Source determination and localization by Atmospheric Transport Modelling

Gerhard Wotawaa

a Zentralanstalt fuer Meteorologie und Geodynamik,

Division Data, Methods and Modelling,

Vienna,

Austria

Abstract. In the last decades, methods to localize and detect sources and to estimate source strengths based on Atmospheric Transport Modelling (ATM) have been very much improved, especially by introducing Lagrangian ATM systems. Backward simulations are accomplished simply by performing the integrations with negative time steps. The introduction of so-called source-receptor sensitivity fields (SRS fields) allowed separating the ATM calculations from the source localization task, making computations less demanding from the CPU perspective. SRS fields can be used to localize sources, or to quantify source strengths in case the location is already known. Applications included the determination of source strengths of aerosols for the Eyjafjallajökull volcano eruption in 2010, the first determination of caesium release rates for the Fukushima nuclear accident 2011, the investigation of Xenon sources influencing detections at the noble gas network of the Preparatory Commission for the Comprehensive-Nuclear-Test Ban Treaty Organization (CTBTO), and the investigation of CTBTO Xenon detections after the announced DPRK nuclear test in 2013. Since more than one decade, SRS computations are operationally performed by CTBTO to interpret data from its two global radionuclide monitoring systems (particulate and radio-xenon). Overall, the results described in this paper demonstrate the high operational usability of these methods, and their future potential. For example, ATM methods could also be used in the non-proliferation framework to detect signatures from undeclared plutonium production and reprocessing activities based on the environmental monitoring of Krypton-85 gases.

1 Introduction

1.1 Atmospheric transport modelling and SRS fields

To establish a relationship between measurements of air constituents (gases, particles) at receptor locations and their respective sources, methods of Atmospheric Transport Modelling (ATM) are applied. Depending on the nature of the problem, ATM is performed either forward or backward in time. Forward modelling is the preferred option if sources are either known or their potential number is small compared with the number of receptor locations (measurements, other points of interest). To determine source-receptor relationships for measurement networks, the concept of source-receptor sensitivity (SRS) fields was defined as follows [1]:

ck = ∑ SRSijkn ∙ Sijn (1)

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ck is the (activity) concentration measured in sample k, SRSijnk the gridded SRS field pertaining to sample k (in this case restricted to surface releases), and Sijn the gridded source field. The sum is taken over all grid cells (i,j) and time intervals n prior to the end of the sampling time. SRS fields can best be computed using ATM models in backward mode, for example the Lagrangian Particle Diffusion Model (LPDM) FLEXPART [2].

In the case of a known release/source location, the analogous concept of a Transfer Coefficient Matrix (TCM) was defined as follows [3]:

cijn = ∑ TCMijkn ∙ Sk (2)

where cijn is the concentration at location (i,j) and time n, TCMijkn the transfer coefficient matrix of release time period k at location (i,j) and time n, and Sk the release vector. The sum is taken over all release time periods. TCMs are computed using ATM models in forward mode. For each release time period, a separate ATM simulation is performed.

1.2 Source determination methods

Source determination methods based on measurements are of high practical relevance, as, e.g. the reactor accidents in Fukushima and Chernobyl have shown. Two cases are of relevance:

Case 1: release location is known; release strength and timing shall be determined Case 2: release location is unknown or uncertain; possible locations shall be identified

Related to case 1, methods applied are (i) SRS or TCM-based bulk-estimates using few measurements, and (ii) inverse modelling.

The first, simplified, method allows for fast, order of magnitude estimates of the emission strength by comparing first-guess, unit-emission model runs with first measurements of the substance(s) released. In case of a SRS computation, the bulk source term Q is simply computed by dividing the measured concentration c by the SRS field value at the known or assumed release location. If TCM fields are used, the bulk source term Q is estimated by dividing the measured concentration by TCM, possibly summed up over a few release time periods, at the measurement location.

The inverse modelling method needs a large number of measurements and is computationally more demanding [4]. The method requires providing an a-priori emission estimate, and uncertainty estimates pertaining to the a-priori, the measurements and the model results. Unlike the first method, however, a time-height estimate of the release evolution consistent with various monitoring results can be obtained.

Related to case 2, the methods usually applied are (i) the source correlation method, and (ii) the source receptor matrix inversion.

SRS fields can be used together with measurements to compute possible source regions. This is done by applying an instantaneous source hypothesis to all possible source locations on the globe, which means that an individual source is assumed at all grid cells acting through one

time interval [1]. This yields ns = nx × ny × nt possible sources, with nx (ny) being the

number of grid cells in x (y) direction, respectively, and nt being the number of simulation time intervals prior to the detection scenario. From the ns different source scenarios, ns

measurement scenarios are computed based on equation (1), and afterwards correlated with the observed scenario. This yields a map of correlation coefficients r2 in space and time. Grid cells with high values of r indicate that the respective source hypothesis would result in a

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measurement scenario largely consistent with the observations. This method is therefore also referred to as “source correlation method”.

Besides the source-correlation method, information on a source field based on an observation vector can be obtained by formally inverting the source-receptor matrix (SRM). The calculation of the elements of the SRM can most efficiently be performed with backward runs of a LPDM [5]. Since the inversion problem is typically strongly underdetermined, regularization assumptions are needed.

2 Applications

2.1 Known sources

In April 2010, the eruption of the volcano Eyjafjallajökull in Iceland caused widespread closures of the European air space, as ash clouds from the volcano were transported towards continental Europe and the Alpine region (see FIG. 1). Based on aerosol/particulates measurements (PM10 concentrations) at Jungfraujoch Observatory, Switzerland and a vertical profile over Leipzig/Germany taken by the DLR Falcon aircraft [6], the total PM10 emission for the main episode (April 14-18, 2010) was operationally estimated by ZAMG. This operational estimate compared very well with the estimate based on the release height as computed with the USGS formula [7], and an inverse-modelling estimate published almost one year later [4] (see Table I).

Table I. Emission estimates of fine ash/PM10 (kg) related to the eruption of Eyjafjallajökull in 2010

Estimate by Method Eruption period Amount of fine ashZAMG (operational) Simplified April 14-18 4 109

USGS-based Based on erupt. hght. April 14-18 2 1010

Stohl et al. [4] Inverse Modelling April and May 8 109

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FIG. 1. Simulation of the transport of the volcanic ash cloud from Eyjafjallajökull towards central Europe on April 16-17, 2010.

After the magnitude-9 earthquake off the coast of Japan on 11 March 2011 at 05:56 UTC and the related tsunami reaching the Eastern coastline of Honshu about one hour later, three reactors at the Fukushima Daiichi nuclear power plants lost their emergency power supply and hence were no longer cooled. In the first days of the accident, predominately westerly winds transported radioactivity towards the Pacific Ocean, reaching the U.S. West Coast on 17 March (see FIG. 2). The first transport event towards inland Japan occurred on 15 March. Based on particulate radionuclide measurements performed by the Vienna-based Preparatory Commission of the Comprehensive-Nuclear-Test Ban Treaty Organization (CTBTO) in Sakramento/USA and Takasaki/Japan, a first quick estimate of the release strength of 137Cs was provided by ZAMG Vienna. This estimate was the first one published world-wide [8], and was on the same order of magnitude compared with subsequent estimates (Table II). Later on, available CTBTO data over the northern hemisphere and Japanese deposition measurements were used to estimate 137Cs by inverse modelling [9]. In this study, the temporal emission profile was estimated, determining that emissions started early after the cooling system failure, and continued into April 2011, but with significantly lower intensity (FIG. 3).

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FIG. 2. Simulation of the transport of radioactivity from the Fukushima Daiichi nuclear power plant location during the first week of the accident.

Table II. Emission estimates of 137Cs (PBq) related to the Fukushima Daiichi nuclear power plant accident 2011

Estimate by Method Release period 137Cs ReleaseZAMG (operational) Simplified (ATM) March 12-15 66IRSN [10] Reactor Physics March 12-22 30Stohl et al. [9] Inverse Modelling March and April 20-53UNSCEAR [11] Literature Review Total 6-20

FIG. 3. Emission estimates of 137Cs after the Fukushima Daiichi nuclear power plant accident based on inverse modelling (extracted from [9]).

2.2 Unknown sources

On February 12, 2013 at 02:58 UTC a seismic event was detected by CTBTO at a location on the territory of the Democratic People’s Republic of Korea (DPRK). The event was consistent with an explosion [12]. Furthermore, the CTBTO seismic event location was close to the location where explosions were detected on October 9, 2006, and May 25, 2009. In all three

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cases, the DPRK government announced that it had conducted a successful nuclear weapons test.

About 7 weeks after the announced nuclear test, on 7 April 2013, a Xenon sample measured at the Japanese CTBTO station Takasaki showed an unusually high 131mXe/133Xe ratio. On April 8-9, other unusual, even higher, detections did occur. The question arose whether these samples could have been influenced from delayed releases that originated from the CTBTO seismic event location, or rather from another site, for example the decommissioned Fukushima Daiichi nuclear accident site. A source-correlation analysis was performed based on the 133Xe measurement time series. Results showed (see FIG. 4) that the Xenon gases could indeed have originated from the DPRK nuclear test location. Also the required source strength, around 1013 Bq, is consistent with the assumption of a late release from a nuclear test back in February. The NPP accident site in Fukushima Daiichi can be excluded as possible source for the measurements. The most likely release date is 7 April 0-6 UTC [13].

FIG. 4. Maximum source correlation for the radionuclide event in Takasaki (left), and minimum source strength of 133Xe needed to explain the detections (right)

In October and November 2011, slightly enhanced 131I concentrations (≈10µBqm-3) were monitored by the national radiation protection networks in some European countries. The exact release location remained unclear. Due to the fact that European national networks collect mainly weekly or monthly samples, it proved to be impossible to localize sources based on these measurements. Within a period of one week, the winds are too variable and the source correlation fields too large.

During the period of regard, however, 131I was also detected in two CTBTO samples, namely at station Dubna, Russia, and at station Stockholm, Sweden (see Table III). Within all other European CTBTO samples Iodine was below the detection limit. Two source-correlation analyses were performed based on the two detection episodes. As can be seen (FIG. 5.), possible source regions are west of Russia and south of Sweden, with an overlap region in Central Europe. The conclusion was that a possible source would likely be in Central Europe. Regarding source strength, the ATM simulations indicated at least 10 GBq.

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FIG. 5. Maximum source correlations for the 131I detection episode in Dubna (left) and Stockholm (right).

A few days after these conclusions were reported to Austrian national authorities and IAEA, there was an official notification that the source of 131I was found in Hungary. The source was located in the Institute of Isotopes in Budapest. The source strength was variable. During the time interval of regard, daily source strengths were between 10 and 30 GBq.

Table III. CTBTO detections of 131I µBqm-3) related to the detection scenario in Europe in October/November 2011

Station Collection Start Collection Stop 131I ConcDubna, Russia 20-OCT-2011 04:26 21-OCT-2011 04:29 19.2Stockholm, Sweden 03-NOV-2011 08:48 05-NOV-2011 08:47 5.6

2.3 Global radioxenon distribution

CTBTO operationally computes SRS fields for all radionuclide sampling stations since more than 10 years [1]. These fields were used to better understand the global radioxenon distribution based on available CTBTO measurements and known locations of emitters [14]. There were two important questions associated, first whether the observed global distribution reflects our knowledge about the emitters, and second whether estimates of emission strengths are correct. As known emitters, four Radioisotope Production Facilities as well as all Nuclear Power Reactors were considered. The study showed that the principal global distribution is well understood (see FIG. 6). Emissions from the radioisotope facilities explain a number of observed peaks, meaning that atmospheric transport modelling is an important tool for the categorization of measurements. On the other hand, Nuclear Power Plant emissions are more difficult to treat in the models, since their temporal variation is high. Generally, the estimates of emission totals per year seem to be consistent with monitoring results, at least on the order of magnitude.

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FIG. 6. Plot of the observed 133Xe concentrations within the CTBTO noble gas network (left), and theoretical influence of the known sources on all (current and future) CTBTO monitoring sites (right). The sites marked in red/with capital H are predicted to be significantly influenced; the sites marked in orange/with capital M occasionally. A significant influence is assumed if the median of the model predicted 133Xe concentrations exceeds 0.1 mBqm-3; an occasional influence if the 95% percentile exceeds 0.1 mBqm-3

3 Summary and conclusions

In this paper, applications of Atmospheric Transport Modelling (ATM) were demonstrated. These methods were successfully used in the last years to localize and detect sources and to estimate source strengths based on monitoring results. In some cases, estimates were available very early, based on a minimum of available measurements, and were still accurate by one order of magnitude or better. This showed that such methods can also operationally be used for a quick situation assessment, for example after a nuclear incident or emergency.

ATM related methods are also a useful tool for the verification of the CTBT based on the radionuclide monitoring system. The SRS fields were used to improve our understanding of the observed concentration patterns in the Xenon network, and of major xenon sources. It was also possible to relate Xenon detections in Japan in 2013 to the CTBTO seismic event location, providing a strong evidence that a nuclear test explosion was conducted there. Also after the DPRK nuclear test of 2006, ATM methods were used to verify the possible release locations following Xenon detections [15]. In the future, such methods could also be applied for non-proliferation applications. For example, Kr-85 is an excellent indicator for clandestine plutonium separation. It is released during reprocessing of spent nuclear fuel rods or plutonium breeding targets. In case that a wide-area sampling network with reasonable temporal resolution (6-24 hours) would exist, ATM methods could be used to detect sources explaining the detected signals [16].

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