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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
21 Sep 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).
Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4
Abdelhadi El Yazidi1, Michel Ramonet1, Philippe Ciais1, Gregoire Broquet1, Isabelle Pison1, Amara Abbaris1, Dominik Brunner2, Sebastien Conil3, Marc Delmotte1, Francois Gheusi4, Frederic Guerin5, Lynn Hazan1, Nesrine Kachroudi1, Giorgos Kouvarakis6, Nikolaos Mihalopoulos6, Leonard Rivier1, and Dominique Serça5 1Laboratoire des sciences du climate et de l'environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
2Laboratory for Air Pollution/Environmental Technology, Swiss Federal Laboratories For Materials Science and Technology, EMPA, Duebendorf, Switzerland
3Direction Recherche & Développement, Andra, CMHM, RD960, 55290 Bure, France
4Laboratoire d'Aérologie, Université de Toulouse, CNRS, UPS, UMR5560, 14 Av. Edouard Belin, 31400 Toulouse, France
5Geosciences Environnement Toulouse UMR 5563 & UR 234 IRD, Université Paul-Sabatier, Avenue Edouard Belin 14, F-31400 Toulouse, France
6Environmental Chemical Processes Laboratory, Department of Chemistry, University of Crete, 71003, Heraklion, Greece
Abstract. This study deals with the problem of identifying atmospheric data that are influenced by local emissions which cause spikes in time series of greenhouse gases and long-lived tracer measurements. We considered three spike detection methods known as coefficient of variation (COV), robust extraction of baseline signal (REBS), and standard deviation of the background (SD), to detect and filter positive spikes in continuous greenhouse gas time series from four monitoring stations representative of the ICOS (Integrated Carbon Observation System) European Infrastructure network. The results of the different methods are compared to each other and against a manual detection performed by station managers. Four stations were selected as test cases to apply the spike detection methods: a continental rural tower of 100 m height in Eastern France (OPE); a high mountain observatory in the south-west of France (PDM); a regional marine background site in Crete (FKL); and a marine clean-air background site in the southern hemisphere in Amsterdam island (AMS). This panel allows addressing the spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analyzed. All the methods were found to be able to detect short-term spikes (lasting from a few seconds to few minutes) in the time series. Analysis of the results of each method leads us to exclude the use of the COV method because of its requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or under-estimate the actual number of spikes. The two other methods freely determine the number of spikes for a given set of parameters, and the values of these parameters were calibrated to provide the best match with spikes known to reflect local emissions episodes well documented by the station managers. More than 96 % of the spikes manually identified by station managers were successfully detected both in the SD and the REBS methods after the best adjustment of parameter values. At PDM, measurements made by two analyzers 200 m from each other allow to confirm that the CH4 spikes identified in one of the time-series but not in the other correspond to a local source from a sewage treatment facility in one of the observatory buildings. From this experiment, we found that the REBS method underestimates the number of positive anomalies in the CH4 data caused by local sewage emissions. As a conclusion, we recommend the use of the SD method, which also appears as the easiest one to implement as automatic data processing, for the operational filtering of spikes in greenhouses gases time series at global and regional monitoring stations of networks like ICOS.

Citation: El Yazidi, A., Ramonet, M., Ciais, P., Broquet, G., Pison, I., Abbaris, A., Brunner, D., Conil, S., Delmotte, M., Gheusi, F., Guerin, F., Hazan, L., Kachroudi, N., Kouvarakis, G., Mihalopoulos, N., Rivier, L., and Serça, D.: Identification of spikes associated with local sources in continuous time series of atmospheric CO, CO2 and CH4, Atmos. Meas. Tech. Discuss.,, in review, 2017.
Abdelhadi El Yazidi et al.
Abdelhadi El Yazidi et al.
Abdelhadi El Yazidi et al.


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