06 Mar 2023
 | 06 Mar 2023
Status: this preprint is currently under review for the journal AMT.

Site and Season Specific Calibrations Improve Low-cost Sensor Performance: Long-term Field Evaluation of PurpleAir Sensors in Urban and Rural India

Mark Joseph Campmier, Jonathan Gingrich, Saumya Singh, Nisar Baig, Shahzad Gani, Adithi Upadhya, Pratyush Agrawal, Meenakshi Kushwaha, Harsh Raj Mishra, Ajay Pillarisetti, Sreekanth Vakacherla, Ravi Kant Pathak, and Joshua S. Apte

Abstract. We report on the long-term performance of a popular low-cost PM2.5 sensor, the PurpleAir PA-II, at multiple sites in India, with the aim of identifying robust calibration protocols. We established 3 distinct sites in India (North India: Delhi, Hamirpur; South India: Bangalore), where we collocated PA-II with reference beta-attenuation monitors to characterize sensor performance and to model calibration relationships between PA-IIs and reference monitors for hourly data. Our sites remained in operation across all major seasons of India. Without calibration, the PA-IIs had high precision (Normalized Root Mean Square Error [NRMSE] among replicate sensors ≤ 15 %) and tracked the overall seasonal and diurnal signals from the reference instruments well (Pearson’s r ≥ 0.9) but were inaccurate (NRMSE ≥ 40 %). We used a comprehensive feature selection process to create optimized site-specific calibrations. Relative to the uncalibrated data, parsimonious least-squares long-term calibration models improved PA-II performance at all sites (cross-validated NRMSE: 20–30 %, R2: 0.82–0.95), particularly by reducing seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed by site. Likewise, using a moving-window calibration, we find a calibration scheme using seasonally specific information somewhat improves performance relative to a static long-term calibration model. In contrast, we demonstrate that a successful short-term calibration exercise for one season may not transfer reliably to other seasons. Overall, we demonstrate how the PA-II, when paired with a careful calibration scheme, can provide actionable information on PM2.5 in India with only modest irreducible uncertainty.

Mark Joseph Campmier et al.

Status: open (until 26 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Mark Joseph Campmier et al.

Mark Joseph Campmier et al.


Total article views: 314 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
231 78 5 314 22 3 3
  • HTML: 231
  • PDF: 78
  • XML: 5
  • Total: 314
  • Supplement: 22
  • BibTeX: 3
  • EndNote: 3
Views and downloads (calculated since 06 Mar 2023)
Cumulative views and downloads (calculated since 06 Mar 2023)

Viewed (geographical distribution)

Total article views: 313 (including HTML, PDF, and XML) Thereof 313 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 24 Mar 2023
Short summary
We studied a low-cost air pollution sensor called PurpleAir PA-II in three different locations in India (Delhi, Hamirpur, and Bangalore) to characterize its performance. We compared its signal to more expensive reference sensors and found that the PurpleAir sensor was precise but inaccurate without calibration. We created a custom calibration equation for each location, which improved the accuracy of the PurpleAir sensor, and found that calibrations should be adjusted for different seasons.