本文選題：重金屬污染 + 污染特征�。� 參考：《湖南大學(xué)》2013年碩士論文

【摘要】：城市河流時(shí)刻接納來自于城市的大量人為污染。在眾多污染中,重金屬由于其在環(huán)境中的持久性、循環(huán)性以及生態(tài)危害性而倍受關(guān)注。沉積物既是重金屬的匯,又是潛在的次生污染源。評(píng)價(jià)沉積物中重金屬的污染有助于了解人類以及不同生物暴露在水生環(huán)境中可能受到的潛在風(fēng)險(xiǎn)以及所處空間的環(huán)境質(zhì)量,進(jìn)行沉積物中重金屬的來源解析則可以幫助人們從根本上有效控制和減少污染源的排放。 本文以洞庭湖沉積物為研究對(duì)象,設(shè)置10個(gè)具有代表性的采樣點(diǎn)位,采用箱式采泥器進(jìn)行沉積物采樣,用電感耦合等離子體質(zhì)譜法和原子熒光法分別測定了樣品中Cd、Pb、Cr、As、Cu和Hg、Zn的含量,通過分析得出：七種重金屬的平均含量均高于洞庭湖水系沉積物背景值,重金屬平均含量由高到低依次是：ZnCrPbCuAsCdHg。隨后運(yùn)用反距離權(quán)重插值法進(jìn)行空間插值,結(jié)果表明Pb、Cd、As、Zn和Cu的空間分布圖相似,Hg和Cr的分布圖各異,重金屬的高值區(qū)主要集中洞庭湖的中下游地區(qū)。 綜合運(yùn)用地累積指數(shù)、潛在生態(tài)風(fēng)險(xiǎn)指數(shù)法并結(jié)合GIS的空間可視化分析確定了洞庭湖沉積物重要污染的重金屬和污染區(qū)域,分析認(rèn)為：洞庭湖沉積物中重金屬重點(diǎn)污染因子為Cd、Hg、Cr和As,重點(diǎn)污染區(qū)域?yàn)槎赐ズ隹诩捌涓浇鼌^(qū)域及各大支流匯入洞庭湖的河口三角洲。 基于模型不確定性的考慮,運(yùn)用相關(guān)分析法、主成分分析法、正矩陣因子分解法、傅立葉和譜分析法分別解析了洞庭湖沉積中重金屬的污染源,綜合四種解析方法的結(jié)果得到三類污染源。第一類因子上的主要污染物為Pb、Cd、As、Zn和Cu,主要來源為人為活動(dòng)中的工業(yè)廢水排放和采礦業(yè)廢水的排放所帶來的污染；第二類因子主要污染物為Hg,主要來源于自然風(fēng)化；第三類因子主要污染物為Cr,主要來源于人類活動(dòng)中的生活污水和農(nóng)業(yè)生產(chǎn)。經(jīng)過各個(gè)模型之間的交叉驗(yàn)證與對(duì)比得出結(jié)論：在數(shù)據(jù)量及數(shù)據(jù)代表性不足情況下的,主成分分析結(jié)合傅立葉和譜分析將會(huì)得到更加準(zhǔn)確可靠的解析結(jié)果。
[Abstract]:Urban rivers always accept a large amount of man-made pollution from the city. Among all kinds of pollution, heavy metals have attracted much attention because of their persistence, recycling and ecological harmfulness in the environment. Sediment is not only a sink of heavy metals, but also a potential secondary pollution source. Assessing the contamination of heavy metals in sediments contributes to an understanding of the potential risks to which humans and different organisms are exposed to the aquatic environment and the environmental quality of the space in which they are exposed, The analysis of the source of heavy metals in sediments can help people to effectively control and reduce the emission of pollution sources. In this paper, taking Dongting Lake sediment as the research object, 10 representative sampling sites are set up, and the sediment sampling is carried out with box mud collector. The contents of Cu and Hgzn in the samples were determined by inductively coupled plasma mass spectrometry (ICP-MS) and atomic fluorescence spectrometry (AFS), respectively. The results show that the average contents of seven heavy metals are higher than the background values of sediments in Dongting Lake. The average content of heavy metals from high to low is: ZnCrPbCuAsCdHg. Then the inverse distance weight interpolation method is used to carry out spatial interpolation. The results show that the spatial distribution maps of Zn and Cu are similar to those of Hg and Cr, and the high value regions of heavy metals are mainly concentrated in the middle and lower reaches of Dongting Lake. Combined with land accumulation index, potential ecological risk index method and spatial visualization analysis of GIS, the heavy metals and polluted areas of sediments in Dongting Lake were determined. It is concluded that the major pollution factors of heavy metals in the sediments of Dongting Lake are CD HgN Cr and As.The main polluted areas are the outlet of Dongting Lake and its adjacent area and the estuarine deltas where the major tributaries converge into Dongting Lake. Based on the uncertainty of the model, correlation analysis, principal component analysis, positive matrix factorization, Fourier and spectral analysis were used to analyze the pollution sources of heavy metals in Dongting Lake sediments. Three kinds of pollution sources are obtained by synthesizing the results of four analytical methods. The main pollutants in the first type of factors are Pb, CD, C, Zn and Cu, which mainly come from the discharge of industrial wastewater from human activities and from the discharge of mining wastewater, and the second type of major pollutants are Hg, mainly from natural weathering. The third factor is Cr, which mainly comes from domestic sewage and agricultural production in human activities. After the cross-validation and comparison among the models, it is concluded that the principal component analysis (PCA) combined with Fourier and spectral analysis will obtain more accurate and reliable analytical results under the condition of the data volume and data representation is insufficient.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P208;X824;X524

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本文編號(hào)：1975978