AI Medical Compendium Journal:
Environmental pollution (Barking, Essex : 1987)

Showing 11 to 20 of 106 articles

SERS-ATB: A comprehensive database server for antibiotic SERS spectral visualization and deep-learning identification.

Environmental pollution (Barking, Essex : 1987)
The rapid and accurate identification of antibiotics in environmental samples is critical for addressing the growing concern of antibiotic pollution, particularly in water sources. Antibiotic contamination poses a significant risk to ecosystems and h...

Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning.

Environmental pollution (Barking, Essex : 1987)
Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of identifying potential particles in micrographs can significantly enhance the research and monitori...

AI-driven approaches for air pollution modelling: A comprehensive systematic review.

Environmental pollution (Barking, Essex : 1987)
In recent years, air quality levels have become a global issue with the rise of harmful pollutants and their effects on climate change. Urban areas are especially affected by air pollution, resulting in a deterioration of the environment and a surge ...

Uncovering key sources of regional ozone simulation biases using machine learning and SHAP analysis.

Environmental pollution (Barking, Essex : 1987)
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, tra...

Prediction of chlorination degradation rate of emerging contaminants based on machine learning models.

Environmental pollution (Barking, Essex : 1987)
Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate const...

PM concentration prediction using a whale optimization algorithm based hybrid deep learning model in Beijing, China.

Environmental pollution (Barking, Essex : 1987)
PM is a significant global atmospheric pollutant impacting visibility, climate, and public health. Accurate prediction of PM concentrations is critical for assessing air pollution risks and providing early warnings for effective management. This stud...

Uncovering soil heavy metal pollution hotspots and influencing mechanisms through machine learning and spatial analysis.

Environmental pollution (Barking, Essex : 1987)
Soil heavy metal (HM) pollution is a significant and widespread environmental issue in China, highlighting the need to quantify influencing factors and identify priority concern areas for effective prevention and management. Based on published litera...

PBScreen: A server for the high-throughput screening of placental barrier-permeable contaminants based on multifusion deep learning.

Environmental pollution (Barking, Essex : 1987)
Contaminants capable of crossing the placental barrier (PB) adversely affect female reproduction and fetal development. The rapid identification of PB-permeable contaminants is urgently needed due to the inefficiencies of conventional cell-based tran...

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment.

Environmental pollution (Barking, Essex : 1987)
Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have e...

Machine learning-based evolution of water quality prediction model: An integrated robust framework for comparative application on periodic return and jitter data.

Environmental pollution (Barking, Essex : 1987)
Accurate water quality prediction is paramount for the sustainable management of surface water resources. Current deep learning models face challenges in reliably forecasting water quality due to the non-stationarity of environmental conditions and t...