Is a land use regression model capable of predicting the cleanest route to school?

Luca Boniardi, Evi Dons, Laura Campo, Martine Van Poppel, Luc Int Panis, Silvia Fustinoni

Research output: Contribution to journalArticlepeer-review

Abstract

Land Use Regression (LUR) modeling is a widely used technique to model the spatial variability of air pollutants in epidemiology. In this study, we explore whether a LUR model can predict home-to-school commuting exposure to black carbon (BC). During January and February 2019, 43 children walking to school were involved in a personal monitoring campaign measuring exposure to BC and tracking their home-to-school routes. At the same time, a previously developed LUR model for the study area was applied to estimate BC exposure on points along the route. Personal BC exposure varied widely with mean ± SD of 9003 ± 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson’s r = 0.74, Lin’s Concordance Correlation Coefficient = 0.6), suggesting that LUR estimates are capable of catching differences among routes and predicting the cleanest route. However, the model tends to underestimate absolute concentrations by 29% on average. A LUR model can be useful in predicting personal exposure and can help urban planners in Milan to build a healthier city for schoolchildren by promoting less polluted home-to-school routes.

Original languageEnglish
Article number90
JournalEnvironments - MDPI
Volume6
Issue number8
DOIs
Publication statusPublished - Aug 1 2019

Keywords

  • Active mobility
  • Air pollution
  • Black carbon (BC)
  • Land use regression (LUR)
  • School streets
  • Schoolchildren
  • Traffic pollution

ASJC Scopus subject areas

  • Environmental Science(all)
  • Ecology, Evolution, Behavior and Systematics
  • Renewable Energy, Sustainability and the Environment

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