Revolutionizing Air Pollution Spikes Analysis With a Blockchain-Driven Machine Learning Framework

Air pollution spikes pose significant health risks and environmental challenges that demand innovative solutions for effective analysis and mitigation. This paper introduces a groundbreaking approach to revolutionize air pollution spikes analysis using a blockchain-driven machine learning framework. Leveraging the transparency and immutability of blockchain technology, coupled with the predictive power of machine learning algorithms, our framework offers real-time monitoring, accurate prediction, and proactive management of air pollution spikes. Our framework provides comprehensive insights into air quality dynamics by integrating data from diverse sources, including IoT sensors. Furthermore, the decentralized nature of blockchain ensures data integrity and enhances trust among stakeholders, including regulatory authorities, industries, and communities. Through case studies and simulations, we demonstrated the efficacy and scalability of our framework in addressing air pollution spikes across diverse geographical regions. The Machine learning techniques for the time series model (RNNs, ARIMA, and Exponential Smoothing) were analyzed and compared using statistical metrics (Mean Absolute Error [MAE], Mean Squared Error [MSE], and R-squared [R²]). The exponential Smoothing model performed well compared to the other two models for all parameters, while both ARIMA and RNNNN models showed negative R² values for certain pollutants, particularly SO₂. For example, the PM10 scored 82.4% for R². This research signifies a paradigm shift in air quality management, empowering stakeholders to make informed decisions and mitigate the adverse impacts of air pollution spikes on public health and the environment. This research demonstrated that machine learning and blockchain can be integrated to analyze data on air pollution spikes and predict pollutant emissions. This solution will help prevent harmful exposure to pollutants, protecting human health and the environment.