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 NOx-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.