Abstract The gradual increase in temperature is one of the most pronounced trendsof climatic changes in the atmosphere. The pollution levels depend essentially on theemissions (both on the human-made emissions and on the biogenic emissions) as wellas on the chemical reactions which take place during the transport of pollutants in theatmosphere. Since both the chemical reactions and the biogenic emissions dependon the temperature, it is obvious that the gradual increase of the temperature willhave some effect on pollution levels. The impact of climatic changes on high ozonelevels, which may have damaging effects on human health, is studied in this paper.Eight European suburban areas were selected. These areas are densely populatedand, therefore, increased ozone pollution levels may cause harm to a great numberof human beings living there. All experiments indicate that, although the changes ofthe ozone concentrations are relatively small, some critical levels, which are relatedto ozone concentrations and which may have damaging effects, will be significantlyexceeded as a result of the warming trend in the future climate.
1 Statement of the problem
Ozone in the troposphere is one of the most harmful pollutants. This is why the ozonelevels must be carefully studied in the efforts
• To predict the appearance of high ozone levels, which may be harmful for somegroups of human beings (people suffering from asthmatic diseases) and/or
• To decide what measures must be taken in order to keep the dangerous ozoneconcentrations under prescribed acceptable limits.
Z. Zlatev (B)National Environmental Research Institute, Aarhus University,Frederiksborgvej 399, P.O. Box 358, 4000 Roskilde, Denmarke-mail: [email protected]
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The control of the ozone levels in highly developed and densely populated regions inthe world is an important task that has to be handled in a systematic way. This is truefor many regions in Europe and North America, but also other parts of the worldare under quick economic development at present and urgent solutions of certainenvironmental problems either are already necessary or will soon become necessaryalso there. The importance of this task has been steadily increased in the beginningof the new millennium. The need to develop reliable and easily applicable controlstrategies for keeping harmful ozone levels under prescribed limits will become evenmore important in the next decades.
Several critical levels for ozone have been established in the European Union(EU) as well as in other parts of the world. Some of these critical levels are legislatedin the EU Ozone Directive. Assume that cmax is the maximum of the 8-hour averagesof the calculated by some model or measured ozone concentrations in a givenday at site A. If the condition cmax > 60 ppb is satisfied at least once in the dayunder consideration, then the expression a “bad day” will be used for such a dayat site A. “Bad days” can have damaging effects on some groups of human beings(as mentioned above, people who suffer from asthmatic diseases). Therefore, thenumber of such days should be reduced as much as possible. Two important aims arestated in the Ozone Directive issued by the EU Parliament in year 2002 (EuropeanParliament 2002):
• Target aim. The number of “bad days” in any site of the European Union shouldnot exceed 25 after year 2010.
• Long-term aim. No “bad day” should occur in the European Union (the yearafter which the long-term aim has to be satisfied is not specified in the EU OzoneDirective).
Climate changes are causing another challenging problem for the modern society.The quick climate changes have many different consequences. The impact of thesechanges on the pollution levels is one of the consequences and this consequence mustbe carefully investigated by studying the relationship between climatic changes andhigh pollution levels.
The impact of the climatic changes on numbers of “bad days” in eight suburbanareas in Europe will be studied in this paper. The areas around the cities listedin Table 1 were selected. These areas are densely populated and increases of thenumbers of “bad days” may cause damaging effects on large number of human beingsin these areas.
This paper is organized as follows:
(a) The main tool (UNI-DEM, the Unified Danish Eulerian Model) and thescenarios used in this study are briefly described in Section 2,
(b) Comparisons of model results and measurements are presented in Section 3,(c) The impact of the climatic changes on the numbers of “bad days” is discussed
in Section 4 and(d) Conclusions and plans for future research are given in Section 5.
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Table 1 The sites selected forthis study
The population is given inmillion inhabitants
No. City Country Population Population inin city metropolitan area
1 Vienna Austria 1.7 2.32 Berlin Germany 3.4 5.03 Madrid Spain 3.2 5.14 Paris France 2.2 12.05 London Great Britain 7.5 8.36 Rome Italy 2.7 4.07 Milan Italy 1.3 7.48 Moscow Russia 10.0
2 The tool used and the selected scenarios
UNI-DEM (the Unified Danish Eulerian Model) is the main tool used in thisstudy. The model is described mathematically by a system of partial differentialequations. Three chemical schemes can be specified (containing 35, 56 and 168species respectively). The condensed CBM IV scheme with 35 species (Gerry et al.1989; Zlatev 1995) was used in all experiments, results of which are reported in thenext two sections. The space domain of the model contains the whole of Europe.Three different meshes can be specified. The fine resolution mesh was chosen forthis study. This means that the space domain is discretized by using 480 × 480 grid-squares into 10 x 10 km surface cells. Ten non-equidistant vertical layers are applied.
Basic EMEP and NERI EMEP and NERI BasicConstant Meteorology for 1989 As in the Basic Scenario As in the Basic Scenario
meteorologyConstant As in the Basic Scenario Emissions for 1989 As in the Basic Scenario
emissionsClimate 1 Increased temperatures As in the Basic Scenario As in the Basic ScenarioClimate 2 As in Climate 1 + diurnal As in the Basic Scenario As in the Basic Scenario
and seasonal variationsClimate 3 As in Climate 2 + new As in the Basic Scenario As in the Basic Scenario
humidity and precipitation2010 As in the Basic Scenario Using IIASA factors As in the Basic ScenarioMFR As in the Basic Scenario Using IIASA factors As in the Basic ScenarioClimate 2010 As in Climate 3 As in Scenario 2010 As in the Basic ScenarioClimate MFR As in Climate 3 As in Scenario MFR As in the Basic ScenarioBiogenic Bassic As in the Basic Scenario As in the Basic Scenario IncreasedBiogenic As in Climate 3 As in the Basic Scenario As in Biogenic Basic
Climate 3Biogenic 2010 As in Climate 3 As in Scenario 2010 As in Biogenic BasicBiogenic MFR As in Climate 3 As in Scenario MFR As in Biogenic Basic
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UNI-DEM is fully described in Zlatev (1995), Zlatev and Dimov (2006). This modelhas been used to study pollution levels in
• Bulgaria (Zlatev and Syrakov 2004a, b),• Denmark (Zlatev 1995; Zlatev et al. 2001),• England (Abdalmogith et al. 2006),• Europe (Ambelas Skjøth et al. 2000; Bastrup-Birk et al. 1997; Zlatev 1995, 2002;
Zlatev and Dimov 2006),• Hungary (Havasi et al. 2001; Havasi and Zlatev 2002) and• North Sea (Harrison et al. 1994).
UNI-DEM has also been used in some inter-comparisons of European large-scale airpollution models (Hass et al. 2004; Roemer et al. 2004).
Recently UNI-DEM was run with 14 scenarios (listed in Table 2). Each scenariowas run on a time-period consisting of 16 consecutive years (from 1989 to 2004).More details about these scenarios can be found in Csomos et al. (2006), Dimovet al. (2008) and Zlatev and Moseholm (2008).
Remarks related to Table 2:
(a) Basic biogenic emissions are produced by applying ideas proposed in Simpsonet al. (1995) and in Lübkert and Schöpp (1989) as described in Geernaert andZlatev (2004).
(b) Increased biogenic emissions are produced by applying ideas from Anastasiet al. (1991).
Fig. 1 Future changes of thetemperatures in Europe and itssurroundings according toIPCC Scenario SRES A2 fromHoughton et al. (2001)
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Table 3 The EMEPmeasurement stationsselected for this study
No. Site Country EMEP abbreviation
1 Illimitz Austria AT022 Langebrugge Germany DE023 San Pablo Spain ES014 Donon France FR085 Harwell Great Britain GB366 Montelibretti Italy IT017 Ispra Italy IT048 Danki Russia RU18
(c) Scenario 2010 and Scenario MFR (Maximal Feasible Reductions) were pre-pared by multiplying the anthropogenic EMEP emissions (EMEP 1999; EMEPHome Web-page 2006) by the factors given in Amann et al. (1999).
Mainly results obtained by the Basic Scenario and Scenario Climate 3 will be usedin this paper, but also results from other scenarios will be shortly discussed. TheBasic Scenario for a given year N (N ∈ [1989, 2004]) is obtained by using emissionsinventories and meteorological data for this year which were prepared either atEMEP, European Monitoring and Evaluation Programme, see EMEP (1999) andEMEP Home Web-page (2006) or at the Danish National Environmental ResearchInstitute, some details can be found in Hertel et al. (2002).
The predictions of the increase in the annual temperatures in Europe according tothe IPCC SRES A2 Scenario as well as several other conclusions, which are related
Fig. 2 The selected cities (left-hand-side plot) and measurement stations (right-hand-side plot)
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to the climatic changes in Europe and which are discussed in Houghton et al. (2001),were used in order to prepare the three climatic air pollution scenarios mentioned inTable 2. The rules which were actually used in the development of these scenariosare sketched below.
Climate scenario 1 The predicted annual changes of the temperature, see Houghtonet al. (2001), were used to produce this climatic scenario. Resulting from this scenariochanges of the temperature in Europe are shown in Fig. 1. Consider any cell of thegrid used to create the plot shown in Fig. 1 and assume that this cell is located in aregion in Fig. 1 where the increase of the temperature is in the interval [a, b]. Thetemperature in the chosen cell at hour n (n being any hour in the interval from 1989to 2004) is increased by an amount a + c(n), where c(n) is randomly generated in theinterval [0, b − a] so that the mathematical expectation of the increase of the annualmean of the temperature at any cell of the space domain is (b − a)/2. This means that(a) only temperatures are varied in this scenario and (b) the mean value of the annualchange of the temperature at a given point will tend to be the same as that prescribedby the IPCC SRES A2 Scenario.
Fig. 3 Comparing calculated and measured annual means of the ozone concentrations for the firstgroup of four measurement stations
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Climate scenario 2 The extreme cases will become even stronger in the futureclimate; see Table 9.6 on p. 575 in Houghton et al. (2001). It is expected that: (a)there will be higher maximum temperatures and more hot days over the land areas,(b) there will be higher minimum temperatures, fewer cold days and fewer frost daysover nearly all land areas and (c) the diurnal temperature range will be reduced overland areas. We increased the temperatures during the night with a factor larger thanthe factor by which the day time temperatures were increased. In this way the secondand the third requirements are satisfied. The first requirement is satisfied as follows:during the summer periods the daytime temperatures are increased by a largeramount in hot days. All these changes are carried out only over land. Furthermore,the temperatures were varied in such a way that the annual means of the changesremained the same, at all cells, as those in the first climatic scenario (i.e. as thoseprescribed in the IPCCS RES A2 Scenario). We also reduced (by 10%) the cloudcovers over land during the summer periods.
Climate scenario 3 It is also expected, as shown in Table 9.6 on p. 575 in Houghtonet al. (2001), that there will be more intense precipitation events but increasedsummer drying and associated risk of drought. We increased the precipitation eventsduring winter (both over land and over water). During summer, the precipitation
COMPARING MODEL RESULTS WITH MEASUREMENTS
THE CHEMICAL COMPOUND IS OZONE
THE UNITS ARE PPB
Fig. 4 Comparing calculated and measured annual means of the ozone concentrations for the secondgroup of four measurement stations
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events in the continental parts of Europe were reduced. Similar changes in thehumidity data were made. The cloud covers during winter were increased (by 10%),while the same cloud covers as in the second climatic scenario were applied inthe third climatic scenario during summer. Again, as in the previous two climaticscenarios, the mathematical expectation of the annual means of the changes of thetemperature is the same as the predictions made in the IPCC SRES A2 Scenario.
A remark about the great computational complexity of problem handled in thisstudy should be given here. The task of running 14 scenarios over a time-periodof 16 years on a fine grid (480 × 480 × 10 cells resulting in systems of 80,640,000equations that are to be handled in 209,664 time-steps per year) is extremelydemanding even when powerful modern computers are available. Therefore, the taskof running so many scenarios over so long time-period can be successfully solved onlyif at least four requirements are simultaneously satisfied: (a) fast but also sufficientlyaccurate numerical methods are to be implemented in the model, (b) the cachememories of the available computers have to be efficiently utilized, (c) codes whichcan be run in parallel have to be developed and used and (d) reliable and robustsplitting procedures have to be implemented. The solution of sub-tasks (a)–(d) is
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Fig. 5 Variations of the anthropogenic (human-made) NOx emissions in seven European countriesand in Europe as a whole in the period 1989–2004
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discussed in detail in Alexandrov et al. (2004) and in Zlatev and Dimov (2006). Itmust be emphasized here that it is impossible to handle the 14 scenarios over a time-period of 16 years on the available super-computers if the sub-tasks (a)–(d) are notefficiently solved. Even when this was done, it took more than two years to computeoutput data from all 2688 runs (14 scenarios × 16 years × 12 months) carried outin this study. This fact illustrates the great computational difficulties that are relatedto the investigation of various impacts of climatic changes on pollution levels. Thestorage requirements (the need for huge input and output files) are also enormous.
The main purpose with the climatic scenarios developed and used in this paper canbe described as follows. It is desirable to be able to compare directly the pollutionlevels obtained by using the predicted future temperatures with the present stateof the pollution levels. To achieve this we fixed the transport and varied only thetemperatures and the emissions as well as some closely related quantities. For thesake of simplicity, assume that only the temperature is varied. Then the approachdiscussed in this paper has the advantage that it allows us to compare directlythe present pollution levels with the pollution levels obtained with the increasedtemperature. Since the temperature is the only parameter that is varied all changes
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Fig. 6 Variations of the anthropogenic (human-made) VOC emissions in seven European countriesand in Europe as a whole in the period 1989–2004
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of the ozone levels are clearly due to the increased temperature levels. It is obviousthat the same conclusion can be drawn if the emissions and some other parametersare also varied (the important issue being to keep the transport the same as that inthe Basic Scenario).
It is also possible to include all meteorological parameters in the set of scenarios(first and foremost the wind fields). This would require running a climatic model.Moreover, it is not very clear in advance how to compare the results so found withthe results obtained with the Basic Scenario (the changes will be caused both bythe increased temperature and by the different transport), but it will be possible todraw useful conclusions by performing runs over sufficiently long time-periods. Themajor problem is that the computational difficulties would be enormous when thefine discretization (10 × 10 km surface cells) used in this paper is to be preserved.
Finally, the problem will become even more challenging if the air pollution modelis to be fully coupled with a climatic model in an attempt to study directly also thefeed back from the increased pollution levels to the climatic changes. At present itis not possible to resolve this problem on the whole European domain when finespatial resolution is to be used. However, the computers are becoming more andmore powerful and it will hopefully be possible to resolve the last two problems inthe near future.
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Fig. 7 Comparing calculated and measured averaged daily maxima the ozone concentrations for thefirst group of four measurement stations
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Some more details about the use of other approaches in the study of the impact offuture climatic changes on air pollution levels can be found in Bell et al. (2007) andJacobson (2008) as well as in the references given in these two papers.
3 Comparison of model results with measurements
Results obtained by the tool used in this study, UNI-DEM, have been many timescompared with measurements. Results from such comparisons were reported inmany publications; see, for example, Abdalmogith et al. (2006), Ambelas Skjøthet al. (2000), Csomos et al. (2006), Zlatev (1995), Zlatev (2002), Zlatev and Dimov(2006), and Zlatev and Syrakov (2004a, b). Nevertheless, it was necessary to testonce again the model results, which were calculated and used in connection withthis particular study, by comparing them with some measurements taken aroundthe selected suburban areas. Three requirements were imposed in the choice ofmeasurement stations:
• There must be at least one measurement station in the neighborhood of each ofthe eight cities listed in Table 1,
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Fig. 8 Comparing calculated and measured averaged daily maxima the ozone concentrations for thesecond group of four measurement stations
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• One should expect the measurements taken at each of the selected stations to beto a certain degree reliable and
• Sufficiently many measurements should be available at each station.
It was relatively easy to satisfy the first two requirements. We decided to usestations from the EMEP network (EMEP Home Web-page 2006). The degree ofreliability of the measurements taken at these stations is increased by the fact thatall measurements are extra checked at the EMEP Chemical Coordinating Center atNILU (Norwegian Institute for Air Research). It was not possible to satisfy fullythe third requirement. The selected measurement stations are listed in Table 3.The locations of the selected cities and the corresponding measurement stations areshown on the two plots in Fig. 2 (the eight cities on the left-hand-side plot and thecorresponding stations on the right-hand-side plot).
The distribution of the nitrogen mono-oxide concentrations in different parts ofEurope for 1990 (right-hand-side plot) and 2004 (left-hand-side plot) is also shownin Fig. 2. It is clearly seen that the 2004 nitrogen mono-oxide concentrations are ingeneral (but not always) considerably smaller than the corresponding 1990 concen-trations. It is also seen that the nitrogen mono-oxide concentrations in the selectedcities are greater than those in the areas around these cities. This should be expectedbecause the traffic is a major source for nitrogen mono-oxide concentrations. Taking
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Fig. 9 Comparing the seasonal variations of calculated and measured ozone concentrations for firstgroup of four measurement stations
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this fact into account, it can be concluded, by studying the plots given in Fig. 2, thatthe model is producing qualitatively correct results.
3.1 Comparison of annual mean values of ozone concentrations
The calculated by UNI-DEM annual mean values of the ozone concentrationsin the selected interval of 16 years (from 1989 to 2004) are compared with thecorresponding measurements in Figs. 3 and 4.
It is seen that the calculated annual means of the ozone concentrations aredecreasing at many of the sites where the measurement stations are located. Thisshould be expected because the EMEP inventories of anthropogenic (human-made)emissions of nitrogen oxides (NOx emissions) and volatile organic compounds (VOCemissions), which are used as input data in the model are also decreasing in manyEuropean countries as well as in Europe as a whole. This fact is illustrated in Fig. 5for the anthropogenic NOx emissions and in Fig. 6 for the anthropogenic VOCemissions.
Note that there is a decreasing trend at San Pablo (ES01) although the human-made NOx emissions in Spain have been increased in the studied period (seeagain Fig. 5). This is probably caused by the fact that (a) the human-made VOC
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Fig. 10 Comparing the seasonal variations of calculated and measured ozone concentrations forsecond group of four measurement stations
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emissions in Spain were decreased (see Fig. 6) and (b) both the human-made NOxemissions and the human-made VOC emissions in Europe as a whole were reducedconsiderably (see Figs. 5 and 6). In connection with (b), it should also be assumedthat a considerable part of the annual means of the ozone concentration in Spain iscaused by long range transport phenomena (this is a reasonable assumption).
The behavior of the annual means of the ozone concentrations at the locationwhere the Russian measurement station is located should be expected. The annualmeans of the ozone concentrations are decreasing in the beginning of the interval andincreasing after 1998; this reflects to a certain degree the behavior of the emissions inRussia (see Figs. 5 and 6), which are also decreasing in the beginning of the intervaland increasing in the end of it. Note too that this station is located far away from themost polluted European regions and, therefore, the influence of the local sources isdominating over the influence of long-range transport phenomena.
There is not a pronounced decreasing trend for the variation of the annual meansof the ozone concentrations at measurement stations. Even a slight increasing trendcan be seen at some stations (see, for example, the results at Donon, FR08, in Fig. 3).It is not very clear what the reason for such behavior is. The following conjectureshould probably be investigated carefully (assuming here that both the emissionsinventories are correctly prepared and the measurements are reliable): the biogenic
Fig. 11 Averaged diurnal variations of calculated and measured ozone concentrations in February2004 for first group of four measurement stations
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emissions used in the models at present are perhaps underestimated.Many scientistsclaim that this is so, see for example Anastasi et al. (1991) and Bouchet et al. (1999a,b). Much more experiments are needed in order to confirm or to reject such aconjecture. It should also be mentioned here that the confirmation of this conjecturewill probably require reconsideration of some other mechanisms commonly used atpresent in the models.
3.2 Comparison of averaged daily maxima of ozone concentrations
Damaging effects on human beings might take place when the ozone concentrationsare high. Therefore, it is necessary to test the reliability of the model results in thecases where the ozone concentrations are really high. The averaged daily maximain the extended summer period (from April 1 to September 30) are appropriatequantities for such tests. Comparisons of calculated and measured averaged dailymaxima of the ozone concentrations are presented in Figs. 7 and 8.
The same conclusions, as those in the previous paragraph, can be drawn in thecase where computed and measured averaged daily maxima of ozone concentrationsare compared.
Fig. 12 Averaged diurnal variations of calculated and measured ozone concentrations in February2004 for second group of four measurement stations
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3.3 Studying the seasonal variations of ozone concentrations
It is well known that ozone concentrations are higher during the summer monthsand lower in the winter. It is important to check whether UNI-DEM is able toreproduce this pattern in the suburban areas around the selected large Europeancities. Results shown in Figs. 9 and 10 indicate that both calculated and measuredozone concentrations are considerably high in the summer and low in the winter.
Although the curves are in general rather smooth, there are some exceptions. Thefollowing additional remarks seem to be appropriate:
• The value of the calculated ozone concentration in Illimitz (AT08) is rather highin April (Fig. 9).
• The value of the calculated ozone concentration in San Pablo (ES01) is ratherlow in June (Fig. 9).
• The value of the calculated ozone concentration in Montelibretti (IT01) is ratherhigh in April, while the corresponding value in June is rather low (Fig. 10).
• The variation of the ozone concentrations in Harwell (GB36) do not follow verywell the behavior observed at the other selected sites. This statement is true bothfor the calculated and the measured results. However, note that the agreementbetween calculated and observed concentrations is fine at this station.
Fig. 13 Averaged diurnal variations of calculated and measured ozone concentrations in July 2004for first group of four measurement stations
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3.4 Studying the diurnal variations of ozone concentrations
The diurnal variation of the ozone concentration is also important. The diurnalvariations during a typical winter month are not very pronounced. During a typicalsummer month the ozone concentrations are usually high during the day and lowduring the night. The truth of these two statements is demonstrated in Figs. 11 and12 for February 2004 and in Figs. 13 and 14 for July 2004. Note that the same scale isused in Figs. 11–14. This allows us to see immediately that:
(a) the ozone concentrations during the winter month are considerably lower thanthe concentrations during the summer month and
(b) as already mentioned above, there are practically no diurnal variations duringthe winter month.
It should be mentioned that in general the concentrations follow some rather smoothpatterns (with no diurnal variations or very weak diurnal variations during thewinter month and rather strong diurnal variations during the summer month). Theonly exception is the behavior of the concentrations at San Pablo (ES01). Theconcentrations at this station change in a rather irregular manner. It is not clear whatthe reason for this behavior is.
Fig. 14 Averaged diurnal variations of calculated and measured ozone concentrations in July 2004for second group of four measurement stations
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Averaged results (for a whole month) are given in Figs. 11–14. Some resultsrepresenting the diurnal variations for every day in a given month can be found inZlatev et al. (1993).
3.5 Numbers of “bad days”: calculated result versus measurements
Model results are traditionally compared with measurements (which was also donein the previous four paragraphs of this section). Many useful conclusions can bedrawn from such comparisons. However, other quantities are normally much moreimportant when the potential damaging effects due to exceeded critical limits haveto be evaluated. In this study we are interested, as stated in Section 1, in evaluatingwhether the EU-limit of “bad days” is exceeded or not in the eight selected suburbanareas. Comparisons between the numbers of “bad days” calculated by the modeland the corresponding numbers observed at measurement stations are very difficult.There are two major sources which cause great troubles:
• The results are extremely sensitive to small errors (if the highest eight-hourmaximum, calculated or measured, at a given day is 60.01 ppb, then the day willbe considered as a “bad day”, while this will not be the case if the maximum is
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Fig. 15 Numbers of measured and calculated “bad days” in the studied interval of 16 years for firstgroup of four measurement stations
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59.99 ppb; i.e. a very small error in calculating or measuring the concentrations,which is much less than 1%, might change the evaluation of the number of “baddays”).
• The stations are in general not providing measurements in all hours within theperiod of interest (this period is from April 1 to September 30 when numbersof “bad days” are studied). Assume that the measurement at 15:00 at a givenday is missing. Then no averaged eight-hour values of the ozone concentrationsin the intervals [8:00,15:00], [9:00,16:00], [10:00,17:00], [11:00,18:00], [12:00,19:00],[13:00,20:00], [14:00, 21:00] and [15:00, 22:00] can be calculated. This means that itis practically impossible to decide whether we have a “bad day” or not when thishappens, because the ozone maxima occur as a rule during the day-time in thesummer period (see Figs. 13 and 14). Of course, one can try to solve the problemby making some assumptions. In the above example, one can assume that themissing measurement is equal to the mean value of the measurements at 14:00and 16:00. It is clear that the mean value will in general differ from the actual(but non-measured) value of the ozone concentration at 15:00. If the mean valueis larger than the actual one, then there is a danger to decide that we have a “badday” when this is actually not true. If it is smaller, then the day might be declaredas a good day while this is actually wrong. It is obvious that the situation becomes
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Fig. 16 Numbers of measured and calculated “bad days” in the studied interval of 16 years for secondgroup of four measurement stations
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much worse when the number of missing measurements is greater than one for agiven day. It should be mentioned here that while the lack of measurements is asa rule causing great problems when computed and calculated numbers of “baddays” are compared, it is not very critical when mean values of calculated andmeasured concentrations are compared. In the latter case the mean value of themeasured concentrations can be obtained by dividing the sum of the availablemeasurements with the number of the measurements and in most of the casesthis approach will give quite satisfactory results.
The two difficulties discussed above explain why comparisons of calculated andmeasured “bad days” are normally not reported in scientific papers. Of course, thegreat uncertainties give a sufficiently good explanation for the reason to avoid suchcomparisons, but it was decided that it is nevertheless worthwhile to present such re-sults. In the calculation of the numbers of measured “bad days” simple interpolationand extrapolation rules were used in the cases of missing measurements. However, itwas required that there are measurements in at least 50% of the hours from 8:00 to18:00 at the day under consideration. No action was carried out when this conditionwas not satisfied (which means in practice that it was assumed that the measurementsindicate that the day is not a “bad day” when this happens).
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Fig. 17 Differences between the annual means of the ozone concentrations obtained by the ClimaticScenario 3 and the Basic Scenario
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Some results are given in Figs. 15 and 16. Three important conclusions can bedrawn by studying the results presented in these two figures:
• Both the model and the measurements indicate that the European Union criticallevel of 25 days (the target aim; see Section 1) is very often exceeded (sometimesby a factor greater than two).
• The numbers of “bad days” calculated by the model are decreasing in the end ofthe studied interval. The observed “bad days” are in general not decreasing. Thiseffect has also been discussed in the previous paragraphs.
• The numbers of “bad days” measured at the Russian station (Danki, RU18) arestrongly increased after 1998. Also the ozone concentrations measured at thisstation are increased after 1998 (see Fig. 4), but the increases are by far not aslarge. This fact shows clearly that even relatively small changes of the ozoneconcentrations may cause rather big changes of the numbers of “bad days”.
3.6 General conclusion from the results presented in this section
Many conclusions were drawn immediately after presenting the results in the previ-ous paragraphs of this section. A general conclusion will be drawn below.
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Fig. 18 The same as Fig. 17 but relative changes (in percent) are calculated instead of differences
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The input data used in the models are very uncertain. The uncertainties of theemissions are as rule evaluated to be about 30% in general, but for some parts ofEurope the uncertainties are even greater. Moreover, only yearly mean values ofthe emissions are available in the inventories. It was necessary to simulate bothseasonal and diurnal variations of the emissions (Zlatev 1995). It is clear that thisis introducing additional uncertainties. The meteorological data are also ratheruncertain. This is especially true for the amount of precipitation in the water areas(the Atlantic Ocean and the seas within the space domain of the model). There arealso uncertainties in the determination of the chemical rates, the vertical turbulence,the upper boundary condition, etc. Taking all this into account and studying theresults presented in this section, we can conclude that the agreement between themodel results and the measurements is rather good. The discrepancies seldom exceed30–40% (and often are much less). It is not reasonable to expect or require somethingmore (the uncertainty on the exit of an information system cannot be less thanthe uncertainty on the entry according to a popular formulation of the entropylaw). On the other hand, all qualitative properties of the ozone concentrations(decreased calculated concentrations and numbers of “bad days” when the emissionsare decreased as well as qualitatively correct seasonal and diurnal patterns) wereclearly verified in the tests of the model results presented in this section.
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Fig. 19 Differences between the averaged daily maxima of the ozone concentrations obtained bythe Climatic Scenario 3 and the Basic Scenario
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Fig. 20 The same as Fig. 19 but relative changes (in percent) are calculated instead of differences
Table 4 The numbers of days in the extended summer period of 2004, in which the differences of thedaily maxima of the ozone concentrations obtained by Climatic Scenario 3 and the Basic Scenarioare equal to the figures given in the first column, are presented in columns 2–9 of this table
4 Impact of climatic changes on ozone concentrations and bad days
It was shown in the previous section that our tool, the Unified Danish EulerianModel (UNI-DEM), can identify different trends and relationships when ozoneconcentrations and associated quantities are studied during a long time-interval of16 years. In this section the impact of the climatic changes will be studied. Resultsobtained with the three climatic scenarios were used in many experiments. Onlyresults from the third climatic scenario will be presented in this section (results fromthe other climatic scenarios, as well as many other results, can be found (Csomoset al. 2006; Zlatev and Dimov 2006).
4.1 Impact of climatic changes on ozone concentrations
Differences between the annual means of the ozone concentrations obtained byrunning the Climatic Scenario 3 and the corresponding quantities calculated by theBasic Scenario are given in Fig. 17. It is seen that the use of the Climatic Scenario 3(instead of the Basic Scenario) leads in most of the cases to an increase of theannual means of the ozone concentrations in the range from 0 ppb to 1.5 ppb. If
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Fig. 21 Seasonal variations of the differences of the ozone concentrations for 2004 obtained by theClimatic Scenario 3 and the Basic Scenario
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the differences are transformed in relative discrepancies (measured in percent), thenmost of the changes are in the range from 0% to 5% (see Fig. 18). It will be shownin Subsection 4.5 that even such small changes in the annual means of the ozoneconcentrations lead to considerable changes of the numbers of “bad days”.
4.2 Impact of climatic changes on averaged daily maxima of the ozoneconcentrations
Differences between the averaged daily maxima of the ozone concentrations ob-tained by running the Climatic Scenario 3 and the corresponding quantities calcu-lated by the Basic Scenario are given in Fig. 19. It is seen that in the most of thecases the use of the Climatic Scenario 3 (instead of the Basic Scenario) leads again,as in the previous sub-section, to an increase of the averaged daily maxima of theozone concentrations in the range from 0 to 1.5 ppb. The changes are slightly biggerthan the corresponding changes in Fig. 17. The same conclusion as in the previoussub-section can be drawn for the relative changes given in percent (see Fig. 20).
The curves given in Figs. 19 and 20 are obtained by finding the daily maximumfor each day of the relevant period (from April 1 to September 30) and calculating
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Fig. 22 The same as Fig. 21, but relative changes (in percent) are considered instead of differences
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the averaged value of these 183 daily maxima. This way of representing the results isgiving us (as in the previous sub-section) very useful qualitative information: the useof the climatic scenarios leads in the most of the cases to an increase of the valuesof the averaged daily maxima of the ozone concentrations (of the annual meansof the ozone concentrations in the previous sub-section). Another representationof the results is needed if we want to get more detailed quantitative information.Such information is presented in Table 4. Consider the seventh row of Table 4.The figures in columns 2 to 9 of this row show in how many cases the differenceof the daily maxima of the ozone concentrations produced by Climatic Scenario3 and by the Basic Scenario is about 5 ppb. The figures in the other rows can beexplained in a similar way. It is seen that the numbers of days in which the changesare approximately 5 ppb (and, thus, an increase of the number of “bad days” could beexpected in most of the days when this happens) is significant for all stations, exceptthe Spanish site. Most significant changes were found at the Russian site (note thatbiggest increases of the annual temperature are predicted, by the IPCC SRES A2Scenario, for this site; see Fig. 1).
The results presented in Table 4 and Figs. 19 and 20 show that sometimesthe climatic scenario results in lower concentrations. The first guess is that this
Fig. 23 Diurnal variations for July 2004 of the differences of the ozone concentrations obtained bythe Climatic Scenario 3 and the Basic Scenario
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behavior might have been caused by introducing changes of the precipitation fieldsand the cloud covers in Climatic Scenario 3. The experiments indicate that this isnot giving the full answer. Results obtained by using Climatic Scenario 1, wherethese parameters were not changed, also show decreases in some cases. Maybethis phenomenon can be explained with domination of winds from areas with bigemission sources. Further research is needed in order to explain better the reason forthe occasional decreases of the number of bad days.
4.3 Impact of climatic changes on seasonal variations of the ozone concentrations
Seasonal variations of the differences of the ozone concentrations obtained withthe third climatic scenario and the Basic Scenario are given in Fig. 21. It is seenthe differences tend to be greater in the summer months (excepting the result forthe Russian site in August). In December all results, excepting again the Russiansite, are negative. The corresponding changes in percent are given in Fig. 22. Similar
Fig. 24 The same as Fig. 23, but the Russian site is removed
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conclusions (as those made above in connection with Fig. 21) can be drawn for theseasonal variation of the relative changes of the ozone concentrations when the twoscenarios are compared.
4.4 Impact of climatic changes on averaged diurnal variations of the ozoneconcentrations
The diurnal variation of the ozone concentrations, and especially for this particularstudy the diurnal variation in the summer months, is very important. It was alreadyshown in the previous section that the ozone concentrations are as a rule highest inthe middle of the day and during the afternoon. The results presented in Fig. 23 showclearly that this is also true for the differences between the results obtained by theBasic Scenario and those obtained by the third climatic scenario at the Russian site.The behavior of the differences at the other seven sites is not very clear, because thevariation at the Russian site is too strong and dominates the variations in the othersites. Therefore, it would be very illustrative to eliminate the Russian site and toshow the variation at the other seven sites on a separate plot. This has been done inFig. 24. It is seen that although the variation is not as strong as at the Russian station,
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Fig. 25 Numbers of measured and calculated “bad days” in the studied interval of 16 years for firstgroup of four cities
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the pattern is essentially the same. It should be mentioned here that differences ofthe diurnal variations averaged for a typical summer month (July) are presented inFigs. 23 and 24. The averaged differences are not very impressive (up to 3 ppb forthe seven sites). However, it is quite obvious that for some individual days thesedifferences might be much larger, which would undoubtedly lead to more “bad days”.The impact of the climatic changes on the numbers of “bad days” will be discussedin the next sub-section.
4.5 Impact of climatic changes on the numbers of “bad days”
Numbers of “bad days”, obtained with the third climatic scenario, are compared withthe corresponding numbers found with the Basic Scenario in Figs. 25 and 26. It isclearly seen that the numbers of “bad days” are in general increased when ScenarioClimatic 3 is used instead of the Basic Scenario. Two important questions are to beanswered in connection with the results presented in Figs. 25 and 26.
The first question is: are these changes serious? More and better visualizationsare needed in order to answer in a more satisfactory manner this question. Thedifferences of the “bad days”, obtained by these two scenarios, are shown on Fig. 27.
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Fig. 26 Numbers of measured and calculated “bad days” in the studied interval of 16 years for secondgroup of four cities
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It is seen that Climatic Scenario 3 leads normally to an increase of the “bad days” inthe interval from one to five days (sometimes the additional “bad days” are evenmore than five). The changes in percent are given on Fig. 28. It is seen that thechanges are as a rule in the interval from 5% to 30% (some times the changesare even more than 30%). This indicates that the climate changes have a ratherconsiderable impact on the numbers of “bad days” and, therefore, the danger that thenumber of “bad days” will exceed 25 (the target aim stated in the Ozone Directive;see European Parliament 2002) is significant.
It is clear that if this behavior is typical (i.e. if it is not restricted only to the eightsites selected for this study), then increases of the “bad days” by up to 30% will causein many parts of Europe situations in which the “bad days” are below the EU limitwhen the Basic Scenario is used and become greater than this limit when ScenarioClimatic 3 is run. Therefore the second important question which has to be answeredis: is the situation at the eight selected for this study sites typical for the whole spacedomain? The results presented in Fig. 29 provide a very clear answer to this question.It is seen that the changes are both very considerable and occur in a large part ofthe space domain. Moreover, the comparison of the results for 2004 (shown in thetwo upper plots in Fig. 29) with results for 2003 (see the two lower plots in Fig. 29)
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Fig. 27 Differences between the numbers of “bad days” obtained by using Climatic Scenario 3 andthe Basic Scenario in the studied interval of 16 years for the eight selected cities
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indicates that also the inter-annual variations of the numbers of “bad days” can bevery significant.
The requirements in the Ozone Directive of the European Union should besatisfied by the member countries after year 2010. Therefore it is interesting to seewhat can be predicted if UNI-DEM is run with the expected anthropogenic emissionsfor 2010. Some results are shown on the two upper plots of Fig. 30. Results obtainedwith the MFR Scenario (MFR stands for maximal feasible reductions) are given onthe two lower plots in Fig. 30.
The anthropogenic (human-made) emissions for Scenario 2010 and the MFRScenario are calculated by using the IIASA factors presented in Amann et al. (1999);the factors related to the sites, which were selected for this study, are listed in Table 5.
Several conclusions can be drawn from the results presented in Fig. 30:
• The reduction of the anthropogenic (human-made) emissions to the levelspredicted in Scenario 2010 will most probably not ensure that the target aim (nomore than 25 “bad days”) posed in the EU Ozone Directive will be fulfilled in the
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Fig. 29 Numbers of bad days when the Basic Scenario is used (for 2004 on the upper left-hand-sideplot and for 2003 on the lower left-hand-side plot) as well as the corresponding changes in percent(on the two right-hand-side plots)
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Fig. 30 Numbers of bad days (when Scenario 2010 and the MFR Scenario are used together withmeteorology for 2004 (on the two left-hand-side plots) as well as the corresponding changes in percent(on the two right-hand-side plots)
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Table 5 Reduction factors, which were used in Amann et al. (1999) to obtain emissions for Scenario2010 and the MFR Scenario, are listed for the countries used in this study
The emissions for these two scenarios are obtained by multiplying the corresponding emissions for1990 with the reduction factors. Note that while only the factors of the involved countries are givenin this table, all the emission factors from Amann et al. (1999) were utilized in the calculation of therequired inventories
whole of Europe (see the left-hand-side upper plot in Fig. 30). It is worthwhileto mention here that the results for Scenario 2010 that are shown in Fig. 30 wereobtained by using with meteorology for 2004. Using meteorology for other yearswill give other results (and often also more “bad days”).
• The reduction of the anthropogenic (human-made) emissions to the levelspredicted in the MFR Scenario will probably ensure that the target aim posedin the EU Ozone Directive will be fulfilled (see the right-hand-side lower plotin Fig. 30). In many parts of Europe also the long term aim will be fulfilled.However, it is not realistic to expect that the emissions will be reduced so much(unless some great technological achievements will be made in the near future).Also here it is worthwhile to mention that the results for the MFR Scenariothat are shown in Fig. 30 were obtained by using meteorology for 2004. Usingmeteorology for other years will give other results (and often also more “baddays”).
• The use of the climatic scenarios (Scenario Climate 2010 and Scenario ClimateMFR) indicates once again that the climatic changes will lead to some increasesof the numbers of “bad days”. In some parts of Europe these changes are ratherconsiderable.
5 Concluding remarks and plans for future work
Only a small part of the enormous sets of output data, which were collected byusing of 14 scenarios over a time-period of 16 years, was used in this study. It wasnevertheless clearly shown that the climatic changes would play an important role inthe selection of measures that are to be taken in the efforts to avoid damaging effectsfrom increased pollution levels. It has been shown that the warming trend will leadto an increase of the high ozone levels up to 30% (and sometimes even more than30%). Therefore, the impact of the warming effect on the environmental pollutionmust be included as a parameter during the development of control strategies forkeeping the damaging concentrations under the prescribed critical levels.
Some of the critical levels, including here most of the critical levels that arerequired in the EU Ozone Directive (European Parliament 2002), are not very
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carefully defined. It was mentioned in Subsection 3.5 that the determination of “baddays” might be an extremely unstable process because in some situations it mightbe enormously sensitive to very small (even negligible) errors of model results ormeasurements. This awkward fact creates great difficulties in the preparation ofreliable and robust control strategies. Moreover, this fact will also cause a lot ofdifficulties in the administration of the directive after 2010. Therefore, it is necessaryto try to stabilize the definitions. In connection with the “bad days”, the use ofthe sharp limit of 60 ppb is not a very good decision (in an extreme situation, thetransition from 59.99 ppb to 60.01 ppb will cause a shift from a “good day” to a “badday”). It would be much more appropriate to introduce an uncertainty zone or a“grey zone”. For example, if the maximal 8-hour averaged ozone concentration is inthe range of 40 to 80 ppb then the day under consideration should be put in the “greyzone”. If this number is under 40 ppb the day can be declared as a “good day”, whilethe day will certainly be “bad” if this number is bigger that 80 ppb. In this way allthe uncertain cases would be accumulated in the “grey zone”. The question is whatto do with the “grey days”. The simplest (but perhaps not the best) strategy will beto declare that n “grey days” are equivalent to one “bad day”. It will be necessaryto perform a long series of systematic experiments in order to decide what value ofn should be chosen. More elaborated strategies can also be tried. For example, itis possible to use some weight coefficients (smaller weight coefficients could be used
Fig. 31 Demonstration of the introduction of an uncertainty zone (“grey zone”)
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when the maximal 8-hour averaged ozone concentration is closer to the lower limit of40 ppb and larger weight coefficients in the opposite case). Finally, it is also possibleto apply two upper limits: one for the “bad days” (say, 0 days) and one for the “greydays” (say 50 days).
The use of an uncertainty zone (“grey zone”) is illustrated in Fig. 31.It must be emphasized that similar rules can be applied in the efforts to stabilize
most of the other critical levels which are based on the use of a sharp limit (the sharplimit used in any critical level should be replaced by some appropriate uncertaintyinterval).
There are some plans to perform appropriate experiments related to the definitionand utilization of “grey zones” instead of sharp critical levels.
Acknowledgements The work on this project was partly supported by the NATO ScientificProgramme (Collaborative Linkage Grants: No. 980505 “Impact of Climate Changes on PollutionLevels in Europe” and No. 98624 “Monte Carlo Sensitivity Studies of Environmental Security”).
The Danish Centre for Supercomputing at the Technical University of Denmark gave me accessto several powerful parallel computers for running the long sequence of scenarios.
Constructive criticism of three unknown referees helped me to improve considerably the presen-tation of the results. I thank them very much.
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