Articles | Volume 25, issue 12
https://doi.org/10.5194/acp-25-6161-2025
© Author(s) 2025. 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-25-6161-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sources and trends of black carbon aerosol in the megacity of Nanjing, eastern China, after the China Clean Action Plan and Three-Year Action Plan
Abudurexiati Abulimiti
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yanlin Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Mingyuan Yu
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yihang Hong
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yu-Chi Lin
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Chaman Gul
Reading Academy, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
Fang Cao
State Key Laboratory of Climate System Prediction and Risk Management, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology, Nanjing 210044, China
Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Related authors
Mingjie Kang, Mengying Bao, Wenhuai Song, Aduburexiati Abulimiti, Changliu Wu, Fang Cao, Sönke Szidat, and Yanlin Zhang
Atmos. Chem. Phys., 25, 73–91, https://doi.org/10.5194/acp-25-73-2025, https://doi.org/10.5194/acp-25-73-2025, 2025
Short summary
Short summary
Reports on molecular-level knowledge of high-temporal-resolution particulate matter ≤2.5 µm in diameter (PM2.5) on hazy days are limited. We investigated various PM2.5 species and their sources. The results show biomass burning (BB) was the main source of organic carbon. Moreover, BB enhanced fungal spore emissions and secondary aerosol formation. The contribution of non-fossil sources increased with increasing haze pollution, suggesting BB may be an important driver of haze events in winter.
Rongshuang Xu, Yu-Chi Lin, Siyu Bian, Feng Xie, and Yan-Lin Zhang
Atmos. Chem. Phys., 25, 12721–12735, https://doi.org/10.5194/acp-25-12721-2025, https://doi.org/10.5194/acp-25-12721-2025, 2025
Short summary
Short summary
Levels of hydroxymethanesulfonate (HMS) in a continental city and, for the first time, a marine atmosphere are reported. The effect of aerosol ionic strength (IS) on HMS formation was quantified; it first rises with increasing IS and then peaks at 4 mol kg−1 before declining. Given the IS range of marine (2–6) and urban (6–20 mol kg−1) aerosols and the clearly negative correlation between humidity and IS, moderate IS levels in humid conditions may notably boost ambient HMS formation.
Xueqin Zheng, Junwen Liu, Nima Chuduo, Bian Ba, Pengfei Yu, Phu Drolgar, Fang Cao, and Yanlin Zhang
Atmos. Chem. Phys., 25, 12451–12465, https://doi.org/10.5194/acp-25-12451-2025, https://doi.org/10.5194/acp-25-12451-2025, 2025
Short summary
Short summary
In this study, we present the first report on the annual variation of stable oxygen isotope anomalies in nitrate (NO3−) collected from the urban area of Lhasa, on the Tibetan Plateau, China. Using a Bayesian isotope mixture model, we found that the relative contribution of the NO3 + volatile organic compound (VOC) pathway to NO3− formation in spring in Lhasa was several times higher than that in urban cities, highlighting the significant influence of VOCs transported from outside the Tibetan Plateau.
Mingjie Kang, Mengying Bao, Wenhuai Song, Aduburexiati Abulimiti, Changliu Wu, Fang Cao, Sönke Szidat, and Yanlin Zhang
Atmos. Chem. Phys., 25, 73–91, https://doi.org/10.5194/acp-25-73-2025, https://doi.org/10.5194/acp-25-73-2025, 2025
Short summary
Short summary
Reports on molecular-level knowledge of high-temporal-resolution particulate matter ≤2.5 µm in diameter (PM2.5) on hazy days are limited. We investigated various PM2.5 species and their sources. The results show biomass burning (BB) was the main source of organic carbon. Moreover, BB enhanced fungal spore emissions and secondary aerosol formation. The contribution of non-fossil sources increased with increasing haze pollution, suggesting BB may be an important driver of haze events in winter.
Tong Sha, Siyu Yang, Qingcai Chen, Liangqing Li, Xiaoyan Ma, Yan-Lin Zhang, Zhaozhong Feng, K. Folkert Boersma, and Jun Wang
Atmos. Chem. Phys., 24, 8441–8455, https://doi.org/10.5194/acp-24-8441-2024, https://doi.org/10.5194/acp-24-8441-2024, 2024
Short summary
Short summary
Using an updated soil reactive nitrogen emission scheme in the Unified Inputs for Weather Research and Forecasting coupled with Chemistry (UI-WRF-Chem) model, we investigate the role of soil NO and HONO (Nr) emissions in air quality and temperature in North China. Contributions of soil Nr emissions to O3 and secondary pollutants are revealed, exceeding effects of soil NOx or HONO emission. Soil Nr emissions play an important role in mitigating O3 pollution and addressing climate change.
Chaman Gul, Shichang Kang, Yuanjian Yang, Xinlei Ge, and Dong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1144, https://doi.org/10.5194/egusphere-2024-1144, 2024
Preprint archived
Short summary
Short summary
Long-term variations in upper atmospheric temperature and water vapor in the selected domains of time and space are presented. The temperature during the past two decades showed a cooling trend and water vapor showed an increasing trend and had an inverse relation with temperature in selected domains of space and time. Seasonal temperature variations are distinct, with a summer minimum and a winter maximum. Our results can be an early warning indication for future climate change.
Mengying Bao, Yan-Lin Zhang, Fang Cao, Yihang Hong, Yu-Chi Lin, Mingyuan Yu, Hongxing Jiang, Zhineng Cheng, Rongshuang Xu, and Xiaoying Yang
Atmos. Chem. Phys., 23, 8305–8324, https://doi.org/10.5194/acp-23-8305-2023, https://doi.org/10.5194/acp-23-8305-2023, 2023
Short summary
Short summary
The interaction between the sources and molecular compositions of humic-like substances (HULIS) at Nanjing, China, was explored. Significant fossil fuel source contributions to HULIS were found in the 14C results from biomass burnng and traffic emissions. Increasing biogenic secondary organic aerosol (SOA) products and anthropogenic aromatic compounds were detected in summer and winter, respectively.
Hao-Ran Yu, Yan-Lin Zhang, Fang Cao, Xiao-Ying Yang, Tian Xie, Yu-Xian Zhang, and Yongwen Xue
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-239, https://doi.org/10.5194/amt-2022-239, 2022
Preprint withdrawn
Short summary
Short summary
We developed a high time resolution method for determining the δ13C values of WSOCp and WSOCg by combination of wet oxidation pretreatment and IRMS. With improvement of oxidation method and determination method, δ13C value of liquid sample with a carbon content between 0.5 to 5 μg can be determined with an accuracy of 0.6 ‰. Using this method, the δ13C value of WSOCp and WSOCg in winter of 2021 at an urban site of Nanjing were determined, which were -25.9 ± 0.7 ‰ and -29.9 ± 0.9 ‰ respectively.
Xipeng Jin, Xuhui Cai, Mingyuan Yu, Yu Song, Xuesong Wang, Hongsheng Zhang, and Tong Zhu
Atmos. Chem. Phys., 22, 11409–11427, https://doi.org/10.5194/acp-22-11409-2022, https://doi.org/10.5194/acp-22-11409-2022, 2022
Short summary
Short summary
Meteorological discontinuities in the vertical direction define the lowest atmosphere as the boundary layer, while in the horizontal direction it identifies the contrast zone as the internal boundary. Both of them determine the polluted air mass dimension over the North China Plain. This study reveals the boundary layer structures under three categories of internal boundaries, modified by thermal, dynamical, and blending effects. It provides a new insight to understand regional pollution.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
Short summary
Short summary
This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Jiyan Wu, Chi Yang, Chunyan Zhang, Fang Cao, Aiping Wu, and Yanlin Zhang
Atmos. Meas. Tech., 15, 2623–2633, https://doi.org/10.5194/amt-15-2623-2022, https://doi.org/10.5194/amt-15-2623-2022, 2022
Short summary
Short summary
We introduced an online method to simultaneously determine the content of inorganic salt ions and reactive oxygen species (ROS) in PM2.5 hour by hour. We verified the accuracy and precision of the instrument. And we got the daily changes in ROS and the main sources that affect ROS. This breakthrough enables the quantitative assessment of atmospheric particulate matter ROS at the diurnal scale, providing an effective tool to study sources and environmental impacts of ROS.
Shichang Kang, Yulan Zhang, Pengfei Chen, Junming Guo, Qianggong Zhang, Zhiyuan Cong, Susan Kaspari, Lekhendra Tripathee, Tanguang Gao, Hewen Niu, Xinyue Zhong, Xintong Chen, Zhaofu Hu, Xiaofei Li, Yang Li, Bigyan Neupane, Fangping Yan, Dipesh Rupakheti, Chaman Gul, Wei Zhang, Guangming Wu, Ling Yang, Zhaoqing Wang, and Chaoliu Li
Earth Syst. Sci. Data, 14, 683–707, https://doi.org/10.5194/essd-14-683-2022, https://doi.org/10.5194/essd-14-683-2022, 2022
Short summary
Short summary
The Tibetan Plateau is important to the Earth’s climate. However, systematically observed data here are scarce. To perform more integrated and in-depth investigations of the origins and distributions of atmospheric pollutants and their impacts on cryospheric change, systematic data of black carbon and organic carbon from the atmosphere, glaciers, snow cover, precipitation, and lake sediment cores over the plateau based on the Atmospheric Pollution and Cryospheric Change program are provided.
Md. Mozammel Haque, Yanlin Zhang, Srinivas Bikkina, Meehye Lee, and Kimitaka Kawamura
Atmos. Chem. Phys., 22, 1373–1393, https://doi.org/10.5194/acp-22-1373-2022, https://doi.org/10.5194/acp-22-1373-2022, 2022
Short summary
Short summary
We attempt to understand the current state of East Asian organic aerosols with both the molecular marker approach and 14° C data of carbonaceous components. A significant positive correlation of nonfossil- and fossil-derived organic carbon with levoglucosan suggests the importance of biomass burning (BB) and coal combustion sources in the East Asian outflow. Thus, attribution of ambient levoglucosan levels over the western North Pacific to the impact of BB emission may cause large uncertainty.
Ahsan Mozaffar, Yan-Lin Zhang, Yu-Chi Lin, Feng Xie, Mei-Yi Fan, and Fang Cao
Atmos. Chem. Phys., 21, 18087–18099, https://doi.org/10.5194/acp-21-18087-2021, https://doi.org/10.5194/acp-21-18087-2021, 2021
Short summary
Short summary
We performed a long-term investigation of ambient volatile organic compounds (VOCs) in an industrial area in Nanjing, China. Followed by alkanes, halocarbons and aromatics were the most abundant VOC groups. Vehicle-related emissions were the major VOC sources in the study area. Aromatic and alkene VOCs were responsible for most of the atmospheric reactions.
Mengying Bao, Yan-Lin Zhang, Fang Cao, Yu-Chi Lin, Yuhang Wang, Xiaoyan Liu, Wenqi Zhang, Meiyi Fan, Feng Xie, Robert Cary, Joshua Dixon, and Lihua Zhou
Atmos. Meas. Tech., 14, 4053–4068, https://doi.org/10.5194/amt-14-4053-2021, https://doi.org/10.5194/amt-14-4053-2021, 2021
Short summary
Short summary
We introduce a two-wavelength method for brown C measurements with a modified Sunset carbon analyzer. We defined the enhanced concentrations and gave the possibility of providing an indicator of brown C. Compared with the strong local sources of organic and elemental C, we found that differences in EC mainly originated from regional transport. Biomass burning emissions significantly contributed to high differences in EC concentrations during the heavy biomass burning periods.
Yunhua Chang, Yan-Lin Zhang, Sawaeng Kawichai, Qian Wang, Martin Van Damme, Lieven Clarisse, Tippawan Prapamontol, and Moritz F. Lehmann
Atmos. Chem. Phys., 21, 7187–7198, https://doi.org/10.5194/acp-21-7187-2021, https://doi.org/10.5194/acp-21-7187-2021, 2021
Short summary
Short summary
In this study, we integrated satellite constraints on atmospheric NH3 levels and fire intensity, discrete NH3 concentration measurement, and N isotopic analysis of NH3 in order to assess the regional-scale contribution of biomass burning to ambient atmospheric NH3 in the heartland of Southeast Asia. The combined approach provides a valuable cross-validation framework for source apportioning of NH3 in the lower atmosphere and will thus help to ameliorate predictions of biomass burning emissions.
Qingcai Chen, Haoyao Sun, Wenhuai Song, Fang Cao, Chongguo Tian, and Yan-Lin Zhang
Atmos. Chem. Phys., 20, 14407–14417, https://doi.org/10.5194/acp-20-14407-2020, https://doi.org/10.5194/acp-20-14407-2020, 2020
Short summary
Short summary
This study found environmentally persistent free radicals (EPFRs) are widely present in atmospheric particles of different particle sizes and exhibit significant particle size distribution characteristics. EPFR concentrations are higher in coarse particles than in fine particles in summer and vice versa in winter. The potential toxicity caused by EPFRs may also vary with particle size and season. Combustion is the most important source of EPFRs (>70 %).
Cited articles
Abulimiti, A.: Data, OSF [data set], https://doi.org/10.17605/OSF.IO/8N32T, 2025.
Bauwens, M., Compernolle, S., Stavrakou, T., Müller, J.-F., van Gent, J., Eskes, H., Levelt, P. F., van der A, R., Veefkind, J. P., Vlietinck, J., Yu, H., and Zehner, C.: Impact of Coronavirus Outbreak on NO2 Pollution Assessed Using TROPOMI and OMI Observations, Geophys. Res. Lett., 47, e2020GL087978, https://doi.org/10.1029/2020GL087978, 2020.
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment, J. Geophys. Res.-Atmos., 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013.
Cao, J. J., Zhu, C. S., Chow, J. C., Watson, J. G., Han, Y. M., Wang, G. H., Shen, Z. X., and An, Z. S.: Black carbon relationships with emissions and meteorology in Xi'an, China, Atmos. Res., 94, 194–202, https://doi.org/10.1016/j.atmosres.2009.05.009, 2009.
Chang, Y., Deng, C., Cao, F., Cao, C., Zou, Z., Liu, S., Lee, X., Li, J., Zhang, G., and Zhang, Y.: Assessment of carbonaceous aerosols in Shanghai, China – Part 1: long-term evolution, seasonal variations, and meteorological effects, Atmos. Chem. Phys., 17, 9945–9964, https://doi.org/10.5194/acp-17-9945-2017, 2017.
Chen, Z., Chen, D., Kwan, M.-P., Chen, B., Gao, B., Zhuang, Y., Li, R., and Xu, B.: The control of anthropogenic emissions contributed to 80 % of the decrease in PM2.5 concentrations in Beijing from 2013 to 2017, Atmos. Chem. Phys., 19, 13519–13533, https://doi.org/10.5194/acp-19-13519-2019, 2019.
Cheng, J., Su, J., Cui, T., Li, X., Dong, X., Sun, F., Yang, Y., Tong, D., Zheng, Y., Li, Y., Li, J., Zhang, Q., and He, K.: Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis, Atmos. Chem. Phys., 19, 6125–6146, https://doi.org/10.5194/acp-19-6125-2019, 2019.
China National Environmental Monitoring Centre (CNEMC): https://air.cnemc.cn:18007/, last access: 18 June 2025.
Chow, J. C., Watson, J. G., Lowenthal, D. H., Antony Chen, L. W., and Motallebi, N.: PM2.5 source profiles for black and organic carbon emission inventories, Atmos. Environ., 45, 5407–5414, https://doi.org/10.1016/j.atmosenv.2011.07.011, 2011.
Chow, W. S., Liao, K., Huang, X. H. H., Leung, K. F., Lau, A. K. H., and Yu, J. Z.: Measurement report: The 10-year trend of PM2.5 major components and source tracers from 2008 to 2017 in an urban site of Hong Kong, China, Atmos. Chem. Phys., 22, 11557–11577, https://doi.org/10.5194/acp-22-11557-2022, 2022.
Dai, M., Zhu, B., Fang, C., Zhou, S., Lu, W., Zhao, D., Ding, D., Pan, C., and Liao, H.: Long-Term Variation and Source Apportionment of Black Carbon at Mt. Waliguan, China, J. Geophys. Res.-Atmos., 126, e2021JD035273, https://doi.org/10.1029/2021JD035273, 2021.
Dai, T., Dai, Q., Ding, J., Liu, B., Bi, X., Wu, J., Zhang, Y., and Feng, Y.: Measuring the Emission Changes and Meteorological Dependence of Source-Specific BC Aerosol Using Factor Analysis Coupled With Machine Learning, J. Geophys. Res.-Atmos., 128, e2023JD038696, https://doi.org/10.1029/2023JD038696, 2023.
Ding, A. J., Huang, X., Nie, W., Sun, J. N., Kerminen, V.-M., Petäjä, T., Su, H., Cheng, Y. F., Yang, X.-Q., Wang, M. H., Chi, X. G., Wang, J. P., Virkkula, A., Guo, W. D., Yuan, J., Wang, S. Y., Zhang, R. J., Wu, Y. F., Song, Y., Zhu, T., Zilitinkevich, S., Kulmala, M., and Fu, C. B.: Enhanced haze pollution by black carbon in megacities in China, Geophys. Res. Lett., 43, 2873–2879, https://doi.org/10.1002/2016GL067745, 2016.
Ding, S., Liu, D., Zhao, D., Tian, P., Huang, M., and Ding, D.: Characteristics of atmospheric black carbon and its wet scavenging in Nanning, South China, Sci. Total Environ., 904, 166747, https://doi.org/10.1016/j.scitotenv.2023.166747, 2023.
Ding, S., Zhao, D., Tian, P., and Huang, M.: Source apportionment and wet scavenging ability of atmospheric black carbon during haze in Northeast China, Environ. Pollut., 357, 124470, https://doi.org/10.1016/j.envpol.2024.124470, 2024.
Drinovec, L., Močnik, G., Zotter, P., Prévôt, A. S. H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., and Hansen, A. D. A.: The ”dual-spot” Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation, Atmos. Meas. Tech., 8, 1965–1979, https://doi.org/10.5194/amt-8-1965-2015, 2015.
Du, H., Li, J., Wang, Z., Chen, X., Yang, W., Sun, Y., Xin, J., Pan, X., Wang, W., Ye, Q., and Dao, X.: Assessment of the effect of meteorological and emission variations on winter PM2.5 over the North China Plain in the three-year action plan against air pollution in 2018–2020, Atmos. Res., 280, 106395, https://doi.org/10.1016/j.atmosres.2022.106395, 2022.
Dumka, U. C., Kaskaoutis, D. G., Devara, P. C. S., Kumar, R., Kumar, S., Tiwari, S., Gerasopoulos, E., and Mihalopoulos, N.: Year-long variability of the fossil fuel and wood burning black carbon components at a rural site in southern Delhi outskirts, Atmos. Res., 216, 11–25, https://doi.org/10.1016/j.atmosres.2018.09.016, 2019.
Fan, M.-Y., Hong, Y., Zhang, Y.-L., Sha, T., Lin, Y.-C., Cao, F., and Guo, H.: Increasing Nonfossil Fuel Contributions to Atmospheric Nitrate in Urban China from Observation to Prediction, Environ. Sci. Technol., 57, 18172–18182, https://doi.org/10.1021/acs.est.3c01651, 2023.
Fuller, G. W., Tremper, A. H., Baker, T. D., Yttri, K. E., and Butterfield, D.: Contribution of wood burning to PM10 in London, Atmos. Environ., 87, 87–94, https://doi.org/10.1016/j.atmosenv.2013.12.037, 2014.
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.
Gul, C., Mahapatra, P. S., Kang, S., Singh, P. K., Wu, X., He, C., Kumar, R., Rai, M., Xu, Y., and Puppala, S. P.: Black carbon concentration in the central Himalayas: Impact on glacier melt and potential source contribution, Environ. Pollut., 275, 116544, https://doi.org/10.1016/j.envpol.2021.116544, 2021.
He, C., Niu, X., Ye, Z., Wu, Q., Liu, L., Zhao, Y., Ni, J., Li, B., and Jin, J.: Black carbon pollution in China from 2001 to 2019: Patterns, trends, and drivers, Environ. Pollut., 324, 121381, https://doi.org/10.1016/j.envpol.2023.121381, 2023.
Helin, A., Niemi, J. V., Virkkula, A., Pirjola, L., Teinilä, K., Backman, J., Aurela, M., Saarikoski, S., Rönkkö, T., Asmi, E., and Timonen, H.: Characteristics and source apportionment of black carbon in the Helsinki metropolitan area, Finland, Atmos. Environ., 190, 87–98, https://doi.org/10.1016/j.atmosenv.2018.07.022, 2018.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Hong, Y., Zhang, Y., Bao, M., Fan, M., Lin, Y. C., Xu, R., Shu, Z., Wu, J. Y., Cao, F., Jiang, H., Cheng, Z., Li, J., and Zhang, G.: Nitrogen-Containing Functional Groups Dominate the Molecular Absorption of Water-Soluble Humic-Like Substances in Air From Nanjing, China Revealed by the Machine Learning Combined FT-ICR-MS Technique, J. Geophys. Res.-Atmos., 128, e2023JD039459, https://doi.org/10.1029/2023JD039459, 2023.
Huang, Z.-J., Li, H., Luo, J.-Y., Li, S., and Liu, F.: Few-Shot Learning-Based, Long-Term Stable, Sensitive Chemosensor for On-Site Colorimetric Detection of Cr(VI), Anal. Chem., 95, 6156–6162, https://doi.org/10.1021/acs.analchem.3c00604, 2023.
IPCC: Climate Change 2022 – Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781009325844, 2023.
Jing, A., Zhu, B., Wang, H., Yu, X., An, J., and Kang, H.: Source apportionment of black carbon in different seasons in the northern suburb of Nanjing, China, Atmos. Environ., 201, 190–200, https://doi.org/10.1016/j.atmosenv.2018.12.060, 2019.
Lee, B. P., Louie, P. K. K., Luk, C., and Chan, C. K.: Evaluation of traffic exhaust contributions to ambient carbonaceous submicron particulate matter in an urban roadside environment in Hong Kong, Atmos. Chem. Phys., 17, 15121–15135, https://doi.org/10.5194/acp-17-15121-2017, 2017.
Li, L., Li, Q., Huang, L., Wang, Q., Zhu, A., Xu, J., Liu, Z., Li, H., Shi, L., Li, R., Azari, M., Wang, Y., Zhang, X., Liu, Z., Zhu, Y., Zhang, K., Xue, S., Ooi, M. C. G., Zhang, D., and Chan, A.: Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation, Sci. Total Environ., 732, 139282, https://doi.org/10.1016/j.scitotenv.2020.139282, 2020.
Li, R., Han, Y., Wang, L., Shang, Y., and Chen, Y.: Differences in oxidative potential of black carbon from three combustion emission sources in China, J. Environ. Manage., 240, 57–65, https://doi.org/10.1016/j.jenvman.2019.03.070, 2019.
Li, W., Liu, X., Duan, F., Qu, Y., and An, J.: A one-year study on black carbon in urban Beijing: Concentrations, sources and implications on visibility, Atmos. Pollut. Res., 13, 101307, https://doi.org/10.1016/j.apr.2021.101307, 2022.
Li, Y., Lei, L., Sun, J., Gao, Y., Wang, P., Wang, S., Zhang, Z., Du, A., Li, Z., Wang, Z., Kim, J. Y., Kim, H., Zhang, H., and Sun, Y.: Significant Reductions in Secondary Aerosols after the Three-Year Action Plan in Beijing Summer, Environ. Sci. Technol., 57, 15945–15955, https://doi.org/10.1021/acs.est.3c02417, 2023.
Lin, Y.-C., Zhang, Y.-L., Xie, F., Fan, M.-Y., and Liu, X.: Substantial decreases of light absorption, concentrations and relative contributions of fossil fuel to light-absorbing carbonaceous aerosols attributed to the COVID-19 lockdown in east China, Environ. Pollut., 275, 116615, https://doi.org/10.1016/j.envpol.2021.116615, 2021.
Liu, D., He, C., Schwarz, J. P., and Wang, X.: Lifecycle of light-absorbing carbonaceous aerosols in the atmosphere, npj Climate and Atmospheric Science, 3, 40, https://doi.org/10.1038/s41612-020-00145-8, 2020a.
Liu, D., Ding, S., Zhao, D., Hu, K., Yu, C., Hu, D., Wu, Y., Zhou, C., Tian, P., Liu, Q., Wu, Y., Zhang, J., Kong, S., Huang, M., and Ding, D.: Black Carbon Emission and Wet Scavenging From Surface to the Top of Boundary Layer Over Beijing Region, J. Geophys. Res.-Atmos., 125, e2020JD033096, https://doi.org/10.1029/2020JD033096, 2020b.
Liu, S., Geng, G., Xiao, Q., Zheng, Y., Liu, X., Cheng, J., and Zhang, Q.: Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000, Environ. Sci. Technol., 56, 16517–16527, https://doi.org/10.1021/acs.est.2c06510, 2022.
Liu, Y., Yan, C., and Zheng, M.: Source apportionment of black carbon during winter in Beijing, Sci. Total Environ., 618, 531–541, https://doi.org/10.1016/j.scitotenv.2017.11.053, 2018.
Lundberg, S. M. and Lee, S.-I.: A unified approach to interpreting model predictions, Adv. Neur. Inf., 30, 4765–4774, https://doi.org/10.5555/3295222.3295230, 2017.
Qin, Y., Ye, J., Ohno, P., Liu, P., Wang, J., Fu, P., Zhou, L., Li, Y. J., Martin, S. T., and Chan, C. K.: Assessing the Nonlinear Effect of Atmospheric Variables on Primary and Oxygenated Organic Aerosol Concentration Using Machine Learning, ACS Earth Space Chem., 6, 1059–1066, https://doi.org/10.1021/acsearthspacechem.1c00443, 2022.
Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to black carbon, Nat. Geosci., 1, 221–227, https://doi.org/10.1038/ngeo156, 2008.
Ran, L., Deng, Z. Z., Wang, P. C., and Xia, X. A.: Black carbon and wavelength-dependent aerosol absorption in the North China Plain based on two-year aethalometer measurements, Atmos. Environ., 142, 132–144, https://doi.org/10.1016/j.atmosenv.2016.07.014, 2016.
Sandradewi, J., Prévôt, A. S. H., Szidat, S., Perron, N., Alfarra, M. R., Lanz, V. A., Weingartner, E., and Baltensperger, U.: Using Aerosol Light Absorption Measurements for the Quantitative Determination of Wood Burning and Traffic Emission Contributions to Particulate Matter, Environ. Sci. Technol., 42, 3316–3323, https://doi.org/10.1021/es702253m, 2008.
Sarigiannis, D., Karakitsios, S. P., Zikopoulos, D., Nikolaki, S., and Kermenidou, M.: Lung cancer risk from PAHs emitted from biomass combustion, Environ. Res., 137, 147–156, https://doi.org/10.1016/j.envres.2014.12.009, 2015.
Seo, J., Park, D.-S. R., Kim, J. Y., Youn, D., Lim, Y. B., and Kim, Y.: Effects of meteorology and emissions on urban air quality: a quantitative statistical approach to long-term records (1999–2016) in Seoul, South Korea, Atmos. Chem. Phys., 18, 16121–16137, https://doi.org/10.5194/acp-18-16121-2018, 2018.
Sun, J., Wang, Z., Zhou, W., Xie, C., Wu, C., Chen, C., Han, T., Wang, Q., Li, Z., Li, J., Fu, P., Wang, Z., and Sun, Y.: Measurement report: Long-term changes in black carbon and aerosol optical properties from 2012 to 2020 in Beijing, China, Atmos. Chem. Phys., 22, 561–575, https://doi.org/10.5194/acp-22-561-2022, 2022a.
Sun, X., Zhao, T., Bai, Y., Kong, S., Zheng, H., Hu, W., Ma, X., and Xiong, J.: Meteorology impact on PM2.5 change over a receptor region in the regional transport of air pollutants: observational study of recent emission reductions in central China, Atmos. Chem. Phys., 22, 3579–3593, https://doi.org/10.5194/acp-22-3579-2022, 2022b.
Wang, Y., Yuan, Y., Wang, Q., Liu, C., Zhi, Q., and Cao, J.: Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions, Sci. Total Environ., 731, 139133, https://doi.org/10.1016/j.scitotenv.2020.139133, 2020.
Wei, C., Wang, M. H., Fu, Q. Y., Dai, C., Huang, R., and Bao, Q.: Temporal Characteristics and Potential Sources of Black Carbon in Megacity Shanghai, China, J. Geophys. Res.-Atmos., 125, e2019JD031827, https://doi.org/10.1029/2019JD031827, 2020.
Wise, E. K. and Comrie, A. C.: Extending the Kolmogorov–Zurbenko Filter: Application to Ozone, Particulate Matter, and Meteorological Trends, J. Air Waste Manage., 55, 1208–1216, https://doi.org/10.1080/10473289.2005.10464718, 2005.
Wu, B., Wu, C., Ye, Y., Pei, C., Deng, T., Li, Y. J., Lu, X., Wang, L., Hu, B., Li, M., and Wu, D.: Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China, Atmos. Environ., 321, 120347, https://doi.org/10.1016/j.atmosenv.2024.120347, 2024.
Xiao, S., Yu, X., Zhu, B., Kumar, K. R., Li, M., and Li, L.: Characterization and source apportionment of black carbon aerosol in the Nanjing Jiangbei New Area based on two years of measurements from Aethalometer, J. Aerosol Sci., 139, 105461, https://doi.org/10.1016/j.jaerosci.2019.105461, 2020.
Yao, X., Chan, C. K., Fang, M., Cadle, S., Chan, T., Mulawa, P., He, K., and Ye, B.: The water-soluble ionic composition of PM2.5 in Shanghai and Beijing, China, Atmos. Environ., 36, 4223–4234, https://doi.org/10.1016/S1352-2310(02)00342-4, 2002.
Yin, C., Deng, X., Zou, Y., Solmon, F., Li, F., and Deng, T.: Trend analysis of surface ozone at suburban Guangzhou, China, Sci. Total Environ., 695, 133880, https://doi.org/10.1016/j.scitotenv.2019.133880, 2019.
Yu, M., Zhang, Y.-L., Xie, T., Song, W., Lin, Y.-C., Zhang, Y., Cao, F., Yang, C., and Szidat, S.: Quantification of fossil and non-fossil sources to the reduction of carbonaceous aerosols in the Yangtze River Delta, China: Insights from radiocarbon analysis during 2014–2019, Atmos. Environ., 292, 119421, https://doi.org/10.1016/j.atmosenv.2022.119421, 2023.
Zhang, L., Shen, F., Gao, J., Cui, S., Yue, H., Wang, J., Chen, M., and Ge, X.: Characteristics and potential sources of black carbon particles in suburban Nanjing, China, Atmos. Pollut. Res., 11, 981–991, https://doi.org/10.1016/j.apr.2020.02.011, 2020.
Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A., Klimont, Z., Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L. T., and Yao, Z. L.: Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153, https://doi.org/10.5194/acp-9-5131-2009, 2009.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang, J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang, Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu, F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu, Z., Yang, F., He, K., and Hao, J.: 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, X., Rao, R., Huang, Y., Mao, M., Berg, M. J., and Sun, W.: Black carbon aerosols in urban central China, J. Quant. Spectrosc. Ra., 150, 3–11, https://doi.org/10.1016/j.jqsrt.2014.03.006, 2015.
Zhang, Y.-L., Li, J., Zhang, G., Zotter, P., Huang, R.-J., Tang, J.-H., Wacker, L., Prévôt, A. S. H., and Szidat, S.: Radiocarbon-Based Source Apportionment of Carbonaceous Aerosols at a Regional Background Site on Hainan Island, South China, Environ. Sci. Technol., 48, 2651–2659, https://doi.org/10.1021/es4050852, 2014.
Zhao, C., Wang, Q., Ban, J., Liu, Z., Zhang, Y., Ma, R., Li, S., and Li, T.: Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01° × 0.01° spatial resolution, Environ. Int., 134, 105297, https://doi.org/10.1016/j.envint.2019.105297, 2020.
Zheng, H., Kong, S. F., Zheng, M. M., Yan, Y., Yao, L., Zheng, S., Yan, Q., Wu, J., Cheng, Y., Chen, N., Bai, Y., Zhao, T., Liu, D., Zhao, D., and Qi, S.: A 5.5-year observations of black carbon aerosol at a megacity in Central China: Levels, sources, and variation trends, Atmos. Environ., 232, 117581, https://doi.org/10.1016/j.atmosenv.2020.117581, 2020.
Zheng, H., Kong, S., Zhai, S., Sun, X., Cheng, Y., Yao, L., Song, C., Zheng, Z., Shi, Z., and Harrison, R. M.: An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models, npj Climate and Atmospheric Science, 6, 214, https://doi.org/10.1038/s41612-023-00536-7, 2023.
Zhou, B., Wang, Q., Zhou, Q., Zhang, Z., Wang, G., Fang, N., Li, M., and Cao, J.: Seasonal Characteristics of Black Carbon Aerosol and its Potential Source Regions in Baoji, China, Aerosol Air Qual. Res., 18, 397–406, https://doi.org/10.4209/aaqr.2017.02.0070, 2018.
Zhou, Y., Ma, X., Tian, R., and Wang, K.: Seasonal transition of Black carbon aerosols over Qinghai-Tibet Plateau: Simulations with WRF-Chem, Atmos. Environ., 308, 119866, https://doi.org/10.1016/j.atmosenv.2023.119866, 2023.
Zhu, C., Kanaya, Y., Takigawa, M., Ikeda, K., Tanimoto, H., Taketani, F., Miyakawa, T., Kobayashi, H., and Pisso, I.: FLEXPART v10.1 simulation of source contributions to Arctic black carbon, Atmos. Chem. Phys., 20, 1641–1656, https://doi.org/10.5194/acp-20-1641-2020, 2020.
Zhuang, B. L., Wang, T. J., Liu, J., Li, S., Xie, M., Yang, X. Q., Fu, C. B., Sun, J. N., Yin, C. Q., Liao, J. B., Zhu, J. L., and Zhang, Y.: Continuous measurement of black carbon aerosol in urban Nanjing of Yangtze River Delta, China, Atmos. Environ., 89, 415–424, https://doi.org/10.1016/j.atmosenv.2014.02.052, 2014.
Zong, Z., Wang, X., Tian, C., Chen, Y., Qu, L., Ji, L., Zhi, G., Li, J., and Zhang, G.: Source apportionment of PM2.5 at a regional background site in North China using PMF linked with radiocarbon analysis: insight into the contribution of biomass burning, Atmos. Chem. Phys., 16, 11249–11265, https://doi.org/10.5194/acp-16-11249-2016, 2016.
Zotter, P., Herich, H., Gysel, M., El-Haddad, I., Zhang, Y., Močnik, G., Hüglin, C., Baltensperger, U., Szidat, S., and Prévôt, A. S. H.: Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol, Atmos. Chem. Phys., 17, 4229–4249, https://doi.org/10.5194/acp-17-4229-2017, 2017.
Short summary
To improve air quality, the Chinese government has implemented strict clean-air measures. We explored how black carbon (BC) responded to these measures and found that a reduction in liquid fuel use was the main factor driving a decrease in BC levels. Additionally, meteorological factors also played a significant role in the long-term trends of BC. These factors should be considered in future emission reduction policies to further enhance air quality improvements.
To improve air quality, the Chinese government has implemented strict clean-air measures. We...
Altmetrics
Final-revised paper
Preprint