Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

Research output: Contribution to journalArticlepeer-review

Standard

Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique. / VU, Van Tuan; Shi, Zongbo; Cheng, Jing; Zhang, Qiang; He, Kebin; Wang, Shuxiao; Harrison, Roy.

In: Atmospheric Chemistry and Physics, Vol. 19, No. 17, 06.09.2019, p. 11303-11314.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{122e726131a546e3bfb2ec931466f752,
title = "Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique",
abstract = "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 PM 2.5 mass concentration would have broken the target of the plan (2017 annual PM 2.5 <60 μg m -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 PM 2.5/span PM 10/NO 2, SO 2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM 2.5 and SO 2 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. ",
author = "VU, {Van Tuan} and Zongbo Shi and Jing Cheng and Qiang Zhang and Kebin He and Shuxiao Wang and Roy Harrison",
year = "2019",
month = sep,
day = "6",
doi = "10.5194/acp-19-11303-2019",
language = "English",
volume = "19",
pages = "11303--11314",
journal = "Atmospheric Chemistry and Physics",
issn = "1680-7316",
publisher = "Copernicus Publications",
number = "17",

}

RIS

TY - JOUR

T1 - Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

AU - VU, Van Tuan

AU - Shi, Zongbo

AU - Cheng, Jing

AU - Zhang, Qiang

AU - He, Kebin

AU - Wang, Shuxiao

AU - Harrison, Roy

PY - 2019/9/6

Y1 - 2019/9/6

N2 - 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 PM 2.5 mass concentration would have broken the target of the plan (2017 annual PM 2.5 <60 μg m -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 PM 2.5/span PM 10/NO 2, SO 2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM 2.5 and SO 2 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.

AB - 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 PM 2.5 mass concentration would have broken the target of the plan (2017 annual PM 2.5 <60 μg m -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 PM 2.5/span PM 10/NO 2, SO 2, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM 2.5 and SO 2 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.

UR - http://www.scopus.com/inward/record.url?scp=85072074987&partnerID=8YFLogxK

U2 - 10.5194/acp-19-11303-2019

DO - 10.5194/acp-19-11303-2019

M3 - Article

VL - 19

SP - 11303

EP - 11314

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

SN - 1680-7316

IS - 17

ER -