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浙江大学学报（农业与生命科学版）
Journal of Zhejiang University (Agriculture and Life Sciences)  2019, Vol. 45 Issue (4): 452-459    DOI: 10.3785/j.issn.1008-9209.2018.09.052
Resource utilization & environmental protection     
Prediction of soil heavy metal content under spatial scale based on Bayesian maximum entropy and auxiliary information
Xufeng FEI1,2(),Zhouqiao REN1,2,Zhaohan LOU3,Rui XIAO4,Xiaonan Lü1,2()
1. Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
2. Key Laboratory of Information Traceability of Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310021, China
3. Ocean College, Zhejiang University, Zhoushan 316021, Zhejiang, China
4. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
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Abstract  

With the rapid urbanization and industrialization process in recent decades, soil heavy metal pollution has been a serious threat to the development of society and human health in China. Mapping the spatial heavy metal distribution is an efficient way to identify high pollution areas, facilitate pollution source apportionment, and formulate prevention and control strategies. Selecting Hangzhou City as a case study, the spatial distribution of heavy metals was predicted by Bayesian maximum entropy (BME) method using the soil parent material as auxiliary information, and the estimation accuracy was compared with the traditional Kriging technique. The results showed that BME method has higher prediction accuracy than Kriging method, which was supported by narrower error distribution, smaller mean absolute error and root mean square error. Although the pollution risk of heavy metals in the study area was relatively low (their mean values were lower than the corresponding secondary soil environmental quality standard values), the contents of cadmium (Cd) and arsenium (As) were significantly higher than their local background values, which were 1.59 and 1.31 times of their corresponding background values, respectively. The contents of lead (Pb) and mercury (Hg) were high in the northeastern urban areas, implying that urbanization, industrialization and transportation may be the pollution sources; Cd and As were relatively high in the southwestern and mid-western rural areas, implying that agricultural activities may be responsible for the pollution source. In addition, Cd also showed some high content areas in the mid-eastern part of the city, which could be attributed to local mining activities. Chromium was mainly affected by natural sources.

Key words： soil heavy metal      soil parent material      Bayesian maximum entropy method      Kriging method      spatial analysis     
Received: 05 September 2018      Published: 17 September 2019
CLC:  X 53  
Corresponding Authors: Xiaonan Lü     E-mail: feixf@mail.zaas.ac.cn;luxn@mail.zaas.ac.cn
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Xufeng FEI
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Cite this article:

Xufeng FEI,Zhouqiao REN,Zhaohan LOU,Rui XIAO,Xiaonan Lü. Prediction of soil heavy metal content under spatial scale based on Bayesian maximum entropy and auxiliary information. Journal of Zhejiang University (Agriculture and Life Sciences), 2019, 45(4): 452-459.

URL:

http://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2018.09.052     OR     http://www.zjujournals.com/agr/Y2019/V45/I4/452


基于贝叶斯最大熵和辅助信息的土壤重金属含量空间预测

预测土壤重金属空间分布对于识别高污染区域、进行污染来源解析和制定预防控制策略具有重要意义。本文选取浙江省杭州市为研究区，以土壤母质类型作为辅助信息，通过贝叶斯最大熵（Bayesian maximum entropy, BME）法，预测土壤重金属的空间分布，并与传统的克里金方法的预测结果进行比较。结果表明：BME在土壤重金属含量空间预测方面精度更高，其残差分布区间、平均绝对误差和均方根误差更小。研究区内重金属污染风险相对较低，其平均值均低于二级土壤环境质量标准值，但镉和砷的含量高于当地背景值，分别是背景值的1.59倍和1.31倍。铅和汞在该研究区东北部的城市地区含量较高，城市化、工业化和交通运输可能是其污染来源；镉和砷在西南部和中西部农村地区含量较高，农业活动可能是其污染来源。此外,镉在中东部还存在一块明显的高含量区域，这与当地矿业活动密切相关。铬主要受自然因素的影响。


关键词： 土壤重金属,  土壤母质,  贝叶斯最大熵法,  克里金法,  空间分析 
Fig. 1 Distribution of soil parent materials and sampling points of heavy metals in Hangzhou City

重金属

Heavy metal

最小值

Min./

(mg/kg)

最大值

Max./

(mg/kg)

平均值

Mean/

(mg/kg)

标准差

s/(mg/kg)

变异系数

CV

偏度

Skewness

峰度

Kurtosis

背景值[20]

Background value[20]/(mg/kg)

二级标准值1）

Grade Ⅱ standard

value1)/(mg/kg)
铬Cr 10.32 104.00 52.88 20.17 0.38 －0.03 －0.58 55.99 150/200/250
铅Pb 16.90 62.60 31.67 8.76 0.28 0.72 0.45 35.70 300/300/300
镉Cd 0.05 1.41 0.27 0.21 0.80 2.41 6.72 0.17 0.3/0.3/0.6
汞Hg 0.03 0.50 0.13 0.08 0.61 1.60 3.07 0.17 0.3/0.5/1.0
砷As 2.63 43.20 9.00 6.24 0.69 2.31 6.21 6.88 30/25/20
Table 1 Descriptive statistics of soil heavy metals in Hangzhou City
Fig. 2 Semi-variance model comparison of Kriging and Bayesian maximum entropy methods
Fig. 3 Predicted residual distribution comparison of Bayesian maximum entropy (BME) and Kriging (OK) methods

重金属

Heavy metal

 平均绝对误差 Mean absolute error (MAE) 均方根误差 Root mean square error (RMSE)
克里金 OK 贝叶斯最大熵 BME 克里金 OK 贝叶斯最大熵 BME
铬Cr 12.023 10.362 16.187 13.865
铅Pb 7.089 5.986 10.645 8.545
镉Cd 0.124 0.122 0.206 0.193
汞Hg 0.067 0.062 0.103 0.094
砷As 3.972 3.199 9.101 5.513
Table 2 Comparison of estimation accuracy of Bayesian maximum entropy (BME) and Kriging (OK) methods
Fig. 4 Spatial distribution of soil heavy metals based on Bayesian maximum entropy (BME) prediction in Hangzhou City
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