Urban green infrastructure plays an increasingly significant role in sustainable urban development planning as it provides important regulating and cultural ecosystem services. Monitoring of such dynamic and complex systems requires technological solutions which provide easy data collection, processing, and utilization at affordable costs. To meet these challenges a pilot study was conducted using a network of wireless, low cost, and multiparameter monitoring devices, which operate using Internet of Things (IoT) technology, to provide real-time monitoring of regulatory ecosystem services in the form of meaningful indicators for both human health and environmental policies. The pilot study was set in a green area situated in the center of Moscow, which is exposed to the heat island effect as well as high levels of anthropogenic pressure. Sixteen IoT devices were installed on individual trees to monitor their ecophysiological parameters from 1 July to 31 November 2019 with a time resolution of 1.5 h. These parameters were used as input variables to quantify indicators of ecosystem services related to climate, air quality, and water regulation. Our results showed that the average tree in the study area during the investigated period reduced extreme heat by 2 °C via shading, cooled the surrounding area by transferring 2167 ± 181 KWh of incoming solar energy into latent heat, transpired 137 ± 49 mm of water, sequestered 8.61 ± 1.25 kg of atmospheric carbon, and removed 5.3 ± 0.8 kg of particulate matter (PM10). The values of the monitored processes varied spatially and temporally when considering different tree species (up to five to ten times), local environmental conditions, and seasonal weather. Thus, it is important to use real-time monitoring data to deepen understandings of the processes of urban forests. There is a new opportunity of applying IoT technology not only to measure trees functionality through fluxes of water and carbon, but also to establish a smart urban green infrastructure operational system for management.

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Palaeoseismology studies the footprints of ancient earthquakes to improve the knowledge about the modern seismicity of the territory. A ground-penetrating radar (GPR), among other geophysical methods, is used for quick determination of shallow stratigraphy -displaced, oblique layers within the fault zone. GPR data interpretation from diverse and complex reflection patterns of the fault zone heavily depends on the interpreter's experience. The range of different fault zone parameters in which this method can be successfully applied has not yet been investigated. We used a numerical simulation of GPR data to determine how GPR images the elements of faults (fault plane, hanging wall, footwall) in comparison with other reflections. Furthermore, we studied which parameters have the most significant impact on GPR wave patterns. We performed a series of numerical models of a fault, changing its geometry with increasing complexity from elementary models to realistic ones. The resulting synthetic profiles allowed finding specific GPR signatures from the fault plane, the hanging wall and the footwall. We collected field GPR data from two different fault zones and examined them for verification.

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