• Journal of Environmental Impact Assessment
• /
• v.31 no.2
• /
• pp.83-94
• /
• 2022

In this study, DNN-based models were learned using air quality determination data for 2017, 2019, and 2020 provided by the National Measurement Network (Air Korea), and this models evaluated using data from 2016 and 2018. Based on Pearson correlation coefficient 0.2, four items (SO₂, CO, NO₂, PM₁₀) were initially modeled as independent variables. In order to improve the accuracy of prediction, monthly independent modeling was carried out. The error was calculated by RMSE (Root Mean Square Error) method, and the initial model of RMSE was 5.78, which was about 46% betterthan the national moving average modelresult (10.77). In addition, the performance improvement of the independent monthly model was observed in months other than November compared to the initial model. Therefore, this study confirms that DNN modeling was effective in predicting PM_2.5 concentrations based on air pollutants concentrations, and that the learning performance of the model could be improved by selecting additional independent variables.

• Journal of Korean Society for Atmospheric Environment
• /
• v.11 no.1
• /
• pp.85-93
• /
• 1995

Measurements of ground level ozone concentrations were made in a rural area of Chongwon (Choongbook Province) from June 1993 to July 1994. High values frequently exceeding 100 ppb (ambient air qualyty standard of Korea) were recorded. High ozone concentrations in the boundary layer were primarily correlated with the several meteorological parameters in warm seasons: pressure, radiation, temperature, precipitation and wind velocity. The annual average concentration of ozone at Chongwon was 17ppb, and this value was relatively higher than those for other cities in Korea. O$\_$3/ concentrations were observed to increase when the ridge of a surface anticyclone was passing over the region, and maximum values(.geq.100 ppb) were observed on the rear sides of high pressure centers and in the warm sectors of cyclones(well head of cold fronts). The ozone concentrations had a negative correlation with the concentration of primary pollutants(e.g., total hydrocarbons).

• Journal of Environmental Science International
• /
• v.26 no.9
• /
• pp.1045-1055
• /
• 2017

Characterization of spatio-temporal variations in $PM_{2.5}$ in Gyeongnam (GN) province during 2015-2016 was investigated to assess the air quality in this area in terms of fine particles. Yearly mean concentrations of $PM_{2.5}$ ranged from 19.1 to $29.5{\mu}gm^{-3}$. High concentrations of $PM_{2.5}$ were observed in spring ($21.2-30.3{\mu}gm^{-3}$) and winter ($20.2-30.3{\mu}gm^{-3}$). Low concentrations of $PM_{2.5}$ were generally observed in fall ($16.2-23.2{\mu}gm^{-3}$). $PM_{2.5}$ concentration was highest in the morning (10 AM). The fractions of $PM_{2.5}$ in $PM_{10}$ were 0.51-0.62 and two were significantly correlated (r=0.779-0.830), suggesting common sources (fossil fuel combustion, mobile sources, etc). CO was significantly correlated with $PM_{2.5}$ in highly urbanized areas such as the city of Changwon (CW, r=0.711), compared to other air pollutants ($SO_2$, $NO_2$, and $O_3$), suggesting dominance of industrial combustion sources.

• Journal of Korean Society for Atmospheric Environment
• /
• v.17 no.1
• /
• pp.31-38
• /
• 2001

Ambient ge-phase $H_2O$$_2$(Hydrogen Peroxide) concentrations were measured at four sites in downtown Seoul Korea. These measurements were mad during winter and summer, February 14~19 and 12~17, 1997. $H_2O$$_2$concentrations were quantified by fluorescence using enzyms. $H_2O$$_2$ concentrations in winter were below the limit of detection and was much higher concentrations in summer. The mean of all observations was 264 ppt and the range measured was 23ppt~1856ppt. The results from the correlation analysis showed that the concentration of gasous $H_2O$$_2$is dependent on the other air pollutants(O$_3$, NO$_2$) and meteorological parameter(solar radiation).

• Development and Reproduction
• /
• v.18 no.4
• /
• pp.187-195
• /
• 2014

Polycyclic aromatic hydrocarbons (PAHs), which are ubiquitous in the air, are present as volatile and particulate pollutants that result from incomplete combustion. Most PAHs have toxic, mutagenic, and/or carcinogenic properties. Among PAHs, benzo[a]pyrene (B[a]P) and dimethylbenz[a]anthracene (DMBA) are suspected endocrine disruptors. The testis is an important target for PAHs, yet effects on steroidogenesis in Leydig cells are yet to be ascertained. Particularly, disruption of testosterone production by these chemicals can result in serious defects in male reproduction. Exposure to B[a]P reduced serum and intratesticular fluid testosterone levels in rats. Of note, the testosterone level reductions were accompanied by decreased steroidogenic acute regulatory protein (StAR) and $3{\beta}$-hydroxysteroid dehydrogenase isomerase ($3{\beta}$-HSD) expression in Leydig cells. B[a]P exposure can decrease epididymal sperm quality, possibly by disturbing the testosterone level. StAR may be a key steroidogenic protein that is targeted by B[a]P or other PAHs.

• Journal of Korean Society for Atmospheric Environment
• /
• v.27 no.1
• /
• pp.73-86
• /
• 2011

Haze phenomena were analyzed to assess the impact of long range transport process on the air quality of Seoul and Busan. We statistically classified haze days observed in both Seoul and Busan into two types of haze cases: stagnant case and long-range transport case, and analyzed the air pollutant levels comparatively for each of the two cases for the period of 2000~2007. The results showed that the long-range transport haze case occurs less frequently with the occurrence frequency of 35.5% than stagnant case with the occurrence frequency of 64.5%. During the observed all haze days, all pollutants have high concentration in comparison with those under other meteorological conditions (Rain, Mist, Dust, Clear, Rain+Mist) except for only $PM_{10}$ of Dust case where its level shows highest among total 6 categorized conditions. The long range transport haze case shows similar levels of $PM_{10}$ and $NO_2$, but higher $SO_2$ and lower $O_3$ compared with stagnant haze cases, suggesting the importance of sulfur chemistry for long range transport haze case and local photochemistry for stagnant haze case. In addition, by employing the NOAA/HYSPLIT-4 backward trajectory model, we subdivided the long range transport haze cases into two different sources: urban anthropogenic high emission areas of central China, and natural emission sources over north China and/or Mongolia. The former long range transport haze case shows higher occurrence (with Seoul 70% and Busan 85%) than the latter haze case (with Seoul 30% and Busan <10%). This is also implying that the long haze phenomena occurred over Korea have been influenced by not only the anthropogenic emissions but also the natural dust emissions. These both emission sources can be good contributors in calculating the source-receptor relationship over Korean atmospheric environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.2
• /
• pp.271-277
• /
• 2017

As a result of rapid industrialization, air pollutants are seriously threatening the health of the people, the forecast is becoming more and more important. In forecasting air quality, it is very important to create a reliable initial field because the initial field input to the air quality forecasting model affects the accuracy of the forecast. There are several methods for enhancing the initial field input. One of the necessary techniques is data assimilation. The number of operations and the time required for such data assimilation is exponentially increased as the forecasting area is widened and the number of observation sites increases. Therefore, as the forecast size increases, it is difficult to apply the existing sequential processing method to a field requiring fast processing speed. In this paper, we propose a method that can process Cresman's method, which is one of the data assimilation techniques, in real time using CUDA. As a result, the proposed parallel processing method using CUDA improved at least 35 times faster than the conventional sequential method and other parallel processing methods.

• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.119-133
• /
• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of Environmental Impact Assessment
• /
• v.24 no.2
• /
• pp.111-118
• /
• 2015

This study was conducted for the establishment of statistical method to determine the representative air quality monitoring station representing long-term ozone trends of Seoul. In this study, hourly ozone concentrations from 2002 to 2011 were used for further analysis. KZ-filter, correlation matrix, cluster analysis, and Kriging method were applied to select the representative station. The analysis based on correlation matrix found that long-term trend of ozone concentrations measured at Sinjung, Sadang, and Bun-dong showed a high correlation. The cluster analysis found that the former three stations belonged to the same cluster. The analysis based on Kriging method also showed that the former three stations were highly correlated with other stations in spatial distribution. Considering these results and the highest correlation coefficient of Sinjung station, the Sinjung station was the most suitable as the representative station used to understand the long-term ozone trend of Seoul. This result could be applied to understand long-term trend of other pollutants. Furthermore, this result can also be used to assess the appropriacy of spatial distribution of national air quality monitoring stations.

• Journal of Korean Society for Atmospheric Environment
• /
• v.21 no.4
• /
• pp.401-411
• /
• 2005

This study was conducted to develop a method to be able to estimate the vehicular emissions according to spatial scales-Seoul province, 25 counties and hundreds of grids $(1km{\times}1km)$. First, the emissions at each spatial scale was calculated by using the road network and the travel volume and speed of each link modeled by travel demand model (TDM). Second, the emission at each spatial scale was calculated on the basis of average speeds estimated by using three kinds of averaging method. These are called the provincial, volume-delay function (VDF) and zonal method, respectively. Third, three kinds of emissions and those by TDM are compared each other at three spatial scales. In Seoul (provincial scale), three kinds of emissions are less than those by TDM, but the differences of TDM from three speed averaging methods (SAMs) are small. The relative ratios of three SAMs to TDM are $88\~90\%\;in\;CO,\;99\~100\%\;in\;NOx,\;84\~85\%$ in VOCs. At county scale, NOx among three pollutants showed the highest correlation between TDM and three SAMs and the zonal method among three SAMs was proven to be the highest correlation with TDM. NOx showed the coefficients $(R^2)$ greater than 0.9 in all three SAMs but CO and VOC showed the coefficients $(R^2)$ greater than 0.9 in only zonal method. Slopes of co..elations of all pollutants showed the values close to '1' in zonal method. In the other two SAMs, slopes of NOx showed the values close to '1', but those of CO and VOC showed the values less than 0.85. At grid scale, correlations between TDM and three SAMs were not high. CO showed $0.68\~0.77\;in\;R^2s\;and\;58\~0.68$ in slopes. NOx showed $0.90\~0.94\;in\;R^2s\;and\;0.86\~0.94$ in slopes. VOC showed $0.56\~0.70\;in\;R^2s\;and\;0.48\~0.57$ in slopes. There are not high correlations between TDM and three SAMs in grid scale. This study showed that there is the most suitable method for calculating the average travel speed at each spatial scale and it is thought that the zonal method is more suitable than the VDF or provincial method.