A Hybrid Dispersion\ Fuzzy Landuse Regression Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning in Isfahan, Iran

This article develops and demonstrates a new quantitative modeling approach for environmental health impact assessment of traffic scenarios. For this purpose, two models based on hierarchical fuzzy inference system (HFIS) are developed. In order to develop HFIS for modeling the effect of transportation system on the PM2.5 concentrations, the data from an air dispersion model are utilized. There are several advantages to this approach such as modeling the spatial variation of PM2.5 with high resolution, suitable processing requirements, and consideration of interaction between emissions and meteorological processes. Moreover, the resulting fuzzy landuse regression (LUR) is capable of using accessible qualitative and uncertain data. In order to develop HFIS for modeling the impact of traffic-related PM2.5 on health, a metric derived from epidemiological studies is employed. The suggested model improved the metric capabilities by modeling the uncertainty of relationships among parameters and parameter value. Two solutions are used to improve the performance of both models. First, the topologies of HFISs are selected according to the problem. Second, used variables, membership functions and rule set is determined together through learning. We examine the capabilities of the proposed approach with assessing the impacts of three traffic scenarios to deal with air pollution in Isfahan, Iran and compare the accuracy of the results with representative models from existing literature. The models are first developed based on the current traffic conditions. Then; Low Emission-Zone and Odd/Even scenarios are examined. The examination shows that, they are the most and least effective scenarios in reducing air pollution and improving environmental health, respectively. The obtained results demonstrate that the proposed approach has desirable accuracy; beside that the model can provide better understanding of phenomena and investigating the impact of each of parameters for the planners.