Identification of NOx and Ozone Episodes and Estimationof Ozone by Statistical Analysis

María Castellano & Amaya Franco &

David Cartelle & Manuel Febrero & Enrique Roca

Received: 10 March 2008 /Accepted: 9 August 2008 / Published online: 28 August 2008# Springer Science + Business Media B.V. 2008

Abstract Frame and daughters directives for eval-uating the ambient air quality have been adopted bythe EU as a part of the new strategies for pollutionprevention and control and environmental manage-ment. Therefore, the prediction of ozone concentra-tion and the identification of episodes by modelingare fundamental for protecting and preventing thepopulation and environment against the harmfuleffects of this species. Under this approach, ambientair quality (immission) data in three zones: AGuarda, Corrubedo and Verín (two coastal and oneinterior) of Galicia (NW Spain), were collected andevaluated using statistical tools. Punctual andfunctional background and standard levels of ozoneand NOx in the three zones have been determined fordetecting abnormal situations and identifying possible

emission sources. With this aim, threshold valueswere established by defining confidence levels.Finally, ozone concentration has been forecasted bytime series modeling. Descriptive and predictivemodels of ozone involving different parametersdepending of the area considered have been devel-oped. Satisfactory estimation of ozone concentrationwas obtained in the three cases with proved efficien-cy, since predictive values did not exceed the 95%confidence level.

Keywords Nitrogen oxides . Ozone .

Confidence levels . Functional random variables .

Time series analysis

1 Introduction

The evaluation of ambient air quality has been animportant environmental concern during last decadesdue to the increment of contaminant emissions to theatmosphere from anthropogenic sources. In the EU,directive 96/62/EC and daughters (1999/30/EC and2002/3/EC) for ambient air quality control andmanagement have been developed in order to estab-lish regulation protocols and plans, establishingthreshold values for NOx and ozone among others.The hourly limit value for NOx and the informationthreshold for ozone are 200 and 180 μg/m3, respec-tively, being the ozone alert threshold 240 μg/m3.

Water Air Soil Pollut (2009) 198:95–110DOI 10.1007/s11270-008-9829-2

M. Castellano :M. FebreroDepartment of Statistics and Operation Research,University of Santiago de Compostela,15782 Santiago de Compostela, Spain

A. Franco : E. Roca (*)Department of Chemical Engineering,School of Engineering,University of Santiago de Compostela,15782 Santiago de Compostela, Spaine-mail: enrique.roca@usc.es

D. CartelleEnvironmental Laboratory of Galicia, Xunta de Galicia,A Coruña, Spain

Under this framework, ambient air quality of theGalicia–North Portugal Euroregion was evaluated in aproject carried out by the cooperation between theEnvironmental Laboratory of Galicia and the Univer-sity of Porto (Environmental Laboratory of Galicia2001). This preliminary assessment was developedwith the aim of confirming the presence of atmo-spheric pollution through indicative measurementsand to identify transboundary contamination flows(Environmental Laboratory of Galicia), comple-mented with the study of soil bacterial micro flora inthe areas of study and the presence of critical levels ofenvironmental contaminants characterized (Universityof Porto; Quinteira et al. 2001).

Tropospheric ozone O3 is the product of complexphotochemical processes which involve some precur-sors as nitrogen oxides NOx and volatile organiccompounds VOCs (the two major classes of directlyemitted precursors). Ozone formation is a highlynonlinear process in relation to NOx and VOC(Sillman 1999). Besides, it is necessary to considerseveral factors that influence the formation anddistribution of ozone. Ozone formation depends ofprecursor concentrations and nature of their emissionsources (traffic, combustion facilities, biogenic, etc).Ozone distribution is highly influenced by meteoro-logical conditions and the topography (heterogeneityof the terrain, the land-use and the types of vegeta-tion) of the area (Jiménez et al. 2006) among others.In fact, a strong influence by meteorological factorson ozone levels have been reported in several works(Bloomfield et al. 1996; Shively and Sager 1999; Sojaand Soja 1999; Gardner and Dorling 2000; Dueñaset al. 2002; Krupa et al. 2003; Tarasova andKarpetchko 2003; Monteiro et al. 2005; Jiménez etal. 2006). Depending on the area, episodes can beoriginated by changes in anthropogenic emissions(Palacios et al. 2002). However, this factor is notenough for explaining ozone episodes, which usuallyinvolved other factors, like long-term transport orseasonal weather conditions (Monks 2000).

Ambient air quality studies in different worldlocations showed that ozone levels increased every yearbetween 0.5–2%, depending on the zone (Logan 1985;Vingarzan and Thomson 2004; Bronnimann et al.2002; Wu and Chan 2001; Lee et al. 1998), being itsconcentration twice higher with respect to the pastcentury (Vingarzan 2004). Significant efforts havebeen made for reducing the emissions of ozone

precursors, like nitrogen oxides and volatile organiccompounds (especially of NOx in Europe and USA),and although trends are still of concern, its emissionvelocities are lower or at least constant in the presentyears (Logan 1994; Pulles et al. 2007). However,ozone levels in many regions are still very high,exceeding in several occasions the exposure thresholdsestablished by the different countries. In SouthernEurope, ozone formation is particularly favored due toparticular meteorological conditions as intense solarradiation, high temperatures and re-circulation of thepolluted air masses (Sanz et al. 2007).

Therefore, prediction models can help for theidentification of episodes which is a key issue forprotecting and preventing the population against theharmful effects on human health from exposure tothese species, being this subject widely investigated(Huang et al. 2001; Elkamel et al. 2001; Lengyel et al.2004; Menut et al. 2005; Ghiaus 2005; Sousa et al.2006). Different Air Quality Models can be consid-ered for the assessment of ozone pollution levels,however two main group can be established deter-ministic and statistical models. The proper selectionof the model will depend of a number of factors i.e.the temporal and spatial scale, the availability of animportant number of physical, chemical, geographicaland meteorological data (Sillman 1999). Amongdeterministic models, those called as chemistry-transport models are the most powerful one toestimate tropospheric pollutant concentration fields,and specifically CHIMERE has been recently appliedin different case studies (Menut et al. 2005; Jiménezet al. 2006; Monteiro et al 2005). On the other hand, anumber of statistical approaches have been applied topredict the air quality and specifically ozone pollutionat a local level, i.e. Functional Analysis (Febrero-Bande et al. 2007), Neural Network models (Soja andSoja 1999; Castellano et al. 2005; Fernandez deCastro et al. 2003), Time Series and AutoRegresivemodels (Dueñas et al. 2005) or Multi-Variate Statis-tical models (Krupa et al. 2003).

In the present study, NOx and ozone air qualitylevels in three different areas sited in Galicia NWSpain (two of them sited in the coast – A Guarda andCorrubedo – and the other – Verín – in the interior)were evaluated by using pattern recognition statisticaltools, with the main objectives of determining thebackground and average levels of both contaminantsin the three areas, the identification of abnormal

96 Water Air Soil Pollut (2009) 198:95–110

situations and ozone concentration forecasting bytime series modeling.

2 Methodology

2.1 Areas of Study and Ambient Air Quality DataCollection

The areas of study are zones declared by the GalicianGovernment (European codes ES 1212, A Guarda andVerín, and ES1214, Corrubedo) in the preliminaryassessment under EC air quality directives. In theselocations, there were not previous representativemeasurements of the levels of pollutants with airquality stations, diffusive sampling methods or othermanual methods in the entire zones.

Air quality measurements in the sampling points(Fig. 1) were carried out by mobile laboratories of theEnvironmental Laboratory of Galicia (Government ofGalicia). These transportable measurement stationswere equipped with automatic sensors, which mea-sured on-line and with real-time response SO2, SH2,NO, NO2, CO, ozone and PM10, and severalmeteorological parameters like temperature, windspeed and direction, rainfall, atmospheric pressure,relative humidity and solar radiation. An acquisitionsystem collected, managed and transmitted the infor-

mation to the Environmental Laboratory of Galicia,which remotely controlled each station. Remotecalibration, real time connection or programmingalarms in case of high levels of pollution are alsoavailable. Nitrogen oxides concentrations were on-line measured by chemiluminescence in a Dasibi2108, with a detection limit of 2 μg/m3, while ozonewas monitored in a Dasibi 1008-AH by ultravioletphotometry (d.l. of 2 μg/m3), both reference mea-surement methods of the 2002/3/EC Directive.

The study comprised measurements in three dif-ferent areas, two of them sited in the coast (A Guardaand Corrubedo) and the other one (Verín) in theinterior. It is assumed that representativeness of theselocations is for sub-regional levels, around 100 km2.Therefore, the three sampling point are considered asrural stations. The selection of the zones of study wasbased not only in its different location (differentmeteorological conditions), but also in its character-istics (a coastal little village centre, a natural reserveand an interior little village), in order to investigatedifferent environments.

Galicia in most of its length has an Atlanticclimate, but has a wide range climate because it is atransitional zone between Oceanic and Mediterraneanregimes. The Atlantic climate is characterized by mildtemperatures, with small annual temperature oscilla-tions and abundant rainfall, while the Mediterraneanclimate is characterized by its humid and mild wintersand hot and dry summers. Thus, A Guarda is a coastalAtlantic zone with Mediterranean influence, deter-mined by a warm and humid climate and hotsummers, Verín is an area in the interior of Galicia,with a climate of Mediterranean influence, andCorrubedo presents an Atlantic climate predominant-ly. The difference between these climatic zonesdetermines weather conditions, and therefore, influ-ences its air quality (Martinez-Cortizas and Pérez-Alberti 2000).

The sampling periods in the coastal areas werecarried out during spring-summer, due to the higherozone formation in these seasons, while in the interiorzone (Verín), the measurements were prolonged inorder to better study the influence of meteorologicalfactors in ozone forecasting. Sampling was developedin different seasons and years, as one of the objectivesof the work was the development of tools for futureepisode identification in each particular location andtherefore, no inter-comparison between the coastal

Corrubedo

A Guarda Verín

Fig. 1 Situation of the sampling points in Galicia (NW Spain)

Water Air Soil Pollut (2009) 198:95–110 97

areas and the interior zone was intended to be done.The minimum time coverage (>10% during summer)recommended in daughter Directive 2002/3/EC fordata quality objectives in indicative measurements, ismet with the duration of sampling carried out. Theseresults are indicative of air quality in each locationand in the sampling period considered, since theseason influences the dispersion and deposition ofcontaminants, which depended mostly on the meteo-rological and climatological conditions (Tarasova andKarpetchko 2003). Ambient air quality was firstmeasured in Torroso mount (41°54.768′ N–8°52.162′ W, 365 m asl) in A Guarda, a locality of10,162 inhabitants in SW Galicia in the frontier withPortugal, and limited by the Miño river in the Southand East, by Vigo city in the North (considered thefirst industrial focus of Galicia) and by the AtlanticOcean in the West. The measures were carried outbetween 6th May 2000 and 23rd August 2000(107 days). During this period, the prevailing windswere in the directions ENE and WSW–SW. After-wards, a second mobile laboratory was placed in theCorrubedo natural reserve (42°33.35′ N–9°1.46′ W, atsea level) near the locality of Ribeira (26,413inhabitants) between 20th June 2000 to 7th September2000 (78 days). Low wind speeds were observedduring the sampling period (58% calm), being E–NEand S–SSW winds predominant. Finally, a thirdmobile laboratory was monitoring ambient air qualityin the Verín-Chaves region, in the Sta. Ana mount(41°53.126′ N–007°29.551′ W, 650 m asl) near thePortuguese limit, between 1st January 2001 and 7thAugust 2001 (220 days). This valley surrounded byseveral mountains has a population density of 28.6inhabitants/km2 and an area of 1,005 km2. Prevailingwinds in this region were those from NW and WNWdirection, being characterized by high speeds.

2.2 NOx and Ozone Levels Evaluation by StatisticalTools

Two punctual NOx and ozone concentration levels forthe three areas of study were defined: a backgroundlevel (minimum concentration) and a medium (typi-cal) level. Background and medium levels weredetermined in an hourly interval basis. The medianof hourly intervals was used for obtaining a charac-teristic concentration profile of the area. This meth-odology allows the episodes identification to be

simplified, as it permits to discriminate those abnor-mal values. Background levels were established at thefifth percentile (Castell et al. 2004; Peña 2002).Defining functional concentration levels was alsopossible, by the calculation of the functional medianof the data (Fraiman and Muniz 2001). In some cases,data may be considered as functional data, instead ofas a large vector obtained by the observation of adetermined phenomenon. In this study the number ofdata depends of the sampling frequency, but the dailyevolution of the variables is the same. The functionallevels were defined considering the day (24 h) as aunit, so the values of a variable in each day is treatedas one functional data, not as 24 isolated points. Thedaily functional data were observations from func-tional random variables, and the definition of func-tional levels is based on the translation of theconcentration measures (mean, median, …) from thepunctual world to the functional world. This transla-tion involves the definition of depth concept in thespace of functions. Some functional location measure-ments have been calculated, like the usual mean, thetrimmed mean, the median and the mode. Thetranslation of the punctual mode to the functional modeis not straightforward. It requires the definition of asuitable density function for the functional data andmaximizing the density function (Cuevas et al. 2006).The use of functional data analysis has been extendedfor solving many different problems (Ramsay andSilverman 1997, 2002) including air pollution applica-tions (Damon and Guillas 2002; Fernández de Castroet al. 2005).

As above-mentioned, punctual limits character-istics for each hour and each zone were alsoestablished with the aim of recognizing immissionlevels episodes. Twenty four hourly series wereconsidered for obtaining those limits, one for eachhour. Data from each of the considered series wereassumed to be independent, as there is no relationbetween data from the same time of consecutive days(US EPA 1999; Dutot et al. 2007). Under thisassumption, an asymptotic upper bound limit wasestimated for identifying episodes with a 95%confidence level (Peña 2002).

The identification of the relationships between theozone concentrations and meteorological conditions(wind speed and direction, temperature and solarradiation) was carried out by time series analysis(Damon and Guillas 2002). The study of the series

98 Water Air Soil Pollut (2009) 198:95–110

can be tackled from two different points of view: (a) aunivariate analysis, in which only the previousevolution or “inertia” of the series is considered; or(b) a dynamic regression model, in which theevolution of the time series is influenced by othervariables (Chatfield 1996). The class of Box–Jenkinsmodels has been the statistical method most widelyused for modeling time series (Box and Jenkins1976). Within this class, it can be emphasized theAutoregressive (AR) models, and the Moving Aver-age (MA) models. A more general Box–Jenkinsmodel, the Auto-regressive Integrated Moving Aver-age (ARIMA) model (Wei 1993), arose from thenecessity of explaining time series with periodicbehaviour. It also incorporates the idea of studyingthe series of the differences rather than the originalseries.

When the behavior of a time series cannot besuccessfully self-explained, i.e. by univariate timeseries analysis, some regressor variables or series(named the exogenous series) can be considered.The dynamic regression (ARIMAX) combines theBox–Jenkins models with the linear regression,obtaining a more general model for the study ofthe time series (Shumway and Stoffer 2000). Underthe assumption of asymptotic normality, predictionintervals can be built for all these time series analysismodels. This is one of the advantages of the statisticalapproach over the mathematical model approach, i.e.the possibility of constructing confidence and predic-tion intervals, based on the limit central theorem(Peña 2002) and the time series model structure.These prediction intervals provide security limits tothe model and a significant amount of data outsidethese limits can be associated to changes in theenvironmental behavior of the pollution sources in theregion.

Time Series models for predicting the next-dayozone concentration were built. Firstly, it was studiedthe temporal behavior of ozone independently fromthe other factors, which was adjusted to a Box–Jenkins model (AR, MA or ARIMA) depending ofdata structure. Afterwards, once the ozone behavior isfitted to a model, the other variables could beincluded in the model considered, with the aim ofanalyzing the dependency between them. This can beused for identifying the variables which more influ-ence actual ozone concentration and the time depen-dency between them and delay.

3 Results and Discussion

3.1 Punctual Background and MediumConcentrations of NOx and Ozone

In Fig. 2, the background (percentile 5%) andmedium (Median and Mean) concentrations ofhourly NOx and ozone, as well as the 95% upper-bond threshold (Up Lim) for A Guarda, Corrubedo,and Verín are shown. The background levels of NOxin all of the studied areas were quite low, around orslightly higher than 5 µg/m3. This value wasmaintained during the whole day. However, thehourly background levels of ozone varied betweenthe three areas considered, due to the differentdegree of atmospheric pollution. Besides, a differentmeteorology pattern can be associated to theselocations. Predominant winds present ENE andWSW direction with daily mean velocities lowerthan 18 km/h and a 32% of null values in A Guarda,NE, ENE, SSW and S direction with velocities lowerthan 11 km/h and a 58.2% of null values inCorrubedo and mostly from NW (28%) and WNW(13%) with velocities usually less than 18 km/h (in a6% of the cases were higher than 25 km/h) and a13.7% of null values in Verín. In this last location,solar radiation and maximum temperature presentedimportant differences during the period of study. Asan example, during the ozone episode occurred sinceJune 19 to 22nd 2001, values between 329–349 W/m2 and 23–25°C were observed, while on the otherhand since January 12 to 16th 2001, values between29–120 W/m2 and 23–25°C were observed. Despitethe different values, similar ozone profiles wereobtained for both the background and the mediumlevels in the studied zones, decreasing its concentra-tion through the night and increasing it along theday. Tracking and control of ozone is difficultbecause ozone formation involved complex reactionsand multiple precursors and because ozone can belong distance transported, as well. Simultaneousincreases and decreases between different stationsconfirmed that ozone oscillations were not onlyobserved at a local level. With independency ofbackground levels produced by anthropogenic andnatural processes, a component of greater magnitude,like exchanges with stratospheric ozone (Fusco andLogan 2003) or transboundary or intercontinentaltransport (Environmental Laboratory of Galicia

Water Air Soil Pollut (2009) 198:95–110 99

2001; Bertschi and Jaffe 2005; Stohl and Trickl1999) may influence ozone concentrations.

Medium (typical) concentration levels of NOx in AGuarda were equal to the minimum (background)level registered (Fig. 2, A Guarda). This fact may alsobe observed in the box plots (Fig. 3), which showedthat the variability of data in A Guarda was very low,being its majority around the smallest values. A

Guarda is a zone with high population and trafficdensity (NOx emissions), and sited in a region withhigh solar radiation. Mean daily traffic intensityvalues of 3,118 vehicles (a 6.5% of those were heavyvehicles) and 8,913 vehicles (a 9.7% of heavyvehicles) were registered during the period of studyon the roads which go from the location of the mobilelaboratory by the west in direction N and by the south

Fig. 2 Background, mean concentrations and 95% upper-bond thresholds of NOx and ozone in the three areas of study

100 Water Air Soil Pollut (2009) 198:95–110

to the NE. A brick and ceramic product factory whichis also a potential source of ozone precursors islocated in the vicinity (NNE) of the monitored place(41°55.956′ N–8°51.708′ W). A municipal solidwaste MSW dump (MSW, plastics and wood resi-dues) in the surroundings (SSW of the monitoringsite) of the village (41°54.210′ N–8°52.346′W, 31 masl) also produced emissions of NOx and other ozoneprecursors, as it suffered daily combustion processes(Environmental Laboratory of Galicia 2001). Thecombination of these factors provoked the fastphotolytic conversion of NOx, this resulting in lowerlevels of this contaminant (Chameides et al. 1992;Sillman 1999). Furthermore, one of the highestindustrial zones of metallic surface treatment andvehicle industry related activities in Galicia (indus-tries and cogeneration installations) is at a middistance (35 km) in E–NE coordinates. This justifiesthe ozone concentrations obtained in A Guarda, thehighest of the three studied areas. NOx only increasedslightly from 9:00 to 12:00 A.M., coinciding with thebeginning of population and industrial activities. Thedecrease of ozone in the first hours of the day may beexplained by the NOx-ozone cycle (Logan 1985).

In Verín, the medium level of NOx was similar tothe background level. The levels of NOx detected inVerín were quite high, because of the particularorography of the zone (a valley completely sur-rounded by mountains), which may favour theaccumulation of these pollutants, and the low solarradiation values in this zone. The ozone concentrationpresented also a smoother profile. Verín is mainly arural area and therefore, NOx and other ozoneprecursor pollutants may be mainly from non localindustrial sources or from biogenic origin. Mean dailytraffic intensity values were very low. Two thousand

one hundred fifty-one vehicles (a 7.9% of those wereheavy vehicles) were registered during the period ofstudy in the roads which surrounds the location of themobile laboratory by the north. A municipal solidwaste MSW dump located in the vicinity of thevillage direction NE from the mobile laboratory (41°57.322′ N–7°23.407′ W, 564 m asl) usually presentedcombustion processes and thus it was the main localsource of anthropogenic emissions of NOx and otherozone precursors (Environmental Laboratory of Gali-cia 2001). In general, the anthropogenic sources ofozone precursors were quite scarce in this location, sobeing the background levels of ozone quite low.

As it can be seen in Fig. 2 Corrubedo, this areapresented the highest and most irregular medium levelof NOx concentration. Taking into account thatCorrubedo is a natural reserve, this is an abnormalbehavior and thus, contamination could be due to atransport phenomenon from mid distances (Dueñaset al. 2004). However, NOx concentration in thisarea presented a particular profile. It increasedduring the night, reaching its maximum values at10 A.M., and decreased drastically to the backgroundlevel until 8 P.M. It is well-known that, during theday, marine breezes are observed in the coastalzones, due to the effect of the sunlight on its surface.However, at night, land surface loses heat faster thansea surface, and a contrary air circulation isproduced, causing the land breezes, this is inagreement with the low values of wind speedobserved in this location during this period. There-fore, pollution generated in those places could betransported to the reserve by the effect of landbreezes during the night. Otherwise, land winds maytransport ozone and its precursors over the sea, beingthe accumulated ozone on the sea returned to the

Ozo

ne(µ

g/m

3 )

VerínCorrubedoA Guarda

Site

25

20

15

10

5

0

VerínCorrubedoA Guarda

Site

100

80

60

40

20

0N

Ox

(µg/

m 3 )

Fig. 3 Box-plots represent-ing data variability andconfidence limits for NOxand ozone concentration(µg/m3) in the three popu-lations (A Guarda, Corru-bedo and Verín) considered

Water Air Soil Pollut (2009) 198:95–110 101

land in the daytime with the sea breeze (Grifoni et al.2004). In the vicinity of this natural reserve, thereare several localities (Ribeira and A Pobra doCaramiñal at 2.5 km to E and 8.5 km to the ENE,respectively) and mean daily traffic intensity valuesof 5,409 vehicles (a 11.9% of those were heavyvehicles) were registered during the period of studyon the road which surrounds the location of themobile laboratory in direction ENE and NE. Anindustry with a cogeneration facility (42°36′ N–8°57′W, 30 m asl) and a MSW dump which scarcelycombusted during the night (42°36.50′ N–8°58.55 ′W, 260 m asl), also exist and may influence on theobserved behavior. Furthermore, the industrial facil-ity dedicated to the processing and preserving of fishand fish products (NOSE-P 105.03) had a yearly NOxemissions declared of 504,000 kg (European Envi-ronment Agency 2004). This facility disposes acogeneration unit which used gas-oil as fuel in theperiod of study. Finally, there is a mid importantharbor (sited 20 km to the east from the monitoringstation in the city of Vilagarcia de Arousa) with animportant activity of 1,808 kt gross register(Environmental Laboratory of Galicia 2001).

Data from Corrubedo were widely dispersed(Fig. 3), indicating a great variability of NOxconcentration in this zone. This fact would confirmthe hypothesis that pollution in this natural reserve ismainly transported from other zones, depending onthe meteorological conditions of a particular day.Considering the prevailing winds (S–SSW and E–NE), NOx may come from the ocean (Galician coasthas an intense maritime traffic) but also from theanthropogenic sources previously mentioned.

In the case of ozone, confidence limits for AGuarda, Corrubedo and Verín were very close to themedium concentration levels (median), being indica-tive of a small data variance. For this reason, ozoneconcentrations would not expected to be higher thanthe characteristic level (mean) established for eachzone. As it can be seen in Fig. 3, ozone concentrationvariability was also very similar in all the zones.

3.2 Functional-Type Medium Concentration Levelsand Episodes Identification

In Fig. 4, NOx and ozone concentration level curvesof functional type for working days are shown.Median and 75% trimmed mean were considered the

representative of the medium concentration level.Median of functional type removed the 50% of theatypical values, this allowing better visualising thetendency of central data in the probability distribu-tion. Trimmed mean is between the median and themean, only eliminating the 25% of outliers. Func-tional type mean was not considered as representativeof data tendency, since it involved the whole data andwas strongly influenced by atypical values, given aninappropriate fitting curve. Something similar oc-curred with the functional mode, which also includenormal and abnormal situations for its determination.

The behavior of outlier curves of NOx and ozonelevels in A Guarda during working days follows asimilar tendency characterized by a decrease until6:00 A.M. and a progressive increase (according toanthropogenic activity) up to a maximum after 6 P.M.Thus it can be related with the presence of industrialfactories, cogeneration installations, traffic roads anda dump in the surroundings of the locality. Roadswere mainly sited in N-NE directions, whereas theindustries and cogeneration installations (one of thehighest industrial zones of metallic surface treatmentand vehicle industry related activities in Galicia) werein E–NE coordinates. On the other hand, meteorolog-ical conditions in both urban and industrial areas likeA Guarda may be different with respect to adjacentland, due to the presence of concentrated heat sources(vehicles, heating, etc.) or to atmospheric pollutionlayers over the village. These factors lead to theproduction of upflow air streams from the innervillage which downflow on their surroundings. Theair circulation caused the transport of contaminantsgenerated by the factories from the outskirts to thevillage centre. The atypical curves of NOx may beexplained by this phenomenon. During days June 6–7th, wind direction changed from NW to NE, and thewind velocity increase from 3.6 to 7.2 km/h, solarradiation and maximum temperature kept almostwithout changes (134 W/m2 and 15°C). An importantincrease to values of 25°C and 223 W/m2 intemperature and solar radiation, respectively, wasobserved on days 12th and 13th of June. Furthermore,wind direction moves from NW to NNE with respectto the day before. NO and NO2 concentration meanvalues increased since 2 to 14 μg/m3 and 2 to 5 μg/m3, respectively, while ozone concentration increased49 to 85 μg/m3 in the last day. The last episodecoincided with two outliers in the ozone concentration

102 Water Air Soil Pollut (2009) 198:95–110

2 days after (June 15th and 16th), when winddirection was still NE and wind speed increased to7.2 km/h, while temperature and solar radiation keptalmost constant (226 and 214 W/m2, 27 and 25°C on

days June 15th and 16th, respectively). Ozone episodesare usually related with mid-high wind speeds mainly incoastal areas (Dueñas et al. 2004; Dabdub et al. 1999),although high ozone concentrations were also reported

Fig. 4 Functional curves of medium concentrations (µg/m3) of NOx and ozone in A Guarda (superior graphs), Corrubedo and Verín(inferior graphs)

Water Air Soil Pollut (2009) 198:95–110 103

with light wind speeds, depending on the area (Topcuet al. 2003; MacDonald et al. 2001; Chen et al. 2003).Besides, atypical curves of ozone always coincided indays with high values of temperature and solarradiation (MacDonald et al. 2001; Bloomfield et al.1996). This is the behavior of this ozone episode in AGuarda, where light wind speed combined with highvalues of temperature and solar radiation occurred.

The number of abnormal situations in Corrubedo(a natural reserve) was higher than in A Guarda (anurban site), this being contradictory if it is taken intoaccount their different characteristics. On day June23rd, wind direction changed from N to NNE, with aspeed mean value of 3.6 km/h, the same of the daybefore, while solar radiation increased since 247 to271 W/m2 and maximum temperature kept constant(18°C). NO and NO2 concentration mean valuesincreased since 9.3 to 12 μg/m3 and 7.3 to 11.3 μg/m3, respectively, while ozone concentration did notsuffer modifications. On day August 25th winddirection changed from SE to E, with an almost nullmean value of velocity while solar radiation increasedsince 207 to 235 W/m2 and maximum temperaturekept constant (20°C). NO and NO2 concentrationmean values increased since 2.4 to 5.5 μg/m3 and 6.3to 21 μg/m3, respectively, while ozone concentrationslightly decreased since 35.9 to 32 μg/m3. Finally, onMonday August 28th wind direction varied sinceNNE to NNW with respect to the day before. Windspeed, solar radiation and temperature did not suffermodifications. NO and NO2 concentration meanvalues increased since 2.3 to 3.8 μg/m3 and 9.2 to21.3 μg/m3, respectively, while ozone concentrationslightly decreased since 34.2 to 29.4 μg/m3. On theother hand, ozone episode on day 21st occurred withWNW wind direction, an almost null value of windspeed and no changes in temperature with respect tothe day before, while solar radiation suffered animportant increase since 97 to 208 W/m2. NO2

concentration mean values decreased since 10.3 to3.3 μg/m3 and NO kept constant (2 μg/m3). Ozoneconcentration increased since 49.6 to 75.3 μg/m3.Finally, the last episode on August 8–9th occurredwhen wind direction changed from ENE to W and analmost null value of wind velocity, while solarradiation and maximum temperature decreased from256 to 205 W/m2 and 23 to 15°C, respectively. NO2

concentration mean values increased since 10 to22.1 μg/m3 and NO kept constant (2 μg/m3).

In Corrubedo, atypical curves of NOx wereobtained in general with low-medium intensity windsin the direction of anthropogenic sources, which thencould transport NOx from those sources. It has to bepointed out that NOx and ozone episodes did nevercoincide and, this being not expected consideringtheir relationship (ozone generation by NOx photol-ysis). However, this fact would confirm the possibilityof short (NOx) and long-term (ozone) atmospherictransport in this area. In case of ozone, sea-landbreezes could favor high concentrations of ozone, asabove-mentioned.

A long ozone episode was observed in Verínbetween 19th and 25th of June 2001. This periodwas characterized by an important increment intemperatures (up to 30°C) with moderate speedwinds 3.6 km/h (except on days 22nd and 25thwhen it was 7.2 and 10.8 km/h, respectively) with adirection which changed from WSW to NNW–NW(an important road was sited in this direction). Highvalues of solar radiation were observed on thosedays (349 to 329 W/m2). Different hypothesis couldbe given to support these results the production ofbiogenic emissions of VOCs or the long distancetransport of pollutants from an energy plant sitedalso in NW direction. In any case, it must be beardin mind the role of other ozone precursors differentto NOx and the development of further researchusing sampling (Sanz et al. 2007) and high-resolu-tion models (Monteiro et al. 2005; Jiménez et al.2006).

Meteorological parameters presented differentdegrees of significance depending on the area. Thisbehavior indicates the importance of developing localmodels which considered the particular characteristicsof each area. As it was explained before (Section 2.2),the temporal structure of each ozone series, withoutconsidering external variables, was studied.

3.3 Time Series Modeling for Ozone Forecasting

Prediction models to forecast ground-level ozone ineach area were developed by time series analysis. Theozone daily mean series of each region wereconsidered for this study. Meteorological parameterspresented different degrees of significance dependingof the area. This behavior indicates the importance ofdeveloping local models which considered the partic-ular characteristics of each area. As it was explained

104 Water Air Soil Pollut (2009) 198:95–110

before (Section 2.2), the temporal structure of eachozone series, without considering external variables,was studied. Ozone concentration presents an ARunderlying structure in all cases. Although ozonehourly series were assumed to be independent forconsecutive days, the analysis of the ozone dailyseries revealed a time dependency due to theapplication of the mean for its construction. In eachcase the possibility of include meteorological varia-bles into the model has been considered. In thefollowing paragraphs, the model selection for eachozone time series will be explained.

3.3.1 Guarda

Fist of all, a univariate time series model wasconsidered. The study of both the estimated partialautocorrelation function and the estimated correla-tion functions indicates an autoregressive model

(AR). Table 1 shows the estimated values of thepartial autocorrelation function, and their signifi-cance. An AR(1) model has been initially selected.The next step was to consider the inclusion of otherregressor series. Cross-correlation function showedcertain dependency between ozone concentrationand solar radiation and temperature of the previousday, although it was not very significant. This partialrelation can be explained by the fact that thesampling period was developed in summer, whensolar radiation and temperatures reached its maxi-mum values. Ozone concentration was also correlat-ed with wind direction (expressed as sine and cosine)of one and two days before, but this correlation wasnot significant. However, a strong correlation be-tween ozone and same-day NOx was observed, thisconfirming the hypothesis of direct generation ofozone from NOx which came from traffic andindustrial activities in the surroundings of A Guarda.Besides, it also was confirmed the influence of winddirection on ozone levels, being the wind directlyinvolved in the transport of NOx from the proxim-ities of the area. Due to the dependence betweenozone and same-day NOx, it was necessary todevelop a descriptive and predictive analysis of thislatter, to subsequently include it the ozone forecastmodel. The simple and partial autocorrelationsshowed that NOx concentration was adjusted to anAR(3) model, i.e., it depended of three-day beforeconcentrations. Despite different models may beemployed (Damon and Guillas 2002; Dueñas et al.2002; Férnandez de Castro et al. 2005; Palacios et al.

Table 1 A Guarda: Ozone estimated partial autocorrelationfunction and limits for the contrast of being equal to zero

Lag EstimatedParameter

StandardError

Lower 95.0%Prob. Limit

Upper 95.0%Prob. Limit

1 0.774 0.095 −0.187 0.1872 −0.173 0.095 −0.187 0.1873 −0.047 0.095 −0.187 0.1874 0.022 0.095 −0.187 0.1875 −0.005 0.095 −0.187 0.1876 −0.012 0.095 −0.187 0.1877 0.092 0.095 −0.187 0.187

80

100

120

140

3 )

A Guarda

0

20

40

60

05-01-00 05-16-00 05-31-00 06-15-00 06-30-00 07-15-00 07-30-00 08-14-00

Ozo

ne(µ

g/m

Date

Ozone Prediction Upper Limit Lower Limit

Fig. 5 Ozone real valuesand confidence limits basedon the predictive model in AGuarda

Water Air Soil Pollut (2009) 198:95–110 105

2002) considering different forecasted variables ordata intervals, in this case the predicted NOx serieswere employed for the ozone prediction because themodel can be used for on line prediction and allowsto work with missed data.

Different alternatives were tested with the aimof establish the predictive model of ozone. Thelowest error was obtained with an AR(1) modelwith five regressors (NOx same day concentration,solar radiation, temperature and wind direction of theprevious day and wind direction of two days before).However, the same model but with only oneregressor (same day NOx concentration) resulted ina very similar prediction. Thus, it is necessary totake into account that the higher the number ofvariables included in the model, the higher thecomplexity and prediction error. Besides, one of theregressors was another prediction model. For that

reason, it was selected the simplest model, whichwas described by Equation 1.

O3 tð Þ¼ O3 t�1ð Þ�0:60þ NOxðtÞ�0:41þ 19:80 ð1ÞBased on this prediction model, 95% upper and

lower-bounds confidence limits for ozone concentra-tions in A Guarda were determined, consideringtypical deviations of NOx and solar radiation pre-dictions. By introducing estimated data of thesevariables, variability of predicted ozone was in-creased, this extending the confidence interval.Estimate 95% confidence upper and lower curvesand real ozone concentrations for A Guarda werepresented in Fig. 5. It can be seen that only in 6occasions (of the 107 days), real data exceededthreshold limits, being the outliers between theremaining 5%.

Data from the last 12 days of the ozone series wereconsidered for model validation. The standard devia-tion of the residues was 10.18 for training data and10.30 for validation data. Validation data and trainingdata errors were very similar, so model adjustment isgood.

3.3.2 Corrubedo

A univariate time series model was again considered,obtaining an AR model. Table 2 shows the estimatedvalues of the partial autocorrelation function, and theirsignificance. An AR(1) model has been initiallyselected. In order to improve the model goodness, the

Table 2 Corrubedo: Ozone estimated partial autocorrelationfunction and limits for the contrast of being equal to zero

Lag EstimatedParameter

StandardError

Lower 95.0%Prob. Limit

Upper 95.0%Prob. Limit

1 0.661 0.113 −0.221 0.2212 −0.054 0.113 −0.221 0.2213 −0.055 0.113 −0.221 0.2214 −0.130 0.113 −0.221 0.2215 −0.209 0.113 −0.221 0.2216 −0.083 0.113 −0.221 0.2217 −0.148 0.113 −0.221 0.221

80

100

120

140

3)

Corrubedo

0

20

40

60

06-16-00 07-01-00 07-16-00 07-31-00 08-15-00 08-30-00

Ozo

ne (

µg/m

Date

Ozone Prediction Upper Limit Lower Limit

Fig. 6 Ozone real valuesand confidence limits basedon the predictive model inCorrubedo

106 Water Air Soil Pollut (2009) 198:95–110

inclusion of other regressor series has been considered.Meteorological factors did not present a direct influ-ence on ozone concentration in Corrubedo, except forsame-day wind direction (expressed as sine of theangle) which indicates a contaminant transport phe-nomenon, and agrees with the hypothesis proposed forthis zone. Therefore, it was necessary to establish apredictive model for this variable, being an AR(1)type. Otherwise, a descriptive and predictive analysiswas carried out for NOx concentration in order to studyits behavior, since correlations between these com-pounds and ozone were not clear. The descriptiveanalysis showed that NOx was adjusted to an AR(1)model, while the predictive analysis showed thedependence between NOx levels and wind direction,according to the hypothesis of NOx transport to thenatural reserve.

Different models were tested for ozone prediction.The model selected was again the simplest one (Eq. 2),an AR(1) model with previous-day NOx concentration

as regressor. In Fig. 6, the real ozone values forCorrubedo, and the 95% confidence level based on theprediction model were presented. It can be observedthat the levels of ozone exceeded the confidence limitsin a few occasions with respect to the total number ofdata, and thus, the model adjustment was adequate.

O3 tð Þ¼ O3 t�1ð Þ�0:67þ NOx t�1ð Þ�0:11þ 11:33 ð2Þ

In this case, the standard deviation of the residueswas 7.27 for training data and 5.33 for modelvalidation with data from the last 12 days of theozone series. Validation error was even lower thantraining error, so the goodness of the model has beenproved.

3.3.3 Verín

A univariate time series model was chosen. The studyof both the estimated partial autocorrelation functionand the estimated correlation functions indicated, likein the other locations, an autoregressive model (AR).Table 3 shows the estimated values of the partialautocorrelation function and their significance. AnAR(2) model has been initially selected. Afterwards,the inclusion of other regressor series has beenconsidered. Significant correlations were obtainedbetween ozone, same-day NOx and previous-dayNOx concentrations in the location of Verín. Theseresults indicated that ozone in this area may begenerated from NOx emitted by a close anthropogenic

Table 3 Verín: Ozone estimated partial autocorrelationfunction and limits for the contrast of being equal to zero

Lag EstimatedParameter

StandardError

Lower 95.0%Prob. Limit

Upper 95.0%Prob. Limit

1 0.757 0.069 −0.135 0.1352 −0.322 0.069 −0.135 0.1353 0.005 0.069 −0.135 0.1354 −0.089 0.069 −0.135 0.1355 −0.022 0.069 −0.135 0.1356 0.017 0.069 −0.135 0.1357 0.049 0.069 −0.135 0.135

80

100

120

140

3 )

Verín

0

20

40

60

12-31-00 03-01-01 04-30-01 06-29-01

Ozo

ne(µ

g/m

Date

Ozone Prediction Upper Limit Lower Limit

Fig. 7 Ozone real valuesand confidence limits basedon the predictive model inVerín

Water Air Soil Pollut (2009) 198:95–110 107

source (the municipal dump of solid urban wastes).Besides, cross-correlation showed the influence ofsolar radiation (same day and previous day) on ozoneconcentration. The correlation between these factorsmay be expected, since solar radiation is involved inozone generation. However, this dependency alsoindicated that ozone concentration in this area wasnot due to transport phenomena. This fact wasconfirmed by the non existence of a clear correlationbetween ozone and wind speed, although winddirection presented a low influence due to the specificand cyclic climatological changes of the area.

Taking into account simple and partial ozoneautocorrelations and cross-correlations with the othervariables, the prediction model for ozone was an AR(2) with two regressors, same day NOx concentrationand solar radiation. Although the proposed model(Equation 3) was complex due to the two predictionmodels included in it for the regressor variables, theprediction error obtained was lower than consideringa simplest model with only one regressor (accordingto Table 1).

O3 tð Þ¼ O3 t�1ð Þ�0:90� O3 t�2ð Þ�0:34� NOx tð Þ�1:37þRad tð Þ�0:024þ 26:33

The model efficiency was confirmed by theestablishment of a 95% confidence interval, sinceozone concentration values were within the limits inthe majority of cases (Fig. 7). The last 12 days of theozone concentration data series of this location wereused for model validation. The standard deviation ofthe residues was 10.38 for training data and 11.18 forvalidation data. Validation data and training dataerrors were quite similar, so model adjustment canbe considered as good.

4 Conclusions

Immission values of NOx and ozone were collected inthree zones of Galicia (NW Spain), in order toestablish characteristics levels (minimum and mediumconcentrations) for each one. The definition of levelsand confidence intervals allowed the identification ofepisodes and, in the case of Verin, its explicitdependency with specific meteorological conditions(solar radiation) through the developed Time SeriesModels. The results showed that ambient air qualityin the three studied areas are in general good, being

warning threshold values never exceeded during thesampling periods. Levels of NOx concentrations werenot very high in A Guarda and Verín, although someoscillations were observed mainly due to nearlyemission sources. In Corrubedo, NOx levels behavedin an anomalous way, reaching high values during thenight, pointing that nitrogen oxides arrived to thisarea favored by transport phenomena. The temporalbehavior of ozone during the day was similar for thethree areas, although different levels were obtaineddue to the specific characteristics of each zone. Thehighest values (episodes) of ozone were favored byhigh ambient temperatures and solar radiation. In AGuarda, several episodes were identified in June2000, coinciding with high ambient temperatures.Descriptive and predictive models of ozone involvingdifferent parameters depending on the area consideredwere developed. Satisfactory forecasting of ozone wasobtained in the three cases, especially in Verín, wheredata obtained from a longer sampling period wereavailable. The results obtained in this work showedthat ozone profile and its dependency with NOxconcentration may vary and be very different inrelatively nearly areas, due to the complex processesinvolved depending on the meteorological conditionsand atmospheric transport at regional, transboundaryand global scale. Further research by using passivesampling and high resolution models like CHIMEREmust be carried out for explaining some particularbehaviors and episodes (Verín) and obtaining predic-tions at a longer temporal horizon.

Acknowledgements This work partially has been carried outwith the support provided by the MEyC project (EuropeanFEDER support included) MTM2005-00820 and by theDirección Xeral de I+D+I (Xunta de Galicia) through theproject SIPENOZON (INCITE-07MDS049E).

References

Bertschi, I. T., & Jaffe, D. A. (2005). Long-range transport ofozone, carbon monoxide, and aerosols to the NE Pacifictroposphere during the summer of 2003: observations ofsmoke plumes from Asian boreal fires. Journal ofGeophysical Research (Atmospheres), 110(D5), D05303/1–D05303/14.

Bloomfield, P., Royle, J. A., Steinberg, L. J., & Yang, Q. (1996).Accounting for meteorological effects in measuring urbanozone levels and trends. Atmospheric Environment, 30,3067–3077. doi:10.1016/1352-2310(95)00347-9.

(3)

108 Water Air Soil Pollut (2009) 198:95–110

Box, G. E., & Jenkins, G. M. (1976). Time series analysis:Forecasting and control, revised edition. San Francisco,CA: Holden Day.

Bronnimann, S., Buchmann, B., & Wanner, H. (2002). Trendsin near-surface ozone concentration in Switzerland: The1990s. Atmospheric Environment, 36, 2841–2852.doi:10.1016/S1352-2310(02)00145-0.

Castell, N., Mantilla, E., Fernández, F., & López, E. (2004).Comparison of the ozone levels temporal variability incoastal and interior environments. In: Climate between thesea and the mountains. Asociación Española de Climato-logía y Universidad de Cantabria. Serie A, 4, 591–598 (InSpanish).

Castellano Méndez, M., Aira, M. J., Iglesias, I., Jato, V., &González-Manteiga, W. (2005). Artificial neural networksas a useful tool to predict the risk level of Betula pollen inthe air. International Journal of Biometeorology, 49, 310–316. doi:10.1007/s00484-004-0247-x.

Chameides, W. L., Fehsenfeld, F., Rodgers, M. O., Cardelino,C., Martínez, J., Parrish, D., et al. (1992). Ozone precursorrelationships in the ambient atmosphere. Journal ofGeophysical Research, 97, 6037–6055.

Chatfield, C. (1996). The analysis of time series. An introduc-tion, fifth edition. In texts in statistical science. London:Chapman and Hall.

Chen, K. S., Ho, Y. T., Lai, C. H., & Chou, Y.-M. (2003).Photochemical modeling and analysis of meteorologicalparameters during ozone episodes in Kaohsiung, Taiwan.Atmospheric Environment, 37, 1811–1823. doi:10.1016/S1352-2310(03)00079-7.

Cuevas, A., Febrero-Bande, M., & Fraiman, R. (2006). On theuse of the bootstrap for estimating functions withfunctional data. Computational Statistics & Data Analysis,51, 1063–1074. doi:10.1016/j.csda.2005.10.012.

Dabdub, D., Dehaan, L. L., & Seinfeld, J. H. (1999). Analysisof ozone in the San Joaquin Valley of California.Atmospheric Environment, 33, 2501–2514. doi:10.1016/S1352-2310(98)00256-8.

Damon, J., & Guillas, S. (2002). The inclusion of exogenousvariables in functional autoregressive ozone forecasting.Environmetrics, 13, 759–774. doi:10.1002/env.527.

Dueñas, C., Fernández, M. C., Cañete, S., Carretero, J., &Liger, E. (2002). Assessment of ozone variations andmeteorological effects in an urban area in the Mediterra-nean Coast. The Science of the Total Environment, 299,97–113. doi:10.1016/S0048-9697(02)00251-6.

Dueñas, C., Fernández, M. C., Cañete, S., Carretero, J., &Liger, E. (2004). Analyses of ozone in urban and ruralsites in Málaga (Spain). Chemosphere, 56, 631–639.doi:10.1016/j.chemosphere.2004.04.013.

Dueñas, C., Fernández, M. C., Cañete, S., Carretero, J., & Liger,E. (2005). Stochastic model to forecast ground-level ozoneconcentration at urban and rural areas. Chemosphere, 61,1379–1389. doi:10.1016/j.chemosphere.2005.04.079.

Dutot, A.-L., Rynkiewicz, J., Steiner, F. E., & Rude, J. (2007).A 24-h forecast of ozone peaks and exceedance levelsusing neural classifiers and weather predictions. Environ-mental Modelling & Software, 22 , 1261–1269.doi:10.1016/j.envsoft.2006.08.002.

Elkamel, A., Abdul-Wahab, S., Bouhamra, W., & Alper, E.(2001). Measurement and prediction of ozone levels

around a heavily industrialized area: a neural networkapproach. Advances in Environmental Research, 5, 47–59.doi:10.1016/S1093-0191(00)00042-3.

Environmental Laboratory of Galicia (2001). Study of atmo-spheric deposition and its characterisation and impact asatmospheric pollution prevention in the Galicia-NorthPortugal Eurorregion. Initiative INTERREG IIC, Opera-tive Programme “Atlantic Space” (In Spanish).

European Environment Agengy.(2004). EPER The EuropeanPollutant Emission Register. http://eper.eea.europa.eu/eper/.

Febrero-Bande, M., Galeano, P., & González-Manteiga, W.(2007). A functional analysis of NOx levels: location andscale estimation and outlier detection. ComputationalStatistics, 22, 411–427. doi:10.1007/s00180-007-0048-x.

Fernández de Castro, B., Guillas, S., & González Manteiga, W.(2005). Functional samples and bootstrap for predictingsulfur dioxide levels. Technometrics, 47, 212–222.doi:10.1198/004017005000000067.

Fernández de Castro, B. M., Prada-Sánchez, J. M., Febrero-Bande, M., Bermúdez-Cela, J. L., Hernández-Hernández,J. J., & González-Manteiga, W. (2003). Prediction of SO2level using neural networks. Journal of the Air & WasteManagement Association, 53, 532–538.

Fraiman, R., & Muniz, G. (2001). Trimmed means forfunctional data. Test, 10, 419–440. doi:10.1007/BF02595706.

Fusco, A. C., & Logan, J. A. (2003). Analysis of 1970–1995trends in tropospheric ozone at Northern Hemispheremidlatitudes with the GEOS-CHEM model. Journal ofGeophysical Research, 108(D15), ACH 4–1–ACH 4-25.

Gardner, M. W., & Dorling, S. R. (2000). Meteorologicallyadjusted trends in UK daily maximum surface ozoneconcentrations. Atmospheric Environment, 34, 171–176.doi:10.1016/S1352-2310(99)00315-5.

Ghiaus, C. (2005). Linear fuzzy-discriminant analysis appliedto forecast ozone concentration classes in sea-breezeregime. Atmospheric Environment, 39, 4691–4702.doi:10.1016/j.atmosenv.2005.04.012.

Grifoni, R. C., Passerini, G., & Tascini, S. (2004). Anassessment of the sea/valley breeze and its impact onozone behaviour. Environmental Studies, 10, 257–266.

Huang, H., Akustu, Y., Arai, M., & Tamura, M. (2001).Analysis of photochemical pollution in summer and winterusing a photochemical box model in the center of Tokyo,Japan. Chemosphere, 44, 223–230. doi:10.1016/S0045-6535(00)00189-2.

Jiménez, P., Jorba, O., Parra, R., & Baldasano, J. M. (2006).Evaluation of MM5-EMICAT2000-CMAQ performanceand sensitivity in complex terrain: High-resolutionapplication to the northeastern Iberian Peninsula. Atmo-spheric Environment, 40, 5056–5072. doi:10.1016/j.atmosenv.2005.12.060.

Krupa, S., Nosal, M., Ferdinand, J. A., Stevenson, R. E., &Skelly, J. M. (2003). A multi-variate statistical modelintegrating passive sampler and meteorology data topredict the frequency distributions of hourly ambientozone (O3) concentrations. Environmental Pollution,124, 173–178. doi:10.1016/S0269-7491(02)00407-4.

Lee, S.-H., Akimoto, H., Nakane, H., Kurnosenko, S., & Kinjo,Y. (1998). Lower tropospheric ozone trend observed in

Water Air Soil Pollut (2009) 198:95–110 109

1989–1997 at Okinawa, Japan. Geophysical ResearchLetters, 25, 1637–1640. doi:10.1029/98GL01224.

Lengyel, A., Héberger, H., Paksy, L., Bánhidi, O., & Rajkó, R.(2004). Prediction of ozone concentration in ambient airusing multivariate methods. Chemosphere, 57, 889–896.doi:10.1016/j.chemosphere.2004.07.043.

Logan, J. A. (1985). Tropospheric ozone: seasonal behav-iour, trends and anthropogenic influence. Journal ofGeophysical Research, 90, 463–482. doi:10.1029/JD090iD06p10463.

Logan, J. A. (1994). Trends in the vertical distribution of ozone:An analysis of ozone sonde data. Journal of GeophysicalResearch, 99, 25553–25585. doi:10.1029/94JD02333.

Martinez-Cortizas, A., & Pérez-Alberti, A. (2000). Galicianclimatic atlas (In Spanish). Environmental Ministry,Galician Government Eds., Santiago de Compostela(Spain).

MacDonald, C. P., Roberts, P. T., Main, H. H., Dye, T. S., Coe,D. L., & Yarbrough, J. (2001). The 1996 Paso del NorteOzone Study: Analysis of meteorological and air qualitydata that influence local ozone concentrations. The Scienceof the Total Environment, 276, 93–109. doi:10.1016/S0048-9697(01)00774-4.

Menut, L., Coll, I., & Cautenet, S. (2005). Impact ofmeteorological data resolution on the forecasted ozoneconcentrations during the ESCOMPTE IOP2a andIOP2b. Atmospheric Research, 74, 139–159. doi:10.1016/j.atmosres.2004.04.008.

Monks, P. S. (2000). A review of the observations and originsof the spring ozone maximum. Atmospheric Environment,34, 3545–3561. doi:10.1016/S1352-2310(00)00129-1.

Monteiro, A., Vautard, R., Lopes, M., Miranda, A., & Borrego,C. (2005). Air pollution forecast in Portugal: A demandfrom the new air quality framework directive. Internation-al Journal of Environment and Pollution, 25, 4–15.doi:10.1504/IJEP.2005.007650.

Palacios, M., Kirchner, F., Martilli, A., Clappier, A., Martín,F., & Rodríguez, M. E. (2002). Summer ozone episodesin the Greater Madrid area. Analyzing the ozoneresponse to abatement strategies by modelling. Atmo-spheric Environment, 36, 5323–5333. doi:10.1016/S1352-2310(02)00590-3.

Peña, D. (2002). Multivariate data analysis (in Spanish).McGraw Hill.–Interamericana de España, S.A.U.

Pulles, T. van het Bolscher, M., Brand, R., & Visschedijk, A.(2007). Assessment of global emissions from fuel com-bustion in the final decades of the 20th century. Applica-tion of the Emission Inventory Model TEAM. TNO Report2007-A-R132/B.

Quinteira, S., Monteiro, N., Cartelle, D., Rodríguez, R., Costoya,M., Roca, E., et al. (2001). Monitorizaçao de metais pesadosem solos por ICP e análise da flora microbiana. XVEncontro Galego-Portugues de Química, Book of Proceed-ings (pp 505–506). A Coruña. (In Spanish).

Ramsay, J. O., & Silverman, B. W. (1997). Functional dataanalysis. New York: Springer. (In Springer series instatistics).

Ramsay, J. O., & Silverman, B. W. (2002). Applied functionaldata analysis. In Springer series in statistics. New York:Springer.

Sanz, M. J., Calatayud, V., & Sánchez-Peña, G. (2007). Measuresof ozone concentrations using passive sampling in forests ofSouth Western Europe. Environmental Pollution, 145, 620–628. doi:10.1016/j.envpol.2006.02.031.

Shively, T. S., & Sager, T. W. (1999). Semiparametricregression approach to adjusting for meteorological vari-ables in air pollution trends. Environmental Science &Technology, 33, 3873–3880. doi:10.1021/es990286b.

Shumway, R. H., & Stoffer, D. S. (2000). Time series analysisand its application. In R. H. Shumway, & D. S. Stoffer(Eds.), Springer text in statistics. New York: Springer.

Sillman, S. (1999). The relation between ozone, NOx andhydrocarbons in urban and polluted rural environments.Atmospheric Environment, 33, 1821–1845. doi:10.1016/S1352-2310(98)00345-8.

Soja, G., & Soja, A. M. (1999). Ozone indices based on simplemeteorological parameters: Potentials and limitations ofregression and neural network models. AtmosphericEnvironment, 33, 4299–4307. doi:10.1016/S1352-2310(99)00126-0.

Sousa, S. I. V., Martins, F. G., Pereira, M. C., & Alvim-Ferraz,M. C. M. (2006). Prediction of ozone concentrations inOporto city with statistical approaches. Chemosphere, 64,1141–1149. doi:10.1016/j.chemosphere.2005.11.051.

Stohl, A., & Trickl, T. (1999). A textbook example of long-range transport: Simultaneous observation of ozone max-ima of stratospheric and North American origin in the freetroposphere over Europe. Journal of Geophysical Re-search (Atmospheres) , 104(D23), 30445–30462.doi:10.1029/1999JD900803.

Tarasova, O. A., & Karpetchko, S. Y. (2003). Accounting forlocal meteorological effects in the ozone time series ofLovozero (Kola Peninsula). Atmospheric Chemistry andPhysics, 3, 941–949.

Topcu, S., Anteplioglu, U., & Incecik, S. (2003). Surface ozoneconcentrations and its relation to wind field in Istanbul.Water Air and Soil Pollution Focus, 3, 53–64.doi:10.1023/A:1026046321511.

US Environmental Protection Agency. (1999). Guideline fordeveloping an ozone forecasting program. Office of AirQuality Planning and Standards, Research Triangle Park,North Carolina 27711.

Vingarzan, R. (2004). A review of surface ozone backgroundlevels and trends. Atmospheric Environment, 38, 3431–3442. doi:10.1016/j.atmosenv.2004.03.030.

Vinagarzan, R., & Thomson, B. (2004). Temporal variation indaily concentrations of ozone and acid related substancesin Saturna Island, British Columbia, Canada. Journal ofthe Air & Waste Management Association, 54, 459–472.

Wei, W. W. S. (1993). Time series analysis: Univariate andmultivariate methods. Wokingham, UK: Addison-Wesley.

Wu, H. W. Y., & Chan, L. Y. (2001). Surface ozone trends inHong Kong in 1985–1995. Environment International, 26,213–222. doi:10.1016/S0160-4120(00)00108-2.

110 Water Air Soil Pollut (2009) 198:95–110