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Estimating Ground‐Level PM 2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Particulate Air Pollution, Ambulatory Heart Rate Variability, and Cardiac Arrhythmia in Retirement Community Residents with Coronary Artery Disease
inferredcites →https://doi.org/10.1289/ehp.1409481openalex-snapshot
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The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions
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National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD
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Investigating the performance of satellite-based models in estimating the surface PM2.5 over China
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2020.127153openalex-snapshot
Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010–2016: A multistage approach
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2020.128801openalex-snapshot
Satellite-based ground PM2.5 estimation using a gradient boosting decision tree
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2022.134003openalex-snapshot
Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree
inferred← citeshttps://doi.org/10.1016/j.chemosphere.2022.134758openalex-snapshot
A mixed spatial prediction model in estimating spatiotemporal variations in benzene concentrations in Taiwan
inferred← citeshttps://doi.org/10.1016/j.compag.2023.107946openalex-snapshot
A novel intelligent approach for predicting meteorological drought based on satellite-based precipitation product: Application of an EMD-DFA-DBN hybrid model
inferred← citeshttps://doi.org/10.1016/j.compag.2025.111352openalex-snapshot
Kriging prior regression: A case for kriging-based spatial features with TabPFN in soil mapping
inferred← citeshttps://doi.org/10.1016/j.earscirev.2023.104461openalex-snapshot
Global synthesis of two decades of research on improving PM2.5 estimation models from remote sensing and data science perspectives
inferred← citeshttps://doi.org/10.1016/j.envadv.2022.100328openalex-snapshot
Impact assessment of COVID-19 global pandemic on water, environment, and humans
inferred← citeshttps://doi.org/10.1016/j.envint.2018.11.075openalex-snapshot
Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations
inferred← citeshttps://doi.org/10.1016/j.envint.2019.105297openalex-snapshot
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
inferred← citeshttps://doi.org/10.1016/j.envint.2020.105821openalex-snapshot
Who are more exposed to PM2.5 pollution: A mobile phone data approach
inferred← citeshttps://doi.org/10.1016/j.envint.2020.105827openalex-snapshot
Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States
inferred← citeshttps://doi.org/10.1016/j.envint.2020.106290openalex-snapshot
The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China
inferred← citeshttps://doi.org/10.1016/j.envint.2023.107941openalex-snapshot
An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak
inferred← citeshttps://doi.org/10.1016/j.envint.2024.108818openalex-snapshot
Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations
inferred← citeshttps://doi.org/10.1016/j.envint.2024.108992openalex-snapshot
Combining Google traffic map with deep learning model to predict street-level traffic-related air pollutants in a complex urban environment
inferred← citeshttps://doi.org/10.1016/j.envpol.2019.02.071openalex-snapshot
The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations
inferred← citeshttps://doi.org/10.1016/j.envpol.2019.113047openalex-snapshot
Advancing the prediction accuracy of satellite-based PM2.5 concentration mapping: A perspective of data mining through in situ PM2.5 measurements
inferred← citeshttps://doi.org/10.1016/j.envpol.2019.113395openalex-snapshot
Estimating PM2.5 concentration of the conterminous United States via interpretable convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.envpol.2020.114257openalex-snapshot
Investigation of the spatially varying relationships of PM2.5 with meteorology, topography, and emissions over China in 2015 by using modified geographically weighted regression
inferred← citeshttps://doi.org/10.1016/j.envpol.2020.115720openalex-snapshot
Estimation of hourly PM1 concentration in China and its application in population exposure analysis
inferred← citeshttps://doi.org/10.1016/j.envpol.2020.116327openalex-snapshot
Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network
inferred← citeshttps://doi.org/10.1016/j.envpol.2021.116459openalex-snapshot
A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5
inferred← citeshttps://doi.org/10.1016/j.envpol.2021.117116openalex-snapshot
Prediction of PM2.5 concentrations at unsampled points using multiscale geographically and temporally weighted regression
inferred← citeshttps://doi.org/10.1016/j.envpol.2021.118159openalex-snapshot
Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach
inferred← citeshttps://doi.org/10.1016/j.envpol.2022.118826openalex-snapshot
Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China
inferred← citeshttps://doi.org/10.1016/j.envpol.2022.119510openalex-snapshot
Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact
inferred← citeshttps://doi.org/10.1016/j.envpol.2022.120630openalex-snapshot
Socio-demographic characteristics and inequality in exposure to PM2.5: A case study in the Sichuan basin, China
inferred← citeshttps://doi.org/10.1016/j.envpol.2023.121508openalex-snapshot
Deep Learning Mapping of Surface Mda8 Ozone: The Impact of Predictor Variables on Ozone Levels Over the Contiguous United States
inferred← citeshttps://doi.org/10.1016/j.envpol.2023.123082openalex-snapshot
Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind
inferred← citeshttps://doi.org/10.1016/j.envres.2020.109120openalex-snapshot
A novel framework for decomposing PM2.5 variation and demographic change effects on human exposure using satellite observations
inferred← citeshttps://doi.org/10.1016/j.envres.2021.111020openalex-snapshot
Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders
inferred← citeshttps://doi.org/10.1016/j.envres.2023.115417openalex-snapshot
Evapotranspiration responses to CO2 and its driving mechanisms in four ecosystems based on CMIP6 simulations: Forest, shrub, farm and grass
inferred← citeshttps://doi.org/10.1016/j.envsci.2020.10.004openalex-snapshot
A Bayesian LSTM model to evaluate the effects of air pollution control regulations in Beijing, China
inferred← citeshttps://doi.org/10.1016/j.envsoft.2019.06.014openalex-snapshot
A review of artificial neural network models for ambient air pollution prediction
inferred← citeshttps://doi.org/10.1016/j.envsoft.2021.104996openalex-snapshot
Incorporating land-use regression into machine learning algorithms in estimating the spatial-temporal variation of carbon monoxide in Taiwan
inferred← citeshttps://doi.org/10.1016/j.envsoft.2023.105653openalex-snapshot
Reformulating land-use regression method as sign-constrained regularized regressions: Advantages and improvements
inferred← citeshttps://doi.org/10.1016/j.eswa.2022.117905openalex-snapshot
An integrated approach of Belief Rule Base and Convolutional Neural Network to monitor air quality in Shanghai
inferred← citeshttps://doi.org/10.1016/j.fmre.2021.04.007openalex-snapshot
Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives
inferred← citeshttps://doi.org/10.1016/j.fraope.2025.100372openalex-snapshot
Role of artificial intelligence and learning techniques in time series forecast of PM2.5 data: An extensive review
inferred← citeshttps://doi.org/10.1016/j.hazadv.2026.101065openalex-snapshot
Assessing the impact of data fusion and data quality on spatiotemporal characteristics of exposure models and probability maps - a case study for hazardous mineral fibres
inferred← citeshttps://doi.org/10.1016/j.heliyon.2023.e17609openalex-snapshot
Impact analysis of meteorological variables on PM2.5 pollution in the most polluted cities in China
inferred← citeshttps://doi.org/10.1016/j.ins.2020.03.012openalex-snapshot
Haze pollution causality mining and prediction based on multi-dimensional time series with PS-FCM
inferred← citeshttps://doi.org/10.1016/j.isci.2023.107652openalex-snapshot
Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2019.03.011openalex-snapshot
A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2019.04.015openalex-snapshot
Deep learning in remote sensing applications: A meta-analysis and review
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2019.08.017openalex-snapshot
Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.05.018openalex-snapshot
Mapping PM2.5 concentration at a sub-km level resolution: A dual-scale retrieval approach
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.06.019openalex-snapshot
Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2021.11.015openalex-snapshot
Global estimates of 500 m daily aerodynamic roughness length from MODIS data
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2021.12.002openalex-snapshot
Multiscale and multisource data fusion for full-coverage PM2.5 concentration mapping: Can spatial pattern recognition come with modeling accuracy?
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2022.03.002openalex-snapshot
Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2022.05.011openalex-snapshot
The estimation of hourly PM2.5 concentrations across China based on a Spatial and Temporal Weighted Continuous Deep Neural Network (STWC-DNN)
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2025.04.019openalex-snapshot
SCAGAT: A scene-aware ensemble graph attention network for global PM2.5 pollution mapping via land–atmosphere interactions
inferred← citeshttps://doi.org/10.1016/j.jag.2021.102356openalex-snapshot
Estimating daily full-coverage surface ozone concentration using satellite observations and a spatiotemporally embedded deep learning approach
inferred← citeshttps://doi.org/10.1016/j.jag.2022.102897openalex-snapshot
Spatiotemporal assessments of nutrients and water quality in coastal areas using remote sensing and a spatiotemporal deep learning model
inferred← citeshttps://doi.org/10.1016/j.jag.2022.102955openalex-snapshot
Ground-level ozone estimation based on geo-intelligent machine learning by fusing in-situ observations, remote sensing data, and model simulation data
inferred← citeshttps://doi.org/10.1016/j.jag.2025.104661openalex-snapshot
Attention mechanism augmented random forest model for multiple air pollutants estimation
inferred← citeshttps://doi.org/10.1016/j.jclepro.2019.02.236openalex-snapshot
Exploring the association between the built environment and remotely sensed PM2.5 concentrations in urban areas
inferred← citeshttps://doi.org/10.1016/j.jclepro.2020.123742openalex-snapshot
Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region
inferred← citeshttps://doi.org/10.1016/j.jclepro.2020.123887openalex-snapshot
Mapping PM2.5 concentration at high resolution using a cascade random forest based downscaling model: Evaluation and application
inferred← citeshttps://doi.org/10.1016/j.jclepro.2021.126493openalex-snapshot
High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model
inferred← citeshttps://doi.org/10.1016/j.jclepro.2022.134011openalex-snapshot
Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model
inferred← citeshttps://doi.org/10.1016/j.jclepro.2022.134890openalex-snapshot
Estimating high-resolution PM2.5 concentration in the Sichuan Basin using a random forest model with data-driven spatial autocorrelation terms
inferred← citeshttps://doi.org/10.1016/j.jclepro.2024.143331openalex-snapshot
Spatiotemporal non-stationarity analysis of urban environment using multi-source remote sensing in Chinese metropolitan areas
inferred← citeshttps://doi.org/10.1016/j.jece.2026.121780openalex-snapshot
Predicting distribution characteristics of PM2.5 concentrations across China combining land use regression model and spatiotemporally weighted stacking machine learning algorithms
inferred← citeshttps://doi.org/10.1016/j.jenvman.2018.12.071openalex-snapshot
Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data
inferred← citeshttps://doi.org/10.1016/j.jenvman.2019.05.146openalex-snapshot
Impacts of urban form on air quality: Emissions on the road and concentrations in the US metropolitan areas
inferred← citeshttps://doi.org/10.1016/j.jenvman.2024.122107openalex-snapshot
Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations
inferred← citeshttps://doi.org/10.1016/j.jhazmat.2026.142660openalex-snapshot
High spatiotemporal resolution assessment of PM2.5 and NO2 exposure and respiratory health inequality in Thailand
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2019.124351openalex-snapshot
Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2020.124828openalex-snapshot
Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2021.125969openalex-snapshot
Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2021.127354openalex-snapshot
Freeze/thaw onset detection combining SMAP and ASCAT data over Alaska: A machine learning approach
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2024.130919openalex-snapshot
Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation
inferred← citeshttps://doi.org/10.1016/j.jhydrol.2025.133223openalex-snapshot
Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity
inferred← citeshttps://doi.org/10.1016/j.jqsrt.2026.110139openalex-snapshot
A hybrid physics-ML framework for aerosol optical depth retrieval from OCM-2 on Oceansat-2 satellite
inferred← citeshttps://doi.org/10.1016/j.jtrangeo.2023.103689openalex-snapshot
Understanding the spatio-temporally heterogeneous effects of built environment on urban travel emissions
inferred← citeshttps://doi.org/10.1016/j.measurement.2023.112742openalex-snapshot
Sensitivity analysis and prediction of diesel particulate matter emissions in Indian underground metalliferous mines using regression and machine learning algorithms
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126280openalex-snapshot
A spatial correlation prediction model of urban PM2.5 concentration based on deconvolution and LSTM
inferred← citeshttps://doi.org/10.1016/j.neunet.2020.10.013openalex-snapshot
PM2.5 concentration modeling and prediction by using temperature-based deep belief network
inferred← citeshttps://doi.org/10.1016/j.oregeorev.2018.10.006openalex-snapshot
Mapping mineral prospectivity through big data analytics and a deep learning algorithm
inferred← citeshttps://doi.org/10.1016/j.pce.2021.103074openalex-snapshot
Retrieving spatial variation of aerosol level over urban mixed land surfaces using Landsat imageries: Degree of air pollution in Dhaka Metropolitan Area
inferred← citeshttps://doi.org/10.1016/j.pce.2025.104264openalex-snapshot
Estimation of PM2.5 concentrations using satellite-derived aerosol metrics over an Indian summer monsoon region
inferred← citeshttps://doi.org/10.1016/j.procs.2021.02.056openalex-snapshot
Application of data fusion based on deep belief network in air quality monitoring
inferred← citeshttps://doi.org/10.1016/j.rineng.2026.112663openalex-snapshot
An energy-efficient regional PM2.5 early-warning pipeline integrating transformer forecasting, residual Kriging, and LLM-based health advisory
inferred← citeshttps://doi.org/10.1016/j.rse.2020.111692openalex-snapshot
Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data
inferred← citeshttps://doi.org/10.1016/j.rse.2020.111716openalex-snapshot
Deep learning in environmental remote sensing: Achievements and challenges
inferred← citeshttps://doi.org/10.1016/j.rse.2020.112005openalex-snapshot
Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches
inferred← citeshttps://doi.org/10.1016/j.rse.2020.112136openalex-snapshot
Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications
inferred← citeshttps://doi.org/10.1016/j.rse.2021.112775openalex-snapshot
Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China
inferred← citeshttps://doi.org/10.1016/j.rse.2021.112827openalex-snapshot
A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data
inferred← citeshttps://doi.org/10.1016/j.rse.2021.112844openalex-snapshot
Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data
inferred← citeshttps://doi.org/10.1016/j.rse.2022.113100openalex-snapshot
Retrieval of high spatial resolution precipitable water vapor maps using heterogeneous earth observation data
inferred← citeshttps://doi.org/10.1016/j.rse.2024.114482openalex-snapshot
First retrieval of 24-hourly 1-km-resolution gapless surface ozone (O3) from space in China using artificial intelligence: Diurnal variations and implications for air quality and phytotoxicity
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2018.11.086openalex-snapshot
A novel spatiotemporal convolutional long short-term neural network for air pollution prediction
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.02.077openalex-snapshot
Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.04.134openalex-snapshot
Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.05.344openalex-snapshot
Assessment of urban environmental change using multi-source remote sensing time series (2000–2016): A comparative analysis in selected megacities in Eurasia
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.07.367openalex-snapshot
Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.134463openalex-snapshot
Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: A review
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.134474openalex-snapshot
Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.134540openalex-snapshot
Mapping human’s digital footprints on the Tibetan Plateau from multi-source geospatial big data
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2019.135755openalex-snapshot
Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013–2017
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2020.138724openalex-snapshot
Estimating surface heat and water vapor fluxes by combining two-source energy balance model and back-propagation neural network
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2020.139761openalex-snapshot
Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2020.140925openalex-snapshot
Spatiotemporal heterogeneity of PM2.5 and its relationship with urbanization in North China from 2000 to 2017
inferred← citeshttps://doi.org/10.1016/j.scitotenv.2020.141034openalex-snapshot
Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?
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