Prakash Doraiswamy

Source Apportionment: Findings from the U.S. Supersites Program

Abstract Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different solutions to the chemical mass balance (CMB) receptor model equations and are implemented on available software. In their more general form, the CMB equations allow spatial, temporal, transport, and particle size profiles to be combined with chemical source profiles for improved source resolution. Although UNMIX and PMF do not use source profiles explicitly as input data, they still require measured profiles to justify their derived source factors. The U.S. Supersites Program provided advanced datasets to apply these CMB solutions in different urban areas. Still lacking are better characterization of source emissions, new methods to estimate profile changes between source and receptor, and systematic sensitivity tests of deviations from receptor model assumptions.

Advances in integrated and continuous measurements for particle mass and chemical composition.

Abstract Recent improvements in integrated and continuous PM2.5 mass and chemical measurements from the Supersite program and related studies in the past decade are summarized. Analytical capabilities of the measurement methods, including accuracy, precision, interferences, minimum detectable levels, comparability, and data completeness are documented. Upstream denuders followed by filter packs in integrated samplers allow an estimation of sampling artifacts. Efforts are needed to: (1) address positive and negative artifacts for organic carbon (OC), and (2) develop carbon standards to better separate organic versus elemental carbon (EC) under different temperature settings and analysis atmospheres. Advances in thermal desorption followed by gas chromatography/mass spectrometry (GC/MS) provide organic speciation of approximately 130 nonpolar compounds (e.g., n-alkanes, alkenes, hopanes, steranes, and polycyclic aromatic hydrocarbons [PAHs]) using small portions of filters from existing integrated samples. Speciation of water-soluble OC (WSOC) using ion chromatography (IC)-based instruments can replace labor-intensive solvent extraction for many compounds used as source markers. Thermal gas-based continuous nitrate and sulfate measurements underestimate filter ions by 10–50% and require calibration against on-site filter-based measurements. IC-based instruments provide multiple ions and report comparable (±10%) results to filter-based measurements. Maintaining a greater than 80% data capture rate in continuous instruments is labor intensive and requires experienced operators. Several instruments quantify black carbon (BC) by optical or photoacoustic methods, or EC by thermal methods. A few instruments provide real-time OC, EC, and organic speciation. BC and EC concentrations from continuous instruments are highly correlated but the concentrations differ by a factor of two or more. Site- and season-specific mass absorption efficiencies are needed to convert light absorption to BC. Particle mass spectrometers, although semiquantitative, provide much information on particle size and composition related to formation, growth, and characteristics over short averaging times. Efforts are made to quantify mass by collocating with other particle sizing instruments. Common parameters should be identified and consistent approaches are needed to establish comparability among measurements.

Comparison of continuous and filter-based carbon measurements at the Fresno supersite.

Abstract Results from six continuous and semicontinuous black carbon (BC) and elemental carbon (EC) measurement methods are compared for ambient samples collected from December 2003 through November 2004 at the Fresno Supersite in California. Instruments included a multi-angle absorption photometer (MAAP; λ = 670 nm); a dual-wavelength (λ = 370 and 880 nm) aethalometer; seven-color (λ = 370, 470, 520, 590, 660, 880, and 950 nm) aethalometers; the Sunset Laboratory carbon aerosol analysis field instrument; a photoacoustic light absorption analyzer (λ = 1047 nm); and the R&P 5400 ambient carbon particulate monitor. All of these acquired BC or EC measurements over periods of 1 min to 1 hr. Twenty-four-hour integrated filter samples were also acquired and analyzed by the Interagency Monitoring of Protected Visual Environments (IMPROVE) thermal/optical reflectance carbon analysis protocol. Site-specific mass absorption efficiencies estimated by comparing light absorption with IMPROVE EC concentrations were 5.5 m2/g for the MAAP, 10 m2/g for the aethalometer at a wavelength of 880 nm, and 2.3 m2/g for the photoacoustic analyzer; these differed from the default efficiencies of 6.5, 16.6, and 5 m2/g, respectively. Scaling absorption by inverse wavelength did not provide equivalent light absorption coefficients among the instruments for the Fresno aerosol measurements. Ratios of light absorption at 370 nm to those at 880 nm from the aethalometer were nearly twice as high in winter as in summer. This is consistent with wintertime contributions from vehicle exhaust and from residential wood combustion, which is believed to absorb more shorter-wavelength light. To reconcile BC and EC measurements obtained by different methods, a better understanding is needed of the wavelength dependence of light-absorption and mass-absorption efficiencies and how they vary with different aerosol composition.

A Retrospective Comparison of Model-Based Forecasted PM2.5 Concentrations with Measurements

Abstract This study presents an assessment of the performance of the Community Multiscale Air Quality (CMAQ) photochemical model in forecasting daily PM2.5 (particulate matter ≤2.5 µm in aerodynamic diameter) mass concentrations over most of the eastern United States for a 2-yr period from June 14, 2006 to June 13, 2008. Model predictions were compared with filter-based and continuous measurements of PM2.5 mass and species on a seasonal and regional basis. Results indicate an underprediction of PM2.5 mass in spring and summer, resulting from under-predictions in sulfate and total carbon concentrations. During winter, the model overpredicted mass concentrations, mostly at the urban sites in the northeastern United States because of overpredictions in unspeciated PM2.5 (suggesting possible overestimation of primary emissions) and sulfate. A comparison of observed and predicted diurnal profiles of PM2.5 mass at five sites in the domain showed significant discrepancies. Sulfate diurnal profiles agreed in shape across three sites in the southern portion of the domain but differed at two sites in the northern portion of the domain. Predicted organic carbon (OC) profiles were similar in shape to mass, suggesting that discrepancies in mass profiles probably resulted from the underprediction in OC. The diurnal profiles at a highly urbanized site in New York City suggested that the over-predictions at that site might be resulting from overpredictions during the morning and evening hours, displayed as sharp peaks in predicted profiles. An examination of the predicted planetary boundary layer (PBL) heights also showed possible issues in the modeling of PBL.

EFFECT OF COUNTY-LEVEL INCOME ON VEHICLE AGE DISTRIBUTION AND EMISSIONS

In conjunction with a statewide emissions inventory of on-road mobile sources in Tennessee, a county-by-county analysis of vehicle registration data was performed. Several interesting trends were observed in the kinds and ages of vehicles driven in Tennessee counties compared with national statistics and compared with the average personal income of county residents. In particular, median vehicle age correlated strongly with average personal income for each county. Vehicle fleets were oldest in lowestincome counties and newest in the highest-income county; median vehicle age was 10.8 years in the former and only 5.9 years in the latter. This difference in vehicle age results in average mobile-source emissions factors 63% higher for nitrogen oxides, 73% higher for carbon monoxide, and 104% higher for volatile organic compounds in the lowest-income counties than in the highest-income counties, based on the MOBILE6 emissions model run for calendar year 2000. The low-income counties also registered 76% more light-duty trucks per capita than the national average, and these trucks were 5 years older than the national median age. It is concluded that county-level personal income is a good predictor of vehicle age and can be used as a readily obtainable indication of whether local vehicle registration data should be used to improve the accuracy of emissions inventories (instead of national defaults or statewide averages). County-level personal income also can be used as a basis for determining whether more than one vehicle age distribution should be used for modeling mobile-source emissions within a state, a metropolitan area, or an airshed.

Air Quality Measurements Inside Diesel Truck Cabs During Long-Term Idling

The overall objective of this study was to measure the air pollutant concentrations inside and outside of diesel truck cabs under conditions of extended idling at a truck stop. The measurements were conducted under different modes of engine and air conditioner operation at different times of day and night. One-hour average concentrations of fine particulate matter, nitrogen oxides, and carbon monoxide were measured. All trucks showed some level of self-contamination of in-cab air quality from engine emissions during idling. Some trucks showed significantly higher in-cab concentrations than outside concentrations, indicating engine compartment leaks into the cab. Other trucks showed in-cab concentrations similar to or even lower than outside concentrations but showed higher outside and in-cab concentrations during engine idling than when the engine was turned off. In these cases, truck emissions raised the outside concentrations, which then migrated into the truck cab. Carbon monoxide concentrations measured in the cab were insignificant compared with relevant air quality standards, but fine particulate matter and nitrogen dioxide concentrations were higher than some U.S. Environmental Protection Agency ambient air quality standards. None of the measurements was higher than Occupational Safety and Health Administration or National Institute for Occupational Safety and Health air quality standards.

Characteristics and emissions of heavy-duty vehicles in Tennessee under the MOBILE6 model

Heavy-duty vehicle (HDV) classifications used for modeling emissions in the MOBILE6 model have been expanded from 2 classifications in MOBILE5 to 16 classifications in MOBILE6. The new classifications are based on vehicle weight and fuel used (i.e., gasoline or diesel). The heavier vehicles have higher emissions, so it is important to use correct vehicle weight distributions. Tennessee’s HDV registration data show a distribution very similar to the national defaults, but with more vehicles in the heaviest weight category (HDV8B). More than 50% of Tennessee’s HDVs fall in the lightest vehicle category (HDV2B). The biggest difference in truck characteristics in Tennessee versus national defaults in MOBILE6 is the higher HDV fraction on Tennessee rural Interstates. Also, the ratio of single-unit trucks to trailer trucks varies considerably by facility type. The emissions of volatile organic compounds and carbon monoxide per mile of travel of gasoline-fueled single-unit trucks can be 2.5 to 5 times higher than those of heavy-duty diesel trailer trucks. The emissions of nitrogen oxides per mile of travel of diesel-fueled tractor–trailer trucks can be five times higher than those of gasoline-fueled single-unit trucks. For these reasons it is important to accurately characterize the HDV fleet. The characteristics of the Tennessee HDV fleet are compared with national defaults used in MOBILE6, and a new scheme for classifying vehicles by road type is presented.

CORRECTIONS TO MILEAGE ACCUMULATION RATES FOR OLDER VEHICLES AND THE EFFECT ON AIR POLLUTION EMISSIONS

The MOBILE6 emissions model, currently under development, uses mileage accumulation rates that assume that the average 25-year-old car has been driven more than 337 962 km (210,000 mi), and that the average 25-year-old pickup truck has been driven more than 402 336 km (250,000 mi). Cumulative mileage is used in the model to calculate emission factors that increase with mileage due to air pollution control device “deterioration.” Odometer readings taken in previous studies by the Environmental Protection Agency and additional data indicate that average mileage accumulations may be much less [i.e., 201 168 km (125,000 mi)] than those used in the MOBILE6 model. This has the effect of overestimating emissions from older vehicles. A simple model is presented that accounts for scrappage of older vehicles as a function of cumulative mileage. This model predicts average cumulative mileage that is much closer to actual odometer readings (taken in Nashville, Tennessee) than the default values used in MOBILE6. The use of more accurate cumulative mileage values for older vehicles should provide improvements to the estimation of emissions from the vehicle fleet.

Source Apportionment of Fine Particles in Tennessee Using a Source-Oriented Model

Abstract Source apportionment of fine particles (PM2.5, particulate matter < 2 µm in aerodynamic diameter) is important to identify the source categories that are responsible for the concentrations observed at a particular receptor. Although receptor models have been used to do source apportionment, they do not fully take into account the chemical reactions (including photochemical reactions) involved in the formation of secondary fine particles. Secondary fine particles are formed from photochemical and other reactions involving precursor gases, such as sulfur dioxide, oxides of nitrogen, ammonia, and volatile organic compounds. This paper presents the results of modeling work aimed at developing a source apportionment of primary and secondary PM2.5. On-road mobile source and point source inventories for the state of Tennessee were estimated and compiled. The national emissions inventory for the year 1999 was used for the other states. U.S. Environmental Protection Agency Models3/ Community Multi-Scale Air Quality modeling system was used for the photochemical/secondary particulate matter modeling. The modeling domain consisted of a nested 36–12–4-km domain. The 4-km domain covered the entire state of Tennessee. The episode chosen for the modeling runs was August 29 to September 9, 1999. This paper presents the approach used and the results from the modeling and attempts to quantify the contribution of major source categories, such as the on-road mobile sources (including the fugitive dust component) and coal-fired power plants, to observed PM2.5 concentrations in Tennessee. The results of this work will be helpful in policy issues targeted at designing control strategies to meet the PM2.5 National Ambient Air Quality Standards in Tennessee.

Meteorology, Emissions, and Grid Resolution: Effects on Discrete and Probabilistic Model Performance

In this study, we analyze the impacts of perturbations in meteorology and emissions and variations in grid resolution on air quality forecast simulations. The meteorological perturbations considered in this study introduce a typical variability of ∼1 °C, 250–500 m, 1 m/s, and 15–30° for temperature, PBL height, wind speed, and wind direction, respectively. The effects of grid resolution are typically smaller and more localized. Results of the air quality simulations show that the perturbations in meteorology tend to have a larger impact on pollutant concentrations than emission perturbations and grid resolution effects. Operational model evaluation results show that the meteorological and grid resolution ensembles impact a wider range of model performance metrics than emission perturbations. Probabilistic model performance was found to vary with exceedance thresholds. The results of this study suggest that meteorological perturbations introduced through ensemble weather forecasts are the most important factor in constructing a model-based O3 and PM2.5 ensemble forecasting system.