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| ■論文No. |
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| ■ページ数 |
6ページ |
■発行日
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2022/10/01 |
| ■タイトル |
Quantitative Analysis for Application Specific Calibration Approaches for Low-Cost Sensors for Air Quality Monitoring |
| ■タイトル(英語) |
Quantitative Analysis for Application Specific Calibration Approaches for Low-Cost Sensors for Air Quality Monitoring |
| ■著者名 |
M V Narayana (Department of Electrical Engineering, Indian Institute of Technology Madras), Devendra Jalihal (Department of Electrical Engineering, Indian Institute of Technology Madras), Shiva Nagendra S M (Department of Civil Engineering, Indian Institu |
| ■著者名(英語) |
M V Narayana (Department of Electrical Engineering, Indian Institute of Technology Madras), Devendra Jalihal (Department of Electrical Engineering, Indian Institute of Technology Madras), Shiva Nagendra S M (Department of Civil Engineering, Indian Institute of Technology Madras) |
| ■価格 |
会員 ¥550 一般 ¥770 |
| ■書籍種類 |
論文誌(論文単位) |
| ■グループ名 |
【C】電子・情報・システム部門 |
| ■本誌 |
電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.10 （2022） 特集：電子材料関連技術の最近の進展
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| ■本誌掲載ページ |
1166-1171ページ |
| ■原稿種別 |
論文／英語 |
| ■電子版へのリンク |
https://www.jstage.jst.go.jp/article/ieejeiss/142/10/142_1166/_article/-char/ja/
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| ■キーワード |
air pollution，low-cost sensors，calibration models，air quality monitoring methods. |
| ■要約(日本語) |
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| ■要約(英語) |
Air quality monitoring (AQM) at fine and granular levels helps in implementing better air pollution control and mitigation plans. Low-cost sensing is a relatively new paradigm for AQM at high spatial and temporal resolutions. However, the data obtained in this technique is less reliable due to various error sources involved. Calibration is a promising approach to counteract error sources and to improve the accuracy of the data obtained from low-cost sensors (LCS). However, fitting a calibration model that works for every application is cumbersome since each application is associated with varying sources of error. Hence, we focus on finding a better calibration model for a given application. Our approach is application-centric. We classify LCS applications into static and mobile, based on sensor deployment in the field. Then, we sub-categorize both stationary and mobile applications further to narrow down to understand each case. Next, we analyze the possible error sources in each application and the corresponding techniques to counteract them. Then, we map applications to the calibration models via possible error sources and assign weights/scores to each parameter based on our analysis. Our quantitative analysis determines scores for each calibration model which results in finding the best suited model for a given set of conditions and applications based on the highest score. We verify our analysis with real time data obtained from LCS deployed in Chennai city, India. |
| ■版 型 |
A4 |
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