A European aerosol phenomenology – 6: scattering properties of atmospheric aerosol particles from 28 ACTRIS sites

Atmos. Chem. Phys., 18, 7877–7911, 2018 https://doi.org/10.5194/acp-18-7877-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. A European aerosol phenomenology – 6: scattering properties of atmospheric aerosol particles from 28 ACTRIS sites Marco Pandolfi1, Lucas Alados-Arboledas2, Andrés Alastuey1, Marcos Andrade3, Christo Angelov4, Begoña Artiñano5, John Backman6,7, Urs Baltensperger8, Paolo Bonasoni9, Nicolas Bukowiecki8, Martine Collaud Coen10, Sébastien Conil11, Esther Coz5, Vincent Crenn12,13, Vadimas Dudoitis14, Marina Ealo1, Kostas Eleftheriadis15, Olivier Favez16, Prodromos Fetfatzis15, Markus Fiebig17, Harald Flentje18, Patrick Ginot19, Martin Gysel8, Bas Henzing20, Andras Hoffer21, Adela Holubova Smejkalova22,23, Ivo Kalapov4, Nikos Kalivitis24,25, Giorgos Kouvarakis24, Adam Kristensson26, Markku Kulmala6, Heikki Lihavainen7, Chris Lunder17, Krista Luoma6, Hassan Lyamani2, Angela Marinoni9, Nikos Mihalopoulos24,25, Marcel Moerman20, José Nicolas27, Colin O’Dowd28, Tuukka Petäjä6, Jean-Eudes Petit12,16, Jean Marc Pichon27, Nina Prokopciuk14, Jean-Philippe Putaud29, Sergio Rodríguez30, Jean Sciare12,a, Karine Sellegri27, Erik Swietlicki26, Gloria Titos2, Thomas Tuch31, Peter Tunved32, Vidmantas Ulevicius14, Aditya Vaishya28,33, Milan Vana22,23, Aki Virkkula6, Stergios Vratolis15, Ernest Weingartner8,b, Alfred Wiedensohler31, and Paolo Laj6,9,19 1Institute of Environmental Assessment and Water Research, c/Jordi-Girona 18–26, 08034, Barcelona, Spain 2Andalusian Institute for Earth System Research, IISTA-CEAMA, University of Granada, Granada 18006, Spain 3Atmospheric Physics Laboratory, ALP, UMSA, Campus Cota Cota calle 27, Endifico FCPN piso 3, La Paz, Bolivia 4Institute for Nuclear Research and Nuclear Energy by the Bulgarian Academy of Sciences, 72 Tsarigradsko Chaussee Blvd, 1784 Sofia, Bulgaria 5Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas, CIEMAT, Unidad Asociada en Contaminación Atmosférica, CIEMAT-CSIC, Avda. Complutense, 40, 28040 Madrid, Spain 6University of Helsinki, UHEL, Division of Atmospheric Sciences, P.O. Box 64, 00014, Helsinki, Finland 7Finnish Meteorological Institute, FMI, Erik Palmenin aukio 1, 00560, Helsinki, Finland 8Paul Scherrer Institut, PSI, Laboratory of Atmospheric Chemistry (LAC), OFLB„ 5232, Villigen PSI, Switzerland 9Institute of Atmospheric Sciences and Climate, ISAC, Via P. Gobetti 101, 40129, Bologna, Italy 10Federal Office of Meteorology and Climatology, MeteoSwiss, Chemin de l’aérologie, 1530 Payerne, Switzerland 11ANDRA – DRD – Observation Surveillance, Observatoire Pérenne de l’Environnement, Bure, France 12LSCE-Orme point courrier 129 CEA-Orme des Merisiers, 91191 Gif-sur-Yvette, France 13ADDAIR, BP 70207 – 189, rue Audemars, 78530, Buc, France 14SRI Center for Physical Sciences and Technology, CPST, Sauletekio ave. 3, 10257, Vilnius, Lithuania 15Institute of Nuclear & Radiological Science & Technology, Energy & Safety, N.C.S.R. “Demokritos”, Athens, 15341, Greece 16Institut National de l’Environnement Industriel et des Risques, Verneuil en Halatte, 60550, France 17Norwegian Institute for Air Research, Atmosphere and Climate Department, NILU, Instituttveien 18, 2007, Kjeller, Norway 18Deutscher Wetterdienst, Met. Obs. Hohenpeissenberg, 82383 Hohenpeissenberg, Germany 19University Grenoble-Alpes, CNRS, IRD, INPG, IGE 38000 Grenoble, France 20TNO B&O, Princetonlaan 6, 3584TA, The Hague, the Netherlands 21MTA-PE Air Chemistry Research Group, Veszprém, P.O. Box 158, 8201, Hungary 22Global Change Research Institute AS CR, Belidla 4a, 603 00, Brno, Czech Republic 23Czech Hydrometeorological Institute, Na Sabatce 17, 143 06, Prague, Czech Republic 24Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, Heraklion, 71003, Greece 25Institute for Environmental Research & Sustainable Development, National Observatory of Athens (NOA), I. Metaxa & Vas. Pavlou, 15236 Palea Penteli, Greece 26Lund University, Department of Physics, P.O. Box 118, 22100, Lund, Sweden 27CNRS-LaMP Université Blaise Pascal 4, Avenue Blaise Pascal, 63178 Aubiere CEDEX, France Published by Copernicus Publications on behalf of the European Geosciences Union. 7878 M. Pandolfi et al.: A European aerosol phenomenology – 6 28School of Physics and Centre for Climate & Air Pollution Studies, Ryan Institute, National University of Ireland Galway, University Road, Galway, Ireland 29EC Joint Research Centre, EC-JRC-IES, Institute for Environment and Sustainability, Via Enrico Fermi 2749, 21027, Ispra, Italy 30Agencia Estatal de Meteorologia, AEMET, Izaña Atmospheric Research Center, La Marina 20, 38071, Santa Cruz de Tenerife, Spain 31Leibniz Institute for Tropospheric Research (TROPOS), Permoserstraße 15, 04318, Leipzig, Germany 32Department of Environmental Science and Analytical Chemistry (ACES) and the Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden 33Space Physics Laboratory, Vikram Sarabhai Space Centre, ISRO, Thiruvananthapuram – 695022, India anow at: EEWRC, The Cyprus Institute, Nicosia, Cyprus bnow at: Institute for Aerosol and Sensor Technology, University of Applied Sciences (FHNW), Windisch, Switzerland Correspondence: Marco Pandolfi ([email protected]) Received: 5 September 2017 – Discussion started: 13 October 2017 Revised: 7 May 2018 – Accepted: 8 May 2018 – Published: 5 June 2018 Abstract. This paper presents the light-scattering properties of atmospheric aerosol particles measured over the past decade at 28 ACTRIS observatories, which are located mainly in Europe. The data include particle light scattering (σsp) and hemispheric backscattering (σbsp) coefficients, scattering Ångström exponent (SAE), backscatter fraction (BF) and asymmetry parameter (g). An increasing gradient of σsp is observed when moving from remote environments (arctic/mountain) to regional and to urban environments. At a regional level in Europe, σsp also increases when moving from Nordic and Baltic countries and from western Europe to central/eastern Europe, whereas no clear spatial gradient is observed for other station environments. The SAE does not show a clear gradient as a function of the placement of the station. However, a west-to-east-increasing gradient is observed for both regional and mountain placements, suggesting a lower fraction of fine-mode particle in western/south-western Europe compared to central and eastern Europe, where the fine-mode particles dominate the scattering. The gdoes not show any clear gradient by station placement or geographical location reflecting the complex relationship of this parameter with the physical properties of the aerosol particles. Both the station placement and the geographical location are important factors affecting the intraannual variability. At mountain sites, higher σsp and SAE values are measured in the summer due to the enhanced boundary layer influence and/or new particle-formation episodes. Conversely, the lower horizontal and vertical dispersion during winter leads to higher σsp values at all low-altitude sites in central and eastern Europe compared to summer. These sites also show SAE maxima in the summer (with corresponding gminima). At all sites, both SAE and gshow a strong variation with aerosol particle loading. The lowest values of gare always observed together with low σsp values, indicating a larger contribution from particles in the smaller accumulation mode. During periods of high σsp values, the variation of gis less pronounced, whereas the SAE increases or decreases, suggesting changes mostly in the coarse aerosol particle mode rather than in the fine mode. Statistically significant decreasing trends of σsp are observed at 5 out of the 13 stations included in the trend analyses. The total reductions of σsp are consistent with those reported for PM2.5and PM10 mass concentrations over similar periods across Europe. 1 Introduction Atmospheric aerosol particles are recognized as an important atmospheric constituent that has demonstrated effects on climate and health. The radiative forcing of aerosol particles, estimated as −0.9 [−1.9 to −0.1]Wm−2(IPCC, 2014), has two competing components: a cooling effect from most particle types and a partially offsetting warming contribution from black carbon (BC) particle light absorption of solar radiation. The aerosol cooling is the dominant effect; thus aerosol particles counteract a substantial portion of the warming effect from well-mixed greenhouse gases (GHGs). This process is driven by the scattering properties of most aerosol particle types (e.g. secondary sulfate and nitrate particles, mineral and organic matter), which reduce the amount of solar radiation reaching the Earth’s surface, instead reflecting it back into space and thus modifying the Earth’s radiative balance. However, the high temporal and spatial variability in atmospheric aerosol particles due to the wide variety of aerosol sources and sinks, together with their short and variable lifetimes (hours to weeks in the planetary boundary layer) and spatial non-uniformity, constitute the largest uncertainties in the estimation of the total radiative forcing. Reducing these uncertainties is mandatory in view of the warming the planet Atmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7879 has experienced over the past 50 years. In fact, there is evidence suggesting that the observed (and projected) decrease in emissions of anthropogenic aerosol particles in response to air quality policies will eventually exert a positive aerosol effective radiative forcing at the top of the atmosphere (Rotstayn et al., 2013). Thus, current emission controls could both enhance climate warming while improving air quality (e.g. Stohl et al., 2015). The measurements of aerosol particle optical properties, such as light scattering and absorption, together with measurements of their physical and chemical properties, are fundamental for understanding the current trade-off between the impacts of aerosols on environmental health and the Earth’s climate. In recent decades, several international projects have provided important information on atmospheric particle properties worldwide. Near-surface in situ observations of aerosol particle properties are being made worldwide under the GAW/WMO (Global Atmosphere Watch; http://www. wmo.int/pages/prog/arep/gaw/gaw_home_en.html, last access: August 2017) programme and are complemented with policy-oriented programmes such as IMPROVE (Interagency Monitoring of Protected Visual Environments; http://vista. cira.colostate.edu/Improve/, last access: August 2017) in the United States and EMEP (European Monitoring and Evaluation Programme; http://www.emep.int/, last access: August 2017) in Europe. Additional information specifically targeting advanced aerosol particle properties has been obtained in Europe using information from the European research infrastructure ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure; http://www.actris.eu, last access: August 2017) and from short-term RTD (Research and Technological Development) projects such as EUCAARI (European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions; http://www.cas.manchester.ac.uk/ resprojects/eucaari/, last access: August 2017). The implementation of the GAW programme in Europe is performed under ACTRIS in regard to the advanced observation of aerosol particle properties. ACTRIS provides harmonized measurements of different (physical, chemical and optical) aerosol properties in a systematic way at major observation sites across Europe. More than 60 measuring sites worldwide are currently providing ground-based in situ aerosol particle light-scattering measurements (EBAS database; http://ebas.nilu.no/, last access: August 2017) and this number has increased substantially in the last decade. However, EBAS also includes data from the IMPROVE network nephelometers, which latter are operated at ambient conditions with no size cut, and as a result these IMPROVE data are not directly comparable to the ACTRIS data set discussed in this investigation. The objective of this work is to integrate the total aerosol light-scattering coefficient (σsp) and hemispheric backscattering coefficient (σbsp) measurements taken over several years at the ground-based in situ ACTRIS stations. A total of 28 stations (26 European+2 non-European) are included in order to document the variability in near-surface aerosol particle light scattering across the ACTRIS network. Moreover, at some of the ACTRIS stations more than 10 years of σsp data are available, allowing us to perform trend analyses. The study of the trend of σsp is important given that a decreasing or increasing trend of σsp over time would be indicative of the effectiveness of the air quality control measures. In fact, many studies have shown that the concentrations of particulate matter (PM) and other air pollutants such as sulfur dioxide (SO2) and carbon monoxide (CO) have clearly decreased over the last 20 years in many European countries (Barmpadimos et al., 2012; Cusack et al., 2012; EEA, 2013; Querol et al., 2014; Guerreiro et al., 2014; Pandolfi et al., 2016; Tørseth et al., 2012, among others). Previous studies presenting multi-site ground-based in situ aerosol particle optical measurements were, for example, taken by Delene and Ogren (2002), Sherman et al. (2015), Collaud Coen et al. (2013) and Andrews et al. (2011). Delene and Ogren (2002) and Sherman et al. (2015) reported on the variability in aerosol particle optical properties at four North American surface monitoring sites. Collaud Coen et al. (2013) presented long-term (>8–9 years) aerosol particle light-scattering and absorption measurements taken at 24 regional/remote observatories located mostly in the United States (although 5 are located in Europe). Andrews et al. (2011) reported aerosol particle optical measurements taken at 12 mountaintop observatories (4 of which are located in Europe, 5 in the United States and Canada and 3 in Asia). Our work is focused mainly on European observatories and aims to present a representative phenomenology of aerosol particle light-scattering coefficients measurements at ACTRIS stations. Thanks to the establishment of European monitoring networks and/or research projects, five papers relating to aerosol phenomenology have been published in Europe: Van Dingenen et al. (2004) and Putaud et al. (2004) respectively studied the physical and chemical characteristics of PM at the kerbside, urban, rural and background sites in Europe; Putaud et al. (2010) studied the physical and chemical characteristics of PM measured at 60 sites across Europe; Cavalli et al. (2016) studied the harmonized concentrations of carbonaceous aerosols at 10 regional background sites in Europe; and Zanatta et al. (2016) presented a climatology of BC optical properties at nine European regional background sites. The importance of these studies and of the present work rests on the premise that a reliable assessment of the physical, chemical and optical properties of aerosol particles at a European scale is of crucial importance for an accurate estimation of the radiative forcing of atmospheric aerosols. This work is the first European phenomenology study dedicated to the light-scattering properties of aerosol particles measured in situ at near-surface ground-based observatories. Moreover, the trend analyses presented can be used to evaluate how the European mitigation strategies adopted to improve air quality have impacted aerosol particle optical properties. www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7880 M. Pandolfi et al.: A European aerosol phenomenology – 6 Table 1. List of ACTRIS observatories providing aerosol particle-scattering measurements. Observatory name/setting1Country Observatory code Lat, long Altitude (ma.s.l.) Geographical location Inlet Nephelometer model Perioda Arctic observatories Zeppelin (ZEP) Svalbard (Norway) NO0042G 78.9067◦N, 11.8883◦E 474 Nordic and Baltic PM10 TSI3563 07/2010–12/2014 Pallas (PAL) Finland FI0096G 67.97◦N, 24.12◦E 565 Nordic and Baltic PM5; PM2.5; PM10b TSI3563 02/2000–12/2015 Antarctic observatories Troll (TRL) Antarctica NO0058G −72.0167◦N, 2.5333◦E 1309 Antarctica Whole air; PM10cTSI3563 02/2007–12/2015 Mountain observatories Puy de Dome (PUY) France FR0030R 45.7667◦N, 2.95◦E 1465 West Whole air TSI3563 01/2007–12/2014 Izaña (IZO) Spain ES0018G 28.309◦N, −16.4994◦E 2373 South-west PM10 TSI3563 03/2008–12/2015 Montsec (MSA) Spain ES0022R 42.0513◦N, 0.44◦E 1570 South-west PM2.5; PM10dECOTECH Aurora3000 01/2013–12/2015 Jungfraujoch (JFJ) Switzerland CH0001G 46.5475◦N, 7.985◦E 3578 Central Whole air TSI3563 07/1995–12/2015 Mt Cimone (CMN) Italy IT0009R 44.1833◦N, 10.7◦E 2165 Central Whole air ECOTECH Aurora M9003; TSI 3563e 05/2007–12/2015 Hohenpeissenberg (HPB) Germany DE0043G 47.8◦N, 11.0167◦E 985 Central PM10 TSI3563 01/2006–12/2015 Beo Moussala (BEO) Bulgaria BG0001R 42.1667◦N, 23.5833◦E 2971 East Whole air TSI3563 03/2007–12/2015 Mt Chacaltaya (CHC) Bolivia BO0001R −16.2000◦N, −68.09999◦E 5240 South America Whole air ECOTECH Aurora3000 01/2012–12/2015f Coastal observatories Preila (PLA) Lithuania LT0015R 55.35◦N, 21.0667◦E 5 Nordic and Baltic PM10 TSI3563 12/2012–04/2014 Mace Head (MHD) Ireland IE0031R 53.3258◦N, −9.8994◦E 5 West Whole air TSI3563 07/2001–12/2013 Finokalia (FKL)2Greece GR0002R 35.3167◦N, 25.6667◦E 250 South-east Whole air; PM1; PM10g RR M903; Ecotech Aurora1000h 04/2004–12/2015 Regional/rural observatories Birkenes II (BIR) Norway NO0002R 58.3885◦N, 8.252◦E 219 Nordic and Baltic PM10 TSI3563 07/2009–12/2015 Hyytiälä (SMR) Finland FI0050R 61.85N, 24.2833◦E 181 Nordic and Baltic PM10 TSI3563 05/2006–12/2015 Vavihill (VHL)3Sweden SE0011R 56.0167◦N, 13.15◦E 175 Nordic and Baltic PM10 ECOTECH Aurora3000 03/2008–04/2014 Observatory Perenne (OPE) France FR0022R 48.5622◦N, 5.505555◦E 392 West Whole air; PM10iECOTECH Aurora3000 09/2012–12/2015 Cabauw (CBW)4The Netherlands NL0011R 51.9703◦N, 4.9264◦E 1 West PM10 TSI3563 01/2008–12/2012 Montseny (MSY) Spain ES1778R 41.7667◦N, 2.35◦E 700 South-west PM10 ECOTECH Aurora3000 01/2010–12/2015 Košetice (KOS) Czech Republic CZ0007R 49.58333N, 15.0833◦E 534 Central PM10 TSI3563 03/2013–12/2015 Melpitz (MPZ)5Germany DE0044R 51.53◦N, 12.93◦E 86 Central PM10 TSI3563 01/2007–12/2015 Ispra (IPR) Italy IT0004R 45.8◦N, 8.6333◦E 209 Central PM10 TSI3563 01/2004–12/2014 K-Puszta (KPS) Hungary HU0002R 46.9667◦N, 19.5833◦E 125 East PM1; PM10jTSI3563 05/2006–12/2014 Urban/suburban observatories SIRTA (SIR) France FR0020R 48.7086◦N, 2.1589◦E 162 West PM1ECOTECH M9003 07/2012–12/2013 Madrid (MAD) Spain ES1778R 40.4627◦N, −3.717◦E 669 South-west PM2.5; PM10kECOTECH Aurora3000 01/2014–12/2014 Granada (UGR) Spain ES0020U 37.164◦N, −3.605◦E 680 South-west Whole air TSI3563 01/2006–12/2015 Athens (DEM) Greece GR0100B 37.9905◦N, 23.8095◦E 270 South-east PM10 ECOTECH Aurora3000 01/2012–12/2015 1Observatory codes from EBAS; 2GAW code: FIK; 3GAW code: VAV; 4GAW code: CES; 5GAW code: MEL; astart-end of measurements; total aerosol particle scattering was used as reference for measurement period; bPM5(2000–08/2005), PM2.5(08/2005–2007) and PM10 (2008–2015); cwhole air (2007–2009) and PM10 (2010–2015); dPM2.5 (2013–03/2014) and PM10 (04/2014–2015); eECOTECH Aurora M9003 during 2007–2013 and TSI 3563 (2014–2015); fonly measurements taken during the year 2012 were used in this investigation; gwhole air (2004–2008), PM10 (2009–2011), PM1(2011–2012), PM10 (2013–2015); hRR M903 during 2004–2011, Ecotech AURORA1000 during 2012–2015; iwhole air (2012–08/2013) and PM10 (09/2014–2015); jPM1(2006–04/2008) and PM10 (05/2008–2014); kPM10 from 03/2014. Atmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7881 Figure 1. Locations of the 28 ACTRIS stations included in this work. 2 Experiment 2.1 Atmospheric observatories Figure 1 shows the location of the observatories which are grouped according to their geographical locations, a grouping employed in other European phenomenology studies (e.g. Putaud et al., 2010). Observatory information (country, code, coordinates, altitude, geographical location, among others) and measurement periods are summarized in Table 1. The observatories are also divided into five different categories depending on their placement within each geographical sector. The Arctic includes stations located in the Arctic/sub-Arctic region; mountains include those observatories located at more than 985ma.s.l.(the lowest altitude among the mountain observatories included here); coastal regions include observatories located close to the coast (<1– 4km); regional/rural areas include those observatories that are representative of large regional areas; and urban/suburban areas include observatories located in the background of an urban or suburban area. Two non-European stations are also included: one Antarctic site and one mountain site in Bolivia. Given that this work mainly focuses on European ACTRIS observatories, the results from these two non-European stations are reported in the Supporting Information. The altitudes of the mountain stations considered here range between 985m at HPB and 5240m at CHC (see Table 1). Some of the mountain stations included in this investigation have already been included in the work of Andrews et al. (2011), namely IZO, JFJ, CMN and BEO. Moreover, the FKL, HPB, JFJ, MHD and PAL stations have been included in the study by Collaud Coen et al. (2013). Both studies presented in situ aerosol particle optical measurements taken at these stations. The main results of these previous investigations are summarized in the Results section. 2.2 Scattering measurements 2.2.1 Instruments The measurements of σsp and σbsp included in this study were obtained from TSI and Ecotech integrating nephelometers (Table 1). These optical instruments measure the amount of light scattered by particles in the visible spectrum and provide σsp and σbsp coefficients of the sampled aerosols. The most common nephelometers in the ACTRIS programme are the TSI3563 and the Ecotech AURORA3000 nephelometers, both of which provide σsp and σbsp. The model TSI3563 measures σsp and σbsp at 450, 550 and 700nm, whereas the Ecotech AURORA3000 measures at 450, 525 and 635nm. Other models used are the M9003 from Ecotech (SIR and CMN) and the RR (Radiance Research) nephelometer model M903 (FKL) measuring σsp at 520 and 532nm respectively. Due to the non-homogeneity of the angular distribution of the light intensity of model M9003 (see Müller et al., 2009), the light source was changed at SIR in 2013 with the AURORA3000 light source and at CMN in 2009 with an opal glass light source. After the change of the light sources, both nephelometers were examined at the World Calibration Center for Aerosol Physics in Leipzig and performed very well (personal communication from Jean Sciare (SIR; 27 July 2017) and Angela Marinoni (CMN; 21 July 2017)). www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7882 M. Pandolfi et al.: A European aerosol phenomenology – 6 The detailed description of the main characteristics and the working principle of the integrating nephelometers can be found in Müller et al. (2011) for the Ecotech AURORA3000 and in Anderson and Ogren (1998) for the model TSI 3563. Recommended quality assurance procedures during an onsite operation, as described in GAW (WMO-GAW report, 2016), help to ensure the quality and comparability of the data. The nephelometers included in this investigation are regularly calibrated using span gas and are zero adjusted using particle-free air. Additionally, most of the integrating nephelometers employed in ACTRIS have undergone a schedule of performance checks at the World Calibration Center for Aerosol Physics of ACTRIS/GAW. 2.2.2 Data treatment Data used in this investigation include hourly averaged level 2 aerosol particle-scattering data downloaded from the ACTRIS/EBAS Data Centre web portals (http://actris.nilu. no; http://ebas.nilu.no; last downloads August 2017). The σsp and σbsp data reported to EBAS and used in this work are referenced to standard T(273.15◦C) and P(1013 hPa) conditions. Data consistency is critical when comparing many years’ worth of data from different stations. In this work, the level 2 scattering data were further reviewed in order to ensure a high quality of presented data. There are, however, station-to-station differences (e.g. sizecut, RH control, wavelength and data processing), which are addressed below. Truncation correction Data from the integrating nephelometers used here are corrected for non-ideal illumination of the light source (deviation from a Lambertian distribution of light) and for truncation of the sensing volumes in the near-forward (around 0–10◦) and near-backward (around 170–180◦) directions (Müller et al., 2009 and Anderson and Ogren, 1998). Correction schemes have been provided by Müller et al. (2009, 2011) for the RR M903 and Ecotech models M9003 and AURORA3000, and by Anderson and Ogren (1998) for the TSI3563. These schemes consist of a simple linear correction based on the scattering Ångström exponent (SAE) determined from the raw nephelometer data to take account of the size-distribution-dependent truncation error. It has been demonstrated that these simple correction schemes are accurate for a wide range of atmospheric aerosols and that the uncertainties in the corrections are not expected to be larger than 2% for an aerosol particle population with a singlescattering albedos (SSA) greater than 0.8 (Bond et al., 2009). The majority of the σsp data in the EBAS database are corrected for non-ideal illumination and for truncation by the data providers. Exceptions are the scattering data submitted for KOS, MHD, PLA, CMN, FKL and SIR. Scattering data from KOS, MHD and PLA were corrected in this work using the correction scheme provided by Anderson and Ogren (1998) (see Table S1 in the Supplement). The σsp data collected at CMN, FKL and SIR are not corrected because the nephelometers deployed at these three stations provide scattering only at one wavelength, thus preventing the estimation of the SAE. Given that the nephelometer correction factors vary as a function of SAE, the assumption of a constant correction factor for the 1-λscattering data could introduce undesired noise. Moreover, at SIR and CMN, the σsp is measured with the single-wavelength Ecotech nephelometer model M9003 (until 2013 at CMN). The correction curve from Müller et al. (2009; Fig. 4) provides a correction factor of around 0.97–1.0 for the M9003 for a SAE of around 1.5–2. Using the TSI3563 scattering measurements taken at CMN during 2014–2015, we estimated a mean SAE of around 2 for CMN (see Table S5 in the Supplement). Thus, given the rather small effect of the correction factor estimated for the Ecotech M9003, scattering data from CMN and SIR were not corrected in this work. At FKL the nephelometer models RR M903 (until 2011) and Ecotech 1000 (from 2012) were used (see Table 1). To the best of our knowledge, no correction scheme has been provided for the Ecotech 1000. Moreover, at FKL, the inlet was changed many times (see Table 1) and the correction factors provided in the literature are a strong function of the size cut-off used. For these reasons, scattering data collected at FKL are not corrected in this investigation. Relative humidity The integrating nephelometer measurements within ACTRIS and WMO-GAW should be taken at a low relative humidity (RH<40 %) in order to avoid enhanced scattering due to water uptake of aerosol particles and in order to make the measurements comparable. For the Ecotech integrating nephelometers, the RH threshold can be set by using a processorcontrolled automatic heater inside the instrument. At some mountain sites, where whole air is sampled (see Table 1), the natural temperature difference between the outside and inside air dries cloud droplets to the aerosol phase when a cloud is present at the station. RH is also controlled by dehumidifying the inlet pipe, as reported in GAW report 226, to ensure a sampling RH of less than 40%. This recommendation is intended to ensure that the data are comparable across the network, as measurements would otherwise would be a strong function of the highly variable sample RH. Currently, at the majority of ACTRIS observatories, the aerosol particle lightscattering measurements are taken at a RH below 40 %. However, given that at some stations the 40% RH threshold is sometimes exceeded, in this work we selected a RH threshold of 50% in order to improve the data coverage. Estimating the aerosol particle light-scattering enhancement due to an increase in RH from 40 to 50% is difficult using the data available here because the σsp measurements at a RH>40 % are not evenly distributed over the measurement periods, with the majority of the stations registering a RH higher than 40% during the summer. Moreover, the chemiAtmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7883 cal composition of atmospheric aerosol particles is an important factor determining the magnitude of the scattering enhancement due to water uptake, which can then change from one site to another (e.g. Fierz-Schmidhauser et al., 2010a,b; Zieger et al., 2014, 2017). However, the scattering enhancement due to a change in RH between 40 and 50% should be small and will not exceed few percent, even for more hygroscopic particles (e.g. Fierz-Schmidhauser et al., 2010a,b). Table S2 in the Supplement reports the percentage of hourly σsp values collected in the range 40% <RH<50%, whereas the frequency distributions of the measured RH are shown in Fig. S1 in the Supplement. Available wavelengths In this work we present and discuss the σsp, backscatter fraction (BF) and asymmetry parameter (g) measurements obtained using the green wavelength of the integrating nephelometers. The available wavelengths ranged from 520nm (2 stations; CMN and VHL) to 550nm (18 stations). Other wavelengths used are 525nm (6 stations) and 532 nm (used at FKL until 2010; see Table 2). An exception is SIR, where only σsp values at 450nm are available. The measurements of σsp reported here are not adjusted to 550nm, which is generally the most common wavelength (e.g. Andrews et al., 2011) because of the different data availability of σsp and SAE at the measuring stations. As discussed in the following sections, the SAE is calculated for σsp data higher than 0.8Mm−1, thus leading to different data coverage for σsp and SAE and preventing the adjustment of all measured σsp to 550nm. Moreover, the SAE is not available at FKL and SIR (or at CMN until 2014), thus preventing any wavelength adjustment at these stations. Using the mean SAE calculated at stations where σsp is measured at wavelengths in addition to 550nm (see Tables S4 and S5 in the Supplement), we estimate differences in the σsp values of less than 6 % after adjusting to 550nm. At FKL and SIR, where the SAE is not available, and assuming a reasonable SAE range between 1.5 and 1.0, the difference due to the adjustment to 550nm is 4.9–3.0% at FKL and 26–18 % at SIR. The higher difference at SIR is due to the fact that measurements at this station are taken at 450nm. Finally, at CMN, the effect of the adjustment of σsp to 550nm (from 520 nm) using a mean SAE of 2 (calculated using the 3-λnephelometer data from 2014; see Table S5) is below 10%. Inlet size cut changes It should be noted that any comparison of the σsp and SAE values among the different stations and the presented trend analyses could be slightly biased by the different particle size cuts upstream of the integrating nephelometers used in this work (see Table 1). Currently, all ACTRIS integrating nephelometers measure whole air or PM10, with the exception of SIR, where the PM1inlet is used. Whole air is currently measured at mountain observatories (BEO, CMN, JFJ, PUY, CHC), one coastal observatory (MHD) and one urban observatory (UGR) (see Table 1). At some stations, the inlet was changed from whole air to PM10 at some point, namely at OPE, FKL and TRL. Given the lower scattering efficiency of aerosol particles larger than 10µm, no important differences in the aerosol particle optical parameters should be expected between aerosol particles sampled with a whole air and a PM10 cut-off. At the other stations the inlet was changed during the measurement period from a cut-off lower than 10 µm (1µm at KPS; 2.5 µm or 5µm at PAL, MSA and MAD) to PM10. For PAL (where a median SAE of around 1.8 was measured; see Sect. 3.2 and Table S5), Lihavainen et al. (2015a) assumed that the inlet changes (from PM5to PM2.5in 2005 and from PM2.5 to PM10; see Table 1) had only minor effects on scattering because the number concentration of coarse particles is very low at PAL. Similarly, the KPS observatory registers among the highest SAE values observed in the network (median value of around 2; see Sect. 3.2 and Table S5), suggesting an aerosol particle size distribution dominated by fine particles. Moreover, at KPS, the inlet was changed in April 2008, less than 1.5 years after the measurements commenced, and thus likely also has a minor effect on the trend analyses and climatology performed at this site over the period 2006–2014. Two stations (MSA and MAD) changed the inlet from a PM2.5 diameter cut-off to PM10. For these two southern European stations the inlet change may have had an effect on the SAE, especially during Saharan dust outbreaks, which are, however, sporadic events. Finally, the FKL observatory was removed from the trend analysis because the inlet was changed from whole air to PM10 in 2009, from PM10 to PM1in 2011 and again from PM1to PM10 in 2013 (see Table 1). These events likely had a major effect on the measured particle optical properties. A sensitivity study (not shown) was carried out to assess the effect of the inlet changes on the SAE values measured at the aforementioned stations. We looked at the climatology of SAE for different inlet sizes and for different time periods (with and without inlet size changes) and we did not observe any obvious change in the climatology as a function of size cut due to interannual variability. Thus, despite the differences in the particle diameter cut-off, the comparison between the different stations seems feasible. 2.2.3 Calculation of aerosol particle intensive optical properties Starting from the spectral σsp measurements taken at the ACTRIS observatories, three intensive aerosol particle optical parameters can be estimated, namely the scattering Ångström exponent (SAE), the backscattering fraction (BF) and the asymmetry parameter (g). These intensive properties do not depend on the PM mass concentration and are directly related to aerosol particle properties such as size, www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7884 M. Pandolfi et al.: A European aerosol phenomenology – 6 shape, size distribution and chemical composition. The SAE can be considered a proxy for the aerosol particle size range with a higher (lower) SAE associated with predominance of fine (coarse) aerosol particles (e.g. Seinfeld and Pandis, 1998; Esteve et al., 2012; Valenzuela et al., 2015 among others). The BF and gparameters are calculated quantities that influence the variability in the radiative forcing efficiency and that represent the angular light scattering of aerosol particles. For computational efficiency, the angular light scattering is often represented by a single value (BF, σsp/σbsp or g) (Andrews et al., 2006). The SAE characterizes the wavelength dependency of σsp and it can be calculated as follows: SAE =− logσλ1 sp /σλ2 sp  log(λ1/λ2).(1) Here, the SAE is derived from a multispectral log linear fit based on the three nephelometer wavelengths. The SAE depends on the particle size distribution and takes values greater than 2 when the light scattering is dominated by fine particles (radii≤0.5 µm as in Schuster et al., 2006), while it is lower than one when the light scattering is dominated by coarse particles (Seinfeld and Pandis, 1998; Schuster et al., 2006). The asymmetry parameter (g) (Andrews et al., 2006; Delene and Ogren, 2002) describes the probability that the radiation is scattered in a given direction and it is defined as the cosine-weighted average of the phase function. Thus, g yields information regarding the amount of radiation that a particle scatters in the forward direction compared to the backward direction. Theoretically, the values of gcan range from −1 for only back scattering to +1 for complete forward scattering, with a value of 0.7 commonly used in radiative transfer models. The gparameter can be estimated from the backscatter fraction (BF), which is the ratio of σbsp and σsp (Andrews et al., 2006): g=−7.14(BF)3+7.46(BF)2−3.96(BF)+0.9893.(2) 2.2.4 Data coverage Table S3 in the Supplement reports the percentage (%) of data coverage at the 28 ACTRIS stations included in this study. Removed data include data flagged as non-valid by the data providers (instrument failure, calibration periods, unspecified contamination or local influence, etc.) or obtained at a RH of greater than 50%. The data coverage for the extensively measured aerosol particle optical properties (σsp and σbsp) is generally high, ranging from around 60 to 95 %. Exceptions are the σsp measurements at CMN in the blue (450nm) and red (700 nm) wavelengths, which have much less data coverage compared to the green wavelength because the three-wavelength nephelometer was implemented at CMN in 2014. Consequently, also the SAE and ghave low data coverage at CMN. Moreover, lower data coverage (<40%) was registered at PLA and VHL. The data coverage for the intensive aerosol particle optical properties (SAE and g) is generally lower compared to the data coverage of σsp and σbsp. This is because the intensive optical properties are calculated from hourly σsp and σbsp data higher than 0.8Mm−1to avoid noise in the calculations. As a consequence, the data coverage of the intensive properties is lower at stations measuring low σsp and σbsp values (e.g. mountain and remote sites). For example, at JFJ, the SAE and gdata coverages are around 54 and 22 % respectively. At TRL, these values are even lower, at 21 and 1%. However, as reported in Table S3, at the majority of the stations the data coverage of SAE and gis higher than 60%. 3 Results and discussion 3.1 Variability of σsp Figure 2 shows the box-and-whisker plots of σsp measured at the stations included in this investigation. In Fig. 2, the observatories are grouped based on their placement and ordered according to their geographical location. Table S4 and Fig. S2 in the Supplement respectively report the statistics of σsp (mean, SD, minimum and maximum values and 5th, 25th, 50th, 75th and 95th percentiles) and frequency and cumulative frequency distributions. In each geographical sector, an increasing gradient of σsp is generally observed when moving from mountain to regional and to urban sites. Thus, the σsp values measured at mountain sites are lower than the measurements made at other locations (coastal to urban), even if exceptions are observed in some sectors. A large range of σsp coefficients is observed across the network, ranging from median values lower than 10Mm−1to values higher than 40Mm−1. Overall, the lowest σsp is on average measured at remote stations because of either (a) their altitude (for example, JFJ is located in central Europe at more than 3500ma.s.l.and CHC in Bolivia is at around 5300ma.s.l.; see Fig. S3 in the Supplement) or (b) because of their large distance from pollution sources, for example the Arctic ZEP and PAL stations, TRL station (see Fig. S3) and some regional sites in the Nordic and Baltic sector such as BIR and SMR. Higher σsp values (medians>40 Mm−1) are on average registered at more polluted sites, such as some urban sites in southern Europe (UGR and DEM), some regional sites in eastern and central Europe (KPS and IPR respectively) and one coastal site in the Nordic and Baltic sector (PLA). The observed variation is consistent with the differences in particulate matter (PM) mass concentrations, PM chemical composition, particle number concentration and absorption coefficients observed across Europe, as described, for examAtmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7885 1 5 50 500 1 5 50 500 ZEP PAL 1 Nordic and Baltic 24563 12Western 4Central 5Eastern 3South-western 6South-eastern PUY JFJCMNHPB PLA Arctic Mountain Coastal BEO IZOMSA MHD FKL Regional/rural BIRSMRVHLOPE CBW KOS MPZ IPR KPS MSY 124563124563 124563124563 SIR MAD UGR DEM Total scattering [Mm-1] Total scattering [Mm-1] Urban/suburban Figure 2. Total aerosol scattering coefficients in the green divided by station setting. Different colours highlight different geographical locations. At SIR, aerosol scattering was available only at 450nm. Medians (horizontal lines in the boxes), percentiles 25 and 75 (lower and upper limits of the boxes) and percentiles 5 and 95 (lower and upper limits of the vertical dashed lines) are reported. Hourly data were used for the statistics. ple, by Putaud et al. (2010), Asmi et al. (2011) and Zanatta et al. (2016). Figure 3a and b show the relationship between the mean particle number concentration measured at different stations from 2008 to 2009 (and reported in Asmi et al., 2011) and the mean σsp measured over the same period (where available). As reported in Fig. 3, positive correlations are observed between N50 (Fig. 3a: mean/median particle number between 50 and 500nm) and N100 (Fig. 3b: mean/median particle number between 100 and 500nm) and mean σsp. Figure 3c shows the relationship (for some stations) between absorption coefficients reported in Zanatta et al. (2016) and the total scattering. The positive correlations reported in Fig. 3c (especially high for the winter and autumn periods) suggest an increase in both scattering and absorption coefficients with increasing aerosol loading. Figure 3c also reports the mean single-scattering albedo (SSA). On average lower SSA is observed at IPR, whereas higher SSA is observed at the Nordic and Baltic VHL and BIR observatories. Finally, at all stations included in this work, the skewness of the σsp distributions (see Table S4) is higher than one and ranges between 1.4 at PLA and 10.6 at TRL (skewness calculated from hourly averaged data). The skewness can be used to evaluate the asymmetry of a distribution. Positive skewness is usually observed for parameters which are defined to be positive and it indicates that the tail on the right side of the distribution is longer or fatter than that on the left side. Thus, for a right-skewed distribution, the mass of the distribution is concentrated on the left, and there is a higher probability of measuring a high value compared to a left-skewed distribution. For example, Querol et al. (2009) used the skewness to assess the importance of Saharan dust outbreaks on PM10 levels measured at different sites across the Mediterranean basin. They found a positive correlation between the calculated skewness and the net dust contribution to the measured PM10 concentration (i.e. the strength of dust pollution episodes; see Fig. 6 in Querol et al., 2009). Figure S2 in the Supporting Material shows the frequency and cumulative frequency distributions for σsp for each station, evidencing the presence of these right-skewed tails. 3.1.1 σsp at Arctic/Antarctic observatories The Arctic (ZEP and PAL; see Fig. 2) and Antarctic (TRL; see Fig. S3) monitoring stations are located in undisturbed environments with minimal influence from the local settlement because they are located above the inversion layer. The mean σsp values measured at ZEP and TRL are by far the lowest across the network, whereas higher σsp values are measured at PAL. PAL is located in a remote continental area characterized by the absence of large local and regional pollution sources (e.g. Aaltonen et al., 2006). However, Lihavainen et al. (2015a) reported that high values of the absorption coefficient and low values of the single-scattering albedo at PAL are rewww.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7892 M. Pandolfi et al.: A European aerosol phenomenology – 6 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 200 250 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 1 2 3 4 5 6 7 8 9 10 11 12 0 20 40 60 80 120 1 2 3 4 5 6 7 8 9 10 11 12 0 50 150 250 350 1 2 3 4 5 6 7 8 9 10 11 12 0 100 300 500 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 200 1 2 3 4 5 6 7 8 9 10 11 12 0 20 60 100 140 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 1 2 3 4 5 6 7 8 9 10 11 12 0 10 30 50 70 Regional/rural 1 2 3 4 5 6 7 8 9 10 11 12 0 10 20 30 40 50 BIR Particle scattering[Mm-1] SMR Particle scattering[Mm-1] VHL Particle scattering [Mm-1] 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 OPE Particle scattering[Mm-1] CBW Particle scattering[Mm-1] MSY Particle scattering[Mm-1] KOS Particle scattering[Mm-1] MPZ Particle scattering[Mm-1] IPR Particle scattering[Mm-1] KPS Particle scattering[Mm-1] Urban/suburban SIR Particle scattering[Mm-1] MAD Particle scattering[Mm-1] UGR Particle scattering [Mm-1] DEM Particle scattering[Mm-1] 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 200 Nordic and Baltic Western Central EasternSouth-western South-eastern Figure 6. Seasonal cycles of σsp (Mm−1) measured in the green nephelometer wavelength. ern Europe (e.g. Pey et al., 2013; Pandolfi et al., 2014a; Rodríguez et al., 2011). At IZO, σsp peaks strongly in July– August because of the very high influence of African mineral dust at this station during these months (e.g. Alastuey et al., 2005; Diaz et al., 2006; Rodríguez et al., 2015). At the mountaintop CHC observatory (see Fig. S8), σsp progressively increases during the dry season, from May to October, reaching lower values during the rainy season (from DeAtmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7893 1 2 3 4 5 6 7 8 9 10 11 12 0.0 0.5 1.0 1.5 2.0 2.5 3.0 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 Arctic ZEP ScatteringÅngströmexp. PAL Mountain PUY ScatteringÅngströmexp. IZO ScatteringÅngströmexp. MSA ScatteringÅngströmexp. JFJ CMN ScatteringÅngströmexp. ScatteringÅngströmexp. HPB ScatteringÅngströmexp. BEO ScatteringÅngströmexp. Coastal PLA ScatteringÅngströmexp. MHD ScatteringÅngströmexp. ScatteringÅngströmexp. 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 4 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 4 1 2 3 4 5 6 7 8 9 10 11 12 0.0 1.0 2.0 3.0 1 2 3 4 5 6 7 8 9 10 11 12 1.0 1.5 2.0 2.5 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 0.0 0.5 1.0 1.5 2.0 2.5 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 Nordicand Baltic Western Central EasternSouth-western South-eastern Figure 7. cember to April). Moreover, during the dry season, the new particle formation events, taking place at CHC with one of the highest frequencies reported in the literature so far (Rose et al., 2015), can introduce very small particles that grow to nucleation and the Aitken mode. At the mountain stations, both SAE and σsp are on average higher in summer compared to the winter period, thus suggesting a higher anthropogenic influence at these sites during the warmest months. The summer SAE increase is more evident at some mountain stations, e.g. HPB, CMN and BEO, compared to other mountain stations such as JFJ and MSA. The less pronounced SAE seasonal variation at JFJ was related to the rather constant composition of the JFJ aerosol by Bukowiecki et al. (2016). At MSA in south-western Europe, the observed less pronounced seasonal cycle of SAE could be due to the contribution of Saharan dust in spring–summer, which contrasts with the PBL transport of fine particles observed at other mountain sites during the warm season. At www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7894 M. Pandolfi et al.: A European aerosol phenomenology – 6 1 2 3 4 5 6 7 8 9 10 11 12 1.0 1.5 2.0 2.5 Regional/rural BIR ScatteringÅngströmexp. SMR ScatteringÅngströmexp. VHL ScatteringÅngströmexp. OPE ScatteringÅngströmexp. CBW ScatteringÅngströmexp. MSY ScatteringÅngströmexp. KOS ScatteringÅngströmexp. MPZ ScatteringÅngströmexp. IPR ScatteringÅngströmexp. KPS ScatteringÅngströmexp. Urban/suburban MAD ScatteringÅngströmexp. UGR ScatteringÅngströmexp. DEM ScatteringÅngströmexp. 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 0.0 0.5 1.0 1.5 2.0 2.5 3.0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 4 1 2 3 4 5 6 7 8 9 10 11 12 0.5 1.0 1.5 2.0 2.5 3.0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 1 2 3 4 5 6 7 8 9 10 11 12 1.0 1.5 2.0 2.5 1 2 3 4 5 6 7 8 9 10 11 12 1.5 2.0 2.5 1 2 3 4 5 6 7 8 9 10 11 12 1.5 2.0 2.5 1 2 3 4 5 6 7 8 9 10 11 12 0.0 1.0 2.0 3.0 1 2 3 4 5 6 7 8 9 10 11 12 0.5 1.0 1.5 2.0 2.5 1 2 3 4 5 6 7 8 9 10 11 12 -1 0 1 2 3 Nordic and Baltic Western Central EasternSouth-western South-eastern Figure 7. Seasonal cycles of SAE (calculated using the three nephelometer wavelengths). IZO, the SAE reaches its lowest values during July–August in conjunction with the peak frequency of dust events (Rodríguez et al., 2015). Overall, the gparameter shows the opposite seasonal cycle to the SAE at almost all mountain stations, with the exception of JFJ and BEO, where gslightly increases with SAE in the summer. At almost all mountain stations, the seasonal variaAtmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7895 1 2 3 4 5 6 7 8 9 10 11 12 0.45 0.55 0.65 1 2 3 4 5 6 7 8 9 10 11 12 0.45 0.55 0.65 Arctic ZEP Asymmetry parameter PAL Asymmetry parameter Mountain PUY Asymmetry parameter IZO Asymmetry parameter MSA Asymmetry parameter JFJ CMN Asymmetryparameter Asymmetry parameter HPB Asymmetry parameter BEO Asymmetryparameter Coastal PLA Asymmetry parameter MHD Asymmetry parameter 1 2 3 4 5 6 7 8 9 10 11 12 0.4 0.5 0.6 0.7 0.8 1 2 3 4 5 6 7 8 9 10 11 12 0.45 0.55 0.65 1 2 3 4 5 6 7 8 9 10 11 12 0.2 0.4 0.6 0.8 1 2 3 4 5 6 7 8 9 10 11 12 0.3 0.4 0.5 0.6 0.7 0.8 1 2 3 4 5 6 7 8 9 10 11 12 0.35 0.45 0.55 0.65 1 2 3 4 5 6 7 8 9 10 11 12 0.45 0.55 0.65 0.75 1 2 3 4 5 6 7 8 9 10 11 12 0.3 0.4 0.5 0.6 0.7 1 2 3 4 5 6 7 8 9 10 11 12 0.55 0.60 0.65 0.70 1 2 3 4 5 6 7 8 9 10 11 12 0.55 0.60 0.65 0.70 0.75 Nordic and Baltic Western Central EasternSouth-western South-eastern Figure 8. tions of SAE and gare less pronounced compared to the seasonal variation of σsp, indicating a larger seasonal variation in the extensive aerosol optical properties than in the intensive properties. At CHC, the SAE decreases as the σsp increases when moving from the wet to the dry season, indicating an increasing effect of coarse particles on the σsp during the dry season. At PUY, σsp peaks from March to September and this increase is accompanied by a small increase in SAE. Venzac et al. (2009) and Boulon et al. (2011) have shown that PUY is more often influenced by the free troposphere or residual layers in winter and spring compared to the summer season. 3.4.3 Seasonal variability at coastal observatories A very different seasonal variation of σsp is observed at the two coastal observatories, MHD and FKL (at PLA, the lack of spring–summer measurements prevents the analysis of the annual cycles). The σsp at MHD (western Europe) peaks in www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7896 M. Pandolfi et al.: A European aerosol phenomenology – 6 1 2 3 4 5 6 7 8 9 10 11 12 0.4 0.5 0.6 0.7 Regional/rural BIR Asymmetry parameter SMR Asymmetry parameter Asymmetry parameter OPE Asymmetry parameter CBW Asymmetryparameter MSY Asymmetry parameter KOS Asymmetryparameter MPZ Asymmetry parameter IPR Asymmetry parameter KPS Asymmetry parameter Urban/suburban MAD Asymmetry parameter UGR Asymmetry parameter DEM Asymmetry parameter 1 2 3 4 5 6 7 8 9 10 11 12 0.45 0.55 0.65 1 2 3 4 5 6 7 8 9 10 11 12 0.40 0.50 0.60 0.70 1 2 3 4 5 6 7 8 9 10 11 12 0.2 0.4 0.6 0.8 1 2 3 4 5 6 7 8 9 10 11 12 0.4 0.5 0.6 0.7 1 2 3 4 5 6 7 8 9 10 11 12 0.40 0.50 0.60 0.70 1 2 3 4 5 6 7 8 9 10 11 12 0.40 0.50 0.60 0.70 1 2 3 4 5 6 7 8 9 10 11 12 0.40 0.50 0.60 0.70 1 2 3 4 5 6 7 8 9 10 11 12 0.45 0.55 0.65 1 2 3 4 5 6 7 8 9 10 11 12 0.3 0.4 0.5 0.6 0.7 1 2 3 4 5 6 7 8 9 10 11 12 0.40 0.50 0.60 1 2 3 4 5 6 7 8 9 10 11 12 0.4 0.5 0.6 0.7 0.8 0.9 Nordic and Baltic Western Central EasternSouth-western South-eastern Figure 8. Seasonal cycles of g(calculated for the green wavelength). winter, whereas a higher σsp is observed in summer at FKL (south-eastern Europe). At FKL, where no intensive optical aerosol properties are available, the higher σsp in summer can be associated with mineral dust storm events, such as those reported by Vrekoussis et al. (2005). However, mineral dust storms in the Mediterranean are not the only reason for the observed increased σsp in the summer at FKL. In fact, as reported by Kalivitis et al. (2011), concentrations of ammoAtmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7897 nium sulfate and particulate organic matter, which increase in summer in the Mediterranean Basin, can also be assumed to be important contributors to σsp during the warm season. At MHD, the higher σsp in winter is related to the higher contribution of wind-speed-generated sea-salt particles in the marine boundary layer during wintertime (Vaishya et al., 2011). At MHD, the SAE (g) is higher (lower) in summer compared to winter. O’Connor et al. (2008) and Vaishya et al. (2011, 2012) showed that the background marine aerosol level measured at MHD contains a strong and significant seasonal cycle with sea salt dominating in winter and biogenic organic aerosols dominating at the submicron scale in summer. This is consistent with the observed seasonal cycles of SAE and g reported here for MHD. 3.4.4 Seasonal variability at regional/rural observatories Regional observatories in central and eastern Europe show marked seasonal cycles of both extensive and intensive aerosol particle optical properties. In these regions, less horizontal and vertical pollutant dispersion in winter, due to a higher frequency of stagnant conditions and temperature inversions, play an important role in the accumulation of aerosols. As a consequence, the σsp is much higher in winter compared to summer. SAE and galso show marked seasonal cycles in these regions, with the SAE (g) being higher (lower) in summer compared to winter. Ma et al. (2014) have shown that, at MPZ, an increased SAE in summer is mainly explained by the variation in the particle number size distribution. Thus, high concentrations in spring and summer of small particles during new particle formation and subsequent growth periods cause the observed increase in SAE (and correspondingly a decrease in g) during the warmest months. At regional sites in the Nordic and Baltic regions, the monthly variation of σsp is on average less pronounced compared to the central or eastern European stations, especially at BIR and SMR (Virkkula et al., 2011). This is likely due to the placement of these stations in remote areas with a different meteorology (e.g. less pronounced PBL variations), where on average much lower σsp values are measured compared to other European sites. Moreover, this could also indicate the importance of anthropogenic sources such as domestic heating in central and eastern Europe in winter. However, both SAE and gshow marked seasonal cycles at these Nordic and Baltic observatories, similar to those reported for central and eastern European observatories with higher (lower) SAE (g) in summer compared to winter. Differences are observed in the annual cycle of σsp at a regional level in south-western Europe (represented by the MSY observatory), where higher σsp values are registered in summer. At the MSY regional site (located at around 720ma.s.l.), the higher efficiency of the sea breeze in transporting pollutants from the urbanized/industrialized coastline toward regional elevated inland areas during the warmer season mainly explains the summer increase in aerosol particle mass concentration and scattering coefficient observed at this site (e.g. Pandolfi et al., 2011). Moreover, the enhanced formation of secondary sulfate and organic matter in the summer, together with frequent Saharan mineral dust outbreaks, strongly contribute to the observed seasonal cycle for σsp and the intensive properties at the MSY site. The σsp peak observed at MSY in March is due to the winter pollution episodes typical of the western Mediterranean Basin (WMB) (e.g. Pandolfi et al., 2014b and references therein). During these episodes, the accumulation of pollutants close to the emission sources is favoured by anticyclonic conditions coupled with strong atmospheric inversions. During such conditions, pollutants accumulate in the PBL and can subsequently reach the MSY station when the PBL height increases. 3.4.5 Seasonal variability at urban/suburban observatories Among the urban sites, marked variations of σsp and the intensive properties are observed at UGR and DEM. At the urban UGR site, the mean aerosol type is very different in winter compared to summer. As evidenced by the seasonal cycles of SAE and g, aerosol particles are generally finer during the winter at UGR compared to the summer season, as already observed, for example, by Lyamani et al. (2010, 2012) and Titos et al. (2012). This is likely due to the accumulation of fine particles, mainly from traffic, domestic heating and biomass burning, favoured by stagnant conditions and atmospheric inversions during winter. In summer, the higher frequency of Saharan mineral dust outbreaks at this site increases the mean size of the particles during the warmest months. At the DEM urban observatories, the high σsp values measured in spring are linked to Saharan dust outbreaks, as also supported by the seasonal cycles of SAE and g, which show the lowest and highest values in spring. 3.5 SAE and gvs. σsp relationships Figure 9 shows the relationships between σsp and SAE and between σsp and gat each station. Mean SAE and gare calculated for each σsp bin and the bin size at each station is calculated following the Freedman–Diaconis rule: Binsize =2IQR(x) 3 √n,(3) where IQR(x) is the interquartile range of the data and nis the number of observations in the sample x. These graphs help in understanding which aerosol type on average dominates the particle light scattering, depending on the degree of scattering measured. It should be noted that, in Fig. 9, the number of samples available at each station are not evenly distributed among the considered bins. Figure S9 in the Supplement shows, for some stations, the SAE–σsp pairs coloured by the number of samples in each bin to highlight how the samples are distributed among the bins. www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7898 M. Pandolfi et al.: A European aerosol phenomenology – 6 3.5.1 g–σsp relationships The asymmetry parameter gshows the lowest values for very low σsp, suggesting the predominance of small fine-mode particles. Andrews et al. (2011) reported similar g–σsp relationships at different mountain sites and suggested that the removal of large particles by cloud scavenging or by deposition during transport could explain the observed low gvalues in a clean atmosphere. They also suggested that the formation of new particles followed by condensation/coagulation could generate small but optically active particles. Here, we show that this behaviour was observed from BF or gas a function of σsp at all sites, not only at mountain sites. The parameter gthen increases with increasing σsp, indicating a shift in the particle number size distribution toward the larger end of the accumulation mode. Delene and Ogren (2002), Andrews et al. (2011), Pandolfi et al. (2014a) and Sherman et al. (2015) showed that the BF tends to decrease with increasing aerosol loading, consistent with the observed increase in g. For comparison with previous works, Fig. S10 in the Supplement shows the BF–σsp relationships for all observatories, evidencing the aforementioned BF decrease with increasing σsp. The shift in the particle number size distribution toward the large end of the fine mode with increasing σsp is probably the main cause of the observed increase in g(and the decrease in BF; see Fig. S10). A possible explanation for this shift is a progressive ageing of atmospheric aerosol particles. Then, at the majority of stations, the variation of gis less pronounced during periods of high particle mass concentration, suggesting changes mostly in the coarse aerosol particle mode rather than in the fine mode. 3.5.2 SAE–σsp relationships As reported in Fig. 9, at some stations the SAE progressively increases with σsp in the σsp range, in which the gparameter also increases. The increase in both gand SAE with σsp, observed for example at the Nordic and Baltic regions, and central and eastern European observatories, could be related to the different effects that different particle sizes have on the SAE and g. A progressive increase in SAE with σsp would suggest an increase in the relative importance of fine aerosol particles. The origin of these fine particles is probably different depending on the location of the measuring site. For the remote PAL site, for example, Lihavainen et al. (2015b) observed an increase in both σsp and SAE with increasing temperature due to the increasing rate of formation of BSOA with increasing ambient temperature, thus likely driving the σsp–SAE relationships reported in Fig. 9 for PAL. The BSOA from gas-to-particle formation over regions substantially lacking in anthropogenic aerosol sources, such as the European boreal region (Tunved et al., 2006), probably contribute strongly to the σsp–SAE relationships observed at other Nordic and Baltic sites, such as SMR. At polluted sites, such as those located in central and eastern Europe, the anthropogenic aerosol emissions and active secondary aerosol production in the region (e.g. Ma et al., 2014) are probably driving the σsp–SAE relationships reported in Fig. 9. For higher σsp, the σsp–SAE relationships change and a progressive shift toward relatively larger particles is on average observed with increasing σsp. However, at the majority of north-western, central and eastern European stations, the SAE maintains values around, or higher than, 1.5 at high particle loads, indicating that the high σsp is dominated by fine particles. An exception is MHD, where the SAE increases with increasing σsp, maintaining values on average lower than 1.4 at high particle loads (see Fig. 9). As already observed, the low SAE at MHD is mainly due to the predominance of coarse sea-salt particles at this site (Vaishya et al., 2011). Conversely, at some sites in southern Europe (e.g. MSA, MSY, IZO, DEM) the SAE reaches values of around one or lower for high particle loads, indicating that, at these stations, the high σsp is dominated by mineral dust coarse particles mainly from African deserts. Exceptions are two urban sites in south-western Europe (UGR and MAD) where fine particles, probably generated for the most part by traffic (and also from biomass burning at UGR), on average dominate the highest measured σsp values. Similar σsp–SAE relationships to those reported in Fig. 9 were observed by Andrews et al. (2011) at mountain sites and by Delene and Ogren (2002) at marine sites. Among the lowest SAE are observed at IZO, the station closest to the African continent. Interestingly, at IZO, the SAE shows the highest gradient for σsp coefficients in the range of 0 to 50Mm−1, whereas the gradient is much lower for σsp values higher than 50Mm−1, with the SAE being almost constant for σsp higher than 100Mm−1. The IZO station is often in the free troposphere and high loadings at this station are only registered during Saharan dust events, thus it is virtually only the mineral dust that is measured at IZO. Normally, the long-range transport mineral dust particles do not represent a significant fraction of the particle population above 10µm because of their short lifetimes, which likely explains the constant SAE observed at the IZO site under high aerosol loading. 3.6 Trends Trends of σsp, SAE and BF are studied for those stations with more than 8 years’ worth of data (13 observatories). Among the ACTRIS stations, PAL, SMR, MHD, HPB, IPR, JFJ and UGR have more than 10 years of data, whereas at PUY, MPZ, CMN, BEO, KPS and IZO, 8 or 9 years of data are available. These stations are included in order to improve the spatial coverage, as is the case in Collaud Coen et al. (2013). The Theil–Sen statistical estimator (Theil, 1950; Sen, 1968) is used here to determine the regression parameters of the data trends, including slope, uncertainty in the slope and pvalue. The Theil–Sen method provides similar results to the Mann– Atmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7899 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 250 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 20 40 60 80 100 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 100 200 300 400 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 150 300 450 600 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 100 200 300 400 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 250 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 0.0 0.5 1.0 1.5 2.0 2.5 0 50 100 150 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 20 40 60 80 100 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 20 40 60 80 100120 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 20 40 60 80 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 250 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 40 80 120 160 200 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 20 40 60 80 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 10 20 30 40 Arctic Mountain Coastal Regional/rural Aerosol scattering coefficient[Mm-1]Aerosol scattering coefficient[Mm-1] gg g g SAE SAE SAE SAE ZEP PAL PUY IZO MSA JFJ CMN 0.0 0.5 1.0 1.5 2.0 2.5 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 50 100 150 200 250 HPB g SAE Aerosol scattering coefficient[Mm-1] BEO PLA MHD BIR SMR VHL OPE CBW MSY g SAE KOS MPZ IPR KPS Aerosol scattering coefficient[Mm-1] Aerosol scattering coefficient[Mm-1] Urban/suburban g SAE Aerosol scattering coefficient[Mm-1] MAD UGR DEM Figure 9. Scatter plots between σsp (xaxis) and SAE (right yaxis; red lines) and g(left yaxis; black lines). Dashed lines represent median σsp values at each station. Different colours highlight different geographical locations as in Figs. 2, 4 and 5. Kendall test and it is implemented in the OpenAir package available for R software (Carslaw, 2012; Carslaw and Ropkins, 2012). The applied method yields accurate confidence intervals, even with non-normal data, and it is less sensitive to outliers and missing values (Hollander and Wolfe, 1999). Monthly means are used for trend analysis and the data are corrected for seasonal effects. The data coverage for σsp is higher than 70 % at all stations included in the trend analyses, with the exception of IZO, where the σsp data coverage is 55 %. For SAE, the data coverage is higher than 65 % at all sites with the exception of PAL (54%), PUY (59 %) and IZO (52%). For BF, the data coverage is higher than 65 % with the exception of PAL (26%), PUY (43 %), BEO (47%) and IZO (27 %). At the remote (PAL) or mountain stations (PUY, BEO and IZO), the percentage for the intensive aerosol particle optical properties is lower because there is a higher probability of measuring σsp lower than the threshold (0.8Mm−1) selected for the calculation of SAE and BF. Table 2 reports the trends observed for σsp, SAE and BF at the 13 observatories included in this analysis. The magnitude and statistical significance of the trends for these parameters are reported in Table S8 in the Supplement. In Table 2, comparisons with the previous trend analysis results presented by Collaud Coen et al. (2013) for aerosol particle optical properties and by Asmi et al. (2013) for particle number concentrations are also reported. www.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7900 M. Pandolfi et al.: A European aerosol phenomenology – 6 Table 2. Trends of aerosol particle-scattering coefficient (σsp), scattering Ångström exponent (SAE) and backscatter fraction (BF). Three trends for SAE are reported: SAE calculated as a linear fit using three wavelengths (b-g-r), the blue and green wavelengths (b-g) and the green and red wavelengths (g-r). Trend results are reported for the whole period available at each station until 2015 (bold) and for the periods reported in Collaud Coen et al. (2013) and Asmi et al. (2013). Trends are considered statistically significant for a pvalue of<0.05. Statistically significant increasing or decreasing trends are highlighted with capital bold I and D letters. Non-statistically significant increasing or decreasing trends are highlighted with lower case italic iand dletters. The dashes in the table cells highlight stations included in this work but not included in the works of Collaud Coen et al. (2013) or Asmi et al. (2013). The symbol $ denotes parameters removed in this work and in the work of Collaud Coen et al. (2013) because of measurement gaps, low data coverage or break points for one or more wavelengths. The symbol # denotes data only available for 2014–2015. Station Period Trend (this work) MK trend (Collaud Coen et al., 2013) MK trend (Asmi et al., 2013) σsp SAE BF σsp SAE BF Particle number b-g-r b-g g-r b-r b-g g-r N N20 (20–500nm) N100 (100–500nm) Nordic and Baltic PAL 2000–2015 iDdD I 2000–2010 DD$ $ idi$ $ i 2001–2010 dD$ $ idi$ $ iD (10–500nm) no trend I 1996–2010 d (10–500nm) SMR 2006–2015 D iIiI––––– 1996–2011 – – – – – D D 2001–2010 – – – – – D D Western MHD 2001–2013 d$ $ $ $ 2000–2010 D (3–500nm) 2001–2010 i$$$$I$$$$i (3–500nm) PUY 2007–2014 dD D D I – – – – – – – – Central HPB 2006–2015 D I I iI 2001–2010 i$ $ $ $ 2002–2010 d$ $ $ $ 1995–2011 i (15–500nm) IPR∗2004–2014 D iiiI– – – – – – – – MPZ 2007–2015 ddddi ––––– 1997–1998 and 2004–2010 ––––– i i JFJ 1995–2015 d$ $ $ $ 1995–2010 i$$$$i$ $ $ $ 1996–2010 i$$$$i$ $ $ $ 2001–2010 d$$$$d$$$$D (10–500nm) 1997–2010 i$ $ $ $ i (10–500nm) CMN 2007–2015 d# # # # Eastern BEO 2007–2015 dDDDd– – – – – – – – KPS 2006–2014 i d Di i – – – – – – – – South-western IZO 2008–2015 D iii$–––––– – – UGR 2006–2015 D I iI I – – – – – – – – ∗A statistically significant decreasing trend of σsp at IPR was also reported by Putaud et al. (2014) for the period 2002–2010. Atmos. Chem. Phys., 18, 7877–7911, 2018 www.atmos-chem-phys.net/18/7877/2018/ M. Pandolfi et al.: A European aerosol phenomenology – 6 7901 3.6.1 Trends of σsp Overall, a statistically significant decreasing trend for σsp is observed at around 50% of the stations considered here (Table 2). Significantly, decreasing trends for σsp are observed at the two Nordic and Baltic observatories (PAL for the period 2000–2010 and SMR), at two (HPB and IPR) out of the five observatories in central Europe and at the two observatories in south-western Europe (IZO and UGR). The trends are not statistically significant in western (MHD and PUY) and eastern (BEO and KPS) Europe. The highest magnitude for the σsp trend [Mm−1yr−2] (see Table S8 in the Supplement) is observed at the polluted IPR observatory. Conversely, the lowest magnitude is observed at the remote PAL observatory. For the periods considered in this work, the total reductions (TRs) for σsp range between approximately 30% (SMR) and 60% (IZO). The high TRs observed at IZO might be affected by the intensity and frequency of Saharan dust outbreaks at this site. However, estimating the effects of these events at IZO is beyond the scope of this study. Overall, the observed decreasing trends of σsp are consistent with a uniform decrease in the aerosol optical depth observed in Europe (AERONET data in Li et al., 2014). The observed statistically significant and decreasing trends of σsp are consistent with the demonstrated reduction of PM concentration in the atmosphere in Europe in recent decades thanks to the implementation of European, national, regional and local mitigation strategies. These decreasing trends are also consistent with the trends in the aerosol chemistry derived from observations in urban environments in Europe (e.g. EEA, 2013; Barmpadimos et al., 2011; Titos et al., 2014; Pandolfi et al., 2016), regional and remote environments in the western Mediterranean (Cusack et al., 2012; Pandolfi et al., 2016) and in general with trends derived for the aerosol chemistry across Europe (Tørseth et al., 2012). Recently, Collaud Coen et al. (2013) showed that trends in σsp are observed at most of the US continental sites and that these trends are generally consistent with the strong SO2and PM reductions observed in the United States (Asmi et al., 2013; EPA, 2011). Conversely, in Europe, the strong decreasing trend observed for SO2(e.g. Tørseth et al., 2012; Henschel et al., 2013) and, with a lower spatial homogeneity and statistical significance, for PM2.5(e.g. EEA, 2016) is not observed for aerosol optical properties. As reported in Collaud Coen et al. (2013) the reasons that no significant trends are observed at some of the European sites might be related to the spatial inhomogeneities and under-representation of continental Europe PBL sites (e.g. Laj et al., 2009) and/or the timing of the SO2and PM trends for the United States and Europe. In Europe, the emission reductions were greater for the period 1980–2000 compared to the period 2000–2010 (e.g. Colette et al., 2016; Tørseth et al., 2012; Manktelow et al., 2007), thus the measurements of optical particle properties in Europe may not go back far enough to reflect the time period with the largest emission reductions. Tørseth et al. (2012) reported average reductions for ambient sulfate and nitrate mass concentrations in Europe of −12 and −1% during 2000–2009 compared to −24 and −7 % during 1990–2000. These authors also reported statistically significant decreases in the PM10 and PM2.5mass concentrations at around 50% of European sites, with total reductions of −18 and −27%, for PM10 (24 sites) and PM2.5(13 sites) during 2000–2009. A direct comparison between the stations included in this work and those included in the study of Tørseth et al. (2012) is not possible because of the different timings of the reported σsp and PM mass concentration measurements. At those stations where a significant decreasing trend for σsp is observed and considering a period of 10 years (even if not coincident for all stations), the total reduction for σsp in Europe is around −35% (see Table S8), consistent with the trend reported by Tørseth et al. (2012) for PM in Europe. Quite good agreement, although again likely biased by the different timings, is also observed when comparing the PM mass concentration and σsp trends by geographical sector. A significant total reduction of around −40 to −30% was reported for PM10 and PM2.5in the Nordic and Baltic sector by Tørseth et al. (2012; see Fig. 7 in Tørseth et al., 2012), in close agreement with the statistically significant total decrease in σsp of around −34% reported for PAL during 2000–2010 (see Table S8). In the western sector (MHD), the decreasing trend for PM2.5during 2000–2009 was insignificant (−10 to 0%) as reported here for σsp during the period 2001–2010. In the central sector, statistically significant decreases for the PM2.5and PM10 mass concentrations ranging between −20 and −40% were observed during a 10-year period (2000–2009) and the total reduction for σsp ranged between −38% (HPB) and around −48 % (IPR). In the south-western European sector the total reduction for σsp is around −32% (at UGR) and −60 % (at IZO), whereas Tørseth et al. (2012) reported decreases of around −20 to −40% for the PM10 mass concentration in the same geographical sector. To further confirm the observed close agreement between the PM trends reported in the literature and the trends of σsp detailed in this work, Table S9 in the Supplement reports the comparison between σsp and PM10 and/or PM2.5mass concentration trends calculated at those stations where simultaneous σsp and PM mass concentration measurements are available. As reported in Table S9, both the observed total reductions and the statistical significance levels of the trends are very similar for σsp and PM10. 3.6.2 Trends of SAE and BF The trends for SAE are estimated for three different quantities, namely the SAE is calculated using the three wavelengths (b-g-r), using the blue and the green wavelengths (b-g) and using the green and red wavelengths (g-r). For the periods considered in this work (in bold in Table 2), the SAE calculated using the three wavelengths (b-g-r) shows statiswww.atmos-chem-phys.net/18/7877/2018/ Atmos. Chem. Phys., 18, 7877–7911, 2018 7908 M. 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