George Pouliot

Model Representation of Secondary Organic Aerosol in CMAQv4.7

Numerous scientific upgrades to the representation of secondary organic aerosol (SOA) are incorporated into the Community Multiscale Air Quality (CMAQ) modeling system. Additions include several recently identified SOA precursors: benzene, isoprene, and sesquiterpenes; and pathways: in-cloud oxidation of glyoxal and methylglyoxal, particle-phase oligomerization, and acid enhancement of isoprene SOA. NO(x)-dependent aromatic SOA yields are also added along with new empirical measurements of the enthalpies of vaporization and organic mass-to-carbon ratios. For the first time, these SOA precursors, pathways and empirical parameters are included simultaneously in an air quality model for an annual simulation spanning the continental U.S. Comparisons of CMAQ-modeled secondary organic carbon (OC(sec)) with semiempirical estimates screened from 165 routine monitoring sites across the U.S. indicate the new SOA module substantially improves model performance. The most notable improvement occurs in the central and southeastern U.S. where the regionally averaged temporal correlations (r) between modeled and semiempirical OC(sec) increase from 0.5 to 0.8 and 0.3 to 0.8, respectively, when the new SOA module is employed. Wintertime OC(sec) results improve in all regions of the continental U.S. and the seasonal and regional patterns of biogenic SOA are better represented.

To what extent can biogenic SOA be controlled

The implicit assumption that biogenic secondary organic aerosol (SOA) is natural and can not be controlled hinders effective air quality management. Anthropogenic pollution facilitates transformation of naturally emitted volatile organic compounds (VOCs) to the particle phase, enhancing the ambient concentrations of biogenic secondary organic aerosol (SOA). It is therefore conceivable that some portion of ambient biogenic SOA can be removed by controlling emissions of anthropogenic pollutants. Direct measurement of the controllable fraction of biogenic SOA is not possible, but can be estimated through 3-dimensional photochemical air quality modeling. To examine this in detail, 22 CMAQ model simulations were conducted over the continental U.S. (August 15 to September 4, 2003). The relative contributions of five emitted pollution classes (i.e., NO(x), NH(3), SO(x), reactive non methane carbon (RNMC) and primary carbonaceous particulate matter (PCM)) on biogenic SOA were estimated by removing anthropogenic emissions of these pollutants, one at a time and all together. Model results demonstrate a strong influence of anthropogenic emissions on predicted biogenic SOA concentrations, suggesting more than 50% of biogenic SOA in the eastern U.S. can be controlled. Because biogenic SOA is substantially enhanced by controllable emissions, classification of SOA as biogenic or anthropogenic based solely on VOC origin is not sufficient to describe the controllable fraction.

Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) Modeling System to Build a National Air Quality Forecasting System

Abstract NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O3) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O3 for the northeastern United States at 12-km horizontal grid spacing. Preoperationa...

Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States

Significance Secondary organic aerosol (SOA) formed from the atmospheric oxidation of gaseous combustion emissions is an important component of global fine-particle pollution, which influences the Earth’s energy budget and affects human health. However, existing models underpredict the amount of SOA measured in laboratory experiments and in the atmosphere. We analyze smog chamber and emissions data to demonstrate that unspeciated organics in combustion emissions are a major class of SOA precursors. We develop source-specific parameterizations for these emissions using surrogate chemical compounds. We find that unspeciated organics dominate SOA mass formed from combustion emissions in the United States; therefore, unspeciated organics must be included in models to simulate ambient fine particulate matter concentrations. Secondary organic aerosol (SOA) formed from the atmospheric oxidation of nonmethane organic gases (NMOG) is a major contributor to atmospheric aerosol mass. Emissions and smog chamber experiments were performed to investigate SOA formation from gasoline vehicles, diesel vehicles, and biomass burning. About 10–20% of NMOG emissions from these major combustion sources are not routinely speciated and therefore are currently misclassified in emission inventories and chemical transport models. The smog chamber data demonstrate that this misclassification biases model predictions of SOA production low because the unspeciated NMOG produce more SOA per unit mass than the speciated NMOG. We present new source-specific SOA yield parameterizations for these unspeciated emissions. These parameterizations and associated source profiles are designed for implementation in chemical transport models. Box model calculations using these new parameterizations predict that NMOG emissions from the top six combustion sources form 0.7 Tg y−1 of first-generation SOA in the United States, almost 90% of which is from biomass burning and gasoline vehicles. About 85% of this SOA comes from unspeciated NMOG, demonstrating that chemical transport models need improved treatment of combustion emissions to accurately predict ambient SOA concentrations.

The development and uses of EPA’s SPECIATE database

SPECIATE is the U.S. Environmental Protection Agency’s (EPA) repository of volatile organic compounds (VOCs) and particulate matter (PM) speciation profiles of air pollution sources. These source profiles can be used to (1) provide input to chemical mass balance (CMB) receptor models; (2) verify profiles derived from ambient measurements by multivariate receptor models (e.g., factor analysis and positive matrix factorization); (3) interpret ambient measurement data; and (4) create speciated emission inventories for regional haze, climate, and photochemical air quality modeling. This paper describes the SPECIATE v4.2 database, provides specific examples of its use, and makes recommendations for future improvements.

Modeling the role of alkanes, polycyclic aromatic hydrocarbons, and their oligomers in secondary organic aerosol formation.

A computationally efficient method to treat secondary organic aerosol (SOA) from various length and structure alkanes as well as SOA from polycyclic aromatic hydrocarbons (PAHs) is implemented in the Community Multiscale Air Quality (CMAQ) model to predict aerosol concentrations over the United States. Oxidation of alkanes is predicted to produce more aerosol than oxidation of PAHs driven by relatively higher alkane emissions. SOA from alkanes and PAHs, although small in magnitude, can be a substantial fraction of the SOA from anthropogenic hydrocarbons, particularly in winter, and could contribute more if emission inventories lack intermediate volatility alkanes (>C(13)) or if the vehicle fleet shifts toward diesel-powered vehicles. The SOA produced from oxidation of alkanes correlates well with ozone and odd oxygen in many locations, but the lower correlation of anthropogenic oligomers with odd oxygen indicates that models may need additional photochemically dependent pathways to low-volatility SOA.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency’s (EPA) Office of Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O3 mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NOx (NO + NO2), VOC and SOx (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.

Assessing Satellite-Based Fire Data for use in the National Emissions Inventory

Biomass burning is significant to emission estimates because: (1) it is a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite.

Development of a biomass burning emissions inventory by combining satellite and ground-based information

A 2005 biomass burning (wildfire, prescribed, and agricultural) emission inventory has been developed for the contiguous United States using a newly developed simplified method of combining information from multiple sources for use in the US EPAs National Emission Inventory (NEI). Our method blends the temporal and spatial resolution of the remote sensing information with the ground based fire size estimate. This method is faster and considerably less expensive than the method used for the 2002 National Emissions Inventory and is more accurate than methods used for 2001 and prior years. In addition, the 2005 fire inventory is the first EPA inventory utilizing remote sensing information. A comparison with the 2002 inventory for wildfire, prescribed, and agricultural fires indicates a large year-to-year variability in wildfire emissions and less variation for prescribed and agricultural fires. Total PM2.5 emissions from wildfires, prescribed burning, and agricultural burning for the contiguous United States were estimated to be 109,000 short tons, 209,000 short tons, and 232,000 short tons, respectively, for 2005. Our total emission estimate for 2005 is 550,000 short tons. Our analysis shows that year-to-year spatial variability accounts for the substantial difference in the wildfire emission estimates.

Predicting the effects of nanoscale cerium additives in diesel fuel on regional-scale air quality.

Diesel vehicles are a major source of air pollutant emissions. Fuel additives containing nanoparticulate cerium (nCe) are currently being used in some diesel vehicles to improve fuel efficiency. These fuel additives also reduce fine particulate matter (PM2.5) emissions and alter the emissions of carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbon (HC) species, including several hazardous air pollutants (HAPs). To predict their net effect on regional air quality, we review the emissions literature and develop a multipollutant inventory for a hypothetical scenario in which nCe additives are used in all on-road and nonroad diesel vehicles. We apply the Community Multiscale Air Quality (CMAQ) model to a domain covering the eastern U.S. for a summer and a winter period. Model calculations suggest modest decreases of average PM2.5 concentrations and relatively larger decreases in particulate elemental carbon. The nCe additives also have an effect on 8 h maximum ozone in summer. Variable effects on HAPs are predicted. The total U.S. emissions of fine-particulate cerium are estimated to increase 25-fold and result in elevated levels of airborne cerium (up to 22 ng/m3), which might adversely impact human health and the environment.