• Asian Journal of Atmospheric Environment
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• v.12 no.1
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• pp.59-66
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• 2018

This study aims to evaluate the performance of the Air Quality Model (AQM) for the seasonal and spatial distribution of the $NH_3$ concentration in the atmosphere. To obtain observational data for the model validation, observations based on biweekly sampling have been conducted using passive samplers since April 2015 at multiple monitoring sites in the Tokyo metropolitan area. AQM, built based on WRF/CMAQ, was applied to predict the $NH_3$ concentration observed from April 2015 to March 2016. The simulation domain includes the Kanto region, which is the most densely populated area in Japan. Because the area also contains large amount of livestock, especially in its northern part, the density of the $NH_3$ emissions derived from human activities and agriculture there are estimated to be the highest in Japan. In the model validation, the model overestimated the observed $NH_3$ concentration in the summer season and underestimated it in the winter season. In particular, the overestimation in the summer was remarkable at a rural site (Komae) in Tokyo. It was found that the overestimation at Komae was caused by the transportation of $NH_3$ emitted in the northern part of the Kanto region during the night. It is suggested that the emission input used in this study overestimated the $NH_3$ emission from human sources around the Tokyo suburbs and agricultural sources in the northern part of the Kanto region in the summer season. In addition, the current emission inventories might overestimate the difference of the agricultural $NH_3$ emissions among seasons. Because the overestimation of $NH_3$ in the summer causes an overestimation of $NO_3{^-}$ in $PM_{2.5}$ in the AQM simulation, further investigation is necessary for the seasonal variation in the $NH_3$ emissions.

• Korean Journal of Environmental Biology
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• v.35 no.1
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• pp.13-20
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• 2017

Greenhouse gas emission from agricultural land is recognized as an important factor influencing climatic change. In this study, the national $CO_2$ emission was estimated for paddy soils, using soil GHG emission model (DNDC) with $1km^2$ scale. To evaluate the applicability of the model in Korea, verification was carried out based on field measurement data using a closed chamber. The total national $CO_2$ emission in 2015 was estimated at $5,314kt\;CO_2-eq$, with the emission per unit area ranging from $2.2{\sim}10.0t\;CO_2-eq\;ha^{-1}$. Geographically, the emission of Jeju province was particularly high, and the emission from the southern region was generally high. The result of the model verification analysis with the field data collected in this study (n=16) indicates that the relation between the field measurement and the model prediction was statistically similar (RMSE=22.2, ME=0.28, and $r^2=0.53$). More field measurements under various climate conditions, and subsequent model verification with extended data sets, are further required.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.1602-1605
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• 2008

We considered most general electron emission caused by temperature as well as electric field with a free electron gas model. The total electron emission current density comes from field emission effect where electron energy is lower than vacuum and from thermionic emission effect where electron energy is higher than vacuum. The total electron emission current density is shown as a function of temperature for constant electric field, and as a function of electric field for constant temperature.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.87-96
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• 2011

In this study, the AERMOD dispersion model was used for predicting odor concentrations and back-calculating industrial area source odor emission rate. The studied area was Sihwa industrial complex in Korea. Odor samples were collected during two days over a year period in 2009. The comparison between the predicted and observed concentrations indicates that the AERMOD model could fairly well predict average downwind odor concentrations. The results show odor emission rates of Sihwa industrial complex area source were ranged from 0.204 to 2.320 $OUms^{-1}$ (average 0.476 $OUms^{-1}$). The results also show wind speed and direction are important parameters to the odor dispersion.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.601-606
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• 1993

From the power plants in a steel plant, environment pollutants such as SOx, NOx, CO are emitted by the combustion reaction between the fuels those are by-product gases and oil. To reduce the amounts of the pollutants, it must be important that build the predictive models for the emission of the pollutants. In this paper, the model that predict the amount of future fuel consumption and the model that predict the amount of generated pollutants for the used fuel amounts is developed by using Gibb's free energy minimization method with the temperature correction techniques and neural network back propagation method. For some data set, the calculation results from this models are compared with the real emission amounts of SOx, NOx and result of the calculation by the ASPEN plus which is a commercial software. The result from this model is better than the result by ASPEN plus for this problem.

• Advances in Energy Research
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• v.8 no.3
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• pp.175-184
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• 2022

The dual-fuel technology, which uses gaseous fuel as the main fuel and liquid as the pilot fuel, is an appealing technology for reducing the exhaust emissions. The current study proposes emission models based on ANFIS for a dual-fuel using producer gas (PG)-diesel engine. Emissions measurements were taken at different engine load levels and fuel injection timings. The proposed model predictions were examined using statistical methods. With R2 values in the range of 0.9903 to 0.9951, the established ANFIS model was found to be consistently robust in predicting emission characteristics. The mean absolute percentage deviate in range 1.9 to 4.6%, and mean squared error varies in range 0.0018 to 13.9%. The evaluation of the ANFIS model developed shows a reliable claim of intrinsic sensitivity, strength, and outstanding generalization. The presented meta-model can be used to simulate the engine's operation in order to create an efficient control tool.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.3
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• pp.282-292
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• 2007

The present paper describes the results of high speed photography, acoustic emission (AE) detection and plasma light emission (LE) measurement during $CO_2$ laser welding of 304 stainless steel in different processing conditions. Video images with high spatial and temporal resolution allowed to observe the melt dynamics and keyhole evolution. The existence of keyhole was confirmed by the slag motion on the weld pool. The characteristic frequencies of flow instability and keyhole fluctuations at different welding speed were measured and compared with the results of Fourier analyses of temporal AE and LE spectra. The experimental results were compared with the newly developed numerical model of keyhole dynamics. The model is based on the assumption that the propagation of front part of keyhole into material is due to the melt ejection driven by laser induced surface evaporation. The calculations predict that a high speed melt flow is induced at the front part of keyhole when the sample travel speed exceeds several 10 mm/s. The numerical analysis also shows the hump formation on the front keyhole wall surface. Experimentally observed melt behavior and transformation of the AE and LE spectra with variation of welding speed are qualitatively in good agreement with the model predictions.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• Journal of the Korean Physical Society
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• v.80
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• pp.953-963
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• 2022

Post-neutron emission fission product mass distributions are calculated by using pre-neutron emission fission product yields (FPYs) and neutron multiplicity. A semi-empirical model is used to calculate the pre-neutron FPY, first. Then the neutron multiplicity for each fission fragment mass is used to convert the pre-neutron FPY to the post-neutron FPY. In doing so, assumptions are made for the probability for a pre-emission fission fragment with a mass number A^* to decay to a post-emission fragment with a mass number A. The resulting post-neutron FPYs are compared with the data available. The systems where the experimental data of not only the pre- and post-neutron FPY but also neutron multiplicity are available are the thermal neutron-induced fission of ²³³U, ²³⁵U and ²³⁹Pu. Thus, we applied the model calculations to these systems and compared the calculation results with those from the GEF and the data from the ENDF and the EXFOR libraries. Both the pre- and post-neutron fission product mass distributions calculated by using the semi-empirical model and the neutron multiplicity reproduce the overall features of the experimental data.

• Advances in environmental research
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• v.9 no.2
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• pp.97-107
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• 2020

The indiscriminate growth in global population poses a threat to the world in handling and disposal of Municipal solid waste. Rapid urban growth increases the production, consumption and generation of Municipal solid waste which leads to a drastic change in the environment. The methane produced from the Municipal Solid waste accounts for up to 11% global anthropogenic emissions, which is a major cause for global warming. This study reports the methane emission estimation using IPCC default, TNO, LandGEM, EPER and close flux chamber from open dump yards at Perungudi and Kodungaiyur in Chennai, India. The result reveals that the methane emission using close flux chamber was in the range of 8.8 Gg/yr-11.3 Gg/yr and 6.1Gg/yr to 9.1 Gg/yr at Kodungaiyur and Perungudi dump yard respectively. The per capita waste generation was estimated based on waste generation and population. The waste generation potential was projected using linear regression model for the period 2017-2050. The trend of CH₄ emission in the actual field measurement were increased every year, similarly the emission trend also increased in IPCC default method (mass balance approach), EPER Germany (zero order decay model) where as TNO and Land GEM (first order decay model) were decreased. The present study reveals that Kodungaiyur dump yard is more vulnerable to methane emission compared to Perungudi dump yard and has more potential in waste to energy conversion mechanisms than compare to Perungudi dump yard.