1



## Residual Risk Assessment for the Steel Pickling Source Category

by EPA’s Office of Air Qualit y Planning and Standards
Office of Air and Radiation August 20121



### Table of Contents

1 Introduction .................................................................................................................. ...... 3
2 Methods ....................................................................................................................... ....... 4
2.1 Emissions and source data ........................................................................................... 4
2.2 Dispersion modeling for inhala tion exposure assessment ........................................... 4
2.3 Estimating human inhalation exposure ....................................................................... 7
2.4 Acute Risk Screening and Refined Assessments ........................................................ 7
2.5 Multipathway and environmen tal risk screening ........................................................ 8
2.6 Dose-Response Assessment ........................................................................................ 9
2.6.1 Sources of chronic dose-response information .................................................... 9
2.6.2 Sources of acute dose-response information ...................................................... 11
2.7 Risk Characterization ................................................................................................ 14
2.7.1 General ............................................................................................................... 14
2.7.2 Mixtures ............................................................................................................. 15
2.7.3 Facility-wide Risks ............................................................................................ 15
3 Risk Results for the Steel Pickling Source Category ................................................... 16
3.1 Source Category Description and Results ................................................................. 16
3.2 Risk Characterization ................................................................................................ 17

4 General Discussion of Uncertainties a nd How They Have Been Addressed................... 18
4.1 Exposure Modeling Uncertainties ............................................................................. 18
4.2 Uncertainties in the Dose-Response Relationships ................................................... 19

5 References .................................................................................................................... .... 23

Index of Tables

Table 2.2-1 AERMOD version 09292 m odel options for RTR modeling ................................ 5 Table 2.6-1 Dose-Response Values for Chroni c Inhalation Exposure to Noncarcinogens .... 11 Table 3.1-1 Summary of Em issions from the Steel Pickling Source Category ...................... 16 Table 3.1-2 Summary of Source Categor y Level Risks for Steel Pickling ............................. 17

Appendices

Appendix 1 Emissions Inventory Support Memorandum Appendix 2 Technical Support Do cument for HEM3 Modeling Appendix 3 Meteorological Data for HEM3 Modeling Appendix 4 Analysis of data on short-term emission rates relative to long-term emission

rates Appendix 5 Detailed Risk Modeling Results

Index of Acronyms2

AERMOD American Meteorological Society/EPA Regulatory Model AEGL Acute Exposure Guideline Level ASTDR US Agency for Toxic Substances and Disease Registry CalEPA California Environmental Protection Agency ERPG Emergency Response Planning Guideline
HAP Hazardous Air Pollutant HEM Human Exposure Model
HI Hazard index HQ Hazard quotient IRIS Integrated Risk Information System MACT Maximum Achievable Control Technology
MIR Maximum Individual Risk
MOA Mode of action NAC National Advisory Committee
NATA National Air Toxics Assessment NEI National Emissions Inventory
PB-HAP Persistent and bioaccumulative - HAP POM Polycyclic organic matter REL Reference exposure level
RfC Reference concentration
RfD Reference dose RTR Risk and Technology Review TOSHI Target-organ-specific hazard index
URE Unit risk estimate3



### 1 Introduction

Section 112 of the Clean Air Act (CAA) establ ishes a two-stage regu latory process for addressing emissions of hazardous air pollutants ( HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agen cy (EPA, or the Agency) to develop technology-based standards for categorie s of sources (e.g., petr oleum refineries, pulp and paper mills, etc.) [ 1 ]. Under section 112(d)(6), EP A must review each of these technology-based Maximum Achi evable Control Technology (M ACT) standards at least every eight years and revise a standard, as necessary, “taking into account developments in practices, processes and control technologies.” In the second stage, EPA is required under section 112(f)(2) to assess th e health and environmental risks that remain after implementation of the MACT standards. If addi tional risk reductions ar e necessary to protect public health with an ample margin of safety or to prevent an adverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of the regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk st age must be completed within eight years of promulgation of the initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the “Risk and Technology Review (R TR) Assessment Plan” and in June of 2007,
we received a letter with the results of that c onsultation. Subsequent to the consultation, in June of 2009 a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology Review (RTR) Risk Assessment Methodologies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review in developing our current risk assessments and we will be continuing our efforts to improve our assessm ents by incorporating updates based on the SAB recommendations as they are developed and be come available. Our responses to the key recommendations of the SAB are outlined in a memo entitled, “EPA’s Actions in Response to Key Recommendations from the SAB Review of RTR Risk Assessment Methodologies” [ 4 ].

This document contains the methods and the resu lts of the baseline risk assessment (i.e., after the implementation of the respective MACT st andards) performed for the Steel Pickling (formally known as Steel Pickling – HCl Pro cess Facilities and Hydrochloric Acid regeneration Plants) source cat egory. The methods discussion includes descriptions of the methods used to develop refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints, as well as descriptions of the methods used to screen for acute health risks, chronic non-inha lation health risks, and adverse environmental effects. None of the HAP reported as being emitte d from the facilities in the source category have cancer dose response values and, therefore, cancer risk was not assessed for the source category. Since none of the screening assessm ents indicated any si gnificant potential for
chronic non-inhalation health effects, or environmental impact s including effects to threatened and endangered species, no further refineme nt of these assessme nts was performed. Screening assessments did not indicate a concer n for acute health effects for this source
category.4



### 2 Methods

2.1 Emissions and source data

The 2005 National-Scale Air Toxics Assessmen t (NATA) National Emissions Inventory (NEI) served as the starting point for this assessment. The 2005 NEI purportedly contains information on actual emissions during the enti re 2005 base year. Using the process MACT
code 1 , we developed a subset of this inventory that contai ns emissions and facility data for the Steel Pickling source category. Next, we performe d an engineering review of the information using EPA engineers who were directly involved in the development of the MACT standards for the source category, and/or who have extens ive knowledge of the char acteristics of this industry. NEI data were updated with industry s upplied data as available. The goal of the engineering review was to identify readily-appa rent limitations and issues with the emissions data (particularly those that w ould greatly influence risk estima tes) and to make changes to the data set where possible to address these issu es and decrease the uncertainties associated
with the assessment.

Details on the development of the emissions a nd source data for this source category are
discussed in section 3. The emissions data and modifications made to the NEI data are discussed in Appendix 1, entitled “Emissi ons Inventory Support Memorandum.”



### 2.2 Dispersion modeling for in halation exposure assessment
Both long- and short-term inhalation exposure co ncentrations and associat ed health risks from each facility in the source category were estimated using the Human Exposure Model (HEM3), which utilizes the American Meteor ological Society/EPA Regulatory Model dispersion modeling system (AERMOD). The approach used in applying this modeling system is outlined below, and further deta ils are provided in Appendix 2. The HEM3 performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimatio n of population risks. This section focuses on the dispersion modeling component. The expos ure and risk characterization components are
discussed in other subsecti ons of sections 2 and 3.

The dispersion model in HEM3, AERMOD versi on 09292, is a state-of-the-science Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and volume
sources of continuous air emissions from facility applications [ 5 ]. Further details on AERMOD can be found in the AERMOD Users Guide [ 6 ]. The model is used to develop annual average ambient concentrations thr ough the simulation of hour-by-hour dispersion from the emission sources into the surrounding at mosphere. Hourly emission rates used for this simulation are generated by evenly dividi ng the total annual emission rate from the inventory into the 8,760 hours of the year.

1 The tagging of data with MACT codes allows EPA to de termine reductions attributable to the MACT program. The NEI associates MACT codes corresponding to MACT source categories with stationary major and area source data. MACT codes may be assigned either at the pro cess level or at the site level in the point source data (e.g., the MACT code for municipal waste combustors (M WCs) is assigned at the site level whereas the MACT code for petroleum refinery catalytic crack ing is assigned at the process level).5

The first step in the application of HEM3 is to predict ambient concentr ations at locations of
interest. The AERMOD mode l options employed are summarized in Table 2.2-1 and are
discussed further below.

Table 2.2-1 AERMOD version 09292 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area

Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site (over 200 stations)

Meteorological data for HEM3 are selected fr om a list of over 200 National Weather Service (NWS) surface observation stations across the c ontinental United States, Alaska, Hawaii, and
Puerto Rico. In most cases the nearest station is selected as representati ve of the conditions at the subject facility. Ideally, wh en considering off-site meteorological data, most site-specific dispersion modeling efforts will employ up to five years of data to capture variability in weather patterns from year to year. However, because we had an insufficient number of appropriately formatted model input files derived from ava ilable meteorological data, we modeled only a single year, typical ly 1991. While the selection of a single year may result in under-prediction of long-term ambient levels at some locations, likewis e it may result in overprediction at others. For each f acility identified by its charac teristic latitude and longitude coordinates, the closest mete orological station was used in the dispersion modeling. The average distance between a modeled facility and the applicable meteorol ogical station was 40 miles (72 km). Appendix 3 (Meteorologica l Data Processing Using AERMET for HEM3) provides a complete listing of stations and assumptions along with further details used in processing the data through AERMET. The sensitivity of model results to the selection of the
nearest weather station a nd the use of one year of meteorolog ical data is discussed in “Risk and Technology Review (RTR) Risk Assessment Methodologies” [2].

The HEM3 estimates ambient concentrations at the geographic centroids of census blocks (using the 2000 Census), and at ot her receptor locations that can be specified by the user. The
model accounts for the effects of multiple facilities when estima ting concentration impacts at each block centroid. Typically we combine onl y the impacts of faciliti es within the same source category, and assess chronic exposure and ri sk only for census blocks with at least one resident (i.e., locations where people may r easonably be assumed to reside rather than6

receptor points at the fenceline of a facility). Chronic ambient concentrations are calculated as the annual average of all estimated short- term (one-hour) concentrations at each block
centroid. Possible future reside ntial use of currently uninhabited areas ar e not considered. Census blocks, the finest resolution available in the census data, are typically comprised of approximately 40 people or about ten households.

In contrast to the developmen t of ambient concentrations fo r evaluating long-term exposures, which is performed only for occupied census blocks, worst-case short-term (one-hour)
concentrations are estimated both at the censu s block centroids and at points nearer the facility that represent locations where people may be present for short periods, but generally
no nearer than 100 meters from the center of the facility (note that for large facilities, this 100-meter ring could still contain locations inside the facility property). Since short-term emission rates are needed to screen for the pote ntial for hazard via acute exposures, and since the NEI contains only annual emission totals, we generally apply the a ssumption to all source categories that the maximum one-hour emission ra te from any source is ten times the average annual hourly emission rate for that source.

The average hourly emissions rate is defined as the total emissions for a year divided by the total number of operating hours in the year. Th e choice of a factor of ten for acute screening was originally based on engineering judgment. To develop a more robust peak-to-mean emissions factor, and in response to one of the key messages from the SAB consultation on our RTR Assessment Plan, we performed an anal ysis using a short-term emissions dataset
from a number of sources located in Texas (originally reported on by Allen et al. 2004)[ 7 ]. In that report, the Texas Environmental Resear ch Consortium Project compared hourly and annual emissions data for volatile organic compounds for all facilities in a heavilyindustrialized 4-county area (Harris, Galvest on, Chambers, and Brazoria Counties, TX) over an eleven-month time period in 2001. We obt ained the dataset and performed our own analysis, focusing that analysis on sources which reported emitting high quantities of HAP over short periods of time (see Appendix 4, “Ana lysis of data on short-term emission rates relative to long-term emission rates”). Most peak emission events were less than twice the annual average, the highest was a factor of 74 times the annual average, and the 99th percentile ratio of peak hourly emission rate to the annua l hourly emission rate was nine. Based on these results, we typically use a factor of ten for the initial screening that is intended to cover all possible hourly peaks associated w ith routinely-variable emissions, as well as startup, shutdown, and malfunction (SSM) emi ssions. While there have been some documented emission excursions above this level, our analysis of the data from the Texas Environmental Research Consortium suggests th at this factor should cover more than 99 percent of the short-term peak gaseous or volatile HAP emi ssions from typical industrial sources. We have no data relating specifically to peak short-term emissions of particulate HAPs. In the absence of source-category-specific data, we use this same default approach for particulate emissions as well.

Census block elevations for HEM3 modeling were determined nationally from the US Geological Survey 1-degree digita l elevation model (DEM) data files, which have a spatial resolution of about 90 meters. Elevations of pol ar grid receptors (points used in estimating short- and long-term ambient co ncentrations) are assi gned the highest eleva tion of any census7

block within the polar grid sect or corresponding to the grid point. If a sector does not contain any blocks, the model defaults to the elevation of the nearest block. If an elevation is not provided for an emission source, the model takes the average elevation of all sectors of the nearest model ring. AERMOD adjusts the plume’s flow if nearby elevated hills are expected to influence the wind patterns. For details on how hill heights were estimated and used in the AERMOD modeling, see Appendix 2.



### 2.3 Estimating human inhalation exposure

We use the estimated annual average ambient ai r concentration of each HAP at each census block centroid as a surrogate fo r the lifetime inhalation exposu re concentrati on of all the people who reside in the census block. That is, the risk analysis does not consider either the short-term or long-term behavior (mobility) of the exposed populati ons and its potential influence on their exposure.

We do not address short-term human activity for tw o reasons. First, ou r experience with the NATA assessments (which modeled daily activ ity using EPA’s HAPEM model) suggests that, given our current understa nding of microenvironment concen trations and daily activities, modeling short-term activity w ould, on average, reduce risk estimates about 25 percent for particulate HAPs; it will also reduce risk estimates for gaseou s HAPs, but typically by much less. Second, basing exposure estimates on average ambient concentrations at census block centroids may underestimate or overestimate actual exposure concentrations at some residences. Further reducing exposure estimates for the most highly-exposed residents by modeling their short-term beha vior could add a systematic low bias to these results.

We do not address long-term mi gration nor population growth or decrease over 70 years, and
instead base the assessment on the assumption that each person’s predicted exposure is
constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer risk, we generally estimate three metrics: th e maximum individual risk (MIR), which is
defined as the risk associated with a lifetime of exposure at the highe st concentration; the population risk distribution; and the cancer incidence. Not c onsidering short or long-term population mobility does not bias the estimate of the theoretical MIR, nor does it affect the
estimate of cancer incidence si nce the total population number re mains the same. It does, however, affect the shape of th e distribution of individual risk s across the affected population, shifting it toward higher estimat ed individual risks at the upper end and reducing the number of people estimated to be at lower risks, ther eby increasing the estimate d number of people at specific risk levels.

When screening for potentially significant acute exposures, we use an estimate of the highest hourly ambient concentration at any off-site location as the surrogate for the maximum potential acute exposure concen tration for any individual.



### 2.4 Acute Risk Screening and Refined Assessments
In establishing a scientifically defensible approa ch for the assessment of potential health risks due to acute exposures to HAPs, we followed the same general approach that has been used8

for developing chronic health ri sk assessments under the residual risk program. That is, we developed a tiered, iterative approach. This a pproach to risk assessment was endorsed by the National Academy of Sciences in its 1993 pub lication “Science and Judgment in Risk Assessment” and subsequently was endorsed in the EPA’s “Residual Risk Report to Congress” in 1999.

The assessment methodology is designed to el iminate from further consideration those
facilities for which we have confidence that no acute adverse health effects of concern will occur. To do so, we use a tiered, iterative approach. This means that we begin with a screening assessment, which relies on readil y-available data and uses conservative assumptions that in combination approximate a worst-case exposure. The result of this screening process is that either the facility be ing assessed poses no potenti al acute health risks (i.e., it “screens out”), or that it requires further, more re fined assessment. A refined assessment could use industry- or site-specific data on the temporal pa ttern of emissions, the layout of emission points at the facility, the boundaries of the f acility, or the local meteorology. In some cases, all of these site-s pecific data may be needed to refine the assessment; in others, lesser amount s of site-specific data can be used to determine that acute exposures are not a concern, and significant ad ditional data collection is not necessary.

Acute health risk screening is performed for each facility as the first step. We use conservative assumptions for emission rates, meteorology, and exposure location. We use the following worst-case assumptions in our screening approach:

 Peak 1-hour emissions assumed to equal 10 times the average 1-hour emission rates.

 For facilities with multiple emission points, peak 1-hour emissions assumed to occur at all emission points at the same time.

 For facilities with multiple emission points, 1-hour concentrations at each receptor assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concen trations occurred dur ing the same hour.

 Worst-case meteorology (from one year of lo cal meteorology) assumed to occur at the same time the peak emission rates occur. The recommended EPA local-scale dispersion model, AERMOD, is used for simulating atmospheric dispersion.

 A person is assumed to be located downwi nd at the point of maximum impact during this same worst-case 1-hour period, but no nearer to the source than 100 meters.

 The maximum impact is compared to multiple short-term health benchmarks for the HAP being assessed to determine if a possibl e acute health risk might exist. These
benchmarks are described in section 2.6 of this report.

The Steel Pickling source category “screened out” for acute risk.



### 2.5 Multipathway and envi ronmental risk screening

The potential for significant hum an health risks due to expos ures via routes other than inhalation (i.e., multipathway exposures) is scre ened by first determining whether any sources emit any hazardous air pollutants known to be persistent and bioaccumulative in the9

environment (PB-HAP). The PB-HAP compounds or compound classes are identified for the screening from the EPA’s Air Toxi cs Risk Assessment Library [ 8 ]. Examples of PB-HAP are cadmium compounds, chlordane, chlorinated di benzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexa ne, lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls, polycyclic organic matter, toxaphene, and
trifluralin. No PB-HAP emissions were iden tified from the Steel Pickling source category, indicating low potential for exposur es due to non-inhalation routes of exposure. The lack of PB-HAP emissions from this source categor y indicates a low potential for adverse
environmental effects due to e xposures resulting from atmosphe ric deposition of PB-HAPs. Thus, even if sensitive species are present in the areas surrounding these facilities with no PBHAP emissions, it is still reasonable to assume that there are no adverse effects to populations in the areas surrounding these facilities.

Additionally, we evaluated the potential for significan t ecological exposures to non PB-HAP
from exceedances of chronic human health inhala tion thresholds in the ambient air near these
facilities. Human health dose -response threshold values are generally derived from studies conducted on laboratory animals (such as rode nts) and developed with the inclusion of uncertainty factors that could be up to 3000. As a result, these human threshold values are often significantly lower than the level expected to cause an adverse effect in an exposed rodent. Therefore, we have concluded that te rrestrial mammalian receptors are unlikely to be
at risk of adverse effects due to inhalation exposures from non PB-HAP. For this source category, EPA considered effects to the environmen t separate from human health risk in order to determine whether it is necessary to set a more stringent standard to prevent an adverse environmental effect. In considering effects to the environment, EPA first determined that some HAPs of potential concern w ith respect to the environment are emitted from sources in this category. These HAPs are hydrogen chloride and chlorine. The agency also determined that there is at least some potential for e xposures to environmental receptors, because the presence of such receptors around the sources in this category ca nnot be ruled out. EPA then determined that given the small amount of HAP that is emitted from sources in this category, the agency does not expect an environment effect to occur.



### 2.6 Dose-Response Assessment

2.6.1 Sources of chronic do se-response information

There are only two HAP emitted by the sources in the Steel Pickling source category, neither of which is considered to be carcinogenic. Dose-response assessment information for the
noncancer effects due to chroni c exposure (either by inhalatio n or ingestion) for the HAPs reported in the emissions invent ory for Steel Pickling sources were based on the EPA Office of Air Quality Planning and Standards’ existing recommendations for HAPs [ 9 ], also used for NATA [ 10 ]. This information has been obtaine d from various sources and prioritized according to (1) conceptual consistency with EP A risk assessment guidelines and (2) level of peer review received. The prioritization pr ocess was aimed at incorporating into our
assessments the best available science with respect to dose-response information. The
recommendations are based on the follo wing sources, in order of priority:10

1) US Environmental Protection Agency (EPA). EPA has developed a dose-response assessmen t for chronic exposure for one of the pollutants in this risk assessment. The dose- response assessment provides a qualitative statement regarding the strength of scientific data and spec ifies a reference concentration (RfC, for inhalation) or referen ce dose (RfD, for ingestion) to protect against effects other
than cancer. The RfC is defined as “an esti mate (with uncertainty spanning perhaps an order of magnitude) of a continuous inha lation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime.” EPA disseminates dose-response assessment information in several forms, based on the level of review. The Integrated Risk Information System (IRIS) [ 11 ] is an EPA database that contains scientific health assessment information, including dose-response information. All IRIS assessments since 1996 have also undergone independent external peer review. The cu rrent IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by independent scien tists external to EPA. New IRIS values are developed and old IRIS values are upda ted as new health effects data become
available. Refer to the “IRIS Track” webs ite for detailed information on status and scheduling of current individua l IRIS assessments and updates ( http://cfpub.epa.gov/ncea/iristrac/index.cfm ).

2) US Agency for Toxic Substan ces and Disease Registry (ATSDR). ATSDR, which is part of the US Department of Health and Human Se rvices, develops and publishes Minimum Risk Levels (MRLs) [ 12 ] for inhalation and oral exposure to many
toxic substances. As stated on the ATSD R web site: “Following discussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to th at of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving s ubstance specific health guidance levels for non neoplastic endpoints.” The MRL is defined as “an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified duration of exposure. ” ATSDR describes MRLs as substancespecific estimates to be used by health assess ors to select environmental contaminants for further evaluation. Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore not intended for use as pr edictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA).The CalEPA Office of Environmental Health Hazard Assessment has developed doseresponse assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorp orates significant exte rnal scientific peer
review. As cited in the CalEPA Technical Support Document for developing their chronic

assessments 2 : “The guidelines for developing ch ronic inhalation exposure levels

2 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document11

incorporate many recommendations of the U.S. EPA [ 13 ] and NAS [ 14 ].” The noncancer
information includes available inhalation health risk guidance values expressed as chronic inhalation reference exposure levels (RELs) [ 15 ]. CalEPA defines the REL as “the
concentration level at or below which no hea lth effects are anticip ated in the general human population”.

The emissions inventory for the source cate gory includes emissions of two HAPs with quantitative inhalation chronic nonc ancer threshold values available. These two HAPs, their threshold values, and the source of th e value are listed in Table 2.6-1.

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous i nhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources : IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environment al Human Health Assessment

Pollutant CAS
Number RfC (mg/m 3 ) Source a

Chlorine 7782505 0.0002 CAL Hydrochloric acid 7647010 0.02 IRIS - L

a The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the overall level of confidence in the RfC value, as reported in the IRIS file.

2.6.2 Sources of acute dose-response information
Hazard identification and dose-response assessment information for preliminary acute inhalation exposure assessments are based on the existing recommenda tions of OAQPS for HAPs [ 16 ]. Depending on availability, the result s from screening acute assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular refe rence level depends on the definition of that level and any limitations expressed therei n. Comparisons among different available
inhalation health effect refere nce values (both acute and chro nic) for selected HAPs can be found in a separate EPA document [ 17].

California Acute Reference Exposure Levels (RELs ). The California Environmental Protection Agency (CalEPA) has developed acu te dose-response refe rence values for many substances, expressing the results as acute i nhalation Reference Exposure Levels (RELs).

The acute REL ( http://www.oehha.ca.gov/air/pdf/acuterel.pdf ) is defined by CalEPA as
“the concentration level at or below which no adverse health effects are anticipated for a specified exposure duration. [ 18 ]. RELs are based on the most sensitive, relevant,

for the Determination of Noncancer Chronic Referen ce Exposure Levels. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)12

adverse health effect reported in the medi cal and toxicological literature. RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety. Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not auto matically indicate an adverse health impact.” Acute RELs are developed fo r 1-hour (and 8-hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs ). AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guideline Levels (AEGLs) for Hazardous Substances, and then reviewed and published by the Natio nal Research Council As described in the Committee’s “Standing Operating Procedures (SOP)” ( http://www.epa.gov/op ptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emer gency exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixe d facilities and from tran sport carriers.” The document states that “the primary purpos e of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetim e, short-term exposures to
airborne concentrations of acute ly toxic, high-priority chemi cals.” In detailing the intended application of AEGL values, the document states that “It is antic ipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in c onjunction with chemical emergency response, planning, and prevention programs. More spec ifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real-time emerge ncy response actions, for accidental chemical
releases at fixed facilities and from transport carriers.”

The NAC/AEGL defines AEGL-1 and AEGL-2 as:

“AEGL-1 is the airborne concentr ation (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and ar e transient and revers ible upon cessation of exposure.”

“AEGL-2 is the airborne concentr ation (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience irreversible or other serious, long-lasting adverse h ealth effects or an impaired ability to escape.”

“Airborne concentrations below AEGL-1 repr esent exposure levels that can produce mild and progressively increasing but transient and nondisabli ng odor, taste, and sensory irritation or certain asymptomatic, nonsenso ry effects. With increasing airborne concentrations above each AEGL, there is a progressive increase in the likelihood of13

occurrence and the severity of effects described for each corresponding AEGL. Although the AEGL values represent threshold levels for the general public, including susceptible subpopulations, such as infants, children, the el derly, persons with asthma, and those with other illnesses, it is recognized that indivi duals, subject to unique or idiosyncratic responses, could experience the effects de scribed at concentr ations below the corresponding AEGL.”

Emergency Response Planni ng Guidelines (ERPGs). The American Industrial Hygiene Association (AIHA) has developed Emergenc y Response Planning Guidelines (ERPGs) [ 19 ] for acute exposures at three different levels of severity. Thes e guidelines represent concentrations for exposure of the general population (but not pa rticularly sensitive persons) for up to 1 hour associated with effects expe cted to be mild or transient (ERPG-1), irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).

ERPG values ( http://www.aiha.org/insideaiha/guideline development/erpg/Pages/default.aspx) are described in their supporting documentati on as follows: “ERPGs are air concentration guidelines for single exposures to agents and are intended for use as tools to assess the adequacy of accident prevention and emergenc y response plans, including transportation emergency planning, community emergency re sponse plans and incident prevention and mitigation.”

ERPG-1 and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild
transient adverse health eff ects or without perceiving a clearly defined, objectionable
odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing
irreversible or other serious h ealth effects or symptoms which could impair an individual's ability to take protective action.”

The emissions inventory for the Steel Pickli ng source category include s emissions of two HAPs with relevant and available quantitative acute dose-response thre shold values. These HAPs, their acute threshold values, and the sour ce of the value are lis ted below in Table 2.6-
2.

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) ERPG-2 (mg/m 3 ) REL (mg/m 3 )

Chlorine 7782505 1.5 5.8 2.9 8.7 0.21 Hydrochloric acid 7647010 2.7 33 4.5 30 2.114



### 2.7 Risk Characterization

2.7.1 General

The final product of the risk as sessment is the risk characteri zation, in which the information from the previous steps is integrated and an overa ll conclusion about risk is synthesized that is complete, informative, and useful for decision ma kers. In general, the nature of this risk characterization depends on the in formation available, the application of the risk information and the resources available. In all cases, majo r issues associated with determining the nature
and extent of the risk are identified and disc ussed. Further, the EPA Administrator’s March
1995 Policy for Risk Characterization [ 20 ] specifies that a risk ch aracterization “be prepared in a manner that is clear, transparent, r easonable, and consistent with other risk characterizations of similar scope prepared acro ss programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency’s Risk Characterization
Handbook [ 21 ], in 2002 by the Agency’s information quality guidelines [ 22 ], and in the OMB/OSTP September 2007 Memorandum on U pdated Principles for Risk Analysis 3 , and are incorporated in these assessments.

Estimates of health risk are pres ented in the context of uncertainties and limitations in the data and methodology. Through our tiered, iterative an alytical approach, we have attempted to reduce both uncertainty and bias to the greate st degree possible in these assessments, within
the limitations of available time and resour ces. We provide summari es of risk metrics (including maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision ma kers with the fullest pictur e of the assessment and its
limitations.

“Risk” for noncancer effects is expressed by comparing an exposur e to a reference level as a ratio. The “hazard quotient” (HQ) is the estim ated exposure divided by a reference level (e.g., the RfC). For a given HAP, exposures at or below the reference level (HQ ≤ 1) are not likely to cause adverse health effects. As expos ures increase above the reference level (HQs increasingly greater th an 1), the potential for adverse effects increases. For exposures predicted to be above the RfC, the risk char acterization includes th e degree of confidence ascribed to the RfC values for the compound( s) of concern (i.e., high, medium, or low confidence) and discusses the impact of this on possible health interpretations.

The risk characterization for chronic effects othe r than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncer tainty factors in the face of uncertain extrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot be translated to a probability that adverse effects will occur,

3 Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis (September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and Depu ty Director for Science, Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)15

and is unlikely to be proportional to advers e health effect outcomes in a population.

Screening for potentially significan t acute inhalation exposures also follows the HQ approach. We divide the maximum estimated acute exposure by each available short-term threshold value to develop an array of HQ values relative to the various acute endp oints and thresholds. In general, when none of these HQ values are grea ter than one, there is no potential for acute risk. In cases where HQ values are above one, ad ditional information is used to determine if there is the potential for significant acute risks.

2.7.2 Mixtures

Since most or all receptors in these assessments receive exposures to multiple pollutants rather than a single pollutant, we estimate the aggregate health risks associated with all the exposures from a particular source category combined.

In assessing noncancer hazard from chronic expo sures, in cases where different pollutants
cause adverse health effects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of th ese mode-of-action differences, the mixtures guidelines support aggregating effects of different substances in specif ic and limited ways. To conform to these guidelines, we aggregate noncancer HQs of HAPs that act by similar toxic modes of action, or (where this information is absent) that affect the same target organ. This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), defined as the sum of hazard quotients for indi vidual HAPs that affect the same organ or organ system. All TOSHI calcu lations presented here were based exclusively on effects occurring at the “critica l dose” (i.e., the lowest dose that pr oduces adverse health effects). Although HQs associated with some pollutants ha ve been aggregated into more than one TOSHI, this has been done only in cases where th e critical dose affects more than one target organ. Because impacts on organs or systems that occur above the critical dose have not been included in the TOSHI calculations, some TOSHIs may have been underestimated. As with the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict
delineation of “safe” and “unsafe” levels. Ra ther, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposur e, and needs to be interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and th e variable nature of emissions and potential exposures, acute impact s were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 Facility-wide Risks

To help place the source category risks in cont ext, we examined “facility-wide” risks using
the results of the 2005 NATA. Fo r the facilities in the Steel Pickling source category, we extracted the NATA results (based on facility ID) for the maximum inhalation cancer and
chronic noncancer risks associated with all HAP emissions sources at the facility, including16

emissions sources that are not part of the s ource category but that are located within a contiguous area and are under comm on control. We analyzed ri sks due to the inhalation of HAP for the populations residing within 50 kilome ters of each facilit y. The results of the “facility-wide” assessment are summarized below in section 3.2. The complete results of the “facility-wide” assessment are provided in Appendix 5.



### 3 Risk Results for the Stee l Pickling Source Category

3.1 Source Category Desc ription and Results

The steel pickling source categor y includes facilities that are e ngaged in the pickling of steel using hydrochloric acid (HCl) as the pickling acid, and facilities that regenerate the HCl after use. Steel pickling is a process in which the heavy oxide crust or mill scale that develops on the surface of steel during hot forming or heat treating is removed chemically in a bath of aqueous acid solution. This source category do es not include facilities which pickle steel using acids other than HCl. Steel pickli ng operations can be conducted via continuous, semicontinuous, or batch processes. Five type s of pickling processes have been identified: (1) continuous strip; (2) push- pull strip; (3) rod/wire; (4) tube; and (5) batch. The HAPs emitted from steel pickling source s are HCl and chlorine (which is generated as a by-product of the regeneration process).

It is currently estimated that there are appr oximately 80 facilities performing steel pickling and/or acid regeneration in the U.S. The data set contains 51 facilities identified with a steel pickling MACT code in the 2005 NATA NEI. While the NEI identifies 25 of these 51 facilities as area sources of HAP (and 26 as majo r sources), the uncertainty associated with these major/area classifications is significant en ough that we considered all of them to be major sources for the purposes of this risk assessment.

Table 3.1-1 summarizes the emissions for the St eel Pickling source cate gory. The total HAP emissions for the source categor y are approximately 265 tons per year. Based on these data, HCl and Chlorine are the only HAP emitted by steel pickling sources. HCl is reported as an
emission at all of the facilities and chlorine is re ported for 6 facilities. There are no persistent and bioaccumulative HAP (PB HAP) emissions included in the data set.

Table 3.1-1 Summary of Emissions fr om the Steel Pickling Source Category

a Specific dose-response values for each chemical are id entified on EPA’s Techno logy Transfer Network website for air toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html. HAP Emissions (tpy) Number of
Facilities Reporting HAP (51 facilities in data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS a

PBHAP

? Unit Risk
Estimate for

Cancer? Reference
Concentration

for Chronic
Noncancer? Health
Benchmark
Values for

Acute
Noncancer? Hydrochloric Acid 247 51  

Chlorine 18 6  17

Table 3.1-2 provides informati on summarizing health risks fo r this source category.

Table 3.1-2 Summary of Source Categor y Level Risks for Steel Pickling

Result HAP “Drivers” Facilities in Source Category
Number of Facilities Estimated to be in the Source Category 80 n/a

Number of Facilities Identified in the NEI and
Modeled in the Risk Assessment 51 n/a

Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) from any Facility in the Category a n/a

Chronic Noncancer Risks Maximum Respiratory Hazard Index 2 chlorine Number of Facilities with Maximu m Respiratory Hazard Index:
Greater than 1 1 n/a Acute Noncancer Screening Results

Maximum Acute Hazard Quotient 0.4
0.2
0.02 chlorine (REL) hydrochloric acid (AEGL-1) hydrochloric acid (AEGL-2)
Number of Facilities With Potential for Acute
Effects 0 n/a

Population Exposure Number of People Living Within 50 Kilometers
of Facilities Modeled 50,000,000 n/a

Number of People Exposed to Cancer Risk: Greater than or equal to 1 in 1 million a n/a Number of People Exposed to Noncancer Respiratory Hazard Index:
Greater than 1 30 n/a Estimated Cancer Incidence (excess cancer cases per year) a n/a

a The facilities in this category do not report emi ssions of any HAP with cancer dose-response values.



### 3.2 Risk Characterization

This section presents the result s of the risk assessment for the Steel Pickling source category. No compounds emitted from the category have can cer potency factors; therefore, we did not assess cancer risks for this source category. The maximum chronic noncancer target-organspecific hazard index (TOSHI) value for the sour ce category is 2. An HI value greater than 1 is found at only one facility. The next highest HI for a source in the category is 0.1, with the remaining HI values significantly lower than 0.1. The highest refi ned worst-case acute HQ value was 0.4 (based on the chlori ne REL), indicating low potential for acute health effects. There were no reported emissions of PB-HAP from this source cat egory; therefore, there is low potential for human health effects from multi-pathway risks from this category, and low potential for adverse environmental effects due to exposures resulting from atmospheric deposition of PB-HAPs.18

The results of the facility-wide assessment indi cate that the Steel Pickling source category’s highest chronic noncancer risk, an HI value of 2 for chlorine, is equal to the facility-wide HI at that facility. The highest facility-wide HI is 10 and at this facility the source category contributes very minimally to the facility-w ide HI (maximum source category HI at this facility is 0.0004). Due to th e absence of HAP w ith cancer dose-response values for the source category emissions, there is no source ca tegory MIR. However, the facility-wide MIR
associated with other emissions sources at facili ties with steel pickling pr ocesses is as high as
100.

While the HI value for one facility exceeds 1, we note that this facility has had compliance
issues with the standard and th at the actual emissions we relied on for this facility include
emissions in excess of what is allowed under the MACT standard. We estimate that if emissions were maintained at levels equal to or lower than the level allowed by the MACT limit (6 ppm) then the HI would be no higher than 1. The next highest HI from any facility in the source category is 0.1.



### 4 General Discussion of Uncertaint ies and How They Have Been
Addressed

4.1 Exposure Modeling Uncertainties

Emission inventory uncertainties were previously discussed for the source category in
sections 2.1 and 3.1. Our estimated number of f acilities indicates that there may be more facilities in this category than represented in the data set. As previously noted, there is significant uncertainty in the identification of sources as major or area in the NEI.

The chronic ambient modeling uncer tainties are considered relatively small since we are using EPA’s refined local dispersion model with site-specific parame ters and reasonably representative meteorology. If anything, the po pulation exposure estimates are biased high by not accounting for short- or long-term popula tion mobility, and by neglecting processes like deposition, plume depletion, and atmospheric degradation. Additionally, risk estimates contain uncertainty because they are derived at census block ce ntroid locations rather than actual residences. This uncertainty is known to create potentia l underestimates and
overestimates of the actual risk values for indivi dual facilities but, overa ll, it is not thought to have a significant impact on th e estimated maximum risk for a source category. Finally, we did not factor in the possibility of a sour ce closure occurring during the 70-year chronic exposure period, leading to a potential upward bi as in both the maximum risk and population risk estimates; nor did we factor in the possi bility of population grow th during the 70-year chronic exposure period, leadi ng to a potential downward bias in both the maximum risk and population risk estimates.

We have purposely biased the acute screening results high, consideri ng that they depend upon the joint occurrence of independent factors, su ch as hourly emissions rates, meteorology and human activity patterns. Furthermore, in cas es where multiple acute threshold values are considered scientifically accepta ble, we have chosen the most conservative of the values,19

erring on the side of overestimating potential he alth risks from acute exposures. In cases where the results indicate the pot ential for exceeding short-term health thresholds, we refine our assessment by developing a better understandi ng of the geography of the facility relative to potential exposure locations and the true variability of short-term emission rates.



### 4.2 Uncertainties in the Do se-Response Re lationships

This section discusses uncertainties associat ed with chronic and acute noncancer reference
values. There are no cancer dos e response values for either of the HAP (HCl and chlorine) assessed and no other carcinogeni c HAP emissions were reported for this source category;
therefore no cancer assessment was done for the Steel Pickling source category. Further, while these two pollutants have been widely st udied, there are no data or studies indicating that either chlorine or HCl might be associated with cancer. Th erefore, we maintain that the
uncertainties associated with the results of our cancer assessment (i.e., that there are no significant cancer risks) are small.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure leve ls that are intended to be health-protective. That is, EPA and other or ganizations which develop noncancer reference values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available informa tion, uncertainty factors (UFs) are applied to
derive reference values that are intended to be protective against appreciable risk of deleterious effects. Uncertainty factors are commonly default values 4 e.g., factors of 10 or 3, used in the absence of compound-specific data; wh ere data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default fact ors are used. Thus there may be a greater tendency to overestimate risk—in the sense th at further study might support development of reference values that are higher (i.e., less pot ent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks ma y be underestimated.

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference C oncentration (RfC) for inhalation exposures. These values provide an estimate (with uncerta inty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a conti nuous inhalation exposure (RfC) to the human

4 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report, Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions
are used to ensure that risk to chemicals is not under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Asse ssment Principles and Practices, EPA/100/B-04/001 available at: http ://www.epa.gov/osa/pdfs/ratf-final.pdf20

population (including sensitive subgroups) that is lik ely to be without an appreciable risk of deleterious effects during a lifetime. 5 To derive values that are intended to be “without appreciable risk,” EPA’s met hodology relies upon an uncertain ty factor (UF) approach [ 23 ],[ 24 ] which includes consideration of both uncertainty and variability.

EPA begins by evaluating all of the availabl e peer-reviewed literature to determine noncancer endpoints of concern, eval uating the quality, strengths and limitations of the available studies. EPA typically chooses th e relevant endpoint that occu rs at the lowest dose, often using statistical modeling of the available data, and then determines th e appropriate point of departure (POD) for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the benchmark dos e (BMD) approach; (2) use of
the dose or concentration at which the toxic response was not signi ficantly elevated (no observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

A series of downward adjustments using defau lt UFs is then applied to the POD to estimate the reference value [ 25 ]. While collectively termed “UFs”, these factors account for a number of different quantitative consid erations when utilizing observe d animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intende d to account for: (1) variation in susceptibility among the members of the human population (i .e., inter-individual variability); (2) uncertainty in extrapolating fr om experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in ex trapolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapolati ng from subchronic to chronic exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are pr oblems with applicability of available studies. When scientifically sound, peer-reviewed assess ment-specific data are not available, default adjustment values are selected for the individua l UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applie s an UF value of 10 or 3 with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the selected POD. An UF of 3 is used when the data do not support the use of a 10-fold factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described more fully below.

1) Heterogeneity among humans is a key sour ce of variability as well as uncertainty. Uncertainty related to human variation is consid ered in extrapolating doses from a subset or smaller-sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger , more diverse population. In the absence of pollutant-specific data on human variation, a 10 -fold UF is used to account for uncertainty
associated with human variation. Human variatio n may be larger or smaller; however, data to examine the potential magnitude of human va riability are often una vailable. In some situations, a smaller UF of 3 ma y be applied to reflect a known l ack of significant variability among humans.

5 See IRIS glossary.21

2) Extrapolation from results of studie s in experimental animals to humans is a necessary step for the majority of chemical ri sk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BM DL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-sta nding scientific support
for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolat ions. In the absence of data to the contrary, the typical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the averag e human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacki ng, thus leading to uncertainty in this extrapolation. Size-related differences (allometric relationships) indicate that typically
humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consis tent with these differences. For a specific chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; however , such dosimetric adjustments are not always possible. Information may not be available to quantitatively a ssess toxicokinetic or toxicodynamic differences between animals a nd humans, and in many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodyna mic components) is used to account for expected species differences and as sociated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value. If information on one or the other of these components is availabl e and accounted for in the cross-species extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for ch ronic exposures where only data from shorter durations are available (e.g., 90-da y subchronic studies in rodent s) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is typically applied unless th e available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the do se or exposure levels that have been tested in individual studies; in an animal study, for ex ample, treatment groups may differ in exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use BMD analysis; however, this approach requires ad equate quantitative results for a meaningful analysis, which is not always possible. Use of a NOAEL is the next preferred approach after BMD analysis in determining a POD for deriving a health effect reference value. However, many studies lack a dose or exposure level at wh ich an adverse effect is not observed (i.e., a NOAEL is not identified). When using data lim ited to a LOAEL, a UF of 10 or 3-fold is often applied.

5) The database UF is intended to account fo r the potential for deriving an underprotective RfD/RfC due to a data gap preven ting complete characterization of the chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a UF of 10 or 3-fold is typically applied.22

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those de veloped for chronic durations, but more often using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific informati on (e.g., simple irritation effects do not vary appreciably between human indivi duals, hence a value of 3 is t ypically used), or based on the purpose for the reference value (see the followi ng paragraph). The UFs applied in acute reference value derivation include: 1) he terogeneity among humans ; 2) uncertainty in extrapolating from animals to humans; 3) unc ertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effect s of potential concern. Additional adjustments are often applied to a ccount for uncertainty in extrapolation from observations at one exposure dura tion (e.g., 4 hours) to arrive at a POD for derivation of an
acute reference value at another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute asse ssment of human health effects relative to the reference value or values being exceeded. Wh ere relevant to the estimated exposures, the
lack of threshold values at di fferent levels of severity shoul d be factored into the risk characterization as poten tial uncertainties.23



### 5 References

1 . US EPA. National Emission Standa rds for Hazardous Air Pollutants. http://www.epa.gov/ttn /atw/mactfnlalph.html

.

2 . US EPA, 2009. Risk and Technology Review (RTR) Risk Assessmen t Methodologies: For Review by the EPA’s Science Advisory Boar d with Case Studies – MACT I Petroleum Refining Sources and Portland Cement Manufacturing. EP A-452/R-09-006. http://www.epa.gov/ttn /atw/rrisk/rtrpg.html

.

3 . US EPA, 2010. SAB’s Res ponse to EPA’s RTR Risk As sessment Methodologies. http://yosemite.epa.gov/sab/sabproduc t.nsf/4AB3966E263D943 A8525771F00668381/$File/E PA-SAB-10-007-unsigned.pdf

4 . US EPA, 2010. EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk Assessment Methodologies. Document ID: EPA-HQ-OAR-2010-0786-0036. http://www.regulations.gov

5 . US EPA, 2005. Revision to the Guideline on Air Quality Models: Adop tion of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Ot her Revisions; Final
Rule. 40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005 /November/Day-09/a21627.htm

6 . US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA-454/B03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf

.

7 . Allen, D., C. Murphy, Y. Kimura, W. Vizuete, T. Edgar, H. Jeffries, B.-U. Kim, M. Webster, and M. Symons, 2004. Variab le industrial VOC emissions and their impact on ozone
formation in the Houston Galveston Area. Final Report: Texas Environmental Research Consortium Project H-13. http://files.harc.edu/Pr ojects/AirQua lity/Projects/H013.2003 /H13FinalReport.pdf

8 . US EPA, 2004. Air Toxics Risk Assessment Reference Library, Vo lume 1. EPA-453-K-04- 001A. http://www.epa.gov/ttn/ fera/risk_atra_vol1.html.

9 . US EPA, 2012. Table 1. Prioritized Chroni c Dose-Response Values (5/21/12). Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/at w/toxsource/table1.pdf

10 . US EPA, 2005. National Air Toxics Risk Assessment. http://www.epa.go v/ttn/atw/nata

11 . US EPA, 2008. Integrated Risk Information System. http://www.epa.gov/iris/index.html

.

12. US Agency for Toxic Substances and Disease Registry. 2006. Minimu m Risk Levels (MRLs)
for Hazardous Substances. http://www.atsdr.cdc. gov/mrls/index.html

.

13 US EPA, 1994. US Environmental Protection Agen cy. Methods for Deriva tion of Inhalation Reference Concentrations and Application of Inhalation Dosimetry. EPA/600/8-90/066F.
Office of Research a nd Development. Washington, DC: U.S.EPA.

14 NRC, 1994. National Research Council. Science and Judgm ent in Risk Assessment. Washington, DC: Nati onal Academy Press.24

15 . CA Office of Environmental Health H azard Assessment, 2005. Chronic Reference Exposure Levels Adopted by OEHHA as of February 2005. http://www.oehha.ca.gov/air/ chronic_rels/AllChrels.html

.

16 . US EPA, 2011. Table 2. Acute Dose-Response Values for Screening Risk Assessments (12/19/2011). Office of Air Qual ity Planning and Standards. http://www.epa.gov/ttn/a tw/toxsource/table2.pdf

17 U.S. EPA, 2009. Graphical Arrays of Chemical -Specific Health Effect Reference Values for Inhalation Exposures [Final Report]. EPA/600/R-09/061, 2009. http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=211003

18 . CA Office of Environmental He alth Hazard assessmen t, 2000. All Acute Reference Exposure Levels developed by OEHHA as of May 2000. http://www.oehha.ca.gov/air/ acute_rels/allAcRELs.html

19 . American Industrial Hygiene Associat ion , 2011. Current AIHA ERPG Values. http://www.aiha.org/insideaiha /GuidelineDevelopment/ERPG/D ocuments/2011erpgweelhandb ook_table-only.pdf

20 . US EPA, 1995. Guidance for Risk Charac terization. Scien ce Policy Council. http://www.epa.gov/OSA /spc/pdfs/rcguide.pdf

.

21 . US EPA, 2000. Risk Characteri zation Handbook. EPA 100-B-00-002.

22 . US EPA, 2002. EPA’s Guidelines for Ensuri ng and Maximizing the Quality, Objectivity, Utility, and Integrity of Inform ation Disseminated by the Envi ronmental Protection Agency.
EPA Office of Environmental In formation. EPA/260R-02-008. http://www.epa.gov/quality/inf ormationguidelines/documents/EP A_InfoQualityGuidelines.pdf

23 US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. http://www.epa.gov/iris/rfd.htm

24 US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and Application of Inha lation Dosimetry. http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=71993.

25 US EPA. 2002. A Review of the Reference Dose and Reference Conc entration Processes. http://www.epa.gov/ncea/iris/RFD_FINAL1.pdfAppendix 1 Emissions Inventory Support MemorandumSteel Pickling1 MEMORANDUM

TO: Anne Pope, EPA

FROM: Darcy Wilson, ERG

DATE: July 15, 2010

SUBJECT: Emissions Data Used in Residual Risk Modeling for Steel Pickling

The purpose of this memorandum is to provide information on the RTR database used in estimating residual risk for the Steel Pick ling Maximum Achievable Control Technology (MACT) source category.

BACKGROUND

Section 112 of the Clean Air Act (CAA) establ ishes a two-stage regu latory process to address emissions of hazardous air pollutants ( HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protecti on Agency (EPA) to develop technology-based standards for categories of industrial sour ces. In the second stage, known as the residual risk stage, section 112( f)(2) requires EPA to a ssess the health and
environmental risks that remain after sour ces come into compliance with MACT. If
additional risk reductions are ne cessary to protect public health with an ample margin of safety or to prevent adverse environmenta l effects, EPA must develop standards to address these remaining risks. As part of this second stage, data were gathered to assess
the residual risks from the Steel Pickling MACT source category.

EMISSIONS DATA USED IN RESIDUAL RISK MODELING

The 2005 National Emissions Inventory (NEI) data that were modeled in the EPA’s
initial 2005 National Air Toxics Assessment (NATA) risk assessment in June of 2009 served as the starting point for this a ssessment. Using the process MACT code, 1 a subset of the NEI was developed that contained faci lity, process, and emissions data for each facility in the Steel Pickling s ource category (MACT code 0310).

After initial 2005 NATA modeling, EPA evalua ted the 2005 NEI to see if missing Steel Pickling facilities could be identified and adde d to the NEI, if any revisions were needed for facilities in the NEI, and if any facilitie s were identified erroneously as Steel Picking facilities, or facilities have closed. To iden tify additional facilities, EPA reviewed the AIST 2009 Directory of Iron and Steel Plants . The AIST directory also helped EPA revise MACT code assignments to identify addi tional facilities as Steel Pickling facilities that were already included in the NEI. Faci lities that were erroneously identified as having Steel Pickling operations were identi fied based on contacts with facilities to

1 Assigning data with MACT codes allows EPA to determine reductions attributable to the MACT program. The NEI associates MACT codes corresp onding to MACT source categories with stationary major and area source data.2 discuss their operations (e.g., they are not s ubject to the Steel Pickling NESHAP because they don’t do steel pickling).

Emissions data for high-risk facilities iden tified in the initial NATA modeling were reviewed and compared against several year s of Toxic Release I nventory (TRI) data and State inventory data. Se veral facilities were contacted to confirm (or correct) emission values. In addition to revising emi ssion estimates based on this analysis, the following types of revisions were also made to improve the 2005 NEI for Steel Pickling
facilities: revised Source Cl assification Codes (SCCs) that provide process information, revised emission release point coordinate locations and type s (reported as either a stack emission source or a fugitive emission source), and revised stack parameters such as the height or diameter of the stack.

EPA also evaluated the facility-wide risks fo r Steel Pickling facili ties using the 2005 NEI
data and risk assessment results that were released in the 2005 NATA Risk Preview with State and local agencies in June of 2010. Emission revisions were made for one facility based on the 2005 NATA Risk Preview comments, but not for steel pi ckling processes at the facility. These revisions were not included in the risk analys es, but will be reflected in
the final NATA risk assessment.Appendix 2 Technical Support Docume nt for HEM-3 Modeling

## Modeling for the Residual Risk and Technology Review Using the Human Exposure Model 3 – AERMOD Version

Updated 8/7/2008

Technical Support Document

Prepared for:

U. S. Environmental Protection Agency
Office of Air and Radiation Office of Air Quality Planning and Standards
Emissions Standards Divisi on, Risk and Exposure Group Research Triangle Park, NC 27711

Prepared by:

EC/R Incorporated 501 Eastowne Drive, Suite 250 Chapel Hill, North Carolina 27514ii 

### Disclaimer

The research described in this document has been funded by the
United States Environmental Pr otection Agency contracts 68-D01-076 and 68-D-06-119 to EC/R Incorporated. Although it has been subject to the Agency's revi ew, it does not necessarily reflect the views of the Agency, and no official endorsement should be
inferred.iii 

## Contents

1. Introduction ..............................................................................................................

................... 1

2. Overview of the HEM3– AERMOD System.............................................................................. 2 2.1 Preparation of Dispersion Modeling Inputs ........................................................................ 3 2.1.1 Compiling Emission Source Data .............................................................................. 3 2.1.2 Defining the Modeling Domain and Receptors ......................................................... 4 Treatment of Nearby Census Blocks a nd Screening for Overlapping Blocks ............... 4 Polar receptor network ................................................................................................... 5 Elevations and hill heights for model receptors ............................................................. 5 2.1.3 Selection of Meteorological Data .............................................................................. 6 2.2 Running of AERMOD ........................................................................................................ 6 2.2.1 AERMOD Dispersion Options Used by HEM3 ........................................................ 6
2.2.2 Use of Dilution Factors .............................................................................................. 7 2.3 Postprocessing of AERMOD Results in HEM3 ................................................................. 7 2.3.1 Calculation of Impacts at Modeled Receptors ........................................................... 8 2.3.2 Interpolation of Impacts at Outer Census Blocks ...................................................... 9 2.3.3 Calculation of Populati on Exposures and Incidence ............................................... 10 2.3.4 Model Outputs ......................................................................................................... 10
2.4 Data Libraries Used in HEM3 .......................................................................................... 11
2.4.1 Chemical Health Effects Information ...................................................................... 11
2.4.2 Census Block Locations and Elevation Data ........................................................... 12 2.4.3 Meteorological Data ................................................................................................. 12 2.4.4 Gaseous Deposition Parameters ............................................................................... 13

3. Modeling for the Residual Risk Technology Review .............................................................. 14 3.1 Emission Source Inputs ...................................................................................................

.. 14 3.2 Pollutant Cross-Referencing ............................................................................................. 15 3.3 Meteorological Data.......................................................................................................

... 15 3.4 Model Options Selected ...................................................................................................

. 18 3.4.1 Urban or Rural Dispersion Characteristics .............................................................. 19 3.4.2 Deposition and Plume Depletion ............................................................................. 19
3.4.3 Cutoff Distance for Mode ling of Individual Blocks ................................................ 20 3.4.4 Plant Boundary Assumptions ................................................................................... 20 3.5 Modeling of Multiple Facilities ........................................................................................ 20

4. Quality Assurance .........................................................................................................

............ 21 4.1 Engineering Review .......................................................................................................

... 21 4.2 Geographic Checks ........................................................................................................

... 21

5. Uncertainties .............................................................................................................

................ 24iv 6. References ................................................................................................................

................. 31



## Figures

Figure 3-1. Meteorological data availabl e in the HEM3 library for AERMOD. .......................... 18 Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on plant
property. .....................................................................................................................

........ 23



## Tables

Table 2-1. Parameters Used to Deli neate the Modeling Domain in HEM3 ................................... 4 Table 3-1. Groupings Used for Po lycyclic Organic Matter in the RTR Phase II Modeling Effort

. . ........................................................................................................................... ............. 16 Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the Residual Risk and Technology Review Assessments ........................................................ 19 Table 5-1. Summary of Genera l Uncertainties Associated wi th Risk and Technology Review
Risk Assessment ...............................................................................................................

. 261 

## 1. Introduction

This document describes the modeling appro ach used to estimate the risks to human populations in support of the Residual Risk and Technology Review currently being carried out by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments was the Human Exposure Model, Version 3 (HEM3) . HEM3 incorporates the AERMOD, a state of the art air dispersion model developed under the direction of the American Meteorological Society / Environmental Protection Agency Regulatory Model Improvement Committee (AERMIC).

Section 2 of this report provides an over view of the HEM3 system; and Section 3 describes inputs and choices made in implementing the model for the RTR program. Quality assurance efforts undertaken in the modeling effort are discussed in Sec tion 4, and uncertainties associated with the modeling effort are discussed in Section 5.2 

## 2. Overview of the HEM3– AERMOD System

HEM3 performs three main operations: di spersion modeling, estimation of population exposure, and estimation of human health risk s. Two options are provided for dispersion modeling. The first is the American Meteor ological Society (AMS) EPA Regulatory Model (AERMOD). 1,2 The second option is the Industrial Source Complex - Short Term, Version 3 (ISCST3) model. Both of these dispersion models handle a wide range of different source types which may be associated with an industrial source complex, including stack sources, area sources, and volume sources. AERMOD was used for the RTR modeling effort.

To prepare dispersion modeling inputs and carry out risk calculations, HEM3 draws on four data libraries, which are provided with the model. The first is a library of meteorological data for over 120 stations, which are used for dispersion calculations. A second library of Census block internal point lo cations and populations provides the basis of human exposure calculations (Census, 2000). This Census library also includes the elevation of each Census block, which can also be used in dispersion calcul ations, at the option of the model user. A third library of pollutant unit risk estimates and re ference concentrations is used to calculate population risks. These unit risk estimates and re ference concentrations are based on the latest values recommended by EPA for hazardous air po llutants (HAP) and other toxic air pollutants (EPA, 2007). The fourth data library is used only with AERMOD and only when the user opts to compute deposition or plume depletion. This da tabase provides deposition parameters for gaseous pollutants.

HEM3 has been implemented in two versi ons: a single-plant ve rsion, and a community
and sector version. The latter was used in this modeling effort. Both versions operate under the same general principles. In e ssence, the community and sector version provides a platform for running the single plant version multiple times. In both versions, source location and emissions data are input through a set of Ex celTM spreadsheets. The main diffe rence is in the user interface for other model inputs. The single plant incl udes a graphical user in terface (GUI) for the selection of various dispersion modeling options. In the comm unity and sector version of HEM3, a control file replaces many of these GUI inputs.

The model estimates cancer risks and noncancer adverse health effects due to inhalation exposure at Census block internal point locations, and at other receptor lo cations that can be specified by the user. Cancer risks are comput ed using EPA’s recommended unit risk estimates for Hazardous Air Pollutants (HAP) and other to xic air pollutants. The resulting estimates
reflect the excess cancer risk for an individual br eathing the ambient air at a given receptor site 24-hours per day over a 70-year lifet ime. The model estimates the numbers of people exposed to various cancer risk levels. In addition, HEM3 estimates the total incremental cancer risks for people living within different distan ces of the modeled emission sources.3 Potential noncancer health e ffects due to chronic exposures are quantified using hazard quotients and hazard indices for various target organs. The “hazard quotient” for a given chemical and receptor site is the ratio of the ambient concentration of the chemical to the
reference concentration. The “hazard index” for a given organ is the sum of hazard quotients for substances that affect that organ. HEM3 comp utes target-organ-specific hazard indices (TOSHI) for HAPs and other toxic air polluta nts, and estimates the numbers of people exposed to different
hazard index levels. In additi on, maximum short term concentrations are computed for all pollutants, and concentrations are compared with threshold levels for acute health effects.

The following sections outline the methodologies used in the HEM3 system. Section 2.1 describes the preparation of di spersion modeling inputs, Secti on 2.2 describes the running of AERMOD, Section 2.3 describes calculations performed by HEM3 to calculate risks and exposures, and Section 2.4 details the sources and methods used to produce HEM3’s data libraries. The HEM3 User’s Manual provides add itional details on the input data and algorithms
used in the model. 3 Specific model options used in the RTR are discussed in Chapter 3.



### 2.1 Preparation of Dispersion Modeling Inputs

HEM3 compiles data that will be needed for dispersion modeling, and prepares an input file suitable for running AERMOD. The disper sion modeling inputs can be divided into three main components: emission source data, inform ation on the modeling domain and receptors for which impacts will be comput ed, and meteorological data.

2.1.1 Compiling Emission Source Data

A series of ExcelTM spreadshee t files are used to specify th e emissions and configuration of the facility to be modeled. At a minimum, tw o files are needed: a po llutant emission file, and
an emission location file. The emission file incl udes an emission source identification code for each emission source at the facility, the name s of pollutants emitted by each source, and the emission rate for each pollutant. In addition, if the model run is to incorporate deposition or plume depletion, the emission file mu st also specify the percentage of each pollutant that is in the form of particulate matter. The bala nce is assumed to be in vapor form.

The emission location file includes the coordi nates of each source, as well as information on the configuration and other characteristics of the source. HEM3 can analyze stack sources, area sources, or volume sources. a For stack sources, the locati on file must provide the stack height, stack diameter, emission ve locity, and emission temperature. The file must also provide dimensions for each area or volume source, as we ll as the height of the source above the ground. For area sources, the angle of rotation from north can also be specified. The user can also

I For definitions of stack, area, and volume sources , please see the AERMOD documentation. AERMOD provides the capability to analyze some other emission sour ce configurations, such as storage piles, however these configurations are not supported by HEM3.4 provide the terrain elevation at the base of each source, and the controlling hill height for each source. (The controlling hill height is used in AERMOD’s flow calculations. Calculation of the hill height is discussed in Section 2.4.2.) If these are not provided, HEM3 will calculate terrain elevations and controlling hill heights based on elevations and hill heights for the Census blocks nearest to the facility.

If particulate deposition or plume depletion are to be cons idered, then HEM-3 requires a third input file to specify the particle size distribution and sc avenging parameters for various particle size ranges. Another opti onal file can be used to specif y building dimensions if building
wake effects are to be modeled.

2.1.2 Defining the Modeling Domain and Receptors

HEM3 defines a modeling domain for each faci lity that is analyzed based on parameters specified by the model user. These parameters are summarized in Table 2-1. The modeling domain is circular, and is centered on the facil ity, with a radius specifi ed by the user. For the RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM3 identifies all of the Census block locations in the modeling domai n from its Census database, and divides the blocks into two groups based on their distance from the facility. For the inner group of Census blocks (closest to the facility ), each block location is modeled as a separate receptor in
AERMOD. The cutoff distance fo r modeling individual Census bl ocks is determined by the model user. The model user can also provide an ExcelTM spreadsheet specifying additional locations to be included as model receptors in AERMOD. These additional discrete receptors may include plant boundary locations, monitoring s ites, individual residen ces, schools, or other
locations of interest.

For Census blocks in the outer group, beyond this modeling cutoff distance, emissions impacts are interpolated based on modeling results for a polar receptor network. The user also specifies an “overlap” distance,
within which Census block coordinates will be considered to be on plant property. The following paragraphs provide more details on the treatment of blocks near the facility, on the polar receptor network, and on the determination of receptor elevations and controlling hill heights to be used in
AERMOD.

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks

Census block locations near the facility are modeled as separate receptors Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM3

Parameter Typical
value

Modeling domain size – maximum
distance to be modeled a 50 km

Cutoff distance for modeling of
individual blocks b 2,000 – 3,000 m

Overlap distance – where receptors will be considered to be on plant property b 30 m

Polar receptor network

Distance to the innermost ring a 100 m

N umber of concentric rings 13

N umber of radial directions 16

a Measured from the center of the facility.

b Measured from each stack at the facility, and from the edges of each area or volume source.5 within AERMOD. The cutoff distance for modeli ng of individual Census blocks is chosen by the user, and is tpically between 2000 and 3000 mete rs. This distance is not measured from the center of the facility, but is the minimum distance from any source at the facility. Therefore, any
Census block location that is within the cutoff distance from any emission source is treated as a discrete AERMOD receptor.

HEM3 checks Census blocks that are very clos e to the facility in order to assess whether they overlap any area or volume emission sources. In addition, the user can specify an overlap distance, within which receptors will be consider ed to be on plant property. The default value for the overlap distance is 30 meters, or approxima tely equal to the width of a narrow buffer and a roadway. HEM3 tests each nearby receptor to dete rmine whether it is within this distance from any stack or from the perimeter of any area or volume source. If a receptor falls within this distance, HEM-3 will not calculate risks based on the location of that receptor, but will instead assume that the risks associated with the recepto r are the same as the highest predicted value for any receptor that is not overlapping. The locat ion for calculating the default impact may be either another Census block, one of the polar gr id receptors, or one of the additional discrete receptor locations provided by the model user.

Polar receptor network

The polar receptor network used in HEM3 serv es three functions. First, it is used to estimate default impacts if one or more Census lo cations are inside the overlap cutoff distance is used to represent the plant boundary. Second, it is used to evaluate potentia l acute effects that may occur due to short-term exposures in unpopu lated locations outside the plant boundary. Third, the polar receptor network is used to in terpolate long-term and short-term impacts at
Census block locations that are outside the cuto ff distance for modeling of individual blocks.

The user defines the inner radius for the pol ar receptor network, th e number of concentric rings to be analyzed, and the number of radial di rections. The inner radius of the polar network should the minimum distance from the facility center that is generally outs ide of plant property. (For complex plant shapes, it is sometimes useful to specify an inner ring that encroaches on facility property in some direc tions.) The default, and also the minimum inner radius for the polar network is 100 meters (m). A typical run includes 13 concentric rings and 16 radial
directions. HEM3 will distribut e the radial directions even ly around the plant. For the concentric rings, the model will generate loga rithmic progression of di stances starting at the inner ring radium and ending at the outer radius of the modeling domain. In the single plant version of HEM3, the user can ch ange these ring distances to meet the needs of a specific study.

Elevations and hill heights for model receptors

The user can specify whether HEM3 will in clude the effects of terrain when running AERMOD. If the terrain option is selected, HEM3 obtains elevations and controlling hill heights for Census block receptors from its internal Ce nsus location library. Section 2.4.2 describes the
derivation of these elevati ons and hill heights.6 Elevations and controlling hill heights for th e polar grid receptors are also estimated based on values from the Census library. HEM3 divides the modeling doma in into sectors based on the polar receptor network, with each Census block assigned to the sector corresponding to the closest polar grid receptor. Each polar grid receptor is then assigne d an elevation based on the highest elevation for any Census block in its sector. The contro lling hill height is also set to the maximum hill height within the sector. If a sector does not contain any blocks, the model defaults to the elevation and controlling hill he ight of the nearest block outside the sector.

2.1.3 Selection of Meteorological Data

In addition to source and receptor information, AERMOD requires surface and upper air meteorological observations in a prescribed format . The model user can select a meteorological station from the HEM3 meteorological data library, or add new files to the library if site-specific data are available. If the user does not spec ify a meteorological station, HEM3 will select the
closest station to the cente r of the modeling domain.



### 2.2 Running of AERMOD

Based on the user input data and other data described in the previous section, HEM3 produces an input file suitab le for AERMOD. HEM3 then runs AERMOD as a compiled executable program. No changes have been made from the version of AERMOD released to the public by EPA. The following sections give additional information on how AERMOD is used
within HEM3.

2.2.1 AERMOD Dispersion Options Used by HEM3

AERMOD provides a wide array of opti ons for controlling dispersion modeling calculations. In general, HEM3 uses the re gulatory default options when running AERMOD. 1 These options include the following:

! consideration of stack-tip downwash (except for Schulman-Scire downwash)

! use of calm processing routines

! use of missing-data processing routines

! consideration of terrain effects

However, the model user can opt to exclude th e effects of terrain when running HEM3. The following additional options are available to the HEM3 user:

! calculation of wet and dr y deposition rates for vapor and particulate mater

! consideration of plume depletion (due to de position) when calculati ng air concentrations

! consideration of building wake effects

! calculation of short term impacts

As noted in Section 2.1, the calculation of de position or depletion and the consideration of building wake effects require additional user inputs.7 The user can opt to analyze short term impacts on a number of different time scales (e.g. 1 hour, 2 hours, etc.), however only one short term time scale can be selected per run. If the user chooses to analyze short term impacts, a multiplier must be specified to reflect the ratio between
the maximum short term emission rate and the long term average emission rate. The default multiplier for short term emissions is a factor of 10. This means that in the default case the
maximum short term emission rate is assumed to be 10 times the long term average emission rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant.

HEM3 uses the AERMOD TOXICS run op tion, which optimizes the area source integration routine to reduce model runtimes. The TOXICS Sampled Chronological Input Model (SCIM) is not used, since the HEM3 AERMOD me teorological library only includes one year of observations for each station. (The SCIM option can be used to skip a portion of the hourly observations when meteorological data are available for multiple years.)

2.2.2 Use of Dilution Factors

To save computer run time when analyz ing the impacts of multiple pollutants, HEM3 does not model each pollutant separately. Instead , AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. The dilution factor for a particular emission source and receptor is defined as the predicted ambient impact from the given source and at the given receptor, divided by th e emission rate from the given source.

If the user chooses not to analyze deposition or plume depletion, the dilution factor does not vary from pollutant to pollutant. If eith er deposition or depletion is chosen with the AERMOD dispersion model option of HEM3, separa te dilution coefficients must be computed for each gaseous pollutant. In addition, separate dilution factors must be computed for different components of particulate matter if the com ponents do not have the same particle size
distribution. In the current ve rsion of HEM3, this can be done by creating a separate emission record for each pollutant emitted by from each source. (Common location data and source configurations can be used for different pol lutant records representing the same emission source.)



### 2.3 Postprocessing of AERMOD Results in HEM3

HEM3 estimates total excess cancer risks and potential chronic noncancer health effects for all Census block locations in the modeling domain, all user-defined receptors, and all points in the polar receptor network. Po tential chronic noncancer health effects are expressed in terms
of TOSHI. Based on the results for Census bl ocks and other receptors, HEM-3 estimates the
maximum individual risk and maximum TOSHI for populated receptors, and determines the locations of these maximum impacts. The model al so determines the concentrations of different pollutants at the sites of the maximum risks, and the contributions of different emission sources
to these maximum estimated risks. It should be noted that the locati ons may differ for the
maximum individual cancer risk and for the haza rd indices for different target organs.8 For acute impacts, HEM3 calculates the ma ximum short term concentrations for all pollutants emitted by the facility. These maximum short term concentrations are compared with
various thresholds for acute health effects.

At the option of the model user, HEM3 will also compute the long term and short term predicted ambient concentrations of all pollutant s emitted by the plant at all of the receptors in the modeling domain. In addition, pollutant co ntributions from each emission source at the facility are computed under this option.

Section 2.3.1. describes methods used to cal culate cancer risks a nd hazard indices for receptors that are explicitly modeled using AE RMOD. Section 2.3.2 describes the interpolation approach used to estimate cancer risks and hazard i ndices at Census blocks that are not explicitly
modeled.

2.3.1 Calculation of Impacts at Modeled Receptors

As noted in Section 2.2.2, HEM3 does not model each pollutant separately unless deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of dilution factors, specific to each emission source and receptor. HEM3 also conserves computer memory by computing cancer risks and hazard indices directly, without recording the concentration of each pollutant at each receptor. The following algorithms are used to compute
cancer risks and TOSHI for chr onic noncancer health effects.

For cancer risk:

CR T = 

###  i, j CR i, j

CR i, j = DF i, j  CF  

###  k 

### [ E i, k  URE k 

### ]

For TOSHI:

TOSHI T = 

###  i, j TOSHI i, j

TOSHI i, j = DF i, j  CF  

###  k 

### [ E i, k  RfC k 

### ]

where:
CR T = total cancer risk at a given receptor (probability for one person)


###  i, j = the sum over all sources i and pollu tant types j (parti culate or gas)

CR i, j = cancer risk at the given recepto r for source i and pollutant type j

DF i, j = dilution factor [(  g/m 3 ) / (g/sec)] at the given recept or for source i and pollutant type j CF = conversion factor, 0.028 77 [(g/sec) / (ton/year)]


###  k = sum over all pollutants k within pol lutant group j (particulate or gas)

E i, k = emissions of pollutant k from source i and in pollutant type j

URE k = cancer unit risk estimate for pollutant k9 TOSHI T = total target-organ -specific hazard index at a given receptor
TOSHI i, j = target-organ-specific hazard index at the given recepto r for source i and pollutant type j
RfC k = noncancer health effect reference concentration for pollutant k

If the cancer unit risk estimate is not availabl e for a given chemical, then that chemical is
not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration is not available for a given chemical, that chem ical is not included in the calculation of hazard indices. Note also that separa te reference concentrations are used for acute and chronic hazard
indices.

HEM3 computes short term concentrati ons and records the highest short term concentration for each pollutant. In addition, the user can opt to compute and record the short term and long concentrations at each receptor. Concentrations are computed as follows.

Long term concentrations:

AC T, k = 

###  i AC i, k

AC i, k = DF i, j  CF  E i, k

Short term concentrations:

AC T = 

###  i AC i, k

AC i, k = DF i, j  CF  E i, k  M

where:
AC T, k = total estimated ambient concentration for pollutant k at a given receptor


###  i = the sum over all sources i (  g/m 3 )

AC i, k = estimated ambient concentration of po llutant k at the given receptor as a

result of emissions from source i (  g/m 3 )
M = ratio between the estimated maximum s hort term emission rate and the long term average emission rate (dimensionless)

2.3.2 Interpolation of Impacts at Outer Census Blocks

For Census blocks outside of the distan ce cutoff for individual block modeling, HEM-3 estimates cancer risks and hazard indices by inte rpolation from the polar receptor network. Impacts at the polar grid receptors are es timated using AERMOD modeling results and the algorithms described in Section 2.3.1. The impact s at each outer Census block are interpolated from the four nearest polar grid receptors. Th e interpolation is linear in the angular direction, and logarithmic in the radi al direction, as summarized in the following equations:

I a, r = I 1, r + (I 2, r – I 1, r )  (a – A1)  (A2 – A1)10 I A1, r = exp{ln(I A1,R1 ) + [ln(I A1,R2 ) – ln(I A1,R1 )]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]}

I A2, r = exp{ln(I A2,R1 ) + [ln(I A2,R2 ) – ln(I A2,R1 )]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]}

where:

I a, r = the impact (cancer risk, h azard index, or concentration) at an angle, a, from north, and radius, r, from the cen ter of the modeling domain a = the angle of the ta rget receptor, from north r = the radius of the target receptor, from the center of the modeling domain A1 = the angle of the polar network recep tors immediately counterclockwise from the
target receptors A2 = the angle of the polar network recepto rs immediately clockwise from the target
receptor R1 = the radius of the polar network recept ors immediately inside the target receptor R2 = the radius of the polar network recept ors immediately outside the target receptor

2.3.3 Calculation of Population Exposures and Incidence

Using the predicted impacts for Census bl ocks, HEM3 estimates the numbers of people exposed to various cancer ri sk levels and TOSHI levels. This is done by adding up the populations for receptors that have predicted can cer risks or TOSHI above the given threshold.

The model also estimates the total annual ex cess cancer risk (incidence) for the entire modeling region. The following equation is used:

TCR = 

### 

m 

### [ CR
m  P
m 

### ]  LT
where: TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the population living within the modeling domain


### 

m = the sum over all Census blocks m within distance the modeling domain

CR
m

= the total lifetime cancer risk (from all modeled pollutants and emission sources) at
Census block m

P
m = the population at Census block m LT = the average lifetime used to develop the cancer unit risk factor, 70 years

2.3.4 Model Outputs

The following is a summary of the outputs pr oduced by HEM3. These are written to a
collection of files in ExcelTM and dBaseTM format.

! Maximum long term impacts at populated locations

" maximum lifetime individual cancer risk

" maximum TOSHI for the following health effects - respiratory system effects
- liver effects11 - neurological system effects - developmental effects - reproductive system effects - kidney effects - ocular system effects - endocrine system effects - hematological system effects - immunological system effects - skeletal system effects - spleen effects - thyroid effects

" locations of the maximum cancer risk and TOSHI

" Census block identification codes for the maximum cancer risk and TOSHI, and number of people in the Census block

" contributions of different chemicals a nd emission sources to the maximum risk
and TOSHI

! Maximum acute impacts

" maximum short term ambient concentration for each chemical

" threshold levels for acute health eff ects of each chemical (compared with the maximum short term concentrations)

" locations of the maximum impacts for different chemicals

" Census block identificati on codes at the locations of maximum concentration, and number of people in the block

" contribution of each emission source at the facility to the maximum short term
concentration of each chemical

! Outputs for all receptors

" maximum individual cancer risk and TOSHI (all target organs) for each Census block and each user-specified discre te receptor (monitoring sites, etc.)

" maximum individual cancer risk and TOSHI (all target organs) for each polar grid
receptor

" estimated deposition (optional)

" predicted ambient concen tration resulting from each emission source at each Census block (optional)

! Population exposures and tota l cancer risk, or incidence

" estimated numbers of people exposed to different levels of lifetime individual cancer risk (1 in a million, 1 in 100,000, etc.)

" estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)

" total cancer risk, or incidence, in estima ted cancer deaths per year, over the entire modeling domain



### 2.4 Data Libraries Used in HEM3

2.4.1 Chemical Health Effects Information12 HEM3 includes a library of available health effects data for HAPs. For each pollutant, the library includes the following parameters, where available:

! unit risk estimate for cancer

! reference concentration for chronic noncancer health effects

! reference concentrations for acute health effects

! target organs affected by the ch emical for chronic health effects

Unit risk estimates and reference concentrations included in the HEM3 chemical library have been taken from EPA’s database of recommended dose-response factors for HAPs. 4

2.4.2 Census Block Locations and Elevation Data

The HEM3 Census library includes Census block identification codes, locations, populations, elevations, and controlling hill height s for all of the over 5 million Census blocks
identified in the 2000 Census. The location coordi nates reflect the intern al point of the block, which is a point selected by the Census to be ro ughly in the center of the block. For complex shapes, the internal point may not be in the geographic center of the block. Locations and population data for Census blocks in the 50 stat es and Puerto Rico were extracted from the
LandView® database. 5 Locations and populations for blocks in the Virgin Islands were obtained
from the Census web site.

The elevation of each Census block in the continental U.S. and Hawaii was estimated by interpolating the elevation at the block’s inte rnal point from the U.S. Geological Service’s 1:250,000 Digital Elevation Model (DEM) data. 6 These data have a resolution of 3 arc seconds,
or about 90 meters. The estimation of the elevati on of each Census block in Alaska and the U.S. Virgin Islands was performed with analysis tool s in the ArcGis 9.1 software application. The point locations of the Census bloc k centroids in Alaska and the Virg in Islands were overlaid with a raster layer of North American Digital Elevation Model elevati ons (in meters). Each Census block point was assigned an elev ation value based on the closest point in the ArcGis elevation
raster file.

Hill heights were determined with base on the algorithm used in AERMAP, the AERMOD terrain processor. 7 In order to save run time and resources, the HEM3 Census block elevation database was substituted for the DEM data generally used in AERMAP. As noted above, the Census block elevations were orig inally derived from the DEM database. To determine the controlling hill height for each Cens us block, a cone was projected away from the block location, representing a 10% elevation grade. The controlling hill height was selected based on the highest elevati on above that 10% grade (in accordance with the AERMAP methodology). The distance cutoff for this calcu lation was 100 km. (This corresponds to an elevation difference, at a 10% grade, of 10,000 m, which cons iderably exceeds the maximum elevation difference in North America.)

2.4.3 Meteorological Data13 HEM3 includes an extensive library of meteorological data to support both the AERMOD and ISCST3 dispersion models. 150 mete orological stations ha ve been preprocessed for AERMOD as part of the RTR effort. In addi tion, 87 stations have previously been processed for ISCST3. Section 3.3 discusses the prepar ation of meteorologica l data for the RTR.

2.4.4 Gaseous Deposition Parameters

HEM3 provides options to compute the deposi tion of air pollutants, and to take into account the impacts of plume depletion due to depos ition of gaseous and particulate pollutants. If either of these options is selected by the model user for gaseous pollutants, a number of pollutant properties are required by AERMOD. Thes e include the diffusivity of the pollutant in air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting the cuticular resistance to uptak e of the pollutant by leaves r CL ). 8 HEM3 includes a library of these parameters for most gaseous HAPs. This library is based on an compendium of gaseous deposition parameters develope d by Argonne National Laboratories. 9 It should be noted, however, that the deposition and depletion options of HEM3 and AERMOD were not used in the this component of the RTR analysis.14 

## 3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the AERMOD version of HEM3 for the RTR modeling analysis. Separate repor ts have been prepared for all of the emission source categories included in the RTR analys is. These reports provide information on the emissions inputs and results for specific emission categories.



### 3.1 Emission Source Inputs

HEM3 and AERMOD require detailed data on emissions from each emission source included in the modeling analysis. These data include:

! pollutants emitted

! emission rate for each pollutant

! emission source coordinates

! stack height (or emission height for fugitive sources)

! stack diameter (or configuration of fugitive sources)

! emission velocity

! emission temperature

Emissions data for the modeling effort were taken from the 2002 National Emissions Inventory (NEI) Final Version 1. 10 These data include HAP emission rates, emission source coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performed an engineering review of the NEI da ta. In cases where new or bette r data were known to exist for a particular source category, that info rmation was integrated into the data used in modeling for that category. For each source category, the emissions ar e summarized in the source category specific report. Detailed computer files containing all emission and release characteristics are available on
the RTR website.

As noted in the previous section, industri al emission sources can be characterized in AERMOD and other dispersion models as point, area, or volume sources. The NEI includes a framework for characterizing area and volume sources ; however, these fields are not filled in the 2002 Final Version 1 of the NEI. Therefore, fug itive emissions are generally characterized as low point sources with minimal exit velocities. Fo r some categories, additional information was available on the configuration of fugitive emission sources. This information was incorporated into the emissions database as part of the engineering review. Thus, fugitive emission sources
were characterized as area or volume sources when sufficient configuration information was
available.15 

### 3.2 Pollutant Cross-Referencing

Because the NEI is developed from a number of different data sources, a single chemical may be listed in the inventory under different names (i.e. a “common name” and one or more structure-based names). In addition, pollutant groupings such as polycyclic organic matter (POM), can be listed in the NEI under the na mes of individual member compounds, and under different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM3 requires an exact match in
the chemical name in order to link emissions to the appropriate dose-response factors. The model will not process any pollutant that is not specifically listed in the chemical library (see Table 2-1). Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in
the HEM3 reference file.

The individual POM chemicals listed in the NEI were grouped into seven subclasses. Table 3-1 shows these groupings, and gives the can cer unit risk estimate used for each subclass. Emissions of metal compounds were also adjusted using algorith ms developed for the Emissions Modeling System for Hazardous Air Pollutants (E MS-HAP) under the National-scale Air Toxics Assessment (NATA). A mass adjustment factor wa s applied to the emissions of metal compounds to account for a particular portion (e .g., the lead portion of lead sulfat e) or to partition them among multiple pollutant categories (e.g. chromium arse nate into chromium VI compounds and arsenic compounds). In addition, where no specific compound information was available, metals were speciated into appropriate oxida tion states (e.g. chromium compounds into chrome VI and chrome III) based on factors that have been developed for specific source categories and average factors that have been developed for the inventory as a whole. The adjustment factors and speciation
factors were taken from the HAP Table module of EMS-HAP. 11 ,12



### 3.3 Meteorological Data

AERMOD meteorological data for the RTR anal ysis were derived from three sources: (1) AERMET processing of 122 National Weather Serv ice (NWS) surface observ ation stations, (2) State agencies, and (3) industry (o n-site). The 122 NWS sites we re selected to provide thorough coverage of the U.S. For the RTR analysis, th e average distance between a modeled facility and the applicable meteorological st ation was 72 km. One year of hour ly data for these sites were processed through the AERMET program, and this is documented in “Meteorological Data Processing Using AERMET.” 13 Four states provided AERMOD -ready meteorology files. These states were: Alabama (5 stations), Florida (4 stations), North Caro lina (6 stations), and Wisconsin (22 stations). Eleven of the state supplied meteor ology sites were co-located with sites in the list of 122. The data at the state suppl ied sites were more recent, so th e state data for the 11 co-located sites were used. In addition, for the RTR petrol eum refinery modeling, two refineries (Fairbanks, Alaska and the Virgin Islands) provided on-site AERMOD-ready meteorology files. These on-site meteorological data files were used with their respective refinery f acilities for the HEM3 refinery modeling. Figure 3-1 shows the location of the 1 50 surface meteorological stations used in the RTR analysis.

As noted above, 122 NWS stations were processed through the AERMET program. To estimate the parameters needed by AERM OD, AERMET requires hourly surface weather16 observations and the full twice-daily upper air soundings (i.e., meteor ological variables reported at all levels). The surface and upper air stations are paired to pr oduce the required input data for AERMOD. To simplify processing and minimize the amount of quality assurance needed, the analysis was restricted to meteor ological data collected prior to the installation of the Automated Surface Observation System (ASOS). The ASOS has previously been found to omit the ceiling height for a large percentage of the observat ions at a number of me teorological stations. Installation and operatio n of ASOS equipment began in 1992; therefore, data for 1991 were processed with AERMET (note; the most recent ve rsion of AERMET has corrected this issue and is now compatible with ASOS data, processing of me teorological data in the future will utilize this revised version of AERMET and more recent years of meteorology). Data were retrieved from products available from the National

Table 3-1. Groupings Used for Polycy clic Organic Matter in the RTR Phase II Modeling Effort

POM subgroup Names used in the NEI Cancer URE
used for the

subgroup (m 3 /g)

POM 71002 16-PAH 0.000055 PAH, total 0.000055 Polycyclic Organic Matter 0.000055

POM72002 2-Chlorona p hthalene 0.000055 2-Methylnaphthalene 0.000055 Acenaphthene 0.000055 Acenaphthylene 0.000055

Anthracene 0.000055 Benzo(c)phenanthrene 0.000055 Benzo[e]Pyrene 0.000055 Benzo[g,h,i,]Perylene 0.000055

Fluoranthene 0.000055

Fluorene 0.000055 Perylene 0.000055

Phenanthrene 0.000055 Pyrene 0.000055

POM 73002 7

, 12-Dimeth y lbenz [ a ] Anthracene 0.1

POM 74002 3-Meth y lcholanthrene 0.01

POM 75002 5-Meth y lchr y sene 0.001 Benzo[a]Pyrene 0.001 Dibenzo[a,h]Anthracene 0.001

POM 76002 B [j] Fluoranthen 0.0001 Benz[a]Anthracene 0.0001 Benzo[b]Fluoranthene 0.0001 Benzo[k]Fluoranthene 0.0001 Indeno[1,2,3-c,d]Pyrene 0.000117 POM 77002 Chr y sene 0.00001

Climatic Data Center (NCDC). The surface data for 1991 were retrieved from the Hourly United States Weather Observation (HUSWO) CD. Up per air soundings were obtained from the Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems Laboratory (FSL).

The input to and output from AERMET were examined for indications of missing input data. The 1200 Greenwich Mean Time (GMT) s ounding is required for AERMET to calculate the convective boundary layer height and several asso ciated parameters. There were many isolated days in which the 1200 GMT sounding was missing for many of the stations. However there also
were several stations for which there were tw o or more consecutive days of missing 1200 GMT soundings. To minimize the impact on the out put from AERMET, upper air data from a representative upper air stati on were substituted for those periods for which the 1200 GMT sounding was missing for two or more consecutive days.

One of the important requirements of applyi ng the AERMOD model is the specification of surface characteristics for use in processing the me teorological data using AERMET. Application of AERMET requires specificati on of the surface roughness length, the Bowen ratio (an indicator of surface moisture), and the albedo (an indi cator of surface reflec tivity). These surface characteristics are used by AERMET to calculate the level of shear-induced mechanical turbulence generated by flow over the surface and for th e energy balance calculations used in the determination of the Monin-Obukhov stability parame ter and the convective ve locity scale. For this application, the following surface characteristics were used:

! Surface roughness length = 0.25 m. At the airport meteorological site, the surface roughness Includes runways, terminal buildings and other airport structures. In addition, off-airport structures often are within 3 kilometers of the measurement site. his combination of land covers suggests a va lue of 0.2 – 0.3 meters is appropriate.

! Bowen ratio = 1.0. Representing an equal partition of the heat fluxes

! Albedo = 0.15. Representing conditions fo r all seasons, including winter without
continuous snow cover.

The file STNS.TXT located on th e HUSWO CD was used for the an emometer heights required by AERMET. These heights are to the nearest meter and were deemed appropriate for use in this application.

Two problems in the AERMET computer prog ram were fixed before completing this application. The first pertained to hourly surface obser vations. Incorrect missi ng indicators in the code for wind speed and direction were output when the data were extracted from the raw input file in Stage 1, which caused th e program to stop running in Stage 3. The AERMET program was changed to use correct missing indicators. The second problem was associated with the upper air soundings. AERMET would not process an entire station’s data if th e surface pressure for the first sounding was less than 850 millibars (mb). Surface pressures less than 850 mb are not uncommon for stations at high elevations . The AERMET program was modified to expand the allowable surface pressures.18 Figure 3.1. Locations of RTR AE RMOD Meteorological Stations

In early 2007, AERMOD was modified to version 07026 which checks the header record of the surface meteorological input file to verify that its version number is 06341. As a result of this modification, the 122 NWS stations were re-processed through a ne wer version of AERMET (version 06431) to create meteor ological data files compatible with AERMOD version 07026. The meteorology data received from the States were alrea dy at version 06341.



### 3.4 Model Options Selected

HEM3 presents a number of options for characterizing the modeling domain and data sources. As many sources were modeled in this assessment, established defaults and common practices were relied on to make these choices. The choices available to a HEM3 user and the
selections that were made in this assessment are presented in Table 3-2. Some of the key
selections are discussed in more detail in the paragraphs below.

Although routine emissions are not expected to vary significantly with time, nonroutine (upset) emissions can be significant relative to routine emissions. Upset emissions occur during periods of startup, shutdown, a nd malfunction. Upset emissions ar e not likely for equipment or storage tanks, but do result from malfunctioning c ontrol devices and leaks in cooling tower heat exchangers. There is some limite d data on upset emissions available, 14 but no facility-specific19 analyses of these data were performed to char acterize short-term emissions from these emission sources, and upset emissions were not m odeled for the RTR risk assessments.

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in
the Residual Risk and Tec hnology Review Assessments

Option Selection

Dispersion model: AERMOD or ISCST3 AERMOD
Type of analysis: chronic, acute, or both Both
Averaging time for short term impacts 1-hour

Multiplier for short term emissions 10 a

Urban or rural dispersion characteristics Rural

Include terrain impacts Yes
Include building wake effects No
Calculate deposition (wet, dry, or both) No

Include impacts of plume depletion due to deposition No

User-specified receptor locations (for plant bou ndary sites or other sites of interest) No
Modeling domain size – maximum distance to be modeled 50 km
Cutoff distance for modeling of individual blocks 3 km b

Overlap distance where receptors are considered to be on plant property – measured
from each source 30 m c

Polar receptor network specifications
Distance from the plant center to the innermost ring 100 m c

Number of rings 13

Number of directions 16
Meteorology data Closest site

a For the secondary aluminum production category, th e ratio of maximum short-term emissions to long-term average emissions was set to 100, based on the engineering review.

b The individual block modeling cutoff was increased for categories and for so me facilities to ensure that the maximum individual risk values were not interpolated.

c The overlap distance and inner ring distance were adjusted for some facilities to avoid modeling locations that are on plant property (see section 4.2).

3.4.1 Urban or Rural Dispersion Characteristics

Since 51 source categories were considered in these screening-level RTR assessments, the rural option was chosen to be most conservative (i.e., more likely to overe stimate risk results).

3.4.2 Deposition and Plume Depletion

This modeling analysis did not take into acc ount the depletion of po llutant concentrations in the plume due to wet or dry deposition. In ad dition, reactivity and decay were not considered.20 It is possible that this approach, may overestimat e air concentrations and therefore risk. However, one of the main metrics used by EPA in the residual risk program is the risk to the individual most exposed (maximum risk). Because the maximum ri sk usually occurs at a receptor very close to the emission source, it is unlikely to be influe nced by altered plume disp ersion characteristics of this type. For more refined, multipathway assessments, EPA will consider deposition and depletion.

3.4.3 Cutoff Distance for Modeling of Individual Blocks

The cutoff distance for modeling individual Census blocks was in itially set to 3 km. This distance generally ensures that the maximum indi vidual cancer risk and the maximum TOSHI are modeled explicitly and not interpol ated. Following the modeling run, the results for each facility
were checked to determine whether the maximum impacts were located inside the modeling cutoff distance. If the maximum impacts were outside the cutoff distance any of the impacts were significant, then HEM3 was rerun for the facility with an expanded modeling distance cutoff. In general, this was done if the cancer risk exceed ed 1 in 10 million or any TOSHI exceeded 0.1. However, the risks for these facilities were gene rally very low, since the maximum impacts were generally only interpolated when the nearest Census block was more than 3 km from the facility.

3.4.4 Plant Boundary Assumptions

The main input mechanisms for incorporati ng plant boundary information in HEM3 are the overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor locations. The NEI does not provide informati on on plant boundaries. Therefore, the userspecified receptor file was not us ed in this analysis. Conservative default assumptions were used for the overlap distance and the innermost polar r eceptor ring. However, these were adjusted for some categories where plant sites are known to be la rge. In addition, satellite imagery was used to check the plant boundary assumptions for facilities with large projected impacts. These checks are
discussed further in the section on Quality Assurance (Section 4).



### 3.5 Modeling of Multiple Facilities

HEM3 models one facility at a time. However, clusters of nearby facilities may impact the same people, resulting in higher risk to those pe ople. To account for this situation, risks were
summed at each Census block for all f acilities affecting the Census block.

As described earlier (Section 2.3.4), HEM3 produces detailed output tables containing the risk and population for every Census block in th e modeling domain. Thes e detailed tables were combined for all facilities in a source categor y and the risk for each Census block was summed. Thus, the effect of multiple facilities in the sa me source category on the same receptor could be estimated. The resulting “combined facility” or “cluster-effect” Ce nsus block risks were used to calculate population exposure to different cance r risk levels and sour ce category incidence.21 

## 4. Quality Assurance

The National Emissions Inventory is subject to an extensive program of quality assurance (QA) and quality control (QC). The QA/QC prog ram for the point source component of the NEI is documented in a separate report, available from the NEI website. 15 This section describes QA activities carried out under the RTR modeling analysis.



### 4.1 Engineering Review

In addition to the standardized QA steps ta ken for the entire NE I, EPA performed an engineering review of NEI data for the emission s ource categories included in the RTR analysis. This engineering review included two main compone nts. The first component addressed the list of facilities included in each source category. EP A engineers reviewed independent sources of information to identify all source s in the category that are include d in the NEI. In addition, EPA reviewed the list of sources repres ented as part of each category in the NEI to make sure that the facilities actually manufacture products char acteristic of the source category.

The second component of the e ngineering review focused on the appropriateness of facility
emissions. EPA reviewed the list of HAPs em itted by each facility to make sure that the pollutants were appropriate to the source cate gory. In addition, EPA engineers review the magnitude of those HAP emissions. In cases wher e new or better data were known to exist for a particular source category, that info rmation was integrated into the data used in modeling for that category. In these cases, the source category sp ecific documents provide additional details on the emissions inputs used.



### 4.2 Geographic Checks

The NEI QA process includes some basic check s on location data for point sources. The
coordinates for each source are checked to ensure that they are in the county that has been specified for the source. If this is not the case, or if no geographic coordinates are available for the emission source, then the coordinates are set to a default location based on the nature of the emission source category. 15 In addition, coordinates for all emi ssion sources at a given facility are checked to ensure that they ar e withing 3 km of one another.

As part of the RTR modeling analysis, a dditional geographical QA checks were made for some facilities. Facilities subjecte d to these additional checks include:

! cases where the initial estimates of maximum risks were particularly high

" maximum individual cancer risk of over 1 in 10,000

" any maximum TOSHI above 10

! cases where no Census blocks were id entified within 3 km of the facility

The emission source coordinates were reviewed for each of these facilities, and compared with the address reported for the facility. Both the coordinates and the facility address were22 entered into Internet mapping sites which provid e the option of viewing aerial or satellite photography. These sites include Google Earth® ( earth.google.com/ ), Google Maps® ( local.google.com/ ), MapQuest® ( www.mapquest.com ) and Microsoft Terraserver® ( www.terraserver.com/ ). If the address and the coordinates represented the same location, then the
coordinates were taken to be correct. If the lo cations were different, th en the aerial photographs
for the address and the coordinate location were reviewed to de termine whether either photograph included an industrial facility. Generally, where the two locations were different, the facility
address was found to be correct. In some cases, this comparison could not be made because the reported address was a Post Office box or a headquart ers address. Where this occurred, the aerial photograph for the coordinate loca tion was reviewed to determine whether an industrial facility
was located at or near the lo cation. If emission source coordinates were found to be incorrect, HEM3 was rerun using corrected coordinates. Th ese changes are described in the source category
documents.

For the high-risk facilities, the coordinates used to represent the most impacted Census
blocks were also reviewed. Th is review drew on detailed Census block boundary maps and aerial photographs. Large industrial facilities will frequen tly occupy one or more entire Census blocks. However, these blocks may also include one or more residences on the periphery of the industrial land. Generally, the coordinates listed for a Cens us block are near the center of the block. In
these cases of mixed industrial a nd residential blocks, the coordinates may be on plant property.

The potential for overlap between an industria l facility and a Census block coordinate is illustrated in Figure 4-1. The figure shows Census blocks divisions around a large industrial facility, with the coordinates of emission sources at the facility illustrated by red squares. Census block boundaries are shown, and th e coordinates given in the Ce nsus data base for populated blocks are indicated by blue circles. As the figur e shows, the facility is spread over five Census blocks. A number of populated bl ocks can be seen to the Southwes t of the facility, and most of the blocks covering the facility are not populated . However, the block containing most of the facility’s emission points also includes at least one residence (the total population given for the block is 5). The location of the residence is at the periphery of the block, near the one of the adjacent populated blocks. However, the coordinates given for the block are near its center, illustrated by the blue star, clearly between various emission sources at the facility.

In general, block coordinates were considered to be on plant property if they were located
between the different emission source locations listed for the facil ity. In these situations, HEM3 was rerun with an expanded overlap distance, in order to exclude the Ce nsus block coordinates that appeared to be located on plant property. The distance to the innermost polar receptor ring was also adjusted to ensure that this ring was not on plant property.23 Figure 4-1. Illustration of a qualit y assurance test for Census block coordinates located on plant property

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## 5. Uncertainties

This analysis is subject to a number of un certainties. For instance, model verification studies for AERMOD showed predicted maximu m annual concentrations ranging from 0.3 to 1.6 times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours)
concentrations were 0.25 to 2.5 times m easured values, with an average of 1.0. 16

In addition, a number of simplifying assump tions have been made in this modeling analysis. First, the c oordinates reported by the Department of Census for Census block “internal points” have been used as a surrogate for l ong-term population exposures . Locations of actual residences have not been modeled. In addition, the current version of HEM3 does not take into account the movement of people from one Census block to another dur ing the course of their lives, or commuting patterns during a given day. Nor doe s the model take into account the attenuation of pollutant from outside emission sources in indoor air. Ideall y, risks to individuals would be modeled as they move through their communities and undertake different activities. However, such modeling is time- and resource-intensive and can only capture a portion of the uncertainty associated with the full range of human activities. In general, it is e xpected that long-term exposures will be overstated for high-end estimate s (as most individuals will not spend all their time at their highly affected residences), but ma y understate the total popul ation exposed (as some individuals living outside the m odeled area may regularly commute into the area for work or school).

When considering long-term or lifetime expos ures, it should be noted that relatively few people in the United States reside in one place for their entire liv es. For the purposes of this assessment, cancer risk estimates are based on a lifetime exposure at the 2000 Census-identified place of residence. While it is impossible to know how this assumption affects the risk experiences by a particular individual (as people ca n move into higher- or lower-risk areas), it is likely that this assumption will overstate the exposure to those most exposed (i.e., people already living in high exposure areas are unlikely to move to yet higher exposure ar eas). However, this assumption will also tend to underestimate the total number of people exposed and population risk (i.e., incidence) because population levels are generally increasing.

In the current analysis, only direct inhalati on has been modeled. Other pathways such as the deposition of pollutants to dri nking water, and to bioaccumula tion of deposited pollutants in the food supply may be a significant source of exposure for persistent and bioaccumulative pollutants. These exposures will be modeled in subsequent anal yses. Because this analysis is restricted to the inhalation pathway, the impact s of plume depletion due to deposition have not been into account. Thus, inha lation impacts may be overestimat ed for some pollutants, but exposures through other pathwa ys would be underestimated.

A number of other simplifications have been made in the dispersion modeling analysis, as has been noted in Table 3-1. For instance, building wake effects have not been considered. Rural dispersi on conditions have also been used for a ll facilities. This is viewed as a conservative assumption, since rural conditions resu lt in less mixing close to the emission source.25 In addition, meteorological observations are based on the closest station in the HEM3 meteorological library (see Figure 2-1). Altern ative meteorological st ations may be more appropriate for some facilities. Ideally, facility-specific meteor ological observations would be used. A single year of meteorological data (1 991) was selected for dispersion modeling. When considering off-site meteorological data most s ite specific dispersion modeling efforts will employ up to five years of data to capture variability in weathe r patterns from year to year. However, because of the large number of f acilities in the analysis and the extent of the dispersion modeling analysis (national scale), it was not practical to m odel five years of data. Other national studies such as NATA have also considered only a single year of meteorological data. A sensitivity analyses performed by the NATA assessment found that variability attr ibutable to the selection of the meteorology location/time (both temporal and spatial) resulted in a 17-84% variation in predicted concentrations at a given station 17

.

Finally, risk and exposure fact ors are also subject to uncer tainty. Not all individuals experience the same degree of e xposure or internal dos e of a given pollutantdue to individualspecific parameters such as weight, age, and gend er. While the health benchmarks used in the analysis crudely account for sensitive populat ions, a prototypical human (e.g., body weight, ventilation rate) is used to define the benchmark. Because of the variability of these parameters in the population, this factor will resu lt in a degree of uncertainty in the resulting risk estimate.

Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analysis. The table also gives a qualitative indication of the potential direction of bias on risk estimates. The sources of uncertainty in Table 5-1 are di vided into four categories, based on the major components of the analysis:

! emissions inventory

! fate and transport modeling

! exposure assessment

! toxicity assessment

It must also be noted that individual source catego ries may be subject to additional uncertainties. These are discussed in separate reports whic h have been prepared for each emission source category included in the RTR.26Table 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk AssessmentParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesEmissions InventoryIndividual HAPemissions rates andfacility characteristics(stack parameters,property boundaries)Emissions and facilitycharacteristics from the NEIprovide an accuratecharacterization of actual sourceemissions.Our emissions inventory is based on the 2002NEI, our internal review, and public commentsreceived. The degree to which the data in ourinventory represents actual emissions is likely tovary across sources. Nearly half of the sourcesin the source category submitted a review oftheir emissions and facility characteristics data.Some detailed data, such as property boundaryinformation was not available for most facility.This is an important consideration indetermining acute impacts.Unbiased overall, magnitudevariableMultiplier for short-termemission ratesMaximum short term emissionrates are estimated by applying asimple multiplier (a factor of 10)to average annual emissions.The ratio between short-term and long-termaverage emission rates may vary from amongthe different emission sources at a facility. Inaddition, the use of a simple multiplier meansthat impacts of maximum short term emissionsare modeled for all meteorological conditions,including the worst-case conditions forpopulation exposure.Potential overestimate due tothe fact that worst-caseemissions are assumed tooccasionally coincide withworst-case meteorology.Overestimate due to lack ofactual information on shortterm emission rates.Fate and Transport ModelingAtmospheric dispersionmodel choiceAERMOD is one of EPA'srecommended models forassessing pollutant concentrationsfrom industrial facilitiesField testing of these models have shown resultsto generally be within a factor of 2 of measuredconcentrations.OverestimateTable 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)27ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesBuilding downwash Not included in assessmentUse of this algorithm in AERMOD couldimprove the dispersion calculations at individualfacilities. However, data were not readilyavailable to utilize this option.Potential underestimate ofmaximum risks near facility.No effect on risks furtherout.Plume depletion anddepositionNot included in assessmentIgnoring these impacts for pollutants thatdeposit minimally and whose risks derivepredominantly from inhalation should haveminimal effect on risk estimates.Unbiased or minimaloverestimate.MeteorologyOne year of meteorological datafrom the nearest weather station(selected from 122 nationwide) isrepresentative of long-termweather conditions at the facility.The use of one year of data rather than the fiveor more adds uncertainty based on whether thatyear is representative of each location’sclimatology. Use of weather station data ratherthan on-site data can add to uncertainty.Additionally, the use of default surfaceparameters in the generation of themeteorological datasets imparts uncertainty tothe results from any individual facility.Minimal underestimate oroverestimate.Reactivity Not included in the assessment.Chemical reactions and transformations ofindividual HAP into other compounds due tosolar radiation and reactions with otherchemicals happens in the atmosphere. However,in general, the HAP in this assessment do notreact quickly enough for these transformationsto be important near the sources, where thehighest individual risks are estimated. Further,most of the HAP do not react quickly enoughfor these transformations to be important to riskestimates in the entire modeled domain (i.e.,within 50 km of the source).No impact on maximum riskestimates. Minimal impacton population risks andincidence.Table 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)28ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesMaximum modelingdistance50 kilometers from center offacilityThis distance is considered to be the maximumdownwind distance for a Gaussian plume modelsuch as AERMOD. This is because, in general,winds cannot be considered to follow straightline trajectories beyond this distance.No effect on maximumindividual risks. Minimalunderestimation ofincidence.Exposure AssessmentLocations and shortterm movements ofindividualsAmbient concentration at centroidof each off-site census block isequal to the exposureconcentration for all people livingin that census block.Effect of human activity patternson exposures is not included in theassessment.People live at different areas within block thatmay have higher or lower exposures than at thecentroid. Individuals also move from outdoorsto indoors and from home to school/work torecreation, etc., and this can affect their totalexposure from these sources.Unbiased across populationfor most pollutants andindividuals, likelyoverestimate for mostexposed and underestimatefor least exposed persons.Long-term movementsof individualsMIR individual is exposedcontinuously to the highestexposure concentration for a 70-year lifetime.Population moves into and out ofexposure area consistent withnational frequency distribution ofresidency.MIR (maximum individual risk) is defined inthis way to be a maximum theoretical risk at apoint where a person can actually reside.Individuals may move into or out of areasimpacted by the facilities of interest in ways thatdon’t correspond exactly with national norms orlocal demographic patterns, and this could leadto localized biases. In general, source categorieswith larger numbers of facilities should carryless potential for bias.Unbiased for mostindividuals, likelyoverestimate for the actualindividual most exposed andlikely underestimate for theleast exposed. Incidenceremains unbiased unlesspopulation around facilitiesincreases or decreases over70 years.Toxicity AssessmentTable 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)29ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesReferenceconcentrations (RfC)Consistent with EPA guidance,RfCs are developed includinguncertainty factors to be protectiveof sensitive subpopulations.Additionally, RfCs are developedbased on the level producing aneffect in the most sensitive targetorgan or system.While other organ systems may be impacted atconcentrations above the RfC, these are notincluded in the calculation of target organspecific hazard indices.In general, EPA derivesRfCs using procedureswhose goal is to avoidunderestimating risks in lightof uncertainty andvariability. The greater theuncertainties, the greater thepotential for overestimatingrisks.Unit Risk Estimate(URE)Use of unit risk estimatesdeveloped from dose-responsemodels such as linear low-doseextrapolation.Uncertainty in extrapolating the impacts fromshort-duration, high-dose animal or work-relatedexposures to longer duration, lower-doseenvironmental impacts.Overestimate of risks fornonlinear carcinogens andfor linear carcinogens withsparse health effects data. Ingeneral, EPA derives UREvalues using proceduresaimed at overestimatingrisks in light of uncertaintyand variability.Toxicity of mixturesCancer risks and noncancer hazardquotients were calculated for eachHAP individually and thensummed into a total risk or hazardindex (assumption of additivity).Concurrent exposures to multiple chemicalsmay result in either increased or decreasedtoxicity due to chemical interactions but the dataneeded to quantify these effects are generallynot available.Unbiased overall. Somemixtures may haveunderestimated risks, someoverestimated, and somecorrectly estimated.Table 5-1. Summary of General Uncertainties Associatedwith Risk and Technology Review Risk Assessment(continued)30ParameterAssumptionUncertainty/Variability DiscussionPotential Direction of Biason Risk EstimatesSurrogate doseresponse values forHAPs without valuesIn the case of groups of HAPssuch as glycol ethers, the mostconservative dose-response valueof the chemical group was used asa surrogate for missing doseresponse values in the group. Forothers, such as unspeciated metals,we have applied speciationprofiles appropriate to the sourcecategory to develop a compositedose-response value for the group.For HAP which are not in a groupand for which no URE’s or RfC’sare available from crediblesources, no assessment of risk ismade.Rather than neglecting the assessment of risksfrom some HAPs lacking dose response values,conservative assumptions allow the examinationof whether these HAPs may pose anunacceptable risk and require furtherexamination, or whether the conservative levelexamination with surrogates screens out theHAPs from further assessment.Overestimate where mostconservative values used.Unbiased where categoryspecific profiles applied.There is the potential tounderestimate risks forpollutants which are notincluded in the assessment.31 

## 6. References

1. EPA. 2004. AERMOD: Description of Model Formulation . EPA-454/R-03-004, U.S. Environmental Protection Agency, Research Triangle Park, NC. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD . EPA454/B-03-001, U.S. Environmental Protection Ag ency, Research Triangle Park, NC. www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EC/R Incorporated. 2006. The HEM-3 User’s Guide . Prepared for the U.S. Environmental Protection Agency, Sector-B ased Assessment Group, Research Triangle Park, NC.

4. EPA. 2007. OAQPS Prioritized Chronic Dose-Response Values (6/12/2007). U.S. Environmental Protection Agency, Resear ch Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5. Census. 2000. LandView® 5 on DVD . U.S. Census Bureau, Washington, DC.

6. USGS. 2003. 1:250,000 Scale Digital Elevation Models (DEM) . U.S. Geologic Survey, Washington, DC. http://edcsgs9.cr.usgs.gov/glis/hype r/guide/1_dgr_demfig/states.html

7. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP) . EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/di spersion_related.htm#aermap

8. EPA. 2004. Addendum: User's Guide for th e AMS/EPA Regulatory Model - AERMOD (related to deposition). U.S. Environmenta l Protection Agency, Research Triangle Park,
NC. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

9. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for the Industrial Source Complex (ISC3) Model . Argonne National Laboratory, Argonne,
Illinois. http://www.epa.gov/scram001/di spersion_prefrec.htm#aermod

10. EPA. 2006. 2002 National Emissions Inventory Data & Documentation . U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/chie f/net/2002inventory.html

11. EPA. 2002. EMS-HAP Modeling System . Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/disp ersion_related.htm#ems-hap32 12. Strum, Madeleine, Richard Mason, and Diane Linderman. 2002. User's Guide for the Emissions Modeling System for Hazar dous Air Pollutants (EMS-HAP) Version 2.0

. EPA-454/B-02-001, Environmental Protection Ag ency, Research Triangle Park, NC. http://www.epa.gov/scram001/di spersion_related.htm#ems-hap

13. MACTEC. 2006. Meteorological Data Processing Using Aermet for HEM-AERMOD

. Prepared by MACTEC Federal Programs, Inc., Durham, NC for EC/R Incorporated, Chapel Hill, NC.

14. Texas Commission on Environmental Qualit y. 2006. Reports on Air Emission Events. http://www.tceq.state.tx.us /compliance/field_ops/eer/

15. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources . U.S. Environmental Protection Agency, Emission Inventory Group, Rese arch Triangle Park,
NC.

16. EPA. 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/7thconf/aermod/eval.pdf

17 EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/atw/nata/peer.htmlAppendix 3 Meteorological Data for HEM-3 Modeling2 

### DRAFT

METEOROLOGICAL DATA PROCESSING
USING AERMET

FOR

HEM-3

Submitted by:

MACTEC Federal Programs, Inc.

5001 S. Miami Blvd., Suite 300 Durham, NC 27703
919-941-0333

July 28, 2006 Revisions May 14, 2012Meteorological Data Processing using AERMET July 28, 2006
For HEM-AERMOD

i TABLE OF CONTENTS

PROJECT BACKGROUND ...............................................................................................1

METEOROLOGICAL DATA PROCESSING ...................................................................1

MISSING DATA .................................................................................................................5

ANEMOMETER HEIGHTS ...............................................................................................6

SURFACE CHARACTERISTICS ......................................................................................6

MODIFICATIONS TO AERMET CODE ..........................................................................7

RESULTS ....................................................................................................................... .....7

LIST OF TABLES

Table

1 SURFACE STATIONS PAIRED WITH UPPER AIR STATION .........................1

2 UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS
AND STATION USED FOR DATA SUBSTITUTION .........................................5

3 SURFACE CHARACTERISTICS ..........................................................................7

4 PERCENT CALM AND MISSING BY STATION ...............................................8

LIST OF FIGURES Figure

1 SURFACE AND UPPER AIR STATIONS ............................................................4Meteorological Data Processing using AERMET July 28, 2006
Rev. 05/12
For HEM-AERMOD

1 PROJECT DESCRIPTIO N AND BACKGROUND

The AERMOD meteorological processor, AERM ET, was used to process one year of hourly data from 122 National Weather Serv ice (NWS) surface observation stations across the continental United States, Alaska, Hawaii, and Puerto Rico.

METEOROLOGICAL DATA PROCESSING

To estimate the boundary layer parameters required by AERMOD, AERMET requires hourly surface weather observations and the fu ll (i.e., meteorological variables reported at all levels) twice-daily upper air soundings. The surface and upper air stations are paired to produce the required input data for AERMOD.

One of the requirements of the project was to use surface observations prior to the introduction of the Automated Surface Observation System (ASOS). Installation and operation of ASOS equipment began in 1992; therefore, data for 1991 were processed
with AERMET.

A list of 86 previously paired surface and upper air stations was provided to MACTEC. On review of these stations, it was determined that data for 1991 for three of the stations – Galveston, TX, Houston-Hobby, TX and B oulder, CO – was not available. The remaining 83 stations did not provide adequa te coverage for the MACT sources to be modeled with AERMOD. As a result, an additi onal 39 surface stations were identified to include for processing. These additional surf ace stations were paired with representative upper air stations. A list of the surface and paired upper air stations are in Table 1.

Data were retrieved from products availabl e from the National Climatic Data Center (NCDC). The surface data for 1991 were retr ieved from the Hourly United States Weather Observation (HUSWO) CD. Uppe r air soundings were obtained from the Radiosonde Data of North America CDs pr oduced by NCDC and the Forecast Systems Laboratory (FSL).

TABLE 1

SURFACE STATIONS WITH PA IRED UPPER AIR STATION

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON

Paducah, KY 03816 37.07 -88.77 Paducah, KY 03816 37.07 -88.77
Huntsville, AL 03856 34.65 -86.78 Nashville, TN 13897 36.13 -86.68 Huntington, WV 03860 38.37 -82.55 Huntington, WV 03860 38.37 -82.55
Greenville, SC 03870 34.90 -82.22 Athens, GA 13873 33.95 -83.32
Fort Worth, TX 03927 32.90 -97.02 Stephenville, TX 13901 32.22 -98.18
Wichita, KS 03928 37.65 -97.45 Topeka, KS 13996 39.07 -95.63
Lake Charles, LA 03937 30.12 -93.23 Lake Charles, LA 03937 30.12 -93.23
Jackson, MS 03940 32.32 -90.08 Jackson, MS 03940 32.32 -90.08Meteorological Data Processing using AERMET July 28, 2006
Rev. 05/12
For HEM-AERMOD

2 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON
Kansas City, MO 03947 39.30 -94.73 Topeka, KS 13996 39.07 -95.63
San Juan, PR 11641 18.43 -66.02 San Juan, PR 11641 18.43 -66.00
Orlando, FL 12815 28.45 -81.32 Tampa, FL 12842 27.70 -82.40
Gainesville, FL 12816 29.68 -82.27 Waycross, GA 13861 31.25 -82.40
Tampa, FL 12842 27.97 -82.53 Ta mpa Bay, FL 12842 27.97 -82.53
W. Palm Beach, FL 12844 26.68 -80.10 W. Palm Beach, FL 12844 26.68 -80.12 Victoria, TX 12912 28.85 -96.92 Corp us Christi, TX 12924 29.77 -97.50
New Orleans, LA 12916 30.00 -90.25 Slidell, LA 53813 30.33 -89.82
Port Arthur, TX 12917 29.95 -94.02 Lake Charles, LA 03937 30.12 -93.23
Brownsville, TX 12919 25.90 -97.43 Brownsville, TX 12919 25.90 -97.43
San Antonio, TX 12921 29.53 -98.47 Del Rio, TX 22010 29.37 -100.92 Corpus Christi, TX 12924 27.77 -97.52 Corpus Christi, TX 12924 27.77 -97.52
Houston, TX 12960 29.98 -95.37 Lak e Charles, LA 03937 30.12 -93.23 Raleigh, NC 13722 35.87 -78.78 Greensboro, NC 13723 36.10 -79.95
Greensboro, NC 13723 36.08 -79.95 Greensboro, NC 13723 36.08 -79.95
Norfolk, VA 13737 36.90 -76.20 Wa llops Island, VA 93739 37.93 -75.47 Philadelphia, PA 13739 39.87 -75.23 Atlantic City, NJ 93755 39.75 -74.67
Richmond, VA 13740 37.50 -77.32 Sterling, VA 93734 38.98 -77.47
Roanoke, VA 13741 37.32 -79.97 Greensboro, NC 13723 36.10 -79.95 Wilmington, NC 13748 34.27 -77.90 Charleston, SC 13880 32.90 -80.03 Wilmington, DE 13781 39.67 -75.60 At lantic City, NJ 93755 39.75 -74.67
Meridian, MS 13865 32.33 -88.75 Jackson, MS 03940 32.32 -90.08
Charleston, WV 13866 38.38 -81.58 Huntington, WV 03860 38.37 -82.55
Atlanta, GA 13874 33.63 -84.45 Athens, GA 13873 33.95 -83.33 Birmingham, AL 13876 33.57 -86.75 C enterville, AL 03881 32.90 -87.25
Bristol, TN 13877 36.47 -82.40 Huntington, WV 03860 38.37 -82.55
Charleston, SC 13880 32.90 -80.03 Charleston, SC 13880 32.90 -80.03
Charlotte, NC 13881 35.22 -80.93 Greensboro, NC 13723 36.08 -79.95
Columbia, SC 13883 33.95 -81.12 Athens, GA 13873 33.95 -83.33
Jacksonville, FL 13889 30.50 -81.70 Waycross, GA 13861 31.25 -82.40
Memphis, TN 13893 35.05 -89.98 N. Little Rock, AR 03952 34.83 -92.27
Mobile, AL 13894 30.68 -88.25 Slidell, LA 53813 30.33 -89.82 Montgomery, AL 13895 32.30 -86.40 Centerville, AL 03881 32.90 -87.25
Nashville, TN 13897 36.13 -86.68 Nashville, TN 13897 36.13 -86.68 Shreveport, LA 13957 32.45 -93.82 Longview, TX 03951 32.35 -94.65
Little Rock, AR 13963 34.73 -92.23 N. Little Rock, AR 03952 34.83 -92.27
Wichita Falls, TX 13966 33.98 -98.50 Stephenville, TX 13901 32.22 -98.18 Oklahoma City, OK 13967 35.40 -97.60 Norman, OK 03948 35.23 -97.47 Baton Rouge, LA 13970 30.53 -91.15 Slidell, LA 53813 30.33 -89.82 Dodge City, KS 13985 37.77 -99.97 Dodge City, KS 13985 37.77 -99.97
St. Louis, MO 13994 38.75 -90.37 Peoria, IL 14842 40.67 -89.68 Springfield, MO 13995 38.82 -92.27 Monet, MO 03946 36.87 -93.97
Buffalo, NY 14733 42.93 -78.73 Buffalo, NY 14733 42.93 -78.73
Newark, NJ 14734 41.72 -75.17 Atl antic City, NJ 93755 39.75 -74.67 Albany, NY 14735 42.75 -73.82 Albany, NY 14735 42.75 -73.82
Allentown, PA 14737 40.65 -75.45 Albany, NY 14735 42.75 -73.82
Boston, MA 14739 42.37 -71.02 Chatham, MA 14684 41.67 -69.97Meteorological Data Processing using AERMET July 28, 2006
Rev. 05/12
For HEM-AERMOD

3 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON
Hartford, CN 14740 41.93 -72.68 Albany, NY 14735 42.75 -73.80 Burlington, VT 14742 44.47 -73.15 Albany, NY 14735 42.75 -73.82
Portland, ME 14764 43.65 -70.30 Portland, ME 14764 43.65 -70.30
Providence, RI 14765 41.73 -71.43 Chatham, MA 14684 41.67 -69.97 Syracuse, NY 14771 43.12 -76.12 Buffalo, NY 14733 42.93 -78.73
Williamsport, PA 14778 41.27 -77.05 Pittsburgh, PA 94823 40.53 -80.23
Cleveland, OH 14820 41.40 -81.85 Pittsburgh, PA 94823 40.53 -80.23
Columbus, OH 14821 39.98 -82.88 Dayton, OH 13840 39.83 -84.05 Fort Wayne, IN 14827 41.00 -85.20 Dayton, OH 13840 39.83 -84.05 Lansing, MI 14836 42.78 -84.58 Flint, MI 14826 42.97 -83.75
Madison, WI 14837 43.13 -89.33 Green Bay, WI 14898 44.48 -88.13
Erie, PA 14860 42.08 -80.18 Buffalo, NY 14733 42.93 -78.73
Akron, OH 14895 40.92 -81.43 Pittsburgh, PA 94823 40.50 -80.23 Green Bay, WI 14898 44.50 -88.12 Green Bay, WI 14898 44.50 -88.12
Duluth, MN 14913 46.83 -92.22 Int'l Falls, MN 14918 48.57 -93.40 Minneapolis, MN 14922 44.88 -93.23 St. Cloud, MN 14926 45.55 -94.05
Moline, IL 14923 41.45 -90.52 Peoria, IL 14842 40.67 -89.68
Rochester, MN 14925 43.92 -92.50 St. Cloud, MN 14926 45.55 -94.08
Sioux Falls, SD 14944 43.57 -96.73 Huron, SD 14936 44.38 -98.22
Eau Claire, WI 14991 44.87 -91.48 St. Cloud, MN 14926 45.55 -94.08
Lihue, HI 22536 21.98 -159.33 Lihue, HI 22536 21.98 -159.33
Midland, TX 23023 31.95 -102.18 Midland, TX 23023 31.95 -102.18
El Paso, TX 23044 31.82 -106.38 El Paso, TX 23044 31.82 -106.38
Amarillo, TX 23047 35.22 -101.70 Amarillo, TX 23047 35.22 -101.70 Albuquerque, NM 23050 35.03 -106.62 Albuquerque, NM 23050 35.03 -106.62
Denver, CO 23062 39.77 -104.87 Denver, CO 23062 39.77 -104.88
Goodland, KS 23065 39.37 -101.70 Do dge City, KS 13985 37.77 -99.97
Grand Junction, CO 23066 39.12 -108.53 Grand Junction, CO 23066 39.12 -108.53
Tonopah, NV 23153 38.05 -117.08 Ely, NV 23154 39.30 -114.85
Tucson, AZ 23160 32.12 -110.93 Tucson, AZ 23160 32.12 -110.93 Daggett, CA 23161 34.87 -116.78 Desert Rock, NV 03160 36.62 -116.02 Los Angeles, CA 23174 33.93 -118.38 Miramar, CA 03190 32.87 -117.15
Prescott, AZ 23184 34.65 -112.43 Tucson, AZ 23160 32.12 -110.93 San Diego, CA 23188 32.73 -117.17 Miramar, CA 03190 32.87 -117.15
Sacramento, CA 23232 38.52 -121.50 Oakland, CA 23230 37.75 -122.22
San Francisco, CA 23234 37.62 -122.40 Oakland, CA 23230 37.72 -122.22
Bismarck, ND 24011 46.78 -100.75 Bismarck, ND 24011 46.78 -100.75 Cheyenne, WY 24018 41.15 -104.77 Denver, CO 23062 39.77 -104.88 Billings, MT 24033 45.80 -108.55 Gr eat Falls, MT 24143 47.47 -111.38 Casper, WY 24089 42.90 -106.47 Lander, WY 24021 42.82 -108.73 Rapid City, SD 24090 44.05 -103.07 Rapid City, SD 24090 44.05 -103.07 Salt Lake City, UT 24127 40.78 -111.97 Salt Lake City, UT 24127 40.78 -111.97
Great Falls, MT 24143 47.47 -111.38 Great Falls, MT 24143 47.47 -111.38 Spokane, WA 24157 47.62 -117.53 Spokane, WA 24157 47.62 -117.53
Medford, OR 24225 42.37 -122.87 Medford, OR 24225 42.37 -122.87
Portland, OR 24229 45.60 -122.60 Salem, OR 24232 44.90 -123.00
Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00Meteorological Data Processing using AERMET July 28, 2006
Rev. 05/12
For HEM-AERMOD

4 SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON
Seattle, WA 24233 47.45 -122.32 Quillayute, WA 94240 47.93 -124.55
Juneau, AK 25309 58.37 -134.58 Annette Is., AK 25308 55.03 -131.57 Cold Bay, AK 25624 55.20 -162.72 Cold Bay, AK 25624 55.20 -162.72
Fairbanks, AK 26411 64.80 -147.88 Fairbanks, AK 26411 64.80 -147.88
Valdez, AK 26442 61.13 -146.35 An chorage, AK 26409 61.22 -149.85 Anchorage, AK 26451 61.20 -150.00 Anchorage, AK 26409 61.22 -149.85
Nome, AK 26617 64.50 -165.43 Nome, AK 26617 64.50 -165.43 Colo. Springs 93037 38.82 -104.72 Denver, CO 23062 39.77 -104.88
Fresno, CA 93193 36.78 -119.72 Oakland, CA 23230 37.72 -122.22
Baltimore, MD 93721 39.18 -76.67 Sterling, VA 93734 38.98 -77.47 Sterling, VA 93738 38.95 -77.45 Sterling, VA 93734 38.98 -77.47
Evansville, IN 93817 38.05 -87.52 Paducah, KY 03816 37.05 -88.77 Indianapolis, IN 93819 39.73 -86.28 Dayton, OH 13840 39.83 -84.05 Lexington, KY 93820 38.03 -84.60 Dayton, OH 13840 39.83 -84.05
Louisville, KY 93821 38.18 -85.73 Dayton, OH 13840 39.83 -84.05 Springfield, IL 93822 39.85 -89.68 Peoria, IL 14842 40.67 -89.68 Pittsburgh, PA 94823 40.53 -80.23 Pittsburgh, PA 94823 40.50 -80.23
Toledo, OH 94830 41.58 -83.80 Flint, MI 14826 42.97 -83.73 Chicago, IL 94846 42.00 -87.93 Peoria, IL 14842 40.67 -89.68
Detroit, MI 94847 42.23 -83.33 Flint, MI 14826 42.97 -83.75

Figure 1 shows the surface stations (green circ les) and upper air stations (red pentagons) used in this study.

FIGURE 1. SURFACE AND UPPER AIR STATIONSMeteorological Data Processing using AERMET July 28, 2006
Rev. 05/12
For HEM-AERMOD

5Meteorological Data Processing using AERMET July 28, 2006
Rev. 05/12
For HEM-AERMOD

6 MISSING DATA

While AERMET will run if input observations are missing, the output will be incomplete, with missing data indicators in the output file. The input to and output from AERMET were examined for indications of missing input data.

The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate the convective boundary layer height and seve ral associated parameters. There were many isolated days in which the 1200 GMT sounding was missing for many of the
stations. However there also were several stations for which there were two or more consecutive days of missing 1200 GMT sounding s. The longest period of consecutive missing 1200 GMT soundings was at Dayton, Ohio with seven days of missing soundings. To minimize the impact on th e output from AERMET and not consume too much time and effort, MACTEC substituted upper air data from a representative upper air station for those periods for which the 1200 GMT sounding was missing for two or more consecutive days. Table 2 s hows the station substitutions.

TABLE 2

UPPER AIR STATIONS WITH MISSI NG 1200 GMT SOUNDINGS AND STATION
USED FOR DATA SUBSTITUTION

Upper Air Station With Missing Data WBAN Station Used for Data
Substitution WBAN

Amarillo, TX 23047 Norman, OK 03948 Athens, GA 13873 Greensboro, NC 13723 Brownsville, TX 12929 Corpus Christi, TX 12924 Centerville, AL 03881 Jackson, MS 03940 Chatham, MA 14684 Portland, ME 14764

Dayton, OH 13840 Paducah, KY 03826

Ely, NV 23154 Winnemucca, NV 24128 Grand Junction, CO 23066 Salt Lake City, UT 24127

Lake Charles, LA 03937 Slidell, LA 53813 Paducah, KY 03826 Nashville, TN 13897

Peoria, IL 14842 Omaha, NE 94918 Pittsburgh, PA 94823 Huntington, WV 03860 Salt Lake City, UT 24127 Ely, NV 23154 St. Cloud, MN 14926 Huron, SD 14936Meteorological Data Processing using AERMET July 28, 2006
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7 ANEMOMETER HEIGHTS

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights required by AERMET. These heights are to the nearest meter and were deemed appropriate for use in this application.

SURFACE CHARACTERISTICS

One of the important requirements of appl ying the AERMOD model is the specification
of surface characteristics for use in processing the meteorological data using AERMET. Application of AERMET requires specifi cation of the surfac e roughness length, the Bowen ratio (an indicator of surface moisture ), and the albedo (an indicator of surface reflectivity). These surface characteristics ar e used by AERMET to calculate the level of shear-induced mechanical turbulence genera ted by flow over the surface and for the energy balance calculations used in the de termination of the Monin-Obukhov stability parameter and the convective velocity scale.

The surface characteristics can be varied by wi nd direction sector (upwind) and by month or season. Current guidance sp ecifies defining the characteri stics for the meteorological measurement site and extending outward fr om the measurement location for three kilometers. In an application of this magnit ude, it is impractical to evaluate and vary the
surface characteristics for each of the 122 st ations, therefore, MACTEC used annual values for one sector, as shown in Table 3.

The albedo used here is repres entative of conditions for all seasons which includes winter
without continuous snow cover. Most stati ons fall into this category and continuous snow coverage for several months even at northern stations in the continental United States is unlikely.

Without a detailed and individual analysis for each of the 122 station locations and for this large an area for an entire year, a value representing an equal partition of the fluxes, or a Bowen ratio of 1.0, is the best represen tation to use in this type of analysis.

The surface roughness not only includes runways and areas between runways, but usually terminal buildings and other airport structur es. In addition, in urban environments, offairport structures often are within 3 kilometers of the measurement site. This combination of land covers suggests a valu e of 0.2 – 0.3 meters is appropriate. MACTEC used 0.25 for the roughness length.Meteorological Data Processing using AERMET July 28, 2006
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8 TABLE 3

SURFACE CHARACTERISTICS

MODIFICATIONS TO AERMET CODE

Recent use of AERMET identified two ‘bugs’ in the code that required fixing before running AERMET. The first had to do with the hourly surface observations. Incorrect missing indicators in the code for wind speed and direction were output when the data were extracted from the raw input file in Stage 1. When AERMET encountered these flags in Stage 3, AERMET simply stopped proce ssing data, resulting in files with less data than requested. MACTEC modified th e code to use correct missing indicators.
MACTEC also checked other meteorological variables to be sure the correct missing indicators were used. No other missing indicators were in error.

The second ‘bug’ was associated with th e upper air soundings. AERMET would not process an entire station’s data if the surface pressu re for the first sounding was less than 850 millibars (mb). Surface pressures less than 850 mb is not uncommon for stations at high elevations such as Denver, CO and Al buquerque, NM. MACTEC modified the code to expand the allowable surface pressures.

Both ‘bugs’ were fixed prior to runni ng AERMET with the result that AERMET successfully ran to completion for all files.

RESULTS

AERMET successfully ran for all 122 stations . While the reports and output from AERMET provide an indication of the success of the runs, MACTEC ran AERMOD with a single source and single receptor to obtai n information on the number of hours with calm winds or missing data. Table 4 shows these values by surface station.

ADDENDUM – May 2012 Since development of the original meteorologi cal data set (122 stat ions) discussed above,
additional sites have been added for Risk and Technology Review (RTR) analyses. These include meteorological data provided by state and local agencies, as well as additional sites processed by EPA risk staff to better represent dispersion conditions at Albedo 0.15

Bowen Ratio 1.0

Surface Roughness (m) 0.25Meteorological Data Processing using AERMET July 28, 2006
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9 specific facility locations. In each case, data were processed using the latest available version of AERMET and processing approach outlined on the USEPA’s SCRAM website. A listing of these additiona l sites is provided in Table 5.Meteorological Data Processing using AERMET July 28, 2006
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10 TABLE 4

PERCENT CALM AND MISSING BY STATION

WBAN Station Calms % Calm Missing % Missing Total % Total
03816 Paducah, KY 820 9.4% 66 0.75% 886 10.1% 03856 Huntsville, AL 719 8.2% 0 0.00% 719 8.2% 03860 Huntington, WV 340 3.9% 28 0.32% 368 4.2% 03870 Greenville, SC 810 9.2% 20 0.23% 830 9.5% 03927 Fort Worth, TX 508 5.8% 24 0.27% 532 6.1% 03928 Wichita, KS 215 2.5% 0 0.00% 215 2.5% 03937 Lake Charles, LA 616 7.0% 15 0.17% 631 7.2% 03940 Jackson, MS 1228 14.0% 58 0.66% 1286 14.7% 03947 Kansas City, MO 231 2.6% 0 0.00% 231 2.6% 11641 San Juan, PR 670 7.6% 11 0.13% 681 7.8% 12815 Orlando, FL 144 1.6% 0 0.00% 144 1.6% 12816 Gainesville, FL 952 10.9% 11 0.13% 963 11.0% 12842 Tampa, FL 572 6.5% 0 0.00% 572 6.5% 12844 W. Palm Beach, FL 566 6.5% 8 0.09% 574 6.6% 12912 Victoria, TX 320 3.7% 0 0.00% 320 3.7% 12916 New Orleans, LA 721 8.2% 8 0.09% 729 8.3% 12917 Port Arthur, TX 736 8.4% 10 0.11% 746 8.5% 12919 Brownsville, TX 418 4.8% 36 0.41% 454 5.2% 12921 San Antonio, TX 913 10.4% 11 0.13% 924 10.5% 12924 Corpus Christi, TX 371 4.2% 0 0.00% 371 4.2% 12960 Houston, TX 1097 12.5% 13 0.15% 1110 12.7% 13722 Raleigh, NC 810 9.2% 0 0.00% 810 9.2% 13723 Greensboro, NC 542 6.2% 24 0.27% 566 6.5% 13737 Norfolk, VA 598 6.8% 39 0.45% 637 7.3% 13739 Philadelphia, PA 154 1.8% 2 0.02% 156 1.8% 13740 Richmond, VA 605 6.9% 0 0.00% 605 6.9% 13741 Roanoke, VA 1287 14.7% 1 0.01% 1288 14.7% 13748 Wilmington, NC 976 11.1% 0 0.00% 976 11.1% 13781 Wilmington, DE 837 9.6% 0 0.00% 837 9.6% 13865 Meridian, MS 1567 17.9% 56 0.64% 1623 18.5% 13866 Charleston, WV 1257 14.3% 29 0.33% 1286 14.7% 13874 Atlanta, GA 309 3.5% 21 0.24% 330 3.8% 13876 Birmingham, AL 1228 14.0% 15 0.17% 1243 14.2% 13877 Bristol, TN 2541 29.0% 28 0.32% 2569 29.3% 13880 Charleston, SC 487 5.6% 0 0.00% 487 5.6% 13881 Charlotte, NC 985 11.2% 0 0.00% 985 11.2% 13883 Columbia, SC 1340 15.3% 20 0.23% 1360 15.5% 13889 Jacksonville, FL 1134 12.9% 12 0.14% 1146 13.1% 13893 Memphis, TN 809 9.2% 0 0.00% 809 9.2% 13894 Mobile, AL 527 6.0% 8 0.09% 535 6.1% 13895 Montgomery, AL 1027 11.7% 19 0.22% 1046 11.9% 13897 Nashville, TN 87 1.0% 0 0.00% 87 1.0% 13957 Shreveport, LA 1088 12.4% 11 0.13% 1099 12.5%Meteorological Data Processing using AERMET July 28, 2006
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11 WBAN Station Calms % Calm Missing % Missing Total % Total
13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4% 13966 Wichita Falls, TX 622 7.1% 23 0.26% 645 7.4% 13967 Oklahoma City, OK 158 1.8% 0 0.00% 158 1.8% 13970 Baton Rouge, LA 787 9.0% 9 0.10% 796 9.1% 13985 Dodge City, KS 30 0.3% 32 0.37% 62 0.7% 13994 St. Louis, MO 416 4.7% 26 0.30% 442 5.0% 13995 Springfield, MO 212 2.4% 39 0.45% 251 2.9% 14733 Buffalo, NY 181 2.1% 0 0.00% 181 2.1% 14734 Newark, NJ 197 2.2% 0 0.00% 197 2.2% 14735 Albany, NY 1217 13.9% 8 0.09% 1225 14.0% 14737 Allentown, PA 465 5.3% 9 0.10% 474 5.4% 14739 Boston, MA 36 0.4% 38 0.43% 74 0.8% 14740 Hartford, CN 204 2.3% 8 0.09% 212 2.4% 14742 Burlington, VT 155 1.8% 9 0.10% 164 1.9% 14764 Portland, ME 381 4.3% 25 0.29% 406 4.6% 14765 Providence, RI 215 2.5% 39 0.45% 254 2.9% 14771 Syracuse, NY 481 5.5% 0 0.00% 481 5.5% 14778 Williamsport, PA 1403 16.0% 24 0.27% 1427 16.3% 14820 Cleveland, OH 271 3.1% 27 0.31% 298 3.4% 14821 Columbus, OH 734 8.4% 0 0.00% 734 8.4% 14827 Fort Wayne, IN 440 5.0% 0 0.00% 440 5.0% 14836 Lansing, MI 632 7.2% 10 0.11% 642 7.3% 14837 Madison, WI 685 7.8% 30 0.34% 715 8.2% 14860 Erie, PA 286 3.3% 4 0.05% 290 3.3% 14895 Akron, OH 232 2.6% 28 0.32% 260 3.0% 14898 Green Bay, WI 414 4.7% 28 0.32% 442 5.0% 14913 Duluth, MN 336 3.8% 0 0.00% 336 3.8% 14922 Minneapolis, MN 311 3.6% 3 0.03% 314 3.6% 14923 Moline, IL 812 9.3% 24 0.27% 836 9.5% 14925 Rochester, MN 10 0.1% 0 0.00% 10 0.1% 14944 Sioux Falls, SD 492 5.6% 0 0.00% 492 5.6% 14991 Eau Claire, WI 1193 13.6% 0 0.00% 1193 13.6% 22536 Lihue, HI 6 0.1% 0 0.00% 6 0.1% 23023 Midland, TX 207 2.4% 19 0.22% 226 2.6% 23044 El Paso, TX 262 3.0% 0 0.00% 262 3.0% 23047 Amarillo, TX 113 1.3% 6 0.07% 119 1.4% 23050 Albuquerque, NM 606 6.9% 0 0.00% 606 6.9% 23062 Denver, CO 530 6.1% 0 0.00% 530 6.1% 23065 Goodland, KS 176 2.0% 41 0.47% 217 2.5% 23066 Grand Junction, CO 295 3.4% 15 0.17% 310 3.5% 23153 Tonopah, NV 341 3.9% 11 0.13% 352 4.0% 23160 Tucson, AZ 154 1.8% 24 0.27% 178 2.0% 23161 Daggett, CA 616 7.0% 11 0.13% 627 7.2% 23174 Los Angeles, CA 944 10.8% 21 0.24% 965 11.0% 23184 Prescott, AZ 376 4.3% 23 0.26% 399 4.6% 23188 San Diego, CA 473 5.4% 21 0.24% 494 5.6% 23232 Sacramento, CA 1324 15.1% 77 0.88% 1401 16.0%Meteorological Data Processing using AERMET July 28, 2006
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12 WBAN Station Calms % Calm Missing % Missing Total % Total 23234 San Francisco, CA 376 4.3% 77 0.88% 453 5.2% 24011 Bismarck, ND 676 7.7% 22 0.25% 698 8.0% 24018 Cheyenne, WY 110 1.3% 0 0.00% 110 1.3% 24033 Billings, MT 273 3.1% 25 0.29% 298 3.4% 24089 Casper, WY 138 1.6% 37 0.42% 175 2.0% 24090 Rapid City, SD 736 8.4% 33 0.38% 769 8.8% 24127 Salt Lake City, UT 581 6.6% 22 0.25% 603 6.9% 24143 Great Falls, MT 111 1.3% 26 0.30% 137 1.6% 24157 Spokane, WA 537 6.1% 36 0.41% 573 6.5% 24225 Medford, OR 1937 22.1% 0 0.00% 1937 22.1% 24229 Portland, OR 1180 13.5% 0 0.00% 1180 13.5% 24232 Salem, OR 1799 20.5% 0 0.00% 1799 20.5% 24233 Seattle, WA 66 0.8% 14 0.16% 80 0.9% 25309 Juneau, AK 1781 20.3% 6 0.07% 1787 20.4% 25624 Cold Bay, AK 119 1.4% 41 0.47% 160 1.8% 26411 Fairbanks, AK 1393 15.9% 0 0.00% 1393 15.9% 26442 Valdez, AK 661 7.5% 2 0.02% 663 7.6% 26451 Anchorage, AK 724 8.3% 0 0.00% 724 8.3% 26617 Nome, AK 358 4.1% 24 0.27% 382 4.4% 93037 Colo. Springs 195 2.2% 0 0.00% 195 2.2% 93193 Fresno, CA 1578 18.0% 76 0.87% 1654 18.9% 93721 Baltimore, MD 361 4.1% 3 0.03% 364 4.2% 93738 Sterling, VA 995 11.4% 0 0.00% 995 11.4% 93817 Evansville, IN 1434 16.4% 66 0.75% 1500 17.1% 93819 Indianapolis, IN 190 2.2% 0 0.00% 190 2.2% 93820 Lexington, KY 234 2.7% 0 0.00% 234 2.7% 93821 Louisville, KY 385 4.4% 0 0.00% 385 4.4% 93822 Springfield, IL 287 3.3% 25 0.29% 312 3.6% 94823 Pittsburgh, PA 493 5.6% 26 0.30% 519 5.9% 94830 Toledo, OH 898 10.3% 13 0.15% 911 10.4% 94846 Chicago, IL 343 3.9% 38 0.43% 381 4.3% 94847 Detroit, MI 468 5.3% 11 0.13% 479 5.5%Meteorological Data Processing using AERMET July 28, 2006
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13 TABLE 5.
ADDITIONAL METEOROLOGICAL STATIONS
ADDED FOR RTR ANALYSES

City State Year

Fairbanks AK 2001

Anaheim CA 2007

Azusa CA 2007 Banning Airport CA 2007

Burbank CA 2007

Central LA CA 2007

Costa Mesa CA 2007

Crestline CA 2007

Fontana CA 2007

Indio CA 2007

La Habra CA 2007

Lake Elsinore CA 2007

LAX CA 2007
Long Beach CA 2007 Lynnwood CA 2007

Mission Viejo CA 2007

Palm Springs CA 2007

Perris CA 2007

Pico Rivera CA 2007

Pomona CA 2007

Redlands CA 2007

Reseda CA 2007

Riverside CA 2007

San Bernardino CA 2007

Santa Clarita CA 2007

Santa Monica CA 2009 Upland CA 2007

Van Nuys Airport CA 2009

West LA CA 2007 Apalachicola FL 2005

Ft. Myers FL 2005

Gainesville FL 2005

Tallahassee FL 2005 Burlington IA 2004

Cedar Rapids IA 2004

Des Moines IA 2004 Dubuque IA 2004

Mason City IA 2004

Sioux City IA 2004Meteorological Data Processing using AERMET July 28, 2006
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14 City State Year

Waterloo IA 2004

Moline IL 2004

Iron Mountain MI 1999

Duluth MN 1999

Red Wing MN 1999

Herculaneum MO 1998

Omaha NE 2004

Sioux Falls SD 2004

Clintonville WI 1999 Eagle River WI 1999

Eau Claire WI 1999

Green Bay WI 1999 Hayward WI 1999

Juneau WI 1999

La Crosse WI 1999

Madison WI 1999

Manitowoc WI 1999

Marshfield WI 1999

Milwaukee WI 1999

Mineral Point WI 1999

Mosinee WI 1999

Oshkosh WI 1999

Rhinelander WI 1999
Sturgeon Bay WI 1999

Wausau WI 1999

Wisconsin Dells WI 1999

Wisconsin Rapids WI 1999 Parkersburg WV 20111 

### Appendix 4:

Analysis of data on short-term emission rates relative to long-term emission rates2 

### Analysis of data on short-term emission rates relative to long-term emission rates

Ted Palma Roy Smith EPA/OAQPS/SBAG

1. Introduction

1.1. The problem

The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, section 112(b)(2)) explicitly includ es acute toxicity as a listing criterion. For this reason, in addition to chronic exposures, EPA considers ac ute exposures in risk-based decision-making for the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input data on hourly meteorological c onditions (available for most ar eas of the US) and short-term emission rates of individual facilities (almost universally absent from the National Emissions Inventory (NEI), the Toxic Release Invent ory (TRI), and state emission databases).

Lacking short-term emission rates, we must estimate peak short-term rates based on annual average rates, which are available. For Ri sk and Technology Review (RTR) rulemakings, we
have assumed that the 1-hour emission rate for each facility could exceed the annual average hourly emission rate by as much as tenfold, and fu rther assumed that this tenfold emission spike could coincide with worst-case meteorological conditions and the presen ce of a human receptor at the facility boundary, as a means of screen ing for potentially signifi cant acute exposures.

In a consultation on the “RTR Assessment Plan”, a panel of the EPA’s Science Advisory Board (SAB), several reviewers questioned the appropri ateness of the factor of ten; some even suggested that this tenfold a ssumption may underestimate actual maximum short-term emissions for some facilities, and thereby also unde restimate maximum acute risks. The SAB recommended an analysis of available short-term emissions data for HAP to test this assumption. This analysis responds to that SAB recommendation and attempts to test th e protectiveness of the tenfold assumption using a database of “event emissions” collected from facilities in the Houston-Galveston area, to compare events repres entative of HAP releases to long-term release rates. We welcome comments from the public on the methods used and the conclusions reached by this analysis.

2. Methods

2.1. Texas Commission on Environmental Qu ality event emissions database

The Texas Commission on Environmental Quality (TCEQ) collects emissions data using online reporting required of any facility releasing 100 pounds or more of a listed chemical (primarily ozone-forming VOCs) during a non-r outine event. The TCEQ data are intended to improve the state’s knowledge of how short-term releases affect tropospheric ozone levels in that area. The database we utilized in our analysis was a subset of the TCEQ data covering emission events that3 occurred in an eight-county area in eastern Texas during a 756-day period between January 31, 2003 and February 25, 2005.

The complete emissions event data were obtained in April 2007 from Cynthia Folsom Murphy, a research scientists with the University of Texas at Austin (UTA) Center for Energy and
Environmental Resources. The da ta were provided in four Excel spreadsheets generated from an original MS Access file. We used these Excel files to recons truct a MS Access database in order to facilitate selection of a representati ve subset of record s for this analysis.

Although some of the released s ubstances were HAPs, this was incidental to the database’s primary purpose of enhancing the TCEQ’s knowledge of photochemical activity. Thus, more
than 80% of the released ma ss was ethene and propene, neither of which are HAPs. The
database included release events caused by accidents, equipment fa ilures, maintenance, startup, and shutdown. It also contained facility names, information on amounts of individual compounds released. To provide a basis for co mparing the event releases with “typical” emissions, the UTA staff included total VOC emissions data for each facility for calendar year 2004, obtained from the EPA Toxic Release Inventory (TRI). The database did not contain any records for facilities that did not experien ce any reportable events during this period.

2.2. Data filtering

Because the event release data were intended for modeling short-term releases of ozoneproducing VOCs, the database includes releas es from accidents (which are regulated under section 112(r) of the CAA and are therefore not considered in residual risk assessments) and releases of light hydrocarbon compounds that ar e not HAPs and are much more volatile than most HAPs. This intent of this analysis, on the other hand, was to evaluate short-term releases of HAPs due to normal process variability or sche duled startups, shutdowns, and maintenance, relative to long-term release rates. Because the full emi ssion events database was not representative of likely HAP em issions normally considered under the residual risk program, we filtered the release data as follows in an attempt to improve its representativeness:

1. Hydrocarbons of C5 or less were droppe d, except that all HAPs (including non-VOCs) were retained regardless of molecular structure;

2. Accidental releases were dropped, but all others (including st artup, shutdown, and maintenance) were retained;

3. Only facilities whose long-term VOC releases exceeded 0.068 tons per day (25 tons per year) were retained, to approximate the populat ion of facilities likely to be subject to residual risk standards (i .e., major facilities);

4. A few release records had to be dropped because their facility number s did not link to any facility in the database;

5. A few facilities had to be dropped because the database did not include their 2004 TRI
VOC release information.

2.3. Analysis4 Annual VOC emissions and emission event release data were both converted to lb/hr. In order to conform to our atmospheric dispersion models, which estimate ambient concentrations for periods of 1 hour or more, amounts released duri ng events shorter than 1 hour were assigned to the whole hour. For example, a release of 100 lb in ten minutes was converted to 100 lb/hr. Events longer than 1 hour were converted norm ally, e.g., a release of 100 lb in 120 minutes was
converted to 50 lb/hr. The ev ent release rates for individual compounds we re summed, yielding
a total release rate for each event. This to tal release rate for each event was divided by the annual VOC release rate for the facility to derive the ratio of peak-to-mean emission rate for the

event.

3. Results and Discussion

3.1. Database filtering

The original database contained 505 individual contaminants, including multiple redundancies.
These redundancies did not affect this analysis, so we did not re solve them. After filtering out light, non-HAP, VOCs, 317 contaminants remained (Table 1).

The database contained release records for 150 unique facilities. Of these, 48 facilities (Table 2) were major VOC emitters that reported releases of at least one of the contaminants in Table 1.

The database contained 3641 individual release events reported by the original 150 facilities. Of these, 319 events involved a Table 1 contaminant released by a Table 2 facility during startup, shutdown, or maintenance. For evaluating short- term releases for residual risk assessments, these 319 events comprise the most representative subset of the full database.

3.2. Descriptive statistics

For this subset of emission events, ratios of even t release rate to long-te rm release rate varied
from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99 th percentile ratio was 9 (i.e., an event release rate nine times the long-term average). Only 3 ratios exceeded our default assumption of 10, and of these only one exceeded 11. The full cumulative probability density of the ratios is shown in Figure 1.

Figure 2 shows the relationship between ratio and event duration. As expected, the ratio declined as duration increased. Only 18 events lasted less than 2 hour s, but these events produced the three highest ratios. Figure 3 is a similar ratio vs. duration plot, but with duration as a percentage of total time. Only 35 events exceeded 1% of the to tal period covered by the database. Figure 4 shows the relationship betwee n ratio and total amount released, and suggests that the highest ratios were pr oduced by facilities whose routine VOC emissions were relatively small. Thus, the events themselves also tended to be relatively small in absolute terms.

3.3. Discussion

These results suggest that the te nfold ratio assumption for short-te rm releases is protective, and that the facilities for which it may underestimate event releases may tend to be smaller emitters.

However, this analysis is limited in the followi ng ways by the nature of the database and the filtering that we applied:5 1. The only long-term release data available fr om the database were total VOC emissions for 2004. Ideally, we would have preferred to have routine release rates for each individual contaminant. However, retrievi ng these data from other sources and linking
them to this database was not feasible.

2. Removing VOCs that are not representative of HAPs, and comparing the releases against all VOCs, would tend to underestimate the true ra tios. This effect could be quantitatively large.

3. Retaining HAPs that are not VOCs (such as toxic metals), and including them in the total to be compared against all VOCs, would tend to overestimate the true ratios. The size of this effect is not known, but seems li kely to be less than for (2) above.

4. The database contains only facilities that had at least one re lease event during the reporting period. The number of facilities in the statistical popul ation that did not experience an event is not known. The lack of data for these facilities (whose ratios in this analysis would have been zero) would cause the descript ive statistics to be skewed
toward an overestimate. The size of this effect is unknown.

Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD 2-Methyloctane No 3221-61-2 90008 2-Methylpentane No 107-83-5 43229 2-methylhexane No 591-76-4 43263 2-Methylpentane No 107-83-5 43229 2,2,3-Trimethylpentane No 564-02-3 2,2,4-Trimethylpentane Yes 540-84-1 43250 dimethyl butane No 75-83-2 43291 2,3-Dimethylbutane No 79-29-8 43276 2,3,4-Trimethylpentane No 565-75-3 43252 2,3-Dimethylbutane No 79-29-8 43276 2,4-Dimethylpentane No 108-08-7 43247 2-methylheptane No 592-27-8 43296 2-methylhexane No 591-76-4 43263 2-Methylpentane No 107-83-5 43229 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 43230 3-Methylheptane No 589-81-1 43253 3-Methylhexane No 589-34-4 43295 3-Methylpentane No 96-14-0 432306 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD
Acetaldehyde Yes 75-07-0 43503

Acetic Acid No 64-19-7 43404

Acetonitrile Yes 75-05-8 70016
Acetophenone Yes 98-86-2

Acrolein Yes 107-02-8 43505 Acrylic acid Yes 79-10-7 43407
Acrylonitrile Yes 107-13-1 43704 alkylphenol No none
Benzene Yes 71-43-2 45201
Benzo[a]anthracene Yes 56-55-3 46716
Benzo[a]pyrene Yes 50-32-8 46719 Benzo[b]fluoranthene Yes 205-99-2 46717
Biphenyl Yes 92-52-4 45226

Butanol No 35296-72-1
Butyl Acrylate No 141-32-2 43440 t-Butyl Alcohol No 75-65-0 43309
butylcyclohexane No 1678-93-9 90101 Butyraldehyde No 123-72-8 43510

C9 Aromatics No none Naphthalene Yes 91-20-3 46701

Nonane No 111-84-2 43235

C9+ No none
Carbon tetrachloride Yes 56-23-5 43804 Carbonyl Sulfide Yes 463-58-1 43933

Chloral No 75-87-6

Trichloromethane Yes 67-66-3 43803

Chlorothalonil No 1897-45-6

Petroleum No 8002-05-9

Petroleum No 8002-05-9

Cumene Yes 98-82-8 45210 Cyclohexane No 110-82-7 43248 Cyclohexanol No 108-93-0 43317 Cyclohexanone No 108-94-1 43561 Cyclohexanone No 108-94-1 43561

Decane No 124-18-5 43238

Decane No 124-18-5 43238
1,2-Dichloroethane No 107-06-2 43815 Diethylbenzene (mixture) No 25340-17-4 45106
Methyl Ether No 115-10-6 43350 Dimethylcyclohexane No 27195-67-1 98059
Dimethylcyclopentane No 28729-52-4 900647 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD
Dimethylcyclopentane No 28729-52-4 90064 Dimethyl formamide Yes 68-12-2 43450
Dimethylhexane No 28777-67-5 90067 Dimethyl pentane No 38815-29-1 90063
Epichlorohydrin Yes 106-89-8 43863 Ethyl Alcohol No 64-17-5 43302
Ethyl Acrylate Yes 140-88-5 43438 Ethyl Alcohol No 64-17-5 43302
Ethyl Benzene Yes 100-41-4 45203 Ethyl Chloride Yes 75-00-3 43812
Ethylcyclohexane No 1678-91-7 43288 ethylacetylene No 107-00-6 43281
Ethyl Benzene Yes 100-41-4 45203 Ethylene Oxide Yes 75-21-8 43601
ethylmethylbenzene No 25550-14-5 45104 formaldehyde Yes 50-00-0 43502

Furfural No 98-01-1 45503 straight-run mi ddle distillate No 64741-44-2

Gasoline No 86290-81-5

Gasoline No 86290-81-5 Heavy Olefins No none n-Heptane No 142-82-5 43232
n-Heptane No 142-82-5 43232 Heptylene No 25339-56-4

hexane Yes 110-54-3 43231

hexane Yes 110-54-3 43231 2-Methylpentane No 107-83-5 43229

hexane Yes 110-54-3 43231

Hexene No 25264-93-1 43289 Indeno[1,2,3-cd]pyrene Yes 193-39-5 46720
Isobutyraldehyde No 78-84-2 43511 2-Methyl-1-propanol No 78-83-1 43306 2-Methyl-1-propanol No 78-83-1 43306 Isobutyraldehyde No 78-84-2 43511 Isoheptanes (mixture) No 31394-54-4 43106 2-Methylpentane No 107-83-5 43229 2,2,4-Trimethylpentane No 540-84-1 43250 2,2,4-Trimethylpentane No 540-84-1 43250
Isopar E No
Isoprene No 78-79-5 43243
2-Propanol No 67-63-0 433048 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD
2-Propanol No 67-63-0 43304

Cumene Yes 98-82-8 45210
Isopropylcyclohexane No 696-29-7 90128 Diisopropyl ether No 108-20-3 85005

Kerosene No 64742-81-0 Methyl ethyl ketone No 78-93-3 43552
Methyl isobutenyl ketone Yes 141-79-7

Methanol Yes 67-56-1 43301
Methyl Acetylene No 74-99-7 43209

Cresol Yes 1319-77-3 45605 Methyl Chloride Yes 74-87-3 43801 methyl cyclohexane No 108-87-2 43261
Methyl ethyl ketone No 78-93-3 43552

Iodomethane No 74-88-4 86025
Methyl Mercaptan No 74-93-1 43901 methyl cyclohexane No 108-87-2 43261
Methylcyclopentane No 96-37-7 43262 2-Methyldecane No 6975-98-0 98155
Methylheptane No 50985-84-7 90045 2-methylheptane No 592-27-8 43296 2-Methyl nonane No 871-83-0 90047 Tert-butyl methyl ether No 1634-04-4 43376
meta-xylene No 108-38-3 45205

Nonane No 111-84-2 43235
Naphtha No 8030-30-6 45101
Naphthalene Yes 91-20-3 46701
Naphtha No 8030-30-6 45101
Naphthalene No 91-20-3 46701
Butyl acetate No 123-86-4 43435 Butyraldehyde No 123-72-8 43510

Nonane No 111-84-2 43235

Nonane No 111-84-2 43235

Octadecene No 27070-58-2

n-Octane No 111-65-9 43233 Octene (mixed isomers) No 25377-83-7 ortho-xylene No 95-47-6 45204

Parathion Yes 56-38-2
4-Aminohippuric Acid No 61-78-9

Phenol Yes 108-95-2 45300

Silicone No 63148-62-9
Naphtha No 8030-30-6 451019 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD
Naphtha No 8030-30-6 45101 Polyethylene No 9002-88-4 Poly(Isobutylene) No 9003-27-4 Chloromethyl pivalate No 18997-19-8 Process fuel gas No none Propionic Acid No 79-09-4 43405
Propylene oxide No 75-56-9 43602 para-xylene No 106-42-3 45206 Styrene Yes 100-42-5 45220

Sulfolane No 126-33-0
t-Butyl Alcohol No 75-65-0 43309 t-Butyl Alcohol No 75-65-0 43309
tert-butyl hydroperoxide No 75-91-2

Toluene Yes 108-88-3 45202 Aqualyte(TM), LSC cocktail No 25551-13-7 45107 1,3,4-Trimethylbenzene No 95-63-6 45208
trimethylcyclopentane No 30498-64-7 98058 trimethylpentane No 29222-48-8 90092

Undecane No 1120-21-4 43241 Vinyl acetate Yes 108-05-4 43453
Vinyl acetate Yes 108-05-4 43453 Vinyl chloride Yes 75-01-4 43860
vinyl resin No none Vinylcyclohexane No 695-12-5
xylenes Yes 1330-20-7 45102 xylenes Yes 1330-20-7 45102
meta-xylene Yes 108-38-3 45205 ortho-xylene Yes 95-47-6 45204
para-xylene Yes 106-42-3 45206
Mineral spirits No 64475-85-0 43118
Propylene glycol No 57-55-6 43369 Vinyl chloride Yes 75-01-4 43860

1-Decene No 872-05-9 90014 2-Ethyl-1-hexanol No 104-76-7 43318 2-Pyrrolidone No 616-45-5

Aromatic No none
Decene No 25339-53-1 90014 2-N,N-Dibutylaminoethanol No 102-81-8 86007
Diisopropanolamine No 110-97-4 86004 N,N-Dimethylethanolamine No 108-01-0 84004

trifluoroethane No 27987-06-010 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD 2,2'-Oxybisethanol No 111-46-6 43367 Hydrocarbons No none Methyl Formate No 107-31-3 43430 Isopropylamine No 75-31-0 86014

n-Butanol No 71-36-3 43305 Polypropylene glycol ether No N-Vinyl-2-Pyrrolidinone No 88-12-0 1,1-Di(t-Amylperoxy) Cyclohexane No 15667-10-4 1,2,3-Trimethyl-4-ethylbenzene No none 2-Methyldecane No 6975-98-0 98155 2-methylheptane No 592-27-8 43296 2-Methyl nonane No 871-83-0 90047 2,5-Dimethylhexane-2,5- dihydroperoxide No 3025-88-5
Butyl ether No 142-96-1 43372
1,2-Dichloroethane Yes 107-06-2 43815 Hydrindene No 496-11-7 98044
Methylheptane No 50985-84-7 90045 methyl methacrylate No 80-62-6 43441
Naphtha No 8030-30-6 45101

hexane Yes 110-54-3 43231
tert-amyl hydroperoxide No 3425-61-4 1,3,4-Trimethylbenzene No 95-63-6 45208

n-Butanol No 71-36-3 43305 2-Butoxy ethanol Yes 111-76-2 43308

hexane Yes 110-54-3 43231 cycloheptane No 291-64-5 43115
n-Heptane No 142-82-5 43232

n-Octane No 111-65-9 43233 Hexyl Carbitol No 112-59-4

Nonene No 27215-95-8 Silane, ethenyltrimethoxy No 2768-02-7 tetrahydrofuran No 109-99-9 70014
Vinyl chloride Yes 75-01-4 43860 Methyl Formate No 107-31-3 43430
Phenyl ether No 101-84-8 phosgene Yes 75-44-5
1,2-Dichloroethane No 107-06-2 43815 2-Butoxy ethanol Yes 111-76-2 43308

Gasoline No 86290-81-5

1-Tridecanol No 112-70-911 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD
1,2,4-Trichlorobenzene Yes 120-82-1 45208 2-(2-Butoxyethoxy)ethanol Yes 112-34-5 43312 2,3,4-trihydroxybenzophenone
Ester No 1143-72-2
Methyl n-amyl ketone No 110-43-0 43562 4,4-Cyclohexylidenebis[phenol] No 843-55-0

Anisole No 100-66-3 2-Butoxy ethanol Yes 111-76-2 43308 Cresol-Formaldehyde novolac
Resin No proprietary

Decane No 124-18-5 43238 gamma-Butyrolactone No 96-48-0
Dimethyl pentane No 38815-29-1 90063 Dodecyl Benzenesulfonic Acid No 27176-87-0

Ethanol Amine No 141-43-5 43777 ethyl lactate No 687-47-8
Hexamethyldisilazane No 999-97-3 Methyl ethyl ketone No 78-93-3 43552

Cresol Yes 1319-77-3 45605 Naphthalene Sulfonic Acid Resin No Naphthalene Sulfonic Acid Resin No

n-Butanol No 71-36-3 43305

Decane No 124-18-5 43238 1-Methyl-2-pyrrolidinone No 872-50-4 70008
Pentyl Ester Acetic Acid No Phenol Formaldehyde Resin,
Novolac No Phenol Formaldehyde Resin,
Novolac No Propylene Glycol Monomethyl
Ether No 107-98-2 70011 Pyrocatechol No 120-80-9

Carbon Disulfide Yes 75-15-0 43934

Hexene No 592-41-6 43245

VOC No none Methacrylic acid No 79-41-4 84009 Methyl 3-hydroxybutyrate No 1487-49-6 t-Butyl Alcohol No 75-65-0 43309
methyl valeraldehyde No 123-15-9 Butyl Methacrylate No 97-88-1 85008
dipropyl ether No 111-43-3
n-Propanol No 71-23-8 43303
Propyl propionate No 106-36-5 8605212 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD 1,2-Epoxybutane Yes 106-88-7 Methylamine No 74-89-5 1,1-Dimethylcyclohexane No 590-66-9 1,1-Dimethylcyclopentane No 1638-26-2 2-Methylpentane No 107-83-5 43229 dimethyl butane No 75-83-2 43291 2,3,3-Trimethylpentane No 560-21-4 2,3-Dimethylhexane No 584-94-1 2,3-Dimethylpentane No 565-59-3 2,4-Dimethylhexane No 589-43-5 2,5-Dimethyl-hexane No 592-13-2 2-Butoxy ethanol Yes 111-76-2 43308
2-mercaptoethanol No 60-24-2
Bisphenol A No 80-05-7
straight-run mi ddle distillate No 64741-44-2 4-Vinylcyclohexene No 100-40-3
straight-run mi ddle distillate No 64741-44-2 Allyl alcohol No 107-18-6
xylenes Yes 1330-20-7 45102
Naphthalene Yes 91-20-3 46701 3-Methylethylcyclohexane No

VOC No none
Gasoline No 86290-81-5 Butyl ether No 142-96-1
dimethyl butane No 75-83-2

Dodecene No 25378-22-7 Styrene Yes 100-42-5 45220 tetrahydrofuran No 109-99-9 70014

hexane Yes 110-54-3 43231
2-Propanol No 67-63-0 43304 liquified petroleum gas No 68476-85-7 Methyl acetylene propadiene No methyl isobutyl ketone Yes 108-10-1 Methyl n-amyl ketone No 110-43-0 43562
Methylpentane No 43133-95-5 Tert-butyl methyl ether Yes 1634-04-4 43376

Toluene Yes 108-88-3 45202

Mineral oil No 8012-95-1

Gasoline No 86290-81-5 2,2-Dimethylpropane No 463-82-1 43222
n-propylbenzene No 103-65-113 Table 1. Event emissions in the Houston-Galveston area. Representative contaminants included in the analysis, selected because they are either HAPs or VOCs with more than 5 carbon atoms. (These data were retrieved directly from t he original database, which included multiple redundancies that did not affect the analysis and were left intact.)

Contaminant HAP CAS SAROAD
propylcyclohexane No 1678-92-8

n-Octane No 111-65-9 43233
ortho-xylene No 95-47-6 45204

Gasoline No 86290-81-5
propylenimine No 75-55-8

Gasoline No 86290-81-5

Technical White Oil No Total Alkylate - non-speciated No Trichloroethylene Yes 79-01-6 Di(2-ethylhexyl) peroxydicarbonate No 16111-62-9
trimethylcyclopentane No 30498-64-7 98058

Ultraformate No 4-Vinylcyclohexene No 100-40-314 Table 2. Event emissions in the Houston-Galveston area. Major emitters reporting at least one release event of a representative substance.

Company Name 2004 VOC Emission Rate (lb/h)
ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88

BALL METAL BEVERAGE CONTAINER CONROE
FACILITY 24.18

BASF FREEPORT SITE 46.47

BELVIEU ENVIRONMENTAL FUELS 112.3

BOC GROUP CLEAR LAKE BOC GASES PLANT 9.52

BP AMOCO CHEMICAL CHOCOLATE BAYOU PLANT 130.4

BP AMOCO CHEMICAL PASADENA PLANT 36.92

BP AMOCO POLYMERS 57.18

BP PRODUCTS NORTH AMERICA TEXAS CITY 737.4

BP TEXAS CITY CHEMICAL PLANT B 112.2

CELANESE BAY CITY PLANT 17.12

CELANESE CLEAR LAKE PLANT 53.11

CELANESE PASADENA PLANT 5.934

CHEVRON PHILLIPS CEDAR BAYOU PLANT 105.3

CHEVRON PHILLIPS CHEMICAL SWEENY COMPLEX 106.7

CHEVRON PHILLIPS HOUSTON CHEMICAL COMPLEX 215.7

CROWN BEVERAGE PACKAGING 18.05

CROWN CENTRAL PETROLEUM PASADENA PLANT 114.3

CROWN CORK & SEAL 18.10
DEER PARK LIQUID STORAGE TERMINAL 124.8

DOW CHEMICAL LA PORTE SITE 5.902

DOW TEXAS OPERATIONS FREEPORT 203.2

E I DUPONT DE NEMOURS AND COMPANY - LA
PORTE PLANT 51.30

EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4
EQUISTAR CHEMICAL S CHOCOLATE BAYOU
COMPLEX 84.87

EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97

EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73

EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL
PLANT 313.7

EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS
PLANT 40.64

GOODYEAR HOUSTON CHEMICAL PLANT 85.68

ISP TECHNOLOGIES TEXAS CITY PLANT 22.12

KANEKA TEXAS CORPORATION 20.55
KINDER MORGAN LIQUID TERMINALS PASADENA 913.9
KINDER MORGAN LIQUIDS TERMINALS 132.7

LBC HOUSTON BAYPORT TERMINAL 12.83

LYONDELL CHEMICAL BAYPORT PLANT 30.04

LYONDELL CHEMICAL CHANNELVIEW 74.15

MARATHON ASHLAND PETROLEUM TEXAS CITY
REFINERY 111.8

MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29

MORGANS POINT PLANT 31.03

PASADENA PLANT 13.4015 Table 2. Event emissions in the Houston-Galveston area. Major emitters reporting at least one release event of a representative substance.

Company Name 2004 VOC Emission Rate (lb/h)
SHELL OIL DEER PARK 405.2

SOLUTIA CHOCOLATE BAYOU PLANT 53.09

STOLTHAVEN HOUSTON TERMINAL 7.347

SWEENY COMPLEX 157.1

UNION CARBIDE TEXAS CITY OPERATIONS 174.4

VALERO REFINING TEXAS CITY 260.1

WHARTON GAS PLANT 7.552

Table 3. Frequency distribution for ratio of event emission rate to long-term emission rate

Bin Frequency Cumulative Frequency

1.00E-08 0 0

3.16E-08 0 0

1.00E-07 2 2

3.16E-07 1 3

1.00E-06 0 3

3.16E-06 2 5

1.00E-05 1 6

3.16E-05 2 8

1.00E-04 5 13

3.16E-04 9 22

1.00E-03 15 37

3.16E-03 28 65

1.00E-02 33 98

3.16E-02 41 139

1.00E-01 59 198

3.16E-01 38 236

1.00E+00 33 269

3.16E+00 31 300

1.00E+01 16 316

3.16E+01 2 318

1.00E+02 1 319

3.16E+02 0 31916 Table 4. Statistics for ratio of event emission rate to long-term emission rate Statistic for Ratio Value
Median 0.043923
75th %ile 0.342655
90th %ile 2.204754
95th %ile 3.344422
96th %ile 3.400832
97th %ile 3.8126
98th %ile 4.790098
99th %ile 8.973897

Max 74.37138 Average 0.815352

Figure 1. Cumulative probability density for ratio of event to routine emission rates.

Cumulative probability of event ratios

0 50 100 150 200 250 300 350

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04

Ratio of event emission rate to long-term emission raten17 Figure 2. Relationship between ratio of ev ent to duration emissi on rate and emission
duration.

Event ratio vs. duration

1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

1 10 100 1,000 10,000 100,000
Event duration (min)Ratio -- event emission rate to long-term emission rate

Figure 3. Relationship between ratio of event to duration emission rate and emission duration, as percentage of total time.

Event ratio vs. duration

1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

0% 1% 2% 3% 4% 5% 6% 7% 8%
Event duration (as % of total time)Ratio -- event emission rate to long-term emission rate18 Figure 4. Relationship between ratio of event to duration emission rate and total amount emitted during the event.

Event ratio vs. 2004 VOC releases -- by event

1.E-08 1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02

1 10 100 1,000
Long-term VOC releases (lb/hr)Ratio -- event emission rate to long-term emission rateAppendix 5 Detailed Risk Modeling ResultsSource CategoryFacility NEI IDFacility NameAddressCity StateSteel Pickling NEI10956U.S. DEPARTMENT OF ENERGY, Y-12PLANT BEAR CREEK ROADOAK RIDGE TNSteel Pickling NEI1120NUCOR STEEL ARKANSAS7301 EAST COUNTYROAD 142BLYTHEVILLE ARSteel Pickling NEI11468WHEELING-PITTSBURGH STEEL CORP.YORKVILLE PLANT 219 PUBLIC RD.YORKVILLE OHSteel Pickling NEI11539VALLEY CITY STEEL LLC. 804 STEEL DR.VALLEY CITY OHSteel Pickling NEI11604AK STEEL CORPORATION1801 CRAWFORDSTREETMIDDLETOWN OHSteel Pickling NEI11747STEEL WAREHOUSE COMPANY, INC.2722 WEST TUCKERDRIVESOUTH BEND INSteel Pickling NEI12964NATIONAL STANDARD CO 3602 N PERKINS RDSTILLWATER OKSteel Pickling NEI13296WESTERN TUBE & CONDUIT CORP 2001 E DOMINGUEZ STLONG BEACH CASteel Pickling NEI19881USS-POSCO INDUSTRIES 900 LOVERIDGE ROADPITTSBURG CASteel Pickling NEI22744CALIFORNIA STEEL INDUSTRIES IN14000 SAN BERNARDINOAVFONTANA CASteel Pickling NEI31812U S STEEL CO GARY WORKSONE NORTHBROADWAY, MS-70GARY INSteel Pickling NEI32262NUCOR STEEL RR 2CRAWFORDSVILLE INSteel Pickling NEI32327BETHLEHEM STEEL CORP. - BURNSHARBOR U.S. 12 & S.R. 149BURNS HARBOR INSteel Pickling NEI32334NATIONAL STEEL CORP 6300 US 12, MS AE-1PORTAGE INSteel Pickling NEI32349WORTHINGTON STEEL 100 WORTHINGTON DR.PORTER INSteel Pickling NEI32409I/N TEK I/N KOTE COMBINED 30755 EDISON ROADNEW CARLISLE INSteel Pickling NEI33221BETHLEHEM STEEL5111 NORTH POINTBOULEVARDSPARROWS POINT MDSteel Pickling NEI34007NATIONAL STEEL CORP 1 QUALITY DRECORSE MISteel Pickling NEI40734WHEELING PGH STEEL/ALLENPORT1 WHEELINGPITTSBURGH STEEL DRALLENPORT PASteel Pickling NEI42439ISG WEIRTON INC.400 THREE SPRINGSDRIVEWEIRTON WVSteel Pickling NEI43399CARGILL STEEL & WIRE 600 COWAN STREETNASHVILLE TNSteel Pickling NEI51000AMERICAN SPRING WIRE CORP 1775 AMERICAN WAYKANKAKEE ILSteel Pickling NEI52149HEIDTMAN STEEL PRODUCTS INC10 NORTHGATEINDUSTRIALGRANITE CITY ILSteel Pickling NEI52655KEYSTONE STEEL & WIRE CO 7000 SW ADAMS STPEORIA ILSteel Pickling NEI52751ISG HENNEPIN INC 10726 ISG DrHENNEPIN ILSteel Pickling NEI53571STERLING STEEL COMPANY, LLC 121 WALLACE STSTERLING ILTable 1 - Facility Identification Information1 of 2Source CategoryFacility NEI IDFacility NameAddressCity StateTable 1 - Facility Identification InformationSteel Pickling NEI55848NATIONAL STEEL CORP - GRANITE CITYDIV RT 203 & 20TH STGRANITE CITY ILSteel Pickling NEI7060AK STEEL CORP/BUTLER WORKSONE ARMCO DRIVE POBOX 232BUTLER PASteel Pickling NEI7194MT JOY WIRE CORP/MT JOY 1000 E MAIN STMOUNT JOY PASteel Pickling NEI8453UNITED STATES STEEL CORPORATIONFAIRFIELD PIPE MIL 5700 VALLEY ROADFAIRFIELD ALSteel Pickling NEI862WHEATLAND TUBE COMPANY - OMEGADIVISION 8200 FRAZIER PIKELITTLE ROCK ARSteel Pickling NEIFL0030004INSTEEL WIRE PRODUCTS INC.S OF US 90, E OFSANDERSONSANDERSON FLSteel Pickling NEIIL0318AAIACME STEEL CO 13500 S PERRY AVERIVERDALE ILSteel Pickling NEIIL1199213Granite City Pickling & Warehousing 1407 State StGranite City ILSteel Pickling NEIIN00384NATIONAL PROCESSING COMPANY4506 WEST CLINEAVENUEEAST CHICAGO INSteel Pickling NEIIN127192MAGNETICS INTERNATIONAL, INC.1111 NORTH STATEROAD 149BURNS HARBOR INSteel Pickling NEIKY021215ATR WIRE & CABLE CO INC1857 S DANVILLEBYPASSDANVILLE KYSteel Pickling NEIMIA8640SEVERSTAL NORTH AMERICA, INC. 3001 MILLER RDDEARBORN MISteel Pickling NEIMIA9960SAMUEL-WHITTAR INC 20001 SHERWOOD AVEDETROIT MISteel Pickling NEIMIB5417NATIONAL STANDARD COMPANY 1631 LAKE STNILES MISteel Pickling NEIMIT$7446DOUBLE EAGLE STEEL COATING CO. 3000 MILLER RD.DEARBORN MISteel Pickling NEIMIT$7474NATIONAL GALVANIZING L.P. 1500 TELB RD.MONROE MISteel Pickling NEIOH0641090WHEELING-PITTSBURGH STEELCORPORATION - STEUBENVILSOUTH 3RD STREETEXTENSIONSTEUBENVILLE OHSteel Pickling NEIOHT$6096SAMUEL STEEL PICKLING CO.CLEVELAND PLANT 4589 JOHNSTON PKY.CLEVELAND OHSteel Pickling NEIOHT$6130AMERICAN SPRING WIRE CORP. 26300 MILES RD.BEDFORD HEIGHTSOHSteel Pickling NEIOHT$6245THOMAS STEEL STRIP CORP. DELAWARE AVE. N.W.WARREN OHSteel Pickling NEIOHT$6432WORTHINGTON STEEL CO. 350 LAWTON AVE.MONROE OHSteel Pickling NEIPA08519-1DUFERCO FARRELL CORP/FARRELL PLT 15 ROEMER BLVDFARRELL PASteel Pickling NEIPA2156US STEEL CORPORATION - IRVIN PLANTCAMP HOLLOW RD, BOX878DRAVOSBURG PASteel Pickling NEIRAL730437BAILEY-PVS OXIDES L.L.C. TIN MILL ROADFAIRFIELD ALSteel Pickling NEISC015366NUCOR STEEL BERKELEY1455 HAGAN AVEHUGER SCSteel Pickling NEITNT$4779INSTEEL WIRE PRODS.600 RAPPAHANNOCKRD.GALLATIN TN2 of 2CancerMIRCancerIncidenceNoncancerMax HI Target OrganCancerMIRNoncancerMax HI Target OrganNEI10956 0.00E+00 0.00E+00 9.54E-02 Respiratory1.00E-052.42E-01 Respiratory 39%NEI1120 0.00E+00 0.00E+00 1.17E-03 Respiratory 1.93E-08 7.78E-03 Neurological 15%NEI11468 0.00E+00 0.00E+00 6.75E-04 Respiratory 5.26E-08 6.75E-04 Respiratory 100%NEI11539 0.00E+00 0.00E+00 8.97E-03 Respiratory 0.00E+00 8.97E-03 Respiratory 100%NEI11604 0.00E+00 0.00E+00 1.64E-03 Respiratory8.00E-059.78E-02 Neurological 2%NEI11747 0.00E+00 0.00E+00 6.39E-03 Respiratory 2.15E-07 3.20E-01 Neurological 2%NEI12964 0.00E+00 0.00E+00 1.24E-03 Respiratory 0.00E+00 3.13E-01 Neurological 0%NEI13296 0.00E+00 0.00E+00 6.14E-04 Respiratory 3.08E-08 7.68E-02 Respiratory 1%NEI19881 0.00E+00 0.00E+00 1.13E-04 Respiratory 5.92E-08 3.94E-03 Respiratory 3%NEI22744 0.00E+00 0.00E+00 3.18E-02 Respiratory 2.74E-08 4.31E-02 Respiratory 74%NEI31812 0.00E+00 0.00E+00 8.60E-03 Respiratory 1.81E-07 4.23E-02 Neurological 20%NEI32262 0.00E+00 0.00E+00 2.68E-03 Respiratory7.00E-05 3.51E+00Neurological 0%NEI32327 0.00E+00 0.00E+00 4.60E-04 Respiratory1.00E-04 1.58E+00Neurological 0%NEI32334 0.00E+00 0.00E+00 7.81E-03 Respiratory 1.17E-07 7.81E-03 Respiratory 100%NEI32349 0.00E+00 0.00E+00 1.70E-03 Respiratory 0.00E+00 1.70E-03 Respiratory 100%NEI32409 0.00E+00 0.00E+00 2.13E-03 Respiratory 3.83E-07 1.29E-02 Respiratory 17%NEI33221 0.00E+00 0.00E+00 3.01E-05 Respiratory1.00E-05 3.18E+00Neurological 0%NEI34007 0.00E+00 0.00E+00 6.95E-04 Respiratory8.00E-05 1.14E+00Neurological 0%NEI40734 0.00E+00 0.00E+00 3.37E-03 Respiratory 2.83E-07 4.37E-03 Respiratory 77%NEI42439 0.00E+00 0.00E+00 4.19E-02 Respiratory 5.91E-07 2.35E-01 Neurological 18%NEI43399 0.00E+00 0.00E+00 1.61E-04 Respiratory 2.07E-08 2.30E-04 Respiratory 70%NEI51000 0.00E+00 0.00E+00 2.20E-03 Respiratory 5.16E-08 1.07E-02 Respiratory 21%NEI52149 0.00E+00 0.00E+00 4.19E-02 Respiratory 4.84E-09 4.19E-02 Respiratory 100%NEI52655 0.00E+00 0.00E+00 9.20E-04 Respiratory7.65E-06 1.85E+00Neurological 0%NEI52751 0.00E+00 0.00E+00 8.32E-03 Respiratory 2.59E-08 8.74E-03 Respiratory 95%NEI55848 0.00E+00 0.00E+00 7.19E-03 Respiratory5.97E-056.11E-01 Neurological 1%NEI7060 0.00E+00 0.00E+00 2.79E-03 Respiratory2.00E-052.05E-02 Respiratory 14%NEI7194 0.00E+00 0.00E+00 7.09E-02 Respiratory 6.30E-08 1.00E+00 Neurological 7%NEI8453 0.00E+00 0.00E+00 3.98E-04 Respiratory1.05E-06 2.39E+00Neurological 0%NEI862 0.00E+00 0.00E+00 2.97E-03 Respiratory 0.00E+00 2.97E-03 Respiratory 100%NEIFL0030004 0.00E+00 0.00E+00 6.96E-03 Respiratory 0.00E+00 7.00E-03 Respiratory 99%NEIIL0318AAI 0.00E+00 0.00E+00 1.58E-07 Respiratory 7.73E-071.62E+00Neurological 0%NEIIL1199213 0.00E+00 0.00E+00 3.50E-03 Respiratory 5.52E-09 4.27E-03 Respiratory 82%NEIIN00384 0.00E+00 0.00E+00 2.63E-04 Respiratory 0.00E+00 2.63E-04 Respiratory 100%NEIIN127192 0.00E+00 0.00E+001.63E+00Respiratory 0.00E+001.64E+00Respiratory 99%NEIKY021215 0.00E+00 0.00E+00 1.01E-03 Respiratory 5.69E-09 1.07E-03 Respiratory 94%NEIMIA8640 0.00E+00 0.00E+00 4.22E-03 Respiratory 7.77E-07 3.63E-01 Neurological 1%NEIMIA9960 0.00E+00 0.00E+00 1.98E-03 Respiratory 0.00E+00 1.99E-03 Respiratory 99%NEIMIB5417 0.00E+00 0.00E+00 4.06E-04 Respiratory 5.83E-07 2.94E-02 Kidney 1%NEIMIT$7446 0.00E+00 0.00E+00 4.65E-03 Respiratory 0.00E+00 4.67E-03 Respiratory 100%NEIMIT$7474 0.00E+00 0.00E+00 1.59E-03 Respiratory 0.00E+00 1.59E-03 Respiratory 100%NEIOH0641090 0.00E+00 0.00E+00 5.31E-03 Respiratory9.29E-061.17E-01 Neurological 5%NEIOHT$6096 0.00E+00 0.00E+00 6.51E-05 Respiratory 0.00E+00 7.24E-05 Respiratory 90%NEIOHT$6130 0.00E+00 0.00E+00 3.91E-03 Respiratory 0.00E+001.02E+01Neurological 0%NEIOHT$6245 0.00E+00 0.00E+00 2.01E-02 Respiratory1.00E-054.34E-01 Respiratory 5%Table 2 – Maximum Predicted HEM-3 Chronic RisksFacility NEI IDSource Category Chronic Risk1Facility-wide Chronic Risk1, 2SC % ofFacility-wideHI Risk1 of 2CancerMIRCancerIncidenceNoncancerMax HI Target OrganCancerMIRNoncancerMax HI Target OrganTable 2 – Maximum Predicted HEM-3 Chronic RisksFacility NEI IDSource Category Chronic Risk1Facility-wide Chronic Risk1, 2SC % ofFacility-wideHI RiskNEIOHT$6432 0.00E+00 0.00E+00 3.97E-04 Respiratory 0.00E+00 4.16E-04 Respiratory 95%NEIPA08519-1 0.00E+00 0.00E+00 5.86E-03 Respiratory 1.43E-08 6.20E-03 Respiratory 95%NEIPA2156 0.00E+00 0.00E+00 1.47E-02 Respiratory 0.00E+00 2.96E-02 Respiratory 50%NEIRAL730437 0.00E+00 0.00E+00 1.04E-01 Respiratory 0.00E+00 1.60E-01 Respiratory 65%NEISC015366 0.00E+00 0.00E+00 1.39E-05 Respiratory1.12E-069.68E-02 Neurological 0%NEITNT$4779 0.00E+00 0.00E+00 1.51E-02 Respiratory 0.00E+00 1.54E-02 Respiratory 98%1BOLDindicates a cancer risk great than 1 in a million or a noncancer risk greater than 12Facility noncancer risk represent MIR at census block locations using Draft 2005 NATA2 of 2REL AEGL1 AEGL2 ERPG1 ERPG2 NEI10956 Hydrochloric acid 2.71E-01 2.11E-01 1.73E-02 1.27E-01 1.90E-02
NEI1120 Chlorine 1.88E-04 2.63E-05 6.81E-06 1.36E-05 4.54E-06 NEI1120 Hydrochloric acid 3.76E-02 2.92E-02 2.39E-03 1.75E-02 2.63E-03 NEI11468 Hydrochloric acid 2.45E-02 1.91E-02 1.56E-03 1.15E-02 1.72E-03 NEI11539 Hydrochloric acid 3.99E-02 3.11E-02 2.54E-03 1.86E-02 2.80E-03 NEI11604 Hydrochloric acid 1.01E-03 7.82E-04 6.40E-05 4.69E-04 7.04E-05 NEI11747 Hydrochloric acid 2.58E-01 2.01E-01 1.64E-02 1.21E-01 1.81E-02
NEI12964 Hydrochloric acid 1.03E-02 8.01E-03 6.55E-04 4.80E-03 7.20E-04 NEI13296 Hydrochloric acid 1.32E-02 1.03E-02 8.42E-04 6.17E-03 9.26E-04 NEI19881 Hydrochloric acid 5.80E-03 4.51E-03 3.69E-04 2.70E-03 4.06E-04 NEI22744 Hydrochloric acid 1.69E-01 1.31E-01 1.07E-02 7.88E-02 1.18E-02 NEI31812 Hydrochloric acid 2.44E-02 1.90E-02 1.55E-03 1.14E-02 1.71E-03 NEI32262 Hydrochloric acid 5.00E-03 3.89E-03 3.18E-04 2.33E-03 3.50E-04 NEI32327 Hydrochloric acid 3.53E-03 2.74E-03 2.24E-04 1.65E-03 2.47E-04 NEI32334 Hydrochloric acid 1.23E-02 9.57E-03 7.83E-04 5.74E-03 8.62E-04 NEI32349 Hydrochloric acid 1.99E-03 1.55E-03 1.27E-04 9.30E-04 1.40E-04 NEI32409 Hydrochloric acid 2.01E-01 1.56E-01 1.28E-02 9.37E-02 1.41E-02 NEI33221 Hydrochloric acid 2.07E-05 1.61E-05 1.31E-06 9.64E-06 1.45E-06 NEI34007 Hydrochloric acid 9.12E-02 7.09E-02 5.80E-03 4.26E-02 6.38E-03 NEI40734 Hydrochloric acid 2.08E-02 1.61E-02 1.32E-03 9.69E-03 1.45E-03
NEI42439 Chlorine 1.52E-02 2.12E-03 5.49E-04 1.10E-03 3.66E-04 NEI42439 Hydrochloric acid 2.42E-02 1.88E-02 1.54E-03 1.13E-02 1.70E-03 NEI43399 Hydrochloric acid 1.14E-04 8.84E-05 7.24E-06 5.31E-05 7.96E-06 NEI51000 Hydrochloric acid 1.11E-02 8.63E-03 7.06E-04 5.18E-03 7.77E-04 NEI52149 Hydrochloric acid 7.71E-02 6.00E-02 4.91E-03 3.60E-02 5.40E-03 NEI52655 Hydrochloric acid 2.05E-03 1.60E-03 1.31E-04 9.58E-04 1.44E-04
NEI52751 Chlorine 2.45E-03 3.44E-04 8.89E-05 1.78E-04 5.93E-05 NEI52751 Hydrochloric acid 1.05E-02 8.19E-03 6.70E-04 4.91E-03 7.37E-04 NEI55848 Hydrochloric acid 3.00E-03 2.33E-03 1.91E-04 1.40E-03 2.10E-04 NEI7060 Hydrochloric acid 1.16E-01 9.00E-02 7.36E-03 5.40E-02 8.10E-03 NEI7194 Hydrochloric acid 7.43E-02 5.78E-02 4.73E-03 3.47E-02 5.20E-03 NEI8453 Hydrochloric acid 5.01E-04 3.90E-04 3.19E-05 2.34E-04 3.51E-05 NEI862 Hydrochloric acid 1.27E-02 9.88E-03 8.08E-04 5.93E-03 8.89E-04 NEIFL0030004 Hydrochloric acid 5.88E-02 4.57E-02 3.74E-03 2.74E-02 4.11E-03 NEIIL0318AAI Hydrochloric acid 1.98E-07 1.54E-07 1.26E-08 9.22E-08 1.38E-08 NEIIL1199213 Hydrochloric acid 1.97E-03 1.53E-03 1.25E-04 9.18E-04 1.38E-04 NEIIN00384 Hydrochloric acid 5.35E-04 4.16E-04 3.40E-05 2.50E-04 3.74E-05
NEIIN127192 Chlorine 3.58E-01 5.02E-02 1.30E-02 2.60E-02 8.65E-03 NEIIN127192 Hydrochloric acid 5.63E-03 4.38E-03 3.59E-04 2.63E-03 3.94E-04 NEIKY021215 Hydrochloric acid 5.55E-03 4.31E-03 3.53E-04 2.59E-03 3.88E-04 NEIMIA8640 Hydrochloric acid 1.09E-02 8.45E-03 6.91E-04 5.07E-03 7.61E-04 NEIMIA9960 Hydrochloric acid 1.06E-03 8.27E-04 6.77E-05 4.96E-04 7.44E-05 NEIMIB5417 Hydrochloric acid 1.78E-02 1.38E-02 1.13E-03 8.29E-03 1.24E-03 NEIMIT$7446 Hydrochloric acid 2.42E-01 1.88E-01 1.54E-02 1.13E-01 1.69E-02 NEIMIT$7474 Hydrochloric acid 7.82E-02 6.08E-02 4.98E-03 3.65E-02 5.48E-03 NEIOH0641090 Hydrochloric acid 1.93E-01 1.50E-01 1.23E-02 8.99E-02 1.35E-02 NEIOHT$6096 Hydrochloric acid 6.80E-05 5.29E-05 4.33E-06 3.18E-05 4.76E-06 NEIOHT$6130 Hydrochloric acid 1.72E-02 1.34E-02 1.10E-03 8.03E-03 1.20E-03 NEIOHT$6245 Hydrochloric acid 1.35E-02 1.05E-02 8.59E-04 6.30E-03 9.44E-04 NEIOHT$6432 Hydrochloric acid 2.37E-02 1.84E-02 1.51E-03 1.11E-02 1.66E-03 NEIPA08519-1 Hydrochloric acid 7.95E-03 6.18E-03 5.06E-04 3.71E-03 5.56E-04
NEIPA2156 Chlorine 2.35E-03 3.29E-04 8.52E-05 1.70E-04 5.68E-05 NEIPA2156 Hydrochloric acid 1.95E-02 1.51E-02 1.24E-03 9.09E-03 1.36E-03 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks (HEM-3)

Maximum Hazard Quotient 1

1 of 2REL AEGL1 AEGL2 ERPG1 ERPG2 Facility NEI ID Pollutant Table 3 – Maximum Predicted Acute Risks (HEM-3)

Maximum Hazard Quotient 1

NEIRAL730437 Chlorine 8.58E-03 1.20E-03 3.10E-04 6.21E-04 2.07E-04 NEIRAL730437 Hydrochloric acid 2.44E-01 1.90E-01 1.55E-02 1.14E-01 1.71E-02 NEISC015366 Hydrochloric acid 4.44E-05 3.45E-05 2.83E-06 2.07E-05 3.11E-06 NEITNT$4779 Hydrochloric acid 2.22E-02 1.73E-02 1.42E-03 1.04E-02 1.56E-03

Note: BOLD indicates acute risks greater than 1 1 Some maximum acute impacts may be at onsite locations.

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