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Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g., concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source-term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation also allows estimation of all tuning parameters from the measurements. The estimation procedure is based on the variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to also estimate all tuning parameters from the observations. The proposed algorithm is tested and compared with the standard methods on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download.
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2019
Aerosols are an important constituent of the atmosphere both influencing the climate system and contributing to increasing pollution of the Arctic. At the same time, their adequate monitoring is a big challenge, as instruments on the ground only can sample aerosols in the lowermost atmosphere. For this reason, these measurements are complemented with observations of aerosol optical depth (AOD) which quantify the total amount of aerosols throughout the atmosphere from the attenuation of direct sunlight (and moonlight). This procedure requires extremely careful instrument calibration and removal of cloud contaminated data. In Svalbard, such measurements have been performed by several research groups with different instruments, mostly in Ny-Ålesund and in Hornsund, but also on research vessels offshore. In the framework of the SSF Strategic Grant project ReHearsol, all AOD data from the Svalbard region since 2002 have been collected and made available to the SIOS research community. They indicate that number and intensity of Arctic haze episodes occurring in late winter and spring have decreased consistently and significantly in the last 20 years, while pollution events in summer/early autumn, caused by boreal biomass burning, are on the rise, though not as consistently. Comparison between in-situ measurements at Gruvebadet Atmosphere Laboratory in Ny-Ålesund and AOD measurements indicate that most (more than 65%) of the episodes with high aerosol load are not captured by surface measurements. This finding does not change when one includes in-situ measurements at Zeppelin Observatory (475 m a.s.l.). Studying extensive high-AOD episodes such as those in summer 2019 requires a multi-tool approach including in-situ and remote-sensing measurements combined with model tools.
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Adequate infrastructure for citizen science is constantly growing and has become increasingly important in providing support to citizen science activities, both nationally and internationally. Many types of citizen science infrastructures exist, with different functionalities. This chapter focuses on current citizen science platforms. The platforms addressed in this chapter are those which display citizen science data and information, provide good practical examples and toolkits, collect relevant scientific outcomes, and are accessible to different stakeholders, ranging from interested citizens to scientific institutions to authorities, politicians, and public media. We present current citizen science platforms in Europe and associated (inter)national citizen science networks and discuss how these platforms have become increasingly vital within citizen science. Based on these examples, we elaborate on challenges for citizen science platforms, such as establishing and financing platforms, designing user interfaces, maintaining platforms, promoting the usage of platforms, etc. We conclude with an outlook into potential development needs of citizen science platforms in the future.
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