Articles | Volume 22, issue 13
https://doi.org/10.5194/acp-22-8597-2022
© Author(s) 2022. 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-22-8597-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jul 2022
Research article |
| 05 Jul 2022
| 05 Jul 2022
Dramatic changes in atmospheric pollution source contributions for a coastal megacity in northern China from 2011 to 2020
Baoshuang Liu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Yanyang Wang
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
He Meng
Qingdao Eco-environment Monitoring Center of Shandong Province, Qingdao 266003, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Liuli Diao
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Jianhui Wu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
Laiyuan Shi
Qingdao Eco-environment Monitoring Center of Shandong Province, Qingdao 266003, China
Jing Wang
Qingdao Eco-environment Monitoring Center of Shandong Province, Qingdao 266003, China
Yufen Zhang
CORRESPONDING AUTHOR
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment–Health Research, Tianjin 300350, China
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Cited articles
Alexander, B., Sherwen, T., Holmes, C. D., Fisher, J. A., Chen, Q., Evans, M. J., and Kasibhatla, P.:
Global inorganic nitrate production mechanisms: comparison of a global model with nitrate isotope observations, Atmos. Chem. Phys., 20, 3859–3877, https://doi.org/10.5194/acp-20-3859-2020, 2020.
Begum, B. A., Biswas, S. K., and Hopke, P. K.:
Key issues in controlling air pollutants in Dhaka, Bangladesh, Atmos. Environ., 45, 7705–7713, https://doi.org/10.1016/j.atmosenv.2010.10.022, 2011.
Beloconi, A., Probst-Hensch, N. M., and Vounatsou, P.:
Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe, Sci. Total Environ., 787, 147607, https://doi.org/10.1016/j.scitotenv.2021.147607, 2021.
Bi, X., Dai, Q., Wu, J., Zhang, Q., Zhang, W., Luo, R., Cheng, Y., Zhang, J., Wang, L., Yu, Z., Zhang, Y., Tian, Y., and Feng, Y.:
Characteristics of the main primary source profiles of particulate matter across China from 1987 to 2017, Atmos. Chem. Phys., 19, 3223–3243, https://doi.org/10.5194/acp-19-3223-2019, 2019.
Bie, S. J., Yang, L. X., Zhang, Y., Huang, Q., Li, J. S., Zhao, T., Zhang, X. F., Wang, P. C., and Wang, W. X.:
Source appointment of PM2.5 in Qingdao Port, East of China, Sci. Total Environ., 755, 142456, https://doi.org/10.1016/j.scitotenv.2020.142456, 2021.
Bove, M. C., Brotto, P., Calzolai, G., Cassola, F., Cavalli, F., Fermo, P., Hjorth, J., Massabò, D., Nava, S., Piazzalunga, A., Schembari, C., and Prati, P.:
PM10 source apportionment applying PMF and chemical tracer analysis to ship-borne measurements in the Western Mediterranean, Atmos. Environ., 125, 140–151, https://doi.org/10.1016/j.atmosenv.2015.11.009, 2016.
Brown, S. G., Eberly, S., Paatero, P., and Norris, G. A.:
Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results, Sci. Total Environ., 518, 626–635, https://doi.org/10.1016/j.scitotenv.2015.01.022, 2015.
Carslaw, D. C.:
Worldmet: Import Surface Meteorological Data from NOAA Integrated Surface Database (ISD), http://github.com/davidcarslaw/ (last access: 5 September 2018), 2017.
Chen, J. Y., Shan, M., Xia, J. J., and Jiang, Y.:
Effects of space heating on the pollutant emission intensities in “2 + 26” cities, Building Environ., 175, 106817, https://doi.org/10.1016/j.buildenv.2020.106817, 2020.
Chen, Y., Zhang, S. M., Peng, C., Shi, G. M., Tian, M., Huang, R. J., Guo, D. M., Wang, H. B., Yao, X. J., and Yang, F. M.:
Impact of the COVID-19 pandemic and control measures on air quality and aerosol light absorption in Southwestern China, Sci. Total Environ., 749, 141419, https://doi.org/10.1016/j.scitotenv.2020.141419, 2020.
Chen, X., Wang, H., Lu, K., Li, C., Zhai, T., Tan, Z., Ma, X., Yang, X., Liu, Y., Chen, S., Dong, H., Li, X., Wu, Z., Hu, M., Zeng, L., and Zhang, Y.:
Field Determination of Nitrate Formation Pathway in Winter Beijing, Environ. Sci. Technol., 54, 9243–9253, https://doi.org/10.1021/acs.est.0c00972, 2020.
Cheng, N. L., Cheng, B. F., Li, S. S., and Ning, T. Z.:
Effects of meteorology and emission reduction measures on air pollution in Beijing during heating seasons, Atmos. Pollut. Res., 10, 971–979, https://doi.org/10.1016/j.apr.2019.01.005, 2019.
Choi, J.-K., Heo, J.-B., Ban, S.-J., Yi, S.-M., and Zoh, K.-D.:
Source apportionment of PM2.5 at the coastal area in Korea, Sci. Total Environ., 447, 370–380, https://doi.org/10.1016/j.scitotenv.2012.12.047, 2013.
Chu, B. W., Zhang, S. P., Liu, J., Ma, Q. X., and He, H.:
Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic, J. Environ. Sci., 99, 346–353, https://doi.org/10.1016/j.jes.2020.06.031, 2021.
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., Pope, C. A., Shin, H., Straif, K., Shaddick, G., Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L., and Forouzanfar, M. H.:
Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015, Lancet, 389, 1907–1918, https://doi.org/10.1016/S0140-6736(17)30505-6, 2017.
Collivignarelli, M. C., De Rose, C., Abbà, A., Baldi, M., Bertanza, G., Pedrazzani, R., Sorlini, S., and Carnevale Miino, M.:
Analysis of lockdown for COVID-19 impact on NO2 in London, Milan and Paris: What lesson can be learnt?, Process Saf. Environ., 146, 952–960, https://doi.org/10.1016/j.psep.2020.12.029, 2021.
Cucciniello, R., Raia, L., and Vasca, E.:
Air quality evaluation during COVID-19 in Southern Italy: the case study of Avellino city, Environ. Res., 203, 111803, https://doi.org/10.1016/j.envres.2021.111803, 2022.
Dall'Osto, M., Booth, M. J., Smith, W., Fisher, R., and Harrison, R. M.:
A study of the size distributions and the chemical characterization of airborne particles in the vicinity of a large integrated steelworks, Aerosol Sci. Technol., 42, 981–991, https://doi.org/10.1080/02786820802339587, 2008.
Dai, Q. L., Liu, B. S., Bi, X. H., Wu, J. H., Liang, D. N., Zhang, Y. F., Feng, Y. C., and Hopke, P. K.:
Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak, Environ. Sci. Technol., 54, 9917–9927, https://doi.org/10.1021/acs.est.0c02776, 2020.
Dai, Q. L., Ding, J., Hou, L. L., Li, L. X., Cai, Z. Y., Liu, B. S., Song, C. B., Bi, X. H., Wu, J. H., Zhang, Y. F., Feng, Y. C., and Hopke, P. K.:
Haze episodes before and during the COVID-19 shutdown in Tianjin, China: Contribution of fireworks and residential burning, Environ. Pollut., 286, 117252, https://doi.org/10.1016/j.envpol.2021.117252, 2021.
Ding, J., Dai, Q. L., Li, Y. F., Han, S. Q., Zhang, Y. F., and Feng, Y. C.:
Impact of meteorological condition changes on air quality and particulate chemical composition during the COVID-19 lockdown, J. Environ. Sci., 109, 45–56, https://doi.org/10.1016/j.jes.2021.02.022, 2021.
Esmaeilirad, S., Lai, A., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., Uzu, G., Daellenbach, K., Canonaco, F., Hassankhany, H., Arhami, M., Baltensperger, U., Prévôt, A. S. H., Schauer, J. J., Jaffrezo, J.-L., Hosseini, V., and El Haddad, I.:
Source apportionment of fine particulate matter in a Middle Eastern Metropolis, Tehran-Iran, using PMF with organic and inorganic markers, Sci. Total Environ., 705, 135330, https://doi.org/10.1016/j.scitotenv.2019.135330, 2020.
Fan, H., Zhao, C. F., and Yang, Y. K.:
A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018, Atmos. Environ., 220, 117066, https://doi.org/10.1016/j.atmosenv.2019.117066, 2020.
Fu, X., Wang, T., Gao, J., Wang, P., Liu, Y. M., Wang, S. X., Zhao, B., and Xue, L. K.:
Persistent Heavy Winter Nitrate Pollution Driven by Increased Photochemical Oxidants in Northern China, Environ. Sci. Technol., 54, 3881–3889, https://doi.org/10.1021/acs.est.9b07248, 2020.
Gao, J., Peng, X., Chen, G., Xu, J., Shi, G. L., Zhang, Y. C., and Feng, Y. C.:
Insights into the chemical characterization and sources of PM2.5 in Beijing at a 1-h time resolution, Sci. Total Environ., 542, 162–171, https://doi.org/10.1016/j.scitotenv.2015.10.082, 2016.
Gao, Y., Shan, H. Y., Zhang, S. Q., Sheng, L. F., Li, J. P., Zhang, J. X., Ma, M. C., Meng, H., Luo, K., Gao, H. W., and Yao, X. H.:
Characteristics and sources of PM2.5 with focus on two severe pollution events in a coastal city of Qingdao, China, Chemosphere, 247, 125861, https://doi.org/10.1016/j.chemosphere.2020.125861, 2020.
Grange, S. K. and Carslaw, D. C.:
Using meteorological normalization to detect interventions in air quality time series, Sci. Total Environ., 653, 578–588, https://doi.org/10.1016/j.scitotenv.2018.10.344, 2019.
Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., and Hueglin, C.:
Random forest meteorological normalisation models for Swiss PM10 trend analysis, Atmos. Chem. Phys., 18, 6223–6239, https://doi.org/10.5194/acp-18-6223-2018, 2018.
Gong, S. L., Zhang, L., Liu, C., Lu, S. H., Pan, W. J., and Zhang, Y. H.:
Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 2. Key weather elements and emissions, Sci. Total Environ., 824, 153847, https://doi.org/10.1016/j.scitotenv.2022.153847, 2022.
Gulia, S., Mittal, A., and Khare, M.:
Quantitative evaluation of source interventions for urban air quality improvement – A case study of Delhi city, Atmos. Pollut. Res., 9, 577–583, https://doi.org/10.1016/j.apr.2017.12.003, 2018.
He, G. J., Pan, Y. H., and Tanaka, T.:
The short-term impacts of COVID-19 lockdown on urban air pollution in China, Nat. Sustainability, 3, 1005–1011, https://doi.org/10.1038/s41893-020-0581-y, 2020.
Hong, Y. W., Xu, X. B., Liao, D., Zheng, R. H., Ji, X. T., Chen, Y. T., Xu, L. L., Li, M. R., Wang, H., Xiao, H., Choi, S. D., and Chen, J. S.:
Source apportionment of PM2.5 and sulfate formation during the COVID-19 lockdown in a coastal city of southeast China, Environ. Pollut., 286, 117577, https://doi.org/10.1016/j.envpol.2021.117577, 2021.
Hopke, P. K., Dai, Q. L., Li, L. X., and Feng, Y. C.:
Global review of recent source apportionments for airborne particulate matter, Sci. Total Environ., 740, 140091, https://doi.org/10.1016/j.scitotenv.2020.140091, 2020.
Hou, L. L., Dai, Q. L., Song, C. B., Liu, B., Guo, F. Z., Dai, T. J., Li, L. X., Liu, B. S., Bi, X. H., Zhang, Y. F., and Feng, Y. C.:
Revealing drivers of haze pollution by explainable machine learning, Environ. Sci. Tech. Let., 9, 112–119, https://doi.org/10.1021/acs.estlett.1c00865, 2022.
Huang, H. Y., Liu, B. S., Li, S., Choe, T.-H., Dai, Q. L., Gu, Y., Diao, L. L., Zhang, S. F., Bi, X. H., Luo, Z. W., Lu, M. M., Zhang, Y. F., and Feng, Y. C.:
An estimation method for regional transport contributions from emission sources based on a high-mountain site: a case study in Zhumadian, China, Atmos. Environ., 263, 118664, https://doi.org/10.1016/j.atmosenv.2021.118664, 2021.
Huang, J., Pan, X. C., Guo, X. B., and Li, G. X.:
2018. Health impact of China's Air Pollution Prevention and Control Action Plan: an analysis of national air quality monitoring and mortality data, Lancet Planetary Health, 2, e313–e323, https://doi.org/10.1016/S2542-5196(18)30141-4, 2018.
Huang, R. J., Zhang, Y. L., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y. M., Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z. S., Szidat, S., Baltensperger, U., El Haddad, I., and Prevot, A. S. H.:
High secondary aerosol contribution to particulate pollution during haze events in China, Nature, 514, 218–222, https://doi.org/10.1038/nature13774, 2014.
Huang, X., Liu, Z., Liu, J., Hu, B., Wen, T., Tang, G., Zhang, J., Wu, F., Ji, D., Wang, L., and Wang, Y.:
Chemical characterization and source identification of PM2.5 at multiple sites in the Beijing–Tianjin–Hebei region, China, Atmos. Chem. Phys., 17, 12941–12962, https://doi.org/10.5194/acp-17-12941-2017, 2017.
Iyer, U. S. and Raj, P. E.:
Ventilation coefficient trends in the recent decades over four major Indian metropolitan cities, J. Earth Syst. Sci., 122, 537–549, https://doi.org/10.1007/s12040-013-0270-6, 2013.
Jain, S., Sharma, S. K., Mandal, T. K., and Saxena, M.:
Source apportionment of PM10 in Delhi, India using PCA/APCS, UNMIX and PMF, Particuology, 37, 107–118, https://doi.org/10.1016/j.partic.2017.05.009, 2018.
Jain, S., Sharma, S. K., Vijayan, N., and Mandal, T. K.:
Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India, Environ. Pollut., 262, 114337, https://doi.org/10.1016/j.envpol.2020.114337, 2020.
Jiang, X., Li, G. L., and Fu, W.:
Government environmental governance, structural adjustment and air quality: A quasi-natural experiment based on the Three-year Action Plan to Win the Blue Sky Defense War, J. Environ. Manage., 277, 111470, https://doi.org/10.1016/j.jenvman.2020.111470, 2021.
Joshi, P., Ghosh, S., Dey, S., Dixit, K., Choudhary, R. K., Salve, H. R., and Balakrishnan, K.:
Impact of acute exposure to ambient PM2.5 on non-trauma all-cause mortality in the megacity Delhi, Atmos. Environ., 259, 118548, https://doi.org/10.1016/j.atmosenv.2021.118548, 2021.
Kleinman, M. T., Kneip, T. J., and Eisenbud, M.:
Seasonal patterns of airborne particulate concentrations in New York City, Atmos. Environ. (1967), 10, 9–11, https://doi.org/10.1016/0004-6981(76)90252-3, 1976.
Kuo, S.-C., Hsieh, L.-Y., Tsai, C.-H., and Tsai, Y. I.:
Characterization of PM2.5 fugitive metal in the workplaces and the surrounding environment of a secondary aluminum smelter, Atmos. Environ., 41, 6884–6900, https://doi.org/10.1016/j.atmosenv.2007.04.038, 2007.
Le, T. H., Wang, Y., Liu, L., Yang, J. N., Yung, Y. L., Li, G. H., and Seinfeld, J. H.:
Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China, Science, 369, 702, https://doi.org/10.1126/science.abb7431, 2020.
Li, H., You, S. J., Zhang, H., Zheng, W. D., and Zou, L. J.:
Analysis of the impacts of heating emissions on the environment and human health in North China, J. Clean Prod., 207, 728–742, https://doi.org/10.1016/j.jclepro.2018.10.013, 2019.
Li, K., Jacob, D. J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K. H., Zhang, Q., and Zhai, S. X.:
A two-pollutant strategy for improving ozone and particulate air quality in China, Nat. Geosci., 12, 906–910, https://doi.org/10.1038/s41561-019-0464-x, 2019.
Li, K., Jacob, D. J., Shen, L., Lu, X., De Smedt, I., and Liao, H.:
Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences, Atmos. Chem. Phys., 20, 11423–11433, https://doi.org/10.5194/acp-20-11423-2020, 2020.
Li, L. Y., Yan, D. Y., Xu, S. H., Huang, M. L., Wang, X. X., and Xie, S. D.:
Characteristics and source distribution of air pollution in winter in Qingdao, eastern China, Environ. Pollut., 224, 44–53, https://doi.org/10.1016/j.envpol.2016.12.037, 2017.
Li, W. J., Shao, L. Y., Wang, W. H., Li, H., Wang, X. M., Li, Y. W., Li, W. J., Jones, T., and Zhang, D. Z.:
Air quality improvement in response to intensified control strategies in Beijing during 2013–2019, Sci. Total Environ., 744, 140776, https://doi.org/10.1016/j.scitotenv.2020.140776, 2020.
Li, Y., Miao, Y. C., Che, H. Z., and Liu, S. H.:
On the heavy aerosol pollution and its meteorological dependence in Shandong province, China, Atmos. Res., 256, 105572, https://doi.org/10.1016/j.atmosres.2021.105572, 2021a.
Li, Y., Xu, H. X., Tang, K. Y., Lau, A. K. H., Fung, J. C. H., and Zhang, X. G.:
An ensemble assessment of the effectiveness of vehicular emission control programs for air quality improvement in Hong Kong, Atmos. Environ., 262, 118571, https://doi.org/10.1016/j.atmosenv.2021.118571, 2021b.
Liang, X., Li, S., Zhang, S. Y., Huang, H., and Chen, S. X.:
PM2.5 data reliability, consistency, and air quality assessment in five Chinese cities, J. Geophys. Res.-Atmos., 121, 10220–10236, https://doi.org/10.1002/2016JD024877, 2016.
Liu, B. S., Song, N., Dai, Q. L., Mei, R. B., Sui, B. H., Bi, X. H., and Feng, Y. C.:
Chemical composition and source apportionment of ambient PM2.5 during the non-heating period in Taian, China, Atmos. Res., 170, 23–33, https://doi.org/10.1016/j.atmosres.2015.11.002, 2016.
Liu, B., Cheng, Y., Zhou, M., Liang, D., Dai, Q., Wang, L., Jin, W., Zhang, L., Ren, Y., Zhou, J., Dai, C., Xu, J., Wang, J., Feng, Y., and Zhang, Y.:
Effectiveness evaluation of temporary emission control action in 2016 in winter in Shijiazhuang, China, Atmos. Chem. Phys., 18, 7019–7039, https://doi.org/10.5194/acp-18-7019-2018, 2018.
Liu, B. S., Wu, J. H., Wang, J., Shi, L. Y., Meng, H., Dai, Q. L., Wang, J., Song, C. B., Zhang, Y. F., Feng, Y. C., and Hopke, P. K.:
Chemical characteristics and sources of ambient PM2.5 in a harbor area: Quantification of health risks to workers from source-specific selected toxic elements, Environ. Pollut., 268, 115926, https://doi.org/10.1016/j.envpol.2020.115926, 2021.
Liu, C., Xu, R., Zhang, T. H., Zhang, H. D., Zhang, B. H., Cong, C. H., and Wu, J. Y.:
Analysis of Ozone Pollution Characteristics and Its Sources During the Shanghai Cooperation Organization Summit in Qingdao, Meteor. Environ. Sci., 43, 51–58, https://doi.org/10.16765/j.cnki.1673-7148.2020.03.007, 2020a (in Chinese).
Liu, C., Zhang, H. D., Zhang, T. H., Xu, R., Zhang, B. H., Lu, M. Y., and Li, G. H.:
The causes of ozone concentration growth in the night during the “Shanghai Cooperation Organization Summit” in Qingdao, China Environ. Sci., 40, 3332–3341, https://doi.org/10.19674/j.cnki.issn1000-6923.2020.0372, 2020b (in Chinese).
Liu, F., Beirle, S., Zhang, Q., van der A, R. J., Zheng, B., Tong, D., and He, K.:
NOx emission trends over Chinese cities estimated from OMI observations during 2005 to 2015, Atmos. Chem. Phys., 17, 9261–9275, https://doi.org/10.5194/acp-17-9261-2017, 2017.
Liu, M., Huang, Y., Ma, Z., Jin, Z., Liu, X., Wang, H., Liu, Y., Wang, J., Jantunen, M., Bi, J. and Kinney, P. L.:
Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012, Environ. Int., 98, 75–81, https://doi.org/10.1016/j.envint.2016.10.003, 2017.
Liu, W. J., Xu, Y. S., Liu, W. X., Liu, Q. Y., Yu, S. Y., Liu, Y., Wang, X., and Tao, S.:
Oxidative potential of ambient PM2.5 in the coastal cities of the Bohai Sea, northern China: Seasonal variation and source apportionment, Environ. Pollut., 236, 514–528, https://doi.org/10.1016/j.envpol.2018.01.116, 2018.
Lyu, X. P., Zeng, L. W., Guo, H., Simpson, I. J., Ling, Z. H., Wang, Y., Murray, F., Louie, P. K. K., Saunders, S. M., Lam, S. H. M., and Blake, D. R.:
Evaluation of the effectiveness of air pollution control measures in Hong Kong, Environ. Pollut., 220, 87–94, https://doi.org/10.1016/j.envpol.2016.09.025, 2017.
Ma, X. W., Li, C. D., Dong, X. Y., and Liao, H.:
Empirical analysis on the effectiveness of air quality control measures during mega events: Evidence from Beijing, China, J. Clean Prod., 271, 122536, https://doi.org/10.1016/j.jclepro.2020.122536, 2020.
Ma, X., Huang, J., Zhao, T., Liu, C., Zhao, K., Xing, J., and Xiao, W.:
Rapid increase in summer surface ozone over the North China Plain during 2013–2019: a side effect of particulate matter reduction control?, Atmos. Chem. Phys., 21, 1–16, https://doi.org/10.5194/acp-21-1-2021, 2021.
Masiol, M., Squizzato, S., Rich, D. Q., and Hopke, P. K.:
Long-term trends (2005–2016) of source apportioned PM2.5 across New York State, Atmos. Environ., 201, 110–120, https://doi.org/10.1016/j.atmosenv.2018.12.038, 2019.
Meng, Z. Y., Ding, G. A., Xu, X. B., Xu, X. D., Yu, H. Q., and Wang, S. F.:
Vertical distributions of SO2 and NO2 in the lower atmosphere in Beijing urban areas, China, Sci. Total Environ., 390, 456–465, https://doi.org/10.1016/j.scitotenv.2007.10.012, 2008.
Munir, S., Chen, H. B., and Ropkins, K.:
Quantifying temporal trends in ground level ozone concentration in the UK, Sci. Total Environ., 458–460, 217–227, https://doi.org/10.1016/j.scitotenv.2013.04.045, 2013.
Nøjgaard, J. K., Nguyen, Q. T., Glasius, M., and Sørensen, L. L.:
Nucleation and Aitken mode atmospheric particles in relation to O3 and NOx at semirural background in Denmark, Atmos. Environ., 49, 275–283, https://doi.org/10.1016/j.atmosenv.2011.11.040, 2012.
Nirel, R. and Dayan, U.:
On the Ratio of Sulfur Dioxide to Nitrogen Oxides as an Indicator of Air Pollution Sources, J. Appl. Meteorol., 40, 1209–1222, https://doi.org/10.1175/1520-0450(2001)040<1209:OTROSD>2.0.CO;2, 2001.
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.
Police, S., Sahu, S. K., and Pandit, G. G.:
Chemical characterization of atmospheric particulate matter and their source apportionment at an emerging industrial coastal city, Visakhapatnam, India, Atmos. Pollut. Res., 7, 725–733, https://doi.org/10.1016/j.apr.2016.03.007, 2016.
Pugliese, S. C., Murphy, J. G., Geddes, J. A., and Wang, J. M.:
The impacts of precursor reduction and meteorology on ground-level ozone in the Greater Toronto Area, Atmos. Chem. Phys., 14, 8197–8207, https://doi.org/10.5194/acp-14-8197-2014, 2014.
Qi, L., Zhang, Y. F., Ma, Y. H., Chen, M. D., Ge, X. L., Ma, Y., Zheng, J., Wang, Z., and Li, S. Z.:
Source identification of trace elements in the atmosphere during the second Asian Youth Games in Nanjing, China: Influence of control measures on air quality, Atmos. Pollut. Res., 7, 547–556, https://doi.org/10.1016/j.apr.2016.01.003, 2016.
Querol, X., Viana, M., Alastuey, A., Amato, F., Moreno, T., Castillo, S., Pey, J., de la Rosa, J., Sánchez de la Campa, A., Artíñano, B., Salvador, P., García Dos Santos, S., Fernández-Patier, R., Moreno-Grau, S., Negral, L., Minguillón, M. C., Monfort, E., Gil, J. I., Inza, A., Ortega, L. A., Santamaría, J. M., and Zabalza, J.:
Source origin of trace elements in PM from regional background, urban and industrial sites of Spain, Atmos. Environ., 41, 7219–7231, https://doi.org/10.1016/j.atmosenv.2007.05.022, 2007.
Ryou, H. G., Heo, J., and Kim, S. Y.:
Source apportionment of PM10 and PM2.5 air pollution, and possible impacts of study characteristics in South Korea, Environ. Pollut., 240, 963–972, https://doi.org/10.1016/j.envpol.2018.03.066, 2018.
Sen, P. K.:
Estimates of the Regression Coefficient Based on Kendall's Tau AU – Sen, Pranab Kumar, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Schleicher, N., Norra, S., Chen, Y., Chai, F., and Wang, S.:
Efficiency of mitigation measures to reduce particulate air pollution—A case study during the Olympic Summer Games 2008 in Beijing, China, Sci. Total Environ., 427–428, 146–158, https://doi.org/10.1016/j.scitotenv.2012.04.004, 2012.
Shi, Z. B., Song, C. B., Liu, B. W., Lu, G. D., Xu, J. S., Vu, T. V., Elliott, R. J. R., Li, W. J., Bloss, W. J., and Harrison, R. M.:
Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns, Sci Adv., 7, eabd6696, https://doi.org/10.1126/sciadv.abd6696, 2021.
Song, L. L., Dai, Q. L., Feng, Y. C., and Hopke, P. K.:
Estimating uncertainties of source contributions to PM2.5 using moving window evolving dispersion normalized PMF, Environ. Pollut., 286, 117576, https://doi.org/10.1016/j.envpol.2021.117576, 2021.
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.
Tsai, D. H., Wang, J. L., Chuang, K. J., and Chan, C. C.:
Traffic-related air pollution and cardiovascular mortality in central Taiwan, Sci. Total Environ., 408, 1818–1823, https://doi.org/10.1016/j.scitotenv.2010.01.044, 2010.
Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, M., Szidat, S., Prévôt, A. S. H., Hueglin, C., Bloemen, H., Wåhlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., and Hitzenberger, R.:
Source apportionment of particulate matter in Europe: A review of methods and results, J. Aerosol Sci., 39, 827–849, https://doi.org/10.1016/j.jaerosci.2008.05.007, 2008.
Vodonos, A. and Schwartz, J.:
Estimation of excess mortality due to long-term exposure to PM2.5 in continental United States using a high-spatiotemporal resolution model, Environ. Res., 196, 110904, https://doi.org/10.1016/j.envres.2021.110904, 2021.
Vu, T. V., Shi, Z., Cheng, J., Zhang, Q., He, K., Wang, S., and Harrison, R. M.:
Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique, Atmos. Chem. Phys., 19, 11303–11314, https://doi.org/10.5194/acp-19-11303-2019, 2019.
Wang, H. L., Miao, Q., Shen, L. J., Yang, Q., Wu, Y. Z., and Wei, H.:
Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: High time-resolution measurements of aerosol chemical compositions and source apportionment, Environ. Pollut., 271, 116298, https://doi.org/10.1016/j.envpol.2020.116298, 2021.
Wang, S. X., Xing, J., Zhao, B., Jang, C., and Hao, J. M.:
Effectiveness of national air pollution control policies on the air quality in metropolitan areas of China, J. Environ. Sci., 26, 13–22, https://doi.org/10.1016/S1001-0742(13)60381-2, 2014.
Wang, Y., Xue, Y. F., Tian, H. Z., Gao, J., Chen, Y., Zhu, C. Y., Liu, H. J., Wang, K., Hua, S. B., Liu, S. H., and Shao, P. Y.:
Effectiveness of temporary control measures for lowering PM2.5 pollution in Beijing and the implications, Atmos. Environ., 157, 75–83, https://doi.org/10.1016/j.atmosenv.2017.03.017, 2017.
Wang, Y. Q., Zhang, X. Y., and Draxler, R.:
TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data, Environ. Model. Softw., 24, 938–939, https://doi.org/10.1016/j.envsoft.2009.01.004, 2009.
Wang, Y. Y., Liu, B. S., Zhang, Y. F., Dai, Q. L., Song, C. B., Duan, L. Q., Guo, L. L., Zhao, J., Xue, Z. G., Bi, X. H., and Feng, Y. C.:
Potential health risks of inhaled toxic elements and risk sources during different COVID-19 lockdown stages in Linfen, China, Environ. Pollut., 284, 117454, https://doi.org/10.1016/j.envpol.2021.117454, 2021.
Xu, H., Xiao, Z. M., Chen, K., Tang, M., Zheng, N. Y., Li, P., Yang, N., Yang, W., and Deng, X. W.:
Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Beijing–Tianjin–Hebei region, Sci. Total Environ., 658, 280–293, https://doi.org/10.1016/j.scitotenv.2018.12.164, 2019.
Xu, L. L., Jiao, L., Hong, Z. Y., Zhang, Y. R., Du, W. J., Wu, X., Chen, Y. T., Deng, J. J., Hong, Y. W., and Chen, J. S.:
Source identification of PM2.5 at a port and an adjacent urban site in a coastal city of China: Impact of ship emissions and port activities, Sci. Total Environ., 634, 1205–1213, https://doi.org/10.1016/j.scitotenv.2018.04.087, 2018.
Xu, M., Qin, Z. F., Zhang, S. H., and Xie, Y.:
Health and economic benefits of clean air policies in China: A case study for Beijing–Tianjin–Hebei region, Environ. Pollut., 285, 117525, https://doi.org/10.1016/j.envpol.2021.117525, 2021.
Xu, W., Liu, X. J., Liu, L., Dore, A. J., Tang, A., Lu, L., Wu, Q. H., Zhang, Y. Y., Hao, T. X., Pan, Y. P., Chen, J. M., and Zhang, F. S.:
Impact of emission controls on air quality in Beijing during APEC 2014: Implications from water-soluble ions and carbonaceous aerosol in PM2.5 and their precursors, Atmos. Environ., 210, 241–252, https://doi.org/10.1016/j.atmosenv.2019.04.050, 2019.
Yin, H., Liu, C., Hu, Q. H., Liu, T., Wang, S., Gao, M., Xu, S. Q., Zhang, C. X., and Su, W. J.:
Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China, Environ. Pollut., 289, 117899, https://doi.org/10.1016/j.envpol.2021.117899, 2021.
Yang, S., Duan, F., Ma, Y., Li, H., Ma, T., Zhu, L., Huang, T., Kimoto, T., and He, K.:
Mixed and intensive haze pollution during the transition period between autumn and winter in Beijing, China, Sci. Total Environ., 711, 134745, https://doi.org/10.1016/j.scitotenv.2019.134745, 2020.
Yu, M. F., Zhu, Y., Lin, C. J., Wang, S. X., Xing, J., Jang, C., Huang, J. Z., Huang, J. Y., Jin, J. B., and Yu, L.:
Effects of air pollution control measures on air quality improvement in Guangzhou, China, J. Environ. Manage., 244, 127–137, https://doi.org/10.1016/j.jenvman.2019.05.046, 2019.
Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.:
Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, https://doi.org/10.5194/acp-19-11031-2019, 2019.
Zhang, D. Z., Shi, G. Y., Iwasaka, Y., Hu, M., and Zang, J. Y.:
Anthropogenic Calcium Particles Observed in Beijing and Qingdao, China, Water Air Soil Poll.:
Focus, 5, 261–276, https://doi.org/10.1007/s11267-005-0743-y, 2005.
Zhang, Q., He, K. B., and Huo, H.:
Cleaning China's air, Nature, 484, 161–162, https://doi.org/10.1038/484161a, 2012.
Zhang, Q., Zheng, Y. X., Tong, D., Shao, M., Wang, S. X., Zhang, Y. H., Xu, X. D., Wang, J. N., He, H., Liu, W. Q., Ding, Y. H., Lei, Y., Li, J. H., Wang, Z. F., Zhang, X. Y., Wang, Y. S., Cheng, J., Liu, Y., Shi, Q. R., Yan, L., Geng, G. N., Hong, C. P., Li, M., Liu, F., Zheng, B., Cao, J. J., Ding, A. J., Gao, J., Fu, Q. Y., Huo, J. T., Liu, B. X., Liu, Z. R., Yang, F. M., He, K. B., and Hao, J. M.:
Drivers of improved PM2.5 air quality in China from 2013 to 2017, P. Natl. Acad. Sci USA, 116, 24463–24469, https://doi.org/10.1073/pnas.1907956116, 2019.
Zhang, Y., Yang, L. X., Bie, S. J., Zhao, T., Huang, Q., Li, J. S., Wang, P. C., Wang, Y. M., and Wang, W. X.:
Chemical compositions and the impact of sea salt in atmospheric PM1 and PM2.5 in the coastal area, Atmos. Res., 250, 105323, https://doi.org/10.1016/j.atmosres.2020.105323, 2021.
Zhao, C. K., Sun, Y., Zhong, Y. P., Xu, S. H., Liang, Y., Liu, S., He, X. D., Zhu, J. H., Shibamoto, T., and He, M.:
Spatio-temporal analysis of urban air pollutants throughout China during 2014–2019, Air Qual. Atmos. Hlth., 14, 1619–1632, https://doi.org/10.1007/s11869-021-01043-5, 2021.
Zhao, S., Tian, H. Z., Luo, L. N., Liu, H. J., Wu, B. B., Liu, S. H., Bai, X. X., Liu, W., Liu, X. Y., Wu, Y. M., Lin, S. M., Guo, Z. H., Lv, Y. Q., and Xue, Y. F.:
Temporal variation characteristics and source apportionment of metal elements in PM2.5 in urban Beijing during 2018–2019, Environ. Pollut., 268, 115856, https://doi.org/10.1016/j.envpol.2020.115856, 2021.
Zong, Z., Wang, X. P., Tian, C. G., Chen, Y. J., Fu, S. F., Qu, L., Ji, L., Li, J., and Zhang, G.:
PMF and PSCF based source apportionment of PM2.5 at a regional background site in North China, Atmos. Res., 203, 207–215, https://doi.org/10.1016/j.atmosres.2017.12.013, 2018.
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.
Understanding effectiveness of air pollution regulatory measures is critical for control policy....
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