Estimating US Background Ozone Using Data Fusion.

Area of Science:

• Atmospheric Chemistry
• Environmental Science
• Air Quality Modeling

Background:

• Background ozone (O3) is the ozone present without US anthropogenic emissions, varying by season and location.
• Quantifying background ozone typically relies on chemical transport models (CTMs), but these estimates have uncertainties and inter-model differences.
• Accurate background ozone estimation is vital for understanding total ozone concentrations and air quality.

Purpose of the Study:

• To develop and apply a novel data fusion method to improve the accuracy of US background ozone (US-B O3) estimates.
• To apportion model bias to both background and anthropogenic ozone components.
• To assess the impact of model scale on ozone bias.

Main Methods:

• Developed a data fusion technique combining observed ozone (O3) with regional CTM (CMAQ) simulated US-B O3.
• Apportioned CTM bias spatially and temporally to US-B O3 and US anthropogenic ozone (US-A O3).
• Analyzed trends in O3 bias across different simulation years (2016-2017) and model resolutions.

Main Results:

• CTM US-B O3 estimates showed a consistent bias, being low in spring and high in fall.
• US-A O3 was generally overestimated, with bias increasing at coarser model resolutions.
• The data fusion bias adjustment method resulted in an estimated 28% improvement in agreement for adjusted US-B O3.
• Adjusted annual mean US-B O3 ranged from 32-33 ppb, and spring means ranged from 37-39 ppb.

Conclusions:

• The developed data fusion method effectively reduces bias in chemical transport model simulations of US background ozone.
• Improved US-B O3 estimates provide more reliable data for air quality management and policy decisions.
• This approach offers a pathway to enhance the accuracy of atmospheric composition modeling.