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

Abstract

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.

Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi

“…Data availability. The data set used in this analysis is available as an open-access Dryad repository (https://doi.org/10.5061/dryad.cz8w9gj4n, Bittner et al, 2022). The repository hosts pre-processed ARISense and reference data sets from the pre-deployment and post-deployment colocations, pre-processed RH-corrected OPC-N2 and MicroPEM data sets from the Malawi colocation, and collated ARISense data sets from the 1 year deployment at each of the three monitoring sites in Malawi.…”

Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network

“…Classic statistical regressions such as Multiple Linear Regression (MLR) are still being employed in recent works [ 6 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. State-of-the-art calibration methods include supervised Machine Learning (ML) techniques such as Support Vector Regression (SVR) [ 34 , 35 , 36 , 37 , 38 , 39 ], ensemble ML techniques, such as Random Forest Regression (RFR) [ 8 , 34 , 36 , 40 , 41 , 42 , 43 ], and Neural Networks (NN) such as Multilayer Perceptron (MLP) [ 25 , 27 , 28 , 37 , 38 , 39 , 43 , 44 ]) and Recurrent Neural Networks (RNN) [ 37 , 38 , 39 , 40 , 45 , 46 ]. Table 1 summarizes the ML techniques used for the calibration of low-cost gas sensors.…”