CCl_4 emissions in eastern China during 2021–2022 and exploration of potential new sources

Domain: earth_science
Published: 2024-02-26
Editorial outcome: accept

Abstract
According to the Montreal Protocol, the production and consumption of ozone-layer-depleting CCl4 for dispersive applications was globally phased out by 2010, including China. However, continued CCl4 emissions were disclosed, with the latest CCl4 emissions unknown in eastern China. In the current study, based on the atmospheric measurements of ~12,000 air samples taken at two sites in eastern China, the 2021–2022 CCl4 emissions are quantified as 7.6 ± 1.7 gigagrams per year. This finding indicates that CCl4 emissions continued after being phased out for dispersive uses in 2010. Subsequently, our study identifies potential industrial sources (manufacture of general purpose machinery and manufacture of raw chemical materials, and chemical products) of CCl4 emissions.

Introduction
Carbon tetrachloride (CCl4) is a first-generation ozone-depleting substance (ODS) with an ozone depletion potential of 0.87 and a potent greenhouse gas with a global warming potential of 21501. According to the Montreal Protocol and its amendments, since 1995, the production and consumption of CCl4 for dispersive uses (i.e., solvents, cleaning agents, and process agents) should have been phased out gradually with 100% reduction by 20102,3. An exemption was its use as a chemical feedstock or processing agent4. Top-down estimates of global emissions, inferred from atmospheric observations, have shown no significant decrease in emissions of CCl4 between 2010 and 2019 (decline rate of 0.1 ± 0.2 Gg yr−1)5. However, a wide gap remains in the emission estimates of CCl4. For instance, the bottom-up emission inventory estimates (obtained by multiplying the activity data and emission factors) imply approximately 26.1 (12.6–40.0) Gg yr−1 emissions in 2019, 17 Gg yr−1 lower than those of the top-down emission estimates (43 Gg yr−1 in 2019)1. Therefore, further atmospheric observations of CCl4 are needed to understand the reason for the gap in bottom-up and top-down emissions.

In 1991, China signed the Montreal Protocol, and in 19936 issued a plan for the production and consumption phase-out of ODSs. Moreover, the application of CCl4 for dispersive uses was to be phased out by 2010. A bottom-up emission inventory showed that in China, emissions of CCl4 from dispersive uses were reduced from 9.3 Gg yr−1 in 2005 to 1.1 Gg yr−1 in 2010 and to zero afterwards7. Meanwhile, a bottom-up inventory for non-emissive uses of CCl4, including coal combustion, tetrachloroethylene conversion, feedstock, laboratory chemical reagents, and CCl4 by-product production, showed that the emissions were reduced from 13 Gg yr−1 in 2005 to 4 Gg yr−1 in 2011, and remained unchanged between 2011 and 2014 (4–5 Gg yr−1)8. Moreover, another independent study showed bottom-up emission estimates for CCl4 in 2014 of 7.3 Gg yr−1 after including non-feedstock emissions from chloromethane and perchloroethylene plants9. Nevertheless, top-down estimates of CCl4 emissions based on field atmospheric observations indicated substantial emissions in China after 2010 (8.8 ± 6.4 Gg yr−1 during 2010–201110, 23.6 ± 7.1 Gg yr−1 during 2011–201511, and 13 (7–19) Gg yr−1 during 2009–201612) that were substantially higher than the bottom-up emission estimates. Thus, wide gaps exist in top-down and bottom-up emission estimates of CCl4; however, the missing sources remain unclear.

Eastern China was identified as a hotspot of CCl4 emissions11–13, with an estimated emission of 10.9 ± 2.0 Gg yr−1 from 2014 to 2017, which decreased to 6.2 ± 1.4 Gg yr−1 in 2018–201914. At least 40–60% of the unexpected increase of trichlorofluoromethane (CFC-11) global emissions during 2014–2017 was also attributed to eastern China with the likely production of CFC-11 and related CCl4 15. Based on the strong correlation between CFC-11 and CCl4 (R2=0.78), Benish et al.13 showed the likely co-production of these two species. To strengthen the regulation of CFC-11 and the production of CCl4, the Ministry of Ecology and Environment of China has dispatched working groups to perform on-site supervision and installed online monitoring systems for enterprises producing CCl4 as a by-product in the country16. The “Supervision program” was implemented to improve the tracking of produced CCl4, thereby preventing its conversion to CFCs (e.g., CFC-11) or its sale for controlled or emissive applications, which may lead to a decline in CCl4 emissions from this source. CCl4 is also used as feedstock, e.g., to produce pentafluoropropane (HFC-245fa) and fluoroolefins (HFOs) (these processes are not controlled by the Montreal Protocol)17. Since the feedstock use of CCl4 for the production of HFCs, HFOs, and other chemicals has increased by ~70% between 2015 and 2019 in China17, an increase in CCl4 emissions from feedstock uses is expected. Given the combined effects of the above activities, the latest emissions of CCl4 in eastern China are unclear. However, no data are available after 2019 on atmospheric concentrations and emissions of CCl4 in eastern China. Furthermore, most atmospheric observation data used to estimate emissions of CCl4 in previous top-down studies11,12,14 were collected from the Gosan station (hereafter named GSN, 33.3°N, 126.2°E) in South Korea. The GSN station is more sensitive to northeastern China than middle and southeastern China (see discussion below). Thus, the emission information regarding CCl4 from middle and southeastern China may not be adequately captured. Although there are reported atmospheric observations of CCl4 within eastern China, namely within Nanjing (120 ± 30 ppt; parts per trillion) in 201818, Dongying (129 ± 62 ppt) in 201719, Hebei (89 ± 21 ppt) in 201613, and Lin’an (111 ± 11 ppt) in 200120, emission estimates of CCl4 using these observations have not been reported. Therefore, an urgent need exists to determine the latest atmospheric concentrations and emissions of CCl4 in eastern China.

Consequently, this study describes observations of atmospheric CCl4 concentrations at two sites in eastern China (as discussed below, their spatial coverages are larger than the previously used GSN site) during 2021–2022. This study reports substantial emissions of CCl4 in eastern China after the dispersive use of CCl4 was phased out in 2010, and the government intensified CCl4 regulations from 2019 to 2022. Finally, through an extensive industrial sampling campaign, we identified potential sources of emissions for CCl4.

Results
Two sites were set up to measure CCl4 and other trace gases (Fig. 1). One observation site was located on the Zijingang Campus of Zhejiang University (hereafter named ZJU, 30.306°N, 120.095°E) in the northwest of Hangzhou City, Zhejiang Province in central-eastern China. The other sampling site was in the Damaojian Mountain in Shanghuang village (hereafter named SHH, 28.583°N, 119.508°E), Wuyi County, Zhejiang Province. Based on the emission sensitivity derived from the backward simulation using the FLEXible PARTicle dispersion (FLEXPART) model, ZJU and SHH were more sensitive to emissions from eastern China (especially in Zhejiang, Anhui, Jiangsu, Jiangxi, and Fujian provinces) than GSN (see Fig. 1 and Supplementary Fig. 2).

During the observation period, the average concentrations of CCl4 were 79.3 ± 14.3 ppt and 80.0 ± 14.6 ppt at ZJU and 76.8 ± 14.3 ppt and 77.9 ± 14.6 ppt at SHH in 2021 and 2022, respectively (Fig. 2a and Supplementary Fig. 3). Using the atmospheric concentration observations at ZJU and SHH and an inverse modeling method, emissions of CCl4 in eastern China were quantified (see Methods for detailed information). Eastern China in this study includes Shandong, Henan, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, and Fujian, whose emissions the mole fractions observed at ZJU and SHH sites were sensitive to (Fig. 1). Emissions of CCl4 in eastern China were estimated to be 7.0 ± 1.6 Gg yr−1 in 2021, and 8.2 ± 1.8 Gg yr−1 in 2022, with a mean emission of 7.6 ± 1.7 Gg yr−1 (Fig. 2b).

Park et al.14 reported that emissions of CCl4 in eastern China (see definition of boundary in Park et al.14) decreased from 11.9 ± 1.5 Gg yr−1 in 2014 to 8.8 ± 1.7 Gg yr−1 in 2017, 6.0 ± 1.5 Gg yr−1 in 2018, and 6.3 ± 1.0 Gg yr−1 in 2019 (Fig. 2b). Meanwhile, the emissions of CCl4 in eastern China (7.6 ± 1.7 Gg yr−1) estimated in this study for 2021–2022 were comparable to (higher than, although not significantly) those in 2019 (6.3 ± 1.0 Gg yr−1)14 within the uncertainties, suggesting persistent emissions of CCl4 in these areas since 2019.

Moreover, emissions of CCl4 in eastern China (7.6 ± 1.7 Gg yr−1) during 2021–2022 were significantly higher than the consumption of CCl4 for dispersive uses (0.12 Gg yr−1 in 2021) as reported by China to the United Nations Environment Program (UNEP, https://wesr.unep.org). The consumption of CCl4 for dispersive uses in China was 0.20–0.26 Gg yr−1 during 2010–2018 and 0.11–0.14 Gg yr−1 during 2019–2021 (Fig. 2b). Similarly, the emission estimates of CCl4 after 2010, as inferred from atmospheric observations (5.6–27 Gg yr−1)10–12,14, were higher than the reported consumption of CCl4 (Fig. 2b). These results suggest (1) larger consumption of CCl4 than that reported, (2) continuous emissions of CCl4 from other sectors in addition to the reported consumption sectors, and (3) a combination of these two scenarios.

The northeastern region of Shandong, the eastern region of Jiangsu, and the southeastern region of Zhejiang were identified in this study as major source regions of CCl4 in eastern China during 2021–2022 (Fig. 3; Supplementary Figs. 4 and 5). Additionally, air masses corresponding to high concentrations of CCl4 typically pass through these regions (Supplementary Fig. 6, see Supplementary Discussion 2 for more details). The high emissions of CCl4 from Shandong, Zhejiang, and Jiangsu were generally consistent with those reported by Park et al.14 based on observation data from GSN, South Korea. However, our study also revealed emissions of CCl4 from Jiangxi (1.1 ± 0.92 Gg yr−1) and Fujian (0.99 ± 0.64 Gg yr−1) provinces (Fig. 3 and Supplementary Table 1) that were not quantified by Park et al.14. This discrepancy is likely due to the GSN observations being less sensitive to emissions in these provinces (Fig. 1). Overall, a large amount of CCl4 emissions were detected in eastern China, emphasizing the continued necessity to perform observations of CCl4.

The emissions of CCl4 from emissive uses in China were estimated to be zero using the bottom-up inventory method by Fang et al.7 and Wan et al.21, as they set the consumption of CCl4 for dispersive uses to zero. Although some bottom-up estimations of CCl4 emissions included CCl4 consumption for dispersive uses and other uses (e.g., feedstock), the emission estimates by Bie et al.8 (5.1 ± 3.5 Gg yr−1 in 2014) and Sherry et al.9 (7.3 Gg yr−1 in 2014) were lower than the top-down estimates for 2014 (26.5 ± 8.1 Gg yr−1 by Park et al.11 and 18.2 ± 5.6 Gg yr−1 by Lunt et al.12). Of note, comparisons between bottom-up and top-down CCl4 estimates after 2014 are not possible as post-2014 bottom-up emission estimates are lacking. Thus, further comprehensive bottom-up and top-down studies are strongly recommended to bridge the associated emission gaps for CCl4.

The relative importance of CCl4 emissions is increasing in China. For instance, the ozone depletion potential (ODP)-weighted emissions (equivalent to CFC-11 emissions) of CCl4 in eastern China estimated in this study (6.6 ± 1.5 CFC-11-eq Gg yr−1) is comparable to the unexpected emissions of CFC-11 (7.0 ± 4.0 Gg yr−1) in eastern China during the 2014–2017 period relative to those during 2008–201214. Since the CFC-11 emissions were reduced between 2017 and 2019, becoming lower than the ODP-weighted CCl4 emissions14, emissions of CCl4 have become increasingly important in eastern China. Moreover, ODP-weighted CCl4 emissions in eastern China are several times higher than the ODP-weighted emissions of other major ODSs, for example, it is 17.6 times higher than the 0.38 ± 0.75 CFC-11-eq Gg yr−1 of dichlorodifluoromethane (CFC-12) in 2019 in eastern China14, 9.5 times the 0.69 (0.38–1.0) CFC-11-eq Gg yr−1 of 1,1-dichloro-1-fluoroethane (HCFC-141b) in 2020 in eastern China22, and 1.4 times the 4.6 (3.9–5.3) CFC-11-eq Gg yr−1 of chlorodifluoromethane (HCFC-22; but for all of China) in 201923. Therefore, this study concludes that further investigations on emissions of CCl4 and the continuation of atmospheric monitoring of CCl4 in East Asia are necessary to preserve the ozone layer.

The emissions of CCl4 in eastern China during 2021–2022 (7.6 ± 1.7 Gg yr−1 in this study) were comparable to those before 2019 (6.1 ± 1.5 Gg yr−1 in 2018 and 6.3 ± 1.0 Gg yr−1 in 2019)16. This comparison indicates that the emissions of CCl4 in eastern China did not fall over this period. Based on the field measurement-based emission factors, Li et al.24 reported that CCl4 emissions from chloromethane production plants were only 2.2 ± 1.6 Gg yr−1 in 2019 in China. The consumption of CCl4 for dispersive uses as reported by China to the UNEP (https://wesr.unep.org) was 0.12 Gg yr−1 in 2021. Thus, the total emissions of CCl4 from chloromethane production plants and dispersive uses in China are smaller than our top-down emission estimates, indicating the presence of other sources of CCl4 emissions.

A comprehensive measurement campaign comprising 456 exhaust samples from 17 industry sectors was performed to determine potential sources of CCl4 (see Methods). This campaign shows that the manufacturing of general-purpose machinery (MGPM, mainly manufacturing engines, excavators, and other heavy machinery; see Supplementary Table 2) sector emits high concentrations of CCl4. In the MGPM sector, the highest concentration of CCl4 was 103 ppb (parts per billion, 1 ppb = 1000 ppt), and the average concentration was 5.4 ppb (Fig. 4). For the manufacture of raw chemical materials and chemical products (MRCM, mainly manufacturing coatings and resins) sector, the concentration of CCl4 reached 38 ppb, with an average concentration of 2.3 ppb. Moreover, the manufacture of electrical machinery and apparatus (MEMA) and manufacture of automobiles (MA) sectors had average CCl4 concentrations of 1.8 ppb and 1.2 ppb, respectively. The average concentrations of CCl4 in MGPM, MRCM, MEMA, and MA sectors were approximately 71, 30, 24, and 16 times the northern hemispheric atmospheric background concentration (~0.076 ppb, http://agage.mit.edu/), respectively, and 67, 29, 23, and 15 times the average concentration of CCl4 at ZJU and SHH (~0.080 ppb), respectively.

The high concentrations of CCl4 in these sectors may arise from (1) byproducts of CCl4 during the use or production of other chemicals/products, (2) using CCl4 as feedstock to produce other chemicals/products, (3) uses of industrial products with impurity of CCl4 (e.g., CCl4 in chloroform17), or (4) other unidentified uses of CCl4. Of note, the first three processes mentioned above are not controlled by the Montreal Protocol, and countries with these processes if any are not against the Montreal Protocol. Nevertheless, our observations reveal that the MGPM, MRCM, MEMA, and MA sectors are potential sources of CCl4 emissions and have not been identified as such in previous studies nor under the on-site enterprise supervision of CCl4. The average concentrations of CCl4 from other sectors (see Supplementary Table 2 for detailed information) sampled in this study were between 0.09 and 0.42 ppb (Fig. 4), only slightly higher than the atmospheric background level (~0.076 ppb) and were, therefore, not deemed to be as important as the MGPM, MRCM, MEMA, and MA sectors.

The number of enterprises has exhibited an obvious upward trend for the MGPM and MEMA industries since 2018 (growth rate: 10–16% yr−1 during 2018–2021)25 (Supplementary Fig. 7). Although the number of enterprises in most other industries was relatively stable during 2011–202126 (Supplementary Fig. 7), the overall trend has been on the rise (Supplementary Fig. 7). Considering that they may represent potential sources of CCl4 emissions, attention should be paid to these industries (particularly MGPM) to evaluate their impact on the ozone layer.

This study quantified substantial emissions of CCl4 in eastern China (7.6 ± 1.7 Gg yr−1) during 2021–2022 based on observations at two sites located in Zhejiang Province, China. By conducting extensive sampling, we detected high concentrations of CCl4 in the exhaust gases from the manufacture of general-purpose machinery and the manufacture of raw chemical materials, and chemical products, implying that these industrial sectors may be potential sources of CCl4. These findings strongly endorse continued monitoring of atmospheric CCl4 to protect the ozone layer.

Methods
At the ZJU site, in-situ hourly samples were collected for 30 min approximately 20 m above the ground between January 2021 and December 2022. In total, 11,174 samples were collected at the ZJU site over two years. A total of 294 samples were collected at the SHH site. Samples at SHH were collected in 3.2-L stainless steel canisters (Entech Instrument, Inc., Simi Valley, CA, USA) at approximately 1100 m asl for 1 min at 14:00 LT (local time) between May 2021 and November 2022 (no sampling in case of rainy or snowy days) and transported to the Zhejiang University laboratory for analysis. There were no apparent CCl4 emission sources in the immediate regions near the ZJU or SHH sites.

Samples from both sites were quantitatively analyzed by gas chromatography coupled with a mass spectrometry detector (GC-MSD; ZF-PKU-VOC1007, Beijing, China). Briefly, after removing H2O and carbon dioxide (CO2), the sample was enriched in a cold trap at −160 °C, then desorbed at 120 °C for 3 min. The desorbed sample was separated in the GC system equipped with a DB-624 column (60 m length × 0.25 mm i.d. × 1.4 μm film thickness; Agilent Technology, Santa Clara, CA, USA) with helium as the carrier gas. The standard gas used for determining CCl4 was TO-15 (Linde Gas North America LLC, Medford, OR, USA). The linear coefficient (expressed as R2) of the five-point calibration curve for CCl4 was 0.998, and the detection limit was 14 ppt (parts per trillion), ~5.5 times lower than the average measured concentrations of CCl4 (80.0 ± 13.6 ppt at ZJU and 77.5 ± 15.3 ppt at SHH).

This study used an inverse modeling technique based on a FLEXPART atmospheric transport simulation model and the Bayesian inversion algorithm to quantify CCl4 emissions. The FLEXPART-based inversion method has been applied in many previous studies27–29. In brief, driven by the meteorological data (European Center for Medium-Range Weather Forecasts) with a spatial resolution of 1° × 1° and a temporal resolution of 3 h, the FLEXPART model was run in backward mode for 20 days. The source–receptor relationship (termed as “emission sensitivity”) matrix was established based on the backward simulation. Combining the derived emission sensitivity matrix, Bayesian inversion technique, and the CCl4 data from ZJU and SHH, yielded the CCl4 emission strength (1° × 1°) in grid cells over eastern China. The associated equations are as follows:

J(x) = 1/2 (x − xa)T Sa−1 (x − xa) + 1/2 (yobs − Hx)T So−1 (yobs − Hx) (1)

By solving ∇x J(x) = 0 yields:

x = xa + Sa HT (H Sa HT + So)−1 (yobs − Hxa) (2)

Sb = (HT So−1 H + Sa−1)−1 (3)

where x represents the state vector of the emission strength, yobs represents the CCl4 measurement vector, xa represents the prior emission vector, H is the emission sensitivity, Sa and Sb are the error covariance matrix of prior and posterior emissions, respectively, and So represents the error covariance matrix of measurement data.

To obtain the prior emission vector (xa), the national total CCl4 emissions in China during 2011–2015 (23.6 ± 7.1 Gg yr−1)11 and emissions in other countries (derived from global emissions [44 ± 15 Gg yr−1 in 2020]1 and emission estimates of the United States [4.0 (2.0–6.5) Gg yr−1 during 2008–2012]30, Japan [0.6 Gg yr−1 in 2014]9, South Korea [0.2 Gg yr−1 in 2014]9, and India [2.8 Gg yr−1 in 2014]9) were assigned to grid cells with a uniform spatial distribution in eastern China, and a population-proxy distribution in other regions (Supplementary Fig. 8a). The inversion results were also evaluated using prior emissions following population distribution in 202031 (Supplementary Fig. 8b).

The robust extraction of baseline signal (REBS) method32 was applied to distinguish background and non-background concentrations of the in-situ CCl4 concentrations at ZJU. The REBS is a statistical method developed by Ruckstuhl et al.32 to extract background signals using a robust local regression model and has been widely applied to determine baselines of trace gases in inversion studies22,33. The observed concentration at a certain time (yi(t)) was divided into three parts as depicted in the following equation:

yi(t) = gi(t) + mi(t) + ei (4)

where g(t) represents the background concentration at time t, m(t) is the enhanced concentration caused by polluted plum during t, and e represents the observational error.

The baseline curve g was obtained using the REBS technique over a sufficiently long bandwidth (90 days) by assuming that most observations are at background levels and that the baseline signal changes slowly relative to the regional signal. In this method, data points closer to the time of consideration were given more weight, and data points outside a specific range (1.5σ in this study) were iteratively excluded. For flask samples at SHH, the background concentration of CCl4 was determined as the lowest concentration measured in a two-month moving window. The observational error (σobs) was calculated as follows:

σobs = sqrt(σobs_precision^2 + σobs_representation^2 + σbackground^2) (5)

where σobs_precision is the measurement precision of CCl4, σobs_representation stands for representation of the observation, σbackground represents the background uncertainty. In this study, σbackground was calculated as 1-Sigma standard deviation of the measurements each day for online samples and as 1-Sigma standard deviation of the measurements in a two-month moving window for flask samples. The σobs_representation was estimated as 1-Sigma standard deviation of the fitted background concentrations during the sampling period. The diagonal elements of So were set to squared σobs.

The diagonal elements of Sa were calculated as squared uncertainty of the prior emission field. The off-diagonal elements of Sa were calculated based on previous studies29,34, with the spatial decorrelation length scale set to 400 km. In this study, three sets of prior emissions were established (i.e., 150%, 100%, and 50% of the reference prior emissions), and three uncertainties (600%, 450%, and 300% for uniformly distributed prior emissions) were set under each set of prior emissions. A total of nine inversions were carried out for each year. The final posterior emissions were the average of the nine inversions (Supplementary Fig. 9). The posterior CCl4 emissions in eastern China under the nine inversions in 2021 and 2022 were loaded in the range of the three sets of prior emissions (Supplementary Fig. 9). The posterior emissions were insensitive (varied <9%) to variations in emission uncertainty, suggesting that the prior emissions used in this study were not systematically high or low, and that the observations constrain posterior emissions well. We also tested baselines from the Advanced Global Atmospheric Gases Experiment (AGAGE) baseline filtering method (baseline obtained by fitting the daily minimum with a second-order polynomial)35 to examine the impact of different baselines on posterior emissions. The inversion ensembles (Supplementary Table 3) using two baselines (AGAGE and REBS methods) and two prior emission fields (uniform and population-proxy distributions) show that the posterior emissions varied by <6% (standard deviation divided by mean) during 2021–2022 (Supplementary Fig. 10).

A comprehensive sampling campaign of 379 industrial enterprises was carried out from September 2021 to January 2022 in a city in eastern China (Fig. 4a; the number of samples in each industry sector is shown in Supplementary Table 2). Finally, 459 valid samples were obtained from the chimney vents of the target enterprises (see sampling method details in Supplementary Information). The industrial samples collected in this study were analyzed using the same analytical system as the ZJU and SHH samples.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Please see attached PDF file.\nReview of NCOMMS-23-29246, Substantial carbon tetrachloride emissions in eastern China, after 2010 dispersive-use phase-out and 2019–2022 strict government supervision: A concern for the ozone layer, by Li et al.\nI shall first briefly respond first to the questions posed on Authors instructions, then expand.\nWhat are the noteworthy results?\nBy far, the most noteworthy results are the measurements of the content of CCl4 in the effluent of 17 types of industries, as detailed mainly in the Supplement and described at the end of the paper.\nThe authors have mainly highlighted their estimate of the emission of CCl4 for Eastern China as a noteworthy result; in my opinion, several aspects of the calculation of the emission of CCl4 for Eastern China are deeply flawed and the uncertainty value for this emission is unjustifiably low.\nWill the work be of significance to the field and related fields? How does it compare to the established literature? If the work is not original, please provide relevant references.\nThe aspect of this work I think is most noteworthy, described just above, is very important, new and unique, and would have very strong impact. The aspect of the work the authors have chosen to emphasize is not, in my opinion, either new enough or strongly enough quantified to warrant publication in Nature Communications.\nDoes the work support the conclusions and claims, or is additional evidence needed?\nNo for the aspect of the work the authors have emphasized (emission of CCl4 for Eastern China); yes for the aspect I feel is most important (emission of CCk4 from various industries).\nAre there any flaws in the data analysis, interpretation and conclusions? Do these prohibit publication or require revision?\nYes, as I will elaborate below. Simply, using the nation-wide estimate of CHCl3 for two years, 2013 and 2015 (Supplemental Table 1) is flawed because the target gas, CCl4, does not exhibit a strong enough correlation with CHCl3 to justify the use of CHCl3 as a tracer for the emission of CCl4. Also, a lot has happened in China since 2015, following the 2018 publication of the Montzka et al. paper. Applying data from 2013 and 2015 to infer emissions in 2020 and 2021 is not sound. Finally and very importantly, the data could be used in an inverse model framework to compute emissions of CCl4 from Eastern China. Yet this approach is not used.\nIs the methodology sound? Does the work meet the expected standards in your field?\nMethods not sound, as noted above.\nIn terms of expected standards in the field, there are a large number of problems including the reader being asked to believe there is no substantial bias between the standard used for Trinidad Head Station (the Scripps Institution of Oceanography USA25 standard) and the standard used at their station (the Linde Gas standard), with reference only in the literature and no support for supplement. In papers on this topic where mixed standards are used, it is common to report side-by-side measurements of a sample from a current lab capable of running both standards, to quantify the actual bias).\nThe quantification of the emission of CCl4 from Eastern China is well below the expected standard, which is to make use of an inverse (or perhaps data assimilation) model.\nIs there enough detail provided in the methods for the work to be reproduced?\nNo. There is minimal discussion of either the Robust Extraction of Baseline Signals (REBS) or the trajectory clustering method in either Main, Methods, or Supplement.\nNow I will summarize the paper and highlight my “Major” and “Minor” points.\nOverview:\nFirst off, the paper is exceptionally well-written. Other than the occasional use of “This” or “These” as a noun, there is little I can offer to improve the communication of the message. I sincerely appreciate being sent a paper to review that is so well-written.\nThe paper reports observations of CCl4 and related species from two sites in Eastern China. The observations are stated to be hourly; 11,174 sample were acquired at one site between Jan 2021 and Dec 2022 whereas 294 samples were acquired at the other site between May 2021 and Nov 2022. For the site with the larger number of samples, that were collected for about 65% of the sampling opportunities (that is, 11,1174 / 2x8760). For the other site, data were collected for only about 2.5% of the sampling opportunities (this is, 294 / 16/12 x 8760)\nThe data are used to compared mean and various percentiles of CCl4 to a time series from the Trinidad Head station, which reveals frequent enhancements of CCl4 in Eastern China and to compute the emission of CCl4 in Eastern China in 2020 and 2021. Finally, observations of CCl4 are reported from the exhaust of 17 types of enterprises in Wuxi, which is in the Jiangsu province of Eastern China. These observations show highly elevated levels of CCl4 in the following sections: Manufacture of General Purpose Machinery mainly manufacturing of engines, excavators, and other heavy machinery); Manufacture of Raw Chemical Materials and Chemical Products industry (mainly manufacturing coatings and resins), Manufacture of Electrical Machinery and Apparatus, and the Manufacture of Automobiles.\nIn my opinion, the comparison of time series for CCl4 to data from Trinidad Head (that is, Figure 1b) is likely not enough to warrant publication in Nature Communications, because the contemporary emission of CCl4 from Eastern China is simply well known. The only possible surprise here would have been the disappearance of elevated CCl4 from Eastern China. I state the elevation of CCl4 in Eastern China is well known based on the content of numerous other studies cited in the paper and that have data which are used in Figure 1b, as well as a very important additional paper (Benish, S. E. et al., Airborne observations of CFCs over Hebei Province, China in Spring, JGR, 126, e2021JD035152, 2021) that is not cited (lots more on the importance of this paper below.\nIn my opinion, the computation of the emission of CCl4 for Eastern China in Nature Communications is also not warranted, because CCl4 exhibits such a poor correlation with CHCl3, the gas selected as the “Tracer” for Equation 4. Figure 2 of the Supplement shows almost no correlation between these two species; the data look like a traditional “shotgun” blast, with the slope clearly influenced by a single outlier. Um, under what conditions was this outlier obtained? And, how does the slope change with the neglect of this single outlier? Any result that depends on a single outlier is highly suspicious. Finally and most importantly, how can the slope of the data (“0.087” with an uncertainty of only “0.001”) be known so well, when the data (outside this one outlier) exhibit almost no correlation? Please note Benish et al. (their Figure 4) report a value of r2 between CCl4 and CHCl3 of only 0.03 based on airborne sampling over the Hebei Province in spring of 2016. A much higher value of r2 of 0.78 is reported between CCl4 and CFC-11.\nFinally, in my opinion, the most important part of this paper is not given due justice. I have extracted from the Potential industrial sectors of CCl4 emissions section of the paper the four dominant industrial sectors. None of these sectors are named in the abstract, which only states:\nSubsequently, our study identified potential industrial sources of CCl4 emissions that were not revealed in previous studies and not controlled by the governmental supervision of CCl4. Therefore, our study can guide mitigations of CCl4 emission and endorse continued monitoring of atmospheric CCl4 to protect the ozone layer.\nI shall provide further detail on a few Major and Minor points below.\nMajor Points\n1) The numerical values of the emission of CCl4 for Eastern China in 2021 and 2022 are highly suspicious, due to the use of CHCl3 as “the tracer”, the use of emissions for CHCl3 from much earlier years (2013 & 2015), and the report by others (that is, Benish et al. 2021) of much higher correlations between CCl4 and other species, such as CFC-11. If the authors see a high correlation of CCl4 with CFC-11 that is not dependent on a single outlier, then perhaps CFC-11 can be used as “the tracer”. Regardless, whatever tracer, if the authors insist on following this approach, upon revision I suggest they:\na) describe the correlation of the tracer with CCl4\nb) state how the uncertainty in the slope is found\nc) justify the use of much older emission estimates (for CHCl3, for 2013 and 2015) to a more recent time period, given so much attention to the emission of ODSs in China following the publication in 2018 of the Montzka et al. paper.\nd) Most importantly show that the selected tracer exhibits a meaningful correlation with CCl4, not only for the ~2 year station data, but also for the samples obtained in the exhaust of 17 specific industries. If CHCl3 show a tight correlation for data from Manufacture of General Purpose Machinery, Raw Chemical Materials and Chemical Products, Electrical Machinery and Apparatus, and Automobiles, then perhaps this gas can be used as a suitable tracer. The submitted paper does not describe any other data for the 17 specific industries, other than CCl4. If there is little to no correlation between CHCl3 and CCl4, then another tracer is needed. Regardless, the selected tracer *MUST* show a significant correlation with CCl4 for both the station data and the exhaust data, for the suspicion noted above to be assuaged.\nAnother option I encourage the authors to consider, should they insist on attempting to quantify the emission of CCl4 for Eastern China in 2021 and 2022, is to use either the inverse model approach or perhaps data assimilation.\nOne more option, one I prefer, is that if this paper proceeds in Nature Communications, the finding of significant emissions of CCl4 from certain industrial sectors be the primary point of emphasis. I expand further on this suggestion in Major Point #4, below.\n2) I suggest the authors provide better justification of the combination of data from Trinidad Head Station obtained using the Scripps Institution of Oceanography USA25 standard with data from their stations, that use the Linde Gas standard. Seeing a comparison of samples run through a lab with one standard, against data measured using the other standard, is common (for example, Tables S2, S3, and S4 of the previously mentioned Benish et al. paper). Also, upon revision, there are many places where the results of Benish et al. 2021 should be cited, and data from this paper should be added to Figure 2b.\n3) As noted above, there is minimal description of either the Robust Extraction of Baseline Signals (REBS) or the trajectory clustering method in either Main, Methods, or Supplement. Upon revision, more details are needed. For the trajectory clustering method, the HYSPLIT model is used, yet there are no references for HYSPLIT apparent in the submitted manuscript. I suggest citing this paper by Stein et al. https://doi.org/10.1175/BAMS-D-14-00110.1. Also, Figure 1 of Supplement is hard to understand. It is not clear which colors correspond to which numbered trajectory, and the use of yellow (for SHH) is ill-advised, as this line is very hard to see. I also suggest writing “ZJU” onto both panels of Fig 1a and “SHH” on both panels of Fig 1b.\n4) As noted above, I feel the most important result of this paper is not highlighted adequately. If this paper proceeds in Nature Communication, I suggest dispensing with the comparison of the new measurements of CCl4 in 2020 and 2021 with data from Trinidad Head quickly, either improving or perhaps striking the attempt to compute emissions of CCl4 for Eastern China over 2020 and 2021, then moving a lot of the material in Supplement regarding what I believe is the key new message of this paper into Main (or Methods).\nMinor points\na) The ODP and GWP for CCl4 given in the most recent Scientific Assessment Ozone Depletion report are 0.87 and 2150, respectively. Upon revision, please adopt these values (since they are current and have been assessed by the relevant experts) on lines 38 and 39, and in the rest of the paper (that is, CFC-11 equivalent emissions) that rely on the use of these numbers.\nb) Line 50. Again, as noted above, the paper is extremely well written! Minor point, but rather than “to restrict CCl4 emissions”, I suggest “to understand the reason for the gap in bottom-up and top-down emissions”. Regardless, if the paper is revised as suggested above, there will certainly be much new text regarding how the observations will be used in the future “to restrict emissions”.\nc) Benish et al., multiple lines: Benish et al. could also be cited on line 68 as well as lines 76 to 79. : Benish et al. also showed, based on their strong correlation of CFC-11 with CCl4, the likely co-production of these two species (line 82).\nd) Line 81. “observed” should be “reported”, since the paper was published in 1981.\ne) Lines 84, 86, and 88. The use of “16” on lines 84 & 86 and “19” on line 88 is confusing. If these numbers are truly different, a bit more explanation would be helpful.\nf) Line 95. Suggest “this study describes observations of …”\ng) Line 99. Here, I prefer “substantial emissions of CCl4” since substantial modifies emissions\nh) Line 121. Not sure “41N” is really “close” to 30.306 and 28.583N; perhaps a different word can be used\ni) Line 125. Suggest “These observations indicate …”\nj) Line 131. In addition to the request for more information about REBS, the inclusion of a reference here would be helpful\nk) Line 135. Suggest “respectively” rather than “in respect”\nl) Lines 146 to 149. Not sure all of this detail is needed, especially if the paper’s focus is altered, but if this text survives, should also cite numerical values from the Benish et al. study\nm) Lines 153 to 156. The use of HYSPLIT for the trajectory clustering should be stated, with a reference to a HYSPLIT paper, perhaps the above mentioned Stein et al. study\nn) Lines 164 to 165. A citation for the CWT method should be given\no) Line 235. Suggest “This comparison indicates … did not fall over this period of time” although, given the inherent problems I have identified with the estimate for the computation of the emission of CCl4 in Eastern China, I have to also state this sentence may need to be altered in a more significant manner, or perhaps removed.\np) Line 268. Suggest “Our observations reveal …”\nq) Line 307. In addition to the website, should also provide a reference here, for HYSPLIT.\nr) Line 506. Figure 1 is very important: this figure is the first graphical component of the paper. The figure should be improved, either by zooming in to make the geographic region of China stand out more clearly, of perhaps some other manner. The three words on panel c are hard to read. Finally, I suggest also pointing out the location of Beijing, so that readers can better orient the locations of ZJU, SHH, and Gossan.\ns) Line 518. I have stared at this figure, and can not find either the black or pink dots. If these dots are truly present, they should be made more visible.\nt) Line 527. Figure 5c is very very important, in my opinion. Figure 5b is easy to “understand”, but hard to decipher, as many of the colors are similar. Perhaps various symbols with a smaller number of colors should be used. Finally, Figure 5a is hard to understand: what is the inset?", "reviewer_2": "This manuscript reports on unique measurements of the ozone-depleting CCl4 from two new sites in China during 2021 and 2022. Quantifying CCl4 emissions from atmospheric measurements is an important issue worth of consideration by this journal because substantial CCl4 emissions persist globally and are delaying recovery of the ozone layer, but the sources responsible for these emissions have not been adequately identified and characterized. Ongoing measurements from these new sites have the potential to inform us more accurately about emissions from unique regions of China than has been possible previously. The authors find that emissions from eastern China are comparable in magnitude to those published elsewhere for this same region through 2019 using measurement data from a different site (Gosan, S. Korea). The authors argue that the emissions they have derived for CCl4 during 2021-2022 are unusually high owing to the “implementation of strict government supervision for CCl4” after 2018. In an intriguing second part of the paper, the authors report on measurements of CCl4 from air collected from the chimney vents of 379 businesses that represented 17 different industry sectors. Unusually high CCl4 concentrations were observed from some sectors that haven’t been associated with CCl4 emissions in the past. While the data presented are unique and relevant to the issue of ozone depletion and ozone layer recovery, I cannot recommend publication at this time for a number of reasons that I delineate below.\nFirst, the importance and uniqueness of this manuscript primarily relates to the availability of new measurement data from new sites AND the assertion that the derived emissions from eastern China are higher than they should be owing to the “implementation of strict government supervision for CCl4” during this period. However, there is no indication that the purpose of the “supervision program” was to limit CCl4 emissions. Certainly, it was instituted to improve tracking of produced CCl4 so that large amounts couldn’t be diverted to produce CFCs, but no indication is provided by the authors to suggest that this program might have had as its goal substantial reductions in CCl4 emission (and I don’t know this independently).\nSecond, the emission derivation is performed here in a manner that substantially underestimates the true uncertainties, making me question the reliability of the CCl4 emission estimates provided. Emissions of CCl4 in this work are derived by scaling the concentration enhancements for CCl4 to those measured concurrently for CHCl3 and multiplying that ratio by the “known” CHCl3 emission. This method works best if the enhancements for the two gases are highly correlated. Figure 2 of the Supplementary material shows the correlation, but its significance isn't quantified or mentioned (and visually does not look very high). Most worrisome, however, is that emissions of CHCl3 from eastern China from 2013 (48.3 Gg) and 2015 (80-95 Gg) (not 2021 or 2022) are used to derive emissions for CCl4 in 2021 and 2022 based on measurements in this latter years. No consideration of what CHCl3 emissions might have been in 2021 or 2022 is mentioned, making the estimate of CCl4 emissions in the key years highly suspect, particularly since the large difference (change?) in CHCl3 emissions during 2013 and 2015 suggests that CHCl3 emissions from eastern China can change substantially from year-to-year.\nThird, the authors also assert that they have found CCl4 emissions in new regions of China (Jiangxi and Fujian provinces). They also acknowledge that the trajectory method can often erroneously place emissions upwind of the actual source region (my interpretation of the “ghost” sources in the ocean discussed in Figure 3). This seems also a possibility for the proposed CCl4 emissions these two regions and would need further investigation before one could reliably assert that there are significant emissions coming from these provinces.\nFourth, in a highly unique section of the paper, concentration measurements were made from the stacks of many industries and potentially suggest the discovery of previously unrecognized CCl4 sources. However, this result needs some further development before such assertions can be made with any reliability. Questions needing to be explored so that the implications of the new results can be better understood include: how extensive is this industry and how many businesses related to this industry exist in China? What are the potential fluxes of CCl4 from these stacks? Can you put those two numbers together to estimate potential emissions from this sector? What are the potential underlying variables affecting the range of CCl4 concentration in stacks associated with the same industry? Particularly MGPM? For this portion of the paper to be useful to a wider audience, it will require some further estimation of these quantities, or reasonable limits applied to them, so that the potential importance of these other sources can be roughly assessed.", "reviewer_3": "Review of the manuscript: Substantial carbon tetrachloride emissions in eastern China, after 2010 dispersive-use phase out and 2019–2022 strict government supervision: A concern for the ozone layer by Li et al., submitted to Nature Comm.\nThe manuscript is related to on-going emissions of CCl4, for which a consistent gap be-tween bottom-up and top-down emissions exists globally, with higher emissions estimated by measurement-based top-down methods. The manuscript discusses enhanced CCl4 concentrations in eastern China and estimates emissions, which are higher than expected. Authors also make use of industrial samples, which show massively high concentrations of CCl4 and related them to certain types of industries.\nI am in favour of publishing this manuscript in Nature Comm. because of its global implications. However, some of the comments should be covered carefully.\nGeneral comments\n1. The most important finding is the massive concentrations close to industrial sites. Here an explanation with a high grade of probability is forbidden emissive uses of CCl4. Authors should clearly state this in the manuscript. This would be a very good point to make and this would also close the gap between the bottom-up and the top-down estimate, as in the bottom-up numbers such illegal practices are simply not taken into account as it was deemed highly unlikely, but obviously not.\n2. Accordingly, point number 1 should also be mentioned in the abstract. At the moment the paper stays very vague on this. The readers want to know the reason for these emissions and it does not make it better to hide it away.\n3. Several citations are at the edge of misquotation. These will be detailed in the specific comments below.\n4. Data must be openly accessible.\nSpecific comments:\nLine 21\n…was globally phased out by 2010, including China.\nLine 23\n…were disclosed, and the latest…\nLine 27\n…respectively, well above the global background.\nLine 29\nIn 2010 and after the implementation…\nLine 42\nFeedstock instead of \"raw material\"\nLine 42\nRef 6 is not needed for this argument and does not really cover this\nLine 45\n[8] is wrongly cited. In the report they say:\nGlobal CCl4 emissions did not significantly decline during the 2010–2019 period\n!%$& , %$' () +*-&\"#\n41±3 and 38±3 are not mentioned in the report (or at least this reviewer has not seen it)\nLine 47\nTake the newest numbers from the most recent ozone assessment (2022)\nLine 54\n…CCl4 emissions from dispersive uses were reduced…\nLine 55\nA bottom-up inventory for non-emissive uses of CCl4, including…\nLine 59\nMoreover, another independent study showed bottom-up emission estimates…\nLine 81\nRigby et al say at least 40-60%\nLine 178\nEquation 4 (see methods)\nLine 187\nReference missing for REBS\nLine 188\n…for total China reported…\nLine 203\n…from emissive uses…\nLine 214\nThe relative importance…\nLine 375\nData have to be open access"}, "author_response": "Responses to Reviewer #1:\nReview of NCOMMS-23-29246, Substantial carbon tetrachloride emissions in eastern\nChina, after 2010 dispersive-use phase-out and 2019–2022 strict government\nsupervision: A concern for the ozone layer, by Li et al.\nI shall first briefly respond first to the questions posed on Authors instructions, then\nexpand.\nWhat are the noteworthy results?\nBy far, the most noteworthy results are the measurements of the content of CCl\n4\nin the effluent of 17 types of industries, as detailed mainly in the Supplement and\ndescribed at the end of the paper.\nThe authors have mainly highlighted their estimate of the emission of CCl for\ncalculation of the emission of CCl for Eastern China are deeply flawed and the\n4\nuncertainty value for this emission is unjustifiably low.\nResponse:\nFollowing the suggestions by reviewers, we have given up the interspecies\ncorrelation emission estimates and used emission inverse modeling (review\ncomments suggest the method) in our revised manuscript. Thus, the flaws have\nbeen avoided, and the uncertainty value for this emission is justified (please see\nthe detailed responses below).\nWill the work be of significance to the field and related fields? How does it compare\nto the established literature? If the work is not original, please provide relevant\nreferences.\nThe aspect of this work I think is most noteworthy, described just above, is very\nimportant, new and unique, and would have very strong impact. The aspect of the\nwork the authors have chosen to emphasize is not, in my opinion, either new\nenough or strongly enough quantified to warrant publication in Nature\nCommunications.\nResponse:\nWe have improved our work and tried to emphasize the new and important\naspects (please see the detailed responses below).\nDoes the work support the conclusions and claims, or is additional evidence needed?\nNo for the aspect of the work the authors have emphasized (emission of CCl for\nyes for the aspect I feel is most important (emission of CCl from various\n4\nindustries).\nResponse:\nFor the aspect of the work of emission of CCl for eastern China, we have\n4\nchanged to a more advanced method than in our first submission. In our revised\nmanuscript, we have used inverse modeling and performed sufficient tests to\nsupport the conclusions and claims.\nFor the aspect of the work of emission of CCl from various industries, thank you\n4\nfor the positive feedback. We have further improved the text. Please see the\ndetailed responses below.\nAre there any flaws in the data analysis, interpretation and conclusions? Do these\nprohibit publication or require revision?\nYes, as I will elaborate below. Simply, using the nation-wide estimate of CHCl\n3\nfor two years, 2013 and 2015 (Supplemental Table 1) is flawed because the target\ngas, CCl , does not exhibit a strong enough correlation with CHCl to justify the\n4 3\nuse of CHCl as a tracer for the emission of CCl . Also, a lot has happened in\n3 4\nChina since 2015, following the 2018 publication of the Montzka et al. paper.\nApplying data from 2013 and 2015 to infer emissions in 2020 and 2021 is not\nsound. Finally and very importantly, the data could be used in an inverse model\nframework to compute emissions of CCl from Eastern China. Yet this approach\n4\nis not used.\nResponse:\nFollowing the suggestions by reviewers, we have given up the interspecies\ncorrelation emission estimates and used emission inverse modeling in our revised\nmanuscript. Thus, the flaws have been avoided, and the uncertainty value for this\nemission is justified (please see the detailed responses below).\nIs the methodology sound? Does the work meet the expected standards in your field?\nMethods not sound, as noted above.\nIn terms of expected standards in the field, there are a large number of problems\nincluding the reader being asked to believe there is no substantial bias between\nthe standard used for Trinidad Head Station (the Scripps Institution of\nOceanography USA25 standard) and the standard used at their station (the Linde\nGas standard), with reference only in the literature and no support for\nsupplement. In papers on this topic where mixed standards are used, it is 2\ncommon to report side-by-side measurements of a sample from a current lab\ncapable of running both standards, to quantify the actual bias).\nThe quantification of the emission of CCl from Eastern China is well below the\n4\nexpected standard, which is to make use of an inverse (or perhaps data\nassimilation) model.\nResponse:\nFollowing the suggestions by reviewers, in our revised manuscript (1), we have\nprovided a citation of the inter-comparison between the SIO and Linde gas\ncalibration standards (the ratio of two standards is 1.02, which provides\njustifications to compare concentration data from stations using two standards),\nand (2) we have switched to use emission inverse modeling to avoid the flaws of\ninterspecies correlation method mentioned above (please see the detailed\nresponses below).\nIs there enough detail provided in the methods for the work to be reproduced?\nNo. There is minimal discussion of either the Robust Extraction of Baseline\nSignals (REBS) or the trajectory clustering method in either Main, Methods, or\nSupplement.\nResponse:\nIn our revised manuscript, we have added detailed descriptions of either the\nrobust extraction of baseline signals (REBS) in the main text or the trajectory\nclustering method in SI.\nNow I will summarize the paper and highlight my “Major” and “Minor” points.\nOverview:\nFirst off, the paper is exceptionally well-written. Other than the occasional use of\n“This” or “These” as a noun, there is little I can offer to improve the\ncommunication of the message. I sincerely appreciate being sent a paper to\nreview that is so well-written.\nResponse:\nThank you. As for our revised manuscript, we have also tried to improve the\ncommunication of messages.\nThe paper reports observations of CCl and related species from two sites in\nacquired at one site between Jan 2021 and Dec 2022 whereas 294 samples were\nacquired at the other site between May 2021 and Nov 2022. For the site with the\nlarger number of samples, that were collected for about 65% of the sampling\nopportunities (that is, 11,1174 / 2x8760). For the other site, data were collected\nfor only about 2.5% of the sampling opportunities (this is, 294 / 16/12 x 8760)\nThe data are used to compared mean and various percentiles of CCl to a time\n4\nseries from the Trinidad Head station, which reveals frequent enhancements of\nCCl in Eastern China and to compute the emission of CCl in Eastern China in\n4 4\n2020 and 2021. Finally, observations of CCl are reported from the exhaust of 17\n4\ntypes of enterprises in Wuxi, which is in the Jiangsu province of Eastern China.\nThese observations show highly elevated levels of CCl in the following sections:\nexcavators, and other heavy machinery); Manufacture of Raw Chemical\nMaterials and Chemical Products industry (mainly manufacturing coatings and\nresins), Manufacture of Electrical Machinery and Apparatus, and the Manufacture\nof Automobiles.\nResponse:\nThank you. Below, we have provided our point-by-point responses to each of the\nreviewers’ comments.\nIn my opinion, the comparison of time series for CCl to data from Trinidad Head\n4\n(that is, Figure 1b) is likely not enough to warrant publication in Nature\nCommunications, because the contemporary emission of CCl from Eastern\ndisappearance of elevated CCl from Eastern China. I state the elevation of CCl\n4 4\nin Eastern China is well known based on the content of numerous other studies\ncited in the paper and that have data which are used in Figure 1b, as well as a\nvery important additional paper (Benish, S. E. et al., Airborne observations of\nCFCs over Hebei Province, China in Spring, JGR, 126, e2021JD035152, 2021)\nthat is not cited (lots more on the importance of this paper below.\nResponse:\nThank you for the comment. The important additional reference of Benish et al.\n(2021) has been added in our revised manuscript and in our new Supplementary Fig.\n3. We agree that based on previous studies, the elevation of CCl concentrations and\n4\nemissions in eastern China before 2019 is already well known; therefore, the presence\nof elevated CCl is not surprising. However, it is surprising that after the 2019–2022\n4\nstrict government supervision on CCl , there were still elevated CCl concentrations\n4 4\nand emissions in eastern China during 2021–2022, as uncovered in this study.\nFollowing the suggestions, we have moved the concentration results and comparison\nwith previous studies from the main text to SI.\nIn my opinion, the computation of the emission of CCl for Eastern China in\n4\ncorrelation with CHCl , the gas selected as the “Tracer” for Equation 4. Figure 2\n3\nof the Supplement shows 3 almost no correlation between these two species; the\ndata look like a traditional “shotgun” blast, with the slope clearly influenced by a\nsingle outlier. Um, under what conditions was this outlier obtained? And, how\ndoes the slope change with the neglect of this single outlier? Any result that\ndepends on a single outlier is highly suspicious. Finally and most importantly,\nhow can the slope of the data (“0.087” with an uncertainty of only “0.001”) be\nknown so well, when the data (outside this one outlier) exhibit almost no\ncorrelation? Please note Benish et al. (their Figure 4) report a value of r2 between\nCCl and CHCl of only 0.03 based on airborne sampling over the Hebei\n4 3\nProvince in spring of 2016. A much higher value of r2 of 0.78 is reported between\nCCl4 and CFC-11.\nResponse:\nThank you for this comment. We ultimately switched to using the inverse modeling\nmethod for emission estimation, and the above flaws of the interspecies correlation\nmethod have been avoided.\nFinally, in my opinion, the most important part of this paper is not given due\njustice. I have extracted from the Potential industrial sectors of CCl emissions\n4\nsection of the paper the four dominant industrial sectors. None of these sectors\nare named in the abstract, which only states:\nSubsequently, our study identified potential industrial sources of CCl emissions\n4\nthat were not revealed in previous studies and not controlled by the governmental\nsupervision of CCl . Therefore, our study can guide mitigations of CCl emission\n4 4\nand endorse continued monitoring of atmospheric CCl to protect the ozone layer.\nThank you for the comment. In our revised manuscript, we have named the potential\nindustrial sectors of CCl emissions in the abstract.\nLines 31–36 in the revised manuscript: “Subsequently, our study identified potential\nindustrial sources (manufacture of general purpose machinery and manufacture of\nraw chemical materials, and chemical products) of CCl4 emissions that were not\nrevealed in previous studies and were not under governmental supervision.”\nI shall provide further detail on a few Major and Minor points below.\nMajor Points\n1) The numerical values of the emission of CCl4 for Eastern China in 2021 and 2022\nare highly suspicious, due to the use of CHCl3 as “the tracer”, the use of emissions for\nCHCl3 from much earlier years (2013 & 2015), and the report by others (that is,\nBenish et al. 2021) of much higher correlations between CCl4 and other species, such\nas CFC-11. If the authors see a high correlation of CCl4 with CFC-11 that is not\ndependent on a single outlier, then perhaps CFC-11 can be used as “the tracer”.\nRegardless, whatever tracer, if the authors insist on following this approach, upon\nrevision I suggest they:\na) describe the correlation of the tracer with CCl4\nb) state how the uncertainty in the slope is found\nc) justify the use of much older emission estimates (for CHCl3, for 2013 and 2015) to\na more recent time period, given so much attention to the emission of ODSs in China\nfollowing the publication in 2018 of the Montzka et al. paper.\nd) Most importantly show that the selected tracer exhibits a meaningful correlation\nwith CCl4, not only for the ~2 year station data, but also for the samples obtained in\nthe exhaust of 17 specific industries. If CHCl3 show a tight correlation for data from\nManufacture of General Purpose Machinery, Raw Chemical Materials and Chemical\nProducts, Electrical Machinery and Apparatus, and Automobiles, then perhaps this gas\ncan be used as a suitable tracer. The submitted paper does not describe any other data\nfor the 17 specific industries, other than CCl4. If there is little to no correlation\nbetween CHCl3 and CCl4, then another tracer is needed. Regardless, the selected\ntracer *MUST* show a significant correlation with CCl4 for both the station data and\nthe exhaust data, for the suspicion noted above to be assuaged.\nAnother option I encourage the authors to consider, should they insist on attempting\nto quantify the emission of CCl4 for Eastern China in 2021 and 2022, is to use either\nthe inverse model approach or perhaps data assimilation.\nOne more option, one I prefer, is that if this paper proceeds in Nature\nCommunications, the finding of significant emissions of CCl4 from certain industrial\nsectors be the primary point of emphasis. I expand further on this suggestion in Major\nPoint #4, below.\nResponse:\nWe agree that the interspecies correlation method used to estimate CCl emissions in\n4\nour previous manuscript has several flaws, as pointed out in this comment. Thus,\nfollowing the suggestions, we have abandoned using the interspecies correlation\nmethod and instead used the inverse modeling method in our revised manuscript. The\ninverse modeling can directly quantify total CCl emissions in eastern China, avoiding\n4\nthe interspecies correlation flaws of (1) good correlations between CCl and tracer and\n4\n(2) known emissions of tracer during 2021–2022. The inverse modeling method can\nalso provide spatial distributions (1°×1° in this study) of CCl while the interspecies\n4,\ncorrelation cannot.\nAs suggested by this comment, the findings of significant emissions of CCl from\n4\nspecific industrial sectors were deepened in our revised manuscript. The specific\nchanges in the article are listed in Major Point #4.\n2) I suggest the authors provide better justification of the combination of data from\nTrinidad Head Station obtained using the Scripps Institution of Oceanography USA25\nstandard with data from their stations, that use the Linde Gas standard. Seeing a\ncomparison of samples run through a lab with one standard, against data measured\nusing the other standard, is common (for example, Tables S2, S3, and S4 of the\npreviously mentioned Benish et al. paper). Also, upon revision, there are many places\nwhere the results of Benish et al. 2021 should be cited, and data from this paper\nshould be added to Figure 2b.\nResponse:\nThank you for this comment. Benish et al. (2021) already did the test on CCl\n4\nconcentrations between Scripps Institution of Oceanography and Linde Gas standards.\nThey found that the ratio of CCl concentrations quantified using the Scripps\nsuggestingthedifference between the CCl concentrations quantified based on these\n4\ntwo standard gases is negligible (2%). Therefore, no additional tests were performed\nin this study. Moreover, concentration data from Trinidad Head Station (and AGAGE\nstations of Mace Head, Jungfraujoch, and Ragged Point are added in our revised\nmanuscript) using the Scripps Institution of Oceanography standard are only\nintroduced for comparison with our measured concentrations at ZJU and SHH in\nSupplementary Fig. 3 Moreover, the CCl concentrations measured by Benish et al.1\n4\nhas been added to original Figure 2b (now Supplementary Fig.3) in our revised\nmanuscript.\nTable S4 in Benish et al.. Summary statistics (N=27) and average CMA/PKU\ncorrection factor for 12 halocarbons (pptv) quantified during ARIAs. The first number\ndenotes the uncorrected value and the second number shows the corrected\nconcentration using the average CMA/PKU ratio obtained from the intercomparison\nexperiment.\nCompound Mean STD Min 25th 50th 75th Max CMA/PKU\nCorrection\nCarbon 88/89 21/21 53/54 74/75 86/88 102/104 138/140 1.02\ntetrachloride\nRevised text:\nLines 15–19 in the revised Supplementary Information: “The four AGAGE stations\nused SIO-05 standard gas (Scripps Institution of Oceanography, USA2) calibration\nscale, while this study used Linde Gas, thus introducing a potential bias. Nevertheless,\nthe differences between these standard gases are likely small1,3,4, e.g., the ratio between\nCCl concentrations determined with SIO-05 standard gas and Linde Gas was 1.021”.\nSupplementary Fig. 3 Concentration levels of CCl in different studies.\nmean levels are shown.\nReference:\nObservations of CFCs Over Hebei Province, China in Spring 2016. J. Geophys. Res-\nAtmos. 126 (2021).\ngases from the Advanced Global Atmospheric Gases Experiment (AGAGE). Earth\nSyst. Sci. Data 10, 985-1018 (2018).\nnearly two decades after its phaseout for dispersive uses. Proc. Natl. Acad. Sci. U. S.\nA. 113, 2880-2885 (2016).\nhydrochlorofluorocarbons and hydrofluorocarbons in 46 Chinese cities. Atmos.\nEnviron. 54, 387-392 (2012).\n3) As noted above, there is minimal description of either the Robust Extraction of\nBaseline Signals (REBS) or the trajectory clustering method in either Main, Methods,\nor Supplement. Upon revision, more details are needed. For the trajectory clustering\nmethod, the HYSPLIT model is used, yet there are no references for HYSPLIT\napparent in the submitted manuscript. I suggest citing this paper by Stein et al.\nhttps://doi.org/10.1175/BAMS-D-14-00110.1. Also, Figure 1 of Supplement is hard to\nunderstand. It is not clear which colors correspond to which numbered trajectory, and\nthe use of yellow (for SHH) is illadvised, as this line is very hard to see. I also suggest\nwriting “ZJU” onto both panels of Fig 1a and “SHH” on both panels of Fig 1b.\nResponse:\nThank you for this comment. Our revised manuscript has added a more detailed\ndescription of REBS and the trajectory clustering method. Reference to Stein et al.\n(2015) has been cited. And we have redrawn the original Supplementary Fig.1 (now\nSupplementary Fig. 6).\nRevised text:\nFor REBS method, more detailed introduction has added in the revised\nmanuscript in lines 295-308: “The robust extraction of baseline signal (REBS)\nmethod5 was applied to distinguish background and non-background concentrations of\nthe in-situ CCl concentrations at ZJU. The REBS is a statistical method developed by\nand has been widely applied to determine baselines of trace gases in inversion studies6,7.\nThe observed concentration at a certain time (y(t)) was divided into three parts as\ni\nshown depicted in the following equation:\ny(t) = g(t)+ m(t)+e\ni i i i\nwhere g(t) represents the background concentration at time t, m(t) is the enhanced\ni i i\nconcentration caused by polluted plum during t, and e represents the observational\ni i\nerror.\nThe baseline curve g was obtained using the REBS technique over a sufficiently\nlong bandwidth (90 days) by assuming that most observations are at background\nlevels and that the baseline signal changes slowly relative to the regional signal.\nIn this method, data points closer to the time of consideration were given more weight,\n-6//-;-8746;: 7<;:4/0-:80.414.9-620!&$(C46;34: :;</@\">0904;09-;4=05@\nexcluded.”\nFor HYSPLIT model, the reference related to HYSPLIT was added in the\nrevised Supplementary Information in line 51-53, “back trajectories of the sampled\nair masses were analyzed using the Hybrid Single-Particle Lagrangian Integrated\nTrajectory Model (HYSPLIT) (http://www.arl.noaa.gov/ready/hysplit4.html)8”.\nCluster method of air mass was added in lines 59-65:“Then, backward trajectories\ngained from HYSPLIT were analyzed based on the Euclidean distance clustering\nalgorithm using the Geographical Information System (GIS)-based TrajStat software\n(http://meteothink.org/docs/trajstat/index.html) developed by Wang et al.9 to\ndistinguish different types of air mass. The equation for calculating Euclidean\ndistance between trajectories is as follows10:\nn\nd 3 º ((X (i)# X (i))2 !(Y(i)#Y (i))2) (1)\n12 1 2 1 2\ni31\nwhere X (Y ) and X (Y ) represent the backward trajectories 1 and 2, respectively.”\n1 1 2 2\nRevised figure:\nSupplementary Fig.6 Clusters of backward trajectories during the sampling period\nand corresponding CCl concentration.a Cluster analysis of 120 h backward\n4\ntrajectories for ZJU during the sampling period calculated using the HYSPLIT model,\nwith the starting height at 100 m above ground level. Running intervals were set as 1\nh for each day; the ratio, moving height, and average concentrations of CCl of each\n4\ncluster are also presented. b Same as (a), excluding the SHH station.\nReference:\nspecies using local regression. Atmospheric Measurement Techniques 5, 2613-2624\n(2012).\nhigh altitude records at Jungfraujoch, Switzerland. Environ. Res. Lett. 16 (2021).\n2017–2021. Atmos. Chem. Phys. 22, 9601-9616 (2022).\nModeling System. Bulletin of the American Meteorological Society 96, 2059-2077\n(2015).\nuses various trajectory statistical analysis methods to identify potential sources from\nlong-term air pollution measurement data. Environ. Model. Software 24, 938-939\n(2009).\nrecord of PAN and O3 ambient air concentrations at Kejimkujik National Park, Nova\nScotia. J. Geophys. Res-Atmos. 100, 2867-2881 (1995).\n4) As noted above, I feel the most important result of this paper is not highlighted\nadequately. If this paper proceeds in Nature Communication, I suggest dispensing\nwith the comparison of the new measurements of CCl4 in 2020 and 2021 with data\nfrom Trinidad Head quickly, either improving or perhaps striking the attempt to\ncompute emissions of CCl4 for Eastern China over 2020 and 2021, then moving a lot\nof the material in Supplement regarding what I believe is the key new message of this\npaper into Main (or Methods).\nResponse:\nThank you for this comment. Following the suggestions, (1) more discussion on\nindustrial sectors investigated in this study has been added; (2) comparison of the new\nmeasurements of CCl in 2020 and 2021 with data from Trinidad Head has been\n4\nmoved to Supplementary Information; (3) a lot of the material in SI has been moved\ninto Main; (4) emissions of CCl for eastern China over 2021 and 2022 was\n4\nrecalculated with inverse model method rather than the interspecies correlation\nmethod previously used.\nFor industrial sector:\nRevised text:\nDiscussion on implications from these newly discovered potential sources of\nCCl has been added in lines 230-242 in the revised manuscript: “Based on the\n4\nresults of this study, high concentrations of CCl in 17 industries were observed in\n4\nenterprises with low annual revenue (Supplementary Fig. 7). Specifically, the CCl\n4\nconcentration was negatively correlated with the logarithm of the enterprise revenue\n(R2 = 0.49, p < 0.01). Similar relationships between CCl concentration and enterprise\n4\nrevenue were also observed in the MGPM (R2 = 0.75, p < 0.01) and MRCM (R2 = 0.49,\np < 0.01) industries.\nThe number of enterprises with annual revenue > 20 million CNY has exhibited an\nobvious upward trend for the MGPM and MEMA industries since 2018 (growth rate:\n10–16% yr-1 during 2018–2021)11 (Supplementary Fig. 8). Although the number of\nenterprises in most other industries was relatively stable during 2011– 202112\n(Supplementary Fig. 8), the overall trend has been on the rise (Supplementary Fig. 8).\nConsidering that they may represent potential sources of CCl emissions, attention\n4\nshould be paid to these industries (particularly MGPM) to evaluate their impact on the\nozone layer.\nThis study identified previously unreported potential industrial sources of CCl\n4\nemissions. These findings can guide mitigation strategies and supervision of CCl\n4\nemissions, while strongly endorsing continued monitoring of atmospheric CCl to\n4\nprotect the ozone layer.”\nRevised figure:\nSupplementary Fig. 7 Relationship betweenCCl concentrations and revenue. a\n4\nexcluding MGPM. c. Same as a, excluding MRCM.The size of the dots represents the\nnumber of enterprises.\nSupplementary Fig. 8 Number of enterprises above designated size (annual\nrevenue > 20 million CNY) for each industry12.\nReference\nStatistics Press, 2021).\nStatistics Press, 2011-2021).\nFor inverse modeling of CCl emission in eastern China:\nDescription on the inverse modelling method has been added in lines 269-338 in\nthe revised manuscript: “This study used an inverse modeling technique based on a\nFLEXPART atmospheric transport simulation model and the Bayesian inversion\nalgorithm to quantify CCl emissions. The FLEXPART-based inversion method has\n4\nbeen applied in many previous studies13-15. In brief, driven by the meteorological data\n(European Centre for Medium-Range Weather Forecasts) with a spatial resolution of\n1° × 1° and a temporal resolution of 3 h, the FLEXPART model was ran in backward\nmode for 20 days. The source–receptor relationship (termed as “emission sensitivity”)\nmatrix was established based on the backward simulation. Combining the derived\nemission sensitivity matrix, Bayesian inversion technique, and the CCl data from ZJU\n4\nand SHH, yielded the CCl emission strength (1° × 1°) in grid cells over eastern China.\n1 1\nJ(x)= !x#x\"TS#1(x#x )! (yobs #Hx)TS#1(yobs #Hx)\n2 a a a 2 o\nBy solving ¦ J(x)30 yields:\nx\nx3x !S HT(HS HT !S )#1(yobs #Hx )\na a a o a\nS 3(HTS#1H !S#1)#1\nb o a\nwhere x represents the state vector of the emission strength, yobs represents the CCl\n4\nmeasurement vector, x represents the prior emission vector, H is the emission sensitivity,\na\nS and S are the error covariance matrix of prior and posterior emissions, respectively,\na b\nand S represents the error covariance matrix of measurement data. To obtain the prior\no\nemission vector (x ), the national total CCl emissions in China during 2011–2015 (23.6\na 4\n±7.1 Gg yr-1)16 and emissions in other countries (derived from global emissions [44 ±\n1 during 2008-2012]3, Japan [0.6 Gg yr-1 in 2014]18, South Korea [0.2 Gg yr-1 in\n2014]18, and India [2.8 Gg yr-1 in 2014]18) were assigned to grid cells with a uniform\nspatial distribution in eastern China, and a population-proxy distribution in other\nregions (Supplementary Fig. 9a). The inversion results were also evaluated using prior\nemissions following population distribution in 202019 (Supplementary Fig. 9b).\nThe robust extraction of baseline signal (REBS) method5 was applied to distinguish\nbackground and non-background concentrations of the in-situ CCl concentrations at\nbackground signals using a robust local regression model, and has been widely applied\nto determine baselines of trace gases in inversion studies6,7. The observed\nconcentration at a certain time (y(t)) was divided into three parts as shown depicted in\ni\nthe following equation:\ny(t) = g(t)+ m(t)+e\ni i i i\nwhere g(t) represents the background concentration at time t, m(t) is the enhanced\ni i i\nconcentration caused by polluted plum during t, and e represents the observational\ni i\nerror.\nThe baseline curve g was obtained using the REBS technique over a sufficiently long\nbandwidth (90 days) by assuming that most observations are at background levels and\nthat the baseline signal changes slowly relative to the regional signal. In this method,\ndata points closer to the time of consideration were given more weight, and data points\n7<;:4/0-:80.414.9-620!&$(C46;34: :;</@\">0904;09-;4=05@0?.5</0/$\nFor flask samples at SHH, the background concentration of CCl was determined as\n4\nthe lowest concentration measured in a two-month moving window. The observational\nerror (= ) was calculated as follows:\nobs\n= =2 !=2 !=2\nobs=\nobs_precision obs_representation background\nwhere = is the measurement precision of CCl , = stands for\nobs_precision 4 obs_representation\nrepresentation of the observation, = represents the background uncertainty. In\nbackground\nthis study, = was calculated as 1-Sigma standard deviation of the\nobs_representation\nmeasurements each day for online samples and as 1-Sigma standard deviation of the\nmeasurements in a two-month moving window for flask samples. The = was\nbackground\nestimated as 1-Sigma standard deviation of the fitted background concentrations during\nthe sampling period. The diagonal elements of S were set to squared= .\no obs\nThe diagonal elements of S were calculated as squared uncertainty of the prior\na\nemission field. The off-diagonal elements of S were calculated based on previous\na\nstudies15,20, with the spatial decorrelation length scale set to 400 Km. In this study,\nthree sets of prior emissions were established (i.e., 150%, 100% and 50% of the\nreference prior emissions), and three uncertainties (600%, 450%, 300% for uniformly\ndistributed prior emissions) were set under each set of prior emissions. A total of nine\ninversions were carried out for each year. The final posterior emissions were the\naverage of the nine inversions (Supplementary Fig. 10). The posterior CCl emissions\n4\nin eastern China under the nine inversions in 2021 and 2022 were loaded in the range\nof the three sets of prior emissions (Supplementary Fig. 10). The posterior emissions\nwere insensitive (varied < 9%) to variations in emission uncertainty, suggesting that\nthe prior emissions used in this study were not systematically high or low, and that the\nobservations constrain posterior emissions well. We also tested baselines from the\nAGAGE baseline filtering method (baseline obtained by fitting the daily minimum with\na second-order polynomial)21 to examine the impact of different baselines on posterior\nemissions. The inversion ensembles (Supplementary Table 3) using two baselines\n(AGAGE and REBS methods) and two prior emission fields (uniform and population-\nproxy distributions) show that the posterior emissions varied by < 6% (standard\ndeviation divided by mean) during 2021–2022 (Supplementary Fig. 11).”\nThe inversed emission of CCl in eastern China was described in lines 122-141\n4\nin the revised manuscript:“Using the atmospheric concentration observations at ZJU\nand SHH and an inverse modeling method, CCl emissions in eastern China were\n4\nquantified (see Methods for detailed information). Eastern China in this study includes\nShandong, Henan, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, and Fujian, the\nemissions from which were detected in the mole fractions observed at ZJU and SHH\nsites (Fig. 1). CCl emissions in eastern China were estimated to be 7.0 ± 2.7 Gg yr-1\n4\nin 2021, and 8.2 ± 3.3 Gg yr-1 in 2022, with a mean emission of 7.6 ± 3.1 Gg yr-1 (Fig.\n2b). Considering that previous top-down studies reported CCl emissions until 2019,\n4\nthis study provides the first top-down estimates for the 2021–2022 period.\nPark et al.22 reported that CCl emissions in eastern China (see definition of\n4\nboundary in Park et al.22) decreased from 11.9 ± 1.5 Gg yr-1 in 2014 to 8.8 ± 1.7 Gg yr-\n1 in 2017, 6.0 ± 1.5 Gg yr-1 in 2018, and 6.3 ± 1.0 Gg yr-1 in 2019 (Fig. 2b). Meanwhile,\nthe CCl emissions in eastern China (7.6 ± 3.1 Gg yr-1) estimated in this study for 2021–\n4\n2022 were comparable to (higher than, although not significantly) those in 2019 (6.3 ±\n1.0 Gg yr-1)22 within the uncertainties, suggesting persistent CCl emissions in these\n4\nareas since 2019. Additionally, the CCl emissions in eastern China reported in this\n4\nstudy (7.6 ± 3.1 Gg yr-1 in 2021–2022) and by Park et al.22 are lower than those for all\nof China reported by Park et al.16 (23.6 ± 7.1 Gg yr-1 during 2011–2015) and Lunt et\nal.23 (17 (11–24) Gg yr-1 during 2009–2016) (Fig. 2b), indicating the presence of CCl\n4\nemissions in non-eastern regions of China.”\nThe spatial distribution of CCl emissions in eastern China based on the inversed\n4\nresults was shown in lines 151-155 in the revised manuscript: “The northeastern\nregion of Shandong, eastern region of Jiangsu, and southeastern region of Zhejiang\nwere identified in this study as major CCl source regions in eastern China during\n4\n2021–2022 (Fig. 3, Supplementary Fig. 4 and Supplementary Fig. 5). Additionally, air\nmass corresponding to high CCl concentrations typically pass through these regions\n4\n(Supplementary Fig. 6, see Supplementary Discussion 2 for more details).”\nRevised figure:\nFig.2Concentration levels and emission estimates of CCl . aTime series of CCl\n4 4\nconcentrations at ZJU and SHH during 2021–2022. b Comparison of CCl emission\n4\nestimates in this study with those from previous studies.\nFig. 1 Mean Spatial distribution of CCl emissions in eastern China derived from\n4\nuniformly distributed prior emissions. a For 2021. b For 2022. Spatial distributions\nof CCl emissions from each inversion using REBS or AGAGE baseline filtering method\n4\nare shown in Supplementary Fig. 4. Supplementary Fig. 5 is the same as Supplementary\nFig. 4 using population-proxy prior emissions.\nSupplementary Fig. 4 Spatial distribution of CCl emissions in eastern China\n4\nderived from uniformly distributed prior emissions. a, b Based on baseline extracted\nwith REBS method. c, d same as a, b, but the baseline is extracted with the method\nused by AGAGE. The blue triangle and dot represent the ZJU and SHH sites,\nrespectively.\nSupplementaryFig. 2Spatial distribution of CCl emissions in eastern China\n4\nderived from population-proxy distributed prior emissions. a, b Based on baseline\nextracted with REBS method. c, d same as a, b, but the baseline is extracted with the\nmethod used by AGAGE. The blue triangle and dot represent the ZJU and SHH sites,\nrespectively.\nSupplementary Fig. 9 CCl prior emissions used in this study. a Prior emissions in\n4\neastern China set to a uniform distribution. b Prior emissions in eastern China\ndistributed based on population density. The blue triangle and dot represent the ZJU\nand SHH sites, respectively.\nSupplementary Fig. 10 Posterior CCl emissions in eastern China. a Nine inversions\n4\n(three magnitudes of 150%, 100%, and 50% of reference prior emission multiplied by\nthe three magnitudes of 600%, 450%, and 300% of reference prior emission uncertainty)\nfor CCl emissions in eastern China in 2021 and 2022 based on the baseline extracted\n4\nwith the REBS method and uniformly distributed prior emissions (Inv-REBS-\nUniformPrior). b Same as a but based on the baseline extracted with the method\nsuggested by AGAGE (Inv-AGAGE-UniformPrior). c Same as a but based on the non-\nuniformly distributed prior emissions (Inv-REBS-PopPrior). d Same as c but based on\nthe baseline extracted with the method suggested by AGAGE (Inv-AGAGE-PopPrior).\nThe three green horizontal solid lines represent three magnitudes of 150%, 100%, and\n50% of reference prior emissions for eastern China.\nSupplementary Fig. 11 CCl emissions from four inversion sets for eastern China.\nmethods) and two prior emission fields (uniform and population-proxy distributions) is\nshown in Supplementary Table 3.\nSupplementary Table 3 The set-up for the four inversion frameworks\nBaseline extraction\nCategory Priori emission distribution\nmethod\nUniformly distributed in eastern China, and distributed\nCase A REBS\nbased on population density in other areas\nUniformly distributed in eastern China, and distributed\nCase B AGAGE\nbased on population density in other areas\nCase C REBS Distributed based on population density\nCase D AGAGE Distributed based on population density\nReference:\nnearly two decades after its phaseout for dispersive uses. Proc. Natl. Acad. Sci. U. S.\nA. 113, 2880-2885 (2016).\nspecies using local regression. Atmospheric Measurement Techniques 5, 2613-2624\n(2012).\nhigh altitude records at Jungfraujoch, Switzerland. Environ. Res. Lett. 16 (2021).\n2017–2021. Atmos. Chem. Phys. 22, 9601-9616 (2022).\nChina. Nature Geosci. 12, 89-93 (2019).\nGeophys. Res. Lett. 46, 10034-10042 (2019).\nEnviron. Sci. Technol. 49, 4345-4353 (2015).\ntetrachloride (CCl4): an analysis of top-down emissions from China. Atmos. Chem.\nPhys. 18, 11729-11738 (2018).\nDepletion: 2022, GAW Report No. 278. 509 (World Meteorological Organization\n(WMO), Geneva, 2022).\nsources of carbon tetrachloride (CCl4) in our atmosphere. Environ. Res. Lett. 13,\n024004 (2018).\nUniversity. (NASA Socioeconomic Data and Applications Center (SEDAC),\nPalisades, New York, 2018).\nestimation. Atmos. Chem. Phys. 12, 3455-3478 (2012).\nAtmospheric Gases Experiment atmospheric research stations from 1994 to 1998. J.\nGeophys. Res. 106, 20429-20444 (2001).\neastern China. Nature 590, 433-437 (2021).\nTetrachloride From Eastern Asia. Geophys. Res. Lett. 45, 11423-11430 (2018).\nMinor points\na) The ODP and GWP for CCl4 given in the most recent Scientific Assessment Ozone\nDepletion report are 0.87 and 2150, respectively. Upon revision, please adopt these\nvalues (since they are current and have been assessed by the relevant experts) on lines\n38 and 39, and in the rest of the paper (that is, CFC-11 equivalent emissions) that rely\non the use of these numbers.\nResponse:\nThank you for this comment. In our revised manuscript, the ODP and GWP values of\n0.87 and 2150 have been used in the original lines 37 and 38 and in the rest of the\npaper (CFC-11 equivalent emissions).\nRevised text:\nThe revised ODP and GWP value was shown in lines 36-38 in the revised\nmanuscript: “Carbon tetrachloride (CCl ) is a first-generation ozone-depleting\n4\nsubstance (ODS) with an ozone depletion potential of 0.87 and a potent greenhouse\ngas with a global warming potential of 215017”.\nThe recalculated ODP and GWP value was shown in lines 175-187 in the revised\nmanuscript: “For instance, the ozone depletion potential (ODP)-weighted emissions\n(equivalent to CFC-11 emissions) of CCl in eastern China estimated in this study (6.6\n4\n± 2.7 CFC-11-eq Gg yr-1) are comparable to the unexpected emissions of CFC-11 (7.0\n± 4.0 Gg yr-1) in eastern China during the 2014–2017 period relative to those during\n2008–201222…… Moreover, ODP-weighted CCl emissions in eastern China (6.6 ±\n4\n2.7 CFC-11-eq Gg yr-1) are several times higher than the ODP-weighted emissions of\nother major ODSs, for example, it is 17.6 times higher than the 0.38 ± 0.75 CFC-11-\neq Gg yr-1 of dichlorodifluoromethane (CFC-12) in 2019 in eastern China22, 8.4 times\nthe 0.79 (0.65–1.1) CFC-11-eq Gg yr-1 of 1,1-dichloro-1-fluoroethane (HCFC-141b)\nin 2020 in eastern China7, and 1.4 times the 4.6 (3.9–5.3) CFC-11-eq Gg yr-1 of\nchlorodifluoromethane (HCFC-22) in 2019 (but for all of China24).”\nReference:\n2017–2021. Atmos. Chem. Phys. 22, 9601-9616 (2022).\nDepletion: 2022, GAW Report No. 278. 509 (World Meteorological Organization\n(WMO), Geneva, 2022).\neastern China. Nature 590, 433-437 (2021).\nNational-Provincial-Gridded Emission Inventories in China and the Analysis of\nEmission Reduction Potential. Environ. Sci. Technol. 56, 814-822 (2022).\nb) Line 50. Again, as noted above, the paper is extremely well written! Minor point,\nbut rather than “to restrict CCl4 emissions”, I suggest “to understand the reason for\nthe gap in bottom-up and top-down emissions”. Regardless, if the paper is revised as\nsuggested above, there will certainly be much new text regarding how the\nobservations will be used in the future “to restrict emissions”.\nResponse:\nThank you for this comment. The “to restrict CCl emissions” has been changed to “to\n4\nunderstand the reason for the gap in bottom-up and top-down emissions” in our\nrevised manuscript.\nRevised text:\nLines 47-49 in the revised manuscript: “Therefore, further CCl atmospheric\n4\nobservations are needed to understandthe reason for the gap in bottom-up and top-\ndown emissions.”\nc) Benish et al., multiple lines: Benish et al. could also be cited on line 68 as well as\nlines 76 to 79. : Benish et al. also showed, based on their strong correlation of CFC-11\nwith CCl4, the likely co-production of these two species (line 82).\nResponse:\nThank you for this comment. In our revised manuscript, the suggested reference of\nBenish et al. (2021) has been added.\nRevised text:\nLines 74-77 in the revised manuscript : “Although there are reported atmospheric\nobservations of CCl within eastern China, namely within Nanjing (120 ± 30 ppt;\n4\nparts per trillion) in 201825#,762@462!&'+DAD)'88;\"46'%&*26, Hebei (89 ± 21 ppt)\nin 20161, and Lin’an (111 ±11 ppt) in 200127”.\nLine 81-83 in the revised manuscript: “Based on the strong correlation between\nCFC-11 and CCl (R2 = 0.78), Benish et al.1 showed the likely co-production of these\n4\ntwo species.”\nReference:\nObservations of CFCs Over Hebei Province, China in Spring 2016. J. Geophys. Res-\nAtmos. 126 (2021).\ncompounds in Nanjing, China during high ozone pollution season. Chemosphere 263,\n128025 (2021).\nYellow River Delta region, northern China. Atmos. Res. 225, 70-80 (2019).\ncharacteristics at Lin'an, a rural site in eastern China, during spring 2001. J. Geophys.\nRes. 109, D19S05 (2004).\nd) Line 81. “observed” should be “reported”, since the paper was published in 1981.\nResponse:\nThank you for this comment. In our revised manuscript, “observed” has been replaced\nby “reported.” The paper was published in 2018 (Montzka, S. A. et al. An unexpected\nand persistent increase in global emissions of ozone-depleting CFC-11. Nature, 2018,\n557, 413-417).\nRevised text:\nLine 81 in the revised manuscript: “In 2018, an unexpected rise in global\ntrichlorofluoromethane (CFC-11) emission was reported.”\ne) Lines 84, 86, and 88. The use of “16” on lines 84 & 86 and “19” on line 88 is\nconfusing. If these numbers are truly different, a bit more explanation would be\nhelpful.\nResponse:\nThank you for the reminder. The number of CCl by-product enterprises under on-site\n4\nsupervision was varied with time, it is 16 in 2019 and 19 in 2021. The sentence has\nbeen revised as follows:\nRevised text:\nLines 89-91 in the revised manuscript: “By 2021, the number of CCl by-product\n4\nenterprises under on-site supervision had increased to 1928, comprising verifiable and\nquantifiable CCl online production monitoring systems connected to the central\n4\ngovernment monitoring platform29.”\nReference:\nenvironmental law enforcement work and next work arrangements (in Chinese),\nhttps://baijiahao.baidu.com/s?id=1698552840313072252&wfr=spider&for=pc\n(2021).\nconference of the Ministry of Ecology and Environment in April (in Chinese),\nhttps://www.mee.gov.cn/xxgk2018/xxgk/xxgk15/202104/t20210428_831177.html\n(2021).\nf) Line 95. Suggest “this study describes observations of …”\nResponse:\nThank you for this comment. We have revised the text following the suggestion.\nRevised text:\nLines 98-99 in the revised manuscript: “Consequently, this study describes\nobservations of atmospheric CCl concentrations at two sites in eastern China…”\n4\ng) Line 99. Here, I prefer “substantial emissions of CCl4” since substantial modifies\nemissions\nResponse:\nThank you for this comment. In our revised manuscript, we changed following the\nsuggestion.\nRevised text:\nLines 101-103 in the revised manuscript: “This study reports substantial emissions\nof in eastern China after the dispersive use of CCl was phased out in 2010.”\n4\nh) Line 121. Not sure “41N” is really “close” to 30.306 and 28.583N; perhaps a\ndifferent word can be used\nResponse:\nThank you for this comment. In our revised manuscript, we changed following the\nsuggestion.\nRevised text:\nLines 5-10 in the revised Supplementary Information: “The concentrations were\nhigher than the measurement in the four remote northern hemispheric stations (Mace\nHead (75.9 ± 0.23 ppt; 53.327°N, 9.904°W), Jungfraujoch (75.4 ± 0.29 ppt;\n46.548°N, 7.985°E), Trinidad Head (76.0 ± 0.22 ppt; 41.054°N, 124.151°W), and\nRagged Point (75.9 ± 0.26 ppt; 13.165°N, 59.432°W)) from the Advanced Global\nAtmospheric Gases Experiment (AGAGE; http://agage.mit.edu/) in 2021.”\ni) Line 125. Suggest “These observations indicate …”\nResponse:\nThank you for this comment. In our revised manuscript, we changed following the\nsuggestion.\nRevised text:\nLines 11-12 in the revised Supplementary Information: “These observations\nindicate the presence of substantial CCl emissions in eastern China…”\n4\nj) Line 131. In addition to the request for more information about REBS, the inclusion\nof a reference here would be helpful\nResponse:\nThank you for this comment. In our revised manuscript, we have changed following\nthe suggestion. The reference to Ruckstuhl et al.5 has been added, and more detailed\ninformation has been presented in the method section (please see the response to\nmajor point 3).\nReference:\nspecies using local regression. Atmospheric Measurement Techniques 5, 2613-2624\n(2012).\nk) Line 135. Suggest “respectively” rather than “in respect”\nResponse:\nThank you for this comment. In our revised manuscript, we have changed following\nthe suggestion.\nRevised text:\nLines 20-22 in the revised Supplementary Information: “approximately 30% of the\nCCl concentrations at ZJU (averaged as 93.9 ± 14.4 ppt) were identified as non-\n4\nbackground concentrations, respectively.”\nl) Lines 146 to 149. Not sure all of this detail is needed, especially if the paper’s focus\nis altered, but if this text survives, should also cite numerical values from the Benish et\nal. study\nResponse:\nThank you for this comment. We believe it is necessary to show the enhanced\nconcentration of CCl to indicate the possible presence of ongoing CCl emissions\n4 4\nafter 2010. Following your suggestion, our revised manuscript has added CCl\n4\nconcentrations reported by Benish et al.\nRevised text:\nLines 28-30 in the revised Supplementary Information: “…e.g., enhanced CCl\n4\nconcentrations in Beijing (approximately 34 ppt in 2015)30, Nanjing (approximately\n32 ppt in 2018)25, Lushan (approximately 94 ppt during 2011–2012)31, Hebei\n(approximately 10 ppt in 2016)1…”\nReference:\nObservations of CFCs Over Hebei Province, China in Spring 2016. J. Geophys. Res-\nAtmos. 126 (2021).\ncompounds in Nanjing, China during high ozone pollution season. Chemosphere 263,\n128025 (2021).\norganic compounds in Beijing, China. Sci. Total Environ. 557-558, 531-541 (2016).\nHalocarbons at a Mountaintop in Southern China. Aerosol Air Qual. Res. 16, 478-491\n(2016).\nm) Lines 153 to 156. The use of HYSPLIT for the trajectory clustering should be\nstated, with a reference to a HYSPLIT paper, perhaps the above mentioned Stein et al.\nstudy\nResponse:\nThank you for this comment. Following the suggestion, the use of HYSPLIT for the\ntrajectory clustering has been stated, and the suggested reference of Stein et al. has\nbeen added:\nRevised text:\nLines 37-38 in the revised Supplementary Information: “Using the Hybrid Single-\nParticle Lagrangian Integrated Trajectory Model (HYSPLIT)\n(http://www.arl.noaa.gov/ready/hysplit4.html)8 , the air masses…”.\nReference:\nModeling System. Bulletin of the American Meteorological Society 96, 2059-2077\n(2015).\nn) Lines 164 to 165. A citation for the CWT method should be given\nResponse:\nThank you for this comment. In our original manuscript, the CWT method was\nadapted from “Ying-Kuang Hsua, T. M. H. & Hopke, P. K. Comparison of hybrid\nreceptor models to locate PCB sources in Chicago. Atmos. Environ. 37, 545–562\n(2003)”. However, in our revised manuscript, CWT results have been removed\nbecause the spatial distributions of CCl emissions have been derived from inverse\n4\nmodeling.\no) Line 235. Suggest “This comparison indicates … did not fall over this period of\ntime” although, given the inherent problems I have identified with the estimate for the\ncomputation of the emission of CCl in Eastern China, I have to also state this\n4\nsentence may need to be altered in a more significant manner, or perhaps removed.\nResponse:\nThank you for this comment. Based on your suggestion, we have altered the emission\nestimation methods to inverse modeling. The inversion results show no decreasing\ntrend of CCl emissions after 2019. Thus, this sentence was revised as follows:\nLines 195-196 in the revised manuscript: “This comparison indicates that the CCl\n4\nemissions in eastern China did not fall over this period.”\np) Line 268. Suggest “Our observations reveal …”\nResponse:\nThank you for this comment. We have revised following the suggestion.\nRevised text:\nLines 223-224 in the revised manuscript: “Our observations reveal that MGPM,\nMRCM, MEMA, and MA sectors are potential sources of CCl emissions…”.\n4\nq) Line 307. In addition to the website, should also provide a reference here, for\nHYSPLIT.\nResponse:\nThank you for the comment. We have added the reference of “Stein, A. F. et al.\nNOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bulletin\nof the American Meteorological Society 96, 2059-2077 (2015)” in the revised\nmanuscript.\nr) Line 506. Figure 1 is very important: this figure is the first graphical component of\nthe paper. The figure should be improved, either by zooming in to make the\ngeographic region of China stand out more clearly, of perhaps some other manner.\nThe three words on panel c are hard to read. Finally, I suggest also pointing out the\nlocation of Beijing, so that readers can better orient the locations of ZJU, SHH, and\nGossan.\nResponse:\nThank you for this comment.To clearly show our inversion region, Figure 1 is\nzoomed in to eastern China, and the boundary of the inversion area is highlighted in\nbold black. In addition, we show the footprint covering the entire China in\nSupplementary Fig. 2 and use bold border lines to highlight the Chinese regions. We\nagree that showing the location of Beijing will help readers understand the location of\nthe ZJU, SHH, and Gosan stations. But instead of showing the location of Beijing to\nhelp readers determine where ZJU, SHH, and Gosan are, we provide a map with the\nname of each province (shown in Supplementary Fig. 1). The revised figures are\nshown below:\nRevised figure:\nFig. 3 Average emission sensitivity derived from FLEXPART simulations and the\naverage emission sensitivity of ZJU and SHH stations to that of the GSN station in\n2021 and 2022. a, b Average emission sensitivities from ZJU and SHH stations. c, d\nAverage emission sensitivities from GSN station. e, f Ratio of the average emission\nsensitivity of ZJU and SHH stations to that of the GSN station. ZJU, SHH, and GSN\nsites are represented by the blue triangle, dot, and cross, respectively. The area framed\nby the bold black line is the target area for the inversion. The name of each Chinese\nprovince is shown in Supplementary Fig. 1. Distribution of the emission sensitivities for\na larger domain than Fig. 1 is shown in Supplementary Fig. 2.\nSupplementary Fig. 4 Provinces in China.\nSupplementary Fig. 5Average emission sensitivity derived from FLEXPART\nsimulations and the ratio of the average emission sensitivity between different\nstations in 2021 and 2022.a Average emission sensitivities from ZJU and SHH\nstations. b Average emission sensitivities from GSN station. c Ratio of the average\nemission sensitivity at ZJU and SHH stations to that at GSN station. Sites at ZJU,\nSHH, and GSN are represented by the blue triangle, dot, and cross, respectively. The\narea outlined by the bold black line is China. The name of each Chinese province is\nshown in Supplementary Fig. 1.\ns) Line 518. I have stared at this figure, and can not find either the black or pink dots.\nIf these dots are truly present, they should be made more visible.\nResponse:\nSorry for the redundant information we provided before. Since spatial distribution of\nemission hotspots are gained from inverse modeling in our revised manuscript,\nthe\nfigure of CWT was replaced with following plots:\nRevised figure:\nFig. 6 Mean Spatial distribution of CCl emissions in eastern China derived from\n4\nuniformly distributed prior emissions. a For 2021. b For 2022. Spatial distributions\nof CCl emissions from each inversion using REBS or AGAGE baseline filtering\n4\nmethod are shown in Supplementary Fig. 4. Supplementary Fig. 5 is the same as\nSupplementary Fig. 4 using population-proxy prior emissions.\nt) Line 527. Figure 5c is very very important, in my opinion. Figure 5b is easy to\n“understand”, but hard to decipher, as many of the colors are similar. Perhaps various\nsymbols with a smaller number of colors should be used. Finally, Figure 5a is hard to\nunderstand: what is the inset?\nResponse:\nThank you for this comment. According to the suggestion, different symbols have\nbeen used to represent different industry sectors in Figure 5b (Figure 4a in our revised\nmanuscript). The original Figure 5a is unnecessary and has been removed accordingly.\nRevised figure:\nFig. 7Measured CCl concentrations from each industry sector.a Spatial\n4\ndistribution of sampling industry sectors with different symbols. b Measured CCl\n4\nconcentrations from each industry sector. Box plots indicate median (middle line),\n25th,75th percentile (box) and 5th and 95th percentile (whiskers) as well as the mean\nlevel (asterisks). Solid dots represent the concentration of each sample.\nEND OF REVIEW\nResponses to Reviewer #2:\nReviewer #2 (Remarks to the Author):\nThis manuscript reports on unique measurements of the ozone-depleting CCl4 from\ntwo new sites in China during 2021 and 2022. Quantifying CCl4 emissions from\natmospheric measurements is an important issue worth of consideration by this\njournal because substantial CCl4 emissions persist globally and are delaying recovery\nof the ozone layer, but the sources responsible for these emissions have not been\nadequately identified and characterized. Ongoing measurements from these new sites\nhave the potential to inform us more accurately about emissions from unique regions\nof China than has been possible previously. The authors find that emissions from\neastern China are comparable in magnitude to those published elsewhere for this same\nregion through 2019 using measurement data from a different site (Gosan, S. Korea).\nThe authors argue that the emissions they have derived for CCl4 during 2021-2022\nare unusually high owing to the “implementation of strict government supervision for\nCCl4” after 2018. In an intriguing second part of the paper, the authors report on\nmeasurements of CCl4 from air collected from the chimney vents of 379 businesses\nthat represented 17 different industry sectors. Unusually high CCl4 concentrations\nwere observed from some sectors that haven’t been associated with CCl4 emissions in\nthe past. While the data presented are unique and relevant to the issue of ozone\ndepletion and ozone layer recovery, I cannot recommend publication at this time for a\nnumber of reasons that I delineate below.\nResponse:\nThank you for reviewing our work. Below we have responded to and addressed each\ncomment.\nFirst,the importance and uniqueness of this manuscript primarily relates to the\navailability of new measurement data from new sites AND the assertion that the\nderived emissions from eastern China are higher than they should be owing to the\n“implementation of strict government supervision for CCl ” during this period.\nlimit CCl emissions. Certainly, it was instituted to improve tracking of produced\n4\nprovided by the authors to suggest that this program might have had as its goal\nsubstantial reductions in CCl emission (and I don’t know this independently).\nThank you for the question. In 2018, an unexpected rise in global\ntrichlorofluoromethane (CFC-11) emission was reported1. At least 40-60% of this\nunexpected rise in emissions was attributed to eastern China, with the likely\nproduction of CFC-11 and related CCl 2. The Chinese government has launched\n4\nspecial inspection law enforcement actions on ODSs (including CFCs, CCl , and\nproduction and consumption of CFC-11 but also the production and consumption of\nother regulated ODSs.\nTo strengthen the regulation of CFC-11 and CCl production, in 2019, the Ministry\n4\nof Ecology and Environment of China dispatched 228 personnel in 16 working groups\nto perform on-site supervision for 16 enterprises producing CCl as a by-product in\n4\nthe country7. In 2019, CCl online monitoring facilities were installed within these 16\n4\nenterprises, and 36 rapid instruments for detecting ODSs in products were dispatched\nto 22 provinces, strengthening law enforcement and monitoring capabilities across\nChina7. By 2021, the number of CCl by-product enterprises under on-site supervision\n4\nhad increased to 198, comprising verifiable and quantifiable CCl online production\n4\nmonitoring systems connected to the central government monitoring platform9. The\n“Supervision program” was implemented to improve tracking of produced CCl to\n4\nprevent its conversion to CFCs (e.g., CFC-11) or its sale for controlled or emissive\napplications. Therefore, as the “Supervision program” proceeds, the CCl emissions\n4\nmight decrease.\nRevised text:\nLines 91-93 in the revised manuscript: “Additionally, the “Supervision program”\nwas implemented to improve tracking of produced CCl to prevent its conversion to\nReference:\nozone-depleting CFC-11. Nature 557, 413-417 (2018).\natmospheric observations. Nature 569, 546-550 (2019).\nODS special law enforcement inspection (in Chinese),\nhttp://www.jiangxi.gov.cn/art/2020/9/14/art_6392_2860755.html (2020).\nfrom the Shandong Provincial Department of Ecology and Environment on the joint\n\"double random, one public\" inspection involving ozone depleting substances (ODS)\nin 2022 (in Chinese),\nhttp://www.sdein.gov.cn/hjjc/zhyw/202211/t20221109_4125354.html (2022).\nEnforcement Work Plan in Jiading District in 2022 (in Chinese),\nhttp://www.jiading.gov.cn/publicity/jcgk/zdgkwj/gbmwj__publicity/fdzdgknr/zfwj/gb\nmwj/151518 (2022).\nof Ecology and Environment: Focus on the Prevention and Control of Air Pollution in\nAutumn and Winter in October (in Chinese),\nhttps://www.solidwaste.com.cn/news/329215.html (2021).\nconference of the Ministry of Ecology and Environment in August 2019 (in Chinese),\nhttps://www.mee.gov.cn/xxgk2018/xxgk/xxgk15/201908/t20190830_730891.html\n(2019).\nenvironmental law enforcement work and next work arrangements (in Chinese),\nhttps://baijiahao.baidu.com/s?id=1698552840313072252&wfr=spider&for=pc (2021).\nconference of the Ministry of Ecology and Environment in April (in Chinese),\nhttps://www.mee.gov.cn/xxgk2018/xxgk/xxgk15/202104/t20210428_831177.html\n(2021).\nSecond, the emission derivation is performed here in a manner that substantially\nunderestimates the true uncertainties, making me question the reliability of the CCl\n4\nemission estimates provided. Emissions of CCl in this work are derived by scaling\n4\nthe concentration enhancements for CCl to those measured concurrently for CHCl\n4 3\nand multiplying that ratio by the “known” CHCl emission. This method works best if\n3\nthe enhancements for the two gases are highly correlated. Figure 2 of the\nSupplementary material shows the correlation, but its significance isn't quantified or\nmentioned (and visually does not look very high). Most worrisome, however, is that\nemissions of CHCl from eastern China from 2013 (48.3 Gg) and 2015 (80-95 Gg)\n3\n(not 2021 or 2022) are used to derive emissions for CCl4 in 2021 and 2022 based on\nmeasurements in this latter years. No consideration of what CHCl3 emissions might\nhave been in 2021 or 2022 is mentioned, making the estimate of CCl emissions in the\n4\nkey years highly suspect, particularly since the large difference (change?) in CHCl\n3\nemissions during 2013 and 2015 suggests that CHCl emissions from eastern China\n3\ncan change substantially from year-to-year.\nResponse:\nThank you for this comment. We highly agree with this comment, which is also\npointed out by reviewer #1. There is indeed a large uncertainty (or poor reliability) in\nestimating emissions using the interspecies correlation method regarding the poor\ncorrelation (Pearson’s r=0.29) between CCl and CHCl and other issues pointed out\n4 3\nin the comment. In our revised manuscript, we have switched to the inverse modeling\nmethod instead of the interspecies correlation method, avoiding the flaws in the\ninterspecies correlation method, which is also suggested by reviewer #1. The details\nof the inverse modeling method description and figures are below.\nRevised text:\nLines 269-338 in the revised manuscript:“This study used an inverse modeling\ntechnique based on a FLEXPART atmospheric transport simulation model and the\nBayesian inversion algorithm to quantify CCl emissions. The FLEXPART-based\n4\ninversion method has been applied in many previous studies10-12. In brief, driven by\nthe meteorological data (European Centre for Medium-Range Weather Forecasts)\nwith a spatial resolution of 1° × 1° and a temporal resolution of 3 h, the FLEXPART\nmodel was ran in backward mode for 20 days. The source–receptor relationship\n(termed as “emission sensitivity”) matrix was established based on the backward\nsimulation. Combining the derived emission sensitivity matrix, Bayesian inversion\ntechnique, and the CCl data from ZJU and SHH, yielded the CCl emission strength\n4 4\n(1° × 1°) in grid cells over eastern China. The associated equations are as follows:\n1 1\nJ(x)= !x#x\"TS#1(x#x )! (yobs #Hx)TS#1(yobs #Hx)\n2 a a a 2 o\nBy solving ¦ J(x)30 yields:\nx\nx3x !S HT(HS HT !S )#1(yobs #Hx )\na a a o a\nS 3(HTS#1H !S#1)#1\nb o a\nwhere x represents the state vector of the emission strength, yobs represents the\nCCl measurement vector, x represents the prior emission vector, H is the emission\n4 a\nsensitivity, S and S are the error covariance matrix of prior and posterior emissions,\na b\nrespectively, and S represents the error covariance matrix of measurement data. To\no\nobtain the prior emission vector (x ), the national total CCl emissions in China\na 4\nduring 2011–2015 (23.6 ±7.1 Gg yr-1)13 and emissions in other countries (derived\nfrom global emissions [44 ± 15 Gg yr-1 in 2020]14 and emission estimates of the\nUnited States [4.0 (2.0–6.5) Gg yr-1 during 2008-2012]15, Japan [0.6 Gg yr-1 in\n2014]16, South Korea [0.2 Gg yr-1 in 2014]16, and India [2.8 Gg yr-1 in 2014]16) were\nassigned to grid cells with a uniform spatial distribution in eastern China, and a\npopulation-proxy distribution in other regions (Supplementary Fig. 9a). The inversion\nresults were also evaluated using prior emissions following population distribution in\n202017 (Supplementary Fig. 9b).\nThe robust extraction of baseline signal (REBS) method18 was applied to\ndistinguish background and non-background concentrations of the in-situ CCl\n4\nconcentrations at ZJU. The REBS is a statistical method developed by Ruckstuhl et\nal.18 to extract background signals using a robust local regression model, and has\nbeen widely applied to determine baselines of trace gases in inversion studies19,20. The\nobserved concentration at a certain time (y(t)) was divided into three parts as shown\ni\ndepicted in the following equation:\ny(t) = g(t)+ m(t)+e\ni i i i\nwhere g(t) represents the background concentration at time t, m(t) is the enhanced\ni i i\nconcentration caused by polluted plum during t, and e represents the observational\ni i\nerror.\nThe baseline curve g was obtained using the REBS technique over a sufficiently\nlong bandwidth (90 days) by assuming that most observations are at background\nlevels and that the baseline signal changes slowly relative to the regional signal. In\nthis method, data points closer to the time of consideration were given more weight,\n-6//-;-8746;: 7<;:4/0-:80.414.9-620 !&$(C 46;34: :;</@\">0904;09-;4=05@0?.5</0/$\nFor flask samples at SHH, the background concentration of CCl was determined as\n4\nthe lowest concentration measured in a two-month moving window. The observational\nerror (= ) was calculated as follows:\nobs\n= =2 !=2 !=2\nobs=\nobs_precision obs_representation background\nwhere = is the measurement precision of CCl , = stands for\nobs_precision 4 obs_representation\nrepresentation of the observation, = represents the background uncertainty. In\nbackground\nthis study, = wascalculated as 1-Sigma standard deviation of the\nobs_representation\nmeasurements each day for online samples and as 1-Sigma standard deviation of the\nmeasurements in a two-month moving window for flask samples. The = was\nbackground\nestimated as 1-Sigma standard deviation of the fitted background concentrations\nduring the sampling period. The diagonal elements of S were set to squared= .\no obs\nThe diagonal elements of S were calculated as squared uncertainty of the prior\na\nemission field. The off-diagonal elements of S were calculated based on previous\na\nstudies12,21, with the spatial decorrelation length scale set to 400 Km. In this study,\nthree sets of prior emissions were established (i.e., 150%, 100% and 50% of the\nreference prior emissions), and three uncertainties (600%, 450%, 300% for uniformly\ndistributed prior emissions) were set under each set of prior emissions. A total of nine\ninversions were carried out for each year. The final posterior emissions were the\naverage of the nine inversions (Supplementary Fig. 10). The posterior CCl emissions\n4\nin eastern China under the nine inversions in 2021 and 2022 were loaded in the range\nof the three sets of prior emissions (Supplementary Fig. 10). The posterior emissions\nwere insensitive (varied < 9%) to variations in emission uncertainty, suggesting that\nthe prior emissions used in this study were not systematically high or low, and that the\nobservations constrain posterior emissions well. We also tested baselines from the\nAGAGE baseline filtering method (baseline obtained by fitting the daily minimum\nwith a second-order polynomial)22 to examine the impact of different baselines on\nposterior emissions. The inversion ensembles (Supplementary Table 3) using two\nbaselines (AGAGE and REBS methods) and two prior emission fields (uniform and\npopulation-proxy distributions) show that the posterior emissions varied by < 6%\n(standard deviation divided by mean) during 2021–2022 (Supplementary Fig. 11).”\nRevised figure:\nSupplementary Fig. 9 CCl prior emissions used in this study. a Prior emissions in\n4\neastern China set to a uniform distribution. b Prior emissions in eastern China\ndistributed based on population density. The blue triangle and dot represent the ZJU\nand SHH sites, respectively.\nSupplementary Fig. 10 Posterior CCl emissions in eastern China. a Nine inversions\n4\n(three magnitudes of 150%, 100%, and 50% of reference prior emission multiplied by\nthe three magnitudes of 600%, 450%, and 300% of reference prior emission uncertainty)\nfor CCl emissions in eastern China in 2021 and 2022 based on the baseline extracted\n4\nwith the REBS method and uniformly distributed prior emissions (Inv-REBS-\nUniformPrior). b Same as a but based on the baseline extracted with the method\nsuggested by AGAGE (Inv-AGAGE-UniformPrior). c Same as a but based on the non-\nuniformly distributed prior emissions (Inv-REBS-PopPrior). d Same as c but based on\nthe baseline extracted with the method suggested by AGAGE (Inv-AGAGE-PopPrior).\nThe three green horizontal solid lines represent three magnitudes of 150%, 100%, and\n50% of reference prior emissions for eastern China.\nSupplementary Fig. 8 CCl emissions from four inversion sets for eastern China.\nmethods) and two prior emission fields (uniform and population-proxy distributions) is\nshown in Supplementary Table 3.\nSupplementary Table 3 The set-up for the four inversion frameworks\nBaseline extraction\nCategory Priori emission distribution\nmethod\nUniformly distributed in eastern China, and distributed\nCase A REBS\nbased on population density in other areas\nUniformly distributed in eastern China, and distributed\nCase B AGAGE\nbased on population density in other areas\nCase C REBS Distributed based on population density\nCase D AGAGE Distributed based on population density\nReference:\nChina. Nature Geosci. 12, 89-93 (2019).\nGeophys. Res. Lett. 46, 10034-10042 (2019).\nEnviron. Sci. Technol. 49, 4345-4353 (2015).\ntetrachloride (CCl4): an analysis of top-down emissions from China. Atmos. Chem.\nPhys. 18, 11729-11738 (2018).\nDepletion: 2022, GAW Report No. 278. 509 (World Meteorological Organization\n(WMO),, Geneva, 2022).\nnearly two decades after its phaseout for dispersive uses. Proc. Natl. Acad. Sci. U. S.\nA. 113, 2880-2885 (2016).\nsources of carbon tetrachloride (CCl4) in our atmosphere. Environ. Res. Lett. 13,\n024004 (2018).\nUniversity. (NASA Socioeconomic Data and Applications Center (SEDAC),\nPalisades, New York, 2018).\nspecies using local regression. Atmospheric Measurement Techniques 5, 2613-2624\n(2012).\nhigh altitude records at Jungfraujoch, Switzerland. Environ. Res. Lett. 16 (2021).\n2017–2021. Atmos. Chem. Phys. 22, 9601-9616 (2022).\nestimation. Atmos. Chem. Phys. 12, 3455-3478 (2012).\nAtmospheric Gases Experiment atmospheric research stations from 1994 to 1998. J.\nGeophys. Res. 106, 20429-20444 (2001).\nThird, the authors also assert that they have found CCl emissions in new regions of\nmethod can often erroneously place emissions upwind of the actual source region (my\ninterpretation of the “ghost” sources in the ocean discussed in Figure 3). This seems\nalso a possibility for the proposed CCl4 emissions these two regions and would need\nfurther investigation before one could reliably assert that there are significant\nemissions coming from these provinces.\nResponse:\nThank you for this comment. In our revised manuscript, the spatial distribution of\nCCl emissions was reanalyzed based on the inverse model method, which performed\n4\nmuch better than the CWT method in locating emission source regions and could\nlargely avoid the flaw of “ghost” sources.23 (please see Fig. 3 in our revised\nmanuscript below).\nRevised text:\nLines 151-155 in the revised manuscript: “The northeastern region of Shandong,\neastern region of Jiangsu, and southeastern region of Zhejiang were identified in this\nstudy as major CCl source regions in eastern China during 2021–2022 (Fig. 3,\ncorresponding to high CCl concentrations typically pass through these regions\n4\n(Supplementary Fig. 6, see Supplementary Discussion 2 for more details).”\nRevised figure:\nFig. 9 Mean Spatial distribution of CCl emissions in eastern China derived from\n4\nuniformly distributed prior emissions.a For 2021. b For 2022. Spatial distributions of\nCCl emissions from each inversion using REBS or AGAGE baseline filtering method\n4\nare shown in Supplementary Fig. 4. Supplementary Fig. 5 is the same as\nSupplementary Fig. 4 using population-proxy prior emissions.\nSupplementary Fig. 4Spatial distribution of CCl emissions in eastern China\n4\nderived from uniformly distributed prior emissions. a, b Based on baseline extracted\nwith REBS method. c, d same as a, b, but the baseline is extracted with the method\nused by AGAGE. The blue triangle and dot represent the ZJU and SHH sites,\nrespectively.\nSupplementary Fig. 10Spatial distribution of CCl emissions in eastern China\n4\nderived from population-proxy distributed prior emissions. a, b Based on baseline\nextracted with REBS method. c, d same as a, b, but the baseline is extracted with the\nmethod used by AGAGE. The blue triangle and dot represent the ZJU and SHH sites,\nrespectively.\nSupplementary Fig.6Clusters of backward trajectories during the sampling period\nand corresponding CCl concentration.a Cluster analysis of 120 h backward\n4\ntrajectories for ZJU during the sampling period calculated using the HYSPLIT model,\nwith the starting height at 100 m above ground level. Running intervals were set as 1\nh for each day; the ratio, moving height, and average concentrations of CCl of each\n4\ncluster are also presented. b Same as (a), excluding the SHH station.\nReference:\nModeling Method in Locating Emission Sources. ACS Earth and Space Chemistry 2,\n843-851 (2018).\nFourth, in a highly unique section of the paper, concentration measurements were\nmade from the stacks of many industries and potentially suggest the discovery of\npreviously unrecognized CCl sources. However, this result needs some further\n4\ndevelopment before such assertions can be made with any reliability. Questions\nneeding to be explored so that the implications of the new results can be better\nunderstood include: how extensive is this industry and how many businesses related\nto this industry exist in China? What are the potential fluxes of CCl4 from these\nstacks? Can you put those two numbers together to estimate potential emissions from\nthis sector? What are the potential underlying variables affecting the range of CCl4\nconcentration in stacks associated with the same industry? Particularly MGPM? For\nthis portion of the paper to be useful to a wider audience, it will require some further\nestimation of these quantities, or reasonable limits applied to them, so that the\npotential importance of these other sources can be roughly assessed.:\nResponse:\nThank you for the suggestion.\n!1\" As for “how extensive is this industry, and how many businesses related to\nthis industry exist in China?”\nThe number of enterprises above the designated size (i.e., annual revenue >20\nmillion CNY) for the industry of MGPM and MEMA shows an apparent\nupward trend in the number of enterprises since 2018. The number of\nenterprises in most other industries shown in Supplementary Fig. 7 is relatively\nstable during 2011– 202124, but the overall trend is still rising.\nThe extensive of each investigated industry was discussed in lines 236-242\nin the revised manuscript: “The number of enterprises with annual revenue >\n20 million CNY has exhibited an obvious upward trend for the MGPM and\nMEMA industries since 2018 (growth rate: 10–16% yr-1 during 2018–2021)25\n(Supplementary Fig. 8). Although the number of enterprises in most other\nindustries was relatively stable during 2011– 202124 (Supplementary Fig. 8), the\noverall trend has been on the rise (Supplementary Fig. 8). Considering that they\nmay represent potential sources of CCl emissions, attention should be paid to\n4\nthese industries (particularly MGPM) to evaluate their impact on the ozone\nlayer.”\n!2\" As for “What are the potential fluxes of CCl4 from these stacks? Can you\ncombine those two numbers to estimate potential emissions from this sector?”\nCCl concentrations were only measured in stacks sampled in this study.\n4\nto measure CCl concentrations in all stacks across China (or major stacks\n4\nacross China) and the exhaust flow in the corresponding stack while these data\nare unavailable. Future studies are encouraged to measure the concentrations\nand flux in more enterprises.\n(3) As for “What are the potential underlying variables affecting the range of CCl\n4\nconcentration in stacks associated with the same industry?”\nWe explore the relationship between revenue and measured concentration of\nCCl to discuss the potential underlying variables affecting the range of CCl\n4 4\nconcentration in stacks.\nThe relationship between revenue and CCl concentrations in stacks was\n4\ndiscussed in lines 230-235 in the revised manuscript: “Based on the results of\nthis study, high concentrations of CCl in 17 industries were observed in\n4\nenterprises with low annual revenue (Supplementary Fig. 7). Specifically, the\nCCl concentration was negatively correlated with the logarithm of the\n4\nenterprise revenue (R2 = 0.49, p < 0.01). Similar relationships between CCl\n4\nconcentration and enterprise revenue were also observed in the MGPM (R2 =\n0.75, p < 0.01) and MRCM (R2 = 0.49, p < 0.01) industries.”\nRevised figure:\nSupplementary Fig. 7 Relationship betweenCCl concentrations and revenue.a\n4\nexcluding MGPM. c. Same as a, excluding MRCM.The size of the dots represents the\nnumber of enterprises.\nSupplementary Fig. 8 Number of enterprises above designated size (annual\nrevenue > 20 million CNY) for each industry24.\nReference:\nStatistics Press, 2011-2021).\nStatistics Press, 2021).\nResponses to Reviewer #3:\nReviewer #3 (Remarks to the Author):\nReview of the manuscript: Substantial carbon tetrachloride emissions in eastern\nChina, after 2010 dispersive-use phase out and 2019–2022 strict government\nsupervision: A concern for the ozone layer by Li et al., submitted to Nature Comm.\nThe manuscript is related to on-going emissions of CCl4, for which a consistent gap\nbe-tween bottom-up and top-down emissions exists globally, with higher emissions\nestimated by measurement-based top-down methods. The manuscript discusses\nenhanced CCl4 concentrations in eastern China and estimates emissions, which are\nhigher than expected. Authors also make use of industrial samples, which show\nmassively high concentrations of CCl4 and related them to certain types of industries.\nI am in favour of publishing this manuscript in Nature Comm. because of its global\nimplications. However, some of the comments should be covered carefully.\nResponse:\nThank you for your support on this work. Below, we have responded to each of the\ncomments.\nGeneral comments\n1. The most important finding is the massive concentrations close to industrial sites.\nHere an explanation with a high grade of probability is forbidden emissive uses of\nCCl4. Authors should clearly state this in the manuscript. This would be a very good\npoint to make and this would also close the gap between the bottom-up and the top-\ndown estimate, as in the bottom-up numbers such illegal practices are simply not\ntaken into account as it was deemed highly unlikely, but obviously not.\nResponse:\nThank you for this comment.\nWe acknowledge that (1) high concentrations of CCl may indicate forbidden\n4\ndispersive uses of CCl , (2) high concentrations of CCl may be formed as a\n4 4\nbyproduct during the use or production of chemicals/products, (3) high concentrations\nof CCl come from using CCl as feedstock to produce other chemicals/products. Due\n4 4\nto the lack of definite evidence, we cannot judge which one or two or three of the\nabove three causes of high concentrations of CCl . Therefore, we cannot clearly state\n4\nthat there is illegal use of CCl . Based on your suggestion, we have added the possible\n4\nexplanations for the high concentration from industrial sectors in our revised\nmanuscript.\nRevised text:\nPossible explanation for the high CCl concentrations was stated in lines 220-223\n4\nin the revised manuscript: “The high CCl concentrations in these sectors may arise\n4\nfrom 1) byproducts of CCl during the use or production of chemicals/products, 2) using\n4\n2. Accordingly, point number 1 should also be mentioned in the abstract. At the\nmoment the paper stays very vague on this. The readers want to know the reason for\nthese emissions and it does not make it better to hide it away.\nResponse:\nWe thank the reviewer for the comments. We agree that revealing the massive\nconcentrations measured at industrial sites is important. Our revised abstract clearly\npresents the MGPM and MRCM sectors of a potential industrial source of CCl . As\n4\nfor the causes of CCl emissions. However, since there is still no conclusive evidence\n4\nto prove whether there are prohibited use of CCl (please see our responses in point\n4\nnumber 1), we only objectively reveal the high concentrations and sectors.\nRevised text:\nLines 28-31 in the revised manuscript: “Subsequently, our study identified potential\nindustrial sources (manufacture of general purpose machinery and manufacture of\nraw chemical materials, and chemical products) of CCl emissions that were not\n4\nrevealed in previous studies and were not under governmental supervision.”\n3. Several citations are at the edge of misquotation. These will be detailed in the\nspecific comments below.\nResponse:\nThank you for this comment. In our revised manuscript, the misquotation has been\ncorrected, and we have responded to the specific comments below.\n4. Data must be openly accessible.\nResponse:\nThe emission sensitivity data sets from backward simulations for each station in this\nstudy are available through XXXXXX URL. Measurement data are available from the\ncorresponding author upon reasonable request.\nSpecific comments:\nLine 21\n…was globally phased out by 2010, including China.\nResponse:\nWe thank the reviewer for this comment. The sentence has been revised as follows:\nLines 20-22 in the revised manuscript: “According to the Montreal Protocol,\nproduction and consumption of ozone-layer-depleting CCl for dispersive\n4,\napplications, was globally phased out by 2010, including China.”\nLine 23\n…were disclosed, and the latest…\nResponse:\nWe thank the reviewer for this comment. The sentence has been revised as follows:\nLines 22-23 in the revised manuscript: “However, continued CCl emissions were\n4\ndisclosed, with the latest CCl emissions unknown in eastern China.”\n…respectively, well above the global background.\nResponse:\nWe thank the reviewer for this comment. This sentence has been removed as the\nconcentration discussion has been shortened as suggested by the reviewer.\nLine 29\nIn 2010 and after the implementation…\nResponse:\nThank you for the suggestion; the sentence has been revised accordingly:\nLines 25-27 in the revised manuscript: This indicates that CCl emissions\n4\ncontinued after being phased out for dispersive uses in 2010 and after the\nimplementation of strict government supervision….\nLine 42\nFeedstock instead of \"raw material\"\nResponse:\nThank you for flagging this; the sentence has been revised accordingly:\nLine 42 in the revised manuscript: “An exemption was in its use as a chemical\nfeedstock or processing agent.”\nLine 42\nRef 6 is not needed for this argument and does not really cover this\nResponse:\nThank you for suggestion. Ref 6 has been deleted in our revised manuscript.\nLine 45\n[8] is wrongly cited. In the report they say:\nGlobal CCl4 emissions did not significantly decline during the 2010–2019 period\n!%$&; %$'+/:7<&\"#)&;(,4-(*;(,7.459 3.49154.-1490.7.6579 !57,9 2.,89 9018\nreviewer has not seen it)\nResponse:\nThanks, 41±3 and 38±3 were extracted from estimate using AGAGE in Figure 3.4. in\nthe “Report on unexpected emissions of CFC-11” (shown below). Following your\nsuggestion, the original sentence has been revised to “Top-down estimates of global\nemissions, inferred from atmospheric observations, have shown no significant\ndecrease in CCl emissions between 2010 and 2019 (decline rate of 0.1 ± 0.2 Gg\n4\nyrB&)”.\nRedacted\nLines 42-45 in the revised manuscript “Top-down estimates of global emissions,\ninferred from atmospheric observations, have shown that there was no significant\ndecrease in CCl emissions between 2010 and 2019 (decline rate of 0.1 ± 0.2 Gg\n4\nyrB&)”.\nLine 47\nTake the newest numbers from the most recent ozone assessment (2022)\nResponse:\nThank you for the comment. The original numbers have been replaced with the\nnewest numbers from the most recent ozone assessment (2022).\nLines 46-47 in the revised manuscript: “…approximately 26.1 Gg (12.6–40.0 Gg)\nemissions in 2019,17 Gg yr-1 lower than those of the top-down emission estimates (43\nGg yr-1 in 2019)1.”\nReference:\nDepletion: 2022, GAW Report No. 278. 509 (World Meteorological Organization\n(WMO), Geneva, 2022).\nLine 54\n…CCl emissions from dispersive uses were reduced…\nThank you for the suggestion; the sentence has been revised accordingly:\nLines 53-54 in the revised manuscript: “… CCl emissions from dispersive uses\n4\nwere reduced from 9.3 Gg yr-1 in 2005 to 1.1 Gg yr-1 in 2010 and to zero afterwards.”\nLine 55\nA bottom-up inventory for non-emissive uses of CCl4, including…\nResponse:\nThank you for the comment; the sentence has been revised accordingly:\nLines 54-55 in the revised manuscript: “a bottom-up inventory for non-emissive\nCCl uses, including coal combustion, tetrachloroethylene conversion,…”\nMoreover, another independent study showed bottom-up emission estimates…\nResponse:\nThank you for the suggestion; the sentence has been revised as follows:\nLines 58-59 in the revised manuscript: “Moreover, another independent study\nshowed bottom-up emission estimates of CCl in 2014 of 7.3 Gg yr-1…”\nRigby et al say at least 40-60%\nResponse:\nThank you for the suggestion. The sentence has been revised as follows:\nLines 80-81 in the revised manuscript: “At least 40–60% of this unexpected\nincrease was attributed to eastern China with the likely production of CFC-11 and\nrelated CCl .”\nEquation 4 (see methods)\nResponse:\nThank you for pointing this out. Since we switched to inverse modeling for estimating\nCCl emissions, the description of the interspecies correlation method has been\n4\nremoved.\nLine 187\nReference missing for REBS\nResponse:\nThank you for pointing this out; we have now added the reference “Ruckstuhl, A. F. et\nal. Robust extraction of baseline signal of atmospheric trace species using local\nregression. Atmospheric Measurement Techniques 5, 2613-2624 (2012).”\nLines 20-22 in the revised Supplementary information: “Using the robust\nextraction of baseline signals (REBS) method2, approximately 30% of the CCl\n4\nconcentrations at ZJU (averaged as 93.9 ± 14.4 ppt) were identified as non-\nbackground concentrations, respectively.”\nReference:\nspecies using local regression. Atmospheric Measurement Techniques 5, 2613-2624\n(2012).\nLine 188\n…for total China reported…\nResponse:\nThank you for the suggestion; the description has been revised as follows:\nLines 136-138 in the revised manuscript: “the CCl emissions in eastern China\n4\nreported in this study… are lower than those for total China reported by Park et al.3\n(23.6±7.1 Gg yr-1 during 2011–2015) …”\nReference:\ntetrachloride (CCl ): an analysis of top-down emissions from China. Atmos. Chem.\nLine 203\n…from emissive uses…\nResponse:\nThank you for pointing this out; the description has been revised accordingly:\nLine 165 in the revised manuscript: “The CCl emissions from emissive uses in\nLine 214\nThe relative importance…\nResponse:\nThank you for the suggestion; the description has been revised accordingly:\nLine 175 in the revised manuscript: “The relative importance of CCl emissions is\n4\nincreasing in China.”\nLine 375\nData have to be open access\nResponse:\nThank you for the suggestion; the description has been revised as follows:\n“The emission sensitivity data sets from backward simulations for each station are\navailable through XXXXXX url. Measurement data are available from the\ncorresponding author upon reasonable request.”\nReviewer #1 (Remarks to the Author):\nPlease see attached PDF.\nReviewer#1A achmentonthefollowingpage\nSecond review of NCOMMS-23-29246A, Substantial carbon tetrachloride emissions in eastern China,\nafter 2010 dispersive-use phase-out and 2019–2022 strict government supervision: A concern for the\nozone layer, by Li et al.\nThe paper is very much improved. The authors have addressed all of my major concerns regarding\nmethodology and the citation of appropriate, prior studies.\nI still question whether the primary result of continued, high emissions of CCl4 from China (that is, the\ntwo red diamonds in Figure 2) are worthy of being so much of the focus of a paper in this journal, given\nthe prior literature on this topic. However, the analysis is sound, the dataset is new and unique, and the\ncontinued emission of CCl4 from China does indeed pose a threat to the ozone layer.\nI have a number of minor comments:\na) assuming the paper is accepted, please try very hard, between now and submission of the final\nmanuscript for production of galleys, to remove each instance of the word “This indicates” being used as\na noun. For example, the sentence “This indicates …” in line 26 reads so much better as “This finding\nindicates …”\nb) the sentence on lines 117 to 121 is longer than needed; suggest ending at after “Fig. 2)” on line 120.\nc) Line 140: text would read better as “are lower than the total emission for all of China”. On line 139,\nperhaps “emissions” would be “emission”.\nd) Lines 145 & 147: in my opinion, the paper is easier to digest if “CCl4” were not used as an adjective, so\noften. Here, I suggest “consumption of CCl4”\ne) Line 157: perhaps “air masses”\nf) Line 159: here, “high emissions of CCl4” reads much better in my opinion\ng) Line 161: how about “indisputable emissions of CCl4” (see\nhttps://forum.wordreference.com/threads/indisputable-and-undisputed.1474726 for some thoughts in\nundisputable versus indisputable\nh) Line 169: perhaps a comma after “Wan et al.”\ni) Line 177: suggest “associated emission gaps for CCl4”.\nj) Line 185: no need in my opinion to repeat the value of 6.6 +/- 2.7 given just above. This sentence is still\nlong and a bit awkward even this is deletion. Perhaps the term CFC-11-eq Gg yr-1 can be given once, early\nin the sentence, so that the reader could more easily focus on the numbers and various species. Finally, I\nsuppose “(but for all of China)” applies only to HCFC-22; the sentence could be modified to that there is\nclarity about how this phrase should be applied.\nk) Line 206: suggest “potential sources of CCl4”\nl) Line 207: suggest “This campaign” rather than “This study” to connect better to prior sentence\nm) line 225: suggest “high concentrations of CCl4”.\n1\nn) lines 235 to 240: not sure what the connection of high concentrations of CCL4 with low annual ravenous\nenterprises means. I wonder if either another sentence could bd added explaining why the authors think\nthis could be important, or else perhaps dropping this paragraph and the associated figures from\nSupplement. If the editor is reading this deep into the minor comments, and wants to shorten the paper,\nI think not only this paragraph but also lines 241 to 248 could be removed.\no) Lines 249 to 252: Sorry, I think this is a rather weak concluding paragraph. Here, I would add in the\nyears of the observations, as well as words in plain language (not abbreviations) of the most egregious\nindustrial sources of CCl4.\nq) The station location on Figure 1 was a bit hard to see based on a printed copy of the paper. I could only\nunderstand by enlarging the PDF. Also, the caption uses “cross” that I interpret to be “+”, rather than “X”.\nIn the interest of getting this back to the journal by the deadline, coupled with the holiday in my home\ncountry, I shall admit I did not review the Methods section in the revised paper. I did however read the\nresponses to my prior comments carefully (I was reviewer 1), and I think the use of FLEXPART to compute\nthe emission of CCL4 rather than the correlation method is a major step forward. I have no concerns\nabout the methodology of the revised paper.\nEND OF REVIEW\nIt remains true that on a global scale substantial gaps still exist in our understanding of the\nmeasured global atmospheric decline of CCl4 concentrations. Emissions derived from global\natmospheric concentration changes (the top-down approach) are larger than can be explained by\nactivity-based estimates of sources (bottom-up approach). Measurements of CCl4 in regions dense\nwith chemical production plants, especially in China, are highly valuable and have the potential to\nimprove our understanding of these important questions regarding CCl4 emissions. As a result, this\nwork reporting high quality and high-frequency measurements at two new sites in China has\nthe potential to add significantly to our understanding of these gaps and of the magnitude and\nnature of CCl4 emissions coming from this important region of the world.\nThe authors have made substantial improvements to their manuscript in response to reviewers’\ncomments, especially in the methods used to derive emissions. In a few instances, however, they\nfailed to address fundamental concerns expressed in the initial review (e.g., accurately conveying\nthe goal of the \"MEE supervision program\", was it for better tracking of production and not, as the\nauthors suggest, related to minimizing residual CCl4 emissions). This point is central to how one\nmight expect emissions from known processes to have evolved over time. As a result, I disagree\nwith their central conclusion about the presence of a substantial gap in our understanding of CCl4\nemissions from China in the most recent years (2021-2022). In previous years, there clearly were\nsubstantial unexpected and unexplained CCl4 emissions, and this point has been made in many\nprevious studies. But the results presented here for 2021-2022, which give a better picture of total\nChinese emissions than previous studies owing to the location of the new sites, suggest that there\nis no longer a gap in our understanding of Chinese emissions in this recent period. This is\nessentially the opposite message being conveyed in the current manuscript. Without substantial\nrevision of the main points of this paper I cannot condone publication in any Journal.\nBottom-up analyses encompassing all known sources not controlled by the Montreal Protocol and,\nin my estimation, not likely to decrease over time are indicated as contributing 7.3 Gg/yr of\nemission in 2014 (lines 56-61). Because the MEE supervision of CCl4 production that was adopted\nafter 2018 was focused primarily on tracking CCl4 production in order to avoid any siphoning of\nthis production to create CFCs, it seems unlikely to expect that it had any effect on residual,\nleakage-related emissions from non-controlled sources. The authors have implied that the MEE\nprogram should have caused emission reductions, but they failed to provide any evidence to back\nup this suggestion in their response. Hence, I have no reason to expect substantial declines in\nCCl4 emissions in the years following 2014 from uncontrolled processes; in fact, I expect that they\ncould have increased in recent years, given the use of CCl4 as feedstock to produce HFCs in larger\namounts in recent years.\nThe new results presented in this manuscript, which the authors rightly suggest provide a more\ncomplete picture of total emissions from China than studies relying on data only from Gosan (see\nFigure 1), yield a CCl4 emission of 7.0-8.2 Gg/yr in 2021 and 2022, with uncertainties of about 3\nGg on each value. Given the consistency of these emissions magnitudes with expectations from\nuncontrolled sources (7.3 in 2014, and perhaps increasing after), it seems reasonable to conclude\nthat there is no longer a gap in our understanding of CCl4 emissions from China.\nThe additional findings discussed in this manuscript related to previously unidentified sources of\nCCl4 is interesting and needs further study, but the evidence presented here suggests that those\nunaccounted sources are likely small relative to Chinese emission totals.\nIn some sections of the manuscript, the authors suggest that the new measurements provide only\nestimates for eastern China, and that some earlier work (Park et al., 2018 and Lunt et al., 2018)\nargues for total Chinese emissions being substantially larger. But I do not think this is an accurate\ninterpretation. The earlier studies cited by the authors are based on very uncertain extrapolations\nof measurements from the Gosan station, which the authors have clearly demonstrated to be\n(Figure 1) much less likely to represent emission magnitudes from China more broadly than the\nresults from their new stations.\nIn summary, this paper provides important new measurements data and better estimates of total\nChinese CCl4 emissions, and the new results are likely more accurately interpreted given the\navailable information to suggest that our understanding of CCl4 emissions from China have\nimproved over time, not the opposite. What would strengthen the manuscript notably is a very\nclose look at the bottom-up emission estimates from non-controlled processes to understand how\nthey have evolved over time instead of presuming without careful consideration what influence the\n\"supervision program\" might have had on emission expectations for CCl4 in years since 2018.\nResponses to Reviewer #1:\nSecond review of NCOMMS-23-29246A, Substantial carbon tetrachloride emissions\nin eastern China, after 2010 dispersive-use phase-out and 2019–2022 strict\ngovernment supervision: A concern for the ozone layer, by Li et al.\nThe paper is very much improved. The authors have addressed all of my major\nconcerns regarding methodology and the citation of appropriate, prior studies.\nResponse:\nThank you for your suggestion to improve the quality of our manuscript. Below,\nwe\nhave responded to each of the comments.\nI still question whether the primary result of continued, high emissions of CCl4 from\nChina (that is, the two red diamonds in Figure 2) are worthy of being so much of the\nfocus of a paper in this journal, given the prior literature on this topic. However, the\nanalysis is sound, the dataset is new and unique, and the continued emission of CCl4\nfrom China does indeed pose a threat to the ozone layer.\nResponse:\nThank you for this comment.\nWe agree with the reviewer that previous studies have reported high CCl\n4\nemissions in China. However, this study has significantly advanced this research field\nbecause: (1) we carried out CCl concentration observations that could better\n4\nconstrain emissions in eastern China than at the site of GSN used in previous studies;\n(2) we focused on the latest period of 2021–2022 in which CCl emissions may have\n4\nchanged; (3) we provide the latest top-down estimates of CCl emissions in eastern\nidentified potential new sources of CCl emissions to better minimize the gaps\n4\nbetween the top-down and bottom-up emissions.\nWe thank the reviewer for the comment that “the analysis is sound, the dataset is\nnew and unique, and the continued emission of CCl from China does indeed pose a\n4\nthreat to the ozone layer”.\nI have a number of minor comments:\nResponse:\nThank you. We have responded to each comment below.\na) assuming the paper is accepted, please try very hard, between now and submission\nof the final manuscript for production of galleys, to remove each instance of the word\n“This indicates” being used as a noun. For example, the sentence “This indicates …”\nin line 26 reads so much better as “This finding indicates …”\nResponse:\nWe thank the reviewer for the suggestion. The “This indicates” in the manuscript was\nrevised as “This finding indicates …”.\nb) the sentence on lines 117 to 121 is longer than needed; suggest ending at after “Fig.\n2)” on line 120.\nResponse:\nWe thank you for the suggestion. The sentence after “Fig. 2)” has been removed, and\nthe original sentence was revised as below:\nLines 111–114 in the revised manuscript: “Based on the emission sensitivity\nderived from the backward simulation using the FLEXible PARTicle dispersion\n(FLEXPART) model, ZJU and SHH were more sensitive to emissions from eastern\nChina (especially in Zhejiang, Anhui, Jiangsu, Jiangxi, and Fujian provinces) than\nGSN (see Fig.1 and Supplementary Fig. 2)”.\nc) Line 140: text would read better as “are lower than the total emission for all of\nChina”. On line 139, perhaps “emissions” would be “emission”.\nResponse:\nWe thank the reviewer for the suggestion. As suggested by Reviewer #2, these\nsentences have been removed from our revised manuscript.\nd) Lines 145 & 147: in my opinion, the paper is easier to digest if “CCl4” were not\nused as an adjective, so often. Here, I suggest “consumption of CCl4”\nResponse:\nWe thank the reviewer for the suggestion. The sentence in Lines 145 & 147 was\nrevised as below, and other similar phrases were revised throughout the manuscript.\nLines 133–136 in the revised manuscript:“Moreover, emissions of CCl in\n4\neastern China (7.6 ± 3.1 Gg yr-1) during 2021–2022 were significantly higher than the\nconsumption of CCl for dispersive uses (0.12 Gg yr-1 in 2021) as reported by China\n4\nto the United Nations Environment Programme (UNEP, https://wesr.unep.org)”.\nLines 136-137 in the revised manuscript: “The consumption of CCl for\n4\ndispersive uses in China was 0.20–0.26 Gg yr-1 …”.\ne) Line 157: perhaps “air masses”\nResponse:\nWe thank the reviewer for the suggestion. The original sentence was revised\naccordingly as “Additionally, air masses corresponding to high CCl concentrations\n4\ntypically pass through these regions” (Lines 146–147).\nf) Line 159: here, “high emissions of CCl4” reads much better in my opinion\nResponse:\nThank you for the suggestion. The sentence was revised accordingly as “The high\nemissions of CCl from Shandong, Zhejiang, and Jiangsu were generally consistent\n4\nwith…” (Line 148)\ng) Line 161: how about “indisputable emissions of CCl ” (see\n4\nhttps://forum.wordreference.com/threads/indisputable-and-undisputed.1474726 for\nsome thoughts in undisputable versus indisputable\nResponse:\nWe thank the reviewer for the suggestion. The sentence was revised accordingly as\n“our study also revealed indisputable emissions of CCl from Jiangxi (1.1± 0.85 Gg\n4\nyr-1) and Fujian (0.99 ± 0.41 Gg yr-1) provinces” (Lines 150–151)\nh) Line 169: perhaps a comma after “Wan et al.”\nResponse:\nWe thank you for the suggestion. We have corrected it in the text.\nLines 158–160 in the revised manuscript: “The emissions of CCl from emissive\n4\nuses inChina were estimated to be zero…and Wan et al.1, as they set consumption of\nCCl for dispersive uses to zero.”\n4\ni) Line 177: suggest “associated emission gaps for CCl4”.\nResponse:\nThe sentence was revised accordingly as “Thus, further comprehensive bottom-up and\ntop-down studies are strongly recommended to bridge the associated emission gaps\nfor CCl ” (Lines 166–167).\n4\nj) Line 185: no need in my opinion to repeat the value of 6.6 +/- 2.7 given just above.\nThis sentence is still long and a bit awkward even this is deletion. Perhaps the term\nCFC-11-eq Gg yr-1 can be given once, early in the sentence, so that the reader could\nmore easily focus on the numbers and various species. Finally, I suppose “(but for all\nof China)” applies only to HCFC-22; the sentence could be modified to that there is\nclarity about how this phrase should be applied.\nResponse:\nWe thank you for this pertinent suggestion. The repeat value of 6.6 +/- 2.7 has been\ndeleted. However, as deleting “CFC-11-eq Gg yr-1” for emissions of each ODS may\nmake the sentence slightly difficult to interpret, we chose to retain “CFC-11-eq Gg yr-\n1” at each instance.\nLines 175–181 in the revised manuscript: “Moreover, ODP-weighted CCl4\nemissions in eastern China are several times higher than those of other major ODSs;\nfor example, they are 17.6 times higher than the 0.38 ± 0.75 CFC-11-eq Gg yr-1 of\ndichlorodifluoromethane (CFC-12) in 2019 in eastern China2, 8.4 times the 0.79\n(0.65–1.1) CFC-11-eq Gg yr-1 of 1,1-dichloro-1-fluoroethane (HCFC-141b) in 2020\nin eastern China3, and 1.4 times the 4.6 (3.9–5.3) CFC-11-eq Gg yr-1 of\nchlorodifluoromethane (HCFC-22; but for all of China) in 20194.”\nk) Line 206: suggest “potential sources of CCl4”\nResponse:\nThank you for the suggestion. The sentence was revised accordingly as “A\ncomprehensive measurement campaign comprising 456 exhaust samples from 17\nindustry sectors was performed to determine potential sources of CCl ”. (Lines 195–\n4\n196).\nl) Line 207: suggest “This campaign” rather than “This study” to connect better to\nprior sentence\nResponse:\nThe sentence was revised accordingly as “This campaign shows that …”. (Line 197)\nm) line 225: suggest “high concentrations of CCl4”.\nResponse:\nWe thank the reviewer for the suggestion. The sentence was revised to “The high\nconcentrations of CCl in these sectors may arise from…”. (Line 211)\n4\nn) lines 235 to 240: not sure what the connection of high concentrations of CCL4 with\nlow annual ravenous enterprises means. I wonder if either another sentence could bd\nadded explaining why the authors think this could be important, or else perhaps\ndropping this paragraph and the associated figures from Supplement. If the editor is\nreading this deep into the minor comments, and wants to shorten the paper, I think not\nonly this paragraph but also lines 241 to 248 could be removed.\nResponse:\nWe agree with you about lines 235 to 240. We have dropped this paragraph.\nHowever, it is necessary to show the scale of the industrial sector observed in this\nstudy and its changing trends, which can help to assess the relative importance of\nthese sectors’ contribution to CCl emissions. Hence, lines 241 to 248 have been\n4\nretained.\no) Lines 249 to 252: Sorry, I think this is a rather weak concluding paragraph. Here, I\nwould add in the years of the observations, as well as words in plain language (not\nabbreviations) of the most egregious industrial sources of CCl4.\nResponse:\nThank you for this insightful suggestion. The original Lines 249 to 252 were\nrewritten.\nLines 230–235 in the revised manuscript:“This study quantified substantial\nemissions of CCl in eastern China (7.6 ± 3.1 Gg yr-1) during 2021–2022 based on\n4\nobservations at two sites located in Zhejiang Province, China. By conducting\nextensive sampling, we detected high concentrations of CCl in the exhaust gases from\n4\nmanufacture of general purpose machinery and manufacture of raw chemical\nmaterials, and chemical products, implying that these previously unreported\nindustrial sectors may be potential sources of CCl .”\n4\nq) The station location on Figure 1 was a bit hard to see based on a printed copy of the\npaper. I could only understand by enlarging the PDF. Also, the caption uses “cross”\nthat I interpret to be “+”, rather than “X”.\nResponse:\nThanks for the suggestion. The symbol representing the location of the sampling point\nhas been enlarged, and the original symbol, “X” has been replaced with “+”.\na b\nc d\ne f\nFig. 1 Average emission sensitivity derived from FLEXPART simulations and the\naverage emission sensitivity of ZJU and SHH stations compared to that of the GSN\nstation in 2021 and 2022.a, b Average emission sensitivities from ZJU and SHH\nstations. c, d Average emission sensitivities from GSN station. e, f Ratio of the\naverage emission sensitivity of ZJU and SHH stations to that of the GSN station. ZJU,\nSHH, and GSN sites are represented by the blue triangle, dot, and cross, respectively.\nThe area framed by the bold black line is the target area for the inversion. The name\nof each Chinese province is given in Supplementary Fig. 1. Distribution of the\nemission sensitivities for a larger domain than that in Fig. 1 is shown in\nSupplementary Fig. 2.\nIn the interest of getting this back to the journal by the deadline, coupled with the\nholiday in my home country, I shall admit I did not review the Methods section in the\nrevised paper. I did however read the responses to my prior comments carefully (I\nwas reviewer 1), and I think the use of FLEXPART to compute the emission of CCL4\nrather than the correlation method is a major step forward. I have no concerns about\nthe methodology of the revised paper.\nResponse:\nWe are grateful to you for suggesting that we improve the emission quantification\nmethod used in this study in the first review round and for pointing out many minor\nerrors in the second review round. Happy holidays.\nEND OF REVIEW\nReferences:\nhalocarbons in China. Atmos. Environ.43, 5822-5829 (2009).\nNature590, 433-437 (2021).\nAtmos. Chem. Phys.22, 9601-9616 (2022).\nGridded Emission Inventories in China and the Analysis of Emission Reduction Potential.\nEnviron. Sci. Technol.56, 814-822 (2022).\nResponses to Reviewer #2:\nIt remains true that on a global scale substantial gaps still exist in our understanding\nof the measured global atmospheric decline of CCl4 concentrations. Emissions\nderived from global atmospheric concentration changes (the top-down approach) are\nlarger than can be explained by activity-based estimates of sources (bottom-up\napproach). Measurements of CCl4 in regions dense with chemical production plants,\nespecially in China, are highly valuable and have the potential to improve our\nunderstanding of these important questions regarding CCl4 emissions. As a result, this\nwork reporting high quality and high-frequency measurements at two new sites in\nChina has the potential to add significantly to our understanding of these gaps and of\nthe magnitude and nature of CCl4 emissions coming from this important region of the\nworld.\nResponse:\nThank you for recognizing the importance of this work. We have responded to each\ncomment point-by-point.\nThe authors have made substantial improvements to their manuscript in response to\nreviewers’ comments, especially in the methods used to derive emissions. In a few\ninstances, however, they failed to address fundamental concerns expressed in the\ninitial review (e.g., accurately conveying the goal of the \"MEE supervision program\",\nwas it for better tracking of production and not, as the authors suggest, related to\nminimizing residual CCl4 emissions). This point is central to how one might expect\nemissions from known processes to have evolved over time. As a result, I disagree\nwith their central conclusion about the presence of a substantial gap in our\nunderstanding of CCl4 emissions from China in the most recent years (2021-2022). In\nprevious years, there clearly were substantial unexpected and unexplained CCl4\nemissions, and this point has been made in many previous studies. But the results\npresented here for 2021-2022, which give a better picture of total Chinese emissions\nthan previous studies owing to the location of the new sites, suggest that there is no\nlonger a gap in our understanding of Chinese emissions in this recent period. This is\nessentially the opposite message being conveyed in the current manuscript. Without\nsubstantial revision of the main points of this paper I cannot condone publication in\nany Journal.\nResponse:\nThank you for this comment. We agree that the total emissions of CCl in China may\n4\nnot have decreased since the feedstock uses of CCl may have substantially increased.\nAs for the “MEE supervision program”, CCl emissions may decline. One factory\n4\nwas illegally producing CFC-11, and substantial discharges of CCl (used as feedstock\n4\nto produce CFC-11) were observed\n(https://sd.ifeng.com/zbc/detail_2013_08/02/1064095_0.shtml; in Chinese).\nThis impliesthat CCl emissions may decline after the MEE supervision program of\n4\ncontrolling the uses of CCl to produce CFC-11. However, we agree with your\n4\nsuggestion that CCl emissions from non-controlled sources may increase in recent\n4\nyears, given the use of CCl as feedstock to produce HFCs in larger amounts.\n4\nlatest changes in CCl emissions in eastern China after 2018 remain unclear.\nChinese emissions in this recent period”, there are still gaps between top-down and\nbottom-up estimates of CCl emissions in China. The CCl emission in China in 2014\n4 4\nbottom-up estimated by Sherry et al.1 was mainly from chloromethane production\nplants (6.6 Gg out of 7.3 Gg of total emissions in 2014), However, based on the field\nmeasurement-based emission factors, Li et al.2 reported that CCl emissions from\n4\nchloromethane production plants were only 2.2 ± 1.6 Gg/yr in 2019, indicating that\nthe emissions from chloromethane production plants and total emissions in China may\nhave been overestimated by Sherry et al.1 The total emissions from chloromethane\nproduction plants (2.2 ± 1.6 Gg/yr in 2019) and dispersive uses (0.21 Gg in 2021,\nhttps://wesr.unep.org) in China are smaller than our top-down emission estimates for\neastern China. Thus, this study aimed to search the potential industrial source of CCl\n4\nemissions. Related revisions in the manuscript are given below:\nLines 74–84 in the revised manuscript: “The “Supervision program” was\nimplemented to improve the tracking of produced CCl , thereby preventing its\n4\nconversion to CFCs (e.g., CFC-11) or its sale for controlled or emissive applications,\nwhich may lead to a decline in CCl emissions from this source. CCl is also used as\n4 4\nfeedstock, e.g., to produce pentafluoropropane (HFC-245fa) and fluoroolefins (HFOs)\n(these processes are not controlled by the Montreal Protocol)3. Since the feedstock use\nof CCl for the production of HFCs, HFOs, and other chemicals has increased by\n4\n~70% between 2015 and 2019 in China3, an increase in CCl emissions from\n4\nfeedstock uses is expected. Given the combined effects of the above activities, the\nlatest emissions of CCl in eastern China are unclear. However, no data…..”\nbased emission factors, Li et al.2 reported that CCl emission from chloromethane\n4\nproduction plants was only 2.2 ± 1.6 Gg yr-1 in 2019 in China. The consumption of\nCCl\n4\nfor dispersive uses as reported by China to the UNEP (https://wesr.unep.org) was\n0.12 Gg yr-1 in 2021. Thus, the total emissions of CCl from chloromethane\n4\nproduction plants and dispersive uses in China are smaller than our top-down\nemission estimates, indicating the presence of other sources of CCl emissions.”\nCCl emissions in eastern China during 2021–2022 and exploration of potential new\n4\nsources”.\nBottom-up analyses encompassing all known sources not controlled by the Montreal\nProtocol and, in my estimation, not likely to decrease over time are indicated as\ncontributing 7.3 Gg/yr of emission in 2014 (lines 56-61). Because the MEE\nsupervision of CCl4 production that was adopted after 2018 was focused primarily on\ntracking CCl4 production in order to avoid any siphoning of this production to create\nCFCs, it seems unlikely to expect that it had any effect on residual, leakage-related\nemissions from non-controlled sources. The authors have implied that the MEE\nprogram should have caused emission reductions, but they failed to provide any\nevidence to back up this suggestion in their response. Hence, I have no reason to\nexpect substantial declines in CCl4 emissions in the years following 2014 from\nuncontrolled processes; in fact, I expect that they could have increased in recent years,\ngiven the use of CCl4 as feedstock to produce HFCs in larger amounts in recent years.\nResponse:\nWe express our thanks for this suggestion. As for the impact of MEE supervision\nprogram on CCl emissions, please see our response above. Meanwhile, we agree that\n4\nthe use of CCl as feedstock to produce HFCs in larger amounts. Therefore, given the\n4\ncombined effects of the above activities, the latest changes in CCl emissions in\n4\neastern China, especially after 2018, remain unclear. Related description on “MEE\nsupervision program” has been substantially revised accordingly.\nLines 71–84 in the revised manuscript:“To strengthen the regulation of CFC-11\nand production of CCl , the Ministry of Ecology and Environment of China has\n4\ndispatched working groups to perform on-site supervision and installed online\nmonitoring systems for enterprises producing CCl as a by-product in the country4.\nCCl , thereby preventing its conversion to CFCs (e.g., CFC-11) or its sale for\n4\ncontrolled or emissive applications, which may lead to a decline in CCl emissions\n4\nfrom this source. CCl is also used as feedstock, e.g., to produce pentafluoropropane\n4\n(HFC-245fa) and fluoroolefins (HFOs) (these processes are not controlled by the\nMontreal Protocol)3. Since the feedstock use of CCl for the production of HFCs,\nChina3, an increase in CCl emissions from feedstock uses is expected. Given the\n4\ncombined effects of the above activities, the latest emissions of CCl in eastern China\n4\nare unclear….”\nThe new results presented in this manuscript, which the authors rightly suggest\nprovide a more complete picture of total emissions from China than studies relying on\ndata only from Gosan (see Figure 1), yield a CCl4 emission of 7.0-8.2 Gg/yr in 2021\nand 2022, with uncertainties of about 3 Gg on each value. Given the consistency of\nthese emissions magnitudes with expectations from uncontrolled sources (7.3 in 2014,\nand perhaps increasing after), it seems reasonable to conclude that there is no longer a\ngap in our understanding of CCl4 emissions from China.\nResponse:\nThank you for this comment. We have provided our responses above; however, we\nhave presented the responses here as well.\nThe CCl emissions estimated in China in 2014 using the bottom-up approach by\nof 7.3 Gg of total emissions in 2014). However, the study by Li et al.2 on field\nmeasurement-based emission factors stated that CCl emissions from chloromethane\n4\nproduction plants were only 2.2 ± 1.6 Gg/yr in 2019 in China, indicating the\nemissions from chloromethane production plants and total emissions in China may\nhave been overestimated by Sherry et al.1. The total emissions from chloromethane\nproduction plants (2.2 ± 1.6 Gg/yr in 2019) and dispersive uses (0.21 Gg in 2021,\nhttps://wesr.unep.org) in China are smaller than our top-down emission estimates.\nTherefore, there are still gaps between top-down and bottom-up estimates of CCl\n4\nemissions in China.\nThe additional findings discussed in this manuscript related to previously unidentified\nsources of CCl4 is interesting and needs further study, but the evidence presented here\nsuggests that those unaccounted sources are likely small relative to Chinese emission\ntotals.\nResponse:\nThank you for this comment. Right now, we cannot reach the conclusion that those\nunaccounted sources are likely small relative to Chinese emission totals. Owing to\nlack of activity data and CCl concentrations of the investigated industrial sectors for\n4\nall of China, we are not in a position to determine the contribution of the investigated\nindustrial sectors to total emissions of CCl in China.\nand MEMA industries since 2018 (growth rate: 10–16% yr-1 during 2018–2021)5\n(Supplementary Fig. 7). Although the number of enterprises in most other industrial\nsectors was relatively stable during 2011– 20216 (Supplementary Fig. 7), the overall\ntrend has been on the rise (Supplementary Fig. 7). Considering that they may\nrepresent potential sources of CCl emissions, attention should be paid to these\n4\nindustries (particularly MGPM) to evaluate their impact on the ozone layer.\nWe agree with your point that their contribution should be studied in the future.\nIn some sections of the manuscript, the authors suggest that the new measurements\nprovide only estimates for eastern China, and that some earlier work (Park et al., 2018\nand Lunt et al., 2018) argues for total Chinese emissions being substantially larger.\nBut I do not think this is an accurate interpretation. The earlier studies cited by the\nauthors are based on very uncertain extrapolations of measurements from the Gosan\nstation, which the authors have clearly demonstrated to be (Figure 1) much less likely\nto represent emission magnitudes from China more broadly than the results from their\nnew stations.\nResponse:\nWe agree with the comment. Accordingly, the original description “Additionally, the\nCCl emissions in eastern China reported in this study (7.6 ± 3.1 Gg yr-1 in 2021–\n4\n2022) and by Park et al.7 are lower than those for total China reported by Park et al.8\n(23.6 ± 7.1 Gg yr-1 during 2011–2015) and Lunt et al.9 (17 (11–24) Gg yr-1 during\n2009–2016) (Error! Reference source not found.b), indicating the presence of CCl\n4\nemissions in non-eastern regions of China” has been removed.\nIn summary, this paper provides important new measurements data and better\nestimates of total Chinese CCl4 emissions, and the new results are likely more\naccurately interpreted given the available information to suggest that our\nunderstanding of CCl4 emissions from China have improved over time, not the\nopposite. What would strengthen the manuscript notably is a very close look at the\nbottom-up emission estimates from non-controlled processes to understand how they\nhave evolved over time instead of presuming without careful consideration what\ninfluence the \"supervision program\" might have had on emission expectations for\nCCl4 in years since 2018.\nResponse:\nWe thank you for the positive comments and feedback.\nWe do agree that there is no longer a gap in our understanding of CCl emissions\n4\nfrom China based on top-down methods, but the gap between top-down and bottom-\nup emission estimates is still large (see our responses above). Based on this review\ncomment, we agree that the changes in CTC emissions cannot be judged by the\nexecution of “supervision program”. CCl is also used as feedstock, e.g., to produce\n4\npentafluoropropane (HFC-245fa) and fluoroolefins (HFOs), the processes for which\nare not controlled by the Montreal Protocol3. Since the feedstock use of CCl for\nChina, an increase in CCl emissions from feedstock uses is expected3.Given the\n4\ncombined effects of the above activities, the latest emissions of CCl in eastern China\n4\nare unclear.\nLines 187–193 in the revised manuscript: “Based on the field measurement-\nbased emission factors, Li et al.2 reported that CCl emission from chloromethane\n4\nproduction plants was only 2.2 ± 1.6 Gg yr-1 in 2019 in China. The consumption of\nCCl\n4\nfor dispersive uses as reported by China to the UNEP (https://wesr.unep.org) was\n0.12 Gg yr-1 in 2021. Thus, the total emissions from chloromethane production plants\nand dispersive uses in China are smaller than our top-down emission estimates,\nindicating the presence of other sources of CCl emissions.”\n4\nproduce pentafluoropropane (HFC-245fa) and fluoroolefins (HFOs) (these processes\nare not controlled by the Montreal Protocol)3. Since the feedstock use of CCl for the\n4\nproduction of HFCs, HFOs, and other chemicals has increased by ~70% between\n2015 and 2019 in China3, an increase in CCl emissions from feedstock uses is\n4\nexpected.”\nReferences:\ntetrachloride (CCl4) in our atmosphere. Environ. Res. Lett.13, 024004 (2018).\nbased on field measurements surrounding a typical chloromethane plant in China. J. Clean.\nProd.414 (2023).\n84/41(b) and (c)). Report No. UNEP/OzL.Pro/ExCom/90/9/Add.1, (United Nations\nEnvironment Programme, Montreal, 2022).\nMinistry of Ecology and Environment in August 2019 (in Chinese),\nhttps://www.mee.gov.cn/xxgk2018/xxgk/xxgk15/201908/t20190830_730891.html (2019).\n2021).\nNature590, 433-437 (2021).\ntetrachloride(CCl4): an analysis of top-down emissions from China. Atmos. Chem. Phys. 18,\n11729-11738 (2018).\nFrom Eastern Asia. Geophys. Res. Lett.45, 11423-11430 (2018)."}]

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