Dense Air Pollution Estimation from Sparse in-situ Measurements and Satellite Data

• URL: http://arxiv.org/abs/2504.17039v1
• Date: Wed, 23 Apr 2025 18:38:16 GMT
• Title: Dense Air Pollution Estimation from Sparse in-situ Measurements and Satellite Data
• Authors: Ruben Gonzalez Avilés, Linus Scheibenreif, Damian Borth,
• Abstract summary: This paper addresses the challenge of estimating ambient Nitrogen Dioxide (NO$$) concentrations, a key issue in public health and environmental policy.<n>Existing methods for satellite-based air pollution estimation model the relationship between satellite and in-situ measurements at select point locations.<n>Motivated by these limitations, this study introduces a novel dense estimation technique.<n>By utilizing a uniformly random offset sampling strategy, our method disperses the ground truth data pixel location evenly across a larger patch.<n>At inference, the dense estimation method can then generate a grid of estimates in a single step, significantly reducing the computational resources required to provide estimates for larger areas.
• Score: 6.206127662604578
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the critical environmental challenge of estimating ambient Nitrogen Dioxide (NO$_2$) concentrations, a key issue in public health and environmental policy. Existing methods for satellite-based air pollution estimation model the relationship between satellite and in-situ measurements at select point locations. While these approaches have advanced our ability to provide air quality estimations on a global scale, they come with inherent limitations. The most notable limitation is the computational intensity required for generating comprehensive estimates over extensive areas. Motivated by these limitations, this study introduces a novel dense estimation technique. Our approach seeks to balance the accuracy of high-resolution estimates with the practicality of computational constraints, thereby enabling efficient and scalable global environmental assessment. By utilizing a uniformly random offset sampling strategy, our method disperses the ground truth data pixel location evenly across a larger patch. At inference, the dense estimation method can then generate a grid of estimates in a single step, significantly reducing the computational resources required to provide estimates for larger areas. Notably, our approach also surpasses the results of existing point-wise methods by a significant margin of $9.45\%$, achieving a Mean Absolute Error (MAE) of $4.98\ \mu\text{g}/\text{m}^3$. This demonstrates both high accuracy and computational efficiency, highlighting the applicability of our method for global environmental assessment. Furthermore, we showcase the method's adaptability and robustness by applying it to diverse geographic regions. Our method offers a viable solution to the computational challenges of large-scale environmental monitoring.

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