Environmental Health

Selectively Quantify Toxic Pollutants in Water by Machine Learning Empowered Electrochemical Biosensors.

Electroactive biofilm (EAB) sensors have become pivotal in water quality detection and early ecological risk warnings due to their remarkable sensitivity. However, it is challenging to identify multiple toxicants in complex water bodies concurrently. This research developed an innovative biosensor detection strategy combined with machine learning. To simultaneously quantify and qualitatively predi...

The Effect of Different Bleaching Techniques Using 6% Hydrogen Peroxide: Penetration Inside the Pulp Cavity, Bleaching Efficacy and Toxicity.

This in vitro study aimed to quantify the penetration of hydrogen peroxide (HP), bleaching efficacy (BE) and toxicity in larvae in different bleaching techniques using 6% HP. Sixty maxillary premolars were divided in six groups (n = 10): Pola Luminate (PL), VivaStyle Paint On PIus (VS), PolaDay (PD), White Class (WC) and Whiteness HP Automixx (AM). A group not exposed to bleaching agents was evalu...

ESR Essentials: how to get to valuable radiology AI: the role of early health technology assessment-practice recommendations by the European Society of Medical Imaging Informatics.

AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large numbe...

Novel method for predicting concentrations of incineration flue gas based on waste composition and machine learning.

The complex composition of solid waste leads to the variability of flue gas emissions during its incineration, which poses a challenge to the stable o...

Evaluating drivers of PM air pollution at urban scales using interpretable machine learning.

Reducing urban fine particulate matter (PM) concentrations is essential for China to achieve the Sustainable Development Goals (SDGs). Identifying the...

Graphene FET biochip on PCB reinforced by machine learning for ultrasensitive parallel detection of multiple antibiotics in water.

Antibiotics like Ciprofloxacin (Cfx), tetracycline (Tet) and Tobramycin (Tob) are commonly used against a broad-spectrum of bacterial infection. Recen...

The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging.

Assessing chemical toxicity in materials like plastic packaging is critical to safeguarding public health. This study presents the development of clas...

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Removing hydrogen sulfide (HS) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers ...

Does industrial robot adoption inhibit environmental pollution in China? An empirical study on energy consumption and green technology innovation.

In this research, we develop a theoretical model to examine the relationship between the adoption of industrial robots and environmental pollution. Ad...

SERS-AI Based Detection and Bioanalysis of Malodorous Components in Kitchen Waste.

The prevention and control of odor gas generated from kitchen waste are significant missions in research on environmental pollution. Because of the hi...

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

Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb)...

Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.

Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different ...

Improving PM and PM predictions in China from WRF_Chem through a deep learning method: Multiscale depth-separable UNet.

Accurate predictions of atmospheric particulate matter can be applied in providing services for air pollution prevention and control. However, the for...

Deployment of intelligent irrigation monitoring system with Android app for machine learning prediction.

Water is a fundamental necessity for humans and a critical resource in agriculture. However, water scarcity poses a significant challenge, especially ...

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO Concentration from Satellite and Ground Monitors.

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. Ho...

Machine learning estimates on the impacts of detection times on wildfire suppression costs.

As climate warming exacerbates wildfire risks, prompt wildfire detection is an essential step in designing an efficient suppression strategy, monitori...

Uncovering global risk to human and ecosystem health from pesticides in agricultural surface water using a machine learning approach.

Pesticides typically co-occur in agricultural surface waters and pose a potential threat to human and ecosystem health. As pesticide screening in glob...

Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India.

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues ...

High-resolution spatiotemporal prediction of PM concentration based on mobile monitoring and deep learning.

Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In or...

hERGBoost: A gradient boosting model for quantitative IC prediction of hERG channel blockers.

The human ether-a-go-go-related gene (hERG) potassium channel is pivotal in drug discovery due to its susceptibility to blockage by drug candidate mol...