Abstract
The issue of air quality has attracted more and more attention. The main methods for monitoring the concentration of pollutants in the air include national monitoring station monitoring and micro air quality detector testing. Since the electrochemical sensor of the micro air quality detector is susceptible to interference, the monitored data has a certain deviation. In this paper, the combined model of partial least square regression and random forest regression (PLS-RFR) is used to correct the detection data of the micro air quality detector. First, correlation analysis is used to find out the factors that affect the concentration of pollutants. Second, partial least squares regression is used to give the quantitative relationship of the influence of each influencing factor on the concentration of pollutants. Finally, the predicted value of partial least squares regression and various influencing factors are used as independent variables, and the pollutant concentration monitored by the national monitoring station is used as the dependent variable, and the PLS-RFR model is obtained with the help of random forest software package. Relative mean absolute percent error, mean absolute error, goodness of fit, and root mean square error are used as evaluation indicators to compare PLS-RFR model, support vector machine model and multilayer perceptron neural network. The results show that no matter which evaluation index, the overall prediction effect of the PLS-RFR model is the best, and the model has a good prediction effect in the training set or the test set, indicating that the model has good generalization ability. This model can play an active role in the promotion and deployment of micro air quality detectors.
Highlights
Air quality is a problem that cannot be ignored
Because the data detected by the electrochemical sensor in the micro air quality detector has errors, it needs to be corrected by the data of the national monitoring station
In order to further verify the reliability of the partial least squares (PLS)-RFR model, support vector machines (SVR) and multilayer perceptron (MLP) neural networks were selected to predict the concentration of pollutants, and the prediction results were compared with the PLS-RFR model
Summary
Air quality is a problem that cannot be ignored. In addition to the harm to the climate, the most important thing is the harm to the human body. 3 million people die every year due to air quality problems. Respiratory diseases, lung cancer and other diseases have a certain relationship with air pollution [1]-[3]. Government departments are working hard to curb the adverse effects caused by air pollution, the air quality in many cities is still very severe. Air quality should be monitored by relevant national departments in real time, so that corresponding countermeasures can be taken in time to reduce the harm to humans and the environment
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