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Local real‐time forecasting of ozone exposure using temperature data
Citation:
Lu, X., A. Gelfand, AND D. Holland. Local real‐time forecasting of ozone exposure using temperature data. ENVIRONMETRICS. John Wiley & Sons Incorporated, New York, NY, 29(7):e2509, (2018). https://doi.org/10.1002/env.2509
Impact/Purpose:
Air pollution continues to be a common health concern. There is now growing interest in providing access to real-time monitoring data and using these data to provide air quality forecasts for local communities. Real-time forecasts and personalized smartphone notifications could allow people to modify their behavior to help reduce pollutant exposures. In the past, continuous, long-term measurement of ambient air pollution has been limited by monitoring restrictions and resource constraints. Recently, there have been monitoring advances in the development of stationary air measurement systems that can provide long-term data collection and be deployed in community environments.
Description:
Rigorous and rapid assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we use hierarchical modeling to enable real‐time forecasting of 8‐hr average ozone exposure. This contrasts with customary retrospective analysis of exposure data. Specifically, our contribution is to show how incorporating temperature data in addition to the observed ozone can significantly improve forecast accuracy, as measured by predictive performance and empirical coverage. We adopt two‐stage autoregressive models, also introducing periodicity and heterogeneity while still maintaining our objective of forecasting in real time. The entire effort is illustrated through modeling data collected at the Village Green monitoring station in Durham, North Carolina.
