Paper:
In the past decade, many urban areas in China have suffered from serious air pollution problems, making air quality forecast a hot spot. Conventional approaches rely on numerical methods to estimate the pollutant concentration and require lots of computing power. To solve this problem, we applied the widely used deep learning methods. Deep learning requires large-scale datasets to train an effective model. In this paper, we introduced a new dataset, entitled as AirNet 1 , containing the 0.25 degree resolution grid map of mainland China, with more than two years of continued air quality measurement and meteorological data. We published this dataset as an open resource for machine learning researches and set up a baseline of a 5-day air pollution forecast. The results of experiments demonstrated that this dataset could facilitate the development of new algorithms on the air quality forecast.

Review:
This paper's main contribution is in the building of a spatio-temporal data set on air pollution indicators as the title states.
The data set is built from open source data to comprise pollutants measured at a number of stations and meteorological data. Then, an air pollutant predictor is built as a baseline machine learning model with a reducedLSTM model. 
Most of the first part's work is in the extraction of the public data from the above mentioned sources, aligning of the two source data and sampling considerations.
The paper lacks detailed explanation of the problem it is actually addressing by omitting the current systems' performance: simply stating: 1.1/page 2 "Thus it became essential and urgent to set up a larger scale training dataset to enhance the accuracy of the forecast results." 
It also lacks definition of certain application domain area terms and acronyms (PM2:5).
Certain paragraphs need rewriting:
     - 2.2/Page 3: "Latitude ranges from 75 degrees to 132 degrees and the north latitude range of is from 18 degrees to 51 degrees".
     - 3.1/Page 4: "We converted the problem of the pollutant prediction as time sequential prediction problems, as in the case of giving the past pollutant concentration x0 to xt􀀀1.".
Also, Table 1: GFS Field Description contains 6 features  not 7 as stated in 2.1
For air pollutant prediction a baseline machine learning model is built with a reducedLSTM model. 
Results seem promising but lack serious comparison with currently obtained results by other approaches as mentioned above. The statement in 5./Page 7:"Furthermore, reduced LSTM is improved than LSTM, we assumed this is because our equation considered air pollutant dynamics, thus we gave more information to model than LSTM while keeping LSTMs advantage." attributes the enhanced results to extra data (quantity) fed to the model rather than the fact (quality) as stated in the paper that the meteorological conditions (dispersion etc.) influence the air pollutant presence/ concentrations in nearby stations.
A rewriting and clarification of certain paragraphs is therefore recommended.