The NDSC datasets were aggregated for the CalEnviroscreen 2.0/3.0 surveys, an environmental justice initiative by CalEPA’s Office of Environmental Health Hazard Assessment that aims to empower communities that experience severe pollution events. Part of their analysis includes the PM2.5 concentration dataset, which was collected by the CalEPA’s California Air Resources Board (CARB)’s air monitoring network of air sampling instruments and satellite data.
The Drinking Water Contamination Index was calculated from California Department of Public Health’s Water Quality Monitoring database on a yearly time grid, with the concentrations of the 13 most prevalent contaminants (ranging from arsenic to nitrate) included in the calculation. For each contaminant, the total contaminant level was compared to other census tracts, and a percentile was assigned. The percentiles of each of the 13 contaminants was summed to yield the contamination index, allowing for comparison between tracts.
The CalEnviroScreen 3.0 survey collected data at a census tract level, with census tracts being defined as small, geographic subdivisions of a county with approximately 4,000 inhabitants. According to the United States Census Bureau, the main factor involved in creating census tracts is balancing each tract’s population. As a result, census tract boundaries can be arbitrary and may not parallel actual neighborhoods, which can obscure possible patterns associated with specific communities. Furthermore, the air and water quality data sets only include environmental measurements, with little contextual background for these disparities. Without information about demographics, socioeconomics, or history, we cannot construct a thorough narrative that accounts for how power dynamics affect environmental justice and lived experiences. To address this limitation, we supplemented environmental data with socioeconomic data, such as the NDSC’s “Race and Ethnicity” dataset.
For air quality, the dataset only measured particulate matter with diameters of less than 2.5 micrometers (PM2.5). This measurement is not as comprehensive as the EPA’s Air Quality Index (AQI), which includes ozone (photochemical smog), larger particulate matter of up to 10 micrometers in diameter (PM10), and other pollutants. For instance, the AQI accounts for CO, which is a toxic gas emitted from wood burning; SO2, a pollutant originating from coal-burning power plants that contributes to acid rain; and NO2, a main contributor to smog formation. As a result, we cannot comprehensively summarize an area’s overall air quality based on PM2.5 alone. Nevertheless, although this dataset may be limited, PM2.5 is still a major factor in the AQI calculation and is especially dangerous, as it can enter the respiratory tract more easily than PM10. Therefore, our dataset still provides information about potential air toxicity that can be corroborated with other atmospheric datasets in future analyses.
For water quality, it is important to note that census tracts “sometimes contain multiple drinking water systems and groundwater resources.” Therefore, where the tract boundaries are drawn may introduce discrepancies in our analysis: for example, data may be aggregated in such a way that it averages out disparities. However, our dataset still portrays a generally accurate picture of the average water quality conditions within that tract — we just need to be cognizant that certain correlations may emerge or disappear depending on each tract’s geographical boundaries.
PM2.5 concentrations reveal how air quality differs within regions in Los Angeles, allowing for spatial analysis into how socioeconomic makeup or other factors affect air quality. Underprivileged communities are typically more at risk for pollution, and the dataset can indicate what characteristics — a neighborhood’s poverty level, average education, and amount of stable housing; the percentage of members belonging to marginalized groups; and any other social or economic factors — most correlate to air pollution levels. Furthermore, given that particulate matter is emitted in large quantities through wildfires, the dataset can also show what areas within Los Angeles are most impacted by fires, an issue that is becoming more relevant in the near future due to the effect of climate change on California’s fire season. As particulate matter is toxic to human health, the dataset may illuminate which neighbourhoods are most severely affected and whether underprivileged communities are more likely to face lasting economic damage and health consequences.
Similarly, the dataset regarding water quality in Los Angeles can reveal information about which neighborhoods are affected by drinking water contaminants and how water pollution affects the residents’ lived experiences. 95% of California residents receive their drinking water from public sources; however, this water can become contaminated with chemicals and bacteria. At times, this contamination can be due to natural causes, such as chemicals originating from rocks and soil; other times, however, the sources can be anthropogenic. These include human waste from factories, sewage treatment plants, and agricultural runoff. Since the dataset contains information about 13 different pollutants, we can determine which pollutants are most prevalent in which areas and explore correlations between water pollution levels, potential sources (e.g. factories), and socioeconomic factors. Ultimately, we hope to use these correlations to investigate how social factors affect pollution levels to illustrate the humanistic side of environmental issues.
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