The Urban Measurement Station SMEAR III: Continuous Monitoring of Air Pollution and Surface–Atmosphere Interactions in Helsinki, Finland

The urban measurement station SMEAR III: Continuous monitoring of air pollution and surface–atmosphere interactions in Helsinki, Finland

Leena Järvi1), Hanna Hannuniemi1), Tareq Hussein1), Heikki Junninen1), Pasi P. Aalto1), Risto Hillamo2), Timo Mäkelä2), Petri Keronen1), Erkki Siivola1), Timo Vesala1) and Markku Kulmala1)

1) Department of Physics, P.O. Box 64, FI-00014 University of Helsinki, Finland 2) Finnish Meteorological Institute, Research and Development, P.O. Box 503, FI-00101 Helsinki, Finland

Received 31 Jan. 2008, accepted 29 May 2008 (Editor in charge of this article: Veli-Matti Kerminen)

Järvi, J., Hannuniemi, H., Hussein, T., Junninen, H., Aalto, P. P., Hillamo, R., Mäkelä, T., Keronen, P., Siivola, E., Vesala, T. & Kulmala, M. 2009: The urban measurement station SMEAR III: Continuous monitoring of air pollution and surface–atmosphere interactions in Helsinki, Finland. Boreal Env. Res. 14 (suppl. A): 86–109.

We present results from the air pollution and turbulent exchange measurements made at the urban measurement station SMEAR III in Helsinki, Finland. First measurements at the station started in August 2004 and since then more measurements have gradually been added. We analyze data until June 2007. Temporal variations and dependencies between the size- fractionated particle number concentrations (both fine and coarse particle concentrations),

gas concentrations (O3, NOx, CO and SO2), turbulent fluxes of momentum, sensible and

latent heat and CO2, and meteorological variables were studied. Most of the air pollutants and turbulent fluxes showed distinct annual and diurnal variation closely related tothe local combustion sources (especially traffic) and the amount of available solar radiation. Ultrafine particles showed the most explicit dependence on traffic and traffic-related -pol lutants, while larger particles were more affected by the meteorological conditions. The surface fluxes were strongly affected by the specific conditions in urban environment.

Introduction ent thermal conditions (Urban heat island effect, Oke 1982), both affecting the spatial and tempo- As compared with natural areas, urban areas ral behaviour of wind field and the strength of create very different circumstances for the lowest turbulent exchange including the turbulent fluxes level of the atmosphere. Most of the air pollution of momentum, energy and matter (Roth 2000, sources (both aerosol particle and gaseous pol- Oke et al. 1989). These have further effect on lutant sources) are concentrated in urbanized pollutant dispersion (Hanna and Britter 2002). areas where also majority of people live and the Previously, atmospheric pollution was linked adverse health effects of air pollutants get the with many type of health problems including greatest interest. In addition, cities are character- cardiopulmonary and respiratory diseases (e.g. ized by high roughness of the surface and differ- Curtis et al. 2006). In addition, air pollutants Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 87 affect visibility and climate (Seinfeld and Pandis and Belcher 2004). Previously, ESCOMPTE 1998). For example ultrafine particles (UFP, campaign brought together simultaneous meas- aerodynamic diameter d < 0.1 µm) can affect urements of turbulent fluxes, aerosol particle human health by penetrating deep into lungs and number and gas concentrations in Marseille, blood circulation (e.g. Nel 2005), and can act France, but the campaign was limited to summer as cloud condensation nuclei and affect cloudi- 2000 (Cros et al. 2004). ness. In urban areas, UFP are mainly produced in The urban measurement station SMEAR III combustion processes which include both traffic (Station for Measuring Ecosystem–Atmosphere and stationary emission sources (e.g. Young and Relationships) was established in Helsinki, Fin- Keeler 2007). UFP can be emitted as a primary land, in autumn 2004. The station is an extension emission or can be produced in secondary reac- to the other SMEAR stations located in different tions from precursor vapours. Nucleation of aer- surroundings around Finland (Fig. 1a). The pur- osol particles may also occur without anthropo- pose of the SMEAR station network is to measure genic precursors and the observations of nuclea- the exchange of momentum, energy and matter in tion events cover various environments from different environments, and to obtain continuous clean arctic areas to polluted cities as reviewed long-term measurements covering chemical and by Kulmala et al. (2004). Accumulation mode physical properties of atmospheric aerosols, gas particles (0.1 < d < 1 µm) can also be produced pollutants, turbulent exchange and basic meteor- in combustion processes but, contrary to UFP, ology. The SMEAR I station is located in Värriö their size is favourable for long-range transport (67°46Ń, 29°36É), eastern Lapland, close to (LRT) in the atmosphere. Coarse particles (d > 1 the Russian border and it represents a remote µm) are mainly re-suspended dust from soil and location where the amount of local emissions is roads by natural and traffic induced turbulence. very low (Hari et al. 1994). The SMEAR II sta- Despite the effects of air pollution, informa- tion is a rural background station located in Scots tion concerning the sources, sinks, mixing and pine forest near Hyytiälä Forestry field station in chemistry of air pollutants in urban areas is still southern Finland (61°51Ń, 24°17É) (Hari and lacking. So far, the simultaneous measurements Kulmala 2005). The SMEAR III station extended of size-fractioned particle number concentrations the measurement network into the city of Hel- and gas pollutant concentrations have been made sinki where the station is situated at two urban in Europe (e.g. Ruuskanen et al. 2001, Wehner background locations (Fig. 1b). The air pollution and Wiedensohler 2003, Ketzel et al. 2004, Aalto measurements together with the meteorological et al. 2005, Hussein et al. 2006), North America and turbulent exchange measurements are made (e.g. Noble et al. 2003, Jeong et al. 2004, Young in Kumpula, 5 km northeast of the Helsinki and Keeler 2007), Australia (e.g. Morawska et centre, while the multidisciplinary ecosystem al. 1998) and Asia (e.g. Shi et al. 2007), but the research is made in Viikki, about 7 km northeast measured variables and the length of the meas- of the Helsinki centre. The station is operated urements varied strongly. Also the measurements together with the University of Helsinki and the of turbulent exchange have been restricted to Finnish Meteorological Institute (FMI). To our few cities in industrialized countries (Grimmond knowledge, this is the first time when simultane- and Oke 2002, Nemitz et al. 2002, Soegaard ous continuous long-term measurements of tur- and Møller-Jensen 2003, Grimmond et al. 2004, bulent parameters and broad aerosol particle size Moriwaki and Kanda 2004, Vogt et al. 2006, spectrum (starting from 3 nm particles) are made Coutts et al. 2007, Vesala et al. 2007). The direct in the same place in urban areas. measurements of turbulent fluxes in urban areas In this study, we focus on the air quality and are required, not only for the better knowledge turbulent exchange measurements made at the of turbulent processes in urban environments Kumpula site. At that site, the number size distri- and their effect on pollutant dispersion, but also bution of fine aerosol particles (UFP + accumu- because urban areas cause friction in the above lation mode particles; d = 3–950 nm) and basic air and can affect mesoscale weather phenom- meteorological variables have been measured ena and local weather forecasts (e.g. Coceal since August 2004. Since then, measurements 88 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A) a b

Fig. 1. (a) The SMEAR station network in Finland. (b) Helsinki metropolitan area and the SMEAR III station measurement sites. The black circle shows the location of the Kumpula site and the black square shows the location of Viikki site. The online traffic monitoring point is shown with black triangle. (c) Schematic map of the SMEAR III Kumpula site. Black circle shows the place of the measurement tower and the container. The meteorological measurements are made from the roof of one of the University of Helsinki buildings. Thick dashed line shows the railway. Contours are plotted with light grey. Also the land use sectors are marked with black straight lines.

have been extended to coarse particle (1–20 µm) ticles. In the analysis, the effect of traffic rates number concentration, pollutant gas concentra- and meteorological variables (including turbu- tions (O3, NOx, CO and SO2) and turbulent fluxes lent fluxes) were studied. In addition, correla- of momentum, heat, H2O and CO2. We utilized tions between gas concentrations and number measurements of these variables until June 2007, concentrations of UFP and accumulation mode covering all four seasons. During the analyzed particles were analyzed. periods, we studied temporal behaviour (with annual and diurnal timescales) of aerosol particle number concentrations, gas concentrations, Materials and methods meteorology and turbulent fluxes. We also inves- tigated wind direction dependencies of pollutant Site description concentrations, and a multiple linear regression (MLR) analysis was made to find the variables The SMEAR III station was officially started in affecting different size-fractionated aerosol par- Helsinki in autumn 2004. Helsinki is located on Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 89 a relatively flat land on the coast of the Gulf of area with one-family houses and green spaces Finland, and together with the three neighbour- is located behind the campus area. The traffic ing cities (Espoo, Vantaa and Kauniainen) Hel- loads on the small roads of the area are low, and sinki forms the Helsinki Metropolitan area with the largest source of atmospheric pollutants is an area of 765 km2 and approximately one mil- the residential activity including wood combus- lion inhabitants. The climate in southern Finland tion. The road sector is dominated by one of the can roughly be classified as either marine or con- main roads leading to the centre of Helsinki. The tinental depending on the air flows and pressure average daily traffic intensity is 50 000 vehicles systems. Either way, the weather is milder than and the amount of heavy duty vehicles on that typically at the same latitude (60°N) mainly due road is considerable. The tower and the road are to the Atlantic Ocean and the warm Gulf Stream. separated by a belt of deciduous forest with a In Helsinki, the 30-year (1971–2000) monthly- width of 150 metres. The other side of the road is average temperatures range from –4.9 °C in Feb- covered by buildings and sea at a distance of one ruary to 17.2 °C in July (Drebs et al. 2002). The kilometre (Fig. 1c). In the road sector, 60% of yearly precipitation is 642 mm being highest in the surface is covered by asphalted area, 30% by late summer and lowest in spring. vegetation and 10% by buildings. The vegetation The air pollution and turbulent exchange sector is mainly covered by green spaces (85%) measurements are made at an urban background and fractions of roads and buildings are only 13% location in Kumpula about 5 km northeast of the and 2%, respectively. Nearby area of the tower Helsinki centre. The turbulent fluxes are meas- is covered by deciduous forest and behind that is ured on the 31-m-high, triangular lattice tower an area of grasses, walkways and gardens in an located on a rocky hill (60°12´N, 24°58´E, 26 m allotment garden and the University Botanical above sea level) next to the University of Hel- garden. On the other side of the allotment garden sinki buildings and the Finnish Meteorological (600 metres), the more urbanized area starts with Institute (Fig. 1c). Next to the tower, an air- blockhouses and roads. A railway, with a couple conditioned measurement container is located, of trains per day, leading to Helsinki harbour is where the aerosol particle and the trace gas meas- passing through the vegetation sector. urement instrumentation is located. In addition, basic meteorological measurements are made from the roof of University of Helsinki buildings Measurements (see also Table 1) (Fig. 1c). The surroundings of the tower and the con- Aerosol particle concentrations tainer are very heterogeneous consisting of buildings, parking lots, roads, patchy forest and low The aerosol particle size range from 3 to 950 vegetation. The area around the tower (within nm has been measured with a twin differential a circle of radius 250 m) has 14% coverage mobility particle sizer (DMPS, e.g. Aalto et al. of buildings, 40% coverage of asphalted area 2001) since spring 2004. The DMPS technique and 46% coverage of vegetation. The land use is based on the bipolar charging of aerosol par- is not evenly distributed and the surrounding ticles, followed by classification of particles into area can be divided into three land use sectors: size classes according to their electrical mobility urban (320°–40°), road (40°–180°) and vegeta- with a differential mobility analyzer (DMA). The tion sector (180°–320°). The FMI and the campus number of particles in each size class is counted area of University of Helsinki are located in with a condensation particle counter (CPC). In the urban sector where the building coverage our setup, one DMPS measures particles in the is 42%. The mean height of the buildings is 20 size range of 3–50 nm and it consists of a Hauke- metres, and the closest of them is situated 55 type DMA (10.9 cm in length) and a TSI Model metres away from the tower. The space between 3025 CPC. The sample and sheath flows are 3 is covered with parking lots with traffic activ- and 171 l min–1, respectively. The other DMPS ity mainly on weekdays. In the urban sector the measures particles in the size range of 10–950 fraction of asphalted area is 51%. A residential nm with a Hauke-type DMA (28 cm in length) 90 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A)

Table 1. Summary of the used parameters and their measurement setups.

Measured quantity technique Equipment Measurement Detection resolution limit

Particle concentration twin differential mobility hauke-type DMA (10.9 cm) + 10 min with size range 3–950 nm particle sizer TSi Model 3025 CPC Hauke–type DMA (28 cm) + TSi Model 3010 CPC

Particle concentration aerodynamic Particle tsi Model 3321 10 min with size range 0.5–20 µm Sizer (APS)

Three wind 3-D ultrasonic Metek USA-1 0.1 s components and anemometer temperature

Friction velocity, Eddy Covariance (EC) metek USA-1 + Open-path 0.1 s sensible and latent infrared absorption gas analyzer heat fluxes, CO2-flux (LI–7500)

Wind direction 2-D ultrasonic Thies Clima ver. 2.1x 10 s anemometer

Air temperature Platinum resistance Pt-100 60 s thermometer

Global radiation and net radiometer and Kipp & Zonen CNR1 + PAR lite 60 s photosynhetically photodiode sensor active radiation (PAR)

Relative humidity Platinum resistance vaisala DPA500 4 min thermometer + thin film polymer sensor

Air Pressure Barometer Vaisala HMP243 4 min

NOx Chemiluminescence tei42S 60 s 0.2 ppb technique + thermal (molybdenum) converter

O3 IR-absorption photometer tei 49 60 s 0.5 ppb

CO Non-dispersive infrared horiba APMA 370 60 s 20 ppb (NDIR) absorption technique

SO2 UV-fluorescence Horiba APSA 360 60 s 0.2 ppb technique

and a TSI Model 3010 CPC. For this system, side the measurement container from the height the sample and sheath flows are 1 and 5 l min–1, of four metres. Time resolution of the combined respectively. Each sheath flow is arranged as a system is 10 minutes (see also Aalto et al. 2001). closed loop with an air filter and aerosol dryer. The aerosol particle size range from 0.5 to The sampling line is 2-m-long stainless steel 20 µm has been measured with an aerodynamic tube with inner diameter of 4 mm and aerosol particle sizer (APS, TSI3321) since May 2005. flow rate of 4 l min–1. Sampled air is drawn out- The APS classifies aerosol particles by using a Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 91 time-of-flight measurement to measure the aero- measured with a platinum resistant thermom- dynamic diameter. The sample flow was 1 l min–1 eter (Pt-100) since May 2005, and total solar and sheath flow 4 l min–1 for the APS, which had radiation and PAR (photosynthetically active a separate sampling line. The time resolution of radiation) with a net radiometer and photodiode the measurements is 10 minutes. sensor (CNR1 + PAR lite, Kipp & Zonen, Delft, the Netherlands), respectively, since July 2005. Time resolution for all of these is one minute. Gas pollutants Air pressure and relative humidity are measured with a barometer (Vaisala DPA500, Vaisala Oyj,

Concentrations of nitrogen oxides (NOx) and Vantaa, Finland), and platinum resistance ther- ozone (O3) have been measured with a chemi- mometer and thin film polymer sensor (Vaisala luminescence analyser (TEI42S, Thermo Envi- HMP243, Vaisala Oyj, Vantaa, Finland) from ronmental Instruments Inc., MA, USA) and an the roof of University of Helsinki Building (Fig. IR-absorption photometer (TEI49, Thermo Envi- 1c), respectively, with a time resolution of four ronmental Instruments Inc., MA, USA), respec- minutes. tively, since November 2005. The measurements of sulphur dioxide (SO2) started with a UV fluorescence analyser (APSA 360, Horiba, Kyoto, Traffic monitoring Japan) in September 2006 and the measurements of carbon monoxide (CO) with an IR-absorption Traffic rates in Helsinki metropolitan area are analyser (Horiba APMA 370, Horiba, Kyoto, monitored by the Helsinki City Planning Depart- Japan) in December 2006. In gas measurements, ment. The nearest continuous calculation point time resolution of one minute is used. is on Itäväylä road, about 2.5 km south from the measurement site (Fig. 1b). The traffic monitoring does not take into consideration the split Turbulent fluxes and meteorological between light- and heavy-duty vehicles. Traffic variables data are logged at 1-hour intrvals, except during rush hours when the logging interval is 15 min- The turbulent fluxes of momentum, sensible and utes. Hourly values were calculated for Decem- latent heat, and CO2 have been measured with ber 2005–August 2007. an eddy covariance (EC) technique on top of the tower at the height of 31 metres since December 2005. The EC setup includes a Metek ultrasonic Data treatment anemometer (USA-1, Metek GmbH, Germany), which measures all three wind components and We divided the aerosol particle size spectrum sonic temperature, and an open path infrared into three size classes: ultrafine (3–100 nm), gas analyzer (LI-7500, Li-Cor Inc., Lincoln, accumulation mode (100 nm–1 µm) and coarse Nebraska, USA) to measure carbon dioxide and particles (1–20 µm). The separation was made water vapour mixing ratios. The gas analyzer due to the deviations in origin, chemical compo- is connected to the anemometer data logger for sition and physical properties of different sized synchronization and the raw data is stored for particles. The UFP and accumulation mode par- calculation of turbulent fluxes. Measurement fre- ticle concentrations were obtained from the twin quency of the EC measurements is 10 Hz. DMPS and the number of coarse particle from Besides the EC system, the horizontal wind the APS. Since the DMPS and APS have differ- speed components and wind direction have been ent measurement principles, we converted the measured on top of the tower with a 2-dimen- aerodynamic diameters measured with APS to sional ultrasonic anemometer (Thies CLIMA the diameters equivalent with DMPS measures. ver. 2.1x, Goettingen, Germany) since Novem- This was done by dividing the aerodynamic ber 2004 with time resolution of 10 seconds. diameter with a square root of the effective den- From the same level, air temperature has been sity of the aerosol particles. The effective density 92 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A) value of 1.5 g cm–3 was used in this study (Stein micrometeorological theories (Monin-Obukhov et al. 1994, McMurry et al. 2002, Khlystov et al. similarity and spectral theories) applying rather 2004). A sensitivity test showed that with effec- well also downwind from the buildings. tive density value ± 0.25 g cm–3, we get differ- Atmospheric stability ζ is an important deriv- ences from 14% to 27% in coarse particle con- ative obtained from the flux measurements. It centrations depending on the season. For aerosol describes the dispersion conditions and it is particle measurements, data from May 2005 to obtained from the relationship between sensi- June 2007 was analyzed (if not mentioned oth- ble heat and momentum fluxes, which roughly erwise) and for this period the data coverage’s describe the thermal and mechanical turbulence were over 96% and 82% for twin DMPS and productions, respectively. ζ gets negative values APS, respectively. Half-hour medians were cal- in unstable atmosphere, positive in stable strati- culated for pollutant concentrations (both aero- fied atmosphere and in neutral situations ζ is sol particle number and gas concentrations). The between –0.01 and 0.01. only exception was the multiple linear regression Size-fractioned aerosol particle number data analysis when hourly values were used due to and meteorological variables (wind speed, wind the measurement resolution of traffic rates. direction, pressure, RH, radiation, PAR) were Turbulent fluxes were calculated as averages divided according to seasons between May 2005 of the covariance of vertical wind speed and and Jun 2007, and a definition of thermal seasons considered scalar, according to common proce- was used. Spring and autumn are the periods dures presented by Aubinet et al. (2000). Before when daily average temperatures are between the flux calculations, data was de-trended and a 0 and 10 °C, and winter and summer are when 2-dimensional coordinate rotation was applied. the temperatures are below 0 and above 10 °C, Fluxes were also corrected for water vapour and respectively. According to this definition winter heating effects according to Webb et al. (1980). was found to be from 16 December 2005 to 7 Clear peaks were removed by visual inspection April 2006 and from 19 January to 6 March and a stationary test was performed (Foken and 2007 with a total number of 160 days. Summer Wichura 1996), where the 30-minute interval covered 21 May–13 October 2005, 4 June–10 used for the calculation of one flux point is com- October 2006 and 17 May–30 June 2007 with pared with the same interval divided into six sub- a total number of 274 days. The number of intervals. If the difference between these two was days in spring and autumn were 148 and 164, more than 60%, the flux data point was rejected respectively. For other variables, same seasonal as non-stationary. The flux data was filtered division was used but for shorter periods. NOx against friction velocity (u*) and data with u* < and O3 concentrations were analyzed between –1 0.1 m s was screen out. Flux data from Decem- November 2005 and June 2007, while for SO2 ber 2005 to June 2007 were analyzed. Missing and CO the analyzed periods covered September data points covered 12% of this period and the 2006–June 2007 and December 2006–June 2007, amount of rejected data points varied between respectively. The turbulent fluxes were analyzed the fluxes and seasons. For momentum and heat between December 2005 and June 2007. fluxes the amount of rejected data was low, between 3%–15% of the measured data. For CO2 flux, 20%–50% of the data was rejected while Multiple linear regression analysis for water vapour the amount was 20%–40%. The amount of rejected data was high, but still typi- Air pollution concentrations are complex func- cal for EC measurements (e.g. Suni et al. 2003). tions of sources, sinks, synoptic and mesos- The EC measurements are done in the vicinity cale meteorology, and turbulence, which all vary of buildings, whose heights are on average 2/3 strongly by time. We tried to distinguish the of the measurement height. This may cause effect of different variables on measured aerosol problems for the EC measurements when wind particle concentrations by means of a multiple is blowing from the direction of these buildings. linear regression analysis (MLR). In MLR, a However, Vesala et al. (2007) showed the basic linear relationship between a dependent variable Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 93

(in this case the aerosol particle concentration) and August 2006 were used. Analysis was done and several independent variables is studied. The for hourly median values (for fluxes average basic idea is to develop a model values were used) and only dry hours were taken into account. The analyzed data accounted 60%

Y = b0 + b1X1 + … + bnXn, (1) of the period. where Y is the modelled concentration, X … X 1 n Results and discussion are independent variables, b0 is the intercept and b1 … bn are regression coefficients (Hair et al. 2006). The MLR models were obtained by con- Annual variations of aerosol particle sidering all possible combinations of independ- number and gas pollutant ent variables, and finding such variables that the concentrations difference between the measured and modelled concentrations is minimized. This provides vari- The highest UFP and NOx concentrations were ables X1 … Xn, which are significant concerning systematically measured in late winter (Febru- the aerosol particle concentrations, and the direc- ary–March) when the concentrations were around tion of the dependence. Traffic rate, turbulent 13 000 cm–3 and 18 ppb (Fig. 2). For CO and fluxes of momentum and heat, wind speed, wind SO2, data over only one year existed and during direction, pressure, RH, solar radiation and PAR that time they had maxima (270 and 1.5 ppb, were taken into account in the analysis. respectively) also in late winter. The elevated Normalization of the dependent and inde- concentrations are caused by the lowered mixing pendent variables before the model development in the boundary layer and also enhanced emis- enables to get so-called beta coefficients, which sions from combustion sources (mainly station- tell the relative importance of each independent ary emission sources) during the coldest periods variable to the dependent variable as compared which usually occur in February (Drebs et al. with other independent variables in the model. 2002). NOx, CO and SO2 are all emitted in fossil

We used bootstrapping to obtain uncertainties fuel burning processes and in the case of NOx for model parameters and performance indi- and CO this mainly refers to traffic while in the ces and to have results more representative. In case of SO2 the main source is energy production. bootstrapping, original data set is divided into More efficient boundary layer mixing could be

100 subsets each including arbitrary 5/6 of the seen as lowered UFP, NOx and CO concentrations data set. Separate MLR models are developed to in summer. Previously also Woo et al. (2001) and each subset and the beta coefficients, R2 and root Aalto et al. (2005) showed higher UFP concen- mean square error are calculated as arithmetic trations in winter than in summer. means from these submodel parameters. Concentrations of UFP and NOx were system- In order to use MLR, used variables should atically higher on weekdays than on weekends be normally distributed. However, some of the (Table 2). In the case of CO, the same pattern variables were not distributed normally and could be seen in winter. The difference between therefore data transformations to correct the non- the weekday and weekend concentrations is normality were used. Logarithmic transforma- caused by additional traffic on weekdays. This tions were used for aerosol particle number and was pronounced in winter when the poor mixing

H2O concentrations. For wind direction compo- conditions cause traffic emissions to be more nents, radiation variables (total solar radiation evidently detected at the measurement point. In and PAR) and stability parameter, inverse trans- spring and summer, deviations between weekday formations were applied. Finally, traffic rates and weekend concentrations were smaller. In were square-transformed. MLR analysis was the case of UFP, the difference can be smoothed made separately for UFP, accumulation mode by the nucleation of new particles which is particle and coarse particle number concentra- most frequent in spring (March–May) and late tions. Due to limited amount of traffic and turbu- summer (September) (e.g. Dal Maso et al. 2005). lent flux data, only data between December 2005 A weekday-related source was also evident in 94 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A)

) 15000 P –3 10000 UF

(cm 5000

) 2000 –3

AP 1000 (cm 0 )

–3 1 (cm Coarse particles 400 Fig. 2. Monthly medians of 3 30 O

(ppb) 20 number concentrations of 10 ultrafine particles (UFP), 20

x accumulation mode particles (AP) and coarse par-

NO 10 (ppb) 0 ticles, and gas concentra- 300 tions of ozone (O3), nitro-

CO gen oxides (NOx), carbon (ppb) 200 monoxide (CO) and sul- 2 phur dioxide (SO2) in Hel- 2 1 sinki in August 2004–June SO (ppb) 0 2007. Error bars show the 2004 2005 2006 2007 quartile deviations.

SO2 concentrations in winter and spring as evi- The winter maximum is related to the low- denced by higher weekday concentrations (see ered mixing and enhanced emissions similarly to Table 2). In Finland, the sulphur content of fuels UFP. In summer, the accumulation mode particle used by road traffic is low (10 ppm since 1995), concentrations are raised by long-range transport suggesting that the higher SO2 concentrations are (LRT) (Laakso et al. 2003) when forest fires/ caused by other weekday-related source, such controlled burning typically take place in Russia as power plant activities and residential heating and eastern Europe bringing polluted air masses with sulphur-containing fuels. Some effect of to southern Finland (e.g. Sillanpää et al. 2005, traffic cannot, however, be ruled out. Due to the Rantamäki et al. 2007). This was especially short measurement period of SO2, these results pronounced in August 2006 when a maximum should be considered with caution. concentration of 1800 cm–3 was measured. The

O3 experienced its maximum concentration whole summer 2006 was exceptionally dry and of 30 ppb in spring and early summer, and a warm, and in August the easterly winds brought minimum concentration of 13 ppb in winter. highly polluted air masses from extensive wild

This annual behaviour of O3 is strongly related fires in Russia and Estonia (Rantamäki et al. to the amount of available solar radiation and 2007). The accumulation mode particle concen- the intensity of photo-oxidation of the precursor trations were also higher on weekdays than on gases (Seinfeld and Pandis 1998, Sillman 1999). weekends in winter and spring when the effect of

Contrary to other gases, O3 concentrations were local emissions is more evident due to the low- higher on weekends than on weekdays. Previous ered mixing. The coarse particles did not have a studies have reported higher O3 concentrations distinct annual pattern (Fig. 2). However, slightly –3 outside cities, since inside urban areas O3 is elevated concentrations (1 cm ) were measured rapidly consumed in chemical reactions (Sillman in spring. This is due to the effective re-suspen- 1999, Noble et al. 2003). The same phenomenon sion of gravelling caused by traffic induced is likely to explain the lower weekday concentra- turbulence and wiping machines after melting of tions. snow and ice. Especially, the effect of studded The annual pattern of accumulation mode tires on coarse particle concentrations in spring particles showed highest concentrations between is a well known phenomenon in Scandinavia February and August (Fig. 2). Pronounced peaks (e.g. Kupiainen et al. 2003, Norman and Johans- were observed in February and in July–August. son 2006, Hussein et al. 2008). Coarse parti- Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 95 6.9 0.3 8.9 0.3 198 (29) (13) 0.37 0.63 0.69 0.58 0.60 0.65 18.2 198) (3.3) (5.9) (0.3) (5.5) (5.7) (0.2) 17.9) (0.18) (0.35) (0.60) (0.35) (0.32) (0.51) S eptember 5.5 0.3 7.7 0.3 198 198 (19) (16) 0.32 0.60 0.87 19.2 0.47 0.53 0.76 20.2 (2.2) (5.0) (0.2) (4.2) (4.5) (0.3) (0.12) (0.28) (0.68) (0.25) (0.26) (0.62) ) for 2 A utumn 9.1 0.5 0.5 M ay 2005–June 2007, 199 211 (27) (19) 0.47 0.87 0.72 14.6 12.5 0.73 0.74 0.62 13.7 (4.3) (5.8) (0.3) (8.5) (5.8) (0.3) (0.26) (0.50) (0.34) (0.48) (0.37) (0.52) 0.3 0.3 229 198 Urb r oad v eg a ll (74) (19) 14.3 0.46 0.64 0.41 15.1 11.1 0.79 0.58 0.49 19.1 (8.1) (6.8) (0.3) (8.3) (8.0) (0.4) (0.29) (0.45) (0.28) (0.50) (0.43) (0.29)

(9) 5.2 0.5 8.2 0.4 172 172 (18) 0.51 1.22 0.46 30.5 0.59 1.22 0.61 25.1 (1.9) (5.5) (0.5) (4.5) (6.6) (0.3) (0.20) (0.54) (0.24) (0.24) (0.55) (0.35) (9) 5.1 0.5 6.6 0.4 172 163 (14) 0.44 1.07 0.47 32.0 0.52 1.11 0.58 25.9 (2.0) (6.0) (0.4) (3.5) (6.5) (0.3) (0.15) (0.53) (0.25) (0.19) (0.47) (0.34) S ummer (9) 5.4 0.6 0.5 163 172 (18) 0.59 1.46 0.51 28.7 10.5 0.71 1.47 0.72 24.9 (2.0) (4.8) (0.7) (5.9) (5.9) (0.5) (0.21) (0.51) (0.22) (0.29) (0.70) (0.34) 5.7 0.4 6.9 0.3 163 Urb r oad v eg a ll 163 (11) (14) 0.59 1.43 0.34 25.4 0.68 1.14 0.56 22.8 (1.9) (4.8) (0.2) (5.0) (5.8) (0.2) (0.19) (0.59) (0.15) (0.26) (0.46) (0.29)

(9) 5.0 9.3 0.5 217 217 (27) 0.58 0.75 0.69 27.0 0.29 0.74 1.11 1.00 24.7 (3.7) (4.5) (7.4) (6.5) (0.3) (0.25) (0.55) (0.43) (0.27) (0.32) (0.65) (0.53) (9) 4.6 0.3 8.0 0.5 199 217 (23) 0.50 0.58 0.71 28.0 0.61 0.84 1.09 26.9 (2.7) (3.3) (0.2) (5.5) (5.5) (0.3) (0.22) (0.30) (0.45) (0.22) (0.45) (0.68) S pring 0.7 0.7 217 245 (27) (27) 10.4 0.74 1.51 0.82 21.0 19.7 1.06 1.72 0.97 20.2 (5.7) (5.4) (0.7) (7.4) (0.6) (0.22) (0.77) (0.39) (11.6) (0.41) (1.07) (0.41) (9) 2.1 0.2 9.6 0.4 217 Urb r oad v eg a ll 226 (32) 0.53 0.45 0.59 27.5 0.82 0.98 0.87 22.7 (1.4) (4.4) (0.1) (7.8) (7.1) (0.2) (0.31) (0.50) (0.63) (0.35) (0.55) (0.57)

7.0 0.3 1.2 236 285 (31) (44) 0.71 0.87 0.56 23.1 21.8 1.27 1.21 0.71 14.5 (3.6) (5.0) (0.4) (5.6) (1.2) (0.40) (0.48) (0.31) (11.1) (0.74) (0.54) (0.30) 6.6 0.3 0.6 247 (30) (55) 0.55 0.60 0.49 25.6 15.4 0.77 0.88 0.60 15.6 (2.9) (4.2) (0.4) (5.5) (0.7) (0.29) (0.35) (0.32) (10.1) (0.48) (0.45) (0.30) 217 (30) Winter N ovember 2005–June 2007, carbon monoxide ( CO ) for December 2006–June 2007 and sulphuric dioxide ( SO (2) 8.2 0.6 0.8 236 302 (14) (32) 0.90 1.29 0.63 28.5 23.6 1.56 1.44 0.71 15.0 (3.3) (0.5) (9.8) (4.9) (0.6) (0.40) (0.57) (0.35) (0.79) (0.54) (0.29) ) for 3 7.0 0.2 2.2 247 Urb r oad v eg a ll 304 (54) (46) 0.82 0.92 0.57 18.7 22.2 1.54 1.30 0.78 12.4 (5.5) (5.3) (0.2) (5.1) (1.7) (0.48) (0.48) (0.29) (11.0) (0.78) (0.58) (0.32) ) –3 ) cm 3 ) and ozone ( O –3 x edians were calculated separately for weekdays (Wd) and weekends (We), and division into land use sectors, urban (Urb), road and vegetation ( V eg), vegetation and road (Urb), urban sectors, use land into division and (We), weekends and (Wd) weekdays for separately calculated were M edians ) –3 T he median seasonal statistics for the measured air pollutants: UFP, accumulation mode particle and coarse particle concentrations for cm 4 . (ppb) We Wd (ppb) We We We We We We Wd Wd Wd Wd Wd Wd x 2 (ppb) 3

A ccum. particles (10 C oarse particles (cm CO (ppb)

2006–June 2007. 2006–June

Table 2 was made. Quartile deviations (half of the difference between 25 and 75 percentiles) are shown in parentheses. UFP (10 NO O SO nitrogen oxides ( NO 96 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A)

2 ) –1 s 1 (m * u

) 600

–2 400 m 200

(W 0 Fig. 3. Time series of the H

) turbulent fluxes (friction

–1 10

s velocity u , sensible heat

* w –2 flux H, water vapour flux F

m 5 Fw, latent heat flux LE and

CO2 flux Fc) measured 0 in Helsinki in December (mmol

) 2005–June 2007. Grey –1

s 50 data points are half-hour

–2 averages, and black lines F m 0 are the daytime (10:00– 14:00) median fluxes cal-

(µmol –50 culated from five days of 2006 2007 data. cles had also higher concentrations on weekdays in summer than in winter (Fig. 3). The median (except in autumn) (Table 2), suggesting the value of H ranged from 20 W m–2 in winter to effect of traffic induced turbulence and/or some 350 W m–2 in summer, while the median value other weekday-related source. of Fw ranged from near zero in winter to 4 The measured UFP and accumulation mode mmol m–2 s–1 in summer, corresponding to latent particle concentrations are comparable to those heat flux (LE) of 230 W m–2. The measured heat previously measured in Helsinki and are at the flux values are similar to H and LE measured in lowest end when compared with those from a residential area of Tokyo in July, where they other cities around the world (Table 3). Coarse reached values of 300 and 200 W m–2 (Moriwaki particle number concentration measurements and Kanda 2004), respectively. Grimmond and were restricted to only few cities and compared Oke (2002) presented heat flux data from 10 with only those, the coarse particle concentra- urban sites in North America and found daily –2 tions in Helsinki are low. Also NOx, CO and SO2 peaks of H ranging from 100 to 300 W m and concentrations were much lower in this study LE ranging from 10 to 240 W m–2. In Basel in than reported in other urban studies (e.g. Table summer, H reached a maximum value of 400 –2 –2 3). Klumpp et al. (2006) reported O3 concentra- W m and LE was below 100 W m (Vogt et al. tions from 11 European cities and found the con- 2006). centrations range from 15.5 to 35.3 ppb. Thus, The u* and CO2 flux (Fc) did not exhibit a the O3 concentrations in Helsinki seem to be clear annual pattern. Median u* ranged between –1 typical for European cities and are mostly higher 0.4 and 1.5 m s and median Fc between – than those listed in Table 3. Only few studies 10 and 25 µmol m–2 s–1. The anthropogenic reported simultaneous measurements of number emissions (especially from traffic) dominated concentrations of size-fractioned aerosol parti- the CO2 exchange, masking the background cles and gas concentrations (Table 3). cycle of Fc. The influence of vegetation CO2

uptake on Fc could only be seen in summer as

a downward (negative) fluxes. Our Fc values Annual variations of turbulent fluxes are comparable to those reported in other stud-

ies. In Edinburgh, Fc ranged between 10 and 40 –2 –1 Sensible heat (H) and water vapour (Fw) flux µmol m s in autumn (Nemitz et al. 2002), had a clear annual pattern with higher values while in the city of Basel the range was 0–25 Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 97 ), 3 Winter s ummer Winter (UFP: 10–100 nm, A P: 100–500 nm) 1998–1999 (UFP: 3–100 nm, A P: 0.1–2 µm S ummer 2005 S ummer 2003 and 2005 (10–100 nm) winter 1999 (UFP: 20–100 nm, A P: 100–700 nm, C oarse: 1–10 µm) winter weekdays 1997–2001 summer weekdays 1997–2001 (UFP: 10–100 nm, A P: 100–800 nm) 10–100 nm, A P: 100–500 nm) Jul 1995– A pr 1997 (UFP: 16–630 nm, c oarse: 0.7–30 µm) C omment Urban background Urban background Urban background m ay 2001–Dec 2003 (7–1000 nm) Urban background Urban Urban background s ep– N ov 2002 (10–700 nm) Urban background Urban background n ov 1996– M ar 1997 (UFP: Downtown description s ite 2 – – – – – s treet canyon – – 0.8 0.4 S 5.9 1.2 3.9 i ndustrial 4.4 (ppb) – – – – – – – 272 172 592 830 630 (ppb) 1100 co so x 7.5 – – – – – – 14.9 17.8 52 14.8 78 34.5 (ppb) no 3 – – – – – – 26 20 29 20 25 16 13 (ppb) ) –3 0.56 – 0.67 – – – – – 3.0 – – – 4.3 ) from this and other urban studies. (cm 2 ) – – – – – –3 905 1200 1100 1690 2050 2107 1383 1670 (cm

) – –3

5600 7700 7400 9500 UFP a P c oarse o (cm 10600 21400 19900 14600 19300 13400 14900 15600 (2001) 2004 (1998) et al. (2001) et al. et al. (2008) et al. (2005) (2003) (2001) et al. et al. et al. et al. ), carbon monoxide ( CO ) and sulphur dioxide SO x M edian number concentrations of ultrafine particles (UFP), accumulation mode particles ( A P) and coarse particles, and median concentrations of ozone ( O . nitrogen oxides ( NO H elsinki, Finland R uuskanen H elsinki, Finland A alto Table 3 S tudy T his study Averages C openhagen, Denmark Ketzel A lkmaar, N etherlands R uuskanen Brisbane, A ustralia M orawska Detroit, M ichigan Young and Keeler (2007) L eipzig, Germany Wehner and Wiedensohler (2003) A tlanta, Georgia Woo

E l Paso, T exas N oble A shdod, I srael moroso

98 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A)

Urb Road Veg Urb Road Veg Urb Fig. 4. Dependence of 104 aerosol particle number 3 concentrations on wind 10 direction in (a) winter, (b) ) 2

–3 10 spring (c) summer, and (d) 1 autumn. Medians for 10°

(cm 10

c sectors were calculated 100 and plotted on a logarith- 10–1 a b mic scale. Black solid lines: ultrafine particle (UFP) con- 4 10 centrations, black dashed 103 lines: accumulation mode ) particle (AP) concentration,

–3 2 10 black dotted lines: coarse

(cm 101 c particles. Quartile deviations are indicated with 100 respective grey lines. The 10–1 c d land use sectors, urban 0 90 180 270 0 90 180 270 360 (Urb), Road and vegetation WD (°) WD (°) (Veg) are separated with UFP AP Coarse particles vertical lines.

Urb Road Veg Urb Urb Road Veg Urb 50 40 a b 40 30 Fig. 5. Seasonal depend- 30 ence on wind direction of 20 (ppb) (a) ozone (O ), (b) nitrogen (ppb)

x 3 3 20 oxides (NO ), (c) carbon O x NO monoxide (CO), and (d) 10 10 sulphur dioxide (SO2). 0 0 Values were calculated as medians from half-hour c d values for O3 and NOx in 8 November 2005–June 300 2007, for CO in Decem- 6 ber 2005–June 2007 and

(ppb) for SO2 in September 2 4 200 2005–June 2007. Grey CO (ppb) SO lines show the respective 2 quartile deviations and the 100 0 black vertical lines show 0 90 180 270 360 0 90 180 270 360 the land use sectors urban WD (°) WD (°) (Urb), road and vegetation Winter Spring Summer Autumn (Veg).

µmol m–2 s–1 in summer. In Chicago and Tokyo Wind direction dependence of aerosol in summer, the daily-average fluxes remained particle number and gas concentrations below 10 µmol m–2 s–1, as reported by Grimmond et al. (2002) and Moriwaki and Kanda (2004), The effect of road leading to the centre of Hel- respectively. Most of the Fc (and also other sinki was evident in the UFP, accumulation mode turbulent flux) measurements are carried out in particle, NOx and CO concentrations which were urban areas where all four seasons are not as higher in the road sector as compared with those distinguishable as in Finland. Thus in this study, in the other land use sectors (Table 2 and Figs. comparisons for winter are quite weak. 4–5). In the case of accumulation mode particles and CO, the concentrations in this direction are Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 99 also affected by potentially more polluted air The highest coarse particle concentrations were masses coming from eastern Europe and Russia. measured downwind from the Botanical Garden The lowest concentrations of these traffic-related (180°–270°) (Fig. 4). The pronounced peak in pollutants (UFP, accumulation mode particles, autumn is likely related to some activity in the

NOx and CO) were measured in the vegetation garden which causes effective re-suspension of sector, where the longest fetch without anthropo- cultivated ground. Small peaks in the direction genic emission sources is enabled. Pronounced of the crossroads were also observed in coarse peaks in traffic related pollutants were observed particle concentrations in winter and autumn in directions 45°–95° and 100°–170° throughout likely due to re-suspension by traffic induced the year (Figs. 4–5), corresponding directions turbulence. where largest crossroads are located, and where the coming airflow remains longer above roads (see also Fig. 1c). Peak concentrations were also Diurnal variability of air pollutants, observed downwind from the city centre (180°– meteorological variables and turbulent 190°) which is roughly the direction of the har- fluxes bour located 6 km away from the measurement site. Ship emissions can affect especially the On weekdays, traffic related pollutants (UFP, black carbon part of accumulation mode particles accumulation mode particles, CO and NOx)

(Pakkanen et al. 2001). The UFP, CO and NOx increased during the morning rush hour (05:00– peaked also in direction 20°–40°. The parking lot, 11:00) and decreased toward the evening (Figs. 6 where numerous cars start their engines during and 7). Peaks related to afternoon rush hour were the day, is located in this direction. In winter, evident only occasionally due to the strength- high concentrations of fine particles, NOx, CO ened turbulent mixing in the boundary layer and SO2 were measured in the urban sector. This (see also Fig. 8h). Similar weekday patterns of might be related to enhanced domestic activities UFP with morning maximum have also been (wood combustion and oil heating) in the residen- observed in Copenhagen (Ketzel et al. 2004), tial area behind the University campus, but also Leipzig (Wehner and Wiedensohler 2003) and a construction site located less than 100 m north Belfast (Harrison and Jones 2005). Noble et al. from the measurements may have its own effect (2003) on the other hand found two clear peaks on measured pollutant concentrations. related to morning and afternoon rush hours in

Outside winter time, SO2 concentrations UFP, accumulation mode particle, CO and NO were slightly higher in the road sector than in the concentrations in El Paso, Texas. The effect of other land use sectors suggesting some effect of lowered mixing could be seen as higher UFP, traffic (Table 2). Increased SO2 concentrations CO and NOx concentrations in winter with daily were measured downwind from the city centre peak values of 24 000 cm–3, 350 ppb and 40 in direction 130°–250° (Fig. 5). The harbour ppb, respectively. In the case of accumulation is also located in this directions and it is likely mode particles, deviations in the diurnal patterns having its own effect on SO2 concentrations, between the seasons were not as much pro- since besides energy production sea traffic is nounced likely due to the effect of LRT which an important source of SO2 in Helsinki (Myl- raised the concentrations especially in summer. lynen et al. 2007). Pronounced SO2 peaks were This was seen as raised nocturnal (roughly the observed in the directions (140° and 225°) of the background) concentrations both on weekdays two power plants, Hanasaari and Salmisaari. and weekends. On weekends, UFP, CO and NOx

O3 and coarse particles did not show dis- increased between 10:00–20:00 following the tinct dependence on land use sectors (Table 2). behaviour of traffic activity.

Minima in O3 concentrations were observed in O3 was clearly sunlight-related with high- directions 45°–95° and 100°–170° corresponding est concentrations after midday. Similar diur- directions of the crossroads. Likely, the amount nal behaviour of O3 has been observed in sev- of pollutants destroying O3 is higher in these eral European cities (Klumpp et al. 2006). The directions causing the lower O3 concentrations. weekday and weekend diurnal patterns deviated 100 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A)

Weekdays Weekends

3 x 104 a

2 x 104

1 x 104

0 b 2000 )

–3 1500 Fig. 6. Median diurnal variation of (a) UFP, (b) (cm 1000 c accumulation mode parti- 500 cles (AP), and (c) coarse particles separately for 2 c weekdays and weekends 1.5 in May 2005–June 2007. Black line shows the 1 variation in winter, black dashed line in spring, grey 0.5 line in summer, and grey 0 dashed line in autumn. 0 6 12 18 0 6 12 18 The quartile deviations for Time Time each season are plotted Winter Spring Summer Autumn with dotted lines.

Weekdays Weekends 40 a 30 20 Fig. 7. Median diurnal (ppb) variation of (a) ozone 3 10 O (O3), (b) nitrogen oxides 0 (NO ), (c) carbon monox- 60 x b ide (CO), and (d) sulphur

40 dioxide (SO2) separately for weekdays and week- (ppb) x 20 ends. Values were cal-

NO culated as medians from 0 400 half-hour values for O and c 3 NOx between November 300 2005 and June 2007, for CO between December 200 2005 and June 2007 and CO (ppb)

for SO2 between Septem- 4 ber 2005 and June 2007. d Black line shows the variation in winter, black 2 (ppb)

2 dashed line in spring, grey line in summer, and grey SO 0 dashed line in autumn. 0 6 12 18 0 6 12 18 The quartile deviations for Time Time each season are plotted Winter Spring Summer Autumn with dotted lines.

between 05:00–8:00 when O3 is rapidly con- nearly equal and the peak O3 concentration was sumed in reactions with other gaseous pollut- also observed after midday. The diurnal pattern ants on weekdays. Noble et al. (2003) found of SO2 showed increased daytime concentra- the weekday and weekend diurnal patterns to be tions on weekdays which were most pronounced Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 101

600 a b ) 45 –2

m 400 315 WD (°)

(W 200

S 225 0 20 c d )

10 –1 0.6 s

Fig. 8. Median diurnal var- (°C)

T 0.5 (m iation of (a) global radia- 0 * u tion (S), (b) wind direction 0.4 (WD), (c) temperature (T ), e ) 100 f ) 200

(d) friction velocity (u ), –2 * –2 m (e) sensible heat flux (H ), m 100 50 (f) latent heat flux (LE), (W H

(g) CO flux F ( ), and (h) 0 LE (W 2 c 0 atmospheric stability ( ) ζ ) 0.1 g in December 2005–June –1 10 s 2007. Winter values are 0 –2

c 5 ζ m plotted with solid line, F –0.1 spring values with dashed 0 h line, summer values with

(µmol –0.2 dotted line and autumn 0 6 12 18 24 0 6 12 18 24 values with dashed-dotted Time Time line. Winter Spring Summer Autumn in winter. As previously mentioned, the higher however, the wind direction had a diurnal cycle weekday concentrations are related to increased related to land sea breeze, which is produced combustion of sulphur-containing fuels and/or by the different heat capacities of sea and land. the effect of traffic. On weekends, SO2 did not The land sea breeze causes the wind to turn exhibit any clear diurnal behaviour. A weak anticlockwise towards the road sector after the effect of traffic could be seen in the diurnal sunrise and in evening returns back to westerly. variation of coarse particles during the morning This turning may have its own effect on the pol- rush hour. The increased coarse particle concen- lutant concentrations measured at the SMEAR trations caused by traffic and wiping machine III. Air temperature had a diurnal pattern with induced turbulence could be seen twice as large lower values at night which increased towards concentrations in spring than during other sea- the afternoon due to sun elevation (Fig. 8c). sons. No traffic related pattern could be seen in Solar radiation is a dominant factor in annual coarse particle concentration in El Paso, where and diurnal behaviour of most of the turbulent the peak concentration was measured in the fluxes. From the diurnal pattern of u* (Fig. 8d), evening (Noble et al. 2003). we can see how the strength of the turbulence In Finland, meteorological conditions vary is higher during the daytime enabling more effi- considerably with season and are strongly cient pollutant dispersion. This pattern was pro- dependent on the amount of solar radiation (Fig. nounced in summer when the amplitude between –1 8). Note that because of the definition of ther- the nocturnal and daytime u* was 0.3 m s . H mal seasons, the lowest sun radiation values followed the diurnal pattern of global radia- were measured in autumn (Fig. 8a). In Helsinki, tion well, reaching 240 W m–2 during summer also the vicinity of sea has a great effect on the days (Fig. 8e). H got negative values (indicat- seasonal changes and behaviour of the meteoro- ing unstable stratification of the atmosphere) logical variables. In Helsinki, the wind typically during nights, except in winter when the noctur- blows from west or south-west, except in winter nal stratification was slightly unstable due to the when air flows from Russia (northeastern) are anthropogenic heat sources (e.g. Salmond et al. dominating (Fig. 8b). In spring and summer 2005). Same pattern could also be distinguished 102 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A) from the diurnal pattern of stability parameter (ζ Results from the multiple linear in Fig. 8h). LE was low in winter and autumn, regression (MLR) analysis and reached 120 W m–2 during summer days following the development of sun elevation and Variables included to the final MLR models growing season. The diurnal patterns of heat varied between the seasons. In the case of UFP, fluxes are also dependent on land use sectors as traffic rate was always included with positive was shown by Vesala et al. (2007). The value of effect on UFP (Table 4). The other variables H was highest in the urban sector and lowest in present in the models were wind speed (U), the vegetation sector where the heat is consumed mixing ratio of H2O, temperature (T), variance in transpiration as could be seen as elevated LE. of the vertical wind speed (σw) and south-west The diurnal behaviour of H and LE followed wind component. Even though the variables those previously reported, even though the peak changed between the seasonal models, the same values range between 50 and 300 W m–2 for H physical effects were present throughout the ana- –2 and between 10 and 240 W m for LE depend- lyzed period. U and σw are indicative of mixing ing on the analyzed season (Grimmond et al. processes affecting the dispersion of pollutant 2002a, Nemitz et al. 2002, Moriwaki and Kanda concentrations. A weaker mixing causes higher

2004). concentrations above the ground. The H2O con- The surrounding area acted most of the time centration is proportional to T and both of them as a source for CO2 and Fc reached a value of 10 are typically lower in high pressure situations µmol m–2 s–1 in winter. In summer, the vegetation when strong inversions and high concentrations uptake of CO2 exceeded the anthropogenic emis- can be measured. Buzorius et al. (2001) reported sions on the footprint area (source area) resulting that on new particle formation days, the H2O in downward fluxes of 3 µmol m–2 s–1. Vesala mixing ratios were smaller than on non-event et al. (2007) showed that traffic is the major days having its own possible contribution to the source of CO2 at our site and the highest Fc negative correlation between H2O and UFP in were measured in the road sector. It should be spring. The available variables explained UFP noted, that Fc is potentially slightly underesti- concentrations better in winter and autumn when mated due to the sensor heat effect as presented traffic is dominating other UFP sources. During by Burba et al. (2006). At our site, the underes- all seasons, the influence of traffic on UFP con- timation increases with decreasing temperature centrations was higher on weekdays than on reaching 2.5 µmol m–2 s–1 (Vesala et al. 2007). weekends, when the meteorological parameters Previous studies have reported similar diurnal became more important. In winter and spring, behaviour of Fc but downward fluxes have only better MLR models were obtained for weekends been observed occasionally (Nemitz et al. 2002, than for weekdays. This might be caused by the Grimmond et al. 2002b, Vogt et al. 2006, Coutts local traffic activity at the parking lot next to et al. 2007). the station or the construction site, which are From the diurnal pattern of atmospheric sta- present mainly on weekdays and which were not bility (Fig. 8h), we see how the atmospheric included on the model parameters. On weekdays, stratification changes from stable to unstable also quality of a simple model may be poorer due after the sunrise. This describes the strengthen- to the more complex distribution of sources. ing of the turbulent mixing in the urban boundary For accumulation mode particles, traffic was layer after the sun starts to heat the ground. As not as dominant factor as in the case of UFP was previously mentioned, on average, the strati- (Table 5). The effect of traffic was systematically fication stays unstable through the day in winter. equally or less important than the meteorologi- During daytime, the atmosphere was more unsta- cal variables and in spring, traffic was not even ble in summer than during other seasons. This included to the final model. The effect of traffic enables higher mixing heights when pollutants decreased on weekends similarly to UFP con- can mix into larger air volume decreasing the centrations. The strong pressure dependence in concentrations above the ground. spring is related to the synoptic weather situation Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 103

Table 4. Results from the multiple linear regression (MLR) analysis of ultrafine particle concentrations p( < 0.05). In the analysis hourly values were used and MLR models were made for each season and for weekdays and weekends, separately. Variables which were used in final models were traffic rate (Tr), wind speed (U ), water vapour mixing ration (H2O), temperature (T ), wind direction component in north-south direction (WD1) and variance of vertical wind speed ( ). The beta coefficients ( ) and their standard deviations ( ), and performance indices, R 2, and σw β σβ root mean square error (rmse), are also listed.

All Weekdays Weekends

β σβ β σβ β σβ Winter tr 0.57 0.00 0.59 0.00 0.45 0.00 U –0.30 0.00 –0.31 0.00 –0.19 0.01

H2O –0.42 0.00 –0.38 0.00 –0.61 0.00 R 2 (%) 56 55 68 rmse 0.66 ± 0.00 0.67 ± 0.00 0.57 ± 0.00 Spring tr 0.35 0.00 0.40 0.00 0.03 0.01 U –0.42 0.00 –0.80 0.00 –0.42 0.00

H2O –0.35 0.00 –0.22 0.00 –0.67 0.01 R 2 (%) 39 42 40 rmse 0.78 ± 0.00 0.76 ± 0.00 0.77 ± 0.00 Summer tr 0.40 0.00 0.42 0.00 0.16 0.00 U –0.40 0.00 –0.44 0.00 –0.35 0.00 T –0.24 0.00 –0.24 0.00 –0.18 0.00 R 2 (%) 26 30 15 rmse 0.86 ± 0.00 0.83 ± 0.00 0.91 ± 0.00 Autumn tr 0.70 0.00 0.73 0.00 0.29 0.01

WD1 0.04 0.01 0.01 0.00 0.27 0.01

σw –0.21 0.00 –0.19 0.01 –0.52 0.01

H2O –0.15 0.00 –0.11 0.00 –0.38 0.01 R 2 (%) 61 64 73 rmse 0.62 ± 0.00 0.60 ± 0.00 0.51 ± 0.01

Table 5. Same as in Table 4 but for accumulation mode particle concentrations (p < 0.05). Variables which were used in final models were traffic rate (Tr), variance of wind speed (σu), temperature (T ), pressure (p), vertical wind speed (w ), water vapour mixing ration (H2O), variance of vertical wind speed (σw) and relative humidity (RH).

All Weekdays Weekends

β σβ β σβ β σβ Winter tr 0.38 0.00 0.39 0.00 0.26 0.01

σu –0.28 0.00 –0.35 0.00 –0.15 0.01 T –0.40 0.00 –0.34 0.00 –0.54 0.00 R 2 (%) 33 33 35 rmse 0.82 ± 0.00 0.82 ± 0.00 0.80 ± 0.00 Spring p 0.54 0.00 0.63 0.00 0.44 0.00 w 0.26 0.00 0.18 0.00 0.45 0.00 R 2 (%) 40 45 43 rmse 0.78 ± 0.00 0.74 ± 0.00 0.74 ± 0.00 Summer tr 0.22 0.00 0.24 0.00 0.15 0.00

σu –0.34 0.00 –0.31 0.00 –0.28 0.00

H2O 0.59 0.00 0.57 0.00 0.68 0.00 R 2 (%) 47 41 62 rmse 0.73 ± 0.00 0.77 ± 0.00 0.62 ± 0.00 Autumn tr 0.47 0.00 0.57 0.00 0.38 0.00

σw –0.46 0.00 –0.25 0.00 –0.33 0.00 RH 0.36 0.00 0.58 0.00 0.07 0.00 R 2 (%) 64 77 31 rmse 0.60 ± 0.00 0.48 ± 0.00 0.82 ± 0.00 104 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A) which evidently affected strongly accumulation explain better models on weekends. U had oppo- mode particle concentrations. In summer, accu- site effect on coarse particles than it had on mulation mode particle concentrations increased UFP. High wind speed increases the re-suspen- as a function of H2O. Same was observed in sion of larger particles from ground while for the case of coarse particles (Table 6). This is smaller particles wind speed is more related to most likely related to the condensation of water the atmospheric stability and mixing (e.g. Hus- vapour on pre-existing particles which increase sein et al. 2006). The appearance of west-east their size to larger classes. Similar results were wind direction component in autumn was evi- also presented by Jamriska et al. (2008), who dent (see also Fig. 4d). found accumulation mode particles to be strongly The only flux variable present in the models affected by RH with positive dependence in was LE, with an inverse effect in the case of Brisbane, Australia. In the same study, particle coarse particles in spring. Re-suspension of dust concentrations with the particle size range of is more efficient in dry conditions when also 15–880 nm were found to be dominated by traf- LE is expected to be lower. The momentum fic, wind speed, temperature and relative humid- flux itself was not present in the models but it is ity similarly to our results. directly proportional to wind speed and standard Overall, the coarse particle concentrations deviation of wind speed components, which were could not be explained as well with the avail- included in the final models. Stability parameter able variables as fine particles. The especially ζ was not present in any of the models (Fig. 8h). low R2 in spring (9%) was most likely caused It is describing the general mixing conditions in by the street dust re-suspended by wiping the boundary layer and it is inadequate in point machines and/or traffic at the parking lot. These by point comparisons. Also the strong diurnal are not included on the available variables and patter of ζ may have its effect. We are not aware are mainly present on weekdays, which would of any study where correlations between aero-

Table 6. Same as in Table 4 but for coarse particle concentrations (p < 0.05). Variables which were used in final models were traffic rate (Tr), vertical wind speed (w), water vapour mixing ration (H2O), pressure (p), latent heat flux

(LE), wind speed (U ) and wind direction component in east-west direction (WD2).

All Weekdays Weekends

β σβ β σβ β σβ

Winter tr 0.25 0.00 0.28 0.00 0.23 0.01 w 0.27 0.00 0.22 0.00 0.37 0.01

H2O –0.37 0.00 –0.35 0.00 –0.51 0.01 R 2 (%) 22 25 16 rmse 0.88 ± 0.00 0.86 ± 0.00 0.91 ± 0.00 Spring tr 0.30 0.00 0.24 0.00 0.08 0.01 p 0.11 0.00 0.02 0.00 0.51 0.01 LE –0.25 0.01 –0.30 0.01 –0.13 0.01 R 2 (%) 9 8 28 rmse 0.95 ± 0.00 0.96 ± 0.00 0.85 ± 0.00 Summer tr 0.18 0.00 0.22 0.00 –0.12 0.00 w 0.05 0.00 0.03 0.00 0.22 0.00

H2O 0.41 0.00 0.33 0.00 0.49 0.00 R 2 (%) 21 16 37 rmse 0.88 ± 0.00 0.92 ± 0.00 0.79 ± 0.00 Autumn tr 0.46 0.00 0.48 0.01 0.15 0.01 U 0.21 0.01 0.36 0.01 –0.11 0.01

WD2 0.12 0.01 0.14 0.01 0.18 0.01 T 0.27 0.01 0.12 0.01 0.70 0.01 R 2 (%) 39 44 46 rmse 0.78 ± 0.00 0.75 ± 0.00 0.73 ± 0.00 Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 105

sol particle number concentrations and turbulent SO2 is considered to be a precursor gas for fluxes have been studied in urban areas. Thus, new particle formation in the atmosphere, as it we do not know are the observed correlations oxides to sulphuric acid which is tied to nuclea- typical for urban areas or only characteristic to tion of new particles (Kulmala 2003, Kulmala our measurement site. et al. 2004, Kulmala et al. 2006). Jeong et al. (2004) found a good correlation between UFP

and SO2 during nucleation events. This could Correlations between aerosol particle at least partly explain the higher correlation of number concentrations and NO , CO and x UFP and SO2 in spring and summer. However, SO 2 longer time series of SO2 and CO are needed for more detailed analysis of the connection Linear correlations of UFP and accumulation between these gases and aerosol particle number mode particle concentrations with NOx, CO and concentrations.

SO2 concentrations were studied separately for each season between December 2006 and June 2007 (Table 7). All concentrations were logarith- Conclusions mically transformed before the analysis. Both UFP and accumulation mode particle Here we report results from the air pollution and concentrations correlated better with NOx and turbulent exchange measurements made at the

CO than with SO2 pointing out the importance urban measurement station SMEAR III. UFP, of traffic as a source of fine particles. Similar accumulation mode particle and coarse particle results have also been reported by Morawska et number concentrations, concentrations of gase- al. (1998) and Roth et al. (2008). The correlation ous pollutants (O3, NOx, CO and SO2), turbulent of UFP with NOx and CO increased in winter fluxes of momentum, heat, H2O and CO2 and indicating the increased effect of traffic due to meteorological were analyzed on annual and e.g. lowered mixing. NOx correlated better with diurnal basis. The measurements of fine aerosol UFP than with accumulation mode particles, particles (UFP and accumulation mode particles) while CO correlated better with accumulation and meteorological variables were started in mode particles. Both NOx and UFP represent pri- August 2004 while the other measurements have mary emissions from combustion sources whose been gradually added with time. In this study, the concentrations are high near emissions sources data until June 2007 were analyzed. The wind

(NOx chemistry fast). The lifetime of CO in the direction dependencies of pollutant concentra- atmosphere is of the same order of magnitude tions were analyzed and the effect of traffic, as the lifetime of accumulation mode particles turbulent fluxes and meteorological variables on (Seinfeld and Pandis 1998), which explains the higher correlation between these two. Contrary Table 7. Squared correlation coefficients (R 2) from a to our results, Roth et al. (2008) found higher linear fitting p( = 0.00) made between aerosol particle NO correlation with accumulation mode parti- number concentration of ultrafine particles (UFP) and x accumulation mode particles (AP), and the gas pollut- cles (size range of 0.1–0.62 µm) than with UFP ants of NOx, CO and SO2. Data in December 2005– in Strasbourg, France. The difference might rise June 2007 was used in the analysis. due to the logarithmic transformation used in no co so this study. Previously, UFP have been found to x 2 correlate better with CO than with NO (Moraw- x Winter UFP 0.74 0.36 0.17 ska et al. 1998, Noble et al. 2003). In Helsinki, AP 0.55 0.69 0.22 the local emissions of CO are lower as compared Spring UFP 0.45 0.15 0.28 with the emissions in these larger cities and, AP 0.63 0.42 0.36 thus, the effect of transported CO likely more Summer UFP 0.54 0.23 0.28 evident. AP 0.28 0.35 0.25 Autumn UFP 0.69 0.24 0.01 SO2 correlated better with fine particles in AP 0.48 0.63 0.08 spring and summer than in winter and autumn. 106 Järvi et al. • Boreal Env. Res. vol. 14 (suppl. A) aerosol particle number concentrations was stud- During other seasons, the nocturnal stratification ied by means of a multiple regression analysis. remained stable. The atmosphere was most unsta- Besides, correlations between fine particles and ble during the summer days indicating stronger gas concentrations of NOx, CO and SO2 were turbulent mixing. The enhanced mixing during analyzed. summer months as compared with that during The pollutant concentrations of UFP, accu- winter months tends to decrease pollutant con- mulation mode particles, NOx, CO and SO2 centrations as observed. On the other hand, also showed a distinct annual pattern with highest emissions during summer months are lower. concentrations in winter due to poor pollutant Variations in UFP were most affected by traf- mixing and increased emissions from stationary fic as was shown by the MLR analysis. However, combustions sources. The annual behaviour of this effect decreased on weekends when the accumulation mode particles is also related to meteorological parameters, especially turbulent the effect of long-range transport which was pro- mixing, became more important. Poorer MLR nounced during the summer season. The coarse models were obtained for weekday than for particle concentration peaked after the melting weekend concentrations in winter and autumn of snow and ice in spring when the re-suspen- most probably due to the emissions just next to sion by traffic and wiping-machine induced tur- the measurement station, which are not included bulence is most efficient. At the measurement in traffic counts and are present only on week- site, the land use cover had a clear effect on the days. On weekdays also the quality of a simple measured traffic related pollutants (UFP, accu- model may be reduced. The importance of traf- mulation mode particles, NOx and CO) with fic as a source of aerosol particles decreased highest values in the road sector and lowest in with increasing particle size when the prevailing the vegetation sector. The diurnal patterns were meteorological conditions and H2O mixing ratios consistent with vehicular activity and the highest became more important. Turbulent mixing had weekday concentrations were measured during always effect on the aerosol particle number con- the morning rush hour. Considering these pol- centrations and its effect depended on the size lutant concentrations, Helsinki seem to be a of the particle. High wind speed increases the relatively clean city when compared with other mechanical mixing and air pollutants can mix cities around the world. However, so far there into larger air volume decreasing the pollutant has been limited amount of studies which simul- concentrations above ground. On the other hand, taneously measure both size-fractioned aerosol higher mixing re-suspends large particles into air particle number concentrations and gas pollut- and thus increases their concentrations. ants. Thus, more measurements are needed for Correlations between fine particles and CO more detailed comparisons. and NO were better than with SO addressing The annual patterns of O , H and LE followed x 2 3 the importance of traffic as a source of these strongly the amount of available solar radiation particles. The effect of long-range transport on as they increased towards summer. O concen- 3 accumulation mode particles and CO was seen as trations were also affected by the amount of the increased correlation between these two. UFP and O -consuming compound which could be seen 3 NO on the other hand are primary emitted and as lower weekday concentrations. The friction x get their highest concentrations near pollution velocity (u ) and F did not have a distinct annual * c sources which increased their correlation. The pattern and on average the urban cover acted as a correlation between SO and both aerosol particle source for CO around the year. However, from 2 2 size classes improved in spring and summer and the diurnal behaviour of Fc could be seen that in the footprint area during the growing season in the case of UFP, higher correlation is likely related to new particle formation events. the vegetation uptake of CO2 exceeded the emissions from anthropogenic sources which could Acknowledgements: For financial support we thank Maj be seen as downward fluxes. The stratification and Tor Nessling Foundation, National technology Agency of nocturnal boundary layer was unstable during (TEKES, Ubicasting project), URPO and REBECCA by Hel- the winter due to the anthropogenic heat sources. sinki University Environmental Research Centre (HERC), Boreal Env. Res. vol. 14 (suppl. A) • The urban measurement station SMEAR III 107

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