Abstract
Particulate matter based pollution has a health impact on ‘at-risk’ individuals requiring monitoring to reduce and inform the population of exposure. These ‘at-risk’ individuals are those with respiratory and pulmonary illnesses. Currently pollution is monitored by High-Cost pollution sensors (HCPS) which can be deployed in limited fashion due to expense. A low-cost alternative can be used to provide greater sensor density for the same budget thereby better informing the ‘at-risk’ population. This study compares the accuracy of low-cost particulate matter sensors as an alternative to a HCPS that are generally used for this purpose. In our evaluation a low-cost sensor was co-located with a HCPS for a period of one month. Data was collected from both sensors in order to enable a comparison. Raw data comparison showed that readings generated from our low-cost sensor fell within the same Air Quality Index (AQI) banding as data from the HCPS. Although the data produced by the low-cost sensor is functionally accurate (classification of pollution within the AQI bands), accuracy could be improved using an algorithmic calibration curve. This could allow for many low-cost systems to be deployed across a wider geographical city area, improving coverage. Helping informing the general public of risks and measure to reduce exposure.
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Kirk, N., Santos, J., Rafferty, J., Nicholl, P., Campbell, C. (2023). An Approach to Monitoring Particulate Matter Based Pollution Using Low-Cost Sensing. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_66
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DOI: https://doi.org/10.1007/978-3-031-21333-5_66
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