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Lead

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Application of supervised learning models for enhanced lead (II) removal from wastewater via modified cellulose nanocrystals (CNCs).

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Heavy metal ions are acknowledged to impact the environment and human health adversely. CNCs are effective materials for removing heavy metal ions in industrial applications and process innovations since they can be used in static and dynamic adsorpt...

Mapping Blood Lead Levels in China during 1980-2040 with Machine Learning.

Environmental science & technology
Lead poisoning is globally concerning, yet limited testing hinders effective interventions in most countries. We aimed to create annual maps of county-specific blood lead levels in China from 1980 to 2040 using a machine learning model. Blood lead da...

Deep Learning-Assisted Colorimetric/Electrical Dual-Sensing System for Ultrafast Detection of Hydrogen Sulfide.

ACS sensors
This study presents a colorimetric/electrical dual-sensing system (CEDS) for low-power, high-precision, adaptable, and real-time detection of hydrogen sulfide (HS) gas. The lead acetate/poly(vinyl alcohol) (Pb(Ac)/PVA) nanofiber film was transferred ...

Microwave detection technique combined with deep learning algorithm facilitates quantitative analysis of heavy metal Pb residues in edible oils.

Journal of food science
The heavy metal content in edible oils is intricately associated with their suitability for human consumption. In this study, standard soybean oil was used as a sample to quantify the specified concentration of heavy metals using microwave sensing te...

Using machine learning to predict soil lead relative bioavailability.

Journal of hazardous materials
Although the relative bioavailability (RBA) can be applied to assess the effects of Pb on human health, there is no definition and no specific data of Pb-RBA to different soil sources and endpoints in vivo. In this study, we estimated the Pb-RBA from...

Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment.

Chemosphere
Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption condit...

Quantifying the contributions of factors to bioaccessible Cd and Pb in soil using machine learning.

Journal of hazardous materials
The bioaccessibility of cadmium (Cd) and lead (Pb) in the gastrointestinal tract is crucial for health risk assessments of contaminated soils. However, variability in In vitro analytical conditions and soil properties introduces bias and uncertainty ...

Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort.

Scientific reports
BACKGROUND: Environmental metal exposure has been implicated in the development of digestive tract cancers, although the specific associations remain poorly defined. This study aimed to investigate the relationship between blood metal levels and the ...

Classification of soil contamination by heavy metals (Cr, Ni, Pb, Zn) in wildfire-affected areas using laser-induced breakdown spectroscopy and machine learning.

Environmental science and pollution research international
The assessment of soil contamination by heavy metals is of high importance due to its impact on the environment and human health. Standard high-sensitivity spectroscopic techniques for this task such as atomic absorption spectrometry (AAS) and induct...

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...