Biogenic New Particle Formation : Field Observations and Chamber Experiments

New particle formation (NPF) is an atmospheric phenomenon, observed in many environments globally, and it contributes to a major fraction of the global aerosol number budget thereby affecting both climate and human health. In this thesis, we investigate the mechanisms behind NPF in the boreal forest environment and analyze the long-term behavior of the variables associated with the occurrence of this phenomenon. In order to improve the classification of atmospheric NPF events, especially when considering the increasing number of measurement campaigns and stations, we developed an automatic framework to classify NPF events based on the 2–4 nm ion and 7–25 nm aerosol particle concentrations in the atmosphere. This approach categorizes days into four defined classes: Regional NPF events, transported NPF events, ion bursts and non-events. For regional NPF events, the approach additionally determined the precise period (start and end-time) during which the event occurred. We show that, in the boreal forest, NPF events tend to occur under clear sky conditions with low condensation sinks and moderate temperatures. Using chamber simulations, we further investigated the mechanisms of new particle formation and growth in the boreal forest environment. While sulfuric acid is known to be the driver of NPF, we found that pure biogenic NPF is possible in the absence of sulfuric acid, and that the nucleation is mediated by dimers of highly oxygenated monoterpene oxidation products. We also found that anthropogenic vapors, such as NOx, attenuate the particle formation and growth by modifying the chemical composition of highly oxygenated molecules (HOMs) necessary for nucleation and growth. In the present-day-time atmosphere, we found that highly oxygenated molecules (HOMs) govern ion-induced new particle formation in the boreal forest when the ratio of biogenic HOMs to H2SO4 is greater than 30. Our results show that non-nitrate HOM dimers mediate ion-induced nucleation not only during daytime but also during night-time. In the absence of H2SO4, we observed pure biogenic ion-induced clustering mediated by non-nitrate HOM dimers and trimers; however, these clusters did not grow past 6 nm due to insufficient photochemistry needed for producing condensable vapors that would ensure cluster survival

Urban Aerosol Particle Size Characterization in Eastern Mediterranean Conditions

Characterization of urban particle number size distribution (PNSD) has been rarely reported/performed in the Middle East. Therefore, we aimed at characterizing the PNSD (0.01–10 µm) in Amman as an example for an urban Middle Eastern environment. The daily mean submicron particle number concentration (PNSub) was 6.5 × 103–7.7 × 104 cm−3 and the monthly mean coarse mode particle number concentration (PNCoarse) was 0.9–3.8 cm−3 and both had distinguished seasonal variation. The PNSub also had a clear diurnal and weekly cycle with higher concentrations on workdays (Sunday–Thursday; over 3.3 × 104 cm−3) than on weekends (below 2.7 × 104 cm−3). The PNSub constitute of 93% ultrafine fraction (diameter < 100 nm). The mean particle number size distributions was characterized with four well-separated submicron modes (Dpg,I, Ni): nucleation (22 nm, 9.4 × 103 cm−3), Aitken (62 nm, 3.9 × 103 cm−3), accumulation (225 nm, 158 cm−3), and coarse (2.23 µm, 1.2 cm−3) in addition to a mode with small geometric mean diameter (GMD) that represented the early stage of new particle formation (NPF) events. The wind speed and temperature had major impacts on the concentrations. The PNCoarse had a U-shape with respect to wind speed and PNSub decreased with wind speed. The effect of temperature and relative humidity was complex and require further investigations

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

Characterization of Urban New Particle Formation in Amman—Jordan

We characterized new particle formation (NPF) events in the urban background of Amman during August 2016–July 2017. The monthly mean of submicron particle number concentration was 1.2 × 104–3.7 × 104 cm−3 (exhibited seasonal, weekly, and diurnal variation). Nucleation mode (10–15 nm) concentration was 0.7 × 103–1.1 × 103 cm−3 during daytime with a sharp peak (1.1 × 103–1.8 × 103 cm−3) around noon. We identified 110 NPF events (≈34% of all days) of which 55 showed a decreasing mode diameter after growth. The NPF event occurrence was higher in summer than in winter, and events were accompanied with air mass back trajectories crossing over the Eastern Mediterranean. The mean nucleation rate (J10) was 1.9 ± 1.1 cm−3 s−1 (monthly mean 1.6–2.7 cm−3 s−1) and the mean growth rate was 6.8 ± 3.1 nm/h (4.1–8.8 nm/h). The formation rate did not have a seasonal pattern, but the growth rate had a seasonal variation (maximum around August and minimum in winter). The mean condensable vapor source rate was 4.1 ± 2.2 × 105 molecules/cm3 s (2.6–6.9 × 105 molecules/cm3 s) with a seasonal pattern (maximum around August). The mean condensation sink was 8.9 ± 3.3 × 10−3 s−1 (6.4–14.8 × 10−3 s−1) with a seasonal pattern (minimum around June and maximum in winter)

Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.Peer reviewe

Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.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

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

Assessment of particle size magnifier inversion methods to obtain the particle size distribution from atmospheric measurements

Accurate measurements of the size distribution of atmospheric aerosol nanoparticles are essential to build an understanding of new particle formation and growth. This is particularly crucial at the sub-3 nm range due to the growth of newly formed nanoparticles. The challenge in recovering the size distribution is due its complexity and the fact that not many instruments currently measure at this size range. In this study, we used the particle size magnifier (PSM) to measure atmospheric aerosols. Each day was classified into one of the following three event types: a new particle formation (NPF) event, a non-event or a haze event. We then compared four inversion methods (stepwise, kernel, Hagen-Alofs and expectation-maximization) to determine their feasibility to recover the particle size distribution. In addition, we proposed a method to pretreat the measured data, and we introduced a simple test to estimate the efficacy of the inversion itself. Results showed that all four methods inverted NPF events well; however, the stepwise and kernel methods fared poorly when inverting non-events or haze events. This was due to their algorithm and the fact that, when encountering noisy data (e.g. air mass fluctuations or low sub-3 nm particle concentrations) and under the influence of larger particles, these methods overestimated the size distribution and reported artificial particles during inversion. Therefore, using a statistical hypothesis test to discard noisy scans prior to inversion is an important first step toward achieving a good size distribution. After inversion, it is ideal to compare the integrated concentration to the raw estimate (i.e. the concentration difference at the lowest supersaturation and the highest supersaturation) to ascertain whether the inversion itself is sound. Finally, based on the analysis of the inversion methods, we provide procedures and codes related to the PSM data inversion.Peer reviewe

Long-term analysis of clear-sky new particle formation events and nonevents in Hyytiälä

New particle formation (NPF) events have been observed all around the world and are known to be a major source of atmospheric aerosol particles. Here we combine 20 years of observations in a boreal forest at the SMEAR II station (Station for Measuring Ecosystem-Atmosphere Relations) in Hyytiala, Finland, by building on previously accumulated knowledge and by focusing on clear-sky (non-cloudy) conditions. We first investigated the effect of cloudiness on NPF and then compared the NPF event and nonevent days during clear-sky conditions. In this comparison we considered, for example, the effects of calculated particle formation rates, condensation sink, trace gas concentrations and various meteorological quantities in discriminating NPF events from nonevents. The formation rate of 1.5 nm particles was calculated by using proxies for gaseous sulfuric acid and oxidized products of low volatile organic compounds, together with an empirical nucleation rate coefficient. As expected, our results indicate an increase in the frequency of NPF events under clear-sky conditions in comparison to cloudy ones. Also, focusing on clear-sky conditions enabled us to find a clear separation of many variables related to NPF. For instance, oxidized organic vapors showed a higher concentration during the clear-sky NPF event days, whereas the condensation sink (CS) and some trace gases had higher concentrations during the nonevent days. The calculated formation rate of 3 nm particles showed a notable difference between the NPF event and nonevent days during clear-sky conditions, especially in winter and spring. For springtime, we are able to find a threshold equation for the combined values of ambient temperature and CS, (CS (s(-1)) > -3.091 x 10(-5) x T (in Kelvin) + 0.0120), above which practically no clear-sky NPF event could be observed. Finally, we present a probability distribution for the frequency of NPF events at a specific CS and temperature.Peer reviewe