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.

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.

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.

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 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.