This article is part of the monograph Advancing Environmental
Justice through Community-Based Participatory Research.
Address correspondence to J. Maantay, Dept. of Geology
and Geography, 250 Bedford Park Blvd. West, City University of New York,
Lehman College, Bronx, NY 10468 USA. Telephone: (718) 960-8574. Fax:
(718) 960-8584. E-mail: maantay@lehman.cuny.edu
This article is based on work supported (in part) by
a grant from the City University of New York PSC-CUNY Research Award
Program. The project title was "Mapping Asthma Incidence and Environmental
Hazards in the Bronx in New York City."
Received 13 August 2001; accepted 14 January 2002.
Mapping Environmental Injustices
Although the mainstream environmental movement of the 1950s and 1960s alerted
the public to the dangers posed by pollution and environmental degradation,
these impacts on people's health and the environment were not generally acknowledged
(or thought) to be spatially or socially differentiated: everyone was presumed
to be affected just about equally. The understanding that environmental problems
may impact certain locations and people more than others (and in a predictable
pattern based on race and income) is a relatively new concept that gained nationwide
attention in the late 1980s.
Environmental injustice can be defined as the disproportionate exposure of
communities of color and the poor to pollution, and its concomitant effects
on health and environment, as well as the unequal environmental protection and
environmental quality provided through laws, regulations, governmental programs,
enforcement, and policies (1-3).
Within the past decade it has become increasingly prevalent to try to map
instances of environmental injustice, usually by geographically plotting facilities
or land uses suspected of posing an environmental and human health hazard or
risk, and then trying to determine the racial, ethnic, and economic characteristics
of the potentially affected populations compared with a reference population.
This often results in dramatic maps showing toxic facilities concentrated in
areas with high proportions of African Americans, Latinos, or Native Americans
(4-8). Mapping became a favored method among researchers
attempting to determine the existence of environmental injustice. Additionally,
the wealth of environmental and demographic data now available on the Internet,
as well as the proliferation of websites with interactive mapping applications
available, have brought environmental justice mapping within reach of virtually
anyone (9).
Although such maps can be unusually effective in visually demonstrating the
disproportionate spatial distribution of noxious or hazardous facilities, these
maps have also come under scrutiny and been criticized for being misleading
and inaccurate, and their findings have often been contradicted by other spatial
analyses. Mapping a phenomenon such as environmental injustice is not a straightforward
exercise, and the difficulties encountered in producing such spatial analyses
leave the maps open to a variety of interpretations and second-guessing. Just
as no map can be viewed as an objective embodiment of the real world, maps depicting
environmental injustice are also social constructions, and therefore subjective
and based on assumptions (10,11).
A fundamental concern with mapping environmental injustice is that it does
not yield definitive findings about differential exposure levels or health outcomes
for the population in proximity to the noxious facilities or land uses. This
drawback makes these studies less useful in conclusively demonstrating (and
measuring) the correspondence between the location of potential environmental
burdens, exposures, and health effects. However, it is feasible to develop methods
and tools for producing more meaningful spatial analyses. Some of the issues
that are contested in mapping environmental injustice, and the technical and
analytic difficulties encountered in such mapping projects, are outlined below
(12), along with some suggestions for using Geographical Information
Systems (GIS) to better assess and predict environmental and health conditions.
The Findings of Environmental Justice Spatial Analyses
The groundbreaking environmental justice study, "Toxic Wastes and Race in
the United States: A National Report on the Racial and Socio-Economic Characteristics
of Communities with Hazardous Waste Sites," was produced in 1987 under the auspices
of the United Church of Christ's Commission for Racial Justice (4). The
report presented maps of the locations of the country's hazardous waste facilities
in conjunction with the characteristics of the nearest populations (by ZIP code),
using indicators such as race, ethnicity, and income. Compared with the areas
that were not hosts to a hazardous waste facility, the host areas showed an
unmistakable statistical and spatial correspondence to minority populations
(13).
If "Toxic Wastes and Race" was the seminal study that helped propel the issue
of environmental justice to the forefront of the public's consciousness in the
late 1980s and early 1990s, it was certainly not the first environmental justice
study. These issues have been researched extensively since at least the late
1960s, and study after study throughout three decades has shown the existence
of disproportionate environmental impacts based on race and/or income (14,15).
Since then, many other researchers have used mapping exercises to try to substantiate
or refute the existence of environmental inequities.
In this article I review 13 GIS-based environmental equity studies conducted
within the past decade (Table 1). In evaluating these studies, it is important
to understand exactly what is being mapped, and how it is being measured.
Limitations of Mapping Environmental Justice Issues
Many environmental justice mapping studies conducted in the early to mid-1990s
had definitional, conceptual, methodologic, and data problems, which limited
their usefulness and raised questions as to the ability of GIS to assess environmental
health or equity. Some of these concerns have since been at least partially
addressed; others have not.
Is Injustice Predicted by Race or Class?
Some commentators have raised what may be termed philosophic questions, such
as Can racism as a phenomenon be isolated? and Can discriminatory intent be
proved? (16,17). Others have questioned whether environmental injustices
are merely
by-products of our market-based economy and due more to differences in land
values than discrimination (18,19). We are not likely to reach consensus
about these issues or effectively prove or disprove them.
Is income or race the deciding variable in exposure to pollution (20)?
The findings of many environmental justice studies are in conflict on this very
point: some clearly show race as the determining variable by controlling for
income and still finding disproportionate burdens on minorities (5,21),
whereas other studies control for race and find that income is the more statistically
significant variable determining disproportionate environmental burdens (22-24).
In exploring which variable, race or economic status, is more important in
predicting environmental injustices, some researchers have found that although
it certainly is not the most affluent communities that bear the burden of pollution,
it is not the poorest communities either (25,26). The relationship between
income and proximity to environmental hazards is nonlinear, according to these
studies, with the working-class locations more often hosting these facilities.
The authors speculate about why this may be the case and suggest that the very
poorest communities have so little economic activity that they are too poor
to attract even a noxious facility. The most affluent communities have the economic
and political power to successfully oppose such facilities from locating in
their area. In these studies, however, race is still strongly associated with
the locations of hazardous facilities.
Of course, the real issue is that minorities are disproportionately represented
in the lowest economic subgroups. Race and low income are inextricably linked,
and therefore it will be difficult to overcome the race/income confound in the
base data (27). As concluded in the article "Environmental Racism in
Southern Arizona":
This research into the geopolitics of pollution finds that economics and
race are inextricably intertwined. Those scholars who attempt to isolate economics
from racism as causal factors in explaining environmental inequity, therefore,
are missing the point. In fact, such efforts to tease out, for analytical
purposes, the effects of each of these discrete variables on pollution impacts
can itself be seen as a form of racism. Certainly, from the perspective of
people of color having to deal with a dirtier environment, the effort to isolate
class and race makes very little sense. (8)
Another angle that has been explored through GIS is that of which came first--the
nuisance or the people. Been and Gupta (22) conducted a longitudinal
study that looked at the population characteristics of the areas surrounding
noxious facilities at the time the facilities were sited, and then every 10
years thereafter. The premise was if it could be shown that the minority population
came to the area after the facility was in place, no discriminatory intent could
be established and presumably there would be no environmental injustice in the
siting. The problem with this line of reasoning is that it does not take into
account that minorities are often very constrained as to where they are able
to live, and is it not racism that restricts their choices to such undesirable
places from which other people with more choice and money have fled?
As to the issue of discriminatory intent,
We think it is irrelevant whether environmental injustice represents conscious
racism, or classism, on the part of policy-makers, either now or in the past.
Attempting to prove intent is a fool's errand, particularly when there are
so many variables in the mix. What truly matters is how the problems are addressed
by policy-makers and business interests in the present. In other words, who
benefits from current siting and pollution control practices, and who pays
the consequences? Further, how are these impacts affected by ongoing policies
and enforcement practices? (28)
The concept of race operates on many levels to influence the potential for
exposure to environmental hazards and disease. Race is often a proxy for other
conditions that pose risks or exacerbate exposures. For instance, minority children
suffer disproportionately from lead poisoning. Poor nutrition has been recognized
as a contributing factor to childhood lead poisoning--nutritional deficiencies,
especially in iron and calcium, increase children's susceptibility to lead toxicity.
According to studies, minority children are more likely to be at greater risk
of marginal nutritional status than White children (29). This is in addition
to the fact that minority and low-income children are disproportionately exposed
to lead in lead-based paints in older housing units as well as in high-traffic
inner city areas where the contamination from lead-based gasoline still remains
years after lead has been banned from gasoline.
Similarly, approximately 75% of the tuberculosis cases in the United States
are people of color (30). Poverty, residential segregation by race, and
housing overcrowding have been found to drive tuberculosis rates, and these
factors disproportionately affect minorities. The discrimination experienced
by minorities in the housing market may be emblematic of a whole range of conditions
helping to make minorities more susceptible to tuberculosis (31-33).
Thus, the context of race, rather than race itself, can be viewed as a risk
factor.
Race and income, though the most prevalent indicators in the selection of
disadvantaged populations for environmental justice research, should not be
the only variables of concern. There are vulnerable populations other than the
poor and minorities who may also be disproportionately at risk, such as the
very young, very old, pregnant, immune compromised, infirm, and future generations.
This poses difficulties in appropriately and comprehensively choosing the populations
to study.
African-American, Hispanic-, Native- and poor Americans seem to be the focus
of attention. The young and the elderly should also be included. But should
recent immigrant groups also be included? Or must they also be poor? In addition,
consideration should be given to future generations. But how should future
generations be represented? One way is by including aquifers and forest areas,
salt-water swamps, and endangered species, all of which may be extremely important
to future generations, as well as those already living. (34)
Environmental justice, in this more inclusive definition, would apply not
only intragenerationally (equity for all people currently alive) but also intergenerationally,
taking into account equity for future generations.
What Is Counted as a Hazard?
What types of facilities should be included in determining the existence of
disproportionate environmental burdens? Many of the studies reviewed focus on
only one set of hazardous land uses, such as transfer, storage, and disposal
facilities for hazardous waste (TSDFs), Superfund sites, Resource Conservation
and Recovery Act (RCRA) facilities, or Toxic Release Inventory (TRI) facilities
(Table 1). This is done primarily because these types of facilities are registered
and tracked on a national level, and consistent information is available on
each facility, thus allowing valid comparisons to be made at the national level.
However, studying the impacts of only one set of facilities produces misleading
and incomplete results. In many communities, the most egregious offenders are
the small electroplating plants, auto-body welding shops, drycleaners, and waste
transfer stations. These types of facilities are typically not required to register
with the federal government, as are TSDFs or TRI facilities. The small polluters,
which cumulatively may be creating more of an environmental burden than one
large facility, are virtually unregulated and undetected. This makes mapping
their impacts problematic. There is no database available for small geographic
areas, and certainly none on a statewide or national level, making statewide
and nationwide comparisons impossible. Most environmental equity mapping has
been restricted to facilities that are on federal lists, because these lists
are standardized and easily obtainable, but this just touches the tip of the
iceberg as far as environmental burdens in many communities. Analyzing only
the impacts of TSDFs or TRI facilities diminishes the magnitude of the total
likely impact. Because of reporting deficiencies and lack of comprehensive data,
total cumulative impacts from all noxious land uses within a given geography
cannot be readily calculated.
An assumption that all noxious facilities are equally noxious is another source
of problems in mapping environmental injustices. Many of the spatial analyses
assume that one TRI facility, for instance, is equivalent to any other, but
amounts of toxic emissions vary widely among TRI facilities, and emission levels
and toxicity are often not mapped or factored into the analysis. Facilities
in communities of color are typically worse polluters than those in White neighborhoods,
receiving less regulatory enforcement and more lenient fines if discovered (1,35).
These factors of difference are mapped more rarely, with many researchers focusing
on simple counts of noxious facilities of one type or another or a binary measure
of "facility" or "no facility" within a certain geography.
How Do We Determine Exposure Potential?
Spatial studies of environmental justice analyze the characteristics of the
population potentially exposed to a hazardous land use. Exposure is often determined
simplistically and defined as whether the population is in the same ZIP code,
census tract, county, or municipal boundary as the noxious facility. This has
the obvious drawback that one could live right across the county line from a
facility just yards away, but for the purposes of the analysis would not be
considered impacted by it, whereas one could live on the opposite side of the
county miles away and still be considered impacted because of being within the
same county as the facility. This becomes less of a problem for the finer geographic
levels of analysis but is nonetheless not a very accurate way of characterizing
the potentially impacted population.
In other analyses actual proximity to the facility is taken into account by
constructing buffer zones of specified distances around the facility, capturing
the demographic data for the entire population within the buffer regardless
of what political or enumeration district they are in. The buffer zones are
intended to act as surrogates for the areas of impact and are usually established
as circles with a radius of one-half mile or 1 mile, or other appropriate distance,
from the noxious land use.
Depending on which method is used, there can be substantial differences in
estimating the magnitude and characteristics of populations affected by noxious
land uses. Figures 1 and 2 illustrate the differences in the findings using
the spatial coincidence method versus proximity analysis. The locations of permitted
waste-related facilities in the Bronx, New York City, have been geocoded
and overlaid on the census tract database (36). Figure 1 indicates the
location of these facilities in relation to the percentage of population in
each census tract that is "minority" (37). Figure 2 shows a comparison
between the spatial coincidence method and proximity analysis in determining
the potentially affected population. Using the demographic information for only
those tracts housing a waste-related facility (spatial coincidence method) does
not adequately capture the potential for exposure, as can be seen by the multitude
of facilities on the edge of tract boundaries. Additionally, some tracts are
very small, whereas some are very large, leading to a misrepresentation of the
exposed population. Average household income was also calculated using the two
methods, with similar results. When the locations of the Bronx TRI facilities
are added to the waste-related facilities and population characteristics are
calculated using the two methods, we again can see the differences in the numbers
obtained (Table 2).
 |
Figure 1. Distribution
of waste-related facilities in relation to minority population, Bronx,
New York. Data from U.S. Bureau of the Census (85), New York
City Department of Sanitation (86), New York State Department
of Environmental Conservation (87), and New York City Department
of City Planning (88).
|
 |
Figure 2. Comparison of
spatial coincidence method and proximity analysis in determining the
characteristics of the population affected by waste-related facilities.
Data from U.S. Bureau of the Census (85), New York City Department
of Sanitation (86), and New York State Department of Environmental
Conservation (87).
|
Proximity analysis is a more useful means of analysis, but it still does not
definitively determine the potential for exposure.
There is little known about the relationship between distance from a pollution
source, such as a hazardous waste site, and actual health risks. . . . Accurate
estimation of human exposures to hazardous air pollutants across all levels
of geographic aggregation is constrained by the paucity of suitable monitoring
methods, relevant ambient measures, and validated models for predicting exposures
to populations of interest. (23).
Assumptions are also made that exposure risk is distributed equally within
a given geography. The studies that try to account for risk based on distance
assume that the risk a facility poses bears some relationship to proximity to
the facility, an assumption that may be inaccurate in many cases.
A better unit of analysis would be one that was based upon the actual distribution
of the risk of the facility, which would depend on the type of substances
the facility handled, wind patterns, the hydrology and geology of the site,
transportation routes to the facility, and many other factors. (22)
Not only are these factors very complicated to assess, but data are often
simply not available, or are not available in a uniform way for the entire study
area and reference comparison areas. Other methods for determining exposure
potential, such as dispersion modeling, are discussed below.
How Do We Measure Exposure?
A critical issue in these environmental justice studies is the lack of a reliable
risk exposure index or proxy.
[Previous studies] of environmental equity lack both a valid measure of
the sources of pollution to which people may be exposed, and relatedly, a
model that describes the relationship between proximity to those sources and
the likelihood of exposure. (21)
Actual risk from TRI facilities, for instance, is dependent on many variables
such as type of facility, substances emitted, quantities emitted, height of
smokestack, exit velocity, wind direction and speed, pollution controls used,
and topographic factors. Simple distance proximity equations are inadequate
for measuring exposure.
We found that risk-based evaluations can lead to different conclusions about
environmental equity than proximity-based evaluations. The differences can
be attributed to two principal factors. One is that the impact areas in risk-based
evaluations are strongly influenced by the direction of the wind. The other
is that the sizes of the impact areas in risk-based evaluations vary and are
generally much larger than the circles used in proximity-based evaluations.
(6)
The Geographic Unit of Analysis
The findings of some environmental justice mapping studies have been diametrically
opposed to those of others. For instance, the nationwide study by Perlin et
al. of TRI facilities at the county level shows a positive correlation between
income and environmentally burdensome land use (23), with household income
increasing in relation to the presence of TRI facilities, whereas the statewide
study by Pollack and Vittas of TRI facilities in Florida at the census block
group level shows a negative correlation (21), with household income
declining in relation to the presence of TRI facilities. Many of these contradictions
and discrepancies can be traced to the geographic unit of analysis used in the
study, often referred to as the Modifiable Area Unit Problem (MAUP). Glickman
and Hersh (6) show that altering the geographic boundaries of the study
area has dramatic implications for the results of the analysis. In their study
of industrial hazards in Allegheny County, Pennsylvania, they found that
The choice of unit of analysis will affect even the most basic findings
of an environmental equity study. Had we used only block groups to define
'community' we would have found contrary to expectations that in TRI communities
the proportion of blacks and minorities is slightly lower than in non-TRI
communities. Similar results hold for census tracts. This pattern is reversed,
however, when we look at the proportions for the combined half-mile radius
circles around TRI facilities vs. the areas beyond the circles. We also see
that the proportion of blacks and minorities is substantially higher in municipalities
with TRI facilities than in those without such facilities. (6)
Generally speaking, data aggregated at higher levels of governmental unit
(county or city, for instance) will be less reliable as indicators of disproportionate
burdens, and less accurate in identifying the affected populations, than data
aggregated by smaller units such as census block groups or blocks. Because there
is so much variation in demographics and facility location within the larger
geographic units, impact and burden are impossible to determine, and comparison
among geographic units becomes almost meaningless. Unfortunately, the availability
of data is often what dictates the level of aggregation.
To reflect a potential environmental health-based concept of risk, the boundaries
should relate to exposure or risk from the site; however, a single boundary
reflecting all variations in toxicity and contaminant fate and transport for
each chemical present plus variabilities in the duration of human exposure
and vulnerability would be virtually impossible...The scale of analysis chosen
is often dictated by expediency, determined by how existing data bases are
aggregated. . . . (38)
Therefore, the selection of the unit of analysis may, in fact, have little
relationship to the actual geographic extent of exposure and risk, yet can shape
the outcome of the analysis.
The Potential of GIS for Environmental Health and Equity
Research
An effective public health management program demands an understanding of
the spatial relationships between pollution and health.
GIS can significantly add value to environmental and public health data
in areas such as exploratory data analysis, hypotheses generation, confirmatory
data analysis, and decision making. (39)
For instance, mapping disease rates geographically and temporally may shed
light on previously unrecognized patterns, which will suggest answers, or at
least provide a focus and direction for further study. GIS can also be used
to select case study areas meeting specific criteria and to create and test
hypotheses relating to environmental risk factors. GIS can combine health outcomes
on the individual level with exposure data aggregated at the geographic unit
level (census tract, health district, etc.) and then model potential exposures,
for use in overlaying the disease incidence data (40).
Although health outcomes were not a specific part of the studies reviewed,
GIS-based environmental equity research nevertheless provides a valuable tool
for health professionals. Decision making and policy formulation are enhanced
by spatial information. Identifying the population likely to be affected by
environmental burdens allows more effective educational intervention and the
planning of health care delivery systems (41). It may also help point
out the geographic areas where health assessments should be a priority.
Integration of Modeling and Statistical Software
with GIS
Some of the problems in using GIS for environmental health and equity research
are due to software deficiencies such as the lack of complex environmental modeling
functions integrated within GIS programs. Having to use external modeling applications
is more cumbersome for the researcher and limits the role of GIS to primarily
data organization and storage, data exploration, and display (42). This
situation could be improved, however, if the software developers had a demand
to respond to. As more researchers use GIS in their work, it will be easier
to justify the costs of development to integrate GIS and modeling software.
This integration has already occurred to a substantial degree with GIS and
complex geostatistical functions. Many geostatistical functions have been incorporated
into a number of major GIS software packages or are available as extensions
(separate add-ons to mapping programs). For instance, the Spatial Analyst extension
is available for ArcView 3.x software (43), and both Spatial Analyst
and ArcGIS Geostatistical Analyst have resident spatiotemporal analytic tools.
These include Inverse Distance Weighting and Spline methods of spatial interpolation
on point data, enabling estimates of data values at unsampled locations (44).
Point data might represent air monitoring sites; facilities emitting pollution;
soil, air, or water sample locations; or ZIP code centroids with disease rates
attached. Kriging, a linear interpolation method, is available in ArcView through
an Avenue script (prewritten applet in the programming language native to ArcView
3.x). Kriging allows predictions of unknown values of a random function from
observations at known locations by using a model of the covariance of the random
function and accommodating and estimating the underlying trend (45).
Spatial regression and geostatistical models can also be employed by using
specialized software such as S+ Spatial Stats, which has been designed by a
software developer (46) to interface almost seamlessly with the ArcView
3.x software (47). Scripts written by individuals and widely accessible
via user group web sites can also be used directly with industry-standard mapping
packages to perform cluster analysis such as K-function and Gi* statistics (48,49).
The ease of surface modeling using these built-in and loosely-coupled local
and global interpolators will change the way we measure and predict environmental
burdens and greatly improve the speed and efficiency of data exploration.
Building Better Databases
The real constraint in using GIS for health and equity research is not software,
however, but data deficiencies. Incomplete, inaccurate, and nonexistent information
does not necessarily reflect our state of knowledge about the issues but may
be merely an indication of our society's informational (and funding) priorities.
For instance, it is virtually impossible to create a measure of exposure and
risk without more detailed and careful data on actual emissions and ambient
conditions. Analysis must be able to take into account measured quantities to
estimate cumulative impacts from multiple sources of pollution and synergistic
impacts from combining pollutants. Studies that investigate exposure to only
one type of hazard are not helpful in determining the full extent of the impacts.
Many of the databases relied upon by researchers, such as TRI, are notoriously
inadequate for detailed modeling. TRI information is self-reported by the facilities
and is based on estimated emissions not measured quantities (50,51).
Some researchers compound the errors by aggregating releases to soil, water,
and land as one quantity of raw pounds, although the effects on human health
and the environment differ markedly by media.
Reliable health assessments are necessary for environmental health and equity
research to progress to the next level. Issues such as patient confidentiality,
lack of data sharing among hospitals, private doctors, and other health care
providers, and few mandatory reporting mechanisms all conspire against comprehensive
health databases. There is no national registry database for chronic diseases
such as asthma, for example.
GIS analysis has been less successful (or less well used) in addressing issues
such as population mobility, occupation, and genetic predisposition, although
these all potentially play a role in the relationship between exposure and health.
An understanding of population mobility, for example, is crucial to tracking
the environmental exposures over time of susceptible populations, but databases
detailing population movement have not been widely developed. An index of residential
neighborhood stability could be created for community comparison purposes, but
this would be of limited value in monitoring spatial correspondence of specific
hazards and health outcomes. Some of the same data deficiency problems exist
for attempts to spatially link occupation and health hazards and to monitor
specific populations by residence location.
For relatively small geographic areas, however, it is possible to develop
something approaching a comprehensive hazards database and to perform a more
complete health and population survey. In the Greenpoint-Williamsburg community
of Brooklyn, New York, publicly available environmental databases were assembled
into a GIS, then supplemented with local knowledge bases, such as a detailed
lot-by-lot land use inventory, and updated regularly to keep the data current
(52,53). Because many hazardous facilities are not tracked at a national,
statewide, or municipal level, community-led inventories and monitoring of local
conditions are essential in assessing environmental loads.
Development of an Exposure Index
Several environmental health and equity studies have employed an exposure
index to reflect some quantification of a population's risk from environmental
burdens beyond simply proximity to or presence of a hazardous facility. In earlier
studies exposure was often a measure of facility capacity, such as tons per
year of hazardous waste handled (26) or total pounds of pollutants released
per year, divided proportional to area in the geography of concern (17)
or divided among the potentially affected population [as in the Population Emission
Index of Perlin et al. (23)].
The total quantity of chemicals handled or released by a facility does not
directly correspond to health impacts, however. Bowen et al, in their study
of TRI facilities in Ohio (24), took into account not only the number
of raw pounds of chemicals released but also pounds adjusted for toxicity, using
Threshold Limit Values (TLVs). Although TLVs are available for many of the chemicals
on the TRI list, it remains a somewhat problematic index for health and equity
assessments, generally having been developed and used to gauge occupational
safety among a healthy worker population. It is unknown how well this index
estimates hazard for more vulnerable populations such as children, the elderly,
pregnant women, and the immune compromised.
The study of hazardous facilities in Minneapolis, Minnesota, by McMaster et
al. (54) refined the measurement of exposures. After ranking and mapping
facilities by proportional symbols according to total chemical poundage released
per year, they applied the Pratt Index, which compares chemicals based on their
environmental behavior and toxicity by calculating a ratio of potential exposure
to toxicity (55). This study found that minority groups and poor people
were not only more likely to live in proximity to hazardous land uses, but were
also burdened by a higher concentration of such facilities, with a higher level
of exposure to toxic substances.
In a study of TRI facility impacts in Oregon, Neumann et al. (56) used
a media-specific Chronic Toxicity Index (CI), which incorporates chronic oral
toxicity factors for carcinogens and noncarcinogens to estimate and compare
relative hazards from TRI releases. Although not useful for identifying population
or individual risk, it is an excellent preliminary screening method.
The ranking of TRI emissions using the CI combined with knowledge about
the demographics of the communities at risk of exposure such as population
density, race, ethnicity, socioeconomic status, and age is an attempt to help
set priorities for future risk or public health assessments, epidemiological
studies, and basic research on cellular mechanisms associated with environmental
health problems. (56)
The limitations of data restrict the completeness of the CI as a measure of
exposure. The CI is based on oral toxicity because inhalation reference doses
for TRI chemicals are not available. However, inhalation reference concentrations
would obviously be more useful in estimating hazard from TRI air releases. The
CI also does not measure acute toxicity, which would be useful in identifying
populations at greatest risk from industrial accidents.
To be more meaningful, an exposure index should reflect an estimate of total
environmental loads resulting from all types of pollutants, i.e., a cumulative
load index. Theoretically, any given location could be assigned a number indicating
the total environmental load borne by people in that geography. A weighting
and ranking index could be developed with a unit of measurement based on toxicity
and concentration of each pollutant, weighted for severity of potential impact
from exposure. This methodology, although useful in a planning and policy context,
would be of more limited use for regulatory and enforcement purposes, given
the structure of current laws, as it is not based on existing legal standards.
However, the cumulative impact index would take into account effects of pollutants
that individually may not exceed thresholds but when considered together may
constitute an impact to human health (57). Linking the cumulative load
model to the GIS would allow visual inspection of the spatial distribution patterns
and complex spatial analyses to be performed and result in a block-specific
score of carcinogenic and noncarcinogenic pollutant loads.
Development of an aggregate environmental load index would be of value in
establishing baseline profiles of communities for comparative purposes and documenting
the relative environmental loads of various communities. The aggregate environmental
load index would also allow communities with the highest environmental loads
to be targeted for pollution prevention and remediation programs and enable
examination of the correspondence between incidence of environmentally linked
diseases and environmental loads. GIS could also facilitate research into the
synergistic effects of toxic substances by pinpointing the geographies subjected
to such environmental loads and comparing them with known or suspected health
problems in these areas.
Clearly, further refinements in exposure indices will help estimate potential
for health effects from hazardous facilities. An index that incorporates not
only a toxicity factor but also information about persistence and environmental
fate of toxic chemicals such as discussed by Jia and Di Guardo (58) will
advance GIS-based health and equity research significantly.
Advanced Proximity Analysis, Dispersion Modeling,
and Fate and Transport Simulation
In addition to exposure indices reflecting toxicity and other measures of
impact, advances have been made in more precisely identifying the geographic
extent of the exposure from hazardous facilities. These methods include dispersion
modeling of airborne pollution and flow and transport modeling of contaminants
in subsurface media, as well as more advanced methods of proximity analysis.
For instance, Sheppard et al. (59), in a study of the distribution
of hazardous land uses in Minneapolis, Minnesota, developed a Proximity Ratio
that was used with both the spatial coincidence and buffering methods to determine
affected populations. The ratio was computed by dividing poverty rates for specific
population groups in geographies containing a hazardous facility by poverty
rates for that group in geographies without such a facility. A high proximity
ratio means that a particular group living near a hazardous facility is more
likely to be in poverty than the equivalent nonproximate group. They found that
for all groups studied (African Americans, Latinos, Native Americans, non-Hispanic
Whites, and children under the age of 5 years) proximity ratios exceeded 1,
meaning that people within certain distances of a hazardous facility are more
likely to live in poverty than their counterparts outside the buffers or census
tracts containing the facilities.
To make sure these ratios reflect a true significance and do not occur just
by chance, a randomization routine was run on the TRI location data. Through
Monte Carlo simulation, 1,500 possible locations for the TRI facilities were
generated to test whether the pattern of higher poverty rates near hazardous
facilities was coincidence and if these high poverty rates would be observed
if TRI sites had been located randomly within Minneapolis. The proximity ratios
were found to be unusually high compared with those that might have resulted
by chance.
Another technique used to improve the assessment of impacted areas and populations
is detailed in the study by Neumann et al. (56). In addition to buffering
facilities and estimating exposure of populations within the TRI buffers using
the CI, as mentioned above, they also buffered the census tract centroids. By
doing this they were able to capture information on populations exposed to multiple
sources of pollution by aggregating emissions from all facilities within the
centroid buffers. This is an important consideration in dense urban areas where
hazardous facilities may be close to other hazardous facilities and proximate
populations are at risk from exposures to emissions from multiple facilities.
This yields a more realistic estimation of actual exposure.
Provided that detailed facility information is available, dispersion modeling
may offer the best means of determining the geographic extent and severity of
exposure. By using mathematic models executed externally to GIS, the spatial
patterns of the average annual concentration of each pollutant emitted to the
air by a hazardous facility can be estimated. Glickman and Hersh used a variety
of models developed by the U.S. Environmental Protection Agency (U.S. EPA) and
the National Oceanic and Atmospheric Administration (NOAA) in their study of
hazardous facilities in Pennsylvania (6). The Areal Locations of Hazardous
Atmospheres (ALOHA) model (60), for instance, was used to determine the
worst-case chemical in each facility, i.e, the one with the longest plume. A
probability distribution of wind speeds and directions was factored in to create
a probable impact area for each facility, creating a plume buffer that was brought
into the GIS and overlaid on the census data (similar to circular buffers used
in other studies) to determine the characteristics of the exposed populations.
Plume extent was combined with dose-response rates to yield risk estimates
(average individual risks) measured in terms of the per capita expected number
of premature deaths in a lifetime. The analysis used two factors to form a toxicity
weight: a measure of potency for carcinogens, and reference dose (RfD), a measure
for noncarcinogens. The total volume of emissions was multiplied by the toxicity
weight to derive a hazard rating.
Dispersion modeling was also used in research by Chakraborty and Armstrong
in Des Moines, Iowa (61). The racial and economic characteristics of
the populations exposed to toxic releases from TRI facilities based on various
circular buffers were compared with those within plume buffers obtained through
dispersion modeling. A composite plume buffer was developed based on the largest
chemical release at each facility and averaged weather conditions. Their research
found that a larger proportion of minorities and people below the poverty line
live within the plume buffers compared with the circular buffers.
A public health assessment study being conducted by the Agency for Toxic Substances
and Disease Registry (ATSDR) is using air dispersion modeling integrated with
GIS to determine the geographic extent of exposure and the demographic characteristics
of the population affected by two phosphate-processing plants near an American
Indian reservation in Idaho (62). Using the U.S. EPA industrial source
complex model, particulate matter (PM) emissions will be modeled based on specific
information about area topography and meteorology. This will produce concentration
isopleths (contour lines) of particles smaller than 2.5 µm in diameter
(PM2.5--those of greatest health concern because they can penetrate
the sensitive areas of the respiratory tract). These isopleths will be imported
into the GIS and transformed into concentration polygons, which will be overlaid
with census data. The overlay analysis will clip the demographic information
of people who have been exposed to PM2.5 above the health-based standard,
reflecting the concentration polygons predicted by the air dispersion modeling.
The demographic data about total population exposed, total susceptible populations
exposed, and the socioeconomic status of persons exposed will be obtained and
compared with address-geocoded mortality data for respiratory and cardiopulmonary
deaths. By performing a point-in-polygon analysis, it can be determined if the
geocoded addresses for the deaths are within the polygons representing a geographic
area where people have been exposed at levels of health concern.
The report concludes with a number of cautions about the proposed methodology,
and the limitations of GIS and air dispersion models in exposure assessments:
The problems of areal interpolation and the fallacy of the homogeneous polygon
must also be considered carefully when evaluating the method used to determine
the demographics of the exposed population defined by the air dispersion model.
The polygons that define the various exposure levels predicted by the air
dispersion model will not correspond to the US Census Bureau's reporting units
(e.g., census tracts or block groups, etc.). Furthermore, the populations
within the census units are not evenly distributed. Therefore, an overlay
analysis method that does not provide some estimate of the population densities
within each census unit will likely produce much exposure misclassification.
ATSDR uses an area proportion program (a script written in Avenue, the programming
language of ArcView GIS [ESRI, Redlands, CA]) that is good for many applications;
however, it assumes that a population within a given census reporting unit
is evenly distributed. . . . Other estimates are being evaluated that provide
better estimates of population densities within the census reporting units.
The two methods currently being evaluated are the kernel density method and
the census control method. Both of these methods use techniques that "disaggregate"
the census reporting units, helping to alleviate the areal interpolation problem
and avoid the fallacy of the homogeneous polygons. . . . An ecologic study
design based on a GIS analysis carries with it unique methodological issues
beyond those that may be encountered in other epidemiologic designs. Ecologic
fallacy, disease and exposure misclassification, and control for confounding
must be carefully considered when designing an ecologic study and in interpreting
its results. (62)
In "Establishing Links between Air Quality and Health: Searching for the Impossible?",
Dunn and Kingham outline some of the problems associated with dispersion modeling
(63). The models require detailed inputs about emissions and facilities
that may not be available or accurate, and they rely on assumptions about meteorologic
and topographic conditions that may not reflect reality. Small differences in
terrain and building configurations can affect the behavior of airborne contaminants,
and these fine differences are difficult to represent adequately in a model.
Additionally, most of the models are based on point-source pollution (from a
smokestack) and do not take into account fugitive emissions (from non-point
sources) or pollution from linear sources such as roads. Results of dispersion
modeling should therefore be treated with caution.
GIS have also been used to assess fate and transport of contaminants in the
subsurface environment. As with air dispersion modeling, groundwater flow models
are generally executed outside the GIS environment, with results brought into
the GIS in the form of contaminant concentration isopleths, which are then overlaid
with the demographic data to assess the extent and characteristics of the exposed
population. An exposure assessment case study conducted by ATSDR attempted to
link contamination from environmental sources with increased health risk to
humans (64). In "Exposure Assessment of Populations, Using Environmental
Modeling, Demographic Analysis, and GIS," an external mathematic model using
a finite-element Galerkin procedure provided the researchers with contours delineating
the geographic extent of groundwater contamination from trichloroethylene released
from an industrial facility (64). Various groundwater modeling and simulation
techniques are well established, and those used in this study include steady
layered aquifer model and contaminant transport in layered aquifer media, which
were run to simulate different scenarios based on various assumptions about
contamination levels and remediation plans. Because the industrial plant had
continued to contaminate the groundwater for more than 20 years, and nearby
residences were eventually connected to town water supplies and thereafter presumably
no longer exposed to the contaminants in the groundwater, the study had a temporal
as well as a spatial component.
By integrating the results of the modeling with the GIS and demographic databases,
the researchers were able to obtain a snapshot of the exposed populations. However,
because this was a longitudinal study exploring exposures over time, population
mobility is a factor in assessing human health impacts. Availability of additional
demographic information on the distribution and mobility of households would
facilitate the generation of more precise spatial and temporal exposure patterns
that could easily be accommodated by methodology described by Maslia et al.
(64). As with many of the other studies discussed, the lack of key data
is a prime impediment to precision and accuracy in exposure assessments.
Conducting Neighborhood-Scale Analyses
Most environmental health and equity studies have been conducted at the national,
statewide, regional, or city level of analysis, as evidenced by the majority
of studies reviewed in this paper. Because of the volume and type of data required
to accurately inventory and assess existing conditions and to model future conditions,
it is likely that neighborhood- or community-level analysis will be more feasible
and useful than studies of larger geographic extents. By definition, studies
covering larger geographies use coarser-resolution data and cannot pinpoint
as accurately the spatial patterns and connections that may exist.
Neighborhood-scale studies also have the advantage of being able to incorporate
local knowledge bases, which can be used to augment (and verify the accuracy
of) publicly available data sources on environment and health. For instance,
communities can inventory the locations of hazardous facilities that do not
appear on any state or national list, such as drycleaners, solid waste-related
facilities, junkyards, auto bodyshops, small industrial facilities that fall
beneath the reporting thresholds for TRI, or coal-burning schools, as well
as confirm and cross-check the locations and types of facilities listed by
governmental permitting agencies. It is also less complex to aggregate exposures
from multiple and varied sources of pollution at a neighborhood scale, and
more likely that necessary informational inputs can be obtained in a comprehensive
way for modeling purposes. The locations of sensitive populations such as
schools, day care centers, hospitals, and nursing homes can also be more accurately
mapped and quantified at the neighborhood scale. There is the potential for
using geodemographic data at a finer resolution, as the census data can be
supplemented in many cases by data collected by the city's planning department
or by community data bases. McMaster et al. discuss using GIS to identify,
map, and monitor potential environmental hazards in four Minneapolis neighborhoods
(54):
Neighborhood-scale analysis and mapping holds great promise for assisting
communities in identifying risks posed by environmental hazards to different
social groups in their neighborhood and for the development of more detailed,
complete, and positionally accurate information by incorporating 'local knowledge'
into the GIS database. (54)
Perhaps the most important benefit of neighborhood-scale analysis is the potential
for direct involvement of the affected people and the intimate knowledge of
their surroundings that they bring to the project, along with the sense of ownership
that their involvement with the project brings to them (65).
Community-based GIS projects have been instrumental in advocacy work and have
proved effective in contributing to community organizing, data collection and
documentation of land use, public health, and environmental conditions of low-income
neighborhoods and communities of color for purposes of influencing policy and
planning decisions. The New York City Environmental Justice Alliance (NYCEJA)
is an example of a nonprofit organization using GIS to promote environmental
justice:
Our vision of technical assistance is not an end, but a means for building
our collective capacity to fight against environmental discrimination. . .
. [D]isparate environmental conditions are often disregarded by policy makers.
Documenting the environmental conditions in underserved communities is important
because data is often out of date, inaccessible, or not available for these
areas. NYCEJA strives to not only assist its members collect and analyze data,
but it also ensures that grassroots communities are directly involved in the
entire process and to advocate for themselves. (66)
Implications of Policy and Planning Decisions on Environmental
Health and Equity
Most environmental equity studies are based on the locations of specific hazardous
facilities. Not only must we prepare more comprehensive analyses based on multiple
sources of pollution, but we must also consider other factors in the potential
for exposure and disproportionate burdens. We need to be concerned not only
with the location of existing and past hazardous land uses and the siting of
such facilities disproportionately in poorer neighborhoods and communities of
color but also with the distribution of locations having the potential to house
hazardous facilities.
My own research on industrial zoning changes and environmental justice in
New York City indicates that the reasons for disproportionate concentrations
of noxious land uses go deep into underlying policies and assumptions (67,68).
In New York City, as in many other places with zoning regulations, noxious facilities
can be located only in areas zoned for manufacturing (M zones), and M zones
tend to be located primarily in or near neighborhoods where residents are poorer
than average and have a higher-than-average likelihood of being African American,
Latino, or other minority. This obviates the principles of the city's Fair Share
guidelines (69), which were intended to ensure that the burdens of urban
(post-) industrial life be shared equally and not fall disproportionately on
any group or area.
This inequitable state of affairs in New York City is perpetuated by city
planning practices and policies that continue to decrease the areal extent of
industrial zones in more affluent and less heavily minority neighborhoods while
increasing the areas of industrial zones in poorer and more heavily minority
neighborhoods. This serves to further concentrate noxious land uses within predominantly
poor and minority communities. Because this situation likely is not unique to
New York City, zoning and other planning policies and practices must be taken
into account when evaluating exposure and risk from hazardous land uses.
Ultimately, any siting of hazardous activities may lead to unjust exposures.
The idea that we can solve the problem of disproportionate toxic exposures by
spreading the pollution around more equitably is absurd. Many believe the real
solution is to be found in eliminating or sharply reducing the need for many
of these noxious facilities to exist (70). This, of course, will require
structural changes in patterns of consumption, waste production and disposal,
transportation, and community governance, planning, and policy making. There
have been instances, however, where "Not In My Back Yard" has become "Not In
Anybody's Back Yard," thereby forcing government and industry to evaluate broader
issues, including "the propriety of a production system under private control
where, in the quest for profit, the public is exposed to known risks" (71).
Another aspect of the long-term solution is to make noxious facilities less
harmful, by pollution prevention techniques, source reduction of toxic substances,
strengthened and evenly applied enforcement of environmental regulations, and
equitable remediation of hazardous conditions.
Making the Connection between Environmental
Justice and Environmental Health
Although showing environmental inequity regarding the distribution of noxious
facilities is certainly of consequence, especially in combating future inequities,
it is probably more critical at this point to demonstrate linkages between environmental
burdens and adverse health impacts. Only when the spatial correspondence is
clear can public health and environmental protection officials, the medical
research community, health care providers, and pollution prevention scientists
begin to develop solutions to existing environmental injustices and resulting
health effects. People within communities disproportionately burdened with pollution
are suffering adverse physical and psychologic impacts, as well as economic
impacts, according to a wealth of anecdotal reports and empirical research (28,72-81).
It is important to show the disproportionate effects of pollution rather than
just the fact that disproportionate distribution of pollution sources exists.
There are encouraging precedents for positive results stemming from such research
and subsequent actions based on the research. For instance, studies from the
1970s suggested that the high rate of childhood lead poisoning in inner cities,
disproportionately affecting minority and low-income children, was connected
to the high traffic volumes in these areas and the concomitant exposure to lead-based
gasoline emissions (82). In large measure because of these findings,
lead in gasoline was phased down by U.S. EPA regulation in the 1980s, and thereafter
the rate of childhood lead poisoning dropped dramatically, demonstrating the
potential for reducing unjust environmental exposures (83).
A more recent example is given in the paper "Impact of Changes in Transportation
and Commuting Behaviors during the 1996 Olympic Games in Atlanta on Air Quality
and Childhood Asthma," which shows that childhood asthma events were significantly
reduced in the 17-day period when vehicular traffic was curtailed in the metropolitan
area because of the Olympic Games of Atlanta (84). Concomitant changes
in air quality were also examined and compared with the 4 weeks preceding and
following the games. Peak daily ozone concentration decreased nearly 28%, peak
weekday morning traffic counts dropped nearly 23%, and the number of asthma
acute-care events decreased 44% during the Olympic Games. This indicates that
the decreased traffic density was "associated with a prolonged reduction in
ozone pollution and significantly lower rates of
