Development of a low-cost black carbon sensor for air quality monitoring in Ghana

The International Energy Agency estimates that 970 million Africans use biomass for cooking [1], emissions from which expose them to pollutants like particulate matter (PM) and black carbon (BC). Due to its small size (range: 135 - 145 nm) [2], BC is easily inhalable and presents worse health impacts than many other PM species [3]. A considerable challenge is accessing affordable standard BC sensors; most cost US$3,000 to US$20,000 and are thus too expensive to deploy in large numbers [3] to provide high spatial resolution. Therefore, in recent low-cost air pollution sensor networks, there has been a noticeable gap in the absence of a BC emissions inventory [3]. In this research, we have designed a BC sensor that costs less than US$200 and incorporates a rechargeable battery & LoRa communication to enable long-term, remote operation. Leveraging Pugh Charts, we chose materials and components available in Ghana. Drawing from recent studies in developing low-cost BC sensors, the sensor uses an optical measurement technique to measure the absorption coefficient from the degree of weakened light intensity of 880 nm wavelength to invert the BC aerosol concentration. We chose absorption measurement at 880 nm to define BC concentration because, at this wavelength, BC is the predominant PM species to absorb light [3]. We developed a low-fidelity prototype using a flame sensor to test the optical measurement concept. The flame sensor detected light wavelengths between 760 nm – 1100 nm with high sensitivity and a resolution of 0.98 mV using a 12-bit analog-to-digital converter (ADC). In the next prototype stage, we aim to achieve a resolution of less than 0.1 mV leveraging a 16-bit ADC. Additionally, components will be integrated to enable the measurement of carbon monoxide (CO) and nitrogen dioxide (NO₂) concentrations as well, leveraging the MiCS-4514 sensor module. Simultaneous detection of high BC & CO and BC & NO₂ concentrations can aid in indicating nearby biomass combustion and diesel engine emissions, respectively [4], thus painting a complete picture of major BC pollution drivers. These emissions data will aid policymakers to devise data-driven solutions to BC-associated human health impacts.

References 

[1] IEA (2022), Africa Energy Outlook 2022, IEA, Paris https://www.iea.org/reports/africa-energy-outlook-2022, License: CC BY 4.

[2] Y. Cheng, S.-M. Li, M. Gordon, and P. Liu, "Size distribution and coating thickness of black carbon from the Canadian oil sands operations," Atmospheric Chem. Phys., vol. 18, no. 4, pp. 2653–2667, Feb. 2018, doi: 10.5194/acp-18-2653-2018.

[3] J. J. Caubel, T. E. Cados, and T. W. Kirchstetter, “A New Black Carbon Sensor for Dense Air Quality Monitoring Networks,” Sensors, vol. 18, no. 3, Art. no. 3, Mar. 2018, doi: 10.3390/s18030738.

[4] B. Alfoldy, A. Gregorič, M. Ivančič, I. Ježek, and M. Rigler, "Source apportionment of black carbon and combustion-related CO2 for the determination of source-specific emission factors," Aerosols/In Situ Measurement/Instruments and Platforms, preprint, Apr. 2022. doi: 10.5194/amt-2022-53.