基于统计动力反演模型分析PM_(2.5)浓度变化的影响因素及其系统演化  

Analysis of Influencing Factors and System Evolution of PM_(2.5) Concentration Change Based on Statistical Dynamic Inversion Model

作　　者：贺祥 林振山[2,3,4,5] 刘会玉 

机构地区：[1]凯里学院旅游学院,贵州凯里556011 [2]南京师范大学地理科学学院,江苏南京210023 [3]虚拟地理环境教育部重点实验室(南京师范大学),江苏南京210023 [4]江苏省地理环境演化国家重点实验室培育建设点,江苏南京210023 [5]江苏省地理信息资源开发与利用协同创新中心,江苏南京210023

出　　处：《生态环境学报》2017年第11期1916-1923,共8页Ecology and Environmental Sciences

基　　金：国家自然科学基金面上项目(31470519);2015年江苏省高校自然科学研究重大项目(15KJA17002);贵州省科技厅基金项目(黔科合LH字[2014]7237);凯里学院2018年度博士专项资助课题(PM_(2.5)浓度变化驱动机制研究);凯里学院规划项目重点课题(S1403)

摘　　要：以杭州市2013—2016年秋冬季PM_(2.5)及影响因素为研究对象,构建统计动力学反演模型,研究PM_(2.5)浓度系统的影响因素与演化特征。结果显示,(1)模型反演预测值与实际监测值的相关系数0.8316,贡献率大于1%的各驱动项之和为90.85%,反演效果较好;模型中线性项贡献率之和为21.65%,非线性项贡献率之和为62.88%,PM_(2.5)浓度变化受非线性影响更加显著。(2)包含NO_2、SO_2、CO的各驱动项贡献率之和分别为10.77%、8.42%、6.58%;包含降雨(PRE)、风速(WIND)的各驱动项贡献率之和分别为20.5%、17.02%,NO_2、SO_2、PRE、WIND是杭州市PM_(2.5)浓度变化的最主要影响因素。(3)贡献率大于2%的各驱动项中,平均气压(PRS)、相对湿度(RHU)、日照时长(SSD)对PM_(2.5)呈负反馈线性稳定影响作用,PM_(2.5)×SO_2、NO_2×PRS、PRE×SSD、PM_(2.5)×WIND等交互项对PM_(2.5)浓度变化呈正反馈非线性影响,PM_(2.5)×O_3、SO_2×NO_2、PRE×WIND等交互项对PM_(2.5)浓度变化呈负反馈非线性稳定作用。(4)杭州市PM_(2.5)浓度变化系统是一元二次非线性驱动演变系统,通过数值模拟分析可知其PM_(2.5)浓度变化系统呈双定态式,PM_(2.5)浓度变化系统在定态值0.2413与0.3769之间波动变化,系统平衡态不稳定。结果表明,统计动力反演模型可定量解析出PM_(2.5)浓度变化的线性驱动项与非线性驱动项的系数值及贡献率,有助于揭示PM_(2.5)浓度变化系统的演化特征,研究结果可应用于大气污染控制与环境管理等方面。To reveal the evolution characteristics of PM2.5 concentration in autumn and winter in 2013-2016, and its relationshipswith the influencing factors, the statistical dynamic inversion model between PM2.5 and its driving factors was constructed by takingHanzhou city as a case study. The results showed that： （1） The correlation coefficient between the predicted value and the actuallymonitoring value was 0.831 6 in the dynamic inversion equations, and the sum contributions of all the drivers with contribution rateabove 1% were 90.85%. The performance of the inversion model was good. The contributions of the linear effects of the drivingfactors were 21.65%, while, the contributions of nonlinear effects were 62.88%. Thus, the changes of PM2.5 concentrations inHangzhou City were more strongly influenced by the nonlinear effects of the driving factors. The contribution rates of NO2, SO2 andCO were 10.77%, 8.42% and 6.58%, respectively. The contribution rates of precipitation （PRE） and wind （WIND） were 20.5% and17.02%, respectively. Therefore, NO2, SO2, PRE and WIND were the most important driving factors of the changes of PM2.5concentration in Hangzhou city. （3） The driving factor with a contribution rate more than 2% was assumed to have an importantinfluence on the change of PM2.5 concentration. Among these factors, the average pressure （PRS）, the relative humidity （RHU） andthe sunshine duration （SSD） had negative feedback effect on the linear stability of PM2.5 concentration. The interaction terms ofPM2.5-SO2, NO2-PRS, PRE-SSD and PM2.5-WIND had positive feedback effect on the nonlinear change of PM2.5 concentration. Theinteractive terms of PM2.5-O3, SO2-NO2 and PRE-WIND had negatively nonlinear feedback effects on the stability of PM2.5concentration. （4） PM2.5 concentration change was a one-place quadratic nonlinear dynamical system. Through numerical simulationanalysis, we found that the system of PM2.5 concentration change had double steady states. The values in steady state of PM2.

关 键 词：PM2.5浓度变化 统计动力反演模型 影响因素 系统演化 杭州市 

分 类 号：X16[环境科学与工程—环境科学]