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.

As an apex predator, the Eurasian otter (Lutra lutra) encounters trace element pollutants in freshwater ecosystems. Our study assessed the accumulation of arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), selenium (Se), copper (Cu), manganese (Mn), and zinc (Zn) in the lung, liver, and kidney tissues of Eurasian otters collected from five regions in South Korea between 2018 and 2024. Comparisons with prior South Korean and European studies indicated regional variations in Se and Mn levels, while other trace element levels remained consistent. Overall concentrations were below known toxicity thresholds, indicating limited immediate risk, although persistent exposure may pose sublethal effects. Organ-specific distribution revealed that As, Cd, and Se accumulated primarily in the kidneys, whereas Hg, Pb, Cu, Mn, and Zn were highest in the liver. The lungs consistently showed the lowest concentrations. Positive correlations were observed between Cd, Hg, and Se, and between Pb and Cu. Age-related differences were identified, with adults exhibiting higher Cd, Hg, and Se levels, whereas juveniles had elevated Pb, Cu, and Zn concentrations. No sex-related differences were observed. These findings enhance understanding of trace element dynamics in Eurasian otters and provide updated insights into freshwater contamination in South Korea.

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.

Plastic waste pollution has become a critical environmental challenge that requires innovative monitoring approaches to support effective environmental management. This systematic review synthesizes recent advancements in remote sensing (RS) technologies for plastic waste detection, analyzing 84 studies published between 2018 and 2024 following PRISMA guidelines. The review evaluates RS platforms, sensor types, spectral ranges, classification methods, and polymer identification across diverse environmental settings. Satellite platforms dominate large-scale marine monitoring (45% of studies), while unmanned aerial vehicles (UAVs) excelled in high-resolution coastal applications (23%). Correspondence analysis identified four distinct research clusters optimized for specific platform-environment combinations. Supervised learning was most prevalent (50%), though deep learning approaches and hybrid models show emerging promise. Polyethylene was most frequently detected across platforms. Limitation of the research field includes geographic bias towards European sites (> 50%), focus on controlled conditions rather than operational deployment, inability to detect microplastics, and lack of standardized protocols. The review also highlights emerging developments in RS technologies, including spectral mechanisms that support polymer discrimination and ongoing gaps in plastic monitoring. An integrated framework is proposed that combines multi-platform Earth Observation (EO), machine learning, and citizen science to enable scalable plastic waste monitoring. The findings provide theoretical and practical insights to guide future sensor design, algorithm development, and global monitoring strategies.

Jellyfish blooms are significant events in marine ecosystems, profoundly impacting carbon and nutrient cycles. During these events, decomposing jellyfish release dissolved organic matter (DOM), which fuels bacterial growth and reshapes nutrient cycling. In this study, we employed an environmental DNA (eDNA) metabarcoding approach to capture bacterial communities associated with Aurelia aurita, and in different body parts, as well as its ambient surface water column during bloom (December 2022) and post-bloom (March 2023) periods in the Golden Horn Estuary, İstanbul, Türkiye. The results reveal distinct temporal and regional variations in bacterial diversity, highlighting the pivotal role of jellyfish blooms in reshaping bacterial communities.

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.

A series of alginate-encapsulated composites (AECs) based on zeolite (NaX) and montmorillonite (MMT) and their magnetite-functionalized derivatives (Alg/NaX/Fe₃O₄ and Alg/MMT/Fe₃O₄) were synthesized using a natural polysaccharide, sodium alginate, cross-linked with calcium ions. The composites were characterized by Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, and scanning electron microscopy. The synthesized AECs showed good adsorption properties for metal ions. The maximum Cd(II) adsorption reached 0.74-0.76 mmol/g for NaX- and MMT-containing AECs and 0.69-0.70 mmol/g for magnetite-incorporated AECs. Fe(II) uptake showed a similar trend. The sorption capacity was 0.43-0.41 mmol/g for AECs without magnetite and 0.38 and 0.28 mmol/g for Alg/NaX/Fe₃O₄ and Alg/MMT/Fe₃O₄, respectively. The time dependence of ion sorption was investigated, and the sorption mechanism was analyzed. The experimental adsorption kinetics aligned with the pseudo-second-order model, indicating that chemisorption is the main mechanism for the interaction of the ions with the surface groups of the sorbents. The AECs achieved 96-99% removal of Cd(II) and 83-97% removal of Fe(II) ions, confirming their high efficiency in extracting metal ions. Thermal analysis showed that the composites exhibit high stability, with decomposition starting above 150 °C. This makes them suitable for use in hot aqueous solutions and enables efficient magnetic separation.

The study examines the development of D-D-π-A organic sensitizers (D2-D4) to use in dye-sensitized solar cell (DSSC) applications. These dyes have been designed by substituting the π-bridge unit (benzothiadiazole) of an existing dye (D1) with diketopyrrolopyrrole (DPP) units. Calculations of theoretical parameters, such as structural, electronic, optical, and photovoltaic characteristics, were carried out using density functional theory (DFT) and time-dependent DFT (TD-DFT). The results indicated that D2-D4 exhibited a higher degree of planarity compared to D1, which is beneficial for intramolecular charge transfer (ICT) processes. Energy level analysis revealed that the HOMO/LUMO states of the sensitizers are aligned with the HOMO/LUMO states of the TiO₂ substrate, thus facilitating electron injection into the conduction band of TiO₂ and subsequent regeneration of the sensitizer. Among all the investigated sensitizers, D4 exhibited the best performance; specifically, it had the least energy gap between the HOMO and LUMO states (1.69 eV), the largest wavelength range within the visible spectrum, and the largest charge transfer capacity. Photovoltaic performance simulations of D2-D4 each showed a larger calculated performance compared to D1. Most notably, D4 exhibited excellent open circuit voltage (V_OC = 0.87 eV) and fill factor (FF = 0.71) values, as well as the largest short circuit current density (J_SC = 24.02 mA cm^-2), resulting in a maximum power conversion efficiency (PCE) of 14.83%. These results demonstrate the potential of DPP as a π-bridge unit to create effective organic sensitizers for DSSC applications.