面向场景的城市PM<sub>2.5</sub>浓度空间分布精细模拟

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Abstract The traditional spatial simulation technologies of PM_2.5 concentration usually ignored the mechanism behind the PM_2.5-urban scenes (e.g. roads, factory, residential area, scenic area) correlation. This study proposed an urban scene assumption of PM_2.5 concentration, namely the PM_2.5 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 PM_2.5 concentration observations from 203 sampling sites and the urban scene map, the urban scene difference of PM_2.5 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 PM_2.5 concentrations were simulated based on GWR-ANN at the 100×100m resolution. Results show that the spatiotemporal variations of PM_2.5 concentration between urban scenes do exist and the difference of PM_2.5 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 PM_2.5 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 PM_2.5 concentration and observed PM_2.5 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 PM_2.5 concentrations based on urban scene enhanced GWR-ANN could be effective in disclosing detail hot-spots and cold-spots of PM_2.5 pollution.

Key words： PM_2.5 pollution mapping urban scene assumption GWR ANN

Received: 22 April 2019

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	XU Shan
	ZOU Bin
	HU Chen-xia

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XU Shan,ZOU Bin,HU Chen-xia. Urban scene-oriented simulation of the spatial distribution of PM_2.5 concentration in an intra-urban area at fine scale[J]. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(11): 4570-4579.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2019/V39/I11/4570

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