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When drinking water quality and hydraulic conditions change, the solid-liquid balance between pipe scales and water in drinking water distribution systems (DWDSs) will be destroyed, causing iron release and secondary pollution of drinking water. In this paper, dynamic experimental systems were set up to analyze the process of iron release in DWDSs under coupled changes of flow rate (v), pH, sulfate (SO42-), and alkalinity (Alk). Principal component regression was used to establish the model for predicting the release of iron. The results indicated that under the condition of v=0.12m/s, pH=6.5, [SO42-]=250mg/L, and Alk=100mg/L CaCO3, the total iron concentrations in steel and cast iron pipes reached the maximum of 1.423mg/L and 0.184mg/L, respectively. A large amount of flaky and scattered spherical structures were observed in steel and cast iron pipe scales, with α-FeOOH being the main component. After the experiment, the contents of α-FeOOH, γ-FeOOH, and Fe2O3 in both pipe scales increased, while those of Fe3O4 decreased. The predictive model showed that the total iron concentrations were negatively correlated with pH and Alk, and positively correlated with SO42- and v. The order of the influences of the four factors was: v > pH > Alk > SO42-.

, correspAuthors=Hui ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Long WANG, Hui ZHANG, Shan ZHANG, Zi-yi MIAO, Zhuo LIU, Ming-zhou FAN, Pei-xin JIA, Yon-jia FENG), CN=ArticleExt(id=1241049994074059763, articleId=1241049979364634978, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=流速、pH值、硫酸根和碱度耦合变化对管网铁释放的影响, columnId=1234106386565624579, journalTitle=中国环境科学, columnName=水污染与控制, runingTitle=null, highlight=null, articleAbstract=

当饮用水水质和水力条件发生变化时,给水管网中管垢和水相之间的固液平衡会被破坏,引起铁释放,造成饮用水的二次污染.本文通过建立动态模拟系统,重点分析了流速(v)、pH值、硫酸根(SO42-)和碱度(Alk)耦合变化影响下的给水管网铁释放过程,并运用主成分回归法构建了铁释放预测模型.结果表明,在v=0.12m/s、pH=6.5、=250mg/L、Alk=100mg/L CaCO3的条件下,钢管和铸铁管总铁浓度达到最大,分别为1.423mg/L和0.184mg/L;2种管道腐蚀产物中均存在大量片状和散射球状结构,主要成分均为α-FeOOH,且实验后钢管和铸铁管管垢中α-FeOOH、γ-FeOOH和Fe2O3含量增加,Fe3O4含量减少;预测模型表明,总铁浓度与pH值和Alk呈负相关关系,与SO42-v呈正相关关系,四种因素的影响程度排序为v>pH>Alk>SO42-.

, correspAuthors=张卉, authorNote=null, correspAuthorsNote=
*责任作者,副教授,
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王龙(2000-),男,陕西咸阳人,西安建筑科技大学硕士研究生,主要从事城市饮用水安全输配技术研究..

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王龙(2000-),男,陕西咸阳人,西安建筑科技大学硕士研究生,主要从事城市饮用水安全输配技术研究..

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王龙(2000-),男,陕西咸阳人,西安建筑科技大学硕士研究生,主要从事城市饮用水安全输配技术研究..

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Environmental Science201738(12):5090-5096., articleTitle=Analysis of relative importance of factors affecting iron release in water supply networks, refAbstract=null)], funds=[Fund(id=1241050020389122787, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, awardId=2021JZ-50, language=CN, fundingSource=陕西省自然科学基础研究计划重点项目(2021JZ-50), fundOrder=null, country=null), Fund(id=1241050020519146215, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, awardId=21JP065, language=CN, fundingSource=陕西省教育厅青年创新团队建设科研计划项目(21JP065), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241049994405408776, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, xref=null, ext=[AuthorCompanyExt(id=1241049994413797389, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, companyId=1241049994405408776, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Environment and Municipal Engineering, Xi'an University of Architecture and Technology, Shanxi Xi'an 710055, China), AuthorCompanyExt(id=1241049994434768910, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, companyId=1241049994405408776, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=西安建筑科技大学环境与市政工程学院,陕西 西安 710055)])], figs=[ArticleFig(id=1241050007281922465, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig.1, caption=Dynamic experimental system, figureFileSmall=juba5+rM9xrLXo/lUalOug==, figureFileBig=vIzS74UNvu+akhsWvRgH9Q==, tableContent=null), ArticleFig(id=1241050007617466800, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图1, caption=动态模拟实验系统装置, figureFileSmall=juba5+rM9xrLXo/lUalOug==, figureFileBig=vIzS74UNvu+akhsWvRgH9Q==, tableContent=null), ArticleFig(id=1241050008024314311, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig.2, caption=Micromorphology of pipe scales, figureFileSmall=F0svNHYtq87ChcLukWKVrg==, figureFileBig=IsI7wWAStfCQEavRjsG8jQ==, tableContent=null), ArticleFig(id=1241050008204669395, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图2, caption=管垢微观形貌

(a)、(b)实验前钢管;(c)、(d)实验前铸铁管;(e)、(f)实验后钢管;(g)、(h)实验后铸铁管

, figureFileSmall=F0svNHYtq87ChcLukWKVrg==, figureFileBig=IsI7wWAStfCQEavRjsG8jQ==, tableContent=null), ArticleFig(id=1241050008443744734, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig.3, caption=XRD spectra of steel pipe and cast iron pipe scales, figureFileSmall=NmhpIROBQYAKjLI25v8YLg==, figureFileBig=qsM45Nea6mkyDwp0xSQgFw==, tableContent=null), ArticleFig(id=1241050010167603693, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图3, caption=钢管和铸铁管管垢晶体组成

(a)初始管垢;(b)水力和水质参数耦合变化后的管垢

, figureFileSmall=NmhpIROBQYAKjLI25v8YLg==, figureFileBig=qsM45Nea6mkyDwp0xSQgFw==, tableContent=null), ArticleFig(id=1241050010339570168, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig.4, caption=Total iron concentrations in the effluents from steel and cast iron pipes under coupled variations of v, pH, SO42-, and Alk, figureFileSmall=7rRPFTBkl6hvM7UwBQ2RsQ==, figureFileBig=YMxtCcWbFoPh8E3uCGt5Hw==, tableContent=null), ArticleFig(id=1241050010486370818, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图4, caption=v、pH值、SO42-和ALk耦合变化下钢管和铸铁管出水总Fe浓度, figureFileSmall=7rRPFTBkl6hvM7UwBQ2RsQ==, figureFileBig=YMxtCcWbFoPh8E3uCGt5Hw==, tableContent=null), ArticleFig(id=1241050010754806290, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig.5, caption=Values of Larsen index and CCPP of the effluents from steel and cast iron pipes under coupled changes of v, pH, SO42-and Alk, figureFileSmall=Z8WD7zDT1n/NpCNf4YTbqw==, figureFileBig=G7PoA5cDrUiwqiOoGOvZrA==, tableContent=null), ArticleFig(id=1241050010943549983, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图5, caption=v、pH值、SO42-和ALk耦合变化下钢管和铸铁管中的出水Larsen指数和CCPP, figureFileSmall=Z8WD7zDT1n/NpCNf4YTbqw==, figureFileBig=G7PoA5cDrUiwqiOoGOvZrA==, tableContent=null), ArticleFig(id=1241050011069379112, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig. 6, caption=Comparison of simulated and predicted values of total iron concentrations in (a)steel (b)cast iron pipes., figureFileSmall=J6XcbWOMb27eqjHg+Cshxw==, figureFileBig=xVsGTg30BzxaweaXCqw1Mw==, tableContent=null), ArticleFig(id=1241050011232956974, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图6, caption=出水总Fe平均浓度模拟值和预测值对比, figureFileSmall=J6XcbWOMb27eqjHg+Cshxw==, figureFileBig=xVsGTg30BzxaweaXCqw1Mw==, tableContent=null), ArticleFig(id=1241050011375563318, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Fig.7, caption=Errors between the simulated and actual values of total iron concentrations in the effluents, figureFileSmall=q0qrzY4iel9hns6LfDw3QA==, figureFileBig=eKTpjW/Nc5177eITUzyoVA==, tableContent=null), ArticleFig(id=1241050011581084224, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=图7, caption=出水总Fe浓度模拟值与真实值的误差, figureFileSmall=q0qrzY4iel9hns6LfDw3QA==, figureFileBig=eKTpjW/Nc5177eITUzyoVA==, tableContent=null), ArticleFig(id=1241050011719496266, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 1, caption=

Tap water quality

, figureFileSmall=null, figureFileBig=null, tableContent=
溶解氧(mg/L)ORP(mV)电导率(µS/cm)pH浊度(NTU)硫酸盐(mg/L)总碱度(mg/L CaCO3)Fe(mg/L)余氯(mg/L)
9.24280.40121.58.280.4016.8059.900.0380.05
), ArticleFig(id=1241050011887268434, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表1, caption=

实验用水水质概况

, figureFileSmall=null, figureFileBig=null, tableContent=
溶解氧(mg/L)ORP(mV)电导率(µS/cm)pH浊度(NTU)硫酸盐(mg/L)总碱度(mg/L CaCO3)Fe(mg/L)余氯(mg/L)
9.24280.40121.58.280.4016.8059.900.0380.05
), ArticleFig(id=1241050012256367198, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 2, caption=

Experimental group

, figureFileSmall=null, figureFileBig=null, tableContent=
实验组v(m/s)pH值SO42-(mg/L)Alk(mg/L CaCO3)
10.046.5050100
20.087.5050200
30.128.5050300
40.086.50150100
50.127.50150200
60.048.50150300
70.126.50250100
80.047.50250200
90.088.50250300
), ArticleFig(id=1241050012579328619, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表2, caption=

实验组别

, figureFileSmall=null, figureFileBig=null, tableContent=
实验组v(m/s)pH值SO42-(mg/L)Alk(mg/L CaCO3)
10.046.5050100
20.087.5050200
30.128.5050300
40.086.50150100
50.127.50150200
60.048.50150300
70.126.50250100
80.047.50250200
90.088.50250300
), ArticleFig(id=1241050012864541299, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 3, caption=

Measurement methods of water quality parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
指标检测方法及仪器仪器型号生产厂家
pH值多参数水质分析仪HQ30d美国HACH
DO多参数水质分析仪HQ30d美国HACH
ORP多参数水质分析仪HQ30d美国HACH
电导率多参数水质分析仪HQ30d美国HACH
浊度便携式浊度仪2100P美国HACH
碱度酸碱指示剂滴定法//
硫酸盐铬酸钡沉淀法UV2800A上海尤尼柯公司
总铁邻菲啰啉分光光度法UV2800A上海尤尼柯公司
二价铁邻菲啰啉分光光度法UV2800A上海尤尼柯公司
), ArticleFig(id=1241050014701646460, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表3, caption=

水质检测方法

, figureFileSmall=null, figureFileBig=null, tableContent=
指标检测方法及仪器仪器型号生产厂家
pH值多参数水质分析仪HQ30d美国HACH
DO多参数水质分析仪HQ30d美国HACH
ORP多参数水质分析仪HQ30d美国HACH
电导率多参数水质分析仪HQ30d美国HACH
浊度便携式浊度仪2100P美国HACH
碱度酸碱指示剂滴定法//
硫酸盐铬酸钡沉淀法UV2800A上海尤尼柯公司
总铁邻菲啰啉分光光度法UV2800A上海尤尼柯公司
二价铁邻菲啰啉分光光度法UV2800A上海尤尼柯公司
), ArticleFig(id=1241050014810698374, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 4, caption=

Component matrix and eigenvalue after

, figureFileSmall=null, figureFileBig=null, tableContent=
变量主成分1主成分2
vPv1Pv2
pH值PpH1PpH2
SO42-PS1PS2
AlkPAlk1PAlk2
主成分Z1特征值为λ1,主成分Z2特征值为λ2
), ArticleFig(id=1241050015062356627, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表4, caption=

铁释放模型降维后的成分矩阵和特征值

, figureFileSmall=null, figureFileBig=null, tableContent=
变量主成分1主成分2
vPv1Pv2
pH值PpH1PpH2
SO42-PS1PS2
AlkPAlk1PAlk2
主成分Z1特征值为λ1,主成分Z2特征值为λ2
), ArticleFig(id=1241050015263683231, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 5, caption=

Crystalline compositions (%) of steel and cast iron pipe scales

, figureFileSmall=null, figureFileBig=null, tableContent=
晶体成分实验前钢管实验前铸铁管实验后钢管实验后铸铁管
α-FeOOH44.5043.6056.8049.20
γ-FeOOH2.202.107.005.00
Fe3O425.6019.202.603.00
Fe2O33.703.304.704.80
CaCO318.505.2024.3011.00
SiO25.5026.604.6027.00
), ArticleFig(id=1241050015439844005, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表5, caption=

钢管和铸铁管中腐蚀产物晶体成分(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
晶体成分实验前钢管实验前铸铁管实验后钢管实验后铸铁管
α-FeOOH44.5043.6056.8049.20
γ-FeOOH2.202.107.005.00
Fe3O425.6019.202.603.00
Fe2O33.703.304.704.80
CaCO318.505.2024.3011.00
SiO25.5026.604.6027.00
), ArticleFig(id=1241050015632781999, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 6, caption=

composition matrix

, figureFileSmall=null, figureFileBig=null, tableContent=
项目Z1Z2
pH值0.9910.119
SO42--0.1790.704
Alk0.9920.118
v-0.1490.732
方差贡献50.496%76.968%
), ArticleFig(id=1241050015746028215, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表6, caption=

成分矩阵

, figureFileSmall=null, figureFileBig=null, tableContent=
项目Z1Z2
pH值0.9910.119
SO42--0.1790.704
Alk0.9920.118
v-0.1490.732
方差贡献50.496%76.968%
), ArticleFig(id=1241050015968326337, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 7, caption=

Iron release models in steel and cast iron pipes

, figureFileSmall=null, figureFileBig=null, tableContent=
管材铁释放模型
钢管
铸铁管
), ArticleFig(id=1241050019718034121, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表7, caption=

钢管和铸铁管铁释放模型

, figureFileSmall=null, figureFileBig=null, tableContent=
管材铁释放模型
钢管
铸铁管
), ArticleFig(id=1241050019877417681, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=EN, label=Table 8, caption=

Standardized regression coefficients of iron release models

, figureFileSmall=null, figureFileBig=null, tableContent=
管材pH值SO42-Alkv
钢管-1.4520.011-0.01525.593
铸铁管-0.4930.001-0.0052.219
), ArticleFig(id=1241050020061967066, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049979364634978, language=CN, label=表8, caption=

铁释放模型标准化回归系数

, figureFileSmall=null, figureFileBig=null, tableContent=
管材pH值SO42-Alkv
钢管-1.4520.011-0.01525.593
铸铁管-0.4930.001-0.0052.219
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流速、pH值、硫酸根和碱度耦合变化对管网铁释放的影响
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王龙 , 张卉 * , 张珊 , 苗梓怡 , 刘卓 , 范明洲 , 贾培馨 , 冯永嘉
中国环境科学 | 水污染与控制 2025,45(1): 124-131
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中国环境科学 | 水污染与控制 2025, 45(1): 124-131
流速、pH值、硫酸根和碱度耦合变化对管网铁释放的影响
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王龙 , 张卉* , 张珊, 苗梓怡, 刘卓, 范明洲, 贾培馨, 冯永嘉
作者信息
  • 西安建筑科技大学环境与市政工程学院,陕西 西安 710055
  • 王龙(2000-),男,陕西咸阳人,西安建筑科技大学硕士研究生,主要从事城市饮用水安全输配技术研究..

通讯作者:

*责任作者,副教授,
Coupling effects of flow rate, pH value, sulfate, and alkalinity on iron release from drinking water distribution systems
Long WANG , Hui ZHANG* , Shan ZHANG, Zi-yi MIAO, Zhuo LIU, Ming-zhou FAN, Pei-xin JIA, Yon-jia FENG
Affiliations
  • School of Environment and Municipal Engineering, Xi'an University of Architecture and Technology, Shanxi Xi'an 710055, China
出版时间: 2025-01-20
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当饮用水水质和水力条件发生变化时,给水管网中管垢和水相之间的固液平衡会被破坏,引起铁释放,造成饮用水的二次污染.本文通过建立动态模拟系统,重点分析了流速(v)、pH值、硫酸根(SO42-)和碱度(Alk)耦合变化影响下的给水管网铁释放过程,并运用主成分回归法构建了铁释放预测模型.结果表明,在v=0.12m/s、pH=6.5、=250mg/L、Alk=100mg/L CaCO3的条件下,钢管和铸铁管总铁浓度达到最大,分别为1.423mg/L和0.184mg/L;2种管道腐蚀产物中均存在大量片状和散射球状结构,主要成分均为α-FeOOH,且实验后钢管和铸铁管管垢中α-FeOOH、γ-FeOOH和Fe2O3含量增加,Fe3O4含量减少;预测模型表明,总铁浓度与pH值和Alk呈负相关关系,与SO42-v呈正相关关系,四种因素的影响程度排序为v>pH>Alk>SO42-.

给水管网  /  管垢  /  铁释放  /  黄水

When drinking water quality and hydraulic conditions change, the solid-liquid balance between pipe scales and water in drinking water distribution systems (DWDSs) will be destroyed, causing iron release and secondary pollution of drinking water. In this paper, dynamic experimental systems were set up to analyze the process of iron release in DWDSs under coupled changes of flow rate (v), pH, sulfate (SO42-), and alkalinity (Alk). Principal component regression was used to establish the model for predicting the release of iron. The results indicated that under the condition of v=0.12m/s, pH=6.5, [SO42-]=250mg/L, and Alk=100mg/L CaCO3, the total iron concentrations in steel and cast iron pipes reached the maximum of 1.423mg/L and 0.184mg/L, respectively. A large amount of flaky and scattered spherical structures were observed in steel and cast iron pipe scales, with α-FeOOH being the main component. After the experiment, the contents of α-FeOOH, γ-FeOOH, and Fe2O3 in both pipe scales increased, while those of Fe3O4 decreased. The predictive model showed that the total iron concentrations were negatively correlated with pH and Alk, and positively correlated with SO42- and v. The order of the influences of the four factors was: v > pH > Alk > SO42-.

drinking water distribution system  /  corrosion scale  /  iron release  /  yellow water
王龙, 张卉, 张珊, 苗梓怡, 刘卓, 范明洲, 贾培馨, 冯永嘉. 流速、pH值、硫酸根和碱度耦合变化对管网铁释放的影响. 中国环境科学, 2025 , 45 (1) : 124 -131 .
Long WANG, Hui ZHANG, Shan ZHANG, Zi-yi MIAO, Zhuo LIU, Ming-zhou FAN, Pei-xin JIA, Yon-jia FENG. Coupling effects of flow rate, pH value, sulfate, and alkalinity on iron release from drinking water distribution systems[J]. China Environmental Science, 2025 , 45 (1) : 124 -131 .
给水管网是城市基础设施的重要组成部分,据统计[1],2022年我国给水管网总长度已达110.2万km,其中,铁质管道的占比达69.7%[2].铁质管道在长期的使用过程中,会在内壁生成大量管垢.当水质或水力条件发生突变时,管垢结构会遭到破坏,引起铁释放,导致“黄水”现象[3-5].
消毒剂、流速(v)、pH值、硫酸盐(SO42-)和碱度(Alk)是影响管网铁释放的重要因素[6-8].其中,消毒剂和SO42-浓度的提高能够促进铁的释放,pH值和Alk的增大则对铁释放起抑制作用,而流速对铁释放的影响则尚不确定.消毒剂大都具有氧化性,其浓度的提高会加剧管道腐蚀,进而导致更多的铁被释放到水中[9];SO42-浓度增加会导致管道中相对厌氧条件的产生,从而促进铁释放[10-11];pH值和Alk的提高会促进铁的氢氧化物的形成,从而在管垢表面形成保护性涂层,抑制铁释放[12];流速的提高一方面可以提升水中溶解氧浓度,抑制铁释放,另一方面也会造成管垢保护层的脱落,形成颗粒性铁释放[13-14].
尽管国内外已经开展了大量上述因素变化下的管网铁释放研究,提出了相应的铁释放机理,然而,已有研究主要针对的是单一因素变化下的铁释放,并未对多因素耦合影响下的铁释放行为进行系统分析.由于不同因素对铁释放的影响具有复杂的非线性特征,其相互作用可能会对铁释放产生增强、削弱或者相反的效果,因此,应开展不同因素复合变动下的给水管网铁释放研究,以进一步完善相关理论体系.
本文以实际给水钢管和铸铁管为研究对象,通过建立动态模拟实验系统,探究了v、pH值、SO42-和Alk耦合变化下的铁释放过程,结合Larsen指数和碳酸钙沉淀势(CCPP)分析了铁释放过程,利用扫描电子显微镜(SEM)、X射线衍射仪技术(XRD)分析了管垢特性变化,并在此基础上,建立了基于主成分回归(PCR)的铁释放预测模型,以期为完善管网铁释放机理与控制理论体系奠定基础.
采集的实验管道均来自北方某城市实际给水管网,材质为碳钢管和灰口铸铁管,均无内衬,管径为100mm,管龄超过15a,钢管和铸铁管内壁存在明显腐蚀管垢.钢管管垢呈黄褐色的块状结构,分布松散;铸铁管管垢形状多样,且质地坚硬不易脱落.
利用实验管道搭建动态模拟实验系统,如图1所示.为了方便安装,将管道切割为200mm的管段,管段两边加盖玻璃板密封,管段之间用硅胶管连接形成钢管和铸铁管回路,每个回路由两个材质相同的管段组成.具体组建方法参见文献[15].系统完整的运行时间设置为48h,以模拟实际管网中的最不利情况.
以实验室自来水为基础,配制实验用水.自来水水质如表1所示.调节离心泵流速达到实验所需的水流速度;利用盐酸(HCl)和氢氧化钠(NaOH)调节pH值,硫酸钠(Na2SO4)和碳酸氢钠(NaHCO3)分别调整SO42-浓度和碱度,以达到实验用水水质.
各因素设置如下水平:(1)v=0.04、0.08和0.12m/s;(2)pH=6.5、7.5和8.5;(3)=50、150和250mg/L;(4)Alk=100、200和300mg/L CaCO3.根据正交实验设计方法得到L9(34)正交测试表,实验组别如表2所示.总共包括9组实验,每组实验的完整周期为10d,每隔46h换水,首先向水箱中加入一定量的实验用水,开泵运行38h后,关闭水泵,使实验系统停滞8h,然后从取水龙头处取样,检测出水总Fe浓度,结束后换水进行下一周期实验.水质指标和检测方法见表3.
使用DZ-2BC型冷冻干燥机对实验前后管道管垢进行干燥,使用Sigma 300型扫描电子显微镜(SEM,Zeiss,Germany)对干燥后管垢表面微观结构进行扫描分析.使用Ultima IV型X射线衍射仪(XRD,Rigaku,Japan)对样品表面进行扫描分析.使用Jade6.0软件拟合XRD结果,分析管垢晶体结构,采用标准峰强度比较法对管垢进行物相半定量分析.
以出水总铁浓度为因变量,选取v、pH值、SO42-和Alk为自变量.由于自变量之间存在共线性,在这种情况下,自变量会给模型提供重复信息,导致模型失真.因此,采用主成分分析法(PCA)对原始自变量进行降维.PCA通过正交性原理和方差最大化原理确保得到的主成分之间相互正交,避免信息重复,同时确保主成分具有最大的方差,即包含了原始自变量中最多的信息.通过这种方法,减少了变量数量,提高了模型的稳定性和解释性.利用SPSS软件对原始自变量进行降维,获得如表4所示的成分矩阵和主成分特征值.
每个变量在主成分中的权重Gij为:
Z1的表达式为:
Z2的表达式为:
式中:Z1是数据中解释方差最大的成分;Z2是数据中解释剩余方差最大的成分,并且与Z1正交.
根据得到的新变量(Z1Z2),采用多元线性回归法建立铁释放模型,其形式为:
由于多元线性回归模型中自变量的单位不同,得到的线性回归系数之间量纲不同,只能用于解释自变量对因变量的作用,无法比较自变量之间的相对重要性.因此,需要通过对自变量进行标准化转换,以减少单位的影响[16].
首先,将铁释放模型(式(4))两边取对数,得到线性铁释放模型为:
式中:Xi为自变量,ai为自变量回归系数,i=1,2,3,…,nn为自变量个数.
然后,对线性模型中的自变量进行标准化:
式中:Zi为标准化后的自变量,Mean(Xi)为自变量Xi的均值,Std(Xi)为自变量Xi的标准差.
最后,对自变量回归系数进行标准化:
式中:βi为标准化系数.
图2表明了v、pH值、SO42-和Alk耦合变化前后钢管和铸铁管的管垢微观形貌.钢管和铸铁管的初始管垢中均存在大量片状(Fe3O4)和散射球状结构(α-FeOOH)[17-18].水力和水质参数耦合变化后,2种管道管垢表面的孔隙率明显增大,钢管管垢主要以球状结构和针状结构为主(α-FeOOH),还出现大量的花簇结构(γ-FeOOH);与钢管的管垢变化类似,铸铁管管垢中同样出现大量棉球状结构(α-FeOOH)以及砂质状结构(γ-FeOOH)[19-20].
水力和水质参数耦合变化前后,钢管和铸铁管管垢组成相同,主要晶体包括α-FeOOH、γ-FeOOH、Fe3O4、Fe2O3[12].实验前,钢管和铸铁管管垢中α-FeOOH峰值强度极其显著,为2种管垢的主要成分,与SEM分析结果和以往研究结果一致[21].钢管管垢中Fe3O4出峰强度相对较高,而铸铁管管垢中SiO2出峰强度则相对较高,两种物质均具有高硬度和化学稳定性,其可以提高管垢的致密性和硬度[11,19].管垢中的γ-FeOOH与α-FeOOH相比,稳定性较差,具有松散的多孔结构,吸附性较强,能够吸附水中的有机物和重金属离子[20].CaCO3和Fe2O3也是铁腐蚀产物中的常见物质[22],可以在管垢表面形成保护层,从而降低管道腐蚀的风险.实验后,管垢中α-FeOOH和γ-FeOOH出峰点增多,而Fe3O4出峰强度显著降低,出峰点数量减少,表明管垢稳定性可能有所下降,即管垢铁释放风险增加.
表5为管垢晶体组成的半定量分析结果.初始管垢中,α-FeOOH和Fe3O4为主要物质(>60%).对比两种管材,铸铁管中Fe3O4、CaCO3和SiO2的含量高于钢管(51%>49.6%),可以看出铸铁管管垢稳定性高于钢管.水力和水质参数耦合变化后,管垢主要物质组成以α-FeOOH和CaCO3为主(>60%).水中不断增加的Alk会使CO32-浓度增加,从而导致CaCO3溶解度降低,更易沉淀形成管垢,这也是实验后管垢中CaCO3含量增加的原因.两种管材中α-FeOOH、γ-FeOOH和Fe2O3的含量有所增加(钢管中的增加量分别为:12.3%、4.8%和1.0%;铸铁管中分别为:5.6%、2.9%和1.5%),Fe3O4的含量下降(钢管中减少量为23.0%;铸铁管中为16.2%).表明水力和水质参数的耦合变化会降低管垢稳定性,更易于发生铁释放.
图4v、pH值、SO42-和Alk耦合变化下钢管和铸铁管中总Fe浓度变化情况.相同实验条件下,随着实验天数的增加,钢管和铸铁管的出水总Fe浓度逐渐增大.并且钢管的出水总Fe浓度高于铸铁管,这一现象可归因于两种管道的管垢特性存在差异,铸铁管的管垢质地较为坚硬且稳定,使得其对水质变化的稳定性优于钢管.在第7组实验条件下,钢管和铸铁管出水总Fe平均浓度达到最大,分别为1.42mg/L和0.18mg/L.当实验条件分别为第6组和第3组时,钢管和铸铁管出水总Fe平均浓度分别达到最小值,分别为0.08mg/L和0.04mg/L.
在1~3组实验条件下,钢管和铸铁管的出水总Fe平均浓度呈现下降趋势,两种管道中的总Fe平均浓度分别从0.15mg/L和0.09mg/L下降至0.10mg/L和0.04mg/L.这可能是多因素叠加的结果.根据以往研究结果,低pH值会增加管道中铁的释放量,随着pH和Alk增加,管道中铁释放量减少,而SO42-v的增大会促进铁释放[23-27].在本研究中,当SO42-浓度为50mg/L时,随着pH值从6.5增大到8.5,Alk从100mg/LCaCO3升高到300mg/LCaCO3,出水总Fe平均浓度逐渐下降,pH值和Alk之间可能存在协同效应,Alk的升高会提高管网水的缓冲能力,有助于保持管垢-液体界面间pH值稳定,而保持界面间高pH值.同时增加pH值和Alk对管道铁释放的抑制效果可能大于单独增加pH值或者Alk的效果.此外pH值和Alk对SO42-v的影响可能存在拮抗作用,从而减弱了SO42-v对铁释放的促进作用,最终使得总Fe浓度随着pH值和Alk的增大而减小.
同样地,在4~6组实验条件下,钢管出水平均Fe浓度从0.87mg/L下降至0.08mg/L,铸铁管从0.18mg/L下降至0.52mg/L.
在7~9组实验条件下,钢管和铸铁管出水Fe浓度随着v变化而变化呈现先下降后升高的趋势,钢管出水总Fe平均浓度先从1.42mg/L下降至0.13mg/L,再升高至0.23mg/L,铸铁管先从0.18mg/L下降至0.07mg/L,再升高至0.11mg/L.由此可见,当SO42-浓度增大到一定阈值时,pH值和Alk对Fe释放的抑制作用减弱,SO42-v对Fe释放的促进作用主导了管道的Fe释放行为,从而使得管道内Fe释放的行为发生突变.
在1、4和7组实验条件下,当v为0.04m/s时,钢管和铸铁管出水总Fe平均浓度为0.15mg/L和0.09mg/L;当v为0.08m/s时,钢管和铸铁管出水总Fe浓度最大增加至1.07mg/L和0.36mg/L;当v增至0.12m/s时,钢管和铸铁管出水总Fe浓度最大可达2.17mg/L和0.59mg/L.随着v的提高,管网出水总Fe浓度逐步增加,v对Fe释放的影响主要表现为:一方面v增大,对管垢内壁的剪切力增大,对管垢的破坏和扰动也越大,从而造成铁释放量的增加.另一方面v增大会导致管网中溶解氧浓度降低,延长循环时间可能使水体缺氧,从而促使管垢中的三价Fe化合物被还原为二价Fe,并释放到水中,增大Fe的释放量.
在3、6和9组实验条件下,当SO42-浓度为50mg/L,v为0.12m/s时,钢管和铸铁管出水总Fe浓度最大可达0.15mg/L和0.11mg/L.当SO42-浓度增至150mg/L,v降低为0.04m/s时,钢管和铸铁管出水总Fe浓度最大可达0.11mg/L和0.08mg/L.当SO42-浓度增至250mg/L,v增至0.08m/s时,钢管和铸铁管出水总Fe浓度最大可达0.26mg/L和0.15mg/L.由此可见,在铁释放影响因素中,v是首要影响因素.
图5v、pH值、SO42-和Alk耦合变化下钢管和铸铁管中Larsen指数和CCPP变化情况.可以看出,Larsen指数越低,钢管和铸铁管的出水总Fe浓度也越低.相反,CCPP越高,钢管和铸铁管的出水总Fe浓度越低.在第7实验组条件下,Larsen指数达到最大,与铁释放趋势相同,而CCPP达到最低,与铁释放趋势相反.这是由于Larsen指数增大,水中Cl-和SO42-浓度升高,水体腐蚀性增强,而CCPP降低,水体形成碳酸盐沉淀的能力降低,不能在管道内壁形成有效的保护层,管道内壁更容易暴露在水体中,提高其被腐蚀的可能性,增大铁释放风险.
根据实验收集到的数据,建立铁释放模型.利用PCA对变量进行降维,消除共线性,得到的成分矩阵如表6所示.
主成分Z1Z2v、pH值、SO42-和Alk 4个自变量的关系为:
利用线性回归法,建立钢管和铸铁管出水总Fe浓度与Z1Z2的关系,即:
将式(8)和(9)分别代入式(10)和(11),得到v、pH值、SO42-和Alk耦合变化下的给水管网铁释放模型,如表7所示.
图6对比了钢管和铸铁管出水总Fe平均浓度的预测值与实测值.可以看出,钢管和铸铁管图中的散点较为均匀的分布在y=x两侧.图7为出水总Fe浓度的模拟值与真实值的误差.由图7可知,误差值均匀的分布在0值附近,大约90%的点在-0.40~0.40mg/L的范围内,有个别点误差值较大,可能是由于相同实验条件下,随着实验时间增加,钢管和铸铁管出水总Fe浓度逐渐升高,组内数据本身存在误差,总体上不影响模型对整体铁释放的预测.
利用铁释放模型标准化回归系数,分析v、pH值、SO42-和Alk的影响程度,其结果如表8所示.可以看出,钢管和铸铁管中pH值和碱度对应的标准化系数为负数,表明两种参数与出水总Fe浓度成负相关关系;SO42-v对应的标准化系数为正值,与出水总Fe浓度成正相关关系.
根据表8中各因素标准化系数的绝对值,得到其对钢管和铸铁管中铁释放的影响程度排序为:v>pH值>Alk>SO42-.可以看出,v对出水总Fe浓度影响最大,SO42-和碱度的影响程度相近,且小于pH值的影响.结合实验结果可知,当SO42-浓度为50mg/L和150mg/L时,pH值和碱度对铁释放的抑制作用远远大于v和SO42-的促进作用,而当SO42-浓度增加到250mg/L时,pH值和碱度的抑制作用减小,SO42-v对铁释放的促进作用显著增加.因此,水质参数耦合变化对铁释放的影响并非单个因素影响效应的简单叠加.因此,在应对管网铁释放或水质二次污染问题时,应考虑多种因素的交互效应,以达到最佳的控制效果,确保供水安全性.
3.1 钢管和铸铁管段内壁管垢主要以铁氧化物为主.α-FeOOH为所有管垢样品中含量最多的成分.钢管和铸铁管管垢实验后Fe3O4的含量分别下降23.0%和19.2%.α-FeOOH、γ-FeOOH和Fe2O3的含量增加(钢管中的增加量分别为:12.3%、4.8%和1.0%;铸铁管中分别为:5.6%、2.9%和1.5%),管垢稳定性下降.
3.2 当pH值=7.5、碱度=100mg/LCaCO3=250mg/L、流速=0.12m/s时,钢管和铸铁管出水总Fe浓度达到最大,其值分别为1.423mg/L和0.184mg/L.
3.3 将硫酸根浓度增加到250mg/L时,铁释放行为发生了显著变化.这表明存在阈值效应,即SO42-浓度超过一定水平后,pH值和碱度对SO42-和流速的拮抗作用消失,铁释放的行为显著改变,SO42-与流速会主导管道系统铁释放行为.
3.4 主成分回归法表明出水总Fe浓度与pH值和碱度呈负相关,与SO42-和流速呈正相关,结论与动态实验结果相同,两种管道中影响程度排序为:流速>pH值>碱度>SO42-.
  • 陕西省自然科学基础研究计划重点项目(2021JZ-50)
  • 陕西省教育厅青年创新团队建设科研计划项目(21JP065)
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陕西省自然科学基础研究计划重点项目(2021JZ-50)
陕西省教育厅青年创新团队建设科研计划项目(21JP065)
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    西安建筑科技大学环境与市政工程学院,陕西 西安 710055

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