• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.1
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• pp.11-23
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• 2000

Photochemical air quality models are essential tools in predicting future air quality and assessing air pollution control strategies. To evaluate air quality using a photochemical air quality model, emission inventories are important inputs to these models. Since most emission inventories are provided at a county-level, these emission inventories need to be geographically allocated to the computational grid cells of the model prior to running the model. The conventional method for the spatial allocation of these emissions uses "spatial surrogate indicators", such as population for mobile source emissions and county area for biogenic source emissions. In order to examine the applicability of such approximations, more detailed spatial surrogate indicators were developed using Geographic Information System(GIS) tools to improve the spatial allocation of mobile and boigenic source emissions, The proposed spatial surrogate indicators appear to be more appropriate than conventional spatial surrogate indicators in allocating mobile and biogenic source emissions. However, they did not provide a substantial improvement in predicting ground-level ozone(O3) concentrations. As for the carbon monoxide(CO) concentration predictions, certain differences between the conventional and new spatial allocation methods were found, yet a detailed model performance evaluation was prevented due to a lack of sufficient observed data. The use of the developed spatial surrogate indicators led to higher O3 and CO concentration estimates in the biogenic source emission allocation than in the mobile source emission allocation.llocation.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.3
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• pp.151-158
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• 1999

Biogenic source emissions refer to naturally occuring emissions from vegetation, microbial activities in soil, lightening, and so on. Vegetation is especially known to emit a considerable amout of volatile organic compounds into the atmosphere. Therefore, biogenic source emissions are an important input to photochemical air quality models. since most biogenic source emissions are calculated at the county-level, they should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation for biogenic source emissions has been to use a "spatial surrogate indicator" such as a county area. In order to examine the applicability of such approximations, this study developed more detailed surrogate indicators to improve the spatial allocation method for biogenic source emissions. Due to the spatially variable nature of biogenic source emissions, Geographic Information Systems(GIS) were introduced as new tools to develop more detailed spatial surrogate indicators. Use of these newly developed spatial surrogate indicators for biogenic source emission allocation provides a better resolution than the standard spatial surrogate indicator.indicator.

• Journal of Environmental Impact Assessment
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• v.14 no.3
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• pp.117-126
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• 2005

This study was executed to know the best matrix of photochemical ozone reduction in the metropolitan area. For this object, we used the OZIPR(Ozone Isopleth Plotting Package for Research) model for comparing the effectiveness of VOCs and NOx amount variation about the ozone creation. Among the various ozone reduction scenarios, 50% reduction of VOCs from organic solvent and road traffic respectively was the best matrix for ozone reduction. Although it needs more accurate assessment and confirmation of VOCs and NOx emission amount data, according to existing data, the control of VOCs is the best way for photochemical ozone reduction in Seoul.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.235-253
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• 2017

This study was conducted for analyzing the contribution factors on ozone concentrations and its long term trends in each major city and province in Korea through several statistical methods such as simple linear regression, generalized linear model, KZ-filer, correlation matrix, Kringing method, and cluster analysis. The overall ozone levels in South Korea have been consistently increasing over the past 10 years. The ozone concentrations in Seoul, the biggest city in Korea, are the lowest in all areas with the highest increasing ratio for $95^{th}%$ ozone. It is thought that the active photochemical reaction could affect the higher ozone concentration increase. On the other hand, the ozone concentrations in Jeju are the highest in Korea with the highest increasing ratio for $5^{th}%$, $33^{th}%$, and $50^{th}%$ ozone. It is also thought that the weak $NO_x$ titration could be the reason of higher ozone concentrations in Jeju. In case of Jeju, transport related factors is the major factor affecting the ozone trend. Thus, it is assumed that the variation of ozone trend of Asian region affecting the ozone trend in Jeju, where domestic ozone photochemical reaction is less active than urban area. It is thought that the photochemical reaction plays the role of increasing of ozone concentrations in the urban area, even though the LRT affected on the increase of ozone concentrations in non-urban area.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.3
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• pp.143-150
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• 1999

Mobile source emissions are important inputs to photochemical air quality models. Since most mobile source emissions are calculated at the county-level, these emission should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation of these emissions has been to use a "spatial surrogate indicator" such as population, since grid-specific emission calculations are very labor-intensive and expensive, plus the necessary data are often not available for such grid resolutions. Accordingly, new spatial surrogate indicators for mobile source emissions(specifically for highway emissions) were developed using Geographic Information Systems(GIS) tools due to the spatially variable nature of mobile source emissions. These newly developed spatial surrogate indicators appear to be more appropriate for the allocation of highway emissions than the population surrogate indicator. It was also revealed that the conventional spatial allocation method underestimates the maximum levels of air pollutant emmissions.mmissions.

• Journal of Environmental Science International
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• v.1 no.1
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• pp.19-33
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• 1992

The high oxidants, which occur the daily maximum concentrations in the afternoon, are transported into the other region via long range transport mechanisms or trapped within the shallow mixing boundary layer and then removed physically (deposition, transport by mountain wind, etc.) and chemically (reaction with local sources). Therefore, modeling formation of photochemical oxidants requires a complex description of both chemical and meteorolog ital processecs . In this study, as a part of air quality studies, we reviewed various aspects of photochemical modeling on the basis of currently available literature. The result of the review shows that the model is based on a set of coupled continuity equations describing advection, diffusion, transport, deposition, chemistry, emission. Also photochemical oxidant models require a large amount of input data concerned with all aspects of the ozone life cycle. First, emission inventories of hydrocarbon and nitrogen oxides, with appropriate spatial and temporal resolution. Second, chemical and photochemical data allowing the quantitative description of the formation of ozone and other photochemically-generated secondary pollutants. Third, dry deposition mechanisms particularly for ozone, PAN and hydrogen peroxide to account for their removal by absorption on the ground, crops, natural vegetation, man-made and water surfaces. Finally, meteorological data describing the transport of primary pollutants away from their sources and of secondary pollutants towards the sensitive receptors where environmental damage may occur. In order to improve our present study, shortcomings and limitation of existing models are pointed out and verification Process through observation is emphasized.

• Journal of Environmental Science International
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• v.11 no.9
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• pp.897-905
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• 2002

A three-dimensional photochemical air pollution model considered advection, dispersion, photochemical reactions, and precipitation processes was developed. The calculated results of meteorological observation clearly exhibited geographical effects of Gwangyang Bay, in which land and sea breezes, mount-valley winds and local circular winds occurred. The observed results of daytime NOx concentrations were slightly higher than the calculated NOx concentrations in Yosu industrial complex, Gwangyang iron mill, and container yard. Eventually, the calculated NOx results generally agreed well with the observed ones.

• Journal of the Korean Solar Energy Society
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• v.31 no.5
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• pp.47-59
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• 2011

The purpose of this paper is to simulate the high ozone concentration in Shiwha Banwol indusrial complex. High pollution episodes (ozone alert) of this area are the results of geographical location and its air pollutants emission. This research has used meteorological model (RAMS) and photochemical air pollution Model (CIT model). As first step of the evaluate of this combined model system simulations are done in terms of meteorological characteristics like wind fields, PBL-height, etc.. Numerical simulations are carried out with real meteorological synoptic data on June. 24-25, 2010. In comparison with real measurement and another research the model reflects well local meteorological phenomena and shows the possibility to be utilized to analyse the pollutant dispersion over irregular terrain region. The high ozone concentration is deeply correlated to the ambient air temperature, wind speed and solar radiation. Local meteorological phenomena like sea-land breeze impact on horizontal dispersion of ozone. This analysis of meteorological characteristics can, in turn, help to predict their influences on air quality and to manage the high ozone episodes.

• Particle and aerosol research
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• v.10 no.1
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• pp.9-17
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• 2014

Several studies have been carried out on the source contribution of the particulate Polycyclic Aromatic Hydrocarbons (PAHs) over Seoul by using the Chemical Mass Balance Model (CMB)(Lee and Kim, 2007; Kim et al., 2013). To confirm the validity of the modeling results, the modified model employing a photochemical loss rate along with varying residence times and the standard model that considers no loss were compared. It was found that by considering the photochemical loss rate, a better performance was obtained as compared to those obtained from the standard model in the CMB calculation. The modified model estimated higher contributions from coke oven, transportation, and biomass burning by 4 to 8%. However, the order of the relative importance of major sources was not changed, coke oven followed by transportation and biomass burning. Thus, it was concluded that the standard CMB model results are reliable for identifying the relative importance of major sources.

• Journal of Environmental Science International
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• v.32 no.8
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• pp.595-611
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• 2023

The purpose of this study is to improve the daily prediction results of PM2.5 from the air quality diagnosis and evaluation system operated by the Busan Institute of Health and Environment in real time. The air quality diagnosis and evaluation system is based on the photochemical numerical model, CMAQ (Community multiscale air quality modeling system), and includes a 3-day forecast at the end of the model's calculation. The photochemical numerical model basically has limitations because of the uncertainty of input data and simplification of physical and chemical processes. To overcome these limitations, this study applied DNN (Deep Neural Network), a deep learning technique, to the results of the numerical model. As a result of applying DNN, the r of the model was significantly improved. The r value for GFS (Global forecast system) and UM (Unified model) increased from 0.77 to 0.87 and 0.70 to 0.83, respectively. The RMSE (Root mean square error), which indicates the model's error rate, was also significantly improved (GFS: 5.01 to 6.52 ug/m³ , UM: 5.76 to 7.44 ug/m³ ). The prediction results for each concentration grade performed in the field also improved significantly (GFS: 74.4 to 80.1%, UM: 70.0 to 77.9%). In particular, it was confirmed that the improvement effect at the high concentration grade was excellent.