Found 10009 publications. Showing page 6 of 401:
2025
2025
2025
2025
2025
2025
2025
2025
Aerosol hygroscopicity influenced by seasonal chemical composition variations in the Arctic region
In this study, we quantified aerosol hygroscopicity parameter using aerosol microphysical observation data (κphy), analyzing monthly and seasonal trends in κphy by correlating it with aerosol chemical composition over 6 years from April 2007 to March 2013 at the Zeppelin Observatory in Svalbard, Arctic region. The monthly mean κphy value exhibited distinct seasonal variations, remaining high from winter to spring, reaching its minimum in summer, followed by an increase in fall, and maintaining elevated levels in winter. To verify the reliability of κphy, we employed the hygroscopicity parameter calculated from chemical composition data (κchem). The chemical composition and PM2.5 mass concentration required to calculate κchem was obtained through Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis data and the calculation of κchem assumed that Arctic aerosols comprise only five species: black carbon (BC), organic matter (OM), ammonium sulfate (AS), sea salt aerosol less than a diameter of 2.5 μm (SSA2.5), and dust aerosol less than a diameter of 2.5 μm (Dust2.5). The κchem had no distinct correlation but had a similar seasonal trend compared to κphy. The κchem value followed a trend of SSA2.5 and was much higher by a factor of 1.6 ± 0.3 than κphy on average, due to a large proportion of SSA2.5 mass concentration in MERRA-2 reanalysis data. This may be due to the overestimation of sea salt aerosols in MERRA-2 reanalysis. The relationship between monthly mean κphy and the chemical composition used to calculate κchem was also analyzed. The elevated κphy from October to February resulted from the dominant influence of SSA2.5, while the maximum κphy in March was concurrently influenced by increasing AS and Dust2.5 associated with long-range transport from mid-latitude regions during Arctic haze periods and by SSA mass concentration obtained from in-situ sampling, which remained high from the preceding winter. The relatively low κphy from April to September can be attributed to low SSA2.5 and the dominance of organic compounds in the Arctic summer. Either natural sources such as those of marine and terrestrial biogenic origin or long-range-transported aerosols may contribute to the increase in organic aerosols in summer, potentially influencing the reduction in κphy of atmospheric aerosols. To our knowledge, this is the first study to analyze the monthly and seasonal variation of aerosol hygroscopicity calculated using long-term microphysical data, and this result provides evidence that changes in monthly and seasonal hygroscopicity variation occur depending on chemical composition.
2025
Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH4 emissions in Australia amount to 6.84 ± 0.51 Tg CH4 yr−1 and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH4 yr−1, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.
2025
2025
Critical review of the atmospheric composition observing capabilities for monitoring and forecasting
WMO
2025
2025
Potato plant disease detection: leveraging hybrid deep learning models
Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate detection vital, especially in staple crops like potatoes. Traditional manual methods, as well as some existing machine learning and deep learning techniques, often lack accuracy and generalizability due to factors such as variability in real-world conditions. This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). It has been trained on a diverse potato leaf image dataset, the “Potato Leaf Disease Dataset”, which reflects real-world agricultural conditions. The proposed model achieved an accuracy of 85.06, representing an 11.43 improvement over the results of the previous study. These results highlight the effectiveness of the hybrid model in complex agricultural settings and its potential to improve potato plant disease detection and identification.
2025
Status report of air quality in Europe for year 2024, using validated and up-to-date data
This report presents summarised information on the status of air quality in Europe in 2024, based on Up-To-Date data (i.e. prior to final quality control) and validated air quality monitoring data officially reported by the member and cooperating countries of the EEA. It aims at giving more timely and preliminary information on the status of ambient air quality in Europe in 2024 for five key air pollutants (PM10, PM2.5, O3, NO2 and SO2). The report also gives a preliminary assessment of the progress towards meeting the European air quality standards for the protection of health and the World Health Organization air quality guideline levels, and compares the air quality status in 2024 with the previous years. The preliminary data reported for 2024 shows that 7% and 13% of the monitoring stations exceeded the EU standards for PM10 and O3, respectively. The WHO AQG for PM2.5, PM10, O3 and SO2 were exceeded by 93%, 59%, 98% and 3%, respectively. Exceedances of the NO2 limit value still occur in 7 reporting countries and NO2 WHO AQG occur in all reporting countries.
ETC/HE
2025
2025
Genotoxicity assessment is essential for ensuring chemical safety and mitigating risks to human health and the environment. Traditional methods, reliant on animal models, are time-consuming, costly, and raise ethical concerns. New Approach Methods (NAMs) offer innovative, cost-effective, and ethical alternatives, playing a pivotal role in both traditional and next-generation risk assessment (NGRA) by minimizing the need for animal testing, particularly in genotoxicity evaluations. However, the development of NAMs often overlooks the particular physicochemical properties of nanomaterials (NMs), which significantly influence their toxicological behaviour and can interfere with genotoxicity evaluation. This underscores an urgent need for the standardization and adaptation of NAMs to address nano- and advanced material-specific genotoxicity challenges. In this review, we summarize the challenges associated with genotoxicity testing of NMs and highlight the suitability of existing in vitro and in silico NAMs for NMs and advanced materials, enabling genotoxicity testing across various exposure routes and organ systems. Despite considerable progress, regulatory validation remains constrained by the absence of approved test guidelines and standardized protocols. To achieve regulatory acceptance, it is crucial to adapt NAMs to NM-specific exposure scenarios, refine test systems to better mimic human biology, develop tailored in vitro protocols, and ensure thorough characterisation of NMs both in pristine form and dispersed in culture medium. Collaborative efforts among scientists, regulators, industry, and advocacy groups are vital to improving the reliability and regulatory acceptance of NAMs. By addressing these challenges, NAMs have the potential to revolutionize genotoxicity risk assessment, advancing it towards a more sustainable, efficient and ethical framework.
2025
2025
2025