• 한국입자에어로졸학회지
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• 제19권4호
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• pp.111-128
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• 2023

Chungcheongnam-do has various emission sources, including large-scale facilities such as power plants, steel and petrochemical industry complexes, which can lead to the severe PM pollution. Here, we measured concentrations of PM₁₀, PM_2.5, and its metallic elements at a suburban site in Taean, Chungcheongnam-do from September 2017 to June 2022. During the measurement period, the average concentrations of PM₁₀ and PM_2.5 were 58.6 ㎍/m³ (9.6~379.0 ㎍/m³) and 35.0 ㎍/m³ (6.1~132.2 ㎍/m³), respectively. The concentration of PM₁₀ and PM_2.5 showed typical seasonal variation, with higher concentration in winter and lower concentration in summer. When high concentrations of PM_2.5 occurred, particulary in winter, the fraction of Zn and Pb components considerably increased, indicating a significant contribution of Zn and Pb to high-PM_2.5 concentration. In addition, Zn and Pb exhibited the highest correlation coefficient among all other metallic elements of PM_2.5. A backward trajectory cluster analysis and CPF model were performed to examine the origin of PM_2.5. The high concentration of PM_2.5 was primarily influenced by emissions from industrial complexes located in the northeast and northwest areas.

• 대한환경공학회지
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• 제36권1호
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• pp.20-28
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• 2014

대구지역의 13개 대기오염측정소 중 PM-10과 PM-2.5를 동시에 측정하는 3개 측정소 즉, 공업지역에 위치한 이현동, 주거지역에 위치한 만촌동, 도로변에 위치한 평리동 측정소를 대상으로 최근 2년간(2011~2012)의 자료를 이용하여 PM-10과 PM-2.5의 농도분포 특성을 연구하였다. PM-10 농도는 이현동($52.5{\mu}g/m^3$)과 평리동($60.9{\mu}g/m^3$) 모두 연평균 기준치인 $50{\mu}g/m^3$을 초과하였고, 만촌동($44.9{\mu}g/m^3$)은 기준치를 만족하였다. PM-2.5 농도는 세 지점 모두 미국의 EPA 연간기준치($15{\mu}g/m^3$)를 초과하였으며, 우리나라에서 2015년부터 시행되는 PM-2.5의 연평균기준치($25{\mu}g/m^3$)도 초과하는 수준이었다. 계절별 변화를 보면, PM-10은 봄철 > 겨울철 > 가을철 > 여름철 순이었고, PM-2.5는 겨울철 > 봄철 > 가을철 > 여름철 순으로 나타나는 특성을 보였다. 월변화 특성을 보면, PM-10과 PM-2.5 모두 겨울철인 2월에 가장 높고 여름철인 9월경에 가장 낮은 농도를 보였다. 일변화 특성을 보면, PM-10과 PM-2.5 모두 오전 7시부터 증가하여 10시~11시경에 최고 농도를 기록하고 오후 6시까지 하강하여 저녁과 새벽까지 일정한 농도를 나타내는 경향을 보였다. 또한, 주중의 미세먼지 농도는 주말보다 높은 농도를 보였으며, 그 변동 폭은 공업지역이 주거지역보다 크게 나타났다. PM-2.5/PM-10 비는 여름철이 높고 봄철이 가장 낮게 나타났고, 황사발생시 PM-2.5/PM-10 비는 비황사시 0.54~0.64에 비해 0.32~0.42로 매우 낮은 특성을 보였다. 본 자료는 대구지역의 미세먼지(PM-10, PM-2.5)의 현황과 특성에 대한 연구로써 향후 미세먼지의 연구 및 대기오염 관리에 유용하게 사용될 것으로 사료된다.

• 한국지리정보학회지
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• 제25권2호
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• pp.1-17
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• 2022

본 연구는 경상남도 창원시 국가산업단지 인근에 설치된 21개의 PM_2.5 간이 측정기에서2020년 6월부터 2021년 5월까지 1년 동안 측정된 자료를 활용하여 PM_2.5의 발생 패턴을 분석하였다. PM_2.5의 발생 패턴은 측정지점 주변의 토지이용현황과 기온 및 풍속의 기상적인 요인을 고려하였다. PM_2.5 농도는 계절별로는 겨울철인 11월부터 3월까지, 시간대별로는 새벽과 이른 아침인 1시부터 9시까지가 높았다. PM_2.5는 공업지역에 인접할수록 농도가 높았으나, 주거지역과 공공시설지역은 농도가 낮았다. 기상적인 요인에서는 높은 기온과 풍속일수록 PM_2.5의 농도는 낮았기 때문에 기상 상태는 PM_2.5의 확산에 영향을 미치는 것으로 판단된다. 본 연구의 결과는 창원국가산업단지 인근의 PM_2.5 발생 패턴을 파악할 수 있었다. 이 결과는 향후 도시지역의 PM_2.5를 포함한 대기질을 개선하기 위해 도시 및 환경계획에서 활용할 수 있는 유용한 자료가 될 것이다.

• 환경영향평가
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• 제19권4호
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• pp.431-441
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• 2010

In this study, inter-comparison for PM-2.5 was undertaken. The PM-2.5 mass concentrations using the gravimetric and beta-attenuation methods were compared during the winter in 2007. Two different types of conventional filter-based measurements (Cyclone type and Impactor type) were also collocated and the measurement data was compared with each other. As a result, continuous PM-2.5 data using beta attenuation method show a comparable mass concentration with gravimetric measurement when the inlet of beta-gauge sampler is heated. The results also showed that the cyclone type shows a little high PM-2.5 concentration than Impactor type. In all the sampling cases, the correlations between measurement methods are high. Subsequently, this study suggests that highly correlated relationship between PM-2.5 measurement instruments can be obtained through the inter-comparison results based on filterb-ased gravimetric method and more intensive measurement and theoretical studies are needed in order to clarify the measurement errors for different sampler types.

• 한국멀티미디어학회논문지
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• 제24권10호
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• The Journal of Korean Physical Therapy
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• 제14권2호
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• pp.172-180
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• 2002

The purpose of this paper was to make an analysis of anaerobic power in professional female basketball players using the Wingate Test Method with bicycle ergometer. Twenty-three subjects(age $21.6\pm2.8years$, body height $178.0\pm7.4cm$, body weight $70.3\pm7.4kg$) were selected from professional female basketball team whose careers were over 10years and participated in this investigation. Each subject peformed a Wingate anaerobic power test to determine total work, peak power, mean power, fatigue index and blood lactate concentration. The following were obtained from result data analysis; 1. The Total Work of athletes was a $1128.7\pm120.6watt$ 2. The Peak Power of athletes was a $449.5\pm53.1watt$ 3. The Mean Power of athletes was a $369.1\pm39.4watt$ 4. The Fatigue Index of athletes was a $33.5\pm6.9\%$ 5. The blood lactate concentration was $1.85\pm0.85mM/L$ at the normal state and $3.16\pm1.53mM/L$ at the after Wingate test. The blood lactate concentration was $6.96\pm0.81mM/L$ after 3 minute and $6.95\pm1.05mM/L$ after 5 minutes.

• Asian Journal of Atmospheric Environment
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• 제12권2호
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• pp.139-152
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• 2018

The urban model inter-comparison study (UMICS) was conducted in order to improve the performance of air quality models (AQMs) for simulating fine particulate matter ($PM_{2.5}$) in the Greater Tokyo Area of Japan. UMICS consists of three phases: the first phase focusing on elemental carbon (UMICS1), the second phase focusing on sulfate, nitrate and ammonium (UMICS2), and the third phase focusing on organic aerosol (OA) (UMICS 3). In UMICS2/3, all the participating AQMs were the Community Multiscale Air Quality modeling system (CMAQ) with different configurations, and they similarly overestimated $PM_{2.5}$ nitrate concentration and underestimated $PM_{2.5}$ OA concentration. Various sensitivity analyses on CMAQ configurations, emissions and boundary concentrations, and meteorological fields were conducted in order to seek pathways for improvement of $PM_{2.5}$ simulation. The sensitivity analyses revealed that $PM_{2.5}$ nitrate concentration was highly sensitive to emissions of ammonia ($NH_3$) and dry deposition of nitric acid ($HNO_3$) and $NH_3$, and $PM_{2.5}$ OA concentration was highly sensitive to emissions of condensable organic compounds (COC). It was found that $PM_{2.5}$ simulation was substantially improved by using modified monthly profile of $NH_3$ emissions, larger dry deposition velocities of $HNO_3$ and $NH_3$, and additionally estimated COC emissions. Moreover, variability in $PM_{2.5}$ simulation was estimated from the results of all the sensitivity analyses. The variabilities on CMAQ configurations, chemical inputs (emissions and boundary concentrations), and meteorological fields were 6.1-6.5, 9.7-10.9, and 10.3-12.3%, respectively.

• 한국환경보건학회지
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• 제50권1호
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• pp.6-15
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• 2024

Background: People's activities have been restricted due to the COVID-19 pandemic. These changes in activity patterns may lead to a decrease in fine particulate matter (PM_2.5) concentrations. Additionally, the level of population exposure to PM_2.5 may be changed. Objectives: This study aimed to analyze the impact of population movement and meteorological factors on the distribution of PM_2.5 concentrations before and after the outbreak of COVID-19. Methods: The study area was Guro-gu in Seoul. The research period was selected as January to March 2020, a period of significant population movement changes caused by COVID-19. The evaluation of the dynamic population was conducted by calculating the absolute difference in population numbers between consecutive hours and comparing them to determine the daily average. Ambient PM_2.5 concentrations were estimated for each grid using ordinary kriging in Python. For the population exposure assessment, the population-weighted average concentration was calculated by determining the indoor to outdoor population for each grid and applying the indoor to outdoor ratio to the ambient PM_2.5 concentration. To assess the factors influencing changes in the ambient PM_2.5 concentration, a statistical analysis was conducted, incorporating population mobility and meteorological factors. Results: Through statistical analysis, the correlation between ambient PM_2.5 concentration and population movement was positive on both weekends and weekdays (r=0.71, r=0.266). The results confirmed that most of the relationships were positive, suggesting that a decrease in human activity can lead to a decrease in PM_2.5 concentrations. In addition, when population-weighted concentration averages were calculated and the exposure level of the population group was compared before and after the COVID-19 outbreak, the proportion of people exceeding the air quality standard decreased by approximately 15.5%. Conclusions: Human activities can impact ambient concentrations of PM_2.5, potentially altering the levels of PM_2.5 exposure in the population.

• 대한원격탐사학회지
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• 제35권6_1호
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• pp.919-932
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• 2019

미세먼지는 인간 활동에 의한 오염물질 배출에 의해 발생하는 것이 일반적이지만, 수문기상 조건에 따라 이동, 심화, 소멸 과정에서 매우 복잡한 메커니즘을 지니고 있으므로 효과적인 미세먼지 대책 마련을 위해서는 수문기상인자와 미세먼지 간의 상관성에 대한 이해가 필수적이다. 현재 우리나라의 미세먼지 농도 관측 및 예보는 지점 측정소에서 농도를 측정하고, 이 자료를 기반으로 측정소가 위치하지 않는 지역의 값을 추정함으로써 이루어지고 있다. 이러한 관측 방식 및 자료는 공간적 대표성을 갖지 못하기 때문에 관측소와의 거리가 먼 지역의 정확한 미세먼지 농도를 파악하는 것이 불가능하며, 미세먼지의 이동, 심화, 소멸 단계를 추적하는데 어려움이 있다. 본 연구에서는 Global Land Data Assimilation System (GLDAS)의 다양한 수문기상인자를 사용하여, 베이지안 모델 평균(Bayesian Model Averaging, BMA)을 통해 초미세먼지(PM2.5)와 유의미한 상관성을 갖는 인자를 선별하였다. 선별된 인자는 MODerate Resolution Imaging Spectroradiometer (MODIS)의 Aerosol Optical Depth (AOD) 자료와 함께 계절별 PM2.5 농도 산출 모델을 구축하는데 활용되었으며, 산출 결과를 매핑하여 PM2.5 농도의 공간 분포를 파악하고자 하였다. 지점 기반 자료와의 비교를 통해 구축된 모델을 검증하였을 때, 측정된 PM2.5 농도와 높은 상관성(R ~0.7; IOA ~0.78; RMSE ~7.66 ㎍/㎥)을 나타냈으며, 지역별로 나누어 비교할 경우 데이터의 분포는 유사하나 상관계수의 차이를 보이는 것을 확인할 수 있었다(R: 0.32-0.82). 모델 산출 자료를 활용하여 PM2.5 농도를 매핑한 결과 기존 내삽 방법에 비해 시공간적 변동성을 더욱 잘 표현하는 것을 확인하였다. 추후 연구 지역을 동아시아 지역으로 확장 시킨다면 국내외 미세먼지 발생원의 파악 및 이동 양상에 대한 분석에 용이할 것으로 기대된다.

• 한국환경과학회지
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• 제31권12호
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• pp.1069-1078
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• 2022

This study investigated the weather conditions, fine particle concentration, and ion components in PM_2.5 when two cold fronts passed through Busan in succession on February 1 and 2, 2021. A analysis of the surface weather chart, AWS, and backward trajectory revealed that the first cold front passed through the Busan at 0900 LST on February 1, 2021, with the second cold front arriving at 0100 LST on February 2, 2021. According to the PM₁₀ concentration of the KMA, the timing of the cold front passage had a close relationship with the occurrence of the highest concentration of fine particles. The transport time of the cold front from Baengnyeongdo to Mt. Gudeok was approximately 11 hours . The PM₁₀ and PM_2.5 concentrations in Busan started to increase after the first cold front had passed, and the maximum concentration occurred two hours after the second cold front passed. The SO₄^2-, NO₃^-, and NH₄⁺ concentration in PM_2.5 started to increase from 1100 to 1200 LST on February 1, after the first cold front passed, and peaked at 0100 LST to 0300 LST on February 2. However, the highest Ca²⁺ concentration was recorded 2-3 hours after the second cold front had passed.