The coronavirus (COVID-19) spread around the world lead to the application of restrictions and strict stay-at-home mandates in the majority of the European countries, during the first quarter of 2020. An intensive observation campaign was organized during May 2020 by the European Aerosol Research Lidar Network (EARLINET), part of ACTRIS (Aerosols, Clouds and Trace Gases Research Infrastructure), to investigate possible aerosol-type changes in the lower troposphere due to the decreased emissions during the COVID-19 lockdown and relaxation period. The current work is part of an extended study related to potential changes in the aerosol load over Europe. The dominant aerosol types are identified with NATALI aerosol classification scheme, applied upon the aerosol lidar-derived intensive products. The aerosol types within January–May 2020 are compared to the aerosol types from the reference period between 2015 and 2019.

The purpose of this study is to investigate the ability of the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) to derive accurate geometrical features of lofted aerosol layers, selecting the Mediterranean Basin as the study area. Comparisons with ground-based correlative measurements constitute a key component in the validation of passive and active satellite aerosol products. For this purpose, we use ground-based observations from quality-controlled lidar stations reporting to the European Aerosol Research Lidar Network (EARLINET). An optimal methodology for validation purposes has been developed and applied using the EARLINET optical profiles and TROPOMI aerosol products, aiming at the in-depth evaluation of the TROPOMI aerosol layer height (ALH) product for the period 2018 to 2022 over the Mediterranean Basin. Seven EARLINET stations were chosen, taking into consideration their proximity to the sea, which provided 63 coincident aerosol cases for the satellite retrievals. In the following, we present the first validation results for the TROPOMI/S5P ALH using the optimized EARLINET lidar products employing the automated validation chain designed for this purpose. The quantitative validation at pixels over the selected EARLINET stations illustrates that the TROPOMI ALH product is consistent with the EARLINET lidar products, with a high correlation coefficient R=0.82 (R=0.51) and a mean bias of -0.51±0.77 km and -2.27±1.17 km over ocean and land, respectively. Overall, it appears that aerosol layer altitudes retrieved from TROPOMI are systematically lower than altitudes from the lidar retrievals. High-albedo scenes, as well as low-aerosol-load scenes, are the most challenging for the TROPOMI retrieval algorithm, and these results testify to the need to further investigate the underlying cause. This work provides a clear indication that the TROPOMI ALH product can under certain conditions achieve the required threshold accuracy and precision requirements of 1 km, especially when only ocean pixels are included in the comparison analysis. Furthermore, we describe and analyse three case studies in detail, one dust and two smoke episodes, in order to illustrate the strengths and limitations of the TROPOMI ALH product and demonstrate the presented validation methodology. The present analysis provides important additions to the existing validation studies that have been performed so far for the TROPOMI S5P ALH product, which were based only on satellite-to-satellite comparisons.

The European Aerosol Research Lidar Network (EARLINET), part of the Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS), organized an intensive observational campaign in May 2020, with the objective of monitoring the atmospheric state over Europe during the COVID-19 lockdown and relaxation period. Besides the standard operational processing of the lidar data in EARLINET, for seven EARLINET sites having collocated sun-photometric observations in the Aerosol Robotic Network (AERONET), a network exercise was held in order to derive profiles of the concentration and effective column size distributions of the aerosols in the atmosphere, by applying the GRASP/GARRLiC (from Generalized Aerosol Retrieval from Radiometer and Lidar Combined data – GARRLiC – part of the Generalized Retrieval of Atmosphere and Surface Properties – GRASP) inversion algorithm. The objective of this network exercise was to explore the possibility of identifying the anthropogenic component and of monitoring its spatial and temporal characteristics in the COVID-19 lockdown and relaxation period. While the number of cases is far from being statistically significant so as to provide a conclusive description of the atmospheric aerosols over Europe during this period, this network exercise was fundamental to deriving a common methodology for applying GRASP/GARRLiC to a network of instruments with different characteristics. The limits of the approach are discussed, in particular the missing information close to the ground in the lidar measurements due to the instrument geometry and the sensitivity of the GRASP/GARRLiC retrieval to the settings used, especially for cases with low aerosol optical depth (AOD) like the ones we show here. We found that this sensitivity is well-characterized in the GRASP/GARRLiC products, since it is included in their retrieval uncertainties.

The SARS-CoV-2 presence and the bacterial community profile in air samples collected at the Intensive Care Unit (ICU) of the Operational Unit of Infectious Diseases of Santa Caterina Novella Hospital in Galatina (Lecce, Italy) have been evaluated in this study. Air samplings were performed in different rooms of the ICU ward with and without COVID-19 patients. No sample was found positive to SARS-CoV-2, according to Allplex 2019-nCoV Assay. The airborne bacterial community profiles determined by the 16S rRNA gene metabarcoding approach up to the species level were characterized by richness and biodiversity indices, Spearman correlation coefficients, and Principal Coordinate Analysis. Pathogenic and non-pathogenic bacterial species, also detected in outdoor air samples, were found in all collected indoor samples. Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and others coagulase-negative staphylococci, detected at high relative abundances in all the patients’ rooms, were the most abundant pathogenic species. The highest mean relative abundance of S. pettenkoferi and C. tuberculostearicum suggested that they were likely the main pathogens of COVID-19 patients at the ICU ward of this study. The identification of nosocomial pathogens representing potential patients’ risks in ICU COVID-19 rooms and the still controversial airborne transmission of the SARS-CoV-2 are the main contributions of this study.

The characterization of the day-to-night changes of the atmospheric particle chemical and optical properties in autumn–winter (AW) and spring–summer (SS) is the main goal of this study to contribute to the characterization and understanding of the particulate matter (PM) impact on the environment and climate at one of the most vulnerable areas of the planet to climate change. To this end, PM10 and PM2.5 samples from 14 January 2016 to 5 January 2017 have been collected in Lecce, a coastal site of South-Eastern Italy (40.33°N, 18.11°E), and day-to-night changes of mass concentrations of both fine (PM2.5) and coarse (PM10-PM2.5) particles and corresponding chemical species have been assessed both in AW and in SS. The statistical analysis of local meteorological parameters and their correlations with PM2.5 and chemical species mass concentrations have indicated that the day-to-night changes of temperature (T) and wind speed (WS) likely affected the day-to-night changes of mass concentrations, because of the T and WS impact on the atmospheric turbulence and air particle dispersion. The daily evolution of the anthropogenic activities and the planetary-boundary-layer height likely contributed to the day-to-night changes of the particle chemical composition. The stagnant atmospheric conditions prevailing in SS all over the Mediterranean basin, which favoured the mixing and the accumulation of atmospheric particles from different pollution sources, likely contributed to the changes of the relationships between chemical species and meteorological parameters from AW to SS at daytime and nighttime. The analysis of the aerosol scattering coefficient, scattering Ångström exponent, and scattering Ångström exponent difference, retrieved from nephelometer measurements co-located in space and time with the PM samplings, has allowed characterizing the day-to-night change of the aerosol optical properties. The relationships between the particle chemical and optical properties allowed a good understanding of their changes both in AW and in SS.

The compositional analysis of 16S rRNA gene sequencing datasets is applied to characterize the bacterial structure of airborne samples collected in different locations of a hospital infection disease department hosting COVID-19 patients, as well as to investigate the relationships among bacterial taxa at the genus and species level. The exploration of the centered log-ratio transformed data by the principal component analysis via the singular value decomposition has shown that the collected samples segregated with an observable separation depending on the monitoring location. More specifically, two main sample clusters were identified with regards to bacterial genera (species), consisting of samples mostly collected in rooms with and without COVID-19 patients, respectively. Human pathogenic genera (species) associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera (species) mainly isolated from soil were detected in the other samples. Propionibacterium acnes, Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and jeikeium were the main pathogenic species detected in COVID-19 patients’ rooms. Samples from these locations were on average characterized by smaller richness/evenness and diversity than the other ones, both at the genus and species level. Finally, the ρ metrics revealed that pairwise positive associations occurred either between pathogenic or non-pathogenic taxa.

Environmental samples collected in Brindisi (Italy) by a Hirst-type trap and in Lecce (Italy) by a PM10 sampler were analysed by optical microscopy and DNA-metabarcoding, respectively, to identify airborne pollen and perform an exploratory study, highlighting the benefits and limits of both sampling/detection systems. The Hirst-type trap/optical-microscopy system allowed detecting pollen on average over the full bloom season, since whole pollen grains, whose diameter vary within 10–100 μm, are required for morphological detection with optical microscopy. Conversely, pollen fragments with an aerodynamic diameter ≤10 μm were collected in Lecce by the PM10 sampler. Pollen grains and fragments are spread worldwide by wind/atmospheric turbulences and can age in the atmosphere, but aerial dispersal, aging, and long-range transport of pollen fragments are favoured over those of whole pollen grains because of their smaller size. Twenty-four Streptophyta families were detected in Lecce throughout the sampling year, but only nine out of them were in common with the 21 pollen families identified in Brindisi. Meteorological parameters and advection patterns were rather similar at both study sites, being only 37 km apart in a beeline, but their impact on the sample taxonomic structure was different, likely for the different pollen sampling/detection systems used in the two monitoring areas.

Results on day-to-night aerosol parameters retrieved from the Cimel CE318-T photometer, operating at Lecce_University (40 degrees 20'N, 18 degrees 6 ' E) since October 2016, are reported. The paper aims to contribute either to the studies on the CE318-T capability for monitoring aerosols in a wide range of moon's illumination and cycles and to the studies on day-to-night columnar aerosol properties by sun-sky-lunar photometry. The reliability of lunar AODs has firstly been evaluated by analysing the AOD differences calculated from the average of the last 1 h of daytime data (AOD(SS)) and the first 1 h of nocturnal data (AOD(MR)), respectively. Similarly, AOD(MS) and AOD(SR) values have been calculated from the average of the last 1 h of nocturnal data and the first 1 h of daytime data, respectively. We found that the AOD differences (AOD(SS) - AOD(MR)) and (AODMS - AODSR) were within the accuracy of solar AODs. Pairwise comparison, correlation coefficients, root mean square errors, and mean bias of AOD(SS) versus AOD(MR) and AOD(MS) versus AOD(SR) also supported the lunar data reliability. The statistical analysis of solar and corresponding lunar daily means has shown that monthly and seasonal AODs from solar and lunar photometry measurements, respectively, are characterized by similar metrics. Monthly and seasonal Angstrom exponents (angstrom) calculated at the 440-870 nm wavelength pair from daily solar and lunar AODs are also characterized by similar metrics. More specifically, pairwise comparisons by the Mann-Whitney test have shown that lunar and solar AOD and angstrom datasets, based on daily means, are not significantly different in all the months and the seasons at the p-level < 0.02. On the contrary, pairwise comparisons have shown that the Angstrom exponent differences (Delta angstrom) calculated from solar and lunar angstrom daily means are statistically different mainly in winter and autumn, likely for the high relative humidity at night-time. Therefore, Delta angstrom has been the only intensive parameter characterized by significant day-to-night changes in autumn and winter. Some case studies have been analysed/discussed to support the paper's main results and show the seasonal changes of the aerosol properties.

A preliminary local database of potential (opportunistic) airborne human and plant pathogenic and non-pathogenic species detected in PM10 samples collected in winter and spring is provided, in addition to their seasonal dependence and relationships with meteorological parameters and PM10 chemical species. The PM10 samples, collected at a Central Mediterranean coastal site, were analyzed by the 16S rRNA gene metabarcoding approach, and Spearman correlation coefficients and redundancy discriminant analysis tri-plots were used to investigate the main relationships. The screening of 1187 detected species allowed for the detection of 76 and 27 potential (opportunistic) human and plant pathogens, respectively. The bacterial structure of both pathogenic and non-pathogenic species varied from winter to spring and, consequently, the inter-species relationships among potential human pathogens, plant pathogens, and non-pathogenic species varied from winter to spring. Few non-pathogenic species and even fewer potential human pathogens were significantly correlated with meteorological parameters, according to the Spearman correlation coefficients. Conversely, several potential plant pathogens were strongly and positively correlated with temperature and wind speed and direction both in winter and in spring. The number of strong relationships between presumptive (human and plant) pathogens and non-pathogens, and meteorological parameters slightly increased from winter to spring. The sample chemical composition also varied from winter to spring. Some potential human and plant pathogens were correlated with chemicals mainly associated with marine aerosol and/or with soil dust, likely because terrestrial and aquatic environments were the main habitats of the detected bacterial species. The carrier role on the species seasonal variability was also investigated.

The atmosphere represents an underexplored temporary habitat for airborne microbial communities such as eukaryotes, whose taxonomic structure changes across different locations and/or regions as a function of both survival conditions and sources. A preliminary dataset on the seasonal dependence of the airborne eukaryotic community biodiversity, detected in PM10 samples collected from July 2018 to June 2019 at a coastal site representative of the Central Mediterranean, is provided in this study. Viridiplantae and Fungi were the most abundant eukaryotic kingdoms. Streptophyta was the prevailing Viridiplantae phylum, whilst Ascomycota and Basidiomycota were the prevailing Fungi phyla. Brassica and Panicum were the most abundant Streptophyta genera in winter and summer, respectively, whereas Olea was the most abundant genus in spring and autumn. With regards to Fungi, Botrytis and Colletotrichum were the most abundant Ascomycota genera, reaching the highest abundance in spring and summer, respectively, while Cryptococcus and Ustilago were the most abundant Basidiomycota genera, and reached the highest abundance in winter and spring, respectively. The genus community structure in the PM10 samples varied day-by-day, and mainly along with the seasons. The impact of long-range transported air masses on the same structure was also proven. Nevertheless, rather few genera were significantly correlated with meteorological parameters and PM10 mass concentrations. The PCoA plots and non-parametric Spearman's rank-order correlation coefficients showed that the strongest correlations generally occurred between parameters reaching high abundances/values in the same season or PM10 sample. Moreover, the screening of potential pathogenic fungi allowed us to detect seven potential pathogenic genera in our PM10 samples. We also found that, with the exception of Panicum and Physcomitrella, all of the most abundant and pervasive identified Streptophyta genera could serve as potential sources of aeroallergens in the studied area.

Lidar, nephelometer, and aethalometer measurements at the surface, co-located in time and space with Particulate Matter (PM) measurements, have been performed to investigate the impact of the daily evolution of the Planetary Boundary Layer (PBL) height on the aerosol optical and microphysical properties. Measurements were performed at a coastal site of southeastern Italy characterized by a shallow (<1000 m) PBL height. The Standard Deviation technique applied to the vertical profiles of both the lidar range corrected signal (RCS) and the linear volume depolarization ratio (δr) has been used to determine the daily evolution of the PBL height and highlight benefits and limits of using RCS and δr vertical profiles. It is shown that the PBL height, which drives the particle dispersion at the surface, significantly affects the optical and microphysical properties of the surface particles since the particle dispersion varies with their size and, consequently, the mean optical and microphysical properties of the surface particles are affected. The impact of meteorological conditions on the daily trend of the PBL height and the surface particle properties has also been highlighted.

The development of analytical methods capable of discriminating between bio-derived and fossil-derived carbon in Particulate Matter (PM) is a crucial aspect in the general efforts of identifying effective strategies for health-risks mitigations. The large difference in the isotopic signature between bio-derived and fossil derived materials in term of the concentration of C-14 the radioactive carbon isotope can be effectively used for this purpose. Indeed, though the detection and measurement of C-14 by AMS (Accelerator Mass Spectrometry) is a well established methodology, the reduced amount of carbon in PM samples (typically in the mu g range) requires the use of proper ion sources with a high efficiency. We present here the set-up developed at CEDAD-University of Salento as based on a gas-accepting hybrid ion source and an in-house designed gas handling interface. The performances of the system are also shown by presenting the firsts tests carried out on PM sampled on quartz filters in Lecce, Italy.

The paper presents the preliminary results of the lidar&radiometer measurement campaign (LRMC2017), estimation of statistical relations between aerosol mode concentrations retrieved from CALIOP and ground-based lidar stations and case study of fire smoke events in the Eurasian regions using combined ground-based and space lidar and radiometer observations.

A new methodology based on optical parameters from integrating nephelometer measurements and chemically speciated PM10 mass concentrations, to associate intensive optical parameters with pollution sources identified by the Positive Matrix Factorization (PMF) technique, is presented. PM10 samplings and integrating nephelometer measurements at 450, 525, and 635 nm, co-located in space and time, were performed from November 2011 to November 2012. The PM10 samples were chemically characterized for 16 species, including ions (Na+, NH4+, K+, Mg2+, Ca2+, Cl-, NO3-, and SO42-), metals (Al, Cd, Cu, Fe, Mn, and Ti), OC, and EC. The scattering sigma(s) and backscattering beta(s) coefficients at 450, 525, and 635 nm, and the PM10 chemically speciated data were used as input of the PMF model. Traffic (TRA, 28.3%), Biomass Burning and Nitrates (BBN, 27.4%), Soil Dust (SDU, 14.7%), ammonium Sulphate (SUL, 17.0%), and Aged Sea-salt (ASS, 12.6%) were the identified pollution sources, according to the PM10 mass apportionment, which did not show any significant difference in terms of source assignment and contribution, with respect to the solution without optical variables. The possibility of retrieving intensive optical parameters associated with the pollution sources from the related spectrally resolved sigma(s) and beta(s) values is the main feature of the proposed approach. The mass scattering efficiency (Sigma(PM10)), the scattering angstrom ngstrom exponent (angstrom), the spectral curvature of the scattering angstrom ngstrom exponent (Delta angstrom), and the asymmetry parameter (g) were the main intensive parameters calculated at different wavelengths or wavelength pairs to characterize the identified pollution sources. Sigma(PM10) and g at 450 nm, angstrom(450, 635 nm) and Delta angstrom were equal to 3.4 m(2) g(-1), 0.57, 0.96, and 0.54 for the TRA-source, to 5.0 m(2) g(-1), 0.58, 1.57, and - 0.06 for the BBN-source, to 5.0 m(2) g(-1), 0.67, 1.54, and 0.24 for the SUL-source, and to 0.6 m(2) g(-1), 0.33, -0.65, and 0.12 for the ASS-source, respectively. The analysis of monitoring days with a prevailing pollution source and the comparison of the paper's results with literature values have demonstrated the reliability of the used methodology.

Aerosol particles are essential constituents of the Earth’s atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence time resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in-situ near-surface segment of the atmospheric observations system. This paper will provide the widest effort so far to document variability of climate-relevant in-situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High quality data from more than 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single scattering albedo and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information including data provision procedures, quality control and analysis, data policy and usage of the ground-based aerosol measurements network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully-characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

The Redundancy Discrimination Analysis (RDA) and Spearman correlation coefficients were used to investigate relationships between airborne bacteria at the phylum and genus level and chemical species in winter and spring PM10 samples over Southeastern Italy. The identification of main chemical species/pollution sources that were related to and likely affected the bacterial community structure was the main goal of this work. The 16S rRNA gene metabarcoding approach was used to characterize airborne bacteria. Seventeen phyla and seventy-nine genera contributing each by mean within-sample relative abundance percentage > 0.01% were identified in PM10 samples, which were chemically characterized for 33 species, including ions, metals, OC, and EC (organic and elemental carbon, respectively). Chemical species were associated with six different pollution sources. A shift from winter to spring in both bacterial community structure and chemical species mass concentrations/sources and the relationships between them was observed. RDA triplots pointed out significant correlations for all tested bacterial phyla (genera) with other phyla (genera) and/or with chemical species, in contrast to correlation coefficient results, which showed that few phyla (genera) were significantly correlated with chemical species. More specifically, in winter Bacillus and Chryseobacterium were the only genera significantly correlated with chemical species likely associated with particles from soil-dust and anthropogenic pollution source, respectively. In spring, Enterobacter and Sphingomonas were the only genera significantly correlated with chemical species likely associated with particles from the anthropogenic pollution and the marine and soil-dust sources, respectively. The results of this study also showed that the correlation coefficients were the best tool to obtain unequivocal identifications of the correlations of phyla (genera) with chemical species. The seasonal changes of the PM10 chemical composition, the microbial community structure, and their relationships suggested that the seasonal changes of atmospheric particles may have likely contributed to seasonal changes of bacterial community in the atmosphere.

Lidar measurements at 355, 532, and 1064 nm have been performed at Lecce (40.3 degrees N, 18.1 degrees E), in south-eastern Italy, to investigate for the first time the impact of the 20 March 2015 solar eclipse on both the planetary boundary layer (PBL) height and the aerosol optical and microphysical properties along the whole aerosol column. The partial solar eclipse lasted from 08:30 up to 10:47 UTC and reached the full phase at 09:37 UTC. The maximum percentage obscuration of the solar disk was 43.6%. The eclipse cooling effect was responsible at the full phase time (t(F)) for the downward solar irradiance decrease at the top of the atmosphere, at the surface, and within the atmosphere of 429.2 +/- 0.6, 373 +/- 25, and 56 +/- 26 W m(-2), respectively. The turbulent kinetic energy, the potential temperature flux, the sensible heat flux, the variance of air temperature, and the vertical wind speed at the surface revealed that the turbulence activity reached the maximum weakening at the time t(F). The standard deviation (SD) technique has been applied to both the lidar range corrected signals (RCS) at 1064 nm and the linear volume depolarization ratio (delta(V)) profiles at 355 nm to determine the time evolution of the shallow PBL height and the aloft aerosol layers. The SD technique applied to RCS and delta(V) profiles revealed similar results within experimental uncertainties. The PBL height, which was equal to 380 +/- 40 m above ground level (AGL) at the eclipse full phase (09:37 UTC), decreased up to 220 +/- 20 m at 09:45 UTC because of the eclipse cooling effect and, then, increased up to 320 +/- 30 mat 10:17 UTC. The determined PBL height time evolution was in good agreement with the ones of the main turbulence parameters at the surface after t(F). The vertical profiles of the aerosol backscatter coefficient (beta beta), the delta(V) at 355 nm, and the extinction-related angstrom ngstrom exponent (angstrom), calculated at the 355-1064 nm wavelength pair revealed a marked decrease of beta, delta(V), and angstrom at the eclipse full phase within the aloft aerosol layers. The abrupt beta, delta(V), and angstrom decrease due to the aerosol concentration and type changes has mainly been associated with the decrease of the fine-mode particle contribution.

A methodology to detect the weekly cycle impact of the particulate matter (PM), and PM sources on the near surface temperature and wind speed is discussed in the paper. Chemically-speciated PM10 and PM2.5 samples are analyzed to detect the weekly cycle of both the PM mass concentrations and the PM sources identified by the Positive Matrix Factorization technique. The average percent departure (APD) of the PM mass concentration from the mean value calculated for each day of the week shows that a positive (higher values during midweek) and a negative (higher values during weekend) weekly cycle characterizes the PM10 and PM2.5 mass concentrations in Autumn-Winter (AW, September–February) and Spring-Summer (SS, March–August), respectively. The westerly transport of pollution seems to have a role on the negative PM weekly cycle found in SS. The analysis of the six identified aerosol sources indicates that in SS the mixed anthropogenic and the reacted dust sources likely impact the PM10 negative weekly cycle and that the mixed anthropogenic source likely impacts the PM2.5 negative weekly cycle. The mixed anthropogenic and soil dust sources likely affect in AW the positive weekly cycle of the PM10 mass concentration. Both sources in addition to the reacted dust source seem to affect the PM2.5 mass concentration in AW. The APD analysis of the temperature (T) and wind speed (WS) at the surface from measurements co-located in space and time with the PM ones reveals that the WS and T values are characterized by a negative weekly cycle in AW. Conversely, in SS, the WS-APD value decreases on Sunday and the T-APD values increase in the second half of the week. These last results likely give evidence of the PM impact on the near-surface temperature and wind speed at the study site.

The performance of aerosol classification schemes based on intensive optical parameters and applied to mixed particle populations monitored at the surface has been investigated to test the ability of optical parameters to identify different types of particles/particle mixtures and explore their complex features. The results show that the combination of two intensive optical parameters does not allow for the unique identification of different particle types. The classification scheme based on the Absorption Angstrom Exponent (AAE) as a function of the Scattering Angstrom Exponent (SAE) and color-coded by the Single Scattering Albedo difference (dSSA) is a good graphical framework to discriminate between different types of particle/particle mixtures. This aerosol classification scheme has been applied to study the optical properties of heterogeneous PM2.5 particles that were monitored at a coastal site of the Central Mediterranean and were significantly affected by both natural and anthropogenic sources also because of long-range transport from surrounding countries. The calculated AAE, SAE, and dSSA hourly means smoothly and continuously vary within their respective range (0.6-3.4, -0.7-3.0, and -0.33-0.52, respectively) because of the different mixing degree of different types of particles. Consequently, the main features of the particle populations depend on the range of the AAE, SAE, and dSSA values. Eight different clusters have been selected within the used graphical framework to identify four key particle populations (dust, marine, OC-dominated, and BC-dominated particles) and four particle mixtures dominated by key aerosol populations. In addition, their main features have been characterized. Particle mixtures consisting of large and low-absorbing particles (LLAP), small and high-absorbing particles (SHAP), dominated by dust, and large organic particles have been characterized. Marine, LLAP, and mixed dust clusters with a SAE value below 1 are responsible for aerosol scattering coefficients (sigma(s); at 470 nm) below 100 Mm(-1). Conversely, SHAP, BC-dominated, and OC-based mixtures are responsible for the highest sigma(s) values and represent dominant particles species.

Six months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EARLINET) from August 2017 to January 2018 are presented. The decay phase of an unprecedented, record-breaking stratospheric perturbation caused by wildfire smoke is reported and discussed in terms of geometrical, optical, and microphysical aerosol properties. Enormous amounts of smoke were injected into the upper troposphere and lower stratosphere over fire areas in western Canada on 12 August 2017 during strong thunderstorm–pyrocumulonimbus activity. The stratospheric fire plumes spread over the entire Northern Hemisphere in the following weeks and months. Twenty-eight European lidar stations from northern Norway to southern Portugal and the eastern Mediterranean monitored the strong stratospheric perturbation on a continental scale. The main smoke layer (over central, western, southern, and eastern Europe) was found at heights between 15 and 20 km since September 2017 (about 2 weeks after entering the stratosphere). Thin layers of smoke were detected at heights of up to 22–23 km. The stratospheric aerosol optical thickness at 532 nm decreased from values > 0.25 on 21–23 August 2017 to 0.005–0.03 until 5–10 September and was mainly 0.003–0.004 from October to December 2017 and thus was still significantly above the stratospheric background (0.001–0.002). Stratospheric particle extinction coefficients (532 nm) were as high as 50–200 Mm−1 until the beginning of September and on the order of 1 Mm−1 (0.5–5 Mm−1) from October 2017 until the end of January 2018. The corresponding layer mean particle mass concentration was on the order of 0.05–0.5 µg m−3 over these months. Soot particles (light-absorbing carbonaceous particles) are efficient ice-nucleating particles (INPs) at upper tropospheric (cirrus) temperatures and available to influence cirrus formation when entering the tropopause from above. We estimated INP concentrations of 50–500 L−1 until the first days in September and afterwards 5–50 L−1 until the end of the year 2017 in the lower stratosphere for typical cirrus formation temperatures of −55 ∘C and an ice supersaturation level of 1.15. The measured profiles of the particle linear depolarization ratio indicated a predominance of nonspherical smoke particles. The 532 nm depolarization ratio decreased slowly with time in the main smoke layer from values of 0.15–0.25 (August–September) to values of 0.05–0.10 (October–November) and < 0.05 (December–January). The decrease of the depolarization ratio is consistent with aging of the smoke particles, growing of a coating around the solid black carbon core (aggregates), and thus change of the shape towards a spherical form. We found ascending aerosol layer features over the most southern European stations, especially over the eastern Mediterranean at 32–35∘ N, that ascended from heights of about 18–19 to 22–23 km from the beginning of October to the beginning of December 2017 (about 2 km per month). We discuss several transport and lifting mechanisms that may have had an impact on the found aerosol layering structures.