Commentary: Opportunities for the application of low-cost sensors in epidemiological studies to advance evidence of air pollution impacts on human health

Wernecke, Bianca; Wright, Caradee Y.; Hey, Joshua Vande; Burger, Roelof; Kunene, Zamantimande; Panchal, Rikesh; Millar, Danielle A.; Oosthuizen, Dina N.; Batini, Chiara; John, Catherine; Guyatt, Anna L; Packer, Richard; Tobin, Martin D.; Hansell, Anna; Gulliver, John; Cordell, Rebecca; Micklesfield, Lisa K.; RamsayI, Michele; Gayenga, Jocelyn; Gómez‐Olivé, F. Xavier; Ngobeni, Khanyisa; Baloyi, Vukosi; Language, Brigitte

doi:10.17159/caj/2021/31/1.11219

Cited by 5 publications

(6 citation statements)

References 14 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.

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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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“…We conducted extensive field comparisons in Ethiopia, at three ambient and four indoor high concentration locations. We collocated the SPSA with itself, with a gravimetric measurement instrument, and with two SSys: IQAir Airvisual Pro (the SSys of Airvisual, which is the largest real-time air quality databank (Wernecke et al, 2021)) and PATS (designed for indoor air pollution measurements). We developed the system and conducted the field comparisons without funding.…”

Section: Introductionmentioning

confidence: 99%

Developing and testing a PM2.5 low-cost sensor in Ethiopia under ambient and indoor air pollution conditions

Dingemanse,

Tademe

2023

Clean Air J.

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PM2.5 low-cost sensors are a promising trend for low-income countries, where the PM2.5 associated burden of disease is high and few measurement instruments are available. Commercially available Sensor Systems (SSys) are relatively affordable and easy to use. They are, however, not designed for or evaluated in contexts characterized by much biomass burning, regular power interruptions and/or low internet coverage, typical for low-income countries. Alternatively, local teams can build a sensor system with PM2.5 sensors from Original Equipment Manufacturers (OEM). Existing African OEM projects depend on international partners and funding. This puts the affordability for local teams without funding into question. Furthermore, field comparisons of such sensors for ambient concentrations and indoor settings are rarely conducted in low-income contexts. In Arba Minch, Ethiopia, we developed a sensor system (SPSA) with the OEM Sensirion SPS30 and other components, together with an Arduino microprocessor, with LoRaWAN data transmission. We used the hardware and software in multiple configurations. The SPSA was used in 14 contexts typical for Ethiopia. During these tests we encountered problems that were easily solved by maintenance on location. On seven locations we collocated the SPSA with itself, gravimetric instruments and/or SSys. Amongst SPSA we found coefficients of determination (R2) of at least 0.98 for three ambient and one indoor location. The accuracy in comparison with the gravimetric method was 16% under ambient and 13% under indoor circumstances. This is lower than the internationally required 25%. The R2 in comparison to two SSys was 0.91-0.98 under ambient and 0.88-1.00 under indoor circumstances. The SPSA is a versatile sensor system that can be used in both ambient and indoor air pollution circumstances. Local development without international partners and funding resulted in local experience gaining, low costs, local ownership, and the possibility of tailoring the system to local needs regarding power and connectivity.

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Commentary: Opportunities for the application of low-cost sensors in epidemiological studies to advance evidence of air pollution impacts on human health

Cited by 5 publications

References 14 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

Developing and testing a PM2.5 low-cost sensor in Ethiopia under ambient and indoor air pollution conditions

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