327 citations as recorded by crossref.

1. Statistical evaluation of the feasibility of satellite-retrieved cloud parameters as indicators of PM2.5 levels C. Yu et al. 10.1038/jes.2014.49
2. Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5 T. Li et al. 10.1016/j.isprsjprs.2020.06.019
3. Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China K. Zhang et al. 10.3390/rs11222679
4. Air Quality Impacts of Shale Gas Development in Pennsylvania R. Zhang et al. 10.1086/721430
5. Satellite AOD conversion into ground PM10, PM2.5 and PM1 over the Po valley (Milan, Italy) exploiting information on aerosol vertical profiles, chemistry, hygroscopicity and meteorology L. Ferrero et al. 10.1016/j.apr.2019.08.003
6. High Spatial Resolution PM2.5 Retrieval Using MODIS and Ground Observation Station Data Based on Ensemble Random Forest X. Chen et al. 10.1109/ACCESS.2019.2908975
7. Aerosol optical and radiative properties and their environmental effects in China: A review H. Che et al. 10.1016/j.earscirev.2023.104634
8. Exploring the relationships between ground-measured particulate matter and satellite-retrieved aerosol parameters in China S. Yin 10.1007/s11356-022-19049-6
9. Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models Q. Xiao et al. 10.1016/j.atmosenv.2020.117921
10. Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models S. Park et al. 10.1016/j.scitotenv.2020.136516
11. Space‐Borne Estimation of Volcanic Sulfate Aerosol Lifetime C. Li & R. Cohen 10.1029/2020JD033883
12. An Efficient and Accurate Model Coupled With Spatiotemporal Kalman Filter and Linear Mixed Effect for Hourly PM2.5 Mapping N. Liu et al. 10.1109/TGRS.2023.3324393
13. Mapping exposure to particulate pollution during severe haze episode using improved MODIS AOT‐PM10 regression model with synoptic meteorology classification K. Leelasakultum & N. Kim Oanh 10.1002/2017GH000059
14. Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States Q. Di et al. 10.1021/acs.est.5b06121
15. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting W. Yu et al. 10.1038/s41598-017-07478-0
16. Real‐time PM2.5 mapping and anomaly detection from AirBoxes in Taiwan G. Huang et al. 10.1002/env.2537
17. The impact of three recent coal-fired power plant closings on Pittsburgh air quality: A natural experiment M. Russell et al. 10.1080/10962247.2016.1170738
18. A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD Y. Bai et al. 10.3390/rs8030262
19. Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure L. McGuinn et al. 10.1016/j.envres.2017.07.041
20. Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations N. Benas et al. 10.1016/j.atmosenv.2013.07.012
21. A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data H. Bagheri 10.1016/j.asr.2022.02.032
22. Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth P. Kulkarni et al. 10.1016/j.atmosenv.2022.119164
23. Seasonal Characteristics of Forecasting Uncertainties in Surface PM2.5 Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region Q. Du et al. 10.1007/s00376-023-3060-3
24. Spatiotemporal mixed effects modeling for the estimation of PM2.5from MODIS AOD over the Indian subcontinent S. Unnithan & L. Gnanappazham 10.1080/15481603.2020.1712101
25. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model Y. Guo et al. 10.1016/j.rse.2017.06.001
26. Spatiotemporal Analysis of Haze in Beijing Based on the Multi-Convolution Model L. Yin et al. 10.3390/atmos12111408
27. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information G. Chen et al. 10.1016/j.scitotenv.2018.04.251
28. A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 L. Li 10.3390/rs12020264
29. Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018 B. Guo et al. 10.1016/j.scitotenv.2020.141765
30. New Directions: Satellite driven PM2.5 exposure models to support targeted particle pollution health effects research Y. Liu 10.1016/j.atmosenv.2012.11.043
31. Spatiotemporal patterns of PM10 concentrations over China during 2005–2016: A satellite-based estimation using the random forests approach G. Chen et al. 10.1016/j.envpol.2018.07.012
32. Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications P. deSouza et al. 10.5194/amt-13-5319-2020
33. Variability, predictability, and uncertainty in global aerosols inferred from gap-filled satellite observations and an econometric modeling approach X. Li et al. 10.1016/j.rse.2021.112501
34. Aerosol data assimilation and forecasting experiments using aircraft and surface observations during CalNex Z. Zang et al. 10.3402/tellusb.v68.29812
35. A geographically and temporally weighted regression model for assessing intra-urban variability of volatile organic compounds (VOCs) in Yangpu district, Shanghai L. Cui et al. 10.1016/j.atmosenv.2019.06.052
36. Estimating the Near-Ground PM2.5 Concentration over China Based on the CapsNet Model during 2018–2020 Q. Zeng et al. 10.3390/rs14030623
37. Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations H. Lee et al. 10.1016/j.envres.2012.06.011
38. Developing a provincial environmental performance index for the environmental health of Vietnam T. Nguyen et al. 10.1007/s10668-021-02072-x
39. Urban Air Quality Assessment by Fusing Spatial and Temporal Data from Multiple Study Sources Using Refined Estimation Methods L. Chen et al. 10.3390/ijgi11060330
40. Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks H. Shen et al. 10.1029/2018JD028759
41. Application of Multiple Linear Regression and Geographically Weighted Regression Model for Prediction of PM2.5 T. Narayan et al. 10.1007/s40010-020-00718-5
42. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. 10.1016/j.rse.2021.112827
43. Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM2.5 Relationships S. Christopher & P. Gupta 10.3390/rs12121985
44. The Effect of Socioeconomic Factors on Spatiotemporal Patterns of PM2.5 Concentration in Beijing–Tianjin–Hebei Region and Surrounding Areas W. Wang et al. 10.3390/ijerph17093014
45. PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India P. Gogikar et al. 10.1007/s12652-020-02457-2
46. Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China X. Mao et al. 10.1016/j.apr.2017.04.002
47. Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics J. Li et al. 10.1016/j.envpol.2018.03.050
48. Estimating spatial variability of ground-level PM2.5 based on a satellite-derived aerosol optical depth product: Fuzhou, China L. Yang et al. 10.1016/j.apr.2018.05.007
49. Spatio-temporal variations in the estimation of PM10 from MODIS-derived aerosol optical depth for the urban areas in the Central Indo-Gangetic Plain S. Chitranshi et al. 10.1007/s00703-014-0347-z
50. How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning? Z. Tian et al. 10.3390/rs15153780
51. Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model Q. He & B. Huang 10.1016/j.envpol.2018.01.053
52. Using satellite data to estimate particulate air quality in a subtropical city: an evaluation of accuracy and sampling issues M. Zhang et al. 10.1080/2150704X.2015.1035771
53. Estimating high-resolution PM1 concentration from Himawari-8 combining extreme gradient boosting-geographically and temporally weighted regression (XGBoost-GTWR) R. Li et al. 10.1016/j.atmosenv.2020.117434
54. Simplicity versus accuracy for estimation of the PM2.5 concentration: a comparison between LUR and GWR methods across time scales B. Zou et al. 10.1080/14498596.2019.1624203
55. WITHDRAWN: Prevention and control of motor vehicle exhaust pollution based on internet of things system and cloud computing Y. Zhang et al. 10.1016/j.micpro.2020.103373
56. Daily estimation of ground-level PM2.5 concentrations at 4 km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations B. Lv et al. 10.1016/j.scitotenv.2016.12.049
57. A systematic review of the physical health impacts from non-occupational exposure to wildfire smoke J. Liu et al. 10.1016/j.envres.2014.10.015
58. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia N. Kamarul Zaman et al. 10.1016/j.atmosres.2017.04.019
59. A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China F. Yao et al. 10.1016/j.scitotenv.2017.08.209
60. Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models C. Samuel et al. 10.1002/gj.4780
61. Interdecadal variation in aerosol optical properties and their relationships to meteorological parameters over northeast China from 1980 to 2017 H. Zhao et al. 10.1016/j.chemosphere.2019.125737
62. Reliability and stability of a statistical model to predict ground-based PM2.5 over 10 years in Karachi, Pakistan, using satellite observations Z. Darynova et al. 10.1007/s11869-022-01296-8
63. Early-life exposure to PM2.5 and risk of acute asthma clinical encounters among children in Massachusetts: a case-crossover analysis R. Khalili et al. 10.1186/s12940-018-0361-6
64. Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models W. You et al. 10.1016/j.scitotenv.2014.11.024
65. Estimation of particulate matter from satellite- and ground-based observations over Hyderabad, India P. Sinha et al. 10.1080/01431161.2015.1112929
66. Estimation of local daily PM2.5 concentration during wildfire episodes: integrating MODIS AOD with multivariate linear mixed effect (LME) models M. Mirzaei et al. 10.1007/s11869-019-00780-y
67. Global and Regional Variations and Main Drivers of Aerosol Loadings over Land during 1980–2018 J. Sun et al. 10.3390/rs14040859
68. Forecasting PM10 Concentrations in the Caribbean Area Using Machine Learning Models T. Plocoste & S. Laventure 10.3390/atmos14010134
69. Gaussian Markov random fields improve ensemble predictions of daily 1 km PM2.5 and PM10 across France I. Hough et al. 10.1016/j.atmosenv.2021.118693
70. Enhancing the Applicability of Satellite Remote Sensing for PM 2.5 Estimation Using Machine Learning Models in China J. Chai et al. 10.1155/2022/7148682
71. Application of an advanced spatiotemporal model for PM2.5 prediction in Jiangsu Province, China T. Zhang et al. 10.1016/j.chemosphere.2019.125563
72. A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China W. Song et al. 10.1016/j.rse.2014.08.008
73. Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling B. Zou et al. 10.3390/rs9010001
74. Spatial characteristics and temporal evolution of the relationship between PM2.5 and aerosol optical depth over the eastern USA during 2003–2017 Q. Jin et al. 10.1016/j.atmosenv.2020.117718
75. Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review F. Bahadur et al. 10.1007/s10661-023-12001-2
76. PM2.5 Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network J. Wang et al. 10.3390/rs15204905
77. Urbanization, ambient air pollution, and prevalence of chronic kidney disease: A nationwide cross-sectional study Z. Liang et al. 10.1016/j.envint.2021.106752
78. Estimation of High-Resolution PM2.5 over the Indo-Gangetic Plain by Fusion of Satellite Data, Meteorology, and Land Use Variables A. Mhawish et al. 10.1021/acs.est.0c01769
79. Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing D. Fu et al. 10.1038/s41598-018-28535-2
80. Association between satellite-based estimates of long-term PM2.5 exposure and coronary artery disease L. McGuinn et al. 10.1016/j.envres.2015.10.026
81. Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison Y. Zhang et al. 10.5194/amt-14-1655-2021
82. Toward Understanding the Differences of PM2.5 Characteristics Among Five China Urban Cities K. Zhang et al. 10.1007/s13143-019-00125-w
83. Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale X. Chen et al. 10.3390/rs13224545
84. A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand P. Tongprasert & S. Ongsomwang 10.3390/atmos13060904
85. Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset Q. Chen et al. 10.1016/j.apr.2019.07.016
86. Estimation of PM2.5 Concentration across China Based on Multi-Source Remote Sensing Data and Machine Learning Methods Y. Yang et al. 10.3390/rs16030467
87. Environmental justice and the COVID-19 pandemic: Evidence from New York State R. Zhang et al. 10.1016/j.jeem.2021.102554
88. Review: Strategies for using satellite-based products in modeling PM2.5 and short-term pollution episodes M. Sorek-Hamer et al. 10.1016/j.envint.2020.106057
89. Spatiotemporal evolution of PM2.5 concentrations in urban agglomerations of China Z. Wang et al. 10.1007/s11442-021-1876-2
90. Assessment of the association between dust storms and COVID-19 infection rate in southwest Iran P. Broomandi et al. 10.1007/s11356-021-18195-7
91. Estimating ground-level PM 10 in a Chinese city by combining satellite data, meteorological information and a land use regression model X. Meng et al. 10.1016/j.envpol.2015.09.042
92. Spatiotemporal Changes in PM2.5 and Their Relationships with Land-Use and People in Hangzhou L. Tian et al. 10.3390/ijerph15102192
93. Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil A. Rudke et al. 10.1016/j.atmosenv.2023.120130
94. Chronic PM2.5 exposure and risk of infant bronchiolitis and otitis media clinical encounters M. Girguis et al. 10.1016/j.ijheh.2017.06.007
95. Spatial and temporal changes analysis of air quality before and after the COVID-19 in Shandong Province, China H. Xing et al. 10.1007/s12145-021-00739-7
96. Performance of MODIS aerosol products at various timescales and in different pollution conditions over eastern Asia Q. Mao et al. 10.1007/s11431-018-9462-5
97. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China W. Wang et al. 10.3390/rs9080858
98. Long Term Monitoring of Air Pollution on Monuments and Cultural Heritage Sites in Cyprus Using Satellite Remote Sensing K. Themistocleous et al. 10.1260/2047-4970.1.1.145
99. Constraining chemical transport PM<sub>2.5</sub> modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley M. Friberg et al. 10.5194/acp-18-12891-2018
100. Improving the estimating accuracy of extinction coefficient of surface aerosol with a new layer-resolved model in China L. Wang et al. 10.1016/j.scitotenv.2019.136443
101. Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters M. Soni et al. 10.1016/j.apr.2018.03.001
102. Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand P. Wongnakae et al. 10.1007/s11356-023-28698-0
103. Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China R. Li et al. 10.3390/rs12040736
104. High resolution aerosol data from MODIS satellite for urban air quality studies A. Chudnovsky et al. 10.2478/s13533-012-0145-4
105. Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States C. Wu et al. 10.3390/rs12223813
106. 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
107. Short period PM2.5 prediction based on multivariate linear regression model R. Zhao et al. 10.1371/journal.pone.0201011
108. Holistics 3.0 for Health D. Lary et al. 10.3390/ijgi3031023
109. Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
110. 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
111. Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network B. Wang et al. 10.1016/j.envpol.2020.116327
112. Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification Q. Pu & E. Yoo 10.1016/j.envpol.2021.116574
113. Quantifying PM2.5 mass concentration and particle radius using satellite data and an optical-mass conversion algorithm M. Liu et al. 10.1016/j.isprsjprs.2019.10.010
114. Trend-attribute forecasting of hourly PM2.5 trends in fifteen cities of Central England applying optimized machine learning feature selection D. Wood 10.1016/j.jenvman.2024.120561
115. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta During the 2014–2017 Period . Cheng et al. 10.3390/ijerph16193522
116. Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS B. Peng-in et al. 10.1007/s11869-022-01238-4
117. Aerosol radiative forcing of forest fires unprecedented in South Korea (2022) captured by Korean geostationary satellites, GK-2A AMI and GK-2B GEMS D. Seong et al. 10.1016/j.envpol.2024.123464
118. Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period S. Ha et al. 10.5194/acp-20-6015-2020
119. A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data Q. Pu & E. Yoo 10.1016/j.envpol.2022.120419
120. A novel multi-pollutant space-time learning network for air pollution inference J. Song & M. Stettler 10.1016/j.scitotenv.2021.152254
121. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain R. Schneider et al. 10.3390/rs12223803
122. The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future L. Remer et al. 10.3390/rs12182900
123. Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach X. Hu et al. 10.1021/acs.est.7b01210
124. 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
125. Coupling the linear mixed effects model with random forest improves hourly PM2.5 estimation from Himawari-8 AOD over the Yangtze River Delta Y. Tan et al. 10.1016/j.apr.2023.101739
126. Estimating Full-Coverage PM2.5 Concentrations Based on Himawari-8 and NAQPMS Data over Sichuan-Chongqing Q. Zeng et al. 10.3390/app12147065
127. Assessment of particulate matter and particle path trajectory analysis using a HYSPLIT model over Dire Dawa, Ethiopia T. Endale et al. 10.1007/s42452-024-05741-4
128. Trend decomposition aids forecasts of air particulate matter (PM2.5) assisted by machine and deep learning without recourse to exogenous data D. Wood 10.1016/j.apr.2022.101352
129. A collaborative analysis framework for distributed gridded environmental data H. Xu et al. 10.1016/j.envsoft.2018.09.007
130. Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD Y. Xie et al. 10.1021/acs.est.5b01413
131. Comparison of Four Ground-Level PM2.5 Estimation Models Using PARASOL Aerosol Optical Depth Data from China H. Guo et al. 10.3390/ijerph13020180
132. Assessment of PM2.5 concentrations over bright surfaces using MODIS satellite observations M. Sorek-Hamer et al. 10.1016/j.rse.2015.03.014
133. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals A. Chudnovsky et al. 10.1016/j.atmosenv.2014.02.019
134. Residential proximity to major roadways, fine particulate matter, and adiposity: The framingham heart study W. Li et al. 10.1002/oby.21630
135. Characterizing spatiotemporal patterns of air pollution in China: A multiscale landscape approach Y. Liu et al. 10.1016/j.ecolind.2017.01.027
136. COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity A. Gayen et al. 10.1016/j.uclim.2021.100786
137. Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters S. Sahu et al. 10.1016/j.chemosphere.2020.126969
138. The Impacts of the COVID-19 Lockdown on Air Quality in the Guanzhong Basin, China K. Zhang et al. 10.3390/rs12183042
139. Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia N. Zaman et al. 10.3390/app11167326
140. Estimating high-spatial-resolution daily PM2.5 mass concentration from satellite top-of-atmosphere reflectance based on an improved random forest model Y. Tang et al. 10.1016/j.atmosenv.2023.119724
141. Satellite-Based Spatiotemporal Trends in PM 2.5 Concentrations: China, 2004–2013 Z. Ma et al. 10.1289/ehp.1409481
142. High-resolution satellite-based analysis of ground-level PM 2.5 for the city of Montreal B. Wang & Z. Chen 10.1016/j.scitotenv.2015.10.024
143. Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES) A. Chudnovsky et al. 10.1080/10962247.2012.695321
144. Development of a model for particulate matter pollution in Australia with implications for other satellite-based models G. Pereira et al. 10.1016/j.envres.2017.07.044
145. A robust calibration approach for PM<sub>10</sub> prediction from MODIS aerosol optical depth X. Yap & M. Hashim 10.5194/acp-13-3517-2013
146. Geographical and temporal encoding for improving the estimation of PM2.5 concentrations in China using end-to-end gradient boosting N. Yang et al. 10.1016/j.rse.2021.112828
147. Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies D. Lary et al. 10.4137/EHI.S15664
148. 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
149. PM2.5 Exposure and Birth Outcomes A. Hyder et al. 10.1097/EDE.0000000000000027
150. Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5 S. Cleland et al. 10.1021/acs.est.0c03761
151. Use of earth observation data for applications in public health Q. Weng et al. 10.1080/10106049.2013.838311
152. PM2.5 over North China based on MODIS AOD and effect of meteorological elements during 2003–2015 Y. Chen et al. 10.1007/s11783-019-1202-8
153. Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States H. Lee et al. 10.1021/acs.est.6b01438
154. Estimation of particulate matter pollution using WRF-Chem during dust storm event over India M. Soni et al. 10.1016/j.uclim.2022.101202
155. Using deep ensemble forest for high-resolution mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran H. Bagheri 10.1007/s10661-023-10951-1
156. Columnar and surface aerosol load over the Iberian Peninsula establishing annual cycles, trends, and relationships in five geographical sectors D. Mateos et al. 10.1016/j.scitotenv.2015.03.002
157. Spatial and Temporal Variations of Satellite-Derived Multi-Year Particulate Data of Saudi Arabia: An Exploratory Analysis Y. Aina et al. 10.3390/ijerph111111152
158. A Locally Weighted Neural Network Constrained by Global Training for Remote Sensing Estimation of PM₂.₅ T. Li et al. 10.1109/TGRS.2021.3074569
159. Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States X. Li et al. 10.3390/rs9060620
160. 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
161. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data I. Kloog et al. 10.1016/j.atmosenv.2014.07.014
162. Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017 S. Li et al. 10.1016/j.scitotenv.2019.135755
163. A Hybrid Approach to Estimate Spatially and Temporally Resolved Pm2.5 Distributions from Multi-Sourced Aod Data Q. Pu & Y. Eun-Hye 10.2139/ssrn.4094009
164. Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model S. Ghotbi et al. 10.1016/j.atmosenv.2016.06.057
165. Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei Q. Wang et al. 10.3390/s19051207
166. Daily Ambient NO2Concentration Predictions Using Satellite Ozone Monitoring Instrument NO2Data and Land Use Regression H. Lee & P. Koutrakis 10.1021/es404845f
167. Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health Y. Tian et al. 10.3389/fpubh.2023.1270033
168. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach J. Wei et al. 10.1016/j.rse.2019.111221
169. Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment H. Bai et al. 10.3390/rs14122933
170. Building Public Health Surveillance 3.0: Emerging Timely Measures of Physical, Economic, and Social Environmental Conditions Affecting Health L. Thorpe et al. 10.2105/AJPH.2022.306917
171. Land Use Regression Modeling of PM2.5 Concentrations at Optimized Spatial Scales L. Zhai et al. 10.3390/atmos8010001
172. Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China J. Liu et al. 10.3390/rs11182120
173. Spatiotemporal dynamics of NO2 concentration with linear mixed models: A Bangladesh case study K. Islam et al. 10.1016/j.pce.2022.103119
174. Scientometric and multidimensional contents analysis of PM2.5 concentration prediction J. Gong et al. 10.1016/j.heliyon.2023.e14526
175. Emissions estimation from satellite retrievals: A review of current capability D. Streets et al. 10.1016/j.atmosenv.2013.05.051
176. 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
177. Estimation of monthly bulk nitrate deposition in China based on satellite NO2 measurement by the Ozone Monitoring Instrument L. Liu et al. 10.1016/j.rse.2017.07.005
178. Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China X. Yan et al. 10.1016/j.atmosenv.2017.09.023
179. Aerosol optical depth retrieval over the city of Abu Dhabi, United Arab Emirates (UAE) using Landsat-8 OLI images K. Omari et al. 10.1016/j.apr.2019.01.015
180. Monitoring and Forecasting Air Pollution Levels by Exploiting Satellite, Ground-Based, and Synoptic Data, Elaborated with Regression Models S. Michaelides et al. 10.1155/2017/2954010
181. Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm J. Liu et al. 10.1016/j.atmosenv.2019.04.002
182. 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
183. Satellite-based ground PM2.5 estimation using timely structure adaptive modeling X. Fang et al. 10.1016/j.rse.2016.08.027
184. COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas I. Mandal & S. Pal 10.1016/j.scitotenv.2020.139281
185. A review of machine learning for modeling air quality: Overlooked but important issues D. Tang et al. 10.1016/j.atmosres.2024.107261
186. Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling Q. He & B. Huang 10.1016/j.rse.2017.12.018
187. Air quality improvements and health benefits from China’s clean air action since 2013 Y. Zheng et al. 10.1088/1748-9326/aa8a32
188. Spatiotemporal ozone pollution LUR models: Suitable statistical algorithms and time scales for a megacity scale J. Wang et al. 10.1016/j.atmosenv.2020.117671
189. A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST A. Verma et al. 10.1007/s10661-023-12020-z
190. The impact of urban expansion on the regional environment in Myanmar: a case study of two capital cities C. Wang et al. 10.1007/s10980-018-0632-1
191. Research on Remote Sensing Retrieval Method of PM2.5 Based on FY-4A Satellite with Multiple Machine Learning Methods W. Li & X. Liu 10.54097/ajst.v4i3.4785
192. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter B. Boys et al. 10.1021/es502113p
193. Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5 Y. Xu et al. 10.1016/j.envpol.2018.08.029
194. Dried bottom of Urmia Lake as a new source of dust in the northwestern Iran: Understanding the impacts on local and regional air quality Y. Alizade Govarchin Ghale et al. 10.1016/j.atmosenv.2021.118635
195. Fusion Method Combining Ground-Level Observations with Chemical Transport Model Predictions Using an Ensemble Deep Learning Framework: Application in China to Estimate Spatiotemporally-Resolved PM2.5 Exposure Fields in 2014–2017 B. Lyu et al. 10.1021/acs.est.9b01117
196. High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique L. Xiao et al. 10.1016/j.atmosenv.2017.10.062
197. Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8 T. Zhang et al. 10.1016/j.scitotenv.2019.04.299
198. A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM2.5 Estimation T. Li et al. 10.1109/JSTARS.2020.2977668
199. Benefits of High Resolution PM2.5 Prediction using Satellite MAIAC AOD and Land Use Regression for Exposure Assessment: California Examples H. Lee 10.1021/acs.est.9b03799
200. Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region K. Gonçalves et al. 10.1016/j.atmosenv.2018.03.057
201. Satellite-based ground PM2.5 estimation using a gradient boosting decision tree T. Zhang et al. 10.1016/j.chemosphere.2020.128801
202. Climatological variations in aerosol optical depth and aerosol type identification in Liaoning of Northeast China based on MODIS data from 2002 to 2019 H. Zhao et al. 10.1016/j.scitotenv.2021.146810
203. Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China X. Xu et al. 10.1371/journal.pone.0240430
204. Spatial variations of PM2.5 in Chinese cities for the joint impacts of human activities and natural conditions: A global and local regression perspective S. Wang et al. 10.1016/j.jclepro.2018.08.249
205. Improving satellite aerosol optical Depth-PM2.5 correlations using land use regression with microscale geographic predictors in a high-density urban context Y. Shi et al. 10.1016/j.atmosenv.2018.07.021
206. Spatiotemporal Variability of Remotely Sensed PM2.5 Concentrations in China from 1998 to 2014 Based on a Bayesian Hierarchy Model J. Li et al. 10.3390/ijerph13080772
207. Estimating urban PM10 and PM2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data A. Beloconi et al. 10.1016/j.rse.2015.10.017
208. Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter N. Grantham et al. 10.1002/env.2499
209. Spatial and temporal variability in desert dust and anthropogenic pollution in Iraq, 1997–2010 A. Chudnovsky et al. 10.1080/10962247.2016.1153528
210. High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China Z. Song et al. 10.1016/j.atmosres.2022.106199
211. Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China B. Lv et al. 10.1021/acs.est.5b05940
212. Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance L. Yang et al. 10.1016/j.jenvman.2020.111061
213. A comparative time series analysis and modeling of aerosols in the contiguous United States and China X. Li et al. 10.1016/j.scitotenv.2019.07.072
214. Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran S. Sotoudeheian & M. Arhami 10.1007/s40201-020-00509-5
215. 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
216. 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
217. The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. 10.1016/j.rse.2022.113426
218. Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count W. You et al. 10.1016/j.rse.2015.07.020
219. Machine learning in geosciences and remote sensing D. Lary et al. 10.1016/j.gsf.2015.07.003
220. 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
221. Estimation of PM2.5 concentrations at a high spatiotemporal resolution using constrained mixed-effect bagging models with MAIAC aerosol optical depth L. Li et al. 10.1016/j.rse.2018.09.001
222. Population exposure across central India to PM2.5 derived using remotely sensed products in a three-stage statistical model P. Maheshwarkar & R. Sunder Raman 10.1038/s41598-020-79229-7
223. 10-year spatial and temporal trends of PM<sub>2.5</sub> concentrations in the southeastern US estimated using high-resolution satellite data X. Hu et al. 10.5194/acp-14-6301-2014
224. Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors C. Ho et al. 10.1016/j.envpol.2020.114810
225. The importance of spatial resolutions of Community Multiscale Air Quality (CMAQ) models on health impact assessment X. Jiang & E. Yoo 10.1016/j.scitotenv.2018.01.228
226. Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017 B. Guo et al. 10.1016/j.scitotenv.2021.146288
227. Interpreting aerosol lidar profiles to better estimate surface PM2.5 for columnar AOD measurements D. Chu et al. 10.1016/j.atmosenv.2013.06.031
228. Advancing Exposure Assessment of PM2.5 Using Satellite Remote Sensing: A Review H. Lee 10.5572/ajae.2020.14.4.319
229. Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain B. Choubin et al. 10.1016/j.scitotenv.2019.134474
230. Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model L. Yang et al. 10.1016/j.jclepro.2019.04.231
231. Global poverty estimation using private and public sector big data sources R. Marty & A. Duhaut 10.1038/s41598-023-49564-6
232. Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth X. Zhang et al. 10.1016/j.scitotenv.2018.02.255
233. Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran A. Mirzaei et al. 10.1007/s12145-022-00912-6
234. Assessment of primary and secondary ambient particle trends using satellite aerosol optical depth and ground speciation data in the New England region, United States H. Lee et al. 10.1016/j.envres.2014.04.006
235. Assessment of PM2.5 concentrations and exposure throughout China using ground observations H. Guo et al. 10.1016/j.scitotenv.2017.05.263
236. Urbanization and regional air pollution across South Asian developing countries – A nationwide land use regression for ambient PM2.5 assessment in Pakistan Y. Shi et al. 10.1016/j.envpol.2020.115145
237. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling L. Li et al. 10.1016/j.rse.2019.111584
238. Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model G. Geng et al. 10.1016/j.rse.2015.05.016
239. 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
240. Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011 M. Lee et al. 10.1038/jes.2015.41
241. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. 10.1016/j.atmosenv.2021.118659
242. Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model Z. Ma et al. 10.1016/j.atmosenv.2016.03.040
243. A nonlinear model for estimating ground-level PM10 concentration in Xi'an using MODIS aerosol optical depth retrieval W. You et al. 10.1016/j.atmosres.2015.09.008
244. 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
245. Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain W. Han & L. Tong 10.3390/atmos10050245
246. An idealized sensitivity study of fine particles' impact on the urban vertical temperature structure H. Wu et al. 10.1016/j.uclim.2023.101492
247. Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States J. Belle & Y. Liu 10.3390/rs8100815
248. A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US Y. Wang et al. 10.3390/ijerph15091999
249. Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models P. Ma et al. 10.3390/rs14030599
250. Predicting spatiotemporal variations of PM2.5 concentrations during spring festival for county-level cities in China using VIIRS-DNB data N. Erkin et al. 10.1016/j.atmosenv.2022.119484
251. Particulate air pollution in Durban: Characteristics and its relationship with 1 km resolution satellite aerosol optical depth B. Mahesh et al. 10.1016/j.asr.2022.04.053
252. Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD W. You et al. 10.1007/s11356-015-6027-9
253. A critical assessment of high-resolution aerosol optical depth retrievals for fine particulate matter predictions A. Chudnovsky et al. 10.5194/acp-13-10907-2013
254. Estimation of surface-level PM concentration from satellite observation taking into account the aerosol vertical profiles and hygroscopicity K. Kim et al. 10.1016/j.chemosphere.2015.09.040
255. Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation N. Zhang et al. 10.1038/s41598-018-27771-w
256. Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research J. le Roux et al. 10.3390/rs13152981
257. Assessing the Potential of Artificial Intelligence (Artificial Neural Networks) in Predicting the Spatiotemporal Pattern of Wildfire-Generated PM2.5 Concentration M. Mirzaei et al. 10.3390/geomatics1010003
258. Improving Satellite-Driven PM2.5 Models with VIIRS Nighttime Light Data in the Beijing–Tianjin–Hebei Region, China X. Zhang & H. Hu 10.3390/rs9090908
259. Assimilating a blended dataset of satellite-based estimations and in situ observations to improve WRF-Chem PM2.5 prediction X. Ma et al. 10.1016/j.atmosenv.2023.120284
260. Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India S. Chitranshi et al. 10.1007/s11869-014-0271-x
261. An intercomparison of AOD-converted PM2.5 concentrations using different approaches for estimating aerosol vertical distribution T. Su et al. 10.1016/j.atmosenv.2017.07.054
262. Satellite mapping of PM<sub>2.5</sub> episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor R. Chatfield et al. 10.5194/acp-20-4379-2020
263. Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data B. Chen et al. 10.3390/ijerph15040573
264. Spatial cluster detection of regression coefficients in a mixed‐effects model J. Lee et al. 10.1002/env.2578
265. Exposure to acute air pollution and risk of bronchiolitis and otitis media for preterm and term infants M. Girguis et al. 10.1038/s41370-017-0006-9
266. Estimating PM2.5 speciation concentrations using prototype 4.4 km-resolution MISR aerosol properties over Southern California X. Meng et al. 10.1016/j.atmosenv.2018.03.019
267. 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
268. Estimating PM 2.5 concentrations in a central region of China using a three-stage model Y. Jing et al. 10.1080/17538947.2023.2175499
269. Overview of atmospheric aerosol studies in Malaysia: Known and unknown K. Kanniah et al. 10.1016/j.atmosres.2016.08.002
270. Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach T. Li et al. 10.1002/2017GL075710
271. Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO S. Zhu et al. 10.1109/TGRS.2023.3248180
272. 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
273. Particulate matter concentration mapping from MODIS satellite data: a Vietnamese case study T. Nguyen et al. 10.1088/1748-9326/10/9/095016
274. Estimation of Particulate Matter (PM2.5) Over Kolkata J. Singh et al. 10.1007/s00024-023-03418-4
275. Spatial prediction of PM2.5 concentration using hyper-parameter optimization XGBoost model in China Y. Song et al. 10.1016/j.eti.2023.103272
276. Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products A. Beloconi et al. 10.1016/j.envint.2018.08.041
277. Estimating PM1 concentrations from MODIS over Yangtze River Delta of China during 2014–2017 K. Qin et al. 10.1016/j.atmosenv.2018.09.054
278. 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
279. Satellite remote sensing in epidemiological studies M. Sorek-Hamer et al. 10.1097/MOP.0000000000000326
280. Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China W. Chen et al. 10.1016/j.scitotenv.2020.141093
281. Estimation of Ultrahigh Resolution PM2.5 Mass Concentrations Based on Mie Scattering Theory by Using Landsat8 OLI Images over Pearl River Delta Y. Tang et al. 10.3390/rs13132463
282. Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa O. Arowosegbe et al. 10.1016/j.envpol.2022.119883
283. Improving satellite‐driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S. X. Hu et al. 10.1002/2014JD021920
284. Interpretation of satellite retrievals of PM2.5 over the southern African Interior M. Kneen et al. 10.1016/j.atmosenv.2015.12.016
285. Exploring the Conversion Model from Aerosol Extinction Coefficient to PM1, PM2.5 and PM10 Concentrations H. Shao et al. 10.3390/rs15112742
286. Mapping nighttime PM2.5 from VIIRS DNB using a linear mixed-effect model D. Fu et al. 10.1016/j.atmosenv.2018.02.001
287. Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations X. Fang et al. 10.3390/rs14143381
288. Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5 Exposures In The Mid-Atlantic States I. Kloog et al. 10.1021/es302673e
289. Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements Y. Zheng et al. 10.1016/j.atmosenv.2015.06.046
290. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information G. Chen et al. 10.1016/j.envpol.2017.10.011
291. 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
292. A new indicator on the impact of large-scale circulation on wintertime particulate matter pollution over China B. Jia et al. 10.5194/acp-15-11919-2015
293. Long-Term Exposure to Ambient PM2.5, Sunlight, and Obesity: A Nationwide Study in China R. Chen et al. 10.3389/fendo.2021.790294
294. Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites D. Li et al. 10.3390/rs14225832
295. Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data W. Han et al. 10.3390/app8122624
296. Studying the Regional Transmission of Air Pollution Based on Spatiotemporal Multivariable Data X. Lu et al. 10.3390/atmos14091438
297. Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 X. Yang et al. 10.3389/fenvs.2022.842237
298. Maternal exposure to traffic-related air pollution and birth defects in Massachusetts M. Girguis et al. 10.1016/j.envres.2015.12.010
299. National scale spatiotemporal land-use regression model for PM2.5, PM10 and NO2 concentration in China Z. Zhang et al. 10.1016/j.atmosenv.2018.08.046
300. Ground Level PM2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO2 and Enhanced Vegetation Index (EVI) T. Zhang et al. 10.3390/ijerph13121215
301. Spatial modeling of PM2.5 concentrations with a multifactoral radial basis function neural network B. Zou et al. 10.1007/s11356-015-4380-3
302. Estimation of Soil Moisture Index Using Multi-Temporal Sentinel-1 Images over Poyang Lake Ungauged Zone Y. Zhang et al. 10.3390/rs10010012
303. 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
304. Validation of MERIS/AATSR synergy algorithm for aerosol retrieval against globally distributed AERONET observations and comparison with MODIS aerosol product N. Benas et al. 10.1016/j.atmosres.2013.05.011
305. Classification of dust days by satellite remotely sensed aerosol products M. Sorek-Hamer et al. 10.1080/01431161.2012.748991
306. Estimation of Ground-Level PM2.5 Concentrations in the Major Urban Areas of Chongqing by Using FY-3C/MERSI Q. Zeng et al. 10.3390/atmos9010003
307. Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region G. Zhang et al. 10.3390/ijgi7090368
308. Inversion of Nighttime PM2.5 Mass Concentration in Beijing Based on the VIIRS Day-Night Band X. Zhao et al. 10.3390/atmos7100136
309. Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing Q. Xu et al. 10.1016/j.apr.2020.03.007
310. Long-term variations of the PM2.5 concentration identified by MODIS in the tropical rain forest, Southeast Asia Y. Ma et al. 10.1016/j.atmosres.2018.12.024
311. Improved retrieval of PM2.5 from satellite data products using non-linear methods M. Sorek-Hamer et al. 10.1016/j.envpol.2013.08.002
312. Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China W. Gong et al. 10.3390/rs9101038
313. Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms S. Nabavi et al. 10.1016/j.apr.2018.12.017
314. Semi-Physical Estimates of National-Scale PM10 Concentrations in China Using a Satellite-Based Geographically Weighted Regression Model T. Zhang et al. 10.3390/atmos7070088
315. Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model C. Brokamp et al. 10.1021/acs.est.7b05381
316. A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth Y. Chu et al. 10.3390/atmos7100129
317. Hourly PM2.5 Estimation over Central and Eastern China Based on Himawari-8 Data Y. Xue et al. 10.3390/rs12050855
318. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model T. Zhang et al. 10.3390/ijerph13100974
319. A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China F. Yao et al. 10.1016/j.isprsjprs.2019.03.011
320. Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China L. Li et al. 10.3390/rs10122006
321. Prediction of hourly PM2.5 using a space-time support vector regression model W. Yang et al. 10.1016/j.atmosenv.2018.03.015
322. Estimating Ground-Level Particulate Matter in Five Regions of China Using Aerosol Optical Depth Q. Zeng et al. 10.3390/rs12050881
323. Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements I. Kloog et al. 10.1016/j.atmosenv.2011.08.066
324. Combining Data from Multiple Sources to Evaluate Spatial Variations in the Economic Costs of PM2.5-Related Health Conditions in the Beijing–Tianjin–Hebei Region . Zhang & . Hu 10.3390/ijerph16203994
325. Using new satellite based exposure methods to study the association between pregnancy pm2.5 exposure, premature birth and birth weight in Massachusetts I. Kloog et al. 10.1186/1476-069X-11-40
326. Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model F. Yao et al. 10.3390/rs11070841
327. Temporal and spatial variation of particulate matter and its correlation with other criteria of air pollutants in Lanzhou, China, in spring-summer periods M. Filonchyk et al. 10.1016/j.apr.2018.04.011