Mitigating the Impact of Humidity on Low-Cost PM Sensors

This preliminary study, conducted in Italy, aims to investigate the potential of growth functions and multi-layer perceptron neural networks (MLP NN) in reducing the impact of humidity on low-cost particulate matter (PM) sensors, with a focus on the sustainability of low-cost sensors compared to reference stations. All over the world, low-cost sensors are increasingly being used for air quality monitoring due to their cost-effectiveness and portability. However, low-cost sensors are susceptible to high humidity, which can lead to inaccurate measurements due to their hygroscopic property. This issue is particularly relevant in Italy, where many cities such as Rome, Milan, Naples, and Turin experience high mean relative humidity levels (>70%) for most months of the year. To improve data quality and gain useful data for quantitative analysis, techniques must be developed to reduce the impact of humidity on the final data. The sensors used in this study were placed in proximity to a reference station, solely for validation purposes in the case of corrective functions and involved in the training phase in the case of MLP NN.