Aerosol retrieval experiments in the ESA Aerosol_cci project

Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (2010–2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust).

Evaluation of water properties in HEA–HEMA hydrogels swollen in aqueous-PEG solutions using thermoanalytical techniques

Hydrogels are polymeric materials used in many pharmaceutical and biomedical applications due to their ability to form 3D hydrophilic polymeric networks, which can absorb large amounts of water. In the present work, polyethylene glycols (PEG) were introduced into the hydrogel liquid phase in order to improve the mechanical properties of hydrogels composed of 2-hydroxyethylacrylate and 2-hydroxyethylmethacrylate (HEA–HEMA) synthesized with different co-monomer compositions and equilibrated in water or in 20 % water–PEG 400 and 600 solutions. The thermoanalytical techniques [differential scanning calorimetry (DSC) and thermogravimetry (TG)] were used to evaluate the amount and properties of free and bound water in HEA–HEMA hydrogels. The internal structure and the mechanical properties of hydrogels were studied using scanning electron microscopy and friability assay. TG “loss-on-drying” experiments were applied to study the water-retention properties of hydrogels, whereas the combination of TG and DSC allowed estimating the total amount of freezable and non-freezing water in hydrogels. The results show that the addition of viscous co-solvent (PEG) to the liquid medium results in significant improvement of the mechanical properties of HEA–HEMA hydrogels and also slightly retards the water loss from the hydrogels. A redistribution of free and bound water in the hydrogels equilibrated in mixed solutions containing 20 vol% of PEGs takes place.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.