Prediction of Regional Groundwater Quality Evolution Affected by Human Activities: A Case Study of Shijiazhuang Area

doi:10.19657/j.geoscience.1000-8527.2020.006

Abstract

Abstract:

Prediction of groundwater quality evolution has always been a difficult issue, with its implications largely limited by the sole dependence of historical data for the evaluation. Taking Shijiazhuang area as the stu-dy area, we put forward a method to revise the water quality evolution trend shown by historical data, and predict the water quality change by using the degree of current human activity influence on groundwater as a correction factor. The four major indices of shallow groundwater at Shijiazhuang (i.e., total hardness, TDS, $NO_3^-$ and Fe) were used as prediction indices for the evolution of representative indices in 2025. The results show that the water quality deterioration areas for TDS and total hardness are mainly in the southeastern part of the Lingshou County, Gaocheng District and Wuji County near the Hutuo River. The deterioration areas for Fe are mainly distributed in the central and southeastern parts of Shijiazhuang City, and those for $NO_3^-$ are near the junction of Luquan District and Lingshou County, northwestern Shijiazhuang City and around the Hutuo River in Gaocheng District. The predicted results are in good agreement with the hydrogeological conditions, the man-made pollution sources and groundwater extraction in the study area.

Key words: Shijiazhuang area, human activity influence, water quality prediction

CLC Number:

P641.6

SHAN Xiaojie, HE Jiangtao, ZHANG Xiaowen, SUN Jichao. Prediction of Regional Groundwater Quality Evolution Affected by Human Activities: A Case Study of Shijiazhuang Area[J]. Geoscience, 2020, 34(01): 189-198.

Figures/Tables 7

Fig.1 Schematic framework of the water quality prediction method

Fig.2 Hydrogeological sketch map of the study area (modified after Zhang et al.[27])

Fig.3 Water quality comprehensive assessment of the study area in 2007 and 2016

Fig.4 Basic element map of total hardness water quality prediction

Fig.5 Water quality classification map for each indicator of current situation (2016) and prediction (2025) in the study area

Fig.6 Basic element maps of the human activity influence on groundwater quality

Fig.7 Maps showing the water quality change for each indicator in the study area

References 32

[1]	向速林, 刘占孟, 马丽平. 地下水水质预测的多元线性回归分析模型研究[J]. 贵州科学, 2006(1):60-62.
[2]	FURLONG B V, RILEY M S, HERBERT A W, et al. Using regional groundwater flow models for prediction of regional well water quality distributions[J]. Journal of Hydrology , 2011,398(1/2):1-16.
[3]	郭庆春, 何振芳, 李力. 人工神经网络模型在黄河水质预测中的应用[J]. 人民黄河, 2011(10):42-43.
[4]	吴吉春, 薛禹群, 谢春红. 越流含水层系统中的溶质运移方程[J]. 水文地质工程地质, 1998,25(1):30-31.
[5]	YI Q, STEWART M. Prediction of groundwater quality trends resulting from anthropogenic changes in southeast Florida[J]. Groundwater, 2018,56(1):46-61.
[6]	王健, 向峰, 邱飞, 等. 水质预测模型研究进展[J]. 环境科学导刊, 2018,37(4):63-67.
[7]	孙兆兵. 基于概率组合的水质预测方法研究[D]. 杭州:浙江大学, 2012.
[8]	JOSHI B P, KUMAR S. Intuitionistic fuzzy sets based method for fuzzy time series forecasting[J]. Cybernetics and Systems: An International Journal, 2012,43(1):34-47.
[9]	MOKARRAM M. Modeling of multiple regression and multiple linear regressions for prediction of groundwater quality (case study: north of Shiraz)[J]. Modeling Earth Systems and Environment, 2016,2(1):3.
[10]	王志广, 黄常晋. 灰色系统理论在岩溶地下水水质污染变化趋势评价中的应用——以桂林市轮胎厂供水井岩溶地下水总硬度变化预测为例[J]. 中国岩溶, 1992(3):42-49.
[11]	王元贞. 沈阳地区地下水污染特征及应用灰色系统理论进行水质预测的研究[J].辽宁地质学报, 1992(2):110-120.
[12]	YILMA M, KIFLIE Z, WINDSPERGER A, et al. Application of artificial neural network in water quality index prediction: a case study in Little Akaki River, Addis Ababa, Ethiopia[J]. Modeling Earth Systems and Environment, 2018,4(1):175-187.
[13]	黄胜伟, 董曼玲. 自适应变步长BP神经网络在水质评价中的应用[J]. 水利学报, 2002,33(10):119-123.
[14]	张小文, 何江涛, 彭聪, 等. 地下水主要组分水化学异常识别方法对比: 以柳江盆地为例[J]. 环境科学, 2017,38(8):3225-3234.
[15]	廖磊. 柳江盆地浅层地下水次要组分和微量组分视背景值研究[D]. 北京:中国地质大学(北京), 2016.
[16]	张人权, 梁杏, 靳孟贵, 等. 水文地质学基础[M]. 第6版.北京: 地质出版社, 2011.
[17]	李培月. 人类活动影响下地下水环境研究[D]. 西安:长安大学, 2014.
[18]	纪轶群, 辛宝东, 郭高轩, 等. 空间插值法在区域地下水质量评价中的应用改进研究[J].工程勘察, 2012(6):42-47, 91.
[19]	MIRZAEI R, SAKIZADEH M. Comparison of interpolation methods for the estimation of groundwater contamination in Andimeshk-Shush Plain, Southwest of Iran[J]. Environmental Science and Pollution Research, 2016,23(3):2758-2769.
[20]	ASHRAF S H, AFSHARI H. Assessment of groundwater chemical quality, using inverse distance weighted method[J]. Journal of Chemical Health Risks, 2014,3(4):1-5.
[21]	蔡文静, 张延平. 基于ArcGIS的DRASTIC 法地下水脆弱性评价应用研究[J]. 地下水, 2017,39(1):18-20.
[22]	于奭, 孙平安, 杜文越, 等. 人类活动影响下水化学特征的影响: 以西江中上游流域为例[J]. 环境科学, 2015,36(1):72-79.
[23]	王焰新, 马腾, 郭清海, 等. 地下水与环境变化研究[J]. 地学前缘, 2005,12(增):14-21.
[24]	彭聪, 何江涛, 廖磊, 等. 应用水化学方法识别人类活动对地下水水质影响程度:以柳江盆地为例[J]. 地学前缘, 2017,24(1):321-331.
[25]	PENG C, HE J T, WANG M L, et al. Identifying and assessing human activity impacts on groundwater quality through hydrogeochemical anomalies and NO₃^-, NH₄⁺, and COD contamination: a case study of the Liujiang River Basin, Hebei Province, P.R. China [J]. Environmental Science Pollution Research Internal, 2017,2:1-18.
[26]	严明疆, 张光辉, 徐卫东. 石家庄市地下水脆弱性评价[J]. 西北地质, 2005,38(3):105-110.
[27]	张兆吉, 费宇红. 华北平原地下水可持续利用图集[M]. 北京: 中国地图出版社, 2009.
[28]	REIMANN C, FILZMOSER P, GARRETT R G. Background and threshold: critical comparison of methods of determination[J]. Science of The Total Environment, 2005,346:1-16.
[29]	张小文, 何江涛, 刘丹丹, 等. 滹沱河冲洪积扇浅层地下水水质外界胁迫作用分析[J]. 水文地质工程地质, 2018,45(5):54-62.
[30]	王贺, 谷洪彪, 迟宝明, 等. 柳江盆地浅层地下水硝酸盐分布特征及影响因素分析[J]. 环境科学, 2016,37(5):1699-1706.
[31]	赵红梅, 赵华, 毛洪亮, 等. 华北平原滹沱河冲洪积扇第四纪地层划分[J]. 地层学杂志, 2014,38(2):137-146.
[32]	朱锦旗, 王彩会, 陆徐荣, 等. 苏锡常地区浅层地下水铁锰离子分布规律及成因分析[J]. 水文地质工程地质, 2006,33(3):30-33,37.