Statistical Analysis of Trends in Urban Ozone Air Quality

The purpose of this paper is to demonstrate the use of some statistical methods for examining trends in ambient ozone air quality downwind of major urban areas. To this end, daily maximum 1 -hr ozone concentrations measured over New Jersey, metropolitan New York City and Connecticut for the period 1980 to 1989 were assembled and analyzed. This paper discusses the application of the bootstrap method, extreme value statistics and a nonparametric test for evaluating trends in urban ozone air quality. The results indicate that although there is an improvement in ozone air quality downwind of New York City, there has been little change in ozone levels upwind of New York City during this ten-year period.     

A review of statistical methods for the meteorological adjustment of tropospheric ozone

A variety of statistical methods for meteorological adjustment of ozone have been proposed in the literature over the last decade for purposes of forecasting, estimating ozone time trends, or investigating underlying mechanisms from an empirical perspective. The methods can be broadly classified into regression, extreme value, and space–time methods. We present a critical review of these methods, beginning with a summary of what meteorological and ozone monitoring data have been considered and how they have been used for statistical analysis. We give particular attention to the question of trend estimation, and compare selected methods in an application to ozone time series from the Chicago area. We conclude that a number of approaches make useful contributions to the field, but that no one method is most appropriate for all purposes and all meteorological scenarios. Methodological issues such as the need for regional-scale analysis, the nonlinear dependence of ozone on meteorology, and extreme value analysis for trends are addressed. A comprehensive and reliable methodology for space–time extreme value analysis is attractive but lacking.     

The Need for a More Robust Ozone Air Quality Standard

The present National Ambient Air Quality Standard for ozone has many statistical problems, including use of extreme values which have inherent large fluctuations, a compliance test that can gradually lower the target of the design value below the standard level, and inconsistencies between the number-of-exceedances criterion and the design value. The above problems can be avoided or minimized by using a more robust statistic, such as the 95th percentile, and applying a statistical compliance test, without sacrificing the stringency of the standard. Analysis of EPA’s ozone data shows that the annual 95th percentiles and their three-year means have less variability than the annual second highest values and the fourth highest values in three years, respectively. A t test for the mean of the annual 95th percentiles is proposed for compliance testing not only to preserve the averaging concept of the present standard, but also to take account of ozone concentration fluctuations in order to increase the stability of the compliance status of a site or a Metropolitan Statistical Area. A procedure is provided to adjust the level of the 95th-percentile standard so that the stringency of the present standard is preserved.     

Method of median semi-variance for the analysis of left-censored data: Comparison with other techniques using environmental data

In environmental monitoring, variables with analytically non-detected values are commonly encountered. For the statistical evaluation of these data, most of the methods that produce a less biased performance require specific computer programs. In this paper, a statistical method based on the median semi-variance (SemiV) is proposed to estimate the position and spread statistics in a dataset with single left-censoring. The performances of the SemiV method and 12 other statistical methods are evaluated using real and complete datasets. The performances of all the methods are influenced by the percentage of censored data. In general, the simple substitution and deletion methods showed biased performance, with exceptions for L/2, Inter and L/√2 methods that can be used with caution under specific conditions. In general, the SemiV method and other parametric methods showed similar performances and were less biased than other methods. The SemiV method is a simple and accurate procedure that can be used in the analysis of datasets with less than 50% of left-censored data.     

A semiparametric statistical approach for forecasting SO2 and NO x concentrations

This study develops a new semiparametric statistical approach for urban air quality forecasting. Compared to conventional approaches, the semiparametric approach allows the model users to benefit from the positive aspects and alleviate the negative ones of parametric and nonparametric approaches. Two advantages of the approach lie in (1) the interpretation of the data set being easily decoded and used by the model and (2) its capability in dependence on prior assumption. To illustrate the performance of the proposed approach, three semiparametric regression models (i.e., linear-, quadratic-, and interactive-based semiparametric regression) are applied to an air quality forecasting problem in the city of Xiamen, China, and satisfactory training and prediction performance are obtained. The three models are also compared to three parametric and two nonparametric regression models. The results indicate that the predictive accuracy of semiparametric regression models is higher than those obtained from the parametric and stepwise cluster analysis models. However, the proposed three semiparametric regression models could be much favored, since they can be achieved more easily and rapidly than the artificial neural network model.     

Trends in meteorologically adjusted ozone concentrations in six Kentucky metro areas, 1998-2002

Meteorologically adjusted ozone (O3) concentrations during five recent O3 seasons (1998-2002) were computed for six Kentucky metro areas using a nonlinear regression model originally developed for forecasting ground-level O3 concentrations. The meteorological adjustment procedure was based on modifying actual measured O3 concentrations according to model-predicted responses to climate departures with respect to a reference year. For all six Kentucky metro areas, meteorologically adjusted O3 concentrations declined over the five-year period. The linear best-fit rate of decline in mean adjusted O3 concentrations ranged from 0.9 to 2.6 ppb/yr for these metro areas; the average rate of decline was 1.6 ppb/yr. The rates of decline in meteorologically adjusted extreme value (e.g., 95th percentile) concentrations were approximately the same, but there is greater statistical uncertainty concerning the extreme value trends.     

Daily surface UV exposure and its relationship to surface pollutant measurements

For the past 30 years, the stratospheric ozone layer has decreased in the Northern Hemisphere. The main effect of this ozone decrease was an expected increase in the UV radiation at the Earth's surface, but there has been no clear evidence of an increasing urban trend in surface UV. This study shows that specific air pollutants can reduce the increased surface levels of UV radiation and offers an explanation for why the expected surface UV increases have not been observed, especially in urban regions. A U.S. Environmental Protection Agency (EPA) UV monitoring site at the University of California at Riverside combined with air pollution data from a site operated by the California Air Resources Board in Rubidoux, CA, provided the basis of this study. The 1997 South Coast Ozone Study (SCOS-97) provided three key ingredients: black carbon, PM10 concentrations, and collocated radiometric measurements. The Total Ozone Mapping Spectrometer (TOMS) satellite data were used to provide the stratospheric ozone levels that were included in the statistical model. All of these input parameters would be used to test this study's hypothesis: the expected increase of surface UV radiation, caused by decreases in stratospheric ozone, can be masked by increases in anthropogenic emissions. The values for the pollutants were 7:00 a.m.-5:00 p.m. averages of the instrument's values taken during summer 1997. A statistical linear regression model was employed using the stratospheric ozone, black carbon, PM10, and surface ozone concentrations, and the sin (theta) and cos (theta). The angle theta is defined by theta = 2pi (Julian date/365). This model obtained a coefficient of determination of 0.94 with an uncertainty level (p value) of less than 0.3% for all of the variables in the model except ground-level ozone. The final model, regressed against a data set from a remote, western North Carolina site, resulted in a coefficient of determination of 0.92. The model shows that black carbon can reduce the Diffey-weighted UV levels that reach the surface by as much as 35%, depending on the season.     

Simultaneous Total Oxidant and Chemiluminescent Ozone Measurements in Ambient Air

The purpose of this project was to investigate the relationship of ambient air quality measurements between two analytical methods, referred to as the total oxidant method and the chemiluminescent method. These two well documented analytical methods were run simultaneously, side by side, at a site on the Houston ship channel. They were calibrated daily. The hourly averages were analyzed by regression techniques and the confidence intervals were calculated for the regression lines. Confidence intervals for point estimates were also calculated. These methods were used with all data sets with values greater than 10 parts per billion and again with values greater than 30 parts per billion. A regression line was also calculated for a second set of data for the preceding year. These data were generated before a chromium triox-ide scrubber was installed to eliminate possible chemical interferences with the Kl method.

The results show that in general the chemiluminescent ozone method tends to produce values as much as two times higher than the simultaneous total oxidant values. In one set of data collected an 80 ppb chemiluminescent ozone value predicted a value of 43.9 ppb total oxidant with a 95% confidence interval of 7.7 to 80.4 ppb. In the second set of data an 80 ppb chemiluminescent ozone value predicted a value of 78 ppb total oxidant with a 95% confidence interval of 0.4 to 156 ppb. Other statistical analyses confirmed that either measurement was a very poor predictor of the other.     

Contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area, Portugal since the 19th century

The main purpose of this study was to evaluate the contribution of anthropogenic pollutants to the increase of tropospheric ozone levels in the Oporto Metropolitan Area (Portugal) since the 19th century. The study was based on pre-industrial and recent data series, the results being analyzed according to the atmospheric chemistry. The treatment of ozone and meteorological data was performed by classical statistics and by time-series analysis. It was concluded that in the 19th century the ozone present in the troposphere was not of photochemical origin, being possible to consider the respective concentrations as reference values. For recent data a cycle of 8h for ozone concentrations could be related to traffic. Compared to the 19th century, the current concentrations were 147% higher (252% higher in May) due to the increased photochemical production associated with the increased anthropogenic emissions.     

Discrimination of air masses near the extratropical tropopause by multivariate analyses from MOZAIC data

Multivariate analyses (MA) are applied to discriminate air masses near the extra-tropical tropopause in a north Atlantic cut-off low (COL) case, using the Measurement of OZone and water vapour by five Airbus In-service airCraft data. Different MA, such as hierarchical clustering methods (HCM) and K-means clustering and the linear and quadratic discriminant analyses are tested and compared. The resulting methodology is based on the successive use of the principal component analysis, HCM with average linkage and K-means clustering, and finally the linear discriminant analysis. It is found that these tools result in a much better discrimination between air masses than the classical selection criteria. From a study based on data selected during seven isobaric flights (230 hPa) crossing the same COL in June 1995, the MA method allows to obtain the physical and chemical structure of the COL and surrounding air masses, and the identification of air mass directly linked to the stratosphere–troposphere exchanges (STE). This air mass corresponds to a mixed zone between stratosphere and troposphere near the jet stream, with intermediate values and weak gradients of ozone, potential temperature and humidity (the tropopause is poorly defined). These STE air masses could not have been found objectively with classical discrimination criteria. A possible hypothesis to explain these STE is the dynamics associated with strong convection beneath and around the southern side of the COL.     

Demonstrating Attainment of the Air Quality Standards: Integration of Observations and Model Predictions into the Probabilistic Framework

ABSTRACT

This paper introduces an integrated observational-modeling approach to transform the deterministic nature of attainment demonstrations of the National Ambient Air Quality Standard (NAAQS) into the probabilistic framework. While the methods presented here can be used to address any air quality standard that is based on extreme values, this paper focuses on the application to the 1-hr and 8-hr NAAQS for ozone. Extreme value statistics and resampling techniques are applied to estimate the probability of exceeding the NAAQS for both 1-hr and 8-hr ozone concentrations. Within the integrated observation-modeling analysis approach, we show that the model-to-model differences in the predicted responses to emission reductions are smaller than the model-to-model differences in predicted absolute ozone concentrations. We illustrate that the emission reductions stemming from a real-world emission control strategy would substantially reduce the probability of exceeding the NAAQS over a large portion of the eastern United States, especially for the 8-hr average ozone concentrations.     

Demonstrating attainment of the air quality standards: integration of observations and model predictions into the probabilistic framework

This paper introduces an integrated observational-modeling approach to transform the deterministic nature of attainment demonstrations of the National Ambient Air Quality Standard (NAAQS) into the probabilistic framework. While the methods presented here can be used to address any air quality standard that is based on extreme values, this paper focuses on the application to the 1-hr and 8-hr NAAQS for ozone. Extreme value statistics and resampling techniques are applied to estimate the probability of exceeding the NAAQS for both 1-hr and 8-hr ozone concentrations. Within the integrated observation-modeling analysis approach, we show that the model-to-model differences in the predicted responses to emission reductions are smaller than the model-to-model differences in predicted absolute ozone concentrations. We illustrate that the emission reductions stemming from a real-world emission control strategy would substantially reduce the probability of exceeding the NAAQS over a large portion of the eastern United States, especially for the 8-hr average ozone concentrations.     

Estimated tropospheric ozone levels on the southeast Spanish Mediterranean coast

The objective of this study is to estimate the spatial variability of tropospheric ozone in an area using a simple model. The area in this case was applied in the western Mediterranean basin. The study period was from May to September in 2003 and 2004.A multiple linear regression between ozone concentrations, altitude and distance to the precursor sources in a fluvial basin can be used to estimate ozone values at other sites during the warmer seasons. The correlation coefficients obtained with 2-week ozone values measured at five points with a passive sampling technique were high enough to apply the model (0.77<r<0.99).To verify the model, ozone concentrations were measured with passive samplers and continuous analyzers at some selected sites, and the values were compared with the estimated concentration. The results of the validations were satisfactory, in 80% of the measurements the estimated levels differ from measured less than 20%, which is included in the bound error for the type of sampler used in this study.     

Estimating the frequency distributions of particulate matter and their metal elements in a temple

Air quality inside Asian temples is typically poor because of the burning of incense. This study measured and analyzed concentrations of fine (PM2.5) and coarse (PM2.5-10) particulate matter and their metal elements inside a temple in central Taiwan. Experimental results showed that the concentrations of metals Cd, Ni, Pb, and Cr inside the temple were higher than those at rural, suburban, urban, and industrial areas in other studies. Three theoretical parent distributions (lognormal, Weibull, and gamma) were used to fit the measured data. The lognormal distribution was the most appropriate distribution for representing frequency distributions of PM10, PM2.5, and their metal elements. Furthermore, the central limit theorem, H-statistic-based scheme, and parametric and nonparametric bootstrap methods were used to estimate confidence intervals for mean pollutant concentrations. The estimated upper confidence limits (UCLs) of means between different methods were very consistent, because the sample coefficient of variation (CV) was < 1. When the sample CV was > 1, the UCL based on H-statistical method tended to overestimate the UCLs when compared with other methods. Confidence intervals for pollutant concentrations at different percentiles were evaluated using parametric and nonparametric bootstrap methods. The probabilities of pollutants exceeding a critical concentration were also calculated.     

Ozone and grosswetterlagen

Meteorological conditions have a decisive impact on surface ozone concentrations. In this study, an empirical model is used to explain the interdependence of ozone and grosswetterlagen. Different meteorological parameters such as air temperature, global solar radiation, relative humidity, wind direction and wind speed are used. Additional nitric oxide (NO) was taken as a representative for the emission situation and ozone maximum of the preceding day in order to evaluate the development of the photochemical situation. The dataset includes data collected over a period of three years (1992–1994) from three stations outside of Munich and one in the center of Munich. All values become variables by calculating means, sums or maxima of the basic dataset consisting of half-hour means. Seasonal periodicity of data is detected with Fourier analysis and eliminated by a division method after computing a seasonal index. The dataset is divided into three different grosswetterlagen groups, depending on main wind direction. One mostly cyclonic (westerly winds), one mixed (alternating winds) and one only anticyclonic (easterly winds). The last is completed with one summertime group including values from April to August. Factor analysis is performed for each group to obtain independent linear variable combinations. Overall, relative humidity is the dominant parameter, a typical value indicating meteorological conditions during a grosswetterlage. Linear multiple regression analysis is performed using the factors obtained to reveal how the ozone concentrations are explained in terms of meteorological parameters and NO. The results improve from cyclonic to anticyclonic grosswetterlagen in conformance with the increasing significance of photochemistry, indicated by the high solar radiation and high temperature, and the low relative humidity and low wind speed. The explained variance r² reaches its maximum with more than 50 % of the time in Munich center. This empirical model is applicable to the forecasting of local ozone maximum concentrations with a total standard error deviation of 8.5 to 12.8 % and, if ozone concentrations exceed 80 ppb, with a standard error deviation of 5.4 to 9.5 %.     

Ozone and grosswetterlagen

Meteorological conditions have a decisive impact on surface ozone concentrations. In this study, an empirical model is used to explain the interdependence of ozone and grosswetterlagen. Different meteorological parameters such as air temperature, global solar radiation, relative humidity, wind direction and wind speed are used. Additional nitric oxide (NO) was taken as a representative for the emission situation and ozone maximum of the preceding day in order to evaluate the development of the photochemical situation. The dataset includes data collected over a period of three years (1992–1994) from three stations outside of Munich and one in the center of Munich. All values become variables by calculating means, sums or maxima of the basic dataset consisting of half-hour means. Seasonal periodicity of data is detected with Fourier analysis and eliminated by a division method after computing a seasonal index. The dataset is divided into three different grosswetterlagen groups, depending on main wind direction. One mostly cyclonic (westerly winds), onemixed (alternating winds) and one onlyanticyclonic (easterly winds). The last is completed with one summertime group including values from April to August. Factor analysis is performed for each group to obtain independent linear variable combinations. Overall, relative humidity is the dominant parameter, a typical value indicating meteorological conditions during a grosswetterlage. Linear multiple regression analysis is performed using the factors obtained to reveal how the ozone concentrations are explained in terms of meteorological parameters and NO. The results improve from cyclonic to anticyclonic grosswetterlagen in conformance with the increasing significance of photochemistry, indicated by the high solar radiation and high temperature, and the low relative humidity and low wind speed. The explained variance r² reaches its maximum with more than 50 % of the time in Munich center. This empirical model is applicable to the forecasting of local ozone maximum concentrations with a total standard error deviation of 8.5 to 12.8 % and, if ozone concentrations exceed 80 ppb, with a standard error deviation of 5.4 to 9.5 %.     

Trends in Meteorologically Adjusted Ozone Concentrations in Six Kentucky Metro Areas, 1998–2002

Abstract

Meteorologically adjusted ozone (O₃) concentrations during five recent O₃ seasons (1998-2002) were computed for six Kentucky metro areas using a nonlinear regression model originally developed for forecasting ground-level O₃ concentrations. The meteorological adjustment procedure was based on modifying actual measured O₃ concentrations according to model-predicted responses to climate departures with respect to a reference year. For all six Kentucky metro areas, meteorologically adjusted O₃ concentrations declined over the five-year period. The linear best-fit rate of decline in mean adjusted O₃ concentrations ranged from 0.9 to 2.6 ppb/yr for these metro areas; the average rate of decline was 1.6 ppb/yr. The rates of decline in meteorologically adjusted extreme value (e.g., 95th percentile) concentrations were approximately the same, but there is greater statistical uncertainty concerning the extreme value trends.     

Selecting ozone exposure statistics for determining crop yield loss from air pollutants

Numerous ozone exposure statistics were calculated using hourly ozone data from crop yield loss experiments previously conducted for alfalfa, fresh market and processing tomatoes, cotton, and dry beans in an ambient ozone gradient near Los Angeles, California. Exposure statistics examined included peak (maximum daily hourly) and mean concentrations above specific threshold levels, and concentrations during specific time periods of the day. Peak and mean statistics weighted for ozone concentration and time period statistics weighted for hour of the day were also determined. Polynomial regression analysis was used to relate each of 163 ozone statistics to crop yield. Performance of the various statistics was rated by comparing residual mean square (RMS) values. The analyses demonstrated that no single statistic was best for all crop species. Ozone statistics with a threshold level performed well for most crops, but optimum threshold level was dependent upon crop species and varied with the particular statistics calculated. The data indicated that daily hours of exposure above a critical high-concentration threshold related well to crop yield for alfalfa, market tomatoes, and dry beans. The best statistic for cotton yield was an average of all daily peak ozone concentrations. Several different types of ozone statistics performed similarly for processing tomatoes. These analyses suggest that several ozone summary statistics should be examined in assessing the relationship of ambient ozone exposure to crop yield. Where no clear statistical preference is indicated among several statistics, those most biologically relevant should be selected.     

A Test Reference Year (TRY) approach to gas dispersion in the atmosphere

For environmental analysis such as the dispersion of pollutants in the atmosphere, it is essential to have meteorological data that are relevant for a long period. In this paper, we explore the possibility of using an environmental Test Reference Year (TRY), i.e., a set of real, contemporaneous and hourly meteorological variables, 'extracted' from a hourly series of at least 10 years, for modelling pollutant dispersion in the atmosphere. The classical approach, based on a statistical data set, implies the loss of important information such as the real correlation between the different meteorological variables, and this implies crude approximation in the simulation results. We compare the simulation results with the long hourly 10 years data set (which can be considered a 'brute force' approach, since it requires a huge amount of data and time processing, but it is here considered the most severe benchmark) and with the statistical data set commonly used. It is shown that the results obtained using the TRY have a good agreement with the ones obtained with the simulation of the 10 years and they are also much better than those obtained using the statistical data set.     

The statistical merits of various methods of calculating transfer coefficients between environmental media--development of the ideal formula for data-sets with a log-normal distribution

The statistical treatment of data-sets from environmental pollutant studies in which different measurements are combined to produce averages or comparative factors (e.g., transfer coefficients (TCs), input-output balance values) are considered here, with particular reference to the analysis of data from input-output balance studies of pollutants such as PCBs in animals and humans. Many methods of statistical analysis ignore the fact that all measurements are subject to error, and generally assume that the normal distribution applies to all data-sets, which is commonly inappropriate for environmental (and particularly biological system) data. Considerably different estimations can be obtained by applying different, commonly used, statistical methods, as shown in a simulation study presented here and when applied to data from an input-output balance study of PCBs in humans. Alternative average and combined factor estimators for the treatment of data from these types of studies that give considerable advantages in terms of bias and the ease of assessment of accuracy are proposed.