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Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi

Cite this dataset

Bittner, Ashley et al. (2022). Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi [Dataset]. Dryad.


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 one 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. Hybrid models also were the most transferable approach when applied to deployment data collected in Malawi. We compared kNN-hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, due to proximity to residential biomass 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 residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the one year deployment to Malawi. Data recovery (30-80%) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory grade monitoring infrastructure. 


Data from ARISense gas and particle sensors were integrated via internal software and saved daily as .txt files stored on an internal USB drive. The USB was manually removed, inserted into a computer or phone device, and sent to North Carolina State University researchers via Google Drive and occasionally through e-mail or WhatsApp, depending on cell service availabiltiy in the study region. 

NC collocation data: ARISense were collocated with EPA and NC-DEQ reference instruments. Reference data was emailed to NC State researchers upon request. ARISense and reference data were manually time-aligned and re-sampled to five-minute averages. ARISense data spikes due to electrical noise following a power loss event were visually identified and removed.

OPC-N2 collocation data: A MicroPEM instrument was collocated with an ARISense in Malawi. Data from each instrument were manually downloaded, time-aligned and re-sampled 1-min averages. OPC-N2 data were RH-corrected as described in accompanying manuscript.

Malawi deployment data: ARISense data were collected from three sites in Malawi over a 1-year period. Data from the 1-year period were collated and re-sampled to five minute averages. 


National Science Foundation, Award: 1617359