Search Results (7,044)

Municipal sludge generation has increased rapidly with urbanization, creating significant challenges for sustainable waste management. This review proposes a system-oriented framework for sludge resource utilization by linking sludge characteristics, conversion technologies, environmental risks, and product applications. Major treatment pathways, including anaerobic digestion, pyrolysis, ozonation, and hydrothermal carbonization, are critically compared, with emphasis on their inherent trade-offs between resource recovery, energy consumption, and contaminant control. Particular attention is given to emerging contaminants, such as microplastics, per- and polyfluoroalkyl substances (PFAS), and antibiotic resistance genes, where the distinction between pollutant removal and actual risk reduction remains insufficiently addressed. The review highlights that no single technology can achieve optimal performance under all conditions, and integrated treatment trains are generally required for sustainable sludge management. Among these pathways, pyrolysis shows considerable potential for applications requiring enhanced contaminant control and value-added biochar production due to its ability to promote organic contaminant degradation, heavy metal immobilization, and carbon storage. However, the feasibility of pyrolysis and other technologies depends strongly on site-specific factors, including sludge properties, energy availability, economic conditions, and regulatory requirements. Future research should focus on integrated process optimization, comprehensive pollutant fate assessment, and standardized evaluation frameworks to advance sludge management toward a circular economy. Full article

Estuaries support distinct spawning and nursery habitats for migratory fishes, yet the differential environmental response patterns between these two critical early life habitats remain poorly understood from a spatial non-stationarity perspective. Based on six ichthyoplankton surveys conducted during peak and late spawning seasons from 2018 to 2020 in the Yangtze Estuary, this study applied geographically weighted regression (GWR) models to quantify the spatially varying effects of sea surface temperature, sea surface salinity, chlorophyll-a, water depth and distance to coast on the distributions of Coilia mystus eggs and larvae. The results reveal clear divergence in both spatial pattern and environmental drivers between spawning and nursery habitats. Spawning grounds were persistently concentrated in the middle reaches of the South Branch, and shifted approximately 10 km upstream during the spring saltwater intrusion event in 2020. Nursery grounds, by contrast, formed a stable dual-core structure, with the northern core at the North Branch mouth consistently supporting higher larval densities than the southern core in the North and South Passages. Salinity was the primary limiting factor for spawning in spring, while temperature dominated in summer, and chlorophyll-a was never retained in optimal egg models. For larvae, chlorophyll-a emerged as a consistent key driver alongside salinity and temperature, and local regression coefficients spanned a wider range than those for eggs, indicating greater spatial heterogeneity in larval distribution–environment relationships. This study provides the first comparative analysis of spatially non-stationary environmental controls on spawning versus nursery habitats of C. mystus, and offers empirical support for stage-specific habitat conservation and fisheries management in the Yangtze Estuary. Full article

The Arctic is warming approximately four times faster than the global mean, accelerating retreat of marine-terminating glaciers. Changes in glacier extent are linked to environmental factors, and their accurate quantification depends on the measurement methods used. This study compares five terminus change quantification methods applied to Austre Torellbreen, analyses terminus position changes of four outlet glaciers of the Amundsenisen Glacial System—Paierlbreen, Austre Torellbreen, Vestre Torellbreen, and Recherchebreen—in SW Svalbard, over 1975–2022, and assesses environmental controls on glacier retreat. Curvilinear box and GTT emerge as the most broadly applicable methods. Multi-centreline, Rectangle box, and Curvilinear box methods form the most internally consistent group, while GTT diverges moderately from this group. The Centreline method deviates most strongly from all others and is unsuitable for short-term analysis. A ~15° change in fjord orientation caused the Rectangle box to underestimate cumulative recession by ~330 m relative to the Curvilinear box, confirming that rectilinear approaches are limited to glaciers with low fjord sinuosity. Fjord depth and surge phase are likely key modulators of the environmental signal: deep-water, marine-terminating fronts show the strongest associations with sea surface temperature and runoff, whereas shallow fjords and restricted near-terminus water circulation weaken the oceanic imprint. Land-terminating sections of glaciers retreat approximately 3.4 times more slowly than marine counterparts and show no significant annual correlations with environmental variables. The terminus record constrains the timing and magnitude of surge-related frontal advance at Paierlbreen (1993–1995, ~280 m), Vestre Torellbreen (2008–2013, ~170 m), and Recherchebreen (2018–2020, ~660 m). Full article

Elevated potentially toxic element (PTE) contents in soils can pose important environmental concerns, particularly in regions where natural metal enrichment associated with ultramafic and mineralized geological settings overlaps with potential anthropogenic inputs. This study evaluates the distribution, enrichment, and probable controls of As, Ni, Co, Cu, Cr, Pb, and Zn in 35 geo-referenced soil samples from the Ortaklar region, Türkiye, with the aim of providing a geochemical basis for environmental monitoring and sustainable land management. PTE contents were determined by ICP-MS (inductively coupled plasma mass spectrometry), and their degree of enrichment was evaluated using the geo-accumulation index (I_geo), contamination factor (CF), and pollution load index (PLI), supported by correlation analysis and multivariate statistical approaches. Mean contents decreased in the order Ni (1972 mg kg⁻¹) > Cr (924 mg kg⁻¹) > Cu (290 mg kg⁻¹) > Zn (188 mg kg⁻¹) > Co (122 mg kg⁻¹) > As (15.9 mg kg⁻¹) > Pb (6.8 mg kg⁻¹). Ni exhibited the highest degree of enrichment and was the dominant contributor to the overall PTE load, while Cr, Cu, and Co also showed substantial enrichment. Mean I_geo values indicated moderate to heavy enrichment for Ni (2.65) and As (2.36), and moderate enrichment for Cr (1.45) and Co (1.02). The mean PLI value of 6.99 indicates a substantial cumulative enrichment of PTEs relative to the adopted reference values. Strong associations among Ni, Cr, and Co are consistent with a dominant geogenic contribution from ultramafic/serpentinitic parent materials and their weathering products, whereas the distributions of As, Cu, and Zn suggest more complex controls involving lithology, mineralization, and potentially localized agricultural or other anthropogenic inputs. However, these statistical relationships indicate probable controls rather than definitively establishing elemental sources. The elevated total PTE contents and contamination indices identify the Ortaklar soils as requiring continued geochemical monitoring; nevertheless, they do not constitute direct evidence of human-health risk because metal speciation, mobility, bioavailability, and exposure pathways were not investigated. The findings provide a regional geochemical framework for distinguishing natural PTE enrichment from anthropogenic influences and for guiding future mineralogical, bioavailability, and spatial investigations in ultramafic and mineralized soil environments. Full article

The behavior of petroleum pool fires has important implications for fire safety and environmental protection due to heat release, smoke generation, and pollutant emissions. In this study, controlled pool-fire experiments were conducted using representative petroleum fuels with multiple pan diameters to investigate the coupled effects of fuel properties and geometric scale on combustion behavior and emission characteristics. Key parameters, including the heat release rate, smoke production rate, mass loss rate, major gaseous emissions, and soot characteristics, were systematically measured. The results show that increasing pan diameter accelerated fire development, increased combustion intensity, and generally enhanced cumulative gaseous emissions. Compared with kerosene, gasoline exhibited more rapid combustion and higher smoke production, whereas kerosene produced a more sustained heat-release process and higher cumulative gaseous emissions. Correlation analysis, principal component analysis, and principal component regression revealed that fuel thermophysical properties and geometric scale are the dominant factors governing combustion behavior and pollutant formation. The proposed statistical framework provides a practical approach for quantitatively relating fuel properties to heat-release characteristics. These findings improve the understanding of the coupled effects of fuel composition and fire scale on petroleum pool-fire behavior and provide experimental support for fire hazard assessment and combustion modeling. Full article

In the Ramis River basin of the Peruvian Andes, flood events have become increasingly frequent and intense due to climate variability. However, the basin has limited hydro-meteorological observation records, and its flood generation mechanisms are extremely complex. This situation not only hinders the accurate identification of flood-prone areas, but also limits the effective implementation of flood risk management measures. This study sets three core objectives: to assess flood sensitivity across the basin, identify the dominant factors that influence flood sensitivity, and verify the flood detection performance of multispectral indices. The study adopts two core methods. First, a multi-criteria framework that integrates the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) is used, incorporating seven flood-related environmental factors and one precipitation triggering variable. Second, the performance of four spectral indices—NDVI, NDWI, SAVI, and MSAVI2 is verified through Spearman correlation analysis, Moran’s I index, and the random forest algorithm. The study finds that landform and geology are the core factors controlling flood sensitivity, with weights of 0.35 and 0.23, respectively. Moderately flood-sensitive areas account for the largest share of the basin, reaching 66% and covering 236.54 km². The flood extent estimated by the spectral indices ranges from 36.73 km² to 101.87 km². Among these indices, NDVI has the strongest spatial correlation with flood-prone areas. The random forest model used in this study has an AUC of 0.6935 and an overall accuracy of 63.51%. The analytical framework proposed in this study is applicable to data-scarce Andean River basins, and the combined use of multispectral indices can provide support for flood risk management and decision-making in this region. Full article

The development of biodegradable materials from renewable sources represents a promising strategy to reduce the environmental impact associated with conventional plastics. This study aimed to optimize potato starch-based bioplastics incorporated with lemongrass essential oil (Cymbopogon citratus) using Response Surface Methodology (RSM) and to evaluate their application in fresh pineapple (Ananas comosus) preservation. A Box–Behnken experimental design with three factors and three levels was applied, considering potato starch concentration (4–8 g), essential oil content (100–300 µL), and glycerol volume (1–2 mL) as independent variables. The effects of these factors on tensile strength, elongation at break, and Young’s modulus were analyzed using a quadratic model. The optimized formulation exhibited a desirability value of 1.00, consisting of 4.13 g of starch, 131.59 µL of essential oil, and 1.43 mL of glycerol, with predicted values of 2.91 MPa tensile strength, 53.18% elongation at break, and 15.18 MPa Young’s modulus. Experimental validation showed good agreement with model predictions, with relative errors below 20%. The optimized bioplastic was subsequently applied as a coating for fresh-cut pineapple stored under refrigeration (4–8 °C), reducing weight loss and improving the stability of physicochemical and textural properties compared with the control treatment. The results demonstrate that potato starch-based bioplastics containing lemongrass essential oil have potential as active biodegradable coatings for extending the quality preservation of fresh pineapple. Full article

Affected by coupled environmental factors including temperature and water activity, food storage is restricted by fungal contamination, quality degradation, and energy limits. Conventional microbial growth prediction models typically rely on given initial states and environmental parameters, making it difficult to account for the effects of prior-parameter errors and thereby limiting the accurate quantification of fungal growth risk and the real-time regulation of storage environments. This paper develops a fungal growth risk prediction and optimal regulation method for food storage based on the forward reachable set (FRS). The method combines a fungal growth kinetic model for Aspergillus flavus with FRS theory to calculate the reachable domains of colony radius and cell states within a finite time horizon, adopts a risk margin to describe the maximum colony expansion relative to deterministic growth trajectories, and constructs a multi-objective index covering energy cost, fungal growth risk, quality loss, and control switching cost to select the optimal environmental control scheme. Numerical simulation results show that the risk margin reflects the expansion of fungal growth risk caused by the propagation and accumulation over time of prior-parameter errors, while the selected regulation strategy exhibits stronger conservatism. Full article

Global plastic waste generation and excessive sand extraction are major environmental challenges, but replacing fine aggregate with plastic waste often degrades concrete performance. This work characterizes concrete incorporating recycled HDPE and PET fine aggregates at a 10% volumetric replacement level, with and without polymer-specific surface treatment, across fresh, mechanical, and durability properties. Untreated plastic aggregate generally lowered mechanical performance due to low polymer stiffness, weak plastic–paste bonding, and greater interfacial void formation. Surface treatment partially offsets these effects by strengthening the plastic–paste bond. H₂O₂-treated HDPE granules recovered the 28-day elastic modulus to within 3% of the control while also improving compressive strength, ultrasonic pulse velocity, and freeze–thaw resistance. H₂O₂-treated HDPE chips showed the highest electrical resistivity and the lowest permeable void content. NaOH-treated PET chips gave the lowest chloride penetrability and the greatest drying shrinkage reduction, approximately 25% relative to the control, though NaOH produced no resistivity gain for PET-C. Freeze–thaw durability factor increased with surface treatment for HDPE-G and PET-C, with HDPE-G-T exhibiting the highest durability factor among the recycled plastic mixtures at 94.20%. These results show that surface-treated recycled HDPE and PET fine aggregate can be incorporated at 10% replacement while maintaining acceptable mechanical and durability performance, supporting recycled plastics as a viable partial fine-aggregate replacement. Full article

Although neonicotinoids (NEOs) insecticides have been extensively detected in environmental matrices, their electron ionization (EI) fragmentation patterns have never been systematically elucidated. Because of the poor thermal stability of NEOs, gas chromatography–mass spectrometry (GC-MS) is rarely used in analysis of NEOs. This work investigated eight representative NEOs by GC-MS using multiple isotopically labeled standards, including ²H and ¹³C labeled standards to clarify fragmentation routes and establish robust structural assignment criteria. We found two main factors affecting EI fragmentation patterns. (1) The pharmacophore controlled the backbone cleavage pathway. Specifically, nitroguanidines underwent transketolation with loss of N₂O, while cyanoamidines fragment via α-cleavage. (2) The heterocycle moiety determined the diagnostic fragment ion. A heterocyclic group with chloropyridine gave the diagnostic fragment ion of m/z 126, and a chlorothiazole moiety gave the diagnostic fragment ion of m/z 132. The lack of m/z 126 and m/z 132 ions in the case of dinotefuran (DNT), which contained neither of these two moieties, indirectly suggested the rules. This set of rules effectively bridged the gap in EI mass spectral interpretation, providing a basis for ion transition selection and fragment assignment during GC-MS method development. Moreover, it could provide supportive mass-spectrometric clues for potential application in the structural elucidation of related compounds. Full article

Emerging industrial agglomeration drives green transformation through knowledge spillovers and economies of scale. However, its effects exhibit pronounced nonlinearity and heterogeneity, shaped by spatial externalities and development stages. This paper investigates 19 Chinese urban agglomerations over the period 2014 to 2023. Kernel density estimation based on enterprise-level Point-of-Interest (POI) data is used to characterize spatial agglomeration patterns across eight emerging sectors. A two-stage dynamic network super-efficiency SBM model decomposes Green Transition Efficiency (GTE) into resource utilization and pollution control sub-stages. An XGBoost-SHAP-GEO analytical framework, combined with partial dependence analysis, then identifies nonlinear driving mechanisms. The main findings are as follows: First, emerging industrial agglomeration intensifies and polarizes toward the eastern coast, whereas GTE displays a “high-west, low-east” pattern. This produces a significant spatial mismatch, rooted in the near-saturation of environmental carrying capacity in eastern regions, where congestion effects exceed knowledge spillover dividends. Second, geographic characteristics constitute the primary factor shaping GTE and operate through nonlinear interactions with industrial agglomeration and R&D investment. Notably, their moderation direction is reversible, suggesting that geographic endowments should be understood as “conditional assets” rather than fixed advantages. Third, nonlinear patterns across sectors are highly heterogeneous. The bio-industry is the only sector to achieve a J-shaped positive breakthrough. Information technology and new materials exhibit persistent inhibition, while related services display an extremely narrow threshold window with the deepest negative reversal. Thus, “moderate agglomeration” is a multidimensional concept that shifts dynamically with industry type and regional endowment. Fourth, driving mechanisms display stage-dependent evolution. The incubation stage relies on natural endowments and basic industrial pull, with the green bottleneck residing in resource utilization efficiency. The growth stage faces multiple tensions from coexisting positive and negative effects. The optimization stage shifts toward R&D innovation and industrial greening, marking a qualitative transformation from MAR externalities to Jacobs externalities. In addition, the non-significant linear coefficient in the 2SLS instrumental variable test is consistent with the inverted U-shaped nonlinear finding, further validating the necessity of a nonlinear analytical framework. These findings provide differentiated governance evidence for balancing industrial agglomeration with green sustainable development. Full article

Environmental conditions have become more unstable; therefore, innovative and eco-friendly methods of food production are urgently required. Most existing hydroponic systems lack the capacity for real-time responses and decision-making based on integrated data, similar to contemporary farms. This document outlines the creation of an advanced hydroponic farming system that utilizes Internet of Things (IoT) sensors and a digital twin (DT) simulator to address these challenges. A completely monitored and continuously assessed hydroponic farming simulator operating on a Raspberry Pi, employing various sensors, data management and processing, and automated environmental regulation. The development of this intelligent hydroponic farming system employs a dual-model machine learning pipeline: one that identifies plant diseases through image analysis, and another that assesses plant nutrient levels based on sensor data. The data from the two models are combined using a cloud-based DT, enabling remote access to the DT and offering closed-loop control for irrigation, nutrient dosing, and management of all environmental factors related to crop growth in a hydroponic setting. This research showcases the capability to develop scalable, data-focused precision agriculture solutions that can adapt to the demands of today’s agricultural environment by combining all elements of IoT sensing, machine learning, and DT simulations into one functional hyperphysical system. Full article

Growing safety-related risks and increasing regulatory pressures have highlighted the importance of safety investment for corporate sustainable development. However, little attention has been paid to whether and how corporate safety investment enhances total factor productivity (TFP). Using panel data from Chinese A-share listed enterprises in high-risk industries over the period 2012–2024, this study examines the impact of safety investment on TFP. The results show that safety investment significantly improves TFP of enterprises in high-risk industries. Mechanism analyses indicate that this positive effect operates primarily through strengthening organizational resilience and alleviating financing constraints. Heterogeneity analyses reveal that the enhancing effect of safety investment is more pronounced in enterprises with high levels of safety investment, state-owned enterprises and enterprises with weaker internal controls. Further analysis shows that safety investment improves corporate sustainable development performance and environmental, social, and governance (ESG) performance by enhancing TFP. These findings contribute to the literature on corporate safety management and operational efficiency by demonstrating that safety investment is not merely a tool for mitigating workplace risks but also a strategic resource that strengthens corporate safety governance, improves operational efficiency, and ultimately promotes sustainable corporate development. Full article

This study investigates recycled low-density polyethylene (LDPE)–sand composites as cement-free materials for selected construction applications. The effects of plastic content (30–50 wt.%), processing temperature (220–260 °C), and particle size (319–1015 µm) on mechanical and physical properties were evaluated using a Taguchi L9 experimental design. Mechanical properties, including compressive, flexural, and tensile strength, and physical properties, including density and water absorption, were assessed using laboratory-scale specimens prepared from moulded composite panels. Processing temperature was the dominant factor controlling strength development and water absorption reduction. The best-performing experimental condition within the investigated range was 30 wt.% LDPE, 260 °C, and 1015 µm particle size, yielding an apparent compressive strength of 65.5 MPa, flexural strength of 20.7 MPa, tensile strength of 4.4 MPa, density of 1595.2 kg/m³, and water absorption of 0.7%. Cross-validation showed good predictive capability for density, tensile strength, flexural strength, and water absorption, but only moderate predictive capability for compressive strength and compressive modulus. Therefore, the regression models are presented as screening tools within the investigated parameter range rather than as general design models. The results indicate that recycled LDPE–sand composites have potential for selected non-structural and limited semi-structural applications, subject to further product-standard testing, durability assessment, fire performance evaluation, and environmental impact analysis. Full article

Aesthetic perception of rural streetscapes reflects individuals’ cognitive responses to their surroundings and is central to understanding how landscape preferences are formed. Existing studies using Scenic Beauty Estimation (SBE) are constrained by incomplete indicator systems and overreliance on linear approaches. This study proposes an interpretable machine learning framework that integrates multi-source data to examine the nonlinear influences of streetscape features on SBE. Using Sanguan Village, a water-networked settlement in Jiangsu, we developed a 24-indicator system spanning color, spatial, natural, artificial, and cultural dimensions. Based on 523 panoramic images and aesthetic ratings from 1175 respondents, we compared OLS, DT, MLP, SVR, RF, and XGBoost models. The best-performing XGBoost, combined with SHAP analysis, revealed threshold effects and interaction patterns among variables. Green visibility, architectural aesthetics, building visibility, sky visibility, environmental coordination, and water are the top six feature variables most strongly associated with rural streetscape aesthetic perception, and each exhibits threshold effects. The saturation threshold for green visibility is 0.153, and architectural aesthetics can only make a positive contribution when its score exceeds 3.815. The appropriate range for building visibility is below 0.452, while the optimal value for sky visibility is approximately 0.194. We also explored the context-dependence of these threshold effects across urban and rural settings. This study proposes streetscape optimization strategies focusing on screening key factors, controlling their thresholds, and coordinating the allocation of streetscape features. The interpretable analytical framework for rural scenic beauty established in this research can facilitate evidence-based landscape optimization and provide scientific support for sustainable rural development and tourism in this case. Full article