Search Results (3)

As an integral part of the 2030 Agenda for Sustainable Development, Disaster Risk Reduction (DRR) is essential for human safety and city sustainability. In recent years, natural disasters, which have had a tremendous negative impact on economic and social development, have frequently occurred in cities. As one of these devastating disasters, earthquakes can severely damage the achievements of urban development and impact the sustainable development of cities. To prepare for potential large earthquakes in the future, efficient evacuation plans need to be developed to enhance evacuation efficiency and minimize casualties. Most previous research focuses on minimization of distance or cost while ignoring risk factors. We propose a multi-objective optimization model with the goal of reducing the risk during the evacuation process, which is called the risk reduction model (RRM). Problem-specific indicators for screening optimal solutions are introduced. The research selects the Ogu area in Tokyo as a case study, where there is a relatively high density of wooden structures, increasing the risks of building collapse and fire spread after an earthquake, and is based on a two-phase evacuation flow that considers secondary evacuation for fire response. The results indicate that, in this case, RRM can, in most situations, reduce the risk level during the evacuation process and improve evacuation efficiency and success rate without significantly increasing the total evacuation distance. It proves to be superior to the traditional distance minimization model (DMM), which prioritizes minimizing the total distance as the objective function. Full article

Densely populated, low-lying coastal areas are most at-risk for negative impacts from increasing intensity of storm-induced flooding. Due to the effects of global warming and subsequent climate change, coastal temperatures and the magnitude of storm-induced flooding are projected to increase, creating a hospitable environment for the aquatic Vibrio spp. bacteria. A relative risk model analysis was used to determine which census block groups in coastal South Carolina have the highest risk of Vibrio spp. exposure using storm surge flooding as a proxy. Coastal block groups with dense vulnerable sub-populations exposed to storm surge have the highest relative risk, while inland block groups away from riverine-mediated storm surge have the lowest relative risk. As Vibriosis infections may be extremely severe or even deadly, the best methods of infection control will be regular standardized coastal and estuarine water monitoring for Vibrio spp. to enable more informed and timely public health advisories and help prevent future exposure. Full article

We conducted a meta-analysis focusing on studies with high potential for trichloroethylene (TCE) exposure to provide quantitative evaluations of the evidence for associations between TCE exposure and kidney, liver, and non-Hodgkin lymphoma (NHL) cancers. A systematic review documenting essential design features, exposure assessment approaches, statistical analyses, and potential sources of confounding and bias identified twenty-four cohort and case-control studies on TCE and the three cancers of interest with high potential for exposure, including five recently published case-control studies of kidney cancer or NHL. Fixed- and random-effects models were fitted to the data on overall exposure and on the highest exposure group. Sensitivity analyses examined the influence of individual studies and of alternative risk estimate selections. For overall TCE exposure and kidney cancer, the summary relative risk (RRm) estimate from the random effects model was 1.27 (95% CI: 1.13, 1.43), with a higher RRm for the highest exposure groups (1.58, 95% CI: 1.28, 1.96). The RRm estimates were not overly sensitive to alternative risk estimate selections or to removal of an individual study. There was no apparent heterogeneity or publication bias. For NHL, RRm estimates for overall exposure and for the highest exposure group, respectively, were 1.23 (95% CI: 1.07, 1.42) and 1.43 (95% CI: 1.13, 1.82) and, for liver cancer, 1.29 (95% CI: 1.07, 1.56) and 1.28 (95% CI: 0.93, 1.77). Our findings provide strong support for a causal association between TCE exposure and kidney cancer. The support is strong but less robust for NHL, where issues of study heterogeneity, potential publication bias, and weaker exposure-response results contribute uncertainty, and more limited for liver cancer, where only cohort studies with small numbers of cases were available. Full article