• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.73-78
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• 2003

In this research, we implement Realtime Air Diffusion Prediction System which is a parallel Fortran model running on distributed-memory parallel computers. The system is designed for air diffusion simulations with four-dimensional data assimilation. For regional air quality forecasting a series of dynamic downscaling technique is adopted using the NCAR/Penn. State MM5 model which is an atmospheric model. The realtime initial data have been provided daily from the KMA (Korean Meteorological Administration) global spectral model output. It takes huge resources of computation to get 24 hour air quality forecast with this four step dynamic downscaling (27km, 9km, 3km, and lkm). Parallel implementation of the realtime system is imperative to achieve increased throughput since the realtime system have to be performed which correct timing behavior and the sequential code requires a large amount of CPU time for typical simulations. The parallel system uses MPI (Message Passing Interface), a standard library to support high-level routines for message passing. We validate the parallel model by comparing it with the sequential model. For realtime running, we implement a cluster computer which is a distributed-memory parallel computer that links high-performance PCs with high-speed interconnection networks. We use 32 2-CPU nodes and a Myrinet network for the cluster. Since cluster computers more cost effective than conventional distributed parallel computers, we can build a dedicated realtime computer. The system also includes web based Gill (Graphic User Interface) for convenient system management and performance monitoring so that end-users can restart the system easily when the system faults. Performance of the parallel model is analyzed by comparing its execution time with the sequential model, and by calculating communication overhead and load imbalance, which are common problems in parallel processing. Performance analysis is carried out on our cluster which has 32 2-CPU nodes.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Journal of the Korean Professional Engineers Association
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• v.42 no.4
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• pp.40-44
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• 2009

The analysis of air quality model applied for EIA in Korea indicates that ISCST3 and CALINE3 dominate the model, and it causes the problem that regional and business characteristics are not taken into account. To solve this problem, it appears necessary to build guideline of the air quality model operation. First of all, to implement the above plan we need to categorize the site into simple and complex terrain, coast to consider regional characteristics, and the sources of pollutants into point/area/line as well. To make the procedure more efficient with reduced time and less cost, we are to apply screening model for prelimninary work of the suggested model.

• Journal of Environmental Science International
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• v.8 no.4
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• pp.485-490
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• 1999

We will calculate concentration of air pollutants using ISCST3, FDM and AERMOD of models recommended in U. S. EPA which are able to predict concentration of short term for point source, complex like industrial complex, power plant and burn-up institution. Before executing model, as analyzing computational result of many cases according to selecting of input data, we will increasing predictable ability of model in limit range of model. Especially, we analyzed three cases-case of considering various emission rate according to time scale and not, case considering effect of atmospheric pollution materials removed by physical process. In our study, after comparing and analyzing results of three model, we choose the atmospheric dispersion model reflected well the characteristic of the area. And we will investigate how large the complex pollutant sources such as industrial complex contribute to atmospheric environment and air quality of the surrounding the area as predicting and estimating chosen model.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Atmosphere
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• v.33 no.5
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• pp.505-517
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• 2023

This study investigates the impacts of carbon neutrality and air quality control policies on near-term climate change in East Asia, by examining three Shared Socioeconomic Pathways (SSPs) scenarios from five climate models. Specifically, low carbon and strong air quality control scenario (SSP1-1.9), high carbon and weak air quality control scenario (SSP3-7.0), and high carbon and strong air quality control scenario (SSP3-7.0-lowNTCF) are compared. For these scenarios, the near-term climate (2045-2054 average) changes are evaluated for surface air temperature (SAT), hot temperature extreme intensity (TXx), and hot temperature extreme frequency (TX90p). In all three scenarios, SAT, TXx, and TX90p are projected to increase in East Asia, while carbon neutrality reduces the increasing rate of SAT and hot temperature extremes. Air quality control strengthens the warming rate. These opposed mitigation effects are robustly forced in all model simulations. Nonetheless, the impact of carbon neutrality overcomes the impact of air quality control. These results suggest that fast carbon neutrality, more effective than an air quality control policy, is necessary to slowdown future warming trend in East Asia.

• Journal of Environmental Health Sciences
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• v.16 no.2
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• pp.1-10
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• 1990

This study was performed to evaluate a applicability of TCM(Texas Climatological Model) model to a industrial area sush as CUNSAN and a possibility to provide necessary informaitons for air quality management. The air pollutants were measured at 6 sampling sites of GUNSAN industrial area from june to july in 1989. The model was checked by comparing the observed data with estimated data. The meteorological data for wind direction and wind speed were obtained from the observatory station in GUNSAN. The results are summarized as follows. 1. Average concentrations of air pollutants at all sampling sites were SO$_{2}$ 0.011-0.019 ppm. NO$_{2}$ 0.012-0.017 ppm. CO 0.6-1.0 ppm. TSP 45.8-64.2 $\mu$g/m$^{3}$. 2. The emission amounts show that point source are in general higher than area source. 3. As a results of correlation analysis, relationship between SO$_{2}$ concentration in the observed value and estimated value showed positive significance.(r = 0.766) 4. The sulfer content of the 1.6% at present to 0.8%, which means a 53.3% reduction. By controlling stack height could be lowered 14.5%, but the effective way of emission control is use of the lower sulfer fuels than controlling stack height. 5. The ratio between SO$_{2}$ contration in the observed value and estimated value showed 1.05. There-fore, the TCM model was quite effective in predicting air quality in GUNSAN industrial area.

• Journal of Environmental Science International
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• v.15 no.10
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• pp.913-918
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• 2006

Despite the wide distribution of air pollutants, the concentrations of indoor air pollutants may be the dominant risk factor in personal exposure due to the fact that most people spend an average of 80% of their time in enclosed buildings. Researches for improvement of indoor air quality have been developed such as installation of air cleaning device, ventilation system, titanium dioxide$(TiO_2)$ coating and so on. However, it is difficult to evaluate the magnitude of improvement of indoor air quality in field study because indoor air quality can be affected by source generation, outdoor air level, ventilation, decay by reaction, temperature, humidity, mixing condition and so on. In this study, evaluation of reduction of formaldehyde and nitrogen dioxide emission rate in indoor environments by $TiO_2$ coating material was carried out using mass balance model in indoor environment. we proposed the evaluation method of magnitude of improvement in indoor air quality, considering outdoor level and ventilation. Since simple indoor concentration measurements could not properly evaluate the indoor air quality, outdoor level and ventilation should be considered when evaluate the indoor net quality.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.3
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• pp.217-223
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• 2001

There exist a number of approaches which can evaluate ventilation and indoor air quality. The measurement and analysis of indoor carbon dioxide concentrations can be useful for evaluating indoor air quality and ventilation. This paper describes a numerical analysis of carbon dioxide concentrations for evaluating indoor air quality and ventilation and the factors the need to be considered in their use. The conditions of this numerical analysis are tow types of positions and inlet velocities of ventilation system in a two-dimensional model of an apartment house. The simulation results could be used as a base data for further analysis for ventilation design of other industrial processes producing a proper ventilation system for a healthier and more comfortable environment in a building.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.2
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• pp.259-270
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• 2008

The regional air movement in a coastal area is generated by the different heat capacities of sea and land sides, which is called sea/land breeze. In the west coast area, the local air quality is significantly influenced by this sea/land breeze. In this study, the mathematical model is proposed to estimate the effective area of sea/land breeze. A commercial air model, that is suggested as an alternative air model by USEPA, is introduced to simulate the mechanism of sea/land breeze generation. From this study, it is confirmed that the numerical approach proposed in this study is reliable to predict the effective area of sea breeze in a coastal area. It implies that the current application of common air model needs to be carefully reviewed especially when dealing with a coastal air quality issue. It is also found that the sea breeze in Incheon area has the impact in the range of approximately 24 km in-land side, so-called penetration length.