Anna Wawrzynczak

The primary aim of this study is to develop an effective decision-support system for managing crises related to the release of hazardous airborne substances. Such incidents, which can arise from industrial accidents or intentional releases, necessitate the rapid identification of contaminant sources to enable timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection and employs the (1 + 1)-Evolution Strategy to solve the inverse problem of source localization. The initial section of this paper reviews existing methods for simulating atmospheric dispersion and reconstructing source locations. In the following sections, we describe the architecture of the proposed system, the modeling assumptions, and the experimental framework. A key feature of the method presented here is its reliance solely on concentration measurements obtained from a distributed network of sensors, eliminating the need for prior knowledge of the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments were conducted to support model calibration and refinement, followed by formal tests to evaluate localization accuracy and robustness. Each test case was completed in under 20 min on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1 + 1)-ES Sandpile model can effectively reconstruct source parameters, staying within the resolution limits of the sensor grid. The system’s speed, simplicity, and reliance exclusively on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications.

The primary aim of this study is to develop an effective decision-support system for managing crises involving the release of hazardous airborne substances. Such scenarios often result from industrial accidents or intentional releases and require rapid contaminant source identification to facilitate timely response measures. This work focuses on a novel approach that integrates a modified Sandpile model with advection with the (1+1) Evolution Strategy to solve the inverse problem of source localisation. The initial part of the paper reviews existing methods for simulating atmospheric dispersion and reconstructing the source location. In the subsequent sections, we describe the proposed system architecture, modelling assumptions, and the experimental framework. A key feature of the presented method is that it relies solely on concentration measurements reported by a distributed network of sensors and does not require any prior knowledge about the source location, release time, or emission strength. The system was validated through a two-stage process using synthetic data generated by a Gaussian dispersion model. Preliminary experiments supported model calibration and refinement, followed by formal tests to evaluate localisation accuracy and robustness. Each test case was completed in under 20 minutes on a standard laptop, demonstrating the algorithm’s high computational efficiency. The results confirm that the proposed (1+1)-ES Sandpile model can effectively reconstruct source parameters within the resolution limits of the sensor grid. The system’s speed, simplicity, and exclusive reliance on sensor data make it a promising solution for real-time environmental monitoring and emergency response applications.

The Quebec blackout on 13 March 1989, has made geomagnetically induced current (GIC) research a socially important field of study. It is widely recognized that the effects of space weather, which may affect the power infrastructure, threaten countries located at high latitudes. However, in recent years, various studies have shown that countries at lower latitudes may also be at risk. One of the best proxies of GIC variability is the local geoelectric field, as measured in Eskdalemuir, Lerwick, and Hartland, by the British Geological Survey or modeled using, e.g., a 1D layered Earth conductivity model. In our article, we present a review of the issues related to the impact of the GIC on transformers and transmission lines in Central and Southern European countries, from Greece, Spain, and Italy to Slovakia, the Czech Republic, Austria, and Poland. The review underlines the importance of the systematic collection of information about power grid failures and the need for further systematic studies of the GIC’s impact on the operation of power grids in mid-latitude countries.

The neural networks are powerful as nonlinear signal processors. This paper deals with the problem of employing the feedforward neural networks (FFNNs) to simulate the time-dependent distribution of the airborne toxin in the urbanized area. The spatial distribution of the contaminant is the multidimensional function dependent on the weather conditions (wind direction and speed), coordinates of the contamination sources, the release rate, and its duration. In this paper, we try to answer what topology should be the multilayered FFNN to forecast the contaminant strength correctly at the given point of the urbanized area at a given time. The comparison between the FFNNs is made based on the standard performance measures like correlation R and mean square error (MSE). Additionally, the new measure estimating the quality of the neural networks forecasts in subsequent time intervals after the release is proposed. In combination with R and MSE, the proposed measure allows identifying the well-trained network unambiguously. Such a neural network may enable creating an emergency system localizing the contaminant source in an urban area in real-time. However, in such a system time of answer depends directly on the multiple times run dispersion model computational time. This time is expected in minutes for custom dispersion models in urban areas and can be shortened to seconds in the case of artificial neural networks.

Solar originating events are continually evident in galactic cosmic ray (GCR) flux registered at the ground by neutron monitors and in situ by space probes. We analyze time intervals of sporadic Forbush decreases (Fd) during the ascending phase of solar cycle 24. We consider cosmic rays flux, as well as, solar, heliospheric and geomagnetic activity parameters, around these periods, using different mathematical tools. Moreover, for this epoch of solar activity we compute geoelectric field for the Poland's region using a 1-D layered conductivity Earth model. Against the background of the above-mentioned parameters, we analyze the number of failures in southern Poland transmission lines. Our results reveal the increase in the superposed averaged number of failures around the appearance of solar transients visible in the GCR flux, suggesting their potential coupling.

Solar originating events are continually evident in galactic cosmic ray (GCR) flux registered at the ground by neutron monitors. We analyze time intervals of sporadic Forbush decreases (Fd) observed by neutron monitors (NM) during the first half of solar cycle 24. We consider NMs data, as well as, solar, heliospheric and geoma - gnetic activity parameters, around those periods, using different mathematical tools. Subsequently, an impact of space weather phenomena on energy infrastructure is well known, in the further step we consider logs from one of the Polish transmission lines operators during the time intervals of Fds. Based on the data from the Ins- titute of Meteorology and Water Management-Polish National Research Institute we exclude from the analysis the weather-related failures. We found that the increase in the superposed averaged number of failures appears around Forbush decreases.