Bulletin of environmental contamination and toxicology
Jan 19, 2026
Per- and polyfluoroalkyl substances (PFAS) are environmentally persistent pollutants, posing challenges for effective remediation. This study presented a machine learning (ML) framework to predict the first-order reaction rate constant (k) of PFAS de...
Environmental monitoring and assessment
Jan 13, 2026
In groundwater contamination source inversion, concentration data from monitoring wells serve as the most crucial known information, directly affecting the inversion accuracy of unknown contamination source parameters. However, existing studies often...
Environmental geochemistry and health
Jan 11, 2026
Heavy metals (HMs) are mostly toxic to all forms of life and are tenacious environmental pollutants. Rapid industrialization, urban development, and unsustainable agricultural implications lead to their accumulation in soil and water ecosystems, prom...
Environmental monitoring and assessment
Jan 7, 2026
Organic pollution in the lake water bodies poses a serious threat to the stability of aquatic ecosystems and human health. Dissolved organic matter (DOM) is a key component of organic pollution. The analysis of its sources is crucial for pollution co...
Environmental geochemistry and health
Dec 22, 2025
Groundwater arsenic contamination poses a significant health risk in coastal region of Bangladesh. However, existing studies have rarely applied advanced machine learning (ML) algorithms to predict arsenic concentrations using comprehensive water qua...
Rivers regulate land-ocean total phosphorus (TP) fluxes critical to ecosystem health and food security, yet global dynamics remain poorly understood due to limited observations. Here, we develop a machine learning framework integrating multimodal dat...
Environmental geochemistry and health
Dec 10, 2025
Pharmaceutical pollutants are increasingly recognized as emerging contaminants in aquatic environments. Their persistence, bioactivity, and resistance to conventional treatment processes raise ecological and human health concerns, including the sprea...
The study investigates the application of Artificial Intelligence (AI) driven neural network time series (NNTS) model for the forecasting prediction of dye removal using ultrasonic activated mixed biomass. Surface and functional characterization of u...
Traditional toxicity testing cannot keep pace with the rapid growth of synthetic chemicals, creating major data gaps that hinder the development of water quality criteria (WQC) for emerging contaminants. This study developed a machine learning model ...
This investigation evaluates the performance of a sequencing batch moving bed biofilm reactor (SB-MBBR) employing Ceramic Rings and K1 biofilters as biofilm carriers for the removal of phenol and chemical oxygen demand (COD) from synthetic landfill l...
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