Predicting and analysing air quality in smart cities using virtual sensors

Virtual sensors have become essential in generating synthetic data to support smart city initiatives, mainly when physical sensors are absent or inaccessible. They play a crucial role in monitoring air pollutants, benefiting those with health sensitivities, and aiding city planning in the context of smart cities. This research focuses on creating reliable victual sensors for air pollutant measurement through deep learning and machine learning models, providing a cost-effective solution to address the scarcity of physical sensors and data inaccuracies. The main contribution lies in improving smart cities by providing essential health-related data to mitigate the harmful effects of air pollution on residents. Continuous monitoring empowers officials and residents to reduce pollution exposure, ultimately improving public health. The study found that gradient-boosted trees are effective for daily and hourly predictions, emphasizing the vital role of virtual sensors in advancing data-driven decision-making and improving environmental quality in smart cities. The results showed that our model was better using the boosted trees method with an RMSE of approximately 10%.