Optical and Microphysical Properties of Aged Biomass Burning Aerosols and Mixtures, Based on 9-Year Multiwavelength Raman Lidar Observations in Athens, Greece

Mean optical and microphysical aerosol properties of long-range transported biomass burning (BB) particles and mixtures are presented from a 9-year (2011–2019) data set of multiwavelength Raman lidar data, obtained by the EOLE lidar over the city of Athens (37.58° N, 23.47° E), Greece. We studied 34 aerosol layers characterized as: (1) smoke; (2) smoke + continental polluted, and (3) smoke + mixed dust. We found, mainly, small-sized aerosols with mean backscatter-related (355 nm/532 nm, 532 nm/1064 nm) values and Ångström exponent (AE) values in the range 1.4–1.7. The lidar ratio (LR) value at 355 nm was found to be 57 ± 10 sr, 51 ± 5 sr, and 38 ± 9 sr for the aerosol categories (1), (2), and (3), respectively; while at 532 nm, we observed LR values of 73 ± 11 sr, 59 ± 10 sr, and 62 ± 12 for the same categories. Regarding the retrieved microphysical properties, the effective radius (reff) ranged from 0.24 ± 0.11 to 0.24 ± 0.14 μm for all aerosol categories, while the volume density (vd) ranged from 8.6 ± 3.2 to 20.7 ± 14.1 μm−3cm−3 with the higher values linked to aerosol categories (1) and (2); the real part of the refractive index (mR) ranged between 1.49 and 1.53, while for the imaginary part (mI), we found values within 0.0108 i and 0.0126 i. Finally, the single scattering albedo (SSA) of the propped particles varied from 0.915 to 0.936 at all three wavelengths (355–532–1064 nm). The novelty of this study is the provision of typical values of BB aerosol properties from the UV to the near IR, which can be used in forecasting the aerosol climatic effects in the European region.

[1]  L. Alados-Arboledas,et al.  Microphysical characterization of long-range transported biomass burning particles from North America at three EARLINET stations , 2016 .

[2]  Ina Mattis,et al.  EARLINET Single Calculus Chain – technical – Part 2: Calculation of optical products , 2016 .

[3]  Y. Wang,et al.  Characterization of forest fire smoke event near Washington, DC in summer 2013 with multi-wavelength lidar , 2014 .

[4]  A. Papayannis,et al.  Influence of Saharan Dust Transport Events on PM2.5 Concentrations and Composition over Athens , 2012, Water, Air, & Soil Pollution.

[5]  Tropospheric vertical profiling of the aerosol backscatter coefficient and the particle linear depolarization ratio for different aerosol mixtures during the PANACEA campaign in July 2019 at Volos, Greece , 2021 .

[6]  M. Komppula,et al.  Optical and microphysical characterization of aerosol layers over South Africa by means of multi-wavelength depolarization and Raman lidar measurements , 2015 .

[7]  Doina Nicolae,et al.  The unprecedented 2017–2018 stratospheric smoke event: decay phase and aerosol properties observed with the EARLINET , 2019, Atmospheric Chemistry and Physics.

[8]  A. Papayannis,et al.  Radiative Effect and Mixing Processes of a Long-Lasting Dust Event over Athens, Greece, during the COVID-19 Period , 2021, Atmosphere.

[9]  V. Freudenthaler,et al.  EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data , 2015 .

[10]  V. Freudenthaler,et al.  Characterization of Saharan dust, marine aerosols and mixtures of biomass-burning aerosols and dust by means of multi-wavelength depolarization and Raman lidar measurements during SAMUM 2 , 2011 .

[11]  C. Samara,et al.  Chemical composition and mass closure of ambient PM10 at urban sites , 2010 .

[12]  E. Martins,et al.  Population differences in a lizard communicative display: evidence for rapid change in structure and function , 1998, Animal Behaviour.

[13]  Alexandros Papayannis,et al.  Canadian Biomass Burning Aerosol Properties Modification during a Long-Ranged Event on August 2018 , 2020, Sensors.

[14]  V. Freudenthaler,et al.  EARLINET: towards an advanced sustainable European aerosol lidar network , 2014 .

[15]  M. Chin,et al.  Six global biomass burning emission datasets: intercomparison and application in one global aerosol model , 2019 .

[16]  J. Burrows,et al.  Impact of the 2009 Attica wild fires on the air quality in urban Athens , 2012 .

[17]  R. Ferrare,et al.  Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples , 2011 .

[18]  S. Gautam.  COVID-19: air pollution remains low as people stay at home , 2020, Air Quality, Atmosphere & Health.

[19]  A. Stohl,et al.  Optical characteristics of biomass burning aerosols over Southeastern Europe determined from UV-Raman lidar measurements , 2008 .

[20]  D. Müller,et al.  Characterization of fresh and aged biomass burning events using multiwavelength Raman lidar and mass spectrometry , 2013 .

[21]  T. Eck,et al.  A review of biomass burning emissions part III: intensive optical properties of biomass burning particles , 2004 .

[22]  Martin Wirth,et al.  Microphysical and optical properties of dust and tropical biomass burning aerosol layers in the Cape Verde region—an overview of the airborne in situ and lidar measurements during SAMUM-2 , 2011 .

[23]  B. Psiloglou,et al.  EARLINET observations of Saharan dust intrusions over the northern Mediterranean region (2014–2017): properties and impact on radiative forcing , 2020 .

[24]  S. K. Akagi,et al.  The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning , 2010 .

[25]  D. Nicolae,et al.  Multiyear Typology of Long-Range Transported Aerosols over Europe , 2019, Atmosphere.

[26]  Iwona S. Stachlewska,et al.  Temporal variations in optical and microphysical properties of mineral dust and biomass burning aerosol derived from daytime Raman lidar observations over Warsaw, Poland , 2017 .

[27]  Alexandros Papayannis,et al.  Using Raman-lidar-based regularized microphysical retrievals and Aerosol Mass Spectrometer measurements for the characterization of biomass burning aerosols , 2015, J. Comput. Phys..

[28]  J. Lamarque,et al.  Aerosol Impacts on Climate and Biogeochemistry , 2011 .

[29]  M. Haeffelin,et al.  Long-range-transported Canadian smoke plumes in the lower stratosphere over northern France , 2019, Atmospheric Chemistry and Physics.

[30]  Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations , 2021 .

[31]  B. Weinzierl,et al.  Aerosol classification by airborne high spectral resolution lidar observations , 2012 .

[32]  R. Draxler.  An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition , 1998 .

[33]  G. Biskos,et al.  Biomass-burning impact on CCN number, hygroscopicity and cloud formation during summertime in the eastern Mediterranean , 2016, Atmospheric Chemistry and Physics.

[34]  Kenneth Sassen,et al.  Cloud effects from boreal forest fire smoke: evidence for ice nucleation from polarization lidar data and cloud model simulations , 2008 .

[35]  Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology , 2020, Atmospheric Chemistry and Physics.

[36]  S. Sherwood,et al.  Climate Effects of Aerosol-Cloud Interactions , 2014, Science.

[37]  M. Vaughan,et al.  Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask , 2013 .

[38]  R. Engelmann,et al.  Depolarization and lidar ratios at 355, 532, and 1064 nm and microphysical properties of aged tropospheric and stratospheric Canadian wildfire smoke , 2018, Atmospheric Chemistry and Physics.

[39]  M. Jacobson,et al.  Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols , 2022 .

[40]  T. Yao,et al.  Black soot and the survival of Tibetan glaciers , 2009, Proceedings of the National Academy of Sciences.

[41]  C. Pérez García-Pando,et al.  Development and evaluation of the BSC-DREAM8b dust regional model over Northern Africa, the Mediterranean and the Middle East , 2012 .