A network-based approach for estimating pedestrian journey-time exposure to air pollution

Abstract

Individual exposure to air pollution depends not only upon pollution concentrations in the surrounding environment, but also on the volume of air inhaled, which is determined by an individual's physiology and activity level. This study focuses on journey-time exposure, using network analysis in a GIS environment to identify pedestrian routes between multiple origins and destinations throughout the city of Lancaster, North West England. For each segment of a detailed footpath network, exposure was calculated accounting for PM2.5 concentrations (estimated using an atmospheric dispersion model) and respiratory minute volume (varying between individuals and with slope). For each of the routes generated the cumulative exposure to PM2.5 was estimated, allowing for easy comparison between multiple routes. Significant variations in exposure were found between routes depending on their geography, as well as in response to variations in background concentrations and meteorology between days. Differences in physiological characteristics such as age or weight were also seen to impact journey-time exposure considerably. In addition to assessing exposure for a given route, the approach was used to identify alternative routes that minimised journey-time exposure. Exposure reduction potential varied considerably between days, with even subtle shifts in route location, such as to the opposite side of the road, showing significant benefits. The method presented is both flexible and scalable, allowing for the interactions between physiology, activity level, pollution concentration and journey duration to be explored. In enabling physiology and activity level to be integrated into exposure calculations a more comprehensive estimate of journey-time exposure can be made, which has potential to provide more realistic inputs for epidemiological studies.

Global and Continuous Pleasantness Estimation of the Soundscape Perceived during Walking Trips through Urban Environments

“…However, although soft transportation modes undoubtedly have a positive global environmental effect, an increased exposure to road traffic pollutants, namely airborne pollutants, fine particles, and noise levels, amplified by the high correlation generally observed between these pollutants [6][7][8], is a harmful counterpart of choosing this transportation mode in urban areas. Moreover, the environmental quality at the neighborhood scale, strongly influences the choice of walking as transportation mode [9][10][11]. Therefore, being able to estimate the exposure associated with an urban walking trip has many potential interests, such as for informing pedestrians about the potential health benefit of their intended walk, or for optimizing the related route choice through specific algorithms [12,13].…”

“…counterpart of choosing this transportation mode in urban areas. Moreover, the environmental quality at the neighborhood scale, strongly influences the choice of walking as transportation mode [9][10][11]. Therefore, being able to estimate the exposure associated with an urban walking trip has many potential interests, such as for informing pedestrians about the potential health benefit of their intended walk, or for optimizing the related route choice through specific algorithms [12,13].…”

Opportunities for meeting sustainability objectives

“…Studies indicated that physical activities require higher volumes of air intake (Klepeis, 2006), and quality of air has direct consequences with human performance. The physiological characteristics such as gender (Clougherty, 2010), age, weight and health condition (Davies and Whyatt, 2014) are also important contributors of human performance when exposed to pollutants. In addition, psychological factors (task stressors in work environment) also have adverse impacts in human performance, which demonstrate a correlation with the increase in air pollutants (Schnell et al, 2013).…”

Contributions of gas flaring to a global air pollution hotspot: Spatial and temporal variations, impacts and alleviation

“…ADMS-Urban, used in this research, models emissions from point, line and area sources over large urban areas (CERC, 2011). It has become an integral part of air quality management in the United Kingdom (Arciszewska and McClatchey, 2001) and has been employed in a number of studies including exposure from traffic pollution and more general air quality assessment (Davies and Whyatt, 2014;Abdul-Raheem and Adejola, 2013). It has however not been previously used in combination with remotely-sensed information to model the impact of gas flares.…”