Population-Level Exposure to Particulate Air Pollution during Active Travel: Planning for Low-Exposure, Health-Promoting Cities

Abstract

Background:Providing infrastructure and land uses to encourage active travel (i.e., bicycling and walking) are promising strategies for designing health-promoting cities. Population-level exposure to air pollution during active travel is understudied.Objectives:Our goals were a) to investigate population-level patterns in exposure during active travel, based on spatial estimates of bicycle traffic, pedestrian traffic, and particulate concentrations; and b) to assess how those exposure patterns are associated with the built environment.Methods:We employed facility–demand models (active travel) and land use regression models (particulate concentrations) to estimate block-level (n = 13,604) exposure during rush-hour (1600–1800 hours) in Minneapolis, Minnesota. We used the model-derived estimates to identify land use patterns and characteristics of the street network that are health promoting. We also assessed how exposure is correlated with indicators of health disparities (e.g., household income, proportion of nonwhite residents). Our work uses population-level rates of active travel (i.e., traffic flows) rather than the probability of walking or biking (i.e., “walkability” or “bikeability”) to assess exposure.Results:Active travel often occurs on high-traffic streets or near activity centers where particulate concentrations are highest (i.e., 20–42% of active travel occurs on blocks with high population-level exposure). Only 2–3% of blocks (3–8% of total active travel) are “sweet spots” (i.e., high active travel, low particulate concentrations); sweet spots are located a) near but slightly removed from the city-center or b) on off-street trails. We identified 1,721 blocks (~ 20% of local roads) where shifting active travel from high-traffic roads to adjacent low-traffic roads would reduce exposure by ~ 15%. Active travel is correlated with population density, land use mix, open space, and retail area; particulate concentrations were mostly unchanged with land use.Conclusions:Public health officials and urban planners may use our findings to promote healthy transportation choices. When designing health-promoting cities, benefits (physical activity) as well as hazards (air pollution) should be evaluated.Citation:Hankey S, Lindsey G, Marshall JD. 2017. Population-level exposure to particulate air pollution during active travel: planning for low-exposure, health-promoting cities. Environ Health Perspect 125:–534; http://dx.doi.org/10.1289/EHP442

Examining ways to enhance active transportation and the impact on commuters' health, wellbeing and sustainable development

“…Based on spatial estimates of cyclists and pedestrians, traffic has been shown to be correlated with street functional class and proximity to high traffic roads. Also, it is seen that the highest rates of active travel are in neighborhoods with high levels of population density, land use mix, open space, and retail area (Hankey et al, 2017). Similarly, Mathews et al (2009) support that there are significant differences among gender and age when it comes to being physically active as well as the type, location, and purpose of the activity.…”

“…Also, cycling is the preferable commuting mode and specifically cycling is three times more likely to be chosen by males (Lawson et al, 2013a). Another factor that influences active transportation is population-level patterns (Hankey et al, 2017). Based on spatial estimates of cyclists and pedestrians, traffic has been shown to be correlated with street functional class and proximity to high traffic roads.…”

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

“…Furthermore, LUR models can give valuable information for public health officials and urban planners to reduce population exposure to air pollution and design health-promoting cities with less polluted routes for pedestrian and cyclists. For instance, Hankey S et al 2016 [21] combined facility-demand and LUR models to highlight exposure patterns during active travel suggesting that it should be possible to reduce exposure by~15% after intervening on the given scenario. Recently, several studies confirmed that the health benefits linked to active travel outweigh the risks such as exposure to air pollution or accidents, suggesting that the attempt to build more walkable and bikeable urban environments is worth the effort [22][23][24].…”

Strategies of method selection for fine-scale PM<sub>2.5</sub> mapping in an intra-urban area using crowdsourced monitoring

“…Due to an increase in low-cost sensing for air pollution monitoring, the real-time strategies for exposure control in cities have been further developed (Kumar et al, 2015). Crowdsourced monitoring that enables citizens to produce geospatial data is constantly growing and shows considerable potential (Heipke, 2010). Large and diverse groups of people who lack formal training can easily describe their environments with a mobile phone or smartphone and upload data via informal social networks and web technology.…”