Found 10066 publications. Showing page 383 of 403:
Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI
Satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO2, however they typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled ‘S-MESH’ (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and modeled meteorological parameters such as planetary boundary layer height, wind velocity, temperature. The overall model evaluation shows a mean absolute error of 7.77 μg/m3, a median bias of 0.6 μg/m3 and a Spearman rank correlation of 0.66. The model performance is found to be influenced by NO2 concentration levels, with the most reliable predictions at concentration levels of 10–40 μg/m3 with a bias of
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2024
Polychlorinated n-alkanes (PCAs) are the main components of chlorinated paraffins (CPs) mixtures, that have been commonly grouped into short-chain (SCCPs, C10–13), medium-chain (MCCPs, C14–17), and long-chain (LCCPs, C18-30) CPs. PCAs pose a significant risk to human health as they are broadly present in indoor environments and are potentially persistent, bioaccumulative, and toxic. The lack of specific terminology and harmonization in analytical methodologies for PCA analysis complicates direct comparisons between studies. The present work summarizes the different methodologies applied for the analysis of PCAs in indoor dust, air, and organic films. The large variability between the reviewed studies points to the difficulties to assess PCA contamination in these matrices and to mitigate risks associated with indoor exposure. Based on our review of physicochemical properties of PCAs and previously reported sum of measurable S/M/LCCPs levels, the homologue groups PCAs–C10–13 are found to be mostly present in the gas phase, PCAs–C14–17 in particulate matter and organic films, and PCAs–C≥18 in settled dust. However, we emphasized that mapping PCA sources and distribution in the indoors is highly dependent on the individual homologues. To further comprehend indoor PCA distribution, we described the uses of PCA in building materials and household products to apportion important indoor sources of emissions and pathways for human exposure. The greatest risk for indoor PCAs were estimated to arise from dermal absorption and ingestion through contact with dust and CP containing products. In addition, there are several factors affecting indoor PCA levels and exposure in different regions, including legislation, presence of specific products, cleaning routines, and ventilation frequency. This review provides comprehensive analysis of available indoor PCA data, the physicochemical properties, applied analytical methods, possible interior sources, variables affecting the levels, human exposure to PCAs, as well as need for more information, thereby providing perspectives for future research studies.
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NILU performed a test campaign for measurements of CF4 and C2F6 for stack emissions at Alcoa Mosjøen Smelter. Time-integrated samples were taken with evacuated canisters combined with low-flow restrictors for continuous sampling periods as long as 4 weeks. The samples were analyzed at NILU with a Medusa preconcentration method combined with GC-MS SIM. As a main conclusion, time integrated sampling together with Medusa GC-MS methodology is a very precise alternative to the traditional attempts to quantify PFC-emission.
NILU
2024
Active vegetation fires in south-eastern (SE) Europe resulted in a notable increase in the number concentration of aerosols and cloud condensation nuclei (CCN) particles at two high latitude locations—the SMEAR IV station in Kuopio, Finland, and the Zeppelin Observatory in Svalbard, high Arctic. During the fire episode aerosol hygroscopicity κ slightly increased at SMEAR IV and at the Zeppelin Observatory κ decreased. Despite increased κ in high CCN conditions at SMEAR IV, the aerosol activation diameter increased due to the decreased supersaturation with an increase in aerosol loading. In addition, at SMEAR IV during the fire episode, in situ measured cloud droplet number concentration (CDNC) increased by a factor of ∼7 as compared to non-fire periods which was in good agreement with the satellite observations (MODIS, Terra). Results from this study show the importance of SE European fires for cloud properties and radiative forcing in high latitudes.
2024
Background
Prioritisation of chemical pollutants is a major challenge for environmental managers and decision-makers alike, which is essential to help focus the limited resources available for monitoring and mitigation actions on the most relevant chemicals. This study extends the original NORMAN prioritisation scheme beyond target chemicals, presenting the integration of semi-quantitative data from retrospective suspect screening and expansion of existing exposure and risk indicators. The scheme utilises data retrieved automatically from the NORMAN Database System (NDS), including candidate substances for prioritisation, target and suspect screening data, ecotoxicological effect data, physico-chemical data and other properties. Two complementary workflows using target and suspect screening monitoring data are applied to first group the substances into six action categories and then rank the substances using exposure, hazard and risk indicators. The results from the ‘target’ and ‘suspect screening’ workflows can then be combined as multiple lines of evidence to support decision-making on regulatory and research actions.
Results
As a proof-of-concept, the new scheme was applied to a combined dataset of target and suspect screening data. To this end, > 65,000 substances on the NDS, of which 2579 substances supported by target wastewater monitoring data, were retrospectively screened in 84 effluent wastewater samples, totalling > 11 million data points. The final prioritisation results identified 677 substances as high priority for further actions, 7455 as medium priority and 326 with potentially lower priority for actions. Among the remaining substances, ca. 37,000 substances should be considered of medium priority with uncertainty, while it was not possible to conclude for 19,000 substances due to insufficient information from target monitoring and uncertainty in the identification from suspect screening. A high degree of agreement was observed between the categories assigned via target analysis and suspect screening-based prioritisation. Suspect screening was a valuable complementary approach to target analysis, helping to prioritise thousands of substances that are insufficiently investigated in current monitoring programmes.
Conclusions
This updated prioritisation workflow responds to the increasing use of suspect screening techniques. It can be adapted to different environmental compartments and can support regulatory obligations, including the identification of specific pollutants in river basins and the marine environments, as well as the confirmation of environmental occurrence levels predicted by modelling tools.
2024
An AI-Enhanced Systematic Review of Climate Adaptation Costs: Approaches and Advancements, 2010–2021
This study addresses the critical global challenge of climate adaptation by assessing the inadequacies in current methodologies for estimating adaptation costs. Broad assessments reveal a significant investment shortfall in adaptation strategies, highlighting the necessity for precise cost analysis to guide effective policy-making. By employing the PRISMA 2020 protocol and enhancing it with the prismAId tool, this review systematically analyzes the recent evolution of cost assessment methodologies using state-of-the-art generative AI. The AI-enhanced approach facilitates rapid and replicable research extensions. The analysis reveals a significant geographical and sectoral disparity in research on climate adaptation costs, with notable underrepresentation of crucial areas and sectors that are most vulnerable to climate impacts. The study also highlights a predominant reliance on secondary data and a lack of comprehensive uncertainty quantification in economic assessments, suggesting an urgent need for methodological enhancements. It concludes that extending analyses beyond merely verifying that benefits exceed costs is crucial for supporting effective climate adaptation. By assessing the profitability of adaptation investments, it becomes possible to prioritize these investments not only against similar interventions but also across the broader spectrum of public spending.
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