• 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.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.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1465-1474
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• 2013

In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.

• Journal of Environmental Health Sciences
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• v.33 no.4
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• pp.255-263
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• 2007

Indoor air quality is the dominant contributor to total personal exposure because most people spend a majority of their time indoors. The purposes of this study were to evaluate the alternative method for improvement of indoor air quality in house after coating titanium dioxide ($TiO_2$) photocatalyst for interior part of the house using nitrogen dioxide ($NO_2$) multiple measurements. To evaluate the alternative method in indoor environment, daily indoor and outdoor $NO_2$ concentrations of an apartment and a detached house were daily measured for consecutive 21 days in winter and summer, respectively, Another daily 21 measurements were carried out after $TiO_2$ coating on wall paper of interior part in houses. All $NO_2$ concentrations were measured by passive filter badges. Indoor air quality models using mass balance are useful tool to quantify the relationship between indoor air pollution levels, ambient concentrations, and explanatory variables. Using a mass balance model and linear regression analysis, penetration factor (ventilation rate divided by sum of ventilation rate and decay rate) and source strength factor (emission rate divided by sum of ventilation rate and decay rate) were calculated. Subsequently, the decay constants were estimated. In this study. magnitude of improvement of indoor air quality could be evaluated by decay constant.

• Journal of Environmental Science International
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• v.16 no.8
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• pp.921-928
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• 2007

The objective of this work is the air quality modeling according to the scenarios of emission on complex terrain. The prognostic meteorological fields and air quality field over complex areas of Seoul, Korea are generated by the PSU/NCAR mesoscale model (MM5) and the Third Generation Community Multi-scale Air Quality Modeling System (Models - 3/CMAQ), respectively. The emission source was driven from the Clean Air Policy Support System of the Korea National institute of Environmental Research (CAPSS), which is a 1 km x 1 km grid in South Korea during 2003. In comparison of air quality fields, the simulated averaged $PM_{10},\;NO_2,\;and\;O_3$ concentration on complex terrain in control case were decreased as compared with base case. Particularly $PM_{10}$ revealed most substantial localized differences by $(18{\sim}24{\mu}g/m^3)$. The reduction rate of $PM_{10},\;NO_2,\;and\;O_3$ is respectively 18.88, 13.34 and 4.17%.

• Journal of Environmental Science International
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• v.11 no.9
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• pp.933-943
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• 2002

The urban pollution if affected by local environmental, so it is necessary to consider area characteristics such as emission source and meteorological phenomena, in studying urban air pollution. Ulsan is laocated on south-east coast and has many industrial facilities, so many people have concerned about air pollution. This study contain conducting numerical simulation of air pollutant concentration considered land and sea breeze in Ulsan area with the numerical model.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.139-146
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• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.5
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• pp.497-514
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• 2017

This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.1
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• pp.9-17
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• 2001

In this study, flow boiling experiments were performed using R-134a on a plain tube bundle. Tests were conducted for the following range of variables; quality from 0.1 to 0.9, mass flux from $8\;kg/m^2s$ to $26\;kg/m^2s$ and heat flux from $10\;kW/m^2s$ to $40\;kW/m^2s$. The heat transfer coefficients were strongly dependent on the heat flux. However, they were almost independent on the mass flux or quality. The data are compared with the modified Chen model, which satisfactorily () predicted the data. Original Chen model, however, did not adequately predict the effect of quality. The reason may be attributed to the flow pattern of the present test, where the bubbly flow prevailed for the entire test range. The heat transfer coefficients of the tube bundle were 6~40% higher than those of the single tube pool boiling.

• Environmental and Resource Economics Review
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• v.30 no.2
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• pp.207-235
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• 2021

This paper examines the effects of ambient air pollution by ozone and particulate matter on traveling by mode of transport. We estimate the SUR model of travel time by different modes of transportation using individual level data of travel diaries. We find that, as air pollution levels rises, traveling by privately-owned vehicles increases but traveling by bus decreases. Our results also show that, when an air quality alert is issued, bus traveling increases in an effort to reduce pollution levels, but traveling by own car does not change and traveling by train declines. This suggests that alert programs may not be highly effective in reducing air pollution emissions from vehicles because voluntary switching to public transportation induced by air quality alerts is outweighed by individual effort of avoiding exposure to pollution.