Black carbon (BC) in haze and deposited on snow and ice can have strong effects on the radiative balance of the Arctic. There is a geographic bias in Arctic BC studies toward the Atlantic sector, with lack of observational constraints for the extensive Russian Siberian Arctic, spanning nearly half of the circum-Arctic. Here, 2 y of observations at Tiksi (East Siberian Arctic) establish a strong seasonality in both BC concentrations (8 ng·m −3 to 302 ng·m −3) and dual-isotopeconstrained sources (19 to 73% contribution from biomass burning). Comparisons between observations and a dispersion model, coupled to an anthropogenic emissions inventory and a fire emissions inventory, give mixed results. In the European Arctic, this model has proven to simulate BC concentrations and source contributions well. However, the model is less successful in reproducing BC concentrations and sources for the Russian Arctic. Using a Bayesian approach, we show that, in contrast to earlier studies, contributions from gas flaring (6%), power plants (9%), and open fires (12%) are relatively small, with the major sources instead being domestic (35%) and transport (38%). The observation-based evaluation of reported emissions identifies errors in spatial allocation of BC sources in the inventory and highlights the importance of improving emission distribution and source attribution, to develop reliable mitigation strategies for efficient reduction of BC impact on the Russian Arctic, one of the fastestwarming regions on Earth.Arctic haze | atmospheric transport modeling | emission inventory | carbon isotopes | climate change B lack carbon (BC) is a short-lived climate pollutant, formed during incomplete combustion of biomass and fossil fuels and contributes to the amplified warming in the Arctic (1-4). However, estimates of the magnitude of added radiative forcing to the global atmosphere by BC span a large range (0.2 W·m −2 to 1 W·m −2 ) (1, 5). Due to its short atmospheric lifetime, BC is a potential target for climate change mitigation. Historically, BC concentrations have been decreasing in the Arctic air (6), but their future fate is unclear. Projections range from increasing concentrations due to a decrease in rainfall (wet scavenging) (7), changes in wind patterns (8), an increase in emissions from wildfires (9), and increased shipping and extraction of natural resources (10) to decreasing concentrations due to more efficient wet scavenging (8). Chemical transport and climate model predictions of BC in the Arctic were, until recently, unsatisfactory and failed to reproduce the observed magnitude and amplitude of BC concentrations (11, 12). However, developments in atmospheric transport models show increasing model skills (12), especially for the European Arctic (13). A key component to the model−observation offset is the large uncertainty connected to emission inventories (EIs) (14-16) used by the models. Implications of these model uncertainties include challenges of accurately assessing the radiative forcing of BC (1, 17). Improveme...

Black carbon (BC) aerosols from incomplete combustion of biomass and fossil fuel contribute to Arctic climate warming. Models—seeking to advise mitigation policy—are challenged in reproducing observations of seasonally varying BC concentrations in the Arctic air. Here we compare year-round observations of BC and its δ13C/Δ14C-diagnosed sources in Arctic Scandinavia, with tailored simulations from an atmospheric transport model. The model predictions for this European gateway to the Arctic are greatly improved when the emission inventory of anthropogenic sources is amended by satellite-derived estimates of BC emissions from fires. Both BC concentrations (R2=0.89, P<0.05) and source contributions (R2=0.77, P<0.05) are accurately mimicked and linked to predominantly European emissions. This improved model skill allows for more accurate assessment of sources and effects of BC in the Arctic, and a more credible scientific underpinning of policy efforts aimed at efficiently reducing BC emissions reaching the European Arctic.

Isotopes pinpoint strong seasonal variations in black carbon sources with consistent patterns at sites around the Arctic.

Black carbon (BC) aerosol particles contribute to climate warming of the Arctic, yet both the sources and the source-related effects are currently poorly constrained. Bottom-up emission inventory (EI) approaches are challenged for BC in general and the Arctic in particular. For example, estimates from three different EI models on the fractional contribution to BC from biomass burning (north of 60° N) vary between 11% and 68%, each acknowledging large uncertainties. Here we present the first dual-carbon isotope-based (Δ(14)C and δ(13)C) source apportionment of elemental carbon (EC), the mass-based correspondent to optically defined BC, in the Arctic atmosphere. It targeted 14 high-loading and high-pollution events during January through March of 2009 at the Zeppelin Observatory (79° N; Svalbard, Norway), with these representing one-third of the total sampling period that was yet responsible for three-quarters of the total EC loading. The top-down source-diagnostic (14)C fingerprint constrained that 52 ± 15% (n = 12) of the EC stemmed from biomass burning. Including also two samples with 95% and 98% biomass contribution yield 57 ± 21% of EC from biomass burning. Significant variability in the stable carbon isotope signature indicated temporally shifting emissions between different fossil sources, likely including liquid fossil and gas flaring. Improved source constraints of Arctic BC both aids better understanding of effects and guides policy actions to mitigate emissions.

Knowledge about personal aerosol exposure in different environments is fundamental for individual and common decision-making, shaping the way we build our infrastructure or change our social behaviours. Aerosols are a leading cause of death and well-known vector for infectious diseases. Yet, passenger exposure to aerosols during long-distance train travel is surprisingly underexplored. Two small, light-weight personal monitoring instruments were employed during a train journey across Europe, to measure the fine particle (PM2.5) and equivalent black carbon (eBC) passenger exposure, respectively. The journey was divided into three legs, inside three different trains, and two layovers in city environments. Highest mean concentrations of PM2.5 and eBC were found within the oldest train type, and revealed PM2.5 concentrations of 58.4 ± 12.7 μg m−3 and eBC of 5.4 ± 2.9 μg m−3. The more modern the train system was, the lower the measured concentrations were to be found. In the newest tested system, the air quality was considerably better inside the train than outdoor air measured by a monitoring network, or simulated by the Copernicus Atmosphere Monitoring Service (CAMS) model ensemble analysis. The mean PM2.5 concentration was roughly 20% lower inside the train than the outdoor air simulated by CAMS. Both the light-weight personal monitoring and the monitoring network indicate that the CAMS ensemble substantially underestimates PM2.5 concentrations for the day of the journey. Effective ventilation and air filtration significantly decrease the passenger’s aerosol exposure, as compared to a stay in outdoor air, leading to a small statistical increase in life expectancy. If this could also reduce the risk of contagion with an infectious disease remains to be explored.

Abstract. Landscape fires are a significant contributor to atmospheric burdens of greenhouse gases and aerosols. Although many studies have looked at biomass burning products and their fate in the atmosphere, estimating and tracing atmospheric pollution from landscape fires based on atmospheric measurements are challenging due to the large variability in fuel composition and burning conditions. Stable carbon isotopes in biomass burning (BB) emissions can be used to trace the contribution of C3 plants (e.g. trees or shrubs) and C4 plants (e.g. savanna grasses) to various combustion products. However, there are still many uncertainties regarding changes in isotopic composition (also known as fractionation) of the emitted carbon compared to the burnt fuel during the pyrolysis and combustion processes. To study BB isotope fractionation, we performed a series of laboratory fire experiments in which we burned pure C3 and C4 plants as well as mixtures of the two. Using isotope ratio mass spectrometry (IRMS), we measured stable carbon isotope signatures in the pre-fire fuels and post-fire residual char, as well as in the CO2, CO, CH4, organic carbon (OC), and elemental carbon (EC) emissions, which together constitute over 98 % of the post-fire carbon. Our laboratory tests indicated substantial isotopic fractionation in combustion products compared to the fuel, which varied between the measured fire products. CO2, EC, and residual char were the most reliable tracers of the fuel 13C signature. CO in particular showed a distinct dependence on burning conditions; flaming emissions were enriched in 13C compared to smouldering combustion emissions. For CH4 and OC, the fractionation was the other way round for C3 emissions (13C-enriched) and C4 emissions (13C-depleted). This indicates that while it is possible to distinguish between fires that were dominated by either C3 or C4 fuels using these tracers, it is more complicated to quantify their relative contribution to a mixed-fuel fire based on the δ13C signature of emissions. Besides laboratory experiments, we sampled gases and carbonaceous aerosols from prescribed fires in the Niassa Special Reserve (NSR) in Mozambique, using an unmanned aerial system (UAS)-mounted sampling set-up. We also provided a range of C3:C4 contributions to the fuel and measured the fuel isotopic signatures. While both OC and EC were useful tracers of the C3-to-C4 fuel ratio in mixed fires in the lab, we found particularly OC to be depleted compared to the calculated fuel signal in the field experiments. This suggests that either our fuel measurements were incomprehensive and underestimated the C3:C4 ratio in the field or other processes caused this depletion. Although additional field measurements are needed, our results indicate that C3-vs.-C4 source ratio estimation is possible with most BB products, albeit with varying uncertainty ranges.

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Abstract. Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact the climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from ground or aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and with airborne aerosol sensors. In this study, we address the main challenges associated with this approach: (1) the degree to which a limited number of samples is representative for the integral smoke plume and (2) the performance of the lightweight aerosol sensors. While aerosol measurements can be made continuously in a UAS set-up thanks to the lightweight analysers, the representativeness of our Tedlar bag filling approach was tested during prescribed burning experiments in the Kruger National Park, South Africa. We compared fire-averaged EFs from UAS-sampled bags for savanna fires with integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R2 ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors with high-fidelity equipment to empirically determine specific calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, although the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multi-wavelength aethalometer (AE33) (linear R2 of 0.95 at λ=880 nm) and the wavelength corrected multi-angle absorption photometer (MAAP, R2 of 0.83 measuring at λ=637 nm). However, the high variability in observed BB mass absorption cross-section (MAC) values (5.2±5.1 m2 g−1) suggested re-calibration may be required for individual fires. Overall, our results indicate that the proposed UAS set-up can obtain representative BB EFs for individual savanna fires if proper correction factors are applied and operating limitations are well understood.