Evaluation of sample preparation methods for suspect and non-target screening in water, sediment, and biota samples using gas chromatography coupled to high-resolution mass spectrometry

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model

Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study therefore demonstrates a novel multi-criteria decision making (MCDM) framework utilizing machine learning (ML) algorithms coupled with toxicological prioritization index tool (i.e., ml_ToxPi) to rank 252 chemicals of interest in groundwater for subsequent targeted analysis. The MCDM framework integrated chemical analysis data (i.e., peak area and detection frequency), toxicity profiles (i.e., bioactivity ratio, human exposure metadata, and carcinogenicity metadata), as well as the environmental fate and transport information (i.e., octanol-water partition coefficient (log Kow), water solubility, biodegradation half-life, and soil adsorption coefficient (Koc)) for ranking the identified pollutants, and the random forest machine learning model was useful for systematically determining the weighting factors of each variable according to their variable importance scores (R2 = 0.808 and 0.778 for training and testing datasets, respectively, while RMSE = 0.042 in both cases). A total of 47 unique high priority compounds (i.e., ml_ToxPi score ≥ 0.55) were identified across the investigated sampling regions, which constituted diverse groups of compounds classified according to their chemical uses, such as alkylated polycyclic aromatic hydrocarbons (alkyl-PAHs), organophosphate flame retardants (OPFRs), parent PAHs, personal care products (PCPs), pesticides, pharmaceuticals, phenols, plasticizers, transformation product (TPs), and other industrial use chemicals. By incorporating relevant variables into the proposed ML-optimized ToxPi MCDM framework, the prioritization approach described here may be adopted in future SNTS assessment of environmental and biological media.

Co-occurrence and spatial distribution of organic micropollutants in surface waters of the River Aconcagua and Maipo basins in Central Chile

Organic Micropollutants (OMPs) might pose significant risks to aquatic life and have potential toxic effects on humans. These chemicals typically occur as complex mixtures rather than individually. Information on their co-occurrence and their association with land use is largely lacking, even in industrialized countries. Furthermore, data on the presence of OMPs in freshwater ecosystems in South America is insufficient. Consequently, we assessed the co-occurrence and distribution of OMPs, including pharmaceuticals, pesticides, personal care products, surfactants, and other industrial OMPs, in surface waters of two river basins in central Chile. We focused on identifying and ranking quantified chemicals, classifying their mode of actions, as well as correlating their occurrence with distinct land uses. We identified and quantified 311 compounds that occurred at least once in the River Aconcagua and River Maipo basins, encompassing compounds from urban, agricultural, industrial, and pharmaceutical sectors. Pharmaceuticals were the most frequently occurring chemicals, followed by pesticides, personal care and household products. OMPs with neuroactive properties dominated surface waters in Central Chile, along with OMPs known to alter the cardiovascular and endocrine systems of humans and aquatic animals. Finally, we observed positive correlations between agricultural and urban land uses and OMPs. Our findings represent a step forward in extending current knowledge on the co-occurrence patterns of OMPs in aquatic environments, particularly in developing countries of the southern hemisphere.

Characterization of AhR-mediated potency in sediments from Kongsfjorden, Svalbard: Application of effect-directed analysis and nontarget screening

In this study, we aimed to identify the major aryl hydrocarbon receptor (AhR) agonists in surface sediments (S1-S10, n = 10) from Kongsfjorden, Arctic Svalbard, using effect-directed analysis. High AhR-mediated potencies were observed in the mid-polar fractions and RP-HPLC subfractions (F2.6-F2.8; log KOW 5-8) in the sediments of sites S2 and S3, which are located near abandoned coal mine areas, as assessed by the H4IIE-luc bioassay. The concentrations of traditional polycyclic aromatic hydrocarbon (t-PAHs), emerging PAHs, alkyl-PAHs, and styrene oligomers ranged from 6.1 to 2100 ng g-1 dry weight (dw), 0.5-1000 ng g-1 dw, 47 to 79,000 ng g-1 dw, and 4.2-130 ng g-1 dw, respectively, with elevated levels in S2 and S3. Principal component analysis coupled with multiple linear regression suggested that t-PAHs in sediments primarily originated from coal, petroleum combustion, and coal combustion. Twenty-four target AhR agonists accounted for 3.2%-100% (mean = 47%) of the total AhR-mediated potencies in S2 and S3. Nontarget screening via GC-QTOFMS in the highly potent fractions identified 48 AhR agonist candidates through four-step selection criteria. Among these, 27 compounds were identified as coal-derived substances. VirtualToxLab in silico modeling predicted that most of the 48 tentative AhR agonist candidates could bind to AhR. Overall, our findings indicate significant contamination of the Kongsfjorden sediments by coal-derived substances, highlighting the need for further studies to assess the ecological risks associated with these contaminants.

Rapid Detection of Total Petroleum Hydrocarbons in Soil Using Advanced Fluorescence Imaging Techniques

Chemical methods for measuring soil organic content are often slow and yield inaccurate results due to significant errors. Simple summation of components may not accurately determine total organic content. In contrast, fluorescence imaging techniques offer rapid, in situ monitoring without complex pretreatment and demonstrate rapid and accurate assessment of soil organic content. Utilizing a soil organic pollutant fluorescence imaging in situ monitoring system that we independently developed, we conducted laboratory experiments to explore methods for acquiring fluorescence signals of petroleum hydrocarbons in soil and extracting image features. We used this monitoring system to obtain fluorescence images of crude oil in standard soil (soil properties are shown in Table S1) samples at concentrations ranging from 0 to 100 g/kg, and the coefficient of determination of the total amount inversion model reached 0.999. Simultaneously, we applied the system to a deserted petroleum storage area, and the relative standard deviation values of 16 of the 18 groups of tests were less than 1%, indicating that the monitoring system is highly stable when applied in the field. This study provides both theoretical foundation and technical support for the rapid and nondestructive detection of total petroleum hydrocarbons in soil at field sites.

Review of carbon dot-hydrogel composite material as a future water-environmental regulator

As water pollution and scarcity pose severe threats to the sustainable progress of human society, it is important to develop a method or materials that can accurately and efficiently detect pollutants and purify aquatic environments or exploit marine resources. The compositing of photoluminescent and hydrophilic carbon dots (CDs) with hydrogels bearing three-dimensional networks to form CD-hydrogel composites to protect aquatic environments is a "win-win" strategy. Herein, the feasibility of the aforementioned method has been demonstrated. This paper reviews the recent progress of CD-hydrogel materials used in aquatic environments. First, the synthesis methods for these composites are discussed, and then, the composites are categorized according to different methods of combining the raw materials. Thereafter, the progress in research on CD-hydrogel materials in the field of water quality detection and purification is reviewed in terms of the application of the mechanisms. Finally, the current challenges and prospects of CD-hydrogel materials are described. These results are expected to provide insights into the development of CD-hydrogel composites for researchers in this field.

Assessment of environmental forensic indicator for anthropogenic groundwater contamination via target/suspect/nontarget analysis using HRMS techniques

This study compared target/suspect/nontarget analysis via liquid chromatography-high-resolution mass spectrometry (LC-HRMS) with traditional environmental forensic methods, specifically nitrate and its stable N isotope, in assessing groundwater pollution from livestock manure and agriculture. Using an in-house database of 1471 target and suspects, 35 contaminants (pesticides, veterinary drugs, surfactants) were identified, some uniquely linked to specific pollution sources, such as sulfamethazine and 4-formylaminoantipyrine in manure-affected areas. Pesticides were widespread, typically showing higher intensity in agricultural zones. On the other hand, the results of stable N isotope analysis (δ15N-NO3: 4.8 to 16.4‰) indicated the influence of human activities such as fertilizers, sewage, and manure in all sampling sites, including the control site far from the pollution sources and cannot differentiate the specific sources. The study underscores LC-HRMS's efficacy in different pollution sources, surpassing the limitations of stable N isotope analysis, and provides valuable insights for polluted groundwater source tracking strategies.

Identification of organic chemical indicators for tracking pollution sources in groundwater by machine learning from GC-HRMS-based suspect and non-target screening data

In this study, the strong analytical power of gas chromatography coupled to a high resolution mass spectrometry (GC-HRMS) in suspect and non-target screening (SNTS) of organic micropollutants was combined with machine learning tools for proposing a novel and robust systematic environmental forensics workflow, focusing on groundwater contamination. Groundwater samples were collected from four different regions with diverse contamination histories (namely oil [OC], agricultural [AGR], industrial [IND], and landfill [LF]), and a total of 252 organic micropollutants were identified, including pharmaceuticals, personal care products, pesticides, polycyclic aromatic hydrocarbons, plasticizers, phenols, organophosphate flame retardants, transformation products, and others, with detection frequencies ranging from 3 % to 100 %. Amongst the SNTS identified compounds, a total of 51 chemical indicators (i.e., OC: 13, LF: 12, AGR: 19, IND: 7) which included level 1 and 2 SNTS identified chemicals were pinpointed across all sampling regions by integrating a bootstrapped feature selection method involving the bootfs algorithm and a partial least squares discriminant analysis (PLS-DA) model to determine potential prevalent contamination sources. The proposed workflow showed good predictive ability (Q2) of 0.897, and the suggested contamination sources were gasoline, diesel, and/or other light petroleum products for the OC region, anthropogenic activities for the LF region, agricultural and human activities for the AGR region, and industrial/human activities for the IND region. These results suggest that the proposed workflow can select a subset of the most diagnostic features in the chemical space that can best distinguish a specific contamination source class.