Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.

Most of the white fumes from the tenter process of a textile plant in an industrial complex are generated by water vapor and oil mist. While general water vapor disappears when the humidity is lowered, the white fume generated in the tenter process does not disappear and is continuously maintained, resulting in environmental problems and complaints. Efforts to reduce white fume are being conducted, but it is vitally important to develop a performance index that quantitatively calculates and deduces the degree by which white fume has been reduced, so that a tangible and visible result can be obtained in the performance evaluation of prevention facilities. In this study, the removal efficiency or performance of a general wet scrubber and a wet electrostatic precipitator (electrical fume collector, EFC) installed in the actual textile tenter process was analyzed by the light scattering method that can measure the concentration of particles up to a high level. The white fume removal efficiency of the EFC was 92%, much higher than the 17% removal efficiency of the general scrubber. In addition, the EFC was more effective in removing toluene, 1,1'- [oxybis(methylene)]bis- Benzene, and benzothiazole, which are the major substances generated from the textile tenter process, as well as complex odors. From these results, it was found that the light scattering method is one of the useful tools to evaluate the performance of white fume prevention facilities in the industrial field in terms of satisfying the urgent need for measurement and the ability to obtain a clear and precise result on site. This approach is meaningful in that real-time quantification is applicable more intuitively than the gravimetric method in assessing the fume removal performance as it can be observed with the naked eye.

The purpose of this study is to develop correction formulas using the results of measurement by PMS 103, which is a weight method measuring device, and by Dusttrak (TSI, USA), DustMate (Turnkey Instrument Ltd., UK), and LD-5 (SIBATA, Japan), which are light scattering measuring devices. The objective is to evaluate and identify new standards (to develop a proposal) in order to complement the limitations of the existing measurement methods of public transportation vehicle indoor air quality by utilizing the three nephelometer type measuring devices. In the case of non-rush hours, the PMS values were estimated using an estimation regression equation. Statistically, the PMS values that were actually measured were not significantly different (p-value=0.4375, 0.4375, 1.000). With respect to the agreement between the two values, ICC was 0.99 in the case of the estimation regression equation using LD-5 values, 0.97 in the case of the estimation regression equation using Dusttrak values, and 0.84 in the case of the estimation regression equation using DustMate values to allow for the identification of agreement at greater levels. In the case of rush hours, the PMS values were estimated using an estimation regression equation. Statistically, the PMS values that were actually measured were not significantly different (p-value=0.3125, 0.6250, 0.8125). With respect to the agreement between the two values, ICC was 0.92 in the case of the estimation regression equation using LD-5 values, 0.91 in the case of the estimation regression equation using Dusttrak values, and 0.89 in the case of the estimation regression equation using DustMate values to allow for the identification of agreement at greater levels.