Steve Hankey

Health impacts of the built environment: within-urban variability in physical inactivity, air pollution, and ischemic heart disease mortality.

Background: Physical inactivity and exposure to air pollution are important risk factors for death and disease globally. The built environment may influence exposures to these risk factors in different ways and thus differentially affect the health of urban populations. Objective: We investigated the built environment’s association with air pollution and physical inactivity, and estimated attributable health risks. Methods: We used a regional travel survey to estimate within-urban variability in physical inactivity and home-based air pollution exposure [particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5), nitrogen oxides (NOx), and ozone (O3)] for 30,007 individuals in southern California. We then estimated the resulting risk for ischemic heart disease (IHD) using literature-derived dose–response values. Using a cross-sectional approach, we compared estimated IHD mortality risks among neighborhoods based on “walkability” scores. Results: The proportion of physically active individuals was higher in high- versus low-walkability neighborhoods (24.9% vs. 12.5%); however, only a small proportion of the population was physically active, and between-neighborhood variability in estimated IHD mortality attributable to physical inactivity was modest (7 fewer IHD deaths/100,000/year in high- vs. low-walkability neighborhoods). Between-neighborhood differences in estimated IHD mortality from air pollution were comparable in magnitude (9 more IHD deaths/100,000/year for PM2.5 and 3 fewer IHD deaths for O3 in high- vs. low-walkability neighborhoods), suggesting that population health benefits from increased physical activity in high-walkability neighborhoods may be offset by adverse effects of air pollution exposure. Policy implications: Currently, planning efforts mainly focus on increasing physical activity through neighborhood design. Our results suggest that differences in population health impacts among neighborhoods are similar in magnitude for air pollution and physical activity. Thus, physical activity and exposure to air pollution are critical aspects of planning for cleaner, health-promoting cities.

Land Use Regression Models of On-Road Particulate Air Pollution (Particle Number, Black Carbon, PM2.5, Particle Size) Using Mobile Monitoring

Land Use Regression (LUR) models typically use fixed-site monitoring; here, we employ mobile monitoring as a cost-effective alternative for LUR development. We use bicycle-based, mobile measurements (∼85 h) during rush-hour in Minneapolis, MN to build LUR models for particulate concentrations (particle number [PN], black carbon [BC], fine particulate matter [PM2.5], particle size). We developed and examined 1224 separate LUR models by varying pollutant, time-of-day, and method of spatial and temporal smoothing of the time-series data. Our base-case LUR models had modest goodness-of-fit (adjusted R(2): ∼0.5 [PN], ∼0.4 [PM2.5], 0.35 [BC], ∼0.25 [particle size]), low bias (<4%) and absolute bias (2-18%), and included predictor variables that captured proximity to and density of emission sources. The spatial density of our measurements resulted in a large model-building data set (n = 1101 concentration estimates); ∼25% of buffer variables were selected at spatial scales of <100m, suggesting that on-road particle concentrations change on small spatial scales. LUR model-R(2) improved as sampling runs were completed, with diminishing benefits after ∼40 h of data collection. Spatial autocorrelation of model residuals indicated that models performed poorly where spatiotemporal resolution of emission sources (i.e., traffic congestion) was poor. Our findings suggest that LUR modeling from mobile measurements is possible, but that more work could usefully inform best practices.

Day-of-Year Scaling Factors and Design Considerations for Nonmotorized Traffic Monitoring Programs

General procedures, including the estimation of annual average daily traffic (AADT) from short-duration counts, have not been established for nonmotorized traffic monitoring programs. Continuous counts of nonmotorized traffic were collected at six locations on the off-street trail network in Minneapolis, Minnesota, in 2011. A new approach for estimating AADT values from short-duration counts, the use of day-of-year factors, is demonstrated. Analyses of variability in count data can be used to design a monitoring program that uses both continuous and short-duration counts of nonmotorized traffic. Five core conclusions may be useful for developing nonmotorized monitoring programs: (a) day-of-year scaling factors have smaller error than does the standard (day-of-week and month-of-year) method of AADT estimation, especially from short-duration counts (<1 week); (b) extrapolation error decreases with short-duration-count length, with only marginal gains in accuracy for counts longer than 1 week; (c) errors in estimating AADT values are lowest when short-duration counts are taken in summer (or spring, summer, and fall) months (April through October); (d) the impact of sampling on consecutive (successive) versus nonconsecutive (separate) days on AADT estimation is minimal but may reduce labor requirements; and (e) the design of a traffic monitoring program depends on the acceptable error, equipment availability, and monitoring period duration. Trade-offs in short-duration-count lengths and estimate accuracy will depend on resource constraints. Analysts can use day-of-year factors to improve the accuracy of AADT estimation. Analyses of variability in traffic counts can strengthen the design of monitoring programs.

Estimating Mixed-Mode Urban Trail Traffic Using Negative Binomial Regression Models

Data and models of nonmotorized traffic on multiuse urban trails are needed to improve planning and management of urban transportation systems. Negative binomial regression models are appropriate and useful when dependent variables are nonnegative integers with overdispersion like traffic counts. This paper presents eight negative binomial models for estimating urban trail traffic using 1,898 daily mixed-mode traffic counts from active infrared monitors at six locations in Minneapolis, Minnesota. These models include up to 10 independent variables that represent sociodemographic, built environment, weather, and temporal characteristics. A general model can be used to estimate traffic at locations where traffic has not been monitored. A six-location model with dummy variables for each monitoring site rather than neighborhood-specific variables can be used to estimate traffic at existing locations when counts from monitors are not available. Six trail-specific models are appropriate for estimating variation in traffic in response to variations in weather and day of week. Validation results indicate that negative binomial models outperform models estimated by ordinary least squares regression. These new models estimate traffic within approximately 16.3% error, on average, which is reasonable for planning and management purposes.

Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures

Several studies show that a significant portion of daily air pollution exposure, in particular black carbon (BC), occurs during transport. In a previous work, a model for the in-traffic exposure of bicyclists to BC was proposed based on spectral evaluation of mobile noise measurements and validated with BC measurements in Ghent, Belgium. In this paper, applicability of this model in a different cultural context with a totally different traffic and mobility situation is presented. In addition, a similar modeling approach is tested for particle number (PN) concentration. Indirectly assessing BC and PN exposure through a model based on noise measurements is advantageous because of the availability of very affordable noise monitoring devices. Our previous work showed that a model including specific spectral components of the noise that relate to engine and rolling emission and basic meteorological data, could be quite accurate. Moreover, including a background concentration adjustment improved the model considerably. To explore whether this model could also be used in a different context, with or without tuning of the model parameters, a study was conducted in Bangalore, India. Noise measurement equipment, data storage, data processing, continent, country, measurement operators, vehicle fleet, driving behavior, biking facilities, background concentration, and meteorology are all very different from the first measurement campaign in Belgium. More than 24h of combined in-traffic noise, BC, and PN measurements were collected. It was shown that the noise-based BC exposure model gives good predictions in Bangalore and that the same approach is also successful for PN. Cross validation of the model parameters was used to compare factors that impact exposure across study sites. A pooled model (combining the measurements of the two locations) results in a correlation of 0.84 when fitting the total trip exposure in Bangalore. Estimating particulate matter exposure with traffic noise measurements was thus shown to be a valid approach across countries and cultures.

Reducing motor vehicle greenhouse gas emissions in a non-California state: a case study of Minnesota.

Approaches for reducing greenhouse gas (GHG) emissions from motor vehicles include more-efficient vehicles, lower-carbon fuels, and reducing vehicle-kilometers traveled (VKT). Many U.S. states are considering steps to reduce emissions through actions in one or more of these areas. We model several technology and policy options for reducing GHGs from motor vehicles in Minnesota. Considerable analysis of transportation GHGs has been done for California, which has a large population and vehicle fleet and can enact unique emissions regulations; Minnesota represents a more typical state with respect to many demographic and transportation parameters. We conclude that Minnesota has a viable approach to meeting its stated GHG reduction targets (15% by 2015 and 30% by 2025, relative to year 2005) only if advancements are made in all three areas-vehicle efficiency, carbon content of fuels, and VKT. If policies focus on only one or two areas, potential improvements may be negated by backsliding in another area (e.g., increasing VKT offsetting improvements in vehicle efficiency).

Monitoring and Modeling of Urban Trail Traffic: Validation of Direct Demand Models in Minneapolis, Minnesota, and Columbus, Ohio

Transportation planners and engineers need estimates of nonmotorized traffic volumes and analytical tools to plan and manage infrastructure for bicycling and walking. Direct demand models are useful, comparatively simple tools for the estimation of volumes from nonmotorized traffic counts and do not require detailed information from travel behavior inventories. However, few demand models for bicycling and walking have been validated. This paper extends the practice of nonmotorized traffic monitoring and modeling in three ways. First, procedures recommended in the FHWA Traffic Monitoring Guide are followed to present estimates of annual average daily traffic (AADT) for each segment of the urban trail networks in two major U.S. cities: Minneapolis, Minnesota, and Columbus, Ohio. Second, independent variables constructed from nationally available data sets and the local characteristics of each trail system are used to estimate and validate direct demand models for AADT. Third, to assess the potential for the general application of the models, the results of cross-city validations are presented. Our results confirm that FHWA monitoring procedures can be used to characterize the variation in traffic flows on urban trail networks. Direct demand models for each city have reasonably good fits, but the predicted traffic volumes for more than one-third of the segments exceed the actual volumes by more than 60%. Cross validation results indicate that these models cannot yet be applied as predictive tools across cities. More experimentation is needed to assess the feasibility of developing generalized direct demand models for trails and other nonmotorized transportation networks.

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. 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

Facility-Demand Models of Peak Period Pedestrian and Bicycle Traffic: Comparison of Fully Specified and Reduced-Form Models

Transportation planners and engineers need better spatial estimates of walking and cycling traffic to assess exposure to hazards, evaluate infrastructure investments, and locate facilities. Facility-demand models are potentially useful for generating spatial estimates of traffic volumes. Few facility-demand models have explored trade-offs between fully specified (i.e., exploratory) models and reduced-form models easily applied in the field. Presented are facility-demand models based on peak period (4 to 6 p.m.) counts of pedestrian and bicycle traffic in Minneapolis, Minnesota. The count database (n = 954 observations; 471 locations) has sufficient spatial density (~3 locations km−2) to develop spatially resolved models (i.e., ~100-m resolution). The modeling approach employs a stepwise linear regression method allowing for varying the spatial scale of independent (land use and transportation) variables. Compared were fully specified (statistically optimal) models and supervised, reduced-form models that included fewer variables based on theoretical validity. Reduced-form core models had modest goodness of fit (adjusted R2: ~.5) and included independent variables with large (industrial area and population density) and small (bicycle facilities, retail area, open space, transit stops) spatial scales. Also developed were reduced-form, time-averaged models for a subset of count sites having multiple observations (n = 84). With the use of reduced-form models (independent variables ranged from four to nine among models), block-level traffic estimates were generated (n = 13,886). Results suggest that reduced-form models perform nearly as well as fully specified models and are easier to apply and interpret. This work could be extended by assessing model performance when estimates of annual average traffic are used in model building.

Urban Form, Air Pollution, and Health

Purpose of ReviewUrban form can impact air pollution and public health. We reviewed health-related articles that assessed (1) the relationships among urban form, air pollution, and health as well as (2) aspects of the urban environment (i.e., green space, noise, physical activity) that may modify those relationships.Recent FindingsSimulation and empirical studies demonstrate an association between compact growth, improved regional air quality, and health. Most studies are cross-sectional and focus on connections between transportation emissions and land use. The physical and mental health impacts of green space, public spaces that promote physical activity, and noise are well-studied aspects of the urban environment and there is evidence that these factors may modify the relationship between air pollution and health.SummaryUrban form can support efforts to design clean, health-promoting cities. More work is needed to operationalize specific strategies and to elucidate the causal pathways connecting various aspects of health.