75 citations as recorded by crossref.

1. Meteorological normalization of NO2 concentrations in the Province of Bolzano (Italian Alps) M. Falocchi et al. 10.1016/j.atmosenv.2020.118048
2. Changes in air pollutants during the COVID-19 lockdown in Beijing: Insights from a machine-learning technique and implications for future control policy J. Hu et al. 10.1016/j.aosl.2021.100060
3. Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model J. Xu et al. 10.5194/acp-21-7321-2021
4. Rapid sulfate formation from synergetic oxidation of SO2 by O3 and NO2 under ammonia-rich conditions: Implications for the explosive growth of atmospheric PM2.5 during haze events in China S. Zhang et al. 10.1016/j.scitotenv.2020.144897
5. Synergetic effects of NH<sub>3</sub> and NO<sub><i>x</i></sub> on the production and optical absorption of secondary organic aerosol formation from toluene photooxidation S. Liu et al. 10.5194/acp-21-17759-2021
6. Importance of meteorology and chemistry in determining air pollutant levels during COVID-19 lockdown in Indian cities L. Crilley et al. 10.1039/D1EM00187F
7. Revealing Drivers of Haze Pollution by Explainable Machine Learning L. Hou et al. 10.1021/acs.estlett.1c00865
8. Two-Dimensional Silicon Fingerprints Reveal Dramatic Variations in the Sources of Particulate Matter in Beijing during 2013–2017 X. Yang et al. 10.1021/acs.est.0c00984
9. Evaluating the meteorological normalized PM2.5 trend (2014–2019) in the “2+26” region of China using an ensemble learning technique L. Qu et al. 10.1016/j.envpol.2020.115346
10. Effect of sub-urban scale lockdown on air pollution in Beijing P. Brimblecombe & Y. Lai 10.1016/j.uclim.2020.100725
11. Satellite Retrieval of Air Pollution Changes in Central and Eastern China during COVID-19 Lockdown Based on a Machine Learning Model Z. Song et al. 10.3390/rs13132525
12. Impact of meteorology and precursor emission changes on O3 variation in Tianjin, China from 2015 to 2021 J. Ding et al. 10.1016/j.jes.2022.03.010
13. Long-term trends in PM2.5 mass and particle number concentrations in urban air: The impacts of mitigation measures and extreme events due to changing climates A. Lorelei de Jesus et al. 10.1016/j.envpol.2020.114500
14. Mutual promotion between aerosol particle liquid water and particulate nitrate enhancement leads to severe nitrate-dominated particulate matter pollution and low visibility Y. Wang et al. 10.5194/acp-20-2161-2020
15. The promotion effect of nitrous acid on aerosol formation in wintertime in Beijing: the possible contribution of traffic-related emissions Y. Liu et al. 10.5194/acp-20-13023-2020
16. Changes of ammonia concentrations in wintertime on the North China Plain from 2018 to 2020 Y. He et al. 10.1016/j.atmosres.2021.105490
17. Air pollutant emissions from coal-fired power plants in China over the past two decades G. Wang et al. 10.1016/j.scitotenv.2020.140326
18. Source apportionment of fine organic carbon (OC) using receptor modelling at a rural site of Beijing: Insight into seasonal and diurnal variation of source contributions X. Wu et al. 10.1016/j.envpol.2020.115078
19. Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China M. Tang et al. 10.1016/j.envpol.2022.119027
20. Influence of weather and air pollution on concentration change of PM2.5 using a generalized additive model and gradient boosting machine B. Cheng et al. 10.1016/j.atmosenv.2021.118437
21. Simulation of surface ozone over Hebei province, China using Kolmogorov-Zurbenko and artificial neural network (KZ-ANN) combined model S. Gao et al. 10.1016/j.atmosenv.2021.118599
22. New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing P. Zuo et al. 10.1021/acs.est.1c05383
23. Spring Festival and COVID‐19 Lockdown: Disentangling PM Sources in Major Chinese Cities Q. Dai et al. 10.1029/2021GL093403
24. Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels S. Ceballos-Santos et al. 10.3390/ijerph182413347
25. Relative importance of meteorological variables on air quality and role of boundary layer height Y. Huang et al. 10.1016/j.atmosenv.2021.118737
26. Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain J. González-Pardo et al. 10.1016/j.scitotenv.2022.153786
27. Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak Q. Dai et al. 10.1021/acs.est.0c02776
28. Haze episodes before and during the COVID-19 shutdown in Tianjin, China: Contribution of fireworks and residential burning Q. Dai et al. 10.1016/j.envpol.2021.117252
29. Separating emission and meteorological contributions to long-term PM<sub>2.5</sub> trends over eastern China during 2000–2018 Q. Xiao et al. 10.5194/acp-21-9475-2021
30. Characterization, possible sources and health risk assessment of PM2.5-bound Heavy Metals in the most industrial city of Iran M. Kermani et al. 10.1007/s40201-020-00589-3
31. Size−resolved source apportionment of particulate matter from a megacity in northern China based on one-year measurement of inorganic and organic components Y. Tian et al. 10.1016/j.envpol.2021.117932
32. The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach M. Cole et al. 10.1007/s10640-020-00483-4
33. The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach B. Ly et al. 10.1016/j.jaerosci.2020.105716
34. Two decades of trends in urban particulate matter concentrations across Australia A. de Jesus et al. 10.1016/j.envres.2020.110021
35. Airborne particulate matter R. Harrison 10.1098/rsta.2019.0319
36. Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna’s first COVID-19 lockdown: Significance for air quality management M. Brancher 10.1016/j.envpol.2021.117153
37. Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations R. Gagliardi & C. Andenna 10.3390/atmos13010064
38. Appreciating the role of big data in the modernization of environmental governance M. Liu et al. 10.1007/s42524-021-0185-x
39. Unexpected Increases of Severe Haze Pollution During the Post COVID‐19 Period: Effects of Emissions, Meteorology, and Secondary Production W. Zhou et al. 10.1029/2021JD035710
40. The complex chemical effects of COVID-19 shutdowns on air quality J. Kroll et al. 10.1038/s41557-020-0535-z
41. More mileage in reducing urban air pollution from road traffic R. Harrison et al. 10.1016/j.envint.2020.106329
42. Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah M. Mallet 10.1016/j.apr.2020.08.001
43. Opinion: Gigacity – a source of problems or the new way to sustainable development M. Kulmala et al. 10.5194/acp-21-8313-2021
44. Air Pollution Control and Public Health Risk Perception: Evidence from the Perspectives of Signal and Implementation Effects Y. Fan et al. 10.3390/ijerph19053040
45. Diurnal and weekly patterns of primary pollutants in Beijing under COVID-19 restrictions P. Brimblecombe & Y. Lai 10.1039/D0FD00082E
46. Significant Changes in Chemistry of Fine Particles in Wintertime Beijing from 2007 to 2017: Impact of Clean Air Actions Y. Zhang et al. 10.1021/acs.est.9b04678
47. Aggravation effect of regional transport on wintertime PM2.5 over the middle reaches of the Yangtze River under China's air pollutant emission reduction process Y. Bai et al. 10.1016/j.apr.2021.101111
48. Using Lidar technology to assess regional air pollution and improve estimates of PM2.5 transport in the North China Plain Y. Xiang et al. 10.1088/1748-9326/ab9cfd
49. Air pollution and tourism development: An interplay N. Zhang et al. 10.1016/j.annals.2020.103032
50. Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China H. Yin et al. 10.1016/j.envpol.2021.117899
51. On the fossil and non-fossil fuel sources of carbonaceous aerosol with radiocarbon and AMS-PMF methods during winter hazy days in a rural area of North China plain Y. Zhang et al. 10.1016/j.envres.2021.112672
52. Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography P. Sekula et al. 10.3390/su14063388
53. Visual Analysis of Odor Interaction Based on Support Vector Regression Method L. Yan et al. 10.3390/s20061707
54. A 5.5-year observations of black carbon aerosol at a megacity in Central China: Levels, sources, and variation trends H. Zheng et al. 10.1016/j.atmosenv.2020.117581
55. Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing D. Srivastava et al. 10.5194/acp-21-14703-2021
56. Increasing impacts of the relative contributions of regional transport on air pollution in Beijing: Observational evidence Q. Tan et al. 10.1016/j.envpol.2021.118407
57. Environmental Effective Assessment of Control Measures Implemented by Clean Air Action Plan (2013–2017) in Beijing, China Y. Xue et al. 10.3390/atmos11020189
58. Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19 H. Zheng et al. 10.1016/j.scitotenv.2020.140000
59. Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation Y. Zhu et al. 10.3390/rs13091798
60. Comprehensive detection of nitrated aromatic compounds in fine particulate matter using gas chromatography and tandem mass spectrometry coupled with an electron capture negative ionization source X. Shi et al. 10.1016/j.jhazmat.2020.124794
61. Chemistry of Atmospheric Fine Particles During the COVID‐19 Pandemic in a Megacity of Eastern China L. Liu et al. 10.1029/2020GL091611
62. Evaluation of black carbon source apportionment based on one year's daily observations in Beijing H. Xiao et al. 10.1016/j.scitotenv.2021.145668
63. Air quality improvement in response to intensified control strategies in Beijing during 2013–2019 W. Li et al. 10.1016/j.scitotenv.2020.140776
64. Long-term variations in ozone levels in the troposphere and lower stratosphere over Beijing: observations and model simulations Y. Zhang et al. 10.5194/acp-20-13343-2020
65. Differential Effects of the COVID-19 Lockdown and Regional Fire on the Air Quality of Medellín, Colombia J. Henao et al. 10.3390/atmos12091137
66. Climate variability or anthropogenic emissions: which caused Beijing Haze? L. Pei et al. 10.1088/1748-9326/ab6f11
67. Variation in Concentration and Sources of Black Carbon in a Megacity of China During the COVID‐19 Pandemic L. Xu et al. 10.1029/2020GL090444
68. Understanding sources of fine particulate matter in China M. Zheng et al. 10.1098/rsta.2019.0325
69. Secondary aerosol formation in winter haze over the Beijing-Tianjin-Hebei Region, China D. Shang et al. 10.1007/s11783-020-1326-x
70. Regional Transport of PM 2.5 and O 3 Based on Complex Network Method and Chemical Transport Model in the Yangtze River Delta, China Q. Wang et al. 10.1029/2021JD034807
71. Adopting urban morphological indicators to land use regression modeling of seasonal mean PM2.5 concentrations for a high-density city Y. Wan et al. 10.1007/s11869-021-01134-3
72. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns Z. Shi et al. 10.1126/sciadv.abd6696
73. Analysis of the air pollution climate of a central urban roadside supersite: London, Marylebone Road A. Kamara & R. Harrison 10.1016/j.atmosenv.2021.118479
74. Heterogeneous effects of COVID-19 lockdown measures on air quality in Northern China J. Wang et al. 10.1016/j.apenergy.2020.116179
75. Introduction to the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing)” Z. Shi et al. 10.5194/acp-19-7519-2019