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Processes
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Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods
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Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 3371

Special Issue Editors

Dr. Jie Yu

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: water enivorment monitoring; optical sensing; artificial intelligence; three-dimensional fluorescence
Prof. Dr. Dibo Hou

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: smart perception and advanced sensing; environmental monitoring and early warning; robotics and unmanned systems
Dr. Xiaoyu Tang

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Interests: detection technology and automation equipment; process detection and information processing; complex fluid monitoring; flow field imaging; machine learning
Dr. Ke Wang

E-Mail Website
Guest Editor Assistant
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
Interests: data mining; artificial intelligence; water environment management; information systems

Special Issue Information

Dear Colleagues,

Water is essential for all known forms of life, and its quality is vital for human health, agricultural productivity and ecological balance. However, water systems worldwide are increasingly becoming contaminated by various pollutants, posing significant risks to public health and the environment. To address these challenges, intelligent recognition, detection and analytical methods are being developed and deployed to monitor and manage water systems effectively.

This Special Issue seeks high-quality works focusing on advanced techniques for identifying and quantifying pollutants, innovative detection methodologies and analytical strategies that leverage intelligent systems for monitoring and mitigating contaminants in various water sources.

Topics include, but are not limited to, the following:

  1. Development and application of novel sensors for contaminant detection in water systems;
  2. Intelligent algorithms and machine learning models for the real-time monitoring and prediction of water quality;
  3. Analytical methods for the identification and quantification of emerging contaminants;
  4. Integration of IoT (Internet of Things) technologies in water quality management;
  5. Advances in spectroscopic and chromatographic techniques for water analysis;
  6. Case studies on the implementation of intelligent systems in municipal and industrial water treatment;
  7. Data-driven approaches for assessing the impact of contaminants on public health and ecosystems;
  8. Remote sensing technologies for large-scale water quality monitoring;
  9. Innovations in portable and field-deployable detection devices for rapid contaminant assessment

Dr. Jie Yu
Prof. Dr. Dibo Hou
Dr. Xiaoyu Tang
Guest Editors

Dr. Ke Wang
Guest Editor Assistant

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. Processes 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 2400 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

  • water contaminant recognition
  • analytical techniques
  • water quality monitoring
  • machine learning
  • IoT in water management
  • spectroscopy and chromatography
  • emerging pollutants
  • remote sensing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 5116 KB  
Article
Design of Portable Water Quality Spectral Detector and Study on Nitrogen Estimation Model in Water
by Hongfei Lu, Hao Zhou, Renyong Cao, Delin Shi, Chao Xu, Fangfang Bai, Yang Han, Song Liu, Minye Wang and Bo Zhen
Processes 2025, 13(10), 3161; https://doi.org/10.3390/pr13103161 - 3 Oct 2025
Viewed by 565
Abstract
A portable spectral detector for water quality assessment was developed, utilizing potassium nitrate and ammonium chloride standard solutions as the subjects of investigation. By preparing solutions with differing concentrations, spectral data ranging from 254 to 1275 nm was collected and subsequently preprocessed using [...] Read more.
A portable spectral detector for water quality assessment was developed, utilizing potassium nitrate and ammonium chloride standard solutions as the subjects of investigation. By preparing solutions with differing concentrations, spectral data ranging from 254 to 1275 nm was collected and subsequently preprocessed using methods such as multiple scattering correction (MSC), Savitzky–Golay filtering (SG), and standardization (SS). Estimation models were constructed employing modeling algorithms including Support Vector Machine-Multilayer Perceptron (SVM-MLP), Support Vector Regression (SVR), random forest (RF), RF-Lasso, and partial least squares regression (PLSR). The research revealed that the primary variation bands for NH4+ and NO3− are concentrated within the 254–550 nm and 950–1275 nm ranges, respectively. For predicting ammonium chloride, the optimal model was found to be the SVM-MLP model, which utilized spectral data reduced to 400 feature bands after SS processing, achieving R2 and RMSE of 0.8876 and 0.0883, respectively. For predicting potassium nitrate, the optimal model was the 1D Convolutional Neural Network (1DCNN) model applied to the full band of spectral data after SS processing, with R2 and RMSE of 0.7758 and 0.1469, respectively. This study offers both theoretical and technical support for the practical implementation of spectral technology in rapid water quality monitoring. Full article
(This article belongs to the Special Issue Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods)
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21 pages, 5660 KB  
Article
EWAIS: An Ensemble Learning and Explainable AI Approach for Water Quality Classification Toward IoT-Enabled Systems
by Nermeen Gamal Rezk, Samah Alshathri, Amged Sayed and Ezz El-Din Hemdan
Processes 2024, 12(12), 2771; https://doi.org/10.3390/pr12122771 - 5 Dec 2024
Cited by 7 | Viewed by 2336
Abstract
In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed [...] Read more.
In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality. Leveraging the strengths of Ensemble Learning models and Explainable Artificial Intelligence (XAI), EWAIS not only enhances the prediction accuracy of water quality but also provides transparent insights into the factors influencing these predictions. EWAIS integrates multiple Ensemble Learning models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, and Voting Ensemble Learning (VEL)—to classify water as drinkable or non-drinkable. The system incorporates advanced techniques for handling missing data and statistical analysis, ensuring robust performance even in complex urban datasets. To address the opacity of traditional Machine Learning models, EWAIS employs XAI methods such as SHAP and LIME, generating intuitive visual explanations like force plots, summary plots, dependency plots, and decision plots. The system achieves high predictive performance, with the VEL model reaching an accuracy of 0.89 and an F1-Score of 0.85, alongside precision and recall scores of 0.85 and 0.86, respectively. These results demonstrate the proposed framework’s capability to deliver both accurate water quality predictions and actionable insights for decision-makers. By providing a transparent and interpretable monitoring system, EWAIS supports informed water management strategies, contributing to the sustainability and well-being of urban populations. This framework has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments. Full article
(This article belongs to the Special Issue Contaminants in Water Systems: Intelligent Recognition, Detection and Analytical Methods)
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