As shown in Fig. G1, all five meteorological variables (10 m eastward wind, U10; 10 m northward wind, V10; 2 m air temperature, T2M; surface pressure, SP; and planetary boundary-layer height, PBLH) at all Guangzhou sites exhibit |z|≤1, indicating that both August and December 2022 were meteorologically typical relative to the same-year seasonal background and the 2010–2022 climatological baselines. At GZ7, December 2010 also shows |z|≤1 relative to both the DJF 2010 seasonal mean and the 2010–2022 DJF climatology, indicating that the 2010 winter sampling month was likewise meteorologically typical (Fig. G1e–f). August 2022 featured slightly weaker easterly winds and near-climatological boundary-layer heights, while December 2022 was characterized by prevailing northerly flow and typical boundary-layer ventilation. Similarly, all five variables for December 2010 at GZ7 remained within ±1σ of both the DJF 2010 mean and the 2010–2022 DJF climatology, confirming that the 2010 winter sampling month was not associated with unusual circulation or mixing conditions.

Complementary ERA5 wind-rose analyses (Fig. G2) and 72 h HYSPLIT back-trajectory simulations (Fig. F1) confirm that both months followed the canonical East Asian monsoon regimes – maritime inflow during summer and continental outflow during winter. Using GZ7 as an illustrative example representative of central Guangzhou, the ERA5 wind roses show dominant east–east-southeasterly (90–135°) winds in August 2022, typically 3–8 ms-1. In comparison, JJA 2022 and the 2010–2021 JJA climatology peak in the south–south-westerly sector (157.5–225°), representing a within-sector rotation (90–225°) rather than a regime change. ERA5 anomalies of U10, V10, and PBLH remain below 1σ, confirming transport typicality. HYSPLIT trajectories indicate that August 2022 air masses primarily originated over the South China Sea, consistent with summer maritime inflow. For December 2022, the ERA5 wind roses display a clear north–north-easterly (0–45°) dominance with 3–8 ms-1 speeds. The DJF 2022 composite and the 2010–2021 DJF climatology show nearly identical northerly continental patterns, typical of the East Asian winter monsoon. HYSPLIT back trajectories confirm that the air parcels predominantly arrived from northern continental China under prevailing northerlies. Similarly, the ERA5 wind roses for December 2010 and DJF 2010 at GZ7 (Fig. G2g–h) show dominant northerly to north-easterly flow, closely matching the DJF climatological wind regime, indicating that the 2010 winter sampling period was also embedded in the canonical East Asian winter monsoon pattern.

To directly compare the meteorological environments of the two sampling years, we further analysed ERA5 diagnostics on flask sampling days at GZ7 (Tables G1 and G2). Both December 2010 and December 2022 were dominated by northerly to north-easterly flow, with winds in the 0–45° sector accounting for 61.7 % and 82.7 % of occurrences, respectively (Table G2). However, December 2022 exhibited stronger winds and deeper boundary layers than December 2010 (mean wind speed: 3.6 vs. 2.6 ms-1; mean PBLH: 476 vs. 377 m; mean ventilation: 2024.5 vs. 1258.0 m2s-1), and these differences are statistically significant (p<0.01 for both Student's t-test and Mann–Whitney U test; Table G1). These conditions would tend to dilute near-surface enhancements in 2022 relative to 2010, implying that the observed decreases in Cff and RCO/CO2ff between the two periods are, if anything, conservative with respect to emission changes.

Overall, these diagnostics suggest that the sampling windows in both 2010 and 2022 were not associated with anomalous large-scale transport. Nevertheless, variability in mixing and transport at sub-monthly scales may still contribute to uncertainty, especially given the limited number of winter flasks in 2022. Accordingly, we treat transport/mixing variability as an uncertainty in the inter-period comparison rather than assuming it to be negligible.

Each flask represents approximately 15–20 min of integrated air, and about 40 samples were collected per month across ten stations, providing broad spatial and temporal coverage. To evaluate how representative these discrete samples are for the respective seasons, we compared ERA5 diagnostics (PBLH, wind speed, and wind direction) during sampling days with the corresponding monthly means. The results show that meteorological conditions during sampling closely matched monthly climatological averages, confirming that no unusual stagnation or transport anomalies occurred on the sampling days. For the December 2010 flask sampling at GZ7, ERA5 diagnostics and wind roses (Figs. G1e, f and G2g, h) likewise show that sampling-day conditions were consistent with the DJF 2010 seasonal mean and the 2010–2022 DJF climatology, indicating that these earlier samples were also collected under typical winter transport regimes.

ERA5 wind roses (Fig. G2) and HYSPLIT 72 h back-trajectories (Fig. F1) further confirm that the flask collection periods coincided with the prevailing summer (90–225°) and winter (0–45°) monsoon sectors. Hence, the samples captured the dominant seasonal transport regimes rather than isolated short-term events. We therefore consider the weekly flask observations to be broadly representative of their seasonal backgrounds in terms of large-scale transport, while noting that the discrete nature of flask sampling (and the small winter 2022 sample size) limits the ability to fully average out synoptic-scale variability.

To ensure comparability, all available historical datasets (Table H1) were harmonized to identical sites, seasons, and local-time windows, and recalculated using unified background references (Table H2 and Fig. 4a). This harmonization reduces methodological differences (e.g., background choice and sampling-window differences) and facilitates an inter-period comparison of Cff mole fractions, while transport and mixing variability remains a source of uncertainty. We emphasize that the following comparison addresses observed near-surface Cff concentrations. Without an atmospheric transport model and inverse modelling, we cannot quantitatively attribute the observed inter-period concentration differences to emission changes.

For Guangzhou, a site-specific long-term comparison was conducted at the GZ7 urban station, which was also used by Ding et al. (2013). In their study, Cff was derived from flask observations collected around 20:00 LT (post-rush-hour) using a Δ(14C) background based on corn-leaf samples from Qinghai, Gansu, and Tibet. Such a background likely represents a different air-mass domain from Guangzhou. In contrast, the present study used atmospheric Δ(14C) observations from the NL regional background site, which directly samples the same regional air masses influencing Guangzhou. To harmonize the background reference used in the Cff calculation between studies, the winter 2010 Cff values from Ding et al. (2013) and the winter 2022 values from this work were recalculated using the NL tree-ring Δ(14C) record (Li et al., 2025b) as a common reference baseline. The NL tree-ring Δ(14C) represents a growing-season (March–October) integrated proxy and the 2022 value is linearly extrapolated from the 2011–2020 record; it is therefore not intended to represent wintertime background variability and is used here only to provide an internally consistent baseline for inter-study comparison. This adjustment changes Cff from 45.6±5.3 to 44.2±5.3µmolmol-1 for 2010, and from 16.8±3.4 to 12.5±3.4µmolmol-1 for 2022.

Because sampling times differ (20:00 vs. 14:00 LT), we quantified the expected diurnal Cff contrast using continuous CO observations near GZ7. ΔCO increased from 168 ppb at 14:00 to 221 ppb at 20:00, corresponding to a 21 % Cff nighttime enhancement (Scheme 1, Appendix H1). A supplementary analysis using the winter 2023–2024 dataset gave a 35 % enhancement (Scheme 2, Appendix H1). These findings suggest that the evening Cff level is typically 21 %–35 % higher than the well-mixed afternoon value due to weaker nocturnal boundary-layer mixing, although a diurnal cycle in emissions may also contribute to this difference. Applying this correction, the 2010 nighttime Cff (44.2±5.3µmolmol-1) corresponds to an afternoon-equivalent concentration between 28.7±3.5 and 34.9±4.2µmolmol-1, which remains substantially higher than the 2022 value of 12.5±3.4µmolmol-1.

In addition to harmonizing background Δ(14C) and sampling times, we explicitly evaluated the impact of changes in boundary-layer mixing between 2010 and 2022 (Sect. 3.4.1 and Table G1). To assess how much of the inter-period difference could plausibly be explained by changes in boundary-layer mixing, we provide a first-order estimate of the sensitivity of near-surface Cff to PBLH variations. Under a well-mixed boundary-layer “box” approximation, the surface enhancement of predominantly surface-emitted tracers scales approximately as Cff∝1/PBLH, implying ΔCff/Cff≈-ΔPBLH/PBLH. Using ERA5 PBLH at the actual flask sampling hours (Fig. G1), the standardized anomaly of PBLH in Dec 2022 at Guangzhou sites is z≈0.17, corresponding to a relative PBLH increase of 11 % (based on the local winter mean and standard deviation used to define z). If emissions and other factors were unchanged, this would translate into an expected dilution of Cff by 11 % (i.e., 1–3 µmolmol-1 for typical wintertime Cff levels). This indicates that the modestly higher PBLH in December 2022 would tend to reduce the observed Cff, but its magnitude is smaller than the observed inter-period difference (16.2–22.4 µmolmol-1).

Taken together, after harmonizing the Δ(14C) background and accounting for sampling-time differences, the observations indicate an indicative inter-period decrease in wintertime Cff in Guangzhou between 2010 and 2022. Using the CO-based diurnal scaling (21 %–35 % nighttime enhancement), the 2010 value corresponds to an afternoon-equivalent Cff of 28.7–34.9 µmolmol-1, compared to 12.5±3.4µmolmol-1 in 2022 (i.e., 56 %–64 % lower; the range reflects uncertainty in the diurnal scaling). This percentage refers to the observed concentration change and may include a modest contribution from differences in boundary-layer mixing; our first-order PBLH-based scaling suggests that the December 2022 mixing anomaly would affect Cff at the ∼10% level (1–3 µmolmol-1). Given the limited number of winter flasks in 2022, we performed a leave-one-out sensitivity test (Appendix H), which shows that the inferred 2010–2022 decrease remains negative for all subsets, although the magnitude varies. Accordingly, we interpret the Guangzhou 2010–2022 difference as an indicative inter-period change rather than a robustly quantified long-term trend. FLEXPART footprint analyses for 2010 and 2022 show similar source-sensitivity patterns centered on the Guangzhou urban core, supporting that GZ7 remains representative of Guangzhou's urban influence domain in both periods.

Comparable harmonized analyses were performed for other Chinese cities (Tables H1 and H2; Fig. 4a). For Beijing, all measurements originate from the urban rooftop site of the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (RCEES). The Δ(14C) background used in Zhou et al. (2020) was based on Qixianling Mountain (QXL), whereas Wang et al. (2022b) adopted the Waliguan (WLG) background. All Cff values were recalculated using WLG as a common reference background with the 2015 value from Niu et al. (2016). After this correction, the 2014–2016 winter Cff value increases slightly from 27.0±0.3 to 27.6±0.3µmolmol-1, ensuring consistency across datasets. Relative to this harmonized baseline, the subsequent decline to 19.7±22.0 µmolmol-1 by winter 2020 (Wang et al., 2022b) represents an approximate 29 % reduction (p<0.05). This trend is consistent with regional fossil-fuel CO2 emission reductions and corroborated by independent Δ(14C) tree-ring records showing a peak near 2010 in Beijing (Niu et al., 2024). For Xi'an, at the Institute of Earth Environment, Chinese Academy of Sciences (IEECAS) urban site, Cff fell by 36 % from (40.1±3.8)µmolmol-1 in 2011–2013 to (25.7±1.1)µmolmol-1 in 2014–2016 (p<0.001) (Zhou et al., 2022). Suburban sites declined by ≈12% from (23.5±6.5)µmolmol-1 in 2016 (Wang et al., 2018) to (13.1±10.9)µmolmol-1 in 2021–2022 (Liu et al., 2024) (p<0.05). These decreases are consistent with independent Δ(14C) tree-ring records indicating emission peak near 2013 in Xi'an (Niu et al., 2024).

Overall, the harmonized, site-specific, and time-of-day-corrected comparisons demonstrate statistically significant reductions in fossil-fuel CO2 across China's major urban centers. For Guangzhou particularly, the combined evidence – consistent background domain, typical meteorology, verified sampling representativeness, and quantified diurnal correction – provides strong support that the observed Cff decline reflects genuine decarbonization rather than artifacts of sampling or transport variability. Furthermore, this observed decline in Cff is consistent with reported emission reductions in major source regions of South and East China (e.g., Hebei, Shandong, Zhejiang, and Guangdong; Fig. F2) according to the MEIC inventory (Shi et al., 2022), supporting the interpretation of a widespread decarbonization trend.

Similar reductions were found in Cff emissions from 2012–2020 according to the MEIC inventory (Shi et al., 2022), such as Guangzhou (by 16 % from 2011), Shenzhen (by 3 %), Zhanjiang (by 0.1 %), Beijing (by 16 %), and Xi'an (by 9 %) (Fig. 4b), particularly in the industrial and power sectors (Li et al., 2017). We also found such declines in the MIXv2 Asian emission inventory (MIXv2, excluding Shenzhen and Shaoguan) (Li et al., 2024) and another carbon inventory for most Chinese cities (Zhang et al., 2024), but not in the ODIAC (Oda and Maksyutov, 2024) and the Emissions Database for Global Atmospheric Research (EDGAR) (Crippa et al., 2023). In fact, the mitigation of Cff emissions in China's MEIC inventory was primarily driven by heterogeneous trends across cities: 38 % exhibited sustained emission reductions, 29 % showed an initial decline followed by a rebound, while 33 % maintained increasing trajectories. Notably, cities achieving sustained reductions were disproportionately concentrated in larger cities, comprising 86 % of megacities, 43 % of supercities, and 43 % of Type I large cities (populations of 3–5 million). In contrast, smaller cities showed lower mitigation prevalence, with only 34 % of Type II large cities (1–3 million) and 38 % of medium/ small cities attaining emission decreases.

We determined the coal, oil, and natural gas fractions of Cff using the Keeling plot of δ(13C) and CO2 (i.e., scatter plot between δ(13C) and inverse of CO2 mole fractions) and the Bayesian mixing model (MixSIAR) (Stock et al., 2018) during winter 2022. The fractions in winter were (49±25) %, (29±22) %, and (22±19) %, respectively, for Guangzhou, (47±25) %, (29±21) %, and (24±20) % for Shenzhen, (43±24) %, (29±21) %, and (28±21) % for Zhanjiang, and (39±24) %, (34±23) %, and (27±21) % for Shaoguan (Table I1). Coal combustion was the largest contributor to Cff emissions, followed in descending order by oil combustion and natural gas combustion. Compared with other cities around the world (Table I1), we found natural gas was the primary fuel type consumed in Paris (70 %) (Lopez et al., 2013) and Beijing (55±9 %) (Wang et al., 2022b), whereas oil was the main fuel type consumed in Los Angeles (>50%) (Djuricin et al., 2010; Newman et al., 2016). Coal remains the primary fossil fuel used in Xi'an ((72.6±10.4) % in 2014 and (54±4) % in 2019) (Wang et al., 2022b; Zhou et al., 2014), Guangzhou (49 % in 2022), and Shenzhen (47 % in 2022). Notably, cities with high Cff emissions consume all three types of fossil fuels, with the dominant fuel type varying by city. Coal remains the primary fossil fuel used in many Chinese cities.

The reduction in Cff concentrations can be attributed to changes in energy systems as a result of China's clean air measures (Shi et al., 2022). A major contribution has been the reduction in coal usage and the shift to low-carbon energy sources such as natural gas. During 2013–2022, the share of coal in the energy mix decreased by 4.9 % in China and by 7.1 % in Guangdong Province, whereas the share of natural gas increased by 3.0 % in China and by 7.2 % in Guangdong Province, according to the MEIC inventory (Li et al., 2017; Zheng et al., 2018; Meic, 2023; Xu et al., 2024). By applying the coal, oil, and natural gas fractions of Cff derived from our measurements, it's likely that coal usage in Guangdong Province since 2013 have decreased ≥21%, and natural gas usage have increased by ≥16% (Fig. 5a). Similarly, in Guangzhou city, it's likely that coal usage since 2011 has decreased by 23 % instead of by 8.8 % (Fig. 5b), and natural gas usage has increased by 17 % instead of by 7.9 %, assuming that the fuel type fractions of Cff in Guangzhou city were the same as those in Guangdong Province in the inventory.

We calculated RCO/CO2ff ratios at each measurement site and found higher ratios in summer than winter (Fig. J1). However, we focused only on observations in winter for four reasons. First, summer CO shows greater instability as its atmospheric lifetime depends on OH radical production, which is enhanced through photochemical reactions (e.g., CH4 oxidation) under intense solar radiation, making CO a less reliable fossil fuel tracer (Rosendahl, 2022). Second, winter exhibits stronger ΔCO-Cff correlations (r>0.6, p<0.01; Fig. J2) with better regional representativeness due to extended CO atmospheric lifetime from slower CO oxidation rates. Third, the winter ΔCO-Cff relationship better captures anthropogenic emission characteristics compared to other seasons. Fourth, weaker vertical mixing in winter accentuates local emission impacts (Wang et al., 2010). In addition, the lower Cff signals observed in summer lead to higher uncertainty in the regression slope and thus greater uncertainty in the RCO/CO2ff ratios, as also noted in Maier et al. (2024), which can be seen in the larger error bars of the summer data in Fig. J2.

We then estimated winter 2022 RCO/CO2ff ratios across Chinese cities using ΔCO-Cff regression slopes (Fig. J2), with spatial variations primarily attributed to differences in fuel composition and combustion efficiency (Graven et al., 2009). CO is generated through incomplete combustion of both fossil fuels and biomass. These spatial patterns are consistent with combustion characteristics showing biomass burning produces higher CO emissions per unit energy than fossil fuel combustion (Akagi et al., 2011). As shown in Fig. J1, suburban/rural sites (GZ1, SZ9, ZJ1, SG1) exhibited significantly higher ratios than urban sites (GZ5, SZ7, ZJ4, SG3): GZ1 > GZ5 ((30.4±10.0) > (19.8±4.6) nmolµmol-1), SZ9 > SZ7 ((41.3±23.0) > (14.8±2.4) nmolµmol-1), ZJ1 > ZJ4 ((41.2±3.6) > (11.9±6.4) nmolµmol-1), and SG1 > SG3 ((26.7±2.9) > (15.1±3.6) nmolµmol-1). This pattern is in agreement with previous studies attributing elevated ratios in non-urban areas to biomass burning contributions (Rosendahl, 2022). In contrast, megacities showed 35 %–40 % lower ratios (Guangzhou: 13.3±3.1nmolµmol-1, Shenzhen: 13.5±2.4nmolµmol-1) compared to smaller cities (Zhanjiang: 22.6±5.0nmolµmol-1, Shaoguan: 21.7±6.4nmolµmol-1; p<0.01; Fig. J2), suggest higher fossil fuel combustion efficiency and/or lower biomass burning inputs. Guangzhou's ratios are dominated by improved fossil fuel combustion efficiency due to having the highest biomass burning emissions among the four studied cities in the EDGAR2024 inventory, while Shenzhen's ratios are attributed to both factors with nearly negligible biomass contributions corresponding to its 2017 biomass boiler phase-out policy.

We retrieved historical RCO/CO2ff data from observations in China by estimation from Δ(14C) measurements and correction from RCO/CO2 (increased by 20 %) (Table J1), and ICO/CO2ff data from the MEIC, MIXv2, and EDGAR inventories (Fig. 6). Because the observational record consists of discrete campaigns, for the observations we assess changes using inter-period differences (rather than fitting a single 1998–2022 linear trend), and we test robustness by comparing the inferred change with the combined 1σ uncertainties (added in quadrature, using the reported vertical 1σ uncertainties for each period). Given the minor contribution of biomass burning (BB)-related CO emissions across all inventories, with ICO/CO2ff ratio below 0.003 (MEIC, incorporating OBBEIC data (Song et al., 2009; Huang et al., 2012)), less than 1.0 (MIXv2), and declining from 3.8 (1990) to 1.1 (2022) in EDGAR, we assume that interannual variability in BB emissions has negligible influence on the overall emission ratios. The compiled observations (1998–2022) and inventories (1990–2022) both indicate RCO/CO2ff and ICO/CO2ff ratios tend to be lower in recent years than in earlier periods (Fig. 6a), consistent with improved combustion efficiency (Wang et al., 2010; Lee et al., 2020), which is another factor contributing to the reduction in Cff concentrations. The MEIC inventory attributes this trend to spatiotemporally heterogeneous mitigation pathways: 72 % of the cities started ICO/CO2ff reductions during 1990–1994, while the remaining 28 % (mainly concentrated in the western provinces) exhibited a delayed start until 1995–2004. The implementation of China's clean air policies since 2013 has systematically phased out small, inefficient combustion facilities and replaced them with centralized, high efficient, and clean energy infrastructure (Shi et al., 2022). The phase-out of coal-fired industrial boilers during 2013–2020 reduced CO2 emissions by (1.5±0.3) Gt, accounting for 12 % of the national industrial emission reduction (Li, 2023). These technological transitions enhanced combustion efficiency by >10%, and reduced coal-dominated energy intensity by 40 % across the sector. The MEIC inventory showed that these synergistic measures resulted in significant energy savings, with a net reduction of 0.25 gigatonnes of coal equivalent (Gtce) in 2020 and a cumulative reduction of 1.06 Gtce over the policy implementation period (Shi et al., 2022). Critically, the efficiency-driven transition decoupled energy demand from Cff emissions, with combustion optimization directly reducing coal consumption 1 %–2 % and Cff emissions by 1–3 Gtyr-1 after 2015 (Le Quéré et al., 2016; Friedlingstein et al., 2023b).

We systematically compared observational RCO/CO2ff values with inventory ICO/CO2ff estimates. Our 2022 measurements of the RCO/CO2ff ratios in megacities (Guangzhou and Shenzhen) were consistent with EDGAR estimates (14.9 nmolµmol-1, 2022), while those in smaller cities (Zhanjiang and Shaoguan) were closer to MEIC values (19.2 nmolµmol-1, 2020) (Fig. 6a) and independent field measurements near Xi'an (23±6 nmolµmol-1, 2021) (Liu et al., 2024). City comparisons of observations against MEIC estimates revealed systematic deviations: Shenzhen's observed ratio fell 42 % below inventory estimates (23.4 nmolµmol-1), whereas Shaoguan's exceeded projections (12.7 nmolµmol-1) by 71 %; Guangzhou's and Zhanjiang's are similar to inventory estimates (14.2 and 23.8 nmolµmol-1, respectively) (Fig. 6b). When a regional background site is used, however, the inferred RCO/CO2ff ratios may be influenced by emissions from outside the target city, so that the observed ratios represent a mixture of urban and regional emission signatures rather than a purely city-scale signal. This background effect may therefore contribute to some of the discrepancies between RCO/CO2ff and ICO/CO2ff.

The 24 year observational record of RCO/CO2ff ratios (1998–2022) are closer to (higher than) the MEIC estimates with a difference of (22±23) % compared with the MIXv2 and EDGAR estimates, when focusing on the ratios over time and ignoring the local deviations caused by the specific cities. These findings indicate that the MEIC inventory is more accurate than the EDGAR inventory for China. For specific cities, we found that the MEIC inventory estimates were deviated less from the observed RCO/CO2ff (based on Δ(14C) measurements) in recent years than the corrected RCO/CO2ff (using RCO/CO2) in earlier years for Beijing and Guangzhou (Fig. 6b). For example, in Beijing, the discrepancy in the ratios between observations and inventories decreased from 22 % in 2006–2007 (RCO/CO2-corrected) (Wang et al., 2010) to 8.7 % in 2009–2010 (Δ(14C)-derived) (Turnbull et al., 2011), and further declined to 7.0 % by 2014 (Δ(14C)-derived) (Niu et al., 2018). Similarly, in Guangzhou, the discrepancy dropped from 84 % in 2009–2010 (RCO/CO2-corrected) (Silva et al., 2013) to 34 % in 2014–2017 (RCO/CO2-corrected) (Mai et al., 2021), and eventually reached 6.4 % by 2022 (Δ(14C)-derived). These results suggest that RCO/CO2 corrections should be carefully interpreted, as the effect of CO2 from non-fossil sources can significantly bias the results, even in megacities with high Cff emissions. For example, human respiration could bias RCO/CO2 low by about 9 % at a rural site near Beijing (Wang et al., 2010; Turnbull et al., 2011).

Despite the relatively good agreement of ratios between observations (RCO/CO2ff) and MEIC inventory (ICO/CO2ff) at the national scale, observational data exhibited significantly greater RCO/CO2ff reduction rates than inventory estimates when examined at the city level. From observations (Fig. 6b), in Guangzhou, RCO/CO2ff decreased by 36 % from 35.8 nmolµmol-1 in 2009–2010 (Silva et al., 2013) to 23.8 nmolµmol-1 in winter of 2014–2017 (Mai et al., 2021) and by 63 % to 13.3 nmolµmol-1 in winter 2022 (partly reflecting seasonal differences, as the Silva et al. (2013) dataset included summer observations, and partly indicating reduced CO emissions relative to Cff due to improved combustion efficiency); in Beijing, RCO/CO2ff decreased by 58 % from 72.3 nmolµmol-1 in 2004 (Han et al., 2009) to 30.4 nmolµmol-1 in 2014 (Niu et al., 2018); in Xi'an, RCO/CO2ff decreased by 50 % from (46±13)nmolµmol-1 in 2016 (Wang et al., 2018) to (23±6)nmolµmol-1 in 2021 (Liu et al., 2024). The MEIC estimates for the above three cities decreased by 36 %, 52 %, and 21 %, respectively, over the same period. Larger reductions of the ratios were found from observations than those from the MEIC inventory (i.e., 63%>36% for Guangzhou, 58%>52% for Beijing, and 50%>21% for Xi'an). This conclusion holds even after artificially biasing the RCO/CO2ff ratio downward by about 9 % to account for human respiration in Beijing (2004) and in Guangzhou (2009–2010 and 2014–2017). These findings suggest that the MEIC inventory may insufficiently capture, or lag, the rapid improvement in combustion efficiency and energy structure transformation in China.

The 24 year decline in China's RCO/CO2ff ratios (1998–2022) demonstrates both improved fossil fuel combustion efficiency and successful implementation of air pollution control policies i.e., the success of air pollution emission reduction efforts. Our observations reveal significantly greater urban RCO/CO2ff reductions than those estimated by the MEIC inventory, indicating potential underestimation of CO emission reductions relative to Cff mitigations in current inventories. This finding aligns with previous reports of inventory underestimates for real-world CO reductions. Mai et al. (2021) showed that the MEIC inventory may underestimate cumulative reductions from fleet turnover and catalytic converter upgrades, despite China's National V standards having achieved the ≤1gkm-1 CO emission limit since 2013. Together, these results imply that the MEIC inventory might systematically underestimate the actual effectiveness of clean air policies in reducing air pollutant emissions.