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Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

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EPIGET—Epidemiology, Epigenetics, and Toxicology Lab, Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 20122 Milan, Italy
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Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium
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Centre for Environmental Sciences, Hasselt University, 3590 Diepenbeek, Belgium
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Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, U.O.S Tossicologia, 20122 Milan, Italy
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Transportation Research Institute (IMOB), Hasselt University, 3590 Diepenbeek, Belgium
*
Author to whom correspondence should be addressed.
Environments 2019, 6(8), 90; https://doi.org/10.3390/environments6080090
Received: 30 June 2019 / Revised: 25 July 2019 / Accepted: 26 July 2019 / Published: 30 July 2019
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. View Full-Text
Keywords: air pollution; black carbon (BC); land use regression (LUR); active mobility; traffic pollution; schoolchildren; school streets air pollution; black carbon (BC); land use regression (LUR); active mobility; traffic pollution; schoolchildren; school streets
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Boniardi, L.; Dons, E.; Campo, L.; Van Poppel, M.; Int Panis, L.; Fustinoni, S. Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School? Environments 2019, 6, 90.

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