A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

Abstract

Abstract:Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM 2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM 2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM 2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners.

Participatory quantitative health impact assessment of urban transport planning: A case study from Eastern Africa

“…We translated the survey participants’ IPAQ physical activity data into a metabolic equivalent of tasks per week (MET/hr/week) based on the type of activity practiced (baseline) and mode of transport used ( Table 4 ). The survey participants’ PA data was assumed to represent the overall PA behaviour of the Port Louis adult population ( Assah et al, 2011 , Guthold et al, 2008 , Mbanya et al, 2010 , Christensen et al, 2009 , Luke et al, 2005 , Sobngwi et al, 2002 , Adeloye et al, 2016 , Murray et al, 2015 , Organization, 2015 , Bonnet et al, 2017 , Sperling and Gordon, 2008 , Rwebangira, 2001 , Kudebong et al, 2011 , De Nazelle et al, 2011 , Mueller et al, 2015 , Mueller et al, 2017 , Pereira et al, 2017 , Winkler et al, 2013 , Winkler et al, 2011 , European Centre for Health Policy WHO, 1999 , Nieuwenhuijsen, 2016 , Chang-Hong et al, 2009 , He et al, 2016 , Vu et al, 2013 , Tashayo et al, 2017 , Aggarwal and Jain, 2015 , Mahendra and Rajagopalan, 2015 , Dhondt et al, 2011 , Ongel and Sezgin, 2016 , Guo et al, 2010 Sep , Permadi et al, 2017 , de Sa et al, 2017 , Ren et al, 2016 , Erlanger et al, 2008 , Thondoo et al, 2019 , Rojas-Rueda et al, 2016 , Kinney et al, 2011 , Utzinger et al, 2005 , Winkler and Utzinger, 2014 , Knoblauch et al, 2017 , Gulis and Mochungong, 2013 , O’Keefe and Scott-Samuel, 2010 , Nieuwenhuijsen et al, 2017 , Government of Mauritius, 2015 , World Bank. Countries by Population Density, 2015 , OECD, 2014 , Ministry of Public Infrastructure and Land Transport, 201...…”

“…HIAs enable identification of the most healthy, feasible and acceptable transport policy measures in cities facing environmental and health hazards and high levels of social inequity ( Nieuwenhuijsen, 2016 ). Transport-related HIAs in LMICs have assessed the impacts of air pollution only ( Chang-Hong et al, 2009 , He et al, 2016 , Vu et al, 2013 , Tashayo et al, 2017 , Aggarwal and Jain, 2015 , Mahendra and Rajagopalan, 2015 , Dhondt et al, 2011 , Ongel and Sezgin, 2016 , Guo et al, 2010 Sep , Permadi et al, 2017 ); of air pollution, road traffic and physical activity combined ( Mahendra and Rajagopalan, 2015 , de Sa et al, 2017 ); air pollution and greenhouse gas emissions combined ( Ren et al, 2016 ); and finally, noise, air pollution and greenhouse gas emissions combined ( Ongel and Sezgin, 2016 ).…”

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

“…Analysis of these patterns plays a significant role in determining the quality of life, site selection for establishing sensitive buildings such as schools or hospitals, and planning traffic routes that minimize the inhalation of pollutants. Patterns also provide valuable information for environmental health impact assessments [11]. At a region of interest, it is assumed that the amounts of pollutants were obtained from observations of the closest monitoring stations.…”

Impact Assessment of Pollutant Emissions in the Atmosphere from a Power Plant over a Complex Terrain and under Unsteady Winds

“…Very few studies in the literature combined both a statistical and a dispersion model [12], moreover, they generally use simplistic dispersion tools (e.g., Gaussian models), which are based on strong assumptions, and suitable only in rarely occurring conditions. The present study combines the use of a statistical and a dispersion modelling tool, and it is featured by a Lagrangian dispersion model, representing a novelty and providing improved concentration fields for reliable economic assessments of the plant and exposure studies.…”