AIMC Topic: Arsenic
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Groundwater quality assessment and health risk evaluation for schoolchildren in Mujibnagar, Bangladesh: safe consumption guidelines using artificial neural network modeling.
Environmental geochemistry and health
Jul 20, 2025
Groundwater is a vital source of drinking water in Bangladesh, with tubewells commonly used, particularly in schools. This study assessed the quality of tubewell water in the southwest region, focusing on iron (Fe), arsenic (As), pH, electrical condu...
A novel method for achieving ecological indicator based on vertical soil bacterial communities coupled with machine learning: A case study of a typical tropical site in China.
Journal of hazardous materials
May 3, 2025
Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM...
Antioxidant activity of Mentha piperita phenolics on arsenic induced oxidative stress, biochemical alterations, and cyto-genotoxicity in fish, Channa punctatus.
Fish physiology and biochemistry
Mar 20, 2025
The study aims to investigate the synergistic antioxidant effects of the phenolics present in Mentha piperita (MP) against arsenic trioxide-induced oxidative stress, biochemical alteration, and cyto-genotoxicity in the fish, Channa punctatus. The phe...
A novel graph convolutional neural network model for predicting soil Cd and As pollution: Identification of influencing factors and interpretability.
Ecotoxicology and environmental safety
Feb 19, 2025
Soil pollution caused by toxic metals poses serious threats to the ecological environment and human well-being. Accurately predicting toxic metal concentrations is critical for safeguarding soil environmental security. However, the distribution of so...
Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.
Scientific reports
Feb 10, 2025
Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and used generalized linear model to investigate the association between urinary heavy metal levels and g...
Assessment of groundwater chemistry to predict arsenic contamination from a canal commanded area: applications of different machine learning models.
Environmental geochemistry and health
Jan 9, 2025
Groundwater arsenic (As), contamination is a significant issue worldwide including China and Pakistan, particularly in canal command areas. In this study, 131 groundwater samples were collected, and three machine learning models [Random Forest (RF), ...
Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.
Journal of hazardous materials
Dec 14, 2024
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...
Reducing cadmium and arsenic accumulation in rice grains: The coupled effect of sulfur's biomass dilution and soil immobilization analyzed using meta-analysis and machine learning.
The Science of the total environment
Oct 30, 2024
The biogeochemical cycling of sulfur (S) in paddy soil influences cadmium (Cd) and arsenic (As) migration. However, the impact of S application on Cd and As within the soil-rice system has not been fully explored. This study aimed to examine the effe...
Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.
Journal of hazardous materials
Oct 2, 2024
Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of int...
Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.
The Science of the total environment
Sep 18, 2024
Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vittata in China and pinpointing the key drivers that influence its distribution can facilitate the id...
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