Articles | Volume 21, issue 21
https://doi.org/10.5194/acp-21-16219-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-16219-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report: Spatiotemporal and policy-related variations of PM2.5 composition and sources during 2015–2019 at multiple sites in a Chinese megacity
Xinyao Feng
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
Yingze Tian
CORRESPONDING AUTHOR
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
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER/CMA-NKU), Tianjin, 300350, China
Qianqian Xue
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
Danlin Song
Chengdu Research Academy of Environmental Sciences, Chengdu, 610072, China
Fengxia Huang
Chengdu Research Academy of Environmental Sciences, Chengdu, 610072, China
Yinchang Feng
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
Related authors
No articles found.
Yuan Li, Qili Dai, Wubin Zhu, Xuan Liu, Jiandong Shen, Renchang Yan, Yunshan Li, Jing Ding, Young Su Lee, Yufen Zhang, and Yinchang Feng
EGUsphere, https://doi.org/10.22541/essoar.174559329.93866726/v2, https://doi.org/10.22541/essoar.174559329.93866726/v2, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Machine learning reveals air quality patterns shaped by holiday activities, with fireworks driving major PM2.5 spikes during Spring Festival.
Baoshuang Liu, Yao Gu, Yutong Wu, Qili Dai, Shaojie Song, Yinchang Feng, and Philip K. Hopke
Atmos. Chem. Phys., 24, 12861–12879, https://doi.org/10.5194/acp-24-12861-2024, https://doi.org/10.5194/acp-24-12861-2024, 2024
Short summary
Short summary
Reactive loss of volatile organic compounds (VOCs) is a long-term issue yet to be resolved in VOC source analyses. We assess common methods of, and existing issues in, reducing losses, impacts of losses, and sources in current source analyses. We offer a potential supporting role for solving issues of VOC conversion. Source analyses of consumed VOCs that reacted to produce ozone and secondary organic aerosols can play an important role in the effective control of secondary pollution in air.
Haoqi Wang, Xiao Tian, Wanting Zhao, Jiacheng Li, Haoyu Yu, Yinchang Feng, and Shaojie Song
Atmos. Chem. Phys., 24, 6583–6592, https://doi.org/10.5194/acp-24-6583-2024, https://doi.org/10.5194/acp-24-6583-2024, 2024
Short summary
Short summary
pH is a key property of ambient aerosols, which affect many atmospheric processes. As aerosol pH is a non-conservative parameter, diverse averaging metrics and temporal resolutions may influence the pH values calculated by thermodynamic models. This technical note seeks to quantitatively evaluate the average pH using varied metrics and resolutions. The ultimate goal is to establish standardized reporting practices in future research endeavors.
Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng
Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023, https://doi.org/10.5194/gmd-16-6757-2023, 2023
Short summary
Short summary
This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
Baoshuang Liu, Yanyang Wang, He Meng, Qili Dai, Liuli Diao, Jianhui Wu, Laiyuan Shi, Jing Wang, Yufen Zhang, and Yinchang Feng
Atmos. Chem. Phys., 22, 8597–8615, https://doi.org/10.5194/acp-22-8597-2022, https://doi.org/10.5194/acp-22-8597-2022, 2022
Short summary
Short summary
Understanding effectiveness of air pollution regulatory measures is critical for control policy. Machine learning and dispersion-normalized approaches were applied to decouple meteorologically deduced variations in Qingdao, China. Most pollutant concentrations decreased substantially after the Clean Air Action Plan. The largest emission reduction was from coal combustion and steel-related smelting. Qingdao is at risk of increased emissions from increased vehicular population and ozone pollution.
Yingze Tian, Xiaoning Wang, Peng Zhao, Zongbo Shi, and Roy M. Harrison
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-1007, https://doi.org/10.5194/acp-2021-1007, 2022
Revised manuscript not accepted
Short summary
Short summary
Chemical mass balance (CMB) is a widely used method to apportion the sources of PM2.5. We explore the sensitivity of CMB results to input data of organic markers only (OM-CMB) with a combination of organic and inorganic markers (IOM-CMB), as well as using different chemical profiles for sources. Our results indicate the superiority of combining inorganic and organic tracers and using locally-relevant source profiles in source apportionment of PM.
Jingsha Xu, Shaojie Song, Roy M. Harrison, Congbo Song, Lianfang Wei, Qiang Zhang, Yele Sun, Lu Lei, Chao Zhang, Xiaohong Yao, Dihui Chen, Weijun Li, Miaomiao Wu, Hezhong Tian, Lining Luo, Shengrui Tong, Weiran Li, Junling Wang, Guoliang Shi, Yanqi Huangfu, Yingze Tian, Baozhu Ge, Shaoli Su, Chao Peng, Yang Chen, Fumo Yang, Aleksandra Mihajlidi-Zelić, Dragana Đorđević, Stefan J. Swift, Imogen Andrews, Jacqueline F. Hamilton, Ye Sun, Agung Kramawijaya, Jinxiu Han, Supattarachai Saksakulkrai, Clarissa Baldo, Siqi Hou, Feixue Zheng, Kaspar R. Daellenbach, Chao Yan, Yongchun Liu, Markku Kulmala, Pingqing Fu, and Zongbo Shi
Atmos. Meas. Tech., 13, 6325–6341, https://doi.org/10.5194/amt-13-6325-2020, https://doi.org/10.5194/amt-13-6325-2020, 2020
Short summary
Short summary
An interlaboratory comparison was conducted for the first time to examine differences in water-soluble inorganic ions (WSIIs) measured by 10 labs using ion chromatography (IC) and by two online aerosol chemical speciation monitor (ACSM) methods. Major ions including SO42−, NO3− and NH4+ agreed well in 10 IC labs and correlated well with ACSM data. WSII interlab variability strongly affected aerosol acidity results based on ion balance, but aerosol pH computed by ISORROPIA II was very similar.
Cited articles
Ait-Helal, W., Borbon, A., Sauvage, S., de Gouw, J. A., Colomb, A., Gros, V., Freutel, F., Crippa, M., Afif, C., Baltensperger, U., Beekmann, M., Doussin, J.-F., Durand-Jolibois, R., Fronval, I., Grand, N., Leonardis, T., Lopez, M., Michoud, V., Miet, K., Perrier, S., Prévôt, A. S. H., Schneider, J., Siour, G., Zapf, P., and Locoge, N.: Volatile and intermediate volatility organic compounds in suburban Paris: variability, origin and importance for SOA formation, Atmos. Chem. Phys., 14, 10439–10464, https://doi.org/10.5194/acp-14-10439-2014, 2014.
Almeida, S. M., Pio, C. A., Freitas, M. C., Reis, M. A., and Trancoso, M.
A.: Source apportionment of fine and coarse particulate matter in a
sub-urban area at the Western European Coast, Atmos. Environ., 39,
3127–3138, https://doi.org/10.1016/j.atmosenv.2005.01.048, 2005.
Amap: POI (point of interest), Amap [data set], available at: http://lbs.amap.com/api/webservice/guide/api/search/, last access: 2 June 2021.
Amato, F. and Hopke, P. K.: Source apportionment of the ambient PM2.5 across
St. Louis using constrained positive matrix factorization, Atmos. Environ., 46, 329–337, https://doi.org/10.1016/j.atmosenv.2011.09.062, 2012.
Amil, N., Latif, M. T., Khan, M. F., and Mohamad, M.: Seasonal variability of PM2.5 composition and sources in the Klang Valley urban-industrial environment, Atmos. Chem. Phys., 16, 5357–5381, https://doi.org/10.5194/acp-16-5357-2016, 2016.
Arimoto, R., Duce, R. A., Savoie, D. L., Prospero, J. M., Talbot, R.,
Cullen, J. D., Tomza, U., Lewis, N. F., and Ray, B. J.: Relationships among
aerosol constituents from Asia and the North Pacific during PEM-West a,
J. Geophys. Res.-Atmos., 101, 2011–2023, https://doi.org/10.1029/95JD01071, 1996.
Bell, M. L., Dominici, F., Ebisu, K., Zeger, S. L., and Samet, J. M.: Spatial and Temporal Variation in PM2.5 Chemical Composition
in the United States for Health Effects Studies, Environ. Health
Persp., 115, 989–995, https://doi.org/10.1289/ehp.9621, 2007.
Bi, X., Feng, Y., Wu, J., Wang, Y., and Zhu, T.: Source apportionment of
PM10 in six cities of northern China, Atmos. Environ., 41, 903–912,
https://doi.org/10.1016/j.atmosenv.2006.09.033, 2007.
Bressi, M., Sciare, J., Ghersi, V., Bonnaire, N., Nicolas, J. B., Petit, J.-E., Moukhtar, S., Rosso, A., Mihalopoulos, N., and Féron, A.: A one-year comprehensive chemical characterisation of fine aerosol (PM2.5) at urban, suburban and rural background sites in the region of Paris (France), Atmos. Chem. Phys., 13, 7825–7844, https://doi.org/10.5194/acp-13-7825-2013, 2013.
Cai, S., Wang, Y., Zhao, B., Wang, S., Chang, X., and Hao, J.: The impact of
the “Air Pollution Prevention and Control Action Plan” on PM2.5
concentrations in Jing-Jin-Ji region during 2012–2020, Sci. Total
Environ., 580, 197–209, https://doi.org/10.1016/j.scitotenv.2016.11.188, 2017.
Chen, L. W. A., Watson, J. G., Chow, J. C., DuBois, D. W., and Herschberger,
L.: PM2.5 Source Apportionment: Reconciling Receptor Models for U.S.
Nonurban and Urban Long-Term Networks, J. Air Waste Manage., 61, 1204–1217, https://doi.org/10.1080/10473289.2011.619082, 2011.
Choi, J.-K., Ban, S.-J., Kim, Y.-P., Kim, Y.-H., Yi, S.-M., and Zoh, K.-D.:
Molecular marker characterization and source appointment of particulate
matter and its organic aerosols, Chemosphere, 134, 482–491, https://doi.org/10.1016/j.chemosphere.2015.04.093, 2015.
Contini, D., Cesari, D., Donateo, A., Chirizzi, D., and Belosi, F.: Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy, Atmosphere, 5, 435-453, doi:10.3390/atmos5020435, 2014.
Dai, F., Chen, M., and Yang, B.: Spatiotemporal variations of PM2.5
concentration at the neighborhood level in five Chinese megacities,
Atmos. Pollut. Res., 11, 190–202, https://doi.org/10.1016/j.apr.2020.03.010, 2020.
Fang, K., Wang, T., He, J., Wang, T., Xie, X., Tang, Y., Shen, Y., and Xu,
A.: The distribution and drivers of PM2.5 in a rapidly urbanizing region:
The Belt and Road Initiative in focus, Sci. Total Environ.,
716, 137010, https://doi.org/10.1016/j.scitotenv.2020.137010,
2020.
Gao, J., Tian, H., Cheng, K., Lu, L., Zheng, M., Wang, S., Hao, J., Wang,
K., Hua, S., Zhu, C., and Wang, Y.: The variation of chemical
characteristics of PM2.5 and PM10 and formation causes during two haze
pollution events in urban Beijing, China, Atmos. Environ., 107, 1–8,
https://doi.org/10.1016/j.atmosenv.2015.02.022, 2015.
Gebhart, K. A., Schichtel, B. A., Malm, W. C., Barna, M. G., Rodriguez, M.
A., and Collett, J. L.: Back-trajectory-based source apportionment of
airborne sulfur and nitrogen concentrations at Rocky Mountain National Park,
Colorado, USA, Atmos. Environ., 45, 621–633, https://doi.org/10.1016/j.atmosenv.2010.10.035, 2011.
Govender, P. and Sivakumar, V.: Application of k-means and hierarchical
clustering techniques for analysis of air pollution: A review (1980–2019),
Atmos. Pollut. Res., 11, 40–56, https://doi.org/10.1016/j.apr.2019.09.009, 2020.
Gurjar, B. R., Ravindra, K., and Nagpure, A. S.: Air pollution trends over
Indian megacities and their local-to-global implications, Atmos. Environ., 142, 475–495, https://doi.org/10.1016/j.atmosenv.2016.06.030, 2016.
Han, L., Wang, X., He, M., and Guo, W.: Inventory and Environmental Impact
of VOCs Emission from the Typical Anthropogenic Sources in Sichuan Province,
Environm. Sci., 34, 4535–4542, https://doi.org/10.13227/j.hjkx.2013.12.043, 2013.
Han, Y.-J., Holsen, T. M., and Hopke, P. K.: Estimation of source locations
of total gaseous mercury measured in New York State using trajectory-based
models, Atmos. Environ., 41, 6033–6047, https://doi.org/10.1016/j.atmosenv.2007.03.027, 2007.
Hasheminassab, S., Daher, N., Saffari, A., Wang, D., Ostro, B. D., and Sioutas, C.: Spatial and temporal variability of sources of ambient fine particulate matter (PM2.5) in California, Atmos. Chem. Phys., 14, 12085–12097, https://doi.org/10.5194/acp-14-12085-2014, 2014.
He, J., Ding, S., and Liu, D.: Exploring the spatiotemporal pattern of PM2.5
distribution and its determinants in Chinese cities based on a multilevel
analysis approach, Sci. Total Environ., 659, 1513–1525,
https://doi.org/10.1016/j.scitotenv.2018.12.402, 2019.
Jang, M., Czoschke, N. M., Lee, S., and Kamens, R. M.: Heterogeneous
atmospheric aerosol production by acid-catalyzed particle-phase reactions,
Science, 298, 814–817, https://doi.org/10.1126/science.1075798, 2002.
Jiang, S. Y. N., Yang, F., Chan, K. L., and Ning, Z.: Water solubility of
metals in coarse PM and PM2.5 in typical urban environment in Hong Kong,
Atmos. Pollut. Res., 5, 236–244, https://doi.org/10.5094/APR.2014.029, 2014.
Kanakidou, M., Seinfeld, J. H., Pandis, S. N., Barnes, I., Dentener, F. J., Facchini, M. C., Van Dingenen, R., Ervens, B., Nenes, A., Nielsen, C. J., Swietlicki, E., Putaud, J. P., Balkanski, Y., Fuzzi, S., Horth, J., Moortgat, G. K., Winterhalter, R., Myhre, C. E. L., Tsigaridis, K., Vignati, E., Stephanou, E. G., and Wilson, J.: Organic aerosol and global climate modelling: a review, Atmos. Chem. Phys., 5, 1053–1123, https://doi.org/10.5194/acp-5-1053-2005, 2005.
Kelly, F. J. and Fussell, J. C.: Size, source and chemical composition as
determinants of toxicity attributable to ambient particulate matter,
Atmos. Environ., 60, 504–526, https://doi.org/10.1016/j.atmosenv.2012.06.039, 2012.
Kleindienst, T. E., Lewandowski, M., Offenberg, J. H., Jaoui, M., and Edney, E. O.: The formation of secondary organic aerosol from the isoprene + OH reaction in the absence of NOx, Atmos. Chem. Phys., 9, 6541–6558, https://doi.org/10.5194/acp-9-6541-2009, 2009.
Kong, S., Han, B., Bai, Z., Chen, L., Shi, J., and Xu, Z.: Receptor modeling
of PM2.5, PM10 and TSP in different seasons and long-range transport
analysis at a coastal site of Tianjin, China, Sci. Total Environ., 408, 4681–4694, https://doi.org/10.1016/j.scitotenv.2010.06.005, 2010.
Kulshrestha, A., Satsangi, P. G., Masih, J., and Taneja, A.: Metal
concentration of PM2.5 and PM10 particles and seasonal variations in urban
and rural environment of Agra, India, Sci. Total Environ., 407,
6196–6204, https://doi.org/10.1016/j.scitotenv.2009.08.050, 2009.
Kulshrestha, U. C., Sunder Raman, R., Kulshrestha, M. J., Rao, T. N., and
Hazarika, P. J.: Secondary aerosol formation and identification of regional
source locations by PSCF analysis in the Indo-Gangetic region of India,
J. Atmos. Chem., 63, 33–47, https://doi.org/10.1007/s10874-010-9156-z, 2009.
Lee, J. H. and Hopke, P. K.: Apportioning sources of PM2.5 in St. Louis, MO
using speciation trends network data, Atmos. Environ., 40, 360–377,
https://doi.org/10.1016/j.atmosenv.2005.11.074, 2006.
Li, G., Fang, C., Wang, S., and Sun, S.: The Effect of Economic Growth,
Urbanization, and Industrialization on Fine Particulate Matter (PM2.5)
Concentrations in China, Environ. Sci. Technol., 50,
11452–11459, https://doi.org/10.1021/acs.est.6b02562, 2016.
Lin, G., Fu, J., Jiang, D., Hu, W., Dong, D., Huang, Y., and Zhao, M.: Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China, International Journal of Environmental Research and Public Health, 11, 173–186, https://doi.org/10.3390/ijerph110100173, 2014.
Liu, G.-R., Shi, G.-L., Tian, Y.-Z., Wang, Y.-N., Zhang, C.-Y., and Feng,
Y.-C.: Physically constrained source apportionment (PCSA) for polycyclic
aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR)
method, Sci. Total Environ., 502, 16–21, https://doi.org/10.1016/j.scitotenv.2014.09.011, 2015.
Luo, K., Li, G., Fang, C., and Sun, S.: PM2.5 mitigation in China:
Socioeconomic determinants of concentrations and differential control
policies, J. Environ. Manage., 213, 47–55, https://doi.org/10.1016/j.jenvman.2018.02.044, 2018.
MeteoInfo: Homepage, MeteoInfo [data set], available at: http://www.meteothinker.com/, last access: 5 September 2021.
Mirowsky, J., Hickey, C., Horton, L., Blaustein, M., Galdanes, K., Peltier,
R. E., Chillrud, S., Chen, L. C., Ross, J., Nadas, A., Lippmann, M., and
Gordon, T.: The effect of particle size, location and season on the toxicity
of urban and rural particulate matter, Inhal. Toxicol., 25, 747–757,
https://doi.org/10.3109/08958378.2013.846443, 2013.
NOAA: Gridded Meteorological Data Archives, NOAA [data set], available at: https://ready.arl.noaa.gov/archives.php, last access: 20 August 2021.
Ostro, B., Lipsett, M., Reynolds, P., Goldberg, D., Hertz, A., Garcia, C.,
Henderson Katherine, D., and Bernstein, L.: Long-Term Exposure to
Constituents of Fine Particulate Air Pollution and Mortality: Results from
the California Teachers Study, Environ. Health Persp., 118,
363–369, https://doi.org/10.1289/ehp.0901181, 2010.
Paatero, P.: Least squares formulation of robust non-negative factor
analysis, Chemometr. Intell. Lab., 37, 23–35,
https://doi.org/10.1016/S0169-7439(96)00044-5, 1997.
Paatero, P. and Tapper, U.: Positive matrix factorization: A non-negative
factor model with optimal utilization of error estimates of data values,
Environmetrics, 5, 111–126, https://doi.org/10.1002/env.3170050203, 1994.
Pant, P. and Harrison, R. M.: Critical review of receptor modelling for
particulate matter: A case study of India, Atmos. Environ., 49,
1–12, https://doi.org/10.1016/j.atmosenv.2011.11.060, 2012.
Philip, S., Martin, R. V., van Donkelaar, A., Lo, J. W.-H., Wang, Y., Chen,
D., Zhang, L., Kasibhatla, P. S., Wang, S., Zhang, Q., Lu, Z., Streets, D.
G., Bittman, S., and Macdonald, D. J.: Global Chemical Composition of
Ambient Fine Particulate Matter for Exposure Assessment, Environ.
Sci. Technol., 48, 13060–13068, https://doi.org/10.1021/es502965b, 2014.
Polissar, A. V., Hopke, P. K., and Poirot, R. L.: Atmospheric Aerosol over
Vermont: Chemical Composition and Sources, Environ. Sci. Technol., 35, 4604–4621, https://doi.org/10.1021/es0105865, 2001.
Reliable Prognosis: Homepage, available at: https://rp5.ru/, last access: 7 June 2021.
Richard, A., Gianini, M. F. D., Mohr, C., Furger, M., Bukowiecki, N., Minguillón, M. C., Lienemann, P., Flechsig, U., Appel, K., DeCarlo, P. F., Heringa, M. F., Chirico, R., Baltensperger, U., and Prévôt, A. S. H.: Source apportionment of size and time resolved trace elements and organic aerosols from an urban courtyard site in Switzerland, Atmos. Chem. Phys., 11, 8945–8963, https://doi.org/10.5194/acp-11-8945-2011, 2011.
Riuttanen, L., Hulkkonen, M., Dal Maso, M., Junninen, H., and Kulmala, M.: Trajectory analysis of atmospheric transport of fine particles, SO2, NOx and O3 to the SMEAR II station in Finland in 1996–2008, Atmos. Chem. Phys., 13, 2153–2164, https://doi.org/10.5194/acp-13-2153-2013, 2013.
Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A.,
Er, M. J., Ding, W., and Lin, C.-T.: A review of clustering techniques and
developments, Neurocomputing, 267, 664–681, https://doi.org/10.1016/j.neucom.2017.06.053, 2017.
Seto, K. C., Golden, J. S., Alberti, M., and Turner, B. L.: Sustainability
in an urbanizing planet, P. Natl. Acad. Sci. USA,
114, 8935, https://doi.org/10.1073/pnas.1606037114, 2017.
Shi, G.-L., Feng, Y.-C., Zeng, F., Li, X., Zhang, Y.-F., Wang, Y.-Q., and
Zhu, T.: Use of a Nonnegative Constrained Principal Component Regression
Chemical Mass Balance Model to Study the Contributions of Nearly Collinear
Sources, Environ. Sci. Technol., 43, 8867–8873,
https://doi.org/10.1021/es902785c, 2009.
Tian, Y. Z., Wang, J., Peng, X., Shi, G. L., and Feng, Y. C.: Estimation of the direct and indirect impacts of fireworks on the physicochemical characteristics of atmospheric PM10 and PM2.5, Atmos. Chem. Phys., 14, 9469–9479, https://doi.org/10.5194/acp-14-9469-2014, 2014.
Tian, Y.-Z., Chen, G., Wang, H.-T., Huang-Fu, Y.-Q., Shi, G.-L., Han, B.,
and Feng, Y.-C.: Source regional contributions to PM2.5 in a megacity in
China using an advanced source regional apportionment method, Chemosphere,
147, 256–263, https://doi.org/10.1016/j.chemosphere.2015.12.132, 2016.
Timmermans, R., Kranenburg, R., Manders, A., Hendriks, C., Segers, A.,
Dammers, E., Zhang, Q., Wang, L., Liu, Z., Zeng, L., van der Gon, H. D.,
and Schaap, M.: Source apportionment of PM2.5 across China using
LOTOS-EUROS, Atmos. Environ., 164, 370–386, https://doi.org/10.1016/j.atmosenv.2017.06.003, 2017.
Vassura, I., Venturini, E., Marchetti, S., Piazzalunga, A., Bernardi, E.,
Fermo, P., and Passarini, F.: Markers and influence of open biomass burning
on atmospheric particulate size and composition during a major bonfire
event, Atmos. Environ., 82, 218–225, https://doi.org/10.1016/j.atmosenv.2013.10.037, 2014.
Wang, N., Zhu, H., Guo, Y., and Peng, C.: The heterogeneous effect of
democracy, political globalization, and urbanization on PM2.5 concentrations
in G20 countries: Evidence from panel quantile regression, J. Clean. Prod., 194, 54–68, https://doi.org/10.1016/j.jclepro.2018.05.092, 2018.
Wang, Q., Fang, J., Shi, W., and Dong, X.: Distribution characteristics and
policy-related improvements of PM2.5 and its components in six Chinese
cities, Environ. Pollut., 266, 115299, https://doi.org/10.1016/j.envpol.2020.115299, 2020.
Wang, Z., Zheng, F., Zhang, W., and Wang, S.: Analysis of SO2 Pollution
Changes of Beijing-Tianjin-Hebei Region over China Based on OMI Observations
from 2006 to 2017, Adv. Meteorol., 2018, 8746068,
https://doi.org/10.1155/2018/8746068, 2018.
Wu, J., Bei, N., Wang, Y., Li, X., Liu, S., Liu, L., Wang, R., Yu, J., Le, T., Zuo, M., Shen, Z., Cao, J., Tie, X., and Li, G.: Insights into particulate matter pollution in the North China Plain during wintertime: local contribution or regional transport?, Atmos. Chem. Phys., 21, 2229–2249, https://doi.org/10.5194/acp-21-2229-2021, 2021.
Xu, G., Ren, X., Xiong, K., Li, L., Bi, X., and Wu, Q.: Analysis of the
driving factors of PM2.5 concentration in the air: A case study of the
Yangtze River Delta, China, Ecol. Indic., 110, 105889, https://doi.org/10.1016/j.ecolind.2019.105889, 2020.
Xu, J., Shi, J., Zhang, Q., Ge, X., Canonaco, F., Prévôt, A. S. H., Vonwiller, M., Szidat, S., Ge, J., Ma, J., An, Y., Kang, S., and Qin, D.: Wintertime organic and inorganic aerosols in Lanzhou, China: sources, processes, and comparison with the results during summer, Atmos. Chem. Phys., 16, 14937–14957, https://doi.org/10.5194/acp-16-14937-2016, 2016.
Xu, Q., Zhang, Q., Liu, J., and Luo, B.: Efficient synthetical clustering
validity indexes for hierarchical clustering, Expert Syst. Appl., 151, 113367, https://doi.org/10.1016/j.eswa.2020.113367, 2020.
Xue, Y.-h., Wu, J.-h., Feng, Y.-c., Dai, L., Bi, X.-h., Li, X., Zhu, T.,
Tang, S.-b., and Chen, M.-f.: Source Characterization and Apportionment of
PM10 in Panzhihua, China, Aerosol Air Qual. Res., 10, 367–377,
https://doi.org/10.4209/aaqr.2010.01.0002, 2010.
Yan, D., Lei, Y., Shi, Y., Zhu, Q., Li, L., and Zhang, Z.: Evolution of the
spatiotemporal pattern of PM2.5 concentrations in China – A case study from
the Beijing-Tianjin-Hebei region, Atmos. Environ., 183, 225–233,
https://doi.org/10.1016/j.atmosenv.2018.03.041, 2018.
Yang, D., Ye, C., Wang, X., Lu, D., Xu, J., and Yang, H.: Global
distribution and evolvement of urbanization and PM2.5 (1998–2015),
Atmos. Environ., 182, 171–178, https://doi.org/10.1016/j.atmosenv.2018.03.053, 2018.
Yang, D., Chen, Y., Miao, C., and Liu, D.: Spatiotemporal variation of PM2.5
concentrations and its relationship to urbanization in the Yangtze river
delta region, China, Atmos. Pollut. Res., 11, 491–498, https://doi.org/10.1016/j.apr.2019.11.021, 2020.
Yang, Y., Pun, V. C., Sun, S., Lin, H., Mason, T. G., and Qiu, H.: Particulate matter components and health: a literature review on exposure assessment, Journal of Public Health and Emergency, 2, 3,
https://doi.org/10.21037/jphe.2018.03.03, 2018.
Yin, H., Yuan, H., Ye, Z., Li, S., and Liang, J.: Temporal and spatial
distribution of VOCs and their OFP in the atmosphere of Chengdu, Acta
Scientiae Circumstantiae, 35, 386–393, https://doi.org/10.13671/j.hjkxxb.2014.0826,
2015.
Zhang, R., Jing, J., Tao, J., Hsu, S.-C., Wang, G., Cao, J., Lee, C. S. L., Zhu, L., Chen, Z., Zhao, Y., and Shen, Z.: Chemical characterization and source apportionment of PM2.5 in Beijing: seasonal perspective, Atmos. Chem. Phys., 13, 7053–7074, https://doi.org/10.5194/acp-13-7053-2013, 2013.
Zhang, R., Wang, G., Guo, S., Zamora, M. L., Ying, Q., Lin, Y., Wang, W.,
Hu, M., and Wang, Y.: Formation of Urban Fine Particulate Matter, Chem. Rev., 115, 3803–3855, https://doi.org/10.1021/acs.chemrev.5b00067, 2015.
Zhang, Y., Shuai, C., Bian, J., Chen, X., Wu, Y., and Shen, L.:
Socioeconomic factors of PM2.5 concentrations in 152 Chinese cities:
Decomposition analysis using LMDI, J. Clean. Prod., 218, 96–107, https://doi.org/10.1016/j.jclepro.2019.01.322, 2019.
Zhao, B., Wang, S., Wang, J., Fu, J. S., Liu, T., Xu, J., Fu, X., and Hao,
J.: Impact of national NOx and SO2 control policies on particulate matter
pollution in China, Atmos. Environ., 77, 453–463, https://doi.org/10.1016/j.atmosenv.2013.05.012, 2013.
Zhao, R., Huang, L., Cheng, J., Ouyang, F., and Zhang, J.: VOC emissions
inventory from the key industries in Chengdu City and its associated spatial
distribution characteristics, Acta Scientiae Circumstantiae, 38, 1358–1367,
https://doi.org/10.13671/j.hjkxxb.2017.0479, 2018.
Zhong, Y., Chen, J., Zhao, Q., Zhang, N., Feng, J., and Fu, Q.: Temporal
trends of the concentration and sources of secondary organic aerosols in
PM2.5 in Shanghai during 2012 and 2018, Atmos. Environ., 261,
118596, https://doi.org/10.1016/j.atmosenv.2021.118596, 2021.
Short summary
This study focused on PM2.5 compositions and sources and explored their spatiotemporal and policy-related variations based on observation at 19 sites during wintertime of 2015–2019 in a fast-developing megacity. We found that PM2.5 compositions for the outermost zone in 2019 were similar to those for the core zone 2 or 3 years ago. Percentage contributions of coal and biomass combustion dramatically declined in the core zone, while the traffic source showed an increasing trend.
This study focused on PM2.5 compositions and sources and explored their spatiotemporal and...
Altmetrics
Final-revised paper
Preprint