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Artificial Intelligence Applications for Sustainable Environment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1433

Special Issue Editor


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Guest Editor
Environmental Engineering Department, Egypt-Japan University of Science and Technology (E-JUST), New Borg El-Arab City, Alexandria 21934, Egypt
Interests: environmental science; energy engineering; chemical engineering; agricultural and biological sciences; biochemistry; computer science
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Special Issue Information

Dear Colleagues,

This Special Issue represents the applications of artificial intelligence (AI) technologies, including artificial neural network, deep neural network, support vector machine, genetic algorithm, fuzzy logic, and adaptive-neuro fuzzy systems, to the field of environmental engineering. These AI techniques have been employed in wastewater degradation processes for predicting the pollutants’ removal efficiencies, optimizing the systems’ operating conditions, and controlling the aeration, flowrates, and tank temperature. Moreover, AI-based digital tools have been utilized for deploying real-time monitors and early warning systems to detect possible contamination in aquatic, terrestrial, and atmospheric environments. AI-aided soft-sensors are also used for collecting, monitoring, and analyzing remote data related to environmental pollution. Furthermore, AI is used to address inputs/outputs nonlinear correlations in chemical and biological environmental-associated processes. Intelligent control systems are employed for smart and wireless monitoring and collection of solid waste management in green cities. Regarding atmospheric environment sustainability, AI-based techniques are used to diagnose, manage, and forecast air-pollution-related diseases. Different artificial intelligence algorithms are utilized to create maps, showing the noise distribution in the area. These advantages are beneficial for fulfilling the sustainable development goals accompanied by pollution control and reduction, protection of human health, and climate change mitigation and adaptation.

  1. AI applications in risk management and assessment related to human health impacts from trace elements, micropollutants, heavy metals, and metalloids;
  2. AI utilization to manage and control several water engineering and water-quality-associated issues;
  3. Environmental sustainability and automated monitoring techniques;
  4. Low-cost and reliable AI-based smart sensors for mitigation of atmospheric pollution;
  5. AI-based methods for the control of urban noise pollution and health effects;
  6. Evaluation of AI-based decision support systems for maintaining socio-economic development and environmental conservation;
  7. Artificial intelligence applications for sustainable solid waste management practices;
  8. Information storage, annotation, and management related to pollutants in water, air, and subsurface environments, using AI based on a digital data collection framework;
  9. Overview of hazardous waste management solutions based on IoT and AI;
  10. Employing AI based on the value chain of green economy, complying with federal and state regulation.

Dr. Mahmoud Nasr
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. Sustainability 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

  • air quality
  • artificial neural network
  • early-warning
  • intelligent control
  • modeling and forecasting
  • noise pollution
  • soft measurement
  • solid waste

Published Papers (1 paper)

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Research

10 pages, 1567 KiB  
Article
Estimating the Amount of Submerged Marine Debris Based on Fishing Vessels Using Multiple Regression Model
by Kyounghwan Song, Seunghyun Lee, Taehwan Joung, Jiwon Yu and Jongkoo Park
Sustainability 2023, 15(20), 15172; https://doi.org/10.3390/su152015172 - 23 Oct 2023
Viewed by 824
Abstract
The majority of marine debris is found in shallow waters; however, submerged debris accumulated at the sea bottom is affected by this kind of pollution. To mitigate the harmful effect of marine debris, we have to recognize its characteristics. However, it is hard [...] Read more.
The majority of marine debris is found in shallow waters; however, submerged debris accumulated at the sea bottom is affected by this kind of pollution. To mitigate the harmful effect of marine debris, we have to recognize its characteristics. However, it is hard to estimate the quantity of submerged marine debris because the monitoring of submerged marine debris requires greater cost and time compared to the monitoring of beach or coastal debris. In this study, we used the data for submerged marine debris surveyed in the sea near the Korean Peninsula from 2017 to 2020 and the data of fishing vessels passing through the areas from 2018 to 2020. In addition, the correlation of major factors affecting the amount of submerged marine debris was analyzed based on the fishing vessel data and the removal project data for submerged marine debris. Moreover, we estimated the amount of submerged marine debris based on the fishing vessels at the collection sites surveyed two or more times using a stepwise regression model. The average amount of submerged marine debris estimated by the model was 6.0 tonnes more than that by the removal project, for which the error was ~26.5% compared to the amount collected by the removal project. The estimation method for submerged marine debris developed in this study can provide crucial information for an effective collection project by suggesting areas that require a collection project for submerged marine debris based on the information of fishing vessels. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Environment)
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