Resources Environment and Sustainability最新文献

Urban water consumption has significant implications for both human welfare and the environment, especially in arid regions. In Saudi Arabia, where 60% of the urban water supply relies on energy-intensive desalination processes, the average per capita water consumption rate of 300 L/day is one of the highest worldwide. This study investigates potential behavioral patterns in residential water consumption among households in Saudi Arabia. A segmentation approach is employed to classify residents into clusters and evaluate their potential water consumption behavioral patterns. Data from 618 households were collected via a structured questionnaire and analyzed using descriptive statistics, principal component analysis (PCA), and cluster analysis (CA). Participants were categorized into six segments based on the relationship between their behaviors and water consumption derived from the factor analysis. The outcomes of PCA and CA analyses indicate a relationship between respondents’ socio-demographic factors and their potential behavioral patterns in residential water consumption. The findings highlight that education, household size, income, housing type, age, and nationality, in increasing order of significance, are the key factors influencing household water consumption and conservation tendencies. The paper concludes that comprehending residential water consumption patterns is crucial for effective interventions, social marketing strategies, and communication campaigns for behavioral changes. The study can inform water conservation policies and programs that promote sustainable water consumption practices, benefiting agencies, policymakers, and scholars in the water sector.

Rapid industrialization and urbanization generate significant waste, including fly ash, posing environmental challenges. Utilizing these materials is essential to mitigate impacts and promote sustainable development. This research paper focuses on the innovative application of geopolymeric fly ash (GEOFA) waste modified with titanium oxide (TiO₂) (GEOFA-TiO₂) as a promising adsorbent for the removal of phenol compounds from contaminated water. Analysis showed that the modification process resulted in the formation of functional groups such as -OH, which aided in the adsorption process. Adsorption studies were conducted to evaluate the influence of pH, temperature, and concentration on phenol removal. The maximum percentage removal of phenol was achieved at optimum conditions of pH 6 and a temperature of 25 °C, with an adsorption capacity of 166.7 mg/g. The adsorption process followed the Langmuir model, suggesting a monolayer adsorption mechanism and the thermodynamic studies revealed that the phenol adsorption onto GEOFA-TiO₂ was an endothermic process with a promising affinity between phenol and the adsorbent. This study demonstrates the effective use of modified fly ash-based adsorbents to remove phenolic contaminants. By repurposing waste materials, this research addresses waste management challenges and promotes sustainable practices, reveling their potential for environmental remediation.

Managing high water content sludge in wastewater treatment is crucial for sustainability. This involves a complex challenge of maximizing the removal of loosely bound extracellular polymeric substances (LB-EPS) while minimizing ultrasonic-Fenton process costs. This study introduces a novel approach to address these conflicting objectives by adopting fuzzy multi-objective optimization. This method reconciles the conflicting objectives by identifying the optimal conditions for ferrous ion (Fe${}^{2+}$) dosage, hydrogen peroxide (H₂O₂) dosage, and ultrasonication time. The optimization model incorporates empirical equations that define the effects of Fenton’s reagent and ultrasonication on LB-EPS removal, as well as considerations for material and electricity usage costs and the cumulative uncertainties associated with experimental runs. To effectively capture the trade-offs between EPS removal and process costs, the $\varepsilon$ -constraint method was utilized to delineate the Pareto front. This approach significantly enhances LB-EPS removal from anaerobically digested sludge and establishes boundary limits within the Pareto front for practical application within the context of fuzzy optimization. The optimized solution derived from this innovative approach resulted in the conditions of 10 mM Fe${}^{2+}$ dosage, 100 mM H₂O₂ dosage, and 10 min of ultrasonication. This configuration achieves an impressive 60.7% $\pm$ 3.7% LB-EPS removal while maintaining a cost of 26.6 USD/L and ensuring 100% overall satisfaction. This research represents a significant advancement in sludge dewatering strategies. It underscores the pivotal role of innovative decision-making approaches in advancing the field of sludge dewatering methodologies for more sustainable wastewater treatment practices.

Seawater intrusion (SWI) stands as a significant challenge impacting coastal aquifers worldwide. The emergence of SWI is a natural phenomenon owing to the density difference between seawater and freshwater. Even without any pumping, the seawater naturally advances inland until a hydrostatic equilibrium between the two water sources is reached. However, human interventions, such as excessive groundwater extraction, coastal developments, and land use as well as climate change intensify the SWI predicament. Efficient solutions to counteract the SWI problem involve the implementation of hydraulic barriers. By employing the Henry problem as a benchmark, the Finite Element Simulator (FEFLOW), which is a numerical modeling software, was used for simulating the flow and transport processes. An integrated FEFLOW-Python code was developed to optimize hydraulic barriers for mitigating SWI. The methodology aims to achieve the maximum possible reduction in SWI by iteratively adjusting the locations and rates of pumping and injection wells. Unlike other investigations, this study explores the utilization of negative SWI barriers to extract brackish water from the dispersion zone and potentially offer a cost-effective alternative water source for desalination plants’ intakes. For the Henry Problem, to maximize the reduction in of SWI, brackish water extraction wells should be situated near the center-bottom of the intrusion zone, with a pumping rate of 2.5 m³/day. For injection wells, the optimal location varies depending on the injection rate (I). Achieving the highest retardation (61%) occurs when the injection rate is 1 m³/day, positioned at the aquifer’s bottom and close to the coastal boundary. However, for injection rates below 0.7 m³/day, the injection well’s optimal location shifts towards the seawater intrusion toe. This study presents a novel approach by establishing explicit correlations for optimizing hydraulic barriers to effectively manage and mitigate SWI.

Banana cultivation plays a pivotal role in Tanzania’s agricultural landscape and food security. Precisely forecasting banana crop yield is essential for resource optimization, market stability, and informed policymaking, particularly in the face of climate change. This study employed time series and ensemble models to forecast banana crop yield in Tanzania, offering crucial insights into future production trends. We utilized Seasonal ARIMA with Exogenous Variables (SARIMAX), State Space (SS), and Long Short-Term Memory (LSTM) models, chosen based on regression analysis and data exploration. Leveraging historical banana yield data (1961–2020) and relevant climate variables, we formulated an ensemble model using a weighted average approach. Our findings underscore the potential of time series and ensemble models for accurate banana crop yield forecasting. Statistical evaluation metrics validate their effectiveness in capturing temporal variations and delivering reliable predictions. This research advances agricultural forecasting by demonstrating the successful application of these models in Tanzania. It emphasizes the importance of considering temporal dynamics and relevant factors for precise predictions. Policymakers, farmers, and stakeholders can leverage this study’s outcomes to make informed decisions on resource allocation, market planning, and agricultural policies. Ultimately, our research bolsters sustainable banana production and enhances food security in Tanzania.

Accurate and rapid censuses can provide detailed basic information for a country, which is useful for resource allocation, disease control, disaster prevention, urban planning, and business management. However, traditional censuses often take up much time, manpower, and financial resources. Population maps are created by national statistical institutes at statistical units. Remote sensing imagery combined with end-to-end deep learning models makes it possible to estimate a wide range of populations at a low cost. This study demonstrates the effectiveness of a local–global dual attention network (LGANet) for population estimation using remote sensing images. The LGANet contains a local attention embranchment and a global attention embranchment on the top of the backbone to adaptively learn and integrate two discriminative features simultaneously. To enhance the precision of population estimation, the outputs from the two attention modules are combined. This method utilizes daytime remote sensing images as input, complemented by nighttime light data, to estimate the population on 1 km grids. Our method exhibits superior accuracy compared to other deep learning methods, as evidenced by an experimental comparison between the estimated population and the ground-truth population in 1 km grids.

Managing mixed waste poses environmental challenges and source separation has been encouraged worldwide. By reviewing 279 relevant papers systematically on waste separation behavior, its definition, influencing factors, commonly applied theories, statistical models, and implications are presented in this paper. Firstly, the term waste separation behavior was defined compared to similar concepts. Taking account of the multi-level attributes of separation behavior, the determinants were divided into micro and macro factors. Among the micro factors, the factors with the most empirical evidence are norms, intention, convenience, and knowledge. Personality and affective evaluation are suggested to be included for future investigation. On the macro level, four factors are analyzed: policies, economy, culture, and market. Possible pathways for research and interventions are given to encourage relevant stakeholders to understand and promote waste separation behavior.

Exploring the impact of technological innovation on carbon efficiency is conducive to achieving a better low-carbon transition and thus reaching sustainable development goals. However, the academic community still has no consensus about the impact of technological innovation on carbon efficiency. China is a vast country with differences in resource endowment and economic development in different regions. Therefore, this paper first assesses the level of technological innovation and carbon emission efficiency in China through panel data of 30 provinces in China. Then, the PVAR (Panel Vector Autoregressive) model is utilized to explore the regional differences in the impact of technological innovation on carbon emission efficiency. The findings reveal that: First, the level of technological innovation in China shows a continuous development trend, but the level as a whole is relatively low. Technological innovation in western China is far behind that in eastern China. Second, China’s carbon emission efficiency is generally at a high level but shows a trend from rise to fall. The efficiency of carbon emission in eastern China is higher than in central China, and the efficiency of carbon emission in central China is higher than in western China. Third, the impulse response reveals that the influence of China’s technological innovation on the efficiency of carbon emission has experienced a change from negative impact to positive impact. The initial influence of technological innovation on carbon emission efficiency is negative, with the largest negative impact seen in central China (−0.100), followed by the eastern area of China (−0.050), and finally the western region of China (−0.005). After one period, technological innovation turned to have positive effect on the efficiency of carbon emission, with eastern China having the most positive impact (0.060), followed by central China (0.010), and western China ranking last (0.001). The above findings have implications for the formulation of technological innovation policies in different regions of China to improve the efficiency of carbon emissions in accordance with local conditions.

The literature review reveals that lean manufacturing tool selection models still have some gaps. These models lack the criteria for selecting LM tools. Only a few of these models adopted hybrid multi-criteria decision-making (MCDM) methods. Obtaining reliable criteria weights in these models is complicated. They lack the consideration of grey uncertainty. Thus, this study is the first to propose a hybrid model for selecting a set of LM tools based on their effect on sustainability. This model combines the best-worst method (BWM) for weighting the criteria and the grey technique for order of preference by similarity to the ideal solution (Grey-TOPSIS) method to rank the alternatives and address the grey uncertainty problem. A set of sustainability metrics (selection criteria) was determined based on a literature review and expert evaluation to prioritize a set of LM tools. An Iraqi cement company was utilized to evaluate the proposed model. The ranking results showed that the value stream mapping (VSM) tool was the most important, whereas the single-minute exchange of die (SMED) tool was the least important. The rankings of the remaining LM tools ranged between these two tools depending on their effects on sustainability. The study conducted a sensitivity analysis using three strategies that verified the model’s robustness and reliability. This research provides 16 applicable sustainability metrics and 12 LM tools that could function as a knowledge foundation for future research. It can help researchers and manufacturers maximize sustainability performance by delivering a hybrid MCDM model to select the appropriate LM tools.