In this study, AERONET (Aerosol Robotic Network) and EARLINET (European Aerosol Research Lidar Network) data from 17 collocated lidar and sun photometer stations were used to characterize the optical properties of aerosol and their types for the 2008–2018 period in various regions of Europe. The analysis was done on six cluster domains defined using circulation types around each station and their common circulation features. As concluded from the lidar photometer measurements, the typical aerosol particles observed during 2008–2018 over Europe were medium-sized, medium absorbing particles with low spectral dependence. The highest mean values for the lidar ratio at 532 nm were recorded over Northeastern Europe and were associated with Smoke particles, while the lowest mean values for the Angstrom exponent were identified over the Southwest cluster and were associated with Dust and Marine particles. Smoke (37%) and Continental (25%) aerosol types were the predominant aerosol types in Europe, followed by Continental Polluted (17%), Dust (10%), and Marine/Cloud (10%) types. The seasonal variability was insignificant at the continental scale, showing a small increase in the percentage of Smoke during spring and a small increase of Dust during autumn. The aerosol optical depth (AOD) slightly decreased with time, while the Angstrom exponent oscillated between “hot and smoky” years (2011–2015) on the one hand and “dusty” years (2008–2010) and “wet” years (2017–2018) on the other hand. The high variability from year to year showed that aerosol transport in the troposphere became more and more important in the overall balance of the columnar aerosol load.

Many previous research studies have shown how local and even regional earthquakes can significantly affect the release of radon in the soil. The aim of this work is to investigate the relationship between radon measurements and the daily seismic activity rate and develop a methodology that allows estimating the seismic activity rate using only radon measurements. To carry out this study, the earthquake catalogue of the Vrancea region (Romania) has been used to estimate the daily seismic activity rate during a given time period, in which radon measurements were also recorded, from January 2016 to September 2020. The Vrancea zone represents the most active seismic zone in Europe and is located on the eastern edge of the strongly bent Carpathian arc. In the case of the radon measurements, seasonal behaviours and linear trends due to non-seismic factors have been identified and subsequently removed. The discrete wavelet transform has been used to analyse the radon signal at two different scales: long and short periods. From the analysis carried out on a long-period scale, an approximate linear relationship has been obtained between the radon series and the daily seismic activity rate, which provides insights into the behaviour of the seismic activity in the study region with only the radon information. In addition, the study reveals certain characteristics that could be used as precursors of earthquakes at different scales: weeks in the case of the estimated daily seismic activity rate, and days in the case of the short-period signal obtained by the wavelet analysis. The results obtained for this region allow us to hope that the analysis of the radon time series can become an effective complement to the conventional seismic analysis used in operational earthquake forecasting.

Large-scale radon monitoring is carried out due to the fact that it is directly responsible for public health. European Directive 2013/59/EURATOM has been transposed into the legislation of several countries and provides for the need for long-term monitoring of radon in homes and workplaces by setting the average annual reference level at 300 Bq/m³. At the same time, radon is a precursor factor, its emission being correlated with seismic and volcanic activity. In this case, the protection of the population is ensured by a forecast similar to a meteorological one. The NIEP (National Institute for Earth Physics) is developing a multidisciplinary real-time monitoring network in the most dangerous seismic area in Romania, Vrancea. This is located at the bend of the Carpathian Mountains and is characterized by deep earthquakes (over 80 km), with destructive effects over large distances. Implementing a multidisciplinary monitoring network that includes radon, involves finding the locations and equipment that will give the best results. There is no generic solution for achieving this, because the geological structure depends on the monitoring area, and in most cases the equipment does not offer the ability to transmit data in real time. The positioning of the monitoring stations was based on fault maps of the Vrancea area. Depending on the results, some of the locations were changed in pursuit of a correlation with zonal seismicity. Through repeated tests, we established the optimal sampling rate for minimizing errors, maintaining measurement accuracy, and ensuring the detection of anomalies in real time. The radon ²²²Rn was determined by the number of counts and ROI1 (region of interest) values, depending on the particularities of the equipment. Finally, we managed to establish a real-time radon monitoring network which transmits data to geophysical platforms and makes correlations with the seismicity in the Vrancea area. The equipment, designed to store data for long periods of time then manually download it with manufacturers’ applications, now works in real time, after we implemented software designed specifically for this purpose.

This study analyzes the possibility to use geophysical and geochemical parameters in an OEF (Operational Earthquake Forecasting) application correlated with short-term changes in seismicity rates using a magnitude–frequency relationship. Tectonic stress over the limits of rock elasticity generates earthquakes, but it is possible that the emission of gases increases as a result of the breaking process. The question is how reliable is the emission of radon-222 and Carbon Dioxide (CO₂), with effects on air ionization and aerosol concentration, in an OEF application? The first step is to select the seismic area (in our study this is the Vrancea area characterized by deep earthquakes at the bend of the Carpathian Mountains), then determine the daily and seasonal evolution of the forecast parameters, their deviations from the normal level, the short-term changes in seismicity rates using a magnitude–frequency relationship and finally to correlate the data with recorded seismic events. The results of anomaly detection, effect evaluation and data analysis alert the beneficiaries specialized in emergency situations (Inspectorate for Emergency Situations, organizations involved in managing special events). Standard methods such as the standard deviation from the mean value, time gradient, cross correlation, and linear regression are customized for the geological specificity of the area under investigation. For detection we use the short-time-average through long-time-average trigger (STA/LTA) method on time-integral data and the daily–seasonal variation of parameters is correlated with atmospheric conditions to avoid false decisions. The probability and epistemic uncertainty of the gas emissions resulting from this study, in addition to other precursor factors such as air ionization, time between earthquakes, temperature in the borehole, telluric currents, and Gutenberg Richter “a-b” parameters, act as inputs into a logical decision tree, indicating the possibility of implementing an OEF application for the Vrancea area. This study is novel in its analysis of the Vrancea area and performs a seismic forecasting procedure in a new form compared to the known ones.

The analysis of the relationship between radon and seismicity was previously carried out in the seismic zone of Vrancea (Romania), positioning the measuring stations on tectonic faults. This article analyzed the evolution of radon under conditions of deep and surface seismicity and the presence of mud volcanoes, as well as fires caused by gasses emanating from the ground. The monitoring area was extended to the Black Sea and the area of the Făgăraș-Câmpulung fault, where a special radon detection system was established and proposed for patenting. The case study was the impact of the earthquakes in Turkey (7.8 R and 7.5 R on 6 February 2023) on the seismically active areas in Romania in terms of gas emissions (radon, CO₂). The main analysis methods for radon (we also included CO₂) were applied to integrated time series and the use of anomaly detection algorithms. Data analysis showed that the effects of global warming led to variations in seasonal gas emissions compared to previous years. This made it difficult to analyze the data and correlate it with seismicity. Several of the cases presented require more in-depth analysis to determine the cause of the unusually high radon levels. The primary purpose of establishing the monitoring network is to use the gas emissions as seismic precursors, but the measurements are affected by the conditions under which the monitoring is conducted. In some cases, we are dealing with the effects of pollution, and in other cases, more extensive studies are required. One solution we plan to use is to expand the measurement points to locate the source of the anomalies and use weather data to determine the impact of global warming on the measurements. The main conclusions related to the development of a radon monitoring network and, in general, to the emission of gasses in earthquake-prone areas relate to the importance of the choice of equipment, monitoring location, and installation method.

Understanding the seismo–ionospheric coupling mechanism requires a quiet geomagnetic condition, as this represents an ideal situation to detect abnormal variations in the geomagnetic field. In reality, continuous interactions between solar wind and Earth’s magnetosphere create many fluctuations in the geomagnetic field that are more related to sun–magnetosphere interactions than to seismotectonic causes. A triaxial magnetometer was installed at the Muntele Rosu Observatory near the Vrancea seismic zone in 1996 to measure the local magnetic field. Since 2002, the data have become more consistent, allowing for the representation of long time series. Since then, variations have been observed on the eastern component (B_y) of the magnetic field, which sometimes overlaps with significant earthquakes. Previous studies have shown that high decreases in amplitude recorded on the B_y component of the magnetic field measured at Muntele Rosu have been accompanied by higher seismicity, while small decreases have been accompanied by lower seismic energy release. This research analyzes the geomagnetic data collected between September 2002 and May 2008 from two geomagnetic observatories, one located in the proximity of the Vrancea seismic zone and another one situated 120 km away. For each geomagnetic anomaly identified, the daily seismic energy released was plotted logarithmically, along with seismicity and Kp indices. Additionally, the daily seismic energy released was also plotted logarithmically for all earthquakes with Mw ≥3. To identify variations in the B_y component, datasets recorded at Muntele Rosu (MLR) were compared with those recorded at Surlari National Geomagnetic Observatory (SUA), to discriminate between global magnetic variations associated with solar activity and possible seismo–electromagnetic variations. The standard deviation (SD_By) was calculated for each anomaly recorded on the B_y component of the magnetic field and compared with the cumulative seismic energy release. To determine if this type of variation was present in other components of the magnetic field, the following ratios were calculated for all data recorded at Muntele Rosu: B_z/B_x, B_z/B_y, and B_z/B_H. The size of the anomalies resulting from the standard deviation measured on the B_y component (SD_By) partially validates the relationship between the size of the anomalies and the seismic energy release during the anomaly. The relationship between the released seismic energy and the anomaly magnitude is vaguely respected, but these variations seem to follow two patterns. One pattern is described by smooth decreases, and the other pattern involves decreases where the B_y component varies significantly over short periods, generating decreases/increases in steps. It was noticed that seismic activity is greater for the second pattern. Additionally, using standard deviation measured on the magnetic field represents a great tool to discriminate external magnetic field variations from local, possibly seismo–magnetic variations.