Attachment 1

MISSOURI

Area Designations For the

24-Hour Fine Particle National Ambient Air Quality Standard

The table below identifies the counties/areas in Missouri that EPA has designated as not attaining the 2006 24-hour fine particle (PM2.5) standard.  A county (or part thereof) is designated as nonattainment if it has an air quality monitor that is violating the standard or if the county is determined to be contributing to the violation of the standard.

Area 	Missouri Recommended Nonattainment Counties	EPA’s Designated Nonattainment Counties

St. Louis, MO-IL	None 	Franklin

Jefferson

St. Charles

St. Louis

City of St. Louis

EPA has designated the remaining counties in the state of Missouri as “attainment/unclassifiable.”

EPA Technical Analysis for St. Louis, MO

Introduction

Pursuant to section 107(d) of the Clean Air Act, EPA must designate as nonattainment those areas that violate the NAAQS and those nearby areas that contribute to violations.  This technical analysis for the St. Louis area identifies the counties with monitors that violate the 24-hour PM2.5 standard and evaluates nearby counties for contributions to fine particle concentrations in the area.  EPA has evaluated these counties based on the weight of evidence of the following nine factors recommended in EPA guidance and any other relevant information:

- pollutant emissions

- air quality data

- population density and degree of urbanization

- traffic and commuting patterns

- growth

- meteorology

- geography and topography

- jurisdictional boundaries

- level of control of emissions sources

EPA also used analytical tools and data such as pollution roses, fine particle composition monitoring data, back trajectory analyses, and the contributing emission score (CES) to evaluate these areas. (See additional discussion of the CES under factor 1 below.)

Figure A is a map of the counties in the area. The Figure includes other relevant information such as the locations and design values of air quality monitors, the metropolitan area boundary, and counties recommended as nonattainment by the State.

Background

The St. Louis metro area was previously established by EPA as a PM2.5 nonattainment boundary for the 1997 annual PM2.5 NAAQS.  That boundary included a total of eight full counties and one city (City of St. Louis), with four of the counties and the city being located in Missouri.  These same counties, and city, are being designated as nonattainment for the 2006 24-hour PM2.5 standard.

In December 2007, Missouri recommended that all areas in the state be designated as attainment  for the 2006 24-hour PM2.5 standard based primarily on its analysis of air quality data from 2004-2006.  All data submitted by Missouri related to the violations occurring in Madison County, IL during 2004-2006, at the Granite City and VFW monitoring sites are included in this rulemaking docket.  These data are from Federal Reference Method (FRM) monitors.  The State based its attainment recommendation on the argument that the two monitors were violating because of the contributions of direct PM2.5 from one local source, US Steel in Granite City, Illinois.

In August 2008, EPA notified Missouri of its intended designations.  In this letter, EPA also requested that if the State wished to provide comments on EPA’s intended designation, it should do so by October 20, 2008.  EPA stated that it would consider any additional information (e.g., on power plants or partial county areas) provided by the state in making final decisions on the designations.  On October 16, 2008, the state of Missouri provided its response to EPA’s August letter.  The information contained in the letter is addressed in EPA’s response to comments and in this document.

Based on EPA's technical analysis described below, EPA is designating portions of the St Louis, Missouri area (previously designated nonattainment for the 1997 annual PM2.5 NAAQS) as nonattainment for the 24-hour PM2.5 NAAQS, based upon currently available information.  These areas are listed in the table below. The EPA must utilize the most recent three years of quality assured monitoring data in making these designations.  In this case, the most current, quality assured monitor data is from the period 2005-2007.  During the period 2005-2007, the violating monitors in the St. Louis metro area were both in Madison County, IL.  These monitors are commonly referred to as the Granite City and Alton monitors.  (It should be noted that Missouri’s analysis in response to EPA’s 120-day letter did not include consideration of violations at the Alton monitor.)

The following is a technical analysis for the EPA Region 7 (Missouri) portion of the St. Louis nonattainment area.

Factor 1:  Emissions data

For this factor, EPA evaluated county level emission data for the following PM2.5 components and precursor pollutants:  “PM2.5 emissions total,” “PM2.5 emissions carbon,” “PM2.5 emissions other,” “SO2,” “NOx,” “VOCs,” and “NH3.”  “PM2.5 emissions total” represents direct emissions of PM2.5 and includes:   “PM2.5 emissions carbon,” “PM2.5 emissions other”, primary sulfate (SO4), and primary nitrate.  (Although primary sulfate and primary nitrate, which are emitted directly from stacks rather than forming in atmospheric reactions with SO2 and NOx, are part of “PM2.5 emissions total,” they are not shown in Table 1 as separate items).  “PM2.5 emissions carbon” represents the sum of organic carbon (OC) and elemental carbon (EC) emissions, and “PM2.5 emissions other” represents other inorganic particles (crustal).  Emissions of SO2 and NOx, which are precursors of the secondary PM2.5 components sulfate and nitrate, are also considered.  VOCs (volatile organic compounds) and NH3 (ammonia) are also potential PM2.5 precursors and are included for consideration.

EPA also considered the Contributing Emissions Score (CES) for each county.  The CES is a metric that takes into consideration emissions data, meteorological data, and air quality monitoring information to provide a relative ranking of counties in and near an area.  Note that this metric is not the exclusive analytical tool used to consider data for these factors.  A summary of the CES is included in attachment 2, and a more detailed description can be found at http://www.epa.gov/ttn/naaqs/pm/pm25_2006_techinfo.html#C.  NOTE:  The emissions data used to derive the CES were taken from the 2005 National Emissions Inventory (NEI), version 1.  See http://www.epa.gov/ttn/naaqs/pm/pm25_2006_techinfo.html.

Table 1 shows emissions of PM2.5 and precursor pollutants components (given in tons per year) and the CES for violating and potentially contributing counties in the St. Louis Area.  Counties that are part of the St. Louis nonattainment area for the 1997 PM2.5 NAAQS are shown in boldface.  Counties are listed in descending order by CES.

County	State Recommended Nonattainment?	CES	PM2.5

emissions

total

(tpy)	PM2.5

emissions

carbon

(tpy)	PM2.5

emissions

other

(tpy)	SO2

(tpy)	NOx

(tpy)	VOCs

(tpy)	NH3

(tpy)

Madison, IL*	Yes	100	4,945	1,148	3,796	27,320	19,373	15,676	1,393

St. Louis, MO*	No	55	4,221	1,707	2,513	29,966	55,605	54,821	2,954

St. Louis City*	No	48	1,686	625	1,060	12,171	24,702	20,647	439

St. Clair, IL*	Yes	22	1,496	487	1,009	2,142	10,233	10,869	1,281

St. Charles, MO*	No	17	3,694	619	3,075	54,561	20,773	12,419	1,182

Jefferson, MO*	No	16	2,945	824	2,121	45,574	16,722	9,273	493

Randolph, IL**	Partial	9	2,505	306	2,199	24,605	9,384	2,331	993

Franklin, MO*	No	5	2,812	621	2,190	56,767	15,595	5,748	1,818

Monroe, IL*	Yes	5	744	235	508	293	3,057	2,529	654

* Counties in bold represent those in the St. Louis nonattainment area for the 1997 PM2.5 NAAQS (This table does not include all counties considered in the 9 factor analysis and those counties not shown had no factors that indicated that they should be candidates for a nonattainment status.)

** Part of Randolph County, Illinois is in the 1997 PM2.5 NAAQS nonattainment area

Table 1.  PM2.5 24-hour Component Emissions, and CESs

EPA notes that Franklin County, the lowest CES ranked county in Missouri which EPA is including in the nonattainment area, has a CES score equal to the lowest scoring Illinois county (Monroe) included in the nonattainment area (by that State of Illinois and EPA Region 5) and more than ten times the total PM2.5 direct and precursor emissions of Monroe County.

By reviewing and comparing 2002 National Emissions Inventory data for Missouri and Illinois counties, Missouri counties’ potential to contribute to the violating monitor can be evaluated relative to other emissions in the metro area.  A review of this data shows that collectively the point sources in the Missouri counties under review emit 50-74% of the primary and precursor pollutants of PM 2.5.  Specifically, Figure 1 and Table 1.1 demonstrate that the Missouri counties emit approximately 50% of the total tons per year (tpy) of direct PM2.5, approximately 57% of the total tpy of NOx, and approximately 74% of the total tpy of SO2 emitted in the nonattainment area.  The figure and table clearly demonstrate Missouri counties have a equal or greater PM2.5, emissions and precursors when compared to the Illinois metro areas included within the nonattainment boundary and therefore, have the potential to contribute air pollution emissions to the violating monitors.

Figure 1:  Missouri counties’ emissions from point sources in the reviewed counties vs. Illinois counties’ emissions from point source in the reviewed counties.  Data from the 2002 NEI.

County	NOx	SO2	PM2.5

Franklin County	7851.837	47612.6224	1005.612776

Jefferson County	9202.056	39281.1982	979.12496

St. Charles County	14691.27	46644.8862	1377.532697

St. Louis city	2014.306	6771.233<PHONE_NUMBER>

St. Louis County	10438.98	16689.3744<PHONE_NUMBER>

Madison County	10608.49	26745.56085	2601.379465

Monroe County	3.49	0.08046	27.57737963

Randolph County	22467.41	26295.97834	1766.661372

St. Clair County	368.7823	1540.66522	271.647678

Total TPY	77646.61<PHONE_NUMBER>	9274.503203

Missouri Total	44198.44	156999.3	4607.237

MO % Contribution	57%	74%	50%

Table 1.1:  2002 NEI TPY by pollutant.

In summary, EPA’s analysis of Factor 1 indicates that based on emission levels and CES values, the Missouri counties are probable contributors to the violating monitors and should remain candidates for a 24-hour PM2.5 nonattainment designation.

Factor 2:  Air quality data

This factor considers the 24-hour PM2.5 design values (in µg/m3) for air quality monitors in counties in the St. Louis Area based on data for the 2005-2007 period.  A monitor’s design value indicates whether that monitor attains a specified air quality standard. The 24-hour PM2.5 standards are met when the 3-year average of a monitor’s 98th percentile values are 35 µg/m3 or less.  A design value is only valid if minimum data completeness criteria are met.

The 24-hour PM2.5 design values for counties in the St. Louis Area are shown in Table 2.

County	State

Recommended

 Nonattainment?	Design Values

2004-06

(µg/m3)

	Design Values

2005-07

(µg/m3)

Madison, IL*(Granite City)	Yes	39	39

Madison, IL (Alton)	Yes	34	36

St. Louis, MO*	No	32	34

St. Louis City, MO*	No	34	35

St. Clair, IL*	Yes	33	34

St. Charles, MO*	No	32	33

Jefferson, MO*	No	32	34

Randolph, IL**	Partial	27	30

Franklin, MO*	No	0***	0***

Monroe, IL*	Yes	0***	0***

*   Counties in bold represent those in the St. Louis nonattainment area for the 1997 PM2.5 NAAQS

** Part of Randolph County, Illinois is in the 1997 PM2.5 NAAQS nonattainment area

*** There are no PM2.5 monitors in this county; therefore there are no monitoring values for this county.

Table 2.  Air Quality Data

Two locations in Madison County, Illinois have recent monitored violations of the 24-hour PM2.5 standard as outlined in Table 2. Those three locations are the Alton, Granite City Steel and VFW monitors.  Therefore, Madison County, Illinois is included in the St. Louis nonattainment area.  However, the absence of a violating monitor alone is not a sufficient reason to eliminate counties as candidates for nonattainment status.  Each county has been evaluated based on the weight of evidence of the nine factors and other relevant information.

g/m3)	Nitrate (g/m3)	Carbon (g/m3)	Crustal (g/m3)	Total (g/m3)	Sulfate Percent	Nitrate Percent	Carbon Percent	Crustal Percent

Total Concentration (Cold)	5.2	0	11.6	2.1	18.9	28	0	61	11

Regional Concentration (Cold)	3	0	2.5	1.3	6.8	44	0	37	19

Urban Increment (Cold)	2.2	0	9.1	0.8	12.1	18	0	75	7

Total Concentration (Warm)	28.4	0	8.2	1	37.7	75	0	22	3

Regional Concentration (Warm)	25.1	0	4.4	1.2	30.7	82	0	14	4

Urban Increment (Warm)	3.3	0	3.8	0	7.1	46	0	54	0

Total Concentration (Ann Avg)	3	2.7	9.2	1.1	16	19	17	58	7

Regional Concentration (Ann Avg)	2.1	0.8	2.8	0.8	6.5	32	12	43	12

Urban Increment (Ann Avg)	0.9	1.9	6.4	0.3	9.5	9	20	67	3

Table 2.1 Speciation data from the Chemical Speciation Network and the IMPROVE monitoring network.

Analysis of data in Table 2.1 indicates that the days with the highest fine particle concentrations occur in both cool and warm seasons, and the average chemical composition of the highest days is typically characterized by high levels of carbon in the cold season, and high levels of sulfates in the warm season.  Table 2.2 denotes all design values in the St. Louis area for 2005-2007.  An evaluation of Table 2.2 indicates that 75% of the violations at the Alton monitor and 73% of the violations at the Granite City monitor occur during the warm season.

 	2nd&Mound	Margaretta	Blair	S. Broadway	Hunter	Sunset Hills	W. Alton Arnold	Ste Genevieve	Swansea	13th&Tudor	Wood River	Alton	Granite City VFW	Jerseyville	Houston- Bkgd	Season

1/28/2005	19.5	18.3	20.8	18.9	20.1	18.8	19.9	17.4	15.6	17.6	17.8	15.7	  19.2	35.1	 	13.2	Cold

1/31/2005	45	41.5	46.1	46.9	43.6	43.8	38.5	41.3	38.3	37.9	 	18.6	  39.6	 	 	 	Cold

2/1/2005	40.8	37.5	43	41.6	36.8	 	 	40.5	 	 	 	 	 	 	 	 	  Cold

2/2/2005	46.7	43.4	47.5	48.3	44.2	 	 	46.3	 	 	 	 	 	 	 	10.1  	Cold

2/3/2005	44.8	38.8	41.6	38.1	43.5	37.3	31.6	33.5	26	44.7	 	41.7	 	41.8 23	 	29.3	Cold

2/27/2005	39.2	38.1	39.7	38.4	36.9	37	32.7	36.7	33.2	35.3	39.5	 	35.5 37.9	36.3	32.8	31.1	Cold

3/17/2005	 	 	 	 	 	 	 	 	 	 	 	 	 	 	37	 	 	Cold

4/4/2005	 	24.8	 	29	24.3	 	 	23.9	 	 	 	 	 	 	38.2	 	 	Cold

6/24/2005	32.4	31.8	33.7	36.9	31.7	29.3	34.2	31.8	32.1	32.4	 	34.7	  36	41.1	 	 	Warm

6/26/2005	40.8	 	40.3	39.5	 	 	 	40.5	 	 	 	 	 	 	 	 	  Warm

6/27/2005	39.4	40	38.6	38.6	38.9	 	41.4	39.1	32.4	37.9	39.6	44.1	45.1 44.1	46.1	43.7	32.4	Warm

6/29/2005	39	 	37.4	38	 	 	 	39.6	 	 	 	 	 	 	 	 	 	Warm

7/23/2005	33.4	 	31.5	31.4	 	 	 	36.6	 	 	 	 	 	 	 	 	  Warm

8/1/2005	45.3	 	41.9	41.5	 	 	 	45.8	 	 	 	 	 	 	 	 	 	Warm

8/2/2005	37.6	40.4	39.3	37	36.9	38.3	37.9	41.6	32.5	35.9	38.6	39.2	38.3 41.2	41.2	40.9	31.3	Warm

8/8/2005	41.6	38.4	38.7	38.4	39.7	36.2	38.2	37	33.2	 	40.4	41.2	39.4 44.7	41	37.2	34.3	Warm

8/9/2005	 	 	36.5	 	 	 	 	39.5	 	 	 	 	 	 	 	 	 	Warm

9/7/2005	39.8	36.4	36.4	36.3	34.5	36.2	34	37.8	36.1	33	 	38.8	35.4	45.8 42.1	33.1	32.1	Warm

9/10/2005	39.4	38.4	39.7	38	38.9	37.7	41.5	39.9	37.7	37	 	40.8	 	42.7 39.1	 	 	Warm

9/11/2005	41.8	 	41.5	39.3	 	 	 	41.7	 	 	 	 	 	 	 	 	  Warm

9/13/2005	 	23.1	 	21.8	22.4	20.6	24.4	22.9	26	24.9	22	27.3	28.6	30.4 36	26.4	28	Warm

2/28/2006	31.8	30.5	32.8	29.3	27.7	23.3	27.2	22.6	15.3	19	29.2	28.3	25.8 40	27	29.5	13	Cold

4/29/2006	17.6	17.7	18	17.6	 	 	19.3	26.1	 	18.7	18.4	17.3	20.2	36.3 28	18.6	19.3	Warm

5/8/2006	20	19.5	20	18.1	19.2	16.6	20.7	18.2	17	 	 	21.9	 	25.1	37.2  	 	Warm

7/20/2006	35.8	 	 	32.5	 	 	 	30.7	 	 	 	 	 	 	 	 	 	Warm

8/12/2006	29.6	30.8	29.2	31.7	31.6	 	28.1	32.4	34.4	28.1	 	25.1	  39.9	32.9	 	 	Warm

8/13/2006	34.2	 	34.2	35.4	 	 	 	39.1	 	 	 	 	 	 	 	 	  Warm

2/21/2007	37.9	0	33.8	30.7	21.2	 	24.5	22.3	 	26.8	 	 	 	 	 	   	Cold

5/24/2007	32.5	32.5	32.1	31.7	28.5	 	32.2	 	 	33.8	34.9	34.3	35.4 38.4	 	 	 	Warm

6/14/2007	36.3	36.3	 	35.8	36.1	 	34.1	35.2	 	32.9	 	31	 	 	 	   	Warm

6/17/2007	34.6	34.2	34.2	34.2	32.7	 	35	34.9	 	30.8	32.7	33.8	34.7 35.3	 	 	 	Warm

7/4/2007	20	 	21.7	22.5	16.8	 	17.5	45.7	 	9.6	 	 	 	 	 	 	  Warm

8/1/2007	25.8	 	26.3	29.2	27	 	25.7	30.4	 	22	 	35	 	24.3	 	 	  Warm

8/2/2007	47.9	 	47.1	41.3	41.9	 	39.4	41.1	 	 	 	 	 	 	 	 	  Warm

8/3/2007	47.9	 	50.4	45	45.8	 	49.7	42.5	 	 	 	 	 	 	 	 	  Warm

8/4/2007	33.8	 	34.2	32.9	29.3	 	33.9	32.6	 	28.4	32.5	38.4	33.4	36  	 	 	Warm

9/21/2007	32.1	 	33.3	33.3	31.7	 	30.5	30.8	 	29.5	 	34.5	35	38.4	   	 	Warm

Table 2.3.  Air Quality Monitoring Data on Exceedance Days.

Note: Eligible monitors for providing design value data generally include State and Local Air Monitoring Stations (SLAMS) at population-oriented locations with an FRM monitor.  All data from Special Purpose Monitors (SPM) using is eligible for comparison to the relevant NAAQS, subject to the requirements given in the October 17, 2006 Revision to Ambient Air Monitoring Regulations (71 FR 61236).  All monitors used to provide data must meet the monitor siting and eligibility requirements given in 71 FR 61236 to 61328 in order to be acceptable for comparison to the 24-hr PM2.5 NAAQS for designation purposes.

In summary, EPA’s review of Factor 2 indicates that both local and regional contributions to the violating monitors are likely.  Due to the spatial separation of the Granite City monitor from the Alton monitor, it is not likely that one source or a small group of localized sources is exclusively impacting both monitors.  The speciation data also indicates that sulfur and organic carbon sources are likely significant contributors to the total mass on the filters.  Both of these PM2.5 precursor emissions are emitted in substantial quantities in upwind Missouri areas.  Consequently, Factor 2 supports broader non-attainment boundaries including all four of the nearby Missouri counties and St. Louis City.

Factor 3: Population density and degree of urbanization (including commercial development)

Table 3 shows the 2005 population for each county in the area being evaluated, as well as the population density for each county in that area.  Population data gives an indication of whether it is likely that population-based emissions might contribute to violations of the 24-hour PM2.5 standards.

County	State Recommended Nonattainment?	2005 Population	2005 Population Density (pop/sq mi)

Madison, IL*	Yes	   263,975 	357

St. Louis, MO*	No	1,002,258 	1914

St. Louis City, MO*	No	  352,572 	5334

St. Clair, IL*	Yes	   259,388 	385

St. Charles, MO*	No	   329,606 	557

Jefferson, MO*	No	   213,011 	321

Randolph, IL**	Partial	     33,116 	55

Franklin, MO*	No	     98,987 	107

Monroe, IL*	Yes	     31,289 	79

*   Counties in bold represent those in the St. Louis nonattainment area for the 1997 PM2.5 NAAQS

** Part of Randolph County, Illinois is in the 1997 PM2.5 NAAQS nonattainment area

Table 3.  Population

A review of the population data for the bi-state metro area indicates that Missouri counties/areas were consistently in the top three rankings for both total population numbers and population density.  All Missouri counties in the ranking for non-attainment consideration have populations of over 90,000 people.  (Of the counties in Missouri, Franklin County is the least populated with 98,987 people).  The total population for Franklin County is approximately 3 times greater than that of Monroe County, IL, which is the lowest population-ranked Illinois County that was recommended for non-attainment by both EPA and the state of Illinois.

In summary, EPA’s review of Factor 3 indicates that 78% of the total population in the bi-state area lives on the Missouri side of the St. Louis area.  While, population and population density alone do not provide absolute justification for determining nonattainment boundaries, this data is indicative of population-based emissions.  Since these highly populated Missouri areas are commonly upwind of the monitors on high PM2.5 monitored days, these Missouri counties/areas remain candidates for consideration in the nonattainment area designation.

Factor 4: Traffic and commuting patterns

This factor considers the number of commuters in each county who drive to another county within the St. Louis area the percent of total commuters in each county who commute to other counties within the area as well as the total Vehicle Miles Traveled (VMT) for each county in millions of miles (see Table 4). A county with numerous commuters is generally an integral part of an urban area and is likely contributing to fine particle concentrations in the area.   The listing of counties on Table 4 reflects a ranking based on the number of people commuting to other counties. The counties that are in the nonattainment area for the 1997 PM2.5 NAAQS are shown in boldface.

County	State Recommended Nonattainment?	2005 VMT

(106 mi)	Number Commuting to any violating counties

	Percent

Commuting to any violating counties

	Number Commuting into statistical area	Percent

Commuting into statistical area

St. Louis, MO*	No	14,165	3,800	1	493,070	99

St. Charles, MO*	No	3,185	740	0	147,420	99

St. Louis City*	No	3,638	1,250	1	139,280	99

Madison, IL*	Yes	2,318	75,490	62	119,590	98

St. Clair, IL*	Yes	3,019	7,040	6	110,870	98

Jefferson, MO*	No	2,241	490	1	96,860	99

Franklin, MO*	No	1,436	150	0	43,600	97

Monroe, IL*	Yes	359	420	3	13,560	95

Randolph, IL**	Partial	261	180	1	2,790	21

*   Counties in bold represent those in the St. Louis nonattainment area for the 1997 PM2.5 NAAQS

** Part of Randolph County, Illinois is in the 1997 PM2.5 NAAQS nonattainment area

Table 4.  Traffic and Commuting Patterns

Although the majority of the populous does not regularly travel directly to/from the county monitoring violations (Madison County), the population does routinely travel to/from and within the MSA as a whole.  Specifically, the information in the table suggests a typical pattern of high urban core traffic with the major interstate highways such as Interstates 70, 270, 44, and 55 located in the Missouri portion of the bi-state area.  The interstate highways outside the core urban area are responsible for the majority of the VMT in those particular counties.

Table 4 also reveals that Missouri counties account for 78% of the total number of commuting miles into the MSA.  Of the Missouri areas: St. Louis County and City of St. Louis and St. Charles rank as the top three commuting areas in the bi-state metro area.

In summary, EPA’s review of Factor 4 indicates that all of the Missouri counties/areas rank high in terms of total VMT and are therefore logical candidates for consideration in the nonattainment area designation

Note:  The 2005 VMT data used for table 4 and 5 of the 9-factor analysis have been derived using methodology such as that described in "Documentation for the  2005 Mobile National Emissions Inventory, Version 2," December 2008, prepared for the Emission Inventory Group, U.S. EPA.  This document may be found at: ftp://ftp.epa.gov/EmisInventory/2005_nei/mobile_sector/documentation/200 5_mobile_nei_version_2_report.pdf

Factor 5:  Growth rates and patterns

This factor considers population growth for 2000-2005 and growth in vehicle miles traveled for 1996-2005 for counties in the St. Louis Area as well as patterns of population and VMT growth.  A county with rapid population or VMT growth is generally an integral part of an urban area and likely to be contributing to fine particle concentrations in the area.

Table 5 below shows population, population growth, VMT and VMT growth for counties that are included in the area.  Counties are listed in descending order based on VMT growth between 1996 and 2005.

County	Population (2005)	Population % change (2000-05)	2005 VMT

(106 mi)	VMT

% change

(1996-2005)

Monroe, IL*	     31,289 	13 	        359 	          47

St. Louis, MO*	 1,002,258 	-1	    14,165 	          33

St. Charles, MO*	    329,606 	15 	     3,185 	          28

Franklin, MO*	     98,987 	5 	     1,436 	          19

St. Clair, IL*	    259,388 	1 	     3,019 	          13

Randolph, IL**	     33,116 	-2	        261 	            2

Jefferson, MO*	    213,011 	7 	     2,241 	            1

St. Louis City, MO*	    352,572 	2 	     3,638 	          -8

Madison, IL*	    263,975 	2 	     2,318 	          -12

*   Counties in bold represent those in the St. Louis nonattainment area for the 1997 PM2.5 NAAQS

** Part of Randolph County, Illinois is in the 1997 PM2.5 NAAQS nonattainment area

Table 5.  Population and VMT Growth and Percent Change.

Data from Table 5 indicate that Missouri counties/areas occupied 4 of the top 5 rankings in terms of total population and population growth between the years 2000-2005.  In addition, Missouri counties/areas, ranked in 3 out of the top 5 rankings in terms of VMT growth.

As listed in Table 5, population growth above 5% occurred in the following counties between 2000 and 2005:  St. Charles, Franklin, and Jefferson counties in Missouri and Monroe County in Illinois. Only two counties had a decline in population from 2000-2005 and those were St. Louis County, Missouri and Randolph County, Illinois.  Madison County, which is the county with the violating monitors, experienced a 12% reduction in VMT between the years 2000 and 2005.  Also, it is notable from Table 5 that no area had a concurrent drop in population and VMT.  In other words, each area has experienced recent growth in at least one or the other indices.

In summary, EPA’s review of Factor 5 indicates that all areas in the bi-state metro area experienced growth in either population or VMT and sometimes both.  All of the Missouri areas consistently rank high in terms of growth rates and growth patterns thereby supporting their candidacy for non-attainment designations.

Factor 6:  Meteorology (weather/transport patterns)

For this factor, EPA considered data from National Weather Service instruments and other meteorological monitoring sites in the area.  Wind direction and wind speed data for 2005-2007 were analyzed, with an emphasis on “high PM2.5 days” for each of two seasons (an October-April “cold” season and a May-September “warm” season).  These high days are defined as days where any FRM or FEM air quality monitors had 24-hour PM2.5 concentrations above 95% on a frequency distribution curve of PM2.5 24-hour values.

Table 6 shows the average prevailing surface wind directions for high PM2.5 days by quadrant for each county in the St. Louis area.  These data show that 24-hour PM2.5 concentrations are influenced by emissions in all directions at various times, but these data also suggest that emissions southeast and southwest of the violating monitor are more likely to contribute to high PM2.5 concentrations than emissions from other directions.

High days

	Prevailing Wind Direction (%)

	NW	SW	SE	NE

Days when only 1 monitor in St. Louis Area violated

2/18/04	0	76	4	4

1/28/05	0	0	96	4

9/13/05	0	59	16	4

2/28/06	0	0	13	42

4/29/06	0	0	96	0

5/8/06	0	29	44	0

8/12/06	0	0	48	28

Days when more than 1 monitor in St. Louis Area violated

9/3/04	4	12	50	8

9/12/04	14	0	41	0

1/31/05	9	0	4	40

2/3/05	17	55	0	0

2/27/05	4	0	58	12

6/24/05	0	54	24	10

6/27/05	0	60	12	0

8/2/05	0	8	54	0

8/8/05	0	12	55	0

9/10/05	0	36	32	0

Data based on EPA Wind Roses for St. Louis International Airport

All percentages approximate. Due N, S, E, and W winds not included

Table 6.  Prevailing Wind Directions for High PM2.5 Days

For each air quality monitoring site, EPA developed a “pollution rose” to understand the prevailing wind direction and wind speed on the days with highest fine particle concentrations.  Figure 3 identifies 24-hour PM2.5 values by color; days exceeding 35 ug/m3 are denoted with a red or black icon.  A dot indicates the day occurred in the warm season; a triangle indicates the day occurred during the cool season.  The center of the figure indicates the location of the air quality monitoring site, and the location of the icon in relation to the center indicates the direction from which the wind was blowing on that day.  An icon that is close to the center indicates a low average wind speed on that day.  Higher wind speeds are indicated when the icon is further away from the center.

Figure 3 Pollution Rose for Madison County, Illinois (County with violating monitor in 2004-2006)

As shown in the pollution rose in Figure 3, the average prevailing surface wind direction for high PM2.5 days in Madison County, Illinois (which is the county with the violating monitors) are from the southeast and southwest.  However, the pollution rose shows that 24-hour PM2.5 concentrations can be influenced by emissions from any direction at various times.

In summary, EPA notes that the bulk of the Missouri portion of the St. Louis bi-state area is located to the south-southwest of the two violating monitors.  A review of the Factor 6 analysis indicates that prevailing winds during PM2.5 episode are commonly from the southeast and southwest.  Under these conditions, the majority of the Missouri emissions for PM2.5 and precursors are upwind of the monitors during these times and thus likely contributors to the total mass on the filters.  Consequently, Factor 6 supports the position that Missouri counties/areas are candidates for consideration in the nonattainment area designation.

Note:  The meteorology factor is also considered in each county’s Contributing Emissions Score because the method for deriving this metric included an analysis of trajectories of air masses for high PM2.5 days.

Factor 7:  Geography/topography (mountain ranges or other air basin boundaries)

The St. Louis area does not have any geographical or topographical barriers significantly limiting air-pollution transport within its air shed.

In summary, there are no physical barriers that would prevent Missouri emissions from traveling downwind to the monitors on high PM2.5 episode days.  Consequently, our review of Factor 7 supports the position that all of the Missouri counties/areas are candidates for consideration in the non-attainment area designation.

Factor 8:  Jurisdictional boundaries (e.g., existing PM2.5 areas)

In evaluating the jurisdictional boundary factor, EPA gave consideration to areas that were already designated nonattainment in 2005 for violating the 1997 fine particle standards.  Analysis of chemical composition data in these areas indicates that the same components that make up most of the PM2.5 mass in the area on an annual average basis (such as sulfate and direct PM2.5 carbon)  also are key contributors to the PM2.5 mass on many days exceeding the 24-hour PM2.5 standard in St. Louis.  These data indicate that in metropolitan areas like St. Louis, the same source categories that contribute to violations of the annual standard also contribute to exceedances of the 24-hour standard.

Most areas that were originally designated nonattainment for the PM2.5 standards still have not attained the standards this is true with the St. Louis metro area, as well.  Thus, EPA has generally concluded that counties that were designated as having emissions sources contributing to fine particle concentrations which continue to exceed the 1997 standards also contribute to fine particle concentrations on the highest days.  For this reason, EPA believes that for most existing nonattainment areas, the nonattainment area for the 2006 24-hour standard should be the same.  Consideration also should be given to existing boundaries and organizations as they may facilitate air quality planning and the implementation of control measures to attain the standard.  Areas already designated as nonattainment represent important boundaries for state air quality planning.

The existing nonattainment area for the 1997 PM2.5   NAAQS is as follows:

In Missouri- St. Charles, Franklin, Jefferson, St. Louis counties and the City of St. Louis.

In Illinois- Madison, Monroe, St. Clair Counties and Baldwin Township, Randolph County

The existing nonattainment area for the 1997 8-hour ozone NAAQS is as follows:

In  Missouri -St. Charles, Franklin, Jefferson, St. Louis counties and the City of St. Louis.

In  Illinois -  Jersey, Madison, Monroe, and St. Clair Counties

In summary, EPA’s review of Factor 8 indicates that the St. Louis metro area has yet to attain the annual PM2.5 standard.   Since, many of the same pollutants and pollutant sources could be implicated in the 24 hour violations as they were for the annual violations, Factor 8 supports including all of the  Missouri counties/areas in the non-attainment designation for the 24-hour standard and including those relevant jurisdictional authorities in the development of the air quality solution.

Factor 9:  Level of control of emission sources

The emission estimates on Table 1 include any control strategies implemented by the States in the St. Louis area before 2002 that may influence emissions of any component of PM2.5 emissions (i.e., total carbon, SO2, NOx, and crustal PM2.5).  In considering county-level emissions, EPA considered 2002 emissions data from the National Emissions Inventory.

EPA recognizes that certain power plants or large sources of emissions in this potential non-attainment area may have installed emission controls or otherwise significantly reduced emissions since 2002 and that this information may not be reflected in this analysis.  However, EPA also notes that many of the controls that Missouri and Illinois will likely rely upon to bring the St. Louis metro area back into attainment with the annual PM2.5 standard have not yet been implemented and thus it is difficult to determine what impact those reductions will have on the monitors measuring violations of the 24 hour standard.

In summary, EPA’s review of Factor 9 is inconclusive.  In other words, no clear conclusions can be drawn regarding the limitation of the non-attainment boundaries for the St. Louis metro area based on these data.

Conclusion

After review of the nine factors for the four counties and the City of St. Louis (including five counties in Illinois) EPA is designating the City of St. Louis, St. Louis County, Franklin County, Jefferson County and St. Charles County in Missouri as nonattainment for the 2006 24-hour PM2.5 standard.  Although the violating monitors are located in Madison County, Illinois, the four Missouri counties and city described above are probable contributors of PM2.5 to the violating monitors based on analysis of all the factors and analytic tools.  Information for each of these Missouri entities is contained in the above discussion of the nine factors.  Conclusions with respect to the entire area in Missouri which we are designating nonattainment are as follows:

Surface wind direction for monitored days with high PM2.5 concentrations in Madison County, Illinois is from the southeast-southwest.  This means that the majority of the Missouri portion of the bi-state area is located upwind of the violating monitors on a significant number of high PM2.5, concentration days.

Missouri counties/areas contribute the majority of PM2.5 and PM2.5 precursor emissions to the bi-state metro area.  Specifically, approximately 50% of the total tons per year (tpy) of direct PM2.5, approximately 57% of the total tpy of NOx, and approximately 74% of the total tpy of SO2 emitted in the bi-state area.

70% or greater of the exceedance days occur during the warm season and are influenced by SO2.  Missouri counties account for 74% of the SO2 emissions in the bi-state area.

78% of the total population and their associated emissions in the bi-state area are located on the Missouri side of the St. Louis bi-state area. While, population and population density alone do not provide absolute justification for determining non-attainment boundaries, this data is indicative of population-based emissions.

Due to the spatial separation of the Granite city monitor from the Alton monitor, it is not likely that one source or a small group of localized sources is exclusively impacting both monitors.  The speciation data also indicates that sulfur and organic carbon sources are likely significant contributors to the total mass on the filters.  Both of these PM2.5 precursor emissions are emitted in substantial quantities in upwind Missouri areas.

All areas in the bi-state metro area experienced growth in either population or VMT and sometimes both in recent years.  However, Missouri areas consistently ranked among the highest in terms of growth rates.

There are no physical barriers that would prevent Missouri emissions from traveling downwind to the Madison County monitors on high PM2.5 episode days.

The St. Louis metro area has yet to attain the annual PM PM2.5standard.  Speciation data and pollution rose data indicate that many of the same pollutants and pollutant sources and source regions that were implicated in the violations of the annual standard could be implicated in the 24 hour violations, as well

The area in Missouri which EPA is including as non-attainment is already part of a nonattainment area for existing Ozone and annual PM2.5standards.  Therefore, the boundary established by EPA is consistent with established jurisdictional boundaries for air quality and transportation planning for the previously mentioned ambient air quality standards.

In conclusion, the above analysis shows that Missouri counties/areas have significant PM2.5 and precursor emissions and substantial growth rates when compared to the rest of the metro area.  Data also show that Missouri areas are commonly upwind of the violating monitors on high PM2.5 episode days.  The air quality monitoring, including the speciation data analysis, the level of control analysis, and the geographic/topographic analysis, indicate that emissions contributions from Missouri counties/areas cannot be ruled out.

None of the analyses conducted by Missouri or EPA under the 9 Factor approach led to a conclusive determination that Missouri counties/areas were not contributing to the filter mass at any of the Monroe County, Illinois violating monitors contribute to the violating monitors.

Based on this review, EPA is designating the Missouri Counties of Franklin, Jefferson, St. Charles and St. Louis, and the City of St. Louis as non-attainment for the 24 hour PM2.5 standard.

Additional information regarding responses to specific State comments can be found in EPA's Response to State Comments document at   HYPERLINK "http://www.epa.gov/pmdesignations/2006standards/tech.htm"  http://www.epa.gov/pmdesignations/2006standards/tech.htm .

Attachment 2

Description of the Contributing Emissions Score

The CES is a metric that takes into consideration emissions data, meteorological data, and air quality monitoring information to provide a relative ranking of counties in and near an area.  Using this methodology, scores were developed for each county in and around the relevant metro area.  The county with the highest contribution potential was assigned a score of 100, and other county scores were adjusted in relation to the highest county.  The CES represents the relative maximum influence that emissions in that county have on a violating county.  The CES, which reflects consideration of multiple factors, should be considered in evaluating the weight of evidence supporting designation decisions for each area.

The CES for each county was derived by incorporating the following significant information and variables that impact PM2.5 transport:

	Major PM2.5 components:  total carbon (organic carbon (OC) and elemental carbon (EC)), SO2, NOx, and inorganic particles (crustal).

	PM2.5 emissions for the highest (generally top 5%) PM2.5 emission days (herein called “high days”) for each of two seasons, cold (Oct-Apr) and warm (May-Sept)

	Meteorology on high days using the NOAA HYSPLIT model for determining trajectories of air masses for specified days

	The “urban increment” of a violating monitor, which is the urban PM2.5 concentration that is in addition to a regional background PM2.5 concentration, determined for each PM2.5 component

	Distance from each potentially contributing county to a violating county or counties

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CES can be found at http://www.epa.gov/ttn/naaqs/pm/pm25_2006_techinfo.html#C.

 EPA designated nonattainment areas for the 1997 fine particle standards in 2005.  In 2006, the 24-hour PM2.5 standard was revised from 65 micrograms per cubic meter (average of 98th percentile values for 3 consecutive years) to 35 micrograms per cubic meter; the level of the annual standard for PM2.5 remained unchanged at 15 micrograms per cubic meter (average of annual averages for 3 consecutive years).

 Under Missouri law, the City of St. Louis is not contained within any county and is therefore not a portion of a county.

 As stated previously, the VFW monitor recorded violations for 2004-2006, but not for 2005-2007.

 PAGE

 PAGE   11

Counties labeled in bold reflect NAAs under 1997 NAAQS

St. Louis, MO-IL


Summary of Public Comments on Materials for the Peer Review of EPA's Draft Report: "Proposed Modeling Approaches for a Health-Based Benchmark for Lead in Drinking Water"
                Prepared by Eastern Research Group, Inc. (ERG)
                                April 20, 2017

 Introduction
This report summarizes public comments on the U.S. Environmental Protection Agency's (EPA's) draft report, "Proposed Modeling Approaches for a Health-Based Benchmark for Lead in Drinking Water", and associated charge questions. Eastern Research Group, Inc. (ERG), a contractor to EPA, is organizing a peer review of this report, and will provide this comment summary and a complete set of public comments and attachments to the reviewers as they prepare their preliminary individual written comments in response to EPA's charge questions. The charge questions seek reviewer comment on the scientific aspects of three potential modeling approaches to associate lead in drinking water with blood lead levels (BLL) for sensitive life stages, including formula-fed infants and children.
In the draft report, EPA's Office of Water (OW) documented three modeling approaches, input parameters, and exposure scenarios that will inform development of a health-based benchmark: the household action level (HAL) for lead in drinking water. OW is considering revising the National Primary Drinking Water Regulations for Lead and Copper (the "Lead and Copper Rule" or LCR) to improve public health by adding this action level. OW will consider the specific role for this value when proposing revisions to the LCR after completion of the peer review. The peer review will focus on scientific aspects of the modeling approaches. Reviewers will not be asked to comment on the specific role for a health-based value.
EPA provided the draft report and charge questions for public comment on January 18, 2017, with an initial deadline of March 6, 2017, which EPA extended to April 5, 2017, based on an early comment requesting an extension. Twelve commenters (see Table 1) submitted written comments, which are summarized in five sections:
 Section 2: General context and purpose of the peer review.
 Section 3: Public comment opportunity.
 Section 4: EPA's draft charge questions.
 Section 5: The three modeling approaches described in EPA's draft report. This section is organized into four areas: model scenarios, model inputs, modeling approaches, and model evaluation/sensitivity analysis.
 Section 6: Interpretation and use of a modeled health-based benchmark for lead in drinking water.
In this summary, organizations commenting are identified by the acronym shown in the first column of Table 1.  Three organizations submitted attachments with their comments, as documented in Table 1.
                          Table 1: Public Commenters
                                 Organization
                                   Signator
                                  Submission
American Water Works Association (AWWA)
G. Tracy Mehan III, Executive Director of Government Affairs
2-page letter requesting extension to comment deadline.
American Water Works Association (AWWA)
G. Tracy Mehan III, Executive Director of Government Affairs
6-page letter and one attachment consisting of a 21-page analysis describing an AWWA consultant's (Dr. Douglas Crawford-Brown's) review of the draft materials.
Anonymous #1
Anonymous
Two-sentence statement.
Association of State Drinking Water Administrators (ASDWA)
J. Alan Roberson, Executive Director
1-page letter.
Anonymous #2
Anonymous
Brief (one-paragraph) statement.
Academy of Nutrition and Dietetics (AND)
Jeanne Blankenship, Vice President for Policy Initiatives and Advocacy; and Pepin Andrew Tuma, Senior Director for Government & Regulatory Affairs
3-page letter.
American Water (AW)
Matthew Corson, Manager for Environmental Compliance and Stewardship
1-page letter.
Environmental Defense Fund (EDF)
Tom Neltner, Chemicals Policy Director; Ananya Roy, Health Scientist; and Lindsay McCormick, Project Manager
4-page letter and an attachment consisting of three recent EDF blog posts: (1) "With draft report, EPA takes major steps to help communities access risks from lead in drinking water"; (2) "When it comes to lead, formula-fed infants get most from water and toddlers from food, but for highest exposed children the main source of lead is soil and dust"; and (3) "EDF's assessment of a health-based benchmark for lead in drinking water."
Earth Justice, Louisiana State University Health Sciences Center, Childhood Lead Action Project, Flint Rising, Freshwater for Life Action Coalition, Parents for Nontoxic Alternatives, Water Alliance, and Women for a Healthy Environment (EJ et al.)
Jennifer C. Chavez, Staff Attorney, et. al.
5-page letter.
Natural Resources Defense Council (NRDC)
Mae Wu, Senior Attorney; and
Miriam Rotkin-Ellman, Senior Scientist
4-page letter and two attachments: (1) State of California. Public Health Goals For Chemicals In Drinking Water Lead (April 2009); (2) California Environmental Protection Agency. Office of Environmental Health Hazard Assessment. Development Of Health Criteria For School Site Risk Assessment Pursuant To Health And Safety Code Section 901(G): Child-Specific Benchmark Change In Blood Lead Concentration For School Site Risk Assessment (April 2007).
Texas Commission on Environmental Quality (TCEQ)
Richard A. Hyde, Executive Director
Cover letter and 5-page comment statement.
Michigan Department of Environmental Quality (MDEQ)
Christine Flaga, Chair of Toxics Steering Group
3-page letter.

 Review Context and Purpose
Several commenters (AND, Anonymous #2, ASDWA, AW, EDF, and MDEQ) acknowledged the importance of a model that defines the relationship between lead in drinking water and blood, and expressed support of EPA's principal goal of developing a health-based benchmark for lead in drinking water. In general, these commenters highlighted the need to develop the proposed models to improve public health protections of the LCR. One commenter (AND) described the cost of inaction, as quantified by childhood lead poisonings in the U.S., as another reason to support the proposed regulatory revisions. Two commenters (AW and MDEQ), however, encouraged EPA to provide further context for the eventual use of modeled outputs. For example, MDEQ found the proposed modeling approaches to be scientifically robust but expressed difficulty evaluating the appropriateness of the different approaches in the absence of a defined adverse health effect association and a specifically defined role for the modeled health-based benchmark. AW expressed similar concerns and suggested that EPA start the formal peer review meeting with a clear, robust discussion of what the modeled numbers represent.

Two commenters did not support development of a health-based benchmark with the proposed modeling approaches (NRDC, and TCEQ). NRDC suggested that EPA base a public notification on the Maximum Contaminant Level Goal (MCLG) or a Maximum Contaminant Level (MCL) that is as close to the MCLG as feasibly possible. This commenter also suggested that EPA consider the approach used by the California Office of Environmental Health Hazard Assessment (OEHHA). TCEQ) argued that the draft report and January 19, 2017, notice in the Federal Register should be withdrawn entirely until the new leadership of EPA has an opportunity to study the materials.

Another commenter (EJ et al.) was cautiously optimistic about EPA's efforts to better characterize the health effects of lead in water on BLLs, but expressed concern with the benchmark concept that EPA is contemplating.
Several commenters (ASDWA, AW, EDF, and EJ et al.) acknowledged general support of EPA's decision to seek expert peer review of the potential modeling approaches that may be used to derive a health-based benchmark for the LCR. For example, one commenter (EJ et al.) stated, "rigorous peer review is needed, at a minimum, to determine whether the models are capable of deriving a conservative value that reliably reflects both potential exposure and effects of exposure on blood lead levels (BLLs) in the most vulnerable population."
 Public Comment Opportunity
Most commenters expressed appreciation for the opportunity to comment on the draft report (AND, ASDWA, AW, AWWA, EDF, MDEQ, NRDC, and TCEQ). One commenter (TCEQ) stressed that EPA should carefully consider and respond to public comments, in a thoughtful, scientific, and common-sense way.
Two commenters (AWWA and TCEQ) did not think the original public comment period was sufficient. The AWWA requested that the public comment period be extended 60 days beyond the original March 6, 2017 deadline. This organization argued that the importance of the modeling efforts warranted additional time for the public to evaluate the materials and provide meaningful comments to the peer review panel. This concern was echoed by TCEQ, who stated that the 45-day public comment period was inadequate to complete an in-depth review of the materials. TCEQ further argued that the 30-day extension, offered two weeks prior to the original deadline, did not help organizations conduct a more thorough analysis. To meet the original due date, organizations would have already shifted their focus to a few key issues, rather than critically reviewing the draft report and proposed modeling approaches, including input parameters and exposure scenarios.
 Draft Charge Questions
Three commenters (ASDWA, AWWA, and MDEQ) commented on EPA's draft charge questions to peer reviewers. MDEQ and ASDWA approved of the questions and found them sufficient to evaluate the proposed modeling approaches. AWWA suggested that the panel charge be expanded to request a recommended minimum appropriate biological averaging period for the models and advice on the minimum duration of exposure that can be appropriately evaluated using the available modeling. AWWA stressed that the peer review panel will need to know the specific risk management strategies to be evaluated with model outputs and the analytical strategies into which the model output will be integrated. AWWA further recommended that EPA provide the peer review panel with the National Drinking Water Advisory Council (NDWAC) recommendation concerning developing a household action level for lead and stressed that the panel be explicitly charged with evaluating modeling approaches with the NDWAC use in mind. Finally, AWWA encouraged the panel to focus on the specific needs of the drinking water program, and not those of the air, waste, and toxic substance control programs.

 Review Materials
5.1	Model Scenarios
The proposed modeling approaches consider lead exposures for three sensitive age groups (i.e., 0- to 6-month-old infants fed formula reconstituted with tap water, 1- to 2-year-old children, and 0- to 7-year-old children). Two commenters (MDEQ and EJ et al.) expressed concern that the models do not evaluate fetal exposures associated with maternal exposures to lead in drinking water. MDEQ encouraged EPA to prioritize their research efforts toward developing a model that would assess exposure risk to the developing fetus while reminding EPA that the existing Adult Lead Model (ALM) only accounts for maternal lead exposures from soil on the fetus. EJ et al. highlighted this as a serious concern and suggested that EPA include, at the very least, a disclaimer concerning the population groups that are not accounted for in model outputs.
One commenter (EDF) was confused by why some sections of the analysis only considered formula-fed infants, while others dealt with all infants. This commenter recommended a comparison of modeling results between formula-fed infants and those not fed formula.
5.2	Model Inputs
Five commenters (EDF, EJ et al., MDEQ, TCEQ, and AWWA) suggested changes to the existing input parameters for the various modeling approaches, and three commenters (Anonymous #1, EDF, and EJ et al.) proposed other parameters.
      Changes to Existing Input Parameters
Two commenters (EDF and EJ et al.) identified drinking water intake calculations as a critical aspect of the proposed modeling approaches. EDF was concerned that the drinking water ingestion rate used for 0- to 6-month-old infants may underestimate true ingestion for infants fed formula reconstituted with tap water (bottle-fed infants). EJ et al. expressed similar concerns regarding the static intake assumptions for formula-fed infants, stressing this population's high water intake on a body weight basis and high absorption rate for lead. Both commenters encouraged EPA to consider the World Health Organization's (WHO's) default value for water intake by bottle-fed infants (0.75 liters/day) and EPA's own exposure guidelines (ranging from 0 liters/day to 1.6 liters/day, with a 90[th] percentile of 0.88 liters/day and 95[th] percentile of 1.0 liter/day). EDF also questioned why the daily water consumption rates for 0- to 7-year-old children drop off from 0.410 liters/day to 0.151 liters/day when a child reaches 1 year of age, and sought clarification on whether this shift was due to consumption of other beverages after that age.
One commenter (MDEQ) identified an internal inconsistency with the geometric mean maternal BLL used in the IEUBK model, referencing the EPA Office of Solid Waste and Emergency Response's OLEM Directive #9285.6-55 released on August 2, 2016. This directive recommended a BLL of 0.7 ug/dL, while the proposed modeling approaches apply a BLL of 0.61 ug/dL.
Another commenter (TCEQ) highlighted an inconsistency with EPA's statement on page 19 of the draft that states "the input parameters used in this analysis do not represent high-end exposure." This commenter provided details on why the proposed outdoor air, soil/dust lead concentration, and soil ingestion rate inputs do include higher-end exposures. Their main points were:
 Outdoor air  -  TCEQ argued that the outdoor air concentration used in the models is based on the average of the highest rolling 3-month average air monitoring data from 50 urban areas and therefore represents "worst-case, high-end, relatively shortterm urban exposure" and not "typical childhood exposure."
 Soil/dust concentrations  -  TCEQ argued that the soil and dust geometric mean lead concentrations used in the models are based on soil sample results from the American Healthy Homes Survey for homes built prior to 1950 and therefore represent high-end lead exposures at homes with lead-based paint. To the contrary, MDEQ was concerned that statistics based on these data may not accurately reflect the disproportionate lead-contaminated housing stock present in older cities. MDEQ cited the number of homes constructed before 1950 from the draft report (approximately 20%), and compared this to Detroit, where approximately 50% of homes were built before 1950. Based on these differences, MDEQ concluded that the soil and dust lead level inputs may underestimate lead levels in urban areas with older housing stock. TCEQ also mentioned that mean concentration inputs likely represent high-end exposure in Texas and other southern states.
 Soil/dust ingestion rates  -  TCEQ argued that central tendency soil and dust ingestion rate inputs may overestimate mean exposure. Based on a meta-analysis of soil ingestion rates, they estimated that the mean soil ingestion rate may overestimate exposure by an order of magnitude for children aged 0 to 2 years and 2-fold for children aged 2 and 3 years.
      Other Input Parameters Proposed
Three commenters (Anonymous #1, EDF, and EJ et al.) mentioned other inputs that EPA should consider in their proposed modeling approaches. One commenter (Anonymous #1) noted that the models do not account for exposure to lead from tobacco smoke and cited an estimate from the European Food Safety Authority that up to 20% of overall lead exposure in infants could be due to environmental tobacco smoke. Another commenter (EJ et al.) questioned why the models do not consider exposure to lead from drinking water through the uptake of cooked food, and suggested that this weakness could greatly underestimate the effects of lead in water on BLLs. A similar concern was expressed by a third commenter (EDF), who questioned whether the water consumption rates included in the model accounted for water retained in food from home cooking.
One commenter (EDF) was concerned that the proposed modeling approaches do not account for the strong seasonal variability of BLLs associated with changes in dust, soil, and paint chip exposures to children, as well as higher lead solubility in water and increased water ingestion during the summer. They argued that lower baseline lead levels from background exposures (e.g., dust, soil, paint chips) coupled with underestimated water consumption during the summer months could result in higher modeled estimates of the water concentration required to reach a given BLL in children.

5.3	Modeling Approaches
EPA proposed three modeling approaches in the draft report, all of which use the Exposure Uptake Biokinetic Model (IEUBK) Model for Children. Two of the approaches are individual-based, while the third approach is population-based and incorporates the Stochastic Human Exposure and Dose Simulation (SHEDS) multimedia model. Several commenters (EDF, NRDC, and TCEQ) discussed concerns that apply to all three of the proposed modeling approaches, as briefly summarized below.
 It was not clear to EDF whether EPA updated the IEUBK model based on 2011 recommendations from the Agency's Science Advisory Board.
 NRDC did not support development of a household action level or health-based benchmark, but suggested EPA consider the California OEHHA approach, if EPA decides to proceed with modeling. NRDC recommended that EPA use the OEHHA-derived level of concern (1 ug/dL) to translate a drinking water level using health protective values.
 TCEQ recommended an approach based on 5% increase in the probability of exceeding 5 ug/dL and justified the approach by claiming it is: "(1) consistent with that used historically by EPA for assessing acceptable lead exposures (i.e., no more than a 5% chance of exceeding 10 ug/dL for children's surface soil impacted by historic Superfund sites); (2) more meaningful in terms of actual risk; and (3) consistent with a more stringent (yet reasonable) probability criterion that may be used by EPA for more serious effects in benchmark dose modeling (i.e., a benchmark response of 5% as opposed to 10%)." TCEQ further recommended the health-based benchmark be derived using geometric mean background soil and dust concentrations of 37 ug/dL and 72 ug/dL, respectively. These conditions result in a drinking water concentration of 15.4 ug/L, which is nearly identical to the current lead action level (15 ug/L). TCEQ concludes that "based on the options currently in the draft report, it appears the current action level that is based on the technical feasibility of corrosion control is also health protective for the formula-fed, average infant scenario recommended by EPA's own NDWAC (as well as all infants combined)."
Comments specific to each approach are summarized below.
 Approach 1: This approach estimates the concentration of lead in drinking water that would result in a 1 to 5 percent increase in the probability of a child having an elevated BLL. One commenter (EDF) thought this approach provided the most useful information because it is grounded in the Reference Level established by the Centers for Disease Control and Prevention (CDC), which is updated every four years to reflect progress made in reducing children's exposure to lead. EDF also emphasized that the approach is conceptually consistent with EPA's method to derive its lead-based paint hazards standard.
 Approach 2: The second modeling approach estimates the concentration of lead in drinking water that would result in a 0.5 ug/dL or 1 ug/dL increase in a child's geometric mean BLL. One commenter (EDF) found this to be a reasonable approach due to its focus on the incremental contribution of lead in drinking water to BLLs. This commenter did, however, question why EPA chose the specified geometric mean BLL increases and stated that those values are not linked to the overall risk to children.
 Approach 3: The third proposed modeling approach estimates the amount of lead in drinking water that would result in a predicted distribution of BLLs for a target population of children with a 95[th] and 97.5[th] percentile of 3.5 ug/dL and 5 ug/dL, respectively. EDF found the description of the proposed approach to be confusing, but thought that the model provided useful insight into the relative contribution of the main sources of lead exposure. This commenter also saw the value in comparing the models to blood lead data recorded from the National Health and Nutrition Examination Survey (NHANES). AWWA (which commented only on third of the three approaches) recommended that EPA consider modifying this approach to first consider all routes of exposure and then examine what happens to the distribution of BLLs when lead in water is reduced below a series of target levels. MDEQ expressed concern regarding whether this modeling approach, which simulates exposure over a 30-day period, violates any underlying assumptions of the models (e.g., the reliability of the IEUBK and ALM has not been assessed for durations less than 3 months). They also asked for justification on how the 5 ug/dL reference BLL from the CDC and the additional modeled value of 3.5 ug/dL are appropriate for the 0- to 6-month scenario since this age group falls outside of CDC's lead reference age range. In general, MDEQ expressed a preference for this approach since the other two approaches rely entirely on the IEUBK model, which assumes a constant concentration of lead in drinking water and thus, may underestimate BLLs. TCEQ cautioned EPA against tying an action level to an ever-moving target such as the 97.5[th] percentile of the U.S. childhood BLL distribution since that value is neither health-based or static.
5.4	Model Evaluation and Multimedia Exposure Pathway/Sensitivity Analyses
The proposed modeling approaches evaluate different lead exposure scenarios including (1) drinking water as the lone source of lead, and (2) drinking water exposures in conjunction with other lead exposure pathways (e.g., air, food, soil, dust, maternal contributions). One commenter (EDF) commended EPA on the inclusion of all likely sources of exposure. However, AWWA commented that the modeling approaches allowed other lead exposures "to occupy the bulk of the lead risk cup and then assume that the small incremental changes in lead exposure through water are determining increases in BLLs." This commenter suggested that the panel evaluate an aggregate lead exposure analysis, where the concentration of lead in water is fixed at candidate pre-defined levels. AWWA also recommended that EPA provide the panel with a sensitivity analysis that tests the model predictions against input data and assumptions. This would offer the panel information to "(1) determine which elements of the model are most influential, (2) evaluate confidence in the model predictions given the primary drivers within the model, (3) inform where the inputs to the model need to be refined or outputs constrained, and (4) judge the appropriate application of specific modeling approaches given the embedded biases within the model algorithms."
AWWA also encouraged EPA and the peer review panel to explore the truncation and correlation of the underlying distribution of the input parameters. This commenter was concerned with the impact of input parameters at the extreme ends of the distribution and does not believe that the model outputs are adequately robust beyond the 90[th] percentile of BLL predictions. The commenter suggested that model predictions be limited accordingly.
Another commenter (EJ et al.) discussed limitations of the proposed modeling approaches and suggested that EPA review the empirical epidemiological and biomonitoring exposure studies, especially those which consider cumulative impacts of chronic exposures to low-dose waterborne lead. This commenter specifically discussed the lack of consideration of absorption factors and variability of lead leaching and lead-scale release, as well as uncertainty surrounding kinetics of lead transfer across the placenta and in breast milk. Citing EPA's draft report, the commenter concluded by stating that "deriving a household‐level benchmark solely on the basis of models with their inherent uncertainties is not only inadequate but dangerous."
 Policy Implications
Several commenters expressed concern, summarized below, about how modeled output will be interpreted and used in practice.
6.1	Interpretation of Modeling Output
Some commenters expressed concern with how the public will interpret a modeled health-based benchmark (EJ et al., NRDC, TCEQ, and AW). These concerns largely stem from the vague description of how model outputs might be used in risk management, and use of the term "health-based benchmark". EJ et al. acknowledged support for EPA's attempt to differentiate the concept of a benchmark derived from health effects (i.e., increase in BLLS) from the existing lead action level. However, the commenter was concerned that a modeled benchmark may create a false sense of security regarding water that tests below the benchmark in any one-time sampling. NRDC noted the same concerns and stated that "any number set above 0 parts per billion (ppb) will give the public the false sense of security that their water is safe, even if it contains lead." Two of these commenters (EJ et al. and TCEQ) encouraged EPA to remove the word "health" or revise the terminology. TCEQ stressed that the term "health-based benchmark" may unnecessarily confuse the public, and recommended that EPA "revise the terminology to accurately depict the derivation and intended use of the value, or provide the necessary documentation to justify a health-based benchmark in a separate document made available for public and peer review." EJ et al. mentioned that the term "health-based" is inherently misleading and ripe for misinterpretation and misapplication. One additional commenter (AW) stated that water systems will lack clear instruction on how to respond to questions raised by customers and the media regarding the health-based benchmark.
6.2	Use of Modeling Output
Numerous commenters cautioned EPA that any modeled numbers will likely be used as point of comparison to existing compliance data (AW, AWWA, and EJ et al.). AW found this troublesome since the current compliance sampling scheme is designed to capture worst-case scenarios, whereas comparing against a typical exposure would be a more appropriate comparison when using a health-based benchmark. AWWA noted that all available data in the U.S. have been obtained by deliberately sampling in a manner that represents higher than typical lead levels and stressed that EPA should therefore caution against direct comparison to any modeled benchmark. EJ et al. questioned how associations between lead-in-water measurements and health effects can be derived from existing lead-in-water data, since they do not accurately capture the concentrations of lead flowing from consumer taps, and requested that EPA defend the scientific validity of such extrapolations.
Related to these concerns, several commenters remarked on the variability inherent with lead exposures from drinking water and questioned how this would affect the use of a modeled benchmark (EJ et al., AWWA, and MDEQ). EJ et al. suggested that EPA consider the variability in lead release and water use patterns, and uncertainties about the presence and nature of lead‐bearing plumbing in homes, daycares, and schools. AWWA echoed these concerns and stressed that the peer review panel be fully informed about the variability of lead exposure through drinking water and the variability in both the form and level of lead present in water as a function of water age, timing of water use, water flow, and other variables.  MDEQ mentioned that the outputs presented in the draft report must reflect specifically defined point-of-use drinking water sampling methodology, and that applicable sampling methodology needs to be standardized before the appropriateness of any downstream drinking water-based health-protective benchmark can be effectively evaluated.