Environmental Science and Pollution Research最新文献

Popular conventional biogas plants in India are reactors operated on cattle dung slurry and cannot accommodate solid biomass. The study elucidates the performance of a digestion system for solid discarded vegetable (DV), subjecting it to aerobic composting and utilising the derived leachate as feedstock for biomethanation, and also accounts for the fugitive greenhouse gases (GHGs) loss from the system. Leachates from the composting substrates had a BOD₅ of 15,305.6 ± 845.1 mg L^-1, which was reduced to 912.3 ± 94.0 mg L^-1 post-biomethanation. The loss of GHGs was highest in the slurry pit (199.33 ± 14.88 g day^-1 CH₄ and 315.25 ± 24.59 g day^-1) and least from the outlet pipe (0.47 ± 0.03 g day^-1 CH₄ and 0.74 ± 0.05 g day^-1 CO₂). Organic leachates from composting DV can be suitably used as substrates for biomethanation. However, attention is needed to minimise the fugitive GHGs loss from the system equivalent to ~2.4 kg CO₂e for producing 1.0 m³ of biogas.

Metal pollution in urban areas has become a serious problem during the last two decades because of vehicular emission, industrial activity, fossil fuel use, and their accumulation constitutes a serious environmental hazard. The aviation sector puts additional impact on the environment further impacting human health. Urban trees can uptake and accumulate pollutants in their tissues, through their roots and leaves. This study aimed to determine whether airport traffic has toxic effects on airport's vegetation, to compare five urban trees with different morphological and silvicultural characteristics (Pinus brutia, Tamarix sp., Populus alba, Olea europaea, Nerium oleander) regarding their foliar metals (Cu, Ni, Pb, Mn, Fe, Co, Cr, Cd, Zn) accumulation, and to find out how proximity to the airport affects above accumulation. Airport of Thessaloniki, northern Greece (SKG) was the case study where data were collected. Results showed that forest tree species presented different heavy metal accumulation patterns. The metals concentration in leaf samples was low and did not exceed toxicity threshold, both inside and outside the airport area. The taller trees with extensive crown surface area i.e., the deciduous and fast-growing tree species P. alba and the evergreen conifer tree species P. brutia, were the most affected. The proximity to the airport area had strong influence on the metal's concentrations in the foliage of P. brutia, while in the other tree species it significantly affected only one or two metals.

The health of children may be adversely influenced by the air quality in schools because they are more sensitive to indoor air pollutants. PM₁₀, which consists of tiny particles that are 10 µm in size or smaller, carries notable dangers associated with breathing issues and the escalation of asthma. This research marks the inaugural continuous monitoring of indoor air quality (IAQ) in Moroccan primary schools, which was conducted in Kenitra from May 27 to September 22, 2023. Indoor monitoring occurred during unoccupied times (May-July), while outdoor data collection was continued until late September. We investigated the PM₁₀ concentrations while considering temperature and humidity using IoT sensor technology. Results from the investigation indicated that the PM₁₀ levels found indoors were moderate, with study-long average concentrations of 15.3 µg/m³ at the urban residential school (Site 1) and 12.3 µg/m³ at the school on a heavy vehicle road near the Sebou River (Site 2). The levels of PM₁₀ outdoors fluctuated significantly, as they were shaped by traffic flow and weather variations. Diurnal patterns revealed morning peaks and afternoon decreases due to natural processes. An exhaustive study was executed, concentrating on three machine learning frameworks-Random Forest, CatBoost, and XGBoost-to reveal the undisclosed indoor PM₁₀ levels. The XGBoost framework demonstrated significant predictive accuracy for hourly PM₁₀ (R² = 0.77; MAE = 2.08 µg/m³; RMSE = 2.82 µg/m³), highlighting the efficacy of ensemble algorithms in environmental forecasting. The machine learning framework enhances confidence in the accurate representation of indoor air quality and provides a robust basis for sustaining pollution oversight. The investigation supplies a fundamental PM₁₀ dataset for Morocco, aiding future epidemiological examinations and specialized indoor air quality actions in academic settings. It emphasizes the necessity of combining cost-effective sensor networks with sophisticated machine learning to address indoor air quality challenges in developing urban environments.

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.