Aerocosmos

In the context of growing demand for middle-level specialists, the study of factors influencing young professionals’ retention in the region becomes particularly relevant. The aim of this research was to identify non-economic factors affecting the decision of engineering graduates to stay and work in their training region. The study was conducted on a sample of 664 engineering students in the Republic of Mordovia using an online survey method. Descriptive statistics, two-dimensional analysis, and binary logistic regression were applied. The results showed that most graduates (53 %) plan to start working after graduation. The defining factors influencing retention in the region were family proximity (increasing the likelihood of staying by 4,2 times), social connections (2,7 times), quick employment opportunities (4 times), and comfortable living conditions (3,2 times). This creates a «home field» effect for future graduates, providing low transaction costs, emotional and material support, and a sense of belonging. Economic factors such as salary and housing availability were found to be less significant. The research demonstrated that effective regional policy should focus not only on material incentives but also on creating conditions for quick employment, preserving social connections, and reducing uncertainty at the career start. The findings can be used by authorities and educational organizations to develop measures for young professionals’ retention.

Currently, geoinformation systems using Earth remote sensing data from space have taken strong positions in the field of economics, politics, and, practically, all spheres of human activity. As a consequence of human activity in urbanized areas, this is the emergence and further spread of unauthorized landfills and industrial waste. The littering of the planet is becoming an increasingly serious problem that we face in the modern world. Plastic waste, air and water pollution, improper waste disposal—all this damages the environment and can have long-term consequences for the planet and life on it. A variety of human diseases, such as chronic lung diseases, malignant tumors, and oncological diseases is directly related to atmospheric pollution, namely the gas component—landfill gas. In order to intervene in time and stop the spread of infection and changes in the soil composition of territories, etc., it is necessary to have a global automatic monitoring system for landfills and industrial waste. The purpose of the work is to design a model of a global automatic monitoring system for waste disposal facilities, including industrial ones, using Earth remote sensing technologies from space. Scientific novelty: a model of a global automatic space monitoring system for the processes of littering of the planet Earth is proposed. A general methodology is given for constructing a geoinformation model that monitors territories for the presence of waste disposal facilities. The analysis of modern prospects in the creation and operation of such automatic geoinformation systems using certain knowledge in the field of ultra-large, distributed and open data archives, as well as modern methods for improving color images, is presented.

We proposed an approach for estimating the shape and geometric parameters of the observed objects from a perspective image based on typed elements, perspective geometry methods and convolutional neural networks. The proposed method uses the assumption that the object under study is rigid. A method is proposed for restoring a 3-D model of an observed object from one perspective image using reference objects and typed elements. Semantic segmentation of typed elements allows to set the photometric parameters of the coordinate system attached to the points on the image. According to the calculated photometric parameters and segmentation of the observed object in the image, its parameters and a 3-D model are estimated. The developed method is applicable for calculating 3-D models from a single perspective image in the vicinity of a road (both road and railway) infrastructure, where there are a large number of typed elements.

The article presents summarized results of the study of registered periods of microseisms of the “voice of the sea” infrasound oscillations, generated due to the influence of wind of tropical cyclones (typhoons) passing in the Sea of Japan. We compared the recorded signals and how they correspond to the microseismic background in the wind wave range. Using satellite monitoring data, we verified and identified the groups of cyclones that excite the “voice of the sea” microseisms with approximately the same characteristics. We found that the bandwidth of the “voice of the sea” range and its central frequency, with the maximum amplitude in this frequency range, are approximately the same for typhoons (cyclones) propagating along the same routes. Wind speed also determines the central frequency of the “voice of the sea” range for a particular typhoon (tropical cyclone) propagation route.

Anomalous variations in various geophysical fields (lineament systems, thermal fields, and ionospheric parameters) during the earthquake with the magnitude M = 5.6, which took place near Lake Baikal on September 22, 2020, and during its preparation, are studied. Features of the geophysical fields are described. Combined analysis of anomalous variations in the geophysical fields in the lithosphere, atmosphere, and ionosphere during the earthquake preparation is carried out. Based on the analysis results, we detect anomalous variations in the lineament systems 15 and 6 days before; in the thermal fields 15 and 14 days before; and in the ionospheric parameters 8, 7, and 6 days before the earthquake. Correspondences are found between the occurrence of the maxima of the lineament systems and the thermal fields 15 and 6 days before the earthquake and changes in the state of the ionosphere and thermal fields 9–10 days and 4 days before the earthquake.

We describe the digital technological platform SODIS Building CM for visual control of construction using a BIM model. It can track the work schedule and control the processes of delivery and acceptance of construction and installation works. BIM model elements can be associated with various tasks, documents, equipment, and processes. Elements of the BIM model can automatically change color depending on the state of the object. Staining changes depending on the percentage of completion and the backlog from the base schedule of work. Using a 3D model of an object allows control the amount of work performed on structural elements and all engineering systems. The article also discusses the capabilities of the SODIS Building FM platform, which allows you to get, instead of a pile of operational documentation, which is extremely inconvenient to use, an almost ideal system for safe operation, where the search for the necessary information takes a matter of seconds. This is especially important in the event of an emergency, when there is an acute shortage of time to make effective management decisions.

In this paper, the causes of the anomalous harmful algal bloom which occurred in the fall of 2020 in Kamchatka have been detected and analyzed using a long-term time series of heterogeneous satellite and simulated data with respect to the sea surface height (HYCOM) and temperature (NOAA OISST), chlorophyll-a concentration (MODIS Ocean Color SMI), slick parameters (SENTINEL-1A/B), and suspended matter characteristics (SENTINEL-2A/B, C2RCC algorithm). It has been found that the harmful algal bloom was preceded by temperature anomalies (reaching 6 °C, exceeding the climatic norm by more than three standard deviation intervals) and intensive ocean level variability followed by the generation of vortices, mixing water masses and providing nutrients to the upper photic layer. The harmful algal bloom itself was manifested in an increase in the concentration of chlorophyll-a, its average monthly value for October 2020 (bloom peak) approached 15 mg/m3, exceeding the climatic norm almost four-fold for the region of interest (Avacha Gulf). The zones of accumulation of a large amount of biogenic surfactant films registered in radar satellite imagery correlate well with the local regions of the highest chlorophyll-a concentration. The harmful bloom was influenced by river runoff, which intensively brought mineral and biogenic suspensions into the marine environment (the concentration of total suspended matter within the plume of the Nalycheva River reached 10 mg/m3 and more in 2020), expanding food resources for microalgae.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Pan-Eurasian Experiment (PEEX) Science Plan, released in 2015, addressed a need for a holistic system understanding and outlined the most urgent research needs for the rapidly changing Arctic-boreal region. Air quality in China, together with the long-range transport of atmospheric pollutants, was also indicated as one of the most crucial topics of the research agenda. These two geographical regions, the northern Eurasian Arctic-boreal region and China, especially the megacities in China, were identified as a “PEEX region”. It is also important to recognize that the PEEX geographical region is an area where science-based policy actions would have significant impacts on the global climate. This paper summarizes results obtained during the last 5 years in the northern Eurasian region, together with recent observations of the air quality in the urban environments in China, in the context of the PEEX programme. The main regions of interest are the Russian Arctic, northern Eurasian boreal forests (Siberia) and peatlands, and the megacities in China. We frame our analysis against research themes introduced in the PEEX Science Plan in 2015. We summarize recent progress towards an enhanced holistic understanding of the land–atmosphere–ocean systems feedbacks. We conclude that although the scientific knowledge in these regions has increased, the new results are in many cases insufficient, and there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures, especially the lack of coordinated, continuous and comprehensive in situ observations of the study region as well as integrative data analyses, hindering a comprehensive system analysis. The fast-changing environment and ecosystem changes driven by climate change, socio-economic activities like the China Silk Road Initiative, and the global trends like urbanization further complicate such analyses. We recognize new topics with an increasing importance in the near future, especially “the enhancing biological sequestration capacity of greenhouse gases into forests and soils to mitigate climate change” and the “socio-economic development to tackle air quality issues”.

Air quality is a major concern for the public; therefore, the reliability of models in predicting the air quality accurately is of a major interest. The objective of this study was to develop an air dispersion model and demonstrate that it can be successfully used in place of or in conjunction with ambient air monitoring stations in determining the local Air Quality Index (AQI). This thesis begins with a review of existing atmospheric dispersion models, specifically, the Gaussian Plume models and their capabilities to handle the atmospheric chemistry of nitrogen oxides (NOx) and sulphur dioxides (SO₂). It also includes a review of wet deposition in the form of in-cloud, below-cloud, and snow scavenging. Existing dispersion models are investigated to assess their capability of representing atmospheric chemistry, specifically in the context of NOx and SO₂x substances and their applications to urban areas. A review was completed of previous studies where Gaussian dispersion models were applied to major cities around the world such as London, Helsinki, Kanto, and Prague, to predict ground level concentrations NOx and SO₂. For the purpose of this thesis, Gaussian air dispersion model was developed, known as the Air dispersion model for the Road Sources in Urban areas (ARSUS) model, which is capable of predicting ground level concentrations for a contaminant of interest. The ARSUS model was validated against the US EPA ISC3 model before it was used to conduct the two studies in this investigation. These two studies simulated weekday morning rush hour tailpipe emissions of CO and predicted ground level concentrations. The first study used the ARSUS model ARSUS model to predict ground level concentrations of CO from the tailpipe emissions of CO for roads and highways located in the vicinity of the Toronto West ambient air monitoring station. The second study involved an expansion of the domain to predict ground level concentrations of CO from tailpipe emissions from highways located in the City of Toronto. The modelled concentrations were then compared to the Toronto West ambient air monitoring station. ARSUS model’s results indicate that air quality in the immediate vicinity of roads or highways is highly impacted by the tailpipe emissions. Higher concentrations are observed for the areas adjacent to the road and highway sources. The tailpipe emissions of CO from highways have a higher contribution to the local air quality. The predicted ground level concentration from the ARSUS model do under-predict when compared to the observed data from the monitoring station; however, despite this a predictive model is viable.

A method has been developed for constructing three-dimensional models of rigid objects on the earth’s surface using one satellite image using the example of railway infrastructure. The method consists in step-by-step processing of satellite images with sequential application of two convolutional neural networks. In the first processing step, a satellite image is segmented by a neural network to select a plurality of objects of predetermined classes. At the second stage of processing with the help of neural network local analysis of image areas detected by results of the first stage of processing is performed. The results of the second processing step are used to estimate the parameters of the 3D model of the object. The possibilities of the method are shown by the example of processing a satellite image of the railway infrastructure. The following classes of informative areas are considered: building, wall edge, roof edge, building shadow, railway infrastructure, car, highway; rails, poles and shadows from poles (taken as reference objects for estimating scaling coefficients in certain directions). An example is given of using the developed method of highlighting typical railway infrastructure objects and for subsequent evaluation of the parameters of a three-dimensional building model partially obscured by trees.