River water quality and organisms have a very close relationship with the human living environment and health, so it is very important to ensure and maintain the ecological integrity of the aquatic ecosystem. In that sense, benthic diatoms have relatively little mobility, can explain the effects of long-term exposed pollution sources, and are very suitable indicator organisms for river ecosystem evaluation. Diatom ecologists have been developed various diatom indices to assess water quality and stream ecosystem over the world. However, they so far have insufficient identification of taxa, are strongly regional, and are difficult to apply as they are domestically. Unfortunately, there has not been developed an independent diatom index suitable for the Korean stream. Therefore, management of water quality and aquatic ecosystem suitable for domestic rivers can be made, and development or improvement of comprehensive multivariate diatom index for the integrated assessment of water quality and aquatic ecosystem is urgently needed.

and evaluated mainly physico-chemical properties. In the present study, several biological indices were applied to determine whether biological indices could provide a more comprehensive understanding in terms of soil quality assessment in urban forests. Microbe and invertebrate biological indices (i.e. biodiversity of both microbe and invertebrate, enzyme activity of microbe, feeding activity of invertebrate) were examined at 6 urban forests with different levels of disturbance in Seoul, Korea. The results showed that feeding activity and biodiversity were significantly and positively intercorrelated, but not with the enzyme activity. We also examined whether these biological indices could be modeled as functions of soil physico-chemical characteristics. To develop a predictive model, we applied principal component regression. The results showed that first principal component represented more than 33% of the total variance of biological indices and gave a good relationship with soil physico-chemical characteristics (R2=0.71). The predictive model developed in this study can be used for qualitative but not for quantitative assessment of soil quality.

본 연구에서는 SWAT 모형과 random forest를 이용하여 미래 기후변화에 따른 한강유역(34,148 km2)의 수생태계 건강성을 평가하였다. 국립 환경과학원에서 8년간(2008~2015년) 봄철(4~6월)에 모니터링한 부착돌말류 지수(TDI), 저서형 대형무척추동물지수(BMI), 어류평가지수(FAI)는 0~100점, A~E등급으로 평가되며, 이를 본 연구에서 사용하였다. 수생태 건강성에 영향을 미치는 변수로는 수질(T-N, NH4, NO3, T-P, PO4)과 수온을 선정하였으며, 수질 오염도가 낮은 경우에는 수생태계 건강성 점수가 광범위하게 분포되지만 수질 오염도가 높은 경우 수생태계 건강성 점수가 낮아지는 역상관관계를 확인하였다. 기계학습의 분류 분석 기법 중 하나인 random forest 모델을 이용한 세 개의 수생태 건강성 지수 등급 분류 결과 정밀도, 재현율, f1-score 모두 0.81 이상의 예측 정확도를 나타내었다. 기상청의 HadGEM3-RA RCP 4.5와 8.5 시나리오를 적용한 미래 SWAT 수문, 수질 결과 기저유출의 증가로 인해 질소 계열 수질 농도는 기준년도 대비 최대 43.2% 증가하였고, 지표유출 감소로 인해 인 계열 수질 오염도는 최대 18.9% 감소하는 것으로 분석되었다. 미래 FAI, BMI의 등급은 개선되는 경향을 보이지만 TDI는 등급이 악화되는 것으로 나타 났다. 이를 통해 TDI는 질소 계열 수질에 민감하고 FAI, BMI는 인 계열 수질에 더 민감하다고 판단하였다.