Emissions of ozone-depleting substances, including trichlorofluoromethane (CFC-11), have decreased since the mid-1980s in response to the Montreal Protocol1,2. In recent years, an unexpected increase in CFC-11 emissions beginning in 2013 has been reported, with much of the global rise attributed to emissions from eastern China3,4. Here we use high-frequency atmospheric mole fraction observations from Gosan, South Korea and Hateruma, Japan, together with atmospheric chemical transport-model simulations, to investigate regional CFC-11 emissions from eastern China. We find that CFC-11 emissions returned to pre-2013 levels in 2019 (5.0 ± 1.0 gigagrams per year in 2019, compared to 7.2 ± 1.5 gigagrams per year for 2008–2012, ±1 standard deviation), decreasing by 10 ± 3 gigagrams per year since 2014–2017. Furthermore, we find that in this region, carbon tetrachloride (CCl4) and dichlorodifluoromethane (CFC-12) emissions—potentially associated with CFC-11 production—were higher than expected after 2013 and then declined one to two years before the CFC-11 emissions reduction. This suggests that CFC-11 production occurred in eastern China after the mandated global phase-out, and that there was a subsequent decline in production during 2017–2018. We estimate that the amount of the CFC-11 bank (the amount of CFC-11 produced, but not yet emitted) in eastern China is up to 112 gigagrams larger in 2019 compared to pre-2013 levels, probably as a result of recent production. Nevertheless, it seems that any substantial delay in ozone-layer recovery has been avoided, perhaps owing to timely reporting3,4 and subsequent action by industry and government in China5,6.
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Rapid increase in dichloromethane emissions from China inferred through atmospheric observations
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Data from Gosan and Cape Grim stations are available from the AGAGE website (https://agage.mit.edu). Hateruma data are available from the World Data Centre for Greenhouse Gases (https://gaw.kishou.go.jp/). Data pertaining to the emissions of CFC-11, CFC-12, and CCl4 are available from the OSF repository ‘Data and code for emissions of CFC-11 and related substances from eastern China’ (https://doi.org/10.17605/OSF.IO/QP2BE).
Licences to use NAME and InTEM are available for research purposes via a request to the UK Met Office or on request from A.J.M. and A.L.R. The code for the NAME-based hierarchical Bayesian inversion (NAME-HB) is available on request from M.R. and L.M.W. The code of the dispersion model FLEXPART is available from https://www.flexpart.eu. The code for the FLEXPART-based Bayesian inversion (FLEXPART-MIT) is available on request from X.F. The inversion code used by Empa is available from https://doi.org/10.5281/zenodo.1194642 or on request from S.H. The algorithm used to estimate the excess emissions of CFC-11, CFC-12 and CCl4, and the production of CFC-11, for eastern China is available from the OSF repository ‘Data and code for emissions of CFC-11 and related substances from eastern China’ (https://doi.org/10.17605/OSF.IO/QP2BE).
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We are indebted to the site operators who oversee the day-to-day running of the AGAGE and NIES stations. We particularly thank the NASA Upper Atmosphere Research Program for its continuing support of AGAGE theory, experiment and selected stations through grant NNX16AC98G to MIT, and grants NNX16AC96G and NNX16AC97G to SIO and their multiple preceding grants. Support also comes from the UK Department for Business, Energy & Industrial Strategy (BEIS contract 1537/06/2018) for Mace Head observations and the maintenance of InTEM. The contributions by A.L.R. and A.J.M. are supported by The Met Office Hadley Centre Climate Programme, funded by the UK’s Department for Business, Energy and Industrial Strategy and Department for Environment, Food and Rural Affairs. Observations at Cape Grim are supported largely by the Australian Bureau of Meteorology, CSIRO, the Australian Department of Agriculture, Water and the Environment (DAWE) and Refrigerant Reclaim Australia (RRA). M.R. and L.M.W. were supported by Natural Environment Research Council (NERC) grants NE/M014851/1 and NE/N016548/1. A.L.G. is supported by NERC Independent Research Fellowship NE/L010992/1. S.P., M.-K.P. and H.P. were supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. 2020R1A2C3003774). Observations at Hateruma were partly supported by the Ministry of the Environment of Japan. NAME-HB simulations were carried out using the University of Bristol BlueCrystal supercomputing facility. FLEXPART calculations by Empa were carried out at the Swiss National Supercomputing Centre (CSCS) under project ID s862. We acknowledge the late Y. Yokouchi (NIES) for her significant contributions to the Hateruma observations. Q.L. thanks NASA’s Modeling, Analysis, and Prediction Program for supporting the GEOSCCM and the NASA High-End Computing (HEC) Program for providing supercomputing resources. We thank N. Harris and P. Seibert for their constructive suggestions to improve the manuscript.
The authors declare no competing interests.
Peer review information Nature thanks Neil Harris, Petra Seibert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 The mean sensitivity of the measurements to emissions of CFC-11.
Each panel shows the sensitivity of the measurements to emissions of CFC-11 for each year between 2008–2019. The black triangle and circle indicate the Gosan and Hateruma stations, respectively. The sensitivities are derived using the NAME model for daily averaged measurement values at each station. Where no measurement data were available from a station, this is not included in the mean sensitivity. The figure shows that the mean sensitivity of the observations to emissions from eastern China did not change substantially throughout this period.
Extended Data Fig. 2 Annual mean above-baseline enhancement in the mole fraction measured at Gosan for trajectories originating from China.
a–c, Annual mean above-baseline mole fraction enhancements are compared to emissions estimates from eastern mainland China from the four inversion systems for CFC-11 (a), CFC-12 (b) and CCl4 (c). The shading shows the estimated emissions for each inversion within their one standard deviation or 68% uncertainty and the black line shows the mean enhancement in mole fraction from China measured at Gosan, with the one-standard-error variability shown by the black dotted line. Baseline mole fractions were determined using a statistical method40, and air masses were classified as originating from China where above-baseline pollution events arriving at Gosan had entered the boundary layer only within the Chinese country domain within their six-day kinematic back trajectories. Back trajectories were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model41 of the NOAA Air Resources Laboratory (ARL) using meteorological information from the Global Data Assimilation System (GDAS) model with 1° × 1° grid cell.
Extended Data Fig. 3 Maps of the mean CFC-11 fluxes from the four inversions.
a–l, Spatial distribution of the mean CFC-11 fluxes from the four inversions: NAME-HB (a–c), NAME-InTEM (d–f), FLEXPART-MIT (g–i), and FLEXPART-Empa (j–l). The plots show the average emissions from 2008–2012 (top row; a, d, g, j); the average emissions for 2014–2017 (middle row; b, e, h, k); and the emissions for 2019 (bottom row; c, f, i, l). The black triangle and circle indicate the Gosan and Hateruma stations, respectively. The hatched areas indicate regions of the domain to which the observations have low sensitivity, and therefore, from which the derived emissions have high uncertainty4. As a result, only emission magnitudes and emission changes for the non-hatched regions are included in the values quoted in the main text.
Extended Data Fig. 4 Maps of the mean flux averaged over the four inversions for CFC-11, CFC-12 and CCl4.
a–i, The mean spatial distribution of the mean fluxes are for CFC-11 (a–c), CFC-12 (d–f), and CCl4 (g–i) from the four inversions for the periods 2008–2012 (top row; a, d, g); 2014–2017 (middle row; b, e, h), and 2019 (bottom row; c, f, i). The black triangle and circle indicate the Gosan and Hateruma stations, respectively, which are the measurement sites used to derive the emissions. The hatched areas indicate regions of the domain to which the observations have low sensitivity, and therefore, from which the derived emissions have high uncertainty4. Only emission magnitudes for the non-hatched regions are included in the values quoted in the main text. The different spatial distributions for the three gases reflect different emissions distributions from their respective banks (primarily for CFC-11 and CFC-12), or differences in production-related emissions (for example, the production of chloromethanes is thought to be a substantial source for CCl4, but not for CFC-11 and CFC-12)16.
Extended Data Fig. 5 A comparison of emissions estimates for eastern mainland China using different measurement datasets.
a–c, Estimated annual emissions of CFC-11 using a priori emissions distributed uniformly over land using measurements from Gosan and Hateruma (a), Gosan (b) and Hateruma (c) stations. The black line shows the mean estimate of the four inversion frameworks and the shading shows the estimates for each inversion within their one standard deviation or 68% uncertainty. Inventory-based estimates of emissions for all four gases from eastern mainland China (determined as the total Chinese emissions scaled by the fraction of the population, 35%, that reside in that part of the domain) are shown as a dashed line (including projected inventory values17 after 2014).
Extended Data Fig. 6 Emissions estimates for eastern mainland China using a priori emissions distributed by population.
a–c, Estimated annual emissions of CFC-11 (a), CFC-12 (b) and CCl4 (c) for eastern mainland China using a priori emissions distributed by population in space. The black line shows the mean estimate of the four inversion frameworks and the shading shows the estimates for each inversion within their one standard deviation or 68% uncertainty. Inventory-based estimates of emissions for all four gases from eastern mainland China (determined as the total Chinese emissions scaled by the fraction of the population, 35%, that reside in that part of the domain) are shown as a dashed line (including projected inventory values17 after 2014). For CCl4, the black symbols show additional bottom-up estimates, shown by a dotted line and black squares with the associated 95% uncertainty21; and by the black diamond16, also scaled by population. The derived emissions are consistent with those derived in the main text, which assume a priori emissions that are spatially uniform over land.
Extended Data Fig. 7 Simulated and observed CFC-11 mole fractions for the four inverse frameworks.
Left, a comparison of the simulated CFC-11 mole fractions from the four different inversion analyses and those that were measured at Gosan and Hateruma. Right, residuals between the simulated and observed mole fractions (data minus model). Shading denotes the 1-sigma model–data mismatch uncertainties assumed in the inversions. Simulated mole fractions are derived from a posteriori emissions. For the NAME-InTEM, FLEXPART-EMPA, NAME-HB and FLEXPART-MIT inversions, 6-hourly, 3-hourly, 24-hourly and 24-hourly averaging, respectively, was applied to the model and data, which represents the temporal resolution at which the model simulates the observed data using derived annual emissions.
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Park, S., Western, L.M., Saito, T. et al. A decline in emissions of CFC-11 and related chemicals from eastern China. Nature 590, 433–437 (2021). https://doi.org/10.1038/s41586-021-03277-w
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