International Journal of Environmental Science and Technology最新文献

In this study, the influence of metal nanoparticle size on treatment efficiency and bacterial community dynamics in a sequencing batch reactor was investigated. The experiments revealed that while the presence of metal nanoparticles did not significantly alter the removal rates of chemical oxygen demand or total phosphorus, the size of the metal nanoparticles clearly affected the nitrogen removal efficiency and bacterial community structure. Smaller metal nanoparticles, such as 20–40 nm copper oxide and < 50 nm zinc oxide, led to reductions in mixed liquor suspended solids, which correlated with decreased removal efficiencies for total nitrogen and ammonium nitrogen. This adverse impact was linked to significant alterations in bacterial community composition, where the dominant genera included Pseudomonas sp., Perlucidibaca sp. and Acinetobacter sp. Bacterial morphological analysis revealed damage to bacterial membranes with relatively small metal nanoparticles. In contrast, larger copper oxides (60–80 nm) increased the mixed liquor suspended solids, improving the nitrogen removal efficiency because microbes secreted extracellular polymeric substances, which reduced the toxicity of metal nanoparticles. The dominant bacteria in this case were Pseudomonas sp., Perlucidibaca sp., and Azospirillum sp. The study concluded that the size of metal nanoparticles critically influences microbial community dynamics and treatment efficiency, emphasizing the need for strategies to reduce the toxicity of metal nanoparticles in wastewater treatment plants. These findings provide valuable insights into environmental management and the design of sustainable wastewater treatment processes.

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Understanding how environmental and topographic factors shape Land Surface Temperature (LST) is essential for effective land use planning and climate adaptation. This study explores the temporal dynamics of LST in relation to land use and land cover (LULC), Normalized Difference Vegetation Index (NDVI), elevation, and slope in the Küçük Menderes Basin, western Türkiye. Landsat imagery was used to derive LULC, NDVI, and LST layers were combined with slope and elevation layers generated from the DEM, and multi-layer spatial signatures were clustered using the open-source R package motif through hierarchical clustering with the Ward agglomeration method. This approach identified recurring landscape configurations that jointly influence LST. Results show substantial LST increased in low-elevation, gently sloping, sparsely vegetated areas, particularly where impervious surfaces expanded, while steeper and densely vegetated areas remained thermally stable, reflecting the moderating influence of topography and vegetation. Cluster validity was confirmed by internal metrics (silhouette widths, Moran’s I) and external predictive benchmarking with Gradient Boosting and Random Forest under spatial cross-validation, demonstrating the robustness and practical relevance of the clustering results. Multivariable regression, partial-correlation, and variance-partitioning analyses further corroborated the independent contributions of spatial drivers. The study highlights the novelty of the pattern-based clustering approach, which uncovers how combined spatial patterns of land use, vegetation cover, and topographic attributes shape thermal behaviour. These findings provide clear and actionable insights for detecting thermally vulnerable zones and guiding climate-resilient land management.

The urban heat island effect, a consequence of rapid urbanization and climate change, threatens urban energy systems, public health, and sustainable development. Mobile observation technology, characterized by high spatiotemporal resolution enabled by well-designed observation networks and inherent flexibility, has become essential for overcoming the limitations of fixed meteorological stations and remote sensing. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study synthesizes 109 publications from four core databases—Scopus, ScienceDirect, Web of Science, and Springer—to trace the century-long evolution of mobile observation from theoretical, methodological, and practical dimensions. Theoretically, advances in algorithms and models have facilitated a shift from descriptive analysis of urban heat island effect phenomena to mechanistic exploration of their formation. Methodologically, observation platforms have evolved from pedestrian and bicycle-based systems to drones, wearables, and multi-platform integrated networks; measured parameters have expanded from air temperature to humidity, wind speed, pollutants, and surface characteristics, with measurement precision improved from ± 0.5 to ± 0.1 °C. In practice, applications now extend to street canyons and are increasingly integrated with information systems for end-to-end data management and simulation-driven decision-making. Challenges remain in standardization, error control, and equitable global monitoring capacity. Future efforts should prioritize interdisciplinary integration, multi-source data synergy, model generalization, and public participation to establish more precise, equitable, and climate-resilient urban thermal governance systems, thereby enhancing urban climate resilience. By synthesizing over a century of innovation, this review provides a foundational framework and scientific insights crucial for advancing urban climatology theory and guiding the development of climate-resilient cities.

Accurate computation of daily reference evapotranspiration (ET0) is fundamental for ensuring the efficient and sustainable governance of water supplies. Consequently, this investigation aims to examine the efficacy of an Artificial Neural Network (ANN) architecture, optimized by two cutting-edge hybrid metaheuristic frameworks—the Innovative Gunner (AIG) and the Black Widow Spider Optimization (BWO)—for ET0 prediction. We assessed the AIG-ANN and BWO-ANN frameworks in estimating daily ET0 across four representative climatic regions in Iran: temperate-humid, cold-mountainous, hot-arid, and hot-humid. These novel hybrid methodologies were benchmarked against established paradigms, namely the WANN and FA-ANN models. Climatic inputs, comprising sunshine duration, maximal and minimal air temperatures (Tmax and Tmin), relative humidity, and air flow velocity, were sourced from four distinct meteorological stations spanning the decade from 2012 to 2022. Data partitioning allocated 2012–2019 for model calibration and 2019–2022 for validation, utilizing nine distinct hybrid input combinations. Model performance assessment relied on established statistical measures: the Coefficient of Correlation, Root Mean Square Error (RMSE), Mean Absolute Error, Normalized RMSERMSE (NRMSE), and the Nash–Sutcliffe Efficiency Index. The findings confirmed that all employed models exhibited superior predictive capability under hybrid input configurations. Notably, the statistical metrics confirmed that the AIG-ANN configuration yielded the highest predictive precision for ET0 across all four evaluated sites, signifying a substantial performance enhancement over conventional estimation techniques. This study substantiates that leveraging advanced metaheuristic enhancements, such as AIG, provides a highly accurate and robust mechanism for refining ET0 estimation, thereby directly facilitating improved regional water stewardship, optimized land management, and the formulation of resilient climate change adaptation strategies.

In this study, the photocatalytic removal of acetamiprid (ACE) was investigated using ZnO and TiO₂-doped ZnO photocatalysts. The effects of different light sources (simulated solar light, UV-A, and UV-C) were evaluated, with the highest performance obtained under UV-C light. Parameters affecting the photocatalytic process, such as pH, catalyst amount, and initial concentration, were examined, and the optimum catalyst amount was determined to be 1 g/L. Kinetic analyses showed that the degradation rate of ACE followed a pseudo-first-order kinetic model. HPLC analyses revealed that ACE was completely degraded (100% removal) after 120 min of reaction; however, UV spectrophotometer analyses identified a removal efficiency of 79% for ZnO and 71% for TiO₂-doped ZnO, due to the presence of intermediate products. LC–MS/MS analyses identified the intermediate products formed during the photocatalytic degradation process of ACE, confirming effective mineralization. The findings demonstrate that ZnO and TiO₂-doped ZnO are effective photocatalysts for the removal of ACE and hold significant potential for environmentally sustainable treatment technologies.

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Phenolic compounds persist in aquatic environments and are toxic, necessitating efficient treatment strategies. This study evaluated phenol adsorption onto three materials—calcined magnesium–aluminum layered double hydroxide, partially reduced graphene oxide, and a calcined magnesium–aluminum layered double hydroxide–graphene composite—using a synthesis route designed to increase surface area and porosity. The methodology combined batch adsorption experiments with response surface methodology to optimize operational conditions, and applied kinetic, isotherm, diffusion, and thermodynamic analyses to elucidate mechanisms. The optimal conditions were 60 mg per liter initial phenol concentration, 50 degrees Celsius, 3 g per liter adsorbent dosage, and 110 min, yielding 93.67 percent removal with the layered double hydroxide–graphene composite. Findings showed adsorbent dosage was the most influential variable. Kinetic analysis followed a pseudo-second-order model, indicating chemisorption. The Freundlich isotherm best captured equilibrium behavior, consistent with multilayer adsorption on a heterogeneous surface, while Temkin and Dubinin-Radushkevich interpretations suggested decreasing adsorption energy and a mixed physical–chemical mechanism. Weber-Morris evaluation indicated contributions from both film diffusion and intra-particle diffusion. Thermodynamics confirmed spontaneous and endothermic adsorption with increased entropy at the solid–liquid interface. In discussion, the composite’s heterogeneous sites and transport characteristics explain its superior capacity and robustness. In conclusion, the composite maintained about 90 percent removal after five regeneration cycles, demonstrating practical promise for wastewater treatment under environmentally relevant conditions.

In this study, we employ an optimization approach for removing cadmium (Cd) and lead (Pb) from aqueous solutions using clay, nanoclay, and magnetic nanocomposite adsorbents. The experimental design was based on the Taguchi method by considering 5 factors: 1- adsorbent type (TA), 2- initial concentration (C) of contaminating metals, 3- adsorbent dosage (D), 4- pH (P), and 5- temperature (T), each of which was considered at 4 levels in the experiment. Based on the results of the ANOVA analysis, the adsorption capacity of Cd and Pb ions was mainly influenced by the initial concentration (56–57%) and the adsorbent dose (35–36%), while pH and temperature had a minimal effect of less than 2%, and the type of adsorbent contributed a small percentage of 4%. By optimizing the metal ion adsorption process the best conditions for Cd were as follows: TA = nanoclay, C = 200 mg L⁻¹, D = 1.6 g L⁻¹, P = 10, and T = 45 °C, and for Pb: TA = clay, C = 200 mg L⁻¹, D = 1.6 g L⁻¹, P = 10, and T = 35 °C. Based on the studied properties of the adsorbents, including FTIR spectra, measurements of specific surface area and SEM and TEM images, clay and nanoclay have been identified as two promising adsorbents for the adsorption of Cd and Pb from solution.

As the global population ages, it is essential to discourage older people from relying heavily on cars and to encourage them to use low-carbon travel modes, such as transit. However, previous studies have paid limited attention to older adults’ satisfaction with transit services (transit satisfaction) and its determinants. More importantly, they typically assume that the effects of service attributes on transit satisfaction are linear and symmetric. This study analyzes questionnaire survey data from 928 older transit users in Chengdu, China. It employs random forest-based regression with dummy variables to examine the effects of service attributes on satisfaction with three types of transit services: metro, bus rapid transit (BRT), and conventional bus services. Additionally, this study incorporates the three-factor theory to identify the factor structure of service attributes, categorizing them into basic, performance, and exciting factors, and determines priorities for service improvement. The results indicate that certain attributes (e.g., seat availability) have consistent effects across all three transit modes, whereas others (e.g., comfort) vary significantly by mode. The key service attributes requiring priority improvement include seat availability and crowding (for metro, BRT, and buses); station accessibility and information at stops (for metro and BRT); and waiting time (for BRT). This study offers mode-specific service enhancement strategies and provides valuable theoretical and empirical insights for optimizing age-friendly transit services.

The present study aims to model the Particulate Matter dispersion and assess the level of exposure and health risk of users in a large-scale sports complex in the center of Tehran. Six stations were selected for measurement utilizing a portable device. Then, the interpolation method with the Kriging technique was applied in the ArcGIS software environment for dispersion modeling. For health risk assessment, the EPA presentation method was employed in sections of carcinogenic and non-carcinogenic risks for children and adults. In the next step, confronting the exposure groups with PM10 from all of the three routes of ingestion, inhalation, and dermal absorption was considered. The results revealed that the current situation is higher than the global standard given that the average annual concentration of PM10 was 81.77 µg/m³. The average maximum daily concentration of PM10 during autumn–winter was higher than that during spring–summer. In addition, the average hourly, eight-hour, daily, and annual PM10 concentrations were 82.39, 73.42, 85.39, and 69.5 µg/m3, respectively, indicating that the simulated maximum 24-h concentrations during both time periods are within the NAAQS standard, yet higher than the WHO standard limit. Further, the simulated maximum annual concentration is higher than the limit of both standards. The results indicated that the PM10 dispersion in the study site is not uniform. PM10 absorption values represented that the highest exposure in both groups was from ingestion, followed by respiratory and dermal absorption, respectively. The PM10 dispersion in the study site does not pose any carcinogenic or non-carcinogenic risk to the target groups due to the small non-carcinogenic cumulative risk index.

Effective solid waste management depends on household-level segregation, yet this remains inadequate in many low- and middle-income countries. While previous studies emphasize infrastructural and behavioural challenges, this paper highlights an under-recognised factor—terminology and classification ambiguity. To examine how inconsistent and unintuitive waste-related terms, signage, and bin colour codes impede effective source segregation in urban low- and middle-income countries particularly in India and South Asia. A structured narrative review of academic and grey literature, including national policies, municipal regulations, and information-education-communication materials, was conducted to explore how misaligned classification systems affect public understanding and compliance. Households often struggle to categorise waste due to overlapping or poorly defined categories (e.g., dry vs. biodegradable), inconsistent colour codes across jurisdictions, and limited culturally relevant communication. This ambiguity, compounded by spatial and economic constraints, leads to widespread non-segregation, contamination of recyclables, and weak policy implementation. We call for standardised and culturally contextualised classification frameworks with intuitive labelling and harmonised colour coding. Integrating clear, locally resonant communication into governance systems can significantly improve segregation behaviour and sustainability outcomes in urban low- and middle-income countries. Beyond infrastructure and awareness drives, semantic clarity and design of waste communication are crucial for enabling behavioural change. Addressing these overlooked dimensions can strengthen urban resilience, improve policy coherence, and accelerate transitions toward circular economy practices in low- and middle-income countries.