AI Medical Compendium Journal:
Environmental science & technology

Showing 11 to 20 of 131 articles

Machine Learning Advancements and Strategies in Microplastic and Nanoplastic Detection.

Environmental science & technology
Microplastics (MPs) and nanoplastics (NPs) present formidable global environmental challenges with serious risks to human health and ecosystem sustainability. Despite their significance, the accurate assessment of environmental MP and NP pollution re...

Decoding the Plastic Patch: Exploring the Global Microplastic Distribution in the Surface Layers of Marine Regions with Interpretable Machine Learning.

Environmental science & technology
The marine environment is grappling with microplastic (MP) pollution, necessitating an understanding of its distribution patterns, influencing factors, and potential ecological risks. However, the vast area of the ocean and budgetary constraints make...

Modeling PFAS Sorption in Soils Using Machine Learning.

Environmental science & technology
In this study, we introduce PFASorptionML, a novel machine learning (ML) tool developed to predict solid-liquid distribution coefficients () for per- and polyfluoroalkyl substances (PFAS) in soils. Leveraging a data set of 1,274 entries for PFAS in ...

Artificial Intelligence for the Discovery of Safe and Effective Flame Retardants.

Environmental science & technology
Organophosphorus flame retardants (OPFRs) are important chemical additives that are used in commercial products. However, owing to increasing health concerns, the discovery of new OPFRs has become imperative. Herein, we propose an explainable artific...

Machine Learning-Enhanced Prediction for Soil-to-Air VOC Emission and Environmental Impact Pertaining Contaminated Fractured Aquifers.

Environmental science & technology
How to scientifically and efficiently quantify the impact and hazards of volatile organic compounds (VOCs) pollution and volatilization from complex groundwater systems on surface air environments is a critical environmental issue. This paper employe...

Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications.

Environmental science & technology
Covalent organic frameworks (COFs) are porous crystalline materials obtained by linking organic ligands covalently. Their high surface area and adjustable pore sizes make them ideal for a range of applications, including CO capture, CH storage, gas s...

Emission Factor Recommendation for Life Cycle Assessments with Generative AI.

Environmental science & technology
Accurately quantifying greenhouse gas (GHG) emissions is crucial for organizations to measure and mitigate their environmental impact. Life cycle assessment (LCA) estimates the environmental impacts throughout a product's entire lifecycle, from raw m...

Machine Learning-Assisted Prediction and Exploration of the Homogeneous Oxidation of Mercury in Coal Combustion Flue Gas.

Environmental science & technology
Mercury emission from coal combustion flue gas is a significant environmental concern due to its detrimental effects on ecosystems and human health. Elemental mercury (Hg) is the dominant species in flue gas and is hard to immobilize. Therefore, it i...

Machine Learning-based Classification for the Prioritization of Potentially Hazardous Chemicals with Structural Alerts in Nontarget Screening.

Environmental science & technology
Nontarget screening (NTS) with liquid chromatography high-resolution mass spectrometry (LC-HRMS) is commonly used to detect unknown organic micropollutants in the environment. One of the main challenges in NTS is the prioritization of relevant LC-HRM...

Predicting Membrane Fouling of Submerged Membrane Bioreactor Wastewater Treatment Plants Using Machine Learning.

Environmental science & technology
Membrane fouling remains a significant challenge in the operation of membrane bioreactors (MBRs). Plant operators rely heavily on observations of filtration performance from noisy sensor data to assess membrane fouling conditions and lab-based protoc...