Black carbon (BC) is a short-lived pollutant with significant environment and human health impacts. Monitoring BC is important, but its spatial coverage is limited. Therefore, predicting BC concentration is crucial in densely populated regions like t...
Identifying and quantifying pollution sources and their associated health risks are essential for formulating effective pollution control policies. This study analyzed PM-bound trace elements based on one year of sampling data collected from a low-PM...
Aerial technogenic pollution from the activity of ferrous and non-ferrous metallurgy resulting in degradation of vulnerable natural ecosystems is a principal environmental problem in Russian Arctic. The industrial barren in the vicinity of Monchegors...
Nitrogen dioxide (NO) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among these, the chemiluminescence method stands out as the most commonly used and is widely regarded as a ref...
Chemicals are misused and released into the environment, causing adverse effects on people and ecosystems. Assessing the potential environmental risks of these chemicals before their use is crucial. The bioconcentration factor (BCF) is a key paramete...
Water environment research : a research publication of the Water Environment Federation
Jul 1, 2025
This study addresses a critical research gap in water quality monitoring, specifically within the Cauvery River basin, where substantial contamination poses significant risks to both human health and aquatic ecosystems. The paper introduces an effect...
In the context of climate change, various countries/regions across East Asia have witnessed severe ground-level ozone (O) pollution, which poses potential health risks to the public. The complex relationships between O and its drivers, including the ...
Harmful algal blooms are critical issues in eutrophic lakes worldwide. However, predicting the spatial distribution of algal blooms at the pixel level is still a challenge. In this study, floating algae cover (FAC) was used to extract algal coverage ...
This study explores the application of deep learning (DL) models to predict methane (CH) emissions from enteric fermentation in dairy cows using performance, feeding, behavioral and weather data from automated milking and feeding systems, behavioral ...
Song et al. (2024), "Prediction of PFAS bioaccumulation in different plant tissues with machine learning models based on molecular fingerprints," employed machine learning methods, such as XGBoost and SHapley Additive exPlanations (SHAP), to predict ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.