ArticleFrontiers in big data2025
Inferring causal interplay between air pollution and meteorology.
Article in Frontiers in big data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Introduction: This study investigates the bidirectional causal interplay between PM Methods: The study employs Convergent Cross Mapping (CCM), a nonlinear empirical dynamic modeling approach. Hourly data were analyzed across four districts in Quito across two distinct time periods: 2004-2005 versus 2022-2024. Robustness of causality was confirmed using surrogate testing techniques. Results: The analysis reveals statistically significant, nonlinear, and time-variant couplings. While RH influenced PM Discussion: The observed suppression of RH by pollution is consistent with urban heat island amplification and radiative effects. These findings underscore the necessity of nonlinear causality frameworks for understanding environmental feedbacks in complex terrains. The study highlights the need for integrated air quality and climate strategies. Future research should expand variables and monitoring sites to further generalize these findings.
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