National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
Environ Int. 2019 Dec;133(Pt A):105161. doi: 10.1016/j.envint.2019.105161. Epub 2019 Oct 11.
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.
To calibrate a low-cost particle monitor (HK-B3, Hike, China) through a controlled exposure experiment.
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 PM 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.
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 (R = 0.98) was higher than that for the linear regression (R = 0.87). The random forest model showed better performance than the traditional linear regression model.
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.
颗粒状空气污染对健康有害，而对大量人群进行低成本监测是进行环境健康研究的有效方法。然而，对低成本监测器准确性的担忧影响了它们在监测项目中的推广。
通过受控暴露实验对低成本颗粒监测器（HK-B3，Hike，中国）进行校准。
本研究使用 MicroPEM 监测器（RTI，美国）作为标准颗粒浓度测量设备来校准 Hike 监测器。建立了一个机器学习模型来校准低成本 PM 监测器获得的颗粒浓度，并使用十折验证来测试该模型。此外，我们使用线性回归模型来比较机器学习模型的结果。建立了一种低成本监测器的校准方法，可用于未来的空气污染监测项目。
随机森林模型校准结果和观察值的数值更集中在回归线 y=0.99x+0.05 周围，R 平方值（R=0.98）高于线性回归（R=0.87）。随机森林模型的表现优于传统的线性回归模型。
本研究提供了一种有效的校准方法，以支持低成本监测器的准确性。基于我们研究中建立的校准模型的机器学习方法可以提高未来空气污染和健康研究的有效性。
