Minimizing the coincidence error in particle size spectrometers with digital signal processing techniques

Abstract

Aerosol quantification is highly coveted for many applications ranging from quality monitoring to healthcare. Optical particle size spectrometers are one of the popular measurement devices used in aerosol characterization, which provide information about the size distribution and concentration of the particles. One drawback of this method is that the spectrometers are limited to applications with relatively low concentrations. At high concentrations, the number concentration is underestimated, while the size distribution is shifted towards larger particles. This phenomenon is commonly referred as coincidence error. However, the counting efficiency, above the lower detection limit, depends i. a. on the detector dead time. In order to minimize the probability of coincidence events, the dead time of the detector has to be as small as possible. The contribution of this work is to minimize the coincidence error by purposefully reducing the detector dead time. Therefore, various digital signal processing methods, to minimize dead time, are investigated and experimentally verified. Wavelet and derivative-based peak detection algorithms were found to be able to separate even strongly overlapping scattered light pulses from each other. The results show that the detector dead time can be reduced by 65% with digital signal processing techniques. Allowing the quantification of aerosols with more than 2.9 times the maximum concentration of current optical aerosol spectrometers, without increasing the coincidence error.