Article(id=1241050007873319359, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717344000000, receivedDateStr=2024-06-03, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773818979470, onlineDateStr=2026-03-18, pubDate=1737302400000, pubDateStr=2025-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773818979470, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773818979470, creator=13701087609, updateTime=1773818979470, updator=13701087609, issue=Issue{id=1241049962679694215, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='1', pageStart='1', pageEnd='592', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773818968696, creator=13701087609, updateTime=1773819749443, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241053237428671382, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241053237428671383, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=583, endPage=592, ext={EN=ArticleExt(id=1241050008372441562, articleId=1241050007873319359, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Comparative study on carbon footprint of sludge ceramsite and fly ash ceramsite, columnId=1234106390126588740, journalTitle=China Environmental Science, columnName=Carbon Emission Control, runingTitle=null, highlight=null, articleAbstract=

Sludge ceramsite and fly ash ceramsite are the two most common types of solid waste ceramsite. To compare and analyze the carbon footprint characteristics of the two types of solid waste ceramsite and quantitatively evaluate the carbon reduction benefits of the products, a carbon footprint accounting model for sludge ceramsite and fly ash ceramsite is constructed from the perspective of carbon footprint. Based on sensitivity analysis, key emission reduction factors are identified, and the carbon reduction potential of sludge ceramsite and fly ash ceramsite is predicted and evaluated through scenario analysis. Meanwhile, using error propagation equations for uncertainty analysis ensures the reliability and effectiveness of carbon footprint results. The results showed that the CO2 emissions from the production of 1kg sludge ceramsite and 1kg fly ash ceramsite were 1.00 and 0.58 kg, respectively. The carbon footprint characteristics of sludge ceramsite and fly ash ceramsite were similar, and the ceramsite production stage was the main link in the carbon emissions of the two ceramsite particle products, accounting for 93.71% and 89.12% of their respective carbon footprints (excluding the raw material acquisition stage), respectively. The raw material structure is the most sensitive factor affecting the carbon footprint of sludge ceramsite and fly ash ceramsite, followed by the transportation structure. Compared with sludge ceramsite, the carbon footprint of fly ash ceramsite is more affected by the adjustment of raw material structure. In the scenario of collaborative optimization, the carbon emission reduction potential of simultaneously optimizing transportation and raw material structure (31%~78%) is far higher than that of simultaneously optimizing transportation and power structure (2%~5%). In addition, the emission reduction potential of the three factors acting simultaneously is the highest, reaching 33%~79%.

, correspAuthors=Shuai DU, Ming-hui XIE, 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=Xiao-cong SONG, Shuai DU, Chen-ning DENG, Peng SHEN, Fang ZHU, Ming-hui XIE), CN=ArticleExt(id=1241050023996224305, articleId=1241050007873319359, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=污泥陶粒与粉煤灰陶粒碳足迹对比研究, columnId=1234106391661704058, journalTitle=中国环境科学, columnName=碳排放控制, runingTitle=null, highlight=null, articleAbstract=

污泥陶粒与粉煤灰陶粒是最为常见的两种固废陶粒,为对比分析两种固废陶粒产品碳足迹特征及量化评估产品碳减排效益,从碳足迹角度构建污泥陶粒与粉煤灰陶粒产品碳足迹核算模型,基于敏感性分析判识关键减排因素,通过情景分析预测评估污泥陶粒与粉煤灰陶粒的碳减排潜力,同时,使用误差传播方程进行不确定性分析保证碳足迹结果的可靠有效.结果表明,生产1kg污泥陶粒和1kg粉煤灰陶粒CO2eq排放量分别为1.00和0.58kg,污泥陶粒和粉煤灰陶粒碳足迹特征相似,陶粒生产阶段是两种陶粒产品碳排放的主要环节,分别占各自碳足迹(除原料获取阶段)的93.71%和89.12%.原料结构是影响污泥陶粒与粉煤灰陶粒碳足迹最敏感的因素,其次就是运输结构,相比于污泥陶粒,粉煤灰陶粒碳足迹受原料结构调整影响更大.协同优化情景中,同时优化运输和原料结构的碳减排潜力(31%~78%)远高于同时优化运输和电力结构(2%~5%),此外,3种因素同时作用的减排潜力最高,达到33%~79%.

, correspAuthors=杜帅, 谢明辉, authorNote=null, correspAuthorsNote=
*责任作者,研究员,
中级,
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宋晓聪(1992-),女,河北石家庄人,工程师,硕士,主要从事固体废物资源化碳评价领域研究.发表论文20余篇..

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宋晓聪(1992-),女,河北石家庄人,工程师,硕士,主要从事固体废物资源化碳评价领域研究.发表论文20余篇..

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宋晓聪(1992-),女,河北石家庄人,工程师,硕士,主要从事固体废物资源化碳评价领域研究.发表论文20余篇..

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Journal of Cleaner Production2022369:133342., articleTitle=Investigating carbon footprint and carbon reduction potential using a cradle-to-cradle LCA approach on lithium-ion batteries for electric vehicles in China, refAbstract=null)], funds=[Fund(id=1241050047249444947, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, awardId=20212ZDYF020047, language=CN, fundingSource=宁波市重大科技攻关项目(20212ZDYF020047), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241050024445014842, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, xref=1., ext=[AuthorCompanyExt(id=1241050024465986368, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, companyId=1241050024445014842, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.Chinese Research Academy of Environmental Sciences, Beijing 100012, China), AuthorCompanyExt(id=1241050024495346496, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, companyId=1241050024445014842, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.中国环境科学研究院,北京 100012)]), AuthorCompany(id=1241050024604398407, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, xref=2., ext=[AuthorCompanyExt(id=1241050024616981319, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, companyId=1241050024604398407, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.Chinese Research Academy of Environmental Sciences Environmental Technology & Engineering Co., Ltd., Beijing 100012, China), AuthorCompanyExt(id=1241050024625369928, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, companyId=1241050024604398407, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.中国环境科学研究院环境技术工程有限公司,北京 100012)])], figs=[ArticleFig(id=1241050039691309059, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Fig.1, caption=System boundary, figureFileSmall=tVUbZ0PZBg+6y/1RzHucWw==, figureFileBig=Gc9pQhVH1Bbon8Z8iW4BCg==, tableContent=null), ArticleFig(id=1241050041448722442, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=图1, caption=系统边界, figureFileSmall=tVUbZ0PZBg+6y/1RzHucWw==, figureFileBig=Gc9pQhVH1Bbon8Z8iW4BCg==, tableContent=null), ArticleFig(id=1241050041700380693, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Fig.2, caption=Carbon footprint of 1kg sludge ceramsite and 1kg fly ash ceramsite based on sensitivity factor changes, figureFileSmall=Vo2F845Cqp4Q+tASb4xY5w==, figureFileBig=G07GaLwmcK8nHiu7h6dDBg==, tableContent=null), ArticleFig(id=1241050041973010459, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=图2, caption=基于敏感性因素变化的1kg污泥陶泥和1kg粉煤灰陶粒碳足迹

图(b)为图(a)的局部放大

, figureFileSmall=Vo2F845Cqp4Q+tASb4xY5w==, figureFileBig=G07GaLwmcK8nHiu7h6dDBg==, tableContent=null), ArticleFig(id=1241050042266611743, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Fig.3, caption=Carbon reduction potential of 1kg sludge ceramsite and 1kg fly ash ceramsite under different scenarios, figureFileSmall=eCYtQiCjLSCCLpJtziDKzw==, figureFileBig=nkcoOb8/adMm+5ANlNRCFQ==, tableContent=null), ArticleFig(id=1241050042384052259, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=图3, caption=不同情景下1kg污泥陶粒和1kg粉煤灰陶粒碳减排潜力, figureFileSmall=eCYtQiCjLSCCLpJtziDKzw==, figureFileBig=nkcoOb8/adMm+5ANlNRCFQ==, tableContent=null), ArticleFig(id=1241050042673459239, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 1, caption=

Carbon footprint factor pool

, figureFileSmall=null, figureFileBig=null, tableContent=
能源碳足迹因子
能源名称碳足迹因子(kg/kg)
柴油3.72
生物质燃料0.12
天然气2.21
电力0.91
原材料开采碳足迹因子
原料类别碳足迹因子(kg/kg)
黏土1.42×10-3
陶粒生产过程碳足迹因子
有机质原料碳足迹因子(kg/kg)
黏土0.60
废弃土0.60
粉煤灰0.01
污泥0.12
交通运输碳足迹因子
交通工具类别碳足迹因子(kg/(t·km))
重型柴油货车运输(载重30t)0.078
纯电动重卡(载重30t)0.046
填埋的碳足迹因子
填埋气类别碳足迹因子(kg/kg)
CH41.84×10-3
CO24.20×10-3
), ArticleFig(id=1241050042795094056, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表1, caption=

碳足迹因子库

, figureFileSmall=null, figureFileBig=null, tableContent=
能源碳足迹因子
能源名称碳足迹因子(kg/kg)
柴油3.72
生物质燃料0.12
天然气2.21
电力0.91
原材料开采碳足迹因子
原料类别碳足迹因子(kg/kg)
黏土1.42×10-3
陶粒生产过程碳足迹因子
有机质原料碳足迹因子(kg/kg)
黏土0.60
废弃土0.60
粉煤灰0.01
污泥0.12
交通运输碳足迹因子
交通工具类别碳足迹因子(kg/(t·km))
重型柴油货车运输(载重30t)0.078
纯电动重卡(载重30t)0.046
填埋的碳足迹因子
填埋气类别碳足迹因子(kg/kg)
CH41.84×10-3
CO24.20×10-3
), ArticleFig(id=1241050043017392172, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 2, caption=

Life cycle inventory for the production of 1kg fly ash ceramsite

, figureFileSmall=null, figureFileBig=null, tableContent=
原料获取阶段
原料类别运输方式运输距离(km)运输量(kg)
原料从开采地污泥公路-30t柴油货车90[53]0.72
到陶粒厂废弃土公路-30t柴油货车90[53]1.53
污泥、废弃土从产生厂到填埋场公路-30t柴油货车302.25
陶粒生产阶段
污泥(kg)0.72
废弃土(kg)1.53
电力消耗(kW·h)3.03×10-3
生物质燃料消耗(kg)0.17
陶粒运输阶段
运输方式运输距离(km)运输量(kg)
从陶粒厂运输至使用现场公路-30t柴油货车70[53]1
陶粒废弃处置阶段
运输方式运输距离(km)运输量(kg)
从使用地运输至填埋场公路-30t柴油货车301
柴油消耗(kg)0.228×10-3[52]
), ArticleFig(id=1241050043264856112, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表2, caption=

1kg污泥陶粒生产的生命周期清单

, figureFileSmall=null, figureFileBig=null, tableContent=
原料获取阶段
原料类别运输方式运输距离(km)运输量(kg)
原料从开采地污泥公路-30t柴油货车90[53]0.72
到陶粒厂废弃土公路-30t柴油货车90[53]1.53
污泥、废弃土从产生厂到填埋场公路-30t柴油货车302.25
陶粒生产阶段
污泥(kg)0.72
废弃土(kg)1.53
电力消耗(kW·h)3.03×10-3
生物质燃料消耗(kg)0.17
陶粒运输阶段
运输方式运输距离(km)运输量(kg)
从陶粒厂运输至使用现场公路-30t柴油货车70[53]1
陶粒废弃处置阶段
运输方式运输距离(km)运输量(kg)
从使用地运输至填埋场公路-30t柴油货车301
柴油消耗(kg)0.228×10-3[52]
), ArticleFig(id=1241050043348742193, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 3, caption=

Life cycle inventory for the production of 1kg fly ash ceramsite

, figureFileSmall=null, figureFileBig=null, tableContent=
原料获取阶段
原料类别运输方式运输距离(km)运输量(kg)
原料从开采地粉煤灰公路-30t柴油货车90[53]1.50
到陶粒厂黏土公路-30t柴油货车90[53]0.90
粉煤灰从产生厂到填埋场公路-30t柴油货车301.50
陶粒生产阶段
粉煤灰(kg)1.50
黏土(kg)0.90
电力消耗(kW·h)0.12×10-3
天然气消耗(m3)4.17×10-3
陶粒运输阶段
运输方式运输距离(km)运输量(kg)
从陶粒厂运输至使用现场公路-30t柴油货车70[53]1
陶粒废弃处置阶段
运输方式运输距离(km)运输量(kg)
从使用地运输至填埋场公路-30t柴油货车301
柴油消耗(kg)0.228×10-3[52]
), ArticleFig(id=1241050043621371957, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表3, caption=

1kg粉煤灰陶粒生产的生命周期清单

, figureFileSmall=null, figureFileBig=null, tableContent=
原料获取阶段
原料类别运输方式运输距离(km)运输量(kg)
原料从开采地粉煤灰公路-30t柴油货车90[53]1.50
到陶粒厂黏土公路-30t柴油货车90[53]0.90
粉煤灰从产生厂到填埋场公路-30t柴油货车301.50
陶粒生产阶段
粉煤灰(kg)1.50
黏土(kg)0.90
电力消耗(kW·h)0.12×10-3
天然气消耗(m3)4.17×10-3
陶粒运输阶段
运输方式运输距离(km)运输量(kg)
从陶粒厂运输至使用现场公路-30t柴油货车70[53]1
陶粒废弃处置阶段
运输方式运输距离(km)运输量(kg)
从使用地运输至填埋场公路-30t柴油货车301
柴油消耗(kg)0.228×10-3[52]
), ArticleFig(id=1241050043814309944, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 4, caption=

Carbon footprint of 1kg sludge ceramsite and 1kg fly ash ceramsite

, figureFileSmall=null, figureFileBig=null, tableContent=
生命周期阶段污泥陶粒CO2eq排放量(kg)粉煤灰陶粒CO2eq排放量(kg)
原料获取阶段-0.11-0.06
陶粒生产阶段1.030.57
陶粒运输阶段0.010.01
陶粒废弃处置阶段0.060.06
合计1.000.58
), ArticleFig(id=1241050043919167549, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表4, caption=

1kg污泥陶粒和1kg粉煤灰陶粒碳足迹

, figureFileSmall=null, figureFileBig=null, tableContent=
生命周期阶段污泥陶粒CO2eq排放量(kg)粉煤灰陶粒CO2eq排放量(kg)
原料获取阶段-0.11-0.06
陶粒生产阶段1.030.57
陶粒运输阶段0.010.01
陶粒废弃处置阶段0.060.06
合计1.000.58
), ArticleFig(id=1241050044053385279, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 5, caption=

Uncertainty in carbon footprint of 1kg sludge ceramsite and 1kg fly ash ceramsite

, figureFileSmall=null, figureFileBig=null, tableContent=
阶段污泥陶粒不确定性(%)粉煤灰陶粒的不确定性(%)
原料获取阶段±13.16±15.98
陶粒生产阶段±10.04±10.71
陶粒运输阶段±11.18±11.18
陶粒废弃处置阶段±10.48±10.48
碳足迹±10.56±10.74
), ArticleFig(id=1241050044346986561, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表5, caption=

1kg污泥陶粒和1kg粉煤灰陶粒碳足迹的不确定性

, figureFileSmall=null, figureFileBig=null, tableContent=
阶段污泥陶粒不确定性(%)粉煤灰陶粒的不确定性(%)
原料获取阶段±13.16±15.98
陶粒生产阶段±10.04±10.71
陶粒运输阶段±11.18±11.18
陶粒废弃处置阶段±10.48±10.48
碳足迹±10.56±10.74
), ArticleFig(id=1241050046037291079, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 6, caption=

Sensitivity analysis results

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因子变动率(%)敏感性系数
污泥陶粒粉煤灰陶粒
运输碳足迹因子±200.030.06
±40
电力碳足迹因子±200.000.00
±40
燃料消耗量±200.020.02
±40
污泥使用量±200.35/
±40/
粉煤灰使用量±20/1.71
±40/
), ArticleFig(id=1241050046314115147, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表6, caption=

敏感性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
影响因子变动率(%)敏感性系数
污泥陶粒粉煤灰陶粒
运输碳足迹因子±200.030.06
±40
电力碳足迹因子±200.000.00
±40
燃料消耗量±200.020.02
±40
污泥使用量±200.35/
±40/
粉煤灰使用量±20/1.71
±40/
), ArticleFig(id=1241050046934872143, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=EN, label=Table 7, caption=

Scenario setting for carbon reduction potential assessment of sludge ceramsite and fly ash ceramsite

, figureFileSmall=null, figureFileBig=null, tableContent=
减碳情景情景说明关键参数选取关键参数设定说明
运输优化使用纯电动重卡替代原有的重型柴油货车运输运输碳足迹因子(kg/(t·km))0.046电力碳足迹因子参考国家发展改革委《以双碳目标为导向的产业链评估与发展路径研究》项目报告数据
原料优化提高污泥和粉煤灰的用量原料中污泥占比(%)60
原料中粉煤灰占比(%)90
协同优化1运输+原料使用纯电动重卡替代原有的重型柴油货车运输,提高污泥和粉煤灰的用量运输碳足迹因子(kg/(t·km))0.046
原料中污泥占比(%)60
原料中粉煤灰占比(%)90
协同优化2运输+电力使用纯电动重卡替代原有的重型柴油货车运输,同时优化电力结构,降低电力碳足迹因子运输碳足迹因子(kg/(t·km))0.024
电力碳足迹因子(t/(MW·h))0.48
协同优化3运输+原料+电力使用纯电动重卡替代原有的重型柴油货车运输,提高污泥和粉煤灰的用量,同时优化电力结构,降低电力碳足迹因子运输碳足迹因子(kg/(t·km))0.024
原料中污泥占比(%)60
原料中粉煤灰占比(%)90
电力碳足迹因子(t/(MW·h))0.48
), ArticleFig(id=1241050047115227217, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241050007873319359, language=CN, label=表7, caption=

污泥陶粒和粉煤灰陶粒的碳减排潜力评估情景设置[12,22,50,51,57-61]

, figureFileSmall=null, figureFileBig=null, tableContent=
减碳情景情景说明关键参数选取关键参数设定说明
运输优化使用纯电动重卡替代原有的重型柴油货车运输运输碳足迹因子(kg/(t·km))0.046电力碳足迹因子参考国家发展改革委《以双碳目标为导向的产业链评估与发展路径研究》项目报告数据
原料优化提高污泥和粉煤灰的用量原料中污泥占比(%)60
原料中粉煤灰占比(%)90
协同优化1运输+原料使用纯电动重卡替代原有的重型柴油货车运输,提高污泥和粉煤灰的用量运输碳足迹因子(kg/(t·km))0.046
原料中污泥占比(%)60
原料中粉煤灰占比(%)90
协同优化2运输+电力使用纯电动重卡替代原有的重型柴油货车运输,同时优化电力结构,降低电力碳足迹因子运输碳足迹因子(kg/(t·km))0.024
电力碳足迹因子(t/(MW·h))0.48
协同优化3运输+原料+电力使用纯电动重卡替代原有的重型柴油货车运输,提高污泥和粉煤灰的用量,同时优化电力结构,降低电力碳足迹因子运输碳足迹因子(kg/(t·km))0.024
原料中污泥占比(%)60
原料中粉煤灰占比(%)90
电力碳足迹因子(t/(MW·h))0.48
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污泥陶粒与粉煤灰陶粒碳足迹对比研究
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宋晓聪 1 , 杜帅 2, * , 邓陈宁 1 , 沈鹏 1 , 朱芳 1 , 谢明辉 1, *
中国环境科学 | 碳排放控制 2025,45(1): 583-592
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中国环境科学 | 碳排放控制 2025, 45(1): 583-592
污泥陶粒与粉煤灰陶粒碳足迹对比研究
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宋晓聪1 , 杜帅2, * , 邓陈宁1, 沈鹏1, 朱芳1, 谢明辉1, *
作者信息
  • 1.中国环境科学研究院,北京 100012
  • 2.中国环境科学研究院环境技术工程有限公司,北京 100012
  • 宋晓聪(1992-),女,河北石家庄人,工程师,硕士,主要从事固体废物资源化碳评价领域研究.发表论文20余篇..

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*责任作者,研究员,
Comparative study on carbon footprint of sludge ceramsite and fly ash ceramsite
Xiao-cong SONG1 , Shuai DU2, * , Chen-ning DENG1, Peng SHEN1, Fang ZHU1, Ming-hui XIE1, *
Affiliations
  • 1.Chinese Research Academy of Environmental Sciences, Beijing 100012, China
  • 2.Chinese Research Academy of Environmental Sciences Environmental Technology & Engineering Co., Ltd., Beijing 100012, China
出版时间: 2025-01-20
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污泥陶粒与粉煤灰陶粒是最为常见的两种固废陶粒,为对比分析两种固废陶粒产品碳足迹特征及量化评估产品碳减排效益,从碳足迹角度构建污泥陶粒与粉煤灰陶粒产品碳足迹核算模型,基于敏感性分析判识关键减排因素,通过情景分析预测评估污泥陶粒与粉煤灰陶粒的碳减排潜力,同时,使用误差传播方程进行不确定性分析保证碳足迹结果的可靠有效.结果表明,生产1kg污泥陶粒和1kg粉煤灰陶粒CO2eq排放量分别为1.00和0.58kg,污泥陶粒和粉煤灰陶粒碳足迹特征相似,陶粒生产阶段是两种陶粒产品碳排放的主要环节,分别占各自碳足迹(除原料获取阶段)的93.71%和89.12%.原料结构是影响污泥陶粒与粉煤灰陶粒碳足迹最敏感的因素,其次就是运输结构,相比于污泥陶粒,粉煤灰陶粒碳足迹受原料结构调整影响更大.协同优化情景中,同时优化运输和原料结构的碳减排潜力(31%~78%)远高于同时优化运输和电力结构(2%~5%),此外,3种因素同时作用的减排潜力最高,达到33%~79%.

污泥陶粒  /  粉煤灰陶粒  /  碳足迹  /  减排潜力  /  敏感性分析

Sludge ceramsite and fly ash ceramsite are the two most common types of solid waste ceramsite. To compare and analyze the carbon footprint characteristics of the two types of solid waste ceramsite and quantitatively evaluate the carbon reduction benefits of the products, a carbon footprint accounting model for sludge ceramsite and fly ash ceramsite is constructed from the perspective of carbon footprint. Based on sensitivity analysis, key emission reduction factors are identified, and the carbon reduction potential of sludge ceramsite and fly ash ceramsite is predicted and evaluated through scenario analysis. Meanwhile, using error propagation equations for uncertainty analysis ensures the reliability and effectiveness of carbon footprint results. The results showed that the CO2 emissions from the production of 1kg sludge ceramsite and 1kg fly ash ceramsite were 1.00 and 0.58 kg, respectively. The carbon footprint characteristics of sludge ceramsite and fly ash ceramsite were similar, and the ceramsite production stage was the main link in the carbon emissions of the two ceramsite particle products, accounting for 93.71% and 89.12% of their respective carbon footprints (excluding the raw material acquisition stage), respectively. The raw material structure is the most sensitive factor affecting the carbon footprint of sludge ceramsite and fly ash ceramsite, followed by the transportation structure. Compared with sludge ceramsite, the carbon footprint of fly ash ceramsite is more affected by the adjustment of raw material structure. In the scenario of collaborative optimization, the carbon emission reduction potential of simultaneously optimizing transportation and raw material structure (31%~78%) is far higher than that of simultaneously optimizing transportation and power structure (2%~5%). In addition, the emission reduction potential of the three factors acting simultaneously is the highest, reaching 33%~79%.

sludge ceramsite  /  fly ash ceramsite  /  carbon footprint  /  emission reduction potential  /  sensitivity analysis
宋晓聪, 杜帅, 邓陈宁, 沈鹏, 朱芳, 谢明辉. 污泥陶粒与粉煤灰陶粒碳足迹对比研究. 中国环境科学, 2025 , 45 (1) : 583 -592 .
Xiao-cong SONG, Shuai DU, Chen-ning DENG, Peng SHEN, Fang ZHU, Ming-hui XIE. Comparative study on carbon footprint of sludge ceramsite and fly ash ceramsite[J]. China Environmental Science, 2025 , 45 (1) : 583 -592 .
我国是陶粒生产大国,陶粒的生产代表着高耗能和高碳排放[1-2],“双碳”战略对陶粒行业提出了绿色低碳转型的要求[3-6].此外,我国固体废物产量与日俱增,年产量超100亿t[7],其大量堆积不仅污染环境,同时也是一种资源浪费.固废陶粒的生产不仅消纳了固废[8-9],也是陶粒行业实现“双碳”目标的主要途径.因此,固废陶粒产品碳足迹研究对陶粒行业实现低碳转型具有重要意义.污泥和粉煤灰因与黏土、页岩等原料成分类似而用于固废陶粒生产[8-14],国内黏土类固废陶粒(污泥、废弃土等)市场占比近85%,再就是粉煤灰陶粒[5-6].国外关于固废陶粒的研究多关注其产品性能及环境影响评价方面.在产品性能方面,陶粒生产过程中单独添加污泥[15]或粉煤灰[16]会导致产品性能退化等问题,Tong等[9]和Mao等[17]通过改变工艺参数、污泥添加量等条件来改善污泥陶粒的性能;Nguyen等[16]和Małaszkiewicz[18]等发现粉煤灰陶粒制成的混凝土随粉煤灰含量的增加抗压强度降低.而环境影响评价上主要是关于陶粒砌砖、陶粒混凝土等陶粒制品的研究[19-20],例如,De Souza等[19]和Letícia等[20]分别对比了加拿大和巴西陶粒砌砖与混凝土砖的环境影响,关于陶粒制品的碳排放情况得出相反的结论,说明陶粒制品碳排放存在地域差异,不同的国家地区需要针对性的研究.
国内有关固废陶粒的研究也大多集中在产品制备技术及性能方面,例如赵飞燕等[21]综述了粉煤灰陶粒制备技术的国内外研究进展,粉煤灰陶粒制备技术主要有焙烧法和免烧法[22-24],污泥陶粒制备方法亦是如此[25-28],与培烧法相比,免烧法相对绿色环保,能耗更低,但是该技术还未成熟[21],国内多采用培烧法制备陶粒.固废陶粒因其优异特性作为建筑材料[29-30]和吸附材料[31-32]应用在建筑、环保等领域.在环保方面,污泥陶粒可用来处理含磷废水[33],粉煤灰陶粒对含铅、铜低浓度重金属废水有较好的净化效果[34],同时污泥陶粒具有类似的净化作用[35].在建筑方面,固废陶粒制备的混凝土可以在盐冻环境下应用[36],此外还可以降低建筑的能耗[37].在碳排放方面的研究大多是关于陶粒制品[38]的鲜少是单独陶粒的并且时间久远,房明慧[39]对粉煤灰陶粒砌砖和加气混凝土砌砖进行生命周期影响评价,其中温室效应是主要的环境影响类别,粉煤灰陶粒砌砖和加气混凝土砌砖CO2排放分别为156和290kg/m3,此项研究虽然单独计算了粉煤灰陶粒的碳排放量,但是只是生产阶段的碳排放,并未涵盖粉煤灰陶粒的全生命周期阶段,不等同于碳足迹.
综上,目前针对固废陶粒产品碳足迹的研究存在空白,大多是通过陶粒制品生命周期评价中的温室效应侧面分析陶粒的碳排放.此外针对陶粒制品碳排放的研究时间久远,并且大多只包含陶粒生产阶段的碳排放,缺少整个生命周期阶段的碳排放.因此,亟需从碳足迹角度开展固废陶粒产品碳排放量化和减排潜力评估工作.本研究以污泥陶粒和粉煤灰陶粒为例,从碳足迹角度构建污泥陶粒与粉煤灰陶粒产品碳足迹核算模型,基于敏感性分析判识影响固废陶粒产品的关键减排因素,通过情景分析预测评估污泥陶粒与粉煤灰陶粒的碳减排潜力,以期为陶粒行业低碳转型提供技术支撑.
从碳足迹视角定义污泥陶粒和粉煤灰陶粒产品的全生命周期系统边界(图1),包括原料获取、原料运输、加工生产、成品运输、应用、废弃处置等阶段.以生产1kg陶粒为功能单位.
根据污泥陶粒和粉煤灰陶粒产品生命周期碳排放核算边界的界定,陶粒生命周期的总碳排放量为原料获取、生产、运输、废物处置阶段的碳排放量之和,具体公式如下:
式中:C为生产1kg陶粒生命周期碳排放,kg(以CO2eq计);C原料为生产1kg陶粒原料获取阶段碳排放,kg;C生产为1kg陶粒生产阶段碳排放,kg;C运输为1kg陶粒由生产地运输到应用地的碳排放,kg;C处置为废陶粒废弃处置阶段的碳排放,kg.
陶粒生产的原材料获取阶段可进一步划分成原料开采、原料运输和固废原料处置.计算公式如下:
式中:C开采为原料开采阶段的碳排放,kg;C运输1为原料运输至陶粒厂的碳排放,kg;C处置1为固废原料填埋阶段的碳排放,kg.
C开采主要指黏土等原料的开采产生的碳排放,计算公式如下:
式中:n为原材料使用种类;Bi为原材料的碳足迹因子,kg/kg;bi为原料消耗量与陶粒产量的比值系数.
C运输1计算公式如下:
式中:n为原材料运输种类;Di为原料运输距离,km;E为所选运输工具的碳足迹因子,kg/(kg·km);Fi为固废原料运输距离,km;g为固废原料消耗量与陶粒产量的比值系数;t为空车修正系数,根据文献[40]可知空载时的环境负荷是满载时的0.67倍,故取t=1.67.
由于固废作为原料再利用,没有填埋,所以需除去其填埋过程造成的碳排放,填埋过程的碳排放源于推土机、挖掘机、装载机、压实机等设备运行的能量消耗和填埋气.计算公式如下:
式中:α为能源类别;Gi为填埋1kg固废的能源用量,kg;Ki为能源的碳足迹因子,kg/kg或kg/(m3);M为1kg固废由产生厂至填埋场的运输距离,km;E为所选运输工具的碳足迹因子,kg/(kg·km);填埋1kg固废的CO2产生量,kg;甲烷全球变暖潜势,kg/(kgCH4),27.9;填埋1kg固废的甲烷产生量,kg.
陶粒生产阶段碳排放主要包括原料中所含的有机质在烘干和煅烧时排放的CO2以及能源燃烧和外购电力热力的碳排放.计算公式如下:
式中:γ为陶粒中含有机质原料的种类;Lj为生产1kg陶粒的有机质原料用量,kg;lj为有机质原料的碳排放因子,kg/kg;Qi为生产1kg陶粒的能源用量,kg;H为生产1kg陶粒消耗的外购电力量,kW·h;h为电力碳足迹因子,kgCO2/(kW·h).H为生产1kg陶粒消耗的外购热力量,MJ;h为热力碳排放因子,kg/MJ.
陶粒运输阶段的碳排放计算公式如下:
式中:β运输方式类别;Ji为1kg陶粒由陶粒厂至使用现场的运输距离,km;Ei为所选运输工具的碳足迹因子,kg/(kg·km).
陶粒废弃处置阶段碳排放计算公式如下:
式中:N为1kg废陶粒由使用地至填埋场的运输距离,km;其他同上.
污泥陶粒和粉煤灰陶粒碳足迹核算依托大量的实景数据和背景数据,因为地域和时间的影响使得碳足迹的核算结果存在一定的误差,为确认碳足迹核算结果真实准确,借助IPCC的误差传播方程[41]对污泥陶粒和粉煤灰陶粒碳足迹核算结果不确定性进行分析.
式中:OSij为第i个阶段中第j种碳源估算的不确定性,%;为碳足迹因子的不确定性,%;为污泥陶粒和粉煤灰陶粒活动水平数据的不确定性,%;Ototal为总的不确定性,%;为第i个阶段的不确定性,%;Si为第i个阶段不同碳源的碳排放量,kg.
通过敏感性系数定量分析影响污泥陶粒和粉煤灰陶粒碳足迹的主要因素[42-43],敏感性系数越大,表示碳足迹结果受该项因素影响越大[43],以此为固废再生陶粒产品降碳提供技术支撑.
式中:T为敏感性系数;ΔC/C为污泥陶粒和粉煤灰陶粒碳排放变化率;Δx/x为影响因素变化率.
背景数据主要指各类碳足迹因子,包括能源碳足迹因子[44-47]、原材料开采碳足迹因子(中国生命周期基础数据库(CLCD))、陶粒生产过程的碳足迹因子[39,48]、交通运输碳足迹因子[49-51]以及填埋的足迹因子[52],具体取值见表1.
文章的实景数据主要通过现场调研污泥陶粒和粉煤灰陶粒代表性企业(生产规模、生产工艺)获得,其中污泥陶粒数据来源于江苏省淮安市陶粒生产厂家,粉煤灰陶粒数据来源于山东省滨州市陶粒生产厂家,数据可代表江苏省和山东省2022年采用回转窑工艺生产的污泥陶粒和粉煤灰陶粒.污泥陶粒和粉煤灰陶粒生产生命周期清单具体见表2表3.
1kg污泥陶粒和1kg粉煤灰陶粒碳足迹如表4所示(2022年数据).研究发现,生产1kg污泥陶粒和1kg粉煤灰陶粒碳足迹分别为1.00和0.58kg.粉煤灰陶粒相比于污泥陶粒可实现约42%的碳减排效益,说明在减碳方面,生产粉煤灰陶粒是中国更优的选择.此外,污泥陶粒和粉煤灰陶粒碳排放特征相似,生命周期阶段的碳排放量由小到大均为:原料获取阶段<陶粒运输阶段<陶粒废弃处置阶段<陶粒生产阶段.污泥陶粒和粉煤灰陶粒原料获取阶段产生了正向的碳减排效益,主要因为使用了污泥、粉煤灰的固体废物作为生产原料,进而避免了填埋处置以上固体废物的能源消耗[2]以及填埋气的产生;陶粒废弃处置阶段的碳排放与填埋处置时填埋气等温室气体的产生息息相关,另外陶粒运输和陶粒废弃处置阶段的碳排放也主要归因于成品场外运输和废陶粒运输至填埋场使用柴油等化石能源的燃烧[20];陶粒生产阶段的碳排放与原料生产工艺过程的碳排放息息相关,污泥陶粒和粉煤灰陶粒生产工艺碳排放占到生产阶段碳排放的90%以上,优化原材料使用结构是降低陶粒生产阶段碳排放重要举措.1kg污泥陶粒生产阶段CO2eq排放量约为1kg,高于周越[38]研究结果(0.5kgCO2),此外,相比于轻质陶粒0.44~0.56kgCO2的碳排放[48,54]也增加了近一倍;1kg粉煤灰陶粒生产阶段CO2eq排放量约为0.57kg,高于房明慧0.36kgCO2[39]的研究结果,主要因为本研究是包含了范围一、二和三的碳足迹结果,而文献只包含了范围一、二的CO2排放,本研究碳核算范围更大,这也说明了范围三的碳排放不可忽视.
文章利用IPCC的误差传播方程定量评估了污泥陶粒和粉煤灰陶粒全生命周期各阶段以及碳足迹的不确定性,结果如表5所示.参考张晓梅研究[55],实景数据不确定性按5%取值,背景数据不确定性按照10%取值,结果显示,污泥陶粒原料获取阶段、生产阶段、运输阶段和废弃处置阶段碳核算结果不确定性分别为13%、10%、11%和10%,最终1kg污泥陶粒生命周期碳足迹不确定性为8%;粉煤灰陶粒除原料获取阶段(16%),其余各生命周期阶段碳排放核算结果不确定性均在11%左右,1kg粉煤灰陶粒碳足迹不确定性略高于污泥陶粒为10.74%.总体分析,文章各项不确定性均在合理范围内[55-57].不确定性通过与碳排放核算相关的研究[41,55]对比在合理范围内.
通过污泥陶粒与粉煤灰陶粒碳足迹分析,发现运输结构、电力结构、原料结构均是影响污泥陶粒与粉煤灰陶粒碳足迹的因素,基于此文章进一步选取运输碳足迹因子、电力碳足迹因子、燃料消耗量、污泥使用量以及粉煤灰使用量4项影响因子进行敏感性分析,判识陶粒碳足迹对于几种影响因子的敏感性.针对运输碳足迹因子、电力碳足迹因子和污泥使用量以及粉煤灰使用量进行±20%和±40%的敏感性分析,敏感性分析结果见表6.
敏感性系数越大,表示碳足迹结果受该项因素影响越大[43].分析发现,原料结构是影响污泥陶粒与粉煤灰陶粒碳足迹最敏感的因素,即固废原料使用量(污泥、粉煤灰),当将污泥和粉煤灰用量比例提高20%时,1kg污泥陶粒与粉煤灰陶粒碳足迹分别可降低约7%(图2(a))和34%(图2(c)),由此分析发现,相比于污泥陶粒,粉煤灰陶粒碳足迹受原料结构调整影响更大.其次是运输碳足迹因子,如果运输碳足迹因子降低20%,1kg污泥陶粒与粉煤灰陶粒碳足迹分别为0.99kg(图2(b))和0.57kg(图2(c)),均实现约1%的碳减排.
另外污泥陶粒与粉煤灰陶粒碳足迹对于电力碳足迹因子和燃料消耗量的影响均不敏感.因此,未来降低陶粒产品的碳排放应该从优化运输和原料结构入手,但是同时需要注意的是,陶粒产品运输结构优化通常是提高电动汽车运输的占比,那么此时,涉及运输的碳排放就与电力碳足迹因子息息相关.所以,在优化运输结构的同时降低电力碳足迹因子也是陶粒产品未来降碳的一个重要方向.
通过敏感性分析可知运输碳足迹因子、固废原料用量是影响陶粒产品碳足迹的重要因素.因此,设置以下5种低碳情景以分析污泥陶粒和粉煤灰陶粒的碳减排潜力(表7).
不同情景下1kg污泥陶粒和粉煤灰陶粒的碳足迹如图3所示.通过提高陶粒产品中污泥和粉煤灰的用量,1kg污泥陶粒和粉煤灰陶粒分别能够实现约31%和75%的碳减排潜力,因减少了黏土、页岩等原料用量而实现减排效益[62].相比于原料优化情景,通过使用电动汽车替代柴油货车运输具有较低的减排潜力,当前,中国电动汽车领域发展迅速,保有量占比已达到全球50%左右[63],在运输优化情景下,1kg污泥陶粒和粉煤灰陶粒仅能实现约2%的碳减排潜力.鉴于电动汽车的碳排放与电力碳足迹因子息息相关,文章设置了3种协同优化情景:协同优化1情景同时优化运输和原料结构,1kg污泥陶粒和粉煤灰陶粒分别实现31.96%和77.42%的碳减排潜力;虽然污泥陶粒与粉煤灰陶粒碳足迹受电力碳足迹因子影响没有运输碳足迹因子敏感,但是相比于单独优化运输结构,联合采取优化运输和电力结构两种措施能够使减排潜力提高70%~80%,协同优化2情景下,1kg污泥陶粒和粉煤灰陶粒分别达到2.26%和4.24%减排潜力;同时优化运输、电力和原料结构,1kg污泥陶粒和粉煤灰陶粒碳足迹分别比基准情景下降32.96%和79.03%,协同优化3情景下碳减排潜力比协同优化1情景稍高.综合政策、经济、时间等因素,优化运输结构的同时提高固废原料使用量或单独优化原料结构是陶粒产品未来降碳的优先选择.
3.1 从碳足迹角度分析,生产1kg粉煤灰陶粒比生产1kg污泥陶粒更低碳.生产1kg污泥陶粒和1kg粉煤灰陶粒碳足迹分别为1.00和0.58kg,粉煤灰陶粒相比于污泥陶粒可实现约42%的碳减排效益.污泥陶粒和粉煤灰陶粒碳排放特征相似,原料获取阶段以及陶粒生产阶段是两种陶粒产品进行碳减排的关键环节,其中原料获取阶段自带11%的碳减排效益;污泥陶粒和粉煤灰陶粒原料获取阶段的碳排放分别占各自碳足迹(除原料获取阶段)的93.71%和89.12%.
3.2 敏感性分析结果显示,原料结构是影响污泥陶粒与粉煤灰陶粒碳足迹最敏感的因素,且相比于污泥陶粒,粉煤灰陶粒碳足迹受原料结构调整影响更大.其次是运输结构,另外,污泥陶粒与粉煤灰陶粒碳足迹对于电力碳足迹因子和燃料消耗量的敏感性较低.鉴于陶粒产品运输结构优化通常是提高电动汽车运输的占比,在优化运输结构的同时降低电力碳足迹因子是陶粒产品未来降碳的一个重要方向.
3.3 单独优化原料结构比优化运输结构具有更大的减排优势.在原料优化情景下,1kg污泥陶粒和粉煤灰陶粒均能够实现约30%~75%的碳减排潜力,而运输优化情景下碳减排潜力低于5%;此外,原料优化情景下粉煤灰陶粒碳减排潜力(75%)是污泥陶粒碳减排潜力(31%)的2.42倍.综合政策、经济、时间等因素,联合优化运输和原料结构或单独优化原料结构是陶粒产品未来降碳的优先选择.协同优化1情景下(优化运输和原料结构),1kg污泥陶粒和粉煤灰陶粒分别达到31.96%和77.42%的碳减排潜力;协同优化2情景下(优化运输和电力结构),1kg污泥陶粒和粉煤灰陶粒仅实现2.26%和4.24%减排潜力.
  • 宁波市重大科技攻关项目(20212ZDYF020047)
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2025年第45卷第1期
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  • 接收时间:2024-06-03
  • 首发时间:2026-03-18
  • 出版时间:2025-01-20
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  • 收稿日期:2024-06-03
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    1.中国环境科学研究院,北京 100012
    2.中国环境科学研究院环境技术工程有限公司,北京 100012

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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