Freezing-enhanced degradation of azo dyes in the chloride-peroxymonosulfate system.

In this study, we investigated the freezing-induced acceleration of dye bleaching by chloride-activated peroxymonosulfate (PMS). It has been observed that the oxidation of chloride by PMS generates a free chlorine species, such as hypochlorous acid (HOCl), under mild acidic and circumneutral pH condition. This process is the major reason for the enhanced oxidation capacity for electron-rich organic compounds (e.g., phenol) in the chloride-PMS system. However, we demonstrated that the chloride-PMS system clearly reduced the total organic carbon concentration (TOC), whereas the HOCl system did not lead to decrease in TOC. Overall, the chemical reaction is negligible in an aqueous condition if the concentrations of reagents are low, and freezing the solution accelerates the degradation of dye pollutants remarkably. Most notably, the pseudo-first order kinetic rate constant for acid orange 7 (AO7) degradation is approximately 0.252 h-1 with 0.5 mM PMS, 1 mM NaCl, initial pH 3, and a freezing temperature of -20 °C. AO7 degradation is not observed when the solution is not frozen. According to a confocal Raman-microscope analysis and an experiment that used an extremely high dose of reactants, the freeze concentration effect is the main reason for the acceleration phenomenon. Because the freezing phenomenon is spontaneous at high latitudes and at mid-latitudes in winter, and the chloride is ubiquitous elsewhere, the frozen chloride-PMS system has potential as a method for energy-free and eco-friendly technology for the degradation of organic pollutants in cold environments.

Target and Suspect Screening for Organic Additives in Six Classifications of Personal Care Products Using Liquid Chromatography-High-Resolution Mass Spectrometry.

Personal care products (PCPs) are integral components of daily human existence, including a large number of chemicals intentionally added for functional attributes (e.g., preservatives and fragrances) or unintentionally present, such as plasticizers. This investigation aimed to optimize the methodology for target and suspect screening via liquid chromatography-high-resolution mass spectrometry, focusing on nine prevalent organic additives (comprising bisphenols A, F, and S, methyl, ethyl, propyl, and butylparaben, 5-chloro-2-methyl-4-isothiazolin-3-one, and 4-hydroxybenzoic acid). A total of 50 high-selling PCPs were purchased from the local online market as samples. In detail, PCP samples were classified into body washes, shampoos, hair conditioners, facial cleansers, body lotions, and moisture creams. For calibration, the quality assurance and quality control results demonstrated a coefficient of determination (R2) surpassing 0.999, with detection and quantification limits ranging from 2.5 to 100.0 ng/g. For recovery experiments, replicate recoveries (n = 5) ranged from 61 to 134%. In purchased PCP samples, five of the nine target compounds were detected via a target screening. Methylparaben exhibited the highest concentration (7860 mg/kg) in a facial cleanser, which is known as an endocrine-disrupting chemical. A total of 248 suspects of organic additives were screened in PCPs, leading to a tentative identification of 9. Confirmation (confidence level 1) via reference standards was achieved for three suspects, while six were tentatively identified with a confidence level of 2. This two-step extraction methodology utilizing methyl tert-butyl ether and isopropyl alcohol enabled simultaneous analysis of diverse chemical groups with distinct properties.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry.

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Proposal for priority emerging pollutants in the Nakdong river, Korea: Application of EU watch list mechanisms.

The Nakdong River, the longest in Korea, has received numerous pollutants from heavily industrialized and densely populated areas while being used as a drinking water source. A number of research have reported occurrences of emerging pollutants (EPs) in the river. The results requested efficient monitoring and systematic management strategies such as EU watch list under Water Framework Directive. The aim of this study is to propose a watch list through preliminary monitoring of the river and risk-based prioritization approach. As candidates for monitoring target, 632 substances were selected based on literature and database searches. Among them, 175 substances were subjected to target screening method whereas 457 were evaluated via suspect screening. A risk-based prioritization was applied to substances quantified through target screening based on concentrations, and a scoring-based prioritization was applied to substances tentatively identified through suspect screening. Sampling campaigns (n = 12) were conducted from October 2020 to September 2021, at 8 sampling sites along the river. As a result, 130 target substances were quantified above the LOQ. Among the 21 substances whose priority score was assigned through risk-based prioritization, telmisartan and iprobenfos were identified with very high environmental risk while candesartan, TBEP, imidacloprid, azithromycin and clotrimazole were classified with high or intermediate risk. As result of the scoring system for 39 tentatively identified substances, 6 substances (benzophenone, caprolactam, metolachlor oxanilic acid, heptaethylene glycol, octaethylene glycol and pentaethylene glycol), which were then confirmed with reference standards, showed a potential environmental risk. Those substances prioritized through target and suspect screening followed by scoring systems can be a subset for the watch list and potential targets for nationwide water quality monitoring program in the future.

Target and non-target analyses of neutral per- and polyfluoroalkyl substances from fluorochemical industries using GC-MS/MS and GC-TOF: Insights on their environmental fate.

Novel and emerging per- and polyfluoroalkyl substances (PFAS) are a key issue of concern in global environmental studies. In this study, air, sediment, and wastewater samples were collected from areas in and/or surrounded by fluorochemical-related industrial facilities to characterize the contamination profiles of neutral and novel PFAS (n-PFAS) using a gas chromatograph-based target and non-target analyses. Fluorotelomer alcohols were predominant in the samples, accounting for 80 % of the n-PFAS, followed by fluorotelomer acrylates. Air samples collected proximate to the durable water repellent (DWR) facility had the highest concentration of n-PFAS, which was approximately two orders of magnitude higher than those found in others. Non-target analysis identified fluorotelomer iodides and fluorotelomer methacrylate in multiple matrices near DWR facilities, indicating significant contamination of n-PFAS. Levels of both C6- and C8-based PFAS reflected a shift in usage patterns from C8- to C6-based fluorochemicals. Matrix-dependent profiles of n-PFAS revealed that shorter-chain (e.g., C6) and longer-chain (>C8) PFAS were predominant in air and sediment, respectively, implying that air and sediment are mobile and secondary sources of PFAS. Untreated and treated industrial wastewater also contained n-PFAS and their transformation products. The findings shed light on our understanding of the multi-matrix distribution and transport of PFAS.

Prioritization of pharmaceuticals and personal care products in the surface waters of Korea: Application of an optimized risk-based methods.

The occurrence of PPCPs in aquatic environments and their potential adverse effects on aquatic organisms have raised worldwide concerns. To address this issue, a study was conducted to analyze 137 selected PPCPs in Korean surface waters, and an optimized risk-based prioritization was performed. The results revealed that 120 PPCPs were detected, with 98 quantified at concentrations ranging from few ng/L to 42,733 ng/L for metformin. The 95% upper confidence limit (UCL95) of the mean value of the measured environmental concentration (MEC) for Metformin was about eight times higher than the second highest compound, dimethyl phthalate, indicating that antidiabetic groups had the highest concentration among the therapeutic groups. An optimized risk-based prioritization was then assessed based on the multiplication of two indicators, the Frequency of Exceedance and the Extent of Exceedance of Predicted No-Effect Concentrations (PNECs), which can be calculated using the traditional risk quotient (RQ) approach. The study found that clotrimazole had the highest risk quotient value of 17.4, indicating a high risk to aquatic organisms, with seven and 13 compounds showing RQ values above 1 and 0.1, respectively. After considering the frequency of exceedance, clotrimazole still had the highest novel risk quotient (RQf) value of 17.4, with 99.6% of its MECs exceeding PNECs. However, the number of compounds with RQf values above 1 decreased from seven to five, with cetirizine and flubendazole being excluded. Furthermore, only 10 compounds exhibited RQf values above 0.1. The study also observed significant differences in the results between risk-based and exposure-based prioritization methods, with only five compounds, cetirizine, olmesartan, climbazole, sulfapyridine, and imidacloprid, identified in both methods. This finding highlights the importance of considering multiple methods for prioritizing chemicals, as different approaches may yield different results.

Comparative insight of pesticide transformations between river and wetland systems.

The widespread use of pesticides threatens the environment and ecosystems. Despite the positive effects of plant protection products, pesticides also have unexpected negative effects on nontarget organisms. The microbial biodegradation of pesticides is one of the major pathways for reducing their risks at aquatic ecosystems. The objective of this study was to compare the biodegradability of pesticides in simulated wetland and river systems. Parallel experiments were conducted with 17 pesticides based on the OECD 309 guidelines. A comprehensive analytical method, such as target screening combined with suspect and non-target screening, was performed to evaluate the biodegradation via identification of transformation products (TPs) using LC-HRMS. As evidence of biodegradation, we identified 97 TPs for 15 pesticides. Metolachlor and dimethenamid had 23 and 16 TPs, respectively, including Phase II glutathione conjugates. The analysis of 16S rRNA sequences for microbials characterized operational taxonomic units. Rheinheimera and Flavobacterium, which have the potential for glutathione S-transferase, were dominant in wetland systems. Estimation of toxicity, biodegradability, and hydrophobicity using QSAR prediction indicated lower environmental risks of detected TPs. We conclude that the wetland system is more favorable for pesticide degradation and risk mitigation mainly attributed to the abundance and variety of the microbial communities.

Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning.

Urban rainfall events can lead to the runoff of pollutants, including industrial, pesticide, and pharmaceutical chemicals. Transporting micropollutants (MPs) into water systems can harm both human health and aquatic species. Therefore, it is necessary to investigate the dynamics of MPs during rainfall events. However, few studies have examined MPs during rainfall events due to the high analytical expenses and extensive spatiotemporal variability. Few studies have investigated the occurrence patterns of MPs and factors that influence their transport, such as rainfall duration, antecedent dry periods, and variations in streamflow. Moreover, while there have been many analyses of nutrients, suspended solids, and heavy metals during the first flush effect (FFE), studies on the transport of MPs during FFE are insufficient. This study aimed to identify the dynamics of MPs and FFE in an urban catchment, using high-resolution monitoring and machine learning methods. Hierarchical clustering analysis and partial least squares regression (PLSR) were implemented to estimate the similarity between each MP and identify the factors influencing their transport during rainfall events. Eleven dominant MPs comprised 75% of the total MP concentration and had a 100% detection frequency. During rainfall events, pesticides and pharmaceutical MPs showed a higher FFE than industrial MPs. Moreover, the initial 30% of the runoff volume contained 78.0% of pesticide and 50.1% of pharmaceutical substances for events W1 (July 5 to July 6, 2021) and W6 (August 31 to September 1, 2021), respectively. The PLSR model suggested that stormflow (m3/s) and the duration of antecedent dry hours (h) significantly influenced MP dynamics, yielding the variable importance on projection scores greater than 1.0. Hence, our findings indicate that MPs in urban waters should be managed by considering FFE.

Transcriptome and computational approaches highlighting the molecular regulation of Zacco platypus induced by mesocosm exposure to common disinfectant chlorine.

Chlorine (Cl2) is a disinfectant often used in swimming pools and water treatment facilities. However, it is released into aquatic ecosystems, where it may harm non-targeted organisms. Here, we performed a mesocosm experiment exposing Zacco platypus (Z. platypus) to biocide Cl2 for 30 days (30 d) at two days' time points 15 days (15 d) and 30 d samples were collected. Here, Z. platypus was exposed to a sublethal concentration (0.1 mg/L) of Cl2, and comparative transcriptomics analyses were performed to determine their response mechanisms at the molecular level. According to RNA sequencing of the whole-body transcriptome, 860 and 1189 differentially expressed genes (DEGs) were identified from the 15 d and 30 d responses to Cl2, respectively. After enrichment analysis of GO (Gene Ontology) functions and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, identified DEGs were demonstrated to be associated with a variety of functions, including "ion binding and transmembrane transporters". Cl2 also induced oxidative stress in Z. platypus by increasing the levels of reactive oxygen species (ROS) while decreasing the catalase (CAT) content and the levels of solute carrier family 22 member 11 (slc22a11), Caspase-8 (casp-8), inducible nitric oxide synthase (NOS2), cytosolic phospholipase A2 gamma (PLA2G4). However, Z. platypus still allows recovery during stress suspension by increasing the expression levels of solute carrier family proteins. The GO and KEGG annotation results revealed that the expression of DEGs were related to the detoxification process, immune response, and antioxidant mechanism. Additionally, protein-protein interaction networks (PPI) and cytoHubba analyses identified sixteen hub genes and their interaction. These findings elucidate the regulation of various DEGs and signaling pathways in response to Cl2 exposure, which will improve our knowledge and laid foundation for further investigation of the toxicity of Cl2 to Z. platypus.

Optimization of the QuEChERS method for multi-residue analysis of pharmaceuticals and pesticides in aquaculture products.

The aim of study was to optimize an QuEChERS extraction procedure for simultaneous determination of organic pollutants in aquaculture products. The QuEChERS extracts were measured using LC-HRMS. The target contaminants include 32 pesticides and 20 pharmaceuticals which have not been regulated for the products in Korea. The method was validated according to CODEX guideline (CAC/GL 71-2009). LOD and LOQ for all analytes ranged from 0.1 to 2 µg/kg and from 0.5 to 5 µg/kg, respectively. Intra-day (n = 5) and inter-day (n = 9) accuracy and precision were evaluated with the guideline. The validated method was applied to aquaculture products (n = 303). As a result, 14 pesticides and 8 pharmaceuticals were quantified. Fluxapyroxad, a fungicide frequently detected in domestic surface waters, was found with relatively higher concentration in 17 out of 23 species. It proves that a hydrophobic inland contaminant can be accumulated in the aquaculture products.

Assessing the Impact of Meteorological Factors on COVID-19 Seasonality in Metropolitan Chennai, India.

Meteorological factors may influence coronavirus disease 2019 (COVID-19) transmission. Due to the small number of time series studies, the relative importance of seasonality and meteorological factors is still being debated. From March 2020 to April 2021, we evaluated the impact of meteorological factors on the transmission of COVID-19 in Chennai, India. Understanding how the COVID-19 pandemic spreads over the year is critical to developing public health strategies. Correlation models were used to examine the influence of meteorological factors on the transmission of COVID-19. The results revealed seasonal variations in the number of COVID-19-infected people. COVID-19 transmission was greatly aggravated by temperature, wind speed, nitric oxide (NO) and barometric pressure (BP) during summer seasons, whereas wind speed and BP aggravated COVID-19 transmission during rainy seasons. Furthermore, PM 2.5, NO and BP aggravated COVID-19 transmission during winter seasons. However, their relationships fluctuated seasonally. Our research shows that seasonal influences must be considered when developing effective interventions.

Long-term degradation of toluene and phenol in soil: Identification of transformation products and pathways via HRMS-based suspect and non-target screening.

In this study, the long-term fate of toluene and phenol in the soil was investigated, and the transformation products (TPs) and pathways of these compounds were studied by a high resolution mass spectrometry (HRMS)-based suspect and non-target screening approach for the first time, and 9 and 12 transformation products were identified for toluene and phenol, respectively in the lab-exposed soil samples. Salicylaldehyde, 4-hydroxybenzaldehyde, and benzaldehyde were identified in toluene-contaminated field soil samples for the first time, and the main mechanisms involved in the biodegradation and detoxification of toluene and phenol in soil were oxidation, carboxylation, dehydroxylation, and ring fission amongst others. 2-oxoglutarate, TP165-A, TP165-B, TP172, and TP195 were identified as novel phenol transformation products, while salicylaldehyde, 2-oxoglutarate, TP165-A, and TP165-B were identified as novel toluene transformation products, providing new possible evidence for additional degradation pathways, which could give new insights into the fate of toluene and phenol during the natural attenuation process in the environment. Finally, salicylaldehyde, 4-OH-benzaldehyde, and 4-OH-benzoic acid which were detected at Level 1 identification confidence were suggested as indicator chemicals of toluene and phenol exposure in the contaminated field.

A novel method for micropollutant quantification using deep learning and multi-objective optimization.

Micropollutants (MPs) released into aquatic ecosystems have adverse effects on public health. Hence, monitoring and managing MPs in aquatic systems are imperative. MPs can be quantified by high-resolution mass spectrometry (HRMS) with stable isotope-labeled (SIL) standards. However, high cost of SIL solutions is a significant issue. This study aims to develop a rapid and cost-effective analytical approach to estimate MP concentrations in aquatic systems based on deep learning (DL) and multi-objective optimization. We hypothesized that internal standards could quantify the MP concentrations other than the target substance. Our approach considered the precision of intra-/inter-day repeatability and natural organic matter information to reduce instrumental error and matrix effect. We selected standard solutions to estimate the concentrations of 18 MPs. Among the optimal DL models, DarkNet-53 using nine standard solutions yielded the highest performance, while ResNet-50 yielded the lowest. Overall, this study demonstrated the capability of DL models for estimating MP concentrations.

Transformation Products of Emerging Pollutants Explored Using Non-Target Screening: Perspective in the Transformation Pathway and Toxicity Mechanism-A Review.

The scientific community has increasingly focused on forming transformation products (TPs) from environmental organic pollutants. However, there is still a lot of discussion over how these TPs are generated and how harmful they are to living terrestrial or aquatic organisms. Potential transformation pathways, TP toxicity, and their mechanisms require more investigation. Non-target screening (NTS) via high-resolution mass spectrometry (HRMS) in model organisms to identify TPs and the formation mechanism on various organisms is the focus of this review. Furthermore, uptake, accumulation process, and potential toxicity with their detrimental consequences are summarized in various organisms. Finally, challenges and future research initiatives, such as performing NTS in a model organism, characterizing and quantifying TPs, and evaluating future toxicity studies on TPs, are also included in this review.

Analysis of micropollutants in a marine outfall using network analysis and decision tree.

The presence of micropollutants (MPs), including pharmaceutical, industrial, and pesticidal compounds, threatens both human health and the aquatic ecosystem. The development and extensive use of new chemicals have also inevitably led to the accumulation of MPs in aquatic environments. Recreational beaches are especially vulnerable to contamination, affecting humans and aquatic animals via the absorption of MPs in water during marine activities (e.g., swimming, sailing, and windsurfing). Additionally, marine outfalls in an urbanized coastal city can cause serious chemical and microbial pollution on recreational beaches, leading to an increase in adverse effects on public health and the ecological system. Therefore, the aim of this study was to, with the use of network and decision tree analyses, identify the features and factors that influence the change in MP concentrations in a marine outfall. These analyses were conducted to inspect the relationship between each MP and its hierarchical structure as well as hydrometeorological variables. Additionally, a risk analysis was conducted in this study in which the MPs were prioritized based on their optimized risk quotient values. During our monitoring of MP concentrations over time at the marine outfall, high concentrations of pharmaceutical and industrial compounds were detected when the tide level was low after rainfall. Furthermore, results of the risk analysis and the prioritization revealed that a total of 18 substances identified in our study posed a risk to the ecosystem; these include major ecotoxicologically hazardous substances such as telmisartan, mevinphos, and methiocarb. Results of the network analysis demonstrated distinct trends for pharmaceutical and industrial substances, whilst those for pesticide compounds were irregular. Additionally, the hierarchical structures for most MPs consisted of rainfall, tide level, and antecedent dry hours; this implies that these factors influence MP dynamics. These findings will be helpful for establishing chemical contamination management plans for recreational beaches in the future.

Identification of biotransformation products of organophosphate ester from various aquatic species by suspect and non-target screening approach.

Organic pollutants that are introduced into the aquatic ecosystem can transform by various mechanisms. Biotransformation is an important process for predicting the remaining structures of pollutants in the ecosystem, and their toxicity. This study focused on triphenyl phosphate (TPHP), which is a commonly used organophosphate flame retardant and plasticizer. Since TPHP is particularly toxic to aquatic organisms, it is essential to understand its biotransformation in the aquatic environment. In the aquatic ecosystem, based on consideration of the producer-consumer-decomposer relationship, the biotransformation products of TPHP were identified, and their toxicity was predicted. Liquid chromatography-high resolution mass spectrometry was used for target, suspect, and non-target analysis. The obtained biotransformation products were estimated for toxicity based on the prediction model. As a result, 29 kinds of TPHP biotransformation products were identified in the aquatic ecosystem. Diphenyl phosphate was detected as a common biotransformation product through a hydrolysis reaction. In addition, products were identified by the biotransformation mechanisms of green algae, daphnid, fish, and microorganism. Most of the biotransformation products were observed to be less toxic than the parent compound due to detoxification except some products (hydroquinone, beta-lyase products, palmitoyl/stearyl conjugated products). Since various species exist in a close relationship with each other in an ecosystem, an integrated approach for not only single species but also various connected species is essential.

Identification, quantification, and prioritization of new emerging pollutants in domestic and industrial effluents, Korea: Application of LC-HRMS based suspect and non-target screening.

The present study was designed to identify recently (or rarely) recognized or unreported substances (RRS or URS) contained in the effluents from water treatment plants in two industrialized urban areas, Gumi and Daegu, in Korea. In addition to 30 initial targets, 72 substances were identified through suspect and non-target screening (SNTS). Among them were 4 RRSs and 22 URSs, respectively. The quantitative analyses were applied to 35 pharmaceuticals, 15 pesticides, 13 poly-/perfluorinated alkyl substances (PFASs), 2 organophosphate flame retardants (OPFRs), 2 corrosion inhibitors, and 3 metabolites. The highest average concentration was observed for benzotriazole, followed by those for niflumic acid, and metformin. Effluents from Gumi mainly contained benzotriazole and metformin whereas niflumic acid and tramadol were the major components in effluents from Daegu. According to a scoring system based on risk relevant parameters, higher priorities were given to telmisartan, PFOA, and cimetidine. Yet, priorities for some substances were area specific (e.g., benzotriazole from Gumi, PFASs from Daegu), reflecting differences in industry profiles and populations. Many of the RRSs and URSs were recognized as potential hazards. The new identifications and evaluations should be taken into consideration for constant monitoring and management, as do the previously recognized contaminants.

Emerging pharmaceuticals and industrial chemicals in Nakdong River, Korea: Identification, quantitative monitoring, and prioritization.

The extensive development and use of new anthropogenic chemicals have inevitably led to their presence in aquatic environments. Surface waters affected by sewage effluents have been exposed to these new substances. In the present study, the occurrence of anthropogenic substances, including pharmaceuticals and industrial chemicals, was investigated in one of the major rivers in Korea, the Nakdong River. Furthermore, seasonal variations in their content were determined via annual monitoring. Through the suspect and non-target screening (SNTS) technique, 58 substances were newly identified in the river and integrated in the quantitative monitoring practice. The results revealed that niflumic acid and melamine exhibited the highest median concentrations, i.e., 320 ng/L and 11,000 ng/L, respectively. The results associated with seasonal change revealed that the concentration of a considerable number of substances increased in winter when the flow rate was low. Conversely, some substances exhibited high concentrations in summer (e.g., polyethylene glycol) and spring (e.g., niflumic acid). This was attributed to the seasonal changes in the consumption, prescriptions, or the application of alternative substances. These changes were also reflected by the risk quotient (RQ) values calculated from the concentration and toxicity values. Pharmaceuticals such as telmisartan and carbamazepine and industrial chemicals such as organophosphorus flame retardants (OPFRs) and melamine accounted for approximately 90% of the total RQ. Major substances prioritized using the production of the RQ value and the detection frequency included OPFRs and telmisartan. It is recommended that these results be reflected in future water quality monitoring plans.

Replacing the internal standard to estimate micropollutants using deep and machine learning.

Similar to the worldwide proliferation of urbanization, micropollutants have been involved in aquatic and ecological environmental systems. These pollutants have the propensity to wreak havoc on human health and the ecological system; hence, it is important to persistently monitor micropollutants in the environment. Micropollutants are commonly quantified via target analysis using high resolution mass spectrometry and the stable isotope labeled (SIL) standard. However, the cost-intensiveness of this standard presents a major obstacle in measuring micropollutants. This study resolved this problem by developing data-driven models, including deep learning (DL) and machine learning (ML), to estimate the concentration of micropollutants without resorting to the SIL standard. Our study hypothesized that natural organic matter (NOM) could replace internal standards if there was a specific mass spectrum (MS) subset, including NOM information, which correlated with an SIL standard peak. Therefore, we analyzed the MS to find the specific MS subsets for replacing the SIL standard peak. Thirty-five alternative MS subsets were determined for applying DL and ML as input data. Thereafter, we trained four different DL models, namely, ResNet101, GoogLeNet, VGG16, and Inception v3, as well as three different ML models, i.e., random forest (RF), support vector machine (SVM), and artificial neural network (ANN). A total of 680 MS data were used for the model training to estimate five different micropollutants, namely Sulpiride, Metformin, and Benzotriazole. Among the DL models, ResNet 101 exhibited the highest model performance, showing that the average validation R2 and MSE were 0.84 and 0.26 ng/L, respectively, while RF was the best in the ML models, manifesting R2 and MSE values of 0.69 and 0.58 ng/L. The trained models showed accurate training and validation results for the estimation of the five micropollutant concentrations. Therefore, this study demonstrates that the suggested analysis has a potential for alternative micropollutant measurement that has rapid and economic vantages.

Identification of transformation products to characterize the ability of a natural wetland to degrade synthetic organic pollutants.

Natural wetlands have been recognized as a natural reactor for degradation and elimination of environmental pollutants. The Upo Wetland, the largest inland wetland in Korea, is mainly surrounded by agricultural lands and it is susceptible to contamination from excess nutrient loads and synthetic organic contaminants (SOCs) (e.g., pesticides). The aim of this study was to identify major SOCs in the wetland and evaluate their degradation. We used high resolution mass spectrometry (HRMS) with a two-step analysis approach (i.e., 1st analysis for target measurement along with suspect and non-target screening (SNTS) and 2nd analysis for complimentary suspect screening) to identify and quantify the transformation products (TPs) of the identified parent SOCs. Quantitative analysis of 30 targets, mainly including pesticides, showed that fungicides were the major SOCs detected in the wetland, accounting for about 50% of the composition ratio of the total SOCs quantified. Orysastrobin occurred at the highest mean concentration (>700 ng/L), followed by two other fungicides, carbendazim and tricyclazole. The first analysis (SNTS) tentatively identified 39 TPs (30 by suspect, 9 by non-target screening) of 14 parent pesticides. Additionally, the second analysis (complimentary suspect screening) identified 9 more TPs. Among the 48 total TPs identified, 7 were confirmed with reference standards. The identification of the remaining TPs had a high confidence level (e.g., level 2 or 3). Regarding transport though the wetland, most TPs showed greater peak area ratios (i.e., the relative portion of chromatographic area of the TPs to the parent compound) at the outlet point of the wetland compared to the inlet point. The risk quotient, which was calculated using the concentrations of parent compounds, decreased toward the outlet, demonstrating the degradation capacity of the wetland. The estimates for biodegradability, hydrophobicity, and toxicity by an in-silico quantitative structure-activity relationship (QSAR) model indicated a lower half-life, lower logDOW, and greater effect concentration for most TPs compared to the parent compounds. Based on these results, we conclude that natural wetlands play a role as an eco-friendly reactor for degrading SOCs to form numerous TPs that are lower risk than the parent compounds.