Published online 20 April 2005
Published in J Environ Qual 34:804-815 (2005)
DOI: 10.2134/jeq2003.0454
© 2005 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
TECHNICAL REPORTS
Ground Water Quality
Identification of the Source of Nitrate Contamination in Ground Water below an Agricultural Site, Jeungpyeong, Korea
Seong-Chun Juna,
Gwang-Ok Baea,
Kang-Kun Leea,* and
Hyung-Jae Chungb
a School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, Korea
b Rural Research Institute, Korea Agricultural & Rural Infrastructure Corporation, Sa-dong 1031-7, Ansan-si, Gyeonggi 426-170, Korea
* Corresponding author (kklee{at}snu.ac.kr)
Received for publication December 31, 2003.
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ABSTRACT
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This study applied hydrogeological characterization and isotope investigation to identify source locations and to trace a plume of ground water contaminated by nitrate. Most of the study site is agricultural fields with the remainder being residential. A poultry farm is also within the study area, so that potential point and nonpoint sources were present. Estimates of seasonal ground water recharge from irrigation and precipitation, leakage of sewage, and the regional ground water flow were linked to the seasonal changes in isotopic values. Ground water recharge largely occurred in spring and summer following precipitation or irrigation, depending on the locations. Natural and fertilized soils were identified as nonpoint sources of nitrate contamination in this area, while septic and animal wastes were identified as small point sources. The seasonal changes in the relative impact of these sources on ground water contamination were related to such factors as source distribution, the aquifer confining condition, precipitation rate, infiltration capacity, recharge rate, and the land use pattern.
Abbreviations: GMWL, Global Meteoric Water Line • LMWL, Local Meteoric Water Line • MCL, maximum contaminant level • PRB, permeable reactive barrier
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INTRODUCTION
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NITRATE CONTAMINATION is a major problem in shallow aquifers in rural areas and poses a threat to ground water supply. In the Republic of Korea, nitrate is the most frequently observed contaminant throughout the nationwide ground water monitoring system. Nitrate has the highest frequency of detection over the Korean maximum contamination level (MCL) for ground water (10 mg L–1 for drinking water and 20 mg L–1 for household water). Tetrachloroethylene (MCL = 0.01 mg L–1 for drinking and household waters), trichloroethylene (MCL = 0.03 mg L–1 for drinking and household waters), and chloride (150 mg L–1 for drinking and household waters) follow nitrate in terms of detection frequency among the 15 constituents listed in the Korean Groundwater Act (Korea Ministry of Environment, 2003).
To protect ground water from contamination, it is important to identify the contamination sources and to delineate pathways of contaminants (Kendall, 1998). Nitrogen compounds in ground water mostly originate from surface or near-surface sources and enter ground water during recharge events (Landon et al., 2000). Thus, the recharge mechanism and its period are important for understanding the spatial and temporal variability of ground water contamination by nitrogen compounds. In cultivated rural areas, the influx of nitrogen compounds from nonpoint sources is related to recharge of irrigation water as well as precipitation. For example, a high concentration of nitrate appears in ground water when spring and summer rainfalls recharge the ground water after the dry Korean winter (Woo et al., 2001). The influx of nitrogen compounds from a point source can also be closely related to ground water recharge. In a residential district of a rural area, recharge and nitrogen input from household sewage may occur (Fogg et al., 1998; Panno et al., 2001). Discharge of wastewater associated with livestock manure is also a potential source of nitrate contamination.
A common method of identifying the recharge rate is the analysis of ground water level fluctuations (Rasmussen and Andreasen, 1959; Sophocleous, 1991; Lee and Lee, 2000; Healy and Cook, 2002). However, this method cannot discriminate between water derived from different sources of recharge, such as precipitation, surface water, irrigation, or leaking of water supply lines or sewers.
Measurement of stable isotopes in the ground water and its sources can provide useful data for the identification of recharge sources and the regional flow pattern of ground water. As oxygen- and hydrogen-stable isotopes are components of water, they are ideal tracers. Stable isotopes have been used to determine the characteristics of ground water recharge, the residence time, and the relationship between surface water and ground water (Mazor, 1997; Clark and Fritz, 1997; Coplen et al., 2000; Koh et al., 2001).
Nitrogen isotopes in nitrate and ammonium ions are useful for the identification of nitrate sources. Each nitrogen source has a typical range of isotopic values (Heaton, 1986; Kendall, 1998). The change in nitrate concentration and isotopic value gives information on the natural attenuation process (Mariotti et al., 1988; Böhlke and Denver, 1995).
The purpose of this research is to identify sources of nitrate contamination in a rural district through hydrological and isotopic investigations. The infiltration and recharge processes of the study area were first analyzed using ground water level fluctuation data and oxygen–hydrogen stable isotope data. Next, the ground water flow pattern was investigated by analyzing oxygen isotopes. Finally, the sources of nitrate were investigated by considering the ground water recharge and flow analysis results together with the nitrogen isotope data.
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MATERIALS AND METHODS
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Study Site and Pollution History
The study site is an agricultural area that includes a rural village, called "Moonhwa Maul," in Jeungpyeong Eup, Chung-buk, Korea (Fig. 1)
. The geology at the site consists of Quaternary alluvium sand, about 20 m thick, underlain by Jurassic porphyritic granite (Korea Agricultural and Rural Infrastructure Corporation, 2001). The water table of the area is 1 to 2.5 m from the surface. Because the vadose zone is so shallow, ground water recharge and rises in the water table follow precipitation events quickly.
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Fig. 1. Location of the study site and monitoring wells. The study site is an agricultural area that includes a rural village. The terms "w99-##" and "w00-##" are the names of monitoring wells. PRB, permeable reactive barrier.
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The village was constructed in 1994 with 11 houses and a population of about 60 to 70. A municipal water supply system supplies all the water used in the houses. An underground sewage treatment system was installed when the village was being built, and all sewage is collected and sent for treatment. The sewage treatment system uses soil filtration and bacterial activity. However, a few years after the construction of the village, the quality of ground water around the village became degraded. Basic investigation of the ground water quality and the flow system during 1999 to 2000 showed that the major contaminants were ammonium nitrogen and nitrate nitrogen. Potential sources of the nitrogen compounds were the discharge of sewage, agricultural fertilizer, and animal waste from a chicken farm in the village. To explore the feasibility of a reactive barrier in reducing nitrate contamination, the Korea Rural Infrastructure Corporation (KARICO) installed a pilot-scale permeable reactive barrier (PRB) of wood chips and permeable sands vertically in the ground water flow system in October 2000. The concept of nitrate reduction is to apply wood chips acting as an electron donor and induce biologically mediated reduction, presumably converting nitrate to nitrogen gas.
Hydrogeological Investigation
In the study area, 28 monitoring wells were installed during 1999–2000. The wells have been installed at a depth of between approximately 2 and 5 m. The holes had been drilled to a 6-m depth, but partly collapsed during the well installation. The screen intervals of wells are between approximately 1- and 5-m depth from the surface, and the casings are located above the screened interval. The depth to the water table is about 2 m though it shows some seasonal variations. Since November 2001, ground water levels have been measured each month in every well. The hydraulic conductivity is in the range of 4.5 to 11 m d–1, estimated using slug tests in seven wells (Korea Agricultural and Rural Infrastructure Corporation, 2001). The Darcian discharge rate, determined by multiplication of aquifer hydraulic conductivity and hydraulic gradient, is in the range of 3.1 x 10–2 to 7.7 x 10–2 m d–1. The annual average amount of precipitation for the last 10 years was about 1230 mm.
To get continuous ground water level data, automatic loggers (DIVER; Van Essen Instruments, Delft, the Netherlands) were installed in two wells in the study area. One well (w00-5) was located between a road and a rice field, and the second well (w00-13) was located near a domestic area and a farm. Data were collected during the period February–December 2002 (Fig. 2a and 2c) .
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Fig. 2. Cross-correlation of ground water level and precipitation rate. (a) Ground water level and precipitation rate in w00-5; (b) result of the cross-correlation in w00-5; (c) ground water level and precipitation rate in w00-13; and (d) result of the cross-correlation in w00-13.
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Characteristics of precipitation recharge were analyzed using the cross-correlation between the ground water level fluctuation data and the rainfall events according to Lee and Lee (2000). The time series data used in this analysis are ground water level fluctuations in wells w00-5 and w00-13 and site rainfall obtained from the Cheongju weather station, about 20 km southwest of the study site. The daily data of ground water levels and rainfall were used for the cross-correlation analysis. To obtain clear relationships between precipitation events and water level rise, the seven-day moving average was subtracted from the ground water level data. The cross-correlation function represents the interrelationship between the input and output series. The cross-correlation was calculated by:
 | [1] |
where d is delay, xi and yi are data from two series, precipitation rates and ground water levels, respectively, and mx and my are the means of two series. The delay, which is the time lag between lag = 0 and the maximum cross-correlation, determines the stress transfer velocity of the system. In this case it represents the delay of the response from precipitation to ground water level, and so is related to the infiltration time. The peak of the cross-correlation coefficient (r) indicates the degree of influence of precipitation on ground water. Other water sources that cause ground water recharge (e.g., irrigation, leaching of water service, and sewage) might also cause a low peak in the analysis by Eq. [1] using the precipitation and ground water level data.
Geochemical and Isotopic Investigation
The isotopic composition of oxygen (
18O) and hydrogen (D) was analyzed monthly. Water samples were taken from 16 wells that had a water column thickness larger than 2 m. However, the wells w99-3 and w99-5 were included in the sampling even though they have less water column thickness. The rainfall water was sampled using precipitation devices installed at the site. Water samples from the Samgi stream and a small irrigation canal at the study site were also obtained. Samples for nitrogen isotopic composition analysis were collected on two occasions in winter and once in summer. The total number of sampling points for nitrogen isotopic analysis was 20: 16 from wells, 1 from the stream, 2 from reservoirs, and 1 from septic tanks.
Water samples for 18O analysis were prepared by H2O–CO2 equilibration (Epstein and Mayeda, 1953). For D analysis, metallic zinc was used to produce hydrogen gas (Coleman et al., 1982). The 18O and D of the samples were determined using a stable isotope ratio mass spectrometer (Prism II, Micromass, Manchester, UK) at the Korea Basic Science Institute (KBSI). The analytical reproducibility was ±0.1
for 18O and ±1 for D. Concentrations of NO3–N and NH4–N were determined using a colorimetric procedure with an N auto-analyzer (FIA Stra 5000; Foss Tecator, Höganäs, Sweden). For the NO3–N isotope composition analysis, N2 gas was prepared according to the method of Hauck (1982). The nitrogen isotopic compositions were determined using a stable isotope ratio mass spectrometer (OPTIMA; Micromass) at the National Instrumentation Center for Environmental Management (NICEM). Multiple replicate analyses indicate the standard deviation of 15N measurements to be <0.2.
The monthly variation of 18O and D was used to analyze the ground water recharge pattern and the ground water flow direction and velocity. If the isotopic precipitation value has distinctive variation each season, the comparison of isotopic variations between the precipitation and the shallow ground water can reveal when the recharge water infiltrated into the soil (Landon et al., 2000). Nonreactive and highly conservative stable isotopes can show distinctive spatial distribution patterns depending on the spatial variations of the recharge rate. In a dry season, the distribution pattern moves laterally according to the ground water flow system. Thus, the spatial correlation analysis of the isotope data was used to compute the ground water flow direction and velocity.
Nitrogen isotope data were used to investigate the types and locations of the nitrogen contamination sources. The analyses of the recharge pattern and the ground water flow system together with the nitrogen isotope data were integrated in the analysis of contamination sources.
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RESULTS AND DISCUSSION
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Temporal Variability in Ground Water Recharge from Different Sources
The time interval for precipitation water to infiltrate from the land surface to the water table was estimated from the cross-correlation analysis. Figures 2a and 2c show the water level and precipitation data in wells w00-5 and w00-13, which represent the different patterns of the relationships between the precipitation event and the water level fluctuation. There was a steep water level rise at well w00-5 in early spring though there was no distinct event of precipitation (Fig. 2a). On the other hand, the major water level fluctuations at well w00-13 responded only to precipitation events (Fig. 2c). This difference is reflected in the cross-correlation analysis. Figure 2b shows the result of the cross-correlation at w00-5. The peak of the lag time is two days, and the correlation coefficient is 0.401. The term lag time represents the delay between when precipitation occurred and the corresponding response at the water table. Figure 2d shows the result of the cross-correlation at w00-13. The peak point of lag time occurs on Day 1, and the correlation coefficient is 0.679. On the basis of the water level fluctuation patterns and the results of cross-correlation analysis, precipitation was the predominant influence on the ground water level near w00-13, but there was another factor influencing the ground water level near w00-5.
To understand the detailed infiltration characteristics over each season, the total period of 334 d was divided into four seasonal periods. According to the seasonal analysis, the time between surface infiltration and ground water recharge in late winter (February to March) is about four to five days, and in spring and early summer (April to June) about two to five days (Table 1). In late summer (July to September), when there is much rain, the recharge time is one day, and in late autumn (October to December) about two to three days. Considering the location of w00-5, the peak interval of five days in springtime was probably caused by the irrigation of stream water into a rice field that was filled with water ready for transplanting rice seedlings.
The cross-correlation analysis in this study, however, was performed with precipitation and ground water level data sets. Thus, the analysis has limitations with respect to identifying influences of sources other than precipitation. What can be decided with the correlation coefficient and time lags is whether the precipitation does or does not strongly influence the ground water level change at a well. Therefore, well locations and surface water applications also should be considered while interpreting the ground water level changes.
Recharge and Ground Water Flow Estimation by Oxygen and Hydrogen Stable Isotopes
The seasonal characteristics of stable isotopes in precipitation give important information on the analysis of the ground water recharge because precipitation is the main component of the recharge. If shallow ground water reflects the seasonal isotopic composition of precipitation, it suggests that the main source of recharge is precipitation. On the other hand, other local sources of recharge may alter the seasonal isotopic pattern. According to the investigation of Kim and Nakai (1988), the precipitation of South Korea does not show very conspicuous seasonal variation such as a sine function, but has a general pattern of variation reflecting the effects of varying temperature and precipitation. The reason for using a smoothed general trend is that the effects of temperature and precipitation occur at the same time and interfere with each other. A temperature increase causes the isotopic composition to become heavier while large amounts of precipitation cause it to become lighter (Dansgaard, 1964), and the two effects mostly occur during the summer. Nevertheless, the temperature effect is shown in dry seasons, and the precipitation effect is shown in the rainy season. This is the reason why seasonal isotopic variation is still valid for the analysis of recharge characteristics.
Most of the isotopic values of ground water in the study area are below the Global Meteoric Water Line (GMWL), and they are distributed parallel to it (Fig. 3
, Table 2). The data points from 28 Nov. 2001 to 22 Mar. 2002 exhibit similar positions. The isotopic values of 30 May 2002 were heavier, and those of 3 Aug. 2002 have a heavy composition but became more distant from the GMWL. On 25 Sept. 2002, the ground water isotopic values became dramatically lighter and were similar to winter values.
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Fig. 3. Isotopic composition and seasonal variation in ground water samples. The values in the small box show the average value of each time. GMWL, Global Meteoric Water Line; LMWL, Local Meteoric Water Line.
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The timing of recharge of precipitation from different seasons can be estimated from the average d values of the ground water and the precipitation along with values of monthly average temperature and the amount of precipitation (Fig. 4
, Table 3). The d value is defined as follows (Craig, 1961):
 | [2] |
Equation [2] equals GMWL when the d value is 10. Thus, the d value can represent the deviation of a water sample's isotopic composition from the GMWL. On 28 Nov. 2001, the ground water isotopes had a relatively light composition, and the d value of the ground water was similar to that of summer precipitation (Kim and Nakai, 1988; Lee et al., 1999). On 23 Jan. 2002, ground water D values were slightly heavier and 18O values were slightly lighter than on 28 Nov. 2001, indicating the recharge of winter precipitation. On 22 Mar. 2002, both D and 18O were slightly heavier and moved toward the GMWL compared with 23 Jan. 2002. This pattern is consistent with recharge of precipitation of a high d value. Because the Korean winter is the dry season and recharge amounts are small, the variations from November 2001 to March 2002 were very slight. The isotopic composition of ground water on 30 May 2002 became much heavier, resulting from recharge by relatively heavy rain and irrigation water. The "temperature effect" of precipitation was reflected in the isotopic composition of the ground water. On 3 Aug. 2002, 18O values of ground water were heavier than before, but D values were lighter. Consequently the d value of ground water was very low, demonstrating the recharge of typical precipitation in summer. Because summer precipitation originates from the North Pacific, it has a relatively higher 18O to 16O ratio than winter precipitation (Kim and Nakai, 1988; Lee and Lee, 1999). On 25 Sept. 2002, the isotopic values of ground water returned to the values characterized by winter. It rained about 500 mm in August, and this "amount effect" of precipitation influenced the isotopic composition of the ground water (Lee et al., 1999).
The recharge ratio is the contribution of precipitation to the storage of shallow ground water, and is different from the ground water recharge ratio of precipitation. To estimate the ground water recharge quantitatively, the recharge ratios were calculated using Eq. [3] as follows (Hooper and Shoemaker, 1986; Koh et al., 2001):
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where Qn is the amount of ground water recharge from precipitation, Qt is the total amount of ground water in the aquifer, t is the isotopic value of ground water after precipitation, 0 is the isotopic value of ground water before precipitation, and n is the isotopic value of precipitation. The ratio of Qn to Qt is calculated to express the temporal and spatial variation of relative ground water recharge.
The recharge ratios calculated by 18O values are represented in Table 4. The isotopic value of the ground water was calculated from the average value for the ground water. The values for April were noteworthy. The recharge ratio for precipitation was small but that for irrigation water was very large. Although an overestimation may have occurred, the irrigation water was considered to be largely responsible for the rise of the ground water level. During May, both recharge ratios by precipitation and irrigation water were large. Considering that about 190 mm of rain fell and that irrigation continued into this period, the recharge ratios were acceptable to some degree.
The ground water flow direction was analyzed based on the measured ground water levels and the hydraulic parameters obtained through field tests. Sequential migration of the distribution pattern of 18O was analyzed through cross-correlation analysis of 18O time series of well pairs. To eliminate the effects of recharge, the daily averages were subtracted from each data set. The distance between the two wells divided by the lag time is the apparent ground water velocity. As a result, the ground water flow direction and velocity were estimated based on the hydrogeological and isotopic analyses. The results for dry season (2002) are given in detail in Fig. 5
. Each arrow shows the direction of flow between two wells, the length represents the scale of the apparent velocity, and the width of the arrow represents the relative correlation coefficient obtained from the well-to-well cross-correlation analysis of 18O time series. The estimated average flow direction was toward the northeast (N65°E) and the flow velocity was 239 m yr–1.
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Fig. 5. Flow analysis by cross-correlation of 18O. Each length of the arrow represents the scale of the apparent velocity, and the width of the arrow represents the relative correlation coefficient. Average velocity was weighted by correlation coefficient.
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Identification of Nitrogen Sources
According to a previous investigation (Korea Agricultural and Rural Infrastructure Corporation, 2001), the northern part of the study area had relatively high nitrate concentrations and the southern areas had relatively low concentrations. The southwestern domestic area had the lowest nitrate concentration (Fig. 6)
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Fig. 6. Spatial distributions and seasonal changes of nitrate nitrogen concentration. Each circle shows the relative concentration. Each number above the circle represents the concentration. All units are mg L–1 as nitrate N.
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Although the zones of high concentration changed each season, it seemed that agricultural fertilizer, animal waste, and the leakage of sewage, independently or combined, could be the sources of nitrogen contamination. To identify the contaminant sources, the isotopic values of nitrogen were used (Tables 5 and 6). If each source type has a typical range of nitrogen isotopic values, the ground water isotopic values may indicate the major contamination source (Heaton, 1986; Kendall, 1998). In summer the isotopic values of 15N-NO3 were mainly in the range of 0 to 15; therefore, the contaminant sources could have been natural soil, fertilized soil, and septic waste (Fig. 7)
. In winter the nitrogen isotopic values were high, in the range of 10 to 40; therefore, septic waste and animal waste were the possible contaminant sources. However, it is also possible that denitrification contributed to the heavier nitrogen isotopic values.
Detailed interpretations of contaminant sources can be made from both spatial distributions of concentrations and from isotopic values (Fig. 8)
. Because w99-5 had an excessively high nitrogen concentration in summer and a high isotopic value in winter in comparison with other values, it was excluded from the spatial distribution. From the general trend of distribution, it can be assumed that the nitrate contamination was due to nonpoint sources that exist in all croplands, and point sources due to leakage of septic wastes and discharge of animal wastes. Nonpoint sources include natural soil and fertilized soil with an isotopic value of about 5 to 10 (Heaton, 1986; Kendall, 1998). The point source occurring in the middle of the study area that displays a high nitrogen concentration could be septic waste. The southwestern domestic area displayed a low nitrogen concentration and a high isotopic value, and that area is considered to be a denitrification zone. Although the northeastern site, w00-15, had a relatively high nitrogen concentration in both winter and summer, values of 15N-NO3 in w00-15 were relatively low (i.e., 14.2 or 6.7 in winter and 2 in summer). The source of nitrogen at this point was considered to be animal waste from poultry farming, on the basis of its location and the ground water velocity. It seems that the reason values of 15N-NO3 were lighter than the values found by earlier researchers was because most of the poultry waste had been removed off-site, and only a small part of the N in poultry waste had been nitrified and washed down to ground water. Also, the mixing of another nitrate source in ground water may contribute to the low values.
Nitrate Transport and Transformation along the Flow Paths
Generally, nitrate concentrations were relatively high in spring and summer due to inputs of nitrate from recharge during these seasons. Natural attenuation tends to reduce nitrate concentrations during the other seasons. To determine the main process of nitrate attenuation would require further investigation. On this site, the mechanisms were inferred from the isotopic value of nitrate nitrogen and the concentration of nitrate.
It is known that the major mechanisms of nitrate attenuation are denitrification and dilution (Mariotti et al., 1988). The former mechanism results in a heavier isotope composition, but the latter causes little change. Thus, it seems that the main mechanism of nitrate attenuation at this site is denitrification because the isotopic values are much higher in winter. Figure 9 is the diagram of nitrate nitrogen concentration versus nitrogen isotopic value in the ground water wells. Results from winter are almost fitted by the Rayleigh equation, excluding the results in w99-5 and w00-12. The approximate Rayleigh equation expresses isotopic fractionation processes as follows (Mariotti et al., 1988):
 | [4] |
where 0 is the initial composition of the substrate, f is the remaining fraction of the substrate, and 
p–s is the enrichment factor. It is known that the enrichment factor is in the range of approximately –5 to –8 when the denitrification occurs in ground water (Kendall, 1998). The enrichment factor value derived from the regression shown in Fig. 9 is –6.33, within that reported range. The average concentration of nitrate nitrogen in summer is 4.35 mg L–1 and the average isotopic value is 8.4. The averaged point is located below the fitting curve. The deviation of the average value from the fitted curve can be explained by dilution.
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Fig. 9. Diagram of nitrate nitrogen concentration versus nitrogen isotopic value. Results from winter are fitted with the Rayleigh equation, excluding the results in w99-5 and w00-12.
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CONCLUSIONS
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This study presented analyses using hydrologic and isotopic data to identify sources of nitrate contamination in ground water below an agricultural site with several potential nitrate contamination sources. Agricultural sites such as the Jeungpyeong site in this study usually have several potential on-ground nitrogen sources such as dairy and poultry manure, fertilizers, septic systems, irrigation, and fertilized soils. Among the potential sources, it is not easy to identify the major source of ground water contamination by chemical indicators only. This study presented a study combining hydrological and isotopic analyses for identifying the nitrogen contamination sources. This study applied time series of ground water level and precipitation data for correlation analysis to analyze the seasonal ground water recharge mechanism and subsequently applied the analysis to the identification of nitrate contamination sources. The result of the correlation analysis at each well indicates what is the dominant recharge for the area near the well. The recharge mechanism and temporal distribution of nitrate concentration and isotopic values such as 15N-NO3 and 18O help with the interpretation of the distribution of major nitrate contamination sources.
The combination of hydrologic and isotopic time series analyses is very useful in the identification of major nitrate contamination sources in an agricultural site of multiple potential contamination sources. The interpretation results in the study site show that ground water recharge largely occurs in spring and summer by infiltration of precipitation water. In spring, however, irrigation water is a large contributor to ground water recharge near a rice field. Nitrogen inputs are mostly related to ground water recharge from precipitation and irrigation, except in some isolated areas where sewage from a poultry farm enters the ground water system.
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ACKNOWLEDGMENTS
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This study was supported by the Agricultural R&D Promotion Center. AEBRC at POSTECH and the BK21 SEES program partly supported this study.
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ABSTRACT
INTRODUCTION
