云南省主要盆地地下水水质监测与变化趋势分析

高瑜; 张华; 康晓莉; 周俊蓉; 武红梅; 刘海峰; 叶咸

doi:10.11932/karst20220404

云南省主要盆地地下水水质监测与变化趋势分析

doi: 10.11932/karst20220404

高瑜^1,,
张华^1, ,,
康晓莉¹,
周俊蓉¹,
武红梅¹,
刘海峰¹,
叶咸²

1.
自然资源部高原山地地质灾害预报预警与生态保护修复重点实验室/云南省地质环境监测院,云南昆明 650216
2.
云南省交通规划设计研究院有限公司云南省数字交通重点实验室（筹），云南昆明 650041

基金项目: 国家重点研发计划项目(2016YFC0502502)；广西岩溶动力学重大科技创新基地开放课题（KDL202101）；国家地下水监测工程（WF202000PB）

详细信息

作者简介:
高瑜（1991－），女，工程师，主要从事水文、工程、环境地质调查与研究。E-mail：993536618@qq.com

通讯作者:
张华（1982－），男，高级工程师，主要从事水文、工程、环境地质调查与研究。E-mail：ybddysghs.zhh@163.com

中图分类号: P641.7
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出版历程
- 收稿日期: 2022-03-10
- 刊出日期: 2022-08-31

Trend analysis of groundwater quality in major basins of Yunnan Province

1.
Key Laboratory of Geohazard Forecast and Geoecological Restoration in Plateau Mountainous Area Plateau mountains, MNR/Yunnan Institute of Geo-Environment Monitoring, Kunming, Yunnan 650216, China
2.
Yunnan Key Laboratory of Digital Communications, Yunnan Communications Planning and Design Institute, Kunming, Yunnan 650041, China

摘要

摘要: 云南省的高原山间盆地是重要的水源汇集区，人口聚集和经济活动中心，也是对水环境影响和变化具有指标意义的环境水文地质单元。文章阐述了云南省高原主要山间盆地地下水质监测概况，并依据多年地下水水质监测数据，按照孔隙水、裂隙水、岩溶水三种地下水类型进行了水质评价。通过数据统计法、Piper三线图、小波神经网络时间序列分析，预测云南省地下水化学特征及水质变化趋势。研究发现：地下水化学类型种类复杂多样，以HCO₃-Ca · Mg、HCO₃-Ca型为主。氨氮、锰、氟化物、硝酸根离子等含量超标率较高，是导致地下水水质超标的主要指标，不同污染指标的污染来源不同，主要为生活污染和工业污染。根据统计分析结果显示云南高原主要盆地水质总体上呈稳定趋势，针对研究结果提出了地下水环境保护的措施建议。
- 水环境 /
- 地下水质 /
- 地下水监测 /
- 污染指标 /
- 高原盆地
Abstract: The plateau intermountain basin in Yunnan Province is an important water source gathering area, a population gathering and economic activity center, as well as an environmental hydrogeological unit with index significance of the impact and change of water environment. This article reviews the general monitoring situation of groundwater quality in the main intermountain basins of Yunnan Province. Among nearly 50 basins with an area of more than 100 square kilometers each, 7 have been laid out groundwater monitoring sites, respectively in Kunming, Yuxi, Dali, Chuxiong, Qujing, Jinghong and Kaiyuan. On the basis of dynamic monitoring data for groundwater quality in these basins from 2016 to 2021, the quality of pore water, fissure water and karst water is evaluated. Besides, the groundwater chemical characteristics are analyzed and the change trend of water quality in Yunnan are predicted with data statistics, Piper trilinear diagrams and time series analyses of wavelet neural network. The simulation results of wavelet neural network show that nitrate ion concentration in Hole 152 of Kunming basin increased in a short term, but then slowed down and leveled off. The concentration of manganese ion in Hole 101 of Chuxiong showed an increasing trend, while the concentration of ammonium ion showed a gradual but continual increase in the recent 1 or 2 years after an initial decrease reached the bottom. The high fitting accuracy of wavelet neural network and the average percentage error (less than 10%) demonstrated a satisfactory prediction, which may provide reference for groundwater treatment, protection and prediction, and the building of an early warning platform.It is found that the chemical types of groundwater are complex and diverse, mainly HCO₃-Ca·Mg and HCO₃-Ca. The content of ammonia nitrogen, manganese, fluoride and nitrate ion exceeds permitted levels, which may mainly indicate the exceedance of an acceptable level of groundwater quality. Over the past six years, the rates of exceeding permitted levels of pore water quality remained stable at about 70%. The components exceeding permitted levels were mainly pH value, total hardness, iodine ion, nitrate, ammonia nitrogen, etc. As for fissure water quality, its rates of exceeding permitted levels were basically unchanged, but the proportion of Class V water was on the rise, mainly in Kunming, Chuxiong and Yuxi. During 2017 and 2019, the rates of exceeding permitted levels were relatively low, but the fissure water quality was deteriorating. The components exceeding permitted levels were mainly manganese ions, pH values, fluorides, etc. The rates of exceeding permitted levels in terms of karst water quality gradually decreased, and in dry season the rate for Class II and Class III water decreased from 23.91% to 15.22%. Components were mainly manganese ions, arsenic ions, ammonia nitrogen, etc. Different indicators show different pollution causes. Pore water may be polluted with infiltration and recharge brought by atmospheric precipitation in pore aquifer. It may also be polluted when laterally recharged by surface water and by agricultural irrigation. Consequently, the deterioration of pore water is closely connected with the discharge of urban domestic sewage, industrial “three wastes” emissions, the use of chemical fertilizer and pesticide, agricultural irrigation and others. Compared with pore water, fissure water was less affected by pollution. Its pollution was mainly caused by the high background values of red beds and metamorphic rocks. Karst water may pollute karst aquifers through low-lying karst fissures, sinkholes and groundwater drainage. According to the statistical results, the water quality in the main basins of Yunnan Plateau shows a stable trend on the whole, which can provide a scientific basis for local economic and social development, territorial space planning, and rational exploitation and effective protection of groundwater resources.
- water environment /
- groundwater quality /
- groundwater monitoring /
- pollution index /
- plateau basin

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图 1 水质监测点分布图

Figure 1. Distribution map of monitoring sites of water quality

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图 2 云南省2016年与2021年piper三线图

Figure 2. Piper trilinear diagram of Yunnan Province in 2016 and 2021

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图 3 孔隙水水质评价结果条形图

Figure 3. Bar chart of quality evaluation of pore water

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图 4 裂隙水水质评价结果条形图

Figure 4. Bar chart of quality evaluation of fissure water

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图 5 岩溶水水质评价结果条形图

Figure 5. Bar chart of quality evaluation of karst water

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图 6 水质污染影响因子变化折线图

Figure 6. Line chart of influence factors of water pollution

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图 7 昆明盆地152号孔硝酸根离子浓度预测结果相关性分析

Figure 7. Correlation analysis of prediction results of nitrate ion concentration in Hole 152 of Kunming basin

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图 8 楚雄101号孔锰离子浓度预测结果相关性分析

Figure 8. Correlation analysis of prediction results of manganese ion concentration in Hole 101 of Chuxiong

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图 9 楚雄101号孔铵根离子浓度预测结果相关性分析

Figure 9. Correlation analysis of prediction results of ammonium ion concentration in Hole 101 of Chuxiong

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表 1 云南省地下水监测站点分布情况表

Table 1. Distribution of groundwater monitoring sites in Yunnan Province

州市	数量	监测项目分类/个
州市	数量	水位	流量	水质	水温	流量、水温共用	水位、水质共用	流量、水质共用	水位、水温共用
昆明	110	68	9	50	14	9	11	4	5
玉溪	12	7	3	6	3	3	1	3	0
开远	21	11	3	5	3	3	0	0	0
大理	11	7	2	5	2	2	1	2	0
楚雄	11	9	1	5	1	1	3	1	0
曲靖	10	6	2	5	2	2	1	2	0
景洪	8	5	2	3	2	2	1	1	0
合计	183	113	22	79	27	22	18	13	5

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表 2 昆明盆地152号孔硝酸根离子浓度预测结果误差分析表

Table 2. Error analysis of prediction results of nitrate ion concentration in Hole 152 of Kunming basin

实际值/ mg·L⁻¹	预测值/ mg·L⁻¹	偏差/ %	平均百分比误差/%	均方根误差
7.974 032	7.836 001	−6.90	7.33	0.16
7.898 648	7.856 258	−2.12
7.599 509	7.661 768	3.11
7.029 46	7.195 764	8.32
6.232 3	6.439 032	10.34
5.279 58	5.494 991	10.77
4.242 854	4.494 204	12.57
3.193 681	3.529 283	16.78
2.203 616	2.663 838	23.01
1.344 214	1.882 355	26.91
0.687 033	1.181 627	24.73
0.303 629	0.635 833	16.61
0.265 557	0.334 812	3.46
0.644 374	0.377 119	−13.36
1.511 637	0.890 394	−31.06
1.75	2.030 851	14.04

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表 3 楚雄101号孔锰离子浓度预测结果误差分析表

Table 3. Error analysis of prediction results of manganese ion concentration in Hole 101 of Chuxiong

实际值 mg·L⁻¹	预测值 mg·L⁻¹	偏差/ %	平均百分比误差/%	均方根误差
0.239 386	0.239 68	0.12	1.84	0.05
0.219 571	0.223 052	1.59
0.197 511	0.203 475	3.02
0.177 614	0.185 628	4.51
0.164 399	0.173 083	5.28
0.162 388	0.167 336	3.05
0.176 101	0.175 076	−0.58
0.210 056	0.200 816	−4.40
0.268 775	0.251 975	−6.25
0.356 778	0.342 704	−3.94
0.478 584	0.487 297	1.82
0.638 714	0.697 465	9.20
0.841 688	0.930 564	10.56

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表 4 楚雄101号孔铵根离子浓度预测结果误差分析表

Table 4. Error analysis of prediction results of ammonium ion concentration in Hole 101 of Chuxiong

实际值 /mg·L⁻¹	预测值 /mg·L⁻¹	偏差 /%	平均百分比误差/%	均方根误差
2.062 402	2.075 19	0.62	0.35	0.01
2.100 669	2.102 612	0.09
2.075 476	2.067 837	−0.37
2.000 217	1.983 028	−0.86
1.889 264	1.880 876	−0.44
1.756 986	1.763 891	0.39
1.617 755	1.632 051	0.88
1.485 943	1.499 617	0.92
1.375 92	1.382 616	0.49
1.302 058	1.297 05	−0.38
1.278 726	1.259 742	−1.48
1.320 298	1.290 263	−2.27
1.441 143	1.410 574	−2.12

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