 220000000 0.02 142621 Hexanoic acid 206440 Fluoranthene 300 370 0.04 544763 Hexadecane, n ( a)

591786 Hexanone, 2 1400 65000 0.04 593453 Octadecane, n ( a)

606202 Dinitrotoluene, 2,6 34 900 0.001 629594 Tetradecane, n ( a)

629970 Docosane, n

630013 Hexacosane, n ( b)

630024 Octacosane, n ( b)

638686 Triacontane, n ( b)

646311 Tetracosane, n ( b)

694804 Bromo 2 chlorobenzene, 1

832699 Methylphenanthrene, 1

1576676 Dimethylphenanthrene, 3,6

1730376 Methylfluorene, 1

2027170 Isopropylnaphthalene, 2

7429905 Aluminum 20000 47000 1 50 7439896 Iron 300 0.3 300 7439921 Lead 15 7439954 Magnesium 7439965 Manganese 50 100 0.14 50 7439976 Mercury 0.05 0.051 2 7439987 Molybdenum 0.005 7440020 Nickel 610 4600 0.02 7440224 Silver 170 110000 0.005 100 7440235 Sodium 7440280 Thallium 1.8 6.5 0.00007 2 Human Health Data for 132 MP& M Pollutants of Concern I 11 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 2:

CAS Number Pollutant Name Human Health AWQC Values Ingesting Water and Organisms Ingesting Organisms Only Slope Factor Reference Dose Drinking Water Criteria (   g/ l) (   g/ l) ( mg/ kg/ day) ( mg/ kg/ day) (   g/ l)

7440315 Tin 0.6 7440326 Titanium 4 7440360 Antimony 14 4300 0.0004 6 7440382 Arsenic 0.02 0.16 1.5 0.0003 50 7440393 Barium 1000 0.07 2000 7440417 Beryllium 66 1100 0.002 4 7440428 Boron 0.09 7440439 Cadmium 14 84 0.0005 5 7440473 Chromium 50000 1000000 1.5 100 7440484 Cobalt 0.06 7440508 Copper 650 1200 0.04 1300 7440575 Gold 7440622 Vanadium 0.007 7440655 Yttrium 7440666 Zinc 9100 69000 0.3 5000 7440702 Calcium 7664417 Ammonia as N 7782492 Selenium 170 11000 0.005 50 14265442 Phosphate 14808798 Sulfate 250000 16887006 Chloride 250000 16984488 Fluoride 0.06 4000 18496258 Sulfide 100 10000 18540299 Chromium hexavalent 100 2000 0.003 100 20324338 Tripropyleneglycolmethyl ether 136777612 Xylene, o & p ( c) 42000 100000 2 10000 179601231 Xylene, m & p ( c) 42000 100000 2 10000 C003 BOD 5 day ( carbonaceous)

C004 Chemical Oxygen Demand ( COD)

C009 Total Suspended Solids ( TSS)

C010 Total Dissolved Solids ( TDS)

C012 Total Organic Carbon ( TOC)

C020 Total Recoverable Phenolics C021 Total Kjeldahl Nitrogen C025 Amenable Cyanide C036 Oil And Grease ( as Hem)

C037 Total Petroleum Hydrocarbons ( as Sgt hem)

C042 Weak acid Dissociable Cyanide Phosphorus ( as PO4)

Oil and Grease Human Health Data for 132 MP& M Pollutants of Concern Sources: U. S. EPA ( 1980), U. S. EPA ( 1984), U. S. EPA ( 1997), U. S. EPA ( 1998), U. S. EPA ( 1998/ 99), Worthing ( 1987).

I 12 MP& M EEBA: Appendices Appendix I: Environmental Assessment     Systemic toxicants System ic toxicants are chemicals that EPA believes can cause significant non carcinogenic health effects when present in the human body above chemical specific toxicity thresholds. These effects may result from acute or chronic chemical exposures, and include:

  systemic health effects ( i. e., loss of one or more neurological, respiratory, reproductive, immunological, or circulatory functions);

  organ specific toxicity ( e. g., liver and kidney effects);

  developmental toxicity ( e. g., reduced weight in newborns or loss of IQ); and   lethality.

EPA typically relies on animal toxicity data to develop RfDs for systemic toxicants that can enter the human body via ingestion. These values represent chemical concentrations expressed in mg of pollutant/ kg body weight/ day. Certain exposed populations are considered to be protected if these chemical concentrations are not exceeded. These populations include sensitive groups, such as young children or pregnant women. EPA included all available RfD data for the MP& M pollutants of concern ( POCs) in the analysis.

    Carcinogens Carcinogens are chemicals that EPA believes can cause or have the potential to cause cellular damage, which can lead to tumors or cancers in humans, either directly or indirectly. Unlike systemic toxicants, most carcinogens are not believed to have a toxicity threshold. Any amount of a carcinogen therefore has the potential to result in a cancer event, even though such a probability can be very small at low concentrations. The Agency has developed SFs, using animal or epidemiological data,

that express the probability that a chemical will induce tumor or cancer development. EPA included all available SF data for the MP& M POCs in the analysis.

    Drinking water criteria EPA developed human health based drinking water criteria to assess the health hazards associated with the presence of certain toxic chemicals in drinking water. The criteria are usually presented as MCLs. MC Ls for non carcinogens represent chemical specific concentrations ( expressed in   g/ l) that are not expected to result in adverse health effects in exposed populations if not exceeded in drinking water. MCLs for carcinogens represent chemical specific concentrations ( expressed in   g/ l) that are expected to result in less than one additional cancer case per million lifetime exposures if not exceeded in drinking water. The Agency also investigated additional drinking water criteria, including:

  Secondary Maximum Contaminant Levels ( SMCLs) established for taste or aesthetic effects,

  MCLs established specifically for trihalomethanes, and   action levels developed on the basis of treatment technology.

EPA included all the available primary and secondary drinking water criteria for the MP& M POCs in the analysis.

    Pollutant uptake via water and/ or organisms EPA has developed WQC for numerous priority toxic pollutants to protect the health of humans who consume water and organisms or only organisms obtained from aquatic habitats contaminated by those PPs. The criteria, expressed in   g/ l,

represent concentrations in surface waters that will cause adverse health effects in humans when exceeded. EPA obtained all available human health WQC for the MP& M POCs and included them in the analysis.

    Priority pollutants ( PPs)

Priority pollutants are 126 individual chemicals, defined by the Agency as toxic, that EPA routinely analyzes when assessing contaminated surface water, sediment, groundwater, or soil samples. These chemicals are of particular concern to the Agency because of their high toxicity or persistence in the environment. EPA identified all MP& M PPs and included them in the analysis.

I 13 MP& M EEBA: Appendices Appendix I: Environmental Assessment     Hazardous air pollutants ( HAPs)

HAPs are compounds that EPA believes may represent an unacceptable risk to human health if present in the air. HAPs,

expressed in   g/ m3 , can be of particular concern to POTW workers if released into the air at high enough concentrations during the wastewater treatment cycle. EPA identified all HAPs among the MP& M POCs analyzed.

b. Aquatic receptor effects The potential impact of chemicals on aquatic receptors can be assessed qualitatively based on five effect and fate parameters:

  aquatic toxicity ( acute and chronic),

  bioconcentration,

  volatilization,

  adsorption, and   biodegradation.

Site specific risks require a measure of exposure and cannot be quantified using this approach. Chemicals can be classified and ranked in terms of their impacts on aquatic receptors, however, by using the five parameters discussed below. Table I. 3 summarizes the measured or estimated values of these parameters for the MP& M POCs. Each effect and fate parameter is described below.

Biological oxygen demand ( BOD), oil and grease ( O& G), pH, and total suspended solids ( TSS): These fate/ effect parameters are relevant only for specific chemicals. These parameters are not available for the conventional pollutants or bulk nonconventional pollutants, such as total petroleum hydrocarbons ( TPH), alkalinity, total organic carbon ( TOC), or total Kjeldahl nitrogen ( TKN). Most of these pollutants are responsible for significant environmental impacts, however. Section 12.2.4 outlines these impacts in greater detail.

    Aquatic toxicity data The Agency addressed two general classes of aquatic toxicity:

  Acute toxicity ( AT) assesses the impacts of a pollutant after a relatively short exposure duration, typically 48 and 96 hours for invertebrates and fish, respectively. The endpoint of concern is mortality, reported as the LC50. This value represents the concentration lethal to 50 percent of the test organisms for the duration of the exposure.

  Chronic toxicity ( CT) assesses the impact of a pollutant after a longer exposure duration, typically from one week to several months. The endpoints of concern are one or more sub lethal responses, such as changes in reproduction or growth in the affected organisms. The results are reported in various ways, including EC1 or EC5 ( i. e., the concentration at which one percent or five percent of the test organisms show a significant sub lethal response),

NOEC ( No Observed Effect Concentration), LOEC ( Lowest Observed Effect Concentration), or MATC ( Maximum Allowable Toxicant Concentration).

    Bioconcentration factor ( BCF) data The bioconcentration factor ( BCF , measured in l/ kg) is a good indicator of the potential for a chemical dissolved in the water column to be taken up by aquatic biota across external surface membranes, usually fish gills. The BCF is defined as follows:

BCF = equilibrium chemical concentration in target organism ( mg/ kg, wet weight)

mean chemical concentration in surrounding water (   g/ L) ( I. 1)

EPA analyzes POCs with elevated BCF values because these pollutants can bioconcentrate in aquatic organisms and transfer up the food chain if they are not metabolized and excreted. This transfer can result in significant exposures to predators ( including humans) consuming contaminated fish or shellfish.

I 14 MP& M EEBA: Appendices Appendix I: Environmental Assessment Although the bioaccumulation factor ( BAF) is a better measure of the potential for a chemical dissolved in the water column to be taken up by aquatic biota, field measured BAFs are not yet available. EPA recognizes that using bioconcentration factors will underestimate the risk to aquatic organisms.

    Volatilization data Volatilization is a process whereby chemicals dissolved in water escape into the air. Chemicals with higher volatilization potential are typically of less concern to aquatic receptors because they tend to be removed quickly from the water column.

These volatile pollutants are a concern to human health when inhaled. For aquatic receptors, however, POCs with higher volatilization potential present lower hazards.

EPA used the air/ water partitioning coefficient H to estimate a chemical s volatilization potential. H represents the ratio of a chemical s aqueous phase concentration to its equilibrium partial pressure in the gas phase ( at 25 ° C); units are typically expressed as atm. m3 / mole. Metals do not have measurable partial pressures ( with some notable exceptions, including several organic mercury compounds), and are therefore considered to be nonvolatile unless otherwise indicated.

    Adsorption data Adsorption is a process whereby chemicals associate preferentially with the organic carbon ( OC) found in soils and sediments. Highly adsorptive compounds tend to accumulate in sludge or sediments. Such chemicals are also more likely to be taken up by benthic invertebrates and to affect local food chains. Both accumulation in sediment and the effect on local food chains make these chemicals more likely to impact higher predators, including humans.

EPA used the adsorption coefficient ( Koc) to assess the potential of organic MP& M POCs to associate with organic carbon. Koc represents the ratio of the target chemical adsorbed per unit weight of organic carbon in the soil or sediment to the concentration of that same chemical in solution at equilibrium. Metals in the aquatic environment typically end up in the sediment phase but do not bind to the organic carbon ( except for nickel). The Agency assumed that all metals show a high affinity for sludge and sediments independent of their negligible Koc values.

    Biodegradation data Biodegradation is a process whereby organic molecules are broken down by microbial metabolism. Biodegradation represents an important removal process: compounds that are readily biodegraded generally represent lower intrinsic hazards because they can be eliminated rapidly. These compounds are therefore less likely to create long term toxicity problems or to accumulate in sludge or sediments and organisms. Chemicals that biodegrade slowly or not at all can accumulate and linger for longer periods of time in sludge or sediments, and represent a higher hazard to aquatic receptors.

EPA used biodegradation half life to estimate the potential for an organic chemical to biodegrade in the aquatic environment. Biodegradation half life represents the number of days a compound takes to be degraded to half of its starting concentration under prescribed laboratory conditions. Metals do not biodegrade.

Table I. 3 summarizes pollutant toxicity data pertaining to aquatic life.

I 15 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 3:

CAS Number Pollutant Name Freshwater Aquatic Life Saltwater Aquatic Life Bio  concentration Factor Henry's Law Constant Adsorption Coefficient ( Koc) Bio  degradation Half Life Acute Value (   g/ l) Chronic Value (   g/ l) Acute Value (   g/ l) Chronic Value (   g/ l) Value ( l/ kg) Value ( atm/

m 3 mole) Value Value ( days)

51285 Dinitrophenol, 2,4 1160 790 1500 940 1.51 0.000000443 2386 263 57125 Cyanide 22 5.2 1 1 1 45 16 59507 Parachlorometacresol 4050 1300 79 0.0000025 604 100 62533 Aniline 250 4 29400 2940 19.9 0.0000019 54 26 62759 Nitrosodimethylamine, N 280000 4000 4300000 430000 0.026 0.000000263 12 180 65850 Benzoic acid 180000 17178 15 0.00000154 182 16 67641 Acetone 6210000 1866000 5640000 10000 0.39 0.00004 18 7 67663 Trichloromethane 13300 6300 19610 1961 3.75 0.00367 40 180 68122 Dimethylformamide, N, N 7100000 710000 0.005 0.000000018 6.1 16 75003 Chloroethane 65614 21069 7.2 0.00882 37.6 28 75092 Dichloromethane 330000 82500 256000 2560 0.91 0.00219 28 28 75150 Carbon disulfide 2100 2 2 11.5 0.0303 89 75354 Dichloroethene, 1,1 11600 5114 224000 22400 5.6 0.0261 343 180 75694 Trichlorofluoromethane 17387 6412 49 0.097 93 360 78591 Isophorone 120000 11000 12900 1290 4.38 0.00000576 25 28 78831 Isobutyl alcohol 949000 4000 600000 60000 2.2 0.0000118 61.7 7.2 78933 Methyl ethyl ketone 3220000 233550 1287000 128700 1 0.00006 5.2 7 79016 Trichloroethene 40700 14850 14000 2000 10.6 0.0103 104 360 80626 Methyl methacrylate 191000 19100 6.6 0.00034 22 28 83329 Acenaphthene 580 208 970 710 242 0.00009 3890 102 84742 Di n butyl phthalate 850 500 450 3.4 89 0.00000181 6310 23 85018 Phenanthrene 180 19 110 11 486 0.00002 18800 200 85687 Butyl benzyl phthalate 820 260 510 400 414 0.00000126 17000 7 86306 Nitrosodiphenylamine, N 5800 1000 3300000 33000 136 0.000005 1200 34 86737 Fluorene 212 8 1000 100 30 0.00006 2830 60 88755 Nitrophenol, 2 160000 3451 32000 16000 13.5 0.00000947 114 28 91203 Naphthalene 1600 370 1200 120 10.5 0.00048 871 20 91576 Methylnaphthalene, 2 1133 417 600 60 2566 0.00052 8500 20 92524 Biphenyl 360 230 4600 460 436 0.0003 1400 7 95476 Xylene, o 3820 1332 6000 600 208 0.00519 129 28 95487 Cresol, o 14000 2251 10200 1020 18 0.0000012 103 7 98555 Terpineol, alpha 12742 4879 48 0.0000544 589 15 Aquatic Life Toxicity Data for 132 MP& M Pollutants of Concern I 16 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 3:

CAS Number Pollutant Name Freshwater Aquatic Life Saltwater Aquatic Life Bio  concentration Factor Henry's Law Constant Adsorption Coefficient ( Koc) Bio  degradation Half Life Acute Value (   g/ l) Chronic Value (   g/ l) Acute Value (   g/ l) Chronic Value (   g/ l) Value ( l/ kg) Value ( atm/

m 3 mole) Value Value ( days)

98862 Acetophenone 162000 31094 11 0.00001 45 16 99876 Cymene, p 6500 237 4400 440 770 0.011 4000 100 100027 Nitrophenol, 4 7680 1300 7170 1900 79 0.000000000415 236 7 100414 Ethylbenzene 9090 4600 430 43 37.5 0.00788 250 10 100425 Styrene 4020 402 9100 910 13.5 0.00283 920 28 100516 Benzyl alcohol 10000 1000 15000 1500 4 0.000000743 6.1 16 100754 Nitrosopiperidine, N 1019538 282592 0.000000275 9 180 101848 Diphenyl Ether 4000 240 930 0.000448 7800 15 105679 Dimethylphenol, 2,4 2120 1970 94 0.000000951 18 7 106445 Cresol, p 7500 2570 17.6 0.000001 49 0.667 107028 Acrolein 14 5.8 55 5.5 215 0.00012 5 28 108101 Methyl isobutyl ketone 505000 50445 812000 81200 2.4 0.00014 19 7 108372 Bromo 3 chlorobenzene, 1 1784 682 190 0.00078 1500 100 108383 Xylene, m 16000 3900 12000 1200 208 0.00718 190 28 108883 Toluene 5500 1000 6300 5000 10.7 0.00664 95 22 108907 Chlorobenzene 2370 2100 10500 1050 10.3 0.00377 275 150 108952 Phenol 4200 200 5800 2410 1.4 0.000000333 30.2 3.5 110861 Pyridine 93800 25000 2 0.00000888 5 7 112403 Dodecane, n ( a) 18000 1300 500000 50000 14500 95000 17 112958 Eicosane, n ( a) 18000 1300 500000 50000 100000 30000000 17 117817 Bis( 2 ethylhexyl) phthalate 130 0.0000001 87420 23 117840 Di n octyl phthalate 690 69 5460 0.000000445 2390 28 120127 Anthracene 2.78 2.2 40 16 478 0.00007 16000 460 122394 Diphenylamine 3790 734 269 0.000000496 1910 20 123911 Dioxane, 1,4 9850000 1457300 0.4 0.0000048 17 180 124185 Decane, n a 18000 1300 500000 50000 8800 58200 17 127184 Tetrachloroethene 4990 510 10200 450 30.6 0.0184 363 360 129000 Pyrene 591 61 1110 0.000011 62700 1900 131113 Dimethyl phthalate 33000 1700 58000 5800 36 0.000000105 40 7 132650 Dibenzothiophene 420 122 1100 0.00002 11000 137304 Ziram \ Cymate 8 1.8 5200 520 0.001 0.4 142621 Hexanoic acid 320000 15170 16 0.0000225 38 12 Aquatic Life Toxicity Data for 132 MP& M Pollutants of Concern I 17 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 3:

CAS Number Pollutant Name Freshwater Aquatic Life Saltwater Aquatic Life Bio  concentration Factor Henry's Law Constant Adsorption Coefficient ( Koc) Bio  degradation Half Life Acute Value (   g/ l) Chronic Value (   g/ l) Acute Value (   g/ l) Chronic Value (   g/ l) Value ( l/ kg) Value ( atm/

m 3 mole) Value Value ( days)

206440 Fluoranthene 45 7.1 40 16 1150 0.0000161 41700 440 544763 Hexadecane, n ( a) 18000 1300 500000 50000 32300 207000 17 591786 Hexanone, 2 428000 38868 6.6 0.000113 12 16 593453 Octadecane, n ( a) 18000 1300 500000 50000 10100 66900 17 606202 Dinitrotoluene, 2,6 18500 60 12 0.000000747 100 180 629594 Tetradecane, n ( a) 18000 1300 500000 50000 19500 126000 17 629970 Docosane, n b 530000 68000 500000 50000 100000 110000000 17 630013 Hexacosane, n ( b) 530000 68000 500000 50000 17 630024 Octacosane, n ( b) 530000 68000 500000 50000 17 638686 Triacontane, n ( b) 530000 68000 500000 50000 17 646311 Tetracosane, n ( b) 530000 68000 500000 50000 100000 420000000 17 694804 Bromo 2 chlorobenzene, 1 2942 1196 240 0.0006 1500 100 832699 Methylphenanthrene, 1 555 54 4790 0.0000078 36000 1576676 Dimethylphenanthrene, 3,6 543 21 33000 0.0000053 330000 20 1730376 Methylfluorene, 1 627 115 3300 0.00008 33000 2027170 Isopropylnaphthalene, 2 540 78 3200 0.00063 33000 7429905 Aluminum 750 87 231 7439896 Iron 1000 33000 3300 7439921 Lead 65 2.5 210 8.1 49 7439954 Magnesium 64700 6470 85215 7439965 Manganese 388 10 7439976 Mercury 1.4 0.77 1.8 0.94 5500 0.018 30000 7439987 Molybdenum 27.8 7440020 Nickel 470 52 74 8.2 47 300 7440224 Silver 3.4 0.34 1.9 0.19 0.5 7440235 Sodium 1640000 1020000 7440280 Thallium 1400 40 2130 213 116 7440315 Tin 18.6 7440326 Titanium 191 7440360 Antimony 3500 1600 4800 2900 1 7440382 Arsenic 340 150 69 36 44 7440393 Barium 410000 2813 Aquatic Life Toxicity Data for 132 MP& M Pollutants of Concern I 18 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 3:

CAS Number Pollutant Name Freshwater Aquatic Life Saltwater Aquatic Life Bio  concentration Factor Henry's Law Constant Adsorption Coefficient ( Koc) Bio  degradation Half Life Acute Value (   g/ l) Chronic Value (   g/ l) Acute Value (   g/ l) Chronic Value (   g/ l) Value ( l/ kg) Value ( atm/

m 3 mole) Value Value ( days)

7440417 Beryllium 130 5.3 19 7440428 Boron 31.6 7440439 Cadmium 4.3 2.2 42 9.3 64 7440473 Chromium 570 74 1100 50 16 7440484 Cobalt 1620 49 10 7440508 Copper 13 9 4.8 3.1 360 7440575 Gold 7440622 Vanadium 11200 9 7440655 Yttrium 7440666 Zinc 120 120 90 81 47 7440702 Calcium 200000 7664417 Ammonia as N 13300 3060 3800 570 0.0000161 3.1 16 7782492 Selenium 12.83 5 290 71 4.8 14265442 Phosphate 14808798 Sulfate 1000000 16887006 Chloride 860000 230000 16984488 Fluoride 1600 160 18496258 Sulfide 2 2 18540299 Chromium hexavalent 16 11 1100 50 16 20324338 Tripropyleneglycolmethylether 2484600 683870 0.2 0.0000000001 46 16 136777612 Xylene, o& p c 2600 1205 6000 600 208 0.0076 260 28 179601231 Xylene, m& p c 2600 1205 6000 600 208 0.0076 260 28 C003 BOD 5 day ( carbonaceous)

C004 Chemical Oxygen Demand ( COD)

C009 Total Suspended Solids ( TSS)

C010 Total Dissolved Solids ( TDS)

C012 Total Organic Carbon ( TOC)

C020 Total Recoverable Phenolics C021 Total Kjeldahl Nitrogen C025 Amenable Cyanide C036 Oil and Grease ( as Hem) Aquatic Life Toxicity Data for 132 MP& M Pollutants of Concern I 19 MP& M EEBA: Appendices Appendix I: Environmental Assessment Table I. 3:

CAS Number Pollutant Name Freshwater Aquatic Life Saltwater Aquatic Life Bio  concentration Factor Henry's Law Constant Adsorption Coefficient ( Koc) Bio  degradation Half Life Acute Value (   g/ l) Chronic Value (   g/ l) Acute Value (   g/ l) Chronic Value (   g/ l) Value ( l/ kg) Value ( atm/

m 3 mole) Value Value ( days)

C037 Total Petroleum Hydrocarbons ( as Sgt hem)

C042 Weak acid Dissociable Cyanide Phosphorus ( as PO4)

Oil and Grease Aquatic Life Toxicity Data for 132 MP& M Pollutants of Concern a Aquatic toxicity data for n decane are reported based on structural similarity b Aquatic toxicity data for n docosane are reported based on structural similarity Values for the most stringent isomer ( p Xylene) are assumed Sources: Arthur D. Little ( 1983), Arthur D. Little ( 1986), Birge et al. ( 1979), Clay ( 1986), Holdway and Spraque ( 1979), ICF, Inc. ( 1985), Leblanc ( 1980), Lyman et al. ( 1981), U. S.

Atomic Energy Commission ( 1973), U. S. EPA ( 1972), U. S. EPA ( 1976), U. S. EPA ( 1980), U. S. EPA ( 1993), U. S. EPA ( 1998/ 99a), U. S. EPA ( 1998/ 99b), Zhang and Zhang ( 1982).

I 20 c MP& M EEBA: Appendices Appendix I: Environmental Assessment I. 1.3 Grouping MP& M Pollutants Based on Risk to Aquatic Receptors The impact assessment for aquatic receptors looks at the six individual fate and effects parameters for each MP& M POC,

including acute and chronic aquatic toxicities, bioconcentration factors, Henry s Law constants, adsorption coefficients, and biodegradation half lives. EPA grouped POCs with similar attributes, and assigned qualitative descriptors of potential environmental behavior and impact to each group. This grouping was used to describe the range of MP& M pollutant characteristics in Chapter 12. The grouping described below focuses specifically on aquatic environments and their biological receptors; it does not cover the human health toxicity data discussed in the previous section.

Table I. 4 provides a summary of the categorization scheme for the six fate and effects parameters.

Table I. 4: Summary of Categorization Scheme For Six Fate and Effects Parameters Parameter High Hazard Moderate Hazard Low Hazard Insignificant Hazard Acute Toxicity ( AT) AT < 100   g/ l 100   AT   1,000   g/ l AT > 1,000   g/ l Chronic Toxicity ( CT) CT < 10   g/ l 10   CT   100   g/ l CT > 100   g/ l Bioconcentration Factor ( BCF) BCF > 500 50   BCF   500 5   BCF < 50 BCF < 5 Henry s Law Constant ( H) H > 10 3 10 5   H   10 3 3.0x10 7   H < 10 5 H < 3.0x10 7 Adsorption Coefficient ( KOC) oc > 10,000 1,000   K oc   10,000 10   Koc < 1,000 K oc < 10 Biodegradation Half Life ( t1/ 2) 1/ 2 < 7 d 7 d   t1/ 2 < 28 d 28 d   t1/ 2 < 180 d t1/ 2   180 d K t Source: U. S. EPA analysis.

a. Acute and chronic aquatic toxicity EPA used the available AT data to group chemicals according to their relative short term effects on aquatic organisms, using the following categories:

  AT < 100   g/ l High acute toxicity   100   g/ l   AT   1,000   g/ l Moderate acute toxicity   AT > 1,000   g/ l Low acute toxicity These categories reflect the fact that acute toxicity decreases when higher concentrations of a pollutant are required to induce short term mortality in the test organisms. EPA s Office of Pollution Prevention and Toxics ( OPPT) uses this categorization as guidance to assess data submitted in Premanufacture Notices ( PMN) ( EPA, 1996).

EPA used the available CT data to group chemicals according to their relative long term effects on aquatic organisms, based on the following categories:

  CT < 10   g/ l High chronic toxicity   10   g/ l   CT   100   g/ l Moderate chronic toxicity   CT > 100   g/ l Low chronic toxicity These categories assume that CT occurs at a concentration averaging one tenth of that responsible for acute toxicity. They also reflect the fact that chronic toxicity decreases when higher concentrations of a pollutant are required to induce longer

term lethal or sub lethal responses in the test organisms.

I 21 MP& M EEBA: Appendices Appendix I: Environmental Assessment b. Bioconcentration factor ( BCF)

EPA used the available BCF data to group chemicals according to their potential to bioconcentrate in aquatic organisms,

based on the following categories:

  BCF > 500 High potential to bioconcentrate   50   BCF   500 Moderate potential to bioconcentrate   5   BCF< 50 Low potential to bioconcentrate   BCF< 5 No significant potential to bioconcentrate These categories reflect the fact that decreased BCF reduces the intrinsic hazard of a chemical to aquatic receptors, because the chemical is less likely to accumulate in biological tissues.

c. Volatilization potential EPA used available H data to group organic chemicals according to their potential to volatilize from water into air, based on the following categories:

  H > 10 3 High potential to volatilize   10 5   H   10 3 Moderate potential to volatilize   3.0 × 10 7   H < 10 5 Low potential to volatilize   H < 3.0 × 10 7 No potential to volatilize Increased volatility decreases a chemical s hazard to aquatic receptors because the chemical is more likely to quickly move from the receiving water into the atmosphere. ( The opposite is true for human health; hazard to human health increases with increased volatility because a volatile chemical is more available for intake by inhalation.)

d. Adsorption potential EPA used the available Koc to group the organic POCs according to their potential to adsorb to sediments, based on the following categories:

  K oc > 10,000 High potential for adsorption   1,000   Koc   10,000 Moderate potential for adsorption   10   K oc < 1,000 Low potential for adsorption   Koc < 10 No significant adsorption A lower adsorption potential indicates a lower potential for a chemical to be a hazard to aquatic receptors. The lower the adsorption potential the less likely a chemical is to accumulate in sediments or to affect benthic invertebrates and to be taken up into local food chains.

e. Biodegradation potential EPA used biodegradation half lives to group organic POCs according to their potential to biodegrade, based on the following categories:

  t1/ 2 < 7 d Rapid rate of biodegradation   7 d   t1/ 2 < 28 d Moderate rate of biodegradation   28 d   t1/ 2 < 180 d Slow rate of biodegradation   t1/ 2   180 d Resistant to biodegradation A faster rate of biodegradation by microbial metabolism decreases an organic chemical s hazard to aquatic receptors. The more rapid the rate of biodegradation, the more quickly a chemical will be removed from the aquatic environment. Most metals occur as inorganic compounds ( notable exceptions include organic forms of certain metals, such as mercury, lead, or selenium), and are not removed by biodegradation. EPA assumes that all metals are resistant to biodegradation for the purposes of this assessment.

I 22 MP& M EEBA: Appendices Appendix I: Environmental Assessment I. 1.4 Assumptions and Limitations The following are the major assumptions and limitations associated with the data compilation and categorization used in the MP& M analysis:

  Some data are estimated, and subject to uncertainty;

  Data are unavailable for some chemicals and parameters;

  The POCs considered in this study do not include all the constituents that may be present in MP& M pollutants;

  Data derived from laboratory tests may not accurately reflect conditions in the field; and   Available aquatic toxicity and bioconcentration test data may not represent the most sensitive species.

I. 2 METHODOLOGY I. 2.1 Sample Set Data Analysis and National Extrapolation This analysis uses discharge information from 862 sample MP& M facilities ( excluding two sample facilities in Puerto Rico)

that discharge directly or indirectly to 607 receiving waterways ( 521 rivers/ streams, 62 bays/ estuaries, and 24 lakes). The in stream water quality analysis excluded eight of the 62 marine reaches due to data limitations. EPA performed environmental assessment on a basis of the sample facility data. The Agency then extrapolated findings from the sample facility analyses to the national level using two alternative extrapolation methods: ( 1) traditional extrapolation and ( 2) post

stratification extrapolation. EPA also used the differential extrapolation technique in addition to both traditional and post

stratification approaches when a sample reach was estimated to receive discharges from multiple facilities. Appendix G provides detailed information on the extrapolation approaches used in this analysis. Based on the extrapolation methods used in this analysis, EPA estimates that approximately 43,901 MP& M facilities discharge to between 29,500 and 40,000 water bodies nationwide. 3 EPA evaluated the national level environmental impacts of reducing pollutant discharges from MP& M facilities to the nation's water bodies for the final rule. EPA considered only pollutant loadings from MP& M facilities to particular water bodies in the national analysis. With one exception, EPA did not take background loadings from other sources into account.

For the analysis of sewage sludge quantity, EPA was able to use information from the Phase 2 Section 308 survey of POTWs to estimate total metal loadings from all sources to a POTW of a given size ( i. e., small, medium, and large). The Agency based this estimate on survey estimates of the average number of small, medium, and large MP& M facilities discharging to a POTW in each size category and the percent contribution of total metal loadings discharged from MP& M facilities.

I. 2.2 Water Quality Modeling EPA used four different equations to model the impacts of MP& M discharges on receiving waterways. EPA used a simple stream dilution model for MP& M facilities that discharge into streams or rivers. This model does not account for fate processes other than complete immediate mixing. 4 EPA derived the facility specific data ( i. e., pollutant loading and facility flow) used in this equation from sources described in Sections 3.1 and 5.2 of this report.

The Agency used one of three receiving stream flow conditions ( the lowest one day average flow with a recurrence interval of 10 years ( 1Q10), the lowest consecutive seven day average flow with a recurrence interval of 10 years ( 7Q10), and the harmonic mean flow), depending on the criterion or toxic effect level being considered.

3 These estimates include facilities that were assessed to be baseline closures by the MP& M economic analysis.

4 EPA used an exponential decay model to estimate pollutant concentrations for the analysis of cancer risk from drinking water consumption for streams. This model is discussed in detail in Appendix G.

I 23 MP& M EEBA: Appendices Appendix I: Environmental Assessment The 1Q10 and 7Q10 flows are used in comparisons of in stream concentrations with acute and chronic aquatic life criteria or toxic effect levels, respectively, as recommended in the Technical Support Document for Water Quality based Toxics Control ( U. S. EPA , 1991).

The harmo nic mean flow, defined as the inverse mean of reciprocal daily arithmetic mean flow values, is used in comparisons of in stream concentrations with human health criteria or toxic effect levels based on lifetime exposure. mmends the long term harmonic mean flow as the design flow for assessing potential long term human health impacts.

flow is preferab le to arithm etic me an flow beca use in strea m po llutant co ncentration is a function of, and inverse ly proportional to, the stream flow downstream of the discharge.

The event frequency represents the number of times an exposure event occurs during a specified time period.

event frequency equal to the facility operating days to assess impacts on aquatic life. The calculated in stream concentration is thus the averag e con centra tion on days the facility is discha rging w astewa ter. A set the event frequency at 365 d ays to assess lo ng term huma n health impacts. e calculated in stream conc entration is thus the a verag e con centra tion on all days of the year. This frequency leads to a lower calculated concentration because of the additional dilution from days when the facility is not operating, but it is consistent with the conservative assumption that the target population is present to consume drinking water every day and contaminated fish throughout an entire lifetime. owing equation calculates in stream concen tration for streams and rivers:

( I. 2)

where:

C is = in stream pollutant concentration (   g/ L);

L = facility pollutant loading (   g/ yr); for indirect dischargers, L = L indirect facility * ( 1 TMT), where TMT is POTW treatment removal efficiency ( unitless);

OD = facility or POT W operating days ( days/ yr);

FF = MP & M facility flow ( L/ day); for indirect dischargers, FF = PO TW flow ( L/ day);

EF = event frequency ( days/ yr); and SF = receiving stream flow ( L/ day).

EP A use d the fo llowing simple steady sta te mo del for facilities that d ischarge into lakes othe r than the Gre at lakes. his model takes into account pollutant degradation and the hydraulic residence time of the lake:

( I. 3)

where:

Clake = stead y state lake conc entratio n of po llutant (   g/ L),

Ci = stead y state inflow conc entratio n of po llutant (   g/ L),

T w = mean hydraulic residence time ( yr),

k = first order pollutant decay rate ( yr 1), and ( I. 4)

where:

V = lake volume ( m3), and Q = mean total inflow rate ( m3/ yr). EPA reco Harmonic mean EPA set the EP Th The foll T I 24 MP& M EEBA: Appendices Appendix I: Environmental Assessment EPA used alternative means to predict pollutant concentrations suitable for comparison with ambient criteria or toxic effect levels for facilities discharging to hydrologically complex waters, such as bays and estuaries. e, EPA employed site specific critical dilutio n fac tors ( CDFs) to predict the concentration at the edge of a mixing zone. re CDFs were not available, EPA used available estuarine diss olve d co nc en tratio n p ote ntia ls ( DCPs).

EPA obtained site specific CDFs from a survey of states and regions conducted by EPA's Office of Pollution Prevention and To xics ( Mixing Z one Dilution F actors for New C hem ical E xpo sure A ssessm ents, U. S. EPA, 1992a). The dilution model for estimating estuary concentrations by using a CDF is presented below:

( I. 5)

where:

C es = estuary pollutant concentration (   g/ L);

L = facility pollutant loading (   g/ yr); for indirect dischargers, L = L indirect facility * ( 1 TMT), where TMT is POTW treatment removal efficiency ( unitless);

EF = event frequency ( days/ yr);

FF = facility flow ( L/ day); for indirect dischargers, FF = PO TW flow ( L/ day); and CDF = critical dilution factor ( unitless).

EP A used a cute CD Fs to evaluate ac ute aquatic life effects and chron ic CD Fs to evaluate ch ronic aqu atic life or adverse human health effects. ng water intake and fishing location are at the edge of the chronic mixing zone. he event frequency equal to the facility operating days for comparison with aquatic life criteria or toxic effect levels, and equ al to 365 days for com parison with hum an health criteria or toxic effect levels.

The National Oceanic and Atmospheric Administration ( NOAA) has developed D CPs to predict pollutant concen trations in various salinity zones for each e stuary in NO AA's Nat iona l Estu arine In ven tory ( NE I).

represents the concentration of a nonreactive dissolved substance under well mixed, steady state conditions given an annual load of 10,000 tons. DCPs account for the effects of flushing by considering the freshwater inflow rate, and dilution by considering the total estuarine volume. s reflect the predicted estuary wide response, and may therefore not be indicative of concentrations at the edge of much smaller mixing zones. mating pollutant concentrations using DCPs is presented below:

( I. 6)

where:

C es = estuary pollutant concentration (   g/ L);

L = facility pollutant loa ding ( k g/ yr); for ind irect disc harge rs,

L = L indirect facility *( 1 TM T), where TM T is PO TW treatment removal efficiency ( unitless);

DCP = dissolved c oncentratio n po tential (   g/ L);

BL = benchmark load ( 10,00 0 tons/ yr); and CF = conversion factor ( 907.2 kg/ ton).

EPA determined potential water quality impacts by comparing projected waterway pollutant concentrations to EPA water quality criteria or toxic effect levels fo r the pr otectio n of aq uatic life an d hum an hea lth. A de termin ed w ater q uality exceedances by dividing the projected waterway pollutant concentration by the EPA water quality criteria or toxic effect levels for the protection of aquatic life and human health. A value greater than one indicates an exceedance.

I. 2.3 a. ition Inhibition of PO TW operations occurs when high levels of toxics, such as metals or cyanide, kill the bacteria required for the wastewater treatment process. on of PO TW operations by comparing calculated POT W influent Where possibl Whe EPA assumed that the drinki EPA set t A DCP DCP The dilution model used for esti EP Impact of Indirect Discharging Facilities on POTW Operations Analysis of biological inhib EPA analyzed inhibiti I 25 MP& M EEBA: Appendices concentrations with available inhibition levels. Exceedances are indicated by a value greater than one. POTW influent concentrations are estimated as: Appendix I: Environmental Assessment ( I. 7)

where:

Cpi = POT W influent concentration (   g/ L),

L = facility pollutant loading (   g/ yr),

O D = facility operating days ( days/ yr), and PF = POT W flow ( L/ day).

b. udge disposal practices EPA also analyzed the effects of MP& M discharges on POTW operations by comparing the estimated concentrations of metals in sewage slud ge with the pub lished m etals co ncen tration lim its for pre ferab le sewa ge slud ge disp osal o r use p ractice s.

In particular, E PA exam ined:

  whether M P& M base line discharge s wou ld pre vent P OT W s from being able to meet the me tals con centra tion limits required for more favorable and lower cost sewage sludge use/ disposal practices ( i. e., beneficial land application and surface disposal); and   whether limitations on the selection of management practices would be removed under the final rule.

EPA estimated the sewage sludge concentrations of eight metals for sample facilities under baseline and post regulatory option discharge levels. EPA compared these concentrations with the relevant metals concentration limits for three sewage sludge management options: Land Application High ( Concentration Limits), Land Application Low ( Ceiling Limits), and Surface D isposal. etal concentration s in sewage sludge are estimated as:

( I. 8)

where:

C sp = sewage sludge pollutant concentration ( mg/ kg),

L = facility pollutant loading (   g/ yr),

T M T = POT W treatment removal efficiency ( unitless),

PART = pollutant specific sludge partition factor ( unitless),

S G F = sludge generation factor ( mg/ kg per   g/ L),

OD = POT W operating days ( days/ yr), and PF = POT W flow ( L/ day).

EPA derived the facility specific data to evaluate POTW operations from the sources described in Sections 3.1 and 5.2. EPA examined multiple MP& M facilities discharging to the same POT W by summing the individual loadings before calculating the PO TW influent and sewage slud ge conc entrations.

The partition factor is a chemical specific value representing the fraction of the load expected to partition to sewage sludge during wastewater treatment. s analysis, EPA used a sludge generation factor of 5.96 mg/ kg per   g/ L. This factor indicated that the resulting concentration in sewage sludge is 5.96 mg/ kg dry weight for every 1   g/ L of pollutant removed from wastewater and partitioned to sewage sludge. Analysis of sl M For thi I 26 MP& M EEBA: Appendices Appendix I: Environmental Assessment I. 2.4 Assumptions and Limitations The following discussion focuses on major assumptions and limitations associated with these in stream water quality analyses.

a. Other source contributions EPA did not account for " other source contributions" of MP& M pollutants to estimate in stream concentrations of these pollutants. Accounting for the discharges from other sources is important because assessing benefits from reduced exceedance of AWQC limits depends on comparing concentrations of pollutants from all sources with applicable thresholds.