Projecting the climate penalty on pm2.5 pollution with spatial deep learning

The climate penalty measures the effects of a changing climate on air quality due to the interaction of pollution with climate factors, independently of future changes in emissions. This work introduces a statistical framework for estimating the climate penalty on soot pollution (PM 2.5), which has been linked to respiratory and cardiovascular diseases and premature mortality. The framework is used to evaluate the disparities in future PM 2.5 exposure across racial/ethnic and income groups. The findings of this study have the potential to inform mitigation policy aiming to protect public health and promote environmental equity in addressing the effects of climate change. The proposed methodology significantly improves upon existing statistical-based methods for estimating the climate penalty. It will use higher-resolution climate inputs---which current statistical approaches cannot accommodate---using an expressive and scalable predictive model based on spatial deep learning with spatiotemporal trend estimation. It will also integrate additional predictive data sources such as demographics and geology. This approach allows us to consider regional dependencies and synoptic weather patterns that influence PM 2.5 and deconvolve them from the effects of exogenous factors, such as the trends in increasing air quality regulations and other sources of unmeasured spatial heterogeneity.