Tutorial : Dynamic organic growth modeling with a volatility basis set

Organic aerosols are ubiquitous in the atmosphere and oxygenated organics are a major driver of aerosol growth. The volatility basis set (VBS) as introduced by Donahue et al. (2006, 2011) is often used to simplify the partitioning behavior of the huge variety of atmospheric organics. Recently, the VBS was used to dynamically model aerosol growth from the smallest sizes onwards. This tutorial is intended to equip the reader with the necessary tools to facilitate organic growth modelling based on gas-phase measurements of oxygenated organics using a 2-dimensional VBS. We start with a contextualization of the VBS in partitioning theory and point out the need for dynamic modeling. We provide an overview on the most common methods to estimate the volatility of oxygenated organics and give detailed instruction on how to construct the binned VBS. We then explain the dynamic condensation model including solution and curvature effects. Furthermore, we provide a python package for VBS growth calculations and show with two examples from ambient and chamber measurements how growth rates can be calculated. Last, we summarize the limitation of this approach and outline necessary future developments.Peer reviewe

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

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.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 un-certainties in the kernel functions and numerical models are known.Peer reviewe

Combining instrument inversions for sub-10 nm aerosol number size-distribution measurements

Resolving aerosol dynamical processes in the sub-10 nm range is crucial for our understanding of the contribution of new particle formation to the global cloud condensation nuclei budget or air pollution. Accurate measurements of the particle size distribution in this size-range are challenging due to high diffusional losses and low charging and/or detection efficiencies. Several instruments have been developed in recent years in order to access the sub-10 nm particle size distribution; however, no single instrument can provide high counting statistics, low systematic uncertainties and high size-resolution at the same time. Here we compare several data inversion approaches that allow combining data from different sizing instruments during the inversion and provide python/Julia packages for free usage of the methods. We find that Tikhonov regularization using the L-curve method for optimal regularization parameter estimation gives very reliable results over a wide range of tested data sets and clearly improves standard inversion approaches. Kalman Filtering or regularization using a Poisson likelihood can be powerful tools, especially for well-defined chamber experiments or data from mobility spectrometers only, respectively. Nullspace optimization and non-linear iterative regression are clearly inferior compared to the aforementioned methods. We show that with regularization we can reconstruct the size-distribution measured by up to 4 different mobility particle size spectrometer systems and several particle counters for datasets from Hyytiala and Helsinki, Finland, revealing the sub-10 nm aerosol dynamics in more detail compared to a single instrument assessment.Peer reviewe

Rapid growth of organic aerosol nanoparticles over a wide tropospheric temperature range

Nucleation and growth of aerosol particles from atmospheric vapors constitutes a major source of global cloud condensation nuclei (CCN). The fraction of newly formed particles that reaches CCN sizes is highly sensitive to particle growth rates, especially for particle sizes <10 nm, where coagulation losses to larger aerosol particles are greatest. Recent results show that some oxidation products from biogenic volatile organic compounds are major contributors to particle formation and initial growth. However, whether oxidized organics contribute to particle growth over the broad span of tropospheric temperatures remains an open question, and quantitative mass balance for organic growth has yet to be demonstrated at any temperature. Here, in experiments performed under atmospheric conditions in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN), we show that rapid growth of organic particles occurs over the range from −25 °C to 25 °C. The lower extent of autoxidation at reduced temperatures is compensated by the decreased volatility of all oxidized molecules. This is confirmed by particle-phase composition measurements, showing enhanced uptake of relatively less oxygenated products at cold temperatures. We can reproduce the measured growth rates using an aerosol growth model based entirely on the experimentally measured gas-phase spectra of oxidized organic molecules obtained from two complementary mass spectrometers. We show that the growth rates are sensitive to particle curvature, explaining widespread atmospheric observations that particle growth rates increase in the single-digit-nanometer size range. Our results demonstrate that organic vapors can contribute to particle growth over a wide range of tropospheric temperatures from molecular cluster sizes onward

Rapid growth of organic aerosol nanoparticles over a wide tropospheric temperature range

Nucleation and growth of aerosol particles from atmospheric vapors constitutes a major source of global cloud condensation nuclei (CCN). The fraction of newly formed particles that reaches CCN sizes is highly sensitive to particle growth rates, especially for particle sizes <10 nm, where coagulation losses to larger aerosol particles are greatest. Recent results show that some oxidation products from biogenic volatile organic compounds are major contributors to particle formation and initial growth. However, whether oxidized organics contribute to particle growth over the broad span of tropospheric temperatures remains an open question, and quantitative mass balance for organic growth has yet to be demonstrated at any temperature. Here, in experiments performed under atmospheric conditions in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN), we show that rapid growth of organic particles occurs over the range from −25 °C to 25 °C. The lower extent of autoxidation at reduced temperatures is compensated by the decreased volatility of all oxidized molecules. This is confirmed by particle-phase composition measurements, showing enhanced uptake of relatively less oxygenated products at cold temperatures. We can reproduce the measured growth rates using an aerosol growth model based entirely on the experimentally measured gas-phase spectra of oxidized organic molecules obtained from two complementary mass spectrometers. We show that the growth rates are sensitive to particle curvature, explaining widespread atmospheric observations that particle growth rates increase in the single-digit-nanometer size range. Our results demonstrate that organic vapors can contribute to particle growth over a wide range of tropospheric temperatures from molecular cluster sizes onward

Counting on chemistry : laboratory evaluation of seed-material-dependent detection efficiencies of ultrafine condensation particle counters

Condensation particle counters (CPCs) are crucial instruments for detecting sub-10 nm aerosol particles. Understanding the detection performance of a CPC requires thorough characterization under well-controlled laboratory conditions. Besides the size of the seed particles, chemical interactions between the working fluid and the seed particles also influence the activation efficiencies. However, common seed particle materials used for CPC characterizations are not chosen with respect to chemical interactions with vapor molecules of the working fluid by default. Here, we present experiments on the influence of the seed particle material on the detection efficiencies and the 50% cutoff diameters of commonly used CPCs for the detection of sub-10 nm particles. A remarkable set consisting of six different and commercially available particle detectors, including the newly developed TSI V-WCPC 3789 and a tuned TSI 3776, was tested. The corresponding working fluids of the instruments are n-butanol, diethylene glycol and water. Among other materials we were able to measure detection efficiencies with nanometer-sized organic seed particles reproducibly generated by the oxidation of beta-caryophyllene vapor in a flow tube. Theoretical simulations of supersaturation profiles in the condensers were successfully related to measured detection efficiencies. Our results demonstrate the importance of chemical similarities between seed particles and the working fluids used when CPCs are characterized. We anticipate our study to contribute to a deeper understanding of chemical interactions during heterogeneous nucleation processes.Peer reviewe

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

The formation and growth of atmospheric particles involving sulfuric acid and organic vapors is estimated to have significant climate effects. To accurately represent this process in large-scale models, the correct interpretation of the observations on particle growth, especially below 10 nm, is essential. Here, we disentangle the factors governing the growth of sub-10 nm particles in the presence of sulfuric acid and organic vapors, using molecular-resolution cluster population simulations and chamber experiments. We find that observed particle growth rates are determined by the combined effects of (1) the concentrations and evaporation rates of the condensing vapors, (2) particle population dynamics, and (3) stochastic fluctuations, characteristic to initial nucleation. This leads to a different size-dependency of growth rate in the presence of sulfuric acid and/or organic vapors at different concentrations. Specifically, the activation type behavior, resulting in growth rate increasing with the particle size, is observed only at certain vapor concentrations. In our model simulations, cluster-cluster collisions enhance growth rate at high vapor concentrations and their importance is dictated by the cluster evaporation rates, which demonstrates the need for accurate evaporation rate data. Finally, we show that at sizes below similar to 2.5-3.5 nm, stochastic effects can importantly contribute to particle population growth. Overall, our results suggest that interpreting particle growth observations with approaches neglecting population dynamics and stochastics, such as with single particle growth models, can lead to the wrong conclusions on the properties of condensing vapors and particle growth mechanisms.Peer reviewe

Survival probability of new atmospheric particles : closure between theory and measurements from 1.4 to 100 nm

The survival probability of freshly nucleated particles governs the influences of new particle formation (NPF) on atmospheric environments and the climate. It characterizes the probability of a particle avoiding being scavenged by the coagulation with pre-existing particles and other scavenging processes before the particle successfully grows up to a certain diameter. Despite its importance, measuring the survival probability has been challenging, which limits the knowledge of particle survival in the atmosphere and results in large uncertainties in predicting the influences of NPF. Here we report the proper methods to retrieve particle survival probability using the measured aerosol size distributions. Using diverse aerosol size distributions from urban Beijing, the Finnish boreal forest, a chamber experiment, and aerosol kinetic simulations, we demonstrate that each method is valid for a different type of aerosol size distribution, whereas misapplying the conventional methods to banana-type NPF events may underestimate the survival probability. Using these methods, we investigate the consistency between the measured survival probability of new particles and the theoretical survival probability against coagulation scavenging predicted using the measured growth rate and coagulation sink. With case-by-case and time- and size-resolved analysis of long-term measurement data from urban Beijing, we find that although both the measured and theoretical survival probabilities are sensitive to uncertainties and variations, they are, on average, consistent with each other for new particles growing from 1.4 (the cluster size) to 100 nm.Peer reviewe

The contribution of new particle formation and subsequent growth to haze formation

We investigated the contribution of atmospheric new particle formation (NPF) and subsequent growth of the newly formed particles, characterized by high concentrations of fine particulate matter (PM2.5). In addition to having adverse effects on visibility and human health, these haze particles may act as cloud condensation nuclei, having potentially large influences on clouds and precipitation. Using atmospheric observations performed in 2019 in Beijing, a polluted megacity in China, we showed that the variability of growth rates (GR) of particles originating from NPF depend only weakly on low-volatile vapor - highly oxidated organic molecules (HOMs) and sulphuric acid - concentrations and have no apparent connection with the strength of NPF or the level of background pollution. We then constrained aerosol dynamic model simulations with these observations. We showed that under conditions typical for the Beijing atmosphere, NPF is capable of contributing with more than 100 mu g m(-3) to the PM2.5 mass concentration and simultaneously >10(3) cm(-3) to the haze particle (diameter > 100 nm) number concentration. Our simulations reveal that the PM2.5 mass concentration originating from NPF, strength of NPF, particle growth rate and pre-existing background particle population are all connected with each other. Concerning the PM pollution control, our results indicate that reducing primary particle emissions might not result in an effective enough decrease in total PM2.5 mass concentrations until a reduction in emissions of precursor compounds for NPF and subsequent particle growth is imposed.Peer reviewe