Department of Earth Sciences

Licentiate Thesis 2015

Atmospheric Dispersion Modelling of Volcanic Emissions

Adam Dingwell

Abstract

Gases and particles released by volcanoes pose a serious hazard to humans and society. Emis-sions can be transported over long distances before being reduced to harmless concentrations.Knowing which areas are, or will be, exposed to volcanic emissions is an important part inreducing the impact on human health or society. In this thesis, the dispersion of volcanic emis-sions is studied using a set of atmospheric models. Two case studies have been performed, onestudying potential ash emission from future eruptions on Iceland, and a second covering SO2emissions from Mt. Nyiragongo in D.R. Congo.

The first study covers long range (∼1,000 km) dispersion of fine ash from explosive erup-tions. Three years of meteorological data are used to repeatedly simulate five eruption scenarios.The resulting concentrations of airborne ash at different times relative the onset of each eruptionis compared to current and previous threshold concentrations used by air traffic controllers. Theash hazard showed a seasonal variation, with a higher probability of efficient eastward transportin winter, compared to summer; summer eruptions pose a more persistent hazard.

In the second study, emissions of SO2 from passive degassing at Mt. Nyiragongo is studiedover a one–year period. The meteorological impact on the dispersion is studied by assigninga fixed emission source. Furthermore, flux measurements from the remote sensing data areused to improve the description of the emission source. Gases are generally transported to thenorth-west in June–August and to the south-west in December–January. A diurnal variation dueto land breeze around lake Kivu contributes to high concentrations of SO2 along the northernshore during the night. Daily averaged concentrations in the city of Goma (∼15 km SW of thesource) exceeded the European Union’s air quality standard (125 μg/m3) for 120-210 days overa one year period.

Sammanfattning

Gas- och partikelutslapp fran vulkaner utgor en fara for manniskor och for vart samhalle. Ut-slappen kan transporteras over langa avstand innan de reduceras till ofarliga halter. Att kannatill vilka omraden som utsatts, eller kommer utsattas, for utslappen ar ett viktigt verktyg for attminska paverkan pa folkhalsa och samhallet. I den har avhandlingen studeras spridningen av ut-slapp fran vulkaner med hjalp av en uppsattning atmosfarsmodeller. Tva fallstudier har utforts,en fokuserar pa vulkanaska fran potentiella framtida utbrott pa Island, den andra studerar SO2-ustlapp fran Nyiragongo i Demokratiska Republiken Kongo.

Den forsta studien beskriver langvaga (∼1,000 km) transport av aska fran explosiva utbrott.Tre ar av meteorologiska data anvands for att modellera spridningen fran fem olika utbrotts-scenarier for varierande vadersituationer. Koncentrationen av luftburen aska studeras vid olikatidpunkter relativt utbrottens starttid och jamfors med tidigare samt befintliga gransvarden forflygtrafik. Sannolikheten for skadliga halter aska varierar med arstid, med en hogre sannolikhetfor effektiv transport osterut under vintermanaderna, jamfort med sommarmanaderna; sommar-utbrott ar istallet mer benagna att orsaka langvariga problem over specifika omraden.

I den andra studien modelleras utslapp av SO2 fran passiva utslapp vid Nyiragongo over enettarsperiod. Den meteorologiska effekten pa spridningen studeras genom att anvanda en kon-stant utslappskalla. Dessutom studeras spridningen mer i detalj genom att anvanda fjarranalysdatafor att battre uppskatta utslappen. Gaserna transporteras i regel mot nordvast i juni–augusti ochmot sydvast i december–februari. En sjo-/landbriscirkulation runt Kivusjon orsakar hoga halterav SO2 langs sjons norra strand nattetid. Dygnsmedelkoncentrationer av SO2 i provinshuvud-staden Goma (∼15 km sydvast om Nyiragongo) overskred EU-riktlinjer (125 μg/m3) under120-210 dagar under en ettarsperiod.

Science is made up of so many thingsthat appear obvious after they are explained

Pardot Kynes, Muad’Dib

List of papers

This thesis is based on the following papers, which are referred to in the textby their Roman numerals.

I Dingwell, A. and Rutgersson, A. (2014). Estimating volcanic ashhazard in European airspace. Journal of Volcanology and GeothermalResearch, 286(0):55–66.

II Dingwell, A.; Rutgersson, A.; Claremar, B.; Arellano, A.; Mapendano,Y. and Galle, B. (2015). Seasonal and diurnal patterns in the dispersionof SO2 from Mt. Nyiragongo. Manuscript prepared for submission

Reprints were made with permission from the publishers.

In Paper I, I had the main responsibility for the data analysis and the writingof the paper.

In Paper II, I was responsible for most of the data analysis, except for theprocessing of the flux-data. I had the main responsibility for writing the paper.

The method for large scale application of the modelling system was mainly de-veloped as part of Paper I. Some adaptations were made in Paper II. Duringthe project, I have also contributed to the development of FLEXPART-WRF.

The following related publication is not included in the thesis:

Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, D.;Seibert, P.; Angevine, W.; Evan, S.; Dingwell, A.; Fast, J. D.;Easter, R. C.; Pisso, I.; Burkhart, J. and Wotawa, G. (2013). TheLagrangian particle dispersion model FLEXPART-WRF version3.1. Geoscientific Model Development, 6(6):1889–1904.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.1 Aim of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 Modelling tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1 Meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1.1 Downscaling meteorological data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.1.2 Meteorological model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Dispersion modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.1 Advection of particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.2 Deposition processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.3 Aggregation and Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.4 Plume rise from strong eruptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3 Model application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.1 Probability of volcanic ash over northern Europe . . . . . . . . . . . . . . . . . . . . . . . . 224.2 Dispersion of SO2 from Mt. Nyiragongo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

6 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

1. Introduction

Volcanoes can emit huge quantities of hazardous ash and gases into the at-mosphere. Both components can have a negative impact on human healthor on important components of our society. Ash from large eruptions can re-main in the atmosphere for days or even weeks before removed or dispersed toharmless concentrations. Gas emissions, either during eruptions or as passivedegassing, can pose an serious hazard for the environment and human health.This can occur either during eruptions or as passive degassing over longer pe-riods. Acidic compounds (e.g. SO2, H2S, HCl, HF) affect cloud chemistryand produce acid rain. Exposure to gas plumes can therefore lead to increasedwear on man-made structures (e.g. roofs, antennas, machinery), and have anirritating effect on airways when inhaled (Delmelle et al., 2002). Fluoride (F),which is mainly emitted as HF, is toxic in high concentrations and can disturbdental development for children (Baxter et al., 1999; Delmelle et al., 2002) oreven prove fatal for grazing animals, which might ingest large quantities offluoride coated plants (Cronin et al., 2000).

The dispersion of volcanic emissions over Europe has occurred severaltimes in history, the most notable caused by the Lakagıgar fissure eruptionin Iceland in 1783–1784. The eruption had severe local impact and likely af-fected large parts of Europe as well. Within a year from the eruption, mostof Icelands livestock died after ingestion of ash coated grass. During thisperiod, famine and direct exposure to toxic emissions reduced the human pop-ulation of Iceland by 19–22 %, or approximately 10,000 people (Demaree andOgilvie, 2001). A persistent haze was reported over Europe in mid to late June1783, lasting for about two months and eventually extending as far as Moscow,Baghdad and northern Africa (Stothers, 1996).

Gas emissions from volcanoes is not uncommon, the main contribution isnot from eruptions, but rather from passive degassing (?). While individualcases of passive degassing are weaker than events like the Lakagıgar eruption,they still pose a regional hazard. Areas downwind of degassing volcanoesoften show negative impact on human health and the environment (?).

In April 2010, the eruption of the Icelandic volcano Eyjafjallajokull re-sulted in almost complete closing of European airspace. Fine grained ashwas transported over 1000 km, reaching continental Europe in a matter ofdays. Between th 15th and 22nd of April, around 104,000 flights were can-celled (EUROCONTROL, 2010). The total emissions of the eruption werenot remarkable, however, the fraction of fine ash produced was unusually highand unusually persistent northerly winds allowed a large portion of this ash

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to reach continental Europe (Stohl et al., 2011). Furthermore, the Europeancountries were not prepared for such an events; as a response, new guide-linesfor volcanic ash were implemented for aviation and regular exercises wereinitiated.

The impact of such extreme events is highly dependent on the informationavailable to decision makers. Increased knowledge and better predictions en-able our society to make preparations and better manage the situation wheneruptions occur.

1.1 Aim of this thesisIn this thesis, exposure to volcanic ash and gases are studied and techniquesfor estimating hazards for different types of volcanoes are presented.

• The probability of encountering volcanic ash in European airspace as theresult of eruptions on Iceland is covered in Paper I.

• Regional exposure to gases emitted from Mt. Nyiragongo (eastern Demo-cratic Republic of Congo) is covered in Paper II.

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2. Modelling tools

The dispersion of pollutants in the atmosphere depends on meteorological con-ditions as well as the properties of the emissions in question. The atmosphereis a complex system with interactions on a wide range of scales, from micro-physical processes and chemistry, to global energy fluxes and Rossby waves.The complexity makes it hard to study the atmosphere as a whole, which iswhy atmospheric models are important tools for studying the fate of pollu-tants in the atmosphere.

A combination of two models was used throughout this project; a meteoro-logical model was used to produce wind fields and precipitation data for usein a dispersion model. The tools used for large scale application of the modelsare similar between the studies but has undergone development throughout theproject. Most of the work has gone into the dispersion model. The dispersionmodel has been improved upon as part of the work.

2.1 Meteorological dataWhen running atmospheric models for research purposes covering past cases,one aims at having the best estimate of the state of the atmosphere to force themodel. One type of product intended to provide such information is reanalysisdata (Warner, 2011). A reanalysis product is the combined result of a large setof observations and a meteorological model, often run over several decadesto provide long time series of consistent data. However, reanalysis data istypically produced by global models— running on relatively coarse resolution.

2.1.1 Downscaling meteorological dataThe initial meteorological data, as was used in this project, was the ERA In-terim reanalysis product (Dee et al., 2011), provided by the European Centrefor Medium range Weather Forecasts (ECMWF). The data was retrieved on a0.75◦ resolution (∼80 km in meridional direction), which is not directly appli-cable for regional studies, where a resolution of several kilometres if typicallydesired. In order to improve the resolution, a limited area model can be used.The limited area model is forced by the reanalysis data but runs on a higherresolution. This process of increasing the resolution by the use of a meteo-rological model is called downscaling. Downscaling requires large computa-tional resources compared to simpler methods (interpolation) but allows small

13

scale variations in land-use and topography to be accounted for. An exampleof this is shown in Figure 2.1; while interpolation produces smooth contours,it lacks the additional information provided by the dynamic downscaling.

Figure 2.1. Comparison of near-surface temperature from basic interpolation (left)versus dynamic downscaling (right) from Dingwell (2012). The left image was pro-duced by interpolating data from ERA-Interim at 0.75 degree resolution (≈ 35 ×85 km) down to 13.5× 13.5 km. The right image has the same resolution, but wasproduced by the WRF-model, using the interpolated data from ERA-Interim as forc-ing. Image

If the desired resolution is differs much from the initial resolution, thendownscaling can be made in several steps, called nesting to further improveresolution (Jacobson, 2005). There are two reasons why nesting is recom-mended rather than simply setting up a single high resolution domain directly.First, there could be characteristics in the terrain (e.g. mountains, islands orlakes) which are too small to be resolved by the global model but which havean important influence of the dynamics of the region. With nesting, these in-fluences can be accounted for. Second, numerical problems may arise if thereis a too large difference between the input data and the computational grid(Warner, 2011).

2.1.2 Meteorological modelThe Advanced Weather Research and Forecasting (WRF) model (Skamarocket al., 2008) was used to downscale meteorological data throughout this project.The WRF-model is a 3-dimensional, non-hydrostatic, mesoscale meteorologi-cal model. Its modular design allows it to be used in various applications fromoperational forecasting to idealized scientific cases.

When initiating a run (i.e. cold starting) with the WRF-model, the modeldomains are initiated in all grid points using interpolated data from the forcingdata. Some time, usually 6-12 simulation hours are needed for the modelto fully develop the small scale dynamics not present in the coarser data when

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running from a cold start. This process is referred to as spin-up. Data producedduring the spin-up is inaccurate and should be discarded.

After initialization, WRF will continue to apply forcing to the boundaries ofthe outer domains but no nudging (i.e. adjustment toward forcing data) takesplace within the domains. Since the WRF-model makes no attempt at nudgingdata inside domains during a simulation, a set-up with large outer domainsrisk deviating too far from the reanalysis data. In order to prevent this, werun the model in segments. Each segment consists of three steps. First, themodel is initialized using the interpolated reanalysis data (cold start) and runfor 6–12 hours to allow the model to reach a state of quasi–equilibrium underthe applied forcing (spin-up). Second, the simulation is paused, and someintermediate processing of model variables is done. The most important partbeing to overwrite accumulated fields (e.g. precipitation) with values from theend of the previous run segment (used in Papers I and II). Third, the modelsimulation is continued running for 24–48 hours for the main production ofdata. We have aimed at keeping each run segment short since this made iteasier to fit jobs into the queues at compute centres.

2.2 Dispersion modellingA dispersion model either works as a stand-alone model using prepared meteo-rological data (offline) or combined with a meteorological model running side-by-side (online). While online models can benefit from allowing emissions toaffect the dynamics through various feedback processes, they are generallymore computationally demanding. An offline model can run independently ofthe meteorological model as long as there is available data.

This project uses the (offline) Lagrangian Particle Dispersion Model(LPDM) FLEXPART-WRF (Brioude et al., 2013; Stohl et al., 2005). An LPDMworksbyreleasinga largenumberofcomputationalparticles (e.g. 100000) froma point, line, area or volume source, located anywhere within the meteorologicalgrid. The computational particles are moved through the domain, according tomeanwindsandturbulencewhichis interpolatedtotheexactpositionofeachpar-ticle. Since computations are only made where pollutants are present, LPDM’sare suitable for simulating emission of chemically stable gases or particles froma limited number of sources over large areas. The main downside of Lagrangianmodels is that they are less suitable for simulating chemistry or aggregation ofparticles.

2.2.1 Advection of particlesThe position of particles in LPDMs is determined by a numerically solving dif-ferential equations. There are several methods for doing this, with the simplest

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being the first order Euler solution (as in FLEXPART-WRF):

r(t +Δt) = r(t)+V (r, t)Δt (2.1)

where r(t) is the particle’s position at any given time, t, Δt is the time step of thesimulation and r(t +Δt) is the particle’s position at the next step. More elaboratemethods can be used to improve precision, such as using a Second order Eulersolution (e.g. the NAME mode: Jones et al., 2007) or by using (2.1) to make aninitial guess position, which can further be improved by determining the meanwindalong theguessedpath (e.g. theHYSPLITmodel:DraxlerandHess,1998).

When simulating particulate matter, the dispersion model should take in toaccount the terminal velocity (i.e. settling due to gravity). For spherical particleslarger than ∼10 μm, the terminal velocity, vg, can be calculated as a balancebetween the downward gravitational force and the upward drag force:

vg =

√43

dpgCD

(ρ −ρair

ρair

)≈√

43

dpgCD

(ρ

ρair

)(2.2)

where dp is the particle’s diameter, g is acceleration due gravity, ρ is the particledensityandρair is thedensityofair. CD is thedragforcewhich, inFLEXPARTandits derivatives, is calculatedusing themethodbyNaslundandThaning (1991). Inorder to accurately describe terminal velocities of particles down to micrometersize, (2.2) can be adjusted by dividingCD with the Cunningham correction factoras proposed by Cunningham (1910); Knudsen and Weber (1911):

Ccun = 1+2λdp

(A0 +Q · e−C dp

2λ

)(2.3)

where λ is the mean free path of gas molecules in air. A0, Q and C are dimen-sionless constants, which in FLEXPART-WRF are set to 1.257, 0.400 and 1.10,respectively.

Furthermore, (2.2) assumes spherical particles, particles of different shapewill have a different terminal velocity. This is usually corrected by assigning anequivalent spherical diameter (i.e. aerodynamic diameter) for particles whensetting up simulations.

2.2.2 Deposition processesDry deposition in models are usually defined in terms of the deposition veloc-ity, Vd , defined as the fraction between the flux, F , and the concentration, C, ofgiven species. FLEXPART-WRF calculates the flux using the resistance method(Wesely, 1989):

Vd =1

rA + rB + rC(2.4)

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where rA is the aerodynamic resistance of the surface layer and rB is the viscousresistanceofthequasi-laminarsublayer, theimplementationoftheseisexplainedby Stohl et al. (2005). The final term, rC is the bulk surface resistance, which isdependent on thickness and type of vegetation, presence soil, water, or buildings,etc., see Wesely (1989); Wesely and Hicks (2000) for details. An illustration ofthis is shown in Figure 2.2.

A

B

C

net flux

Figure 2.2. Illustration of the different sub-processes of dry deposition. (A) repre-sents the surface layer, where the resistance is determined by turbulent eddies. (B) isthe laminar sublayer, a thin layer of air in contact with surfaces, where turbulence isnegligible and viscous forces dominate. Transport through this layer depend on Brow-nian motion and inertia of heavier particles. (C) represents the bulk surface properties,which combines numerous processes for absorption of gases by the surface.

In FLEXPART-WRF rC is estimated using land-use categories following We-sely (1989); Wesely and Hicks (2000). A problem with the land-use data was en-countered in Paper II, when the spatial resolution was set to a much higher valuethan the typical resolution in FLEXPART. The land-use data was on a coarse0.3◦ grid, resulting in patchy deposition in high resolution grids. Therefore,FLEXPART-WRF was modified to read land-use data from the WRF model. Ata horizontal resolution of 2 km, the model results differ significantly dependingon which land-use data is used, an example of this is shown in Figure 2.3.

Particles behave differently than gases, mainly by the exclusion of surfaceresistances and the added effect of gravitational settling. In FLEXPART-WRFthe deposition velocity of particles is given by:

vd =1

rA + rB + rArBvg+ vg (2.5)

where rB is the same as for gases but including an impaction factor.Wet deposition in FLEXPART-WRF has recently undergone major changes.

Wet deposition was first officially added by Brioude et al. (2013), following thesame model as was used in FLEXPART 9.02. Currently, the model documenta-tion describes a slightly different approach than what the model i programmedto do. An up-to-date description of wet deposition of particles was thereforeincluded in Paper I.

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0.75

1.25

0.951.05

0.85

1.15

1.5

1.3

1.10.9

0.7

0.5

(B)(A)

Figure 2.3. Difference between FLEXPART-WRF simulations using different land-use data (figure from Paper II. Both figures show average values from a simulationwith land-use data from WRF divided by results from a simulation using the old built-in data. (A) shows differences in accumulated dry-deposition of SO2 over one year.(B) shows difference in average SO2-concentration 0-500 m a.g.l. during September–November 2010.

2.2.3 Aggregation and ChemistryChemistry and particle aggregation affect the aerodynamic properties of emis-sions as well as the removal rate. These processes are not easily included inLPDMs, some attempts exist where gas-phase chemistry is calculated on fixedEulerian grids (e.g. Oettl and Uhrner, 2011) or with simple redistribution of masswithin computational particles (e.g. Businger et al., 2015). FLEXPART-WRFcan only handle chemistry as an additional removal process, conversion betweenmodelled species is not possible. An attempt to estimate the model error arisingfrom the lack of sulphur chemistry is made in Paper II.

2.2.4 Plume rise from strong eruptionsIn Paper I, the dispersion of volcanic ash from a range of eruption scenarios wasstudied. An important part in setting up the study was assigning the emissionaltitude. In reality, the emissions take place at the vent of the volcano, a mixtureof ash and gases are released into the atmosphere at high velocity. Ambient airentrained into the plume is heated; convection carries the emissions to higher al-titudes (Sparks and Wilson, 1976; Sparks, 1986). This process can be studied inadvanced Eulerianmodels (Textor et al., 2006), however, thehighcomputationaldemand of such models, currently make them unsuitable for long range simula-tions. Instead, simplifications need to be made regarding the emission source.A common method is to set up a source volume were the ash is released. Thisvolume typically represent part of the plume where convection is still important.

Emission sources in earlier studies and operational models were assigned asvertical columns with uniform mass released throughout the column (Withamet al., 2007). However, since volcanic plumes often are convective (Sparks,

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1986), the majority of fine ash should be expected at the top of the eruption col-umn due to their smaller settling velocity (Carey and Sparks, 1986; Koyaguchiand Ohno, 2001). Other studies, (e.g. Peterson and Dean, 2008; Steensen et al.,2013), use a top-weighted function to determine the particle release in the sourcecolumn. However, this introducesanothererror, sinceconcentrationinthesourcecolumn increases with altitude. If the plume is convective, concentration shoulddecrease with altitude due to volume expansion. This process becomes moreimportant with taller the eruption columns. In Paper I, the source volume wasdesigned to account for an increase in fine ash at higher altitudes by distributingmass over ten stacked source segments according to a Poisson distribution. Sim-ilar methods have previously been used by Peterson and Dean (2008); Steensenet al. (2013), but without accounting for varying sizes of the source volumes. Thewidth of each segment, d, varied between layers determined by the mass release:

d =M(zi)

U(zi)CΔz(2.6)

where M(zi) is the mass release rate in a given source segment (zi), U(zi) is theaverage wind speed within a segment, Δz is the segment thickness, and C is aconstant proportional to the concentration within each segment. This methodreduces the overestimation of concentration in the upper portions of the plume.The method can be further improved by including atmospheric density profiles.

InPaper II, theplumerisewas typicallymuch lower (i.e. several100metres),making plume expansion less important. Therefore, the classic approach of auniform source was used instead.

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3. Model application

The studies covered in this thesis apply the same modelling system to two highlydifferent cases. Different methods for building statistics over exposure to emis-sions were used. In Paper I, five different eruption scenarios were studied, rep-resenting potential future eruptions on Iceland. Each scenario was repeatedlysimulated, for varying meteorological conditions, to determine which areas aremost likely to be exposed to hazardous concentrations of ash. The area coveredby the study is shown in Figure 3.1A. In order to minimize systematic errors fromany diurnal variation, eruption scenarios were initiated every six hours, produc-ing four cases for each scenario and day. Each of these cases was simulated for96 hours. In total, the meteorological model runs covered 3 years, correspondingto an equivalent of 240 years of dispersion simulations. The data was analyzedrelative to the onset of each eruption case, and hourly average concentrationswere compared to thresholds used by air traffic control (ATC) for declaration ofno-flight zones.

~Provincial capital Goma(population: ~1 000 000) Lake Kivu

Mt. Nyamuragira (3058 m)Mt. Nyiragongo (3470 m)

d04

60°N

50°N

40°N

30°N

(A) (B)

Figure 3.1. Geographical coverage of the two studies included in this thesis. (A)shows three nested domain, all of which were used in the dispersion simulations inPaper I. (B) shows the single domain (third order nest) used in the dispersion simula-tions in Paper II

In Paper II, a case of passive degassing was studied. Flux data from the Net-work for Observation of Volcanic and Atmospheric Change (NOVAC) by Galleet al. (2010) was used to study the dispersion of emissions from Mt. Nyiragongo,an active volcano in eastern Democratic Republic of Congo (DRC), see Fig-ure 3.1B. The flux data was used as emission strength when available, however,since the observations could only be made in daylight, there were regular gaps in

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the data. In order to cover night-time cases as well as other periods lacking data,any gaps in the data were filled by randomly sampling flux data from the avail-able measurements. This procedure was repeated 30 times, creating 30 differenttime series but with common emissions whenever observations were available.A dispersion simulation was conducted for each of the emissions cases, formingan ensemble with 30 members.

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4. Results

4.1 Probability of volcanic ash over northern EuropeThe probability of exposure to ash as a result of future volcanic eruptions wasstudied in Paper I. The hazard was found to vary with season, with the strongestdifferenceseenbetweensummer(June–August)andwinter(December–January).An example of this is shown in Figure 4.1; the hazard from a summer time erup-tion is more persistent but initially lower for areas east of Iceland. Wintertimeeruptions have a higher probability of affecting most of the Scandinavian penin-sula and areas around the Baltic sea. Since the polar front is weaker during sum-mer, the westerly winds are weaker and less persistent, resulting in a higher prob-ability of ash being transported in other directions. In winter, however, the strongpolar front, results in strong westerly winds most of the time, transporting asheastward in most of the cases. This transport is both more frequent as well asmore efficient, resulting in high concentrations of ash at greater distances fromthe source.

A comparison was made between different eruption scenarios, an exampleof this is shown in Figure 4.2A-E, using a threshold value of 0.2 mg/m3; thisthreshold corresponds to the no-flight condition in use prior to the eruption in2010 (Webster et al., 2012). Note that the Eyafjallajokull eruption case (coveringthemostintensephasesoftheeruption)hasaprobabilitybelow10%formostlandareas beyond Iceland. The weakest scenario (Mt. Spurr) shows no exceedancesfor this period, partly due to the short duration of the eruption. In general, ashat higher altitude is more consistently transported eastwards compared to lowerlevels. However, the longest transport is seen in the mid-level (∼6–11 km) wherethe jet stream is expected.

A comparison is also made with earlier results by Leadbetter and Hort (2011)(Figure 4.2F), who used a similar approach. The scenario set up by Leadbetterand Hort (2011) should, in terms of emission strength, correspond to the weakestscenario in Paper I (i.e. Figure 4.2A). However, it turns out to be more similar tothe Askja-1875 scenario (Figure 4.2C), suggesting that ash hazard is much lowerthan previously estimated.

4.2 Dispersion of SO2 from Mt. NyiragongoThe geographical distribution of gases emitted from Mt. Nyiragongo was stud-ied in Paper II. At first only the meteorological influence on the dispersion was

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Figure 4.1. Probability of ash concentration exceeding the no-flight threshold (2.0mg/m3) for an eruption similar to Askja-1875 (from Paper I). This scenario is de-signed to be roughly 10 times stronger than the Eyjafjallajokull eruption in 2010. Theprobability is calculated for three different periods: yearly (A–D), winter (E–H) andsummer (I–L). Different time periods are given, specifically (top to bottom) 0–24, 24–48, 48–72 and 72–96 hours relative the onset of the eruption. All cases are for flightlevels FL200–FL350 (∼ 6–11 km a.s.l.).

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Figure 4.2. Areas where the probability to of exceeding hourly average concentrationsof 0.2 mg/m3 is at least 10 % within 24–48 h after the onset of an eruption (fromPaper I). Five different eruption scenarios, based on historic events, are presented(A-E), as well as results from Leadbetter and Hort (2011) (F). Contours are given forthree different flight levels (given in units of 100 ft.) as indicated by the grey-scale.

studied by using a constant emission source. The mean dispersion direction overthe whole year was to the west (as expected). However, a seasonal shift was seenwiththehigherportionoftheplumebeingtransportedfurthernorthinDecember–Januarycomparedto theannualmean. Thiscanbeseenin theplumecrosssectionshowninFigure4.3,whichstretchesalong the linemarked inFigure3.1B.Lowerportions of the plume was instead transported to the south. In June–August, thepattern was reversed with northward transport of the lower and southward trans-port of the higher parts of the plume. This skewness corresponds with the migra-tion of the Inter Tropical Convergence Zone (ITCZ), which is located to the northin July, and to the southwest in January. Surface winds converge at the ITCZ,which is why lower portions of the plume vary with season. At higher altitudes,air flows away from the ITCZ which causes the skewness seen in Figure 4.3.

Figure 4.3 also shows a diurnal variation. In daytime, emissions are morelikely to remain over land (i.e. north of 1.6◦S in Figure 4.3). In nighttime, emis-sions tend to form a shallow layer with high concentration of SO2 over lake Kivu.This is related to a lake-/land breeze cycle forming over lake Kivu, especiallyevident around the equinoxes, when the ITCZ has the least meridional influenceon the flow. Lake Kivu lies on a rift valley with steep slopes (escarpments) on itseast and west sides, with a valley located about halfway on the eastern side. Theescarpments create a channel effect, preventing air exchange other than at the

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Figure 4.3. Modelled seasonal average diurnal variation of SO2 along the cross sectionmarked in Figure 3.1. The leftmost column shows average concentrations for each 3-month period of a full year. The middle middle column shows average concentrationsbetween 05:30–06:30, local time (i.e. around sunrise) while the rightmost columnsshows average concentrations between 12:30–13:30 (i.e. at maximum insolation).The two leftmost columns represent the diurnal extremes regarding the concentrationsover lake Kivu. The data covers one year (April 2010 through March 2011) and usesa constant emission source. Figure from Paper II

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Figure 4.4. Monthly variation of SO2 exposure in populated areas near Mt. Nyi-ragongo (from Paper II). (A) Map showing the total number of days during which the24-hour average SO2 concentration exceeded 125 μg m−3 (reference value used by theEuropean Union). (B) Fraction of day when the reference value was exceeded eachmonth for locations marked in (A). Values are presented for 12 months, from April2010 to February 2011. The lines represent the average result from an ensemble of30 dispersion simulations, the shaded areas show the 25–75 percentiles of ensemblemembers.

northern and southern shores (or through the eastern valley). This concentratesthe lake-/land breeze flow at the northern and southern shores.

Using thefluxdata fromtheNOVACnetwork, anestimateofpastSO2 concen-trations in populated areas was made. Results from this are shown in Figure 4.4,where average concentrations are compared to reference values in use by the Eu-ropean Union. Exceeding these values over longer periods could be connectedto an increase in mortality (WHO, 2006). In the city of Goma (∼1,000,000 in-habitants), the concentrations exceeded the reference value a total of 120–210days, with the highest exposure in the western parts. Over the region, the highestexposure was seen along the shore of Lake Kivu (Goma and Sake) and to the NWof Mt. Nyiragongo (Kahe). The area with at least 10 days above the referencevalue extends to about 170 km NW of Mt. Nyiragongo.

The exposure strongly depends on the season. In April–August (2010), areastotheNWsawthehighestexposure. Kitchanga(∼85kmNW)andKahe(∼35kmNW) exceeded the reference value ∼45 % and >80 % of the time, respectively.Duringthesameperiod,concentrationsinSake(W)andWesternGoma(S)whereabove the the reference value∼20 % of the days, while eastern Goma was belowmost of the time. Most of the exceedances in Sake and Goma were related tothe land-breeze forming during the night. In September, the conditions changed.Goma and Sake saw a steep increase in SO2 while concentrations in Kitchanga

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and Kahe decreased. The highest exposure at the norhtern shore of lake Kivu wasreached in November 2010 through March 2011. Western Goma exceeded thereference value 90 % of the days, with slightly better conditions in Sake.

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5. Concluding remarks

In Paper I we showed seasonal variations in dispersion for different eruptionscenarios on Iceland. The more persistent westerly winds in winter, makes itmore likely for ash to be transported westward, typically reaching further than ifthe same eruption would occur in summer. Summertime eruption are less pre-dictable over long term, since the otherwise dominating westerly winds are lessfrequent. However, we also found that winter conditions vary more between theyears studied compared to summer. In order to further improve the accuracy ofthe probability analysis, it might be more important to study additional wintersthan full years.

Paper II demonstrated the first attempt at a regional dispersion study aroundMt. Nyiragongo. The combination of observational data from DOAS retrievalwith a regional modelling system is an important step in studying health prob-lems in the region. The study shows the importance of studying both daytime andnighttime conditions as well as seasonal variations. The modelled air concentra-tions of SO2 suggest suggest that a large number of people are being exposed tohazardous amounts of SO2, which can have both long- and short term negativeimpact on human health.

While it has been shown that the modelling system can be applied to differ-ent cases of volcanic emissions, continued development is still important. Adiagnostic plume rise scheme could be included to better describe weaker vol-canic plumes (or industrial sources), instead of using external tools to adjust it. Abuilt-in scheme would benefit from continuously updated meteorological fields,allowing development over time. HCl and HF are two important gases in vol-canic plumes, which are currently not included in FLEXPART-WRF. Addingthem would require new wet scavenging and dry deposition coefficients. In addi-tion, SO2 is only removed through dry deposition, despite being a highly solublegas; adding wet deposition for SO2 should be considered.

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6. Acknowledgments

I would like to thank my supervisors Anna Rutgersson and Bjorn Claremar, fortheir support throughout this project. Special thanks to Anna for giving me thisopportunity in the first place.

I thank Jerome Brioude (NOAA) for being so supportive with addressing is-sues with the dispersion model and helping me join the development team. San-tiago Arellano helped a lot with the NOVAC-data and had great patience with mymany questions.

I would also like to thank the many PhD-students in CNDS and at the depart-ment, especially my office buddies, Marc and Jean-Marc for plenty of disastrousdiscussions.

Finally, a big thank you to friends and family, for putting up with my weirdworking hours and all modelling jargon which I keep blabbing on about.

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References

Baxter, P. J., Baubron, J.-C., and Coutinho, R. (1999). Health hazards and disasterpotential of ground gas emissions at furnas volcano, sao miguel, azores. Journal ofVolcanology and Geothermal Research, 92(1–2):95 – 106.

Brioude, J., Arnold, D., Stohl, A., Cassiani, M., Morton, D., Seibert, P., Angevine,W., Evan, S., Dingwell, A., Fast, J. D., Easter, R. C., Pisso, I., Burkhart, J., andWotawa, G. (2013). The Lagrangian particle dispersion model FLEXPART-WRFversion 3.1. Geoscientific Model Development, 6(6):1889–1904.

Businger, S., Huff, R., Pattantyus, A., Horton, K., Sutton, A. J., Elias, T., andCherubini, T. (2015). Observing and forecasting vog dispersion from Kilaueavolcano, Hawaii. Bulletin of the American Meteorological Society.

Carey, S. and Sparks, R. (1986). Quantitative models of the fallout and dispersal oftephra from volcanic eruption columns. Bulletin of Volcanology, 48:109–125.

Cronin, S. J., Manoharan, V., Hedley, M. J., and Loganathan, P. (2000). Fluoride: Areview of its fate, bioavailability, and risks of fluorosis in grazed-pasture systemsin new zealand. New Zealand Journal of Agricultural Research, 43(3):295–321.

Cunningham, E. (1910). On the velocity of steady fall of spherical particles throughfluid medium. Proceedings of the Royal Society of London. Series A, ContainingPapers of a Mathematical and Physical Character, 83(563):357–365.

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S.,Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes,M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen,L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut,J.-N., and Vitart, F. (2011). The ERA-Interim reanalysis: configuration andperformance of the data assimilation system. Quarterly Journal of the RoyalMeteorological Society, 137(656):553–597.

Delmelle, P., Stix, J., Baxter, P., Garcia-Alvarez, J., and Barquero, J. (2002).Atmospheric dispersion, environmental effects and potential health hazardassociated with the low-altitude gas plume of masaya volcano, nicaragua. Bulletinof Volcanology, 64:423–434. 10.1007/s00445-002-0221-6.

Demaree, G. R. and Ogilvie, A. E. J. (2001). History and Climate: Memories of theFuture?, chapter Bons baisers d’Islande: Climatic, environmental, and humandimensions impacts of the Lakagıgar eruption (1783–1784) in Iceland, pages219–246. Plenum Publishing Co.,N.Y.

Dingwell, A. (2012). Dispersion of ash and gases from volcanic eruptions.Introduction essay for PhD-studies at the Department of Earth Sciences, UppsalaUniversity and the Centre for Natural Disaster Science.

Draxler, R. R. and Hess, G. D. (1998). An overview of the HYSPLIT 4 modellingsystem for trajectories, dispersion and deposition. Aust. Met. Mag., 47:295–308.

30

EUROCONTROL (2010). Ash-cloud of April and May 2010: Impact on Air Traffic.Galle, B., Johansson, M., Rivera, C., Zhang, Y., Kihlman, M., Kern, C., Lehmann, T.,

Platt, U., Arellano, S., and Hidalgo, S. (2010). Network for Observation ofVolcanic and Atmospheric Change (NOVAC)–A global network for volcanic gasmonitoring: Network layout and instrument description. Journal of GeophysicalResearch (Atmospheres), 115:D5304.

Jacobson, M. Z. (2005). Fundamentals of Atmospheric Modeling. CambridgeUniversity Press, 2 edition.

Jones, A., Thomson, D., Hort, M., and Devenish, B. (2007). The U.K. met office’snext-generation atmospheric dispersion model, NAME III. In Borrego, C. andNorman, A.-L., editors, Air Pollution Modeling and Its Application XVII, pages580–589. Springer US. 10.1007/978-0-387-68854-1 62.

Knudsen, M. and Weber, S. (1911). Luftwiderstand gegen die langsame bewegungkleiner kugeln. Annalen der Physik, 341(15):981–994.

Koyaguchi, T. and Ohno, M. (2001). Reconstruction of eruption column dynamicson the basis of grain size of tephra fall deposits: 1. methods. Journal ofGeophysical Research: Solid Earth, 106(B4):6499–6512.

Leadbetter, S. J. and Hort, M. C. (2011). Volcanic ash hazard climatology for aneruption of hekla volcano, iceland. Journal of Volcanology and GeothermalResearch, 199(3–4):230 – 241.

Naslund, E. and Thaning, L. (1991). On the Settling Velocity in a NonstationaryAtmosphere. Aerosol Science and Technology, 14(2):247–256.

Oettl, D. and Uhrner, U. (2011). Development and evaluation of GRAL-C dispersionmodel, a hybrid Eulerian–Lagrangian approach capturing NO–NO2–O3 chemistry.Atmospheric Environment, 45(4):839 – 847.

Peterson, R. A. and Dean, K. G. (2008). Forecasting exposure to volcanic ash basedon ash dispersion modeling. Journal of Volcanology and Geothermal Research,170(3–4):230 – 246.

Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G.,Huang, X.-Y., Wang, W., and Powers, J. G. (2008). A Description of the AdvancedResearch WRF Version 3. NCAR technical note, National Center for AtmosphericResearch, Boulder, Colorado, USA.

Sparks, R. S. J. (1986). The dimensions and dynamics of volcanic eruption columns.Bulletin of Volcanology, 48:3–15.

Sparks, R. S. J. and Wilson, L. (1976). A model for the formation of ignimbrite bygravitational column collapse. Journal of the Geological Society, 132(4):441–451.

Steensen, T., Stuefer, M., Webley, P., Grell, G., and Freitas, S. (2013). Qualitativecomparison of Mount Redoubt 2009 volcanic clouds using the PUFF andWRF-Chem dispersion models and satellite remote sensing data. Journal ofVolcanology and Geothermal Research, 259(0):235 – 247. The 2009 Eruption ofRedoubt Volcano, Alaska.

Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G. (2005). Technical note:the Lagrangian particle dispersion model FLEXPART version 6.2. AtmosphericChemistry and Physics, 5(9):2461–2474.

Stohl, A., Prata, A. J., Eckhardt, S., Clarisse, L., Durant, A., Henne, S., Kristiansen,N. I., Minikin, A., Schumann, U., Seibert, P., Stebel, K., Thomas, H. E.,Thorsteinsson, T., Tørseth, K., and Weinzierl, B. (2011). Determination of time-

31

and height-resolved volcanic ash emissions and their use for quantitative ashdispersion modeling: the 2010 Eyjafjallajokull eruption. Atmospheric Chemistryand Physics, 11(9):4333–4351.

Stothers, R. B. (1996). The great dry fog of 1783. Climatic Change, 32:79–89.Textor, C., Graf, H., Herzog, M., Oberhuber, J., Rose, W. I., and Ernst, G. (2006).

Volcanic particle aggregation in explosive eruption columns. part i:Parameterization of the microphysics of hydrometeors and ash. Journal ofVolcanology and Geothermal Research, 150(4):359 – 377.

Warner, T. T. (2011). Numerical Weather and Climate Prediction. CambridgeUniversity Press.

Webster, H. N., Thomson, D. J., Johnson, B. T., Heard, I. P. C., Turnbull, K.,Marenco, F., Kristiansen, N. I., Dorsey, J., Minikin, A., Weinzierl, B., Schumann,U., Sparks, R. S. J., Loughlin, S. C., Hort, M. C., Leadbetter, S. J., Devenish, B. J.,Manning, A. J., Witham, C. S., Haywood, J. M., and Golding, B. W. (2012).Operational prediction of ash concentrations in the distal volcanic cloud from the2010 Eyjafjallajokull eruption. Journal of Geophysical Research (Atmospheres),117.

Wesely, M. (1989). Parameterization of surface resistances to gaseous dry depositionin regional-scale numerical models. Atmospheric Environment (1967),23(6):1293–1304.

Wesely, M. and Hicks, B. (2000). A review of the current status of knowledge on drydeposition. Atmospheric Environment, 34(12–14):2261–2282.

WHO (2006). Air quality guidelines: global update 2005: particulate matter, ozone,nitrogen dioxide, and sulfur dioxide. World Health Organization, 3rd edition.

Witham, C. S., Hort, M. C., Potts, R., Servranckx, R., Husson, P., and Bonnardot, F.(2007). Comparison of VAAC atmospheric dispersion models using the 1November 2004 Grimsvotn eruption. Meteorological Applications, 14(1):27–38.

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