• Environmental and Resource Economics Review
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• v.28 no.4
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• pp.467-495
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• 2019

Uncertainty of renewable energy such as photovoltaic(PV) power is detrimental to the flexibility of the power system. Therefore, precise prediction of PV power generation is important to make the power system stable. The purpose of this study is to forecast PV power generation using meteorological data including particulate matter(PM). In this study, PV power generation is predicted by support vector machine using RBF kernel function based on machine learning. Comparing the forecasting performances by including or excluding PM variable in predictor variables, we find that the forecasting model considering PM is better. Forecasting models considering PM variable show error reduction of 1.43%, 3.60%, and 3.88% in forecasting power generation between 6am~8pm, between 12pm~2pm, and at 1pm, respectively. Especially, the accuracy of the forecasting model including PM variable is increased in daytime when PV power generation is high.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.83-92
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• 2022

In this paper, we designed an algorithm that can control the state of PM by learning the chain failure pattern of PHM based air conditioning facility. It is an inevitable spread of PM due to the downtime caused by the failure of the air conditioning facility. The algorithm developed by us is to establish a PM management system through PHM, and it is an algorithm that maintains a constant stabilization state through learning the stop/operation pattern of the air conditioner and manages PM based on this. As a result of the simulating at a subway station for the performance qualification of the algorithm, it was verified that the concentration of PM reduces by 30% on average. In the case of stations with many passengers using the subway, the concentration of PM exceeded the Ministry of Environment Standards(100 ㎍/m³), but it was verified that the concentration of PM was improved at all stations where the simulation was conducted. In the future research is to expand the system to comprehensively manage not only PM but also pollutants such as CO₂, CO, and NO₂ in subway stations.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.277-286
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• 2019

The construction industry is known to be one of the representative industries that generate fine dust. Therefore, reducing the amount of fine dust generated in construction sites is very important for the overall fine dust management. Based on this, this study proposed the fine dust measurement and removal technology combined with advanced technologies such as autonomous mobile vehicle IoT and DFS. The qualitative, quantitative and risk elimination effects that can be expected when applying the proposed technique are analyzed. The proposed technique will be validated through system development and field application and evaluated specific economics through cost analysis.

• Bulletin of the Korea Photovoltaic Society
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• v.4 no.3
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• pp.36-41
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• 2018

연일 뉴스에 보도되고 있는 미세먼지는 노약자의 호흡기는 물론 발암물질을 포함하고 있어 국민건강에 악영향을 미치기 때문에 반드시 해결되어야 할 사회 문제이다. 미세먼지는 해외 유입 인자와 국내 유발인자를 정확하게 구분하기 어려우나 약 50%의 미세먼지는 2차 발생에 의한 국내 요인으로 발생하고 있는 것으로 파악되고 있으며 화력발전소가 미세먼지 유발 물질의 주요 생성원으로 지목되어왔다. 국내 화력발전소의 50%가 충남 서해안이 위치하고 있어 수도권에 가장 큰 영향을 주는 것으로 밝혀져 충남은 내구연한에 도달한 노후 화력발전소의 폐쇄 및 발전량 감축 조절을 통해 미세먼지 발생을 최소화 하기 위해 노력해왔다. 그러나 태양전지 주도의 신재생에너지 발전으로 전환하는 것만이 미세먼지를 저감시킬 근본적인 해결책이라고 할 수 있다. 충남은 2050년까지 화력발전 비중을 0%로 낮추고 필요한 전력은 에너지컨슈머들이 생산하는 신재생에너지로 생산하는 내용을 골자로 하는 에너지 전환비전을 선포하였다. 이 비전이 선언에 그치지 않고 목표를 달성하기 위해서 이에 대한 세부 이행계획을 수립하고 충남에 맞는 태양광 발전 산업 육성을 위한 정책을 병행해야 할 것이다. 이번 글에서는 충남의 여건을 고려하여 태양광을 이용한 수소 생산 및 인공광합성을 연계한 고부가가치 화학물질의 생산 연구 및 실증 과제를 추진할 것을 제안하였다. 이러한 충남의 노력은 수도권 수요 지역에 대한 전력 공급기지에서 친환경에너지로 유지되는 '청정남도'로서의 재도약을 가능하게 할 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.508-511
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• 2020

미세먼지(PM10, PM2.5)는 배출가스 증가와 함께 빠르게 악화되어 왔으며, 다양한 화학성분 뿐만 아니라 금속 성분이 포함되어 있어 인체에 큰 유해성을 발생한다. 이에 정부는 미세먼지 저감 정책 및 법률을 통해 개선하고자 했지만, 2013년부터 그 효력을 잃기 시작하였다. 이에 본 연구에서는 미세먼지 저감 정책 및 법률을 수립하는데 있어 가장 중요한 요소인 미세먼지 농도를 예측하는 연구를 진행한다. 이전 연구들에서 미세먼지 영향 요소들이 시계열 기반의 데이터(기상인자와 대기오염 인자)인 것을 확인하였기에, 시계열 데이터에 좋은 성능을 보이는 LSTM 알고리즘을 사용하여 학습 후, 서울시 '구별' '시간단위' 미세먼지 농도 예측에 대한 예측 오차(RMSE, MAE) 성능을 비교하였다. 실험 결과 PM10의 경우 (7.2, 4.78), PM2.5의 경우 (4.7, 3.2)의 예측 오차를 보였으며, 금천구의 경우 PM10이 (5.3, 3.71), PM2.5에서 (3.5, 2.5)로 가장 좋은 성능을 보였다.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.437-449
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• 2023

This study measured PM₁₀ concentrations and wind speeds in buffer green spaces and neighborhood parks located along the road, and compared them with roadside measurementresults to understand the effect of mitigating PM₁₀ concentrations by type of green space and the influence of wind speeds on it. As a result of the analysis, the effect of mitigating PM₁₀ concentration was different depending on the type of roadside green space, and an increase in wind speed had a significant effect on reducing PM₁₀ concentration. In buffer green areas with high planting density, wind speed was low and PM₁₀ stagnated inside, resulting in the highest concentration. On the other hand, green areas in neighborhood parks with relatively low planting density had high wind speeds and the lowest PM₁₀ concentration. The non-green area within the neighborhood park recorded the highest wind speed, which was advantageous for the spread of PM₁₀, but the concentration was higherthan that of the green area. Therefore, in orderto reduce PM₁₀ concentration in roadside green space, it is necessary to create green space with good ventilation, and the combined effect of green space and wind speed seems to be more advantageous in reducing PM₁₀ concentration. Green spaces capture and remove PM inside, contributing to reducing the concentration of PM outside. In order to manage PM in the entire city and on roads, it is necessary to increase planting density and leaf area in roadside green spaces, such as buffer green spaces, so that PM can be removed within the green spaces. However, in green spaces such as neighborhood parks that are actively used by city residents, in orderto minimize damage to users due to PM, it is desirable to create green spaces with a structure that allows PM to spread to the outside rather than stagnate inside.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1258-1261
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• 2017

1급 발암물질인 미세먼지 중 44.3%를 차지하고 있는 도로 비산먼지는 효과적인 미세먼지 농도 저감 대책의 방안 중 하나이다. 도로 비산먼지 제거는 일반적으로 특수 차량을 이용, 정해진 경로와 주기에 따라 운행된다. 이러한 운행방식은 도로의 오염 현황에 따른 효과적 경로 선정 및 운영이 어렵다. 본 논문에서는 도로 비산먼지 제거의 효율적인 경로 제안을 위해 대구지역에 분포된 KISTI 이동형 도시센싱 테스트베드에서 수집되는 고해상도의 실시간 지역별 오염 현황 데이터를 활용하여 실시간 오염도를 분석하고, LSTM(LONG SHORT-TERM MEMORY) 알고리즘을 활용하여 미래의 미세먼지 농도를 예측하였다. 기존 연구와 달리 지역별 상황을 고려한 데이터를 사용하여 선형 회귀 분석을 수행하였다. 실험 결과, 시간 속성을 고려한 LSTM이 MLP 보다 평균 제곱근 오차 값이 경우에 따라 최대 30% 더 작음을 확인했다. 본 연구를 기반으로 고해상도 사물 데이터 기반 예측 연구의 가능성을 보였으며, 미세먼지 예측 결과를 활용 유연하고 효과적인 도로 청소차량의 운행 경로를 설정에 활용될 수 있을 것으로 기대한다.

• 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.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.6
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• pp.40-49
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• 2017

The purpose of the research is to analyze the recent cases of green space planning and planting design in Beijing, one of the cities having the worst particulate matters (PM) pollution. This study comprehensively reviewed Chinese academic literature addressing green space planning to reduce PM pollution. In addition, we conducted field observations and interviews with public officials from Beijing Municipal Bureau of Landscape Design in charge of planning and management of green spaces in Beijing. After the extensive review of literature we derived tree planting principles to mitigate the impact of PM from urban road system, residential area, and industrial district. Using the principles we evaluated the three recent cases of planting design to mitigate PM: Beijing Fuxing Road, Fu Run Residential Area and Beijing Beiqi Multipurpose Vehicle Factory. We conclude that green space planning and trees planting are not effective in mitigating negative impact of PM pollution because of inadequate selection of trees and inconsiderate planting composition. We proposed to replace the tree species with the ones capable of reducing the spread of PM, and reorganize planting compositions that consider the directions and characteristics of urban wind flow. This study suggests desirable types of tree species and planting compositions for road, residential and industrial districts, and we expect that it provides helpful guidelines for making planting design and species selection to mitigate the impact of PM in urban landscape.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.620-628
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• 2021

This study was conducted to investigate public's perception of the effects of particulate matter (PM) in oral health and to provide specific motivation to prevent oral disease by PM. A total of 134 adults were selected as final analysis subjects from some people all over the country. The data collected is analyzed using SPSS 21.0 for windows. Frequency analysis was used to identify general characteristics and hygiene habit. For identifying perception of effects of PM on oral health, crossover analysis was used. The largest number of people recognized that the level of PM had deteriorated, compared to five years ago. That perception was highest among those in 30 years of age and service professions. Those who check the concentration of PM are more concerned with oral health care when the PM is occurred in high concentration. People who perceive PM as a threat to the oral health are more concerned about oral health care when the PM is occurred in high concentration. It is concerned those who are aware of the relationship between PM and oral health specifically manage the oral health to protect the oral cavity from PM.