Exposure to secondhand and thirdhand smoke in private vehicles: Measurements in air and dust samples.

BACKGROUND: This study aimed to estimate airborne nicotine concentrations and nicotine, cotinine, and tobacco-specific nitrosamines (TSNAs) in settled dust from private cars in Spain and the UK. METHODS: We measured vapor-phase nicotine concentrations in a convenience sample of 45 private cars from Spain (N = 30) and the UK (N = 15) in 2017-2018. We recruited non-smoking drivers (n = 20), smoking drivers who do not smoke inside the car (n = 15), and smoking drivers who smoke inside (n = 10). Nicotine, cotinine, and three TSNAs (NNK, NNN, NNA) were also measured in settled dust in a random subsample (n = 20). We computed medians and interquartile ranges (IQR) of secondhand smoke (SHS) and thirdhand smoke (THS) compounds according to the drivers' profile. RESULTS: 24-h samples yielded median airborne nicotine concentrations below the limit of quantification (LOQ) (IQR:

Toxicity of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in early development: A wide-scope metabolomics assay in zebrafish embryos.

The tobacco-specific nitrosamine 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is a carcinogenic and ubiquitous environmental pollutant for which toxic activity has been thoroughly investigated in murine models and human tissues. However, its potential deleterious effects on vertebrate early development are yet poorly understood. In this work, we characterized the impact of NNK exposure during early developmental stages of zebrafish embryos, a known alternative model for mammalian toxicity studies. Embryos exposed to different NNK concentrations were monitored for lethality and for the appearance of malformations during the first five days after fertilization. LC-MS based untargeted metabolomics was subsequently performed for a wide-scope assay of NNK-related metabolic alterations. Our results revealed the presence of not only the parental compound, but also of two known NNK metabolites, 4-Hydroxy-4-(3-pyridyl)-butyric acid (HPBA) and 4-(Methylnitrosamino)-1-(3-pyridyl-N-oxide)-1-butanol (NNAL-N-oxide) in exposed embryos likely resulting from active CYP450-mediated α-hydroxylation and NNK detoxification pathways, respectively. This was paralleled by a disruption in purine and pyrimidine metabolisms and the activation of the base excision repair pathway. Our results confirm NNK as a harmful embryonic agent and demonstrate zebrafish embryos to be a suitable early development model to monitor NNK toxicity.

Whole Exome Sequencing Provides the Correct Diagnosis in a Case of Osteopathia Striata with Cranial Sclerosis: Case Report of a Novel Frameshift Mutation in AMER1.

The diagnosis of rare diseases with multisystem manifestations can constitute a difficult process that delays the determination of the underlying cause. Whole exome sequencing (WES) provides a suitable option to examine multiple target genes associated with several disorders that display common features. In this study, we report the case of a female patient suspected of having Sotos syndrome. Screening for the initially selected genes, considering Sotos syndrome and Sotos-like disorders, did not identify any pathogenic variants that could explain the phenotype. The extended analysis, which considered all genes in the exome associated with features consistent with those shown by the studied patient, revealed a novel frameshift variant in the AMER1 gene, responsible for osteopathia striata with cranial sclerosis. WES analysis and an updated revision of previously reported disease-causing mutations, proved useful to reach an accurate diagnosis and guide further examination to identify critical abnormalities.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine. With the rise of synthetic and new engineered materials, alongside ongoing prioritisation needs in chemical risk assessment for existing chemicals, early predictive evaluations are becoming of utmost importance to both scientific and regulatory purposes. ELIXIR is an intergovernmental organisation that brings together life science resources from across Europe. To coordinate the linkage of various life science efforts around modern predictive toxicology, the establishment of a new ELIXIR Community is seen as instrumental. In the past few years, joint efforts, building on incidental overlap, have been piloted in the context of ELIXIR. For example, the EU-ToxRisk, diXa, HeCaToS, transQST, and the nanotoxicology community have worked with the ELIXIR TeSS, Bioschemas, and Compute Platforms and activities. In 2018, a core group of interested parties wrote a proposal, outlining a sketch of what this new ELIXIR Toxicology Community would look like. A recent workshop (held September 30th to October 1st, 2020) extended this into an ELIXIR Toxicology roadmap and a shortlist of limited investment-high gain collaborations to give body to this new community. This Whitepaper outlines the results of these efforts and defines our vision of the ELIXIR Toxicology Community and how it complements other ELIXIR activities.

Unravelling the metabolic alterations of liver damage induced by thirdhand smoke.

BACKGROUND: Thirdhand smoke (THS) is the accumulation of tobacco smoke gases and particles that become embedded in materials. Previous studies concluded that THS exposure induces oxidative stress and hepatic steatosis in liver. Despite the knowledge of the increasing danger of THS exposure, the metabolic disorders caused in liver are still not well defined. OBJECTIVES: The aim of this study is to investigate the metabolic disorders caused by THS exposure in liver of male mice and to evaluate the effects of an antioxidant treatment in the exposed mice. METHODS: We investigated liver from three mice groups: non-exposed mice, exposed to THS in conditions that mimic human exposure and THS-exposed treated with antioxidants. Liver samples were analyzed using a multiplatform untargeted metabolomics approach including nuclear magnetic resonance (1H NMR), liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and laser desorption/ionization mass spectrometry imaging (MSI), able to map lipids in liver tissues. RESULTS: Our multiplatform approach allowed the annotation of eighty-eight metabolites altered by THS exposure, including amino acids, nucleotides and several types of lipids. The main dysregulated pathways by THS exposure were D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism and oxidative phosphorylation and glutathione metabolism, being the last two related to oxidative stress. THS-exposed mice also presented higher lipid accumulation and decrease of metabolites involved in the phosphocholine synthesis, as well as choline deficiency, which is related to Non-Alcoholic Fatty Liver Disease and steatohepatitis. Interestingly, the antioxidant treatment of THS-exposed mice reduced the accumulation of some lipids, but could not revert all the metabolic alterations, including some related to the impairment of the mitochondrial function. CONCLUSIONS: THS alters liver function at a molecular level, dysregulating many metabolic pathways. The molecular evidences provided here confirm that THS is a new factor for liver steatosis and provide the basis for future research in this respect.

rMSIKeyIon: An Ion Filtering R Package for Untargeted Analysis of Metabolomic LDI-MS Images.

Many MALDI-MS imaging experiments make a case versus control studies of different tissue regions in order to highlight significant compounds affected by the variables of study. This is a challenge because the tissue samples to be compared come from different biological entities, and therefore they exhibit high variability. Moreover, the statistical tests available cannot properly compare ion concentrations in two regions of interest (ROIs) within or between images. The high correlation between the ion concentrations due to the existence of different morphological regions in the tissue means that the common statistical tests used in metabolomics experiments cannot be applied. Another difficulty with the reliability of statistical tests is the elevated number of undetected MS ions in a high percentage of pixels. In this study, we report a procedure for discovering the most important ions in the comparison of a pair of ROIs within or between tissue sections. These ROIs were identified by an unsupervised segmentation process, using the popular k-means algorithm. Our ion filtering algorithm aims to find the up or down-regulated ions between two ROIs by using a combination of three parameters: (a) the percentage of pixels in which a particular ion is not detected, (b) the Mann-Whitney U ion concentration test, and (c) the ion concentration fold-change. The undetected MS signals (null peaks) are discarded from the histogram before the calculation of (b) and (c) parameters. With this methodology, we found the important ions between the different segments of a mouse brain tissue sagittal section and determined some lipid compounds (mainly triacylglycerols and phosphatidylcholines) in the liver of mice exposed to thirdhand smoke.

Biomarkers of Exposure to Secondhand and Thirdhand Tobacco Smoke: Recent Advances and Future Perspectives.

Smoking is the leading preventable disease worldwide and passive smoking is estimated to be the cause of about 1.0% of worldwide mortality. The determination of tobacco smoke biomarkers in human biological matrices is key to assess the health effects related to the exposure to environmental tobacco smoke. The biomonitoring of cotinine, the main nicotine metabolite, in human biofluids-including urine, serum or saliva-has been extensively used to assess this exposure. However, the simultaneous determination of cotinine together with other tobacco biomarkers and the selection of alternative biological matrices, such as hair, skin or exhaled breath, would enable a better characterization of the kind and extent of tobacco exposure. This review aims to perform a critical analysis of the up-to-date literature focused on the simultaneous determination of multiple tobacco smoke biomarkers studied in different biological matrices, due to the exposure to secondhand smoke (SHS) and thirdhand smoke (THS). Target biomarkers included both tobacco-specific biomarkers-nicotine and tobacco specific nitrosamine biomarkers-and tobacco-related biomarkers, such as those from polycyclic aromatic hydrocarbons, volatile organic compounds, metals and carbon monoxide. To conclude, we discuss the suitability of determining multiple biomarkers through several relevant examples of SHS and THS exposure.

Assessing the potential of sputtered gold nanolayers in mass spectrometry imaging for metabolomics applications.

Mass spectrometry imaging (MSI) is a molecular imaging technique that maps the distribution of molecules in biological tissues with high spatial resolution. The most widely used MSI modality is matrix-assisted laser desorption/ionization (MALDI), mainly due to the large variety of analyte classes amenable for MALDI analysis. However, the organic matrices used in classical MALDI may impact the quality of the molecular images due to limited lateral resolution and strong background noise in the low mass range, hindering its use in metabolomics. Here we present a matrix-free laser desorption/ionization (LDI) technique based on the deposition of gold nanolayers on tissue sections by means of sputter-coating. This gold coating method is quick, fully automated, reproducible, and allows growing highly controlled gold nanolayers, necessary for high quality and high resolution MS image acquisition. The performance of the developed method has been tested through the acquisition of MS images of brain tissues. The obtained spectra showed a high number of MS peaks in the low mass region (m/z below 1000 Da) with few background peaks, demonstrating the ability of the sputtered gold nanolayers of promoting the desorption/ionization of a wide range of metabolites. These results, together with the reliable MS spectrum calibration using gold peaks, make the developed method a valuable alternative for MSI applications.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications.

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.

rMSI: an R package for MS imaging data handling and visualization.

SUMMARY: R platform provides some packages that are useful to process mass spectrometry imaging (MSI) data; however, none of them provide an easy to use graphical user interface (GUI). Here, we introduce rMSI, an R package for MSI data analysis focused on providing an efficient way to manage MSI data together with a GUI integrated in R environment. MS data is loaded in rMSI custom format optimized to minimize the memory footprint yet maintaining a fast spectra access. The rMSI GUI is designed for simple and effective data exploration and visualization. Moreover, rMSI is designed to be integrated in the R environment through a library of functions that can be used to share MS data across others R packages. The release of rMSI for R environment establishes a novel and flexible platform for MSI data analysis, completely free and open-source. AVAILABILITY AND IMPLEMENTATION: The code, the documentation, a tutorial and example data are available open-source at: github.com/prafols/rMSI. CONTACT: jesus.brezmes@urv.cat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unravelling and Quantifying the "NMR-Invisible" Metabolites Interacting with Human Serum Albumin by Binding Competition and T2 Relaxation-Based Decomposition Analysis.

Quantitative profiling of low-molecular-weight metabolites (LMWMs) by 1H NMR is routinely used in high-throughput serum metabolomics. First, the protein background is attenuated using a T2 filter; then, the LMWM signals are resolved by line-shape fitting. However, protein-binding modifies the motional properties of LMWM, and their signal partially attenuates with the T2 filter, along with the protein background. Consequently, the quantified LMWM signals do not reflect the total concentration in serum but the nonbinding part. Here we present a novel strategy based on binding competition to promote the release of the "NMR-invisible" metabolites from serum proteins and achieve quantifications closer to total concentrations. The study focuses on five clinically relevant amino acids with different binding properties (valine, isoleucine, leucine, tyrosine, and phenylalanine). We analyzed their binding affinity to human serum albumin (HSA) in serum mimic samples and promoted the release of their bound fraction by TSP titration. Furthermore, we used a novel combination of pseudo-2D CPMG and multivariate curve resolution analysis, allowing the separation of LMWM and protein signals and providing LMWM quantifications corrected for transverse relaxation effects. We found that TSP concentrations larger than 3 mM released most of the bound fraction and validated these findings in real serum/plasma samples.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics.

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah .

Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics.

Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R(2) coefficients of determination were in good agreement (R(2)>0.90) while up to 24 cases exhibited an excellent linear relation (R(2)>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS.

Comparative study of comprehensive gas chromatography-nitrogen chemiluminescence detection and gas chromatography-ion trap-tandem mass spectrometry for determining nicotine and carcinogen organic nitrogen compounds in thirdhand tobacco smoke.

Thirdhand tobacco smoke (THS) constitutes a poorly understood pathway of exposure of non-smokers, especially toddlers, to tobacco-related carcinogens. However, to date most of the carcinogens present in tobacco smoke have not been detected in THS and, therefore, the significance of THS health risk is still unknown. In this study, we have compared the performance of two analytical methods - one based on gas chromatography coupled to ion trap mass spectrometry detection (GC-IT-MS) and the other on comprehensive two-dimensional gas chromatography coupled to a nitrogen chemiluminescence detector (GC×GC-NCD) - for simultaneously determining, in settled house dust, the presence of 16 organic nitrogen carcinogens already detected in tobacco smoke. The target compounds included four aromatic amines, two nitrocompounds, eight N-nitrosamines and two tobacco-specific nitrosamines, as well as nicotine as a tobacco marker. Dust samples were extracted using in-cell clean up pressurized liquid extraction with silica as clean up sorbent and ethyl acetate as the organic solvent, with average recovery of 89%. Although GC-IT-MS, using chemical ionization with methanol and tandem MS, performed well, the optimized GC×GC-NCD gave lower limits of detection (from 4 to 22ngg(-1)) and better repeatability and reproducibility a low concentration levels (%RSD<8%) and, therefore, was applicable for determining these different groups of carcinogens without the need for derivatization prior to the GC analysis. The performance of the optimized PLE/GC×GC-NCD method was tested by quantifying the target compounds in house dust samples from smokers' and non-smokers' homes. The median carcinogen compounds detected was 3.8µgg(-1) and 1.1µgg(-1) in smokers' and non-smokers' house dust, respectively. In this study, we have detected highly carcinogenic aromatic amines and nitro compounds for the first time in settled house dust complementing the state of knowledge of THS composition and providing fresh evidence about THS health risks.

Estimated Exposure Risks from Carcinogenic Nitrosamines in Urban Airborne Particulate Matter.

Organic nitrogen (ON) compounds are present in atmospheric particulate matter (PM), but compared to their inorganic, hydrocarbon, and oxygenated counterparts, they are difficult to characterize due to their low concentrations in complex matrices. Nitrosamines are a class of ON compounds known to be highly carcinogenic and include species formed from nicotine degradation, but there are no detailed estimates of their abundance in ambient air. We use a highly sensitive analytical method, which is capable of separating over 700 ON compounds, to determine daily variability in nicotine, and 8 nonspecific and 4 tobacco-specific nitrosamines in ambient PM from central London over two periods in winter and summer. The average total nitrosamine concentration was 5.2 ng m(-3), substantially exceeding a current public recommendation of 0.3 ng m(-3) on a daily basis. The lifetime cancer risk from nitrosamines in urban PM exceeded the U.S. Environmental Protection Agency guideline of 1 excess cancer case per 1 million population exposed after 1 h of exposure to observed concentrations per day over the duration of an adult lifetime. A clear relationship between ambient nitrosamines and total PM2.5 was observed with 1.9 ng m(-3) ± 2.6 ng m(-3) (total nitrosamine) per 10 µg m(-3) PM2.5.

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation.

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices.

Exposure to nitrosamines in thirdhand tobacco smoke increases cancer risk in non-smokers.

In addition to passive inhalation, non-smokers, and especially children, are exposed to residual tobacco smoke gases and particles that are deposited to surfaces and dust, known as thirdhand smoke (THS). However, until now the potential cancer risks of this pathway of exposure have been highly uncertain and not considered in public health policy. In this study, we estimate for the first time the potential cancer risk by age group through non-dietary ingestion and dermal exposure to carcinogen N-nitrosamines and tobacco-specific nitrosamines (TSNAs) measured in house dust samples. Using a highly sensitive and selective analytical approach we have determined the presence of nicotine, eight N-nitrosamines and five tobacco-specific nitrosamines in forty-six settled dust samples from homes occupied by both smokers and non-smokers. Using observations of house dust composition, we have estimated the cancer risk by applying the most recent official toxicological information. Calculated cancer risks through exposure to the observed levels of TSNAs at an early life stage (1 to 6years old) exceeded the upper-bound risk recommended by the USEPA in 77% of smokers' and 64% of non-smokers' homes. The maximum risk from exposure to all nitrosamines measured in a smoker occupied home was one excess cancer case per one thousand population exposed. The results presented here highlight the potentially severe long-term consequences of THS exposure, particularly to children, and give strong evidence of its potential health risk and, therefore, they should be considered when developing future environmental and health policies.

Simultaneous determination of parabens and synthetic musks in water by stir-bar sorptive extraction and thermal desorption-gas chromatography-mass spectrometry.

This study focuses on a method for simultaneously determining personal care products in a wide range of polarities in different water matrices. The method is based on stir-bar sorptive extraction followed by thermal desorption-gas chromatography-mass spectrometry. Prior to extraction, the parabens were acetylated to improve their affinity for the polydimethylsiloxane phase of the stir bar. The method showed good linearity, repeatability and reproducibility between days for all compounds and limits of detection at low ng/L levels (between 0.02 and 0.3 ng/L). The proposed method is also environmentally friendly, because it does not use organic solvents, and reduces the risk of external pollution, due to the minimal manipulation of the sample required. The method developed was successfully applied for the analysis of personal care products in different kinds of water matrices: influents and effluents of urban and industrial wastewater treatment plants, effluents of a reverse osmosis treatment plant and river waters. The influents of urban treatment plants generally showed the highest values for synthetic musks, with concentrations of up to 2219 ng/L of galaxolide, whereas the highest concentrations of parabens were detected in the industrial treatment plants influents.

Chronic risk assessment of exposure to volatile organic compounds in the atmosphere near the largest Mediterranean industrial site.

This study focuses on characterising the risk of exposure to volatile organic compounds (VOCs) by means of inhalation in people living in the vicinity of the largest chemical production site in the Mediterranean area. Eighty-six VOCs were initially selected for this study based on their adverse environmental and health effects. The monitoring campaign was conducted for 276 days in three different locations around the chemical site. The analytical method used for the characterisation was based on European standard method EN-14662-2, which consists of the active sampling of air for 24h in charcoal tubes, followed by extraction with carbon disulphide and GC-MS analysis. Forty-four VOCs with toxicological data available concerning their carcinogenic and non-carcinogenic health effects were quantified during the monitoring campaign. None of the quantified VOCs showed average concentrations exceeding their chronic reference concentrations and, therefore, no non-carcinogenic health effects are expected as a result of this exposure. However, the global average cancer risk due to VOC exposure in the area (3.3×10(-4)) was found to be above the values recommended by the WHO and USEPA. The influence of the analytical method was also evaluated by comparing cancer risk estimates using a thermal desorption (TD) method based on method EN-14662-1. The results of the 24-h samples for the solvent extraction method were compared with the average of 12 daily samples of 2-h for the TD method for 24 sampling days. Although the global estimated lifetime cancer risk was statistically comparable for both methods, some differences were found in individual VOC risks. To our knowledge, this is the first study that estimates the carcinogenic and non-carcinogenic risks posed by the inhalation of VOCs in people living near a chemical site of this size, and compares the estimated cancer risk obtained using two different standard analytical methods.

Determination of nicotine and N-nitrosamines in house dust by pressurized liquid extraction and comprehensive gas chromatography--nitrogen chemiluminiscence detection.

A novel, highly selective method for the determination of nicotine, N-nitrosamines and tobacco-specific nitrosamines (TSNAs) in indoor dust samples is presented in this study. Samples were extracted by in-cell clean-up pressurized liquid extraction (PLE) that allows high extraction efficiency with moderate consumption of organic solvents. The extracts were analyzed by comprehensive gas chromatography and detected with a nitrogen chemiluminiscence detector (GC×GC-NCD) that provided enhanced selectivity and sensitivity for organic nitrogen containing compounds. Method validation showed good linearity, repeatability and reproducibility (%RSD<8%). Recovery was higher than 80% for most target compounds and limits of detection lower than 16 ng g(-1). The method was used for the determination of the nitrosamine target compounds in house dust samples from both smoking and non-smoking households. All the analytes were found in the samples, nicotine being the most abundant compound in smokers' dust and one of the most abundant in non-smokers' dust. To our knowledge this is the first time that volatile N-nitrosamines and TSNAs have been determined in indoor dust samples. The results demonstrate the presence of these highly carcinogenic compounds in house dust, with inherent human exposure through inhalation and/or involuntary ingestion of house dust.