This study evaluated the importance of assessing personal exposure to volatile organic compounds (VOCs) by monitoring indoor, outdoor, and personal VOC levels in 15 Seoul residents over a 3-month period using passive samplers. Results indicated that limonene had the highest concentrations across indoor, outdoor, and personal samples, with this compound primarily originating from household cleaners and air fresheners. Other VOCs, such as 2-butanone and toluene, also varied by location. Health risk assessments showed that most VOCs had a Hazard Index (HI) below 1, while the HI of individual exposures were relatively higher. Notably, cancer risk assessments for chloroform and ethylbenzene exceeded permissible levels in some scenarios, suggesting potential cancer risks. This underscores the importance of diverse microenvironment monitoring for accurate health risk evaluations, as relying solely on indoor and outdoor levels can underestimate actual exposure risks. This study highlights the need for future research to monitor VOC levels in various microenvironments, in addition to the necessity of investigating personal activity patterns in depth to accurately assess personal exposure levels. Such an approach is crucial for precise health risk assessments, and it provides valuable foundational data for evaluating personal VOC exposures.

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.