Distinction of Elemental Fingerprints from Wood Burning and Traffic

Distinction of Elemental Fingerprints from Wood Burning and Traffic Highly Time-Resolved Rotating Drum Impactor Samples Analyzed by Synchrotron X-Ray Fluorenscence Spectrometry

Diploma Thesis Presented by Lukas P. Kammermann Environmental Sciences ETH Zürich April - September 2005 Supervisors Prof. Dr. Ulrike Lohmann Dr. André Prévôt Dr. Nicolas Bukowiecki

Distinction of Elemental Fingerprints from Wood Burning and Traffic Highly Time-Resolved Rotating Drum Impactor Samples Analyzed by Synchrotron X-Ray Fluorescence Spectrometry Lukas P. Kammermann September 2005

Contents 1 Introduction

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2 Materials and Methods 2.1 The Rotating Drum Impactor (RDI) . . . . . . . . .

2.2 Sampling . . . . . . . . . . . . . . . . . . . . . . . .

2.2.1 Laboratory Study: Wood Burning at EMPA 2.2.2 Field Studies in Ticino . . . . . . . . . . . . .

2.2.3 Field Study on Jungfraujoch . . . . . . . . .

2.3 Analysis with Synchrotron-XRF . . . . . . . . . . .

2.3.1 Setup and Calibration . . . . . . . . . . . . .

2.3.2 First Session at HASYLAB-beamline . . . . .

2.3.3 Second Session at HASYLAB-beamline . . .

2.3.4 Testing Session at SLS-beamline . . . . . . .

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3 Results and Discussion 21 3.1 Laboratory Study: Wood Burning at EMPA . . . . . . . . . . . . 21 3.1.1 Fingerprints of Wood Species . . . . . . . . . . . . . . . . 21 3.1.2 Median Variations of Fire Content . . . . . . . . . . . . . 31 3.1.3 Validation of the Data . . . . . . . . . . . . . . . . . . . . 35 3.1.4 General Uncertainties in the Results . . . . . . . . . . . . 36 3.2 Field Studies in Ticino . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.1 Fingerprints of the Three Field Studies . . . . . . . . . . 38 3.2.2 Qualitative Source Apportionment . . . . . . . . . . . . . 46 3.2.3 Time Series . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.4 Selected Key-Playing Elements for Wood Burning and Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 3.2.5 Validation of the Data . . . . . . . . . . . . . . . . . . . . 74 3.3 Field Study on Jungfraujoch . . . . . . . . . . . . . . . . . . . . . 78 4 Conclusions and Outlook 4.1 Sampling with the Rotating Drum Impactor (RDI) 4.2 Laboratory Study: Wood Burning at EMPA . . . .

4.3 Field Studies in Ticino . . . . . . . . . . . . . . . .

4.4 Field Study on Jungfraujoch . . . . . . . . . . . . .

4.5 Analysis with Synchrotron-XRF . . . . . . . . . .

5 Acknowledgements

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80 80 81 81 86 86 88

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Bibliography

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A Schemes

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B Photos

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II

Abstract The distinction of the elemental composition of ambient aerosols, focusing on the contribution of wood burning emissions to air pollution from traffic, was assessed within the scope of the Swiss aerosol project AEROWOOD. A three-stage rotating drum impactor (RDI) was used to sample aerosol in a laboratory study on wood burning, in a field study in Ticino and in a field study on Jungfraujoch. Additionally, data from two winter field studies in Ticino was analyzed and included in this thesis.

Analysis results of the samples from the laboratory study showed, that mainly sulphur, chlorine, potassium and calcium are present in wood burning emissions. Potassium was the only element that was permanently present in higher amounts in the fine mode (0.1 − 1 µm). All other elements were of small importance. Expected substances emitted by wood burning experiments were CaSO4 , CaCl2 , K2 SO4 and KCl. The main elemental components of aerosols sampled in the three field studies in Ticino were sulphur, chlorine (only in the winter field studies), potassium, calcium and iron. Most of these elements were present in the coarse mode (2.5 − 10 µm). Elemental fingerprints indicated the presence of similar compounds as found in the laboratory study: CaSO4 and K2 SO4 . In a qualitative source apportionment, size fractions of selected elements were attributed to seven sources. An analysis of three regions of interest in each time series from the data of the field studies in Ticino pointed out that the elemental concentrations of the aerosol were strongly influenced by different weather conditions. However, in a village in which residential heating by burning wood is commonly performed, diurnal concentration patterns of wood burning activities were clearly visible in winter. In a different site next to a motorway concentration patterns for elements indicating traffic showed high emissions during morning and afternoon rush hour traffic. High concentrations of elements from wood burning emissions were found in this site due to accumulation, which was influenced by the weather condition of a stable inversion.

The RDI could only declare 25 % of the total aerosol concentration. That is why high concentrations of carbonaceous aerosol were expected. Thus, for a quantitative declaration of wood burning emissions to emissions from traffic, a potential is seen in the analysis of data from EC/OC measurings.

Aerosol samples were analyzed with synchrotron-XRF in two sessions at a beamline of the Hamburger Synchrotronstrahlungslabor (HASYLAB) at primary energies of 16 keV , 25.6 keV and 28 keV . In a testing session at a beamline of the Swiss Light Source (SLS) at Paul Scherrer Institut (PSI), the setup and the analysis parameters were assessed at 9.8 keV .

The analysis of RDI samples from the field study on Jungfraujoch is a novelty. The elemental composition on the High Alpine Research Station on Jungfraujoch had never been assessed by an RDI. However, first analysis of the spectra showed the presence of only iron and nickel in very low concentrations.

Chapter 1

Introduction Aerosol is the term for a dispersion of either liquid or solid particles in a gas.

Usually this gas is air. Aerosol particles exist in sizes from one nanometer to several micrometers. In some places, higher concentrations of aerosol particles are expected: Concentrations of 100’000 particles cm−3 are common in towns, whereas in the countryside 10’000 particles cm−3 are measured. In remote areas of high altitude — as for example on Jungfraujoch (section 2.2.3) — only 1’000 particles cm−3 air are found [1].

Especially fine aerosol particles have several effects on the human health [30]. Because of their small size, some particles are able to pass the defense mechanisms of the human respiratory system (figure 1.1), while larger particles are stopped by several filter systems of the human body. The most obvious filter system for particles is the nose, where particles larger than 10 µm are completely absorbed by fine hairs and nasal mucosa. Inside the trachea, particles with a size of 3 − 5 µm diameter are stopped, whereas in the bronchial tubes, particles with 2 − 3 µm diameter are stopped. Particles with a diameter of 1 − 2 µm can follow the air flow until they reach the bronchioli, the ramifications of the bronchia. Ultrafine particles with diameters < 100 nm penetrate to the very end of the respiratory tract, namely to the alveoli of the lung, where the exchange of oxygen and carbon dioxide takes place. By definition, the particle size fraction with diameters in the range of 2.5 − 10 µm is called coarse mode. Particles with diameters < 2.5 µm are said to be the fine mode. Nanoparticles is the name of the smallest particle fraction, which has diameters of only < 50 nm. Figure 1.2 visualizes the mentioned facts and shows which particles make the highest contributions to the total mass and total number, relative to all size fractions.

It is evident that substances on such particles or even smaller particles can take part in the air exchange reactions in the lung. Still, health effects are by far not entirely understood, but it is known that aerosols are a stressor for the immune system. This causes the death of 3’300 people and health costs of 6.6 billion swiss francs per year only in Switzerland [25]. Health effects were first thought to be only a consequence of the mass of coarse particles [16]. With further research, especially with findings about the presence of nanoparticles, it was evident that the number of particles must be an essential factor for health effects [18]. Nanoparticles with diameters from 5−50 nm have a high deposition efficiency in the lungs as well. This is a crucial fact because nanoparticles usually appear in high numbers (figure 1.2). Because of their size, their appearance in 1

Figure 1.1: Scheme of the human respiratory tract with targets for the mentioned particle sizes (adapted from [5]).

high numbers and the good deposition efficiency, small size aerosols could have a potential in pharmacy: A better understanding of the alveolar epithelium could lead to the development of more specific drug targeting approaches (by means of distribution as an aerosol) [23]. One of the possibilities to overcome the blood-brain barrier â&#x20AC;&#x201D; an insurmountable obstacle for a large number of drugs â&#x20AC;&#x201D; for drug delivery in the brain could be the use of nanoparticles [29].

Different sources report an increase in respiratory symptomps such as (chronic) bronchitis or asthma attacks, hospitalization and cardiovascular diseases in humans with an increase of aerosol pollution in the atmosphere [28, 36]. Pathogene effects are expected from certain trace elements (for the most part metals) that are found on aerosols or form aerosols themselves. Known health effects from trace metals are the development of respiratory inflammation to cancer and the damage of vital organs [33, 38]. For several effects, as the induction of inflammatory proteins and cytoxicity, the composition and the solubility of the particles seem to play an important but still unclarified role. Soluble polyvalent transition metals can cause oxidative stress after having produced free radicals [17].

The Swiss aerosol project AEROWOOD, which started in December 2004 and lasts until December 2007, tries to estimate the contribution of wood smoke to the total particle concentrations of ambient air. This estimation is done by measuring several air parameters. A field campaign and a laboratory study performed during this thesis could make a contribution to the AEROWOOD project. Field measurements of the project take place in an area in southern Switzerland, where a major motorway causes extensive traffic emissions. Rural villages are present in this area as well where domestic heating is often performed by wood burning. Additionally, in some seasons the influence of the mentioned sources on air pollution is strongly increased due to a stationary temperature inversion. This effect, which is common in mountain valleys is well documented in other studies [34, 24].

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Figure 1.2: Scheme of the mass contribution, of the number contribution, and of the alveolar deposition fraction of aerosol fractions from 0.001 µm to 10 µm diameter. Mostly ultrafine particles (< 100 µm diameter), which are present in highest amounts, remain in the alveoli. Particles with diameters between 0.1 − 1 µm make highest contributions to the normalized mass weight of all size fractions [27].

Effects on human health by aerosols from wood smoke are reported in several sources [35, 37]. Associations between wood smoke and adverse health effects such as irritations of eyes, nose and throat, decrements in lung function and increased respiratory infections are reported as well. Children with asthma seem to be particularly susceptible to effects of wood smoke. Reported symptoms include increased respiratory symptoms, lower respiratory infections, and decreased pulmonary function [46]. Glowing combustion of softwood pellets is reported to release carcinogenic benzene as the predominant aromatic compound. However, a glowing combustion emits lower benzene concentrations than a flaming burning. Benzene emissions from different pellet burners varied by a factor 60 [39]. By comparing the mentioned benzene concentrations to results of a different study, which measured benzene concentrations in a smoky caf´e, inside a car and in a tunnel [9], it was shown that some pellet burners emitted three times lower benzene concentrations than present in a smoky caf´e (inside a car similar concentrations as in a smoky caf´e were present). Other pellet burners, however, emitted benzene concentrations, which were twice as high as present in an urban road tunnel!

Not only human health is influenced by aerosols. Fine particles in the atmosphere reduce visibility, directly and indirectly affect the earth’s radiative balance, and influence the fate of hazardous pollutants in the environment. The properties of clouds are changed by aerosols, and the atmospheric budget of a range of elements and compounds is influenced [1]. Older studies report that residential wood burning has been shown to be a very important source to air pollution including particles, polycyclic aromatic hydrocarbons and mutagenic 3

compounds [43]. Wood smoke emissions from residential fireplaces are a major source of atmospheric fine particulate matter in several communities in the United States. Wood smoke from wild fires are responsible for severe episodes of air pollution. In developing countries, where biomass fuels are still the major source of energy for cooking and heating, indoor exposures of up to several mg/m3 have been measured [46]. By burning biomass, mainly a broad group of carbonaceous particles is produced which has highly variable atmospheric effects [40]. But apart from carbonaceous particles, important quantities of potassium, sulphur, chlorine and silicone are emitted from biomass burning.

Although, emission factors have been measured and calculated for several vegetations, details of the distribution of the elements on individual particles are not known. Because of these facts, it is important to understand the processes influencing the concentrations of particles in the atmosphere, including the role of anthropogenic activities, and to determine their origin.

To distinguish and avoid particle emissions, the knowledge of processes that produce emissions is necessary. Such processes can be described by analyzing the concentrations of elements produced exclusively by the processes, so called tracer elements. If a substance is mainly produced by a focused process, but can be a product from a different source, it is not seen as a tracer element. A tracer should feature the following three main characteristics: uniqueness (originating only from one source), constancy (operational and environmental conditions do not effect the emission factor), inertness (the tracer does not get lost between the source and the receptor) [26].

In the next paragraph, every element which was analyzed in the frame of this thesis is introduced [8, 6].

Sulphur is found for example in meteorites, volcanoes or hot springs. Among other elements, sulphur is a major component of crustal materials and soil and so predominantly attributed to resuspension of road dust due to the large amounts present in PM10 [32]. On construction sites it is found as a content of gypsum (CaSO4 ). Between the early and mid 1990s, high sulphur concentrations of sulphur dioxide (SO2 ) emissions from metal processing industries decreased rapidly [30]. Today in most countries sulphur dioxide emissions are no longer present in high amounts due to restrictions imposed by law. In atmospheric processes, sulphur in the form of sulphate (SO4â&#x2C6;&#x2019;2 ) is an important fine mode secondary aerosol [44].

Chlorine is mainly found in nature as a salt in sodium chloride (N aCl) and calcium chloride (CaCl2 ). It is expected to be mainly sampled in winter due to the use of deicing salt on roads. In sites near oceans, chlorine is often found in sea salt aerosols from sea-spray [44]. Chlorine is commonly used as a tracer of wood burning [26].

Potassium is the seventh most abundant element and makes up about 1.5% by weight of the earthâ&#x20AC;&#x2122;s crust. It is an essential constituent for plant growth and often found in plants as potassium chloride (KCl). Potassium and chlorine are organically bound or present in the fluids of the vegetation.

Therefore it is expected to be a tracer for wood burning [26]. Some studies detected organic particles with inorganic inclusions in biomass burning emissions: The latter were mainly potassium-salts as KCl, potassium 4

sulphate (K2 SO4 ) or potassium nitrate (KN O3 ) [40]. Fine potassium which is not attributable to mineral dust or sea salt is an indicator of biomass burning [31]. It is found in most soils, from which it can get resuspended to the aerosol phase [32].

Calcium is fifth in abundance in the earth’s crust, of which it forms more than 3%. Calcium is found in nature as limestone (mainly consisting of CaSO4 ) and gypsum. It is an essential constituent of leaves, bones, teeth, and shells.

Titanium is the ninth most abundant element. In the earth’s litosphere it only occurs as oxide. As a metal it has a low density, good strength and an excellent corrosion resistance. That is why it is used in automotive engineering for example in dampers in the chassis of cars. Titanium is expected to be present when sampling traffic emissions.

Vanadium is used as an additive to improve steels. The resulting ferrovanadium is suitable for further processing. Steels containing vanadium are used in automotive engineering to produce shafts and gears for the gearbox. Therefore, vanadium is expected to be found in aerosol samples of traffic emissions. Furthermore, Vanadium is a byproduct in mineral-oil processing industry due to its presence in petroleum.

Chromium is often used to galvanize materials such as steel, copper or aluminium after coating it with a special layer. Chromium plated aluminium cylinders are used in automotive engineering. Therefore, chromium is thought to be present in traces in traffic emissions.

Manganese is present in the earth’s crust and after iron the second in abundance among the heavy metals. It is used in metal processing industries to enhance the quality of steel. The latter is used in different parts of cars.

As titanium, manganese is expected to be present when sampling traffic emissions.

Iron represents 5% of the earth’s crust’s content and therefore it is one of the most abundant elements. In automotive engineering it is used in steels for car bodies. Needless to say, that there are many other parts in cars made of iron. Iron is a vital constituent of plant and animal life as well.

Copper is chosen to coat surfaces of metals, and due to its good conductibility it is often used for electric cables. Its good resistance against corrosion makes it a common material for tubes and mechanical parts which afford high precision. Copper is used with zinc in alloys. In other studies copper was found in aerosol near railway tracks due to pantographs’ abrasion of contact wires [13].

Zinc is used to galvanize iron to protect it from corrosion. This is applied in different branches, such as in automotive engineering which connects zinc to traffic emissions. Tire wear from cars brings remarkable amounts of zinc to the environment: Estimations came to the result that for each driven kilometer by car, 0.05 g of tire wear is produced. With this assumption, an amount of 9’200 tons of zinc would have been emitted to the environment in 1995 only by tire wear [14]!

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Bromine was added to leaded fuel as a scavenger to countertrack the effects of impurities. This could be measured (mainly as P bBrCl) as a main component in exhaust emissions [10]. Moreover, it is used in brominated flame retardants for example on circuit boards (as polybrominated biphenyls [PBB]) or in rubber for tires.

Rubidium is only present in low amounts in engineering due to the expensive production. Alloys of rubidium are formed with gold, caesium, sodium and potassium. With mercury rubidium forms amalgam. Rubidium is present in heavy oils and found in traces in mineral dust.

Strontium is as rubidium of subordinate use for engineering. Some special steels are hardened with strontium.

Cadmium was commonly used in pigments of red and yellow colour and plastics. In automotive engineering, cadmium is used as lubricant in disc brakes. Moreover, it is contained in solder and used to produce semiconductors. Therefore, cadmium is expected to be present in some car components.

Lead concentrations were extremely high in the atmosphere in the 1970s due to the use of leaded gasonline but declined in the 1980s [14]. Lead in the form of tetraethyl- and tetramethyl lead (P b(C2 H5 )4 and P b(CH3 )4 ) was used as antiknock additives in leaded gasoline [10]. In older studies, 100 times higher concentrations of lead in the atmosphere were measured, compared to measurements from more recent studies. Therewith, serious problems in the central and peripheral nervous system and in the vascular system of humans were more present in the past [48].

The goal of this thesis was to distinguish emissions from wood burning and traffic with the known facts of every element and so to gain more insight into tracer elements for the mentioned processes. This is the topic of the Swiss aerosol project AEROWOOD too: High emissions from a major traffic route and wintertime weather situations are believed to cause high air pollution. Additionally, wood burning is common in the observed area. Therefore, the effect of wood smoke is of interest. In the area where the measurements were done either emissions from mainly wood burning, traffic or a mix of both were expected. Additionally, data from a recently performed field study in this area was analyzed and included in the results of this thesis.

Sampling was performed during a laboratory study and several field studies, during which aerosol samples were collected with a three-stage impactor. Each stage contained a drum-like sampling wheel that could keep up to 140 samples.

The samples from a laboratory study represent a novelty in highly time-resolved impactor sampling: In a wood chips oven, which was automatically supplied with wood chips, and a stove operated with logs, the smoke from fires of different wood species was sampled. Collecting samples with the impactor at the High Alpine Research Station on Jungfraujoch has never been done before. However, this data is not yet completely processed and only an excerpt is presented in this thesis. The analysis of the samples from Jungfraujoch are believed to elucidate the elemental composition of a secondary, low concentrated aerosol. This issue is the subject of a different project at the Labor f¨ ur Atmosph¨arenchemie.

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All the samples were analyzed during two sessions by means of synchrotronXRF at the Hamburger Synchrotronstrahlungslabor (HASYLAB/DESY Hamburg, Germany, DORIS beamline L) with beam energies of 16 keV , 25.6 keV and 28 keV . Although the method of synchrotron-XRF is well established, the used geometry to analyze wheels with aerosol samples on a mylar film, as performed in this thesis, is a more recent technique. Testing analysis, experiments with a goniometer table and the assessment of a batch analysis software for the samples were performed as well at the Swiss Light Source (SLS) at Paul Scherrer Institut (PSI Villigen, Switzerland, beamline X05LA). Analysis results from both beamlines were satisfactory, although optimizations in the geometry and the automation, the improvement of settings and configurations of the tools are open tasks and believed to bear a big potential.

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Chapter 2

Materials and Methods 2.1

The Rotating Drum Impactor (RDI)

The impactor used in this thesis collected aerosol particles of three different size fractions (RDI, Lundgren type 3-DRUM impactor). Each one of the three stages contained a wheel-like sampling drum, to which a substrate-film was attached. A step motor revolved the drums simultaneously after a period of time, depending on the expected dilution of the sampled air. Figure 2.1 shows a scheme of the three sampling stages in the impactor. The mylar film (length 190 mm, width 15 mm, thickness 1.8 µm, Du Pont Inc.) was treated with a five weight percent solution of APIEZON (M+I Materials Inc., Manchester UK) and toluene (p.a., Fluka AG, Switzerland). Mylar was chosen because of its contents: Mylar is mainly a very thin polyester film and contains a constant contamination of calcium. So measured calcium peaks from the sampling substrate could be excluded and the contamination by the calcium could be set as background concentration. To make the aerosol particles stick better on the mylar film and to avoid particle re-bouncing, greasing with the mentioned solution was performed before the sampling. With orifice-slits, which lead defined cutoffs of the total aerosol to the RDI drums, three aerosol sizes were collected (table 2.1). The impactor cutoffs — defined by the orifice-slit dimensions, the air pressure and the distance between the orifice-slits and the mylar film — were calculated as described in [12]. For all described laboratory and field studies, a constant flux of 16.6 l/min brought air to the RDI. The RDI was used with a PM10 impactor. The distance between two sampling spots on the mylar film was usually 1.2 mm. So each sampling wheel was able to record up to 140 spots of accumulated aerosol particles. A full-scale drawing of a mylar film with aerosol samples is shown on the front page of this thesis below the subtitle line.

To calibrate the synchrotron-XRF analysis for absolute concentrations (section 2.3.1), wheelsets with wider distances between the samples were collected.

Figure B.1 in the appendix shows the opened RDI-box with the three sampling wheels fixed in the drums.

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Figure 2.1: Scheme of the RDIâ&#x20AC;&#x2122;s three sampling stages

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Table 2.1: RDI sampling size fractions.

RDI stage first stage second stage third stage

2.2

drum wheel A B C

size fraction 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm

Sampling

During a laboratory study on wood burning, in field campaigns in a village, next to a motorway and in an alpine research station (section 2.2), samples of aerosols were taken using a rotating drum impactor (section 2.1). Analysis was performed using synchrotron X-ray fluorescence spectrometry (section 2.3) at the Hamburger Synchrotronstrahlungslabor (HASYLAB/DESY Hamburg, Germany, DORIS beamline L) and at the Swiss Light Source SLS at Paul Scherrer Institut (PSI Villigen, Switzerland, beamline X05LA). With this, concentrations of several trace elements could be calculated. Figure 2.2 shows a schematic overview of all sampling sites and one of the two analysis sites.

Figure 2.2: Schematic map of Switzerland. Light coloured shadings indicate the areas of the different cantons of Switzerland. Red circles indicate sampling sites of the field studies: In the north of Bellinzona lies the site of Moleno, Roveredo is located in the east of Bellinzona. Jungfraujoch is marked with the third red circle. EMPA D¨ ubendorf, where the laboratory study was performed is marked with the blue circle close to Z¨ urich. The green circle marks the location of the Swiss Light Source (SLS) at Paul Scherrer Institut (PSI) where a testing analysis was performed. Map reproduced with the permission of swisstopo (BA057259).

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2.2.1

Laboratory Study: Wood Burning at EMPA

From April 18 until April 29 samples were taken within the scope of a wood burning campaign at the Swiss Federal Laboratories for Materials Testing and Research (EMPA) in D¨ ubendorf.

During the first six days, aerosols from a wood chip fire were sampled with the RDI. The fire was automatically supplied with chips, so the fire had a longer constant burning phase after the starting phase. During three days, chips of beech wood were burned, followed by two days of firing chips of larch wood. After that, beech chips were burned for one more day due to an error in the setting of the first phase of beech wood firing. The RDI sampling was not influenced by this error. In the setting of this phase of chips fires, the RDI was placed after three dilutors, where the first dilution stage was either heated up to 70 or 150 . The fume from the chip fires was diluted by factors ranging from 9’054 to 9’739. In all results in this thesis, the dilution factors were included and, therefore, the concentrations stand for undiluted emissions from the fires.

After one hour, the wheel was revolved to start a new sample on the mylar film.

Small logs of different wood types were incinerated from April 27 until April 29. Amounts of five small logs were weighed and burned in a small fireplace (Haas + Sohn) as used in living rooms. A period of one firing lasted — depending on the wood type — between 38 and 59 minutes. At the beginning of every day, a preheating fire was lit to start all sampled fires from a similar temperature. On the first day of this phase, beech wood logs were fired three times with having the first dilution stage heated up to 70 , afterwards the same kind of wood was burned three times with heating the first dilution stage up to 150 .

This phase was sampled by the RDI with an interval of 20 min. The next day, larch wood was burned and sampling was performed with an interval of 10 min.

There were four fires with heating the first dilution stage up to 70 and three fires where the diluter was heated up to 150 . On the last day, four firings with wood of white fir and four firings with oak wood were performed. Both fires were sampled with heating the first dilution stage up to 70 . These last fires were sampled with an interval of 10 min. The fume from the log fires was diluted by factors ranging from 1’170 to 1’968. These dilution factors were as well included in all the results.

Tables 2.2 and 2.3 give an overview of the experiments performed within the scope of the wood burning campaign at EMPA D¨ ubendorf. Figures A.1 and A.2 in the appendix show schemes of all devices that were involved in the EMPA laboratory study.

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Table 2.2: Overview of the wood chips experiments at the wood burning campaign at EMPA D¨ ubendorf. start of experiment April 18, 2005 09:20 April 19, 2005 10:50 April 19, 2005 13:40 April 19, 2005 16:15 April 20, 2005 09:43 April 20, 2005 12:52 April 20, 2005 14:55 April 21, 2005 09:41 April 21, 2005 12:33 April 21, 2005 15:00 April 22, 2005 09:19 April 22, 2005 11:30 April 22, 2005 16:20 April 25, 2005 10:09 April 25, 2005 11:52 April 25, 2005 13:56 April 25, 2005 16:38

experiment condition tests with beech wood chips, exhaust gas 150 beech wood chips, exhaust gas 150 , starting phase beech wood chips, exhaust gas 150 , constant phase beech wood chips, exhaust gas 70 , constant phase beech wood chips, exhaust gas 70 , starting phase beech wood chips, exhaust gas 70 , constant phase beech wood chips, exhaust gas 150 , constant phase larch wood chips, exhaust gas 70 , starting phase larch wood chips, exhaust gas 70 , constant phase larch wood chips, exhaust gas 150 , constant phase larch wood chips, exhaust gas 150 , starting phase larch wood chips, exhaust gas 150 , constant phase larch wood chips, exhaust gas 70 , constant phase beech wood chips, exhaust gas 70 , starting phase beech wood chips, exhaust gas 150 , starting phase beech wood chips, exhaust gas 150 , constant phase beech wood chips, exhaust gas 70 , constant phase

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sampling time 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min 60 min

Table 2.3: Overview of the log experiments at the wood burning campaign at EMPA D¨ ubendorf. start of experiment April 27, 2005 11:31 April 27, 2005 12:09 April 27, 2005 12:50 April 27, 2005 14:48 April 27, 2005 15:28 April 27, 2005 16:08 April 28, 2005 10:40 April 28, 2005 11:30 April 28, 2005 12:20 April 28, 2005 13:11 April 28, 2005 15:24 April 28, 2005 16:14 April 28, 2005 17:15 April 29, 2005 09:10 April 29, 2005 09:35 April 29, 2005 10:25 April 29, 2005 11:17 April 29, 2005 12:08 April 29, 2005 13:44 April 29, 2005 14:20 April 29, 2005 15:10 April 29, 2005 16:00

2.2.2

experiment condition beech logs, exhaust gas 70 beech logs, exhaust gas 70 beech logs, exhaust gas 70 beech logs, exhaust gas 150 beech logs, exhaust gas 150 beech logs, exhaust gas 150 larch logs, exhaust gas 70 larch logs, exhaust gas 70 larch logs, exhaust gas 70 larch logs, exhaust gas 70 larch logs, exhaust gas 150 larch logs, exhaust gas 150 larch logs, exhaust gas 150 white fir logs, exhaust gas 70 white fir logs, exhaust gas 70 white fir logs, exhaust gas 70 white fir logs, exhaust gas 70 white fir logs, exhaust gas 70 oak logs, exhaust gas 70 oak logs, exhaust gas 70 oak logs, exhaust gas 70 oak logs, exhaust gas 70

sampling time 20 min 20 min 20 min 20 min 20 min 20 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min 10 min

Field Studies in Ticino

During two field studies in Ticino, aerosol was sampled in a rural village and in a site next to a motorway. Figure B.2 in the appendix shows an overview of the sampling sites in Ticino: Both sites are visible in the aerial view.

The village Roveredo in canton Graub¨ unden lies in the valley of Misox, which ranges from the pass of San Bernardino heading south until the town of Bellinzona. Roveredo has a population of 2’195 inhabitants and covers an area of 39 km2 [4]. In the valley of Misox cold air is often covered with a stable layer of warm air during the cold seasons. A stationary temperature inversion is present, and only a low exchange of air takes place, which is reported by meteorological data (e.g. temperature) [47]. This inhibits the dilution of air as well, and only strong downslope winds or solar radiation can cause a mixing of the air layers in such weather [41]. Due to the presence of domestic wood burning for heating, smog over the villages is often visible in winter, as seen on the background photo on the front page of this thesis. Contributions of air pollutants from the nearby valley of Leventina, which features an important traffic transit route, are probably not negligible. Additionally, the main road from San Bernardino to Bellinzona is passing Roveredo. Therefore, an elemental mix of traffic tracer elements, wood burning tracer elements and a background concentration was expected in Roveredo. The RDI was placed next to the main

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road in the middle of the village for sampling. Power for the operation was provided by a permanent monitoring station. The RDI was operated during winter 2004 / 2005 (December 12, 2004 until December 21, 2004 and January 4, 2005 until January 22, 2005). Figure B.3 in the appendix shows a detailed aerial view of the sampling site in Roveredo.

Moleno is a little village in canton Ticino, about 10 km north of Bellinzona.

It has a population of 150 inhabitants and covers an area of 7.4 km2 [6]. The sampling site was situated some kilometers out of the village, directly next to a motorway in the valley of Leventina. This valley features one of the main traffic routes in Switzerland which brings a lot of emissions from traffic to the area. As in Roveredo, domestic wood burning for heating purposes is common, so contributions to air pollution from this source was expected. To be best accessible, the RDI was placed between the access road from a small motorway station and the motorway itself. The close-by permanently installed monitoring station for measuring different air pollution parameters provided power for the RDI and the pump during the sampling period. It was expected to sample an elemental mix of traffic tracer elements and a background concentration.

Sampling in this site took place in winter 2004 / 2005 (January 25, 2005 until February 2, 2005). The RDI in Moleno was operated as well in summer 2005 (June 20, 2005 until July 1, 2005). Figure B.3 in the appendix shows a detailed aerial view of the sampling site in Moleno.

The samples in Ticino were collected within the scope of the AEROWOOD project, a Swiss project that tries to characterize aerosol from wood combustion (chapter 1).

Roveredo and Moleno in Winter 2004 / 2005 During this thesis, RDI wheelsets were analyzed that were sampled during a field study in Ticino in winter 2004 / 2005. On December 13, 2004 the RDI was established in Roveredo and sampling was started at 13:57 with a sampling interval of two hours per each sample. Sampling was stopped on December 22, at 16:00. Restart of the sampling took place on January 4, 2005 with the same sampling parameters. After some days of sampling, on January 13, 2005 the pump was stopped at 09:20 to check and clean the RDI. On the same day at 09:40, sampling was restarted with the same set of sampling wheels. Finally the sampling in Roveredo was stopped on January 22, 2005 at 10:02. Table 2.4 shows the schedule of the sampling in Roveredo.

The RDI was brought to Moleno and installed on January 25, 2005. On that day at 15:30 sampling until February 4, 2005 11:45 was started. The sampling wheels were changed at that time, and sampling with a new set of wheels was restarted at 12:00. This lasted until February 14, 2005 when the sampling was stopped and the wheels were brought back to PSI. Table 2.5 gives an overview of the schedule of the sampling session in Moleno.

Moleno in Summer 2005 As in winter 2004 / 2005, aerosol samples were taken within the scope of a field study in Ticino in summer 2005 with the RDI. Due to logistic reasons it was only possible to sample the site of Moleno in summer 2005 but not Roveredo as it was done in winter 2004 / 2005.

14

Table 2.4: Overview of the experiments in Roveredo in winter 2004 / 2005. start of sampling December 13, 2004 13:57 December 22, 2004 16:00 January 4, 2005 14:05 January 13, 2005 09:40 January 22, 2005 10:02

sampling condition ambient air in village stop of experiments ambient air in village ambient air in village stop of experiments

sampling time 120 min 120 min 120 min

Table 2.5: Overview of the experiments in Moleno in winter 2004 / 2005. start of sampling January 25, 2005 15:30 February 4, 2005 12:00 February 14, 2005 20:00

sampling condition ambient air next to motorway ambient air next to motorway stop of experiments

sampling time 120 min 120 min

On June 20, the RDI in Moleno was cleaned and restarted after the off period since the campaign in winter 2004 / 2005. Sampling was started at 14:46 with pumping ambient air through the RDI for two hours per each sample.

A permanently installed PM10 impactor on the roof of the small container with the RDI inside was used. To get a calibration wheelset, the wheels were replaced and restarted on June 29, 11:55 with the same sampling parameters but one change: Due to the fact that the calibration wheels have to be cut apart after the analysis (section 2.3.1), this wheelset was sampled with wider distances between the spots on the mylar film: The spots had a distance of 4.8 mm between each other instead of 1.2 mm.

Sampling in Moleno was stopped on July 1 at 15:51 and the RDI was brought to PSI for maintenance work. Table 2.6 shows an overview of the sampling schedule in Moleno.

Table 2.6: Overview of the experiments in Moleno in summer 2005. start of sampling June 20, 2005 16:46 June 29, 2005 11:55 July 1, 2005 15:51

2.2.3

sampling condition ambient air next to motorway ambient air next to motorway, calibration wheelset stop of experiments

sampling time 120 min 120 min

Field Study on Jungfraujoch

For the first time, the RDI was sampling in the High Altitude Research Station on Jungfraujoch to analyze the composition of an aged, highly diluted aerosol.

Figure B.2 in the appendix shows an aerial view of the High Altitude Research Station on Jungfraujoch in Central Switzerland.

The lab on Jungfraujoch is situated on a saddle between the mountains Jungfrau and M¨ onch, the research station has an altitude of 3â&#x20AC;&#x2122;450 meters above

15

sea level. The Sphinx laboratory, where the sampling session took place, has an altitude of 3’580 meters above sea level and provides well equipped labs for aerosol sampling and a terrace with permanently installed heated inlets. The site is of big interest because of several reasons: Altitude: Due to the high altitude, in the past the site was often used for medical experiments. Today this is rarely the case. Still, the average air pressure at Sphinx laboratory is only 653.3 mbar which can be a challenge for instruments operated with pumps. But the main reason is that due to the high altitude there are few sources of aerosol pollution expected on Jungfraujoch.

Saddle: The location of the research area is ideal for cloud formation and therefore as well for aerosol research because of its location on a saddle between the mountains Jungfrau and M¨onch. Air is forced to ascend — and so to condensate — to pass the saddle.

Accessibility: It is possible to reach Jungfraujoch by train from Grindelwald or Lauterbrunnen. The two underground station halls on Jungfraujoch house Europe’s highest altitude railway station. Only with that it was affordable and efficient to bring the sampling equipment to the site.

Equipment: Several other studies and a permanently operated meteorological station set the frame for a fast setup of the RDI in one of the labs. A heated inlet gave the possibility to sample indoors and so to protect the sensitive parts of the RDI from the cold weather. Comfortable accommodation for the scientific staff ensured a sampling session without extensive preparations.

A testing wheelset was installed on Jungfraujoch on July 6 at 18:14 with a sampling time of 360 min for each sample on the mylar film. The day after, the wheels were checked and a new wheelset for a long-term sampling session was installed. Due to the long sampling period for one aerosol sample, it was possible to get continuous data from one month with a time resolution of 6 hours. The wheelset for the long term sampling was started on July 8 at 19:00.

To avoid freezing up of the inlet, a permanently installed inlet was used which was heated to 25 . On August 8 at 13:40, the sampling was stopped and the samples were brought back to PSI. Table 2.7 gives an overview of the sampling schedule of the measurements on Jungfraujoch.

Table 2.7: Overview of the experiments on Jungfraujoch. start of sampling July 6, 2005 18:14 July 7, 2004 14:21 July 8, 2005 19:00 August 8, 2005 13:40

sampling condition test with ambient air Sphinx laboratory stop of experiments ambient air Sphinx laboratory stop of experiments

16

sampling time 360 min

360 min

2.3

Analysis with Synchrotron-XRF

During two sessions at Hamburger Synchrotronstrahlungslabor (HASYLAB / DESY Hamburg, Germany, DORIS beamline L), the RDI samples from the field and laboratory studies were analyzed using energy-dispersive synchrotron radiation X-ray fluorescence spectrometry (SR-XRF). A testing session at the Swiss Light Source (SLS) at Paul Scherrer Institut (PSI Villigen, Switzerland, beamline X05LA) was performed to assess the analysis setup.

The Hamburger Synchrotronstrahlungslabor (HASYLAB) is a national user research facility. Experiments from scientists from all over the world are performed. At HASYLAB researchers from universities, industries, and government laboratories investigate the fields of physics, biology, chemistry and crystallography, materials and geological sciences as well as medical applications. Synchrotron radiation emitted from the DORIS and PETRA storage rings is used at about 40 experimental stations with about 80 instruments in many ways in fundamental and applied research. The beamline L of the DORIS ring (figure A.3) was used for analysis of the samples with synchrotron radiation in this thesis. DORIS was the first storage ring at DESY. Today, after its third conversion it is called DORIS III and operates exclusively as a synchrotron radiation source for HASYLAB [3].

The PSI allows researchers from universities and industry to use its facility with a user laboratory: With the Swiss Light Source (SLS), a synchrotron light source with an energy of 2.4 GeV is provided. The SLS provides photon beams of high brightness for research in different subjects such as materials science, chemistry and biology. The testing session which is described in this thesis was performed at the beamline X05LA (microXAS) of the SLS (see figure A.4). This beamline focusses on X-ray absorption spectroscopy (XAS) and Xray fluorescence (XRF) experiments that require high spatial resolution. The design of the microXAS beamline is conceptualized to yield monochromatic Xray beams (5 − 20 keV ) with a high energy resolution. The spectrum of the synchrotron light is ranging from infrared to hard X-rays [2]. An advantage compared to the HASYLAB-beamline is the top-up injection of the SLS which produces a constant beam intensity for the experiments .

Figure B.5 in the appendix shows a panorama picture from the experimental hutch of the HASYLAB-beamline and the SLS-beamline.

2.3.1

Setup and Calibration

For the SR-XRF measurements, the mylar films were transferred from the RDI sampling wheels to slitted aluminium wheels that were used as sample holders.

The slits had the effect that the incoming primary beam only hits the mylar film and therefore prevents any scattering of aluminium. For the analysis, the wheels were placed on a goniometer table (Huber Diffraktionstechnik GmbH, Germany, Model 410), which enabled the wheels to turn step by step. The setup of the analysis was chosen so that the synchrotron beam hit the sampling wheel in an angle of 45◦ to the wheel’s tangential line. The detector (HPGe GEMINI 30/Be/XRF) was set to be 45◦ to the tangential line of the wheelset (figure 2.3). The beam cross section was measured with a thermo-sensitive paper on an aerosol measuring wheel: A small piece of the paper was attached to the measuring wheel. The X-ray beam hit the paper for some minutes, therewith 17

the paper was heated up and the colour changed from green to red. Figure B.6 in the appendix shows a piece of thermo-sensitive paper on a measuring wheel after the beam hit the paper for some five minutes. The size for the beam was chosen to be 0.9 mm in height and 1.5 mm in width. The beam cross section corresponded only to some 10 % of the aerosol impaction areas of the RDI stages (depending on the stages and therefore as well depending on the size fractions).

Analyzed concentrations on the spots of the mylar film had to be extrapolated to assumed areas of particle impaction on the mylar film.

So far, there was no possibility to carry out the experiments in a helium atmosphere or in vacuum. This would help to filter out argon and krypton lines and to prevent absorption of low energy fluorescence by the air for elements with Z < 20. The fluorescence spectra for the hourly RDI impaction spots were obtained by scanning the samples across the beam in increments of 1.93 degrees, given by the RDI step width.

The calibration procedure consisted of two parts: First, a relative sensitivity curve was determined for the considered range of elements. Afterwards, an absolute sensitivity (counts per second/pg) was determined for a selected element within the range of the relative sensitivity curve. In this way, the measured count rate for every element was linked to a mass density (pg/mm2 ). The main condition for the applied calibration method was an unmodified sampling setup (sample and detector geometry, beam sizes and energy) both for the sample analysis and the calibration procedure. As an additional part of the calibration procedure, the impurities of the mylar film were determined by blank spectra measurements. Calculation of the relative sensitivity curve and the results of the blank spectra measurements are reported in [12].

The calibration for calculating absolute concentrations for the content of the analyzed aerosol samples was performed in both sessions at HASYLAB with a standard addition procedure as follows: A previously analyzed wheel with a mylar film was cut so that a single spot of one aerosol sample was on the small mylar piece. Four such samples were brought to seperate test-tubes. In each of them the sample mass was washed out with 0.5 ml of concentrated nitric acid (HN O3 cc.). In order to accelerate the dissolving procedure the liquid was heated and shaken during two hours. To each sample, 1 µg copper and 1 µg selenium internal standard was added. From this solution 0.5 µl was pipetted on the blank mylar film and slowly dried. The dissolved samples have been analyzed with the synchrotron beam in the same way as the aerosol samples.

Relative calibration factor measurements were performed with four calibration solutions containing 0.5 mg/ml (solution 1 and solution 3) or 5 µg/l (solution 2 and solution 4) of different elements: solution 1: K, V , F e, Cu, Rb, Sr, M o, P t solution 2: K, V , F e, Cu, Rb, Sr, M o, P t, P d solution 3: K, Co, Cu, Se, Rb, P d solution 4: K, Co, Cu, Se, Rb, P d, Zr, Rh Solution 2 and solution 4 were the final calibration solutions. These two solutions were pipetted on the same blank wheel as the above mentioned solutions of the absolute calibration. To get relative calibration factors, the Kα 18

peak areas of all spectra were normalized to the area of the 29 Cu Kα peak.

Absolute calibration was performed for the F e mass according to the following formula for mass calculation: F e mass [µg] = (F eKα /CuKα ) / (F e, Cu cal.f actor) × m(Cu) [µg]

(2.1)

F e mass [µg] = (F eKα /SeKα ) / (F e, Se cal.f actor) × m(Se) [µg]

(2.2)

Where F e, Cu cal.f actor = 0.548 and F e, Se cal.f actor = 0.382.

The mass of the internal standards that were added to the solutions was: m(Cu) = m(Se) = 1.0 µg Minimal detection limits (MDL) for the calibration solutions were calculated according to: p (2.3) M DL [ng] = 3 × background × m[ng]/(peak area) Where m is the mass of any element in 0.5 µl solution (= 2.5 ng).

Different size fractions of the same element had different MDL because of the analyzed area on the mylar film: The area of the sampled aerosol was depending on the size of the RDI’s orifice-slits. That is why concentrations on the analyzed sample areas had to be extrapolated to the total sample area on the mylar using different extrapolation factors for the three size fractions.

Figure 2.3: Alignment of the analysis setup. To get the best spectra, the detector was moved the closest possible to the sampling wheel. Best spectra for the analyzed elements resulted by performing the analysis with a tantalum collimator on the detector.

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2.3.2

First Session at HASYLAB-beamline

All the data was analyzed in the first analysis-session at the HASYLAB-beamline in Hamburg at 28 keV (May 9, 2005 07:00 until May 12, 2005 07:00). The results from the analysis at 16 keV of this session were used to validate the 28 keV -data because only two wheels from the RDI stage C (0.1 − 1 µm) could be analyzed at this energy. One of the two analyzed wheelsets contained samples of the wood burning study at EMPA D¨ ubendorf from the experiments on April 25, 2005 (beech wood chips, 60 min sampling interval) and April 27, 2005 (beech wood chips, 20 min sampling interval). From this wheelset, only the RDI-stages B (1 − 2.5 µm) and C (0.1 − 1 µm) were analyzed due to a lack of time at the beamline. The other wheelset contained samples from the experiments on April 28, 2005 (larch logs, 10 min sampling interval) and April 29, 2005 (white fir and oak logs, 10 min sampling interval). All three wheels from this second set were analyzed during this session. The two wheelsets which contained samples from the laboratory study on wood burning were scanned for 30 seconds. Additionally, six wheels containing samples from the field studies in Ticino in winter 2004 / 2005 were analyzed. All samples of these two wheelsets (Roveredo and Moleno) were scanned for 15 seconds.

2.3.3

Second Session at HASYLAB-beamline

The second analysis session lasted from July 30, 2005 07:00 until August 1, 2005 07:00. Samples from the field study in Ticino of summer 2005 and a testing wheelset, sampled on Jungfraujoch, were analyzed. The primary energy was set to 25.6 keV , a silicon detector was used with a molybdenum collimator.

Tests with a tantalum detector were made as well and showed good results. All samples were scanned according to the mentioned procedure; each spot on the mylar film was scanned for 15 seconds.

2.3.4

Testing Session at SLS-beamline

Tests at the beamline of the Swiss Light Source (SLS) were performed from June 16 until June 18, 2005. The aim of this session was to check the automation of the data acquisition system which had recently been programmed. It is the idea to install a similar system at the SLS as it is present at the HASYLAB-beamline in Hamburg. A goniometer was installed and a software that was able to rotate the table, scan the sample for a defined time, save the counts of the spectra in a file and finally rotate the goniometer to the next sample on the wheel. The idea was to assess the current experimental setup (collimator and detector position, influence of the analysis in air).

Additionally, previously analyzed RDI samples were measured at a lower energy (less than 10 keV ). However, data from this analysis was not included in this thesis.

20

Chapter 3

Results and Discussion 3.1

Laboratory Study: Wood Burning at EMPA

Averages of all concentrations of this laboratory study were listed in an overview (table 3.1). Additionally the minimal detection limit, the average concentration and the standard deviation of every analyzed element and size fraction was listed.

3.1.1

Fingerprints of Wood Species

To get first insights, all the data of the laboratory study at EMPA was plotted in an overview box plot. The resulting figure 3.1 contained only those experiments of which all three wheels were analyzed, so every box contained the same amount of data (N = 82). Therefore, this box plot did not contain data from experiments with beech wood. Concentrations of the elements sulphur, chlorine, potassium and calcium were in general higher than all other analyzed elements. It would be wrong to call all these four ”tracer elements” for wood fires but due to their high concentrations they were ”key players” in the experiments. It seemed to be a fact, that for all elements in the overview box plot, the concentrations of the three RDI stages behaved like A > B > C. This was not the case for potassium: The distribution of the concentrations in the fine mode was similar to the one of the coarse mode. So looking at other elements and comparing the other size fractions of potassium with potassium’s stage C concentrations, there must have been a remarkably high amount of potassium in the fine mode. The size fractions of different elements sometimes showed similar concentrations: For example calcium had only slightly lower median and average values in stage A (2.5 − 10 µm) than sulphur. Because of the much lower concentrations of the elements titanium, manganese, iron, zinc, bromine and lead, these elements won’t be described in detail.

Depending on the elements, a minimal detection limit (MDL) could be calculated for every size fraction (table 3.1). An overview of the MDLs of all elements and size fractions is shown as well in figure 3.1; in the box plot with all tree species the MDL levels were plotted as well, indicated with a star. Below this threshold, concentrations were only detectable with a high uncertainty by the method. The minimal detection limits were in some cases similar to the

21

detected concentrations. This was especially the case for the coarse mode fractions of the major elements sulphur, chlorine, potassium. For other elements, as well only coarse mode size fractions are expected to be in the region of the MDL: Titanium, manganese, iron, bromine and lead’s stage A concentrations had high uncertainties.

For beech chips, only 9 data points were available for each element, for beech log experiments, 17 data points were included in the fingerprint box plot (figure 3.2). For the beech experiments, only stages B and C were analyzed. As seen in the overview plot of all wood species, the elements sulphur, chlorine, potassium and calcium showed as well here the major concentrations. The mentioned pattern of the concentrations distribution in the three stages (A > B > C) was not obviously given for sulphur: The coarse mode and stage B showed similar distributions of the concentrations. Note, that these values were below the given minimal detection limit and had with that high uncertainties. As well potassium didn’t follow the pattern. Potassium in stage C was for the beech logs experiments remarkably higher whereas for beech logs experiments only a slightly higher distribution than in stage B was the fact. Calcium and sulphur showed in the coarse mode similar concentrations, which was especially seen for beech chips. Potassium and chlorine showed similar concentrations in the fine mode for beech chips. Results from the beech logs experiments showed similar concentrations for sulphur and potassium in RDI stage B (1 − 2.5 µm) as well in stage C (0.1 − 1 µm).

For the box plots of larch, 46 data points were included for each box, for white fir 26 points (figure 3.3) and for oak 10 points (figure 3.4). Besides the common elemental pattern for the ”key player” elements (A > B > C), larch, white fir, and oak showed a different pattern of the concentrations distributions for potassium among the three size fractions. Larch showed similar concentrations for potassium in all three size fractions whereas white fir experiments showed in the coarse mode even lowest potassium concentrations. In oak experiments the lowest values for potassium concentrations were found in the medium size fraction. Corresponding elemental concentrations were found for larch, white fir and oak experiments in sulphur and calcium’s coarse mode. Less corresponding this was as well the case for the stages B and C (oak was not at all corresponding in these stages). A similar distribution was found as well for chlorine and potassium’s fine mode in larch experiments, which was the case for all three wood species. An additional interesting fact is, that white fir shows a very similar distribution of the three size fractions of chlorine. This was only found for this tree species.

To distinguish differences in the smoke emission content between the tree species, box plots with data from several tree species were plotted. In a plot with data from beech chips and beech logs experiments was proved, if there are any significant differences between the tree species concerning a certain element or size fraction (figure 3.5). Therefore, a one-way ANOVA (means comparison using Bonferroni test) with OriginPRO 7.5 was performed. Only concentrations of calcium in stage B and potassium in stage C varied significantly between the logs and the chips experiments (for both p < 0.05).

In a comparing box plot of larch, white fir and oak (figure 3.5), sulphur varied significantly between oak and white fir in stage B, chlorine between larch and white fir in stage C, potassium between larch and white fir in stage C and as well between oak and white fir in stage C (all mentioned p < 0.05).

22

The box plots of the wood species fingerprints showed, that some elements were present in much higher concentrations in all experiments such as sulphur, chlorine, potassium and calcium. The concentrations of the group of these elements were significantly different from the concentrations of the other elements (two sample independent t-test with OriginPRO 7.5: p < 0.05). Sulphur, chlorine, potassium and calcium were known to be present in smoke from wood fires from several studies [11, 19, 20, 21, 22, 26, 31, 40]. Some elements were not expected from a laboratory study on wood burning. These were mainly the elements titanium, manganese, iron, zinc, bromine and lead. The detection limits were decreasing rapidly with the increasing element number, so the elements, which were not expected didn’t seem to be artefacts of the analysis. Therefore, it is suggested to treat them as impurities of the used stoves. An interesting fact was that zinc in the fine mode correlated rather good with the variation of the major elements!

A contamination to subsequent experiments from condensating aerosol at the wall of the stoves might be a problem in the experiments as they were performed during the wood burning study at EMPA. However it is said, that these effects are negligible: Carry-over contaminations from compounds in high levels present in one experiment but not expected to be present in a second experiment were less than 5 % [20].

The presence of similar concentrations of different elements in the same size fraction gave a hint about possible substances that are emitted by burning wood: CaSO4 , CaCl2 , K2 SO4 , KCl and similar compounds were expected in the smoke. More KCl particles occur in young smoke, whereas more K2 SO4 and KN O3 particles are present in aged smoke. This change indicates that with transport processes in the atmosphere, photochemical oxidation and aqueousphase reactions between KCl particles and SO2 could form particulate K2 SO4 and released hydrogen chloride (HCl). The formation of KN O3 , as well a fate of aged KCl particles, probably results from the reaction of KCl with gaseous nitric acid (HN O3 ) that was formed through photochemical reaction of nitrogen oxides (N Ox ) emitted by the fires. A high chlorine-content on particles in young smoke is expected whereas the Cl/S-Ratio is a function of both fuel composition and the type of fire [31]. Smoke particles seem to provide a favorable surface for the nucleation of sulphate, coagulation can bring soot and sulphate together.

After long-range transport biomass smoke aerosol consists primarily of internally mixed organic/sulphate and soot/sulphate particles [40].

Most fine particle mass emitted from fireplace combustion of wood is comprised of organic carbon which makes up over 74 % of the fine particle mass [20]. Organic particles with and without potassium-salt inclusions were the most abundant particle type in the smoke from biomass-burning fires in southern Africa [31]. These particles accounted for 70 − 83 % of the total particle number in the smoke. However, emission rates per kilogram of wood burned vary roughly by a factor of 5 comparing different source tests. In atmospheric air during winter months, 20 − 30 % of the fine particle mass concentration often can be attributed to wood smoke [19]. Potassium-salt particles are the most abundant inorganic aerosol constituents in the smoke from biomass burning [31].

They include KCl, K2 SO4 , KN O3 and minor potassium- and calcium-bearing sulphate with variable K/Cl ratios. Aggregates of calcium-bearing particles are present in the length of 2 − 15 µm. Single particles have diameters from 23

50 â&#x2C6;&#x2019; 300 nm, some were up to 500 nm. They included the carbonates aragonite and calcite, gypsum and phosphate in the form of apatite. Calcium sulphate and sulphate containing both potassium and calcium have been obseverd in smoke from African savanna and wetlands fires and originate from biomass burning.

The morphology and fine grain size of the calcium-bearing particles in the smoke samples from this study suggest that they are not mineral dust, although it is a fact that intensive outdoor-fires can mobilize soil dust from the ground. The aggregates have large sizes and for that believed to have a short residence time in the atmosphere.

24

Table 3.1: Average concentrations, standard deviations and minimal detection limits (multiplied with the lowest (1’170) and the highest dilution factor (9’739) from the setup) of the experiments performed at the wood burning laboratory study at EMPA D¨ ubendorf. For the averaged concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires. element name S

Cl

K

Ca

Ti

Mn

Fe

Zn

Br

Pb

size fraction 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm 2.5 − 10 µm 1 − 2.5 µm 0.1 − 1 µm

avg. conc. [µg/m3 ] 151.606 73.024 42.279 63.625 31.829 24.599 30.981 32.193 48.886 99.725 45.269 18.870 1.809 0.933 0.264 0.635 0.323 0.212 3.188 1.750 3.221 0.818 0.539 0.342 0.109 0.059 0.044 0.274 0.126 0.095

25

std. dev. [µg/m3 ] 146.948 69.911 40.416 36.874 25.184 20.220 41.025 51.844 65.861 24.142 24.895 8.416 0.992 0.712 0.248 0.507 0.332 1.108 12.689 8.364 28.554 0.782 0.985 0.340 0.044 0.042 0.035 0.093 0.077 0.068

mdl min. [µg/m3 ] 385.205 56.965 11.810 89.036 13.109 2.659 12.983 1.925 0.377 6.408 0.941 0.179 2.117 0.314 0.056 0.617 0.092 0.016 0.461 0.071 0.013 0.191 0.030 0.006 0.118 0.021 0.005 0.259 0.044 0.008

mdl max. [µg/m3 ] 3206.420 474.172 98.305 741.128 109.116 22.137 108.074 16.021 3.136 53.341 7.830 1.490 17.618 2.610 0.467 5.132 0.769 0.136 3.837 0.594 0.107 1.587 0.253 0.049 0.984 0.175 0.039 2.152 0.370 0.068

Figure 3.1: Top: Fingerprint box plot of data from the white fir, oak, and larch log experiments from the wood burning study at EMPA. Because of having analyzed only the RDI stages B and C of the beech wood experiments, this data was excluded in the plot. Stars: minimal detection limits (MDL) of each element; a dilution factor of 1000 was assumed for the plotted MDL. Symbols in all box plots: boxes: 1st , 2nd , 3rd quartile; square: average values; error bars: 0.05-, 0.95-percentiles; ×: 0.01-, 0.99-percentiles; small dashes: minima, maxima. A: 2.5 − 10 µm, B: 1 − 2.5 µm, C: 0.1 − 1 µm. Bottom: MDL (dilution factor: 1000) for all elements and size fractions in the wood burning experiments. For all box plot concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

26

Figure 3.2: Fingerprint box plot of beech chips fire and beech log fire experiments. B: 1 − 2.5 µm, C: 0.1 − 1 µm. Stage A was not analyzed. For all concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

27

Figure 3.3: Fingerprint box plot of larch log fire and white fir log fire experiments. A: 2.5 − 10 µm, B: 1 − 2.5 µm, C: 0.1 − 1 µm. For all concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

28

Figure 3.4: Fingerprint box plot of oak log fire experiments. A: 2.5 − 10 µm, B: 1 − 2.5 µm, C: 0.1 − 1 µm. For all concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

29

Figure 3.5: Fingerprint box plots comparing larch, white fir and oak (top) and beech chips and beech logs (bottom). A: 2.5−10 µm, B: 1−2.5 µm, C: 0.1−1 µm.

For all concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

30

3.1.2

Median Variations of Fire Content

Typical runs for fire experiments for each wood species were distinguished by plots showing medians of datapoints for points in time of the experiments. Obvious courses were only in a few cases for higher concentrated elements found (sulphur, chlorine, potassium, calcium). Even median values gave often not a characteristic variation. For white fir and beech log experiments, reasonable variations were found by plotting median data points versus a time scale.

From the data of the white fir experiments, five (four) data points were available for the first two (last three) points in time of an experiment (figure 3.6). The plotted points were median values from the mentioned amount of data points, and the resolution of the time scale was 10 minutes for the white fir experiments. A typical experimental run was given for potassium in the fine mode: The median of five experiments started at a concentration of 149.1 µg/m3 and decreased with time to a minimum of 45.7 µg/m3 after 30 minutes of firing.

A similar pattern with lower concentrations (maximum value for sulphur and chlorine was around 60 µg/m3 ) was plotted for sulphur and chlorine in this size fraction: Excepting the first median point (t0), the two lines were almost in parallel and as well they had a variation, which was similar to the one of potassium.

Calcium showed almost constant median concentrations of around 20 µg/m3 in the fine mode. Generally for white fir most obvious variations during the firing experiments were distinguished for the fine mode.

In the RDI stage B (1 − 2.5 µm), which had in general the lowest median concentrations, potassium was present in low concentrations (maximum median value: 62.0 µg/m3 , minimum median value: 16.3 µg/m3 ) but showed the most probable typical experimental variation of all elements. Sulphur and chlorine showed a rather parallel run but an obvious variation for the experiment was not visible. Calcium concentrations were almost on a constant level of about 50 µg/m3 during all the white fir experiments in stage B. The maximum median concentration value of chlorine and calcium was very similar and as well similar to the one of potassium. Sulphur showed an extraordinary high median concentration in the last median point (t4).

Concentrations of stage A were mostly higher than the other size fractions but not for potassium, as already seen in the fingerprint box plots for white fir. Only potassium with very low concentrations showed a typical experimental run if ignoring the first data point. The least obvious variations showed sulphur in the coarse mode, although this element was present in highest concentrations for all elements and size fractions in the white fir experiments. Chlorine variations were in parallel with sulphur variations for the first three points but showed lower concentrations than sulphur by at least 60 µg/m3 . Calcium was as well in the coarse mode surprisingly constant: All median concentration values varied only slightly from 80 µg/m3 .

The dataset for the beech logs experiments had a resolution of 20 minutes (figure 3.7). For the first point in the experiment, the median of 5 data points was plotted, the second median came from 6 points, the third median from three data points, the fourth from two data points and the last median value was the concentration value from one experiment. As white fir, beech showed in the fine mode fraction most obvious courses during the experiments. Potassium concentrations were present in highest values of all elements: At the beginning of an experiment the median started from 196.4 µg/m3 and decreased during

31

the experiment to 17.5 µg/m3 . The increase after 40 minutes (t4) is probably caused by an increase of the emissions at the beginning of a further experiment.

Plots of sulphur and chlorine median concentrations showed almost parallel courses: A decrease from 76.4 µg/m3 to 25.6 µg/m3 for sulphur and a decrease from 45.7 µg/m3 to 8.3 µg/m3 for chlorine during 20 minutes after the start of the experiment was found. After this decrease, both sulphur and chlorine on RDI stage C remained more or less constant during the rest of the beech logs experiments. Fine mode calcium remained constant in even lower median concentrations during the experiments.

The experiment’s variations were most obvious for potassium in RDI stage B (1 − 2.5 µm), which corresponds to observations made from white fir experiments. As well in this size fraction, potassium decreases in the first 20 minutes of the experiment from 97.9 µg/m3 to 27.7 µg/m3 and remains constant after that. No obvious variations during the experiments were observed for chlorine and calcium: the median values for these elements remained constant during the experiments between a median concentration of 10 µg/m3 to 20 µg/m3 .

Considering the second median value of sulphur in stage B as an outlier, it is possible that a variation during the experiments is visible but in very low concentrations.

RDI stage A samples of the beech log fire experiments were not analyzed.

However, very low concentrations were expected on this stage. No grey or black lines (as it was the case for other samples) were visible on the mylar film of the RDI wheel A due to the high dilutions of the setup (section 2.2.1).

32

Figure 3.6: Median concentration variations for all white fir log fire experiments in three size fractions. For all concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

33

Figure 3.7: Median concentration variations for all beech log fire experiments in two size fractions. Stage A (2.5 â&#x2C6;&#x2019; 10 Âľm) was not analyzed. For all concentrations dilution factors were included to get concentrations of the undiluted emissions from the fires.

34

3.1.3

Validation of the Data

To validate the data, sulphur concentrations of the laboratory study on wood burning were compared to sulphate concentrations from an aerosol mass spectrometer (AMS) which was involved in the setup at the wood buring study. For that, sums of all size fractionâ&#x20AC;&#x2122;s concentrations of sulphur were calculated for each sample and oxygen masses for sulphate were added. With this approach it was assumed that all sulphur atoms which were sampled by the RDI were present in the form of sulphate. A plot of the experimentâ&#x20AC;&#x2122;s run of the beech logs firing experiments showed that the two data sets corresponded well (figure 3.8). A problem of comparing the two data sets was that the AMS data had a much higher time resolution than the RDI data. Therefore, concentrations from the RDI always have to be treated as sum concentrations over the sampling period. For the comparing plot, the sampling interval for the RDI was 20 minutes, whereas the the AMS data originally had a resolution of 2 minutes. Out of the AMS data set concentrations were chosen for the plot which were sampled at the end of a 20 minutes sampling period of the RDI.

Figure 3.8: Comparing aerosol mass spectrometer (AMS) sulphate data to modelled sulphate concentrations from all three size fractions of the sulphur concentrations sampled by the RDI. The sampling interval for the RDI lasted 20 minutes.

35

3.1.4

General Uncertainties in the Results

One aim of the wood burning campaign at EMPA was, to determine emissions from different species and to show characteristics in the emissions of burning different wood types. This was not properly possible with the measured elements by the RDI. In fact, significant differences are only obvious by analyzing organic compounds: It is said, that hardwood combustion emissions contain greater amounts of substituted syringols than softwoods. Resin acids are significant components of softwood emissions but are barely found in emissions from hardwood . As a species-specific organic tracer, betulin (a single triterpenoid) can be used for paper birch; juvabion and dehydrojuvabione are known components of balsam fir wood [19]. A study about aerosol compositions from biomass burning in southern Africa confirms that particulate emissions vary with the vegetation types burnt and the burn intensities (flaming or smoldering) [31].

Other studies suggest not to use the chosen wood burning tracers (as e.g. nonmineral potassium) because they are found as well in fine particle emissions from other processes as e.g. meat cooking [19, 20]. It is said, that potassium and chlorine have large emission variabilities which leads to uncertain estimates for emission factors [26]. Potassium can be present in significant amounts from sources as e.g. refuse incinerators or lime kilns [45]. Potassium emissions analyzed by XRF from six typical wood types from the southern United States showed, that the data range is too large (0.2 â&#x2C6;&#x2019; 1.8 % by weight of the fine particle mass) to be a proper tracer. It is suggested to use particle-phase organic compounds as chemical tracers, as for example levoglucosan, a cellulose pyrolysis product. Between 3 % and 12 % by weight of the fine particle organic compound emissions are accounted for by lecoglucosan in wood burning experiments with six different wood types grown in the northeastern United States [19]. Levoglucosan is told to be present in high emission rates with biomass burning and as well told to be unique with such processes [20]. A problem of using levoglucosan as a wood tracer is its lifetime in the atmosphere which is told to be in maximum 10 days. This is a general problem with organic compounds: Unlike elements, they may be formed or degraded in the atmosphere by chemical reactions obscuring their origin.

It is said, that retene is correlated highly with wood carbon during the day.

This finding supports the use of retene as a tracer of softwood combustion [42].

The ratio of elemental and organic carbon (EC/OC ratio) is suggested to be excellent tracer for wood burning [26]. Further, it is suggested to use the method of radioactive 14 C (radiocarbon method): In fossil fuels such as coal and oil, the decay of this isotope is complete, while living or recently living matter as wood or paper contains a known amount of 14 C. Hence, the relative contributions of fossil and modern sources to a sample can be determined by measuring the 14 C/12 C or 14 C/13 C ratio [45].

A list of the composition in weight percent of aspen wood indicates as well that organic compounds might be better tracer elements for wood combustion [11]: Carbon (51.57 wt%), hydrogen (6.27 wt%), oxygen (39.52 wt%) show more than ten times higher concentrations than all other elements. The following values were given for other elements: nitrogen (0.47 wt%), calcium (0.1524 wt%), potassium (0.0810 wt%), magnesium (0.0256 wt%), sulphur (0.0250 wt%), phosphorus (0.0085 wt%), iron (0.001872 wt%), aluminium (0.001008 wt%), manganese (0.001008 wt%), boron (0.000360 wt%), copper (0.000216 wt%), zinc

36

(0.00245 wt%).

Particulate pyrene is well-correlated with fossil carbon (as e.g. present in emissions from motor vehicles), but only 0.3 â&#x2C6;&#x2019; 11% of the total pyrene is estimated to be particle-bound during the day with the remainder in the gas phase [45].

37

3.2

Field Studies in Ticino

As for the analyzed concentrations in the wood burning study, all averaged elemental concentrations, the standard deviations and the minimal detection limits of the analysis were listed in an overview (table 3.2). Because the samples from the two Ticino field studies in winter were analyzed in the same session with the same analysis parameters at the energy of 28 keV , both data sets had the same minimal detection limits (MDL). The samples of the summer field study in moleno were analyzed in a second analysis session at the energy of 25.6 keV . For that session, separate MDL-values were calculated.

Lower MDL-values resulted from a higher signal-to-noise ratio in the second session. This was reached due to a more optimized geometry: The detector was moved the closest possible to the sampling wheel to have less disturbing ambient air between the the samples and the detector during the analysis (figure 2.3).

Additionally, for several elements, the concentration varied less in the summer data, therefore, low concentrations were less often below the minimal detection limit.

RDI stage A was as well for the results from the field studies in Ticino influenced by uncertainties: MDL values were higher expecially for the winter data of sulphur’s stage A, but other elements as vanadium, chromium, cadmium (concentrations mainly lower than MDL), bromine, rubidium and lead (only fine mode fraction elements were clearly above the MDL) had high uncertainties as well. For a better visualization, the MDL-values were as well included in the fingerprints of the three sampling sessions in Ticino (figures 3.9, 3.10). Concentrations for the summer data varied less than for the winter data, for that summer data often lay completely above the MDL. MDL values were slightly lower for this second analysis session. However, for stage B of chlorine, for all size fractions of vanadium, for chromium’s stage A and B as well for bromine in stage A and B this was not the case: The concentrations were often lower than the MDL for the analysis. Strontium and cadmium concentrations were not calculated for the summer data, because the used fitting line of the calculation for the absolute elemental concentrations did not properly include the low concentrations of these two elements in the spectra. Mainly coarse mode and fine mode size fractions were discussed for the Ticino field studies.

3.2.1

Fingerprints of the Three Field Studies

Aerosol samples from the field study in Roveredo in winter (December 13, 2004 14:00 until January 22, 2005 10:00) resulted in the largest set of data. Each box of the Roveredo fingerprint box plot contained 325 data points of elemental concentrations (figure 3.9). Key player elements were not as obviously to distinguish as in the wood burning campaing at EMPA, due to a big variation of all elements during time. The pattern, that coarse mode size fractions showed higher concentrations in general than fine mode fractions, was found for most elements of these samples as well. Sulphur, potassium, bromine, rubidium and lead showed a different pattern: While the medium size fraction (1 − 2.5 µm) showed clearly lower concentrations than the coarse mode, the fine mode fraction showed similar concentrations as the coarse mode. Dominating elements in the field studies in Roveredo in winter 2004 / 2005 were sulphur, chlorine, potassium, calcium and iron. They were mainly present in the coarse mode.

38

Highest concentrations were sampled for chlorine in the coarse mode.

The fingerprint box plot of the field study in Moleno in winter (January 25, 2005 15:00 until February 14, 2005 20:00) contained 244 data points in each box (figure 3.9). Different from the common pattern that the concentrations in the three RDI size fractions behave like A > B > C, were the distributions of the concentrations for the elements sulphur, potassium, bromine and lead.

While fine and coarse mode concentrations of sulphur were very similarly distributed, stage B concentrations were slightly lower. For potassium, both stage B and fine mode stage C were lower than concentrations in the coarse mode.

For bromine, fine mode concentrations were even the highest of the three size fractions, whereas for lead all three size fractions showed similar concentrations.

Dominating elements in the field studies in Moleno in winter 2004 / 2005 were sulphur, and coarse modes of chlorine, potassium, calcium and iron.

The smallest data set resulted from the field study in Moleno in summer 2005 (June 20, 2005 17:00 until June 29, 2005 13:00): For each box in the fingerprint box plot 107 data points were present (figure 3.10). In this dataset, sulphur, chlorine, zinc and bromine varied from the mentioned pattern of the distribution of the concentrations in the three size fractions (A > B > C). Sulphur and bromine showed highest concentrations in the fine mode while coarse and medium size fraction concentrations were similarly distributed. For chlorine, the medium size fraction showed lowest concentrations. Fine mode concentrations were lower than concentrations in the coarse mode. Vanadium showed for the data of the summer field study big variations and low concentrations (mainly below the MDL) such that a pattern of the concentrations distributions was not clearly to detect. Dominating elements in the field studies in Moleno in summer were sulphur, potassium, calcium and iron.

The box plots showed, that corresponding concentrations were commonly sampled for calcium, potassium, iron and sulphur in the coarse mode. This could indicate the presence of some of the compounds that were expected in the laboratory study from wood burning: CaSO4 and K2 SO4 .

In a next step, concentrations of the three sites of the Ticino field studies were compared. Elements were listed, that showed a pattern which was different from the mentioned concentrationâ&#x20AC;&#x2122;s distribution A > B > C (table 3.3). Sulphur was present in all three field studies in higher concentrations in the fine mode. This indicates the presence of sulphate as a secondary aerosol. It is found mainly in periods of sunny weather because its formation is promoted by sunlight.

As well high concentrations of fine mode bromine were found in all three field studies. Bromine was used as a fuel additive [10] and is still today present in a rest fraction. For that it was present in higher average concentrations in Moleno than in Roveredo. Fine mode potassium was mainly present in Moleno in winter. The high concentrations were present due to the the combustion of wood for residential heating in periods of cold weather. As well in Roveredo high concentrations were sampled, while concentrations in Moleno in summer were much lower. Additionally, rubidium showed a different pattern: Concentrations in the fine mode were higher than concentrations in the medium RDI stage (1 â&#x2C6;&#x2019; 2.5 Âľm) in Roveredo. Rubidium is thought to have its origin in the heavy oil and is found as well in traces in mineral dust.

As for the wood burning study at EMPA, a comparing box plot was made to visualize the differences between the field studies in winter 2004 / 2005 and the field studies in summer 2005 in Moleno (figure 3.10). To proof differences, 39

a one-way ANOVA (means comparison using Bonferroni test) with OriginPRO 7.5 was performed. Significant differences between the winter and the summer studies in Moleno in the elemental concentrations were found for the coarse mode of chlorine, calcium and iron, the medium size fraction (1 â&#x2C6;&#x2019; 2.5 Âľm) of sulphur, and the fine mode of sulphur (for all five p < 0.05). Coarse mode chlorine was clearly higher in winter due to the use of deicing salt on roads (mainly N aCl, sometimes as well CaCl2 ). Higher concentrations of coarse mode calcium could be attributed to a higher presence of mineral dust during summer or a construction site which was operated during summer times. The existence of a nearby construction site was not prooved. Iron concentrations were higher in summer, however looking at the comparing box plot (figure 3.10), significant differences were not visible. Therefore, it was not clear if the used means comparison (Bonferroni) made sense in this case. As well no source was known that had a higher iron output in summer than in winter. Sulphurâ&#x20AC;&#x2122;s fine mode differences in concentrations between the summer and winter field studies were already discussed.

40

41

element name S

Roveredo winter size avg. conc. std. dev. fraction [ng/m3 ] [ng/m3 ] 2.5 − 10 µm 78.657 63.838 1 − 2.5 µm 30.074 42.705 0.1 − 1 µm 52.922 57.112 Cl 2.5 − 10 µm 764.894 984.391 1 − 2.5 µm 60.692 167.620 0.1 − 1 µm 4.650 5.128 K 2.5 − 10 µm 16.715 13.990 1 − 2.5 µm 4.200 3.445 0.1 − 1 µm 10.751 8.834 Ca 2.5 − 10 µm 57.701 42.132 1 − 2.5 µm 10.265 6.731 0.1 − 1 µm 2.218 1.203 Ti 2.5 − 10 µm 2.750 5.169 1 − 2.5 µm 0.399 0.339 0.1 − 1 µm 0.044 0.053 V 2.5 − 10 µm 0.319 0.272 1 − 2.5 µm 0.050 0.045 0.1 − 1 µm 0.016 0.023 Cr 2.5 − 10 µm 0.584 0.471 continued on next page

Moleno winter avg. conc. std. dev. [ng/m3 ] [ng/m3 ] 136.260 107.504 78.858 89.195 144.301 133.399 644.848 643.899 20.500 18.976 9.357 11.183 65.144 57.928 11.827 10.596 19.928 19.431 178.770 148.909 19.323 15.212 2.957 2.031 7.294 5.890 1.015 0.823 0.106 0.094 0.977 0.766 0.193 0.180 0.039 0.035 1.820 1.588 mdl [ng/m3 ] 329.235 48.688 10.094 76.099 11.204 2.273 11.097 1.645 0.322 5.477 0.804 0.153 1.809 0.268 0.048 1.127 0.168 0.030 0.749

Moleno summer avg. conc. std. dev. [ng/m3 ] [ng/m3 ] 183.566 82.373 156.375 107.422 688.886 670.492 45.173 78.219 3.300 4.355 8.229 5.327 108.696 40.217 16.207 6.876 6.560 4.674 244.240 155.988 27.891 10.889 4.648 2.395 7.333 3.782 1.208 0.530 0.243 0.198 0.541 1.038 0.026 0.141 0.142 0.206 0.653 0.550

Table 3.2: Average concentrations, standard deviations and the minimal detection limits of the field studies in Roveredo and Moleno in winter 2004 / 2005 and of the field studies in Moleno in summer 2005.

mdl [ng/m3 ] 81.574 29.561 39.677 41.794 13.037 7.678 4.011 1.256 0.530 2.136 0.701 0.297 0.940 0.313 0.131 1.239 0.416 0.180 0.600

42

Table 3.2 – continued from previous page Roveredo winter element size avg. conc. std. dev. name fraction [ng/m3 ] [ng/m3 ] 1 − 2.5 µm 0.107 0.077 0.1 − 1 µm 0.030 0.040 Mn 2.5 − 10 µm 1.362 1.013 1 − 2.5 µm 0.324 0.257 0.1 − 1 µm 0.221 0.306 Fe 2.5 − 10 µm 80.393 54.195 1 − 2.5 µm 15.811 10.026 0.1 − 1 µm 3.110 3.002 Cu 2.5 − 10 µm 3.819 3.014 1 − 2.5 µm 0.770 0.532 0.1 − 1 µm 0.149 0.146 Zn 2.5 − 10 µm 1.949 1.672 1 − 2.5 µm 0.761 1.608 0.1 − 1 µm 0.490 0.477 Br 2.5 − 10 µm 0.047 0.090 1 − 2.5 µm 0.024 0.030 0.1 − 1 µm 0.065 0.062 Rb 2.5 − 10 µm 0.055 0.053 1 − 2.5 µm 0.008 0.008 0.1 − 1 µm 0.019 0.018 Sr 2.5 − 10 µm 0.196 0.157 1 − 2.5 µm 0.034 0.028 0.1 − 1 µm 0.007 0.005 Cd 2.5 − 10 µm <0.001 0.004 continued on next page Moleno winter avg. conc. std. dev. [ng/m3 ] [ng/m3 ] 0.369 0.337 0.049 0.044 4.285 3.411 0.870 0.726 0.197 0.163 276.311 241.447 53.580 47.364 7.924 6.854 5.554 4.327 1.460 1.147 0.332 0.253 6.225 4.819 2.482 2.764 1.495 1.386 0.073 0.170 0.056 0.057 0.177 0.159 0.269 0.261 0.040 0.041 0.032 0.036 0.552 0.515 0.067 0.062 0.015 0.014 <0.001 0.006 mdl [ng/m3 ] 0.113 0.020 0.527 0.079 0.014 0.394 0.061 0.011 0.189 0.030 0.005 0.163 0.026 0.005 0.101 0.018 0.004 0.092 0.017 0.004 0.089 0.017 0.004 0.127

Moleno summer avg. conc. std. dev. [ng/m3 ] [ng/m3 ] 0.106 0.122 0.016 0.046 3.287 1.628 0.678 0.376 0.182 0.131 208.445 91.134 45.385 21.825 11.467 8.016 4.274 1.894 1.109 0.519 0.330 0.192 3.841 2.373 1.780 1.840 1.508 1.377 0.044 0.029 0.021 0.015 0.160 0.126 0.684 0.176 0.180 0.045 0.081 0.022 – – – – – – – – mdl [ng/m3 ] 0.201 0.089 0.361 0.125 0.055 0.288 0.101 0.046 0.134 0.049 0.023 0.131 0.049 0.024 0.069 0.029 0.015 0.072 0.030 0.016 – – – –

43

Moleno winter avg. conc. std. dev. [ng/m3 ] [ng/m3 ] 0.001 0.004 0.007 0.008 0.986 0.899 0.522 0.549 0.828 1.083 mdl [ng/m3 ] 0.023 0.004 0.221 0.038 0.007

Moleno summer avg. conc. std. dev. [ng/m3 ] [ng/m3 ] – – – – 0.565 0.298 0.241 0.143 0.225 0.151 mdl [ng/m3 ] – – 0.109 0.043 0.021

Roveredo winter 2004 / 2005 Moleno winter 2004 / 2005 Moleno summer 2005

sampling site

high concentrations in coarse mode P b, rest P b, rest P b, rest

high concentrations in fine mode S, K, Br, Rb S, K, Br S, Br

Table 3.3: Overview of size fractions with high elemental concentrations of the field studies in Ticino. Elements are listed, which differed from the common pattern A > B > C and had high concentrations in the fine mode. Most of all elements were present in highest concentrations in the coarse mode (rest). The origin of highest concentrations in the coarse mode of lead was not clear.

Table 3.2 – continued from previous page Roveredo winter element size avg. conc. std. dev. name fraction [ng/m3 ] [ng/m3 ] 1 − 2.5 µm <0.001 0.002 0.1 − 1 µm 0.002 0.002 Pb 2.5 − 10 µm 0.313 0.252 1 − 2.5 µm 0.127 0.123 0.1 − 1 µm 0.178 0.183

Figure 3.9: Fingerprint box plots of data from field studies in Roveredo (top) and Moleno (bottom) in winter 2004 / 2005. Stars indicate minimal detection limits of each element and size fraction. A: 2.5 − 10 µm, B: 1 − 2.5 µm, C: 0.1 − 1 µm.

44

Figure 3.10: Top: Fingerprint box plot of data from field studies in Moleno in summer 2005. Stars indicate minimal detection limits of each element and size fraction. Bottom: Comparing fingerprint box plot between data from the field study in winter 2004 / 2005 and summer 2005 in Moleno. A: 2.5 − 10 µm, B: 1 − 2.5 µm, C: 0.1 − 1 µm.

45

3.2.2

Qualitative Source Apportionment

With the mentioned patterns and comparings, a simple qualitative source apportionment was performed. Neither a positive matrix factorization (PMF), nor a chemical mass balancing (CMB) method was used. The following list shows expected sources for selected elements and size fractions that were found in the field studies in Ticino.

Mineral Dust and Background Concentrations: Scoarse , Kcoarse , Cacoarse .

Distribution of the Concentrations: Roveredo winter < Moleno winter < Moleno summer Main influence on concentration: Meteorology Secondary Sulphate: Sf ine Distribution of the Concentration: Roveredo winter < Moleno winter < Moleno summer Main influence on concentration: Photochemistry due to meteorology Wood Burning: Kf ine Distribution of the concentrations: Moleno winter < Moleno summer < Roveredo winter Main influence on concentration: Wood burning in winter (residential heating) Traffic (Non-Exhaust as Steels, Alloys etc.): Crcoarse , M ncoarse , F ecoarse , Cucoarse , Zncoarse Distribution of the concentrations: Roveredo winter < Moleno summer < Moleno winter Main influence on concentration: Proximity to road, meteorology Traffic (Exhaust): M nf ine , Brf ine , P bf ine Distribution of the concentrations: Roveredo winter < Moleno summer < Moleno winter Main influence on concentration: Proximity to road, meteorology Deicing Salt: Clcoarse Distribution of the concentrations: Moleno summer < Moleno winter = Roveredo winter Main influence on concentration: Deicing of the roads in cold periods in winter Heavy Oil, Mineral Dust etc.: Rbcoarse , Srcoarse Distribution of the concentrations: Roveredo winter < Moleno winter (< Moleno summer)1 Main influence on concentration: Meteorology

1 Data

for strontium was not analyzed for the summer field study in Moleno.

46

3.2.3

Time Series

A confirmation for the fingerprint patterns of the field studies in Ticino was thought to be found out by means of time series of all sampled elements. Similar patterns for some elements as well the detection of the meteorology’s influence were expected to be visualized. To get an insight into the sampled time series from the three field studies in Ticino, for each time series three different regions of interest (ROI) were chosen and the concentrations and local variations of selected analyzed elements were discussed. In all plots, the ROIs were marked with a light grey vertical bar for a better visualization.

Automatic monitoring stations in Roveredo and Moleno provided data for total aerosol concentrations (PM10, in Moleno for PM2.5 as well) and N Ox .

The latter was used as a short-distance tracer for the amount of traffic. Data for air pressure, temperature, wind speed, global radiation and precipitation was as well available from the monitoring station. For Roveredo, precipitation values were available in the unit %. Data from both monitoring stations was recorded in the interval of 30 minutes, so the unit % indicated, how much percent of the 30 minute period’s time precipitation was present. In Moleno precipitation was measured at standard conditions in the unit mm. As well PM2.5-values were not recorded by the monitoring station in Roveredo and PM10 values had to be interpolated by a statistical software (OriginPRO 7.5) due to diurnal missing data points. Temperature data was measured in Roveredo 4 metres above ground, while in Moleno, temperature was measured at standard conditions (2 metres above ground).

As it was the idea to get insight into the influence of weather on the aerosol concentrations, interpretations were given for the concentrations of the discussed elements in the context of the weather situation. Mainly elements in the fine mode and in the coarse mode size fraction were discussed.

Roveredo Time Series: Regions of interest See figures 3.11, 3.12, 3.13, 3.14.

1. December 16, 2004 14:00 until Dezember 17, 2004 04:00 was the date when highest concentrations of PM10 were sampled by the automatic monitoring station in Roveredo. High concentrations of N Ox forming a wide peak were as well present. Precipitation was measured during the whole period, air pressure was low and decreasing on that date. The global radiation reached only very low values, temperature was stable, it was not too windy.

Chlorine in coarse mode showed a wide peak during this period ranging from December 16, 14:00 to December 17, 06:00. Sulphur and potassium showed a slight but very similar peak as almost every other element in the coarse mode: From 14:00 until 20:00 an increase in concentration while at 22:00 a local minimal concentration was present. From then, another peak was formed until midnight. Until 04:00 concentrations were again on a low level. Fine mode size fractions showed a similar variation for sulphur, potassium — fine mode concentrations were for these two even higher than coarse mode concentrations! — and chlorine. Bromine and lead showed a special pattern for fine mode concentrations during this period: Much higher concentrations and a decrease to a low level only 47

on December 17, 08:00. Zinc showed highest fine mode concentrations on December 16, 18:00 (3.048 ng/m3 ).

Low wind speeds, decreasing air pressure, stable temperatures and high emissions indicated a period of a strong and stable atmospheric inversion.

Dilution by wind or mixing of the air masses (e.g. by sunlight heated air masses) didnâ&#x20AC;&#x2122;t take place, that is why emissions reached highest concentrations. Due to the stable atmosphere, peaks of traffic were clearly visible, mainly in the N Ox concentrations: Just before the ROI, at 10:00 a sharp peak from morning traffic and a strong increase in concentration at 16:00 from traffic in the evening was measured. A sharp peak in fine mode potassium and sulphur at 18:00 was attributed to residential wood burning. A sharp peak two hours later could not be clearly attributed: Coarse mode titanium and vanadium indicate emissions from petroleum.

However, this peak was visible in almost all elemental time series. During the whole period, coarse mode calcium showed constant and high concentrations, which is clearly attributed to deicing salt on roads (N aCl) during periods of cold weather. High zinc concentrations in the fine mode indicated periods of traffic. Zinc is found in vehicle tire-wear [14].

2. January 9, 2005 12:00 will be focussed in a next discussion. While N Ox showed a small peak on that date and global radiation showed a rather wide peak, in any other meteorological data set no exceptional peaks were present (constant, rather high air pressure, low wind speed, no rain).

This ROI was chosen because of coarse mode sulphur and calcium showed a broad peak. Potassium showed a slightly higher peak in the coarse mode than on two following dates but for coarse mode chlorine high values but not a sharp peak was present. Coarse mode titanium, vanadium and chromium showed broad peak areas which were permanently high during the afternoon. Extreme peaks in the coarse mode showed manganese, iron, copper and bromine. These elements showed almost highest concentrations in the whole time series in the region of January 9. For copper and strontium the highest concentrations in Roveredo were sampled on January 9, 12:00 (copper: 0.943 ng/m3 , strontium: 17.006 ng/m3 ). An additional interesting fact of the peaks in the coarse mode on this date is that for some elements double-peaks were sampled: Chromium, manganese, iron, copper, zinc, rubidium and lead showed two maxima in the broad peak around January 9, 12:00.

A broad peak of the global radiation data, a high air pressure, no precipitation and rather high and increasing temperatures set the frame for a sunny day on January 9. PM10 and N Ox showed a peak at 12:00 from traffic but due to the good weather, the atmosphere was well mixed and concentrations were diluted. Extreme peaks of coarse mode sulphur, potassium and calcium were attributed to mineral dust resuspension. Several metals showed as well a high peak in the coarse mode (e.g. manganese, iron, copper) â&#x20AC;&#x201D; this was interpreted as an effect of non-exhaust emissions from traffic. A second peak from the mentioned elements was found at 16:00.

A clear attribution to traffic could not be done in this case: N Ox from the monitoring station didnâ&#x20AC;&#x2122;t show high values at 16:00. Increasing fine mode sulphur concentrations were found to be an effect of a period of sunny weather on Janaury 9, 12:00. Sulphur in this size fraction is thought to 48

be present in the form of sulphate, an important secondary aerosol which is produced with the aid of sunlight [44].

3. January 10, 2005 10:00 and Janaury 11, 2005 10:00 were the dates when two sharp and high peaks of sulphur in the coarse mode were detected. N Ox did not reach a peak in the morning on January 10 and January 11 but was strongly increasing, reaching a maximum on both days at 14:00. An interesting fact is, that N Ox was on January 11, 10:00 on a local minimum while a small peak was present that morning at 8:00 and an extensive peak was present some hours later at 14:00. Air pressure was rather high on both dates, temperatures were below 0 Â&#x2030;, having on both days almost lowest values in the morning at 10:00. Highest temperatures for both winter days were present at 14:00. Only a slight wind and no rain was present on both mornings in Roveredo.

On both January 10 and January 11, chlorine, potassium and calcium showed very high peaks in the coarse mode as well. Other elements in the coarse mode size fraction as titanium and vanadium reached only two hours later their maximum concentration. Several coarse mode elements showed as well peaks on January 10, 10:00. This was the case for chromium, manganese, iron, copper, zinc, rubidium, and lead. Strontium and bromine in coarse mode reached almost their highest concentrations on the two dates. On Janaury 11, for most elements the peaks were not as extreme as for January 10. For coarse mode titanium and vanadium not a peak but an area of high concentrations in the time series was present on January 11. As well coarse mode chromium, iron and lead did not reach a maximum, whereas manganese, copper, zinc, bromine and especially strontium showed a clear peak in the coarse mode on that date. Some elements showed a peak in the concentration only two hours later on January 11 (e.g. lead or chromium). In the fine mode, only potassium showed peaks on both dates. Fine mode manganese and iron showed a maximum on January 10. Fine mode rubidium and strontium showed only slightly higher concentrations on both dates.

As well during this ROI, sunny weather was present in Roveredo. A very regular temperature pattern with maximum values at 14:00 and minimum values at 10:00 showed a constant weather situation without rain and almost highest air pressures in the whole time series. High concentrations of fine mode sulphur confirmed the fact that sunny weather was present.

Because of low wind speeds traffic emissions were clearly visible in two N Ox peaks which showed traffic after lunchtime (14:00) and traffic in the late afternoon (20:00). Especially with late afternoon traffic exhaust and non-exhaust emissions were sampled as e.g. coarse mode manganese, iron, copper and zinc. The emission peak at 20:00 was visible in the interpolated PM10 variation as well, because no wind was present to dilute the high concentrations. The peak was visible for corse modes of most elements. At night, beginning on January 10, 22:00, wind was present which diluted concentrations of all elements. This is commonly the case in the area of Roveredo [41]. On January 11, even a slight peak for morning traffic (08:00) was registered in the N Ox time series. This peak was not visible in the other elementâ&#x20AC;&#x2122;s time series. Peaking coarse mode potassium and calcium on both mornings at 10:00 pointed out emissions from wood 49

burning in Roveredo. However, one should have in mind, that the time scale of the RDI samples always indicated ending times of a two hours sampling interval, while the automatic monitoring station was measuring values online. This is an explanation why peaking concentrations appeared sometimes earlier in the data set of the automatic monitoring station than in the RDIâ&#x20AC;&#x2122;s elemental concentrations.

50

Figure 3.11: Time series of the elements sulphur, chlorine, potassium, calcium, titanium and vanadium for the the analyzed RDI stages from Roveredo from December 13, 2004 13:00 until January 22, 2005 00:00. Interruption lasted from December 22, 2004 00:00 until January 4, 2005 14:00.

51

Figure 3.12: Time series of the elements chromium, manganese, iron, copper, zinc and bromine for the the analyzed RDI stages from Roveredo from December 13, 2004 13:00 until January 22, 2005 00:00. Interruption lasted from December 22, 2004 00:00 until January 4, 2005 14:00.

52

Figure 3.13: Time series of the elements rubidium, strontium, cadmium and lead for the the analyzed RDI stages from Roveredo from December 13, 2004 13:00 until January 22, 2005 00:00. Interruption lasted from December 22, 2004 00:00 until January 4, 2005 14:00.

53

Figure 3.14: Time series of meteorological data: PM10 (because of missing data, interpolation was performed), N Ox , air pressure, temperature, wind speed, global radiation and precipitation from Roveredo from December 13, 2004 13:00 until January 22, 2005 00:00. Interruption lasted from December 22, 2004 00:00 until January 4, 2005 14:00.

54

Moleno Winter Time Series: Regions of interest See figures 3.15, 3.16, 3.17, 3.18.

4. January 30, 2005 21:00 was the date when coarse mode chlorine showed a maximum concentration in the whole time series (4118.012 ng/m3 ).

Data from the monitoring station showed high concentrations for the total PM10 and a strongly increasing concentration for PM2.5. N Ox showed a little peak, embedded in an area of higher concentrations. Air pressure was rather high but low temperatures were present (-3.6 Â&#x2030;). It was neither windy nor rainy at that time.

For coarse mode sulphur, as well for vanadium and zinc a clear peak was present. This was not the case for coarse mode potassium and calcium: Calcium showed a local maximum point but not a clear peak, the same was the case for coarse mode potassium, titanium, manganese, copper and for fine mode bromine and cadmium. High concentrations were present for rubidium and lead. Fine mode sulphur and potassium showed as well elevated concentrations. Zinc and rubidium showed as potassium rather well correlating fine mode concentrations. The variations in the fine mode for most elements went almost in parallel with the elementâ&#x20AC;&#x2122;s coarse mode fraction in the period around January 30. Cadmium and bromine showed higher concentrations in the fine mode size fraction than in the coarse mode during that time, both elements showed a surprising parallel variation.

A cold winter night at that date was present due to the low and decreasing temperature with a minimum temperature at 03:00 on January 31. The absence of wind caused an increase of all concentrations as clearly visible for the PM10 and PM2.5 data of the monitoring station. The peak in N Ox was probably caused due to the absence of wind: high traffic emissions (for N Ox , PM10 and PM2.5 starting at 17:00) reached a peak when the wind speed was very low. The highest peak in the coarse mode chlorine time series at that time can be only an effect of the extensive use of deicing salt on the motorway. Besides sulphur, as well other elements showed peaking concentrations. A possible explanation might be, that after deicing salt was applied it was possible to drive faster and therewith, much more emissions were produced. Peaks in N Ox , coarse mode zinc (from tire-wear [14]), fine mode bromine (in traces a fuel additive [10]), coarse mode sulphur (from dust resuspension) as well as coarse mode iron and copper (non-exhaust emission from steels and alloys in cars) confirmed this hypothesis. The source of fine mode cadmium was not distinguished. The periodical 6 hours pattern of coarse mode vanadium emissions is thought to have its origin from an oil heating, that burned petroleum in a six hours interval during the cold night. As well potassium in the coarse and the fine mode size fractions showed increasing concentrations during the cold night, which indicates residential wood burning.

5. January 31, 2005 21:00 as a ROI was chosen because of peaks of rubidium and strontium in coarse mode and the clear peak of bromine and cadmium in the fine mode. The peaks that were measured on January 31, 21:00 were as well present in the data of the monitoring station. PM10 and

55

PM2.5 showed clear and almost highest peaks for the whole time series.

N Ox formed a peak as well, which had its minimum at 13:00, came to a plateau at 19:00 until 21:00 and decreased fast to reach its minimum on February 1, 01:00. Air pressure was slowly decreasing during that date, temperature showed a typical diurnal pattern and reached its minimum at 23:00 on January 31. Thereafter, a sharp increase in temperature was the case (January 31, 23:00: -3.4 ; February 1, 01:00: 10.4 ). The increase in temperature went in parallel with an increase in wind speed (January 31, 23:00: 0.7 m/s; February 1, 01:00: 6.1 m/s); there was no precipitation present.

Although strontium reached its maximum in this ROI already at 19:00, concentrations were two hours later not much lower. Sulphur showed on that day not an extreme but a clear peak in all size fractions, coarse and fine mode concentrations varied almost in parallel around 21:00. Chlorine, potassium, calcium and titanium showed in the coarse mode a clear peak which started to increase at 15:00 that day and reached its minimum not before 03:00 on February 1. Coarse mode vanadium and chromium showed a similar peak but reached maximum concentrations at 23:00.

Coarse mode manganese, iron, copper and zinc reached as well maximum concentrations on that date. All size fractions showed a peak, a rather parallel variation was present. Fine mode bromine showed much higher concentrations at that date than coarse mode bromine. This was as well the case for lead and cadmium, as mentioned.

Very low wind speeds and a stable atmosphere following an afternoon of light wind caused an accumulation of all emissions on that date. Additionally a lot of traffic (high N Ox concentrations) on that evening was the reason for high PM10 and PM2.5 concentrations. A continuously decreasing air pressure indicated an oncoming change in weather: This was in fact the case on January 31, 23:00, when a strong wind started to mix the atmosphere (and for that all emissions were diluted). Temperature rose by approximately 10 during that night. The result was a strong windy foehn coming from the north which compensated differences in air pressure. At 03:00 on February 1, almost lowest concentrations in most elment’s concentrations of the whole time series were found due to the windy weather. An interesting fact was that as soon as the wind stopped for some hours (February 1, 09:00), all emissions increased again rapidly: This was especially the case for non-exhaust traffic tracers as copper, iron or manganese. A N Ox -peak confirmed the presence of traffic. Coarse mode chlorine showed as well an extreme peak at 09:00. Deicing salt was thought to be applied on February 1, 09:00 due to frost on the motorway.

In the following period, again windy weather was present which caused a longer period of very low elemental concentrations.

6. February 10, 2005 08:00 was chosen because of having sampled high concentrations in a trace metal’s variation (e.g. fine mode lead). Peaks were found as well in the data of the automatic monitoring station: For PM10 and PM2.5 a periodical pattern of peaks was present during these days, a much lower concentration was present at 08:00 on February 10 for both PM10 and PM2.5. N Ox showed the highest concentrations on that date (551.8 ppb), as well air pressure was present in almost highest levels 56

from the whole time series. The temperature was at a rather low level with -4.5 Â&#x2030;, however the minimum temperature in the time series was -8.9 Â&#x2030; on January 30, 2005 07:00. Neither wind speed nor precipitation, nor global radiation showed special values.

Fine mode lead showed highest concentrations on that date. This was even the highest value of all lead concentrations in the three field studies in Ticino. Sulphur showed as well a slight peak in the coarse mode fraction on that date. Very high concentrations were found for the coarse mode of chromium, manganese, iron and copper on that date. For all four elements, the peak at 08:00 was the second of a double-peak, whereas the first peak was around midnight. As lead, cadmium and bromine showed highest concentrations of all size fractions in the fine mode on February 10, 08:00. For rubidium and strontium high concentrations were found on February 10, 08:00.

The focussed date is embedded in a period of five days of stable weather and slowly increasing temperatures. Highest temperatures during one day were registered in this period regularly at 14:00, often two hours after the global radiation reached its maximum. Constant high air pressures and only a light wind usually at 12:00 set the frame for a regular diurnal pattern of many elements (e.g. coarse mode chromium, manganese, iron and copper). This pattern went exactly in parallel with the PM10 and PM2.5 concentrations measured by the monitoring station, which indicates that these elements (mainly present in non-exhaust emissions from traffic) were the main contents of the measured PM10 and PM2.5. The diurnal pattern of decreasing elemental concentrations were attributed to a daily oncoming wind which reached the maximum speed usually at 14:00.

As measured on several days, a high peak in N Ox -emissions was present at 08:00 indicating rush hour traffic. Regular coarse mode sulphur concentrations were attributed to the resuspension of mineral dust due to traffic. A clear explanation for highest fine mode bromine, cadmium and lead concentrations was not found. A possible reason is traffic with cars using old leaded fuel or other fuel additives containing these elements.

57

Figure 3.15: Time series of the elements sulphur, chlorine, potassium, calcium, titanium and vanadium for the the analyzed RDI stages from Moleno from January 25, 2005 15:00 until February 14, 2005 20:00.

58

Figure 3.16: Time series of the elements chromium, manganese, iron, copper, zinc and bromine for the the analyzed RDI stages from Moleno from January 25, 2005 15:00 until February 14, 2005 20:00.

59

Figure 3.17: Time series of the elements rubidium, strontium, cadmium and lead for the the analyzed RDI stages from Moleno from January 25, 2005 15:00 until February 14, 2005 20:00.

60

Figure 3.18: Time series of meteorological data: PM10, PM2.5, N Ox , air pressure, temperature, wind speed, global radiation and precipitation for Moleno from January 25, 2005 15:00 until February 14, 2005 20:00.

61

Moleno Summer Time Series: Regions of interest See figures 3.19, 3.20, 3.21, 3.22.

7. June 22, 2005 07:00 was the date when the highest N Ox -peak was measured by the automatic monitoring station in Moleno. As well PM10 and PM2.5 were on a high level just before decreasing rapidly. Air pressure showed one of the highest level in this summer time series (990 hP a), while only during one different period in the time series lower temperatures were measured. The temperature reached a miniumum at 07:00 (16.6 Â&#x2030;). A low wind speed â&#x20AC;&#x201D; as on other days in this time series â&#x20AC;&#x201D; was present just before a diurnal maximum peak which was usually reached in the early afternoon (13:00 or 15:00). No rain and already at 07:00 a rather high global radiation was present (33.7 W/m2 ).

Sulphur reached slight peaks during this period in all size fractions. Coarse mode chlorine showed increasing concentrations which lead to a small peak at 09:00. For coarse mode potassium, titanium and vanadium a peak was present at that date. Calcium showed rather high concentrations in the coarse mode while peaking in the fine mode size fraction at 09:00. A peak at 09:00 was as well present for all chromium size fractions. Coarse mode manganese, iron, copper and zinc showed a peak at 07:00. Comparing the coarse and the fine mode size fractions, a peaking coarse mode at 07:00 and a peaking fine mode at 09:00 additionally was present for potassium, vanadium, manganese, iron, copper, zinc and rubidium. Lead showed decreasing concentrations in all size fractions at 07:00 while having a slight peak in the fine mode size fraction at 09:00.

High N Ox -peaks were measured almost every moring at 07:00 in this time series due to rush hour traffic in the early morning and weather conditions that made this effect clearly visible: Highest air pressure values of nearly every day were at 07:00, indicating stable conditions with low wind speeds and for that no dilution of the emissions. Lowest temperatures during the whole day were usually measured at 05:00 or 07:00; this enforced the effect that cold vehicle engines produced more emissions on the motorway.

With the slight increase of wind speed (and probably the lower amount of traffic later in the morning) all emissions decreased rapidly as seen in the variations of PM10 and PM2.5. A peak in coarse mode sulphur at 07:00 was sampled due to traffic-made particle resuspension and an increase in fine mode sulphur as an effect of the global radiation forming secondary sulphate (note the wide range of the y-axis of the Moleno summer time series!). The peaks of coarse mode chlorine at 09:00 and potassium at 07:00 could not be attributed to a certain process. However, compared to the coarse mode chlorine concentrations that were sampled in winter in Moleno due to the use of deicing salt, chlorine peaks of the summer time series were negligible. Manganese, iron, copper and zinc peaks at 07:00 are thought to have their origin in non-exhaust emissions from traffic. The fact that some elements showed a peak in coarse mode at 07:00 and a peak in fine mode at 09:00 was attributed to the process of non-exhaust emission particles being pulverized by traffic. This was suggested to be the case for lead wheel weights which are dropped from vehicle wheels [32].

Still, it is not clear if this assumption can be assigned to other non-exhaust 62

vehicle emissions.

8. June 23, 2005 23:00 was the date when coarse mode vanadium reached by far highest concentrations (8.76 ng/m3 ) of all three field studies in Ticino. Additionally this point was of interest because none of the other elements showed peaks. PM10 and PM2.5 values measured from the automatic monitoring station reached very low values on June 23, 23:00, just after PM10 reached the maximum value in the whole time series at 19:00 (62.6 Âľg/m3 )! As well PM2.5 was present in high concentrations at 19:00.

PM2.5 reached a minimum exactly at 23:00 while PM10 showed minimal values already at 21:00. N Ox was present in low concentrations after a peak at 19:00 and before a peak at 07:00 the next day. The diurnal pattern of air pressure was interrupted on June 23, 21:00: At 23:00 usually the slope got flatter and reached a plateau until 09:00. On June 23, 23:00, the curve had a maximum, slightly decreased for six hours and reached a maximum at 07:00, where it restarted the pattern. The temperature pattern was slightly different than on other days: Although a decrease from 15:00 in the afternoon was commonly the case, the decrease stopped for a constant temperature at 19:00 and on 21:00 even a slight increase was the case. At 23:00 the decrease continued. In the region of June 23, 23:00 no rain was present. Wind speed reached its maximum that day at 21:00 (3.77 m/s), which was the highest value in this time series. At 23:00 wind speed was again only 0.6 m/s.

Fine mode sulphur reached a slight peak on that day at 21:00, coarse mode sulphur showed a peak at 19:00. Chlorine was even present in a low concentration between two peaks while coarse mode potassium, calcium, titanium, manganese, iron and copper showed strongly decreasing values.

Coarse mode chromium, zinc and lead already reached a minimum at that time while all size fractions of bromine were present in low concentrations.

Rubidium in coarse mode reached a constant concentration in a decreasing period.

The low concentrations of the PM10 and PM2.5 values measured from the monitoring station were present due to a peak in the wind time series just two hours before: On June 23, 21:00 a peak of highest wind speed of the whole summer time series was present. This went in parallel with a period of stable air pressure during the night. After the peaking wind speed at 23:00, an increase in PM10 was again measured. The slight increase in temperature at the time of the peak of the wind speed differed from the common diurnal pattern and indicated a period of warm wind, probably a light windy foehn was present as seen in the fifth ROI (January 31, 21:00 in Moleno). Wind from the foehn was able to dilute most of the elements that were sampled, therefore, e.g. potassium, calcium and titanium in the coarse mode showed a strong decrease in concentration.

Only shortly after the peak in the wind time series, some emissions could accumulate again, as e.g. coarse mode chlorine, which was as well in this ROI only present in minor concentrations, compared to the winter time series. The concentrations peak in the coarse mode of vanadium was not clearly attributable to a source, however, higher coarse mode vanadium concentrations seemed to be a common fact in periods of foehn in Moleno, as seen already in the fifth ROI (January 31, 21:00 in Moleno). The only 63

explanation for the high peak of coarse mode vanadium (the highest peak of vanadium in all three field studies in Ticino!) in this period of windy weather is a strong source in the north of Moleno. A peak in coarse mode vanadium concentrations was present as well in other periods of low concentrations for other elements, indicating foehn: On January 30, 13:00 in Moleno and on June 25, 13:00 in Moleno.

9. June 24, 2005 13:00 - June 26, 2005 15:00 was chosen as a ROI because of a period of high concentrations for sulphur and bromine in the fine mode size fraction. Data from the monitoring station showed a high peak in PM10 and PM2.5 at June 25, 13:00. The period from June 24, 13:00 to June 25, 13:00 was suggested to be a period of higher concentrations of PM10 and PM2.5, while the period thereafter until June 26, 15:00 was suggested to be a period of lower concentrations. To check the difference, the PM10 and PM2.5 concentrations of the two periods were compared with a paired t-test in OriginPRO 7.5 and resulted in a significant difference (p < 0.01) for both PM10 and PM2.5 for the two periods. N Ox showed low values without remarkable peaks during the whole period, while air pressure reached a maximum during the focussed time period: On June 25, 15:00 990.6 hP a was reached. The diurnal temperature pattern, which was present for almost the whole summer time series in Moleno was interrupted by a peak at 11:00 on June 25 (26.9 ), while usually the maximum temperature (between 30.2 and 32.7 ) was reached at 15:00. The temperature peak decreased already at 13:00 and reached a minimum temperature at 15:00 (18.0 ). This decrease went in parallel with the global radiation, additionally, wind speed reached a peak at 13:00 (2.8 m/s). A short period of slight precipitation followed the peaking wind speed: The 0.2 mm of rain during 30 min happened at the same time as the temperature reached its minimum of 18.0 . During the summer field studies only on that date rain was present.

Sulphur and bromine in the fine mode showed the highest concentrations for the whole time series during that period (sulphur: 30 610 ng/m3 , bromine: 0.544 ng/m3 , both on June 25, 09:00). An interesting fact for this period was that coarse mode size fractions from sulphur and bromine showed low concentrations and neither a peak nor a parallel variation with the fine modes were present. The two variations for fine mode sulphur and fine mode bromine had a surprising parallel variation! Coarse mode chlorine showed a peak at 17:00 on June 24 that corresponded with peaks of fine mode sulphur, calcium, vanadium, manganese, copper, bromine and zinc (this was the maximum concentration of zinc (8.686 ng/m3 )) and all size fractions of lead. The high peak of coarse mode chlorine on June 25, 19:00 did neither correspond with fine mode sulphur nor with coarse mode sulphur but with coarse mode chromium, with an area of high concentrations of iron and a peak of copper and zinc.

The weather in this period showed a regular pattern, interrupted by a peak in wind speed on June 26, 13:00 and two hours later a peak of 0.2 mm of rainfall in the 30 min measuring period (the only rainfall in the whole time series). This was attributed to a period of stormy weather.

Therewith, as well global radiation decreased between 11:00 and 17:00, as well air temperature was decreasing. Wind and rain were able to dilute 64

and clean the atmosphere from particles, therefore the period thereafter showed significantly lower concentrations of PM10 and PM2.5. After the passing by of the weather front, air pressure increased rapidly, the maximum was reached on June 25, 15:00. The high values of fine mode sulphur, which are common in the form of the secondary aerosol sulphate in periods of intense sunlight gave a clear indication to the good weather at the beginning of this ROI as well: Highest concentrations of all three field studies were sampled between June 24, 17:00 and June 25, 09:00 which were periods of intense sunlight and warm temperatures as the meteorological data confirmed. Fine mode bromine went in parallel with fine mode sulphur. Bromine is known to be used as an additive for car fuels [10] but probably today of less importance than it used to be. A source which emitts sulphur and bromine in the fine mode was not found. On June 24, 17:00 many elements showed peaking concentrations. This was surprising because a slight wind was present â&#x20AC;&#x201D; usually this caused a dilution of all emissions. A possible explanation is a different wind direction which brought not diluting, clean air to Moleno, but different air pollutants. Traffic emissions could be excluded in the assumption, because of low N Ox -concentrations. Sampled air pollutants could be coarse mode potassium, all size fractions of chlorine, calcium, lead and peaks of fine mode zinc (this peak showed hightest fine mode zinc concentrations of all three field studies in Ticino), bromine and sulphur. Parallel peaks of coarse mode chlorine and chromium on June 25, 19:00 were attributed to particle resuspension. This is confirmed by a correlation with coarse mode iron, copper and zinc â&#x20AC;&#x201D; the presence of non-exhaust particles after the period of storm in the afternoon of June, 25 is quite possible. On that date, N Ox as an indicator of traffic was not present in extensive concentrations as well.

65

Figure 3.19: Time series of the elements sulphur, chlorine, potassium, calcium, titanium and vanadium for the analyzed RDI stages from Moleno from June 20, 2005 17:00 until June 29, 2005 13:00.

66

Figure 3.20: Time series of the elements chromium, manganese, iron, copper, zinc and bromine for the analyzed RDI stages from Moleno from June 20, 2005 17:00 until June 29, 2005 13:00.

67

Figure 3.21: Time series of the elements rubidium and lead for the analyzed RDI stages from Moleno from June 20, 2005 17:00 until June 29, 2005 13:00.

68

Figure 3.22: Time series of meteorological data: PM10, PM2.5, N Ox , air pressure, temperature, wind speed, global radiation and precipitation for Moleno from June 20, 2005 17:00 until June 29, 2005 13:00.

69

3.2.4

Selected Key-Playing Elements for Wood Burning and Traffic

To focus tracer elements for the three sources ”wood burning”, ”traffic (nonexhaust as steels, alloys etc.)” and ”traffic (exhaust)”, average diurnal variations were plotted in box plots (figures 3.23, 3.24, 3.25).

As a tracer for wood burning, potassium in the fine mode was chosen as suggested by [26]. Potassium chloride and potassium sulphate are important contents of wood fires, as well potassium salts and potassium nitrates were detected in smoke from burning wood [40, 31]. Fine mode potassium was found in highest concentrations in Moleno in winter, due to an accumulation of emissions from surrounding areas. Much lower concentrations were sampled in Roveredo, lowest concentrations were present in the summer field study in Moleno. The lower concentrations were interpreted as an effect of meteorology, as will be discussed in detail in the following paragraphs.

Manganese in the coarse mode was chosen as a tracer for non-exhaust traffic emissions. It is known to be present in metal PM10-emissions from cars [32].

As for coarse mode potassium, highest concentrations were found in Moleno in the field studies in winter. Slightly lower concentrations were found in the field studies in Moleno in summer, while for the field study in winter in Roveredo approximately half as much manganese was found.

Lead was used as a fuel additive [10] and is still found in low concentrations when sampling car emissions. Therefore, fine mode lead was used as a tracer for exhaust emissions from traffic. Highest fine mode lead concentrations were found in the field studies in Moleno in winter. Half as high concentrations as in winter in Moleno were found in the field study in Moleno in summer. Only slightly lower concentrations were present in Roveredo in winter.

For the traffic tracers manganese and lead, highest concentrations in Moleno in winter 2004 / 2005 were thought to be an effect of accumulation, as it was the case for the wood burning tracer potassium. Lowest concentrations for the traffic tracers were found in Roveredo in winter 2004 / 2005 due to the absence of an intense source like the motorway in Moleno and the presence of a characteristic meteorology.

Potassium as a Tracer for Wood Burning A clear indication for wood burning was found in Roveredo during moring times while fine mode potassium concentrations were increasing from 06:00 (figure 3.23). At 11:00 concentrations reached a maximum. Higher concentrations were present in some afternoons in Roveredo. Some high values were present during periods of cold weather where heating produced more emissions. Slightly lower concentrations were sampled in Roveredo during the late afternoon (boxes 16:00 - 17:00 and 18:00 - 19:00), probably heating was at that time not yet necessary because either temperatures were higher or many people were not at home. Additionally, atmospheric dilution and therewith mixing of the air could have been the reason for lower concentrations. A different effect was attributed to lower concentrations at night: As reported in other studies [41], often high wind speeds at night and in the morning were measured in the valley of Misox where Roveredo is located. The wind has its origin in northern mountains (San Bernardino) and valleys (valley of Calanca).

70

Highest fine mode potassium concentrations were sampled in Moleno in winter 2004 / 2005. This was especially the case between 02:00 and 07:00 which is taken as a result of the accumulation of wood burning emissions and a low mixing of the atmosphere. A dilution of the emissions took place in the afternoon (14:00 - 17:00) due to sunlight: Because of heating up lower air layers and the ground, the air got mixed and the concentrations decreased. With lower intensity of sunlight, again the air is less mixed and concentrations start to accumulate (starting at 18:00 and reaching a maximum between 02:00 and 03:00).

Because of less residential heating and a better dilution of the the atmospheric air (more sunlight), concentrations were in summer in Moleno much lower than in winter. The minimum concentrations between 14:00 and 15:00 were interpreted as a result of a well mixed atmosphere. However, the data set from Moleno in summer contains only data from one week of sampling. Therefore, interpretations of averaged diurnal variations are based on a statistic of only seven days.

Figure 3.23: Box plot of average diurnal variations of potassium fine mode concentrations from field studies in Roveredo and Moleno in winter 2004 / 2005 and from Moleno in summer 2005. Time scale indicates ending times of the two hours sampling of the RDI.

Manganese as a Tracer for Non-Exhaust Traffic Emissions Coarse mode manganese concentrations in Roveredo in winter 2004 / 2005 were mainly influenced by the effect of meteorology (figure 3.24): Winds from the northern mountains clean the air at night and in the early morning [41], which caused lowest concentrations between 02:00 and 05:00. However the slight increase of manganese concentrations in the late morning (increase at 10:00) was 71

interpreted as the resuspension and emission of particles due to traffic on the nearby main road.

The influence of traffic in the morning was clearly visible in the coarse mode manganese variations in Moleno in winter 2004 / 2005. Highest concentrations were sampled from 06:00 to 09:00 which gave a clear indication to morning traffic. During the afternoon, the common dilution, caused by the mixing of the atmosphere and higher sunlight intensities was as well observed for coarse mode manganese concentrations: Lowest concentrations were sampled between 14:00 and 17:00. Accumulation at the beginning of the night was as well the case. An increase of the concentrations was registered starting from 18:00.

Much lower concentrations in summer in Moleno were caused by the atmospheric dilution. Although the concentrations were rather low, slight peaks of traffic emissions were visible: In the morning, an increase of the concentrations (starting at 06:00) was observed. As well at 16:00, concentrations increased.

Median values lying above average values (10:00 - 11:00 and 16:00 - 21:00) indicated periods of high concentrations of manganese from traffic emissions and particle resuspension due to traffic.

Figure 3.24: Box plots of average diurnal variations of manganese coarse mode concentrations from field studies in Roveredo and Moleno in winter 2004 / 2005 and from Moleno in summer 2005. Time scale indicates ending times of the two hours sampling of the RDI.

Lead as a Tracer for Exhaust Traffic Emissions Similarly to the variation of coarse mode manganese, the fine mode lead concentrations were in Roveredo in winter 2004 / 2005 mainly influenced by meteorology (figure 3.25): Wind caused low concentrations during nights and early morning hours (00:00 - 09:00), while slightly higher lead concentrations from 72

10:00 are thought to have their origin in an increase in traffic. Higher concentrations were present from 10:00 until 19:00, thereafter a decrease took place.

Morning traffic emissions were visible in the variation of fine mode lead in Moleno in winter 2004 / 2005. Remarkably higher concentrations were already present between 04:00 and 07:00, a second period of high lead emissions was registered between 10:00 and 11:00. Atmospheric dilution and sunlight caused low concentrations from 14:00 to 17:00. An increase in concentration thereafter was taken for more traffic and less mixing of the atmosphere.

Fine mode lead concentrations in Moleno in summer showed a similar diurnal variation as manganese: Rather constant concentrations during the night and from 06:00 to 09:00 high outlier concentrations due to morning traffic. In the afternoon, a dilution of the concentrations due to a better mixing of the atmosphere was found. Concentrations started to increase at 18:00 when sunlight was less intense and an accumulation of the emissions took place.

Figure 3.25: Box plots of average diurnal variations of lead fine mode concentrations from field studies in Roveredo and Moleno in winter 2004 / 2005 and from Moleno in summer 2005. Time scale indicates ending times of the two hours sampling of the RDI.

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3.2.5

Validation of the Data

To validate the data, sampled elemental concentrations from the field study in Moleno in winter 2004 / 2005 were compared with data from the automatic monitoring station in Moleno. Therefore, a simplyfied mass balance was performed. From the data set of the monitoring station, concentrations of PM2.5 were subtracted from concentrations of PM10 and with that, the RDI’s coarse mode size fraction (2.5 − 10 µm) was compared. For some coarse mode elements that were sampled in Moleno in winter 2004 / 2005, corresponding oxygen and sodium concentrations were calculated. With that it was reached, that in the mass balance the predominant chemical coarse mode species were present: N aCl, CaO, SiO2 , F e2 O3 . Silicon was estimated to be twenty times more frequent than titanium. All other analyzed coarse mode elements were added up as a single fifth species.

A time series was plotted which compared the mentioned species calculated from the RDI’s elemental concentrations and the differences of PM10 and PM2.5 concentrations of the automatic monitoring station. A comparing time series for the winter data set from the field study in Moleno was plotted (figure 3.26).

Big differences were found for some dates in the time series (e.g. January 31, 2005 19:00: concentrations of the automatic monitoring station were nine times higher than concentrations calculated from the RDI samples) while on other dates, both data sets corresponded rather good (February 1, 2005 11:00: concentrations of the automatic monitoring station was only 1.3 times higher than concentrations calculated from the RDI samples). The time series showed as well, that N aCl is the most abundant species in the coarse mode and present in Moleno mainly from deicing salts. The fact, that potassium is present in low concentrations compared to the focussed compounds, indicated high concentrations of organic compounds from wood burning which were not found with the presented method.

An averaged contribution of every one of the calculated compounds was shown in a pie chart (figure 3.27). The whole pie discribes the averaged concentration of the automatic monitoring station (difference between PM10 and PM2.5). The yellow part of the pie indicates the average difference to the compounds found by the RDI. The pie chart shows, that only 24.27 % of the total aerosol is described by the elements sampled by the RDI. Again it was assumed that N aCl, CaO, SiO2 and F e2 O3 were the most abundant species in the coarse mode. N aCl contributed in average most of all to the analyzed concentrations with 9.87 % of the total coarse mode concentration. Only slightly lower average concentrations were sampled for F e2 O3 (7.27 %). Each one from the compounds CaO, SiO2 , and as well all other analyzed elements contributed only less than 3 % to the total aerosol concentration.

The time series showed that differences in concentration between the automatic monitoring station and the RDI were not always in the same amount. For that, the differences were visualized in a box plot showing the distribution of the concentrations for all days, grouped according to the time of the day (figure 3.27). With a one-way ANOVA (OriginPRO 7.5, means comparison using Bonferroni test) significant differences between two boxes were checked. It was shown that differences in the concentrations of the monitoring station and the species calculated from the RDI’s elemental data differed significantly between certain times during the day: Ending sampling periods between 00:00 and 01:00

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varied significantly from sampling times ending at 02:00 and 03:00, at 04:00 and 05:00, at 06:00 and 07:00, at 08:00 and 09:00, at 10:00 and 11:00, at 12:00 and 13:00 and as well at 14:00 and 15:00 (for all seven comparisons p < 0.05). As well, a significant difference from sampling times ending at 06:00 and 07:00 from sampling times ending at 20:00 and 21:00 was shown (p < 0.05). However, differences in concentrations had a wide range for sampling times ending at 20:00 and 21:00.

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Figure 3.26: Mass balance of coarse mode concentrations in the time series from sampling in Moleno in winter 2004 / 2005. Oxygen and sodium was added by calculating all the required masses to the RDIâ&#x20AC;&#x2122;s elemental concentrations of chlorine, calcium and iron. Silicon was estimated to have 20 times higher concentrations than titanium. â&#x20AC;?Analyzed restâ&#x20AC;? summarizes all other analyzed coarse mode elemental concentrations from the RDI. The difference between PM10 and PM2.5 was calculated from concentrations of the automatic monitoring station.

Figure 3.27: Top: Mass balance of averaged coarse mode concentrations from sampling in Moleno in winter 2004 / 2005. Oxygen and sodium was added by calculating all the required masses to the RDIâ&#x20AC;&#x2122;s elemental concentrations of chlorine, calcium and iron. Silicon was estimated to have 20 times higher concentrations than titanium. â&#x20AC;?Analyzed restâ&#x20AC;? summarizes all other analyzed coarse mode elemental concentrations from the RDI. The whole pie stands for the concentration of the difference between PM10 and PM2.5 of the automatic monitoring station. Bottom: Differences in concentration between the automatic monitoring station and the RDI seemd to differ during the whole day not in the same amount. X-axis scale stands for ending times of RDI sampling periods. N = 20 for each box.

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3.3

Field Study on Jungfraujoch

The long term time series of the Jungfraujoch field study was not yet analyzed when this thesis was finished. Only a testing wheel that was sampling for one day (table 2.7) was analyzed in the second session at HASYLAB. By looking at an output spectra from the beamline detector, almost none of the analyzed elements were visible on the Jungfraujoch wheel. A spectra was compared in figure 3.28 with the spectra of an empty analysis wheel, with the one of a greased and an ungreased blank and with a sample from Moleno (from one of the field studies in Ticino). In a zoomed view, very low concentrations of iron and even lower concentrations of nickel were apparent. The low concentrations of iron were mostly present in the two large size fractions (2.5 − 10 µm and 1 − 2.5 µm). For nickel in the fine mode, concentrations of about half of the difference between the sample from Moleno and the empty analysis wheel was expected.

Although the sampling wheel from the four weeks sampling duration was not analyzed, it was brought to PSI. Accidentially the RDI was stopped during the field study after about two weeks of aerosol sampling without having stopped the pump. This resulted in a time series of only two weeks instead of four weeks.

The precise time when the RDI was unplugged will be detected by analyzing the samples. The location of the last aerosol sample on the sampling wheels showed an extremely high amount of dust because the running pump brought aerosol from two weeks on one single spot (figure B.4 in the appendix)!

Particles of the size between 0.1 − 1 µm have the longest residence time in atmosphere among all size fractions. Trace element particles of this size can have an age of 100 days [48]. Therefore, especially on Jungfraujoch, where no direct emission sources are expected, very old trace elements were expected on the third stage of the RDI.

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Figure 3.28: Top: Overview spectra comparing an empty analysis wheel and a greased and an ungreased blank mylar with spectra from Jungfraujoch samples of three size fractions and a coarse mode sample from one of the field studies in Ticino. Bottom: A zoomed view showed that on Jungfraujoch a very low concentration of iron â&#x20AC;&#x201D; mostly in coarse mode â&#x20AC;&#x201D; and an even lower concentration of nickel, most of it in the fine mode, was sampled.

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Chapter 4

Conclusions and Outlook 4.1

Sampling with the Rotating Drum Impactor (RDI)

The sampling method with the RDI was found to be a suitable technique for sampling highly time-resolved elemental compositions of aerosols. To get a better overview of the total aerosol composition, results should be compared to data from other instruments. A comparison with a mass spectrometer’s sulphate data from the wood burning study at EMPA showed a good consistency of the data. A comparison with data from an automatic monitoring station in Ticino showed that only about 25 % of the total aerosol was registered by the RDI. Probably a larger quantity of carbonaceous aersosol was not found with this method — further comparisons, e.g. with EC/OC data, could confirm this hypothesis.

Several parameters of the RDI should be checked to avoid uncertainties in the sampling. An incorrect position of the orifice-slits, which is sometimes caused by setting up or cleaning the device, could be avoided with a different construction of the orifice-slits. To evaluate the correct position and verify the precise cutoffs of the three stages, an artificial aerosol should be produced.

Comparison of RDI samples and simultaneously performed sampling with an electrical low pressure impactor (ELPI, DEKATI Ltd.) will clearly determine the precision of the RDI cutoffs. For some samples, distances between two samples on the mylar film were not precisely the same. This indicated a problem with the step motor of the RDI. The device should be checked to make sure that the analysis at the beamline with the goniometer leads to valid results: With precise distances between two samples errors due to analyzing only a small part of the aerosol sample or the empty mylar film between two samples can be eliminated. Problems with earthing the RDI made it difficult to configure the sampling parameters with the laptop. Especially for the RDI, which was used in the field studies on Jungfraujoch, this was an issue. This task will be easily solved by adding a proper earthing to the RDI’s power cable.

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4.2

Laboratory Study: Wood Burning at EMPA

The pattern, that higher concentrations were present in the coarse mode than in the fine mode size fractions was found for most of the elements in the wood burnig study. Key-playing elements in the experiments were sulphur, chlorine, potassium and calcium. Other elements were present in much lower concentrations and therefore not discussed in detail. They were interpreted as impurities of the used stoves. Potassium — often mentioned to be a tracer for wood burning — was present in highest concentrations in the fine mode. This was confirmed in experiments with all tree species. A comparison between beech logs and chips experiments showed significant differences in calcium’s stage B (1 − 2.5 µm) and in the fine mode of potassium. A comparison between log burning experiments of larch, white fir and oak showed significant differences in some elements and size fractions as well: Oak and white fir had significantly different sulphur emissions in the RDI’s stage B (1 − 2.5 µm), larch and white fir had significantly different chlorine emissions in the fine mode while potassium emissions differed significantly between larch and white fir in the fine mode and between oak and white fir in the fine mode. Different elements which were present in the same size fraction gave a hint to expected substances emitted by the wood burning experiments: CaSO4 , CaCl2 , K2 SO4 , KCl. However, to distinguish tree species by their elemental fingerprint, sampled with the presented method, was not possible due to different reasons.

The main problem was certainly that the firing cycles showed an irregular pattern of elemental concentrations. To gain more information about certain tree species, more experiments with the same firing parameters should be done and other parameters as e.g. the weight of the burned wood or the temperature of the chimney should remain constant. To distinguish different wood types better during wood burning experiments, organic tracers such as levoglucosan should be focussed [20]. Substituted syringols are more present in hardwood than in softwoods, while resin acids are significant components of softwood emissions. For two tree species which are present in the northeastern United States, specific organic tracers are presented [19]. However, the question remains which compounds are proper tracers for common tree species which are fired in Switzerland.

In further wood burning studies, aerosol from small outdoor campfires could be sampled to get an insight into the elemental composition of wood burning smoke performed in less optimized conditions.

Minimal detection limits (MDL) given by the analysis especially caused high uncertainties for concentrations of coarse modes of some elements (e.g. sulphur and chlorine). However, elements besides the ”key players” (which had higher atomic numbers) were less influenced by that, because the MDL were decreasing with the increase of the atomic numbers. Therefore, the presence of the elements besides the key-playing elements can’t be neglected.

4.3

Field Studies in Ticino

As shown in studies using the same method [13], weather conditions have an important influence on the aerosol concentrations. By comparing the climate data from Roveredo and Moleno as done for the time series, it was possible to get

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an insight into the influence of the weather conditions. Measurements pointed out that in Roveredo a daytime atmospheric instability was present which caused a mixing of the air layers while temperature inversions were present in periods of cold and cloudy weather or at night [47]. Diurnal patterns of temperature inversion breakup were observed in other valleys in Switzerland as well [34].

A study in a Greek valley reports, that characteristics like length, depth and the ridgetop width of the valley play an important role in the mechanism of the destruction of the temperature inversion [24]. The resulting pattern of fine particles which have their origin from anthropogenic activities (e.g. residential wood burning) but are as well strongly influenced by the mentioned weather conditions is known as well from other studies [15].

In Roveredo, often clean air from northern mountains (e.g. San Bernardino) and valleys (e.g. valley of Calanca) is able to dilute the inversion and therewith the concentrations of air pollutants as well. This is especially the case during the nights or early in the morning. In Roveredo smaller amounts of air contribute to the inversion than in Moleno, therefore a diurnal dilution is more common than in Moleno. This was the case for the period just before January 20, 2005 (figures 3.11, 3.12, 3.13, 3.14). A majority of the elements showed low concentrations on that date.

Moleno is situated in a much wider valley, air masses are often too large to be diluted. That is why concentrations were often higher in Moleno than in Roveredo. However, strong and windy foehn coming from the north can cause very low concentrations in Moleno during winter as it was the case during February 2 or February 3, 2005 (figures 3.15, 3.16, 3.17, 3.18).

Roveredo is assumed to be close to wood burning sources. The concentrations of elements as fine mode potassium (commonly present in wood burning smoke) in the atmosphere showed variations mainly influenced by the sources and the atmospheric dilution. Moleno is further away from immediate wood burning sources, however, in Moleno residential wood burning is as well performed on a smaller scale. While assuming a constant concentration in the atmosphere in Moleno, the diurnal fine mode potassium variation showed mainly the pattern of the atmospheric dilution. Higher concentrations of fine mode potassium in Moleno are caused due to the more stable air masses, as mentioned. Probably, air masses from the valley of Misox are brought to Moleno and are accumulated there. The discussed situation was mainly found in the data from the winter field studies. A scheme of fine mode potassium sources and the main effects of the weather conditions is shown in figure 4.1.

High concentrations of coarse mode chlorine, calcium or potassium during sunny weather were caused due to particle resuspension by cars. Especially in Moleno, next to the motorway, this is certainly of importance: Dust and plant material which is placed on the road during the night can be resuspended during periods of high traffic in the morning. This can cause periods of very high potassium concentrations in the fine mode as sampled in the morning of June 27, 2005 in Moleno where a peak of coarse mode potassium (09:00) and N Ox (07:00) was present (figures 3.19, 3.20, 3.21, 3.22).

Emissions from traffic were present in high concentrations in Moleno due to the motorway. A diurnal concentration pattern of fine mode lead, which is connected to traffic emissions, showed peaks from morning and afternoon rush hour traffic. Between the two periods of heavy traffic, the atmosphere was mixed on some days. In Roveredo the main road from San Bernardino to Bellinzona 82

caused traffic emissions, which is seen in the diurnal pattern of fine mode lead.

Due to the presence of wind from the valley of Calanca and San Bernardino, as mentioned, the emissions were lower than in Moleno. The dilution caused by wind was mainly sampled during the night. The discussed situation was found in the data from field studies in winter and in summer. A scheme of fine mode lead sources and the main effects of the weather conditions is shown in figure 4.2.

To gain a more detailed insight into the influence of different weather conditions on the presence of the elemental composition, additional parameters should be included in a further analysis: Wind direction, traffic data (number of cars, number of trucks). Additionally, some sources as an assumed source of vanadium in the north of Moleno should be detected by backtrack air masses.

A fourth data set of a summer field study in Roveredo is a different approach in evaluating different weather conditions. This would give more insight into seasonal fluctuations of the aerosol, influenced by the weather conditions in Roveredo.

To clearly assign the effects of traffic, other parameters should be included in a further analysis. Only the number of cars that were passing the sampling site in Moleno will give a clear answer if there was really more traffic or only different traffic (e.g. short periods in winter where a lot of trucks were passing).

An intercomparison between different measuring devices is a good validation for the method. Due to the fact, that only about 25 % of the total aerosol was analyzed by the presented method, high amounts of carbonaceous aerosol are probably present from wood burning. That is why a potential is seen in including data from EC/OC measurements in Ticino. A different approach to characterize the components of the aerosol is the analysis of the present ratio of 14 C/12 C or 14 C/13 C. With this method, the two origins of carbonaceous aerosol can be distinguished: An attribution either to recently living plant material or fossil energy sources is possible. Only with the complete, quantitative characterization of the aerosol, the contribution of wood burning emissions to emissions from traffic is possible.

Emissions from a dynamometer-testbench, sampled by the RDI, could be compared in a further study to evaluate the elemental content of car emissions.

Suggestions for the presence of trace elements in fuel (e.g. lead) could be proved with this method. A better qualitative source apportionment of elemental peaks in the time series would be possible.

83

84

Figure 4.1: Scheme of fine mode potassium sources in Roveredo and Moleno in winter. Weather conditions cause an accumulation of the emissions in Moleno. In Roveredo the emissions were commonly diluted by wind from the valley of Calanca and San Bernardino during the night and in the early morning.

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Figure 4.2: Scheme of fine mode lead sources in Roveredo and Moleno in winter and in summer. Weather conditions and rush hour traffic cause an accumulation of the emissions in Moleno. Although a main road is passing Roveredo, traffic emissions are lower due to the presence of wind from the valley of Calanca and San Bernardino during the night and in the early morning.

4.4

Field Study on Jungfraujoch

Although the samples from the testing wheel on Jungfraujoch showed only the presence of a low amount of a few elements, the concentrations should be calculated. Analysis of the long term sampling will show periods of higher concentrations, as clearly visible on the mylar. The accidentially stopped sampling could bear some potential as well: Because of having sampled on one spot of the mylar aerosol during two weeks, much higher concentrations are expected on this spot in all three size fractions. This could lead to a better detection of the present elements. A modified analysis setup with the ability to analyze silicon could bear a different potential: Because of yellowish spots on the mylar in the coarse mode, silicon of dust is expected to be sampled (e.g. dust from the Sahara desert). With the ability to detect silicon, the presence of dust could be proven.

Comparisons with other devices (e.g. a simultaneously operated EC/OC) could give an insight into the presence of carbon. Carbonaceous aerosol is thought to be sampled because of visible grey and black spots on the mylar.

4.5

Analysis with Synchrotron-XRF

The extrapolation factor that took the fuzzy edges of the aerosol samples and the synchrotron-beam into consideration was an important issue in calculating elemental concentrations. Zoomed views (photos made with a digital camera on a microscope) of the beam dimensions on the coloured thermo sensitive paper were hard to process. A simple, automated procedure to evaluate the precise size of one sample is not yet found. Image processing tracer softwares are seen as a potential in defining the beam dimensions.

Still, a fast and precise setup procedure for the analysis at the beamlines has not been found. For future analysis, an automation would shorten the time of setting up and aligning the samples to the beam.

A well working positioning procedure was defined as follows (figure B.6 in the appendix): 1. Adjust vertical positions of detector and samples.

2. Adjust detector and sample wheel: detector in 45â&#x2014;Ś position to the tangential line of the wheel (figure 2.3).

3. Put a blank wheel on the goniometer with a copper filament at the tangential position.

4. Pull the detector back so it doesnâ&#x20AC;&#x2122;t get overloaded by the following steps.

5. Open the beam shutter.

6. Move main table horizontally until the detected copper peak reaches a maximum. This determines the proper positioning.

7. Change to aerosol sample wheels, push the detector nose as close as possible to the source.

8. Put an X-ray sensitive paper on a sample wheel to determine actual beam position. The mark of the beam on the paper has to be in the middle of the wheel slit.

86

To get proper beam dimensions, the following procedure for slitting the beam is suggested: 1. Measure a sample wheel with a rather high aerosol loading.

2. Slit beam horizontally to the desired width and fix it there.

3. Slit beam vertically to optimize the detector count rate.

4. Check the beam size with a X-ray sensitive paper.

Next steps in improving the experimental setup for an optimized analysis at the SLS-beamline is suggested as follows: Put the goniometer on a z-table for a less improvised vertical adjustment (a manual ”Laborboy” may be sufficient).

Think about a better collimator system. The best material of the collimator is not yet found (this influences the detected elemental composition).

The question remains how to move the detector as close as possible to the sample without destroying it.

Design a helium or a vacuum chamber to exclude the effect of ambient air.

Helium is possibly easier for sample change.

Optimize settings for the detector.

Adaptation of the software scanning routine: Fix bugs of the current version and store all beamline parameters which are of possible interest in the parameter file.

To improve the scanning procedure, an adaptation in the software could improve the results. The following idea drafts a possible algorithm: 1. Adjust the beam size to the exact RDI slit width (done manually).

2. Scan the RDI wheels in tiny steps and simply record the ROI of an abundant element.

3. Determine the intensity peak maxima (that is where the spot centers are) and record their position (goniometer position).

4. Return to the beginning of the tape and scan the wheel once more according to the array of given positions.

In this way, the scan procedure is expected to yield a considerable increase in precision and accuracy of the entire method. This algorithm should be easy to integrate into the software routine.

87

Chapter 5

Acknowledgements Thank you,... ...Urs Baltensperger. Only his offer, to write a diploma thesis at the Labor f¨ ur Atmosph¨ arenchemie, made the interesting work possible. ...Andr´ e Pr´ evˆ ot. A lot of hints and new ideas for analyzing results made us forget the time during the talks... ...Nicolas Bukowiecki. One of the essential things in doing a thesis is an excellent supervisor. The latter was indeed the case — the collaboration was a great pleasure for me! ...staff of the EMPA wood burning laboratory study. Although being a freshman in sampling aerosols, everything turned out to run very well due to the friendly help of all involved people! Thanks to Rami Alfarra for organizing the PSI-part of the laboratory study and to Daniel Schreiber and Martin Mohr for the technical part of EMPA. ...staff of the field studies in Ticino. Car shuttle-service to the motorway station, helping to set up the infrastructure and troubleshooting in cases of blackouts were issues I was lucky to get help. The campaign only worked properly due to the good organization by Jisca Sandradewi. ...staff of the field study on Jungfraujoch. The spontaneous idea to sample aerosols on Jungfraujoch with the RDI was fully supported by Julie Cozic.

Her advice from experiences of several sampling campaigns in the High Altitude Research Station were of importance during a phase of nasty health troubles...

During troubles with programming the RDI on Jungfraujoch, I was lucky to get help from G¨ unther Wehrle. ...staff of the HASYLAB-beamline L. Gerald Falkenberg and Karen Rickers helped installing the setup at the beamline. As well we got important hints for the beam parameters and the handling of the controls for the analysis.

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...Ferenc Heged¨ us. His huge experience in synchrotron-XRF was important for the choice of the best analysis parameters in the two sessions in Hamburg.

Additionally, with creative new ideas the new calibration technique was performed and documented by him. ...staff of the SLS-beamline 5L. It was due to several hints of Daniel Grolimund that the operation of the installed goniometer could work properly and a testing session could be performed at the SLS. Rafael Abela helped to set up the analysis geometry and provided important ideas in aligning the aerosol samples to the synchrotron beam. ...my dear officemates. With the help of Robin Riedmann, it was possible to solve a couple of language problems in this thesis. Stefan Zeller kindly helped with problems concering statistics and LATEX. He amply provided sophisticated code fragments all the time... ...Dieter Studer. Who could have better found language mistakes than a student in English Studies? Thanks a lot for proofreading this thesis!

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Bibliography [1] Website u ¨ber die Aerosolforschung des PSI. http://aerosolforschung.web.psi.ch, visited 22 July 2005.

[2] Website der Synchrotron Lichtquelle Schweiz (SLS) am PSI. http://sls.web.psi.ch/, visited August 16, 2005.

[3] Website des Hamburger Synchrotronstrahlungslabor (HASYLAB). http://www.hasylab.de/, visited August 16, 2005.

[4] Website des Kantons Graub¨ unden. http://www.gr.ch/, visited August 9, 2005.

[5] Website des Umwelt-und Gesundheitsschutz Z¨ urich. http://www3.stzh.ch/internet/ugz/home.html, visited July 27, 2005.

[6] Wikipedia die freie Enzyklop¨adie. http://de.wikipedia.org, visited July 18, 2005.

[7] Search.ch The Swiss Search Engine. http://www.map.search.ch/, visited September 6, 2005.

[8] WebElements Periodic Table. http://www.webelements.com, visited July 20, 2005.

[9] Barrefors G., Petersson G. Assessment of ambient volatile hydrocarbons from tobacco smoke and from vehicle emissions. Journal of Chromatography, 643:71–76, 1993.

[10] Biggins P. D. E., Harrison R. M. Atmospheric chemistry of automotive lead. Environmental Science and Technology, 13 (5):558–565, 1979.

[11] Blander M. The inorganic chemistry of the combustion of aspen wood with added sulphur. Biomass and Bioenergy, 12 (4):289–293, 1997.

[12] Bukowiecki N. P., Hill M., Gehrig R., Zwicky C. N., Lienemann P., Heged¨ us F., Falkenberg G., Weingartner E., Baltensperger U. Trace metals in ambient air: Hourly size-segregated mass concentrations determined by synchrotron-xrf. Environmental Science and Technology, 39:5754–5762, 2005.

[13] Bukowiecki N. P., Weingartner E., Baltensperger U. Verifikation der PM10-Emissionen des Schienenverkehrs - Schlussbericht Teil PSI. Technical report, Paul Scherrer Institut, 5232 Villigen PSI, April 2005.

90

[14] Councell T. B., Duckenfield K. U., Landa E. R., Callender E. Tire-wear particles as a source of zinc to the environment. Environmental Science and Technology, 39 (15):4206–4214, 2004.

[15] Currie L. A. Improved atmospheric understanding through exploratory data analysis and complementary modeling: the urban K-Pb-C system.

Atmospheric Environment, 28 (8):1359–1369, 1994.

[16] Dockery D. W., Pope C. A., Xu, X. P., Spengler, J. D., Ware, J. H., Fay M. E., Ferris B. G., Speizer F. E. An association between air-pollution and mortality in 6 United States cities. New England Journal of Medicin, 329:1753–1759, 1993.

[17] Donaldson K., Beswick P. H., Gilmour P. S. Free radical activity associated with the surface of particles: a unifying factor in determining biological activity? Toxicology Letters, 88:293–298, 1996.

[18] Donaldson K., Li X. Y., MacNee W. Ultrafine (nanometre) particle mediated lung injury. Journal of Aerosol Science, 29 (5):553–560, 1998.

[19] Fine P. M., Cass G. R., Simoneit B. R. T. Chemical characterization of fine particle emissions from fireplace combustion of woods grown in the northeastern United States. Environmental Science and Technology, 35 (13):2665–2675, 2001.

[20] Fine P. M., Cass G. R., Simoneit B. R. T. Chemical characterization of fine particle emissions from the fireplace combustion of woods grown in the southern United States. Environmental Science and Technology, 36 (7):1442–1451, 2002.

[21] Fine P. M., Cass G. R., Simoneit B. R. T. Chemical characterization of fine particle emissions from the fireplace combustion of wood types grown in the midwestern and western United States. Environmental Engineering Science, 21 (3):387–409, 2004.

[22] Fine P. M., Cass G. R., Simoneit B. R. T. Chemical characterization of fine particle emissions from the wood stove combustion of prevalent United States tree species. Environmental Engineering Science, 21 (6):705– 721, 2004.

[23] Gumbleton, M. Caveolae as potential macromolecule trafficking compartments within alveolar epithelium. Advanced Drug Delivery Reviews, 49:281–300, 2001.

[24] Helmis C. G., Asimakopoulos D. N., Deligiorgi D. G. Some observations on the destruction of the morning temperature inversions in a large and broad mountain valley. Journal of Applied Meteorology, 29:396–400, 1990.

[25] Jordi B., Bundesamt f¨ ur Umwelt, Wald und Landschaft BUWAL. Feinstaub in der Atemluft schleicht sich heimlich ins Blut. UMWELT, 2:12– 16, 2000.

91

[26] Khalil M. A. K., Rasmussen R. A. Tracers of wood smoke. Atmospheric Environment, 37:1211–1222, 2003.

[27] Kittelson D. B., Johnson J., Watts W., Wei Q., Drayton M., Paulsen D., Bukowiecki N. Diesel aerosol sampling in the atmosphere.

SAE Technical Paper Series, No. 2000-01-2122, 2000.

[28] K¨ unzli N., Kaiser R., Medina S., Studnicka M., Chanel O., Filliger P., Herry M., Horak F. Jr., Puybonnieux-Texier V., Qu´ enel P., Schneider J., Seethaler R., Vergnaud J-C., Sommer H. Publichealth impact of outdoor and traffic-related air pollution: a european assessment. The Lancet, 356:795–801, 2000.

[29] Kreuter, J. Nanoparticulate systems for brain delivery of drugs. Advanced Drug Delivery Reviews, 47:65–81, 2001.

[30] Lee P. K. H., Brook J. R., Dabek-Zlotorzynska E., Mabury S. A. Identification of the major sources contributing PM2.5 observed in Toronto. Environmental Science and Technology, 37 (21):4831–4840, 2003.

[31] Li J., P´ osfai M., Hobbs P. V., Buseck P. R. Individual aerosol particles from biomass burning in southern Africa: 2. compositions and aging of inorganic particles. Journal of Geophysical Research, 108 (D13):20(1)– 20(12), 2003.

[32] Lough G. C., Schauer J. J., Park J., Shafer M. M., Deminter J. T., Weinstein J. P. Emissions of metals associated with motor vehicle roadways. Environmental Science and Technology, 39 (3):826–836, 2005.

[33] Mandal B. K., Suzuki K. T. Arsenic round the world: a review. Talanta, 58:201–235, 2002.

[34] M¨ uller H., Whiteman C. D. Breakup of a nocturnal temperature inversion in the Dischma valley during DISKUS. Journal of Applied Meteorology, 27:188–194, 1988.

[35] Musthapa M. S., Lohani M., Tiwari S., Mathur N., Prasad R., Rahman Q. Cytogenetic biomonitoring of Indian women cooking with biofuels: Micronucleus and chromosomal aberration tests in peripheral blood lymphocytes. Environmental and Molecular Mutagenesis, 43 (4):243–249, 2004.

[36] Natusch D. F. S., Wallace J. R. Urban aerosol toxicity: the influence of particle size. Science, 186:695–699, 1974.

[37] Neufeld L. M., Haas J. D., Ruel M. T., Grajeda R., Naeher L. P. Smoky indoor cooking fires are associated with elevated hemoglobin concentration in iron-deficient women. Revista panamericana de salud publica - pan American journal of public health, 15 (2):110–118, 2004.

[38] Newhook R., Hirtle H., Byrne K., Meek M. E. Releases from copper smelters and refineries and zinc plants in Canada: human health exposure and risk characterization. Science of the total Environment, 301:23–41, 2003.

92

[39] Olsson M., Kjallstrand J. Emissions from burning of softwood pellets.

Biomass and Bioenergy, 27 (6):607–611, 2004.

[40] P´ osfai M., Simonics R., Li J., Hobbs P. V., Buseck P. R. Individual aerosol particles from biomass burning in Southern Africa: 1. compositions and size distributions of carbonaceous particles. Journal of Geophysical Research, 108 (D13):19(1)–19(13), 2003.

[41] Pr´ evˆ ot A. S. H., Dommen J., B¨ aumle M., Furger M. Diurnal variations of volatile organic compounds and local circulation systems in an alpine valley. Atmospheric Envirnoment, 34:1413–1423, 2000.

[42] Ramdahl T. Retene - a molecular marker of wood combustion in ambient air. Nature, 306:580–582, 1983.

[43] Ramdahl T., Schjoldager J., Currie L. A., Hanssen J. E., Møller M., Klouda G. A., Alfheim I. Ambient impact of residential wood combustion in Elverum, Norway. The Science of the Total Environment, 36:81–90, 1984.

[44] Seinfeld J. H., Pandis S. N. Atmospheric Chemistry and Physics. John Wiley & Sons, Inc., 1998.

[45] Sheffield A. E., Gordon G. E., Currie L. A., Rieder G. E. Organic, elemental, and isotopic tracers of air pollution sources in Albuquerque, NM. Atmospheric Environment, 28 (8):1371–1384, 1994.

[46] Simpson C. D., Paulsen M., Dills R. L., Liu L.-J. S., Kalman D. A. Determination of methoxyphenols in ambient atmospheric particulate matter: Tracers for wood combustion. Environmental Science and Technology, 39 (2):631–637, 2005.

[47] Tiefbauamt Graub¨ unden. Umfahrung Roveredo: Luftbelastung 1988 bis 2004. Technical report, Amt f¨ ur Natur und Umwelt Graub¨ unden, Amt f¨ ur Natur und Umwelt, G¨ urtelstrasse 89, 7000 Chur, September 2004.

[48] Utsunomiya S., Jensen K. A., Keeler G. J., Ewing R. C. Direct identification of trace metals in fine and ultrafine particles in the Detroid urban atmosphere. Environmental Science and Technology, 38 (8):2289– 2297, 2004.

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Appendix A

Schemes

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Figure A.1: Scheme of all the sampling devices that were involved in the wood chips experiments at the laboratory study at EMPA D¨ ubendorf.

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Figure A.2: Scheme of all the sampling devices that were involved in the log experiments at the laboratory study at EMPA D¨ ubendorf.

Figure A.3: Layout of the DORIS-beamlines at Hamburger Synchrotronstrahlungslabor (HASYLAB). Analysis of the samples were performed at the beamline L. In the upper left corner of the scheme, the electrons from the DORIS and PETRA storage ring enter the DORIS III-ring that leads the synchrotron radiation to the different experimental hutches.

Figure A.4: Layout of the beamlines at the Swiss Light Source (SLS). Testing the automation and the data acquisition was performed at the beamline X05LA (microXAS).

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Appendix B

Photos

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Figure B.1: Opened RDI box with the three stages on the right: Coarse mode particles impact on the upper sampling wheel, particles with 1 â&#x2C6;&#x2019; 2.5 Âľm in diameter on the sampling wheel in the middle and fine mode particles impact on the sampling wheel on the bottom.

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Figure B.2: Aerial overview pictures of sampling sites. Top: Part of Ticino with the sampling sites Roveredo and Moleno (red circles). In the lower left corner: Parts of the Lake Maggiore. In the lower right corner the borderline to Italy. Bottom: Central Switzerland with the sampling site on Jungfraujoch (red circle). In the upper part of the picture: The lake of Thun and the lake of Brienz (adapted from [7]).

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Figure B.3: Aerial pictures of sampling sites. Top: Sampling site in the village of Roveredo (marked with a red circle). Bottom: Sampling site on a motorway station near the village Moleno (marked with a red circle)(adapted from [7]).

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Figure B.4: Top: RDI samples from Jungfraujoch. Due to an accidental stop of the RDI, the pump brought aerosol of two weeks on one single spot on the mylar film. RDI stage A (coarse mode) on the top of the stack, RDI stage C (fine mode) at the bottom of the stack. Bottom: RDI stage A (coarse mode) in a zoomed view.

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Figure B.5: Panorama-pictures of analysis sites. Top: Experimental hutch of SLS-beamline 5L at PSI Villigen (90◦ -panorama). Bottom: Experimental hutch of DORIS-beamline L at HASYLAB/DESY Hamburg (180◦ -panorama).

Figure B.6: Top: Picture and description of the analysis setup from the testing session at the SLS-beamline. Bottom: A piece of thermo sensitive paper was used to estimate the beam dimensions and position.

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