Environmental geochemistry of higher radioactivity in a transboundary Himalayan river sediment (Brahmaputra,Bangladesh): potential radiation exposure and health risks

Environmental Science and Pollution Research - This study of a downstream segment (Brahmaputra, Bangladesh) of one of the longest transboundary (China-India-Bangladesh) Himalayan rivers reveals...     

Ecological treatment of El Kriymat boiler ash for recovering vanadium,nickel and zinc from sulfate leach liquor

The primary goal of this work is to develop a technology that allows for the recovery of metal values from waste products, thereby promoting the wise and efficient use of our nation's resources. To achieve this goal, an industrial waste of El Kriymat boiler fly Ash was used for recovering its content of vanadium, nickel and zinc. About 97, 95 and 99% respectively of these economic elements were first dissolved from boiler fly ash magnetic concentrate (after physical concentration). Leaching experiments using optimum conditions include: 180 g/L sulfuric acid concentration and 4% solid/solid proportion manganese dioxide acts as an oxidant at 80 °C. The recovery of vanadium (V) metal ions was carried out using 3% Alamine 336 in kerosene at an equilibrium pH value of 0.9. Subsequently, 15% sodium sulfide solution was used for co-precipitation of nickel and zinc metal ions in the raffinate solution at pH value of 3.5.

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Passive aerated in-vessel composting of food waste with the addition of Sabah ragi for campus sustainability: mass balance,dwarf crape jasmine growth and techno-economic study

Journal of Material Cycles and Waste Management - Composting is a sustainable solution on campus for waste management as it is essential for achieving the ideal circular economy. This study aims to...     

Feature selection approaches for predictive modelling of cadmium sources and pollution levels in water springs

The World Health Organization lists cadmium (Cd) as one of the top ten chemicals of public health concern. Cd is toxic at relatively low exposure levels and has acute and chronic effects on both health and the environment. In this study, we investigate a suite of data-driven methods that could assist decision-makers in estimating Cd levels in water springs, and in identifying polluting sources. Machine learning (ML) regression models were used to identify sources of contamination and predict Cd levels based on support vector machines and a variety of tree-based models, including Random Forests, M5Tree, CatBoost, and gradient boosting. Feature selection analysis revealed that heavy traffic and distance to a major power plant in the sampled area play a leading role in springs Cd contamination, together with precipitation levels and average of slopes of the closest waste dumps upstream to sampled springs. Our best performing ML model was the Adaboost regression tree using all the features (RMSE = 19.36, R^2 = 0.64). Our findings highlight the effectiveness of predictive data-driven modeling in addressing environmental challenges, particularly in high-risk areas with low resources.