Authors: Dongyang Yang*, , Debin Lu, East China Normal University, Jianhua Xu, East China Normal University
Topics: Geographic Information Science and Systems, Spatial Analysis & Modeling, Environmental Science
Keywords: Particulate matter (PM2.5); Spatio-temporal modeling; weekly average PM2.5 concentrations; Yangtze River Delta
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Bayside A, Sheraton, 4th Floor
Presentation File: No File Uploaded
High spatiotemporal resolution pollutant concentration could decrease biased exposure assessment. Thus, the accurate estimation of PM2.5 concentrations at fine spatial and temporal resolutions is essential for data collection to assess the health effects of exposure. The current study aims to provide spatial predictions of weekly average PM2.5 concentrations by using a spatio-temporal model. The GIS-based land use data, including the area of cultivated land, construction land, forest land, and normalized difference vegetation index (NDVI) with spatial differences and meteorological variables, including precipitation, air pressure, relative humidity, temperature, and wind speed with temporal and spatial variations, were respectively served as spatial covariates and spatiotemporal covariates for the spatio-temporal modeling. The results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration and the selected land use data and meteorological data were favorable predictors of PM2.5, with the cross-validated R2 of 0.86, and a 93.2% coverage of a 95% confidence interval of observed values. The result provided accurate spatio-temporal predictions of PM2.5 concentration which can contribute to performing an accurate assessment of potential health effects of air pollution.