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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.

Background. Although thousands of different chemicals have been identified in cigarette smoke, the characterization of their urinary metabolites still requires significant research. The aim of this work was to perform an untargeted metabolomic approach to a pilot cross-sectional study conducted on subjects with different smoking habits and to compare the results with those of the targeted measurement of mercapturic acids. Methods. Urine samples from 67 adults, including 38 non-smokers, 7 electronic cigarette users, and 22 traditional tobacco smokers were collected. Samples were analysed by liquid chromatography/time-of flight mass spectrometry. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. One-way ANOVA test was used to identify different features among groups. Quantitative determination of 17 mercapturic acids was available from a previous study. Results. One hundred and seventeen features, out of 3613, were different among groups. They corresponded to 91 potential metabolites, 5 of which were identified vs authentic standards, 43 were putatively annotated and 13 were attributed to chemical classes. Among identified compounds there were the mercapturic acids of acrolein, 1,3-butadiene, and crotonaldehyde; among putatively annotated compounds there were the glucuronide conjugated of 3-hydroxycotinine and the sulfate conjugate of methoxyphenol; with the lowest degree of confidence several sulfate conjugates of small molecules were annotated. Considering mercapturic acids, the coherence between the targeted and untargeted approach was found for a limited number of chemicals, typically the most abundant. Conclusions. Differences in the urinary levels of several compounds were associated to the different smoking habits, suggesting that the proposed approach is useful for the investigation of the metabolite patterns related to the exposure to toxicants. However, limitations were highlighted, in particular regarding the identification of low concentration compounds. Highlights• LC-MS/MS untargeted metabolomics applied to subjects with different smoking habits • 91 potential urinary metabolites out of 3613 features were different among groups • 61 potential metabolites were annotated with various degree of confidence • Annotated metabolites derived from smoke pollutants and metabolism modifications • Among different features, 3 corresponded to mercapturic acids previously measured *Highlights (for review) Urinary biomonitoring of subjects with different smoking habits.

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