Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China

Liu, Baoshuang; Song, Na; Dai, Qili; Mei, Rubo; Sui, Benhui; Bi, Xinhui; Feng, Yinchang

doi:10.1016/j.atmosres.2015.11.002

Cited by 143 publications

(70 citation statements)

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“…As coal combustion was an important source of SO 2− 4 , Cl − , OC, and EC Liu et al, 2015;Liu et al, 2016Liu et al, , 2017a, it can be inferred that the influence of coal combustion might increase noticeably during the CAHP compared to the NCAHP, which was likely due to the increased usage of coal for domestic heating during winter (Table 1). Additionally, unfavourable meteorological conditions during the CAHP can have an offset effect on the control measures for coal combustion sources.…”

Section: Variations In Chemical Speciesmentioning

confidence: 99%

“…9 and 10). The chemical profile of factor 1 was mainly represented by Si (72.3 %), Ca 2+ (74.0 %), Mg 2+ (43.9 %), and Al (71.3 %), which were derived mainly from crustal dust Liu et al, 2016 (Santacatalina et al, 2010;Srimuruganandam and Nagendra, 2012;Liu et al, 2015;Liu et al, 2016Liu et al, , 2017a. The contribution proportions of factor 2 to PM 2.5 ranged from 29.5 % (66.4 µg m −3 ) during the NCANHP, 30.8 % (87.9 µg m −3 ) during the NC-AHP, and 31.6 % (84.8 µg m −3 ) during the CAHP to 32.7 % (64.6 µg m −3 ) during the WY, and decreased to 28.8 % (85.2 µg m −3 ) during the ACA.…”

Section: Variations In Pm 25 Source Contributionsmentioning

confidence: 99%

“…As these species (i.e. SO 2− 4 , Cl − , OC, EC, Si, Al, Ca 2+ , and Mg 2+ ) were closely associated with coal combustion Liu et al, 2015;Liu et al, 2016;Liu et al, 2017a, b, c), coal combustion for heating in winter probably had a great impact on increasing these chemical species in PM 2.5 . Furthermore, compared to the NCANHP, the concentrations of Cr, Cu, Fe, Mn, Ti, Zn, and Pb increased by 0.02, 0.02, 0.34, 0.02, 0.02, 0.28, and 0.07 µg m −3 , respectively, and the P i−heating values of these species were 72.7, 33.1, 34.4, 21.0, 45.8, 48.3, and 36.2 %, respectively, during the NCAHP.…”

Section: Variations In Chemical Speciesmentioning

confidence: 99%

“…However, compared to the NCAHP, the concentrations of Si, Al, Ca 2+ , and Mg 2+ during the CAHP decreased by 1.1, 0.1, 0.6, and 0.1 µg m −3 , respectively, and the P i−action values were up to 30.3, 4.5, 47.0, and 45.2 %, respectively. As Si, Al, Ca 2+ , and Mg 2+ mainly originated from the crustal dust Liu et al, 2016;, the influence of crustal dust on PM 2.5 during the CAHP might decrease clearly compared to the NCAHP. This is closely related to the control measures on dust emission during the TECA period (as shown in Sect.…”

Section: Variations In Chemical Speciesmentioning

confidence: 99%

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Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

et al. 2018

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Abstract. To evaluate the environmental effectiveness of the control measures for atmospheric pollution in Shijiazhuang, China, a large-scale controlling experiment for emission sources of atmospheric pollutants (i.e. a temporary emission control action, TECA) was designed and implemented during 1 November 2016 to 9 January 2017. Compared to the no-control action and heating period (NCAHP), under unfavourable meteorological conditions, the mean concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and chemical species (Si, Al, Ca 2+ , Mg 2+ ) in PM 2.5 during the control action and heating period (CAHP) still decreased by 8, 8, 5, 19, 30.3, 4.5, 47.0, and 45.2 %, respectively, indicating that the control measures for atmospheric pollution were effective. The effects of control measures in suburbs were better than those in urban area, especially for the control effects of particulate matter sources. The control effects for emission sources of carbon monoxide (CO) were not apparent during the TECA period, especially in suburbs, likely due to the increasing usage of domestic coal in suburbs along with the temperature decreasing.The results of positive matrix factorization (PMF) analysis showed that crustal dust, secondary sources, vehicle emissions, coal combustion and industrial emissions were main PM 2.5 sources. Compared to the whole year (WY) and the no-control action and no-heating period (NCANHP), the contribution concentrations and proportions of coal combustion to PM 2.5 increased significantly during other stages of the TECA period. The contribution concentrations and proportions of crustal dust and vehicle emissions to PM 2.5 decreased noticeably during the CAHP compared to other stages of the TECA period. The contribution concentrations and proportions of industrial emissions to PM 2.5 during the CAHP decreased noticeably compared to the NCAHP. The pollutants' emission sources during the CAHP were in effective control, especially for crustal dust and vehicles. However, the necessary coal heating for the cold winter and the unfavourable meteorological conditions had an offset effect on the control measures for emission sources to some degree. The results also illustrated that the discharge of pollutants might still be enormous even under such strict control measures.

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Section: Variations In Chemical Speciesmentioning

confidence: 99%

Section: Variations In Pm 25 Source Contributionsmentioning

confidence: 99%

Section: Variations In Chemical Speciesmentioning

confidence: 99%

Section: Variations In Chemical Speciesmentioning

confidence: 99%

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Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

et al. 2018

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“…With the rapidly increasing amount of vehicles in use, automobile exhaust gases are becoming the main sources of PMs in urban areas (Goel and Guttikunda, 2015;Calvo et al, 2013;Karar and Gupta, 2007;Liu et al, 2016a;Kleeman et al, 2000;Police et al, 2016). In particular, fine PMs (particle diameter ≤ 2.5 μm) arouse greater worldwide concerns than coarse PMs (diameter of particle ≤ 10 μm) because fine PMs are able to penetrate lung cells, giving increased toxicity, and scatter light efficiently, leading to extensive visibility reduction (Choi et al, 2012;Chen et al, 2016bChen et al, , 2016cLiu et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Mass spectral chemical fingerprints reveal the molecular dependence of exhaust particulate matters on engine speeds

Zhang

Zhao

et al. 2018

Journal of Environmental Sciences

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Available online xxxxParticulate matters (PMs) emitted by automobile exhaust contribute to a significant fraction of the global PMs. Extractive atmospheric pressure chemical ionization mass spectrometry (EAPCI-MS) was developed to explore the molecular dependence of PMs collected from exhaust gases produced at different vehicle engine speeds. The mass spectral fingerprints of the organic compounds embedded in differentially sized PMs (e.g., 0.22-0.45, 0.45-1.00, 1.00-2.00, 2.00-3.00, 3.00-5.00, and 5.00-10.00 μm) generated at different engine speeds (e.g., 1000, 1500, 2000, 2500, and 3000 r/min) were chemically profiled in the mass range of mass to charge ratio (m/z) 50-800. Organic compounds, including alcohols, aldehydes, and esters, were detected in all the PMs tested, with varied concentration levels for each individual PM sample. At relatively low engine speeds (≤ 1500 r/min), the total amount of organic species embedded in PMs of 0.22-1.00 μm was greater than in PMs of other sizes, while more organic species were found in PMs of 5.00-10.00 μm at high engine speeds (≥3000 r/min), indicating that the organic compounds distributed in different sizes of PMs strongly correlated with the engine speed. The experimental data showed that the EAPCI-MS technique enables molecular characterization of PMs in exhaust, revealing the chemical dependence of PMs on the engine speeds (i.e., the combustion conditions) of automobiles.

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An Amended Chemical Mass Balance Model for Source Apportionment of PM_2.5 in Typical Chinese Eastern Coastal Cities

Wang

Cheng

et al. 2019

CLEAN Soil Air Water

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An adequate air quality analysis requires interpretation regarding not only how severe pollution is but also how the pollution is formed. In this work, an amendment to the chemical mass balance (CMB) model is proposed to obtain better source apportionment of PM2.5. First, localized PM2.5 source profiles for Chinese eastern coastal cities are obtained by in situ sampling. Then, a judgment diagram is established for quick preliminary judgment. CMB models, including an original standard model and an amended two‐step model, are applied to apportion highly mixed samples. For the amended CMB model, simulation is first executed with the ratios of metal elements to Al, to apportion windowsill dust into soil dust, road dust, construction dust, and vehicle dust. Then, a virtual mixed source profile was built with the proportion ratio. The second CMB model is executed to apportion ambient dust based on the virtual mixed source profile instead of divided dusts. Finally, the proportions of soil dust, road dust, and construction dust are obtained by redistributing the virtual mixed sources within ambient dust. The modeling results show that the amended CMB method could separate different fugitive dusts and obtain reasonable results: secondary sulfate contributes the most to PM2.5 pollution in Chinese eastern coastal cities at 44.50%, followed by vehicle exhaust dust and ammonium nitrate; the fugitive dusts are more precisely apportioned and soil dust, road dust, construction dust, and windowsill dust contribute 2.24, 0.47, 0.09, and 1.84%, respectively.

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Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China

Cited by 143 publications

References 82 publications

Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

Mass spectral chemical fingerprints reveal the molecular dependence of exhaust particulate matters on engine speeds

An Amended Chemical Mass Balance Model for Source Apportionment of PM_2.5 in Typical Chinese Eastern Coastal Cities

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Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China

Cited by 143 publications

References 82 publications

Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China

Mass spectral chemical fingerprints reveal the molecular dependence of exhaust particulate matters on engine speeds

An Amended Chemical Mass Balance Model for Source Apportionment of PM2.5 in Typical Chinese Eastern Coastal Cities

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An Amended Chemical Mass Balance Model for Source Apportionment of PM_2.5 in Typical Chinese Eastern Coastal Cities