B33A-0637
Methodology for Airborne Quantification of NOx fluxes over Central London and Comparison to Emission Inventories

Wednesday, 16 December 2015
Poster Hall (Moscone South)
Adam Robert Vaughan1, James D Lee2, Alastair C Lewis3, Ruth Purvis2, David Carslaw2, Pawel K Misztal4, Stefan Metzger5, Sean Beevers6, Allen H Goldstein4, C Nick Hewitt7, Marvin Shaw3 and Thomas Karl8, (1)University of York, Department of Chemistry, York, United Kingdom, (2)University of York, York, United Kingdom, (3)Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, United Kingdom, (4)University of California Berkeley, Berkeley, CA, United States, (5)NEON, Fundamental Instrument Unit, Boulder, CO, United States, (6)Kings College London, London, United Kingdom, (7)University of Lancaster, Lancaster, United Kingdom, (8)University of Innsbruck, Institute for Meteorology and Geophysics, Innsbruck, Austria
Abstract:
The emission of pollutants is a major problem in today’s cities. Emission inventories are a key tool for air quality management, with the United Kingdom’s National and London Atmospheric Emission Inventories (NAEI & LAEI) being good examples. Assessing the validity of such inventoried is important. Here we report on the technical methodology of matching flux measurements of NOx over a city to inventory estimates.

We used an eddy covariance technique to directly measure NOx fluxes from central London on an aircraft flown at low altitude. NOx mixing ratios were measured at 10 Hz time resolution using chemiluminescence (to measure NO) and highly specific photolytic conversion of NO2 to NO (to measure NO2). Wavelet transformation was used to calculate instantaneous fluxes along the flight track for each flight leg. The transformation allows for both frequency and time information to be extracted from a signal, where we quantify the covariance between the de-trended vertical wind and concentration to derive a flux.

Comparison between the calculated fluxes and emission inventory data was achieved using a footprint model, which accounts for contributing source. Using both a backwards lagrangian model and cross-wind dispersion function, we find the footprint extent ranges from 5 to 11 Km in distance from the sample point. We then calculate a relative weighting matrix for each emission inventory within the calculated footprint. The inventories are split into their contributing source sectors with each scaled using up to date emission factors, giving a month; day and hourly scaled estimate which is then compared to the measurement.

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