Baseline air mass selection at Cape Point, South Africa: application of 222Rn and other filter criteria to CO2

Brunke, E.-G.; Labuschagne, Casper; Parker, B.; Scheel, H.E.; Whittlestone, S.

doi:10.1016/j.atmosenv.2004.04.024

Cited by 68 publications

(85 citation statements)

References 12 publications

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“…The station receives clean marine air masses for most of the time. Occasional events with continental and polluted air can easily be filtered out using a combination of the CO and 222 Rn measurements (Brunke et al, 2004). Gaseous elemental mercury (GEM) was measured by a manual amalgamation technique (Slemr et al, 2008) • 33 E).…”

Section: Methodsmentioning

confidence: 99%

Comparison of mercury concentrations measured at several sites in the Southern Hemisphere

et al. 2015

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Abstract. Our knowledge of the distribution of mercury concentrations in air of the Southern Hemisphere was until recently based mostly on intermittent measurements made during ship cruises. In the last few years continuous mercury monitoring has commenced at several sites in the Southern Hemisphere, providing new and more refined information. In this paper we compare mercury measurements at several remote sites in the Southern Hemisphere made over a period of at least 1 year at each location. Averages of monthly medians show similar although small seasonal variations at both Cape Point and Amsterdam Island. A pronounced seasonal variation at Troll research station in Antarctica is due to frequent mercury depletion events in the austral spring. Due to large scatter and large standard deviations of monthly average median mercury concentrations at Cape Grim, no systematic seasonal variation could be found there. Nevertheless, the annual average mercury concentrations at all sites during the 2007-2013 period varied only between 0.85 and 1.05 ng m −3 . Part of this variability is likely due to systematic measurement uncertainties which we propose can be further reduced by improved calibration procedures. We conclude that mercury is much more uniformly distributed throughout the Southern Hemisphere than the distributions suggested by measurements made onboard ships. This finding implies that smaller trends can be detected in shorter time periods. We also report a change in the trend sign at Cape Point from decreasing mercury concentrations in 1996-2004 to increasing concentrations since 2007.

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Section: Methodsmentioning

confidence: 99%

Comparison of mercury concentrations measured at several sites in the Southern Hemisphere

et al. 2015

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“…1 shows an updated time series of GEM median concentrations of all measurements at Cape Point since September 1995 until the end of 2011. Annual medians and averages from the "all data set" were found to be statistically the same as the annual medians and averages for the baseline data (defined as GEM concentrations at 222 Rn ≤ 250 mBq m -3 (Brunke et al, 2004;Whittlestone et al, 2009)). All measurements were thus used in Fig.…”

Section: Methodsmentioning

confidence: 68%

Trend and seasonal variation of atmospheric mercury concentrations at the Cape Point GAW observatory, South Africa

Brunke

Ebinghaus

Kock

et al. 2013

E3S Web of Conferences

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“…• (Antarctic region) (Brunke et al, 2004). Therefore background measurements at Cape Point are well representative of the background CO 2 signal influencing the Cape Peninsula.…”

Section: Concentration Measurements -C 15mentioning

confidence: 96%

Estimates of CO<sub>2</sub> fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

Nickless¹,

Engelbrecht²,

Brunke³

et al. 2017

Preprint

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Abstract. The results of a high resolution Bayesian inversion over the City of Cape Town, South Africa, are presented, which used observations of atmospheric CO 2 from sites at Robben Island and Hangklip lighthouses collected over a sixteen month period from March 2012 until June 2013. A Lagrangian particle dispersion model driven by the regional climate model Conformal Cubic Atmospheric Model (CCAM) was used to provide the sensitivities of the observations to the surface sources and boundary concentrations. This regional climate model was dynamically coupled to the CABLE (Community Atmosphere 5Biosphere Land Exchange) model, which provided prior estimates of the biogenic fluxes. Prior estimates of the fossil fuel emissions were obtained from an inventory analysis specifically carried out for this inversion exercise, making use of vehicle count data, population census data, fuel usage at industrial point sources, and aviation and shipping vessel counts. The inversion solved for the actual concentration measurements at each site, which was made possible by the use of the Cape Point background site to provide information on the boundaries, and was necessary due to the effect of topography on the atmospheric 10 transport, affecting particularly the sensitivity of the Robben Island site to the surface fluxes. Night-time observations were included, but allocated much larger errors compared to the daytime observations. The inversion was able to substantially improve the agreement between the modelled and observed concentrations, and able to better represent the diurnal cycle in the concentrations compared with the prior modelled concentrations. The mean bias in the modelled concentrations was reduced from -2.9 ppm, with interquartile range -9.1 to 3.7, for the prior modelled 15 concentrations, to 0.5, with interquartile range -1.5 to 1.5, for the posterior modelled concentrations at Robben Island, and from a bias of 2.4 ppm in the prior modelled concentrations at the Hangklip site, with interquartile range -2.3 to 6.5, to a bias of 0.04, with interquartile range -1.1 to 0.8. The standard deviations of the posterior residuals at both sites were reduced to values below that of the observed concentrations.The inversion solved for working week and weekend fossil fuel emissions, and weekly biogenic fluxes, each split into day model errors or the uncertainty in the fluxes was not specified high enough for some months. A companion paper on sensitivity analyses will address different options for the specification of the correlations between errors in the modelled concentrations, how these prior errors are determined, how correlations are determined between the prior fluxes, and how the state vector is specified. Greater confidence is given to the inversion's ability to correct the total flux within each pixel, rather than the individual flux estimates.

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Baseline air mass selection at Cape Point, South Africa: application of 222Rn and other filter criteria to CO2

Cited by 68 publications

References 12 publications

Comparison of mercury concentrations measured at several sites in the Southern Hemisphere

Comparison of mercury concentrations measured at several sites in the Southern Hemisphere

Trend and seasonal variation of atmospheric mercury concentrations at the Cape Point GAW observatory, South Africa

Estimates of CO<sub>2</sub> fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

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Baseline air mass selection at Cape Point, South Africa: application of 222Rn and other filter criteria to CO2

Cited by 68 publications

References 12 publications

Comparison of mercury concentrations measured at several sites in the Southern Hemisphere

Comparison of mercury concentrations measured at several sites in the Southern Hemisphere

Trend and seasonal variation of atmospheric mercury concentrations at the Cape Point GAW observatory, South Africa

Estimates of CO&lt;sub&gt;2&lt;/sub&gt; fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling

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Estimates of CO<sub>2</sub> fluxes over the City of Cape Town, South Africa, through Bayesian inverse modelling