72 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 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. 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. 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. 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. 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. 10.1080/10962247.2016.1218393
7. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. 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. 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. 10.1016/j.envpol.2023.121505
10. Satellite data of atmospheric pollution for U.S. air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid B. Duncan et al. 10.1016/j.atmosenv.2014.05.061
11. Investigating the Variability in Aerosol Optical Depth and Associated Population Exposure Risk in Pakistan Using MAIAC Data M. Nawaz et al. 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. 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. 10.3390/rs14010069
14. Sensors and systems for air quality assessment monitoring and management: A review D. Singh et al. 10.1016/j.jenvman.2021.112510
15. MODIS high-resolution MAIAC aerosol product: Global validation and analysis W. Qin et al. 10.1016/j.atmosenv.2021.118684
16. The unexpected smoke layer in the High Arctic winter stratosphere during MOSAiC 2019–2020 K. Ohneiser et al. 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. 10.3390/rs14122933
18. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals A. Chudnovsky et al. 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. 10.3390/rs12040736
20. An algorithm for retrieving aerosol optical depth from Landsat 8 Operational Land Imager in Vietnam V. Tran Tuan et al. 10.1080/10106049.2023.2228748
21. Satellite remote sensing in epidemiological studies M. Sorek-Hamer et al. 10.1097/MOP.0000000000000326
22. 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. 10.1080/01431161.2016.1220031
23. Spatiotemporal estimates of daily PM2.5 concentrations based on 1-km resolution MAIAC AOD in the Beijing–Tianjin–Hebei, China X. Yang et al. 10.1016/j.envc.2022.100548
24. Validation of satellite and model aerosol optical depth and precipitable water vapour observations with AERONET data over Pune, India K. Vijayakumar et al. 10.1080/01431161.2018.1476789
25. 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. 10.1016/j.atmosenv.2020.118163
26. 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. 10.1016/j.apr.2018.08.016
27. Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals T. Zhang et al. 10.1016/j.rse.2018.06.030
28. Effect of COVID-19 Response Policy on Air Quality: A Study in South China Context X. Jin et al. 10.3390/atmos13050842
29. 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. 10.1016/j.atmosenv.2017.06.047
30. An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10 R. Stirnberg et al. 10.3390/rs10091353
31. Assessment of PM2.5 concentrations and exposure throughout China using ground observations H. Guo et al. 10.1016/j.scitotenv.2017.05.263
32. Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria T. Etchie et al. 10.1016/j.scitotenv.2021.145187
33. 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. 10.1007/s12524-024-02096-6
34. Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA A. Just et al. 10.3390/rs10050803
35. The development of Air Quality Indices using AOD-Retrieved Images during haze events in Peninsular Malaysia K. Ali Ahmed Ben Youssef et al. 10.1088/1755-1315/373/1/012027
36. Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning R. Stirnberg et al. 10.1029/2019JD031380
37. 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. 10.1016/j.atmosres.2022.106481
38. Characterization of temporal and spatial variability of aerosols from ground-based climatology: towards evaluation of satellite mission requirements C. Chen et al. 10.1016/j.jqsrt.2021.107627
39. 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. 10.3390/ijgi10010031
40. Seasonal monitoring and estimation of regional aerosol distribution over Po valley, northern Italy, using a high-resolution MAIAC product B. Arvani et al. 10.1016/j.atmosenv.2016.06.037
41. Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia X. Ma et al. 10.1016/j.scitotenv.2021.151558
42. Spatial and temporal variations of air quality and six air pollutants in China during 2015–2017 H. Guo et al. 10.1038/s41598-019-50655-6
43. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges A. Ranjan et al. 10.1007/s13143-020-00215-0
44. A novel calibration approach using satellite and visibility observations to estimate fine particulate matter exposures in Southwest Asia and Afghanistan S. Masri et al. 10.1080/10962247.2016.1230079
45. 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. 10.1109/LGRS.2021.3049887
46. 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. 10.1016/j.atmosenv.2022.119362
47. The observation‐based relationships between PM2.5 and AOD over China J. Xin et al. 10.1002/2015JD024655
48. 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. 10.1016/j.atmosenv.2015.10.004
49. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. 10.1021/acs.est.1c01863
50. 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. 10.5194/amt-13-3873-2020
51. Developing particle emission inventories using remote sensing (PEIRS) C. Tang et al. 10.1080/10962247.2016.1214630
52. 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. 10.1016/j.atmosenv.2019.117016
53. 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. 10.1016/j.atmosres.2025.107948
54. 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. 10.1016/j.apr.2019.11.020
55. Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data S. Lee et al. 10.1016/j.atmosenv.2020.117737
56. Spatial and Temporal Variation of Aerosol Optical Depth in Huaihai Economic Zone from 1982 to 2021 S. Wu et al. 10.3390/atmos14050822
57. Spatial and temporal variability in desert dust and anthropogenic pollution in Iraq, 1997–2010 A. Chudnovsky et al. 10.1080/10962247.2016.1153528
58. MODIS Collection 6 MAIAC algorithm A. Lyapustin et al. 10.5194/amt-11-5741-2018
59. Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain W. Han & L. Tong 10.3390/atmos10050245
60. MODIS AOD sampling rate and its effect on PM2.5 estimation in North China Z. Song et al. 10.1016/j.atmosenv.2019.04.020
61. Observational Constraints on the Aerosol Optical Depth–Surface PM2.5 Relationship during Alaskan Wildfire Seasons T. Zhao et al. 10.1021/acsestair.4c00120
62. Estimating Air Particulate Matter Using MODIS Data and Analyzing Its Spatial and Temporal Pattern over the Yangtze Delta Region J. Xu et al. 10.3390/su8090932
63. Monthly analysis of PM ratio characteristics and its relation to AOD M. Sorek-Hamer et al. 10.1080/10962247.2016.1208121
64. 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. 10.3390/atmos9020078
65. Satellite Aerosol Retrieval Using Scene Simulation and Deep Belief Network C. Jia et al. 10.1109/TGRS.2021.3116154
66. 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. 10.3390/rs11192223
67. The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis I. Rogozovsky et al. 10.1016/j.atmosenv.2023.119930
68. Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data M. Al-Hamdan et al. 10.1007/s10661-019-7414-3
69. Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth R. Saunders et al. 10.1016/j.atmosenv.2014.03.060
70. Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model X. Hu et al. 10.1016/j.rse.2013.08.032
71. Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data S. Alexeeff et al. 10.1038/jes.2014.40
72. A study of the impact of spatial resolution on the estimation of particle matter concentration from the aerosol optical depth retrieved from satellite observations L. Mei et al. 10.1080/01431161.2019.1601279