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Concentration-Temporal Multilevel Calibration of Low-Cost $PM_{2.5}$ Sensors
| Content Provider | MDPI |
|---|---|
| Author | Day, Rong-Fuh Yin, Peng-Yeng Huang, Yuh-Chin T. Wang, Cheng-Yi Tsai, Chih-Chun Yu, Cheng-Hsien |
| Copyright Year | 2022 |
| Description | Ambient aerosols have a significant impact on plant species mortality, air pollution, and climate change. It is critical to monitor the concentrations of aerosols, especially particulate matter with an aerodynamic diameter ≤ 2.5 μm $(PM_{2.5}$), which has a direct relationship with human respiratory diseases. Recently, low-cost $PM_{2.5}$ sensors have been deployed to provide a denser monitoring coverage than that of government-built monitoring supersites, which only give a macro perspective of air quality. To increase the measurement accuracy, low-cost sensors need to be calibrated. In current practice, regression techniques are used to calibrate sensors. This paper proposes a concentration-temporal multilevel calibration method to cope with the varying regression relation in different concentration and temporal domains. The performance of our method is evaluated with real field data from a supersite sensor and a low-cost sensor deployed in Puli, Taiwan. The experimental results show that our calibration method significantly outperforms linear regression in terms of $R^{2}$, Root Mean Square Error, and Normalized Mean Error. Moreover, our method compares favorably with a machine learning calibration method based on gradient regression tree boosting. |
| Starting Page | 10015 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su141610015 |
| Journal | Sustainability |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-12 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Industrial Engineering Pm2.5 Supersite Sensor Low-cost Sensor Multilevel Calibration Linear Regression |
| Content Type | Text |
| Resource Type | Article |
