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Long-term Field Evaluation of Low-cost Particulate Matter Sensors in Nanjing

Category: Aerosol Physics and Instrumentation

Volume: 20 | Issue: 2 | Pages: 242-253
DOI: 10.4209/aaqr.2018.11.0424

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To cite this article:
Bai, L., Huang, L., Wang, Z., Ying, Q., Zheng, J., Shi, X. and Hu, J. (2020). Long-term Field Evaluation of Low-cost Particulate Matter Sensors in Nanjing. Aerosol Air Qual. Res. 20: 242-253. doi: 10.4209/aaqr.2018.11.0424.

Lu Bai1, Lin Huang1, Zhenglu Wang2, Qi Ying 1,2, Jun Zheng1, Xiaowen Shi1, Jianlin Hu 1

  • 1 Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2 Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA


  • 18-month field evaluation on low-cost aerosol monitors was performed.
  • Linear, power-law, and ANN techniques were used for data calibration.
  • Low-cost monitors are more accurate for concentrations over 35 µg m–3.
  • High humidity could cause larger errors for low-cost monitors.
  • A clear sensor deterioration trend is found over the 18-month field calibration.


Low-cost particulate matter (PM) sensors can be widely deployed to measure aerosol concentrations at higher spatial and temporal resolutions than traditional instruments, but they need to be carefully calibrated under ambient conditions. In this study, a long-term field experiment was conducted from December 2015 to May 2017 at a site in Nanjing to evaluate the capabilities of in-house built low-cost PM monitors using the Shinyei PPD42NS sensor for ambient PM2.5 monitoring. A BAM-1020 particulate monitor was co-located with the low-cost sensors to provide reference readings. Least-square regressions with linear and power-law functions, and an artificial neural network (ANN) technique were used to convert electrical instrument readings to ambient aerosol concentrations. Applying the ANN technique resulted in the best estimation of the hourly PM2.5 (R2 = 0.84; mean normalized bias = 12.7% and mean normalized error (MNE) = 29.7%). The low-cost sensors displayed relatively good performance with high aerosol concentrations but larger errors with concentrations below 35 µg m–3. High humidity (RH > 75%) can cause a larger MNE for these sensors, but the impact of temperature was negligible in this study. A clear sensor deterioration trend was observed during the 18-month field calibration. High correlations were found between the data from a single low-cost sensor and the data from the BAM-1020 when the low-cost sensor was individually calibrated, but the correlations between measurements taken by different low-cost sensor units were only moderate, possibly due to internal sensor variations. The results suggest that these low-cost sensors can measure ambient PM2.5 concentrations with an acceptable level of accuracy, which can and should be improved by calibrating each sensor individually. Special attention should be paid to the accuracy of these sensors after long-term application and in highly humid environments.


PPD42NS Field calibration Long-term Artificial neural network

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