우리나라에 축산물을 수출한 국가들이 2022년과 2023년 에 제출한 2019-2022년 국가별 잔류물질검사프로그램(NRP) 자료를 바탕으로 축산물 유형별, 국가별로 잔류허용기준 을 위반한 동물용의약품과 환경오염물질의 현황을 분석하 였다. 가장 많이 위반된 잔류물질은 네오마이신 2 4 1건, 페 니실린G 183건, 겐타마이신 151건, 세프티오퍼 113건, 니카 바진 103건 등이였고 축산물별로는 소고기, 돼지고기, 가 금육, 우유, 계란 등의 순서로 나타났다. 잔류 위반이 가 장 많았던 소고기에서는 페니실린G, 세프티오퍼, 플루니신 , 네오마이신, 벤질페니실린, 멜록시캄, 플로르페니콜, 질 파테롤 등 전염병 치료에 사용되는 항생제와 해열제 및 성장 촉진에 사용되는 동물용의약품이 주로 검출되었지만 , 돼지고기에서는 성장촉진을 목적으로 사용되는 락토파민 과 항생제인 독시사이클린, 클로람페니콜, 벤질페니실린, 난드롤론, 티오라실, 스트렙토마이신 및 임신 촉진에 사용 되는 호르몬이 가장 흔하게 검출된 것으로 나타났다. 가 금육에서는 콕시듐증 치료에 사용되는 항생제인 니카바진 이 가장 많이 위반되었으며, 스트렙토마이신, 아미노글리 코사이드 항생제, 벤질페니실린, 독시사이클린 및 기타 항 생제가 그 뒤를 이었다. 동물용의약품의 잔류 위반은 많 은 국가에서 보고되고 있으나, 다이옥신과 PCB와 같은 환 경오염 물질의 잔류는 주로 유럽 국가에서 보고되었다. 국 가별 잔류물질의 차이는 각국에서 사육되는 가축과 사육 관리 방식의 차이에 의한 것으로, 소고기의 잔류 수준이 높은 것은 소는 다른 동물에 비해 질병 발생 시 개체별로 치료되기 때문일 것으로 추정된다. 본 연구 결과는 수입 축산물의 안전성 확보를 위하여 국가별, 축산물별로 중점 적으로 검사가 필요한 물질을 제시함으로써 매년 실시하 는 수입 축산물 잔류물질검사를 보다 과학적이고 효율적 으로 수행할 수 있는 기반을 제공하였다.

전 세계적으로 기후변화 등의 환경 문제와 자원고갈 문제가 심각해짐에 따라 화석연료의 의존 도를 낮추고 탄소중립을 실현할 수 있는 대체 에너지원 확보가 중요해졌다. 이에 따라 국내 발전분야에서 는 중유를 대체할 수 있는 폐자원 유래 바이오중유를 2014년부터 시범보급하여 2019년 상용화를 시작하였 다. 본 연구에서는 2014년 시범보급 시작부터 2023년까지 상용화 기간을 포함한 10년간 국내에서 발전용 연료로 사용된 바이오중유를 대상으로 원료 수급 데이터 및 연료 품질 모니터링 데이터를 연도별로 분석하 여 주요 품질 특성의 변화를 평가하였다. 시범보급 초기에는 저가 원료의 사용 및 연료 제조기술의 부족 등으로 인화점, 동점도, 황분, 회분, 전산가, 금속분, 질소분, 인함량 등 대부분 품질 항목의 변동성이 컸으 나, 2018년까지 시범보급 기간을 거치며 바이오중유 생산사들의 제조기술 및 발전소의 구매 연료 품질 관 리 체계의 발전 등으로 연료 품질이 빠르게 안정화되었다. 이후 상용화 기간을 포함한 2023년까지 인화점 과 황분 함량은 안정적으로 유지되었고, 동점도, 회분, 유동점, 전산가, 금속분, 인 함량 등은 감소 추세를 나타내었으며, 발열량은 증가 추세를 보이는 것으로 나타났다. 10년간 바이오중유의 품질은 시범보급 첫해 를 제외하고 모두 법적인 품질기준을 만족하였고 연료로서의 품질 특성은 안정화되며 개선되고 있는 것으 로 나타났다.

This study analyzed IoT-based indoor air quality monitoring data in a cooking room at a high school in Seoul. As a result of measuring the type and concentration change of cooking fumes generated during roasting, frying, and stir-fry, each cooking method showed a different pattern. Some cooking fumes were observed high during the distribution process, not during cooking, and it is necessary to observe and control indoor air quality during the entire process of cooking, storage, and distribution as well as various elements of cooking fumes. Through these results, we propose the addition of an IoT-based real-time indoor air quality monitoring system and ventilation facilities linked to it.

PURPOSES : This study aimed to compare the object detection performance based on various analysis methods using point-cloud data collected from LiDAR sensors with the goal of contributing to safer road environments. The findings of this study provide essential information that enables automated vehicles to accurately perceive their surroundings and effectively avoid potential hazards. Furthermore, they serve as a foundation for LiDAR sensor application to traffic monitoring, thereby enabling the collection and analysis of real-time traffic data in road environments. METHODS : Object detection was performed using models based on different point-cloud processing methods using the KITTI dataset, which consists of real-world driving environment data. The models included PointPillars for the voxel-based approach, PartA2-Net for the point-based approach, and PV-RCNN for the point+voxel-based approach. The performance of each model was compared using the mean average precision (mAP) metric. RESULTS : While all models exhibited a strong performance, PV-RCNN achieved the highest performance across easy, moderate, and hard difficulty levels. PV-RCNN outperformed the other models in bounding box (Bbox), bird’s eye view (BEV), and 3D object detection tasks. These results highlight PV-RCNN's ability to maintain a high performance across diverse driving environments by combining the efficiency of the voxel-based method with the precision of the point-based method. These findings provide foundational insights not only for automated vehicles but also for traffic detection, enabling the accurate detection of various objects in complex road environments. In urban settings, models such as PV-RCNN may be more suitable, whereas in situations requiring real-time processing efficiency, the voxelbased PointPillars model could be advantageous. These findings offer important insights into the model that is best suited for specific scenarios. CONCLUSIONS : The findings of this study aid enhance the safety and reliability of automated driving systems by enabling vehicles to perceive their surroundings accurately and avoid potential hazards at an early stage. Furthermore, the use of LiDAR sensors for traffic monitoring is expected to optimize traffic flow by collecting and analyzing real-time traffic data from road environments.

The objective of this study is to analyze the indoor air quality of multi-use facilities using an IoT-based monitoring and control system. Thise study aims to identify effective management strategies and propose policy improvements. This research focused on 50 multi-use facilities, including daycare centers, medical centers, and libraries. Data on PM10, PM2.5, CO2, temperature, and humidity were collected 24 hours a day from June 2019 to April 2020. The analysis included variations in indoor air quality by season, hour, and day of the week (including both weekdays and weekends). Additionally, ways to utilize IoT monitoring systems using big data were propsed. The reliability analysis of the IoT monitoring network showed an accuracy of 81.0% for PM10 and 76.1% for PM2.5. Indoor air quality varied significantly by season, with higher particulate matter levels in winter and spring, and slightly higher levels on weekends compared to weekdays. There was a positive correlation found between outdoor and indoor pollutant levels. Indoor air quality management in multi-use facilities requires season-specific strategies, particularly during the winter and spring. Furhtermore, enhanced management is necessary during weekends due to higher pollutant levels.

복숭아혹진딧물(Myzus persicae)은 다식성으로 담배, 감자, 고추, 배추, 복숭아 등에 심각한 피해를 입히는 대표적인 농업해충이다. 본 연구에 서는 국내 복숭아혹진딧물 야외개체군의 λ-cyhalothrin, imidacloprid 및 flupyradifurone에 대한 약제 저항성 발달 수준과 점 돌연변이(R81T, L1014F, M918L)의 발생 여부를 확인하였다. 또한, qRT-PCR을 통해 사이토크롬 P450 유전자인 CYP6CY3 발현량을 확인하였다. 그 결과, λ -cyhalothrin은 저항성비(Resistance Ratio, RR)가 12개 모든 지역이 > 200으로 높은 저항성을 보였다. Imidacloprid와 flupyradifurone은 YS, UR, HY, 그리고 WJ 개체군에서 > 200의 저항성비로 높은 저항성을 나타냈다. R81T는 12개 집단 중 약 50%, L1014F는 약 33.3%, M918L은 100%에서 발현하였다. 또한 qRT-PCR을 통해 imidacloprid 저항성 개체에서 subunit CYP6CY3의 발현량이 높게 나타난 것을 확인하였다. 이러 한 결과는 M918L 점 돌연변이는 λ-cyhalothrin 저항성 진단마커로, R81T와 CYP6CY3의 높은 발현은 imidacloprid 저항성 진단마커로 활용이 가능하다는 것을 시사한다.

PURPOSES : The evaluation of the low-temperature performance of an asphalt mixture is crucial for mitigating transverse thermal cracking and preventing traffic accidents on expressways. Engineers in pavement agencies must identify and verify the pavement sections that require urgent management. In early 2000, the research division of the Korea Expressway Corporation developed a three-dimensional (3D) pavement condition monitoring profiler vehicle (3DPM) and an advanced infographic (AIG) highway pavement management system computer program. Owing to these efforts, the management of the entire expressway network has become more precise, effective, and efficient. However, current 3DPM and AIG technologies focus only on the pavement surface and not on the entire pavement layer. Over the years, along with monitoring, further strengthening and verification of the feasibility of current 3DPM and AIG technologies by performing extensive mechanical tests and data analyses have been recommended. METHODS : First, the pavement section that required urgent care was selected using the 3DPM and AIG approaches. Second, asphalt mixture cores were acquired from the specified section, and a low-temperature fracture test, semi- circular bending (SCB) test, was performed. The mechanical parameters, energy-release rate, and fracture toughness were computed and compared. RESULTS : As expected, the asphalt mixture cores acquired from the specified pavement section ( poor condition – bad section) exhibited negative fracture performances compared to the control section (good section). CONCLUSIONS : The current 3DPM and AIG approaches in KEC can successfully evaluate and analyze selected pavement conditions. However, more extensive experimental studies and mathematical analyses are required to further strengthen and upgrade current pavement analysis approaches.

Benthic attached diatoms (BADs), a major primary producer in lotic stream and river ecosystems are micro-sized organisms and require a highly magnified microscopic technique in the observation work. Thus, it is often not easy to ensure accuracy and precision in both qualitative and quantitative analyses. This study proposed a new technique applicable to improve quality control of aquatic ecosystem monitoring and assessment using BADs. In order to meet the purpose of quality control, we developed a permanent mounting slide technique which can be used for both qualitative and quantitative analyses simultaneously. We designed specimens with the combination of grid on both cover and slide glasses and compared their efficiency. As a result of observation and counting of BADs, the slide glass designed with the color-lined grid showed the highest efficiency compared to other test conditions. We expect that the method developed in this study could be effectively used to analyze BADs and contributed to improve the quality control in aquatic ecosystem health monitoring and assessment.

사물인터넷(IoT) 기술을 활용한 전력 사용량 모니터링은 스마트팜 운영비 절감 기술 개발을 위한 기초자료로 필요성이 부각되고 있다. 본 연구에서는 멜론 생산 스마트팜 운영 중 실시간 전력사용량 모니터링 시스템을 설치한 예를 소개하고 이 를 이용하여 수집된 데이터를 실시간으로 활용하는 방법을 제 안한다. 전력사용량 모니터링 시스템의 실증을 위하여 멜론 스마트팜에서 3개월의 멜론 재배기간 동안 보일러, 양분분배 시스템, 자동제어기, 순환팬, 보일러제어기, 기타 IoT 관련 유 틸리티 등 스마트팜 시설에서 사용하는 개별 전원 기구들의 전력사용량 데이터를 수집하였다. 모니터링 결과를 이용하여 전기에너지 소비패턴의 예시를 분석하고, 측정 데이터를 최 적으로 활용하기 위해 필요한 고려사항을 제시하였다. 본 논 문은 전력사용량 모니터링 시스템을 새로이 구축하고자 하는 유저들에게 기술적 진입장벽을 낮추고 생성된 데이터 활용 시 시행착오를 줄이는 데 유용한 자료가 될 것으로 사료된다.

The frequent detection and occurrence of micropollutants (MPs) in aquatic ecosystems has raised public health concerns worldwide. In this study, the behavior of 50 MPs was investigated in three different domestic wastewater treatment plants (WWTPs). Furthermore, the Kruskal-Wallis test was used to assess the geographical and seasonal variation of MPs in the WWTPs. The results showed that the concentrations of 43 MPs ranged from less than 0.1 to 237.6 μg L-1, while other seven MPs including 17-ethynylestradiol, 17-estradiol, sulfathiazole, sulfamethazine, clofibric acid, simvastatin, and lovastatin were not detected in all WWTPs. Among the detected MPs, the pharmaceuticals such as metformin, acetaminophen, naproxen, and caffeine were prominent with maximum concentrations of 133.4, 237.6, 71.5, and 107.7 μg L-1, respectively. Most perfluorinated compounds and nitrosamines were found at trace levels of 1.2 to 55.3 ng L-1, while the concentration of corrosion inhibitors, preservatives (parabens), and endocrine disruptors ranged from less than 0.1 to 4310.8 ng L-1. Regardless of the type of biological treatment process such as MLE, A2O, and MBR, the majority of pharmaceuticals (except lincomycin, diclofenac, iopromide, and carbamazepine), parabens (except Methyl paraben), and endocrine disruptors were removed by more than 80%. However, the removal efficiencies of certain MPs such as atrazine, DEET, perfluorinated compounds (except PFHxA), nitrosamines, and corrosion inhibitors were relatively low or their concentration even increased after treatment. The results of statistical analysis reveal that there is no significant geographical difference in the removal efficacy of MPs, but there are temporal seasonal variations in all WWTPs.