Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?
| Autor: | Luc Int Panis, Evi Dons, Silvia Fustinoni, Martine Van Poppel, L. Boniardi, Laura Campo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
air pollution Air pollution school streets 010501 environmental sciences Land use regression medicine.disease_cause 01 natural sciences lcsh:TD1-1066 Air pollutants Statistics medicine black carbon (BC) Active mobility lcsh:Environmental technology. Sanitary engineering Ecology Evolution Behavior and Systematics 0105 earth and related environmental sciences General Environmental Science land use regression (LUR) Renewable Energy Sustainability and the Environment Traffic pollution schoolchildren traffic pollution Concordance correlation coefficient active mobility Environmental science Spatial variability |
| Zdroj: | Environments, Vol 6, Iss 8, p 90 (2019) Article Environments Volume 6 Issue 8 |
| ISSN: | 2076-3298 |
| DOI: | 10.3390/environments6080090 |
| Popis: | 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&rsquo s r = 0.74, Lin&rsquo 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. |
| Databáze: | OpenAIRE |
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