**APPENDIX B:**

**Modeled Outdoor Concentrations of Hazardous Air Pollutants:**

**Analysis of Data from the Cumulative Exposure Project**

**for the Urban Area Source Program**

**May 1998**

Office of Policy, Planning and Evaluation

Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency

# AUTHORS

Daniel Axelrad

U.S. EPA Office of Policy, Planning and Evaluation

Tracey Woodruff

U.S. EPA Office of Policy, Planning and Evaluation

Jane Caldwell

U.S. EPA Office of Air Quality Planning and Standards

Raquel Morello-Frosch

University of California-Berkeley

Arlene Rosenbaum

Systems Applications International, Inc.

# CONTENTS

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

2.  Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
    . . . . . . . . . . . . . . . . . . . . . . . . . . .1

3.  Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
    . . . . . . . . . . . . . . . . . . . . . . . . . . .18

4.  Limitations and Uncertainties. . . . . . . . . . . . . . . . . . . .
    . . . . . . . . . . . . . . . . . . . 27

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . .40

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . .47

Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . .60

Attachment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . .62

##### 1 INTRODUCTION

In order to gain an expanded understanding of the national distribution
of air toxics concentrations, EPA's Office of Policy, Planning and
Evaluation has conducted a national air toxics modeling study as part of
its Cumulative Exposure Project. Outdoor concentrations of 148 air
toxics were modeled at the census tract level for the entire continental
U.S., in both urban and rural areas. To evaluate the potential impacts
of air toxics in urban areas, modeled concentrations were compared with
potential regulatory thresholds of concern or "benchmark
concentrations." Modeled concentrations greater than these benchmark
concentrations provide an indication of a potential health risk to the
general population. The frequency and magnitude of modeled
concentrations greater than benchmark concentrations provide an
indication of those hazardous air pollutants having the greatest
potential to pose health risks to the general population.

This chapter presents the methods for and results of this
screening-level study of national urban air toxics concentrations. In
addition, results for urban areas are compared with those for rural
areas. Contributions of area sources of emissions, relative to point
sources and mobile sources, are also assessed.

**2 METHODS**

There are three primary components to this analysis of urban air toxics:

1.  Estimating Air Toxics Emissions and Modeling Air Toxics
    Concentrations

2.  Identifying Benchmark Concentrations

3.  Comparing Modeled Concentrations to Benchmark Concentrations.

The methods used in each of these components are discussed below.

**[2.1 Estimating Air Toxics Emissions and Modeling Air Toxics
Concentrations]{.underline}**

**2.1.1 Atmospheric Modeling Methodology**

To meet this study's objectives of estimating long-term concentrations
of HAPs on a national scale with reasonable geographic resolution, a
long-term Gaussian dispersion modeling approach was selected. The
Assessment System for Population Exposure Nationwide (ASPEN) used in
this study is a modified version of EPA's Human Exposure Model (HEM).
The HEM is a standard modeling tool used by EPA staff to support
regulatory activity and special studies, such as EPA's electric utility
study (U.S. Environmental Protection Agency 1996d). The HEM utilizes a
Gaussian dispersion model­ing approach for point sources with optional
first-order decay and a simple deposition algorithm. For this study, the
deposition algorithm has been improved and treatment of area and mobile
source emissions has been modified. In addition, methods to estimate
secondary formation of seven HAPs (formaldehyde, acetaldehyde,
propionaldehyde, methyl ethyl ketone, acrolein, phosgene, and cresol)
have been incorporated.

For each source, the model calculates ground-level concentrations as a
function of radial distance and direction from the source for a set of
receptors laid out in a radial grid pattern. These concentrations
represent the steady-state concentrations that would occur with constant
emissions and meteorological parameters, and are calculated for model
receptor locations up to 50 kilometers (km) from each source.
Concentrations resulting from any number of sources are extrapolated
from model receptor locations to the centroids of population
subdivisions, such as census tracts, block groups, or blocks. The model
may be used to simulate any size modeling domain for which appropriate
data are available.

In recog­ni­tion of the potential for a large degree of spatial variation
in ambient concentrations, geographic resolution at the census tract
level was selected for this study. There are approxi­mately 60,000 census
tracts in the continental United States. Census tracts generally contain
roughly equal populations, and therefore tend to be smaller in urban
areas and larger in rural areas. This level of resolution focuses
greater computing resources in urban locations, and appropriately
balances the desire for high spatial resolution against the limitations
of the model and the available emissions and meteorological databases
that preclude accurate modeling at higher resolution. The modeled
concentrations approximate the population-weighted average of outdoor
HAP concentrations experienced within a census tract over the course of
a year.

Modeling was conducted separately for each source category group
identified in the following section. For each HAP, the modeled
concentrations for each category group, along with the background
concentrations identified below, were summed together to arrive at a
modeled concentration for each census tract. Details of the ASPEN model
are found in Chapter 5 of the technical report on the modeling portion
of this study (Rosenbaum et al. 1998).

**2.1.2 Emissions Inventory**

### 2.1.2.1 Emissions categories and data sources

HAPs are emitted from a variety of sources, including large
manufacturing facilities such as chemical production plants, combustion
facilities such as waste incinerators, small commercial operations such
as dry cleaners, and both onroad and nonroad mobile sources. For this
analysis, sources of HAP emissions were aggregated into six source
category groups:

-   manufacturing point sources (e.g., chemical manufacturing,
    refineries, primary metals)

-   nonmanufacturing point sources (e.g., electric utility generators,
    municipal waste combustors)

-   manufacturing area sources (e.g., wood products manufacturing,
    degreasing)

-   nonmanufacturing area sources (e.g., dry cleaning, consumer
    products, small medical waste incinerators)

-   onroad mobile sources (e.g., cars, buses, trucks)

-   nonroad mobile sources (e.g., farm equipment, airplanes, boats).

In this study, emissions from manufacturing point sources are
represented by data from EPA's 1990 Toxics Release Inventory (TRI) (U.S.
Environmental Protection Agency 1991). TRI is an annual compilation of
facility-reported estimates of releases and transfers for over 300
pollutants. TRI is a comprehensive database of estimated emissions for
large manufacturing sources, but does not address many important sources
of toxics emissions, including mobile sources, combustion sources such
as incinerators, and small industrial, commercial, consumer and
agricultural sources (i.e. area sources). For these other source
category groups, this study estimates HAP emissions from EPA's extensive
1990 base year national emissions inventories for volatile organic
compounds (VOCs) and particulate matter (PM) (Pechan 1994; U.S.
Environmental Protection Agency 1993b). These inventories contain
facility-specific data on point sources and county-level emissions
totals for mobile sources and area sources.

HAP emissions estimates are derived from VOC and PM emission estimates
through the application of speciation profiles. Speciation profiles are
industry-specific and/or process-specific estimates of the presence of
particular chemical constituents in a VOC or PM emissions stream. For
example, estimates of gaseous HAP emissions from automobile refinishing
operations can be derived by combining the estimated total VOC emissions
from automobile refinishing with a speciation profile, which provides
estimates of the percentage of automobile refinishing VOC emissions made
up of individual chemical constituents, such as benzene and toluene.
Speciation profiles are available from EPA's SPECIATE database (U.S.
Environmental Protection Agency 1992) and from the technical literature
(Battye and Williams 1994; Burnet et al. 1990; California Air Resources
Board 1991; Edgerton et al. 1985; Hare and White 1991; Harley and Cass
1994; Harley et al. 1992; Hildemann et al. 1991; Ingalls 1991; Lipari et
al. 1984; Miller et al. 1994; Sagebiel et al. 1996; Scheff et al. 1992;
Scheff et al. 1994; U.S. Environmental Protection Agency 1989; U.S.
Environmental Protection Agency 1996d). Speciation profiles are not
available for all source categories; for categories without profiles,
emissions are estimated using profiles for source categories judged to
be similar. This introduces some uncertainty into the characterization
of emissions. However, it is necessary to use estimates with
uncertainties in order to approximate actual HAP concentrations, since
many sources and source categories have not been characterized in
detail. To omit such sources could result in significant underestimates
of HAP concentrations.

In this analysis, the definition of an area source is not precisely
equivalent to the CAA Section 112 definition, nor is the definition of a
point source precisely equivalent to the CAA section 112 definition of a
major source. Area source emissions in this analysis are derived from
EPA's national inventories of VOC and PM emissions. In these
inventories, area sources are defined as those that do not emit more
than 100 tons per year of any criteria pollutant (VOCs, PM, carbon
monoxide, nitrogen oxides, or sulfur dioxide). Some of the emissions in
this analysis attributed to area sources may actually be emitted by
facilities which are defined as major sources under CAA section
112---that is, facilities which emit more than 10 tons per year of any
single HAP or 25 tons per year of any combination of HAPs. There also
may be facilities classified as point sources in this analysis which are
considered area sources under CAA section 112. The potential
implications of these different definitions of area sources are
discussed in Section 4 below.

The scope of the modeling exercise was limited to a certain extent by
the available emissions data. The domain for the modeling exercise is
comprised of the continental United States. The national VOC and PM
inventories used to develop much of the HAP emissions estimates for this
study do not include data for Alaska or Hawaii; these states were
therefore not included in the modeling study. In addition, 148 out of
the 188 HAPs listed in CAA section 112(b)(1) are included in the
modeling study. There were no emissions data identified for the
remaining HAPs in the data sources used.[^1]

### 2.1.2.2 Spatial Allocation

For point sources, emissions data are available for specific facility
locations. All emissions data for area sources and mobile sources,
however, are estimated as county totals. Before HAP emissions derived
from these inventories can be used effectively in dispersion modeling,
they should be allocated to smaller geographic units within the counties
to approximate the spatial distribution of actual emissions.

To allocate emissions from the county level to the census tract level,
20 different surrogates were developed, as shown in Table 1. Each
surrogate is based on data available at the census tract level, and
represents an approximation of the distribution of emissions-generating
activities across the census tracts within a county for at least one
area source or mobile source category of emissions. Surrogates were
developed using data on population (Bureau of the Census 1990a; Bureau
of the Census 1990b), roadway miles and railway miles (Bureau of the
Census 1993), and land use (U.S. Geological Survey various dates) for
each census tract. For each area source and mobile source category,
county emissions were allocated to constituent census tracts in
proportion to the fraction of the total county value of the
corresponding surrogate. For example, each county's emissions from lawn
and garden equipment are allocated to each census tract in proportion to
the tract's percentage of residential land area within the county, while
emissions from industrial sources are allocated in proportion to
industrial land use, and mobile source emissions are allocated using
data on population and roadway miles in each tract.

**2.1.3 Background Concentrations**

The emissions inventory and modeling methodologies described above are
used to estimate long-term outdoor concentrations of HAPs attributable
to 1990 anthropogenic emissions, within 50 kilometers of each source.
For many HAPs, however, outdoor concentrations may include "background"
components attributable to long-range transport, re-suspension of
historical emissions, and non-anthropogenic sources. To accurately
estimate 1990 outdoor concentrations of HAPs, it is necessary to account
for these background concentrations which are not represented by
atmospheric modeling of 1990 anthropogenic emissions.

In this study, background concentrations are represented by inclusion of
concentration values measured at "clean air locations" remote from the
impact of local anthropogenic sources. Background concentrations were
identified from the literature (Grosjean 1991; Howard 1989; Howard 1990;
Howard 1991; Howard 1992; Howard 1997; Khalil and Rasmussen 1984;
Panshin and Hites 1994a; Panshin and Hites 1994b; Singh et al. 1985;
U.S. Environmental Protection Agency 1994a; U.S. Environmental
Protection Agency 1996c; Wiedmann et al. 1994; World Meteorological
Organization 1991) for 28 HAPs, and are shown in Table 2. These values
were obtained from measurement studies characterized as rural/remote,
hemispheric baseline, remote ocean, global background, or other terms
denoting contributions from only natural sources or long-range
transport. For these HAPs, the estimated concentration in each census
tract is determined by the summing together the estimated background
concentrations and the modeled concentrations arising from 1990
emissions. Background concentrations are assumed to be constant across
all census tracts; the available data are insufficient to address any
possible geographic variations in background. Because no background
concentration values were identified in the technical literature for
other 120 HAPs included in this study, their background concentrations
are assumed to be zero. This may result in underestimation of outdoor
concentrations for some HAPs.

**2.1.4 Evaluation of Modeled Concentrations**

Performance of the model was evaluated by comparing modeled
concentrations of HAPs to available monitored concentrations. In
addition, modeled and monitored carbon monoxide (CO) concentrations were
also compared for evaluation of model performance. Although CO is not a
HAP, it was included in the model simulations specifically for model
evaluation purposes, because the CO measurement data base contains
significantly more monitoring sites than the HAP measurement data base.
In terms of dispersion and other atmospheric processes after release, CO
is expected to behave similarly to gaseous HAPs with very low reactive
decay rates. In addition, CO is measured hourly throughout the year,
whereas HAP measurements are typically 24-hour averages taken
approximately every twelfth day. The greater temporal coverage reduces
uncertainty in annual average statistics, and allows for time-of-day
comparisons.

***2.1.4.1 Monitoring Data Sets***

Quantitative comparisons of 1990 annual average ASPEN model predictions
with observed HAP concentrations were made for several monitoring
programs summarized in Table 3. Some of the programs were not operating
in 1990, so measurements for other years between 1988 and 1992 were used
in some cases, introducing some uncertainty into the comparisons.

HAP measurement data from the monitoring programs identified in the
table were not used for the quantitative model performance evaluation in
any cases where more than 10 percent of the measurements were below the
minimum detection levels (MDLs). The uncertainty in estimating an annual
average concentration from monitoring data sets with large numbers of
non-detects limits the usefulness of such data sets in quantitative
model performance evaluation. More qualitative analysis of some of these
data sets was used to supplement the information from the quantitative
model performance evaluation. For data sets used in the quantitative
analysis, those values below the MDL were set equal to half the
detection limit as a default in calculating annual average
concentrations.

CO data from 259 monitoring sites were extracted from EPA's Aerometric
Information Retrieval System (AIRS) for comparison with ASPEN
predictions. In selecting the sites, an attempt was made to eliminate
those monitors identified as microscale or middle-scale and/or as
maximum concentration or source-oriented. Because these monitors are
located in order to detect extreme concentrations, or "hot spots", they
are likely to record concentrations that are significantly higher than
the ASPEN estimates for the corresponding census tracts, which represent
tract averages. However, not all monitor records contained these
identifiers, and some are likely to be incorrectly identified.
Therefore, a certain amount of underprediction of CO concentrations for
this set of 259 monitor locations is expected. A separate analysis was
conducted for the 100 CO monitors out of the set of 259 that were
specifically designated as representative of neighborhood scale (0.5 to
4 km), urban scale (4 to 50 km), or regional scale (more than 50 km).
Comparison of model outputs with measured concentrations from this
subset of monitors provides an analysis that is not influenced by any
unidentified "hot spot" monitors included in the larger set of 259.

### 2.1.4.2 Evaluation Methods

For each HAP at each monitoring location with a full year of data and
fewer than 10 percent of observations below the MDL, the ratio of the
predicted (modeled) concentration to the observed (monitored)
concentration was calculated. In order to account for the possibility
that a pollutant monitor may be nearly equidistant from multiple census
tract centroids, measured concentrations were compared with a
distance-weighted average of the nearest six tract concentration
predictions, weighting each centroid value by 1/distance^2^. Note that
the ASPEN algorithms are designed to estimate concentrations that
represent the average throughout the census tract. Although the
monitored HAP values are point measurements, they are typically made in
locations where concentration gradients are not expected to be steep,
because the long-term monitoring programs from which they are taken are
intended to represent general population exposures. Sensitivity analysis
showed that results are not substantially different when only the
closest census tract is used for comparison.

The same evaluation methods were also used for the CO model-monitor
comparisons. An assumed CO background concentration of 125 ppb (144
μg/m^3^) was added to the sum of predicted anthropogenic contributions
to CO concentrations for this comparison. This value is based on
1989-1990 measurements at Niwot Ridge, Colorado (Novelli et al. 1992), a
remote land site at approximately intermediate U.S. latitude (40N).

**[2.2 Benchmark Concentrations]{.underline}**

Toxicological information on health effects and dose-response
relationships for the 148 hazardous air pollutants included in this
study was assembled from a variety of sources, evaluated comparatively,
and assigned to a series of tiers defined by quality and availability of
information, and consistency of methodology. Much of the needed health
effects information was previously compiled for EPA's proposed
rulemaking under the authority of section 112(g) of the Clean Air Act.
For this study, the 112(g) information was updated, and information from
several additional data sources was incorporated (Caldwell et al. 1998).

Hazard information and dose-response data for the HAPs were used to
develop benchmark concentrations for carcinogenic hazard and for
noncarcinogenic hazard from both long-term and short-term exposure. For
each hazard category, a benchmark concentration representing a
presumptive health protective value was selected. For carcinogenic
hazard, the benchmark was selected to be the concentration of a known,
probable, or possible human carcinogen representing an upper bound
one-in-one-million excess probability of contracting cancer over a
lifetime of exposure. The selection of this benchmark is based on
provisions of CAA sections 112(c)(9) and 112(f) that allow source
categories to be exempted from regulation when posing less than a
one-in-one-million lifetime cancer risk to the most exposed individual.

For noncarcinogenic hazards, the benchmark was selected to be the
concentration of a HAP likely to be without appreciable risk of
noncancer effects from long-term or short-term exposures. This benchmark
is based on the provision of section 112(f) of the CAA requiring
"residual risk" emissions standards to "provide an ample margin of
safety to protect public health" from non-cancer effects. Similar
language is also found in CAA section 112(c)(9). EPA inhalation
reference concentrations (RfCs), or similar values developed by other
agencies, representing levels below which long-term exposure is not
expected to result in any adverse health effects, were selected as the
benchmark concentrations for non-cancer health effects from long-term
exposure.

Benchmark concentrations for potential non-cancer hazards from
short-term exposures to HAPs were developed using EPA's Levels of
Concern (LOC). LOCs are established for chemicals on the Superfund
Amendments and Reauthorization Act section 302 list of "extremely
hazardous substances" (U.S. Environmental Protection Agency et al.
1987). LOCs are airborne concentrations of chemicals for which no
serious irreversible health effects are expected to occur following a
short-term exposure of 30 minutes. To derive a short-term benchmark
concentration, the LOC was divided by a safety factor of 1000 to address
the fact that the LOC is based on lethality as an endpoint and to
address the uncertainty in the derivation of the LOC.

Wherever available, EPA estimates of inhalation unit risks (IURs) for
cancer and EPA reference concentrations (RfCs) for non-cancer effects
were used in developing benchmark concentrations. When these values were
not available, other available values were used as benchmark
concentrations, including: EPA estimates of cancer risks from oral
exposure, converted into inhalation units; California EPA inhalation
unit risks and Reference Exposure Levels (RELs); and Minimal Risk Levels
(MRLs) developed by the Agency for Toxic Substances and Disease Registry
(ATSDR).

Fourteen of the 148 HAPs included in this study are chemical groups. It
is difficult to assess the toxicity of chemical groups because each is
comprised of a number of different constituents that may have varying
levels of toxicity. For this analysis, toxicity values that can be
assigned to an entire chemical group are included (Caldwell et al.
1998). Toxicity values applicable only to individual constituents of
chemical groups are not included, because the modeled concentrations
developed in this study represent the entire group.

The various benchmark concentrations were grouped into tiers to account
for differences in methodology, inherent uncertainty of data used in
derivation, and level of peer review. Tier I includes values derived
from EPA IURs and RfCs, and represents those values with the most
consistency in derivation and highest level of peer review. Tier II
includes other categories of EPA data, as well as quantitative
information from California EPA and ATSDR.

## [2.3 Comparison of Modeled Concentrations to Benchmark Concentrations]{.underline}

This study's modeled concentrations are estimates of annual average
outdoor HAP concentrations for 1990. To screen for whether a modeled
concentration represents a potential health risk, it is compared to
benchmark concentrations for cancer and chronic noncancer effects,
assuming long-term exposure. These benchmark concentrations represent an
estimated concentration at which a lifetime daily exposure is unlikely
to result in adverse health effects, based on available hazard
assessment data. A modeled long-term concentration greater than a cancer
or chronic benchmark is therefore an indicator of some potential for
adverse health effects.

In addition, estimated ambient concentrations are also compared to
benchmarks for health effect concerns from short-term exposure. While
the estimated concentrations in this analysis do not represent
short-term peak concentrations typically used to assess acute effects,
exceedance of short-term benchmarks by long-term average concentrations
is an indication of a potential health concern, because short-term peak
concentrations will be higher than annual average concentrations.
However, the uncertainties in the benchmarks for short-term exposure are
relatively large.

Comparison of estimated HAP concentrations to benchmark concentrations
implicitly treats outdoor concentrations as equivalent to exposure
concentrations. Outdoor concentrations are a reasonable proxy for
exposures that occur both outdoors and indoors, given the high rates of
penetration into indoor environments for various HAPs. A field sampling
study of indoor and outdoor concentrations of volatile organic compounds
(VOCs) reported by Lewis (Lewis 1991) and Lewis and Zweidinger (Lewis
and Zweidinger 1992) found that penetration of VOCs from outdoor to
indoor air is complete, even when air exchange rates are low. Similar
results have been found for particulates less than 10 micrograms in
aerodynamic diameter\--that is, the penetration of such pollutants from
outdoor to indoor air is virtually 100 percent (U.S. Environmental
Protection Agency 1996a). Therefore, long term indoor concentrations of
both gaseous and particulate HAPs can, in the absence of indoor sources,
be assumed to be approximately equal to long term outdoor concentrations
in the same location. Indoor removal mechanisms may reduce indoor
concentrations to some extent for some HAPs.

To evaluate the potential for individual HAPs to pose health risks,
hazard ratios were computed by dividing each estimated HAP concentration
by its corresponding benchmark concentration for both cancer and
noncancer health effects. Hazard ratios greater than one indicate the
estimated concentration was in excess of the benchmark concentration.
Hazard ratios were computed for each available benchmark for each census
tract.

To evaluate the impacts of air toxics in urban areas, exceedances of
benchmark concentrations are evaluated separately for urban census
tracts and rural census tracts. Each census tract was designated as
either "urban" or "rural" as part of the dispersion modeling
methodology, since dispersion parameters differ for these two types of
locations. All census tracts with population density greater than 750
people/km^2^ are designated as urban, while census tracts with lower
population density are designated as rural, based on EPA modeling
guidance (U.S. Environmental Protection Agency 1996b). This results in
an approximately even split of census tracts into the urban and rural
designations, meaning that many suburban areas are classified as "rural"
rather than "urban." Characteristics of urban and rural census tracts
are shown in Table 4.

**3 RESULTS**

**[3.1 HAP Modeling]{.underline}**

**3.1.1 Pollutant Concentrations**

Figure 1 shows boxplots of the modeled concentrations of 38 selected
HAPs in the 28,314 urban census tracts. The HAPs shown are those
identified in section 3.3 below as having at least 50 urban census
tracts with modeled concentrations exceeding health hazard benchmark
concentrations. Pollutants are separated into four groups in the figure,
according to the magnitude of the modeled concentrations shown on the
vertical axes. The vertical lines for each HAP in the figure show the
range from the 1st percentile to the 99th percentile of modeled
concentrations, while the box shows the range from the 25th to the 75th
percentile, and the horizontal line shows the median modeled
concentration for urban census tracts. Modeled concentrations of zero
were set equal to a minimal value (1 x 10^-13^) to accommodate a
logarithmic scale. In the fourth panel of the figure, pollutants for
which the modeled concentration is zero in a majority of the urban
census tracts are shown with a median concentration equal to this
minimal value.

For many of the HAPs shown, the distribution of modeled concentrations
spans several orders of magnitude; however, the range from the 25th to
the 75th percentile is one order of magnitude or less for a majority of
the pollutants. Greater variations in the modeled concentrations are
seen for pollutants that have a relatively large proportion of emissions
from point sources, such as propylene dichloride and hydrazine.

**3.1.2 Model Performance**

Table 5 summarizes predicted-to-observed concentration ratios for all
HAPs for which a significant amount of monitoring data above the minimum
detect level was identified. All available observed data from the
monitoring programs listed in Table 3 were combined for each HAP. The
results for these HAPs show an overall tendency for the model to
underestimate the observed concentrations, with geometric mean
predicted/observed ratios generally less than 1.0, ranging from 0.09 to
1.0.

P-dichlorobenzene, methylene chloride, and styrene appear to be
underpredicted to a greater degree than other gaseous HAPs, with
geometric mean predicted-to-observed ratios less than 0.33, suggesting
that significant sources have been omitted from the emissions inventory
for these pollutants.

Model-monitor comparisons for CO also indicate a tendency for
underestimation of concentrations. For the full set of 259 monitors, the
geometric mean ratio is 0.52, and for the subset of 100 monitors
specifically identified as not related to "hot spots," the geometric
mean ratio is 0.62. Further analysis of the predicted-to-observed ratios
for CO, described in Chapter 7 and Attachment 5 of the modeling report
(Rosenbaum et al. 1998) suggests that much of the model's tendency to
underestimate pollutant concentrations may be explained by limitations
of the Gaussian model formulation, such as neglect of calm wind
conditions, poor representation of stable atmospheric conditions, and
the 50 kilometer downwind distance limit. Uncertainties in the HAP
emissions inventory may also explain the tendency to underpredict.

Figure 2 presents a comparison of predicted and observed concentrations
for 13 gaseous HAPs at 5 locations in the city of Baltimore, Maryland.
The overall Spearman correlation coefficient is 0.82 (p=0.00001). The
high correlation coefficient is an indicator of good performance in
distinguishing the relative magnitude of concentrations among the
different HAPs included in the data set.

#### 3.2 Benchmark Concentrations

A total of 183 benchmark concentrations were identified for the 148 HAPs
in this study, as summarized in Table 6. Seventy-seven of the benchmarks
are for cancer, 90 for non-cancer effects from long-term exposure, and
16 for non-cancer effects from short-term exposure. No quantitative
benchmarks were identified for 29 of the 148 HAPs, while benchmarks for
more than one of the three hazard categories were identified for 60
HAPs.

Out of the 14 pollutant groups included in this study, benchmarks
appropriate for applications to the entire group were identified for
nine: arsenic compounds, beryllium compounds, cadmium compounds,
chromium compounds, cobalt compounds, lead compounds, manganese
compounds, nickel compounds, and selenium compounds. For five pollutant
groups, no benchmarks applicable to the entire group were identified for
antimony compounds, cyanide compounds, glycol ethers, mercury compounds,
and polycyclic organic matter. Values of all benchmark concentrations
used in this analysis are shown in Attachment 1.

#### 3.3 Benchmark Exceedances

### 3.3.1 Exceedances of Benchmark Concentrations in Urban and Rural Census Tracts

Table 7 shows the percentage of urban and rural census tracts with
modeled 1990 average outdoor concentrations that exceed benchmark
concentrations. Results are shown for 38 HAPs with exceedances in more
than 50 urban census tracts[^2]. An additional 12 HAPs not shown in the
table have exceedances in between 10 and 50 urban census tracts, and an
additional 17 have exceedances in between 1 and 10 urban census tracts.
Three pollutants have exceedances in rural census tracts only; in each
case, the number of census tracts is less than five.

A majority of the benchmark concentrations exceeded are for cancer,
rather than noncancer effects. This reflects the fact that the cancer
benchmarks, set at a one-in-one-million risk level, are generally much
lower concentrations than the noncancer benchmarks.

There are eight HAPs (benzene, bis (2-ethylhexyl) phthalate, carbon
tetrachloride, chloroform, ethylene dibromide, ethylene dichloride,
formaldehyde, and methyl chloride) with benchmark exceedances in all
urban and rural census tracts. For each of these HAPs, the background
concentration alone, as defined in section 2.1.3 above, exceeds the
benchmark concentration for cancer, as shown in Table 8. To evaluate the
impact of 1990 anthropogenic emissions, the background concentration was
subtracted from the total estimated concentrations, and the remaining
concentrations (i.e., modeled 1990 anthropogenic concentrations) were
compared to benchmark concentrations, with results also shown in Table
8. Modeled 1990 anthropogenic concentrations for benzene and
formaldehyde are estimated to exceed the benchmark concentration in
almost all urban census tracts and in more than 87 percent of rural
census tracts. Modeled anthropogenic ethylene dichloride concentrations
exceed the benchmark concentration in 32 percent of urban census tracts
and 11 percent of rural census tracts. The remaining five HAPs have 1990
modeled anthropogenic concentrations that exceed benchmark
concentrations in less than 10 percent of both urban and rural census
tracts.

For most other HAPs in Table 7, the percentage of urban census tracts
with exceedances of benchmark concentrations is two to three times
greater than the percentage for rural census tracts for most pollutants.
For some pollutants, however, including 1,3-dichloropropene,
p-dichlorobenzene and lead, the relative frequency of exceedances in
urban areas is much greater. For example, modeled concentrations of
1,3-dichloropropene exceed the cancer Tier II benchmark for this
pollutant in 56 percent of urban census tracts and in only 5 percent of
rural census tracts. Almost all estimated emissions of this pollutant
are associated with consumer lawn care products. National emissions
estimates from this category are spatially allocated to census tracts in
proportion to residential population, resulting in greater emissions
density in urban tracts. For this category, this allocation method may
overstate the share of emissions in urban census tracts relative to
rural census tracts. P-dichlorobenzene and lead also have large
proportions of emissions that are allocated to tracts in proportion to
population.

Of the pollutants with more than 50 urban exceedances, only
benzotrichloride has a higher frequency of exceedances in rural tracts
than in urban tracts. Virtually all of the estimated benzotrichloride
emissions used in this analysis were reported to the TRI by four
facilities. Two of these facilities, accounting for about 68 percent of
the reported emissions, are in rural locations.

**3.3.2 Relative Contributions of Area, Point and Mobile Sources**

To evaluate the contribution of three broad categories of sources---area
sources, point sources, and mobile sources---to exceedances of benchmark
concentrations in urban areas, the estimated number of exceedances
resulting from each category's emissions was calculated separately. That
is, the modeled concentrations associated with area source emissions
were compared to the benchmark concentrations for each HAP in each
census tract, and the same was done for point source and mobile source
emissions.

Area source emissions estimates were developed for 73 of the HAPs
included in this analysis. Table 9 lists the HAPs for which area source
emissions alone are estimated to result in long-term outdoor
concentrations greater than the benchmark concentrations. There are 30
HAPs with modeled exceedances of benchmark concentrations resulting from
estimated area source emissions. Six HAPs---acrolein, benzene,
1,3-butadiene, 1,3-dichloropropene, formaldehyde, and chromium---are
estimated to have concentrations greater than the benchmarks, from area
source emissions alone, in more than 90 percent of urban census tracts.
Four of the HAPs shown in the table are estimated to have area source
exceedances in less than 0.1 percent of urban census tracts.

Point source emissions have been estimated for all 148 HAPs included in
this analysis. There are 63 HAPs with benchmark exceedances in urban
census tracts resulting from estimated point source emissions alone; 34
of these have exceedances in more than 50 urban census tracts and are
shown in Table 10. In general, point source emissions of individual HAPs
tend to result in exceedances in a smaller number of census tracts than
area sources: only seven HAPs---arsenic, benzene, 1,3-butadiene, dioxin,
chromium, formaldehyde, and nickel---are estimated to exceed the
benchmark concentration in ten percent or more of the urban census
tracts due to point source emissions alone. Each of these HAPs, except
dioxin, also has a large number of exceedances from area source
emissions alone.

Mobile source emissions were estimated for 35 HAPs in this analysis. As
shown in Table 11, ten HAPs exceed benchmark concentrations in urban
census tracts when only mobile source emissions are considered. Five of
these HAPs---acrolein, benzene, 1,3-butadiene, chromium, and
formaldehyde---have modeled mobile source concentrations that exceed
benchmarks in more than 90 percent of urban census tracts, while
acetaldehyde is estimated to exceed its cancer benchmark in two-thirds
of the urban tracts from mobile source emissions alone. The results
shown in Table 11 for particulate HAPs are attributable to estimated
emissions from diesel vehicles (for arsenic, chromium, and nickel, based
on speciation data in the technical literature (Hildemann et al. 1991;
and commercial marine vehicles powered by residual oil (for cadmium and
nickel).

The results presented in Tables 9, 10 and 11 consider the impacts of
area, point and mobile sources in isolation. For some HAPs, only one of
these source category groups appears to account for most of the
benchmark exceedances, while for other HAPs the contributions of two or
all three of the source category groups are very significant. Table 12
compares the number of estimated exceedances from modeled concentrations
for each of the three broad source category groups alone (from Tables
9-11), along with exceedances for all source categories and background
concentrations combined (from Table 7). This comparison is shown for
each of the 38 HAPs with estimated exceedances in more than 50 urban
census tracts. HAPs are listed in order of the number of benchmark
exceedances resulting from modeled area source emissions only.

Table 12 shows that modeled concentrations of 1,3-butadiene from area
sources alone exceed the cancer benchmark concentration for this HAP in
almost every urban census tract, and that the same is also true of
modeled mobile source concentrations for this HAP. This suggests that
emissions from both area sources and mobile sources would have to be
reduced in order to realize urban concentrations of 1,3-butadiene that
are lower than this HAP's cancer benchmark concentration. This finding
also applies to acrolein, benzene, formaldehyde, and chromium compounds,
since each of these HAPs also exceed benchmark concentrations from
modeled area sources alone and from modeled mobile sources alone in
nearly every urban census tract. Each of these pollutants also has
smaller, but still significant, contributions from point sources. A
different finding applies to 1,3-dichloropropene. Modeled concentrations
of this HAP from area sources alone (consumer lawn care products) also
exceed the benchmark concentration in nearly every urban census tract,
but there are no estimated emissions of this HAP from mobile sources,
and minimal contributions from point sources.

Table 12 also shows that vinyl chloride and p-dichlorobenzene each have
modeled concentrations from estimated area source emissions alone that
exceed benchmark concentrations in more than 35 percent of urban census
tracts. Both pollutants have smaller estimated contributions from other
source types. For both of these pollutants, however, the number of
benchmark exceedances resulting from area source emissions may be
overstated due to uncertainties in the emissions inventory; estimated
area source chemical manufacturing emissions of these pollutants have
high uncertainties and may be overestimates.

The benchmark exceedances for most of the other pollutants in Table 12
appear to be due to the combined contributions of area sources and point
sources, with significant contributions for some HAPs from mobile
sources (nickel compounds, arsenic compounds, and particularly
acetaldehyde) and from background concentrations. For the last nine HAPs
listed in the table, benchmark concentrations are exceeded in fewer than
500 hundred urban census tracts each, and these exceedances are due
almost entirely to emissions from point sources.

A correlation analysis of the number of benchmark exceedances for each
source category group and the total number of exceedances, for the set
of HAPs in Table 12, shows that exceedances from area sources are most
strongly correlated with total exceedances. The relationship between the
exceedances from area sources and the total, while controlling for
mobile sources and point sources, is a correlation of 0.68 (p=0.0001).
The correlation between the exceedances from the point sources and the
total, while controlling for the area and mobile sources is 0.48
(p=0.0001). The relationship between the exceedances from mobile sources
and the total, while controlling for the area and point sources, is a
correlation of 0.14 (p=0.0001). This indicates that the exceedances from
area sources have the strongest association with total exceedances, and
that there is also an important contribution from point sources. Mobile
source exceedances do not appear to be associated with total exceedances
when considering the full set of HAPs in Table 12. As discussed above,
mobile source contributions are very important for several individual
HAPs; but for HAPs with large mobile source contributions, there are
also large contributions from area sources.

4.  **LIMITATIONS AND UNCERTAINTIES**

The analysis presented in this chapter uses available emissions data,
modeling techniques and hazard evaluation data to estimate the frequency
with which long-term average concentrations of hazardous air pollutants
at the census tract level may be greater than benchmark concentrations.
While modeled concentrations are subject to uncertainties arising from
both emissions estimates and the modeling methodology, the available
monitoring data support the conclusion that exceedances of benchmark
concentrations are common. For example, several sources of long-term
monitoring data for benzene and 1,3-butadiene show that measured
concentrations routinely exceed benchmark concentrations (California Air
Resources Board 1995; New York State 1993; Texas Natural Resource
Conservation Commission 1997).

**[4.1 Emissions Estimates]{.underline}**

The majority of HAP emissions estimates used in this study were
developed through the application of speciation profiles to the 1990
base year national interim emissions inventories for VOCs (1993 version)
and particulate matter (PM) (1995 version). The speciation methodology
starts with a large data set---national emissions of total VOCs and
PM---and breaks it down into relevant component parts, i.e. emissions of
each of the HAPs. The strength of this approach is in its comprehensive
coverage of sources and in applying a consistent methodology nationally.
Uncertainties in this approach are due to uncertainties in the VOC and
PM inventories and in the speciation profiles used to estimate the HAP
components of the VOC and PM emissions.

Another approach to estimating national HAP emissions for a modeling
study is to assemble a variety of different data sources that each
address a particular set of HAP emitters (source categories) or a
particular geographic area. The strength of this approach is that it
frequently has more detailed emissions estimation methodologies.
Uncertainties in this approach are due to possible inconsistencies that
are introduced by combining data from disparate sources, and the
potential for overlooking important emitters.

An important goal for the modeling portion of this study was to
approximate actual outdoor concentrations of air toxics in 1990.
Therefore, key objectives included comprehensive treatment of emissions
and emissions sources, and a consistent approach applicable to the
entire continental U.S. The speciation approach to emissions inventory
development was chosen for this study because it was the best approach
available for developing a comprehensive national modeling emissions
inventory within budget and time constraints.

In 1997, EPA released its 1990 National Emission Trends (NET) inventory
(U.S. Environmental Protection Agency 1997). This inventory is a revised
version of the 1990 interim inventories for VOC and PM used in this
study. National emissions totals in the NET inventory are lower than in
the interim inventories by 33 percent for VOC emissions and 42 percent
for PM. General reductions in VOC and PM emissions would suggest general
reductions in the emissions of toxics estimated for this study. However,
many of the large reductions in the VOC and PM emissions estimates are
concentrated in source categories for which the interim inventory VOC
and PM estimates were not used in this study. These categories include:
chemical manufacturing and other manufacturing point sources (1990 TRI
data were used instead for this study); waste treatment, storage and
disposal facilities (alternate EPA data source (Coburn 1995) used
instead for this study); and PM area source dust emissions for paved and
unpaved roads, wind erosion construction, geogenic wind erosion, and
agricultural crops tilling (emissions for these categories were not
included in this study because of high uncertainties). In addition, the
NET inventory reflects increases of 36-46 percent, compared with the
interim inventory used for this study, in PM emissions from point
sources and nondust area sources. More detailed analysis of the
differences between the interim inventory and the NET inventory, and
their relationship to the emissions estimates used in this study, may be
found in Chapter 3 of the modeling technical report (Rosenbaum et al.
1998).

Development of HAP emissions estimates from VOC and PM emissions
estimates requires the application of speciation profiles. The primary
source of many of these profiles is EPA's SPECIATE database (U.S.
Environmental Protection Agency 1992). Previous studies have noted
problems with specific profiles in the SPECIATE database (Harley et al.
1992; Korc and Chinkin 1993; Ligocki et al. 1992). As a result, profiles
from SPECIATE were supplemented or revised using numerous other data
sources in the technical literature. These profiles are detailed in
Attachment 3 of the modeling technical report (Rosenbaum et al. 1998).

Some of the remaining uncertainties in the emissions inventory used in
this study may be particularly important for the development of
strategies for reducing risks from area sources. First, there are eight
pollutants---1,1,2,2-tetrachloroethane, acrylamide,
bis(2-ethylhexyl)phthalate; ethylene dibromide, hydrazine, methylene
diphenyl diisocyanate, quinoline, and vinylidene chloride\--with no area
source emissions estimates in this study, but which do have area source
emissions in the emissions inventory for 40 HAPs recently developed as
part of the urban area source program. These area source emissions could
result in exceedances of benchmark concentrations that have not been
estimated in this study. Second, this study's emissions inventory has
relatively high contributions for area source chemical manufacturing and
pharmaceutical manufacturing for several HAPs, including acrylonitrile,
vinyl chloride, ethylene dichloride, chloroform, carbon tetrachloride,
and ethyl acrylate. Both the VOC emissions estimates and the speciation
profiles for this categories have large uncertainties\--possibly
resulting in the overestimation of benchmark concentration exceedances
resulting from area source emissions for these HAPs.

The comparison of impacts from area sources, point sources, and mobile
sources presented in this chapter also has uncertainties attributable to
the definition of an "area source." As noted above, the area source
definition used in constructing the emissions inventory for this
analysis is not equivalent to the statutory definition of area source in
CAA section 112. Some emissions treated as area source emissions in this
analysis may actually be associated with section 112 major sources
rather than section 112 area sources. An evaluation of the emissions
inventory compiled for this study found that more than 70 percent of the
estimated area source emissions are associated with categories which
almost certainly are not CAA section 112 major sources, such as
agricultural sources, dry cleaners, consumer and commercial products,
and residential fuel combustion. However, significant portions of the
area source emissions estimates for some other source categories, such
as chemical manufacturing, petroleum refining, oil and gas production,
and industrial surface coating, may be attributable to emitters which
would be classified as major sources under section 112. Consequently,
the relative contributions of area sources to benchmark exceedances in
this analysis may be greater than the contributions of emitters defined
as area sources under section 112.

**[4.2 Modeled Concentrations]{.underline}**

Comparison of modeled concentrations to the available monitoring data
for air toxics indicates that the model estimates have an overall
tendency to underestimate measured concentrations (Rosenbaum et al.
1998). A ratio of the modeled concentration to the monitored
concentration was calculated for a total of 736 annual averaged
monitored concentrations, obtained for 19 HAPs at 81 monitoring
locations. The geometric mean of the set of model-monitor ratios is
0.53, and 73 percent of the ratios are less than one. As described in
Section 2.1.4 above, only monitoring data sets with measurements below
the minimum detection level (MDL, or "non-detect level") totaling less
than 10 percent of measurements taken in a year were used in this
quantitative model performance evaluation.

It is possible that the findings of an overall tendency to underestimate
are biased by the exclusion of measurement data sets dominated by
observations below the non-detect level, since the data sets eliminated
will tend to be those with lower concentrations. To test this
hypothesis, additional model-monitor comparisons were conducted for the
13 HAPs measured in the Urban Air Toxics Monitoring Program with
measurements dominated by individual observations below the non-detect
level (this analysis is described in Attachment 5 of the modeling report
(Rosenbaum et al. 1998)). For these non-detect data sets, the modeled
concentration was compared to a range of possible concentrations, which
was calculated by assigning two values to each measurement below the
non-detect level: a lower bound of zero, and an upper bound of the
non-detect level itself.

For these more qualitative model-monitor comparisons, the modeled
concentrations were lower than the lower bound of the possible monitored
concentration range---an indication of model underestimation---in 57
percent of the 156 cases. The modeled concentrations were greater than
the upper bound of the possible monitored concentration range---an
indication of model overestimation---in 14 percent of the cases. The
modeled concentrations were between the lower bound and the upper bound
of the range in 29 percent of the cases, offering no clear information
about the comparison between modeled and monitored concentrations. The
high frequency of cases in which the modeled concentration is lower than
the lower bound of the possible range of modeled concentrations is
consistent with the conclusion that the general tendency is for the
modeled concentrations to underestimate concentrations found by
monitoring.

The tendency to underestimate outdoor concentrations of air toxics could
result in underestimation of the frequency with which benchmark
concentrations are exceeded. In addition, the modeled concentration
outputs do not capture spatial or temporal peak concentrations that
could be significant. The modeling approach used for this study
estimates annual average concentrations at the census tract level. A
census tract average concentration will not reflect areas within a
census tract, such as locations close to a stationary source or a major
roadway, which may have concentrations substantially greater than the
average across the census tract. Also, a long-term average concentration
will not reflect short-term elevated concentrations that may also have
important health effects. Consequently, a HAP concentration modeled in
this study which is less than a benchmark concentration for the HAP does
not mean that the benchmark is never exceeded within that census tract;
consideration of alternate spatial and temporal scales could potentially
identify additional benchmark exceedances.

In addition, the analysis in this chapter only presents the frequency
with which benchmark concentrations are exceeded, and does not consider
the magnitude of exceedance. A HAP which exceeds its benchmark
concentration in a relatively small number of census tracts may exceed
that benchmark by a large magnitude, and therefore may pose a greater
potential risk than another HAP which exceeds its benchmark in more
tracts but with a small magnitude of exceedance.

**[4.3 Benchmark Concentrations]{.underline}**

For this study, a set of benchmark concentrations was compiled from a
number of data sources, as described in Caldwell et al (Caldwell et al.
1998). Benchmark concentrations represent an estimated concentration at
which a lifetime daily exposure is unlikely to result in adverse health
effects, based on available hazard assessment data.

The benchmark concentration for cancer hazard is derived from the unit
risk, an upper-bound estimate of the excess cancer risk over background
incidence associated with a continuous lifetime exposure. Factors
including use of sensitive animal strains, tumor sites of uncertain
human relevance, and linear extrapolation to low doses can contribute to
uncertainty in estimating the risk in human population (Cogliano 1997).
Differences in the pharmacokinetics of pollutants between exposure
routes and species are expected, and can have influence on extrapolation
of observed responses in animals and humans (U.S. Environmental
Protection Agency 1994b).

This analysis emphasizes the inhalation route of exposure as benchmark
concentrations were applied to modeled ambient air concentrations.
However, health effects information is not always available for the
inhalation route of exposure. For cancer benchmarks, extrapolations were
needed to use available information from other routes of exposure. When
extrapolating between two different routes of exposure (e.g., inhalation
vs. oral), a number of factors are important for determining the
association between a specific dose and the degree of toxic response
engendered by a pollutant. These factors include differences by route of
exposure in (1) tissue distribution, (2) rate of delivery leading to
differing concentration profiles, (3) degree of metabolism, and (4)
response caused by an agent at its site of action across species and
among target tissue.

How such uncertainties affected the application of dose-response
information for this type of analysis is not clear (U.S. Environmental
Protection Agency 1994b). However, in limited comparisons of differences
between oral and inhalation dose routes, Pepelko concluded that the
carcinogenic potencies are not substantially influenced by dose route
(Pepelko 1990). However, the use of information extrapolated from oral
to the inhalation route of exposure involves greater uncertainty than
using that based on the inhalation route. This uncertainty is addressed
by assignment of cancer benchmark concentrations based on extrapolated
data to Tier II rather than Tier I.

Benchmarks representing noncancer risks from long-term exposure make use
of USEPA Reference Concentrations (RfCs) or similar values representing
nonccancer inhalation risks developed by other agencies. The RfC is by
definition an estimate with an uncertainty spanning perhaps an order of
magnitude. Although severity of effect is considered in the development
of RfCs, there is no numerical adjustment for severity. Considerations
of uncertainty are numerically represented in the derivation of RfCs to
account for differences in human sensitivity, extrapolation from animals
to humans, length of study, use of an observed rather than non-observed
effect level, and completeness of the database. These uncertainties are
address by use of conservative safety factors in derivation of the RfC;
however, an RfC is not derived when it is determined that the
uncertainties are too great (U.S. Environmental Protection Agency 1990).

In this study, only data on health effects via the inhalation route of
exposure were used in establishing noncancer benchmark concentrations.
No benchmarks for noncancer effects were developed through extrapolation
from data for the oral route of exposure; oral studies are limited as
indicators of non-cancer inhalation toxicity because of factors such as
portal of entry effects and liver "first-pass effects," as well as lack
of consideration of dosimetric considerations (U.S. Environmental
Protection Agency 1994b). For HAPs with no EPA inhalation RfCs,
California EPA reference exposure levels (RELs) and ATSDR minimal risk
levels (MRLs) were used and assigned to Tier II.

Limitations in the availability of toxicity data for HAPs must be
considered when assessing potential health impacts of these pollutants.
Approximately 20 percent of the modeled HAPs with a weight of evidence
indicating potential carcinogenicity do not have a cancer potency
estimate and half do not have a benchmark concentration for noncancer
health effects (Caldwell et al. 1998). Seventeen of the HAPs considered
in this analysis have either an EPA weight-of-evidence determination as
known, probable or possible human carcinogens or a recent National
Toxicology Program study reporting clear evidence of animal
carcinogenicity, but do not have carcinogenic potency estimates. For
example, styrene is considered to be a possible (Group C) human
carcinogen, but because it has no potency estimate, it was not possible
to determine the frequency with which modeled styrene concentrations
exceed a benchmark concentration. If styrene were assigned a default
potency estimate that is consistent with other Group C carcinogens,
modeled concentrations in a number of census tracts would exceed the
benchmark concentration.

Even for some of the ubiquitous pollutants identified in this analysis,
there is incomplete toxicity information. For example, benzene and
1,3-butadiene have both been associated with reproductive and
developmental effects (U.S. Environmental Protection Agency 1994b), but
currently have no benchmark concentration for such effects. In addition,
29 of the 148 HAPs included in this study have no Tier I or Tier II
benchmark concentrations for any effects even though there are previous
studies indicating some of these HAPs are of potential health concern
(U.S. Environmental Protection Agency 1994b). For example,
n,n-dimethylaniline is ranked by EPA as being of high concern for
noncancer effects, but quantitative hazard information is not available.

Another limitation in the toxicity information for the HAPs is in hazard
evaluation for chemical groups. Outdoor concentrations were modeled for
14 HAP chemical groups. It is difficult to assess the toxicity of
chemical groups, because they are comprised of a number of different
species. For example, the HAP listed as "mercury compounds" is made up
of several different constituents, including mercuric chloride,
elemental mercury, mercuric nitrate, and mercury (aceto) phenyl, all
with potentially different levels and types of toxicity. Also, the
toxicity of the individual members of the polycyclic organic matter
(POM) category varies significantly. This category is very broad and the
toxicity of many of its members has not been characterized. However,
many studies have shown the potential carcinogenic potency of polycyclic
aromatic hydrocarbons---a subset of the POM category---to be large (U.S.
Environmental Protection Agency 1993a). Assignment of an appropriate
benchmark to this category depends on the extent to which particular POM
constituents contribute to overall POM concentrations; differing
assignments of hazard potential estimates for POM may profoundly affect
estimates of the health risk posed by HAPs.

A further limitation of this analysis is that it only considers the
potential health impact of individual pollutants. Additive or
synergistic interactions among HAPs may pose a threat to public health
beyond that identified in this chapter. HAP concentrations that are less
than benchmark concentrations may pose a risk to health when they are
considered in combination with concentrations of other HAPs. Currently,
too little is known about how pollutants interact to fully evaluate the
potential health risks posed by exposure to multiple HAPs at
concentrations below toxicity benchmarks.

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[^1]: A few HAPs with available emissions data, such as chlorine and
    titanium tetrachloride, were not included in the modeling exercise
    because of their physical/chemical properties which make dispersion
    modeling difficult.

[^2]: Modeled concentrations for many of the HAPs may exceed more than
    one benchmark, as 60 HAPs have more than one benchmark concentration
    (separate benchmarks for cancer, non-cancer effects from long-term
    exposure, and non-cancer effects from short-term exposure). In
    Tables 7-12, HAP results are shown only for the benchmark with the
    most exceedances for each HAP.