本文選題：PM2.5 + 時間序列　； 參考：《重慶理工大學》2015年碩士論文

【摘要】：霧霾已成為中國日�？諝馕廴镜耐怀鰡栴}。而PM2.5是霧霾的主要構(gòu)成成分,也是衡量空氣質(zhì)量的重要指標,其濃度的變化直接反映了空氣質(zhì)量好壞的變化。因此研究PM2.5的形成機理和構(gòu)成成分就顯得極其重要。然而,到目前為止,人們對PM2.5的形成機理和構(gòu)成成分還沒有形成一致認識。因此,從不同的角度運用不同的方法對PM2.5的發(fā)生和演變規(guī)律進一步研究是必要的。本文運用時間序列分析的方法,對重慶市PM2.5濃度以日期為單位的時序變化規(guī)律進行分析。首先根據(jù)相關(guān)文獻資料篩選出與PM2.5相關(guān)性較大的變量,如溫度、CO、PM10、NO2和SO2等,并通過網(wǎng)絡(luò)渠道收集相關(guān)數(shù)據(jù)并對數(shù)據(jù)進行整理和預(yù)處理;其次分別研究各個變量序列自身的變化規(guī)律,得出各自的適應(yīng)性模型;再次將PM2.5作為輸出變量序列,分別將最高溫度、最低溫度、CO、PM10、NO2和SO2等作為輸入變量序列,構(gòu)建單輸入變量傳遞函數(shù)模型;再逐步構(gòu)建多輸入變量的綜合性傳遞函數(shù)模型,用來研究PM2.5與溫度、CO、PM10、NO2和SO2之間的內(nèi)在關(guān)系。最后,為了評價上述綜合性傳遞函數(shù)模型,分別構(gòu)建PM2.5與溫度、CO、PM10、NO2和SO2之間的普通多元線性回歸模型及協(xié)整模型,通過三種模型的對比進一步明確了PM2.5與其影響因素之間的關(guān)系。結(jié)果表明:每一個變量序列本身都不平穩(wěn),有其自身的變化規(guī)律;PM2.5分別與各個輸入變量有顯著的相關(guān)關(guān)系;而且PM2.5與各個因素間的多輸入變量傳遞函數(shù)模型的擬合效果更好,充分說明了PM2.5濃度變化規(guī)律明顯受到各個因素的綜合影響。另外,通過多元線性回歸模型和協(xié)整模型的對比分析可知,這種綜合影響不是簡單的線性相關(guān)關(guān)系,PM2.5濃度值不僅受到當期相關(guān)因素數(shù)值的影響以及隨機誤差的干擾,而且還要受到其自身及各因素前期數(shù)值的顯著影響。
[Abstract]:Haze has become a prominent problem of daily air pollution in China. PM2.5 is the main component of haze and an important index to measure air quality. The change of its concentration directly reflects the change of air quality. Therefore, it is very important to study the formation mechanism and composition of PM2.5. However, up to now, there is no consensus on the formation mechanism and composition of PM2.5. Therefore, it is necessary to further study the occurrence and evolution of PM2.5 by using different methods from different angles. In this paper, time series analysis is used to analyze the time series of PM2.5 concentration in Chongqing. Firstly, according to the relevant literature, the variables which have a great correlation with PM2.5, such as temperature, PM10, NO2 and SO2, are selected, and the relevant data are collected through the network channel, and the data are sorted out and preprocessed. Secondly, the variation law of each variable sequence itself is studied, and their adaptive models are obtained. Thirdly, PM2.5 is taken as the output variable sequence, and the highest and lowest temperatures, such as COP10, NO2 and SO2, are taken as input variable sequences, respectively. The transfer function model of single input variable is constructed, and the comprehensive transfer function model of multiple input variables is constructed step by step, which is used to study the relationship between PM2.5 and temperature COP10 PM10NO2 and SO2. Finally, in order to evaluate the comprehensive transfer function model mentioned above, the general multivariate linear regression model and cointegration model between PM2.5 and COP10 / NO2 and SO2 are constructed, respectively. The relationship between PM2.5 and its influencing factors is further clarified by comparing the three models. The results show that each variable sequence itself is not stable and has its own variation law. PM2.5 has a significant correlation with each input variable, and the model of transfer function of multiple input variables between PM2.5 and each factor has a better fitting effect. It is fully explained that the variation of PM2.5 concentration is obviously influenced by various factors. In addition, through the comparative analysis of multivariate linear regression model and cointegration model, it can be seen that this kind of comprehensive influence is not a simple linear correlation relation. The concentration of PM2.5 is not only affected by the value of relevant factors in the current period, but also interfered by random errors. It is also subject to its own and various factors of the value of the previous significant impact.
【學位授予單位】：重慶理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：X513

【參考文獻】

相關(guān)碩士學位論文 前1條

1 楊天智;長沙市大氣顆粒物PM2.5化學組分特征及來源解析[D];中南大學;2010年

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本文編號：1946739