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In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers - the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models - the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CARTBagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM. [ABSTRACT FROM AUTHOR]

Thermal variations in surface and atmosphere observed from multiple satellites prior to strong earthquakes have been widely reported ever since seismic thermal anomalies were discovered three decades ago. These thermal changes are related to stress accumulation caused by the tectonic activities in the final stage of earthquake preparation. In the present paper, we focused on the thermal changes associated with the 2021 Madoi M 7.3 earthquake in China and analyzed the temporal and spatial evolution of the Index of Microwave Radiation Anomaly (IMRA) and the Index of Longwave Radiation Anomaly (ILRA) based on 8-year microwave brightness temperature (MWBT) and 14-year outgoing longwave radiation (OLR) data collected by satellites. We also explored their responses in different tectonic units (seismogenic fault zone and active tectonic block). Our results indicated that the enhanced IMRA was distributed along the seismogenic fault since mid-February and reappeared for a longer time and with stronger intensity in March and April 2021. The pronounced enhancement in the ILRA was observed within one month over Bayan Har tectonic and adjacent blocks. The higher ILRA over the tectonic blocks in the southern Tibet Plateau at the beginning of 2021 could be associated with the regional stress accumulation, as proven by the occurrences of two moderate earthquakes during this period. [ABSTRACT FROM AUTHOR]

PM2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM2.5 exposure. This study analyzed the spatial distribution of PM2.5 concentration (45.9 μg/m3 national average in 2016) and premature mortality attributed to PM2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 per 10,000 persons in the year 2016, resulting in higher estimates compared to the integrated exposure‐response model. We assessed the premature mortality attributed to PM2.5 through common diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). The premature mortality due to IHD and CEV accounted for 68.5% of the total mortality, and the per capita mortality (per 10,000 persons) for all ages due to IHD was 3.86, the highest among diseases. For the spatial distribution of disease‐specific premature mortality, the top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively, were found in Chongqing and Beijing. In 338 cities of China, we have found a significant positive spatial autocorrelation of per capita premature mortality, indicating the necessity of coordinated regional governance for an efficient control of PM2.5. Plain Language Summary: Fine particulate matter (PM2.5) concentrations have increased in general, in most developing countries in recent decades. In China, PM2.5 pollution has become a major component of air pollution and has serious health impacts. To obtain a comprehensive understanding of the national health impacts of PM2.5 in China, we have used the Global Exposure Mortality Model (GEMM) to estimate the premature mortality associated with PM2.5 exposure in 338 cities in China at the prefectural level and above. In addition, we analyzed the spatial distribution of premature mortality attributed to PM2.5 for five diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). Our study finds that the total premature mortality associated with PM2.5 exposure in China for 2016 was 1.55 million persons. The top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively were found in Chongqing and Beijing. Furthermore, cities with high per capita premature mortality tended to be spatially connected with other cities with high per capita premature mortality, indicating the coordinated regional governance should be adopted to reduce the impact of PM2.5 on human health. Key Points: Based on an extended observational network in China, 1.55 million premature deaths attributed to PM2.5 is estimated in 2016Higher premature mortality using the Global Exposure Mortality Model compared to the Integrated Exposure‐Response modelA positive spatial autocorrelation of the per capita premature mortality is found at the city levels [ABSTRACT FROM AUTHOR]

Various physical, geophysical, geochemical, electrical and hydrological parameters are measured on the surface and in shallow/deep boreholes throughout mainland China to obtain early warning signals of impending earthquakes. Numerous wells are equipped with water level and temperature sensors for continuous observations of the water level and groundwater temperature. An analysis of water temperature data from boreholes equipped with water temperature sensors reveals that nearly half of the boreholes show coseismic response associated with the Wenchuan earthquake (Mw 7.9, 12 May, 2008). The coseismic response of the groundwater temperature cannot be differentiated from the groundwater flow or movement when the earthquake occurred, but there is no fixed relationship between the temperature variation and the water flow. At the same time, we observed that the rock temperature in dry wells can record the seismic events and even the pre-seismic abnormal information. The spatial distribution of the coseismic groundwater temperature response is random and irregular, which does not support the dislocation model of seismic faults at the regional or larger scale. Changes in the groundwater temperature are closely related to the borehole temperature gradient, lithology profile and geological environment of the borehole and depths of the aquifers. The mechanism of the coseismic groundwater temperature response can be explained by an enhanced permeability induced by an earthquake. The groundwater temperature increases if the temperature sensor in the borehole is located near the deep-circulating aquifer and decreases if the sensor is near the shallow-circulating aquifer when seismic waves arrive. The groundwater temperature may be slightly affected or even unchanged if the temperature sensor is far from the aquifer during the propagation of seismic waves. However, it was hard to conclude the changes of rock temperature observed in six dry wells. [ABSTRACT FROM AUTHOR]

The thermal anomalies associated with earthquakes using satellite infrared data are being studied in different parts of the world for more than three decades. The thermal anomalies have emerged as one of the potential earthquake precursor. However, often cloud cover obstructs detection of the thermal anomalies. Compared to infrared, passive microwave sensors provide information about the thermal radiations under any weather conditions. In the present study, we have carried out detailed analysis of brightness temperature data derived from the Defense Meteorological Space Program (DMSP) Special Sensor Microwave/Imager (SSM/I) to determine thermal anomalies associated with the 1997 Manyi and 2001 Kokoxili earthquakes. Brightness temperature data for 13 years period from 1996 to 2008 observed from F13 satellite were considered to avoid difference in the sensor sensitivity. Based on 9 years background data that ignoring data for the years in which strong earthquake occurred, we computed Index of Microwave Radiation Anomaly (IMRA) over the Manyi-Yushu Fault (MYF) and Kun Lun Fault (KLF) zones, Tibetan Plateau. Our results indicate that the microwave brightness temperature at 19.35 GHz has higher sensitivity to the seismic anomalies in comparison to the other higher frequency channels. The IMRA with multi-region, multi-frequency, and multi-parameter variation were analyzed to validate our results. In addition, variation of different parameters (microwave brightness temperature, near surface air temperature and carbon monoxide-CO) observed for Kokoxili earthquake shows the transfer process of thermal anomalies from the focal region to the atmosphere during the preparation and occurrence of earthquake. Passive microwave satellite data combined with other surface and atmospheric parameters provide better understanding of physical mechanism of thermal anomalies associated with earthquakes. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]

Air pollution contributes to a large fraction of the total mortality estimated under the global burden of disease project (GBD) of the World Health Organization (WHO). This paper discusses an integrated study to obtain the spatiotemporal characteristics of particulate matter (PM2.5 and PM10) and trace gases (O3, SO2, NO2, and CO) pollutants in Hangzhou City (China) for the years 2014–2016. Our detailed analysis shows a relationship between air pollutants and land-use/land-cover change. Air quality parameters (PM2.5 and PM10) and trace gases (SO2, NO2, and CO) show strong monthly variations in the months of January (higher values) and July (lower values). During monsoon and summer seasons, air quality and trace gases show low values, whereas ozone (O3) is higher in the summer and lower in the winter. The spatial distribution of air pollutants is retrieved using the kriging method at the monitoring sites in Hangzhou City. We have considered normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Landsat 8 data. The correlation between air pollutants and land use at the street-town unit suggests that areas with low NDVI, high road density, large built-up density, and LST are consistent with high concentrations of particulate matter and SO2, NO2, and CO. Among the trace gases, NO2 is found to be the most sensitive element affected by land use patterns, and O3 shows weak correlation with land use. SO2 shows a strong positive correlation with road density and LST, whereas CO shows positive correlation with the built-up density, LST, and population density. [ABSTRACT FROM AUTHOR]

Air pollution represents a significant fraction of the total mortality estimated by the World Health Organization (WHO) global burden of disease project (GBD). The present paper discusses the characteristics of trace gases (O, NO, NO, and CO) and particulate matter (PM and PM) in two Asian megacities, Delhi (India) and Beijing (China). A continuous measurement of trace gases and particulate matter are considered from 12 measuring sites in Beijing and 8 sites in Delhi. Over Beijing, the annual average of PM, PM, O, NO, and CO is, respectively, 85.3, 112.8, 58.7, and 53.4 μg/m and 1.4 mg/m, and, respectively, over Delhi 146.5, 264.3, 24.7,and 19.8 μg/m and 1.73 mg/m. From the spatial variations of pollutants, the concentrations of particulate matter and trace gases are observed to be much higher in the urban areas compared to the suburban areas. The higher average concentrations of PM and PM over Delhi and Beijing are observed during winter season compared with other seasons. The maximum diurnal variation of PM concentration is observed during winter season over Beijing and Delhi. The comparison of trace gases shows that the O concentrations during daytime are obviously higher compared with nighttime, and the highest diurnal variation of O is observed during summer. The concentrations of CO are highest during winter season, and higher concentrations are observed during nighttime compared to daytime. The O and CO show negative correlation over Beijing and Delhi. The negative correlation between O and NO is merely observed over Beijing, while CO and NO concentrations, in contrast, show positive correlation over Beijing. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to discuss an integrated approach to efficiently obtaining earthquake damage information. We developed a framework to rapidly obtain earthquake damage information using post-earthquake airborne optical images. The framework is a standard process that includes data selection, preprocessing, damage factor identification, damage factor evaluation and the development of an earthquake damage information map. We can obtain damage information on severely affected regions using this framework, which will aid in planning rescue and rehabilitation efforts following disasters. We used the integrated approach to obtain damage information using the Lushan earthquake (magnitude 7.0, 20 April 2013) as a case study. The result were as follows: (1) 644 collapsed buildings and 4599 damaged buildings accounted for 13.90% and 96.24%, respectively, of the total number of buildings in the study area; (2) 334 landslides (total area of 691,674.5 m2) were detected and were found at greater probabilities at elevations of 1400–1500 m and higher slope; (3) no secondary disasters, such as barrier lakes, were detected; (4) 15 damaged sections (total of 306 m) were detected in the lifelines, and road sections that are at a high risk of damage (total of 2.4 km) were identified; and (5) key structures, including Yuxi River Dam and three bridges, were intact. Integrating the earthquake damage factor information generated a comprehensive Lushan earthquake damage information map. The integrated approach was proven to be effective using the Lushan earthquake as a case study and can be applied to assess earthquake damage to facilitate efficient rescue efforts. [ABSTRACT FROM AUTHOR]

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making efforts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two different paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner. [ABSTRACT FROM AUTHOR]

The number of vehicle ownership keeps increasing with the fast development of China's society and economy. Consequently, there are surging road traffic accidents, which seriously endanger the safety of people's lives and property. Hence, the present work aims to reduce the probability of traffic accidents and improve the efficiency of driver risk identification. The driver is taken as the research sample. Driver's physiological and Pleasure Arousal Dominance (PAD) data are collected using virtual driving equipment. Recurrent Neural Network (RNN) is applied to identify multiple physiological signals. Support Vector Machine (SVM) is utilized to classify the data. The performance of the proposed model is validated through data analysis of multiple volunteers. Results demonstrate that the PAD physiological signals can improve the rate of drivers recognizing risks. In the meantime, these signals help to understand the driver's emotions, thereby identifying the risks faced by the driver. By combining SVM and RNN, the accuracy of risk identification reaches 91.44%, which to some extent proves the effectiveness of the proposed model. The results have vital reference value for reducing traffic accidents and ensuring driving safety. [ABSTRACT FROM AUTHOR]

• Shows relations with the hydroseismogram and the earthquake parameters. • Uncertain relations when the epicentral distance exceeds 4000 km. • The vibration ratio between the hydroseismogram and ground is frequency dependent. • The dominant hydroseismogram period increases with the epicentral distance. • Hydroseismograms are influenced by anisotropy and seismic wave superposition. Hydroseismograms reflect the oscillations of well water levels caused by seismic waves. In this study, the relationships between the hydroseismogram oscillation amplitude and three factors (seismic magnitude, epicentral distance, and focal depth) in Jingle well, China were analyzed. The results revealed that hydroseismogram amplitude increases exponentially with seismic magnitude as H mag = 7.848 × 10 - 8 × e 2.1690 × m + 0.047 (m > M w 5.8) (m – seismic magnitude), decreases exponentially with epicentral distance as H dis = - 2.1108 × 10 - 5 × e 2.3864 × γ + 0.011 (γ – epicentral distance), and decreases exponentially with focal depth as H dep = - 1.6438 × 10 - 3 × e 0.0254 × d - 0.203 (d is focal depth). However, these relationships become uncertain when the epicentral distance exceeds 4000 km and are not applicable to nuclear explosions. Based upon the single well–aquifer model constructed for Jingle well, the amplification (μ) of the water level oscillation by ground vibrations μ = A b A a = R 2 - r 2 r 2 depends on the frequency of the water-level oscillations. For relatively high-frequency oscillations (caused by earthquakes with relatively small epicentral distances), the amplification will be small, while for low-frequency oscillations (caused by earthquakes with relatively large epicentral distances), the amplification will be large. Using the S-transform, the dominant hydroseismogram period increases with the epicentral distance as τ = 0.5826 × γ + 12.51 , which means that the amplification (μ) increases with the epicentral distance. Of course, the amplitude of ground oscillations will be greatly attenuated with increasing epicentral distance. Due to the anisotropy of the earth and seismic wave superposition, the relationships between the hydroseismogram amplitude and the seismic magnitude, epicentral distance, and focal depth become blurred or even uncertain with increasing epicentral distance and focal depth, leading to poor goodness-of-fit between the dominant hydroseismogram period and epicentral distance. The quantitative relationships between the hydroseismogram amplitude and the seismic magnitude, epicentral distance, and focal depth, which determine the ratio between the amplitudes of water-level oscillations and ground vibrations and the relationship between groundwater oscillations and seismic wave transmission, are proposed for the first time in the present work. These results will improve understanding of hydrogeological conditions and could also be important in identifying reliable earthquake precursors. [ABSTRACT FROM AUTHOR]

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years.