meteorological conditions reduced no2 gotheburg

Have Meteorological Conditions Reduced NO2Concentrations from Local Emission Sourcesin Gothenburg?

Lin Tang & David Rayner &Marie Haeger-Eugensson

Received: 22 November 2010 /Accepted: 2 March 2011 /Published online: 24 March 2011# Springer Science+Business Media B.V. 2011

Abstract The risks of exceeding EU limit values forNO2 concentrations have increased in many Europeancities, and compliance depends strongly on meteorolog-ical conditions. This study focuses on meteorologicalconditions and their influences on urban backgroundNO2 concentrations in the city of Gothenburg for 19992008. The relations between observed NO2 concen-trations and meteorological conditions are constructedusing two modelling approaches: multiple linear regres-sion and synoptic regression. Both approaches assumeno trends in emissions over the study period. Themultiple linear regression model is established onobserved local meteorological variables. The synoptic-regression model first groups days according to synopticconditions using Lamb Weather Types and then useslinear regression on each group separately. A modelcomparison shows that linear regression model andsynoptic-regression model perform satisfactory. Thesynoptic-regression model gives higher explained vari-ance (R2) against observations during the calibrationyears (19992007), in particular for the morning peakand afternoonevening peak concentrations, but the

improvement in the validation period is weak. Theannual mean NO2 variations, and their trends during thestudy period, were assessed using the synoptic-regression model. The synoptic-regression model is ableto explain 54%, 42% and 80% of the annual variabilityof daily mean, morning peak and afternooneveningpeak NO2 concentrations, respectively. The observedand modelled annual means of the daily mean andmorning/afternoonevening peak NO2 concentrationsshow decreasing trends from 1999 to 2008. All trends,except the trend in annual-average observed morningpeak NO2 are statistically significant. The presence oftrends in the modelled NO2 concentrationseventhough emissions are assumed to be constantleadsus to conclude that weather and climate alone areresponsible for a substantial fraction of the recentdeclines in observed NO2 concentrations in Gothenburg.Favourable meteorological conditions may have miti-gated increases in local NO2 emissions during 1999 to2008.

Keywords NO2 concentrations . Dispersionconditions . Statistic downscaling . Linear regressionmodel . Synoptic-regression model . Gothenburg

1 Introduction

Despite significant decreases in nitrogen dioxide(NO2) concentrations in the last 15 years, the EUstandard for annual-average NO2 concentration(40 g/m3) was exceeded at a city street site and a

Water Air Soil Pollut (2011) 221:275286DOI 10.1007/s11270-011-0789-6

L. Tang :M. Haeger-EugenssonIVL Swedish Environmental Research Institute Ltd,P.O. Box 5302, 40014 Gothenburg, Sweden

L. Tang (*) :D. RaynerDepartment of Earth Sciences, University of Gothenburg,Sweden,P.O. Box 460, 405 30 Gothenburg, Swedene-mail: [email protected]

traffic route site in Gothenburg during all years from2003 to 2006 (Pleijel et al. 2009). During 19902000,anthropogenic nitrogen oxide (NOx=NO+NO2)emissions in Western Europe declined steeply, dueto the introduction of improved vehicle technologiesand stringent inspection systems, while from 2000 to2005, the downward emission trend had flattened out(Vestreng et al. 2009). In addition, increased "direct"NO2 emissions, due to increased use of oxidativecatalytic converters in diesel vehicles, and increasedbackground ozone concentrations, are thought to beoffsetting the effect of reductions in NOx emissionson NO2 concentrations (Carslaw et al. 2007; Grice etal. 2009). The NO2 fraction in primary emissions ishigher for diesel-fuelled cars (Pleijel et al. 2009), andthe fraction of vehicle-kilometres using diesel hasincreased by a factor of 2.5 in Sweden between 1994and 2008 (Haeger-Eugensson et al. 2010).

Local NOx emissions from different sources contrib-ute to local NO2 concentrations. Road transport hasbeen the dominating source of NOx emissions overEurope since 1970 and contributes with 40% to thetotal emissions in 2005 (Vestreng et al. 2009). In thecity of Gothenburg, NOx emissions from vehicles andships occupy the major portion of total emissions, with40% and 38% in 2005 (Haeger-Eugensson et al. 2010).The NOx emissions from vehicles and ships in the cityof Gothenburg slowly deceased from 9,300 ton/year in1998 to 5,930 ton/year in 2004 but increased again to7,104 ton/year by 2007, with particular increases fromships (Haeger-Eugensson et al. 2010).

The variation of air pollutant concentrations iscontrolled by not only natural and anthropogenicemissions but also chemical transformations, removalprocesses (wet and dry deposition), local dispersionconditions and long-range transport. Chemical lossfor NO2 in the atmosphere occurs during daytime andnight-time. During daytime, one important removalpath is the photolysis of NO2 to generate oxygenatoms driven by solar actinic flux (Eqs. 13), which isthe key reaction in photochemical smog formation.

NO2 hv > 340 nm ! NO O 1

O O2 ! O3 2

NO O3 ! NO2 O2 3

The other is the formation of nitric acid (HNO3) bythe reaction of gas phase NO2 and hydroxyl radical(OH; Eq. 4)

NO2 OH ! HNO3 4During the night-time, the appearance of nitrate

radical (NO3), formed by the reaction of NO2 andozone (O3; Eq. 5), further reacts with NO2 to formdinitrogen pentoxide (N2O5; Eq. 6). A considerableloss of N2O5 is assumed to occur on the surface ofaqueous aerosol particles by reaction Eq. 7 (Platt et al.1984). Thus, the hydrolysis of N2O5 on the surfacesof aerosol particles is the dominant removal channelfor nitrogen oxides at night (Riemer et al. 2003).

NO2 O3 ! NO3 O2 5

NO3 NO2 ! N2O5 6

N2O5 H2O ! 2HNO3 aerosol 7

Large-scale meteorological fluctuation and localmeteorological conditions speed up or slow downthese processes through the change of temperature,humidity, wind speed, etc., and finally decide theinter-annual variation of air pollutants (Velders andMatthijsen 2009). Carslaw et al. (2007) predicted theimpact of changes in the NO2 fraction of primaryemissions on future ambient concentrations in Londonand highlighted that the risks of exceeding the EU limitvalue in 2010 depended strongly on the prevailingmeteorological conditions. Grundstrm et al. (2011)found that the number winter-time NO2 and NO air-quality standard exceedances in Gothenburg is corre-lated with the North Atlantic Oscillation (NAO). Theyfound a positive trend in the number of hourly NO2concentrations exceeding 90 g/m3/year over 19972006, which they concluded was related to anincreasing trend in the NAO.

In line with this, climate change has been identifiedas an important factor influencing air quality andshould be taken into account for future policy design(Giorgi and Meleux 2007; Jacob and Winner 2009).Therefore, constructing impact models with appropri-ate meteorological conditions and climate changesignals is becoming essential, both to guide policy

276 Water Air Soil Pollut (2011) 221:275286

and to understand the effect of meteorological con-ditions and climate change on regional/local airquality.

A conventional way to analyse the response of airquality to meteorological conditions and climatechange is to run complex chemical transport models(CTMs) driven by fields generated from a regionalclimate model (RCM). The RCM has itself beenforced with boundary conditions from a globalclimate model, a process known as dynamicaldownscaling (Giorgi et al. 2001; Schmidli et al.2007). Dynamical downscaling involves explicitlysolving equations that describe the physical dynamicsof the atmospheric system (Giorgi and Mearns 1991).However, running a high-resolution CTM is timeconsuming and expensive work (Giorgi and Meleux2007). Alternatively, statistical downscaling methods,where relationships in the system are derived fromobservational data, are relatively simple and easilyimplemented. Statistical downscaling methods havebeen widely applied in Sweden, for studies coveringmonthly and daily precipitation (Hanssen-Bauer et al.2005; Chen et al. 2006; Wetterhall et al. 2009); annualsurface ozone levels (Tang et al. 2009) and urbanclimate (Thorsson et al. 2011).

According to Wilby and Wigley (1997), statisticaldownscaling techniques can be roughly grouped intothree categories: regression methods, circulation-based methods and weather generator methods.Regression methods are most common in statisticaldownscaling studies, including linear (such as linearregression, multivariate regression and canonicalcorrelation analysis) and non-linear methods (neuralnetworks). Circulation-based methods classify atmo-spheric circulation into a limited number of classesand simulate variables based on the circulation types.Recently, a new downscaling approach combiningregression methods and circulation-based methodshas been applied in air quality research (Cheng et al.2007a, b; Demuzere and van Lipzig 2010a, b). Thissynoptic-regression approach, applying a circulationpattern classification prior to the multiple linearregression analysis, has proven to be superior to asimple regression approach.

This study focuses on the impact of meteorologicalconditions on urban background NO2 concentrationsduring 19992008 in the city of Gothenburg. Twostatistical downscaling methodsmultiple linear re-gression and synoptic regressionwere established

and evaluated against observed daily mean, morningpeak and afternoonevening peak NO2 concentrations.Both models produced results that were highlycorrelated with observed concentrations. We then usedthe synoptic-regression model to further investigate theinter-annual variations and trends in observed NO2concentrations. The hypotheses of this study were:

& The variation of NO2 concentrations in the city ofGothenburg would be strongly influenced by localmeteorological conditions as well as synopticweather patterns.

& The inter-annual variations and trends of NO2concentrations depend strongly on the eventualmeteorological conditions.

2 Data and Methods

2.1 Data

2.1.1 Air Quality and Local Meteorological Data

Gothenburg is a city on the western coast of Sweden,with about 600,000 inhabitants and an area of451 km2. The air pollution monitoring site Femman,an urban background site, is located on a rooftop(25 m) in the city centre and close to the harbour. Thesite has been maintained by the Environmental Officein Gothenburg since 1965. We complemented themeteorological records from Femman (see below)with data from the nearby sites Lejonet (locatedaround 1.3 km north-east of the Femman site) andJrnbrott (located approximately 7 km south-west ofGothenburg city centre). Jrnbrott, a meteorologicalsite with a 100-m mast, is the nearest site to the cityequipped with vertically distributed temperature andwind instruments. The meteorological parameters thatdetermine the dispersion conditions are rather similarbetween the Jrnbrott and Femman site, even if theremay be a small time lag and somewhat differentinversion heights (Haeger-Engensson 1999). Thetemperature gradient at Jrnbrott has been used toinvestigate the relationship between winter inversionsand urban aerosol by Janhll et al. (2006), demon-strating that the temperature gradient at Jrnbrott cancapture the urban morning ground-level inversion.

The predictands in this study were daily mean(24-h average), morning peak (hourly maximum

Water Air Soil Pollut (2011) 221:275286 277

during 0500 to 1200 hours local time (LT)) andafternoonevening peak (hourly maximum during1400 to 0200 hours LT) NO2 concentrations atFemman. The predictors were the local meteorolog-ical variables measured at Femman, rainfall andglobal radiation at Lejonet and vertical temperaturedifference between 3 and 73 m at Jrnbrott.

In this study, the impact of meteorological con-ditions on local NO2 concentrations is our focus. Inorder to minimise the influence from emissionreductions, we selected the time period from 1999 to2008 during which there was a modest downwardtrend in NOx emissions over Europe (Vestreng et al.2009).

2.1.2 Lamb Weather Classification

Daily and 6-h mean sea level pressures (SLP) for a16-point gird centred over the Gothenburg city centre(57.7 N, 11.97 E) were obtained from the NCEP/NCAR Reanalysis database 2.52.5 degree pressurefields (Kalnay et al. 1996). Circulation indices(describing the geostrophic winds) and Lamb weathertypes (Jenkinson and Collison 1977) were thencalculated following Chen (2000). This classificationscheme has 27 weather types: anticyclone (A),cyclone (C), eight directional types (NE, E, SE, ),16 hybrid types (ANE, AE, ASE, CNE, CE, CSE,)and one unclassified type (U). In this study, the 27weather types were consolidated into 11 weathertypes according to their directions: U, A, C, NE, E,SE, S, SW, W, NW, N. For a more detaileddescription for each weather type, see Chen (2000)and Tang et al. (2009).

2.2 Methods

2.2.1 Multiple Linear Regression Method

Multiple linear regression is used to establish aquantitative relationship between a group of predictorvariables and a predictant. The relationship is useful tounderstand which predictors have greatest effect andthe direction of the effect, and to predict future valuesof the predictant. The stepwise linear regressionmethod has been widely used in synoptic climatolog-ical air pollution studies due to its ability to identifysequentially the optimum subset of predictor variables(Lam and Cheng 1998; Demuzere et al. 2009; Tang et

al. 2009). In this study, a backward step-wise methodwas conducted to find predictive equations for theNO2 concentrations with the meteorological variablesas the predictor variables. The independent meteoro-logical variablestemperature, relative humidity,wind speed, rainfall, etc.were included in the initialmodel, then the least-relevant variables were removedbased on the p value of an F statistic. The objective isto find the variables that best correlate with the NO2concentrations and the order-of-magnitude of influ-ence. MATLAB was used to conduct the backwardstep-wise regression for each season separately, withthe exit tolerance (minimum p value for a predictor tobe removed) of 0.10.

2.2.2 Synoptic-Regression Method

Synoptic regression is a combination of circulation-based and regression-based methods. The circulation-based method contains information about the large-scale atmospheric conditions. The regression method,based on local meteorological variables, reflects theimpact of local meteorological conditions. Thesynoptic-regression model, thus reflecting both localmeteorological conditions as well as atmosphericcirculation, has been applied in air-quality assessment(Cheng et al. 2007a, b). Table 1 shows that the threecirculation indices used for classification of circula-tion patternswesterly wind component (u), southerlywind component (v) and vorticity ()show signifi-cant correlations with NO2 concentrations. Therefore,Lamb weather type based on daily mean SLP and SLPat 0600 and 1800 hours were used for the synoptic-regression models for daily mean, morning peak andafternoonevening peak NO2 concentrations, respec-

Table 1 Correlation coefficient for circulation indices anddaily mean, morning peak, afternoonevening peak NO2 during19992008 after detrending

Dailymean

Morningpeak

Afternoonevening peak

Westerly wind component (u) 0.23 0.18 0.23Southerly wind component (v) 0.28 0.30 0.26Vorticity () 0.19 0.15 0.20

Westerly wind component (u), southerly wind component (v) andvorticity (). All correlations are statistically significant at p

tively. With 11 consolidated weather types (A, C, NE,), 44 (11 weather types4 seasons) regression modelsin total were established for each predictant.

2.2.3 Predictor Selection

With statistical downscaling, an important step is toselect appropriate predictors. One of the demands inselection of appropriate predictors is that the large-scale variables should be strongly correlated with thesurface variables of interest (Wetterhall et al. 2009).Table 2 lists local observed meteorological variablesused as predictors in the multiple regression models.Daily mean meteorological variables are used aspredictors for daily mean NO2 concentrations, thesub-daily meteorological conditions at 0600 and 1200hours are used to model the morning peak and thoseat 1800 and 0000 hours for the afternooneveningpeak. Most of predictors are significantly correlatedwith corresponding NO2 concentrations, except forrelative humidity at 0600 hours and global radiation at1800 and 0000. Among those predictors, local windspeed and near-surface vertical temperature differ-ence, dictated by atmospheric stability, are the mostsignificant variables influencing NO2 dispersion andtransport. The multiple regression models were con-structed for seasonal and annual time series.

2.2.4 Weekly Emission Index

The variability of city activities induces significantshort-term variations of emissions, especially in urbanareas (Ziomas et al. 1995). The weekly emissionindex, one of the significant predictors in Table 2,should be taken into account in any prediction model(Ziomas et al. 1995; Cheng et al. 2007a). A weekly

NOx emission index in Gothenburg was calculatedfor a typical road in the city centre (Grda) for year2006. It was determined from actual traffic intensitymeasurements and type of vehicles. NOx emissionfactors for each type of vehicles were based on theArtemis emission model (Andre 2005). The calcula-tions were carried out for daily mean, morning peakand afternoonevening peak of NOx emissions,respectively (see Fig. 1). These emission indices werethen used as one of the predictors in the respondingprediction models.

2.2.5 Model Evaluation Measures

A set of statistical measures was used to quantitative-ly measure and compare the performances of thelinear regression model and the synoptic-regressionmodel. The following measures were used in thisstudy: correlation coefficient (r), mean bias error, rootmean square error (RMSE) and explained variance(R2). In addition, the MannKandell method (Yue etal. 2002) was used to assess whether trends werestatistically significant, and the TheilSen method(Sen 1968) was used to estimate trends.

3 Results

3.1 Seasonal, Weekly and Diurnal Cycles

Diurnal, seasonal and weekly variations of NO2concentrations depend on NOx emissions, meteoro-logical conditions, chemical formation, chemical loss(Boersma et al. 2008) and loss through deposition.Figure 2a shows two daily peaks in NO2 concen-trations. The morning peak NO2 concentrations are

Table 2 Predictors for daily mean, morning peak and afternoonevening peak NO2 concentrations

Daily mean Weekly emission index, daily maximum temperature, daily minimum temperature, daily mean relative humidity,daily mean wind speed, daily mean surface pressure, daily mean global radiation, daily total rainfall, dailymean vertical temperature difference between 73m and 3m

Morning peak Weekly emission index, temperature at 0600 and 1200 hours, relative humidity at 0600 and 1200 hours, windspeed at 0600 and 1200 hours, surface pressure at 0600 and 1200 hours, global radiation at 0600 and 1200hours, daily total rainfall, vertical temperature difference between 73m and 3m at 0600 and 1200 hours.

Afternoon-eveningpeak

Weekly emission index, temperature at 1800 and 0000 hours, relative humidity at 1800 and 0000 hours, windspeed at 1800 and 0000 hours, surface pressure at 1800 and 0000 hours, global radiation at 1800 and 0000hours, daily total rainfall, vertical temperature difference between 73m and 3m at 1800 and 0000 hours.

Except for relative humidity at 0600 hours and global radiation at 1800 and 0000 hours, all the other meteorological variables havesignificant correlation with responding predictors at p

explained by the higher traffic volume in urban areasand poor dispersion before daytime vertical mixing isestablished. The afternoonevening peak NO2 con-centrations typically peak later than the rush hourmaximum, largely due to decreased mixing height andstagnant atmospheric conditions in the evening. Themorning peak occurs at a similar time for all seasons,while afternoonevening peaks occur at differenttimes in different seasons: around 17001800 hoursin winter and fall, 23000000 hours in spring andsummer. The delayed spring/summer afternooneven-ing peak is associated with delayed evening/night-time traffic volume and a later decline in the windspeed and mixing height (Bigi and Harrison 2010).

In the northern countries, higher NO2 concentra-tion in winter time (Fig. 2b) is mainly due to higheremissions from combustion and traffic in combination

Sun. Mon. Tue. Wed. Thu. Fri. Sat.0

0.2

0.4

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1

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1.4Tr

affic

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issio

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dex

Daily meanMorning peakAfternoonevening peak

Fig. 1 Calculated weekly emission index for daily mean,morning peak and afternoonevening peak in Gothenburg

00:00 04:00 08:00 12:00 16:00 20:0015

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a

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Fig. 2 a Durinal cycle of NO2 concentrations for each seasonduring 1999 to 2008. b Seasonal cycle of daily mean, morningpeak, afternoonevening peak NO2 concentrations in Gothenburg

for 19992008. c Weekly cycle of daily mean, morning peak,afternoonevening peak NO2 concentrations for 19992008


with frequently occurring poor dispersion in theearly morning during high-pressure situations(Haeger-Engensson 1999). Another reason forhigher NO2 concentrations is lower OH concentra-tions in winter, which results in the weak chemicalloss of NO2 during the daylight hours (Boersma etal. 2009). Apart from better dispersion in summer,higher water vapour concentrations and more ultra-violet flux lead to higher OH concentrations in thatseason (Spivakovsky et al. 2000). Therefore, stron-ger daytime chemical loss is strengthening the lowNO2 levels in summer.

The weekly variability of NO2 is entirely aconsequence of human activity (Flemming et al.2005). Similar with USA, Europe and Japan (Beirleet al. 2003), the weekly cycle of NO2 in Gothenburg(Fig. 2c) reflects the life-style of highly developedindustrialised society: industrial activity and trafficare reduced during weekends and Sunday/Saturdayminimum of NO2 are expected. NO2 concentrationsare kept at a similar high level from Monday toFriday.

3.2 Model Comparison

Our comparison of the linear regression and synoptic-regression models was based on the statisticalmeasures shown in Table 3 and Fig. 3. The R2 ofboth models against observations were calculated ineach season for calibration years 19992007 andvalidation year 2008 separately (Table 3). Figure 3shows that both models were able to reproduce muchof the variation in observed NO2. However, thesynoptic-regression model captured more of theobserved variability in daily mean, morning peak

and afternoonevening peak NO2 concentrationsduring the calibration years. In particular, thesynoptic-regression model was better able to representthe higher observed values, especially for the morningpeaks. RMSE and MAE for calibration years, given inFig. 3, show better performance for the synoptic-regression model, especially in simulating the morn-ing and afternoonevening peaks. For the validationyear 2008, the performance of the two models is moresimilar. Disappointingly, the highest observed morn-ing peak value that year is severely underestimated.

3.3 Assessment of Annual Mean NO2 Variationsand Trends

In order to display how much of the year-to-yearvariability and trends in NO2 concentrations arecaptured by the synoptic-regression model, the annualmean variation and the trend of observed andmodelled daily mean, morning peak and afternoonevening peak NO2 concentrations from 1999 to 2008are shown in Fig. 4. Annual mean values from thesynoptic-regression model were based on calibrationyear 19992007 and validation year 2008.

The synoptic-regression model is able to explain54%, 42% and 80% of the annual variability of dailymean, morning peak and afternoonevening peakNO2 concentrations, respectively. Furthermore, themodelled annual trends follow the observed decreas-ing trends. The slopes of linear trends, estimated byTheilSen method, for observed daily mean, morningpeak and afternoonevening peak annual mean NO2concentrations are 0.45, 0.20 and 0.78 g/m3/year and 0.23, 0.27 and 0.65 g/m3/year forsynoptic-regression modelled values. All trends,

Calibration years (19992007) Validation year (2008)

Multiple linear regression model (daily mean/morning peak/afternoonevening peak)

Winter (DJF) 0.73/0.58/0.57 0.57/0.44/0.57

Spring (MAM) 0.66/0.57/0.38 0.71/0.64/0.50

Summer (JJA) 0.61/0.49/0.36 0.71/0.50/0.37

Annual 0.71/0.59/0.50 0.68/0.55/0.49

Synoptic-regression model (daily mean/morning peak/afternoonevening peak)

Winter (DJF) 0.80/0.71/0.67 0.59/0.34/0.49

Spring (MAM) 0.77/0.71/0.54 0.70/0.58/0.49

Summer (JJA) 0.71/0.62/0.50 0.71/0.60/0.49

Annual 0.78/0.72/0.61 0.71/0.50/0.51

Table 3 Explained variance(R2) of the multiple linearregression and synoptic-regression models againstobservations for each season

Calculations were conductedfor daily mean, morning peakand afternoonevening peakNO2 concentrations duringcalibration years (19992007)and validation year (2008),respectively. All correlationsare statistically significant atp

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Obs NO2 (g/m3)

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Calibration year 19992007Verification year 2008

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except the trend in annual-average observed morningpeak NO2, are statistically significant.

4 Discussion and Conclusions

The study presents firstly the characteristics of NO2concentrations in the urban background site inGothenburg by investigating the diurnal, weekly andannual cycles. Secondly, the daily mean, morning peakand afternoonevening peak NO2 concentrations are

modelled using a multiple linear regression model and asynoptic-regression model. The two models are inreasonable agreement with observed NO2 concentra-tions. The synoptic-regression model captured more ofthe daily and yearly variability in mean and extremeNO2 concentrations. Different from the multiple linearregression model, the synoptic-regression modelincludes circulation pattern classification which hasclear physical links with high/low air pollution in thisregion (Tang et al. 2009). The circulation patternclassification provides a clear insight in typical large-scale atmospheric structures and associated anomalies inmeteorological variables during high/low pollutionevents (Demuzere et al. 2009). The synoptic-regressionapproach has been demonstrated to be suitable for ruralO3 concentration for a mid-latitude area, and the betterperformance of the synoptic-regression model is bene-

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 200922

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a

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Fig. 4 Annual variations and linear trends for observed andsynoptic-regression model based NO2 concentrations from1999 to 2008. a Annual-average daily mean, b morning peak

and c afternoonevening peak NO2 concentrations, respective-ly. Trends marked with asterisk in the legend representstatistically significant linear trends at p

ficial for representing higher-order statistical momentsof the air quality level distributions (Demuzere and vanLipzig 2010a). Our study extends the application ofsynoptic-regression approach to the urban environmentand NO2 concentrations, even though urban NO2variations depend dramatically on local emissions. Theresults confirm the hypothesis that local and synopticweather conditions strongly influence urban NO2 con-centrations in the city of Gothenburg, as well asdemonstrating the advantage of the synoptic-regressionapproach for air quality assessment.

It appears that for the study period 1999 to 2008,54%, 42% and 80% of the annual variability of dailymean, morning peak and afternoonevening peakNO2 concentrations, respectively, can be ascribed tovariations in local and synoptic weather conditions.Although both the primary emitted NO2 fraction andthe total NOx emissions from transportation have infact increased in city of Gothenburg during recentyears (Haeger-Eugensson et al. 2010), statisticallysignificant decreasing trends were detected in bothobserved and modelled NO2 concentrations at theurban background site. Comparing the trends in themodelled and observed NO2, it appears that weatherand climate alone are responsible for 51% (0.23/0.45), 135% (0.27/0.20) and 83% (0.65/0.78)of the declines in annual-average daily mean, morningpeak and afternoonevening peak NO2, respectively.That is, our modelling suggests that the decline indaily mean and afternoonevening peak NO2 concen-trations would have been much less if it were not forfavourable meteorological conditions and that morn-ing peak NO2 concentrations would in fact have risenunder average conditions.

Our finding of statistically significant, decreasingtrends in average NO2 concentrations might seem tocontradict Grundstrm et al. (2011), who found apositive trend in the number of hourly NO2 concen-trations exceeding 90 g/m3/year in Gothenburg inwinter. This apparent discrepancy is explained by thedetails of the two studies. Firstly, Grundstrm et al.(2011) analysed 19972006, whereas we analysed19992008 and the number of winter exceedences in20062007 and 20072008 were comparatively low.When data for 20062007 and 20072008 areincluded, the trend in exceedences disappears. Sec-ondly, in our study, we modelled average daily mean,morning peak and afternoonevening peak NO2concentrations rather than threshold exceedences.

Average concentrations are statistically more sensitiveto trends than exceedence counts, although exceed-ence counts are more closely aligned with policyobjectives. Finally, our study investigated trends inannual averages, whereas Grundstrm et al. restrictedtheir study to winter season. Only 40% of the hourlyNO2 concentrations exceeding 90 g/m

3/year in cityof Gothenburg occur during wintertime.

At this stage, we are not able to determine whetherthe trends in modelled NO2 are specific to the studyperiod or whether they are part of a longer-term trend.However, it appears thatat least in the recent pastweather and climate conditions in Gothenburg havebeen an important factor contributing to the trendtowards lower average NO2 concentrations.

Acknowledgements This work was supported by the GMV(Centre for Environment and Sustainability, Gothenburg,Sweden) and GAC (Gothenburg Atmospheric Science Centre)foundations. The authors appreciate the assistance of Mr. JanBrandberg from Environmental Agency in Gothenburg inproviding measured meteorological and air quality data forFemman. We gratefully acknowledge the NOAA/OAR/ESRLPSD, Boulder, Colorado, USA, for providing the NCEPReanalysis data. Finally, we would like to thank an anonymousreviewer for the careful reading and interesting suggestions.

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Have Meteorological Conditions Reduced NO2 Concentrations from Local Emission Sources in Gothenburg?AbstractIntroductionData and MethodsDataAir Quality and Local Meteorological DataLamb Weather Classification

MethodsMultiple Linear Regression MethodSynoptic-Regression MethodPredictor SelectionWeekly Emission IndexModel Evaluation Measures

ResultsSeasonal, Weekly and Diurnal CyclesModel ComparisonAssessment of Annual Mean NO2 Variations and Trends

Discussion and ConclusionsReferences

meteorological conditions reduced no2 gotheburg

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