基于随机森林的光散射法传感器微站PM_(2.5)监测值的校正方法研究

Research on the correction method of PM_(2.5)monitoring data of light scattering sensor micro-stations based on random forest

光散射法传感器微站以其体积小、反应迅速、成本低等优点,已成为城市PM_(2.5)规模化移动监测的新选择.由于其标准与传统标准台站不同,必须对这类微站的监测数据进行准确地校正.本研究利用2021年06月—2022年02月武汉市江夏区标准台站及同期传感器微站监测数据,探讨传感器微站监测误差与温度、相对湿度的关系,并通过随机森林回归(Random Forest Regressor,RFR)校正传感器微站PM_(2.5)监测数据.对比单一RFR模型、按气象因素分类后RFR模型、“小波去噪+RFR”组合模型、“加权滑动平均去噪+RFR”组合模型校正效果,结果表明:RFR模型和分类后RFR模型均出现泛化能力差的问题,不能满足校正需求;“小波去噪+RFR”组合模型、“加权滑动平均去噪+RFR”组合模型平均绝对误差分别为8.77μg·m^(-3)和4.78μg·m^(-3),平均相对误差分别为40.80%和18.13%.去噪组合模型能满足校正需求,且“加权滑动平均+RFR”组合模型校正效果明显优于“小波去噪+RFR”组合模型.研究结果可为光散射法传感器微站PM_(2.5)监测值校正提供有益参考.

Light scattering sensor micro-stations have become a new choice for the large-scale mobile monitoring of PM_(2.5)in cities,mainly by virtue of their small size,rapid response,and low cost.Due to the differences between light scattering sensor micro-stations and traditional stations in monitoring standards,it is necessary to accurately correct the monitoring data of such micro-stations.Relying on the monitoring data of the standard station and micro-station in Jiangxia District,Wuhan from June 2021 to February 2022,this study explored the relationships of the monitoring errors of the sensor micro-station with temperature and relative humidity,and corrected its PM_(2.5)monitoring data using random forest regression(RFR).Then,this study compared the correction effects of the single RFR model,the RFR model based on classification of meteorological factors,the combined model of"wavelet denoising+RFR",and the combined model of"weighted moving average denoising+RFR".The results showed that both the single RFR model and the post-classification RFR model had poor generalization ability,which could not meet the correction requirements.The mean absolute errors of the combined model of"wavelet denoising+RFR"and the combined model of"weighted moving average denoising+RFR"were 8.77μg·m^(-3) and 4.78μg·m^(-3),respectively.In addition,their mean relative errors were 40.80% and 18.13%,respectively.The denoising combination model can meet correction requirements.The correction effect of the"weighted moving average+RFR"combination model was obviously superior to that of the"wavelet denoising+RFR"combination model.The results provide useful references for correcting the PM_(2.5)monitoring data of light scattering sensor micro-stations.