With global climate change, cities face the challenge of increasing flood disaster caused by heavy rainfall, and the prediction and assessment of flood disaster risk is a crucial step towards risk mitigation and adaptation planning. In this study, a method combining Bayesian network (BN) model and geographic information system (GIS), which can capture the potential relationships between factors impacting flood disaster and has capacity of quantifying uncertainty and utilizing both data and knowledge-based sources, was proposed to assess flood disaster risk. The proposed methodology was applied in a case study to assess flood disaster risk and to diagnose the reason for flood disaster in Zhengzhou City, and the results were validated by comparing with actual situation. The results show that that the relative error of very-low, low, moderate, high and very-high risk predicted by the proposed model is 12.57%, 13.21%, 2.23%, 19.63% and 21.65%, respectively, which demonstrates the discriminative power of the established model. Based on the spatial distribution of different risk levels, it can be recognized that the flood disaster risk in Zhengzhou City is decreasing from the middle to the surroundings. The results provide some basis for the field control and management of urban flood disaster.

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).

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.