Water

The coal mining subsidence area constitutes a distinct ecotone in the transition from agricultural soil to sediment, yet the microbially mediated nitrogen cycle within it remains inadequately understood. This investigation comprehensively analyzed physicochemical properties, microbial communities, functional genes, and co-occurrence networks along a 0–6500 mm depth gradient. Results indicated that pH transitioned from acidic to alkaline, while TN, TP, OM, and NH₄⁺–N accumulated with depth. NO₃⁻–N decreased rapidly within 1000 mm and then stabilized. Alpha-diversity showed an S-shaped increase in richness, with Shannon index peaking at 1500 mm. Beta-diversity shifted along PC1, and the shallow subsidence area (SS) influenced by NO₃⁻–N; the transition zone (TZ) regulated by OM, TN, and NH₄⁺–N; deep subsidence area (DS) was constrained by TP and pH. Microbial communities transitioned from aerobic/facultative to strictly anaerobic phyla, yet Pseudomonadota remained dominant (24–32%) across depths. With increasing depth, gene abundances for denitrification, assimilatory nitrate reduction to ammonium (ANRA), and nitrate assimilation declined, while those for dissimilatory nitrate reduction to ammonium (DNRA) and nitrification increased; nitrogen fixation remained weak. Co-occurrence networks shifted from highly connected, short-pathlength, and clustered in TZ to highly modular and long-pathlength in DS, with Aminicenantes, Syntrophus, and Methanoregula as key taxa. Overall, the thick and stable reducing zone in the subsidence area restructured the nitrogen cycle, shifting terminal products from N₂ removal to NH₄⁺ retention. These findings advance the understanding of nitrogen transformation in soil-sediment ecotones and provide a mechanistic framework for nitrogen cycling in mining-affected ecosystems.

Hydrochar is a carbon-rich material produced through the hydrothermal carbonization (HTC) of wet biomass such as sewage sludge. Its nitrogen content is a critical quality parameter, influencing its suitability for use as a soil amendment and its potential environmental impacts. This study develops a high-accuracy ensemble machine learning framework to predict the nitrogen content of hydrochar derived from sewage sludge based on feedstock compositions and HTC process conditions. Four ensemble algorithms—Gradient Boosting Regression Trees (GBRTs), AdaBoost, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)—were trained using an 80/20 train–test split and evaluated through standard statistical metrics. GBRT and XGBoost provided the best performance, achieving R² values of 0.993 and 0.989 and RMSE values of 0.169 and 0.213 during training, while maintaining strong predictive capabilities on the test dataset. SHAP analyses identified nitrogen content, ash content, and heating temperature as the most influential predictors of hydrochar nitrogen levels. Predicting nitrogen behaviour during HTC is environmentally relevant, as the improper management of nitrogen-rich hydrochar residues can contribute to nitrogen leaching, eutrophication, and disruption of aquatic biogeochemical cycles. The proposed ensemble-based modelling approach therefore offers a reliable tool for optimizing HTC operations, supporting sustainable sludge valorisation, and reducing environmental risks associated with nitrogen emissions.

Drinking water contamination during the treatment process remains a major problem for decision-makers responsible for the collection and supply of water to recipients. This article presents measurements of 33 parameters of drinking water quality in the years 2009–2023, taken from the Dobromierz reservoir in Poland, with particular emphasis on the stages of raw water, water undergoing treatment, and utility water. The results showed that the raw water tested is contaminated microbiologically (presence of coliform bacteria), organoleptically (worse turbidity, odor, color), and chemically (increased PAHs, nitrites, benzo(α)pyrene). This indicates improper maintenance of the areas around the reservoir, i.e., agricultural areas (the existing nutrient runoff), residential areas (the lack of stringent records of cesspools and septic tanks), and roadside (improper maintenance of ditch slopes). In most cases, water at the treatment stage and at the end recipients was effectively purified (in most cases, the analyzed parameters achieved a degree of compliance with drinking water standards of at least 95%). Only for the turbidity in the network, the standards did not reach the adopted minimum level. This suggests the need to conduct systematic investment activities in order to reduce failures in the network (average system failure rate of 34%). Moreover, the statistical analysis of the results showed significant changes in the parameters between raw water and water in the water supply network and at end recipients (p < 0.05). Therefore, it is necessary to focus on protecting the quality of raw water resources for more effective treatment and ensuring human health safety.