Towards near real-time SEMS size distribution measurements under up-scan operation

Accelerated measurements of aerosol size distributions by continuously scanning the aerodynamic aerosol classifier

“…Similar to inversions for other aerosol classifiers, such as the DMPS (Knutson, 1976), SMPS (Wang and Flagan, 1990) and steady-state AAC (Johnson et al, 2018a), the inversion parameters for the scanning AAC were derived assuming the concentration of the aerosol is constant over the width of its average transfer function ( sc  ). To avoid this assumption and also allow faster scans, future work could investigate the possibility of using higher-order inversion schemes, such as the Twomey algorithm used by Collins et al (2002) or the L-curve algorithm used by Talukdar and Swihart (2003) or Kanaparthi et al (2018) to improve the inversion of SMPS data.…”

“…Collins et al (2002) found similar outcomes, but simplified the approach by assuming an exponential decay to remove the effect of the mixing within the detector first, then inverting the data A c c e p t e d M a n u s c r i p t based on the DMA response. Kanaparthi et al (2018) also accounted for these smearing effects from downstream flows. They took a more comprehensive approach by incorporating these effects, as well as the impacts of the plumbing delay and counting times, directly into the arrival time transfer function of the SMPS.…”

“…This requirement could be relaxed in the future by following similar approaches as those previously discussed (i.e. Russell et al, 1995, Collins et al, 2002, Kanaparthi et al, 2018 and accounting for the smearing effects of the flow downstream of the AAC within or before the data inversion. However, similar to the viscous flow effects in the classifier, additional testing is required with AAC scans significantly faster than those in this study to amplify these flow effects and investigate their impact on the measurements.…”

“…For example, as the scans of the SMPS are accelerated the effect of viscous flow in the DMA classifier or in the downstream plumbing/detector become significant. Therefore, additional theory is required to account for the smearing of particle residence times (Collins et al, 2004;Mamakos et al, 2008;Dubey and Dhaniyala, 2008) and detection times (Russell et al, 1995;Collins et al, 2002;Kanaparthi et al, 2018). The effects of particle diffusion (Dubey and Dhaniyala, 2011;Huang et al, 2020) and entrance/exit geometry of the classifier on the transfer function of the SMPS have also been investigated.…”

“…The effects of particle diffusion (Dubey and Dhaniyala, 2011;Huang et al, 2020) and entrance/exit geometry of the classifier on the transfer function of the SMPS have also been investigated. These studies have lead to the recent work of and Kanaparthi et al (2018), which independently developed more advanced inversion theories to account for many of these effects on SMPS measurements.…”