Over a period of 45 days, about 65,000+ data points were collected by each sensor. The data were cleaned up by removing single peaks using pandas libraries. After 10 rounds, 3.4% of the total rows were removed from the dataset with mask (referred as the mask data), and 1.7% of the total rows were removed from the dataset of the background concentration (referred as the background data). Fig 1 shows the data before and after clean up.
The PMS7003 with 1-minute interval sampling yielded large datasets. Single peaks were shown in Fig. 4 indicated sudden busts in concentration with no clear reason behind. The author accepted that the peaks as the artifact of low-cost sensors and decided to remove those peaks before analyzing the effectiveness of mask to screen out PMs.
After peaks were removed, the cleaned dataset was segmented into a pair for each experiments. One included a background and a filtering period. The background period lasted for 90 to 120 minutes to cross check the PM sensor for monitoring filtered air with the PM sensor monitoring the ambient air, also called background PM concentration.
The removal efficiency (RE) was as the portion of PM filtered out to the total PM contained in incoming air. Each of the graphs below presented the average RE, RE calculated for the crosscheck windows, and of PM2.5 and PM10 during the experiments.The results are presented in Figs. 4-7. For the boxplot graph, each boxplot marked the medium, 25th, 75th percentile and the outliers that were processed by seaborn library on the top of Matplotlib of Python package.
Details of the removal during crosscheck, of PM2.5 and PM10 with fan duty are listed in Table 2. Fan duty indicates the speed of the fan which 1 as on all the time, and 0.5 is 50% on. The fan duty was used as the surrogate for the air flow through one mask.
Removal during the crosscheck is closed to zero as shown Fig. 4 and Table 2 confirmed that the setup is adequate to compare the PMs concentration of the filtered air and the background. Because the numbers are closed to zero and much smaller than the standard deviation, the RE of PMs was reported without deducting the difference during the crosschecks.
|FU2 test #1||0.01||0.09±0.06||0.08±0.05||0.63|
|FU2 test #2||0.01||0.15±0.09||0.12±0.01||0.51|
|S2 test #1||0.01||0.29±0.13||0.34±0.14||0.51|
|S2 test #2||0.03||0.33±0.06||0.33±0.08||0.55|
|3M2 test #1||0.01||0.57±0.09||0.59±0.09||0.60|
|3M2 test #2||0.01||0.56±0.09||0.62±0.07||0.62|
The experiment setup with a mask mounted on a PVC pile limits the scope of testing to the filter efficiency of the mask's material. The construction of each mask was presented on Figs. 7-10 with an USB-microscopic webcam.
The construction of the farbic masks (F1U and FN) includes 3 layers with a cotton-like cloth in the outer layer, a thick polyester layer in the middle and the support layer in the inner layer. The middle layer is loosely packed with made of the thickness of the masks.
Surgical masks are 4-5 layers packed tightly. One middle layer is made of tiny fibers and densely packed. Two out the experiments.The results are presented in Figs. 4-6. Each boxplot marked the medium, 25th, 75th percentile and the outliers that were processed by Seaborn library on the top of Matplotlib of Python package.
In two masks purposely against particulate pollution, A1 is constructed with a single layer with randomly woven polyester. The size of pore compared to the hair string is not much different. Meanwhile, PM2.5 is about 30 times smaller. This visual supports the measurement on Table 2 as no effective to filter out PMs. The other mask (A2) is constructed with 5 layers with very fine fibers as shown in the bottom row in Fig. 9.
Brand-name particulate respirators, 3M 9001 with KN90 (3M1) and 3M Aura 9332+, are composed of 5 layers. The later model has the middle layer thick and pillow-like shape. The former model has two layers in the middle is composed of very fine fibers.
A mask is an equipment that is very specific in use; however, the working principle is as a filter. When airflow moves into the mask, the friction the mask layer creates turbulence around the pores. A higher airflow is translated to a shorter contact time with materials and a lower probability that the particles to be adsorb onto the fiber matrix. For the mechanism of filtering, please refer to this link for more information.
Figs. 12-15 presents a snapshot analysis for 4 candidates of each type. Those graphs provide detailed visuals. The correlation of the fan duty to RE is presented in Fig. 15. The author used the fan duty as the surrogate for flow rate indicator.
The results in Fig. 16 is consistent with the RE in Table 2. The fabric mask is porous to PMs that leads to marginal RE. The RE is not correlated with airflow because the friction is minimal. With surgical and air masks, a negative correlation indicates at a higher flow rate, a lower RE of PMs. The 3M Aura with FFP3 standard shows a correlation of -0.51, which is in line with the two above but the effect of airflow to the RE is smaller. This suggests improvements in 3M mask that allows a higher airlow with a less nagative on RE.
The problem associated with PMs pollution is not singular to Vietnam. Other countries such as China and India have been experienced unhealthy levels. Exisiting blog posts and journal research are relevant to users in Vietnam to understand the basic and consider the recommendations. For example, Yu at el., 2014 found that the fitting of standard N95 mask to Chinese works is "poor". Informative blogs posts on the testing methods and performance of masks are useful for the "citizen science" approach.
In a research journal, the limitation section is not included. Nevertheless, the goal of this study is to inform the mask users and some limitation the author should spell out.