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The Xiahe Ms5.7 earthquake occurred in Xiahe county, Gannan prefecture, China (35.10°N, 102.69°E) on October 28, 2019, with a source depth of 10 km. This study investigates the spatial and temporal evolution characteristics of cross-fault soil gas concentrations prior to the Xiahe Ms5.7 earthquake by analyzing Rn, Hg, H2, and CO2 data collected from 11 profiles across the northern margin of the West Qinling fault zone from 2016 to 2019. The spatial distribution of these gases showed varying trends, with Rn concentration intensity decreasing from the Wushan segment to the east and west sections, while Hg, H2, and CO2 all broke the trend in the West Qinling fault zone's northern margin. The soil gas concentration intensity demonstrated a significant response to the Xiahe Ms5.7 earthquake, particularly in the west Ganjia sections. By integrating the seismogenic model and numerical simulation results, we explored the physical mechanism underlying these abnormal trends. Our findings suggest that the continuous decline characteristic of fault gas could be a valuable indicator of fracture tectonic activity, while an upward trend after continuous decline may signal a medium and short-term seismogenic event in the source area. These results provide a foundation for improved tracking of earthquake location and timing in a fault zone through cross-fault soil gas methods, highlighting the importance of enhancing deep fluid flow monitoring and seismogenic model research in fault zones.

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The urban on-road CO2 emissions will continue to increase, it is therefore essential to manage urban on-road CO2 concentrations for effective urban CO2 mitigation. However, limited observations of on-road CO2 concentrations prevents a full understanding of its variation. Therefore, in this study, a machine learning-based model that predicts on-road CO2 concentration (CO2traffic) was developed for Seoul, South Korea. This model predicts hourly CO2traffic with high precision (R2 = 0.8 and RMSE = 22.9 ppm) by utilizing CO2 observations, traffic volume, traffic speed, and wind speed as the main factors. High spatiotemporal inhomogeneity of hourly CO2traffic over Seoul, with 14.3 ppm by time-of-day and 345.1 ppm by road, was apparent in the CO2traffic data predicted by the model. The large spatiotemporal variability of CO2traffic was related to different road types (major arterial roads, minor arterial roads, and urban highways) and land-use types (residential, commercial, bare ground, and urban vegetation). The cause of the increase in CO2traffic differed by road type, and the diurnal variation of CO2traffic differed according to land-use type. Our results demonstrate that high spatiotemporal on-road CO2 monitoring is required to manage urban on-road CO2 concentrations with high variability. In addition, this study demonstrated that a model using machine learning techniques can be an alternative for monitoring CO2 concentrations on all roads without conducting observations. Applying the machine learning techniques developed in this study to cities around the world with limited observation infrastructure will enable effective urban on-road CO2 emissions management.

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We investigated the distribution of air temperature (Ta) and the factors affecting it in low-rise areas surrounding an isolated high-rise building during the Japanese winter. The study site was the central part of a regional city in Japan (36°5' N, 140°12' E), lying north-east of the Tokyo metropolitan area. The daytime surface temperature (Ts) in the shade is generally considered to be comparable to Ta; however, according to airborne remote sensing conducted in December 2009 where a multi-spectral scanner was installed on a fixed-wing aircraft, Ts for pavements in the shade of a high-rise building was significantly lower than Ta of sub-urban areas, indicating an influence of cold storage on Ts. Then, we conducted mobile observations using instruments (thermocouple, four component radiometer, and so on) installed on a bicycle in January 2016 to investigate the detailed distribution of Ta and the factors affecting it. The results showed the Ta over the pavements in the shade of the high-rise building was lower than the Ta of sunlit areas in the same urban area by -2 °C and lower than the Ta of sub-urban areas by -1-1.5 °C, although the advection effect was large due to strong winds around the building. In conclusion, a locally lower Ta compared to the surrounding areas can develop during the day in winter, even in spaces that are open to areas beyond the canopy.