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
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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[Space-Time Estimations and Mapping of PM 2.5 Fine Particulates Based on Multi-source Data].
TL;DR: A new technique, Bayesian maximum entropy (BME) combined with geographically weighted regression (GWR), is used to evaluate the spatial and temporal characteristics of PM2.5 exposure in an eastern region of China in winter, and the prediction by the BME were greatly improved.
Journal ArticleDOI
Estimating the spatial distribution of PM 2.5 concentration by integrating geographic data and field measurements
TL;DR: Wang et al. as mentioned in this paper utilized multiple sources of remote sensing and GIS data to estimate the spatial distribution of PM 2.5 concentration in Shijiazhuang, China, by utilizing multivariate linear regression modeling, and integrating year average values of PM2.5 collected from local environment observing stations.
Journal ArticleDOI
Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia.
Xiaofei Ma,Yu Ding,Haiyang Shi,Wei Yan,Xin Dou,Friday Uchenna Ochege,Geping Luo,Chengyi Zhao +7 more
TL;DR: In this article, the authors evaluated population exposure risk to AOD in six ecological zones (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert areas (JBDA), Tarim Basin desert area,TBDA and Hexi corridor desert area(HCDA)).
Journal ArticleDOI
Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia
TL;DR: In this paper , the authors evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area, TBDA, and Hexi corridor desert areas (HCDA)).
Journal ArticleDOI
Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran
TL;DR: In this paper, the authors developed an optimized prediction model to estimate a fine-resolution grid of ground-level PM25 levels over Tehran using remote sensing data to obtain fineresolution grids of particulate levels in highly polluted environments in areas such as Middle East with the abundance of brightly reflecting deserts.
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.