A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations

Abstract

As a primary air pollutant, fine particulate matter (PM2.5) is increasingly attracting attention. Crowdsourcing observations based methods are thought to be the best solutions for identifying the spatio-temporal distribution of PM2.5 in intra-urban areas. However, inconsistent timing in the collection of crowdsourced data has typically been ignored in previous studies. To address this issue, a temporally calibrated method (TCM) was introduced in this study. By interpolating TCM-estimated observations using the inverse distance weighted (IDW) method, variations of PM2.5 concentrations across the urban areas of Changsha City were captured. The results demonstrate that TCM can efficiently resolve the inconsistent timing defects of raw crowdsourcing observations (R2 was 0.73 and the RMSE was 7.65 μg/m3). Furthermore, PM2.5 distributions developed using TCM-based interpolations are of a finer spatial scale than those developed from raw observations at crowdsourcing locations. With a lack of funds to build sufficient stationary monitoring sites, developing crowdsourcing observation-based technology is the most promising solution for revealing intra-urban PM2.5 variations at a higher spatio-temporal- resolution.