Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: Demonstrating the potential of low-cost air quality sensor for epidemiologic research.

BACKGROUND: There is currently a scarcity of air pollution epidemiologic data from low- and middle-income countries (LMICs) due to the lack of air quality monitoring in these countries. Additionally, there is limited capacity to assess the health effects of wildfire smoke events in wildfire-prone regions like Brazil's Amazon Basin. Emerging low-cost air quality sensors may have the potential to address these gaps. OBJECTIVES: We investigated the potential of PurpleAir PM2.5 sensors for conducting air pollution epidemiologic research leveraging the United States Environmental Protection Agency's United States-wide correction formula for ambient PM2.5. METHODS: We obtained raw (uncorrected) PM2.5 concentration and humidity data from a PurpleAir sensor in Rio Branco, Brazil, between 2018 and 2019. Humidity measurements from the PurpleAir sensor were used to correct the PM2.5 concentrations. We established the relationship between ambient PM2.5 (corrected and uncorrected) and daily all-cause respiratory hospitalization in Rio Branco, Brazil, using generalized additive models (GAM) and distributed lag non-linear models (DLNM). We used linear regression to assess the relationship between daily PM2.5 concentrations and wildfire reports in Rio Branco during the wildfire seasons of 2018 and 2019. RESULTS: We observed increases in daily respiratory hospitalizations of 5.4% (95%CI: 0.8%, 10.1%) for a 2-day lag and 5.8% (1.5%, 10.2%) for 3-day lag, per 10 µg/m3 PM2.5 (corrected values). The effect estimates were attenuated when the uncorrected PM2.5 data was used. The number of reported wildfires explained 10% of daily PM2.5 concentrations during the wildfire season. DISCUSSION: Exposure-response relationships estimated using corrected low-cost air quality sensor data were comparable with relationships estimated using a validated air quality modeling approach. This suggests that correcting low-cost PM2.5 sensor data may mitigate bias attenuation in air pollution epidemiologic studies. Low-cost sensor PM2.5 data could also predict the air quality impacts of wildfires in Brazil's Amazon Basin.

Air Pollution and Environmental Justice in Latin America: Where Are We and How Can We Move Forward?

PURPOSE OF REVIEW: Air pollution in Latin America is a major environmental threat, yet few studies have focused on aspects of environmental justice with regard to air pollution in the region. We examined the scientific literature and described whether and how this issue has been addressed, identify possible gaps in knowledge, and offer suggestions for future research to contribute to policies that seek greater equity concerning air pollution impacts in Latin America. RECENT FINDINGS: There is a limited literature that has addressed issues of environmental justice or environmental health inequalities about air pollution in Latin America, with studies concentrated in Brazil, Mexico, and Chile. Studies that examined disparities in exposure to air pollution found a clear pattern of higher exposure in socially deprived areas. Studies that examined disparities in health impacts associated with air pollution have mixed results, but many found a clear modification of effect with those in the lower socioeconomic groups presenting greater effects. Despite Latin America's colonial and slavery history, no studies have considered ethnicity or minority populations. The literature shows that health risks (exposure and susceptibility) associated with air pollution are unevenly distributed among Latin American populations. Methodological approaches varied and can be improved in future studies, especially for exposure assessment to air pollution, as well as for assigning socioeconomic position to individuals. Using smaller geographic units and spatial regression techniques will allow a reduction in measurement error. Attempts should be made to include both individual and contextual socioeconomic indicators in the analysis. Better quality information will help understand these differential exposures and effects and provide inputs to policies to tackle these inequalities.