# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 1 Introduction
### IoT Employed in Air Quality Measurement  
Multiple studies have demonstrated that ML algorithms successfully predict air quality index (_AQI_) and pollutant concentrations utilizing a range of meteorological and environmental variables.  
Integrating IoT technologies into AQ monitoring systems has transformed data collection, analysis, and dissemination. IoT-based systems enable continuous, real-time monitoring of air pollutants, which permits timely responses to pollution incidents and informs public health policies in real-time. Some platforms [43] can be accessed onlineand offer daily forecasting information regarding the weather parameters for the next 7 days. By accessing the URL's parameters platform [43], it can be identified that the PM\({}_{2.5}\) concentration is currently 8.5 \(\upmu\)g/m\({}^{3}\) (10 April 2025).  
IoT devices are integrated into AQ monitoring systems due to their capacity for remote operation without human intervention [44]. These devices automatically collect and analyze data, replacing the fixed stations that are costly and limited in spatial coverage [45; 46]. For example, low-cost sensors can be deployed in diverse environments, facilitating a more comprehensive understanding of pollution distribution across various regions [47; 48]. This is especially important in indoor AQ, where people spend a significant portion of their time [49; 50; 51]. Modern sensors measure a wide range of pollutants, including PM, NO\({}_{2}\), and volatile organic compounds (VOCs) [52]. These sensors provide real-time feedback on AQ fluctuations, enabling citizens to participate in measurement initiatives and fostering greater community awareness of air pollution [53; 54; 55]. Integrating ML algorithms with IoT systems enhances data analysis capabilities, allowing for predictive AQ modeling based on historical and real-time data [56].  
An air quality monitoring system (AQMS) using Global System for Mobile Communications (GSM) communication is presented, enabling real-time measurement of CO\({}_{2}\), CO, NO\({}_{2}\), and SO\({}_{2}\) via electrochemical and infrared sensors. The system features wireless data transmission, a user-friendly Graphical user interface (GUI) for gas concentration visualization, and periodic calibration to ensure accuracy, addressing design challenges from prior studies [57]. A heuristic recurrent air quality predictor (RAQP) is proposed by [58], using meteorology- and pollution-related variables to infer PM\({}_{2.5}\) concentrations. By addressing non-linear declines in correlations over time, RAQP recursively applies a one-hour prediction model, outperforming state-of-the-art accuracy methods for multi-hour forecasts. Massive-scale AQ sensing with millions of sensors offers fine spatial-temporal monitoring. Challenges include calibration and deployment feasibility, explored via a Helsinki testbed [59].  
Another technical solution in AQ is using mobile sensing networks, such as sensors attached to vehicles or drones [60; 61]. Data collected through these networks are uploaded to cloud platforms, ensuring accessibility for the public and policymakers [62; 63].