1. Common Sample Air Inlet Inlet to the AE51 MicroAeth Inlet to the HHPC-6  1510 measurements of BC and PM concentration were recorded simultaneously at the clearing site and the vegetation barrier site.  Winds were divided into 4 categories: winds with speeds <0.1m/s (low/variable), winds directly from the highway (downwind), winds parallel to the highway from the north or south (parallel), and winds blowing towards the highway (upwind).  Similar hourly trends were observed at both sites. The differences between the clearing and vegetation barrier sites were less than instrument noise for both mean PM (0.5-2.0µm) and BC concentrations. Growing evidence exists that populations spending significant amounts of time near major roads face increased risks for several adverse health effects. 1 These effects may be attributable to increased exposure to particulate matter (PM), gaseous criteria pollutants, and air toxics. Roadway design, including the presence of roadside vegetation, may be a means of reducing air pollutant concentrations near roads 2. Vegetation in urban areas, particularly trees, can directly remove air pollution and can also provide barriers between sources and exposed populations. Trees remove gaseous air pollution primarily by uptake via leaf stomata, though some gases are removed by the plant surface. Trees also remove pollution by intercepting and diffusing airborne particles. The particles may be re- suspended to the atmosphere, washed off by rain, or dropped to the ground with leaf and twig fall after deposition. 3 Consequently, vegetation provides a temporary retention site for many atmospheric particles. This study employed portable, hand-held monitoring devices to investigate the effect of a roadside tree stand on near-road black carbon (BC) and particulate matter (PM) concentrations in Detroit, Michigan. Mobile Monitoring Instruments Using Portable Samplers to Determine the Effect of Roadside Vegetation on Near-Road Air Quality Halley L. Brantley 1, Gayle S. W. Hagler 1, Parikshit Deshmukh 2, Richard W. Baldauf 1,3 1 U.S. EPA, Office of Research and Development, National Risk Management Research Laboratory, 109 TW Alexander Drive, E343-02, Research Triangle Park, NC 27711 2 ARCADIS Inc., 4915 Prospectus Drive, Suite F, Research Triangle Park, NC 27713, USA 3 U.S. EPA, Office of Transportation and Air Quality, 2000 Traverwood Drive, Ann Arbor, MI 48105 Introduction Mobile Monitoring Methods and Results References: 1. Health Effects Institute. 2009. Traffic-related air pollution: a critical review of the literature on emissions, exposure, and health effects. Preprint Special Report 17. Health Effects Institute, Boston, MA. 2. Baldauf R.W., C. Bailey, J.R. Cook, T.A. Cahill, A. Khlystov, K.M. Zhang, C. Cowherd, G.E. Bowker. 2009. Can Roadway Design be used to Mitigate Air Quality Impacts from Traffic? Environmental Manager, August edition. 3. Smith, W. H. 1990. Air pollution and forests. New York: Springer-Verlag. 618 p. 4. Hagler, G.S.W., Yelverton, T. L. B., Vedantham, R., Hansen, A. D. A., and Turner, J. R., 2011. Post-processing method to reduce noise while preserving high time resolution in Aethalometer real-time black carbon data. Aerosol and Air Quality Research, 11: 539–546. Measurement Parameter Sampling Approach Instrument Make/Model Sample Frequency Black CarbonMicro- Aethalometer Model AE-51, Magee Scientific 1 sec Particle Size and Number Handheld PM sampler HHPC-6, MetOne1 min Latitude and Longitude GPSVGPS 900, Visiontac Instrument Inc. 1 sec 3D Wind Speed and Direction Ultrasonic Anemometer RM Young, Model 8100V 1 sec Leaf Area IndexLAI2000LI_COR Biosciences-- Mobile Monitoring Methods  Two backpack sampling systems were equipped with an HHPC-6, AE-51, and GPS. The instruments used in these systems are equipped with sufficient battery life to be used for nearly 8 hours of mobile (backpack) monitoring. Alternatively, when connected to an appropriate power source, they can be used for extended stationary sampling.  The backpack sampling system incorporates a common sampling inlet for both instruments. The HHPC-6 is placed at the center of the backpack to ensure a smooth and almost straight path for the larger sized particles to enter the HHPC inlet. The AE-51 inlet tubing is kept short and attaches to the side of the tee. Since black carbon measurements by the AE-51 typically measure particles in the sub-micron size range, this tee configuration has minimal impact on the removal of these small particles.  The HHPC-6 measures particle counts along 6 size channels from 0.5 µm – >10.0 µm in diameter.  The AE-51 frequently reports negative values so the post- processing algorithm developed by Hagler, et. al. 4 was used to reduce noise. One minute averaging with ∆ATN =0 was used for the mobile monitoring data in order to retain high spatial resolution. Ten minute averaging with ∆ATN = 0.05 was used for the comparison of the stationary monitoring data to further reduce noise. Stationary Monitoring Results  Co-located sampling was conducted for a total of 190 minutes during the study.  Measurements of particle concentration in the largest 3 size bins were not strongly correlated and were not used in the analysis.  The slope of the regression lines for the smaller three size bins was within 15% of 1 with R 2 >0.9,. With the understanding that differences less than 15% may be due to instrument noise, the counts of the particles in the smaller three bins were summed for analysis.  The slope of the regression line for the BC data was within 5% of 1, although the R 2 was only 0.525. Ten minute averaging was used for the stationary samples in an attempt to reduce noise. HHPC-6 MicroAeth AE-51 Comparison of co-located particle counts by size bin. Comparison of co-located BC measurements. Ratio of concentration of (a) PM (0.5-0.7 µm), (b) PM (0.7-1.0 µm), (c) PM (1.0-2.0 µm), and (d) BC at the mobile sites to levels at the clearing site by distance from the highway. LAI for each site is shown in (d). Locations of mobile monitoring sites and distance from highway edge. The star symbols indicate the locations of the clearing site and the vegetation barrier site.  Sampling was conducted between May 20 and June 18, 2011 near I-275 in Detroit, Michigan. I-275 is a six-lane highway that supports approximately 120,000 vehicles/day at the study site.  Backpack one was used for continuous sampling at the clearing site during the entire study period. Backpack two was moved on 10 of the sampling days to the clearing site for co-located sampling and to each of the mobile sampling sites twice for approximately 10 minutes of sampling per stop. When not being used for mobile sampling, backpack two was used for continuous sampling at the vegetation barrier site.  The wind direction and speed were recorded at the clearing site with a sonic anemometer during the entire sampling period.  Leaf area index (LAI) was measured at each of the sites on 14 of the sampling days.  For PM 0.5-0.7 µm and BC no difference was observed between the sites.  For PM 0.7-1.0 µm and PM 1.0-2.0 µm the mobile sites had slightly lower concentrations than the clearing site, but the differences were not greater than the instrument noise.  There does seem to be a slight decrease in pollutant concentration with increased LAI, but the differences are not significant when distance is taken into account. Wind roses by category for the entire sampling period. Hourly trends in wind direction, BC and PM (0.5-2.0 µm) concentration. Ratio of vegetation to clearing site PM (0.5-2.0 µm) (a) and BC (b) versus wind speed under downwind conditions. Outliers not shown.  At no time of day was there a significant difference between PM concentration in the clearing and behind the vegetation barrier.  A large difference in BC concentration was observed between 8-9am when the winds were parallel to the road. Under these conditions, mean BC concentration behind the vegetation barrier was ~20% (675 ng/m 3 ) lower than the mean concentration in the clearing (p- value=0.15). In contrast, PM concentration under these conditions was higher behind the vegetation barrier. PM (0.5-2.0 µm) and BC concentrations by wind category and hour of day in a clearing site and behind a tree stand: under low speed wind conditions (a), downwind of road (b), winds parallel to the road (c), and upwind of road (d). Values shown are means ± standard error. Each marker is labeled by hour of day. Means for the shown hours and meteorological categories are only provided for N > 10 (100 minutes of data).  Portable backpack sampling systems with micro-aethalometers and hand-held particle counters made it possible to efficiently collect data at 9 different locations in a clearing and behind a tree stand at a variety of distances from the highway.  The PM size bins showed high variability in results. The largest three PM bin sizes (2.0 µm to >10 µm) couldn’t be used because of the lack of correlation in the co-located measurements. The co-located PM measurements in the lower bin sizes (0.5-2.0 µm) were strongly correlated but still showed some bias: measurements from backpack 2 were 7% and 5% lower than backpack 1 for bins 1 and 2, respectively, and 13% higher for bin 3. In addition, there was variability in the field measurements among the lower size bins that initially defy explanation.  The co-located measurements of BC did not seem to be biased, but were more weakly correlated than the PM measurements due to instrument noise. This study highlights the importance of co-located sampling when using mobile monitoring devices in order to understand the limitations of the instruments and field measurements.  At the mobile sites, PM (0.5-2.0 µm) and BC concentrations seemed to decrease with increased LAI, but the differences were not significant and may be attributed to instrument noise and variation in traffic activity and weather along with distance from the highway.  No significant difference was observed between PM (0.5-2.0 µm) or BC concentrations at the clearing site and those at the vegetation barrier site; however, this may be a result of using less precise instruments or the PM characteristics measured, because other studies have shown tree stands to have an effect on PM in other size ranges and on gaseous air pollution. Further research is needed on these instruments and the ability of vegetation to mitigate near-road air pollution. Conclusions Mean ratio ± standard error of concentration at the mobile sites to concentration at the clearing site compared to LAI. Co-located Sampling Results