102 citations as recorded by crossref.

1. A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US Y. Wang et al.
2. Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China L. Li et al.
3. A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020 B. Chen et al.
4. Sensors and systems for air quality assessment monitoring and management: A review D. Singh et al.
5. Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data W. Han et al.
6. A Comparative Assessment of Multisensor Data Merging and Fusion Algorithms for High-Resolution Surface Reflectance Data X. Wei et al.
7. Satellite‐Based Daily PM2.5 Estimates During Fire Seasons in Colorado G. Geng et al.
8. Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM2.5 and aerosol optical depth H. Zhu et al.
9. Performance comparison of Fengyun-4A and Himawari-8 in PM2.5 estimation in China Y. Hu et al.
10. Comparison of PMCAMx aerosol optical depth predictions over Europe with AERONET and MODIS measurements A. Panagiotopoulou et al.
11. Pollution and its Impacts on the South American Cryosphere L. Molina et al.
12. Long-Term Exposure to Ambient PM2.5 and Increased Risk of CKD Prevalence in China G. Li et al.
13. Assessing the Effect of the Long-Term Variations in Aerosol Characteristics on Satellite Remote Sensing of PM2.5 Using an Observation-Based Model C. Lin et al.
14. Estimating PM2.5 in Southern California using satellite data: factors that affect model performance J. Stowell et al.
15. Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements Y. Zheng et al.
16. Establishment of PM2.5 Prediction Model Based on MAIAC AOD Data of High Resolution Remote Sensing Images X. Han et al.
17. A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth Y. Chu et al.
18. Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach W. He et al.
19. Epigenetic mechanisms of particulate matter exposure: air pollution and hazards on human health D. Gavito-Covarrubias et al.
20. Spatiotemporal Distributions of PM2.5 Concentrations in the Beijing–Tianjin–Hebei Region From 2013 to 2020 X. Yang et al.
21. 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
22. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015 C. Jung et al.
23. Cross-comparison and evaluation of air pollution field estimation methods H. Yu et al.
24. Source characteristics of non-refractory particulate matter (NR-PM1) using high-resolution time-of-flight aerosol mass spectrometric (HR-ToF-AMS) measurements in the urban industrial city in India A. Vispute et al.
25. High-resolution satellite remote sensing of provincial PM2.5 trends in China from 2001 to 2015 C. Lin et al.
26. Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation X. Lu et al.
27. Influence of 2000–2050 climate change on particulate matter in the United States: results from a new statistical model L. Shen et al.
28. An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China Y. Wu et al.
29. Retrospective assessment of pregnancy exposure to particulate matter from desert dust on a Caribbean island: could satellite-based aerosol optical thickness be used as an alternative to ground PM10 concentration? S. Tuffier et al.
30. Estimating Ground-Level Hourly PM2.5 Concentrations in Thailand Using Satellite Data: A Log-Linear Model With Sum Contrast Analysis S. Buya et al.
31. Spatiotemporal dynamics and exposure analysis of daily PM2.5 using a remote sensing-based machine learning model and multi-time meteorological parameters B. Chen et al.
32. Satellite-based ground PM2.5 estimation using a gradient boosting decision tree T. Zhang et al.
33. Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018 L. Gladson et al.
34. 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.
35. Air pollution and lung cancer: a comprehensive review V. Héluain et al.
36. Strong Dependence of U.S. Summertime Air Quality on the Decadal Variability of Atlantic Sea Surface Temperatures L. Shen et al.
37. Estimation of PM2.5 and PM10 Mass Concentrations in Beijing Using Gaofen-1 Data at 100 m Resolution S. Wu et al.
38. Spatial regression modelling of particulate pollution in Calgary, Canada S. Bertazzon et al.
39. Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model Z. Ma et al.
40. Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model P. Kim et al.
41. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach J. Wei et al.
42. Estimating ground-level PM2.5 concentrations using two-stage model in Beijing-Tianjin-Hebei, China W. Guo et al.
43. Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach X. Hu et al.
44. An Ensemble Machine-Learning Model To Predict Historical PM2.5Concentrations in China from Satellite Data Q. Xiao et al.
45. Seasonal monitoring and estimation of regional aerosol distribution over Po valley, northern Italy, using a high-resolution MAIAC product B. Arvani et al.
46. Spatial patterns and temporal variations of traffic-related air pollutants and estimating its health effects in Isfahan city, Iran Y. Hajizadeh et al.
47. Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01° × 0.01° spatial resolution C. Zhao et al.
48. Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China Q. Xiao et al.
49. Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011 M. Lee et al.
50. 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.
51. Methods, availability, and applications of PM2.5 exposure estimates derived from ground measurements, satellite, and atmospheric models M. Diao et al.
52. Regional differences of pollution emissions in China: contributing factors and mitigation strategies B. Xu & B. Lin
53. Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain W. Han & L. Tong
54. Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data B. Chen et al.
55. Estimating PM2.5 concentrations at public schools in North Carolina using multiple data sources and interpolation methods R. Carroll et al.
56. Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model S. Ghotbi et al.
57. Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model F. Yao et al.
58. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information G. Chen et al.
59. Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data M. Li et al.
60. Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes S. Ebelt et al.
61. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset G. Song et al.
62. A Clearer Picture of China’s Air: Using Satellite Data and Ground Monitoring to Estimate PM 2.5 over Time N. Seltenrich
63. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model Y. Guo et al.
64. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al.
65. Clarifying Relationship between PM2.5 Concentrations and Spatiotemporal Predictors Using Multi-Way Partial Dependence Plots H. Shi et al.
66. Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter B. Boys et al.
67. Air Pollution and Lung Cancer: A Review by International Association for the Study of Lung Cancer Early Detection and Screening Committee C. Berg et al.
68. A new indicator on the impact of large-scale circulation on wintertime particulate matter pollution over China B. Jia et al.
69. Long-term trend of PM2.5 over five Indian megacities using a new statistical approach K. Ravindra et al.
70. Data fusion for air pollution monitoring: satellite, ground based and ensemble models for PM 2.5 prediction A. Ullah et al.
71. Stacking machine learning model for estimating hourly PM2.5 in China based on Himawari 8 aerosol optical depth data J. Chen et al.
72. Maternal exposure to traffic-related air pollution and birth defects in Massachusetts M. Girguis et al.
73. Evaluation of gap-filling approaches in satellite-based daily PM2.5 prediction models Q. Xiao et al.
74. Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8 T. Zhang et al.
75. Improving satellite‐driven PM2.5 models with Moderate Resolution Imaging Spectroradiometer fire counts in the southeastern U.S. X. Hu et al.
76. Estimation of Ultrahigh Resolution PM2.5 in Urban Areas by Using 30 m Landsat-8 and Sentinel-2 AOD Retrievals H. Lin et al.
77. Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth X. Jin et al.
78. Satellite-Based Spatiotemporal Trends in PM 2.5 Concentrations: China, 2004–2013 Z. Ma et al.
79. 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.
80. Toward Understanding the Differences of PM2.5 Characteristics Among Five China Urban Cities K. Zhang et al.
81. Air Pollution Monitoring for Health Research and Patient Care. An Official American Thoracic Society Workshop Report K. Cromar et al.
82. Spatiotemporal pattern analysis of PM2.5 and the driving factors in the middle Yellow River urban agglomerations Y. Mi et al.
83. Satellite data for environmental justice: a scoping review of the literature in the United States T. Sayyed et al.
84. Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid C. Keet et al.
85. Evaluation of linear, nonlinear, and hybrid models for predicting PM2.5 based on a GTWR model and MODIS AOD data M. Mirzaei et al.
86. Constraining chemical transport PM2.5 modeling outputs using surface monitor measurements and satellite retrievals: application over the San Joaquin Valley M. Friberg et al.
87. PM2.5 Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network J. Wang et al.
88. Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment T. Li et al.
89. Pediatric Emergency Visits and Short-Term Changes in PM 2.5 Concentrations in the U.S. State of Georgia M. Strickland et al.
90. Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health Y. Tian et al.
91. Recent literature in cartography and geographic information science M. Finn & D. Thunen
92. Concentrations and mortality due to short- and long-term exposure to PM2.5 in a megacity of Iran (2014–2019) Y. Hajizadeh et al.
93. High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model Z. Wang et al.
94. A comprehensive review delineates advancements in retrieving particulate matter utilising satellite aerosol optical depth: Parameter consideration, data processing, models development and future perspectives S. Padimala & C. Matli
95. Disparities in air quality downscaler model uncertainty across socioeconomic and demographic indicators in North Carolina S. Zhou et al.
96. Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective Z. Ma et al.
97. New Homogeneous Spatial Areas Identified Using Case-Crossover Spatial Lag Grid Differences between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Hospitalizations J. Braggio et al.
98. How to reduce PM2.5? Perspective from a spatial autoregressive threshold panel model F. Li et al.
99. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information G. Chen et al.
100. Satellite-based ground PM2.5 estimation using timely structure adaptive modeling X. Fang et al.
101. Long-Term Exposure to Air Pollution and Survival After Ischemic Stroke G. Chen et al.
102. Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter B. Sadeghi et al.