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

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

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

Un approccio metabolomico non mirato per indagare l'esposizione a sostanze tossiche nel fumo di sigaretta

Introduzione: Nel fumo di sigaretta siano state identificate migliaia di diverse sostanze chimiche pericolose; ci\uf2 nonostante la caratterizzazione dei metaboliti urinari di queste sostanze a seguito di esposizione nell'uomo \ue8 stata effettuata sono parzialmente. Obiettivo: Lo studio si propone di applicare un approccio metabolomico non mirato all'analisi di campioni di urina di soggetti con diversa abitudine al fumo, allo scopo di identificare i metaboliti derivanti da sostanze tossiche associati. Metodi: Sono stati raccolti campioni estemporanei di urina da 67 soggetti suddivisi in tre gruppi sulla base della loro abitudine al fumo: 38 soggetti erano non fumatori, 7 erano fumatori di sigaretta elettronica e 22 erano fumatori di tabacco. I campioni sono stati analizzati utilizzando la cromatografia liquida accoppiata ad uno spettrometro di massa con tempo di volo, raccogliendo i segnali degli ioni negativi. I dati sono stati processati utilizzando i pacchetti R IPA e MXCMS per correggere i tempi di ritenzione ed effettuare l'allineamento tra i cromatogrammi. Il test ANOVA \ue8 stato utilizzato per identificare gli elementi caratteristici che distinguono tra loro i gruppi. Il software BEAMS, sviluppato dall'universit\ue0 di Birmingham, \ue8 stato applicato per raggruppare gli addotti e gli isotopi riferiti ad una stessa sostanza ed effettuare una prima annotazione dei picchi. L'annotazione \ue8 stata completata confrontando gli spettri di frammentazione ottenuti da standard puri e con il database Metlin, usando il software MS-FINDER Risultati: Nei cromatogrammi ottenuti sono stati identificati complessivamente 3613 segnali, di cui 117 sono risultati diversi nei gruppi studiati. Questi segnali sono stati attribuiti a circa 80 diversi metaboliti, dei quali siamo riusciti ad annotarne putativamente circa la met\ue0. L\u2019identificazione, con un grado di confidenza pari a 1, degli acidi mercapturici dell\u2019acroleina, del 1,3-butadiene, e della crotonaldeide, sostanze risaputamene presenti nel fumo di tabacco, supportano la validit\ue0 dell\u2019approccio adottato (il grado di confidenza 1 si attribuisce alle molecole identificate con certezza per confronto con lo standard puro). Con un grado di confidenza minore (pari a 2) sono state identificati: il coniugato glucuronide della 3-idrossicotinina e il coniugato solfato del metossifenolo. Infine, con un grado di confidenza 3, sono state identificate numerose altre piccole molecole, escrete come coniugati solfati. Conclusione: L\u2019approccio proposto sembra utile per indagare l\u2019esposizione a miscele di sostanze tossiche nell\u2019uomo. Dato che l\u2019esposizione a miscele di sostanze chimiche, piuttosto che a singoli composti, \ue8 una caratteristica peculiare di molti ambienti di lavoro, si reputa che questo approccio apra interessanti prospettive per la medicina del lavoro

Spontaneous sparse learning for PCM-based memristor neural networks

Neural networks trained by backpropagation have achieved tremendous successes on numerous intelligent tasks. However, naive gradient-based training and updating methods on memristors impede applications due to intrinsic material properties. Here, we built a 39nm 1Gb phase change memory (PCM) memristor array and quantified the unique resistance drift effect. On this basis, spontaneous sparse learning (SSL) scheme that leverages the resistance drift to improve PCM-based memristor network training is developed. During training, SSL regards the drift effect as spontaneous consistency-based distillation process that reinforces the array weights at the high-resistance state continuously unless the gradient-based method switches them to low resistance. Experiments show that the SSL not only helps the convergence of network with better performance and sparsity controllability without additional computation in handwritten digit classification. This work promotes the learning algorithms with the intrinsic properties of memristor devices, opening a new direction for development of neuromorphic computing chips

Olive mill wastewater as valuable source of animal feed and energy

An alternative process to treat olive mill wastewater has been proposed and its potential demonstrated in a pilot-scale plant. The treatment consists of an integrated physico-chemical system and a biological uni