초록 ▼

Ⅳ. 연구개발결과
하천수계에 대한 농약의 주기적 잔류조사 목적으로 한강, 낙동강, 금강-새만금, 섬진-영산강수계 본류 및 지천 94개소를 선정하여 2012년과 2014년에 농약사용 비수기 1시기를 포함 4회 분석하였고, 농약의 포장 유출, 소수계 유입 및 대하천 희석 양상 파악을 위해 2013년 농업용 저수지 52개소 및 2014년 배 재배지역에 소재한 8개의 저수지와 연못을 선정하여 잔류 조사를 실시하였으며, 금강-새만금 지역 집중잔류조사는 농약사용 성수기 위주로 2013년에 11회 분석하였다.
환경 중 행적을 예측하기

Ⅳ. 연구개발결과
하천수계에 대한 농약의 주기적 잔류조사 목적으로 한강, 낙동강, 금강-새만금, 섬진-영산강수계 본류 및 지천 94개소를 선정하여 2012년과 2014년에 농약사용 비수기 1시기를 포함 4회 분석하였고, 농약의 포장 유출, 소수계 유입 및 대하천 희석 양상 파악을 위해 2013년 농업용 저수지 52개소 및 2014년 배 재배지역에 소재한 8개의 저수지와 연못을 선정하여 잔류 조사를 실시하였으며, 금강-새만금 지역 집중잔류조사는 농약사용 성수기 위주로 2013년에 11회 분석하였다.
환경 중 행적을 예측하기 위해 예측모형으로 과수용 농약은 PA5를, 벼재배용 농약은 RICEWQ와 SCI-GROW를 선정하고 우리나라의 기상자료, 작물 재배력 및 토양통을 이용하여 butachlor, iprobenfos, carbofuran, tebuconazole을 대상으로 수계 중 잔류농도를 추정하였다. 수계모니터링에서 butachlor와 iprobenfos의 최대값은 예측모형의 peak 농도보다 낮았고 최소값은 예측모형의 연평균농도보다 낮아 RICEWQ를 벼 재배환경 중 잔류농약의 농도 추정에 이용할 수 있음을 확인하였다.

Abstract ▼

To evaluate residues of environmental concerned pesticides which mainly include pesticides used for rice cultivation, total ninety four sampling sites were selected through main streams and branch streams of four major river basins. And the water samples at these sites were collected four times per

To evaluate residues of environmental concerned pesticides which mainly include pesticides used for rice cultivation, total ninety four sampling sites were selected through main streams and branch streams of four major river basins. And the water samples at these sites were collected four times per year, April, May-June, July-August, and September-October or November-December in 2012 and 2014. Besides, the water samples at sites of Keum, Mangyung and Dongjin rivers belong to the Keum river basin were regularly collected with a month interval, especially biweekly from May to August in 2013. Of the pesticides monitored, fenoxanil, hexaconazole, isoprothiolane iprobenfos and thifluzamide as fungicides were mainly detected in rice season. While other fungicides including diniconazole, propiconazole, fenarimol, nuarimol and boscalid, were detected with low frequencies and their average residue levels in positive samples were also fairly low. Of the insecticides monitored, some organophosphoruses, cadusafos, diazinon, fenitrothion, fenthion, phenthoate and prothiofos, two carbamates, carbofuran and fenobucarb, and endosulfan were detected with low frequencies and low residue levels. Of the herbicides monitored, nine pesticides which include alachlor, butachlor, dimethametryn, dithiopyr, ethalfluralin, metolachlr, oxadiazon, simetryn and thiobencarb were detected with frequencies of 1∼48% and in their residue level of 0.01∼1.9 μg/L. Detection frequencies and residue levels of insecticides and herbicides were the highest in waters sampled in May and June. Almost pesticides detected were for the rice plants and their residue levels were very low to compared with standard values.
Pesticide is used to protect the crops, but also become a cause of polluting the environment. Perform a risk assessment using physical and chemical properties, environmental fate and toxicity data in order to determine the pesticide registration. The aquatic model estimates pesticide concentrations in water bodies that result from pesticide applications to rice paddies and apple orchard. The used models are the PRZM, EXAMS and AGRO shell (PA5), Rice Water Quality Model (RICEWQ) and Screening Concentration In GROund Water (SCI-GROW). The residual concentration of water body was estimated using meteorological data, crop calendar and soil series of Korea. The chosen pesticides were butachlor, carbofuran, iprobenfos, azoxystrobin and tebuconazole. It has shown the potential that the RICEWQ is possible to predict residue level in water of butachlor and iprobenfos, because the maximum value in water monitoring data is lower than the peak concentration of the model, and the minimum value is lower than the average annual concentration of the model. But RICEWQ was insufficient to predict exposure concentrations in ground water. The estimated exposure concentrations of carbofuran in ground water is very higher than in surface water because of its low soil adsorption coefficient. Although tebuconazole were not detected in the water monitoring that means very low concentration, it is possible that the PA5 can be used to predict residue level in water. The case study is necessary in order to supplement and validate the predictive model. The proximity of the water model should be determined by performing a run off test in the field reflected the parameters of the model.