the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Recent ozone trends and changepoints in the Greater Region: The role of meteorology and NOₓ emission reductions
Abstract. We analysed long-term records of NO2 and O3 in Luxembourg and the Greater Region with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to identify probabilistic changepoints in time series. Seasonal components of NO2 and long-term components of O3 revealed a significant changepoint in 2017/2018 that is more prominent than the effect of the short-term emission reductions in 2020/2021 and the subsequent post-Covid rebound in urban areas. NO2 concentrations decrease by at least 50 % for all station categories from 2009 to 2024. Linear trends for O3 during this period based on quantile regression and Theil-Sen trend estimator were positive, with highest values at urban stations (~0.75 µg m-3 yr-1), while increases at peri-urban, rural, and forest sites were smaller (~0.4–0.6 µg m-3 yr-1). Spring and autumn are the dominant seasons contributing to the O3 increase. The decline of the positive weekend effect, together with the reduced MDA8 O3 sensitivity to maximal air temperatures above ~20 °C after 2018, indicates an ongoing transition from predominantly VOC-limited towards NOₓ-limited chemistry, particularly at peri-urban, rural, and forest stations during summer. However, our results suggest that the impact of reduced O3 titration by NO, particularly at low to moderate temperatures has sustained increasing O3 in the Greater Region. Simultaneously, the reduction in peak O3 due the influence of NOx-limited chemistry was partly offset by the increase in daytime maximum boundary layer height observed after the 2017/2018 changepoint.
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Status: open (until 10 Nov 2026)
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RC1: 'Comment on egusphere-2026-5341', Anonymous Referee #1, 17 Oct 2026
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This manuscript presents a comprehensive analysis of long-term NO2 and O3 observations spanning up to 25 years (2000–2024, with a detailed focus on 2009–2024) across 15 monitoring stations in the Greater Region of Western Europe (Luxembourg, Belgium, France, and Germany). The study investigates seasonal and long-term trend changepoints using primarily the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), Quantile Regression with Moving Block Bootstrap (QR-MBB), and non-parametric Theil-Sen slope estimates combined with SSA and STL seasonal decompositions. The authors report a consistent downward trend in NO2 concentrations (>50% reduction from 2009 to 2024 across all station categories) alongside widespread positive trends in surface O3. A distinct probabilistic changepoint was identified around 2017/2018, driven by shifts in winter/autumn NO2 seasonality, fleet composition changes, and meteorological changes such as deeper daytime BLH. Overall, the manuscript is well-structured, thoroughly executed, and addresses crucial policy and chemical-regime questions regarding surface ozone formation in Western Europe.
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Definition of changepoint; This study does not adequately distinguish between abrupt changes and changes of trend magnitudes. Abrupt changes typically refer to step changes, not necessarily implying the long-term trends have changed. Changes of trend magnitudes can indicate turnaround, acceleration, and deceleration. Currently those two concepts are mixed badly in the manuscript. See Weatherhead et al. (2017), Lund et al. (2023) and Chang et al. (2025) for further discussion. Since the authors hardly discuss trends before changepoints, This is not obvious to me if authors want to discuss abrupt changes or trend changes. The authors are required to report the scenario for each site type, e.g., which sites truly have trends of turnaround, merely an anomalous year (see my next comment), or other cases (changes of data variability). A plot similar to Figure 2 of Chang et al. (2025) would be helpful.
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The BEAST algorithm detects a major changepoint in autumn 2017/2018 for O3 trends and NO2 seasonality. The manuscript attributes this partially to vehicle fleet composition shifts (e.g., Euro 6 SCR systems, rising electric/hybrid registrations starting ~2017/2018) and partially to meteorological shifts (step-like increase in maximum daytime BLH). How confident are the authors that the BEAST seasonal/trend changepoint in late 2017 was not artificially accentuated by the extreme European heatwave of summer 2018? Indeed, based on Figure 3d, it looks like the gray stripes coincide with heatwave years 2003, 2006 and 2018 (much better correlations than 3a and 3c). Heatwaves appear to be the main driver of abrupt changes, instead of NO2.
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Section 2.1 mentions that early NO2 records before 2008 were flagged for seasonal irregularities and removed from the final dataset for several stations. Could the authors specify which exact stations required pre-2008 NO2 removal, and how this affects the analysis of 2000–2024 time series in Figure 3 and relevant figures? I am very skeptical about such a data removal. Seasonal irregularities appear to be commonly observed in NO2 data. Recently available satellites and Pandora network often see seasonal irregularities in total column NO2. Why are those considered suspicious? Evidence and references are needed.
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Data availability: I know the "data upon request" statement is now prohibitive for many journals, but I am not sure it is still allowed for ACP.
Minor comments: L14, specify the exact number of sitesChang, K. L., McDonald, B. C., Harkins, C., & Cooper, O. R. (2025). Surface ozone trend variability across the United States and the impact of heat waves (1990–2023). Atmospheric Chemistry and Physics, 25(10), 5101-5132.Lund, R. B., Beaulieu, C., Killick, R., Lu, Q., & Shi, X. (2023). Good practices and common pitfalls in climate time series changepoint techniques: A review. Journal of Climate, 36(23), 8041-8057.Weatherhead, E. C., Harder, J., Araujo-Pradere, E. A., Bodeker, G., English, J. M., Flynn, L. E., ... & Woods, T. N. (2017). How long do satellites need to overlap? Evaluation of climate data stability from overlapping satellite records. Atmospheric Chemistry and Physics, 17(24), 15069-15093.ReplyCitation: https://doi.org/10.5194/egusphere-2026-5341-RC1 -
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