Correspondence Comment on “Geographic Sensitivity of Fine Particle Mass to Emissions of SO2 and NOx” Mueller et al. (1) used the URM-1ATM air quality model developed by Boylan et al. (2) to evaluate the sensitivity of fine particulate matter (PM2.5) to SO2 and NOx emissions in the eastern United States. The model’s sensitivity analysis capability for particulate matter (PM) as used in this paper is a product of our work-in-progress. We started the development of this capability as part of the Southern Appalachian Mountains Initiative (SAMI). In this project, we collaborated with other researchers at Georgia Tech to derive the mathematical equations, code the algorithm, test the source-code, and evaluate URM-1ATM with the new capability. Finally, we performed a detailed analysis of the ozone, PM, and wet deposition sensitivities to SO2, ground-level and elevated NOx, and ammonia emissions. That analysis was performed for six modeling episodes using slightly different source regions than those used in Mueller et al. (1). During the course of this project, SAMI entered into an agreement with the Tennessee Valley Authority (TVA), the organization of Mueller et al., to apply our model to three additional episodes. We gave TVA our code along with critical inputs for its execution to help SAMI meet their schedule. The results of the combined analysis for all nine episodes, which together provide estimates of seasonal or annual average sensitivities, can be found in Chapter 12 of the SAMI Air Quality Modeling Final Report (3). During the peer review of the SAMI Air Quality Modeling Final Report (3), we were unable to explain some counterintuitive behavior of the model’s response to emission changes. This was stated in Chapter 10 (pp 10-13) of the report: “The low response in nitrate wet deposition is counterintuitive and contradicts with other studies (e.g., Shin and Carmichael, 1992)” (4). Later, we discovered that this behavior was due to errors in the code as well as the input files. Our preliminary analysis showed that these errors could significantly affect the findings drawn from the original code. Therefore, we chose not to publish our results until those errors were corrected, and the sensitivity analysis was redone. We corrected the code and the input files and made a public release in December 2002. With little leftover funding from SAMI and no help from TVA, we decided to redo the analysis for only the six episodes that we originally modeled. In 2003, we finished re-running the model for these episodes and revised the seasonal/annual averaging procedure to work with six episodes instead of nine. Soon we will submit our papers for publication. The results in Mueller et al. (1) are products of the original code and input files that contained errors. The corrections to the code and inputs consisted of (i) correcting the boundary conditions of PAN, (ii) correcting the solar radiation scaling factors used to calculate photolysis rates, and (iii) correcting a mass conservation problem related to reduced nitrogen species in the aerosol module. The PAN boundary conditions were 7-8 times too high, and the solar radiation scaling factors were low by a factor of 30%. Also, the mass conservation correction in the aerosol routine decreased the total reduced nitrogen species in the system by a factor of 2, leading to significant changes in nitrate concentrations * Corresponding author present address: Georgia Dept. of Natural Resources, 4244 International Pkwy., Suite 120, Atlanta, GA 30354; phone: (404)362-4851; fax: (404)363-7100; e-mail: james_boylan@ dnr.state.ga.us. 4910
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 38, NO. 18, 2004
(over-predictions became under-predictions). Mueller et al. (1) did examine the effect of reducing the PAN boundary condition and saw large reductions in nitrate and modest reductions in organics and sulfate concentrations but only slight changes in the sensitivity of these species to NOx emission reductions. However, they did not investigate the effects of correcting the solar radiation scaling factors or the mass conservation problem. These corrections significantly altered the sensitivities of some PM species to emissions of SO2 and NOx. For example, the sensitivity of nitrate to SO2 emissions decreased by a factor of 2-5, and the sensitivity of organic carbon mass to SO2 emissions decreased by more than an order of magnitude in some cases. Both of these sensitivities showed a significantly different spatial pattern in addition to the changes in magnitude. On the other hand, the sensitivity of sulfate to NOx emissions increased by a factor of 2, and the sensitivity of organic carbon mass to NOx emissions increased by up to 30%. These differences are also reflected in the magnitude and spatial patterns of PM2.5 sensitivities to SO2 and NOx emissions. On the positive side, the sensitivity of sulfate particles to SO2 emissions was not significantly altered. There are also inaccurate statements about DDM in Mueller et al. (1). The authors state “Major limitations on the use of DDM results are as follows: (1) DDM cannot provide reliable quantitative estimates of air quality changes for emission changes in excess of ∼30%. (2) DDM cannot provide quantitative or qualitative estimates of air quality changes in response to simultaneous changes in multiple emissions species.” Both of these statements are incorrect. Emission changes in excess of 30% were not evaluated as part of the SAMI project, but follow-on research has shown that some DDM results may be reliable to as much as 50% (4). Also, it is not true that DDM cannot provide estimates of changes in response to simultaneous changes in multiple emission species (5). We hope this correspondence will prevent further dissemination of erroneous results from our work-in-progress and inaccurate statements about our research. We also hope this correspondence will help to minimize the potential confusion due to major differences between the results of Mueller et al. and our results, once we publish them.
Literature Cited (1) Mueller, S. F.; Bailey, E. M.; Kelsoe, J. J. Environ. Sci. Technol. 2004, 38, 570. (2) Boylan, J. W.; Odman, M. T.; Wilkinson, J. G.; Russell, A. G.; Doty, K. G.; Norris, W. B.; McNider, R. T. Atmos. Environ. 2002, 36, 3721. (3) Odman, M. T.; Boylan, J. W.; Wilkinson, J. G.; Russell, A. G.; Mueller, S. F.; Imhoff, R. E.; Doty, K. G.; Norris, W. B.; McNider, R. T. SAMI Air Quality Modeling Final Report; Report for the Southern Appalachian Mountains Initiative: Asheville, NC, 2002. (4) Shin, W.-C.; Carmichael, G. R. Environ. Sci. Technol. 1992, 26, 715. (5) Hakami, A.; Odman, M. T.; Russell, A. G. Environ. Sci. Technol. 2003, 37, 2442. (6) Hakami, A.; Odman, M. T.; Russell, A. G. Non-linearity in atmospheric response: A direct sensitivity analysis approach. J. Geophys. Res. (in press).
James W. Boylan* and Mehmet T. Odman Georgia Institute of Technology School of Civil & Environmental Engineering 311 Ferst Drive Atlanta, Georgia 30332-0512 ES040404H 10.1021/es040404h CCC: $27.50
2004 American Chemical Society Published on Web 08/03/2004
