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Review of receptor-based source apportionment research of fine particulate matter and its challenges in China

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 586, Issue -, Pages 917-929

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2017.02.071

Keywords

PM2.5; Source apportionment; Receptor model; Review Source profile; China

Funding

  1. National Natural Science Foundation of China [41571130033, 41121004, 41430646]
  2. Ministry of Science and Technology Of China [2014BAC06B02]
  3. MEP (Ministry of Environmental Protection of China) Special Funds for Research on Public Welfare [201409002]

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As the key for haze control, atmospheric fine particulate matter with aerodynamic diameter <2.5 mu m (or PM2.5) is of great concern lately in China. It is closely linked to fast pace of urbanization, industrialization and economic development, especially in eastern China. A good understanding of its sources is required for effective pollution abatement. Receptor models are one of the major methods for source apportionment used in China. The major objective of this study is to understand sources that contribute to fine particulate matter in China and key challenges in this area. Spatial distribution of fine particulate matter concentration, chemical composition and dominant sources in North and South China are summarized. Based on chemical speciation results from 31 cities and source apportionment results from 21 cities, it is found that secondary sources and traffic emission have higher contribution in South China while the percentage of coal combustion, dust and biomass burning to total PM2.5 are higher in North China. Source profiles established in China from 44 cities and areas are also summarized as references for future source apportionment studies. Suggestions for future research are also provided including methods for evaluating source apportionment results, ways for integrating multiple source apportionment methods, the need for standardizing protocols and developing tools for high-time resolution source apportionment. (C) 2017 Elsevier B.V. All rights reserved.

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