Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts

Richardson, Alexandra K.; Chadha, Marcus; Rapp-Wright, Helena; Mills, Graham A.; Fones, Gary R.; Gravell, Anthony; Stürzenbaum, Stephen R.; Cowan, David; Neep, David J.; Barron, Leon

doi:10.1039/d0ay02013c

Cited by 21 publications

(7 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, its consistent occurrence across sites could be explained by continual release to the environment from such items as well as leaching from crops [44]. Fenuron has been measured by our group [7,8,45,46] and the Environment Agency in the United Kingdom in urban and rural rivers to varying degrees, and mostly at concentrations < 50 ng/L [47]. Total CEC concentrations fell by 3-fold at SZ10~200 m downstream.…”

Section: Methods Performance Assessmentmentioning

confidence: 99%

“…However, these can still suffer limited selectivity for application to large numbers of chemically diverse CECs as well as the potential for analyte and matrix carryover if the methods are not properly optimised, especially in complex samples. While direct-injection MS methods are still not widely considered for the quantitation of large numbers of CECs due to the potential for significant matrix effects, recently, direct-injection LC-MS of CECs in water has emerged, usually with gradient pre-separation to minimise these effects [6][7][8][9][10][11][12][13][14]. In many cases, this requires the large volume injection (LVI) of sample (typically between 80-5000 µL) and can help overcome the issue of analyte selectivity loss during pre-treatment.…”

Section: Introductionmentioning

confidence: 99%

“…Smaller injection volumes (10 µL) and standard flow rates (0.5 mL/min) enabled very stable measurement performance over longer batches of samples. We subsequently evaluated its quantitative performance for 33 CECs as part of a small monitoring campaign in brackish river water (River Thames, UK) and passive sampler extracts [7]. The method yielded promising performance for this small set of CECs with detection limits at 3-8 ng/L and excellent linearity overall.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quantitative Determination and Environmental Risk Assessment of 102 Chemicals of Emerging Concern in Wastewater-Impacted Rivers Using Rapid Direct-Injection Liquid Chromatography—Tandem Mass Spectrometry

et al. 2021

Self Cite

View full text Add to dashboard Cite

The rapid source identification and environmental risk assessment (ERA) of hundreds of chemicals of emerging concern (CECs) in river water represent a significant analytical challenge. Herein, a potential solution involving a rapid direct-injection liquid chromatography–tandem mass spectrometry method for the quantitative determination of 102 CECs (151 qualitatively) in river water is presented and applied across six rivers in Germany and Switzerland at high spatial resolution. The method required an injection volume of only 10 µL of filtered sample, with a runtime of 5.5 min including re-equilibration with >10 datapoints per peak per transition (mostly 2 per compound), and 36 stable isotope-labelled standards. Performance was excellent from the low ng/L to µg/L concentration level, with 260 injections possible in any 24 h period. The method was applied in three separate campaigns focusing on the ERA of rivers impacted by wastewater effluent discharges (1 urban area in the Basel city region with 4 rivers, as well as 1 semi-rural and 1 rural area, each focusing on 1 river). Between 25 and 40 compounds were quantified directly in each campaign, and in all cases small tributary rivers showed higher CEC concentrations (e.g., up to ~4000 ng/L in total in the R. Schwarzach, Bavaria, Germany). The source of selected CECs could also be identified and differentiated from other sources at pre- and post- wastewater treatment plant effluent discharge points, as well as the effect of dilution downstream, which occurred over very short distances in all cases. Lastly, ERA for 41 CECs was performed at specific impacted sites, with risk quotients (RQs) at 1 or more sites estimated as high risk (RQ > 10) for 1 pharmaceutical (diclofenac), medium risk (RQ of 1–10) for 3 CECs (carbamazepine, venlafaxine, and sulfamethoxazole), and low risk (RQ = 0.1–1.0) for 7 CECs (i.e., RQ > 0.1 for 11 CECs in total). The application of high-throughput methods like this could enable a better understanding of the risks of CECs, especially in low flow/volume tributary rivers at scale and with high resolution.

show abstract

Section: Methods Performance Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Determination and Environmental Risk Assessment of 102 Chemicals of Emerging Concern in Wastewater-Impacted Rivers Using Rapid Direct-Injection Liquid Chromatography—Tandem Mass Spectrometry

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Codex Aliment Arius Commission (Codex) techniques have also utilized a method devised by the Collaborative FAO (Food and Agriculture Organization)/WHO (World Health Organization) Gathering on Pesticide Residues (JMPR-Joint Meeting on Pesticide Residues) to observe the exact allowable maximum amount of such a pesticide residue that could legitimately be contained in or on food. Also, the maximum residue level (MRL), which is stated in mg/kg, is the accepted safe limit [4]. Similarly, MRL is determined by the quantitative measurement of a specific active component found in food samples with essential Good Agricultural Practices (GAP).…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Sellamuthu¹,

Kaliappan²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Pesticides have become more necessary in modern agricultural production. However, these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem. Due to a shortage of basic pesticide exposure awareness, farmers typically utilize pesticides extremely close to harvesting. Pesticide residues within foods, particularly fruits as well as veggies, are a significant issue among farmers, merchants, and particularly consumers. The residual concentrations were far lower than these maximal allowable limits, with only a few surpassing the restrictions for such pesticides in food. There is an obligation to provide a warning about this amount of pesticide use in farming. Previous technologies failed to forecast the large number of pesticides that were dangerous to people, necessitating the development of improved detection and early warning systems. A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified, named as the Hybrid Chronic Multi-Residual Framework (HCMF), in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing (CEP) by processing given spatial and sequential data. The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers. Overall, the technique is carried out in a Python environment, with the results showing that the proposed model has a 98.57% accuracy and a training loss of 0.30.

show abstract

“…Our specific objectives of this study were (i) to perform suspect screening (enabled by liquid chromatography–high-resolution mass spectrometry (LC-HRMS)) and source-related clustering of OMPs in POCIS and grab samples collected from 20 sites on streams draining mixed-use watersheds in upstate New York; (ii) to develop a nontarget screening approach for prioritization and identification of OMPs by examining the connection between chemical features in POCIS and watershed attributes; and (iii) to evaluate the comparability of POCIS and grab sampling for OMP load estimation and screening-level risk assessments across stream sites. Only a few recent studies have explored the in situ enrichment feature afforded by passive samplers for suspect or nontarget screening of OMPs in mixed-use watersheds. − Our work stands in the gap between studies that focused on the development of qualitative screening workflows ,, and the spatiotemporal profiling of passive sampler data − with the goal of applying a multi-watershed sampling and screening framework to comparatively assess OMP contamination in streams.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Basijokaite

Murphy

et al. 2022

Environ. Sci. Technol.

View full text Add to dashboard Cite

Organic micropollutants (OMPs) represent an anthropogenic stressor on stream ecosystems. In this work, we combined passive sampling with suspect and nontarget screening enabled by liquid chromatography−high-resolution mass spectrometry to characterize complex mixtures of OMPs in streams draining mixed-use watersheds. Suspect screening identified 122 unique OMPs for target quantification in polar organic chemical integrative samplers (POCIS) and grab samples collected from 20 stream sites in upstate New York over two sampling seasons. Hierarchical clustering established the co-occurrence profiles of OMPs in connection with watershed attributes indicative of anthropogenic influences. Nontarget screening leveraging the time-integrative nature of POCIS and the cross-site variability in watershed attributes prioritized and confirmed 11 additional compounds that were ubiquitously present in monitored streams. Field sampling rates for 37 OMPs that simultaneously occurred in POCIS and grab samples spanned the range of 0.02 to 0.22 L/d with a median value of 0.07 L/d. Comparative analyses of the daily average loads, cumulative exposure−activity ratios, and multi-substance potentially affected fractions supported the feasibility of complementing grab sampling with POCIS for OMP load estimation and screening-level risk assessments. Overall, this work demonstrated a multi-watershed sampling and screening approach that can be adapted to assess OMP contamination in streams across landscapes.

show abstract

Rapid direct analysis of river water and machine learning assisted suspect screening of emerging contaminants in passive sampler extracts

Abstract: Machine-learning assisted suspect screening in passive sampler extracts and rapid direct injection LC-MS/MS of river water from the River Thames, London, UK in 2018/9.

Cited by 21 publications

References 57 publications

Quantitative Determination and Environmental Risk Assessment of 102 Chemicals of Emerging Concern in Wastewater-Impacted Rivers Using Rapid Direct-Injection Liquid Chromatography—Tandem Mass Spectrometry

Quantitative Determination and Environmental Risk Assessment of 102 Chemicals of Emerging Concern in Wastewater-Impacted Rivers Using Rapid Direct-Injection Liquid Chromatography—Tandem Mass Spectrometry

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

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

Contact Info

Product

Resources

About