本文關鍵詞： 土壤砷 土壤氮 空間變異 RBF神經(jīng)網(wǎng)絡 協(xié)同克里格　出處：《華南農(nóng)業(yè)大學》2016年博士論文　論文類型：學位論文

【摘要】：土壤是農(nóng)業(yè)生產(chǎn)最基本的生產(chǎn)資料,全面了解土壤質(zhì)量信息是展開土壤生態(tài)保護和修復等工作的基礎,而土壤養(yǎng)分的豐缺程度是土壤質(zhì)量高低的重要指標,同時土壤污染物含量的高低也是影響土壤質(zhì)量的重要指標。本研究以廣州市增城區(qū)為研究區(qū)域,在耕地地力調(diào)查成果的基礎上,選取117個土壤樣點,用不同的插值方法對土壤砷和土壤全氮含量的空間變異特征及其相互關系進行了研究。主要研究內(nèi)容和結論如下:(1)通過對117個土壤樣品數(shù)據(jù)的數(shù)理統(tǒng)計分析得出增城區(qū)土壤砷和土壤全氮含量的分布范圍都較廣,土壤全氮變異系數(shù)為38.97%屬于中等變異,從偏度上看盡管大于零但數(shù)值比較小,其分布趨于對稱;土壤砷變異系數(shù)為84.41%屬于強變異,從偏度上看,土壤砷含量有較大的正偏離,其分布較正態(tài)分布向右偏離。(2)分別利用普通克里格方法和RBF神經(jīng)網(wǎng)絡方法對土壤砷含量進行空間插值,對插值結果進行對比分析發(fā)現(xiàn)在研究區(qū)范圍內(nèi)RBF神經(jīng)網(wǎng)絡方法在擬合能力和插值精度等方面都優(yōu)于普通克里格方法。(3)增城區(qū)土壤砷含量范圍主要分布在0~15mg/kg,增城區(qū)的東北部和南部土壤砷含量較高,且自東北至西南方向呈現(xiàn)逐漸降低的趨勢,西部含量較低。土壤砷含量較高(9~15mg/kg)地區(qū)主要分布在派潭鎮(zhèn)、正果鎮(zhèn)、小樓鎮(zhèn)、增江街道、荔城街道的東北部、石灘鎮(zhèn)的中南部和新塘鎮(zhèn)的南部,土壤砷含量較低(小于9mg/kg)地區(qū)主要分布在中新鎮(zhèn)、朱村街道、荔城街道的西南部、石灘鎮(zhèn)的西北部和新塘鎮(zhèn)的北部。(4)分別利用普通克里格方法和RBF神經(jīng)網(wǎng)絡方法對土壤全氮含量進行空間插值,對插值結果進行對比分析發(fā)現(xiàn)在研究區(qū)范圍內(nèi)RBF神經(jīng)網(wǎng)絡方法在擬合能力和插值精度等方面都占優(yōu)。(5)增城區(qū)土壤全氮含量高級水平(1~2.5g/kg)地區(qū)主要分布在新塘鎮(zhèn)、石灘鎮(zhèn)的西部、朱村街道和派潭鎮(zhèn)的中南部,且自中部向東西兩方向呈現(xiàn)逐漸降低的趨勢,土壤全氮含量中級水平(0.75~1g/kg)地區(qū)主要分布在派潭鎮(zhèn)、荔城街道和石灘鎮(zhèn)的東部,土壤全氮含量低級水平(小于0.75g/kg)地區(qū)主要分布在中新鎮(zhèn)、小樓鎮(zhèn)、正果鎮(zhèn)和增江街道。(6)研究區(qū)灌木林、有林地、果園、水田、旱地、水澆地中土壤砷和土壤全氮的含量都是呈現(xiàn)逐步上升的趨勢,且土壤全氮和土壤砷含量在各土地利用類型上的相關系數(shù)R2達到了0.987,這種相關性不僅說明了土壤砷和土壤氮含量受人為活動影響較大,也說明了在研究區(qū)范圍內(nèi)土壤砷和土壤氮含量之間存在顯著正相關關系。(7)以土壤砷含量為協(xié)變量對土壤氮含量進行協(xié)同克里格插值,協(xié)同克里格方法在模型的擬合能力和插值精度等方面較普通克里格方法和RBF神經(jīng)網(wǎng)絡方法都有一定程度的提高,也證明了土壤砷和土壤氮含量之間存在顯著相關關系,土壤砷的含量會對土壤氮含量的空間變異造成影響。
[Abstract]:Soil is the most basic means of production in agricultural production. Comprehensive understanding of soil quality information is the basis of soil ecological protection and remediation, and the abundance of soil nutrients is an important indicator of soil quality. At the same time, the content of soil pollution is also an important index to affect soil quality. In this study, 117 soil samples were selected on the basis of the results of cultivated land fertility investigation in Zengcheng District of Guangzhou City. The spatial variation characteristics of soil arsenic and soil total nitrogen contents and their relationships were studied by different interpolation methods. The main contents and conclusions are as follows: 1). Based on the statistical analysis of 117 soil samples, it was found that the distribution range of soil arsenic and soil total nitrogen content in Zengcheng area was wide. The coefficient of variation of soil total nitrogen (TNA) of 38.97% belongs to medium variation, although the deviation is greater than zero, the value is smaller, and its distribution tends to be symmetrical. The coefficient of variation of arsenic in soil is 84.41%, which is a strong variation, and the soil arsenic content has a large positive deviation from the degree of deviation. Its distribution deviates to the right than the normal distribution.) the spatial interpolation of arsenic content in soil is carried out by using the ordinary Kriging method and the RBF neural network method respectively. Comparing and analyzing the interpolation results, it is found that the RBF neural network method is superior to the ordinary Kriging method in terms of fitting ability and interpolation accuracy in the study area. The range of arsenic content in Zengcheng area was 15 mg / kg. The content of arsenic in the northeast and south of Zengcheng area is higher and the content of arsenic is decreasing gradually from northeast to southwest. The content of arsenic in the west is relatively low. The content of arsenic in soil is higher than that in 15 mg 路kg ~ (-1) of soil. It is mainly distributed in the northeast of Pitan Town, Zhengguo Town, Xialou Town, Zengjiang Street and Licheng Street. The low arsenic content (less than 9 mg / kg) in the south of Shitan town and the south part of Xintang town is mainly distributed in Zhongxin town, Zhucun street and southwest of Licheng street. The general Kriging method and RBF neural network method were used to interpolate soil total nitrogen content in the northwestern part of Shitan town and the northern part of Xitang town respectively. The comparison and analysis of the interpolation results show that the RBF neural network method is superior in fitting ability and interpolation accuracy in the study area.) the higher level of soil total nitrogen content in Zengcheng area (. (1) the area of 2.5 g / kg is mainly distributed in Xintang Town. The west of Shitan Town, the street of Zhucun and the central and southern part of Pitan Town show a decreasing trend from the middle to the east and west. The intermediate level of soil total nitrogen content was 0.75g / kg) in Pitan Town, Licheng Street and the eastern part of Shitan Town. The low level of soil total nitrogen content (< 0.75 g / kg) was mainly distributed in shrub forest, woodland, orchard, paddy field and dryland in Zhongxin, Xiaolou, Zhengguo and Zengjiang streets. The contents of soil arsenic and soil total nitrogen in irrigated land showed a trend of gradual increase, and the correlation coefficient R2 of soil total nitrogen and soil arsenic content in each land use type reached 0.987. This correlation not only indicates that soil arsenic and soil nitrogen content are greatly affected by anthropogenic activities. It also shows that there is a significant positive correlation between soil arsenic and soil nitrogen content in the study area. The cooperative Kriging method can improve the model fitting ability and interpolation accuracy to some extent compared with the common Kriging method and the RBF neural network method. It is also proved that there is a significant correlation between soil arsenic and soil nitrogen content, and the content of soil arsenic will affect the spatial variation of soil nitrogen content.
【學位授予單位】：華南農(nóng)業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：S153.6

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