Pollution Detection Algorithm (PDA)
The Pollution Detection Algorithm (PDA) is an algorithm to identify and flag periods of primary polluted data in remote atmospheric time series 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 g...
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ftdatacite:10.5281/zenodo.5761100 2023-05-15T14:58:42+02:00 Pollution Detection Algorithm (PDA) Ivo, Beck Hélène, Angot Andrea, Baccarini Markus, Lampimäki Boyer Matthew Julia, Schmale 2021 https://dx.doi.org/10.5281/zenodo.5761100 https://zenodo.org/record/5761100 unknown Zenodo https://dx.doi.org/10.5281/zenodo.5761101 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY MOSAiC Aerosol Trace gas Pollution SoftwareSourceCode article Software 2021 ftdatacite https://doi.org/10.5281/zenodo.5761100 https://doi.org/10.5281/zenodo.5761101 2022-02-08T18:05:53Z The Pollution Detection Algorithm (PDA) is an algorithm to identify and flag periods of primary polluted data in remote atmospheric time series 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 is written in python and runs from the command line. No GUI installation is needed. The script only relies on the target dataset file 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. : {"references": ["Beck, I., Angot, H., Dada, L., Baccarini, A., Qu\u00e9l\u00e9ver, L. L. J., Jokinen, T., Laurila, T., Lampimaki, M., Bukowiecki, N., Boyer, M., Gong, X., Gysel-Beer, M., Pet\u00e4j\u00e4, T., and Schmale, J.: Automated identification of local contamination in remote atmospheric composition time series, Atmos. Meas. Tech., in prep."]} Software Arctic DataCite Metadata Store (German National Library of Science and Technology) Angot ENVELOPE(-61.676,-61.676,-63.816,-63.816) Arctic Beck ENVELOPE(67.017,67.017,-71.033,-71.033) Boyer ENVELOPE(-116.086,-116.086,58.467,58.467) Jokinen ENVELOPE(25.083,25.083,66.133,66.133) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
MOSAiC Aerosol Trace gas Pollution |
spellingShingle |
MOSAiC Aerosol Trace gas Pollution Ivo, Beck Hélène, Angot Andrea, Baccarini Markus, Lampimäki Boyer Matthew Julia, Schmale Pollution Detection Algorithm (PDA) |
topic_facet |
MOSAiC Aerosol Trace gas Pollution |
description |
The Pollution Detection Algorithm (PDA) is an algorithm to identify and flag periods of primary polluted data in remote atmospheric time series 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 is written in python and runs from the command line. No GUI installation is needed. The script only relies on the target dataset file 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. : {"references": ["Beck, I., Angot, H., Dada, L., Baccarini, A., Qu\u00e9l\u00e9ver, L. L. J., Jokinen, T., Laurila, T., Lampimaki, M., Bukowiecki, N., Boyer, M., Gong, X., Gysel-Beer, M., Pet\u00e4j\u00e4, T., and Schmale, J.: Automated identification of local contamination in remote atmospheric composition time series, Atmos. Meas. Tech., in prep."]} |
format |
Software |
author |
Ivo, Beck Hélène, Angot Andrea, Baccarini Markus, Lampimäki Boyer Matthew Julia, Schmale |
author_facet |
Ivo, Beck Hélène, Angot Andrea, Baccarini Markus, Lampimäki Boyer Matthew Julia, Schmale |
author_sort |
Ivo, Beck |
title |
Pollution Detection Algorithm (PDA) |
title_short |
Pollution Detection Algorithm (PDA) |
title_full |
Pollution Detection Algorithm (PDA) |
title_fullStr |
Pollution Detection Algorithm (PDA) |
title_full_unstemmed |
Pollution Detection Algorithm (PDA) |
title_sort |
pollution detection algorithm (pda) |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://dx.doi.org/10.5281/zenodo.5761100 https://zenodo.org/record/5761100 |
long_lat |
ENVELOPE(-61.676,-61.676,-63.816,-63.816) ENVELOPE(67.017,67.017,-71.033,-71.033) ENVELOPE(-116.086,-116.086,58.467,58.467) ENVELOPE(25.083,25.083,66.133,66.133) |
geographic |
Angot Arctic Beck Boyer Jokinen |
geographic_facet |
Angot Arctic Beck Boyer Jokinen |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://dx.doi.org/10.5281/zenodo.5761101 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.5761100 https://doi.org/10.5281/zenodo.5761101 |
_version_ |
1766330822630047744 |