• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.987-998
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• 2013

This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.

• Journal of Bio-Environment Control
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• v.31 no.2
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• pp.79-89
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• 2022

With the wide use of greenhouses, the working hours have been increasing inside the greenhouse for workers. In the closed ventilated greenhouse, the internal environment has less affected to external weather during making a suitable temperature for crop growth. Greenhouse workers are exposed to organic dust including soil dust, pollen, pesticide residues, microorganisms during tillage process, soil grading, fertilizing, and harvesting operations. Therefore, the health status and working environment exposed to workers should be considered inside the greenhouse. It is necessary to secure basic data on particulate matter (PM) concentrations in order to set up dust reduction and health safety plans. To understand the PM concentration of working environment in greenhouse, the PM concnentrations were monitored in the cut-rose and Hallabong greenhouses in terms of PM size, working type, and working period. Compare to no-work (move) period, a significant increase in PM concentration was found during tillage operation in Hallabong greenhouse by 4.94 times on TSP (total suspended particle), 2.71 times on PM-10 (particle size of 10 ㎛ or larger), and 1.53 times on PM-2.5, respectively. During pruning operation in cut-rose greenhouse, TSP concentration was 7.4 times higher and PM-10 concentration was 3.2 times higher than during no-work period. As a result of analysis of PM contribution ratio by particle sizes, it was shown that PM-10 constitute the largest percentage. There was a significant difference in the PM concentration between work and no-work periods, and the concentration of PM during work was significant higher (p < 0.001). It was found that workers were generally exposed to a high level of dust concentration from 2.5 ㎛ to 35.15 ㎛ during tillage operation.

• Applied Chemistry for Engineering
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• v.25 no.2
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• pp.222-226
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• 2014

In this study, the purposes are investigating and analyzing car emission factors for estimating road emissions and the legal framework for the control of particulate matters. At the result, when input emission data are not realistic, the modeling output concentration distributions can lead to a serious distortion of the results. So, the spatial analysis of the dust emission vehicles have to be based on the actual traffic volumes. Because dust emission factors used in the car by National Institute of Environmental Research Method (2010) are mainly targeted for 2003-2007 cars these could not reflect the effect of DPF and the dust emission of gasoline passenger car. So, the real dust emission factors of diesel and gasoline cars need to be developed.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.249-264
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• 2011

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 ($PM_{10}$) concentration mapping in Incheon Metropolitan city using monthly $PM_{10}$ concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series $PM_{10}$ concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.61-69
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• 2014

In this study spatial analysis of PM10 was performed to Particulate Materials(PM) less than $10{\mu}m$ in diameter in Seoul city. Because PM10 are responsible for the increasing mortality rate of lung cancer and cardiovascular diseases, spatial distribution of PM10 are special interest in air pollution of Seoul. In this study, spatial analysis of Particulate Materials were monitored by monthly averaged PM10 concentration of 2010, 2011. The monthly spatial patterns of PM10 showed the west area of Seoul(Youngdungpo) higher PM10 concentration than northern part of Seoul in early spring and winter seasons. In the comparison of PM10 concentration distribution patterns in 2010 and 2011, the PM10 concentration of 2011 at Gangnam and Songpa-gu were more increased than yearly averaged patterns of 2010. The distribution patterns of PM10 in Seoul city showed the high concentration PM10 of several areas with Youngdungpo-gu, Gangnam-gu and Cheongnyangni. Therefore we need to establish PM10 management strategy for these area.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.365-366
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• 2008

광학입자 계수기는 실내환경, 대기오염 및 콜린룸 등 입자크기분포 측정장비로 가장 많이 사용된다. 광학입자 계수기에 샘플링된 업자는 관측체적 내로 1개씩 통과 하며 산란된 빚은 집광장치에 의해 광검출기로 전달한다. 이때 산란광의 양에 비례하여 전압 (전류)의 세기로 변환하여 전기적 선호로서 나타나는 Pulse의 높이는 Calibration Data에 따라 업자의 크기로 변환하고 Pulse의 개수는 입자의 개수로 표시된다. 입자의 크기와 개수등 이용하여 부피로 환산 한 후 부유하는 입자의 평균 밀드를 이용하여 질량으로 환산시킨다. 이렇게 측정된 미세먼지 농도는 ZigBee 통신을 사용하여 구축한 시스템을 통해서 중앙부에서 실시간으로 먼지 농도를 알 수 있다. 특히 멀티흡 기능을 이용하여 건물 구조가 복잡하거나 층간의 통신, 꺾인 부분이나 사무실 안과 밖과 같은 무선 통신이 원할 하지 못하는 경우를 극복하여 미세먼지의 농도 값을 측정 할 수도 있다.

• Journal of Environmental Impact Assessment
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• v.29 no.3
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• pp.210-218
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• 2020

Even though PM₁₀ in ambient air has been steadily reduced, the perception of it has been deteriorated. Forthatreason, first, it can still be mentioned the annual average concentration of PM₁₀ exceeding WHO standards, second, an increase in the number of high concentration days of PM₁₀, and third, lack of consideration for differences in causes and phenomena of PM₁₀ by regions. Therefore, this study was aimed to suggest management types for PM₁₀ in ambient air by clustering 69 cities based on the trends and current levels of PM₁₀. In addition, we proposed complementary measures such as the green infrastructure, ventilation corridors and adaptation measures (limit of exposure) for type III (distribution in the central inner region) and IV (metropolitan city, south-east coast region) where improvement of PM₁₀ was insufficient. Although this study did not consider the cause of PM₁₀ together, there is a significance that the scientific basis for responding to the near future is conducted based on past trends of PM₁₀.

• Particle and aerosol research
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• v.16 no.3
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• pp.65-72
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• 2020

In this study, the indoor fine dust concentration in an office of the Korea Knowledge Industry Center was measured for about 80 hours when the concentration of atmospheric PM_2.5 was very high. The effect of the operation of the air cleaner and the forced ventilation system on the indoor PM_2.5 was investigated, and the particle size distribution of the indoor and outdoor particles was analyzed. When forced ventilator and air purifiers were partially used, the indoor PM_2.5 concentrations were maintained between 27.7 ㎍/㎥ and 32.9 ㎍/㎥ when the atmospheric PM_2.5 was 127.7 ㎍/㎥ to 141.6 ㎍/㎥ during working hours. It is more effective to operate the air purifier without operating the forced ventilation system when the concentration of the PM_2.5 is high since the PM_2.5 penetrating the installed filter is continuously introduced indoor from the outside.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.341-343
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• 2019

As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).

• Journal of Environmental Impact Assessment
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• v.27 no.6
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• pp.717-726
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• 2018

PM(Particulate Matter) cause serious diseases of air pollution. Most of the studies have analyzed local distribution trends using satellite images or modeling techniques. However,the method using the spatial interpolation method based on the meteorological value is insufficient in Korea. In this study, monthly spatial distribution of $PM_{10}$ and $PM_{2.5}$ in January, February, March, and April of 2018 Seoul Metropolitan City were analyzed based on 39 PM monitoring networks. In addition, a distribution map showing the difference between $PM_{10}$ and $PM_{2.5}$ was based on the distribution obtained through this study. The regions of high $PM_{10}$ and $PM_{2.5}$ emissions were selected. In addition, the correlation between $PM_{10}$ and $PM_{2.5}$ was confirmed through the distribution map. This study analyzed the spatial distribution variation results of analyzing $PM_{10}$ and $PM_{2.5}$ in Seoulthrough spatial analysis technique. As a result of this study, it was confirmed that $PM_{10}$ shows high measured value on the roadside measurement station.