Estimating ground-level particulate matter concentrations using satellite-based data: a review
出版年份 2019 全文链接
标题
Estimating ground-level particulate matter concentrations using satellite-based data: a review
作者
关键词
-
出版物
GIScience & Remote Sensing
Volume 57, Issue 2, Pages 174-189
出版商
Informa UK Limited
发表日期
2019-12-17
DOI
10.1080/15481603.2019.1703288
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Toward Improving Short‐Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals
- (2019) Rajesh Kumar et al. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
- Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors
- (2019) Aaron van Donkelaar et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Predicting ground-level PM2.5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach
- (2019) Xintong Li et al. ENVIRONMENTAL POLLUTION
- Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model
- (2019) Massimo Stafoggia et al. ENVIRONMENT INTERNATIONAL
- Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China
- (2019) Zhao-Yue Chen et al. ATMOSPHERIC ENVIRONMENT
- A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China
- (2019) Fei Yao et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm
- (2019) Jianjun Liu et al. ATMOSPHERIC ENVIRONMENT
- Two-stage model for estimating the spatiotemporal distribution of hourly PM1.0 concentrations over central and east China
- (2019) Wei Wang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model
- (2019) Zhenqun Hua et al. Remote Sensing
- An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution
- (2019) Qian Di et al. ENVIRONMENT INTERNATIONAL
- Deep Learning Architecture for Estimating Hourly Ground-Level PM2.5 Using Satellite Remote Sensing
- (2019) Yibo Sun et al. IEEE Geoscience and Remote Sensing Letters
- Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model
- (2019) Lijuan Yang et al. JOURNAL OF CLEANER PRODUCTION
- Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8
- (2019) Taixin Zhang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Review of surface particulate monitoring of dust events using geostationary satellite remote sensing
- (2018) M. Sowden et al. ATMOSPHERIC ENVIRONMENT
- High-resolution spatiotemporal mapping of PM 2.5 concentrations at Mainland China using a combined BME-GWR technique
- (2018) Lu Xiao et al. ATMOSPHERIC ENVIRONMENT
- Assimilating AOD retrievals from GOCI and VIIRS to forecast surface PM 2.5 episodes over Eastern China
- (2018) Jiongming Pang et al. ATMOSPHERIC ENVIRONMENT
- An improved geographically weighted regression model for PM 2.5 concentration estimation in large areas
- (2018) Liang Zhai et al. ATMOSPHERIC ENVIRONMENT
- High-resolution satellite remote sensing of provincial PM 2.5 trends in China from 2001 to 2015
- (2018) C.Q. Lin et al. ATMOSPHERIC ENVIRONMENT
- Improving satellite aerosol optical Depth-PM 2.5 correlations using land use regression with microscale geographic predictors in a high-density urban context
- (2018) Yuan Shi et al. ATMOSPHERIC ENVIRONMENT
- Modelling daily PM 2.5 concentrations at high spatio-temporal resolution across Switzerland
- (2018) Kees de Hoogh et al. ENVIRONMENTAL POLLUTION
- Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM 2.5 concentrations in Taiwan from 2005 to 2015
- (2018) Chau-Ren Jung et al. ENVIRONMENTAL POLLUTION
- Satellite-based high-resolution PM 2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model
- (2018) Qingqing He et al. ENVIRONMENTAL POLLUTION
- Estimating hourly PM 1 concentrations from Himawari-8 aerosol optical depth in China
- (2018) Lin Zang et al. ENVIRONMENTAL POLLUTION
- Estimating spatiotemporal distribution of PM 1 concentrations in China with satellite remote sensing, meteorology, and land use information
- (2018) Gongbo Chen et al. ENVIRONMENTAL POLLUTION
- Predicting monthly high-resolution PM 2.5 concentrations with random forest model in the North China Plain
- (2018) Keyong Huang et al. ENVIRONMENTAL POLLUTION
- Local pollutants go global: The impacts of intercontinental air pollution from China on air quality and morbidity in California
- (2018) Nicole S. Ngo et al. ENVIRONMENTAL RESEARCH
- Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model
- (2018) Cole Brokamp et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Estimation of ultrahigh resolution PM 2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals
- (2018) Tianhao Zhang et al. REMOTE SENSING OF ENVIRONMENT
- Satellite-based mapping of daily high-resolution ground PM 2.5 in China via space-time regression modeling
- (2018) Qingqing He et al. REMOTE SENSING OF ENVIRONMENT
- Predicting daily PM 2.5 concentrations in Texas using high-resolution satellite aerosol optical depth
- (2018) Xueying Zhang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information
- (2018) Gongbo Chen et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Land-Use Regression Modelling of Intra-Urban Air Pollution Variation in China: Current Status and Future Needs
- (2018) Baihuiqian He et al. Atmosphere
- Satellite-based daily PM2.5 estimates during fire seasons in Colorado
- (2018) Guannan Geng et al. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
- Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5
- (2018) Yongming Xu et al. ENVIRONMENTAL POLLUTION
- A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels
- (2018) Ruixin Zhang et al. ENVIRONMENTAL POLLUTION
- Estimating PM1 concentrations from MODIS over Yangtze River Delta of China during 2014–2017
- (2018) Kai Qin et al. ATMOSPHERIC ENVIRONMENT
- The sensitivity of satellite-based PM2.5 estimates to its inputs: Implications to model development in data-poor regions
- (2018) Guannan Geng et al. ENVIRONMENT INTERNATIONAL
- Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms
- (2018) Seyed Omid Nabavi et al. Atmospheric Pollution Research
- Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China
- (2018) Lijuan Li et al. Remote Sensing
- Ground-Level PM2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm
- (2018) Ying Li et al. Remote Sensing
- Estimation of High-Resolution Daily Ground-Level PM2.5 Concentration in Beijing 2013–2017 Using 1 km MAIAC AOT Data
- (2018) Weihong Han et al. Applied Sciences-Basel
- Estimating regional ground‐level PM 2.5 directly from satellite top‐of‐atmosphere reflectance using deep belief networks
- (2018) Huanfeng Shen et al. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
- Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth
- (2018) Lin Zang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Impacts of snow and cloud covers on satellite-derived PM2.5 levels
- (2018) Jianzhao Bi et al. REMOTE SENSING OF ENVIRONMENT
- Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data
- (2018) Kaixu Bai et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States
- (2018) Daniel L. Goldberg et al. ATMOSPHERIC ENVIRONMENT
- Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology
- (2017) Massimo Stafoggia et al. ENVIRONMENT INTERNATIONAL
- A satellite-based model for estimating PM 2.5 concentration in a sparsely populated environment using soft computing techniques
- (2017) Bijan Yeganeh et al. ENVIRONMENTAL MODELLING & SOFTWARE
- Development of PM 2.5 and NO 2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China
- (2017) Xiaofan Yang et al. ENVIRONMENTAL POLLUTION
- Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach
- (2017) Xuefei Hu et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products
- (2017) Fan Chen et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Estimating ground-level PM 2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model
- (2017) Yuanxi Guo et al. REMOTE SENSING OF ENVIRONMENT
- Full-coverage high-resolution daily PM 2.5 estimation using MAIAC AOD in the Yangtze River Delta of China
- (2017) Qingyang Xiao et al. REMOTE SENSING OF ENVIRONMENT
- Daily estimation of ground-level PM 2.5 concentrations at 4 km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations
- (2017) Baolei Lv et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Future global mortality from changes in air pollution attributable to climate change
- (2017) Raquel A. Silva et al. Nature Climate Change
- Space-time mapping of ground-level PM2.5 and NO2 concentrations in heavily polluted northern China during winter using the Bayesian maximum entropy technique with satellite data
- (2017) Qutu Jiang et al. Air Quality Atmosphere and Health
- Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China
- (2017) Xi Mao et al. Atmospheric Pollution Research
- Impact and Suggestion of Column-to-Surface Vertical Correction Scheme on the Relationship between Satellite AOD and Ground-Level PM2.5 in China
- (2017) Wei Gong et al. Remote Sensing
- Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta
- (2017) Man Jiang et al. Remote Sensing
- Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory
- (2016) Rodica Claudia Dumitrache et al. ATMOSPHERIC ENVIRONMENT
- Estimating ground-level PM 2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements
- (2016) Yixuan Zheng et al. ATMOSPHERIC ENVIRONMENT
- Estimating urban ground-level PM 10 using MODIS 3km AOD product and meteorological parameters from WRF model
- (2016) Saba Ghotbi et al. ATMOSPHERIC ENVIRONMENT
- A nonlinear model for estimating ground-level PM10 concentration in Xi'an using MODIS aerosol optical depth retrieval
- (2016) Wei You et al. ATMOSPHERIC RESEARCH
- Identification of nitrogen dioxide and ozone source regions for an urban area in Korea using back trajectory analysis
- (2016) Kowsalya Vellingiri et al. ATMOSPHERIC RESEARCH
- Development of West-European PM 2.5 and NO 2 land use regression models incorporating satellite-derived and chemical transport modelling data
- (2016) Kees de Hoogh et al. ENVIRONMENTAL RESEARCH
- Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States
- (2016) Qian Di et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- 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
- (2016) Baolei Lv et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Enhancing the Applicability of Satellite Remote Sensing for PM2.5 Estimation Using MODIS Deep Blue AOD and Land Use Regression in California, United States
- (2016) Hyung Joo Lee et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
- (2016) Aaron van Donkelaar et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression
- (2016) Bin Zou et al. IEEE Geoscience and Remote Sensing Letters
- Recent advances in (soil moisture) triple collocation analysis
- (2016) A. Gruber et al. International Journal of Applied Earth Observation and Geoinformation
- Satellite-based ground PM 2.5 estimation using timely structure adaptive modeling
- (2016) Xin Fang et al. REMOTE SENSING OF ENVIRONMENT
- 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)
- (2016) Tianhao Zhang et al. International Journal of Environmental Research and Public Health
- Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling
- (2016) Bin Zou et al. Remote Sensing
- A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD
- (2016) Yang Bai et al. Remote Sensing
- A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth
- (2016) Yuanyuan Chu et al. Atmosphere
- Estimating daily PM 2.5 and PM 10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data
- (2015) Itai Kloog et al. ATMOSPHERIC ENVIRONMENT
- Improved aerosol retrieval algorithm using Landsat images and its application for PM10 monitoring over urban areas
- (2015) Nana Luo et al. ATMOSPHERIC RESEARCH
- Formation of Urban Fine Particulate Matter
- (2015) Renyi Zhang et al. CHEMICAL REVIEWS
- Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013
- (2015) Zongwei Ma et al. ENVIRONMENTAL HEALTH PERSPECTIVES
- Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD
- (2015) Yuanyu Xie et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America
- (2015) Aaron van Donkelaar et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Estimating long-term PM2.5 concentrations in China using satellite-based aerosol optical depth and a chemical transport model
- (2015) Guannan Geng et al. REMOTE SENSING OF ENVIRONMENT
- Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation
- (2015) Ying Zhang et al. REMOTE SENSING OF ENVIRONMENT
- A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data
- (2014) Itai Kloog et al. ATMOSPHERIC ENVIRONMENT
- Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals
- (2014) Alexandra A. Chudnovsky et al. ATMOSPHERIC ENVIRONMENT
- Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth
- (2014) Rolando O. Saunders et al. ATMOSPHERIC ENVIRONMENT
- Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
- (2014) Zongwei Ma et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China
- (2014) Weize Song et al. REMOTE SENSING OF ENVIRONMENT
- Impacts of intercontinental transport of anthropogenic fine particulate matter on human mortality
- (2014) Susan C. Anenberg et al. Air Quality Atmosphere and Health
- Improved retrieval of PM2.5 from satellite data products using non-linear methods
- (2013) M. Sorek-Hamer et al. ENVIRONMENTAL POLLUTION
- Evaluation of Atmospheric Aerosol Optical Depth Products at Ultraviolet Bands Derived from MODIS Products
- (2012) Qian Li et al. AEROSOL SCIENCE AND TECHNOLOGY
- Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression
- (2012) Xuefei Hu et al. ENVIRONMENTAL RESEARCH
- The Retrieval of Profiles of Particulate Extinction fromCloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO) Data: Uncertainty and Error Sensitivity Analyses
- (2012) Stuart A. Young et al. JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY
- Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements
- (2011) Itai Kloog et al. ATMOSPHERIC ENVIRONMENT
- Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach
- (2009) Pawan Gupta et al. JOURNAL OF GEOPHYSICAL RESEARCH
- Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method
- (2009) Zifeng Wang et al. REMOTE SENSING OF ENVIRONMENT
- A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements
- (2009) Jie Tian et al. REMOTE SENSING OF ENVIRONMENT
- Spatiotemporal Associations between GOES Aerosol Optical Depth Retrievals and Ground-Level PM2.5
- (2008) Christopher J. Paciorek et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
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