Preprints
https://doi.org/10.5194/amt-2021-372
https://doi.org/10.5194/amt-2021-372

  23 Nov 2021

23 Nov 2021

Review status: this preprint is currently under review for the journal AMT.

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Ashley S. Bittner1, Eben S. Cross2, David H. Hagan2, Carl Malings3, Eric Lipsky4, and Andrew Grieshop1 Ashley S. Bittner et al.
  • 1Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27606, USA
  • 2QuantAQ, Inc., Somerville, MA 02143, USA
  • 3NASA Postdoctoral Program Fellow, Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 4Department of Energy Engineering, Penn State Greater Allegheny University, McKeesport, PA 15132, USA

Abstract. Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated; they also appeared to be the most transferable approach when applied to field data collected in Malawi. We compared calibrated CO observations to remote sensing data in two regions in Malawi and found qualitative agreement in spatial and annual trends. However, the monthly mean surface observations were 2 to 4 times higher than the remote sensing data, possibly due to proximity to small-scale combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70 %) conditions and influence from emissions from nearby biomass cookstoves. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi; however, overall data recovery was limited by insufficient power and access to technical resources at deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regulatory grade regional infrastructure.

Ashley S. Bittner et al.

Status: open (until 13 Jan 2022)

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Ashley S. Bittner et al.

Model code and software

Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" Malings, Carl; Tanzer, Rebecca; Hauryliuk, Aliaksei; Kumar, Sriniwasa P.N.; Zimmerman, Naomi; Kara, Levent B.; Presto, Albert A.; and Subramanian, R. https://doi.org/10.5281/zenodo.1482011

Ashley S. Bittner et al.

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Short summary
We present findings from a 1-year pilot deployment of low-cost integrated air quality sensor packages in rural Malawi using calibration models developed during collocation with US regulatory monitors. We compare the results with data from remote sensing products and previous field studies. We conclude that while the remote calibration approach can help extract useful data, great care is needed in assessing low-cost sensor data collected in regions without reference instrumentation.