| Literature DB >> 31437705 |
Yaling Lu1, Yuan Wang2, Like Wang2, Hongyu Zhang3, Si Zhou4, Fenfen Bi3, Wei Zhang3, Yu Liu5, Hongqiang Jiang6, Wenbo Xue7.
Abstract
The "Joint Prevention and Control of Regional Air Pollution in China" has been put forward to solve serious regional air pollution. However, there is no geographical division covering the whole country that could reveal the similarities and differences among regions to support implementing this policy. This paper applied an air quality model and a multiregional input-output model to analyze the impacts and patterns of atmospheric transmission and trade transfer of regional air pollution, then compared the differences among typical provinces in the northern, central and southern regions. The social network analysis method was used to analyze the interacting pattern of regional atmospheric transmission and trade transfer, and 31 provinces in mainland China were divided into 5 clusters to reveal the interprovincial relationship of atmospheric transmission; then, the 31 provinces were divided into 3 clusters to reveal the interprovincial relationship of trade transfer. Based on these results, the provincial geographical division of "Nine Atmospheric Environmental Transmission Zones" covering mainland China was carried out. In addition, the key regions linking atmospheric transmission and trade transfer were also found. For example, Inner Mongolia is a typical key linkage region. From an atmospheric transmission perspective, it is linked to the northeast area. However, from a trade transfer perspective, it has a close relationship with the southern provinces. The linkage among provinces provides support for the interregional interaction mechanism of air pollution and the formulation of "Joint Prevention and Control of Regional Air Pollution in China".Keywords: Air pollution; Atmospheric transmission; China; Geographical division; Trade transfer
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Year: 2019 PMID: 31437705 DOI: 10.1016/j.jenvman.2019.109377
Source DB: PubMed Journal: J Environ Manage ISSN: 0301-4797 Impact factor: 6.789