Jeong Eun Kim , In Sun Kim , Soo Ran Won , Daehyun Wee
{"title":"基于预定义离散化的简化势源密度函数","authors":"Jeong Eun Kim , In Sun Kim , Soo Ran Won , Daehyun Wee","doi":"10.1016/j.jer.2024.02.009","DOIUrl":null,"url":null,"abstract":"<div><div>The potential source contribution function (PSCF) method is widely used in the analysis of air pollutant source areas, but it also faces several limitations. To address such limitations, the potential source density function (PSDF) method was developed based on Gaussian process regression (GPR). However, the PSDF model requires more computational resources than the PSCF model. Here, we present an enhanced model with improved speed. We discretized the PSDF method by assigning a predetermined spatial correlation between cells through a priori known correlation length scale. The time taken was reduced by 25–30% from that of the original PSDF method, while the values representing the air pollution sources exhibited only a slight difference from the original ones. Our new method reduces the time required for computational calculations, measures potential sources with comparable precision, and ensures the reliability and source intensity of the results.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"13 2","pages":"Pages 1487-1495"},"PeriodicalIF":2.2000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A simplified potential source density function based on predefined discretization\",\"authors\":\"Jeong Eun Kim , In Sun Kim , Soo Ran Won , Daehyun Wee\",\"doi\":\"10.1016/j.jer.2024.02.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The potential source contribution function (PSCF) method is widely used in the analysis of air pollutant source areas, but it also faces several limitations. To address such limitations, the potential source density function (PSDF) method was developed based on Gaussian process regression (GPR). However, the PSDF model requires more computational resources than the PSCF model. Here, we present an enhanced model with improved speed. We discretized the PSDF method by assigning a predetermined spatial correlation between cells through a priori known correlation length scale. The time taken was reduced by 25–30% from that of the original PSDF method, while the values representing the air pollution sources exhibited only a slight difference from the original ones. Our new method reduces the time required for computational calculations, measures potential sources with comparable precision, and ensures the reliability and source intensity of the results.</div></div>\",\"PeriodicalId\":48803,\"journal\":{\"name\":\"Journal of Engineering Research\",\"volume\":\"13 2\",\"pages\":\"Pages 1487-1495\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering Research\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2307187724000373\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2307187724000373","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
A simplified potential source density function based on predefined discretization
The potential source contribution function (PSCF) method is widely used in the analysis of air pollutant source areas, but it also faces several limitations. To address such limitations, the potential source density function (PSDF) method was developed based on Gaussian process regression (GPR). However, the PSDF model requires more computational resources than the PSCF model. Here, we present an enhanced model with improved speed. We discretized the PSDF method by assigning a predetermined spatial correlation between cells through a priori known correlation length scale. The time taken was reduced by 25–30% from that of the original PSDF method, while the values representing the air pollution sources exhibited only a slight difference from the original ones. Our new method reduces the time required for computational calculations, measures potential sources with comparable precision, and ensures the reliability and source intensity of the results.
期刊介绍:
Journal of Engineering Research (JER) is a international, peer reviewed journal which publishes full length original research papers, reviews, case studies related to all areas of Engineering such as: Civil, Mechanical, Industrial, Electrical, Computer, Chemical, Petroleum, Aerospace, Architectural, Biomedical, Coastal, Environmental, Marine & Ocean, Metallurgical & Materials, software, Surveying, Systems and Manufacturing Engineering. In particular, JER focuses on innovative approaches and methods that contribute to solving the environmental and manufacturing problems, which exist primarily in the Arabian Gulf region and the Middle East countries. Kuwait University used to publish the Journal "Kuwait Journal of Science and Engineering" (ISSN: 1024-8684), which included Science and Engineering articles since 1974. In 2011 the decision was taken to split KJSE into two independent Journals - "Journal of Engineering Research "(JER) and "Kuwait Journal of Science" (KJS).