The "Carbonaceous Aerosol in Rome and Environs" (CARE) experiment took place at a Mediterranean urban background site in Rome (Italy) deploying a variety of instrumentation to assess aerosol physical-chemical and optical properties with high-time resolution (from 1 min to 2 h). In this study, aerosol optical properties, chemical composition, and size distribution data were examined with a focus on the analysis of several intensive optical properties obtained from multi-wavelength measurements of aerosol scattering and absorption coefficients. The spectral behaviour of several quantities related to both aerosol composition and size was explored, analysing their high-time resolved temporal patterns and combining them in order to extract the maximum information from all the available data.A methodology to separate aerosol types using optical data only is here proposed and applied to an urban area characterised by a complex mixture of particles. A key is given to correctly disentangle cases that could not be distinguished observing only one or few parameters, but that can be clearly separated using a suitable ensemble of optical properties.The SSCAAE, i.e. the wavelength dependence of the Single Scattering co-albedo 1-SSA (where SSA is the Single Scattering Albedo) -that efficiently responds to both aerosol size and chemical composition -resulted to be the best optical intensive parameter to look at for the discrimination between episodes characterised by specific aerosol types (e.g. sea salt, Saharan dust) and more mixed conditions dominated by local emissions. However, this study also highlighted that it is necessary to combine temporal patterns of different optical parameters to robustly associate SSCAAE features to specific aerosol types. In addition, the complete chemical speciation and the hightime resolved size distribution were used to confirm the aerosol types identified via a combination of aerosol optical properties. Look-up tables with most suitable ranges of values for optical variables were produced; therefore, these pieces of information can be used at the same site or at locations with similar features to quickly identify the occurrence of aerosol episodes. Graphical frameworks (both from the literature and newly designed) are also proposed; for each scheme features, advantages, and limitations are discussed.

Abstract. In this paper, a new methodology coupling aerosol optical and chemical parameters in the same source apportionment study is reported. In addition to results on source contributions, this approach provides information such as estimates for the atmospheric absorption Ångström exponent (α) of the sources and mass absorption cross sections (MACs) for fossil fuel emissions at different wavelengths. A multi-time resolution source apportionment study using the Multilinear Engine (ME-2) was performed on a PM10 dataset with different time resolutions (24, 12, and 1 h) collected during two different seasons in Milan (Italy) in 2016. Samples were optically analysed by an in-house polar photometer to retrieve the aerosol absorption coefficient bap (in Mm−1) at four wavelengths (λ=405, 532, 635, and 780 nm) and were chemically characterized for elements, ions, levoglucosan, and carbonaceous components. The dataset joining chemically speciated and optical data was the input for the multi-time resolution receptor model; this approach was proven to strengthen the identification of sources, thus being particularly useful when important chemical markers (e.g. levoglucosan, elemental carbon) are not available. The final solution consisted of eight factors (nitrate, sulfate, resuspended dust, biomass burning, construction works, traffic, industry, aged sea salt); the implemented constraints led to a better physical description of factors and the bootstrap analysis supported the goodness of the solution. As for bap apportionment, consistent with what was expected, biomass burning and traffic were the main contributors to aerosol absorption in the atmosphere. A relevant feature of the approach proposed in this work is the possibility of retrieving a lot of other information about optical parameters; for example, in contrast to the more traditional approach used by optical source apportionment models, here we obtained source-dependent α values without any a priori assumption (α biomass burning =1.83 and α fossil fuels =0.80). In addition, the MACs estimated for fossil fuel emissions were consistent with literature values. It is worth noting that the approach presented here can also be applied using more common receptor models (e.g. EPA PMF instead of multi-time resolution ME-2) if the dataset comprises variables with the same time resolution as well as optical data retrieved by widespread instrumentation (e.g. an Aethalometer instead of in-house instrumentation).

Abstract. In the frame of the EMEP/ACTRIS/COLOSSAL campaign in Milan during winter 2018, equivalent black carbon measurements using the Aethalometer 31 (AE31), the Aethalometer 33 (AE33), and a Multi-Angle Absorption Photometer (MAAP) were carried out together with levoglucosan analyses on 12 h resolved PM2.5 samples collected in parallel. From AE31 and AE33 data, the loading-corrected aerosol attenuation coefficients (bATN) were calculated at seven wavelengths (λ, where λ values are 370, 470, 520, 590, 660, 880, and 950 nm). The aerosol absorption coefficient at 637 nm (babs_MAAP) was determined by MAAP measurements. Furthermore, babs was also measured at four wavelengths (405, 532, 635, 780 nm) on the 12 h resolved PM2.5 samples by a polar photometer (PP_UniMI). After comparing PP_UniMI and MAAP results, we exploited PP_UniMI data to evaluate the filter multiple-scattering enhancement parameter at different wavelengths for AE31 and AE33. We obtained instrument- and wavelength-dependent multiple-scattering enhancement parameters by linear regression of the Aethalometer bATN against the babs measured by PP_UniMI. We found significant dependence of the multiple-scattering enhancement parameter on filter material, hence on the instrument, with a difference of up to 30 % between the AE31 and the AE33 tapes. The wavelength dependence and day–night variations were small – the difference between the smallest and largest value was up to 6 %. Data from the different instruments were used as input to the so-called “Aethalometer model” for optical source apportionment, and instrument dependence of the results was investigated. Inconsistencies among the source apportionment were found fixing the AE31 and AE33 multiple-scattering enhancement parameters to their usual values. In contrast, optimised multiple-scattering enhancement parameters led to a 5 % agreement among the approaches. Also, the component apportionment “MWAA model” (Multi-Wavelength Absorption Analyzer model) was applied to the dataset. It was less sensitive to the instrument and the number of wavelengths, whereas significant differences in the determination of the absorption Ångström exponent for brown carbon were found (up to 22 %).

In this study, the applicability of a benchtop polar photometer (PP_UniMI) to retrieve multi-wavelength aerosol absorption coefficient data by off-line measurements of the Multi-Angle Absorption Photometer (MAAP) sample spots is presented. MAAP is a widespread single wavelength online instrument providing the aerosol absorption coefficient and the equivalent black carbon concentration. In this work, MAAP samples collected during four different campaigns were analysed off-line with PP_UniMI.First of all, data from PP_UniMI and MAAP were compared to investigate contributions to measurement uncertainties. In particular, the role of the following assumptions performed in the MAAP was investigated:-reconstructing angular distribution of light scattered by filter samples from measurements at three fixed angles using analytical functions; -setting the fraction of the back-scattered radiation by the blank filter (BM) at a fixed value BM=0.7:-assuming a fixed value for the asymmetry factor g=0.75.Samples collected at several sites showed an agreement within 5% when data from the two instruments were retrieved using the same approximations (i.e. backscattered radiation from the filter matrix BM set at a fixed value, phase functions reconstructed by analytical functions from measurements at 3 angles, same asymmetry factor) in the data retrieval algorithm. Conversely, larger differences (14% on average) between off-line measurements and averaged MAAP data were obtained when the high-angular resolved information available by PP_UniMI was exploited. By analysing the role of MAAP assumptions for σap retrieval, it resulted that fixing BM=0.7 was the main responsible for the detected differences. Indeed, highangular resolved off-line measurements by PP_UniMI allow to directly measure BM, obtaining BM=0.88 on white spots. It is noteworthy that the observed results were similar at all considered sites, so they proved to be independent of the aerosol type and can likely be attributed to instrumental effects. Moreover, a sensitivity test was carried out also to check the impact of the fixed value used for the asymmetry factor (set at g=0.75 in both instruments). Varying g values within the typical range for ambient aerosol (0.50-0.75) the estimate of aerosol absorbance ABS (directly obtained from PP_UniMI measurements and linked to σap) was affected by 8% at most, thus being a minor source of uncertainty in the calculation. The effect of the variability in blank spots used for off-line analyses was also evaluated and resulted in a contribution smaller than 3% to the uncertainty of the methodology employed.Finally, the possibility of exploiting multi-wavelength assessment of absorption coefficients is an added value of PP_UniMI; indeed, it allows to estimate the contribution of different aerosol sources and components to the absorption coefficient using MAAP tapes used in present or past campaigns.