Concentration-Temporal Multilevel Calibration of Low-Cost PM 2.5 Sensors

Rong-Fuh Day, Peng-Yeng Yin (), Yuh-Chin T. Huang, Cheng-Yi Wang, Chih-Chun Tsai and Cheng-Hsien Yu
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Rong-Fuh Day: Department of Information Management, National Chi Nan University, No. 1, University Rd., Puli 545, Nantou County, Taiwan
Peng-Yeng Yin: Information Technology and Management Program, Ming Chuan University, No. 5 De Ming Rd., Taoyuan City 333, Gui Shan District, Taiwan
Yuh-Chin T. Huang: Department of Medicine, Duke University Medical Center, 10 Duke Medicine Circle, Durham, NC 27710, USA
Cheng-Yi Wang: Department of Internal Medicine, Cardinal Tien Hospital and School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City 231, Taishan District, Taiwan
Chih-Chun Tsai: Department of Information Management, National Chi Nan University, No. 1, University Rd., Puli 545, Nantou County, Taiwan
Cheng-Hsien Yu: Department of Information Management, China University of Technology, No. 56, Sec. 3, Xinglong Rd., Taipei City 116, Wunshan District, Taiwan

Sustainability, 2022, vol. 14, issue 16, 1-12

Abstract: 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.

Keywords: PM 2.5; supersite sensor; low-cost sensor; multilevel calibration; linear regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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