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Remote Sens. 2017, 9(3), 221; doi:10.3390/rs9030221

Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China

1
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
2
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
3
The Collaborative Innovation Center for Regional Environmental Quality, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Müller and Prasad S. Thenkabail
Received: 15 December 2016 / Revised: 25 January 2017 / Accepted: 27 February 2017 / Published: 1 March 2017
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
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Abstract

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. Through assembling techniques, including linear mixed effect regressions with a spatial-varying coefficient, a maximum likelihood estimator and the spatiotemporal Kriging together, we develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1° over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.0 μg/m3 and R2 = 0.72) and outperforms either AOD-derived PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-derived PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides spatiotemporally complete predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.7 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81 across China during 2014. Fused estimates will be publicly available for future health-related studies. View Full-Text
Keywords: fine particulate matter (PM2.5); aerosol optical depth (AOD); community multi-scale air quality (CMAQ) model; data fusion; exposure assessment fine particulate matter (PM2.5); aerosol optical depth (AOD); community multi-scale air quality (CMAQ) model; data fusion; exposure assessment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Xue, T.; Zheng, Y.; Geng, G.; Zheng, B.; Jiang, X.; Zhang, Q.; He, K. Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China. Remote Sens. 2017, 9, 221.

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