• Ocean and Polar Research
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• 제34권3호
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• pp.305-323
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• 2012

The ocean's response to the Pinatubo and 1259 volcanic eruptions was investigated using an ocean general circulation model equipped with an energy balance model. Volcanic eruptions release gases into the atmosphere which increases the aerosol optical depth and acts to reduce the incoming short-wave radiation. For example, there was a huge volcanic eruption (Pinatubo) in 1991 which reduced the global mean radiative forcing by about 3 W $m^{-2}$. Two numerical experiments were simulated. The first experiment features the Pinatubo eruption and the second experiment simulates the much larger volcanic eruption that occurred in 1259 when the radiative forcing was reduced by 7 times compared to the Pinatubo event. With the reduced radiative forcing due to the Pinatubo eruption at about 3 W $m^{-2}$ and 1259 eruption at about 21 W $m^{-2}$, the global mean sea surface temperature (SST) decreased to its lowest in the second year after each event by about $0.4^{\circ}C$ and $1.6^{\circ}C$, respectively. Sea surface salinity (SSS) increased substantially in the northern North Pacific, northern North Atlantic, and the Southern Ocean. The reduced SST together with SSS increased ocean convection, which yielded an increase in North Atlantic Deep Water, Antarctic Bottom Water, and North Pacific Intermediate Water production and their outflows. The increase in overturning circulation eventually increased the pole-ward ocean heat fluxes. In conclusion, huge volcanic eruptions perturb the ocean substantially and their hallmarks last for more than a decade, confirming the importance of volcanic eruptions in illustrating the decadal-climate variability recorded in the paleoclimate proxy data for the past million years.

• 한국산업보건학회지
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• 제16권2호
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• pp.101-109
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• 2006

The objective of this study is to provide fundamental data for pertinent management of indoor air quality through investigating the size-based characteristics of bioaerosol distributed in the general hospital. Measurement sites are main lobby, ICU, ward and laboratory and total five times were sampled with six-stage cascade impactor. Based on the result of this study, concentrations of airborne bacteria and fungi were the highest in main lobby as followed by an order of ward, ICU and laboratory. Concentrations of airborne bacteria was generally higher than those of airborne fungi and the ratio of indoor and outdoor concentration of both exceeded 1.0 in all the measurement sites of the general hospital. The predominant genera of airborne bacteria identified in the general hospital were Staphylococcus spp.(50%), Micrococcus spp.(15-20%), Corynebacterium spp.(5-20%), and Bacillus spp.(5-15%). On the other hand, the predominant genera of airborne fungi identified in the general hospital were Cladosporium spp.(30%), Penicillium spp.(20-25%), Aspergillus spp.(15-20%), and Alternaria spp.(10-20%). In regard to size distribution of bioaerosol, the detection rate was generally highest on 5 stage($1.1-2.1{\mu}m$) for airborne bacteria and on 1 stage(>$7.0{\mu}m$) for airborne fungi. Cleanliness of facilities in the general hospital and condition of HVAC system should be monitored regularly to prevent indoor air contamination by airborne microorganisms.

• 한국대기환경학회지
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• 제22권5호
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• pp.535-553
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• 2006

2003년 12월 '수도권 대기환경 개선에 관한 특별법'이 국회를 통과하면서, 서울을 포함한 수도권 대기에서 미세먼지 농도를 저감하는 것을 주요 목표로 하는 '수도권 대기환경관리 기본계획'이 수립되었다. 효과적인 미세먼지 저감 대책을 수립하기 위해서는 정확한 추이와 현황, 그리고 주요 기여원을 알아야만 한다. 이 연구에서는 2006년 9월까지 국내외 학술지와 학술대회 등에서 발표된 연구 결과를 종합적으로 검토, 분석하여 서울 대기의 미세먼지에 대해 (1)변화 추이를 파악하고, (2)농도에 영향을 미치는 주요 변수는 무엇인지를 찾고자 하였다. 아황산가스, 미세먼지 등의 대기환경기준물질 농도와 체감 대기오염의 지표인 시정의 지난 20여 년간의 변화를 검토한 결과 서울의 대기환경은 1990년 이후 개선되고 있음을 알았다. 그러나 서울의 미세먼지 관련 대기환경은 아직 우리나라 다른 대도시나 외국의 대도시에 비해 미세먼지 질량 농도나, 시정, 먼지의 화학조성 등 여러 면에서 나쁘다. 서울 대기의 미세먼지 농도에 영향을 미치는 주요 인자는 자체에서의 배출, 반응에 의한 생성, 외부로부터의 유입 세 가지이다. 현재의 측정 자료는 서울 대기의 미세먼지의 화학조성의 추이를 이해하기에 충분하지 않아, 외부로부터의 장거리이동과 반응에 의한 생성을 구분하기 힘들다. 그럼에도 불구하고, 측정 자료와 모사 결과를 종합하면 서울의 경우에는 배출과 외부로부터의 유입만큼이나 반응에 의한 생성의 영향이 큰 것으로 나타났다. 이는 서울의 미세먼지 농도를 줄이기 위해서는 직접 배출을 줄이는 것도 중요하지만, 광화학반응에 의한 생성과 외부에서의 유입을 줄이는 것도 중요함을 의미한다. 보다 효과적인 미세먼지 저감대책을 수립하기 위해서는 시공간적으로 대표성 있는 미세먼지 성분 분석 결과가 필요함을 다시 한 번 확인하였다.

• 대기
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• 제28권2호
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• pp.211-222
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• 2018

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

• 한국산업보건학회지
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• 제29권3호
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• pp.358-367
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• 2019

Objectives: This study aimed to review model algorithms and input parameters applied to some exposure models and to compare the simulated estimates using an exposure scenario from each model. Methods: A total of five exposure models which can estimate inhalation exposure were selected; the Korea Ministry of Environment(KMOE) exposure model, European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment(ECETOC TRA), SprayExpo, and ConsExpo model. Algorithms and input parameters for exposure estimation were reviewed and the exposure scenario was used for comparing the modeled estimates. Results: Algorithms in each model commonly consist of the function combining physicochemical properties, use characteristics, user exposure factors, and environmental factors. The outputs including air concentration ($mg/m^3$) and inhaled dose(mg/kg/day) are estimated applying input parameters with the common factors to the algorithm. In particular, the input parameters needed to estimate are complicated among the models and models need more individual input parameters in addition to common factors. In case of CEM, it can be obtained more detailed exposure estimates separating user's breathing zone(near-field) and those at influencing zone(far-field) by two-box model. The modeled exposure estimates using the exposure scenario were similar between the models; they were ranged from 0.82 to $1.38mg/m^3$ for concentration and from 0.015 to 0.180 mg/kg/day for inhaled dose, respectively. Conclusions: Modeling technique can be used for a useful tool in the process of exposure assessment if the exposure data are scarce, but it is necessary to consider proper input parameters and exposure scenario which can affect the real exposure conditions.

• Journal of Veterinary Science
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• 제24권2호
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• pp.22.1-22.12
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• 2023

Background: Citric acid (CA) and sodium hypochlorite (NaOCl) have been used to disinfect animals to protect them against avian influenza and foot-and-mouth disease. Objectives: We performed a good laboratory practice (GLP)-compliant animal toxicity study to assess the acute toxic effects of CA and NaOCl aerosol exposure in Sprague-Dawley rats. Methods: Groups of five rats per sex were exposed for 4 h to four concentrations of the two chemicals, i.e., 0.00, 0.22, 0.67, and 2.00 mg/L, using a nose-only exposure. After a single exposure to the chemicals, clinical signs, body weight, and mortality was observed during the observation period. On day 15, an autopsy, and then gross findings, and histopathological analysis were performed. Results: After exposure to CA and NaOCl, body weight loss was observed but recovered. Two males died in the CA 2.00 mg/L group and, two males and one female died in the 2.00 mg/L NaOCl group. In the gross findings and histopathological analysis, discoloration of the lungs was observed in the CA exposed group and inflammatory lesions with discoloration of the lungs were observed in the NaOCl exposed group. These results suggest that the lethal concentration 50 (LC50) of CA is 1.73390 mg/L for males and > 1.70 mg/L for females. For NaOCl, the LC50 was 2.22222 mg/L for males and 2.39456 mg/L for females. Conclusions: The Globally Harmonized System is category 4 for both CA and NaOCl. In this study, the LC50 results were obtained through a GLP-based acute inhalation toxicity assessment. These results provide useful data to reset safety standards for CA and NaOCl use.

• 대한원격탐사학회지
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• 제37권2호
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• pp.321-335
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• 2021

미세먼지 (PM10) 및 초미세먼지 (PM2.5)는 인체에 흡수 가능하여 호흡기 질환 및 심장 질환과 같이 인체건강에 악영향을 미치며, 심각할 경우 조기 사망에 영향을 줄 수 있다. 전 세계적으로 현장관측기반의 모니터링을 수행하고 있지만 미 관측지역에 대한 대기질 분포의 공간적인 한계점이 존재하여 보다 광범위한 지역에 대한 지속적이고 정확한 모니터링이 필요한 상황이다. 위성기반 에어로졸 정보를 사용함으로써 이러한 현장 관측자료의 한계점을 극복할 수 있다. 따라서 본 연구에서는 다양한 위성 및 모델자료를 활용하여 2019년도에 대해 한 시간 단위의 지상 PM10 및 PM2.5 농도를 추정하였다. GOCI 위성의 관측영역을 포함하는 동아시아 지역에 대해 트리 기반 앙상블 방법을 사용하는 Boosting 기법인 GBRTs (Gradient Boosted Regression Trees)와 LightGBM (Light Gradient Boosting Machine)을 활용하여 모델을 구축하였다. 또한, 기상변수 및 토지피복변수의 사용유무에 따른 모델의 성능을 비교하기 위해 두 가지 festure set으로 나누어 테스트하였다. 두 기법 모두 주요 변수인 AOD (Aerosol Optical Depth), SSA (Single Scattering Albedo), DEM (Digital Eelevation Model), DOY (Day of Year), HOD (Hour of Day)와 기상변수 및 토지피복변수를 함께 사용한 Feature set 1을 사용하였을 때 높은 정확도를 보였다. Feature set 1에 대해 GBRT 모델이 LightGBM에 비해서약 10%의 정확도 향상을 보였다. 가장 정확도가 높았던 기상 및 지표면 변수를 포함한 Feature set1을 사용한 GBRT기반 모델을 최종모델로 선정하였으며 (PM10: R2 = 0.82 nRMSE = 34.9%, PM2.5: R2 = 0.75 nRMSE = 35.6%), 계절별 및 연평균 PM10 및 PM2.5 농도에 대한 공간적인 분포를 확인해본 결과, 현장관측자료와 비슷한 공간 분포를 보였으며, 국가별 농도 분포와 계절에 따른 시계열 농도 패턴을 잘 모의하였다.

• 한국지구과학회지
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• 제33권2호
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• pp.139-147
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• 2012

몽골의 태양-기상자원지도는 위성자료 및 재분석 자료를 이용하여 개발되었다. 태양복사량은 단층 태양복사모델을 이용하였으며 입력자료는 SRTM, MODIS, OMI, MTSAT-1R 등의 위성관측자료와 전구모델의 재분석자료를 이용하였다. 계산된 결과는 NCEP/NCAR 재분석 DSWRF 자료를 이용하여 계산된 일사량을 검증하였다. 몽골은 서부의 산악지역과 중남부의 사막 및 반사막지대로 이루어져 있으며 대륙 내부에 위치하여 강수량이 적고 맑은 날이 많아 동일 위도상의 다른 지역과 비교하여 높은 일사량이 나타난다. 서부 산악지역은 고도가 높아 태양에너지가 많이 도달되는 곳임에도 불구하고 일사량이 낮게 나타난다. 그 이유는 산악지역에 존재하는 연중 적설이 위성자료의 구름탐지 알고리즘에서 구름으로 오탐지 되기 때문이다. 따라서 청천지수뿐만 아니라 일사량 또한 낮게 계산된다. 남부지역은 상대적으로 높은 가강수량과 에어로솔 광학두께가 나타났으나 다른 지역에 비해 위도가 낮고 청천지수가 높아 일사량이 높게 나타나는 것으로 분석된다. 계산된 월 누적 일사량은 547.59 MJ로써 전 지점에서 약 2.89 MJ로 높게 계산되었으며 상관성은 0.99였고 평방근오차(Root Mean Square Error; RMSE)는 6.17 MJ 이었다. 월별 통계 값을 계산하였을 때 상관성이 가장 높은 월은 10월로 0.94였고 3월은 0.62로 가장 낮게 나타났다.

• 한국대기환경학회지
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• 제29권5호
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• pp.642-655
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• 2013

In this study, semi-continuous measurements of $PM_{2.5}$ mass, organic and elemental carbon (OC and EC), black carbon (BC), and ionic species concentrations were made for the period of April 03~13, 2012, at a South Area Supersite at Gwangju. Possible sources causing the high concentrations of major chemical species in $PM_{2.5}$ observed during a haze episode were investigated. The measurement results, along with meteorological parameters, gaseous pollutants data, air mass back trajectory analyses and PSCF (potential source contribution function) results, were used to study the haze episode. Substantial enhancements of OC, EC, BC, $K^+$, $SO{_4}^{2-}$, $NO{_3}{^-}$, $NH{_4}{^+}$, and CO concentrations were closely associated with air masses coming from regions of forest fires in southeastern China, suggesting likely an impact of the forest fires. Also the PSCF maps for EC, OC, $SO{_4}^{2-}$, and $K^+$ demonstrate further that the long-range transport of smoke plumes of forest fires detected over the southeastern China could be a possible source of haze phenomena observed at the site. Another possible source leading to haze formation was likely from photochemistry of precursor gases such as volatile organic compounds, $SO_2$, and $NO_2$, resulting in accumulation of secondary organic aerosol, $SO{_4}^{2-}$ and $NO{_3}{^-}$. Throughout the episode, local wind directions were between 200 and $230^{\circ}C$, where two industrial areas are situated, with moderate wind speeds of 3~5 m/s, resulting in highly elevated concentration of $SO_2$ with a maximum of 15 ppb. The $SO{_4}^{2-}$ peak occurring in the afternoon hours coincided with maximum ambient temperature ($24^{\circ}C$) and ozone concentration (~100 ppb), and were driven by photochemistry of $SO_2$. As a result, the pattern of $SO{_4}^{2-}$ variations in relation to wind direction, $SO_2$ and $O_3$ concentrations, and the strong correlation between $SO_2$ and $SO{_4}^{2-}$ ($R^2=0.76$) suggests that in addition to the impact of smoke plumes from forest fires in the southeastern China, local $SO_2$ emissions were likely an important source of $SO{_4}^{2-}$ leading to haze formation at the site.

• 한국대기환경학회지
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• 제27권2호
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• pp.201-208
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• 2011

Recently, a real-time, pocket-sized aethalometer (microAeth$^{(R)}$ model AE51) has been developed by Magee Scientific Inc. for measuring the concentration of black carbon in the atmosphere. In this study, two aethalometers, models AE-16 and AE-51, which measure the optical absorption of carbon particles at infrared 880 nm, were operated at time interval of 5-min between January 9 and February 10, 2010 at an urban site of Gwangju, to compare the accuracy of black carbon (BC) concentrations reported from the AE-51 model and to investigate reasonable sampling time of filter media in the AE-51. The air samples in the AE-51 and AE-16 models are collected on T60 (Teflon coated glass fiber) filter media (filter spot area: 0.07 $cm^2$) and quartz fiber roll-tape filter (filter spot area: 1.67 $cm^2$), respectively. Real-time measurement results indicate that when the filters were clean, the AE-51 BC was greater than or similar to the AE-16 BC data. However as the filter spots become darker, the AE-16 BC concentrations were higher than the AE-51 BC data and the difference in the BC concentrations from two AE models becomes gradually increased. Relative error in the AE-51 and AE-16 BC concentrations showed significance difference depending on used time of the filter in the AE-51 model, weather pattern, levels of air pollution, etc, ranging from 11.5% (used time of the filter in AE-51: 1,595 min) to 52.5% (used time of the filter in AE-51: 2,085 min). When considering the used time of one filter ticket in the AE-51 model and difference (or relative error %) between AE-16 and AE-51 BC concentrations, it is recommended that the standard sampling time per one filter ticket within the AE-51 model be less than approximately 24 hr (1,440 min) under the normal weather conditions except for severe haze and mist events.