Characterizing Determinants of Near-Road Ambient Air Quality for an Urban Intersection and a Freeway Site.
Near-road ambient air pollution concentrations that are affected by vehicle emissions are typically characterized by substantial spatial variability with respect to distance from the roadway and temporal variability based on the time of day, day of week, and season. The goal of this work is to identify variables that explain either temporal or spatial variability based on case studies for a freeway site and an urban intersection site. The key hypothesis is that dispersion modeling of near-road pollutant concentrations could be improved by adding estimates or indices for site-specific explanatory variables, particularly related to traffic. Based on case studies for a freeway site and an urban intersection site, the specific aims of this project are to (1) develop and test regression models that explain variability in traffic-related air pollutant (TRAP) ambient concentration at two near-roadway locations; (2) develop and test refined proxies for land use, traffic, emissions and dispersion; and (3) prioritize inputs according to their ability to explain variability in ambient concentrations to help focus efforts for future data collection and model development.
The key pollutants that are the key focus of this work include nitrogen oxides (NO), carbon monoxide (CO), black carbon (BC), fine particulate matter (PM; PM ≤ 2.5 μm in aerodynamic diameter), ultrafine particles (UFPs; PM ≤ 0.1 μm in aerodynamic diameter), and ozone (O). NO, CO, and BC are tracers of vehicle emissions and dispersion. PM is influenced by vehicle table emissions and regional sources. UFPs are sensitive to primary vehicle emissions. Secondary particles can form near roadways and on regional scales, influencing both PM and UFP concentrations. O concentrations are influenced by interaction with NO near the roadway. Nitrogen dioxide (NO), CO, PM, and O are regulated under the National Ambient Air Quality Standards (NAAQS) because of demonstrated health effects. BC and UFPs are of concern for their potential health effects. Therefore, these pollutants are the focus of this work.
The methodological approach includes case studies for which variables are identified and assesses their ability to explain either temporal or spatial variability in pollutant ambient concentrations. The case studies include one freeway location and one urban intersection. The case studies address (1) temporal variability at a fixed monitor 10 meters from a freeway; (2) downwind concentrations perpendicular to the same location; (3) variability in 24-hour average pollutant concentrations at five sites near an urban intersection; and (4) spatiotemporal variability along a walking path near that same intersection.
The study boundary encompasses key factors in the continuum from vehicle emissions to near-road exposure concentrations. These factors include land use, transportation infrastructure and traffic control, vehicle mix, vehicle (traffic) flow, on-road emissions, meteorology, transport and evolution (transformation) of primary emissions, and production of secondary pollutants, and their resulting impact on measured concentrations in the near-road environment. We conducted field measurements of land use, traffic, vehicle emissions, and near-road ambient concentrations in the vicinity of two newly installed fixed-site monitors. One is a monitoring station jointly operated by the U.S. Environmental Protection Agency (U.S. EPA) and the North Carolina Department of Environmental Quality (NC DEQ) on I-40 between Airport Boulevard and I-540 in Wake County, North Carolina. The other is a fixed-site monitor for measuring PM at the North Carolina Central University (NCCU) campus on E. Lawson Street in Durham, North Carolina. We refer to these two locations as the freeway site and the urban site, respectively. We developed statistical models for the freeway and urban sites.
We quantified land use metrics at each site, such as distances to the nearest bus stop. For the freeway site, we quantified lane-by-lane total vehicle count, heavy vehicle (HV) count, and several vehicle-activity indices that account for distance from each lane to the roadside monitor. For the urban site, we quantified vehicle counts for all 12 turning movements through the intersection. At each site, we measured microscale vehicle tailpipe emissions using a portable emission measurement system.
At the freeway site, we measured the spatial gradient of NO, BC, UFPs, and PM, quantified particle size distributions at selected distances from the roadway and assessed partitioning of particles as a function of evolving volatility. We also quantified fleet-average emission factors for several pollutants.
At the urban site, we measured daily average concentrations of nitric oxide (NO), NO, O, and PM at five sites surrounding the intersection of interest; we also measured high resolution (1-second to 10-second averages) concentrations of O, PM, and UFPs along a pedestrian transect. At both sites, the Research LINE-source (R-LINE) dispersion model was applied to predict concentration gradients based on the physical dispersion of pollution.
Statistical models were developed for each site for selected pollutants. With variables for local wind direction, heavy-vehicle index, temperature, and day type, the multiple coefficient of determination (R) was 0.61 for hourly NO concentrations at the freeway site. An interaction effect of the dispersion model and a real-time traffic index contributed only 24% of the response variance for NO at the freeway site. Local wind direction, measured near the road, was typically more important than wind direction measured some distance away, and vehicle-activity metrics directly related to actual real-time traffic were important. At the urban site, variability in pollutant concentrations measured for a pedestrian walk-along route was explained primarily by real-time traffic metrics, meteorology, time of day, season, and real-world vehicle tailpipe emissions, depending on the pollutant. The regression models explained most of the variance in measured concentrations for BC, PM, UFPs, NO, and NO at the freeway site and for UFPs and O at the urban site pedestrian transect.
Among the set of candidate explanatory variables, typically only a few were needed to explain most of the variability in observed ambient concentrations. At the freeway site, the concentration gradients perpendicular to the road were influenced by dilution, season, time of day, and whether the pollutant underwent chemical or physical transformations. The explanatory variables that were useful in explaining temporal variability in measured ambient concentrations, as well as spatial variability at the urban site, were typically localized real-time traffic-volume indices and local wind direction. However, the specific set of useful explanatory variables was site, context (e.g., next to road, quadrants around an intersection, pedestrian transects), and pollutant specific. Among the most novel of the indicators, variability in real-time measured tailpipe exhaust emissions was found to help explain variability in pedestrian transect UFP concentrations. UFP particle counts were very sensitive to real-time traffic indicators at both the freeway and urban sites. Localized site-specific data on traffic and meteorology contributed to explaining variability in ambient concentrations. HV traffic influenced near-road air quality at the freeway site more so than at the urban site. The statistical models typically explained most of the observed variability but were relatively simple. The results here are site-specific and not generalizable, but they are illustrative that near-road air quality can be highly sensitive to localized real-time indicators of traffic and meteorology.
Frey HC
，Grieshop AP
，Khlystov A
，Bang JJ
，Rouphail N
，Guinness J
，Rodriguez D
，Fuentes M
，Saha P
，Brantley H
，Snyder M
，Tanvir S
，Ko K
，Noussi T
，Delavarrafiee M
，Singh S
... -
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Development and application of an aerosol screening model for size-resolved urban aerosols.
Predictive models of vehicular ultrafine particles less than 0.1 microm in diameter (UFPs*) and other urban pollutants with high spatial and temporal variation are useful and important in applications such as (1) decision support for infrastructure projects, emissions controls, and transportation-mode shifts; (2) the interpretation and enhancement of observations (e.g., source apportionment, extrapolation, interpolation, and gap-filling in space and time); and (3) the generation of spatially and temporally resolved exposure estimates where monitoring is unfeasible. The objective of the current study was to develop, test, and apply the Aerosol Screening Model (ASM), a new physically based vehicular UFP model for use in near-road environments. The ASM simulates hourly average outdoor concentrations of roadway-derived aerosols and gases. Its distinguishing features include user-specified spatial resolution; use of the Weather Research and Forecasting (WRF) meteorologic model for winds estimates; use of a database of more than 100,000 road segments in the Los Angeles, California, region, including freeway ramps and local streets; and extensive testing against more than 9000 hours of observed particle concentrations at 11 sites. After initialization of air parcels at an upwind boundary, the model solves for vehicle emissions, dispersion, coagulation, and deposition using a Lagrangian modeling framework. The Lagrangian parcel of air is subdivided vertically (into 11 levels) and in the crosswind direction (into 3 parcels). It has overall dimensions of 10 m (downwind), 300 m (vertically), and 2.1 km (crosswind). The simulation is typically started 4 km upwind from the receptor, that is, the location at which the exposure is to be estimated. As parcels approach the receptor, depending on the user-specified resolution, step size is decreased, and crosswind resolution is enhanced through subdivision of parcels in the crosswind direction. Hourly concentrations and size distributions of aerosols were simulated for 11 sites in the Los Angeles area with large variations in proximal traffic and particle number concentrations (ranging from 6000 to 41,000/cm3). Observed data were from the 2005-2007 Harbor Community Monitoring Study (HCMS; Moore et al. 2009), in Long Beach, California, and the Coronary Health and Air Pollution Study (CHAPS; Delfino et al. 2008), in the Los Angeles area. Meteorologic fields were extracted from 1-km-resolution meteorologic simulations, and observed wind direction and speed were incorporated. Using on-road and tunnel measurements, size-resolved emission factors ranging from 1.4 x 10(15) to 16 x 10(15) particles/kg fuel were developed specifically for the ASM. Four separate size-resolved emissions were used. Traffic and emission factors were separately estimated for heavy-duty diesel and light-duty vehicles (LDV), and both cruise and acceleration emission factors were used. The light-duty cruise size-resolved number emission factor had a single prominent mode at 12 nm. The diesel cruise size-resolved number emission factor was bimodal, with a large mode at 16 nm and a secondary mode at around 100 nm. Emitted particles were assumed to be nonvolatile. Data on traffic activity came from a 2008 travel-demand model, supplemented by data on diurnal patterns. Simulated ambient number size distributions and number concentrations were compared to observations taking into account estimated losses from particle transmission efficiency in instrument inlet tubing. The skill of the model in predicting number concentrations and size distributions was mixed, with some promising prediction features and some other areas in need of substantial improvement. For long-term (-15-day) average concentrations, the variability from site to site could be modeled with a coefficient of determination (r2) of 0.76. Model underprediction was more common than overprediction. The average of the absolute normalized bias was 0.30; in other words, long-term mean particle concentrations at each site were on average predicted to within 30% of the measured values. Observed 24-hour number concentrations were simulated to within a factor of 1.6 on 48% of days at HCMS sites and 81% at CHAPS sites, lower than the original design goal of 90%. Extensive evaluation of hourly concentrations, diurnal patterns, sizedistributions, and directional patterns was performed. At two sites with heavy freeway and heavy-duty-vehicle (HDV) influences and extensive size-resolved measurements, the ASM made significant errors in the diurnal pattern, concentration, and mode position of the aerosol size distribution. Observations indicated a shift in concentrations and size distributions corresponding to the afternoon development of offshore wind at the HCMS sites. The model did not reproduce the changes in particles associated with this wind shift and suffered from overprediction for particles of less than 15 nm and underprediction for particles of between 15 and 500 nm, raising doubt about the applicability of the HDV emission factors and the model's assumptions that particles were nonvolatile. The model's temporal prediction skill at individual monitoring sites was variable; the index of agreement (IOA) for hourly values at single sites ranged from 0.30 to 0.56. The model's ability to reproduce diurnal patterns in aerosol concentrations was site dependent; midday underprediction as well as underprediction for particle sizes greater than 15 nm were typical errors. Despite some problems in model skill, the number of time periods and locations evaluated as well as the extent of our qualitative and quantitative evaluations versus physical measurements well exceeded other published size-resolved modeling efforts. As a trial of a typical application, the sensitivity of the concentrations at each receptor site to LDV traffic, HDV traffic, and various road classes was evaluated. The sensitivity of overall particle numbers to all types of traffic ranged from 0.87 at the site with the heaviest traffic to 0.28 at the site with the lightest traffic, meaning that a 1% reduction in traffic could yield a reduction in particle number of 0.87% to 0.28%. Key conclusions and implications of the study are the following: 1. That variable-resolution (down to 10 m) modeling in a relatively simple framework is feasible and can support most of the applications mentioned above; 2. That model improvements will be required for some applications, especially in the areas of the HDV emission factor and the parameterization of meteorologic dispersion; 3. That particle loss from instrument transmission efficiency can be significant for particles smaller than 50 nm, and especially significant for particles smaller than 20 nm. In cases where loss corrections are not accounted for, or are inaccurate, this loss can cause disagreements in observation-model and observation-observation comparisons. 4. That LDV traffic exposures likely exceed HDV traffic exposures in some locations; 5. That variable step size and adaptive parcel width are critical to balancing computational efficiency and resolution; and 6. That the effects of roadways on air quality depend on both traffic volume and distance--in other words, low traffic volumes at close proximity need to be considered in health and planning studies just as much as do high traffic volumes at distances up to several kilometers. Future improvements to the model have been identified. They include improved emission factors; integration with the U.S. Environmental Protection Agency (EPA) Motor Vehicle Emission Simulator (MOVES) model; nesting with three-dimensional (3D) Eulerian models such as the Community Multi-scale Air Quality (CMAQ) model; increased emission dependence on acceleration, load, grade, and speed as well as evaporation and condensation of semivolatile aerosol species; and modeling of carbon dioxide (CO2) as an on-road and near-road dilution tracer. In addition, comparison with other statistically and physically based models would be highly beneficial.
Stanier CO
，Lee SR
，HEI Health Review Committee
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