FEMTOSAT-Based Air Quality Monitoring: Leveraging Satellite Data and LoRa Communication for Improved AQI Predictions

Abstract

The exponential growth of the global population has given rise to an alarming surge in air pollution levels, casting profound repercussions on economies, ecosystems, and human well-being. The predominant method for assessing air quality involves the deployment of sensors atop buildings at regular intervals. However, this approach faces significant drawbacks, including heightened energy consumption associated with each building's sensor infrastructure and its inapplicability in sparsely populated rural areas. Addressing these limitations, the utilization of FEMTOSAT technology emerges as a solution, capitalizing on satellite data for autonomous air pollution monitoring, analysis, and mitigation. Amidst the evolving scientific landscape, the integration of Low Power Wide Area Network (LoRa) technology assumes a pivotal role within the Internet of Things (IoT), facilitating long-range data communication with minimal power consumption. In this context, LoRa communication serves as the conduit for transmitting and receiving data from satellites via RF signals. The satellite-derived environmental data, thus collected, serves as the foundation for computing the Air Quality Index (AQI) at specific locations, a critical metric that informs us about air quality conditions, whether pristine or contaminated. The AQI computation factors in various pollutants, including NO2, CO, O3, PM2.5, SO2, and PM10, all of which significantly influence air quality. This study employs a range of machine learning (ML) techniques, including time series analysis, linear regression, Support Vector Machines (SVM), and logistic regression, to predict and forecast AQI values. These models amalgamate AQI data from diverse sources, yielding robust and dependable AQI prediction models. Notably, modern sensor technology simplifies and enhances data collection accuracy. In the realm of environmental data analysis, only ML algorithms can grapple with the complexity of processing vast datasets to generate precise and trustworthy predictions. The incorporation of integrated sensors as payload in the Femto Sat mission epitomizes the mission's objectives. This system is characterized by its cost-effectiveness, lightweight design, durability, redundancy, and user-friendly interface, requiring minimal power consumption for operation.