Study of Microstructural Characteristics Using Mathematical Morphology

In NDE images, one often comes across overlapping or connected regions of interest. Segmentation of these regions becomes one of the preprocessing stages before any kind of image analysis. A secondary electron image showing silicon carbide particles dispersed in aluminum metal matrix composites is given in Figure 1. To study the microstructural characteristics of these composites, segmentation of the various particles is essential. Segmentation of these particles using conditional skeleton was about forty percent successful [1]. Three different approaches with varying degrees of complexity and accuracy are discussed in this paper. All three techniques are based on the principles of mathematical morphology [2]. The first step in all three techniques is to generate a marker or seed for each particle in the image. Once the seeds are generated, the techniques differ in the manner in which the seeds are grown. All the techniques work on binary images. Experimental results with microstructural statistics for each technique are presented in the paper. Some of the advanced morphological tools used are defined in the next sectio

Deformation Quantization of a Certain Type of Open Systems

We give an approach to open quantum systems based on formal deformation quantization. It is shown that classical open systems of a certain type can be systematically quantized into quantum open systems preserving the complete positivity of the open time evolution. The usual example of linearly coupled harmonic oscillators is discussed.Comment: Major update. Improved main statements. 21 page

Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations

We proviede an atmospheric aerosol characterization for North Africa, Northeastern Atlantic, Mediterranean and Middle East based on the analysis of quality-assured direct-sun observations of 39 stations of the AErosol RObotic NETwork (AERONET) which include at least an annual cycle within the 1994–2007 period. We extensively test and apply the recently introduced graphical method of Gobbi and co-authors to track and discriminate different aerosol types and quantify the contribution of mineral dust. The method relies on the combined analysis of the Ångström exponent (α) and its spectral curvature δα. Plotting data in these coordinates allows to infer aerosol fine mode radius (Rf) and fractional contribution (η) to total Aerosol Optical Depth (AOD) and separate AOD growth due to fine-mode aerosol humidification and/or coagulation from AOD growth due to the increase in coarse particles or cloud contamination. Our results confirm the robustness of this graphical method. Large mineral dust is found to be the most important constituent in Northern Africa and Middle East. Under specific meteorological conditions, its transport to Southern Europe is observed from spring to autumn and decreasing with latitude. We observe "pure Saharan dust" conditions to show AOD>0.7 (ranging up to 5), α1.5 and δα~−0.2 corresponding to η>70% and Rf~0.13 μm. Here, dust mixed with fine pollution aerosols shifts the observations to the region α<0.75, in which the fine mode contribution is less than 40%.Peer ReviewedPostprint (published version

Aerosols in the CALIOPE air quality modelling system: evaluation and analysis of PM levels, optical depths and chemical composition over Europe

The CALIOPE air quality modelling system is developed and applied to Europe with high spatial resolution (12 km × 12 km). The modelled daily-to-seasonal aerosol variability over Europe in 2004 is evaluated and analysed. Aerosols are estimated from two models, CMAQv4.5 (AERO4) and BSC-DREAM8b. CMAQv4.5 calculates biogenic, anthropogenic and sea salt aerosol and BSC-DREAM8b provides the natural mineral dust contribution from North African deserts. For the evaluation, we use daily PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt; and aerosol components data from 55 stations of the EMEP/CREATE network and total, coarse and fine aerosol optical depth (AOD) data from 35 stations of the AERONET sun photometer network. Annual correlations between modelled and observed values for PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; are 0.55 and 0.47, respectively. Correlations for total, coarse and fine AOD are 0.51, 0.63, and 0.53, respectively. The higher correlations of the PM&lt;sub&gt;10&lt;/sub&gt; and the coarse mode AOD are largely due to the accurate representation of the African dust influence in the forecasting system. Overall PM and AOD levels are underestimated. The evaluation of the aerosol components highlights underestimations in the fine fraction of carbonaceous matter (EC and OC) and secondary inorganic aerosols (SIA; i.e. nitrate, sulphate and ammonium). The scores of the bulk parameters are significantly improved after applying a simple model bias correction based on the observed aerosol composition. The simulated PM&lt;sub&gt;10&lt;/sub&gt; and AOD present maximum values over the industrialized and populated Po Valley and Benelux regions. SIA are dominant in the fine fraction representing up to 80% of the aerosol budget in latitudes north of 40° N. In southern Europe, high PM&lt;sub&gt;10&lt;/sub&gt; and AOD are linked to the desert dust transport from the Sahara which contributes up to 40% of the aerosol budget. Maximum seasonal ground-level concentrations (PM&lt;sub&gt;10&lt;/sub&gt; &gt; 30 μg m&lt;sup&gt;−3&lt;/sup&gt;) are found between spring and early autumn. We estimate that desert dust causes daily exceedances of the PM&lt;sub&gt;10&lt;/sub&gt; European air quality limit value (50 μg m&lt;sup&gt;−3&lt;/sup&gt;) in large areas south of 45° N with more than 75 exceedances per year in the southernmost regions

COST Lecture 2019 AE GM Barcelona: International Network to Encourage the Use of Monitoring and Forecasting Dust Products (InDust)

Amongst the most significant extreme meteorological phenomena are the Sand and Dust Storms (SDS). Owing to significant amounts of airborne mineral dust particles generated during these events, SDS have impacts on climate, the environment, human health, and many socio-economic sectors (e.g. aviation, solar energy management). Many studies and reports have underlined that the society has to understand, manage and mitigate the risks and effects of SDS on life, health, property, the environment and the economy in a more unified way. The EU-funded European Cooperation in Science and Technology (COST) Action 'InDust: International network to encourage the use of monitoring and forecasting Dust products' has an overall objective to establish a network involving research institutions, service providers and potential end users on airborne dust information. We are a multidisciplinary group of international experts on aerosol measurements, aerosol modelling, stakeholders and social scientists working together, exchanging ideas to better coordinate and harmonize the process of transferring dust observation and prediction data to users, as well as to assist the diverse socio-economic sectors affected by the presence of high concentrations of airborne mineral dust. This article highlights the importance of being actively engaged in research networking activities, supported by EU and COST actions since common efforts help not only each scientist by shaping their expertise and strengthening their position, but also all communities

Optimizing CALIPSO Saharan dust retrievals

We demonstrate improvements in CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations) dust extinction retrievals over northern Africa and Europe when corrections are applied regarding the Saharan dust lidar ratio assumption, the separation of the dust portion in detected dust mixtures, and the averaging scheme introduced in the Level 3 CALIPSO product. First, a universal, spatially constant lidar ratio of 58 sr instead of 40 sr is applied to individual Level 2 dust-related backscatter products. The resulting aerosol optical depths show an improvement compared with synchronous and collocated AERONET (Aerosol Robotic Network) measurements. An absolute bias of the order of −0.03 has been found, improving on the statistically significant biases of the order of −0.10 reported in the literature for the original CALIPSO product. When compared with the MODIS (Moderate-Resolution Imaging Spectroradiometer) collocated aerosol optical depth (AOD) product, the CALIPSO negative bias is even less for the lidar ratio of 58 sr. After introducing the new lidar ratio for the domain studied, we examine potential improvements to the climatological CALIPSO Level 3 extinction product: (1) by introducing a new methodology for the calculation of pure dust extinction from dust mixtures and (2) by applying an averaging scheme that includes zero extinction values for the non-dust aerosol types detected. The scheme is applied at a horizontal spatial resolution of 1×1 degrees for ease of comparison with the instantaneous and collocated dust extinction profiles simulated by the BSC-DREAM8b dust model. Comparisons show that the extinction profiles retrieved with the proposed methodology reproduce the well-known model biases per subregion examined. The very good agreement of the proposed CALIPSO extinction product with respect to AERONET, MODIS and the BSC-DREAM8b dust model makes this dataset an ideal candidate for the provision of an accurate and robust multiyear dust climatology over northern Africa and Europe

Investigating the quality of modeled aerosol profiles based on combined lidar and sunphotometer data

In this study we present an evaluation of the Comprehensive Air Quality Model with extensions (CAMx) for Thessaloniki using radiometric and lidar data. The aerosol mass concentration profiles of CAMx are compared against the PM2.5 and PM2. 5−10 concentration profiles retrieved by the Lidar-Radiometer Inversion Code (LIRIC). The CAMx model and the LIRIC algorithm results were compared in terms of mean mass concentration profiles, center of mass and integrated mass concentration in the boundary layer and the free troposphere. The mean mass concentration comparison resulted in profiles within the same order of magnitude and similar vertical structure for the PM2. 5 particles. The mean centers of mass values are also close, with a mean bias of 0.57 km. On the opposite side, there are larger differences for the PM2. 5−10 mode, both in the boundary layer and in the free troposphere. In order to grasp the reasons behind the discrepancies, we investigate the effect of aerosol sources that are not properly included in the model's emission inventory and in the boundary conditions such as the wildfires and the desert dust component. The identification of the cases that are affected by wildfires is performed using wind backward trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model in conjunction with satellite fire pixel data from MODerate-resolution Imaging Spectroradiometer (MODIS) Terra and Aqua global monthly fire location product MCD14ML. By removing those cases the correlation coefficient improves from 0.69 to 0.87 for the PM2. 5 integrated mass in the boundary layer and from 0.72 to 0.89 in the free troposphere. The PM2.5 center of mass fractional bias also decreases to 0.38 km. Concerning the analysis of the desert dust component, the simulations from the Dust Regional Atmospheric Model (BSC-DREAM8b) were deployed. When only the Saharan dust cases are taken into account, BSC-DREAM8b generally outperforms CAMx when compared with LIRIC, achieving a correlation of 0.91 and a mean bias of −29.1 % for the integrated mass in the free troposphere and a correlation of 0.57 for the center of mass. CAMx, on the other hand, underestimates the integrated mass in the free troposphere. Consequently, the accuracy of CAMx is limited concerning the transported Saharan dust cases. We conclude that the performance of CAMx appears to be best for the PM2.5 particles, both in the boundary layer and in the free troposphere. Sources of particles not properly taken into account by the model are confirmed to negatively affect its performance, especially for the PM2. 5−10 particles.The authors would like to acknowledge the EU projects MACC-III (Monitoring Atmospheric Composition and Climate – III, grant agreement no. 633080) and MACC-II project (Monitoring Atmospheric Composition and Climate – Interim Implementation, grant agreement no. 283576). The simulated results presented in this research paper have been produced using the EGI and HellasGrid infrastructures. The ACTRIS-2 project from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 654109 is gratefully acknowledged. The authors would also like to acknowledge the support provided by the Scientific Computing Center at Aristotle University of Thessaloniki throughout the progress of the work on air quality forecasting. BSC-DREAM8b simulations were performed on the Mare Nostrum supercomputer hosted by Barcelona Supercomputing Center-Centro Nacional de Supercomputacion (BSC-CNS). S. Basart wants to acknowledge the CICYT project (CGL2013-46736). Elina Giannakaki acknowledges the support of the Academy of Finland (project no. 270108).Peer ReviewedPostprint (published version

How bias-correction can improve air quality forecast over Portugal

Currently three air quality modelling systems operate routinely with high-resolution over mainland Portugal for forecasting purposes, namely MM5-CHIMERE, MM5-EURAD, and CALIOPE. They each operate daily using different horizontal resolutions (10 km × 10 km, 5 km × 5 km, and 4 km × 4 km, respectively), specific physical and chemical parameterizations, and their own emission pre-processors (with a common EMEP emission database source but different spatial disaggregation methodologies). The operational BSC-DREAM8b model is coupled offline within the aforementioned air quality systems to provide the Saharan dust contribution to particulate matter. Bias-correction studies have demonstrated the benefit of using past observational data to reduce systematic model forecast errors. The present contribution aims to evaluate the application of two bias-correction techniques, the multiplicative ratio and the Kalman filter, in order to improve air quality forecasts for Portugal. Both techniques are applied to the three modelling systems over the full year of 2010. Raw and unbiased model results for the main atmospheric pollutants (O3, NO2, SO2, PM10, and PM2.5) are analysed and compared with data from 18 monitoring stations distributed within inland Portugal on an hourly basis. Statistical analysis shows that both bias-correction techniques improve the raw forecast skills (for all the modelling systems and pollutants). In the case of O3 max-8 h, correlation coefficients improve by 19-45%, from 0.56-0.81 (raw models) to 0.78-0.86 (corrected models). PM2.5 also presents significant improvements, for example correlation coefficients increase by more than 50% (with both techniques), reaching values between 0.50 and 0.64. The corrected primary pollutants NO2 and SO2 demonstrate significant relative improvements compared to O3, mostly because the original modelling system skills are lower for those species. Although the applied techniques have different mathematical formulations and complexity levels, there are comparable answers for all of the forecasting systems. Analysis performed over specific situations such as air quality episodes and cases of unvalidated or missing data reveals different behaviours of the bias-correction techniques under study. The results confirm the advantage of the application of bias-correction techniques for air quality forecasts. Both techniques can be applied routinely in operational forecast systems and they will be useful to provide accurate alerts about exceedances to the population

An Assessment of the Efficiency of Dust Regional Modelling to Predict Saharan Dust Transport Episodes

Aerosol levels at Mediterranean Basin are significantly affected by desert dust that is eroded in North Africa and is transported northwards. This study aims to assess the performance of the Dust REgional Atmospheric Model (BSC-DREAM8b) in the prediction of dust outbreaks near the surface in Eastern Mediterranean. For this purpose, model PM10 predictions covering a 7-year period and PM10 observations at five surface monitoring sites in Greece are used. A quantitative criterion is set to select the significant dust outbreaks defined as those when the predicted PM10 surface concentration exceeds 12 μg/m3. The analysis reveals that significant dust transport is usually observed for 1–3 consecutive days. Dust outbreak seasons are spring and summer, while some events are also forecasted in autumn. The seasonal variability of dust transport events is different at Finokalia, where the majority of events are observed in spring and winter. Dust contributes by 19–25% to the near surface observed PM10 levels, which can be increased to more than 50 μg/m3 during dust outbreaks, inducing violations of the air quality standards. Dust regional modeling can be regarded as a useful tool for air quality managers when assessing compliance with air quality limit values