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Proposing a novel algorithm for identifying atmospheric pollutant transport pathways by coupling optical flow vectors with movement distance constraints—Farneback-Distance Transmission Path Recognition Algorithm (FBD-TPRA). Based on high spatiotemporal-resolution (2km spatial resolution, 1h temporal resolution) PM_2.5 concentration satellite remote sensing inversion data generated via a one-dimensional deep convolutional neural network (1D-CNN), this algorithm extracts inter-frame motion features of polluted air masses using optical flow and incorporates movement distance constraints to achieve continuous and refined reconstruction of pollutant transport pathways. Validation experiments conducted during three typical air pollution events in Changsha, Hunan Province (January 14, October 12, and October 27, 2024) demonstrate that the transport paths identified by the FBD-TPRA algorithm can reasonably explain the temporal relationships of urban concentration variations (Xiantao-Jingzhou-Yueyang-Changsha, Yueyang-Changde-Yiyang-Changsha, and Yueyang-Yiyang-Changsha). Compared with the widely used HYSPLIT model, the FBD-TPRA achieves strong consistency in net displacement direction(cosine similarity up to 0.99), but differ in path morphology, as reflected by dynamic time warping (DTW) values of 2.99°, 9.58°, and 3.69°, respectively. The study indicates that the FBD-TPRA algorithm enhances the characterization of pollutant transport processes by integrating high-resolution spatiotemporal pollutant concentration data and movement distance constraints, providing a new technical approach for identifying atmospheric pollution transport pathways.

, authors=Wei-wen HE¹, Bin ZOU^{1, *}, Yong XU^{1, 2}, Qin-ting ZHANG¹, Jie XU³, Jian WU⁴, authorsList=Wei-wen HE, Bin ZOU, Yong XU, Qin-ting ZHANG, Jie XU, Jian WU, authorCompany=null, correspAuthors=Bin ZOU, 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, fund=null), CN=ArticleExt(id=1314917648874430605, articleId=1314917648324976779, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=耦合光流矢量与移动距离约束的大气污染传输路径识别算法, columnId=1234106388364981004, journalTitle=中国环境科学, columnName=大气污染与控制, runingTitle=null, highlight=null, articleAbstract=

提出一种耦合光流矢量与移动距离约束的大气污染传输路径识别算法——Farneback-Distance Transmission Path Recognition Algorithm（FBD-TPRA）．该算法基于一维深度卷积神经网络（1D-CNN）反演生成的高时空分辨率（空间分辨率为 2km，时间分辨率为1h）PM_2.5浓度卫星遥感反演数据，通过光流法提取污染气团帧间运动特征，引入移动距离约束，实现污染气团传输路径的连续精细重构．湖南省长沙市三次典型大气污染事件（2024年1月14日、10月12日和10月27日）验证实验显示，FBD-TPRA算法所识别的传输路径能够合理解释城市浓度变化的时序关系（仙桃-荆州-岳阳-长沙、岳阳-常德-益阳-长沙、岳阳-益阳-长沙）；相比广泛应用的HYSPLIT模拟路径，FBD-TPRA算法识别的传输路径与其在净位移方向上具有较高的一致性，余弦相似度最高达0.99，但在路径形态上存在差异，动态时间规整（DTW值）分别为2.99°、9.58°和3.69°．研究表明，FBD-TPRA算法通过结合高时空分辨率污染物浓度数据和移动距离约束，增强了对污染物传输过程的刻画能力，为大气污染传输路径的识别提供了新的技术支持．

, authors=贺威文¹, 邹滨^{1, *}, 徐勇^{1, 2}, 张钦挺¹, 许杰³, 吴坚⁴, authorsList=贺威文, 邹滨, 徐勇, 张钦挺, 许杰, 吴坚, authorCompany=null, correspAuthors=邹滨, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=BIsILYzCUqHBT7qvgzlcMA==, magXml=2suCalkrIkcgnHHLrc+NoA==, pdfUrl=null, pdf=ETeTmBqnvR78Lu752HLlCQ==, pdfFileSize=4622003, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=PrvCANRPqdpNDwTW5VzNIw==, mapNumber=null, fund=null)}, authors=[Author(id=1314918056707580839, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917648324976779, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=956811749@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1314918056845992873, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917648324976779, authorId=1314918056707580839, language=EN, stringName=Wei-wen HE, firstName=Wei-wen, middleName=null, lastName=HE, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Geosciences and Info-physics, Central South University, Changsha 410083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1314918056908907434, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1314917648324976779, authorId=1314918056707580839, language=CN, stringName=贺威文, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.中南大学地球科学与信息物理学院，湖南 长沙 410083, bio={"content":"

贺威文（2000-），男，重庆人，中南大学硕士研究生，主要研究方向为城市大气污染跨区域传输的遥感定性定量解析．发表论文1篇．956811749@qq.com.

"}, bioImg=null, bioContent=

贺威文（2000-），男，重庆人，中南大学硕士研究生，主要研究方向为城市大气污染跨区域传输的遥感定性定量解析．发表论文1篇．956811749@qq.com.

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