Stochastic Monte Carlo Simulation Research Articles

Quantifying future carbon emissions uncertainties under stochastic modeling and Monte Carlo simulation: Insights for environmental policy consideration for the Belt and Road Initiative Region

This study critically examines future carbon (CO2) emissions in the Belt & Road Initiative (BRI) region, considering factors such as energy consumption, economic growth, population growth, and population density. The objective of this study is to identify critical areas of higher emissions, which require policy intervention capable of strengthening sustainability in the BRI compact. A combined approach of stochastic modeling and Monte Carlo simulations was employed, utilizing panel data from 45 countries in the BRI region from 1990 to 2021. Results confirm that emissions are higher in all scenarios in direct proportion to electric power consumption, population growth, and Gross Domestic Product (GDP) growth. In scenarios with high emissions, a continuous and significant upward trend in CO2 emissions was observe. The medium emissions scenario exhibited a more moderated rise in emissions, suggesting a balance between economic development and environmental considerations. Critical areas for future environmental policy-making resides in electric power consumption, population growth, and GDP growth. The study strongly recommends for a shift from the current focus on road and railway infrastructure to renewable energy infrastructure, green innovations and efficient technology transfer to member countries. Without this, the BRI region is likely to face increased emissions, posing significant challenges to future sustainable development and global environmental sustainability.

Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations

We study the ferromagnetic transverse-field Ising model with quenched disorder at T = 0T=0 in one and two dimensions by means of stochastic series expansion quantum Monte Carlo simulations using a rigorous zero-temperature scheme. Using a sample-replication method and averaged Binder ratios, we determine the critical shift and width exponents \nu_\mathrm{s}νs and \nu_\mathrm{w}νw as well as unbiased critical points by finite-size scaling. Further, scaling of the disorder-averaged magnetisation at the critical point is used to determine the order-parameter critical exponent \betaβ and the critical exponent \nu_{\mathrm{av}}νav of the average correlation length. The dynamic scaling in the Griffiths phase is investigated by measuring the local susceptibility in the disordered phase and the dynamic exponent z'z′ is extracted. By applying various finite-size scaling protocols, we provide an extensive and comprehensive comparison between the different approaches on equal footing. The emphasis on effective zero-temperature simulations resolves several inconsistencies in existing literature.

Investigating the closure stress and crack initiation stress in fractured rocks using the student t distribution and Monte Carlo simulation method.

Traditional method of determining closure and initiation stress of fractured rocks by analyzing the stress-strain curve has problems such as strong subjectivity and large errors. This study utilized the rock closure stress values and onset stress values determined by three traditional methods, namely, axial strain method, fracture volume method and empirical value taking method, as the base database. The Student t distribution theory was used to obtain a confidence interval based on its overall distribution of values and to achieve a combination of the advantages of multiple methods. Within confidence interval, the Monte Carlo stochastic simulation was used to determine the convergence interval of the second stage to further improve the accuracy. Finally, mean value of the randomly sampled values after reaching the convergence stage was taken as the probability value of rock closure and crack initiation stress. The results showed that the 3 traditional methods for calculating rock closure and initiation stresses are significantly different. In contrast, the proposed method biases more towards multi-numerical distribution intervals and also considers the preference effects of different calculation methods. In addition, this method does not show any extreme values that deviate from the confidence intervals, and it has strong accuracy and stability compared to other methods.

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach

Urban energy poverty remains a critical challenge affecting millions worldwide, with significant implications for socio-economic development and sustainability. This study employs Monte Carlo simulations to analyze the variability in energy poverty, as measured by the Multidimensional Energy Poverty Index (MEPI). Through this approach, we explored how different socio-economic predictors affect MEPI scores across five scenarios: economic growth and diversification, energy transition and technological innovation, climate change impacts and vulnerability, policy intervention and social safety nets, and stagnation and rising inequality. The sensitivity analysis revealed that household income, size, and food security are paramount in influencing urban energy poverty levels. Scenarios involving economic growth and technological innovation demonstrated positive impacts on reducing energy poverty, while scenarios of climate change and economic stagnation highlighted the vulnerabilities and widening disparities within populations. The results suggest that to effectively alleviate urban energy poverty, policy interventions must be comprehensive, targeting the identified influential factors. The study emphasizes the need for targeted policy interventions on social safety nets to alleviate energy poverty.

Buckling behavior of rotor blade sandwich panels with spatially distributed material uncertainties

The study evaluates the impact of material uncertainties on the buckling behavior of sandwich panels in wind turbine rotor blades. The analysis is limited to linear buckling and is performed using stochastic finite element Monte Carlo simulation on a rectangular and flat submodel of the rotor blade’s trailing edge panel. The finite element model of the panels is simply supported on all edges. To generate the spatial material property distributions, the Karhunen-Loève expansion is used in combination with Latin hypercube sampling. The results compare the effects of various correlation lengths of the spatial distributions. The buckling loads vary in correlation to the average panel stiffness caused by the random distributions. The spatial distribution has a less dominant effect, reducing the mean value of the buckling load results. The amount of reduction in buckling load is highest when the correlation length of the distribution is close to the harmonic half-wave of the dominant buckling shape.

Stochastic dynamics of mechanical systems with impacts via the Step Matrix multiplication based Path Integration method

AbstractIn this work we propose the Step Matrix Multiplication based Path Integration method (SMM-PI) for nonlinear vibro-impact oscillator systems. This method allows the efficient and accurate deterministic computation of the time-dependent response probability density function by transforming the corresponding Chapman–Kolmogorov equation to a matrix–vector multiplication using high-order numerical time-stepping and interpolation methods. Additionally, the SMM-PI approach yields the computation of the joint probability distribution for response and impact velocity, as well as the time between impacts and other important characteristics. The method is applied to a nonlinear oscillator with a pair of impact barriers, and to a linear oscillator with a single barrier, providing relevant densities and analysing energy accumulation and absorption properties. We validate the results with the help of stochastic Monte-Carlo simulations and show the superior ability of the introduced formulation to compute accurate response statistics.

Stochastic modeling of a neutron imaging center at the Brazilian Multipurpose Reactor

Neutron imaging is a non-destructive technique for analyzing a wide class of samples, such as archaeological or industrial material structures. In recent decades, technological advances have had a great impact on the neutron imaging technique, which has meant an evolution from simple radiographs using films (2D) to modern tomography systems with digital processing (3D). The 5 MW research nuclear reactor IEA-R1, which is located at the Instituto de Pesquisas Energéticas e Nucleares (IPEN) in Brazil, possesses a neutron imaging instrument with 1.0×106n/cm2s in the sample position. IEA-R1 is over 60 years old and the future of neutron science in Brazil, including imaging, will be expanded to a new facility called the Brazilian Multipurpose Reactor (RMB, Portuguese acronym), which will be built soon. The new reactor will house a suite of instruments at the Neutron National Laboratory, including the neutron imaging facility, viz., Neinei. Inspired by recent author’s works, we model the Neinei instrument through stochastic Monte Carlo simulations. We investigate the sensitivity of the neutron imaging technique parameter (L/D ratio) with the neutron flux, and the results are compared to data from the Neutra (PSI), Antares (FRM II), BT2 (NIST) and DINGO (OPAL) instruments. The results are promising and provide avenues for future improvements.

Stochastic Excitation-Incorporated Seismic Fragility Assessment Using Monte Carlo Simulations

Structures are susceptible to damage from natural disasters like tornadoes, floods, and seismic events. A resilient structure can withstand such damage and ensure cost-effectiveness. Therefore, accurately assessing the probability of structural failure is imperative for structural designers. This study employs an innovative fragility methodology to assess seismic damage in structures. In comparison to conventional fragility approaches, this method is cost-effective, universally applicable across various seismic sources, and highly precise. Specifically, this method involves the generation of artificial seismic records using the point-source stochastic seismological model and Monte Carlo simulation method. These synthetic seismic records offer distinct advantages when compared to the seismic data collected from real earthquakes at recording stations, such as the ability to adjust the epicentral distance of the synthetic waves as needed. Therefore, this approach enables a comprehensive analysis of structural responses under seismic waves characterized by different attributes. For example, the influence of different seismological parameters such as wave magnitude and epicentral distance are well studied and compared. The advanced finite element software, OpenSees, serves as the computational platform for examining how structures react to diverse seismic waves. The research findings suggest that, compared to traditional methods, utilizing OpenSees can significantly enhance computational efficiency. Furthermore, the methods employed introduce a novel technique for factoring in wave magnitudes and distances during the calculation of structural seismological responses/fragility. This innovation holds significance for the development of next-generation structural resilience designs.

Stochastic Economic Ranking Addresses Risk and Uncertainty for Decision-Makers

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213385, “Stochastic Economic Ranking: A Prudent Way To Address Risk and Uncertainty for Decision-Makers,” by Mahesh Narayanan, SPE, Mehaboob Abdulazeez, SPE, and Khadija Bukhamsin, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. _ Project screening and ranking is inevitable even when an organization faces no resource bottlenecks. In project economic predictions, the stochastic approach is prevalent and proves to be the most natural approach to address technical and commercial uncertainties. In contrast with a deterministic approach, a stochastic Monte Carlo simulation helps in understanding the range of possible outcomes to enable effective decision-making. The objective of the complete paper is to present an integrated stochastic work flow that addresses the subsurface, surface, and cost uncertainties to screen, rank, and grade opportunities early in the derisking process. Introduction The proposed work flow simultaneously can address all technical and commercial uncertainties with a Monte Carlo model and predict a range of possible economic outcomes with customized economic analysis tools. In other words, it can achieve complete unbiased sample randomness in expected project outcomes. The work flow also creates a stage-gate decision-tree approach and reviews economics as a series of staged investments. This helps to expose incremental capital in the life cycle of a project responsibly and help with course correction where necessary. Value Addition: Stochastic Approach A common practice in the oil and gas industry is to simply examine low, middle, and high cases. This is a limited view of possible scenarios and provides a false sense of precision regarding expected project outcomes. The term “stochastic” refers to the property of being well-described by a random probability distribution. A stochastic approach features the following advantages: - A stochastic model addresses uncertainties in inputs with complete unbiased randomness in the outcomes. - By running thousands of calculations and using many different estimates of future economic conditions, stochastic models predict a range of possible future investment results, showing the potential upside and downside of each. - Once the work flow is followed and the model is set up, multiple realizations can be created with a single click. Proposed Integrated Stochastic Methodology In the world of project economics, classical stochastic economics is a tool to address uncertainty and risk. In simple terms, a mean or deterministic view of a production profile, along with one view of a schedule, is taken and blended with probabilistic distribution of cost inputs. This data is then taken through a Monte Carlo model to create multiple scenarios to predict a range of possible economic outcomes. This narrow range of economic outcomes only covers the uncertainty in cost but does not cover variability in well performance and time.

Estimating RQD for Rock Masses Based on a Comprehensive Approach

Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size D, beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size D (RQD(D)) and (4) determining the representative RQD (rRQD), defined as the specific value which, once D exceeds, the RQD(D) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates.

Nonequilibrium steady states in coupled asymmetric and symmetric exclusion processes.

We propose and study a one-dimensional (1D) model consisting of two lanes with open boundaries. One of the lanes executes diffusive and the other lane driven unidirectional or asymmetric exclusion dynamics, which are mutually coupled through particle exchanges in the bulk. We elucidate the generic nonuniform steady states in this model. We show that in a parameter regime, where hopping along the TASEP lane, diffusion along the SEP lane, and the exchange of particles between the TASEP and SEP lanes compete, the SEP diffusivity D appears as a tuning parameter for both the SEP and TASEP densities for a given exchange rate in the nonequilibrium steady states of this model. Indeed, D can be tuned to achieve phase coexistence in the asymmetric exclusion dynamics together with spatially smoothly varying density in the diffusive dynamics in the steady state. We obtain phase diagrams of the model using mean field theories, and corroborate and complement the results with stochastic Monte Carlo simulations. This model reduces to an isolated open totally asymmetric exclusion process (TASEP) and an open TASEP with bulk particle nonconserving Langmuir kinetics (LK), respectively, in the limits of vanishing and diverging particle diffusivity in the lane executing diffusive dynamics. Thus, this model works as an overarching general model, connecting both pure TASEPs and TASEPs with LK in different asymptotic limits. We further define phases in the SEP and obtain phase diagrams and show their correspondence with the TASEP phases. In addition to its significance as a 1D driven, diffusive model, this model also serves as a simple reduced model for cell biological transport by molecular motors undergoing diffusive and directed motion inside eukaryotic cells.

Rapid evolution of phenotypic plasticity in patchy habitats

Phenotypic plasticity may evolve rapidly, enabling a population’s persistence in the face of sudden environmental change. Rapid evolution can occur when there is considerable genetic polymorphism at selected loci. We propose that balancing selection could be one of the mechanisms that sustain such polymorphism for plasticity. We use stochastic Monte Carlo simulations and deterministic analysis to investigate the evolution of a plasticity modifier locus in structured populations inhabiting favorable and adverse environments, i.e. patchy habitats. We survey a wide range of parameters including selective pressures on a target (structural) locus, plasticity effects, population sizes, and migration patterns between demes including periodic or continuous bidirectional and source-sink dynamics. We find that polymorphism in phenotypic plasticity can be maintained under a wide range of environmental scenarios in both favorable and adverse environments due to the balancing effect of population structure in patchy habitats. This effect offers a new plausible explanation for the rapid evolution of plasticity in nature: Phenotypic plasticity may rapidly evolve from genetic variation maintained by balancing selection if the population has experienced immigration from populations under different selection regimes.

Evolution of 3D X-Ray Dose Computation Algorithms

Radiation treatment planning of individual cancer patients relies on the accurate computation of dose distributions in irradiated tissue. Inaccurate dose maps have the potential to mislead clinical decision-making and compromise the balance between effective tumour control and side effects in surrounding normal tissue. In the context of this conference, 3D dosimetry is important for the experimental validation of computed dose distributions. Dose computation methods for external beams of high energy x rays have evolved over the past decade with computer simulation models more closely aligned with the fundamental physics of x-ray scattering and absorption in heterogeneous tissue. In this article, we first present a historical review from a Canadian perspective, followed by an introductory intuitive description of contemporary algorithms used in clinical treatment planning: (1) Convolution-superposition algorithm fundamentally based on the Green’s function method; (2) Stochastic Monte Carlo simulation of x-ray interactions in tissue, and (3) Deterministic numerical solution of a system of Boltzmann transport equations. In principle, all these methods solve the same problem of predicting x-ray scattering and absorption in heterogeneous tissue. However, the mathematical tools differ in their approach and approximations to achieve sufficient speed for routine clinical application. In the conclusion of this article, the evolution of 3D x-ray dose computation is summarized, in terms of accuracy and computational speed.

Stochastic model to assess bioeconomic impact of PRRS on pig farms in Costa Rica.

Despite the economic importance of PRRS and its high prevalence in Costa Rica, there are no studies on the bioeconomic impact of the disease in the country or, even, in Central America. Such studies are essential in finding cost-effective preventive measures tailored for different production circumstances. Therefore, the objective of this study was to evaluate economic and production parameters of a PRRSV-infection for a medium-sized farrow-to-finish pig farm system in Costa Rica with a farm-level stochastic Monte Carlo simulation model. The effect of PRRS was assessed by scenario analysis, in which a baseline PRRS-free situation was compared against three alternative scenarios that assumed low, medium and high PRRS effects. The PRRS effects were based on data from local farms, scientific literature and expert opinion. Sensitivity analyses were performed to assess the impact of key input parameters on output variables. Results show that at the animal level, changes between the baseline and the PRRS-high scenario were estimated as: +25 d in age to slaughter, -9.9 pigs to slaughter (per breeding sow/yr), +6% annual replacement rate, -255 d in sow productive lifetime, - 6.9 mo in age at culling of sows, and +24 non- productive days. For a medium size local farm (n=588 sows), a reduction of 5826 fat pigs to slaughter per farm/yr from baseline compared to PRRS-high scenario was observed. PRRS-induced loss per farm per year was estimated at -US $142,542, US $180,109 and -US $524,719 for PRRS-low, medium and high scenarios, respectively. Revenues/costs ratio changed from 1.12 in the baseline to 0.89 in the PRRS-high scenario. The production cost per kg carcass weight increased from US $2.63 for the baseline to US $3.35 in the PRRS-high scenario. PRRS-induced loss was estimated at US $77.1 per slaughtered pig/yr and US $892 per breeding sow/yr for the PRRS-high scenario. Results from the model indicate that pig farms with medium to high prevalence of PRRS will require optimal market conditions in order to have positive economic outcomes. These results can be helpful in the design of better control strategies for PRRS.

A stochastic numerical model for wave-induced dynamic response in a spatially heterogeneous seabed around structures

The interaction between waves, seabed, and structures is a complex phenomenon that involves multiple physical domains and processes. The spatial variability of properties in marine sediment can significantly affect the dynamic response of seabed. A stochastic numerical model is developed to investigate the response in a spatially heterogeneous poro-elastic seabed around a submerged breakwater. The model is implemented using the COMSOL platform, which allows for solving the wave and seabed sub-models within the same framework. The spatial variability of soil properties is simulated using the random field method employing the Karhunen-Loève expansion. To couple the generated random fields in MATLAB with the integrated model in COMSOL, a LiveLink program is developed, which incorporates a cycle algorithm for stochastic finite element analysis, enabling hundreds or thousands of runs. The model is validated by comparing its results with analytical solutions and experimental data from literature. Probabilistic analysis is performed using the stochastic numerical model and Monte Carlo simulations. The effects of coefficient of variation and correlation lengths in random field on the probabilistic analysis are investigated. The present study provides a valuable tool and insights for the probabilistic assessment of soil response in a spatially heterogeneous seabed around structures.

One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level

BackgroundPrimary healthcare systems require adequate staffing to meet the needs of their local population. Guidelines typically use population ratio targets for healthcare workers, such as Ethiopia’s goal of two health extension workers for every five thousand people. However, fixed ratios do not reflect local demographics, fertility rates, disease burden (e.g., malaria endemicity), or trends in these values. Recognizing this, we set out to estimate the clinical workload to meet the primary healthcare needs in Ethiopia by region.MethodsWe utilize the open-source R package PACE-HRH for our analysis, which is a stochastic Monte Carlo simulation model that estimates workload for a specified service package and population. Assumptions and data inputs for region-specific fertility, mortality, disease burden were drawn from literature, DHS, and WorldPop. We project workload until 2035 for seven regions and two charted cities of Ethiopia.ResultsAll regions and charted cities are expected to experience increased workload between 2021 and 2035 for a starting catchment of five thousand people. The expected (mean) annual clinical workload varied from 2,930 h (Addis) to 3,752 h (Gambela) and increased by 19–28% over fifteen years. This results from a decline in per capita workload (due to declines in fertility and infectious diseases), overpowered by total population growth. Pregnancy, non-communicable diseases, sick child care, and nutrition remain the largest service categories, but their priority shifts substantially in some regions by 2035. Sensitivity analysis shows that fertility assumptions have major implications for workload. We incorporate seasonality and estimate monthly variation of up to 8.9% (Somali), though most services with high variability are declining.ConclusionsRegional variation in demographics, fertility, seasonality, and disease trends all affect the workload estimates. This results in differences in expected clinical workload, the level of uncertainty in those estimates, and relative priorities between service categories. By showing these differences, we demonstrate the inadequacy of a fixed population ratio for staffing allocation. Policy-makers and regulators need to consider these factors in designing their healthcare systems, or they risk sub-optimally allocating workforce and creating inequitable access to care.

Simulations of the effect of smectite‐to‐illite transition in shales on permeability and overpressures using a stochastic approach, a Norwegian margin case study

AbstractThe smectite‐illite transition in shales due to subsidence, temperature changes and diagenesis influences many processes in a sedimentary basin that can contribute to overpressure build up like reducing the shale permeability. The smectite‐rich layers can form sealing barriers to fluid flows that will influence pore pressure prognosis for drilling campaigns, contribute to sealing caprocks for possible CO2 storage and to sealing of plugging and abandonment wells. In this work, we have included the diagenetic smectite‐illite transition into a three‐dimensional pressure simulation model to simulate its effect on pressure build‐up due to reduced shale permeabilities over geological time scale. We have also tested effect of thermal history and potassium concentration on the process of smectite‐illite transition and the associated smectite‐illite correction on permeability. A new smectite‐illite correction has been introduced, to mimic how shale permeability will vary dependent on the smectite‐illite transition. Stochastic Monte Carlo simulations have been carried out to test the sensitivity of the new correction parameters. Finally, a 3D Monte Carlo pore pressure simulation with 1000 drawings has been carried out on a case study covering Skarv Field, and Dønna Terrace offshore Mid‐Norway. The simulated mean overpressures are in range with observed overpressures from exploration wells in the area for the Cretaceous sandy Lysing Formation and for the two Cretaceous Intra Lange Formation sandstones. The simulated smectite content versus depth is in line with published XRD dataset from wells. The corresponding modelled present‐day permeabilities for the shales including the smectite‐illite transition are two magnitudes higher than measured permeabilities on small samples in the laboratory using transient decay method. The measured permeabilities are in the range of 2.66·10−18 to 3.94·10−22 m2 (2695 to 0.39 nD) for the North Sea database and represent the end members for shales‐permeabilities with the lowest values, since the small samples are selected with no or minor natural fractures. This work shows that by upscaling shale permeabilities from mm‐scale to km scale, natural fractures and sedimentary heterogeneities will increase the shale permeabilities with a factor of two and that by including permeability correction controlled by the smectite fraction, pressure ramp can be simulated due to diagenesis effect in shales.

Potential Ecological Risk Assessment and Source Analysis of Heavy Metals in Soil-crop System in Xiong'an New District

In order to evaluate the distribution characteristics of fluorine geochemistry in the surface soil and human health risk in Xiong'an New District, GIS spatial analysis and correlation analysis were used to analyze the depleted and enriched features and influencing factors of soil fluoride and to carry out the soil fluoride health risk assessment. The uncertainty of the health risk assessment results was studied based on the Monte Carlo stochastic simulation. The results showed that the average content of fluorine was 641 mg·kg-1, which was 1.34 times the background value of the national A-layer soil. The excess fluorine and high-grade samples accounted for more than 85%, and the overall soil fluorine content was relatively high. The average content of fluoride of the irrigation water samples was 0.85 mg·L-1, the spatial distribution characteristics of which were affected by the hydrochemical type and flow direction of shallow groundwater. The vertical spatial variation of soil fluoride, mainly affected by the vertical distribution of soil physicochemical properties such as soil organic carbon and texture, was not obvious. The depletion and enrichment of topsoil fluorine was mainly controlled by the geological background, and its spatial distribution was affected by external inputs, such as human factors (agricultural irrigation water, fertilization, and atmospheric dry and wet deposition). The soil fluoride content was significantly correlated with the iconic indicators of the geomorphological environment, including the content of Al2O3, Fe2O3, MgO, K2O, soil organic carbon (Corg.), cation exchange capacity (CEC), clay, and silt (P<0.01). The results of human health risk assessment showed that oral intake was the main exposure risk route of soil fluoride. The non-carcinogenic health risk index HQ of adults was less than 1, and the harm could be ignored. The probability of non-carcinogenic health risk exceeding the threshold for adults and children was 34.3% and 27.6%, respectively, and daily soil intake was the most sensitive parameter.