Abstract With the acceleration of urbanisation, waterspace faces many challenges and problems. This study aims to explore the integration and system optimisation strategy of waterspace based on the overall development model of landscape system. Through the study of related theories and practical cases, the current problems of waterspace are analysed, and the principles and methods of integration and optimisation are proposed. Taking specific waterspace as an example, an empirical study was conducted and the optimisation effect was evaluated. The results show that the integration and optimisation of waterspace based on the overall development model of landscape system can effectively enhance the ecological, social and economic values of waterspace and promote the sustainable development of cities.

Abstract Wild edible mushrooms are a popular food considering their nutritional value. However, some mushroom species can harm human health by accumulating some elements excessively. To evaluate the pollution level of toxic elements in wild edible and non-edible mushrooms from two private natural areas in the Altos de Cantillana mountain range in Central Chile (Altos de Cantillana Natural Reserve and Cerro Poqui Nature Sanctuary) present in them were quantified. All mushrooms contained Pb, Zn, Fe, Cu, and Ni. Mushrooms obtained in Los Altos de Cantillana have higher amounts of metals. In mushrooms of the type Bovista brunnea (sample 27) there are maximum amounts of Pb (566.8 μg/g), Zn (1152.3 μg/g), and Cu (568.6 μg/g) while those of the type Lycoperdon sp. (sample 14) have maximum amounts of Fe (17806.9 μg/g) and Ni (27.6 μg/g). On the other hand, only the species Stereum hirsutum (samples 1 and 4) has very low amounts of As (3.9 μg/g and 6.5 μg/g) and only this one and Phaeoclavulina flaccida contain low amounts of Cd (0.02 μg/g and 0.04 μg/g). On the other hand, Sb and Au were not found in any sample; all values were < LOQ (Limit of quantification). Although intraspecies differences were observed, not all were significant. It is important to highlight the analysis of wild mushroom species that people can consume, such as the genus Cyttaria, which should be evaluated for trace element content.

Abstract This study presents an advanced real-time monitoring system integrating wireless sensor networks with machine learning to assess water quality in the Gulf of Aqaba. Our hybrid machine learning framework combines Random Forest (500 trees, node size = 5) for feature selection with Artificial Neural Network ensembles (3-layer MLP with Monte Carlo dropout) for probabilistic forecasting. The system continuously monitors six critical parameters, demonstrating strong predictive performance through rigorous validation: dissolved oxygen (R² = 0.92, RMSE = 0.45 mg/L, 95 % CI (Confidence interval): 0.41 - 0.49), nitrite (R² = 0.85, RMSE = 0.08 mg/L, CI: 0.07 - 0.09), and turbidity (R² = 0.89, RMSE = 2.3 FTU, CI: 2.1 - 2.5). Comprehensive uncertainty analysis revealed prediction intervals of ±0.38 mg/L for DO and ±0.10 mg/L for nitrite, with spatial variability lowest in open waters (CV = 8.2 %) and highest near coastal zones (CV = 15 %). Residual autocorrelation analysis confirmed model reliability (Moran’s I < 0.12, p > 0.05) across the study area. Spatial-temporal analysis identified nitrite as a sensitive pollution indicator, with concentrations reaching 0.12 mg/L near urban outflows compared to background levels (< 0.05 mg/L). The system achieved 92 % accuracy in the early detection of environmental risks, including coral bleaching precursors (temperature anomalies > 1 °C) and pollution events (nitrite spikes > 0.1 mg/L). Compared to conventional monitoring, the platform demonstrated 20.4 % greater predictive accuracy (ΔR² = +0.17, p < 0.01) while reducing operational costs by 30.2 %, primarily through automated data collection and reduced manual sampling. The integration of high-frequency sensing, adaptive machine learning, and cloud-based analytics establishes a replicable framework for coastal ecosystem management, particularly in anthropogenically stressed environments like the Red Sea.

Abstract Oil contamination of soils remains an acute environmental problem, particularly in oil-producing regions such as western Kazakhstan. In this study, we explored whether a microbial consortium - specifically Rhodococcus erythreus AT7 and Dietzia maris 22K - could work better when immobilised on agricultural waste like buckwheat and rice husks for cleaning up oil-contaminated soils. The adsorption immobilisation method was applied and compared with free cell systems over 45 days using model soils contaminated with crude oil from the Karazhanbas field. Our results showed that when we immobilised cells on buckwheat and rice husks, they achieved significantly higher TPH degradation (58.4 ±3.7) % and (52.1 ±4.2) %, respectively) compared to free cells. Kinetic modelling revealed first-order degradation kinetics (R 2 > 0.95) with rate constants of 0.0187 d– 1 for buckwheat husks, 0.0162 d– 1 for rice husks, and 0.0108 d– 1 for free cells, representing 73 % enhancement for buckwheat husk. Why did this work so well? We found several reasons: excellent cell retention (92.3 ±2.1) %, better moisture retention (55 % compared to just 38 % in controls), and - perhaps most interestingly - favourable chemical properties, especially higher antioxidant content. Under optimal conditions (10 % carrier ratio, 60 % - 70 % moisture, pH 7.0 - 7.5), projected cleanup timelines of 3-4 months are achievable. These findings suggest that agricultural waste carriers represent a promising and cost-effective approach for bioremediation of oil-contaminated soils.

Abstract In the era of Industry 4.0, accurate prediction of industrial process parameters is essential for optimising operations, lowering costs, and enhancing product quality. Traditional statistical methods often struggle to capture the complex temporal dependencies within industrial processes. This study explores the use of Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Q-Network models to predict material quantities in an industrial dataset. The dataset was pre-processed to address missing values and outliers, and the models were evaluated based on Mean Squared Error (MSE), R 2, and accuracy. The results show that the LSTM model achieved an MSE of 14.253 and an R 2 of 0.700. The BiLSTM model greatly outperformed it, with an MSE of 0.714 and an R 2 of 0.985. The Q-Network model produced an MSE of 0.005 and an R 2 of 0.992. These findings demonstrate the Q-Network’s superior ability to capture temporal dependencies within the data.

Abstract Sustainable and equitable utilisation of natural resources without exceeding the planetary boundaries is imperative for one and all as envisioned in the UN-SDGs. With rapid urbanisation, cities are the main human settlements, so adaptation to climate-resilient and livable cities is an important theme with considerations of Good Health and Well-being (SDG#3), Quality Education in Sustainability Science (SDG#4), Sustainable Cities and Communities (SDG#11) and Climate Actions (SDG#13) under the UN Sustainable Development Goals. The Earth’s climate is changing, and now it stands at a position of 1.0 °C - 1.1 °C above pre-industrial level. Climate change alteration is due to anthropogenic activities resulting in loss of biodiversity and an altered biosphere by man, termed Noosphere. Unfortunately, society is not well-prepared to act for adaptation due to limited understanding of the impacts of climate change on human health and safety, lack of awareness and knowledge of climate-induced risks, vulnerabilities and solutions, and slow actions for climate adaptation transformation at the nexus of water, energy and food. Novel solutions for sustainable interaction between climate-controlled energy and food nexus include international cooperation, sustainable economic growth, increasing climate mitigation strategies, using efficient resource management, pollution abatement and to developing technological advancements such as machine learning.

Abstract Cadmium, a toxic element, poses significant environmental and health risks, especially when released into water systems. Mining and natural processes contribute to elevated cadmium levels in surface waters, often surpassing permissible limits set by the Water Framework Directive. This study investigates the potential of natural limestone as a cost-effective and sustainable adsorbent for cadmium removal from aqueous solutions. Cadmium concentrations were analysed using inductively coupled plasma optical emission spectrometry (ICP-OES). Adsorption isotherms and the influence of pH, particle size, adsorbent dosage, and competing metals were examined. The results showed that cadmium adsorption efficiency increased with pH and decreased with larger particle sizes or higher metal concentrations. Under optimised conditions, the maximum adsorption capacity was determined to be 8.87 mg/g, indicating limestone’s suitability for cadmium removal. Further application in acidic mining waters demonstrated lower removal efficiency due to competitive sorption, suggesting the need for process optimisation.

Abstract Biostalactite produced as a result of the activities of the insectivorous bat species Eptesicus serotinus is described. A guano (bat faeces) deposit was found in Poland, which suggests the possibility of creating such structures not only in warm and arid climatic zones but also outside caves in temperate climate countries. The research revealed that the temperate climate bat stalactite is made of urea, accompanied by a small and variable amount of non-crystalline organic substance that comes from bat guano. Lack of contact with cave minerals is a way to save the original composition without intrusions of calcite or other minerals which come from the bottom or vault of the cave. An X-ray diffractometric analysis shows a number of dominant minerals in crystalline form present in the examined material. Detected minerals are mainly taranakite H6K3Al5(PO4)8·18H2O, urea crystals and ammonium aluminium hydrogen phosphate Al2(NH4)OH(PO4)2·H2O. Formation of bat biostalactites is often observed in hot and dry caves, but the temperate climate in Poland eliminates this phenomenon due to high air moisture and quick microbiological digestion processes.

Abstract The objective of this study was to evaluate the efficiency of the emulsion liquid membrane (ELM) technique for cadmium removal from aqueous solutions through systematic assessment of key operating parameters. Kerosene was used as the diluent, sorbitan monooleate (Span 80) as the surfactant, Di-(2-ethylhexyl) phosphoric acid (D2EHPA) as the carrier, and hydrochloric acid (HCl) as the stripping agent. The effects of surfactant concentration, agitation speed, internal to membrane phase ratio, emulsion to feed ratio, and HCl concentration in the internal phase were investigated. The best conditions yielded a maximum cadmium removal efficiency (η Cd) of 97.4 % at 4 % surfactant concentration, 450 rpm agitation speed, 1 : 4 internal to membrane ratio, 1 : 3 emulsion to feed ratio, and 0.25 M HCl. The results of cadmium removal follow the second-order kinetic model. The results demonstrate that the developed ELM system is an effective and rapid method for cadmium removal, with strong potential for application in industrial wastewater treatment and heavy metal remediation.

Abstract This study presents an innovative method for predicting ambient temperature fluctuations using a robust mathematical model that incorporates both environmental pollutants and other meteorological factors across different months. The primary objective was to establish a correlation model that integrates variables such as carbon monoxide, CO, nitrogen dioxide, NO2, carbon dioxide, CO2, ozone, O3, along with model-specific parameters like baseline temperature, Y 0, phase shift, X c, angular frequency, w, and amplitude, A). Data was collected monthly, from January to June, to analyse the direct impact of these pollutants and parameters on ambient temperature. The correlation constants calculated for each month demonstrate how environmental conditions and pollution levels dynamically influence temperature predictions. Initial findings reveal significant variations in the constants that correlate with changes in pollutant concentrations, suggesting a sensitive interplay between environmental quality and temperature. This study enhances our understanding of temperature dynamics in urban settings and could contribute to more effective environmental monitoring and climate management strategies. The approach underscores the importance of integrating comprehensive environmental data in predictive models to better anticipate temperature changes and potentially mitigate adverse climate impacts.