Detoxification of 4-chlorophenol in TiO2 sunlight system: effect of raw and treated solution on seed germination and plants growth of various sensitive vegetables

The mineralization and detoxification of 4-chlorophenol (4-CP) were studied using a TiO₂-paper/sunlight system. The possibility of reusing the photocatalyst was examined to determine the cost effectiveness of the method. Experiments were performed to establish optimum conditions for 4-CP removal. Phytotoxicity of photo-treated and raw 4-CP (100 mg L^?1) solutions on seed germination and plant growth were carried out with the aim of water reuse and environment protection. The seeds irrigated with raw 4-CP solution showed lower sprout length while increase in sprout length was observed with the photo-treated solution for tomato (Lycopersicon esculentum), lettuce (Lactuca sativa), onion (Allium cepa), and turnip (Brassica rapa). Plant growth tests with the photo-treated 4-CP solution did not affect the leaf numbers compared to those irrigated with tap water. Photo-treated 4-CP solution can be used for irrigation in agriculture.     

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.