The goal of this research is to assess hazardous heavy metal levels in PM_2.5 fractioned road dust in order to quantify the risk of inhalation and potential health effects. To accomplish this, Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) was used to determine concentrations of eight heavy metals (Cr, Cu, Ni, Zn, Cd, As, Pb, and Hg) in the PM_2.5 portion of road dust samples from five different land use areas (commercial, residential, industrial, parks, and educational) in Zhengzhou, China. The following were the average heavy metal concentrations in the city: Cr 46.26 mg/kg, Cu 25.13 mg/kg, Ni 12.51 mg/kg, Zn 152.35 mg/kg, Cd 0.56 mg/kg, As 11.53 mg/kg, Pb 52.15 mg/kg, and Hg 0.32 mg/kg. Two pollution indicators, the Pollution Index (PI) and the Geoaccumulation Index (I_geo), were used to determine the degree of contamination. Both PI and I_geo indicated the extreme pollution of Hg and Cd, while PI also ranked Zn in the extreme polluted range. The US Environmental Protection Agency (USEPA) model for adults and children was used to estimate health risks by inhalation. The results identified non-carcinogenic exposure of children to lead (HI > 0.1) in commercial and industrial areas. Both children and adults in Zhengzhou’s commercial, residential, and park areas are exposed to higher levels of copper (Cu), lead (Pb), and zinc (Zn).

Despite the rapid development of risk analysis in dam engineering, there is a relative absence of research on the environmental impact of dam break. As a systematic theory, set pair analysis has a good effect in dealing with uncertainties, although the result is relatively rough and easy to distort. A connection degree of five grades and a generalized set of potential are introduced to improve traditional set pair analysis. Combined with the index system, the evaluation model of the environmental impact of dam break is established, which is based on generalized set pair analysis. Taking Sheheji Reservoir dam as an example, a comparison of evaluation results of fuzzy mathematics theory and generalized set pair analysis is made, which verifies the scientificity and practicability of the method proposed in this paper. The results show that the evaluation grade of the environmental impact of dam break at Sheheji Reservoir is serious, and appropriate management measures should be taken to reduce the risk.

Confronting the frequent flood disasters triggered by torrential downpour, the vulnerability of urban rainstorm flood disasters was analyzed with one highly popular area of research in mind: big data. Web crawler technology was used to extract text information related to floods from Internet and popular social media platforms. Combining these text data with traditional statistical data, a flood disaster vulnerability assessment model based on Analytic Hierarchy Process (AHP) was established to evaluate rainstorm and flood disaster vulnerability, and the spatial distribution characteristics of vulnerability to pluvial flooding were analyzed based on Geographic Information System (GIS). The established model was applied in Zhengzhou, a city that often suffers from heavy rainstorms. The results show that the areas located near downtown Zhengzhou were more vulnerable to rainstorm and flooding than others, and most of the city could be at moderate and high vulnerability. Finally, the waterlogging spots extracted from various sources were used to evaluate the performance of the proposed model. The results show that most of waterlogging spots were located in very-high and high risk zones, while less waterlogging spots were found in districts with low vulnerability, which demonstrates the discriminative power of the established model based on big data sources. This study overcomes limited data in flood disaster vulnerability assessment methods and provides a basis for flood control and management in cities.

Flood risk assessment is an important tool for disaster warning and prevention. In this study, an integrated approach based on a D-number-improved analytic hierarchy process (D-AHP) and a self-organizing map (SOM) clustering algorithm are proposed for urban flooding risk assessment. The urban flood inundation model and geographic information system (GIS) technology were used to quantify the assessment indices of urban flood risk. The D-AHP approach was adopted to determine the weights of the indices, which effectively makes up for the shortcomings of the AHP in dealing with uncertain evaluation information (such as fuzzy and incomplete information). In addition, the SOM clustering algorithm was applied to determine the flood risk level. It is a data-driven approach that avoids the subjective determination of a flood risk classification threshold. The proposed approach for flood risk assessment was implemented in Zhengzhou, China. The flood risk was classified into five levels: highest risk, higher risk, medium risk, lower risk, and the lowest risk. The proportion of the highest risk areas was 9.86%; such areas were mainly distributed in the central and eastern parts of the Jinshui District, the eastern part of the Huiji District, and the northeastern part of the Guancheng District, where there were low terrain and serious waterlogging. The higher risk areas accounted for 24.26% of the study area, and were mainly distributed in the western and southern parts of the Jinshui District, the southern part of the Huiji District, the middle and eastern parts of the Zhongyuan District, the northeastern part of the Erqi District, and the northwestern part of the Guancheng District, which consisted of economically developed areas of dense population and buildings, matching well with historical flooding events. To verify the effectiveness of the proposed approach, traditional approaches for risk assessment were compared. The comparison indicated that the proposed approach is more reasonable and accurate than the traditional approaches. This study showed the potential of a novel approach to flood risk assessment. The results can provide a reference for urban flood management and disaster reduction in the study area.