Adam Moore

Impact of Bicycle Lane Characteristics on Exposure of Bicyclists to Traffic-Related Particulate Matter

Bicycling as a mode of transportation is increasingly seen as a healthy alternative to motorized transportation modes. However, in congested urban areas, the health benefits of bicycling can be diminished by the negative health effects associated with inhalation of particulate matter. Particles of small size (ultrafine particles <0.1 μm) are the most harmful, even during short-duration exposure. Because vehicular exhaust is the major source of ultrafine particles, the impact of traffic levels and bicycle lane characteristics on exposure of bicyclists was studied. Ultrafine particle exposure concentrations were compared in two settings: (a) a traditional bicycle lane adjacent to the vehicular traffic lanes and (b) a cycle track design with a parking lane separating bicyclists from vehicular traffic lanes. Traffic measurements were made alongside air quality measurements. The cycle track design mitigated ultrafine particle exposure concentrations for cyclists. Results showed statistically significant differences in terms of exposure levels for the two bike facilities, as well as correlations between traffic levels and exposure level differences. Results also suggested that ultrafine particle levels and spatial distribution were sensitive to proximity to signalized intersections. Findings of this research indicated that, in high traffic areas, bicycle facility design had the potential to lower air pollution exposure levels of bicyclists.

Air Quality at Bus Stops: Empirical Analysis of Exposure to Particulate Matter at Bus Stop Shelters

Congested traffic corridors in dense urban areas are key contributors to the degradation of urban air quality. While waiting at bus stops, transit patrons may be exposed to greater amounts of vehicle-based pollution, including particulate matter (PM), because of their proximity to the roadway. Current guidelines for the location and the design of bus stops do not take into account air quality or exposure considerations. This study compared the exposure of transit riders waiting at three-sided bus stop shelters that either faced the roadway traffic or faced away from the roadway traffic. Shelters were instrumented with air quality monitoring equipment, sonic anemometers, and vehicle counters. Data were collected for 2 days at three shelters during both the morning and the afternoon peak periods. Bus shelter orientation was found to significantly affect concentration of four sizes of PM: ultrafine particles, PM1, PM2.5, and PM10. Shelters with an opening oriented toward the roadway were consistently observed to have higher concentrations inside the shelter than outside the shelter. In contrast, shelters oriented away from the roadway were observed to have lower concentrations inside the shelter than outside the shelter. The differences in PM concentration were statistically significant across all four sizes of particulate matter studied. Traffic flow was shown to have a significant relationship with all sizes of particulate concentration levels inside bus shelters. Microscale anemometer measurements were made next to bus shelters. Both wind speed and direction were shown to affect particulate concentrations differently, depending on shelter orientation.

Statistical Study of Variables Associated with Particulate Matter Exposure Levels at Bus Shelters

This study expands on previous work that examined differences in exposure to particulate matter in and around bus stop shelters for passengers waiting along a busy urban corridor in Portland, Oregon. An extensive body of literature has demonstrated the negative health effects of exposure to particulate matter. Although concentrations of particulate matter were known to be greater near busy roadways, little research has been conducted on exposure in and around bus stop shelters. Two sizes of particulate matter were examined in this study: fine particulate matter of less than 2.5 microns in aerodynamic diameter (PM2.5) and ultrafine particles. Pearson association tests were run between particulate concentrations and three categories of independent variables: location, traffic, and weather. Significant correlations were observed primarily between particulates and weather (temperature and relative humidity). With 1-min data intervals, a series of log-linear regression models with and without lagged variables was used to estimate the effects of location, traffic, and weather variables on particulate concentrations. The presence of a transit bus stopped at the shelter significantly increased both sizes of particulate matter concentrations. Wind, temperature, and shelter location also had significant effects on ultrafine and PM2.5 levels. The estimated models for particulate concentrations inside and outside the bus stop shelters were compared to demonstrate differences in particulate behavior. Suggestions are made for shelter configuration given environmental and traffic considerations.

Modeling Impact of Traffic Conditions on Variability of Midblock Roadside Fine Particulate Matter: Case Study of an Urban Arterial Corridor

The objective of this study was to examine the concentration variation of midblock roadside particulate matter less than 2.5 Μm (PM2.5) as a function of very high resolution meteorological and traffic data. Morning peak period measurements were taken at a midblock roadside location on an urban arterial commuter roadway. For the impact of dynamic traffic conditions to be captured, data were analyzed at 10-s intervals, a substantially higher resolution than that used in typical roadside air quality study designs. Particular attention was paid to changes in traffic conditions, including fleet mix, queuing, and vehicle platooning over the course of the study period, and the effect of these changes on PM2.5. Significant correlations were observed between vehicle platoons and increases in PM2.5 concentrations. Traffic state analysis was employed to determine median PM2.5 levels before and after the onset of congestion. A multivariate regression model was estimated to determine significant PM2.5 predictors while controlling for autocorrelation. Significance was found not only in the simultaneous traffic variables but also in lagged traffic variables; in addition, the effects of vehicle types and wind direction were quantified. Modeling results indicated that traffic state (e.g., congestion) and vehicle type had a significant impact on roadside PM2.5 concentrations. This study serves as a demonstration of the abilities of very-high-resolution data to identify the effects of relatively minute changes in traffic conditions on air pollutant concentrations.