• Journal of Korean Society for Atmospheric Environment
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• v.15 no.5
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• pp.613-623
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• 1999

A Lagrangian particle dispersion mode l(LPDM) coupled with the prognostic flow model based on nonequilibrium level 2.5 turbulence closure has been dcveloped to simulate the dispersion from an elevated emission source. The proposed model did not require any empirical formula or data for the turbulent statistics such as velocity variances and Lagrangian time scales since the turbulence properties for LPDM were calculated from results of the flow model. The LPDM was validated by comparing the model results against the wind tunnel tracer experiment and ISCST3 model. The calculated wind profile and turbulent velocity variances were in good agreement with those measured in the wind tunnel. The ground level concentrations along the plume centerline as well as the dispersion codfficients also showed good agreement in comparison with the wind tunnel tracer experiment. There were some discrepancies on the horizontal spread of the plume in comparison with the ISCST3 but the maximum ground level concentrations were in a good confidence range. The results of comparisons suggested that the proposed LPDM with the flow model was an effective tool to simulate the dispersion in the flow situation where the turbulent characteristics were not available in advance.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.757-765
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• 1999

Lagrangian particle dispersion model(LPDM) is an effective tool to calculate the dispersion from a point source since it dose not induce numerical diffusion errors in solving the pollutant dispersion equation. Fictitious particles are released to the atmosphere from the emission source and they are then transported by the mean velocity and diffused by the turbulent eddy motion in the LPDM. The concentration distribution from the dispersed particles in the calculation domain are finally estimated by applying a particle count method or a Gaussian kernel method. The two methods for calculating concentration profiles were compared each other and tested against the analytic solution and the tracer experiment to find the strength and weakness of each method and to choose computationally time saving method for the LPDM. The calculated concentrations from the particle count method was heavily dependent on the number of the particles released at the emission source. It requires lots fo particle emission to reach the converged concentration field. And resulting concentrations were also dependent on the size of numerical grid. The concentration field by the Gaussian kernel method, however, converged with a low particle emission rate at the source and was in good agreement with the analytic solution and the tracer experiment. The results showed that Gaussian kernel method was more effective method to calculate the concentrations in the LPDM.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2000.04a
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• pp.167-168
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• 2000

복잡한 구조를 갖고 시간에 따라서 변하는 바람장내에서 공장굴뚝과 같은 점오염원에서 배출되는 오염물질의 확산을 계산하기 위해서 라그란지안 입자확산모텔(Lagrangian Particle Dispersion Model, LPDM)을 사용하는 것이 최근의 연구 동향이다. 구윤서(1999a, 1999b)는 중립 및 안정한 대기조건에서 바람장 계산시 비평형 2.5 난류모델을 이용한 LPDM을 개발하여 복잡한 대기흐름내 확산현상을 보다 정확히 모사할 수 있는 LPDM을 제시하였다. (중략)

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.3
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• pp.249-261
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• 2003

A three-dimensional mesoscale atmospheric dispersion modeling system consisting of the Lagrangian particle dispersion model (LPDM) and the meteorological mesoscale model (MM5) was employed to simulate the transport and dispersion of non-reactive pollutant during the nuclear spill event occurred from Sep. 31 to Oct. 3, 1999 in Tokaimura city, Japan. For the comparative analysis of numerical experiment, two more sets of foreign mesoscale modeling system; NCEP (National Centers for Environmental Prediction) and DWD (Deutscher Wetter Dienst) were also applied to address the applicability of air pollution dispersion predictions. We noticed that the simulated results of horizontal wind direction and wind velocity from three meteorological modeling showed remarkably different spatial variations, mainly due to the different horizontal resolutions. How-ever, the dispersion process by LPDM was well characterized by meteorological wind fields, and the time-dependent dilution factors ($\chi$/Q) were found to be qualitatively simulated in accordance with each mesocale meteorogical wind field, suggesting that LPDM has the potential for the use of the real time control at optimization of the urban air pollution provided detailed meteorological wind fields. This paper mainly pertains to the mesoscale modeling approaches, but the results imply that the resolution of meteorological model and the implementation of the relevant scale of air quality model lead to better prediction capabilities in local or urban scale air pollution modeling.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.1
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• pp.73-84
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• 2006

Hazardous Air Pollutants (HAPs) are characterized by being relatively heavier and denser than that of ambient air due to the various reasons such as higher molecular weight, low temperature and other complicated chemical transformations (Witlox, 1994). In an effort to investigate transport and diffusion from instantaneous emission of heavy gas, Lagrangian Particle Dispersion Model (LPDM) coupled with the RAMS output was employed. Both deposition process and buoyancy term were added on the atmospheric diffusion equations of LPDM, and the locations and concentrations of dense gas particle released from instantaneous single point source (emitting initially for 10 minutes only) were analyzed. The result overall shows that adding deposition process and buoyancy terms on the diffusion equation of LPDM has very small but detectable effect on the vertical and horizontal distribution of Lagrangian particles that especially transported for a fairly long traveling time. Also the slumping of dense gas can be found to be ignored horizontally compared to the advection by the horizontal wind suggesting that it was essential to couple the Lagrangian particle dispersion model coupled with the RAMS model in order to explain the dispersion of HAPs more accurately. However, during the initial time of instantaneous emission, buoyancy term play an important role on the vertical locations of dense particles for near surface atmosphere and around source area, indicating the importance of densities of HAPs in the beginning stage or short duration for the risk assessment of HAPs or management of heavy vapors during the explosive accidents.

• Atmosphere
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• v.29 no.5
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• pp.551-566
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• 2019

Quantitative understanding of a random error that is associated with Lagrangian particle dispersion modeling is a prerequisite for backward-in-time mode simulations. This study aims to quantify the random error of the WRF-FLEXPART model and suggest an optimum number of the Lagrangian particles for backward-in-time simulations over the Seoul metropolitan area. A series of backward-in-time simulations of the WRF-FLEXPART model has conducted at two receptor points by changing the number of Lagrangian particles and the relative error, as a quantitative indicator of random error, is analyzed to determine the optimum number of the release particles. The results show that in the Seoul metropolitan area a 1-day Lagrangian transport contributes 80~90% in residence time and ~100% in atmospheric enhancement of carbon monoxide. The relative errors in both the residence time and the atmospheric concentration enhancement are larger when the particles release in the daytime than in the nighttime, and in the inland area than in the coastal area. The sensitivity simulations reveal that the relative errors decrease with increasing the number of Lagrangian particles. The use of small number of Lagrangian particles caused significant random errors, which is attributed to the random number sampling process. For the particle number of 6000, the relative error in the atmospheric concentration enhancement is estimated as -6% ± 10% with reduction of computational time to 21% ± 7% on average. This study emphasizes the importance of quantitative analyses of the random errors in interpreting backward-in-time simulations of the WRF-FLEXPART model and in determining the number of Lagrangian particles as well.