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Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China

Yang, Dongyang, Lu, Debin, Xu, Jianhua, Ye, Chao, Zhao, Jianan, Tian, Guanghui, Wang, Xinge, Zhu, Nina
Stochastic environmental research and risk assessment 2018 v.32 no.8 pp. 2445-2456
air pollution, atmospheric pressure, confidence interval, data collection, land use, meteorological data, models, particulates, prediction, relative humidity, river deltas, spatial variation, temperature, temporal variation, wind speed, China, Yangtze River
The prediction of PM₂.₅ concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM₂.₅ concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM₂.₅ concentrations. We validated the estimated effects by using the cross-validated R² and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM₂.₅ concentration, with a reasonably large R² of 0.86 and a small RMSE of 8.15 (μg/m³). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM₂.₅ concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.