Journal Article10.1016/J.ENVRES.2012.11.003
Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression
Xuefei Hu,Lance A. Waller,Mohammad Z. Al-Hamdan,William L. Crosson,Maurice G. Estes,Sue Estes,Dale A. Quattrochi,Jeremy A. Sarnat,Yang Liu +8 more
TL;DR: A geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information, and suggested that North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.
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About: This article is published in Environmental Research. The article was published on 01 Feb 2013. The article focuses on the topics: Data assimilation & Cross-validation.
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Citations
OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
Florian Lautenschlager,Martin Becker,Konstantin Kobs,Michael Steininger,Padraig Davidson,Anna Krause,Andreas Hotho +6 more
TL;DR: This work introduces OpenLUR, an off-the-shelf approach for modeling air pollution that works on a set of novel features solely extracted from the globally and openly available data source OpenStreetMap and is based on state-of- the-art machine learning featuring automated hyper-parameter tuning in order to minimize manual effort.
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Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques
Hussein Habeeb Hamed,Huda Jamal Jumaah,Bahareh Kalantar,Naonori Ueda,Vahideh Saeidi,Shattri Mansor,Zainab Ali Khalaf +6 more
TL;DR: In this article, the authors proposed a global model for PM2.5 particle estimation disregarding the local changes and factors, and the global model was used to estimate the PM 2.5 concentration.
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Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth
TL;DR: In this article, the upwind (Lagrangian) MODIS-AOD along with in situ AOD were used as predictors in empirical models of ground-level PM2.5.
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Hourly PM2.5 Estimates from a Geostationary Satellite Based on an Ensemble Learning Algorithm and Their Spatiotemporal Patterns over Central East China
TL;DR: The spatial agglomeration patterns of the estimated PM2.5 concentrations over central East China show a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer, which can improve the understanding of the formation and transportation processes of regional pollution episodes.
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Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression
TL;DR: In this article , the authors used geographically weighted panel regression (GWPR) to examine the relationship between satellite-derived data, measured ground-level NO2 concentrations, and several controlling meteorological variables from January 2015 to October 2021.
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Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
A. Fotheringham,Chris Brunsdon,Martin Charlton +2 more
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North american regional reanalysis
Fedor Mesinger,Geoff DiMego,Eugenia Kalnay,Kenneth E. Mitchell,Perry Shafran,Wesley Ebisuzaki,Dusan Jovic,John S. Woollen,Eric Rogers,Ernesto Hugo Berbery,Michael Ek,Yun Fan,Robert Grumbine,Wayne Higgins,Hong Li,Ying Lin,Geoff Manikin,David F. Parrish,Wei Shi +18 more
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