Software for Evaluating Relevance of Steps in Algebraic Transformations

Students of our department solve algebraic exercises in mathematical logic in a computerized environment. They construct transformations step by step and the program checks the syntax, equivalence of expressions and completion of the task. With our current project, we add a program component for checking relevance of the steps.Comment: CICM 2013, Bat

Evaluation of the performance of four chemical transport models in predicting the aerosol chemical composition in Europe in 2005

© Author(s) 2016.Four regional chemistry transport models were applied to simulate the concentration and composition of particulate matter (PM) in Europe for 2005 with horizontal resolution 20 km. The modelled concentrations were compared with the measurements of PM chemical composition by the European Monitoring and Evaluation Programme (EMEP) monitoring network. All models systematically underestimated PM10 and PM2:5 by 10–60 %, depending on the model and the season of the year, when the calculated dry PM mass was compared with the measurements. The average water content at laboratory conditions was estimated between 5 and 20% for PM2:5 and between 10 and 25% for PM10. For majority of the PM chemical components, the relative underestimation was smaller than it was for total PM, exceptions being the carbonaceous particles and mineral dust. Some species, such as sea salt and NO3, were overpredicted by the models. There were notable differences between the models’ predictions of the seasonal variations of PM, mainly attributable to different treatments or omission of some source categories and aerosol processes. Benzo(a)pyrene concentrations were overestimated by all the models over the whole year. The study stresses the importance of improving the models’ skill in simulating mineral dust and carbonaceous compounds, necessity for high-quality emissions from wildland fires, as well as the need for an explicit consideration of aerosol water content in model–measurement comparison.Peer reviewedFinal Published versio

A refinement of the emission data for Kola Peninsula based on inverse dispersion modelling

Peer reviewe

Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. (C) 2012 Elsevier Ltd. All rights reserved.Peer reviewe

Identication of pollution sources and characteristics of atmospheric composition via forward and inverse dispersion modelling

Atmospheric composition has strong influence on human health, ecosystems and also Earth's climate. Among the atmospheric constituents, particulate matter has been recognized as both a strong climate forcer and a significant risk factor for human health, although the health relevance of the specific aerosol characteristics, such as its chemical composition, is still debated. Clouds and aerosols also contribute the largest uncertainty to the radiative budget estimates for climate projections. Thus, reliable estimates of emissions and distributions of pollutants are necessary for assessing the future climate and air-quality related health effects. Chemistry-transport models (CTMs) are valuable tools for understanding the processes influencing the atmospheric composition. This thesis consists of a collection of developments and applications of the chemistry-transport model SILAM. SILAM's ability to reproduce the observed aerosol composition was evaluated and compared with three other commonly used CTM-s in Europe. Compared to the measurements, all models systematically underestimated dry PM10 and PM2.5 by 10-60%, depending on the model and the season of the year. For majority of the PM chemical components the relative underestimation was smaller than that, exceptions being the carbonaceous particles and mineral dust - species that suffer from relatively small amount of available oservational data. The study stressed the necessity for high-quality emissions from wild-land fires and wind-suspended dust, as well as the need for an explicit consideration of aerosol water content in model-measurement comparison. The average water content at laboratory conditions was estimated between 5 and 20% for PM2.5 and between 10 and 25% for PM10. SILAM predictions were used to assess the annual mortality attributable to short-term exposures to vegetation-fire originated PM2.5 in different regions in Europe. PM2.5 emitted from vegetation fires was found to be a relevant risk factor for public health in Europe, more than 1000 premature deaths per year were attributed to vegetation-fire released PM2.5. CTM predictions critically depend on emission data quality. An error was found in the EMEP anthropogenic emission inventory regarding the SOx and PM missions of metallurgy plants on the Kola Peninsula and SILAM was applied to estimate the accuracy of the proposed correction. Allergenic pollen is arguably the type of aerosol with most widely recognised effect to health. SILAM's ability to predict allergenic pollen was extended to include Ambrosia Artemisiifolia - an invasive weed spreading in Southern Europe, with extremely allergenic pollen capable of inducing rhinoconjuctivitis and asthma in the sensitive individuals even in very low concentrations. The model compares well with the pollen observations and predicts occasional exceedances of allergy relevant thresholds even in areas far from the plants' habitat. The variations of allergenicity in grass pollen were studied and mapped to the source areas by adjoint computations with SILAM. Due to the high year-to-year variability of the observed pollen potency between the studied years and the sparse observational network, no clear geographic pattern of pollen allergenicity was detected

Airborne olive pollen counts are not representative of exposure to the major olive allergen Ole e 1

Pollen is routinely monitored, but it is unknown whether pollen counts represent allergen exposure. We therefore simultaneously determined olive pollen and Ole e 1 in ambient air in C ordoba, Spain, and Evora, Portugal, using Hirst-type traps for pollen and high-volume cascade impactors for allergen. Pollen from different days released 12-fold different amounts of Ole e 1 per pollen (both locations P < 0.001). Average allergen release from pollen (pollen potency) was much higher in C ordoba (3.9 pg Ole e 1/pollen) than in Evora (0.8 pg Ole e 1/pollen, P = 0.004). Indeed, yearly olive pollen counts in C ordoba were 2.4 times higher than in Evora, but Ole e 1 concentrations were 7.6 times higher. When modeling the origin of the pollen, >40% of Ole e 1 exposure in Evora was explained by high-potency pollen originating from the south of Spain. Thus, olive pollen can vary substantially in allergen release, even though they are morphologically identical

Educational Research Association

n/