Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.5194/acp-19-11303-2019 |
Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique | |
Vu, Tuan V.1; Shi, Zongbo1; Cheng, Jing2; Zhang, Qiang2; He, Kebin3,4; Wang, Shuxiao3; Harrison, Roy M.1,5 | |
2019-09-06 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2019 |
卷号 | 19期号:17页码:11303-11314 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Peoples R China; Saudi Arabia |
英文摘要 | A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM2.5 mass concentration would have broken the target of the plan (2017 annual PM2.5 < 60 mu gm(-3)) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013-2017), the primary emission controls required by the action plan have led to significant reductions in PM2.5, PM10, NO2, SO2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000484560600003 |
WOS关键词 | METEOROLOGICAL NORMALIZATION ; POLLUTION SOURCES ; SEVERE HAZE ; CHINA ; EMISSIONS ; PM2.5 ; MODEL ; TIME ; OZONE ; ACCOUNTABILITY |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/186947 |
专题 | 地球科学 |
作者单位 | 1.Univ Birmingham, Sch Geog Earth & Environm Sci, Div Environm Hlth & Risk Management, Birmingham B1 52TT, W Midlands, England; 2.Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China; 3.Tsinghua Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Con, Beijing 100084, Peoples R China; 4.State Environm Protect Key Lab Sources & Control, Beijing 100084, Peoples R China; 5.King Abdulaziz Univ, Ctr Excellence Environm Studies, Dept Environm Sci, POB 80203, Jeddah, Saudi Arabia |
推荐引用方式 GB/T 7714 | Vu, Tuan V.,Shi, Zongbo,Cheng, Jing,et al. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2019,19(17):11303-11314. |
APA | Vu, Tuan V..,Shi, Zongbo.,Cheng, Jing.,Zhang, Qiang.,He, Kebin.,...&Harrison, Roy M..(2019).Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique.ATMOSPHERIC CHEMISTRY AND PHYSICS,19(17),11303-11314. |
MLA | Vu, Tuan V.,et al."Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique".ATMOSPHERIC CHEMISTRY AND PHYSICS 19.17(2019):11303-11314. |
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