AIMC Topic: Lead

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Explainable machine-learning-based predictions of blood lead levels and school drinking water contamination among children: a case study in Washington DC.

Scientific reports
Water quality degradation poses significant risks to human health, ecosystem, and community. Many cities continue to rely on outdated pipes and water distribution networks that are highly susceptible to leaks, corrosion, and lead contamination. The p...

Mapping hotspots and identifying drivers of lead bioaccumulation in Oryza sativa L. in tropical agroecosystems.

Journal of hazardous materials
Tropical rice systems exhibit high annual rates of heavy metal accumulation, requiring accurate identification of accumulation drivers in rice-growing ecosystems to ensure regional food security. Therefore, we collected 229 paired soil and rice sampl...

Distribution mapping and risk assessment of lead in topsoil across the Tibetan Plateau.

Ecotoxicology and environmental safety
Lead exposure poses substantial long-term health risks, accounting for over 900,000 annual deaths worldwide and impairing cognitive development in more than 800 million children. Recent studies have indicated elevated soil lead contamination levels o...

Stricter cadmium and lead standards needed for organic fertilizers in China.

The Science of the total environment
This study aims to evaluate the adequacy of China's national standards for heavy metals in organic fertilizers by predicting their concentrations in grains using machine leaning. A comprehensive dataset was collected from literature, including soil p...

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...

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...

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 ...

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...

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...