National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

Tianjun Lu, Julian D. Marshall, Wenwen Zhang, Perry Hystad, Sun Young Kim, Matthew J. Bechle, Matthias Demuzere, Steve Hankey

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO2, PM2.5, O3, CO, PM10, SO2) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R2 increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R2 = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

Original languageEnglish (US)
Pages (from-to)15519-15530
Number of pages12
JournalEnvironmental Science and Technology
Volume55
Issue number22
DOIs
StatePublished - Nov 16 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Environmental Chemistry

Keywords

  • Empirical models
  • exposure assessment
  • machine learning
  • street-level features
  • urban form

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