AIMC Topic: Soil Pollutants
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Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach.
Chemosphere
Jun 24, 2024
The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil sp...
Enhanced biosynthesis of coated silver nanoparticles using isolated bacteria from heavy metal soils and their photothermal-based antibacterial activity: integrating Response Surface Methodology (RSM) Hybrid Artificial Neural Network (ANN)-Genetic Algorithm (GA) strategies.
World journal of microbiology & biotechnology
Jun 24, 2024
This study explores the biosynthesis of silver nanoparticles (AgNPs) using the Streptomyces tuirus S16 strain, presenting an eco-friendly alternative to mitigate the environmental and health risks of chemical synthesis methods. It focuses on optimizi...
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network.
Chemosphere
Jun 21, 2024
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high ...
Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis.
Journal of hazardous materials
Jun 17, 2024
The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-c...
Phytobial remediation advances and application of omics and artificial intelligence: a review.
Environmental science and pollution research international
May 23, 2024
Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation...
Development and application of machine learning models for prediction of soil available cadmium based on soil properties and climate features.
Environmental pollution (Barking, Essex : 1987)
May 10, 2024
Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consum...
Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite.
Environmental pollution (Barking, Essex : 1987)
May 10, 2024
Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and r...
Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic.
Chemosphere
May 6, 2024
The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery...
Comparative role of charcoal, biochar, hydrochar and modified biochar on bioavailability of heavy metal(loid)s and machine learning regression analysis in alkaline polluted soil.
The Science of the total environment
Apr 26, 2024
Pot experiment was performed aimed to assess the comparative role of charcoal, biochar, hydrochar and thiourea-vegetable modified biochar at 1 and 2 % doses, and <1 mm particle size on the bioavailability of Cd, Pb, As, Ni, Cu and Zn, and enhance NPK...
Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.
Journal of hazardous materials
Apr 23, 2024
Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated exce...
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