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Sensors for Water Quality Monitoring and Assessment

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 14180

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Prof. Dr. Joaquín Ángel Ortuño

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Department of Analytical Chemistry, Faculty of Chemistry, University of Murcia, 30100-Murcia, Spain
Interests: chemical sensors; ion-selective electrodes; electrochemistry at the interface between two immiscible electrolyte solutions (ITIESs)
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Dr. José M. Olmos

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Guest Editor

Department of Analytical Chemistry, Faculty of Chemistry, University of Murcia, 30100 Murcia, Spain
Interests: ion-selective electrodes; voltametric techniques at solid/liquid and liquid/liquid interfaces; modelling chemical systems
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Special Issue Information

Dear Colleagues,

This Special Issue covers all topics related to the use of sensors in general, and chemical sensors in particular, to assess an aspect or aspects of water quality, including waters of any origin or use.

Water quality monitoring is crucial across a wide variety of cases. Water quality is related to a number of physical, chemical and biological parameters that are determined by the use or origin of the water. Water’s uses include drinking, irrigation, aquaculture and industry. The origins of water include groundwater, rivers, lakes, reservoirs, seawater, wastewater and waters from aquaculture. The water’s quality is indicative of environmental pollution.

Chemical sensors are a very useful tool for water quality monitoring. This is due to the wide variety of chemical sensors available and their continuous innovation, which allows many different chemical species to be monitored. Additionally, these sensors have suitable performance characteristics for real-time, in situ measurements.

Prof. Dr. Joaquín Ángel Ortuño
Dr. José M. Olmos
Guest Editors

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Keywords

sensors
water quality
chemical sensors
water monitoring
real-time monitoring
drinking water
seawater
aquaculture
environmental pollution
wastewater
groundwater

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Published Papers (10 papers)

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Research

23 pages, 3691 KB

Open AccessArticle

Development of a Cost-Effective Multiparametric Probe for Continuous Real-Time Monitoring of Aquatic Environments

by Samuel Fernandes, Alice Fialho, José Maria Santos, Teresa Ferreira and Ana Filipa Filipe

Sensors 2025, 25(23), 7110; https://doi.org/10.3390/s25237110 - 21 Nov 2025

Viewed by 438

Abstract

Continuous, real-time measurements are essential for informed water resource management and the development of strategies for the protection of aquatic ecosystems. Traditional methods of water quality assessment often fail to adequately capture seasonal trends, and the frequency and rapidity of fluctuations. To address this challenge, a standalone, low-cost (<EUR 1000), autonomous multisensor prototype for remote assessment was developed. The design of the system was optimized with a hardware-centric approach to minimize costs, whilst providing reliability and high precision and accuracy. Based on embedded systems and capable of long-range communication through GSM/GPRS, the device operates with minimal human intervention, ensuring timely data availability for analysis and decision-making. The multisensor instrument determines four important water quality parameters: pH, conductivity, temperature, and water level. Calibration and sensitivity analyses were performed; 1000 measurements per sensor indicated distributions consistent with normality for pH, conductivity, and water level. The results demonstrated high performance in pH measurements (mean: 5.65 on the Sørensen scale, R² = 0.9992, expanded uncertainty: ±0.4), conductivity (R² = 0.9999, expanded uncertainties: ±56.52 to ±3200.00 µS/cm for various standards), and water level (R² = 0.9952, expanded uncertainty: ±5.2 cm). Capable of providing continuous, accurate data at low cost, this multiparameter probe has broad applicability in environmental regulation compliance, pollution control, and sustainable ecosystem management. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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17 pages, 2557 KB

Open AccessArticle

In Situ Water Quality Monitoring for the Assessment of Algae and Harmful Substances in Water Bodies with Consideration of Uncertainties

by Stefanie Penzel, Thomas Mayer, Helko Borsdorf, Mathias Rudolph and Olfa Kanoun

Sensors 2025, 25(22), 7055; https://doi.org/10.3390/s25227055 - 19 Nov 2025

Viewed by 331

Abstract

Harmful algal blooms, particularly those caused by cyanobacteria (blue-green algae) and green algae, pose an increasing risk to aquatic ecosystems and public health. This risk is intensified by climate change and nutrient pollution. This study presents a methodology for in situ monitoring and assessment of algal contamination in surface waters, combining UV/Vis and fluorescence spectroscopy with a fuzzy pattern classifier for consideration of uncertainties. The system incorporates detailed data pre-processing to minimise measurement uncertainty and uses full-spectrum feature extraction to enhance classification accuracy. To assess the methodology under both controlled and real-world conditions, a mobile submersible probe was tested alongside a laboratory setup. The results demonstrate a high degree of agreement between the two systems, showing particular sensitivity to biological signals, such as the presence of algae. The assessment method successfully identified cyanobacterial and green algal contamination, and its predictions aligned with external observations, such as official warnings and environmental changes. By explicitly accounting for measurement uncertainty and employing a comprehensive spectral analysis approach, the system offers robust and adaptable monitoring capabilities. These findings highlight the potential for scalable, field-deployable solutions for the early detection of harmful algal blooms. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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21 pages, 2981 KB

Open AccessArticle

A Multi-Sensing Technology Approach for the Environmental Monitoring of the Ystwyth River

by Edore Akpokodje, Nnamdi Valbosco Ugwuoke, Mari Davies, Syeda Fizzah Jilani, Maria de la Puera Fernández, Lucy Thompson and Elizabeth Hart

Sensors 2025, 25(21), 6743; https://doi.org/10.3390/s25216743 - 4 Nov 2025

Viewed by 639

Abstract

Monitoring water quality in Welsh rivers has become a critical public concern, particularly in efforts to address pollution and protect the environment. This study presents the development and assessment of an interactive web and mobile application, featuring a real-time mapping interface built using the Mapbox framework. The platform provides stakeholders, including farmers, environmental agencies, and the public, with easy access to real-time water quality data using the Ystwyth River in Mid-Wales as a trial system. Users can click on map markers to view sensor readings for key water quality parameters. These include pH, electrical conductivity (EC), temperature, dissolved oxygen (DO), total dissolved solids (TDS) and nutrients levels such as nitrate (NO₃). This paper focuses on the feasibility of combining in situ sensor technology with a user-friendly mobile app to enable stakeholders to visualize the impact of land management practices and make informed decisions. The system aims to enhance environmental surveillance, increase transparency, and promote sustainable agricultural practices by providing critical water quality information in an accessible format. Future developments will explore the integration of artificial intelligence (AI) for predictive modelling and satellite data for broader spatial coverage, with the goal of scaling up the system to other catchments and improving proactive water quality management. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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23 pages, 26050 KB

Open AccessArticle

A Portable Measurement System Based on Nanomembranes for Pollutant Detection in Water

by Luca Tari, Maria Cojocari, Gabriele Cavaliere, Sarah Sibilia, Francesco Siconolfi, Georgy Fedorov, Luigi Ferrigno, Polina Kuzhir and Antonio Maffucci

Sensors 2025, 25(21), 6557; https://doi.org/10.3390/s25216557 - 24 Oct 2025

Viewed by 428

Abstract

This work presents the design, the development and the experimental validation of a portable, low-cost sensing system for the detection of waterborne pollutants. The proposed system is based on Electrochemical Impedance Spectroscopy and PPF+Ni nanomembrane sensors. Designed in response to the increasing demand for in situ water quality monitoring, the system integrates a simplified, scalable EIS acquisition architecture compatible with microcontroller-based platforms. The sensing configuration utilises the voltage divider principle, ensuring simplicity in signal conditioning by allowing compatibility with different electrode types through passive impedance matching. In addition, new merit figures have been proposed and implemented to analyse the measures. The proposed platform was experimentally characterised for its measurement stability, accuracy and environmental robustness. Sensitivity tests using benzoquinone as a target analyte demonstrated the capability of detecting concentrations as low as 0.1 mM with a monotonic response over increasing concentrations. A comparative study with a commercial electrochemical system (PalmSens4) under identical conditions highlighted the higher resolution and practical advantages of the proposed method despite operating with a lower impedance range. Additionally, the system exhibited reliable discrimination across tested concentrations and greater adaptability for integration into field-deployable environmental monitoring platforms. Future developments will focus on optimising selectivity through new sensor materials and analytical modelling of uncertainty propagation in the analysis based on defined figures of merit. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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26 pages, 11096 KB

Open AccessArticle

A Novel ML-Powered Nanomembrane Sensor for Smart Monitoring of Pollutants in Industrial Wastewater

by Gabriele Cavaliere, Luca Tari, Francesco Siconolfi, Hamza Rehman, Polina Kuzhir, Antonio Maffucci and Luigi Ferrigno

Sensors 2025, 25(17), 5390; https://doi.org/10.3390/s25175390 - 1 Sep 2025

Cited by 1 | Viewed by 977

Abstract

This study presents a comprehensive analysis aimed at validating the use of an innovative nanosensor based on graphitic nanomembranes for the smart monitoring of industrial wastewater. The validation of the potential of the nanosensor was carried out through the development of advanced analytical methodologies, a direct experimental comparison with commercially available electrode sensors commonly used for the detection of chemical species, and the evaluation of performance under conditions very similar to real-world field applications. The investigation involved a series of controlled experiments using an organic pollutant—benzoquinone—at varying concentrations. Initially, data analysis was performed using classical linear regression models, representing a conventional approach in chemical analysis. Subsequently, a more advanced methodology was implemented, incorporating machine-learning techniques to train a classifier capable of detecting the presence of pollutants in water samples. The study builds upon an experimental protocol previously developed by the authors for the nanomembranes, based on electrochemical impedance spectroscopy. The results clearly demonstrate that integrating the nanosensor with machine-learning algorithms yields significant performance. The intrinsic properties of the nanosensor make it well-suited for potential integration into field-deployable platforms, offering a real-time, cost-effective, and high-performance solution for the detection and quantification of contaminants in wastewater. These features position the nanomembrane-based sensor as a promising alternative to overcome current technological limitations in this domain. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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14 pages, 9364 KB

Open AccessArticle

Development of Autonomous Electric USV for Water Quality Detection

by Chiung-Hsing Chen, Yi-Jie Shang, Yi-Chen Wu and Yu-Chen Lin

Sensors 2025, 25(12), 3747; https://doi.org/10.3390/s25123747 - 15 Jun 2025

Viewed by 2182

Abstract

With the rise of industry, river pollution has become increasingly severe. Countries worldwide now face the challenge of effectively and promptly detecting river pollution. Traditional river detection methods rely on manual sampling and subsequent data analysis at various sampling sites, requiring significant time and labor costs. This article proposes using an electric unmanned surface vehicle (USV) to replace manual river and lake water quality detection, utilizing a 2.4 G high-power wireless data transmission system, an M9N GPS antenna, and an automatic identification system (AIS) to achieve remote and unmanned control. The USV is capable of autonomously navigating along pre-defined routes and conducting water quality measurements without human intervention. The water quality detection system includes sensors for pH, dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP). This design uses a modular structure, it is easy to maintain, and it supports long-range wireless communication. These features help to reduce operational and maintenance costs in the long term. The data produced using this method effectively reflect the current state of river water quality and indicate whether pollution is present. Through practical testing, this article demonstrates that the USV can perform precise positioning while utilizing AIS to identify potential surrounding collision risks for the remote planning of water quality detection sailing routes. This autonomous approach enhances the efficiency of water sampling in rivers and lakes and significantly reduces labor requirements. At the same time, this contributes to the achievement of the United Nations Sustainable Development Goals (SDG 14), “Life Below Water”. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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28 pages, 13302 KB

Open AccessArticle

Feasibility of Oil Spill Detection in Port Environments Based on UV Imagery

by Marian-Daniel Iordache, Françoise Viallefont-Robinet, Gert Strackx, Lisa Landuyt, Robrecht Moelans, Dirk Nuyts, Joeri Vandeperre and Els Knaeps

Sensors 2025, 25(6), 1927; https://doi.org/10.3390/s25061927 - 20 Mar 2025

Cited by 2 | Viewed by 1413

Abstract

Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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17 pages, 3603 KB

Open AccessArticle

pH Sensing Properties of Co₃O₄-RuO₂-Based Electrodes and Their Application in Baltic Sea Water Quality Monitoring

by Kiranmai Uppuluri, Dorota Szwagierczak, Krzysztof Zaraska, Piotr Zachariasz, Marcin Stokowski, Beata Synkiewicz-Musialska and Paweł Krzyściak

Sensors 2025, 25(4), 1065; https://doi.org/10.3390/s25041065 - 11 Feb 2025

Viewed by 1210

Abstract

Water is critical for the sustenance of life and pH is an important parameter in monitoring its quality. Solid-state pH sensors provide a worthy alternative to glass-based electrodes due to many advantages such as low cost, longer shelf life, simpler manufacturing, easier operation, miniaturization, and integration into electronic systems. Cobalt oxides are relatively cheaper and more abundantly available than ruthenium oxide. This work aims to reduce the environmental impact of screen-printed pH sensors by mixing Co₃O₄ and RuO₂ in five molar proportions (30%, 40%, 50%, 60%, and 70%) and investigating the influence of oxide proportions on the pH-sensing properties of the resulting composition using potentiometric characterization, scanning electron microscopy, X-ray diffraction, surface profilometry, and electron dispersive spectroscopy. Although all the developed compositions showed super- or near-Nernstian sensitivity with good linearity, the sensors based on 50 mol% Co₃O₄-50 mol% RuO₂ were the best due to superior sensitivity, selectivity, and stability. Fabricated sensors were applied in real-life environmental, municipal, and commercial water samples, including those from various depths in the Baltic Sea, and were found to be accurate in comparison to a glass electrode. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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19 pages, 8885 KB

Open AccessArticle

Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning

by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan and Yuhong Li

Sensors 2024, 24(22), 7345; https://doi.org/10.3390/s24227345 - 18 Nov 2024

Cited by 4 | Viewed by 2958

Abstract

The colorimetric method, due to its rapid and low-cost characteristics, demonstrates a wide range of application prospects in on-site water quality testing. Current research on colorimetric detection using deep learning algorithms predominantly focuses on single-target classification. To address this limitation, we propose a multi-task water quality colorimetric detection method based on YOLOv8n, leveraging deep learning techniques to achieve a fully automated process of “image input and result output”. Initially, we constructed a dataset that encompasses colorimetric sensor data under varying lighting conditions to enhance model generalization. Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). Experimental results demonstrate that the optimized deep learning algorithm excels in precision (96.4%), recall (96.2%), and mAP50 (98.3), significantly outperforming other mainstream models. Furthermore, compared to YOLOv8n, the parameter count and computational load were reduced by 25.8% and 25.6%, respectively. Additionally, precision improved by 2.8%, recall increased by 3.5%, mAP50 enhanced by 2%, and mAP95 rose by 1.9%. These results affirm the substantial potential of our proposed method for rapid on-site water quality detection, offering new technological insights for future water quality monitoring. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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20 pages, 16413 KB

Open AccessArticle

A Wireless Network for Monitoring Pesticides in Groundwater: An Inclusive Approach for a Vulnerable Kenyan Population

by Titus Mutunga, Sinan Sinanovic and Colin Harrison

Sensors 2024, 24(14), 4665; https://doi.org/10.3390/s24144665 - 18 Jul 2024

Cited by 4 | Viewed by 2144

Abstract

Safe drinking water is essential to a healthy lifestyle and has been recognised as a human right by numerous countries. However, the realisation of this right remains largely aspirational, particularly in impoverished nations that lack adequate resources for water quality testing. Kenya, a Sub-Saharan country, bears the brunt of this challenge. Pesticide imports in Kenya increased by 144% from 2015 to 2018, with sales data indicating that 76% of these pesticides are classified as highly hazardous. This trend continues to rise. Over 70% of Kenya’s population resides in rural areas, with 75% of the rural population engaged in agriculture and using pesticides. Agriculture is the country’s main economic activity, contributing over 30% of its gross domestic product (GDP). The situation is further exacerbated by the lack of monitoring for pesticide residues in surface water and groundwater, coupled with the absence of piped water infrastructure in rural areas. Consequently, contamination levels are high, as agricultural runoff is a major contaminant of surface water and groundwater. The increased use of pesticides to enhance agricultural productivity exacerbates environmental degradation and harms water ecosystems, adversely affecting public health. This study proposes the development of a wireless sensor system that utilizes radio-frequency identification (RFID), Long-range (LoRa) protocol and a global system for mobile communications (GSM) for monitoring pesticide prevalence in groundwater sources. From the system design, individuals with limited literacy skills, advanced age, or non-expert users can utilize it with ease. The reliability of the LoRa protocol in transmitting data packets is thoroughly investigated to ensure effective communication. The system features a user-friendly interface for straightforward data input and facilitates broader access to information by employing various remote wireless sensing methods. Full article

(This article belongs to the Special Issue Sensors for Water Quality Monitoring and Assessment)

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