Design and Evaluation of a Dryer System for IoT Hyperlocal Particulate Matter Monitoring Device

Particulate matter (PM) monitoring and climate change mitigation actions have been promoted due to the Paris agreement because of their impact on health and high mortality rate. High-resolution networks based on hyperlocal Internet of Things (IoT) sensors can play a fundamental role in improving data quality. In this context, hyperlocal refers to air quality IoT systems that allow collecting data in real time and in the cheapest way in comparison with local reference stations. Despite these methods are powerful and widely used by the scientific community, the signal is highly affected by relative humidity (RH). In this article, we present a system for measuring nanoparticles based on drying the air sampled and avoiding the hygroscopic growing of the particles. To the best of our knowledge, this is the first dryer system approach developed for IoT hyperlocal sensors. In addition, the relevance of our solution is supported by the following points: 1) we propose a new dryer system that has been patented; 2) our solution can be integrated into an IoT infrastructure that allows it to interact with other services; and 3) our solution has been validated in a real scenario in the city of Madrid. We have observed that the integration of a dryer system improves the performance of the OPC-N3 sensor and that we can measure the PM10 and PM2.5 fractions with high precision, ${R}^{{2}} = {0}.{83}$ . In addition, our solution can measure small particles, such as PM1, with a good correlation against the reference air quality stations. Thus, our work contributes by improving high-spatial-resolution nanoparticle monitoring in correlation to official measurements to mitigate climate change.