Assessing air pollution exposure misclassification using high-resolution PM2.5 concentration model and human mobility data

This study explores exposure measurement errors associated with ignoring human mobility and its impact on exposure-health effect estimates. Using a random forest classification model, this study examines the impact of a variety of factors on potential measurement errors in personal exposure to outdoor PM2.5. Mobility data at the individual level was combined with hourly PM2.5 surfaces at the neighborhood level to estimate and compare residence-based and mobility-based exposures for 100,784 Los Angeles County residents. The results show that exposure measurement errors increase for individuals with high mobility levels. Significant sociodemographic disparities are observed across different exposure classification groups. Exposures of low-income people who have high mobility and reside in polluted neighborhoods tend to be overestimated. In contrast, exposures of high-income people living in neighborhoods with cleaner air are likely to be underestimated. The result on the exposure-health effect suggests that health risks of the socially disadvantaged after exposure to PM2.5 is likely to be underestimated due to the exposure measurement error introduced by ignoring human mobility.
Source: Air Quality, Atmosphere and Health - Category: Environmental Health Source Type: research