A new approach to spatial source apportionment of haze pollution in large scale and its application in China. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Liu, Huajun; Du, Guangjie; Liu, Yanli
• Is Part Of: Chinese journal of population resources and environment. Volume 16:Issue 2(2018)
• Page Start: 131
• Page End: 148
• Publication Date: 2018-04-03
• Subjects: Haze pollution -- spatial source apportionment -- spatial interaction -- variance decomposition -- PM2.5; Population -- Environmental aspects -- Periodicals; Natural resources -- China -- Periodicals; Environmental protection -- China -- Periodicals; Environmental protection; Natural resources; Population -- Environmental aspects; China; Periodicals; 333.705
• DOI: 10.1080/10042857.2018.1480687
• Languages: English
• ISSNs: 1004-2857

Abstract

ABSTRACT: Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter (PM2.5 ) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.