  Air quality is closely related to public health. Health issues such as
cardiovascular diseases and respiratory diseases, may have connection with long
exposure to highly polluted environment. Therefore, accurate air quality
forecasts are extremely important to those who are vulnerable. To estimate the
variation of several air pollution concentrations, previous researchers used
various approaches, such as the Community Multiscale Air Quality model (CMAQ)
or neural networks. Although CMAQ model considers a coverage of the historic
air pollution data and meteorological variables, extra bias is introduced due
to additional adjustment. In this paper, a combination of model-based strategy
and data-driven method namely the physical-temporal collection(PTC) model is
proposed, aiming to fix the systematic error that traditional models deliver.
In the data-driven part, the first components are the temporal pattern and the
weather pattern to measure important features that contribute to the prediction
performance. The less relevant input variables will be removed to eliminate
negative weights in network training. Then, we deploy a long-short-term-memory
(LSTM) to fetch the preliminary results, which will be further corrected by a
neural network (NN) involving the meteorological index as well as other
pollutants concentrations. The data-set we applied for forecasting is from
January 1st, 2016 to December 31st, 2016. According to the results, our PTC
achieves an excellent performance compared with the baseline model (CMAQ
prediction, GRU, DNN and etc.). This joint model-based data-driven method for
air quality prediction can be easily deployed on stations without extra
adjustment, providing results with high-time-resolution information for
vulnerable members to prevent heavy air pollution ahead.
