A data-driven exploration of Stratospheric Ozone dynamics : Bridging regional Ozone insights with environmental policy

Abstract

The stratospheric ozone can be very crucial in protecting the Earth against harmful UV radiation, as well as its restoration after the 1987 Montreal protocol, is unevenly spread in various regions. This research is a data-based examination of the longterm dynamics of the ozone in various countries despite having different climatic and geographical settings, which showed specific recovery in various regions. The study models complex seasonal and nonlinear ozone behavior, using the support of more complex feature engineering, which incorporates lag variables, rolling averages, and temporal indicators based on advanced deep-learning models: LSTM, GRU, TCN, Transformer, and hybrid solutions. Model assessment, which is based on the combination of accuracy measures and uncertainty estimation, indicates that LSTM is the best in terms of explanatory performance, and GRU achieves the lowest in terms of MAE and RMSE in all six climatic regions. Another meta-analysis conducted across all regions further synthesizes recovery slopes and prediction error and levels of uncertainty, with strong recovery rates in the Tropical, Temperate regions, and slower or more erratic rates in Polar, Subpolar, and Arid regions. A policy modeling framework based on the use of data-driven insights to inform climate-aligned policies in SDG 13 (Climate Action), the mitigation of UV-risks in SDG 3 (Good Health and Well-being), and the improvement of environmental planning in SDG 11 (Sustainable Cities and Communities) is also introduced in the study. This framework offers an evidence-based and scalable policy instrument to monitor the environment in the long term and make decisions to bridge long-term ozone recovery and policy action recommendations to sustainable climate decisions.