ORCID Profile
0000-0001-8270-6979
Current Organisation
University of Amsterdam
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Environmental Chemistry (incl. Atmospheric Chemistry) | Environmental Monitoring | Other Chemical Sciences | Water Treatment Processes | Environmental Science and Management | Separation Science | Analytical Spectrometry |
Environmental Health | Expanding Knowledge in the Environmental Sciences | Expanding Knowledge in the Chemical Sciences | Health Protection and/or Disaster Response
Publisher: American Chemical Society (ACS)
Date: 26-06-2020
Publisher: Springer Science and Business Media LLC
Date: 20-07-2020
DOI: 10.1186/S12302-020-00375-W
Abstract: The Partnership for Chemicals Risk Assessment (PARC) is currently under development as a joint research and innovation programme to strengthen the scientific basis for chemical risk assessment in the EU. The plan is to bring chemical risk assessors and managers together with scientists to accelerate method development and the production of necessary data and knowledge, and to facilitate the transition to next-generation evidence-based risk assessment, a non-toxic environment and the European Green Deal. The NORMAN Network is an independent, well-established and competent network of more than 80 organisations in the field of emerging substances and has enormous potential to contribute to the implementation of the PARC partnership. NORMAN stands ready to provide expert advice to PARC, drawing on its long experience in the development, harmonisation and testing of advanced tools in relation to chemicals of emerging concern and in support of a European Early Warning System to unravel the risks of contaminants of emerging concern (CECs) and close the gap between research and innovation and regulatory processes. In this commentary we highlight the tools developed by NORMAN that we consider most relevant to supporting the PARC initiative: (i) joint data space and cutting-edge research tools for risk assessment of contaminants of emerging concern (ii) collaborative European framework to improve data quality and comparability (iii) advanced data analysis tools for a European early warning system and (iv) support to national and European chemical risk assessment thanks to harnessing, combining and sharing evidence and expertise on CECs. By combining the extensive knowledge and experience of the NORMAN network with the financial and policy-related strengths of the PARC initiative, a large step towards the goal of a non-toxic environment can be taken.
Publisher: American Chemical Society (ACS)
Date: 30-08-2023
Publisher: American Chemical Society (ACS)
Date: 13-01-2023
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.TALANTA.2018.11.039
Abstract: Guaranteeing clean drinking water to the global population is becoming more challenging, because of the cases of water scarcity across the globe, growing population, and increased chemical footprint of this population. Existing targeted strategies for hazard monitoring in drinking water are not adequate to handle such erse and multidimensional stressors. In the current study, we have developed, validated, and tested a machine learning algorithm based on the data produced via non-targeted liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) for the identification of potential chemical hazards in drinking water. The machine learning algorithm consisted of a composite statistical model including an unsupervised component (i.e. principal component analysis PCA) and a supervised one (i.e. partial least square discrimination analysis PLS-DA). This model was trained using a training set of 20 drinking water s les previously tested via conventional suspect screening. The developed model was validated using a validation set of 20 drinking water s les of which 4 were spiked with 15 labeled standards at four different concentration levels. The model successfully detected all of the added analytes in the four spiked s les without producing any cases of false detection. The same validation set was processed via conventional trend analysis in order to cross validate the composite model. The results of cross validation showed that even though the conventional trend analysis approach produced a false positive detection rate of ≤5% the composite model outperformed that approach by producing zero cases of false detection. Additionally, the validated model went through an additional test with 42 extra drinking water s les from the same source for an unbiased examination of the model. Finally, the potentials and limitations of this approach were further discussed.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Elsevier BV
Date: 08-2023
Publisher: American Chemical Society (ACS)
Date: 05-10-2020
Publisher: Springer Science and Business Media LLC
Date: 21-10-2022
DOI: 10.1186/S12302-022-00680-6
Abstract: The NORMAN Association ( www.norman-network.com/ ) initiated the NORMAN Suspect List Exchange (NORMAN-SLE ds/SLE/ ) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water s les by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The in idual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community ( ommunities/norman-sle ), with a total of 40,000 unique views, 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem ( pubchem.ncbi.nlm.nih.gov/ ) and the US EPA’s CompTox Chemicals Dashboard ( ashboard/ ), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser ( lassification/#hid=101 ). The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website ( ds/SLE/ ).
Publisher: American Chemical Society (ACS)
Date: 29-11-2021
Publisher: Elsevier BV
Date: 04-2022
Publisher: American Chemical Society (ACS)
Date: 03-2023
DOI: 10.26434/CHEMRXIV-2023-VKQ8Q
Abstract: Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for ex le, wrong identification further down the workflow. Commonly used methods for deconvolution of fragment signals rely on the presence of a time domain (e.g., peak apex retention time difference and correlation analysis). However, when there is no or insufficient MS2 information in the time domain, these methods are unusable and only the mass domain remains. A probability based cumulative neutral loss (CNL) model for fragment deconvolution using the mass domain information was thus developed to allow deconvolution for such cases. The optimized model, with a mass tolerance of 0.005 Da and a CNL score threshold of -0.95, was able to achieve true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 25.6%, and a reduction rate of 39.9%. Additionally, the CNL model was extensively tested on real s les containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 95% with FD rates between 45% and 77% and reduction rates between 10% and 24%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 93.1%, a FDr of 57.2%, and a reduction rate of 42.6%.
Publisher: American Chemical Society (ACS)
Date: 13-09-2023
Publisher: Elsevier BV
Date: 09-2022
DOI: 10.1016/J.ENVINT.2022.107436
Abstract: Analysis of untreated municipal wastewater is recognized as an innovative approach to assess population exposure to or consumption of various substances. Currently, there are no published wastewater-based studies investigating the relationships between catchment social, demographic, and economic characteristics with chemicals using advanced non-targeted techniques. In this study, fifteen wastewater s les covering 27% of the Australian population were collected during a population Census. The s les were analysed with a workflow employing liquid chromatography high-resolution mass spectrometry and chemometric tools for non-target analysis. Socioeconomic characteristics of catchment areas were generated using Geospatial Information Systems software. Potential correlations were explored between pseudo-mass loads of the identified compounds and socioeconomic and demographic descriptors of the wastewater catchments derived from Census data. Markers of public health (e.g., cardiac arrhythmia, cardiovascular disease, anxiety disorder and type 2 diabetes) were identified in the wastewater s les by the proposed workflow. They were positively correlated with descriptors of disadvantage in education, occupation, marital status and income, and negatively correlated with descriptors of advantage in education and occupation. In addition, markers of polypropylene glycol (PPG) and polyethylene glycol (PEG) related compounds were positively correlated with housing and occupation disadvantage. High positive correlations were found between separated and orced people and specific drugs used to treat cardiac arrhythmia, cardiovascular disease, and depression. Our robust non-targeted methodology in combination with Census data can identify relationships between biomarkers of public health, human behaviour and lifestyle and socio-demographics of whole populations. Furthermore, it can identify specific areas and socioeconomic groups that may need more assistance than others for public health issues. This approach complements important public health information and enables large-scale national coverage with a relatively small number of s les.
Publisher: American Chemical Society (ACS)
Date: 09-07-2020
Publisher: American Chemical Society (ACS)
Date: 04-04-2018
Abstract: A key challenge in the environmental and exposure sciences is to establish experimental evidence of the role of chemical exposure in human and environmental systems. High resolution and accurate tandem mass spectrometry (HRMS) is increasingly being used for the analysis of environmental s les. One lauded benefit of HRMS is the possibility to retrospectively process data for (previously omitted) compounds that has led to the archiving of HRMS data. Archived HRMS data affords the possibility of exploiting historical data to rapidly and effectively establish the temporal and spatial occurrence of newly identified contaminants through retrospective suspect screening. We propose to establish a global emerging contaminant early warning network to rapidly assess the spatial and temporal distribution of contaminants of emerging concern in environmental s les through performing retrospective analysis on HRMS data. The effectiveness of such a network is demonstrated through a pilot study, where eight reference laboratories with available archived HRMS data retrospectively screened data acquired from aqueous environmental s les collected in 14 countries on 3 different continents. The widespread spatial occurrence of several surfactants (e.g., polyethylene glycols ( PEGs ) and C12AEO-PEGs ), transformation products of selected drugs (e.g., gabapentin-lactam, metoprolol-acid, carbamazepine-10-hydroxy, omeprazole-4-hydroxy-sulfide, and 2-benzothiazole-sulfonic-acid), and industrial chemicals (3-nitrobenzenesulfonate and bisphenol-S) was revealed. Obtaining identifications of increased reliability through retrospective suspect screening is challenging, and recommendations for dealing with issues such as broad chromatographic peaks, data acquisition, and sensitivity are provided.
Publisher: American Chemical Society (ACS)
Date: 07-08-2023
Publisher: Elsevier BV
Date: 11-2023
Publisher: Elsevier BV
Date: 08-2021
Publisher: American Chemical Society (ACS)
Date: 16-11-2020
Publisher: American Chemical Society (ACS)
Date: 08-12-2022
Publisher: Elsevier BV
Date: 12-2020
Publisher: Proceedings of the National Academy of Sciences
Date: 07-10-2019
Abstract: To date, wastewater-based epidemiology has focused on reporting drug and pharmaceutical consumption patterns by analyzing domestic wastewater. Here we explore the relationships between chemicals in wastewater and social, demographic, and economic parameters of the respective populations. We show the extent to which consumption of chemicals such as opioids and illicit drugs are associated with sociodemographics. We also examine chemicals that reflect in iduals’ consumption of food components in wastewater and show that disparities in diet are associated with educational level. Our study shows that chemicals in wastewater reflect the social, demographic, and economic properties of the respective populations and highlights the potential value of wastewater in studying the sociodemographic determinants of population health.
Publisher: American Chemical Society (ACS)
Date: 21-06-2022
DOI: 10.26434/CHEMRXIV-2022-B8T79
Abstract: Most chemicals present in the human and environmental exposome are structurally unknown (i.e. ≤ 1%). The European Chemicals Agency (ECHA) and US Environmen- tal Protection Agency (EPA) have listed approximately 800k chemicals that must be further investigated for their potential environmental and/or human health risk. A sig- nificant number of these chemicals have large enough global volumes of consumption (e.g. industrial and agrochemical) to reach the limits of detection of our analytical chemistry methods and may be toxic. In this study we present a supervised classification model that directly connects the molecular descriptors of chemicals to their toxicity. As a proof of concept we used 907 experimentally defined LC50 values for acute fish toxicity. Our classification model explained ≈ 90% of variance in our data for the training set and ≈ 80% for the test set. Direct comparison of our classification model with the conventional strategy (i.e. QSAR regression models) resulted in a 5 fold decrease in the wrong chemical categorization for our model. This optimized model was employed to predict the toxicity categories of ≈ 32k chemicals (from the Norman SusDat). Finally, a comparison between the model based applicability domain (AD) vs the training set AD was performed, suggesting that the training set based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to conventionally employed QSAR methods, makes this approach a viable tool for hazard identification and risk assessment of chemicals.
Publisher: Elsevier BV
Date: 02-2022
DOI: 10.1016/J.JHAZMAT.2021.127092
Abstract: Tire and road wear particles may constitute the largest source of microplastic particles into the environment. Quantification of these particles are associated with large uncertainties which are in part due to inadequate analytical methods. New methodology is presented in this work to improve the analysis of tire and road wear particles using pyrolysis gas chromatography mass spectrometry. Pyrolysis gas chromatography mass spectrometry of styrene butadiene styrene, a component of polymer-modified bitumen used on road asphalt, produces pyrolysis products identical to those of styrene butadiene rubber and butadiene rubber, which are used in tires. The proposed method uses multiple marker compounds to measure the combined mass of these rubbers in s les and includes an improved step of calculating the amount of tire and road based on the measured rubber content and site-specific traffic data. The method provides good recoveries of 83-92% for a simple matrix (tire) and 88-104% for a complex matrix (road sediment). The validated method was applied to urban snow, road-side soil and gully-pot sediment s les. Concentrations of tire particles in these s les ranged from 0.1 to 17.7 mg/mL (snow) to 0.6-68.3 mg/g (soil/sediment). The concentration of polymer-modified bitumen ranged from 0.03 to 0.42 mg/mL (snow) to 1.3-18.1 mg/g (soil/sediment).
Publisher: Elsevier BV
Date: 12-2020
DOI: 10.1016/J.SCITOTENV.2020.141175
Abstract: An emission source of microplastics into the environment is laundering synthetic textiles and clothing. Mechanical drying as a pathway for emitting microplastics, however, is poorly understood. In this study, emissions of microplastic fibres were s led from a domestic vented dryer to assess whether mechanical drying of synthetic textiles releases microplastic fibres into the surrounding air or are captured by the inbuilt filtration system. A blue polyester fleece blanket was repeatedly washed and dried using the 'Normal Dry' program of a common domestic dryer operated at temperatures between 56 and 59 °C for 20 min. Microfibres in the ambient air and during operation of the dryer were s led and analysed using microscopy for particle quantification and characterisation followed by Fourier-Transform Infrared Spectroscopy (FTIR) and Pyrolysis Gas Chromatography-Mass Spectrometry (Pyr-GC/MS) for chemical characterisation. Blue fibres averaged 6.4 ± 9.2 fibres in the room blank (0.17 ± 0.27 fibres/m
Publisher: Elsevier BV
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 04-09-2023
Publisher: American Chemical Society (ACS)
Date: 29-07-2019
DOI: 10.1021/ACS.ANALCHEM.9B02422
Abstract: Nontargeted feature detection in data from high resolution mass spectrometry is a challenging task, due to the complex and noisy nature of data sets. Numerous feature detection and preprocessing strategies have been developed in an attempt to tackle this challenge, but recent evidence has indicated limitations in the currently used methods. Recent studies have indicated the limitations of the currently used methods for feature detection of LC-HRMS data. To overcome these limitations, we propose a self-adjusting feature detection (SAFD) algorithm for the processing of profile data from LC-HRMS. SAFD fits a three-dimensional Gaussian into the profile data of a feature, without data preprocessing (i.e., centroiding and/or binning). We tested SAFD on 55 LC-HRMS chromatograms from which 44 were composite wastewater influent s les. Additionally, 51 of 55 s les were spiked with 19 labeled internal standards. We further validated SAFD by comparing its results with those produced via XCMS implemented through MZmine. In terms of ISs and the unknown features, SAFD produced lower rates of false detection (i.e., ≤ 5% and ≤10%, respectively) when compared to XCMS (≤11% and ≤28%, respectively). We also observed higher reproducibility in the feature area generated by SAFD algorithm versus XCMS.
Publisher: Research Square Platform LLC
Date: 10-03-2023
DOI: 10.21203/RS.3.RS-2120496/V2
Abstract: Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate the advancements in data processing. Here we present InSpectra, a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.
Publisher: Springer Science and Business Media LLC
Date: 24-08-2021
DOI: 10.1038/S41597-021-01002-W
Abstract: Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental s les. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of s les based on the passive s ling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.
Publisher: Research Square Platform LLC
Date: 04-10-2022
DOI: 10.21203/RS.3.RS-2120496/V1
Abstract: Non-target analysis (NTA) employing high-resolution mass spectrometry (HRMS) coupled with liquid chromatography is increasingly being used to identify chemicals of biological relevance. HRMS datasets are large and complex making the identification of potentially relevant chemicals extremely challenging. As they are recorded in vendor-specific formats, interpreting them is often reliant on vendor-specific software that may not accommodate the advancements in data processing. Here we present InSpectra , a vendor independent automated platform for the systematic detection of newly identified emerging chemical threats. InSpectra is web-based, open-source/access and modular providing highly flexible and extensible NTA and suspect screening workflows. As a cloud-based platform, InSpectra exploits parallel computing and big data archiving capabilities with a focus for sharing and community curation of HRMS data. InSpectra offers a reproducible and transparent approach for the identification, tracking and prioritisation of emerging chemical threats.
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 05-2020
Publisher: American Chemical Society (ACS)
Date: 21-01-2022
Abstract: Wastewater-based epidemiology is a potential complementary technique for monitoring the use of performance- and image-enhancing drugs (PIEDs), such as anabolic steroids and selective androgen receptor modulators (SARMs), within the general population. Assessing in-sewer transformation and degradation is critical for understanding uncertainties associated with wastewater analysis. An electrospray ionization liquid chromatography mass spectrometry method for the quantification of 59 anabolic agents in wastewater influent was developed. Limits of detection and limits of quantification ranged from 0.004 to 1.56 μg/L and 0.01 to 4.75 μg/L, respectively. Method performance was acceptable for linearity (
Location: Italy
Start Date: 04-2019
End Date: 04-2023
Amount: $515,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2021
End Date: 06-2025
Amount: $563,412.00
Funder: Australian Research Council
View Funded Activity