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 …

Abstract Machine learning (ML) exhibits substantial potential for predicting the properties of
solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML …

As urbanization accelerates, understanding and managing carbon emissions from urban
sewer networks have become crucial for sustainable urban water cycles. This review …

Air pollution is a major issue all over the world because of its impacts on the environment
and human beings. The present review discussed the sources and impacts of pollutants on …

Air pollution, a pervasive environmental threat that spans urban and rural landscapes alike,
poses significant risks to human health, exacerbating respiratory conditions, triggering …

With the background of China's carbon peak, the low-carbon and sustainable development
of the coal industry is vital to China's national energy security. Because the underground …

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for
climate change mitigation and sustainable waste management. However, optimally …

Commercial instrumentation for measurement of various wastewater treatment processes
parameters is costly and time-consuming in wastewater treatment plants (WWTPs). Long …

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce
energy consumption and greenhouse gas emissions significantly. Machine learning (ML) …

Using piezoelectric materials for advanced oxidation is a green and viable means of water
purification and environmental restoration. However, unmodified piezoelectric catalysts …