An enhanced ozone forecasting model using air mass trajectory analysis

An enhanced ozone forecasting model using nonlinear regression and an air mass trajectory parameter has been developed and field tested. The model performed significantly better in predicting daily maximum 1-h ozone concentrations during a five-year model calibration period (1993–1997) than did a previously reported regression model. This was particularly true on the 28 “high ozone” days ([O₃]>120 ppb) during the period, for which the mean absolute error (MAE) improved from 21.7 to 12.1 ppb. On the 77 days meteorologically conducive to high ozone, the MAE improved from 12.2 to 9.1 ppb, and for all 580 calibration days the MAE improved from 9.5 to 8.35 ppb. The model was field-tested during the 1998 ozone season, and performed about as expected. Using actual meteorological data as input for the ozone predictions, the MAE for the season was 11.0 ppb. For the daily ozone forecasts, which used meteorological forecast data as input, the MAE was 13.4 ppb. The high ozone days were all anticipated by the ozone forecasters when the model was used for next day forecasts.     

Urban heat island (UHI) influence on secondary pollutant formation in a tropical humid environment

The combined action of urbanization (change in land use) and increase in vehicular emissions intensifies the urban heat island (UHI) effect in many cities in the developed countries. The urban warming (UHI) enhances heat-stress-related diseases and ozone (O₃) levels due to a photochemical reaction. Even though UHI intensity depends on wind speed, wind direction, and solar flux, the thermodynamic properties of surface materials can accelerate the temperature profiles at the local scale. This mechanism modifies the atmospheric boundary layer (ABL) structure and mixing height in urban regions. These changes further deteriorate the local air quality. In this work, an attempt has been made to understand the interrelationship between air pollution and UHI intensity at selected urban areas located at tropical environment. The characteristics of ambient temperature profiles associated with land use changes in the different microenvironments of Chennai city were simulated using the Envi-Met model. The simulated surface 24-hr average air temperatures (11 m above the ground) for urban background and commercial and residential sites were found to be 30.81 ± 2.06, 31.51 ± 1.87, and 31.33 ± 2.1ºC, respectively. The diurnal variation of UHI intensity was determined by comparing the daytime average air temperatures to the diurnal air temperature for different wind velocity conditions. From the model simulations, we found that wind speed of 0.2 to 5 m/sec aggravates the UHI intensity. Further, the diurnal variation of mixing height was also estimated at the study locations. The estimated lowest mixing height at the residential area was found to be 60 m in the middle of night. During the same period, highest ozone (O₃) concentrations were also recorded at the continuous ambient air quality monitoring station (CAAQMS) located at the residential area.

Implications: An attempt has made to study the diurnal variation of secondary pollution levels in different study regions. This paper focuses mainly on the UHI intensity variations with respect to percentage of land use pattern change in Chennai city, India. The study simulated the area-based land use pattern with local mixing height variations. The relationship between UHI intensity and mixing height provides variations on local air quality.     

A climatology of regional background ozone at different elevations in Switzerland (1992–1998)

The ozone records of several monitoring stations in Switzerland from 1992 to 1998 are investigated with respect to the variability observed during regional background conditions, i.e. conditions with little detectable local or regional-scale influences as evident by NO_x and CO concentrations. The sites cover different altitudes between 490 and 3600 m asl. They are characteristic of near-surface conditions, the top of the planetary boundary layer or residual layer, the complex atmosphere in an alpine valley, and the free troposphere. The results reveal a distinctly different ozone variability (diurnal cycles, seasonal cycles, trends) during regional background conditions compared to all days. The estimated annual average ozone concentration under these conditions is between 33 and 50 ppb, dependent on altitude, with a spring maximum and an autumn/winter minimum. Differences in background ozone are found depending on the synoptic weather type. For all sites a positive ozone trend is calculated for background conditions that is larger than for all data. For the latter, the trends appear to be stronger positive for the last 7 years than for the last 11 years.     

On ground truth in cross-border ozone transport

Cross-border transport of ozone is one of the most contentious issues of air pollution management in the U.S. Yet, both the modeling and observational studies are lacking. Models are normally validated by comparing predicted and observed ozone concentrations. However, proper validation of cross-border transport model requires a comparison of predictions against observation-based benchmarks of cross-border ozone transport. Such benchmarks are unavailable, as published observation-based studies always deal only with a combination of local production and cross-border transport, not a cross-border transport itself. We show how to extract necessary benchmarks from observations of rural monitoring sites near state borders. On example of the western border of New York, we find that in about two-thirds of the most polluted days all the ozone came in a steady cross-border inflow after previously passing over one or more large urban areas to the west. In all the enumerated days with direct cross-border inflow, daily maximum 8-hr concentrations of ozone just upwind of the border were over 60 ppb, with an average value of 68 ppb, just short of the 70 ppb ozone regulatory threshold, information also useful to state air pollution authorities.

Implications: The purpose of the cross-border ozone pollution models is to predict cross-border transport of ozone, so the ability of the model to accurately represent observed ozone concentrations is necessary but not sufficient for model validation. The accuracy of predicted ozone concentrations is not necessarily the same as the accuracy of the predictions of ozone transport. Proper model validation requires comparisons against observation-based benchmarks of cross-border transport. Such observations, so far absent, can be obtained from rural monitoring sites near state borders, as illustrated by the example of western New York.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Long-term changes in the tropospheric column ozone from the ozone soundings over Europe

The tropospheric column of ozone is analyzed from the measurements of the vertical profile of ozone by balloon-born ozonesondes. The data base includes ∼16,000 ozone profiles collected above six European stations—three of them have begun the ozonesoundings since 1970. We present a trend analysis (with data up to 2005) focusing on detection of the long-term tropospheric ozone variability over the European network. The ozone time series have been examined separately for each station and season (DJF, MAM, JJA, SON) using a flexible trend model. A trend component of the model is taken as a smooth curve without a priori defined shape. A large increase in the European tropospheric ozone since the beginning of the 1970s (net change of ∼10% in summers and ∼30% in winters) and a kind of stabilization in the early 1990s have been corroborated by the study. This pattern comes from the most extensive data set of ozonesoundings over Hohenpeissenberg and Payern. The decadal differences in the trend pattern between these and other European stations are disclosed. The results of a stepwise regression model using various characteristics of the ozone and temperature profiles as explanatory variables for the tropospheric column ozone (TCO₃) variations show that the ozone changes may be reconstructed using the ozone mixing ratio at 500 hPa, the thermal tropopause (TT) height, and the difference between ozonepause and TT heights. It appears that the last two factors induce 20–30% of the net TCO₃ change over Hohenpeissenberg in the 1970–2004 period.     

Ozone pollution and ozone biomonitoring in European cities. Part I: Ozone concentrations and cumulative exposure indices at urban and suburban sites

In the frame of a European research project on air quality in urban agglomerations, data on ozone concentrations from 23 automated urban and suburban monitoring stations in 11 cities from seven countries were analysed and evaluated. Daily and summer mean and maximum concentrations were computed based on hourly mean values, and cumulative ozone exposure indices (Accumulated exposure Over a Threshold of 40 ppb (AOT40), AOT20) were calculated. The diurnal profiles showed a characteristic pattern in most city centres, with minimum values in the early morning hours, a strong rise during the morning, peak concentrations in the afternoon, and a decline during the night. The widest amplitudes between minimum and maximum values were found in central and southern European cities such as Düsseldorf, Verona, Klagenfurt, Lyon or Barcelona. In the northern European cities of Edinburgh and Copenhagen, by contrast, maximum values were lower and diurnal variation was much smaller. Based on ozone concentrations as well as on cumulative exposure indices, a clear north–south gradient in ozone pollution, with increasing levels from northern and northwestern sites to central and southern European sites, was observed. Only the Spanish cities did not fit this pattern; there, ozone levels were again lower than in central European cities, probably due to the direct influence of strong car traffic emissions. In general, ozone concentrations and cumulative exposure were significantly higher at suburban sites than at urban and traffic-exposed sites. When applying the newly established European Union (EU) Directive on ozone pollution in ambient air, it was demonstrated that the target value for the protection of human health was regularly surpassed at urban as well as suburban sites, particularly in cities in Austria, France, northern Italy and southern Germany. European target values and long-term objectives for the protection of vegetation expressed as AOT40 were also exceeded at many monitoring sites.     

Operational ozone forecasts for the region of Copenhagen by the Danish Meteorological Institute

The Danish Meteorological Institute (DMI) has developed an operational forecasting system for ozone concentrations in the Atmospheric Boundary Layer; this system is called the Danish Atmospheric Chemistry FOrecasting System (DACFOS). At specific sites where real-time ozone concentration measurements are available, a statistical after-treatment of DACFOS’ results adjusts the next 48 h ozone forecasts. This post-processing of DACFOS’ forecasts is based on an adaptive linear regression model using an optimal state estimator algorithm. The regression analysis uses different linear combinations of meteorological parameters (such as temperature, wind speed, surface heat flux and atmospheric boundary layer height) supplied by the Numerical Weather Prediction model DMI-HIRLAM. Several regressions have been tested for six monitoring stations in Denmark and in England, and four of the linear combinations have been selected to be employed in an automatic forecasting system. A statistical study comparing observations and forecasts shows that this system yields higher correlation coefficients as well as smaller biases and RMSE values than DACFOS; the present post-processing thus improves DACFOS’ forecasts. This system has been operational since June 1998 at the DMI's monitoring station in the north of Copenhagen, for which a new ozone forecast is presented every 6 h on the DMI's internet public homepage.     

GARCH modelling in association with FFT–ARIMA to forecast ozone episodes

In operational forecasting of the surface O₃ by statistical modelling, it is customary to assume the O₃ time series to be generated through a homoskedastic process. In the present work, we’ve taken heteroskedasticity of the O₃ time series explicitly into account and have shown how it resulted in O₃ forecasts with improved forecast confidence intervals. Moreover, it also enabled us to make more accurate probability forecasts of ozone episodes in the urban areas. The study has been conducted on daily maximum O₃ time series for four urban sites of two major European cities, Brussels and London. The sites are: Brussels (Molenbeek) (B1), Brussels (PARL.EUROPE) (B2), London (Brent) (L1) and London (Bloomsbury) (L2). Fast Fourier Transform (FFT) has been used to model the periodicities (annual periodicity is especially distinct) exhibited by the time series. The residuals of “actual data subtracted with their corresponding FFT component” exhibited stationarity and have been modelled using ARIMA (Autoregressive Integrated Moving Average) process. The MAPEs (Mean absolute percentage errors) using FFT–ARIMA for one day ahead 100 out of sample forecasts, were obtained as follows: 20%, 17.8%, 19.7% and 23.6% at the sites B1, B2, L1 and L2. The residuals obtained through FFT–ARIMA have been modelled using GARCH (Generalized Autoregressive Conditional Heteroskedastic) process. The conditional standard deviations obtained using GARCH have been used to estimate the improved forecast confidence intervals and to make probability forecasts of ozone episodes. At the sites B1, B2, L1 and L2, 91.3%, 90%, 70.6% and 53.8% of the times probability forecasts of ozone episodes (for one day ahead 30 out of sample) have correctly been made using GARCH as against 82.6%, 80%, 58.8% and 38.4% without GARCH. The incorporation of GARCH also significantly reduced the no. of false alarms raised by the models.     

Increasing interannual and altitudinal ozone mixing ratios in the Catalan Pyrenees

Interannual, seasonal, daily and altitudinal patterns of tropospheric ozone mixing ratios, as well as ozone phytotoxicity and the relationship with NO_x precursors and meteorological variables were monitored in the Central Catalan Pyrenees (Meranges valley and Forest of Guils) over a period of 5 years (2004–2008). Biweekly measurements using Radiello passive samplers were taken along two altitudinal transects comprised of thirteen stations ranging from 1040 to 2300 m a.s.l. Visual symptoms of ozone damage in Bel-W3 tobacco cultivars were evaluated biweekly for the first three years (2004–2006). High ozone mixing ratios, always above forest and vegetation protection AOT40 thresholds, were monitored every year. In the last 14 years, the AOT40 (Apr–Sept.) has increased significantly by 1047 μg m^?3 h per year. Annual means of ozone mixing ratios ranged between 38 and 67 ppb_v (38 and 74 ppb_v during the warm period) at the highest site (2300 m) and increased at a rate of 5.1 ppb_v year^?1. The ozone mixing ratios were also on average 35–38% greater during the warm period and had a characteristic daily pattern with minimum values in the early morning, a rise during the morning and a decline overnight, that was less marked the higher the altitude. Whereas ozone mixing ratios increased significantly with altitude from 35 ppb_v at 1040 m–56 ppb_v at 2300 m (on average for 2004–2007 period), NO₂ mixing ratios decreased with altitude from 5.5 ppb_v at 1040 m–1 ppb_v at 2300 m. The analysis of meteorological variables and NO_x values suggests that the ozone mainly originated from urban areas and was transported to high-mountain sites, remaining aloft in absence of NO. Ozone damage rates increased with altitude in response to increasing O₃ mixing ratios and a possible increase in O₃ uptake due to more favorable microclimatic conditions found at higher altitude, which confirms Bel-W3 as a suitable biomonitor for ozone concentrations during summer time. Compared to the valley-bottom site the annual means of ozone mixing ratios are 37% larger in the higher sites. Thus the AOT40 for the forest and vegetation protection threshold is greatly exceeded at higher sites. This could have substantial effects on plant life at high altitudes in the Pyrenees.     

Modeled response of ozone to electricity generation emissions in the northeastern United States using three sensitivity techniques

Electrical generation units (EGUs) are important sources of nitrogen oxides (NO_x) that contribute to ozone air pollution. A dynamic management system can anticipate high ozone and dispatch EGU generation on a daily basis to attempt to avoid violations, temporarily scaling back or shutting down EGUs that most influence the high ozone while compensating for that generation elsewhere. Here we investigate the contributions of NO_x from individual EGUs to high daily ozone, with the goal of informing the design of a dynamic management system. In particular, we illustrate the use of three sensitivity techniques in air quality models—brute force, decoupled direct method (DDM), and higher-order DDM—to quantify the sensitivity of high ozone to NO_x emissions from 80 individual EGUs. We model two episodes with high ozone in the region around Pittsburgh, PA, on August 4 and 13, 2005, showing that the contribution of 80 EGUs to 8-hr daily maximum ozone ranges from 1 to >5 ppb at particular locations. At these locations and on the two high ozone days, shutting down power plants roughly 1.5 days before the 8-hr ozone violation causes greater ozone reductions than 1 full day before; however, the benefits of shutting down roughly 2 days before the high ozone are modest compared with 1.5 days. Using DDM, we find that six EGUs are responsible for >65% of the total EGU ozone contribution at locations of interest; in some locations, a single EGU is responsible for most of the contribution. Considering ozone sensitivities for all 80 EGUs, DDM performs well compared with a brute-force simulation with a small normalized mean bias (–0.20), while this bias is reduced when using the higher-order DDM (–0.10).

Implications: Dynamic management of electrical generation has the potential to meet daily ozone air quality standards at low cost. We show that dynamic management can be effective at reducing ozone, as EGU contributions are important and as the number of EGUs that contribute to high ozone in a given location is small (<6). For two high ozone days and seven geographic regions, EGUs would best be shut down or their production scaled back roughly 1.5 days before the forecasted exceedance. Including online sensitivity techniques in an air quality forecasting model can provide timely and useful information on which EGUs would be most beneficial to shut down or scale back temporarily.     

Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories. Evaluation of individual model and ensemble forecasts of surface ozone and particulate matter (PM) was performed using data from 119 EPA AIRNow ozone sites and 38 PM sites during a 50-day period in August and September of 2006. From the original set of models, two new bias-corrected model data sets were built, either by applying a simple running mean average to the past 7 days of data or by a Kalman-Filter approach. From the original and two bias-corrected data sets, three ensembles were created by a simple averaging of the seven models. For further improvements three additional weighted model ensembles were created, where individual model weights were calculated using the singular value decomposition method. All six of the ensembles are compared to the individual models and to each other in terms of root mean square error, correlation, and contingency and probabilistic statistics. In most cases, each of the ensembles show improved skill compared to the best of the individual models. The over all best ensemble technique was found to be the combination of Kalman-Filtering and weighted averaging. PM_2.5 aerosol ensembles demonstrated significant improvement gains, mostly because the original model's skill was very low.     

Forecasting Daily Maximum Surface Ozone Concentrations in Brunei Darussalam—An ARIMA Modeling Approach

Abstract

A time series approach using autoregressive integrated moving average (ARIMA) modeling has been used in this study to obtain maximum daily surface ozone (O₃) concentration forecasts. The order of the fitted ARIMA model is found to be (1,0,1) for the surface O₃ data collected at the airport in Brunei Darussalam during the period July 1998-March 1999. The model forecasts of one-day-ahead maximum O₃ concentrations have been found to be reasonably close to the observed concentrations. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Fractional Bias, Normalized Mean Square Error, and Mean Absolute Percentage Error as 0.025, 0.02, and 13.14% respectively.     

Estimating contribution of wildland fires to ambient ozone levels in National Parks in the Sierra Nevada, California

Data from four continuous ozone and weather monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an important source of pollution in these National Parks. Comparisons of forecasted and observed values demonstrated that accurate forecasts of next-day hourly ozone levels may be achieved by using a time series model with historic averages, expected local weather and modeled PM values as explanatory variables. Results on fire smoke influence indicated occurrence of significant increases in average ozone levels with increasing fire activity. The overall effect on diurnal ozone values, however, was small when compared with the amount of variability attributed to sources other than fire.     

Improved space–time forecasting of next day ozone concentrations in the eastern US

There is an urgent need to provide accurate air quality information and forecasts to the general public and environmental health decision-makers. This paper develops a hierarchical space–time model for daily 8-h maximum ozone concentration (O₃) data covering much of the eastern United States. The model combines observed data and forecast output from a computer simulation model known as the Eta Community Multi-scale Air Quality (CMAQ) forecast model in a very flexible, yet computationally fast way, so that the next day forecasts can be computed in real-time operational mode. The model adjusts for spatio-temporal biases in the Eta CMAQ forecasts and avoids a change of support problem often encountered in data fusion settings where real data have been observed at point level monitoring sites, but the forecasts from the computer model are provided at grid cell levels. The model is validated with a large amount of set-aside data and is shown to provide much improved forecasts of daily O₃ concentrations in the eastern United States.     

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.     

Comparison of measured and modeled surface ozone concentrations at two different sites in Europe during the solar eclipse on August 11, 1999

The effects of the solar eclipse on 11 August 1999 on surface ozone at two sites, Thessaloniki, Greece (urban site) and Hohenpeissenberg, Germany (elevated rural site) are investigated in this study and compared with model results. The eclipse offered a unique opportunity to test our understanding of tropospheric ozone chemistry and to investigate with a simple photochemical box model the response of surface ozone to changes of solar radiation during a photolytical perturbation such as the solar eclipse. The surface ozone measurements following the eclipse display a decrease of around 10–15 ppbv at the urban station of Eptapyrgio at Thessaloniki while at Hohenpeissenberg, the actual ozone data do not show any clear effect of eclipse on surface ozone. For Thessaloniki, the model results suggest that solely photochemistry can account for a significant amount of the observed surface ozone decrease during the eclipse but transport effects mask part of the photochemical effect of eclipse on surface ozone. For Hohenpeissenberg, the box model predicted an ozone decrease, due to the eclipse, of about 2 ppbv in relative agreement with the magnitude of the observed ozone decrease from the 2 h moving average while at the same time it inhibits the foreseen diurnal ozone increase. However, this modeled ozone decrease during the eclipse is small compared to the diurnal ozone variability due to transport effects, and hence, transport really masks such relative small changes. The different magnitude of the surface ozone decrease between the two sites indicates mainly the role of the NO_x levels. Measured and modeled NO and NO₂ concentrations at Hohenpeissenberg during the eclipse are also compared and indicate that the partitioning of NO and NO₂ in NO_x is influenced clearly from the eclipse. This is not observed at Thessaloniki due to local NO_x sources.     

European abatement of surface ozone in a global perspective

EU's programme Clean Air for Europe (CAFE) is presently revising the policy on air quality which will lead to the adoption of a thematic strategy on air pollution under the Sixth Environmental Action Programme by mid-2005. For the abatement of surface ozone it is becoming evident that processes outside European control will be crucial for meeting long-term aims and air quality guidelines in Europe in the future. Measurements and modelling results indicate that there is a strong link between climate change and surface ozone. A warmer and dryer European climate is very likely to lead to increased ozone concentrations. Furthermore, increased anthropogenic emissions in developing economies in Asia are likely to raise the hemispheric background level of ozone. A significant increase in the background concentration of ozone has been observed at several sites in Northern Europe although the underlying causes are not settled. The photochemical formation of tropospheric ozone from increased concentrations of methane and CO may also lead to a higher ozone level on a global scale. Gradually, these effects may outweigh the effect of the reduced European ozone precursor emissions. This calls for a global or hemispheric perspective in the revision of the European air quality policy for ozone.     

Air quality in Yanbu,Saudi Arabia

Yanbu, on the Red Sea, is an affluent Saudi Arabian industrial city of modest size. Substantial effort has been spent to balance environmental quality, especially air pollution, and industrial development. We have analyzed six years of observations of criteria pollutants O₃, SO₂, particles (PM_2.5 and PM₁₀) and the known ozone precursors—volatile organic compounds (VOCs) and nitrogen oxides (NO_x). The results suggest frequent VOC-limited conditions in which ozone concentrations increase with decreasing NO_x and with increasing VOCs when NO_x is plentiful. For the remaining circumstances ozone has a complex non-linear relationship with the VOCs. The interactions between these factors at Yanbu cause measurable impacts on air pollution including the weekend effect in which ozone concentrations stay the same or even increase despite significantly lower emissions of the precursors on the weekends. Air pollution was lower during the Eids (al-Fitr and al-Adha), Ramadan and the Hajj periods. During Ramadan, there were substantial night time emissions as the cycle everyday living is almost reversed between night and day. The exceedances of air pollution standards were evaluated using criteria from the U.S. Environmental Protection Agency (EPA), World Health Organization (WHO), the Saudi Presidency of Meteorology and Environment (PME) and the Royal Commission Environmental Regulations (RCER). The latter are stricter standards set just for Yanbu and Jubail. For the fine particles (PM_2.5), an analysis of the winds showed a major impact from desert dust. This effect had to be taken into account but still left many occasions when standards were exceeded. Fewer exceedances were found for SO₂, and fewer still for ozone. The paper presents a comprehensive view of air quality at this isolated desert urban environment.

Implications: Frequent VOC-limited conditions are found at Yanbu in Saudi Arabia that increase ozone pollution if NO_x is are reduced. In this desert environment, increased nightlife produces the highest levels of VOCs and NO_x at night rather than the day. The effects increase during Ramadan. Fine particles peak twice a day—the morning peak is caused by traffic and increases with decreasing wind, potentially representing health concerns, but the larger afternoon peak is caused by the wind, and it increases with increasing wind speeds. These features suggest that exposure to pollutants must be redefined for such an environment.     

The dispersion of the Buncefield oil fire plume: An extreme accident without air quality consequences

The dispersion of pollutants from the huge Buncefield oil depot fire that occurred on 11 December 2005 is simulated using a regional Eulerian chemistry-transport model. We analyse the transport and mixing of the fire plume. We show that the hot plume never reached the ground. Instead, it pierced the thin wintertime boundary layer and was injected into the free troposphere at higher altitudes. This is in agreement with data from many air quality stations. This high injection was fortunate because the fine aerosol particles (PM₁₀) mass column generated by the fire smoke exceeded that of ordinary pollution by an order of magnitude. Our regional chemistry-transport modelling is able to predict the early development of the plume dispersion, as shown by a qualitative comparison between simulated PM₁₀ columns and a satellite image obtained by the EOS-TERRA-MODIS sensor.If the accident had occurred in summer when boundary layers are much deeper and convective, a severe degradation in air quality due to PM₁₀ could have occurred, as shown by a sensitivity simulation assuming a similar fire during one of the hottest days of August 2003. The modelled impact of the fire on regional and European air quality levels strongly depends on the altitude reached by the buoyant plume, as shown by a set of sensitivity simulations with variable injection heights. However, in all cases we found that the fire only affected surface aerosol concentrations without increasing photochemical pollution.