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 (https://doi.org/10.5194/amt-15-4195-2022). {"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., 2022"]}