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

Abstract

Estimating ground surface PM 2.5 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 PM 2.5 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 PM 2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM 2.5 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 PM 2.5 is in good agreement with the observational data (RMSE = 23.0 µg/m 3 and R 2 = 0.72) and outperforms either AOD-derived PM 2.5 (R 2 = 0.62) or CMAQ simulations (R 2 = 0.51). According to step-specific CVs, in data fusion, AOD-derived PM 2.5 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 PM 2.5 . Based on the products, averaged concentration of annual exposure to PM 2.5 was 55.7 µg/m 3 , while averaged count of polluted days (PM 2.5 > 75 µg/m 3 ) was 81 across China during 2014. Fused estimates will be publicly available for future health-related studies.

Air quality improvements and health benefits from China’s clean air action since 2013

“…In this study, we applied a three-stage data fusion model proposed in our previous study [17] to predict PM 2.5 concentrations in China (excluding Taiwan, map of China is illustrated in figure S1) from 2013-2015, whose spatial resolution is 0.1 • × 0.1 • . Our first-stage model utilized a modified linear mixed-effects (LMEs) model [25] to predict surface PM 2.5 by calibrating satellite-based AOD and PM 2.5 simulations from the WRF-CMAQ model using routine measurements.…”

“…All aftermentioned national and provincial PM 2.5 concentrations present population-weighted values. Details on the PM 2.5 predictions are documented in Xue et al (2017) [17].…”

“…Nonetheless, because of the unavoidable missing AOD measurements, AOD-based statistical approaches are hard to provide spatially continuous PM 2.5 predictions. To tackle the spatial discontinuity of PM 2.5 predictions, our previous study [17] developed a data fusion model that integrates PM 2.5 simulations from air quality models in addition to satellite-based AOD measurements and PM 2.5 observations and provides spatially continuous estimates of PM 2.5 on a daily basis in 2014.…”

Synoptic meteorological modes of variability for fine particulate matter (PM<sub>2.5</sub>) air quality in major metropolitan regions of China

“…PM 2.5 monitoring has been introduced in the national air quality monitoring network in China since 2012 with the published third revision of the "National Ambient Air Quality Standards" (NAAQS) (Zhang and Cao, 2015). Before that, observational spatial distribution of PM 2.5 was mostly estimated by 30 satellite retrievals (Ma et al, 2015;van Donkelaar et al, 2010;Xue et al, 2017;Zheng et al, 2016). One of the disadvantages of PM 2.5 monitoring at present is that there are very few sites with detailed speciation data in China, although short-period studies of PM 2.5 speciation have been conducted (Cao et al, 2012;Huang et al, 2014;Yang et al, 2005Yang et al, , 2011.…”

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

“…Many studies have been published on aerosols in relation to air quality in the eastern part of China, including satellite remote sensing, ground-based measurements, modeling and combinations thereof, which often focus on local or regional aspects (e.g., Song et al, 2009;Ma et al, 2016;Zou et al, 2017;Xue et al, 2017;Miao et al, 2017;Guo et al, 2017). Satellites offer the opportunity to obtain information, using the same instruments and methods, over a large area during a longer period of time.…”