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Journal ArticleDOI

Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression

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.
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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.

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Citations
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Journal ArticleDOI

Neighborhood diversity and the creative class in Chicago

TL;DR: This paper used OLS regression and geographically weighted regression (GWR) to test the hypothesis that there exists significant associations between particular types of neighborhood diversity (i.e., sexual orientation, language, race, and income) and the proportion of workers with specific creative class occupations.
Journal ArticleDOI

Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions.

TL;DR: The results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation.
Journal ArticleDOI

A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China

TL;DR: In this article, the authors used the VIIRS AOD, together with multi-source auxiliary variables, to develop a spatially structured adaptive two-stage model to estimate ground-level PM2.5 concentrations at a 6-km spatial resolution.
Journal ArticleDOI

Spatial Variation of the Relationship between PM2.5 Concentrations and Meteorological Parameters in China

TL;DR: The results indicated that PM2.5 had a strong and stable correlation with meteorological parameters and their spatial variance in China for the period 2001–2010, and the relationship between the variables changed over space.
Journal ArticleDOI

Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer

TL;DR: The result indicates that the optimal subset regression model with the parameters of the depth and temperature difference of the inversion can significantly improve the accuracy of the predictions of surface PM2.5 concentrations.
References
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Journal ArticleDOI

Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution

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)

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?

Pierre Legendre
- 01 Sep 1993 - 
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

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.
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