气候变化领域集成服务门户(CCPortal): Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models

CCPortal

DOI	10.1016/j.atmosenv.2024.120374
	Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models
	Kim, Honghyok; Son, Ji-Young; Junger, Washington; Bell, Michelle L.
发表日期	2024
ISSN	1352-2310
EISSN	1873-2844
起始页码	322
卷号	322
英文摘要	South America is underrepresented in research on air pollution exposure disparities by sociodemographic factors, although such disparities have been observed in other parts of the world. We investigated whether exposure to and information about air pollution differs by sociodemographic factors in the city of Rio de Janeiro, the second most populous city in Brazil with dense urban areas, for 2012-2017. We developed machine learning-based models to estimate daily levels of O-3, PM10, and PM2.5 using high-dimensional datasets from satellite remote sensing, atmospheric and land variables, and land use information. Cross-validations demonstrated good agreement between the estimated levels and measurements from ground-based monitoring stations: overall R-2 of 76.8 %, 63.9 %, and 69.1 % for O-3, PM2.5, and PM10, respectively. We conducted univariate regression analyses to investigate whether long-term exposure to O-3, PM2.5, PM10 and distance to regulatory monitors differs by socioeconomic indicators, the percentages of residents who were children (0-17 years) or age 65+ years in 154 neighborhoods. We also examined the number of days exceeding the Brazilian National Air Quality Standard (BNAQS). Long-term exposures to O-3 and PM2.5 were higher in more socially deprived neighborhoods. An interquartile range (IQR) increment of the social development index (SDI) was associated with a 3.6 mu g/m(3) (95 % confidence interval [CI]: 2.9, 4.4; p-value <= 0.001) decrease in O-3, and 0.3 mu g/m(3) (95 % CI: 0.2, 0.5; p-value = 0.010) decrease in PM2.5. An IQR increase in the percentage of residents who are children was associated with a 4.1 mu g/m(3) (95 % CI: 3.1, 5.0; p-value <= 0.001) increase in O-3, and 0.4 mu g/m(3) (95 % CI: 0.3, 0.6; p-value = 0.009) increase in PM2.5. An IQR increase in the percentage of residents age >= 65was associated with a 3.3 mu g/m(3) (95 % CI: 2.4, 4.3; p-value=<0.001) decrease in O-3, and 0.3 mu g/m(3) (95 % CI: 0.1, 0.5; p-value = 0.058) decrease in PM2.5. There were no apparent associations for PM10. The association for daily O-3 levels exceeding the BNAQS daily standard was 0.4 %p-0.8 %p different by the IQR of variables, indicating a 7-15 days difference in the six-year period. The association for daily PM2.5 levels exceeding the BNAQS daily standard showed a 0.7-1.5 %p difference by the IQR, meaning a 13-27 days difference in the period. We did not find statistically significant associations between the distance to monitors and neighborhood characteristics but some indication regarding SDI. We found that O-3 levels were higher in neighborhoods situated farther from monitoring stations, suggesting that elevated levels of air pollution may not be routinely measured. Exposure disparity patterns may vary by pollutants, suggesting a complex interplay between environmental and socioeconomic factors in environmental justice.
英文关键词	Environmental justice; Air pollution modeling; Air pollution disparity; Information disparity; Socioeconomic inequality; Climate change
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001179067700001
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/306717
作者单位	University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Yale University; Universidade do Estado do Rio de Janeiro
推荐引用方式 GB/T 7714	Kim, Honghyok,Son, Ji-Young,Junger, Washington,et al. Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models[J],2024,322.
APA	Kim, Honghyok,Son, Ji-Young,Junger, Washington,&Bell, Michelle L..(2024).Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models.ATMOSPHERIC ENVIRONMENT,322.
MLA	Kim, Honghyok,et al."Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning models".ATMOSPHERIC ENVIRONMENT 322(2024).