본 연구는 새우젓(5%, 10%) 및 찹쌀풀(2.5%)의 첨가가 김치발효중 저급 질소화합물들의 함량변화에 미치는 영향을 살펴 본 것이다. 김치를 10℃에서 14일간 저장하면서 pH와 총산도, 유리당을 측정하여 김치의 발효정도를 조사하고, nitrite, dimethylamine(DMA)과 Volatile Basic Nitrogen(VBN) 함량을 정량하였다. 김치 발효초기의 아질산염 함량은 0.16-0.31 mg/kg의 수준을 보였으며, 그 중에서도 10% 새우젓 첨가시료들 (S2, GS2)이 비교적 높은 것으로 나타났다. 그러나 적숙기인 숙성 6일 이후에는 모든 시료들이 검출한계미만으로 흔적정도를 나타냈다. 발효가 진행되면서 시료중의 DMA 함량은 점차적으로 증가하였으며, 젓갈첨가량이 높을 수록 많이 검출되었다. 새우젓+찹쌀죽 첨가구(GS1, GS2)는 새우젓만 첨가한 시료들(S1, S2)보다 더 많이 검출되었다. 새우젓을 첨가한 시료들의 DMA 함량은 발효초기에 0.03-1.2 mg/kg이었으며, 발효말기에는 1.6-2.26 mg/kg이었다. VBN 함량은 발효가 진행되면서 점점 증가하여 새우젓 첨가구들(S1, S2) 또는 새우젓과 찹쌀풀을 첨가한 시료들(GS1, GS2)이 새우젓을 첨가하지 않은 시료들(C, G)보다 분명하게 높았으며, 새우젓 첨가구와 새우젓+찹쌀풀 첨가구는 유사한 양상을 보였으므로, 찹쌀풀을 첨가하는 것이 VBN 함량에 영향을 주지 않는 것으로 보인다.

The free-living amoeba and Acanthamoeba sp. are widely distributed in fresh water, soil, air and dust in the world. We studied distribution of amoeba from low Nakdong River(Mulgum and Maeri) and removal efficiency in water treatment process of Busan metropolitan city. During this investigation, water quality showed pH 7.4～9.6(±1.1), water temperature 2.0～29.0(±17)℃, turbidity 4.8～27.4(±11.0) NTU, chlorophyll-a 10.3～109.0(±44.3) mg/m3, BOD 1.7～4.9(±2.6) mg/L, COD 3.1～6.9(±5.0) mg/L and total coliform 17～920(±200.5) MPN/100 mL. The free-living amoeba were detected highly than Acanthamoeba sp., 11 out of 22 in raw water samples were positive (50%) for Acanthamoeba sp. from February 2005 to December 2005. The seasonal characteristics of free-living amoeba and Acanthamoeba sp. in raw water were mainly distributed through the spring to the early fall. When free-living amoeba and Acanthamoeba sp. were passed through the water treatment of pilot-plant, approximately 80% was sure to be removed through pre-ozonation, sedimentation, send filtration. 100% was removed after post-ozonation process. All of the isolated amoebas from Nakdong River were Acanthamoeba sp. AC311 18S ribosomal RNA gene with 98% nucleotide sequence homology.

We have been proposed model equation which is able to predict the trihalomethane producing concentration formation, that is one of byproduct, in the water treatment processes. In proposed model, the effects of trihalomethane factors like chlorine contact time, pH, temperature, TOC and UV-254 are considered. The concentration of the trihalomethane produced is proportion to the contact with chlorine, pH of water, temperature of water TOC and UV-254, respectively. This proposed model could be predicted the formed concentration of trihalomethanes by trihalomethane factors.