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http://hdl.handle.net/1942/30355
Title: | Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School? | Authors: | BONIARDI, Luca DONS, Evi Campo, Laura Van Poppel, Martine INT PANIS, Luc Fustinoni, Silvia |
Issue Date: | 2019 | Publisher: | MDPI | Source: | ENVIRONMENT, 6 (8) (Art N° ARTN 90) | 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. | Keywords: | air pollution;black carbon (BC);land use regression (LUR);active mobility;traffic pollution;schoolchildren;school streets | Document URI: | http://hdl.handle.net/1942/30355 | e-ISSN: | 2076-3298 | DOI: | 10.3390/environments6080090 | ISI #: | WOS:000482963600007 | Rights: | 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2022 |
Appears in Collections: | Research publications |
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Boniardi,2019b.pdf | Published version | 5.43 MB | Adobe PDF | View/Open |
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