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

Abstract. Ozone (O₃), a major tropospheric air pollutant, poses significant threats to public health and ecosystems, especially across Africa, where O₃ 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 O₃ 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 O₃ variations. Results reveal that as a NO_x-limited region, increased biogenic isoprene emissions contribute to a slight reduction in O₃ levels (< 0.5 ppb). Conversely, favorable meteorological conditions elevate O₃ levels over Africa, with a maximum projected increase of 2.0 ppb in 2050 relative to 2020, dominating the O₃ variations driven by climate change. The low-emission SSP scenarios are projected to prompt less increases in O₃ levels than the high-emission SSPs. Moreover, elevated air temperatures associated with global warming magnify the health burden across Africa, as O₃ 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.