Ionic composition variation characteristics and regression analysis of spring water in Jinan City
-
摘要: 以济南市城区泉水SO4 2−、NO3 −、Cl−为研究对象,从降水、补源、人类活动3个维度选取影响因子,研究影响因子与3种离子浓度的相关性;以通径分析判定影响因子的直接作用和间接作用,通过计算决策系数判定影响因子对离子浓度变化的作用大小和方向;通过建立的回归方程预测泉水NO3 −浓度和环境可承载的废水排放总量。结果表明:2008—2019年,城区泉水SO4 2−、Cl−、NO3 −浓度均呈上升趋势,SO4 2−和Cl−浓度为显著上升(P<0.01)。废水排放总量与SO4 2−、Cl−浓度呈显著正相关(P<0.01,P<0.05),相关系数分别为0.811和0.577;补源水库H+浓度与NO3 −浓度呈显著正相关(P<0.05),相关系数为0.692。各影响因子中,对泉水SO4 2−、NO3 −、Cl−浓度直接作用最大的均为废水排放总量。废水排放总量在SO4 2−、Cl−浓度变化过程中起增进作用;水库H+浓度、降水量、废水排放总量在泉水NO3 −浓度变化过程中起增进作用,水库H+浓度为主要决策变量。通过方程预测2020年泉水NO3 −浓度为8.42 mg/L,满足GB/T 14818—2017《地下水质量标准》Ⅲ类标准。如将泉水NO3 −浓度保持在10 mg/L以下,废水排放总量应控制在6.324×109 m3以内。Abstract: Taking SO4 2−, NO3 −, Cl− in spring water in Jinan City as the research objects, the influence factors from three aspects of precipitation, source supplement and human activities were selected, and the correlation between the influence factors and the concentrations of the three ions was studied. The path analysis was used to determine the direct and indirect effects of the influencing factors. The action size and direction of the influencing factors on the change of ion concentrations were determined by calculating the decision coefficient. The regression equation was established and used to predict the concentration of NO3 − in spring water and the total amount of wastewater that could be carried by environment. The results showed that the concentration of SO4 2−, Cl− and NO3 − in spring water showed an upward trend, and SO4 2− and Cl− increased significantly (P < 0.01) from 2008 to 2019. There was a significant positive correlation between the amount of wastewater and the concentration of SO 4 2− and Cl− (P < 0.01, P < 0.05), and the correlation coefficients were 0.811 and 0.577, respectively. There was a significant positive correlation between the concentration of H + and NO3 − in source supplement reservoir (P < 0.05), and the correlation coefficient was 0.692. Among the influencing factors, the amount of wastewater had the greatest direct effect on the concentration of SO 4 2−, Cl− and NO3 − in spring water. The amount of wastewater played an increasing role in the change of SO4 2− and Cl− concentrations. Precipitation, H+ concentration and the amount of wastewater in reservoir played an increasing role in NO3 − concentration change of spring water , and H+ in reservoir was the main decision variable. The equation predicted that NO3 − concentration of spring water would be 8.42 mg/L in 2020, meeting Class Ⅲ standard of Quality Standard for Ground Water (GB/T 14818-2017). If NO3 − concentration of spring water kept below 10 mg/L, the total amount of wastewater could be controlled within 6324 million cubic meters.
-
Key words:
- spring water /
- ionic composition /
- influence factors /
- correlation analysis /
- multiple regression analysis /
- Jinan City
-
表 1 研究选取的影响因子
Table 1. Influence factors selected in the research
影响因子 泉水SO4 2−浓度 泉水NO3−浓度 泉水Cl−浓度 降水 降水量 √ √ √ 降水SO4 2−浓度 √ 降水NO3 −浓度 √ 降水Cl−浓度 √ 补源 水库H+浓度 √ √ √ 水库SO4 2−浓度 √ 水库NO3 −浓度 √ 水库Cl−浓度 √ 人类活动 废水排放总量 √ √ √ SO2排放总量 √ NOx排放总量 √ 表 2 2008—2019年泉水离子浓度测定结果
Table 2. Measurement results of ion concentrations in spring water from 2008 to 2019
mg/L 项目 SO4 2− NO3 − Cl− 均值 76.65 8.31 46.44 最小值 62.10 6.85 37.50 最大值 96.01 10.26 64.06 Ⅰ类标准 ≤50 ≤2.0 ≤50 Ⅱ类标准 ≤150 ≤5.0 ≤150 Ⅲ类标准 ≤250 ≤20.0 ≤250 表 3 2008—2019年降水、补源、人类活动指标结果
Table 3. Index results of precipitation, source supplement and human activities from 2008 to 2019
项目 降水 补源 人类活动 降水量/
mmSO4 2−浓度/
(mg/L)NO3 −浓度/
(mg/L)Cl−浓度/
(mg/L)H+浓度/
(10−6mg/L)SO4 2−浓度/
(mg/L)NO3 −浓度/
(mg/L)Cl−浓度/
(mg/L)废水排放总量/
109 m3SO2排放总量/
104 tNOx排放总量/
104 t均值 684.29 15.89 8.89 3.40 6.04 109.95 5.21 33.40 3.44 7.81 8.16 最小值 520.50 10.00 4.10 0.86 3.01 89.22 2.53 22.32 2.40 1.14 2.33 最大值 880.00 27.75 14.33 7.62 12.7 152.42 11.43 67.90 4.20 12.06 11.68 表 4 Spearman相关系数计算结果
Table 4. Calculation results of Spearman correlation coefficient
项目 年份 泉水SO4 2−浓度 泉水NO3 −浓度 泉水Cl−浓度 年份 1.000 泉水SO4 2−浓度 0.825** 1.000 泉水NO3 −浓度 0.252 0.364 1.000 泉水Cl−浓度 0.797** 0.867** 0.042 1.000 注:**表示在置信度(双测)为 0.01 时,显著相关;*表示在置信度(双测)为 0.05 时,显著相关。全文同。 表 5 影响因子与泉水 SO4 2−浓度相关性分析结果
Table 5. Correlation analysis results of influence factors and SO4 2− concentration in spring water
项目 泉水SO4 2−浓度 降水量 降水SO4 2−浓度 水库H+浓度 水库SO4 2−浓度 废水排放总量 SO2排放总量 泉水SO4 2−浓度 1.000 降水量 −0.011 1.000 降水SO4 2−浓度 −0.269 −0.076 1.000 水库H+浓度 −0.198 −0.477 0.385 1.000 水库SO4 2−浓度 0.514 0.570 −0.185 −0.438 1.000 废水排放总量 0.811** 0.195 0.073 −0.011 0.580* 1.000 SO2排放总量 −0.383 −0.317 0.630* 0.597* −0.466 −0.350 1.000 表 6 影响因子与泉水NO3 −浓度相关性分析结果
Table 6. Correlation analysis results of influence factors and NO3 − concentration in spring water
项目 泉水NO3 −浓度 降水量 降水NO3 −浓度 水库H+浓度 水库NO3 −浓度 废水排放总量 NOx排放总量 泉水NO3 −浓度 1.000 降水量 −0.570 1.000 降水NO3 −浓度 0.486 −0.018 1.000 水库H+浓度 0.692* −0.477 0.067 1.000 水库NO3 −浓度 −0.243 0.086 −0.332 −0.091 1.000 废水排放总量 0.483 0.195 0.746** −0.011 −0.363 1.000 NOx排放总量 0.111 0.012 −0.240 0.493 0.116 −0.301 1.000 表 7 影响因子与泉水Cl−浓度相关性分析结果
Table 7. Correlation analysis results of influence factors and Cl− concentration in spring water
项目 泉水Cl−浓度 降水量 降水Cl−浓度 水库H+浓度 水库Cl−浓度 废水排放总量 泉水Cl−浓度 1.000 降水量 0.062 1.000 降水Cl−浓度 0.109 0.444 1.000 水库H+浓度 −0.473 −0.477 −0.466 1.000 水库Cl−浓度 0.205 0.594* 0.543 −0.494 1.000 废水排放总量 0.577* 0.195 0.445 −0.011 0.345 1.000 表 8 各影响因子与泉水离子浓度通径分析结果
Table 8. Results of path analysis between influence factors and ion concentration in spring water
泉水
离子浓度影响因子 Pearson相关系数 直接通径系数 间接通径系数 决策系数 降水量 水库H+浓度 废水排放总量 合计 SO4 2− 废水排放总量 0.811 0.811 0.657 Cl− 废水排放总量 0.577 0.577 0.333 NO3 − 降水量 −0.570 −0.452 −0.230 0.112 −0.118 0.311 水库H+浓度 0.692 0.482 0.216 −0.006 0.210 0.435 废水排放总量 0.483 0.577 −0.088 −0.005 −0.093 0.224 表 9 各影响因子与泉水离子浓度多元回归分析结果
Table 9. Multiple regression analysis results of influence factors and ion concentration in spring water
泉水离子 方程 项目 系数 P 拟合度R2 SO4 2− 1 常量 28.376 0.030 0.657 废水排放总量 14.020 0.001 Cl− 1 常量 23.228 0.052 0.333 废水排放总量 6.741 0.049 NO3 − 1 常量 6.877 0.000 0.479 水库H+浓度 237041.035 0.013 2 常量 4.427 0.001 0.720 水库H+浓度 238951.513 0.003 废水排放总量 0.708 0.021 3 常量 6.884 0.000 0.870 水库H+浓度 165292.914 0.011 废水排放总量 0.832 0.002 降水量 −0.004 0.016 表 10 配对t检验结果
Table 10. Result of paired t test
污染物 预测值 实测值 t P 2005年 2006年 2007年 2005年 2006年 2007年 SO4 2− 60.06 61.46 62.30 47.31 52.20 52.15 −10.249 0.009 NO3 − 5.74 7.65 7.77 6.80 7.64 6.80 0.043 0.970 Cl− 38.46 39.14 39.54 22.16 30.25 32.99 −3.599 0.069 -
[1] 路洪海, 陈诗越, 张重阳.济南泉域排泄区地下水演变趋势分析[J]. 人民黄河,2009,31(6):80, 82. [2] 于苗, 邢立亭, 吴吉春, 等.基于时间序列分形的济南岩溶大泉动态研究[J]. 地质学报,2020,94(8):2509-2519. doi: 10.3969/j.issn.0001-5717.2020.08.025YU M, XING L T, WU J C, et al. Study of large karst springs using the time series fractal method in Jinan[J]. Acta Geologica Sinica,2020,94(8):2509-2519. doi: 10.3969/j.issn.0001-5717.2020.08.025 [3] KAUFMANN G, BRAUN J. Karst Aquifer evolution in fractured, porous rocks[J]. Water Resources Research,2000,36(6):1381-1391. doi: 10.1029/1999WR900356 [4] ABUSAADA M, SAUTER M. Studying the flow dynamics of a karst aquifer system with an equivalent porous medium model[J]. Groundwater,2013,51(4):641-650. [5] BARBERÁ J A, ANDREO B. Functioning of a karst aquifer from S Spain under highly variable climate conditions, deduced from hydrochemical records[J]. Environmental Earth Sciences,2012,65(8):2337-2349. doi: 10.1007/s12665-011-1382-4 [6] HARTMANN A, BARBERÁ J A, LANGE J, et al. Progress in the hydrologic simulation of time variant recharge areas of karst systems:exemplified at a karst spring in Southern Spain[J]. Advances in Water Resources,2013,54:149-160. doi: 10.1016/j.advwatres.2013.01.010 [7] HARTMANN A, GOLDSCHEIDER N, WAGENER T, et al. Karst water resources in a changing world:review of hydrological modeling approaches[J]. Reviews of Geophysics,2014,52(3):218-242. doi: 10.1002/2013RG000443 [8] 王家乐. 济南岩溶水系统多级次循环模式分析及识别方法研究[D]. 武汉: 中国地质大学, 2016. [9] 马玉亮. 济南泉域岩溶水水文地球化学及其环境意义[D]. 青岛: 青岛大学, 2017. [10] 李波. 卧虎山水库对济南泉域岩溶水补给的影响[D]. 济南: 济南大学, 2011. [11] 李健, 张文河, 马宇熹.济南市卧虎山水库回灌补源对泉域修复分析[J]. 山东水利,2013(11):28-29. doi: 10.3969/j.issn.1009-6159.2013.11.014 [12] 王东海, 李春, 高焰, 等.人类活动对济南泉域地下水水质的影响[J]. 中国环境监测,2003,19(5):18-21. doi: 10.3969/j.issn.1002-6002.2003.05.006WANG D H, LI C, GAO Y, et al. Effect on groundwater quality of Jinan spring region by human activity[J]. Environmental Monitoring in China,2003,19(5):18-21. doi: 10.3969/j.issn.1002-6002.2003.05.006 [13] 高旭波, 王万洲, 侯保俊, 等.中国北方岩溶地下水污染分析[J]. 中国岩溶,2020,39(3):287-298.GAO X B, WANG W Z, HOU B J, et al. Analysis of karst groundwater pollution in Northern China[J]. Carsologica Sinica,2020,39(3):287-298. [14] LANG Y C, LIU C Q, ZHAO Z Q, et al. Geochemistry of surface and ground water in Guiyang, China: water/rock interaction and pollution in a karst hydrological system[J]. Applied Geochemistry,2006,21(6):887-903. doi: 10.1016/j.apgeochem.2006.03.005 [15] JIANG Y J, WU Y X, GROVES C, et al. Natural and anthropogenic factors affecting the groundwater quality in the Nandong karst underground river system in Yunnan, China[J]. Journal of Contaminant Hydrology,2009,109(1/2/3/4):49-61. [16] GRASBY S E, HUTCHEON I, MCFARLAND L. Surface-water–groundwater interaction and the influence of ion exchange reactions on river chemistry[J]. Geology,1999,27(3):223. doi: 10.1130/0091-7613(1999)027<0223:SWGIAT>2.3.CO;2 [17] ROEHRDANZ P R, FERAUD M, LEE D G, et al. Spatial models of sewer pipe leakage predict the occurrence of wastewater indicators in shallow urban groundwater[J]. Environmental Science & Technology,2017,51(3):1213-1223. [18] 王珺瑜, 王家乐, 靳孟贵.济南泉域岩溶水水化学特征及其成因[J]. 地球科学,2017,42(5):821-831.WANG J Y, WANG J L, JIN M G. Hydrochemical characteristics and formation causes of Karst water in Jinan spring catchment[J]. Earth Science,2017,42(5):821-831. [19] 赵占锋, 欧璐, 秦大军, 等.济南岩溶水水化学特征及影响因素[J]. 中国农村水利水电,2012(7):31-37.ZHAO Z F, OU L, QIN D J, et al. Factors controlling hydrochemical characteristics of karstic water in Jinan[J]. China Rural Water and Hydropower,2012(7):31-37. [20] 徐慧珍, 段秀铭, 高赞东, 等.济南泉域排泄区岩溶地下水水化学特征[J]. 水文地质工程地质,2007,34(3):15-19. doi: 10.3969/j.issn.1000-3665.2007.03.005XU H Z, DUAN X M, GAO Z D, et al. Hydrochemical study of karst groundwater in the Jinan spring catchment[J]. Hydrogeology & Engineering Geology,2007,34(3):15-19. doi: 10.3969/j.issn.1000-3665.2007.03.005 [21] HANSHAW B B, BACK W. Major geochemical processes in the evolution of carbonate-aquifer systems[J]. Developments in Water Science,1979,12:287-312. [22] 万利勤, 徐慧珍, 殷秀兰, 等.济南岩溶地下水化学成分的形成[J]. 水文地质工程地质,2008,35(3):61-64. doi: 10.3969/j.issn.1000-3665.2008.03.016WAN L Q, XU H Z, YIN X L, et al. Formation of hydrochemistry components of karst groundwater in Jinan[J]. Hydrogeology & Engineering Geology,2008,35(3):61-64. doi: 10.3969/j.issn.1000-3665.2008.03.016 [23] 王兆林, 高宗军, 徐源, 等.济南泉域岩溶水水化学特征[J]. 山东国土资源,2013,29(2):27-29. doi: 10.3969/j.issn.1672-6979.2013.02.009WANG Z L, GAO Z J, XU Y, et al. Hydrochemical characteristics of karst water in Jinan spring region[J]. Shandong Land and Resources,2013,29(2):27-29. doi: 10.3969/j.issn.1672-6979.2013.02.009 [24] ZHOU J, XING L T, ZHANG F J, et al. Chemical characteristics research on karst water in Jinan spring area[J]. Advanced Materials Research,2015,1092/1093:593-596. doi: 10.4028/www.scientific.net/AMR.1092-1093.593 [25] PLAGNES V, BAKALOWICZ M. The protection of a karst water resource from the example of the Larzac Karst Plateau (south of France): a matter of regulations or a matter of process knowledge[J]. Engineering Geology,2002,65(2/3):107-116. [26] 苟睿坤, 陈佳琦, 段高辉, 等.基于GF-2的油松人工林地上生物量反演[J]. 应用生态学报,2019,30(12):4031-4040.GOU R K, CHEN J Q, DUAN G H, et al. Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data[J]. Chinese Journal of Applied Ecology,2019,30(12):4031-4040. [27] SWETS J A. Measuring the accuracy of diagnostic systems[J]. Science,1988,240(4857):1285-1293. doi: 10.1126/science.3287615 [28] 钟艮平, 沈文君, 万方浩, 等.用GARP生态位模型预测刺萼龙葵在中国的潜在分布区[J]. 生态学杂志,2009,28(1):162-166.ZHONG G P, SHEN W J, WAN F H, et al. Potential distribution areas of Solanum rostratum in China: a prediction with GARP Niche Model[J]. Chinese Journal of Ecology,2009,28(1):162-166. [29] 周超, 方秀琴, 吴小君, 等.基于三种机器学习算法的山洪灾害风险评价[J]. 地球信息科学学报,2019,21(11):1679-1688. doi: 10.12082/dqxxkx.2019.190185ZHOU C, FANG X Q, WU X J, et al. Risk assessment of mountain torrents based on three machine learning algorithms[J]. Journal of Geo-Information Science,2019,21(11):1679-1688. doi: 10.12082/dqxxkx.2019.190185 [30] 杜鹃.通径分析在Excel和SPSS中的实现[J]. 陕西气象,2012(1):15-18. doi: 10.3969/j.issn.1006-4354.2012.01.005 [31] 宋小园, 朱仲元, 刘艳伟, 等.通径分析在SPSS逐步线性回归中的实现[J]. 干旱区研究,2016,33(1):108-113.SONG X Y, ZHU Z Y, LIU Y W, et al. Application of path analysis in stepwise linear regression SPSS[J]. Arid Zone Research,2016,33(1):108-113. [32] 袁志发, 周静芋, 郭满才, 等.决策系数: 通径分析中的决策指标[J]. 西北农林科技大学学报(自然科学版),2001,29(5):131-133.YUAN Z F, ZHOU J Y, GUO M C, et al. Decision coefficient: the decision index of path analysis[J]. Journal of Northwest Sci-Tech University of Agriculture and Forestry,2001,29(5):131-133. [33] 陈赞宇.基于线性回归模型的用水需求量分析及预测: 以长春市为例[J]. 中小企业管理与科技(中旬刊),2021(5):88-89.CHEN Z Y. Analysis and forecast of water demand based on linear regression model: taking Changchun City as an example[J]. Management & Technology of SME,2021(5):88-89. [34] 曹喜果, 张永涛, 李雅恬.基于IQGA-GRNN模型的SCR脱硝出口NOx质量浓度预测方法[J]. 华电技术,2021,43(5):9-14. doi: 10.3969/j.issn.1674-1951.2021.05.002CAO X G, ZHANG Y T, LI Y T. Prediction method for NOx discharged from SCR denitrification systems based on IQGA-GRNN model[J]. Huadian Technology,2021,43(5):9-14. doi: 10.3969/j.issn.1674-1951.2021.05.002 [35] 刘宇宏, 沈恒根.多跨厂房内跨电焊烟吹吸式通风预测模型研究[J]. 建筑热能通风空调,2020,39(8):41-44.LIU Y H, SHEN H G. Study on prediction model of welding smoke push-pull ventilation in inner span of multi-span workshop[J]. Building Energy & Environment,2020,39(8):41-44. [36] 赵晓萌, 蔡新玲, 雷向杰, 等.基于Logistic回归的陕南秦巴山区降雨型滑坡预测方法[J]. 冰川冻土,2019,41(1):175-182.ZHAO X M, CAI X L, LEI X J, et al. Prediction method of rainfall-induced landslides in Qinba Mountains of south Shaanxi Province based on Logistic regression[J]. Journal of Glaciology and Geocryology,2019,41(1):175-182. [37] 李大秋, 高焰, 王志国, 等.济南泉域岩溶地下水水质变化分析[J]. 中国岩溶,2002,21(3):202-205. doi: 10.3969/j.issn.1001-4810.2002.03.009LI D Q, GAO Y, WANG Z G, et al. Analysis on the variations of groundwater quality in Jinan spring basin[J]. Carsologica Sinica,2002,21(3):202-205. doi: 10.3969/j.issn.1001-4810.2002.03.009 [38] 张喜山, 姜文志.地下水中硝酸盐的污染原因及防治对策[J]. 地下水,1995,17(2):82-84. [39] 朱济成.关于地下水硝酸盐污染原因的探讨[J]. 北京地质,1995(2):22-26.ZHU J C. Research on the nitrate contamination in groundwater[J]. Beijing Geology,1995(2):22-26. [40] 国家环境保护总局. 污水综合排放标准: GB 8978—1996[S]. 北京: 中国标准出版社, 1998. [41] 国家环境保护总局. 城镇污水处理厂污染物排放标准: GB 18918—2002[S]. 北京: 中国环境出版社, 2003. [42] 刘文杰. 小清河流域水环境保护政策回顾性评价[D]. 济南: 山东大学, 2017. [43] 山东省环境保护厅. 流域水污染物综合排放标准 第3部分: 清河流域: DB 37/ 3416.3—2018.[S/OL].[2021-04-07].http://epb.zibo.gov.cn/attach/0/d1b43602bfa74e1ab4830d10e5da4bf8.pdf 2022/01/06 10:29 [44] 张光辉, 刘中培, 连英立, 等.河北平原地下水质变及农药化肥施用量变化影响[J]. 南水北调与水利科技,2009,7(2):50-54. doi: 10.3969/j.issn.1672-1683.2009.02.017ZHANG G H, LIU Z P, LIAN Y L, et al. Variation of groundwater quality and influence of pesticide and fertilizer on Hebei plain[J]. South-to-North Water Transfers and Water Science & Technology,2009,7(2):50-54. doi: 10.3969/j.issn.1672-1683.2009.02.017 [45] 王志强, 廖媛, 顾栩, 等.污水灌溉对地下水水质的影响效应研究: 以栾城污灌区为例[J]. 环境科学与技术,2016,39(3):117-125.WANG Z Q, LIAO Y, GU X, et al. Environmental effects of the sewage irrigation on groundwater quality: a case study of Luancheng sewage irrigation area[J]. Environmental Science & Technology,2016,39(3):117-125. ⊗