Yang Hao, Song Ying Qiang, Hu Yue Ming, Chen Fei Xiang, Zhang Rui
College of Natural Resources and Environment, South China Agricultural University, Guang-zhou 510642, China.
Key Laboratory of Construction Land Improvement, Ministry of Land and Resources, Guangzhou 510642,China.
Ying Yong Sheng Tai Xue Bao. 2018 May;29(5):1695-1704. doi: 10.13287/j.1001-9332.201805.037.
The heavy metals in soil have serious impacts on safety, ecological environment and human health due to their toxicity and accumulation. It is necessary to efficiently identify the risk area of heavy metals in farmland soil, which is of important significance for environment protection, pollution warning and farmland risk control. We collected 204 samples and analyzed the contents of seven kinds of heavy metals (Cu, Zn, Pb, Cd, Cr, As, Hg) in Zengcheng District of Guangzhou, China. In order to overcame the problems of the data, including the limitation of abnormal values and skewness distribution and the smooth effect with the traditional kriging methods, we used sequential indicator simulation method (SISIM) to define the spatial distribution of heavy metals, and combined Hakanson index method to identify potential ecological risk area of heavy metals in farmland. The results showed that: (1) Based on the similar accuracy of spatial prediction of soil heavy metals, the SISIM had a better expression of detail rebuild than ordinary kriging in small scale area. Compared to indicator kriging, the SISIM had less error rate (4.9%-17.1%) in uncertainty evaluation of heavy-metal risk identification. The SISIM had less smooth effect and was more applicable to simulate the spatial uncertainty assessment of soil heavy metals and risk identification. (2) There was no pollution in Zengcheng's farmland. Moderate potential ecological risk was found in the southern part of study area due to enterprise production, human activities, and river sediments. This study combined the sequential indicator simulation with Hakanson risk index method, and effectively overcame the outlier information loss and smooth effect of traditional kriging method. It provided a new way to identify the soil heavy metal risk area of farmland in uneven sampling.
土壤中的重金属因其毒性和累积性，对安全、生态环境及人类健康有着严重影响。高效识别农田土壤重金属风险区域很有必要，这对环境保护、污染预警及农田风险管控具有重要意义。我们采集了204个样本，分析了中国广州增城区七种重金属（铜、锌、铅、镉、铬、砷、汞）的含量。为克服数据问题，包括异常值和偏态分布的局限性以及传统克里金法的平滑效应，我们采用顺序指示模拟法（SISIM）来定义重金属的空间分布，并结合哈坎森指数法识别农田重金属潜在生态风险区域。结果表明：（1）基于土壤重金属空间预测精度相近，SISIM在小尺度区域对细节重建的表达优于普通克里金法。与指示克里金法相比，SISIM在重金属风险识别不确定性评估中的错误率更低（4.9%-17.1%）。SISIM的平滑效应更小，更适用于模拟土壤重金属空间不确定性评估及风险识别。（2）增城农田未受污染。因企业生产、人类活动及河流沉积物，研究区域南部发现存在中度潜在生态风险。本研究将顺序指示模拟与哈坎森风险指数法相结合，有效克服了传统克里金法的异常值信息损失和平滑效应。为不均匀采样情况下识别农田土壤重金属风险区域提供了新途径。
