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Gap-filling Fast Electrical Mobility Spectrometer Measurements of Particle Number Size Distributions for Eddy Covariance Application

Category: Urban Air Quality

Volume: 19 | Issue: 12 | Pages: 2721-2731
DOI: 10.4209/aaqr.2019.06.0291
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To cite this article:
Meyer-Kornblum, A., Gerling, L. and Weber, S. (2019). Gap-filling Fast Electrical Mobility Spectrometer Measurements of Particle Number Size Distributions for Eddy Covariance Application. Aerosol Air Qual. Res. 19: 2721-2731. doi: 10.4209/aaqr.2019.06.0291.

Agnes Meyer-Kornblum , Lars Gerling, Stephan Weber

  • Climatology and Environmental Meteorology, Institute of Geoecology, Technische Universität Braunschweig, 38106 Braunschweig, Germany

Highlights

  • Eddy covariance shall be used to measure size-resolved particle number fluxes.
  • Fast measurements (≥ 10 Hz) of particle number size distributions are needed.
  • Size-channel concentrations can fall below the minimum threshold of analyser.
  • Missing concentrations are calculated by three different gap-filling methods.
  • Three different gap-filling methods are evaluated for data set from Berlin, Germany.

Abstract

To estimate the spatial and temporal variation in urban particle number concentrations (PNCs), e.g., for exposure studies, a better knowledge of the exchange of particles between the urban surface and the atmosphere is important. Size-resolved fluxes of PNCs were quantified in Berlin, Germany, using the micrometeorological eddy covariance technique. The method requires measurements of particle number size distributions (PNSDs) by a fast particle spectrometer. The Engine Exhaust Particle Sizer (EEPS) Spectrometer 3090 (TSI Inc.) is designed for fast (10 Hz), high-concentration measurements of particles in the size range of 5.6–560 nm, e.g., in the exhaust plume of engines. In the urban background environment of Berlin, however, PNCs in some size channels can temporarily fall below the minimum threshold concentration of the analyser, resulting in missing concentrations that lead to gaps in the PNSD. In the present study, three gap-filling methods were applied to derive complete PNSDs: linear interpolation (LI), natural spline interpolation (NSI) and log-normal fitting (LNF). To evaluate the methods, different numbers of artificial gaps were inserted into 105 gapless PNSDs. Using three different data sets, the results demonstrate that LI and NSI (LI: R2 = 0.84–0.94; NSI: R2 = 0.84–0.95) outperform LNF (R2 = 0.78–0.88). With regard to the Berlin data set, NSI is the recommended gap-filling method since it results in a lower average uncertainty of 10.5–21.8% vs. 13.3–22.9% for LI. It is advisable to reject the boundary areas of the PNSD from gap filling, i.e., Dp < 10 nm and Dp > 200 nm, as this considerably improves the gap-filling quality. This study provides recommendations on how to gap-fill incomplete PNSD data sets obtained with an EEPS.

Keywords

Micrometeorology Engine Exhaust Particle Sizer Spectrometer Berlin Particle fluxes Ultrafine


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