| 基于APCS-MLR受体模型的贵州喀斯特矿区水田土壤重金属源解析 |
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| 引用本文： | 张旺,高珍冉,邰粤鹰,陈小然,黄啸云,何腾兵.基于APCS-MLR受体模型的贵州喀斯特矿区水田土壤重金属源解析[J].农业工程学报,2022,38(3):212-219. |
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| 作者姓名： | 张旺 高珍冉 邰粤鹰 陈小然 黄啸云 何腾兵 |
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| 作者单位： | 1. 贵州大学农学院,贵阳 550025; 2. 贵州大学新农村发展研究院,贵阳 550025; |
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| 基金项目： | 国家自然科学基金委员会-贵州喀斯特科学研究中心项目（U1612442） |
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| 摘 要： | 水田重金属污染对粮食生产和人体健康造成严重危害,喀斯特矿区周边土壤受到地质和工矿活动的双重污染,而备受关注。为探讨贵阳市开阳县喀斯特矿区水田土壤重金属污染来源,应用绝对主成分得分-多元线性回归（APCS-MLR）与地统计学分析相结合,对水田土壤中重金属Cd、Hg、As、Pb、Cr、Cu、Zn、Ni的来源进行解析。结果表明：研究区Hg的变异系数最强（384.56%）,其均值（1.51 mg/kg）是贵州省土壤背景（0.11 mg/kg）的13.73倍,表现出很高的外源Hg富集;8项重金属均有点位超农用地土壤污染风险筛选值,Cd超的比例最高（47.54%）,污染风险最为突出。Cd、Cr、Cu、Zn和Ni的高值区主要分布于中部,且位置相对一致;Hg的高值区分布于西南部;As的高值区分布于西北部、中部和西南部,具有明显的连续性;Pb的高值区主要分布在西部。各重金属在空间分布上具有一定的相似特征,高值区以点状形式分布,并未出现明显的大范围聚集区域。通过分析最终解析出3个主要污染源,Cd、Cr、Cu和Ni主要受自然源影响,其中Cd的污染来源较为复杂,受人为源的影响也较大;Pb和Zn主要是受工矿业与农业混合源的影响;Hg和As主要受到大气沉降与农业混合源,特别是Hg受到极强的人为活动影响,应引起相关部门的重视,采取措施对其进行污染防治。研究结果可为喀斯特高背景矿区水田重金来源解析、水田土壤重金属综合防控和水稻安全生产提供参考和科学依据。
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| 关 键 词： | 重金属 土壤 地统计 源解析 采矿区 |
| 收稿时间： | 2021/8/18 0:00:00 |
| 修稿时间： | 2021/12/20 0:00:00 |
Source analysis of the heavy metals in paddy field soils in Karst mining areas of Guizhou using APCS-MLR receptor model |
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| Zhang Wang,Gao Zhenran,Tai Yueying,Chen Xiaoran,Huang Xiaoyun,He Tengbing.Source analysis of the heavy metals in paddy field soils in Karst mining areas of Guizhou using APCS-MLR receptor model[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(3):212-219. |
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| Authors: | Zhang Wang Gao Zhenran Tai Yueying Chen Xiaoran Huang Xiaoyun He Tengbing |
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| Institution: | 1. College of Agriculture, Guizhou University, Guiyang 550025, China; 2. Institute of New Rural Development, Guizhou University, Guiyang 550025, China; |
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| Abstract: | Heavy metals contamination in soil has posed a serious threat to human and the ecosystem. Particularly, there is a significantly higher background of soil heavy metals in the Karst areas of southwest China. Human activities (such as industry, mining and agriculture) have aggravated to seriously endanger the food safety and human health in recent years. Therefore, it is highly urgent to quantitatively assess on the main sources of heavy metals for the safe production of rice in paddy fields. Taking the rice fields around a typical karst industrial and mining area in a county of Guiyang City, Guizhou Province, China as the research object, this study aims to implement a source analysis of heavy metals using the absolute principal component score-multiple linear regression (APCS-MLR) model combined with geographical and correlation analysis, according to the geographic location of industrial and mining sites. A total of 122 topsoil samples were collected in paddy fields, where eight heavy metals were measured, including Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni. The results showed that Hg was the most abnormal element in the study area, and its coefficient of variation (384.56%) was the maximum, followed by Cd (129.99%). The average content of the eight heavy metals was 1.51 mg/kg, where the average of Hg was 13.73 times more than of the soil background value in the whole Province. The other selected elements were all higher than the background value, except for Cr and Ni less than or equal to the background. Specifically, the Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni in some sampling points exceeded the risk screening value that realized by the "Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard" (GB 15618-2018), and their exceeded rates were 26.23%, 31.97%, 5.74%, 0.82%, 7.38%, 7.38%, and 4.10%, respectively. The exceeded proportion of Ca was the highest, followed by As and Hg, indicating the most prominent pollution risk. A correlation analysis showed that the correlation coefficients of Cd-Cr, Cd-Zn, Cr-Ni, Cr-Cu, and Cu-Ni were greater than 0.6, exhibiting a strong correlation (P<0.01). Furthermore, the high-value areas of Cd, Cr, Cu, Zn, and Ni were mainly distributed in the middle of the study area, indicating relatively consistent locations in the spatial distribution. The high-value areas of Hg and Pb were mainly distributed in the southwest and the west of the study area, respectively, whereas, those of As were in the northwest, the middle and southwest, indicating an outstanding continuity. Correspondingly, there were similar characteristics of spatial distribution of heavy metals. As such, three main pollution sources were achieved in APCS-MLR and geostatistical interpolation, including the natural resources, mixed sources of industry, mining and agriculture, as well the atmospheric deposition and mixed agricultural sources. Among them, the natural resources dominated the elements of Cd, Cr, Cu, and Ni. Additionally, there were much more complex pollution sources of Cd, indicating a greater influence of anthropogenic sources. Pb and Zn were mainly affected by the mixed sources of industry, mining and agriculture, whereas, Hg and As were mainly depended on the mixed sources of atmospheric deposition and agriculture. Especially, Hg was extremely strong affected by human activities in the Karst areas of southwest China. |
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| Keywords: | heavy metals soil geostatistics source analysis mining area |
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