| 02 Mar 2026
Long-term mercury isotope evidence for a shift toward background-dominated urban atmospheric mercury in North China under sustained emission controls
Abstract. Evaluating the effectiveness of the Minamata Convention requires a clear understanding of how emission controls reshape atmospheric mercury (Hg) budgets. Here, we present a multi-year investigation of gaseous elemental Hg (GEM) concentrations and isotope compositions in urban Tianjin, China, spanning three distinct periods: a pre-control phase (2018), the COVID-19 lockdown (2021–2022), and a post-pandemic phase under strengthened controls (2024–2025). By integrating long-term monitoring with isotope-based source apportionment, we capture changes in Hg sources and processes that are not evident from concentration data alone. GEM concentrations declined sharply from pre-control levels (~4.6 ng m⁻³) to regional background values (~1.5 ng m⁻³) during the COVID-19 lockdown, with no rebound following the resumption of socioeconomic activities. This sustained decline was accompanied by a pronounced isotopic transition, from negative δ202Hg and near-zero Δ199Hg and Δ200Hg values characteristic of primary anthropogenic emissions to near-zero to positive δ202Hg and negative Δ199Hg and Δ200Hg values indicative of the regionally well-mixed background Hg pool. Comparisons with other cities in China and South Asia further demonstrate that effective emission controls drive convergence toward background-like GEM concentrations and isotopic signatures. Isotopic mixing models indicate that the collapse of primary anthropogenic emissions accounted for nearly all of the observed concentration decline since the 2020s. Together, our results reveal a fundamental regime shift in urban atmospheric Hg cycling from local primary emission-dominated to background-dominated conditions modulated by secondary surface processes.
The study by Zhang et al. measured atmospheric GEM concentrations and isotopic compositions at long-term monitoring urban site and multiple short-term monitoring urban, suburban, rural sites in China and Pakistan. Based on the comprehensive observations, the authors show notable declines in GEM concentrations and clear changes in the isotopic compositions in the urban atmosphere. By using a Hg isotope mixing model, they quantify the relative contributions of anthropogenic emissions and evidence that the GEM declines are mainly driven by the control of anthropogenic emissions. This research is well designated. I broadly agree with the interpretations throughout the manuscript. I would suggest a minor revision of this manuscript (the manuscript is well written currently). Some of the minor suggestions are presented below.
The source apportionment: in this study, the authors use a δ202Hg and Δ199Hg mixing model to quantify the source contributions. I would suggest to use the Δ199Hg and Δ200Hg mixing model as previous studies. This is because the GEM δ202Hg in the boundary layer apt to modified by vegetation uptake process (particularly at the suburban and rural sites in this study), while the Hg-MIF signals are relative stable. As seen from Fig. 3a, some of the δ202Hg and Δ199Hg assemble especially in the background areas are not fully encompassed by δ202Hg and Δ199Hg of the three source endmembers. The authors argue that the Δ200Hg of anthropogenic emissions overlap surface re-emissions. However, the Δ199Hg of anthropogenic emissions and surface re-emissions are distinguishable, and this could enable the source quantification of these two sources. In addition, the Tianjin sampling site is close to seas, and the seawater re-emissions should be also considered. A recent study reported mean Δ199Hg and Δ200Hg values of -0.13‱ and 0.02‱, respectively, for ocean emissions (Fu et al., 2026, NSR). This oceanic signature is identical to soil re-emissions. Therefore, using the Δ199Hg and Δ200Hg mixing model would help to understand the contributions from the two most important natural emissions (e.g., soil + seawater reemission).
Line 13: please add ‘mean’ before GEM concentration.
Fig.1: better to show the location of Karachi in this figure.
Line 127: please specify whether the sampling flow rate is operated under the standard temperature and air pressure.
Section 2.2: please provide the sampling interval or duration for each sample at the sampling sites, including the pump-trap and passive samples.
Section 2.5: please add the method for the calculation of GEM concentrations using the pump-trap. Are all concentrations reported for the STP conditions?
Line 221: add ‘mean±1sd’ after the values.
Line 223: please note ‘mean±1sd’ for the values reported in Beijing and Shijiazhuang.
Line 235: add ‘mean,’ before n =35.
Line 238: add ‘mean,’ before n =35.
Line 240: better to show the exact p value when it is higher than 0.05.
Line 245: add ‘mean’ before δ202Hg.
Line 246: change ‘directly’ to ‘mainly’?
Line 247-248: add ‘mean±1sd’ before n=16. Same in line 249, 251 and 255.
Line 257: show the real value instead of >0.05.
Line 262: add mean before GEM.
Line 267-268: not note these are mean values.
Line 277: add ‘mean’ before values.
Line 303: please add ‘mean±1sd’ after the D200Hg values.
Line 309: add ‘mean’ before GEM.
Line 324: please add ‘mean’ before δ202Hg and D199Hg. please also check other place if the values are referred to mean values.
Line 343: please add ‘on average’ before contributing.