Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining.
Alexander C BradleyBart E CroesColin HarkinsBrian C McDonaldJoost de GouwPublished in: Environmental science & technology (2024)
Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO 2 columns and modeled PM 2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM 2.5 surface concentrations of 6.5 μg m -3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 μg m -3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.
Keyphrases
- particulate matter
- air pollution
- african american
- heavy metals
- lung function
- high resolution
- health risk assessment
- risk assessment
- affordable care act
- electronic health record
- drinking water
- genetic diversity
- healthcare
- big data
- health risk
- cystic fibrosis
- machine learning
- data analysis
- water quality
- climate change
- artificial intelligence