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31.
32.
The impact of cloudiness and cloud type on the atmospheric heating rate of black and brown carbon in the Po Valley
Niccolò Losi, Ezio Bolzacchini, Gian Paolo Gobbi, Luca Di Liberto, Luca Ferrero, Asta Gregorič, Griša Močnik, Francesca Barnaba, Sergio Cogliati, Martin Rigler, 2021, original scientific article

Abstract: We experimentally quantified the impact of cloud fraction and cloud type on the heating rate (HR) of black and brown carbon (HRBC and HRBrC). In particular, we examined in more detail the cloud effect on the HR detected in a previous study (Ferrero et al., 2018). High-time-resolution measurements of the aerosol absorption coefficient at multiple wavelengths were coupled with spectral measurements of the direct, diffuse and surface reflected irradiance and with lidar–ceilometer data during a field campaign in Milan, Po Valley (Italy). The experimental set-up allowed for a direct determination of the total HR (and its speciation: HRBC and HRBrC) in all-sky conditions (from clear-sky conditions to cloudy). The highest total HR values were found in the middle of winter (1.43 ± 0.05 K d−1), and the lowest were in spring (0.54 ± 0.02 K d−1). Overall, the HRBrC accounted for 13.7 ± 0.2 % of the total HR, with the BrC being characterized by an absorption Ångström exponent (AAE) of 3.49 ± 0.01. To investigate the role of clouds, sky conditions were classified in terms of cloudiness (fraction of the sky covered by clouds: oktas) and cloud type (stratus, St; cumulus, Cu; stratocumulus, Sc; altostratus, As; altocumulus, Ac; cirrus, Ci; and cirrocumulus–cirrostratus, Cc–Cs). During the campaign, clear-sky conditions were present 23 % of the time, with the remaining time (77 %) being characterized by cloudy conditions. The average cloudiness was 3.58 ± 0.04 oktas (highest in February at 4.56 ± 0.07 oktas and lowest in November at 2.91 ± 0.06 oktas). St clouds were mostly responsible for overcast conditions (7–8 oktas, frequency of 87 % and 96 %); Sc clouds dominated the intermediate cloudiness conditions (5–6 oktas, frequency of 47 % and 66 %); and the transition from Cc–Cs to Sc determined moderate cloudiness (3–4 oktas); finally, low cloudiness (1–2 oktas) was mostly dominated by Ci and Cu (frequency of 59 % and 40 %, respectively). HR measurements showed a constant decrease with increasing cloudiness of the atmosphere, enabling us to quantify for the first time the bias (in %) of the aerosol HR introduced by the simplified assumption of clear-sky conditions in radiative-transfer model calculations. Our results showed that the HR of light-absorbing aerosol was ∼ 20 %–30 % lower in low cloudiness (1–2 oktas) and up to 80 % lower in completely overcast conditions (i.e. 7–8 oktas) compared to clear-sky ones. This means that, in the simplified assumption of clear-sky conditions, the HR of light-absorbing aerosol can be largely overestimated (by 50 % in low cloudiness, 1–2 oktas, and up to 500 % in completely overcast conditions, 7–8 oktas). The impact of different cloud types on the HR was also investigated. Cirrus clouds were found to have a modest impact, decreasing the HRBC and HRBrC by −5 % at most. Cumulus clouds decreased the HRBC and HRBrC by −31 ± 12 % and −26 ± 7 %, respectively; cirrocumulus–cirrostratus clouds decreased the HRBC and HRBrC by −60 ± 8 % and −54 ± 4 %, which was comparable to the impact of altocumulus (−60 ± 6 % and −46 ± 4 %). A higher impact on the HRBC and HRBrC suppression was found for stratocumulus (−63 ± 6 % and −58 ± 4 %, respectively) and altostratus (−78 ± 5 % and −73 ± 4 %, respectively). The highest impact was associated with stratus, suppressing the HRBC and HRBrC by −85 ± 5 % and −83 ± 3 %, respectively. The presence of clouds caused a decrease of both the HRBC and HRBrC (normalized to the absorption coefficient of the respective species) of −11.8 ± 1.2 % and −12.6 ± 1.4 % per okta. This study highlights the need to take into account the role of both cloudiness and different cloud types when estimating the HR caused by both BC and BrC and in turn decrease the uncertainties associated with the quantification of their impact on the climate.
Found in: osebi
Keywords: black carbon, brown carbon, cloud, atmospheric heating rate, climate change
Published: 29.03.2021; Views: 116; Downloads: 0
.pdf Fulltext (8,61 MB)

33.
Determination of Aethalometer multiple-scattering enhancement parameters and impact on source apportionment during the winter 2017/18 EMEP/ACTRIS/COLOSSAL campaign in Milan
Asta Gregorič, Dario Massabò, Griša Močnik, Paolo Prati, Martin Rigler, Luca Santagostini, Ezio Bolzacchini, Alice Corina Forello, Vera Bernardoni, Luca Ferrero, 2021, original scientific article

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 %).
Found in: osebi
Keywords: black carbon, filter photometer, Aethalometer, source apportionment
Published: 16.04.2021; Views: 145; Downloads: 0
.pdf Fulltext (1,95 MB)

34.
35.
Performance of microAethalometers: Real-world Field Intercomparisons from Multiple Mobile Measurement Campaigns in Different Atmospheric Environments
Honey Alas, Thomas Mueller, Kay Weinhold, Sascha Pfeifer, Kristina Glojek, Asta Gregorič, Griša Močnik, Luka Drinovec, Francesca Costabile, Martina Ristorini, A. Wiedensohler, 2020, original scientific article

Abstract: Small aethalometers are frequently used to measure equivalent black carbon (eBC) mass concentrations in the context of personal exposure and air pollution mapping through mobile measurements (MM). The most widely used is the microAethalometer (AE51). Its performance in the laboratory and field is well documented, however, there is not sufficient data in the context of its performance in different environments. In this investigation, we present the characterization of the performance of the AE51 through field unit-to-unit intercomparisons (IC), and against a reference absorption photometer from three MM campaigns conducted in drastically different environments. Five IC parameters were considered: i) study area, ii) location of IC, iii) time of day, iv) duration of IC, and v) correction for the filter-loading effect. We can conclude that it is crucial where and how long the IC have been performed in terms of the correlation between the mobile and reference instruments. Better correlations (R2 > 0.8, slope = 0.8) are achieved for IC performed in rural, and background areas for more than 10 minutes. In locations with more homogenous atmosphere, the correction of the loading effect improved the correlation between the mobile and reference instruments. In addition, a newer microAethalometer model (MA200) was characterized in the field under extreme cold conditions and correlated against another MA200 (R2 > 0.8, slope ≈ 1.0), AE51(R2 > 0.9, slope ≈ 0.9), and a stationary Aethalometer (AE33) across all wavelengths (R2 > 0.8, slope ≈ 0.7). For MA200, the loading effect was more pronounced, especially at the lower wavelengths, hence the correction of the loading effect is essential to improve the correlation against the AE33. The MA200 and AE51 proved to be robust and dependable portable instruments for MM applications. Real-world quality assurance of these instruments should be performed through field IC against reference instruments with longer durations in areas of slowly changing eBC concentration.
Found in: osebi
Keywords: Portable instruments, Mobile monitoring, Black carbon, Instrument intercomparisons
Published: 15.09.2020; Views: 484; Downloads: 21
.pdf Fulltext (3,54 MB)

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