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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A novel multi-factor & multi-scale method for PM2.5 concentration forecasting.
TL;DR: The empirical study focuses on the PM2.5 of Cangzhou, which is one of the most air-polluted cities in China, and indicates that the proposed multi-factor & multi-scale learning paradigms statistically outperform their corresponding original techniques and similar counterparts in terms of prediction accuracy.
Journal ArticleDOI
Ground Level PM 2.5 Estimates over China Using Satellite-Based Geographically Weighted Regression (GWR) Models Are Improved by Including NO₂ and Enhanced Vegetation Index (EVI)
TL;DR: A newly developed GWR model combined with a fused Aerosol Optical Depth product and meteorological parameters could explain approximately 87% of the variability in the corresponding PM2.5 mass concentrations by introducing NO2 and Enhanced Vegetation Index into the Geographically Weighted Regression (GWR) model.
Journal ArticleDOI
Estimating Ground-Level PM2.5 Using Fine-Resolution Satellite Data in the Megacity of Beijing, China
TL;DR: In this paper, the authors used a mixed effects model to calibrate the day-to-day relationship between satellite AOD and ground-level PM2.5 in urban areas from satellite-retrieved AOD data.
Journal ArticleDOI
Application of Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and Weather Research Forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM2.5) over India
Yogesh Sathe,Santosh H. Kulkarni,Pawan Gupta,Akshara Kaginalkar,Sahidul Islam,Prashant Gargava +5 more
Journal ArticleDOI
A Validation Approach Considering the Uneven Distribution of Ground Stations for Satellite-Based PM 2.5 Estimation
TL;DR: A CV-based validation approach considering the uneven spatial distribution of monitoring stations (denoted as SDCV) is proposed, which can yield a more complete and effective evaluation for the popular PM2.5 estimation models than the traditional validation approaches.
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.