Dealing with large particle counting data sets

A particle count survey of 21 South African water treatment plants over a period of 15 months presented the authors with challenges similar to those experienced by other workers in the field. The amount of data generated was staggering and had to be dealt with in an orderly and structured way to derive the maximum benefit from the survey. Furthermore, the number of significant data points involved if the entire count is to be taken into consideration complicated interpretation of particle counting data. This led to the application of several data reduction techniques to reduce the number of significant parameters that had to be considered during analysis. The most common conventional method is the use of the total particle count larger than a given size, for example total count per ml > 2 μm. This method, however, negates one of the most powerful abilities of the particle counter, namely the ability to indicate particle size distribution. The application of the power law, a common alternative, provides a more detailed description but has its flaws. In this paper the authors illustrate how many particle counts were successfully handled in a purpose-designed database and how the power law concept was improved to provide a better particle counting data-reduction methodology.