4 years ago

Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures

Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures
The accurate measurement of particulate matter (PM) provides a crucial basis for health impact assessment and pollution management and control. However, monitoring stations of air pollution are limited worldwide. Recently, some researchers have attempted to estimate the levels of PM based on remote sensing data, but the methods still need to be validated and further improved. Objectives This study aimed to develop a new model, to estimate daily ground-level PM2.5 concentrations using the fused aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectro radiometer and meteorological information. Methods We combined generalized additive mixed-effects model with the log-linked Gaussian error distribution and non-linear exposure-lag-response model for AOD and meteorological measures, to estimate daily ground-level PM2.5 concentrations in 2014–2015 in Guangzhou, China. Results The PM2.5 concentration was significantly associated with AOD and meteorological measures. Compared to the log-linear model, the non-linear exposure-lag-response model had better model performance with a higher temporal (spatial) cross-validation R–square (0.81 (0.81) vs 0.67 (0.67)), and a smaller mean absolute percentage error (17.65% (16.90%) vs 21.22% (21.01%)). AOD explained about 15% variations of PM2.5 in the mixed-effect model. The planetary-boundary -layer-height-revised AOD and relative-humidity-revised PM2.5 did not significantly improve the model performance. Conclusion Considering the non-linear exposure-lag-response association between PM2.5 and AOD and meteorological factors can significantly increase the modelling ability to estimate PM2.5 concentrations.

Publisher URL: www.sciencedirect.com/science

DOI: S135223101730729X

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