Venue: ICLR 2018 conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: 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.
review_title: Main contribution of the paper is building a spatio-temporal data set on air pollution indicators from open source data and a baseline machine learning prediction model. The paper needs rewriting of certain of its paragraphs (bad phrasings and missing details).

final_score: rating: 4: Ok but not good enough - rejection
review_content: The major contribution lies in the producing of the data. There are several concerns.
1. Since the major contribution lies in the production of the data, it is required for the authors to justify the quality of data. How accurate they are? What are the error bounds in terms of devices of measurement? What is the measurement precision? There is no such discussion for the data source in this submission, and thus it would be really hard for the reviewer to judge the validity of the dataset. The authors claim this is the largest dataset of such purpose, but they didn't demonstrate that the smaller datasets offered previously is indeed less competitive.

2. Using interpolation to align data is questionable. There are obviously many better ways to do so.

3. I would suggest the authors should use the two baseline models on other air-quality datasets for comparison. It can then convince the readers this dataset is indeed a better choice for the designed task. 

4. This paper is not very well written. The English has certain room for improvement, and some details are missing. For instance, in Table1, Table2 and Table3, there are no captions . It is also unclear what's the purpose of Figure3 for?
review_title: This work offers a new air quality dataset. The authors describe the collection of data sources, and the processing of them. They then defined the machine learning task of predicting future air quality. Two baseline models are offered – ReducedLSTM and WIPENET. Experiments are done to demonstrate that this dataset is suitable to train deep models that predicts air quality. 

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper is about open sourcing AirNet, a database that has interpolated air quality metrics in a spatial form along with matching meteorological data obtained elsewhere. In addition, the paper also develops a few baseline methods and evaluated using standard metrics such as detection rate, false alarms etc. The work is original and significant from an applications point of view. It looks like the dataset is useful but the model development and experimental sections are weak.

Strengths:
- open source data set for air quality monitoring that is significantly better than existing ones.
- baseline models using standard methods including RNN.

Weaknesses:
- The air quality data is measured at point locations (stations) which are interpolated to obtain spatial data. There is no evaluation on this step to make sure the interpolated data indeed reflects truth. 
- Experiments doesn't seem to be carefully done using hyper-parameter tuning/ cross-validation. The model results may be misleading.
- Writing and formatting needs to be improved. Some examples - "quality of air quality", "people attempted to apply deep learning", "in the computer vision field .", "Some people also used the hidden Makov model", "radial of longitude", "in 2:00AM, January 23". The paper in general was not easy to follow at many places.
- Is Table 3 incomplete with one box unlabeled?
- Figure 3 is not clear. It is suggested to follow standard notations to represent the RNN structure (see Jurgen Schmidhuber's paper)
- "DEV" in table 4 is not explained. Is this a development set? If so, what does it mean?
- It is said that "reduced LSTM is improved than LSTM". But the test results in Table 4 shows that LSTM is better.
review_title: It looks like the dataset is useful but the model development and experimental sections are weak.

Meta-review: This is an interesting application area, but the quality of the presentation and experimental work here is not sufficient for acceptance. The numerical ratings from reviewers are just not high enough to warrant acceptance. 
Decision: Reject