Source Location Identification Research Articles

Particulate black carbon mass concentrations and the episodic source identification driven by atmospheric blocking effects in Astana, Kazakhstan

Black carbon (BC) is a component of fine particulate matter (PM2.5) that is a key contributor to adverse human health effects and climate forcing. To date, BC mass concentrations and possible sources in Kazakhstan have not been studied. Thus, understanding the temporal variations of BC for a large developing region with a complex climate is useful. In this study, measurements of fine particulate BC mass concentrations in Astana were made from June 2020 to October 2021 by measuring light absorption of PM2.5 on filters. The mean BC concentration was 2.56 ± 1.29 μg m−3 with maximum and minimum monthly mean BC concentrations being 4.56 ± 2.03 μg m−3 and 1.12 ± 0.42 μg m−3 in January 2021 and June 2020, respectively. Temporal analyses of BC, SO2, PM10, NOx, CO, meteorological and atmospheric stability parameters were performed. Aggregated pollutant ‘episodic loadings’ during the heating and non-heating periods were identified. Their relationships with blocking anticyclones and cyclones were investigated by examining the reversal of meridional gradients at 500 hPa geopotential height (GPH) maps and identifying Omega (Ω) and Rex blocking types. Astana has some of the highest BC concentrations of cities worldwide. Seasonal BC source location identification using Conditional Bivariate Probability Function (CBPF) analysis implicated combined heat and power (CHP) plant emissions as the major BC source in Astana. Significant increases in BC concentrations were observed during the cold season due to numerous sources, generally poorer atmospheric dispersion and blocking events. The Concentration Weighted Trajectory (CWT) analysis results showed that the distribution of the 75th percentile of BC during episodic periods actively controlled by blockings exceeding than the entire measurement period, which may reflect cross-border transport and adjacent countries.

A new sensor configuration design method for source term estimation in urban neighborhood with complex conditions under different wind directions

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.

Source Location Identification in an Ideal Urban Street Canyon with Time-Varying Wind Conditions under a Coupled Indoor and Outdoor Environment

Source location identification methods are typically applied to steady-state conditions under pure indoor or outdoor environments, but under time-varying wind conditions and coupled indoor and outdoor environments, the applicability is not clear. In this study, we proposed an improved adjoint probability method to identify the pollutant source location with time-varying inflows in street canyons and used scaled outdoor experiment data to verify the accuracy. The change in inflow velocity will affect the airflow structure inside the street canyons. Outdoor wind with a lower temperature will exchange heat with the air with a higher temperature inside the street canyon, taking away part of the heat and reducing the heat of the air inside the street canyons. Moreover, the room opening will produce some air disturbance, which is conducive to the heat exchange between the air near the opening and the outdoor wind. Furthermore, the fluctuations of the upper wind will influence the diffusion of the tracer gas. We conducted three cases to verify the accuracy of the source identification method. The results showed that the conditioned adjoint location probability (CALP) of each case was 0.06, 0.32, and 0.28. It implies that with limited pollutant information, the improved adjoint probability method can successfully identify the source location in the dynamic wind environments under coupled indoor and outdoor conditions.

Seismic monitoring of rockfalls using distributed acoustic sensing

Distributed acoustic sensing (DAS) using fiber-optic cables shows promise for rockfall monitoring by potentially offering high spatial resolution and wide coverage at lower costs. However, further research is needed to fully evaluate DAS performance for rockfall applications. This study assesses DAS capabilities for rockfall monitoring by analyzing DAS and seismometer recordings of 38 artificially triggered rockfalls with block masses ranging from 139.6 to 638 kg. Results demonstrate strong correlations between DAS waveforms and spectra with colocated seismometers, validating DAS data for detecting rockfall impacts. Fiber configuration impacted DAS sensitivity: fully in-contact cables improved signal-to-noise ratios and detection of high frequencies from 55 to 86 Hz. Analyses of seismic waves identified three distinct rock block propagation modes associated with intact, fragmenting, and collapse-triggering movements. While DAS signal feature values agreed with seismometers, no clear mass correlation emerged within the examined range. However, spectral features like frequency centroid (48.8 ± 6.5 Hz) and predominant frequency (41.0 ± 12.1 Hz) exhibited spatial stability across channels, indicating potential for event identification. Preliminary attempts to locate rockfall sources from DAS arrays proved feasible but require optimization. Overall, enhanced fiber coupling, identification of propagation modes, spatially-dependent analyses, and dense-array source location may enable DAS to complement sparse seismic networks for higher resolution, cost-effective rockfall monitoring. These insights could inform the development of improved rockfall monitoring strategies leveraging distributed fiber-optic sensing.

Groundwater contamination source identification based on Sobol sequences-based sparrow search algorithm with a BiLSTM surrogate model.

In the traditional linked simulation-optimization method, solving the optimization model requires massive invoking of the groundwater numerical simulation model, which causes a huge computational load. In the present study, a surrogate model of the origin simulation model was developed using a bidirectional long and short-term memory neural network method (BiLSTM). Compared with the surrogate models built by shallow learning methods (BP neural network) and traditional LSTM methods, the surrogate model built by BiLSTM has higher accuracy and better generalization performance while reducing the computational load. The BiLSTM surrogate model had the highest R2 of the three with 0.9910 and the lowest RMSE with 3.7732g/d. The BiLSTM surrogate model was linked to the optimization model and solved using the sparrow search algorithm based on Sobol sequences (SSAS). SSAS enhances the diversity of the initial population of sparrows by introducing Sobol sequences and introduces nonlinear inertia weights to control the search range and search efficiency. Compared with SSA, SSAS has stronger global search ability and faster search efficiency. And SSAS identifies the contamination source location and release intensity stably and reliably. The average relative error of SSAS for the identification of source location is 9.4%, and the average relative error for the identification of source intensity is 1.83%, which are both lower than that of SSA at 11.12% and 3.03%. This study also applied the Cholesky decomposition method to establish a Gaussian field for hydraulic conductivity to evaluate the feasibility of the simulation-optimization method.

Identification of source location in a single-sided building with natural ventilation: Case of interunit pollutant dispersion

A sudden outbreak of COVID-19 occurred in December 2019 and its rapid spread over the last two years caused a global pandemic. A special airborne transmission via aerosols called interunit dispersion is risky in a high-density urban environment, which needs more attention. In order to identify the source location of pollutants or viruses under the interunit transmission condition with natural ventilation, this study adopted the inverse Computational Fluid Dynamics (CFD) simulation with the adjoint probability method. The detailed process of the inverse modeling was presented. Also, the possible interunit transmission routes of the pollutants or viruses were analyzed. A three-story building model with single-sided openings was built. Six different combinations of fixed sensor locations were tested, and it was determined that setting sensors in the four corner regions of the building was the optimist strategy. A total of 25 cases with five different wind directions (0°, 45°, 90°, 135°, and 180°) were tested to verify the accuracy of the source location with inverse modeling. The results showed that 67%–78% of the rooms in the building can be identified with a limited number of pollutant sensors and all rooms can be identified with one additional sensor in the downstream room of the building under different wind direction. This research revealed that the inverse modeling method could be used to identify the pollutant source in the coupled indoor and outdoor environment. Further, this work can provide guidance for the pollutant monitor positions in the applications.

Identification of harmonic source location in power distribution network

This paper presents the experimental set-up of identification of harmonic source location in the power distribution network using time-frequency analysis, known as S-transform (ST) at the point of common coupling (PCC). S-transform offers high frequency resolution in analyzing the low frequency component and able to represent signal parameters in time-frequency representation (TFR) such as TFR impedance (ZTFR). The proposed method is based on IEEE Std. 1459-2010, ST, and the significant relationship of spectral impedances components (ZS) that been extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). This experiment was conducted out on an IEEE 4-bus test feeder with a harmonic producing load in numerous different scenarios. The experimental was tested and verified for three consecutive months. The findings of this study reveal that the proposed method provides 100 percent correct identification of harmonic source location.

Inverse identification of source location in a single-sided natural ventilation building

Natural ventilation is a common way of ventilation for urban residents, which is simple and energy-efficient. However, this ventilation not only introduces fresh air from outdoors into the indoor environment but also brings various pollutants from outdoors into the indoor environment, thus reducing indoor air quality and causing a series of human respiratory diseases in severe cases, such as asthma, pneumonia, bronchitis, etc. In a high-density urban environment, the proximity of rooms within a building can easily lead to cross-infection between occupants in the event of a public health emergency. Therefore, it is of great significance to quickly and accurately find the source of viruses or pollutants. The objective of this study is to accurately locate pollutant source which spread between units by wind effect. A model with three-storey building of wind-driven single-sided ventilation was built. Carbon dioxide (CO2) was used as a tracer of indoor pollutants. Computational Fluid Dynamics (CFD) is used to accurately simulate and predict airflow and concentration fields in and around the building. The results indicated that the location of the predicted pollution source is close to the position where the pollutant is released. The results of this paper can provide vital information for preventing the spread of contaminants in buildings.

Contaminant Source Identification from Finite Sensor Data: Perron–Frobenius Operator and Bayesian Inference

Sensors in the built environment ensure safety and comfort by tracking contaminants in the occupied space. In the event of contaminant release, it is important to use the limited sensor data to rapidly and accurately identify the release location of the contaminant. Identification of the release location will enable subsequent remediation as well as evacuation decision-making. In previous work, we used an operator theoretic approach—based on the Perron–Frobenius (PF) operator—to estimate the contaminant concentration distribution in the domain given a finite amount of streaming sensor data. In the current work, the approach is extended to identify the most probable contaminant release location. The release location identification is framed as a Bayesian inference problem. The Bayesian inference approach requires considering multiple release location scenarios, which is done efficiently using the discrete PF operator. The discrete PF operator provides a fast, effective and accurate model for contaminant transport modeling. The utility of our PF-based Bayesian inference methodology is illustrated using single-point release scenarios in both two and three-dimensional cases. The method provides a fast, accurate, and efficient framework for real-time identification of contaminant source location.

An experiment-based impulse response method to characterize airborne pollutant sources in a scaled multi-zone building

Promptly locating the airborne pollutant sources in indoor environments is of great importance for improving air quality and ensuring indoor safety, especially in the context of controlling airborne pathogens. However, most existing studies focused on short-range airborne transmission in close contact and identified pollution sources based on numerical simulations. In this study, an experiment-based impulse response method was established to estimate the source term in a multi-zone building, and uncertainty analysis (UA) was conducted by combining the errors from the sensors and systematic responses. An acrylic scaled multi-zone building was built to investigate and validate the proposed method. The prior impulse response vectors between the potential sources and sensor network were obtained through impulse response experiments. Next, four sets of representative experiments were conducted with the scaled model to evaluate the source identification method. The results showed that the source release rate and source location could be identified using the impulse response method, and most of the relative errors of the source release rate were within 30%. For source location identification, the location probabilities of the constant and dynamic real sources were 44% and 100%, respectively. This study provides a novel perspective for source term estimation (STE) in multi-zone environments.

Identification of clandestine groundwater pollution sources using heuristics optimization algorithms: a comparison between simulated annealing and particle swarm optimization.

Groundwater pollution is the biggest threat to sustainability of groundwater resources and even more difficult to detect in case of clandestine sources. At the time when pollution is first detected in randomly located sparse wells, very little is known about the pollution sources. Finding the precise locations of clandestine sources of pollution and their release flux history is the biggest challenge and often termed as a problem belonging to the class of environmental forensics. In this study, two linked simulation optimization-based novel techniques are developed to estimate locations and release flux history from clandestine point sources of groundwater pollution. Simulation model is clubbed with optimization solver to determine the locations and release flux histories of groundwater pollution sources by minimizing the residual error between observed and simulated concentration values. Simulated annealing (SA) and particle swarm optimization (PSO) are used as optimization algorithms. A detailed comparative analysis of these two meta-heuristic optimization algorithms in minimizing the residual error is presented in this study. The performance evaluation of both the algorithms in identifying the sources locations and release flux history is carried out for two synthetic cases and a real-life scenario of groundwater pollution in an aquifer in New South Wales, Australia, which has not been attempted in the past. The results of source location identification and release flux history show the selective applicability of each algorithm in solving real-life scenarios of groundwater pollution.

Simultaneous identification of groundwater pollution source location and release concentration using Artificial Neural Network

Groundwater pollution source identification problem plays an important role in designing the groundwater remediation measures. This study aims at identifying the groundwater pollutant source location and its strength given a set of concentration breakthrough curves at different locations. Feed forward three layered artificial neural network (ANN) has been used to identify these two source parameters: location, i.e., the distance of source from the observation point and strength, i.e., the release concentration. A simplified ANN architecture is achieved by presenting the available concentration breakthrough curves in a specific manner as input to the model. The database of 1750 patterns for training and testing of the model is generated employing analytical solution of one dimensional steady flow and transient contaminant transport in a homogeneous aquifer. The performance of the ANN model was evaluated using standard statistical methods. A network with architecture 22-16-2 was found to be optimum and resulted in a normalized root mean square error (NRMSE) of 0.014 for identifying the source location and NRMSE of 0.044 for identifying the source release concentration. The results show that ANN can be a very efficient tool for locating pollution sources and estimating the release concentration at source. A good ANN model performance was obtained even with a simple architecture and with a small number of input variables.

Accurate harmonic source identification using S-transform

This paper introduces the accurate identification of harmonic sources in the power distribution system using time-frequency distribution (TFD) analysis, which is S-transform. The S-transform is a very applicable method to represent signals parameters in time-frequency representation (TFR) such as TFR impedance ( Z TFR ) and the main advantages of S-transform it can provide better frequency resolution for low frequency components and also offers better time resolution for high-frequency components. The identification of multiple harmonic sources are based on the significant relationship of spectral impedances ( Z S ) that extracted from the Z TFR , consist of the fundamental impedance ( Z 1 ) and harmonic impedance ( Z h ). To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior, with 100% correct identification of harmonic source location. It is proven that the method is accurate, fast and cost-efficient to localize harmonic sources in the power distribution system.

Identification of intermittent point source capacity and location of the initial contamination plume

While working on a model of mass impurity transition for the waters of the Sea of Azov and the Kerch Strait, we calculated the capacity of the immediate point source and identified the location of the initial contamination plume. Contamination source capacity identification is based on minimizing the quadratic functional of the forecasting quality and solving associated problems. Associated problem solving is also used in order to identify the location of the initial contamination plume in the body of water under analysis. The first goal was reached for the Gulf of Taganrog area, and the second one – for the Kerch Strait area. It must be noted that both areas have heavy marine traffic and experience the construction of new transport corridors. Simulation results were produced taking into consideration north-western winds that are dominant in the region. Quantitative experiments show that it is possible to identify the capacity of an intermittent point source of contamination using a three-dimensional model of mass impurity migration, as well as to identify the location of the initial contamination plume.

Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection

Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems.

산업단지 지하수 오염원 위치 및 누출 이력 규명 기술의 특성 분석

산업단지 지하수 오염원 위치 및 누출 이력 규명 기술의 특성 분석

Identification of Potential Source Location of Sulfur at Urban Area Bandung using Conditional Probability Function (CPF)

Bandung area is known as the center of economic growth in West Java. Bandung is also surrounding by mountains, therefore the morphological form of its territory is like a giant bowl. The previous study showed that the concentration of Sulfur in air particulate matter from Bandung is high. The objective of this study is to examine the use of conditional probability function (CPF) to identify directions of Sulfur sources using data collected from Bandung. CPF method is an advanced tool that analyzes pollutant source contributions in relation to the winds at the site and has been applied to find source directions. Data collection were done by air particulate sampling using GENT Sampler for 24-hours, once a week in Bandung during 2012-2014. EDXRF technique was applied for Sulfur analysis. The CPF values for Sulfur presented in polar plots, for fine and coarse particles. From this polar plot, the contributions of Sulfur in fine particles were coming from all sectors in Bandung whereas, in coarse particles, Sulfur dominated from the northern sector of Bandung.

Inverse tracking of an airborne pollutant source location in a residential apartment by joint simulation of CFD and a multizone model

Prompt identification of an indoor air pollutant source location is important for the safety of building residents in gaseous contaminant leakage incidents. Using the computational fluid dynamics (CFD) method in such inverse modeling is time consuming, especially for naturally ventilated residential buildings, which have multiple rooms and require consideration both indoor and outdoor environments. This paper compares the results of the pollutant source location identification and the simulation time based on two different inverse modeling methods: the CFD method and joint modeling of the multizone and CFD methods, to discuss the consumption of the computing time, as well as the accuracy of the location identification result. An instantaneous airborne pollutant source is assumed in a typical residential apartment that utilizes natural ventilation. A CFD model with the computational domain of the whole apartment and surrounding environment is built, for which the adjoint probability method is applied to simulate the source location probability from limited sensor readings. Meanwhile, a multizone model of the apartment is built to simulate and identify the room in which the source is located using the adjoint probability method. The CFD method is applied to the identified room afterwards to identify the exact location of the source within that room. The joint simulation of CFD and the multizone model is verified by a scaled model experiment of the apartment. It is found that the joint simulation method can significantly reduce the computing time and provides a good alternative for real-time inverse tracking of the indoor airborne pollutant.

Acoustic emission Bayesian source location: Onset time challenge

Robust identification of the most accurate observed input data among a pool of observations is key in modeling and decision making. A statistically biased observed measurement deteriorates the predictive power of a model and affects decision-making ability based on the prediction of the model. When two competing methods of measurement are available, such as methods which identify arrival times in acoustic emission (AE) signals, a principal question is whether one of the two obtained datasets, or a combination of the two, should be used later on, for example, to localize an AE source. This question becomes more important when collecting not repeatable data such as AE signals created by a propagating crack. This paper considers an inverse source location problem in a concrete block to address the mentioned issue, a proposed methodology which also has wider application in competitive data selection. Elastic energy released by an AE event, such as a propagating crack, is recorded by acoustic emission data acquisition system. The onset time of AE signals is often used to locate the source of the event, and its accuracy directly affects the precision of source identification. This research proposes an innovative approach to select the most probable onset time obtained from two automatic picker methods. The proposed method selects the most probable onset times, which are observed by each picker for each sensor, in a probabilistic fashion. To validate the proposed method, the most accurate onset time observed by each picker is identified by visual inspection and is compared with the one is selected by the proposed method. Finally, the dataset is used for source location identification. Results show that picked onset times determined by the proposed method generate more accurate source identification when compared with coordinates obtained using each dataset individually.

Fracture studies on synthetic fiber reinforced cellular concrete using acoustic emission technique

Cellular lightweight concrete (CLC) is increasingly used for low strength non-structural and structural applications. The effects of synthetic fiber reinforcement on the fracture behavior of CLC is investigated. In particular, acoustic emission (AE) technique is employed to study the influence of macro (structural), micro polyolefin synthetic fibers and their combinations on the fracture behavior of CLC beams. Notched fiber reinforced CLC beams were tested to study the crack initiation and propagation characteristics using AE sensors. Different AE parameters are correlated with the crack growth and damage accumulation. An attempt has been made to correlate the crack mouth opening displacement (CMOD) with the number of AE hits. The variation of cumulative acoustic energy release of the cracks is studied with respect to applied load and CMOD. Three dimensional source location of cracks is carried out based on the AE events picked by the sensors bonded to the CLC specimens. The analysis of AE results indicates that the crack source location identification from AE is consistent with the actual crack development. Analysis of AE signals reveal that the CLC matrix cracking produces signals with less number of hits that lie in the notched plane in bending. Moreover, the signals from the post peak regime correspond to more number of hits which tend to be scattered around the plane of notch due to the fiber pull out.