Ambient air pollution is known to be a serious issue that has an impact on human health and the environment. Assessing
air quality is of the utmost importance to protect human health and the environment. Different tools are available,
from monitoring stations to complex models. These systems are capable of accurately predicting air quality levels, but
they are often computationally very expensive which makes them poorly efficient. In this paper, we developed a novel
model called Dynamic Neural Assimilation (DyNA) integrating Recurrent Neural Networks and Data Assimilation methods
to derive a physics-informed system capable of accurately forecasting air pollution tendencies and investigating
the relationship with industrial statistics. DyNA is trained in historical data and is fine-tuned as soon as new data comes
available. We trained and tested the system on real data provided by the air quality monitoring stations located in Italy
from the European Environment Agency and simulated results derived from the air quality modelling system Atmospheric
Modelling System-Model to support the International Negotiation on atmospheric pollution on a National Italian level.