2014, Liu, H.J., Zhao, C.S., Nekat, B., Ma, N., Wiedensohler, A., van Pinxteren, D., Spindler, G., Müller, K., Herrmann, H.

Hygroscopic growth of aerosol particles is of significant importance in quantifying the aerosol radiative effect in the atmosphere. In this study, hygroscopic properties of ambient particles are investigated based on particle chemical composition at a suburban site in the North China Plain during the HaChi campaign (Haze in China) in summer 2009. The size-segregated aerosol particulate mass concentration as well as the particle components such as inorganic ions, organic carbon and water-soluble organic carbon (WSOC) are identified from aerosol particle samples collected with a ten-stage impactor. An iterative algorithm is developed to evaluate the hygroscopicity parameter κ from the measured chemical composition of particles. During the HaChi summer campaign, almost half of the mass concentration of particles between 150 nm and 1 μm is contributed by inorganic species. Organic matter (OM) is abundant in ultrafine particles, and 77% of the particulate mass with diameter (Dp) of around 30 nm is composed of OM. A large fraction of coarse particle mass is undetermined and is assumed to be insoluble mineral dust and liquid water. The campaign's average size distribution of κ values shows three distinct modes: a less hygroscopic mode (Dp < 150 nm) with κ slightly above 0.2, a highly hygroscopic mode (150 nm < Dp < 1 μm) with κ greater than 0.3 and a nearly hydrophobic mode (Dp > 1 μm) with κ of about 0.1. The peak of the κ curve appears around 450 nm with a maximum value of 0.35. The derived κ values are consistent with results measured with a high humidity tandem differential mobility analyzer within the size range of 50–250 nm. Inorganics are the predominant species contributing to particle hygroscopicity, especially for particles between 150 nm and 1 μm. For example, NH4NO3, H2SO4, NH4HSO4 and (NH4)2SO4 account for nearly 90% of κ for particles of around 900 nm. For ultrafine particles, WSOC plays a critical role in particle hygroscopicity due to the predominant mass fraction of OM in ultrafine particles. WSOC for particles of around 30 nm contribute 52% of κ. Aerosol hygroscopicity is related to synoptic transport patterns. When southerly wind dominates, particles are more hygroscopic; when northerly wind dominates, particles are less hygroscopic. Aerosol hygroscopicity also has a diurnal variation, which can be explained by the diurnal evolution of planetary boundary layer, photochemical aging processes during daytime and enhanced black carbon emission at night. κ is highly correlated with mass fractions of SO42−, NO3− and NH4+ for all sampled particles as well as with the mass fraction of WSOC for particles of less than 100 nm. A parameterization scheme for κ is developed using mass fractions of SO42−, NO3−, NH4+ and WSOC due to their high correlations with κ, and κ calculated from the parameterization agrees well with κ derived from the particle's chemical composition. Further analysis shows that the parameterization scheme is applicable to other aerosol studies in China.

2018, Papagiannopoulos, Nikolaos, Mona, Lucia, Amodeo, Aldo, D'Amico, Giuseppe, Gumà Claramunt, Pilar, Pappalardo, Gelsomina, Alados-Arboledas, Lucas, Guerrero-Rascado, Juan Luís, Amiridis, Vassilis, Kokkalis, Panagiotis, Apituley, Arnoud, Baars, Holger, Schwarz, Anja, Wandinger, Ulla, Binietoglou, Ioannis, Nicolae, Doina, Bortoli, Daniele, Comerón, Adolfo, Rodríguez-Gómez, Alejandro, Sicard, Michaël, Papayannis, Alex, Wiegner, Matthias

We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80%. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.