Block Group Level Factors

The unit of analysis for the neighborhood are census block groups, the smallest level at which information about the racial and socioeconomic composition of the

neighborhood is available. The data on air quality are from the Environmental Protection Agency’s (EPA) Risk-Screening Environmental Indicators Geographic Microdata (RSEI-GM) for the year 2012. I use the geographically aggregated data for RSEI Version 2.3.4.

The RSEI-GM uses EPA Toxic Release Inventory (TRI) data that documents pollutants from more than 20,000 industrial facilities in the United States. Facilities meet the requirements for TRI reporting if they meet each of three criterions: employ at least 10 full-time employees, are within a specific industry sector such as manufacturing or mining, and manufacture or process greater than 25,000 pounds of a TRI chemical, and use more than 10,000 pounds of a TRI chemical in a given year. In 2012, more than 400 chemicals were utilized for the RSEI-GM data from more than 20,000 facilities.

To create the dependent variable, toxic concentration, the RSEI-GM uses TRI data to model the health risk posed by chemicals from large industrial facilities. The EPA utilizes plume modeling techniques that measure the fate of chemicals by accounting for

the amount of the chemical, the toxicity of the chemical, the source of release (e.g. stack, valve), its transport through space, and the route’s relation to human exposure

(Environmental Protection Agency 2015). Facilities are located at the center of an 810 m² grid cell nested within a grid that covers the contiguous United States. All grid cells in a 49 kilometer radius of a facility are assigned a pollution value based on the likely pounds of releases indexed to the toxicity of the chemicals in that particular grid cell. The grid cells receive different values of this toxic concentration variable depending on the fate of the release of the pollutant. For instance, grid cells closer to the facility typically have a higher toxic concentration than those further away.

The block group toxic concentration is created by determining the proportion of the block group area that is covered by a grid cell, and aggregating the toxic

concentration proportionally (see Ard 2015 for an example of this approach). For

example, if a block group contains three grid cells with toxic concentration values of 500, 400, and 1,000, respectively, and the grid cells comprise 50 percent, 25 percent, and 25 percent of the area, respectively, the toxic concentration would be calculated by

multiplying each grid cell’s proportion and its toxic concentration value: ((500*0.5) + (400*0.25) + (1000*0.25)). The estimated toxic concentration for this block group is 600.

Because the toxic concentration measure is highly skewed (see below; Collins, Munoz, and JaJa 2016), I log transform the variable.

The RSEI-GM has been used by researchers to predict environmental inequalities particularly for smaller levels of geographies (Ard 2015; Ard 2016; Ash and Fetter 2004;

Ash and Boyce 2011; Ash et al. 2013; Boyce, Zwickl, and Ash 2016; Collins, Munoz, and JaJa 2016; Downey 2007; Downey et al 2008; Downey and Hawkins 2008; Zwickl,

Ash and Boyce 2014). RSEI-GM is a major advance in measuring industrial air pollution from earlier environmental justice research that focuses on incidence of facilities, or the pounds of pollutant releases, without considering the toxicity of the chemicals at hand (e.g. Bryant and Mohai 1992; Pais, Crowder, and Downey 2013). Limitations of the data include that it focuses solely on large industrial facilities, and therefore misses the many small and medium-sized polluters that go unregulated by the TRI (Elliott and Frickel 2015). The RSEI-GM’s use of industrial pollution data also does not measure other types of pollutants, such as those from transportation sources like the National Air Toxics Assessment (e.g. Liévanos 2015). Finally, while the data are one of the best tools with which to denote pollution data in neighborhoods, it is an estimation, not a direct measurement of neighborhood air quality.

Block groups were included from all metropolitan areas in the contiguous United States. Independent variables at the block group level were obtained using 2008-2012 American Community Survey (ACS) estimates. Block groups were excluded from the analysis if they had an estimated total working population less than 100. The exclusion of these block groups, in addition to using the five-year pooled data, helps to maximize the reliability of ACS estimates by minimizing the possible size of the margins of error for the estimates. A limitation of using five-year data (instead of 2012 or 2010-2012 estimates) is one of validity, as estimates are drawn from data across a larger period of time than that of the dependent variable.

The following independent variables at the block group level are included in the analysis. Race is measured by the proportion of blacks and proportion of Hispanics in a block group. Following Ard (2015), household median income is measured in two

categories: less than or equal to $50,000, and greater than $50,000 (the latter is the reference category). Income is an important predictor as class has long been a focus of environmental justice research (Downey and Hawkins 2008). Proportion of

manufacturing workers is often associated with greater pollution because this industrial class of workers is associated with environmentally risky work (Sicotte and Swanson 2007). Median commute time has been found to be a significant predictor in some studies such that longer commutes allow workers to find cleaner air (Liévanos 2015). The

population density of the block group is also included, as places that are more densely populated may have more commercial activity and thereby possibly more pollution. All neighborhood measures are group mean centered within each metropolitan area, and all metropolitan covariates are grand mean centered.

2.3. Results