Vladimír Ždímal 1, Marek Brabec2,3, Zdeněk Wagner4

  • 1 Laboratory of Aerosol Chemistry and Physics, Institute of Chemical Process Fundamentals of the AS CR, v. v. i., Rozvojová 135, Praha 6, 165 02, Czech Republic
  • 2 Department of Biostatistics and Informatics, National Institute of Public Health, Šrobárova 48, Praha 10, 100 42, Czech Republic
  • 3 Department of Nonlinear Modeling, Institute of Computer Science, Pod Vodárenskou věží 2, Praha 8, 182 07, Czech Republic
  • 4 E. Hála Laboratory of Thermodynamics, Institute of Chemical Process Fundamentals of the AS CR, v. v. i., Rozvojová 135, Praha 6, 165 02, Czech Republic

Received: November 30, 2008
Revised: November 30, 2008
Accepted: December 29, 2016
Download Citation: ||https://doi.org/10.4209/aaqr.2008.11.0051  

  • Download: PDF


Cite this article:
Ždímal, V., Brabec, M. and Wagner, Z. (2008). Comparison of Two Approaches to Modeling Atmospheric Aerosol Particle Size Distributions. Aerosol Air Qual. Res. 8: 392-410. https://doi.org/10.4209/aaqr.2008.11.0051


 

ABSTRACT


This paper compares two approaches to modeling (smoothing) aerosol particle size distribution (particle counts for specified diameter intervals): i) the semiparametric approach based on a maximum likelihood fitting of lognormal (LN) mixtures at each time separately, followed by smoothing parameter tracks, ii) the nonparametric approach based on a kernel-like smoothing as an application of the gnostic theory of uncertain data. The specific advantages and disadvantages of both the semiparametric and nonparametric approaches are discussed and illustrated using real data containing a day-long time series of size spectra measurements.


Keywords: Particle size distribution; Lognormal mixture; Semiparametric modeling; Nonparametric modeling; Gnostic theory of uncertain data

Don't forget to share this article 

 

Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.