AI Article Synopsis
- This study showcases how Riemannian geometry enhances the analysis of complex atmospheric monitoring data from multiple sites and pollutants.
- By using covariance matrices, researchers capture spatial and temporal variability, leveraging Riemannian metrics for improved data processing.
- The research highlights its application in a year-long analysis of data from 34 monitoring stations in Beijing, leading to superior dimensionality reduction and outlier detection compared to traditional methods.
Article Abstract
We demonstrate the benefits of using Riemannian geometry in the analysis of multi-site, multi-pollutant atmospheric monitoring data. Our approach uses covariance matrices to encode spatio-temporal variability and correlations of multiple pollutants at different sites and times. A key property of covariance matrices is that they lie on a Riemannian manifold and one can exploit this property to facilitate dimensionality reduction, outlier detection, and spatial interpolation. Specifically, the transformation of data using Reimannian geometry provides a better data surface for interpolation and assessment of outliers compared to traditional data analysis tools that assume Euclidean geometry. We demonstrate the utility of using Riemannian geometry by analyzing a full year of atmospheric monitoring data collected from 34 monitoring stations in Beijing, China.
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| http://dx.doi.org/10.1016/j.scitotenv.2023.164064 | DOI Listing |
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