Calibration of a low-cost PM2.5 monitor using a random forest model

Abstract

BACKGROUND: Particle air pollution has adverse health effects, and low-cost monitoring among a large population group is an effective method for performing environmental health studies. However, concern about the accuracy of low-cost monitors has affected their popularization in monitoring projects. OBJECTIVE: To calibrate a low-cost particle monitor (HK-B3, Hike, China) through a controlled exposure experiment. METHODS: monitors, and ten-fold validation was used to test the model. In addition, we used a linear regression model to compare the results of the machine learning model. A calibration method was established for the low-cost monitors, and it can be used to apply the monitors in future air pollution monitoring projects. RESULTS: = 0.87). The random forest model showed better performance than the traditional linear regression model. CONCLUSIONS: Our study provided an effective calibration method to support the accuracy of low-cost monitors. The machine learning method based on the calibration model established in our study can increase the effectiveness of future air pollution and health studies.