关键词: 人工神经网络, 城市空气质量, 影响因子

Abstract:

Based on the air quality data of five indices in 2010 for 78 main cities of China, the research calculated the comprehensive score of urban air quality, selected ten out of 48 variables describing the climate, topography, urban development and environment management of these cities with multivariate linear regression analysis, and quantified their contribution to the urban air quality. Based on the comprehensive score of urban air quality, the authors used a stratified random sample of 30 from the 78 cities, as a training sample, to construct a radial basis function network (RBFN) model, which was used to simulate air quality of 173 main cities in China based on the natural and social-economic features, and environmental management of the cities. The results indicated that the average saturation vapor pressure, built-up urban area, elevation range, and the percentage of industry in GDP as four major dominants of urban air quality, accounting for the variation by 14.7%, 12.8%, 8.8% and 7.2%, respectively. This study broke the limitation of most previous air quality assessment models, which merely took air pollutants and meteorological factors as input. The result showed a high accuracy (R²=0.658, p<2.2×10^-14) of the RBFN model.

Key words: RBF neural network, urban air quality, causal factors