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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Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5.
TL;DR: Comparing the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.
Journal ArticleDOI
10-year spatial and temporal trends of PM 2.5 concentrations in the southeastern US estimated using high-resolution satellite data
TL;DR: A new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used, and time-series analysis showed that PM2.5 levels in the southeastern US have decreased by ∼20 % over the 10-year study period.
Journal ArticleDOI
Using the geographical detector technique to explore the impact of socioeconomic factors on PM2.5 concentrations in China
TL;DR: In this paper, the authors explored the impact of socioeconomic factors on PM2.5 concentrations and provided insights into air quality improvement by using satellite observations over the years from 2000 to 2015.
Journal ArticleDOI
Estimating Regional Ground-Level PM 2.5 Directly From Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks
TL;DR: In this article, the authors established the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref-PM modeling).
Journal ArticleDOI
The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations.
TL;DR: The results showed that the performance of retrievals is also decreasing while PM2.5-AOD relationship getting weaker, and the temporal variations in terms of interannual variations were investigated.
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.