AI Medical Compendium Journal:
Environment international

Showing 11 to 20 of 55 articles

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Environment international
Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human re...

Ecological risks of PFAS in China's surface water: A machine learning approach.

Environment international
The persistence of per- and polyfluoroalkyl substances (PFAS) in surface water can pose risks to ecosystems, while due to data limitations, the occurrence, risks, and future trends of PFAS at large scales remain unknown. This study investigated the e...

Forecasting O and NO concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach.

Environment international
Ozone (O) is a significant contributor to air pollution and the main constituent ofphotochemical smog that plagues China. Nitrogen dioxide (NO) is a significant air pollutant and a critical trace gas in the Earth's atmosphere. The presence of O and N...

NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion.

Environment international
Chemically induced neurotoxicity is a critical aspect of chemical safety assessment. Traditional and costly experimental methods call for the development of high-throughput virtual screening. However, the small datasets of neurotoxicity have limited ...

Identifying human activities causing water pollution based on microbial community sequencing and source classifier machine learning.

Environment international
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts...

Machine learning helps reveal key factors affecting tire wear particulate matter emissions.

Environment international
Tire wear particles (TWPs) are generated with every rotation of the tire. However, obtaining TWPs under real driving conditions and revealing key factors affecting TWPs are challenging. In this study, we obtained a TWPs dataset by simulating tire wea...

Improving WRF-Chem PM predictions by combining data assimilation and deep-learning-based bias correction.

Environment international
In numerical model simulations, data assimilation (DA) on the initial conditions and bias correction (BC) of model outputs have been proven to be promising approaches to improving PM (particulate matter with an aerodynamic equivalent diameter of ≤ 2....

A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa).

Environment international
Quantitative structure-activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application...

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

Environment international
Pesticides typically co-occur in agricultural surface waters and pose a potential threat to human and ecosystem health. As pesticide screening in global agricultural surface waters is an immense analytical challenge, a detailed risk picture of pestic...

Global meta-analysis and machine learning reveal the critical role of soil properties in influencing biochar-pesticide interactions.

Environment international
Biochar application in soils is increasingly advocated globally for its dual benefits in enhancing agricultural productivity and sequestering carbon. However, lingering concerns persist regarding its environmental impact, particularly concerning its ...