## 4 Discussion
The only significant change over the years is the ability to measure and monitor finer particulate matters with the help of new sensors (Figure 8). After finding out the main pollutant of the selected papers, it is interesting to explore countries distribution per pollutant. In Figure 9, we can see that the case study of 18 publications out of 19, having PM\({}_{2.5}\) as a pollutant target, is China. _Time Granularity_: is time resolution which is considered as the prediction interval. In Figure 10, we can notice that most used time resolutions were 24 h in 17 papers, and just in four papers the authors tried to make weekly prediction. The main reason is the issue related to the accuracy of long term predictions (for example, in [12] RMSE for the next 1h was 9.3953, for the next 5 h it was 37.6874 and for the next 10 h it was 65.7108). Figure 8: Number of the publications focused on the prediction of PM\({}_{2.5}\) and PM\({}_{10}\) over the years. Figure 9: Countries distribution per pollutant. _Data Rates_: is a frequency of the data acquisition from the sensors. Figure 11 presents data obtainment frequency for the selected studies. As it might be seen, in the majority of the papers (27) sensors provided hourly data, in nine studies sensors provide daily data, in one paper the frequency was 15 min, and the rest includes publications which did not provide any information about data rates. _Dataset Types_: include types of data which were used in order to perform analysis. The used dataset types involve _AQ_: air quality data, _MET_: meteorological data, _Temporal_: include the day of the month (values from 1 to 31), day of the week (values from 1 to 7), the hour of the day (values from 1 to 24), _WFD_: weather forecast data, _Spatial_: in one paper it refers to proximity to transportation, topographical characteristics, neighbourhood characteristics, and for the rest cases it indicates the locations of the stations, _Social Media_: microblog data, _Chemical_: chemical component forecast data (organic carbon, black carbon, sea salt, etc.) and _TIF_: traffic intensity features, which contains information about traffic level in the surrounding stations (vehicles/hour). As shown in Figure 12, in the majority of the papers (36) air quality data was combined with meteorological data, considering the importance of the latter one in air quality prediction. The next types are temporal data in six papers, weather forecast data  
Figure 11: Data rates. Figure 10: Time granularity. in five papers, spatial data in five papers, and social media, chemical forecast data, traffic intensity features, each of them in one paper. _Open Data:_ includes information about data accessibility. Taking into consideration the role of reproducibility nowadays, we explored to know which papers provide a link to the dataset used to carry out the experiments. However, it is worth mentioning that reproducibility is not only data; it also refers to code availability [69]. No paper provided code scripts, although the algorithms were available and were explained in the papers. Figure 13 shows data accessibility of the selected studies. We can see that 20 papers for their analysis used open data, 16 papers used private data, and _Others_ includes five papers; in the first paper the authors mentioned that data can be available through the request, the second paper used partially available data, the third study provided link to access to the data, but now the link is not available, and the other two studies mentioned that they took data from Hong Kong Environmental Protection Department without providing any reference. Figure 14 displays the number of publications for data accessibility over the years. We can notice that since 2010 the authors have started to use open data portals to capture data to perform analysis and since 2017, in contrast to papers using private data, the number of publications having open data increased. Figure 12: Dataset types used in the selected publications.