76 citations as recorded by crossref.

1. Meteorological parameters and gaseous pollutant concentrations as predictors of daily continuous PM2.5 concentrations using deep neural network in Beijing–Tianjin–Hebei, China X. Wang & W. Sun https://doi.org/10.1016/j.atmosenv.2019.05.004
2. Satellite-based view of the aerosol spatial and temporal variability in the Córdoba region (Argentina) using over ten years of high-resolution data L. Della Ceca et al. https://doi.org/10.1016/j.isprsjprs.2018.08.016
3. Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China X. Zhang et al. https://doi.org/10.1016/j.atmosres.2020.105063
4. Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset S. Chowdhury et al. https://doi.org/10.1016/j.atmosenv.2019.02.029
5. Spatio-temporal variations of aerosol optical depth over Ukraine under the Russia-Ukraine war D. Jiadan et al. https://doi.org/10.1016/j.atmosenv.2023.120114
6. Trends and spatial patterns of fine-resolution aerosol optical depth–derived PM2.5 emissions in the Northeast United States from 2002 to 2013 C. Tang et al. https://doi.org/10.1080/10962247.2016.1218393
7. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. https://doi.org/10.5194/amt-13-877-2020
8. Characteristics of the Spatio-Temporal Dynamics of Aerosols in Central Asia and Their Influencing Factors Y. Zhou et al. https://doi.org/10.3390/rs14112684
9. Influence of spatial resolution of PM2.5 concentrations and population on health impact assessment from 2010 to 2020 in China H. Bai et al. https://doi.org/10.1016/j.envpol.2023.121505
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11. Investigating the Variability in Aerosol Optical Depth and Associated Population Exposure Risk in Pakistan Using MAIAC Data M. Nawaz et al. https://doi.org/10.1007/s11270-023-06661-6
12. Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance Y. Xie et al. https://doi.org/10.1016/j.scitotenv.2018.12.365
13. Hospitalization Due to Fire-Induced Pollution in the Brazilian Legal Amazon from 2005 to 2018 W. Campanharo et al. https://doi.org/10.3390/rs14010069
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16. The unexpected smoke layer in the High Arctic winter stratosphere during MOSAiC 2019–2020 K. Ohneiser et al. https://doi.org/10.5194/acp-21-15783-2021
17. Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment H. Bai et al. https://doi.org/10.3390/rs14122933
18. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals A. Chudnovsky et al. https://doi.org/10.1016/j.atmosenv.2014.02.019
19. Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China R. Li et al. https://doi.org/10.3390/rs12040736
20. Vertical Aerosol Structure Matters: Improving the AOD–PM2.5 Link for Air Quality and Exposure I. Rogozovsky et al. https://doi.org/10.1021/acs.est.6c00095
21. An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam V. Tran Tuan et al. https://doi.org/10.1080/10106049.2023.2228748
22. Satellite remote sensing in epidemiological studies M. Sorek-Hamer et al. https://doi.org/10.1097/MOP.0000000000000326
23. Estimating and source analysis of surface PM2.5 concentration in the Beijing–Tianjin–Hebei region based on MODIS data and air trajectories Y. Wang et al. https://doi.org/10.1080/01431161.2016.1220031
24. Spatiotemporal estimates of daily PM2.5 concentrations based on 1-km resolution MAIAC AOD in the Beijing–Tianjin–Hebei, China X. Yang et al. https://doi.org/10.1016/j.envc.2022.100548
25. Validation of satellite and model aerosol optical depth and precipitable water vapour observations with AERONET data over Pune, India K. Vijayakumar et al. https://doi.org/10.1080/01431161.2018.1476789
26. Impact of aerosol layering, complex aerosol mixing, and cloud coverage on high-resolution MAIAC aerosol optical depth measurements: Fusion of lidar, AERONET, satellite, and ground-based measurements I. Rogozovsky et al. https://doi.org/10.1016/j.atmosenv.2020.118163
27. Application of Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and Weather Research Forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM2.5) over India Y. Sathe et al. https://doi.org/10.1016/j.apr.2018.08.016
28. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals T. Zhang et al. https://doi.org/10.1016/j.rse.2018.06.030
29. Effect of COVID-19 Response Policy on Air Quality: A Study in South China Context X. Jin et al. https://doi.org/10.3390/atmos13050842
30. An example of aerosol pattern variability over bright surface using high resolution MODIS MAIAC: The eastern and western areas of the Dead Sea and environs L. Sever et al. https://doi.org/10.1016/j.atmosenv.2017.06.047
31. An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10 R. Stirnberg et al. https://doi.org/10.3390/rs10091353
32. Assessment of PM2.5 concentrations and exposure throughout China using ground observations H. Guo et al. https://doi.org/10.1016/j.scitotenv.2017.05.263
33. Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria T. Etchie et al. https://doi.org/10.1016/j.scitotenv.2021.145187
34. Integrated Approach (MCD19A2 and PM10 Datasets) for Spatiotemporal Assessment of Aerosol and Revealing Approachable Predictive Model Across the Mega-Mining Region (Jharkhand), India Along with its Accuracy Measures S. Priya et al. https://doi.org/10.1007/s12524-024-02096-6
35. Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA A. Just et al. https://doi.org/10.3390/rs10050803
36. The development of Air Quality Indices using AOD-Retrieved Images during haze events in Peninsular Malaysia K. Ali Ahmed Ben Youssef et al. https://doi.org/10.1088/1755-1315/373/1/012027
37. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. https://doi.org/10.1029/2019JD031380
38. Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020 Q. He et al. https://doi.org/10.1016/j.atmosres.2022.106481
39. Characterization of temporal and spatial variability of aerosols from ground-based climatology: towards evaluation of satellite mission requirements C. Chen et al. https://doi.org/10.1016/j.jqsrt.2021.107627
40. Estimate Particulate Matter PM2.5 Concentration Impact of Wildfires Using Machine Learning in Chiang Mai Province, Thailand T. Sena & P. Soytong https://doi.org/10.63271/rmuttorj.v18i2.265288
41. PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China N. Chen et al. https://doi.org/10.3390/ijgi10010031
42. A spatial lag model analysis of lung cancer incidence and satellite-derived data on air pollution in Thailand from 2020 to 2023 W. Silangam & A. Luenam https://doi.org/10.4081/gh.2025.1424
43. Seasonal monitoring and estimation of regional aerosol distribution over Po valley, northern Italy, using a high-resolution MAIAC product B. Arvani et al. https://doi.org/10.1016/j.atmosenv.2016.06.037
44. Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia X. Ma et al. https://doi.org/10.1016/j.scitotenv.2021.151558
45. Linking land development to ecological and atmospheric change: remote sensing insights from Egypt’s new delta A. El-Zeiny et al. https://doi.org/10.1186/s40068-026-00471-5
46. Spatial and temporal variations of air quality and six air pollutants in China during 2015–2017 H. Guo et al. https://doi.org/10.1038/s41598-019-50655-6
47. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. https://doi.org/10.1007/s13143-020-00215-0
48. A two-stage algorithm to estimate ground-level PM2.5 concentration levels in Madrid (Spain) from AOD satellite data and surface proxies J. Cordero https://doi.org/10.1016/j.apr.2025.102678
49. A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan S. Masri et al. https://doi.org/10.1080/10962247.2016.1230079
50. Multitechnique Observations on the Impacts of Declining Air Pollution on the Atmospheric Convective Processes During COVID-19 Pandemic at a Tropical Metropolis G. Rakshit et al. https://doi.org/10.1109/LGRS.2021.3049887
51. Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM L. Miao et al. https://doi.org/10.1016/j.atmosenv.2022.119362
52. The observation‐based relationships between PM2.5 and AOD over China J. Xin et al. https://doi.org/10.1002/2015JD024655
53. Estimating daily PM 2.5 and PM 10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data I. Kloog et al. https://doi.org/10.1016/j.atmosenv.2015.10.004
54. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. https://doi.org/10.1021/acs.est.1c01863
55. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa C. Malings et al. https://doi.org/10.5194/amt-13-3873-2020
56. Developing particle emission inventories using remote sensing (PEIRS) C. Tang et al. https://doi.org/10.1080/10962247.2016.1214630
57. Evaluating the use of satellite observations to supplement ground-level air quality data in selected cities in low- and middle-income countries M. Alvarado et al. https://doi.org/10.1016/j.atmosenv.2019.117016
58. Impact of maritime and industrial activities on air quality along the Egyptian Mediterranean coast using data observation techniques A. El-Zeiny et al. https://doi.org/10.1007/s41207-025-00803-4
59. Monitoring and simulation of a 7-day dust episode and associated dust radiative forcing over the Middle East via synergy of satellite observations, reanalysis datasets and regional/numerical models K. Mohammadpour et al. https://doi.org/10.1016/j.atmosres.2025.107948
60. Decades of land use change and its impact on air quality in Egypt’s Middle Nile Delta observed from space A. El-Zeiny et al. https://doi.org/10.1038/s41598-025-17019-9
61. Mitigating MODIS AOD non-random sampling error on surface PM2.5 estimates by a combined use of Bayesian Maximum Entropy method and linear mixed-effects model D. Fu et al. https://doi.org/10.1016/j.apr.2019.11.020
62. Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data S. Lee et al. https://doi.org/10.1016/j.atmosenv.2020.117737
63. Spatial and Temporal Variation of Aerosol Optical Depth in Huaihai Economic Zone from 1982 to 2021 S. Wu et al. https://doi.org/10.3390/atmos14050822
64. Spatial and temporal variability in desert dust and anthropogenic pollution in Iraq, 1997–2010 A. Chudnovsky et al. https://doi.org/10.1080/10962247.2016.1153528
65. MODIS Collection 6 MAIAC algorithm A. Lyapustin et al. https://doi.org/10.5194/amt-11-5741-2018
66. Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain W. Han & L. Tong https://doi.org/10.3390/atmos10050245
67. MODIS AOD sampling rate and its effect on PM2.5 estimation in North China Z. Song et al. https://doi.org/10.1016/j.atmosenv.2019.04.020
68. Observational Constraints on the Aerosol Optical Depth–Surface PM2.5 Relationship during Alaskan Wildfire Seasons T. Zhao et al. https://doi.org/10.1021/acsestair.4c00120
69. Estimating Air Particulate Matter Using MODIS Data and Analyzing Its Spatial and Temporal Pattern over the Yangtze Delta Region J. Xu et al. https://doi.org/10.3390/su8090932
70. Monthly analysis of PM ratio characteristics and its relation to AOD M. Sorek-Hamer et al. https://doi.org/10.1080/10962247.2016.1208121
71. Spatiotemporal Distribution of Satellite-Retrieved Ground-Level PM2.5 and Near Real-Time Daily Retrieval Algorithm Development in Sichuan Basin, China C. Gao et al. https://doi.org/10.3390/atmos9020078
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73. Impacts of AOD Correction and Spatial Scale on the Correlation between High-Resolution AOD from Gaofen-1 Satellite and In Situ PM2.5 Measurements in Shenzhen City, China J. Wu et al. https://doi.org/10.3390/rs11192223
74. The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis I. Rogozovsky et al. https://doi.org/10.1016/j.atmosenv.2023.119930
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