• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.909-912
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• 2006

In this paper a method for remotely-sensed assessment of eutrophication was experimented. The water samples were collected for analysis of COD (chemical oxygen demand) and nutrients concentration, and the remote sensing reflectance data at the sampling points were synchronously measured using above-water method in two cruises, which were conducted in the Pearl River Estuary in January 2003 and January 2004 respectively. Based on the in-situ data the local algorithms for estimation of concentration of nutrients (P and N) and COD were developed by Partial Least Squares (PLS) regression. The algorithms were then applied to atmospheric-corrected SeaWiFS data and the COD and nutrients concentration in Pearl River Estuary were estimated. And then the assessment of eutrophication was carried out by comparison of the estimated nutrients and COD value with the water quality standard. The results show that the whole estuary is seriously in eutrophication.

• Environmental Analysis Health and Toxicology
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• 제19권1호
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• pp.65-72
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• 2004

Environmental risk assessment of cadmium compounds was conducted using national monitoring data of aquatic and terrestrial compartments of local area. Aquatic and terrestrial toxicities of cadmium compounds on algae, daphnid, fish, earthworm, springtails and other species were evaluated. The toxicity data evaluated in this study were mainly from ECOTOX database provided by US EPA. Assessment factors were determined according to the EU technical guidance document and/or OECD proposal. Predicted no effect concentration (PNEC) values of aquatic and terrestrial toxicity were 25$\mu\textrm{g}$/L and 0.2 mg/kg, respectively and they were compared with cadmium exposure data of several local areas, which were used as Predicted exposure concentration(PEC) values. Most of the local area were found to be not risky. However, the risk values (PEC/NEC) of some metropolitan areas were greater than 1 when the most conservative PNEC value was applied.

• 한국대기환경학회지
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• 제15권2호
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• pp.139-150
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• 1999

The ozone concentrations measured at the National Air Monitoring Stations between 1990 and 1995 were reviewed to detect any anomalies in the measurements. By screening the cases, in which variation of the ozone concentration from the previous measured value is greater than 75ppb, 125 station-days were identified as the test cases for the anomaly test. Historical and parallel consistencies of the measured concentrations were examined by plotting data for each test case. The detected anomalies can be classified into four categories; single outliers, anomalous variations during the startup period, baseline rises, and fluctuations in th diurnal variations. Anomalies were detected in as many as 80 cases among 125 test cases. Because of these anomalies, the number of hours exceeding 100ppb in the areas other than the Greater Seoul Area(GSA) could decrease from 157 to 107. Further studies for developing the methodology for eliminating the abnormal monitoring data are warranted for the data from the National Air Monitoring Stations are official to the both inside and outside of the country.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.913-916
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• 2006

Spatial distribution, seasonal and interannual variability of chlorophyll a concentration in Okhotsk Sea from SeaWiFS data between 2001 and 2004 were describe. An Empirical Orthogonal Function method was applied for analysis data. The ten modes described about 85% of total variance. Two maxima were defined - more intensive in spring and weaker in autumn. The first mode showed zones with chlorophyll a concentration during maximum bloom. The second mode specified timing of spring bloom in various regions in Okhotsk Sea. Analysis of SeaWiFS data indicated connection between highest chlorophyll a concentration and sea surface temperature limits during spring bloom. Similar relation was not found during fall bloom.

• 한국환경과학회지
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• 제11권12호
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• pp.1243-1252
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• 2002

This paper aims to find the characteristics of concentration distribution of coastal urban air pollutants. For this purpose, It was used the daily meteorological data and the hourly concentration data for $O_3$and NO$_2$ in Busan metropolitan city from 1994 to 1996. It was investigated the annual and monthly distribution of ozone and nitrogen dioxide concentration at each site in Busan, and also investigated the characteristics of concentration change of air pollutants with time under the sea breeze. As a results, the concentration of nitrogen dioxide and ozone tend to be increased every year and nitrogen dioxide concentration is higher than ozone concentration at all sites in Busan. The concentration of ozone is high in summer season and low in winter season, but the concentration of nitrogen dioxide have a reversed trend. The monthly peak concentration of ozone occurred in April and September, while the monthly minimum concentration of nitrogen dioxide occurred in August. Their trend were identified by sites near the coastline than sites stands apart from the coastline. The sea breeze occurred annual mean 81 day in Busan from 1994 to 1996. The main wind direction of sea breeze was classified into southwesterly and southeasterly. In case of southwesterly, It was pronounced the south wind and southwest wind. In case of southeasterly, the occurrence frequency of east wind was high. Especially, the concentrations of urban air pollutants, such as ozone and nitrogen dioxide, were high on time which the sea breeze flow, and the areas that ozone concentration was high moved from outside part to central part of city with time. In costal urban such as Busan, the wind direction of sea breeze is influenced the change of ozone and nitrogen dioxide concentration on time which the sea breeze flow at each site and also influenced the change of air pollutants concentration of sites on the pathway of sea breeze.

• 대한원격탐사학회지
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• 제28권6호
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• pp.635-651
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• 2012

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.

• 한국대기환경학회지
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• 제24권5호
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• 한국멀티미디어학회논문지
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• 제23권6호
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• pp.747-755
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• 2020

In this paper, we deal with outlier data problems that occur when constructing a PM_2.5 fine dust forecasting system using a neural network. In general, when learning a neural network, some of the data are not helpful for learning, but rather disturbing. Those are called outlier data. When they are included in the training data, various problems such as overfitting occur. In building a PM_2.5 fine dust concentration forecasting system using neural network, we have found several outlier data in the training data. We, therefore, remove them, and then make learning 3 ways. Over_outlier model removes outlier data that target concentration is low, but the model forecast is high. Under_outlier model removes outliers data that target concentration is high, but the model forecast is low. All_outlier model removes both Over_outlier and Under_outlier data. We compare 3 models with a conventional outlier removal model and non-removal model. Our outlier removal model shows better performance than the others.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.19-22
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.191-194
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• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.