There is an increasing body of evidence showing the impact of air pollutants on human health such as on the respiratory, and cardio- and cerebrovascular systems. In China, as people begin to pay more attention to air quality, recent research focused on the quantitative assessment of the effects of air pollutants on human health. To assess the health effects of air pollutants and to construct an indicator placing emphasis on health impact, a generalized additive model was selected to assess the health burden caused by air pollution. We obtained Baidu indices (an evaluation indicator launched by Baidu Corporation to reflect the search popularity of keywords from its search engine) to assess daily query frequencies of 25 keywords considered associated with air pollution-related diseases. Moreover, we also calculated the daily concentrations of major air pollutants (including PM₁₀, PM_2.5, SO₂, O₃, NO₂, and CO) and the daily air quality index (AQI) values, and three meteorological factors: daily mean wind level, daily mean air temperature, and daily mean relative humidity. These data cover the area of Beijing from 1 March 2015 to 30 April 2017. Through the analysis, we produced the relative risks (RRs) of the six main air pollutants for respiratory, and cardio- and cerebrovascular diseases. The results showed that O₃ and NO₂ have the highest health impact, followed by PM₁₀ and PM_2.5. The effects of any pollutant on cardiovascular diseases was consistently higher than on respiratory diseases. Furthermore, we evaluated the currently used AQI in China and proposed an RR-based index (health AQI, HAQI) that is intended for better indicating the effects of air pollutants on respiratory, and cardio- and cerebrovascular diseases than AQI. A higher Pearson correlation coefficient between HAQI and RR_Total than that between AQI and RR_Total endorsed our efforts.

Disaster preparedness is crucial for providing an effective response to, and reducing the possible impacts of, disasters. Although volunteers’ participation plays an important role in disaster preparedness, their actual participation in disaster preparedness activities is still low. To find ways to encourage more volunteers to participate, this study analyzed the social background and organizational and attitudinal factors affecting the volunteers’ willingness to participate. Questionnaires were distributed to 990 registered disaster volunteers across Beijing and the data were analyzed using linear regression models. Results revealed a weak willingness to participate in disaster preparedness. Only 28.08% of the respondents indicated that they were “very ready” to participate in voluntary disaster preparedness, and 14.65% showed “a little bit” of interest. The following was concluded: (1) Disaster volunteers’ social background variables were related to their willingness to participate in disaster preparedness. Compared to male volunteers, female volunteers were more willing to participate. Chinese Communist Party members were more willing to participate than non-members. (2) Providing accidental life insurance for the volunteers had a positive effect on their willingness to participate in disaster preparedness. Provision of more training had a negative effect on the volunteers’ willingness to participate, indicating a low quality of training. (3) Organizational identification was positively related to the volunteers’ willingness to participate. According to these results, we suggest that volunteer organizations should improve their standards and procedures for disaster volunteer recruitment and selection, and gain a deeper understanding of the needs of the disaster volunteers in order to better motivate them to participate.

Urban storm inundation, which frequently has dramatic impacts on city safety and social life, is an emergent and difficult issue. Due to the complexity of urban surfaces and the variety of spatial modeling elements, the lack of detailed hydrological data and accurate urban surface models compromise the study and implementation of urban storm inundation simulations. This paper introduces a Constrained Delaunay Triangular Irregular Network (CD-TIN) to model fine urban surfaces (based on detailed ground sampling data) and subsequently employs a depression division method that refers to Fine Constrained Features (FCFs) to construct computational urban water depressions. Storm-runoff yield is placed through mass conservation to calculate the volume of rainfall, runoff and drainage. The water confluences between neighboring depressions are provided when the water level exceeds the outlet of a certain depression. Numerical solutions achieved through a dichotomy are introduced to obtain the water level. Therefore, the continuous inundation process can be divided into different time intervals to obtain a series of inundation scenarios. The main campus of Beijing Normal University (BNU) was used as a case study to simulate the “7.21” storm inundation event to validate the usability and suitability of the proposed methods. In comparing the simulation results with in-situ observations, the proposed method is accurate and effective, with significantly lower drainage data requirements being obtained. The proposed methods will also be useful for urban drainage design and city inundation emergency preparations.

Purpose: A bad safety accident at a manufacturing company usually results in casualties and economic losses. The company affected by such an accident must deal with pressure from multiple stakeholder groups. Employees, in particular, play a key role in pushing the affected company to develop strategies to improve occupational safety and health. The purpose of this paper is to seek answers to two questions: does a safety accident affect employee behavior in terms of giving up prospects to develop a career at the affected company? If yes, could innovation initiatives adopted by the company help moderate the negative consequences from a safety accident? Design/methodology/approach: By investigating 120 safety accidents reported by publicly listed Chinese manufacturing companies between 2009 and 2016, the authors conduct an empirical study using regression-based statistical hypotheses testing to describe the companies’ responses and prospects for their employees. Findings: The results show that the magnitude of the accident and the accident being caused by an employee error positively affect the turnover of employees. Furthermore, technical innovation initiatives, such as spending on R&D, by the accident-affected companies increase the positive effect of the accident magnitude on employee turnover. On the contrary, management innovation initiatives, such as corporate social responsibility activities, weaken the impact of the accident magnitude and employee error on employee turnover. Originality/value: This study contributes to knowledge development by adding a crisis perspective in human resource management research. It helps to better understand the impact of safety accidents on employee behavior and the response taken by companies through innovation initiatives.