107 citations as recorded by crossref.

1. Elucidating Contributions of Anthropogenic Volatile Organic Compounds and Particulate Matter to Ozone Trends over China C. Li et al. 10.1021/acs.est.2c03315
2. Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change J. Laughner et al. 10.1073/pnas.2109481118
3. Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau X. Chen et al. 10.1039/D2EA00168C
4. Impact Assessment of COVID‐19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX‐DOAS Observations and Machine Learning Models S. Zhang et al. 10.1029/2021JD036377
5. Comparison of PM10 Levels in Three of the Most Polluted Cities in Romania over the Periods Related to Lower Traffic—Implications for Human Health G. Burghelea et al. 10.3390/app14188169
6. Impacts of COVID-19 Lockdown on Traffic Flow, Active Travel and Gaseous Pollutant Concentrations; Implications for Future Emissions Control Measures in Oxford, UK A. Singh et al. 10.3390/su142316182
7. A traffic-induced shift of ultrafine particle sources under COVID-19 soft lockdown in a subtropical urban area T. Chen et al. 10.1016/j.envint.2024.108658
8. Impact of 2020 COVID-19 lockdowns on particulate air pollution across Europe J. Putaud et al. 10.5194/acp-23-10145-2023
9. Urban–Rural Disparities in Air Quality Responses to Traffic Changes in a Megacity of China Revealed Using Machine Learning Y. Wen et al. 10.1021/acs.estlett.2c00246
10. Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013–2020 L. Zhang et al. 10.1016/j.scitotenv.2023.166182
11. Pop-up cycling infrastructure as a niche innovation for sustainable transportation in European cities: An inter- and transdisciplinary case study of Berlin S. Becker et al. 10.1016/j.scs.2022.104168
12. The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects P. Huszar et al. 10.5194/acp-21-14309-2021
13. Response of atmospheric composition to COVID-19 lockdown measures during spring in the Paris region (France) J. Petit et al. 10.5194/acp-21-17167-2021
14. Compliance with 2021 WHO air quality guidelines across Europe will require radical measures D. Bowdalo et al. 10.1088/1748-9326/ac44c7
15. Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021 J. Ding et al. 10.1016/j.jes.2022.03.010
16. Secondary PM<sub>2.5</sub> decreases significantly less than NO<sub>2</sub> emission reductions during COVID lockdown in Germany V. Balamurugan et al. 10.5194/acp-22-7105-2022
17. Impacts of the COVID-19 lockdown on air pollution at regional and urban background sites in northern Italy J. Putaud et al. 10.5194/acp-21-7597-2021
18. Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning K. Patel et al. 10.1080/02786826.2023.2193237
19. Integrated process analysis retrieval of changes in ground-level ozone and fine particulate matter during the COVID-19 outbreak in the coastal city of Kannur, India F. Ye et al. 10.1016/j.envpol.2022.119468
20. The air quality impacts of pre-operational hydraulic fracturing activities S. Wilde et al. 10.1016/j.scitotenv.2022.159702
21. 污染减排与气象因素对我国主要城市2015~2021年环境空气质量变化的贡献评估 启. 戴 et al. 10.1360/SSTe-2022-0271
22. Ozone formation sensitivity study using machine learning coupled with the reactivity of volatile organic compound species J. Zhan et al. 10.5194/amt-15-1511-2022
23. The contribution of transport emissions to ozone mixing ratios and methane lifetime in 2015 and 2050 in the Shared Socioeconomic Pathways (SSPs) M. Mertens et al. 10.5194/acp-24-12079-2024
24. Estimating the potential health effects of cleaner air in the initial stages of the COVID-19 pandemic: a study in Malmö, Sweden E. Malmqvist et al. 10.1080/16549716.2024.2416291
25. Did unprecedented air pollution levels cause spike in Delhi’s COVID cases during second wave? S. Kolluru et al. 10.1007/s00477-022-02308-w
26. Emerging Investigator Series: COVID-19 lockdown effects on aerosol particle size distributions in northern Italy J. Shen et al. 10.1039/D1EA00016K
27. Long-term airborne measurements of pollutants over the United Kingdom to support air quality model development and evaluation A. Mynard et al. 10.5194/amt-16-4229-2023
28. Air Quality in the Italian Northwestern Alps during Year 2020: Assessment of the COVID-19 «Lockdown Effect» from Multi-Technique Observations and Models H. Diémoz et al. 10.3390/atmos12081006
29. Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania M. Zoran et al. 10.1016/j.psep.2022.08.042
30. Measuring the Emission Changes and Meteorological Dependence of Source‐Specific BC Aerosol Using Factor Analysis Coupled With Machine Learning T. Dai et al. 10.1029/2023JD038696
31. COVID-19 PANDEMİSİ DÖNEMİNDE PLASTİK ATIK TEMELLİ ÇEVRE KİRLİLİĞİNİ KONU ALAN KAMU SPOTLARI A. DARI 10.14520/adyusbd.1055931
32. Wavelet Based Semblance Analysis of Anthropogenic Aerosol Control on Air Quality During the COVID-19 Pandemic Period J. Asir et al. 10.1007/s41810-024-00250-8
33. 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
34. Characteristic changes of ozone and its precursors in London during COVID-19 lockdown and the ozone surge reason analysis C. Zhang & D. Stevenson 10.1016/j.atmosenv.2022.118980
35. Impact of the 2020 COVID-19 lockdown on the concentration of non-methane volatile organic compounds in a UK urban atmosphere E. Bernard et al. 10.1016/j.atmosenv.2024.120836
36. Green recovery or pollution rebound? Evidence from air pollution of China in the post-COVID-19 era T. Feng et al. 10.1016/j.jenvman.2022.116360
37. Assessing the Impact of Straw Burning on PM2.5 Using Explainable Machine Learning: A Case Study in Heilongjiang Province, China Z. Xu et al. 10.3390/su16177315
38. Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control Z. Tan et al. 10.1021/acs.est.3c08761
39. Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach M. Hassan et al. 10.1007/s11356-023-29665-5
40. Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic A. Taloor et al. 10.3390/rs14184650
41. SARS-CoV-2 and other airborne respiratory viruses in outdoor aerosols in three Swiss cities before and during the first wave of the COVID-19 pandemic Y. Tao et al. 10.1016/j.envint.2022.107266
42. Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects V. Calatayud et al. 10.1016/j.envres.2023.115835
43. Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning F. Tuluri et al. 10.3390/ijerph20116022
44. Quantifying changes in ambient NOx, O3 and PM10 concentrations in Austria during the COVID-19 related lockdown in spring 2020 C. Staehle et al. 10.1007/s11869-022-01232-w
45. Tropospheric ozone production and chemical regime analysis during the COVID-19 lockdown over Europe C. Nussbaumer et al. 10.5194/acp-22-6151-2022
46. Changes in a Bottom-Up Vehicular Emissions Inventory and Its Impact on Air Pollution During COVID-19 Lockdown in São Paulo, Brazil S. Ibarra-Espinosa et al. 10.3389/frsc.2022.883112
47. Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway E. Bertazza et al. 10.1002/met.2123
48. Increased tropospheric ozone levels as a public health issue during COVID-19 lockdown and estimation the related pulmonary diseases K. Anbari et al. 10.1016/j.apr.2022.101600
49. A parent-school initiative to assess and predict air quality around a heavily trafficked school P. Kumar et al. 10.1016/j.scitotenv.2022.160587
50. The greatest air quality experiment ever: Policy suggestions from the COVID-19 lockdown in twelve European cities M. Volta et al. 10.1371/journal.pone.0277428
51. 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
52. Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning M. Wang et al. 10.5194/acp-23-10313-2023
53. Insights into India's temporary air pollution relief: A systematic review for green recovery amid and post-COVID-19 B. Singh 10.1557/s43581-024-00100-7
54. Assessing the effects of significant activity changes on urban-scale air quality across three European cities M. Ramacher et al. 10.1016/j.aeaoa.2024.100264
55. Lessons learnt for air pollution mitigation policies from the COVID-19 pandemic: The Italian perspective M. D'Isidoro et al. 10.1016/j.apr.2022.101620
56. Watch out for trends: did ozone increased or decreased during the COVID-19 pandemic? R. Tavella & F. da Silva Júnior 10.1007/s11356-021-17142-w
57. A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region Y. Ren et al. 10.1016/j.scitotenv.2023.163190
58. Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic? J. Moreda-Piñeiro et al. 10.3390/atmos12050562
59. Dramatic Lockdown Fossil Fuel CO2 Decrease Detected by Citizen Science-Supported Atmospheric Radiocarbon Observations J. Turnbull et al. 10.1021/acs.est.1c07994
60. Spring Festival and COVID‐19 Lockdown: Disentangling PM Sources in Major Chinese Cities Q. Dai et al. 10.1029/2021GL093403
61. Different Response Mechanisms of N‐Bearing Components to Emission Reduction Across China During COVID‐19 Lockdown Period R. Li et al. 10.1029/2023JD039496
62. Staggered-peak production is a mixed blessing in the control of particulate matter pollution Y. Wang et al. 10.1038/s41612-022-00322-x
63. Unraveling the O3-NOX-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai B. Lu et al. 10.1016/j.atmosenv.2023.119864
64. A quantitative analysis of causes for increasing ozone pollution in Shanghai during the 2022 lockdown and implications for control policy Y. Zhang et al. 10.1016/j.atmosenv.2024.120469
65. Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021 Q. Dai et al. 10.1007/s11430-022-1128-1
66. Determining the impacts of the incineration of sacrificial offerings on PM2.5 pollution in Lanzhou, Northwest China X. Liu et al. 10.1016/j.atmosenv.2022.119155
67. A case study application of machine-learning for the detection of greenhouse gas emission sources J. Shaw et al. 10.1016/j.apr.2022.101563
68. Modelling the impacts of emission changes on O3 sensitivity, atmospheric oxidation capacity, and pollution transport over the Catalonia region A. Badia et al. 10.5194/acp-23-10751-2023
69. Quantification of COVID-19 impacts on NO2 and O3: Systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods Y. Wong et al. 10.1016/j.atmosenv.2023.119677
70. Changing Air Quality and the Ozone Weekend Effect during the COVID-19 Pandemic in Toronto, Ontario, Canada W. Gough & V. Anderson 10.3390/cli10030041
71. Evaluating long-term reductions in trace metal emissions from shipping in Shanghai M. Wang et al. 10.1016/j.jhazmat.2024.136367
72. The air quality and well-being effects of low emission zones L. Sarmiento et al. 10.1016/j.jpubeco.2023.105014
73. Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China Q. Wang et al. 10.1016/j.envpol.2024.125071
74. The effects of the COVID-19 lockdowns on the composition of the troposphere as seen by In-service Aircraft for a Global Observing System (IAGOS) at Frankfurt H. Clark et al. 10.5194/acp-21-16237-2021
75. Future impacts of O3 on respiratory hospital admission in the UK from current emissions policies H. Macintyre et al. 10.1016/j.envint.2023.108046
76. Slower than Expected Reduction in Annual Pm2.5 in Northwest China Revealed by Machine Learning-Based Meteorological Normalization M. Wang et al. 10.2139/ssrn.4096148
77. Changes in Air Pollutants from Fireworks in Chinese Cities Y. Lai & P. Brimblecombe 10.3390/atmos13091388
78. A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina M. Diaz Resquin et al. 10.5194/essd-15-189-2023
79. Unusual response of O 3 and CH 4 to NO 2 emissions reduction in Japan during the COVID-19 pandemic A. Phan & H. Fukui 10.1080/17538947.2023.2297844
80. A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID‐19 lockdown G. Fioravanti et al. 10.1002/env.2723
81. Spatial heterogeneity in global atmospheric CO during the COVID–19 lockdown: Implications for global and regional air quality policies M. Pathak et al. 10.1016/j.envpol.2023.122269
82. Slower than expected reduction in annual PM2.5 in Xi'an revealed by machine learning-based meteorological normalization M. Wang et al. 10.1016/j.scitotenv.2022.156740
83. The impact of Chinese new year on air quality in north China based on machine learning Y. Ren et al. 10.1016/j.atmosenv.2024.120874
84. Source-apportionment and spatial distribution analysis of VOCs and their role in ozone formation using machine learning in central-west Taiwan M. Mishra et al. 10.1016/j.envres.2023.116329
85. Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach D. Akritidis et al. 10.3390/atmos12111500
86. Particulate Pollution from New Year Fireworks in Honolulu P. Brimblecombe & Y. Lai 10.3390/environments10040068
87. A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions R. Sokhi et al. 10.1016/j.envint.2021.106818
88. Did Germany reach its 2020 climate targets thanks to the COVID-19 pandemic? S. Shammugam et al. 10.1080/14693062.2022.2063247
89. Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China L. Zhang et al. 10.1016/j.scitotenv.2024.171295
90. Quantifying the impacts of the COVID-19 pandemic lockdown and the armed conflict with Russia on Sentinel 5P TROPOMI NO 2 changes in Ukraine A. Phan & H. Fukui 10.1080/20964471.2023.2265105
91. Impacts of transportation emissions on horizontal and vertical distributions of air pollutants in Shanghai: Insights from emission reduction in COVID-19 lockdown Y. Chen et al. 10.1016/j.aeaoa.2024.100267
92. Anomalies of O3, CO, C2H2, H2CO, and C2H6 detected with multiple ground-based Fourier-transform infrared spectrometers and assessed with model simulation in 2020: COVID-19 lockdowns versus natural variability I. Ortega et al. 10.1525/elementa.2023.00015
93. IMPACT OF THE COVID-19 LOCKDOWN ON AIR POLLUTION IN AN INDUSTRIAL CITY IN NORTHEASTERN CHINA X. Wang et al. 10.3846/jeelm.2023.19472
94. Machine learning integrated PMF model reveals influencing factors of ozone pollution in a coal chemical industry city at the Jiangsu-Shandong-Henan-Anhui boundary C. Wang et al. 10.1016/j.atmosenv.2024.120916
95. Air pollution and health impacts during the COVID-19 lockdowns in Grenoble, France M. Aix et al. 10.1016/j.envpol.2022.119134
96. The positive impact of the Omicron pandemic lockdown on air quality and human health in cities around Shanghai Y. Wang & Q. Ge 10.1007/s10668-023-03071-w
97. European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions M. Guevara et al. 10.5194/essd-14-2521-2022
98. Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia M. Lovrić et al. 10.3390/ijerph19116937
99. Drivers of High Concentrations of Secondary Organic Aerosols in Northern China during the COVID-19 Lockdowns X. Chang et al. 10.1021/acs.est.2c06914
100. The impact of lockdown on nitrogen dioxide (NO2) over Central Asian countries during the COVID-19 pandemic Z. Zhang et al. 10.1007/s11356-021-17140-y
101. Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe S. Solberg et al. 10.3390/atmos12020131
102. 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
103. Positive global environmental impacts of the COVID-19 pandemic lockdown: a review H. Loh et al. 10.1007/s10708-021-10475-6
104. Impact of Environmental Indicators on the COVID-19 Pandemic in Delhi, India S. Mangla et al. 10.3390/pathogens10081003
105. What Are the Different Measures of Mobility Telling Us About Surface Transportation CO2 Emissions During the COVID‐19 Pandemic? J. Gensheimer et al. 10.1029/2021JD034664
106. Comprehensive analysis of ambient air quality during second lockdown in national capital territory of Delhi G. Sharma et al. 10.1016/j.hazadv.2022.100078
107. Monitoring the impact of the COVID-19 lockdown on air quality in Lanzhou: Implications for future control strategies H. Liu et al. 10.3389/feart.2022.1011536