| 南昌市浅层地下水水质评价及监测指标优化 |
| 摘要点击 1804 全文点击 454 投稿时间：2022-08-01 修订日期：2022-09-27 |
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| 中文关键词 地下水水质 熵权水质指数(EWQI) 逐步多元线性回归 关键指标 优化方法 |
| 英文关键词 groundwater quality entropy-water quality index(EWQI) stepwise multiple linear regression key indicators optimization method |
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| 中文摘要 |
| 地下水环境监测过程中保证地下水水质评价代表性的同时尽量优化监测指标数量，对地下水环境管理具有重要的意义.选取南昌市地区2014年和2019年浅层地下水监测数据，运用统计分析、Piper三线图和熵权水质指数（EWQI）分析水化学特征及水质变化，并耦合逐步多元线性回归分析构建基于水质评价的关键指标优化方法，评估此方法应用的可行性.结果表明，南昌市地下水2014年和2019年水化学类型主要为HCO3-Ca型；pH值、NO3-、I-、Fe和Mn等5项超标指标是水质变化的主要影响因子；2019年水质状况总体上高于2014年，基本为"中等"水质，二者EWQI平均值分别为53.72和82.34；基于关键指标优化方法构建的最优模型EWQImin-4能够较好地代表实际的EWQI，关键指标包括Mn、NO3-、TH、Fe、pH值和I-，其决定系数（R2）和百分比误差（PE）值分别为0.865和10.61%.因此，基于熵权水质指数构建的地下水监测指标优化方法可作为优化指标重要参考，为区域地下水环境管理提供技术方法. |
| 英文摘要 |
| In the process of groundwater environmental monitoring, while ensuring the representativeness of groundwater quality evaluation, the number of monitoring indicators should be optimized as much as possible, which is of great significance to groundwater environmental management. Based on monitoring data of shallow groundwater in Nanchang in 2014 and 2019, the chemical characteristics of water and the changes in water quality were analyzed via statistical analysis, a Piper three-line diagram, and the entropy-weighted water quality index (EWQI). Furthermore, a key indicator optimization method based on water quality evaluation was constructed by coupling stepwise multiple linear regression analysis. The feasibility of this method was also evaluated. The results showed that the water chemistry type of groundwater in 2014 and 2019 was mainly HCO3-Ca, and the five abnormal indicators pH value, NO3-, I-, Fe, and Mn were the main influencing factors of water quality change. The water quality in 2019 was generally higher than that in 2014, which was considered as overall "moderate," and the average EWQI values of the two years were 53.72 and 82.34, respectively. The optimal model EWQImin-4 constructed based on the key indicator optimization method could better represent the actual EWQI; the key indicators included Mn, NO3-, TH, Fe, pH value, and I-; and the determination coefficient (R2) and percentage error (PE) values were 0.865 and 10.61%, respectively. Therefore, the optimization method of groundwater monitoring indicators based on entropy-weighted water quality evaluation could be used as an important reference for optimizing monitoring indicators and provide a method for regional groundwater environmental management. |
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