Spatial cluster detection of air pollution exposure inequities across the United States

• Published in: PloS one Vol. 9; no. 3; p. e91917
• Main Authors: Zou, Bin, Peng, Fen, Wan, Neng, Mamady, Keita, Wilson, Gaines J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 19-03-2014; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Age; Age Factors; Air pollution; Air Pollution - analysis; Air Pollution - economics; Air quality; Analysis; Autocorrelation; Benzene; Benzene - analysis; Black people; Census; Cluster Analysis; Clustering; Clusters; Coastal zone; Computer and Information Sciences; Data processing; Demographics; Demography; Earth Sciences; Ecology and Environmental Sciences; Education; Educational Status; Environmental conditions; Environmental Exposure - analysis; Environmental Exposure - economics; Exposure; Geography; Health aspects; Health disparities; Healthcare Disparities - economics; Humans; Income; Medicine and Health Sciences; Middle Aged; Minority & ethnic groups; Native Americans; Odds Ratio; Outdoor air quality; Pollution; Pollution detection; Quality of life; Racial Groups; Regression analysis; Social Sciences; Socioeconomic factors; Spatial analysis; Spatial distribution; Statistical analysis; Studies; United States; Young Adult; United States; United States > US; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0091917

Air quality is known to be a key factor in affecting the wellbeing and quality of life of the general populous and there is a large body of knowledge indicating that certain underrepresented groups may be overexposed to air pollution. Therefore, a more precise understanding of air pollution exposure as a driving cause of health disparities between and among ethnic and racial groups is necessary. Utilizing 52,613 urban census tracts across the United States, this study investigates age, racial, educational attainment and income differences in exposure to benzene pollution in 1999 as a case. The study examines spatial clustering patterns of these inequities using logistic regression modeling and spatial autocorrelation methods such as the Global Moran's I index and the Anselin Local Moran's I index. Results show that the age groups of 0 to 14 and those over 60 years old, individuals with less than 12 years of education, racial minorities including Blacks, American Indians, Asians, some other races, and those with low income were exposed to higher levels of benzene pollution in some census tracts. Clustering analyses stratified by age, education, and race revealed a clear case of disparities in spatial distribution of exposure to benzene pollution across the entire United States. For example, people aged less than 4 years from the western south and the Pacific coastal areas exhibit statistically significant clusters. The findings confirmed that there are geographical-location based disproportionate pattern of exposures to benzene air pollution by various socio-demographic factors across the United States and this type of disproportionate exposure pattern can be effectively detected by a spatial autocorrelation based cluster analysis method. It is suggested that there is a clear and present need for programs and services that will reduce inequities and ultimately improve environmental conditions for all underrepresented groups in the United States.