## 3 Results and Discussion
### Particle Filtration Efficiency as a Function of Size  
To determine the particle FE as a function of diameter, each individual averaged number size distribution observed at pre- and post-filter locations (i.e., Figure 3) were first fitted by a log-normal distribution to reduce measurement noise. Then, the pre- and post-filter fitted log-normal distribution measured at the two closest time periods were compared to determine the particle number (PN) _FE_ over various diameters, _FE_(\(D\)), which is defined as  
\[FE(D)=\frac{N_{pre,Di}-N_{post,Di}}{N_{pre,Di}}\times 100\%, \tag{2}\]  
where \(N_{pre,Di}\) and \(N_{post,Di}\) are the number concentrations at diameter \(D_{i}\) measured at the pre- and post-filter locations, respectively. Figure 5 summarizes the PN FE as a function of particle diameter for all 11 filters in a box plot. In general, all FE curves possess a typical inverted V-shape, indicating the minimum FE at about 100 nm. This is similar to what would be predicted from theory and also consistent with the observations from other studies [38; 56; 57]. There were large variations in filtration performance among different filters. For example, filters 2, 4, and 8 had substantially better filtration performance than other filters. These filters were capable of removing 10 nm particles with close to 100% efficiency. They also provide FE up to 60% for 520 nm particles. The smallest FE of about 40% was observed for 100 nm particles as expected. Importantly, these filters had electrostatic properties, which also explain the high initial collection efficiencies of these filters compared to the non-electrostatic filters and the subsequent reduction in efficiency as the electrostatic charge was shadowed or reduced. Filters 5 and 6 had the worst performance as they typically provided 0% FE at 100 nm-sized particles. For all other sizes, the FE of these filters were typically 10-30% lower than the rest of other filters. The FE for the rest of the filters (i.e., 1, 3, 7, 9, 10, and 11) appears to fall in between the FE for the electrostatic filters and the underperforming filers groups. Based on this preliminary analysis, the cabin filters were categorized into three groups. Group I represents the nominal filters (filters 1, 3, 7, 9, 10, 11), which represents what one would expect for a typical standard cabin filter. Group II is the electrostatic filters (filter 2, 4, 8), which have better FE than normal FE when new. Group III is the underperforming filters (filters 5, 6). Figure 4: Comparison of the calculated (**a**) BC mass and (**b**) PM mass with the measured masses. Figure 5: (**a–k**) Particle number filtration efficiency (PN FE) as a function of particle diameter for different cabin filters. Individual box represents the 30 th, 50 th, and 60 th percentiles of the FE values over the course of the experiment, 20 th and 80 th percentiles are represented by the bottom and top whiskers, respectively. The dotted and solid curve represent the average PN FE at the beginning and the end of the experiment. To understand the relative change in PN FE among Group II and Group III filters with respect to Group I, all PN FE for the same group were combined together to obtain the average PN FE as a function of sizes which are summarized in Figure 6. Results demonstrate that the presence of electrostatic fibers (red top) clearly enhance the particle capture across all sizes when compared to those without (black middle). For example, a 20% better FE was observed for 6 nm particles (Figure 7). The FE improvement slowly increased with particle size and reached 59% at 110 nm before it steadily declined to about 26% at 520 nm. Particles with diameter of about 100 nm are typically the most penetrating particle size in most typical sampling conditions because these particles are not heavy or light enough to be effectively removed by impaction and diffusion, respectively. This study shows that the electrostatic filtration mechanism can be an effective means to remove these particles from ambient air.