• Agricultural and Biosystems Engineering
• /
• v.4 no.2
• /
• pp.45-49
• /
• 2003

Achievements in the real-time soil spectro-photometer are: an improved soil penetrator to ensure a uniform soil surface under high speed conditions, real-time collecting of underground soil reflectance, getting underground soil color images, use of a RTK-GPS, and all units are arranged for compactness. With the soil spectrophotometer, field experiments were conducted in a 0.5 ha paddy field. With the original reflectance, averaging and multiple scatter correction, Kubelka-Munk (KM) transformation as soil absorption, its 1st and 2nd derivatives were calculated. When the spectra was highly correlated with the soil parameters, stepwise regression analysis was conducted. Results include the best prediction models for moisture, soil organic matter (SOM), nitrate nitrogen ($NO_3-N$), pH and electric conductivity (EC), and soil maps obtained by block kriging analysis.

• Proceedings of the Korea Air Pollution Research Association Conference
• /
• 2003.05b
• /
• pp.347-348
• /
• 2003

대기 중의 질산에 의해 생성되는 질산염은 해염성분이나 토양성분과 만나 조대입자 영역에 머물거나 암모늄과 만나 미세입자로 존재할 수 있다. 미세입자로 존재하는 질산염은 여름철과 같은 광화학 반응이 활발할 때 2차적으로 생성되는 물질로, 반휘발성 특성 때문에 측정하는 과정에서 오차가 발생할 가능성도 크다. Seinfeld (1986)에 의하면 미국의 도심 지역에서 미세입자 중 황산염이나 질산염 등 2차 이온 성분의 비율이 전체 입자의 40∼60 %를 차지한다고 보고되고 있으며, 대표적인 도심 지역인 서울에서도 비슷하다 (강충민 등, 1999). (중략)

• Agricultural and Biosystems Engineering
• /
• v.4 no.1
• /
• pp.28-33
• /
• 2003

Achievements in the real-time soil spectro-photometer are: an improved soil penetrator to ensure a uniform soil surface under high speed conditions, real-time collecting of underground soil reflectance, getting underground soil color images, use of a RTK-GPS, and all units are arranged for compactness. With the soil spectrophotometer, field experiments were conducted in a 0.5 ha paddy field. With the original reflectance, averaging and multiple scatter correction, Kubelka-Munk (KM) transformation as soil absorption, its 1st and 2nd derivatives were calculated. When the spectra was highly correlated with the soil parameters, stepwise regression analysis was conducted. Results include the best prediction models for moisture, soil organic matter (SOM), nitrate nitrogen (NO$_3$-N), pH and electric conductivity (EC), and soil maps obtained by block kriging analysis.

• Communications for Statistical Applications and Methods
• /
• v.27 no.5
• /
• pp.547-568
• /
• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• Ocean Science Journal
• /
• v.43 no.4
• /
• pp.195-208
• /
• 2008

This paper evaluates whether a thermodynamic ocean-carbon model can be used to predict the monthly mean global fields of the surface-water partial pressure of $CO_2$ ($pCO_{2SEA}$) from sea surface salinity (SSS), temperature (SST), and/or nitrate ($NO_3$) concentration using previously published regional total inorganic carbon ($C_T$) and total alkalinity ($A_T$) algorithms. The obtained $pCO_{2SEA}$ values and their amplitudes of seasonal variability are in good agreement with multi-year observations undertaken at the sites of the Bermuda Atlantic Timeseries Study (BATS) ($31^{\circ}50'N$, $60^{\circ}10'W$) and the Hawaiian Ocean Time-series (HOT) ($22^{\circ}45'N$, $158^{\circ}00'W$). By contrast, the empirical models predicted $C_T$ less accurately at the Kyodo western North Pacific Ocean Time-series (KNOT) site ($44^{\circ}N$, $155^{\circ}E$) than at the BATS and HOT sites, resulting in greater uncertainties in $pCO_{2SEA}$ predictions. Our analysis indicates that the previously published empirical $C_T$ and $A_T$ models provide reasonable predictions of seasonal variations in surface-water $pCO_{2SEA}$ within the (sub) tropical oceans based on changes in SSS and SST; however, in high-latitude oceans where ocean biology affects $C_T$ to a significant degree, improved $C_T$ algorithms are required to capture the full biological effect on $C_T$ with greater accuracy and in turn improve the accuracy of predictions of $pCO_{2SEA}$.

• Journal of the Korean GEO-environmental Society
• /
• v.5 no.4
• /
• pp.25-31
• /
• 2004

In conventional activated sludge process, COD fractions in wastewater are important parameters, significantly. Depending on characteristics of influent COD fractionation, activated sludge process requires a major change of a process operation to ensure meeting a stricter standards. In order to validate and evaluate the accuracy of the traditional COD fractionation methodologies, readily and slowly biodegradable COD was mixed using glucose and peptone as a sole carbon source in a synthetic wastewater, respectively. In this research, prediction of the COD fraction was made using the OUR(Oxygen Utilization Rate) and the NUR(Nitrate Utilization Rate) experiments. The result showed that COD fractions calculated by OUR experiment were similar to the composition of synthetic wastewater. On the other hand, it was found that an error was generated during the NUR experiment. This error was due to the intracellular storage period for storage microorganisms such as PAOs, and the error in COD fraction was observed about 8-14 % in terms of Total COD.

• Journal of Korean Society of Forest Science
• /
• v.95 no.5
• /
• pp.556-561
• /
• 2006

This study was conducted to choose end-members and tracers for application of End Member Mixing Analysis (EMMA) model for the coniferous forest catchment, Gwangneung Gyeongi-do near Seoul metropolitan of South Korea (N $37^{\circ}$ 45', E $127^{\circ}$ 09'). This coniferous forest of Pinus Korainensis and Abies holophylla was planted at stocking rate of $3.0stems\;ha^{-1}$ in 1976. Thinning and pruning were carried out two times in the spring of 1996 and 2004 respectively. We monitored two successive rainfall events during ten days from June 26, 205 to July 5, 2005. Two storm events were selected to determine the end members and natural traces for hydrograph separation. The event 1 amounts to 161.9 m for two days from June 26 to 27, 2005. The event 2 precipitates to 139.2 mm for one day of July 1, 205. Throughfall, groundwater, soil water and stream water of the two events above were sampled through the bulk and automatic sampler. Their chemical properties were analyzed for prediction of the main tracer. The end members that contribute to the stream runoff were identified from the three components including groundwater, soil water and throughfall. Each component and stream water in the two events formed the suitable mixing diagram in case of chloride-nitrate ion and sulfate-potassium ion. Especially, chloride-nitrate ion was found to be the most suitable tracers for EMMA model in the two events.

• Economic and Environmental Geology
• /
• v.44 no.2
• /
• pp.135-152
• /
• 2011

Nitrate concentrations in springs at the southern slope of Jeju Island were predicted using multiple linear regression (MLR) of spatial variables including hydrogeological parameters and land use characteristics. Springs showed wide range of nitrate concentrations from <0.02 to 86 mg/L with a mean of 20 mg/L. Spatial variables were generated for the circular buffer when the optimal buffer radius was assigned as 400 m. Selected regression models were tested using the p values and Durbin-Watson statistics. Explanatory variables were selected using the adjusted $R^2$, Cp (total squared error) and AIC (Akaike's Information Criterion), and significance. In addition, mutual linear relations between variables were also considered. Small portion of springs, usually <10% of total samples, were identified as outliers indicating limitations of MLR using circular buffers. Adjusted $R^2$ of the proposed models was improved from 0.75 to 0.87 when outliers were eliminated. In particular, the areal proportion of natural area had the greatest influence on the nitrate concentrations in springs. Among anthropogenic land uses, the influence of nitrate contamination is diminishing in the following order of orchard, residential area, and dry farmland. It is apparent quality of springs in the study area is likely to be controlled by land uses instead of hydrogeological parameters. Most of all, it is worth highlighting that the contamination susceptibility of springs is highly sensitive to nearby land uses, in particular, orchard.

• Journal of Soil and Groundwater Environment
• /
• v.13 no.5
• /
• pp.57-73
• /
• 2008

Nitrate concentrations were measured up to 49 mg/L (as $NO_3$-N) and 22% of the samples exceeded drinking water standard in shallow and bedrock groundwater of the northern Nonsan area. Nitrate concentrations showed a significant difference among land use groups. To predict nitrate concentration in groundwater, multiple regression analysis was carried out using hydrogeologic parameters of soil media, topography and land use which were categorized as several groups, well depth and altitude, and field parameters of temperature, pH, DO and EC. Hydrogeologic parameters were quantified as area proportions of each category within circular buffers centering at wells. Regression was performed to all the combination of variables and the most relevant model was selected based on adjusted coefficient of determination (Adj. $R^2$). Regression using hydrogelogic parameters with varying buffer radii show highest Adj. $R^2$ at 50m and 300m for shallow and bedrock groundwater, respectively. Shallow groundwater has higher Adj. $R^2$ than bedrock groundwater indicating higher susceptibility to hydrogeologic properties of surface environment near the well. Land use and soil media was major explanatory variables for shallow and bedrock groundwater, respectively and residential area was a major variable in both shallow and bedrock groundwater. Regression involving hydrogeologic parameters and field parameters showed that EC, paddy and pH were major variables in shallow groundwater whereas DO, EC and natural area were in bedrock groundwater. Field parameters have much higher explanatory power over the hydrogeologic parameters suggesting field parameters which are routinely measured can provide important information on each well in assessment of nitrate contamination. The most relevant buffer radii can be applied to estimation of travel time of contaminants in surface environment to wells.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.3
• /
• pp.531-538
• /
• 2024

While the importance of the water quality environment is being emphasized, the water quality index for improving the water quality of urban rivers in Gwangju Metropolitan City is an important factor affecting the aquatic ecosystem and requires accurate prediction. In this paper, the XGBoost and LightGBM machine learning algorithms were used to compare the performance of the water quality inspection items of the downstream Pyeongchon Bridge and upstream BanghakBr_Gwangjucheon1 water systems, which are important points of Gwangju Stream, as a result of statistical verification, three water quality indicators, Nitrogen(TN), Nitrate(NO3), and Ammonia amount(NH3) were predicted, and the performance of the predictive model was evaluated by using RMSE, a regression model evaluation index. As a result of comparing the performance after cross-validation by implementing individual models for each water system, the XGBoost model showed excellent predictive ability.