An integrated approach to monitoring and estimating COVID-19 risk exposure among leisure-time physical activity participants.

BACKGROUND: Leisure time physical activity (LTPA) provides both health benefits and risks, particularly during a pandemic. During the COVID-19 pandemic, significant increases in close-to-home LTPA raised concerns for public health and land managers alike. This project illustrates a novel, integrated monitoring approach to estimating COVID-19 risk exposure during trail-related LTPA, with implications for other public spaces. METHODS: COVID-19 risk exposure was conservatively calculated from the integration of in-person observations of LTPA trail groups and automated monitoring of trail traffic volumes in spring 2020. Trained observers tracked 1,477 groups. Traffic volume estimates and observed distance data were integrated, considering occlusion and total trail traffic volume. RESULTS: 70% of groups had one or more encounters. Among individual users, 38.5% were 100% compliant across all events observed but 32.7% were not compliant. Considering trail traffic volumes and annual daily traffic volume, exposure to risk of COVID-19 was conservatively estimated at 61.5% among individual trail users. CONCLUSIONS: Monitoring opportunities and challenges of health risk exposure exist. Adjusted exposure measures based on volume counts can approximate numbers of unique individuals exposed, inform management actions, efficacy and policy decisions.

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

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.

Ambient air conditions and variation in urban trail use.

This study examines the effect of air quality and administrative policies on use of urban trails in Indianapolis, IN. Attention is focused on two policy variables: (1) issuance of air pollution advisories and (2) the adoption of Daylight Savings Time. Results suggest that while trail use varies with air quality, current public advisories regarding air pollution may be of limited effectiveness in reducing trail users' exposures to hazardous pollutants. In contrast, the adoption of Daylight Savings Time was associated with a statistically significant increase in traffic levels.

Neighborhood Correlates of Urban Trail Use.

PURPOSE: To model urban trail traffic as a function of neighborhood characteristics and other factors including weather and day of week. METHODS: We used infrared monitors to measure traffic at 30 locations on five trails for periods ranging from 12 months to more than 4 y. We measured neighborhood characteristics using geographic information systems, satellite imagery, and US Census and other secondary data. We used multiple regression techniques to model daily traffic. RESULTS: The statistical model explains approximately 80% of the variation in trail traffic. Trail traffic correlates positively and significantly with income, neighborhood population density, education, percent of neighborhood in commercial use, vegetative health, area of land in parking, and mean length of street segments in access networks. Trail traffic correlates negatively and significantly with the percentage of neighborhood residents in age groups greater than 64 and less than 5. CONCLUSIONS: Trail traffic is significantly correlated with neighborhood characteristics. Health officials can use these findings to influence the design and location of trails and to maximize opportunities for increases in physical activity.