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Due to the influences of buildings, geographical and geomorphological environments, road conditions, etc., the probabilities and numbers of casualties in different areas after an earthquake are different. Accordingly, we propose the concept of the lethal level, which attains different grades representing the mortality rate of differing intensities. Different regions have unique lethal levels, and regional lethal levels are affected mainly by the proportion of each building type and the corresponding lethal level, as different types of buildings also have unique lethal levels. Based on data of 52 historical earthquake disasters, we constructed a lethal level calculation model and obtained the lethal level of each building type. The results reveal that the lethal level ranges of different building types are fixed and unequal; moreover, the ranges of different building types overlap each other. The lethal level range of adobe structures is 0.85–1, that of civil structures is 0.75–0.95, that of brick-wood structures is 0.6–0.9, that of brick-concrete structures is 0.33–0.6, that of wood structures is 0.2–0.35, and that of reinforced concrete structures is 0.1–0.25. Based on the lethal levels of these building types, the overall level of a region can be quantified and graded, and this classification does not depend on the geographical location or administrative boundaries. In pre-earthquake evaluation efforts, the lethal level of an area can be derived through field research. After an earthquake, the number of casualties can be quickly assessed based on the mortality rate corresponding to the intensity of the area. This approach can further provide scientific support for risk zoning and risk assessment research.

Surface longwave radiation (SLR) plays a pivotal role in the Earth's energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth's energy dynamics. [ABSTRACT FROM AUTHOR]

Earthquake disaster risk analyses provide significant scientific guidance for reducing earthquake disaster losses etc. The current commonly used method is based on the vulnerability of different types of buildings to evaluate, and may ignore the difference between the seismic resistance of the same type of buildings in different areas. The lethal level considers a series of reasons such as different types of buildings, resulting in different capacities for casualties, the lethal levels of the cities in the same subregion are considerably different and exhibit obvious distribution characteristics. In Yancheng and Suqian, the lethal levels in urban areas are lower than those in rural areas, but the lethal levels in Guangzhou show the opposite trend (the lethal levels are higher in urban areas than in other towns and rural areas). The average lethal level in Guangzhou is 0.3856, that in Suqian is 0.5844, and that in Yancheng is 0.5912. This study based on the lethal levels, conduct a comparative analysis of the earthquake disaster risk of cities throughout eastern China, and a map of the earthquake disaster risk in each city is obtained. The overall lethal level and risk in Guangzhou is much lower than those in Yancheng and Suqian. The main influencing factors in the different cities also diverge. One reason for the lower lethal levels and risk in Guangzhou is that most of the brick-concrete structures are equipped with ring beams and structural columns; conversely, Yancheng and Suqian exhibit higher lethal levels and risk because the brick-concrete structures have ring beams but no structural columns. These maps provide a technical reference and solid scientific and technological support for further earthquake disaster risk analyses and for disaster prevention, mitigation and disaster relief planning, and this method improves the accuracy of rapid postearthquake assessment.

After an earthquake, the important task of emergency rescue work is to minimize casualties, but due to the suddenness of earthquake disasters, it is difficult to obtain enough disaster information immediately, especially personnel distribution and movement information. The traditional methods of obtaining disaster data are through reports from the disaster area or field investigations by the emergency rescue team; this work lags, and its efficiency is low. This paper analyzes the feasibility of using mobile phone location signal data in earthquake emergency rescue work in several respects, such as quantity, location, change rate, and epicentral distance. The results show that mobile phone location signal data can quickly obtain the situation of personnel distribution and quantity after an earthquake, and we find the change rate, distance, etc., can determine the approximate range of the earthquake impact field. Through the data distribution in different time periods, the movement of personnel after the earthquake can be obtained. Based on several situations, we can determine the basic situation of the disaster-stricken areas in times after the earthquake, especially the personnel relevant to the situation, and these data can provide a scientific basis for emergency rescue decision making.

Considering the significant influence of clouds on thermal infrared (TIR) data sea surface temperature (SST) retrieval, this study focuses on the reduction of cloud influence on Moderate Resolution Imaging Spectroradiometer (MODIS) longwave SST retrieval. First, the quality level (QL) 3 MODIS SSTs are classified into cloudy or clear-sky pixels based on MODIS cloud mask products. The cloudy SST pixels, flagged as confident cloudy or probably cloudy in the cloud masks, are further classified into three groups according to their associated daytime cloud-top and optical properties. Taking the SSTs measured by 11 buoys over one year as the reference data, those three groups of cloudy SSTs are significantly underestimated, with biases of −33.45 °C, −6.35 °C, and −4.72 °C. The QL 3 clear-sky SSTs are identified as probably and confident clear in the cloud masks and are not influenced by clouds, with a bias of nearly 0 °C. Then, three support vector regression (SVR) models are individually proposed for the three groups of cloudy SSTs. Taking the cloud-top and optical parameters as inputs of the proposed SVR models, we can obtain SSTs with a bias of 0 °C and a root-mean-square error (RMSE) of less than 1.6 °C for the three groups of cloudy SSTs. The method proposed in this study shows the potential for TIR SST estimation under some cloudy conditions using satellite remote sensing cloud products. Considering the RMSE of 0.4 °C in operational sea surface temperature and sea ice analysis, further study is needed before actual applying the method proposed in this study. [ABSTRACT FROM AUTHOR]

After an earthquake, rapidly assessing the affected areas, particularly the meizoseismal area, is crucial to emergency rescue work. Currently, many methods exist for delimiting the range of the area affected by an earthquake. The main method for estimating this area is to use an empirical model of the seismic intensity attenuation relationship. In contrast, this article uses the change rate (CR) of the phones' location data in an area after the earthquake to infer the extent of the meizoseismal area. This study selects phone location data from eight earthquakes for experimentation and analysis. The results show a correlation between the phone location data and the actual distribution of the meizoseismal area based on intensity maps that were issued by the China Earthquake Administration. According to the results, the extent of the meizoseismal area can be estimated by the magnitude, the focal depth, the CR, the time of the earthquake, and the distance to the epicenter. The magnitude, CR, and focal depth are dominant and are positively correlated with the intensity. Based on the aforementioned parameters, a calculation model for delimiting the extent of the meizoseismal area based on phone location data can be constructed. The accuracy of the correlation coefficient of fitting result is 0.7826, and the R² value is 0.6125. The results demonstrate that phone location data can be used to quickly delimit the meizoseismal area and can provide a basis for earthquake emergency rescue work. [ABSTRACT FROM AUTHOR]