Study after study has shown the relationship between environmental pollution and the population’s wealth and race. For example, Andrew Hurley’s study of pollution in Gary, Indiana found that “The skewed social distribution of toxic waste sites represented the most marked example of an environmental regime that discriminated along the lines of race and class” (Hurley 172). Another study of the same effect in a Chinese province showed that “townships in Jiangsu province with large populations of rural migrants are disproportionately exposed to industrial pollution” (Schoolman). The effect wealth has on a person’s environment is an effect that reaches across cultures and countries. However, very few have examined these effects in Los Angeles county. Thus, we will take a look at the pollution levels around various areas of the county and examine if the same effect will be present here, and speculate on the causes of it.
In this examination of pollution we will first need to define a measurable index for pollution. Luckily, the US Environmental Protection Agency provides indices to quantify the amount of different particle pollutants in the air (George et al.). The primary measurement we will pull from will be the PM2.5 index, which indicates the density of fine particulates in the air that are 2.5 micrometers or smaller. We will also make use of indexes provided by the National Air Toxics Assessment (NATA), which measure the amount of toxins in the air (George et al.).
The EPA provides data on these pollutants divided into census blocks, as determined by the US
Census. The Census (and all federal agencies) divides and identifies geographical areas of the US
using ANSI codes, which are unique 12-digit codes representing a geographical area. The first two
numbers represent the state, and the next three a county within that state. The remaining 7 numbers
represent an area within that county. In this case, we will focus on the ANSI codes within Los
Angeles County, of which there are 6,425. These begin with the county’s code,
The EPA packges the data discussed above into a user-friendly map-based format called the EJSCREEN Mapping Tool, which is available here (Corrales). The following image uses this tool to overlay the hazardous waste proximity of various census tracts in Los Angeles County to their population densities.
While this map is easy for a human to use, this map interface is unreadable to a machine, which
makes it hard to draw statistical conclusions from. Thus, we will use the raw data from the
EJSCREEN_2020_USPR.csv file available here,
which contains absolute numerical data for each census block.
This data table contains data for every single county in every single state, which is far more data
than we need. Therefore, we can run the following to import the CSV as a
pandas dataframe and then
filter out ANSI codes not in Los Angeles county:
import pandas as pd # low_memory=False is needed as it's a pretty big CSV df = pd.read_csv('EJSCREEN_2020_USPR.csv', low_memory=False) df = df.loc[(df['ID'] > 60370000000) & (df['ID'] < 60380000000)]
Here’s what the data looks like when we’ve filtered it:
This table includes both pollutant data including PM2.5 and NATA indexes, in addition to demographics data such as percentage of the residents who are of color, and the percentage of residents under the poverty line. We’ll examine this data for insights.
Let’s start by looking at the demographics of the people who live within Los Angeles. We’ll look at
LOWINCPCT columns in the dataset, which measure the percentage of the
population who are minorities, as well as the percentage of the population who are low income. We
can’t use the raw counts of these values, since these counts will tend to increase as the population
increases, while percentages will stay relatively constant.
Running a regression will show if there’s a statistical relationship between these two variables. In
this case, we’ll do a linear regression, which will show if the data tends to trend in a direction.
This can be done easily using the
import statsmodels.api as sm MINORPCT = df[['MINORPCT']].apply(pd.to_numeric).values.reshape(-1, 1) LOWINCPCT = df[['LOWINCPCT']].apply(pd.to_numeric).values.reshape(-1, 1) ols = sm.OLS(LOWINCPCT, MINORPCT) result = ols.fit() prediction = result.predict(MINORPCT)
Once we have a function, we can graph it with the
matplotlib library and the following snippet:
import matplotlib.pyplot as plt plt.figure(figsize=(25, 15)) plt.scatter(MINORPCT, LOWINCPCT) plt.scatter(MINORPCT, prediction, color='red') plt.show()
With a mean-squared error of just 883.80, we can safely conclude that a person’s race in Los Angeles correlates with their wealth. In the same vein, a Duke University study also “revealed major disparities in wealth accumulation across various racial and ethnic groups in Los Angeles” (De La Cruz-Viesca 5). The study also shows that the median net worth of Mexicans and blacks in the county is between $3500 and $4000, while the median net worth of a white household is #355,000, a whole 2 degrees of magnitude higher (De La Cruz-Viesca et al. 40).
Airborne Pollutant Analysis
We can now also check for correlations between environmental pollution, race, and class. We’ll start
with the PM2.5 index which we discussed above. This data is stored in the
PM25 statistic. Also, we
will use a demographic index calculated by EJSCREEN to be the average of the low income percentage
and the minority population percentage. Graphing the relation between the PM2.5 index and this new
index, we can see that this data is significantly messier than before, with nearly equal
distributions of particulate matter pollution throughout the county.
This is to be expected however, since air pollution doesn’t usually to stay put in a single area, but rather tends to move along the wind (Lu et al. 1500). In Los Angeles’ case, these winds tend to blow East from the Pacific Ocean towards Riverside county (Lu et al. 1504). Such a pattern can be seen when the data is examined on a map of Los Angeles, as shown below. Coastal areas are significantly less polluted than their inland counterparts, with particulate matter levels seeing a large drop across mountainous areas.
This lack of correlation between race and class for airbone pollution plays out for every statistic provided by the EPA on airbone pollution. For example, the EPA detected significantly higher amounts of diesel particulates in the Long Beach Dock area, which is most likely due to the high amount of shipping and diesel-based container trucks passing through the port. Similarly, the NATA respriatory hazard and cancer risk indices almost perfectly overlap, with significantly higher results in areas of heavy vehicle traffic.
Note that this analysis only examinines data from 2020. A similar analysis of NATA data from 1996 found a significant correlation between air pollution, wealth, and minority population (Pastor et al. 144). The significant decrease in airbone pollutant concentrations may be due to international treaties aiming to limit emissions adopted by the US in the period, such as the Kyoto Protocol and the Paris Accords.
Other Hazardous Pollutants
Of course, air pollution is far from the only type of pollution posing a risk to our health. The EPA also provides data on each census tract’s proximity to various areas posing risks to the population, including:
- Treatment Storage and Disposal (TSDF) facilities, which is just a fancy word for landfill
- Risk Management Plan (RMP) facilities, which are locations that deal with highly hazardous substances
- National Priorities List (NPL) sites, which are hazardous waste cleanup sites being cleaned up under the Superfund program. They are more commonly known as “Superfund sites.”
When we run the same analysis we did in the last section here, we see a much stronger correlation between distance from these locations and the demographic index.
Let’s take a closer look at the Montrose Chemical Co. Superfund site. This was the site of a DDT manufacturing facility from 1947 to 1982 which contaminated the soil and groundwater in the surrounding neighborhood (US Environmental Protection Agency). An extremely interesting fact to note is that the former site of this plant corresponds almost perfectly with a marked increase in the demographic index of the area.
The same effect can be seen in multiple other Superfund sites, including the three sites in the city of South Gate, just southeast of downtown Los Angeles. There, industrial activities polluted the surrounding area, which also house a significant minority population.
This correlation can be traced back to the same phenomenon that caused the demographics of Compton to shift from a middle-class white population to a lower-class minority population in the 1950s. Compton was close to many of the factory jobs that sustained the middle class of the city, and middle-class black workers began moving in to the area, while white workers fled for farther suburbs. As the Superfund program was only established in 1980, it would follow that most of the superfund sites would be in industrial areas, and since minority populations were more likely to live near these areas they would also be more likely to be affected by these sites.
Looking at RMP and TSDP facilities tells a more nuanced story. There are only 16 sites that process waste recieved from other areas in the entire county, all of which are in areas with a high demographic index (Envirofact). This obviously cannot be a coincidence. One factor that may be causing this may be that riskier jobs are taken by those with less schooling, which is strongly negatively correlated with being a minority (Leigh 63). It may be that poorer minorities are forced to take jobs in places that are at a higher risk of contaminating the area, and to live near these jobs.
We have shown that there is a strong negative correlation between wealth and minority status caused by a hsitory of discrimination and racism. Furthermore, atmospheric pollution in Los Angeles is uniform across the county due to Eastward winds that disperse strong concentrations of it. Furthermore, we have also discovered a significantly higher low-income and minority population near RMP, TSDP, and NPL sites with dangerous levels of pollution, which may either be due to historical demographic shifts that coincided with a period of industrial irresponsibility or be the inevitable outcome of an economic and governmental system that proritizes white working power.
Lu, Rong, and Richard P. Turco. “Air pollutant transport in a coastal environment—II. Three-dimensional simulations over Los Angeles basin.” Atmospheric Environment 29, no. 13 (1995): 1499-1518.
Corrales, Mark. “EJSCREEN: EPA’s Environmental Justice Mapping Tool.” In APHA 2016 Annual Meeting & Expo (Oct. 29-Nov. 2, 2016). American Public Health Association, 2016.
De La Cruz-Viesca, Melany, Zhenxiang Chen, Paul M. Ong, Darrick Hamilton, and William A. Darity Jr. “The Color of Wealth in Los Angeles.” Durham, NC/New York/Los Angeles: Duke University/The New School/University of California, Los Angeles (2016).
“Envirofact.” EPA. US Environmental Protection Agency. https://enviro.epa.gov/index.html.
George, Barbara Jane, Bradley D. Schultz, Ted Palma, Alan F. Vette, Donald A. Whitaker, and Ronald W. Williams. “An evaluation of EPA’s National-Scale Air Toxics Assessment (NATA): comparison with benzene measurements in Detroit, Michigan.” Atmospheric Environment 45, no. 19 (2011): 3301-3308.
Hurley, Andrew. Environmental inequalities: Class, race, and industrial pollution in Gary, Indiana, 1945-1980. Univ of North Carolina Press, 1995.
Leigh, J. Paul. “Who chooses risky jobs?.” Social Science & Medicine 23, no. 1 (1986): 57-64.
Office of Environmental Justice, EJSCREEN Environmental Justice Mapping and Screening Tool: EJSCREEN Technical Documentation § (2019).
US Environmental Protection Agency, Montrose & Del Amo Superfund Sites Fact Sheet § (2018).
US Environmental Protection Agency, South Gate Superfund Sites Fact Sheet § (2017).
Pastor Jr, Manuel, Rachel Morello-Frosch, and James L. Sadd. “The air is always cleaner on the other side: Race, space, and ambient air toxics exposures in California.” Journal of urban affairs 27, no. 2 (2005): 127-148.
Schoolman, Ethan D., and Chunbo Ma. “Migration, class and environmental inequality: Exposure to pollution in China’s Jiangsu Province.” Ecological Economics 75 (2012): 140-151.
This post was written as part of the final project for USC’s AMST 101 class.