Unshielded Magnetocardiography in Clinical Practice: Detection of Myocardial Damage in CAD Patients and in Patients Recovered from COVID-19

The chapter deals with magnetocardiography—a specific section of electrocardiography, which is designed to analyze the magnetic component of the electromagnetic field of the heart. Magnetocardiography is described as clinical information technology (IT), i.e., a set of methods, software, and hardware combined into a technological chain, the product of which is an automated diagnostic report. There are several examples of magnetocardiographic information technology implementation in clinical routine, aiming to register and evaluate subtle changes in the electromagnetic field of the heart for early diagnosis of the most common and dangerous heart diseases, especially coronary heart disease. It is shown that new metrics of analysis of spatial structure of 2D and 3D magnetocardiographic maps of current density distribution allow diagnosis with high accuracy of various forms of myocardial ischemia as well as myocardial damage in patients, recently recovered from COVID-19

Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth

Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data density, we merged AERONET observations with a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, we estimated AOD as a linear combination of observational data and a model forecast, with weighting coefficients chosen to minimize a mean-square error in the calculation, assuming a negligible error of AERONET AOD observations. To obtain weight coefficients, we used correlations between model errors in different grid points. In contrast with classical OI, where only spatial correlations are considered, we developed the spatial-temporal optimal interpolation (STOI) technique for atmospheric applications with the use of spatial and temporal correlation functions. Using STOI, we obtained estimates of the daily mean AOD distribution over Europe. To validate the results, we compared daily mean AOD estimated by STOI with independent AERONET observations for two months and three sites. Compared with the GEOS-Chem model results, the averaged reduction of the root-mean-square error of the AOD estimate based on the STOI method is about 25%. The study shows that STOI provides a significant improvement in AOD estimates

Spatio-Temporal Optimal Interpolation of Aerosol Optical Depth Observations Using a Chemical Transport Model

To estimate the spatial and temporal distribution of aerosol optical depth (AOD), we used the optimal interpolation (OI). In OI, observational data and a model forecast are linearly combined according to their relative accuracies. Weight coefficients are chosen to minimize the mean-square error in the estimate. To obtain weight coefficients, correlations between model errors in the different grid points are used. In classical OI, only spatial correlations are considered. We used spatial and temporal correlation functions. To obtain error statistics, we used observations from European stations of ground-based sun photometers, the Aerosol Robotic Network (AERONET), and simulations by a chemical transport model GEOS-Chem, assuming a negligible error of AERONET AOD observations. The estimates of the daily mean AOD distribution over Europe are obtained. The reduction of the root-mean-square error of the AOD estimate based on the OI method in comparison with the GEOS-Chem model results is discussed

Co-ordinated vertical profiling of Saharan dust over western and central Europe with a continental lidar network : The 11-16 October 2001 outbreak

The spread of mineral particles over southwestern, western, and central Europe resulting from a strong Saharan dust outbreak in October 2001 was observed with the European Aerosol Research Lidar Network (EARLINET). For the first time, a dense desert dust plume over Europe was characterized coherently with high vertical resolution on a continental scale

Optimization of lidar data processing: a goal of the EARLINET-ASOS project

EARLINET-ASOS (European Aerosol Research Lidar Network - Advanced Sustainable Observation System) is a 5-year EC Project started in 2006. Based on the EARLINET infrastructure, it will provide appropriate tools to improve the quality and availability of the continuous observations. The EARLINET multi-year continental scale data set is an excellent instrument to assess the impact of aerosols on the European and global environment and to support future satellite missions. The project is addressed in optimizing instruments and algorithms existing within EARLINET-ASOS, exchanging expertise, with the main goal to build a database with high quality aerosol data. In particular, the optimization of the algorithms for the retrieval of the aerosol optical and microphysical. properties is a crucial activity. The main objective is to provide all partners with the possibility to use a common processing chain for the evaluation of their data, from raw signals to final products. Raw signals may come from different types of systems, and final products are profiles of optical properties, like backscatter and extinction, and, if the instrument properties permit, of microphysical properties. This will have a strong impact on the scientific community because data with homogeneous well characterized quality will be made available in nearly real time

EARLINET: the European Aerosol Research Lidar Network for the Aerosol Climatology on Continental Scale

Lidar techniques represent the most suitable tool to obtain information on the aerosol vertical distribution and therefore to close this kind of observational gap. Lidar networks are fundamental to study aerosol on large spatial scale and to investigate transport and modification phenomena. These are the motivations why EARLINET, the European Aerosol Research Lidar Network, was established in 2000, as a research program funded by the European Commission in the frame of the 5(th) framework program. After the end of the project, the network activity continued on the base of a voluntary association. At present, EARLINET consists of 25 lidar stations distributed over Europe. On March 2006, the EC Project EARLINET-ASOS (Advanced Sustainable Observation System) started on the base of the EARLINET infrastructure. This infrastructure project will enhance the operation of the network. EARLINET data can contribute significantly to the quantification of aerosol concentrations, radiative properties, long-range transport and budget, and prediction of future trends on European and global scale. It can also contribute to improve model treatment on a wide range of scales and to a better exploitation of present and future satellite data

EARLINET all observations (2000-2010)

This collection contains all measurements that have been performed in the frame of the EARLINET project during the period April 2000 - December 2010. Some of these measurements are also part of the collections 'Calipso', 'Climatology', 'SaharanDust' or 'VolcanicEruption'. In addition this collection also contains measurements from the categories 'Cirrus', 'DiurnalCycles', 'ForrestFires', 'Photosmog', 'RuralUrban', and 'Stratosphere'. This collection also contains measurements not devoted to any of the above categories. More information about these categories and the contributing stations can be found in the file 'EARLINET_general_introduction.pdf' accompanying this dataset

EARLINET correlative observations for CALIPSO (2006-2010)

A European Aerosol Research Lidar Network to Establish an Aerosol ClimatologyAerosols affect life on earth in several ways. They play an important role in the climate system; the effect of aerosols on the global climate system is one of the major uncertainties of present climate predictions. They play a major role in atmospheric chemistry and hence affect the concentrations of other potentially harmful atmospheric constituents, e.g. ozone. They are an important controlling factor for the radiation budget, in particular in the UV-B part of the spectrum. At ground level, they can be harmful, even toxic, to man, animals, and plants. Because of these adverse effects that aerosols can have on human life, it is necessary to achieve an advanced understanding of the processes that generate, redistribute, and remove aerosols in the atmosphere.A quantitative dataset describing the aerosol vertical, horizontal, and temporal distribution, including its variability on a continental scale, is necessary. The dataset is used to validate and improve models that predict the future state of the atmosphere and its dependence on different scenarios describing economic development, including those actions taken to preserve the quality of the environment. The EARLINET data set is the most comprehensive compilation of data available for this purpose.This project description is taken from: http://www.earlinet.org/index.php?id=earlinet_homepageSummary: Since the beginning of CALIPSO observations in June 2006 EARLINET has performed correlative measurements during nearby overpasses of the satellite at individual stations following a dedicated observational strategy. The EARLINET-CALIPSO correlative measurement plan considers the criteria established in the CALIPSO validation plan (http://calipsovalidation.hamptonu.edu). Participating EARLINET stations perform measurements, as close in time as possible and for a period of at least 30 min up to several hours, when CALIPSO overpasses their location within a horizontal radius of 100 km. Within the 16-day observational cycle of CALIPSO each station is overpassed within this distance 1-2 times during daytime (typically between 1100 and 1400 UTC) and 1-2 times during night time (typically between 0000 and 0300 UTC). Additional measurements are performed, mainly on a non-regular basis, when CALIPSO overpasses a neighboring station in order to study the horizontal variability of the aerosol distribution. The time schedule for correlative observations is calculated starting from the high-resolution ground-track data provided by NASA, and is updated and distributed to whole network weekly. The EARLINET-CALIPSO correlative dataset represents a statistically significant data set to be used for the validation and full exploitation of the CALIPSO mission, for studying the representativeness of cross sections along an orbit against network observations on a continental scale, and for supporting the continuous, harmonized observation of aerosol and clouds with remote-sensing techniques from space over long time periods