인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
The scope of this work was to characterize PM concentration in inland and coastal areas in Korea and to examine the relative contribution of the outdoor environment and time factor. The coastal area was designated as the case where there was a measuring station within 10 km of the coastline, and the other areas were defined as inland areas. PM10•PM2.5 concentrations and other environmental factors were all measured by the national measuring station in units of one hour in 2019. Through the prediction accuracy analysis by machine learning algorithm using these data, it was confirmed that the boosted decision tree had the highest accuracy in PM analysis, and the accuracy was lowered in PM10 and coastal areas due to factors that were not reflected in the feature such as sea salt. After that, by using variable weights derived from linear regression, we found the main causes of the PM concentration increase were PM precursor and the seasonal characteristics of winter and spring. And main causes of PM concentration decrease were dew point temperature, wind speed and direction. Also, the PM increase or decrease due to environmental variables was larger in the inland area than in the coastal area.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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