2013 Progress Report: Improved Prediction of the Vertical Profile of Atmospheric Black Carbon: Development and Evaluation of WRF-CMAQ

EPA Grant Number: R835041
Title: Improved Prediction of the Vertical Profile of Atmospheric Black Carbon: Development and Evaluation of WRF-CMAQ
Investigators: Carlton, Annmarie
Institution: Rutgers, The State University of New Jersey
EPA Project Officer: Hunt, Sherri
Project Period: September 1, 2011 through August 31, 2014 (Extended to February 29, 2016)
Project Period Covered by this Report: September 1, 2012 through August 31,2013
Project Amount: $449,916
RFA: Black Carbon's Role In Global To Local Scale Climate And Air Quality (2010) RFA Text |  Recipients Lists
Research Category: Global Climate Change , Climate Change , Air


To improve the model predictions of the vertical profile of particulate carbon through better representation of condensed phase organic chemistry in the 3-dimensional photochemical model "CMAQ."

Progress Summary:

The project goals for the second year were to test a new solver approach for CMAQ’s aqueous cloud chemistry in a box model and develop mass yields for secondary organic aerosol (SOA) production. The goals also include testing the sensitivity of CMAQ particulate matter (PM) predictions to different emissions inputs with and without aqueous chemistry.  We have a Kinetic Pre Processor (KPP) version of an expanded aqueous phase chemical mechanism working in a box model.  We have expanded the mechanism to include other SOA precursors (e.g., glycoaldehyde and other water-soluble species in the SAPRC07 chemical mechanism), and additionally photo Fenton chemistry.  Te determine which organic compounds are most critical and likely to influence CMAQ predictions we calculated a ‘solubility index’ that normalizes all organic species by their solubility and potential to form SOA (see Figure 1).  We added the photo Fenton chemistry in response to a recent Science paper (Harris et al., Science, 2013) that indicates transition metals can dominate oxidant production in clouds globally.

We have tested the new explicit chemistry against CMAQ’s yield-based approach for SOA in hundreds of box model simulations (Figure 2).  We find that CMAQ’s yield-based approach for SOAAQ formation does describe mean SOA values well when compared to explicit chemistry in, but episodic peak values with the explicit approach are not captured by the yield approach.  We have been working with CMAQ model developers at EPA to add photolysis rates consistent with the larger CMAQ model to our mechanism.  We plan to conduct comparison simulations of the yield and explicit chemistry approaches with sensitivities related to individual source sector emission intputs in the full 3-dimensional photochemical model in 2014 with our EPA colleagues.  Additionally, we learned that colleagues unaffiliated with this project published SOA yields for use in photochemical models developed from box model simulations. (Lim, et al., ACP 2013) employed an aqueous chemical mechanism similar to what we proposed.  We are experimenting with those yields in the full photochemical CMAQ model, having decided that developing our own yields would not be scientifically efficient.  This freed us and allowed further extension of CMAQ cloud chemistry modeling in two ways not originally planned in the initial proposal.  While thinking about cloud chemistry and aloft processes that could affect climate, we decided to add cloud ice chemistry of inorganic species in CMAQ.  Predictions of particulate nitrate changed dramatically in the highest regions of the model (e.g., 100mb) (see Figure 3).  Because scattering is altitude dependent, there are potential regional climate implications associated with this ice chemistry. This work currently is in press (Marmo, et al., Atmos Environ 2013). Also, we worked with EPA to change the aqueous chemical mechanism in the multipollutant version of CMAQ. Observational evidence suggests atmospheric mercury appears to be reduced clouds. Previously the CMAQ model used an HO2 reduction pathway that was recently demonstrated in laboratory experiments to not be plausible.  Without an aqueous pathway for mercury reduction, model performance is poor.  We added a dicarboxylic acid reduction pathway to the aqueous chemical mechanism in the multipollutant version of CMAQ and this work just published (Bash, et al.,Atmosphere 2014)

Another aspect of this project is organic chemistry in aqueous aerosol. We recently published a paper (Carlton and Turpin, Atmos Chem Phys 2013) that describes the potential of organic compounds to partition from the gas phase to the condensed phase, either through an aqueous pathway or a more traditional dry pathway.  Our CMAQ modeling suggests that gas phase partitioning to aerosol liquid water has a higher potential than the dry pathway by more than an order of magnitude.


Figure 1. Organic gas concentrations normalized by solubility and SOAAQ-forming potential.  A representative order of SOAAQ-forming potential for gas phase precursors which laboratory experiments have been done:  glyoxal > methylglyoxal > glycoaldehyde (not shown)> acetaldehyde (“CCHO”) > methacrolein




Figure 2. Explicit organic cloud chemistry with KPP (RODAS 3 solver) enables simultaneous solving of multiphase chemistry: partitioning, equilibrium and oxidation reactions. In the current aqchem.F subroutine, multiphase chemistry is solved sequentially. Oxidation reactions are solved with a forward Euler solver within a bisection method for pH. Comparing the modeled SOAAQ mass predictions, we find that explicit and expanded chemistry increases the amount of SOA mass produced, which is consistent with the noted low bias in CMAQ SOA predictions. Inclusion of explicit chemistry does not degrade CMAQ’s performance for sulfate mass prediction (bottom two panels). We further note that inclusion of the explicit chemistry creates a prediction profile that seems to have a Poisson distribution for SOAAQ similar to sulfate mass predictions. The yield approach (left side in top 4 panels) tends to have a maximum ‘cutoff.’
Figure 3.jpg

Text Box: ΔHNO3 (ppb) 

Figure 4. Example absolute difference plot between two different simulations of the 100% ice partitioning case, indicating that numerical noise between simulations is not seen at the scale shown in the manuscript’s Figure 1.

Future Activities:

Year 3 activities are focused on full CMAQ simulations and comparison with ambient observational data.  

Journal Articles on this Report : 2 Displayed | Download in RIS Format

Other project views: All 50 publications 17 publications in selected types All 17 journal articles
Type Citation Project Document Sources
Journal Article Carlton AG, Turpin BJ. Particle partitioning potential of organic compounds is highest in the Eastern US and driven by anthropogenic water. Atmospheric Chemistry and Physics 2013;13(20):10203-10214. R835041 (2013)
R835041 (Final)
  • Full-text: ACP-Full Text-PDF
  • Abstract: ACP-Abstract
  • Other: ResearchGate-Abstract & Full Text-PDF
  • Journal Article Pratt KA, Fiddler MN, Shepson PB, Carlton AG, Surratt JD. Organosulfates in cloud water above the Ozarks’ isoprene source region. Atmospheric Environment 2013;77:231-238. R835041 (2013)
    R835041 (Final)
  • Full-text: Science Direct-Full Text HTML
  • Abstract: Science Direct-Abstract
  • Other: Science Direct-Full Text PDF
  • Supplemental Keywords:

    cloud processing, secondary organic aerosol

    Relevant Websites:

    http://envsci.rutgers.edu/~acarlton/ Exit

    http://climate.envsci.rutgers.edu/SOAS Exit

    Progress and Final Reports:

    Original Abstract
    2012 Progress Report
    2014 Progress Report
    2015 Progress Report
    Final Report