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Emissions of Tetrafluoromethane (CF4) and Hexafluoroethane (C2F6) From East Asia: 2008 to 2019
The perfluorocarbons (PFCs), tetrafluoromethane (CF4) and hexafluoroethane (C2F6), are potent greenhouse gases with very long atmospheric lifetimes. They are emitted almost entirely from industrial sources, including the aluminum and rare earth metal smelting industries that emit them as by-products, and the semiconductor and flat panel display manufacturing industries that use them and vent unutilized amounts to the atmosphere. Despite extensive industrial efforts to quantify and curb these emissions, “top-down” PFC emission estimates derived from atmospheric measurements continue to rise and are significantly greater than reported process- and inventory-based “bottom-up” emissions. In this study, we estimate emissions of CF4 and C2F6 from East Asia, where PFC emitting industries are heavily concentrated, using a top-down approach (a Bayesian inversion) with high-frequency atmospheric measurements at Gosan (Jeju Island, South Korea) for 2008–2019. We also compile and analyze the available bottom-up CF4 and C2F6 emissions in East Asia from industrial and government reports. Our results suggest that the observed increases in global PFC emissions since 2015 are driven primarily by China's aluminum industry, with significant contributions from Japan's and Korea's semiconductor industry. Our analysis suggests that Chinese emissions occur predominantly from the aluminum industry, although their emissions per production ratio may be improving. Our results for Japan and Korea find significant discrepancies between top-down and bottom-up emissions estimates, suggesting that the effectiveness of emission reduction systems (abatement) used in their semiconductor industries may be overestimated. Overall, our top-down results for East Asia contribute significantly to reducing the gap in the global PFC emission budgets.
2021
2021
Moving forward in microplastic research: A Norwegian perspective
Given the increasing attention on the occurrence of microplastics in the environment, and the potential envi-ronmental threats they pose, there is a need for researchers to move quickly from basic understanding to applied science that supports decision makers in finding feasible mitigation measures and solutions. At the same time, they must provide sufficient, accurate and clear information to the media, public and other relevant groups (e.g., NGOs). Key requirements include systematic and coordinated research efforts to enable evidence-based decision making and to develop efficient policy measures on all scales (national, regional and global). To achieve this, collaboration between key actors is essential and should include researchers from multiple disciplines, policy-makers, authorities, civil and industry organizations, and the public. This further requires clear and informative communication processes, and open and continuous dialogues between all actors. Cross-discipline dialogues between researchers should focus on scientific quality and harmonization, defining and accurately communi-cating the state of knowledge, and prioritization of topics that are critical for both research and policy, with the common goal to establish and update action plans for holistic benefit. In Norway, cross-sectoral collaboration has been fundamental in supporting the national strategy to address plastic pollution. Researchers, stakeholders and the environmental authorities have come together to exchange knowledge, identify knowledge gaps, and set targeted and feasible measures to tackle one of the most challenging aspects of plastic pollution: microplastic. In this article, we present a Norwegian perspective on the state of knowledge on microplastic research efforts. Norway’s involvement in international efforts to combat plastic pollution aims at serving as an example of how key actors can collaborate synergistically to share knowledge, address shortcomings, and outline ways forward to address environmental challenges.
2021
The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies
Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.
2021
2021
Potential use of CAMS modelling results in air quality mapping under ETC/ATNI
ir quality European-wide annual maps based on the Regression – Interpolation – Merging Mapping (RIMM) data fusion methodology have been regularly produced, using the Air Quality e-Reporting validated (E1a) monitoring data, the EMEP modelling data and other supplementary data. In this report, we examine the use of the preliminary (E2a) monitoring data as provided up-to-date (UTD) by many European countries and as also stored in the Air Quality e-Reporting database, together with the EMEP or the Copernicus Atmospheric Monitoring Service (CAMS) modelling data in two variants (i.e. CAMS Ensemble Interim Reanalysis and CAMS Ensemble Forecast) for potential preparing of preliminary spatial maps. With respect to the availability, the CAMS Ensemble Forecast is the most useful in the potential interim mapping. Such preliminary maps could be constructed approximately one year earlier than the validated maps. Even though we have demonstrated the feasibility, the mapping performance presented in the report is influenced by the lack of the E2a data in some areas.
Next to the evaluation of potential interim maps, regular RIMM maps based on the validated E1a measurement data using three different chemical transport model outputs have been compared, i.e. using the CAMS Ensemble Forecast, the CAMS Ensemble Interim Reanalysis and the EMEP model outputs. Based on the evaluation of the results presented, it is not possible to conclude that any of the three model datasets gives definitively better results compared to the others. The results do not provide strong reasons for a potential change of the model used in the regular mapping.
In addition, the RIMM mapping results have been compared with the CAMS Ensemble Forecast and the CAMS Ensemble Interim Reanalysis outputs. The comparison shows that the data fusion RIMM method gives better results, both in the rural and urban background areas, presumably because of the higher spatial resolution, introduction of additional ancillary data in the data fusion and not fully reduced bias in some data assimilation methods used in CAMS.
ETC/ATNI
2021
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2021
Long-term trends of air pollutants at national level 2005-2019
Trend calculations of air pollutants for the periods 2005-2019 have been applied. Sulphur dioxide shows the largest decrease of all pollutants with a reduction of the order of 60-70 %. The agreement between reported emission data and measured concentrations are quite good. For NO2, a mismatch between the trend in air concentrations and NOx emissions is found. While the overall NOx emissions are reported to be reduced by 45 %, the measured NO2 data indicate a decline of the order of 30 % although marked differences between the countries are found. This mismatch could not be explained by changes in meteorology as this is accounted for. Possible reasons for the mismatch could be the NO2/NOx ratio of the emissions, changes in baseline hemispheric ozone concentration and natural emissions. For PM data (PM10 and PM2.5) we find an opposite mismatch, meaning that the PM concentrations show stronger downward trends than the reported emissions. This is likely an effect of the importance of secondary aerosols which are mitigated by other activities than the direct PM emissions. An overall reduction in PM10 of the order of 30-38 % is found during 2005-2019 while the direct emissions give a reduction that is 5-10 percentage units smaller. Similar results are found for PM2.5, but these findings are uncertain due to the less amount of long-term data. For O3, our findings are in line with earlier studies noting that the annual mean ozone concentration has increased while the high peaks have been reduced. But the reduction of the peaks is now within only a few percent and non-significant, while for the 2000-2017 period it was significant and about 10%.
ETC/ATNI
2021