Simulation Analysis Of The Impact Of COVID-19 Epidemic Control On Air Quality Based On Machine Learning

The problem of air quality changes caused by environmental pollution has always attracted much attention.The sudden outbreak of COVID-19 has a positive impact on air quality changes to a certain extent,providing favorable conditions for studying air quality changes.Based on this,the main work of this paper is as follows:（1）Considering that the principal component analysis method can effectively reduce the data dimension,the attention mechanism can capture important information,and the long-term and short-term memory neural network has the characteristics of long-term memory function,this paper proposes a long-term and short-term memory neural network model based on principal component analysis and attention mechanism（PCA-Attention-LSTM）.The PCA-Attention-LSTM model proposed in this paper is compared with the classical support vector regression model（SVR）,random forest model（RF）,BP neural network model（BPNN）,and long short-term memory neural network model based on convolutional neural network（CNN-LSTM）.The atmospheric pollutant data,meteorological element data and working day data of Xinjiang,Gansu,Qinghai,Shaanxi and Ningxia in Northwest China from 2016 to 2020 were analyzed.The results show that the correlation coefficients between the predicted and true values of the PCA-Attention-LSTM model in the five regions are 0.92,0.93,0.92,0.91 and 0.91,respectively.The average absolute errors are 5.63,4.13,5.12,4.98 and 4.51,and the mean square errors are74.32,38.6,58.17,63.71 and 51.10,respectively.The performance of the CNN-LSTM model is the second.The correlation coefficients of CNN-LSTM model in five regions were 0.88,0.89,0.89,0.90 and 0.87,respectively.The mean absolute error was between4.78 and 6.12,and the mean square error was between 46.53 and 101.24.The prediction effect of SVR model is poor.The correlation coefficients of SVR model in five regions of Northwest China are 0.74,0.76,0.80,0.81 and 0.79,respectively.The mean absolute error is between 8.25 and 10.52,and the mean square error is between 78.9 and 198.73.（2）During the period of COVID-19,people adopted epidemic prevention measures such as shutdown and home isolation,which had a certain impact on the concentration of atmospheric pollutants,and was conducive to studying the impact of human activity level on air quality.In this paper,the PCA-Attention-LSTM model is used to predict the concentration of air pollutants without control during COVID-19.In addition,the model is used to analyze the improvement value and improvement rate of pollutant concentration reduction by meteorological sources and anthropogenic sources during COVID-19.The air quality data before and after COVID-19 control in Northwest China in 2020 and the same period of COVID-19 control in the past five years were used to analyze the air quality changes caused by epidemic control by using inverse distance weighted interpolation method and numerical feature comparison.The results show that the improvement rates of PM_2.5,PM₁₀,SO₂,NO₂ and CO concentrations in Northwest China due to COVID-19control are 34%,21.6%,12%,41.8%and 31.2%,respectively.The improvement rates of meteorological source simulation are 25.68%,10.36%,6.21%,20.5%and 22.26%,respectively.The improvement rates of anthropogenic source simulation are 8.32%,11.24%,5.79%,21.3%and 8.94%,respectively.The improvement rate of O₃ pollutants was-111.8%,the improvement rate of O₃ simulated by meteorological source was-56.82%,and the improvement rate of O₃ simulated by human source was-54.98%.