Long-term changes of ozone and traffic in Bilbao

It is well known that ozone levels are the result of the interaction among emissions of VOC's and NO_x, on the one hand, and meteorological effects on the other hand. In this work, using the low-pass KZ filter developed by Kolmogorov and Zurbenko, the original time series consisting of the logarithm of daily maximum ozone concentrations measured at three locations in the Bilbao area, are splitted into long-term, seasonal and short-term effects. Next, meteorological effects are moderated or removed from filtered ozone series using multiple linear regression. The long-term evolution of ozone forming capability due to changes in precursor emissions can be obtained applying the KZ filter to the residuals of this regression.The present work is an application of the widely used and well known KZ filter technique and focuses on analyzing the joint evolution of the long-term components of ozone time series on the one hand, and mean traffic in the Bilbao area (Spain) on the other hand during years 1993, 1994, 1995 and 1996.To that end, regression analysis between the long-term fractions of ozone and traffic has been performed. The results show that long-term changes of the mean traffic flow are responsible for the long-term changes in ozone forming capability due to changes in precursor emissions of the area. Long-term trend of daily mean traffic can explain between 81% and 99.6% of the total variance of long-term ozone changes at the three locations of Bilbao studied.     

Artificial neural network-derived trends in daily maximum surface ozone concentrations

Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.     

Long-term meteorologically independent trend analysis of ozone air quality at an urban site in the greater Houston area

The Houston-Galveston-Brazoria (HGB) area of Texas has a history of ozone exceedances and is currently classified under moderate nonattainment status for the 2008 8-hr ozone standard of 75 ppb. The HGB area is characterized by intense solar radiation, high temperature, and high humidity, which influence day-to-day variations in ozone concentrations. Long-term air quality trends independent of meteorological influence need to be constructed for ascertaining the effectiveness of air quality management in this area. The Kolmogorov-Zurbenko (KZ) filter technique, used to separate different scales of motion in a time series, is applied in the current study for maximum daily 8-hr (MDA8) ozone concentrations at an urban site (U.S. Environmental Protection Agency [EPA] Air Quality System [AQS] Site ID: 48-201-0024, Aldine) in the HGB area. This site, located within 10 miles of downtown Houston and the George Bush Intercontinental Airport, was selected for developing long-term meteorologically independent MDA8 ozone trends for the years 1990–2016. Results from this study indicate a consistent decrease in meteorologically independent MDA8 ozone between 2000 and 2016. This pattern could be partially attributed to a reduction in underlying nitrogen oxide (NO_x) emissions, particularly lowering nitrogen dioxide (NO₂) levels, and a decrease in the release of highly reactive volatile organic compounds (HRVOCs). Results also suggest solar radiation to be most strongly correlated to ozone, with temperature being the secondary meteorological control variable. Relative humidity and wind speed have tertiary influence at this site. This study observed that meteorological variability accounts for a high of 61% variability in baseline ozone (low-frequency component, sum of long-term and seasonal components), whereas 64% of the change in long-term MDA8 ozone post 2000 could be attributed to NO_x emission reduction. Long-term MDA8 ozone trend component was estimated to be decreasing at a linear rate of 0.412 ± 0.007 ppb/yr for the years 2000–2016 and 0.155 ± 0.005 ppb/yr for the overall period of 1990–2016.

Implications: The effectiveness of air emission controls can be evaluated by developing long-term air quality trends independent of meteorological influences. The KZ filter technique is a well-established method to separate an air quality time series into short-term, seasonal, and long-term components. This paper applies the KZ filter technique to MDA8 ozone data between 1990 and 2016 at an urban site in the greater Houston area and estimates the variance accounted for by the primary meteorological control variables. Estimates for linear trends of MDA8 ozone are calculated and underlying causes are investigated to provide a guidance for further investigation into air quality management of the greater Houston area.     

Artificial Neural Network-Derived Trends in Daily Maximum Surface Ozone Concentrations

ABSTRACT

Interannual variability in meteorological conditions can confound attempts to identify changes in ozone concentrations driven by reduced precursor emissions. In this paper, a technique is described that attempts to maximize the removal of meteorological variability from a daily maximum ozone time series, thereby revealing longer term changes in ozone concentrations with increased confidence. The technique employs artificial neural network [multilayer perceptron (MLP)] models, and is shown to remove more of the meteorological variability from U.S. ozone data than does a Kolmogorov-Zurbenko (KZ) filter and conventional regression-based technique.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

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.     

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.     

Long-term trends of primary and secondary pollutant concentrations in Switzerland and their response to emission controls and economic changes

A detrending technique is developed for short-term and yearly variations in order to identify long-term trends in primary and secondary pollutants. In this approach, seasonal and weekly variations are removed by using a mean year; the residual meteorological short-term variation is removed by using a multiple linear regression model. This methodology is employed to detrend ozone (O₃), NO_x, VOC and CO concentrations in Switzerland. We show that primary pollutants (NO_x,VOC and CO) at urban and sub-urban stations show a downward trend over the last decade which correlates well with the reductions in the estimated Swiss emissions. In spite of these large decreases achieved in precursor emissions, summer peak ozone concentrations do not show any statistically significant trend over the last decade. Application of this method to ozone concentrations measured at the Jungfraujoch (3580 m a.s.l.) also shows no trend over the last 10 years. Detrended summer ozone correlates well with European Union gross national product and industrial production growth rates. These results suggest that if substantial reductions in summer peak ozone in Switzerland are desired, emissions reduction strategies must be part of control program involving a much larger region.     

The effects of meteorology on ozone in urban areas and their use in assessing ozone trends

The United States Environmental Protection Agency issues periodic reports that describe air quality trends in the US. For some pollutants, such as ozone, both observed and meteorologically adjusted trends are displayed. This paper describes an improved statistical methodology for meteorologically adjusting ozone trends as well as characterizes the relationships between individual meteorological parameters and ozone. A generalized linear model that accommodates the nonlinear effects of the meteorological variables was fit to data collected for 39 major eastern US urban areas. Overall, the model performs very well, yielding R² statistics as high as 0.80. The analysis confirms that ozone is generally increasing with increasing temperature and decreasing with increasing relative humidity. Examination of the spatial gradients of these responses show that the effect of temperature on ozone is most pronounced in the north while the opposite is true of relative humidity. By including HYSPLIT-derived transport wind direction and distance in the model, it is shown that the largest incremental impact of wind direction on ozone occurs along the periphery of the study domain, which encompasses major NO_x emission sources.     

Extending the Kolmogorov–Zurbenko Filter: Application to Ozone,Particulate Matter,and Meteorological Trends

Abstract

Tropospheric ozone (O₃) and particulate matter (PM) are pollutants of great concern to air quality managers. Federal standards for these pollutants have been promulgated in recent years because of the known adverse effects of the pollutants on human health, the environment, and visibility. Local meteorological conditions exert a strong influence over day‐to‐day variations in pollutant concentrations; therefore, the meteorological signal must be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for the future. Although the Kolmogorov–Zurbenko (KZ) filter has been widely used for this type of trend separation in O₃ studies in the eastern United States, this article aims to extend the method in three key ways. First, whereas the KZ filter is known as a useful tool for O₃ analysis, this study also evaluates its effectiveness when applied to PM. Second, the method was applied to Tucson, AZ, a city in the semi‐arid southwestern United States (Southwest), to evaluate the appropriateness of the method in a region with weaker synoptic weather controls on air quality than the eastern United States. Third, additional forms of output were developed and tailored to be more applicable to decision-makers’ needs through a partnership between academic researchers and air quality planners and managers. Results of the study indicate that the KZ filter is a useful method for examining emissions‐related PM trends, resulting in small, but potentially significant, differences after adjustment. For the Tucson situation with weaker synoptic controls, the KZ method identified mixing height as a more important variable than has been found in other cities.     

Modelling emission trends from non-constant time series of PM10 concentrations in Christchurch, New Zealand

This paper presents an attempt to model the trend of emissions through analysis of a time series of PPM₁₀ concentrations in Christchurch, New Zealand. Emissions are not constant over time, but show high seasonality. Fluctuations are removed by creating a time series in which concentrations do not show dependency on ambient air temperature. Remaining meteorological influences are removed through multiple linear regression. Finally, a moving average filter is applied to reveal the low-frequency trend in the residuals of the meteorologically adjusted time series. The modelled trend shows a peak in emissions in 2001-2002 with a steady decrease thereafter.     

Extending the Kolmogorov-Zurbenko filter: application to ozone, particulate matter, and meteorological trends

Tropospheric ozone (O3) and particulate matter (PM) are pollutants of great concern to air quality managers. Federal standards for these pollutants have been promulgated in recent years because of the known adverse effects of the pollutants on human health, the environment, and visibility. Local meteorological conditions exert a strong influence over day-to-day variations in pollutant concentrations; therefore, the meteorological signal must be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for the future. Although the Kolmogorov-Zurbenko (KZ) filter has been widely used for this type of trend separation in O3 studies in the eastern United States, this article aims to extend the method in three key ways. First, whereas the KZ filter is known as a useful tool for O3 analysis, this study also evaluates its effectiveness when applied to PM. Second, the method was applied to Tucson, AZ, a city in the semi-arid southwestern United States (Southwest), to evaluate the appropriateness of the method in a region with weaker synoptic weather controls on air quality than the eastern United States. Third, additional forms of output were developed and tailored to be more applicable to decision-makers' needs through a partnership between academic researchers and air quality planners and managers. Results of the study indicate that the KZ filter is a useful method for examining emissions-related PM trends, resulting in small, but potentially significant, differences after adjustment. For the Tucson situation with weaker synoptic controls, the KZ method identified mixing height as a more important variable than has been found in other cities.     

Meteorologically adjusted long-term trend of ground-level ozone concentrations in Kaohsiung County,southern Taiwan

Since meteorological changes strongly affect ambient ozone concentrations, trends in concentrations of ozone upon the adjustment of meteorological variations are important of evaluating emission reduction efforts. The goal of this work is to study meteorological effects on the long-term trends of ozone concentration using a multi-variable additive model. Data on the hourly concentrations of ozone were collected from four air-quality stations from 1997 to 2006 in Kaohsiung County to determine the monthly, seasonal and annual average concentrations of ozone. The model incorporates seven meteorological parameters – pressure, temperature, relative humidity, wind speed, wind direction, duration of sunshine and cloud cover. The simulated results show that the long-term ozone concentration increases at 13.84% (or 13.06%) monthly (or annually) after meteorological adjustments, less than at 26.10% (or 23.80%) without meteorological adjustments. Wind speed, duration of sunshine and pressure are the three dominant factors that influence the ground-level ozone levels.     

Determining temporal and spatial variations in ozone air quality

Unless the change in emissions is substantial, the resulting improvement in ozone air quality can be easily masked by the meteorological variability. Therefore, the meteorological and chemical signals must be separated in examining ozone trends. In this paper, we discuss the use of the Kolmogorov-Zurbenko filter in evaluating the temporal and spatial variations in ozone air quality utilizing ozone concentration data from several monitoring locations in the northeastern United States. The results indicate a downward trend in the ozone concentrations during the period 1983-1992 at most locations in the northeastern United States. The results also reveal that ozone is a regional-scale problem in the Northeast.     

Detecting and Tracking Changes in Ozone Air Quality

This paper presents a statistical method for filtering out or moderating the influence of meteorological fluctuations on ozone concentrations. Use of this technique in examining trends in ambient ozone air quality is demonstrated with ozone data from a monitoring location in New Jersey. The results indicate that this method can detect changes in ozone air quality due to changes in emissions in the presence of meteorological fluctuations. This method can be useful in examining the effectiveness of regulatory initiatives in improving ozone air quality.     

Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations

A model which quantifies the relationship between the monthly time series for CO emissions, the monthly time series in ambient CO concentration, and meteorologically driven dispersion was developed. Fifteen cities representing a wide range of geographical and climatic conditions were selected. An eight-year time series (1984–1991 inclusive) of monthly averaged data were examined in each city. A new method of handling missing ambient concentration values which is designed to calculate city-wide average concentrations that follow the trend seen at individual monitor sites is presented. This method is general and can be used in other applications involving missing data. The model uses emissions estimates along with two meteorological variables (wind speed and mixing height) to estimate monthly averages of ambient air pollution concentrations. The model is shown to have a wide range of applicability; it works equally well for a wide range of cities that have very different temporal CO distributions. The model is suited for assessing long-term trends in ambient air pollutants and can also be used for estimating seasonal variations in concentration, estimation of trends in emissions, and for filling in gaps in the ambient concentration record.     

Synoptic-scale transport of ozone into Southern Ontario

This study focuses on synoptic-scale transport of ozone as it affects Southern Ontario. This process has been analyzed for the summer in 2001, as an example period of a frequent event that usually occurs during summer in this region. The work was carried out using the mesoscale modeling system generation 5 (MM5)/sparse matrix operator kernel emission modeling system (SMOKE)/community multiscale air quality (CMAQ) regional air quality modeling system, together with observational data from monitoring stations located throughout the modeling domain. Other different analyses have been carried out to supply more information apart from that obtained by the modeling system. A back-trajectory cluster methodology was used to evaluate the magnitude of the effects studied and an analysis of wind direction and cloud cover revealed a significant correlation with ozone concentration (R²=0.5–0.6). Synoptic sea-surface level pressure (SLP) patterns were also analyzed to examine other meteorological aspects. The contribution of natural background ozone to the total amount within the region was compared with that from synoptic-scale transport. The influence of emission of pollutants from selected areas on ozone concentrations in Southern Ontario was also analyzed. As relevant results of these analyses, the model predicts that background ozone is the largest contribution to the ground-level ozone concentration during days in which low values were recorded. However, when smog episodes occurred, the model predicts that around 60% of the ozone formed by anthropogenic emissions of pollutants is due to releases from nearby US states.     

Trends in near-surface ozone concentrations in Switzerland: the 1990s

A time series analysis of ozone monitoring data from several locations in Switzerland from 1991 to 1999 is presented. Different methods are used to address changes in the ozone level during these years and to account for the influence of changing meteorological conditions. The results show a slight decrease of the peaks but a highly significant increase of the mean value of around 0.5–0.9 ppb yr⁻¹. The frequency distribution has changed in the sense that very low values have become less frequent and that there is a strong increase in frequency of occurrence of half-hourly mean values between about 45 and 55 ppb. A selection procedure reveals slight tendencies towards different trends of afternoon ozone peaks in summer depending on weather and pollution situations. Ozone peaks tend to decrease on fair weather days at rural sites (but increase at urban sites) and show a small increase on cloudy and windy days. A non-linear regression model is used to estimate trends of summertime afternoon ozone peaks in the presence of meteorological variability. In the model, the long-term signal is additively split into a linear part and a part which is modulated by global radiation. The coefficients for both terms are statistically significant at many sites, with an increasing linear trend at the sites north of the Alps of around 1 ppb yr⁻¹ and a decrease of ozone peaks under fair weather conditions relative to cloudy conditions. When additionally considering the effect of precursor concentrations in the regression models, both trends are weakened, which means that they can partly be explained by changes in local to regional emissions. However, at the sites north of the Alps remains a tendency towards a positive linear “base trend” of around 0.4 ppb yr⁻¹. This could possibly be due to increasing background ozone concentrations.     

Dynamic evaluation of a regional air quality model: Assessing the emissions-induced weekly ozone cycle

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system for the “weekend ozone effect” to determine if observed changes in ozone due to weekday-to-weekend (WDWE) reductions in precursor emissions can be accurately simulated. The weekend ozone effect offers a unique opportunity for dynamic evaluation, as it is a widely documented phenomenon that has persisted since the 1970s. In many urban areas of the Unites States, higher ozone has been observed on weekends than weekdays, despite dramatically reduced emissions of ozone precursors (nitrogen oxides [NO_x] and volatile organic compounds [VOCs]) on weekends. More recent measurements, however, suggest shifts in the spatial extent or reductions in WDWE ozone differences. Using 18 years (1988–2005) of observed and modeled ozone and temperature data across the northeastern United States, we re-examine the long-term trends in the weekend effect and confounding factors that may be complicating the interpretation of this trend and explore whether CMAQ can replicate the temporal features of the observed weekend effect. The amplitudes of the weekly ozone cycle have decreased during the 18-year period in our study domain, but the year-to-year variability in weekend minus weekday (WEWD) ozone amplitudes is quite large. Inter-annual variability in meteorology appears to influence WEWD differences in ozone, as well as WEWD differences in VOC and NO_x emissions. Because of the large inter-annual variability, modeling strategies using a single episode lasting a few days or a few episodes in a given year may not capture the WEWD signal that exists over longer time periods. The CMAQ model showed skill in predicting the absolute values of ozone concentrations during the daytime. However, early morning NO_x concentrations were underestimated and ozone levels were overestimated. Also, the modeled response of ozone to WEWD differences in emissions was somewhat less than that observed. This study reveals that model performance may be improved by (1) properly estimating mobile source NO_x emissions and their temporal distributions, especially for diesel vehicles; (2) reducing the grid cell size in the lowest layer of CMAQ; and, (3) using time-dependent and more realistic boundary conditions for the CMAQ simulations.     

Modelling and assessing trends in traffic-related emissions using a generalised additive modelling approach

A generalised additive modelling (GAM) approach is used to model daily concentrations of nitrogen oxides (NO_X), nitrogen dioxide (NO₂), carbon monoxide (CO), benzene and 1,3-butadiene at a busy street canyon location in central London. The models were developed for the period July 1998–June 2005 using appropriate meteorological and road traffic covariates. For all models, the complex and localised wind-flow patterns resulting from the street canyon location of the monitoring site, which can be difficult to model deterministically, have a large influence on the model predictions. It is shown that GAMs built using simple covariates explain a large amount of the daily variation for these pollutants (mean r²=0.86). It is found that concentrations of benzene and 1,3-butadiene have declined in line with detailed calculations of emissions trends, with some evidence to suggest that reductions in benzene have been greater than estimated reductions in emissions. Although measured concentrations of NO_X have declined from 1998 to 2005, much of the decline appears to be associated with reductions in overall traffic and meteorological factors rather than reduced emissions of NO_X. Unadjusted NO_X trends show a 28.6% reduction (95% confidence interval from 21.2% to 35.8%) from 1998 to 2005, whereas meteorologically adjusted trends show a 19.3% decline (95% confidence interval from 14.8% to 23.5%) over this period. Analysis shows that there were a higher number of occasions in the early part of the time series that led to strong recirculation of exhaust emissions and higher NO_X concentrations at this location, thus affecting observed trends in concentration.