Dispersion modeling of particulate matter concentration reductions due to alternative aviation fuels

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1 Dispersion modeling of particulate matter concentration reductions due to alternative aviation fuels James Keehn and Donald Hagen Missouri University of Science and Technology World Weather Open Science Conference Montreal, QC August 20, 2014

2 Outline Introduction Alternative Aviation Fuels Emissions Inventories and Dispersion Modeling Airport Simulation Summary

3 Jet Emissions The projected growth of the airline industry necessitates further research into jet emissions and into options for mitigating their effects on climate change, local air quality, and human health Airport ground crews, as well as residents of nearby neighborhoods, are especially at risk with respect to the PM emissions of jet engines Dispersion modeling is a good tool to determine pollutant concentrations at small spatial scales

4 Study Goals Use engine exit plane emissions data to investigate the air quality impacts of alternative jet fuel use near airports Follow procedures outlined in Donohoo 2010 and the Airport Cooperative Research Program (ACRP) Reports 46 and 71 for the generation of an emissions inventory and a dispersion modeling study using the United States Federal Aviation Administration s Emissions and Dispersion Modeling System (EDMS) As a first order approximation, apply emissions data for CFM56 engines to all gas turbine engines at the airport

5 Alternative Aviation Fuels Alternative aviation fuels (alt fuels) are a promising method for reducing emissions from gas turbine engines Fuels that are compatible with current fueling and aircraft infrastructure are termed drop in alt fuels, and are more suitable for immediate use. This study models emissions from aircraft burning standard Jet A fuel, as well as blends of 50% Jet A with Fischer Tropsch (FT) fuel.

6 PM Measurement The Missouri University of Science and Technology s Center of Excellence for Aerospace Particulate Emissions Reduction Research performs PM emissions measurement experiments for both traditional and alt fuels Because EDMS and AERMOD model emissions in terms of mass, non volatile soot PM (PMNV) emissions data with units of g/kg fuel were used to develop emissions reduction factors for the alt fuel blends

7 Alt fuels Emissions Data Mass based PM data for a 50% blend of standard Jet A fuel with fuel produced using the Fischer Tropsch process were taken from two experimental campaigns, the GE alt fuels test in 2007 and the AAFEX campaign in 2009 After fitting the percent reduction in emissions, the reductions at each engine power setting in the International Civil Aviation Organization Landing Takeoff Cycle were averaged over the different times in mode and weighted using the fuel burn during each stage; this process is detailed in Donohoo 2010

8 Engine Power Dependence % Emissions Reduction CFM56 engines, 50% FT fuel blends 0.00% 0.00% 10.00% 20.00% 40.00% 60.00% 80.00% % % % Emissions Reduction 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% y = x R² = y = x R² = AAFEX 1 GE Linear (AAFEX 1) Linear (GE) 90.00% Engine Power

9 Alt fuels Emissions Scenarios Increasing fuel sulfur content AAFEX HS AAFEX LS GE HS GE LS Increasing alt-fuel NVPM emissions

10 EDMS Introduction The Emissions and Dispersion Modeling System (EDMS) was developed by the United States Federal Aviation Administration (FAA) to assist airports in performing regulatory modeling of their environmental impacts EDMS includes the AMS/EPA Regulatory Model (AERMOD), which uses the emissions inventories produced by EDMS to perform dispersion modeling of desired pollutants

11 Modeling System Structure Sources (flight schedule) Emissions Inventory Emissions Weather EDMS Hourly emissions file Airport layout and operations AERMOD

12 Input Data For the United States, flight schedule data for domestic flights can be obtained from the Bureau of Transportation Statistics (BTS); this database was used for the present study This database includes only domestic passenger flights and does not have gate or runway assignments Both surface and upper air data for this project were taken from databases maintained by the US National Oceanic and Atmospheric Administration (NOAA) The data set used for this study contained a significant amount of missing records; EDMS and AERMOD will complete successfully in this case but the effects of this on the results are unknown

13 Hartsfield Jackson Atlanta International Airport (ATL) ATL was chosen for this study because of its size and capacity (it is one of the busiest airports in the world) and because previous research has been done on emissions there (Donohoo 2010) The complexity of the airport layout will provide opportunities for further study: 5 domestic concourses and 2 international concourses 5 runways >90 separate taxiways 1854 unique aircraft serviced in one month just from domestic passenger flights using gas turbine engines

14 Modeling the Airport Because of the large number of gates at ATL, each concourse is modeled as one gate for the purposes of the simulation This vastly decreases the number of taxipaths necessary to model aircraft movement through the airport Because each terminal services certain airlines, flights operated by each airline were distributed evenly between applicable terminals (EDMS gates ) Runways were assigned to each flight based on their percent usages for arrivals and departures as well as the wind conditions (other criteria are available)

15 ATL Layout

16 EDMS to AERMOD EDMS writes particulate emissions to the hourly emissions file as PM 10 or PM 2.5 only examining PMNV alone is not possible by default In order to account for the change in emissions due to different fuels, the hourly emissions were multiplied by one PM 2.5 emissions reduction factor which was calculated based on the change in emissions of each PM 2.5 component:

17 Variation with Direction, Jet A Fuel 7.00E 01 Concentration vs. Distance 6.00E 01 Concentration (µg/m3) 5.00E E E E E 01 E SE S SW W NW N NE 0.00E Distance (m)

18 Variation with Distance & Fuel Type 0.4 Avg. Concentration vs. Distance Concentration (µg/m3) Jet A GE HS AAFEX HS GE LS AAFEX LS Distance from ATL (km)

19 Further Research Obtain input data of higher quality and compare with studies of a similar level of complexity to this one JGK1 Apply modifications to EDMS such as using different reduction factors for different entries in the hourly emissions file Run more scenarios by including more sources and fuel types and by simulating different airports and time periods or weather conditions Use chemical transport models such as the Community Multiscale Air Quality model to compare with dispersion modeling results and include other pollutants

20 Slide 19 JGK1 Reword Keehn, James G. (S&T-Student), 8/13/2014

21 Summary EDMS/AERMOD can be used as a tool to examine the impacts of alternative aviation fuel use on air quality near airports In agreement with other studies, reducing the sulfur content of jet fuel produces the greatest reduction in aircraft PM emissions (Donohoo 2010) Blending standard Jet A fuel with Fischer Tropsch fuels allows for further reductions in PM emissions and less dependence on oil without any required changes in fueling infrastructure There are many opportunities to expand on this project by studying the effects of input data, model settings, and the modeled scenarios on the dispersion results

22 References Kim, B., Rachami, J., Robinson, D., Robinette, B., Wyle, K. N., Arunachalam, S., Davis, N., Baek, B. H., Shankar, U., Talgo, K., Yang, D., Hanna, A. F., Wayson, R. L., Noel, G., Cliff, S. S., Zhao, Y., Hopke, P. K., and Kumar, P. ACRP Report 71: Guidance for Quantifying the Contribution of Airport Emissions to Local Air Quality, Transportation Research Board, National Research Council, Washington, D.C., 2012 Morser, F., Soucacos, P. ACRP Report 46: Handbook for Analyzing the Costs and Benefits of Alternative Aviation Turbine Engine Fuels at Airports, Transportation Research Board, National Research Council, Washington, D.C., Scaling air quality effects from alternative jet fuel in aircraft and ground support equipment by Donohoo, Pearl, M.S. thesis, Massachusetts Institute of Technology, Whitefield, P.D., Lobo, P., Hagen, D.E., Timko, M.T., Miake Lye, R.C., Taylor, C., Ratliff, G., Lukachko, S., Sequeira, C., Hileman, J., Waitz, I., Webb, S., Thrasher, T. G., Ohsfeldt, M. R., Kaing, H. K., and Essama, S. C., ACRP Report 9: Summarizing and Interpreting Aircraft Gaseous and Particulate Emissions Data, Transportation Research Board, National Research Council, Washington, D.C., 2008.

23 Acknowledgments Thanks to Don Hagen and Prem Lobo for advice and assistance during this project. Thanks also to Brian Badger and Matt Davis for providing information regarding operations at Hartsfield Jackson Atlanta Airport. Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. FAA, EUROCONTROL or ICAO

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