Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging

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dc.contributor.authorDi, Qian
dc.contributor.authorAmini, Heresh
dc.contributor.authorShi, Liuhua
dc.contributor.authorKloog, Itai
dc.contributor.authorSilvern, Rachel
dc.contributor.authorKelly, James
dc.contributor.authorSabath, M. Benjamin
dc.contributor.authorChoirat, Christine
dc.contributor.authorKoutrakis, Petros
dc.contributor.authorLyapustin, Alexei
dc.contributor.authorWang, Yujie
dc.contributor.authorMickley, Loretta J.
dc.contributor.authorSchwartz, Joel
dc.date.accessioned2020-02-06T17:31:43Z
dc.date.available2020-02-06T17:31:43Z
dc.date.issued2019-12-18
dc.description.abstractNO₂ is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO₂ levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO₂ model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R² of 0.788 overall, a spatial R² of 0.844, and a temporal R² of 0.729. The relationship between daily monitored and predicted NO₂ is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO₂ levels. This NO₂ estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO₂ in unmonitored areas. We found the highest NO₂ levels along highways and in cities. We also observed that nationwide NO₂ levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.en_US
dc.description.sponsorshipThis publication was made possible by U.S. EPA grant numbers RD-834798, RD-835872, and 83587201; HEI grant 4953-RFA14-3/16-4. The publication was supported by Beijing Key Laboratory of Indoor Air Quality Evaluation and Control. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the U.S. Environmental Protection Agency (EPA). The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the publication. Research described in this article was also conducted under contract to the Health Effects Institute (HEI), an organization jointly funded by the U.S. EPA (Assistance Award No.CR-83467701) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. The computations in this paper were run on the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University.en_US
dc.description.urihttps://pubs.acs.org/doi/abs/10.1021/acs.est.9b03358en_US
dc.format.extent2 filesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2esxg-k0fh
dc.identifier.citationDi, Qian; Amini, Heresh; Shi, Liuhua; Kloog, Itai; Silvern, Rachel; Kelly, James; Sabath, M. Benjamin; Choirat, Christine; Koutrakis, Petros; Lyapustin, Alexei; Wang, Yujie; Mickley, Loretta J.; Schwartz, Joel; Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging; Environmental Science and Technology 54, 3, 1372-1384 (2019); https://pubs.acs.org/doi/abs/10.1021/acs.est.9b03358en_US
dc.identifier.urihttps://doi.org/10.1021/acs.est.9b03358
dc.identifier.urihttp://hdl.handle.net/11603/17226
dc.language.isoen_USen_US
dc.publisherACS Publicationsen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsPublic Domain Mark 1.0*
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleAssessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averagingen_US
dc.typeTexten_US

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