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EST Publication: Estimating US background ozone levels using data fusion
Citation:
Skipper, T., Y. Hu, M. Odman, B. Henderson, C. Hogrefe, R. Mathur, AND A. Russell. EST Publication: Estimating US background ozone levels using data fusion. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 55(8):4504-4512, (2021). https://doi.org/10.1021/acs.est.0c08625
Impact/Purpose:
Accurately quantifying background ozone is important for many air quality management applications. Often, background ozone estimates are based solely on chemical transport models and are thus uncertain due to potential errors in model process descriptions and inputs. The manuscript develops and applies a method to combine model-based estimates of background ozone with observations to account for model biases and increase confidence is estimated background concentrations.
Description:
US background (US-B) ozone (O3) is the O3 that would be observed in the absence of US anthropogenic emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of overall O3. Typically, US-B O3 is quantified using a chemical transport model (CTM), though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US anthropogenic (US-A) O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the original CTM US-B O3 estimate is typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 is biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3.
URLs/Downloads:
DOI: EST Publication: Estimating US background ozone levels using data fusion
https://pubs.acs.org/doi/10.1021/acs.est.0c08625