Long term trend of PM2.5 in Beijing, China

碩士 === 崑山科技大學 === 環境工程研究所 === 104 === In this study we analyze open 6-year PM2.5 (particles with aerodynamic diameters less than 2.5 μm) data for tipping points of high haze pollution events in Beijing, China. First, we analyze basic statistic parameters, such as annual mean and standard deviation....

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Main Authors: Hsien-Hua Li, 李顯華
Other Authors: Chih-Sheng Lee
Format: Others
Language:zh-TW
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/48796602809768633442
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spelling ndltd-TW-103KSUT05150232016-12-30T04:07:13Z http://ndltd.ncl.edu.tw/handle/48796602809768633442 Long term trend of PM2.5 in Beijing, China 北京PM2.5長期趨勢分析 Hsien-Hua Li 李顯華 碩士 崑山科技大學 環境工程研究所 104 In this study we analyze open 6-year PM2.5 (particles with aerodynamic diameters less than 2.5 μm) data for tipping points of high haze pollution events in Beijing, China. First, we analyze basic statistic parameters, such as annual mean and standard deviation. Second, we select three statistic indicators (standard deviation, skewness, and autocorrelation) and try various sliding windows (300, 370, 500, 1000, and 1200-days). Finding that 500-day of leading indicators can extract clear signals of critical transitions (CTs). Based on this, we identify five CTs, four are flickering and one is critical slowing down (CSD) in the period of 2009–2014. Third, we also find alternative stable states are continuing in process that implies atmospheric PM2.5 variation caused by various external driving forces, e.g., wind speeds and aerosol optical depth. That implies need effective pre-warning technologies to predict heavy haze pollution events. Chih-Sheng Lee 李志賢 2016 學位論文 ; thesis 95 zh-TW
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language zh-TW
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description 碩士 === 崑山科技大學 === 環境工程研究所 === 104 === In this study we analyze open 6-year PM2.5 (particles with aerodynamic diameters less than 2.5 μm) data for tipping points of high haze pollution events in Beijing, China. First, we analyze basic statistic parameters, such as annual mean and standard deviation. Second, we select three statistic indicators (standard deviation, skewness, and autocorrelation) and try various sliding windows (300, 370, 500, 1000, and 1200-days). Finding that 500-day of leading indicators can extract clear signals of critical transitions (CTs). Based on this, we identify five CTs, four are flickering and one is critical slowing down (CSD) in the period of 2009–2014. Third, we also find alternative stable states are continuing in process that implies atmospheric PM2.5 variation caused by various external driving forces, e.g., wind speeds and aerosol optical depth. That implies need effective pre-warning technologies to predict heavy haze pollution events.
author2 Chih-Sheng Lee
author_facet Chih-Sheng Lee
Hsien-Hua Li
李顯華
author Hsien-Hua Li
李顯華
spellingShingle Hsien-Hua Li
李顯華
Long term trend of PM2.5 in Beijing, China
author_sort Hsien-Hua Li
title Long term trend of PM2.5 in Beijing, China
title_short Long term trend of PM2.5 in Beijing, China
title_full Long term trend of PM2.5 in Beijing, China
title_fullStr Long term trend of PM2.5 in Beijing, China
title_full_unstemmed Long term trend of PM2.5 in Beijing, China
title_sort long term trend of pm2.5 in beijing, china
publishDate 2016
url http://ndltd.ncl.edu.tw/handle/48796602809768633442
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