Kai Zhang , Yaqi Peng , Hong Yu , Pei Ning , Xueyong Hou , Ling Zhu , Shengyong Lu
{"title":"浓度和粒度对电荷感应法颗粒物检测误差的影响","authors":"Kai Zhang , Yaqi Peng , Hong Yu , Pei Ning , Xueyong Hou , Ling Zhu , Shengyong Lu","doi":"10.1016/j.apr.2024.102254","DOIUrl":null,"url":null,"abstract":"<div><p>Particulate matters generated from waste incineration carry charge due to collision and friction. By using charge induction method, it becomes feasible to detect particulate matter concentration by capturing the electrical signals emitted by particulate matters. In this study, a new charge induction device was constructed and tested. The investigation revealed a linear relationship between the sine wave eigenvalues of the electrical signals and the concentration of particulate matters. The corresponding formulas for peak-to-peak value, root mean square, and standard deviation were calculated, with R<sup>2</sup> values greater than 0.98. Additionally, the influence of concentration and size on detection error was studied. The results showed that as the concentration increased or the size decreased, the detection error decreased. Furthermore, the study found that the impact of particulate matter concentration on detection results mitigated that of particulate matter size. The detection device, correlation formulas and influencing factors proposed in this study are expected to provide technical support and theoretical basis for particulate matter detection, offering significant value in the field of air pollution control.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 11","pages":"Article 102254"},"PeriodicalIF":3.9000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The influence of concentration and size on the error of particulate matter detection using charge induction method\",\"authors\":\"Kai Zhang , Yaqi Peng , Hong Yu , Pei Ning , Xueyong Hou , Ling Zhu , Shengyong Lu\",\"doi\":\"10.1016/j.apr.2024.102254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Particulate matters generated from waste incineration carry charge due to collision and friction. By using charge induction method, it becomes feasible to detect particulate matter concentration by capturing the electrical signals emitted by particulate matters. In this study, a new charge induction device was constructed and tested. The investigation revealed a linear relationship between the sine wave eigenvalues of the electrical signals and the concentration of particulate matters. The corresponding formulas for peak-to-peak value, root mean square, and standard deviation were calculated, with R<sup>2</sup> values greater than 0.98. Additionally, the influence of concentration and size on detection error was studied. The results showed that as the concentration increased or the size decreased, the detection error decreased. Furthermore, the study found that the impact of particulate matter concentration on detection results mitigated that of particulate matter size. The detection device, correlation formulas and influencing factors proposed in this study are expected to provide technical support and theoretical basis for particulate matter detection, offering significant value in the field of air pollution control.</p></div>\",\"PeriodicalId\":8604,\"journal\":{\"name\":\"Atmospheric Pollution Research\",\"volume\":\"15 11\",\"pages\":\"Article 102254\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Atmospheric Pollution Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1309104224002198\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Atmospheric Pollution Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1309104224002198","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
The influence of concentration and size on the error of particulate matter detection using charge induction method
Particulate matters generated from waste incineration carry charge due to collision and friction. By using charge induction method, it becomes feasible to detect particulate matter concentration by capturing the electrical signals emitted by particulate matters. In this study, a new charge induction device was constructed and tested. The investigation revealed a linear relationship between the sine wave eigenvalues of the electrical signals and the concentration of particulate matters. The corresponding formulas for peak-to-peak value, root mean square, and standard deviation were calculated, with R2 values greater than 0.98. Additionally, the influence of concentration and size on detection error was studied. The results showed that as the concentration increased or the size decreased, the detection error decreased. Furthermore, the study found that the impact of particulate matter concentration on detection results mitigated that of particulate matter size. The detection device, correlation formulas and influencing factors proposed in this study are expected to provide technical support and theoretical basis for particulate matter detection, offering significant value in the field of air pollution control.
期刊介绍:
Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.