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 %.     

Synoptic weather patterns and their relationship to high ozone concentrations in the Taichung Basin

Frequent high ozone days (defined as daily maximum ozone concentration ⩾80 ppb) during recent years in the Taichung Basin have caused much concern. High ozone days occur mainly during autumn and spring. Statistically, there is no clear linear relationship between a single meteorological variable and ozone concentration. In this study, data from 1996–2000 has shown that high ozone concentrations occur during two types of synoptic weather patterns. The first type is a continental cyclone emanating from mainland China, the southern part of it swept towards Taiwan by easterly winds. The second pattern is a tropical depression moving northwards toward the region, the northern part of it affecting Taiwan via easterly winds. Both types cover Taiwan with easterly winds, which are blocked by the Central Mountain Ranges (altitude of 2000–3000 m). The ranges create lee cyclogenesis to the west, which is unfavorable for pollutant dispersion and leads to serious air pollution episodes.The statistical results of the synoptic weather patterns in relation to ozone concentrations are based on the 5 yr data (1996–2000). This was obtained from a network of air-pollution monitoring sites in the study area, while the vertical data come from two 3-day tethersonde experimental campaigns conducted during March and October 2000, measuring air pressure, air temperature, relative humidity, wind speed and direction, non-methane hydrocarbons, NO_x and O₃.     

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.     

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.     

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.     

A study of surface ozone and the relation to complex wind flow in Hong Kong

Ozone measurements made from 5 sites in Hong Kong have been analyzed, including those from one upwind, one downwind, and three urban locales. The data are analyzed in terms of the seasonal and diurnal trends. A subset of data in autumn is further analyzed to study the relationship between the ozone spatial pattern and wind flow as well as other meteorological parameters. The results show that averaged ozone levels at most sites exhibit maxima in autumn, which appears to be a unique feature for eastern Asia. On average the daily maximum 1-h concentrations are found to be higher in the western (normally downwind) site than those on the eastern side and in urban areas. Examination of surface wind patterns and other meteorological parameters suggest that elevated ozone concentrations on the western side occur during the days with intense solar radiation, light winds, and in the presence of a unique wind circulation. The wind reversal in the western parts under the “convergence” flow is believed to be an important cause of the high-ozone events observed there. Such wind flow may re-circulate/transport nearby urban plumes (in this case the Hong Kong–Shenzhen urban complex). Examination of chemical data from the western site has shown that averaged afternoon SO₂ to NO_x ratios on days with wind reversal are larger than those of typical urban Hong Kong and that a significant SO₂ enhancement was clearly indicated on several occasions. The SO₂ enhancement may be interpreted as being the evidence to suggest the contribution of regional sources and/or Hong Kong’s power plants (both containing high SO₂). A case study has shown that when moderately strong northwesterly wind prevails, elevated ozone and SO₂ can be transported to western Hong Kong from the inner Pearl Delta region. This study has also indicated that under the impact of ENE winds the eastern side of Hong Kong is not frequently affected by the re-circulating ozone plumes present in the western side.     

A statistical model for predicting carbon monoxide levels

This paper presents a statistical model that is able to predict carbon monoxide (CO) concentrations as a function of meteorological conditions and various air quality parameters. The experimental work was conducted in an urban atmosphere, where the emissions from cars are prevalent. A mobile air pollution monitoring laboratory was used to collect data, which were divided into two groups: a development group and a testing group. Only the development dataset was used for developing the model. The model was determined by using a stepwise multiple regression modelling procedure. Thirteen independent variables were selected as inputs: non-methane hydrocarbon (NMHC), methane (CH₄), suspended dust, carbon dioxide (CO₂), nitrogen oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), wind speed, wind direction, temperature, relative humidity and solar energy. It was found that NO has the most effect on the predicted CO concentration. The contribution of NO to the CO concentration variations was 91.3%. Adding in the terms for NO₂), NMHC and CH₄ improved the model by only a further 2.3%. The derived model was shown to be statistically significant, and model predictions and experimental observations were shown to be consistent.     

Seasonal evolutions of ozone (O3) and its nitrogen precursors (NO, NO2) in urban Sfax (Tunisia)

Seasonal evolution of ozone (O3) and its nitrogen precursors (NO, NO2) in downtown Sfax (Tunisia) was monitored. Nitrogen oxides are shown to be closely related to local vehicle sources. Seasonal ozone levels, however, are shown to be dependent on regional meteorological conditions. High ozone levels are due to the effect of anticyclones and stratosphere intrusions (cut-off lows). Low levels are associated with cyclonic conditions of small vertical range of motion. Other than these particular conditions, ozone levels are shown to be relatively higher in fall and winter seasons, characterised by a very steady atmosphere. Overall, the examined meteorological conditions, the ozone concentrations observed in downtown Sfax are characterised by clear day/night cycles, which can be explained by the significant ventilation of the region.     

一次光化学污染过程中O3和NOx的垂直梯度观测研究

为了研究2008年北京奥运会前期污染物浓度变化特征,对北京气象塔3层高度上的大气污染物（NO2和O3）进行加强观测,分析其变化特征。观测结果表明,由于北京奥运会前期采取了严格的空气质量控制措施,NO2浓度相对车辆限行前下降了45.3%,且随着高度递增逐渐降低;O3浓度最大值和日均值有所降低,其最大值出现时间较10年前提前了12 h,且有4 h左右处于相对平稳状态。O3浓度峰值主要是受NO2的控制,O3浓度峰值出现时间提前反映出北京大气氧化效率不断提高。对于观测期间出现光化学污染事件,利用同期气象资料和大气污染监测数据分析,发现造成这次大气污染的主要原因是气象因子：地面多处于弱高压场控制中,大气层结稳定,风力较弱（小于2 m/s）,并伴随着连续高温、强辐射和低湿。     

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.     

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.     

An assessment of the impact of climate change on air quality at two UK sites

Possible effects of climate change on air quality are studied for two urban sites in the UK, London and Glasgow. Hourly meteorological data were obtained from climate simulations for two periods representing the current climate and a plausible late 21st century climate. Of the meteorological quantities relevant to air quality, significant changes were found in temperature, specific humidity, wind speed, wind direction, cloud cover, solar radiation, surface sensible heat flux and precipitation. Using these data, dispersion estimates were made for a variety of single sources and some significant changes in environmental impact were found in the future climate. In addition, estimates for future background concentrations of NO_x, NO₂, ozone and PM₁₀ upwind of London and Glasgow were made using the meteorological data in a statistical model. These showed falls in NO_x and increases in ozone for London, while a fall in NO₂ was the largest percentage change for Glasgow. Other changes were small. With these background estimates, annual-average concentrations of NO_x, NO₂, ozone and PM₁₀ were estimated within the two urban areas. For London, results averaged over a number of sites showed a fall in NO_x and a rise in ozone, but only small changes in NO₂ and PM₁₀. For Glasgow, the changes in all four chemical species were small. Large-scale background ozone values from a global chemical transport model are also presented. These show a decrease in background ozone due to climate change. To assess the net impact of both large scale and local processes will require models which treat all relevant scales.     

Sensitivity of ozone to summertime climate in the eastern USA: A modeling case study

The goal of this modeling study is to determine how concentrations of ozone respond to changes in climate over the eastern USA. The sensitivities of average ozone concentrations to temperature, wind speed, absolute humidity, mixing height, cloud liquid water content and optical depth, cloudy area, precipitation rate, and precipitating area extent are investigated individually. The simulation period consists of July 12–21, 2001, during which an ozone episode occurred over the Southeast. The ozone metrics used include daily maximum 8 h average O₃ concentration and number of grid cells exceeding the US EPA ambient air-quality standard. The meteorological factor that had the largest impact on both ozone metrics was temperature, which increased daily maximum 8 h average O₃ by 0.34 ppb K⁻¹ on average over the simulation domain. Absolute humidity had a smaller but appreciable effect on daily maximum 8 h average O₃ (−0.025 ppb for each percent increase in absolute humidity). While domain-average responses to changes in wind speed, mixing height, cloud liquid water content, and optical depth were rather small, these factors did have appreciable local effects in many areas. Temperature also had the largest effect on air-quality standard exceedances; a 2.5 K temperature increase led to a 30% increase in the area exceeding the EPA standard. Wind speed and mixing height also had appreciable effects on ozone air-quality standard exceedances.     

Surface ozone measurements and meteorological influences in the urban atmosphere of Istanbul

Hourly measurements of ozone concentration in the urban atmosphere of Istanbul were carried out from February 1998 to July 1999. An assessment of the annual variations and relationships of ozone concentrations and meteorological variables was made. Annual variations were first examined without considering meteorological variables, and meteorological influences on ozone seasonal values were then examined. Furthermore, a typical ozone threshold period was analysed by considering meteorological variables for a case study. Meteorological conditions favourable for high ozone concentrations appeared when Istanbul and its surrounding region were dominated by an anticyclonic pressure system. During conducive ozone days, southerly and southwesterly winds with low speeds (daytime mean value <11m1sSUP align=right>-1) influence Istanbul.     

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.     

Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting

Introduction

This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O₃) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons.

Methods

Two of the methodologies define threshold models, which assume that the behaviour of the dependent variable (O₃ concentrations) changes when it enters in a different regime (two and four regimes were considered in this study). The change from one regime to another depends on a specific value (threshold value) of an explanatory variable (threshold variable), which is also defined by GAs. The predictor variables were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide, nitrogen dioxide (NO₂), and O₃ (recorded in the previous day at an urban site with traffic influence) and also meteorological data (hourly averages of temperature, solar radiation, relative humidity and wind speed). The study was performed for the period from May to August 2004.

Results and discussion

Several models were achieved and only the best model of each methodology was analysed. In threshold models, the variables selected by GAs to define the O₃ regimes were temperature, CO and NO₂ concentrations, due to their importance in O₃ chemistry in an urban atmosphere.

Conclusion

In the prediction of O₃ concentrations, the threshold model that considers two regimes was the one that fitted the data most efficiently.     

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.     

Measurements of ammonia concentrations,fluxes and dry deposition velocities to a spruce forest 1991–1995

The dry deposition velocities and fluxes of ammonia have been estimated from measurements of the vertical gradient of ammonia and micrometeorology above a spruce forest in western Jutland, Denmark. Measurements have been made in seven periods, each lasting about one week and covering all seasons and different meteorological situations. Different deposition characteristics were observed, depending on the ammonia concentration and the relative humidity. At conditions with westerly winds, the wind brings air masses from the North Sea with low concentration levels of ammonia to the site, while at conditions with easterly winds, the air have passed central Jutland with large emission areas. Some of the relatively low deposition velocities or emissions were observed during conditions with low ammonia concentration and westerly winds. These observations might relate to a compensation point of the forest, i.e. an ammonia concentration below which the trees and/or the surface emit ammonia due to an equilibrium with the ammonia inside the needles or on the surface. Emission of ammonia was also observed at relatively high ammonia concentration levels (above 2 μg NH₃–N m^-3), mainly during one measuring period characterized by easterly winds with dry conditions and high ammonia concentrations, and the emissions might relate to evaporation from ammonia saturated surfaces or emission from mineralization in the forest soil. In general, relatively high net deposition velocities were observed during conditions with relative humidity above 80% or at ammonia concentrations moderate higher than a given (temperature dependent) compensation point. During stable conditions some observations revealed that the gradient above the canopy not necessarily represents the exchange with the canopy.     

Nanoparticle and ultrafine particle events at the Fresno supersite

Continuous measurements of particle size distributions of 3-407 nm were collected from August 2002 to July 2004 at the Fresno Supersite to understand their number concentrations, size distributions, and formation processes. Measurements for fine particulate matter (PM2.5) mass, sulfate (SO4(2-)), nitrate (NO3-), black carbon (BC), particle-bound polycyclic aromatic hydrocarbons (PAHs), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O3), and meteorological data (wind speed, wind direction, temperature [T], relative humidity [RH], and solar radiation) were used to determine the causes of nanoparticle (3-10 nm) and ultrafine (10-100 nm) particle events. These events were found to be divided into four types: (1) 3- to 10-nm morning nucleation; (2) 10- to 30-nm morning traffic; (3) 10- to 30-nm afternoon photochemical; and (4) 50- to 84-nm evening home heating, including residential wood combustion. Intense examples of the first type (>10(4) number [#]/cm3) were observed on 29 days, nearly always during the summer. The second type of event was observed on more than 73 days and occurred throughout the year. The third type was observed on 36 days, from spring through summer. The fourth type was found on 109 days, all of them during the winter. Although sulfur dioxide (SO2) emissions in Central California are low, the small residual amounts in gasoline and diesel fuel are apparently sufficient to initiate nucleation events. These were measured in the morning, soon after the shallow surface inversion coupled with layers aloft where nucleation probably was initiated. PM2.5 concentrations were poorly correlated with nanoparticle number.     

A simple semi-empirical photochemical model for the simulation of ozone concentration in the Seoul metropolitan area in Korea

O₃ concentrations were simulated over the Seoul metropolitan area in Korea using a simple semi-empirical reaction (SEGRS) model which consists of generic reaction set (GRS), photochemical reaction set, and the diagnostic wind field generation model. The aggregated VOC emission strength was empirically scaled by the comparison of the simulated slope of (O₃–2NO–NO₂) concentration as a function of cumulative actinic light flux against measurements on high surface ozone concentration days with the relatively weak easterly geostrophic winds at the 850 hPa level in summer when the effect of horizontal advection was fairly small. The results indicated that the spatial distribution patterns and temporal variations of spatially averaged ground-level ozone concentrations were quite well simulated compared with those of observations with the modified volatile organic compound (VOC) emission strength. The diurnal trend of the surface ozone concentration and the maximum concentration compared observations were also quite reasonably simulated. However, the maximum ozone concentration occurring time at Seoul lagged about 2 h and the ozone concentration in the suburban area was slightly overestimated in the afternoon due to the influx of high ozone concentration from the urban area. It was found that the SEGRS model could be effectively used to simulate or predict the ground-level ozone concentration reasonably well without heavy computational cost provided the emission of ozone precursors are given.