Abstract
Grid monitoring is the current development direction of atmospheric monitoring. The micro air quality detector is of great help to the grid monitoring of the atmosphere, so higher requirements are put forward for the accuracy of the micro air quality detector. This paper presents a model to calibrate the measurement data of the micro air quality detector using the monitoring data of the air quality monitoring station. The concentration of six types of air pollutants is the research object of this study to establish a calibration model for the measurement data of the micro air quality detector. The first step is to use correlation analysis to find out the main factors affecting the concentration of the six types of pollutants. The second step uses Ridge Regression (RR) to select variables, find out the factors that have significant effects on the concentration of pollutants, and give the quantitative relationship between these factors and the pollutants. Finally, the predicted value of the ridge regression model and the measurement data of the micro air quality detector are used as input variables, and the Extreme Gradient Boosting (XGBoost) algorithm is used to give the final pollutant concentration prediction model. We named the combined model of ridge regression and XGBoost algorithm RR-XGBoost model. Relative Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), goodness of fit (R2), and Root Mean Square Error (RMSE) were used to evaluate the prediction accuracy of the RR-XGBoost model. The results show that the model is superior to some commonly used pollutant prediction methods such as random forest, support vector machine, and multilayer perceptron neural network in the evaluation of various indicators. The model not only has a good prediction effect on the training set but also on the test set, indicating that the model has good generalization ability. Using the RR-XGBoost model to calibrate the data of the micro air quality detector can make up for the shortcomings of the data monitoring accuracy of the micro air quality detector. The model plays an active role in the deployment of micro air quality detectors and grid monitoring of the atmosphere.
Highlights
Introduction to pollutant concentration prediction modelAir pollutants mainly include O3, PM2.5, PM10, CO, NO2, and SO2 (“two dust and four gases”)
Joharestani et al used Random Forest, XGBoost, and Deep Learning to predict PM2.5 concentration, and the results showed that the model performance obtained by using the XGBoost algorithm was the b est[29]
The second set of data is provided by the micro air quality detector and the location of the micro air quality detector is juxtaposed with the air quality monitoring station
Summary
Introduction to pollutant concentration prediction modelAir pollutants mainly include O3, PM2.5, PM10, CO, NO2, and SO2 (“two dust and four gases”). A variety of algorithm models have been used by scholars at home and abroad to predict the concentration of pollutants in the atmosphere, and relatively good results have been achieved. These model algorithms mainly include time series models, chemical transmission models, machine learning models, etc. The time series models used to predict air quality include: Moving Average (MA) model, Autoregressive (AR) model, Autoregressive Moving Average (ARMA) model, Autoregressive Integral Moving Average (ARIMA) model, fuzzy time series model, etc. Koo et al used ARIMA and Singh fuzzy time series model and other models to predict the air pollution index of Kuala Lumpur, Malaysia in 2017. It is found that the Singh fuzzy time series model is the most accurate and effective forecasting m odel[9]
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