Evaluating Emergency Response Capacity of Chemical Industrial Park Using an Hybrid Fuzzy AHP Method

This paper presents an expert-based fuzzy analytic hierarchy process( AHP) model for evaluating emergency response capacity of Chemical Industrial Park( ERCCIP) by jointly using an improved fuzzy preference programming( FPP) and 2-tuple fuzzy linguistic approach. An evaluation index system for ERCCIP is proposed. The weight of sub-criteria and criteria of the evaluation index system for ERCCIP are determined using the improved FPP. And the ratings of sub-criteria are assessed in linguistic values according to the experts' subjective opinions. Finally,the aggregated ratings of criteria and the overall ERCCIP are calculated.

An novel identification method of the environmental risk sources for surfacewater pollution accidents in chemical industrial parks

The chemical industry is a major source of various pollution accidents. Improving the management level of risk sources for pollution accidents has become an urgentdemand for most industrialized countries. In pollution accidents, the released chemicals harm the receptors to some extentdepending on their sensitivity or susceptibility. Therefore, identifying the potential risk sources from such a large number of chemical enterprises has become pressingly urgent. Based on the simulation of thewhole accident process, a novel and expandable identification method for risk sources causingwater pollution accidents is presented. The newlydeveloped approach, by analyzing and stimulating thewhole process of a pollution accident between sources and receptors, can be applied to identify risk sources, especially on the nationwide scale. Three major types of losses, such as social, economic and ecological losses,were normalized, analyzed and used for overall consequence modeling. A specific case study area, located in a chemical industry park （CIP） along the Yangtze River in Jiangsu Province, China,was selected to test the potential of the identification method. The results showed that therewere four risk sources for pollution accidents in this CIP. Aniline leakage in the HS Chemical Plantwould lead to the most serious impact on the surroundingwater environment. This potential accidentwould severelydamage the ecosystem up to3.8 kmdownstream of Yangtze River, and lead to pollution over adistance stretching to 73.7 kmdownstream. The proposed method is easily extended to the nationwide identification of potential risk sources.

Application of k-means clustering to environmental risk zoning of the chemical industrial area

The homogeneous risk characteristics within a sub-area and the heterogeneous from one sub-area to another are unclear using existing environmental risk zoning methods. This study presents a new zoning method by determining and categorizing the risk characteristics using the k-means clustering data mining technology. The study constructs indices and develops index quantification models for environmental risk zoning by analyzing the mechanism of environmental risk occurrence. We calculate the source risk index, air risk field index, water risk field index, and target vulnerability of the study area with Nanjing Chemical Industrial Park using a 100 m - 100 m mesh grid as the basic zoning unit, and then use k-means clustering to analyze the environmental risk in the area. We obtain the optimal clustering number with the largest average silhouette coefficient by calculating the average silhouette coefficients of clustering at different k-values. The clustering result with the optimal clustering number is then used for the environmental risk zoning, and the zoning result is mapped using the geographic information system. The study area is divided into five sub-areas. The common environmental risk characteristics within the same sub-area, as well as the differences between sub- areas, are presented. The zoning is helpful in risk management and is convenient for decision makers to distribute limited resources to different sub-areas in the design of risk reducing intervention.