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Research Progress on Water Quality and Sediment Quality

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 1434

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Guest Editor
Department of Marine Sciences, University of the Aegean; Lesbos 811 00, Greece
Interests: water and sediment quality; toxic pollutants; disinfection by-products; pharmaceuticals; analytical methods; occurrence/fate; green chemistry; circular economy
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Special Issue Information

Dear Colleagues,

Environmental pressures have been proven to increasingly influence water quality and sediment quality on a global scale, resulting in a range of adverse effects to organisms, ecosystems and, ultimately, human health. Research efforts during the recent years have focused on the characterization of the quality of water and sediment on the basis of the levels and speciation of pollutants, toxicity to different organisms, regulated parameter concentrations, and pollution indicators. As more and more information, as well as technological advances, become available, relevant research is progressing with the aim of gaining more insight into the factors and processes that affect water and sediment quality; “old” and “new” pollutants, their transformation products occurrence, and effects; analytical methods optimization and toxicity estimation; and development of quality indicators. Increased efforts are also directed toward the achievement of these research goals while simultaneously minimizing time, effort, cost, and exposure to toxic reagents and solvents, both for the analysts and for the environment, in the framework of green chemistry and circular economy. The present Special Issue aims to present and highlight the most recent advances in this field, including characterization and identification techniques and applications, toxicity tests, indicator development, new insights in processes and approaches, and optimization of methodologies towards green chemistry and circular economy, for the protection of the environment and human health.

Dr. Anastasia Nikolaou
Guest Editor

Manuscript Submission Information

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Keywords

  • water quality
  • sediment quality
  • toxic pollutants
  • analytical methods
  • occurrence/fate
  • pollution indicators
  • green chemistry
  • circular economy

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Published Papers (1 paper)

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Research

17 pages, 4861 KiB  
Article
Water Quality Assessment and Forecasting Along the Liffey and Andarax Rivers by Artificial Neural Network Techniques Toward Sustainable Water Resources Management
by Eyad Abushandi
Water 2025, 17(3), 453; https://doi.org/10.3390/w17030453 - 6 Feb 2025
Cited by 3 | Viewed by 938
Abstract
This research evaluates water quality in two contrasting hydro-climatic regions: the River Liffey in Ireland and the Andarax River in Spain. It utilizes an Artificial Neural Network (ANN) to simulate potential changes in key water-quality parameters based on field measurements. The ANN models [...] Read more.
This research evaluates water quality in two contrasting hydro-climatic regions: the River Liffey in Ireland and the Andarax River in Spain. It utilizes an Artificial Neural Network (ANN) to simulate potential changes in key water-quality parameters based on field measurements. The ANN models showed strong predictive efficiency and performance, achieving R2 values of 0.89 for dissolved oxygen (DO), 0.98 for electrical conductivity (EC), 0.87 for pH, 0.95 for total dissolved solids (TDS), and 0.96 for turbidity. The root mean-square-error (RMSE) values for important parameters were DO (1.25 mg/L), EC (48.06 µS/cm), and turbidity (8.9 FNU). The models were able to capture complex nonlinear relationships under different environmental conditions. The results showed that DO levels in the Liffey will decline by up to 20% over the next decade due to rising nutrient pollution, while TDS levels in the Andarax River are expected to rise by approximately 15% during the same period as a result of ongoing agricultural runoff. The study also simulated potential future hypothetical scenarios by applying the model to four different “what-if” situations. Overall, the research underscores the significance of machine learning in understanding intricate water-quality dynamics. Full article
(This article belongs to the Special Issue Research Progress on Water Quality and Sediment Quality)
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