Integrated Approach for Impact Analysis of Urban Traffic on Population Exposure to Air Pollution
Research Idea Scope
An efficient and robust surface highway transportation system is essential for continued economic growth of the US. Conversely, vehicle emission has increasingly resulted in adverse impact on air quality and environmental health. Auto exhaust is a major contributor to toxic air pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), and PM2.5 (particulate matter with diameters of 2.5 micrometers or less). Time-series analyses suggest that chronic exposure to PM2.5 has detrimental effects on respiratory health and motor vehicles contribute from 25% to 35% of direct PM2.5 emissions. Infants living near traffic or elementary students at schools near heavy transportation infrastructures are particularly susceptible to such air pollutants. Despite plenty of studies conducted on travel demand forecasting models, vehicle emission and dispersion models, and exposure-health models that address aerosol-related effect to children’s health, no effort has been put on integrating those models and embedding them into a Geographical Information System (GIS) environment. Such an integration effort will be greatly beneficial to identifying strategic solutions to design and operate transportation systems in a way that meets transportation needs while protecting the environmental health of all people.
The goal of the proposed research is to fill in this gap through developing the GIS-based system using an integrated approach as a tool for analyzing environmental and exposure-health impact due to traffic emission related air pollution. This project is to provide a proof of concept of the proposed approach through a case study. In this project, categorized models, namely, travel forecasting model, vehicle emission model, air pollution dispersion model, and exposure-health model will be integrated heuristically, mathematically with data flows via input/output (I/O) interfaces. Additionally, the air quality index based on NAAQS (National and State of Ambient Air Quality Standards) and American Lung Association (ALA)’s health indices will be embedded into the GIS environment.
The research includes two parts: integrate the categorized models into GIS for developing the prototype of the integrated system; and use collected data for the case study to validate the integrated model and demonstrate the interactive changes between traffic, air pollutant dispersion level (CO and PM2.5 in this case), and exposure level to the air pollutant, as well as risk level to respiration-related health problem versus he time horizon. The integration mechanism proposed in this research lies in mathematical interconnections of all involved models with data flows in a heuristic way. A framework for integrating the categorized models into GIS is proposed to develop the prototype of the integrated impact analysis system and three main layers are suggested be involved in the system, namely, data layer, scenario interactive analysis layer, and assumptions layer.
The data layer receive, store and manipulate all the data that will be resulted from either the procedure of simulation environmental analysis, which is regarding the evaluation procedure of operation improvement and engineering design alternatives using a simulation based tool. It is expected to enable inputting and storing the outputs from macro- and micro- traffic simulation models into the integrated system. Assumptions layer is for users to set up assumptions for specifying suitability of a scenario versus concerned assessment criteria within the impact analysis system. The user has the ability to modify this assumption and dynamically view the differing outcome that results. Assumptions give the user to specify data querying criteria and threshold values. A vibrant visualization of the analysis scenario is expected to be allowed in any case where assumptions must be made. In the proposed research, the conformity analysis of a projected or improved transportation infrastructure will be selected as a case study of the scenario analysis. Scenario interactive analysis layer will be capable of aiding interactive scenario analysis based on guidance of the US EPA’s project-level conformity analysis procedure. Criteria for setting up scenarios (e.g., assumptions, dynamic attributes, and indicators like NAAQS) are manifested by user-defined values and formulas that relate one component to another. Regression models for school-age population exposure levels to air pollution will be used to enhance the function for display of “black spots”.
Related Research (Added by TERI Administrator; April, 2011)
Development of a Model to Predict and Mitigate Environmental and Public Health Impacts of Traffic Flows and Traffic Management Policies in Urban Transportation Microenvironments; Oregon Transportation Research and Education Consortium, 2009, Active
Urgency and Payoff
The proposed research and impact analysis system will provide an integrated tool to facilitate the PM 2.5 project-level conformity analysis with easy function to identify the requirement by using the existing US EPA guidelines. It has flexibility to be adjusted based on localized suitability requirements in different states. It allows users to make interactive analysis by building different scenarios via combinations of data and assumption layers, and the results could be intuitionally visualized in the GIS platform. In this way, the analysis would become much friendly. Furthermore, it could be expanded into a tool to integrate results of multiple models (such as macro and micro simulation models, and other public health exposure impact models) into one single platform, so that a comprehensive transportation conformity project could be conducted in one single system. In addition, the proposed research attempts to provide a proof-of-concept study for the integration of theoretical, methodological, and practical models. It will also be beneficial to developing an education plan and help raise general public awareness of traffic-related environmental health concerns.
Heng Wei, University of Cincinnati