Automated identification of local contamination in remote atmospheric composition time series
- 1Extreme Environments Research Laboratory, École Polytechnique fédérale de Lausanne, Switzerland
- 2Institute for Atmospheric and Earth System Research, INAR/Physics, FI-00014 University of Helsinki, Finland
- 3Climate & Atmosphere Research Centre (CARE-C), The Cyprus Institute, P.O. Box 27456, Nicosia, 1645, Cyprus
- 4Atmospheric Sciences, Department of Environmental Sciences, University of Basel, Switzerland
- 5Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- 6Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen PSI, Switzerland
Abstract. Atmospheric observations in remote locations offer a possibility to explore trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this pollution is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local pollution in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with low background signal compared to the pollution signal. The Pollution Detection Algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the gradient (time-derivative) of a concentration over time. If this gradient exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or less data points as polluted).
The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Central Arctic. Using strict settings, we identified 62 % of the data as influenced by local pollution. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied on a trace gas dataset (CO2), also collected during MOSAiC, and on another particle number concentration dataset, collected at the high altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local pollution in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open access code enables reproducible application to a wide suite of different datasets. It is available at: https://doi.org/10.5281/zenodo.5761101.
Ivo Beck et al.
Ivo Beck et al.
Model code and software
Pollution Detection Algorithm (PDA) https://doi.org/10.5281/zenodo.5761101
Ivo Beck et al.
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