• Economic and Environmental Geology
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• v.52 no.1
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• pp.13-28
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• 2019

This study analyzed precipitation environment, heavy metal contamination, and mineral composition of white, reddish brown and mixed precipitates occurring at the Osip stream drainage, Gangwondo. Furthermore, spectral characteristics of the precipitates associated with heavy metal contamination and mineral composition was investigated based on spectroscopic analysis. The pH range of the precipitates was 4.43-6.91 for white precipitates, 7.74-7.94 for reddish brown precipitates, and 7.59-7.9 for the mixed precipitates, respectively. XRF analysis revealed that these precipitates were contaminated with Ni, Cu, Zn, and As. The white precipitates showed high Al concentration compared to reddish brown precipitates as much as 3.3 times, and the reddish brown precipitates showed high Fe concentration compared to white precipitates as much as 15 times. XRD analysis identified that the mineral composition of the white participates was aluminocoquimbite, gibbsite, quartz, saponite, and illite, and that of reddish brown precipitates was aluminum isopropoxide, kaolinite, goethite, dolomite, pyrophyllite, magnetite, quartz, calcite, pyrope. The mineral composition of the mixed precipitates was quartz, albite, and calcite. The spectral characteristics of the precipitates was manifested by gibbsite, saponite, illite for white precipitates, goethite, kaolinite, pyrophyllite for reddish brown precipitates, and albite for the mixed precipitates, respectively. The spectral reflectance of the precipitates decreased with increase in heavy metal contamination, and absorption depth of the precipitates indicated that the heavy metal ions were adsorbed to saponite and illite for white precipitates, and goethite and magnetite for reddish brown precipitates.

• Journal of Animal Science and Technology
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• v.49 no.1
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• pp.137-146
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• 2007

This study was conducted to determine fertilizer nutrient and pollutant production of Holstein dairy cattle by estimating manure characteristics. The moisture content of feces was 83.9% and 95.1% for urine. The pH of feces and urine were in the ranges of 7.0～7.4 and 7.5～7.8, respectively. The average BOD5, COD, SS, T-N, T-P concentrations of the dairy feces were 18,294, 52,765, 102,889, 2,575, and 457mg/ℓ, respectively. Dairy urine showed lower levels of BOD5(5,455mg/ℓ), COD(8,089mg/ℓ), SS(593mg/ℓ), T-N(3,401mg/l), and T-P(13mg/ℓ) than feces. The total daily produced pollutant amounts of a dairy cow were 924.1g(Milking cow), 538.8g(Dry cow), 284.4g(Heifer) of BOD5, 2,336.5g (Milking cow), 1,651.8g(Dry cow), 734.1g(Heifer) of COD and 4,210.1g(Milking cow), 2,417.1g(Dry cow), 1,629.1g(Heifer) of SS and 194.8g(Milking cow), 96.4g(Dry cow), 58.3g(Heifer) of T-N and 24.0g(Milking cow), 10.2g(Dry cow), 6.1g(Heifer) of T-P. The calculated amount of pollutants produced by a 450kg dairy cow for one year were 181.3kg of BOD5, 492.5kg of COD, 899.9kg of SS, 36.0kg of T-N and 4.1kg of T-P. The total yearly estimated pollutant production from all head(497,261) of dairy cattle in Korea is 90,149 tons of BOD5, 244,890 tons of COD, 447,491 tons of SS, 17,898 tons of T-N and 2,008 tons of T-P. The fertilizer nutrient concentrations of dairy feces was 0.26% N, 0.1% P2O5 and 0.14% K2O. Urine was found to contain 0.34% N, 0.003% of P2O5 and 0.31% K2O. The total daily fertilizer nutrients produced by dairy cattle were 197.4g (Milking cow), 97.4g(Dry cow), and 57.9g(Heifer) of Nitrogen, 54.2g(Milking cow), 22.2g(Dry cow), and 14.2g(Heifer) of P2O5 and 110.8g(Milking cow), 80.4g (Dry cow), and 39.5g(Heifer) of K2O. The total yearly estimated fertilizer nutrient produced by a 450kg dairy animal is 36.2kg of N, 8.8kg of P2O5, 24.6kg of K2O. The estimated yearly fertilizer nutrient production from all dairy cattle in Korea is 18,000 tons of N, 4,397 tons of P2O5, 12,206 tons of K2O. Dairy manure contains useful trace minerals for crops, such as CaO and MgO, which are contained in similar levels to commercial compost being sold in the domestic market. Concentrations of harmful trace minerals, such as As, Cd, Hg, Pb, Cr, Cu, Ni, Zn, met the Korea compost standard regulations, with some of these minerals being in undetected amounts.

• Korean Journal of Ecology and Environment
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• v.35 no.1 s.97
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• pp.52-61
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• 2002

This research was conducted to evaluate the effect of forest management practices on pH and electrical conductivity to get fundamental information on water purification capacity after forest operation. Rainfall, throughfall and stemflow　were　sampled at the study sites which consist of Abies holophylla and Pinus koraiensis　in Gwangreung Experimental Forest for S months from May to November 1999. Mean pH of the throughfall of the beginning of the event was higher in management (thinning and pruning) sites of Abies holophylla and Pinus koraiensis stands than nonmanagement site of Abies holophylla and Pinus koraiensis stands. In addition, pH of the throughfall of the total amount of the event showed similar trends which are higher pH in the management sites compared with the non- management sites. This result indicates that managements such as thinning and pruning improve tree butler capacity of rainfall pH. According to the linear regression results, pH of the throughfall of the total amount of the event in non-management sites = 0.735${\times}$pH of the throughfall of the beginning of the event in non-management sites+1.849 ($R^2\;=\;0.82$) and pH of the throughfall of the total amount of the event in management sites= 0.863${\times}$pH of the throughfall of the beginning of the event in management sites +1.0242 ($R^2\;=\;0.87$). In case of stemflow pH, pH of the sternflow of the total amount of　the event in non-management sites = 0.53${\times}$pH of the stemflow of the beginning of　the event in non- management sites+2.7709 ($R^2\;=\;0.64$) and pH of the stemflow of the total amount of the event in management sites = 0.5854${\times}$pH of the stemflow of the beginning of the event in management sites+2.7045 ($R^2\;=\;0.65$). Electrical conductivity (EC) of the throughfall of the beginning and total amount of the event was highest in non- management site in Abies holophylla, followed by management sites in fsies Abies holophylla, non-management site in Pinus koraiensis, and management sites in Pinus koraiensis stands, respectively. According to the linear regression results, EC of the throughfall of the total amount of the event in non-managementsites = 0.4045${\times}$EC of the throughfall of the beginning of the event in non-management sites+26.766 ($R^2\;=\;0.69$) and EC of the throughfall of the total amount of the event in management sites = 0.6002${\times}$EC of the throughfall of the beginning of the event in management sites+8.0184 ($R^2\;=\;0.54$). In case of stemflow EC, EC of thestemflow of the total amount of the event in non-management sites = 0.6298${\times}$EC of the stemflow of the beginning of the event in non-management sites+11.582 ($R^2\;=\;0.72$) and pH of the stemflow of the total amount of the event in management sites =0.602${\times}$pH of the stemflow of the beginning of the event in management sites+20.783($R^2\;=\;0.49$).