Combining Passive Sampling with Suspect and Nontarget Screening to Characterize Organic Micropollutants in Streams Draining Mixed-Use Watersheds

Analysis strategy of contamination source using chemical fingerprint information based on GC-HRMS: A case study of landfill leachate

With the increasing prevalence of emerging contaminants (ECs) in the environment, gaining a deeper understanding of the chemical information pertaining to the contamination source is a crucial step toward effective prevention and control of these ECs. This study presents a novel strategy for analyzing the chemical information of contamination sources using gas chromatography-high resolution mass spectrometry (GC-HRMS) and demonstrates it on landfill leachate, a common and representative environmental contamination source. Initially, a non-targeted screening approach using HRMS was used to characterize a total of 5344 organic compounds with identification confidence levels 1 and 2 in 14 landfill leachate samples. Leveraging this as a base data set, the similarity analysis was first performed, and the classification fingerprints exhibited a pronounced level of similarity. Second, 169 characteristic marker contaminants with important and significant differences were identified in the 3 groups of landfill leachate with different solid waste compositions (mostly kitchen waste, mostly plastic & daily chemical product waste, and proportion average) by difference analysis. Finally, 101 hazardous chemicals (HCs) were screened in the data set. The results demonstrated that a class of contamination source exhibited certain common characteristics, while different groups of samples had their own distinct contamination signatures. This work offers a unique perspective on the interpretation of chemical information from contamination sources, aiming to provide a valuable reference for environmental pollution management.

Increasing chemical coverage, accuracy, and reproducibility of the processing method for polar organic chemical integrative samplers

Despite their diffusion in research studies, passive samplers are rarely used in regulatory applications. To expand the employment of passive samplers in regulatory environmental studies, standardized procedures for processing each sampler type should be proposed and accepted, but currently, each study develops its own protocol based on previous knowledge and specific needs. In this work, six identical polar organic chemical integrative samplers in seawater were deployed to understand the importance of the sorbent transfer method prior to the elution step. A common "wet transfer" with ultra-pure water was compared to a less diffused "dry-transfer," assessing recoveries and matrix effects of 38 target compounds of emerging concern, including polar pesticides, recreational and food-related substances, pharmaceuticals, industrial additives, and ultra-violet (UV) filters. The dry-transfer procedure generally allowed better recoveries, especially for the more polar compounds, without affecting matrix effects (which remained in the range 40-130%). Along with the recovery assessment, the analysis of the non-spiked sorbent extracts revealed traces of many of the targeted emerging contaminants, quantifying perfluorooctanoic acid, UV-filters, carbamazepine, diclofenac, and triclosan. Furthermore, other compounds were found below their limits of quantitation. Ten analytes were detected only in the extracts of the dry-transferred passive samplers, highlighting the importance of applying this protocol, especially when dealing with polar compounds. This refined processing method, therefore, permits a more standardized and reproducible strategy, at the same time enlarging the set of analytes which could be detected and quantified.

Photochemistry of dissolved organic matter derived from compost

The extensive application of compost to enhance soil quality highlights the crucial role of dissolved organic matter (DOM) derived from compost in both terrestrial and aquatic ecosystems, influencing carbon cycling and the fate of contaminants. However, the photochemical behavior of compost-derived DOM (DOMCOM) remains poorly understood. In this study, we investigated the photochemical transformation and photoactivity of DOM derived from typical composts produced from cow manure (CDOM) and pig manure (PDOM). The results indicated that the initial CDOM exhibited higher molecular weight, aromaticity, humification, and photoactivity compared to PDOM. Under UV irradiation, both DOMCOM underwent photobleaching and photo-humification, resulting in a decrease in the average molecular weight by 23.68 % for CDOM and 3.82 % for PDOM, with CDOM being particularly affected. Meanwhile, 2D-COS analysis revealed that the fulvic-like fluorescence fraction was first to respond to photoirradiation in both DOM, followed by the protein-like and microbial humic-like fluorescence fractions, which showed contrasting response trends in CDOM and PDOM. Furthermore, CDOM with a higher concentration of humic-like substances efficiently generated 3DOM*, 1O2 and •OH (4.09 × 10-8, 1.17 × 10-8 and 7.05 × 10-12, respectively) under UV radiation, which were apparently greater than those produced by PDOM (3.30 × 10-8, 8.38 × 10-9 and 4.99 × 10-12, respectively).

Per- and polyfluoroalkyl substances (PFAS) at low concentration improve coagulation efficiency but induce higher membrane fouling in drinking water treatment

The presence of per- and polyfluoroalkyl substances (PFAS) in surface water has been widely reported in recent years. Many techniques, e.g., adsorption, have been investigated to remove PFAS from contaminated waters. However, the underlying impacts of PFAS on conventional drinking water treatment have been overlooked so far. In this study, we hypothesized whether PFAS have significant impacts on algae in surface water, which in turn may influence the performance of typical treatment processes (e.g., coagulation/membrane filtration). Therefore, we sampled a representative surface water (drinking water source) in Beijing, China, and dosed 2 representative PFAS compounds, at environment concentrations, to conduct bench-scale treatment tests. Results showed that the presence of PFAS caused larger flocs during coagulation and more severe ultrafiltration (UF) membrane fouling, compared with a control solution without PFAS. Specifically, PFAS at a low concentration (0.1 μg/L) led to the greatest influence on floc growth and UF membrane fouling; compared with the solution without PFAS, the floc size increased by 1.6 times and membrane flux declined more than 10%. These effects were evidenced by the stress response of algae under PFAS stimulus, secreting more biopolymers (mainly polysaccharides), rather than by PFAS directly. Overall, this study has demonstrated that the presence of PFAS can have both beneficial, and undesirable, indirect effects on water treatment in real applications, through its impact on algae in surface water sources.

Investigating the chemical space coverage of multiple chromatographic and ionization methods using non-targeted analysis on surface and drinking water collected using passive sampling

Multiple non-targeted analysis tools were used to look for a broad range of possible chemical contaminants present in surface and drinking water using liquid chromatography separation and high-resolution mass spectrometry detection, including both quadrupole time of flight (Q-ToF) and Orbitrap instruments. Two chromatographic techniques were evaluated on an LC-Q-ToF with electrospray ionization in both positive and negative modes: (1) the traditionally used reverse phase C18 and (2) the hydrophilic interaction liquid chromatography (HILIC) aimed to capture more polar contaminants that may be present in water. Multiple ionization modes were evaluated with an LC-Orbitrap, including electrospray (ESI) and atmospheric pressure chemical ionization (APCI), also in both positive and negative modes. A suspect screening library of over 1300 possible environmental contaminants, including pesticides, pharmaceuticals, personal care products, illicit drugs/drugs of abuse, and various anthropogenic markers was made with experimentally collected data with the LC-Q-ToF with both column types, with 227 chemicals being retained by the HILIC column. The non-targeted methods using multiple chromatographic and ionization modes were applied to environmental water samples collected with polar organic chemical integrative samplers (POCIS), including surface water upstream and downstream from wastewater effluent discharge, and the downstream drinking water intake and treated drinking water for three distinct sampling events. For the LC-Q-ToF, 442 chemical features were detected on the C18 column and 91 with the HILIC column in the POCIS extracts, while 556 features were found on the Orbitrap workflow by ESI and 131 features detected by APCI. Over 100 chemicals were tentatively identified by suspect screening and database searching. The comprehensive and systematic evaluation of these methods serve as a step in characterizing the chemical space covered when utilizing different chromatography and ionization methods, or different instrument workflows on complex environmental mixtures.

Exploring micropollutants in polar environments based on non-target analysis using LC-HRMS

The routine use of chemicals in polar regions contributes to unexpected occurrence of micropollutants, with sewage discharge as a prominent pollution source. The aim of this study was to identify and quantify micropollutants in polar environments near potential point sources using non-target analysis (NTA) with liquid chromatography high-resolution mass spectrometry. Seawater samples were collected from Ny-Ålesund, Svalbard and Marian Cove, King George Island, in 2023. We tentatively identified 32 compounds with NTA, along with 105 homologous series substances. Of these, 18 substances were confirmed, and 13 were quantified using the internal standard method. Most quantified substances in the Ny-Ålesund, including caffeine, naproxen, and polyethylene glycols (PEGs), exhibited concentrations ranged from 0.9 to 770,000 ng/L. In Marian Cove, the analysis predominantly detected acetaminophen, with concentrations ranging from 13 to 35 ng/L. The findings underscore the presence and spatial distribution of emerging micropollutants resulting from wastewater discharge in polar regions.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

De facto Water Reuse: Investigating the Fate and Transport of Chemicals of Emerging Concern from Wastewater Discharge through Drinking Water Treatment Using Non-targeted Analysis and Suspect Screening

Wastewater is a source for many contaminants of emerging concern (CECs), and surface waters receiving wastewater discharge often serve as source water for downstream drinking water treatment plants. Nontargeted analysis and suspect screening methods were used to characterize chemicals in residence-time-weighted grab samples and companion polar organic chemical integrative samplers (POCIS) collected on three separate hydrologic sampling events along a surface water flow path representative of de facto water reuse. The goal of this work was to examine the fate of CECs along the study flow path as water is transported from wastewater effluent through drinking water treatment. Grab and POCIS samples provided a comparison between residence-time-weighted single-point and integrative sample results. This unique and rigorous study design, coupled with advanced analytical chemistry tools, provided important insights into chemicals found in drinking water and their potential sources, which can be used to help prioritize chemicals for further study. K-means clustering analysis was used to identify patterns in chemical occurrences across both sampling sites and sampling events. Chemical features that occurred frequently or survived drinking water treatment were prioritized for identification, resulting in the probable identification of over 100 CECs in the watershed and 28 CECs in treated drinking water.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

Mechanistic and Kinetic Consideration of the Photochemically Generated Oxidative Organic Radicals in Dissolved Black Carbon Solutions under Simulated Solar Irradiation

The photochemically generated oxidative organic radicals (POORs) in dissolved black carbon (DBC) was investigated and compared with that in dissolved organic matter (DOM). POORs generated in DBC solutions exhibited higher one-electron reduction potential values (1.38-1.56 V) than those in DOM solutions (1.22-1.38 V). We found that the photogeneration of POORs from DBC is enhanced with dissolved oxygen (DO) increasing, while the inhibition of POORs is observed in reference to DOM solution. The behavior of the one-electron reducing species (DBC•-/DOM•-) was employed to explain this phenomenon. The experimental results revealed that the DO concentration had a greater effect on DBC•- than on DOM•-. Low DO levels led to a substantial increase in the steady-state concentration of DBC•-, which quenched the POORs via back-electron reactions. Moreover, the contribution of POORs to the degradation of 19 emerging organic contaminants (EOCs) in sunlight-exposed DBC and DOM solutions was estimated. The findings indicate that POORs play an important role in the photodegradation of EOCs previously known to react with triplets, especially in DBC solutions. Compared to DOM solutions, POOR exhibits a lower but considerable contribution to EOC attenuation. This study enhances the understanding of pollutant fate in aquatic environments by highlighting the role of DBC in photochemical pollutant degradation and providing insights into pollutant transformation mechanisms involving POORs.

Non-target analysis for water characterization: wastewater treatment impact and selection of relevant features

Non-target analyses were conducted to characterize and compare the molecular profiles (UHPLC-HRMS fingerprint) of water samples from a wastewater treatment plant (WWTP). Inlet and outlet samples were collected from three campaigns spaced 6 months apart in order to highlight common trends. A significant impact of the treatment on the sample fingerprints was shown, with a 65-70% abatement of the number of features detected in the effluent, and more polar, smaller and less intense molecules found overall compared to those in WWTP influent waters. Multivariate analysis (PCA) associated with variations of the features between inlets and outlets showed that features appearing or increasing were correlated with effluents while those disappearing or decreasing were correlated with influents. Finally, effluent features considered as relevant to a potentially adverse effect on aqueous media (i.e. those which appeared or increased or slightly varied from the influent) were highlighted. Three hundred seventy-five features common with the 3 campaigns were thus selected and further characterized. For most of them, elementary composition was found to be C, H, N, O (42%) and C, H, N, O, P (18%). Considering the MS2 spectra and several reference MS2 databases, annotations were proposed for 35 of these relevant features. They include synthetic products, pharmaceuticals and metabolites.

Bypassing the Identification: MS2Quant for Concentration Estimations of Chemicals Detected with Nontarget LC-HRMS from MS2 Data

Nontarget analysis by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is now widely used to detect pollutants in the environment. Shifting away from targeted methods has led to detection of previously unseen chemicals, and assessing the risk posed by these newly detected chemicals is an important challenge. Assessing exposure and toxicity of chemicals detected with nontarget HRMS is highly dependent on the knowledge of the structure of the chemical. However, the majority of features detected in nontarget screening remain unidentified and therefore the risk assessment with conventional tools is hampered. Here, we developed MS2Quant, a machine learning model that enables prediction of concentration from fragmentation (MS2) spectra of detected, but unidentified chemicals. MS2Quant is an xgbTree algorithm-based regression model developed using ionization efficiency data for 1191 unique chemicals that spans 8 orders of magnitude. The ionization efficiency values are predicted from structural fingerprints that can be computed from the SMILES notation of the identified chemicals or from MS2 spectra of unidentified chemicals using SIRIUS+CSI:FingerID software. The root mean square errors of the training and test sets were 0.55 (3.5×) and 0.80 (6.3×) log-units, respectively. In comparison, ionization efficiency prediction approaches that depend on assigning an unequivocal structure typically yield errors from 2× to 6×. The MS2Quant quantification model was validated on a set of 39 environmental pollutants and resulted in a mean prediction error of 7.4×, a geometric mean of 4.5×, and a median of 4.0×. For comparison, a model based on PaDEL descriptors that depends on unequivocal structural assignment was developed using the same dataset. The latter approach yielded a comparable mean prediction error of 9.5×, a geometric mean of 5.6×, and a median of 5.2× on the validation set chemicals when the top structural assignment was used as input. This confirms that MS2Quant enables to extract exposure information for unidentified chemicals which, although detected, have thus far been disregarded due to lack of accurate tools for quantification. The MS2Quant model is available as an R-package in GitHub for improving discovery and monitoring of potentially hazardous environmental pollutants with nontarget screening.