Advancing Air Quality Monitoring: Metrological Challenges and Innovations in Low-Cost Sensor Networks

Abstract

Air pollution is a global environmental and health issue that has adverse effects on climate change and human health and contributes to ecosystem degradation and biodiversity loss. Air quality monitoring is therefore crucial to understand the sources and impacts of air pollution and to assess mitigation interventions. However, the number of operative monitoring stations are usually limited in air quality monitoring networks due to the high cost of the instrumentation used to monitor selected air pollutants. This limits the number of stations and results in spatial gaps. However, air pollution is highly heterogeneous. The recent development of sensor technology has offered a broad range of gas and particulate matter sensors of small size and relatively low energy consumption and cost. These features make low-cost sensors (LCS) an attractive alternative to increase the spatial coverage of air quality networks and to deploy affordable networks in countries with lower resources. Nonetheless, LCS are faced with limitations such as cross-sensitivity and drift, while sensor networks are characterized by their scale, mixed quality, data volatility, interdependencies and correlations among others. These factors, in conjunction with the inherent complexity of the entire LCS network, lead to the emergence of novel metrological challenges. Moreover, some of the approaches used in sensor networks like the use of sensor redundancy for fault detection and self- or co-calibration, require a more holistic metrological assessment than traditional metrology methods. These metrological challenges must be addressed to ensure the data quality and comparability of LCS networks. For that purpose, several sensor network metrology techniques are currently under development in the European Partnership on Metrology project "Fundamentals of Sensor Network Metrology" (FunSNM). Some of these techniques include uncertainty propagation in sensor networks using Bayesian approaches and mathematical tools like Laplace tools, sensor fusion, correlation analysis, self- and co-calibration methods using optimised spatial statistics and machine learning algorithms among others. Current outputs of the projects will be included in this poster, showing the essential role of sensor network metrology in advancing the adaptability and reliability of sensor networks.