International Journal of Environmental Science and Technology最新文献

Air quality is a very important factor for environmental health and ecosystem integrity; its assessment is very important for implementing mitigation strategies that can limit its impact on the environment and humans. This study leverages climatic remote sensing data to monitor urban air quality in Baghdad, providing insights into pollution distribution and dynamics. This approach offers added value by integrating satellite-derived climatic indicators with air quality analysis, which previous urban studies in the region have not employed. This study uses Sentinel 5P and MODIS data and the MERRA-2 model to investigate air pollution parameters such as NO₂, SO₂, CO, AOD, temperature, and wind speed. According to spatial analysis, high NO₂ levels are noted in the Eastern part covering about 9.96% of the total study area. High SO₂ concentrations were found in the Eastern and Northern parts, covering about 11% of the study area. High CO concentrations (0.035–0.038 molecules/m²) were found in about 39% of the study area. The high levels of aerosol optical depth (AOD) were found in 9.11% of the region. Temperature gradients reveal different thermal zones ranging from 27 to 29 °C, with 50.11% of the studied area experiencing high temperatures. Wind speed patterns depict prevalent airflow dynamics, with a significant wind speed range described by 46.07% of the area. Pearson correlation coefficients reveal connections between air contaminants and meteorological factors. Air pollution in Baghdad has been historically under-monitored due to limited ground stations. The findings of this study directly inform practical air-quality management, by identifying pollution hotspots and peak periods, local authorities can target interventions such as traffic regulation or industrial emissions control. Ultimately, this research supports the development of data-driven strategies to improve urban air quality and public health in Baghdad, with methods that can be extended to other cities facing similar data limitations.

This systematic review was conducted to examine relationships between sample matrices and predominant analytical methods in microplastics research. An extensive literature search was performed across Springer, MDPI, and Scopus databases to identify relevant publications through October 2022. Through systematic screening, 180 studies were selected for full-text analysis. The reviewed methodologies revealed two primary sample preparation approaches: density separation and organic matter digestion. For organic matter digestion, oxidation using 30% hydrogen peroxide (H₂O₂) was identified as the predominant technique. In density separation protocols, zinc chloride (ZnCl₂) emerged as the most frequently employed chemical compound. Regardless of the sample matrix, the main identification methods were Fourier transform infrared spectroscopy (µFTIR), µRaman and pyrolysis mass spectrometry (Pyr/GC–MS). The choice of methodology is heavily influenced by the sample matrix, however, other aspects are relevant, such as the type of material and concentration of organic matter in the sample. To address challenges in methodological standardization and result comparability, it is recommended that future studies be categorized according to identification method, as this parameter significantly influences the detectable size range of microplastics. Furthermore, density separation techniques should be carefully considered, as solution density may affect the recovery of higher-density polymers. Implementation of these methodological considerations could enhance the accuracy of comparative analyses regarding polymer types and particle size distributions in environmental microplastic samples. The findings suggest that while methodological approaches vary substantially, systematic classification of analytical protocols could facilitate more robust inter-study comparisons in microplastics research.

Reliable prediction of future land use and land cover change is essential for sustainable spatial planning in rapidly transforming African landscapes, including Ghana. Ghana has experienced accelerated urbanisation, agricultural expansion, and forest degradation. Yet the predictive performance of machine learning and cellular automata-based models applied in this context have not been systematically synthesised. This study conducts a systematic review and meta-analysis to evaluate the effectiveness of machine learning models, cellular automata models, and hybrid machine learning–cellular automata approaches for land use and land cover change prediction in Ghana between 2010 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, eighty-five peer-reviewed studies were selected from an initial pool of 12,008 records. Quantitative synthesis focused on standardised accuracy metrics, including overall accuracy, the Kappa coefficient, and the figure of merit. Results demonstrate that hybrid models consistently outperform standalone approaches across diverse land use contexts. Cellular automata–machine learning frameworks, including cellular automata-Markov-artificial neural network and future land use simulation models, achieved overall accuracy values exceeding 85 percent and Kappa coefficients commonly ranging from 0.80 to 0.89 in urban, forest, wetland, and river basin applications. Standalone machine learning classifiers attained high classification accuracies of up to 95–98 percent but showed limited capacity for spatial–temporal forecasting when applied without cellular automata integration. In contrast, standalone cellular automata–Markov models exhibited moderate predictive performance, with Kappa values typically between 0.75 and 0.82, nationally relevant.

This study utilized natural and economical biomass of Lavandula stoechas L. to eliminate methylene blue dye from water and investigate its ability to be an antioxidant source within a zero-waste framework. A Taguchi design of experiments (L25) was used to evaluate the impacts of four factors: beginning concentration (10–50 mg/L), adsorbent dosage (0.01–0.15 g/50 mL), contact time (0–150 min), and pH (2–12), across four distinct levels. Machine learning models were integrated after the Taguchi analysis to validate the robustness of the optimal parameter combination, predict adsorption efficiency under different experimental conditions, and quantitatively assess the relative importance of process variables. The machine learning results supported the Taguchi and ANOVA findings by confirming the dominant influence of pH and adsorbent dosage on adsorption performance. The adsorption raw data were analyzed using non-linear Langmuir, Freundlich, Dubinin–Radushkevich, Temkin, and Sips isotherm models. The maximum adsorption capacities were determined to be 49.09 mg/g and 47.88 mg/g, respectively, based on the Langmuir and Sips isotherms. The study concluded that the adsorption kinetics adhered to the pseudo-second order kinetic model, and that many mechanisms, including intraparticle and film diffusion, were significant in the adsorption as per the Weber Morris and Boyd models. Reusability experiments demonstrated that L. stoechas L. biomass yielded highly successful results for a maximum of five cycles. All findings demonstrated that L. stoechas L. biomass, regarded as a zero-waste biomass, serves as a promising and eco-friendly adsorbent for the elimination of cationic MB dye.

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This study synthesized ZnO-based nanoparticles into mono- (ZnO), bi- (ZnO/Ag, ZnO/Fe), and tri-metallic (ZnO/Ag/Fe) forms, using extracts from Trichoderma asperellum to generate nanoparticles (NPs) for the removal of Malachite Green (MG) dye in aqueous solution. The NPs were characterized based on UV/Vis Spectroscopy, Field Emission Scanning Electron Microscopy, High Resolution Transmission Electron Microscopy, Energy Dispersive X-Ray, Fourier Transform Infrared Spectroscopy, and Dynamic Light Scattering. Results revealed that fungal-mediated biosynthesis successfully produced typical ZnO-based NPs that were spherical-shaped with sizes from 13.89 to 31.28 nm. The biogenic nature of the NPs was validated by the detection of functional groups (N–H, –OH, –COOH, –CH) originating from fungal extracts. All ZnO-based NPs achieved more than 89% of removal efficiencies using only 2 mg of NPs, with multi-metallic forms considerably outperforming their mono-metallic ZnO counterparts. The adsorption by the NPs is likely via multi-layered chemisorption as evidenced by the Freundlich isotherm (R² = 0.9661–0.9937) and pseudo-second-order model (R² = 0.9899–0.9991). The maximum monolayer adsorption capacities of ZnO, ZnO/Ag, ZnO/Fe, and ZnO/Ag/Fe NPs were determined to be 142.86, 37.45, 42.55, and 33.22 mg/g, respectively. As such, adsorption of MG dye particles may have involved hydrogen bonding, π–π stacking, and electrostatic interactions. These findings underscore the potential of fungal-mediated ZnO-based NPs, especially tri-metallic forms, as promising adsorbents for removal of MG or other dyes, with possibilities as well for other hazardous pollutants.

The composition and operating conditions of electrospun polyethyleneimine (PEI)/polystyrene (PS) composite nanofibers were optimized for CO₂ adsorption. A maximum capacity of 1.16 mmol CO₂/g was achieved at a 60 °C isotherm using 20 wt% PEI with respect to PS. This adsorption capacity is comparable to that of many PEI-impregnated nanofibers and porous sorbents. Despite the exothermic nature of CO₂ adsorption, operating at higher temperatures (up to 60 °C) increased the adsorption capacity. Thermal pre-treatment of PEI/PS nanofibers showed a similar trend, with nanofibers pre-treated at increasing temperatures (up to 60 °C) exhibiting greater capacity when operated at 30 °C. Pre-treatment at 60 °C led to similar capacities for isotherms at 30 °C and 60 °C, suggesting that temperature-induced changes to the nanofibers increased the capacity. Kinetic analysis of all PEI/PS nanofibers showed that pre-treatment did not affect the CO₂ adsorption mechanism. However, pre-treatment above 60 °C decreased capacity, showing the thermal sensitivity of these materials. Temperature swing cycle testing (30 °C adsorption, 70 °C desorption) found desorption to be 6 times slower than adsorption, showing the constraints of operating within the fiber’s small range of thermal stability.

Per- and polyfluoroalkyl substances (PFAS) represent a diverse class of persistent organic pollutants that are ubiquitously detected across water and soil environments, posing serious ecological and human health risks. PFAS compositions vary by environmental matrices. Long-chain compounds dominate groundwater, short-chain forms are common in stormwater, mixed PFAS appear in surface waters, and landfill leachates show the highest diversity, while soils and sediments serve as long-term PFAS sinks. Therefore, different strategies are needed for different aquatic and soil systems. This review comprehensively examines biomass-based approaches for PFAS removal in various environments for the first time, including adsorption and phytoremediation. In water environments, biomass-derived adsorbents such as activated carbon, hydro char, and cationic nanocellulose remove PFAS primarily through hydrophobic partitioning, electrostatic interaction, ion-pair formation, and metal-fluoride complexation. In soil environments, plant-based remediation relies on root absorption, translocation, and rhizospheric transformation, where short-rotation crops preferentially accumulate short-chain PFAS, and deep-rooted grasses aided by mild surfactant amendments improve long-chain PFAS uptake.

This review also highlights critical gaps in current biomass-based PFAS remediation, including limited field-scale validation, insufficient treatment of ultrashort-chain PFAS (e.g., TFA and PFPrA), and strategies for managing PFAS-laden spent biomass. We recommend future research to prioritize (i) hybrid treatment systems that integrate biomass adsorption with phytoremediation or advanced oxidation/reduction processes, (ii) computationally guided design of functionalized biomass sorbents to improve selectivity in real matrices, and (iii) regeneration and disposal pathways that ensure permanent PFAS destruction. Overall, these advances are essential for translating biomass-based PFAS remediation from laboratory studies to sustainable real-world applications.

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Global warming and nano-plastic pollution are widespread problems affecting the aquatic environment. Still, there are substantial gaps in their combined effects on aquatic species, which require further study. Thus, the present study investigates the biological alterations caused by the interactive toxicity between increasing water temperatures and polyvinyl chloride nano-plastics (PVC-NPs) in Oreochromis niloticus. Using hematological parameters, acetylcholinesterase activity (AChE), DNA damage, and histopathological alterations. Fish aquaria were divided into 0 mg/L and 10 mg/L PVC-NPs exposure groups at 30 °C, 32 °C, and 34 °C in a duplicate manner. After 4 days, the hematological parameters of groups subjected to PVC-NPs and temperatures exhibited a significant decrease. Additionally, AChE activity in the muscle and brain tissues of all groups was substantially reduced. DNA fragmentation rate also showed an elevation in all fish groups’ liver, gills, and brain tissues. The liver, gills, kidney, and brain exhibited various histological changes. These changes ranged from mild and moderate alterations to severe damage, especially in groups exposed to high temperatures with PVC-NP toxicity. Finally, all current results indicated a synergistic impact between temperature and PVC-NPs on O. niloticus.

Excessive leaching of metamitron into soil and water systems poses significant environmental and public health risks. This study integrates biochar physicochemical characterization, hyperspectral reflectance profiling (400–1000 nm), and comparative machine-learning models (PLSR, SVM, RF, and ANN) to predict metamitron attenuation in soil–biochar systems. Three biochar types derived from hazelnut shells, apricot kernel shells, and waste car tires were evaluated at application rates of 5%, 15%, and 25% (w/w). GC–MS analyses revealed statistically significant differences in residual metamitron concentrations among biochar types (p < 0.01), with lignocellulosic biochar showing substantially lower residues than tire-derived biochar. The lowest residual concentration (5.18 µg L⁻¹) was observed for apricot kernel shell biochar at the 25% application rate, while higher overall residues persisted in tire-derived biochar treatments. Hyperspectral analysis identified the 600–690 nm region as the most diagnostic spectral window, with reflectance at 670 nm exhibiting the strongest correlation with metamitron concentration (r = 0.81–0.82, p < 0.01). Comparative modeling demonstrated marked performance differences among algorithms. Support Vector Regression showed limited predictive capability (R² = 0.016, RMSE = 11.43), while Random Forest achieved moderate accuracy (R² = 0.753, RMSE = 5.73). The Artificial Neural Network (ANN) model outperformed all alternatives, achieving the highest accuracy (R² = 0.965, RMSE = 0.122, MAE = 0.080) and demonstrating consistent generalization across the training, validation, and test datasets. Relative importance analysis (Olden and Jackson method) identified biochar type (+ 29.7%) and application rate (− 33.3%) as the dominant predictors, confirming a nonlinear, threshold-dependent adsorption response. In contrast, BET surface area showed a weak negative contribution (− 1.4%). Overall, these findings demonstrate that integrating hyperspectral reflectance with ANN modeling provides a rapid, non-destructive, and mechanistically informative framework for assessing biochar-based pesticide attenuation under controlled laboratory conditions. External field validation across diverse soils and environmental settings is required to fully assess model transferability.

Benthic foraminiferal assemblages were investigated as bioindicators of metal pollution and environmental gradients in Gorgan Bay, a semi-enclosed basin in the southeastern Caspian Sea. Surface sediment samples from fifteen stations were analyzed for species composition, sediment characteristics, water quality parameters, and heavy metal concentrations. Multivariate analyses revealed distinct spatial patterns in community structure, driven by both natural and anthropogenic factors. Total organic matter, water depth, mud content, and copper concentration emerged as the most influential variables, collectively accounting for 38.86–44.6% of the observed variation. The assemblage was dominated by Ammonia beccarii caspica (68%) and Elphidium littorale caspicus (22%), with rare taxa such as Cornuspira sp. exhibiting restricted distributions. These findings demonstrate that benthic foraminiferal communities in Gorgan Bay are sensitive to environmental gradients and metal contamination, supporting their utility as effective bioindicators in vulnerable coastal ecosystems under increasing anthropogenic pressure.