Fig. 1

Model Architecture of BlaSt. Here, MinMaxScaler scales features to a 0–1 range, aiding LSTM neural networks by ensuring consistent input feature scaling for better convergence and performance. linear interpolation is used to smoothly estimate missing data, preserving time series integrity by considering trends in adjacent points. Also, the choice of 12 LSTM units in the model is based on a balance between complexity and performance. This specific number was determined through empirical experimentation, where we found that 12 units provided sufficient capacity to capture the temporal dependencies and patterns in the air quality data without leading to overfitting. Additionally, 12 units aligns with the 12 time step resolution we are working with, ensuring that the model effectively captures the necessary temporal dynamics for accurate predictions