2. Allen S. Lefohn, Christopher S. Malley, Gina Mills, Luther Smith, Milan Hazucha, Vaishali Naik, Martin G. Schultz, Heather Simon, Benjamin Wells, Elena Paoletti, Alessandra De Marco, Xu Xiaobin, Howard Neufeld, Robert Musselman, David Tarasick, David Parrish, Michael Brauer, Mhairi Coyle, Zhaozhong Feng, Tang Haoye, Kazuhiko Kobayashi, ….. 28 Scientists are Actively Contributing to Chapter 3 Disciplines Include: Statistics Human Health (Clinical and Epidemiology) Vegetation Effects Climate Change Modeling Chemistry Meteorology

3. Which regions of the world have the greatest exposure to ozone pollution relevant to human health and vegetation (i.e., crops and ecosystems) ? What conclusions can be derived regarding the interactions between climate change and temporally related tropospheric ozone concentrations ? What changes in ozone concentrations are occurring in regions where strong emissions controls are implemented ? What changes in ozone concentrations are occurring in regions with increasing emissions of ozone precursors ? Adopted from Overview of TOAR (http://www.igacproject.org/TOAR).

4. While Chapter 3 describes the form of each metric, it also provides a holistic characterization of how a metric works, and why metrics developed to quantify the same impact work in different ways and provide different results. A complete understanding of how the metrics operate as changes in distributions occur places into perspective the results presented in Chapters 4, 5, 6, and 7.

5. Glazebury, UK

6. The rural site at Glazebury in the UK illustrates not only significant changes in some exposure metrics, but also insignificant changes in other metrics; and Depending upon the metric selected, it is possible to conclude that health and vegetation ozone has increased, decreased, or not changed at all between 1989 and 2013 at Glazebury.

7. Description and rationale for each TOAR metric; Description of what part of the distributions of hourly average O 3 concentrations affect the magnitude of each TOAR exposure and dose metric; Description of statistical methods used by TOAR to identify trend patterns over the entire time series, as well as specific periods when emission changes and/or climate changes may have occurred; and The magnitude of the response of each TOAR metric (i.e., applicable to surface measurements) as changes occur to O3 concentration distributions over time.

9. Identification of possible candidate metrics for model- measurement comparisons, free troposphere, human health, and vegetation began in December 2014 in Boulder, CO at first TOAR meeting; A list of candidate metrics resulted from the discussions at the second TOAR meeting in Madrid in April 2015; The candidate list of TOAR metrics was presented to the TOAR research community in June 2015 and 30 days was provided for comment; and Based on comments, the final list of TOAR metrics was adopted by the TOAR Steering Committee and posted on the TOAR website on 31 July 2015.

15. Discussion of statistical methods available for TOAR were initiated in December 2014 in Boulder, CO at the first TOAR meeting; At the Madrid workshop in April 2015, the Statistics and Database working group discussed statistical tests associated with analyzing the data, the design of the database, and the candidate exposure and dose metrics to be used in the TOAR analyses; At the Madrid workshop, the statistical methods to be used by TOAR were finalized; and The adopted statistical methods were described in the workshop summary, which was placed on the TOAR website on 25 June 2015.

16. Nonparametric statistical test (Mann-Kendall) is utilized to test for significant trend; Nonparametric statistic (Theil-Sen) is used to estimate the magnitude of the trend; Nonparametric approaches were selected because they require no assumptions regarding functional form or statistical distribution for the data and are resistant to outliers; and Parametric statistical test (linear regression) can be used if the following assumptions are met: (1) a linear model is appropriate to describe the data and (2) the variable is normally distributed, has constant variance, and that the data are independent observations.

17. To facilitate analyses in Chapters 4, 5, 6, and 7 focusing on present-day distributions and temporal trends…. We investigate how the distribution of O3 concentrations has changed over the measurement period at a site; We compare and contrast the different types of trends which have occurred at different sites, and across different types of site; and We calculate the response of the TOAR metrics to changes in O3 concentration distribution at each site, highlighting the relevant trends in the O3 distribution which result in the trend(s) in the different metrics (i.e., do the changes in high/low/moderate concentrations determine the trends in the metrics ? ).

18. Summary of changes in O 3 concentration distribution and human health O 3 metrics at an urban site in Berlin, Germany (EU AirBase ID: DEBE034): a) Proportion of hourly O 3 concentrations in 5 ppb O 3 concentration bins for each year, b) Theil-Sen percent trend in proportion of O 3 concentrations in each bin between 1990 and 2013 (significance determined by the Mann-Kendall test), and c) Theil-Sen percent trend in 6 human health O 3 metrics between 1990 and 2013.

19. Summary of changes in O 3 concentration distribution and vegetation metrics at a rural site at Yarner Wood, UK (EU AirBase ID: GB0013R): a) Proportion of hourly O 3 concentrations in 5 ppb O 3 concentration bins for each year, b) Theil-Sen percent trend in proportion of O 3 concentrations in each bin between 1990 and 2013 (significance determined by the Mann-Kendall test), and c) Theil-Sen percent trend in 8 vegetation O 3 metrics between 1990 and 2013.

20. Summary of changes in O 3 concentration distribution and vegetation metrics at a rural site at Beltsville, Maryland (EPA AQS ID: 240339991-1): a) Proportion of hourly O 3 concentrations in 5 ppb O 3 concentration bins for each year, b) Theil-Sen percent trend in proportion of O 3 concentrations in each bin between 1990 and 2013 (significance determined by the Mann-Kendall test), c) Theil-Sen percent trend in 6 human health O 3 metrics between 1990 and 2013, and d) Theil-Sen percent trend in 8 vegetation O 3 metrics between 1990 and 2013.

21. Suffolk County, New York AQS ID: 361030002-1

22. Although not shown, the higher hourly average concentrations are shifting downward, while the lower concentrations are shifting upward toward the middle. As a result of this pattern: The monthly average concentrations are either significantly increasing or not changing due to the shifting of the lower concentrations toward the mid-level values; and The two human health exposure metrics selected are decreasing due to the reduction of the higher concentrations.

23. All examples shown here demonstrate compressions of the ozone distribution: low concentrations increasing and high concentrations decreasing This trend type was used for examples because it is most likely to produce different trends in TOAR metrics This is a very common trend type but not the only type observed at US and European sites Observed trend types in US and Europe Type 0: No trend Type 1: Low end shifts up, high end shifts down Mid range may shift up, shift down, or stay the same Type 2: Low end shifts up, high end does not change Type 3: High end shifts down, low end does not change Type 4: Entire distribution shifts down Other types may be observed in other parts of the world

24. Metrics defined to quantify the same impact (human health, vegetation, or climate change) do not all provide a consistent picture of the magnitude of spatial and temporal variation; The metrics are influenced to a different degree by high, moderate, and low hourly average concentrations; and Different temporal patterns in these concentrations may result in a different behavior for each metric.

25. In the chapter, we have determined temporal trends at only EU and US sites; We invite other investigators from other locations around the globe, who have access to data with sufficiently long time series, to join us in our current research effort ; and It is anticipated that on a global basis, this analysis will quantify the extent to which each metric is influenced by changes in high, moderate, and low concentrations using empirical data. The relationship between the ozone distribution and relevant exposure and dose metrics impacts both predicted effects and policy considerations.

26. It is more like a book chapter but it reads very well and has so much useful information and introduces the topic in a logical and flowing order; Chapter is too technical and too long; What magnitude of the metrics elicit effects ?; Chapter 3 should consider investigating interannual variability; Does TOAR wish to address changes in the rate of change of the trends across the time series ?; and A reviewer has recently questioned the decision reached in Madrid in April where TOAR recommended the use of the Mann-Kendall test and Theil-Sen statistic as the prime approaches for analyzing thousands of sites. The reviewer recommends the use of the parametric statistical test (i.e., linear regression).

29. The nature of the TOAR project is that the same analysis is applied across thousands of measurement sites (calculation of metrics etc.). This large amount of data presumably precludes a detailed survey of every site to determine an appropriate functional form for the data and whether a linear or nonlinear regression approach would be appropriate. Hence, the sheer amount of data being processed in TOAR means that the nonparametric approach has an advantage.

30. For the purposes of TOAR, when comparing trends on a site-by-site basis, based on its universal applicability, the nonparametric approach is utilized because assumptions required for using a parametric approach may not be met when assessing trends at each monitoring site, with the result that some sites would have to be rejected.

31. When the number of observations is small (as it is in our trends data), the assumption of normally distributed errors becomes problematic if the underlying data are not normally distributed about the regression line; Since the non-parametric tests are based on the median instead of the mean, they are more robust to outliers than the parametric tests. Outliers are fairly common in air quality and other environmental data; Since we are dealing with trends data, the assumption of uncorrelated errors may be inappropriate if long-term climate patterns may also impact the observations across multiple years; The assumption of constant variance may also be inappropriate, especially for the ozone data since the inter-annual variability tends to decrease as precursor emissions are reduced and concentrations approach background levels; Parametric regression methods are not particularly well suited to working with count data, especially if the counts are small. For example, we were able to use the Mann-Kendall test to show significance in situations where the frequency of high ozone concentrations dropped to zero over time, whereas simple linear regression would have simply returned a non- significant result.

32. The approach makes no distributional assumption; It does not require the assumption of any particular functional form for the behavior of the data through time. Thus, it would be universally applicable across all sites, seasons, and different continuous summary statistics (e.g., percentiles, means, and cumulative exposure metrics, such as the W126 and AOT40 exposure metrics); and The technique is resistant to the effects of outlying observations. Thus, the results are not unduly affected by particularly high or low values that occur during the time series. In most cases, an important advantage of using the Mann-Kendall test is its universal applicability.

33. It possesses the same attributes described for the Mann- Kendall test (i.e., there are no distributional or functional form assumptions and the estimator is resistant to outliers); The Theil-Sen estimator, similar to the Mann-Kendall technique, is also universally applicable; and In cases, where simple linear regression is appropriate, the slope of the regression line and the Theil-Sen estimator are asymptotically equivalent.

34. Trends of low, mid-range, and high concentrations may go in different directions; Relative frequency of low, mid-range, and high concentrations: at most locations, low to mid-range ozone concentrations occur much more frequently than high concentrations (i.e. lognormal or Weibull distribution); High and low concentration hours are not spread evenly across hours-of-the day and months-of the year. Metrics that focus on a certain time of day or certain seasons will have different weighting from low, mid-range, and high concentrations than metrics which focus on different time periods. Additionally trends in low, mid-range, and high ozone concentrations may vary by time of year (i.e., low concentrations in U.S. have increased more during winter/spring than they have during summer).

35. A description of a metric (one of the main goals of Chapter 3) is not complete without understanding how the metric behaves. Understanding the mechanics of these metrics is therefore central to the aim of Chapter 3, and provides the basis for Chapter 4 and Chapter 5 to communicate the important conclusions about the spatial and temporal changes in health and vegetation-relevant O3. The analysis described in Chapter 3 fulfills one aspect of defining the TOAR metrics; Having analysis of changes in distribution and the response of the TOAR metrics in a single chapter (i.e., Chapter 3) creates a linkage between health and vegetation metrics by identifying similarity and differences in the response of health and vegetation metrics to changes across the O3 distribution; and The analysis in Chapter 3 does not replicate analyses currently included in the health and vegetation chapters.

36. Chapter 3 provides the information for the use of the exposure and dose metrics, with the implication that the subsequent chapters actually will use them. On this basis, for inclusion in Chapter 3, the analysis showing the response of the metrics to changes in the distribution, provides useful information for investigators wishing to apply these metrics.

37. Performing a trending analysis over the entire period of record may not provide a precise assessment of trending at a specific site. It may be possible that the trending pattern changes over time as a function of, for example, emission reduction strategies implemented during the monitoring period of record. It is important to examine whether the rate of change in O 3 concentrations at a site is itself changing with time.

38. Look Rock, TN

39. The site is located in the Great Smoky Mountains National Park in Tennessee. The annual 95 th percentile of the hourly values was calculated for each year from 1990 to 2013, inclusive. The data completeness criterion applied was that at least 75% of the hourly values had to be present for the 95 th percentile to be considered valid; all years met this criterion. A plot of the resultant 95 th percentile suggested that a change may have occurred between the 1990-1997 and 1998-2013 periods. Exploring this observation further, the series was split into the two sections. For both the 1990-1997 and 1998-2013 periods, Theil-Sen estimators (absolute and relative) and their associated asymptotic 95% confidence intervals were calculated. The results were that the 1990-1997 period had a Theil-Sen estimator of 2.25 ppm/yr with a 95% confidence interval of (0.33, 2.67) while the 1998-2013 period had an estimated rate of change of -1.35 ppm/yr with a 95% confidence interval of (-2.00, -0.67).