Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research

Abstract

Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the Tropospheric Ozone Assessment Report (TOAR) is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.

Keywords: ground-level ozone; metrics; ozone distributions; shifting ozone concentrations; trends; tropospheric ozone.

Figure 1:

Sine function fits to monthly average data from Mace Head, Ireland. The black curves give the least-squares regressions to the fundamental (upper black curve) and second harmonic (lower black curve) terms, and the blue curve shows their sum. The data points about the x-axis are the residuals between the measurements and the fundamental fit. The fit parameters with 95% confidence limits are annotated. A small, long-term trend has been removed from the monthly average data before fitting (data from Parrish et al., 2016). DOI: https://doi.org/10.1525/elementa.279.f1

Figure 2:

The weighting applied to hourly average ozone values for the calculation of the W90 exposure index (see Lefohn et al., 2010b). DOI: https://doi.org/10.1525/elementa.279.f2

Figure 3:

The weighting applied to hourly average ozone values for the calculation of the W126 exposure index (see Lefohn et al., 1988). DOI: https://doi.org/10.1525/elementa.279.f3

Figure 4:

Time series for (a) Harwell, UK (1984–2013) for the 4^th highest MDA8 level and (b) Look Rock, Tennessee (1990–2013) for the 95^th percentile. DOI: https://doi.org/10.1525/elementa.279.f4

Figure 5:

Comparison of selected annual percentiles of ozone levels at Look Rock, TN during the summer months (April–September) with the same percentiles derived from the entire annual data. DOI: https://doi.org/10.1525/elementa.279.f5

Figure 6:

Theil-Sen (%/year) trend for a) hourly ozone levels in each bin, and b) 6 human health and 8 vegetation ozone metrics for a site at Glazebury, UK between 1989 and 2013, and c) hourly ozone levels in each bin, and d) 6 human health and 8 vegetation ozone metrics for a site at Yuen Long, Hong Kong, China between 1995 and 2015 (significance determined by the Mann-Kendall test at p < 0.05). (Data characterized as per Lefohn et al., 2017). DOI: https://doi.org/10.1525/elementa.279.f6

Figure 7:

Map of (a) EU, (b) mainland China and Hong Kong, China, and (c) US sites selected for the study (Lefohn et al., 2017). DOI: https://doi.org/10.1525/elementa.279.f7

Figure 8:

Percent of combined EU and US sites that exhibited specific trend types that occurred. (Data results summarized from Lefohn et al., 2017). DOI: https://doi.org/10.1525/elementa.279.f8

Figure 9:

Percentage of EU and US sites combined in each trend type (e.g., 0, 1a, 1b, etc.) with trends in (a) A4MDA8, (b) 3-month 12-h W126, (c) SOMO35, and (d) 3-month AOT40. Summarized results from Lefohn et al. (2017). Trend Types 5, 6, and 8 did not occur and Trend Type X occurred infrequently. DOI: https://doi.org/10.1525/elementa.279.f9

Figure 10:

Percent of EU and US sites combined in each trend type (e.g., 0, 1a, 1b, etc.) with trends in (a) 6-month 12-h W126, (b) 6-month 12-h daily average, and (c) SOMO10. Summarized results from Lefohn et al. (2017). Trend Types 5, 6, and 8 did not occur and Trend Type X infrequently. DOI: https://doi.org/10.1525/elementa.279.f10

Figure 11:

The Theil-Sen (%/year) trend in monthly average ozone levels and the annual SOMO35 and 4 th highest MDA8 human health metrics (A4MDA8) for a suburban site for 1980–2013 in Philadelphia, Pennsylvania (US EPA AQS ID: 421010024–1). The p < 0.05 value was used to determine significance using the Mann-Kendall test. DOI: https://doi.org/10.1525/elementa.279.f11