Journal ArticleDOI
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
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TLDR
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.About:
This article is published in Environmental Research.The article was published on 2013-02-01. It has received 288 citations till now. The article focuses on the topics: Data assimilation & Cross-validation.read more
Citations
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Journal ArticleDOI
Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013
Michael Brauer,Greg Freedman,Joseph Frostad,Aaron van Donkelaar,Randall V. Martin,Frank Dentener,Rita Van Dingenen,Kara Estep,Heresh Amini,Joshua S. Apte,Kalpana Balakrishnan,Lars Barregard,David M. Broday,Valery L. Feigin,Santu Ghosh,Philip K. Hopke,Luke D. Knibbs,Yoshihiro Kokubo,Yang Liu,Stefan Ma,Lidia Morawska,José Luis Texcalac Sangrador,Gavin Shaddick,H. Ross Anderson,Theo Vos,Mohammad H. Forouzanfar,Richard T. Burnett,Aaron Cohen +27 more
TL;DR: In this paper, the authors combined satellite-based estimates, chemical transport model simulations, and ground measurements from 79 different countries to produce global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0. 1° spatial resolution for five-year intervals from 1990 to 2010 and the year 2013.
Ambient air pollution exposure estimation for the global burden of disease 2013
Michael Brauer,Greg Freedman,Joseph Frostad,Aaron van Donkelaar,Randall V. Martin,Frank Dentener,Rita Van Dingenen,Kara Estep,Heresh Amini,Joshua S. Apte,Kalpana Balakrishnan,Lars Barregard,David M. Broday,Valery L. Feigin,Santu Ghosh,Philip K. Hopke,Luke D. Knibbs,Yoshihiro Kokubo,Yang Liu,Stefan Ma,Lidia Morawska,José Luis Texcalac Sangrador,Gavin Shaddick,H. Ross Anderson,Theo Vos,Mohammad H. Forouzanfar,Richard T. Burnett,Aaron Cohen +27 more
TL;DR: In this article, the authors combined satellite-based estimates, chemical transport model (CTM) simulations and ground measurements from 79 different countries to produce new global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0. 1° spatial resolution for five-year intervals from 1990-2010 and the year 2013.
Journal ArticleDOI
Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013
Zongwei Ma,Xuefei Hu,Andrew M. Sayer,Robert C. Levy,Qiang Zhang,Yingang Xue,Shilu Tong,Jun Bi,Lei Huang,Yang Liu +9 more
TL;DR: Li et al. as discussed by the authors developed a two-stage spatial statistical model using the MODIS Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network.
Journal ArticleDOI
Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
TL;DR: A national-scale geographically weighted regression model was developed to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor and confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM 2.5 monitoring network in China.
Journal ArticleDOI
Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach.
Xuefei Hu,Jessica H. Belle,Xia Meng,Avani Wildani,Lance A. Waller,Matthew J. Strickland,Yang Liu +6 more
TL;DR: A random forest model incorporating aerosol optical depth data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM2.5 concentrations over the conterminous United States in 2011 is developed.
References
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Journal ArticleDOI
Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS
TL;DR: In this article, the authors compared the ability of the aerosol optical thickness (AOT) retrieved by the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer(MODIS) to predict ground-level PM2.5 concentrations in St. Louis, MO and its surrounding areas.
Journal ArticleDOI
An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan
TL;DR: The relationship between aerosol optical depth estimated from satellite data at 5 km spatial resolution and the mass of fine particles ≤2.5 μm in aerodynamic diameter is examined to examine the time-space dynamics of air pollution in Delhi following recent air quality regulations, and to assess exposure to air pollution before and after the regulations and its impact on health.
Journal ArticleDOI
Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands
Martijn Schaap,Arnoud Apituley,Renske Timmermans,R. B. A. Koelemeijer,G. de Leeuw,G. de Leeuw +5 more
TL;DR: In this article, the authors used LIDAR observations to detect residual cloud contamination in the AERONET L1.5 data at Cabauw and showed a low correlation between the two properties.
Journal ArticleDOI
Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems
Quan Wang,Jian Ni,John Tenhunen +2 more
TL;DR: In this paper, a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non-stationarity in the parameters estimated for forest ecosystems in China, was obtained.
Journal ArticleDOI
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
Jie Tian,Dongmei Chen +1 more
TL;DR: In this paper, a semi-empirical model is developed to predict the hourly concentration of ground-level fine particulate matter (PM 2.5 ) coincident to satellite overpass, at a regional scale.