Application of multiple wind-roses to improve the modelling of ground-level ozone in the UK

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Atmospheric Environment 40 (2006) 7480–7493 www.elsevier.com/locate/atmosenv

Application of multiple wind-roses to improve the modelling of ground-level ozone in the UK J. Strong, J.D. Whyatt, C.N. Hewitt Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK Received 27 March 2006; received in revised form 7 July 2006; accepted 10 July 2006

Abstract The Edinburgh Lancaster Model for Ozone (ELMO) has been previously used to predict ground-level ozone concentrations in the UK. Here we make significant improvements to its modelling performance by the application of more representative meteorology. We find that when ELMO is used with a series of distance weighted (DW) wind-roses, distributed across the whole of the UK, it closely predicts the 98th percentile of the annual hourly ozone concentration typical of summertime ozone episodes as validated against observations at 15 rural monitoring sites. There is, however, still some over-prediction of this metric in urban and sub-urban areas impacted by large emissions of NOx. We use ELMO with DW wind-roses to predict the effects of reductions of precursor emissions on ozone concentrations and demonstrate the extent to which the UK Air Quality Standard (AQS) for ozone is likely to be exceeded across the country in 2010. r 2006 Elsevier Ltd. All rights reserved. Keywords: ELMO; Master Chemical Mechanism; Summertime peak ozone; Air Quality Standards

1. Introduction Ozone occurs naturally in the lower atmosphere where it plays an essential role by scavenging some pollutants and greenhouse gases via the formation of the hydroxyl radical. Ground-level ozone concentrations have risen in Europe in response to increases in precursor gas emissions from human activity since the industrial revolution (Guicherit and Roemer, 2000). This is of concern as elevated ozone levels are injurious to human health (e.g. WHO, 2003, 2004), harmful to vegetation including agricultural crops and forestry (e.g. Ashmore, 2005)

Corresponding author.

E-mail address: [email protected] (C.N. Hewitt). 1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.07.007

and potentially damaging to some materials (Weatherley and Timmis, 2001). Ozone is formed in the troposphere by photochemical reactions involving NOx (NO and NO2) and volatile organic compounds (VOCs) in the presence of sunlight. This chemistry is complex and non-linear (for recent reviews see Atkinson, 2000; Jenkin and Clemitshaw, 2000; Monks, 2003; Varotsos et al., 2005). Sillman (1999) describes how the ratio of [NOx] to [VOC] can affect the overall ozone concentration and its persistence, since NO is both an ozone precursor and sink. Areas of high NOx emissions tend to have lower ozone concentrations, since much of the ozone is scavenged by its rapid reaction with NO. Plumes of high ozone concentration form downwind of major precursor emissions if

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meteorological conditions are favourable. These plumes can persist for several days before being depleted by deposition and chemical degradation, or being dispersed to contribute to the atmospheric background burden. Biological effects of ozone can result from acute and chronic exposures at different concentration thresholds. Three cycles of elevated ozone concentrations at ground level have been identified. These are seasonal (e.g. Derwent et al., 1998a; Monks, 2000), weekly (Jenkin et al., 2002) and daily (e.g. UK PORG, 1997) and reflect changes in both anthropogenic and biogenic emission patterns, meteorology and photochemistry. Within the UK, a network of rural ground-level ozone monitoring stations has been established by Government to record ozone concentrations and the 15 operational in 1995 are displayed in Fig. 1(a). Observational data collected in the last 30 years indicate that background concentrations have increased and are predicted to continue rising (Derwent et al., 2006) whereas episodic peak levels are falling (Derwent et al., 2003) in response to reduced precursor emissions. DEFRA have defined an hourly mean concentration of ozone above 50 ppb (100 mg m
3) as ‘moderate air quality’ (DEFRA, 2001) and damage to human health can occur at these levels (e.g. Ponce de Leon et al., 1996; WHO, 2003). The Air Quality Standard (AQS) for the protection of human health in the UK is defined as the daily maximum of the eight hourly running mean not to exceed 50 ppb (100 mg m
3) more than 10 times per year and implemented at the end of 2005 (DETR, 2000). A variety of models have been developed to predict ozone levels, representing specific episodes, background or typical summertime concentrations, over a range of scales. Such models are important for understanding the mechanisms responsible for ozone concentration and assessing the impacts of precursor emission reductions. Two three-dimensional Lagrangian models, STOCHEM (Collins et al., 1997, 2000) and EMEP (EMEP, 2003) cover the British Isles but, because their domains are global and European respectively, they lack sufficient spatial resolution to allow satisfactory evaluation of the fine ground-level scale ozone concentration structures encountered in the UK. Metcalfe et al. (2002) developed and utilised the ELMO model (Edinburgh Lancaster Model for Ozone) to reconstruct 1995 UK ozone levels across the UK at 10 km resolution. Output was compared

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against percentiles of annual observed hourly ozone concentrations recorded at the UK rural ozone monitoring sites. ELMO was also used to investigate the ozone concentrations attributable to UK or non-UK (EMEP) precursor emissions, the effects of the Sillman ratio and to predict possible typical summertime ozone levels assuming implementation of the Gothenburg Protocol (UNECE, 1999). However, as meteorological data recorded at Heathrow Airport were used to construct a single wind-rose which was assumed to be representative of wind patterns across the whole of the country, model performance was compromised. Here we show how the use of meteorological data which is more representative of the entire UK allows ELMO to give better simulations of ground-level ozone. Specifically, this is done by creating series of windroses representing the wind climatology over a range of spatial scales across the UK. 1.1. Model description ELMO has been described in detail by Metcalfe et al. (2002) and its main features are summarised here. ELMO is a receptor-based Lagrangian model with 72 equally spaced straight-line trajectories (51 spacing) arriving at each specified receptor. Air-parcels are advected along individual trajectories towards the receptor at a constant 3.6 m s
1 in 120 s time steps for a period of up to 96 h. At each time step, the parcels pick up emissions from either the underlying EMEP emissions grid (resolved to 50 km) or the UK grid (10 km) and a simplified form of the Master Chemical Mechanism (MCM) (Jenkin et al., 1997; Derwent et al., 1998b) is applied. The chemistry used in this study comprises 17 photochemical and 186 chemical reactions. Instant mixing within the planetary boundary layer (PBL) with uniform depth of 1400 m is assumed at a constant ambient temperature of 300 K. These are typical values associated with ozone episodes. Orography is not considered. The only temporally variable factors are photodissociation reaction rates which are dependent on a daily sunlight intensity of 17.2 h calculated at a latitude of 501N. A receptor point arrival time of 1800 h is assumed. Ozone concentrations at a receptor point are obtained by weighting the ozone present in each air parcel arriving at that receptor point by wind direction frequency determined by the wind-rose. Only summertime ‘ozone days’ are used in the calculation of wind direction frequency. ‘Ozone

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Fig. 1. (a) Locations of rural ozone monitoring sites (1995), meteorological stations and MO district boundaries in the UK. (b) ELMO output weighted by the ‘original’ Heathrow and DW-08 wind-rose configurations against the observed 90th, 95th, 98th and 99th percentiles of annual hourly ozone concentrations at each rural ozone monitoring site.

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Fig. 2. Radar plots of: (a) the ‘original’ Heathrow, (b) E Scotland ‘district’, (c) SW England and S Wales ‘district’ and (d) SE England ‘district’ wind-roses.

days’ are defined as days in which hourly ozone concentrations exceeded 50 ppb (100 mg m
3) at any of the rural ozone monitoring sites between 0000 and 2300 h in 1995 (Fig. 1(a)). A total of 101 ‘ozone days’ occurred between 22 March and 08 October 1995. These days were then matched to a groundlevel wind direction climatology resolved to 101 recorded at Heathrow Airport between 1200 and 1800 h (inclusive) (data from the British Atmospheric Data Centre (BADC)) and the relative

frequencies of each wind direction were then used to construct an average wind-rose for all 1995 ‘ozone days’ (Fig. 2(a)). Emissions inventories used by Metcalfe et al. (2002) include anthropogenic non-methane volatile organic compounds (NMVOCs) undifferentiated by species but apportioned to be representative of 11 abundant compounds, mainly short chained aliphatics. Additional biogenic inventories for isoprene and monoterpenes (treated as pinene) are also

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available for the UK domain (Stewart et al., 2003) and are used in this study. Maritime DMS is also processed chemically in the model. Initial concentrations are set for NO, NO2, CO, CH4, H2 and H2O2 to accepted global background levels, although ozone is set at 30 ppb (60 mg m
3), which is lower than current background estimates of between 35 and 39 ppb (70–78 mg m
3) for this domain (Derwent et al., 2003; Simmonds et al., 2004). Diurnal and seasonal variations in emission rates are not considered. An important characteristic of ELMO is its ability to handle differentiated input emission data which makes it suitable to investigate the relative significance of precursor emissions to the spatial patterns of ozone concentrations. The emission inventories can be scaled by pollutant (e.g. NOx or NMVOC), sector type (e.g. transport) or location and applied to individual sectors across either the UK or EMEP grid domains. Thus, ELMO can aid formulation of national and regional policies. A major disadvantage of ELMO is that it assumes meteorological conditions at Heathrow are representative of the whole of the UK during elevated ozone periods. In SE England, ELMO predicts ozone concentrations which are close to the 98th or 99th percentiles of annual hourly observations for 1995, but for sites further away from Heathrow, the modelled concentrations are closer to the 95th percentiles of the hourly observations (Fig. 1(b)). To improve the consistency of ELMO’s output across the UK, we use three different wind-rose configurations constructed using data from the UK Meteorological Office Numerical Weather Prediction model (UKMO NWP) for 51 UKMO stations across the UK, also shown in Fig. 1(a). 2. Methodology: derivation of wind-roses The derivations of the three wind-rose configurations are explained below and ELMO has been run using each of these. The resultant ozone predictions have been evaluated against hourly observation data at receptor sites, interpolated observation data and the original Metcalfe et al. (2002) modelled results. 2.1. Derivation of ‘nearest wind-roses’ For each of the 15 ozone monitoring sites, the number of ‘ozone days’ was determined and a separate wind-rose constructed for each site using

the data from the nearest UK Meteorological Office (MO) station. As some sites only recorded a low number of ‘ozone days’, wind-roses derived from directional data recorded between 1200–1800 h produced ‘spiky’ wind-roses and were considered unrepresentative. Using these to weight incoming trajectories could cause disproportional ozone contributions from some trajectories. However, by extending the hours used from each ‘ozone day’ to cover the full 24 h (0000–2300 h), this anomaly was removed. Subsequent wind-roses also use 24 h wind data. The ‘nearest wind-roses’ are not illustrated here. 2.2. Derivation of ‘district wind-roses’ The UK is divided into 10 MO districts (depicted in Fig. 1(a)) partially based on climatological conditions and a wind-rose has been constructed for each. ‘Ozone days’ were determined for each district from the ozone monitoring sites falling within the district boundaries and then wind-roses based on each district-bound MO station were constructed. Figs. 2(b)–(d) illustrate ‘district windroses’ for E Scotland, SW England and S Wales and SE England districts respectively, and their shapes describe the prevailing wind directions when an anticyclone is positioned over Western Europe, as would be expected on ‘ozone days’ since these meteorological conditions are favourable for elevated ozone in the UK. 2.3. Derivation of ‘distance weighted (DW) windroses’ Unique wind-roses were constructed for each ozone monitoring site by using wind meteorology from a variable number of MO stations. Wind-roses for each MO station were prepared using ‘ozone days’ derived from the nearest ozone monitoring site. The proportional influence of a MO station’s wind-rose on a receptor site was calculated by using the inverse square of the distance to the receptor site. Participating MO stations were determined by proximity to the receptor site and are described as DW-xx where xx indicates the number of contributing MO stations. Distance-weighted windroses were initially derived for each of the 15 rural ozone monitoring sites and subsequently generated for the centre point of 3012  10 km grid cells across of the UK. These are not illustrated here.

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2.4. Implementation of ELMO In this study we ran ELMO using: (i) 1995 emissions data and corresponding meteorology with four different wind-rose types as described above, (ii) 1999 and 2002 emissions data and corresponding meteorology with the four different wind-rose types and (iii) projected 2010 emissions data and 1995 meteorology with two wind-rose types. All other constants remained unaltered. 3. Results ELMO was run with 1995 emissions and corresponding meteorological data to the 15 rural ozone monitoring sites using four different wind-rose sets (Heathrow, as in the original ELMO work; ‘nearest’; ‘district’; and ‘distance weighted’ (DW)), to predict typical summer episodic ozone concentrations at these sites. Fig. 1(b) shows comparison of the ‘original’ and DW-08 weighted ELMO predictions against the 95th, 98th and 99th percentiles of annual observed ozone concentrations. These percentiles were chosen as the metrics for comparing model output with observations, as previously in Metcalfe et al. (2002). To assess the performance, or goodness-of-fit, of modelled data against observations, statistical

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measures recommended by Legates and McCabe (1999) were utilised. Commonly, the coefficient of determination (r2) is used to judge model performance, but correlation-based measures are often inappropriate since a model may systematically over- or under-estimate observations but still yield good correlation. Wilmott (1981, 1982) and Wilmott et al. (1985) recommend the index of agreement (IoA) and error indices (EI) as alternate forms of assessment. The IoA quantifies the error contained within the modelled prediction and can be expressed in two forms, d1 and d2, where d1 is based on simple differences between observed and modelled data, whereas d2 uses squared differences and is therefore more sensitive to outliers. Both have a range between 0 and 1 where 1 represents a perfect agreement. The EI are the mean bias error (MBE) which describes the under- or over-prediction of the model, the mean absolute error (MAE) and the root mean-square error (RMSE), which describe the residual error and are expressed in the same units as the measurement. This approach to atmospheric model assessment has been applied previously, e.g. Sturman and Zawar-Reza (2002) and Gardner and Dorling (2000). Table 1 presents the IoA and EI statistics calculated for ELMO output for 1995 weighted by the various wind-roses configurations compared

Table 1 Error indices (EI) and index of agreement (IoA) for ELMO output weighted by different wind-rose configurations (1995 emissions and meteorology) Percentile

Wind-rose type Original (Heathrow)

Nearest

District

DW-05

DW-08

DW-20

Mean bias error (MBE)

99 98 95


14.7
5.1 8.9


5.8 3.8 17.8


7.0 2.6 16.6


6.4 3.2 17.2


6.5 3.1 17.1


6.9 2.7 16.7

Mean absolute error (MAE)

99 98 95

14.7 9.0 10.8

7.1 6.4 17.8

8.0 5.6 16.6

7.8 6.1 17.2

7.9 6.0 17.1

8.2 6.3 16.7

Root mean- square error (RMSE)

99 98 95

16.7 10.1 13.4

8.7 8.1 19.3

9.7 7.1 17.7

9.2 7.9 18.9

9.3 7.8 18.8

9.7 7.8 18.5

Index of agreement (IoA, d2)

99 98 95

0.64 0.78 0.49

0.82 0.82 0.37

0.76 0.83 0.40

0.80 0.83 0.38

0.80 0.84 0.39

0.79 0.83 0.39

Modified index of agreement (IoA, d1)

99 98 95

0.39 0.51 0.38

0.58 0.63 0.21

0.52 0.64 0.22

0.54 0.65 0.38

0.54 0.65 0.22

0.52 0.64 0.39

Note: IoA values range between 0 and 1, where 1 represents a perfect fit. EI values are expressed as ppb.

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against the 95th, 98th and 99th percentiles of the hourly average observations of ozone from the 15 rural monitoring sites. Results for the ELMO predictions using 1999 and 2002 emissions and meteorology are not shown.

4. Discussion 4.1. Evaluation of ELMO output against observation sites, 1995 The highest IoA values are obtained against the observed 98th percentile of hourly concentrations by all wind-rose weightings, although the ‘original’ ELMO wind-rose weighting does not perform as well as the other model derivations when compared to this metric. The use of the ‘nearest’ wind-rose generally gives poorer agreement with observations than either the ‘district’ or the DW wind-rose implementations (Table 1). The distance from a MO station to an ozone monitoring site varies considerably, from 0.3 km at Eskdalemuir to 110 km at Lough Navar (see Fig. 1(a)). Clearly, more distant MO stations are less representative of the meteorological conditions at an ozone monitoring site than stations close by. Also, the association of a MO station to its nearest monitoring site is not necessarily appropriate as their geographical influences may be significantly different. MO districts are based on the annual climate but ozone episodes tend to occur in the summer. Therefore districts may not be appropriate divisions when considering ozone. Additionally, MO stations and ozone monitoring sites are not uniformly distributed so the resultant wind-roses are likely to be inconsistent close to district boundaries. Despite this, evaluating the IoA and EI statistics suggest district weighted output performance is comparable with some of the best DW results. Using the 98th percentile of hourly observations as a reference, the best model results are achieved by DW wind-rose weightings using wind direction data from 5 to 20 MO stations (DW-05–DW-20). According to most of the IoA and EI statistics, the most favourable is the DW wind-rose configuration using eight MO stations (DW-08). Comparison of the respective means of the modelled (65 ppb or 130 mg m
3) and observed (62 ppb or 124 mg m
3) ozone concentrations indicate that when ELMO is weighted by the DW-08 wind-roses, it predicts

ozone concentrations just above the 98th percentile of the hourly observed data. Performance deteriorates when less than three MO stations are used to build a DW wind-rose for similar reasons described for the ‘nearest’ weightings. Increasing the number of contributing MO stations improves the DW wind-rose accuracy by incorporating wind direction characteristics from other nearby MO stations. When more than about 20 MO stations are integrated, performance declines.

4.2. ELMO output compared with the 98th percentile of observations at each monitoring site Figs. 3(a) and (b) show the under- and overpredictions using the ‘original’ (Heathrow windrose) ELMO and the DW-08 weightings against the 98th percentile of hourly observations, respectively. Generally, the original ELMO weighting underpredicted the 98th percentile value at most sites by up to 20 ppb (40 mg m
3); Glazebury, Bottesford and Sibton are over-predicted by between 7 or 8 ppb (14 or 16 mg m
3) and Ladybower by 3 ppb (6 mg m
3). When ELMO output is weighted by the DW-08 wind-rose configuration, these four sites are again the most over-predicted. Glazebury, Bottesford and Ladybower were classified as ‘rural’ sites by UK PORG (1997) and NEGTAP (2001), but DETR (DETR, 1998) reclassifies Glazebury and Bottesford as ‘sub-urban’ while the Ladybower site is located between the Manchester and Sheffield conurbations. It is likely that ozone concentrations at these sites are depressed below the ELMO predicted values by enhanced local emissions of NOx. Sibton is overpredicted with both the original and DW-08 weightings and here the effects of emissions and ozone plumes generated over continental Europe are important during ozone episodes. Because of the coarser resolution of the EMEP emission inventories, accurate modelling of ozone concentrations is more difficult to attain over the EMEP domain and these inaccuracies are extended to UK locations close to the continent. Observational data for Bush are incomplete for July and August 1995 when the rest of the UK experienced episodes of high ozone levels. Consequently, the percentiles of hourly observations are lowered, giving an apparent over-prediction of ozone concentration of 7 ppb (14 mg m
3) when the DW-08 weighting is used.

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Fig. 3. Comparison of over- and under-estimates of ELMO modelled concentrations weighted by (a) the ‘original’ Heathrow and (b) the DW-08 wind-rose configurations relative to the 98th observed percentile of observed annual hourly ozone concentrations.

With the exception of the sites discussed above, ELMO output weighted by DW-08 predicted all ozone sites to within 5 ppb (10 mg m
3) of their respective 98th observed hourly percentiles. Recalculating the IoA and EI statistics without these five anomalous sites improves model prediction to this percentile. However, care must be taken in interpretation as the sample size is reduced to 10. The MO stations used in this study are all land-based and receptor sites close to the coast do not have wind data from their seaward side included in their wind-roses. This does not appear to be a major factor as both the South Coast sites, Lullington Heath and Yarner Wood, are each only underpredicted by 5 ppb (10 mg m
3). 4.3. ELMO extended to the UK grid domain We have demonstrated that ELMO output weighted by the DW-08 wind-roses gives a good prediction of the 98th percentile of hourly observed data at the 15 rural sites and can therefore be applied confidently across the UK, even though there is the potential for local over-estimation of ozone concentration in areas of high NOx emissions due to ozone titration. We now consider when ELMO is run to a grid of equally spaced receptor points covering the whole of

the UK. The results using the original wind-rose for 3012 receptor points (spaced at 10 km) have been published by Metcalfe et al. (2002) and are reproduced in Fig. 4(a). The concentration structure is concentrically arranged over SE England although there is a noticeable ridge running up the Pennines resulting from the southerly arm of the ‘original’ Heathrow wind-rose (see Fig. 2(a)). There are also several areas of lower ozone concentrations and these coincide with areas of high NOx emissions, the most prominent being over London. Other conurbations are also apparent and small areas where ozone is depressed coincide with large point sources (e.g. power stations). Little concentration structure is apparent over Scotland and Northern Ireland. Fig. 4(b) shows modelled ozone concentrations obtained by running ELMO with DW-08 weightings to the same 3012 receptors. The south-east northwest gradient is still apparent, but the concentric feature is replaced by a series of high ozone concentration plumes downwind of areas of high emissions from both continental Europe and UK conurbations, notably in Kent, the Thames Valley, the NW Midlands and NW England. The plume over the Pennines is augmented and extends northwards into the NE Scotland where it is interrupted and reinforced by emissions from central Scotland.

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Fig. 4. Maps of the UK showing predicted ozone concentrations: (a) ELMO using 1995 emissions and ‘original’ wind-rose weighting, (b) ELMO using 1995 emissions and DW-08 wind-rose weighting, (c) interpolation of the 98th percentile of observed 1995 ozone concentrations with NOx correction and (d) ELMO using NECD target emissions and DW-08 wind-rose weighting for 1995.

One striking difference between the original implementation of ELMO (Fig. 4(a)) and ELMO using DW-08 meteorology (Fig. 4(b)) is the occurrence of fine structure in the London ozone plume predicted by the latter. The Harwell monitoring site can be

used to assess the representativeness of concentrations in the area of predicted high ozone levels down wind (west) of London; ELMO DW-08 gives a slight over-prediction (5 ppb or 10 mg m
3) of the 98th percentile observed value at this site.

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Fig. 4(c) depicts observed ozone concentrations interpolated across the UK at a spatial resolution of 10 km. This is derived from the interpolation of the 98th percentile of hourly observations in 1995 from the 15 rural monitoring sites using methods similar to those described in UK PORG (1997), NEGTAP (2001) and Coyle et al. (2002). To account for the scavenging effects of NOx, ozone concentrations have been reduced by the estimated NOx emissions from each respective grid square (1996 data) factored by 0.006. S England still shows the highest levels of ozone and these decrease northwards. However, there is a band of lower ozone concentration running across Central England caused by the interpolation of low ozone levels observed at Glazebury and Bottesford. Detailed ozone structures are only apparent close to observation sites or areas of high NOx emissions. Thus, S Wales, SW England, Scotland and Northern Ireland show little variation in ozone concentrations. It is not possible to validate or assess the accuracy of the interpolated representation of the 98th percentile of hourly ozone concentrations shown in Fig. 4(c) since no independent observations are available for rural areas; the interpolation uses data from all the rural ozone monitoring sites. There are clearly significant differences between the interpolated ozone observations (Fig. 4(c)) and the ELMO DW-08 predictions (Fig. 4(b)). The most striking difference between the two occurs in parts of NW England where the interpolated observations predict 98th percentile values as less than 55 ppb (110 mg m
3), but ELMO DW-08 predicts 98th percentile values greater then 75 ppb (150 mg m
3). As discussed above, ELMO DW-08 over-predicts the 98th percentile ozone value in areas of high NOx emissions. While it is clear that this weighting of ELMO output will normally over-predict the 98th percentile value in urban areas for this reason, this may be extended to semi-rural or sub-urban areas with relatively high NOx emissions such as much of central England, where ozone production is VOC limited. 4.4. ELMO applied to 1999 and 2002 To test the robustness of the new wind-rose implementation, ELMO has been run using similar emissions inventories to 1995 for 1999 and 2002 (where available) (NAEI; Vestreng et al., 2005) with a range of wind-rose weightings derived for the corresponding year. Modelling the ozone climatol-

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ogy encountered in 1999 and 2002 is more difficult as they are both considered to be ‘lower’ ozone years with fewer episodes and lower ozone maxima than 1995. The overall mean of the observed hourly 98th percentiles for all 15 sites is 54 and 49 ppb (108 and 98 mg m
3) for 1999 and 2002, respectively, compared with 61 ppb (122 mg m
3) for 1995. ELMO output weighted by a single wind-rose derived from the relevant year’s meteorology at Heathrow fails to predict the 98th percentile of hourly observations as accurately as all other weightings for both years and less decisively than for 1995 (results not shown). For 1999, the percentile best modelled by ELMO DW-08 is the 98th percentile with over-predictions for Glazebury, Ladybower, Bottesford and Sibton similar to those discussed for 1995 previously. For 2002, ELMO DW-08 predictions were closest to the 99th percentile of hourly observations. For both years (1999 and 2002), the weightings with the most consistently accurate predictions were DW-03–DW20, although no one wind-rose configuration was superior on all statistical measures. ELMO is configured to model ozone more efficiently in conditions favourable for ozone generation so these findings are not unexpected. 4.5. Modelling the effects of future emission requirements Future emission targets for Europe for SO2, NOx, NH3 and NMVOCs have been agreed for 2010 under the Gothenburg Protocol (UNECE, 1999) and the National Emissions Ceiling Directive (NECD) (EC, 2001). Here, we have used ELMO to model the potential UK ozone climatology for 2010 using both the 1995 ‘original’ (Heathrow) and DW-08 wind-rose weightings and the target NECD emissions. The ozone concentrations predicted using the DW-08 weightings are shown in Fig. 4(d). It is apparent that the proposed reductions in precursor emissions will lead to a reduction in peak (98th percentile) ozone concentrations over the whole of the UK. The UK Government has set an AQS objective for ozone to protect human health from 2005 (the maximum daily eight hourly running mean of 50 ppb (100 mg m
3) not to be exceeded more than 10 times per year). For all rural and non-rural ozone monitoring sites operational in 1995, the 98th percentile of hourly observations have been plotted against the number of exceedances of the AQS (Fig. 5) to give a coefficient of determination of

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Fig. 5. Relationship between number exceedance days of the UK AQS for ozone against 98th percentile of hourly observed ozone concentrations (1995 data) from rural and urban/sub-urban monitoring sites. (Rural sites are labelled.)

r2 ¼ 0.96. The regression equation was used to convert the modelled concentrations to AQS exceedances for 2010. Figs. 6(a) and (b) show the predicted number of AQS exceedances derived from using ELMO with the ‘original’ 1995 Heathrow wind-rose and the 1995 DW-08 wind-rose weightings respectively. A clear limitation of these predictions is the use of 1995, rather than 2010, meteorology. Nevertheless, it can be seen that ELMO output weighted by the ‘original’ Heathrow wind-rose predicts fewer exceedances of the AQS than does ELMO using the DW08 wind-rose weighting over the country. Previously, ELMO predicted that only southern and central England and E Wales would have more than 20 days exceedances of the AQS in 2010 (Fig. 6(a)). Using the DW-08 wind-rose configurations, ELMO now predicts that all of England and Wales will have greater than 20 days exceedances and most of Scotland will experience greater than 10 days of exceedances (Fig. 6(b)). 5. Conclusions ELMO has been previously used to model the spatial distribution of ground-level peak ozone

concentrations over the UK. However, reliance on a single wind-rose from Heathrow led to variations in performance across the country relative to observed data. The availability of meteorological data covering the whole of the UK has led to the development of a more realistic representation of wind directions encountered during ozone episodes associated with anticyclonic conditions by constructing individual DW wind-roses for each receptor point. Using these wind-rose configurations, we have calculated ozone concentrations for 1995 and these have been shown to be close to the 98th percentile of hourly observations at the 15 rural ozone monitoring sites operational in 1995. The best fit between ELMO predictions and the 98th percentile observations occurred when DW windroses using data from eight MO stations were used (DW-08). The revised ozone output using ELMO DW-08 indicated that the original ELMO weighting previously under-estimated ozone over most of the UK especially at locations outside SE England, by between 10–20 ppb (20–40 mg m
3). Importantly, this also led to an over-estimate of the benefits of future emission reductions.

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Fig. 6. Number of exceedance days of the UK AQS standard (1995) after the implementation of the NECD emission targets for 2010 predicted by ELMO using: (a) the ‘original’ and (b) DW-08 wind-rose weightings.

Despite these improvements to ELMO, the ozone concentrations predicted in plumes downwind from areas of high emissions cannot be validated due to the shortage of observational data. When extended to the whole of the UK, ELMO DW-08 predicts ozone concentrations that we believe give a good representation of the 98th percentile of hourly values, except in areas significantly impacted by high NOx emissions. The potential effects of climate change on ground-level ozone concentrations are complex since commitant changes may occur to mechanisms involving precursor emissions, ozone chemistry and meteorology. The MCM, upon which ELMO’s chemistry is based, has been developed to be representative of conditions encountered in NorthWest Europe during the latter part of the 20th century (Jenkin and Clemitshaw, 2000). Stohl et al. (1996) describe increased NO emission rates from soil with rising temperature, and biogenic NMVOC emissions are similarly dependent on climatic parameters, e.g. Stewart et al. (2003) and Utembe et al. (2005). Human behaviour in response to climate change is likely to influence rates of anthropogenic ozone precursor emissions (IPCC, 2001). Hulme et al. (2002) and Stott et al. (2004) estimate the conditions favourable for ozone

formation will become more common in Europe by the middle of this century. As has been shown in this study, wind direction frequency is an important factor in determining spatial ozone concentration patterns. Climate change (IPCC, 2001) and the North Atlantic Oscillation (Trigo et al., 2002) will also influence tropospheric synoptic weather patterns, meteorological conditions and hence the wind direction frequencies and speeds. Each of these factors taken individually will not necessarily cause increased ozone concentrations, and considered together will result in many interactions. Other sources of ozone not originating from North-West Europe need to be considered, e.g. changes to the rate of stratosphere–troposphere exchange and a global increase in background ozone concentrations. Future development of ELMO’s modular structure will allow it to incorporate the potential effects of climate change on air quality in the UK. Acknowledgements We are grateful for advice and comments provided by Dick Derwent (RGD Scientific) and Sarah Metcalfe (University of Nottingham), Derek Ryall (Meteorological Office) for supplying meteorological data and Mhairi Coyle (CEH Edinburgh)

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for providing the UK interpolated ozone concentrations. We also thank Simon Chew (Lancaster University) for cartographic work and the Geography Department, Lancaster University and the Marie Curie RTN ‘ISONET’ for partial funding.

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