小波分析等方法在兰州盆地大气污染物变化特征预测中的应用

Research on the Application of Wavelet analysis and Other Methods in Predicting the Characteristics of Atmospheric Pollutant Changes in Lanzhou Basin

文章根据2019—2023年兰州盆地大气监测数据,结合小波分析、Mann-Kendall突变检测、ARIMA模型等方法,研究了CO、NO2、O3、PM10、PM2.5、SO2这6项大气污染物浓度变化特征,并对2024年6项污染物浓度变化进行预测。研究结果显示,此6项污染物均呈现出6个月左右的明显周期。2019—2023年,CO、NO2、SO2浓度年总量整体呈下降趋势;但是O3浓度年总量仍处于上升趋势,PM2.5、PM10浓度年总量则呈现上下波动变化。CO、NO2、SO2浓度在冬季、春季呈现上升趋势;而O3浓度在冬季、春季呈现下降趋势;PM2.5、PM10颗粒物浓度突变时间点在2021年3月重叠。2024年的预测结果表明,除O3外,其余5项污染物浓度均存在一定的下降趋势。

Based on the atmospheric monitoring data of Lanzhou Basin from 2019 to 2023,this article employs wavelet analysis,Mann Kendall mutation detection,ARIMA model and other methods to study the concentration of six atmospheric pollutants,CO,NO2,O3,PM10,PM2.5,SO2,and predict the concentration change of them in 2024.The research findings suggest that all six pollutants exhibit a significant cycle of about 6 months.From 2019 to 2023,the annual total concentration of CO,NO2,and SO2 showed an overall downward trend;However,the concentration of O3 is still on the rise,while the annual total concentration of PM2.5 and PM10 shows fluctuations.In addition,the concentrations of CO,NO2,and SO2 show an upward trend in winter and spring;while that of O3 shows a decreasing trend in winter and spring;Furthermore,the time point of sudden changes in PM2.5 and PM10 concentrations overlapped in March 2021.In summary,the forecast results for 2024 indicate that,except for O3,the other five pollutants all show a downward trend.