Ridiculously simple data-driven air pollution interpolation method

Air pollution interpolation is crucial for civil management: it is used to transform limited sensor data into comprehensive pollution maps. Various methods, including deterministic, geostatistical, and Machine Learning (ML)-based techniques have been utilized for this purpose. Deterministic methods rely on mathematical rules for estimation, whereas geostatistical techniques are based on spatial correlations. ML leverages historical data for predictions. Each method has limitations: deterministic methods do not adequately model environmental complexity, geostatistical methods struggle with small-scale areas, and ML depends heavily on data availability. On the other hand, air pollution simulators based on physicochemical dispersion models capture intricate pollution dispersion patterns, yet running them at high resolution for continuous interpolation is often computationally demanding. Recent advancements in ML offer a complementary pathway by leveraging simulated data to enable rapid, real-time interpolation. This study introduces an air pollution interpolation approach combining simulated air-dispersion patterns through an educated machine that includes machine learning and environmental modeling that considers boundaries, pollution sources, obstacles, wind dynamics, and topography. Specifically, we present a combined ML-based linear regression model designed to infer dense concentration maps from a sensor array using state-of-the-art simulation methods. We dub this the Ridiculously Simple data-driven air pollution Interpolation Method (RSIM). The RSIM method was evaluated on both synthetic and real-life-based simulation models. The synthetic scenarios included an industrial area with point pollution sources and an urban road surrounded by buildings simulating traffic-related pollution. The real-world environment consisted of sensor data and simulations from Antwerp, Belgium. The results indicate that this method outperforms standard techniques for reconstructing dense pollution maps from sparse sensing, and demonstrates significant promise for other real-world applications. • We propose a novel method for interpolating air pollution using machine learning. • The approach is validated on both synthetic data and real data from Antwerp, Belgium. • Our method outperforms classical techniques in generating dense pollution maps. • The workflow integrates simulated dispersion fields with real-world sensor readings. • The methodology supports scalable, high-resolution mapping for urban air quality.