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Ridiculously simple data-driven air pollution interpolation method
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DOI：10.1016/j.envsoft.2026.106918.png)
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Abstract
En 中文
• 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.
Keywords:
air pollution interpolation
machine learning
urban air quality
dense pollution maps
data-driven method
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