P. Steven Porter

Separating Different Scales of Motion in Time Series of Meteorological Variables

Abstract 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.

Interpreting the Information in Ozone Observations and Model Predictions Relevant to Regulatory Policies in the Eastern United States

Abstract 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 importan...

Ozone Air Quality over North America: Part II—An Analysis of Trend Detection and Attribution Techniques

ABSTRACT Assessment of regulatory programs aimed at improving ambient O3 air quality is of considerable interest to the scientific community and to policymakers. Trend detection, the identification of statistically significant long-term changes, and attribution, linking change to specific clima-tological and anthropogenic forcings, are instrumental to this assessment. Detection and attribution are difficult because changes in pollutant concentrations of interest to policymakers may be much smaller than natural variations due to weather and climate. In addition, there are considerable differences in reported trends seemingly based on similar statistical methods and databases. Differences arise from the variety of techniques used to reduce nontrend variation in time series, including mitigating the effects of meteorology and the variety of metrics used to track changes. In this paper, we review the trend assessment techniques being used in the air pollution field and discuss their strengths and limitations in discerning and attributing changes in O3 to emission control policies.

Observation-Based Assessment of the Impact of Nitrogen Oxides Emissions Reductions on Ozone Air Quality over the Eastern United States

Abstract 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 an...

Modeling analyses of the effects of changes in nitrogen oxides emissions from the electric power sector on ozone levels in the eastern United States.

Abstract In this paper, we examine the changes in ambient ozone concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for summer 2002 under three different nitrogen oxides (NOx) emission scenarios. Two emission scenarios represent best estimates of 2002 and 2004 emissions; they allow assessment of the impact of the NOx emissions reductions imposed on the utility sector by the NOx State Implementation Plan (SIP) Call. The third scenario represents a hypothetical rendering of what NOx emissions would have been in 2002 if no emission controls had been imposed on the utility sector. Examination of the modeled median and 95th percentile daily maximum 8-hr average ozone concentrations reveals that median ozone levels estimated for the 2004 emission scenario were less than those modeled for 2002 in the region most affected by the NOx SIP Call. Comparison of the “no-control” with the “2002” scenario revealed that ozone concentrations would have been much higher in much of the eastern United States if the utility sector had not implemented NOx emission controls; exceptions occurred in the immediate vicinity of major point sources where increased NO titration tends to lower ozone levels.

A comparison of four techniques for separating different time scales in atmospheric variables

Abstract In this paper, four methods for spectrally decomposing time series of atmospheric variables are compared. Two of these methods have been previously applied to the analysis of time series of atmospheric variables, while the others are being applied for the first time. This paper focuses on the practical applications of time scale separation techniques rather than on an in-depth comparison of the mathematical features of the filtering techniques. The performance of the above filtering methods is illustrated and evaluated using both simulated and observed ozone time series data. The adaptive window Fourier transform filter is shown to extract fluctuations of known frequency as cleanly as the Morlet wavelet and, therefore, is a useful new tool for time–frequency analyses of atmospheric variables. Simulation results indicate that all four of these filters provide qualitatively similar results when used to extract the energy in five frequency bands of particular interest in time series of atmospheric variables. However, differences can exist when different filters are used to study the temporal variations of the extracted components. In addition, it is shown that all filters are able to capture the year-to-year fluctuations in the magnitudes of individual components. Such analysis can be used to discern the time scales that cause long-term changes in pollutant concentrations. As no single filter performs best in separating the various time scales, great care has to be taken to match the filter characteristics with the objectives of a given analysis.

Small sample properties of nonparametric bootstrap t confidence intervals

Confidence interval construction for central tendency is a problem of practical consequence for those who must analyze air contaminant data. Determination of compliance with relevant ambient air quality criteria and assessment of associated health risks depend upon quantifying the uncertainty of estimated mean pollutant concentrations. The bootstrap is a resampling technique that has been steadily gaining popularity and acceptance during the past several years. A potentially powerful application of the bootstrap is the construction of confidence intervals for any parameter of any underlying distribution. Properties of bootstrap confidence intervals were determined for samples generated from lognormal, gamma, and Weibull distributions. Bootstrap t intervals, while having smaller coverage errors than Students t or other bootstrap methods, under-cover for small samples from skewed distributions. Therefore, we caution against using the bootstrap to construct confidence intervals for the mean without first considering the effects of sample size and skew. When sample sizes are small, one might consider using the median as an estimate of central tendency. Confidence intervals for the median are easy to construct and do not under-cover. Data collected by the Northeast States for Coordinated Air Use Management (NESCAUM) are used to illustrate application of the methods discussed.

Ozone air quality over North America: part I--a review of reported trends.

ABSTRACT Ozone and precursor trends can be used to measure the effectiveness of regulatory programs that have been implemented. In this paper, we review trends in the concentrations of O3, NOx, and HCs over North America that have been reported in the literature. Although most existing trend studies are confounded by meteorological variability, both the raw data trends and the trends adjusted for meteorology collectively indicate a general decreasing trend in O concentrations in most areas of the United States during 1985-1996. In Canada, mean daily maximum 1-hr O3 concentrations at urban sites show mixed trends with a majority of sites showing an increase from 1980 to 1993. Mean daily maximum 1-hr O3 at most regionally representative Canadian sites appears to decrease from 1985 to 1993 or shows no significant change. There are far fewer data and analyses of NOx and HC trends. Available studies covering various ranges of years indicate decreases in ambient NOx and HC concentrations in Los Angeles, CA, decreases in HC concentrations in northeastern U.S. cities, and decreases in NO concentrations in Canadian cities. Two key needs are long-term HC and NOx measurements, particularly at rural sites, and a systematic comparison of trend detection techniques on a reference data set.

A reduced form model for ozone based on two decades of CMAQ simulations for the continental United States

A Reduced Form Model (RFM) is a mathematical relationship between the inputs and outputs of an air quality model, permitting estimation of additional modeling without costly new regional-scale simulations. A 21-year Community Multiscale Air Quality (CMAQ) simulation for the continental United States provided the basis for the RFM developed in this study. Predictors included the principal component scores (PCS) of emissions and meteorological variables, while the predictand was the monthly mean of daily maximum 8-hour CMAQ ozone for the ozone season at each model grid. The PCS form an orthogonal basis for RFM inputs. A few PCS incorporate most of the variability of emissions and meteorology, thereby reducing the dimensionality of the source-receptor problem. Stochastic kriging was used to estimate the model. The RFM was used to separate the effects of emissions and meteorology on ozone concentrations. by running the RFM with emissions constant (ozone dependent on meteorology), or constant meteorology (ozone dependent on emissions). Years with ozone-conducive meteorology were identified, and meteorological variables best explaining meteorology-dependent ozone were identified. Meteorology accounted for 19% to 55% of ozone variability in the eastern US, and 39% to 92% in the western US. Temporal trends estimated for original CMAQ ozone data and emission-dependent ozone were mostly negative, but the confidence intervals for emission-dependent ozone are much narrower. Emission-driven changes in monthly mean ozone levels for the period 2000-2010 ranged from 6.4 to 10.9 ppb for the eastern US and from 1.4 to 2.5 ppb for the western US.

Effects of changes in data reporting practices on trend assessments

This paper illustrates inhomogeneities in pollutant time-series data caused by changes in data reporting practices. These inhomogeneities may have a substantial effect on trend assessments. Therefore, in estimating trends or mean levels in pollutant time series or attributing changes in pollutant levels to an emission control policy, it is important to homogenize time-series data.