Estimation of non-stationary aerosol size distributions using the state-space approach

All algorithms proposed for the reconstruction of aerosol size distribution s are based on the assumption that the size distribution to be determined i s time invariant during each measurement cycle. However, this assumption do es not hold true in the general case. In this paper we consider the problem of estimating time-varying aerosol size distributions from DMPS measuremen ts. The problem is formulated as a discrete-time state estimation problem a nd the reconstruction approach is based on the use of the Kalman filter and fixed-interval smoother algorithms. Temporal changes in the size distribut ion function are assumed to occur between DMA voltage changes and the obser vation errors are assumed to be Gaussian with zero mean. We also exploit ad ditional information on the solution to prevent non-physical features. The performance of the proposed method is evaluated with synthetic measurement data and it is shown that the temporal resolution and the accuracy of the e stimates can be considerably enhanced with the proposed approach when compa red to traditional approaches. (C) 2001 Elsevier Science Ltd. All rights re served.