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....
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2016
|
Online Access: | http://ndltd.ncl.edu.tw/handle/48796602809768633442 |
id |
ndltd-TW-103KSUT0515023 |
---|---|
record_format |
oai_dc |
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 |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
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 |
work_keys_str_mv |
AT hsienhuali longtermtrendofpm25inbeijingchina AT lǐxiǎnhuá longtermtrendofpm25inbeijingchina AT hsienhuali běijīngpm25zhǎngqīqūshìfēnxī AT lǐxiǎnhuá běijīngpm25zhǎngqīqūshìfēnxī |
_version_ |
1718405822804918272 |