摘要
为了解阿拉尔市大气颗粒物PM_(10)、PM_(2.5)的污染状况,便于合理制定大气污染治理措施。本文收集阿拉尔市2017—2023年PM_(10)、PM_(2.5)的监测数据,构建季节性差分自回归滑动平均(SARIMA)模型,系统性分析了阿拉尔市大气颗粒物PM_(10)和PM_(2.5)浓度的变化特征,并预测未来变化趋势。研究结果表明:阿拉尔市的大气颗粒物PM_(10)、PM_(2.5)的质量浓度较高,且呈现明显的季节性变化;基于SPSSAU软件自动寻优的大气颗粒物PM_(10)、PM_(2.5)最优化模型分别为SARIMA(0,0,0)(2,0,0)_(12)和SARIMA(1,0,0)(3,0,0)_(12);运用最优模型对阿拉尔市2023年大气颗粒物PM_(10)、PM_(2.5)的月平均进行拟合与比对发现,总体相对误差在15%以内,一定程度上反映了模型的拟合效果良好;运用最优模型对阿拉尔市2024年大气颗粒物PM_(10)、PM_(2.5)质量浓度进行预测发现,预测值与2024年1—6月实测值吻合度较好,进一步表明该模型的准确性。以上研究结果得出,SARIMA(0,0,0)(2,0,0)_(12)和SARIMA(1,0,0)(3,0,0)_(12)适用于阿拉尔市大气颗粒物的污染状况预测,可为阿拉尔市大气颗粒物污染的环境调控策略提供技术参考。
In order to understand the pollution status of atmospheric particulate matter PM_(10) and PM_(2.5) in recent years in Alar City,and adjust the air pollution control strategies,this work collected the monitoring data of atmospheric particulate matter PM_(10) and PM_(2.5) in Alar City from 2017 to 2023,and constructed a seasonal autoregressive integrated moving average(SARIMA)model to systematically analyzes the variation characteristics of the concentration of atmospheric particulate matter PM_(10) and PM_(2.5) in Alar City,and predict their future trends.The results show that the mass concentrations of atmospheric particulate matter PM_(10) and PM_(2.5) in Alar City are relatively high and exhibit significant seasonal variations.The optimized models for atmospheric particulate matter PM_(10) and PM_(2.5) ,automatically selected by the SPSSAU software,are SARIMA(0,0,0)(2,0,0)_(12) and SARIMA(1,0,0)(3,0,0)_(12),respectively.By applying the optimized models to fit and compare the monthly averages of atmospheric particulate matter PM_(10) and PM_(2.5) in Alar City in 2023,it was found that the overall relative error is within 15%,to some extent reflecting the good fitting effect of the model.The application of the optimized models to predict the mass concentrations of atmospheric particulate matter PM_(10) and PM_(2.5) in Alar City in 2024 revealed a good agreement between the predicted values and the measured values from January to June 2024,further demonstrating the accuracy of the model.The above research results indicate that SARIMA(0,0,0)(2,0,0)_(12) and SARIMA(1,0,0)(3,0,0)_(12) can be used to predict the pollution status of atmospheric particulate matter in Alar City,providing technical reference for environmental regulation strategies for atmospheric particulate matter pollution in Alar City.
作者
江宜霖
王建
梁朵朵
张文刚
杨波
李煜
JIANG Yi-lin;WANG Jian;LIANG Duo-duo;YANG Bo;ZHANG Wen-gang;LI Yu(Xinjiang Production and Construction Corps Ecological Environment Second Monitoring Station,Alar Xinjiang 843300,China)
出处
《干旱环境监测》
2024年第4期179-185,共7页
Arid Environmental Monitoring
基金
新疆生产建设兵团第一师阿拉尔市科技计划项目《阿拉尔市沙尘天气与工业污染源对城市大气环境质量的影响及大气颗粒物溯源关键技术研究》(项目编号:2022HB01)。
关键词
SARIMA模型
颗粒物
阿拉尔
污染特征
预测
SARIMA model
particulate matter
Alar
pollution characteristics
prediction
