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
Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model
Xuefei Hu,Lance A. Waller,Alexei Lyapustin,Yujie Wang,Yujie Wang,Mohammad Z. Al-Hamdan,William L. Crosson,Maurice G. Estes,Sue Estes,Dale A. Quattrochi,Sweta Jinnagara Puttaswamy,Yang Liu +11 more
TL;DR: In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields and land use parameters (e.g., forest cover, road length, elevation, and point emissions).
Journal ArticleDOI
Daily Estimation of Ground-Level PM2.5 Concentrations over Beijing Using 3 km Resolution MODIS AOD.
TL;DR: A mixed effects model is developed to derive daily estimations of surface PM2.5 levels in Beijing, using the 3 km resolution satellite aerosol optical depth calibrated daily by the newly available high-density surface measurements, and it is demonstrated that a good calibration of the satellite data requires a relatively large number of ground-level PM 2.5 monitoring sites.
Journal ArticleDOI
Satellite-based mapping of daily high-resolution ground PM 2.5 in China via space-time regression modeling
Qingqing He,Bo Huang +1 more
TL;DR: In this article, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations.
Journal ArticleDOI
Full-coverage high-resolution daily PM 2.5 estimation using MAIAC AOD in the Yangtze River Delta of China
Qingyang Xiao,Yujie Wang,Yujie Wang,Howard H. Chang,Xia Meng,Guannan Geng,Alexei Lyapustin,Alexei Lyapustin,Yang Liu +8 more
TL;DR: In this article, a two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM 2.5 concentrations in 2013 and 2014 at 1-km resolution with complete coverage in space and time.
Journal ArticleDOI
A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China
TL;DR: In this article, a satellite-based geographically weighted regression (GWR) model was developed to estimate the daily concentration of ground-level PM2.5 coincident to satellite overpass at regional scale.
References
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Journal ArticleDOI
Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution
C. Arden Pope,Richard T. Burnett,Michael J. Thun,Eugenia E. Calle,Daniel Krewski,Kazuhiko Ito,George D. Thurston +6 more
TL;DR: Fine particulate and sulfur oxide--related pollution were associated with all-cause, lung cancer, and cardiopulmonary mortality and long-term exposure to combustion-related fine particulate air pollution is an important environmental risk factor for cardiopULmonary and lung cancer mortality.
Journal ArticleDOI
NCEP–DOE AMIP-II Reanalysis (R-2)
Masao Kanamitsu,Wesley Ebisuzaki,John S. Woollen,Shi-Keng Yang,J. J. Hnilo,M. Fiorino,Gerald L. Potter +6 more
TL;DR: The NCEP-DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the "50-year" (1948-present) N CEP-NCAR Reanalysis Project.
Journal ArticleDOI
Spatial Autocorrelation: Trouble or New Paradigm?
TL;DR: The paper discusses first how autocorrelation in ecological variables can be described and measured, and ways are presented of explicitly introducing spatial structures into ecological models, and two approaches are proposed.
Journal ArticleDOI
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
TL;DR: The North American Regional Reanalysis (NARR) project as mentioned in this paper uses the NCEP Eta model and its Data Assimilation System (at 32-km-45-layer resolution with 3-hourly output) to capture regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.
Book
Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
TL;DR: In this paper, the basic GWR model is extended to include local statistics and local models for spatial data, and a software for Geographically Weighting Regression is described. But this software is not suitable for the analysis of large scale data.