Estimation of time-varying aerosol size distributions—exploitation of modal aerosol dynamical models

We present a method for improved estimation of size distributions from DMPS measurements in case of a non-steady input aerosol. We assume that the size distribution can be represented using a parametric model and in a simulation study we describe the size distribution as a bi-modal log-normal function. We have previously studied the non-parametric approach for the estimation of time-varying size distributions and in the simulation studies we used the random-walk model to describe the evolution of the size distribution. The use of the parametric approach enables the feasible use of the more realistic evolution models in real-time estimation. The objective is to track the time-evolution of the model parameters using state space approach. Since the problem is non-linear, the extended Kalman filter is employed to determine estimates for the parameters assuming that observation errors are zero-mean Gaussian. The focus is in on-line data processing and two different evolution models are implemented in the computations. The estimates are compared to the estimates obtained with a traditional least-squares approach. The results show that in real-time data processing the accuracy of the estimates can be improved significantly by using more accurate evolution models.