Underestimated or overestimated? Dynamic assessment of hourly PM2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations.

Abstract

Spatio-temporal distributions of air pollution and population are two important factors influencing the patterns of mortality and diseases. Past studies have quantified the adverse effects of long-term exposure to air pollution. However, the dynamic changes of air pollution levels and population mobility within a day are rarely taken into consideration, especially in metropolitan areas. In this study, we use the high-resolution PM2.5 data from the micro-air monitoring stations, and hourly population mobility simulated by the heatmap based on Location Based Service (LBS) big data to evaluate the hourly active PM2.5 exposure in a typical Chinese metropolis. The dynamic "active population exposure" is compared spatiotemporally with the static "census population exposure" based on census data. The results show that over 12h on both study periods, 45.83% of suburbs' population-weighted exposure (PWE) is underestimated, while 100% of rural PWE and more than 34.78% of downtown's PWE are overestimated, with the relative difference reaching from -11μg/m3 to 7μg/m3. More notably, the total PWE of the active population at morning peak hours on weekdays is worse than previously realized, about 12.41% of people are exposed to PM2.5 over 60μg/m3, about twice as much as that in census scenario. The commuters who live in the suburbs and work in downtown may suffer more from PM2.5 exposure and uneven environmental resource distribution. This study proposes a new approach of calculating population exposure which can also be extended to quantify other environmental issues and related health burdens.