Non-target Analysis of Danish Wastewater Treatment Plant Effluent: Statistical Analysis of Chemical Fingerprinting as a Step Toward a Future Monitoring Tool.

In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.

In-depth analysis of volatolomic and odorous profiles of novel craft beer by permutation test features selection and multivariate correlation analysis.

This research explored the impact of binary cereal blends [barley with durum wheat (DW) and soft wheat (CW)], four autochthonous yeast strains (9502, 9518, 14061 and 17290) and two refermentation sugar concentrations (6-9 g/L), on volatolomics (VOCs) and odour profiles of craft beers using unsupervised statistics. For the first time, we applied permutation test to select volatiles with higher significance in explaining variance among samples. The unsupervised approach on the 19 selected VOCs revealed cereal-yeast interaction to be the main source of variability and DW-9502-6/9, DW-17290-6, CW-17290-6 and CW-9518-6 being the best technological strategies. In particular, in samples DW-9502-6/9, concentrations of some of the selected volatiles were observed to be approximately three to more than seven times higher than the average. PLS-correlation between VOCs and odour profiles proved to be very useful in assessing the weight of each of the selected VOCs on the perception of odour notes.

Residual loads from tilapia farming on the sediment of a Brazilian reservoir.

This study investigated the sediment geochemistry of a fish farming area in net cage tanks in the Rosário reservoir, Brazil. Three areas were investigated: reference (RA), fish farming (FFA), and dispersion (DA). The results were analyzed through correlation, similarity, principal component analysis, comparison with legislation, sediment quality guidelines, and sediment pollution indices. The mean concentrations for RA, FFA, and DA areas were respectively: Cu (mg.kg-1) 37.74, 62.23, and 71.83; Mn (mg.kg-1) 22.55, 66.48, and 55.90; Zn (mg.kg-1) 9.13, 114.83, and 94.27; Fe (%) 0.28, 0.40, and 0.43; OM (%) 15.84, 21.95, and 18.45; TOC (%) 1.86, 3.69, and 6.05; TN (mg.kg-1) 2365.00, 5015.00, and 3447.51; TP (mg.kg-1) 780.00, 6896.00, and 2585.50; ORP (mV) -95.50, -135.20, and -127.10; pH 6.60, 6.58, and 6.05; <63 µm 90.59, 78.68, and 87.30. Statistically, the influence of fish farming on sediment, organic matter, and pollutant sedimentation was demonstrated. Cu and Zn concentrations were below sediment quality guidelines. Regarding legal limits (resolution 454/2012/CONAMA), nutrients in the FFA area exceeded by 60% (TN) and 100% (TP), while in DA and RA areas they were 100% lower. TOC was 100% lower in all areas. Organic matter exceeded the limit by 100% in all areas. Pollution indices resulted in: low contamination factor 78%; unpolluted for 87% of pollution load and 83% of combined pollution; moderately polluted for 75% of the Nemerow index. The greatest impacts and influence of farming on pollutant sedimentation were more concentrated in the fish farming area. In terms of legal aspects and pollution indices, fish farming produced low levels of trace metal pollution and nutrient concentrations exceeded legal limits.

Exploring the bioaccessibility of polyphenols and glucosinolates from Brassicaceae microgreens by combining metabolomics profiling and computational chemometrics.

Microgreens constitute natural-based foods with health-promoting properties mediated by the accumulation of glucosinolates (GLs) and phenolic compounds (PCs), although their bioaccessibility may limit their nutritional potential. This work subjected eight Brassicaceae microgreens to in vitro gastrointestinal digestion and large intestine fermentation before the metabolomics profiling of PCs and GLs. The application of multivariate statistics effectively discriminated among species and their interaction with in vitro digestion phases. The flavonoids associated with arugula and the aliphatic GLs related to red cabbage and cauliflower were identified as discriminant markers among microgreen species. The multi-omics integration along in vitro digestion and fermentation predicted bioaccessible markers, featuring potential candidates that may eventually be responsible for these functional foods' nutritional properties. This combined analytical and computational framework provided a promising platform to predict the nutritional metabolome-wide outcome of functional food consumption, as in the case of microgreens.

Population exposure to emerging perfluoroalkyl acids (PFAAs) via drinking water resources: Application of multivariate statistics and risk assessment models.

This study assessed the occurrence, origins, and potential risks of emerging perfluoroalkyl acids (PFAAs) for the first time in drinking water resources of Khyber Pakhtunkhwa, Pakistan. In total, 13 perfluoroalkyl carboxylic acids (PFCAs) with carbon (C) chains C4-C18 and 4 perfluoroalkyl sulfonates (PFSAs) with C chains C4-C10 were tested in both surface and ground drinking water samples using a high-performance liquid chromatography system (HPLC) equipped with an Agilent 6460 Triple Quadrupole liquid chromatography-mass spectrometry (LC-MS) system. The concentrations of ∑PFCAs, ∑PFSAs, and ∑PFAAs in drinking water ranged from 1.46 to 72.85, 0.30-8.03, and 1.76-80.88 ng/L, respectively. Perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA), and perfluoropentanoic acid (PFPeA) were the dominant analytes in surface water followed by ground water, while the concentration of perfluorobutane sulfonate (PFBS), perfluorooctanoic acid (PFOA), perfluoroheptanoic acid (PFHpA), perfluorooctane sulfonate (PFOS), perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), and perfluorododecanoic acid (PFDoDA) were greater than long-chain PFOA and PFOS. The correlation statistics, which showed a strong correlation (p < 0.05) between the PFAA analytes, potentially indicated the fate of PFAAs in the area's drinking water sources, whereas the hierarchical cluster analysis (HCA) and principal component analysis (PCA) statistics identified industrial, domestic, agricultural, and commercial applications as potential point and non-point sources of PFAA contamination in the area. From risk perspectives, the overall PFAA toxicity in water resources was within the ecological health risk thresholds, where for the human population the hazard quotient (HQ) values of individual PFAAs were < 1, indicating no risk from the drinking water sources; however, the hazard index (HI) from the ∑PFAAs should not be underestimated, as it may significantly result in potential chronic toxicity to exposed adults, followed by children.

Graphical, statistical and index-based techniques integrated for identifying the hydrochemical fingerprints and groundwater quality of In Salah, Algerian Sahara.

Groundwater, a predominant reservoir of freshwater, plays a critical role in providing a sustainable potable water and water for agricultural and industry uses in the In Salah desert region of Algeria. This research collected 82 underground water samples from Albian aquifers to assess water quality and identify hydrogeochemical processes influencing mineralization. To achieve this objective, various methods were employed to evaluate water quality based on its intended uses. The drinking water quality index utilized revealed the water potability status, while the indicators of irrigation potability were employed to evaluate its quality for agricultural purposes. Additionally, an assessment of groundwater susceptibility to corrosion and scaling in an industrial context was conducted using several indices, e.g., Langelier index, Larson-Skold index, Ryznar index, chloride-sulfate mass ratio, Puckorius index, aggressiveness index, and the Revelle index. The findings of this study revealed that the groundwater quality for consumption fell into four categories: good (2.44%), fair (29.27%), poor (65.85%), and non-potable (2.44%). Concerning agricultural irrigation, the indexical results indicated that 15.85% of the waters exhibited adequate quality, while 84.15% were questionable for irrigation. Calculations based on various corrosion and scaling evaluation indices showed that most wells were prone to corrosion, with a tendency for calcium bicarbonate deposit formation. Furthermore, the hydrochemical study identified three water types: Na-Cl (53.66%), Ca-Mg-Cl (37.80%), and Ca-Cl (8.54%) waters. Analyses of correlation matrices, R-type clustering, factor loadings, Gibbs diagrams, scatterplots, and chloro-alkaline indices highlighted that the chemistry of the Albian groundwater is fundamentally impacted by a number of processes such as silicate weathering, evaporite dissolution, ionic exchange, and anthropogenic inputs, that played impactful role in the aquifer's water chemistry.

An analytical strategy for challenging members of the microplastic family: Particles from anti-corrosion coatings.

Potentially hazardous particles from paints and functional coatings are an overlooked fraction of microplastic (MP) pollution since their accurate identification and quantification in environmental samples remains difficult. We have applied the most relevant techniques from the field of microplastic analysis for their suitability to chemically characterize anti-corrosion coatings containing a variety of polymer binders (LDIR, Raman and FTIR spectroscopy, Py-GC/MS) and inorganic additives (ICP-MS/MS). We present the basis of a possible toolbox to study the release and fate of coating particles in the (marine) environment. Our results indicate that, due to material properties, spectroscopic methods alone appear to be unsuitable for quantification of coating/paint particles and underestimate their environmental abundance. ICP-MS/MS and an optimized Py-GC/MS approach in combination with multivariate statistics enables a straightforward comparison of the multi-elemental and organic additive fingerprints of paint particles. The approach can improve the identification of unknown particles in environmental samples by an assignment to different typically used coating types. In future, this approach may facilitate allocation of emission sources of different environmental paint/coating particles. Indeed, future work will be required to tackle various remaining analytical challenges, such as optimized particle extraction/separation of environmental coating particles.

The Shelf Life of Milk-A Novel Concept for the Identification of Marker Peptides Using Multivariate Analysis.

The quality of food is influenced by several factors during production and storage. When using marker compounds, different steps in the production chain, as well as during storage, can be monitored. This might enable an optimum prediction of food's shelf life and avoid food waste. Especially, proteoforms and peptides thereof can serve as indicators for exogenous influences. The development of a proteomics-based workflow for detecting and identifying differences in the proteome is complex and time-consuming. The aim of the study was to develop a fast and universal workflow with ultra-high temperature (UHT) milk as a proteinaceous model food with expectable changes in protein/peptide composition. To find an optimum shelf life without sticking to a theoretically fixed best-before date, new evaluation and analytical methods are needed. Consequently, a modeling approach was used to monitor the shelf life of the milk after it was treated thermally and stored. The different peptide profiles determined with high-resolution mass spectrometry (HRMS) showed a significant difference depending on the preparation method of the samples. Potential marker peptides were determined using orthogonal projections to latent structures discriminant analysis (OPLSDA) and principal component analysis (PCA) following a typical proteomics protocol with tryptic hydrolysis. An additional Python-based algorithm enabled the identification of eight potential tryptic marker peptides (with mass spectrometric structural indications m/z 885.4843, m/z 639.3500, m/z 635.8622, m/z 634.3570, m/z 412.7191, m/z 623.2967, m/z 880.4767, and m/z 692.4041), indicating the effect of the heat treatment. The developed workflow is flexible and can be easily adapted to different research questions in the field of peptide analysis. In particular, the process of feature identification can be carried out with significantly less effort than with conventional methods.

Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese.

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Natural Radioactivity in Raw Building Materials for Underground Parking Lots and Assessment of Radiological Health Risk for the Population.

This article reports the results of an investigation into the activity concentration of natural radionuclides in raw building materials for underground parking lots, together with the assessment of the radiation hazard for the public related to exposure to ionizing radiations. To this purpose, high-purity germanium (HPGe) γ-ray spectrometry was employed in order to quantify the average specific activity of 226Ra, 232Th, and 40K natural radioisotopes. With the aim to assess any possible radiological health risk for the population, the absorbed γ-dose rate (D), the annual effective dose equivalent outdoor (AEDEout) and indoor (AEDEin), the activity concentration index (I), and the alpha index (Iα) were also estimated, resulting in values that were lower than the maximum recommended ones for humans. Finally, the extent of the correlations existing between the observed radioactivity and radiological parameters and of these parameters with the analyzed samples was quantified through statistical analyses, including Pearson's correlation, a principal component analysis (PCA), and a hierarchical cluster analysis (HCA). As a result, three clusters of the investigated samples were recognized based on their chemical composition and mineralogical nature. Noteworthily, this paper covers a certain gap in science since its topic does not appear in literature in this form. Thus, the authors underline the importance of this work to global knowledge in the environmental research and public health fields.

Trace the origin of yak meat in Xizang based on stable isotope combined with multivariate statistics.

In this study, the feasibility of tracing the origin of yak meat in Xizang Autonomous Region based on stable isotope combined with multivariable statistics was researched. The δ13C, δ15N, δ2H and δ18O in yak meat were determined by stable isotope ratio mass spectrometry, and the data were analyzed by analysis of variance, fisher discriminant analysis (FDA), back propagation (BP) neural network and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the δ13C, δ15N, δ2H and δ18O had significant differences among different origins (P < 0.05). The overall original correct discrimination rate of fisher discriminant analysis was 89.7 %, and the correct discrimination rate of cross validation was 88.2 %. The correct classification rate of BP neural network based on training set was 93.38 %, and the correct classification rate of BP neural network based on test set was 89.83 %. The OPLS-DA model interpretation rate parameter R2Y was 0.67, the model prediction rate parameter Q2 was 0.409, which could distinguish yak meat from seven different producing areas in Xizang Autonomous Region. The results showed that the origin of yak meat in Xizang Autonomous Region can be traced based on stable isotope combined with multivariate statistics.

Multi-Omics Approaches for Freshness Estimation and Detection of Illicit Conservation Treatments in Sea Bass (Dicentrarchus Labrax): Data Fusion Applications.

Fish freshness consists of complex endogenous and exogenous processes; therefore, the use of a few parameters to unravel illicit practices could be insufficient. Moreover, the development of strategies for the identification of such practices based on additives known to prevent and/or delay fish spoilage is still limited. The paper deals with the identification of the effect played by a Cafodos solution on the conservation state of sea bass at both short-term (3 h) and long-term (24 h). Controls and treated samples were characterized by a multi-omic approach involving proteomics, lipidomics, metabolomics, and metagenomics. Different parts of the fish samples were studied (muscle, skin, eye, and gills) and sampled through a non-invasive procedure based on EVA strips functionalized by ionic exchange resins. Data fusion methods were then applied to build models able to discriminate between controls and treated samples and identify the possible markers of the applied treatment. The approach was effective in the identification of the effect played by Cafodos that proved to be different in the short- and long-term and complex, involving proteins, lipids, and small molecules to a different extent.

Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data.

We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from eight characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli), and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena such as the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.

Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion.

This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.

Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil.

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple water uses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed that the area occupied by fish farms has increased in the reservoir from 2016 to 2022 and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir.

Multi-layered physical factors govern mercury release from soil: Implications for predicting the environmental fate of mercury.

Despite the recognised risks of human exposure to mercury (Hg), the drivers of gaseous elemental mercury (GEM) emissions from the soil remain understudied. In this study, we aimed to identify the environmental parameters that affect the GEM flux from soil and derive the correlations between environmental parameters and GEM flux. Principal component analysis (PCA), factor analysis (FA), and structural equation modelling (SEM) were performed on samples from forest and non-forest sites. The associated results revealed the impact of each environmental parameter on GEM flux, either due to the interaction between the parameters or as a coherent set of parameters. An introductory correlation matrix examining the relationship between two components showed a negative correlation between GEM flux and atmospheric pressure at the two sites, as well as strong correlations between atmospheric pressure and soil temperature. In cases of non-forest open sites with no trees, the PCA and FA results were consistent, indicating that atmospheric pressure, solar irradiance, and soil moisture-defined as primary causality-are largely independent drivers of GEM flux. In contrast, the PCA and FA results for the forest areas with high humidity, tree coverage, and shade were inconsistent, confirming the hypothesis that primary causality affects GEM flux rather than consequent parameters driven by primary causality, such as air and soil temperature and atmospheric humidity. The SEM results provided further evidence for primary and consequent causality as crucial drivers of the GEM flux. This study demonstrates the importance of key primary parameters, such as atmospheric pressure, solar irradiance, and soil moisture content, that can be used to predict mercury release from soils, as well as the importance of consequent parameters, such as air and soil temperature and atmospheric humidity. Monitoring the magnitude of these environmental parameters alone may facilitate the estimation of mercury release from soils and be useful for detailed modelling of soil-air Hg exchange.

An integrative multi-omics approach aimed to gain insight on the effect of composition, style, yeast, and wheat species on wheat craft beer flavour.

This study was aimed to unravel the effect of raw materials (barley and wheat), wheat concentration (0, 25, 40, and 100 %), wheat species (common and durum), beer style (Blanche and Weiss), and yeast (US-05 and WB-06) on the chemical composition, volatiles, and sensory profile of wheat craft beers by using a multivariate statistical approach. Beer samples were analysed for their composition, volatiles and sensory profile and data were processed using unsupervised multivariate analyses, PLS regression and a multi-omics approach using multi-block PLS-DA. Multi-block variable sparsification was used as an embedded dimension reduction step. The adopted multi-omics approach permitted to correctly classify beers with different styles and wheat concentration, and to accurate classify (95 % accuracy) beers according to yeast type. Wheat species was of lower importance since it permitted a classification with 49 % accuracy which increased to 74 % in Blanche beers, thus suggesting that malting flattened differences determined by wheat species.

Predictive role of NICU-related stress, postpartum depression trajectory and family coping on growth trajectory of moderate-to-late preterm infants: A longitudinal study.

AIMS: To describe the changes in moderate-to-late preterm infants' (MLPIs) growth during 12 months of corrected age (CA) and to examine the predictive role of NICU-related stress, postpartum depression trajectory and family coping ability on the physical developmental trajectory of MLPIs. DESIGN: A prospective longitudinal study. METHODS: There were 237 mother-infant dyads with at least two follow-up data records included. General characteristics and NICU-related stress were recorded from medical records at baseline. Infants' physical growth was measured at 40 weeks, 1, 3, 6, 9 and 12 months CA during outpatient follow-up. Maternal postpartum depressive symptoms and family coping ability were assessed by questionnaires at 1, 3, 6, 9 and 12 months CA and 1 month CA respectively. We investigated the modifiable factors inside and outside of NICU on the trajectories of physical growth in the first year in MLPIs, mainly by using latent growth curve models with time-varying covariates. RESULTS: The curved trajectories of weight, length and head circumference in the first year in MLPIs demonstrated gradually slowed growth rates and these infants were above the WHO growth standards for the same age and sex. The latent growth curve models indicated that more NICU-related stress was negatively associated with the weight and length at 40 weeks CA, and family coping ability (parent-child relationship) at 1 month CA was associated with the growth rate of weight. Besides, more NICU-related stress predicted faster length growth rate. The infants of mothers who were in the group of high-level postpartum depression trajectory had a slower growth rate of head circumference. CONCLUSIONS: Our study identified the modifiable factors along the care continuum influencing the trajectory of MLPIs' physical growth. Nurses should receive more training about infant stress measurement and family-centred care to work in partnership with parents so that MLPIs can reach their full developmental potential. Also, multidisciplinary interventions including stress reduction strategies, close psychological monitoring and education improving parent-infant relationships should be further developed to achieve optimizing growth in the first year of MLPIs. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: It is recommended that nurses pay attention to the long-term physical growth status of MLPIs, and closely support their families. Quantifying NICU-related stress and developing reduction strategies should be the priority for clinical staff during hospitalization. After discharge, persistent screening of depressive symptoms, psychological intervention and education about the parent-child relationship need to be included in the follow-up visits. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. The study only included patients who were research participants.

Exploring geochemical distribution of potentially toxic elements (PTEs) in wetland and agricultural soils and associated health risks.

This study is carried out to understand the degree of soil pollution, transport mechanism, and distribution pattern of potentially toxic elements (PTEs), including the exposure effects on human health. Towards this, topsoil samples were collected from the Saman wetland and surrounding agricultural fields in the Gangetic plain, India. The results show that the mean concentration of Cu, Hg, Zn, Pb, Th, As, U, and Cd of both soil types exceed the natural background values. The multivariate analysis suggests the soils are moderately contaminated with As, Cd, Zn, Pb, and Hg (possibly from anthropogenic sources) and heavily contaminated with Th and U, likely ascended from geogenic sources. The GIS-based geostatistical plots coupled with principal component analysis (PCA) and hierarchical cluster analysis (HCA) apportion the sources of these toxic elements, which vary greatly and are closely correlated to the geogenic processes and local anthropogenic sources like pesticides and agrochemicals. The health risk assessment revealed that the cumulative hazard index (HI) values of PTEs are lower than the safe level, suggesting no significant noncarcinogenic effect for adults and children. However, excess cancer risk (ECR) values exceed the permissible limit (1 × 10-6), signifying that exposure to the toxic element concentration may cause cancer in the exposed population, most probably in the children subpopulation. Thus, this study highlights the importance of local compliance, ensuring the quality checks and management policies in using pesticides and other agrochemicals containing PTEs to control the imposed cancer risks.

Detection of fragrances on the skin and study of their interaction using infrared and Raman spectroscopy.

Nowadays, fragrances belong to the widely used cosmetics. Their composition is designed in a way that it evolves and changes over time. In this work, the effect of fragrances on the skin was studied - the interactions between pig skin samples and fragrances and the possibility of their detection and mutual differentiation. Non-invasive techniques of vibrational spectroscopy were used to obtain the data, namely FT-IR spectroscopy with attenuated total reflection accessory and Raman microspectroscopy. Vibrational spectra were measured within 8 h with different time intervals and after 22 h from the application of fragrance for FT-IR and Raman measurements, respectively. The obtained spectra were pre­processed and subsequently evaluated by multivariate statistical methods. The study showed that skin treated by fragrances is well distinguishable from untreated skin, even after 22 h. In addition, it is possible to differentiate individual fragrances from each other; therefore, the use of spectroscopical techniques could be a potential tool for forensic analysis of fragrances.