Identification Of Contaminants Research Articles

Identification and trend analysis of organic cationic contaminants via non-target screening in suspended particulate matter of the German rivers Rhine and Saar

Non-target screening of suspended particulate matter (SPM), collected from the German rivers Rhine and Saar, was conducted with the goal of identifying organic, permanent cationic contaminants and of estimating their temporal trends over an extended period. Therefore, annual composite samples of SPM, provided by the German Environmental Specimen Bank, were extracted and analyzed with high resolution LC-QToF-MS/MS. To facilitate the identification of substances belonging to the class “permanent cations”, prioritization methods were applied utilizing the physicochemical properties of these compounds. These methods include both interactions of the analyte molecules with cation exchange resins and analyzing mass deviations when changing from non-deuterated to deuterated mobile phase solvents during LC-MS analysis. By applying both methods in a combined approach, 123 of the initially detected 2695 features were prioritized, corresponding to a 95% data reduction. This led to the identification of 22 permanent cationic species. The organic dyes Basic Yellow 28 and Fluorescent Brightener 363 as well as two quaternary ammonium compounds (QACs) were detected in environmental samples for the first time to best of or knowledge. The other compounds include additional QACs, as well as quaternary tri-phenylphosphonium compounds (QPC/TPP). In addition to identification, we determined temporal trends of all compounds over a period of 13 years and assessed their ecotoxicological relevance based on estimated concentrations. The two QACs oleyltrimethylammonium and eicosyltrimethylammonium show significant increasing trends in the Rhine SPM and maximum concentrations in the Saar SPM of about 900 and 1400 µg/kg, respectively. In the case of the dyes, constant trends have been observed at the end of the studied period, but also maximum concentrations of 400 µg/kg for Basic Yellow 28 in 2006 and 1000 µg/kg for Fluorescent Brightener 363 in 2015, potentially indicating a strong ecotoxicological risk.

Impedimetric study of the electrocatalytic oxidation of alcohols by nickel-Schiff base metallopolymer: Potential application for forensic identification of alcoholic beverage contaminants by multivariate data analysis

The present paper presents a systematic electrochemical impedance study on the electrooxidation of methanol, ethanol, 1-propanol, ethylene glycol and glycerol by a sensor based on nickel-salen metallopolymer. As a second step, the application simulated the device in the identification of contamination in alcoholic beverages by principal component analysis (PCA). By studying electrochemical impedance, we verified that the reactivity of alcohols on the device is directly related to pKa values and polarity index. By multivariate analysis, it was possible to set up a system that differentiates the types of alcohol with impedimetric variables. The identification distance between the groups allowed the differentiation of mixed samples from the groups containing ethanol-methanol and ethanol-ethylene glycol.

Identification of Potential Waste Fluid Contamination with Geoelectric Method in Tebing Tinggi Subdistrict, West Tanjung Jabung Regency, Jambi

In the study area of Tebing Tinggi Subdistrict, Tanjung Jabung Barat Regency, Jambi Province, there are several companies engaged in the manufacturing, agriculture, and plantation industries. This company uses the Pengabuan river for transportation. Therefore, the study area has the potential to be contaminated by the waste fluid produced by the company. In this study, the geoelectric resistivity method was used because it is good for identifying potential contamination at the subsurface. The Wenner array was used because it is good in a study with shallow surfaces, so it is good at identifying contamination. Data processing is done by inversion process using Res2Dinv software to get 2D modeling and Voxler to get 3D modeling. Based on the 2D modeling, it is identified that the contamination is on lines 1, 2, and 4 with a resistivity range of ±0.059-2 Ωm. Also, in the study area, it is identified that there are alternating tuffaceous claystone, claystone, tuffaceous sandstone, sandstone, and shallow aquifer. Based on the 3D modeling, it is identified that there is a potential continuity of contamination between line 3 and line 4, which is at a distance of 0-8 m from line 4 to line 3.Keywords: Contamination, Geoelectric Resistivity Method, Res2Dinv, Voxler, Wenner Array

Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods

High amounts of industrial wastewater are generated by the ever-growing demand and production of paint and coating materials. These effluents have negative effects on human health and the environment. The source of industrial effluents highly influences the properties, composition, and content of pollutants. The manufacturing of paint and coatings uses huge volumes of water and chemical reagents, consequently producing huge volumes of heavily polluted wastewater. This review is focused on summarizing various methods of industrial wastewater treatment from the paint manufacturing industry. Current trends in paint industry wastewater treatment processes have resulted in high efficiency of the reduction of chemical oxygen demand. Factors affecting the treatment processes are discussed and future trends are outlined. The effectiveness of the recently used methods is compared and the limitations of advanced treatment systems are highlighted. The review of recent developments in paint industry wastewater treatments points to the need for paying great attention to advanced analytical methods allowing the identification of individual contaminants to guarantee safe disposal limits.

Technical note: Assessment of radiation measurement devices for the detection of [177 Lu]Lu-labeled radiopharmaceuticals containing [177m Lu]Lu-impurities.

Nuclear medicine therapies using [177 Lu]Lu-labeled radiopharmaceuticals have increased significantly in recent years. Some of these radiopharmaceuticals contain long-lived impurities of [177m Lu]Lu. Identification of this specific contamination and its quantification is important in different scenarios. In this study, standard measurement hardware used for handling radioisotopes was evaluated for the measurement of [177m Lu]Lu and [177 Lu]Lu at equilibrium. Device-specific detection limits (LODs) for [177m Lu]Lu were determined according to international standards and then validated. The LODs were determined according to the international standard ISO 11929-1 for [177m Lu]Lu for five identical portable contamination monitors (PCMs), a wastewater counter (WWC), and a release counter system (RCS) at different measuring times. Subsequent activity measurements of the defined samples were used to validate the linearity of the measurement instruments down to the LOD for each system. The average LOD across all PCMs was 0.249±0.009 and 0.129±0.005Bq/cm2 for 10 and 30s measurements, respectively. The LODs of WWC varied between 3.3 and 4.7Bq/L for measurement times of 300s and 0.8-1.3Bq/L for 3600s depending on the energy window studied. The LOD of the RCS depended on the container volume and was 0.08Bq/g for the 50L container at 60s measurement. The measurements for all examined devices were linear down to the LOD (correlation coefficient R≥0.96; coefficient of determination R2 ≥0.92). All investigated measuring instruments (PCM, WWC, RCS) were suitable for the determination of [177m Lu]Lu at equilibrium, and their specific LODs were determined. Based on the measurements performed, activity is overestimated; however, this is tolerable because assumptions and measurements in the context of radiation protection should be conservative.

Improving the Identification of Fecal Contamination in Recreational Water through the Standardization and Normalization of Microbial Source Tracking

Improving the Identification of Fecal Contamination in Recreational Water through the Standardization and Normalization of Microbial Source Tracking

Delineation of Hydrochemical Characteristics and Tracing Nitrate Contamination of Groundwater Based on Hydrochemical Methods and Isotope Techniques in the Northern Huangqihai Basin, China

Hydrochemical research and identification of nitrate contamination are of great significant for the endorheic basin, and the Northern Huangqihai Basin (a typical endorheic basin) was comprehensively researched. The results showed that the main hydrochemical facies were HCO3–Mg·Ca and HCO3–Ca·Mg. Spatial variation coefficients of most indices were greater than 60%, which was probably caused by human activities. The hydrochemical evolution was mainly affected by rock weathering and also by cation exchange. The D–18O relationship of groundwater was δD = 5.93δ18O − 19.18, and the d–excess range was −1.60–+6.01‰, indicating that groundwater was mainly derived from precipitation and that contaminants were very likely to enter groundwater along with precipitation infiltration. The NO3(N) contents in groundwater exceeded the standard. Hydrochemical analyses indicated that precipitation, industrial activities and synthetic NO3 were unlikely to be the main sources of nitrate contamination in the study area. No obvious denitrification occurred in the transformation process of nitrate. The δ15N(NO3) values ranged from +0.29‰ to +14.39‰, and the δ18O(NO3) values ranged from −6.47‰ to +1.24‰. Based on the δ15N(NO3) – δ18O(NO3) dual isotope technique and hydrochemical methods, manure, sewage and NH4 fertilizers were identified to be the main sources of nitrate contamination. This study highlights the effectiveness of the integration of hydrochemical and isotopic data for nitrate source identification, and is significant for fully understanding groundwater hydrochemistry in endorheic basins and scientifically managing and protecting groundwater.

Spatial distribution and risk identification of arsenic contamination in water and soil through GIS-based interpolation techniques in Jianghan Plain, Central China

Arsenic is considered a poison because of its seriously toxic effects on the human body; elevated concentrations of arsenic in drinking water have been reported in different parts of the world. Investigating the arsenic distributions in soil, surface water (SW), and groundwater (GW) is an interesting topic of research, along with probing its correlations with local factors of the ecosystem and other hydrogeochemical parameters. This study mainly aims to investigate the impacts of various factors on elevated arsenic concentrations in water and soil. The following factors are assessed for their relationship to the propagation of arsenic in Jianghan Plain, which is the study area: population density, pumping rate, rain, land use, surface elevation, water level, and heavy metal contamination. The arsenic contamination potential prediction map and categories were developed using GIS-based techniques, such as ordinary kriging and quantile methods. Then, the “raster calculator” tool was applied to verify the impacts of the abovementioned factors on arsenic concentration. Eighty-four single-factor, bi-factor, and multi-factor models were established to investigate the effective combinations among the factors. Land use and pumping rate were identified from the soil through an equal frequency tool, whereas water population density and pumping rate were obtained with high matching percentages. The arsenic concentrations varied in the ranges of 0.0001–0.1582 mg/L in GW, 0.0003–0.05926 mg/L in SW, and 1.820–46.620 mg/kg in soil sediment. The single factors showed the best equal frequency of arsenic concentration in water for population density (68.62%) and in soil for land use (65.57%) and pumping (63.66%). Statistical calculations with percentage frequency factors also depicted a positive trend. Arsenic was reported to have high correlations with Fe in GW (r2 = 0.4193), with EC in SW (r2 = 0.4817), and with Cu in soil (r2 = 0.623). It is observed that the alkaline behaviors of water bodies are associated with arsenic mobility. Elevated arsenic values were observed in grids along surface flows with high anthropogenic activities and urbanization. Additionally, low concentrations of Fe depicted reduced activities in aquifer systems. Filtering drinking water as well as controlling the suspected sources and factors affecting concentrations of arsenic in the three phases are options for reducing the health risks of the local populations.

Hygiene and Sanitation Factors Affecting Contamination of Soil-Transmitted Helminths in Household Water Sources in Jember, Indonesia

Background: Water sources used by households are at risk of contamination by soil-transmitted helminths (STH). Hygiene and sanitation have an essential role in the transmission and contamination of STH. The objective of this study was to analyze whether there was an association between hygiene and sanitation factors with STH contamination in household water sources in Jember Regency, East Java Province, Indonesia.Methods: This cross-sectional analytic study was carried out from December 2020 to March 2021, including 46 samples from 23 respondents in Sukowono District and 23 respondents in Sukorambi District, Jember Regency, selected using a purposive sampling technique. Primary data on hygiene and sanitation risk factors were obtained from questionnaire interviews. The STH contamination in household water sources was examined by sedimentation and flotation methods. Data were analyzed using Fisher Exact test.Results: The identification of STH contamination in household water sources using microscope examination found hookworm species contaminated 8.7% (n=4) of water sources. There was a significant association between the type of human sewage disposal with STH contamination in household water sources (p=0.037). There was no association between hygiene and other sanitation factors with STH contamination in household water sources (p>0.05).Conclusions: Human sewage disposal that goes directly to sewers and rivers can cause contamination of water sources by STH. Therefore, it is important to dispose of human waste in septic tanks that comply with health requirements to prevent the transmission of STH to humans through water sources.

Modification of Glassy Carbon Electrode Using Microcrystalline Cellulose-Ethylenediaminetetraacetic Acid for the Detection of Lead and Cadmium Ions

There is a greater need for the identification and monitoring of metal contaminants as a result of the growing hazard that heavy metal contamination of water poses to the environment. In this work, microcrystalline cellulose was functionalised with ethylenediaminetetraacetic acid, and the product analysed using scanning electron microscopy and X-ray diffraction techniques. The functionalised microcrystalline cellulose-ethylenediaminetetraacetic acid was employed to modify the glassy carbon electrode. Detection for Pb2+ and Cd2+ ions was determined using square wave anodic stripping voltammetric analysis at square wave potential scan of -1.0 V to -0.2 V, and deposition potential of -1.0 V in 0.1M acetate buffer for 240 sec. Higher current response of Pb2+ was obtained at -0.6 V and that of Cd2+ at -0.8V. Limit of detection for Pb2+ was 1.8 ppb (MCC-EDTA GCE) and 5.0 ppb (MCC-GCE) , while that of Cd2+ was 7 ppb (MCC-EDTA GCE) and 10 ppb (MCC-GCE), these indicate MCC EDTA GCE has higher sensitivity towards detection of the metal ions and selectivity of modifiers for detection of Pd2+ and Cd2+ was achieved successfully.

THE USE OF MOLECULAR GENETIC METHODS BASED ON MLVA ANALYSIS TO CONFIRM THE UNIQUENESS OF COLLECTION STRAINS OF BRUCELLA

Collections of pathogenic bacterial cultures are necessary for the storage of commercially valuable and typical strains for the purpose of patenting, production and testing of diagnostic and preventive drugs, as well as epidemiological strains isolated during the diagnosis of infectious diseases for detailed study and monitoring of changes in sanitary and epidemiological well-being. Collections are replenished with new unique strains, so it is very important that the characteristics of these strains should be carried out using highly discriminatory molecular genetic methods. This article presents the results of the evaluation of the genetic diversity of 36 collection strains of Brucella abortus and 57 strains of Brucella melitensis by the Multilocus variable number tandem repeat (VNTR) analysis (MLVA-16). Generic identification and absence of contamination by other bacterial species was confirmed by sequencing of the 16S rRNA gene. MLVA analysis allowed to establish contamination of 4 strains with different genotypes of brucella and to reveal that the deposited 36 strains isolated in 14 outbreaks of brucellosis are identical genotypes of the pathogen. Our studies have confirmed the presence of identical brucella strains in the collection. The use of MLVA-16 in testing isolated bacterial cultures from a single outbreak will allow differentiating unique strains for their subsequent deposition. Verification of deposited strains with MLVA characteristics with a detailed analysis of metadata about deposited strains will optimize the collection, in terms of preserving only unique strains, which will reduce economic costs and risks to ensure biological safety.

Source identification and health risks of nitrate contamination in shallow groundwater: a case study in Subei Lake basin.

Nitrate pollution of groundwater has become a global concern as it can affect drinking water quality and human health. In this paper, an extensive hydrochemical investigation was performed to assess the spatial distribution, source identification, and health risk of groundwater nitrate pollution in the Subei Lake basin. The prevalent pollutant, nitrate (NO3-), was identified based on descriptive statistical method and box plots, and most of the other parameters of groundwater samples met water standards and can be used for drinking purpose. The results showed that nearly 23.53% of groundwater samples displays the NO3- concentrations higher than the limit of 50 mg/L recommended by the World Health Organization, and the highest nitrate content (199 mg/L) is mainly distributed around the Mukai Lake. Piper triangle diagram demonstrated that the dominated anions of hydrochemical types exhibit a gradual evolving trend from HCO3- to SO42- and Cl- with increasing nitrate concentration. The correspondence analysis suggested that agricultural activities are identified as the most possible source of nitrate contamination, while the higher content of other parameters in individual groundwater samples may be controlled by natural factors. The impacts of pollutant NO3- on human health were quantified using human health risk assessment method, and results showed that the order of non-carcinogenic health risk values through drinking water intake is Infants>Children>Adult males>Adult females, and 65%, 53%, 41%, and 35% of samples exceed the acceptable risk level (hazard quotient=1), respectively. The main findings obtained from this study can provide valuable insight on drinking water safety and groundwater pollution prevention.

Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles.

A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms’ response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from Daphnia magna, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems.

Machine learning for identification of silylated derivatives from mass spectra

MotivationCompound structure identification is using increasingly more sophisticated computational tools, among which machine learning tools are a recent addition that quickly gains in importance. These tools, of which the method titled Compound Structure Identification:Input Output Kernel Regression (CSI:IOKR) is an excellent example, have been used to elucidate compound structure from mass spectral (MS) data with significant accuracy, confidence and speed. They have, however, largely focused on data coming from liquid chromatography coupled to tandem mass spectrometry (LC–MS).Gas chromatography coupled to mass spectrometry (GC–MS) is an alternative which offers several advantages as compared to LC–MS, including higher data reproducibility. Of special importance is the substantial compound coverage offered by GC–MS, further expanded by derivatization procedures, such as silylation, which can improve the volatility, thermal stability and chromatographic peak shape of semi-volatile analytes. Despite these advantages and the increasing size of compound databases and MS libraries, GC–MS data have not yet been used by machine learning approaches to compound structure identification.ResultsThis study presents a successful application of the CSI:IOKR machine learning method for the identification of environmental contaminants from GC–MS spectra. We use CSI:IOKR as an alternative to exhaustive search of MS libraries, independent of instrumental platform and data processing software. We use a comprehensive dataset of GC–MS spectra of trimethylsilyl derivatives and their molecular structures, derived from a large commercially available MS library, to train a model that maps between spectra and molecular structures. We test the learned model on a different dataset of GC–MS spectra of trimethylsilyl derivatives of environmental contaminants, generated in-house and made publicly available. The results show that 37% (resp. 50%) of the tested compounds are correctly ranked among the top 10 (resp. 20) candidate compounds suggested by the model. Even though spectral comparisons with reference standards or de novo structural elucidations are neccessary to validate the predictions, machine learning provides efficient candidate prioritization and reduction of the time spent for compound annotation.

Sundry Bacteria Contamination Identification of Lentinula Edodes Logs Based on Deep Learning Model

Lentinula edodes logs are susceptible to sundry bacteria contamination during the culture process, and the manual identification of contaminated logs is difficult, untimely, and inaccurate. Aiming to solve this problem, this paper proposes a method for the identification of contaminated Lentinula edodes logs based on the deep learning model Ghost–YOLOv4. Firstly, a data set of Lentinula edodes log sundry bacteria contamination was constructed. Secondly, in view of the problems that the YOLOv4 network parameters are too large and that the detection speeds of Lentinula edodes log videos are slow, the backbone feature extraction network was replaced with a lightweight network, GhostNet, and the YOLOv4 enhancement feature extraction network PANet and the Yolo Head modules were completed. The modification of the network reduced the number of parameters of the network and improved the detection speed of the network. Finally, the feature extraction network introduced the migration learning pre-training model, which reduced the computational pressure and overfitting problems of the model and further improved the performance of the Ghost–YOLOv4 network. Not only did the constructed Ghost–YOLOv4 ensure the accuracy of the identification and detection of Lentinula edodes log sundry bacteria contamination, but it also had better results in detection speed and real-time performance, and it provides an effective solution for the lightweight deployment of a target detection model on embedded equipment in culture sheds.

Blockchain-Enabled Supply Chain platform for Indian Dairy Industry: Safety and Traceability.

Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

Stored Reference Samples Enable Efficient Non-Target HRMS Screening for Novel Chemical Contamination in Drinking Water

Producers of drinking water (DW) occasionally require chemical identification of new or unexpected contamination, e.g., caused by an incident. The state-of-the-art technique for the identification of organic compounds is High-Resolution Mass Spectrometry (HRMS). The ability to discover unexpected compounds at low concentrations in any sample by HRMS is facilitated by comparison to non-contaminated reference samples. Samples of raw and drinking water were collected regularly over one whole year from six Swedish drinking water treatment plants (DWTPs). The samples were analyzed by LC-HRMS together with spiked samples mimicking an incident. This setup enabled evaluation of the significance of having access to a collection of matrix-matched reference samples. The main variation in the organic compound profile in the data set was explained by the uniqueness of the raw water and purification steps in the individual DWTPs. Seasonal variations were also significant but subordinate. This subject was further explored by the analysis of drinking water sampled once from 90 of the 1750 Swedish DWTPs, where a similarity between DW originating from rock aquifers was observed. No other significant correlation between samples was observed—e.g., other types of raw water, which types of purification steps were involved, or which additives or process chemicals were added—which could aid in the selection of relevant reference samples. The conclusion from the study is that it would be imperative for the DWTPs to have access to their individual reference samples for use in the investigation of an incident. A library of such reference samples, e.g., collected monthly and covering the last 12 months, could be stored and used together with a fresh “suspected sample” for non-target HRMS investigations as described.

How to Verify Non-Presence-The Challenge of Axenic Algae Cultivation.

Many phycological applications require the growth and maintenance of pure algae cultures. In some research areas, such as biochemistry and physiology, axenic growth is essential to avoid misinterpretations caused by contaminants. Nonetheless, axenicity—defined as the state of only a single strain being present, free of any other organism—needs to be verified. We compare the available methods to assess axenicity. We first purified unialgal Limnospira fusiformis cultures with an established series of axenicity treatments, and by including two additional treatment steps. The presumable axenic cultures were then tested for their axenic state by applying conventional tests on LB (lysogeny broth) agar-plates, 16S rRNA gene amplicon sequencing, flow-cytometry and epifluorescence microscopy. Only the plate tests indicated axenic conditions. We found a linear relationship between total cell counts of contaminants achieved by flow cytometry and epifluorescence microscopy, with flow cytometry counts being consistently higher. In addition, 16S rRNA gene amplicon sequencing demonstrated its superiority by not only being an efficient tool for axenicity testing, but also for identification of persistent contaminants. Although classic plate tests are still commonly used to verify axenicity, we found the LB-agar-plate technique to be inappropriate. Cultivation-independent methods are highly recommended to test for axenic conditions. A combination of flow-cytometry and 16S rRNA gene amplicon sequencing complement each other and will yield the most reliable result.

Visualizing Dynamic Environmental Processes in Liquid at Nanoscale via Liquid-Phase Electron Microscopy.

Visualizing the structure and processes in liquids at the nanoscale is essential for understanding the fundamental mechanisms and underlying processes of environmental research. Cutting-edge progress of in situ liquid-phase (scanning) transmission electron microscopy (LP-S/TEM) and inferred possible applications are highlighted as a more and more indispensable tool for visualization of dynamic environmental processes in this Perspective. Advancements in nanofabrication technology, high-speed imaging, comprehensive detectors, and spectroscopy analysis have made it increasingly convenient to use LP S/TEM, thus providing an approach for visualization of direct and insightful scientific information with the exciting possibility of solving an increasing number of tricky environmental problems. This includes evaluating the transformation fate and path of contamination, assessing toxicology of nanomaterials, simulating solid surface corrosion processes in the environment, and observing water pollution control processes. Distinct nanoscale or even atomic understanding of the reaction would provide dependable and precise identification and quantification of contaminants in dynamic processes, thus facilitating trouble-tracing of environmental problems with amplifying complexity.

Comprehensive suspect screening for the identification of contaminants of emerging concern in urine of Flemish adolescents by liquid chromatography high-resolution mass spectrometry

The increasing human exposure to contaminants of emerging concern (CECs) cannot be fully assessed by targeted biomonitoring methods alone as these are limited to a subset of known analytes. On the contrary, suspect screening approaches based on liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allow the simultaneous detection of a high number of CECs and/or their (predicted) metabolites leading to a more comprehensive assessment of possible human exposure to these compounds. Within this study, 83 urine samples of Flemish adolescents (47 males, 36 females) collected in the frame of the 4th cycle of the Flemish Environment and Health Study (FLEHS IV) were selected with the aim of including a high and a low exposure group based on the overall exposure of 45 known contaminants. Samples were analyzed using a previously developed method involving a suspect screening approach to annotate CECs and their metabolites. The applied suspect list contained a total of >12,500 CECs and their known and predicted metabolites resulting from metabolization reactions, such as hydroxylation, glucuronidation and methylation. In total, 63 compounds were annotated at a confidence level of 3 or better, with most of the detected compounds not included in current biomonitoring programs. 5 out of the 63 compounds could be assigned with confidence level 2. Five compounds could unequivocally be identified (confidence level 1) through the comparison with reference standards. Personal care products were the main detected compound class (42% of detected compounds). Additionally, a detailed literature search indicated potential toxic effects for several of the detected CECs. Lastly, in the urine samples, a significantly higher number (p < 0.05) of compounds was detected in the high exposure group as opposed to the low exposure group. This difference could only be observed between high and low exposure load samples of female participants (p < 0.01).