Disproportionate proximity to environmental health hazards: methods, models, and measurement

Abstract

We sought to provide a historical overview of methods, models, and data used in the environmental justice (EJ) research literature to measure proximity to environmental hazards and potential exposure to their adverse health effects. We explored how the assessment of disproportionate proximity and exposure has evolved from comparing the prevalence of minority or low-income residents in geographic entities hosting pollution sources and discrete buffer zones to more refined techniques that use continuous distances, pollutant fate-and-transport models, and estimates of health risk from toxic exposure. We also reviewed analytical techniques used to determine the characteristics of people residing in areas potentially exposed to environmental hazards and emerging geostatistical techniques that are more appropriate for EJ analysis than conventional statistical methods. We concluded by providing several recommendations regarding future research and data needs for EJ assessment that would lead to more reliable results and policy solutions.

FIGURE 1

Spatial definition of proximity to environmental hazards using (a) spatial coincidence to select host census units, (b) circular buffers of uniform radius around facilities of concern, and (c) plume footprint for a hypothetical chlorine release scenario using the Areal Locations of Hazardous Atmospheres model.

FIGURE 1

Spatial definition of proximity to environmental hazards using (a) spatial coincidence to select host census units, (b) circular buffers of uniform radius around facilities of concern, and (c) plume footprint for a hypothetical chlorine release scenario using the Areal Locations of Hazardous Atmospheres model.

FIGURE 1

Spatial definition of proximity to environmental hazards using (a) spatial coincidence to select host census units, (b) circular buffers of uniform radius around facilities of concern, and (c) plume footprint for a hypothetical chlorine release scenario using the Areal Locations of Hazardous Atmospheres model.

FIGURE 2

Use of areal interpolation to select census units within a circular buffer using (a) polygon containment, (b) centroid containment, and (c) buffer containment.

FIGURE 2

Use of areal interpolation to select census units within a circular buffer using (a) polygon containment, (b) centroid containment, and (c) buffer containment.

FIGURE 2

Use of areal interpolation to select census units within a circular buffer using (a) polygon containment, (b) centroid containment, and (c) buffer containment.

FIGURE 3

Cadastral dasymetric mapping: using land parcels to estimate households within a circular buffer zone.

FIGURE 4

Using geographically weighted regression to explore the relationship between cancer risk from non–point (area) sources of air toxics (1999 National-Scale Air Toxic Assessment) and various explanatory variables (2000 Census) in Florida: Distribution of local t statistic by census tract.

FIGURE 4

Using geographically weighted regression to explore the relationship between cancer risk from non–point (area) sources of air toxics (1999 National-Scale Air Toxic Assessment) and various explanatory variables (2000 Census) in Florida: Distribution of local t statistic by census tract.

FIGURE 4

Using geographically weighted regression to explore the relationship between cancer risk from non–point (area) sources of air toxics (1999 National-Scale Air Toxic Assessment) and various explanatory variables (2000 Census) in Florida: Distribution of local t statistic by census tract.

FIGURE 4

Using geographically weighted regression to explore the relationship between cancer risk from non–point (area) sources of air toxics (1999 National-Scale Air Toxic Assessment) and various explanatory variables (2000 Census) in Florida: Distribution of local t statistic by census tract.