AIMC Topic: Arsenic
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Environmentally Sustainable Detection of Arsenic using Convolutional Neural Networks and Imidazole-Based Organic Probes: Application in Food Samples and Arsenic Album.
Chemical research in toxicology
Sep 12, 2024
Arsenic contamination poses a significant health risk, particularly when it infiltrates water supplies. While current detection methods offer precise analysis, they often involve complex instrumentation not suitable for field use. This study presents...
Quantifying the impact of factors on soil available arsenic using machine learning.
Environmental pollution (Barking, Essex : 1987)
Jul 17, 2024
Arsenic (As) can accumulate in edible plant parts and thus pose a serious threat to human health. Identifying the contributions of various factors to soil available As is crucial for evaluating environmental risks. However, research quantitatively as...
Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.
Environmental science and pollution research international
Jul 9, 2024
Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The stud...
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...
Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.
Water research
Apr 6, 2024
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to uti...
Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach.
Ecotoxicology and environmental safety
Apr 4, 2024
The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, widespread presence, and persistence. The Kangdian metallogenic province, famous for its iron-copper dep...
A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.
Environmental science and pollution research international
Mar 6, 2024
Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over ...
Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning.
Journal of environmental sciences (China)
Nov 27, 2023
Arsenic (As) pollution in soils is a pervasive environmental issue. Biochar immobilization offers a promising solution for addressing soil As contamination. The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics...
Analysis of Prospective Genetic Indicators for Prenatal Exposure to Arsenic in Newborn Cord Blood of Using Machine Learning.
Biological trace element research
Sep 23, 2023
Using a machine learning methods, we aim to find biological effect biomarkers of prenatal arsenic exposure in newborn cord blood. From the Gene Expression Omnibus (GEO) database, two datasets (GSE48354 and GSE7967) pertaining to cord blood sequencing...
An insight into the act of iron to impede arsenic toxicity in paddy agro-system.
Journal of environmental management
May 20, 2022
Surplus research on the widespread arsenic (As) revealed its disturbing role in obstructing the metabolic function of plants. Also, the predilection of As towards rice has been an interesting topic. Contrary to As, iron (Fe) is an essential micronutr...
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