the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Disappearing day-of-week ozone patterns in US nonattainment areas
Christian Hogrefe
Andrew Whitehill
Kristen M. Foley
Jennifer Liljegren
Norm Possiel
Benjamin Wells
Barron H. Henderson
Lukas C. Valin
Gail Tonnesen
K. Wyat Appel
Shannon Koplitz
Abstract. Past work has shown that traffic patterns in the US and resulting NOX emissions vary by day of week, with NOX emissions typically higher on weekdays than weekends. This pattern of emissions leads to different levels of ozone on weekends versus weekdays and can be leveraged to understand how local ozone formation changes in response to NOX emissions perturbations in different urban areas. Specifically, areas with lower NOX but higher ozone on the weekends (the weekend effect) can be characterized as NOX -saturated and areas with both lower NOX and ozone on weekends (the weekday effect) can be characterized as NOX-limited. In this analysis we assess ozone weekend-weekday differences across US nonattainment areas using 18 years of observed and modeled data from 2002–2019 using two metrics: mean ozone and percentage of days > 70 ppb. In addition, we quantify the modeled and observed trends in these weekend-weekday differences across this period of substantial NOX emissions reductions in the US. The model assessment is carried out using EPA’s Air QUAlity TimE Series Project (EQUATES) CMAQ dataset. We identify 3 types of ozone trends occuring across the US: disappearing weekend effect, disappearing weekday effect, and no trend. The disappearing weekend effect occurs in a subset of large urban areas that were NOX -saturated (i.e., VOC-limited) at the beginning of the analysis period but transitioned to mixed chemical regimes or NOX-limited conditions by the end of the analysis period. Nine areas have disappearing weekend effect trends in both datasets and with both metrics indicating strong agreement that they are shifting to more NOX-limited conditions: Milwaukee, Houston, Phoenix, Denver, Northern Wasatch Front, Southern Wasatch Front, Las Vegas, Los Angeles – San Bernardino County, Los Angeles – South Coast, and San Diego. The disappearing weekday effect was identified for multiple rural and agricultural areas of California which were NOX -limited for the entire analysis period but appear to become less influenced by local day of week emission patterns in more recent years. Finally, we discuss a variety of reasons why there are no statistically significant trends in certain areas including complex impacts of heterogeneous source mixes and stochastic impacts of meteorology. Overall, this assessment finds that the EQUATES modeling simulations indicate more NOX-saturated conditions than the observations but do a good job of capturing year-to-year changes in weekend-weekday ozone patterns.
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Heather Simon et al.
Status: open (until 20 Oct 2023)
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RC1: 'Comment on egusphere-2023-1974', Anonymous Referee #1, 25 Sep 2023
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This is a very good study, written by experts at EPA and it will be a welcome addition to the literature. I find the research question to be important and the conclusions are clearly supported by the analysis. I don’t have any concerns regarding the analysis or conclusions, my only recommendation is that the authors update their terminology regarding the reporting of statistical findings, to be more consistent with the Tropospheric Ozone Assessment Report and current thinking regarding the limitations of the expression “statistically significant”, as described below.
Regarding the use of the Theil-Sen/Mann-Kendall method for calculating trends, the authors state that they chose this method because of the small sample sizes and because it does not require assumptions about the distribution of the residuals. Another reason that is often given for the choice of this method is that it is resistant to outliers. The problem is that in order to remove the impact from outliers, this method automatically ignores up to 29% of the data points in a sample (see Section 2 of Chang et al., 2021). This would be fine if the analyst believed that the outliers are due to instrument errors, but there is no reason to throw out data if they are believed to be reliable. In your case there is no reason to believe that your samples contain erroneous data points that should be ignored. For this reason, the Tropospheric Ozone Assessment Report has abandoned the Theil-Sen/Mann-Kendall method that was used in the first phase of TOAR (2014-2019). A further problem with the Theil-Sen method is that it produces unrealistically narrow 95% confidence intervals. This is shown in Figure 1 of the TOAR-II Recommendations for Statistical Analyses (available at https://igacproject.org/activities/TOAR/TOAR-II). Figure 1 compares the trend and 95% confidence interval calculated by 10 different methods for the ozone time series at Mace Head, Ireland. The Theil-Sen method has the narrowest 95% confidence interval by far, and the reason is that this method ignores 29% of the data; by throwing out all of the extreme values the sample has very little variability and therefore a straight line can be fit through the remaining data within a very narrow range. The second phase of TOAR-II is now recommending the use of quantile regression, as described in the TOAR-II Recommendations for Statistical Analyses. Quantile regression was used to good effect in the very nice paper by co-author B. Wells (Wells et al., 2021), and it could easily be applied to your current analysis.
Throughout the paper the authors use the expression “statistically significant”, however this expression is now recognized as being problematic and it should be abandoned and replaced by the more useful method of reporting all trends (with uncertainty) and all p-values, followed by a discussion of the trends and the author’s opinion regarding their confidence in the trend values. This advice comes from a highly influential paper by Wasserstein et al. (2019), published in the journal, The American Statistician, that has already been cited over 1300 times (according to Web of Science). This advice was adopted by the first phase of TOAR (Tarasick et al., 2019) and will also be used by TOAR-II. Some other recent papers on ozone trends that have taken this advice are: Chang et al., 2020; Cooper et al., 2020; Gaudel et al., 2020; Chang et al., 2022; Wang et al., 2022; Mousavinezhad et al., 2023. Because these papers report all trend values, uncertainties, and all p-values, and also discuss the trend results, there is no confusion regarding the findings, and one does not even notice that the term “statistically significant” is not used at all.
The authors describe a trend as “no trend” when the p-value is greater than 0.05. There are two problems with this approach:
1) as described above the expression “statistically significant” which is tied to the p-value of 0.05 should be abandoned. Just because a trend has a p-value of 0.06, it does not mean that there is absolutely no trend, it just means that there is a gray area and the trend is not as robust as one that has a p-value of 0.02. Chang et al. (2017) provide a nice demonstration of the useful information that can be gleaned from a trend with a p-value greater than 0.05 (see their Figure 13). They calculated a regional ozone trend for the eastern USA using all available ozone monitors (in summer the trend was strongly negative for the period 2000-2014). They then conducted an exercise to see what would happen to the regional trend if they threw out all time series with a p-value less than 0.05. The result was almost the same because the time series with p-values greater than 0.05 still reflected the overall regional decrease of ozone.
2) The authors are using the Theil-Sen method to calculate trends and p-values. As described above the 95% confidence intervals are unrealistically narrow using this method, and therefore the p-values are also too low. This means that too many sites are classified as having a real trend, according to the 0.05 p-value threshold. If the authors use another method for calculating trends (like quantile regression) the p-values will increase and they would then have to classify more sites as having “no trend”. Given the gray area around p-values, and given that trends with p-values greater than 0.05 can still be reliable, there is no justification for dichotomizing ozone time series as “trend” or “no trend” based on a p-value.
When I look at the maps in Figure 4 and 5 I am left wondering about the non-attainment regions labeled as “no trend”. Is there really no trend here, i.e. a flat line, or is there still a decrease, but it just doesn’t reach the arbitrary threshold of p<0.05? A good example is Tuscan Buttes. Table S-1 shows the observed and modelled trend is the same (0.14) but because the model has a p-value of 0.02 this trend is considered to be real, while the observations have a p-value of 0.06 and are classified as “no trend”. The TOAR papers report all trends and all p-values and the trend values in their map plots are colored according to p-value (Fleming et al., 2018). This allow the reader to see if a trend is still notable (e.g. a p-value between 0.05 and 0.10) or if there really and truly is no trend (e.g. a p-value > 0.33). It would be very helpful to the reader if the authors can color their maps according to p-value, in a manner similar to TOAR.References:
Chang, K-L, et al 2017 Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia. Elem Sci Anth, 5: 50, DOI: https://doi.org/10.1525/elementa.243
Chang, K.-L., et al. (2020), Statistical regularization for trend detection: An integrated approach for detecting long-term trends from sparse tropospheric ozone profiles, Atmos. Chem. Phys., 20, 9915–9938, https://doi.org/10.5194/acp-20-9915-2020
Chang, K-L, et al. 2021. Trend detection of atmospheric time series: Incorporating appropriate uncertainty estimates and handling extreme events. Elem Sci Anth, 9: 1. DOI: https://doi.org/10.1525/elementa.2021.00035
Chang, K.-L., (2022), Impact of the COVID-19 economic downturn on tropospheric ozone trends: an uncertainty weighted data synthesis for quantifying regional anomalies above western North America and Europe, AGU Advances, 3, e2021AV000542. https://doi.org/10.1029/2021AV000542
Cooper, et al. 2020. Multi-decadal surface ozone trends at globally distributed remote locations. Elem Sci Anth, 8: 23. DOI: https://doi.org/10.1525/elementa.420
Fleming, Z. L., R. M. Doherty, E. von Schneidemesser, C. S. Malley, O. R. Cooper et al. (2018), Tropospheric Ozone Assessment Report: Present-day ozone distribution and trends relevant to human health, Elem Sci Anth, 6(1):12, DOI: https://doi.org/10.1525/elementa.273
Gaudel, A., et al. (2020), Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere. Sci. Adv. 6, eaba8272, DOI: 10.1126/sciadv.aba8272
Mousavinezhad, S., Ghahremanloo, M., Choi, Y., Pouyaei, A., Khorshidian, N. and Sadeghi, B., 2023. Surface ozone trends and related mortality across the climate regions of the contiguous United States during the most recent climate period, 1991–2020. Atmospheric Environment, p.119693.
Tarasick, D. W. et al. (2019), Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties. Elem Sci Anth, 7(1), DOI: http://doi.org/10.1525/elementa.376
Wang, H., et al. (2022), Global tropospheric ozone trends, attributions, and radiative impacts in 1995–2017: an integrated analysis using aircraft (IAGOS) observations, ozonesonde, and multi-decadal chemical model simulations, Atmos. Chem. Phys., 22, 13753–13782, https://doi.org/10.5194/acp-22-13753-2022
Wasserstein, R. L., Schirm, A. L., and Lazar, N. A.: Moving to a world beyond p < 0:05, Am. Stat., 73, 1–29,
https://doi.org/10.1080/00031305.2019.1583913, 2019.Citation: https://doi.org/10.5194/egusphere-2023-1974-RC1
Heather Simon et al.
Heather Simon et al.
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