High aerosol loading over mega city Delhi in the western Indo-Gangetic plain : Optical characteristics

Measurements of aerosol optical properties were carried out at an urban mega city Delhi, which is situated in the western Indo-Gangetic Plain (IGP) region in north India using an automatic sun/sky radiometer during 2006-2008. The present study revealed high aerosol loading over the station, which could be due to its topography surrounded by different natural and anthropogenic emission sources, and may have major implications towards health, air quality and climate system. Results show a large variability in AOD during the study period, with nearly equal values during winter (0.67 ± 0.06) and summer (0.71 ± 0.11). The Ångström exponent (AE) values were found to be relatively higher during winter (1.19 ± 0.07, suggests dominance of fine-mode aerosols) and lower during summer (0.74±0.06, suggests dominance of coarse-mode aerosols). A slight decrease in single scattering albedo (SSA) was observed during the study period, with a mean value of ~0.9. SSA was found to be about 0.93 during post-monsoon and 0.96 during the winter period whereas during summer and monsoon, SSA was about 0.95. The estimated monthly absorption Ångström exponent (AAE) values over the station varied from 0.11 to 1.87, which were found to be less than 1.0 by ~55% time (mostly during winter and monsoon), and greater than 1.0 by ~45% time (mostly during summer and post-monsoon).

The Impact of Intense Winter Saharan Dust Events on PM and Optical Properties at Urban Sites in the Southeast of the Iberian Peninsula

The influence of three Saharan dust events (SDE) on particulate matter (PM) concentrations and aerosol optical properties (AOP) during February 2021 was studied. The physical characteristics of the African aerosol were different for each episode. Therefore, the impacts of the three events on PM and AOP were analyzed separately. The monitoring sites were placed in Elche, in the southeast of the Iberian Peninsula. The sites can be classified as urban background locations. The procedure used to obtain the contribution of SDE to PM10 mass concentrations was the 40th percentile method. Nearly half of the days during the study period were under the influence of Saharan air masses. The average contribution of mineral dust (MD) to the PM10 mean concentration was ~50%, which was the highest contribution during the month of February in the last 14 years. The results show that those events characterized by a high input of fine particles (PM1 and PM2.5) caused larger increases in the absorption (σap) and scattering (σsp) coefficients than SDE in which coarse particles predominated. Nevertheless, as expected, SAE (Scattering Angström Exponent) values were lowest during these episodes. AAE (Absorption Angström Exponent) values during SDE were slightly higher than those observed in the absence of African dust, suggesting some contribution from MD to the absorption process.

Biomass burning events measured by lidars in EARLINET – Part 2: Optical properties investigation

Biomass burning episodes measured at 14 stations of the European Aerosol Research Lidar Network (EARLINET) over 2008–2017 were analysed using the methodology described in "Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology" (Adam et al., 2020, this issue). The smoke layers were identified in lidar optical properties profiles. A number of 795 layers for which we measured at least one intensive parameter was analysed. These layers were geographically distributed as follows: 399 layers observed in South-East Europe, 119 layers observed in South-West Europe, 243 layers observed in North-East Europe, and 34 layers observed in Central Europe. The mean layer intensive parameters are discussed following two research directions: (I) the long-range transport of smoke particles from North America, and (II) the smoke properties (fresh versus aged), separating the smoke events into four continental source regions (European, North American, African, Asian or a mixture of two), based on back trajectory analysis. The smoke detected in Central Europe (Cabauw, Leipzig, and Hohenpeißenberg) was mostly transported from North America (87 % of fires). In North-East Europe (Belsk, Minsk, Warsaw) smoke advected mostly from Eastern Europe (Ukraine and Russia), but there was a significant contribution (31 %) from North America. In South-West Europe (Barcelona, Evora, Granada) smoke originated mainly from the Iberian Peninsula and North Africa (while 9 % were originating in North America). In the South-East Europe (Athens, Bucharest, Potenza, Sofia, Thessaloniki) the origin of the smoke was mostly local (only 3 % represented North America smoke). The following features, correlated with the increased smoke travel time (corresponding to aging) were found: the colour ratio of the lidar ratio (i.e., the ratio of the lidar ratio at 532 nm to the lidar ratio at 355 nm) and the colour ratio of the backscatter Ångström exponent (i.e., the ratio of the backscatter-related Angstrom exponent for the pair 532 nm – 1064 nm to the one for the pair 355 nm – 532 nm) increase, while the extinction Ångström exponent and the colour ratio of the particle depolarization ratio (i.e., the ratio of the particle linear depolarization ratio at 532 nm to the particle depolarization ratio at 355 nm) decrease. The smoke originating from all continental regions can be characterized on average as aged smoke, with a very few exceptions. In general, the long range transported smoke shows higher lidar ratio and lower depolarization ratio compared to the local smoke.

Effects of different correction algorithms on absorption coefficient – a comparison of three optical absorption photometers at a boreal forest site

We present a comparison between three absorption photometers that measured the absorption coefficient (σabs) of ambient aerosol particles in 2012–2017 at SMEAR II (Station for Measuring Ecosystem–Atmosphere Relations II), a measurement station located in a boreal forest in southern Finland. The comparison included an Aethalometer (AE31), a multi-angle absorption photometer (MAAP), and a particle soot absorption photometer (PSAP). These optical instruments measured particles collected on a filter, which is a source of systematic errors, since in addition to the particles, the filter fibers also interact with light. To overcome this problem, several algorithms have been suggested to correct the AE31 and PSAP measurements. The aim of this study was to research how the different correction algorithms affected the derived optical properties. We applied the different correction algorithms to the AE31 and PSAP data and compared the results against the reference measurements conducted by the MAAP. The comparison between the MAAP and AE31 resulted in a multiple-scattering correction factor (Cref) that is used in AE31 correction algorithms to compensate for the light scattering by filter fibers. Cref varies between different environments, and our results are applicable to a boreal environment. We observed a clear seasonal cycle in Cref, which was probably due to variations in aerosol optical properties, such as the backscatter fraction and single-scattering albedo, and also due to variations in the relative humidity (RH). The results showed that the filter-based absorption photometers seemed to be rather sensitive to the RH even if the RH was kept below the recommended value of 40 %. The instruments correlated well (R≈0.98), but the slopes of the regression lines varied between the instruments and correction algorithms: compared to the MAAP, the AE31 underestimated σabs only slightly (the slopes varied between 0.96–1.00) and the PSAP overestimated σabs only a little (the slopes varied between 1.01–1.04 for a recommended filter transmittance >0.7). The instruments and correction algorithms had a notable influence on the absorption Ångström exponent: the median absorption Ångström exponent varied between 0.93–1.54 for the different algorithms and instruments.

Impacts of the Variations of Aerosols Components and Relative Humidity on the Visibility and Particles Size Distribution of the Desert Atmosphere

This paper investigates the Impact of relative humidity, varying the concentrations of water-soluble aerosol particle concentrations (WASO), Mineral Nuclei Mode Aerosols Particle Concentration (MINN), mineral accumulation mode, nonspherical (MIAN) aerosol particles concentrations and Mineral Coarse Mode Aerosols Particle Concentration (MICN) on the visibility and particles size distribution of desert aerosols based on microphysical properties of desert aerosols. The microphysical properties (the extinction coefficients, volume mix ratios, dry mode radii and wet mode radii) were extracted from Optical Properties of Aerosols and Clouds (OPAC 4.0) at eight relative humidities, RHs (00 to 99%) and at the spectral visible range of 0.4-0.8mm, the concentrations were varied to obtain five different models for each above-mentioned component. Regression analysis of some standard equations were used to determine the Angstrom exponent (α), the turbidity coefficient (β), the curvature (α2), humidification factor (), the mean exponent of aerosol growth curve (µ) and the mean exponent of aerosol size distributions (n). The values of angstrom exponent (α) were observed to be less than 1 throughout the five models at all RHs for the four studied components, and this signifies the dominance of coarse mode particles over fine mode particles. But the magnitude of the angstrom exponent (α) fluctuates all through the studied components except for WASO which increased with the increase in RH across the models and this also signifies the dominance of coarse mode particles with some traces of fine mode particles. The investigation also revealed that the curvature (α2) has both monomodal (negative signs) and bimodal (positive signs) types of distributions all through the five models and this also signifies the dominance of coarse mode particles with some traces of fine mode particles across the individual models for all the studied components. it was also found that the visibility decreased with the increase in RH and increased with the increase in wavelength. The investigation further revealed that the turbidity coefficient (β) fluctuates with the increase in RH and the particles concentrations, and this might be due to major coagulation and sedimentation. The analysis further found that there is a direct inverse power relation between the humidification factor and the mean exponent of aerosols size distribution with the mean exponent of aerosols growth curve. It was also found that as the magnitude of µ increased for MIAN, MINN and MICN, the effective hygroscopic growth decreased. For WASO, it was found that as the magnitude of µ decreased, the effective hygroscopic growth increased with the increase in particles concentrations and RH. The decreased in the magnitude of µ for WASO might be due to the fact that as we increase the non-hygroscopic particles, we decrease the deliquescence. The mean exponent of aerosol size distribution (n) being less than 3 shows foggy condition of the desert atmosphere the four investigated components and five studied models.

Aerosol classification using sun photometer and lidar data within a machine learning algorithm-a possible nowcasting application

<p>The Met Office operates a ground based operational network of nine polarisation Raman lidars (aerosol profiling instruments) and sun photometers (column integrated information). An aerosol classification scheme using supervised machine learning has been developed. The concept of Mahalanobis (~normalized) distance to identify the aerosol type  from individual Aerosol Robotic Network (AERONET) measurements including Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo and Index of refraction is used for a subset of AERONET stations around the globe of known main aerosol types (training set). The aerosol types  include maritime, urban industrial, biomass burning and dust. We build a predictive model from this training set using K nearest neighbour machine learning algorithms. The relation of particle polarisation ratio and lidar ratio from the Raman lidar is used as a sanity check.  We apply the model to 3- 4 years of AERONET and profiling data across the UK, with instruments evenly distributed across the country, from Camborne in Cornwall to Lerwick in the Shetland Islands. We are showing more detailed data of a dust event in May 2016, dust/biomass burning aerosol mix from October 2017 (hurricane Ophelia) and more recent aerosol transported from the Canadian wild fires in September 2020. AERONET Level 2.0  data is compared to level 1.5 in order to determine the implications for the aerosol classification. Level 1.5 data are cloud-screened, but not quality assured and may not have the final calibration applied. Level 2.0  data have pre- and post-field calibration applied, are cloud-screened, and quality-assured data. As level 2.0 data is usually only available after 1-2 years (after a new calibration has been performed), it is important to understand the  usefulness of more readily available level 1.5 (cloud screened) data.</p><p>The aim is to build a real time aerosol classification application that can be used in Nowcasting.</p>