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

Abstract

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 PM_2.5.

Methods: We obtained raw (uncorrected) PM_2.5 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 PM_2.5 concentrations. We established the relationship between ambient PM_2.5 (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 PM_2.5 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 PM_2.5 (corrected values). The effect estimates were attenuated when the uncorrected PM_2.5 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 PM_2.5 sensor data may mitigate bias attenuation in air pollution epidemiologic studies. Low-cost sensor PM_2.5 data could also predict the air quality impacts of wildfires in Brazil's Amazon Basin.

Keywords: Amazon basin; Low-cost sensors; Particulate matter; Respiratory; Wildfires.