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^☆

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 O₃, PM₁₀, and PM_2.5 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 O₃, PM_2.5, and PM₁₀, respectively. We conducted univariate regression analyses to investigate whether long-term exposure to O₃, PM_2.5, 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 O₃ and PM_2.5 were higher in more socially deprived neighborhoods. An interquartile range (IQR) increment of the social development index (SDI) was associated with a 3.6 μg/m³ (95 % confidence interval [CI]: 2.9, 4.4; p-value≤0.001) decrease in O₃, and 0.3 μg/m³ (95 % CI: 0.2, 0.5; p-value = 0.010) decrease in PM_2.5. An IQR increase in the percentage of residents who are children was associated with a 4.1 μg/m³ (95 % CI: 3.1, 5.0; p-value≤0.001) increase in O₃, and 0.4 μg/m³ (95 % CI: 0.3, 0.6; p-value = 0.009) increase in PM_2.5. An IQR increase in the percentage of residents age ≥65was associated with a 3.3 μg/m³ (95 % CI: 2.4, 4.3; p-value=<0.001) decrease in O₃, and 0.3 μg/m³ (95 % CI: 0.1, 0.5; p-value = 0.058) decrease in PM_2.5. There were no apparent associations for PM₁₀. The association for daily O₃ 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 PM_2.5 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 O₃ 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.