An analysis of roadside particulate matter pollution and population exposure over the Pearl River Delta region of China under clear-sky condition using new ultra-high-resolution PM2.5 satellite-retrieval algorithms

Abstract

Abstract Roadside air pollution is one of the serious air pollution problems in urban areas. Even though roadside air pollution has been reported to cause adverse human health impacts, the spatial distribution of roadside air pollution in a large urban agglomeration has yet to be fully assessed. This study aimed to analyse roadside fine particulate matter (PM2.5) pollution and the population exposure in 11 cities in the Pearl River Delta (PRD) region of China. We developed satellite-retrieval algorithms with dark target method, vector support machine model and random forest model to retrieve the spatial distribution of PM2.5 at an ultra-high-spatial-resolution (30 m) based on 30 m Landsat-8 L1 data. Our results show that the retrieved PM2.5 had a promising consistency with PM2.5 measurements at general and roadside stations (R 2 = 0.86; RMSE = 7.72 µg m−3). Moreover, on average, the roadside PM2.5 in Dongguan, Foshan, and Guangzhou was relatively higher (up to 107.60 µg m−3) whereas that in Hong Kong was relatively lower (up to 30.40 µg m−3). The roadside PM2.5 pollution typically occurred in roads for motorized vehicles i.e. motorway, trunk, primary and secondary road. Our results also show that roadside PM2.5 was up to 17% higher in holidays than in workdays in all the PRD cities except Hong Kong that showed roadside PM2.5 higher in workdays than in holidays. The population-weighted PM2.5 decreased with increasing distances from roads in every PRD city, and population-weighted PM2.5 was estimated to be up to 22% higher at roadsides than at distances of 1500 m away from roads. This study pinpointed the seriousness of roadside air pollution in the PRD region.