Smoking habit in parents and exposure to environmental tobacco smoke in elementary school children of Milan

Children with smoking parents are potentially exposed to Environmental Tobacco Smoke (ETS). The aims of this study were: 1) to assess ETS exposure in Milan schoolchildren, by measuring urinary cotinine (COT-U), 2) to compare the parents' perception of children ETS exposure, with the actual ETS exposure measured by COT-U, 3) to explore the factors influencing COT-U, including smoking bans at home, the season, and children characteristics.One-hundred school children (7-11 years) and their parents were recruited for the study in Spring 2018 (n = 81) and in Winter 2019 (n = 94), 75 children participated to both campaigns, for a sum of 175 observations. A questionnaire was submitted to parents to collect information about smoking habits in the house. COT-U was measured by LC-MS/MS in spot urine sample collected in the morning from children.Detectable COT-U levels were found in 42% and 57% of children, in spring and winter, in contrast with 17% and 13% of parents acknowledging ETS exposure. Children living with smokers or e-cigarette users (vapers) (30% of the participants) had higher COT-U levels than children not living with smokers or vapers (median 0.67, 0.46, and0.1 μg/L in spring, and 0.98, 0.85, and 0.11 μg/L in winter, respectively). Increasingly higher COT-U levels were observed in children living in homes where smoking was completely banned, allowed in the external parts of the home, or allowed in some rooms. The multiple regression analysis confirmed the positive significant effect of living with smokers, a partial smoking ban and absence of smoking ban at home, the winter season, and BMI as determinants of COT-U.ETS exposure resulted in measurable urinary cotinine in children. Smoking parents underestimate exposure to ETS of their children. Living with smokers is a determinant of COT-U, only slightly mitigated by adopting partial smoking ban

Phosphorus recovery from a pilot-scale grate furnace: influencing factors beyond wet chemical leaching conditions

Phosphorus is a non-renewable resource going to exhaustion in the future. Sewage sludge ash is a promising secondary raw material due to its high phosphorus content. In this work, the distribution of 19 elements in bottom and cyclone ashes from pilot-scale grate furnace have been monitored to determine the suitability for the phosphorus acid extraction. Moreover, the influence of some parameters beyond wet chemical leaching conditions were investigated. Experimental results showed that bottom ash presented lower contamination in comparison to cyclone ash and low co-dissolution of heavy metals (especially Cr, Pb and Ni), while high phosphorus extraction efficiencies (76-86%) were achieved. High Al content in the bottom ash (9.4%) negatively affected the phosphorus extraction efficiency as well as loss on ignition, while the particle size reduction was necessary for ensuring a suitable contact surface. The typology of precipitating agents did not strongly affect the phosphorus precipitation, while pH was the key parameter. At pH 3.5-5, phosphorus precipitation efficiencies higher than 90% were achieved, with a mean phosphorus content in the recovered material equal to 16-17%, comparable to commercial fertilizers. Instead, the co-precipitation of Fe and Al had a detrimental effect on the recovered material, indicating the need for additional treatments

Development, validation and application of an HPLC-MS/MS method to quantify urinary mercapturic acids

Introduction Mercapturic acids are metabolic end products of some occupational and environmental toxicants such as volatile organic compounds. They are metabolites formed by the conjugation of an electrophilic compound with glutathione. These electrophilic metabolic intermediates are believed to be the active species able to react with DNA and responsible for the genotoxicity associated with parent compounds [1]. Mercapturates can be found in urine and, therefore, they can be considered useful non-invasive biomarkers of exposure. Although several analytical methods were reported for the analysis of single or small groups of mercapturates [2], only two papers describes the analysis of several mercapturates [3,4]. The aim of this work was to set up a LC-MS/MS method able to determine mercapturic acids derived from different toxicants. Experimental For the preparation of standard solution, the majority of standard compounds were purchased from Toronto Research Chemicals (Ontario, Canada), along with relative isotopically labelled standards. The complete list of analytes is reported in Table 1. The simple sample preparation developed includes dilution with formic acids (0.2 M), addition of an internal standard mixture of 16 deuterated analogs and filtration with 0.45 \u3bcm regenerated cellulose membrane filter (Agilent Technologies, Santa Clara, California). Analysis were carried out using a hybrid triple quadrupole/linear ion trap mass spectrometer (QTRAP 5500, AB Sciex, Monza, Italy) interfaced with an ultrahigh pressure liquid chromatograph (UHPLC, Agilent 1220, Cernusco sul Naviglio, Italy) equipped with a Betasil C18 column (150 x2.1 mm, 5 \u3bcm; Thermo Fisher Scientific, Rodano, Italy) and a pre-column BETASIL C18 (10 x 2,1 mm, 5\u3bc; Thermo Fisher Scientific, Rodano, Italy). Chromatographic separation was performed using a linear gradient with an aqueous mobile phase composed by an aqueous solution of ammonium formiate 5 mM and 0.1% formic acid and an organic mobile phase composed by acetonitrile. A complete validation was carried out: linearity, sensitivity, accuracy, precision, selectivity, matrix effect, recovery and process efficiency were evaluated according to both FDA guidelines and the considerations reported in the review written by Gonz\ue1lez and co-workers [5,6]. The method was then applied to the analysis of urine samples from adult subjects with different smoking habits: non-smokers, electronic cigarette smokers, and traditional tobacco smokers. Results Results from linearity assays showed that correlation coefficients (R2) were close to 1 for most of compounds, demonstrating optimal linear responses for the considered concentrations ranges, although a polynomial regression was necessary for AAMA since it showed a saturation at high concentrations. Limits of quantitation (LOQ) values were between 0.15 and 1 \u3bcg/L, except for HEMA and AAMA (1.93 and 1.30 \u3bcg/L respectively). Precision, evaluated as relative standard deviations (RDS), was below 15% for most analytes in both intra-day and inter-day tests. Accuracy was between 85 and 110 % of expected values, with few exceptions exceeding 120% at the lowest concentrations. Selectivity was verified by injection of a blank sample (synthetic urine) showing no chromatographic peak having an area at 20% of LOQ at the relative retention time and mass transition of compounds of interest. The same condition was verified analysing a blank sample immediately after the injection of the standard mixture at the highest concentration of the calibration curve, indicating the absence of carry-over. Results from the matrix effect, recovery and process efficiency tests were suitable in most of the cases, with some exceptions that were partially corrected using the internal standards. Results from urine samples of individuals with different smoking habit showed significant differences between smokers and non-smokers: 11 different mercapturic acids were significantly higher (P-value 640.005) in traditional tobacco smokers than in non-smokers (an example is illustrated in Figure 1). Conclusion In this work, we developed a method useful to quantify mercapturic acids in urine samples. The method was subjected to a complete validation and showed to be suitable for most of the considered analytes. Despite some critical issues with some analytes (in particular HEMA), it demonstrated to be an useful tool for fast determination of mercapturates. The first application carried out using human urine samples suggests that mercapturic acids are suitable biomarkers for toxicants in tobacco smoke. References 1. B.M. De Rooij, J.N.M. Commandeur, N.P.E; Biomarkers, 3 (1998), pp 239-303. 2. P.I. Mathias, C. B'hymer; Biomarkers, 21 (2016), pp 293-315. 3. K.U. Alwis, B.C. Blount, A.S. Britt, D. Patel, D.L. Ashley; Analytica Chimica Acta, 750 (2012), pp 152-160. 4. N. Pluym, G. Gilch, G. Scherer, M. Scherer; Analytical and Bioanalytical Chemistry, 407 (2015), pp 5463-5476. 5. FDA. Guidance for Industry - Bioanalytical Method Validation. (2013) Available at: https://www.fda.gov/downloads/Drugs/Guidances/ucm368107.pdf 6. O. Gonz\ue1lez, M.E. Blanco, G. Iriarte, L. Bartolom\ue9, M.I. Maguregui, R.M. Alonso; Journal of Chromatography A, 1353 (2014), pp10-27

Insight into the Cycling Behaviour of Metal Anodes, Enabled by X-ray Tomography and Mathematical Modelling

This work tackles the methodological challenge of rationalizing symmetric-cell cycling data from a materials-science perspective, through experiment replication, mathematical modelling, and tomographic imaging. Specifically, we address Zn electrode cycling in alkaline electrolyte with and without adding tetrabutylammonium bromide (TBAB). This additive is known from literature, but its practical impact is jeopardized by lack of in-depth understanding of its behaviour. Electrochemical testing was carried out at practically relevant current densities and the effect of variations of operating conditions was taken into account. The physical chemistry underlying cell potential profiles, has been modelled mathematically, accounting for: electrokinetics, mass-transport, electrode shape change and passivation. In particular, we disclosed an unexpected joint effect of TBAB and current density on passivation time: tomography allowed to rationalise this behaviour in terms of precipitate morphology

Untargeted metabolomics in urine to investigate smoking exposure

Background: Although thousands of different chemicals have been identified in cigarette smoke, the characterization of urinary metabolites derived from those compounds is still not completely achieved. The aim of this work was to perform an untargeted metabolomic experiment on a pilot cross-sectional study conducted on subjects with different smoking habits. Methods: Urine samples were collected from 67 adults; including 38 non-smokers, 7 electronic cigarette smokers, and 22 traditional tobacco smokers. Samples were analyzed by liquid chromatography/time-of flight mass spectrometer operating in data dependent mode. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The ANOVA test was used to detect significant features among groups. The software BEAMS (University of Birmingham) was implemented for grouping adducts and isotopes, and to perform a first annotation. Annotation was completed by comparing fragmentation patterns with on-line databases as Metlin, and using the software MS-FINDER. Results: One hundred and seventeen features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites, of which we were able to putatively annotate about half. The identification of the mercapturic acids of acrolein, 1,3-butadiene, and crotonaldeide, chemicals known to be present in tobacco smoke, supports the validity of the proposed approach. With a lower level of confidence, we annotated the glucuronide conjugated of 3-hydroxycotinine and the sulfate conjugate of methoxyphenol; finally, with the lowest degree of confidence, several other sulfate conjugates of small molecules were annotated. Short discussion/conclusions: The proposed approach seems to be useful for the investigation of exposure to toxicants in humans

Investigation of urine metabolites related to tobacco smoke chemicals using an untargeted metabolomic approach

Although thousands of different chemicals have been identified in cigarette smoke, the characterization of urinary metabolites derived from those compounds is still not completely achieved. The aim of this work was to perform an untargeted metabolomic experiment on a pilot cross-sectional study conducted on subjects with different smoking habits. Urine samples were collected from 67 adults; including 38 non-smokers, 7 electronic cigarette smokers, and 22 traditional tobacco smokers. Samples were analyzed by liquid chromatography/time-of flight mass spectrometer operating in data dependent mode. Data were processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The ANOVA test was used to detect significant features among groups. The software BEAMS (University of Birmingham) was implemented for grouping adducts and isotopes, and to perform a first annotation. Annotation was completed by comparing fragmentation patterns with on-line databases as Metlin, and using the software MS-FINDER. One hundred and seventeen features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites, for which we were able to putatively annotate about half. Among these, the identification of the glucuronide conjugated of 3-hydroxycotinine supports the validity of the proposed approach. Furthermore, several metabolites, mostly as sulfate conjugates, derived from chemicals known to be present in tobacco smoke, were annotated, among which the metabolite of methoxyphenol, acrolein, 1,3-butadiene, and crotonaldeide

A workflow for data integration, analysis, and metabolite annotation for untargeted metabolomics

Metabolomics is the youngest of the \u201comics\u201d disciplines and it is regarded as a promising approach to understand the metabolic changes that can occur in particular conditions and to identify new biomarkers. We present here a workflow for data integration, analysis, and metabolite annotation to be applied to untargeted metabolomic experiments. Data acquired with LC-MS/MS, operating in data dependent mode, are processed using the R-packages IPO and XCMS to perform feature detection, retention time correction and alignment. The data-table obtained is elaborated and submitted to statistical analysis using the on-line software MetaboAnalyst. Multivariate analysis, in particular principal component and partial least squares discriminant analysis are performed for data visualization. Univariate analysis, in particular T-test for pairwise and ANOVA for multi-groups comparison, are performed to detect significant features among groups. The software BEAMS, developed by the University of Birmingham, is then implemented for grouping adducts and isotopes, and to perform a first annotation. Metabolite annotation is finally completed by comparing the fragmentation pattern obtained from each parent ion corresponding to a significant feature with data stored in on-line databases as Metlin, and with the help of the software MS-FINDER, which performs in-silico fragmentation. We applied this workflow to an untargeted metabolomic experiment performed on 67 urine samples obtained from adult subjects with different smoking habits: non-smokers, electronic cigarette smokers, and traditional tobacco smokers. 117 features, out of 3613, were statistically different among groups. We estimated that they correspond to about 80 metabolites. We were able to putatively annotate compound classes of most of the significant metabolites (level 3 according to the \u201cProposed minimum reporting standards\u201d; Sumner et al., 2007) and to putatively annotate some of them (level 2). Among them, the glucuronide conjugated of 3-hydroxycotinine supports the validity of the proposed approach

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

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 \ub1 SD of 9003 \ub1 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson\u2019s r = 0.74, Lin\u2019s 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

Abitudine al fumo di sigaretta e profilo di escrezione di acidi mercapturici urinari : confronto tra tabacco e sigaretta elettronica

Introduzione. Nel fumo di sigaretta sono presenti moltissime molecole, centinaia delle quali riconosciute cancerogene per l\u2019uomo. Il contenuto del fumo di sigaretta elettronica \ue8 meno noto e sulla sua pericolosit\ue0 per la salute umana \ue8 in corso un acceso dibattito. Molte sostanze cancerogene contenute nel fumo di tabacco, dopo assorbimento nel nostro organismo, subiscono biotrasfomazione a dare composti elettrofili che sono ritenuti responsabili della loro genotossicit\ue0/cancerogenicit\ue0. In molti casi questi intermedi reattivi vengono eliminati nelle urine come acidi mercapturici. Scopo. Scopo di questo lavoro \ue8 stato verificare se l\u2019abitudine al fumo di tabacco e la sigaretta elettronica rappresentano sorgenti di esposizione a composti elettrofili, eliminati nell\u2019urine come acidi mercapturici (MA). Metodo. Sono stati raccolti campioni di urina di soggetti adulti con diversa abitudine al fumo di sigaretta: 22 soggetti fumatori (FT) di sigaretta tradizionale, 7 soggetti fumatori di sigaretta elettronica (FE) e 38 soggetti non fumatori (NF). Nei campioni di urina \ue8 stata ricercata la presenza di 18 acidi mercapturici derivati dalla biotrasformazione di acroleina, acrilamide, acrilonitrile, benzene, 1,3-butadiene, crotonaldeide, N,N-dimetilformammide, etilene, ossido di etilene, cloruro di vinile, ossido di propilene, stirene, toluene nonch\ue9 agenti metilanti ed etilanti. Le determinazioni sono state eseguite con un metodo basato sulla cromatografia liquida accoppiata a spettrometria di massa. Risultati. Dal confronto dei livelli di MA nei soggetti con diverse abitudini al fumo sono state evidenziate differenze significative tra fumatori e non fumatori: 11 diversi acidi mercapturici erano significativamente pi\uf9 alti (valore p 640.005) nei fumatori tradizionali rispetto ai non fumatori. I livelli mediani di MA sono risultati variare tra

FORENSIC ENGINEERING SURVEYS WITH UAV PHOTOGRAMMETRY AND LASER SCANNING TECHNIQUES

This work aims at presenting the use of new technologies in the field of forensic engineering. In particular, the use of UAV photogrammetry and laser scanning is compared with the traditional methods of surveying an accident site. In this framework, surveys must be carried out promptly, executed in a short time and performed so that the greatest possible amount of information is collected with sufficient accuracy to avoid the possibility of neglecting details once that the scene is no longer preserved. The combination of modern surveying techniques such UAV photogrammetry and laser scanning can properly fulfill these requirements. An experimental test has been arranged and instruments, procedures, settings, practical limits and results have been evaluated and compared with respect to the usual way of performing the survey for forensic purposes. In particular, both qualitative and quantitative considerations are given, assessing the completeness of the reconstructed model, the statistical evaluation of the errors and the accuracy achieved