AIMC Topic: Arsenic

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Adsorptive removal of arsenic by novel iron/olivine composite: Insights into preparation and adsorption process by response surface methodology and artificial neural network.

Journal of environmental management
Olivine, a low-cost natural material, impregnated with iron is introduced in the adsorptive removal of arsenic. A wet impregnation method and subsequent calcination were employed for the preparation of iron/olivine composite. The major preparation pr...

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

International journal of environmental research and public health
Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this stu...

Arsenic treatment and power generation with a dual-chambered fuel cell with anionic and cationic membranes using NaHCO anolyte and HCl or NaCl catholyte.

Chemosphere
Dual-chambered fuel cells with an iron anode and an air-carbon cathode separated by an ion exchange membranes have been used to treat arsenate during power production. To select an effective catholyte, the dual-chambered fuel cell consisted 90 mL of ...

Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.

Journal of environmental sciences (China)
Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) a...

Insights into the comparison of machine learning models on rice grain arsenic prediction: Interplay of rice cultivation systems and soil environmental factors.

Environmental pollution (Barking, Essex : 1987)
Arsenic (As) exposure to rice threatens food safety while transferring As to rice from paddy soils significantly impacts increasing As levels in rice. This study explores establishing an efficient model for predicting As accumulation in rice grain us...

Machine learning-driven optimization of arsenic phytoextraction using amendments.

Ecotoxicology and environmental safety
Exogenous amendments are crucial for enhancing the remediation efficiency of arsenic-contaminated soils by Pteris vittata. However, their effectiveness is unstable due to various factors, and neglecting their economic costs hinder broader application...

Electrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis.

Bioresource technology
Alum sludge (AlS) has emerged as an effective adsorbent for anionic contaminants, with traditional activation methods like acid/base treatments and calcination employed to enhance its adsorption capacity. However, these approaches encounter significa...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Journal of pharmaceutical and biomedical analysis
Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine lea...

ArsenicNet: An efficient way of arsenic skin disease detection using enriched fusion Xception model.

PloS one
Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh's rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is c...

[Condition optimization for bio-oxidation of high-S and high-As gold concentrate].

Wei sheng wu xue bao = Acta microbiologica Sinica
OBJECTIVE: To study the effects of temperature and lixivium return on the concentrate bio-oxidation and rate of gold cyanide leaching.