基于线性回归的空气质量监测仪数据校准方法研究

Research on Data Calibration Method of Air Quality Monitor Based on Linear Regression

针对城市空气质量监测仪数据准确性不够,提出一种基于线性回归的数据校准方法。文章以某空气质量监测仪2018年11月14日10:02至2019年6月11日16:32监测的PM2.5和PM10这两种污染物浓度为实验数据。通过与同时段国控点数据进行细致的数据分析,发现造成各污染物数据差异与零点漂移和量程漂移有关,进而结合各污染物本身的特点,建立不同的线性回归模型,并验证模型的校准效果,为城市空气质量监测仪的数据校准提供参考。

In view of the lack of data accuracy of urban air quality detector,a data calibration method based on linear regression is proposed.In this paper,the concentrations of PM2.5 and PM10 measured by an air quality detector from 10:02 on November 14,2018 to 16:32 on June 11,2019 are used as experimental data.Through the detailed data analysis with the national control point data in the same period,it is found that the data difference of each pollutant is related to zero drift and range drift,and then combined with the characteristics of each pollutant,different linear regression models are established.The calibration effect of the model is verified to provide reference for the data calibration of urban air quality detector.