This study evaluated changes in volatile organic compounds (VOCs) and aerosol particle concentrations following the use of certain spray-type household chemical products in an indoor environment. Four commercially available products in South Korea (including sticker/tar removers, a razor cleaner, and a fabric stain remover) were analyzed using real-time VOC monitoring with proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) and particle number concentration measurement with an Optical Particle Sizer. Immediately after spraying 17 grams of each product, VOC concentrations increased sharply, and hazardous substances such as benzene, 1,3-butadiene, formaldehyde, and acrolein remained at elevated levels for a certain period. Additionally, some products exhibited benzene concentrations exceeding levels of potential exposure concern, and the operation of an air purifier reduced VOC levels by 73%. This research provides insight into changes in indoor air quality and associated exposure risks due to household chemical use, and it may serve as a basis for future air quality management and regulatory standards.

This study investigated the spatial distribution of dissolved organic carbon and the changes in tidal flat soil characteristics following the flooding season in the closed estuary of the Geumgang River basin. We found that after the flooding season, a brackish water area expanded due to increased river discharge, which also introduced low concentrations of dissolved organic carbon from the river. Dissolved inorganic carbon predominantly originated from the sea in proportion to salinity, indicating that in the closed estuary area, dissolved carbon supplied from the sea remained a significant source even after the flooding season. Additionally, a substantial amount of tidally transported silt and clay was deposited near the estuary dam after the flood, and its effects continued to influence the sediment characteristics downstream in the intertidal zone. As an empirical study in the closed estuary, our results are expected to contribute to conservation measures for the World Heritage Site in the area.

Along with the increase in the number of vehicles in circulation, the indoor air quality in automobiles is attracting attention as another possible health concern. However compared to data regarding indoor air quality in other spaces, there are insufficient data on indoor air quality in automobiles. In addition, there is no standard for the evaluation method. In this study, the change in the concentration of particulate matter in the vehicle while driving under real road conditions was analyzed in order to use it as basic data for a method to evaluate vehicle indoor air quality. Through the selection of measurement target materials and test vehicles and the preparation of test methodologies, evaluation was performed on vehicle, route, and HVAC modes. The concentration of particulate matter in the vehicle was the lowest in the RC (In-vehicle recirculation) condition, and it was confirmed that it decreased with time. The highest average concentration was confirmed in the OA (Outside air ventilation) condition, and the concentration change according to the changing HVAC mode was observed in the Auto condition. The concentration of pollutants inside the vehicle showed a significant correlation with factors such as season, external concentration, and HVAC conditions, along with a weak correlation to powertrain type. The results of this study can be used as basic data for developing methods for evaluating vehicle interior air quality in future work.

The occurrence of cyanobacteria and the formation of cyanotoxins in lakes and reservoirs are influenced by many environmental factors. In this study, the harmful cyanobacteria occurrence and cyanotoxins concentrations was investigated in three agricultural reservoirs in the southern Gyeonggi-do in 2023. Although harmful cyanobacteria growth and cyanotoxins formation are influenced by various environmental factors in the actual ecosystem, the cell density of harmful cyanobacteria reached its maximum in September when the water temperature was relatively high, and the highest cyanotoxins (microcystin and anatoxin-a) concentrations were observed in October when the water temperature dropped to the range of 20~23℃. From this investigation, harmful cyanobacteria blooms and cyanotoxins residence in investigated three agricultural reservoirs was observed.

To utilize textured vegetable protein (TVP) in food manufacturing, TVP was soaked in salt solutions of different concentrations. Physicochemical quality characteristics of TVP were then measured. When TVP was soaked in a salt solution, the pH tended to increase compared to the control. However, the pH decreased after 18 hours of soaking. The salinity of the control decreased slightly from the initial value depending on the soaking time. The group treated with salt solution showed higher salinity than the control. Water absorption capacity of the control increased as the soaking time increased. However, water absorption capacity of the group treated with salt solution decreased as the concentration of salt solution increased. Lightness of the group treated with salt solution showed less change than the control during soaking. The redness increased as the concentration of salt solution increased. The yellowness increased compared to the control during soaking. Hardness, gumminess, and chewiness of the control decreased during soaking in water. The group treated with salt solution showed significantly higher hardness, gumminess, and chewiness as the concentration of the salt solution increased. However, adhesiveness, elasticity, and cohesiveness generally did not show significant differences among samples.

2019년부터 2023년까지 서울지역에 유통되는 식약공용 농·임산물 60품목 1,340건을 대상으로 ICP-MS와 수은분 석기, 모니어-월리엄스 변법을 사용하여 중금속(납, 카드 뮴, 비소, 수은) 및 이산화황의 함량을 조사하고 위해성 평 가를 수행하였다. 중금속의 평균 검출량 및 범위는 납 0.327 mg/kg (ND-36.933), 카드뮴 0.083 mg/kg (ND-1.700), 비소 0.075 mg/kg (ND-2.200), 수은 0.004 mg/kg (ND- 0.047)으로 나타났다. 품목별은 2023년에 복령 1건에서 납 이 36.933 mg/kg으로 기준을 초과하였고 카드뮴은 2022년 에 구절초 2건이 각각 1.700 mg/kg, 0.650 mg/kg으로 기준 를 초과하였다. 나머지 품목은 모두 허용기준 이내였다. 이 산화황의 평균 검출량 및 범위는 0.75 mg/kg (ND-192.00)이 였으며, 2019년에 천마 2건에서 각각 192.00 mg/kg과 42.00 mg/kg으로 기준치을 초과하였다. 약용부위별 중금속 평균 검출량은 납은 버섯류(1.377 mg/kg), 카드뮴은 수·근피 류(0.156 mg/kg)와 등목류(0.144 mg/kg), 비소는 엽류 (0.149 mg/kg), 수은은 전초류(0.009 mg/kg), 엽류(0.009 mg/ kg)에서 높게 검출되었다. 이산화황 평균 검출량은 근경류 (4.12 mg/kg)에서 높게 검출되었다. 원산지별로 중금속 및 이산화황 함량을 비교한 결과, 납, 카드뮴 및 수은은 국내 산, 중국산, 중국 외 수입산 간에 차이는 없었으나, 비소 와 이산화황은 중국산이 국내산과 중국 외 수입산보다 높 게 검출되었다. 위해성 평가 결과, 납은 대부분의 품목에 서 노출안전역(MOE)값이 1보다 커서 인체 위해성이 낮았 지만, 복령에서 MOE 값이0.66으로 1보다 낮아 위해성이 있는 것으로 나타났다. 카드뮴, 비소 및 수은의 위해도(HI) 는 각각 0.2740-1.0702%, 0.0049-0.0335% 및 0.0041- 0.0287%로 매우 낮은 수준으로 평가되었다. 이산화황의 위해도(HI)는 모든 품목에서1을 초과하지 않아 안전한 수 준이었다. 앞으로도, 식약공용 농·임산물의 안전성에 대한 지속적인 관심과 모니터링이 필요하다.

본 연구의 연구목적은 20세기 초 중국인 농과 도일 유학생 규모의 변 화 요인을 심층적으로 고찰하는 데 있다. 이 시기는 중국 국내에서 교육 과 정치의 변화가 급격하게 일어나던 시기로, 이러한 변화가 중국인 유 학생 수에 미친 영향을 분석하는 것은 매우 필수적인 작업이다. 이에 본 연구는 1900-1910년대 일본에서 유일한 농업 특성화 제국대학으로서의 역할을 수행했던 홋카이도제국대학을 사례로 삼아, 당시 중국의 교육 '장 '과 정치 '장'이 홋카이도제국대학의 중국인 유학생 규모에 미친 영향에 대해 자세히 고찰해 보고자 하였다. 이를 위해 피에르 부르디외의 '장' 이론을 적용하여 교육사회학·교육인류학적 해석을 시도하였으며, 이를 통 해 20세기 초 홋카이도제국대학 중국인 유학생들의 규모 변화에 미친 영 향을 체계적으로 분석함으로써, 궁극적으로는 당시 중국의 국내 및 국제 사회 '장'의 구조적 요인이 중국인 도일 유학생들의 학문적 경로에 미친 영향에 대해 심층적인 이해를 도모하고자 하였다.

In order to determine the future direction of Busan City’s tree planting policy in accordance with changes in automobile fuel and air pollutants, this study selected representative tree species planted in Busan and identified the biogenic volatile organic compounds (BVOCs) emission rate and characteristics of each species. First, representative tree species were selected for each street tree species, forest tree species, and park tree species, and the emission rate and major components of BVOCs were investigated for each tree species. Furthermore, by comparing the ozone generation potential (POCP) for each tree species, tree species with a low emission rate were selected. According to the POCP comparison, P. yedoensis, G. biloba, Z. serrata and C. retusus were selected as roadside tree species, P. densiflora and C. obtusa as forest species, and A. palmatum, C. japonica, and Q. myrsinaefolia were deemed suitable for park species. However, in the case of P. occidentalis, Quercus, and M. glyptostroboides, the emission rates of BVOCs were found to be high. Despite this, these tree species were found to display excellent CO2 absorption and carbon storage. The concentration of NOx in the city center is likely to decrease due to the current trend of transitioning to eco-friendly cars worldwide, resulting in less cars that rely on fossil fuels. Therefore, in the current climate where NOx emissions are still high, planting tree species with a low BVOCs emission rate would be an optimal approach. On the other hand, if the NOx concentration in the city is found to be very low due to changes in automobile fuel use, planting tree species with excellent BVOCs emission capacity and CO2 absorption would be ideal.

Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.

This study investigates the influence of particulate matter concentrations on the incidence of asthma, focusing on the delayed onset of symptoms and subsequent medical consultations. Analysis incorporates a four-day lag from the initiation of fine dust exposure and compares asthma patterns before and after the World Health Organization's (WHO) classification of fine dust as a Group 1 carcinogen in November 2013. Utilizing daily PM10 data and asthma-related medical visit counts in Seoul from 2008 to 2016, the study additionally incorporates Google search frequencies and newspaper article counts on fine dust to assess public awareness. Results reveal a surge in search frequencies and article publications after WHO announcement, indicating heightened public interest. To standardize the long-term asthma occurrence trend, the daily asthma patient numbers are ratio-adjusted based on annual averages. The analysis uncovers an increase in asthma medical visits 2 to 3 days after fine dust events. Additionally, greater public awareness of fine dust hazards correlates with a significant reduction in asthma occurrence after such events, even within 'normal' fine dust concentrations. Notably, behavioral changes, like limiting outdoor activities, contribute to this decrease. This study highlights the importance of analyzing accumulated medical data over an extended period to identify general public behavioral patterns, deviating from conventional survey methods in social sciences. Future research aims to extend data collection beyond 2016, exploring recent trends and considering the potential impact of decreased fine dust awareness amid the COVID-19 pandemic.

This study was aimed to determine the changes in CO2 concentration according to the temperatures of daytime and nighttime in the CO2 supplemental greenhouse, and to compare calculated supplementary CO2 concentration during winter and spring cultivation seasons. CO2 concentrations in experimental greenhouses were analyzed by selecting representative days with different average temperatures due to differences in integrated solar radiation at the growth stage of leaf area index (LAI) 2.0 during the winter season of 2022 and 2023 years. The CO2 concentration was 459, 299, 275, and 239 μmol·mol-1, respectively at 1, 2, 3, and 4 p.m. after the CO2 supplementary time (10:00-13:00) under the higher temperature (HT, > 18°C daytime temp. avg. 31.7, 26.8, 23.8, and 22.4°C, respectively), while it was 500, 368, 366, 364 μmol·mol-1, respectively under the lower temperature (LT, < 18°C daytime temp. avg. 22.0, 18.9, 15.0, and 13.7°C, respectively), indicating the CO2 reduction was significantly higher in the HT than that of LT. During the nighttime, the concentration of CO2 gradually increased from 6 p.m. (346 μmol·mol-1) to 3 a.m. (454 μmol·mol-1) in the HT with a rate of 11 μmol·mol-1 per hour (240 tomatoes, leaf area 330m2), while the increase was very lesser under the LT. During the spring season, the CO2 concentration measured just before the start of CO2 fertilization (7:30 a.m.) in the CO2 enrichment greenhouse was 3-4 times higher in the HT (>15°C nighttime temperature avg.) than that of LT (< 15°C nighttime temperature avg.), and the calculated amount of CO2 fertilization on the day was also lower in HT. All the integrated results indicate that CO2 concentrations during the nighttime varies depending on the temperature, and the increased CO2 is a major source of CO2 for photosynthesis after sunrise, and it is necessary to develop a model formula for CO2 supplement considering the nighttime CO2 concentration.

PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

In this study, we analyzed the changes in concentrations of volatile fatty acids (VFA), phenols, and indoles, as well as odor contribution in pig slurry. The pig slurry was stored for approximately two months after the manure excretion of pigs which had been fed 3% level of peat moss additive. The investigation was carried out through lab-scale experiments simulating slurry pit conditions within pig house. Throughout the storage period, the concentration of VFA exhibited a tendency to be 11%-32% higher in the pig manure treated with peat moss as compared to the control group. From a concentration perspective, phenol and acetic acid accounted for the majority of the total odor compounds produced during the pig slurry storage period. However, their significance diminished when the concentration of odoros compounds are converted into odor activity value and odor contribution. Despite the odor reduction effect of the ammonia (NH3) adsorption by peat moss, if it cannot effectively reduce the high odor-contributing compounds such as indoles and p-cresol, the sole use of peat moss may not be considered an effective means of mitigating odors produced by pig slurry. According to this study, indoles, p-cresol, skatole, and valeric acid were consistently revealed as major odor-contributing substances during the two-month storage of pig slurry. Therefore, a comprehensive odor mitigation methodology should be proposed, taking into consideration the odor generation characteristics (including temporal concentration and odor contribution) of pig slurry-derived odors during storage.

본 연구에서는 복숭아혹진딧물의 포식 기생자인 콜레마니진디벌의 기능반응을 조사하였다. 이산화탄소 농도별(400, 600, 1000ppm) 복 숭아혹진딧물 밀도를 달리하여(2, 4, 8, 16, 32, 64, 128마리) 콜레마니진디벌 한 마리를 24시간 동안 노출시켰다. 각 이산화탄소 처리에서 콜레 마니진디벌은 제 3 유형의 기능반응을 보였다. 600 ppm (0.015 day)과 1000 ppm (0.014 day)에서 추정된 처리시간은 400 ppm에서 추정된 결 과(0.017 day)보다 짧았다. 이산화탄소 농도별 복숭아혹진딧물 기생률은 유사한 특징을 나타내었다. 가장 높은 기생률은 400, 600, 1000 ppm 에서 복숭아혹진딧물 16, 32, 32마리에 대해 0.57, 0.61, 0.70이었다. 이산화탄소 농도 증가는 콜레마니진디벌의 기능반응에서 공격율에는 영향 을 주지 않았지만 처리시간에는 영향을 주었다.

This study was carried out to examine the concentration and distribution characteristics of total airborne bacteria (TAB) and airborne mold in non-regulated public-use facilities. The arithmetic mean (AM) of the TAB in all facilities was 356.5 ± 419.3 CFU/m3, and the geometric means (GM) was 157.8 CFU/m3, which did not exceed the standard value of 800 CFU/m3. The highest concentration was 637.3 ± 372.0 CFU/m3 (GM: 534.9 CFU/m3) in the underground shopping mall. The AM of airborne mold in all facilities was 448.2 ± 429.6 CFU/m3 (GM: 285.4 CFU/m3), which did not exceed the standard value of 500 CFU/m3, but was close to it. In particular, subway station (AM: 661.5 ± 441.2 CFU/m3, GM: 540.0 CFU/ m3), large-scale store (AM: 587.6 ± 683.2 CFU/m3, GM: 297.8 CFU/m3), and private educational institute (AM: 528.8 ± 379.6 CFU/m3, GM: 373.7 CFU/m3) exceeded the standard. Operational taxonomic unit of 16S rDNA and ITS2 rDNA region was analyzed to profile bacteria and mold component in the air of the public-use facilities. As a result, Pseudomonas and Morganella are the major bacterial groups. Regarding mold, Aspergillus, Candida, Malassezia, and Penicillium are the major groups. Component of each airborne bacterial and mold groups varied depending on the type of public-use facilities.

본고는 현대중국어에서 대표적인 형식동사(dummy verb) 중 하나인 ‘弄’이 문장에 서 결합하는 성분에 따라 상이한 의미항을 분석하고 각 의미항이 발생하게 된 연유 를 프레임 의미론을 적용하여 분석한 연구이다. ‘弄’은 대다수 동사로 쓰이며 ‘보어’, ‘목적어’와 결합하여 문장을 이룬다. 정도보어와 결합할 때 문장에서 사동의 의미가 발현되어 보어 뒤에 따르는 대상이나 상황의 변화를 발생시킨다는 의미를 가진다. 결과보어와 결합할 때는 선행한 동사를 대체하거나 결과를 나타내는 단어 앞에 쓰여 결과를 강조하기 위한 문법적 장치로 작용하는데, 이때 ‘弄’에는 사동의 의미가 내포 되어 있다. 또한 바로 목적어를 이끌 때는 ‘획득’, ‘제작’, ‘다루다’ 등의 의미를 가진 다. 이 같은 다양한 의미가 생성될 수 있는 가능성은 ‘弄’의 원형인 손으로 옥을 다 루는 형상에서 유추할 수 있는데, 프레임 의미론의 관점에서 윤곽부여를 통해 초점 이 손에 부여될 때와 옥에 부여될 때 서로 다른 의미항이 생성될 수 있다.

The purpose of this study was to explore the potential of agricultural heritage as a sustainable agricultural and rural paradigm with a focus on the “Argan-based agro-sylvo-pastoral system in the area of Ait Souab-Ait Mansour”, a Globally Important Agricultural Heritage Systems (GIAHS) site in Morocco. Based on the inscription criteria of the GIAHS, we analyzed the economic-industrial, sociocultural, and ecological-environmental perspectives and presented strategies for revitalizing agricultural and rural development cooperation through the Moroccan Argan GIAHS. The argan tree has been a source of economic, cultural, and environmental stability for the Berber people for centuries, but today it is exposed to many threats. In particular, the declining consumption of argan oil by Berbers, the lack of financial independence of women's cooperatives, and the over-exploitation of the tree suggest that it is time to balance the three pillars of environmental, economic, and social sustainability that development has sought to achieve. Agricultural heritage can be preserved when local people take ownership of their heritage and utilize it to generate economic activities. Only a symbiotic way of life between humans and agricultural heritage can overcome the possibilities and limitations of the ecological environment and generate local value through the accumulation of knowledge, technology, and culture. Only on these premises, can local self-sustaining development based on the pluralistic values and public functions of the world's important agricultural heritage be possible.