Abstract：The traditional spatial simulation technologies of PM2.5 concentration usually ignored the mechanism behind the PM2.5-urban scenes (e.g. roads, factory, residential area, scenic area) correlation. This study proposed an urban scene assumption of PM2.5 concentration, namely the PM2.5 concentration is rather homogeneous within an urban scene while heterogeneous between different urban scenes. Taking the intra-urban area of Changsha, Hunan as an example, the spatial distribution of urban scenes was manually interpreted using a priori knowledge and a high-density monitoring sampling campaign was conducted for two periods in December 24~25, 2015. Based on the hourly PM2.5 concentration observations from 203 sampling sites and the urban scene map, the urban scene difference of PM2.5 concentration was explored and an urban scene enhanced two-stage modelling strategy of geographically weighted regression and artificial neural networks (GWR-ANN) was developed. The spatial patterns of PM2.5 concentrations were simulated based on GWR-ANN at the 100×100m resolution. Results show that the spatiotemporal variations of PM2.5 concentration between urban scenes do exist and the difference of PM2.5 concentration for sampling sites with the same land use/cover in two different types of urban scenes varied with time. The urban scene enhanced GWR-ANN could be effective in spatial simulation of PM2.5 concentrations at fine scale. The GWR-ANN model with urban scene variable performed better than the GWR-ANN model without urban scene variable. Except for five sampling hours with rather close statistics, the cross-validation R2 between estimated PM2.5 concentration and observed PM2.5 concentration for GWR-ANN with urban scene were higher than GWR-ANN without urban scene (0.76~0.84vs. 0.57~0.81). The spatial patterns of PM2.5 concentrations based on urban scene enhanced GWR-ANN could be effective in disclosing detail hot-spots and cold-spots of PM2.5 pollution.
许珊, 邹滨, 胡晨霞. 面向场景的城市PM2.5浓度空间分布精细模拟[J]. 中国环境科学, 2019, 39(11): 4570-4579.
XU Shan, ZOU Bin, HU Chen-xia. Urban scene-oriented simulation of the spatial distribution of PM2.5 concentration in an intra-urban area at fine scale. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(11): 4570-4579.
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