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Abstract:
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Meteorological conditions are a driver of ambient air quality and pollution levels. Hence, there is increasing interest in quantifying the impacts of climate change on future air pollution levels and their associated health effects. We describe a statistical modeling framework for projecting future ambient ozone levels. Previous studies have typically utilized outputs from numerical models for projecting future ozone levels; however, these models provide only deterministic projections. In contrast, a statistical approach, driven by meteorology and precursor variables, can flexibly incorporate various sources of uncertainties in the future projections, which may be useful to inform public health risk assessment. We present future ozone projections and their health impacts across 28 US cities for the period 2041-2070 based on simulations from multiple climate models and emission scenarios. We also find that health impact projection uncertainty is driven predominantly by uncertainty in the health effect association and climate model variability. However, calibrating climate simulations with historical observations can greatly reduce differences in projections across climate models.
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