ABSTRACTepubs.surrey.ac.uk/857126/1/Frey Kumar et al (2020... · Web viewPhoenix, AZ 85016 [email protected] Sanjam Singh Senior Staff Tora Consulting, LLC 509 Harwich

1Frey, Gadre, Singh, and Kumar

Quantification of Sources of Variability of Air Pollutant Exposure Concentrations Among Selected Transportation Microenvironments

H. Christopher Frey, Ph.D.Glenn E. Futrell Distinguished University Professor

Department of Civil, Construction and Environmental EngineeringNorth Carolina State University

Campus Box 7908, Raleigh, NC [email protected]

ORCID ID: 0000-0001-9450-0804

Disha Gadre Senior Environmental Consultant

Trinity Consultants1661 E Camelback Road , Suite 290

Phoenix, AZ [email protected]

Sanjam SinghSenior Staff

Tora Consulting, LLC509 Harwich Ct.

Lexington, SC 29072

Prashant Kumar, Ph.D.Professor and Chair in Air Quality and Health

Director, Global Centre for Clean Air ResearchDepartment of Civil and Environmental Engineering and Physical Sciences

University of SurreyGuildford, UK GU2 7XH

[email protected]

March 24, 2020

7,680 Text Words + 2 Tables (500 words) = 8,180 Words

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mailto:[email protected]

mailto:[email protected]


ABSTRACT

The National Research Council has identified the lack of sufficient microenvironmental air pollution exposure data as a significant barrier to quantification of human exposure to air pollution. Transportation microenvironments, including pedestrian, transit bus, car, and bicycle, can be associated with higher exposure concentrations than many other microenvironments. Data are lacking that provide a systematic basis for comparing exposure concentrations in these transportation modes that account for key sources of variability, such as time of day, season, and types of location along a route such as bus stops and intersections. The objectives of this work are to: quantify and compare PM2.5, CO, and O3 exposure concentrations in selected active and passive transportation microenvironments; and quantify the effect of season, time of day, and location with respect to variability in transportation mode exposure concentrations. Measurements were made with an instrumented backpack and were repeated for multiple days in each season to account for the impact of inter-run variability. Results include mean trends, spatial variability, and contribution to variance. Pedestrian and cycle mode exposure concentrations were approximately similar to each other and were substantially higher than for bus and car cabins for both PM2.5 and O3. Based on over 30 days of field measurements conducted over three seasons and for two times of day on weekdays, transportation modes and season were the largest contributors to variability in exposure for PM2.5 and O3, whereas location type alone and in combination with transport mode helped explain variability in CO exposures.

Keywords: air pollution, exposure, health, particulate matter, ozone, carbon monoxide

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Introduction

Vehicle emissions and traffic-related air pollution have long been recognized as a threat to public health. Pollutants that are emitted directly from vehicle operations include carbon monoxide (CO), particulate matter (PM), including fine particles less than 2.5 micrometers in aerodynamic diameter (PM2.5), and others such as oxides of nitrogen. Vehicle emissions also lead to the formation of tropospheric ozone.(1) For CO, short-term exposures are likely to be causal for cardiovascular morbidity.(2) Short-term and long-term exposures to PM2.5 were found to cause cardiovascular morbidity and premature mortality, and likely to cause respiratory morbidity.(3) Short-term exposure to ozone was determined to be causal for respiratory morbidity and likely to be causal for cardiovascular morbidity and premature mortality.(4)

Chambliss et al. (5) estimated the global health burden from exposure to PM2.5 from surface transportation to be approximately 240,000 premature deaths in 2005. Land transportation was estimated to be a leading cause of air pollutant-related mortality in North America, accounting for 32 percent of all PM2.5-related mortality.(6)

Approximately 60 million Americans live within 500 meters of roads with greater than 25,000 average annual daily traffic volume (Rowangould, 2013).(7) Approximately 86% of US commuters use a personal vehicle.(8) People of all ages average 1.3 hours daily in a vehicle and average working age adults (18-65 years old) spend nearly 1.8 hours per day in a vehicle.(9,10) People receive substantial exposure to pollutants in near-roadway and in-vehicle microenvironments (2-4).

In vehicle and near road exposures represent a large share of total exposure.(11) Vehicles have higher air exchange rates (ACHs) than buildings, leading to the potential for high exposure to on-road pollutants.(12) There is intermodal variability regarding in-vehicle and other transport-related exposures. For example, PM2.5 and CO exposure concentrations differed between cars and transit buses.(13) Although transit bus ridership is relative low in the U.S. except in larger cities, many state and local agencies are interested in promoting increased transit bus ridership.(14) In some cases, depending on urban morphology and the location of bicycle lanes, bicyclists can have lower exposure to traffic-related air pollution than pedestrians,(15,16) but their exposure depends on proximity to traffic and whether bicycle lanes are separated from roadways.(17). However, active commuters, which refers to persons who walk or cycle rather than use motorized transport, may have larger risk from exposure to traffic-related air pollution because they have higher breathing ventilation rates compared to the more passive activity of sitting in a vehicle. Thus, they may inhale a larger potential dose of pollutants even though the exposure concentration may be lower.(18,19) Although there is considerable heterogeneity in exposure concentrations among transportation modes (20), and although there have been numerous studies that have focused on individual modes such as cars, there are relatively fewer studies that systematical compare exposure concentrations among transport modes.

The U.S. National Research Council identified key challenges, barriers, and opportunities for the advancement of exposure science, including the need for more extensive microenvironmental air quality data.(21) A microenvironment is a location in which people spend time for which pollutant concentrations can be well-characterized. Examples of microenvironments include vehicle cabins in cars and buses, as well as outdoor locations for pedestrians and cyclists.

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Transportation mode exposure concentrations can vary with factors such as season and time of day, which are related to atmospheric stability and dispersion and traffic flow, as well as by proximity to locations that may be emissions hotspots, such as intersections or bus stops, or that may otherwise be influenced by emissions of other vehicles.(22-28) Given the diversity of influential factors, exposure concentrations can be highly variable.

The objectives of this work are to: (a) quantify and compare PM2.5, CO, and O3 exposure concentrations in selected active and passive transportation microenvironments; and (b) quantify the effect of season, time of day, and location with respect to variability in transportation mode exposure concentrations.

METHODSMeasurements of PM2.5, CO, and O3 exposure concentrations were made for pedestrian,

car, bus, and bicycle transportation modes over multiple seasons. Measurements were made for two times of day that represent off-peak and peak traffic conditions. Measurements were made with an instrumented backpack and were repeated for multiple days in each season to account for the impact of inter-run variability. Results are analysed in terms of mean trends, spatial variability, and contribution to variance.

Data were collected in the winter, spring, and summer of 2015 in the vicinity of the North Carolina State University campus. Over 30 weekdays were measured. The local climate is temperate, with cold temperatures in the winter that motivate the use of heating inside vehicle cabins and warm and humid conditions in summer that motivate the use of air conditioning.

Transport ModesPrior research has clearly established that the ratio of in-cabin to outdoor air pollutant

concentrations in vehicles are highly dependent on whether windows are open or closed, whether mechanical ventilation is used if windows are closed, and whether mechanical ventilation is based on fresh air intake or recirculation of cabin air, as well as other factors such as vehicle age and cabin air filtration.(13,22,23,29-32) Cabin air filters reduce in-vehicle exposures to particles by approximately 40 percent to 70 percent depending on the filter and particle size range.(33) In-cabin to outdoor (I/O) ratios for PM2.5 depend on these factors and can vary from approximately 0.82 to 0.99 for high advection of outside air, and from 0.5 to 0.8 for air recirculation and closed windows.(11,34) In-cabin PM2.5 exposures are typically lowest with closed windows and air recirculation.(30)

Given that prior work by many others has focused on sources of variability in the in-cabin concentration and the I/O ratio for cars, this work instead focuses on a typical ventilation operating mode for cars that is common in the local area given prevailing weather conditions. Specifically, all car measurements are based on windows closed, air recirculation, with heat or air conditioning depending on the season. Likewise, these are the typical operating conditions for local transit buses.

Buses are of interest because they are a common local public transit mode but also because of prior observations that pollutant concentrations can be higher inside buses compared to ambient levels.(35,36) Another study estimated that self-pollution of LDGVs contributed approximately 15 percent of CO and 30 percent of PM2.5 in-cabin exposure concentration.(37) In some studies, the highest exposure concentrations for some pollutants, including PM2.5, were found on buses compared to cars.(29)

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Pedestrian exposure concentrations are of concern because pedestrians often use sidewalks adjacent to roadways and, thus, are exposed near-road in very close proximity to traffic related air pollution. Pollutants emitted from vehicles typically lead to elevated concentrations within 300 meters downwind of a road.(38-40) Thus, pedestrian exposures may be high. As noted earlier, cyclist exposures are expected to depend on whether a cyclist rides with traffic, is close to traffic on a sidewalk, or is away from traffic on low-traffic roads or dedicated bike lanes that are separated from roads.

RoutesThe routes for each transportation mode were selected, as illustrated in Figure 1, to allow

for comparisons between the modes while also taking into account routing decisions that may be unique to a given mode. Furthermore, to allow for sequential data collection of each mode within a relatively short period of time, the amount of time allocated to each mode was approximately 30 minutes, such that all four modes could be measured within a two hour period. Given the differences in typical average speed of pedestrian, bicycle, bus, and car transport modes, the distances covered in approximately 30 minutes are different. The typical distance of each route was: 1.2 miles for the pedestrian roundtrip; 6.2 miles for the bus roundtrip; 7.5 miles for the car roundtrip; and 4 miles for the bicycle circuit.

The pedestrian route was partly through campus and partly along Hillsborough Street, which is a minor arterial with one lane of traffic in each direction. The pedestrian route included time at a traffic circle at the intersection of Hillsborough St. and Pullen Road, as well as time near a signalized intersection at Horne Street and bus stops on Hillsborough St.

The Wolfline bus route ran through the center of the main campus but most of the distance traveled by bus was on Avent Ferry Road to the south of campus, with some time spent at the intersection with Western Blvd and on Western Blvd. Both Avent Ferry Road and Western Blvd have two through lanes in each direction and are major arterials.

The car route was designed to overlap as much as possible with the bus route; however, cars do not have the same on-campus access as transit buses. The car route includes the same road segments as the bus on Western Blvd and Avent Ferry Road, but additionally circumnavigates the campus on Gorman Street, Hillsborough Street, and Pullen Road, each of which is a minor arterial road. The pedestrian, bus, and car routes all share common segments on Hillsborough Street, including the traffic circle at Pullen Road.

The selection of the bicycle route was the most challenging because of the flexibility of choosing paths in the study area while constrained by safety considerations. For example, a decision was made not to cycle on a road with traffic unless the road was marked with a sharrow or a bicycle lane. Thus, although it was desirable to align the bicycle route to the extent possible to include the full length of the pedestrian route, this was not possible because there was not a safe place to cycle on Hillsborough Street along the full length. Thus, the bicycle route includes a shorter portion of Hillsborough for which a sharrow was marked, including the entrance to the traffic circle at Pullen Road. The bicycle path was traveled as a one-way circuit. There is a

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Figure 1. Study routes used for real-world measurements of air pollutant exposure concentrations for pedestrian, transit bus, car, and bicycle transportation modes in the vicinity of the North Carolina State University campus near downtown Raleigh, North Carolina. Map data ©2015 Google. Used under terms of permissions.

sidewalk along Pullen Road that transitions to a bicycle path on approach to the intersection with Western Blvd. From Pullen Road to Avent Ferry Road along Western Blvd, the bicycle path is a sidewalk that is curbside with the road. From Avent Ferry to Gorman Street, there is no sidewalk and the bicycle route takes the nearest parallel low traffic service road. On Gorman Street the lanes are wide enough to accommodate bicycles. From Gorman Street to Varsity Drive, the bicycle route uses part of the Rocky Creek Greenway, which runs parallel to the very low traffic Sullivan Drive. From there, the bicycle route uses campus streets that have low traffic to return to the start point. Thus, the bicycle route is based on a combination of on-road cycling, near-road cycling, and cycling away from main roads on cycle paths.

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The routes shown in Figure 1 were for the winter and spring data collection period. The routes were similar but not identical for other data collection periods because, for example, the university operates the campus transit buses on slightly different routes in the summer. However, in all seasons, the bus routes were between the main campus and the intersection of Gorman Street with Avent Ferry Road, which is the lowest terminal point of the bus route shown in Figure 1. In winter, because of somewhat unusually snow and ice conditions, bicycling was not attempted for safety reasons.

Seasons and Time of DayMeasurements were made in winter, spring, and summer seasons of 2015. All

measurements were made on weekdays. Measurements were made for mid-day, starting at 11:00 am, and for evening rush hour, starting at 4:00 pm. Measurements were made sequentially for the four transport modes, except that bicycle mode was not measured in the winter because of icy road conditions. Typically, it took approximately 30 minutes to conduct a measurement for one transport mode. Thus, it typically took 2 hours to conduct the measurements for all four modes. The order in which the modes were measured was varied from day to day.

The winter measurements were made over eight weekdays in February. Because of changing weather conditions, it was not always possible to measure both the mid-day and evening time periods on the same day. Spring measurements were made over seven weekdays in April. It was possible to measure both time periods of the day for five of these days. However, because of variable rainy weather, only one time period could be measured on two of these days. Summer measurements took place during three periods. The first period included five weekdays in the first half of June. The second period included ten weekdays starting on June 30 through July 30. The third period included four weekdays in early August. The results for these three summer measurement periods were aggregated into one summer season dataset.

Instrument PackageThe instrument package used for measurement is shown in Figure 2. The instruments

include a TSI DustTrak DRX 8533 for measuring PM2.5, a Langan T15n or Langan L76x for measuring CO, a 2B Technologies Portable Ozone Monitor (POM), a Garmin Oregon 550 GPS receiver with barometric altimeter, and a HOBO data logger for temperature and relative humidity.

The DustTrak is a light scattering instrument based on photometry and single particle sizing.(41) The DRX was compared by Wang et al. (41) to a tapered element oscillating microbalance (TEOM) Federal Equivalent Method instrument, and the two instruments had a very high coefficient of determination of 0.992. The DustTrak actively samples air via an inlet tube which was placed so that the air inlet was approximately at nose level. The DustTrak is factory span-calibrated. It was zero calibrated prior to each measurement.

The POM measures ozone based on the attenuation of light from a low pressure mercury lamp measured by a photodiode. The POM was calibrated to multiple samples of known ozone concentration by 2B Technologies and holds calibration for a year. The POM actively samples via an tube whose inlet was placed next to that of the DustTrak, as shown in Figure 2(a). The POM samples every 5 seconds and averages the two values and gives out a reading at the 10th second. The POM is a Federal Equivalent Method instrument.

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Figure 2. Instrumented backpack. From top left clockwise: (a) configured backpack illustrating location of air sample inlet and GPS receiver; (b) interior instrument rack of backpack with monitors for particulate matter, temperature (T), relative humidity (RH), carbon monoxide (CO), and ozone; (c) side view of backpack illustrating air vents for passive sampling instruments; (d) view of instrument rack; (e) close-up view of second row of instrument rack with T&RH sensor and CO sensor; and (f) closeup of top row of instrument rack illustrating placement of the portable ozone monitor in a protective bracket. Not shown: data collected starting in spring was based on a combined CO and CO2 monitor in place of the CO monitor shown here, and USB battery-powered fans were added to promote airflow to the T&RH and CO sensors.

During the winter measurement, the Langan T15n CO Measurer was used. The instrument uses a CiTicel passive electrochemical sensor based on diffusion airflow to measure CO with a range of 0 to 200 ppm and resolution of 0.5 ppm (42). In subsequent seasonal periods, the Langan L76x was used. The L76x measures CO using the same method as the T15n and additionally measures CO2. The L76x uses the Telaire 7001 measurement system which is based on dual beam infrared. The Langan CO instruments were zero and span calibrated. Span calibration was with cylinder gas containing 10 ppm of CO.

All of the instruments were synchronized to the US Naval Observatory Master Clock eastern time as the reference Readings were taken on a 1 Hz basis for all instruments except for the POM, for which readings were reported every 10 seconds.

Data Collection ProtocolsData were collected by members of the research team and by students enrolled in the

CE/NE 772 Environmental Exposure and Risk Analysis course. To assure consistency of paths

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taken, a detailed data collection protocol was developed and shared with all participants. Logsheets were used to note observations of such as traffic count, time of arrival and departure at various checkpoints, wind speed and indications of emissions such as odor, people smoking and visible pollutant plumes from vehicles and the passing by of heavy duty vehicles such as trucks and buses. For the pedestrian and bicycle modes, traffic counts were recorded for a full cycle of traffic light phases at selected intersections. For pedestrian mode, data collectors were instructed to walk within 1 meter of the curbside. All checkpoints were indicated in photographs provided to participants to ensure consistency of data collection on different days with different data collectors.

For the bus mode, log sheets were used to note the occupancy of the bus at every bus stop. The location of backpack inside the bus was also noted on the log sheets. Inside the bus, the backpack was placed on a seat adjacent to the researcher. Data collection was typically conducted in the rear of the bus, farther back than the middle door, unless vehicle occupancy conditions precluded access, in which case the actual location was noted on a schematic diagram. The cars used for data collection ranged from 2001 to 2015 model years. Participants were provided with a Polder Digital Clock with neck strap, that was synchronized to all of the instruments, for use in recording time stamps in the logsheets.

Data AnalysisData were analyzed using SAS software according to a predetermined protocol. Data

from each of the instruments was downloaded, converted to a common format, and combined into a master data file. Data were indexed by date, time of day, and transportation mode.

Exposure to traffic-related air pollution is dependent in part on the emissions source strength of nearby vehicles. There is substantial variability in vehicle emissions along a corridor or a route.(43) Tailpipe emissions at intersections are primarily attributable to acceleration after a stop.(44) On-road median concentrations of PM2.5 were found to be 16 percent higher in the vicinity of signalized intersection compared to free-flow segments between intersections.(25) Cyclists exposures were found to be higher near intersections than other locations.(17) Exposure concentrations are also expected to be higher near bus stops.(45) The local area includes signalized intersections, traffic circles, and bus stops. Thus, the travel routes for each transport mode were segmented into intersections (INT), traffic circles (TRC), bus stops (BST), with the remainder categorized as other segments (SEG). Data were linked marked based on the GPS latitude and longitude.

For intersections, including INT and TRC, a circle with a radius of 100 meters was assigned from the center of the intersection as an influence area. The coordinates of the center of the intersection and the end points of the influence area were obtained using Google Earth Pro. Similarly bus stops (BST) were assigned an influence area of a circle with a radius of 35 meters.

Inter-modal variability was explored by comparing mean exposure concentrations among transportation modes by time of day and season. Temporal variability was analyzed by comparing mean concentrations between the mid-day and evening time periods within a season for each transport mode, and by comparing seasonal means for each time period by transport mode. Inter-location variability was assessed by comparing mean values of exposure for each type of segment for a given mode.

High resolution spatial variability was assessed by plotting measured exposure concentrations on a map using a geographic information system (GIS). Multi-factor analysis of

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variance (ANOVA) was used to identify which factors are statistically significant in explaining variability. Factors that were considered include mode, location, time of day, and season.

Data analysis was conducted iteratively during the course of the work. In particular, detailed analysis was undertaken of the winter and spring data prior to conducting summer measurements. Therefore, lessons learned from applications of the data collection protocols and analysis procedures from the first seasonal periods were applied to later seasonal periods. In some cases, standard operating procedures developed in the project to guide field data collectors and analysts were refined for efficiency and clarity. One key lesson from the first data collection period was that even with eight days of data collection, the inter-run variability was sufficiently large that it was difficult to infer statistically significant differences when comparing mean values, such as between transport modes, between times of day, or between location categories. This early finding motivated the continuation of data collection through the summer so as to obtain a larger number of runs.

RESULTSResults are given with a focus on PM2.5 for intermodal variability, seasonal and time of

day variability, location variability, and spatial variability. Based on ANOVA, the relative importance of mode, season, time of day, location, and their interactions are quantified for PM2.5, CO, and O3.Overview of Results

A summary of the measured mean microenvironmental concentrations by transport mode, location type, time of day, season, and pollutant is given in Table 1. The mean concentrations for a given transport mode differed by season, location type, and time of day. For example, PM2.5 concentrations were higher for all modes in summer than spring, were higher for buses near bus stops than at other locations, and were typically higher in the evening than midday. The mean concentrations were substantially different for various pairwise combinations of transport modes. For example, PM2.5 concentrations for car cabins were much less than for pedestrians. Specific sources of variability in these results are detailed below.

Inter-Modal Variability

Inter-modal variability in PM2.5 concentration is illustrated in Figure 3 based on data collected in both the mid-day and evening time periods over all three measured seasons. Although data were collected over 34 weekdays spread over winter, spring, and summer, the number of days of data for each time period ranges from 22 to 29. On some days, weather conditions differed between mid-day and evening such that data could be collected in only one of these time periods. Weather conditions were especially a limiting factor during winter. There were also some episodes of equipment failure that lead to loss of data for some attempted data collection periods. For CO, data were not collected during the winter season because of instrument problems, which were resolved in time for the spring measurements. No bicycle data were collected in the winter because of icy road conditions. Overall, approximately 100 hours of data were collected over the two times of day and three seasons for each pollutant.

The data are normalized by dividing each modal average concentration for a given mode, time of day, and day by the corresponding pedestrian modal concentration. This was done because the pedestrian concentration is an indicator of near-road ambient concentration. The results clearly indicate that cyclist exposure concentrations, on average, are similar to those of pedestrians, for each pollutant and time of day.

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Table 1. Mean exposure concentrations for PM2.5, CO, and O3 by Season, Time of Day, Transport Mode, and Location

Pollutant Seasona

Timeof

Day

Transport Mode and Location TypePedestrianb Busb Carb Bicycleb

B I S T Av B I S T Av B I S T Av B I S T Av

PM2.5

(g/m3)

Winter 1100 21 20 22 21 21 13 10 9 10 11 7 5 5 5 61600 21 23 21 22 22 20 11 11 18 15 10 10 10 10 10

Spring 1100 9 8 8 9 9 9 4 5 4 5 5 5 6 5 5 9 10 9 9 91600 8 8 8 8 8 8 4 5 4 5 6 6 6 5 6 9 10 9 9 9

Summer 1100 28 27 27 27 27 15 14 13 15 14 16 15 16 15 16 30 27 27 31 291600 29 29 29 29 29 17 15 15 15 16 18 18 19 19 18 31 29 29 30 30

All 1100 23 22 22 23 22 14 12 12 13 13 12 12 12 11 12 25 23 22 26 241600 23 23 23 23 23 17 13 13 15 14 15 14 15 15 15 25 24 24 25 25

CO(ppm)

Spring

1100 0.4 1.2 0.3 0.6 0.7 2.1 2.2 0.6 1.4 0.5 0.5 0.5 0.5 0.5 0.3 0.5 0.6 0.3 0.4

1600 0.4 1.3 0.4 0.7 0.6 0.8 2.3 0.6 1.1 0.8 0.9 0.7 0.9 0.8 0.5 0.6 0.9 0.4 0.6

Summer

1100 0.5 1.2 0.7 0.5 0.7 1.1 0.6 0.6 0.5 0.7 1.0 1.0 1.0 1.1 1.0 1.0 0.6 0.6 0.8 0.81600 0.5 1.2 0.7 0.5 0.7 1.2 0.8 0.8 0.7 0.9 1.1 1.0 1.0 1.2 1.1 0.5 0.7 0.6 0.5 0.6

All

1100 0.5 1.2 0.8 0.5 0.7 1.1 0.8 0.8 0.5 0.8 0.9 0.9 0.9 1.0 0.9 0.8 0.6 0.6 0.7 0.71600 0.5 1.2 0.8 0.5 0.8 1.1 0.8 1.0 0.7 0.9 1.0 1.0 0.9 1.1 1.0 0.5 0.7 0.7 0.5 0.6

O3

(ppb)

Winter 1100 31 29 27 31 29 16 5 6 11 9 5 4 6 4 51600 31 32 30 35 32 15 6 8 7 9 4 4 4 2 3

Spring 1100 44 43 41 43 43 13 10 12 8 11 21 21 21 18 20 43 37 38 35 381600 44 44 39 44 43 25 13 13 20 18 19 22 23 23 22 44 42 44 46 44

Summer 1100 40 40 40 42 41 12 8 8 10 10 10 9 10 11 10 41 41 42 43 421600 43 41 42 43 42 9 7 6 8 8 6 7 8 7 7 42 41 42 43 42

All 1100 40 39 38 41 39 12 8 8 10 10 11 11 12 11 11 41 40 41 41 411600 42 41 40 42 41 12 7 7 9 9 9 10 10 10 10 42 41 42 43 42

a Winter measurements spanned 8 weekdays, with sample sizes ranging from 3 to 6 depending on mode, time of day and pollutant, except for bicycle mode, which was not measured because of icy road conditions, and except for CO, for which no measurements were made because of instrument failure. Spring measurements spanned 7 weekdays, with sample sizes ranging from 3 to 6 depending on mode, time of day and pollutant. Summer measurements spanned 19 weekdays, with sample sizes ranging from 17 to 19 depending on mode, time of day and pollutant. Here, sample size refers to the number of runs for a given season, mode, and time of day. Each run, including all location types, typically included approximately 30 minutes of measurements.

b B = Bus Stop; I = Intersection; S = Other Road Segments; T = Traffic Circle

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Figure 3. Measured mean ratio of PM2.5, CO, and O3 for bus, car, and bicycle modes with respect to pedestrian mode by time of day (1100 = mid day; 1600 = evening). Data were collected on 22 to 29 days, depending on the pollutant and transport mode, over winter, spring, and summer seasons. On each day, data were collected for approximately 30 minutes per transport mode.

PM2.5 exposure concentrations in bus and car cabins are approximately 40 percent lower compared to those for pedestrians and cyclists. The mean differences between car versus pedestrian and bus versus pedestrian are statistically significant for both times of day. Likewise, the mean differences between car versus bicycle and bus versus bicycle are statistically significant for both times of day. The mean differences between bus and car are not significantly different for either time of day. The mean ratio of in-cabin to pedestrian exposure of approximately 0.6 is consistent with other data that indicates that PM2.5 infiltrates only partially into cabins with closed windows and air recirculation, and may additionally indicate the effectiveness of cabin air filters in filtering out ambient PM2.5. The results are insensitive to time of day, indicating that relative differences among the modes are not sensitive to factors such as traffic or atmospheric conditions that may differ from mid-day to evening. Although not measured, the relative difference in potential dose between active and passive transport modes is higher than the indicated difference in exposure concentration, since pedestrians and, perhaps moreso, cyclists, are likely to have higher breathing rates than sedentary vehicle occupants. The comparison of modes was similar for each of the seasonal measurement periods, including winter, spring, and summer. Pedestrian and cyclist PM2.5 exposure concentrations were consistently higher than for bus and car, and bus and car exposure concentrations were approximately similar.

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For CO, compared to pedestrians, the modal average concentration for bicyclists was not significantly different for the mid-day time period but was significantly lower, by 20 percent, for the evening period. Some of the difference, however, may be related to portions of the bicycle route being located away from high traffic roads. In-car CO exposure concentrations were significantly higher than for pedestrians by an average of 32 percent over both time periods, whereas in-bus exposures were significantly higher by 15 percent. The difference in mean exposure between pedestrians and buses was, however, not statistically significant. As noted earlier, the elevated CO levels in vehicle cabins compared to near roadways could be in part because of vehicle self-pollution.

For O3, pedestrians and cyclists had similar average exposures, with pairwise t-test p-values of 0.40 and 0.47 for mid-day and evening, respectively. However, O3 exposure concentrations were significantly lower by approximately 75 percent in bus and car cabins compared to pedestrians. Ozone is a highly reactive oxidant that tends to react with surfaces in enclosed environments.(4) Thus, the relatively low ratio of in-cabin to near-road exposure concentration for ozone is expected. An implication is that active commuters are not only more highly exposed to ozone, but because of their higher ventilation rates, they would receive substantially higher potential doses of ozone compared to cabin occupants.

The measured microenvironmental exposure concentrations were not found to be significantly correlated with observed traffic counts. This is likely because the traffic counts were consistent for each time of day and location from day to day, but ambient concentrations were more highly variable. This is not to say that traffic count is not an influential factor in general, but only that it was not found to be significant here.

Seasonal and Temporal Variability

Seasonal and temporal variability in mean exposure concentrations is illustrated in Figure 4 for pedestrian PM2.5 exposure. For PM2.5, the inter-seasonal variability is greater than the variation between time periods in a given season. The seasonal average concentrations range in the afternoon from approximately 8 μg/m3 in the spring period to nearly 29 μg/m3 in the summer period, a range of 21 μg/m3. In contrast, the average range of variability from mid-day to evening is less than 1 μg/m3 in each season. The relative seasonal trend in mean concentration for other modes is approximately similar to that for the pedestrian mode. Cyclist exposure concentrations are typically similar to those of pedestrians. Cabin occupant exposure are lower but follow the same general seasonal pattern, with higher exposure concentrations in summer compared to winter and spring. The PM2.5 exposure concentrations are compared to 24-hour average mean values from the nearest fixed site regulatory monitor, which is located approximately 7 miles from the study area at the Millbrook monitoring site on Spring Forest Road in Raleigh, NC. Daily 24-hour PM2.5 concentrations were available for only 26 of the 34 days of exposure measurement. The ambient monitoring data illustrate that PM2.5 daily average concentrations were lowest in the spring compared to winter and summer, which is consistent with the trend in measured exposure concentration. The magnitude of the measurements is similar for spring, but higher for the exposure measurements in winter and summer. This difference may be in part related to different detection methods (laser light-scattering for the portable monitor versus the beta-attenuation Federal equivalent method used at the regulatory monitor) and to differences in proximity to nearby traffic. Generally, the exposure and ambient monitoring concentrations are of similar magnitude, although they are numerically different.

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(a) Particulate Matter

(b) Carbon Monoxide

(c) OzoneFigure 4. Measured mean pedestrian PM2.5, CO, and O3 exposure concentrations: Comparison of seasons and time of day and comparison to nearest fixed site ambient monitoring station.

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CO concentrations were generally very low in all seasons, with mean modal concentrations of typically 1.1 ppm or less. Although the mean values average 11 percent higher in summer than spring, there was not a significant different in mean CO concentration when comparing times of day or seasons. The CO exposure concentrations were approximately twice as high as the daily maximum eight-hour average concentrations measured at the fixed site monitor, which averaged approximately 0.3 ppm in spring and summer. The higher CO exposure concentrations are attributable to closer proximity to traffic at the study area. The observed CO concentrations, even close to or on roadways, were typically low and well below levels of health concern.(2)

For O3, the seasonal average pedestrian exposures ranged from 29 ppb in the winter to 43 ppb in the summer during mid-day, and from 32 ppb in winter to 43 ppb in the spring during evening. O3 levels typically peak in early to mid-afternoon.(1) Mixing heights can grow substantially during the afternoon on warm summer days. Thus, the observed trend in pedestrian ozone concentration is reasonable. Moreover, near roadways, ozone can be titrated by reactions with fresh vehicle exhaust NO2, thereby depressing ozone close to the roadway. The typically higher traffic volumes in the evening versus mid-day, therefore, could explain the lower average ozone exposures for pedestrians, especially in the high ozone formation summer season. In each season, O3 exposure concentrations for cyclists were approximately the same as for pedestrians, and were significantly lower for bus and car cabins by approximately 75 percent. The mean O 3

exposure concentrations, which ranged from 29 ppb to 43 ppb, were comparable to the daily maximum eight-hour average concentrations measured at the fixed site monitor, which ranged from 34 ppb in the winter to 41 ppb in the spring.

Location Variability

Figure 5 compares mean PM2.5 exposure concentrations by location type for pedestrian, bus, car and bicycle modes based on 34 weekdays of measurements inclusive of winter, spring, and summer. The average sample size is 52 time periods, representing an average of 26 mid-day and evening time periods over 26 days. Each time period for each mode, inclusive of all location types, is based on approximately 30 minutes of measurements. Even with the large sample size, the confidence intervals for mean values are wide relative to the differences in means for different location types. However, the sample results are highly correlated. For example, the time-period average PM2.5 concentrations, for both time periods and all days, for pedestrians have correlations of approximately 99 percent among location types. Therefore, pairwise t-tests were used to infer whether pairwise combinations of location type mean concentrations for a given pollutant and mode were statistically significantly different.

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(a) Particulate Matter

(b) Carbon Monoxide

(c) OzoneFigure 5. Measured mean PM2.5, CO, and O3 concentrations for pedestrian, transit bus, private car, and bicycle modes by type of location: bus stop (BST); intersection (INT); road segment (SEG); and traffic circle (TRC). Based on an average of 52 time periods (lunch, evening)

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measured over 34 days spanning three seasons. For CO, data are based only on spring and summer seasons.

For pedestrian PM2.5 exposure concentrations, bus stop and traffic circle locations had 0.4 g/m3 higher average concentration than other road segments. Even though this difference is small, the difference is statistically significant because of the high correlation of 0.994 in concentrations between bus stop and other segment concentrations and of 0.989 between traffic circles and other segments. However, from a practical perspective, these differences are not substantial. For transit buses, the average exposure concentration for bus stops was 24 percent significantly higher than that for intersections and other road segments, and 15 percent significantly higher than for traffic circles. However, there was not a significant difference for buses between intersections and other road segments. For private cars, the highest PM 2.5

exposure concentrations were for other road segments. Although significant, the mean concentration difference for cars between other road segments and intersections was only 3 percent, and no other comparisons were significant. The in-car PM2.5 concentrations were low for all location types, ranging from 13.2 g/m3 for intersections and traffic circles to 13.6 g/m3 for other road segments. For bicycles, the location type with the highest mean concentration was traffic circles. The bicycle traffic circle PM2.5 concentrations were significantly higher than those for intersections and other road segments by 6.6 and 9.2 percent, respectively. Bicycle PM2.5

concentrations near bus stops were significantly higher than those for other road segments by 7.1 percent.

Overall, there was relatively little location variability in mean PM2.5 concentration for pedestrians and car occupants. For buses and bicycles, one location type (bus stops and traffic circles, respectively) had higher PM2.5 concentrations than all others. Higher in-bus exposures at bus stops is related to the bus door being open. Higher bicycle exposure to PM2.5 is related to the close proximity of the bicycle to circulating traffic in the single-lane roundabouts characteristic of the study area.

For CO exposure concentrations for a given transport mode, there were typical multiple pairwise location type comparisons that were significantly different. For example, for pedestrian mode, bus stops had 60 and 40 percent lower mean CO concentrations than intersections and other road segments, respectively, whereas intersections had mean CO concentrations higher by 50 percent than other road segments and higher by 150 percent than traffic circles. However, for buses, bus stops had the highest mean CO concentrations, which were significantly higher by 37 percent and 80 percent compared to intersections and traffic circles, respectively. For cars, the highest exposure concentration was at traffic circles, for which the mean CO concentrations were significantly higher by 6.8 percent, 6.7 percent, and 13 percent compared to bus stops, intersections, and other road segments, respectively. In contrast, for bicycles, most of the pairwise comparisons of location type were not significantly different, except for bus stops and traffic circles, for which the mean difference was 12 percent. The highest exposure location differed by mode: intersections for pedestrians; bus stops for buses; traffic circles for cars, and bus stops for bicylists. Pedestrian CO exposure at intersections is likely influenced by vehicle acceleration. Bus CO exposure at bus stops is at least partly related to the bus door being open. Cars in traffic circles may be accepting gaps with short headway (close spacing) to other cars, and cyclists at bus stops may be approaching and navigating around a stopped bus, which may also place the bicycle in closer proximity to vehicle traffic.

For ozone exposure concentrations, there was relatively little variability among location types for pedestrians and bicycles, for which the average difference between the highest and

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lowest mean concentrations were 6.3 and 3.5 percent, respectively. However, for the pedestrian mode, each of the location type mean concentrations was significantly different than the other types. For example, even though the traffic circle mean concentration was only 1.5 percent higher than for bus stops, the difference was significant. For cars, the largest significant difference of 12.0 percent was between the mean O3 concentration for other road segments compared to bus stops, but the absolute difference was only 1.2 ppb. Thus, from a practical perspective, differences in ozone concentrations among locations for pedestrian, car, and bicycle modes are not substantial. However, for bus mode, the mean O3 concentration at bus stops was 61 percent higher compared to intersections, and the absolute difference was 4.7 ppb. The relatively high bus O3 exposure at bus stops is related to the bus door being open, allowing more ambient air to infiltrate into the bus.

Figure 5 also illustrates inter-modal differences. For PM2.5, the average bicycle concentration was just 6 percent, but not significantly, higher than that for pedestrians. However, the approximately 40 percent lower average exposure concentrations for bus and cars compared to pedestrians were significant, with p-values below 0.0005. Buses and cars also had approximately 45 percent significantly lower exposure concentrations than bicycles. For CO, the average exposure concentrations were significantly higher for cars than for all other transport modes by 32, 10, and 50 percent compared to pedestrians, buses, and bicycles, respectively. Bus CO exposures were significantly higher than those for bicycles, by 36 percent, but significantly lower than those for cars by 10 percent. The difference between pedestrians and bicycles was not significant. For ozone, bus exposure concentrations were significantly lower than for pedestrians, by 76 percent, and for bicyclists, by 78 percent, but not significantly different from those of cars. The small average difference between pedestrians and bicycles of 4 percent was not significant.

Spatial Variability

An example of spatial variability in exposure concentration is shown in Figure 6 for PM2.5

for car mode based on evening measurements on one of the winter days. Although the absolute PM2.5 concentrations are relatively low, the spatial variability is illustrative of the substantial effect of intersections, bus stops, and low speed vehicle activity on exposures. For example, one of the exposure hotspots is at the intersection of Western Blvd and Avent Ferry Road, while other hotspots occur at bus stops near the southern terminus of the bus route. Hot spots inside the campus, on Hillsborough Street, and on Pullen Road are associated with idling or accelerations related to stop-and-go vehicle movements. Although the health implications of exposures to sub-daily exposures to PM2.5, including second-by-second peaks, are largely unknown (46), quantification of spatial variability can help prioritize locations and interventions that might reduce trip average exposures. For example, interventions that would smooth traffic flow may help reduce high end exposures, which in turn could reduce average exposures.

Comparison of Sources of Variability

An integrated analysis of the relative importance of mode, season, time, and location is given in Table 2 for PM2.5, CO, and O3. For both PM2.5 and O3, the transportation mode is the most important factor leading to variability in exposure concentrations. For all three of the pollutants, there are differences based on location for one or more modes. PM2.5 exposure concentrations are mostly sensitive to the main effects of mode, followed by time and season, and lastly location, but there are also secondary interaction effects among mode, season, and time that contribute to variability. This implies that the effect of some of these factors differs as

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other factors vary. For example, time of day may be more or less important in some seasons or for some modes.

For CO, interaction effects were approximately as important as the only identified significant main effect. For O3, mode was the biggest contributor to variability, related to the large difference between in-cabin versus outdoor concentration.

Figure 6. Illustrative example of spatial variability in exposure concentrations for car in-cabin PM2.5 exposure concentrations based on an evening time period in spring. Map © 2015 HERE and © 2015 Microsoft Corporation. Used under terms of print rights.

Table 2. Significant Sources of Variability in PM2.5, CO, and O3 Exposure Concentrations PM2.5 CO O3

Factor F-Ratio P-Value Factor F-Ratio P-Value Factor F-Ratio P-ValueSeason 13.3 <0.01 Location 3.0 <0.03 Season 72.6 <0.01Location 3.6 <0.02 Season*Mode 2.5 <0.01 Location 5.5 <0.01Time 14.4 <0.01 Season*Location 2.8 <0.01 Mode 2370 <0.01Mode 159.1 <0.01 Mode*Location 2.6 <0.01 Season*Mode 32.1 <0.01Season*Time 5.8 <0.01 Season*Time 9.4 <0.01Season*Mode 5.2 <0.01 Mode*Location 2.9 <0.01Time*Mode 4.5 <0.01

Comparison to Companion Study

The study conducted in Raleigh, NC (USA) is companion to a study conducted in Guilford, Surrey (UK) using similar study design, instruments, and analysis methods.(47) In the Guildford study, measurements were made between February and March 2015 of pedestrian,

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transit bus, private car, and bicycle modes on typical commuting routes between the University of Surrey and the Guildford city centre. Measurements were made for three times of day, including morning peak, afternoon off-peak, and evening peak. The number of runs for each mode and time of day was 10 for pedestrian, bus, and bicycle modes and ranged from 15 to 21 for car mode. The same concept for carrying an instrumented backpack was applied, and the data collection and analysis protocols were similar. The Guildford study focused mainly on particulate matter, including PM1, PM2.5, and PM10 size ranges.

With respect to the common pollutant of PM2.5, the results of the Guildford and Raleigh studies were more dissimilar than similar. For example, the lowest mean PM2.5 exposure concentration in Guildford was for bicycle mode for all three times of day, ranging from 8 g/m3

to 10 g/m3, whereas exposure concentrations were approximately similar among the other three modes, ranging from approximately 20 g/m3 to 30 g/m3 in most cases. The bus route was 13.8 km and overlapped only partly with the car, cycle, and walk (CCW) route, which was 5.3 km long. Unlike the transit buses in the Raleigh study area, the buses in the Guildford study area used a central bus terminal and thus had some stops in proximity to a typically larger number of other buses. However, in-cabin exposure concentrations in buses can be substantially affected by the bus HVAC system and cabin air filtration. Although bicycles followed the same general route as cars and pedestrians, cyclists separated from traffic and sidewalks in some places, such as because of road maintenance. At the time of the field study, the light duty vehicle fleet in the UK was approximately 49 percent diesel (48), whereas the light duty vehicle fleet in the U.S. was nearly entirely gasoline fueled.(49) As a result, the direct on-road emissions of primary particulate matter from light duty vehicles would generally be higher in the UK study area, leading to higher exposure concentrations for pedestrians and cars. Thus, differences in results between the Raleigh and Guildford studies are at least in part because of differences in the vehicle fuel mix and vehicle emissions, and may also be related to differences in land-use, urban morphology, and transit operations.

The results of the Raleigh study agree well with studies cited earlier that have generally found that in-cabin exposures in buses and cars to fine particles and ozone are lower than for pedestrians, whereas elevated CO concentrations in vehicle cabins appears to be related to some combination of proximity to onroad emissions and possible vehicle self-pollution. For the Raleigh study, bicycle exposure concentrations were similar to pedestrian concentrations in part because some of the safest paths for bicycles are the same sidewalks that are used by pedestrians, but may also represent compensating factors. For some parts of the bicycle route, the path is a sharrow on the road in very close proximity to moving vehicles, for which exposure concentrations are likely to be higher, while for some other parts of the bicycle route, the path was well-separated from vehicle traffic, for which exposure concentrations are likely to be lower.

CONCLUSIONSBased on over 30 days of field measurements conducted over three seasons and for two

times of day on weekdays, PM2.5, CO, and O3 exposure concentrations were quantified for selected active and passive transportation microenvironments. The effect of season, time of day, and location with respect to variability in transportation mode exposure concentrations was assessed. The precision with which comparisons can be made is limited by substantial inter-run variability that leads to confidence intervals in mean values that are comparable or larger than, in many cases, observed differences in means. Nonetheless, statistically significant results were

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obtained from multi-factor ANOVA that enable comparison of the relative importance of measured and observed factors.

Pedestrian and cycle mode exposure concentrations were approximately similar to each other and were substantially higher than for bus and car cabins for both PM2.5 and O3. Results for CO were less conclusive in part because measured CO concentrations were typically very low, at less than 2 ppm. However, there was evidence that CO concentrations were higher in vehicle cabins compared to ambient levels faced by pedestrians, which is in accord with prior studies that have found evidence of vehicle self-pollution. There were strong seasonal effects in ambient concentration that lead to seasonal variation in exposure concentrations in each microenvironment, especially for PM2.5 and O3. Although there was some variability in exposure concentrations by time of day when comparing mid-day and evening, this source of variability was relatively weak for PM2.5 and was not identified as a main factor in ANOVA for CO or O 3. There is some evidence that the type of location along a route contributes to variation in mean concentrations. For example, depending on the mode and pollutant, locations such as bus stops, traffic circles, or intersections were associated with higher average exposure concentrations compared to other locations. There is substantial spatial variability in 1 Hz exposure concentrations which, while perhaps not directly relevant to known health effects, could assist in pinpointing opportunities to intervene to reduce emissions or exposures.

Although this study was comprehensive in some ways, in terms of addressing selected key traffic related pollutants, key transportation models, seasonal variability, and accounting for inter-run variability by repetitive sampling for over 30 days, there are many opportunities to extend this type of work. For example, although there was variability in road types in the study area, it was not possible to sample all road types of broader policy interest, such as interstates and ramps. Although temporal variability during the day was a relatively weak contributor to variability, had other time periods been measured, such as morning rush hour, the importance of time period may have differed. More repetitive sampling of key location features such as bus stops, intersections, and traffic circles may be needed to obtain more clarity regarding mean differences in exposures. Studies such as this could be repeated in other locations, to assess robustness of findings with regard to factors such as climate zones and differences in urban morphology, such as street canyons. The comparison of results for Raleigh, NC and Guildford, UK illustrate that results may also be sensitive to the local mix of vehicle fuels and technologies. Thus, there are numerous promising opportunities to extend and refine studies such as this that contribute to the need for improved microenvironmental exposure concentration data.

ACKNOWLEDGMENTS

This work was funded in part by the University Global Partnership Network, which is a multilateral network of university partners including North Carolina State University, University of Surrey, and others, and in part by a pilot project of the NCSU Center for Human Health and the Environment under grant P30ES025128 from the National Institutes of Health. Data collection in the winter season was assisted by students in CE/NE 772 Environmental Exposure and Risk Analysis. Data collection in the summer periods was conducted by Michael Cuffney, Fareed Farhat, and Yejin Kim.

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AUTHOR CONTRIBUTION STATEMENT

The authors confirm contribution to the paper as follows: study conception and design: H.C. Frey and P. Kumar; data collection: D. Gadre; analysis and interpretation of results: D. Gadre, S. Singh, H.C. Frey, P. Kumar; draft manuscript preparation: H.C. Frey. All authors reviewed the results and approved the final version of the manuscript.

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Documents

ABSTRACTepubs.surrey.ac.uk/857126/1/Frey Kumar et al (2020... · Web viewPhoenix, AZ 85016 [email protected] Sanjam Singh Senior Staff Tora Consulting, LLC 509 Harwich