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.     

Meteorologically adjusted trends of daily maximum ozone concentrations in Taipei,Taiwan

The meteorological conditions exert large impacts on ozone concentrations, and may mask the long-term trends in ozone concentrations resulting from precursor emissions. Estimation of long-term trends of ozone concentrations due to the changes in precursor emissions is important for corresponding control strategy. Multiple linear regression (method I), multilayer perceptron (MLP) neural network (method II) and Komogorov-Zurbenko (KZ) filter method plus MLP methodology (method III), are used to estimate the meteorologically adjusted long-term trends of daily maximum ozone concentrations by removing the masking effects of meteorological conditions in this study. The daily maximum ozone concentrations and relative meteorological variables were extracted from six air-monitoring stations in Taipei area from 1994 to 2001. The data collected during 1994–2000 period were used as modeling set and utilized to estimate the meteorologically adjusted trends, and the data of 2001 were used as the validation data. The meteorologically adjusted trends of ozone for these three methods were calculated and compared. The results show that both MLP and KZ filter +MLP models are more suitable than multiple linear regression for estimating the long-term trends of ozone in Taipei, Taiwan. The long-term linear trends of meteorologically adjusted ozone concentrations due to the precursor emissions show an increase trend at all stations, and the percent changes per year range from 1.0% to 2.25% during the modeling period in Taipei area.     

Predicting ozone levels

This paper presents a statistical model that is capable of predicting ozone levels from precursor concentrations and meteorological conditions during daylight hours in the Shuaiba Industrial Area (SIA) of Kuwait. The model has been developed from ambient air quality data that was recorded for one year starting from December 1994 using an air pollution mobile monitoring station. The functional relationship between ozone level and the various independent variables has been determined by using a stepwise multiple regression modelling procedure. The model contains two terms that describe the dependence of ozone on nitrogen oxides (NOx) and nonmethane hydrocarbon precursor concentrations, and other terms that relate to wind direction, wind speed, sulphur dioxide (SO₂) and solar energy. In the model, the levels of the precursors are inversely related to ozone concentration, whereas SO₂ concentration, wind speed and solar radiation are positively correlated. Typically, 63 % of the variation in ozone levels can be explained by the levels of NOx. The model is shown to be statistically significant and model predictions and experimental observations are shown to be consistent. A detailed analysis of the ozone-temperature relationship is also presented; at temperatures less than 27 °C there is a positive correlation between temperature and ozone concentration whereas at temperatures greater than 27 °C a negative correlation is seen. This is the first time a non-monotonic relationship between ozone levels and temperature has been reported and discussed.     

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.     

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 %.     

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 %.     

Long-term variation in surface ozone and its precursors in Athens,Greece

INTENTION, GOAL, SCOPE, BACKGROUND: Photochemical pollution is a very complex process involving meteorological, topographic, emission and chemical parameters. The most important chemical mechanisms involved in the atmospheric process have already been identified and studied. However, many unknown parameters still exist because of the large number of participating chemical reactions. OBJECTIVE: The present study investigates the processes involved in the photochemical pollution effect of an urban station located in the greater area of the Athens basin and gives a plausible explanation for the different seasonal ozone development between that station and another rural one. Furthermore, the distribution of the mean monthly surface ozone observed at the urban station during 1987-2001 is examined in order to create a relevant forecasting tool. METHODS: Averaged hourly data of O3 and NOx observations monitored at the above mentioned stations, during 1987-2001, have been used in order to derive the daytime (7:00-15:00) values. Trajectories calculated by using a 2D-trajectory code and meteorological data, during the period 1988-1996, have also been used. RESULTS AND DISCUSSION: At the urban station, the percentage negative trend of NO and NOx data in winter and summer is higher than that in spring and autumn, while the percentage ozone trend is maximum in the summer. On the contrary, the negative surface ozone trend at the rural station exhibits a minimum in summer and a maximum in autumn and winter. The mean seasonal wind-rose for the selected months shows that the northward wind flow dominates during June, the month of the lowest negative ozone trend in the rural station. Finally, the development of the forecasting tool shows that the mean monthly surface ozone data during the period (1987-2001) demonstrates a semi-log distribution. CONCLUSIONS: Air transport effect on the air pollution of the rural station (not blocked by mountains) is deduced as a possible reason for the different seasonal ozone development observed between the rural and the urban station. Finally, the discrepancies between the theoretical probabilities deduced by the model and the empirical ones appear to be very small, and the corresponding correlation coefficient is 0.99. RECOMMENDATION AND OUTLOOK: However, to interpret the aforementioned statistical results about the negative trends in ozone and its precursors, additional parameters can be taken into account. Changes in NOx concentrations, for instance, can result not only from changes in emissions or meteorological conditions. There might also be a contribution through changes in the atmospheric composition. A study of the contribution of changes in atmospheric composition to trends of observed NOx concentrations requires that a series of steps be taken (removal of meteorological influence in the time series, calculation of trends in OH concentrations, etc.).     

A hierarchical Bayesian model to estimate and forecast ozone through space and time

A Bayesian hierarchical regime switching model describing the spatial–temporal behavior of ozone (O₃) within a domain covering Lake Michigan during spring–summer 1999 is developed. The model incorporates linkages between ozone and meteorology. It is specifically formulated to identify meteorological regimes conducive of high ozone levels and allow ozone behavior during these periods to be different from typical ozone behavior. The model is used to estimate or forecast spatial fields of O₃ conditional on observed (or forecasted) meteorology including temperature, humidity, pressure, and wind speed and direction. The model is successful at forecasting the onset of periods of high ozone levels, but more work is needed to also accurately identify departures from these periods.     

Extreme value modeling for the analysis and prediction of time series of extreme tropospheric ozone levels: a case study

The occurrence of high concentrations of tropospheric ozone is considered as one of the most important issues of air management programs. The prediction of dangerous ozone levels for the public health and the environment, along with the assessment of air quality control programs aimed at reducing their severity, is of considerable interest to the scientific community and to policy makers. The chemical mechanisms of tropospheric ozone formation are complex, and highly variable meteorological conditions contribute additionally to difficulties in accurate study and prediction of high levels of ozone. Statistical methods offer an effective approach to understand the problem and eventually improve the ability to predict maximum levels of ozone. In this paper an extreme value model is developed to study data sets that consist of periodically collected maxima of tropospheric ozone concentrations and meteorological variables. The methods are applied to daily tropospheric ozone maxima in Guadalajara City, Mexico, for the period January 1997 to December 2006. The model adjusts the daily rate of change in ozone for concurrent impacts of seasonality and present and past meteorological conditions, which include surface temperature, wind speed, wind direction, relative humidity, and ozone. The results indicate that trend, annual effects, and key meteorological variables along with some interactions explain the variation in daily ozone maxima. Prediction performance assessments yield reasonably good results.     

Interannual variation in meteorologically adjusted ozone levels in the eastern United States: A comparison of two approaches

Assessing the influence of abatement efforts and other human activities on ozone levels is complicated by the atmosphere's changeable nature. Two statistical methods, the dynamic linear model (DLM) and the generalized additive model (GAM), are used to estimate ozone trends in the eastern United States and to adjust for meteorological effects. The techniques and resulting estimates are compared and contrasted for four monitoring locations chosen through principal components analysis to represent regional patterns of ozone concentrations. After adjustment for meteorological influence, overall downward trends are evident at all four locations from 1997 to 2004. The results indicate that the two methods’ estimates of ozone changes agree well. When such estimates are needed quickly, or when many similar, but separate analyses are required, the ease of implementation and relative simplicity of the GAMs are attractive. The DLMs are much more flexible, readily addressing such issues as autocorrelation, the presence of missing values, and estimation of long-term trends or cyclical patterns. Implementation of DLMs, however, is typically more difficult, and especially in the absence of an experienced practitioner, they may be better reserved for in-depth analyses.     

An ozone climatology: relationship between meteorology and ozone in the Southeast USA

A statistical analysis of ozone (O₃) concentrations and meteorological parameters was performed to determine the relationship between meteorological changes and ambient O₃ concentrations in the Southeast United States. The correlation between average daily maximum O₃ concentration and various meteorological variables was analysed on a monthly basis from April through October during 1980-1994. The correlations were strongest during the summer months, particularly June, July, and August. Analysis of long term O₃ concentration trends indicates increasing trends during the 1980s and decreasing trends during the early 1990s.     

Air quality modeling in the South Coast Air Basin of California: what do the numbers really mean?

This study evaluates air quality model sensitivity to input and to model components. Simulations are performed using the California Institute of Technology (CIT) airshed model. Results show the impacts on ozone (O3) concentration in the South Coast Air Basin (SCAB) of California because of changes in: (1) input data, including meteorological conditions (temperature, UV radiation, mixing height, and wind speed), boundary conditions, and initial conditions (ICs); and (2) model components, including advection solver and chemical mechanism. O3 concentrations are strongly affected by meteorological conditions and, in particular, by temperature. ICs also affect O3 concentrations, especially in the first 2 days of simulation. On the other hand, boundary conditions do not significantly affect the absolute peak O3 concentration, although they do affect concentrations near the inflow boundaries. Moreover, predicted O3 concentrations are impacted considerably by the chemical mechanism. In addition, dispersion of pollutants is affected by the advection routine used to calculate its transport. Comparison among CIT, California Photochemical Grid Model (CALGRID), and Urban Airshed Model air quality models suggests that differences in O3 predictions are mainly caused by the different chemical mechanisms used. Additionally, advection solvers contribute to the differences observed among model predictions. Uncertainty in predicted peak O3 concentration suggests that air quality evaluation should not be based solely on this single value but also on trends predicted by air quality models using a number of chemical mechanisms and with an advection solver that is mass conservative.     

Variation of surface ozone in Campo Grande,Brazil: meteorological effect analysis and prediction

The effect of meteorological variables on surface ozone (O₃) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O₃ concentration, relative humidity and solar radiation. The threshold model that considers two O₃ regimes was the one that correctly describes the effect of important meteorological variables in O₃ behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O₃ regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O₃ concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O₃ behaviour, being useful to define policy strategies for human health protection regarding air pollution.     

Modelling stomatal ozone flux across Europe

A model has been developed to estimate stomatal ozone flux across Europe for a number of important species. An initial application of this model is illustrated for two species, wheat and beech. The model calculates ozone flux using European Monitoring and Evaluation Programme (EMEP) model ozone concentrations in combination with estimates of the atmospheric, boundary layer and stomatal resistances to ozone transfer. The model simulates the effect of phenology, irradiance, temperature, vapour pressure deficit and soil moisture deficit on stomatal conductance. These species-specific microclimatic parameters are derived from meteorological data provided by the Norwegian Meteorological Institute (DNMI), together with detailed land-use and soil type maps assembled at the Stockholm Environment Institute (SEI). Modelled fluxes are presented as mean monthly flux maps and compared with maps describing equivalent values of AOT40 (accumulated exposure over threshold of 40 ppb or nl l(-1)), highlighting the spatial differences between these two indices. In many cases high ozone fluxes were modelled in association with only moderate AOT40 values. The factors most important in limiting ozone uptake under the model assumptions were vapour pressure deficit (VPD), soil moisture deficit (for Mediterranean regions in particular) and phenology. The limiting effect of VPD on ozone uptake was especially apparent, since high VPDs resulting in stomatal closure tended to co-occur with high ozone concentrations. Although further work is needed to link the ozone uptake and deposition model components, and to validate the model with field measurements, the present results give a clear indication of the possible implications of adopting a flux-based approach for future policy evaluation.     

An Empirical Model for Forecasting Maximum Daily Ozone Levels in the Northeastern U.S.

A stepwise multiple regression procedure was employed to develop the best .fit equation relating maximum afternoon ozone concentrations to meteorological and emission factors along a 24h upwind air parcel trajectory. The equation was developed using ozone data from receptor sites in Northern New Jersey and the resulting correlation coefficient was 0.96. The four most significant variables were the upwind ozone maximum on the previous day, today’s maximum temperature, the previous day’s upwind maximum temperature and the mean wind speed from the surface to 1000 m. The model was also successfully tested at 5 other sites in the Northeastern Quadrant of the United States. The results indicate that the model could be a potentially useful tool for air pollution forecasters in predicting maximum ozone concentrations in this quadrant of the country.     

Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain

The photochemical grid model, UAM-V, has been used by regulatory agencies to make decisions concerning emissions controls, based on studies of the July 1995 ozone episode in the eastern US. The current research concerns the effect of the uncertainties in UAM-V input variables (emissions, initial and boundary conditions, meteorological variables, and chemical reactions) on the uncertainties in UAM-V ozone predictions. Uncertainties of 128 input variables have been estimated and most range from about 20% to a factor of two. 100 Monte Carlo runs, each with new resampled values of each of the 128 input variables, have been made for given sets of median emissions assumptions. Emphasis is on the maximum hourly-averaged ozone concentration during the 12–14 July 1995 period. The distribution function of the 100 Monte Carlo predicted domain-wide maximum ozone concentrations is consistently close to log-normal with a 95% uncertainty range extending over plus and minus a factor of about 1.6 from the median. Uncertainties in ozone predictions are found to be most strongly correlated with uncertainties in the NO₂ photolysis rate. Also important are wind speed and direction, relative humidity, cloud cover, and biogenic VOC emissions. Differences in median predicted maximum ozone concentrations for three alternate emissions control assumptions were investigated, with the result that (1) the suggested year-2007 emissions changes would likely be effective in reducing concentrations from those for the year-1995 actual emissions, that (2) an additional 50% NO_x emissions reductions would likely be effective in further reducing concentrations, and that (3) an additional 50% VOC emission reductions may not be effective in further reducing concentrations.     

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.     

Meteorological factors of ozone predictability at Houston, Texas

Several ozone modeling approaches were investigated to determine if uncertainties in the meteorological data would be sufficiently large to limit the application of physically realistic ozone (O3) forecast models. Three diagnostic schemes were evaluated for the period of May through September 1997 for Houston, TX. Correlations between measured daily maximum and model calculated O3 air concentrations were found to be 0.70 using a linear regression model, 0.65 using a non-advective box model, and 0.49 using a three-dimensional (3-D) transport and dispersion model. Although the regression model had the highest correlation, it showed substantial underestimates of the highest concentrations. The box model results were the most similar to the regression model and did not show as much underestimation. The more complex 3-D modeling approach yielded the worst results, likely resulting from O3 maxima that were driven by local factors rather than by the transport of pollutants from outside of the Houston domain. The highest O3 concentrations at Houston were associated with light winds and meandering trajectories. A comparison of the gridded meteorological data used by the 3-D model to the observations showed that the wind direction and speed values at Houston differed most on those days on which the O3 underestimations were the greatest. These periods also tended to correspond with poor precipitation and temperature estimates. It is concluded that better results are not just obtained through additional modeling complexity, but there needs to be a comparable increase in the accuracy of the meteorological data.     

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.     

Air Quality Modeling in the South Coast Air Basin of California: What Do the Numbers Really Mean?

Abstract

This study evaluates air quality model sensitivity to input and to model components. Simulations are performed using the California Institute of Technology (CIT) airshed model. Results show the impacts on ozone (O₃) concentration in the South Coast Air Basin (SCAB) of California because of changes in: (1) input data, including meteorological conditions (temperature, UV radiation, mixing height, and wind speed), boundary conditions, and initial conditions (ICs); and (2) model components, including advection solver and chemical mechanism. O₃ concentrations are strongly affected by meteorological conditions and, in particular, by temperature. ICs also affect O₃ concentrations, especially in the first 2 days of simulation. On the other hand, boundary conditions do not significantly affect the absolute peak O₃ concentration, although they do affect concentrations near the inflow boundaries. Moreover, predicted O₃ concentrations are impacted considerably by the chemical mechanism. In addition, dispersion of pollutants is affected by the advection routine used to calculate its transport. Comparison among CIT, California Photochemical Grid Model (CALGRID), and Urban Airshed Model air quality models suggests that differences in O₃ predictions are mainly caused by the different chemical mechanisms used. Additionally, advection solvers contribute to the differences observed among model predictions. Uncertainty in predicted peak O₃ concentration suggests that air quality evaluation should not be based solely on this single value but also on trends predicted by air quality models using a number of chemical mechanisms and with an advection solver that is mass conservative.