[PM2.5 Source Apportionment Based on a Variety of New Receptor Models]

Zhen-Yu Wang; Yong-Bin Li; Ling Guo; Zhi-Qiang Song; Yan-Ling Xu; Feng Wang; Wei-Qing Liang; Guo-Liang Shi; Yin-Chang Feng

doi:10.13227/j.hjkx.202106199

[PM_2.5 Source Apportionment Based on a Variety of New Receptor Models]

Huan Jing Ke Xue. 2022 Feb 8;43(2):608-618. doi: 10.13227/j.hjkx.202106199.

[Article in Chinese]

Authors

Zhen-Yu Wang^{1

2}, Yong-Bin Li³, Ling Guo³, Zhi-Qiang Song³, Yan-Ling Xu⁴, Feng Wang^{1

2}, Wei-Qing Liang^{1

2}, Guo-Liang Shi^{1

2}, Yin-Chang Feng^{1

2}

Affiliations

¹ State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
² China Meteorological Administration-Nankai University(CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China.
³ Jinzhong Municipal Bureau of Ecology and Environment, Jinzhong 030600, China.
⁴ Center for Regional Air Quality Simulation and Control, Chinese Academy for Environmental Planning, Beijing 100012, China.

PMID: 35075835
DOI: 10.13227/j.hjkx.202106199

Abstract

In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM_2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM_2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.

Keywords: PM2.5; new receptor models; partial target transformation-positive matrix factorization (PTT-PMF); positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR); source apportionment.

MeSH terms

Air Pollutants* / analysis
Dust / analysis
Environmental Monitoring
Particulate Matter / analysis
Vehicle Emissions / analysis

Substances

Air Pollutants
Dust
Particulate Matter
Vehicle Emissions