AIMC Journal:
The Science of the total environment

Showing 221 to 226 of 226 articles

A ternary classification using machine learning methods of distinct estrogen receptor activities within a large collection of environmental chemicals.

The Science of the total environment Dec 20, 2016
Endocrine-disrupting chemicals (EDCs), which can threaten ecological safety and be harmful to human beings, have been cause for wide concern. There is a high demand for efficient methodologies for evaluating potential EDCs in the environment. Herein ...
Humans Machine Learning Support Vector Machine Discriminant Analysis Endocrine Disruptors Receptors, Estrogen
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Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis.

The Science of the total environment Sep 28, 2015
The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concen...
Neural Networks, Computer Environmental Monitoring Water Pollutants, Chemical Water Pollution, Chemical
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Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles.

The Science of the total environment Apr 3, 2015
The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can pre...
Artificial Intelligence Algorithms Soil Europe Environmental Restoration and Remediation Models, Chemical Radium North America Hazardous Waste Sites Radiation Monitoring Radioactive Waste
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Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques.

The Science of the total environment Jan 20, 2015
This study focuses on correlations between total number concentrations, road traffic emissions and noise levels in an urban area in the southwest of Spain during the winter and summer of 2009. The high temporal correlation between sound pressure leve...
Environmental Monitoring Fuzzy Logic Noise Air Pollutants Air Pollution Particulate Matter Data Mining Spain
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Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.

The Science of the total environment Sep 19, 2014
Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vecto...
Neural Networks, Computer Artificial Intelligence Republic of Korea Environmental Monitoring Water Pollutants, Chemical Fresh Water Models, Chemical Chlorophyll Chlorophyll A Estuaries Water Pollution, Chemical
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Advancements in artificial intelligence-based technologies for PFAS detection, monitoring, and management.

The Science of the total environment Jun 10, 2025
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with strong carbon‑fluorine (CF) bonds that contribute to bioaccumulation and long-term environmental and health risks. Traditional PFAS detection and treatment meth...
Artificial Intelligence Environmental Monitoring Water Pollutants, Chemical Environmental Pollutants Fluorocarbons
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