A comprehensive modeling framework for transportation-induced population exposure assessment

Highlights

• A comprehensive modeling framework for the impact of vehicle emissions on air quality and population exposure.

• A holistic approach to analyzing the cause-and-effect chain between transportation demand and population exposure.

• Highly resolved spatio-temporal identification of high levels of localized concentrations and population exposure.

• The importance of taking into account dynamic population distribution in various microenvironments in population exposure assessment.

Abstract

This research is motivated by the need to improve transportation policy analysis through the development of a holistic framework to evaluate transportation externalities. Traditionally, transportation planning has been focused primarily on the improvement of transportation infrastructure and network performance and little attention has been paid to the resulting externalities that negatively impact public health. The paper presents a holistic analysis framework that enables policy makers analyze the chain effect of transportation demand on air quality and population health exposure. Holism is achieved by incorporating the interactions between transportation demand, network performance measurement, vehicular emissions, air quality modeling and population exposure assessment. The eventual impact of vehicular emissions on population is measured through the use of an intake fraction metric, which measures the fraction of pollutant inhaled by an exposed population over a defined period of time. The proposed framework takes advantage of the existing state-of-the-art domain specific models so there is no need to re-invent the wheels. Instead, the focus of the this research is to provide a prescriptive process of addressing data gaps and resolution matching between these models as well as other models alike. The proposed population exposure assessment incorporates key parameters including different microenvironments and inhalation rates not accounted for in the existing literature of exposure assessment. The entire framework is evaluated with the three city sub-region of Maricopa County in Arizona. Further investigations demonstrate the importance of differentiating microenvironments and inhalation rates to properly capturing population exposure.

Introduction

Vehicular emissions are a major source of a number of pollutants such as greenhouse gases, particulate matter, mobile source air toxics, hydrocarbons, nitrogen oxides, and carbon-monoxide. Epidemiology and toxicology research has documented adverse respiratory and cardiovascular effects for populations living within the near road environment (Brugge et al., 2007). Short-term exposures experienced by vehicle occupants, cyclists or pedestrians have also been associated with negative health responses (Peters et al., 2013, McCreanor et al., 2007). Despite all the field observations and experimental findings, health researchers are in need of improved assessment of exposure to traffic-specific emissions to better quantify health impacts and to support more definitive findings about causality (Adar and Kaufman, 2007, HEI, 2010); policy makers must make informed decisions to address the transportation equity and environmental justice issues (Rowangould, 2013); and transportation researchers have started incorporating some health related measures into transportation policy scenario analyses (Vallamsundar et al., 2016).

This research attempts to address the need for an improved assessment of traffic-specific population exposure. This is accomplished by building a modeling framework that estimates the chain effect starting from the need that drives travel demand and ending with population exposure. The framework integrates models of activity based travel demand (ABM), dynamic traffic assignment (DTA), vehicle emissions, air dispersion and population exposure assessment. Thus, it provides a holistic approach to capturing the correlation between daily activities of people, their resulting travel patterns and roadway traffic, vehicular emissions, pollutant concentration and exposure levels. The methodology exploits the detailed travel time activity and location estimates from ABM to refine the estimation of traffic induced pollutant concentrations and population exposure. Such detailed spatial and temporal information is often lacking in exposure assessment.

The proposed framework takes advantage of the existing state-of-the-art domain specific models and envelops them into a unified modeling platform through necessary data processing interfaces between models. Developing advanced transportation models is expensive both in terms of cost and time needed to develop and calibrate them (SHRP2, 2015). Further, major metropolitan planning organizations (MPOs) across the country have been investing in activity-based regional transportation models. Specifically in this paper, OpenAMOS (Konduri, 2012) and DTALite (Zhou and Taylor, 2014) are chosen as the transportation models in our modeling framework. On the mobile emission and pollution modeling side, the U.S. Environmental Protection Agency’s MOVES emissions model and AERMOD air dispersion model are regulatory models presumably representing the state of art and practice in modeling. So there is no need to re-invent the wheels. Instead, the focus of the this research is to provide a prescriptive process of addressing data gaps and resolution matching between these models as well as other models alike.

In this paper, the modeling framework is demonstrated through estimating the population exposure of fine particulate matter particles with a diameter size up to 2.5 μm (PM_2.5) due to their harmful health effects (Krewski et al., 2009, Pope et al., 2009, Karner et al., 2010, Bigazzi and Figliozzi, 2014). PM_2.5 and ultrafine particles are found to cause adverse health effects as they can easily pass through the respiratory system and penetrate deeper into the tissues, making them more harmful than coarser PM (U.S.EPA, 1991). It is worth mentioning that although only PM_2.5 is analyzed in this paper, the methodology could be applied to any primary, non-reactive pollutant released from vehicular exhaust.