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A platform for research: civil engineering, architecture and urbanism
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 Our study used a MicroPEM monitor (RTI, America) as a standard particle concentration measurement device to calibrate the Hike monitors. A machine learning model was established to calibrate the particle concentration obtained by the low-cost PM2.5 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 The values of the random forest model calibration results and observations were more condensed around the regression line y = 0.99x + 0.05, and the R squared value (R2 = 0.98) was higher than that for the linear regression (R2 = 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.
Highlights A machine learning method was used to establish a random forest model for calibrating low-cost PM2.5 monitors. The random forest model showed better performance than the traditional linear regression model. Our study provided a calibration method for low-cost monitors under indoor air pollution conditions.
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 Our study used a MicroPEM monitor (RTI, America) as a standard particle concentration measurement device to calibrate the Hike monitors. A machine learning model was established to calibrate the particle concentration obtained by the low-cost PM2.5 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 The values of the random forest model calibration results and observations were more condensed around the regression line y = 0.99x + 0.05, and the R squared value (R2 = 0.98) was higher than that for the linear regression (R2 = 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.
Highlights A machine learning method was used to establish a random forest model for calibrating low-cost PM2.5 monitors. The random forest model showed better performance than the traditional linear regression model. Our study provided a calibration method for low-cost monitors under indoor air pollution conditions.
Calibration of a low-cost PM2.5 monitor using a random forest model
Wang, Yanwen (author) / Du, Yanjun (author) / Wang, Jiaonan (author) / Li, Tiantian (author)
2019-09-05
Article (Journal)
Electronic Resource
English
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