Article(id=1240655023273726902, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.04.021, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1732550400000, receivedDateStr=2024-11-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724807804, onlineDateStr=2026-03-17, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724807804, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724807804, creator=13701087609, updateTime=1773724807804, updator=13701087609, issue=Issue{id=1240655017040991028, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='4', pageStart='1', pageEnd='199', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724806318, creator=13701087609, updateTime=1773725219619, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656750618145316, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656750618145317, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655017040991028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=183, endPage=191, ext={EN=ArticleExt(id=1240655024213251011, articleId=1240655023273726902, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Study on the method for monitoring the fuel sulfur content of vessels based on emission and diffusion characteristics, columnId=1240655020656489358, journalTitle=Navigation of China, columnName=Green Shipping, runingTitle=null, highlight=null, articleAbstract=

To effectively identify vessels using fuel with excessive sulfur content, a reverse calculation method for determining fuel sulfur content was developed based on emission and diffusion characteristics. A Gaussian puff compensation model was applied to estimate the emission source strength of vessels from monitored SO₂ concentrations at designated points. In addition, a computational model for vessel fuel consumption was established using key vessel parameters, including the power and fuel consumption rates of main and auxiliary engines. The proposed method demonstrated superior performance compared to the mainstream carbon balance method in detecting vessels with non-compliant sulfur content, achieving detection and false detection rates of 86.60% and 2.06%, respectively. Over a 30-day continuous monitoring period, the fuel sulfur content of 2,743 vessels was successfully determined, representing an effective detection rate of 82.72%. Among these, 131 vessels were identified as potentially exceeding sulfur limits. Subsequent verification confirmed that 111 vessels used non-compliant fuel, resulting in an assessment accuracy of 84.73%. These findings demonstrate the method's capability to enable real-time monitoring of fuel sulfur content without requiring CO₂ concentration data.

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文章提出了一种以排放扩散特征为基础的在航船舶燃油硫含量监测方法，通过分析监测点获取的SO₂扩散浓度，并运用高斯烟团修正模型进行计算，最终推导出船舶排放源的强度。进一步，利用船舶主机与辅机的基本参数，构建燃油消耗量计算模型。最后，基于SO₂排放速率与燃油消耗量的比值，反算出船舶燃油硫含量。经验证，本方法在超标船舶检出率和检测异常率方面均优于当前广泛采用的碳平衡法，分别达到86.60%和2.06%。在连续30天的实际监测中，共成功检测2 743艘船舶的燃油硫含量，有效检出率为82.72%；其中131艘涉嫌硫含量超标，经核实111艘确属不合格，检测准确率为84.73%。实测数据证实，该方法能够在不获取CO₂的条件下，实现对在航船舶燃油硫含量的有效实时监测。

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