Application of RR-XGBoost combined model in data calibration of micro air quality detector

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.

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

“…In order to comprehensively compare the accuracy of the PCA–RVM–NAR model with other commonly used air quality prediction models, four commonly used indicators are used to compare the models in this paper 32 , 39 . These four indicators include Root Mean Square Error, Goodness of fit (R 2 ), Mean Absolute Error (MAE) and relative Mean Absolute Percent Error (MAPE).…”

“…Different geographical environments have different influence factors on the concentration of air pollutants. The Pearson correlation coefficient is used in this paper to screen the main factors affecting air quality 25 , 32 . Equation ( 1 ) is its expression, where is the value of the first variable, is the value of the second variable, is the mean of , is the mean of , and represents the number of samples.…”

A method for calibrating measurement data of a micro air quality monitor based on MLR-BRT-ARIMA combined model

“…The Pearson correlation coefficient is used to measure the correlation between two variables. 26,38 In eqn (1), x i and y i respectively represent the i-th sample value of the two variables. The value range of the Pearson correlation coefficient is [−1,1].…”

Supplementary material to "Long-term Variability in Black Carbon Emissions Constrained by Gap-filled Absorption Aerosol Optical Depth and Associated Premature Mortality in China"

“…The XGBoost algorithm is an additive model based on hundreds of decision tree models. XGBoost first builds multiple CART (Classification and Regression Trees) models to predict the data set, and then integrates these trees as a new tree model (Liu et al, 2021). The model will continue to iteratively improve, and the new tree model generated in each iteration will fit the residual of the previous tree.…”