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. 2022 Feb 22;13(1):984.
doi: 10.1038/s41467-022-28560-w.

Black carbon footprint of human presence in Antarctica

Affiliations

Black carbon footprint of human presence in Antarctica

Raúl R Cordero et al. Nat Commun. .

Abstract

Black carbon (BC) from fossil fuel and biomass combustion darkens the snow and makes it melt sooner. The BC footprint of research activities and tourism in Antarctica has likely increased as human presence in the continent has surged in recent decades. Here, we report on measurements of the BC concentration in snow samples from 28 sites across a transect of about 2,000 km from the northern tip of Antarctica (62°S) to the southern Ellsworth Mountains (79°S). Our surveys show that BC content in snow surrounding research facilities and popular shore tourist-landing sites is considerably above background levels measured elsewhere in the continent. The resulting radiative forcing is accelerating snow melting and shrinking the snowpack on BC-impacted areas on the Antarctic Peninsula and associated archipelagos by up to 23 mm water equivalent (w.e.) every summer.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Human presence in Antarctica has surged in recent decades.
a Blue dots represent research stations in the area of interest according to the Council of Managers of National Antarctic Programs (COMNAP). Dotted lines represent popular tourist routes. Most of the visitors travelling to Antarctica with International Association of Antarctica Tour Operators (IAATO) embark on a ship for cruising the Antarctic Peninsula, where about half of the research facilities in the continent are located. Around 1% of all visitors fly to the interior of Antarctica where they stay in field camps such as Union Glacier Camp. IAATO has now more than 50 operators whose fleet of 54 vessels (including 6 large cruise ships) registered a total of 378 departures in the 2019–2020 season. b Visitors have been growing steadily since 2011–2012. Of the 74,401 visitors who travelled with IAATO members to the region, about 25% travelled on cruise-only vessels (i.e., vessels carrying more than 500 passengers) and did not set foot on the continent. c There are currently 76 research stations in Antarctica (including seasonal facilities) with a combined accommodation capacity (for both scientists and staff) of about five thousand beds. There are 11 research stations (with a total of 700 beds) on King George Island only. Plots were generated by using Python’s Matplotlib Library.
Fig. 2
Fig. 2. Black Carbon (BC) concentration around research stations and popular tourism destinations is considerable higher than elsewhere in Antarctica.
a Red dots represent the snow sampling sites. We sampled at 15 sites on the South Shetland Islands (where 16 research stations are located), five sites in the Palmer Archipelago, six sites on or near the coast of the Antarctic Peninsula, and at two deep-field sites on Union Glacier (Ellsworth Mountains); see Table 1 for details. b Boxplots of the BC concentrations in samples from different locations. In each box, the central mark (red stripe) indicates the median, and the edges indicate the 25th and 75th percentiles. The whiskers extend to the maximum and minimum data excluding outliers. c Mean of the absorption Ångström exponent of light-absorbing impurities in samples from different locations. Locations, given in order of latitude, in plots b and c, may combine samples from nearby sites. Measurements in plots b and c were subjected to the statistical significance tests whose results are presented in the Supplementary Information. Plots were generated by using Python’s Matplotlib Library.
Fig. 3
Fig. 3. Black Carbon (BC) concentration is above background levels even around deep-field tourist destinations.
a C-130 aircraft about to land on the blue-ice runway at Union Glacier (79°S, Ellsworth Mountains), an increasingly popular deep-field destination (a private four-engine turbofan Ilyushin Il-76 landed on the runway 30 times in the 2019–2020 season only). b, c Snowpit for snow sampling (6 km east of the blue-ice runway and about 1 km west of the designed landing sites for ski-equipped airplanes). d BC concentrations measured at different snow layers. BC concentration peaked at about 3 ng/g during the 2013–2014 season. Although private activities began years earlier, the Chilean Union Glacier Camp became operational during the 2013–2014 season. In each box, the central mark (red stripe) indicates the median, and the edges indicate the 25th and 75th percentiles. The whiskers extend to the maximum and minimum data excluding outliers. e Mean of the absorption Ångström exponent of light-absorbing impurities in snow from different layers. Photographs taken by the authors. Plots were generated by using Python’s Matplotlib Library.
Fig. 4
Fig. 4. Black Carbon (BC) from local activities in Antarctica darkens the snow and makes it melt sooner.
a Broadband shortwave (SW) albedo averaged for December, January and February (DJF) days over the period 2004–2020. Data from MERRA-2 were used. b Downwelling shortwave (SW) all-sky irradiance averaged for DJF days over the period 1981–2019. The all-sky DJF SW irradiance is about 250 W/m2. Data from the ERA5 Atmospheric Reanalysis were used. c Probability density function of the snow that melts sooner due to the BC footprint of a researcher (red line) and a tourist (blue line). These probabilities are based on the simulations shown in Supplementary Figs. 7-8. In the case of the snow that melts sooner due to the BC footprint attributable to a researcher, we assumed that the area impacted by the presence of 11 research stations (with a total of 700 beds) on King George Island ranges from 6 to 48 km2. In the case of the snow that melts sooner due to the BC footprint attributable to a tourist, we assumed that the area impacted by BC emissions related to tourism ranges from 100 to 500 km2; we also considered that, on average, 53,000 tourists visited Antarctica annually from season 2016–2017 to the season 2019–2020. Plots were generated by using Python’s Matplotlib Library.

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