A comparison of statistical and machine learning methods for creating national daily maps of ambient PM2.5 concentration

• Type of Material: Article
• Authors: Veronica J. Berrocal, University of California Irvine; Yawen Guan, University of Nebraska; Amanda Muyskens, Lawrence Livermore National Laboratory; Haoyu Wang, North Carolina State University; Brian J. Reich, North Carolina State University; James A. Mulholland, Georgia Institute of Technology; Howard Chang, Emory University
• Language: English
• Date: 2020-02-01
• Publisher: Pergamon-Elsevier Science Ltd.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 Elsevier Ltd. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.atmosenv.2019.117130
• Title of Journal or Parent Work: Atmospheric Environment
• Volume: 222
• Grant/Funding Information: National Institutes of Health (NIH) Grant R01ES027892; National Institutes of Health (NIH) Grant P30ES017885; Department of Interior Grant DOI-JFSP 14-1-04-9 and King Abdullah University of Science and Technology Grant 3800.2.; National Science Foundation Grant DMS-1638521; This material was based upon work partially supported by the National Science Foundation under Grant DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute.; BJR is also supported by NIH R01ES027892, DOI-JFSP 14-1-04-9 and KAUST 3800.2; VJB is supported by NIH P30ES017885.
• Keywords: Nox; Science & Technology; Air pollution; US State; Environmental Sciences & Ecology; Land-use regression; Pollutants; Preterm birth; Ozone; Meteorology & Atmospheric Sciences; Modeling approach; Environmental Sciences; Life Sciences & Biomedicine; Physical Sciences
• Research Categories: Statistics; Environmental Sciences; Biology, Biostatistics

Abstract

A typical challenge in air pollution epidemiology is to perform detailed exposure assessment for individuals for which health data are available. To address this problem, in the last few years, substantial research efforts have been placed in developing statistical methods or machine learning techniques to generate estimates of air pollution at fine spatial and temporal scales (daily, usually) with complete coverage. However, it is not clear how much the predicted exposures yielded by the various methods differ, and which method generates more reliable estimates. In this paper, we aim to address this gap by evaluating a variety of exposure modeling approaches, comparing their predictive performance. Using PM2.5 in year 2011 over the continental U.S. as a case study, we generate national maps of ambient PM2.5 concentration using: (i) ordinary least squares and inverse distance weighting; (ii) kriging; (iii) statistical downscaling models, that is, spatial statistical models that use the information contained in air quality model outputs; (iv) land use regression, that is, linear regression modeling approaches that leverage the information in Geographical Information System (GIS) covariates; and (v) machine learning methods, such as neural networks, random forests and support vector regression. We examine the various methods’ predictive performance via cross-validation using Root Mean Squared Error, Mean Absolute Deviation, Pearson correlation, and Mean Spatial Pearson Correlation. Additionally, we evaluated whether factors such as, season, urbanicity, and levels of PM2.5 concentration (low, medium or high) affected the performance of the different methods. Overall, statistical methods that explicitly modeled the spatial correlation, e.g. universal kriging and the downscaler model, outperform all the other exposure assessment approaches regardless of season, urbanicity and PM2.5 concentration level. We posit that the better predictive performance of spatial statistical models over machine learning methods is due to the fact that they explicitly account for spatial dependence, thus borrowing information from neighboring observations. In light of our findings, we suggest that future exposure assessment methods for regional PM2.5 incorporate information from neighboring sites when deriving predictions at unsampled locations or attempt to account for spatial dependence.