發(fā)布時(shí)間：2018-05-14 13:42

  本文選題：PM10 + 空間分布��； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：隨著各種工業(yè)過程中超細(xì)顆粒物的排放以及大氣中二次形成的超細(xì)顆粒物與氣溶膠等原因,大氣中的可吸入顆粒物(PM10)逐漸成為成為全國大中城市的首要空氣污染物,并嚴(yán)重影響城市生態(tài)環(huán)境及居民身體健康。探討區(qū)域內(nèi)PM10濃度在地理上的空間分布以及空間點(diǎn)模式具有重要的價(jià)值與現(xiàn)實(shí)意義。本文以甘肅省為研究區(qū),以PM10為研究對象,利用甘肅省2016年夏季33個(gè)空氣污染監(jiān)測站點(diǎn)的六項(xiàng)空氣指標(biāo)(PM10, PM2.5, CO, NO2, 03,SO2)的75天的日數(shù)據(jù)進(jìn)行建模研究。首先利用提出的基于Elman神經(jīng)網(wǎng)絡(luò)的平均影響值(MIV)的機(jī)器學(xué)習(xí)算法來確定其它五個(gè)空氣指標(biāo)對PM10的影響重要程度,篩選出其中對PM10影響最大的指標(biāo)PM2.5,并且利用統(tǒng)計(jì)方法相關(guān)系數(shù)和散點(diǎn)圖驗(yàn)證了該結(jié)論的正確性。然后分別利用普通克里金方法和以篩選出來的PM2.5為輔助變量的協(xié)同克里金方法對研究區(qū)每天的PM10進(jìn)行插值,通過交叉驗(yàn)證的方式比較兩種插值模型的精度。實(shí)驗(yàn)對比結(jié)果表明,協(xié)同克里金插值方法在插值精度上優(yōu)于普通克里金,并且以協(xié)同克里金方法對研究區(qū)進(jìn)行插值,以了解PM10的空間分布情況。最后,本文利用PM10濃度極大值點(diǎn)進(jìn)行了空間點(diǎn)模式分析。利用協(xié)同克里金插值出來的75天的PM10空間分布,找出每天具有PM10濃度極大值的地理位置,將這些地理位置全部繪制在研究區(qū)內(nèi)進(jìn)行空間點(diǎn)模式分析。分析表明研究區(qū)夏季PM10的分布情況主要集中在城市人口密度大的區(qū)域,具有強(qiáng)烈的聚集性。
[Abstract]:With the emission of ultrafine particles in various industrial processes and the secondary formation of superfine particles and aerosols in the atmosphere, PM10 in the atmosphere has gradually become the main air pollutant in large and medium-sized cities in China. And seriously affect the urban ecological environment and the health of residents. It is of great value and practical significance to study the geographical distribution of PM10 concentration and the spatial point model. In this paper, taking Gansu Province as the research area and PM10 as the research object, the 75-day daily data of PM10, PM2.5, CO, NO2, and SO2) from 33 air pollution monitoring stations in Gansu Province in the summer of 2016 are used to model the model. Firstly, the machine learning algorithm based on the average influence value of Elman neural network is proposed to determine the importance of the other five air indexes to the PM10. The index PM2.5, which has the greatest influence on PM10, is selected, and the validity of the conclusion is verified by using the correlation coefficient and scatter plot of statistical method. Then the common Kriging method and the cooperative Kriging method with the filtered PM2.5 as the auxiliary variable are used to interpolate the daily PM10 in the study area, and the accuracy of the two interpolation models is compared by cross-validation. The experimental results show that the interpolation accuracy of cooperative Kriging interpolation method is better than that of common Kriging method, and the cooperative Kriging method is used to interpolate the study area to understand the spatial distribution of PM10. Finally, the spatial point model is analyzed by using the maximum of PM10 concentration. By using the spatial distribution of PM10 which is interpolated by co-Kriging for 75 days, the geographical location with maximum PM10 concentration per day is found out, and all these geographical positions are plotted in the study area for spatial point pattern analysis. The analysis shows that the distribution of PM10 in summer in the study area is mainly concentrated in the area with high urban population density and has a strong agglomeration.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：X513

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本文編號：1888078