Journal ArticleDOI
Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
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TLDR
A national-scale geographically weighted regression model was developed to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor and confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM 2.5 monitoring network in China.Abstract:
Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that ex...read more
Citations
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Journal ArticleDOI
Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors
Aaron van Donkelaar,Randall V. Martin,Randall V. Martin,Michael Brauer,N. Christina Hsu,Ralph A. Kahn,Robert C. Levy,Alexei Lyapustin,Andrew M. Sayer,David M. Winker +9 more
TL;DR: This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM 2.5 characterization on a global scale.
Journal ArticleDOI
Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013
Zongwei Ma,Xuefei Hu,Andrew M. Sayer,Robert C. Levy,Qiang Zhang,Yingang Xue,Shilu Tong,Jun Bi,Lei Huang,Yang Liu +9 more
TL;DR: Li et al. as discussed by the authors developed a two-stage spatial statistical model using the MODIS Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network.
Journal ArticleDOI
Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling
TL;DR: This study developed a generalized additive modeling (GAM) method for satellite-based PM2.5 concentration mapping that outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R2 and lower RMSEs.
Journal ArticleDOI
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.
TL;DR: This work develops geoscience-derived estimates of PM2.5 composition from a chemical transport model and satellite observations of aerosol optical depth and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation.
Journal ArticleDOI
Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach.
Xuefei Hu,Jessica H. Belle,Xia Meng,Avani Wildani,Lance A. Waller,Matthew J. Strickland,Yang Liu +6 more
TL;DR: A random forest model incorporating aerosol optical depth data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM2.5 concentrations over the conterminous United States in 2011 is developed.
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
AERONET-a federated instrument network and data archive for aerosol Characterization
Brent N. Holben,Thomas F. Eck,Ilya Slutsker,Didier Tanré,J. P. Buis,Alberto Setzer,Eric Vermote,John A. Reagan,Yoram J. Kaufman,Teruyuki Nakajima,François Lavenu,I. Jankowiak,Alexander Smirnov +12 more
TL;DR: The operation and philosophy of the monitoring system, the precision and accuracy of the measuring radiometers, a brief description of the processing system, and access to the database are discussed.
Journal ArticleDOI
The MODIS Aerosol Algorithm, Products and Validation
Lorraine A. Remer,Yoram J. Kaufman,Didier Tanré,Shana Mattoo,D. A. Chu,J. V. Martins,Rong-Rong Li,Charles Ichoku,Robert C. Levy,Richard G. Kleidman,Thomas F. Eck,Eric Vermote,Brent N. Holben +12 more
TL;DR: In this article, the spectral optical thickness and effective radius of the aerosol over the ocean were validated by comparison with two years of Aerosol Robotic Network (AERONET) data.
Journal ArticleDOI
Fine Particulate Air Pollution and Hospital Admission for Cardiovascular and Respiratory Diseases
Francesca Dominici,Roger D. Peng,Michelle L. Bell,Luu Pham,Aidan McDermott,Scott L. Zeger,Jonathan M. Samet +6 more
TL;DR: Short-term exposure to PM2.5 increases the risk for hospital admission for cardiovascular and respiratory diseases and was higher in counties located in the Eastern region of the United States, which included the Northeast, the Southeast, the Midwest, and the South.
Journal ArticleDOI
Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
TL;DR: A technique is developed, termed geographically weighted regression, which attempts to capture variation by calibrating a multiple regression model which allows different relationships to exist at different points in space by using Monte Carlo methods.