Cargando…

Real-Time Estimation of Satellite-Derived PM(2.5) Based on a Semi-Physical Geographically Weighted Regression Model

The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM(2.5)) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM(2.5) mass concentrations at national scale using the Aqua Moderate Reso...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Tianhao, Liu, Gang, Zhu, Zhongmin, Gong, Wei, Ji, Yuxi, Huang, Yusi
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2016
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5086713/ https://www.ncbi.nlm.nih.gov/pubmed/27706054 http://dx.doi.org/10.3390/ijerph13100974

Ejemplares similares

Ground Level PM(2.5) Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO(2) and Enhanced Vegetation Index (EVI)
por: Zhang, Tianhao, et al.
Publicado: (2016)

Implications of Nonstationary Effect on Geographically Weighted Total Least Squares Regression for PM(2.5) Estimation
por: Mokhtari, Arezoo, et al.
Publicado: (2021)

The View from Afar: Satellite-Derived Estimates of Global PM(2.5)
por: Konkel, Lindsey
Publicado: (2015)

Estimation of On‐Road PM(2.5) Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning
por: Tong, Chengzhuo, et al.
Publicado: (2022)

Study on PM2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model
por: Cao, Qilong, et al.
Publicado: (2018)

Global and Geographically and Temporally Weighted Regression Models for Modeling PM(2.5) in Heilongjiang, China from 2015 to 2018
por: Wei, Qingbin, et al.
Publicado: (2019)

Spatiotemporal Pattern of PM(2.5) Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression
por: Luo, Jieqiong, et al.
Publicado: (2017)

PM(2.5) concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China
por: Quan, Weilin, et al.
Publicado: (2023)

PM(2.5) Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression
por: Liaw, Jiun-Jian, et al.
Publicado: (2020)

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM(2.5) Components
por: Zhang, Tianyu, et al.
Publicado: (2020)

Non-Iterative Multiscale Estimation for Spatial Autoregressive Geographically Weighted Regression Models
por: Gao, Shi-Jie, et al.
Publicado: (2023)

A modification to geographically weighted regression
por: Leong, Yin-Yee, et al.
Publicado: (2017)

Estimating the Impact of COVID-19 on the PM(2.5) Levels in China with a Satellite-Driven Machine Learning Model
por: Li, Qiulun, et al.
Publicado: (2021)

Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information
por: Liu, Yang, et al.
Publicado: (2009)

A Clearer Picture of China’s Air: Using Satellite Data and Ground Monitoring to Estimate PM(2.5) over Time
por: Seltenrich, Nate
Publicado: (2016)

Improving satellite-based PM(2.5) estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
por: Yu, Wenxi, et al.
Publicado: (2017)

A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
por: Wang, Fang, et al.
Publicado: (2018)

Satellite-Based Spatiotemporal Trends in PM(2.5) Concentrations: China, 2004–2013
por: Ma, Zongwei, et al.
Publicado: (2015)

Bayesian geostatistical modelling of PM(10) and PM(2.5) surface level concentrations in Europe using high-resolution satellite-derived products
por: Beloconi, Anton, et al.
Publicado: (2018)

Spatio-Temporal Variation of PM(2.5) Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China
por: Lin, Gang, et al.
Publicado: (2013)

10-year spatial and temporal trends of PM(2.5) concentrations in the southeastern US estimated using high-resolution satellite data
por: Hu, X., et al.
Publicado: (2014)

A Satellite–Ground Hybrid Approach: Relative Risks for Exposures to PM(2.5) Estimated from a Combination of Data Sources
por: Seltenrich, Nate
Publicado: (2017)

Keeping an Eye on PM(2.5): Satellite Data Reveal Global Picture of Particulate Pollution
por: Spivey, Angela
Publicado: (2010)

Geographically weighted temporally correlated logistic regression model
por: Liu, Yang, et al.
Publicado: (2018)

Satellite-Based Estimation of Hourly PM(2.5) Concentrations Using a Vertical-Humidity Correction Method from Himawari-AOD in Hebei
por: Zeng, Qiaolin, et al.
Publicado: (2018)

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM(2.5) levels during the Camp Fire episode in California
por: Vu, Bryan N., et al.
Publicado: (2022)

Estimation of ground-level PM(2.5) concentration using MODIS AOD and corrected regression model over Beijing, China
por: Xu, Xinghan, et al.
Publicado: (2020)

Forecasting PM(2.5)-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM(2.5) data from 1998 to 2016: a modeling study
por: Liao, Wei-Bin, et al.
Publicado: (2020)

Spatial distribution characteristics of PM(2.5) and PM(10) in Xi’an City predicted by land use regression models
por: Han, Li, et al.
Publicado: (2020)

Geographic Variation in the Association between Ambient Fine Particulate Matter (PM(2.5)) and Term Low Birth Weight in the United States
por: Hao, Yongping, et al.
Publicado: (2015)

Long-term variation of satellite-based PM2.5 and influence factors over East China
por: He, Qianshan, et al.
Publicado: (2018)

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM(2.5) Mass and Composition
por: Belle, Jessica H., et al.
Publicado: (2017)

Using satellite data on remote transportation of air pollutants for PM(2.5) prediction in northern Taiwan
por: Kibirige, George William, et al.
Publicado: (2023)

Using new satellite based exposure methods to study the association between pregnancy pm(2.5) exposure, premature birth and birth weight in Massachusetts
por: Kloog, Itai, et al.
Publicado: (2012)

Low birth weight and PM(2.5) in Puerto Rico
por: Kirwa, Kipruto, et al.
Publicado: (2019)

Wavelet geographically weighted regression for spectroscopic modelling of soil properties
por: Song, Yongze, et al.
Publicado: (2021)

Short period PM(2.5) prediction based on multivariate linear regression model
por: Zhao, Rui, et al.
Publicado: (2018)

A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM(2.5) Concentration by Integrating Multisource Datasets
por: Shi, Yuan, et al.
Publicado: (2021)

Estimating ground-level PM(2.5) using subset regression model and machine learning algorithms in Asian megacity, Dhaka, Bangladesh
por: Islam, Abu Reza Md. Towfiqul, et al.
Publicado: (2023)

Real-Time Estimation of Population Exposure to PM(2.5) Using Mobile- and Station-Based Big Data
por: Chen, Bin, et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_v3ikcc424mp05knro5egck87l9