Please wait a minute...
浙江大学学报（农业与生命科学版）
浙江大学学报（农业与生命科学版）  2019, Vol. 45 Issue (4): 452-459    DOI: 10.3785/j.issn.1008-9209.2018.09.052
资源利用与环境保护     
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
费徐峰1,2(),任周桥1,2,楼昭涵3,肖锐4,吕晓男1,2()
1. 浙江省农业科学院数字农业研究所，杭州 310021
2. 农业农村部农产品信息溯源重点实验室，杭州 310021
3. 浙江大学海洋学院，浙江 舟山 316021
4. 武汉大学遥感与信息工程学院，武汉 430072
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
 全文: PDF(4583 KB)   HTML
摘要：

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    
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
收稿日期: 2018-09-05 出版日期: 2019-09-17
CLC:  X 53  
基金资助: 国家重点研发计划(2017YFD0200600);国家自然科学基金(41801302)
通讯作者: 吕晓男     E-mail: feixf@mail.zaas.ac.cn;luxn@mail.zaas.ac.cn
作者简介: 费徐峰（https://orcid.org/0000-0001-5431-8397），Tel：+86-571-86419079，E-mail：feixf@mail.zaas.ac.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
费徐峰
任周桥
楼昭涵
肖锐
吕晓男

引用本文:

费徐峰,任周桥,楼昭涵,肖锐,吕晓男. 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[J]. 浙江大学学报（农业与生命科学版）, 2019, 45(4): 452-459.

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.

链接本文:

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

图1  杭州市土壤母质及重金属采样点分布图

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
表1  杭州市土壤重金属含量描述性统计
图2  克里金和贝叶斯最大熵半方差模型对比
图3  贝叶斯最大熵（BME）和克里金（OK）法预测的残差分布对比图

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
表2  土壤重金属BME和OK预测方法的计算精度对比
图4  基于贝叶斯最大熵（BME）预测的杭州市土壤重金属空间分布图
1 JIANG Y X , CHAO S H , LIU J W , et al . Source apportionment and health risk assessment of heavy metals in soil for a township in Jiangsu Province, China. Chemosphere, 2017,168:1658-1668.
2 ZHANG C S . Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland. Environmental Pollution, 2006,142(3):501-511.
3 ZANG F , WANG S L , NAN Z R, et al . Accumulation, spatio-temporal distribution, and risk assessment of heavy metals in the soil-corn system around a polymetallic mining area from the Loess Plateau, northwest China. Geoderma, 2017,305:188-196.
4 吕建树,张祖陆,刘洋，等 .日照市土壤重金属来源解析及环境风险评价.地理学报,2012,67(7):109-122.
Lü J S , ZHANG Z L , LIU Y , et al . Sources identification and hazardous risk delineation of heavy metals contamination in Rizhao City. Acta Geographica Sinica, 2012,67(7):109-122. (in Chinese with English abstract)
5 张贝,李卫东,杨勇,等 .贝叶斯最大熵地统计学方法及其在土壤和环境科学上的应用.土壤学报,2011,48(4):831-839.
ZHANG B , LI W D , YANG Y , et al . The Bayesian maximum entropy geostatistical approach and its application in soil and environmental sciences. Acta Pedologica Sinica, 2011,48(4):831-839. (in Chinese with English abstract)
6 张若兮,杨勇,张楚天 .基于范畴型变量和贝叶斯最大熵的土壤有机质空间预测.土壤通报,2015(2):312-318.
ZHANG R X , YANG Y , ZHANG C T . Prediction of spatial distribution of soil organic matter based on categorization variables and BME methods. Chinese Journal of Soil Science, 2015(2):312-318. (in Chinese with English abstract)
7 CHRISTAKOS G , LI X . Bayesian maximum entropy analysis and mapping: a farewell to Kriging estimators? Mathematical Geology , 1998,30(4):435-462.
8 CHRISTAKOS G . Modern Spatiotemporal Geostatistics. New York, US: Oxford University Press, 2000:50-55.
9 CHRISTAKOS G . Spatiotemporal information systems in soil and environmental sciences. Geoderma, 1998,85(2/3):141-179.
10 CHRISTAKOS G , SERRE M L . Spatiotemporal analysis of environmental exposure-health effect associations. Journal of Exposure Analysis & Environmental Epidemiology, 2000,10(2):168-87.
11 YANG Y , CHRISTAKOS G , GUO M , et al . Space-time quantitative source apportionment of soil heavy metal concentration increments. Environmental Pollution, 2017,223:560-566.
12 QIAO M , CAI C , HUANG Y , et al . Characterization of soil heavy metal contamination and potential health risk in metropolitan region of northern China. Environmental Monitoring & Assessment, 2011,172(1/2/3/4:353-365.
13 HU B F , JIA X L , HU J , et al . Assessment of heavy metal pollution and health risks in the soil-plant-human system in the Yangtze River Delta, China. International Journal of Environmental Research & Public Health, 2017,14(9):1-18.
14 吴嘉平,胡义镰,支俊俊,等 .浙江省1∶5万大比例尺土壤数据库.土壤学报,2013,50(1):30-40.
WU J P , HU Y L , ZHI J J , et al . A 1∶50 000 scale soil database of Zhejiang Province, China. Acta Pedologica Sinica, 2013,50(1):30-40. (in Chinese with English abstract)
15 史舟,李艳 .地统计学在土壤学中的应用.北京:中国农业出版社,2006:54-60.
SHI Z , LI Y . Application of Geostatistics in Soil Science. Beijing: China Agricultural Press, 2006:54-60. (in Chinese)
16 OLEA R A . A six-step practical approach to semivariogram modeling. Stochastic Environmental Research & Risk Assessment, 2006,20(5):307-318.
17 CHRISTAKOS G . Random Field Models in Earth Sciences. Salt Lake City, US: Academic Press, 1992:140-145.
18 章明奎,王浩,张慧敏 .浙东海积平原农田土壤重金属来源辨识.环境科学学报,2008,28(10):1946-1954.
ZHANG M K , WANG H , ZHANG H M . Distinguishing different sources of heavy metals in soils on the coastal plain of Eastern Zhejiang Province.Acta Scientiae Circumstantia, 2008,28(10):1946-1954. (in Chinese with English abstract)
19 唐诵六 .用主成分分析研究土壤中重金属含量与母质的关系.环境科学,1985,6(3):2-6.
TANG S L . Study on the relationship between heavy metal content in soil and parent material by principal component analysis. Environmental Science, 1985,6(3):2-6. (in Chinese)
20 徐玉裕,史坚,周侣艳,等 .杭州地区农业土壤中重金属的分布特征及其环境意义.中国环境监测,2012,28(4):74-81.
XU Y Y , SHI J , ZHOU L Y , et al . Characteristics of heavy metals distribution in agricultural soils of Hangzhou and its environment significances. Environmental Monitoring in China, 2012,28(4):74-81. (in Chinese with English abstract)
21 LIANG J , FENG C T , ZENG G M , et al . Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China. Environmental Pollution, 2017,225:681-690.
22 LIU H W , ZHANG Y , ZHOU X , et al . Source identification and spatial distribution of heavy metals in tobacco-growing soils in Shandong Province of China with multivariate and geostatistical analysis. Environmental Science & Pollution Research, 2017,24(6):5964-5975.
23 ENGLE M A , GUSTIN M S , LINDBERG S E , et al . The influence of ozone on atmospheric emissions of gaseous elemental mercury and reactive gaseous mercury from substrates. Atmospheric Environment, 2005,39(39):7506-7517.
24 WANG Y , YANG L , KONG L , et al . Spatial distribution, ecological risk assessment and source identification for heavy metals in surface sediments from Dongping Lake, Shandong, East China. Catena, 2015,125(2):200-205.
25 MARRUGO-NEGRETE J , PINEDO-HERNáNDEZ J , DíEZ S . Assessment of heavy metal pollution, spatial distribution and origin in agricultural soils along the Sinú River Basin, Colombia. Environmental Research, 2017,154:380-388.
26 杨平舒,郑平,张美琴 .浙江省富阳市东坞山银多金属矿床地质特征及控矿因素分析.矿产与地质,2010,24(1):64-69.
YANG P S , ZHENG P , ZHANG M Q . Study on geological characteristics and ore-controlling factors of Dongwushan silver polymetallic deposit in Fuyang, Zhejiang Province. 2010,24(1):64-69. (in Chinese with English abstract)
[1] 杨勇*， 张楚天， 贺立源. 基于贝叶斯最大熵的多因子空间属性预测新方法[J]. 浙江大学学报(农业与生命科学版), 2013, 39(6): 636-644.
[2] 李海光， 施加春*， 吴建军. 污染场地周边农田土壤重金属含量的空间变异特征及其污染源识别[J]. 浙江大学学报(农业与生命科学版), 2013, 39(3): 325-334.
[3] 孙华生　黄敬峰　金艳　邓睿　王杰　. 基于GIS技术的县域居民点空间分布特征分析及其优化布局[J]. 浙江大学学报(农业与生命科学版), 2007, 33(3): 348-354.
[4] 陶星名　张建英　王宇峰等. 杭州市生态敏感性及其空间分布[J]. 浙江大学学报(农业与生命科学版), 2006, 32(1): 27-30.
[5] 方慧　 邵永华　 何勇. 基于掌上电脑的农田信息空间分析系统的研究[J]. 浙江大学学报(农业与生命科学版), 2003, 29(6): 679-683.