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

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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.

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Citations
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An operational monitoring method for full coverage pollution enterprises based on satellite remote sensing

TL;DR: Based on the satellite remote sensing technology, the authors explores an operational method of monitoring the whole coverage of pollution enterprises from macro to micro, focusing on monitoring the regional pollution status results of the pollution enterprises and identifying the key monitoring objects including the small and medium-sized ones.
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Estimating pm2.5 in xi’an , china using aerosol optical depth of npp viirs data and meteorological measurements

TL;DR: Wang et al. as mentioned in this paper used Visible Infrared Imaging Radiometer Suite (VIIRS) to retrieve aerosol optical depth (AOD) with the way of dark pixels, and several other major meteorological variables (wind speed, relative humidity, NO2 concentration, ground surface relative humidity and planetary boundary layer height) were combined with AOD to construct a nonlinear multiple regression mode for establishing the relationship between AOD and PM2.5.
Journal ArticleDOI

The deposition mapping of polycyclic aromatic hydrocarbons in megacity Shanghai, China.

TL;DR: In this paper , a deposition map of polycyclic aromatic hydrocarbons (PAHs) with a resolution of 2 × 2 km in megacity Shanghai, China was established.
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Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression

TL;DR: Li et al. as discussed by the authors used a newly developed geographically neural network weighted regression (GNNWR) model to predict 750 m resolution ground-level PM2.5 in China with the high-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) intermediate product (IP) AOD data.
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Real-time estimation of PM2.5 concentrations at high spatial resolution in Busan by fusing observational data with chemical transport model outputs

TL;DR: In this article , a data fusion approach was developed to predict PM2.5 concentrations on an hourly basis with high spatial resolution, this was done by regressing the observed data with the 1 km2 CMAQ-derived spatial coefficients after obtaining site-pairs from a seasonal cluster analysis.
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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