- Email: [email protected]
Meteorologically adjusted trends in UK daily maximum surface ozone concentrations
Atmospheric Environment 34 (2000) 171}176
Meteorologically adjusted trends in UK daily maximum surface ozone concentrations M.W. Gardner, S.R. Dorling* School of Environmental Sciences, University of East Anglia, Norwich, UK Received 13 April 1999; accepted 21 July 1999
Abstract A methodology to meteorologically adjust daily UK surface ozone data is presented which reveals the impact of longer-term variations in precursor emissions more clearly. Based on this approach a general site-dependant decline in meteorologically adjusted summer daily maximum ozone concentrations has occurred between 1994 and 1998 and is between 0.7 and 2.3 ppb yr~1. ( 1999 Elsevier Science Ltd. All rights reserved. Keywords: Precursor emissions; Emission reduction policies; Background trace gas concentrations; Arti"cial neural network
1. Introduction Current and proposed European Union directives aim to reduce ozone concentrations through reduction in emissions of nitrogen oxides and volatile organic compounds. Through national programmes such directives will ultimately impact "nancially and operationally on polluters; quantifying the e!ectiveness of the policies is therefore important. Identifying long-term trends in surface ozone concentrations is confounded by meteorological variability at a range of timescales. The interannual meteorologically driven #uctuations are typically much larger than any trends in concentrations due to precursor emission reduction policies, thereby hindering attempts to assess their e!ectiveness (PORG, 1997). Analysis of 1986}1991 data from the UK ozone monitoring network by Dollard et al. (1995) indicated an increase (decrease) in annual mean concentrations at rural (urban) sites. Analysis of 1986}1995 data from UK sites (PORG, 1997) indicated that either no change or a small positive trend (average#0.5 ppb yr~1) in annual mean ozone concentrations occurred at most rural sites. * Corresponding author. Fax: #441603-507719 E-mail address: [email protected] (S.R. Dorling)
These analyses did not account for meteorological variability and the latter demonstrated, for example at Eskdalemuir (a rural site in southern Scotland), that an apparent linear trend of #0.59 ppb yr~1 between 1986 and 1995 was not observed when the years 1987 and 1988 (`low ozone yearsa) were removed from the analysis. One attempt to address the impact of meteorology on surface ozone concentrations was made by Simmonds et al. (1997) by classifying days as either being `backgrounda or `polluteda depending on the air mass origin at Mace Head (Ireland) over the period 1987}1995. A downward trend in the `polluteda data of !0.39 ppb yr~1 was tentatively attributed to a combination of decreasing European carbon monoxide, nitric oxide and non-methane volatile organic compound emissions. Simpson et al. (1997) described the application of a photochemical oxidant model to investigate the relative e!ects of meteorological variability, emissions and background concentration changes on surface ozone concentrations for one model grid point in Europe over the period 1985}1995. The results indicated that meteorological variability was almost entirely responsible for the observed trend in ozone. This work also indicated that mean summer ozone concentrations could vary by up to 12 ppb due to meteorological interannual variability.
1352-2310/99/$ - see front matter ( 1999 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 3 1 5 - 5
172
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
Historical data indicated that it would take 30 yr in order to observe a reduction in ozone concentrations of more than 12 ppb that could be attributed to precursor emission changes. The study concludes It follows that long-term monitoring networks are essential if trends due to emission changes are to be detected. Models and statistical analysis will also be required to disentangle the various factors contributing to measured trends. Recently, a new technique which utilises multilayer perceptron (MLP) neural network models has been developed. The technique, herein refered to as the MLP methodology, was originally developed and applied to identify trends in daily maximum ozone data from the US (Gardner, 1999; Gardner and Dorling, 1999a). The MLP methodology was shown to remove more of the meteorological variability than an alternative technique (based on time series "lters and regression models) which had been previously applied to the same data (Milanchus et al., 1998). In this paper the MLP methodology is used to meteorologically adjust UK ozone data, to investigate whether any discernible temporal and spatial trends have occurred in response to changing precursor emissions.
2. Data Time series of daily maximum ozone concentrations, from the Department of the Environment, Transport and the Regions (DETR) automatic air quality monitoring network (http://www.aeat.co.uk/netcen/airqual/), and concurrent daily meteorological data, from the British Atmospheric Data Centre (http://www.badc.rl.ac.uk/), were analysed for "ve UK `rurala sites over the period 1979}1997. Rural sites were selected based on their relatively long length of record and with the expectation that nationwide, non-local trends would be more clearly revealed by avoiding the confounding e!ect of local urban vehicular NO emissions. x In all cases the nearest climate station to the ozone monitoring station was selected. In some instances the nearest meteorological station was a considerable distance from the ozone monitoring station (up to 50 km) however they were included to increase the number of sites analysed. Sites were chosen which measured all the required meteorological variables: daily maximum temperature, total daily sunshine (a surrogate for daily maximum solar radiation which was unavailable), mean daily wind speed, vapour pressure and total cloud cover. These variables were chosen from those available based on previous experience (Gardner, 1999). Five pairs of ozone and meteorological stations were found to conform to the selection criteria (Harwell and Wallingford, 12 km distant; Lullington Heath and Herstmonceux, 17 km;
Sibton and Hemsby, 50 km; Yarner Wood, sites co-located; Eskdalemuir, sites co-located). The location of the ozone monitoring sites is shown at http://www.aeat.co.uk/netcen/airqual/networks/o3map. html. Time of year was also used as an ozone predictor variable and was presented to the network as sin(2pd/365) and cos(2pd/365), where d is the Julian day number.
3. MLP methodology The MLP methodology is fully described in both Gardner (1999) and Gardner and Dorling (1999a) and hence only a brief outline will be provided here. In the same manner as noted by Rao and Zurbenko (1994) the technique supposes that a time series of ozone O(t) can be expressed as the sum of a long-term e(t), a seasonal S(t) and short-term =(t) component, O(t)"e(t)#S(t)#=(t). The long-term component represents changes in ozone due to climate or precursor emission changes and also variations in the background concentrations of some related tropospheric trace gases. The seasonal component corresponds to the annual cycle in solar radiation and vertical transport of ozone from the stratosphere, whilst the short-term component is associated with the day-to-day variations in weather. The MLP methodology involved the development of a statistical model of daily maximum ozone based on meteorological and seasonal predictor variables described above. The residuals from such a model will be strongly related to changes in ozone concentrations due to changing precursor emissions (and meteorological variables not considered) and will be refered to hereafter as the meteorologically adjusted ozone. MLP neural network models were used since they can represent complex functional non-linear relationships with arbitrary interactions amongst predictor variables. The application of MLP neural networks within the atmospheric sciences is reviewed by Gardner and Dorling (1998). MLP models have been successfully applied to the modelling of primary and secondary pollutants in a range of urban and rural settings (Gardner, 1999; Gardner and Dorling, 1999b). MLP models include as a special case all forms of regression model and o!er a more #exible framework for the development of statistical models. Recently generalized additive models (GAMs) have been used to meteorologically adjust US ozone data (Holland et al., 1999). The use of GAMs recognises the fact that the relationship between meteorology and ozone is non-linear. However such models do not allow interactions between the predictors and are therefore less #exible than MLP models.
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
The MLP models were trained to learn the relationship between the meteorological and seasonal predictors and daily maximum ozone concentrations using data taken from all but one year of the time series. The remaining year was used for validation during training in order to protect against over"tting. Full details concerning the training of the MLP models are provided in Gardner (1999) and Gardner and Dorling (1999a). Following training, the model residuals were calculated and interpreted as the meteorologically adjusted daily maximum ozone concentrations. Since the model residuals were expected to contain some remaining variability at both the short-term and seasonal time scales, due to meteorological variables not considered, measurement error or noise, the residuals were smoothed using a Kolmogorov}Zurbenko (KZ) "lter (Eskridge et al., 1997). A KZ "lter involves the multiple application of a moving average "lter and can be used to remove high frequency variability from data. Here a KZ(365,3) "lter was used, with a window size of 365 d applied 3 times, resulting in a cut-o! frequency of 3.8 yr. KZ "lters were used in a similar manner by Milanchus et al. (1998).
173
4. Results and discussion Table 1 lists the performance of the MLP models developed on the data from each site. Bootstrap estimates of standard error (at the 95% con"dence level) were calculated by randomly sampling the test data, with replacement, 1000 times. In the case of Yarner Wood, the apparent poor performance over the validation data is probably due to the large amount of missing data during the validation period, resulting in a small data set mostly covering late summer and winter. Over the entire training data period the models have captured between 52% and 70% of the variability in the daily maximum ozone concentrations. The lower explained variance at Sibton is most likely due to the larger distance between the meteorological station and the ozone monitoring site. The RMSE for the models varies between 7.3 ppb at Eskdalemuir and 12.2 ppb at Sibton, also demonstrating the e!ect of distance between the meteorological and ozone monitoring sites. Fig. 1a shows the meteorologically adjusted and unadjusted long-term components of the daily maximum ozone time series at each site. The unadjusted component
Table 1 The performance of the MLP models for each site over (a) the training data period and (b) the validation data period Eskdalemuir
Harwell
Lullington Heath
Sibton
Yarner Wood
(a) Performance on training data o6 34.61(0.36) MBE !0.38(0.24) s 11.13(0.46) 0 s 8.29(0.32) 1 RMSE 7.31(0.27) R2 0.57(0.03)
37.63(0.48) 0.73(0.29) 17.01(0.54) 12.76(0.39) 10.42(0.33) 0.63(0.03)
40.76(0.57) !0.01(0.31) 17.09(0.72) 14.23(0.58) 9.32(0.33) 0.70(0.02)
36.36(0.46) !0.42(0.33) 17.45(0.67) 12.28(0.35) 12.15(0.56) 0.52(0.03)
39.31(0.51) !0.22(0.32) 15.10(0.70) 11.80(0.58) 9.43(0.42) 0.61(0.03)
(b) Performance on validation data o6 29.17(1.28) MBE !5.47(0.76) s 10.72(2.42) 0 s 7.56(1.43) 1 RMSE 8.32(0.80) R2 0.67(0.13)
52.35(3.59) 5.66(2.14) 20.05(4.21) 13.87(2.47) 13.58(2.86) 0.63(0.13)
38.06(1.38) !0.14(0.86) 13.52(1.56) 9.48(0.93) 8.40(0.83) 0.62(0.07)
32.73(2.29) !5.71(1.42) 16.05(2.22) 11.30(1.50) 11.11(1.12) 0.66(0.10)
29.70(1.52) !5.62(1.35) 9.76(2.95) 7.69(1.61) 10.34(1.24) 0.27(0.20)
(c) Trends 1994}1998 Met-Adj !0.8$0.0
!0.7$0.0
!0.7$0.0
!2.3$0.0
!0.7$0.0
(d) Trends 1989}1994 Mon-95% !0.8$1.0 Met-Adj #0.2$0.0
!0.8$1.6 #1.3$0.1
!2.1$1.5 #0.0$0.0
#0.1$1.0 #0.3$0.0
!2.3$1.3 !0.2$0.0
Note: The lower portion of the table lists (c) the trend in 1994}1998 meteorologically adjusted summer ozone (met-adj) concentrations derived here ($2]standard error) and (d) the trend (ppb yr~1) in 1989}1994 monthly 95th percentile ozone concentrations (mon-95%) described in PORG (1997) and also the trend in 1989}1994 meteorologically adjusted summer ozone (met-adj) derived here ($2]standard error). o6 } mean observed concentration (ppb). MBE } mean bias error (ppb). s , s } observed and predicted 0 1 concentration variance (ppb). RMSE } root mean squared residual error (ppb). R2 } percentage of the variance in the observed data explained by the model. Bootstrap estimates of standard error at the 95% con"dence level listed in brackets.
174
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
Fig. 1. The long-term component of (a) the meteorologically adjusted daily maximum ozone at each site (black) and the unadjusted ozone (grey) and (b) the meteorologically adjusted daily maximum summer season ozone at each site (black) and the unadjusted ozone (grey).
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
was obtained by applying a KZ(365,3) "lter directly to the raw ozone data and subtracting the mean. This plot shows that the adjusted and unadjusted long-term components for each site are di!erent and hence the residuals from the MLP models are not simply reproducing the long-term mean concentrations. At all sites except Sibton the unadjusted ozone concentrations were higher in 1990 than the meteorologically adjusted ozone concentrations, suggesting that this peak was indeed due to the exceptionally warm summer in that year (PORG, 1997). Similarly in 1993, at all sites except Eskdalemuir and Lullington Heath, the unadjusted concentrations were lower than the meteorologically adjusted ozone concentrations. The pro"les for Sibton, Harwell and Yarner Wood indicate that the 1986 and/or 1987 `low ozonea years were not only due to meteorological e!ects. Even when the dominant meteorological signal has been removed from the ozone time series the adjusted concentrations are still low suggesting that the combined e!ect of both meteorology and lower precursor emissions, or background concentrations, could have been contributing to lower ozone concentrations during this period. Between 1990 and 1997 the meteorologically adjusted ozone concentrations show a general increase and then a slight downward trend at Yarner Wood, Harwell and Sibton. However at Eskdalemuir and Lullington Heath the meteorologically adjusted ozone concentrations remain essentially unchanged. Fig. 1b shows the meteorologically adjusted and unadjusted long-term component of the summer (April} September) ozone time series at each site. Trends in ozone concentrations due to changes in precursor concentrations should be more evident in the summer months since this is the period when photochemical episodes are most prevalent. Of most interest is the change in meteorologically adjusted summer ozone concentrations between 1990 and 1997. At all sites, the concentrations are seen to increase or remain constant until 1994/1995. After this period there is evidence of a slight downward trend in the concentrations at all sites. The downward trend is most marked at Sibton and least evident at Eskdalemuir, perhaps a function of their location and proximity to emission sources. Table 1 lists the size of the trend in meteorologically adjusted summer ozone which, depending on site, varied between !0.7 and !2.3 ppb yr~1 over the period 1994}1998. Compared to trends in peak concentrations calculated in PORG (1997) over the period 1989}1994 for the sites considered here, the changes in meteorologically adjusted ozone concentrations show little correspondence. Table 1 lists the trends derived by PORG (1997), obtained when "tting a linear trend model to the monthly 95th percentile ozone concentrations (including 12 parameters to capture the seasonal cycle), alongside the linear rate of change in the meteorologically adjusted summer ozone, derived here, both between 1989 and 1994. It
175
should be pointed out that a linear trend model is inappropriate for representing a non-linear trend (such as those observed here) and was only carried out for comparison purposes. The monthly 95th percentile ozone concentrations should give a better indication of the long-term trend in ozone (due to changes in precursor concentrations) than higher percentile concentrations since it should be less in#uenced by meteorology associated with high ozone concentration episodes. In fact, the large observed di!erences in the trends found here suggests that monthly 95th percentile ozone concentration may be a poor indicator of changes in ozone due to changes in precursor concentrations and may be more highly in#uenced by meteorology than previously considered. 5. Conclusions This paper has suggested that a general decline in the meteorologically adjusted summer daily maximum ozone concentrations has occurred since 1994. Previous analyses of UK ozone trends, which do not account for the e!ects of daily meteorological variability, have not detected a consistent downward trend in concentrations due to changes in precursor emissions. Meteorologically adjusting ozone time series from neighbouring European ozone monitoring stations would increase con"dence in the results obtained here. Coincident measurements of volatile organic compound concentrations may provide one method to validate the results of future analyses. These changes in the UK meteorologically adjusted long-term ozone concentrations should be considered within a broader European and Global framework. The meteorologically adjusted ozone concentrations represent the e!ects of changing background concentrations of global trace gases as well as the more localised changes in emissions. As such the meteorologically adjusted ozone time series should be a good indicator of the overall impact of emissions reduction policy upon surface ozone concentrations. Acknowledgements We are grateful to the BADC and DETR/NETCEN for making the meteorological and ozone data freely available and also to the School of Environmental Sciences, University of East Anglia, for supporting this work. References Dollard, G., Fowler, D., Smith, R.I., Hjellbrekke, A.G., Uhse, K., Wallasch, M., 1995. Ozone measurements in Europe. Water, Soil and Air Pollution 85, 1949}1954.
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Eskridge, R.E., Ku, J.Y., Rao, S.T., Porter, P.S., Zurbenko, I.G., 1997. Separating di!erent scales of motion in time series of meteorological variables. Bulletin of the American Meteorological Society 78 (7), 1473}1483. Gardner, M.W., 1999. The advantages of arti"cial neural network and regression tree based air quality models. Ph.D. Thesis, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK. Gardner, M.W., Dorling, S.R., 1998. Arti"cial neural networks (the multilayer perceptron) } a review of applications in the atmospheric sciences. Atmospheric Environment 32 (14}15), 2627}2636. Gardner, M.W., Dorling, S.R., 1999. Neural network modelling of hourly NO and NO concentrations in urban air in x 2 London. Atmospheric Environment 33 (5), 709}719. Gardner, M.W., Dorling, S.R., 1999a. Arti"cial neural network derived trends in surface ozone concentrations. Journal of the Air and Waste Management Association. Submitted for publication. Gardner, M.W., Dorling, S.R., 1999b. Statistical ozone models: the importance of non-linearities and interactions between predictor variables at the hourly timescale. Atmospheric Environment. Submitted for publication.
Holland, D.M., Principe, P.P., Vorburger, L., 1999. Rural ozone: trends and exceedances at CASTNet sites. Environmental Science and Technology 33, 43}48. Milanchus, M.L., Rao, S.T., Zurbenko, I.G., 1998. Evaluating the e!ectiveness of ozone management e!orts in the presence of meteorological variability. Journal of the Air and Waste Management Association 48, 201}215. PORG, 1997. Ozone in the United Kingdom. Photochemical Oxidants Review Group. Rao, S.T., Zurbenko, I.G., 1994. Detecting and tracking changes in ozone air quality. Journal of the Air and Waste Management Association 44, 1089}1092. Simmonds, P.G., Seuring, S., Nickless, G., Derwent, R.G., 1997. Segregation and interpretation of ozone and carbon monoxide measurements by air mass origin at the TOR station Mace Head from 1987 to 1995. Journal of Atmospheric Chemistry 28, 45}59. Simpson, D., OlendrzynH ski, K., Semb, A., St+ren, E., Unger, S., 1997. Photochemical oxidant modelling in Europe: multiannual modelling and source}receptor relationships, Technical Report EMEP MSC-W Report 3/97, Norwegian Meteorological Institute, P.O. Box 43-Blindern, N-0313 Oslo 3, Norway.
Meteorologically adjusted trends in UK daily maximum surface ozone concentrations M.W. Gardner, S.R. Dorling* School of Environmental Sciences, University of East Anglia, Norwich, UK Received 13 April 1999; accepted 21 July 1999
Abstract A methodology to meteorologically adjust daily UK surface ozone data is presented which reveals the impact of longer-term variations in precursor emissions more clearly. Based on this approach a general site-dependant decline in meteorologically adjusted summer daily maximum ozone concentrations has occurred between 1994 and 1998 and is between 0.7 and 2.3 ppb yr~1. ( 1999 Elsevier Science Ltd. All rights reserved. Keywords: Precursor emissions; Emission reduction policies; Background trace gas concentrations; Arti"cial neural network
1. Introduction Current and proposed European Union directives aim to reduce ozone concentrations through reduction in emissions of nitrogen oxides and volatile organic compounds. Through national programmes such directives will ultimately impact "nancially and operationally on polluters; quantifying the e!ectiveness of the policies is therefore important. Identifying long-term trends in surface ozone concentrations is confounded by meteorological variability at a range of timescales. The interannual meteorologically driven #uctuations are typically much larger than any trends in concentrations due to precursor emission reduction policies, thereby hindering attempts to assess their e!ectiveness (PORG, 1997). Analysis of 1986}1991 data from the UK ozone monitoring network by Dollard et al. (1995) indicated an increase (decrease) in annual mean concentrations at rural (urban) sites. Analysis of 1986}1995 data from UK sites (PORG, 1997) indicated that either no change or a small positive trend (average#0.5 ppb yr~1) in annual mean ozone concentrations occurred at most rural sites. * Corresponding author. Fax: #441603-507719 E-mail address: [email protected] (S.R. Dorling)
These analyses did not account for meteorological variability and the latter demonstrated, for example at Eskdalemuir (a rural site in southern Scotland), that an apparent linear trend of #0.59 ppb yr~1 between 1986 and 1995 was not observed when the years 1987 and 1988 (`low ozone yearsa) were removed from the analysis. One attempt to address the impact of meteorology on surface ozone concentrations was made by Simmonds et al. (1997) by classifying days as either being `backgrounda or `polluteda depending on the air mass origin at Mace Head (Ireland) over the period 1987}1995. A downward trend in the `polluteda data of !0.39 ppb yr~1 was tentatively attributed to a combination of decreasing European carbon monoxide, nitric oxide and non-methane volatile organic compound emissions. Simpson et al. (1997) described the application of a photochemical oxidant model to investigate the relative e!ects of meteorological variability, emissions and background concentration changes on surface ozone concentrations for one model grid point in Europe over the period 1985}1995. The results indicated that meteorological variability was almost entirely responsible for the observed trend in ozone. This work also indicated that mean summer ozone concentrations could vary by up to 12 ppb due to meteorological interannual variability.
1352-2310/99/$ - see front matter ( 1999 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 3 1 5 - 5
172
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
Historical data indicated that it would take 30 yr in order to observe a reduction in ozone concentrations of more than 12 ppb that could be attributed to precursor emission changes. The study concludes It follows that long-term monitoring networks are essential if trends due to emission changes are to be detected. Models and statistical analysis will also be required to disentangle the various factors contributing to measured trends. Recently, a new technique which utilises multilayer perceptron (MLP) neural network models has been developed. The technique, herein refered to as the MLP methodology, was originally developed and applied to identify trends in daily maximum ozone data from the US (Gardner, 1999; Gardner and Dorling, 1999a). The MLP methodology was shown to remove more of the meteorological variability than an alternative technique (based on time series "lters and regression models) which had been previously applied to the same data (Milanchus et al., 1998). In this paper the MLP methodology is used to meteorologically adjust UK ozone data, to investigate whether any discernible temporal and spatial trends have occurred in response to changing precursor emissions.
2. Data Time series of daily maximum ozone concentrations, from the Department of the Environment, Transport and the Regions (DETR) automatic air quality monitoring network (http://www.aeat.co.uk/netcen/airqual/), and concurrent daily meteorological data, from the British Atmospheric Data Centre (http://www.badc.rl.ac.uk/), were analysed for "ve UK `rurala sites over the period 1979}1997. Rural sites were selected based on their relatively long length of record and with the expectation that nationwide, non-local trends would be more clearly revealed by avoiding the confounding e!ect of local urban vehicular NO emissions. x In all cases the nearest climate station to the ozone monitoring station was selected. In some instances the nearest meteorological station was a considerable distance from the ozone monitoring station (up to 50 km) however they were included to increase the number of sites analysed. Sites were chosen which measured all the required meteorological variables: daily maximum temperature, total daily sunshine (a surrogate for daily maximum solar radiation which was unavailable), mean daily wind speed, vapour pressure and total cloud cover. These variables were chosen from those available based on previous experience (Gardner, 1999). Five pairs of ozone and meteorological stations were found to conform to the selection criteria (Harwell and Wallingford, 12 km distant; Lullington Heath and Herstmonceux, 17 km;
Sibton and Hemsby, 50 km; Yarner Wood, sites co-located; Eskdalemuir, sites co-located). The location of the ozone monitoring sites is shown at http://www.aeat.co.uk/netcen/airqual/networks/o3map. html. Time of year was also used as an ozone predictor variable and was presented to the network as sin(2pd/365) and cos(2pd/365), where d is the Julian day number.
3. MLP methodology The MLP methodology is fully described in both Gardner (1999) and Gardner and Dorling (1999a) and hence only a brief outline will be provided here. In the same manner as noted by Rao and Zurbenko (1994) the technique supposes that a time series of ozone O(t) can be expressed as the sum of a long-term e(t), a seasonal S(t) and short-term =(t) component, O(t)"e(t)#S(t)#=(t). The long-term component represents changes in ozone due to climate or precursor emission changes and also variations in the background concentrations of some related tropospheric trace gases. The seasonal component corresponds to the annual cycle in solar radiation and vertical transport of ozone from the stratosphere, whilst the short-term component is associated with the day-to-day variations in weather. The MLP methodology involved the development of a statistical model of daily maximum ozone based on meteorological and seasonal predictor variables described above. The residuals from such a model will be strongly related to changes in ozone concentrations due to changing precursor emissions (and meteorological variables not considered) and will be refered to hereafter as the meteorologically adjusted ozone. MLP neural network models were used since they can represent complex functional non-linear relationships with arbitrary interactions amongst predictor variables. The application of MLP neural networks within the atmospheric sciences is reviewed by Gardner and Dorling (1998). MLP models have been successfully applied to the modelling of primary and secondary pollutants in a range of urban and rural settings (Gardner, 1999; Gardner and Dorling, 1999b). MLP models include as a special case all forms of regression model and o!er a more #exible framework for the development of statistical models. Recently generalized additive models (GAMs) have been used to meteorologically adjust US ozone data (Holland et al., 1999). The use of GAMs recognises the fact that the relationship between meteorology and ozone is non-linear. However such models do not allow interactions between the predictors and are therefore less #exible than MLP models.
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
The MLP models were trained to learn the relationship between the meteorological and seasonal predictors and daily maximum ozone concentrations using data taken from all but one year of the time series. The remaining year was used for validation during training in order to protect against over"tting. Full details concerning the training of the MLP models are provided in Gardner (1999) and Gardner and Dorling (1999a). Following training, the model residuals were calculated and interpreted as the meteorologically adjusted daily maximum ozone concentrations. Since the model residuals were expected to contain some remaining variability at both the short-term and seasonal time scales, due to meteorological variables not considered, measurement error or noise, the residuals were smoothed using a Kolmogorov}Zurbenko (KZ) "lter (Eskridge et al., 1997). A KZ "lter involves the multiple application of a moving average "lter and can be used to remove high frequency variability from data. Here a KZ(365,3) "lter was used, with a window size of 365 d applied 3 times, resulting in a cut-o! frequency of 3.8 yr. KZ "lters were used in a similar manner by Milanchus et al. (1998).
173
4. Results and discussion Table 1 lists the performance of the MLP models developed on the data from each site. Bootstrap estimates of standard error (at the 95% con"dence level) were calculated by randomly sampling the test data, with replacement, 1000 times. In the case of Yarner Wood, the apparent poor performance over the validation data is probably due to the large amount of missing data during the validation period, resulting in a small data set mostly covering late summer and winter. Over the entire training data period the models have captured between 52% and 70% of the variability in the daily maximum ozone concentrations. The lower explained variance at Sibton is most likely due to the larger distance between the meteorological station and the ozone monitoring site. The RMSE for the models varies between 7.3 ppb at Eskdalemuir and 12.2 ppb at Sibton, also demonstrating the e!ect of distance between the meteorological and ozone monitoring sites. Fig. 1a shows the meteorologically adjusted and unadjusted long-term components of the daily maximum ozone time series at each site. The unadjusted component
Table 1 The performance of the MLP models for each site over (a) the training data period and (b) the validation data period Eskdalemuir
Harwell
Lullington Heath
Sibton
Yarner Wood
(a) Performance on training data o6 34.61(0.36) MBE !0.38(0.24) s 11.13(0.46) 0 s 8.29(0.32) 1 RMSE 7.31(0.27) R2 0.57(0.03)
37.63(0.48) 0.73(0.29) 17.01(0.54) 12.76(0.39) 10.42(0.33) 0.63(0.03)
40.76(0.57) !0.01(0.31) 17.09(0.72) 14.23(0.58) 9.32(0.33) 0.70(0.02)
36.36(0.46) !0.42(0.33) 17.45(0.67) 12.28(0.35) 12.15(0.56) 0.52(0.03)
39.31(0.51) !0.22(0.32) 15.10(0.70) 11.80(0.58) 9.43(0.42) 0.61(0.03)
(b) Performance on validation data o6 29.17(1.28) MBE !5.47(0.76) s 10.72(2.42) 0 s 7.56(1.43) 1 RMSE 8.32(0.80) R2 0.67(0.13)
52.35(3.59) 5.66(2.14) 20.05(4.21) 13.87(2.47) 13.58(2.86) 0.63(0.13)
38.06(1.38) !0.14(0.86) 13.52(1.56) 9.48(0.93) 8.40(0.83) 0.62(0.07)
32.73(2.29) !5.71(1.42) 16.05(2.22) 11.30(1.50) 11.11(1.12) 0.66(0.10)
29.70(1.52) !5.62(1.35) 9.76(2.95) 7.69(1.61) 10.34(1.24) 0.27(0.20)
(c) Trends 1994}1998 Met-Adj !0.8$0.0
!0.7$0.0
!0.7$0.0
!2.3$0.0
!0.7$0.0
(d) Trends 1989}1994 Mon-95% !0.8$1.0 Met-Adj #0.2$0.0
!0.8$1.6 #1.3$0.1
!2.1$1.5 #0.0$0.0
#0.1$1.0 #0.3$0.0
!2.3$1.3 !0.2$0.0
Note: The lower portion of the table lists (c) the trend in 1994}1998 meteorologically adjusted summer ozone (met-adj) concentrations derived here ($2]standard error) and (d) the trend (ppb yr~1) in 1989}1994 monthly 95th percentile ozone concentrations (mon-95%) described in PORG (1997) and also the trend in 1989}1994 meteorologically adjusted summer ozone (met-adj) derived here ($2]standard error). o6 } mean observed concentration (ppb). MBE } mean bias error (ppb). s , s } observed and predicted 0 1 concentration variance (ppb). RMSE } root mean squared residual error (ppb). R2 } percentage of the variance in the observed data explained by the model. Bootstrap estimates of standard error at the 95% con"dence level listed in brackets.
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M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
Fig. 1. The long-term component of (a) the meteorologically adjusted daily maximum ozone at each site (black) and the unadjusted ozone (grey) and (b) the meteorologically adjusted daily maximum summer season ozone at each site (black) and the unadjusted ozone (grey).
M.W. Gardner, S.R. Dorling / Atmospheric Environment 34 (2000) 171}176
was obtained by applying a KZ(365,3) "lter directly to the raw ozone data and subtracting the mean. This plot shows that the adjusted and unadjusted long-term components for each site are di!erent and hence the residuals from the MLP models are not simply reproducing the long-term mean concentrations. At all sites except Sibton the unadjusted ozone concentrations were higher in 1990 than the meteorologically adjusted ozone concentrations, suggesting that this peak was indeed due to the exceptionally warm summer in that year (PORG, 1997). Similarly in 1993, at all sites except Eskdalemuir and Lullington Heath, the unadjusted concentrations were lower than the meteorologically adjusted ozone concentrations. The pro"les for Sibton, Harwell and Yarner Wood indicate that the 1986 and/or 1987 `low ozonea years were not only due to meteorological e!ects. Even when the dominant meteorological signal has been removed from the ozone time series the adjusted concentrations are still low suggesting that the combined e!ect of both meteorology and lower precursor emissions, or background concentrations, could have been contributing to lower ozone concentrations during this period. Between 1990 and 1997 the meteorologically adjusted ozone concentrations show a general increase and then a slight downward trend at Yarner Wood, Harwell and Sibton. However at Eskdalemuir and Lullington Heath the meteorologically adjusted ozone concentrations remain essentially unchanged. Fig. 1b shows the meteorologically adjusted and unadjusted long-term component of the summer (April} September) ozone time series at each site. Trends in ozone concentrations due to changes in precursor concentrations should be more evident in the summer months since this is the period when photochemical episodes are most prevalent. Of most interest is the change in meteorologically adjusted summer ozone concentrations between 1990 and 1997. At all sites, the concentrations are seen to increase or remain constant until 1994/1995. After this period there is evidence of a slight downward trend in the concentrations at all sites. The downward trend is most marked at Sibton and least evident at Eskdalemuir, perhaps a function of their location and proximity to emission sources. Table 1 lists the size of the trend in meteorologically adjusted summer ozone which, depending on site, varied between !0.7 and !2.3 ppb yr~1 over the period 1994}1998. Compared to trends in peak concentrations calculated in PORG (1997) over the period 1989}1994 for the sites considered here, the changes in meteorologically adjusted ozone concentrations show little correspondence. Table 1 lists the trends derived by PORG (1997), obtained when "tting a linear trend model to the monthly 95th percentile ozone concentrations (including 12 parameters to capture the seasonal cycle), alongside the linear rate of change in the meteorologically adjusted summer ozone, derived here, both between 1989 and 1994. It
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should be pointed out that a linear trend model is inappropriate for representing a non-linear trend (such as those observed here) and was only carried out for comparison purposes. The monthly 95th percentile ozone concentrations should give a better indication of the long-term trend in ozone (due to changes in precursor concentrations) than higher percentile concentrations since it should be less in#uenced by meteorology associated with high ozone concentration episodes. In fact, the large observed di!erences in the trends found here suggests that monthly 95th percentile ozone concentration may be a poor indicator of changes in ozone due to changes in precursor concentrations and may be more highly in#uenced by meteorology than previously considered. 5. Conclusions This paper has suggested that a general decline in the meteorologically adjusted summer daily maximum ozone concentrations has occurred since 1994. Previous analyses of UK ozone trends, which do not account for the e!ects of daily meteorological variability, have not detected a consistent downward trend in concentrations due to changes in precursor emissions. Meteorologically adjusting ozone time series from neighbouring European ozone monitoring stations would increase con"dence in the results obtained here. Coincident measurements of volatile organic compound concentrations may provide one method to validate the results of future analyses. These changes in the UK meteorologically adjusted long-term ozone concentrations should be considered within a broader European and Global framework. The meteorologically adjusted ozone concentrations represent the e!ects of changing background concentrations of global trace gases as well as the more localised changes in emissions. As such the meteorologically adjusted ozone time series should be a good indicator of the overall impact of emissions reduction policy upon surface ozone concentrations. Acknowledgements We are grateful to the BADC and DETR/NETCEN for making the meteorological and ozone data freely available and also to the School of Environmental Sciences, University of East Anglia, for supporting this work. References Dollard, G., Fowler, D., Smith, R.I., Hjellbrekke, A.G., Uhse, K., Wallasch, M., 1995. Ozone measurements in Europe. Water, Soil and Air Pollution 85, 1949}1954.
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Eskridge, R.E., Ku, J.Y., Rao, S.T., Porter, P.S., Zurbenko, I.G., 1997. Separating di!erent scales of motion in time series of meteorological variables. Bulletin of the American Meteorological Society 78 (7), 1473}1483. Gardner, M.W., 1999. The advantages of arti"cial neural network and regression tree based air quality models. Ph.D. Thesis, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK. Gardner, M.W., Dorling, S.R., 1998. Arti"cial neural networks (the multilayer perceptron) } a review of applications in the atmospheric sciences. Atmospheric Environment 32 (14}15), 2627}2636. Gardner, M.W., Dorling, S.R., 1999. Neural network modelling of hourly NO and NO concentrations in urban air in x 2 London. Atmospheric Environment 33 (5), 709}719. Gardner, M.W., Dorling, S.R., 1999a. Arti"cial neural network derived trends in surface ozone concentrations. Journal of the Air and Waste Management Association. Submitted for publication. Gardner, M.W., Dorling, S.R., 1999b. Statistical ozone models: the importance of non-linearities and interactions between predictor variables at the hourly timescale. Atmospheric Environment. Submitted for publication.
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