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Author Notes:

Correspondence and requests for materials should be addressed to Z.M. (email: njumazw@163.com) or J.B. (email: jbi@nju.edu.cn)

Z.M. and J.B. conceived this study, W.Y. and Z.M. performed the data processing and model development, Y.L. and W.Y. analyzed the results, W.Y. wrote the manuscript.

All authors reviewed the manuscript.

Helpful comments from the anonymous reviewers are greatly appreciated.

Competing Interests: Te authors declare that they have no competing interests.

Subjects:

Research Funding:

This work was supported by the National Natural Science Foundation of China (71433007, 91644220, and 41601546) and Jiangsu Natural Science Foundation of China (BK20160624).

Keywords:

  • Science & Technology
  • Multidisciplinary Sciences
  • Science & Technology - Other Topics
  • GROUND-LEVEL PM2.5
  • GEOGRAPHICALLY WEIGHTED REGRESSION
  • PARTICULATE AIR-POLLUTION
  • AEROSOL OPTICAL DEPTH
  • MODIS
  • STATES
  • LAND
  • AOD
  • Atmospheric science
  • Environment sciences

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting

Tools:

Journal Title:

Scientific Reports

Volume:

Volume 7

Publisher:

, Pages 7048-7048

Type of Work:

Article | Final Publisher PDF

Abstract:

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2 = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

Copyright information:

© The Author(s) 2017

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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