On the hygroscopic growth of ammoniated sulfate particles of non-stoichiometric composition

International audienceThe hygroscopic growth of ammoniated sulfate particles was studied by measurements and model calculations for particles with varying ammonium-to-sulfate ratio. In the measurements, the ammonium-to-sulfate ratio was adjusted by using mixtures of ammonium sulfate and ammonium bisulfate in generating the solid particles. The hygroscopic growth was measured using a tandem differential mobility analyzer. The measurements were simulated using a thermodynamical equilibrium model. The calculations indicated that the solid phases in particle with ammonium-to-sulfate ratio between 1.5?2, were ammonium sulfate and letovicite. Both in the calculations and in the experiments the hygroscopic growth was initiated at relative humidities less than the theoretical deliquescence relative humidity of these particles. This indicates that the particles were multi-phase particles including solids and liquids. The equilibrium model yielded a satisfactory prediction of the hygroscopic growth of particles generated from a solution with 1:1 mass ratio between dissolved ammonium sulfate and ammonium bisulfate. However, for particles with 3:1 and 10:1 mass ratios, the model predictions overestimated the growth at relative humidities between about 60% and the point of complete deliquescence (close to 80% RH). In contrast, a model, in which letovicite was allowed to dissolve only after complete dissolution of ammonium sulfate, reproduced the observations well. This indicates that the dry particles had a letovicite core surrounded by an ammonium sulfate shell

A method for detecting the presence of organic fraction in nucleation mode sized particles

New particle formation and growth has a very important role in many climate processes. However, the overall knowlegde of the chemical composition of atmospheric nucleation mode (particle diameter, d<20 nm) and the lower end of Aitken mode particles (d&#x2264;50 nm) is still insufficient. In this work, we have applied the UFO-TDMA (ultrafine organic tandem differential mobility analyzer) method to shed light on the presence of an organic fraction in the nucleation mode size class in different atmospheric environments. The basic principle of the organic fraction detection is based on our laboratory UFO-TDMA measurements with organic and inorganic compounds. Our laboratory measurements indicate that the usefulness of the UFO-TDMA in the field experiments would arise especially from the fact that atmospherically the most relevant inorganic compounds do not grow in subsaturated ethanol vapor, when particle size is 10 nm in diameter and saturation ratio is about 86% or below it. Furthermore, internally mixed particles composed of ammonium bisulfate and sulfuric acid with sulfuric acid mass fraction &#x2264;33% show no growth at 85% saturation ratio. In contrast, 10 nm particles composed of various oxidized organic compounds of atmospheric relevance are able to grow in those conditions. These discoveries indicate that it is possible to detect the presence of organics in atmospheric nucleation mode sized particles using the UFO-TDMA method. In the future, the UFO-TDMA is expected to be an important aid to describe the composition of atmospheric newly-formed particles

Ion production rate in a boreal forest based on ion, particle and radiation measurements

International audienceIn this study the ion production rates in a boreal forest were studied based on two different methods: 1) cluster ion and particle concentration measurements, 2) external radiation and radon concentration measurements. Both methods produced reasonable estimates for ion production rates. The average ion production rate calculated from aerosol particle size distribution and air ion mobility distribution measurements was 2.6 ion pairs cm-3s-1, and based on external radiation and radon measurements, 4.5 ion pairs cm-3s-1. The first method based on ion and particle measurements gave lower values for the ion production rates especially during the day. A possible reason for this is that particle measurements started only from 3nm, so the sink of small ions during the nucleation events was underestimated. It may also be possible that the hygroscopic growth factors of aerosol particles were underestimated. Another reason for the discrepancy is the nucleation mechanism itself. If the ions are somehow present in the nucleation process, there could have been an additional ion sink during the nucleation days

Nanoparticle formation by ozonolysis of inducible plant volatiles

International audienceWe present the first laboratory experiments of aerosol formation from oxidation of volatile organic species emitted by living plants, a process which for half a century has been known to take place in the atmosphere. We have treated white cabbage plants with methyl jasmonate in order to induce the production of monoterpenes and certain less-volatile sesqui- and homoterpenes. Ozone was introduced into the growth chamber in which the plants were placed, and the subsequent aerosol formation and growth of aerosols were monitored by measuring the particle size distributions continuously during the experiments. Our observations show similar particle formation rates as in the atmosphere but much higher growth rates. The results indicate that the concentrations of nonvolatile oxidation products of plant released precursors needed to induce the nucleation are roughly an order-of-magnitude higher than their concentrations during atmospheric nucleation events. Our results therefore suggest that if oxidized organics are involved in atmospheric nucleation events, their role is to participate in the growth of pre-existing molecular clusters rather than to form such clusters through homogeneous or ion-induced nucleation

Using discriminant analysis as a nucleation event classification method

More than three years of measurements of aerosol size-distribution and different gas and meteorological parameters made in Po Valley, Italy were analysed for this study to examine which of the meteorological and trace gas variables effect on the emergence of nucleation events. As the analysis method, we used discriminant analysis with non-parametric Epanechnikov kernel, included in non-parametric density estimation method. The best classification result in our data was reached with the combination of relative humidity, ozone concentration and a third degree polynomial of radiation. RH appeared to have a preventing effect on the new particle formation whereas the effects of O₃ and radiation were more conductive. The concentration of SO₂ and NO₂ also appeared to have significant effect on the emergence of nucleation events but because of the great amount of missing observations, we had to exclude them from the final analysis

Changes in the production rate of secondary aerosol particles in Central Europe in view of decreasing SO2 emissions between 1996 and 2006

In anthropogenically influenced atmospheres, sulphur dioxide (SO2) is the main precursor of gaseous sulphuric acid (H2SO4), which in turn is a main precursor for atmospheric particle nucleation. As a result of socio-economic changes, East Germany has seen a dramatic decrease in anthropogenic SO2 emissions between 1989 and present, as documented by routine air quality measurements in many locations. We have attempted to evaluate the influence of changing SO2 concentrations on the frequency and intensity of new particle formation (NPF) using two different data sets (1996–1997; 2003–2006) of experimental particle number size distributions (diameter range 3–750 nm) from the atmospheric research station Melpitz near Leipzig, Germany. Between the two periods SO2 concentrations decreased by 65% on average, while the frequency of NPF events dropped by 45%. Meanwhile, the average formation rate of 3 nm particles decreased by 68% on average. The trends were statistically significant and therefore suggest a connection between the availability of anthropogenic SO2 and freshly formed new particles. In contrast to the decrease in new particle formation, we found an increase in the mean growth rate of freshly nucleated particles (+22%), suggesting that particle nucleation and subsequent growth into larger sizes are delineated with respect to their precursor species. Using three basic parameters, the condensation sink for H2SO4, the SO2 concentration, and the global radiation intensity, we were able to define the characteristic range of atmospheric conditions under which particle formation events take place at the Melpitz site. While the decrease in the concentrations and formation rates of the new particles was rather evident, no similar decrease was found with respect to the generation of cloud condensation nuclei (CCN; particle diameter >100 nm) as a result of atmospheric nucleation events. On the contrary, the production of CCN following nucleation events appears to have increased by tens of percents. Our aerosol dynamics model simulations suggest that such an increase can be caused by the increased particle growth rate

Nucleation and growth of new particles in Po Valley, Italy

Aerosol number distribution measurements are reported at San Pietro Capofiume (SPC) station (44&deg;39&apos; N, 11&deg;37&apos; E) for the time period 2002&ndash;2005. The station is located in Po Valley, the largest industrial, trading and agricultural area in Italy with a high population density. New particle formation was studied based on observations of the particle size distribution, meteorological and gas phase parameters. The nucleation events were classified according to the event clarity based on the particle number concentrations, and the particle formation and growth rates. Out of a total of 769 operational days from 2002 to 2005 clear events were detected on 36% of the days whilst 33% are clearly non-event days. The event frequency was high during spring and summer months with maximum values in May and July, whereas lower frequency was observed in winter and autumn months. The average particle formation and growth rates were estimated as ~6 cm^&minus;3 s^&minus;1 and ~7 nm h^&minus;1, respectively. Such high growth and formation rates are typical for polluted areas. Temperature, wind speed, solar radiation, SO₂ and O₃ concentrations were on average higher on nucleation days than on non-event days, whereas relative and absolute humidity and NO₂ concentration were lower; however, seasonal differences were observed. Backtrajectory analysis suggests that during majority of nucleation event days, the air masses originate from northern to eastern directions. We also study previously developed nucleation event correlations with environmental variables and show that they predict Po Valley nucleation events with variable success

A statistical proxy for sulphuric acid concentration

Gaseous sulphuric acid is a key precursor for new particle formation in the atmosphere. Previous experimental studies have confirmed a strong correlation between the number concentrations of freshly formed particles and the ambient concentrations of sulphuric acid. This study evaluates a body of experimental gas phase sulphuric acid concentrations, as measured by Chemical Ionization Mass Spectrometry (CIMS) during six intensive measurement campaigns and one long-term observational period. The campaign datasets were measured in Hyytiälä, Finland, in 2003 and 2007, in San Pietro Capofiume, Italy, in 2009, in Melpitz, Germany, in 2008, in Atlanta, Georgia, USA, in 2002, and in Niwot Ridge, Colorado, USA, in 2007. The long term data were obtained in Hohenpeissenberg, Germany, during 1998 to 2000. The measured time series were used to construct proximity measures ("proxies") for sulphuric acid concentration by using statistical analysis methods. The objective of this study is to find a proxy for sulfuric acid that is valid in as many different atmospheric environments as possible. Our most accurate and universal formulation of the sulphuric acid concentration proxy uses global solar radiation, SO2 concentration, condensation sink and relative humidity as predictor variables, yielding a correlation measure (R) of 0.87 between observed concentration and the proxy predictions. Interestingly, the role of the condensation sink in the proxy was only minor, since similarly accurate proxies could be constructed with global solar radiation and SO2 concentration alone. This could be attributed to SO2 being an indicator for anthropogenic pollution, including particulate and gaseous emissions which represent sinks for the OH radical that, in turn, is needed for the formation of sulphuric acid

Identification of new particle formation events with deep learning

New particle formation (NPF) in the atmosphere is globally an important source of climate relevant aerosol particles. Occurrence of NPF events is typically analyzed by researchers manually from particle size distribution data day by day, which is time consuming and the classification of event types may be inconsistent. To get more reliable and consistent results, the NPF event analysis should be automatized. We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification. To our knowledge, this is the first time that a deep learning method, i.e., transfer learning of a convolutional neural network (CNN), has successfully been used to automatically classify NPF events into different classes directly from particle size distribution images, similarly to how the researchers carry out the manual classification. The developed method is based on image analysis of particle size distributions using a pretrained deep CNN, named AlexNet, which was transfer learned to recognize NPF event classes (six different types). In transfer learning, a partial set of particle size distribution images was used in the training stage of the CNN and the rest of the images for testing the success of the training. The method was utilized for a 15-year-long dataset measured at San Pietro Capofiume (SPC) in Italy. We studied the performance of the training with different training and testing of image number ratios as well as with different regions of interest in the images. The results show that clear event (i.e., classes 1 and 2) and nonevent days can be identified with an accuracy of ca. 80 %, when the CNN classification is compared with that of an expert, which is a good first result for automatic NPF event analysis. In the event classification, the choice between different event classes is not an easy task even for trained researchers, and thus overlapping or confusion between different classes occurs. Hence, we cross-validated the learning results of CNN with the expert-made classification. The results show that the overlapping occurs, typically between the adjacent or similar type of classes, e.g., a manually classified Class 1 is categorized mainly into classes 1 and 2 by CNN, indicating that the manual and CNN classifications are very consistent for most of the days. The classification would be more consistent, by both human and CNN, if only two different classes are used for event days instead of three classes. Thus, we recommend that in the future analysis, event days should be categorized into classes of quantifiable (i.e., clear events, classes 1 and 2) and nonquantifiable (i.e., weak events, Class  3). This would better describe the difference of those classes: both formation and growth rates can be determined for quantifiable days but not both for nonquantifiable days. Furthermore, we investigated more deeply the days that are classified as clear events by experts and recognized as nonevents by the CNN and vice versa. Clear misclassifications seem to occur more commonly in manual analysis than in the CNN categorization, which is mostly due to the inconsistency in the human-made classification or errors in the booking of the event class. In general, the automatic CNN classifier has a better reliability and repeatability in NPF event classification than human-made classification and, thus, the transfer-learned pretrained CNNs are powerful tools to analyze long-term datasets. The developed NPF event classifier can be easily utilized to analyze any long-term datasets more accurately and consistently, which helps us to understand in detail aerosol–climate interactions and the long-term effects of climate change on NPF in the atmosphere. We encourage researchers to use the model in other sites. However, we suggest that the CNN should be transfer learned again for new site data with a minimum of ca. 150 figures per class to obtain good enough classification results, especially if the size distribution evolution differs from training data. In the future, we will utilize the method for data from other sites, develop it to analyze more parameters and evaluate how successfully CNN could be trained with synthetic NPF event data.</p