• Journal of Korean Society for Atmospheric Environment
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• v.14 no.3
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• pp.177-190
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• 1998

The concentrations of air pollutants In large cities such as Pusan area have been increased every year due to the increasing of fuels consumption at factories and by vehicles as well as the gravitation of the population. In addition to the pollution sources, time and spatial variation of air pollutants concentration and meteorological factors have a great influence on the air pollution problem. Especially , its concentration is governed by wind direction, wind speed, precipitation, solar radiation, temperature, humidity and cloud amounts, etc. In this study, we have analyzed various data of meteorological factors using typical patterns of the air pressure to investigate how the concentration of air pollutants is varied with meteorological condition. Using the relationship between meteorological factors (air temperature, relative humidity, wind speed and solar radiation) and the concentration of air pollutants (SO2, O3) , experimental prediction formulas for their concentration were obtained. Therefore, these prediction formulas at each meteorological factor in a pressure pattern may be roughly used to predict the air pollutants concentration and contributed to estimate the variation of its value according to the weather condition in Pusan city.

• Journal of Environmental Science International
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• v.6 no.4
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• pp.351-357
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• 1997

In the Atmosphere under the various physical and chemical condition different chemical reactions occur and there are a number of air pollutants which are generated by photochemical reaction by absorbing solar energy. Therefor various testing simulation was done as foundation work to develop the numerical model for the prediction of concentration of air pollutants. It was shown change of msjor air pollutants concentration In according to variation of photodissociation speed constant, Kl and Initial condition of air pollutants concentration which plays major role In photochemical reaction. The photochemical reaction model which was used In this study Is found to be useful for understanding relationship among the concentration of reacting air pollutants and the prediction of concentration of air pollutants in urban atmosphere.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.86-87
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• 2021

As non-road mobile pollutants such as construction equipment are emerging as the main cause of air pollutants emission, construction equipment regulations are gradually strengthening. Research was conducted by correcting the emission coefficient to calculate and predict air pollutant emissions of construction equipment, but it did not reflect site variables such as field and equipment conditions that affect actual emissions. This study derived an Artificial Neural Network emission prediction model based on the actual emission data of excavators and trucks measured at the site and proposed a platform to predict the emission of air pollutants at the site according to the working size and conditions. Through this, it is possible to establish an eco-friendly process plan using a model from the construction plan.

• Journal of Environmental Science International
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• v.10 no.5
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• pp.343-350
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• 2001

To investigate air quality away from the coastal urban source region, we used a hybrid Eulerian - Lagrangian method which can describe the formation, transport, transform and deposition processes in complex terrain, with inclusion of shipping sources that were considered to be important emission in the coastal urban region. The result of the Eulerian advection - diffusion prediction was quite similar to that of the Lagrangian particle diffusion prediction. It showed that pollutants emitted from Sasang and Janglim industrial complexes can affect Hwamyeong and the coastal, respectively. During the daytime the concentration was low due to large deposition flux and terrain effect.

• Journal of Korean Society for Atmospheric Environment
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• v.8 no.1
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• pp.74-83
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• 1992

Photochemical reactions are important for the diurnal variation of the concentrations of air pollutants in the urban atmosphere. A photochemical reaction model was developed, which includes in terms of the effective chemical reaction. Various experimental results were introduced to the construction of model. To verify the applicability of the model, the simulated results were compared with those observed. By comparing the simulated results with those observed, it was shown that those two are in good agreement qualitatively. As a result, the photochemical reaction model which has been developed in this study is found to be useful for the prediction of concentrations of air pollutants in the atmosphere.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.1
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• pp.13-23
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• 2003

A local wind model and a three dimensional local environmental model including advection, diffusion, deposition. and photochemical reactions were performed at Gwangyang Bay, Korea, to predict air flow and air pollutants concentrations. A large grid was used, and nesting method was employed for small grid calculation. From the meterological module simulation, we were able to reproduce local wind characteristics such as sea/land winds and mountain/valley winds simulation at Gwangyang Bay. In addition, the concentration module showed high concentration regions at Yosu industrial complex, Gwangyang steel company. and Container anchor. It was also seen that air pollutants were dispersed by sea/land winds. A comparison between the measurement and the prediction of sulfur dioxide and nitric oxide, which are relatively low-reacted pollutants, was performed. However, the measured nitrogen dioxide and ozone concentrations were higher than the simulated ones. Particularly, ozone concentration between 8 a..m. and 8 p.m. agreed well, but the measured ozone during the rest of time were generally higher.

• Asian Journal of Atmospheric Environment
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• v.10 no.4
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• pp.197-207
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• 2016

Large quantities of air pollutants are released into the atmosphere and hence, must be monitored and routinely assessed for their health implications. This paper proposes a stochastic technique to predict unobserved hazardous air pollutants (HAPs), especially Benzo[a]pyrene (BaP), which can have negative effects on human health. The proposed approach constructs a nearest-neighbor structure by incorporating the linkage between BaP and meteorology and meteorological effects. This approach is adopted in order to predict unobserved BaP concentrations based on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing the cancer risk. The efficient prediction provides useful information relating to the optimal monitoring period and projections of future BaP concentrations for both industrial and residential areas within Korea.

• JOURNAL OF ELECTRICAL WORLD
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• no.9 s.93
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• pp.11-16
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• 1984

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.5
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• pp.625-638
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• 1999

Air quality modeling for coastal urban region has been composed of a complex system including meteorological, chemical and physical processes and emission characteristics in complex terrain. In this study, we studied about an effect prediction for deposition phenomenon affecting on air quality in Pusan metopolitan metropolitan city. In air quality modeling including ship sources, a situation considered deposition process habe better result than not considered when compared with observed value. Air pollutants emitted into urban air during the daytime nearly removed through urban atmosphere polluted. Also these phenomena correlated concentration variation connent with sea/land breezes and terrain effect. Therefore we conclude that the concentration was low at daytime when deposition flux is high, and deposition effect on industrial complex and Dongrae region is considerable in particular.

• Advances in environmental research
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• v.4 no.4
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.