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Applied Sciences
Special Issues
Water Footprint and Life Cycle Assessment
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Water Footprint and Life Cycle Assessment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 October 2021) | Viewed by 4170

Special Issue Editors

Prof. Dr. Jacek Makinia

E-Mail Website
Guest Editor
Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, Gdańsk, Poland
Interests: wastewater treatment; activated sludge systems; nutrient removal and recovery; mathematical modeling; process optimization
Prof. Dr. Anna Mikola

E-Mail Website
Guest Editor
Water and Wastewater Engineering Research Group, School of Engineering, Aalto University, PO Box 15200, FI-00076 Aalto, Finland
Interests: nutrient removal and recovery; removal of micropollutants and microplastics and control of GHG emissions in municipal wastewater treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable water resource management is gaining prominence due to the increasing water demand and protection of limited water resources. The concept of water footprinting has emerged as a consumption-based indicator of sustainability in water use. Water footprint (WF) can be viewed as a stand-alone inventory method in terms of the volume of water consumed to produce the goods and services. On the other hand, WF can also be integrated with life cycle assessment. With this approach, WF assesses the potential water-related impacts on human health, ecosystem quality, and available resources.

This Special Issue on “Water Footprint and Life Cycle Assessment” seeks high-quality works that focus on (i) methods and tools that enable analysis and support decision making in relations to water use, (ii) water-related impacts of specific production processes and stages, and (iii) WF of organizations throughout their supply chains.

Prof. Dr. Jacek Makinia
Prof. Dr. Anna Mikola
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 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. Applied Sciences 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 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

  • Green water footprint
  • Blue water footprint
  • Grey water footprint
  • Decision making
  • LCA
  • Process optimization
  • Sustainability
  • Water management

Published Papers (2 papers)

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Research

18 pages, 7654 KiB  
Article
Multivariate Analysis Applied to Aquifer Hydrogeochemical Evaluation: A Case Study in the Coastal Significant Subterranean Water Body between “Cecina River and San Vincenzo”, Tuscany (Italy)
by Alessia Bastianoni, Enrico Guastaldi, Alessio Barbagli, Stefano Bernardinetti, Andrea Zirulia, Mariantonietta Brancale and Tommaso Colonna
Appl. Sci. 2021, 11(16), 7595; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167595 - 18 Aug 2021
Cited by 7 | Viewed by 1931
Abstract
The hydrogeochemical characteristics of the significant subterranean water body between “Cecina River and San Vincenzo” (Italy) was evaluated using multivariate statistical analysis methods, like principal component analysis and self-organizing maps (SOMs), with the objective to study the spatiotemporal relationships of the aquifer. The [...] Read more.
The hydrogeochemical characteristics of the significant subterranean water body between “Cecina River and San Vincenzo” (Italy) was evaluated using multivariate statistical analysis methods, like principal component analysis and self-organizing maps (SOMs), with the objective to study the spatiotemporal relationships of the aquifer. The dataset used consisted of the chemical composition of groundwater samples collected between 2010 and 2018 at 16 wells distributed across the whole aquifer. For these wells, all major ions were determined. A self-organizing map of 4 × 8 was constructed to evaluate spatiotemporal changes in the water body. After SOM clustering, we obtained three clusters that successfully grouped all data with similar chemical characteristics. These clusters can be viewed to reflect the presence of three water types: (i) Cluster 1: low salinity/mixed waters; (ii) Cluster 2: high salinity waters; and (iii) Cluster 3: low salinity/fresh waters. Results showed that the major ions had the greater influence over the groundwater chemistry, and the difference in their concentrations allowed the definition of three clusters among the obtained SOM. Temporal changes in cluster assignment were only observed in two wells, located in areas more susceptible to changes in the water table levels, and therefore, hydrodynamic conditions. The result of the SOM clustering was also displayed using the classical hydrochemical approach of the Piper plot. It was observed that these changes were not as easily identified when the raw data were used. The spatial display of the clustering results, allowed the evaluation in a hydrogeological context in a quick and cost-effective way. Thus, our approach can be used to quickly analyze large datasets, suggest recharge areas, and recognize spatiotemporal patterns. Full article
(This article belongs to the Special Issue Water Footprint and Life Cycle Assessment)
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14 pages, 1599 KiB  
Article
Sustainable Management of Water Resources in Supplementary Irrigation Management
by Monika Marković, Goran Krizmanić, Andrija Brkić, Atilgan Atilgan, Božica Japundžić-Palenkić, Davor Petrović and Željko Barač
Appl. Sci. 2021, 11(6), 2451; https://0-doi-org.brum.beds.ac.uk/10.3390/app11062451 - 10 Mar 2021
Cited by 6 | Viewed by 1668
Abstract
Watermark, Tensiometer and Time Domain Reflectometry (TDR) are commonly used soil water sensors in irrigation practice whose performance depends on soil type, depth and growing conditions. Here, the results of sensor performance evaluation in different soil depths as well as the field and [...] Read more.
Watermark, Tensiometer and Time Domain Reflectometry (TDR) are commonly used soil water sensors in irrigation practice whose performance depends on soil type, depth and growing conditions. Here, the results of sensor performance evaluation in different soil depths as well as the field and laboratory testing in silty clay loamy soil are presented. Gravimetric soil moisture samples were taken from sensor installation depths (10, 20, 30 and 45 cm) and used as reference Soil Water Content (SWC). The measurements varied significantly (p < 0.05) across the monitoring depths. On average across the soil depths, there was a strong negative linear relationship between Watermark (r = −0.91) and TDR (r = 0.94), and a moderate negative (r = −0.75) linear relationship between SWC and Tensiometer. In general, Watermark and Tensiometer measured SWC with great accuracy in the range of readily available water, generated larger Mean Difference (MD) than TDR and overestimated SWC, while TDR underestimated SWC. Overall, laboratory testing reduced the root mean square error (RMSE, Watermark = 1.2, Tensiometer = 2.6, TDR = 1.9) and Mean Average Error (MAE, Watermark = 0.9, Tensiometer = 2.04. TDR = 1.04) for all tested sensors. Full article
(This article belongs to the Special Issue Water Footprint and Life Cycle Assessment)
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