期刊
OPEN GEOSCIENCES
卷 9, 期 1, 页码 314-321出版社
SCIENDO
DOI: 10.1515/geo-2017-0026
关键词
Grey relation; air pollutants; social-economic activities; quantitative assessment
资金
- China Postdoctoral Science Foundation [2016M592647]
- National Natural Science Foundation of China [61305022]
- Fund for International S&T Cooperation and Exchange R&D Project of Sichuan Province [2017HH0054]
- Opening fund of State Key Laboratory of Virtual Reality Technology and Systems (Beihang University) [BUAA-VR-16KF11]
With the rapid economic development, the serious air pollution in Beijing attracts increasing attention in the last decade. Seen as one whole complex and grey system, the causal relationship between the social development and the air pollution in Beijing has been quantitatively analyzed in this paper. By using the grey relational model, the aim of this study is to explore how the socio-economic and human activities affect on the air pollution in the city of Beijing, China. Four air pollutants, as the particulate matter with size 2.5 micrometers or less (PM2.5), particulate matter with size 10 micrometers or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NOx), are selected as the indicators of air pollution. Additionally, fifteen socio-economic indicators are selected to account for the regional socio-economic characteristics (economy variables, energy consumption variables, pollution emissions variables, environment and construction activity variables). The results highlight that all variables are associated with the concentrations of the four selected air pollutants, but with notable differences between the air pollutants. Most of the socio-economic indicators, such as industrial output, total energy consumption are highly correlated with PM2.5, while PM10, SO2, and NOx present in general moderate correlations with most of the socioeconomic variables. Contrary to other studies and reports this study reveals that vehicles and life energy do not have the strongest effect on air pollution in Beijing. This study provides useful information to reduce air pollution and support decision-making for sustainable development.
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