Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor
Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impedin...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/13/3617 |
| _version_ | 1797563988095008768 |
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| author | Hoochang Lee Jiseock Kang Sungjung Kim Yunseok Im Seungsung Yoo Dongjun Lee |
| author_facet | Hoochang Lee Jiseock Kang Sungjung Kim Yunseok Im Seungsung Yoo Dongjun Lee |
| author_sort | Hoochang Lee |
| collection | DOAJ |
| description | Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 <inline-formula> <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math> </inline-formula>g/m<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mrow></mrow> <mn>3</mn> </msup> </mrow> </semantics> </math> </inline-formula>) and increases the correlation (e.g., R<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mn>2</mn> </msup> </semantics> </math> </inline-formula>: 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network. |
| first_indexed | 2024-03-10T18:50:59Z |
| format | Article |
| id | doaj.art-0d29337c8a4e479091db8ceb7df7a65e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:50:59Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0d29337c8a4e479091db8ceb7df7a65e2023-11-20T05:07:30ZengMDPI AGSensors1424-82202020-06-012013361710.3390/s20133617Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) SensorHoochang Lee0Jiseock Kang1Sungjung Kim2Yunseok Im3Seungsung Yoo4Dongjun Lee5Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, KoreaDepartment of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, KoreaDepartment of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, KoreaAir Quality Analysis and Control Center, Seoul Metropolitan Research Institute of Public Health and Environment, 30, Janggunmaeul 3-gil, Gwacheon-si, Gyeonggi-do, Seoul 08826, KoreaAir Quality Analysis and Control Center, Seoul Metropolitan Research Institute of Public Health and Environment, 30, Janggunmaeul 3-gil, Gwacheon-si, Gyeonggi-do, Seoul 08826, KoreaDepartment of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, KoreaLow-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 <inline-formula> <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math> </inline-formula>g/m<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mrow></mrow> <mn>3</mn> </msup> </mrow> </semantics> </math> </inline-formula>) and increases the correlation (e.g., R<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mn>2</mn> </msup> </semantics> </math> </inline-formula>: 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.https://www.mdpi.com/1424-8220/20/13/3617particulate matter (PM)low-cost sensorcalibrationmultivariate linear regression (MLR)multilayer perceptron (MLP)segmented model and residual treatment (SMART) calibration |
| spellingShingle | Hoochang Lee Jiseock Kang Sungjung Kim Yunseok Im Seungsung Yoo Dongjun Lee Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor Sensors particulate matter (PM) low-cost sensor calibration multivariate linear regression (MLR) multilayer perceptron (MLP) segmented model and residual treatment (SMART) calibration |
| title | Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor |
| title_full | Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor |
| title_fullStr | Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor |
| title_full_unstemmed | Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor |
| title_short | Long-Term Evaluation and Calibration of Low-Cost Particulate Matter (PM) Sensor |
| title_sort | long term evaluation and calibration of low cost particulate matter pm sensor |
| topic | particulate matter (PM) low-cost sensor calibration multivariate linear regression (MLR) multilayer perceptron (MLP) segmented model and residual treatment (SMART) calibration |
| url | https://www.mdpi.com/1424-8220/20/13/3617 |
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