4.7 Article

Seasonal and annual variation of carbon dioxide surface fluxes in Helsinki, Finland, in 2006-2010

Journal

ATMOSPHERIC CHEMISTRY AND PHYSICS
Volume 12, Issue 18, Pages 8475-8489

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-12-8475-2012

Keywords

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Funding

  1. Academy of Finland [138328, 1118615]
  2. Nessling foundation
  3. public works department of City of Helsinki
  4. EU
  5. Academy of Finland (ICOS-Finland) [263149]
  6. Academy of Finland (AKA) [263149, 138328, 263149, 138328] Funding Source: Academy of Finland (AKA)

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Five years of carbon dioxide exchange measured with the eddy covariance technique at the world's northernmost urban flux station SMEAR III located in Helsinki, Finland, were analyzed. The long-term measurements and high-latitude location enabled us to examine the seasonal and annual variations of CO2 exchange, and to identify different factors controlling the measured exchange. Online traffic counts and soil respiration measurements were utilized in the study. Furthermore, the advantage of the station is that the complex surrounding area enables us to distinguish three different surface cover areas that can be evaluated separately. We also tested different methods (artificial neural networks and median diurnal cycles) to fill gaps in CO2 flux time series and examined their effect on annual emission estimates. The measured fluxes were highly dependent on the prevailing wind direction with the highest fluxes downwind from a large road and lowest downwind from the area of high fraction of vegetation cover. On an annual level, the area of the road emitted 3500 g Cm-2 whereas the area of high fraction of vegetation cover emitted only 870 g Cm-2 showing the effect of surface cover to be large in urban areas. Seasonal differences in the CO2 exchange downwind from the road were mainly caused by reduced traffic rates in summer, whereas in other directions seasonality was more determined by vegetation activity. Differences between the gap filling methods were small, but slightly better (0.6 mu mol m(-2) s(-1) smaller RMSE) results were obtained when the artificial neural network with traffic counts was used instead of the one without traffic network and method based on median diurnal cycles. The measurement site was a net carbon source with average annual emissions of 1760 g Cm-2, with a biased error of 6.1 g Cm-2 caused by the gap filling. The annual value varied 16% between the different years.

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