Roseane A. S. Albani, Vinicius V. L. Albani, Luiz E. S. Gomes, Helio S. Migon, Antonio J. Silva Neto
{"title":"通过贝叶斯推理估算大气释放数量和其他参数","authors":"Roseane A. S. Albani, Vinicius V. L. Albani, Luiz E. S. Gomes, Helio S. Migon, Antonio J. Silva Neto","doi":"10.1007/s11869-023-01497-9","DOIUrl":null,"url":null,"abstract":"<div><p>We propose a methodology to estimate unknown atmospheric releases, including the number of emissions, addressing overfitting, and using an economical number of unknowns. It is based on the combination of accurate modeling to solve the dispersion problem with Bayesian inference to identify the parameters from observed concentrations. The estimation tool is tested with the Fusion Field Trial 2007 (FFT-07) data set.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 5","pages":"1007 - 1019"},"PeriodicalIF":2.9000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating the number of atmospheric releases and other parameters by Bayesian inference\",\"authors\":\"Roseane A. S. Albani, Vinicius V. L. Albani, Luiz E. S. Gomes, Helio S. Migon, Antonio J. Silva Neto\",\"doi\":\"10.1007/s11869-023-01497-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We propose a methodology to estimate unknown atmospheric releases, including the number of emissions, addressing overfitting, and using an economical number of unknowns. It is based on the combination of accurate modeling to solve the dispersion problem with Bayesian inference to identify the parameters from observed concentrations. The estimation tool is tested with the Fusion Field Trial 2007 (FFT-07) data set.</p></div>\",\"PeriodicalId\":49109,\"journal\":{\"name\":\"Air Quality Atmosphere and Health\",\"volume\":\"17 5\",\"pages\":\"1007 - 1019\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Air Quality Atmosphere and Health\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11869-023-01497-9\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Air Quality Atmosphere and Health","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s11869-023-01497-9","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
我们提出了一种估算未知大气排放的方法,包括排放数量、解决过度拟合问题以及使用经济的未知数数量。该方法将精确建模解决扩散问题与贝叶斯推理相结合,通过观测浓度确定参数。该估算工具利用 Fusion Field Trial 2007 (FFT-07) 数据集进行了测试。
Estimating the number of atmospheric releases and other parameters by Bayesian inference
We propose a methodology to estimate unknown atmospheric releases, including the number of emissions, addressing overfitting, and using an economical number of unknowns. It is based on the combination of accurate modeling to solve the dispersion problem with Bayesian inference to identify the parameters from observed concentrations. The estimation tool is tested with the Fusion Field Trial 2007 (FFT-07) data set.
期刊介绍:
Air Quality, Atmosphere, and Health is a multidisciplinary journal which, by its very name, illustrates the broad range of work it publishes and which focuses on atmospheric consequences of human activities and their implications for human and ecological health.
It offers research papers, critical literature reviews and commentaries, as well as special issues devoted to topical subjects or themes.
International in scope, the journal presents papers that inform and stimulate a global readership, as the topic addressed are global in their import. Consequently, we do not encourage submission of papers involving local data that relate to local problems. Unless they demonstrate wide applicability, these are better submitted to national or regional journals.
Air Quality, Atmosphere & Health addresses such topics as acid precipitation; airborne particulate matter; air quality monitoring and management; exposure assessment; risk assessment; indoor air quality; atmospheric chemistry; atmospheric modeling and prediction; air pollution climatology; climate change and air quality; air pollution measurement; atmospheric impact assessment; forest-fire emissions; atmospheric science; greenhouse gases; health and ecological effects; clean air technology; regional and global change and satellite measurements.
This journal benefits a diverse audience of researchers, public health officials and policy makers addressing problems that call for solutions based in evidence from atmospheric and exposure assessment scientists, epidemiologists, and risk assessors. Publication in the journal affords the opportunity to reach beyond defined disciplinary niches to this broader readership.