Metal-organic frameworks (MOFs) are widely investigated for water purification, yet conventional materials are often limited by saturated metal nodes that restrict active-site accessibility and by microporous channels that impede mass transport. Defect engineering provides a means to generate unsaturated metal sites and hierarchical porosity while preserving framework integrity. Quasi-MOFs occupy a distinct position within this landscape, retaining partial long-range order and local coordination environments of the parent MOF while incorporating controlled defects that yield high densities of coordinatively unsaturated sites and multimodal pore structures. In this review, we summarize synthetic strategies that enable precise control of defect type, density, and distribution in quasi-MOFs, including thermal activation, post-synthetic ligand exchange, and modulated coordination approaches. We examine advanced characterization techniques that reveal correlations between engineered defects and enhanced pollutant diffusion and catalytic activation. Applications in adsorptive removal and advanced oxidation/reduction processes are analyzed, highlighting performance advantages derived from improved site accessibility and transport kinetics relative to pristine MOFs. Finally, we discuss persisting challenges, including hydrolytic stability, scalable synthesis, and detailed structure-activity relationships, and outline future directions for translating quasi-MOFs into practical water-treatment technologies.

Freshwater reservoirs are critical for water management but face increasing impacts from climate change, which alters their thermal regimes and affects ecosystem functions globally. In temperate regions, surface water temperatures have risen at rates often surpassing those of air temperature, driven by atmospheric warming, hydrological processes, and reservoir morphometry. However, long-term studies on reservoir-specific thermal responses, particularly short-term variability, remain scarce. An important question is how environmental drivers influence both long-term warming trends and daily thermal fluctuations in managed water bodies. Here we show that over 31 years (1991–2021), surface water temperatures in 35 Czech reservoirs increased by an average of 0.59 °C per decade, with air temperature, altitude, and retention time as primary predictors of mean temperatures. A novel corrected metric for day-to-day variability (DTDV) revealed that inflow rate, depth, and retention time strongly influence short-term fluctuations, and DTDV trends positively correlated with warming rates, indicating linked drivers of thermal reorganization. Seasonal patterns showed strongest warming in April, with an anomaly of minimal change in May, likely tied to regional climatic shifts. These findings elucidate climate-driven thermal dynamics in reservoirs, highlighting the interaction of climatic and local factors. By combining statistical modeling with process-based indicators, this study informs adaptive strategies to mitigate impacts on water quality, stratification, and biodiversity under changing climates.

Non-point source pollution from agricultural activities poses a significant threat to water quality by introducing excess nutrients like nitrogen into aquatic ecosystems, leading to issues such as eutrophication and groundwater contamination. In agricultural watersheds, nitrate transport involves intricate physical, chemical, and biological processes influenced by meteorological conditions, hydrological features, and spatial topologies, making accurate short-term predictions challenging. Traditional data-driven deep learning models often fail to incorporate physical constraints and complex spatiotemporal dynamics, limiting their interpretability and predictive accuracy. Here we show a hierarchical transformer and graph neural network model that accurately predicts watershed nitrate concentrations by integrating multi-source data and simulating pollutant migration. The model captures nonlinear multivariate temporal patterns through hierarchical transformers, fuses global meteorological and local hydrological features via neural networks, and models runoff topologies with physically constrained graph neural networks. For predicting the concentration changes of pollutants discharged from watersheds, it outperforms baselines like multi-layer perceptrons, recurrent neural networks, and long short-term memory networks, with state-of-the-art performance in root mean square error, mean absolute error, and R2. Ablation studies confirm the essential roles of multi-source data integration and watershed topological modeling in enhancing performance. This method of directly modeling physical processes by leveraging the characteristics of different neural network architectures opens up a new path for addressing the interpretability problem in neural earth system modeling, apart from the process-guided deep learning and differentiable modelling methods.

Microbial contamination in building plumbing systems poses significant risks to public health at the point of use. Stagnation and warm temperatures are well-known drivers of microbial regrowth, but the effects of common short-term stagnation in touchless sensor faucets—widely used for hygiene and comfort—remain poorly understood. Here we show that microbial water quality in touchless sensor faucets changes during short-term stagnation (0.25–10 h) at varying temperatures (10, 30, and 40 °C). We identify two pivotal time points—2 and 4 h—where microbial diversity decreases and Legionella pneumophila concentrations increase significantly, driven by accelerated chlorine decay and biofilm contributions. Heating to 30 °C maximizes microbial biomass (measured as ATP) but minimizes L. pneumophila proliferation, whereas 40 °C reduces biomass while promoting L. pneumophila growth. These findings reveal a temperature-dependent microbial water quality guarantee period of 2–4 h, beyond which flushing is necessary to mitigate health risks. Optimizing faucet temperatures between 30 and 40 °C could balance microbial safety, user comfort, and energy efficiency, offering practical guidance for managing water quality in modern plumbing systems.