Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings.

In last years, atmospheric dispersion models have reached considerable popularity in environmental research field. In this regard, given the difficulties associated to the estimation of emission rate for some kind of sources, and due to the importance of this parameter for the reliability of the results, Backward dispersion models may represent promising tools. In particular, by knowing a measured downwind concentration in ambient air, they provide a numerical value for the emission rate. This paper discusses a critical validation of the WindTrax Backward model: the investigation does not only deal with the strict reliability of the model but also assesses under which conditions (i.e. stability class, number, and location of the sensors) the model shows the greatest accuracy. For this purpose, WindTrax results have been compared to observed values obtained from available experimental datasets. In addition, a sensitivity study regarding model-specific parameters required by WindTrax to replicate the physics and the random nature of atmospheric dispersion processes is discussed. This is a crucial point, since, for these settings, indications on the numerical values to be adopted are not available. From this study, it turns out that the investigated model specific settings do not lead to a significant output variation. Concerning the validation study, a general tendency of the model to predict the observed values with a good level of accuracy has been observed, especially under neutral atmospheric conditions. In addition, it seems that WindTrax underestimates the emission rate during unstable stratification and overestimates during stable conditions. Finally, by the definition of alternative scenarios, in which only a portion of the concentration sensors was considered, WindTrax performance appears better than acceptable even with a small number of concentration sensors, as long as the positioning is in the middle of the plume and not in the strict vicinity of the source.

Hydrogeochemical characterization and geochemical modeling for the evaluation of groundwater quality and health risk assessment in the Varuna River basin, India.

This study focuses on determining significant controlling factors of chemical consequences, inverse geochemical modeling, water quality, and human health risk in the Varuna River basin of India. The study interprets that according to pH, total dissolved solids, and total hardness, the maximum number of groundwater samples are alkaline, fresh, and have substantial hardness. The abundance of major ions follows a pattern: Na > Ca > Mg > K, and HCO3 > Cl > SO4 > NO3 > F. Piper diagram shows that Ca-Mg-HCO3 facies are predominant during both seasons. Na-normalized molar ratios of HCO3/Na, Mg/Na, and Ca/Na are 0.62, 0.95, and 1.82 (pre-monsoon) and 0.69, 0.91, and 1.71 (post-monsoon), respectively, elucidating the coupled silicate and carbonate weathering (dolomite dissolution) sources. The Na/Cl molar ratio is 5.3 (pre-monsoon) and 3.2 (post-monsoon), indicating silicate alteration as the primary process rather than halite dissolution. The chloro-alkaline indices confirm the presence of reverse ion- exchange. Geochemical modeling using PHREEQC identifies the formation of secondary kaolinite minerals. The inverse geochemical modeling categorizes the groundwaters along the flow path from recharge area waters (Group I: Na-HCO3-Cl), transitional area waters (Group II: Na-Ca-HCO3), and discharge area waters (Group III: Na-Mg-HCO3). The model demonstrates the prepotency of water-rock interactions in pre-monsoon justified by the precipitation of Chalcedony and Ca-montmorillonite. The mixing analysis shows that in the alluvial plains, groundwater mixing is a significant hydrogeochemical process that affects groundwater quality. The Entropy Water Quality Index ranks 45% (pre-monsoon) and 50% (post-monsoon) of samples as an excellent category. However, the non-carcinogenic health risk assessment shows that children are more susceptible to fluoride and nitrate contamination.

Influence of tapentadol and oxycodone on the spinal cord and brain using electrophysiology: a randomized, placebo-controlled trial.

AIMS: The aim of this study was to investigate the effects of tapentadol and oxycodone using the nociceptive withdrawal reflex and sensory evoked potentials. METHODS: Twenty-one healthy volunteers completed a cross-over trial with oxycodone (10 mg), tapentadol (50 mg) extended-release tablets, or placebo treatment administered orally BID for 14 days. Electrical stimulations were delivered on the plantar side of the foot to evoke a nociceptive withdrawal reflex at baseline and post-interventions. Electromyography, recorded at tibialis anterior, and electroencephalography were recorded for analysis of: number of reflexes, latencies, and area under the curve of the nociceptive withdrawal reflex as well as latencies, amplitudes and dipole sources of the sensory-evoked potential. RESULTS: Tapentadol decreased the odds ratio of eliciting nociceptive withdrawal reflex by -0.89 (P = .001, 95% confidence interval [CI] -1.46, -0.32), whereas oxycodone increased the latency of the N1 component of the sensory-evoked potential at the vertex by 12.5 ms (P = .003, 95% CI 3.35, 21.69). Dipole sources revealed that the anterior cingulate component moved caudally for all three interventions (all P < .02), and the insula components moved caudally in both the oxycodone and tapentadol arms (all P < .03). CONCLUSION: A decrease in the number of nociceptive withdrawal reflex was observed during tapentadol treatment, possibly relating to the noradrenaline reuptake inhibition effects on the spinal cord. Both oxycodone and tapentadol affected cortical measures possible due to µ-opioid receptor agonistic effects evident in the dipole sources, with the strongest effect being mediated by oxycodone. These findings could support the dual effect analgesic mechanisms of tapentadol in humans as previously shown in preclinical studies.

Inference of brain networks with approximate Bayesian computation - assessing face validity with an example application in Parkinsonism.

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.

The added value of satellite observations of methane forunderstanding the contemporary methane budget.

Surface observations have recorded large and incompletely understood changes to atmospheric methane (CH4) this century. However, their ability to reveal the responsible surface sources and sinks is limited by their geographical distribution, which is biased towards the northern midlatitudes. Data from Earth-orbiting satellites designed specifically to measure atmospheric CH4 have been available since 2009 with the launch of the Japanese Greenhouse gases Observing SATellite (GOSAT). We assess the added value of GOSAT to data collected by the US National Oceanic and Atmospheric Administration (NOAA), which have been the lynchpin for knowledge about atmospheric CH4 since the 1980s. To achieve that we use the GEOS-Chem atmospheric chemistry transport model and an inverse method to infer a posteriori flux estimates from the NOAA and GOSAT data using common a priori emission inventories. We find the main benefit of GOSAT data is from its additional coverage over the tropics where we report large increases since the 2014/2016 El Niño, driven by biomass burning, biogenic emissions and energy production. We use data from the European TROPOspheric Monitoring Instrument to show how better spatial coverage and resolution measurements allow us to quantify previously unattainable diffuse sources of CH4, thereby opening up a new research frontier. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

Inverse identification of pollution source release information for surface river chemical spills using a hybrid optimization model.

To evaluate the liability of the spilled contaminant and to design comprehensive emergency response schemes, it is essential to estimate the contaminant source characteristic and identify where, when and how much the spilled contaminant is injected into a surface river. In this study, an effective pollution source inverse method is developed to reconstruct the release history of the injection location, time, and quantity, and provide appropriate emergency response schemes for dealing with surface river environmental pollution. The pollution source inverse method IGSAA is developed by an integration of genetic algorithm (IGA) and simulated annealing algorithm (SAA) in order to guarantee both the global searching ability and convergence speed. The pollution source inverse method IGSAA is then applied to a hypothetical study, comparing with the traditional GA-based and SAA-based methods, to verify the accuracy and efficiency of the contaminant source inverse, and to a trace study of Truckee River in west America to identify the contaminant source release history and characteristic under different scenarios. The pollution source inversion results can help decision-makers (DMs) to identify the contaminant source characteristics of a chemical spill, and carry out emergency disposal scheme for an emergency rescue in a quick response, and enhance the supervision and management ability for a real surface river system.

Extending the range of applicability of the semi-empirical ecosystem flux model PRELES for varying forest types and climate.

Applications of ecosystem flux models on large geographical scales are often limited by model complexity and data availability. Here we calibrated and evaluated a semi-empirical ecosystem flux model, PREdict Light-use efficiency, Evapotranspiration and Soil water (PRELES), for various forest types and climate conditions, based on eddy covariance data from 55 sites. A Bayesian approach was adopted for model calibration and uncertainty quantification. We applied the site-specific calibrations and multisite calibrations to nine plant functional types (PFTs) to obtain the site-specific and PFT-specific parameter vectors for PRELES. A systematically designed cross-validation was implemented to evaluate calibration strategies and the risks in extrapolation. The combination of plant physiological traits and climate patterns generated significant variation in vegetation responses and model parameters across but not within PFTs, implying that applying the model without PFT-specific parameters is risky. But within PFT, the multisite calibrations performed as accurately as the site-specific calibrations in predicting gross primary production (GPP) and evapotranspiration (ET). Moreover, the variations among sites within one PFT could be effectively simulated by simply adjusting the parameter of potential light-use efficiency (LUE), implying significant convergence of simulated vegetation processes within PFT. The hierarchical modelling of PRELES provides a compromise between satellite-driven LUE and physiologically oriented approaches for extrapolating the geographical variation of ecosystem productivity. Although measurement errors of eddy covariance and remotely sensed data propagated a substantial proportion of uncertainty or potential biases, the results illustrated that PRELES could reliably capture daily variations of GPP and ET for contrasting forest types on large geographical scales if PFT-specific parameterizations were applied.

Magma Degassing as a Source of Long-Term Seismicity at Volcanoes: The Ischia Island (Italy) Case.

Transient seismicity at active volcanoes poses a significant risk in addition to eruptive activity. This risk is powered by the common belief that volcanic seismicity cannot be forecast, even on a long term. Here we investigate the nature of volcanic seismicity to try to improve our forecasting capacity. To this aim, we consider Ischia volcano (Italy), which suffered similar earthquakes along its uplifted resurgent block. We show that this seismicity marks an acceleration of decades-long subsidence of the resurgent block, driven by degassing of magma that previously produced the uplift, a process not observed at other volcanoes. Degassing will continue for hundreds to thousands of years, causing protracted seismicity and will likely be accompanied by moderate and damaging earthquakes. The possibility to constrain the future duration of seismicity at Ischia indicates that our capacity to forecast earthquakes might be enhanced when seismic activity results from long-term magmatic processes, such as degassing.

Predicting the abundance of forest types across the eastern United States through inverse modelling of tree demography.

Global environmental change is expected to induce widespread changes in the geographic distribution and biomass of forest communities. Impacts have been projected from both empirical (statistical) and mechanistic (physiology-based) modelling approaches, but there remains an important gap in accurately predicting abundance across species' ranges from spatial variation in individual-level demographic processes. We address this issue by using a cohort-based forest dynamics model (CAIN) to predict spatial variation in the abundance of six plant functional types (PFTs) across the eastern United States. The model simulates tree-level growth, mortality, and recruitment, which we parameterized from data on both individual-level demographic rates and population-level abundance using Bayesian inverse modelling. Across a set of 1° grid cells, we calibrated local growth, mortality, and recruitment rates for each PFT to obtain a close match between predicted age-specific PFT basal area in forest stands and that observed in 46,603 Forest Inventory and Analysis plots. The resulting models produced a strong fit to PFT basal area across the region (R2  = 0.66-0.87), captured successional changes in PFT composition with stand age, and predicted the overall stem diameter distribution well. The mortality rates needed to accurately predict basal area were consistently higher than observed mortality, possibly because sampling effects led to biased individual-level mortality estimates across spatially heterogeneous plots. Growth and recruitment rates did not show consistent directional changes from observed values. Relative basal area was most strongly influenced by recruitment processes, but the effects of growth and mortality tended to increase as stands matured. Our study illustrates how both top-down (population-level) and bottom-up (individual-level) data can be combined to predict variation in abundance from size, environmental, and competitive effects on tree demography. Evidence for how demographic processes influence variation in abundance, as provided by our model, can help in understanding how these forests may respond to future environmental change.

A baseline for source localisation using the inverse modelling tool FREAR.

A global network of monitoring stations is set up that can measure tiny concentrations of airborne radioactivity as part of the verification regime of the Comprehensive Nuclear-Test-Ban Treaty. If Treaty-relevant detections are made, inverse atmospheric transport modelling is one of the methods that can be used to determine the source of the radioactivity. In order to facilitate the testing of novel developments in inverse modelling, two sets of test cases are constructed using real-world 133Xe detections associated with routine releases from a medical isotope production facility. One set consists of 24 cases with 5 days of observations in each case, and another set consists of 8 cases with 15 days of observations in each case. A series of inverse modelling techniques and several sensitivity experiments are applied to determine the (known) location of the medical isotope production facility. Metrics are proposed to quantify the quality of the source localisation. Finally, it is illustrated how the sets of test cases can be used to test novel developments in inverse modelling algorithms.

Inverse resolution of spatially varying diffusion coefficient using Physics-Informed neural networks.

Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally, these systems often have spatial variation in the diffusion coefficient which must be determined, such as for injectable drug-eluting implants into heterogeneous tissues. Unfortunately, obtaining the diffusion coefficient from images in such cases is an inverse problem with only discrete data points. The development of a robust method that can work with such noisy and ill-posed datasets to accurately determine spatially-varying diffusion coefficients is of great value across a large range of disciplines. Here, we developed an inverse solver that uses physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying biological and engineering applications. The residual of the transient diffusion equation for a concentration field is minimized to find the diffusion coefficient. The robustness of the method as an inverse solver was tested using both numerical and experimental datasets. The predictions show good agreement with both the numerical and experimental benchmarks; an error of less than 6.31% was obtained against all numerical benchmarks, while the diffusion coefficient calculated in experimental datasets matches the appropriate ranges of other reported literature values. Our work demonstrates the potential of using PINNs to resolve spatially-varying diffusion coefficients, which may aid a wide-range of applications, such as enabling better-designed drug-eluting implants for regenerative medicine or oncology fields.

Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling.

Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g., from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g., agricultural runoff, wastewater treatment plants). To address this challenge, a novel inverse modelling approach is presented. Using waterway network topology, it quantifies locations and concentrations of contaminant sources upstream by inverting concentrations measured in water samples. It is computationally efficient and quantifies uncertainty. The approach is demonstrated for 13 contaminants of emerging concern (CECs) in an urban stream, the R. Wandle (London, UK). Mixing (the forward problem) was assumed to be conservative, and the location of sources and their concentrations were treated as unknowns to be identified. Calculated CEC source concentrations, which ranged from below detection limit (a few ng/L) up to 1µg/L, were used to predict concentrations of chemicals downstream. Using this approach, >90% of data were predicted within observational uncertainty. Principal component analysis of calculated source concentrations revealed signatures of two distinct chemical sources. First, pharmaceuticals and insecticides were associated with a subcatchment containing a known point source of treated effluent from a wastewater treatment plant. Second, illicit drugs and salicylic acid were associated with multiple sources, interpreted as input from untreated sewage including Combined Sewer Overflows (CSOs), misconnections, runoff and direct disposal throughout the catchment. Finally, a simple algorithmic approach that incorporates network topology was developed to design sampling campaigns to improve resolution of source apportionment. Inverse modelling of contaminant measurements can provide objective means to apportion sources in waterways from spot samples in catchments on a large scale.

Influence of non-equilibrium and nonlinear sorption of 137Cs in soils. Study with stirred flow-through reactor experiments and quantification with a nonlinear equilibrium-kinetic model.

This paper addresses the modelling of cesium sorption in non-equilibrium and nonlinear conditions with a two-site model. Compared to the classical Kd approach, the proposed model better reproduced the breakthrough curves observed during continuous-flow stirred tank reactor experiments conducted on two contrasted soils. Fitted parameters suggested contrasted conditions of cesium sorption between 1) equilibrium sites, with low affinity and high sorption capacity comparable to CEC and 2) non-equilibrium sites, with a fast sorption rate (half-time of 0.2-0.3 h), a slow desorption rate (half-time of 3-9 days) and a very low sorption capacity (0.02-0.04% of CEC). Comparison of EK sites densities with sorption capacities derived from the literature suggests that the EK equilibrium and kinetic sites might correspond to ion exchange and surface complexation of soil clay minerals respectively. This work stresses the limits of the Kd model to predict 137Cs sorption in reactive transport conditions and supports an alternative non-equilibrium nonlinear approach.

Evolution Mechanism of Arsenic Enrichment in Groundwater and Associated Health Risks in Southern Punjab, Pakistan.

Arsenic (As) contamination in groundwater is a worldwide concern for drinking water safety. Environmental changes and anthropogenic activities are making groundwater vulnerable in Pakistan, especially in Southern Punjab. This study explores the distribution, hydrogeochemical behavior, and pathways of As enrichment in groundwater and discusses the corresponding evolution mechanism, mobilization capability, and health risks. In total, 510 groundwater samples were collected from three tehsils in the Punjab province of Pakistan to analyze As and other physiochemical parameters. Arsenic concentration averaged 14.0 µg/L in Vehari, 11.0 µg/L in Burewala, and 13.0 µg/L in Mailsi. Piper-plots indicated the dominance of Na+, SO42-, Ca2+, and Mg2+ ions in the groundwater and the geochemical modeling showed negative saturation indices with calcium carbonate and salt minerals, including aragonite (CaCO3), calcite (CaCO3), dolomite (CaMg(CO3)2), and halite (NaCl). The dissolution process hinted at their potential roles in As mobilization in groundwater. These results were further validated with an inverse model of the dissolution of calcium-bearing mineral, and the exchange of cations between Ca2+ and Na+ in the studied area. Risk assessment suggested potential carcinogenic risks (CR > 10-4) for both children and adults, whereas children had a significant non-carcinogenic risk hazard quotient (HQ > 1). Accordingly, children had higher overall health risks than adults. Groundwater in Vehari and Mailsi was at higher risk than in Burewala. Our findings provide important and baseline information for groundwater As assessment at a provincial level, which is essential for initiating As health risk reduction. The current study also recommends efficient management strategies for As-contaminated groundwater.

Lagrangian Particle Dispersion Models in the Grey Zone of Turbulence: Adaptations to FLEXPART-COSMO for Simulations at 1 km Grid Resolution.

Lagrangian particle dispersion models (LPDMs) are frequently used for regional-scale inversions of greenhouse gas emissions. However, the turbulence parameterizations used in these models were developed for coarse resolution grids, hence, when moving to the kilometre-scale the validity of these descriptions should be questioned. Here, we analyze the influence of the turbulence parameterization employed in the LPDM FLEXPART-COSMO model. Comparisons of the turbulence kinetic energy between the turbulence schemes of FLEXPART-COSMO and the underlying Eulerian model COSMO suggest that the dispersion in FLEXPART-COSMO suffers from a double-counting of turbulent elements when run at a high resolution of 1 × 1 km 2 . Such turbulent elements are represented in both COSMO, by the resolved grid-scale winds, and FLEXPART, by its stochastic parameterizations. Therefore, we developed a new parametrization for the variations of the winds and the Lagrangian time scales in FLEXPART in order to harmonize the amount of turbulence present in both models. In a case study for a power plant plume, the new scheme results in improved plume representation when compared with in situ flight observations and with a tracer transported in COSMO. Further in-depth validation of the LPDM against methane observations at a tall tower site in Switzerland shows that the model's ability to predict the observed tracer variability and concentration at different heights above ground is considerably enhanced using the updated turbulence description. The high-resolution simulations result in a more realistic and pronounced diurnal cycle of the tracer concentration peaks and overall improved correlation with observations when compared to previously used coarser resolution simulations (at 7 km × 7 km). Our results indicate that the stochastic turbulence schemes of LPDMs, developed in the past for coarse resolution models, should be revisited to include a resolution dependency and resolve only the part of the turbulence spectrum that is a subgrid process at each different mesh size. Although our new scheme is specific to COSMO simulations at 1 × 1 km 2 resolution, the methodology for deriving the scheme can easily be applied to different resolutions and other regional models. Supplementary Information: The online version contains supplementary material available at 10.1007/s10546-022-00728-3.

Field leaching study - Inverse estimation of degradation and sorption parameters for a mobile soil metabolite and its pesticide parent.

Pesticide concentration measurements from field studies under real-world conditions can improve the derivation of more representative modelling input parameters for the exposure assessment of agrochemicals in the authorization process of plant protection products. The pertinent guidance documents foresee the application of inverse modelling approaches in combination with environmental fate and transport models to estimate e.g., soil dissipation rates that are solely based on microbial degradation and are not lumped with contributions from other dissipation processes such as leaching, plant uptake, volatilization and photodegradation. Field leaching studies can be used to estimate both degradation and sorption of chemicals in the soil matrix. In this study, inverse modelling of environmental fate parameters is presented based on solute concentrations from a field leaching study sampling pore water from five different depths down to 1.5 m. The leaching model PEARL and the universal optimization tool PEST were coupled, and sorption and degradation of the fungicide fluopicolide and its soil metabolite BAM (2,6-dichlorobenzamide) were quantified. Soil degradation half-lives were not different from results obtained in regular field degradation studies sampling residues in the total soil matrix (236 d vs. 158 d for fluopicolide and 53 d vs. 45 d for BAM); whereas a sorption increase with time (time-dependent sorption) was observed for the parent compound. This work aims at pointing out the feasibility to include field leaching studies with measurements at different soil depths in regulatory exposure assessment, since a statistically significant derivation of degradation and sorption parameters is presented, along with low uncertainties in the estimated parameter values of ±10%.

The effects of tapentadol and oxycodone on central processing of tonic pain.

OBJECTIVE: The present study investigated differences between opioids to experimental tonic pain in healthy men. METHODS: Twenty-one males participated in this cross-over-trial. Interventions twice daily were oxycodone (10 mg), tapentadol (50 mg) and placebo for 14 days. Tonic pain was induced on day 1, 4 and 14 by immersing the hand in 2 °C water for 120 s. Electroencephalography was recorded during test pain at baseline and after 14 days. Spectral analysis and source localization were investigated in predefined frequency bands. RESULTS: A decreased perception of pain on day 4 persisted throughout the 14 days compared to baseline (p < 0.006). Oxycodone decreased the electroencephalography spectral power in the delta and theta bands and increased power in the alpha1, alpha2 and beta1 bands (p < 0.03). Tapentadol increased spectral power in the alpha1 band (p < 0.001). Source localization revealed that oxycodone decreased activity of the temporal and limbic region in the delta band, and frontal lobe in the alpha2 and beta1 bands, whereas tapentadol decreased alpha1 band activity in the temporal lobe compared to placebo. CONCLUSION: Oxycodone and tapentadol reduced pain perception and changed the central processing of tonic pain. SIGNIFICANCE: Different mechanisms of action were involved, where oxycodone affected cortical structures more than tapentadol.

Sorption- and diffusion-induced isotopic fractionation in chloroethenes.

Isotopic fractionation of groundwater contaminants can occur due to degradation, diffusion and sorption. Of these, only degradation has been extensively explored, yet diffusive isotopic fractionation (DIF) and sorptive isotopic fractionation (SIF) can have significant effects on the isotopic enrichment of groundwater contaminants. Understanding how to mathematically describe and model these processes is vital to the correct interpretation of compound-specific isotope analysis (CSIA) data in the field. Here, models for these physical fractionation processes are developed and described, including the definition of a sorption enrichment factor. These models are then implemented numerically using inverse and finite-element methods to investigate two scenarios, diffusion-sorption and diffusion-sorption-advection, that have been measured in the laboratory. Concentration, δ37Cl, and δ2H data from cis-dichloroethene (cDCE) and trichloroethene (TCE) are used as inputs to the models. Unknown transport parameters including diffusive fractionation exponents are determined from an inverse modelling approach. DIF is shown to have a stronger influence on chlorine isotopologues than on hydrogen isotopologues. For both cDCE and TCE, the sorption enrichment factor of chlorine is found to be negative while that of hydrogen is positive. The presented approach and results provide novel tools and insight into DIF and SIF and underline that these processes should be taken into account when using CSIA to assess contaminant fate.

Detection of radioactivity of unknown origin: Protective actions based on inverse modelling.

The detection of radioactivity of unknown origin necessitates the use of models that can quantify unknown corresponding source term parameters. In this work, a method for solving this inverse problem is described. The main goal of the method is that it can be used in emergency response. Therefore, the full modelling chain dealing with the collection and pre-processing of measurement data, source term estimation, (forward) dispersion modelling, and consequence assessment are discussed. Firstly, to verify this inverse model SHERLOC, the part of the modelling chain concerning the source term estimation based on measurement data, is applied to the first episode of the European Tracer Experiment (ETEX). Secondly, the complete model chain is applied to a release that is still unaccounted for; the 106Ru measured in the atmosphere of Europe in September and October of 2017. It is estimated that during the night of the 25th to the 26th of September 2017 approximately 1.33 PBq (1.33×1015 Bq) of 106Ru was emitted at a location in the region of the Southern Urals in the Russian Federation. Statistical indicators show that the modelled levels of concentration are in good agreement with the measurements. The radiological consequences of the release are estimated to be minor at distances farther than 22 km from the estimated source. However, in the vicinity of the emission the maximum committed dose received by the public may have exceeded 100 mSv. Since the presented approach can be executed within few hours after the collection of measurement data it can be used in the emergency response following the detection of radioactivity of unknown origin.

Source estimation of an unexpected release of Ruthenium-106 in 2017 using an inverse modelling approach.

For the first time since the Chernobyl accident, detectable concentrations of ruthenium-106 were measured across Europe in September and October 2017. The source of this radioactive cloud remains unconfirmed. In this paper we present a forensic inverse modelling study to simultaneously estimate the source location, timing and magnitude of the unexpected ruthenium-106 release using 473 measurements of atmospheric concentration. To do this, we introduce a novel method, which estimates the uncertainty in the often unknown transport error using a Markov chain Monte Carlo approach. We corroborate the conclusions of other studies which suggest the source location is in the Southern Ural region of Russia, where the Mayak nuclear complex is located. Assuming that the Mayak nuclear complex is the most plausible release location, the method estimates that 441±13 TBq was released 12:00-18:00 UTC 24 September 2017, assuming a six hour release window.