Environmental Science and Pollution Research最新文献

Understanding the spatiotemporal analysis of air pollutants is crucial for identifying hotspots, local sources, and devising mitigation strategies, but this requires faster, more efficient approaches to support decision-making. For the first time in this study, Spatiotemporal Trend, Emerging Hot Spot (EHSA) and Time Series Cluster (TSC) analysis have been performed by creating a Space Time Cube (STC) at the neighbourhood level. The analyses were conducted for key air pollutants (PM₁₀, PM_2.5, NO₂) measured between 2015 and 2023 in Istanbul. For three pollutants, 9855 concentration maps were generated using Inverse Distance Weighted (IDW) for each day. The regions classified as Oscillating Hot Spot for all pollutants are generally 4 times higher than the intersection cluster of Anselin Local Moran's I (LISA) and Optimised Hot Spot (OHSA). Although there is a downward trend in the majority of the urban area of Istanbul, increasing trends and hot spots are evident in urban transformation, dense traffic-industrial and touristic areas. NO₂, PM_2.5 and PM₁₀ values decreased by 43%, 8.9% and 31.6%, respectively, when the NDVI value increased approximately 2 times. Through this approach, sociospatial variables at the neighbourhood level can be synthesised with the spatiotemporal consequences of air pollution. This research identifies key areas contributing to environmental justice, providing decision-makers with detailed, comprehensive data to advance critical social and environmental justice initiatives.

Climate change is a critical global challenge driven by rising greenhouse gas emissions, particularly carbon dioxide CO $_2^{}$ . Accurate forecasting of CO $_2^{}$ emissions is essential for developing effective mitigation strategies. This study focuses on modeling and forecasting CO $_2^{}$ emissions in Iraq based on data from 1937 to 2023, incorporating climatic variables such as temperature and precipitation as exogenous variables to enhance forecast accuracy using multiple models, including traditional time series ARIMAX, Feedforward Neural Networks (FNN), Recurrent Neural Networks (RNN), and hybrid FNN-RNN. ARIMAX requires the assumption of linearity, FNN alone can model complex nonlinear interactions for each observation, while the RNN capture temporal relationships in sequential data. The hybrid configuration combining FNN and RNN models provides a learning of both linear and nonlinear structures. Empirical results indicate that the hybrid FNN-RNN model outperforms other models using key evaluation metrics, including $R^2$ , MSE, RMSE, and MAE. The hybrid model shows that both training and validation losses decrease steadily and converge to very low values without overfitting. The close alignment of the two curves indicates good generalization, and the slight dip in validation loss suggests effective regularization. Additionally, the study forecasts a significant 9.18% rise in Iraq's CO $_2^{}$ emissions over the 5 years from 2024 to 2028, and the forecast showed its highest recorded value in 2028. These findings may support policymakers in designing more accurate and proactive emission control strategies. While focused on climatic variables, the model offers a strong basis for future research to focus on socioeconomic factors such as GDP and population growth.

The atmospheric dynamics of glyphosate and AMPA was investigated in an agricultural area in the Netherlands over eight weeks following glyphosate application to sandy soil. Airborne sediment was collected every two weeks, at five different heights, and analyzed for glyphosate and AMPA. Results showed that the glyphosate content in the samples was initially high, almost 6000 µg kg^-1 two weeks after application, decreasing to about 2300 µg kg^-1 eight weeks after application. AMPA content showed less variation and fluctuated between 1000 and 1700 µg kg^-1. Airborne concentrations ranged from 0.01 to 1 µg m^-3 for glyphosate and from 0.005 to 0.5 µg m^-3 for AMPA. They showed a clear and systematic decrease with height. Elevated airborne concentrations were measured up to approximately six weeks after application. Horizontal transport flux followed a similar pattern, decreasing with height and remaining elevated up to six weeks after application. Both glyphosate and AMPA were substantially enriched in the fine particle fractions of the soil, with higher enrichment ratios in finer sediments. More than half of the glyphosate and AMPA that was collected in the airborne samples was transported in suspension. The transport pathway was calculated for two days with high emissions and indicated that long-distance travelling of pesticides is a matter of concern. Analysis of the glyphosate and AMPA amounts in the PM10 fraction of the airborne samples suggests that residents in agricultural areas where glyphosate is frequently applied may be at risk of inhalation exposure.

The presence of antibiotics in aquaculture wastewater poses environmental and public-health risks by disrupting aquatic ecosystems and promoting the spread of antibiotic-resistant bacteria. This study evaluates pine-bark biochars activated under different atmospheres for the removal of tetracycline from real aquaculture wastewater and examines their combined use with peroxymonosulfate as an oxidant. The biochars were produced by pyrolysis and activated using carbon dioxide or humid argon. Carbon-dioxide activation generated a larger surface area and a more developed porous structure than humid-argon activation, which resulted in higher adsorption performance. Batch experiments achieved 80-100% tetracycline removal in real aquaculture wastewater containing competing ions and dissolved organic matter. Adsorption kinetics followed the pseudo-second-order model, indicating that chemisorption governed the process, while intraparticle diffusion contributed but was not the controlling step. The solution pH strongly influenced adsorption, with maximum removal under alkaline conditions. Results suggest that aromatic ring interactions, hydrogen bonding and surface complexation were predominant adsorption mechanisms. Combining biochar with peroxymonosulfate enhanced tetracycline removal through a synergistic effect, reaching up to 99% with very low oxidant dosages. These findings highlight pine-bark biochar as a promising and sustainable metal-free material for treating contaminants of emerging concern in aquaculture wastewater.

Calcium oxide (CaO) was synthesized from waste chicken eggshells through calcination at 900 °C and examined to determine catalytic efficiency in degrading Rhodamine B (RhB) dye. Although CaO exhibits photocatalytic properties, to enhance its catalytic performance, a tribocatalytic and solar-tribocatalytic approach was utilized for RhB dye degradation. The material was characterized using X-ray diffraction, Raman spectroscopy, Photoluminescence, Diffuse reflectance spectrum, X-ray photoelectron spectroscopy and Scanning electron microscopy with energy-dispersive X-ray spectroscopy to analyze its structural and morphological properties. The degradation percentages achieved within 240 min were 33% ± 1.4% for photocatalysis, 75% ± 2.0% for tribocatalysis, and 91% ± 1.6% for solar-tribocatalysis, respectively. Furthermore, the effects of key experimental factors, including solution pH, presence of light source and surface area of polytetrafluoroethylene (PTFE), were investigated under tribocatalytic conditions. Scavenger analysis identified superoxide radicals (O₂^.-) as the primary agents responsible for degrading the chromophoric structure of RhB.

The increasing demand for accurate toxicity assessment and to minimise or eliminate the use of animal testing has accelerated the development of numerous computational models, AI/ML models, and online resources that support research in computational toxicology. This review addresses toxicity prediction and chemical safety evaluation, focusing on computational models and data coverage, Molecular Descriptors, QSAR models, AI/ML-based approaches, Explainable AI, predictive methodologies, regulatory relevance, and accessibility. This collectively enables the identification, prediction, and analysis of chemical toxicity across various biological endpoints. In addition, the review highlights AI/ML tools for predicting toxicity endpoints, such as neurotoxicity, hepatotoxicity, cardiotoxicity, genotoxicity, and environmental toxicity. Regulatory limitations vary significantly among countries and jurisdictions, exhibiting a marked absence of convergence. Current debates regarding regulatory norms focus on achieving global conformity. Regulatory adaptability is the key as AI evolves rapidly. The promotion of AI/ML tool integration and interoperable frameworks could substantially enhance the future of predictive toxicology.

Plant-derived essential oils are attractive corrosion inhibitors due to their low cost, availability,::::and eco-friendliness. In this work, essential oil of Zingiber mioga (ZM), obtained by hydro-distillation, was studied as a green inhibitor for mild steel in 1 M HCl. The oil was characterized by FTIR and GC-MS, while its inhibition performance was evaluated by gravimetric studies and electrochemical techniques (EIS and PDP). Surface morphology was examined using SEM, SEM-EDS, and FTIR, and density functional theory (DFT) was applied to assess the reactivity of key phytochemicals. Results revealed that inhibition efficiency increased with concentration and reached a maximum at 5 g/L (93%). At this concentration, the charge transfer resistance (R_ct) improved from 66.1 Ω cm² (blank) to 313.94 Ω cm², and weight-loss studies confirmed a significant reduction in corrosion rate. The inhibition efficiency was observed to decrease with increasing temperature, dropping from 93% at 303 K to 77% at 333 K. PDP showed mixed-type inhibition with anodic predominance, while adsorption followed the Langmuir isotherm (ΔG°_ads < 20 kJ/mol), indicating physisorption. GC-MS identified α-pinene, α-terpineol, and caryophyllene as major components, and DFT confirmed that 2-[4-methyl-6-(2,6,6-trimethylcyclohex-1-enyl)-hexa-1,3,5-trienyl]cyclohex-1-en-1-carboxaldehyde, with ΔE = 5.03 eV, was the most active inhibitor. Zingiber mioga essential oil acts as a promising eco-friendly corrosion inhibitor, and future studies should explore its industrial applications and synergistic effects with other natural inhibitors.

Rare Earth Elements (REEs) are essential for advanced technologies and high-tech applications, yet their presence and impact in marine and transitional ecosystems remain poorly understood. This study provides a preliminary assessment of their distribution in the Venice Lagoon (LoV), a large Mediterranean coastal lagoon in northeastern Italy, by analyzing three environmental matrices: water, sediments, and mesozooplankton. Particular attention was given to Acartia (Acartiura) clausii and Acartia (Acanthacartia) tonsa, two dominant copepod species. Seasonal samples were collected from three sites characterized by different environmental conditions and varying degrees of anthropogenic influence. REE concentrations were determined using ICP-MS after acid digestion. Results indicate that REE concentrations in water were below the quantification limit, while sediments exhibited an enrichment of light REEs (LREEs) over heavy REEs (HREEs). In mesozooplankton, bioaccumulation was limited but detectable, with a spatial gradient showing higher concentrations in areas under strong anthropogenic pressure (San Giuliano) and lower values in marine-influenced sites. Seasonal patterns suggest that temperature and primary productivity influence REE uptake. These findings provide a baseline for monitoring REEs in the Venice Lagoon and underscore the need for further research on their environmental fate and potential ecotoxicological impact, particularly on zooplankton communities.