TY - JOUR
AU - Wang, Hantao
AU - Miyazaki, Kazuyuki
AU - Sun, Haitong Zhe
AU - Qu, Zhen
AU - Liu, Xiang
AU - Inness, Antje
AU - Schultz, Martin
AU - Schröder, Sabine
AU - Serre, Marc
AU - West, J. Jason
TI - Intercomparison of global ground-level ozone datasets for health-relevant metrics
JO - Atmospheric chemistry and physics
VL - 25
IS - 22
SN - 1680-7316
CY - Katlenburg-Lindau
PB - EGU
M1 - FZJ-2026-00098
SP - 15969 - 15990
PY - 2025
AB - Ground-level ozone is a significant air pollutant that detrimentally affects human health and agriculture. Global ground-level ozone concentrations have been estimated using chemical reanalyses, geostatistical methods, and machine learning, but these datasets have not been compared systematically. We compare six global ground-level ozone datasets (three chemical reanalyses, two machine learning, one geostatistics) relative to observations and against one another, for the ozone season daily maximum 8 h average mixing ratio, for 2006 to 2016. Comparing with global ground-level observations, most datasets overestimate ozone, particularly at lower observed concentrations. In 2016, across all stations, grid-to-grid R2 ranges from 0.50 to 0.75 and RMSE 4.25 to 12.22 ppb. Agreement with observed distributions is reduced at ozone concentrations above 50 ppb. Results show significant differences among datasets in global average ozone, as large as 5–10 ppb, multi-year trends, and regional distributions. For example, in Europe, the two chemical reanalyses show an increasing trend while other datasets show no increase. Among the six datasets, the share of population exposed to over 50 ppb varies from 61 % [28 %, 94 %] to 99 % [62 %, 100 %] in East Asia, 17 % [4 %, 72 %] to 88 % [53 %, 99 %] in North America, and 9 % [0 %, 58 %] to 76 % [22 %, 96 %] in Europe (2006–2016 average). Although sharing some of the same input data, we found important differences, likely from variations in approaches, resolution, and other input data, highlighting the importance of continued research on global ozone distributions. These discrepancies are large enough to impact assessments of health impacts and other applications.
LB - PUB:(DE-HGF)16
DO - DOI:10.5194/acp-25-15969-2025
UR - https://juser.fz-juelich.de/record/1050288
ER -