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This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and longlived 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 European ICOS (Integrated Carbon Observation System) Research 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 100m 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 cleanair background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate 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 that are 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 located 200m from each other allow us 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 also 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 to be the easiest one to implement in automatic data processing, used for the operational filtering of spikes in greenhouse gases time series at global and regional monitoring stations of networks like that of the ICOS atmosphere network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study deals with the problem of identifying atmospheric data influenced by local emissions that can result in spikes in time series of greenhouse gases and longlived 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 European ICOS (Integrated Carbon Observation System) Research 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 100m 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 cleanair background site in the Southern Hemisphere on Amsterdam Island (AMS). This selection allows us to address spike detection problems in time series with different variability. Two years of continuous measurements of CO2, CH4 and CO were analysed. All methods were found to be able to detect short-term spikes (lasting from a few seconds to a few minutes) in the time series. Analysis of the results of each method leads us to exclude the COV method due to the requirement to arbitrarily specify an a priori percentage of rejected data in the time series, which may over- or underestimate 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 that are 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 located 200m from each other allow us 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 also 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 to be the easiest one to implement in automatic data processing, used for the operational filtering of spikes in greenhouse gases time series at global and regional monitoring stations of networks like that of the ICOS atmosphere network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]