Low-cost sensors, an interesting alternative for air quality monitoring in Africa.

Rahal, Farid

doi:10.17159/caj/2020/30/2.9223

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…A growing body of literature highlights the potential value of LCS technologies for sub-Saharan Africa and other lowresource settings (Subramanian and Garland, 2021;Wernecke and Wright, 2021;Rahal, 2020;Sewor et al, 2021;Awokola et al, 2020). We found that our LCS surface observations were consistent with the only other available data sources in this region (remote sensing data and model products) and data from similar studies across SSA.…”

Section: Discussionmentioning

confidence: 99%

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

Bittner

Cross²,

Hagan³

et al. 2022

Atmos. Meas. Tech.

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Abstract. Low-cost gas and particulate matter 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 colocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modeling approaches to calibrate the electrochemical gas sensors: k-nearest neighbors (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 were also 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, partly 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 colocation 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 1-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, low-income settings would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory-grade monitoring infrastructure.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Bittner

Cross²,

Hagan³

et al. 2022

Atmos. Meas. Tech.

View full text Add to dashboard Cite

show abstract

“…A growing body of literature highlights the potential value of LCS technologies for Sub-Saharan Africa and other low-resource settings (Subramanian and Garland, 2021;Wernecke and Wright, 2021;Rahal, 2020;Sewor et al, 2021;Awokola et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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

Bittner

Cross²,

Hagan³

et al. 2021

Preprint

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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.

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“…To accomplish the first of these goals, the physical infrastructure for air quality monitoring, AfriqAir is helping implement hybrid networks of low-cost air quality monitors centered around reference-grade monitors. The benefits of using low-cost sensors, particularly in Africa, to increase the spatiotemporal resolution of air quality monitoring efforts have been noted elsewhere (Rahal, 2020). The drawback of low-cost sensors, however, is that they require calibration to ensure their output can be trusted.…”

Section: Commentary Afriqair's Mission Towards Cleaner Air For Africa and A Call To Actionmentioning

confidence: 99%

Commentary: AfriqAir’s Mission Towards Cleaner Air for Africa and a Call to Action

Giordano¹,

Jaramillo

2021

Clean Air J.

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Low-cost sensors, an interesting alternative for air quality monitoring in Africa.

Cited by 4 publications

References 12 publications

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

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

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

Commentary: AfriqAir’s Mission Towards Cleaner Air for Africa and a Call to Action

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