248 citations as recorded by crossref.

1. Large-scale and rapid perception of regional economic resilience from data-driven insights T. Cheng et al. 10.1080/17538947.2024.2365971
2. A new optimized hybrid approach combining machine learning with WRF-CHIMERE model for PM10 concentration prediction Y. Chelhaoui et al. 10.1007/s40808-024-02086-0
3. Portfolio diversification with varying investor abilities N. James & M. Menzies 10.1209/0295-5075/ad1ef2
4. Anthropogenic Drivers of Hourly Air Pollutant Change in an Urban Environment during 2019–2021—A Case Study in Wuhan Y. Zhang et al. 10.3390/su152416694
5. Multi-model revealing the driving factors of black carbon pollution in Hohhot, the largest city on the Mongolian plateau Y. Wu et al. 10.1016/j.uclim.2024.102055
6. Machine learning unveils the impact of anthropogenic emission changes on urban PM2.5 and O3: A case study in Wuhu H. Xu et al. 10.1016/j.jes.2024.10.028
7. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns Z. Shi et al. 10.1126/sciadv.abd6696
8. Variations of Wintertime Ambient Volatile Organic Compounds in Beijing, China, from 2015 to 2019 J. Li et al. 10.1021/acs.estlett.2c00919
9. Method to retrieve cloud condensation nuclei number concentrations using lidar measurements W. Tan et al. 10.5194/amt-12-3825-2019
10. 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
11. Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning S. Lv et al. 10.3389/ffgc.2024.1338795
12. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
13. 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
14. Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover Y. Chen et al. 10.1038/s41561-022-00991-6
15. Description of the NASA GEOS Composition Forecast Modeling System GEOS‐CF v1.0 C. Keller et al. 10.1029/2020MS002413
16. Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction K. Srijiranon & N. Eiamkanitchat 10.3390/app11146590
17. Identifying the key drivers in retrieving blue sky during rapid urbanization in Shenzhen, China X. Peng et al. 10.1016/j.jclepro.2022.131829
18. High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia I. Jaya & H. Folmer 10.3390/math12182899
19. Improving the Air Pollution Control Measures More Efficiently and Cost-Effectively: View from the Practice in the 7th Military World Games in Wuhan S. Kong et al. 10.1007/s41810-024-00245-5
20. Machine Learning Uncovers Aerosol Size Information From Chemistry and Meteorology to Quantify Potential Cloud‐Forming Particles A. Nair et al. 10.1029/2021GL094133
21. Variations of air pollutant response to COVID-19 lockdown in cities of the Tibetan Plateau X. Chen et al. 10.1039/D2EA00168C
22. Abrupt exacerbation in air quality over Europe after the outbreak of Russia-Ukraine war X. Meng et al. 10.1016/j.envint.2023.108120
23. High loadings of carbonaceous aerosols from wood smoke in the atmosphere of Beijing from 2015 to 2017: Implications for energy transition policy Q. Liu et al. 10.1016/j.envpol.2023.123240
24. COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas S. Grange et al. 10.5194/acp-21-4169-2021
25. The air quality impacts of pre-operational hydraulic fracturing activities S. Wilde et al. 10.1016/j.scitotenv.2022.159702
26. Causes of the unexpected slowness in reducing winter PM2.5 for 2014–2018 in Henan Province X. Chen et al. 10.1016/j.envpol.2022.120928
27. A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions Y. Mo et al. 10.3390/rs13142838
28. Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application L. Qu et al. 10.1016/j.jes.2022.10.003
29. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022 H. Zheng et al. 10.1016/j.envint.2023.108361
30. 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
31. Enhanced natural releases of mercury in response to the reduction in anthropogenic emissions during the COVID-19 lockdown by explainable machine learning X. Qin et al. 10.5194/acp-22-15851-2022
32. Quantitative assessment of the impact of biomass burning episodes on surface solar radiation using machine learning technology: A case study of a pollution event in Beijing Z. Li et al. 10.1016/j.jastp.2023.106022
33. The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events Z. Song et al. 10.3390/rs16183363
34. Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory–based approach N. Aman et al. 10.1007/s11356-024-34548-4
35. How international conflicts and global crises can intertwine and affect the sources and levels of air pollution in urban areas O. Ghaffarpasand et al. 10.1007/s11356-024-34648-1
36. The AirGAM 2022r1 air quality trend and prediction model S. Walker et al. 10.5194/gmd-16-573-2023
37. Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning M. Lovrić et al. 10.1016/j.envpol.2020.115900
38. 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
39. Switzerland's PM10 and PM2.5 environmental increments show the importance of non-exhaust emissions S. Grange et al. 10.1016/j.aeaoa.2021.100145
40. Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning? E. Kalantari et al. 10.1007/s11356-024-35404-1
41. 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 Y. Hong et al. 10.1029/2023JD039459
42. Machine learning revealing key factors influencing HONO chemistry in Beijing during heating and non-heating periods W. Zhang et al. 10.1016/j.atmosres.2023.107130
43. Reviewing the Crop Residual Burning and Aerosol Variations during the COVID-19 Pandemic Hit Year 2020 over North India M. Hari et al. 10.3390/pollutants1030011
44. A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1 J. Nicely et al. 10.5194/acp-20-1341-2020
45. Trends of source apportioned PM2.5 in Tianjin over 2013–2019: Impacts of Clean Air Actions Q. Dai et al. 10.1016/j.envpol.2023.121344
46. 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
47. Spring Festival and COVID‐19 Lockdown: Disentangling PM Sources in Major Chinese Cities Q. Dai et al. 10.1029/2021GL093403
48. Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020 S. Wang et al. 10.1016/j.chemosphere.2023.138742
49. Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions Y. Wong et al. 10.1016/j.jclepro.2022.132893
50. Meteorological normalization of NO2 concentrations in the Province of Bolzano (Italian Alps) M. Falocchi et al. 10.1016/j.atmosenv.2020.118048
51. 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
52. Aqpet — An R package for air quality policy evaluation Y. Dai et al. 10.1016/j.envsoft.2024.106052
53. Improved estimation of trends in U.S. ozone concentrations adjusted for interannual variability in meteorological conditions B. Wells et al. 10.1016/j.atmosenv.2021.118234
54. Comparison of daily streamflow forecasts using extreme learning machines and the random forest method X. Li et al. 10.1080/02626667.2019.1680846
55. Meteorological impacts on the unexpected ozone pollution in coastal cities of China during the unprecedented hot summer of 2022 X. Ji et al. 10.1016/j.scitotenv.2024.170035
56. Evaluating urban and nonurban PM 2.5 variability under clean air actions in China during 2010–2022 based on a new high-quality dataset B. Liu et al. 10.1080/17538947.2024.2310734
57. Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model J. Khayyam et al. 10.1016/j.scitotenv.2024.171488
58. 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
59. 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
60. Quantifying vehicle restriction related PM2.5 reduction using field observations in an isolated urban basin Y. Guo et al. 10.1088/1748-9326/ad2238
61. Yearly variations of water-soluble ions over Xi'an, China: Insight into the importance contribution of nitrate to PM2.5 X. Yang et al. 10.1016/j.apr.2024.102296
62. More mileage in reducing urban air pollution from road traffic R. Harrison et al. 10.1016/j.envint.2020.106329
63. The role of sources and meteorology in driving PM2.5-bound chlorine X. Wang et al. 10.1016/j.jhazmat.2022.129910
64. 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
65. Air quality and health impact of 2019–20 Black Summer megafires and COVID-19 lockdown in Melbourne and Sydney, Australia R. Ryan et al. 10.1016/j.envpol.2021.116498
66. Influences of synoptic circulations on regional transport, local accumulation and chemical transformation for PM2.5 heavy pollution over Twain-Hu Basin, central China J. Yao et al. 10.1016/j.jes.2024.06.007
67. Factors affecting the different growth rates of PM2.5：Evidence from composition variation, formation mechanisms, and importance analysis of water-soluble inorganic ions with case study in northern China H. Li et al. 10.1016/j.atmosenv.2024.120913
68. Linking Switzerland's PM<sub>10</sub> and PM<sub>2.5</sub> oxidative potential (OP) with emission sources S. Grange et al. 10.5194/acp-22-7029-2022
69. 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
70. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions M. Qiu et al. 10.5194/acp-22-10551-2022
71. Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone C. Keller et al. 10.5194/acp-21-3555-2021
72. Chang impact analysis of level 3 COVID-19 alert on air pollution indicators using artificial neural network G. Lin et al. 10.1016/j.ecoinf.2022.101674
73. Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies A. Santos & S. Mendonça 10.1007/s11192-022-04327-4
74. 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
75. High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning Y. Yang et al. 10.3390/rs16132498
76. Evaluating emissions and meteorological contributions to air quality trends in northern China based on measurements at a regional background station W. Pu et al. 10.1039/D4EA00070F
77. Assessing the Nonlinear Effect of Atmospheric Variables on Primary and Oxygenated Organic Aerosol Concentration Using Machine Learning Y. Qin et al. 10.1021/acsearthspacechem.1c00443
78. Trends of peroxyacetyl nitrate and its impact on ozone over 2018–2022 in urban atmosphere Z. Lin et al. 10.1038/s41612-024-00746-7
79. An intercomparison of weather normalization of PM2.5 concentration using traditional statistical methods, machine learning, and chemistry transport models H. Zheng et al. 10.1038/s41612-023-00536-7
80. Different approaches to explore the impact of COVID-19 lockdowns on carbonaceous aerosols at a European rural background site S. Mbengue et al. 10.1016/j.scitotenv.2023.164527
81. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique T. Vu et al. 10.5194/acp-19-11303-2019
82. A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019 X. Weng et al. 10.5194/acp-22-8385-2022
83. Meteorologically normalized spatial and temporal variations investigation using a machine learning-random forest model in criteria pollutants across Tehran, Iran M. Ali-Taleshi et al. 10.1016/j.uclim.2023.101790
84. Spatial-temporal characterization of air pollutants using a hybrid deep learning/Kriging model incorporated with a weather normalization technique G. Lin et al. 10.1016/j.atmosenv.2022.119304
85. Why is ozone in South Korea and the Seoul metropolitan area so high and increasing? N. Colombi et al. 10.5194/acp-23-4031-2023
86. Can Building Subway Systems Improve Air Quality? New Evidence from Multiple Cities and Machine Learning L. Xie et al. 10.1007/s10640-024-00852-3
87. Using machine learning to improve the estimate of U.S. background ozone F. Hosseinpour et al. 10.1016/j.atmosenv.2023.120145
88. Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models F. Yu et al. 10.1029/2022GL098551
89. Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022 B. Zhang et al. 10.1016/j.scitotenv.2023.163189
90. Vertically resolved meteorological adjustments of aerosols and trace gases in Beijing, Taiyuan, and Hefei by using RF model J. Khayyam et al. 10.1016/j.scitotenv.2024.174795
91. Kolmogorov-Zurbenko filter coupled with machine learning to reveal multiple drivers of surface ozone pollution in China from 2015 to 2022 T. Yao et al. 10.1016/j.scitotenv.2024.175093
92. 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
93. Effects of heat waves on ozone pollution in a coastal industrial city: Meteorological impacts and photochemical mechanisms D. Liao et al. 10.1016/j.apr.2024.102280
94. True Reduction in the Air Pollution Levels in the Community of Madrid During the COVID-19 Lockdown J. Cordero et al. 10.3389/frsc.2022.869000
95. Meteorology-driven variability of air pollution (PM<sub>1</sub>) revealed with explainable machine learning R. Stirnberg et al. 10.5194/acp-21-3919-2021
96. Marine fuel restrictions and air pollution: A study on Chinese ports considering transboundary spillovers X. Chen et al. 10.1016/j.marpol.2024.106136
97. 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
98. Decoupling impacts of weather conditions on interannual variations in concentrations of criteria air pollutants in South China – constraining analysis uncertainties by using multiple analysis tools Y. Lin et al. 10.5194/acp-22-16073-2022
99. Molecular-scale investigation on the composition, sources and secondary generation of organic aerosols in polluted central China Z. Dong et al. 10.1016/j.jclepro.2023.139830
100. Growth of nitrate contribution to aerosol pollution during wintertime in Xi'an, northwest China: Formation mechanism and effects of NH3 H. Su et al. 10.1016/j.partic.2023.09.014
101. Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods Q. Wu et al. 10.1016/j.atmosenv.2022.119083
102. The effect of national protest in Ecuador on PM pollution R. Zalakeviciute et al. 10.1038/s41598-021-96868-6
103. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia N. Zaman et al. 10.3390/app11167326
104. Nonlinear shifts and dislocations in financial market structure and composition N. James & M. Menzies 10.1063/5.0209904
105. Long-term trends in particulate matter from wood burning in the United Kingdom: Dependence on weather and social factors A. Font et al. 10.1016/j.envpol.2022.120105
106. Increased contribution to PM2.5 from traffic-influenced road dust in Shanghai over recent years and predictable future M. Wang et al. 10.1016/j.envpol.2022.120119
107. Do city lockdowns effectively reduce air pollution? W. Lin et al. 10.1016/j.techfore.2023.122885
108. Using machine learning to quantify drivers of aerosol pollution trend in China from 2015 to 2022 Y. Ji et al. 10.1016/j.apgeochem.2023.105614
109. How much urban air quality is affected by local emissions: A unique case study from a megacity in the Pearl River Delta, China M. Tang et al. 10.1016/j.atmosenv.2023.119666
110. Evaluation of equivalent black carbon source apportionment using observations from Switzerland between 2008 and 2018 S. Grange et al. 10.5194/amt-13-1867-2020
111. Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates Z. Luo et al. 10.1016/j.scitotenv.2024.172038
112. Machine learning elucidates ubiquity of enhanced ozone air pollution in China linked to the spring festival effect B. Zhu et al. 10.1016/j.apr.2024.102127
113. 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
114. A comparison of meteorological normalization of PM2.5 by multiple linear regression, general additive model, and random forest methods L. Qi et al. 10.1016/j.atmosenv.2024.120854
115. A Comparative and Systematic Study of Machine Learning (ML) Approaches for Particulate Matter (PM) Prediction A. Pandya et al. 10.1007/s11831-023-09994-x
116. Novel quantification of regional fossil fuel CO2reductions during COVID-19 lockdowns using atmospheric oxygen measurements P. Pickers et al. 10.1126/sciadv.abl9250
117. 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
118. Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China Y. Wang et al. 10.1021/acs.estlett.0c00605
119. 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
120. Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China B. Wu et al. 10.1016/j.atmosenv.2024.120347
121. Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches X. Yao & L. Zhang 10.5194/acp-24-7773-2024
122. Data imbalance causes underestimation of high ozone pollution in machine learning models: A weighted support vector regression solution L. Zhen et al. 10.1016/j.atmosenv.2024.120952
123. Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models . Doreswamy et al. 10.1016/j.procs.2020.04.221
124. Impact from the evolution of private vehicle fleet composition on traffic related emissions in the small-medium automotive city X. Tian et al. 10.1016/j.scitotenv.2022.156657
125. Machine learning for air quality prediction using meteorological and traffic related features I. Gryech et al. 10.3233/AIS-200572
126. 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
127. Has the Three-Year Action Plan improved the air quality in the Fenwei Plain of China? Assessment based on a machine learning technique X. Dai et al. 10.1016/j.atmosenv.2022.119204
128. Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method J. Xue et al. 10.1007/s11783-023-1738-5
129. A Portable and Smartphone-Based Plasmonic System for On-Site Measurement of Airborne Redox-Active Compounds by Light-Initiated Redox Reaction R. Yu et al. 10.2139/ssrn.4098777
130. Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California L. Weissert et al. 10.1016/j.atmosenv.2020.117287
131. Response of PM2.5 chemical composition to the emission reduction and meteorological variation during the COVID-19 lockdown Y. Gong et al. 10.1016/j.chemosphere.2024.142844
132. Climate modulation of Niño3.4 SST-anomalies on air quality change in southern China: Application to seasonal forecast of haze pollution X. Cheng et al. 10.1016/j.atmosres.2019.04.002
133. Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning F. Tuluri et al. 10.3390/ijerph20116022
134. Characteristics, sources, and health risks of PM2.5-bound trace metals in northern Zhejiang Province: The effects of meteorological variables based on machine learning F. Zhang et al. 10.1016/j.jclepro.2024.142089
135. Technical note: Improving the European air quality forecast of the Copernicus Atmosphere Monitoring Service using machine learning techniques J. Bertrand et al. 10.5194/acp-23-5317-2023
136. Revealing Drivers of Haze Pollution by Explainable Machine Learning L. Hou et al. 10.1021/acs.estlett.1c00865
137. Revealing the drivers of surface ozone pollution by explainable machine learning and satellite observations in Hangzhou Bay, China T. Yao et al. 10.1016/j.jclepro.2024.140938
138. LIFE Monza: comparison between ante andpost-operamnoise and air quality monitoring activities in a Noise Low Emission Zone R. Silvaggio et al. 10.1515/noise-2020-0015
139. Can satellite data on air pollution predict industrial production? J. Bricongne et al. 10.2139/ssrn.3967146
140. Change Points Detection and Trend Analysis to Characterize Changes in Meteorologically Normalized Air Pollutant Concentrations R. Gagliardi & C. Andenna 10.3390/atmos13010064
141. Impacts of aquaculture on the area and soil carbon stocks of mangrove: A machine learning study in China M. Xu et al. 10.1016/j.scitotenv.2022.160173
142. Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford, UK A. Singh et al. 10.1016/j.envpol.2021.118584
143. Improving the design stage of air pollution studies based on wind patterns L. Zabrocki et al. 10.1038/s41598-022-11939-6
144. Regional background ozone estimation for China through data fusion of observation and simulation Z. Sun et al. 10.1016/j.scitotenv.2023.169411
145. Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015–2022 in a Coastal City in Eastern China Z. Qian et al. 10.3390/toxics11060481
146. Assessment of human and meteorological influences on PM10 concentrations: Insights from machine learning algorithms P. Verma et al. 10.1016/j.apr.2024.102123
147. Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China S. Guha et al. 10.1016/j.atmosenv.2024.120793
148. Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion X. Zheng et al. 10.1016/j.scitotenv.2024.175073
149. 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
150. Characteristics and Formation Mechanism of Ozone Pollution in Demonstration Zone of the Yangtze River Delta, China Y. Wu et al. 10.3390/atmos15030382
151. Meteorology-normalized impact of the COVID-19 lockdown upon NO<sub>2</sub> pollution in Spain H. Petetin et al. 10.5194/acp-20-11119-2020
152. Reconstructing multi-decadal airborne birch pollen levels based on NDVI data and a pollen transport model W. Verstraeten et al. 10.1016/j.agrformet.2022.108942
153. Decisive role of ozone formation control in winter PM2.5 mitigation in Shenzhen, China M. Tang et al. 10.1016/j.envpol.2022.119027
154. A portable and smartphone-based plasmonic system for on-site measurement of airborne redox-active compounds by light-initiated redox reaction R. Yu et al. 10.1016/j.snb.2022.132505
155. Surface ozone trends and related mortality across the climate regions of the contiguous United States during the most recent climate period, 1991–2020 S. Mousavinezhad et al. 10.1016/j.atmosenv.2023.119693
156. 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
157. A Bagging-GBDT ensemble learning model for city air pollutant concentration prediction X. Liu et al. 10.1088/1755-1315/237/2/022027
158. Ozone and its precursors at an urban site in the Yangtze River Delta since clean air action plan phase II in China H. Fang et al. 10.1016/j.envpol.2024.123769
159. How to better predict the effect of urban traffic and weather on air pollution? Norwegian evidence from machine learning approaches C. Cao 10.1016/j.jebo.2024.03.018
160. An advection-diffusion equation-based approach to discern the meteorological factor effects on particle concentrations J. Ren et al. 10.1016/j.atmosres.2023.107213
161. Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements A. Nair & F. Yu 10.5194/acp-20-12853-2020
162. 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
163. Elucidating Contributions of Anthropogenic Volatile Organic Compounds and Particulate Matter to Ozone Trends over China C. Li et al. 10.1021/acs.est.2c03315
164. Chemistry of Atmospheric Fine Particles During the COVID‐19 Pandemic in a Megacity of Eastern China L. Liu et al. 10.1029/2020GL091611
165. Gradient Boosting Machine to Assess the Public Protest Impact on Urban Air Quality R. Zalakeviciute et al. 10.3390/app112412083
166. COVID-19-Related Changes in NO2 and O3 Concentrations and Associated Health Effects in Malta S. Fenech et al. 10.3389/frsc.2021.631280
167. The effect of quarantine policy on pollution emission and the usage of private transportation in urban areas Y. Hong & K. Lu 10.1038/s41598-024-66685-8
168. Changing supersites: assessing the impact of the southern UK EMEP supersite relocation on measured atmospheric composition H. Walker et al. 10.1088/2515-7620/ab1a6f
169. Exploring the effect of waterbodies coupled with other environmental parameters to model PM2.5 over Delhi-NCT in northwest India B. Gayen et al. 10.1016/j.apr.2022.101614
170. 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
171. 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
172. Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito P. Chau et al. 10.3389/fdata.2022.842455
173. Equivalence relations andLpdistances between time series with application to the Black Summer Australian bushfires N. James & M. Menzies 10.1016/j.physd.2023.133693
174. Long-term meteorology-adjusted and unadjusted trends of PM2.5 using the AirGAM model over Delhi, 2007–2022 . Chetna et al. 10.1016/j.aeaoa.2024.100255
175. 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
176. Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution Z. Zhang et al. 10.1016/j.envres.2022.113322
177. 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
178. 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
179. Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis Y. Li et al. 10.1016/j.envsoft.2022.105329
180. Green infrastructure and air pollution: Evidence from highways connecting two megacities in China W. Lee et al. 10.1016/j.jeem.2023.102884
181. Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western Taiwan M. Mishra et al. 10.1016/j.envpol.2024.123662
182. A review about COVID-19 in the MENA region: environmental concerns and machine learning applications H. Meskher et al. 10.1007/s11356-022-23392-z
183. Exploring formation mechanism and source attribution of ozone during the 2019 Wuhan Military World Games: Implications for ozone control strategies L. Zhang et al. 10.1016/j.jes.2022.12.009
184. Anthropogenic-driven changes in concentrations and sources of winter volatile organic compounds in an urban environment in the Yangtze River Delta of China between 2013 and 2021 Z. Zhang et al. 10.1016/j.scitotenv.2024.173713
185. 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
186. Impact of COVID-19 restrictions liberalization on air quality: a case study of Chongqing, Southwest China H. Wang et al. 10.1007/s10661-024-13213-w
187. 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
188. Revisiting the dynamics of gaseous ammonia and ammonium aerosols during the COVID-19 lockdown in urban Beijing using machine learning models Y. Lyu et al. 10.1016/j.scitotenv.2023.166946
189. Meteorological variability and predictive forecasting of atmospheric particulate pollution W. Hong 10.1038/s41598-023-41906-8
190. Surface, Satellite Ozone Changes in Northern South America During Low Anthropogenic Emission Conditions: A Machine Learning Approach A. Casallas et al. 10.2139/ssrn.4016140
191. Keeping air pollution policies on track G. Fuller & A. Font 10.1126/science.aaw9865
192. Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach G. Fioravanti et al. 10.1016/j.atmosenv.2021.118192
193. Elucidating ozone and PM2.5 pollution in the Fenwei Plain reveals the co-benefits of controlling precursor gas emissions in winter haze C. Lin et al. 10.5194/acp-23-3595-2023
194. Measurement report: Size-resolved secondary organic aerosol formation modulated by aerosol water uptake in wintertime haze J. Duan et al. 10.5194/acp-24-7687-2024
195. Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning H. Wang et al. 10.1016/j.scitotenv.2024.174452
196. Impact of Recent Meteorological Changes on Surface Ozone Increases in Seoul Metropolitan Area H. Lee et al. 10.5572/KOSAE.2024.40.4.467
197. Using meteorological normalisation to detect interventions in air quality time series S. Grange & D. Carslaw 10.1016/j.scitotenv.2018.10.344
198. Evaluating long-term reductions in trace metal emissions from shipping in Shanghai M. Wang et al. 10.1016/j.jhazmat.2024.136367
199. Exploring the driving factors of haze events in Beijing during Chinese New Year holidays in 2020 and 2021 under the influence of COVID-19 pandemic L. Luo et al. 10.1016/j.scitotenv.2022.160172
200. Predictions of the Optical Properties of Brown Carbon Aerosol by Machine Learning with Typical Chromophores Y. Wang et al. 10.1021/acs.est.4c09031
201. Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis S. Sohrab et al. 10.1186/s12302-024-00972-z
202. Aqueous‐Phase Secondary Processes and Meteorological Change Promote the Brown Carbon Formation and Transformation During Haze Events H. Jiang et al. 10.1029/2023JD038735
203. Analyzing the Contribution of Human Mobility to Changes in Air Pollutants: Insights from the COVID-19 Lockdown in Wuhan J. Wu et al. 10.3390/ijgi10120836
204. Impact and prediction of pollutant on mangrove and carbon stocks: A machine learning study based on urban remote sensing data M. Xu et al. 10.1016/j.gsf.2023.101665
205. An exploration of the mathematical structure and behavioural biases of 21st century financial crises N. James & M. Menzies 10.1016/j.physa.2023.129256
206. Characteristics and sources of carbonaceous aerosols in a semi-arid city: Quantifying anthropogenic and meteorological impacts P. Liu et al. 10.1016/j.chemosphere.2023.139056
207. Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach Y. Rybarczyk & R. Zalakeviciute 10.1029/2020GL091202
208. Meteorologically normalised long-term trends of atmospheric ammonia (NH3) in Switzerland/Liechtenstein and the explanatory role of gas-aerosol partitioning S. Grange et al. 10.1016/j.scitotenv.2023.165844
209. Examining trends and variability of PM2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations Y. Liu et al. 10.1016/j.scitotenv.2024.173331
210. 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
211. Spatial, temporal features and influence of meteorology on PM2.5 and O3 association across urban and rural environments of India A. Krishnaveni et al. 10.1016/j.aeaoa.2024.100265
212. Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost L. Le et al. 10.3390/app9132714
213. 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
214. Assessing the Impacts of Birmingham’s Clean Air Zone on Air Quality: Estimates from a Machine Learning and Synthetic Control Approach B. Liu et al. 10.1007/s10640-023-00794-2
215. 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
216. Differential Effects of the COVID-19 Lockdown and Regional Fire on the Air Quality of Medellín, Colombia J. Henao et al. 10.3390/atmos12091137
217. Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013–2020 Y. Guo et al. 10.1016/j.chemosphere.2022.134608
218. Staggered-peak production is a mixed blessing in the control of particulate matter pollution Y. Wang et al. 10.1038/s41612-022-00322-x
219. 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
220. Applying machine learning methods to better understand, model and estimate mass concentrations of traffic-related pollutants at a typical street canyon I. Šimić et al. 10.1016/j.envpol.2020.114587
221. Discrepant Global Surface Ozone Responses to Emission- and Heatwave-Induced Regime Shifts C. Tao et al. 10.1021/acs.est.4c08422
222. Changes in Air Pollutants from Fireworks in Chinese Cities Y. Lai & P. Brimblecombe 10.3390/atmos13091388
223. Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation W. Zeng et al. 10.1016/j.jenvman.2024.122615
224. Enhanced ozone pollution in the summer of 2022 in China: The roles of meteorology and emission variations H. Zheng et al. 10.1016/j.atmosenv.2023.119701
225. Winter-autumn air pollution control plan in North China modified the PM2.5 compositions and sources in Central China S. Jiang et al. 10.1016/j.atmosenv.2023.119827
226. Wind power forecasting based on a machine learning model: considering a coastal wind farm in Zhejiang as an example G. Gu et al. 10.1080/15435075.2024.2319228
227. 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
228. 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
229. Study on spatiotemporal characteristics and influencing factors of pedestrian-level PM2.5 concentrations in outdoor open spaces of Harbin in winter, using a generalized additive model (GAM) D. Han et al. 10.1016/j.uclim.2022.101313
230. Arctic/North Atlantic atmospheric variability causes Severe PM10 events in South Korea J. Kim et al. 10.1016/j.scitotenv.2023.169714
231. Quantifying the drivers of surface ozone anomalies in the urban areas over the Qinghai-Tibet Plateau H. Yin et al. 10.5194/acp-22-14401-2022
232. Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries Y. Ma et al. 10.1016/j.envint.2024.108668
233. Contrasting effects of emission control on air pollution in Central China during the 2019 Military World Games based on satellite and ground observations M. Tao et al. 10.1016/j.atmosres.2021.105657
234. 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
235. 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
236. Machine learning elucidates the impact of short-term emission changes on air pollution in Beijing W. Zhou et al. 10.1016/j.atmosenv.2022.119192
237. 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
238. The impact of urban mobility on air pollution in Kampala, an exemplar sub-Saharan African city O. Ghaffarpasand et al. 10.1016/j.apr.2024.102057
239. Estimation of hygroscopic growth properties of source-related sub-micrometre particle types in a mixed urban aerosol T. Vu et al. 10.1038/s41612-021-00175-w
240. Impact of Exposure to Environmental Particulate Matter on the Onset of Giant Cell Arteritis M. Bond et al. 10.1002/acr.25404
241. Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning H. Andersen et al. 10.1080/16000889.2021.1971925
242. Drivers and impacts of decreasing concentrations of atmospheric volatile organic compounds (VOCs) in Beijing during 2016–2020 Y. Liu et al. 10.1016/j.scitotenv.2023.167847
243. Decrease of atmospheric black carbon and CO2 concentrations due to COVID-19 lockdown at the Mt. Waliguan WMO/GAW baseline station in China F. Xie et al. 10.1016/j.envres.2022.112984
244. 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
245. Dramatic changes in atmospheric pollution source contributions for a coastal megacity in northern China from 2011 to 2020 B. Liu et al. 10.5194/acp-22-8597-2022
246. Multiple driving factors and hierarchical management of PM2.5: Evidence from Chinese central urban agglomerations using machine learning model and GTWR C. Ou et al. 10.1016/j.uclim.2022.101327
247. Quantify the role of anthropogenic emission and meteorology on air pollution using machine learning approach: A case study of PM2.5 during the COVID-19 outbreak in Hubei Province, China H. Liu et al. 10.1016/j.envpol.2022.118932
248. Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review Y. Rybarczyk & R. Zalakeviciute 10.3390/app8122570