• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Korean Journal of Remote Sensing
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• v.33 no.2
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• pp.111-123
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• 2017

The phycocyanin pigment (PC) is a marker for cyanobacterial presence in eutrophic inland water. Accurate estimation of low PC concentration in turbid inland water is challenging due to the optical complexity and criticalforissuing an early warning of potentialrisks of cyanobacterial bloom to the public. To monitor cyanobacterial bloom in eutrophic inland waters, an approach is proposed to partition non-water absorption coefficient from measured reflectance and to retrieve absorption coefficient of PC with the aim of improving the accuracy in remotely estimated PC, in particular for low concentrations. The proposed inversion model retrieves absorption spectra of PC ($a_{pc}({\lambda})$) with $R^2{\geq}0.8$ for $a_{pc}(620)$. The algorithm achieved more accurate Chl-a and PC estimation with $0.71{\leq}R^2{\leq}0.85$, relative root mean square error (rRMSE) ${\leq}39.4%$ and mean relative error(RE) ${\leq}78.0%$ than the widely used semi-empirical algorithm for the same dataset. In particular, low PC ($PC{\leq}50mg/m^3$) and low PC: Chl-a ratio values of for all datasets used in this study were well predicted by the proposed algorithm.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1647-1661
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• 2011

The Government had devised legislation of Special Act and drew up guidelines for improving air quality in Seoul Metropolitan area. In 2007 local government of Seoul, Incheon and Gyeonggi conducted the results of application policy by reduced air pollutants emission for the first time. Although there was reduction of air pollutant emission in each local government, it was ineffective as expected using air pollution monitoring database. Therefore we worked out a way to prepare modeling input data using the results of enforcement plan. And we simulated surface $NO_2$ and PM10 before and after decrease in air pollutants emission and examine reduction effects of air pollution according to enforcement regulation except other influence, by using MM5-SMOKE-CMAQ system. Each local government calculated the amount of emission reduction under application policy, and we developed to prepare input data so as to apply to SMOKE system using emission reduction of enforcement plan. Distribution factor of emission reduction were classified into detailed source and fuel codes using code mapping method in order to allocate the decreased emission. The code mapping method also included a way to allocate spatial distribution by CAPSS distribution. According to predicted result using the reduction of NOx emission, $NO_2$ concentration was decreased from 19.1 ppb to 18.0 ppb in Seoul. In Gyeonggi and Incheon $NO^2$ concentrations were down to 0.65 ppb and 0.68 ppb after application of enforcement plan. PM10 concentration was reduced from 18.2 ${\mu}g/m^3$ to 17.5 ${\mu}g/m^3$ in Seoul. In Gyeonggi PM10 concentration was down to 0.51 ${\mu}g/m^3$ and in Incheon PM10 concentration was decreased about 0.47 ${\mu}g/m^3$ which was the lower concentration than any other cities.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.3
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• pp.192-197
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• 2005

The effects of macronutrients $(NO_3^-,\; NH_4^+\;and\;PO_4^{3-})$ on cell growth and triterpenoids production in Centella asiatica cell suspension cultures were analyzed using the Box­Behnken response surface model experimental design. In screening and optimization experiments, $PO_4^{3-}$ as a single factor significantly influenced cell growth where increasing the phosphate level from 0.1 to 2.4 or 2.6 mM, elevated cell growth from 3.9 to $14\~16g/L$. The optimum values predicted from the response surface model are 5.05mM $NH_4^+$, 15.0mM $NO_3^-$ and 2.6mM $PO_4^{3-}$, yielding 16.0g/L cell dry weight with $99\%$ fitness to the experimental data. While the $NH_4^+-NO_3^-$ interaction influenced cell growth positively in the optimization experiment, $NH_4^+$ and $NO_3^-$ as single factors; and interactions of $NO_3^--PO_4^{3-},\;NH_4^+-PO_4^{3-}$ and $NH_4^+-NO_3^-$ were all negative in the screening experiment. Cell growth and the final pH level were positively affected by $PO_4^{3-}$, but negatively affected by $NH_4^+\;and\;NH_4^+-PO_4^{3-}$ interactions. The different effects of factors and their interactions on cell growth and final pH are influenced by a broad or narrow range of macronutrient concentrations. The productions of triterpenoids however were lower than 4mg/g cell dry weight.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.49-56
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• 2001

The water quality simulation was carried out to predict water quality in Tan stream of the Han river using water quality model, QUAL2E. In the end, the future variations in water quality of Tan stream were simulated and the prediction of the impacts of Tan stream on water quality in the Han river was carried out by applying the Tan stream simulation results into the model. The results are as follows. The predicted results of future water quality of Tan stream suggested that the concentrations of BOD, T-N and T-P at Chungdam bridge would increase to 0.68~0.77 mg/$\ell$, 1.33~1.62 mg/$\ell$ and 0.05~0.06 mg/$\ell$, respectively in 2006 and 2011 and that with the implementation of advanced treatment in Sungnam and Tanchun sewage treatment plants, the concentration of T-N would be reduced more as the amount of treated sewage increase, while the concentration of T-P would stay 0.49 mg/$\ell$. The results obtained from simulation of the impacts of future Tan stream water quality improvement on the main stream of the Han river showed that with implementation of advanced treatment in both Sungnam and Tanchun sewage treatment plants, the concentration of T-N, T-P and chlorophyll-a at Hangang bridge and Heangju bridge would be reduced by 11.6%, 7.7% and 20.9%, respectively in 2..6 and by 13.6%, 9.4% and 22.2%, respectively in 2011, which indicates that the effect on the reduction of T-N and T-P would be relatively significant while the effect on the decrease of algae would be slight.

• Korean Journal of Environmental Agriculture
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• v.17 no.4
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• pp.341-345
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• 1998

Soil solutions were collected by zero-tension lysimeters at Kwangju, Kyunggi Province to estimate differences in ion concentration among species and horizons. Zero-tension lysimeters were installed in O, A, and B horizons in Larix leptolepis, Pinus koraiensis, and Quercus mongolica stands. Soil solution samples were collected from September 1996 through August 1998 and analyzed for $K^+$, $NA^+$, $NH_4^+$, $Mg^{2+}$, $Ca^{2+}$, $Al^{3+}$, $Cl^-$, $SO_4^{2-}$, $NO_3^-$, and $PO_4^{3-}$. The experimental site had high nitrogen loading from the atmosphere, and $NO_3^-$ was positively correlated with $K^+$ and $Mg^{2+}$. However, $NO_3^-$ and $NH_4^+$ showed a positive correlation only in the O horizon of Q. mongolica stand. Mg^(2+) deficit in the soil was predicted owing to the positive relationship of $Mg^{2+}$ with $NO_3^-$. Concentrations of $K^+$ and $Ca^{2+}$in soil solution were estimated higher in L. leptolepis than in other stands because of high leaching from the plant. Concentration of $Al^{3+}$ in soil solution was negatively correlated with soil solution pH. Mean soil solution pH of A and B horizons in P. koraiensis was lower than 4.7, however the $Al^{3+}$ concentration was lower than the toxic level to plants.

• Environmental Engineering Research
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• v.25 no.4
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• pp.479-487
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• 2020

Current assumptions are used in the formulation of pseudo-first (PFO) and second-order (PSO) models to describe the kinetic data of filtration based on ideal operating conditions. This paper presents a new model developed with pseudo n^th order and based on real assumption. A comparison was performed between PFO, PSO and the new model to highlight their performance and the optimisation of the pseudo-order equation, using MATLAB software. Adsorption characteristic of bovine serum albumin adsorption on the track-etched membrane are used as a medium based on protein filtration data were extracted from the literature for different concentrations to demonstrate the comparison between PFO/PSO and the new model. The pseudo first and second-order kinetic models were applied to test the experimental data and they did not provide reasonable values. The results show that the predicted values are consistent with experimental values giving a good correlation coefficient R² = 0.997 and a minimum root mean squared error RMSE = 0.0171. Indeed, the experimental results follow the new model and the optimal pseudo equation order n = 1.115, the most suitable curves for the new model. As a result, we used different experimental adsorption data from the literature to examine and check the applicability and validity of the model.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.6
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• pp.666-682
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• 2010

The forecasting system for Today's and Tomorrow's PM10 was developed based on the statistical model and the forecasting was performed at 9 AM to predict Today's 24 hour average PM10 concentration and at 5 PM to predict Tomorrow's 24 hour average PM10. The Today's forecasting model was operated based on measured air quality and meteorological data while Tomorrow's model was run by monitored data as well as the meteorological data calculated from the weather forecasting model such as MM5 (Mesoscale Meteorological Model version 5). The observed air quality data at ambient air quality monitoring stations as well as measured and forecasted meteorological data were reviewed to find the relationship with target PM10 concentrations by the regression analysis. The PM concentration, wind speed, precipitation rate, mixing height and dew-point deficit temperature were major variables to determine the level of PM10 and the wind direction at 500 hpa height was also a good indicator to identify the influence of long-range transport from other countries. The neural network, regression model, and decision tree method were used as the forecasting models to predict the class of a comprehensive air quality index and the final forecasting index was determined by the most frequent index among the three model's predicted indexes. The accuracy, false alarm rate, and probability of detection in Tomorrow's model were 72.4%, 0.0%, and 42.9% while those in Today's model were 80.8%, 12.5%, and 77.8%, respectively. The statistical model had the limitation to predict the rapid changing PM10 concentration by long-range transport from the outside of Korea and in this case the chemical transport model would be an alternative method.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.9
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• pp.783-792
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• 2009

This study investigated 31 selected EDCs(Endocrine Disrupting Compounds) and PPCPs(Pharmaceutical and Personal Care Products) in the influent and effluent of a wastewater treatment plant(WWTP) nearby Seoul metropolitan area. The chemical compounds of EDC/PPCPs detected from the plant influent sample include stimulant, X-ray contrast media and fire retardant. The total amount of each compound class were 59.67%, 20.20% and 9.00% respectively. However, in the effluent sample, the major micropolutants detected were oral beta-blocker(30.54%), fire retardant(20.49%), X-ray contrast media(18.17%). The EDC/PPCPs occurrence levels of this study were somewhat lower than previous domestic studies'. When compared to those of overseas, the values were even lower. Some pharmaceutical compound levels particularly measured in European studies were even several thousand times high. This study then compared PECs(Predicted Environmental Concentration) and MECs(Measured Environmental Concentration) of 9 selected pharmaceuticals compounds. The calculated PECs were substantially different with the MECs, while the occurrence order between the PECs and MECs in terms of concentrations of the compounds were similar.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.1
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• pp.67-74
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• 2005

Toxicity tests were performed to evaluate the feasibility of application with prediction models to 10 mixture chemicals (chloroneb, butylbenzylphthalate, pendimethaline, di-n-butylphthalate, di-iso-butylphthalate, diazinon, isofenphos, 2-chlorophenol, 2,4,6-trichlorophenol and p-octylphenol) detected in effluents from wastewater treatment plants (WWTPs). Ten chemicals were selected in the basis of their toxicities to Daphnia magna and the concentrations in effluents measured by GC/MS. Three models including concentration addition (CA), independent action (IA) and effect summation (ES) were employed for the comparison of the predicted and the observed mortality of D. magna exposed to 10 mixture chemicals for 48 hours. With a comparative study it was ineffective to predict the mortality through the CA and the ES prediction model, while the IA prediction model showed a high correlation($r^2\;=\;0.85$). Moreover, the ES model over-estimated the toxicity observed by bioassay experiments about five-fold. Consequently, IA model is a reasonable tool to predict the mixture toxicity of the discharging water from WWTPs.