Skip to main content

Advertisement

SpringerLink
Log in

Adaptive Grid Modeling with Direct Sensitivity Method for Predicting the Air Quality Impacts of Biomass Burning

  • Published:
Water, Air, and Soil Pollution Aims and scope Submit manuscript

Abstract

The objective of this study was to improve the ability to model the air quality impacts of biomass burning on the surrounding environment. The focus is on prescribed burning emissions from a military reservation, Fort Benning in Georgia, and their impact on local and regional air quality. The approach taken in this study is to utilize two new techniques recently developed: (1) adaptive grid modeling and (2) direct sensitivity analysis. An advanced air quality model was equipped with these techniques, and regional-scale air quality simulations were conducted. Grid adaptation reduces the grid sizes in areas that have rapid changes in concentration gradients; consequently, the results are much more accurate than those of traditional static grid models. Direct sensitivity analysis calculates the rate of change of concentrations with respect to emissions. The adaptive grid simulation estimated large variations in O3 concentrations within 4 × 4-km2 cells for which the static grid estimates a single average concentration. The differences between adaptive average and static grid values of O3 sensitivities were more pronounced. The sensitivity of O3 to fire is difficult to estimate using the brute-force method with coarse scale (4 × 4 km2) static grid models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Battye, W., & Battye, R. (2002). Development of emissions inventory methods for wildland fires. Report Prepared for US Environmental Protection Agency, Durham, NC.

  • Boylan, J., Wilkinson, J., Odman, T., Russell, A., & Imhoff, R. (2001). Response and sensitivity of PM2.5 in the southern Appalachian mountains. A&WMA Annual Conference and Exhibition, Orlando, Florida, June 24–28.

  • Cheng, L., McDonald, K. M., Angle, R. P., & Sandhu, H. S. (1998). Forest fire enhanced photochemical air pollution: A case study. Atmospheric Environment, 32, 673–681. doi:10.1016/S1352-2310(97)00319-1.

    Article  CAS  Google Scholar 

  • EPD (2003). “Exceedances of Federal Air Quality Standards in Georgia”, 2000 Georgia Environmental Protection Division, http://www.air.dnr.state.ga.us/amp Located: November 5, 2008.

  • Hakami, A., Odman, M. T., & Russell, A. G. (2003). High-order, direct sensitivity analysis of multidimensional air quality models. Environmental Science & Technology, 37(11), 2442–2452. doi:10.1021/es020677h.

    Article  CAS  Google Scholar 

  • Hardy, C. C., & Leenhouts, B. (2001) Introduction section. 2001 Smoke management guide for prescribed and wildland fire. Prepared by National Wildfire Coordinating Group, NFES-1279.

  • Hu, Y., Odman, M. T., & Russell, A. (2003a) Meteorological modeling of the first base case episode for the fall line air quality study. Prepared for Georgia Department of Natural Resources, Environmental Protection Division, February 2003.

  • Hu, Y., Odman, M. T., & Russell, A. (2003b) Air quality modeling of the first base case episode for the fall line air quality study. Prepared for Georgia Department of Natural Resources, Environmental Protection Division, June 2003.

  • Jang, J. C., Jeffries, H. E., Byun, D., & Pleim, J. E. (1995). Sensitivity of ozone to model grid resolution-I. Application of high-resolution regional acid deposition model. Atmospheric Environment, 29(21), 3085–3100. doi:10.1016/1352-2310(95)00118-I.

    Article  CAS  Google Scholar 

  • Karamchandani, P., Santos, I., Sykes, I., Zhang, Y., Tonne, C., & Seigneur, C. (2000). Development and evaluation of a state-of-the-science reactive plume model. Environmental Science & Technology, 34, 870–880. doi:10.1021/es990611v.

    Article  CAS  Google Scholar 

  • Khan, M. N., Odman, M. T., & Karimi, H. A. (2005). Evaluation of algorithms developed for adaptive grid air quality modeling using surface elevation data. Computers, Environment and Urban Systems, 29(6), 718–734. doi:10.1016/j.compenvurbsys.2004.08.002.

    Article  Google Scholar 

  • Larimore, R. (2000). Prescribed Burning on the Fort Benning Military Reservation. Presentation to the Columbus Environmental Committee, August 8, 2000.

  • Odman, M. T., & Ingram, C. L. (1996). Multiscale air quality simulation platform (MAQSIP): Source code documentation and validation. Technical Report, ENV-96TR002, MCNC—North Carolina Supercomputing Center, Research Triangle Park, NC, pp. 83.

  • Odman, M. T., Mathur, R., Alapaty, K., Srivastava, R. K., McRae, D. S., & Yamartino, R. J. (1997). Nested and adaptive grids for multiscale air quality modeling. Next generation environmental models computational methods. G. Delic and M. F. Wheeler, SIAM, Philadelphia, pp. 59–68.

  • Odman, M. T., Khan, M. N., & McRae, D. S. (2001). Adaptive grids in air pollution modeling: Towards an operational model. In S.-E. Gryning, & F. A. Schiermeier (Eds.), Air pollution modeling and its application XIV (pp. 541–549). New York: Kluwer.

    Google Scholar 

  • Odman, M. T., Khan, M. N., Srivastava, R. K., & McRae, D. S. (2002). Initial application of the adaptive grid air pollution model. In C. Borrego, & G. Schayes (Eds.), Air pollution modeling and its application XV (pp. 319–328). New York: Kluwer.

    Google Scholar 

  • Ottmar, R. D. (2001). Smoke source characteristics. 2001 Smoke management guide for prescribed and wildland fire. Prepared by National Wildfire Coordinating Group, NFES-1279.

  • Srivastava, R. K., McRae, D. S., & Odman, M. T. (2000). An adaptive grid algorithm for air quality modeling. Journal of Computational Physics, 165, 437–472. doi:10.1006/jcph.2000.6620.

    Article  CAS  Google Scholar 

  • Srivastava, R. K., McRae, D. S., & Odman, M. T. (2001a). Simulation of a reacting pollutant puff using an adaptive grid algorithm. Journal of Geophysical Research, 106, 24245–24257.

    Google Scholar 

  • Srivastava, R. K., McRae, D. S., & Odman, M. T. (2001b). Simulation of dispersion of a power plant plume using an adaptive grid algorithm. Atmospheric Environment, 35, 4801–4808.

    Google Scholar 

  • Unal, A., Tian, D., Hu, Y., & Russell, T. (2003). 2000 Emissions Inventory for Fall Line Air Quality Study (FAQS). Prepared for Georgia Department of Natural Resources, Environmental Protection Division.

  • Yang, Y.-J., Wilkinson, J. G., & Russell, A. G. (1997). Fast direct sensitivity analysis of multidimensional photochemical models. Environmental Science & Technology, 31, 2859–2868. doi:10.1021/es970117w.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Alper Unal

  2. Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Turkey

    Alper Unal

Authors
  1. Alper Unal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alper Unal.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Unal, A. Adaptive Grid Modeling with Direct Sensitivity Method for Predicting the Air Quality Impacts of Biomass Burning. Water Air Soil Pollut 200, 47–57 (2009). https://doi.org/10.1007/s11270-008-9892-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11270-008-9892-8

Keywords

Search

Navigation