Using spatiotemporal prediction models to quantify PM2.5 exposure due to daily movement

Abstract

To date, epidemiological studies have generally not accounted for the spatiotemporal variations in PM_2.5 concentration that populations experience. These studies typically infer exposure using home address and annually-averaged concentrations measured by a few centrally-located monitors. To quantify the impact of spatiotemporal variation on exposure estimates, this study uses land-use random forest models to estimate daily-average ambient PM_2.5 concentrations in Allegheny County, USA. The data were collected using a network of 47 low-cost air quality sensors, and predictions were made for 50 × 50 m grids in Pittsburgh. Residential (P_R) and commercial (P_C) probability weighting values were assigned to each grid. The daily-average predictions were divided into “weekday” and “weekend” concentrations for each grid and averaged annually to estimate total annual exposure. Weighted stratified sampling was conducted using P_R and P_C values as probabilities, and weekdays and weekends as strata. Static models (population spends 24 hours per day in a fixed residential area) and dynamic models (estimates that account for time spent in residential and commercial areas) were created using these samples. The daily-average predicted concentrations across all grids ranged from 4–75 μg m⁻³ (μ = 12.0 μg m⁻³). Weekend concentrations were 10% higher than weekday concentrations, and commercial area concentrations were 9% higher than residential areas. These results support the hypotheses that exposure profiles vary due to movement between different areas and that exposure is underestimated when residents' mobility is ignored. Furthermore, exposure estimates may be affected due to the observed existence of temporal variations between weekdays and weekends. As low-cost sensor networks adoption grows, this work suggests that epidemiological exposure models can leverage these data to further refine exposure estimates and identify behaviors that may reduce exposure.