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Frontiers of Environmental Science & Engineering

Front. Environ. Sci. Eng.    2016, Vol. 10 Issue (5) : 6     https://doi.org/10.1007/s11783-016-0839-9
RESEARCH ARTICLE |
Source apportionment of PM2.5 in Tangshan, China—Hybrid approaches for primary and secondary species apportionment
Wei WEN1,2,Shuiyuan CHENG1,4,*(),Lei LIU3,*(),Gang WANG1,Xiaoqi WANG1
1. Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
2. Environmental Meteorology Forecast Center of Beijing-Tianjin-Hebei, Chinese Meteorological Administration, Beijing 100089, China
3. Department of Civil and Resource Engineering, Dalhousie University, Halifax, NS B3H 4RZ, Canada
4. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
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Abstract

The new hybrid approaches for the source apportionment of PM2.5 were proposed.

The hybrid approach can be used for source apportionment of secondary species.

The metallurgy industry was the biggest contribution source to PM2.5 of Tangshan.

In winter, the contribution from the coal-fired boilers was the largest one.

The objective of this paper is to propose a hybrid approach for the source apportionment of primary and secondary species of PM2.5 in the city of Tangshan. The receptor-based PMF (Positive Matrix Factorization) is integrated with the emission inventory (EI) to form the first hybrid method for the source apportionment of the primary species. The hybrid CAMx-PSAT-CP (Comprehensive Air Quality Model with Extensions – Particulate Source Apportionment Technology – Chemical Profile) approach is then proposed and used for the source apportionment of the secondary species. The PM2.5 sources identified for Tangshan included the soil dust, the metallurgical industry, power plants, coal-fired boilers, vehicles, cement production, and other sources. It is indicated that the PM2.5 pollution is a regional issue. Among all the identified sources, the metallurgy industry was the biggest contribution source to PM2.5, followed by coal-fired boilers, vehicles and soil dust. The other-source category plays a crucial role for PM2.5, particularly for the formation of secondary species and aerosols, and these other sources include non-specified sources such as agricultural activities, biomass combustion, residential emissions, etc. The source apportionment results could help the local authorities make sound policies and regulations to better protect the citizens from the local and regional PM2.5 pollution. The study also highlights the strength of utilizing the proposed hybrid approaches in the identification of PM2.5 sources. The techniques used in this study show considerable promise for further application to other regions as well as to identify other source categories of PM2.5.

Keywords Secondary species      Emission inventory      PM2.5      Source apportionment     
This article is part of themed collection: Understanding the processes of air pollution formation (Responsible Editors: Min SHAO, Shuxiao WANG & Armistead G. RUSSELL)
Corresponding Authors: Shuiyuan CHENG,Lei LIU   
Issue Date: 13 May 2016
 Cite this article:   
Wei WEN,Shuiyuan CHENG,Lei LIU, et al. Source apportionment of PM2.5 in Tangshan, China—Hybrid approaches for primary and secondary species apportionment[J]. Front. Environ. Sci. Eng., 2016, 10(5): 6.
 URL:  
http://journal.hep.com.cn/fese/EN/10.1007/s11783-016-0839-9
http://journal.hep.com.cn/fese/EN/Y2016/V10/I5/6
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Wei WEN
Shuiyuan CHENG
Lei LIU
Gang WANG
Xiaoqi WANG
Fig.1  The location of observation station and designed of modeling domain
species parameter January April July October
PM2.5 simulated 219 56 68 85
monitored 214.1 78.9 77.1 96.2
NMB/% 2.3 -28.6 –12.2 –11.9
NME/% 42.2 43.2 31.9 32.2
SO2 simulated 221 85.5 129.4 141.5
monitored 215.8 120.6 104.8 109.6
NMB/% 2.4 -29 23.5 29.1
NME/% 25.3 34.7 42.1 36.6
NO2 simulated 109 58.1 68.3 73.1
monitored 90 55.4 66 67.1
NMB/% 20.6 4.9 3.3 9.2
NME/% 31.7 18.8 21.9 18.4
NH4+ simulated 13.6 2.5 2.8 2.7
monitored 13.4 3.8 8.5 5.7
NMB/% 1.4 –35 –67.9 –51.6
NME/% 58.9 48.7 68 58
SO42 simulated 13 4 8 4
monitored 21.8 5.6 15.9 9.1
NMB/% –40.3 –32.1 –49.6 –54.3
NME/% 50.4 50.6 73.1 60.0
NO3 simulated 16 3.2 4.5 4.1
monitored 10.0 7.5 8.5 12.1
NMB/% 57.7 –46.6 –47.4 –63.9
NME/% 65.1 66.3 72.7 65.9
Tab.1  Comparison of simulated results with monitored data (24h average concentrations /(μg·m–3) )
composition industrial subdivision residential subdivision
spring summer autumn winter mean spring summer autumn winter mean
POA 16.25 8.00 10.66 14.68 12.40 16.88 10.84 13.32 13.61 13.66
SOA 5.58 9.29 15.48 16.81 11.79 7.38 9.77 14.26 12.64 11.01
EC 4.00 5.81 4.28 3.93 4.51 6.38 5.52 7.02 3.98 5.73
soil dust 22.22 16.61 18.24 10.81 16.97 20.84 14.69 17.73 10.39 15.91
pollution elements 6.54 2.00 6.09 2.28 4.23 7.25 1.00 3.94 3.03 3.81
SO42 8.52 24.01 10.07 13.00 13.90 7.07 20.81 8.44 14.97 12.82
NO3 7.00 10.62 10.88 7.78 9.07 8.77 11.27 10.44 8.51 9.75
NH4+ 3.94 11.05 10.56 7.40 8.24 4.79 11.71 7.96 8.01 8.12
unidentified 25.95 12.63 13.75 23.32 18.91 20.63 14.40 16.89 24.86 19.20
Tab.2  Mass percentage of chemical compositions in PM2.5 samples on a monthly basis at both subdivisions (unit:%)
Fig.2  Preliminary source apportionment results for primary species in PM2.5 at the residential subdivision using PMF only
soil dust metallurgical industry cement production power plants coal-fired boilers vehicles otherssources
spring 27.0 17.8 3.6 8.5 21.7 16.7 4.6
summer 17.1 22.3 12.8 8.5 14.6 14.5 10.1
autumn 25.4 11.8 7.2 6.9 15.3 15.2 18.0
winter 17.0 17.2 10.1 5.0 25.0 19.0 6.6
annual average 21.6 17.2 8.4 7.2 19.1 16.3 9.8
Tab.3  Complete source apportionment results for primary species in PM2.5 at the residential subdivision using the hybrid PMF-EI approach (unit:%)
soil dust metallurgical industry cement production power plants coal-fired boilers vehicles otherssources
spring 25.4 28.0 5.9 6.1 9.1 12.0 13.4
summer 22.0 25.3 5.8 8.5 12.6 14.3 11.4
autumn 22.8 23.3 6.2 6.0 9.0 12.2 20.4
winter 13.0 27.6 5.6 9.6 14.2 14.1 15.8
annual average 20.8 26.0 5.8 7.5 11.2 13.1 15.2
Tab.4  Complete source apportionment results for primary species in PM2.5 at the industrial subdivision using the hybrid PMF-EI approach (unit:%)
Fig.3  Contribution percentages from each source/group to the gaseous precursors in PM2.5
winter spring summer autumn annual
industrial subdivision
power plants 2.7 2.3 4.8 2.5 3.1
vehicles 5.1 3.4 4.8 7.4 5.2
metallurgical industry 10.6 6.8 15.2 11.9 11.1
cement production 2.2 2.4 4.5 3.6 3.2
coal-fired boilers 13.1 3.6 8.0 4.9 7.4
others sources 11.1 6.2 17.4 16.3 12.8
residential subdivision
power plants 3.0 2.4 4.5 2.3 3.1
vehicles 4.9 4.3 5.0 6.9 5.3
metallurgical industry 10.3 7.4 14.4 10.7 10.7
cement production 2.3 2.5 4.3 3.2 3.1
coal-fired boilers 12.3 3.8 7.4 4.4 7.0
others sources 11.0 7.2 17.7 13.3 12.3
Tab.5  Source apportionment results for the secondary species in PM2.5 at both subdivisions (unit:%)
Fig.4  Complete source apportionment results of PM2.5 at both subdivisions
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