Special Issue on “Synthesis and Characterization of Nanomaterials”

Nanomaterial is defined a natural, incidental or manufactured material containing particles, in an unbound state, as an aggregate, or as an agglomerate, and where, for 50% or more of the particles in the number size distribution, one or more external dimensions is in the size range 1–100 nm [...]

A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modeled as a superposition of log-normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes and perturbed by Gaussian noise as well as on three datasets obtained from AERONET. We show that the proposed algorithm provides good results when the right number of modes is selected. In general, an overestimate of the number of modes provides better results than an underestimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

Look−up tables resolved by complex refractive index to correct particle sizes measured by common research−grade optical particle counters

Optical particle counters (OPC) are widely used to measure the aerosol particle number size distribution at atmospheric ambient conditions and over a large size range. Their measurement principle is based on the dependence of light scattering on particle size. However, this dependence is not monotonic at all sizes and light scattering also depends on the particle composition (i.e., the complex refractive index, CRI) and morphology. Therefore, the conversion of the measured scattered intensity to the desired particle size depends on the microphysical properties of the sampled aerosol population and might not be unique at all sizes. While these complexities have been addressed before, corrections are typically applied ad-hoc and are not standardised. This paper addresses this issue by providing a consistent and extended database of pre−computed correction factors for a wide range of complex refractive index values representing the composition variability of atmospheric aerosols. These correction factors are calculated for five different commercial OPCs (USHAS, PCASP, FSSP, GRIMM and its airborne version Sky− GRIMM, CDP) by assuming Mie theory for homogeneous spherical particles, and by varying the real part of the CRI between 1.33 and 1.75 in steps of 0.01 and the imaginary part between 0.0 and 0.4 in steps of 0.001. Correction factors for mineral dust are provided at the CRI of 1.53 – 0.003i and account for the asphericity of these particles. The datasets described in this paper are distributed at open-access repository: https://doi.org/10.25326/234 (license CC BY, Formenti et al., 2021) maintained by the French national center for Atmospheric data and services AERIS to data users/geophysicists who number size distribution measurements from OPC for their research on atmospheric aerosols. Application and caveats of the CRI-corrections factors are presented and discussed. The dataset presented in this paper is not only useful for correcting the size distribution from an OPC when the particle refractive index is known, but even when only assumptions can be made. Furthermore, this dataset can be useful in calculating uncertainties or sensitivities of aerosol volume/mass/extinction from OPCs given no or limited knowledge of refractive index.

Removal of Inclusions from Melts

Inclusion origins and the methods for determining the content of inclusions in a melt are described. Removal of inclusions by flotation/settling is demonstrated. The method for removing inclusions from molten metals by bubbling is described in detail with attachment mechanism to bubbles. Use of microbubbles are included. Filtration capture mechanisms of inclusions, cake and deep bed mode, are derived. A model for removal of inclusions by ceramic foam filters is introduced. Re-entrainment of inclusions are examined. In addition the use of rotational and electromagnetic forces to remove inclusions is explained. The number size distribution of inclusions is taken into account, both the change in the distribution function and the growth of inclusions with time. In the end the interaction of dissolved elements and inclusions is described.

Development and evolution of an anomalous Asian dust event across Europe in March 2020

This paper concerns an in-depth analysis of an exceptional incursion of mineral dust over Southern Europe in late March 2020. This event was associated with an anomalous circulation pattern leading to several days of PM10 exceedances in connection with a dust source located in Central Asia a rare source of dust for Europe, more frequently affected by dust outbreaks from the Sahara desert. The synoptic meteorological configuration was analyzed in detail, while aerosol evolution during the transit of the dust cloud over Northern Italy was assessed at high time resolution by means of optical particle counting at three stations, namely Bologna, Trieste, and Mt. Cimone allowing to reveal transport timing among the three locations. Back-trajectory analyses supported by AOD (Aerosol Optical Depth) maps allowed to locate the mineral dust source area in the Aralkum region. The event was therefore analyzed through the observation of particle number size distribution with the support of chemical composition analysis. It is shown that PM10 exceedance recorded is associated with a large fraction of coarse particles in agreement with mineral dust properties. Both in-situ number size distribution and vertical distribution of the dust plume were cross-checked by Lidar Ceilometer and AOD data from two nearby stations, showing that the dust plume, differently from those originated in the Sahara desert, traveled close to the ground up to a height of about 2 km. The limited mixing layer height caused by high concentrations of absorbing and scattering aerosols caused the mixing of mineral dust with other locally-produced ambient aerosols, thereby potentially increasing its morbidity effects.

Retrieving the atmospheric number size distribution from lidar data

We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modelled as a superposition of log–normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes, and perturbed by Gaussian noise, and on three real datasets obtained from AERONET. We show that the proposed algorithm provides satisfactory results even when the assumed number of modes is different from the true number of modes, and substantially excellent results when the right number of modes is selected. In general, an over-estimate of the number of modes provides better results than an under-estimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

Formation and growth of aerosol particles in boreal forest of Siberia

<p>New Particle Formation (NPF) is a process in which a large number of particles is formed in the atmosphere via gas-to-particle conversion. Previous research shows the important role of formation of new particles for atmosphere, clouds and climate (Kerminen, V.-M. et al. 2018).</p><p>              There exist measurements from different parts of the world which show that NPF is happening worldwide (Kerminen, V.-M. et al. 2018). Measurements at SMEAR II station in Hyytiälä, Finland (Hari P. and Kulmala M., 2005), show that NPF is a common process in Finland’s boreal forests. However, measurements at Zotto station in Siberia, Russia, show that NPF events are very rare in that area (Wiedensohler A. et al., 2018). Measurements in Siberian boreal forests are sparse. We have conducted new measurements at Fonovaya station near Tomsk (Siberia, Russia) using Neutral cluster Air Ion Spectrometer (NAIS), Particle Size Magnifier (PSM), Differential Mobility Particle Sizer (DMPS) and the Atmospheric Pressure interface Time-Of-Flight mass spectrometer (APi-TOF). Those instruments measure aerosol particle number size distribution (NAIS, DMPS), ion number size distribution (NAIS), size distribution of small particles (PSM) and chemical composition of aerosol particles (APi-TOF). The novelty of this work is that such complex measurements have not been done in Siberia before.</p><p>              Here we report the first results of our research on NPF phenomenon in Siberian boreal forest. We present detailed statistics of NPF events, as well as formation rates (J) and growth rates (GR) of aerosol particles. The results from Fonovaya station are compared with those from SMEAR II station and from SMEAR Estonia station in Järvselja, Estonia.</p><p>               </p><p> </p><p> </p><p>Literature</p><ul><li>[1] Kerminen V.-M. et al. “Atmospheric new particle formation and growth: review of field observations”. In: Environmental Research Letters 10 (2018), p. 103003.</li> <li>[2] Wiedensohler A. et al. “Infrequent new particle formation over the remote boreal forest of Siberia”. In: Atmospheric Environment 200 (2019), pp. 167–169.</li> <li>[3] Dada L. et al. “Long-term analysis of clear-sky new particle formation events and nonevents in Hyytiälä”. In: Atmospheric Chemistry and Physics 10 (2017), pp. 6227–6241.</li> </ul><p> </p>