An Ensemble Spatiotemporal Model for Predicting PM2.5 Concentrations

International Journal of Environmental Research and Public Health
Lianfa LiYing Fang

Abstract

Although fine particulate matter with a diameter of <2.5 μm (PM2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 μm (PM10), measurements of PM2.5 have only recently been performed, and the spatial coverage of these measurements is limited. Comprehensively assessing PM2.5 pollution levels and the cumulative health effects is difficult because PM2.5 monitoring data for prior time periods and certain regions are not available. In this paper, we propose a promising approach for robustly predicting PM2.5 concentrations. In our approach, a generalized additive model is first used to quantify the non-linear associations between predictors and PM2.5, the bagging method is used to sample the dataset and train different models to reduce the bias in prediction, and the variogram for the daily residuals of the ensemble predictions is then simulated to improve our predictions. Shandong Province, China, is the study region, and data from 96 monitoring stations were included. To train and validate the models, we used PM2.5 measurement data from 2014 with other predictors, including PM10 data, meteorological parameters, remote sensing data, and land-use data. The validation results revealed that ...Continue Reading

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Citations

Nov 15, 2019·Current Epidemiology Reports·Christine ChoiratMarianthi-Anna Kioumourtzoglou
Dec 19, 2019·International Journal of Environmental Research and Public Health·Qingbin WeiZhen Zhen
Nov 22, 2019·Environmental Science & Technology·Alexandra ShteinMassimo Stafoggia

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Methods Mentioned

BETA
GAM

Software Mentioned

mgcv
pcaMethods
R
ArcGIS
CHEM
GEOS

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