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A major challenge in accidental or unregulated releases is the ability to identify the pollutant source, especially if the location is in a large industrial area. Usually in such cases, only a few sensors provide non-zero signal. A crucial issue is therefore the ability to use a small number of sensors in order to identify the source location and rate of emission. The general problem of characterizing source parameters based on real-time sensors is known to be a difficult task. As with many inverse problems, one of the main obstacles for an accurate estimation is the non-uniqueness of the solution, induced by the lack of sufficient information. In this study, an efficient method is proposed that aims to provide a quantitative estimation of the source of hazardous gases or breathable aerosols. The proposed solution is composed of two parts. First, the physics of the atmospheric dispersion is utilized by a well-established Lagrangian stochastic model propagated backward in time. Then, a new algorithm is formulated for the prediction of the spacial expected uncertainty reduction gained by the optimal placement of an additional sensor. These two parts together are used to construct an adaptive decision support system for the dynamical deployment of detectors, allowing for an efficient characterization of the emitting source. This method has been tested for several scenarios and is shown to significantly reduce the uncertainty that stems from the insufficient information. [ABSTRACT FROM AUTHOR]

Identifying unknown sources of air pollutants is vital for protecting public health, especially in cases involving the emission of toxic substances. The efficiency of this process depends highly on the accuracy of Source Term Estimation (STE) methods and the availability of robust measurements. Therefore, it is important to examine how sensor network characteristics affect STE accuracy. This study investigates the impact of different sensor configurations on STE results for a stationary point source in a complex, urban-like environment. The STE methodology employs the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm alongside numerical simulations of a Computational Fluid Dynamics (CFD) model. The STE algorithm is applied across several sensor configurations in three distinct release scenarios and real sensor observations from the Michelstadt wind tunnel experiment, assessing both the number of sensors used and the agreement between measured and modeled concentrations. In general, the results indicate that increasing the number of sensors and the model's accuracy improves the source parameters estimations. However, there is a specific number of sensors in each release scenario where STE outcomes from randomly selected, high-accuracy, and low-accuracy sensors converge to similar solutions. Overall, the findings provide valuable information for designing sensor configurations in urban areas. [ABSTRACT FROM AUTHOR]

Mathematical models serve as crucial tools for quantitatively assessing the environmental and population impact resulting from the release of hazardous substances. Often, precise source parameters remain elusive, leading to a reliance on rudimentary assumptions. This challenge is particularly pronounced in scenarios involving releases that are accidental or deliberate acts of terrorism. A conventional method for estimating the source term involves the construction of backward plumes originating from various sensors measuring tracer concentrations. The area displaying the highest overlap of these backward plumes typically offers an initial approximation for the most probable release location. The backward plume (BP) method has been compared with a machine learning based method. Both methods use data from a field campaign and from a synthetic dataset built from a simple setup featuring receptors arranged linearly downwind from the release point. A substantial number (~ 1500) of forward plume simulations are conducted, each initiated from random locations and under varying meteorological conditions. This extensive dataset encompasses critical meteorological variables and concentration measurements recorded by idealized receptors. Subsequently, the dataset has been partitioned into training and testing subsets. A feed-forward neural network (NN) has been employed. This NN is trained using the concentration data from the receptors and the associated meteorological variables as input, with the source location coordinates serving as the output. Subsequent verification is carried out using the testing dataset, facilitating a comparison between the NN's and BP's predictions and the actual source locations. One of the key advantages of the NN-based approach is its ability to rapidly estimate the source term, typically within a fraction of a second on a standard laptop. This speed is of paramount significance in scenarios involving accidental releases, where swift response is essential. Notably, the computationally intensive tasks of dataset construction and NN training can be conducted offline, providing preparedness in areas where accidental releases may be anticipated. [ABSTRACT FROM AUTHOR]

Estimating the parameters of an unidentified toxic pollutant source is crucial for public safety, especially in densely populated urban areas. Implementing source term estimation methods in real-world urban environments is challenging due to complex phenomena and the absence of concentration observational data. This work combines a computational fluid dynamics numerical simulation with the Metropolis–Hastings MCMC algorithm to identify the location and quantify the release rate of an unknown source within the geometry of Augsburg city center. To address the lack of concentration measurements, synthetic observations are generated by a forward dispersion model. The methodology is tested using these datasets, both as directly calculated by the forward model and with added Gaussian noise under different source release and wind flow scenarios. The results indicate that in most cases, both the source location and the release rate are estimated accurately. Although a higher performance is achieved using synthetic datasets without additional noise, high accuracy predictions are also obtained in many applications of noisy measurement datasets. In general, the outcomes demonstrate that the presented methodology can be a useful tool for estimating unknown source parameters in real-world urban applications. [ABSTRACT FROM AUTHOR]

Air pollution poses a major threat to health, environment, and global climate. Characterizing the emission parameters responsible for air contamination can help formulate appropriate response plans. We propose an advanced methodology that uses Markov Chain Monte Carlo (MCMC) sampling within a Bayesian inference framework to invert for emission parameters of air contamination in an urban environment. We also use a high-resolution Lagrangian dispersion model to provide microscale wind computations as well as pollution concentration values in the presence of urban features with high complexity. Buildings and land use features were all integrated in a realistic urban setup that represents the region of King Abdullah University of Science and Technology, KSA. Boundary meteorological conditions acquired from a Weather Research and Forecasting (WRF) model simulation were employed to obtain the mesoscale wind field. We design numerical experiments to infer two common types of reference observations, a pollutant concentration distribution and point-wise discrete concentration values. The local L 2 norm and global Wasserstein distance are investigated to quantify the discrepancies between the observations and the model predictions. The results of the conducted numerical experiments demonstrate the advantages of using the global optimal transport metric. They also emphasize the sensitivity of the inverted solution to the available observations. The proposed framework is proven to efficiently provide robust estimates of the emission parameters. [ABSTRACT FROM AUTHOR]

• Dynamic mode decomposition is proposed to improve the efficiency of source term estimation. • The storage demand for source term estimation is reduced when applying dynamic mode decomposition. • The future flow required by source term estimation can be predicted rapidly when applying dynamic mode decomposition. • The appropriate selection of DMD parameters in investigated. When estimating source parameters in the unsteady flow, the flow information of pollution dispersion is indispensable. It is common practice to save the flow information in the computer in advance but it requires large storage space. Besides, when contaminants are released after a time period of the flow field saved before, calculating the flow field by Computational Fluid Dynamics (CFD) model demands massive computational cost. Dynamic Mode Decomposition (DMD) is thereby proposed to solve the problems mentioned above. Firstly, the fields are decomposed by DMD. Then, the simulated concentrations are acquired by the adjoint equation based on the field synthesized by DMD. Finally, the measured concentrations and the simulated concentrations are taken into Bayesian inference to accomplish source term estimation (STE). The results show that the estimated results with high accuracy are obtained both in the reconstruction stage and in the prediction stage when using the fields obtained by DMD. Also, the efficiency of predicting the future flow by DMD is much higher than that by CFD simulation, suggesting that DMD can improve the efficiency of STE in some cases. As DMD uses a small number of dominant modes to synthesize the approximate fields with minor errors, it reduces the storage demand of flow information in STE. The sampling range and sampling resolution should be properly selected to ensure the accuracy of STE. [ABSTRACT FROM AUTHOR]

During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios. [ABSTRACT FROM AUTHOR]

The use of autonomous vehicles for source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate from the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in an adaptive framework. The proposed system intelligently produces potential gas sampling locations that will reliably inform the estimation engine by not sampling in the wake of buildings as frequently. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations.The proposed informed tree planning algorithm is then tested against the standard Entrotaxis and Entrotaxis-Jump techniques in a series of high fidelity simulations. The proposed system is found to reduce source estimation error far more efficiently than its competitors in a feature rich environment, whilst also exhibiting vastly more consistent and robust results. [ABSTRACT FROM AUTHOR]

Source term estimation (STE) is crucial for understanding and addressing hazardous gas leakages in the chemical industry. Most existing methods basically use an atmospheric transport and dispersion (ATD) model to predict the concentrations of hazardous gas leakages from different possible sources, compare the predicted results with multi-sensor data, and use the deviations to search and derive information on the real sources of leakages. Although performing well in principle, complicated computations and the associated computer time often make these methods difficult to apply in real time. Recently, many machine learning methods have also been proposed for the purpose of STE. The idea is to build offline a machine-learning-based STE model using data generated with a high-fidelity ATD model and then apply the machine learning model to multi-sensor data to perform STE in real time. The key to the success of a machine-learning-based STE is that the machine-learning-based STE model has to cover all possible scenarios of concern, which is often difficult in practice because of unpredictable environmental conditions and the inherent robust problems with many supervised machine learning methods. In order to address challenges with the existing STE methods, in the present study, a novel multi-sensor data-driven approach to STE of hazardous gas leakages is proposed. The basic idea is to establish a multi-sensor data-driven STE model from historical multi-sensor observations that cover the situations known as the independent hazardous-gas-leakage scenarios (IHGLSs) in a chemical industry park of concern. Then the established STE model is applied to online process multi-sensor data and perform STE for the chemical industry park in real time. The new approach is based on a rigorous analysis of the relationship between multi-sensor data and sources of hazardous gas leakages and derived using advanced data science, including unsupervised multi-sensor data clustering and analysis. As an example of demonstration, the proposed approach is applied to perform STE for hazardous gas-leakage scenarios wherein a Gaussian plume model can be used to describe the atmospheric transport and dispersion. Because of no need of ATD-model-based online optimization and supervised machine learning, the new approach can potentially overcome many problems with existing methods and enable STE to be literally applied in engineering practice. [ABSTRACT FROM AUTHOR]

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency.Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.

This study evaluates two local‐scale atmospheric inversion approaches for the monitoring of methane (CH4) emissions from industrial sites based on in situ atmospheric CH4 mole fraction measurements from stationary or mobile sensors. We participated in a two‐week campaign of CH4 controlled‐release experiments at TotalEnergies Anomaly Detection Initiatives (TADI) in Lacq, France in October 2019. We analyzed releases from various points within a 40 m × 50 m area with constant rates of 0.16 to 30 g CH4 s−1 over 25 to 75 mins, using fixed‐point and mobile measurements, and testing different inversion configurations with a Gaussian dispersion model. An inlet switching system, combining a limited number (6–7) of high‐precision gas analyzers with a higher number (16) of sampling lines, ensured that a sufficient number of fixed measurement points sampled the plume downwind of the sources and the background mole fractions for any wind direction. The inversions using these fixed‐point measurements provide release rate estimates with approximately 23%–30% average errors and estimates of the location of the releases with approximately 8–10 m average errors. The inversions using the mobile measurements provide estimates with approximately 20%–30% average errors for the release rates and approximately 30 m average errors for the release locations. The precision of the release rate estimates from both inversion frameworks corresponds to the best estimation precision documented on site‐scale CH4 inversions. However, the use of continuous measurements from fixed stations provides much more robust estimates of the source locations than that of the mobile measurements. [ABSTRACT FROM AUTHOR]

In hazardous situations involving the dispersion of chemical, biological, radiological, and nuclear pollutants, timely containment of the emission is critical. A contaminant disperses as a dynamically evolving plume into the atmosphere, introducing complex difficulties in predicting the dispersion trajectory and potential evacuation sites. Strategies for predictive modeling of rapid contaminant dispersion demand localization of the emission source, a task performed effectively via unmanned mobile-sensing platforms. With vast possibilities in sensor configurations and source-seeking algorithms, platform deployment in real-world applications involves much uncertainty alongside opportunity. This work aims to develop a plume source detection simulator to offer a reliable comparison of source-seeking approaches and performance testing of ground-based mobile-sensing platform configurations prior to experimental field testing. Utilizing ROS, Gazebo, MATLAB, and Simulink, a virtual environment is developed for an unmanned ground vehicle with a configurable array of sensors capable of measuring plume dispersion model data mapped into the domain. For selected configurations, gradient-based and adaptive exploration algorithms were tested for source localization using Gaussian dispersion models in addition to large eddy simulation models incorporating the effects of atmospheric turbulence. A unique global search algorithm was developed to locate the true source with overall success allowing for further evaluation in field experiments. From the observations obtained in simulation, it is evident that source-seeking performance can improve drastically by designing algorithms for global exploration while incorporating measurements of meteorological parameters beyond solely concentration (e.g. wind velocity and vorticity) made possible by the inclusion of high-resolution large eddy simulation plume data. [ABSTRACT FROM AUTHOR]

Air pollution of chemical industrial parks (CIPs) is becoming increasingly severe, which has major impacts on the health of local residents. Therefore, source term estimation (STE) is of high importance to find the source locations and back-calculate the source emission rates based on the ambient concentration measurements and meteorological information. However, the number of the ambient sensors is far less than the one of pollution sources for CIPs. This issue of sensor deficiency makes the unknown source parameters untraceable. In this paper, the concept of traceability is introduced to propose a condition to explain when the STE problem has a unique solution. Then an approach using measurements at multiple time points for estimating the emission rate of each source is proposed. In order to satisfy the condition of traceability, the coefficient matrix can be expanded by augmenting measurement samples collected at different time instances with different wind directions when the emission rates keep unchanged. Furthermore, based on the rank of the coefficient matrix, the problem can be classified as fully traceable, partially traceable, and untraceable. Then, the regularized least squares method is applied to estimate the source rates in real-time. Some test results with a simulated scenario demonstrate that the source rate can be reliably estimated with the method proposed. Finally, the limitations and conclusions of the method are stated. [ABSTRACT FROM AUTHOR]

Air pollution monitoring for chemical industrial parks suffers from sensor deficiency. To address this problem, this work focuses on mobile monitoring station solutions to complement the fixed measurement deficiency. Flexibility in measurement location makes the mobile solution advantageous over fixed stations not only in providing sufficient number of measurements to satisfy the traceability condition, but also in the possibility to design monitoring route optimally according to real-time wind directions. By taking these advantages, this work proposes a new method for optimal mobile route design so that the inference of uncertainties on the accuracy of source rate estimation is minimized. Based on the linear relationship between the concentration measurements and source emission rates, the effect of measurement noises on the estimation error is derived as an amplification factor through numerical analysis, then, the optimal monitoring route can be determined by minimizing the amplification. Numerical and real case studies are presented to test the performance of the method. The results suggest that the approach proposed can effectively improve the performance of source rate estimation by optimally choosing the monitoring route. [ABSTRACT FROM AUTHOR]

In air pollution monitoring, source term estimation (STE) is a problem to inversely estimate the source parameters, such as their locations and emission rates, with available information of meteorology, ground geometry, as well as concentration measurements and their locations. For chemical industrial parks (CIPs), a major challenge of the estimation is the ill-posedness caused by the lack of sufficient measurements to match a vast number of undetermined source parameters. This ill-posedness results in incapacity in estimating unique solution for unknown source parameters, hence they are untraceable. In this paper, a theoretical definition of traceability is introduced to give a general criterion to determine whether a STE problem has a unique solution. This is derived from an atmospheric transport and dispersion model with description of linear sensitivity of measurements to source parameters. Based on this traceability criterion, an equivalent source method to convert an untraceable problem to traceable is proposed. The performance of this method is evaluated with a numerically simulated case whose configuration is based on information collected from a CIP in Shangyu, China. The results suggest that traceability is vital to STE problems. The method is effective for solving daily STE problems with sensor deficiency encountered in CIPs. [ABSTRACT FROM AUTHOR]

The growing concern of the effects of potential releases of chemical, biological or radiological materials in populated areas has led to an increase in urban dispersion modelling over the past several decades. More recently, there has been a surge of research in the area of source term estimation (STE), in which inverse computational methods are used to predict a source (release) location and strength based on sensor readings. Many studies to date have focused on idealized, free-field scenarios estimating continuous or instantaneous gaseous releases. There have been limited efforts including geometry (e.g. terrain or urban structures) effects using computational fluid dynamics (CFD) and no efforts towards estimating highly complex, transient sources. The first contribution of this work is the development of a proposed methodology to approximate the strength and location of transient source terms, whether mobile or changing in strength. The transient prediction tool is demonstrated to accurately predict the location and strength of sources exhibiting low to moderate transient behaviour. For fast moving or rapidly changing sources, the model becomes heavily reliant on adequate sensor positioning. The second contribution of this work is to quantify the uncertainty of the STE tool given uncertain measurements in atmospheric conditions (e.g. wind speed, wind angle and surface roughness) which are often sparse and prone to variations. The uncertainty quantification study is performed on steady, instantaneous, mobile and variable-strength sources in an idealized free-field setting. The wind angle was found to have the most effect on the prediction of the release position. The true release location was within 10-90th percentiles, with standard deviations on the order of one CFD cell size, for all cases assessed indicating a robustness of the algorithm to handle uncertain inputs. The free-field analysis is used as a baseline for applying the uncertainty quantification to predictions in a full-scale urban environment using the Joint Urban 2003 experimentation. Despite uncertain atmospheric conditions in the urban setting, the predicted source location was generally in the correct vicinity, although sometimes in the adjacent upwind street. It is recommended that the uncertainty quantification be applied to a probabilistic prediction tool to quantify the uncertainty of a statistical source term representation. Further, the analysis could be applied for more complex, highly transient and multi-source scenarios to fully assess the robustness of the algorithm. Article highlights: A source term estimation (STE) methodology is proposed for approximating the strength and location of transient atmospheric releases based on sensor concentrations. An error analysis is performed on the methodology to bound the predictive errors based on the level of transiency of the release. An uncertainty quantification study is performed to characterize the uncertainty in the prediction given uncertain measurements in atmospheric conditions. [ABSTRACT FROM AUTHOR]

Estimating the sources of contaminant or hazard emissions is important for pollution control and safety management. Markov chain Monte Carlo (MCMC), combined with Bayesian inference, was used to identify the source terms of pollutants. However, the efficiency and accuracy of the forward dispersion model greatly impacted the performance of the estimation method. Therefore, a machine learning algorithm (MLA) model with high prediction accuracy and efficiency was proposed and coupled with MCMC method to estimate the source terms. A previously proposed MLA model was used to obtain the expected concentrations in Bayesian estimation. The Delayed Rejection Adaptive Metropolis (DRAM) method was applied to sample particles in order to form Markov chains. To evaluate the performance of the MCMC–MLA method, a Gaussian dispersion model was selected as the forward model. The performances of MCMC–MLA and MCMC–Gaussian models were then compared with release cases in Prairie Grass experiment and the results showed that the MCMC–MLA method converged more rapidly than the MCMC–Gaussian model. Nevertheless, release cases in the Round Hill experiment were also used to test the generalisability of the MCMC–MLA. The results indicated that the performance of MCMC–MLA was better than that of the MCMC–Gaussian model for estimating source terms in estimation accuracy. Hence, the MCMC–MLA method proposed here is potentially a useful tool for identifying emissions source parameters with high accuracy and efficiency, as well as reasonable probability estimates. [ABSTRACT FROM AUTHOR]

Estimating gas source terms is essential and significant for managing a gas emission accident. Optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

When hazardous substances leak into the atmosphere, Source Term Estimation (STE) can quickly determine the information of the leaked substance to control the threat of the leakage event to the environment and public safety. Reasonable sensor configuration is the foundation of STE. Existing sensor configuration schemes have limited applicability in STE processes. When urban neighborhood has complex building structures and airflow conditions, there is a lack of a method that can provide reasonable sensor configurations to ensure good STE performance. In this study, a new sensor configuration design method is proposed, which weights the joint entropy of adjoint concentration information of multi wind directions to obtain the cost function, and then uses Simulated Annealing (SA) algorithm to minimize the cost function. This article selects a real complex urban neighborhood for simplified modeling, and designs the optimal sensor array for it. STE results of several leakage scenarios are compared with the optimal and uniform sensor configurations at different leakage locations and wind directions. Compared to traditional uniform sensor configurations, the optimal sensor configuration has significantly improved estimation performance in both source location and strength, with a reduction of 27 %–51 % in the bias of source location identification and 17 %–39 % in the bias of source strength estimation. • A design method for sensor configuration in solving Source Term Estimation (STE) problems is proposed. • This method can cope with STE problems in urban neighborhood facing complex structures and wind conditions. • A complex urban neighborhood is selected to validate the rationality of the proposed method. • This method is making possible to apply sensor optimization strategies in a real urban neighborhood. [ABSTRACT FROM AUTHOR]

A computationally efficient source inversion algorithm was developed and applied with the Lagrangian atmospheric dispersion model DIPCOT. In the process of source location estimation by minimizing a correlation-based cost function, the algorithm uses only the values of the time-integrated concentrations at the monitoring stations instead of all of the individual measurements in the full concentration-time series, resulting in a significant reduction in the number of integrations of the backward transport equations. Following the source location estimation the release start time, duration and emission rate are assessed. The developed algorithm was verified for the conditions of the ETEX-I (European Tracer Experiment—1st release). Using time-integrated measurements from all available stations, the distance between the estimated and true source location was 108 km. The estimated start time of the release was only about 1 h different from the true value, within the possible accuracy of estimate of this parameter. The estimated release duration was 21 h (the true value was 12 h). The estimated release rate was 4.28 g/s (the true value was 7.95 g/s). The estimated released mass almost perfectly fitted the true released mass (323.6 vs. 343.4 kg). It thus could be concluded that the developed algorithm is suitable for further integration in real-time decision support systems.

Abstract One of the difficulties encountered in source-term analyses for airborne contaminants is the large computational effort required to predict air concentrations for all possible release scenarios. In some cases, analysts use atmospheric ATM runs with complex models done in the reverse-time direction because one ATM run done backwards in time for each sample can yield as much information as potentially hundreds or thousands of ATM runs done forwards in time. Unfortunately, the effective atmospheric dilution between the source and sampling locations differ depending on the time direction of the ATM run, with runs in the forward time direction being more realistic. No general studies have been published comparing the agreement between runs in the two time directions. Over 18000 ATM runs at 14 release locations were used to explore the agreement between dilution factors for the forward and reversed time directions at distances up to 1000 km from the release point. Ten of the release locations have a correlation below 0.9, with the lowest correlations occurring over mountainous terrain. The release locations were estimated using the time-reversed ATM runs, with 26% of the estimated release points being within 10 km of the modeled release point, 61% within 25 km, and 80% within 50 km. Most of the location differences greater than 50 km occur for two release locations in mountainous terrain. Good time-reversibility cannot be guaranteed for a new analysis, so we recommend any source-term solution using time-reversed ATM runs should include comparisons based on forward time ATM runs. Highlights • Empirical comparison of dilution factors for paired ATM runs in forward and backwards time directions. • Land elevation changes have a large negative impact on the time reversibility of the ATM runs. • Source term estimation using time-reversed ATM runs is very useful. • Source term estimation using time-reversed ATM runs needs confirmation using forward ATM runs. [ABSTRACT FROM AUTHOR]

The aim of this study is to optimize sensor networks for fast deployment in order to reconstruct an unknown source of intentional or accidental release in local urban topography. In such emergency circumstances, only the meteorological conditions are available in real time and the network deployed must be efficient enough regardless of a source's position and intensity. To determine the optimal positions to be instrumented by the sensors, an adequate cost function is defined based on the renormalization inversion method. This function, named the entropic criterion, quantifies the amount of information contained in a network of the sensors to estimate the intensity and the location of an unknown source. The optimal design is approached as combinatorial optimization (NP‐Hard) and a stochastic algorithm (simulated annealing, SA) is employed to solve this problem. The computation is performed by coupling the CFD adjoint fields in an urban environment, the renormalization algorithm and the SA. The optimization is evaluated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment for the reconstruction of a continuous point release in an idealized urban geometry using optimal networks of sizes 10 and 13 sensors. The process is achieved successfully and the results showed that the reduction of an original network of 40 sensors to one third (13) and one quarter (10) does not degrade the performance of this network. Also, a comparison of the optimal design efficiency based on apriori information and without apriori information about the source showed that the present entropic criterion leads to network design and performance that can accurately retrieve an unknown emission source in an urban environment. [ABSTRACT FROM AUTHOR]

Stand-off detection of airborne chemical compounds has proven to be a useful method that is gaining popularity following technical progress. There are obvious advantages compared to in situ measurements when it comes to the security aspect and the ability to measure at locations otherwise hard to reach. However, an inherent limitation in many of the stand-off detection techniques lies in the fact that the measured signal from a chemical depends nonlinearly on the distance to the detector. Furthermore, the measured signal describes the summation of the responses from all chemicals spatially distributed in the line of sight of the instrument. In other words, the three dimensional extension of the chemical plume is converted into a two-dimensional image. Not only is important geometric information per se lost in this process, but the measured signal strength itself depends on the unknown plume distribution which implies that the interpretation of the observation data suffers from significant uncertainty. In this paper we investigate different and novel approaches to reconstruct the original three-dimensional distribution and concentration of the plume by implementation of atmospheric dispersion models and numerical retrieval methods. In particular our method does not require a priori assumptions on the three-dimensional distribution of the plume. We also strongly advocate the use of proper constraints to avoid unphysical solutions being derived (or post-process 'adjustments' to correct unphysical solutions). By applying such a reconstruction method, both improved and additional information is obtained from the original observation data, providing important intelligence to the analysts and decision makers. [ABSTRACT FROM AUTHOR]

Abstract Source term estimation (STE) of atmospheric dispersion plays an important role in public safety, environmental protection and many other application fields. In this paper, several new composite cost functions for STE using hybrid genetic algorithm are proposed and compared using Nemenyi test based on 68 STE tasks from Prairie Grass field experiment. Results show one of the new composite cost functions, named as WSD, has outstanding performance in estimating both source location and emission rate. Then the patterns in STE results using different cost functions are analyzed based on the 68 tasks mentioned above, which provides further insights into what to expect from STE. At last, the relationship between composite cost functions and multi-objective optimization is analyzed to facilitate the understanding of composite cost functions. To summarize, composite cost functions such as WSD has the potential to achieve a better balance between sensitivity and robustness of cost functions applied in STE, providing the most accurate estimates. Statistical algorithm comparison techniques like Nemenyi test can help us better understand the characteristics and performance of specific settings in STE methods. Graphical abstract Highlights • New composite cost functions are introduced to source term estimation. • Nemenyi test is used to compare different cost functions using multiple data sets. • One of the new composite cost function WSD has outstanding performance. • Bias patterns of the results using different cost functions are analyzed. • The relationship with multi-objective optimization is demonstrated. [ABSTRACT FROM AUTHOR]

Abstract The estimation of a hazardous contaminant unknown source characteristics (i.e., rate and location) in a complex urban environment using efficient inverse modelling techniques is a challenging problem that involves advanced computational fluid dynamics combined with appropriate mathematical algorithms. In this paper we further assess our recently proposed inverse source term estimation method (Efthimiou et al., 2017, Atmos. Environ., 170, 118–129) by applying it in two wind tunnel experiments simulating atmospheric flow and tracer dispersion following a stationary release in realistic urban settings, namely Michelstadt and Complex Urban Terrain Experiment (CUTE). The method appears to be robust and to predict with encouraging accuracy the source location and emission rate for both wind tunnel experiments. Highlights • Evaluation of an inverse source term identification method. • Variational, 2-steps method: (1) source coordinates (2) emission rate estimation. • Successful validation through simulations of Michelstadt and CUTE wind tunnel experiments. • The performance of the method varies with the examined scenario. [ABSTRACT FROM AUTHOR]

Source location is crucial to manage contaminant emissions in atmosphere, In order to determine the source location without dependence on the absolute measurement data, a method based on optimal correlated matching of concentration distribution (OCMCD) was proposed. First, the estimation efficiency, accuracy and dependence on source strength of OCMCD were compared with the common method which estimates multiple parameters of the source term simultaneously. The results show that the method of OCMCD performs better than the common multiple parameters estimation method based on the mean errors between prediction and measurement in both estimation accuracy and efficiency. The test results with different sets of source strength manifest that OCMCD relies minimally on the source strength Then, a wind direction correction parameter and a weighted term of normalization concentration error were introduced into the model to compensate some missed information and improve the location results. The influence of data noises on the estimation accuracy of OCMCD method was also verified by adding extra manual noises on the measurement data. Then, the dependence of estimation performance with OCMCD method on atmosphere conditions were investigated statistically with experiment release cases. The results showed that source location was identified well in most of cases. Finally, OCMCD method was extended to determine the source location during the source trace process with a mobile sensor. The test results with a simulation scenario based on Zigzag search strategy demonstrate that the source location determined by OCMCD source criterion is much closer to the real source position than that determined by the criterion of the maximum concentration. Therefore, the results have proven the feasibility and superiority of OCMCD proposed in this paper to estimate source location in cases of both static sensor distribution and mobile sensors. OCMCD will be a potentially useful method to identify emission source location in atmosphere. [ABSTRACT FROM AUTHOR]

In this work, we present an inverse computational method for the identification of the location, start time, duration and quantity of emitted substance of an unknown air pollution source of finite time duration in an urban environment. We considered a problem of transient pollutant dispersion under stationary meteorological fields, which is a reasonable assumption for the assimilation of available concentration measurements within 1 h from the start of an incident. We optimized the calculation of the source-receptor function by developing a method which requires integrating as many backward adjoint equations as the available measurement stations. This resulted in high numerical efficiency of the method. The source parameters are computed by maximizing the correlation function of the simulated and observed concentrations. The method has been integrated into the CFD code ADREA-HF and it has been tested successfully by performing a series of source inversion runs using the data of 200 individual realizations of puff releases, previously generated in a wind tunnel experiment. [ABSTRACT FROM AUTHOR]

The estimation of source information from limited measurements of a sensor network is a challenging inverse problem, which can be viewed as an assimilation process of the observed concentration data and the predicted concentration data. When dealing with releases in built-up areas, the predicted data are generally obtained by the Reynolds-averaged Navier-Stokes (RANS) equations, which yields building-resolving results; however, RANS-based models are outperformed by large-eddy simulation (LES) in the predictions of both airflow and dispersion. Therefore, it is important to explore the possibility of improving the estimation of the source parameters by using the LES approach. In this paper, a novel source term estimation method is proposed based on LES approach using Bayesian inference. The source-receptor relationship is obtained by solving the adjoint equations constructed using the time-averaged flow field simulated by the LES approach based on the gradient diffusion hypothesis. A wind tunnel experiment with a constant point source downwind of a single building model is used to evaluate the performance of the proposed method, which is compared with that of the existing method using a RANS model. The results show that the proposed method reduces the errors of source location and releasing strength by 77% and 28%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the Socialtaxis approach for efficient source search and accurate estimation in a turbulent atmospheric environment using multiple cooperative agents. The original Socialtaxis, one of the decentralized information-theoretic source search methods for multiple agents, maximizes individual entropy reduction as well as group information diversity for rapid and efficient source search. However, it has several drawbacks: high computational complexity due to the grid-based environment, bias towards exploration, the lack of cooperation among agents (e.g., not sharing measurements and decisions), and the assumption of network connectivity between agents at all times. In order to address the above issues, we improve Socialtaxis in terms of the following aspects. First, we modify the grid representation of the environment of Socialtaxis to the continuous domain using the Rao-Blackwellized particle filter to reduce computational loads and enable more efficient Bayesian estimation of the source parameters. Second, we introduce a new utility function that balances the exploration and exploitation better by using a distance to the estimated source. Third, the sequential greedy algorithm is applied while sharing measurements with one another to realize a fully decentralized decision-making system. Lastly, to prevent network disconnection among agents, we ensure connectivity preservation in a decentralized way so that the agents are within the communication range for information sharing. Extensive numerical simulation results show that the proposed improved Socialtaxis outperforms the original Socialtaxis as well as other existing state-of-the-art source search strategies. The considered aspects of the improved Socialtaxis are proven to be crucial elements of decentralized information-theoretic source search, which can be robust to different wind conditions and encourage multiple agents to cooperate more. • Multi-agent information-theoretic source search is proposed by improving Socialtaxis. • The high computation load is alleviated from the Rao-Blackwellized particle filter. • Cooperative and balanced source search in exploration and exploitation is achieved. • The network connectivity preservation is considered for continuous information sharing. • The proposed algorithm is verified by comparing with other source search strategies. [ABSTRACT FROM AUTHOR]

Finding sources of airborne chemicals with mobile sensing systems finds applications across safety, security, environmental monitoring, and medical domains. In this paper, we present an algorithm based on Source Term Estimation for odor source localization that is coupled with a navigation method based on partially observable Markov decision processes. We propose a novel strategy to balance exploration and exploitation in navigation. Moreover, we study two variants of the algorithm, one exploiting a global and the other one a local framework. The method was evaluated through high-fidelity simulations and in a wind tunnel emulating a quasi-laminar air flow in a controlled environment, in particular by systematically investigating the impact of multiple algorithmic and environmental parameters (wind speed and source release rate) on the overall performance. The outcome of the experiments showed that the algorithm is robust to different environmental conditions in the global framework, but, in the local framework, it is only successful in relatively high wind speeds. In the local framework, on the other hand, the algorithm is less demanding in terms of energy consumption as it does not require any absolute positioning information from the environment and the robot travels less distance compared to the global framework.

An improved inverse modelling method to estimate the location and the emission rate of an unknown point stationary source of passive atmospheric pollutant in a complex urban geometry is incorporated in the Computational Fluid Dynamics code ADREA-HF and presented in this paper. The key improvement in relation to the previous version of the method lies in a two-step segregated approach. At first only the source coordinates are analysed using a correlation function of measured and calculated concentrations. In the second step the source rate is identified by minimizing a quadratic cost function. The validation of the new algorithm is performed by simulating the MUST wind tunnel experiment. A grid-independent flow field solution is firstly attained by applying successive refinements of the computational mesh and the final wind flow is validated against the measurements quantitatively and qualitatively. The old and new versions of the source term estimation method are tested on a coarse and a fine mesh. The new method appeared to be more robust, giving satisfactory estimations of source location and emission rate on both grids. The performance of the old version of the method varied between failure and success and appeared to be sensitive to the selection of model error magnitude that needs to be inserted in its quadratic cost function. The performance of the method depends also on the number and the placement of sensors constituting the measurement network. Of significant interest for the practical application of the method in urban settings is the number of concentration sensors required to obtain a “satisfactory” determination of the source. The probability of obtaining a satisfactory solution – according to specified criteria –by the new method has been assessed as function of the number of sensors that constitute the measurement network. [ABSTRACT FROM AUTHOR]

Severe nuclear power plant (NPP) accidents are those which involve significant core degradation and lead the plant to conditions more severe than a design basis accident. Under such conditions the accident progression might become unpredictable and the source term estimation, imprecise by orders of magnitude. The consequence is a dose assessment very far from the reality and a deficient decision making support. This work presents a novel approach to improve accuracy of dose estimation, based on field measurements and particle swarm optimization (PSO) algorithm. The main idea is to determine a correction matrix, which once applied to the originally estimated (incorrect) dose distribution map, generates a corrected one, which better fits to the field measurements. The proposed correction matrix is the result of a concatenation of geometric transformations and an amplification/attenuation factor, aimed to fit the shape of the original map and radiation intensities in order to match the field measurements. Finding the optimum transformations (correction matrix) is, however a complex nonlinear optimization problem, which has been successfully solved by using a PSO algorithm. Results demonstrate that PSO was able to find good correction transformations, which can be used to better project future dose distributions and, consequently, improve decision making support. [ABSTRACT FROM AUTHOR]

Source term estimation (STE) addresses the retrieval of emission source information, including location and strength, based on available information. STE can be viewed as an assimilation process of the observed concentration data measured by a sensor network and the predicted concentration data provided by a dispersion model. When considering emissions in complex urban areas, computational fluid dynamics (CFD) approaches are generally used to provide building-resolving results; however, the value of a key parameter, the turbulent Schmidt number S c t , has remained an arbitrary choice. Therefore, it is important to investigate the role of S c t in STE problems and determine its optimum value for the purpose of obtaining better estimation results. In this paper, the impact of S c t on STE problems is examined, and Bayesian inference is used to improve estimation accuracy by treating S c t as an extra unknown parameter. A wind tunnel experiment with a constant point tracer source in an urban-like geometry is used for demonstration. The results show that S c t has a major impact on estimation. Larger S c t values shift the estimated location towards the upwind direction and decrease the estimated strength. Compared with a conventional estimation method performed by using a pre-assigned value of S c t = 0.7 , treating S c t as an unknown improves point estimates, while the uncertainty increases since the proposed method introduces an extra unknown parameter. For source strength, more improvement in point estimates and a larger increase in uncertainty are shown due to its greater sensitivity of S c t compared with the source location. [ABSTRACT FROM AUTHOR]

In order to prevent the uncontrolled large release of radioactive materials to the environment due to containment failure, Containment Filtered Venting System (CFVS) has been considered as the one of effective measure to prevent the containment over-pressurization. Because the filtered radioactive materials are captured and accumulated in the CFVS components, the CFVS becomes the source of the radiation and this radiation should be properly shielded to protect the field workers. In this study, the effect of CFVS operation pressure on the required shielding wall thickness is investigated in various accident scenarios. The accident scenarios considering the type of initial accident and condition of external safety injection besides the CFVS operation pressure were simulated. The source terms were obtained using the MAAP5 and ORIGEN-ARP code calculation. The effective dose rate and required shielding wall thickness were estimated by the MICROSHIELD code. Consequently, the shielding wall was necessary and the CFVS operation pressure affected the required shielding differently depending on the conditions of external safety injection. As a result, the lower operation pressure of CFVS was favorable in perspective with the low demand of shielding wall. [ABSTRACT FROM AUTHOR]

This paper presents a two-step deterministic approach for identifying an unknown point source with a constant emission rate in built-up urban areas. The analytic form of the marginal posterior probability density function of the source location is derived to estimate the source location. The emission rate is then estimated using the conditional posterior distribution. Such a procedure deconstructs the calculation of the joint posterior distribution of the source parameters into calculations of two separate distributions and can thus be easily calculated directly and accurately without stochastic sampling. The proposed method is tested using real data obtained in two wind tunnel scenarios of contaminant dispersion in typical urban geometries represented by block arrays. Computational fluid dynamics (CFD) modeling and the adjoint equations are used to calculate the building-resolving source-receptor relationship required in the identification. The estimated source parameters in both cases are close to true values. In both cases, the source locations are identified with errors less than half of the block size, and the emission rates are well estimated, with only slight overestimation. Moreover, in this paper, we test two potential performance indicators for a posteriori evaluation of the credibility of a certain estimation. One indicator is the size of the highest probability density region, and the other is the angle between the observed and predicted concentration vectors, which is derived from the analytic form of the marginal posterior distribution of the source location. Synthetic concentration data are generated to test the validity of both indicators. It is found that the former is not appropriate for denoting the credibility of estimations but that the latter shows a strong correlation with estimation performance and is likely to be an effective performance indicator for Bayesian source term estimation. [ABSTRACT FROM AUTHOR]

Source term information of radioactive release in a nuclear accident is important for emergency response. Two major categories of source term estimation techniques are forward method based on the status data of nuclear power plant and backward method based on environmental monitoring data. Both data contain considerable uncertainties which can propagate through estimation and result in a biased estimate. To solve the problem, a coupled estimation method is proposed in this study, which combines forward method (Response Technical Manual, RTM-96) and backward method (ensemble Kalman filter). The coupled method builds an evolution model based on a forward estimate and uses it to constrain the temporal correlation of estimates in the backward part, so that the propagation of uncertainties is reduced. Numerical experiments and sensitivity analysis are performed to verify the proposed method, based on a hypocritical loss-of-coolant (LOCA) accident process and the records of a tracer field experiment for Sanmen nuclear power plant. The results demonstrate that the coupled method provides the most accurate estimate in all tests and is more robust to uncertainties in various parameters than both RTM-96 and ensemble Kalman filter. [ABSTRACT FROM AUTHOR]

Modeling the downwind hazard area resulting from the unknown release of an atmospheric contaminant requires estimation of the source characteristics of a localized source from concentration or dosage observations and use of this information to model the subsequent transport and dispersion of the contaminant. This source term estimation problem is mathematically challenging because airborne material concentration observations and wind data are typically sparse and the turbulent wind field chaotic. Methods for addressing this problem fall into three general categories: forward modeling, inverse modeling, and nonlinear optimization. Because numerous methods have been developed on various foundations, they often have a disparate nomenclature. This situation poses challenges to those facing a new source term estimation problem, particularly when selecting the best method for the problem at hand. There is, however, much commonality between many of these methods, especially within each category. Here we seek to address the difficulties encountered when selecting an STE method by providing a synthesis of the various methods that highlights commonalities, potential opportunities for component exchange, and lessons learned that can be applied across methods. [ABSTRACT FROM AUTHOR]

• BI-IEnKF coupling model for gas leakage estimation in utility tunnels is developed. • Source term estimation and gas concentration revision are performed simultaneously. • The implementation of mobile sensors benefits both model prediction accuracy and investment costs. As an effective way to facilitate the increasing demand for reliable infrastructure, energy supply and sustainable urban development, underground utility tunnels have been developed rapidly in recent years. Due to the widespread distribution of utility tunnels, the safe operation of natural gas pipelines accommodated in utility tunnels has caused great concern considering fire, explosion, and other coupling consequences induced by the gas pipeline leakage. However, the limited information on leakage source terms in accidental leakage scenarios could preclude timely consequence assessment and effective emergency response. In this study, a BI-IEnKF coupling source term estimation (STE) model is developed, with the combination of gas dispersion model, Bayesian inference (BI) and iterative ensemble Kalman filter (IEnKF) method, to achieve the effective source term estimation (including leakage location and leakage rate) and gas concentration distribution prediction. The newly developed model is first evaluated by the twin experiment with good reliability and accuracy. Furthermore, three contributing factors affecting the performance of the developed BI-IEnKF coupling STE model were investigated to assist parameter selection for practical use. Additionally, the novel application of mobile sensors serving as an alternative for fixed sensors is explored, and an application framework is sequentially given to guide the deployment of the developed coupling model in utility tunnels. The results show that the developed model has great performance in accuracy, efficiency and robustness, as well as the potential to be applied in actual utility tunnel scenarios. This study can provide technical supports for safety control and emergency response in the case of natural gas pipeline leakage accidents in utility tunnels. Also, it could be helpful to reasonable references for gas lekage monitoring system design. [ABSTRACT FROM AUTHOR]