2010 Progress Report: Changes in Climate, Pollutant Emissions, and US Air Quality: An Integrating Modeling Study

EPA Grant Number: R833374
Title: Changes in Climate, Pollutant Emissions, and US Air Quality: An Integrating Modeling Study
Investigators: Adams, Peter , Pandis, Spyros N.
Institution: Carnegie Mellon University
EPA Project Officer: Ilacqua, Vito
Project Period: March 1, 2007 through February 28, 2011 (Extended to February 28, 2012)
Project Period Covered by this Report: March 1, 2010 through February 28,2011
Project Amount: $896,596
RFA: Consequences of Global Change For Air Quality (2006) RFA Text |  Recipients Lists
Research Category: Global Climate Change , Climate Change , Air

Objective:

The goals of the project remain the same as in the proposed work. During this past year, much of the work related to Tasks 2-4 of the original proposal, namely the extension of the GRE-CAPS (Global-Regional Climate Air Pollution Modeling System) model to include a state-of-the-art treatment of organic PM (using the volatility basis set), ultrafine particles, and atmospheric mercury has been completed and papers submitted for publication. Emphasis now is on estimating the impact of climate change on OA, particle microphysics, and mercury. It is expected that there will be sufficient time to complete these tasks before the end of the project in February, 2012.

Progress Summary:

5.1 Development of OA Models

Development of global and regional OA models has largely been completed, and the scientific details have been documented in earlier reports. Here, we provide a brief summary of the approach and updates on the publication status of the related papers.

5.1.1 Global SOA

As described in more detail in our previous report, a new treatment of organic aerosol (OA) has been implemented on the global scale. This treatment uses the “volatility basis set” approach (VBS) of Donahue et al. [2006] to represent the volatility distribution and gas-particle phase partitioning of OA. The manuscript describing the treatment of secondary organic aerosol (SOA) [Farina et al., 2010] was published in Atmospheric Chemistry and Physics in May, 2010, and a copy is attached with this report.

5.1.2 Global POA

The new treatment for primary OA (POA) builds on the work just noted [Farina et al., 2010] for secondary OA (SOA) from “traditional” biogenic and anthropogenic sources. The POA-related updates include the following major revisions to the treatment of POA: POA is treated as semi-volatile, intermediate volatility organic compounds (IVOCs) are also emitted from primary sources, semi-volatile POA and IVOCs undergo oxidative aging to form “nontraditional” SOA. The manuscript describing this work was submitted to Atmospheric Chemistry and Physics in January, 2011 and is attached with this report [Jathar et al., 2011].

5.1.3 Regional OA

Our previous report described a regional-scale budget approach used to compare OA mass concentrations predicted by the regional chemical transport model, PMCAMx, to STN and IMPROVE data sets. A major conclusion of that work was that an OA formation rate of 22 +/- 5 ktons per day was consistent with observed OA levels in that region. This work was submitted to Journal of Geophysical Research in April 2010 and published in December, 2010.

Our results (see below, Section 5.3) indicate that the major impact of temperature changes on the atmospheric OA cycle is “indirectly” via changes in biogenic emissions rather than “direct” changes in gas-aerosol partitioning or T-dependent oxidation rates. Therefore, we are in the process of updating the representation of biogenic emissions in PMCAMx, the regional component of the GRE-CAPS modelling system.

Two popular models exist for estimating biogenic emissions for input to chemical transport models in the U.S.; the Biogenic Emissions Inventory System (BEISv3.13), which is currently providing inputs to PMCAMx-2008, and the Model of Emissions of Gases and Aerosols from Nature (MEGAN, v2.04). Both models generate net terrestrial biosphere emissions with a 1-km spatial resolution, but deviate in several ways. MEGAN includes biological, physical and chemical emissions driving variables, and was developed by Guenther et al. [2006] to replace both previous global emission models by the same group and the BEIS family of regional emission models. Guenther et al. [2006] reports that, compared to BEIS, MEGAN incorporates updated emission factors and land cover data, more controlling variables over the emissions, and explicit sesquiterpene emissions. MEGANv2.04 is chosen for dynamic emissions generation in this work based on its more extensive and current inventories.

MEGAN generates gridded, hourly biogenic emissions estimates based on:

 

[Emissions] = [ε][γ][ρ]                                                                                                      Equation 1

 

where the emissions are in mg (m-2 earth surface) h-1, ε is the emission factor of the compound into the canopy at standard conditions, γ is the emission activity factor, and ρ accounts for production and loss within plant canopies.

The emission factor, ε, has units of mg m-2 h-1 and is dependent on plant type. The surface of each grid cell is divided into different plant functional types and non-vegetated surfaces. There are currently four plant types in use: broadleaf trees, needle leaf trees, shrub-bush, and “other.” Classifications are applied based on high-resolution geographically referenced tree inventories, and values are assigned based on enclosure studies.

The emission activity factor, γ, is a normalized ratio that accounts for deviations from standard canopy and weather conditions. The emission rate is also dependent upon ρ, a normalized ratio representing production and loss within a canopy. This includes molecules consumed by biological, chemical, and physical processes on soil and vegetation surfaces. Molecules that react within the canopy atmosphere are also included.

MEGAN tracks emission rates for 138 chemical species, which can be mapped to several gas and aerosol chemistry mechanisms. PMCAMx uses the SAPRC99 gas-phase chemistry mechanism, which estimates the reactivities of explicit species and lumped chemical classifications. Several individual species from MEGAN such as isoprene, methanol, acetone and others are explicitly represented in SAPRC99, but MEGAN includes many more biogenic species than SAPRC99. In this case, several related species are combined into lumped SAPRC99 classifications. Notable examples of this include the monoterpene class, which is lumped from myrcene, sabinene, limonene, carene, β-ocimene, α- and β-pinene, and others, and the sesquiterpene class, which is a combination of β-caryophyllene, α-farnesene, and others.

The process of generating a biogenic emissions inventory involves several steps. Meteorology files in the form of the MM5 (Mesoscale Model 5) [Grell et al., 1994] regional meteorological model are converted and input to MEGAN. Emission factor and plant functional type data are taken from the inventory publicly available on the NCAR community data portal (cdp.ucar.edu). The MEGAN output is then converted into PMCAMx-ready emissions inventory format. Streamlining the connectivity of various MEGAN subroutines with both the host computer and PMCAMx was a significant task, but the task is at a stage where only minor debugging remains.

Meteorology is integral to the emissions generation process. MEGAN uses MM5 output; this is generated by the Global-Regional Climate-Air Pollution modeling System (GRECAPS) developed by Dawson et al. [2008]. This system links the Goddard Institute for Space Studies general circulation model II’ (GISS GCM II’) to MM5 and PMCAMx. Global-scale meteorology and species concentrations are generated on a 4º x 5º grid by GISS II’; GRECAPS downscales and realigns these data to the 36 x 36 km PMCAMx grid. “Present” (1990s) and future (2050s) datasets, both ten years in duration, will be used. The present meteorology is based on work described in Racherla and Adams [2006] and its use with GRE-CAPS was shown by Dawson et al. [2008] to perform well against other models and measurements. The resulting MM5 meteorology will be passed through MEGAN to obtain biogenic emissions, which are finally input to PMCAMx.

5.2 Development of Ultrafine Model

Recent efforts have focused on making the global aerosol microphysics model faster and more accurate, especially for studies of atmospheric new particle formation. The following sections document work done to develop the “Fast-TOMAS” algorithms and to investigate how much of the nucleation process can be parameterized (versus represented explicitly) in global models.

5.2.1 Fast TOMAS

The development of a numerically faster and more efficient version of TOMAS by reducing the number of size sections (described in the previous annual report, Section 5.3.1) has been completed. A manuscript is under preparation for submission to Aerosol, Science and Technology.

5.2.2 Nucleation Model Evaluation

Earlier in the project, we have evaluated the amount of detailed required for an accurate numerical simulation of nucleation mode dynamics in a global aerosol microphysics model (Section 5.3.2 of the previous year’s report). A manuscript describing this work is in preparation.

Moving beyond this numerical assessment, we have been evaluating the nucleation and growth events predicted by GEOS-Chem-TOMAS versus observations at five sites: Pittsburgh, St. Louis, Atlanta, Hyytiälä, and the Po Valley. Metrics used to compare model to observations include J3 (formation rate of 3 nm clusters due to nucleation), GR (growth rate of nucleated particles due to condensation), SP50 and SP100 (survival probability to 50 and 100 nm, respectively), J50 and J100 (formation rate of 50 and 100 nm particles due to nucleation and growth), and the contribution of single-day nucleation and growth events to CCN concentrations. At each site, we compare daily statistics for these quantities between the model and observations.

A summary of these comparisons is shown in Figure 1, which shows the concentrations of CN50 and CN100 (particles larger than 50 and 100 nm, surrogates for CCN) attributable to single-day nucleation and growth events in model and observations at the five sites. Due to the nature of the observations, it should be stressed that these results only account for nucleated particles that grow to the designated size within the same day; accounting for growth of nucleated particles on later days would increase these values but is difficult to do in the observations due to changes in air masses. The red percentage values show, for the observations, what fraction of the total CN50 and CN100 can be attributed to single-day nucleation and growth events. For CN100, the contribution is always small (less than 3%) because it is rare for particles to grow to 100 nm on the same day. Results for CN50 are more variable; single-day nucleation and growth makes a small contribution at Pittsburgh, St. Louis, and the Po Valley but is significant for Atlanta and Hyytiälä. Comparing model to observations, we see that, using two different nucleation schemes (ternary and activation), annually averaged CN50 and CN100 are reasonably represented by the model, which tends to overpredict slightly the number of particles from nucleation.

Figure 1: Panel A shows the annual-average number concentrations of particles 50 nm and larger in diameter (CN50) attributable to new particle formation events and growth within the same day. Panel B shows the same for number concentrations of particles 100 nm and larger in diameter (CN100). Number concentrations are in units of cm-3. Ternary and activation refer to model-predicted results using the corresponding nucleation parameterization. Red numbers indicate the percentage of nucleation-produced CCN compared to CCN from all sources (measurements only).

5.3 Air Quality and Climate Change Assessment

A manuscript is now published discussing the sensitivity of OA to increased temperatures [Day and Pandis, 2011]. This work goes beyond that published in our previous project [Dawson et al., 2007] by using the updated OA model that uses the volatility basis set framework. Changes in OA mass concentrations due to temperature alone (holding biogenic emissions constant) are modest, typically less than 1% per K. However, allowing biogenic emissions to increase with temperature, results in larger changes: increases of around 4% per K in OA mass concentrations. This work suggests, therefore, that changes in biogenic emissions will be the most important aspect of climate change for OA.

We have preliminary results for the impact of climate change on biogenic emissions based on the implementation of MEGAN described above (Section 5.1.3). Figure 2 shows the comparison of the MEGANv2.04 emissions for a year in the 1990s and a year in the 2050s. The 2050s scenario has emissions that are 7.6% higher than the 1990s scenario on average. These are preliminary results based on two sample years; additional simulations will be performed to get an ensemble of present and future emissions. The updated emissions will then feed into an assessment of their impacts on future OA concentrations. This will be a significant improvement over the work of Dawson et al. (2009) because of the improved simulation of biogenic emissions and organic aerosol concentrations.

Figure 2: Average summer-time isoprene emissions in kmol/hr for (a) the MEGANv4.02 model in the 1990s; and (b) the MEGANv4.02 model in the 2050s.

Future Activities:

In the final year of the project (March 2011 through February 2012), we are working to complete the assessments of the sensitivity of organic aerosol, mercury, and ultrafine particles to climate change (Task 5 of the original proposal).


Journal Articles on this Report : 11 Displayed | Download in RIS Format

Other project views: All 29 publications 21 publications in selected types All 21 journal articles
Type Citation Project Document Sources
Journal Article Dawson JP, Adams PJ, Pandis SN. Sensitivity of PM2.5 to climate in the Eastern US: a modeling case study. Atmospheric Chemistry and Physics 2007;7(16):4295-4309. R833374 (2007)
R833374 (2008)
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R830961 (Final)
  • Full-text: Atmospheric Chemistry and Physics-Full Text PDF
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  • Abstract: Atmospheric Chemistry and Physics-Abstract
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  • Journal Article Dawson JP, Racherla PN, Lynn BH, Adams PJ, Pandis SN. Simulating present-day and future air quality as climate changes: model evaluation. Atmospheric Environment 2008;42(19):4551-4566. R833374 (2007)
    R833374 (2008)
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  • Journal Article Day MC, Pandis SN. Predicted changes in summertime organic aerosol concentrations due to increased temperatures. Atmospheric Environment 2011;45(36):6546-6556. R833374 (2007)
    R833374 (2008)
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  • Journal Article Farina SC, Adams PJ, Pandis SN. Modeling global secondary organic aerosol formation and processing with the volatility basis set: implications for anthropogenic secondary organic aerosol. Journal of Geophysical Research–Atmospheres 2010;115(D9):D09202. R833374 (2007)
    R833374 (2008)
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  • Full-text: Wiley-Full Text HTML
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  • Journal Article Jathar SH, Farina SC, Robinson AL, Adams PJ. The influence of semi-volatile and reactive primary emissions on the abundance and properties of global organic aerosol. Atmospheric Chemistry and Physics 2011;11(15):7727-7746. R833374 (2007)
    R833374 (2008)
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    R833748 (2010)
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  • Journal Article Jung JG, Pandis SN, Adams PJ. Evaluation of nucleation theories in a sulfur-rich environment. Aerosol Science and Technology 2008;42(7):495-504. R833374 (2007)
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  • Full-text: AS&T-Full Text PDF
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  • Journal Article Jung J, Fountoukis C, Adams PJ, Pandis SN. Simulation of in situ ultrafine particle formation in the eastern United States using PMCAMx-UF. Journal of Geophysical Research–Atmospheres 2010;115(D3):D03203 (13 pp.). R833374 (2007)
    R833374 (2008)
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  • Journal Article Lane TE, Donahue NM, Pandis SN. Effect of NOx on secondary organic aerosol concentrations. Environmental Science & Technology 2008;42(16):6022-6027. R833374 (2007)
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  • Journal Article Lane TE, Donahue NM, Pandis SN. Simulating secondary organic aerosol formation using the volatility basis-set approach in a chemical transport model. Atmospheric Environment 2008;42(32):7439-7451. R833374 (2007)
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  • Journal Article Lee YH, Adams PJ. Evaluation of aerosol distributions in the GISS-TOMAS global aerosol microphysics model with remote sensing observations. Atmospheric Chemistry and Physics 2010;10(5):2129-2144. R833374 (2007)
    R833374 (2008)
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  • Full-text: Atmospheric Chemistry and Physics-Full Text PDF
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  • Journal Article Racherla PN, Adams PJ. The response of surface ozone to climate change over the Eastern United States. Atmospheric Chemistry and Physics 2008;8(4):871-885. R833374 (2007)
    R833374 (2008)
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    R830961 (Final)
  • Full-text: Atmospheric Chemistry and Physics-Full Text PDF
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  • Supplemental Keywords:

    RFA, Scientific Discipline, Air, climate change, Air Pollution Effects, Environmental Monitoring, Atmospheric Sciences, Ecological Risk Assessment, Atmosphere, air quality modeling, particulate matter, atmospheric models

    Progress and Final Reports:

    Original Abstract
  • 2007 Progress Report
  • 2008 Progress Report
  • 2009
  • Final Report