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Modeling and Monitoring Water Quality Management in Support of Public Health

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

Deadline for manuscript submissions: 10 June 2025 | Viewed by 1803

Special Issue Editor

Dr. Jianyong Wu

E-Mail Website
Guest Editor
Division of Environmental Health Sciences, Collage of Public Health, The Ohio State University, Columbus, OH, USA
Interests: GIS; data analytics; GeoAI; water and health; mental health; blue space; green space
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With increasing concerns about waterborne diseases and contaminants, understanding the dynamics of water quality is essential for safeguarding public health. This Special Issue aims to explore the application of cutting-edge technologies in the modeling and monitoring of water quality for public health protection. Leveraging advancements in machine learning, Geographic Information Systems (GIS), geospatial artificial intelligence (GeoAI), and remote sensing, this issue seeks to showcase innovative approaches to addressing public health challenges posed by emerging contaminants in water. The topics of this Special Issue include, but are not limited to, the following:

  • Application of machine learning algorithms for water quality prediction and monitoring.
  • Integration of GIS techniques for spatial analysis and visualization of the health risk posed by emerging contaminants in water.
  • Utilization of remote sensing technologies for large-scale monitoring of emerging contaminants in water.

Dr. Jianyong Wu
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. 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

  • machine learning
  • GIS
  • remote sensing
  • GeoAI
  • emerging contaminants
  • public health

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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

17 pages, 5459 KiB  
Article
Water-Quality Spatiotemporal Characteristics and Their Drivers for Two Urban Streams in Indianapolis
by Rui Li, Gabriel Filippelli, Jeffrey Wilson, Na Qiao and Lixin Wang
Water 2025, 17(8), 1225; https://doi.org/10.3390/w17081225 - 20 Apr 2025
Viewed by 141
Abstract
Water quality in urban streams is critical for the health of aquatic and human life, as it impacts both the environment and water availability. The strong impacts of changing climate and land use on water quality necessitate a better understanding of how stream [...] Read more.
Water quality in urban streams is critical for the health of aquatic and human life, as it impacts both the environment and water availability. The strong impacts of changing climate and land use on water quality necessitate a better understanding of how stream water quality changes over space and time. To this end, four key water-quality parameters—Escherichia coli (E. coli), nitrate (NO3−), sulfate (SO42−), and chloride (Cl−)—were collected at 12 sites along Fall Creek and Pleasant Run streams in Indianapolis, Indiana USA from 2003 to 2021 on a seasonal basis: March, July, and October each year. Two-way ANOVA tests were used to determine the impacts of seasonality and location on these parameters. Correlation and RDA (redundancy analysis) were used to determine the importance of climatic drivers. Linear regressions were used to quantify the impacts of land-use types on water quality integrating buffer zone size and sub-watershed analysis. Strong seasonal variations of the water-quality parameters were found. March had higher levels of NO3−, SO42−, and Cl− than other months. July had the highest E. coli concentrations compared to March and October. Seven-days antecedent snow and precipitation were found to be significantly related to Cl− and log10(E. coli) and can explain up to 53% and 31% of their variations, respectively. Spatially, urban built-up land in a 1000 m buffer around the sampling sites was positively correlated with the log10(E. coli) variation, while lawn cover was positively related to NO3− concentrations within 500 m buffers. Conversely, NDVI (Normalized Difference Vegetation Index) values were negatively related to all variables. In conclusion, E. coli is more impacted by higher precipitation and urban land coverage, which could be related to more combined sewer overflow events in July. Cl− peaking in March and its relationship with snow indicate salt runoff during snow melting events. NO3− and SO42− increases are likely due to fertilizer input from residential lawns near streams. This suggests that Indianapolis stream water-quality changes are influenced by both changing climate and land-cover/-muse types. Full article
(This article belongs to the Special Issue Modeling and Monitoring Water Quality Management in Support of Public Health)
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16 pages, 4585 KiB  
Article
Application of Machine Learning to Identify Influential Factors for Fecal Contamination of Shallow Groundwater
by Jianyong Wu, Yanni Cao, Md. Sirajul Islam and Michael Emch
Water 2025, 17(2), 160; https://doi.org/10.3390/w17020160 - 9 Jan 2025
Cited by 1 | Viewed by 1273
Abstract
Understanding influential factors for fecal contamination in groundwater is critical for ensuring water safety and public health. The objective of this study is to identify key factors for fecal contamination of shallow tubewells using machine learning methods. Three methods, including recursive feature elimination [...] Read more.
Understanding influential factors for fecal contamination in groundwater is critical for ensuring water safety and public health. The objective of this study is to identify key factors for fecal contamination of shallow tubewells using machine learning methods. Three methods, including recursive feature elimination (RFE) with XGBoost, Random Forest, and mutual information, were implemented to examine E. coli presence and concentration in 1495 tubewell water samples in Matlab, Bangladesh. For E. coli presence, climatic variables, including average rainfall and temperature over the 30, 15, and 7 days preceding sampling, as well as ambient temperature and rainfall on the sampling day, emerged as critical predictors. Land cover characteristics, such as the percentages of urban and agricultural areas within 100 m of a tubewell, were also significant. For E. coli concentration, land cover characteristics within 100 m, the number of hot and heavy-rain days in the 30 days preceding sampling, average rainfall and temperature in the 3 days preceding sampling, and ambient temperature on the sampling day were identified as key drivers. Random Forest and mutual information yielded results that were more similar to each other than to those of RFE with XGBoost. The findings highlight the interplay between climatic factors, land use, and population density in determining fecal contamination in shallow well water and demonstrate the power of machine learning algorithms in ranking these factors. Full article
(This article belongs to the Special Issue Modeling and Monitoring Water Quality Management in Support of Public Health)
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