Distribution and Detection of Toxic Elements in Soil and Sediments
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2600
Interests: geology; geochemistry; mining; environment; pollution
Pollutants seep into the soils and sediments that act as nature’s decontaminate agent, where a combination of interactive processes cleanse our environment of them. Trace elements can be released into the soil by natural processes, such as chemical weathering, but their concentration and distribution in soil layers particles change due to pedogenic processes or anthropogenic elements as a direct or indirect result of human activities such as mining and energy production, agriculture and industrial activities, and careless waste disposal. Minerals are the main repositories of chemical elements in the Earth's crust, and thus, they are the main sources of elements needed for the development of civilization, as well as contaminating and polluting elements that affecting global and local ecosystems. The mining and processing of metal ores causes important environmental degradation and destruction. In mining and smelting areas, soils and sediments are affected by the disposal of mine tailings, acid mine drainage, and aerial deposition of contaminants from smelters. The exposed soils and sediments become acidified and contaminated with potentially toxic trace elements associated with polymetallic sulphides. The contamination and acidification of the soil reduce its fertility and reduce biodiversity and alter the relationship between species in the soil biota.
This Special Issue invites research papers on the various environmental aspects of soil and sediment pollution, with an emphasis on predictive soil mapping techniques to better understand the relationships between soil and the environment. Predictive soil mapping was created after the increase in computer efficiency and capacity, geo-information technology, and data availability, and it requires accurate and reliable maps. The application of novel modelling techniques and the development of realistic models play important roles in determining toxic elements and reconstructing major distribution pathways. Soil and sediment field measurements with multi-source geoscience datasets are being applied, developed, and incorporated into spatial distribution models. Regulatory issues and legal considerations are also of interest in this Special Issue. Submissions with results from different regions of the world are especially welcome to ensure a worldwide perspective on this topic.
Dr. Robert Šajn
Prof. Dr. Trajče Stafilov
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. Minerals is an international peer-reviewed open access monthly 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.
- potentially toxic elements
- machine learning
- artificial neural network