ABSTRACT Beginning in 2013, China launched two phases (2013–2017 and 2018–2020) of clean air actions that have led to substantial reductions in PM2.5 concentrations. However, improvement in

PM2.5 pollution was notably slowing down during Phase II. Here we quantify the efficacy and drivers of PM2.5 improvement and evaluate the associated cost during 2013–2020 using an integrated

framework that combines an emission inventory model, a chemical transport model and detailed cost information. We found that national population-weighted mean PM2.5 concentrations decreased

by 19.8 μg m−3 and 10.9 μg m−3 in the two phases, and the contribution of clean air policies in Phase II (2.3 μg m−3 yr−1) was considerably lower than that of Phase I (4.5 μg m−3 yr−1),

challenging, with the average cost of reducing one unit of PM2.5 concentration in Phase II twice that of Phase I. Our results suggest there is a need for strengthened, well-balanced,

emission control strategies for multi-pollutants. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS

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Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS DRIVERS OF PM2.5 AIR POLLUTION DEATHS IN CHINA 2002–2017 Article 26 July 2021 OPTIMAL REACTIVE NITROGEN CONTROL PATHWAYS

IDENTIFIED FOR COST-EFFECTIVE PM2.5 MITIGATION IN EUROPE Article Open access 17 July 2023 COMBINED SHORT-TERM AND LONG-TERM EMISSION CONTROLS IMPROVE AIR QUALITY SUSTAINABLY IN CHINA Article

Open access 17 June 2024 DATA AVAILABILITY The emission data developed by this work are publicly available from http://meicmodel.org.cn. The source data for figures presented in the main

text and extended data are available at the figshare repository (https://doi.org/10.6084/m9.figshare.26411008 (ref. 58)). Source data are provided with this paper. CODE AVAILABILITY The code

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pollution between 2013 and 2020. _figshare_ https://doi.org/10.6084/m9.figshare.26411008 (2024). Download references ACKNOWLEDGEMENTS This study was supported by the National Natural Science

Foundation of China (42222507 to G.G. and 41921005 to Q.Z.) and the New Cornerstone Science Foundation through the XPLORER PRIZE to Q.Z. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * State

Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China Guannan Geng, Yang Liu, Liu Yan, Hanwen Hu & Kebin He *

Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China Yuxi Liu, Shigan Liu, Jing Cheng, Nana Wu, Dan Tong 

& Qiang Zhang * State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control, Chinese Academy of Environmental Planning, Beijing, China Yuxi

Liu * Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China Bo Zheng * Key Laboratory of Meteorological Disaster,

Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China

Zhicong Yin Authors * Guannan Geng View author publications You can also search for this author inPubMed Google Scholar * Yuxi Liu View author publications You can also search for this

author inPubMed Google Scholar * Yang Liu View author publications You can also search for this author inPubMed Google Scholar * Shigan Liu View author publications You can also search for

this author inPubMed Google Scholar * Jing Cheng View author publications You can also search for this author inPubMed Google Scholar * Liu Yan View author publications You can also search

for this author inPubMed Google Scholar * Nana Wu View author publications You can also search for this author inPubMed Google Scholar * Hanwen Hu View author publications You can also

search for this author inPubMed Google Scholar * Dan Tong View author publications You can also search for this author inPubMed Google Scholar * Bo Zheng View author publications You can

also search for this author inPubMed Google Scholar * Zhicong Yin View author publications You can also search for this author inPubMed Google Scholar * Kebin He View author publications You

can also search for this author inPubMed Google Scholar * Qiang Zhang View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS G.G. and Q.Z.

conceived the study. Yang Liu, J.C., L.Y., N.W., H.H., B.Z., D.T., G.G., K.H. and Q.Z. estimated China’s emissions. Yang Liu, D.T. and G.G. estimated the drivers of emission changes. Yuxi

Liu, S.L. and J.C. conducted CMAQ simulations. Z.Y. contributed to the analysis of meteorological impacts. G.G. and Q.Z. interpreted the results. G.G. and Q.Z. wrote the paper, with input

from all co-authors. CORRESPONDING AUTHOR Correspondence to Qiang Zhang. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW

INFORMATION _Nature Geoscience_ thanks Monica Crippa, Zbigniew Klimont, Jean-Francois Lamarque and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Primary Handling Editor: Tom Richardson, in collaboration with the _Nature Geoscience_ team. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA EXTENDED DATA FIG. 1 SUMMARY OF MAJOR CONTROL MEASURES IMPLEMENTED DURING PHASE I (2013–2017) AND II

(2018–2020). The footnotes represent the title of each standard. EXTENDED DATA FIG. 2 ANTHROPOGENIC EMISSIONS BY SECTOR IN CHINA DURING 2013–2020. This is a supplement of Fig. 1, which

presents the emission trends for PM10, BC, OC, and CO. Source data EXTENDED DATA FIG. 3 EMISSION TRENDS COMPARED WITH SATELLITE- AND GROUND-BASED OBSERVATIONS. The 2013–2020 trends in SO2

(blue solid curve) and NOx (orange solid curve) emissions are compared with OMI SO2 (blue dashed curve) and NO2 (orange dashed curve) tropospheric columns for eastern China (29°N–41°N,

108°E–123°E), respectively. The 2013–2020 trends in ground-based observations of SO2 (blue dotted curve) and NO2 (orange dotted curve) are also presented. Data are normalized by their

corresponding value in 2013. Source data EXTENDED DATA FIG. 4 DRIVERS OF EMISSION CHANGES OF MAJOR AIR POLLUTANTS IN CHINA FROM 2013–2020. Drivers of the national emission changes in (A)

SO2, (B) NOx, (C) PM2.5, (D) NMVOC, (E) NH3, (F) PM10, (G) BC, (H) OC, and (I) CO. For each pollutant, the changes in emissions are decomposed into the drivers of activity rates and

pollution controls by sector during Phases I and II. Numbers less than 0.01 Tg are not presented. Pow, Ind, Sol, Res, Tra and Agr represent power, industry, solvent use, residential,

transportation and agriculture sector, respectively. Source data EXTENDED DATA FIG. 5 COMPARISON OF METEOROLOGICALLY DRIVEN PM2.5 CONCENTRATIONS BETWEEN 2017 AND 2020. PM2.5 simulations

using fixed emissions at 2020 and meteorological conditions for 2017 and 2020. Source data EXTENDED DATA FIG. 6 DRIVERS OF PM2.5 VARIATIONS FROM 2017 TO 2020 OVER THE THREE KEY REGIONS.

Estimations of drivers in (A) BTHSA, (B) YRD and (C) FWP. Values in parentheses show the 95% CI of our estimates. Source data EXTENDED DATA FIG. 7 METEOROLOGICALLY DRIVEN VARIATIONS IN PM2.5

CONCENTRATIONS. Monthly percentage anomalies of simulated meteorologically driven PM2.5 variations (population-weighted) and occurrence frequency of air stagnation days

(population-weighted) from their 2017–2020 means for individual months in (A) China, (B) BTHSA, (C) YRD and (D) FWP. Source data EXTENDED DATA FIG. 8 RELATIVE CHANGES OF MAJOR INDUSTRIAL AND

SOCIAL-ECONOMIC ACTIVITIES OVER CHINA IN EACH MONTH BETWEEN 2019 AND 2020. The row denotes different activities and the column represent each month. Source data EXTENDED DATA FIG. 9 REDUCED

PM2.5 CONCENTRATIONS FROM STRENGTHENING INDUSTRIAL EMISSION STANDARDS AND PROMOTING CLEAN FUELS IN THE RESIDENTIAL SECTORS. Spatial distributions of the reduced PM2.5 concentrations

contributed by (A) strengthening industrial emission standards and (B) promoting clean fuels in the residential sectors. (C) Daily variations of national population-weighted mean PM2.5

reductions contributed by these two measures. Source data EXTENDED DATA FIG. 10 COMPARISON OF THE CHANGES BETWEEN PM2.5 CHEMICAL COMPOSITION CONCENTRATIONS AND THEIR PRECURSOR EMISSIONS

DURING 2013–2020. Comparison between (A) SO2 emissions and sulfate concentrations, (B) NOx emissions and nitrate concentrations, (C) NH3 emissions and ammonium concentrations, and (D) BC

emissions and BC concentrations. PM2.5 chemical composition concentrations are national population-weighted mean excluding the impacts from changes in meteorological conditions. All data are

percent changes relative to the value in 2013. The shades of the symbols’ colors denote the year. Source data SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Methods,

Figs. 1–15 and Tables 1–8. SOURCE DATA SOURCE DATA FIG. 1 Estimated anthropogenic emissions by sector in China from 2013–2020. SOURCE DATA FIG. 2 PM2.5 concentrations 2017–2020 in China and

the three key regions from CMAQ simulations, TAP dataset and ground observations. SOURCE DATA FIG. 3 Estimated impacts from meteorological variations, anthropogenic emission control and

COVID-19 lockdown in China for phases I and II. SOURCE DATA FIG. 4 Estimated contributions of the eight control measures to emission reduction, PM2.5 abatement and avoided premature deaths.

SOURCE DATA FIG. 5 Estimated cost for the eight control measures. SOURCE DATA EXTENDED DATA FIG. 2 Estimated anthropogenic emissions by sector in China from 2013–2020. SOURCE DATA EXTENDED

DATA FIG. 3 Satellite- and ground-based observations from 2013–2020. SOURCE DATA EXTENDED DATA FIG. 4 Estimated drivers of emission changes in major air pollutants in China from 2013–2020.

SOURCE DATA EXTENDED DATA FIG. 5 Monthly PM2.5 simulations under E20M17 and BASE20 scenario. SOURCE DATA EXTENDED DATA FIG. 6 Estimated impacts from meteorological variations, anthropogenic

emission control and COVID-19 lockdown in BTHSA, YRD and FWP in Phase II. SOURCE DATA EXTENDED DATA FIG. 7 Monthly percentage anomalies of simulated meteorologically driven PM2.5 variations

and occurrence frequency of air stagnation days from their 2017–2020 means for individual months. SOURCE DATA EXTENDED DATA FIG. 8 Data of major industrial and social-economic activities

over China in each month between 2019 and 2020. SOURCE DATA EXTENDED DATA FIG. 9 Simulated national population-weighted mean PM2.5 from strengthening industrial emission standards and

promoting clean fuels in the residential sectors. SOURCE DATA EXTENDED DATA FIG. 10 Annual data for PM2.5 chemical composition concentrations and their precursor emissions during 2013–2020.

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permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Geng, G., Liu, Y., Liu, Y. _et al._ Efficacy of China’s clean air actions to tackle PM2.5 pollution between 2013 and 2020. _Nat. Geosci._ 17,

987–994 (2024). https://doi.org/10.1038/s41561-024-01540-z Download citation * Received: 05 July 2023 * Accepted: 22 August 2024 * Published: 18 September 2024 * Issue Date: October 2024 *

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