Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data

Gurram, S and Stuart, AL and Pinjari, AR (2019) Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data. In: Computers, Environment and Urban Systems, 75 . pp. 22-34.

PDF com_env_urb_75_22-34_2019.pdf - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

Better understanding of the complex links between urban transportation, land use, air quality, and population exposure is needed to improve urban sustainability. A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. We aimed to characterize exposure and exposure inequality for traffic-related air pollution, and to investigate the impacts of high-resolution information on estimated exposure. To do these, we developed and applied a modeling framework that combines the DaySim activity-based travel demand model, the MATSim dynamic traffic assignment model, the MOVES mobile source emissions estimator, and the R-LINE dispersion model. Resulting spatiotemporal distributions of daily individual human activity and pollutant concentration were matched to analyze population and subgroup exposure to oxides of nitrogen (NO x ) from passenger car travel for an average winter day in 2010. Four scenarios using data with different spatiotemporal resolutions were considered: a) high resolution for both activities and concentrations, b) low resolution for both activities and concentrations, c) high resolution for activities, but low resolution for concentrations, and d) vice versa. For the high-resolution scenario, the mean daily population exposure concentration of NO x from passenger cars was 10.2 μg/m 3 ; individual exposure concentrations ranged from 0.2 to 145 μg/m 3 . Subgroup mean exposure was higher than the population mean for individuals living below-poverty (by ~16%), those with daily travel time over one hour (8%), adults aged 19–45 (7%), blacks (6%), Hispanics (4%), Asians (2%), combined other non-white races (2%), people from middle income households (2%), and residents of urban areas (2%). The subgroup inequality index (a measure of disparity) largely increased with concentration up to the 90th percentile level for these groups. At higher levels, disparities increased sharply for individuals from below poverty households, blacks, and Hispanics. Low-resolution simulation of both activities and concentrations decreased the exposure estimates by 10% on average, with differences ranging from eight times higher to ~90% lower.

Item Type:	Journal Article
Publication:	Computers, Environment and Urban Systems
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd.
Keywords:	Air quality; Automobiles; Autonomous agents; Computational methods; Land use; Motor transportation; Nitrogen oxides; Sustainable development; Traffic control; Travel time, Activity-based travels; Agent based simulation; Environmental justice; Human exposures; Model resolution; Traffic-related air pollution, Urban transportation, atmospheric pollution; computer simulation; environmental justice; estimation method; modeling; pollution exposure; traffic emission; travel demand; urban atmosphere, Florida United States; Tampa; United States
Department/Centre:	Division of Interdisciplinary Sciences > Center for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP) Division of Mechanical Sciences > Civil Engineering
Date Deposited:	30 Nov 2022 05:41
Last Modified:	30 Nov 2022 05:41
URI:	https://eprints.iisc.ac.in/id/eprint/78420

Actions (login required)

View Item