63 citations as recorded by crossref.

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22. Limits to Vehicle Emission Control: A Case of Guangzhou A. Lee https://doi.org/10.1016/j.proenv.2016.09.020
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24. Haze pollution under a high atmospheric oxidization capacity in summer in Beijing: insights into formation mechanism of atmospheric physicochemical processes D. Zhao et al. https://doi.org/10.5194/acp-20-4575-2020
25. Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms I. Yeo et al. https://doi.org/10.1007/s00521-021-06082-8
26. Amplified ozone pollution in cities during the COVID-19 lockdown P. Sicard et al. https://doi.org/10.1016/j.scitotenv.2020.139542
27. Long-term trends of fine particulate matter and chemical composition in the Pearl River Delta Economic Zone (PRDEZ), China X. Wang et al. https://doi.org/10.1007/s11783-014-0728-z
28. Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period Y. Chen et al. https://doi.org/10.5194/acp-20-499-2020
29. Regional Policies Targeting Residential Solid Fuel and Agricultural Emissions Can Improve Air Quality and Public Health in the Greater Bay Area and Across China L. Conibear et al. https://doi.org/10.1029/2020GH000341
30. A Modeling Study of Impact of Emission Control Strategies on PM2.5Reductions in Zhongshan, China, Using WRF-CMAQ J. Mai et al. https://doi.org/10.1155/2016/5836070
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34. Can urban rail transit systems alleviate air pollution? Empirical evidence from Beijing S. Guo & L. Chen https://doi.org/10.1111/grow.12266
35. COVID-19 lockdown: a boon in boosting the air quality of major Indian Metropolitan Cities D. Rathore et al. https://doi.org/10.1007/s10453-020-09673-5
36. Spatial and temporal variability of ozone sensitivity over China observed from the Ozone Monitoring Instrument X. Jin & T. Holloway https://doi.org/10.1002/2015JD023250
37. Ozone Pollution and Its Response to Nitrogen Dioxide Change from a Dense Ground-Based Network in the Yangtze River Delta: Implications for Ozone Abatement in Urban Agglomeration Z. He et al. https://doi.org/10.3390/atmos13091450
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39. Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms Y. Ma et al. https://doi.org/10.5194/acp-21-17003-2021
40. Understanding ozone variability in spatial responses to emissions and meteorology in China using interpretable machine learning X. Zhang et al. https://doi.org/10.1016/j.isci.2025.113036
41. Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning Y. Lv et al. https://doi.org/10.1016/j.scitotenv.2022.159339
42. Air pollution characteristics and human health risks in key cities of northwest China H. Luo et al. https://doi.org/10.1016/j.jenvman.2020.110791
43. Highly spatial and temporal bottom-up vehicle emission characterization and its control in a typical ecology-preservation area P. Bie et al. https://doi.org/10.1016/j.eehl.2022.09.003
44. Vertical distributions of tropospheric formaldehyde, nitrogen dioxide, ozone and aerosol in southern China by ground-based MAX-DOAS and LIDAR measurements during PRIDE-GBA 2018 campaign Y. Luo et al. https://doi.org/10.1016/j.atmosenv.2020.117384
45. Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China H. Zhao et al. https://doi.org/10.3390/su10124519
46. Interannual and Decadal Changes in Tropospheric Ozone in China and the Associated Chemistry-Climate Interactions: A Review Y. Fu et al. https://doi.org/10.1007/s00376-019-8216-9
47. An investigation into the role of VOCs in SOA and ozone production in Beijing, China Q. Li et al. https://doi.org/10.1016/j.scitotenv.2020.137536
48. Statistical analysis of PM2.5 observations from diplomatic facilities in China F. San Martini et al. https://doi.org/10.1016/j.atmosenv.2015.03.060
49. Impacts of irregular and strategic lockdown on air quality over Indo-Pak Subcontinent: Pre-to-post COVID-19 analysis F. Saleem et al. https://doi.org/10.1016/j.chaos.2023.114255
50. Emission-driven changes in anthropogenic aerosol concentrations in China during 1970–2010 and its implications for PM2.5 control policy W. Chang et al. https://doi.org/10.1016/j.atmosres.2018.05.008
51. Modeling of the health impacts of ambient ozone pollution in China and India L. Liu et al. https://doi.org/10.1016/j.atmosenv.2021.118753
52. COVID-19 lockdown closures of emissions sources in India: Lessons for air quality and climate policy K. Tibrewal & C. Venkataraman https://doi.org/10.1016/j.jenvman.2021.114079
53. Diverse spillover effects of COVID-19 control measures on air quality improvement: evidence from typical Chinese cities L. Zhao et al. https://doi.org/10.1007/s10668-022-02353-z
54. Spatiotemporal assessment of health burden and economic losses attributable to short-term exposure to ground-level ozone during 2015–2018 in China Z. Zhang et al. https://doi.org/10.1186/s12889-021-10751-7
55. Mechanisms of ozone formation in heavily polluted cities using interpretable machine learning B. Sun et al. https://doi.org/10.1016/j.uclim.2026.102954
56. Ozone pollution in Chinese cities: Assessment of seasonal variation, health effects and economic burden K. Maji et al. https://doi.org/10.1016/j.envpol.2019.01.049
57. Source assessment of atmospheric fine particulate matter in a Chinese megacity: Insights from long-term, high-time resolution chemical composition measurements from Shanghai flagship monitoring supersite C. Zhang et al. https://doi.org/10.1016/j.chemosphere.2020.126598
58. Examining the implications of photochemical indicators for O3–NOx–VOC sensitivity and control strategies: a case study in the Yangtze River Delta (YRD), China X. Li et al. https://doi.org/10.5194/acp-22-14799-2022
59. Ground-level ozone pollution and its health impacts in China H. Liu et al. https://doi.org/10.1016/j.atmosenv.2017.11.014
60. Increased nitrogen deposition and airborne particulate matter pollution in the vicinity of intensive animal farms caused by ammonia emissions W. Yi et al. https://doi.org/10.1016/j.agee.2025.109634
61. Research on prediction of environmental aerosol and PM2.5 based on artificial neural network X. Wang & B. Wang https://doi.org/10.1007/s00521-018-3861-y
62. A 10-year assessment of ambient fine particles and related health endpoints in a large Mediterranean city Y. Khaniabadi & P. Sicard https://doi.org/10.1016/j.chemosphere.2021.130502
63. Understanding primary and secondary sources of ambient carbonyl compounds in Beijing using the PMF model W. Chen et al. https://doi.org/10.5194/acp-14-3047-2014