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Application of Smart Technologies in Integrated Water Quality Modeling, 2nd Edition

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

Deadline for manuscript submissions: 24 July 2026 | Viewed by 2826

Special Issue Editors

Dr. Tianxiang Wang

E-Mail Website
Guest Editor
School of Ocean Science and Technology, Dalian University of Technology, Panjin, China
Interests: nitrogen; phosphorus; sediment; ice-covered; model; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Haiyan Zhang

E-Mail Website
Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin, China
Interests: ecological environment; mechanistic model; integrated water quality model; environment carrying capacity; aquatic ecosystems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A healthy ecological environment is the foundation for sustainable development. However, human activities, while driving social and economic development, have introduced unpredictable impacts on the ecological environment, presenting challenges for its effective management. Among these challenges, fluctuations in water quality are particularly critical, as they are intricately intertwined with water resources, aquatic ecosystems, and human activities. Accurate water quality prediction is essential for enhancing ecological management, necessitating the exploration of comprehensive water quality models. This Special Issue aims to explore methodologies related to water quality, encompassing water resources, aquatic ecosystems, and the assessment of water environment carrying capacities, using case studies of representative regions. The scope of this Special Issue encompasses several dimensions: first, the refinement of mechanistic models through the assimilation of regional parameters acquired via on-site investigations or experiments; second, the development of nonlinear models tailored to special regions; and third, the exploration of integrated water quality models grounded in extensive datasets, intergrating machine learning techniques. Ultimately, we aim to advance the synergistic integration of conventional surveys, empirical studies, and smart technologies, thus propelling the advancement of comprehensive water quality modeling.

Dr. Tianxiang Wang
Dr. Haiyan Zhang
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Water is an international peer-reviewed open access semimonthly 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 model
  • assessment method
  • statistic model
  • mechanism model
  • regional indicators
  • machine learning
  • water resource

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Published Papers (3 papers)

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Research

17 pages, 2031 KB  
Article
AGConvLSTM: An Adaptive Graph Convolutional LSTM Network for Multi-Station Water Quality Classification
by Yali Zhao, Xuecheng Wang, Fansen Meng and Xiaoyan Chen
Water 2026, 18(9), 1073; https://doi.org/10.3390/w18091073 - 30 Apr 2026
Viewed by 480
Abstract
Water quality classification is essential for freshwater ecosystem protection but faces challenges posed by spatiotemporal dependencies and class imbalance. To address these issues, this paper proposes the Adaptive Graph Convolutional Long Short-Term Memory Network (AGConvLSTM), which integrates adaptive graph convolution into the LSTM [...] Read more.
Water quality classification is essential for freshwater ecosystem protection but faces challenges posed by spatiotemporal dependencies and class imbalance. To address these issues, this paper proposes the Adaptive Graph Convolutional Long Short-Term Memory Network (AGConvLSTM), which integrates adaptive graph convolution into the LSTM gating mechanism to explicitly capture spatiotemporal dependencies. As complementary components, station-wise Principal Component Analysis (PCA) preserves spatial heterogeneity in feature structures, while DTW-SMOTE with adaptive sampling and dynamic denoising mitigates class imbalance. Evaluated on five-year water quality data from 13 stations in the Taihu Basin, China, AGConvLSTM achieves a test accuracy of 69.34% and an F1 score of 69.68%, outperforming baseline models. Station-wise accuracy ranges from 49.12% to 88.48%, reflecting spatial heterogeneity. These results suggest that spatiotemporal fusion within recurrent units provides an effective pathway for multi-station water quality classification and offers practical value for watershed early warning systems. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling, 2nd Edition)
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22 pages, 6786 KB  
Article
Hydrochemical Characteristics and Nitrate Health Risk Assessment in a Shallow Aquifer: Insights from a Typical Low-Mountainous Region
by Xia Li, Jiaxin Song, Junjian Liu, Wenda Liu, Jingtao Shi, Suduan Hu, Jiangyulong Wang and Xueyao Niu
Water 2025, 17(24), 3516; https://doi.org/10.3390/w17243516 - 12 Dec 2025
Cited by 1 | Viewed by 905
Abstract
Wolong Town, Pingquan City, is located in a typical low-mountainous area of northern China, where groundwater is a crucial drinking water resource, thus, investigating groundwater’s hydrochemical characteristics and assessing nitrate-related health risks are vital for protecting, developing, and utilizing water resources. In this [...] Read more.
Wolong Town, Pingquan City, is located in a typical low-mountainous area of northern China, where groundwater is a crucial drinking water resource, thus, investigating groundwater’s hydrochemical characteristics and assessing nitrate-related health risks are vital for protecting, developing, and utilizing water resources. In this study, 66 groundwater samples were collected and analyzed for physicochemical parameters and major ion concentrations. Results showed that the groundwater in Wolong Town was weakly alkaline (average pH = 7.6), and classified as fresh water with TDS ranging from 90.0 to 900 mg/L. The dominant hydrochemical type was identified as HCO3−-Ca2+. Hydrochemical evolution was jointly regulated by natural water-rock interaction, anthropogenic nitrogen input, and environmental redox differentiation. Among these, water-rock interaction was the primary driver, where the hydrochemical composition was mainly shaped by the dissolution of halite, calcite, dolomite, and gypsum, coupled with cation exchange. Nitrate was the primary groundwater pollutant, with concentrations varying from 0.94 to 259 mg/L; elevation, soil type, and population density were key drivers influencing nitrate distribution. Health risk assessment indicated that nitrate posed significantly higher non-carcinogenic risks to infants and children than to adults, and long-term consumption of groundwater with excessive nitrate might induce adverse health effects. This study enhances understanding of shallow groundwater’s hydrochemical evolution and nitrate contamination-related health risks, thereby providing theoretical support for the sustainable development, utilization, and quality protection of groundwater resources in semi-arid low-mountainous areas. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling, 2nd Edition)
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18 pages, 3716 KB  
Article
Spatial Distribution and Environmental Impacts of Soil Nitrogen and Phosphorus in the Downstream Daliao River Basin
by Tianxiang Wang, Yexin Liu, Zixiong Wang, Tianzi Wang, Zipeng Zhang, Runfa Cui, Rongyue Ma and Guangyu Su
Water 2025, 17(22), 3267; https://doi.org/10.3390/w17223267 - 15 Nov 2025
Viewed by 824
Abstract
Soil nitrogen (N) and phosphorus (P) loss in watersheds is a critical source of water pollution. This study explores the spatial distribution, release potential, and environmental impacts of soil N and P in the downstream Daliao River basin by integrating field investigations and [...] Read more.
Soil nitrogen (N) and phosphorus (P) loss in watersheds is a critical source of water pollution. This study explores the spatial distribution, release potential, and environmental impacts of soil N and P in the downstream Daliao River basin by integrating field investigations and simulation experiments. Results showed that total nitrogen content in soils ranged from 256.09 to 3362.75 mg/kg, while that in sediments ranged from 114.85 to 1640.54 mg/kg. Total phosphorus content in soils varied from 250.18 to 1142.69 mg/kg, whereas in sediments it ranged from 327.23 to 586.24 mg/kg. The ammonia nitrogen release potentials of soils collected from rice paddies, corn farmlands, roadsides, and reed wetlands were 0.75, 0.86, 0.70, and 8.65 mg/L, respectively, with corresponding total phosphorus release potentials of 0.61, 1.01, 0.31, and 1.52 mg/L. For sediments, ammonia nitrogen and total phosphorus release potentials ranged from 0.96 to 1.21 mg/L and 0.44 to 0.52 mg/L, respectively. Temperature, pH, and dissolved oxygen were important factors influencing nitrogen and phosphorus release from soils and sediments. The export of nitrogen and phosphorus from soil reached 50.50 t/a and 21.63 t/a, respectively. During the soil erosion process in the Daliao River Basin, phosphorus exhibited a high release potential and served as the primary pollutant, whereas the release mechanism of ammonia nitrogen was more complex, showing seasonal variability. Soils in the downstream Daliao River basin have large specific surface areas and may pose a high pollution risk after discharge into water bodies due to prolonged adsorption of pollutants. It is recommended to propose promoting soil testing-based fertilization, constructing ecological engineering projects, developing sponge cities, and conducting environmental dredging to reduce N and P release from agricultural lands, construction areas, natural wastelands, and sediments. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling, 2nd Edition)
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