Aerosol formation and growth rates from chamber experiments using Kalman smoothing

Abstract

Abstract. Bayesian state estimation in the form of Kalman smoothing was applied to differential mobility analyser train (DMA-train) measurements of aerosol size distribution dynamics. Four experiments were analysed in order to estimate the aerosol size distribution, formation rate, and size-dependent growth rate, as functions of time. The first analysed case was a synthetic one, generated by a detailed aerosol dynamics model and the other three chamber experiments performed at the CERN CLOUD facility. The estimated formation and growth rates were compared with other methods used earlier for the CLOUD data and with the true values for the computer-generated synthetic experiment. The agreement in the growth rates was very good for all studied cases: estimations with an earlier method fell within the uncertainty limits of the Kalman smoother results. The formation rates also matched well, within roughly a factor of 2.5 in all cases, which can be considered very good considering the fact that they were estimated from data given by two different instruments, the other being the particle size magnifier (PSM), which is known to have large uncertainties close to its detection limit. The presented fixed interval Kalman smoother (FIKS) method has clear advantages compared with earlier methods that have been applied to this kind of data. First, FIKS can reconstruct the size distribution between possible size gaps in the measurement in such a way that it is consistent with aerosol size distribution dynamics theory, and second, the method gives rise to direct and reliable estimation of size distribution and process rate uncertainties if the uncertainties in the kernel functions and numerical models are known.

What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?

“…Moreover, they can retrieve both size- and time-dependence of growth rates, which makes it easier to evaluate the effect of vapor time-dependence on the behavior of the growth rate. Recently, Ozon et al 78 introduced a method based on applying a Kalman smoother to a finite difference solution of GDE that can also provide the uncertainty range for measured growth rate. The uncertainties of sub-10 nm particle size distribution measurements are significant, 34 which propagates in the growth rate values and should thus be considered when interpreting particle growth observations.…”

“…When modeling particle growth, the stochastic uctuations and population dynamics effects can be considered by using particle population simulations. 9,10,73 Regarding measured growth rates, GDE-based methods 29,77,78 to determine growth rate should be preferred over simpler ones, such as the appearance time method. This is because GDE-based methods can separate the effects of condensation and coagulation on particle growth rate, although they neglect stochastic effects.…”

Role of sesquiterpenes in biogenic new particle formation

“…The uncertainty is calculated from the uncertainties of the linear regression, which includes the fitting uncertainties in the determination of the individual appearance times. Systematic effects such as coagulation or wall losses on the growth rate are neglected because of their minor influence at typical CLOUD conditions [see also the work of Ozon et al ( 71 )].…”

Improved Counting Statistics of an Ultrafine DMPS System

“…These sources can additionally contribute to the uncertainties of the This study shows the significant improvement in the determination of the formation and growth rate during NPF by the deployment of a DMPS with improved counting statistics. The wide deployment of such instrumentation which is optimized for sub-10 nm measurements could significantly reduce our uncertainties in formation and growth rate determination or even allow for the application of better analysis tools due to the increased statistics (Pichelstorfer et al, 2018;Ozon et al, 2021) and hence boost our understanding of NPF, e.g. the provide better mass closure in aerosol growth (Stolzenburg et al, 2018).…”