Emission Source Parameters Research Articles

Femtoscopy measurements of two-kaon combinations in Au+Au collisions at the STAR experiment

Abstract. Relativistic heavy-ion collisions can study the properties of nuclear matter in high-energy experiments like the STAR experiment. One of the methods to learn about the properties of the bulk matter is the femtoscopy technique, which relies on information carried by the particles produced during the collisions. The emission source parameters, like space-time characteristics, can be extracted using the technique. High statistics data from RHIC make it possible to study the correlations between strange particles, like charged and neutral kaons. The pair-wise interactions between the identical kaons that form the basis for femtoscopy are quantum statistics and the Coulomb interaction for K±K±, and quantum statistics and the final-state interaction (FSI) through the f0(980)/a0(980) threshold resonances for KS0KS0. The interactions between non-identical kaons pairs of KS0K± are essential, as the strong FSI is described only by the a0(98O) resonance, which could be a four-quark state. These proceedings will present the femtoscopic measurements of strange particles with charged and neutral kaons correlations in Au+Au collisions at the RHIC energy.

Influencing factors of shielding effectiveness test of electromagnetic shielding clothing

There is no perfect method to test and evaluate electromagnetic shielding (EMS) clothing up to now. The main reason is that the influence laws of the emission source parameters and the test position on the shielding effectiveness (SE) of the EMS clothing has not been clarified. In this paper, the SE of the EMS clothing is tested using a semi anechoic chamber in the frequency range of 1–18 GHz. The variation of the SE of the clothing is explored when the emission source parameters and the test positions are different. It is found that the SE of the EMS clothing decreases obviously with the increase of the testing distance, and the decline degrees are different with different frequencies. The decline trend slows down and approaches stability as the distance increases to a certain extent. The influence of the emission source angle on the SE is closely related to the frequency and positive or negative effects are produced at different frequency points. The SE are low when the test points are in the leakage areas with seams and openings, and the SE are high in the normal area. Under certain conditions, the measured SE of the clothing is close to zero or even less than zero. That is, a dangerous frequency point is formed. The mechanism of the above-mentioned laws is analyzed, and a new multi-index composite evaluation method is proposed to scientifically evaluate the EMS clothing based on the comprehensive consideration of the influencing factors. This research has great significance to promote the scientific test, evaluation and relevant standard formulation of the EMS clothing, and provides a reference role for the design and production of the EMS clothing.

Failure phenomenon of electromagnetic shielding clothing on human health protection in electromagnetic radiation environment

Electromagnetic shielding (EMS) clothing is an important product to protect human body from electromagnetic damage, but the change law and influencing factors of its protective effect have not been clarified so far. In this paper, an important phenomenon is found that the shielding effectiveness (SE) value of the EMS clothing is abnormally reduced to 0 or even less than 0 under certain conditions, which is named failure phenomenon. A semi anechoic chamber is selected to build a testing system to carry out the experiments. The occurring rule and mechanism of the failure phenomenon is explored when the emission source parameters and the test positions are different. The projection method is proposed to obtain the equivalent hole and equivalent seam of the clothing. According to the electromagnetic theory, the estimation method of whether the failure phenomenon appears is established. The results show that the failure phenomenon of the EMS clothing is a serious hidden danger to human health, and the parameters of the emission source and test location have an important impact on the generation of the failure phenomenon. Its essence is determined by the distribution and characteristics of the leakage area and the normal area of the clothing area incident by electromagnetic wave. The estimation method of the failure phenomenon and the design principle of the clothing can prevent the occurrence of the failure phenomenon. This paper can provide reference for the design, production, test and evaluation of the EMS clothing and related scientific research, and has great significance for really protection of human health by the EMS clothing.

Application and improvement of swarm intelligence optimization algorithm in gas emission source identification in atmosphere

Hazardous gas emissions could cause serious consequences for ecology, environment, human life and even society. Thus gas emission source term identification is crucial for emergency response and safety management. Based on experimental data, swarm intelligent optimization (SIO) algorithms including particle swarm optimization (PSO), ant colony optimization algorithm (ACO) and firefly algorithm (FA), are compared to identify the gas emission source parameters including source strength and location parameters. The results show that all three SIO methods used in this work have similar performances in terms of source parameter estimation, and all of them depend slightly on initial range set for individuals in the population. However, PSO method is superior in computational efficiency compared with ACO and FA methods. The convergence rate of FA is faster than that of ACO. PSO method can obtain satisfied estimation results under different boundary constraints, while the estimation results of FA and ACO will become unrealistic under too wide boundary constraints. The impact of atmospheric conditions on estimated results is also discussed. The results under extreme atmospheric conditions are worse than that in other conditions. Finally, SIO method coupled with a new model, correlated matching of concentration distribution (CMCD) model, is applied to the source location estimation. Test results prove that SIO-CMCD model can obtain a satisfied estimation as well as greatly enhanced computational efficiency when only location parameters are required to be determined. Hence, SIO is a useful tool to estimate emission source term for the storage and transportation process of hazardous gas or volatile materials.

Gas emission source term estimation with 1-step nonlinear partial swarm optimization–Tikhonov regularization hybrid method

Abstract Source term identification is very important for the contaminant gas emission event. Thus, it is necessary to study the source parameter estimation method with high computation efficiency, high estimation accuracy and reasonable confidence interval. Tikhonov regularization method is a potential good tool to identify the source parameters. However, it is invalid for nonlinear inverse problem like gas emission process. 2-step nonlinear and linear PSO (partial swarm optimization)–Tikhonov regularization method proposed previously have estimated the emission source parameters successfully. But there are still some problems in computation efficiency and confidence interval. Hence, a new 1-step nonlinear method combined Tikhonov regularization and PSO algorithm with nonlinear forward dispersion model was proposed. First, the method was tested with simulation and experiment cases. The test results showed that 1-step nonlinear hybrid method is able to estimate multiple source parameters with reasonable confidence interval. Then, the estimation performances of different methods were compared with different cases. The estimation values with 1-step nonlinear method were close to that with 2-step nonlinear and linear PSO–Tikhonov regularization method. 1-step nonlinear method even performs better than other two methods in some cases, especially for source strength and downwind distance estimation. Compared with 2-step nonlinear method, 1-step method has higher computation efficiency. On the other hand, the confidence intervals with the method proposed in this paper seem more reasonable than that with other two methods. Finally, single PSO algorithm was compared with 1-step nonlinear PSO–Tikhonov hybrid regularization method. The results showed that the skill scores of 1-step nonlinear hybrid method to estimate source parameters were close to that of single PSO method and even better in some cases. One more important property of 1-step nonlinear PSO–Tikhonov regularization method is its reasonable confidence interval, which is not obtained by single PSO algorithm. Therefore, 1-step nonlinear hybrid regularization method proposed in this paper is a potential good method to estimate contaminant gas emission source term.

Parameter identification for continuous point emission source based on Tikhonov regularization method coupled with particle swarm optimization algorithm

In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear Tikhonov-PSO regularization method. The regularization parameters were selected by L-curve method. The estimation results with different regularization matrixes showed that the confidence interval with high-order regularization matrix is narrower than that with zero-order regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear Tikhonov-PSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear Tikhonov–PSO method with transformed linear inverse model has higher computation efficiency than nonlinear Tikhonov-PSO method. The confidence intervals from linear Tikhonov-PSO are more reasonable than that from nonlinear method. The estimation results from linear Tikhonov-PSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by Tikhonov-PSO method. Therefore, the presented linear Tikhonov-PSO regularization method is a good potential method for hazardous emission source parameters identification.

Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.

Estimation of Appropriate Stack Height at Industrial Layout, Makurdi, Benue State

The prediction of appropriate stack height at the Makurdi industrial layout in Benue State of Nigeria is carried out using the point source Gaussian plume model. The model takes into account the ambient environmental conditions and emission source parameters to calculate and estimate the appropriate stack height for three prevailing atmospheric stability classes, A, D and F within the Makurdi Industrial layout. The research estimates an appropriate stack height of ~65.0m for the Makurdi industrial layout. The results set a baseline for environmental safety standards for industries operating at the industrial layout and can be adopted by the National Environmental Standard Enforcement and Regulatory Agency (NESERA), and other concerned authorities in environmental monitoring and pollution control.

A simple inversion method for determining optimal dispersion model parameters from satellite detections of volcanic sulfur dioxide

AbstractA simple inversion scheme for optimizing volcanic emission dispersion model parameters with respect to satellite detections is presented in this paper. In this scheme, multiple dispersion model simulations, obtained by varying relevant model parameters, are created and compared against satellite detections using pattern correlation as a measure of model agreement with observations. It is shown that the scheme is successful in inferring emission source parameters such as those describing the vertical extent of the nascent sulfur dioxide emissions in the November 2010 Mount Merapi eruption in Java, Indonesia. These optimal parameter values then become a basis for improved forecasts of the transport of volcanic emissions.

Pollutant dispersion characteristics in Dhaka city, Bangladesh

Air pollution is a major environmental concern in major cities around the world. The major causes of air pollution include rapid industrialization/urbanization and increased non environment-friendly energy production. This paper analyses the atmospheric pollutant such as carbon monoxide (CO) and particulate matter (PM) dispersion characteristics of Dhaka city. The yearly and diurnal variations of pollutant concentration are described by taking into consideration of both meteorological and emission source parameters highlighting washout effect due to rainfall and inversion phenomena. Concentration of PM (both PM2.5 and PM10) and CO in the ambient air are measured for a period of one year with Airmetric Minivol air samplers and Gas Chromatographic (GC) technique, respectively. The trend over the year shows an increase in the monthly average hourly PM and CO concentrations in winter months (November to March) when both PM10 and PM2.5 annual average concentrations (about 130 and 95 μg m−3, respectively) exhibit levels exceeding World Health Organization (WHO) guidelines as well as exceed more than twice the national standards of annual PM10 (50 μg m−3) and PM2.5 (15 μg m−3) concentrations. Such high pollutant concentrations may have significant health implications for residents of Dhaka city. It is also found that the PM concentration increases with the increase of wind speed during dry winter season and is also influenced by transboundary air pollution. The data and subsequent recommendations can be useful in formulating air quality management strategies for the Dhaka city.

Risk Assessment Using EUSES; Refinement Options to Estimate Atmospheric Transportation by its Operational Priority Substances Model (OPS)

ABSTRACT The Operational Priority Substances (OPS) model is part of the European Union System for the Evaluation of Substances (EUSES) and is used to estimate air concentrations and atmospheric depositions for risk assessment of chemicals. Traditionally, EUSES assesses environmental exposure using a standardized scenario based on explicit assumptions and default parameter settings. Under the new European Union REACH legislation—which stands for Registration, Evaluation, Authorisation, and restriction of Chemicals—industry is responsible for the risk assessment of chemicals. The current work investigates the potential impact and importance of source parameter refinement on the emission distribution and environmental fate of chemicals as modelled by OPS. In total, 60 scenarios with different combinations of (non) default values for emission heat content, emission source height, and emission source radius, were run for a typical particle-bound substance. Increasing heat content and emission source height reduced the modelled air concentrations and depositions and caused maximum air concentrations to occur further away from the emission source. Results show that estimated air concentrations and total depositions used for risk assessment can become more realistic by using more site-specific emission source parameters. This article proposes risk assessment refinement options and provides recommendations on future updates of EUSES.