Atmospheric Measurement Techniques (Feb 2025)

Performance evaluation of Atmotube PRO sensors for air quality measurements in an urban location

  • A. I. Shittu,
  • A. I. Shittu,
  • K. J. Pringle,
  • S. R. Arnold,
  • R. J. Pope,
  • R. J. Pope,
  • A. M. Graham,
  • C. Reddington,
  • R. Rigby,
  • J. B. McQuaid

DOI
https://doi.org/10.5194/amt-18-817-2025
Journal volume & issue
Vol. 18
pp. 817 – 828

Abstract

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This study presents a performance evaluation of eight Atmotube PRO sensors using US Environmental Protection Agency (US EPA) guidelines. The Atmotube PRO sensors were collocated side by side with a reference-grade Fidas monitor in an outdoor setting for a 14-week period in the city centre of Leeds, UK. We assessed the linearity and bias for PM1, PM2.5, and PM10. The result of the PM2.5 assessment showed the Atmotube PRO sensors had particularly good precision with a coefficient of variation (CoV) of 28 %, 18 %, and 15 % for PM2.5 data averaged every minute, hour, and day, respectively. The inter-sensor variability assessment showed two sensors with low bias and one sensor with a higher bias in comparison with the sensor average. Simple univariate analysis was sufficient to obtain good fitting quality to a Fidas reference-grade monitor (R2>0.7) at hourly averages, although poorer performance was observed using a higher time resolution of 15 min averaged PM2.5 data (R2 of 0.48–0.53). The average error bias, root mean square error (RMSE), and normalized root mean square error (NRMSE) were 3.38 µg m−3 and 0.03 %, respectively. While there were negligible influences of temperature on Atmotube PRO-measured PM2.5 values, substantial positive biases (compared to a reference instrument) occurred at relative humidity (RH) values > 80 %. The Atmotube PRO sensors correlated well with the PurpleAir sensor (R2 of 0.88, RMSE of 2.9 µg m−3). In general, the Atmotube PRO sensors performed well and passed the base-testing metrics as stipulated by recommended guidelines for low-cost PM2.5 sensors. Calibration using the multiple linear regression model was enough to improve the performance of the PM2.5 data of the Atmotube PRO sensors.