Assessment of IoT low-cost sensor networks for long-term outdoor and indoor air quality monitoring: a case study in Dublin

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This study provides a framework for Internet of Things based low-cost sensors (LCS) network implementation, using office environments in Dublin, Ireland, as a case study for long-term indoor air quality (IAQ) monitoring. It covers options and key decisions related to sensor technology, reporting systems and data management. Environmental and indoor data were collected from 1 June 2023 to 20 June 2024, using Smart Citizen Kit 2.1 and PurpleAir devices, and data retrieved from cloud-based data platforms for analysis. The standard deviation and coefficient of variation were calculated to evaluate intra-sensor precision. To improve data quality of LCS various correction models were tested, considering the impact of temperature and relative humidity. A multilinear model with additive relative humidity, using the piecewise regression, provided better performance (R 2 > 0.7, RMSE < 5 μg/m 3 ) and accuracy (> 0.88) for 24-h fine particulate matter (PM 2.5 ) and respiratory particulate matter (PM 10 ). This study bridges the data gap by incorporating multi-brand LCS network for further application in outdoor supplementary and IAQ reporting. The results showed corrected indoor PM 2.5 data in offices complies with WHO air quality guidelines, and carbon dioxide (CO 2 ) levels in naturally ventilated conditions remained below 800 ppm. Additionally, diurnal patterns reveal elevated levels of CO 2 and total volatile organic compounds during core office hours, while the contrasting patterns for PM 2.5 suggest outdoor infiltration as the dominant source. This study demonstrates the potential of data-driven techniques for real-time IAQ monitoring and reporting, providing valuable insights for promoting healthier IAQ for occupants.

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