Search Results (2,192)

Driven by the need for sustainable and efficient energy systems, the optimal management of distributed generation, including photovoltaic systems and battery energy storage systems within prosumer households, is of crucial importance. This requires a comprehensive cost–benefit analysis to assess their viability. In this study, an optimization model formulated as a mixed-integer linear programming problem is proposed to evaluate the integration of battery storage systems for 10 prosumers on the radial feeder in Croatia and to quantify the benefits both from the prosumers’ perspective and that of the reduction in grid losses. The results show significant annual cost reductions for prosumers, totaling EUR 1798.78 for the observed feeder, with some achieving a net profit. Grid losses are significantly reduced by 1172.52 kWh, resulting in an annual saving of EUR 216.25 for the distribution system operator. However, under the current Croatian market conditions, the integration of battery storage systems is not profitable over the entire lifetime due to the high initial investment costs of EUR 720/kWh. The break-even analysis reveals that investment cost needs to decrease by 52.78%, or an inflation rate of 4.87% is required, to reach prosumer profitability. This highlights the current financial barriers to the widespread adoption of battery storage systems and emphasizes the need for significant cost reductions or targeted incentives. Full article

Background: Thallium is a metal that is ubiquitous in our natural environment. Despite its potential for high toxicity, thallium is understudied and not regulated in food. The California Department of Public Health was alerted to a household cluster of elevated urine thallium levels noted among a mother (peak 5.6 µg/g creatinine; adult reference: ≤0.4 µg/g creatinine) and her three young children (peak 10.5 µg/g creatinine; child reference: ≤0.8 µg/g creatinine). Objectives: This case report identifies questions raised after a public health investigation linked a household’s thallium exposure to a commercially available food product. We provide an overview of the public health investigation. We then explore concerns, such as gaps in toxicological data and limited surveillance of thallium in the food supply, which make management of individual and population exposure risks challenging. Methods: We highlight findings from a cross-agency investigation, including a household exposure survey, sampling of possible environmental and dietary exposures (ICP-MS analysis measured thallium in kale chips at 1.98 mg/kg and 2.15 mg/kg), and monitoring of symptoms and urine thallium levels after the source was removed. We use regulatory and research findings to describe the challenges and opportunities in characterizing the scale of thallium in our food supply and effects of dietary exposures on health. Discussion: Thallium can bioaccumulate in our food system, particularly in brassica vegetables like kale. Thallium concentration in foods can also be affected by manufacturing processes, such as dehydration. We have limited surveillance data nationally regarding this metal in our food supply. Dietary reviews internationally show increased thallium intake in toddlers. Limited information is available about low-dose or chronic exposures, particularly among children, although emerging evidence shows that there might be risks associated at lower levels than previously thought. Improved toxicological studies are needed to guide reference doses and food safety standards. Promising action towards enhanced monitoring of thallium is being pursued by food safety agencies internationally, and research is underway to deepen our understanding of thallium toxicity. Full article

Background: Concerns over women’s mental health have intensified globally, especially among mothers managing dual careers and family responsibilities. Positive Psychology Interventions (PPIs), such as gratitude journaling and well-being workshops, have demonstrated promise in enhancing mental health; however, their applicability in Arab contexts remains underexplored. This study aims to investigate the effectiveness of PPIs on mothers’ well-being, gratitude, depression, anxiety, and stress in Saudi Arabia. Methods: This quasi-experimental, one-group pretest–posttest study assessed the effects of a four-week PPI on 37 Saudi working mothers (aged 21–50 years) employed at a private school in Riyadh. The intervention included guided gratitude journaling thrice weekly and two workshops on positive psychology and gratitude. Pre- and post-intervention assessments used validated Arabic versions of the Depression Anxiety Stress Scale (DASS-21), WHO-5 Well-being Index, and Gratitude Questionnaire (GQ-6). Results: Significant improvements were found post-intervention: depression, anxiety, and stress scores decreased (p < 0.001), while well-being and gratitude increased (p = 0.001). However, participants with lower household income (<50,000 SAR) showed less improvement, indicating a potential moderating effect of socioeconomic status. Conclusions: The intervention demonstrated promising short-term improvements in mental health and well-being among Saudi mothers. The findings underscore the importance of culturally appropriate PPIs and highlight the need for further research using controlled, long-term designs. Limitations include the small, non-random sample, absence of a control group, and restriction to a single geographic region. Full article

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. Full article

Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m², and those in the high-rise and low-rise building zones were 51.28 m² and 300 m², respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones. Full article

Water is essential for ecological balance, environmental sustainability, and food security, particularly in arid regions where effective water management increasingly depends on farmer cooperation. The Samarkand region of Uzbekistan, known for its favorable climate and leading role in grape production, is facing rising drought conditions. This study explores the factors influencing grape farmers’ willingness to collaborate on water management in the districts of Ishtikhan, Payarik, and Kushrabot, which together produce 75–80% of the region’s grapes. A quantitative survey of 384 grape-producing households was conducted across 19 county citizens’ gatherings (38.7% of such gatherings), and structural equation modeling was employed to analyze a framework consisting of four dimensions: norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices. The results indicate that norms and environmental concerns positively influence collaboration, suggesting a collective orientation toward sustainability. In contrast, economic barriers such as high costs and limited financial capacity significantly hinder cooperative behavior. Furthermore, a strong individual intention to adopt sustainable practices was associated with a greater likelihood of collaboration. These findings highlight the critical drivers and constraints shaping collective water use in agriculture and suggest that targeted policy measures and community-led efforts are vital for promoting sustainable water governance in drought-prone regions. Full article

The sustainable management of Municipal Solid Waste (MSW) relies heavily on community participation in separating it at the source and delivering it to collection systems. These practices are crucial for reducing pollution, protecting ecosystems, and maximizing resource recovery. However, in the Global South context, with conditions of socioeconomic vulnerability, community participation in the sustainable management of MSW remains limited, highlighting the need to generate context-specific interventions. MSW includes items such as household appliances, batteries, and electronic devices, which require specialized handling due to their size, hazardous components, or material complexity. This study implemented a Community-Based Social Marketing approach during the research and design phases of an intervention focused on promoting source separation and management of hard-to-manage MSW in five municipalities within the administrative region of Norte de Santander (Colombia), which borders Venezuela. Using a mixed-methods approach, we collected data from 1775 individuals (63.83% women; M _age = 33.48 years; SD = 17.25), employing social mapping, focus groups, semi-structured interviews, participant observation, and a survey questionnaire. The results show that the source separation and delivery of hard-to-manage MSW to collection systems are limited by a set of psychosocial, structural, and institutional barriers that interact with each other, affecting communities’ willingness and capacity for action. Furthermore, a prediction model of willingness to engage in separation and delivery behaviors showed a good fit (R2 = 0.83). The strongest predictors were awareness of the negative consequences of non-participation and perceived environmental benefits, with subjective norms contributing to a lesser extent. Based on these results, we designed a context-specific intervention focused on reducing these barriers and promoting community engagement in the sustainable management of hard-to-manage MSW. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Housing affordability is a major determinant of quality of life. Despite the relatively high GDP per capita in Hong Kong (HK) (USD 460,000), about one-third of the population lives in public rental housing (PRH) because they cannot afford private housing. Existing research estimating the benefits of PRH or direct housing supports faces methodological limitations. Addressing this research gap, our study adopts an “in-kind subsidy” approach to estimate the monetary value of PRH, quantifying how much less PRH households might save monthly if they resided in private rental units, after controlling for confounding factors. This paper examines the association of housing types and household savings by applying ordinary least squares (OLS) regression to compare savings among PRH tenants, non-PRH tenants, and mortgage-free homeowners, based on seven rounds of the Household Expenditure Survey data (1989/1990 to 2019/2020). PRH tenants saved significantly more than private housing tenants. In terms of household savings, the value of HK PRH has steadily increased from HKD 4483 in 1999/2000, to HKD 9187 in 2019/2020. For every dollar increase in income, a household would have the propensity to save 0.7 dollars in 2019/2020. Given limited public resources, our findings offer robust evidence regarding the value of public housing. The results underscore the importance of the equitable allocation and effective management of current PRH stock to enhance the upward mobility of low- to middle-income households amid limited housing resources in HK. Full article

The construction and development of digital infrastructure have emerged as a crucial indicator of national competitiveness, which holds significant importance in driving the sustained growth of the national economy and the comprehensive advancement of society. This paper explores the impact of digital infrastructure on farm households’ scale management, aiming to reveal the role and potential of digital technology in agricultural modernization. Additionally, it seeks to offer a scientific foundation for the government to formulate agricultural policies and advance agricultural modernization. Using the OLS (Ordinary Least Squares) model, moderating effect model, and other methods, this study investigates how digital infrastructure affects farm households’ scale management based on micro-level research data of 2510 farm households from the CRRS (China Rural Revitalization Survey). The following conclusions are drawn: Firstly, the enhancement of digital infrastructure can motivate farm households to expand the land management area and increase the unit output of land. Secondly, farm households’ digital literacy positively moderates the effect of digital infrastructure on their land unit output; moreover, digital skills training for farm households exhibits a positive moderating effect on the influence of digital infrastructure on their management area. Finally, there is a heterogeneity in the impact of digital infrastructure on farm households’ scale management. Specifically, the promotion of farm households’ scale management is stronger in plain areas and weaker in hilly and mountainous areas; stronger for middle-aged and older and small-scale farm households; and weaker for youth groups and large-scale farm households. Based on this, this paper suggests increasing the investment in digital infrastructure construction, improving farm households’ digital literacy, carrying out digital skills training, and formulating differentiated regional policies for reference. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

The burgeoning crisis of plastic waste accumulation necessitates innovative approaches towards sustainable packaging solutions. Polylactic acid (PLA), a leading biopolymer, emerges as a promising candidate in this realm, especially for environmentally friendly packaging. PLA is renowned for its compostable properties, offering a strategic avenue to mitigate plastic waste. However, its dependency on specific industrial composting conditions, characterized by elevated temperatures, humidity, and thermophilic microbes, limits its utility for household composting. This study aims to bridge the research gap in PLA’s recyclability and explore its feasibility in mechanical recycling processes. The research focuses on assessing the mechanical characteristics of PLA and PLA-based composites post-recycling. Specifically, we examined the effects of two extrusion methods—conical and parallel—on PLA and its composites containing 20 wt.% basalt fibers (BF). The recycling process encompassed repeated cycles of hot melt extrusion (HME), followed by mechanical grinding to produce granules. These granules were then subjected to injection moulding (IM) after 1, 3 and 5 recycling cycles. The tensile properties of the resulting IM-produced bars provided insights into the material’s durability and stability. The findings reveal that both PLA and PLA/BF composites retain their mechanical integrity through up to 5 cycles of mechanical recycling. This resilience underscores PLA’s potential for integration into existing recycling streams, addressing the dual challenges of environmental sustainability and waste management. The study contributes to the broader understanding of PLA’s lifecycle and opens new possibilities for its application in eco-friendly packaging, beyond the limits of composting. The implications of these findings extend towards enhancing the circularity of biopolymers and reducing the environmental footprint of plastic packaging. Full article

Groundwater contamination by nitrates (NO₃⁻) and nitrites (NO₂⁻) is an urgent problem in rural areas of Eastern Europe, with profound public health and sustainability implications. This paper presents an integrated assessment of groundwater vulnerability and water quality in rural wells in the Ceanu Mare commune, Cluj County, Romania—a representative area of the Northern Transylvania Basin, characterized by diverse geological structures, intensive agricultural activities, and incomplete public water infrastructure. This study combines detailed hydrochemical analyses, household-level studies, and geological context to identify and quantify key factors influencing nitrate and microbial contamination in rural wells, providing a comprehensive perspective on water quality challenges in the central part of Romania. This study adopts a multidisciplinary approach, integrating detailed geotechnical investigations conducted through four strategically located boreholes. These are complemented by extensive hydrogeological and lithological characterization, as well as rigorous chemical and microbiological analyses of nearby wells. The results reveal persistently elevated concentrations of NO₃⁻ and NO₂⁻, commonly associated with inadequate livestock waste management and the proximity of manure storage areas. Microbiological contamination was also frequent. In this study, the NO₃⁻ levels in well water ranged from 39.7 to 48 mg/L, reaching up to 96% of the EU/WHO threshold (50 mg/L), while the NO₂⁻ concentrations varied from 0.50 to 0.69 mg/L, exceeding the legal limit (0.5 mg/L) in 87% of the sampled wells. Ammonium (NH₄⁺) was detected (0.25–0.34 mg/L) in all the wells, below the maximum allowed limit (0.5 mg/L) but indicative of ongoing organic pollution. All the well water samples were non-compliant for microbiological parameters, with E. coli detected in 100% of cases (5–13 CFU/100 mL). The regional clay–marl substrate offers only limited natural protection against pollutant infiltration, primarily due to lithological heterogeneity and discontinuities observed within the clay–marl layers in the study area. This research delivers a replicable model for rural groundwater assessment and addresses a critical gap in regional and European water safety studies. It also provides actionable recommendations for sustainable groundwater management, infrastructure development, and community risk reduction in line with EU water directives. Full article

Rural areas around the world are increasingly dealing with energy and environmental challenges. These challenges are particularly acute in developing countries, where persistent reliance on traditional energy sources—such as wood fuel—intersects with concerns about forest conservation and energy sustainability. While wood fuel use is often portrayed as unsustainable, it is important to acknowledge that much of it remains ecologically viable and socially embedded. This study explores the role of rural entrepreneurs in shaping low-carbon transitions at the intersection of household energy practices and environmental stewardship. Fieldwork was carried out in four rural Zambian communities in 2016 and complemented by 2024 follow-up reports. It examines the connections between household energy choices, greenhouse gas emissions, and forest resource dynamics. Findings reveal that over 60% of rural households rely on charcoal for cooking, with associated emissions estimated between 80 and 150 kg CO₂ per household per month. Although this is significantly lower than the average per capita carbon footprint in industrialized countries, such emissions are primarily biogenic in nature. While rural communities contribute minimally to global climate change, their practices have significant local environmental consequences. This study draws attention to the structural constraints as well as emerging opportunities within Zambia’s rural energy economy. It positions rural entrepreneurs not merely as policy recipients but as active agents of innovation, environmental monitoring, and participatory resource governance. A model is proposed to support sustainable rural energy transitions by aligning forest management with context-sensitive emissions strategies. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article