Measurement of the quality of water before usage is important; however, this is challenging in developing countries, where water testing equipment is expensive and inaccessible. A Water Quality Monitoring System (WQMS) was developed to ease these unsafe water usage practices and improved water quality monitoring and data management. WQMS consisted of 5 different water quality sensors built on Arduino microprocessors to measure multiple water quality parameters, pH, EC, TDS, temperature and turbidity values simultaneously. Water samples from 5 sampling points S1, S2, S3, S4, and S5 along River Ogun, Nigeria were tested and their Water Quality Status (WQS) was calculated using the Weighted Arithmetic Water Quality Index. WQMS has 78% efficiency and able to measure multiple WQIs. All studied parameters were significantly different, probably attributed to WQMS calibration methods and sensor performances. The WQS of the sampled water ranged from 26 to 50, which, according to standards, was within good water quality.
A novel heterogeneous electro-Fenton (HEF) composite catalyst based on activated carbon (AC) as the support material was prepared with the inclusion of Fe@Fe2O3 for its HEF catalytic activity and cerium oxide (CeO2) for its redox properties. The effect of different Fe/Ce molar ratios on the activity of the prepared catalysts was investigated. XRD, BET, FTIR, SEM, and EDS techniques were used to characterize the prepared catalysts. The catalyst activity was examined by its ability to degrade 50 mg/L of methylene blue (MB) at 20 mA/cm² and pH 3 within 150 min. A higher removal rate of 91% was achieved when AC+Fe/Ce (2:1) was used as a HEF catalyst. The results confirmed the synergy between Fe and Ce at a Fe/Ce molar ratio of 2:1. This improved catalyst was used to remove chemical oxygen demand (COD) from petroleum refinery wastewater. The optimum conditions were a current density of 10 mA/cm², pH of 3, and a catalyst dosage of 1 g/L, achieving a COD removal rate of 83.4%, with an energy consumption of 9.22 kWh/kg COD. Reusability testing confirmed the good catalytic activity of AC+Fe/Ce (2:1) catalyst after five cycles. Furthermore, the COD degradation over time was found to follow pseudo-first-order kinetics. These findings demonstrate that the prepared AC+Fe/Ce (2:1) catalyst is a highly efficient and energy-effective material for degrading organic pollutants in wastewater.
The scientific and efficient use of water and land resources is an inevitable requirement for China’s ecological civilization and achieve high-quality development. Investigating the spatiotemporal evolution of the utilisation efficiency of water and land resources in the Poyang Lake Urban Agglomeration, as well as the factors influencing water environment governance, can provide strategic support for the coordinated development of these resources in this urban agglomeration. This study examines the non-desired output Super-SBM model and the coupling coordination degree model to examine the spatiotemporal evolution of water and land resource utilization efficiency in the Poyang Lake Urban Agglomeration from 2009 to 2022. The results show that the efficiency of the utilization of water and land resources increased with spatial heterogeneity. The coupling coordination degree evolved from impending imbalance to medium-level coordination, with a spatial pattern of “high in the east and low in the west”. Urbanization, land resource utilization efficiency, and foreign investment positively affect water resource utilization efficiency, while industrial structure, agricultural mechanization, and economic development level have negative impacts. The negative effect of R&D expenditure and the positive effect of grade highway construction have a lag effect. Overall, although the mean coupling-coordination degree improved, it remained between “marginal” and “primary” coordination, indicating modest rather than high-quality synergy. This study therefore propose efficiency-based zoning and cross-city spillover compensation as replicable tools for other lake-type urban agglomerations.
Slope stability of dumps in the mining industry is a critical issue due to frequent incidents involving loss of life, equipment damage, and operational disruptions. This study presents an innovative methodology integrating unmanned aerial vehicle (UAV)-based 3D photogrammetric reconstruction, point cloud analysis, change detection, and numerical modeling to assess slope stability. High-resolution data were acquired using a DJI drone. Geometric features were analyzed at multiple scales using advanced point cloud techniques. A key advancement was the implementation of a Hybrid registration approach that yielded the lowest RMS error (0.642), which ensures precise spatial alignment. Change detection analysis identified a maximum displacement of 31.13 m in the active dumping zone. Numerical modeling of the critical section with a Factor of Safety (FOS) of 1.107, confirming near-instability conditions. This multi-modal approach not only improves assessment precision but also promotes proactive slope management, offering broad applicability across various geotechnical contexts.
Building sustainability assessment increasingly demands life cycle coverage that captures environmental burdens and trade-offs beyond operational energy, including embodied impacts and end of life (EoL) outcomes under circular economy (CE) pathways. This review synthesises recent research on how Life Cycle Assessment (LCA) supports the formation of Building Sustainability Index (BSI), with particular attention to the stage-dependent distribution of impacts and the growing significance of embodied and EoL stages in low-energy buildings, methodological sensitivities associated with system boundaries, datasets, and scenario definitions, and the implications of EoL modelling and allocation choices for CE-oriented sustainability claims. The paper further examines how life cycle thinking is operationalised within major certification schemes (LEED, BREEAM and DGNB) and discusses comparability limitations when certification outcomes are interpreted as sustainability indices. By consolidating methodological evidence and practice-oriented pathways, the paper clarifies requirements for robust, transparent and comparable Life Cycle Assessment (LCA) informed sustainability indices aligned with whole-life and circularity objectives.
The research proposes a novel DeepClusGAN, namely an intelligent combination of Deep Convolutional Generative Adversarial Network (DCGAN) and deep embedding cluster (DEC) to optimize urban building cluster layouts. DCGAN generates innovative and intelligent spatial layouts by learning patterns from existing urban residential building data sources and guarantees diversity and adherence to urban design principles. DEC analyzes the learned spatial features from the previous phase and performs clustering to identify functional zones and spatial relationships between metrics like proximity, compactness, and accessibility. The research maintains a spatial pattern and adjacency of building arrangements by optimizing objectives to meet urban planning goals, such as maximizing space usage, enhancing urban environmental growth, and promoting building architectural harmony. The research achieves an improved silhouette score, an adjusted rand index for validating the building cluster, and a 20.27% improvement in spatial diversity compared to existing models while maintaining an enhanced optimal building layout efficiency.
In this study, Geographical Information Systems and Remote Sensing techniques were used to analyse the temporal spatial dynamics of land use on Marmara Island for 1991, 2001, 2011, and 2023 years representing major socio-economic transitions in Türkiye nd to model future land use for 2055 using the CA–Markov approach. NDVI values derived from multi-temporal Landsat imagery were used to assess vegetation responses to LULC change. Results indicate substantial declines in forests (38.4%), grasslands (28.3%), shrublands (54%), olive groves (15.4%) and agricultural areas (56.4%) between 1991 and 2023, while mining areas increased by 52% and are projected to expand by an additional 46% by 2055. NDVI values (–0.26 to +0.69) show pronounced vegetation loss in northern mining zones and relatively stable agricultural–forest mosaics in the south. Overall, the findings demonstrate that mining driven anthropogenic pressures are reshaping the island’s ecological structure, underscoring the need for sustainable land management.
Public open spaces serve as vital components of urban well-being, offering spaces for recreation, social interaction, and environmental balance. In hill cities, however, the steep terrain and irregular street networks create distinctive challenges for pedestrian accessibility and route selection. This study investigates how users navigate and choose routes to access public open spaces (POS) in the hill city of Aizawl, Mizoram, employing the space syntax approach to analyze spatial configuration and pedestrian movement patterns. By examining route connectivity, visual integration, and topographic influence, the study identifies how spatial hierarchy and elevation differences affect pedestrian safety, comfort, and decision-making. The analysis inte-grates spatial metrics with field observations to reveal critical barriers and preferred pathways that influence accessibility and user experience. The findings highlight the need for designing safe, inclusive, and topographically sensitive pedestrian networks that enhance access to public open spaces. Ultimately, this research advances the discourse on three-dimensional urban morphology in hill cities and provides actionable insights for planners to improve pedestrian mobility and spatial equity in complex urban terrains.
This study employs computational intelligence techniques – gene expression programming (GEP), back-propagation neural network (BPNN), support vector regression (SVR) and linear regression (LR)–to model the quantitative relationship between pollutant gases (PGs) and PM2.5 concentrations using 2021 environmental data from 12 Chinese cities. A comparative analysis was conducted to evaluate model performance using the correlation coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results showed that the correlation coefficients (R) between predicted and actual PM2.5 concentrations ranged from –0.7579 to 0.9802 across all models. SVR and LR demonstrated the most robust performance, achieving high average R values of 0.8656 and 0.8671, respectively. LR also yielded the lowest average RMSE (0.12) and MAE (0.06) across the cities. GEP proved capable of finding highly accurate explicit models, achieving a maximum R of 0.9766. A key finding from the LR models is that CO and PM10 consistently had the most significant impact on PM2.5 concentrations. Correlation formulas derived from GEP and LR can support further PM2.5 analysis. These findings offer insights into PM2.5 formation mechanisms and inform pollution control strategies.
The promoting effect of agricultural land circulation on the enhancement of agricultural green total factor productivity (AGTFP) and the high-quality development of China’s agriculture still requires in-depth empirical verification. This study employs the super-efficiency SBM method to quantitatively evaluate AGTFP. Based on panel data from 30 provinces in China spanning the period 2005–2022, it conducts an empirical analysis using fixed-effects models and mediation-effects models. The main research conclusions are as follows: Firstly, the empirical findings indicate that agricultural land circulation has a positive impact on AGTFP, and this conclusion remains robust after undergoing endogeneity tests and robustness checks. Secondly, mechanism analysis reveals that agricultural land circulation effectively elevates the level of AGTFP by promoting economies of scale, accelerating the transfer of rural labor, and facilitating capital deepening, among other pathways (Hu et al., 2025). Meanwhile, economies of scale, labor mobility, and capital deepening also have common and synergistic effects. Thirdly, heterogeneity analysis demonstrates that the enhancing effect of agricultural land circulation on AGTFP is more pronounced in the eastern and central regions, major grain-producing areas, and cultivated regions. Based on the aforementioned research findings, it is recommended that China expedite the reform of its agricultural land circulation system to achieve a steady increase in AGTFP, thereby fostering the development of agriculture.
The optimization of urban infrastructure (UI) placement relies on the evaluation of the current situation, which refers to neighboring capacities of transport, retail, and landscape. These factors play a critical role in determining the holding capacity of EIs in urban environments and directly influence urban planning and resource allocation. The placement of educational institutions (EI) is critical for the sustainable development of a city because it regulates population movements and attracts services. Points of interest (POI) are novel and flexible instruments for landscape evaluation and optimization based on remote geographical information. Changchun was chosen as the study area, where POIs of EIs were collected through web scraping from five types of educational institutions (EIs): training centers, public services, preschools, schools, and colleges. This method allowed us to gather data on the spatial distribution and characteristics of these institutions systematically. Regional placement of EIs showed agglomerated distributions in six areas of interest (AOIs). Both retail numbers (food, hotel, and market) and road density (classes I–IV) had positive relationships with the number of EIs. Landscape metrics did not have a direct impact on the EI number, but areas of green space and impervious land had contrasting effects on the parameter estimates of the EI number of public services. Both retail numbers and road density also showed positive relationships with the capacity to hold EIs for public services per greenspace area and built-up land area. Compared with the current placement of EIs, the optimization scheme indicated that the number of EI providing public services was far higher than that held by surrounding retailers. In contrast, more schools should be planned in downtown areas because of the large gap in traffic capacity.
The coordinated development of agricultural economy and ecological protection is essential for achieving sustainable agriculture, as the resulting ecological benefits have significant implications for both environmental security and economic stability. However, existing ecological benefit evaluation models often suffer from limited indicator coverage and insufficient intelligence in weight assignment, making it difficult to capture the coupled relationship between ecological and economic dimensions. To address these issues, this study proposes a comprehensive evaluation model based on fuzzy logic and a particle swarm optimized backpropagation neural network (PSO-FBP). A multi-level indicator system integrating ecological and economic value is constructed, and fuzzy logic is introduced to manage uncertainty, while PSO enables adaptive weight optimization. The proposed model demonstrates strong learning capability and robustness, enabling a comprehensive quantification of agricultural ecosystem services. A case study of a province shows that waste treatment, agricultural production, and soil conservation are the main contributors to ecological value, confirming the model’s effectiveness in real-world agricultural contexts. This research provides a scientific and practical tool for ecological benefit assessment, offering valuable support for decision-making in sustainable agricultural policy.
Urban landscape architecture design plays an essential role in public health because it is influenced by several factors such as social connection, safety, walkability and amenities access. Traditional planning techniques are time-consuming and often result in suboptimal or aesthetically incoherent layout decisions, which diminish the accessibility and safety of landscape design. The research issue is addressed by integrating artificial intelligence (AI) techniques in architectural design to improve the overall aesthetic design while handling health decisions. This study uses Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to satisfy the health objectives via landscape layouts. The GAN uses the generator and discriminator to design the landscape layout that covers inputs such as functional requirements, site dimension, zone restrictions and health design principles. The combination of generator and discriminator helps to maximize the outcomes in injury prevention, mental restoration and physical activities. The generated layouts are further explored using RL rewards regarding usage, safety, and access, ensuring the layout appearance and aesthetics are directly tailored to health. The incorporation of new technology into landscape architecture can offer evidence-based approaches for constructing salutogenic landscapes. The scalable computational technique enables faster scenario evaluation, providing information for informed planning policies. Then, the excellence of the landscape layout is evaluated using experimental results.
The advanced persulfate oxidation method is known for its efficiency, stability, and capacity to breakdown organic contaminants without secondary contamination. In this study, biochar derived from sludge containing iron and manganese from domestic wastewater treatment was produced via pyrolysis and utilized to activate persulfate for the degradation of benzo(a)pyrene (BaP) in sediments. Results demonstrated that biochar pyrolyzed at 800 °C for 2 hours exhibited the highest catalytic performance. Under optimal conditions: a catalyst concentration of 1 g/L, persulfate concentration of 0.4 mM, and a reaction pH of 3, BaP removal efficiency reached 99.85% after 180 min. Characterization of the biochar showed that it was rich in Fe and Mn oxides, along with functional groups such as C-O-C, C=C, and C=O, which facilitated the adsorption and catalytic degradation of BaP. Quenching experiments revealed that the degradation of BaP was driven by reactive species such as •SO₄⁻ and •OH radicals, as well as singlet oxygen (¹O₂). The integration of radical dand non-radical pathways was essential for effective BaP removal. This research presents an innovative method for managing polycyclic aromatic hydrocarbons (PAHs) contamination in sediments by utilizing sludge-derived biochar in conjunction with activated persulfate.
The aim of this study is to analyze the impact of financial development, trade openness, renewable energy, and nonrenewable energy consumption on CO2 emissions in India by analyzing the quarterly data from 1980 to 2020. Quantile ARDL and Wavelet Coherence methods are employed to examine the nexus. In the long and short run, nonrenewable energy consumption, financial development, and trade openness have a positive impact on CO2 emissions. When emissions are already high, it suggests that financial development may also lead to increased CO2 emissions. Moreover, Renewable energy consumption has a negative impact on CO2 emissions irrespective of the emission level that whether it is high or low in the nations, which shows that if financial enhancement increases, carbon emissions decrease. Finally, we test the EKC hypothesis, and the QARDL findings support the EKC in India. Additionally, the wavelet coherence study found a causal relationship between the CO2 emissions and independent variables, and the findings under the Wald test reject the parameter constancy for all variables. To create effective policies for environmental deterioration, the empirical findings of the current analysis can be used as guidelines for policy implications.
The search for sustainable alternatives has positioned banana pseudostems as a promising material for the production of bioplastics, biopolymers, and eco-friendly composites. This bibliometric review of 119 publications (2003–2024) reveals growing academic interest in its use as a biodegradable material, with an annual growth rate of 8.91% and an average of 15.25 citations per article. Pseudostem fibers have superior mechanical properties, with a tensile strength of 458 MPa and a modulus of 17.14 GPa. Bioplastics derived from this biomass are not only viable alternatives to conventional plastics, especially in packaging, but also provide antibacterial and UV protection. In addition, these fibers function as efficient biosorbents, achieving up to 100% arsenic removal in water. Sentiment analysis of scientific abstracts indicates a predominant feeling of confidence and optimism, reinforcing the potential of banana pseudostems in sustainable industrial applications and highlighting their role in advancing the circular economy.
Using a novel integration of Environmental Literacy Theory, the Theory of Planned Behavior, and Sustainable Development Theory, this study models the determinants of environmentally responsible behavior (often termed organizational citizenship behavior for the environment [OCBE]) among undergraduates at private universities in Malaysia. Data were collected via stratified random sampling of 450 students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that environmental attitudes and a sense of responsibility partially mediate the effect of environmental knowledge on OCBE, highlighting their pivotal roles. These results suggest that educational interventions should not only impart environmental knowledge but also foster pro-environmental attitudes and responsibility. The framework provides guidance for educators and policymakers to design curricula and policies that align with Malaysia’s Sustainable Development Goals (SDGs) and national environmental education targets. By highlighting these mediating factors, the study offers actionable insights for universities and policymakers aiming to advance Malaysia’s sustainability education agenda and SDGs.
The ecological sensitivity of rural landscapes exhibits complexity and diversity. Traditional evaluation methods, which merely take into account a single factor or a limited number of factors, struggle to effectively manage uncertain information. This leads to inaccurate classification of ecological units in rural landscape ecological images, thereby undermining the precise assessment of the distribution of ecological sensitivity in rural landscapes. Therefore, a deep learning based algorithm for dividing rural landscape ecological sensitive areas is proposed. By selecting six major factors that affect the ecological sensitivity of rural landscape, such as geological environment, ecological and hydrological conditions, an ecological sensitivity evaluation index system is constructed, which is used as an input vector, and fuzzy neural network is used to output the ecological sensitivity of rural landscape; In addition, support vector machine is used to divide the ecological units of the collected rural landscape ecological images, and the division results and the sensitivity evaluation results of each unit of rural landscape are used as the input data of ArcGIS software to realize the visual presentation of the unit division results of rural landscape ecological sensitive areas. The results showed that with the increase of slope, the ecological sensitivity of rural landscape showed a trend of first increasing and then decreasing, the vegetation coverage rate decreased, and the ecological sensitivity of rural landscape showed a trend of gradually increasing; This algorithm can effectively evaluate the sensitivity of each unit of rural landscape, and visually present the unit division results of ecological sensitive areas of rural landscape. This algorithm can compare and analyze the changes of ecological sensitivity under different time dimensions.
Heavy rainfall is a significant challenge for marginal farmers in the aspect of sustainable agriculture. This research analyzed data from eight grid points over 42 years to determine rainfall criteria: 50.91 mm to 79.65 mm for heavy rain, 76.95 mm to 101.21 mm for extreme rain, and 101.21 mm for rare 24-hour occurrences. The vulnerability mapping found 58 agriculturally susceptible communities. Research shows cashew nuts, coriander, sugarcane, sweet potatoes, and turmeric are the five main crops in the 58 most susceptible villages to heavy rainfall. These villages contain a greater number of marginal farmers. The DELPHI method revealed that coriander is the most susceptible crop. In this study, climate-smart agricultural practices such as Integrated Pest Management methods, shifting crop seasons, and Meghdoot application projections are used to minimize the damages caused by heavy rainfall. This includes protecting crops before heavy rainfall and monitoring them after heavy rainfall. For emission reduction as one of the pillars of Climate Smart Agriculture, biochar from biomass breakdown without oxygen is suggested. A poll found that 44% of respondents would use social entrepreneurship for biochar kilns. As, a result 33 farmers from 7 villages used the suggested Integrated Pest Management Technique and Meghdoot to harvest their second season with minimum losses.
Green supplier selection is an important component of sustainable supply chain management, particularly in emerging markets where environmental rules and institutional support may be inadequate. This study addresses the need for a structured, sustainability-focused decision-making strategy by presenting a novel hybrid framework that merges the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The suggested methodology enables systematic evaluation of suppliers by including both subjective expert judgment and objective performance data under conditions of uncertainty. A case study conducted inside the Sri Lankan construction sector demonstrates the practical applicability of the concept. Suppliers were evaluated on four important criteria: environmental, economic, social, and technological. The data reveal that Supplier A is the most suited choice, excelling mainly in environmental and social dimensions. Environmental compliance emerged as the most relevant element in the selection process. By merging fuzzy language assessments with quantitative analysis, the hybrid Fuzzy AHP–TOPSIS method promotes the accuracy, transparency, and consistency of supplier evaluation. The results give valuable insights for industry practitioners, procurement managers, and legislators aiming to connect purchasing choices with long-term sustainability goals. Additionally, the framework gives an accurate platform for future research and application in similar scenarios, helping the growth of green purchasing practices in developing nations.
