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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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2018 Impact Factor: 3.883

Front. Environ. Sci. Eng.    2018, Vol. 12 Issue (1) : 12    https://doi.org/10.1007/s11783-018-1009-z
RESEARCH ARTICLE |
Seasonal variations of transport pathways and potential sources of PM2.5 in Chengdu, China (2012–2013)
Yuan Chen1, Shaodong Xie2(), Bin Luo3
1. School of Safety and Environmental Engineering, Capital University of Economics and Business, Beijing 100070, China
2. College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
3. Sichuan Provincial Environmental Monitoring Center, Chengdu 610041, China
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Abstract

PM2.5 in Chengdu showed clear seasonal and diurnal variation.

5, 5, 5 and 3 mean clusters are generated in spring, summer, autumn, and winter.

Short-distance air masses are important pathways in Chengdu.

Emissions within the Sichuan Basin contribute significantly to PM2.5 pollution.

Long-range transport from Southern Xinjiang is a dust invasion path to Chengdu.

Seasonal pattern of transport pathways and potential sources of PM2.5 in Chengdu during 2012–2013 were investigated based on hourly PM2.5 data, backward trajectories, clustering analysis, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) method. The annual hourly mean PM2.5 concentration in Chengdu was 97.4 mg·m–3. 5, 5, 5 and 3 mean clusters were generated in four seasons, respectively. Short-distance air masses, which travelled within the Sichuan Basin with no specific source direction and relatively high PM2.5 loadings (>80 mg·m–3) appeared as important pathways in all seasons. These short pathways indicated that emissions from both local and surrounding regions of Chengdu contributed significantly to PM2.5 pollution. The cities in southern Chengdu were major potential sources with PSCF>0.6 and CWT>90 mg·m–3. The northeastern pathway prevailed throughout the year with higher frequency in autumn and winter and lower frequency in spring and summer. In spring, long-range transport from southern Xinjiang was a representative dust invasion path to Chengdu, and the CWT values along the path were 30-60 mg·m–3. Long-range transport was also observed in autumn from southeastern Xinjiang along a northwesterly pathway, and in winter from the Tibetan Plateau along a westerly pathway. In summer, the potential source regions of Chengdu were smaller than those in other seasons, and no long-range transport pathway was observed. Results of PSCF and CWT indicated that regions in Qinghai and Tibet contributed to PM2.5 pollution in Chengdu as well, and their CWT values increased to above 30 mg·m-3 in winter.

Keywords Transport pathway      Backward trajectory      Clustering analysis      Potential source      Chengdu     
Corresponding Authors: Shaodong Xie   
Issue Date: 26 December 2017
 Cite this article:   
Yuan Chen,Shaodong Xie,Bin Luo. Seasonal variations of transport pathways and potential sources of PM2.5 in Chengdu, China (2012–2013)[J]. Front. Environ. Sci. Eng., 2018, 12(1): 12.
 URL:  
http://academic.hep.com.cn/fese/EN/10.1007/s11783-018-1009-z
http://academic.hep.com.cn/fese/EN/Y2018/V12/I1/12
Sampling site Longitude Latitude Sampling site Longitude Latitude
Chengdu 104º6′E 30º36′N Xinjiang 73º40′E–96º23′E 34º22′N–49º10′N
Zigong 104º46′E 29º22′N Qinghai 89º35′E–103º4′E 31º9′N–39º19′N
Neijiang 105º2′E 29 º35′N Inner Mongolia 97º12′E–126º4′E 37º24′N–53º23′N
Yibin 104º34′E 29º46′N Tibet 78ºE–99ºE 27ºN–37ºN
Luzhou 105º23′E 28º55′N Ningxia 104º17′E–107º39′E 35º15′N–39º53′N
Meishan 103º51′E 30º5′N Gansu 92º14′E–108º46′E 32º11′N–42º58′N
Leshan 103º44′E 29º35′N Hubei 108º22′E–116º8′E 29º6′N–33º20′N
Chongqing 105º11′E–110º11′E 28º10′N–
32º13′N
Henan 110º22′E–116º40′E 31º24′N–36º23′N
Badain Juran desert 98º30′E–104ºE 39º30′N–42ºN Shaanxi 105º29′E–111º16′E 31º42′N–39º35′N
Tibetan Plateau 74ºE–104ºE 25ºN–40ºN Guizhou 103º37′E–109º35′E 24º38′N–29º13′N
Tab.1  Longitude andlatitude of the sampling site in Chengdu and other mentioned sitesin the study
Fig.1  (a) Probably distributionfunction and statistical summary, and (b) monthly variations of PM2.5 levels in Chengdu (Bottom and top of the boxes representthe 25% and 75% limits, respectively; bottom and top whiskers representthe 5 th and 95 th percentiles, respectively; and bottom and top shortlines represent the minimum and maximum values, respectively. Thesquare denotes the mean value, and the bottom and top crosses denotethe 1 st and 99 th percentiles, respectively)
Fig.2  Diurnal variations of PM2.5 concentrations in Chengdu (Explanation to the box-whiskerplot is the same as Fig. 1(b))
Fig.3  Distribution of 100 m backwardtrajectories in each season of Chengdu
Fig.4  Mean trajectory of each clusterin each season of Chengdu
Season Cluster All trajectories Polluted trajectories
Number of trajectories Percent of total (%) PM2.5
(mg·m–3)
Number of trajectories Percent of total (%) PM2.5
(mg·m–3)
Spring 1 869 39.4 89.3±48.6 439 36.2 125.4±40.4
2 112 5.1 67.4±61.2 28 2.3 147.5±66.8
3 845 38.3 109.9±51.7 615 50.6 128.2±48.0
4 332 15.0 66.4±37.5 104 8.6 110.1±35.6
5 48 2.2 149.8±97.8 28 2.3 225.4±46.6
All 2206 100 94.1±53.7 1214 100 128.3±47.6
Summer 1 1078 48.8 80.2±37.5 543 64.0 108.1±28.9
2 404 18.3 50.9±26.8 68 8.0 97.1±18.2
3 188 8.5 74.0±32.2 80 9.4 100.0±26.0
4 347 15.7 71.7±43.3 143 16.8 111.3±37.5
5 191 8.7 46.9±19.9 15 1.8 91.5±12.9
All 2208 100 70.0±37.4 849 100 106.7±29.7
Autumn 1 464 21.5 65.5±38.8 166 15.2 106.5±31.0
2 90 4.2 66.1±40.6 26 2.4 114.4±28.0
3 733 33.9 91.0±55.2 389 35.5 125.9±53.5
4 422 19.5 103.4±55.2 264 24.2 130.7±52.0
5 451 20.9 96.1±54.4 248 22.7 131.2±49.5
All 2160 100 88.0±52.9 1093 100 125.1±49.6
Winter 1 1294 59.9 121.1±69.9 922 54.5 147.0±65.4
2 235 10.9 153.9±80.5 191 11.3 178.0±69.3
3 631 29.2 165.6±71.5 579 34.2 174.8±66.8
All 2160 100 137.8±74.4 1692 100 160.0±67.8
Tab.2  Trajectory numberand averaged PM2.5 concentration of each clusterin spring, summer, autumn and winter of Chengdu
Fig.5  PSCF maps for PM2.5 of Chengdu in each season
Fig.6  CWT maps for PM2.5 of Chengdu in each season
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