基于PCA-SOM的北京市平谷区地下水污染溯源

林斯杰; 齐永强; 杨梦曦; 杨庆; 杨梦凡; 刘毅; 胡清

doi:10.13198/j.issn.1001-6929.2020.05.28

基于PCA-SOM的北京市平谷区地下水污染溯源

doi: 10.13198/j.issn.1001-6929.2020.05.28

林斯杰^{1, 2, 4,},
齐永强^{1, 4},
杨梦曦¹,
杨庆³,
杨梦凡^{1, 4},
刘毅²,
胡清^1, ,

1.
南方科技大学环境科学与工程学院, 广东深圳 518055
2.
清华大学环境学院, 北京 100091
3.
北京市地质矿产勘查院, 北京 100195
4.
北京环丁环保大数据研究院, 北京 100083

基金项目:

国家水体污染控制与治理科技重大专项 2018ZX07109-002

详细信息

作者简介:
林斯杰(1985-), 男, 福建厦门人, linsj@sustech.edu.cn

通讯作者:
胡清(1964-), 女, 北京人, 教授, 博士, 博导, 主要从事土壤地下水污染防治与修复和生态环境大数据研究, huq@sustech.edu.cn

中图分类号: X523
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-31
- 修回日期: 2020-04-29
- 刊出日期: 2020-06-25

Source Analysis of Groundwater Pollution in Pinggu District of Beijing Using PCA-SOM

LIN Sijie^{1, 2, 4
,},
QI Yongqiang^{1, 4},
YANG Mengxi¹,
YANG Qing³,
YANG Mengfan^{1, 4},
LIU Yi²,
HU Qing^{1
, ,}

1.
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2.
School of Environment, Tsinghua University, Beijing 100091, China
3.
Beijing Geological and Mineral Exploration Institute, Beijing 100195, China
4.
Beijing Huanding Environmental Big Data Institute, Beijing 100083, China

Funds:

National Major Science and Technology Program for Water Pollution Control and Treatment, China 2018ZX07109-002

摘要

摘要: 为了解北京市平谷区地下水污染物来源，以平谷区2010—2018年监测数据为基础，使用PCA(主成分分析法)识别了地下水水质指标因子，使用自组织映射识别了污染物的空间分布.结果表明：通过监测指标间的Pearson检验发现, 平谷区地下水电导率与ρ(Ca²⁺)(p=0.936)、总碱度与ρ(HCO₃^2-)(p=0.981)、ρ(Mg²⁺)与总硬度(p=0.944)指标之间显著相关.地下水化学类型主要以HCO₃-Ca型为主，其次为HCO₃-Mg型.NH₄⁺、SO₄^2-、Cd、Fe(Ⅱ)、NO₂指标空间分布离散性和差异性较大，存在局部富集现象.通过因子分析法筛选出影响平谷区地下水水质的8个公因子，首要影响因子为溶滤-富集作用(贡献率为22.398%)，次要影响因子为农业、养殖业和填埋场等人为活动作用(贡献率为16.533%)，雨水下渗作用(贡献率为8.035%)、工业源人为活动(贡献率为7.466%)对地下水也有一定影响.通过比较各指标的SOM(Self-Organizing Map，自组织映射)特征图像和监测井映射特征图像，发现NH₄⁺受山前地带林业、种植业和平原地带农业、养殖业的双重影响，Na⁺、Mn受平原地带人为活动的影响；同时，NH₄⁺、NO₃^-、NO₂三者之间及Fe(Ⅱ)与Fe(Ⅲ)之间来源不同，Cd、Al、氰化物三者具有同一来源.研究显示，PCA-SOM(PCA与SOM相结合)可以对地下水化学组分来源进行定性识别与定量分析.
- 自组织映射(SOM) /
- 地下水污染溯源 /
- 主成分分析法(PCA)
Abstract: In order to understand the source of groundwater pollutants in Pinggu District of Beijing, based on the monitoring data of Pinggu District from 2010 to 2018, this study used PCA to identify the groundwater quality index factors, and identified the spatial distribution of pollutants using SOM to map the pollution index and the location of the monitoring wells and compare mapping feature by comparison images. The Pearson test between monitoring indicators showed that there was a significant correlation between electroconductibility and ρ(Ca²⁺)(p=0.936), total alkalinity and ρ(HCO₃^2-) (p=0.981), ρ(Mg²⁺) and total hardness (p=0.944) in Pinggu District groundwater, and the chemical type was mainly HCO₃-Ca, followed by HCO₃-Mg type. The spatial distribution of NH₄⁺, SO₄^2-, Cd, Fe(Ⅱ) and NO₂ was very discrete and different, and there was local enrichment. Eight common factors affecting groundwater quality in Pinggu District were screened out by factor analysis. The primary impact factor was leaching-beneficiation (contribution rate 22.398%), and the secondary impact factors were human activities such as agriculture, aquaculture, and landfills (contribution rate of 16.533%), rainwater seepage (contribution rate of 8.035%), and industrial source. Human activities (contribution rate of 7.466%) also had a certain effect on its groundwater. By comparing the SOM mapping characteristic images of various indicators, NH₄⁺ was affected by the dual effects of forestry, plantation, and agricultural production in the plain area. The characteristic images of the Na⁺, Mn response area and the plain area coincide, showing the influence of human activities. At the same time, NH₄⁺-NO₃^--NO₂, Fe(Ⅱ)-Fe(Ⅲ) were not from the same source, and Cd, Al, and cyanide had the same source. The results show that PCA and SOM can be used to identify and analyze the chemical components of groundwater.
- Self-Organizing Map (SOM) /
- source analysis of groundwater pollution /
- PCA

HTML全文

图 1 平谷区浅层地下水污染监测井位置和岩层富水性分布^[6]

注：极富水区，岩性为卵石含漂石；富水区，岩性为砾卵石；中等富水区，岩性为卵砾石夹粗砂；弱富水区，岩性为砂、卵石夹中粗砂；极弱富水区，岩性为粗中砂夹卵砾石.

Figure 1. Location of shallow groundwater pollution monitoring wells and water abundance of rock stratum in Pinggu District^[6]

下载: 全尺寸图片幻灯片

图 2 主要监测指标映射到SOM上的特征图谱

Figure 2. Result of main components in SOM picture

下载: 全尺寸图片幻灯片

图 3 监测井映射到SOM上的特征图谱

Figure 3. SOM pictures of all wells

下载: 全尺寸图片幻灯片

图 4 研究区域NH₄⁺、NO₃^-和NO₂污染物空间分布

Figure 4. Spatial distribution of NH₄ ⁺, NO₃^- and NO₂ pollutants in the study area

下载: 全尺寸图片幻灯片

表 1 平谷区地下水化学成分统计表

Table 1. Statistical parameters of groundwater chemical component in Pinggu District

指标	最小值/(mg/L)	最大值/(mg/L)	变异系数	GB/T 14848—2017《地下水质量标准》Ⅲ类标准限值/(mg/L)
ρ(K⁺)	0.050	20.600	0.757	—
ρ(Na⁺)	3.710	116.000	0.785	200
ρ(Ca²⁺)	15.000	430.000	0.469	—
ρ(Mg²⁺)	2.400	329.900	0.623	—
ρ(NH₄⁺)	0.019	9.280	3.402	0.50
ρ(HCO₃^-)	140.000	720.000	0.335	—
ρ(CO₃^2-)	0	48.000	1.704	—
ρ(Cl^-)	0.400	274.000	1.500	250
ρ(SO₄^2-)	0.300	1 415.000	2.967	250
ρ(F^-)	0.049	4.600	0.855	1.0
ρ(NO₃^-)	0.049	373.000	1.484	20.0
ρ(CO₂)	0	36.100	1.416	—
ρ〔Fe(Ⅱ)〕	0.004	25.400	2.700	0.3
ρ〔Fe(Ⅲ)〕	0	15.200	2.317	0.3
ρ(Cr⁶⁺)	0.001	0.021	1.163	0.05
ρ(Cu)	0	0.008	0.751	1.00
ρ(Zn)	0.001	1.500	1.729	1.00
ρ(Cd)	0	0.008	5.021	0.005
ρ(Se)	0	0.001	0.887	0.01
ρ(Mn)	0.001	4.930	1.779	0.10
ρ(Al)	0.001	2.410	1.966	0.20
ρ(As)	0.001	0.147	1.873	0.01
ρ(Hg)	0	0	0.121	0.001
ρ(VP)	0.001	0.010	0.739	0.002
ρ(CN)	0.001	0.004	0.162	0.05
ρ(NO₂)	0.001	15.700	5.836	1.00
ρ(DO)	0.080	9.980	1.517	1.00
总硬度	153.000	2 267.000	0.506	450
ρ(TDS)	128.750	3 044.000	0.439	1 000
注：加粗体为变异系数大于2的指标.

下载: 导出CSV

表 2 主要因子的特征值和累计方差贡献率

Table 2. Characteristic values and variance contribution rates of main factors

成分	初始特征值			提取载荷平方和			旋转载荷平方和
成分	总计	方差百分比/%	累计方差贡献率/%	总计	方差百分比/%	累计方差贡献率/%	总计	方差百分比/%	累计方差贡献率/%
1	9.312	32.109	32.109	9.312	32.109	32.109	6.495	22.398	22.398
2	3.007	10.369	42.478	3.007	10.369	42.478	4.795	16.533	38.931
3	2.097	7.230	49.708	2.097	7.230	49.708	2.330	8.035	46.967
4	1.668	5.751	55.459	1.668	5.751	55.459	2.165	7.466	54.433
5	1.480	5.104	60.563	1.480	5.104	60.563	1.677	5.784	60.217
6	1.136	3.916	64.479	1.136	3.916	64.479	1.155	3.982	64.198
7	1.029	3.547	68.026	1.029	3.547	68.026	1.093	3.768	67.966
8	1.022	3.524	71.550	1.022	3.524	71.550	1.039	3.584	71.550

下载: 导出CSV

表 3 旋转因子荷载矩阵

Table 3. Loading matrix of rotated factors

指标	公因子
指标	F₁	F₂	F₃	F₄	F₅	F₆	F₇	F₈
ρ(K⁺)	0.196	0.400	-0.139	-0.286	0.021	-0.091	-0.483	-0.090
ρ(Na⁺)	0.270	0.708	0.162	0.428	0.095	-0.022	-0.021	-0.055
ρ(Ca²⁺)	0.865	0.304	0.218	-0.105	-0.018	0.070	-0.045	-0.053
ρ(Mg²⁺)	0.947	0.178	0.053	0.030	-0.004	0.009	0.040	0.036
ρ(NH₄⁺)	0.082	0.707	0.182	0.036	0.050	-0.013	-0.184	0.041
ρ(HCO₃^-)	0.352	0.576	0.337	0.562	0.053	0.004	0.049	-0.024
ρ(CO₃^2-)	-0.112	-0.023	-0.852	0.031	-0.070	0.116	-0.097	0.023
ρ(Cl^-)	0.469	0.694	0.175	-0.218	0.045	0.017	-0.013	-0.035
ρ(SO₄^2-)	0.947	-0.113	-0.010	-0.078	-0.020	-0.028	-0.031	-0.021
ρ(F^-)	-0.047	-0.166	-0.135	0.620	-0.067	-0.036	-0.081	-0.050
ρ(NO₃^-)	0.224	-0.254	0.108	-0.674	-0.102	0.195	-0.086	0.102
ρ(CO₂)	0.202	0.531	0.610	-0.155	-0.007	0.090	-0.098	-0.010
ρ〔Fe(Ⅱ)〕	0.021	0.176	0.082	0.135	0.842	-0.027	0.019	0.013
ρ〔Fe(Ⅲ)〕	-0.022	-0.019	-0.060	-0.174	0.162	0.599	0.178	-0.477
ρ(Cr⁶⁺)	-0.109	-0.093	0.087	-0.374	-0.075	-0.446	0.060	-0.049
ρ(Mn)	0.334	0.761	0.204	0.247	0.040	0.015	0.040	-0.017
ρ(Al)	0.015	-0.011	0.010	-0.031	0.145	-0.666	0.102	-0.195
ρ(As)	-0.023	-0.006	0.006	0.593	0.031	0.170	0.034	0.036
ρ(Hg)	-0.021	0.025	-0.019	-0.096	0.031	0.139	0.056	0.828
ρ(VP)	0.140	0.258	0.074	-0.068	0.032	-0.106	0.749	-0.015
ρ(CN)	0.533	-0.157	-0.125	-0.028	0.234	-0.096	0.288	0.128
ρ(NO₂)	0.076	0.717	-0.064	-0.105	-0.043	0.055	0.253	-0.105
ρ(DO)	0.707	0.556	0.033	-0.055	0.014	-0.010	0.111	-0.047
总硬度	0.947	0.250	0.142	-0.040	-0.013	0.043	-0.003	-0.008
pH	-0.187	-0.289	-0.860	0.094	-0.015	0.017	-0.056	-0.028
EC	0.850	0.426	0.202	-0.006	0.001	0.044	-0.032	-0.028
色度	0.088	0.583	0.062	-0.006	0.077	0.027	0.096	0.142
浊度	0.005	-0.014	0.005	-0.055	0.900	-0.007	0.011	-0.018
ρ(TDS)	0.887	0.327	0.195	0.092	0.005	0.029	0.001	-0.010
注：加粗体为荷载因子矩阵进行正交旋转后得分较高的指标.

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基于PCA-SOM的北京市平谷区地下水污染溯源

doi: 10.13198/j.issn.1001-6929.2020.05.28

作者简介:
林斯杰(1985-), 男, 福建厦门人, linsj@sustech.edu.cn

通讯作者:
胡清(1964-), 女, 北京人, 教授, 博士, 博导, 主要从事土壤地下水污染防治与修复和生态环境大数据研究, huq@sustech.edu.cn

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Source Analysis of Groundwater Pollution in Pinggu District of Beijing Using PCA-SOM

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基于PCA-SOM的北京市平谷区地下水污染溯源

doi: 10.13198/j.issn.1001-6929.2020.05.28

作者简介: 林斯杰(1985-), 男, 福建厦门人, linsj@sustech.edu.cn

通讯作者: 胡清(1964-), 女, 北京人, 教授, 博士, 博导, 主要从事土壤地下水污染防治与修复和生态环境大数据研究, huq@sustech.edu.cn

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Source Analysis of Groundwater Pollution in Pinggu District of Beijing Using PCA-SOM

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出版历程

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作者简介:
林斯杰(1985-), 男, 福建厦门人, linsj@sustech.edu.cn

通讯作者:
胡清(1964-), 女, 北京人, 教授, 博士, 博导, 主要从事土壤地下水污染防治与修复和生态环境大数据研究, huq@sustech.edu.cn