RT journal article
T1 Leveraging Low-Cost Sensor Data and Predictive Modelling for IoT-Driven Indoor Air Quality Monitoring
A1 Camacho Magriñán, Patricia
A1 Sales Lérida, Diego
A1 Lara Doña, Alejandro
A1 Sánchez Morillo, Daniel
A2 Ingeniería en AutomáticaElectrónica, Arquitectura y Redes de Computadores
K1 air quality
K1 CO2
K1 eCO2
K1 indoor
K1 VOC
K1 PM
K1 low-cost sensors
K1 particulate matter
K1 pollutants
K1 volatile organic compounds
K1 IoT
K1 iAQ
AB Indoor air quality (IAQ) in residential settings is often dominated by high-concentration pollutant events from activities such as cooking and occupancy, which are overlooked by traditional 24 h average assessments. In this, we have designed and implemented a low-cost unit for remote IAQ monitoring. We deployed these units for high-resolution remote monitoring of CO2, particulate matter (PM), and volatile organic compounds (VOCs) in three different domestic environments: a kitchen, a living room, and a bedroom. The monitoring campaign confirmed that, while daily averages frequently remained below guideline limits, transient peaks (e.g., CO2 exceeding 2800 ppm in bedrooms and significant increases in PM during cooking) posed acute exposure risks. This dataset was used to train and evaluate machine learning models for 10 min ahead pollutant forecasting. Ensemble tree-based methods (Random Forest) and gradient boosting algorithms (XGBoost, LGBM, and CatBoost) were effective and robust. The predictability of the models correlated with room dynamics: performance improved under clear cyclical patterns (bedroom) and remained stable under stochastic events (kitchen). This work shows that integrating low-cost IoT sensing with machine learning enables proactive IAQ management, supporting health interventions driven by predictive risk rather than static averages.
PB MDPI
SN 2624-6511
YR 2025
FD 2025-11-28
LK http://hdl.handle.net/10498/38912
UL http://hdl.handle.net/10498/38912
LA eng
NO This contribution has been supported by grant PID2021-126810OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU.
DS Repositorio Institucional de la Universidad de Cádiz
RD 21-sep-2026