The features of the selected papers in the field of air quality prediction

<p>The table is a part of a submitted manuscript (Iskandaryan, D., Ramos, F., &amp; Trilles, S. The Role of Datasets in Air Quality Prediction. &nbsp;Submitted to Atmosphere.)&nbsp;and includes the following features extracted from the selected papers: <em>Year, Case Study, Prediction Target, Dataset Type, Data Rate, Period (Days), Open Data, Algorithm, Time Granularity and Evaluation Metric</em>. The relevant papers&nbsp;were selected from a systematic review in <em>Air Quality Prediction Using Machine Learning Technologies. </em>The works were&nbsp;queried in Association for Computing Machinery, IEEE Xplore, Scopus and Web of Science databases using the following query: (&quot;machine learning&quot;) AND (&quot;prediction&quot;OR &quot;forecast&quot;) AND (&quot;air quality&quot; OR &quot;air pollution&quot;), which was being applied to title, abstract and keywords. After filtering the results guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, &nbsp;ninety-three papers were selected. The goal of this review is to understand which features are used in the field, in particular to answer the following questions:&nbsp;&nbsp;1) What types of datasets are used to improve air quality predictions?; and 2) What characteristics of the dataset are important for efficient and effective air quality forecasting?&nbsp;<br> Twenty-six datasets were used by the authors as supplemental air quality data in order to predict air quality more accurately. Those datasets are: &quot;MET&quot;- meteorological data; &quot;Spatial&quot;- topographical characteristics, the locations of the stations; &quot;Temporal&quot;-includes the day of the month, day of the week, the hour of the day; &quot;AOD&quot;- aerosol optical depth; &quot;Social Media&quot;- microblog data; &quot;Traffic&quot;; &quot;PBL Height&quot;- planetary boundary layer height; &nbsp;&quot;Land Use&quot;; &quot;BEV&quot;- Built Environment Variables; &quot;UV Index&quot;; &quot;SP&quot;- Sound Pressure; &quot;PD&quot;-Population Density; &quot;Human Movements&quot;- floating population and estimated traffic volume; &nbsp;&quot;Altitude&quot;; &nbsp;&quot;OMI-SO2&quot;-Satellite-retrieved SO2 from Ozone Monitoring Instrument-SO2; &quot;PPS&quot;- Pollution Point Source; &quot;TS&quot;-Transportation Source; &quot;WFD&rsquo;&quot;- weather forecast data; &quot;POI Distribution&quot;; &quot;FAPE&quot;- factory air pollution emission; &quot;RND&quot;- Road Network Distribution; &quot;Elevation&quot;; &quot;AEI&quot;- Anthropogenic Emission Inventory; &quot;NDVI&quot;; &quot;Chemical&quot;- chemical component forecast data (organic carbon, black carbon, sea salt, etc.); &quot;Emission&quot;.</p>