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

Abstract

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 (R² > 0.7, RMSE <5 μg/m³) and accuracy (>0.88) for 24-h fine particulate matter (PM_2.5) and inhalable particulate matter (PM₁₀). 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 (CO₂) levels in naturally ventilated conditions remained below 800 ppm. Additionally, diurnal patterns reveal elevated levels of CO₂ 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 to promote healthier IAQ for occupants.