Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique
Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique
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DOI：
10.5194/acp-19-11303-2019
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发表时间：
2019-09-06
影响因子：
6.3
通讯作者：
Harrison, Roy M.
中科院分区：
文献类型：
--
作者：
Vu, Tuan V.;Shi, Zongbo;Harrison, Roy M.
2013年，北京实施了五年清洁空气行动计划，以减少空气污染物排放，改善环境空气质量。评估这一行动计划是审查其效力和制定新政策的决策过程的一个重要部分。统计模型和化学输运模型以前曾用于评估这一行动计划的效力。然而，这些方法固有的不确定性意味着需要新的和独立的方法来支持评估过程。在这里，我们应用了一种基于机器学习的随机森林技术，通过解耦气象对环境空气质量的影响来量化北京行动计划的有效性。研究结果表明，气象条件对环境空气质量的年际变化具有重要影响。进一步分析表明，如果不是2017年冬季有利于空气污染物扩散的气象条件，PM2.5质量浓度可能会突破计划目标（2017年全年PM2.5 < 60亩-3）。然而，在整个时期（2013-2017年），行动计划要求的主要排放控制导致PM2.5、PM10、NO2、SO2和CO在2013年至2017年期间，经气象校正后分别显著减少约34%、24%、17%、68%和33%。PM2.5和SO2的显著下降在很大程度上归因于煤炭燃烧的减少。研究结果表明，该行动计划在减少一次污染排放和改善北京空气质量方面取得了非常有效的效果。该行动计划为中国其他地区和其他发展中国家制定空气质量政策提供了成功的范例。
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.