미세먼지 (PM<sub>10</sub>) 및 초미세먼지 (PM<sub>2.5</sub>)는 인체에 흡수 가능하여 호흡기 질환 및 심장 질환과 같이 인체 건강에 악영향을 미치며, 심각할 경우 조기 사망에 영...
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https://www.riss.kr/link?id=A107392684
2021
-
KCI등재,SCOPUS,ESCI
학술저널
321-335(15쪽)
1
0
상세조회0
다운로드국문 초록 (Abstract)
미세먼지 (PM<sub>10</sub>) 및 초미세먼지 (PM<sub>2.5</sub>)는 인체에 흡수 가능하여 호흡기 질환 및 심장 질환과 같이 인체 건강에 악영향을 미치며, 심각할 경우 조기 사망에 영...
미세먼지 (PM<sub>10</sub>) 및 초미세먼지 (PM<sub>2.5</sub>)는 인체에 흡수 가능하여 호흡기 질환 및 심장 질환과 같이 인체 건강에 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 PM<sub>10</sub> 및 PM<sub>2.5</sub> 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 (PM<sub>10</sub>: R<sup>2</sup> = 0.82 nRMSE = 34.9%, PM<sub>2.5</sub>: R<sup>2</sup> = 0.75 nRMSE = 35.6%), 계절별 및 연평균 PM<sub>10</sub> 및 PM<sub>2.5</sub> 농도에 대한 공간적인 분포를 확인해본 결과, 현장관측자료와 비슷한 공간 분포를 보였으며, 국가별 농도 분포와 계절에 따른 시계열 농도 패턴을 잘 모의하였다.
다국어 초록 (Multilingual Abstract)
Particulate matter (PM<sub>10</sub> and PM<sub>2.5</sub> with a diameter less than 10 and 2.5 μm, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based ...
Particulate matter (PM<sub>10</sub> and PM<sub>2.5</sub> with a diameter less than 10 and 2.5 μm, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learningbased retrieval algorithm for ground-level PM<sub>10</sub> and PM<sub>2.5</sub> concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R<sup>2</sup>) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis (PM<sub>10</sub>: R<sup>2</sup> = 0.82, nRMSE = 34.9 %, PM<sub>2.5</sub>: R<sup>2</sup> = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.
참고문헌 (Reference)
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1 임현광, "일본 정지궤도 기상위성 Himawari-8을 이용한 에어로졸 광학정보 산출 및 검증" 대한원격탐사학회 32 (32): 681-691, 2016
2 Du, C., "Urban boundary layer height characteristics and relationship with particulate matter mass concentrations in Xi’an, central China" 13 (13): 1598-1607, 2013
3 Wang, S., "The impacts of different kinds of dust events on PM10 pollution in northern China" 40 (40): 7975-7982, 2006
4 Friedman, J. H., "Stochastic gradient boosting" 38 (38): 367-378, 2002
5 Zhan, Y., "Spatiotemporal prediction of continuous daily PM2. 5 concentrations across China using a spatially explicit machine learning algorithm" 155 : 129-139, 2017
6 Pedregosa, F., "Scikit-learn : Machine learning in Python" 12 : 2825-2830, 2011
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8 She, Q., "Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China" 239 : 124678-, 2020
9 Rodrıguez, S., "Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain" 35 (35): 2433-2447, 2001
10 Qadeer, K., "Prediction of PM10Concentration in South Korea Using Gradient Tree Boosting Models" 1-6, 2019
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17 Jin, K., "LEO and GEO satellite programs for space-borne measurement of aerosol" 16 (16): 53-62, 2018
18 Krasnov, H., "Increase in dust storm related PM10 concentrations : A time series analysis of 2001-2015" 213 : 36-42, 2016
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농림위성 활용 수종분류 가능성 평가를 위한 래피드아이 영상 기반 시험 분석
SAR 검보정 Site 구축을 위한 후방 산란 특성 분석
우리나라 시군단위 벼 수확량 예측을 위한 다종 기상자료의 비교평가
천리안위성2A호 기상탑재체 Best Detector Select 맵 평가 및 업데이트
학술지 이력
연월일 | 이력구분 | 이력상세 | 등재구분 |
---|---|---|---|
2027 | 평가예정 | 재인증평가 신청대상 (재인증) | |
2021-01-01 | 평가 | 등재학술지 유지 (재인증) | ![]() |
2018-01-01 | 평가 | 등재학술지 유지 (등재유지) | ![]() |
2015-01-01 | 평가 | 등재학술지 유지 (등재유지) | ![]() |
2011-01-01 | 평가 | 등재학술지 유지 (등재유지) | ![]() |
2009-01-01 | 평가 | 등재학술지 유지 (등재유지) | ![]() |
2007-01-01 | 평가 | 등재학술지 유지 (등재유지) | ![]() |
2006-07-24 | 학술지등록 | 한글명 : 대한원격탐사학회지외국어명 : Korean Journal of Remote Sensing | ![]() |
2005-01-01 | 평가 | 등재학술지 유지 (등재유지) | ![]() |
2002-07-01 | 평가 | 등재학술지 선정 (등재후보2차) | ![]() |
2000-01-01 | 평가 | 등재후보학술지 선정 (신규평가) | ![]() |
학술지 인용정보
기준연도 | WOS-KCI 통합IF(2년) | KCIF(2년) | KCIF(3년) |
---|---|---|---|
2016 | 0.52 | 0.52 | 0.54 |
KCIF(4년) | KCIF(5년) | 중심성지수(3년) | 즉시성지수 |
0.53 | 0.44 | 0.725 | 0.12 |