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In summer 2020 the energy company St1 carried out its second stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing an automatic phase picking on the ISUH 2020 event and waveform data catalogue (doi:10.23729/cdfd937c-37d5-46b0-9c16-f6e0c10bc81f) using an algorithm based on machine learning (doi: 10.1785/0220210068). The dataset provided by IMS was later transferred to formats used by ISUH. The resulting dataset comprises of phase pickings and relevant waveforms of 85 induced earthquakes that occurred between 8 March 2020 to 8 December, 2020, with local magnitudes between -1.1 and 1.4. Note that the event location and other metadata of the resulting dataset are still based on the ISUH 2020 catalogue in order to preserve the consistency within the dataset as some events did not have enough automatic phase picks for reliable relocation. Waveform, location and timing data have been produced at ISUH using seismic stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) including the Helsinki local broadband network, the temporary HEL broadband network in Helsinki and Espoo, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201925.1; SmartSolos and Refteks, doi: 10.1785/0220210195). The deployment is described in Rintamäki et al., 2021, A Seismic Network to Monitor the 2020 EGS Stimulation in the Espoo/Helsinki Area, Southern Finland, doi:10.1785/0220210195. Event data, event metadata, and station metadata are provided in distinct directories, and for event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards.

In summer 2018 the energy company St1 carried out its first stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. The Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing seismic location and time data based on ISUH waveform data, and the IMS algorithm that is based on machine learning (doi: 10.1785/0220210068). The IMS location and time data were later transferred to formats used by ISUH. The resulting dataset consists of seismic waveforms of 483 induced earthquakes that occurred between 5 June 2018 to 26 July 2018, with local magnitudes between -0.7 and 2.0. Seismic data derive from stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) the temporary HEL broadband network, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201802.1, ). The deployment is described in Hillers et al., 2020, The 2018 Geothermal Reservoir Stimulation in Espoo/Helsinki, Southern Finland: Seismic Network Anatomy and Data Features, doi:10.1785/0220190253. Event data, event metadata, and station metadata are provided in distinct directories. For event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards. Due to an unfortunate miscommunication, the IMS amplitude readings in this data product need to be halved in order to be compatible with the ISUH 2018 dataset. This also has the effect of reducing the local magnitude values reported herein by ~0.25 units.

The geography of Kazakhstan is characterized by a diverse landscape and a small population. Therefore, certain automobile roads pass through unpopulated mountain regions, where physical road diagnostics are rare or almost absent, while landscape factors continue to affect the road. However, modern geo-information approaches and remote sensing could effectively provide the road diagnostics necessary to make timely control decisions regarding a road’s design, construction, and maintenance. To justify this assumption, we researched the deformation of a mountain road near Almaty city. Open access satellite images of and meteorological archival data for the region were processed. The resulting data were compared to validate the road’s deformation triggers. Extreme weather conditions’ impacts could be identified via road destruction (nearly 40 m longitudinal cracks, 15 m short transversal cracks, and two crack networks along a 50 m road section). The remotely sensed parameters (vertical displacement velocity, slope exposure, dissections, topographic wetness index, aspect, solar radiation, SAVI, and snow melting) show the complexity of triggers of extensive road deformations. The article focuses only on open access data from remote sensing images and meteorological archives. All the resulting data are available and open for all interested parties to use.

Many regions, not only in the Europe, introduce plans for the modernization of energy systems so that in a few or several years most of the demand for electricity was being able to cover using renewable energy sources. The aim of this paper is to present the possibility of estimation of appropriate power supply based on the renewable energy sources in the context of the whole energy system in the annual balance, taking into account the technical and the economic optimization strategies. The article presents also the simplified structure of the 100% renewable energy system supported by energy storage systems and the production of synthetic fuels.

The renewable biomass material obtained from rice husk, a low-cost agricultural waste, was used as a precursor to synthesize a highly porous graphene-based carbon as electrode material for supercapacitors. Activated graphene-based carbon (AGC) was obtained by a two-step chemical procedure and exhibited a very high specific surface area (SSA) of 3292 m2 g−1. The surface morphology of the synthesized materials was studied using scanning and transmission electron microscopy (SEM, TEM). Furthermore, the AGC was modified with nickel hydroxide Ni(OH)2 through a simple chemical precipitation method. It was found that the most significant increase in capacitance could be reached with Ni(OH)2 loadings of around 9 wt.%. The measured specific capacitance of the pure AGC supercapacitor electrodes was 236 F g−1, whereas electrodes from the material modified with 9 wt.% Ni(OH)2 showed a specific capacitance of up to 300 F g−1 at a current density of 50 mA g−1. The increase in specific capacitance achieved due to chemical modification was, therefore 27%.

The present research is dedicated to the study of anaerobic co-digestion process of different biomass materials. Anaerobic co-digestion of digested sludge, grass silage, haylage and cattle manure was evaluated in mesophilic tank reactors in the lab-scale experiment. Twelve laboratory scale tank reactors (1.5 L) were used during the incubation period of 45 days. First triplet of reactors was fed with pure digested sludge and the other three with different mixtures having the volumetric ratios of 30/35/25/10, 40/30/20/10 and 50/25/15/10 for digested sludge/corn silage/grass haylage/cattle manure. Methane production was analyzed for all labscale reactors individually. The resulting specific methane production of above-mentioned batches was 336.34, 238.1 and 233.23 LSTP[CH4] kg-1 [TVS], respectively. Other results such as cumulative biogas and methane yield, volumetric biogas and methane yield, volumetric biogas and methane yield per day were also assessed. These results had the highest meaning when complex substrate had no more than 30% of inoculum

Long-term spatiotemporal Land Use and Land Cover (LULC) analysis is an objective tool for assessing patterns of sustainable development (SD). The basic purpose of this research is to define the Driving Mechanisms (DM) and assess the trend of SD in the Burabay district (Kazakhstan), which includes a city, an agro-industrial complex, and a national natural park, based on the integrated use of spatiotemporal data (STD), economic, environmental, and social (EES) indicators. The research was performed on the GEE platform using Landsat and Random Forest. The DM were studied by Multiple Linear Regression and Principal Component Analysis. SD trend was assessed through sequential transformations, aggregations, and integrations of 36 original STD and EES indicators. The overall classification accuracy was 0.85–0.97. Over the past 23 years, pasture area has changed the most (−16.69%), followed by arable land (+14.72%), forest area increased slightly (+1.81%), and built-up land—only +0.16%. The DM of development of the AOI are mainly economic components. There has been a noticeable drop in the development growth of the study area in 2021, which is apparently a consequence of the COVID-19. The upshots of the research can serve as a foundation for evaluating SD and LULC policy.

Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.