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Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit data

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dc.contributor.authorLu, Tianjunen
dc.contributor.authorLansing, Jenniferen
dc.contributor.authorZhang, Wenwenen
dc.contributor.authorBechle, Matthew J.en
dc.contributor.authorHankey, Steven C.en
dc.date.accessioned2019-08-22T17:03:31Zen
dc.date.available2019-08-22T17:03:31Zen
dc.date.issued2019-08-10en
dc.description.abstractLand Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R-2: 0.56; Root Mean Square Error [RMSE]: 032 mu g/m(3)) as compared to the permit data model (0.42; 037) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data. (C) 2019 The Authors. Published by Elsevier B.V.en
dc.description.notesThis publication was developed as part of the Center for Air, Climate, and Energy Solutions (CACES), which was supported under Assistance Agreement No. R835873 awarded by the U.S. Environmental Protection Agency. It has not been formally reviewed by EPA. The views expressed in this document are solely those of authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. The authors acknowledge Patrick Hanlon for providing the VOC sampling data and funding from Minneapolis Health Department.en
dc.description.sponsorshipU.S. Environmental Protection Agency [R835873]; Minneapolis Health Departmenten
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.scitotenv.2019.04.285en
dc.identifier.eissn1879-1026en
dc.identifier.issn0048-9697en
dc.identifier.pmid31054441en
dc.identifier.urihttp://hdl.handle.net/10919/93218en
dc.identifier.volume677en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectHazardous air pollutantsen
dc.subjectVolunteer-based monitoringen
dc.subjectLocal emissionsen
dc.subjectExposure assessmenten
dc.titleLand Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit dataen
dc.title.serialScience of the Total Environmenten
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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