Smart Air Pollution Predictor for Smart Cities

Abstract

Air pollution remains a critical threat to human health, leading to respiratory issues and fatalities worldwide. With industrialization and urbanization on the rise, monitoring and predicting Air Quality Index (AQI) levels, especially in cities, becomes increasingly challenging. Cluster analysis emerges as a crucial tool for discerning patterns in air pollutant data. This study introduces the Common Mahalanobis Distance-based Fuzzy C-Means algorithm (CM-FCM) for air pollution data analysis, evaluating its effectiveness against k-means Clustering Algorithm and Euclidean Fuzzy C-Means Clustering Algorithm in terms of accuracy. The CM-FCM algorithm identified non-spherical clusters that accurately captured pollution patterns, enabling precise hotspot detection. Applied to datasets from Byrnihat and Indira Gandhi International Airport (LGBI) Airport, India, it categorized LGBI Airport as “Moderately Polluted” and Byrnihat as “Most Polluted”, with PM10 levels exceeding World Health Organization (WHO) standards on 99% of days. By considering pollutant correlations, CM-FCM provides valuable tools for policymakers to address pollution hotspots and enhance public health strategies.