Article(id=1223193063992971681, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20220607, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1648483200000, receivedDateStr=2022-03-29, revisedDate=1650384000000, revisedDateStr=2022-04-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1769561552283, onlineDateStr=2026-01-28, pubDate=1674576000000, pubDateStr=2023-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769561552283, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769561552283, creator=13701087609, updateTime=1769561552283, updator=13701087609, issue=Issue{id=1223193053830169317, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='1', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769561549861, creator=13701087609, updateTime=1769567790701, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223219229885857794, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223219229885857795, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223193053830169317, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=63, endPage=67, ext={EN=ArticleExt(id=1223193065221902852, articleId=1223193063992971681, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Identifying Optimal Minimum Water Quality Index for Weihe River Tributaries in Xi’an City, columnId=1222925279849341011, journalTitle=Water Resources and Power, columnName=HYDROLOGY, WATER RESOURCES AND ENVIRONMENT, runingTitle=null, highlight=null, articleAbstract=

To determine the key water quality indicators for influencing the Weihe River tributaries of Chanhe, Bahe, Fenghe and Zaohe Rivers and the minimum water quality index (W_WQImin) is of great significance for simplifying water quality evaluation. The research set up a total of 30 sampling points in the 4 rivers and collected water quality samples in April, August, and December 2019, respectively. Fifteen water quality indicators were analyzed by laboratory experiment and mathematical statistics. It was found that the content of DO in Fenghe River was obviously highest, and the contents of EC and nitrite in Zaohe River and the Bahe River reach after the convergence of Chanhe River were higher than other rivers. In addition, based on 15 water quality indicators, the water quality index (W_WQI) was calculated to evaluate the temporal and spatial changes of water quality of the 4 rivers. This study shows that the water quality indices were generally above the medium level (W_WQI≥51) in all four rivers, and the water quality of the Fenghe River was better among the four rivers. On time scale, the water quality in the spring of 2019 was better than the other two periods. Besides, stepwise multiple linear regression analysis and empirical method were used to determine the appropriate minimum water quality index (W_WQImin), which contains DO, ammonia nitrogen, COD_Mn, turbidity, nitrate nitrogen, $\mathrm{SO}_{4}^{2-}$, and EC. Compared with the W_WQI based on 15 water quality indicators, the relative error (P_PE) is only 4.78%, the linear correlation coefficient R² reaches 0.95, and the root mean square error (R_RMSE) is only 3.99. Finally, the spatial and temporal distribution of W_WQI and the appropriate W_WQImin in the 4 rivers are consistent. Therefore, it can be considered that the selected appropriate W_WQImin can be replaced of WQI, which is more efficient for time-saving, low-cost and high-efficiency water quality evaluation in the basin near the Chanhe, Bahe, Fenghe and Zaohe Rivers.

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分析影响渭河支流浐河、灞河、沣河和皂河水系的关键水质指标，进而确定最小水质指数（W_WQImin），对简化水质评价意义重大。为此，在浐河、灞河、沣河和皂河4条河流共设30个采样点，于2019年4、8、12月分别进行采样，结合室内试验和数理统计的方法对浐河、灞河、沣河、皂河的15个水质指标进行分析，发现沣河DO含量最高，皂河和浐河交汇后的灞河段的EC、亚硝氮含量相比其他河流较高。此外，基于15个水质指标应用水质指数（W_WQI）对浐灞沣皂河水质的时空变化进行评价，发现水质总体达中等以上水平（W_WQI≥51），4条河流中沣河水质更好，而2019年春季的水质相比另外两个时期更好。同时，应用逐步多元线性回归分析法和经验法确定出了最佳的最小水质指数（W_WQImin），该W_WQImin包含DO、氨氮、COD_Mn、浊度、硝氮、$\mathrm{SO}_{4}^{2-}$、EC共7个水质指标，与基于15个水质指标得出的W_WQI相比的相对误差P_PE仅为4.78%，确定性系数R²则达到了0.95，均方根误差R_RMSE仅为3.99。最后，将W_WQI与最佳W_WQImin在浐河、灞河、沣河、皂河进行时空对比发现二者在时空分布上具有一致性。可见选出的最佳W_WQImin可较好地替代W_WQI对浐河、灞河、沣河、皂河附近流域进行省时、低成本、高效的水质评价。

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徐新涵（1994-），女，硕士研究生，研究方向为水环境，E-mail：xuxinhan0720@163.com

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徐新涵（1994-），女，硕士研究生，研究方向为水环境，E-mail：xuxinhan0720@163.com

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