DOI: 10.5061/dryad.cz8w9gj4n
title: Data accompanying: Performance characterization of low-cost air sensors for off-grid deployment in rural Malawi

description: 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 &gt; 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. 
description: 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.