Multivariate Time-Series forecasting using LSTM
What you could try to do is the following (pseudocode ahead):

input_past_24 = Input(shape=(24,5))
input_today = Input(shape(1,4))
output_from_LSTM = LSTM(some_args, ...)(input_past_24) # this has some size of your choice, let's say N
# if you want to add other layers, you'd do it here.
prediction = Dense(some_args, ...)(concatenate(input_today,output_from_LSTM))
model = Model(inputs=[input_past_24,input_today], output=prediction)
model.compile(some_args)
model.fit([data_from_past_days,data_today],expected_pollution, some_other_args...)


Above, the crucial point is to concatenate the output of the LSTM layer with the input of the day, before sending this to a final Dense layer for the final prediction.

For extra details, you may want to check Keras' Guide to the functional API.