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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Estimating PM2.5 in Southern California Using Satellite Data: Factors that Affect Model Performance.
Jennifer D. Stowell,Jianzhao Bi,Mohammad Z. Al-Hamdan,Hyung Joo Lee,Sang-Mi Lee,Frank R. Freedman,Patrick L. Kinney,Yang Liu +7 more
TL;DR: In this paper, the authors examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model, where the first stage estimated the temporal PM2.5/AOD relationships using a linear mixed effects model at 1 km resolution.
Journal ArticleDOI
A grouped spatial-temporal model for PM2.5 data and its applications on outlier detection
Baishan Guo,Rui Pan,Xuening Zhu +2 more
TL;DR: Wang et al. as mentioned in this paper adopted a grouped spatial-temporal model to depict the distribution of PM2.5, capturing the spatial heterogeneity in the patterns of PM 2.5 concentrations.
Proceedings ArticleDOI
Satellite data supporting to monitor air quality from pm2.5 indicator
Tran Thi Van,Vo Quoc Bao +1 more
TL;DR: In this paper, the authors presented the research to simulate air quality from PM2.5 indicator determined by satellite data and established a regression equation for mapping PM 2.5 distribution in Ho Chi Minh city.
Proceedings ArticleDOI
Integration of Remote Sensing and Geographic Information System for Urban Air Quality Assessment
Zhonghua Jin,Yan Yuan +1 more
TL;DR: In this article, the authors used remote sensing and Geographic Information System (GIS) technologies to examine the urban area air quality in Houston-Galveston region, where air pollution has become a major environmental pollution concern.
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.