Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …

Advanced oxidation processes (AOPs) can degrade a wide range of trace organic
contaminants (TrOCs) to improve the quality of potable water or discharged wastewater …

Environmental pollution is escalating due to rapid global development that often prioritizes
human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the …

Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

The complexity and dynamics of the environment make it extremely difficult to directly predict
and trace the temporal and spatial changes in pollution. In the past decade, the …

Interfacial reactions drive all elemental cycling on Earth and play pivotal roles in human
activities such as agriculture, water purification, energy production and storage …

With the development of Raman spectroscopy and the expansion of its application domains,
conventional methods for spectral data analysis have manifested many limitations. Exploring …

Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Controlling heavy metal pollution in agricultural soil has been a significant challenge. These
heavy metals seriously threaten the surrounding ecological environment and human health …

Converting biomass into biochar (BC) as a functional biocatalyst to accelerate persulfate
activation for water remediation has attracted much attention. However, due to the complex …