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A Plume-in-grid Approach to Characterize Air Quality Impacts of Aircraft Emissions at the Hartsfield–jackson Atlanta International Airport : Volume 13, Issue 18 (16/09/2013)

By Rissman, J.

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Book Id: WPLBN0003989789
Format Type: PDF Article :
File Size: Pages 18
Reproduction Date: 2015

Title: A Plume-in-grid Approach to Characterize Air Quality Impacts of Aircraft Emissions at the Hartsfield–jackson Atlanta International Airport : Volume 13, Issue 18 (16/09/2013)  
Author: Rissman, J.
Volume: Vol. 13, Issue 18
Language: English
Subject: Science, Atmospheric, Chemistry
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Binkowski, F. S., West, J. J., Woody, M., Rissman, J., Bendor, T., & Arunachalam, S. (2013). A Plume-in-grid Approach to Characterize Air Quality Impacts of Aircraft Emissions at the Hartsfield–jackson Atlanta International Airport : Volume 13, Issue 18 (16/09/2013). Retrieved from http://community.worldlibrary.net/


Description
Description: Energy Innovation: Policy and Technology LLC, 98 Battery St. Ste. 202, San Francisco, CA 94111, USA. This study examined the impacts of aircraft emissions during the landing and takeoff cycle on PM2.5 concentrations during the months of June and July 2002 at the Hartsfield–Jackson Atlanta International Airport. Primary and secondary pollutants were modeled using the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). AMSTERDAM is a modified version of the Community Multiscale Air Quality (CMAQ) model that incorporates a plume-in-grid process to simulate emissions sources of interest at a finer scale than can be achieved using CMAQ's model grid. Three fundamental issues were investigated: the effects of aircraft on PM2.5 concentrations throughout northern Georgia, the differences resulting from use of AMSTERDAM's plume-in-grid process rather than a traditional CMAQ simulation, and the concentrations observed in aircraft plumes at subgrid scales. Comparison of model results with an air quality monitor located in the vicinity of the airport found that normalized mean bias ranges from −77.5% to 6.2% and normalized mean error ranges from 40.4% to 77.5%, varying by species. Aircraft influence average PM2.5 concentrations by up to 0.232 μg m−3 near the airport and by 0.001–0.007 μg m−3 throughout the Atlanta metro area. The plume-in-grid process increases concentrations of secondary PM pollutants by 0.005–0.020 μg m−3 (compared to the traditional grid-based treatment) but reduces the concentration of non-reactive primary PM pollutants by up to 0.010 μg m−3, with changes concentrated near the airport. Examination of subgrid-scale results indicates that median aircraft contribution to grid cells is higher than median puff concentration in the airport's grid cell and outside of a 20 km × 20 km square area centered on the airport, while in a 12 km × 12 km square ring centered on the airport, puffs have median concentrations over an order of magnitude higher than aircraft contribution to the grid cells. Maximum puff impacts are seen within the 12 km × 12 km ring, not in the airport's own grid cell, while maximum grid cell impacts occur within the airport's grid cell. Twenty-one (21)% of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg m−3 PM2.5 concentrations. Near the airport, median daily puff concentrations vary between 0.017 and 0.134 μg m−3 (0.05 and 0.35 μg m−3 at ground level), while maximum daily puff concentrations vary between 6.1 and 42.1 μg m−3 (7.5 and 42.1 μg m−3 at ground level) during the 2-month period. In contrast, median daily aircraft contribution to grid concentrations varies between 0.015 and 0.091 μg m−3 (0.09 and 0.40 μg m−3 at ground level), while the maximum varies between 0.75 and 2.55 μg m−3 (0.75 and 2.0 μg m−3 at ground level). Future researchers may consider using a plume-in-grid process, such as the one used here, to understand the impacts of aircraft emissions at other airports, for proposed future airports, for airport expansion projects under various future scenarios, and for other national-scale studies specifically when the maximum impacts at fine scales are of interest.

Summary
A plume-in-grid approach to characterize air quality impacts of aircraft emissions at the Hartsfield–Jackson Atlanta International Airport

Excerpt
Eyers, C., Gilboy, M.: Revision of Calvert method for filling in missing smoke number data, QinetiQ, 2007.; Air New Zealand: Aircraft Statistics, available at: http://www.airnewzealand.co.nz/aircraft-statistics (last access: 22 June 2013), 2013.; Airports Council International: ACI releases World Airport Traffic Report 2009, available at: http://www.aci.aero/Media/aci/file/Press Releases/2010/PR_WATR2009_050810_FINAL.pdf (last access: 4 January 2013), 2010.; Airports Council International: Annual Traffic Data (Movements) for years 2002 and 2005, available at: http://www.aci.aero/Data-Centre/Annual-Traffic-Data/Movements/ (last access: 16 February 2013), 2013.; Arunachalam, S., Baek, B. H., Holland, A., Adelman, Z., Binkowski, F. S., Hanna, A., Thrasher, T., and Soucacos, P.: An Improved Method to Represent Aviation Emissions in Air Quality Modeling Systems and their Impacts on Air Quality, in: Proceedings of the 13 Conference on Aviation, Range and Aerospace Meteorology, New Orleans, LA, January 2008, 135626, available at: https://ams.confex.com/ams/pdfpapers/135626.pdf (last access: 30 November 2012), 2008.; Arunachalam, S., Wang, B., Davis, N., Baek, B. H., and Levy, J. I.: Effect of Chemistry-Transport Model Scale and Resolution on Population Exposure to PM2.5 from Aircraft Emissions during Landing and Takeoff, Atmos. Environ., 45, 3294–3300, doi:10.1016/j.atmosenv.2011.03.029, 2011.; Barrett, S. R. H., Britter, R. E., and Waitz, I. A.: Global mortality attributable to aircraft cruise emissions, Environ. Sci. Technol., 44, 7736–7742, 2010.; Aviation Environmental Design Tool: http://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/aedt/ (last access: 25 October 2010), 2010.; Baek, B. H., Arunachalam, S., Holland, A., Adelman, Z., Hanna, A., Thrasher, T., and Soucacos, P.: Development of an Interface for the Emissions Dispersion and Modeling System (EDMS) with the SMOKE Modeling System, in: Proceedings of the 16th Annual Emissions Inventory Conference, Emissions Inventories: Integration, Analyses and Communication, Raleigh, NC, May 2007, available at: http://www.epa.gov/ttn/chief/conference/ei16/session1/baek.pdf (last access: 30 November 2012), 2007.; Byun, D. W. and Schere, K. L.: Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System, J. Appl. Mech. Rev., 59, 51–77, doi:10.1115/1.2128636, 2006.; Cimorelli, A., Perry, S., Venkatram, A., Weil, J., Paine, R., Wilson, R., Lee, R., Peters, W., and Brode, R.: AERMOD: A dispersion Model for Industrial Source Applications. Part I: General Model Formulation and Boundary Layer Characterization, J. Appl. Meteorol., 44, 682–693, doi:10.1175/JAM2227.1, 2005.; FAA and EPA: Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet, and Turboprop Engines, http://www.faa.gov/regulations_policies/policy_guidance/envir_policy/media/FAA-EPA_RBP_Speciated OG_Aircraft_052709.pdf (last access:

 

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