Abstract
We present an overview of results from an enhanced sub-grid scale approach to characterize air quality impacts of aircraft emissions at the Hartsfield-Jackson Atlanta International airport (in the U.S.) for June and July 2002 using an adaptation of CMAQ called the Advanced Modeling System for Transport, Emissions, Reactions, and Deposition of Atmospheric Matter (AMSTERDAM). Aircraft emissions during the landing and takeoff cycle (LTO) and below 3,000 m were represented as plume-in-grid (PInG) emissions using AMSTERDAM’s PInG treatment. Initial results from CMAQ-AMSTERDAM focusing on impacts from aircraft emissions to inorganic PM2.5 and total PM2.5 indicated aircraft increased average total PM2.5 concentrations by up to 235 ng m−3 near the airport and by 1–7 ng m−3 throughout the Atlanta metro area. However, aircraft reduced concentrations by 0.5–1 ng m−3 downwind of the airport, attributable to reductions in sulfate aerosol. The subgrid-scale concentrations were an order of magnitude higher than the grid-based concentrations due to aircraft. In an earlier study when aircraft emissions were modeled by CMAQ as traditional point sources within the ATL airport grid cell, we showed that modeled secondary organic aerosol (SOA) concentrations increased by 2 % due to primary organic aerosol (POA) emissions from aircraft, which provided additional surface area for SOA to partition onto. We now present results from additional modeling work performed to a) enhance organic treatment in a 1-D aerosol microphysics model used to provide engine-specific emissions parameters for CMAQ, and b) examine aircraft’s impacts on secondary organic aerosol concentrations using the volatility basis set (VBS) within CMAQ-AMSTERDAM to represent the formation and aging of organic aerosols. Parameterization for the VBS components was determined using current and ongoing field study measurements, chamber studies, and box models specific to aircraft SOA formation, which showed that non-traditional precursors of SOA (NTSOA) were a much higher contributor to aircraft-specific SOA than previously understood.
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Acknowledgements
This work was funded by PARTNER under a grant to UNC. Opinions, findings, conclusions and recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of PARTNER sponsoring organizations. PARTNER is funded by FAA, NASA, Transport Canada, DOD &EPA.
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Questions and Answers
Questioner Name: Stefano Galmarini
Q: Would your new approach/improvements be more visible if you considered an airport less busy than the Atlanta one? Preferential pathways may lead to differences that grid method would smear down.
A: Yes, we agree that the grid-based approach would dilute some of these impacts due to preferential pathway changes. However, that limitation is precisely the reason we need an enhanced subgrid-scale treatment of aircraft sources to characterize their air quality impacts in the near field. We believe that our new approach for modeling aircraft emissions will likely show improvements in characterizing air quality even for less busy airports, specifically where commercial aircraft activity that have bigger engines dominate, but unlikely for small airports where most of the activity is dominated by regional jets.
Questioner Name: Jeff Weil
Q1. How far downstream (in space) do you go before the puff concentrations blend into the background, and do not stand out?
A: Based upon this modeling application, up to 99 % of all the aircraft-related puffs are chemically active at a distance of 60 km from the airport, with a few puffs active even at a distance of 145 km, before they are “handed over” to the host grid in CMAQ, based upon physical or chemical criterion for puff maturity.
Q2. Do you see the 3D puff of PInG contribution when you compare with observations?
A: We don’t have direct comparison of 3D puffs from aircraft with observations. However, since 21 % of all aircraft-related puffs from the Atlanta airport have at least 0.1 μg/m3 PM2.5 concentrations (mean of 0.14 μg/m3and max of 42.1 μg/m3from all puffs during the 2-month simulation), these concentrations are definitely measurable.
Q3. If the 3D puffs do matter, should you consider a TC0 case where you explicitly model individual aircraft plumes as “line-thermal” or 2D puffs because they would better retain their buoyancy?
A: We haven’t considered that. But we will investigate this in ongoing work, which is focused on improving treatment of aircraft sources in 3-D models, and understanding subgrid-scale variability due to these sources.
Questioner Name: Biliaiev N
Q: From the physical point of view, there must be the interaction between puffs. I don’t see this in these results. Does your model take into account this interaction?
A: Yes, CMAQ-AMSTERDAM supports both intra-plume and inter-plume interactions by treating both splitting and merging of puffs from aircraft to account for wind shear effects, and varying chemistry across the plume.
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Arunachalam, S. et al. (2014). An Enhanced Sub-grid Scale Approach to Characterize Air Quality Impacts of Aircraft Emissions. In: Steyn, D., Builtjes, P., Timmermans, R. (eds) Air Pollution Modeling and its Application XXII. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5577-2_55
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DOI: https://doi.org/10.1007/978-94-007-5577-2_55
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