AI Medical Compendium Journal:
Environmental science & technology

Showing 121 to 130 of 131 articles

Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach.

Environmental science & technology
A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of microplastics controlling this process are elusive. Here, we show that the gastrointestinal bioaccessibil...

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Environmental science & technology
Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with acc...

Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features.

Environmental science & technology
Identifying potential persistent organic pollutants (POPs) and persistent, bioaccumulative, and toxic (PBT) substances from industrial chemical inventories are essential for chemical risk assessment, management, and pollution control. Inspired by the...

The Fourth-Revolution in the Water Sector Encounters the Digital Revolution.

Environmental science & technology
The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is rev...

Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models.

Environmental science & technology
Postcombustion CO capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO capture processes are considered too energetically...

Rapid Life-Cycle Impact Screening Using Artificial Neural Networks.

Environmental science & technology
The number of chemicals in the market is rapidly increasing, while our understanding of the life-cycle impacts of these chemicals lags considerably. To address this, we developed deep artificial neural network (ANN) models to estimate life-cycle impa...

Predicting Microbial Fuel Cell Biofilm Communities and Bioreactor Performance using Artificial Neural Networks.

Environmental science & technology
The complex interactions that occur in mixed-species bioelectrochemical reactors, like microbial fuel cells (MFCs), make accurate predictions of performance outcomes under untested conditions difficult. While direct correlations between any individua...

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

Environmental science & technology
Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which ...

Magnetic Properties as a Proxy for Predicting Fine-Particle-Bound Heavy Metals in a Support Vector Machine Approach.

Environmental science & technology
The development of a reasonable statistical method of predicting the concentrations of fine-particle-bound heavy metals remains challenging. In this study, daily PM samples were collected within four different seasons from a Chinese mega-city. The an...