基于贝叶斯模型的地下水风险源污染概率估计方法研究

李璐; 殷乐宜; 牛浩博; 刘伟江; 陈坚

doi:10.13198/j.issn.1001-6929.2020.05.25

基于贝叶斯模型的地下水风险源污染概率估计方法研究

doi: 10.13198/j.issn.1001-6929.2020.05.25

李璐^1,,
殷乐宜¹,
牛浩博¹,
刘伟江^2, ,,
陈坚^1, ,

1.
生态环境部环境规划院, 长江经济带生态环境联合研究中心, 北京 100012
2.
生态环境部土壤与农业农村生态环境监管技术中心, 北京 100012

基金项目:

国家水体污染控制与治理科技重大专项 2018ZX07109-001

详细信息

作者简介:
李璐(1988-), 女, 黑龙江哈尔滨人, 助理研究员, 硕士, 主要从事地下水污染防治研究, lilu@caep.org.cn

通讯作者:
刘伟江(1977-), 男, 河北秦皇岛人, 高级工程师, 硕士, 主要从事地下水污染防治研究, liuwj@caep.org.cn

陈坚(1981-), 男, 吉林长春人, 副研究员, 博士, 主要从事地下水污染防治研究, chenjian@caep.org.cn

中图分类号: X523
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-23
- 修回日期: 2020-05-11
- 刊出日期: 2020-06-25

Contamination Probability of Groundwater Risk Sources by Bayesian

LI Lu^1
,,
YIN Leyi¹,
NIU Haobo¹,
LIU Weijiang^{2
, ,},
CHEN Jian^{1
, ,}

1.
Center for Ecological Environment in Yangtze River Economic Belt, Chinese Academy of Environmental Planning, Beijing 100012, China
2.
Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China

Funds:

National Water Pollution Control and Treatment Science and Technology Major Project, China 2018ZX07109-001

摘要

摘要: 我国地下水环境风险源点多面广，但风险源周边地下水监测水平较低，尤其是在单个监测点指标异常时，监测数据异常值的来源及风险源造成污染概率的判定方面存在较大不足.为了解决此类问题，提出了基于贝叶斯模型的地下水风险源污染概率估计方法，并以石家庄市某工业集聚区下游一个农灌井中Cr⁶⁺含量和CHCl₃含量异常事件为研究案例，计算了指标异常来源于工业集聚区内8个风险源的污染概率.结果表明：①通过结合风险源的建成时间、废水排放量等软数据及对流弥散方程，优化先验概率、似然度以及后验概率求解方法，提出了基于贝叶斯模型的地下水风险源污染概率估计方法.②该工业集聚区下游农灌井中Cr⁶⁺含量和CHCl₃含量异常事件的案例应用结果显示，Cr⁶⁺含量异常来源于S6风险源的后验概率为76.2%，即Cr⁶⁺含量异常最有可能由某无机盐制造业污染源造成；CHCl₃含量异常来源于S1和S3风险源的后验概率分别为32.7%和23.6%，监测点CHCl₃含量异常最有可能由一个或两个化学农药制造业污染源造成.研究显示，建立的地下水风险源污染概率估计方法初步解决了监测数据不足时指标异常的来源识别问题，可用于未开展详细调查前地下水污染来源的快速锁定，也可使后期的地下水污染调查更具有针对性，对地下水污染风险防控具有重要科学意义.
- 地下水风险源识别 /
- 贝叶斯 /
- 污染概率
Abstract: Groundwater risk sources are widely distributed in China. However, the groundwater monitoring level around the risk sources was low. Especially when the index of a single monitoring point was abnormal, there were major deficiencies in the determination the source of the abnormal monitoring data and the probability of pollution caused by the risk source. In order to solve this problem, a method for identifying the probability of groundwater risk source based on the Bayesian formula was proposed. Taking the abnormal events of Cr⁶⁺ and CHCl₃ in an agricultural irrigation well downstream of an industrial agglomeration area in Shijiazhuang as the research object, the probability of contamination for 8 risk sources was calculated. The results showed that: (1) Through the combination of the groundwater risk source soft data and the convection dispersion equation, the posterior probability of the abnormal observation point caused by the risk source was obtained, and the identification of contamination sources with insufficient observation data in multiple groundwater risk sources was solved. (2) Based on inversion calculations of the probability of 8 different industry categories of groundwater risk sources which could be caused Cr⁶⁺ and CHCl₃ observation anomalous. The probability of the S6 was 76.2%, indicating that the anomaly of observation data was mostly caused by the salt manufacturing industry. For the abnormal values of CHCl₃, the probability of the S1 and S3 points was 32.7% and 23.6% respectively, which was mostly caused by the chemical pesticide industry. The research shows that this method can solve the problem of source identification of indicator outliers when the observation data is insufficient. It can be used to quickly identify the sources of groundwater contamination before conducting a detailed investigation. It can also make the following groundwater contamination investigation more targeted. This method has important scientific significance for the prevention and control of groundwater pollution.
- identification of groundwater risk sources /
- Bayesian /
- the probability of contamination

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图 1 地下水风险源及监测点位置及地下水位等值线示意

Figure 1. Sketch of the groundwater risk sources location, the monitoring point location and the groundwater table

下载: 全尺寸图片幻灯片

表 1 研究区域内风险源相关信息及先验概率值

Table 1. Information of the risk sources and calculation result of priori probability in the study area

风险源	行业类别	p₀(S_i)		T_i/a	废水排放量/(m³/a)	防渗措施	渗水面积/m²	L_i	Q_i	p(S_i)
风险源	行业类别	Cr⁶⁺	CHCl₃	T_i/a	废水排放量/(m³/a)	防渗措施	渗水面积/m²	L_i	Q_i	Cr⁶⁺	CHCl₃
S1	化学农药制造	0.5	0.5	22	56 000	—	—	0.8	0.4	0.16	0.16
S2	有机化学原料制造	0.5	0.5	12	68 400	—	—	0.5	0.4	0.10	0.10
S3	化学农药制造	0.5	1.0	8	17 800	—	—	0.2	0.4	0.04	0.08
S4	其他基础化学原料制造	0.5	0.5	7	18 000	—	—	0.2	0.4	0.04	0.04
S5	其他合成材料制造	0.5	0.5	19	4 000	—	—	0.5	0.2	0.05	0.05
S6	无机盐制造	1.0	0.0	41	0	有防渗措施，时间＞5 a	21 000	0.8	0.8	0.64	0.00
S7	其他基础化学原料制造	0.5	0.5	13	99 000	—	—	0.5	0.4	0.10	0.10
S8	有机化学原料制造	0.5	0.5	17	5 500	—	—	0.5	0.2	0.05	0.05

下载: 导出CSV

表 2 似然度计算结果

Table 2. Calculation result of the likelihood

风险源	α/(°)	Δh_i/m	L_i/km	p(S_i, m)/W
S1	14	7.3	2.82	0.890 7
S2	51	6.2	2.90	0.463 9
S3	18	5.3	1.98	1.285 7
S4	25	4.9	1.96	1.156 0
S5	37	4.4	1.92	0.953 2
S6	10	4.1	1.45	1.920 4
S7	64	5.4	2.85	0.291 4
S8	58	4.5	2.41	0.410 6

下载: 导出CSV

表 3 后验概率计算结果

Table 3. Calculation result of posterior probability

风险源	p(S_i)		p(S_i, m)/W	p(m, S_i)/%
风险源	Cr⁶⁺	CHCl₃	p(S_i, m)/W	Cr⁶⁺	CHCl₃
S1	0.16	0.16	0.891	8.84	32.74
S2	0.10	0.10	0.464	2.88	10.66
S3	0.04	0.08	1.286	3.19	23.63
S4	0.04	0.04	1.156	2.87	10.62
S5	0.05	0.05	0.953	2.95	10.95
S6	0.64	0.00	1.920	76.20	0.00
S7	0.10	0.10	0.291	1.81	6.69
S8	0.05	0.05	0.411	1.27	4.72

下载: 导出CSV

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