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Although various methods have been proposed for source term estimation, they mainly focus on hazardous gases behaving as passive scalars. These methods will struggle in identifying unknown sources of reactive gases, which are common in complex urban environments. Focusing on one of the most severe atmospheric pollutions in urban areas, the NOx-O3 photochemical reactive gas dispersion, this research proposed a novel source term estimation method based on Bayesian inference and the adjoint method. Utilizing sparse measurements and background concentrations, this method can effectively identify and quantify the source terms of NO and NO2. To evaluate the performance of the proposed method, a case of an urban canyon in Paris was studied. The NOx gases were released from the traffic, which was a line source at the central bottom of the canyon, and the strength was measured on-site. The measurements of multiple sensors were synthesized by numerical simulations validated by on-site concentration measurements. First, deploying sensors in the target canyon, the proposed method was coupled with a super-Gaussian function to estimate the location, shape, and strength of NOx sources. Our method successfully estimated the length and width of sources by using 4 sensors located at roadsides and roofs. When sensors increased to 7, the strength estimation errors could be controlled within 5%. The heights of sources were found to be the most difficult term because sources were close to the bottom wall and flows were strongly circulated in the canyon, making the adjoint concentration simulation biased. Secondly, assuming the unknown sources are points, the proposed method was applied to estimate source terms in continuous urban street canyons based on sensors on each roof. It was found that our method can identify the exact canyon where sources were located and estimate their strength with about 10% errors. In addition, the conventional Bayesian approach for passive scalars has failed in both cases, demonstrating the necessity and advantages of our method.

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.

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.

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.Graphic abstract

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.

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.

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts.

During nuclear accidents, decision-makers need to handle considerable data to take appropriate protective actions to protect people and the environment from radioactive material release. In such scenarios, machine learning can be an essential tool in facilitating the protection action decisions that will be made by decision-makers. By feeding machines software with big data to analyze and identify nuclear accident behavior, types, and the concentrations of released radioactive materials can be predicted, thus helping in early warning and protecting people and the environment. In this study, based on the ground deposition concentration of radioactive materials at different distances offsite in an emergency planning zone (EPZ), we proposed classification and regression models for three severe accidents. The objective of the classification model is to recognize the transient situation type for taking appropriate actions, while the objective of the regression model is to estimate the concentrations of the released radioactive materials. We used the Personal Computer Transient Analyser (PCTRAN) Advanced Power Reactor (APR) 1400 to simulate three severe accident scenarios and to generate a source term released to the environment. Additionally, the Radiological Consequence Analysis Program (RCAP) was used to assess the off-site consequences of nuclear power plant accidents and to estimate the ground deposition concentrations of radionuclides. Moreover, ground deposition concentrations at different distances were used as input data for the classification and regression tree (CART) models to obtain an accident pattern and to establish a prediction model. Results showed that the ground deposition concentration at a near distance from a nuclear power plant is a more informative parameter in predicting the concentration of radioactive material release, while the ground deposition concentration at a far distance is a very informative parameter in identifying accident types. In the regression model, the R-square of the training and test data was 0.995 and 0.994, respectively, showing a mean strong linear relationship between the predicted and actual concentration of radioactive material release. The mean absolute percentage error was found to be 26.9% and 28.1% for the training and test data, respectively. In the classification model, the model predicted a scenario (1) of 99.8% and 98.9%, scenario (2) of 98.4% and 91.6%, and scenario (3) of 98.6% and 94.7% for the training and test data, respectively.

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.

This paper describes a new methodology implemented in the ESTE decision support system for evaluating the source term resulting from a nuclear weapon detonation. The methodology is based on a model of a stabilized radioactive mushroom cloud, parameterized as the source term for a Lagrangian particle dispersion model. It includes radionuclide composition, spatial distribution of aerosol and gaseous particles, and particle size distribution. This method is designed for rapid assessment of radiological impacts primarily at medium- and long-range distances, for example, in neighboring countries. The parametrization has been calibrated and adjusted using data from historical nuclear tests, and its performance is evaluated in terms of impacted area, range, and spatial overlap of fallout regions. A comparison is presented between ESTE calculations and field measurements obtained after the British nuclear tests conducted in the 1950s at the Maralinga Range (Australia), using historical ERA5 meteorological reanalyses from ECMWF.