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Toxics
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Data Science for Environmental Chemical Monitoring
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Data Science for Environmental Chemical Monitoring

A special issue of Toxics (ISSN 2305-6304). This special issue belongs to the section "Novel Methods in Toxicology Research".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3485

Special Issue Editor

Dr. Nikiforos Alygizakis

E-Mail Website
Guest Editor
1. Department of Chemistry, Laboratory of Analytical Chemistry, National and Kapodistrian University of Athens, Athens, Greece
2. Environmental Institute, Koš, Slovakia
Interests: analytical chemistry; non-target screening; high-resolution mass spectrometry; environmental chemistry; chemicals' management; cheminformatics; data science; machine learning

Special Issue Information

Dear Colleagues,

It is my great pleasure to invite you to participate in this Special Issue on "Data Science for Environmental Chemical Monitoring".

Environmental chemical monitoring is an ever-evolving field that plays a crucial role in understanding and mitigating the impacts of hazardous substances on our environment, and in order to address the intricate challenges posed by environmental chemical monitoring, innovative data science approaches are essential. Consequently, this Special Issue aims to gather the latest research advances in this area and provide a forum for scientists, engineers, and practitioners to share their experiences, ideas, and innovations.

The scope of this Special Issue includes, but is not limited to, the following topics:

  1. Computational mass spectrometry for the analysis of environmental data.
  2. Novel prioritization approaches in non-target screening and metagenomics.
  3. Statistical methods, machine learning, and data mining techniques for environmental chemical monitoring.
  4. Computational models for environmental exposure assessment.
  5. New ecotoxicological approaches and their applications in environmental chemical monitoring.
  6. Advances in understanding adverse outcome pathways and their impact in ecosystem services.
  7. Integrating big data sources into environmental chemical monitoring.
  8. Emerging trends and challenges in environmental data science.

This Special Issue will serve as a platform for disseminating cutting-edge research findings, encouraging cross-disciplinary collaboration, and facilitating the exchange of ideas among researchers and practitioners working in the field of environmental chemical monitoring.

We warmly welcome original research articles, review articles, and perspectives from experts in the field. All submissions will undergo a rigorous peer-review process to ensure the highest quality of published articles.

The deadline for submitting articles for consideration is 31 May 2024.

We hope that you will seize this opportunity to contribute to this exciting Special Issue and join us in advancing the field of environmental data science for environmental chemical monitoring.

Sincerely,

Dr. Nikiforos Alygizakis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • environmental chemical monitoring
  • computational mass spectrometry
  • non-target screening
  • statistical methods
  • machine learning
  • computational models
  • big data

Published Papers (3 papers)

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Research

18 pages, 6488 KiB  
Article
Characteristics of DOM and Their Relationships with Potentially Toxic Elements in the Inner Mongolia Section of the Yellow River, China
by Kuo Wang, Juan Jiang, Yuanrong Zhu, Qihao Zhou, Xiaojie Bing, Yidan Tan, Yuyao Wang and Ruiqing Zhang
Toxics 2024, 12(4), 250; https://doi.org/10.3390/toxics12040250 - 29 Mar 2024
Viewed by 596
Abstract
The characterization of dissolved organic matter (DOM) is important for better understanding of the migration and transformation mechanisms of DOM in water bodies and its interaction with other contaminants. In this work, fluorescence characteristics and molecular compositions of the DOM samples collected from [...] Read more.
The characterization of dissolved organic matter (DOM) is important for better understanding of the migration and transformation mechanisms of DOM in water bodies and its interaction with other contaminants. In this work, fluorescence characteristics and molecular compositions of the DOM samples collected from the mainstream, tributary, and sewage outfall of the Inner Mongolia section of the Yellow River (IMYR) were determined by using fluorescence spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). In addition, concentrations of potentially toxic elements (PTEs) in the relevant surface water and their potential relationships with DOM were investigated. The results showed that the abundance of tyrosine-like components increased significantly in downstream waters impacted by outfall effluents and was negatively correlated with the humification index (HIX). Compared to the mainstream, outfall and tributaries have a high number of molecular formulas and a higher proportion of CHOS molecular formulas. In particular, the O5S class has a relative intensity of 41.6% and the O5-7S class has more than 70%. Thirty-eight PTEs were measured in the surface water samples, and 12 found above their detective levels at all sampling sites. Protein-like components are positively correlated with Cu, which is likely indicating the source of Cu in the aquatic environment of the IMYR. Our results demonstrated that urban wastewater discharges significantly alter characteristics and compositions of DOM in the mainstream of IMYR with strongly anthropogenic features. These results and conclusions are important for understanding the role and sources of DOM in the Yellow River aquatic environment. Full article
(This article belongs to the Special Issue Data Science for Environmental Chemical Monitoring)
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11 pages, 9592 KiB  
Article
Triplex DNA Helix Sensor Based on Reduced Graphene Oxide and Electrodeposited Gold Nanoparticles for Sensitive Lead(II) Detection
by Jing Gao, Piao Xu, Lu Qiao, Yani Tao, Yao Xiao, Hong Qin, Yuan Zhu and Yi Zhang
Toxics 2023, 11(9), 795; https://doi.org/10.3390/toxics11090795 - 20 Sep 2023
Viewed by 931
Abstract
A triplex DNA electrochemical sensor based on reduced graphene oxide (rGO) and electrodeposited gold nanoparticles (EAu) was simply fabricated for Pb2+ detection. The glass carbon electrode (GCE) sequentially electrodeposited with rGO and EAu was further modified with a triplex DNA helix that [...] Read more.
A triplex DNA electrochemical sensor based on reduced graphene oxide (rGO) and electrodeposited gold nanoparticles (EAu) was simply fabricated for Pb2+ detection. The glass carbon electrode (GCE) sequentially electrodeposited with rGO and EAu was further modified with a triplex DNA helix that consisted of a guanine (G)-rich circle and a stem of triplex helix based on T-A•T base triplets. With the existence of Pb2+, the DNA configuration which was formed via the Watson–Crick and Hoogsteen base pairings was split and transformed into a G-quadruplex. An adequate electrochemical response signal was provided by the signal indicator methylene blue (MB). The proposed sensor demonstrated a linear relationship between the differential pulse voltammetry (DPV) peak currents and the logarithm of Pb2+ concentrations from 0.01 to 100.00 μM with a detection limit of 0.36 nM. The proposed sensor was also tested with tap water, river and medical wastewater samples with qualified recovery and accuracy and represented a promising method for Pb2+ detection. Full article
(This article belongs to the Special Issue Data Science for Environmental Chemical Monitoring)
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26 pages, 5048 KiB  
Article
Stability Constant and Potentiometric Sensitivity of Heavy Metal–Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands
by Phan Thi Diem-Tran, Tue-Tam Ho, Nguyen-Van Tuan, Le-Quang Bao, Ha Tran Phuong, Trinh Thi Giao Chau, Hoang Thi Binh Minh, Cong-Truong Nguyen, Zulayho Smanova, Gerardo M. Casanola-Martin, Bakhtiyor Rasulev, Hai Pham-The and Le Canh Viet Cuong
Toxics 2023, 11(7), 595; https://doi.org/10.3390/toxics11070595 - 07 Jul 2023
Cited by 1 | Viewed by 1429
Abstract
Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods [...] Read more.
Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure–property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities. Full article
(This article belongs to the Special Issue Data Science for Environmental Chemical Monitoring)
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