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In order to understand the disaster-causing laws of earthquakes, formulae the disaster prevention and mitigation planning effectively, and mitigate earthquake disaster losses, earthquakes data with the magnitude not less than 5.0 and their losses in China and surrounding areas were sorted out from 1949 to 2021. Their distribution characteristics in terms of time and space were studied in detail, and the following conclusions were obtained, 453 disaster earthquakes caused about 350,000 deaths, 760,000 injuries, and 1124.9 billion RMB of direct economic losses (converted value), of which caused by major seismic hazards accounted for a huge proportion. From the time perspective, there are obvious earthquake-prone and less earthquake-prone years, and the number of catastrophic earthquakes has increased in chronological terms. The economic losses caused by earthquakes accelerated quickly, and the casualties decreased by varying degrees. Geographically, the frequency of earthquakes on the west side of the HU Huanyong Line was higher, but the average loss was lower. In the economic subregions, the western region had the highest frequency of earthquakes and the lowest average loss. The eastern region had the second highest frequency of earthquakes and the highest average loss. The central and northeastern regions had the lowest frequency of earthquakes and medium average losses. Yunnan and Sichuan provinces had the highest frequency of earthquakes and the most severe earthquake losses. For earthquakes with similar intensity in the same period, the casualties and economic losses were more serious in economically developed and densely populated areas. In the 21st century, the frequency of earthquakes with low magnitudes but large-scale disasters increased, and most of them occurred on the east side of the HU Huanyong Line and in the eastern and central parts in the economic subregion. In the events of major disasters caused by minor earqhquakes, the focus has shifted from a high number of casualties to significant economic losses. [ABSTRACT FROM AUTHOR]

Background Chronic diseases have become an important public health problem that affects the economic and social development of country and population health. Quantifying the disease and economic burden of the elderly can provide a better understanding of the impact of chronic diseases on individuals and society. Objective To understand the prevalence of common chronic diseases among the elderly in Shanxi Province and explore the quality-adjusted life year (QALY) loss and economic loss which caused by chronic diseases. Methods From June to August in 2019, a multi-stage random sampling method was used to select 3 250 elderly people aged≥ 60 years from 11 cities in Shanxi Province as the research subjects for a questionnaire survey. The questionnaire included general demographic information, disease status survey, and health-related quality of life status. The European Five-Dimensional Health Scale (EQ-5D-5L) was used to assess the health-related life quality of the elderly. The QALY loss caused by chronic diseases was calculated based on the Chinese EQ-5D-5L utility value score system, and the QALY loss and economic loss caused by common chronic diseases in Shanxi Province in 2019 were estimated by using population and GDP per capita from statistical bulletin. Results The results of univariate analysis showed that there were significant differences in the utility value of EQ-5D-5L among the elderly with different ages, living regions, educational levels, marital status, and family per capita monthly income (P

Based on the direct economic losses data of typhoon disasters in county-based units from 2008 to 2019, ten explanatory variables are selected by considering disaster-causing factors and environmental factors. Algorithms including Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Extreme Gradient Boosting Machine (XGBoost) and Light Gradient Boosting Machine (LightGBM) are used to build typhoon economic losses risk models respectively. The performance is evaluated to identify the model with the best accuracy. The result show that the accuracy of typhoon economic losses risk model using RF algorithm is highest; the accuracy of models using RF, XGBoost, LightGBM, AdaBoost and SVM are 0.69, 0.63, 0.62, 0.45 and 0.41, respectively. According to the model based on RF, disaster-causing factors are the dominant explanatory variable, and the importance of rainfall-induced losses exceeded that of wind speed. The model is good at economic losses risk classification, with TS scores of 0.55 and 0.51 for the training and test sets. However, the model has different ability to distinguish between each category. The model has a good classification effect on the lowest risk and highest risk, but has a certain deficiency in higher risk and medium risk. This model is applied to Typhoon Hato, the 13th typhoon in 2017, and the results are consistent with the actual economic losses risk level, with the accuracy above 0.7, the TS score above 0.58, and the false alarm rate and false alarm rate below 0.31 and 0.25, respectively. [ABSTRACT FROM AUTHOR]

By establishing an objective and effective indicator system, we can accurately estimate the economic losses brought by typhoon storm surge. With Guangdong selected as the area for our case study, we used confirmatory factor analysis to identify and select three types of risk factors generating economic losses from such disasters:hazard, vulnerability, and disaster resilience.Moreover, by dividing the study area into three time periods based on economic development conditions, we analyzed the indicator changes in various risk factors within them. Studies have shown that impact indicators from various risk factors at different time periods have not changed significantly, while their degree of relevance have varied with each risk factor. Compared with the traditionally established indicator systems, the basic indicators based on historical cases in this paper are more factual, and the application of confirmatory factor analysis in the selection of indicators has eliminated the interference of subjective factors, the correlation between the indicators and the research object is enhanced, and the indicators redundancy is reduced,rendering the results more rational and scientific, and provides a new idea for the research of index system. [ABSTRACT FROM AUTHOR]

Vehicle exhaust emissions are posing an increasingly adverse impact on urban air quality. The emission characteristics analysis and health effect assessment of specific air pollution sources can provide scientific evidence for environmental air quality management. The characteristics and health effects of PM 2.5 emissions from vehicles and economic losses caused by them in the Beijing-Tianjin-Hebei Region were analyzed from 2010 to 2020. From 2010 to 2020, PM 2.5 emissions from vehicles in the Beijing-Tianjin-Hebei Region showed an annual increase at first, followed by a slow decrease. According to the emission sharing ratios of different vehicle types, heavy-duty trucks and buses were the main contributors to PM 2.5 , with a total contribution rate of over 65.27%. The emission characteristics of vehicle pollutants varied in different cities. The contribution rate of pollutants in Beijing decreased significantly, and the emission reduction in other cities was also dramatic. The evaluation results of the impact of PM 2.5 emissions from vehicles on human health showed that the number of health endpoints in the Beijing-Tianjin-Hebei Region was on the rise. In 2020, PM 2.5 pollution caused approximately 34337 premature deaths (95% CI:9025-57209), 45500 hospitalizations (95% CI:10800-80200), 282300 outpatients (95% CI:140500-416300), and 439000 people to fall ill (95% CI:160300-679200). Beijing had the largest number of patients that presented different health endpoints. The total health and economic losses caused by PM 2.5 emissions from vehicles in 2010, 2015, and 2020 were 27.742 billion yuan (95% CI:8.616-44.643 billion yuan), 90.608 billion yuan (95% CI:28.476-144.050 billion yuan), and 129.965 billion yuan (95% CI:40.829-205.245 billion yuan), respectively. In addition, due to the differences in vehicle ownership, PM 2.5 concentrations, population, and economic losses per case of health outcome, the health effects and economic losses varied in different cities within the region. Among these cities, Beijing, Tianjin, Baoding, and Tangshan were at higher health risks and suffered more economic losses. The results of this study will help reduce the adverse effects on health and economic losses caused by pollution discharge and provide scientific evidence for environmental protection authorities to implement targeted pollution prevention and control.

Ambient air pollution is a dominant determinant of health. The health effects and economic losses due to air pollution are very important for decision-making. Since the implementation of the "Air Pollution Prevention and Control Action Plan" and "blue sky defense war" policies, the air quality of Tianjin has changed significantly. Here, the health effects and economic losses attributable to ambient air pollution in Tianjin from 2013 to 2020 wereestimated. For the particulate matter which has complex components, we assessed the inhalation health risks of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in PM 2.5 . The variation in the concentration of the main components of PM 2.5 was also analyzed. The results showed that improved air quality had positive health benefits. The health benefits from SO 2 were the highest among the six air pollutants, and 3786 deaths were avoided in 2020 compared to in 2013 due to lower SO 2 concentration. The economic losses caused by air pollutants ranged from several billion to ten billion yuan. Among the six air pollutants, particulate matter and ozone had higher health losses in recent years. The health risks of heavy metals and PAHs in PM 2.5 showed a decreasing trend. However, Cr(Ⅵ), As, Cd, and Ni in PM 2.5 in the winter of 2020 still had respiratorysystem carcinogenic risk, whereas there was no health risk of PAHs in PM 2.5 in 2019-2020. The concentrations of main components of PM 2.5 have decreased significantly. In the future, the reduction of health loss caused by air pollution depends on synergy governance of particulate matter and ozone and further research on health effects.

To understand the situation of the financial loss caused by the outflow of scientific articles published in foreign journals, a model for evaluating it based on literature loss was established in this study. Firstly, Loss of single equivalent for single outflow article includes publication fee, counter purchase fee and translation fee. The total loss of outflow was calculated using Equivalent loss of single article multiplying by number of outflow articles. Equivalent loss of single article was 3 348 yuan. The total number of outflow articles in agricultural engineering field published in foreign journals was 1991 in 2012, and its total financial losses were 6.67 million. And the total loss for all disciplines was 733 million, which originated from state scientific program, leading to loss of state resource. Therefore, we encourage appropriate amount of scientific articles published in foreign journals for international communication, but not the blind pursuit of SCI papers. [ABSTRACT FROM AUTHOR]

It is important to carry out research on health risks and associated economic losses caused by air pollution using a large sample city in order to control air pollution and improve public health literacy. Using environmental health risk and environmental value assessment methods, the health risks of PM 2.5 pollution in 2015, as well as the corresponding economic losses, were estimated in this study using the environmental monitoring data of 62 key cities as samples. The results show that significant impacts due to PM 2.5 pollution occurred in 2015, including about 125.1[95% confidence interval (CI):33.29-205.90] thousand premature deaths and 10.10 (95% CI:4.70-15.02) million cases of illness, hospital admissions, and outpatients, which accounted for 3.53% (95% CI:1.64%-5.26%) of the total urban population. The total economic loss associated with the health risks was approximately 570.6 (95% CI:193.08-874.21) billion yuan, which accounts for 1.53% (95% CI:0.52%-2.35%) of the total GDP in 2015. The per capita loss was 1970 (95% CI:667-3018) yuan for these cities. Among the four major urban agglomerations of Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and Northeast China, Beijing-Tianjin-Hebei has the highest health risks, associated economic losses, the proportion of associated economic losses to GDP, and associated economic loss per capita because of its high concentration of PM 2.5 and dense population than other three urban agglomerations. Among the three economic areas of Eastern, Central, and Western China, the health risks and associated economic losses were highest in the former. However, economic loss per capita did not differ greatly in the three economic areas. The difference in economic losses was very small between South and North China. However, the proportion of economic loss to GDP and per capita loss were much higher in the North than in the South. As to specific cities, the problems of health risks and economic losses were outstanding in Baoding, Zhengzhou, Jinan, and Beijing, which have higher PM 2.5 concentrations.