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Edith Gégo, P. Steven Porter, Alice Gilliland, and S. Trivikrama Rao


Ozone is produced by chemical interactions involving nitrogen oxides (NOx) and volatile organic compounds in the presence of sunlight. At high concentrations, ground-level ozone has been shown to be harmful to human health and to the environment. It has been recognized that ozone is a regional-scale problem and that regionwide control strategies would be needed to improve ozone air quality in the eastern United States. To mitigate interstate transport of ozone and its precursors, the U.S. Environmental Protection Agency issued a regional rule in 1998 known as the “NOx State Implementation Plan (SIP) Call,” requiring 21 states in the eastern United States to reduce their summertime NOx emissions by 30 May 2004. In this paper, the effectiveness of the new emission control measures mandated by the NOx SIP Call is assessed by quantifying the changes that occurred in the daily maximum 8-h ozone concentrations measured at nearly 50 locations, most of which are rural (33 sites of the Clean Air Status and Trend Network and 16 sites of the Air Quality System), over the eastern United States. Given the strong dependence of ozone formation and accumulation on meteorological conditions, the incidence of the latter is first mitigated, and meteorologically adjusted ozone concentrations are extracted using a multiple regression technique. By examining the differences between the cumulative distribution functions of the meteorologically adjusted ozone concentrations, it is shown that ozone concentrations in the eastern United States are now on average 13% less than those prior to the NOx SIP Call. Using back-trajectory analyses, it is also shown that emission controls on the electricity-generating units located in the Ohio River Valley have contributed toward the improvement of ozone air quality in downwind regions, especially east and northeast of the Ohio River Valley.

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Christian Hogrefe, S. Trivikrama Rao, Igor G. Zurbenko, and P. Steven Porter

To study the underlying forcing mechanisms that distinguish the days with high ozone concentrations from average or nonepisodic days, the observed and model-predicted ozone time series are spectrally decomposed into different temporal components; the modeled values are based on the results of a three-month simulation with the Urban Airshed Model—Variable Grid Version photochemical modeling system. The ozone power spectrum is represented as the sum of four temporal components, ranging from the intraday timescale to the multiweek timescale. The results reveal that only those components that contain fluctuations with periods equal to or greater than one day carry the information that distinguishes ozone episode days from nonepisodic days. Which of the longer-term fluctuations is dominant in a particular episode varies from episode to episode. However, the magnitude of the intraday fluctuations is nearly invariant in time. The promulgation of the 8-h standard for ozone further emphasizes the importance of longer-term fluctuations embedded in ozone time series data. Furthermore, the results indicate that the regional photochemical modeling system is able to capture these features. This paper also examines the effect of simulation length on the predicted ozone reductions stemming from emission reductions. The results demonstrate that for regulatory purposes, model simulations need to cover longer time periods than just the duration of a single ozone episode; this is necessary not only to perform a meaningful model performance evaluation, but also to quantify the variability in the efficacy of an emission control strategy.

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Robert E. Eskridge, Jia Yeong Ku, S. Trivikrama Rao, P. Steven Porter, and Igor G. Zurbenko

The removal of synoptic and seasonal signals from time series of meteorological variables leaves datasets amenable to the study of trends, climate change, and the reasons for such trends and changes. In this paper, four techniques for separating different scales of motion are examined and their effectiveness compared. These techniques are PEST, anomalies, wavelet transform, and the Kolmogorov–Zurbenko (KZ) filter. It is shown that PEST and anomalies do not cleanly separate the synoptic and seasonal signals from the data as well as the other two methods. The KZ filter method is shown to have the same level of accuracy as the wavelet transform method. However, the KZ filter method can be applied to datasets with missing observations and is much easier to use than the wavelet transform method.

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