A temporally-calibrated method for crowdsourcing based mapping of intra-urban PM2.5 concentrations. (1st October 2020)

• Record Type: Journal Article
• Authors: Zheng, Zhong; Zou, Bin; Wang, Yongqian; Li, Shenxin; Gao, Yanghua; Yang, Shiqi
• Is Part Of: Journal of cleaner production. Volume 269(2020)
• Publication Date: 2020-10-01
• Subjects: Crowdsourcing observation -- Data calibration -- Fine particular matter -- Spatial mapping -- Temporal variation; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.122347
• Languages: English
• ISSNs: 0959-6526

Abstract

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 ( R 2 was 0.73 and the RMSE was 7.65 μg/m 3 ). 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. Graphical abstract: Image 1 Highlights: A temporal calibration method was introduced for crowdsourcing air quality data. TCM can eliminate time inconsistence of original crowdsourcing based PM2.5 concentrations. Intra-urban variations of PM2.5 concentrations were revealed by densely observation. Crowdsourcing observation is effective for fine scale air quality mapping in urban area.