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Long-term measurements of the mass concentration of black carbon (BC) in the atmosphere (MBC) with well-constrained accuracy are indispensable to quantify its emission, transport, and deposition. The aerosol light absorption coefficient (babs), usually measured by a filter-based absorption photometer, including an Aethalometer (AE), is often used to estimate MBC. The measured babs is converted to MBC by assuming a value for the mass absorption cross section (MAC). Previously, we derived the MAC for AE (MAC (AE)) from measured babs and independently measured MBC values at two sites in the Arctic. MBC was measured with a filter-based absorption photometer with a heated inlet (COSMOS). The accuracy of the COSMOS-derived MBC (MBC (COSMOS)) was within about 15%. Here, we obtained additional MAC (AE) measurements to improve understanding of its variability and uncertainty. We measured babs (AE) and MBC (COSMOS) at Alert (2018–2020), Barrow (2012–2022), Ny-Ålesund (2012–2019), and Pallas (2019–2022). At Pallas, we also obtained four-wavelength photoacoustic aerosol absorption spectrometer (PAAS-4λ) measurements of babs. babs (AE) and MBC (COSMOS) were tightly correlated; the average MAC (AE) at the four sites was 11.4 ± 1.2 m2 g−1 (mean ± 1σ) at 590 nm and 7.76 ± 0.73 m2 g−1 at 880 nm. The spatial variability of MAC (AE) was about 11% (1σ), and its year-to-year variability was about 18%. We compared MAC (AE) in the Arctic with values at mid-latitudes, measured by previous studies, and with values obtained by using other types of filter-based absorption photometer, and PAAS-4λ. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Aerosol light absorption was measured during a 1-month field campaign in June–July 2019 at the Pallas Global Atmospheric Watch (GAW) station in northern Finland. Very low aerosol concentrations prevailed during the campaign, which posed a challenge for the instruments' detection capabilities. The campaign provided a real-world test for different absorption measurement techniques supporting the goals of the European Metrology Programme for Innovation and Research (EMPIR) Black Carbon (BC) project in developing aerosol absorption standard and reference methods. In this study we compare the results from five filter-based absorption techniques – aethalometer models AE31 and AE33, a particle soot absorption photometer (PSAP), a multi-angle absorption photometer (MAAP), and a continuous soot monitoring system (COSMOS) – and from one indirect technique called extinction minus scattering (EMS). The ability of the filter-based techniques was shown to be adequate to measure aerosol light absorption coefficients down to around 0.01 Mm-1 levels when data were averaged to 1–2 h. The hourly averaged atmospheric absorption measured by the reference MAAP was 0.09 Mm-1 (at a wavelength of 637 nm). When data were averaged for >1 h, the filter-based methods agreed to around 40 %. COSMOS systematically measured the lowest absorption coefficient values, which was expected due to the sample pre-treatment in the COSMOS inlet. PSAP showed the best linear correlation with MAAP (slope=0.95 , R2=0.78), followed by AE31 (slope=0.93). A scattering correction applied to PSAP data improved the data accuracy despite the added noise. However, at very high scattering values the correction led to an underestimation of the absorption. The AE31 data had the highest noise and the correlation with MAAP was the lowest (R2=0.65). Statistically the best linear correlations with MAAP were obtained for AE33 and COSMOS (R2 close to 1), but the biases at around the zero values led to slopes clearly below 1. The sample pre-treatment in the COSMOS instrument resulted in the lowest fitted slope. In contrast to the filter-based techniques, the indirect EMS method was not adequate to measure the low absorption values found at the Pallas site. The lowest absorption at which the EMS signal could be distinguished from the noise was >0.1 Mm-1 at 1–2 h averaging times. The mass absorption cross section (MAC) value measured at a range 0–0.3 Mm-1 was calculated using the MAAP and a single particle soot photometer (SP2), resulting in a MAC value of 16.0±5.7 m2g-1. Overall, our results demonstrate the challenges encountered in the aerosol absorption measurements in pristine environments and provide some useful guidelines for instrument selection and measurement practices. We highlight the need for a calibrated transfer standard for better inter-comparability of the absorption results. [ABSTRACT FROM AUTHOR]

Light-absorbing impurities (LAIs) deposited in snow have the potential to substantially affect the snow radiation budget, with subsequent implications for snow melt. To more accurately quantify the snow albedo, the contribution from different LAIs needs to be assessed. Here we estimate the main LAI components, elemental carbon (EC) (as a proxy for black carbon) and mineral dust in snow from the Indian Himalayas and paired the results with snow samples from Arctic Finland. The impurities are collected onto quartz filters and are analyzed thermal-optically for EC, as well as with an additional optical measurement to estimate the light-absorption of dust separately on the filters. Laboratory tests were conducted using substrates containing soot and mineral particles, especially prepared to test the experimental setup. Analyzed ambient snow samples show EC concentrations that are in the same range as presented by previous research, for each respective region. In terms of the mass absorption cross section (MAC) our ambient EC surprisingly had about half of the MAC value compared to our laboratory standard EC (chimney soot), suggesting a less light absorptive EC in the snow, which has consequences for the snow albedo reduction caused by EC. In the Himalayan samples, larger contributions by dust (in the range of 50% or greater for the light absorption caused by the LAI) highlighted the importance of dust acting as a light absorber in the snow. Moreover, EC concentrations in the Indian samples, acquired from a 120 cm deep snow pit (possibly covering the last five years of snow fall), suggest an increase in both EC and dust deposition. This work emphasizes the complexity in determining the snow albedo, showing that LAI concentrations alone might not be sufficient, but additional transient effects on the light-absorbing properties of the EC need to be considered and studied in the snow. Equally as imperative is the confirmation of the spatial and temporal representativeness of these data by comparing data from several and deeper pits explored at the same time. [ABSTRACT FROM AUTHOR]

Several types of filter-based instruments are used to estimate aerosol light absorption coefficients. Two significant results are presented based on Aethalometer measurements at six Arctic stations from 2012 to 2014. First, an alternative method of post-processing the Aethalometer data is presented, which reduces measurement noise and lowers the detection limit of the instrument more effectively than boxcar averaging. The biggest benefit of this approach can be achieved if instrument drift is minimised. Moreover, by using an attenuation threshold criterion for data post-processing, the relative uncertainty from the electronic noise of the instrument is kept constant. This approach results in a time series with a variable collection time (Δ) but with a constant relative uncertainty with regard to electronic noise in the instrument. An additional advantage of this method is that the detection limit of the instrument will be lowered at small aerosol concentrations at the expense of temporal resolution, whereas there is little to no loss in temporal resolution at high aerosol concentrations (>2.1-6.7Mm-1 as measured by the Aethalometers). At high aerosol concentrations, minimising the detection limit of the instrument is less critical. Additionally, utilising co-located filter-based absorption photometers, a correction factor is presented for the Arctic that can be used in Aethalometer corrections available in literature. The correction factor of 3.45 was calculated for low-elevation Arctic stations. This correction factor harmonises Aethalometer attenuation coefficients with light absorption coefficients as measured by the co-located light absorption photometers. Using one correction factor for Arctic Aethalometers has the advantage that measurements between stations become more inter-comparable. [ABSTRACT FROM AUTHOR]

Several types of filter-based instruments are used to estimate aerosol light absorption coefficients. Two significant results are presented based on Aethalometer measurements at six Arctic station from 2012-2014. First, an alternative method of post-processing the Aethalometer data is presented which reduces measurement noise and lowers the detection limit of the instrument more effectively than boxcar averaging. The biggest benefit of this approach can be achieved if instrument drift is minimized. Moreover, by using an attenuation threshold criterion for data post-processing, the relative uncertainty from the electronic noise the instrument is kept constant. This approach results in a time series with a variable collection time (Δt), but with a constant relative uncertainty with regard to electronic noise in the instrument. An additional advantage of this method is that the detection limit of the instrument will be lowered at small aerosol concentrations at the expense of temporal resolution, whereas there is little to no loss in temporal resolution at high aerosol concentrations (>2.1-6.7 Mm-1 as measured by the Aethalometers). At high aerosol concentrations, minimising the detection limit of the instrument is less critical. Additionally, utilising co-located filter-based absorption photometers, a correction factor is presented for the Arctic that can be used in Aethalometer corrections available in literature. The correction factor of 3.45 was calculated for low-elevation Arctic stations. This correction factor harmonises Aethalometer attenuation coefficients with light absorption coefficients as measured by the co-located light absorption photometers. Using one correction factor for Arctic Aethalometers has the advantage that measurements between stations become more inter-comparable. [ABSTRACT FROM AUTHOR]