98 citations as recorded by crossref.

1. COVID‐19 Impact on the Oil and Gas Industry NO2 Emissions: A Case Study of the Permian Basin R. Serrano‐Calvo et al. 10.1029/2023JD038566
2. 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
3. Shaping the Future of Science: COVID‐19 Highlighting the Importance of GeoHealth M. Gorris et al. 10.1029/2021GH000412
4. Did Germany reach its 2020 climate targets thanks to the COVID-19 pandemic? S. Shammugam et al. 10.1080/14693062.2022.2063247
5. Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China L. Lin et al. 10.3390/rs14205239
6. COVID-19 induced lower-tropospheric ozone changes M. Mertens et al. 10.1088/1748-9326/abf191
7. Significant reduction of ultrafine particle emission fluxes to the urban atmosphere during the COVID-19 lockdown A. Straaten et al. 10.1016/j.scitotenv.2022.156516
8. Activity-based integrated modelling for assessing COVID-19 impacts on transport operations and emissions M. Alam et al. 10.1080/19427867.2022.2122110
9. Unique impacts of strong and westward-extended western Pacific subtropical high on ozone pollution over eastern China M. Li et al. 10.1016/j.envpol.2024.124515
10. Emissions Background, Climate, and Season Determine the Impacts of Past and Future Pandemic Lockdowns on Atmospheric Composition and Climate J. Hickman et al. 10.1029/2022EF002959
11. Implications of COVID-19 Restriction Measures in Urban Air Quality of Thessaloniki, Greece: A Machine Learning Approach D. Akritidis et al. 10.3390/atmos12111500
12. 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
13. Quantifying urban, industrial, and background changes in NO<sub>2</sub> during the COVID-19 lockdown period based on TROPOMI satellite observations V. Fioletov et al. 10.5194/acp-22-4201-2022
14. Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and western Europe D. Putero et al. 10.5194/acp-23-15693-2023
15. Local integrated air quality predictions from meteorology (2015 to 2020) with machine and deep learning assisted by data mining D. Wood 10.1016/j.samod.2021.100002
16. Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. 10.5194/acp-23-3311-2023
17. Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder J. Xing et al. 10.1021/acs.est.1c08337
18. Tracking air quality trends and vehicle traffic dynamics at urban scale using satellite and ground data before and after the COVID-19 outbreak D. De Santis et al. 10.1016/j.scitotenv.2023.165464
19. Changes in tropospheric ozone concentration over Indo-Gangetic Plains: the role of meteorological parameters S. Payra et al. 10.1007/s00703-022-00932-3
20. Study on the characteristics of urban background ozone pollution based on long-term observations from mountain sites in China during 2015–2022 W. Zhao et al. 10.1016/j.atmosres.2024.107696
21. Effects of the COVID-19 lockdown and recovery on People's mobility and air quality in the United Arab Emirates using satellite and ground observations A. Shanableh et al. 10.1016/j.rsase.2022.100757
22. 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
23. TROPOMI NO2 Shows a Fast Recovery of China’s Economy in the First Quarter of 2023 H. Li & B. Zheng 10.1021/acs.estlett.3c00386
24. PM10 variation, composition, and source analysis in Tuscany (Italy) following the COVID-19 lockdown restrictions F. Giardi et al. 10.5194/acp-22-9987-2022
25. Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning C. Fan et al. 10.3390/atmos15111337
26. Air quality and urban climate improvements in the world’s most populated region during the COVID-19 pandemic A. Damiani et al. 10.1088/1748-9326/ad25a2
27. Spatiotemporal variability of tropospheric NO2 over four megacities in Southern Africa: Implications for transboundary regional air pollution N. Matandirotya & R. Burger 10.1016/j.envc.2021.100271
28. Screening Approach for Short-Term PM2.5 Health Co-Benefits: A Case Study from 15 Metropolitan Cities around the World during the COVID-19 Pandemic Y. Lam et al. 10.3390/atmos13010018
29. Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities Q. Zhu et al. 10.1021/acs.est.1c05636
30. 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
31. Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources B. Duncan et al. 10.1029/2021GH000451
32. Ozone Trends and the Ability of Models to Reproduce the 2020 Ozone Concentrations in the South Coast Air Basin in Southern California under the COVID-19 Restrictions L. Parker et al. 10.3390/atmos13040528
33. Global tropospheric ozone responses to reduced NO x emissions linked to the COVID-19 worldwide lockdowns K. Miyazaki et al. 10.1126/sciadv.abf7460
34. 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
35. Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth X. Ye et al. 10.1021/acs.est.2c05712
36. Global fine-scale changes in ambient NO2 during COVID-19 lockdowns M. Cooper et al. 10.1038/s41586-021-04229-0
37. Changing Air Quality and the Ozone Weekend Effect during the COVID-19 Pandemic in Toronto, Ontario, Canada W. Gough & V. Anderson 10.3390/cli10030041
38. Inter-regional environmental inequality under lasting pandemic exacerbated by residential response C. Li et al. 10.1016/j.scitotenv.2023.163191
39. Quantifying anomalies of air pollutants in 9 U.S. cities during 2020 due to COVID-19 lockdowns and wildfires based on decadal trends J. Peischl et al. 10.1525/elementa.2023.00029
40. A satellite-data-driven framework to rapidly quantify air-basin-scale NO<sub><i>x</i></sub> emissions and its application to the Po Valley during the COVID-19 pandemic K. Sun et al. 10.5194/acp-21-13311-2021
41. Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric modeling T. Doumbia et al. 10.5194/essd-13-4191-2021
42. Estimates of the spatially complete, observational-data-driven planetary boundary layer height over the contiguous United States Z. Ayazpour et al. 10.5194/amt-16-563-2023
43. Influence of biomass burning on ozone levels in the Megalopolis of Central Mexico during the COVID-19 lockdown V. Almanza et al. 10.1016/j.jes.2023.07.031
44. Editorial: Cities in Lockdown: Implications of COVID-19 for Air Quality and Urban Environmental Health J. Salmond et al. 10.3389/frsc.2022.913629
45. Assessing geographical differences of the COVID-19 impact on fuel consumption: the case of Italy R. Borgoni et al. 10.1007/s12076-023-00361-9
46. Diesel passenger vehicle shares influenced COVID-19 changes in urban nitrogen dioxide pollution G. Kerr et al. 10.1088/1748-9326/ac7659
47. Increasing importance of ammonia emission abatement in PM2.5 pollution control W. Xu et al. 10.1016/j.scib.2022.07.021
48. Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic A. Taloor et al. 10.3390/rs14184650
49. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review M. Rahman et al. 10.1109/ACCESS.2021.3079121
50. Regional impacts of COVID-19 on carbon dioxide detected worldwide from space B. Weir et al. 10.1126/sciadv.abf9415
51. COVID-19 perturbation on US air quality and human health impact assessment J. He et al. 10.1093/pnasnexus/pgad483
52. Impact of the COVID‐19 Economic Downturn on Tropospheric Ozone Trends: An Uncertainty Weighted Data Synthesis for Quantifying Regional Anomalies Above Western North America and Europe K. Chang et al. 10.1029/2021AV000542
53. NASA Satellite Measurements Show Global‐Scale Reductions in Free Tropospheric Ozone in 2020 and Again in 2021 During COVID‐19 J. Ziemke et al. 10.1029/2022GL098712
54. The drivers and health risks of unexpected surface ozone enhancements over the Sichuan Basin, China, in 2020 Y. Sun et al. 10.5194/acp-21-18589-2021
55. The AirGAM 2022r1 air quality trend and prediction model S. Walker et al. 10.5194/gmd-16-573-2023
56. Comparing different approaches for assessing the impact of COVID-19 lockdown on urban air quality in Reading, UK S. Munir et al. 10.1016/j.atmosres.2021.105730
57. Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models J. Barré et al. 10.5194/acp-21-7373-2021
58. Tropospheric and Surface Nitrogen Dioxide Changes in the Greater Toronto Area during the First Two Years of the COVID-19 Pandemic X. Zhao et al. 10.3390/rs14071625
59. 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
60. Assessing air pollution changes during the COVID-19 and its impact on the urban environment using remote sensing and neural networks M. Mokarram et al. 10.1016/j.asr.2023.11.039
61. 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
62. Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning technique Q. Tu et al. 10.5194/amt-16-2237-2023
63. A spatiotemporal analysis of NO2 concentrations during the Italian 2020 COVID‐19 lockdown G. Fioravanti et al. 10.1002/env.2723
64. Spatial and temporal characteristics of air pollutants and their health effects in China during 2019–2020 R. Lei et al. 10.1016/j.jenvman.2022.115460
65. The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget T. Sekiya et al. 10.1126/sciadv.adh2688
66. 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
67. Unprecedented decline in summertime surface ozone over eastern China in 2020 comparably attributable to anthropogenic emission reductions and meteorology H. Yin et al. 10.1088/1748-9326/ac3e22
68. Role of different types of RNA molecules in the severity prediction of SARS-CoV-2 patients P. Jeyananthan 10.1016/j.prp.2023.154311
69. Errors and uncertainties associated with the use of unconventional activity data for estimating CO2 emissions: the case for traffic emissions in Japan T. Oda et al. 10.1088/1748-9326/ac109d
70. New sights on the impact of spatial composition of production factors for socioeconomic recovery in the post-epidemic era: a case study of cities in central and eastern China S. LIU et al. 10.1016/j.scs.2022.104061
71. Air pollution benefits from reduced on-road activity due to COVID-19 in the United States C. Arter et al. 10.1093/pnasnexus/pgae017
72. Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach A. Casallas et al. 10.1007/s11869-023-01303-6
73. 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
74. On ozone's weekly cycle for different seasons in Arizona M. Greenslade et al. 10.1016/j.atmosenv.2024.120703
75. Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China Y. Hu et al. 10.5194/acp-22-13183-2022
76. 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
77. Deciphering decadal urban ozone trends from historical records since 1980 H. Wang et al. 10.1093/nsr/nwae369
78. A Nudge to the Truth: Atom Conservation as a Hard Constraint in Models of Atmospheric Composition Using a Species-Weighted Correction P. Sturm & S. Silva 10.1021/acsestair.4c00220
79. Tracking NO2 Pollution Changes Over Texas: Synthesis of In Situ and Satellite Observations M. Gyawali et al. 10.1029/2022JD037473
80. Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
81. Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches Z. Li 10.3390/toxics12080580
82. Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau X. Chen et al. 10.1039/D2EA00168C
83. Impacts of greenhouse gases and anthropogenic aerosols changes on surface air temperature in East Asia under different post-pandemic period emission scenarios J. He et al. 10.1016/j.accre.2022.10.004
84. Diverging Ozone Trends Above Western North America: Boundary Layer Decreases Versus Free Tropospheric Increases K. Chang et al. 10.1029/2022JD038090
85. Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements C. Malings et al. 10.1029/2021EA001743
86. Tropospheric NO2 and O3 Response to COVID‐19 Lockdown Restrictions at the National and Urban Scales in Germany V. Balamurugan et al. 10.1029/2021JD035440
87. Assessing progress toward the Paris climate agreement from space B. Weir et al. 10.1088/1748-9326/ac998c
88. Koopman-inspired approach for identification of exogenous anomalies in nonstationary time-series data A. Mallen et al. 10.1088/2632-2153/acdd50
89. Using machine learning approach to reproduce the measured feature and understand the model-to-measurement discrepancy of atmospheric formaldehyde H. Yin et al. 10.1016/j.scitotenv.2022.158271
90. The International Global Atmospheric Chemistry project comments on the revised WHO air quality guidelines C. Paton-Walsh et al. 10.1088/1748-9326/ad039f
91. A fuel-based method for updating mobile source emissions during the COVID-19 pandemic C. Harkins et al. 10.1088/1748-9326/ac0660
92. Air pollution in Vietnam during the COVID-19 social isolation, evidence of reduction in human activities T. Ngo et al. 10.1080/01431161.2021.1934911
93. What can we learn about urban air quality with regard to the first outbreak of the COVID-19 pandemic? A case study from central Europe I. Salma et al. 10.5194/acp-20-15725-2020
94. COVID-19-Related Changes in NO2 and O3 Concentrations and Associated Health Effects in Malta S. Fenech et al. 10.3389/frsc.2021.631280
95. Effective Radiative Forcings Due To Anthropogenic Emission Changes Under Covid‐19 and Post‐Pandemic Recovery Scenarios X. Yu et al. 10.1029/2021JD036251
96. The Climate Response to Emissions Reductions Due to COVID‐19: Initial Results From CovidMIP C. Jones et al. 10.1029/2020GL091883
97. Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe S. Solberg et al. 10.3390/atmos12020131
98. Negative ozone anomalies at a high mountain site in northern Italy during 2020: a possible role of COVID-19 lockdowns? P. Cristofanelli et al. 10.1088/1748-9326/ac0b6a