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Atmospheric Environment 45 (2011) 7396e7405

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Atmospheric Environment

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The determination of a “regional” atmospheric background mixing ratio foranthropogenic greenhouse gases: A comparison of two independent methods

U. Giostra a, F. Furlani a, J. Arduini a, D. Cava b, A.J. Manning c, S.J. O’Doherty d, S. Reimann e, M. Maione a,*

aDepartment of Basic Sciences (DiSBeF), Università degli Studi di Urbino “Carlo Bo”, Scientific Campus “Sogesta”, 61029 Urbino, Italyb Institute of Atmospheric and Climate Sciences, CNR, Str. Prov. Lecce - Monteroni Km 1, 20073100 Lecce, ItalycAtmospheric Dispersion Group, Met Office, FitzRoy Road, Exeter, EX1 3PB, United KingdomdAtmospheric Chemistry Research Group (ACRG), School of Chemistry, University of Bristol, BS8 1TS, United Kingdome Empa, Swiss Federal Laboratories for Materials Science and Technology, Uberlandstrasse 129, 8600 Dubendorf, Switzerland

a r t i c l e i n f o

Article history:Received 22 February 2011Received in revised form23 June 2011Accepted 27 June 2011

Keywords:HalocarbonsBaselineContinuous observationsLong term trendsSources

* Corresponding author.E-mail addresses: umberto.giostra@uniurb.it (U.Gios

it (F. Furlani), jgor.arduini@uniurb.it (J. Arduini), d.cavamanning@metoffice.gov.uk (A.J. Manning), s.odohertStefan.Reimann@empa.ch (S. Reimann), michela.maione

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.06.076

a b s t r a c t

Halocarbons are powerful greenhouse gases capable of significantly influencing the radiative forcing ofthe Earth’s atmosphere. Halocarbons are monitored in several stations which are globally distributed inorder to assess long term atmospheric trends and to identify source regions. However, to achieve theseaims the definition of background mixing ratios, i.e. the mixing ratio in a given air mass when the recentcontribution of local sources is absent, is necessary. This task can be accomplished using differentmethods. This paper presents a statistical methodology that has been devised specifically for a mountainsite located in Continental Europe (Monte Cimone, Italy), characterised by the vicinity of strong sources.The method involves the decomposition of the observed data distribution into a Gaussian distribution,representative of background values, and a Gamma distribution, ascribable to contribution from strongersources. The method has been applied to a time series from a European marine remote station (MaceHead, Ireland) as well as to time series from Monte Cimone. A comparison of the methodology describedin this paper with a well-established meteorological filtering procedure at Mace Head has shown anexcellent agreement. A comparison of the baselines at Mace Head, Mt. Cimone and the Swiss alpinestation of the Jungfraujoch highlighted the occurrence of a specific background concentration. Althoughthis paper presents the application of the method to three hydrofluorocarbons, the proposed method-ology can be extended to any long lived atmospheric component for which a long term time series isavailable and at any location even if affected by strong source regions.

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1. Introduction

The relevance of man-made halocarbons lies in their capabilityto alter our climate in two ways: by absorbing long-wave radiationemitted by the earth’s surface and by acting as a major source ofozone-depleting halogens in the stratosphere. Halocarbons arecharacterised by prolonged residence times in the atmosphere.Therefore, continuous emissions resulting from their widespreaduse has contributed over the years to a build-up of their back-ground mixing ratios.

Long term and continuous in situ observations of halocarbonsare carried out in several globally distributed research stations in

tra), francesco.furlani@uniurb.@isac.cnr.it (D. Cava), alistair.y@bris.ac.uk (S.J. O’Doherty),@uniurb.it (M. Maione).

All rights reserved.

the frame of long term programmes, like AGAGE (Advanced GlobalAtmospheric Gases Experiment), or European funded projects, likeSOGE (System for Observation of Halogenated Greenhouse Gases inEurope).

One of the aims of long term observations is the definition ofatmospheric trends, while a high temporal resolution of themeasurements is needed in order to identify source regions andquantify emissions. In this way, compliance with internationalagreements regulating production (the Montreal Protocol) andemissions (the Kyoto Protocol) of these gases can be ascertained.

As mentioned above, continuous observations are conducted atdifferent stations in both hemispheres: most of them are classifiedas “baseline stations”, which are mainly under the influence of anairflow coming from “clean” sectors. Other stations, closer to sourceregions, are more frequently influenced by polluted air masses. Thelatter, even if less appropriate for the identification of a clearbaseline, are more effective for the identification of source regionsand quantification of emissions.

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e7405 7397

A general definition of the background mixing ratio of a givenspecies is the mixing ratio in a given air mass when the recentcontribution of local sources is absent. Such conditions occurwhenever the measurement site is under the influence of baselineairflow. Pollution events superimpose upon such background levels(Derwent et al., 2006) giving rise to the so-called “above thebackground peaks”.

A careful evaluation of the background mixing ratios is crucialnot only for estimating atmospheric trends and, consequently,annual growth rates, but also for emission evaluation, because backattribution techniques used for assessing emissions are based onthe clear identification of “above the background” data.

On the basis of the application described above, as a way ofidentifying a background mixing ratio, we consider that subset ofdata which, being produced by sources on sufficiently long timescales, has reached the condition of “well mixed”: this subset doesnot provide any information useful for source allocation and it isdistinguishable in the time series because it follows a Gaussiandistribution.

A Gaussian distribution can be produced, as well as by a spatiallyhomogeneous concentration field, also by a concentration fieldwith a linear spatial gradient. In the latter case, the resultingbackgroundwill be site-specific of the station, and that is true for allstations, including the so-called baseline stations.

In the case of more complex gradients, the background coulddeviate from a Gaussian condition and in any case, the stronger isthe gradient, the larger is the width of the Gaussian.

So far, several methods have been used for the identification ofthe background mixing ratio at the different stations (see e.g. Prinnet al., 2000; Reimann et al., 2005).

For “baseline” stations a common approach is the use ofa meteorological filter, i.e. discarding all those data that are relatedto air masses coming from not-clean sectors or taken when the airwas not well mixed (i.e. in stable atmospheric conditions)(Manning et al., 2003, 2011). Such a method requires that a cleansector can be identified for the station and that air from that sectorarrives at the station with sufficient frequency. As an alternative,and to be independent from the availability of meteorological dataand back trajectories, we propose a different approach based ona statistical method, using the assumption that the background ischaracterised by a Gaussian distribution.

In the following discussion, a comparison between the twodifferent approaches is presented based on observations carriedout at the “baseline” station of Mace Head (Ireland). Moreover,results obtained applying the statistical filter to two “continental”stations, Mt. Cimone (Italy) and Jungraujoch (Switzerland), arereported as well, along with a discussion on the concept of“regional background”.

In this study, we have focused on the three major hydro-fluorocarbons HFC-125, HFC-134a, and HFC-152a, whose charac-teristics are reported in Table 1 and on measurements conducted inthe time frame spanning from January 2001 to May 2009 at MaceHead and Jungfraujoch, and from June 2001 to May 2009 at Mt.Cimone.

Table 1Halocarbons considered in this study.

compound chemicalformula

GWPa lifetime(y)b

principal uses

HFC-125 C2HF5 3500 29 component in refrigerant blendsHFC-134a C2H2F4 1430 14 refrigerant in automotive

air conditionersHFC-152a C2H4F2 124 1.4 blowing agent, aerosol propellant

a IPCC AR4, 2007.b IPCC/TEAP, 2005.

2. Halocarbon analysis

High frequency observations of halogenated hydrocarbons areconducted at both stations via gas chromatographyemass spec-trometry (GCeMS) preceded by on-line sample enrichment usingadsorbent material. Although the analytical techniques are basedon the same principle they differ in the pre-concentration tech-niques used. A detailed description of the instrumentation used atMace Head and Mt. Cimone is reported in Miller et al. (2008) andMaione et al., 2004, respectively. Average instrumental precisionfor HFC-125, HFC-134a, and HFC-152a is 1.3, 0.6, and 1.5%, respec-tively at Mace Head and 3.0, 1.2, and 2.0% respectively at MonteCimone.

3. The observation stations

The Mace Head (MH) station is located on the West coast ofIreland (53�200 N, 9�540 W). It is one of a few clean baselineWesternEuropean stations, thus providing essential baseline input for inter-comparisons with continental Europe, whilst also acting as a base-line site representative of mid-latitude Northern Hemispheric air.Polluted European air masses as well as tropical maritime airmasses also cross the site periodically. The area immediatelysurrounding Mace Head is very sparsely populated providing verylow local anthropogenic emissions (Grant et al., 2010).

The Mt. Cimone (MtC) station is located in the highest moun-tains of the Northern Apennines in Italy (44�110 N, 10�420 E). It isconsidered representative of the European continental backgroundconditions (Fischer et al., 2003; Bonasoni et al., 2000) and due to itsaltitude (2165 m asl) and geographical position to the south of theAlps and the Po Valley and to the north of the Mediterranean Sea,this measurement site is suitable to study a wide spectrum ofatmospheric processes. Moreover, the site is characterised bya completely free horizon for 360�, and it has the highest averagewind speed among the Italian meteorological stations, with pre-vailing winds from SW and NE.

4. Baseline evaluation

4.1. The meteorological filter

The NAME model, a three-dimensional Lagrangian atmosphericdispersion model (Ryall et al., 2001) is run in backwards mode toestimate the impact of surface sources (assumed within 100 m ofthe ground) within 12 days of travel en-route to Mace Head. Thecomputational domain covers 100.0� We45.125� E longitude and10.0� Ne80.125� N latitude and extends to more than 10 kmvertically. For each 3-h period 33,000 inert model particles wereused to describe the dispersion.

Baseline concentrations are defined as those that have not beeninfluenced by significant regional emissions, i.e. those that are wellmixed and are representative of the mid-latitude Northern Hemi-sphere background concentrations. A 3-h period is classed as‘baseline’ if emissions from Europe or local toMace Head would notsignificantly contribute or if there is not significant influence fromsoutherly latitudes. The ‘local’ criterion is designed to exclude lowwind and stable boundary layer situations when local topographicor heating effects can result in complex local wind features, e.g.land or sea breezes, which are not resolved by the underpinningmodelled meteorology. Southerly air masses are excluded becauseof the impact of potentially strong hemispheric gradients. Theremaining ‘baseline’ data points, i.e. those observed during ‘base-line’ periods, are statistically filtered, removing outlying pointswithin a moving 40-day window, before smoothing within the

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e74057398

same time window. The method is fully described in (Manninget al., 2011).

Monthly baseline mixing ratios are estimated by averaging all ofthe smoothed hourly baseline values within the appropriate monthprovided there is a good representation of the whole month.

4.2. The statistical filter

Our alternative approach to identify the baseline in a conti-nental remote station is based on a two-step procedure. The firststep consists of detrending the time series using an appropriatetime interval.

The second step is aimed at estimating the amplitude of theoverall error, which includes the instrumental error and the naturalbackground variability, through the hypothesis that such overallerror follows a Gaussian distribution. A well-mixed pollutant ina non-stationary state also displays a Gaussian distribution.

In detail, in the first step we evaluate a thirty-day runningsixteenth percentile on the whole data set (ca. 16.000 data for eachcompound). We are assuming � 15 days as the time scale ofsynoptic variability; whereas the sixteenth percentile, which ina central Gaussian corresponds to �1 s (Sigma ¼ standard devia-tion), has been chosen being more stable than the runningminimum concentration value. Once we have identified therunning sixteenth percentile, we calculate the probability densityfunction (PDF) of the deviations of all data from the sixteenthpercentile itself. Finally, the absolute minimum value is translatedto zero to allow the use of a Gamma distribution.

Fig. 1 shows the PDF, represented by black dots, for HFC-134ameasured at Mace Head in the time interval January 2001eMay2009.

Fig. 1 also shows how the overall observed PDF is decomposedinto the sum of a Gaussian distribution, (red line) and a Gammadistribution (green line). The Gaussian corresponds to the well-mixed state; the sigma value of the distribution decreases withthe increase of the extent of mixing, converging towards theinstrumental error in the limit of “perfect” mixing. The Gammacorresponds to a non-well-mixed state. As described by Yee et al.(1993), a Gamma distribution can be used to simulate the PDF of

Fig. 1. PDF of the detrended (see text) HFC-134a data measured at Mace Head from Jan2001 to May 2009 (black dots). Red and green lines represent the Gaussian andGamma distributions, respectively, derived by the decomposition of the above PDF. Theblue curve is the sum of the red and of the green line. The upper X axis scale shows sunits of the Gaussian and is centred on the Gaussian mean. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version ofthis article.)

concentrations deriving from a nearby plume, i.e. a non-well-mixedpollutant.

We assign to the baseline all data lower than a threshold (Tb)corresponding to the intersection of the right-hand branch of theGaussian (red curve) and the Gamma distribution (green curve).

An appropriate algorithm (Maximum likelihood estimatesFunction, Statistics Toolbox�) is applied in order to evaluate theGaussian and Gamma PDF distributions whose sum is the best fitfor the observed PDF (blue curve in Fig. 1).

Fig. 2. Comparison of baseline data PDF distributions of HFC-125, HFC-134a, and HFC-152a at Mace Head obtained with the meteorological filter (blue curve) and thestatistical filter (red curve). Black dots represent the PDF distributions of all data.(For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e7405 7399

5. Discussion of results

5.1. Comparison between two independent methods

In order to ascertain the effectiveness of the method,a comparison with the well-established methodology based on themeteorological filter, developed for the station of Mace Head, hasbeen carried out. In the following the PDFs of time series obtainedfor the three compounds at Mace Head using the two methods arereported. In Fig. 2 the PDFs for the three HFCs are shown. Specifi-cally, the black dots represent the PDF distribution of total data, theblue curves represent data identified as “baseline” by the meteo-rological filter, and the red curves show the data identified as“baseline” by the statistical filter.

The graphs show that, according to the well mixed nature of thebackground, the subset of baseline data identified by the meteoro-logicalfilter shows a good agreementwith theGaussian distributionof the statistical filter in the identified baseline concentration value(i.e. the average of the PDF). The differences between the total dataPDFs and Gaussian PDFs could be due to source contributions soweak that they cannot be distinguished from the background, andtherefore cannot be allocated. The differences between the totaldata and the meteorological filter PDFs are also due to the choice ofusing only data related to air masses coming from specific sectors,a priori defined as “clean sectors” (i.e. under different constraint,like source regions, latitude, etc.).

The statistical filter already described has been applied to thetime series of the three halocarbons obtained at Mace Head. Fig. 3

Fig. 3. Percent relative bias between the two methods appli

reports the percent relative bias between the two methods appliedto the time series recorded at Mace Head for the three HFCs,showing that for HFC-125 and HFC-134a the percent relative bias ismost of the time below 1%, only occasionally it is greater but nevermore than 2%, and with only one data point between 2 and 3%. ForHFC-152a the bias is normally below 3%, with only four data pointsaround 4%.

5.2. Data analysis

In the following section, a detailed description of the dataanalysis for the time series of HFCs 134a, 152a and 125 at MaceHead and Mt. Cimone is reported. The results of the decompositionof the PDFs into Gaussian and Gamma distributions for the threeHFCs at the two sites are shown in the plots in Fig. 4.

The parameters describing the Gaussian (i.e. the average M andthe sigma s) and the Gamma (A and B) distributions for the abovetime series are reported in Table 2, along with the term W, whichrepresents the weighting of the Gaussian with respect to the totalPDF, and 1-W, which represents the weighting of the Gamma. Inother words, the two terms describe the ratio between baseline (W)and above the baseline (1-W) data. It should be noticed that, foreach compound,W is always greater at Mace Head compared to Mt.Cimone, reflecting the cleaner condition of the marine site.

The calculated s values represent the variability of baseline data.For the Mace Head data, the s of the two longer-lived compounds(HFC-134a (14 yrs) and 125 (29 yrs)) is close to the instrumentalerror, indicating that at Mace Head the two compounds have

ed to time series of three HFCs recorded at Mace Head.

Fig. 4. As Fig. 1, for the three HFCs at Mace Head (left panels) and at Mt. Cimone (right panels).

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e74057400

reached the well-mixed status. The greater s value of HFC-152a(1.4 yrs), which is twice the instrumental error, suggests that forthis compound, which ismuchmore reactive, thewell-mixed statushas not yet been reached and a spatial gradient is still occurring.

Table 2Parameters describing the Gaussian and the Gamma distributions for the three HFCs at

Compound M s A

Mace HeadHFC-125 0.58 � 0.004 0.08 � 0.004 6.99 � 0.52HFC-134a 3.49 � 0.02 0.41 � 0.02 7.20 � 0.52HFC-152a 2.59 � 0.01 0.18 � 0.01 16.88 � 1.31Mt. CimoneHFC-125 1.60 � 0.04 0.35 � 0.05 6.91 � 1.26HFC-134a 12.2 � 0.26 2.38 � 0.22 7.58 � 0.62HFC-152a 2.37 � 0.06 0.64 � 0.08 5.38 � 0.61

It should be noticed how, for the Mace Head data, the lowerconcentrations are obtained at many s below the Gaussianmean, asa consequence of the contribution of air masses from the SouthernHemisphere. That is especially true for HFC-152 for which such an

the two measurement Stations. For explanation of symbols, see text.

B W% 1-W% BF% 1-BF%

0.13 � 0.01 61 39 65 350.69 � 0.04 57 43 64 360.18 � 0.01 53 47 70 30

0.39 � 0.05 51 49 60 402.54 � 0.23 47 53 55 450.81 � 0.10 50 50 58 42

Fig. 5. PDF distributions of HFC-134a at Mace Head (a) and Mt. Cimone (b). Areasshaded in red represent data assigned to the baseline on the base of the proposedapproach.

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e7405 7401

effect is emphasised by the marked Meridional gradient of thiscompound.

In the case of Mt. Cimone, s values are always much higher(from 3 to 6 times) than instrumental error and this is due to themuch more complex source field surrounding the station. This isreflected in a broader baseline bandmasking data corresponding toweak sources that cannot be allocated.

The greater sigma value of the baseline distributions masksspecific sources which make weak contributions to the measuredconcentrations, either because they are nearby but small, or largeremitters but further away from the measurement site.

At Mace Head, W values, representing the frequency of occur-rence of data contributing to the baseline, are always greater than50%, consistent with the remote location of the station. An inter-esting comparison can be made between the behaviour of HFC-125and HFC-152a, whose average baseline concentrations are quitesimilar, despite HFC-152a having twice the emissions of HFC-125(Greally et al., 2007, O’Doherty et al., 2009) but with differentlifetimes (29 and 1.4 years, respectively). The lowerW value of 152aaccounts for the greater emission strengths for this compound.

Instead, at Mt. Cimone the frequency of occurrence of datacontributing to baseline and elevation above the baseline are quitebalanced, as a consequence of the proximity of strong sourceregions.

Typically, data are attributed to the baseline below a giventhreshold (Tb) (See e.g. Prinn et al., 2000; Manning et al., 2003;Reimann et al., 2005). In the proposed approach Tb is the inter-cept between the descending branch of the Gaussian and theGamma. Therefore, as a consequence of the greater sigma values ofthe Mt. Cimone time series compared with Mace Head, the numberof data not distinguishable from the baseline (even if due to sourcecontribution) is greater for the continental site, as shown in Fig. 5.

In the last two columns of Table 2, the percent relativeweighting of data belonging to the baseline (BF) and of data above(1-BF) the baseline, identified with the above described thresholdcriterion, are reported. An advantage of the proposed method isthat, through the comparison between percent W and BF values, itis possible to quantify, for each compound and for each station, towhat extent the number of data belonging to the baseline is over-estimated with respect to the actual distribution (represented byWand 1-W).

5.3. Baselines at Monte Cimone and Jungfraujoch

The statistical method described above has been applied to thetime series of the three HFCs recorded at Mt. Cimone. In Fig. 6, wereport a comparison between the resulting baselines (red curves)and those calculated with the same method at Mace Head (blackcurves). In addition, to corroborate the findings of this work for thebaseline at Mt. Cimone, the analysis has been applied to the timeseries recorded at the Swiss alpine station on the Jungfraujoch at3580 m asl (46� 320 N, 7� 590 E) (blue curves), which being locatedin central Europe is surrounded by an emission field similar to theMt. Cimone site. At the Jungfraujoch, halocarbons measurementshave been carried out since 2000 using the AGAGE instrumentationand calibration protocol (Reimann et al., 2004), andwith an averageinstrumental precision for HFC-125, HFC-134a, and HFC-152a of2.2, 0.6, and 1.1%, respectively.

The Figure shows that, even if the trends at the three stations areconsistent, the baselines at Mt. Cimone and Jungfraujoch onlysporadically (and always during the winter) coincide with thebaseline at Mace Head and are often higher. The phenomenon ismuch more evident during the warmmonths when the differencesbetween Mace Head and the other two baselines reaches itsmaximum, confirming the influence of the surrounding emission

field on the resulting baselines. This was already discussed byO’Doherty et al. (2009). In addition especially for Jungfraujochperiods exist where the station is for extended time periods underthe influence of extremely clean air masses, which results inbaseline concentrations being lower than Mace Head and MonteCimone.

As shown in Fig. 7, where the HFC-125minimawithin the thirty-day running window at the three stations are reported, throughoutthe whole year on many occasions, the minima at the two conti-nental stations are as low as those at Mace Head, indicating thatalso the continental stations are able to detect hemispheric back-ground values during the whole year.

In order to evaluate to what extent the separation of the base-lines shown in Fig. 6 is driven by the inclusion of the Mt. Cimonemeasurement site in the planetary boundary layer (PBL) due tochanges in solar radiation, differences among the seasonal andhourly PDF distributions have been investigated.

In Fig. 8, the normalised PDF distributions for HFC-125 in thefour seasons at Mt. Cimone and Mace Head are reported. Asexpected, at the continental site a different behaviour of the PDFdistribution, compared to Mace Head, is observed during thesummer, as a consequence of the PBL influence. In particular, a riseof the tail of the PDF deriving from the high frequency of occur-rence of above baseline data is observed. The consequent loweringof the summer PDF maximum is due to the normalisation of thecurves.

Fig. 6. Baseline values of time series of the three HFCs at Mt. Cimone (red curves) Mace Head (black curves) and Jungfraujoch (blue curves), obtained using the statistical filteringprocedure.

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e74057402

Fig. 9 shows the average diurnal variation of HFC-125 at Mt.Cimone and Mace Head. The concentrations measured at MaceHead do not show any significant daily cycle, meanwhile at Mt.Cimone a strong cycle is evident during the Summer, weaker inSpring and Autumn, and absent in the Winter. However, asexpected, the night-time values are always unaffected by the PBL,

Fig. 7. HFC-125a minima within the thirty-day running window at Mace Head (black), Mt. Cthis figure legend, the reader is referred to the web version of this article.)

suggesting that the Mt. Cimone station at night-time is in the freeatmosphere even during the summer.

To investigate this further, calculation of the baseline taking intoconsideration for Spring, Summer and Autumn (SSA) night-timedata only (20:00e10:00 h), has been carried out, while for thewinter (W) season all daily data have been used. The resulting plot

imone (red), and Jungfraujoch (blue). (For interpretation of the references to colour in

Fig. 9. Average diurnal behaviour for HFC-125 at Mace Head (top panel) and Mt.Cimone (bottom panel) expressed as the average of the detrended baseline.

Fig. 8. PDF distributions of HFC-125 data at Mace Head (left panel) and Mt. Cimone (right panel) in the different seasons.

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e7405 7403

(red curve) is reported in Fig. 10 together with the baselineobtained using the whole data set (black curve). In the same Figure,the blue dots represent the percent relative bias between the twotime series (monthly means), which is always below 2%.

Fig. 10. Baselines of the HFC-125 time series at Mt. Cimone. Red curve night-time data for Sbias between the two time series (monthly means). (For interpretation of the references to

Therefore, it can be concluded that the rise of the baselineduring the non-winter periods is not dominated by the influence ofthe PBL, but rather is due to an actual and systematic increase of theunderlying concentration.

Such an increase could be due to changes in the circulationduring the different seasons or to an increase in source strengths.

In order to evaluate the possible role of atmospheric circulation,a comparison has been made between the modelled atmosphericcirculation of air masses reaching the Mt. Cimone site during thesummer and the winter seasons. For this purpose, a constantrelease from sources homogeneously spread over the Europeandomain has been simulated, employing MM5 v 3.7 (Grell et al.,1994: Dudhia et al., 2005) and FLEXPART v 6.2 for MM5 (Stohlet al., 1998, 2005) models to simulate atmospheric circulation andcontaminants dispersion, respectively.

The mean contributions of each cell to the concentrationmeasured at Mt. Cimone are reported in Fig. 11a and b, for thesummer and winter season, respectively. The summer/winter ratio(Fig. 11c) shows how winter circulation is contributing moresignificantly to the signal detected at Mt. Cimone for continentalareas, whereas transport during the summer is more intense fromsea areas. Since the investigated compounds are of solely anthro-pogenic origin and emitted by land based sources, it suggests that,on the base of atmospheric circulation, the contribution to baselinedata by land based sources should be larger in winter.

Therefore, once both the PBL and the atmospheric circulationcontribution are ruled out, the summer rise in the baselineobserved at Mt. Cimone can be only attributed to the systematicincrease of emissions of the investigated compounds during thewarm season. In particular, emissions from diffuse and weaksources whose signal is detected at Mt. Cimone, but which are notable to rise above the baseline concentration, contribute to itsmagnitude.

SA and all data for W; black curve all data. The blue dots represent the percent relativecolour in this figure legend, the reader is referred to the web version of this article.)

Fig. 11. Modelled mean contribution to concentration measured at Mt. Cimone site after a constant release from the European domain in a) summer, and b) winter. c) summer/winter ratio.

U. Giostra et al. / Atmospheric Environment 45 (2011) 7396e74057404

6. Conclusions

A statistically based methodology for the identification of theatmospheric baseline in a remote continental site surrounded bya diffuse emission field has been proposed. In order to highlight thecapability of the approach, the application of the method to man-made halocarbons continuously measured in three remote sites,Mace Head, Mt. Cimone and Jungfraujoch, has been discussed.

The method requires that a clean sector can be identified for thestation and that air from that sector arrives at the station withsufficient frequency. In fact, the statistical approach is based on theidentification of a PDF distribution representative of baseline valuesand of a Gamma distribution ascribable to source contributions.The s of such a Gaussian distribution (sb), compared with theinstrumental s (si), is an indicator of the proximity of the concen-trations at the measurement station to those of the global back-ground. When sb is ysi, the station can be considered a planetarybackground station for the considered compounds. When sb > si,the station is a regional background site but the analysedcompound has not yet reached the well-mixed status and a spatialgradient is still occurring.

Therefore, the statistical approach allows an assessment of towhat extent the site is representative of a global rather thana regional background concentration.

The comparison of the results obtained at Mace Head, Mt.Cimone and Jungfraujoch highlights and confirms the previousstatement.

For Mace Head, the procedure has been validated againsta meteorological filtering method based on the identification of thebaseline contributions obtained by selecting air masses comingfrom clean sectors.

HFC-125 and 134a measured at Mace Head exhibit sbvalues y si. This is expected considering that their atmosphericlifetimes are much greater than the global mixing time scale. HFC-152a measured at the same site exhibit sb values much greater thansi, and this is representative of a marked Meridional gradient, dueto a lifetime comparable with the global mixing time scale.

The analysis carried out at Mt. Cimone, highlights sb valuesmuch > si for all the compounds, suggesting the occurrence ofa marked regional concentration gradient. Furthermore, theproposed approach allows the identification of a seasonal growth ofthe baseline, reaching its maximum in the summer, and ascribableto the contribution of a number of diffuse sources whose contri-bution is not significantly greater than the baseline and/or reachesthe receptor in a well-mixed state.

A similar situation is observed at the Swiss alpine station of theJungfraujoch, where sb values are 0.22, 1.34, and 0.55 for HFC-125,

HFC-134a, and HFC-152a, respectively thus confirming that thebackground shows features that are site-specific.

Sources whose contributions are always below the thresholdvalue Tb (see Data Analysis section) are undetectable. However,provided that the emission strength is constant in time, a sourcecan give a distinguishable contribution as a function of dynamicalparameters (as length of the trajectory followed by the airmass, turbulence, etc.). Such a source will be detectable throughan appropriate back attribution statistical approach (Manninget al., 2003).

Acknowledgements

This research started under the EU FP5 Project SOGE. ScrippsInstitution of Oceanography and the SIO2005 scale, The Universityof Bristol and the UB 98 scale are gratefully acknowledged, as wellas the science teams of the SOGE and AGAGE consortia. PaoloBonasoni, station chief, and all the staff of the CNR (Italian NationalResearch Council) “O. Vittori” research station at Monte Cimone aredeeply acknowledged as well.

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