Environmental Health

Leveraging machine learning for sustainable cultivation of Zn-enriched crops in Cd-contaminated karst regions.

Karst soils often exhibit elevated zinc (Zn) levels, providing an opportunity to cultivate Zn-enriched crops. (meanwhile) However, these soils also frequently contain high background levels of toxic metals, particularly cadmium (Cd), posing potential health risks. Understanding the bioaccumulation of Cd and Zn and the related drivers in a high geochemical background area can provide important insi...

Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon.

Climate change pressures include the dissolved oxygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygen concentrations, which have consequences to ecosystem functioning including biogeochemical cycling from mild to severe disruption. The study investigates the potential of machine learning (ML) and deterministic models to predict future hypoxia events. Employing ML m...

Hourly PM concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model.

Accurate prediction of PM concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of por...

Machine learning-assisted laccase-like activity nanozyme for intelligently onsite real-time and dynamic analysis of pyrethroid pesticides.

The intelligently efficient, reliable, economical and portable onsite assay toward pyrethroid pesticides (PPs) residues is critical for food safety an...

Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm.

Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-va...

Co-exposure to microplastics and soil pollutants significantly exacerbates toxicity to crops: Insights from a global meta and machine-learning analysis.

Environmental contamination of microplastics (MPs) is ubiquitous worldwide, and co-contamination of arable soils with MPs and other pollutants is of i...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and l...

Explainable machine learning for predicting diarrhetic shellfish poisoning events in the Adriatic Sea using long-term monitoring data.

In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by ...

Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach.

Metal contamination in soil poses environmental and health risks requiring effective remediation strategies. This study introduces an innovative appro...

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with rai...

Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.

Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vi...

Enhancing shipboard oil pollution prevention: Machine learning innovations in oil discharge monitoring equipment.

Maritime operations face significant challenges in environmental stewardship, particularly in managing oil discharges from tankers as mandated by the ...

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se...

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models.

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical...

Deciphering the cytotoxicity of micro- and nanoplastics in Caco-2 cells through meta-analysis and machine learning.

Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exposing humans through various routes. Despite human...

Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework.

Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to fo...

Modeling health outcomes of air pollution in the Middle East by using support vector machines and neural networks.

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. A...

Performance analysis of machine learning models for AQI prediction in Gorakhpur City: a critical study.

Air pollution and climate change are two complementary forces that directly or indirectly affect the environment's physical, chemical, and biological ...

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.

BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient sel...

Detecting floating litter in freshwater bodies with semi-supervised deep learning.

Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These metho...