Partnership for AiR Transportation Noise and Emission Reduction An FAA/NASA/TC-sponsored Center of Excellence Matthew Woody and Saravanan Arunachalam Institute for the Environment, UNC Chapel Hill October 19-21, th Annual CMAS User’s Conference, Chapel Hill, NC Secondary Organic Aerosol Produced from Aircraft Emissions at the Atlanta Airport – An Advanced Diagnostic Investigation Using Process Analysis

2 Overall Motivation for FAA Research Aviation activities have emissions of CO, NO x, VOC, SO x, PM 2.5 and numerous hazardous air pollutants –Critical to understand exposure to these to protect public health Compared to all other sources that impact air quality, aviation emissions are usually small –For e.g. in the U.S., NO x from aviation contributes < 1% in 77% of counties, PM 2.5 contributes < 1% in 94% of counties –However, in some counties, airport contribution could be significant Concerns about potential growth of aviation emissions in the future Aviation emissions vary in 4-D (in space and time), undergo complex chemical transformation in the atmosphere –Need to be characterized accurately for better understanding their atmospheric impacts

3 Background Emissions at ATL airport CMAQ modeling performed to determine effects of aircraft emissions on air quality at 3 airports –Atlanta Hartsfield (ATL), Chicago O’Hare (ORD) and Providence T.F. Green (PVD) airports MM5-SMOKE-CMAQ (v4.6) modeling system used along with the FAA Emissions Dispersion and Modeling System (EDMS) –Emissions from Landing- TakeOff cycle (LTO) up to 10,000 ft –Aircraft emissions processed through SMOKE using EDMS2INV interface (Baek et al, 2007)

4 Background CMAQ Modeling Scenarios –base02 (NEI-2002 based emissions for non-aviation sources) –sens_airc (above plus aircraft emissions from ATL, ORD, and PVD) June and July modeled at 36k, 12k, and 4k resolutions Contributions from aircraft computed from the difference of sens_airc (with aircraft emissions) minus base02 (without) Model performance evaluated using AMET for PM 2.5 data from various networks (Arunachalam et al, 2008) Modeling Domains

5 Monthly Average Change in PM 2.5 due to Aircraft Emissions at ATL 0.50% 1.47% 5.66% 0.51% 1.18% 5.61% Overall, aircraft emissions increase PM 2.5 concentrations at the grid- cell containing the airport. However, aircraft emissions reduce SOA concentrations at 36k and 12k grid resolutions while SOA concentrations increase at the 4k resolution.

6 Overall Goals Determine the chemical and physical processes behind the changing concentrations of SOA at the various grid resolutions –SOA reduced at 36k and 12k, increase at 4k –Focus on grid-cell containing airport Test modeling updates in CMAQ v4.7 (aero5) with new SOA pathways and precursors, and determine the impacts of changes in SOA concentrations due to aircraft emissions, relative to CMAQ v4.6 (aero4)

7 Process Analysis Approach Chose the 2 days from June and July which exhibited the largest reduction of SOA concentrations in the 12k and 36k grid resolutions –June 6 and 7, 2002 Two days rerun in CMAQ v4.6 with Process Analysis –Integrated Process Rates –Integrated Reaction Rates 04-km grid-cell 12-km grid-cell 36-km grid-cell JST Compare results at 3 resolutions (36k, 12k, and 4k) for the single grid-cell containing the airport and for the 9 grid-cells (at 12k resolution) and 81 grid-cells (at 4k resolution) that match the spatial extents of the 36k grid-cell

8 Changes in SOA Concentrations Due to Aircraft at ATL 36k 4k 12k SOA changes due to aircraft are driven by aerosol process. Since aircraft emissions do not contain SOA precursors, what is happening to free radical budgets that oxidize SOA precursors?

9 Changes in NO 3 Concentrations Due to Aircraft at ATL 36k 4k 12k Aircraft emissions cause reductions in NO 3, hindering oxidation of SOA precursors. Vertical diffusion drives the changes in NO 3.

10 Changes in NO 3 Concentrations Due to Aircraft at ATL – Layers 1 through 14 36k 4k 12k Reductions of NO 3 concentrations from aircraft in lower 14 lowers driven by chemistry. NO x emissions from aircraft react to reduce NO 3 concentrations (not shown)

11 Changes in Primary Organic Aerosol Concentrations Due to Aircraft at ATL 36k 4k 12k Increase in Primary Organic Aerosol (POA) from direct aircraft emissions at the 4k resolution provide additional surface area for SOA to partition onto.

12 Changes in POA Emissions Due to Aircraft at ATL Concentration ( μ g/m 3 ) Based Emissions Mass Per Time (g/s) Based Emissions Emissions are equivalent on a mass basis but are diluted over larger grid cells at coarse resolution.

13 Changes in SOA Concentrations – Analysis for Equivalent Areas 36k, 12k, and 4k resolutions Equivalent Areas at 36k, 12k (3x3), and 4k (9x9) resolutions Equivalent spatial extents at 3 grid resolutions exhibit similar patterns – gas phase chemistry dominates change in SOA concentrations due to aircraft SOA

14 Changes in SOA Concentrations Due to Aircraft at ATL – v4.6 vs. v4.7 36k 12k 4k Solid lines indicate v4.6 Dotted lines indicate v4.7 Change in SOA concentrations follow similar diurnal patterns in v4.6 and v4.7, but with differing magnitudes

15 Changes in NO 3 and POA Concentrations Due to Aircraft at ATL – v4.6 vs. v4.7 Solid lines = v4.6 Dotted lines = v4.7 No change in NO 3 or POA from v4.6 to v4.7 (expected) NO 3 POA 36k 4k 12k

16 Comparison of Total Carbon Against Jefferson Street (JST) SEARCH Monitor 36k 12k 4k CMAQ v4.7 performs better at the 4k resolution while performance at 36k and 12k resolutions remains relatively unchanged

17 Conclusions Used Process Analyses to explain CMAQ sensitivity to aircraft emissions at different grid resolutions –SOA changes due to aircraft emissions are dependent on grid resolution, and the relative magnitude of POA and radical budgets At 36k and 12k, SOA concentrations are reduced at the grid-cell containing the airport due to gas phase chemistry –NO x emissions react with NO 3, preventing NO 3 from oxidizing SOA precursors At the 4k grid resolution, SOA concentrations increase due to POA emissions from aircraft at the airport –POA provides additional surface area for SOA to partition onto Changes in SOA concentrations exhibit similar diurnal patterns in CMAQ v4.6 and v4.7 –Smaller magnitude of changes in SOA concentrations due to aircraft in v4.7 Comparison against Total Carbon at JST indicates v4.7 performs better at 4k resolution than 4.6 while performance remains relatively unchanged at 36k and 12k resolutions

18 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the FAA, NASA or Transport Canada. This work was funded by FAA and Transport Canada under FAA Award Nos.: 07-C-NE-UNC, Amendment Nos. 001, 002, 003 and 004 The Investigation of Aviation Emissions Air Quality Impacts project is managed by Christopher Sequeira. Acknowledgements CSSI, Inc. for EDMS outputs Jim Boylan, GA DNR for providing ATL 4k input datasets Barron Henderson, for assistance with PERM