Benford's law applied to aerobiological data and its potential as a quality control tool

Benford's phenomenological law gives the expected frequencies of the first significant digits of any given series of numbers. According to this law, the frequency of 1 is higher than that of 2; this in turn appears more often 3, and so on. Similarly, Benford's law can also be applied to the first two significant digits (i.e., from 10 to 99), and so on. Here, we show that gross data sets of daily pollen counts from three aerobiological stations (located in European cities with different features regarding vegetation and climatology) fit Benford's law for the first significant digits, but this is not always true for the data transformed by a correction factor used in aerobiological studies. That is to say, the biases introduced by rounding and lower and upper built-in limits in pollen counts are detected by Benford's law analysis. The analysis of the first two significant digits from transformed data is better explained by a Power law than Benford's law. We propose that Benford's law could be used as a quality control tool for numerical aerobiological data sets.