Spatial Variation of Aerosol Chemical Composition and Organic

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Spatial Variation of Aerosol Chemical Composition and Organic Components Identified by Positive Matrix Factorization in the Barcelona Region Claudia Mohr, Peter DeCarlo, Maarten F. Heringa, Roberto Chirico, Rene Richter, Monica Crippa, Xavier Querol, Urs Baltensperger, and Andre Prevot Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b02149 • Publication Date (Web): 03 Aug 2015 Downloaded from http://pubs.acs.org on August 11, 2015

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Environmental Science & Technology

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Spatial Variation of Aerosol Chemical Composition and Organic

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Components Identified by Positive Matrix Factorization in the Barcelona

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Region

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Claudia Mohr1,2,*, Peter. F. DeCarlo1,3, Maarten. F. Heringa1, Roberto Chirico1,4, René Richter1, Monica Crippa1,5, Xavier Querol6, Urs Baltensperger1, and André S. H. Prévôt1

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14 15 16 17 18 19 20 21 22

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Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen, Switzerland

now at: Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany

now at: Department of Civil, Architectural, and Environmental Engineering and Department of Chemistry, Drexel University, Philadelphia, PA, USA now at: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), UTAPRAD-DIM, Frascati, Italy

now at: European Commission Joint Research Centre (JRC), Institute for Environment and Sustainability, Ispra, Italy

Institute for Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain *Corresponding author: Claudia Mohr, Karlsruhe Institute of Technology, Hermannvon-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany Phone: +49 -721 / 608 - 2 2696 Fax: +49 -721 / 608 - 2 4332 [email protected]

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ACS Paragon Plus Environment

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Abstract

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The spatial distribution of PM1 components in the Barcelona metropolitan area

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was investigated using on-road mobile measurements of atmospheric particle- and gas-

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phase compounds during the DAURE campaign in March 2009. Positive matrix

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factorization (PMF) applied to organic aerosol (OA) data yielded 5 factors: hydrocarbon-

29

like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), and low volatility

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and semi-volatile oxygenated OA (LV-OOA and SV-OOA). The area under investigation

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(~ 500 km2) was divided into six zones (city center, harbor, industrial area, pre-coastal

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depression, 2 mountain ranges) for measurements and data analysis. Mean zonal OA

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concentrations are 4.9 - 9.5 µg m-3. The area is heavily impacted by local primary

34

emissions (HOA 14 – 38%, COA 10 – 18%, BBOA 10 – 12% of OA); concentrations of

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traffic-related components, especially black carbon, are biased high due to the on-road

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nature of the measurements. The formation of secondary OA adds more than half of the

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OA burden outside the city center (SV-OOA 14 – 40%, LV-OOA 17 – 42 % of OA). A

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case study of one measurement drive from the shore to the pre-coastal mountain range

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furthest downwind of the city center indicates the importance of non-fossil over

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anthropogenic secondary OA based on OA/CO.

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Introduction

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Adverse health effects of atmospheric aerosols are of special concern in highly populated

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areas 1. The identification and quantification of their sources, basis for the design of

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mitigation strategies, remains challenging due to their complex nature. The organic

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fraction of atmospheric aerosols (organic aerosol, OA), often the major component in

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submicron particulate matter (PM1) 2, is composed of 1000s of compounds

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chemical reactions within the particles

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condensation, evaporation, gas-phase oxidation, and recondensation processes 6-8. Recent

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advances in the apportionment of OA sources have been achieved through the

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combination of aerosol mass spectrometer (AMS) measurements

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analytical model positive matrix factorization (PMF)

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has led to the identification of diverse OA components: Hydrocarbon-like OA (HOA,

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related to fresh traffic emissions), biomass burning OA (BBOA, from domestic wood

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burning or wildfire emissions), cooking OA, and different types of oxygenated OA

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(OOA, related to secondary OA), i.e., low volatility OOA (LV-OOA), characterized as a

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regional, heavily aged OA, and the less aged semi-volatile OOA (SV-OOA) 11, 12 11, 13-16.

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Most of these studies were based on stationary measurements during a certain period of

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time. However, the evolution of pollutants in the plume of a major source region such as

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an urban center or the characterization of the sources and processes of atmospheric

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pollutants of a whole region are best assessed by mobile measurements, ground based or

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using aircraft 17-25.

4, 5

3

subject to

and the surrounding gas phase via

10

9

and the factor

. The application of this method

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The densely populated metropolitan area of Barcelona, Spain (5.4 million

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inhabitants) is subject to very high air pollution levels due to the combination of its


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Mediterranean climate, abundant biogenic and anthropogenic emissions, orography and

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atmospheric dynamics 26, 27. Limited to the SE by the Mediterranean Sea and to the NW-

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SW by the Catalan coastal ranges, Barcelona experiences a daily cycle of nocturnal

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offshore flows, transporting air masses from the surrounding valleys (e. g. the densely

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populated Vallés) to the city center, and diurnal sea breeze, advecting polluted coastal air

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masses inland 28. The Llobregat river valley SW of the city center contains both extensive

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industrial and agricultural areas.

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This paper investigates the sources, processes and evolution of PM1 in the

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Barcelona metropolitan area during late winter (when frequent cyclonic episodes further

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enhance pre-existing high PM1 concentrations

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fraction. Mobile measurements were performed in March 2009 within the international

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DAURE campaign (Determination of the sources of atmospheric Aerosols in Urban and

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Rural Environments in the western Mediterranean)

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measurements of various particle- and gas-phase parameters at an urban background site

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investigated and quantified PM sources such as road traffic, construction-demolition

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works, shipping emissions, and photochemical processes

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burning

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carbon

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results from the mobile measurements complement these findings by adding information

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on the evolution of the Barcelona city plume and the emission sources in the regions

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surrounding the city. Since measurements were carried out on-road, levels and

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composition of PM1 are influenced by close traffic emissions from circulating vehicles.

31

32

or cooking

21

29

), with a special focus on the organic

26

30

. Concurrent stationary

. Contributions of biomass

emissions to OA, the fossil and non-fossil fractions of organic

, and the particulate trace metal content

33

were assessed, among others. The

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Experimental Section

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Mobile measurements. 16 days of mobile measurements (61 sampling hours)

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were performed in the Barcelona metropolitan area with the Paul Scherrer Institute

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mobile laboratory, an IVECO Daily van equipped with several continuous online gas-

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and particle-phase instruments 34, between March 1 and 26, 2009. Figure 1 shows a map

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of the Barcelona metropolitan area and the driving route. It was divided into 6 different

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zones for both the measurements and the data analysis: City center (zone 1), harbor (zone

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2), the industrialized Llobregat valley (zone 3), the coastal mountain range (zone 4), the

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pre-coastal mountain range (zone 6) and the heavily populated pre-coastal depression

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Vallés (zone 5). Table 1 summarizes the zones, driving dates and times. For a discussion

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of zone identification see SI.

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Instrumentation. Table S1 in the Supporting Information (SI) lists the

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instruments deployed in the mobile laboratory and gives information on the parameters

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measured, the detection limits and the time resolution of the data acquisition. Details are

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provided elsewhere 34; only deviations from this setup are provided here. The focus of the

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present analysis is on size-resolved chemical composition data of non-refractory PM1

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measured by the Aerodyne high-resolution time-of-flight aerosol mass spectrometer

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(AMS). AMS data were acquired in the higher sensitivity V-Mode 35 at a time resolution

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of 6 s. The PM1 equivalent black carbon fraction (EBC) was derived from aerosol light

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absorption measurements at 670 nm by a multi-angle absorption photometer (MAAP

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5012, Thermo), time resolution 1 s. An ozone monitor (Dual Beam, Model 205, 2B

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Technologies, Inc.) added for this campaign measured O3 concentrations based on the

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absorption of UV light at 254 nm. NO2 concentrations are available from March 10, 2009


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onward (when the chromium trioxide converter used to measure NOx

ceased to

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function; earlier data, where a separation of NO and NO2 is not possible, are excluded

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from the analysis). All gases were measured with a time resolution of 1 s.

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Data Analysis. AMS data were processed using the software SQUIRREL v.1.51B

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and PIKA v1.1.10B within Igor Pro 6.22A (Wavemetrics). The collection efficiency (CE)

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was determined based on a) the nitrate fraction fNO3 of the species measured by AMS

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and b) comparison of the AMS when the mobile laboratory was measuring at the urban

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background site (location shown in Figure 1) with a scanning mobility particle sizer

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(SMPS, TSI 3071 type differential mobility analyzer 37 custom built by PSI, condensation

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particle counter CPC 1022, TSI), a second AMS, and PM1 measured by an optical

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particle counter (1107, Grimm Labortechnik GmbH & Co. KG) and corrected with PM1

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sample loadings collected on filters 21. This resulted in a CE = 0.7 for fNO3 ≤ 0.25, CE = 1

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for fNO3 ≥ 0.78, and a linear increase between those values for 0.25 ≤ fNO3 ≤ 0.78 (Figure

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S1 in the SI). The CO2+ ion signal was corrected for elevated gas-phase contributions by

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a time-dependent factor based on gaseous CO2 measurements.

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PMF was applied to organic high resolution (HR) mass spectral data (time

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resolution 6 s). The input matrices, comprising the complete mobile data set, were

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prepared as described previously34. All data are in local standard time (UTC + 1) and at

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local pressure and temperature conditions (10 - 25 ° C and 943 - 1048 hPa).

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Mobile measurements are usually carried out during short periods of time only

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(few hours per day during few weeks per year). Analyses of the spatial distribution of

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parameters based on mobile measurements thus always bear the risk of being overly


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influenced by meteorological conditions and diurnal patterns 23. This is especially true for

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the present study where the mean concentration values for the different locations are

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based on aggregate data from several measurement drives spread out across several

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weeks. To reduce the influence of the temporal component on the spatial distribution, the

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time series of each parameter X measured in the mobile laboratory during a single drive

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(see Table 1) was divided by a normalization factor N based on the concentration of X

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measured simultaneously at the urban background station. The normalization factor NDy,

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of species X for an individual drive, Dy, is given as:

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𝑁𝐷𝑦 =

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with 𝑋_𝐵𝐺𝐷𝑦 denoting the background station mean value of X for the time interval of Dy

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and 𝑋_𝐵𝐺 the background station campaign mean value of X. Individual factors for

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particle components instead of a single factor for all components based on total PM were

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used since different meteorological conditions (e. g. relative humidity or radiation) can

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influence individual particle components differently. For PMF factors the Org

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normalization factor was used (normalization was done after PMF analysis).

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Normalization factors per drives and species are presented in the SI, as well as a detailed

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discussion on the influence of the normalization on zonal mean values, and limitations of

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the method employed. We ask the reader to keep in mind that the mass concentrations

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reported in Figures 3 and 4 are normalized data. In addition, the data were binned into

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different time of day intervals to give an indication of the influence of the diurnal pattern

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of parameters on the spatial variation. Table 1 specifies the time of day bins and the

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prevailing atmospheric scenarios as defined by Pandolfi et al.

𝑋_𝐵𝐺𝐷𝑦

(1),

𝑋_𝐵𝐺


26

which influenced

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meteorological conditions, strength of the sea breeze cycle, mixing/residual layer height

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and thus pollutant concentrations in the region. Briefly, scenario A is characterized by the

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most severe pollution episodes, recirculation of air masses, the pre-coastal mountain

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range lying within the mixing and residual layers, and a weakened sea breeze cycle;

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scenario C by North Atlantic advection flushing the planetary boundary layer; T denotes

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the transition periods with strong sea breeze cycles and a decoupling of pollutant

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concentrations between the city center and the pre-coastal mountain range.

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Results and Discussion

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PMF results. Five OA factors were identified in the mobile dataset, similar to the

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parallel stationary AMS data from the urban background site 21. These factors are shown

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in Figure 2, and are identified as the following: Hydrocarbon-like OA (HOA) from traffic

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(mostly diesel) emissions; biomass burning OA (BBOA) mostly from open agricultural

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fires

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secondary OA: Low-volatility OOA (LV-OOA), and semi-volatile OOA (SV-OOA). The

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R2 values of the correlations of the stationary and the mobile factor profiles, and of the

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time series of the PMF factors and ancillary data are shown in Table S3 in the SI, as well

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as the factors’ atomic oxygen to carbon (O:C) and organic matter (OM) to organic carbon

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(OC) ratios for both solutions. The noisy characteristics of the 6-s mobile data and

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different magnitudes of the peaks resulted in high scatter in the correlations, thus 5-min

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averages were used for most of the time series correlations (numbers in italics). PMF was

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run in the robust mode and the solution was analyzed as outlined in the SI from Mohr et

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al.

21

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; cooking OA (COA); and two different oxygenated OA (OOA), related to

, and the 5-factor solution was chosen. The most central rotation (fpeak = 0) and a


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pseudorandom start value SEED = 23 were selected based on the correlations displayed

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in Table S3, SI. Figures S4 and S5 in the SI show the mass fractions of the 5-factorial

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solution as a function of fpeak and SEED values tested, respectively. The ratio of Q

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(parameter to be minimized by the PMF algorithm

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freedom of the fitted data is) 1.18 for five factors, indicating a good error estimation12, 38.

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The 4-factor solution was discarded due to one factor being a combination of COA and

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BBOA (Figure S6, SI); the 6-factor solution yielded a “residual” factor (Figure S7, SI).

10

) over Qexp (expected Q, degree of

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Mobile and stationary HOA, COA and LV-OOA factor mass spectra are

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correlated with R2 values of 0.99, 0.92, and 0.96, respectively (Table S3). The O:C ratios

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of the mobile and stationary HOA factors are similarly low (0.01 and 0.03, respectively).

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For the COA, the stationary factor exhibits a higher O:C ratio (0.21) than the mobile

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factor (0.1), indicating that measurements on-road were closer to the cooking sources

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with lower atmospheric residence time and lower exposure to oxidants. The mobile LV-

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OOA factor has a higher O:C ratio (0.98) than the stationary LV-OOA factor (0.75),

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likely due to mobile measurements including more aged and processed air masses

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downwind of the city center. The BBOA spectra (R2 = 0.77) exhibit differences in the

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signals at m/z 44 (and 28), which are higher in the mobile spectrum. This is also reflected

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in the O:C ratios (0.31 and 0.24 for the mobile and stationary factor profile, respectively).

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The SV-OOA spectra show the least similarities (R2 = 0.69), mostly due to the signal of

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the CHO ion (R2 = 0.86 for the linear regression fit excluding CHO). Again, the O:C ratio

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of the mobile factor is higher than that of the stationary factor (0.54 and 0.32,

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respectively).


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Correlation of EBC with HOA (R2 = 0.63) is better than with BBOA (R2 = 0.24)

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and is likely due in part to sample bias from on-road sampling and variability in emission

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of BC from biomass combustion sources 39 (Table S3). This indicates that the majority of

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EBC sampled originated from traffic related fossil fuel combustions (supported by the R2

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= 0.71 of the correlation of HOA and CO, and R2 = 0.52 for HOA and NO2). These

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values are comparable to correlation factors found for measurements of traffic-related

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pollutants in Queens, New York with a soot particle AMS (R2 = 0.5 and 0.6 for HOA

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(including refractory BC) and BC or NOx, respectively)

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the time series of LV-OOA and SO4 are well correlated due to their regional

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characteristics

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shipping emissions which occur spatially and temporally separated from urban emissions

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locally influence the SO4 concentrations 41, 42. LV-OOA exhibits a temporal evolution (R2

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= 0.40) similar to e.g. the organic fraction of m/z 44 (f44), a surrogate for the degree of

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oxidation and thus photochemical age of the air mass

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NO3 (R2 = 0.74) due to its volatile nature, as observed in other datasets e. g. 11, 21.

11

40

. Usually in ambient datasets

. This is not the case for the present data (R2 = 0.30). We speculate that

43

. SV-OOA correlates well with

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Regional overview. Figure 3 gives an overview of the normalized mean

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concentrations of PM1 components measured on-road per zone and time interval. For a

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comparison of normalized to measured mean values see Table S2. Corrected particle

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number and gas phase concentrations are given in Table S4 in the SI. Apparent from

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Figure 3 is the relatively uniform distribution of PM1 component concentrations across

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the Barcelona metropolitan region. EBC is the major component in all zones, with the

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highest zonal mean value measured in the heavily populated Vallés region (zone 5)

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followed by the city center (zone 1). These EBC values are biased high due to the


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influence of surrounding vehicles when measuring on-road. This influence is also visible

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in the large differences of EBC mean zonal values between different times of day

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(horizontal bars). In addition, urban EBC background concentrations might be more

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reduced compared to on-road concentrations (due to tailpipe emissions being a major

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source) than for AMS species. This effect is even more distinct during daytime when

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traffic activity is high. Mobile measurements were only performed during the day. The

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normalization to relatively lower background data will thus bias EBC values high relative

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to AMS species and make the direct comparison difficult. Consequently, zonal mean

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EBC values should only be interpreted qualitatively for concentration levels in different

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zones, but at the same time are indicative of high emissions in the entire region.

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The inorganic PM1 components NO3, NH4, SO4, and Chloride (Chl) show little

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variability in concentration and fractional contribution across the Barcelona metropolitan

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area. An exception are the industrial zones of the Llobregat valley, where Chl shows

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significantly higher concentrations, most likely due to HCl emissions from industries

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(not discernible in Figure 3, compare SI, Figure S8). The ammonium nitrate/ammonium

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sulfate ratio is < 1 except in zone 3 and zone 6 (3.6 and 4.4 µg m-3 of NO3, respectively,

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compared to ~2.5 µg m-3 in the other zones), which can be attributed to higher NOx

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emissions (in the industrial area) and/or traffic activity while measuring. Shipping

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emissions in front of the Barcelona coast are very high due to the high traffic density of

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ships traveling from all across the Mediterranean towards the Gibraltar straight and

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impacting coastal air quality 41. The measured SO4 concentrations can thus to some extent

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be related to oxidation of SO2 or direct emissions of H2SO4 from diesel fuel combustion

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in ship engines

45

44

. The diurnal differences of the inorganic PM1 component


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concentrations are relatively small and indicate that they are less influenced by very local

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emissions but more part of the regional pollution problem. Zone 4 shows slight decreases

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of all components in the afternoon compared to morning hours. With the geographical

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orientation of the Llobregat valley (perpendicular to the shoreline, not downwind of the

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city center), the sea breeze might have a slight cleansing effect on this region.

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OA exhibits the second highest concentrations of PM1 components in all zones.

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Total OA concentrations are similar across all zones (zonal mean values between 4.9

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(zone 4, t4) and 9.5 (zone 1, t1) µg m-3) and relatively high compared to concentrations

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measured across Europe

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PMF vary more considerably. Figure 4 shows the spatial variation of OA and the

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corrected relative contributions of the PMF factor loadings to OA per zone and time

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interval (for a comparison of normalized to measured zonal mean values of PMF factors

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see Table S2). The highest organic concentrations are measured in the city center during

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t1. The contribution to OA from OOA (sum of SV- and LV-OOA) is only 34%, HOA

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dominates the primary components with 38% (3.3 µg m-3) followed by COA with 16%

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(1.4 µg m-3). Similar HOA fractions were found for roadside measurements with a mobile

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laboratory in New York City 46. In the other zones, HOA makes up between 14% (1.1 µg

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m-3, zone 4, t5) and 29% (2.4 µg m-3, zone 4, t1). Note again that these values are highly

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influenced by traffic conditions during on-road measurements. For all zones, BBOA

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makes up between 10 and 12% of OA (range of absolute values: 0.6 – 1.1 µg m-3) and is

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attributed to a mix of domestic solid fuel combustion

267

(observed especially in zone 3, where the highest absolute concentration was measured).

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Relative COA contributions to OA are highest in zone 4 (14 – 18%, 0.7 – 1.3 µg m-3),

15

. The contributions of the different components as found by

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and open agricultural fires

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10% in the remote zone 6 (0.7 µg m-3) and between 10 and 15% (0.7 – 0.9 µg m-3) in

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zones 2, 3, and 5. Even though relative contributions of COA to OA exhibit similar

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values in the entire region, absolute values decrease from city center to zone 6 by a factor

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of 2. There is a slight increase in relative HOA and COA contributions in the afternoon

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hours, likely influenced by the diurnal wind pattern. In the afternoon, sea breezes advect

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air masses from the city center 26 to the zones north of the city. These air masses contain

275

relatively more HOA and COA leading to a slight decrease in fractional SV-OOA

276

contribution, formed in-situ during the morning hours by photochemical reactions of

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gaseous precursors. The relative contribution of the more regional LV-OOA stays

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constant during this meteorological pattern. Exceptions are zone 4 (coastal range) which

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is less impacted by local emissions and often above the city’s mixing layer 26, and zone 2

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(harbor), where the fractional contribution of LV-OOA to OA increases in the afternoon

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due to the sea breeze advecting aged air masses.

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In all zones except zone 1, OOA mostly dominates (48 – 63%), as observed in

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other locations (both urban and rural) in the northern hemisphere 2. Biogenic or, more

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generally, non-fossil emissions might contribute substantially to OOA, especially

285

downwind of the city center 32. The highest OOA contribution (40% SV-OOA, 2.7 µg m-

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3

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We attribute the high SV-OOA level to newly formed OA during hours with high

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photochemical activity from gaseous precursors emitted in the city center, which can be

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upwind of the harbor at this time of day 28. The high LV-OOA contribution to OA (32%,

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2.2 µg m-3) may be partly due to aging of SV-OOA and advection of processed air

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masses by the sea breeze. In addition, the two afternoon drives in zone 2 were both

, and 23% LV-OOA, 1.6 µg m-3) was observed in zone 2 (harbor) in the late morning.


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during meteorological episode A, characterized by the most severe pollution episodes and

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high LV-OOA levels due to recirculation of air masses. The LV-OOA concentrations

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might thus be biased high despite normalization to urban background values. Zone 6

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(Montserrat), a forested, mountainous, and sparsely populated region, exhibits the

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second-highest contribution of OOA. In contrast to zone 2, t1, LV-OOA (42%, 2.9 µg m-

297

3

298

general trend in increasing contributions from (LV-)OOA, indicative of formation and

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aging of OA in the plume as it evolves in the atmosphere downwind of the city. This will

300

be investigated in the next section.

) is higher than SV-OOA (21%, 1.5 µg m-3). From the city center to zone 6 there is a

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OOA formation in the urban plume. The formation of secondary OA from

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anthropogenic precursors in the plume downwind of a city can be investigated by the

303

ratio of OA to CO

304

value) is used as an inert tracer of urban emissions including aerosol precursors to

305

account for dilution effects (evaporation upon dilution is assumed not to affect this

306

relationship 24, 49). We use the portion of the measurement drive on March 20 from zone 1

307

(city center) to zone 6 (Montserrat, pre-coastal mountain range) to estimate

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anthropogenic secondary OOA formation downwind of the Barcelona city center. The

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meteorological conditions were governed by a transition period with low mixing layer

310

height and a strong sea breeze effect

311

well after the sea breeze was established, at the shore upwind of the city center and

312

reached Montserrat, with 711 m. a. s. l. the highest point in the measurement route, at

313

16:05 local time (see portion of the route highlighted in red, Figure 1). Figure 5 shows the

314

measured OA plotted vs CO concentrations. Note that no CO background was subtracted

22, 47-49

. Generally, ΔCO (the CO concentrations minus a background

26

. The measurement started at ~14:00 local time,


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here (issues with CO background subtractions were also reported by e. g. Kleinman et al.

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47

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well below the urban background mean of 110 ± 15 ppbv. In addition, the OA, but more

318

so the CO concentrations measured on-road are heavily impacted by the emissions from

319

traffic close by and not necessarily representative of plume mean values (see Figure S9a

320

in the SI). OA/CO ratios can also vary depending on the influence of primary sources

321

such as biomass burning. The numbers reported here should thus be regarded as a case

322

study only and are prone to high levels of uncertainties. Two subsets of data, marked by

323

the stars in Figure 1 and representative of a near-source (blue) and distant-from-source

324

(orange) OA/CO ratio, respectively, were linearly fit (Figure 5). The slope for the city

325

center data points (near-source) equals ~10 µg m-3 ppmv-1, comparable to values found

326

for e. g. Mexico City 47. Fitting the data points measured in zone 6 at Montserrat yields a

327

slope of ~85 µg m-3 ppmv-1, comparable to slopes found for aged (> 1 day) air masses in

328

other studies (pink slope in Figure 5 e. g. 47-49). Since the aging time of this case study is

329

much less than a day (~2.5 hours), the secondary OA mass formed downwind of the

330

Barcelona city center might not be purely of anthropogenic origin. In fact, Minguillón et

331

al.

332

coastal mountain range, ~72% of secondary OA carbon was non-fossil and from

333

biogenic, biomass burning, and urban non-fossil sources (e. g. cooking). Ignoring that

334

urban non-fossil sources also count as anthropogenic precursors, and taking only the

335

fossil 28% of secondary OA into account, yields a slope of OA/CO of ~ 24 µg m-3 ppmv-1

336

(black slope in Figure 5). Kleinman et al.

337

photochemical lifetimes – a rough comparison shows that a slope of 24 µg m-3 ppmv-1

) due to the measurement route crossing two mountain ranges with CO concentrations

32

found that at Montseny, a measurement station during DAURE on the same pre-

32

calculated OA/CO ratios for different


15


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338

corresponds to ~0.1 unit of photochemical age which in turn corresponds to ~2.5 hours,

339

in good agreement with the time difference of 2 hours between the start and end point of

340

this measurement drive. The calculated secondary OA mass yield of 75 µg m-3 ppmv-1

341

(difference between ~85 µg m-3 ppmv-1 at Montseny and ~10 µg m-3 ppmv-1 in the city

342

center) was compared to the mass increase in SV- and LV-OOA (normalized to the fit

343

CO plume mean values scaled to 1, Figure S9) between start and end point of the

344

measurement drive and showed good agreement as well (Figure S9b, SI).

345

Overall, the spatial distribution of on-road measured PM1 components shows that

346

during March 2009 the entire Barcelona metropolitan area was heavily impacted by local

347

emissions from traffic, cooking activities and, to a lesser extent, biomass burning.

348

Industrial areas, the Barcelona airport, harbor, an extensive highway network and

349

agricultural areas surround the city center. During the measurements several open fires

350

were observed by the drivers (zone 3). The municipality of Barcelona has one of the

351

highest car densities per km2 in Europe 50, and (primary) road traffic emission effects also

352

include high concentrations of NO2 (48 ppbv), CO (556 ppbv), and particle number

353

concentrations (115000 cm-3). Note that since measurements were done on road, the

354

concentrations of compounds related to traffic emissions will be enhanced compared to

355

background concentrations, but may be indicative of near-road concentrations. The

356

formation of secondary aerosol, mostly OA, favored by high precursor emissions of both

357

anthropogenic and biogenic origin, and high photochemical activity in the Mediterranean

358

climate adds more than half of the OA burden outside the city center. We conclude that

359

the Barcelona metropolitan area is an important source of regional PM1, compared to e. g.

360

Paris, which has a very low impact on the air quality of its surroundings


51

. In addition,

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361

the diurnal cycle of mountain wind and sea breeze can lead to recirculation of processed

362

air masses mixing with new emissions, possibly causing slight changes in the relative

363

contributions of the various components as a function of time of day. However, since this

364

affects both the city center and the surrounding areas, and since the whole region is

365

heavily populated and has abundant primary emissions sources, the overall concentrations

366

and composition of PM1 are relatively similar across the whole area.

367

368

Acknowledgement

369

We thank the organizers of the DAURE project and the staff at IDAEA for their support.

370

We acknowledge the IMBALANCE project of the Competence Center Environment and

371

Sustainability (CCES) and the EU-FP7 project EUCAARI for financial support and

372

“Accion Complementaria DAURE” from the Spanish Ministry of Science and Innovation

373

(CGL2007-30502-E/CLI) for infrastructure support. P. F. DeCarlo is grateful for the

374

postdoctoral support from the US-NSF (IRFP# 0701013).

375 376

Supporting Information Available

377

This information is available free of charge via the Internet at http://pubs.acs.org.

378


17


379 380 381 382 383 384 385 386

Page 18 of 30

Table 1: Zones the Barcelona measurement route was divided into, their driving times, and dates, the prevailing atmospheric scenarios as defined by Pandolfi et al. 26, and the time intervals the data were binned to for the investigation of the influence of the time of day on the spatial variation. A single drive corresponds to a single time interval in a zone on a particular day, e. g. 10:30 – 12:45 in zone 1 on 19.03. The times for AM drives are printed in bold for clarity. *For a short description of the atmospheric scenarios see main text

387 388 389 390


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391 392 393 394 395 396 397 398 399


Figure 1: Route driven for the mobile measurements, and zones defined for the analysis. The part highlighted in red shows the traverse driven on 20 March. The two stars with the same color bracket the subparts of the route used for the calculation of the OA/CO slopes in the two different regions, respectively. The red dot depicts the location of the urban background station during the DAURE campaign 21.


19


400 401 402 403 404 405

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Figure 2: Factor profiles of the 5-factor high resolution PMF solution found for the organic data matrix measured by AMS. Peaks are colored according to their elemental composition.


20

Page 21 of 30


406

407 408 409 410 411 412

Figure 3: Average normalized concentrations for PM1 components per zone. Horizontal lines give the average concentrations for the different time intervals. The error bars represent the standard error of the mean.


21


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413

414 415 416 417 418 419 420

Figure 4: Spatial variation of the normalized OA (Org) concentration and average relative composition of OA per zone and time interval. The standard errors of the mean of the reported percentage values are below 1% except for HOA, where they are up to 3%. The higher uncertainty for HOA is due to the influence of surrounding traffic on-road.


22

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421

422 423 424 425 426 427 428 429 430 431

Figure 5: OA (Org, µg m-3) vs CO (ppbv) for the portion of the drive highlighted in Figure 1 on March 20, colored by local time. The subsets of the data used to calculate the slopes using orthogonal distance regression are indicated by the stars in Figure 1 and color coded accordingly. The black curve gives the slope for purely fossil secondary OA based on the findings by Minguillón et al. 32. a and b denotes slopes and intercepts ( only where data was fit, see text).


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200x143mm (150 x 150 DPI)


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Spatial Variation of Aerosol Chemical Composition and Organic

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