key: cord-0694646-771n4y6c
authors: Dong, Changgui; Li, Jiaying; Qi, Ye
title: Decomposing PM(2.5) air pollution rebounds in Northern China before COVID-19
date: 2022-01-06
journal: Environ Sci Pollut Res Int
DOI: 10.1007/s11356-021-17889-2
sha: 64f4c78438bb4bc99fc0741d43120be08f2fe707
doc_id: 694646
cord_uid: 771n4y6c

China’s efforts to curb air pollution have drastically reduced its concentrations of fine particulate matter (PM(2.5)) from 2013 to 2018 nationwide. However, few studies examined the most recent changes in PM(2.5) concentrations and questioned if the previous PM(2.5) declining trend was sustained or not. This study took a deep dive into the PM(2.5) trend for 136 northern cities of China from 2015 to early 2020 before the coronavirus disease 2019 (the COVID-19 hereafter) crisis, using ground-based PM(2.5) data notably adjusted for a key measurement method change. We find that mean PM(2.5) concentrations in northern China increased by 5.16 µg/m(3) in 2019, offsetting 80% of the large reduction achieved in 2018. The rebound was more significant during the heating seasons (HS; Nov to next Mar) over the 2 years: 10.49 µg/m(3) from the 2017 HS to the 2019 HS. A multiple linear regression analysis further revealed that anthropogenic factors contributed to around 50% of the PM(2.5) rebound in northern cities of China. Such a significant role of anthropogenic factors in driving the rebound was tightly linked to deep cuts in PM(2.5) concentrations in the previous year, systemic adjustment of policy targets and mitigation measures by the government, and the rising marginal cost of these measures. These findings suggest the need to chart a more sustainable path for future PM(2.5) emission reductions, with an emphasis on key regions during key pollution periods. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-021-17889-2.

Air pollution is a key environmental threat to human health worldwide (Cohen et al. 2017; Burnett et al. 2018) . In China, long-term exposure to fine particulate matter (PM 2.5 ) may have caused 1.5 to 2 million premature deaths annually (Liang et al. 2020) , prompting the government to place air pollution control as a top priority in its policy agenda. Since 2013, the central government has promulgated two largescale national action plans to tackle the PM 2.5 air pollution problem: first the 2013-2017 Air Pollution Prevention and Control Action Plan (the 2013-2017 Action Plan hereafter) with ambitious reduction targets set to be achieved by the end of 2017, and then the Three-year Action Plan to Win the Blue-Sky Defense War (2018-2020) as a spinoff (Feng et al. 2019; Li et al. 2020b; Jiang et al. 2021 ). These two national movements together with stringent enforcement have reduced PM 2.5 concentrations and improved the air quality greatly (Zheng et al. 2018; Ma et al. 2019; Editorial 2019) , and the annual average PM 2.5 concentrations declined by 34-49% across China from 2013 to 2018 (Zhang et al. 2019b) .

However, few studies have examined the post-2018 changes in PM 2.5 concentrations in China in detail (Zhai et al. 2019; Chen et al. 2020; Bae et al. 2021 ) (see Table 3 for more detail), or questioned if PM 2.5 concentration levels had continued to decline after the successful experience by the Action Plan (Yin and Zhang 2020; Zhong et al. 2021) .

One recent study on the PM 2.5 pollution in Beijing-Tianjin-Hebei and the surrounding areas (the so-called "2 + 26" cities) estimated that PM 2.5 concentration levels increased by 6.8 µg/m 3 (9.46%) in the winter of 2018 (Dec-Feb). More recently, a few studies discovered that PM 2.5 air pollution also deteriorated during the coronavirus disease 2019 (the COVID-19 hereafter) epidemic in early 2020 (Wang et al. 2020a; Le et al. 2020 ). However, one big issue with these studies was that such a rebound in PM 2.5 air pollution was more than 100% attributed to meteorological conditions, and human efforts continued to reduce PM 2.5 concentrations (Yin and Zhang 2020) . These results and conclusions run contrary to the observation that government policies and actions may have been relaxed after the great success in 2017 (Ministry of Ecology and Environment of China 2017 China , 2018 China , 2019 . According to the official statistics (Ministry of Ecology and Environment of China 2020), the national average PM 2.5 concentration levels in 2019 stayed the same as that in 2018, both at 36 µg/m 3 , pointing to a strong possibility of a reversed trend for at least some regions.

Our study aims to reassess the changes in China's PM 2.5 pollution from 2018 to early 2020 before the outbreak of COVID-19 based on a large sample of cities with adjusted PM 2.5 data and decompose the relative contributions of the meteorological and anthropogenic factors to the recent changes. The contributions of our study are threefold: First, by focusing on the most polluting areas in China with district heating in the north of the Huai River (Ebenstein et al. 2017 ), we expand the study area to 136 cities in 15 heating provinces in northern China from 2015 to early 2020 right before the COVID-19 crisis. Second, we adjust the PM 2.5 concentration data following the official monitoring method change for comparison consistency (see "Materials and methods" for more detail). Third, we apply a stepwise multiple linear regression (MLR) analysis (Li et al. 2019a (Li et al. , 2020a Zhai et al. 2019; Chen et al. 2020; Bae et al. 2021) to decompose PM 2.5 changes into contributions from meteorological and anthropogenic factors. We further relate the human factor contributions to actual policy adjustment and the rising marginal cost of these policy measures.

We used data of 136 northern cities with central heating in China. These cities spread over 15 northern provinces: Beijing, Tianjin, Hebei, Shanxi, Heilongjiang, Jilin, Liaoning, Inner Mongolia, Shandong, Henan, Shaanxi, Ningxia, Gansu, Qinghai, and Xinjiang (Fig. 1) . Among those 136 cities, "2 + 26" cities were the most polluted area in China, and the targeted region by the Ministry of Ecology and Environment to mitigate PM 2.5 pollution in February 2017 (Ministry of Ecology and Environment of China 2017); other northern cities were not targeted at this time. Given the heterogeneity in these two regions, below we analyze the changes in PM 2.5 concentrations of "2 + 26" cities and other northern cities separately.

The data source for ground-based PM 2.5 concentrations is an online platform (https:// www. aqist udy. cn/ histo rydata/), which has been used by several studies before Zhu et al. 2020) . This data source collects real-time data of national monitoring sites from the Ministry of Ecology and Environment of China (https:// www. mee. gov. cn/ hjzl/) and then calculates monthly mean PM 2.5 concentrations for each city in China. For the purpose of this research, we collected monthly PM 2.5 concentrations data for each northern city from 2015 to early 2020 before the COVID-19 crisis.

The biggest challenge to use ground-based PM 2.5 concentrations data is that the Ministry of Ecology and Environment changed its monitoring method of PM 2.5 concentrations in September 2018 (Li et al. 2020a) , so that PM 2.5 concentration data are not directly comparable before and after September 2018. According to the Amendment for Ambient Air Quality Standards (hereafter referred to as "the Amendment"), the local ambient state instead of the standard state (273 K, 1013 hPa) should be used during the monitoring process of PM 2.5 concentrations before this Amendment. The local ambient state refers to the real-time condition of monitoring stations, i.e., real-time temperature and surface pressure. Thus, to achieve a consistently measured time series of PM 2.5 concentrations, we followed the literature (Laugier and Garai 2007; Li et al. 2020a ) and applied the Ideal Gas Equation of State to adjust pre-September 2018 PM 2.5 concentration data:

where PM adj and PM obs are PM 2.5 concentrations with and without adjustment, respectively; T is the real-time temperature, and Pressure is the real-time surface pressure. Since the temperature and surface pressure should be roughly the same within a city, we directly applied the above adjustment to our city-level PM 2.5 concentration data.

We integrated meteorological data from several sources. Meteorological variables were mainly from Airwise, an online data platform (http:// hz. zc123 69. com/ home) that has been used by previous research (Xu et al. 2020; Li et al. 2020b ). The original data source of Airwise is the groundbased data from the National Meteorology Center (NMC) of (1) PM adj = PM obs × 273+0 273+T × Pressure China (http:// www. nmc. cn/). This platform collects the realtime data of national sites from NMC and provides monthly meteorological data for 137 northern cities from 2015 to 2020. We used six meteorological variables including mean temperature, humidity, wind speed, surface pressure, total precipitation, and total cloud cover. We chose these variables since they are strongly correlated with PM 2.5 concentrations (Xu et al. 2018; Liu et al. 2020) . To fill in possible missing values, we further collected surface pressure data from the China Meteorological Data Service Center (http:// data. cma. cn; last accessed: 2020-10-17) and the National Climate Data Center (https:// quots oft. net/ air/# archi ve; last accessed: 2020-11-25). We also collected three more meteorological variables used in the sensitivity analyses (described below): 850 hPa meridional wind velocity (V850), 10 m meridional wind (V10), and zonal wind (U10), all from MERRA-2 reanalysis by NASA Global Modeling and Assimilation Office (https:// gmao. gsfc. nasa. gov/ reana lysis/ MERRA-2, last accessed: 2021-01-10). The MERRA-2 data have a spatial resolution at 0.5° × 0.625° and we further averaged these data to the city level.

Several studies have applied stepwise MLR models to quantify the relative impacts of meteorological and anthropogenic factors on air pollutant trends (Li et al. 2019a (Li et al. , 2020a Zhai et al. 2019) . We employed the same method to estimate the contributions of human and nature factors to the PM 2.5 rebound. First, we regressed monthly PM 2.5 concentrations on six meteorological variables from February 2017 to January 2020, which are highly relevant to PM 2.5 concentrations.

We specified the following MLR model for each city:

(2) PM adj = 0 + where PM adj is the monthly mean PM 2.5 concentrations after adjusting, X 1 -X 6 are six meteorological variables as described above, and k are the corresponding regression coefficients. We further conducted a series of sensitivity analyses by adding more meteorological variables as well as the second order effect of all variables into the regression. Specifically, we added V850, V10, and U10 into the baseline model. Since V850 has a strong correlation with PM 2.5 concentrations in Northern China, we also ran a MLR model with V850 and our first six meteorological variables (Cai et al. 2017 ). For all model runs at the city level, only the first three key variables based on R-square were kept in the final regression to avoid overfitting (Li et al. 2020a ). These three key variables varied from one city to another (Supplementary Figs. S1-3). The fitted values of the MLR model reflect the contributions from meteorological factors.

To estimate the contributions from anthropogenic factors, we further distilled the nonlinear trend in the residuals from the MLR model. Specifically, we included seven dummy variables in the regression of MLR residuals from the last step:

where Res is the residual from Eq. (2) and T 1 -T 7 are timedummy variables covering the whole study period and indicating whether each month belongs to the heating season or non-heating season in a specific year (e.g., 2017 heating season and 2017 non-heating season, and so on). The regression coefficients k are the anthropogenic contributions for each season.

Annual mean PM 2.5 concentrations in northern cities declined from 2015 to 2018 but rebounded in 2019. Table 1 (3)

shows that the annual mean PM 2.5 concentrations in northern China dropped continuously from 2015 to 2018, with a total effect of 16.56 µg/m 3 . However, this declining trend was reversed in 2019, when PM 2.5 concentrations increased by 5.16 µg/m 3 (14.60%), or 80% of the reduction (6.4 µg/ m 3 ) achieved in 2018. Figure 1c further shows that such a PM 2.5 rebound in 2019 occurred to most cities in northern China, though some of them continued to reduce their PM 2.5 concentrations in 2019, such as Beijing (Li et al. 2020b ). The rebound was even more significant during the heating seasons (HS; Nov to next Mar) when the PM 2.5 pollution was the most severe ( Fig. 1d ). In the 2018 HS, northern cities saw the PM 2.5 concentrations rebounded by 5.71 µg/m 3 (10.80%) after a drastic drop of 16.17 µg/m 3 in the 2017 HS (Table 1) . However, PM 2.5 concentrations declined remarkably in the 2019 HS due to the impact of COVID-19 lockdown (Supplementary Table S1 ) (Huang et al. 2021 ), resulting in a smaller rebound in northern cities during 2017-2019 HS (Supplementary Table S2 ). Given that lockdown only had a short-term impact on PM 2.5 trends (Lu et al. 2021) , we excluded its impacts by removing data in February and March 2020 (post-Feb 2020) from the baseline results. As such, PM 2.5 rebound continued into the 2019 HS, with the PM 2.5 concentrations further increased by 4.79 µg/m 3 (8.1%). These two rebounds increased PM 2.5 concentrations by 10.49 µg/m 3 and offset 65% of the largest reduction in PM 2.5 concentrations during the 2017 HS.

The so-called "2 + 26" cities, consisting of Beijing, Tianjin, and 26 surrounding cities in Northern China, are the key area in China's air pollution control program since 2013, and also the targeted region in the annual action plan during heating seasons since 2017. Compared with other northern cities, "2 + 26" cities had a greater drop of 26.67 µg/m 3 (33.43%) in their annual mean PM 2.5 concentrations from 2015 to 2018, but with a smaller rebound of 3.02 µg/m 3 (5.68%) in 2019 (Table 1) . During heating seasons, these 28 cities also reduced their PM 2.5 concentrations much more than other cities in the 2017 HS and then experienced a greater rebound in PM 2.5 pollution in the 2018 HS. Mean Fig. S4 ). Such a rebound effect continued into the 2019 HS (2.14 µg/m 3 , 2.57%). These two rebounds together offset 29.44% of the biggest reduction of 32.06 µg/m 3 in the 2017 HS. Other northern cities had an even larger and more evenly distributed rebounding effect over the two heating seasons: 5.35 µg/m 3 in the 2018 HS and 5.38 µg/m 3 in the 2019 HS. It is worth noting that the maximum of PM 2.5 concentrations consistently show greater rebound than the means, implying that the PM 2.5 rebound made the worst pollution month even worse (Table 1) .

We decomposed the contribution of meteorological and anthropogenic factors to the rebound of PM 2.5 concentrations using a stepwise MLR model (Li et al. 2019a ), one of the two common methods used in this task (see Table 3 for our summary). The results showed that anthropogenic factors contributed to 55% (2.82 µg/m 3 ) of the PM 2.5 rebound in northern cities from 2018 to 2019, while meteorological factors only contributed to 45% (2.34 µg/m 3 ) of the rebound (Fig. 2e) . A similar pattern appeared over the 3-year heating seasons from 2017 to 2019, though with a much larger absolute rebound (Fig. 2f ). For the "2 + 26" cities, around 40-50% of the PM 2.5 rebound was attributed to anthropogenic factors, and the rebound during the heating seasons was much larger, too. Results from sensitivity analyses with respect to variable choices and functional forms further show a range of 50-60% of human contributions for northern cities, and 20-80% for "2 + 26" cities from 2018 to 2019 (Supplementary Table S3 ). Our decomposition results are also robust even if post-Feb 2020 data are included in the analysis (Supplementary Table S3 ).

The contributions of anthropogenic drivers in different cities varied greatly. For the 136 northern cities used in our MLR model, the contributions of anthropogenic drivers to PM 2.5 rebound during the 2017-2019 HS ranged from − 10.82 to 32.73 µg/m 3 , and 112 of them had seen positive contributions of anthropogenic factors to the PM 2.5 rebound ( Supplementary Fig. S6 ). Considering the important role of anthropogenic factors in PM 2.5 rebound, we focus on the adjustment of policy targets and mitigation measures below, which would directly affect emissions from human activity. That being said, the underlying physical and chemical mechanisms of PM 2.5 rebounds also merit further investigation.

The significant PM 2.5 rebound driven by anthropogenic factors in the heating seasons was tightly linked to the significant reduction of PM 2.5 before. Anthropogenic contributions to PM 2.5 concentrations were correlated over space and time. As Fig. 3 shows, for every 100 km reduction in the distance to Beijing (within a 400 km range), the rebounding effect of PM 2.5 concentrations driven by human factors increased by about 3 µg/m 3 during the 2018 HS for the "2 + 26" cities. This seems to suggest a counterintuitive and declining pressure from Beijing to surrounding cities for the latter to reduce PM 2.5 emissions. However, it might be well explained by the more significant reduction in PM 2.5 concentrations due to human factors by cities closer to Beijing in the 2017 HS. For every 100 km reduction in the distance to Beijing, the PM 2.5 concentrations caused by human factors dropped by 11 μg/m 3 in the 2017 HS, corresponding to a 27% rebounding effect in the 2018 HS. Interestingly, the total rebound effect during the 2017-2019 HS had little to do with the city distance to Beijing (Supplementary Fig. S7 ).

As for other northern cities, the magnitude of the rebounding effect due to human factors in the 2018 HS was also negatively related to their contribution to the reduction in PM 2.5 concentrations in the previous year. Specifically, for 1 µg/m 3 reduction in PM 2.5 concentrations resulting from human factors in the 2017 HS, PM 2.5 concentrations increased by 0.18 µg/m 3 in the 2018 HS (Fig. 4) , pointing to an 18% rebounding effect in the 2018 HS. This rebounding effect was less than the 26% rebound effect for "2 + 26" cities in the 2018 HS (Fig. 4) , probably because other northern cities did not reduce their PM 2.5 concentrations in the 2017 HS as much as "2 + 26" cities did. However, the total humanrelated rebound effect over the 2-year heating seasons from 2018 to 2019 amounted to 40% of the reduction in the 2017 HS for other northern cities, which became even larger than the 28% for "2 + 26" cities ( Supplementary Fig. S8 ). The differences in the rebounding effect driven by human factors between the two regions point to different reasons behind their PM 2.5 rebounds.

Policy and cost reasons behind PM 2.5 rebounds PM 2.5 rebound was closely related to the policy re-adjustment and rising marginal cost of mitigation measures, at least for the "2 + 26" cities. By comparing policy targets and mitigation measures from 2017 to 2019 during the heating seasons for the "2 + 26" cities ( Fig. 5) , we find that the targets for reducing PM 2.5 concentrations and severe haze-polluted days decreased from 15% in the 2017 HS to 3% in the 2018 HS and 4-6% in the 2019 HS. Furthermore, specific regulations on mitigating PM 2.5 pollution also loosened in their intensity and stringency, though with more flexibility being added. For instance, the strong onesize-fits-all requirements were removed with respect to the coal-to-gas and coal-to-power programs; as a replacement, it was then encouraged to use power, gas, coal, and centralized heating in their best suitable way. Furthermore, industrial production was staggered, coal-fired boilers were phased out, and dust control was managed with a new credit system. The shrunken policy goal and increased flexibility and freedom were consistent with the observed rebounding effect of PM 2.5 concentrations and the significant role of human factors in the "2 + 26" cities during the heating seasons, irrespective of the influence of natural factors. One possible reason for the systemic policy re-adjustment after 2017 was due to the economic losses born by local governments to achieve the milestone targeted by the Action Plan . Moreover, the policy re-adjustment for "2 + 26" cities in the 2018 HS might contribute to the greater PM 2.5 rebound than that of other northern cities.

The rising marginal cost of PM 2.5 mitigation measures was also astonishing. As illustrated in Fig. 6 , only four out of 13 measures cost less than 100 yuan (~ $15.64) per kg of PM 2.5 emission reduction, including removing small coal-fired boilers, lowering pollution in key industries, controlling road dust, and phasing out outdated industrial capacities. These measures together account for roughly 70% of the existing PM 2.5 emission reduction capacity. By contrast, the heavily used coal-to-gas and coal-to-power programs were very costly with the highest average abatement cost at 583 yuan/kg (~ $91.20/kg), though these programs brought about significant PM 2.5 emission reduction in the 2017 HS (Li et al. 2020c; Wang et al. 2020b) . After a dramatic cut in PM 2.5 emissions in 2017 to meet the policy goal stipulated by the Action Plan, there was little room for these measures to further reduce emissions cost-effectively-an inconvenient truth consistent with the PM 2.5 rebound.

Thanks to rigid mitigation measures and favorable meteorological conditions, China has achieved significant reduction in PM 2.5 concentrations during 2013-2018, especially in northern cities; nevertheless, the post-2018 rebound in PM 2.5 pollution cannot be overlooked. This study found a PM 2.5 rebound in northern China in 2019 and early 2020, even though the nationwide PM 2.5 concentrations remained unchanged from 2018 to 2019. To mitigate the health risk 

Phase out coal-fired boilers

Phase out all 44,000 coalfired boilers in the elimination list, and hold local officials accountable.

Implement heating supply plans according to local conditions. Speed up the construction of central heating pipe network, and prioritize clean heating methods to eliminate coal-fired boilers.

Complete the task of coal-togas and coal-to-power. Remove scattered coal use Completely.

Use electricity, gas, coal and heat in their best way. Replace scattered coal first in regions with largest impact.

Shut down production in the building materials industry and VOC-related industries.

Plan staggered production in industrial enterprises according to local conditions. Use technologies that are sustainable, effective, reliable, and popular with the residents.

Exempt A-level firms from reducing emissions, and Blevel firms partially reduce their emissions.

Suspend all earthwork and house demolition during the heating season with a few exceptions.

Establish a credit management system for the construction industry and a blacklist of some construction market entities.

An average cost curve for PM 2.5 abatement in China. Data on the abatement effect and average cost of each measure are obtained from Zhang et al. (2019a) linked to PM 2.5 pollution, how to maintain a sustainable PM 2.5 reduction merits more attention. So, it is necessary to quantify the relative contribution of both anthropogenic and meteorological factors to the PM 2.5 rebound and link decomposition results to underlying human factors, such as the actual policy readjustment and the increasing marginal cost of mitigation measures. Our decomposition results differ considerably from the existing study (Yin and Zhang 2020) claiming that meteorological conditions explained 122% of the PM 2.5 rebound in the 2018 winter and that human factors offset rather than contributed to the PM 2.5 rebound in the "2 + 26" cities. There may be two explanations for such a qualitative difference. First, we used adjusted PM 2.5 concentration data to start with, while the previous study did not (see Table 2 for a direct comparison with the literature and the impact of such our data adjustment). The second reason is that different methods were used to fit the observed PM 2.5 trend: While we applied a stepwise MLR model, the previous study used a chemical transport model (CTM). Though there is no consensus concerning which method performs better, our model had a better goodness-of-fit. The mean correlation of model fitted values with actual PM 2.5 concentrations was 0.86 ( Fig. 2a; Supplementary Fig. S5 ), compared to a range of 0.67-0.72 in the previous study based on CTM (Yin and Zhang 2020) .

Moreover, our results were generally consistent with those from most previous studies (Table 3) . Although few studies investigated the drivers of PM 2.5 concentrations in post-2018 China, many studies on the previous declining trend of PM 2.5 suggested that meteorology only played a minor role in the process, regardless of the method being used. For example, a study using MLR indicated that 12% of the PM 2.5 reduction from 2013 to 2018 was attributable to meteorology (Zhai et al. 2019) , while another study used the CTM model and found that meteorology contributed 16% to the reduction from 2013 to 2017 (Zhang et al. 2019b ). Thus, it is unreasonable to fully attribute the PM 2.5 rebound to meteorology while neglecting the anthropogenic factors (Yin and Zhang 2020) .

On the methodology front, both MLR and CTM have been widely used to decompose the meteorological and anthropogenic factors behind pollution level changes; however, it is still too early to determine which one is better. While CTM can simulate the chemical process and predict chemical species with more detail (Bey et al. 2001) , one limitation of this method is its inherent uncertainty regarding its emission inventory (Chen et al. 2019; Zhong et al. 2021) : Even if two studies used the same CTM and focused on the same domain, their decomposition results can vary much (Ding et al. 2019; Dong et al. 2020) . By contrast, the uncertainty of the MLR mainly comes from model specifications and its sensitivity to data outliers. Moreover, MLR is unable to provide detailed information on physical and chemical mechanisms of PM 2.5 trends as CTM did in previous studies (Wang et al. 2020a; Le et al. 2020; Huang et al. 2021 ). This, admittedly, is one limitation of our research that merits more future research. Nevertheless, MLR is sufficient to fulfill the main research objective of the paper, i.e., to analyze the long-term trend of PM 2.5 and decompose the PM 2.5 rebounds into human and natural factors. Such a statistical approach has been used in other similar research such as Li et al. (2019a) and Zhai et al. (2019) . The obtained MLR results can provide useful hints on high-level factors behind the PM 2.5 rebound and thus help achieve sustainable PM 2.5 emission reductions. Broadly speaking, the PM 2.5 rebound during the heating season characterizes the typical campaign-style governance model of China (Liu et al. 2015; Wang et al. 2021) . First is the top-down campaign-style governance with quick and highly visible effects, which may create long-term issues and side effects (Zhao et al. 2020b) . Specifically, this model focuses on solving the most-pressing issue with top priorities, at a very quick pace. To solve this problem, the governments set an ambitious goal that needs to be achieved within a limited amount of time and then mobilize and coordinate all types of resources to fulfill that goal. Such a campaignstyle model has the merit of taking effect very quickly but cannot sustain the effort due to high costs of implementation. Once the initial goal is reached, the governments will shift their attention and focus to other important issues. This is the case for PM 2.5 rebound in northern China. The policy goal of a 10% reduction in annual PM 2.5 from 2012 to 2017 set up by the Action Plan was first over-met by governments leveraging heavy one-size-fits-all measures regardless of their relative cost-effectiveness (Zhang et al. 2019b ). However, this was later followed by a rebounding effect of as high as 65% in the following heating seasons for northern cities in China.

Given the increasing marginal costs of emission reductions (Fig. 6) , we do not believe that the Chinese government intended to adjust the goals and measures to result in the PM 2.5 rebounds, but only the result of a dynamic process characterized by the campaign-style governance model. In other words, the PM 2.5 rebound we found for northern cities only followed after deep cuts in PM 2.5 concentrations previously. It is interesting to note that the magnitude of and the reasons for the PM 2.5 rebounds in "2 + 26" cities and other northern cities was somewhat different. Specifically, the rebounding effect in "2 + 26" cities was greater than that of other northern cities in the 2018 HS. This is because "2 + 26" cities were chosen as the targeted region in the annual action plan since 2017 and thus had to reduce PM 2.5 pollution more seriously in the 2017 HS. Owing to the policy re-adjustment and increasing marginal costs later on, PM 2.5 concentrations in "2 + 26" cities rebounded more in the 2018 HS than in other northern cities. If without the previous deeper cuts in PM 2.5 concentrations, we should not expect "2 + 26" cities to perform worse than other cities in terms of either PM 2.5 mitigation or PM 2.5 rebounds. Such a comparison between "2 + 26" and other northern cities suggests that China's campaignstyle governance model had its spatial range limitations. Furthermore, the campaign on PM 2.5 also produced other repercussions. Studies found that southern cities experienced natural gas shortages owing to the hyped coal-togas program in the north and these cities had to revert to coal for production and heating ). The decrease in PM 2.5 concentrations in northern China even contributed to the increase in O 3 concentrations in the summer owing to the excessive attention on PM 2.5 controls (Li et al. 2019b; Wang et al. 2020c; Zhao et al. 2020a) .

Second is the system's ability to adapt to emerging problems and new situations in the process. In the case of controlling China's PM 2.5 pollution, the re-adjustment of policy targets and the insertion of flexibilities into policy measures after 2017 may reflect certain degrees of self-learning and system re-calibration, rather than pure rebounding. After all, the rising abatement cost and potential GDP loss ) cannot simply be ignored and must be carefully balanced with PM 2.5 emission reduction. The end result is that even considering the relaxation of policy stringency and the rebound effect, China's overall speed in reducing PM 2.5 concentrations is still faster than that of its counterparts from the developed world (Greenstone et al. 2021) . Similar findings also exist in relation to China's reduction of its coal overcapacity (Dong et al. 2021 ). Whether a smooth or curly path can better address these complex collective action problems deserves more attention and fundamental research.

In sum, the main contribution of this paper is to confirm the PM 2.5 rebound in northern China from Nov 2018 to Jan 2020. After decomposing the changes in PM 2.5 concentrations via a statistical approach, this study highlights the important role played by human factors, in particular the policy re-adjustment and the increasing marginal costs of PM 2.5 abatement. However, there are still some data and methodological limitations that need to be addressed in future research. First, though the statistical approach was straightforward, it was unable to offer detailed information about the contribution of different mitigation measures. Future studies could collect more data and quantify the relative contributions of those different measures to the PM 2.5 rebound. Second, due to the COVID-19 lockdown, the statistical approach could not separate its confounding effect on PM 2.5 from the other non-COVID-related human factors that we care about. More future work should be done to control for confounding factors such as COVID-19 in order to examine more recent trends of PM 2.5 . Lastly, it would also be interesting to compare the relative performance of our statistical approach and chemical transport modeling approach with more data inputs and more research efforts in the future.

This study found that mean PM 2.5 concentrations in northern China continued to decline from 2015 to 2018. However, the PM 2.5 pollution in this heavily polluted region has rebounded in 2019 and early 2020 before COVID-19, mostly due to changes during the heating season. Furthermore, anthropogenic factors have contributed roughly 50% of the rebound on average to this rebound after contributing to a severe cut of PM 2.5 emissions in 2017. The underlying reasons for this PM 2.5 rebound and the contribution from human factors have a lot to do with the systemic re-adjustment of policy targets and mitigation measures by the central government, as well as the rising abatement cost of many mitigation measures, rather than mere meteorological factors. In other words, such a human-induced PM 2.5 rebound is the result of a dynamic process under the campaign-style governance model. Regardless of the merits or demerits of China's governance model, successfully addressing PM 2.5 pollution brings about substantial environmental and human health benefits, as well as co-benefits for climate change. Challenges, however, still exist concerning how to maintain the previous efforts and momentums, potentially in a smarter and more cost-effective way. Based on our results and discussion, we make the following policy recommendations. First, chart a more sustainable path for future PM 2.5 emission reductions to meet the 2035 policy target of 35 µg/m 3 while avoiding possible rebounding effects. This includes paying more attention to key cities and key polluting seasons, both in the north and south, while not only focusing on the national annual mean concentration levels. Second, dynamically adjust the portfolio of mitigation measures based on their relative cost-effectiveness and social acceptance, strike a good balance between policy stringency and flexibility, and differentiate the contributions from natural and human factors. Third, address PM 2.5 pollution together with other energy and environmental issues, leverage system synergies and co-benefits, and employ a multi-goal governance system.

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Anthropogenic drivers of 2013-2017 trends in summer surface ozone in China

A two-pollutant strategy for improving ozone and particulate air quality in China

Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences

Does China's air pollution abatement policy matter? An assessment of the Beijing-Tianjin-Hebei region based on a multi-regional CGE model

Air quality improvement in response to intensified control strategies in Beijing during 2013-2019

Integrated assessment of the environmental and economic effects of "coal-to-gas conversion" project in rural areas of northern China

The 17-y spatiotemporal trend of PM 2.5 and its mortality burden in China

Campaign-style enforcement and regulatory compliance

Exploring the relationship between air pollution and meteorological conditions in China under environmental governance

COVID-19-induced lockdowns indicate the short-term control effect of air pollutant emission in 174 cities in China

Effects of air pollution control policies on PM 2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective

Action plan for the integrated air pollution control in the Beijing-Tianjin-Hebei region and surrounding areas during 2017-2018 autumn and winter. Ministry of Ecology and Environment of China

Action plan for the integrated air pollution control in the Beijing-Tianjin-Hebei Region and surrounding areas during 2019-2020 autumn and winter. Ministry of Ecology and Environment of China (2020) The Ministry of Ecology and Environment announces the national surface water and ambient air quality status in 2019

Pollutant emission reductions deliver decreased PM 2.5 -caused mortality across China during 2015-2017

Can campaign-style enforcement work: when and how? Evidence from Straw Burning Control in China

Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak

Natural gas shortages during the "coal-to-gas" transition in China have caused a large redistribution of air pollution in winter 2017

Effects of atmospheric circulations on the interannual variation in PM 2.5 concentrations over the Beijing-Tianjin-Hebei region in 2013-2018

Contrasting trends of PM2.5 and surface-ozone concentrations in China from

Possible environmental effects on the spread of COVID-19 in China

Impact of meteorological conditions on PM2.5 pollution in China during winter

Climate anomalies contributed to the rebound of PM2.5 in winter 2018 under intensified regional air pollution preventions

2018: separating contributions from anthropogenic emissions and meteorology

Cost-benefit analysis of China's Action Plan for Air Pollution Prevention and Control

Drivers of improved PM 2.5 air quality in China from 2013 to 2017

Time-resolved spread of antibiotic resistance genes in highly polluted air

and O3 pollution during 2015-2019 over 367 Chinese cities: spatiotemporal variations, meteorological and topographical impacts

Evaluating the effects of campaign-style environmental governance: evidence from Environmental Protection Interview in China

Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions

reductions in Chinese cities from 2013 to 2019 remain significant despite the inflating effects of meteorological conditions

Association between short-term exposure to air pollution and COVID-19 infection: evidence from China