Multivariate and multi-series LSTM
I would say that option 1 will not work out too well: in my experience, the model will either only be good for the first or last model you train, depending on how much freedom you give the algorithm to change weights as time goes on (e.g. with the learning rate).

You really need to decide what you are going to be predicting. Is it the pollution level for a single city: Which features do you have for each city?

It could can make sense to train all cities at the same time if the features you have are also general ones that really can explain the target variable. So if you have temperature, humidity, some transport statistics for that city etc. then training everything together could make sense.

I would think about each sample leading to one target pollution level, and if that sample has enough information (based on the features) to distinguish itself from samples of the other cities, the model should pick up on and leverage those subtleties in the data.