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

Tianjun Lu, Jennifer Lansing, Wenwen Zhang, Matthew J. Bechle, Steve Hankey

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Land 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]: 0.32 μg/m 3 ) as compared to the permit data model (0.42; 0.37) 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.

Original languageEnglish (US)
Pages (from-to)131-141
Number of pages11
JournalScience of the Total Environment
Volume677
DOIs
StatePublished - Aug 10 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

Keywords

  • Exposure assessment
  • Hazardous air pollutants
  • Local emissions
  • Volunteer-based monitoring

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