{"title":"基于大气CO2变化复时频谱的CO2泄漏识别方法","authors":"Denglong Ma*, Xiuben Wu, Jianmin Gao, Zaoxiao Zhang, Xin Zuo","doi":"10.1021/acs.chas.1c00025","DOIUrl":null,"url":null,"abstract":"<p >It is a challenging problem to monitor atmospheric CO<sub>2</sub> leakage due to the complex variation of the atmosphere background. In this research, a new CO<sub>2</sub> leakage identification method in the atmosphere based on the complex time–frequency spectrum of atmospheric CO<sub>2</sub> variation was proposed. First, the complex continuous wavelet transform (CWT) was utilized to analyze the experimental data without and with CO<sub>2</sub> leakage. It was found that CWT could provide distinguished features for atmospheric CO<sub>2</sub> leakage by calculating the time–frequency spectrum and modulus of CWT for the cases with a leakage rate from 5 to 25 m<sup>3</sup>/h. Further, the atmospheric CO<sub>2</sub> concentration and CO<sub>2</sub> variation rate were compared to recognize abnormal leakage. The results indicated that the CWT spectrum of the CO<sub>2</sub> variation rate performed better than that of concentration. Moreover, the CWT spectrum of the atmospheric CO<sub>2</sub> variation rate with the real-valued wavelet function was also utilized to recognize CO<sub>2</sub> leakage. The tests showed that the CWT spectrum with the complex Morlet wavelet demonstrated a more obvious and wider hot spot than that with the real-valued Morlet wavelet. In addition, a pretreatment method with principal component analysis (PCA) was applied to extract the features of original monitoring signals. It was proved that more obvious abnormal signals in the time–frequency spectrum and modulus variation PCA–CWT method could be captured than that from the original CWT analysis, even for a small leakage. Therefore, it is a feasible method to monitor and recognize atmospheric CO<sub>2</sub> leakage with the complex CWT of the CO<sub>2</sub> variation rate in the atmosphere combined with PCA processing.</p>","PeriodicalId":12,"journal":{"name":"ACS Chemical Health & Safety","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1021/acs.chas.1c00025","citationCount":"0","resultStr":"{\"title\":\"CO2 Leakage Identification Method Based on Complex Time–Frequency Spectrum of Atmospheric CO2 Variation\",\"authors\":\"Denglong Ma*, Xiuben Wu, Jianmin Gao, Zaoxiao Zhang, Xin Zuo\",\"doi\":\"10.1021/acs.chas.1c00025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >It is a challenging problem to monitor atmospheric CO<sub>2</sub> leakage due to the complex variation of the atmosphere background. In this research, a new CO<sub>2</sub> leakage identification method in the atmosphere based on the complex time–frequency spectrum of atmospheric CO<sub>2</sub> variation was proposed. First, the complex continuous wavelet transform (CWT) was utilized to analyze the experimental data without and with CO<sub>2</sub> leakage. It was found that CWT could provide distinguished features for atmospheric CO<sub>2</sub> leakage by calculating the time–frequency spectrum and modulus of CWT for the cases with a leakage rate from 5 to 25 m<sup>3</sup>/h. Further, the atmospheric CO<sub>2</sub> concentration and CO<sub>2</sub> variation rate were compared to recognize abnormal leakage. The results indicated that the CWT spectrum of the CO<sub>2</sub> variation rate performed better than that of concentration. Moreover, the CWT spectrum of the atmospheric CO<sub>2</sub> variation rate with the real-valued wavelet function was also utilized to recognize CO<sub>2</sub> leakage. The tests showed that the CWT spectrum with the complex Morlet wavelet demonstrated a more obvious and wider hot spot than that with the real-valued Morlet wavelet. In addition, a pretreatment method with principal component analysis (PCA) was applied to extract the features of original monitoring signals. It was proved that more obvious abnormal signals in the time–frequency spectrum and modulus variation PCA–CWT method could be captured than that from the original CWT analysis, even for a small leakage. Therefore, it is a feasible method to monitor and recognize atmospheric CO<sub>2</sub> leakage with the complex CWT of the CO<sub>2</sub> variation rate in the atmosphere combined with PCA processing.</p>\",\"PeriodicalId\":12,\"journal\":{\"name\":\"ACS Chemical Health & Safety\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2021-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1021/acs.chas.1c00025\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Chemical Health & Safety\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acs.chas.1c00025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Chemical Health & Safety","FirstCategoryId":"1","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.chas.1c00025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
CO2 Leakage Identification Method Based on Complex Time–Frequency Spectrum of Atmospheric CO2 Variation
It is a challenging problem to monitor atmospheric CO2 leakage due to the complex variation of the atmosphere background. In this research, a new CO2 leakage identification method in the atmosphere based on the complex time–frequency spectrum of atmospheric CO2 variation was proposed. First, the complex continuous wavelet transform (CWT) was utilized to analyze the experimental data without and with CO2 leakage. It was found that CWT could provide distinguished features for atmospheric CO2 leakage by calculating the time–frequency spectrum and modulus of CWT for the cases with a leakage rate from 5 to 25 m3/h. Further, the atmospheric CO2 concentration and CO2 variation rate were compared to recognize abnormal leakage. The results indicated that the CWT spectrum of the CO2 variation rate performed better than that of concentration. Moreover, the CWT spectrum of the atmospheric CO2 variation rate with the real-valued wavelet function was also utilized to recognize CO2 leakage. The tests showed that the CWT spectrum with the complex Morlet wavelet demonstrated a more obvious and wider hot spot than that with the real-valued Morlet wavelet. In addition, a pretreatment method with principal component analysis (PCA) was applied to extract the features of original monitoring signals. It was proved that more obvious abnormal signals in the time–frequency spectrum and modulus variation PCA–CWT method could be captured than that from the original CWT analysis, even for a small leakage. Therefore, it is a feasible method to monitor and recognize atmospheric CO2 leakage with the complex CWT of the CO2 variation rate in the atmosphere combined with PCA processing.
期刊介绍:
The Journal of Chemical Health and Safety focuses on news, information, and ideas relating to issues and advances in chemical health and safety. The Journal of Chemical Health and Safety covers up-to-the minute, in-depth views of safety issues ranging from OSHA and EPA regulations to the safe handling of hazardous waste, from the latest innovations in effective chemical hygiene practices to the courts'' most recent rulings on safety-related lawsuits. The Journal of Chemical Health and Safety presents real-world information that health, safety and environmental professionals and others responsible for the safety of their workplaces can put to use right away, identifying potential and developing safety concerns before they do real harm.