Inferring causal interplay between air pollution and meteorology - Rybarczyk, Yves Philippe; Dave, Niralkumar Hemantbhai; Tapia-Flores, Tobias Isaac; Zalakeviciute, Rasa (Frontiers in Big Data, 2025)
IntroductionThis study investigates the bidirectional causal interplay between PM2.5 and relative humidity (RH) in Quito, Ecuador. Focusing on a high-altitude city with complex terrain, the objective is to understand pollution-climate feedbacks over a two-decade span.MethodsThe 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.ResultsThe analysis reveals statistically significant, nonlinear, and time-variant couplings. While RH influenced PM2.5 in the early 2000s, the relationship inverted, with PM2.5 increasingly driving RH by the early 2020s. Partial-derivative analyses indicate shifting interaction signs and strengths. Notably, pollution was found to increasingly suppress RH, particularly in northern districts.DiscussionThe 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.