Combined short-term and long-term emission controls improve air quality sustainably in China

Domain: earth_science
Published: 2024-06-17
Editorial outcome: accept

Abstract
The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by “blue skies” type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action.

Introduction
China’s economy has rapidly grown over the past 40 years. At the same time, environmental issues have become increasingly severe, damaging ecosystems and human health1,2. People’s experience of air pollution is widespread, direct, and intense. Consequently, there is a strong demand from the public for improved air quality. Starting in the 1980s, China began to focus on the damage and necessary control measures of sulfur dioxide (SO2) emissions and acid rain3. After 2010, attention to air quality improvement shifted to reactive nitrogen (Nr) pollution and fine particulate matter (PM2.5)4,5. During this period, as the economy steadily developed, some important international mega-events were organized in China. To ensure “blue skies” (i.e. clean air) the Chinese government implemented many short-term pollutant emission control measures during the events (each lasting for about a month), including the suspension of industrial activities to decrease SO2 and nitrogen oxides (NOx) emissions, and the reduction of vehicle movements in and around the event locations mainly focused on NOx, i.e. minimizing atmospheric pollution, yet at large economic costs. In order to sustain “blue skies”, the Chinese government has issued a series of policies aimed at long-term reductions in pollutant emissions (i.e. that should persist for years), such as the release of the Air Pollution Prevention and Control Action Plan in 2013 (referred to as the Atmospheric Ten Actions), the Three-Year Action Plan for Winning the Blue-Sky Defense Battle in 2018 (referred to as the 3-year Defense Battle), and the second phase of the Action Plan. Long-term mitigation strategies are expected to be more likely to achieve environmental goals6,7. Large coal consumers, power plants, industry, and the transport sector were priority targets and have significantly reduced emissions of SO2 and NOx8. This has effectively mitigated acid rain and reduced PM2.5 concentrations, as confirmed by emission inventories, environmental monitoring, and model simulations9,10. So is it logical and necessary to introduce short-term, provisional measures as well when long-term emission control policies appear to be working?

PM2.5 concentrations can characterize air pollution variation at a daily scale, responding rapidly to changes in strong emissions or meteorological conditions. Atmospheric nitrogen (N) deposition, as a sink for Nr emissions, can be used as an indicator to evaluate the degree of Nr pollution at the monthly/yearly scale. Recent observations and modeling evidence show that PM2.5 concentrations and N deposition in China experienced a rapid increase and then gradually stabilized with the implementation of long-term emission control policies7,11–13. In addition, global warming may contribute to changes in spatial patterns and interannual trends in atmospheric Nr emissions and N deposition due to the high correlation between temperature and NH3 emissions.

Here, we aim to gain new insight into the response of air quality to Nr emission changes driven by short-term compared to long-term policies and to understand how PM2.5 and N deposition are changing under the influence of anthropogenic activities and climate change. Our research is based on the Nationwide Nitrogen Deposition Monitoring Network (NNDMN), which contains monitoring data from nearly 60 monitoring sites with more than 40,000 samples over the 10 years from 2011 to 2020, and an integrated observation dataset of PM2.5 and precursors collected during four international mega-events. Our analysis highlights the trade-off between short-term measures and long-term policies, and the source-sink relationships between Nr emissions and N deposition and uncovers hidden challenges in creating effective improvements in air quality.

Results
Short-term measures impact PM2.5 levels
Many short-term measures have been implemented by the Chinese government to prevent air pollution from becoming a distraction during important international events, such as the suspension of manufacturing facilities and vehicle restrictions for SO2, NOx, and PM2.5 emissions (Table S1). These strong, effective measures were not only implemented in Beijing but extended to surrounding regions (i.e., Tianjin and Hebei provinces). Accordingly, the concentration of major gaseous pollutants and secondary inorganic components in PM2.5 during the Olympic Games (Olympic), Asia-Pacific Economic Cooperation Summit (APEC), and Military Parades (Parade) were significantly lower than those before the event (Fig. 1a–c). The concentrations of SO2 and NO2 decreased by 30%–56%, the PM2.5 concentrations decreased by about 60%, and secondary inorganic ions (SNA: SO42−, NO3−, NH4+) declined by more than 70%, indicating that the air quality was noticeably improved during the events. However, there was no substantial decrease in atmospheric NH3 concentrations during each event and even a slight increase during the APEC. Also, the rigorous regulations led to a temporary pause in economic development and inconvenient living conditions for residents in the control areas. During the Belt and Road Summit (BRS), a series of “non-disturbance on residents” air quality control measures introduced by the government produced no statistical difference in pollutant concentrations before and during the BRS, whether for PM2.5 or precursors (Fig. 1d). Meteorological conditions need to be taken into account to determine the actual impact of the control measures on pollution mitigation, which is important for short-term case studies. Air pollutant concentrations were mostly significantly negatively linked with wind speed, precipitation, and relative humidity, but positively correlated with atmospheric pressure. The effect of temperature on air pollution has high uncertainty14. In this study, some of the meteorological conditions were favorable for air pollutant dispersion during the events, such as the occurrence of precipitation during the Parade and stronger southerly winds during APEC. However, emission reduction measures remained the most important driver of pollution mitigation. For example, higher temperatures, elevated relative humidity, lower atmospheric pressure, and frequent northerly winds did not reduce significant decreases in PM2.5 concentration during the Olympic compared to the pre- and post-Olympic periods (Figs. S1 and S2).

PM2.5 concentrations increased by 157%, 156%, and 139%, respectively, and showed no significant change (−1%) during the post-Olympic, post-APEC, post-Parade, and post-BRS periods compared to concentrations during the events. Similarly, NO2 and SO2 concentrations returned to “pre-event” levels, and no significant trends in NH3 concentrations were observed. Except for BRS, SNA concentrations increase from “event” to “post-event”, with a 5-fold growth after the Parade. Similarly, a decrease of 12% in PM2.5 and 12% in SNA concentrations was observed for the Hangzhou G20 Summit compared to the period before aggressive mitigation measures were introduced. However, after the various control measures were lifted, all air pollutant concentrations increased back to initial levels15. During the 2022 Beijing Winter Olympics, the intense emission controls significantly reduced PM2.5 and SNA concentrations, with source control measures contributing 54% to the total reduction in PM2.5 concentrations16. In summary, “Olympic Blue”, “APEC Blue” and “Military Parade Blue” impressed the world, and the “blue skies” increased people’s determination to defeat air pollution and showed the potential for air quality improvement. Mandatory administrative measures to reduce air pollution, such as those in Table S1, were temporary and unsustainable in the long term. However, such short-term abatement measures are essential because the moral incentive probably outweighs the rebound after the decrease in PM2.5 concentration during the event. This means that, in order to improve air quality standards in the short term, it is necessary to adopt some unconventional emission reduction measures at the expense of the economy to remind the public that pollution can be prevented and so increase their confidence in policies and practices.

From August 2005 to August 2020, the concentrations of PM2.5, SNA, and gaseous precursors in Beijing decreased by 68%, 66%, and 52%, respectively (Fig. 1e). Notably, the reduction in SO2 was as high as 90%, while the decreases in NO2 and NH3 were limited to 38% and 26%. Although PM2.5 concentrations in Beijing are significantly higher in winter than in summer, they still showed a continuous downward trend over the 15 years: from January 2005 to January 2020, PM2.5, SNA, SO2, and NO2 decreased by 65%, 51%, 94%, and 50%, respectively (Fig. 1f). In contrast to the summer months, NH3 concentrations increased by about 50% in winter and stabilized at 7 μg m−3 after 2013. China effectively controls SO2 emissions through strengthened industrial emission standards, upgrades on industrial boilers, and the phasing out of outdated industrial capacity7. The “Olympic Blue” in August 2008 was a phenomenon that astonished the nation at the time. In August 2009, a significant rebound in the concentrations of PM2.5, ionic components, and gaseous precursors was observed, returning to the levels documented in August 2007, which underscored the limitations of the short-term emission reduction measures. Nonetheless, after approximately six years, the concentrations of pollutants in all subsequent years were consistently lower than those recorded in 2008. Similarly, the air pollution episode in January 2013 caused strong dissatisfaction among the government and the public of Beijing. However, from that time until 2020, the PM2.5 concentration in Beijing decreased by approximately 54%, demonstrating the effectiveness of sustained emission control policies, including the Atmospheric Ten Actions (2013–2017) and the 3-year Defense Battle (2018–2020).

Short-term measures were virtually unaffected by long-term emission reductions, with short-term net effects of 67% and 61% of APEC and Parade events, respectively. Short-term measures also did not inherently alter the slope of the decline in long-term pollution reduction (p>0.05, Note S1). This is because the decrease in concentrations from long-term policies is stable and gradual. The >50% reduction in PM2.5 concentration achieved in Beijing through one month of short-term measures has in fact taken nearly a decade to stabilize. With continuous adjustments in energy and industrial structures, the concentration of atmospheric pollutants is expected to continue to decline. When the concentration of pollutants is extremely low, it shows that long-term emission reduction policies have played an extremely significant role, and so the implementation of short-term measures might be ineffective and unnecessary under this condition.

Long-term controls affect N deposition trends
The average concentration of atmospheric Nr (NH3, NO2, HNO3, pNH4+, pNO3−) over the 10 years from 2011 to 2020 was 22.3 ± 1.34 μg N m−3. It peaked in 2013, and then slowly decreased by 19% to 2020 (Fig. 2a). The average Nr concentration in precipitation was 2.86 ± 0.39 mg N L−1 (NO3−, NH4+), with the highest value occurring in 2012, decreasing by 36% to 2020 (Fig. 2b). The site-averaged dry, bulk and total N deposition in China were estimated at 20.2 ± 1.60, 19.0 ± 0.82 and 39.2 ± 2.42 kg N ha−1 yr−1 during the 10 years, respectively. Given the spatial heterogeneity of monitoring sites, the geographic annual mean fluxes were 14.8 ± 1.71, 12.8 ± 0.82 and 27.7 ± 2.42 kg N ha−1 yr−1 for dry, bulk, and total N deposition, which are in good agreement with satellite observations and model simulations17,18. Annual N deposition was estimated to continuously decline in China by about 19%, 26%, and 23% for dry, bulk, and total deposition from 2012 to 2020, respectively (Fig. 2c–e, p<0.01). A strong positive relationship was observed between Nr emissions and N deposition, implying a rapid response of N deposition to changes in emissions (R2 = 0.90, Fig. 2e). These trends are consistent with the strict control of Nr emissions since 2012, which caused annual NOx emissions to decline by 33% from 2012 to 2020 (MEIC, http://www.meicmodel.org). However, NH3 emissions declined only from 10.6 to 9.07 Tg during the same period due to the few national policies implemented for NH3 emissions control. In contrast to PM2.5 concentrations, monthly averaged atmospheric N deposition was significantly higher in summer than in winter by 32% (Fig. S3). However, similar to PM2.5 concentrations, under the influence of long-term emission reductions, the alleviation of summer pollution is greater than that in winter. Winter air quality clearly needs to be improved.

According to a Mann–Kendall test of the data, the decline in N deposition slowed in 2015. We divided the 10 years into 3 stages, 2011–2014 (period of severe air pollution), 2015–2017 (period of air pollution mitigation), and 2018–2020 (period of significant air quality improvement). The changes in dry, bulk, and total N deposition from 2011–2014 to 2015–2017 were −14%, −13%, and −13% (consistent with the reductions in Nr emissions of 14% during the same period), followed by smaller reductions from 2015–2017 to 2018–2020 of −11%, 1.3%, and −5.2% (Fig. S4). Similar trends are revealed in the interannual variation of Nr emission inventories, implying a decline in emission reductions in recent years, as the most easily achievable reductions, especially for NOx, had already worked through in the early stages of pollution emission control19,20. Future air quality improvement needs an even greater focus on technological innovation to create breakthroughs, which has not been emphasized in previous research. Compared to immediate, effective but short-lived short-term pollution control measures, long-term policies take more time to have an impact, but their effects are more permanent. Even though the decline in N deposition and the improvement in air quality slowed over the 10 years, there was no significant rebound.

The components and forms of Nr concentrations and N deposition have undergone significant changes over the 10-year study period, revealing new challenges for future air pollution control. NH3 was the largest component of total deposition (26%) during 2011-2020, followed by bulk NH4+, bulk NO3−, HNO3, NO2 and pNH4+, and pNO3− (Fig. 3a). The reduced N (NH3, NH4+, pNH4+) in dry deposition plus NH4+ in bulk deposition were therefore the main forms deposited (57%) in total deposition. As shown in Fig. 3b, the dry deposition of NO2, HNO3, pNH4+, and pNO3− significantly decreased (−36%, −64%, −33%, −47%) from 2011 to 2020, whereas that of NH3 increased. The decline in oxidized N deposition (NOy: NO2, HNO3, pNO3− in dry deposition plus NO3− in bulk deposition) is in agreement with the decline in NOx emissions (of NOy deposition by 31% vs NOx emission by 33%, Fig. 3c). The proportion of HNO3 and NO2 gaseous forms in total oxidized N deposition decreased from 0.45 in 2011 to 0.28 in 2020 (p<0.01), but the variation in dry oxidized deposition was negligible (Fig. S5). The percentage of HNO3 decreased while that of NO2 increased annually, which may be related to the homogeneous and heterogeneous photolysis of nitrate in the atmosphere21–23. In contrast, the gaseous fraction of NH3 increased from 0.39 to 0.52 over the 10 years. Ambient mean NH3 concentrations and NH3 deposition in 2020 were 34% and 27% higher than in 2011, counteracting the decrease in pNH4+ concentrations, confirming the stability of NH3 emissions and indicating that more NH3 is present as a gas in the atmosphere due to the reductions in acid gas emissions24,25. The decrease in NH4+ bulk deposition (from 10.3 to 7.80 kg N ha−1 yr−1, p=0.01) offset the increasing gaseous NH3 so that total reduced N deposition presented a small decrease, which was generally consistent with the change in NH3 emissions (NH3 deposition decreased by 13% vs NH3 emission by 14%, Fig. 3d). The NH3-rich atmospheric environment (AdjGR > 1, defined in Note S2) and the small increase of NO3− bulk deposition (8%, p>0.05) show that NOx emission reduction policies need to be more stringent and placed alongside NH3 emission controls, especially for non-point source emissions, as opportunities for acid gas emission reduction decrease.

The 10-year average N deposition at urban, rural, and background monitoring sites was 47.8 ± 7.32, 39.3 ± 2.32, and 24.6 ± 6.49 kg N ha−1 yr−1, respectively. Deposition of NHx and NOy at urban monitoring sites was 10% and 37% higher than at rural sites. Annual N deposition exhibited different interannual variability at the three land-use types (Figs. 3e–g and S6). NHx and NOy deposition at urban monitoring sites decreased at an annual rate of 3.5% and 3.9% from 2011 to 2020 (p<0.01). At rural monitoring sites, NOy deposition declined more slowly because the energy structure transformation and stricter SO2 and NOx emission standards of industry and transportation mainly affected urban areas. NHx deposition showed irregular variation in rural areas because the slight increase in NH3 dry deposition offset the decrease in NH4+ bulk deposition. Air pollutant emissions were greatly reduced at national scales during the coronavirus 2019 pandemic lockdown period (COVID-19). The mean dry deposition of reduced N continued to increase from 2019 to 2020 in rural regions but decreased at urban sites. One possible explanation is that non-agricultural emissions were the major NH3 sources in urban areas but agricultural NH3 volatilization resulted in persistent high values in rural areas26,27.

Agricultural sources account for more than 80% of total NH3 emissions, so the control of NH3 emissions from non-point sources is imperative28,29. According to Gu et al. 30, the cost of technological measures to reduce global agricultural NH3 emissions by 32% is around 19 billion USD. However, the technologies mentioned do not take into account the difficulty of technology diffusion and acceptability for farmers, so the actual cost of reducing NH3 emissions from agricultural sources may be higher than this. Very few studies have addressed urban NH3 emission reduction actions. Nonetheless, with adjustments in urban industrial structures and the promotion of electric vehicles and other measures that are mainly focused on NOx and other pollutants, it is possible to achieve a “free ride” on reducing urban NH3 emissions, which could significantly lower the economic cost of urban NH3 emission reduction.

Regional differences and transboundary effects
Driven by the effects of short-term policy implementation and an earlier and broader transformation of energy structures in North China (NC), this region saw a more dramatic abatement of Nr emissions than other regions, a faster annual reduction in N deposition at −3.9% since 2011 (p<0.01), and a clear decoupling of N deposition and economic development (Fig. 4a). In contrast, the changes in total N deposition in other regions were not significant (Fig. 4b). The decrease in the dry deposition of oxidized N ranged from 43 to 57% across all regions, except for Northeast China (NE) where it was reduced by about 21%. For NHx deposition, the spatial analysis showed more complex features. The concentration of NH3+pNH4+ in NC increased until 2014 and leveled off after 2015, and in Southwest China (SW), NH3+pNH4+ showed non-significant changes until 2017 and then increased. The trends in oxidized N concentrations can explain the differences in NH3+pNH4+ concentrations between the two regions. Oxidized N concentrations have continued to decline in NC (both in air and precipitation). This suggests that NH3 emissions have declined significantly in this region in recent years, causing the smaller oxidized N concentrations and the stabilization of NH3+pNH4+ concentrations. In contrast, the NH3 emission inventory for the Southwest region shows a 10% decrease from 2017–2020. Therefore, an observed increase in atmospheric NH3+pNH4+ concentrations may be attributed to a decrease in acid gas emissions (SO2 emission declined by 36%, NOx emission decline by 11%) and fewer neutralization reactions, resulting in more NH3 present in the atmosphere. Inter-regional differences in NH3 emissions are understandable. The decreased NH3 emissions in NC are due to the previous agricultural policies introduced by the government, e.g., the Three-year action plan to win the Blue-Sky Protection Campaign (https://www.gov.cn/zhengce/content/2018-07/03/content_5303158.htm), and the promotion of numerous NH3 emission reduction measures, e.g., the reduction of farmland NH3 volatilization through better controls on N-fertilizer applications, and the introduction and promotion of urease inhibitors31,32.

The spatial correlations of N deposition and Nr emissions vary from one region to another (Fig. 4e, f). NH3 emissions and NHx deposition were higher in NC, Southeast and SW, consistent with regions that have frequent and intensive agricultural activities. NH3 emissions and NHx deposition were generally comparable within regions, exhibiting near-source deposition of NH3. By comparison, the highest NOy deposition is mainly concentrated in northern China, such as Beijing, Shandong and Hebei provinces, with their highly developed industry and transportation sectors. Topography and meteorological conditions determine that the spatial characteristics of the different air pollutants in China are very similar, and N deposition is no exception. For NC, NOx emissions were generally higher than NOy deposition at the provincial scale, while the surrounding areas show the opposite relationship. The gap between them implies that there is transboundary export of pollution from the NC: about 50% of NOx is deposited in the region and the rest is “exported” to the northwest or southeast regions. Pollution transport not only affects N inputs to ecosystems in the surrounding area, but also harms human health. A recent study showed that approximately half of the PM2.5-related mortality rates were attributed to emissions from sources outside the regions in which the deaths occurred33,34. This shows the importance of strengthening the joint prevention and control of air pollution at the regional level.

Future air quality improvement potential
It is now accepted that global temperatures will increase slowly and extreme precipitation will intensify in the future35 (https://www.ipcc.ch/sr15/), and that improvements in air quality could be negated by climate change36. In this study, we found the effect of meteorology is more clearly reflected in the monthly changes of N deposition (Fig. S7), with precipitation and temperature being the significant explanatory variables (Fig. S8). Wang et al. 37 discovered a previously unrecognized mechanism showing that synoptic climate change might have a bigger effect in increasing the concentration of PM2.5 in low quartiles than in upper quartiles. Meanwhile, the Chinese government will not relent in its efforts to address air pollution issues, with its commitment to “Carbon Emission Peaking in 2030” and “Carbon Neutrality in 2060”. We simulated the response of air quality to changes in precursor emissions: a base simulation (“Base: 2017”) and two scenarios (“Case 1: 2030”, Carbon Emission Peaking; “Case 2: 2060, Carbon Neutrality”) were developed to quantify the contribution of near-term (7–10 years) or long-term (30–40 years) emission reductions to PM2.5 concentrations and N deposition (simulation details are in the “Methods” section and Table S2). Atmospheric emissions of acid gases and NH3 are expected to decline by, respectively, ~40% and ~10% by 2030, and ~90% and ~50% by 2060, with the increasing impacts of low-carbon policies (Fig. S9). Overall, the control of agricultural emissions is lagging behind the implementation of stringent regulations on power plants, industry and vehicle emissions. PM2.5-SNA concentrations are predicted to decrease significantly by 2030 and 2060, and the national average concentrations to decrease by 44% and 76% compared to 2017, respectively (Fig. 5). The national percentage area exceeding 15 μg m−3 PM2.5-SNA is predicted to be 51%, 20% and 0.3% in 2017, 2030 and 2060, respectively. In contrast, the response of N deposition to emission reductions is weaker, with the mean N deposition decreasing by 33% and 57% by 2030 and 2060, respectively, compared to 2017, which illustrates the complex multifactorial processes occurring from precursor release to gas-particle transformation, and deposition.

In view of the impact of climate change on air quality (global warming and increased extreme precipitation), policies and the feasibility of emission reduction technologies need to be rationalized. We have shown that emission control policies are effective, both in the short term for preventing haze pollution during important international events and in the long term for national air quality improvement. Even though there is a significant rebound in air quality after short-term, campaign-style political controls, the effectiveness of such approaches has greatly increased public confidence in obtaining “daily blue skies”, i.e. cleaner air, and has shown the potential for air quality improvement. This is particularly important for heavily polluting developing countries38. Compared to short-term policies that sacrifice economic benefits and cause some public inconvenience, long-term policies are more likely to achieve the dual objectives of not damaging economic development and improving environmental protection39. However, they inevitably take several years to have an impact, with the risk that those involved in implementation lose confidence and patience. Therefore, a combination of long-term and short-term emission controls is most appropriate for air quality improvement, delivering an immediate and observable effect of short-term measures that reduce pollutant emissions, and the stabilizing effect of long-term policies contributing to the sustainability of cleaner air.

Policy implications
In contrast to acid gas emission reductions, which can be achieved with both short-term measures and long-term policies, the control of non-point source NH3 emissions can only be implemented through agricultural innovations that improve crop N use efficiency and reduce environmental impacts. Currently, the measures for air quality control mainly focus on end-of-pipe treatments and cleaner production, which are beneficial for air pollution emission control targets but may not be able to reach the dual objectives of air quality improvement and climate change mitigation40,41. Therefore, policymakers should promote sustainable approaches impacting all necessary sectors and objectives, such as low/zero/negative-carbon production methods for industry through raw material, product structure and production equipment upgrades, ultra-low pollutant emissions from transportation through the popularization of electric vehicles and synergistic pollution control on diesel trucks, aircraft and ships, and green development in agriculture through the application of precise management in crop breeding and production42–44.

Compared to current policies, three trade-off relationships should be considered for policy development. One is the already-mentioned trade-off between inspiring but aggressive short-term measures and effective but durable long-term policies. The combination of long-term and short-term emission control policies is the sustainable solution for air quality improvement. Frequent or prolonged implementation of high-intensity short-term control measures will disrupt the development of industry, transport and residential life. Therefore, at a time when the effectiveness of long-term emission control is still tentative, a short but major emission control exercise should be carried out once every 1–2 years to clarify the regional emission reduction potential. Policymakers should gradually adjust the intensity of the controls, and align them with long-term goals, ensuring a smooth transition and continuous progress towards objectives, defining specific and achievable long-term targets for air quality improvements that are informed by the success of short-term reductions. In the long term, two aspects need to be clarified. Firstly, “polluter identification”. For example, sintering in the steel industry is a major source of NOx emissions, while in agriculture, high amounts of NH3 are emitted from the treatment of manure from livestock. Therefore, to increase pollution management efficiency, the specific targets of emission reduction must be identified (including key emitting sectors and critical emission stages). Secondly, the principle of “polluter pays” should be achieved. To ensure fairness in environmental and economic terms within pollution management, it is essential to identify the input and output of pollutants between regions. There need to be a more precise quantification of transboundary contributions, which can provide data support for subsequent compensatory mechanisms between regions.

The second is the oxidized–reduced N deposition/dry–wet N deposition trade-off, as the reduction in oxidized N dry deposition is offset by the increase in bulk deposition, and the decrease in acid gas emissions increases NH3 concentrations. To avoid this, synergistic controls of multiple precursors are necessary. Compared to SO2 and NOx, it is almost impossible to achieve rapid reductions in NH3 concentrations quickly. Our case study during the COVID-lockdown period has proven that, after a drastic reduction of transport and industrial NH3 emissions in February 2020, ambient NH3 concentrations from January to February (17%) still exceeded the levels of previous years (9%), while concentrations of SO2 and NO2 decreased by 24% and 49%, respectively26. This indicates that, as opposed to the intense reductions of acid gases, NH3 emissions can only be gradually reduced through better fertilizer and manure management. Due to the lack of a national NH3 monitoring system overseen by the government, the impact of NH3 on air quality and the environment remains a matter of theoretical debate45. Currently, “Green Demonstration Zones” have been established across China, where systemic transformations in precision and smart agriculture will improve nitrogen use efficiency and reduce NH3 volatilization. We suggest that research on NH3, PM2.5 and atmospheric N deposition should be uniformly strengthened in this area, treating spatial “regional areas” analogously to temporal “short-term” studies, thereby identifying the national potential and environmental effects of NH3 emission reduction through case studies.

Third is the trade-off between inter-regional Nr pollution and controls. Regional-specific and cross-regional prevention measures are needed in the face of spatial heterogeneity and transboundary air pollution. In the case of cross-boundary pollution transport, the implementation of stricter emission standards and production technology innovation could deliver win-win air pollution reductions for origin and destination regions46. Our regionalization results will help in the design of region-specific pollution control strategies that should be further investigated. Moreover, it is important to include public opinion in the process of strategy- and policy-making and implementation, balancing the desire of citizens to improve air quality against ensuring the normal conduct of their lives47. In particular, for the control of non-point source NH3 emissions, and to achieve comprehensive and sustainable air quality improvement, it is crucial to focus on targeted strategies that promote sustainable agricultural practices and involve collaboration between all stakeholders, including government agencies, farmers and the public. Quantifying the economic benefits and environmental costs associated with pollution transport contributes to revealing the environmental inequalities of regional emissions and pollution. As reflected in this study, economically developed regions export Nr emissions to neighboring regions, which results in these suffering both economic and environmental losses. Currently, economic compensation between regions under multiple constraints (air pollution, climate change, ecological effects) is hardly discussed. Regions more economically developed are encouraged to increase green investments in emission-reducing technological transformations48 and may need to compensate polluted regions around them. This could be through economic measures such as tax incentives, subsidies and loans, encouraging enterprises and consumers to make more environmentally friendly choices. For instance, financial support could be provided to enterprises and farms that utilize clean energy and energy-efficient technologies, and tax relief given to consumers for purchasing environmentally friendly products and services. Our work also shows that long-term monitoring is essential for an effective understanding of environmental change and air quality improvement that is not possible with short-term studies alone.

Methods
Daily average PM2.5 and its precursor concentrations
Daily average PM2.5 concentrations and ionic components during the Olympic Games, APEC and Military Parades periods were obtained from published literature49–51. PM2.5 concentrations and ionic concentrations during the Belt and Road Summit were taken from our group observation results. Daily concentrations of gas precursors (SO2, NO2) were obtained from the China National Environmental Monitoring Centre (CNEMC, http://www.cnemc.cn/sssj/). PM2.5 and ionic component concentrations are from Tracking Air Pollution in China (TAP, http://tapdata.org.cn/). Since NH3 data are unavailable from CNEMC, they were taken from our group’s monthly sampling results and used as an approximate substitute for atmospheric NH3 concentrations during events. ALPHA passive samplers were used to obtain monthly NH3 samples for the four study periods (Adapted Low-cost High Absorption, Center for Ecology and Hydrology, Edinburgh, UK). Detailed measurement and analysis procedures for the ALPHA samplers are described in Note S3. The durations of the pre-event, event window and post-event for each event are listed in Table S3.

Monthly Nr deposition estimations
The monitoring of pollutants in Beijing began with the measurement of acid rain in 1998. The Nationwide Nitrogen Deposition Monitoring Network (NNDMN) has been in operation since 2011 and collects samples, including gaseous and particulate Nr and precipitation. Ambient gaseous (NH3 and HNO3) and particulate (pNH4+ and pNO3−) samples were collected monthly with active DELTA samplers (Denuder for Long-Term Atmospheric sampling, Center for Ecology and Hydrology, Edinburgh, UK), and NO2 samples were collected monthly using Gradko passive diffusion tubes (Gradko International Limited, UK). NH3 was also sampled using an ALPHA passive sampler at all monitoring sites as a check on results. Passive samplers were deployed in duplicate or triplicate. Precipitation samples were collected after each precipitation event in rain gauges (SDM6, Tianjin Weather Equipment Inc., China).

Gaseous and particulate samples were stored at 4°C, and precipitation samples stored at −4°C until analyzed. More details of the sample processing and quality control procedures are shown in Note S3. Dry N deposition (Fd) was calculated as the product of the Nr concentration (Cd) and deposition velocity (Vd) (Eq. (1)). Hourly Vd for 5 Nr species were simulated by the GEOS-Chem chemical transport model (Note S4, Fig. S10)52. Innovations in industrial or agricultural technology could directly alter the emissions and concentrations of Nr, while the stability of land-use types maintains the resistance of gases to capture by surface plants or soil and so to Vd, thereby emphasizing the key impact of Nr concentration on the trend of dry deposition. Bulk N deposition (Fb) was calculated based on the Nr concentration (Cb) and precipitation amount (Pt) (Eq. (2)).

Fd = Cd × Vd (1)

Fb = Pt × Cb = 100 (2)

F = Fd + Fb (3)

The 59 sites were selected from the NNDMN for data analysis (sampling period of more than one year) to cover major land-use types: urban, cropland, coastal, forest and grassland. More details on each site including location, monitoring period, monitoring method and land-use type can be found in Table S4. We analyzed the spatial patterns of dry/bulk deposition using both arithmetical averaging of the sites and weighting based on regional area to avoid overestimation or underestimation of deposition fluxes due to uneven sized distribution (Eq. (4), Eq. (5)).

Site average N deposition = (FNC1 + FNC2 + ... + FSE1 + FSE2 + ... + FSW1 + FSW2 + ... + FNW1 + FNW2 + ... + FNE1 + FNE2 + ... + FTP1 + FTP2 + ...) = 59 (4)

Geographic average N deposition = [(FNC1 + FNC2 + ...) = ANC = 960 + (FSE1 + FSE2 + ...) = ASE = 960 + (FSW1 + FSW2 + ...) = ASW = 960 + (FNW1 + FNW2 + ...) = 960 + (FNE1 + FNE2 + ...) = ANE = 960 + (FTP1 + FTP2 + ...) = ATP = 960] (5)

F represents total N deposition at the NCi site, and Ai represents area of region i.

For the quantification of pollutant transport between regions, we assumed that Nr emissions and deposition in China are balanced, with no pollutants being transported from China from other countries; the emission-to-deposition between regions is defined simply as all pollutants will preferentially be deposited in their own region.

Factors vary N deposition
Random forest (RF) evaluates the complex relationships between the response variable and interpretation variables and has been widely used for the prediction of air pollution or identifying key determining factors. The random forest modeling program was run by using the “randomforest” package in R software (version 3.6.1). We constructed the functions of “oxidized /reduced /total N deposition ~ acid gas emissions + ammonia emission + meteorological parameters + soil parameters + NDVI”. The robustness of the RF model was tested by 10-fold cross-validation.

Monthly oxidized, reduced and total N deposition was derived from the NNDMN. Monthly SO2, NOx and NH3 emissions from 2011 to 2020 in China were obtained from the Model of Multi-resolution Emission Inventory (MEIC, http://www.meicmodel.org). Meteorological parameters included precipitation amount, temperature at 2m, wind speed at 10m, surface pressure and boundary layer height. Soil parameters included soil temperature, soil moisture and evapotranspiration. The monthly precipitation amount was obtained from NNDMN, and other meteorological and soil factors were downloaded from the European Centre for Medium-Range Weather Forecasts (https://apps.ecmwf.int, 0.25°×0.25°). Data for NDVI were derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 product (https://neo.sci.gsfc.nasa.gov, 0.1°×0.1°). The deposition at each sampling site corresponds to emissions, meteorological, soil and NDVI parameters through latitude and longitude.

The future trends in PM2.5 concentrations and N deposition
The nonlinear responses of PM2.5 concentrations and N deposition to the precursor emissions (SO2, NOx, NH3, etc) and meteorological factors were simulated using the WRF (version 3.9.1) - EMEP (version rv4.17) model system (27 km × 27 km). The meteorological conditions were obtained from Final Operational Global Reanalysis (FNL) data and the Representative Concentration Pathway (RCP). For anthropogenic emissions, the MEIC and Asian anthropogenic emission inventory (MIX) were used. Here, the ground observation data of PM2.5 concentrations and N deposition were used to evaluate the model performance. PM2.5 concentrations were taken from TAP, and the spatial correlation R2 between them reached 0.77 in 2017 (Fig. S11). The N deposition simulation results were calibrated against NNDMN monitoring data from 2008 to 201718.

To evaluate the impact of anthropogenic emissions and climate change on PM2.5 concentrations and N deposition, we performed the following simulation scenarios using the WRF-EMEP model: Base scenario: the scenario to simulate the spatiotemporal of PM2.5 concentrations and N deposition in 2017; the meteorological conditions and anthropogenic emissions were kept at the level of 2017. Case 1 (2030, Carbon Emission Peaking)/Case 2 (2060, Carbon Neutrality): these scenarios simulated the spatiotemporal PM2.5 concentrations and N deposition under the most stringent policy implementations at these two future dates, with climate change taken into account, with rising temperatures and increased extreme precipitation (RCP4.5_2030, RCP4.5_2060).

Data analysis
A one-way analysis of variance (ANOVA) and nonparametric t-tests were used to examine the differences between land-use types and regions. A Mann–Kendall (MK) test was conducted to analyze the trends in N deposition and Nr emission and Sen’s slope was used to calculate the trend slope. Linear or nonlinear regression analyses related to bulk N deposition, precipitation amount, and Nr concentrations in precipitation. All data were analyzed with SPSS statistical software or R software. N deposition spatial patterns were drawn with ArcGIS software. Statistically significant differences were set at p values < 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This arficle seeks to invesfigate the favorable outcomes resulfing from the integrafion of short-term and long-term pollufion control measures for improving air quality. Through the formulafion of a baseline scenario and two addifional scenarios, the study quanfifies the impact of short-term and long-term emission reducfions on PM2.5 concentrafions and Nr deposifion. The findings suggest that both short-term measures and long-term policies prove effecfive in enhancing air quality.\nSeveral issues necessitate further detailed argumentafion and supplementafion by the author:\n1.Firstly, while this paper quanfifies the impacts of short-term measures and long-term policies on PM2.5 concentrafions and Nr deposifion through scenario simulafions, it does not simulate the policy effects of combining the short-term measures and long-term policies. Drawing the conclusion that the combinafion of short-term and long-term policies can effecfively achieve pollutant reducfion solely based on the observafion that short-term measures or long-term policies contributes to emissions reducfion lacks credibility and rigor. Addifionally, in quanfifying the policy effects, the author overlooks potenfial interference between short-term measures and long-term policies. For example, the short-term measures highlighted, such as those during the Asia-Pacific Economic Cooperafion Summit and Military Parades, occurred after the implementafion of long-term measures covered in this paper, specifically, the Atmospheric Ten Acfions (2013-2017) and the 3-year Defense Baftle (2018-2020). The paper does not differenfiate the emission reducfion effects influenced by short-term measures from those resulfing from long-term policies. Consequently, the author does not calculate the “net effects” of short-term or long-term policies, significantly compromising the reliability of the baseline results in this paper.\nThe author may contemplate the following suggesfions: Firstly, quanfifying the cost and efficacy of short-term pollufion reducfion policies after controlling the impact of long-term policies; secondly, assessing the effecfiveness of long-term policies on pollufion reducfion after isolafing the influence of short-term policies; thirdly, evaluafing the combined effects of short-term measures and long-term policies on pollufion reducfion; fourthly, conducfing a comparafive analysis of the implementafion costs and effect of these three types of policies, and ulfimately deriving conclusions.\n2.This paper reviews certain policies and short-term measures aimed at long-term reducfion of pollutant emissions. However, there seems to be a lack of specificity in addressing targeted policies for the two pollutants of primary concern in this study, namely PM2.5 and reacfive nitrogen (Nr) pollufion. Could there be considerafion for enhancing the specificity in the review of policies?\n3.This paper summarizes the significance of exploring Nr pollufion but does not idenfify the reasons of invesfigafing the trends in PM2.5. Please provide addifional informafion.\n4.Lines 128-129: There is a lack of evidence to demonstrate that control over SO2 has been achieved through end-of-pipe treatment rather than source control. Please provide addifional informafion.\n5.Lines 165-166: Please provide scienfific jusfificafion for dividing the fime into three stages, namely 2011-2014, 2015-2017, and 2018-2020.\n6.Lines 179-182: The argumentafion process seems somewhat absolute. Long-term emission control policies consistently exert pressure on pollufing enfifies, making it less likely to observe pollufion rebound after short-term emission control measures conclude. However, this does not necessarily imply that policy implementafion has achieved a win-win situafion for both environmental protecfion and economic development. The author is encouraged to further elaborate on how long-term emission control measures can indeed achieve this dual benefit of environmental protecfion and economic development.\n7.Lines 233-236: The citafion of references 27-29 appears to lack a clear connecfion to the discussed content in this paragraph. The focus of this paragraph is primarily on urban and rural areas. Could there be considerafion for introducing a discussion on the divergence in marginal abatement costs of NH3 emissions between urban and rural areas?\n8.Lines 283-285: The statement “A recent study ... occurred region” seems somewhat unrelated to the central focus of this paragraph on Nr emissions. Please provide further clarificafion. Addifionally, please review the coherence between the referenced literature and the arguments presented in this paper, ensuring a more logical connecfion in the reasoning process.\n9.Please further review the units of various pollutants in the figures of this paper to ensure accuracy, for instance, “ug N m-3” in Figure 4.\n10.How short-term measures are specifically integrated with long-term policies needs further clarificafion. The author is encouraged to provide addifional details on this aspect.\n11.Should regional heterogeneity be considered when combining short-term and long-term control policies, given the significant differences in economic development stages, industrial structures, energy consumpfion pafterns, and geographical climafic condifions across various regions in China?\n12.When formulafing pollufion control policies, enhancing efficiency and equity is crucial. Please elaborate on how the combinafion of short-term and long-term control policies reflects efficiency and fairness in the implementafion process.\n13.Due to the limited feasibility of implemenfing uniform emission standards for pollutants across different regions, controlling regional air pollufion requires not only command-and-control policies but also the integrafion of economic incenfive policies, which aims to harmonize interests among various regions and ensure the full internalizafion of negafive externalifies from pollufion emissions and posifive externalifies from pollufion control. Please provide addifional details on how to establish a sustainable mechanism for pollufion control among regions.", "reviewer_2": "This manuscript provides interesfing results. I recommend it be accepted for publicafion after my following comments are addressed.\nLine 28-30. Why are PM2.5 and nitrogen trends compared here when PM2.5 appears to be presented as results only for August, while nitrogen corresponds to the annual trend? Besides, considering the author presents the reducfion rate, it would be beneficial to specify the temporal range rather than using the ambiguous statement \"for decades.\"\nLine 34. What does \"Nr emissions\" refer to? The text lacks an abbreviafion note for \"Nr,\" and earlier, the abbreviafion for nitrogen was noted as \"N.\" It seems the author may be using \"Nr\" to represent nitrogen, but clarificafion is needed for consistency.\nLine 47. What does the author indicate by \"People's experience of air pollufion is widespread, direct and intense\"?\nLine 57. \"nafional government\" ? I recommend using the \"Chinese government\" directly.\nLine 69-70. Why can the “N deposifion” be used to “indicate the direcfion of air quality improvement”?\nLine 75-76. What is the relafionship between Nr emission (or N deposifion) and climate change? The author does not menfion this above.\nLine 106. What is “BR”?\nLine 112. What is “moral incenfive”? I'm not sure about the message the author intends to convey with this sentence.\nLine 114-120. The current discussions are not suitable or necessary, as the main text should center around the actual results obtained from the data. Content unrelated to the data of this study should not be addressed here; instead, it could be incorporated into the concluding remarks at the end.\nFigure 1. The definifion of the periods before and after a specific event are inconsistent in terms of fime length, why? Besides, a note of “Table S3” should be placed here for befter understanding.\nFigure 1e. Why does the author only present the August data here? How about the annual trend?\nLine 145. What does the “(NO3-, NH4+)” mean in “2.86 ± 0.39 mg N L-1 (NO3-, NH4+)”, unit? And what does the other “N” refer to in the unit in this paragraph?\nLine 146-149. What is the difference between “site average” and “geographic annual mean”?\nLine 162-166. What’s the point of parficularly dividing the 10-year period into three stages?\nLine 171-173. How is this conclusion reached? The impact of long-term emission control is deemed equally significant as short-term acfions.\nLine 207-211. How is this conclusion reached? How does the author define “NH3-rich atmospheric environment”? why “especially for non-point source emissions”? There is no specific statement about the contribufion of non-point source emissions here.\nLine 255-257. How is this conclusion reached? Why “the increase in NH3+pNH4+ concentrafions from 2018 may be aftributed to acid gas emission reducfions, despite a reducfion shown in the NH3 emission inventory for this region.”\nLine 274-276. How is this conclusion reached? How does the author calculate the “transboundary export of pollufion”?\nLine 296. Why “improvements in air quality could be negated by climate change”? Following the author's reasoning, with climate change leading to increased precipitafion, the concentrafion of pollutants in the air should theorefically decrease. If this holds true, could climate change potenfially contribute to improved air quality?", "reviewer_3": "This manuscript aimed to provide opfions for short-term and long-term emission controls in China. This is important to sustainably reduce atmospheric pollutant emissions. However, several aspects should be explained more carefully before the paper can be considered for publicafion. Some suggesfions are offered below:\nGeneral comments:\n1. It is logical that short-term emergency control measures can quickly improve air quality, while long-term policies take longer to take effect, but are more sustainable. how does your analysis differ from others? This manuscript should present novel findings in this aspect.The analysis of the deposifion trends of Nr and NH3 appears to be novel, more discussions should be paid on the effect of short and long term controls in China.\n2. In this manuscript, discussions on short-term controls mostly focused on North China. Other regions like Yangtze River Delta, Fenwen Plain shoule be added, because there were a lot of short-term controls(for example 2016 G20 Hangzhou sumimit, and many Regional Air Quality Warnings).\n3. I am puzzled about why the author chose to discuss PM2.5 in summer instead of autumn or winter(Figure 1e). After all, China's control measures are more focused on winter, and the most significant effects are also seen in winter. The author should separately discuss the responses of PM2.5 and N deposifion in autumn and winter to short-term early warning control and long-term policies.\nspecific comments:\n1. Line 88-101 the difference between pre-, during and post Olympic, APEC et al, is diffcult to describe the effect of short-term measures. This because that the meteorology is likely different, which is very important to short-term air quality.\n2. Figure 1, Why did the NH3 concentrafions seems no difference during Olympics, APEC abd BRs compared to post-APEC abd BRs. In urban areas, NH3 largely came from mobiles and industries, which were reduced during these campaigns.\n3. Is the landuse changed at at sites in Table S1 since 2010-2017? It is key to calculate the deposifion velocity in the Formula 1 and analyze its trends.\n4. Line 141, this manuscript discussed the long-term trends of N deposifion. The short effects are suggested fi added. Discussing only the concentrafion of PM2.5 and its precursors is not enough to fully evaluate the short-term impact. Many other studies have done so."}, "author_response": "Dear Editor:\nPlease find below our itemized responses to the reviewer’s comments. We have\naddressed the comments raised by three reviewers and incorporated their\ncomments/suggestions in the revised manuscript.\nThank you very much for your consideration.\nSincerely,\nXuejun Liu\nOn behalf of all co-authors\nReviewer #1 (Remarks to the Author):\nThis article seeks to investigate the favorable outcomes resulting from the integration\nof short-term and long-term pollution control measures for improving air quality.\nThrough the formulation of a baseline scenario and two additional scenarios, the study\nquantifies the impact of short-term and long-term emission reductions on PM2.5\nconcentrations and Nr deposition. The findings suggest that both short-term measures\nand long-term policies prove effective in enhancing air quality. Several issues\nnecessitate further detailed argumentation and supplementation by the author:\n[Response]: The authors thank the reviewer for the valuable comments and\nsuggestions that have improved our paper. Below we provide a point-by-point\nresponse to the reviewer’s comments, together with proposed changes in the revised\nmanuscript (in blue).\n1. Firstly, while this paper quantifies the impacts of short-term measures and long-\nterm policies on PM2.5 concentrations and Nr deposition through scenario\nsimulations, it does not simulate the policy effects of combining the short-term\nmeasures and long-term policies. Drawing the conclusion that the combination of\nshort-term and long-term policies can effectively achieve pollutant reduction solely\nbased on the observation that short-term measures or long-term policies contributes to\nemissions reduction lacks credibility and rigor. Additionally, in quantifying the policy\neffects, the author overlooks potential interference between short-term measures and\nlong-term policies. For example, the short-term measures highlighted, such as those\nduring the Asia-Pacific Economic Cooperation Summit and Military Parades,\noccurred after the implementation of long-term measures covered in this paper,\nspecifically, the Atmospheric Ten Actions (2013-2017) and the 3-year Defense Battle\n(2018-2020). The paper does not differentiate the emission reduction effects\ninfluenced by short-term measures from those resulting from long-term policies.\nConsequently, the author does not calculate the “net effects” of short-term or long-\nterm policies, significantly compromising the reliability of the baseline results in this\npaper.\nThe author may contemplate the following suggestions: Firstly, quantifying the cost\nand efficacy of short-term pollution reduction policies after controlling the impact of\nlong-term policies; secondly, assessing the effectiveness of long-term policies on\npollution reduction after isolating the influence of short-term policies; thirdly,\nevaluating the combined effects of short-term measures and long-term policies on\npollution reduction; fourthly, conducting a comparative analysis of the\nimplementation costs and effect of these three types of policies, and ultimately\nderiving conclusions.\n[Response]: Thank you for this valuable suggestion. Yes, we agree that quantifying\nthe net effect of long-term policies and short-term measures is a more important\nconclusion of the article. We now specifically discuss the interaction between short-\nterm measures and long-term policies. Firstly, in Res-Figure 1, we present three\nscenarios with a high level of consensus: Scenario 1, assumes no emission reduction\nmeasures (and assuming emissions do not rise), where pollutant emissions and\nconcentrations remain stable. Scenario 2, considers only short-term measures, in\nwhich pollutant emissions and concentrations experience a brief decline followed by a\nrapid rebound. Scenario 3, considering only long-term policies, results in pollutant\nemissions and concentrations continuing to decline linearly or non-linearly over time.\nThe impact of short-term measures on long-term policies could be as follows (Res-\nFigure 2): Hypothesis 1, short-term measures do not enhance the effectiveness of\nlong-term emission reductions. Pollutant emissions and concentrations decline sharply\nduring the event period, followed by a rapid return to initial levels, and continue to\ndecline linearly or non-linearly with long-term mitigation policy until the reduction\ntarget is achieved at time T2. Hypothesis 2, short-term measures will facilitate long-\nterm reductions. The occurrence of the event accelerates the decrease in pollutant\nemissions and concentrations and the emission reduction target is achieved at an\nearlier period (T1). In this study, we observed that concentrations of both PM and\n2.5\nits precursors almost immediately rebounded to 100% of their pre-event levels after\nthe suspension of the short-term measures. Therefore, we inferred that short-term\nmeasures have not significantly altered the slope of emission reductions. Through the\nDifference-in-Differences (DID) method, based on monthly scales, we further\nquantified the effects of short-term measures on pollutant concentrations and\ndeposition fluxes. The results showed p=0.685>0.05 for PM concentration,\n2.5\np=0.197>0.05 for oxidized N deposition, and p=0.325>0.05 for reduced N deposition,\nreaffirming that the trend in long-term measures is not influenced by short-term\nemission reduction efforts. This could be attributed to the limited scope of emission\nreductions (Nr emissions from the Beijing-Tianjin-Hebei region account for only 7.5%\nof the national total emissions) and the short duration of such measures (at most 1\nmonth), which results in the negligible impact of these short-lived reductions on the\nlong-term trend.\nWe agree that long-term policies may partially overshadow the benefits of short-\nterm measures. Assuming a daily decrease in Nr emissions without taking into\naccount the meteorological factors or transport effects, theoretically, the pollution\nconcentrations of each month should be lower than that of the previous month.\nTherefore, PM and its precursor concentrations, following short-term measures,\n2.5\nshould be slightly lower than those before emission reduction. In this study, the\ndifference in PM concentration during the Per-parade and Parade event was 68%,\n2.5\nwhich is significantly higher than the average monthly PM concentration reduction\n2.5\nrate of 0.7% in 2015, suggesting that the net effect of the short-term measures is\napproximately 67%. Similarly, the net effect of the short-term measures during the\nAPEC event was about 61%.\nWe have included the following discussion: “Short-term measures were virtually\nunaffected by long-term emission reduction, with short-term net effects of 67% and\n61% for APEC and Parade events, respectively. Short-term measures also do not\ninherently alter the slope of the decline in long-term pollution reduction (p>0.05).\nThis is because the decrease in concentrations from long-term policies is stable and\ngradual. The >50% reduction in PM concentration achieved in Beijing through one\n2.5\nmonth of short-term measures has in fact taken nearly a decade to stabilize. With\ncontinuous adjustments in energy and industrial structures, the concentration of\natmospheric pollutants is expected to continue to decline. When the concentration of\npollutants is extremely low, it shows that long-term emission reduction policies may\nhave played an extremely significant role, and so the implementation of short-term\nmeasures might be ineffective.”.\nRes-Figure 1 Effects of long-term and short-term measures on pollutant emissions\nand concentrations under three scenarios\nRes-Figure 2 Two hypotheses on the impact of short-term measures on long-term\nemission reduction trends\n2. This paper reviews certain policies and short-term measures aimed at the long-term\nreduction of pollutant emissions. However, there seems to be a lack of specificity in\naddressing targeted policies for the two pollutants of primary concern in this study,\nnamely PM2.5 and reactive nitrogen (Nr) pollution. Could there be consideration for\nenhancing the specificity in the review of policies?\n[Response]: We have inserted the target pollutants addressed by the measures and\npolicies in the paper. Including: “…including the suspension of industrial activities to\ndecrease SO and NO emissions, and the reduction of vehicle movements in and\naround the event locations mainly for NO …”, “such as the suspension of\nX\nmanufacturing facilities and vehicle restrictions for SO , NO and PM emission…”.\nTable S1 Control measures implemented in Beijing-Tianjin-Hebei during the\nOlympic Games, APEC, Military Parade, and BRS\nProvinces Control measures\nBeijing Strict and provisional driving restrictions on motor\n1\nvehicles by odd-even number(for NO and PM );\nX 2.5\nSuspended the use of 70% of government motor\n2\nvehicles(for NO and PM );\nX 2.5\nStopped some construction site operations (for SO\n3 2\nand PM );\n2.5\n2.5\nStopped production and limited production in key\n5\npolluting enterprises(for SO and NO );\nOlympic 2 X\nGames\nEmission pollutants (SO , NO , etc.) decreased by\n6 2 X\n30% from fuel combustion(for SO and NO );\n(Aug. 8– 2 X\n24,\nOrganic emissions at gas stations, tankers, and storage\n2008) 7\ndepots reduced(for PM );\n2.5\nTianjin Strict and provisional driving restrictions on motor\n1\nvehiclesby odd-even number(for NO and PM );\nX 2.5\nSome gas stations in key areas stopped refueling\n2\noperations during key hours (for NO and PM );\nX 2.5\nOpen burning of straw strictly prohibited (for PM\n3 2.5\nand NH );\n4\npower plants ceased operation(for SO and NO );\nSelected pollutant emissions suspended (for SO and\n5 2\nNO );\nX\nIncreased road flushing and sprinkling for dust\n6\nsuppression(for PM );\n2.5\nHebei All production activities related to construction works\n1\nare suspended(for PM );\n2.5\nIncrease in the frequency of road water spraying (for\n2.5\nStrict control of straw burning and coal burning (for\n2.5 X 2 3\nStrengthened the inspection of vehicles entering\nX 2.5\nBeijing Strict supervision and driving restrictions on motor\n1\nvehicles(for NO and PM );\nX 2.5\nDriving ban on 70% of vehicles belonging to\n2 government and state-owned enterprises (for NO and\nX\nPM );\n2.5\nAll construction sites within the fifth ring road\n3\nsuspended(for PM );\n2.5\nProduction by coal-fired and industrial companies\n4\nsuspended(for SO and NO );\nEmission standard of pollutants (SO , NO , etc.)\n5 2 X\nincreased by 50%(for SO and NO );\nKey roads required to be cleaned at a high frequency\nAPEC 6\nevery day (for PM );\n2.5\n(Nov. 1–\nStrict control of express deliveries in Beijing (for\n12, 7\nPM );\n2.5\n2014)\nTianjin 50% of motor vehicles limited and public transit\n1\ncapacity enhanced(for NO and PM );\nX 2.5\nAll production activities related to construction works\n2\nsuspended(for PM );\n2.5\nEmission standard of pollutants (SO , NO , etc.)\n3 2 X\nincreased by 30%(for SO and NO );\nMain roads in the central area cleaned once a day (for\n2.5\nStrict control of straw burning and coal burning (for\n2.5 3\nHebei Strengthened inspection of vehicles entering Beijing\n1\n(for NO and PM );\nX 2.5\nReal-time monitoring of straw burning and (for PM\n2 2.5\nand NH );\nConstruction sites and key enterprises suspended or\n4\nrestricted in key areas (for SO , NO and PM ).\nBeijing Strict and provisional driving restrictions on motor\n1\nvehicles by odd-even number(for NO and PM );\nX 2.5\nDriving ban on 80% of vehicles belonging to\n2 government and state-owned enterprises (for NO and\nX\nPM );\n2.5\nWidespread surprise inspections and rectification of\n3\npolluting enterprises(for SO , NO and PM );\nAll construction works within the fifth ring road\n4\nsuspended(for PM );\n2.5\nRemoved from use 217,000 old vehicles and 244\n5 heavy polluting enterprises in advance (for SO , NO\nand PM );\n2.5\n58 important roads cleaned at high frequency every\n6\nday(for PM );\n2.5\nEnhanced supervision of thermal power plants and\nMilitary 7 steel companies in west and south Beijing based on\nParade scientific research(for SO , NO and PM );\n(Sep. 3, Tianjin Increased emission reduction standard of coal power\n1\n2015) plants by more than 50%(for SO );\n2\nmonths in advance(for NO and PM );\nX 2.5\nStrengthened efforts to rectify environmental pollution\n3\nincidents (for SO , NO and PM );\nCars that did not meet national emission standards\n4\nwere not allowed to enter Beijing (for NO and PM );\nX 2.5\nMain roads in the central area and Binhai area required\n5\nto be cleaned once a day (for PM );\n2.5\nHebei Set up an inspection team to account for environmental\n1\nviolations (for SO , NO and PM );\nDriving restrictions on motor vehicles by odd-even\n2\nnumber(for NO and PM );\nX 2.5\nProduction of key companies suspended in Baoding\n3\nand other cities(for SO , NO and PM );\nEnhanced the desulfurization facilities in ANSTEEL in\nAll construction works and pollutant companies\n5\nsuspended or restricted(for SO , NO and PM ).\nBeijing 1 The principle of “non-disturbance of the resident”;\nProvisional traffic restrictions on vehicles with\n2\ndangerous chemicals(for NO and PM );\nX 2.5\nSome logistics vehicles are completely banned (for\nX 2.5\nEnterprises that could not meet emissions standards or\n4 with excessive emissions suspended (for SO , NO\nBelt and 2 X\nand PM );\nRoad 2.5\nSet up a special inspection team to ensure air quality\n(May. 8– 5\n(for SO , NO and PM );\n17, 2 X 2.5\n2017)\nTianjin Strengthened control of coal pollution, industrial\n1\npollution and dust pollution(for SO , NO and PM );\nStrengthened the assessment and accountability\n2\nmechanism;\nHebei 1 10-day corporate inspection(for SO , NO and PM );\nStrengthened environmental law enforcement and data\n2\nmonitoring (for SO , NO and PM ).\n3. This paper summarizes the significance of exploring Nr pollution but does not\nidentify the reasons of investigating the trends in PM2.5. Please provide additional\ninformation.\n[Response]: Thank you for pointing this out. We note in the Introduction: “PM\n2.5\nconcentrations can characterize air pollution variation at a daily scale, responding\nrapidly to changes in strong emission or meteorological conditions. Atmospheric N\ndeposition, as a sink for Nr emissions, can be used as an indicator to evaluate the\ndegree of Nr pollution at the monthly/yearly scale.”.\n4. Lines 128-129: There is a lack of evidence to demonstrate that control over SO has\n2\nbeen achieved through end-of-pipe treatment rather than source control. Please\nprovide additional information.\n[Response]: We revised the text of “end-of-pipe” to “strengthen industrial emission\nstandards, upgrades on industrial boilers, and phase out outdated industrial capacity1”.\n5. Lines 165-166: Please provide scientific justification for dividing the time into\nthree stages, namely 2011-2014, 2015-2017, and 2018-2020.\n[Response]: The division of the past decade into three stages is based on the different\ndevelopment phases of atmospheric environmental issues. The period from 2011 to\n2014 was marked by severe air pollution, which gradually attracted the attention of\nthe public and the government. However, during this stage, emission reduction\nmeasures were limited. Between 2015 and 2017, air pollution control was of high\npriority, and the implementation of the “Air Pollution Prevention and Control Action\nPlan” resulted in the strict control of pollutant emissions, strengthened adjustment of\nthe energy structure, technological transformations in industries, and the enhancement\nof joint prevention and control mechanisms. From 2018 to 2020, air pollution\ncampaigns achieved remarkable improvements. During this stage, air quality has\nimproved significantly, although the stabilization of these positive trends remains a\nchallenge. In the revised manuscript, we now state that “2011-2014 (period of severe\nair pollution), 2015-2017 (period of air pollution mitigation), and 2018-2020 (period\nof air quality significant improvement)”.\n6. Lines 179-182: The argumentation process seems somewhat absolute. Long-term\nemission control policies consistently exert pressure on polluting entities, making it\nless likely to observe pollution rebound after short-term emission control measures\nconclude. However, this does not necessarily imply that policy implementation has\nachieved a win-win situation for both environmental protection and economic\ndevelopment. The author is encouraged to further elaborate on how long-term emission\ncontrol measures can indeed achieve this dual benefit of environmental protection and\neconomic development.\n[Response]: Agreed. We have removed the sentence and added a discussion of\nenvironmental protection and economic benefits in the response to Question #12 and\nQuestion #13.\n7. Lines 233-236: The citation of references 27-29 appears to lack a clear connection\nto the discussed content in this paragraph. The focus of this paragraph is primarily on\nurban and rural areas. Could there be consideration for introducing a discussion on the\ndivergence in marginal abatement costs of NH3 emissions between urban and rural\nareas?\n[Response]: This is a very good question. Current research on urban NH focuses on\n3\nsource quantification based on isotope techniques, on-site monitoring, emission\ninventories, etc., but there are few studies on the differences in the cost of NH\n3\nemission abatement for urban and rural areas. We believe this is a good idea that can\nbe addressed in our future research. Here we add some text: “According to Gu et al.\n(2023)2, the cost of technological measures to reduce global agricultural NH\n3\nemissions by 32% is around 19 billion USD. However, the technologies used do not\ntake into account the difficulty of technology diffusion and acceptability for farmers,\nso the actual cost of reducing NH emissions from agricultural sources may be higher\n3\nthan this. Very few studies have been addressed urban NH emission reduction actions.\nelectric vehicles and other measures that are mainly focused on NO and other\nX\npollutants, it is possible to achieve a “free ride” on reducing urban NH emissions,\n3\nwhich could significantly lower the economic cost of urban NH emission reduction.”.\n3\n8. Lines 283-285: The statement “A recent study ... occurred region” seems somewhat\nunrelated to the central focus of this paragraph on Nr emissions. Please provide\nfurther clarification. Additionally, please review the coherence between the referenced\nliterature and the arguments presented in this paper, ensuring a more logical\nconnection in the reasoning process.\"\n[Response]: We have added the clarification that: “Pollution transport not only affects\nN inputs to ecosystems in the surrounding area but also harms human health.”.\n9. Please further review the units of various pollutants in the figures of this paper to\nensure accuracy, for instance, “ug N m-3” in Figure 4.\n[Response]: Corrected.\n10. How short-term measures are specifically integrated with long-term policies needs\nfurther clarification. The author is encouraged to provide additional details on this\naspect.\n[Response]: Combining this question with Question #12, we have now discussed how\nshort-term policies can be integrated with long-term strategies in our manuscript.\nFurthermore, we have added additional content: “Compared to SO and NO , it is\n2 2\nalmost impossible to achieve rapid reductions in NH concentrations quickly. Our\n3\ncase study during the COVID-lockdown period has proven that, after a drastic\nreduction of transport and industrial NH emissions in February 2020, ambient NH\n3 3\nconcentrations from January to February (17%) still exceeded the levels of previous\nyears (9%), while concentrations of SO and NO decreased by 24% and 49%,\n2 2\nrespectively3. This indicates that, as opposed to the intense reductions of acid gases,\nNH emissions can only be gradually reduced through better fertilizer and manure\n3\nmanagement. Due to the lack of a national NH monitoring system overseen by the\n3\ngovernment, the impact of NH on air quality and the environment remains a matter\n3\nof theoretical debate4. Currently, “Green Demonstration Zones” have been established\nacross China, where systemic transformations in precision and smart agriculture will\nimprove nitrogen use efficiency and reduce NH volatilization. We suggest that\n3\nresearch on NH , PM and atmospheric N deposition should be uniformly\n3 2.5\nstrengthened in this area, treating spatial “regional areas” analogously to temporal\n“short-term” studies, thereby identifying the national potential and environmental\neffects of NH emission reduction through case studies.”.\n3\n11. Should regional heterogeneity be considered when combining short-term and\nlong-term control policies, given the significant differences in economic development\nstages, industrial structures, energy consumption patterns, and geographical climatic\nconditions across various regions in China?\n[Response]: We agree that there are differences in economic development, energy\nstructures, industrial levels, and meteorological and soil conditions between regions.\nIn the light of these differences, the government has taken them into account when\nformulating policies or applying emission reduction technologies. From the\nperspective of combining short-term and long-term controls, there is no directional\ndifference in technology or policy within regions, but only between regions. Within a\nregion, short-term measures aim for immediate effects (such as the implementation of\nodd-even license plate restrictions), while long-term emission reductions should seek\na socially, economically and environmentally sustainable path (such as promoting\nelectric and hybrid vehicles, enhancing fuel quality and encouraging urban planning\nthat reduces the need for private vehicle use). Besides, for regions experiencing little\nor no air pollution, short-term emission reduction measures might not yield significant\noutcomes. However, for areas that suffer serious pollution, a combination of short-\nterm and long-term strategies proves to be more effective.\n12. When formulating pollution control policies, enhancing efficiency and equity is\ncrucial. Please elaborate on how the combination of short-term and long-term control\npolicies reflects efficiency and fairness in the implementation process.\n[Response]: Thank you for this suggestion. We have added text to the discussion:\n“Frequent or prolonged implementation of high-intensity short-term control measures\nwill disrupt the development of industry, transport and the residential life. Therefore,\nat a time when the effectiveness of long-term emission control is still tentative, it is\nrecommended that a short but major emission control exercise be carried out once\nevery 1-2 years to clarify the regional emission reduction potential. Policymakers\nshould gradually adjust the intensity of the controls, and align them with long-term\ngoals, ensuring a smooth transition and continuous progress towards objectives,\ndefining specific and achievable long-term targets for air quality improvements that\nare informed by the success of short-term reductions. In the long-term emission\nreduction process, two aspects need to be clarified. Firstly, “polluter identification”.\nFor example, sintering in the steel industry is a major source of NOx emissions, while\nin agriculture, high amounts of NH are emitted from the treatment of manure from\n3\nlivestock. Therefore, to increase pollution management efficiency, the specific targets\nof emission reduction must be identified (including key emitting sectors and critical\nemission stages). Secondly, the principle of “polluter pays” should be achieved. To\nensure fairness in environmental and economic terms within pollution management, it\nis essential to identify the input and output of pollutants between regions. There needs\nto be a more precise quantification of transboundary contributions, which can provide\ndata support for subsequent compensatory mechanisms between regions.”.\n13. Due to the limited feasibility of implementing uniform emission standards for\npollutants across different regions, controlling regional air pollution requires not only\ncommand-and-control policies but also the integration of economic incentive policies,\nwhich aims to harmonize interests among various regions and ensure the full\ninternalization of negative externalities from pollution emissions and positive\nexternalities from pollution control. Please provide additional details on how to\nestablish a sustainable mechanism for pollution control among regions.\n[Response]: Thank you for this insightful comment. We have added relevant text to\nthe discussion: “Quantifying the economic benefits and environmental costs\nassociated with pollution transport contributes to revealing the environmental\ninequalities of regional emissions and pollution. As reflected in this study,\neconomically developed regions export Nr emissions to neighbouring regions, which\nresults in these suffering both economic and environmental losses. Currently,\neconomic compensation between regions under multiple constraints (air pollution,\nclimate change, ecological effects) is hardly discussed. Regions more economically\ndeveloped are encouraged to increase green investments in emission-reducing\ntechnological transformations5 and may need to compensate polluted regions around\nthem. This could be through economic measures such as tax incentives, subsidies and\nloans, encouraging enterprises and consumers to make more environmentally friendly\nchoices. For instance, financial support could be provided to enterprises and farms\nthat utilize clean energy and energy-efficient technologies, and tax relief is given to\nconsumers for purchasing environmentally friendly products and services.”.\nReviewer #2 (Remarks to the Author):\nThis manuscript provides interesting results. I recommend it be accepted for\npublication after my following comments are addressed.\n[Response]: We thank the reviewer for recommending publication. Below we provide\na point-by-point response to the reviewer’s comments, together with proposed\nchanges in the revised manuscript (in blue).\n1. Line 28-30. Why are PM2.5 and nitrogen trends compared here when PM2.5\nappears to be presented as results only for August, while nitrogen corresponds to the\nannual trend? Besides, considering the author presents the reduction rate, it would be\nbeneficial to specify the temporal range rather than using the ambiguous statement\n\"for decades.\"\n[Response]: In combination with reviewer #3's Question #3, we have answered this\nby adding more details about winter variations (Figure 1f) of precursor, PM and\n2.5,\nSNA concentrations for the period of 2005-2020 in the revised paper: “Although\nPM concentrations in Beijing are significantly higher in winter than in summer, they\n2.5\nstill showed a continuous downward trend over the 15 years: from January 2005 to\nJanuary 2020, PM , SNA, SO and NO decreased by 65%, 51%, 94% and 50%,\n2.5 2 2\nrespectively. In contrast to the summer months, NH concentrations increased by\n3\nabout 50% in winter and stabilized at 7 μg m-3 after 2013.”, and “Similarly, the air\npollution episode in January 2013 caused strong dissatisfaction among the\ngovernment and public of Beijing. However, from that time until 2020, the PM\n2.5\nconcentration in Beijing decreased by approximately 58%, ...”. In the Abstract, we\nclarify this: “Long-term adherence to sustained and strict emission reduction policies\nled to successful decreases of 58% in PM concentrations in Beijing, and of 23% in\n2.5\natmospheric nitrogen (N) deposition in China during the period of 2011-2020…”.\nshort-term\nlong-term\nlong-term\nFigure 1 PM , gaseous precursors (SO , NO , NH ) and secondary inorganic ion\n2.5 2 2 3\n(SNA: SO 2-, NO -, NH +) concentrations (average ± SD) in Beijing during (a) pre-\n4 3 4\nOlympic Games (Jun.1-30, 2008), Olympic Games (Aug.8-24, 2008) and post-\nOlympic Games (Sep.24-Oct.23, 2008); (b) pre-APEC (Oct.1-31, 2014), APEC\n(Nov.1-12, 2014) and post-APEC (Nov.13-Dec.31, 2014); (c) pre-Military Parades\n(Aug.3-19, 2015), Military Parades (Aug.20-Sep.3, 2015) and post-Military Parades\n(Sep.3-29, 2015); (d) pre-Belt and Road Summit (Apr.28-May 7, 2017), Belt and\nRoad Summit (May 8-17, 2017) and post- Belt and Road Summit (May 18-Jun.17,\nperiod of January 2005 to January 2020.\n2. Line 34. What does \"Nr emissions\" refer to? The text lacks an abbreviation note for\n\"Nr,\" and earlier, the abbreviation for nitrogen was noted as \"N.\" It seems the author\nmay be using \"Nr\" to represent nitrogen, but clarification is needed for consistency.\n[Response]: Here Nr emissions refer to emissions from various reactive N\ncompounds (i.e., NH and NO ). We have added an abbreviation note for Nr in the\nAbstract at line 39 in the revised paper.\n3. Line 47. What does the author indicate by \"People's experience of air pollution is\nwidespread, direct and intense\"?\n[Response]: It means that air pollution is common and serious giving those who\nbreathe polluted air a strong uncomfortable feeling. This is widely experienced by\nmost Chinese people, so we need to formulate a series of policies and implement\nmeasures to mitigate air pollution.\n4. Line 57. \"national government\"? I recommend using the \"Chinese government\"\ndirectly.\n[Response]: Agreed and done.\n5. Line 69-70. Why can the “N deposition” be used to “indicate the direction of air\nquality improvement”?\n[Response]: The trend in N deposition can be used to indicate the direction of air\nquality improvement. This is because atmospheric Nr (reactive nitrogen) components\nare constituents of PM (such as NH + and NO - in fine particles) as well as direct\n2.5 4 3\ncomponents of Nr in air (NH , NO and HNO ). N deposition also contains\n3 2 3\ninformation about the variation of Nr concentrations in precipitation. If N deposition\ndecreases, it means that the atmospheric Nr concentrations have decreased, so that the\nPM concentration, which is closely related to air quality, decreases accordingly.\n2.5\nCombined with reviewer #1's comments on Question #3, we now explain this in the\nintroduction: “PM concentrations can characterize air pollution variations at a daily\n2.5\nscale, responding rapidly to changes in strong emissions or meteorological conditions.\nAtmospheric N deposition, as a sink for Nr emissions, can be used as an indicator to\nevaluate the degree of Nr pollution at the monthly/yearly scale.”.\n6. Line 75-76. What is the relationship between Nr emission (or N deposition) and\nclimate change? The author does not mention this above.\n[Response]: Thank you for pointing this out. We now state in the Introduction “In\naddition, global warming may contribute to changes in spatial patterns and interannual\ntrends in atmospheric Nr emissions and N deposition due to the high correlation\nbetween temperature and NH emissions.”.\n3\n7. Line 106. What is “BR”?\n[Response]: We apologize for this mistake, it should be BRS, representing the Belt\nand Road Summit. This is now explained in the text (line 117).\n8. Line 112. What is “moral incentive”? I'm not sure about the message the author\nintends to convey with this sentence.\n[Response]: The sentence has been revised as “This means that, in order to improve\nair quality standards in the short term, it is necessary to adopt some unconventional\nemission reduction measures at the expense of the economy to remind the public that\npollution can beprevented and so increase their confidence in policies and practices.”.\n9. Line 114-120. The current discussions are not suitable or necessary, as the main\ntext should center around the actual results obtained from the data. Content unrelated\nto the data of this study should not be addressed here; instead, it could be incorporated\ninto the concluding remarks at the end.\n[Response]: Agreed and revised accordingly. These sentences have been incorporated\ninto the concluding remarks.\n10. Figure 1. The definition of the periods before and after a specific event are\ninconsistent in terms of time length, why? Besides, a note of “Table S3” should be\nplaced here for better understanding.\n[Response]: For Olympic, APEC and parade events, the data are from the literature.\nThe three periods are categorized as pre-event (one month before the event),\nevent, and post-event (one month after the event). For the Belt and Road Summit event,\nsince no significant emission reduction measures were implemented, our sampling\nfrequency at that time was 1 time per day for both pre-event and event, and once 3\ndays after the event. This ensured that there were 10 PM samples available for\n2.5\nanalysis during the period of pre-BRS, BRS and post-BRS. For a clearer explanation\nof this, in Figure 1, we labeled the duration of each event. Please also see our\nresponse to Question #1.\n11. Figure 1e. Why does the author only present the August data here? How about the\nannual trend?\n[Response]: See our previous answer and that to Question #1. We have added the\nJanuary trend in the revised paper.\n12. Line 145. What does the “(NO3-, NH4+)” mean in “2.86 ± 0.39 mg N L-1 (NO3-,\nNH4+)”, unit? And what does the other “N” refer to in the unit in this paragraph?\n[Response]: The Nr in the air (including 5 Nr species: NH , NO , HNO , pNH +,\n3 2 3 4\npNO -) and Nr in precipitation (including 2 Nr species: NO -, NH +) are not the same.\n3 3 4\nThe mean concentrations of Nr in precipitation (2.86 ± 0.39 mg N L-1) are in different\nunits to those N species in the air (units = µg N m-3). However, expressing these in\nterms of N rather than NO - or NO allows the different dry and wet deposition\n3 2\ncomponent fluxes to be added together, after they have been calculated, based on\ndeposition velocity and rainfall amount as kg N ha-1 yr-1.\n13. Line 146-149. What is the difference between “site average” and “geographic\nannual mean”?\n[Response]: The “site average” is the mean value of atmospheric N deposition at 59\nmonitoring sites. These are not evenly distributed across the region. The number of\nmonitoring sites is higher in the North China Plain and Northeast China (more\npopulous, more economically developed, and more polluted) than in other regions,\nand the lower number of sites in some regions is constrained by electric power and\nlabor. Using “site averages” may overestimate the true level of N deposition across\nChina. Therefore, we calculated the “geographic mean”, which is the average N\ndeposition in each region multiplied by the proportion of the area that each region\naccounts for the entire country, summing the means. This reduces the overestimation\nof the true N deposition flux and “site averages”. The relevant equations are as\nfollows, now included in Materials and Methods.\n𝐹 = 𝐹𝑑 +𝐹𝑏\n𝑆𝑖𝑡𝑒 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑁 𝑑𝑒𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛\n= (𝐹𝑁𝐶1+𝐹𝑁𝐶2 +⋯+𝐹𝑆𝐸1 +𝐹𝑆𝐸2 +⋯+𝐹𝑆𝑊1 +𝐹𝑆𝑊2 +⋯+𝐹𝑁𝑊1\n+𝐹𝑁𝑊2 +⋯+𝐹𝑁𝐸1 +𝐹𝑁𝐸2 +⋯+𝐹𝑇𝑃1 +𝐹𝑇𝑃2 +⋯)/59\n𝐺𝑒𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑁 𝑑𝑒𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛\n𝐴𝑁𝐶 𝐴𝑆𝐸\n= [(𝐹𝑁𝐶1+𝐹𝑁𝐶2 +⋯)/( )]+[(𝐹𝑆𝐸1 +𝐹𝑆𝐸2 +⋯)/( )]\n960 960\n𝐴𝑆𝑊 𝐴𝑁𝑊\n+[(𝐹𝑆𝑊1 +𝐹𝑆𝑊2 +⋯)/( )]+[(𝐹𝑁𝑊1 +𝐹𝑁𝑊2+⋯)/( )]\n960 960\n𝐴𝑁𝐸 𝐴𝑇𝑃\nrepresents t+ota[l( 𝐹N𝑁 𝐸1de+po𝐹s𝑁it𝐸i2on+ a⋯t )s/it(e NC)i], +an[d(𝐹 𝑇𝑃 1re+pr𝐹e𝑇s𝑃e2nt+s ⋯the) /a(rea o)f] that\n960 960\n𝐹 p𝑁ar𝐶t𝑖icular region i. 𝐴𝑖\n14. Line 162-166. What’s the point of particularly dividing the 10 years into three\nstages?\n[Response]: Please see our response to Reviewer #1's Question #5.\n15. Line 171-173. How is this conclusion reached? The impact of long-term emission\ncontrol is deemed equally significant as short-term actions.\n[Response]: We want to emphasize the greater effectiveness of long-term policies\ncompared to the quick rebound of pollutant concentrations under short-term measures.\nTo avoid confusion, we have revised the text as: “Compared to immediate, effective\nbut short-lived short-term pollution control measures, long-term policies take more\ntime to have an impact, but their effects are more permanent.”.\n16. Line 207-211. How is this conclusion reached? How does the author define “NH3-\nrich atmospheric environment”? why “especially for non-point source emissions”?\nThere is no specific statement about the contribution of non-point source emissions\nhere.\n[Response]: Thank you for pointing it out. The “NH -rich atmospheric environment”\n3\nhere represents a relatively high concentration of NH in the atmosphere. We added\n3\nthe categorization of “NH -rich” or “NH -poor” to further quantify the degree of\n3 3\n“NH -richness”6. The formula is as follows:\n3\n−\n𝑓𝑟𝑒𝑒 𝑎𝑚𝑚𝑜𝑛𝑖𝑎 𝑇𝐴−𝐷𝑆𝑁×𝑇𝑆 [𝑁𝐻3](𝑔)+[𝑁𝑂3](𝑝)\n𝐴𝑑𝑗𝐺𝑅 = = = −\n𝑡𝑜𝑡𝑎𝑙 𝑛𝑖𝑡𝑟𝑎𝑡𝑒 𝑇𝑁 [𝑁𝑂3 ](𝑝)+[𝐻𝑁𝑂3](𝑔)\n+ −\n[𝑁𝐻4](𝑝)−[𝑁𝑂3](𝑝)\n𝐷𝑆𝑁 = 2−\n[𝑆𝑂4 ](𝑝)\n+\n𝑇𝐴 = [𝑁𝐻3](𝑔)+[𝑁𝐻4](𝑝)\n−\n𝑇𝑁 = [𝐻𝑁𝑂3](𝑔)+[𝑁𝑂3](𝑝)\n2−\nThe concentration unit of𝑇 g𝑆a=seo[u𝑆s𝑂 2a]n(d𝑔 )pa+rt[ic𝑆u𝑂l4ate] (p𝑝o)llutants is µmol m-3. Where\nof <1 indicates an NH -poor regime, indicating that small perturbations in\n𝐴𝑑𝑗3𝐺𝑅\nindicates NH -rich conditions that have a free NH ratio capable of neutralizing\n3 3 𝐴𝑑𝑗𝐺𝑅\nexcess HNO produced by the additional increase in NOx emission. The national\n3\naverage value of is 3.24, which is more than 1, so we consider that our\ncountry is generally NH -rich atmospheric environment.\n𝐴𝑑𝑗𝐺𝑅3\n17. Line 255-257. How is this conclusion reached? Why “the increase in\nNH3+pNH4+ concentrations from 2018 may be attributed to acid gas emission\nreductions, despite a reduction shown in the NH3 emission inventory for this region.”\n[Response]: The increase of NH concentrations in the atmosphere can be attributed\n3\nto two main factors. On the one hand, it may be due to a direct increase in NH source\n3\nemissions. On the other hand, it may be a consequence of reduced emissions of acid\nprecursors (SO , NO ), leading to fewer neutralization reactions with NH and so\nmore remaining of gaseous NH in the air. Both scenarios are reasonable contributors\n3\nto rising atmospheric NH levels. In southwest China, emission inventories have\n3\nshown a decreasing trend in atmospheric NH emissions (MEIC, declined by 10%\n3\nfrom 2017 to 2020). However, there has been an observed increase in NH\nX\nconcentrations. Consequently, it is concluded that the reduction in NH emissions is\n3\nrelatively minor and insufficient to offset the concentration increase caused by the\ndecreased acidic gas emissions (SO emission declined by 36%, NO emission\ndeclined by 11%, and the decrease in observed oxidized N concentrations in this study\nis also evidence of a reduction in acidic gas emissions). Similarly, Liu et al. (2021)7\nfound that the considerable decrease of atmospheric nitric acid concentrations in\nSouthern China resulted in an increase of gaseous NH concentrations by 8.0% and\n3\nconsequently enhanced NH dry deposition.\n3\nthe Southwest region shows a 10% decrease from 2017-2020. Therefore, an observed\nincrease in atmospheric NH +pNH + concentrations may be attributed to a decrease in\n3 4\nacid gas emissions (SO emission declined by 36%, NO emission decline by 11%)\nand fewer neutralization reactions, resulting in more NH present in the atmosphere.”.\n3\n18. Line 274-276. How is this conclusion reached? How does the author calculate the\n“transboundary export of pollution”?\n[Response]: For the quantification of pollution transport between regions, we assume\nthat Nr emissions and deposition in China are balanced, with no pollutants being\ntransported to China from other countries. The emission-to-deposition process\nbetween regions is simplified: all pollutants will preferentially be deposited in their\nregion and is the difference between the provincial Nr emissions and N\ndeposition. If is positive, Nr emissions > N deposition and the residual\n𝑇𝑟𝑎𝑛\npollutants will be transported to other regions. If is negative, Nr emissions < N\n𝑇𝑟𝑎𝑛\ndeposition and all pollutants are deposited in their region. We have added a\n𝑇𝑟𝑎𝑛\ndescription of this assumption to Methods.\n19. Line 296. Why “improvements in air quality could be negated by climate change”?\n𝑇𝑟𝑎𝑛 = 𝑁𝑟 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛−𝑁 𝑑𝑒𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛\nFollowing the author's reasoning, with climate change leading to increased\nprecipitation, the concentration of pollutants in the air should theoretically decrease. If\nthis holds true, could climate change potentially contribute to improved air quality?\n[Response]: The relationship between air quality and climate change is not simple.\nFirst, climate change includes extreme precipitation, consisting of droughts and\nstorms. Focusing on precipitation, heavy rainfall contributes to the removal of\npollutants from the air, whereas droughts may lead to an increase in pollutant\nconcentrations. On the other hand, the reduction in the concentration of\nparticulates/aerosols in the atmosphere is followed by more solar radiation being\ncaptured by the surface (reduced reflectivity)8. The accelerated emission due to the\nwarming effect is probably an important contributor to the increased pollution.\nOverall, substantial emission reductions are needed to offset the effects of global\nwarming.\nReviewer #3 (Remarks to the Author):\nThis manuscript aimed to provide options for short-term and long-term emission\ncontrols in China. This is important to reduce atmospheric pollutant emissions\nsustainably. However, several aspects should be explained more carefully before the\npaper can be considered for publication. Some suggestions are offered below:\n[Response]: We thank the reviewer for the comments. Below we provide a point-by-\npoint response to the reviewer’s comments and proposed changes in the revised\nmanuscript (in blue).\nGeneral comments:\n1. It is logical that short-term emergency control measures can quickly improve air\nquality, while long-term policies take longer to take effect, but are more sustainable.\nhow does your analysis differ from others? This manuscript should present novel\nfindings in this aspect. The analysis of the deposition trends of Nr and NH3 appears to\nbe novel, more discussions should be paid on the effect of short and long-term\ncontrols in China.\n[Response]: Most previous studies have demonstrated either the effectiveness of\nshort-term pollution control measures or long-term emission reduction policies.\nHowever, the issue of pollution rebound from short-term measures, and the\ncontribution of short-term measures to long-term emission reductions has been little\ndiscussed. Firstly, in the short term, where the improvement of air pollution is\ntemporary and the economic costs are extremely high, is it necessary to implement\nshort-term abatement measures only to safeguard national image? Secondly, most\nstudies on the effectiveness of long-term policies concentrate on trends in PM and\n2.5\nprecursor concentrations. In addition to the much-discussed PM pollution, we have\n2.5\nincluded N deposition as an indicator, which includes air quality information as\nconcentrations of gaseous, particulate Nr in air, and Nr in precipitation, and the\necological impacts of surface N inputs via atmospheric deposition. This provides a\nmore comprehensive understanding of the future challenges that need to be addressed\nfor better air quality and eco-environmental protection.\nCombining our responses to Reviewer #3 with those of Reviewer #1 and Reviewer\n#2’s suggestions, we have added relevant details to highlight the novelty of the article.\nFirst, we clarify the research objectives of this article in the Introduction, i.e., “To\nensure “blue skies”…”, “In order to sustain “blue skies”, the Chinese government has\nissued a series of policies aimed at long-term reductions in pollutant emissions (i,e,\nthat should persist for years) …”, “…and uncovers hidden challenges in creating\neffective improvements in air quality”, “PM concentrations can characterize air\n2.5\npollution variations at a daily scale, responding rapidly to changes in strong emission\nor meteorological conditions. Atmospheric N deposition, as a sink for Nr emissions,\ncan be used as an indicator to evaluate the degree of Nr pollution at the\nmonthly/yearly scale.”.\nSecond, we further quantify the net effect of short-term and long-term emission\nreductions (please see response to Reviewer #1, Question #1). In brief, our study\nreveals that short-term measures have negligible effects on the slope of long-term\nemission reductions based on a Difference in Differences (DID) analysis. Short-term\nmeasures are more important in demonstrating the potential for emission reduction\nand enhancing people's confidence in the likely effectiveness of long-term emission\nreductions. Furthermore, short-term measures were virtually unaffected by long-term\nemission reduction. Additionally, we have expanded the discussion on how short-term\nmeasures and long-term policies can be integrated, covering the coupling of emission\nreduction targets, the synergy between different precursors, and the economic costs of\nregional transfers, to highlight the theme of the paper.\nThird, short-term measures have an insignificant impact on N deposition on a\nmonthly scale, as N deposition comprises 7 Nr species and is the combination of the\ndeposition velocity and Nr concentrations in gaseous form, and the rainfall amount\nand Nr concentrations in precipitation. Consequently, our article focuses on the day-\nscale variations in PM , SNA, and precursor concentrations, as well as the annual\n2.5\ntrends in N deposition.\n2. In this manuscript, discussions on short-term controls mostly focused on North\nChina. Other regions like Yangtze River Delta, Fenwen Plain should be added,\nbecause there were a lot of short-term controls (for example 2016 G20 Hangzhou\nsummit, and many Regional Air Quality Warnings).\n[Response]: Thank you for this suggestion. Due to the limitations of the monitoring\ndata, we were unable to cover all short-term emission reductions, but we have added\nsome text to the discussion: “Similarly, a decrease of 12% in PM and 12% in SNA\n2.5\nconcentrations was observed for the Hangzhou G20 Summit compared to the period\nbefore aggressive mitigation measures were introduced. However, after the various\ncontrol measures were lifted, all air pollutant concentrations increased back to initial\nlevels9. During the 2022 Beijing Winter Olympics, the intense emission controls\nsignificantly reduced PM and SNA concentrations, with source control measures\n2.5\ncontributing 54% to the total reduction in PM concentrations10.”.\n2.5\n3. I am puzzled about why the author chose to discuss PM2.5 in summer instead of\nautumn or winter (Figure 1e). After all, China's control measures are more focused on\nwinter, and the most significant effects are also seen in winter. The author should\nseparately discuss the responses of PM2.5 and N deposition in autumn and winter to\nshort-term early warning control and long-term policies.\n[Response]: Combining with Reviewer #2’s suggestions, we have added a description\nof winter PM pollution, please see our response to Reviewer #2’s Question #1.\n2.5\nWe also added the text in the revised paper: “In contrast to PM concentrations,\n2.5\nmonthly averaged atmospheric N deposition was significantly higher in summer than\nin winter by 32% (Figure S1). However, similar to PM concentrations, under the\n2.5\ninfluence of long-term emission reductions, the alleviation of summer pollution is\ngreater than that in winter. Winter air quality clearly needs to be improved.”.\nFigure S1 Average monthly values of atmospheric N deposition in winter and\nsummer from 2011 to 2020.\nspecific comments:\n1. Line 88-101 the difference between pre-, during and post Olympic, APEC et al, is\ndifficult to describe the effect of short-term measures. This because that the\nmeteorology is likely different, which is very important to short-term air quality.\n[Response]: Thank you for pointing this out. We have added the relevant discussion:\n“Meteorological conditions need to be taken into account to determine the actual\nimpact of the control measures on pollution mitigation, which is important for short-\nterm case studies. Liu et al.11 showed that emission control measures during the APEC\nreduced PM concentrations in Beijing by an average of 41%, and by more than 50\n2.5\nμg m-3 under unfavorable meteorological conditions. At the time of the Parade and\nPre-Parade, the integrated analyses of modeling and monitoring demonstrate that\npollutant emission reduction measures are the main contributor to this as there were\nno significant differences in the two periods between the temperature, wind speed,\nand relative humidity12.”.\n2. Figure 1, Why did the NH3 concentrations seems no difference during Olympics,\nAPEC and BRs compared to post-APEC and BRs. In urban areas, NH3 largely came\nfrom mobiles and industries, which were reduced during these campaigns.\n[Response]: Partly correct. During the event, the concentration of atmospheric NH\n3\ndid not show a significant decreasing or increasing trend, whereas the amount of\naerosol NH + was reduced by more than 50%, and the overall NH concentration\ndecreased, reflecting the impact of emissions source changes on NH . The\n3\ninsignificant variation in atmospheric NH can be attributed to two causes. Firstly, the\n3\nbackground level of atmospheric NH emissions is relatively high in the North China\nthere is an excess of atmospheric NH . Even though some NH has been converted to\n3 3\naerosol NH + through reactions with acidic gases, a large amount of NH still exists in\n4 3\nthe atmosphere in gaseous form, resulting in stable atmospheric NH concentrations.\nmonths, and most studies indicate that agriculture remains the main source of NH in\natmospheric NH in summer originates from agriculture, and about 20%-30% from\n3\nsewage treatment13,14. Therefore, even if transportation and industrial activities were\nsuspended during an event, there would still be the other dominant sources of NH .\nconcentration of atmospheric NH in Beijing in winter still increased, highlighting the\n3\nimportance of transported agricultural and human sources for urban NH .\n3\n3. Is the landuse changed at sites in Table S1 since 2010-2017? It is key to calculate\nthe deposition velocity in the Formula 1 and analyze its trends.\n[Response]: Land use types at sites in Table S1 did not change from 2011-2020. The\nGEOS (Goddard Earth Observing System)-Chem chemical transport model (CTM;\nhttp://geos-chem.org) was used to simulate the V values of five Nr species (gases\nd\nNH , NO , HNO , and aerosols pNH +, pNO -) every hour at each monitoring site\n3 2 3 4 3\nfrom 2011 to 2020. Hourly V values were averaged to obtain a monthly V , which\nd d\nwas multiplied by the monthly species concentration to estimate the dry deposition\nflux. The V values of gaseous NH , NO , HNO and particulate NH + and NO - all\nd 3 2 3 4 3\nshowed no significant differences from 2011 to 2020 (p>0.05) (Figure S8). Therefore,\ncalculated trends in dry deposition are not a function of V , but follow the measured\nd\nNr concentrations in air.\nFigure S8 Variations in the dry deposition velocities of Nr species (a: NH , b: HNO ,\n3 3\nc: NO , d: pNH +, e: pNO -) at sites within the National Nitrogen Deposition\n2 4 3\nMonitoring Network (NNDMN) from 2011 to 2020.\n4. Line 141, this manuscript discussed the long-term trends of N deposition. The short\neffects are suggested to be added. Discussing only the concentration of PM2.5 and its\nprecursors is not enough to fully evaluate the short-term impact. Many other studies\nhave done so.\n[Response]: Thank you for this suggestion. Please see our response to Question #1\nwhere we explain our response and our enhanced discussion of short-term measures.\nReference\n1. Zhang Q, et al. Drivers of improved PM air quality in China from 2013 to 2017. Proceedings of\n2.5\nthe National Academy of Sciences of the United States of America116, 24463-24469 (2019).\n2. Gu, B. et al. Cost-effective mitigation of nitrogen pollution from global croplands. Nature 613,\n77-84 (2023).\n3. Xu W, et al. Increasing importance of ammonia emission abatement in PM pollution control.\n2.5\nScience bulletin67, 1745-1749 (2022).\n4. Liu M, et al. Ammonia emission control in China would mitigate haze pollution and nitrogen\ndeposition, but worsen acid rain. Proceedings of the National Academy of Sciencesof the United\nStates of America116, 7760 (2019).\n5. Cao S, Nie L, Sun H, Sun W, Taghizadeh-Hesary F. Digital finance, green technological\ninnovation and energy-environmental performance: Evidence from China's regional economies.\nJournal of Cleaner Production327, 129458 (2021).\n6. Pinder RW, Dennis RL, Bhave PV. Observable indicators of the sensitivity of PM nitrate to\n2.5\nemission reductions—Part I: Derivation of the adjusted gas ratio and applicability at regulatory-\nrelevant time scales. Atmospheric Environment42, 1275-1286 (2008).\n7. Liu M, et al. Unexpected response of nitrogen deposition to nitrogen oxide controls and\nimplications for land carbon sink. Nature Communications13, 3126 (2022).\n8. Wang P, et al. Aerosols overtake greenhouse gases causing a warmer climate and more weather\nextremes toward carbon neutrality. Nature Communications14, 7257 (2023).\n9. Ji Y, et al. Counteractive effects of regional transport and emission control on the formation of\nfine particles: a case study during the Hangzhou G20 summit. Atmospheric Chemistry and\nPhysics18, 13581-13600 (2018).\n10. Liu Y, Xu X, Yang X, He J, Ji D, Wang Y. Significant Reduction in Fine Particulate Matter in\nBeijing during 2022 Beijing Winter Olympics. Environmental Science & Technology Letters 9,\n822-828 (2022).\n11. Liu H, et al. The blue skies in Beijing during APEC 2014: A quantitative assessment of emission\ncontrol efficiency and meteorological influence. Atmospheric Environment167, 235-244 (2017).\n12. Xu W, et al. Air quality improvement in a megacity: implications from 2015 Beijing Parade Blue\npollution control actions. Atmospheric Chemistry and Physics17, 31-46 (2017).\n13. Li Y, et al. Apportioning Atmospheric Ammonia Sources across Spatial and Seasonal Scales by\nTheir Isotopic Fingerprint. Environmental Science & Technology57, 16424-16434 (2023).\n14. Zhang Y, Benedict KB, Tang A, Sun Y, Fang Y, Liu X. Persistent Nonagricultural and Periodic\nAgricultural Emissions Dominate Sources of Ammonia in Urban Beijing: Evidence from 15N\nStable Isotope in Vertical Profiles. Environmental Science & Technology54, 102-109 (2020).\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\n1.The paper aims to enhance the credibility of the effecfiveness of short-term and long-term emission\nreducfion strategies. To further quanfify the impact of short-term measures on pollutant concentrafions,\nthe author employs the Difference-in-Differences (DID) method. To bolster the credibility of the\nconclusions drawn from the DID model and demonstrate that long-term measures remain unaffected by\nshort-term emission reducfion efforts, it is essenfial to present the constructed DID model. This should\nencompass details on the treatment group, control group, selecfion of control variables, chosen\nexogenous shocks related to short-term measures, and a discussion on potenfial endogeneity issues.\nPlease elaborate further.\n2.Figure 5: Is this the complete version of the nafional map?\n3.Lines 409-412: It menfions that current air quality control measures, focused on end-of-pipe treatment\nand clean producfion, are conducive to achieving air pollufion emission control targets but carry the risk\nof suppressing reducfions in CO2 emissions. I don’t quite understand why air quality control measures\ncould pose a risk of suppressing reducfions in CO2 emissions. The author cites two references here, and\nbased on Reference 41, the conclusion is that the contribufion of low-carbon policies to improving air\nquality is greater than end-of-pipe control. Addifionally, Reference 41 menfions that since CO2 as an air\npollutant shares sources with emissions, climate policies can reduce pollutant emissions, improve air\nquality, and simultaneously reduce carbon emissions. It seems that this does not lead to the conclusion\nof \"the risk of suppressing reducfions in CO2 emissions\". Please explain.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my comments well.\nReviewer #3 (Remarks to the Author):\nThe revised manuscript responded to most of my previous concerns, and seems to have made significant\nimprovements. However, Minor comments are recommended before the publicafion.\n1. The paper invesfigates the impact of meteorological fields on short-term PM2.5 through citafions of\nprior research. Nevertheless, I believe that this examinafion is inadequate. studies to disfinguish\nbetween short-term controling measures and meteorological fields should be fully discussed.\n2. This manuscript employed the GEOS-Chem to esfimate the dry deposifion and revealed the absence\nof any notable pafterns.The authors note that the land use representafion in GEOS-Chem remains\nunchanged, contradicfing real situafion. I proposed at least a brief discussion on the impact of land use\nchanges over the past ten years.\nDear Editor:\nPlease find below our itemized responses to the reviewer’s comments. We have\naddressed all comments raised by three reviewers and incorporated their\ncomments/suggestions in the revised manuscript.\nThank you very much for your consideration.\nSincerely,\nXuejun Liu\nOn behalf of all co-authors\nReviewer #1 (Remarks to the Author):\n1. The paper aims to enhance the credibility of the effectiveness of short-term and long-\nterm emission reduction strategies. To further quantify the impact of short-term\nmeasures on pollutant concentrations, the author employs the Difference-in-Differences\n(DID) method. To bolster the credibility of the conclusions drawn from the DID model\nand demonstrate that long-term measures remain unaffected by short-term emission\nreduction efforts, it is essential to present the constructed DID model. This should\nencompass details on the treatment group, control group, selection of control variables,\nchosen exogenous shocks related to short-term measures, and a discussion on potential\nendogeneity issues. Please elaborate further.\n[Response]: We have employed the DID method to quantitatively examine whether and\nto what extent the short-term emission control measures influence the long-term\npolicies. DID recognizes the NC1(Beijing) as the treatment group, while the other sites\nare regarded as the control group. Differences for each group in PM concentrations,\n2.5\noxidized N deposition, reduced N deposition during APEC, and Parade (emissions data\nwere lacking of Olympics events, and limited data available after the implementation\nof short-term measures during the BRS period and therefore did not take part in the\nassessment) when compared before and after the implementation of the short-term\nmeasures are then calculated. According to the result of Random Forest (in the\nMaterials and Methods), the important variables were selected as emission data (SO\n2\nemission, NO emission, NH emission), meteorological parameters (2 m temperature,\nX 3\natmospheric pressure, 10 m wind speed, boundary layer height) and NDVI. The\ndifference in the abovementioned differences thus represents the net effect on PM\n2.5\nconcentration, oxidized N deposition, and reduced N deposition brought by the short-\nterm measures, and statistically significant differences were set at p values < 0.05. The\nDID method requires there to be a similarity between the treatment group and the\ncontrol group. In response to this, parallel trends were tested by the event analysis\nmethod, with p values > 0.05 indicated pass the test. All actions were performed in Stata\nMP version 18.\n2. Figure 5: Is this the complete version of the national map?\n[Response]: Thank you for pointing this out. Corrected.\nFigure 5 Annual average PM -SNA concentrations and N deposition in China in 2017,\n2.5\n2030 and 2060, using the WRF-EMEP model under the most stringent policy\nimplementations and rising temperatures and increased extreme precipitation\n(RCP4.5_2030, RCP4.5_2060) in 2030 and 2060.\n3. Lines 409-412: It mentions that current air quality control measures, focused on end-\nof-pipe treatment and clean production, are conducive to achieving air pollution\nemission control targets but carry the risk of suppressing reductions in CO2 emissions.\nI don’t quite understand why air quality control measures could pose a risk of\nsuppressing reductions in CO2 emissions. The author cites two references here, and\nbased on Reference 41, the conclusion is that the contribution of low-carbon policies to\nimproving air quality is greater than end-of-pipe control. Additionally, Reference 41\nmentions that since CO2 as an air pollutant shares sources with emissions, climate\npolicies can reduce pollutant emissions, improve air quality, and simultaneously reduce\ncarbon emissions. It seems that this does not lead to the conclusion of \"the risk of\nsuppressing reductions in CO2 emissions\". Please explain.\n[Response]: Previous studies have shown that end-of-pipe treatment has drastically\nreduced the emissions of air pollutants in the past years, while have not achieved the\nco-benefits of reduced carbon emissions1, 2. We have revised the sentence from “risks\ninhibiting CO emission reductions” to “may not be able to reach the synergistic\n2\nobjectives of air quality improvement and climate change mitigation”. Replace\nreference #41 with “Cheng J, et al. A synergistic approach to air pollution control and\ncarbon neutrality in China can avoid millions of premature deaths annually by 2060.\nOne Earth 6, 978-989 (2023)”3.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed my comments well.\n[Response]: We thank the reviewer's recommendation for publication.\nReviewer #3 (Remarks to the Author):\nThe revised manuscript responded to most of my previous concerns, and seems to have\nmade significant improvements. However, Minor comments are recommended before\nthe publication.\n1. The paper investigates the impact of meteorological fields on short-term PM2.5\nthrough citations of prior research. Nevertheless, I believe that this examination is\ninadequate. Studies to distinguish between short-term controlling measures and\nmeteorological fields should be fully discussed.\n[Response]: We have added the analysis and description of four event meteorological\nparameters, including temperature, precipitation, atmospheric pressure, relative\nhumidity, wind speed, and direction. Now state that “Air pollutant concentrations were\nmostly significantly negatively linked with wind speed, precipitation, and relative\nhumidity, but have positive correlation with atmospheric pressure. The effect of\ntemperature on air pollution has high uncertainty4. In this study, some of the\nmeteorological conditions were favorable for the air pollutants dispersion during the\nevent, such as the occurrence of precipitation during the Parade and stronger northerly\nwinds during the APEC. However, emissions reduction measures remained the most\nimportant driver of pollution mitigation. Higher temperatures, elevated relative\nhumidity, lower atmospheric pressure, and frequent southerly winds were ineffective in\nhalting significant decreases in PM concentration during the Olympics compared to\n2.5\nthe pre- and post-Olympics periods.”.\nFigure S1 Temperature (a), precipitation (b), atmospheric pressure (c), relative\nhumidity (d) during the Olympics, APEC, Parade, and BRS.\na\nb\nc\nd\nFigure S2 Wind speed and direction during the Olympics (a), APEC (b), Parade (c),\nand BRS (d), denoting three subperiods of the pre-event (left), event (middle), and post-\nevent (right), respectively.\n2. This manuscript employed the GEOS-Chem to estimate the dry deposition and\nrevealed the absence of any notable patterns. The authors note that the land use\nrepresentation in GEOS-Chem remains unchanged, contradicting real situation. I\nproposed at least a brief discussion on the impact of land use changes over the past ten\nyears.\n[Response]: The trend variation of dry deposition is primarily influenced by the\natmospheric Nr concentration ( ) and deposition velocity ( ), as expressed in the\nfollowing formula: 𝐶𝑑 𝑉𝑑\nThe is obtained through samp\n𝐹\nl𝑑e\n=\nco\n𝐶\nll𝑑ec\n×\ntio\n𝑉\nn𝑑, measurement and analysis, and\nis simul\n𝐶\na𝑑ted via the GEOS-Chem model. We found decreased by 19% from 201\n𝑉\n2𝑑\nto 2020, while exhibited no significant ch\n𝐶\na𝑑nge (see previous response).\nConsequently, the d\n𝑉\nr𝑑y deposition decreased by approximately 19% during this period.\nTherefore, we infer that this temporal variation in dry deposition is determined by\nchanges in atmospheric Nr concentration.\nStability in land use type and underlying surface is the main reason for the\ninsignificant trend in . For example, at NC9, a rural site in Hebei province, the land\nuse type has always be\n𝑉\ne𝑑n agricultural. The wheat-maize rotation system has remained\nstable for many years, although recent improvements in agricultural technology may\noccurred due to economic development, such as changes in fertilization rates and field\nmanagement practices.\nWe have added a brief discussion: “Innovations in industrial or agricultural\ntechnology could directly alter the emissions and concentrations of Nr. While the\nstability of land use types maintains the resistance of gases to capture by surface plants\nor soil and , thereby emphasizing the key impact of Nr concentration on the trend of\ndry depositi\n𝑉\no𝑑n in this study.”.\nReference\n1. Lei Y, et al. The 2022 report of synergetic roadmap on carbon neutrality and clean air\nfor China: Accelerating transition in key sectors. Environmental Science and\nEcotechnology19, 100335 (2024).\n2. Zhang Q, et al. Drivers of improved PM air quality in China from 2013 to 2017.\n2.5\nProceedings of the National Academy of Sciences of the United States of America116,\n24463-24469 (2019).\n3. Cheng J, et al. A synergistic approach to air pollution control and carbon neutrality in\nChina can avoid millions of premature deaths annually by 2060. One Earth6, 978-989\n(2023).\n4. Liu Y, Zhou Y, Lu J. Exploring the relationship between air pollution and\nmeteorological conditions in China under environmental governance. Scientific\nReports10, 14518 (2020).\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have addressed my comments well."}]

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