Regional background ozone estimation for China through data fusion of observation and simulation

Zhixu Sun; Jiani Tan; Fangting Wang; Rui Li; Xinxin Zhang; Jiaqiang Liao; Yangjun Wang; Ling Huang; Kun Zhang; Joshua S Fu; Li Li

doi:10.1016/j.scitotenv.2023.169411

Regional background ozone estimation for China through data fusion of observation and simulation

Sci Total Environ. 2024 Feb 20:912:169411. doi: 10.1016/j.scitotenv.2023.169411. Epub 2023 Dec 19.

Authors

Zhixu Sun¹, Jiani Tan¹, Fangting Wang¹, Rui Li¹, Xinxin Zhang¹, Jiaqiang Liao¹, Yangjun Wang¹, Ling Huang¹, Kun Zhang¹, Joshua S Fu², Li Li³

Affiliations

¹ School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China.
² Deparent of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USA.
³ School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China; Key Laboratory of Organic Compound Pollution Control Engineering (MOE), Shanghai University, Shanghai 200444, China. Electronic address: [email protected].

PMID: 38123088
DOI: 10.1016/j.scitotenv.2023.169411

Abstract

Regional background ozone (O₃__RBG) is an important component of surface ozone (O₃). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O₃__RBG in China is challenging. In this study, we calculated the O₃__RBG concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O₃ with the multiple linear regression (MLR) model. The annual average O₃__RBG concentration in China region in 2020 is 35 ± 4 ppb, accounting for 81 ± 5 % of the maximum 8-h average O₃ (MDA8 O₃). We applied the random forest and Shapley additive explanations based on meteorological standardization techniques to separate the contributions of meteorology and natural emissions to O₃__RBG. Natural emissions contribute more significantly to O₃__RBG than meteorology in various Chineses regions (30-40 ppb), with higher contributions during the warm season. Meteorological factors show higher contributions in the spring and summer seasons (2-3 ppb) than the other seasons. Temperature and humidity are the primary contributors to O₃__RBG in regions with severe O₃ pollution in China, with their individual impacts ranging from 30 % to 62 % of the total impacts of all meteorological factors in different seasons. For policy implications, we tracked the contributions of O₃__RBG and local photochemical reaction contributions (O₃__LC) to total O₃ concentration at different O₃ levels. We found that O₃__LC contribute over 45 % to MDA8 O₃ on polluted days, supporting the current Chinese policy of reducing O₃ peak concentrations by cutting down precursor emissions. However, as the contribution of O₃__RBG is not considered in the policy, additional efforts are needed to achieve the control groal of O₃ concentration. As the implementation of stringent O₃ control measurements in China, the contribution of O₃__RBG become increasingly significant, suggesting the need for attention to O₃__RBG and regional joint prevention and control.

Keywords: Brute force method; Chemical transport model; China; Multiple linear regression; Regional background O(3).