모기류는 흡혈을 통해 원충, 바이러스, 사상충 등 다양한 병원체를 보유하며 말라리아, 일본뇌염, 웨스트나일열, 뎅기열 등을 사람에게 매 개하는 위해성이 있는 위생해충이다. 이번 연구에서는 해외유입 모기류 감시를 위해 경상남도 고성군에 스마트 고공포집기를 설치하여 2022년 부터 2023년까지 모기류들을 모니터링하였다. 조사기간 동안, 총 3속 5종 43개체가 채집되었으며, 이중 삼일열말라리아를 매개하는 벨렌얼룩날 개모기(Anopheles belenrae)를 경남 지역에서 처음으로 발생함을 확인하였다. 본 연구에서는 스마트 고공포집기를 통해 해외에서 유입가능한 모 기류에 대한 실시간 모니터링이 가능함을 확인하였다.

We analyzed risk factors of coastal gillnet fishers during fishing process and considered work safety measures to reduce safety accidents during fishing using a UWB (ultra wideband) based positioning system. The static position accuracy of the UWB based positioning system was 45 cm. When entering a port, there is a risk of falling overboard. When casting a net, there was a risk of falling overboard due to being hit by fishing gear or guards, or getting caught in a buoy line or sinker line. When hauling a net, there is a risk of getting caught between fishing gear and net hauler, and the risk of musculoskeletal disorders due to repetitive work over a long period of time. Most safety accidents during work on fishing boats are blamed on human errors of the fisher and skipper, but safety accidents occur due to a mixture of mechanical and equipment factors, work and environmental factors, and management factors in addition to human errors. Therefore, the 4E were presented as countermeasures against the 4M, which are causes of safety accidents, and the proposed measures were used to identify risk factors for operation process, comply with work safety rules, and ensure the wearing of personal protective equipments. We need to reduce safety accidents during work by making it part of our daily routine. These research results can be used in the future for optimal placement of fishing gear and fishing nets in other coastal industries where safety accidents occur frequently.

Recently, domestic fishing production of Japanese horse mackerel has been continuously decreasing. To achieve sustainable fishing of this species, it is essential to acquire its target strength (TS) for accurate biomass estimation and to study its ecological characteristics. To date, there has been no TS research using a broadband echosounder targeting Japanese horse mackerel. In this study, for the first time, we synchronized an underwater camera with a broadband frequency (nominal center frequency of 200 kHz, range: 160-260 kHz) to measure the TS according to the body size (16.8-35.5 cm) and swimming angle of the species. The relationship between Japanese horse mackerel length and body weight showed a general tendency for body weight to increase as length increased. The pattern of the frequency spectra (average values) by body length exhibited a similar trend regardless of body length, with no significant fluctuations in frequency observed. The lowest TS value was observed at 243 kHz while the highest TS values were recorded at 180 and 257.5 kHz. The frequency spectra for the swimming angles appeared to be flat at angles of –5, 0, 30, 60, 75, and 80° while detecting more general trends of frequency spectra for swimming angle proved challenging. The results of this study can serve as fundamental data for Japanese horse mackerel biomass estimation and ecological research.

This study investigated the species composition and community structure of aquatic organisms captured using coastal beam trawling in Gomso Bay, Jeollabuk-do, from January to December 2022. Throughout the experimental period, a total of 20,246 individuals belonging to 94 marine species were captured, with a combined biomass of 602,828 g. Fish exhibited the highest abundance, comprising 56 species, followed by crustacea (21 species), bivalvia (8 species), cephalopoda (5 species), gastropoda (3 species), and holothuroidea (1 species). The dominant species was Leiognathus nuchalis, constituting 14.0% of the total individuals, followed by Portunus trituberculatus at 12.1%, Oratosquilla oratoria at 10.4%, Crangon hakodatei at 9.9%, and Metapenaeus joyneri at 7.9%. The diversity index ranged from 1.72 to 2.55, with the lowest diversity observed in March and the highest in July. Cluster analysis based on species composition of the 27 most common species showed that aquatic organisms were divided into three groups: spring and summer organisms (Group A) and summer organisms (Group B) and autumn and winter organisms (Group C).