Environmental Health

Intelligent monitoring of coastal outfalls via multi-source remote sensing image fusion.

Coastal outfalls are major point sources of pollutants entering the marine environment, severely threatening coastal water quality and marine ecosystems. Effective monitoring of these outfalls is therefore essential for pollution control and ecosystem protection. Existing monitoring approaches face significant limitations: manual inspections are inefficient and labor-intensive, visible light UAV i...

Seasonal synergistic management of PM2.5-O3-CO2 in key Chinese cities: analysis integrating synergistic indexes and machine learning models.

China confronts dual environmental challenges: PM2.5-O3 synergistic pollution and mounting CO2 emissions, demanding a strategic shift from single-pollutant control to coordinated management. In response, this study develops a trivariate synergy index (TSI) based on the bivariate synergy index (BSI), integrating PM2.5, O3, and CO2 into a single metric to quantify differences in synergy levels acros...

Spatiotemporal evolution and ecological risk assessment of heavy metals in agricultural black soils of Northeast China.

Black soils are globally important agricultural resources, yet comprehensive trans-provincial assessments of heavy metal contamination and its spatiot...

Leveraging machine learning and ten-cities observations to disentangle emission and meteorological controls on secondary inorganic aerosols in China.

Secondary inorganic aerosols ((NH4)2SO4 and NH4NO3) play a crucial role in air pollution and climate change, yet the differences in the key drivers an...

Refinement and performance benchmark for range-separated water force field.

In our previous work, we developed a CCSD(T)-level range-separated water force field that combines the power of physics-driven and machine learning mo...

Advances in single-molecule junctions for the detection of hazardous pollutants.

With the continuous development of the global economy, environmental pollution has emerged as a significant challenge impeding sustainable economic de...

Predicting Catalytic Pathways for Thiophenol Decomposition on TM-Doped MoS2: A Comparative Machine Learning Study.

Thiophenol (TP), a high-toxicity compound prevalent in pharmaceuticals and industrial products, necessitates efficient catalytic decomposition methods...

Environmental drivers of soil potentially toxic metals identified through multi-scenario machine learning in an arid region of Southeastern Iran.

Interpretable QSAR models for acute oral toxicity via tuned XGBoost and hybrid data sampling techniques.

Accurate, interpretable prediction of acute oral toxicity (LD₅₀) is challenged by severe dataset imbalance and complex structure-activity relationship...

Design and laboratory verification of an AI-driven plant protection robot with a custom communication protocol.

Traditional agricultural plant protection primarily relies on manual labor and indiscriminate chemical spraying, resulting in inefficiency, increased ...

Safe-by-redesign guidance for toxic industrial chemicals using explainable artificial intelligence: Introducing the DETOX-QSAR model.

Exploring the potential targets and mechanisms of malachite green induced lung adenocarcinoma through network toxicology and molecular docking.

Malachite green (MG), a synthetic dye, has turned into a major risk to human health, because of its toxicity of teratogenic, genotoxic, carcinogenic, ...

A multisource dataset for anomaly detection and fault prediction in urban water distribution networks.

This multi-source dataset was compiled to support research on anomaly-based leak detection in urban water distribution networks (WDNs). It contains on...

Inverting a new hydrogen map on the moon derived from Chang'E-2 gamma-ray spectrum data.

This study addresses the challenge of extracting subtle 2H characteristic γ ray ([email protected] MeV) count from the Chang'e-2 Gamma-Ray Spectrometer (CE2-GR...

Deep learning-guided discovery and engineering of binding peptides for accelerated enzymatic degradation of polyethylene terephthalate.

Enzymatic polyethylene terephthalate (PET) degradation holds promise for environmental restoration. However, limited substrate catalytic capacity hind...

Seasonal variations in sources and dynamics of surface water-groundwater nitrate in a plain river network area: Based on hydrochemical, dual isotope approach and feature importance interpretation.

Understanding the seasonal dynamics of nitrate pollution in interconnected surface water (SW) and groundwater (GW) systems is critical for effective n...

Machine learning-driven source apportionment and health risk assessment of multi-source groundwater pollution in highly permeable aquifers: A case study in Southwest China.

Industrial expansion releases multiple pollutants into groundwater, whose migration in highly permeable aquifers is governed by rapid flow through coa...

Sustainable Materials Design With Multi-Modal Artificial Intelligence.

The growing scarcity of critical minerals, coupled with high embodied carbon emissions and persistent pollution from material smelting, highlights the...

Integrating ensemble and contrastive learning with explainable AI for modeling eutrophication-driven algal risks.

This study develops a Composite Eutrophication Index (CEI) based on four water-quality parameters to predict river eutrophication risk, using ten quan...

Dissolved organic matter regulating the speciation and transport of potentially toxic metals in groundwater: Environmental implications and perspectives.

Groundwater, as an important freshwater resource, faces an increasingly severe problem of contamination by potential toxic metals (PTEs). Dissolved or...