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Article

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components

1
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
2
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
3
Gangarosa Department of Environmental Health, Emory University, Atlanta, GA 30322, USA
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(11), 1233; https://doi.org/10.3390/atmos11111233
Received: 22 September 2020 / Revised: 5 November 2020 / Accepted: 13 November 2020 / Published: 16 November 2020
(This article belongs to the Special Issue Statistical Approaches to Investigate Air Quality)
Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R2 from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM2.5 components could be estimated with good accuracy, especially when collocated PM2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses. View Full-Text
Keywords: regression trees; machine learning; Bayesian model; particulate matter; Community Multiscale Air Quality (CMAQ); aerosol optical depth regression trees; machine learning; Bayesian model; particulate matter; Community Multiscale Air Quality (CMAQ); aerosol optical depth
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MDPI and ACS Style

Zhang, T.; Geng, G.; Liu, Y.; Chang, H.H. Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components. Atmosphere 2020, 11, 1233. https://doi.org/10.3390/atmos11111233

AMA Style

Zhang T, Geng G, Liu Y, Chang HH. Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components. Atmosphere. 2020; 11(11):1233. https://doi.org/10.3390/atmos11111233

Chicago/Turabian Style

Zhang, Tianyu, Guannan Geng, Yang Liu, and Howard H. Chang 2020. "Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components" Atmosphere 11, no. 11: 1233. https://doi.org/10.3390/atmos11111233

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