Estimation of aerosol particle number distributions with Kalman Filtering - Part 1: Theory, general aspects and statistical validity

  • Viskari T
  • Asmi E
  • Kolmonen P
 et al. 
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Aerosol characteristics can be measured with different instruments
providing observations that are not trivially inter-comparable. Extended
Kalman Filter (EKF) is introduced here as a method to estimate aerosol
particle number size distributions from multiple simultaneous
observations. The focus here in Part 1 of the work was on general
aspects of EKF in the context of Differential Mobility Particle Sizer
(DMPS) measurements. Additional instruments and their implementations
are discussed in Part 2 of the work. University of Helsinki
Multi-component Aerosol model (UHMA) is used to propagate the size
distribution in time. At each observation time (10 min apart), the time
evolved state is updated with the raw particle mobility distributions,
measured with two DMPS systems. EKF approach was validated by
calculating the bias and the standard deviation for the estimated size
distributions with respect to the raw measurements. These were compared
to corresponding bias and standard deviation values for particle number
size distributions obtained from raw measurements by a inversion of the
instrument kernel matrix method. Despite the assumptions made in the EKF
implementation, EKF was found to be more accurate than the inversion of
the instrument kernel matrix in terms of bias, and compatible in terms
of standard deviation. Potential further improvements of the EKF
implementation are discussed.

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  • T. Viskari

  • E. Asmi

  • P. Kolmonen

  • H. Vuollekoski

  • T. Petäjä

  • H. Järvinen

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