Journal of Environmental Engineering and Landscape Management
The Journal of Environmental Engineering and Landscape Management publishes original research about the environment with emphasis on sustainability. More information ...
Copyright © 2021 The Author(s). Published by Vilnius Gediminas Technical University. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Activated carbon loaded with Fe@Fe2O3 and CeO2 as a novel composite catalyst for heterogeneous electro-Fenton process
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.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Coupling of water-land resources and water governance in China’s Poyang Lake Urban Agglomeration
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.
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University. https://creativecommons.org/licenses/by/4.0
Insights into the entire building life cycle assessment (LCA): existing trends, challenges and prospective framework
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.
A novel 3D geomorphic change detection framework for large-scale active dump slope using multi-temporal imagery
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.
Analysing the route choices to access public open spaces in hill cities using space syntax approach
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.
Land transfer and green productivity synergies for sustainable agriculture
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.
green total factor productivity in agriculture
Intelligent layout and spatial planning of green urban buildings based on deep learning
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.
Modelling spatial and temporal changes in land use/cover of Marmara Island (Türkiye) using CA-Markov model
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.
A PSO-optimized fuzzy neural model for evaluating the coordinated development of agricultural economy and ecological protection
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.
Modeling the effect of pollutant gas on PM2.5 in China with computational intelligence
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.
From waste to resource: bibliometric evaluation of the use of banana pseudostems as biodegradable material
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.
Modeling sustainable environmental responsibility behavior of students in private university
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.
A deep learning-based algorithm for unitizing ecologically sensitive areas in rural landscapes
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.
Impact of financial development, trade, and energy consumption on environmental footprint in India: evidence from QARDL and wavelet coherence approach
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.
renewable and nonrenewable energy consumption
Does environmental decentralization promote green technology innovation? —Empirical evidence based on River Chief System
Based on the River Chief System policy characterized by Environmental Decentralization, a double difference model was used to empirically test the impact of Environmental Decentralization on Green Technology Innovation using panel data from 283 cities in China from 2005 to 2022. Research has found that there is a significant positive impact between Environmental Decentralization and Green Technology Innovation. Furthermore, the introduction of the spatial double difference model verifies the significant positive spatial spillover effect of Green Technology Innovation. Robustness testing is also conducted through a series of methods, such as replacing explanatory variables, to confirm the robustness and credibility of the results.
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University. http://creativecommons.org/licenses/by/4.0
Health sensitive decision-making for integrating artificial intelligence and landscape architecture in urban cities
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.
Generative Adversarial Networks (GAN) and Reinforcement Learning (RL)
Optimizing spatial placement of educational institutions using POI and geo-information
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.
Modeling soil retention, erosion potential, and sedimentation risk using the InVEST SDR model
This study conducts an examination of the Ilam watershed, utilizing the InVEST and SDR models to assess soil retention, erosion, and transport. It incorporates factors like rainfall erosivity, soil erodibility, DEM, land use, vegetation, and conservation practices to explore the complex interplay between ecosystem services (ES) and disservices. The study found that the average soil retention in the watershed is 94.5 tons/ha/year, the average erosion potential is 62.8 tons/ha/year, and the average sediment transport is 10.5 tons/ha/year. Forest areas retain a significant portion of sediment (60%) with low discharge (13%), while agricultural and urban regions contribute more to erosion. This highlights the importance of integrating ES into land management strategies to mitigate environmental degradation. The study highlights the crucial role of ES in maintaining ecological balance and supporting human well-being. It advocates for innovative policies and customized solutions to mitigate land use impacts on soil conservation and sediment retention, thereby fostering awareness among managers and decision-makers for more sustainable land use planning.
A Hybrid Fuzzy AHP-TOPSIS approach for green supplier selection: a case study in Sri Lanka
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.
Spatial pattern and its driving forces analysis of soil available nitrogen, phosphorus, and potassium in semi-arid grassland surface coal mining areas
Mining activities not only provide a large amount of material basis for socio-economic development but also cause great damage to the environment. It is of great significance to study the spatial variation characteristics of soil Available Nitrogen (AN), Available Phosphorus (AP), and Available potassium (AK) in mining areas for land reclamation and ecological protection. Currently, research on soil AN, AP, and AK in mining areas is lacking in large-scale survey, sampling, spatial pattern, and driving force research for surface coal mines in semi-arid grassland areas, and it is not possible to comprehensively grasp the distribution characteristics and driving forces of soil AN, AP, and AK in surface coal mines. Given this, this study took the Shengli Coal Field in Xilinhot City, the hinterland of Xilingol Grassland, as an example to study the spatial pattern and driving forces of soil AN, AP, and AK in the semi-arid grassland surface coal mining areas. The results showed that: (1) There was no strong spatial correlation among AN, AP, and AK in the soil of the study area, and the spatial pattern heterogeneity was strong. The content of AN, AP, and AK in southeast soil was relatively low. (2) The influence degree of each factor on the spatial pattern of AN was ranked as follows: NDVI > Agriculture > Water > Town > Mining > Industry; (3) The influence degree of each factor on the spatial pattern of soil AP was ranked as follows: NDVI > Mining > Water > Industry > Town > Agriculture; (4) The influence degree of each factor on the spatial pattern of soil AK was ranked as follows: NDVI > Town > Agriculture > Water > Mining > Industry; (5) The interaction between two factors presented two relationships: nonlinear enhancement and dual-factor enhancement. The interaction between various factors was higher than that of a single factor.
available nitrogen, phosphorus, and potassium
Sludge-based catalysts for the remediation of PAH-contaminated sediments
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.
Enhanced nitrogen removal and sludge characteristic in aerobic SBR by simultaneous nitrification and denitrification bacteria
Four efficient heterotrophic nitrification and aerobic denitrification (HN-AD) strains were applied in sequencing batch reactors (SBRs) via two bioaugmentation strategies to enhance the nitrogen removal and sludge characteristic. Synthetic domestic wastewater with NH4-N concentrations of 30~50 mg L–1, was treated in three SBRs, with DO maintained at 4±0.5 mg L–1. Compared with the control (crude activated sludge), bioaugmentation improved TN removal by 7% averagely, increased nitration rate by 0.54 mg g–1 h–1, and reduced sludge volume index at 30 min (SVI30) by 3.5~14.7 mL g–1. The maximum TN removal efficiency reached 50.37% with effluent TN concentration of 14.64 mg L–1, meeting China’s Class 1A discharge standard (TN ≤ 15 mg L–1). SBR started by bacterial suspension without activated sludge exhibited high adaptability to low carbon/nitrogen ratio, achieving 35% TN removal at C/N = 3 (vs <10% in control), with <5% MLSS fluctuation versus 30% decline in control. Microbial community analysis revealed Saccharibacteria dominance (15.34% vs control’s 8.48%) coupled with 7.6% reduction in filamentous Saprospiraceae (12.78% to 5.18%), collectively explaining the enhanced nitrogen removal and sludge settleability. This study provides the first evidence of granular sludge formation via HN-AD bacterial coaggregation under low C/N conditions, offering a novel strategy for energy-efficient wastewater treatment.
simultaneous nitrification and denitrification
Motivation to pangolin conservation among Gen Z: applying the extended Theory of Planned Behavior
The study explores the factors influencing pangolin conservation intentions among Gen Z using the extended Theory of Planned Behavior (TPB). Despite ongoing conservation efforts, pangolins remain one of the most trafficked animals globally, facing severe threats from illegal wildlife trade. To address this, the research investigates the roles of attitudes, subjective norms, and perceived behavioral control, along with knowledge and experience, in shaping conservation behaviors. A quantitative approach was employed, utilizing a structured questionnaire to gather data from 377 respondents. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for data analysis. The results show that subjective norms and perceived behavioral control are the most significant predictors of behavioral intentions, whereas attitudes, knowledge, and past experiences related to wildlife conservation or pangolins have a relatively weak influence. While they still have a positive effect, these factors do not influence the intention to engage in pangolin conservation behavior as strongly. These findings suggest that conservation campaigns should focus on leveraging social influences and enhancing individuals’ perceived control over their actions rather than solely increasing factual knowledge.
the extended of Theory of Planned Behavior
Changes in land use conflict and zoning optimization in rapidly developing mountainous cities: a case study of Guiyang, China
Mountainous cities, influenced by complex human activities and terrain, face severe land use conflict issues. However, the spatiotemporal characteristics of land use conflict changes and the scientific questions regarding their management in rapidly developing mountainous cities remain unresolved. Therefore, Guiyang, a typical mountainous city in China, was chosen as the study area. With the support of ArcGIS technology, this research analyzed land use conflict changes and conducted zoning optimization. The study reveals a rapid intensification of land use conflicts, with high conflict areas increasing by 369%, while moderate, general, and low conflict areas declined. The conflict between production and living spaces is the predominant one, expanding by 385%. Additionally, high conflict areas between production and ecological spaces rose by 760%. Integrating land suitability, conflict, and functional differences in zoning optimization enhances core functions, mitigates conflicts, and guides land use management. The results are valuable for optimizing land use patterns and promoting healthy urban development in mountainous cities.
Integration of ethnoecopreneuship, collaborative, and creative economy as an effort for sustainability of mangrove ecotourism
In the face of global tourism industry challenges, innovation in tourism services is essential for achieving sustainable development goals. This research aims to address the multifaceted challenges of sustainable mangrove ecotourism in Indonesia through an integrative approach combining ethnoecopreneuship, collaborative, and creative economy. Utilizing participatory rural appraisal (PRA) and focus group discussion (FGD), data analysis combines strengths, weaknesses, opportunities, threats (SWOT) analyses and analytic hierarchy process (AHP) method. The results emphasize the importance of community involvement, ecological considerations, effective promotion, and adoption of digital technology in the sustainability of mangrove ecotourism. The research calls for collaboration among stakeholders, policy emphasis on local government responsibilities, and active involvement of local communities and non-governmental organizations in mangrove conservation and management. This research underscores the potential for sustainable development, aligning with the sustainable development goals (SDGs) and enhancing economic and social outcomes in Indonesia’s mangrove ecotourism sector.
The impact of lakeshore modifications and constructions on visual landscape quality: a mixed methods study
Lakeshore areas continue to be threatened by increasing human activities and land use. Development and large construction projects in lakeshore areas affect both the lake’s ecological condition and its landscape quality and aesthetics. To minimize and prevent the occurrence of significant visual impacts, it is important to understand and evaluate the magnitude of damage and the factors contributing to such impacts from development activities. In this study, a mixed methods approach is used to assess the visual impact of modifications and constructions on the lakeshore landscape. This includes (1) an objective landscape indicator-based assessment method to measure the extent of construction and modification impacts on the visual landscape, and (2) a visual perception-based assessment method to capture receptors’ evaluations of the visual landscape changes and visual impact factors on the lakeshore. Integrating the results from both methods yields a comprehensive assessment of visual impact. The results of both assessment methods indicate that the visual quality of the lakeshore landscape declined significantly during the construction phase. In addition, this study concludes that this mixed approach to visual impact assessment has greater advantages than a single approach and provides more dimensional information, criteria, and perspectives.
Emergency risk assessment and rescue of groundwater pollution caused by mining based on 5G network
Recently, heavy metal pollution of soil, land degradation and groundwater pollution generated due to the lack of supporting environmental protection measure, which not only endanger human health, but also affect the sustainable development of mining industry. In order to avoid groundwater pollution, and improve risk assessment and rescue levels of underground water pollution, and a wireless remote emergency risk assessment and rescue system based on 5G core network is constructed, and emergency risk assessment model of groundwater pollution caused by mining is designed. The types of ground water pollution are analyzed. The emergency risk assessment of groundwater pollution is carried out, and the evaluation method of groundwater pollution is designed. A data mining algorithm is designed for optimizing 5G communication network. Taking groundwater in a mining area as the research object, 15 heavy metal indexes of 12 monitoring wells in the mining area were monitored and analyzed. The results show that proposed emergency risk evaluation and rescue of groundwater pollution caused by mining based on 5G network has better communication performance, indexes of as, Sb, Co, Fe and Mn in underground water exceed the standard. And then 5G core network construction of groundwater pollution emergency risk assessment and rescue concludes user plane function/mobile edge computing (UPF/MEC) sink and independent private network is achieved. A case study is carried out using a coal mine as researching object, results showed that proposed remote emergency risk assessment and rescue of groundwater pollution has less time delay and packet loss rate, therefore proposed emergency risk assessment and rescue of groundwater pollution has better communication performance. Finally, the emergency rescue countermeasures of groundwater pollution are taken according to evaluation results.
Study on suitability assessment method of vegetation restoration species in disturbed sites of transmission line engineering construction
Screening species that can adapt to the disturbed habitats resulting from transmission line construction is of great practical significance for the ecological restoration of primary vegetation degradation caused by such construction. Therefore, a suitability evaluation method for vegetation restoration species in disturbed areas of transmission line engineering construction is studied to improve the ecological restoration. Based on the principles of stability and durability, adapting to local conditions and trees, and comprehensive and dominant screening, an index system for evaluating the adaptability of vegetation restoration species is established. Determine the subjective weight of evaluation index by G1 method and G2 method. The objective weight of evaluation index is determined by improving entropy weight method and deviation method. Through the optimal combination weighting method, the final evaluation index weight is obtained by combining subjective weight and objective weight. Using cloud model combined with index weight to calculate the certainty of each index. According to the principle of maximum certainty, the evaluation grade corresponding to the maximum certainty is selected as the final suitability evaluation result. The experiments have demonstrated that the suitability evaluation index system of this method exhibits high reliability, and it can effectively determine the weights of the suitability evaluation indices and complete the suitability assessment for vegetation restoration species. Among these species, Elymus chinensis, Panicum miliaceum, Brassica napus, arugula, and rye exhibit the most promising ecological restoration effects.
Heavy rainfall resilience: adoption of climate smart agriculture among marginal farmers in a sub-basin of India
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.
Landscape change and perception along historical railway: the case of Yunnan-Vietnam Railway, China
Against the background of environmental change, this study investigates the relationship between landscape changes and perceptions along the historical railway using a combination of Historical Geographic Information Systems (HGIS) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). After quantifying the changes by GIS, the research aims to understand how different types of landscape changes affect physical, psychological, and cultural perceptions among locals. By integrating historical maps with remote sensing data and questionnaire, the study provides a comprehensive analysis of landscape evolution over time. The findings reveal that cropland and urban areas both showed significant increases along the railway; forest and water body both decreased over 70 years. The presence of forest change led to significant cultural perceptions and cropland change influenced the psychological perceptions of landscape. This research contributes to the understanding of the interplay between landscape change and human perception, offering valuable insights for sustainable landscape management and heritage conservation.