Scenario-driven ozone projections and associated impact on mortality over Africa with an integrated machine learning framework

Ozone (O ₃ ), a major tropospheric air pollutant, poses significant threats to public health and ecosystems, especially across Africa, where O ₃ concentrations have experienced pronounced increases in recent decades. This study employs an interpretable machine learning (ML) model integrated with multi-source data to predict near-surface O ₃ levels over Africa from 2020 to 2050 driven by climate change under four Shared Socioeconomic Pathways (SSPs). We quantitatively investigate the respective roles of climate-driven changes in meteorological conditions and biogenic isoprene emissions in affecting future O ₃ variations. Results reveal that, in an annual mean sensitivity experiment that isolates climate-driven changes in biogenic isoprene, increased biogenic isoprene emissions contribute to a slight reduction in O ₃ levels (<0.5 ppb). Conversely, favorable meteorological conditions elevate O ₃ levels over Africa, with a maximum projected increase of 2.0 ppb in 2050 relative to 2020, dominating the O ₃ variations driven by climate change. The low-emission SSP scenarios are projected to lead to smaller increases in O ₃ levels than the high-emission SSPs. Moreover, elevated air temperatures associated with global warming magnify the health burden across Africa, as O ₃ pollution acts as an additional stressor in a warming climate. This highlights the urgency for robust air pollution control and climate mitigation strategies to alleviate future health impacts in Africa.