本文關(guān)鍵詞：北京市MODIS氣溶膠光學(xué)厚度與PM2.5濃度關(guān)系研究　出處：《成都理工大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： MODIS 氣溶膠光學(xué)厚度(AOD) PM2.5 相關(guān)分析 回歸分析

【摘要】：近年來(lái)全國(guó)各地頻現(xiàn)的霧霾天氣,不但制約和影響著我們國(guó)民經(jīng)濟(jì)的發(fā)展,更威脅著我們每個(gè)人的生命健康。氣溶膠是霧霾形成的基礎(chǔ)和前提,人類(lèi)活動(dòng)排放的污染物中包括直接排放的氣溶膠和各種氣態(tài)污染物,通過(guò)光化學(xué)轉(zhuǎn)化,這些物質(zhì)可形成二次氣溶膠,進(jìn)而演變成灰霾,這就使得危害人體健康的細(xì)顆粒物PM2.5的濃度進(jìn)一步升高。目前我國(guó)大氣環(huán)境常規(guī)監(jiān)測(cè)手段仍是通過(guò)建立地面監(jiān)測(cè)站的方式,我國(guó)幅員遼闊,想要監(jiān)測(cè)大區(qū)域尺度的空氣質(zhì)量狀況,實(shí)現(xiàn)區(qū)域、全球的大氣環(huán)境質(zhì)量監(jiān)測(cè),顯然現(xiàn)有的地面監(jiān)測(cè)站的數(shù)量遠(yuǎn)不能滿足需求。衛(wèi)星遙感監(jiān)測(cè)手段為我們提供了天地一體化的監(jiān)測(cè)體系,衛(wèi)星遙感在大氣環(huán)境監(jiān)測(cè)方面具有廣覆蓋、連續(xù)性、空間性和預(yù)測(cè)性的獨(dú)特優(yōu)勢(shì),能夠在更大尺度的空間范圍內(nèi)快速、實(shí)時(shí)、準(zhǔn)確地獲取大氣環(huán)境狀況。氣溶膠光學(xué)厚度是氣溶膠膠體的重要光學(xué)特征,通過(guò)對(duì)氣溶膠光學(xué)厚度的反演,建立其和地基空氣質(zhì)量監(jiān)測(cè)的PM2.5濃度的關(guān)系模型,可以獲得大尺度區(qū)域的近地表PM2.5的濃度,彌補(bǔ)了地基空氣質(zhì)量監(jiān)測(cè)中由于地面監(jiān)測(cè)站的數(shù)量有限而無(wú)法監(jiān)測(cè)大范圍區(qū)域空氣質(zhì)量指標(biāo)的不足。本文以近年來(lái)空氣污染較為嚴(yán)重的北京市為研究區(qū)域,選取2013年到2014年晴朗天氣拍攝的Terra-MODIS衛(wèi)星遙感影像數(shù)據(jù)作為研究區(qū)的樣本數(shù)據(jù),利用當(dāng)前發(fā)展較成熟的暗像元方法,基于ENVI5.0平臺(tái)反演出北京各天的氣溶膠光學(xué)厚度。與此同時(shí)收集與遙感影像獲取相同時(shí)間段內(nèi)監(jiān)測(cè)得到的北京市12個(gè)監(jiān)測(cè)站點(diǎn)PM2.5的瞬時(shí)濃度值,并利用空間地統(tǒng)計(jì)分析原理對(duì)北京市PM2.5濃度的整體特征分別在空間和時(shí)間維度進(jìn)行分析。由于利用暗像元算法反演氣溶膠光學(xué)厚度有其特定的適用條件,比如在植被密度較高的地方反演效果較低植被覆蓋度的區(qū)域好,在春夏季節(jié)由于植被較秋冬季節(jié)密度高,所以春夏季節(jié)的反演效果好,秋冬季節(jié)存在反演結(jié)果誤差大的情況,所以要選取氣溶膠光學(xué)厚度反演結(jié)果中質(zhì)量好的部分,對(duì)應(yīng)各個(gè)監(jiān)測(cè)站點(diǎn)的位置提取出瞬時(shí)氣溶膠光學(xué)厚度值和監(jiān)測(cè)站得到的PM2.5的濃度值,然后對(duì)這兩個(gè)值做相關(guān)分析和回歸分析,分別建立以線性函數(shù)、二次函數(shù)、三次函數(shù)、指數(shù)函數(shù)、對(duì)數(shù)函數(shù)和乘冪函數(shù)為基礎(chǔ)的回歸模型,通過(guò)對(duì)回歸模型擬合優(yōu)度R2及模型檢驗(yàn)精度的對(duì)比選擇出最優(yōu)擬合模型。論文基于上述研究方法,取得的成果如下:(1)利用暗像元算法對(duì)北京地區(qū)的氣溶膠光學(xué)厚度值進(jìn)行了反演,并篩選出滿足要求的反演結(jié)果,得到6幅夏季的反演結(jié)果圖像,3幅春季的反演結(jié)果圖像,1幅秋季的反演結(jié)果圖像。(2)對(duì)北京市PM2.5值的變化趨勢(shì)分別在時(shí)間維度和空間維度上進(jìn)行了分析,冬季PM2.5值普遍較高,春季次之;夏、秋季節(jié)PM2.5值較低。從空間變化趨勢(shì)上看PM2.5值呈現(xiàn)由北向南逐漸遞增的趨勢(shì)。(3)證明了氣溶膠光學(xué)厚度與PM2.5相關(guān)性的存在。并且分季節(jié)進(jìn)行相關(guān)性分析后,氣溶膠光學(xué)厚度與PM2.5的pearson相關(guān)系數(shù)與雙側(cè)顯著性都有了明顯的提高,具有統(tǒng)計(jì)學(xué)意義,可以進(jìn)行回歸分析并建立回歸模型。(4)通過(guò)回歸分析,針對(duì)夏季和春季建立了氣溶膠光學(xué)厚度與PM2.5值的6種回歸模型,并篩選出擬合優(yōu)度R2較高的三次模型、指數(shù)模型、乘冪模型做模型驗(yàn)證,最終確定乘冪模型為夏季和春季的最優(yōu)擬合模型。
[Abstract]:The haze weather around the country are frequent in recent years, not only affects the development of our national economy, more threatening our lives. Aerosol health is the basis and prerequisite for the formation of haze, including direct emissions of aerosols and various gaseous pollutants emissions of human activities, through photochemical transformation, these substances can form two secondary aerosol, and then evolved into the haze, which makes the concentration of fine particles PM2.5 harmful to human health is still further increased. The conventional means of monitoring the atmospheric environment in China through the establishment of ground monitoring station, China's vast territory, the status of air quality monitoring, to achieve large scale regional atmospheric environmental quality monitoring the world, obviously the existing ground monitoring stations can not meet the demand. The number of satellite remote sensing monitoring provides world integrated monitoring for us Measuring system of satellite remote sensing in atmospheric environmental monitoring has wide coverage, continuity, the unique advantages of space and predictive, can quickly, in the space scope of the larger scale in real-time, accurately obtain atmospheric environmental conditions. The aerosol optical thickness is an important optical characteristics of aerosol colloid, through the inversion of aerosol optical thickness the relationship between the concentration of PM2.5, the model and establish the foundation for air quality monitoring, can obtain the PM2.5 concentration near the surface of large scale area, make up the foundation of air quality monitoring due to the limited number of ground monitoring stations to monitoring large area air quality index in this paper. In recent years, air pollution is more serious in Beijing city as the study area, from 2013 to the Terra-MODIS satellite remote sensing data in 2014 sunny weather shooting as the sample data of the study area, using the current development Dark pixel method is more mature, the anti ENVI5.0 platform performance in Beijing every day based on the instantaneous concentration of aerosol optical thickness. At the same time collecting and remote sensing images obtained in the same time monitoring of the 12 monitoring stations in Beijing city PM2.5, and using the spatial statistical analysis of overall characteristics of the concentration of PM2.5 in Beijing city in space and principle. The time dimension is analyzed. Due to the use of dark pixel algorithm to retrieve aerosol optical thickness has its special application condition, such as in the local areas of high vegetation density inversion effect of low vegetation coverage, in spring and summer than in autumn and winter festival because the vegetation density is high, so the inversion effect of spring and summer, autumn and winter are the inversion results the error is large, so we should choose the aerosol optical thickness inversion results in good quality parts, each monitoring site location to extract transient The concentration of aerosol optical thickness and the value of air monitoring station get the value of PM2.5, then do the correlation analysis and regression analysis of these two values were established with linear function, quadratic function, cubic function, exponential function, logarithmic function and power function regression model as the foundation, through the comparison of the goodness of fit of R2 and the model testing precision of regression model to select the best fitting model. The method is based on the above research, the results are as follows: (1) the dark pixel algorithm of aerosol optical depth in Beijing area were selected and inversion, the inversion results meet the requirements, the inversion results obtained 6 summer images, 3 pieces of spring the inversion results of images, 1 images of the image retrieval results fall. (2) the change trend of Beijing city PM2.5 values in the dimension of time and space to carry on the analysis, the PM2.5 value is generally higher in winter, summer, autumn and spring; Seasonal low PM2.5 value. The PM2.5 value showed gradually increasing trend from the south to the north from the spatial change trend. (3) proved that the aerosol optical thickness and PM2.5 correlation. And seasonal correlation analysis, Pearson correlation coefficient and significant bilateral aerosol optical thickness and PM2.5 have been significantly improved, have statistical significance, can carry out regression analysis and the regression model (4). Through regression analysis, 6 Regression Model of aerosol optical thickness and PM2.5 value have been set up for the summer and spring, and selected the three model, the goodness of fit R2 high index model, model verification power model, and ultimately determine the power the model was the best fitting model for the spring and summer months.

【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：X513

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