Real-Time Systems for Air Quality Forecasting: A Review of Sensor Networks, Data Fusion, and Modeling Approaches

An important environmental concern that has an impact on people's health and quality of life is air quality. For early detection of possible air quality issues and the creation of efficient mitigation plans, accurate air quality forecasting is crucial. To offer timely updates on the state of the air and increase the precision of air quality forecasts, real-time systems can be employed. These systems include sensor networks, data fusion, and modeling techniques. With an emphasis on these three strategies, this study examines the state of the art of research on real-time systems for air quality forecasting. Sensor networks are groups of sensors placed all over an area to gather information on variables affecting air quality, such as temperature, humidity, and pollution concentrations. This work covers recent studies on the application of sensor networks for air quality forecasting going through the difficulties and advantages of this strategy. Data fusion consists of combining information from several sources, including sensor networks and satellite data, to produce an accurate picture of the current state of the air. In this study, we cover recent research on data fusion methods for predicting air quality and highlight the advantages and drawbacks of this strategy. Using modeling techniques, it is possible to simulate how various environmental elements affect air quality and receive real-time updates on forecasts. Therefore, this work presents a review on recent research on modeling methods for predicting air quality and outlines also the difficulties and advantages of this method. Moreover, this work also emphasizes the main research gaps and areas of future research in real-time systems for air quality forecasting.