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'Extreme' events - including climatic events, such as hurricanes, tornadoes, drought - can cause massive disruption to society, including large death tolls and property damage in the billions of dollars. Events in recent years have shown the importance of being prepared and that countries need to work together to help alleviate the resulting pain and suffering. This volume presents an integrated review of the broad research field of large-scale disasters. It establishes a common framework for predicting, controlling and managing both manmade and natural disasters. There is a particular focus on events caused by weather and climate change. Other topics include air pollution, tsunamis, disaster modeling, the use of remote sensing and the logistics of disaster management. It will appeal to scientists, engineers, first responders and health-care professionals, in addition to graduate students and researchers who have an interest in the prediction, prevention or mitigation of large-scale disasters.

Earthquakes, floods, drought, and other natural hazards cause tens of thousands of deaths, hundreds of thousands of injuries, and billions of dollars in economic losses each year around the world. Many billions of dollars in humanitarian assistance, emergency loans, and development aid are expended annually. Yet efforts to reduce the risks of natural hazards remain largely uncoordinated across different hazard types and do not necessarily focus on areas at highest risk of disaster. Natural Disaster Hotspots presents a global view of major natural disaster risk hotspots – areas at relatively high risk of loss from one or more natural hazards. It summarizes the results of an interdisciplinary analysis of the location and characteristics of hotspots for six natural hazards – earthquakes, volcanoes, landslides, floods, drought, and cyclones. Data on these hazards are combined with state-of-the-art data on the subnational distribution of population and economic output and past disaster losses to identify areas at relatively high risk from one or more hazards.

Industrial cities are hotspots for many hazardous air pollutants (HAPs), which are detrimental to human health. We devised an identification method to determine priority HAP monitoring areas using a comprehensive approach involving monitoring, modeling, and demographics. The methodology to identify the priority HAP monitoring area consists of two parts: (1) mapping the spatial distribution of selected categories relevant to the target pollutant and (2) integrating the distribution maps of various categories and subsequent scoring. The identification method was applied in Ulsan, the largest industrial city in South Korea, to identify priority HAP monitoring areas. Four categories related to HAPs were used in the method: (1) concentrations of HAPs, (2) amount of HAP emissions, (3) the contribution of industrial activities, and (4) population density in the city. This method can be used to select priority HAP monitoring areas for intensive monitoring campaigns, cohort studies, and epidemiological studies.

High levels of nitrates in groundwater pose a risk to human health. In this study, we selected areas with typical agricultural nitrate pollution in northeast China as study sites. We then collected groundwater samples for nitrate nitrogen content analysis using the Four Step method developed by the United State Environmental Protection Agency (USEPA) in conjunction with the non-carcinogens health risk model (R = CDI/RfD) to determine the health risk associated with nitrate pollution of groundwater. The reference value of nitrates in drinking water was set at 10 mg/L (measured as nitrogen) and the intake reference dose of nitrate was set at 1.6 mg•kg −1 •d −1 based on the EPA’s IRIS(Integrated Risk Information System). The water intake reference values were set at 2.3 L/d and 1.5 L/d based on the EPA values and actual values observed in the study area. The average exposure time was the ED (exposure duration) × 365d/a. Weights refer to the 2002 national urban and rural average weight of residents of different genders and different ages. Health hazard index calculation was based on the above information, and the index less than 1 is acceptable (U.S. EPA’s Risk Assessment Guide). Health risk assessment maps were then drawn by Arcgis software. The results indicated that agricultural sewage irrigation areas in the study area showed strong health risks, but that those of the city were relatively small. Moreover, the results indicated that children’s health risks are greater than those of adults.

The use of remote sensing techniques is now common practice in different working environments, including engineering geology. Moreover, in recent years the development of structure from motion (SfM) methods, together with rapid technological improvement, has allowed the widespread use of cost-effective remotely piloted aircraft systems (RPAS) for acquiring detailed and accurate geometrical information even in evolving environments, such as mining contexts. Indeed, the acquisition of remotely sensed data from hazardous areas provides accurate 3-D models and high-resolution orthophotos minimizing the risk for operators. The quality and quantity of the data obtainable from RPAS surveys can then be used for inspection of mining areas, audit of mining design, rock mass characterizations, stability analysis investigations and monitoring activities. Despite the widespread use of RPAS, its potential and limitations still have to be fully understood.In this paper a case study is shown where a RPAS was used for the engineering geological investigation of a closed marble mine area in Italy; direct ground-based techniques could not be applied for safety reasons. In view of the re-activation of mining operations, high-resolution images taken from different positions and heights were acquired and processed using SfM techniques to obtain an accurate and detailed 3-D model of the area. The geometrical and radiometrical information was subsequently used for a deterministic rock mass characterization, which led to the identification of two large marble blocks that pose a potential significant hazard issue for the future workforce. A preliminary stability analysis, with a focus on investigating the contribution of potential rock bridges, was then performed in order to demonstrate the potential use of RPAS information in engineering geological contexts for geohazard identification, awareness and reduction.

National-level studies examining racial disparities around hazardous waste treatment, storage, and disposal facilities have been very influential in defining the academic and political debates about the existence and importance of \"environmental injustice.\" However, these studies tend to employ methods that fail to adequately control for proximity between environmentally hazardous sites and nearby residential populations. By using GIS and applying methods increasingly used in environmental inequality research that better control for proximity, we conduct a comprehensive reassessment of racial inequality in the distribution of the nation's hazardous waste facilities. We compare the magnitude of racial disparities found with those of prior studies and test competing racial, economic, and sociopolitical explanations for why such disparities exist. We find that the magnitude of racial disparities around hazardous waste facilities is much greater than what previous national studies have reported. We also find these disparities persist even when controlling for economic and sociopolitical variables, suggesting that factors uniquely associated with race, such as racial targeting, housing discrimination, or other race-related factors are associated with the location of the nation's hazardous waste facilities. We further conclude that the more recent methods for controlling for proximity yield more consistent and definitive results than those used previously, and therefore argue for their wider utilization in environmental inequality research.

The objectives are exploring the impacts of geologic hazards on the construction of homestays, improving the safety of homestay buildings, guaranteeing the safety of tourists, and enhancing the disaster-resistance of homestays in scenery spots. The computer simulation system and Building Information Modeling (BIM) technology are employed to construct a geologic hazard prediction model for homestays. The model utilizes a time history method to establish a complete early-warning and monitoring system by learning the geologic disaster data. The detection of various geologic hazards has verified the effectiveness of the proposed model. The results show that the model can reduce the losses in the case of water accumulation and landslides during storms, and the BIM technology-based homestay buildings will suffer fewer losses. In the case of earthquakes, BIM technology-based homestay buildings have no noticeable shaking and displacement. Compared to traditional construction methods, the displacement is reduced by 49.15%. In the case of a spontaneous fire, the burning area of the BIM technology-based homestay building is only 270m2. The most severe factors affecting the construction of homestay buildings are earthquakes and landslide risks. The BIM technology generates 3D building planning; therefore, planners can fully understand the problems in the building. In the meantime, the multi-source monitoring data of multiple geologic hazards can be monitored and fed back, thereby improving the timeliness of early-warning of geologic hazards. The results are of considerable significance to the prevention of losses caused by geologic hazards, which can significantly improve the understanding of geologic hazards.

PurposeAdverse health impact of air pollution on health may not only be associated with the level of exposure, but rather mediated by perception of the pollution and by top-down processing (e.g. beliefs of the exposure being hazardous), especially in areas with relatively low levels of pollutants. The aim of this study was to test a model that describes interrelations between air pollution (particles < 10 μm, PM10), perceived pollution, health risk perception, health symptoms and diseases.MethodsA population-based questionnaire study was conducted among 1000 Estonian residents (sample was stratified by age, sex, and geographical location) about health risk perception and coping. The PM10 levels were modelled in 1 × 1 km grids using a Eulerian air quality dispersion model. Respondents were ascribed their annual mean PM10 exposure according to their home address. Path analysis was performed to test the validity of the model.ResultsThe data refute the model proposing that exposure level significantly influences symptoms and disease. Instead, the perceived exposure influences symptoms and the effect of perceived exposure on disease is mediated by health risk perception. This relationship is more pronounced in large cities compared to smaller towns or rural areas.ConclusionsPerceived pollution and health risk perception, in particular in large cities, play important roles in understanding and predicting environmentally induced symptoms and diseases at relatively low levels of air pollution.

We sought to provide a historical overview of methods, models, and data used in the environmental justice (EJ) research literature to measure proximity to environmental hazards and potential exposure to their adverse health effects. We explored how the assessment of disproportionate proximity and exposure has evolved from comparing the prevalence of minority or low-income residents in geographic entities hosting pollution sources and discrete buffer zones to more refined techniques that use continuous distances, pollutant fate-and-transport models, and estimates of health risk from toxic exposure. We also reviewed analytical techniques used to determine the characteristics of people residing in areas potentially exposed to environmental hazards and emerging geostatistical techniques that are more appropriate for EJ analysis than conventional statistical methods. We concluded by providing several recommendations regarding future research and data needs for EJ assessment that would lead to more reliable results and policy solutions.

Industrial accidents have shown that many people can be affected, such as in Seveso, Italy, in 1976. Industrial accidents in nuclear power plants have also led to fatalities and evacuations. To better guide preparedness against and mitigation of industrial accidents, an assessment is necessary to evaluate hazard exposure and the type of potentially vulnerable social groups that need to be taken into account. This study conducted a spatial assessment of three types of industrial facilities in Germany: facilities registered under the Seveso Directive, chemical parks, and nuclear power plants. The method consisted of a spatial assessment using a Geographic Information System of exposure around hazardous sites registered under the Seveso Directive in Germany and of census data to analyze social vulnerability. Hazards analyzed included industrial accidents and earthquakes. The results revealed that most industrial sites are in urban areas and that population density, the numbers of foreigners, and smaller housing unit sizes are higher in close proximity to these sites. The buffer zones analyzed in circles between 1 and 40 km show a decreasing vulnerability with more distance. This can guide emergency management planners and other stakeholders to better prepare for major accidents and better devise disaster risk reduction strategies specifically for different social groups.