Time-delayed causal network analysis of meteorological variables and air pollutants in Baguio city

Abstract

Air pollution contributes significantly to climate change and has recently become a significant concern. Previous studies on the interplay between air quality and the environment, such as meteorological variables, have hinted at their relevance in predicting air quality. While air quality prediction has been extensively explored, only a few studies have investigated the causal interactions, and no study has looked into identifying time-delayed causality between individual meteorological factors and air pollutants. In this work, we have deployed sensors in Baguio City, renowned as the “Summer Capital of the Philippines”, and collected data from September 2022 to April 2023. Convergent Cross Mapping (CCM) is then used to infer the time-delayed causation network for PM1, PM2.5, PM10, VOC, and local meteorological variables. Baguio City faces environmental challenges from urbanization and influx of tourists. Here, the top meteorological variables are identified and characterized according to the strength and delay of influences on air pollutants. Wind direction emerges as a leading influencer driving air quality instantly, while pollutants reciprocate with delayed feedback. Temperature holds sway after a day, with pollutants impacting it after eight days on average. Furthermore, links from wind speed to PM concentrations are observed as mediators in causal pathways. Here, the maximum lag delay is only nine days. This work highlights the intricate interdependence of local meteorological factors and air quality, pioneering the identification of time delays that optimize causal influences between these variables. The results of this work offer valuable insights into environmental management and the formulation of mitigation measures.