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.

1. 서 론
2. 연구방법
    2.1 연구 방법
3. 결 과
    3.1 미세먼지 농도 측정 비교
    3.2 구간 분류 여부에 따른 보정계수 산출 결과
    3.3 의사결정트리를 활용한 농도 구간 분류
    3.4 회귀분석 기반 구간별 보정 결과
4. 고 찰
5. 결 론

• 박신영(서경대학교 환경화학공학과) | Shin-Young Park (Department of Environmental & Chemical Engineering, Seokyeong University)
• 장 혁(서경대학교 나노화학생명공학과) | Hyeok Jang (Department of Nano Chemical & Biological Engineering, Seokyeong University)
• 권재민(캘리포니아 주립대학교 공중보건학과) | Jaymin Kwon (Department of Public Health, California State University, Fresno, CA 93740, USA)
• 조용성(서경대학교 환경화학공학과, 서경대학교 나노화학생명공학과) | Yong-Sung Cho (Department of Environmental & Chemical Engineering, Seokyeong University, Department of Nano Chemical & Biological Engineering, Seokyeong University)
• 이철민(서경대학교 환경화학공학과, 서경대학교 나노화학생명공학과) | Cheol-Min Lee (Department of Environmental & Chemical Engineering, Seokyeong University, Department of Nano Chemical & Biological Engineering, Seokyeong University) Corresponding author