• Journal of Preventive Medicine and Public Health
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• v.40 no.5
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• pp.363-370
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• 2007

Objectives: The principal objective of this study was to determine the relationship between maternal exposure to air pollution and low birth weight and to propose a possible environmental health surveillance system for low birth weight. Methods: We acquired air monitoring data for Seoul from the Ministry of Environment, the meteorological data from the Korean Meteorological Administration, the exposure assessments from the National Institute of Environmental Research, and the birth data from the Korean National Statistical Office between January 1, 2002 and December 31, 2003. The final birth data were limited to singletons within $37{\sim}44$ weeks of gestational age. We defined the Low Birth Weight (LBW) group as infants with birth weights of less than 2500g and calculated the annual LBW rate by district. The air monitoring data were measured for $CO,\;SO_2,\;NO_2,\;and\;PM_{10}$ concentrations at 27 monitoring stations in Seoul. We utilized two models to evaluate the effects of air pollution on low birth weight: the first was the relationship between the annual concentration of air pollution and low birth weight (LBW) by individual and district, and the second involved a GIS exposure model constructed by Arc View 3.1. Results: LBW risk (by Gu, or district) was significantly increased to $1.113(95%\;CI=1.111{\sim}1.116)\;for\;CO,\;1.004(95%\;CI=1.003{\sim}1.005)\;for\;NO_2,\;1.202(95%\;CI=1.199{\sim}1.206\;for\;SO_2,\;and\;1.077(95%\;CI=1.075{\sim}1.078)\;\;for\;PM_{10}$ with each interquartile range change. Personal LBW risk was significantly increased to $1.081(95%\;CI=1.002{\sim}1.166)\;for\;CO,\;1.145(95%\;CI=1.036{\sim}1.267)\;for\;SO_2,\;and\;1.053(95%\;CI=1.002{\sim}1.108)\;for\;PM_{10}$ with each interquartile range change. Personal LBW risk was increased to $1.003(95%\;CI=0.954{\sim}1.055)\;for\;NO_2$, but this was not statistically significant. The air pollution concentrations predicted by GIS positively correlated with the numbers of low birth weights, particularly in highly polluted regions. Conclusions: Environmental health surveillance is a systemic, ongoing collection effort including the analysis of data correlated with environmentally-associated diseases and exposures. In addition. environmental health surveillance allows for a timely dissemination of information to those who require that information in order to take effective action. GIS modeling is crucially important for this purpose, and thus we attempted to develop a GIS-based environmental surveillance system for low birth weight.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.5
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• pp.228-235
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• 2016

Nitrite accumulation is essential for constructing an anammox process. As the pH in the reactor exerts a complicated and strong influence on the reaction rate, we investigated its effects upon treatment of an ammonic wastewater (2,000 mgN/L) through modeling and experiment. The modeling results indicated that the reaction stability is strongly affected by pH, which results in a severe reduction of the 'stable region' of operation under alkaline environments. On a coordinate of the total ammonia nitrogen (TAN) concentration vs. pH, the maximal stable reaction rates and the maximal nitrite accumulation potentials could be found on the 'stability ridge' that separates the stable region from the unstable region. We achieved a stable and high ammonia oxidation rate (${\sim}6kgN/m^3-d$) with a nitrite accumulation ratio of ~99% when operated near the 'stability ridge'. The optimum pH that can be observed in experiments varies with the TAN concentrations utilized, although the intrinsic optimum pH is fixed. The direction of change is that the optimum operational pH falls as the TAN concentration increases, which is in excellent accordance with the observations in the literature. The optimum operational pH for 95% nitritation was predicted to be ~8.0, whereas it was ~7.2 for 55% partial nitritation to produce an anammox feed in our experimental conditions.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.5
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• pp.590-603
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• 2006

Size-and time-resolved aerosol samples were collected using an eight-stage Davis rotating unit for monitoring (DRUM) sampler from 23 March to 29 April 2001 at Gosan, Jeju Island, Korea, which is one of the super sites of Asia-Pacific Regional Aerosol Characterization Experiment(ACE-Asia). These samples were analyzed using synchrotron X-ray fluorescence for 3-hr average concentrations of 19 elements including Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, and Pb. The size-resolved data sets were then analyzed using the positive matrix factorization(PMF) technique to identify possible sources and estimate their contributions to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Twelve sources were resolved in eight size ranges($0.09{\sim}12{\mu}m$) and included continental soil, local soil, sea salt, biomass/biofuel burning, coal combustion, oil combustion, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, and volcanic emission. The PMF result of size-resolved source contributions showed that natural sources represented by local soil, sea salt, continental soil, and volcanic emission contributed about 79% to the predicted primary particulate matter(PM) mass in the coarse size range ($1.15{\sim}12{\mu}m$) while anthropogenic sources such as coal combustion and biomass/biofuel burning contributed about 58% in the fine size range($0.56{\sim}2.5{\mu}m$). The diesel vehicle source contributed mostly in ultra-fine size range($0.09{\sim}0.56{\mu}m$) and was responsible for about 56% of the primary PM mass.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.435-444
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• 2023

Suitable environmental conditions in Saemangeum frequently favor phytoplankton growth. There have been occurrences of sudden phytoplankton blooms, surpassing the algae management standards. A model was designed to prevent such blooms using scientific predictive techniques to forecast and regulate the possibility of phytoplankton blooms. We propose effective and efficient algae control measures concerning every phytoplankton species optimized through the policy control of nutrients (DIN, PO4-P) from rivers and controlling lake salinity using gate operations. The probability of phytoplankton blooms was initially forecast using an artificial neural network algorithm based on observations. The model's Kappa number fluctuated from 0.7889 to 1.0000, indicating good to excellent predictive power. The Garson algorithm was then utilized to assess the significance of explanatory variables for every species. Meanwhile, the probability of phytoplankton blooms was anticipated depending on the DIN and salinity value changes. Therefore, the model predicted the precise DIN and salinity concentrations to inhibit phytoplankton blooms for each species. Hence, the green algae model can create effective proactive measures to avoid future phytoplankton blooms in enormous artificial lakes.

• Journal of the korean society of oceanography
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• v.35 no.1
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• pp.16-33
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• 2000

In order to measure the vertical fluxes of particles and reactive radionuclides such as thorium and polonium isotopes, Dunbar-type sediment traps were freely deployed at the Ulleung Basin and in warm and cold water masses around the polar front of the East Sea. We estimated the ratios of the catched (F) to the predicted $^234$Th fluxes (P) using natural tracers pair $^234$Th-$^238$U. The F/P ratios are decreased with increasing water depth. Whereas the concentrations of suspended particles are homogeneous in water column, the mass fluxes are also decreased with increasing water depth like the F/P ratios. These facts indicate that organic matters of settling particles are destructed within the euphotic layer due to decomposition. Whereas regenerations of sinking particles are negligible in the cold water mass, about 80% of them are regenerated in the warm water mass during falling of large particles. These downward mass fluxes are closely correlated with their primary productions in euphotic zone. The activities of $^234$Th, $^228$Th and $^210$Po in the sinking material were increased with water depth. Because $^234$Th steadily produced in the water column are cumulatively adsorbed on the surface of sinking particles, vertical $^234$Th fluxes were observed to increase with water depth. Therefore, these sinking particles play important roles in transporting the particle reactive elements like thorium from surface to the deep sea. The scavenging processes including adsorption and settling reactions generate radio-disequilibrium between daughter and parent nuclides in water column. The activity ratios of $^234$Th/$^238$U and $^228$Th/$^228$Ra were observed to be < 1.0 in the surface water and approached to be equilibrium below the thermocline. The extent of the deficiency of daughter nuclides compared to the parents nuclide was highly correlated with the vertical particle flux. Because most of the $^210$Po in the surface water are scavenged on a labile phase and are recycled at sub-surface depths (< 200 m), the $^210$Po are always observed to be excess activities compared to $^226$Ra in surface water.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.5
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• pp.643-648
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• 2005

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal grains and forages. If samples could be analyzed without drying and grinding, then sample preparation time and costs may be reduced. This study was conducted to develop robust NIRS equations to predict fermentation quality of corn (Zea mays) silage and to select acceptable sample preparation methods for prediction of fermentation products in corn silage by NIRS. Prior to analysis, samples (n = 112) were either oven-dried and ground (OD), frozen in liquid nitrogen and ground (LN) and intact fresh (IF). Samples were scanned from 400 to 2,500 nm with an NIRS 6,500 monochromator. The samples were divided into calibration and validation sets. The spectral data were regressed on a range of dry matter (DM), pH and short chain organic acids using modified multivariate partial least squares (MPLS) analysis that used first and second order derivatives. All chemical analyses were conducted with fresh samples. From these treatments, calibration equations were developed successfully for concentrations of all constituents except butyric acid. Prediction accuracy, represented by standard error of prediction (SEP) and $R^2_{v}$ (variance accounted for in validation set), was slightly better with the LN treatment ($R^2$ 0.75-0.90) than for OD ($R^2$ 0.43-0.81) or IF ($R^2$ 0.62-0.79) treatments. Fermentation characteristics could be successfully predicted by NIRS analysis either with dry or fresh silage. Although statistical results for the OD and IF treatments were the lower than those of LN treatment, intact fresh (IF) treatment may be acceptable when processing is costly or when possible component alterations are expected.

• Asian Journal of Atmospheric Environment
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• v.8 no.4
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• pp.212-224
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• 2014

There are several studies on the effects of emissions of highly reactive volatile organic compounds (HRVOC) from the industrial sources in the Houston-Galveston-Brazoria (HGB) area on the high ozone events during the Texas Air Quality Study (TexAQS) in summer of 2000. They showed that the modeled atmosphere lacked reactivity to produce the observed high ozone event and suggested "imputation" of HRVOC emissions from the base inventory. Byun et al. (2007b) showed the imputed inventory leads to too high ethylene concentrations compared to the measurements at the chemical super sites but still too little aloft compared to the NOAA aircraft. The paper suggested that the lack of reactivity in the modeled Houston atmosphere must be corrected by targeted, and sometimes of episodic, increase of HRVOC emissions from the large sources such as flares in the Houston Ship Channel (HSC) distributed into the deeper level of the boundary layer. We performed retrospective meteorological and air quality modeling to achieve better air quality prediction of ozone by comparison with various chemical and meteorological measurements during the Texas Air Quality Study periods in August-September 2006 (TexA QS-II). After identifying several shortcomings of the forecast meteorological simulations and emissions inputs, we prepared new retrospective meteorological simulations and updated emissions inputs. We utilized assimilated MM5 inputs to achieve better meteorological simulations (detailed description of MM5 assimilation can be found in F. Ngan et al., 2012) and used them in this study for air quality simulations. Using the better predicted meteorological results, we focused on the emissions uncertainty in order to capture high peak ozone which occasionally happens in the HGB area. We described how the ozone predictions are affected by emissions uncertainty in the air quality simulations utilizing different emission inventories and adjustments.

• Advances in environmental research
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• v.5 no.3
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• Journal of Korea Soil Environment Society
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• v.4 no.1
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• pp.109-118
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• 1999

Recently, transport parameters of conservative solutes such as KCl in a porous medium have been successfully determined using time domain reflectometry (TDR) . This study was initiated to Investigate the applicability of TDR technique to monitoring the fate of a heavy metal ion in a sandy soil and the distribution of its concentration along travel distance with time. A column test was conducted in a laboratory that consists of monitoring both resident and flux concentrations of $ZnCl_2$in a sandy soil under a breakthrough condition. A tracer of $ZnCl_2$(10 g/L) was injected onto the top surface of the sample as pulse type as soon as a steady-state condition was achieved. Time-series measurements of resistance and electrical conductivity were performed at 10 cm and 20 cm of distances from the inlet boundary by horizontal-positioning of parallel TDR metallic rods and using an EC-meter for the effluent exiting the bottom boundary respectively. In addition. Zn ions of the effluent were analyzed by ICP-AES. Since the mode and position of concentration detected by TDR and effluent were different, comparison between ICP analysis and TDR-detected concentration was made by predicting flux concentration using CDE model accommodating a decay constant with the transport parameters obtained from the resident concentrations. The experimental results showed that the resident concentration resulted in earlier and higher peak than the flux concentration obtained by EC-meter, implying the homogeneity of the packed sandy soil. A close agreement was found between the predicted from the transport parameters obtained by TDR and the measured $ZnCl_2$concentration. This indicates that TDR technique can also be applied to monitoring heavy metal concentrations in the soil once that a decay constant is obtained for a given soil.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.37-48
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• 2021

A new high speed rotation type-passive sampling device (HSR-PSD), which can rotate seawater at high speed and absorb easily and quickly the freely dissolved hydrophobic organic contaminants from seawater, was developed and then applied around the Korean Peninsula. Freely dissolved concentrations (Cfree) of polycyclic aromatic hydrocarbons (PAHs) were determined using the HSR-PSD with low density polyethylene (LDPE) sheets as a passive sampler. Furthermore, dissolved concentrations (Cdissolved) of PAHs in seawater were also obtained from high volume water sampling as a conventional method to account for actual bioavailability. When the LDPE sheets were rotated in the HSR-PSD at 900 rpm, PAHs with log KOW 3.4 ~ 5.2 were equilibrated between the LDPE and water in 5 hours. Although the high molecular weight PAHs with log KOW 5.6 ~ 6.8 was expected to be 2 to 30 days to reach the equilibrium, the Cfree of the PAHs at equilibrium could be corrected using performance reference compounds in 5 hours. Meanwhile, the total Cfree of PAHs were from 0.32 to 1.2 ng/L, which were higher than reported values in other oceans, but lower than in coastal water such as estuary, harbor, or shore. A bioavailability from the detected PAHs was highest at the sampling line near the dumping site of the Yellow Sea. Predicted residual concentrations in biota were relatively higher in offshore including the dumping site than in coastal regions.