Search Results (6)

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies. Full article

Air pollution, soil contamination, and rising illness demand integrated, nature-based solutions. Willow trees (Salix spp.) uniquely combine ecological resilience with therapeutic value, remediating polluted environments while supporting human well-being. This review synthesizes recent literature on the established role of Salix spp. in phytoremediation and growing contribution to forest therapy through emissions of biogenic volatile organic compounds (BVOCs). As urbanization accelerates and environmental pressures intensify globally, the surprising adaptability and multifunctionality of Salix justify the utilization of this genus in building resilient and health-promoting ecosystems. The major points discussed in this work include willow-based phytoremediation strategies, such as rhizodegradation, phytoextraction, and phytostabilization, contributing to restoring even heavily polluted soils, especially when combined with specific strategies of microbial augmentation and trait-based selection. Salix plantations and even individual willow trees may contribute to forest therapy (and ‘forest bathing’ approaches) through volatile compounds emitted by Salix spp. such as ocimene, β-caryophyllene, and others, which exhibit neuroprotective (against Parkinson’s disease), anti-inflammatory, and mood-enhancing properties. Willow’s significantly extended foliage season in temperate regions allows for prolonged ‘forest bathing’ opportunities, enhancing passive therapeutic engagement in urban green infrastructures. Remarkably, the pharmacological potential of willow extends beyond salicin, encompassing a diverse array of phytocompounds with applications in phytomedicine. Finally, willow’s ease of propagation and adaptability make this species a convenient solution for multifunctional landscape design, where ecological restoration and human well-being converge. Overall, this review demonstrates the integrative value of Salix spp. as a keystone genus in sustainable landscape planning, combining remarkable environmental resilience with therapeutic benefits. Future studies should explore standardized methods to evaluate the combined ecological and therapeutic performance of Salix spp., integrating long-term field monitoring with analyses of BVOC emissions under varying environmental stresses. Full article

The study aims to evaluate the demand for forest bathing in northern Italy’s regions, underlining the willingness to pay and the potential tourists’ habits and flows on attending alpine contexts. Several studies have analyzed the psychophysical benefits of forest bathing, and there is a growing interest in assessing its economic value for tourism purposes. The sample’s answers have been analyzed employing a stratified sampling methodology considering three different macro areas (northwest regions, northeast regions, and the Friuli Venezia Giulia region). After analyzing the sample’s attitudes, the research approached the estimation section by calculating the flow as the number of potential forest bathing hikes per year and the willingness to pay through the contingent valuation method. The main results indicate that forest bathing activity in northern Italy has a considerable monetary value, and it is generally compared with positive feelings, particularly concerning well-being and fascination factors. Full article

Forest-bathing experiences can be seen as guided recreational activities led by non-clinical trained practitioners in the context of green, slow, and mindful tourism. Notwithstanding its growing practice, there is a lack of research on the psychological benefits which can help support nature-based tourism destination managers in unlocking the potential of this emerging tourism demand. This study will fill in this gap by testing the hypothesis that a three-day forest-bathing retreat, which fits with the most common type of holidays in Europe, will enhance positive affect, vitality, optimism, and gratitude as indicators of hedonic and eudaimonic well-being. Forty-four adults were involved in the residential program and filled-in self-report questionnaires before and after the experience to assess the well-being dimensions considered. Results showed a significant increase for all the assessed variables. We conclude that forest bathing as a mindful tourism practice carried out in natural settings dominated by forests can favor hedonic and eudaimonic well-being, thus becoming a potential source of attractiveness for nature-based touristic destinations, but more transdisciplinary efforts are needed to exploit this potential. In particular, research gaps still exist in Europe on cause–effect relations between forest features and psychological benefits, how forests could be managed to guarantee these benefits to the advantage of a tourism destination, and how tourism and forest management sectors could collaborate in this direction. Full article

Cancer can have a significant impact on one’s life situation, with many patients reporting psychosocial discomfort, worry, anxiety, fear of recurrence, depression, tiredness/fatigue, sleep problems, pain and numerous other problems even long after active medical treatment. Psychosocial support during cancer rehabilitation has proven to be insufficient. In a recent debate article, the scientific committee of CancerRehabFund, Sweden, demands more rehabilitation alternatives for individuals living with cancer. Nature-based treatment is one of the alternatives mentioned as the way forward, but more research is needed. Therefore, we want to evaluate the patient’s experience of a ten-week forest bathing intervention, as an add-on to the standard care, and whether it can improve general health and well-being in women suffering or recovering from gynaecological cancer. The study will run between the autumn of 2022 and until the end of 2023. It is a prospective single-case study, including quantitative and qualitative approaches using validated self-administered instruments (pre–post measurements) and semi-structured interviews (post) on women’s lived experience of the 10-week forest bathing intervention. The quantitative outcome measurements will be the quality of life, fatigue and depression/anxiety. There will also be a questionnaire on perceived sensory dimensions experienced in the forest environment. The study will include 24 participants, divided into four groups of 6 participants. Once a week for ten weeks, the participants will be offered a session of a 2.5-hour stay in the forest with breathing exercises, slow movement, time in silence and privacy and a social gathering to conclude each session. Before and after each session, the participants will be invited to fill in the Profile of their mood state to describe their mood/feelings. There will be three different forest locations with varied forest cover types, i.e., evergreen, deciduous and mistands. Participation in this study will be voluntary, and all results will be anonymously presented on a group level. This paper is a protocol paper describing in detail the venues/forest sites, the forest therapy intervention and the scientific methodological approach for evaluating the ten-week intervention. To our knowledge, this is the first study on forest bathing for cancer survivors in Sweden. The Swedish Ethical Review Authority has approved the study [Dnr 2022-02083-01]. Full article

Water scarcity is a growing threat to humankind. At university campuses, there is a need for shared shower room managers to forecast the demand for bath water accurately. Accurate bath water demand forecasts can decrease the costs of water heating and pumping, reduce overall energy consumption, and improve student satisfaction (due to stability of bath water supply and bathwater temperature). We present a case study conducted at Capital Normal University (Beijing, China), which provides shared shower rooms separately for female and male students. Bath water consumption data are collected in real-time through shower tap controllers to forecast short-term bath water consumption in the shower buildings. We forecasted and compared daily and hourly bath water demand using the autoregressive integrated moving average, random forests, long short-term memory, and neural basis expansion analysis time series-forecasting models, and assessed the models’ performance using the mean absolute error, mean absolute percentage error, root-mean-square error, and coefficient of determination equations. Subsequently, covariates such as weather information, student behavior, and calendars were used to improve the models’ performance. These models achieved highly accurate forecasting for all the shower room areas. The results imply that machine learning methods outperform statistical methods (particularly for larger datasets) and can be employed to make accurate bath water demand forecasts. Full article