Introduction

Atmospheric visibility provides intuitive grasp of air quality for public1. China has suffered substantial visibility deterioration in the past years2,3,4,5, which adversely impacts traffic6 and human happiness7. Intensive occurrences of haze with low visibility have raised public awareness8,9,10,11,12. Since the promulgation of Air Pollution Prevention Control and Action Plan in 201313, the national emissions of SO2, NOx, and primary fine particle (PM2.5) declined by 59, 21 and 33%, respectively14,15,16. The PM2.5 mass concentrations in Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta reduced by 28–40% during 2013–201717. However, such great mitigations in air pollution are not directly visible to the general population because the ambient visibility seems less improved, especially in winter11,12,14,16,18,19. For example, the frequency of low visibility events only decreased by 5% despite the reduction in PM2.5 > 30% in 2018 in Southern China when compared with 201319. This depressing visibility improvement is also found in Eastern China even though PM2.5 has lowered by 50.8 μg m−3 from 2013 to 201816. The annual average visibilities for Fenwei Plain and Central China are still < 10 km, and the haze days are still > 71 days20. All these mask the intense and painstaking efforts that the government devoted for defending the blue sky.

Aerosol light extinction (bext) including aerosol absorption (bap) and scattering (bsp) was the key deciding ambient visibility4,21. The aerosol chemical compositions and hygroscopic properties impact bext substantially19,22,23,24,25. Organic matter (OM, 29–52%)26, (NH4)2SO4 (29%)27, and NH4NO3 (31–45%)28 were the main contributors to bext in the megacities of China. The contribution from sulfate-nitrate-ammonium (SNA) to bext even increased to nearly 80% under polluted atmospheric conditions22. Typically, the haze events are associated with elevated ambient relative humidity (RH), which promotes SNA formation29,30,31,32,33 and enhances bsp25,34,35. The aerosol hygroscopicity increased bsp by 1.8 times at RH of 80%, compared with that of dry conditions (RH < 40%)36. For improving visibility, it is pivotal to identify the key chemical components impacting bext and control their precursor gases.

Temporary emission control measures have been frequently implemented during mega-event periods, aimed at reducing the mass concentrations of PM2.5, SO2, NOx, and O324,37,38. The mass concentrations of PM2.5 decreased obviously by 40–49% in these events37,38, which actuated the appearances of blue sky37,39. Li et al.40 observed that the frequency of hazy days decreased to 36% during the Beijing Olympics because the bext contributions of (NH4)2SO4 and NH4NO3 decreased by 17.1 and 13%, respectively. Tao et al.41 found that the “APEC blue” was mainly raised by SNA extinction reduction (30%), with 5 days holding ambient visibility > 20 km. However, the frequency of haze occurrence increased by 7% during the 16th Asian Games period though PM2.5 decreased by 32%42. The unexpected haze during the former mega-event periods implies that the temporal pollution control measures for air pollutants at a regional scale may be not effective for improving the ambient visibility necessarily.

The lockdown events due to coronavirus disease 2019 (COVID-19) provide the widest, longest, and thorough “controlled experiment,” to investigate the impacts of unexpected control measures on reducing air pollutant concentrations and improving visibility. Abundant studies have reported large decreases in CO243,44,45, NOx46,47,48, particulate matter49,50,51, and associated chemical components52,53,54,55,56. While the reduced anthropogenic emissions did not hold back the occurrences of severe haze events in China because of unfavorable meteorology57,58, enhanced secondary formation18,59, and regional transport60,61. In addition, the aerosol optical depth (AOD) was less affected by the reductions47,57. In fact, the impacts of the strictest lockdown measures on aerosol optical properties are puzzled and till now no studies have focused on this point.

Wuhan is the first locked city and the lockdown measures are the strictest. This study analyzed the online monitoring datasets including bsp, bap, and chemical components for PM2.5 in Wuhan for pre-lockdown period (PLP) and lockdown period (LP). The impacts of chemical compositions and hygroscopic growth on bext and corresponding mechanisms were investigated, and the key chemical component impacting bext was identified. Priority policies for reducing PM2.5 and improving ambient visibility effectively were proposed. Results here can provide a reference for policy making from the view of improving ambient visibility.

Results and discussion

Substantial PM2.5 reduction and visibility improvement

The average PM2.5 mass concentration decreased by 37.8% in LP (47.8 ± 25.5 μg m−3) compared with that for PLP (76.8 ± 34.0 μg m−3, Fig. 1a), since the lockdown measures actually reduced the anthropogenic emissions45,59,61. The decrements in the average mass concentrations of major compounds varied from 0.8 μg m−3 (elemental carbon (EC)) to 24.8 μg m−3 (NH4NO3) except for secondary organic aerosol (SOA) (Fig. 1b). The decrease in NH4NO3 made up 63.6% of PM2.5 mass reduction. The average SOA concentration showed an increase of 1.6 μg m−3 and its mass percentage in PM2.5 raised by 6.9%, verifying the enhanced secondary formation18.

Fig. 1: Variations in PM2.5 and ambient visibility between PLP and LP.
figure 1

a Time series of PM2.5, aerosol scattering coefficient (bsp), aerosol absorption coefficient (bap), single scattering albedo (SSA), ambient visibility (VIS), and relative humidity (RH) for pre-lockdown period (PLP, 2019/12/23–2020/01/22) and lockdown period (LP, 2020/01/23–2020/02/22) in Wuhan. The dashed lines mean the average value; **p < 0.01. b The differences in the mass concentrations and fractions of major chemical components in PM2.5 for PLP and LP. c The differences in the contributions of major chemical components in PM2.5 to aerosol extinction coefficient (bext) for PLP and LP. EC elemental carbon, POA primary organic aerosol, SOA secondary organic aerosol, FS fine soil.

Full size image

The mean bsp and bap decreased by 39.0% (151.2 Mm−1) and 31.4% (8.9 Mm−1) during LP, respectively (Fig. 1a). The single scattering albedo decreased only by 1.1% during LP (0.91), implying the strong scattering ability of particle. The RH slightly descended by 8.8% from PLP (78.4 ± 13.8%) to LP (71.4 ± 15.7%). The visibility displayed a remarkable (p < 0.01) increase of 106.7% (14.4 km) during LP, demonstrating that the strict control measures were effective to improve the ambient visibility along with the decrease of PM2.5. While in Eastern16 and Southern China19 the PM2.5 substantially decreased, the ambient visibility was less improved due to its nonlinear relationship with PM2.516,19,22,28.

During LP, severe haze events with ambient visibility < 10 km occurred on 3 and 5 February with the maximal bsp and bap as 689.0 and 53.7 Mm−1, respectively. The SNA contributed highest to PM2.5 mass (66.7–67.4%) and bext (56.0–59.8%) (Supplementary Fig. 1). The haze events on 3 and 5 February were related with local accumulation and regional transport of air pollutants, respectively60 (Supplementary Fig. 1), while the dominant chemical species of the 2 days were similar under the two different atmospheric circulations, highlighting the substantial role in improving ambient visibility by regional-joint control of the precursor gas emissions for SNA.

Increases in PTV10

Figure 2a shows the non-linear responses of visibility to PM2.5 under different RH intervals, with strong negative power function relationships (R2 > 0.71, p < 0.01). The PM2.5 thresholds corresponding to visibility of 10 km (PTV10) varied from 54 to 175 μg m−3 and decreased with RH increasing due to rapid hygroscopic growth of particles27. The spatiotemporal variabilities of PTV10, e.g., 50–63 μg m−3 in Sichuan basin in 201462 and 66 μg m−3 in Wuhan during 2018 winter28, reduced the consistency and reliability of air quality studies using a fixed visibility (10 km) to reflect the occurrence of haze without considering air pollution intensity63. Meanwhile, the PTV10 for RH > 90% was 13–28% lower than the Chinese secondary PM2.5 standard (75 μg m−3), implying that the air quality standard was not always able to keep the visibility > 10 km. Thus, a strict hourly PM2.5 standard value should be emphasized to prevent haze formation. In this study, it is 54 μg m−3.

Fig. 2: Drivers for the elevations in PTV10.
figure 2

a Scatter plots of ambient visibility (VIS) and PM2.5 mass concentrations under different relative humidity (RH) ranges. The curves represent the fitting lines. b Scatter plots of aerosol scattering (bsp) and absorption (bap) coefficients versus PM2.5 mass concentrations. The gray zones represent the 95% confidence intervals. c Relationships between the optical hygroscopicity (f(RH)) of PM2.5 and RH. The upper and lower boundaries of boxes (c) represent the 75th and 25th percentiles, respectively; the lines with the boxes mark the median; the whiskers above and below boxes indicate the maximum and minimum values, respectively; the diamonds along the boxes represent the average values; the dots indicate the potential outliers.

Full size image

Compared with those (54–126 μg m−3) during PLP, the PTV10 increased by 9–58 μg m−3 (p < 0.01) for different RH ranges during LP. In other words, the visibilities were higher in LP than those for PLP under the same RH and PM2.5 concentration. Moreover, the elevations in PTV10 implied that the reductions in PM2.5 and RH could not thoroughly explain the increase in visibility. Other parameters including mass scattering efficiency (MSE), mass absorption efficiency (MAE), and optical hygroscopicity (f(RH)) for PM2.5 should be also considered. In Fig. 2b, PM2.5 was highly correlated with bsp and bap (R2 ≥ 0.58, p < 0.01). The slopes of the linear regressions can be considered as the bulk MSE and MAE38,42. During LP, the MSE decreased by ~5% (0.26 m2 g−1) compared with that for PLP. While due to the aerosol aging64, MAE raised by 24% (0.06 m2 g−1), partly counteracting the MSE reduction. However, compared with the decrease in MSE, the decrements in f(RH) were more obviously as 6–14% (p < 0.01) for various RH ranges from PLP (1.57–3.46) to LP (1.48–3.12) (Fig. 2c). Since the ambient visibility was highly sensitive to f(RH) changes under high RH conditions19, the decline in f(RH) was one of the key reasons for the increase of PTV10. It is worth noting that the hygroscopic behavior of SOA has not been considered due to its minor contributions to bsp and f(RH).

The driver for ambient visibility improvement

The MSEs and MAEs of major chemical components in PM2.5 were estimated by multiple linear regression (MLR) (Supplementary Table 1). The MSEs for NH4NO3 varied slightly from 5.9 m2 g−1 for PLP to 4.2 m2 g−1 for LP, while the values for (NH4)2SO4 changed dramatically from 1.2 to 4.4 m2 g−1. These MSEs were comparable to the values of 1.7–17.4 m2 g−1 65,66 for NH4NO3 and 1.9–9.2 m2 g−1 23,67 for (NH4)2SO4 in previous studies, which are summarized in Supplementary Table 2. For (NH4)2SO4 and NH4NO3, the variations in MSEs are related to particle size distribution, with particles in droplet mode (600–700 nm) holding the highest values23,68,69. The large increase in the MSE for (NH4)2SO4 was likely due to the fact that its particle size increased and approached to droplet mode during the aerosol aging processes as the COVID-19 lockdown significantly reduced primary emissions45,59,61. For primary organic aerosol (POA), the calculated MSEs (8.4–9.3 m2 g−1) were within the range of 1.0–16.7 m2 g−1 67,70 (Supplementary Table 2). The MSEs for SOA, varying from 0.3 m2 g−1 for PLP to 2.6 m2 g−1 for LP, were lower than those for POA. They were also acceptable when compared with the values (1.1–8.5 m2 g−1 71) in the former studies (Supplementary Table 2) though the MSE estimation of SOA for PLP did not pass the significance test (p = 0.17, Supplementary Table 1). From Supplementary Table 2, the MSEs for POA and SOA do not show necessarily high or low relationships as they are determined by the integral effects of mass concentrations, size distributions, emission sources, and morphology72,73. The increment in MSE for SOA might be associated with the variation in emission sources and the enlargement in particle size during LP. The MAEs for EC increased from 11.6 m2 g−1 for PLP to 12.1 m2 g−1 for LP due to the aerosol aging and were consistent with 7–14 m2 g−1 66,74. MAE of EC depends on its size distribution and coating64. It is noteworthy that there are no studies reporting MSEs of POA and SOA in PM2.5 for comparison.

From Fig. 1c, NH4NO3 contributed the highest fractions to bext by 61.8% and 31.8% for the PLP and LP, respectively. Then POA (22.1 and 28.1%, respectively) and (NH4)2SO4 (6.0 and 25.6%, respectively) followed. Other chemical species contributed < 7%. The estimated bext contributed from all chemical species decreased by 8.7–179.5 Mm−1 during LP compared with those for PLP, except for (NH4)2SO4 and SOA. As the decreases in the mass concentration (56.1%) and MSE (28.8%), NH4NO3 held the highest bext reduction and its contribution to bext displayed a decrease of 30%, which actuated the visibility improvement during LP. Liu et al.16 found that from 2013 to 2017 the increased contributions of nitrate to particle mass and bext elevated the f(RH) and mass extinction efficiency of PM2.5 in Eastern China, which hindered the visibility improvement. From PLP to LP, the estimated bext of SOA and (NH4)2SO4 increased by 15.0 and 40.3 Mm−1, although the mass concentration of (NH4)2SO4 decreased by 29.4%. It indicated that the reduction strategies of PM2.5 and associated chemical components currently in China would not reduce bext and improve the ambient visibility necessarily. A significant cutting down of NH4NO3 and its precursor (NOx or NH3) can serve as the most effective way to improve ambient visibility in the future.

Weakened mutual promotion between AWC and NH4NO3

Previous studies revealed the vital role of aerosol composition alterations on hygroscopicity and bext16,19,75,76. The bext induced by aerosol hygroscopicity (Δbext) was estimated, which was the difference between the ambient and measured bext. In Fig. 3a, the Δbext and aerosol water content (AWC) displayed a similar temporal pattern and decreased by ~60% from PLP to LP averagely. The AWC was significantly (p < 0.01) correlated with Δbext (R2 = 0.86) and ambient visibility (R2 = 0.72) (Fig. 3b), indicating that the reduction in AWC would be another reason for improving ambient visibility. Besides the decreases in ambient RH and PM2.5, 5.0–22.4 and 4.1–37.1% of the reductions in Δbext and AWC could be ascribed to the aerosol composition variations, respectively (Fig. 4).

Fig. 3: Mechanisms for the further improvement of ambient visibility.
figure 3

a Time series of the aerosol water content (AWC) and the differences (Δbext) between ambient and measured bext. b Scatter plots of AWC versus Δbext and visibility (VIS). c Scatter plots of AWC versus NH4NO3 concentration and the mass ratio of NH4NO3/(NH4NO3 + HNO3). The circles and crosses are colored based on ambient visibility.

Full size image
Fig. 4: Roles of aerosol composition variations on AWC and ∆bext.
figure 4

Comparisons of the normalized aerosol water content (AWC/PM2.5) and the differences (Δbext) between ambient and measured bext (∆bext/PM2.5) for pre-lockdown period and lockdown period under various relative humidity (RH) ranges. *p < 0.05; **p < 0.01. The error bar represents one standard deviation.

Full size image

NH4NO3 and (NH4)2SO4 are the main hygroscopic compounds in aerosols and their abilities of water uptake are comparable with the same particle size and RH33,77,78. However, compared to (NH4)2SO4 (80% at 298 K), NH4NO3 has a lower deliquescence RH (62% at 298 K)77 and is more easily liquefied79. Following the method in Liu et al.16 and Wexler and Seinfeld80, the average PM2.5 deliquescence humidity for LP (71.3%) was significantly (p < 0.01) higher than that for PLP (70.0%) as the decrease in NH4NO3 mass percentage (Supplementary Fig. 2). It means higher ambient RH requirement for hygroscopic growth16. In addition, the aerosol water facilitates NH4NO3 formation81,82,83 and the enhanced NH4NO3 fraction will promote water uptake correspondingly33,84,85. Such mutual promotion between the aerosol water and NH4NO3 degraded ambient visibility effectively (Fig. 3c), while the decreases in NH4NO3 and RH weakened the promotion and then reduced AWC and Δbext, which further improved the ambient visibility during LP.

Priority policies for co-regulating PM2.5 and visibility

Reducing NH4NO3 can substantially reduce PM2.5 and improve ambient visibility. This can be realized by reducing NOx to lower HNO3, which further transfers to particle phase, or reducing NH3 to lower aerosol pH and keep HNO3 in the gas phase86. In Fig. 5, all variables responded to NHx (NH3 + NH4+) reduction nonlinearly. They flattened out until 36% (9.2 μg m−3) and 43% (6.9 μg m−3) NHx reductions achieved for PLP and LP, respectively, at which points they started to decrease rapidly. The sweet spots for NHx reduction (36 and 43%) were determined by a critical pH of 3, which balanced the partition between HNO3 and NO3− 86. It increased by 7% (Fig. 5a) due to the reductions of TNO3 (NO3− + HNO3) and SO42− converting more NHx into gas phase during LP87, reflecting that the ambient visibility improvement would become more difficult via NHx control. Fu et al.88 also reported that increases in free NH3 concentration could decrease the sensitivity of PM2.5 reduction to NHx emission control.

Fig. 5: Relative merits of NHx and TNO3 controls on various aspects of PM2.5.
figure 5

Aerosol pH (a), mass concentration of [NH4+ + NO3−] (b), proportion of particle nitrate (ε(NO3−)) (c), and PM2.5 mass concentration (d) predicted by ISORROPIA-II model. The simulations are based on the average values, with changes only from NHx or TNO3. The horizontal gray solid line in a identifies the critical pH value of 386.

Full size image

The impacts of reducing TNO3 showed different responses. Decreasing TNO3 did not obviously change pH due to the buffering by NH3-NH4+ partitioning86,89. While the pH decreased clearly when NHx reduction exceeded its sweet spots due to the increase of TNO3 partitioning to HNO3, the decrease of AWC, and the increase of hydrogen ion concentration in aerosol water87. A linear reduction in TNO3 caused a linear decrease in [NH4+ + NO3−] as the NO3− was nearly equal to TNO3 (Fig. 5c). Then the decrease in TNO3 was transmitted directly to [NH4+ + NO3−]86. Thus, controlling TNO3 was a more direct and effective way to elevate ambient visibility than NHx. However, if the NHx reduction surpassed 69% (17.5 μg m−3) and 73% (11.7 μg m−3) for PLP and LP, respectively (Fig. 5b), it would become more effective in increasing ambient visibility than TNO3 reduction. Wu et al.90 suggested that the measures to reduce NHx pollution should be focused on non-agricultural emission sources in both local and surrounding areas of urban regions as the NHx emitted from agricultural sources has been highly overrated90,91.

For guaranteeing blue sky in the future, the responses of average PM2.5 concentration for PLP to NHx or TNO3 reduction were roughly simulated given that the anthropogenic emissions have rebounded to pre-pandemic levels after Wuhan reopened45,48. A reduction of 51% in TNO3 (17.9 μg m−3) or 59% in NHx (15.0 μg m−3) could make the PM2.5 concentration < 54 μg m−3 and ensure the ambient visibility > 10 km (Fig. 5d). When the NHx reduction exceeded 64% (16.3 μg m−3), it might be more effective in improving ambient visibility than TNO3 reduction. The simultaneous reductions of NHx and TNO3 with different ratios did not decrease their reduction threshold percentages corresponding to PM2.5 of 54 μg m−3 (Fig. 6), which meant that just control TNO3 was enough for improving ambient visibility. However, there are other welfares to control NHx emissions, for instance, reducing nitrogen deposition and minimizing eutrophication in aqueous system92. Thus, multi-pollutant control but more priority given to TNO3 reduction is proposed from the view of improving ambient visibility in China.

Fig. 6: Effects of simultaneous reductions of NHx and TNO3.
figure 6

The reduction threshold percentages of NHx and TNO3 corresponding to average PM2.5 mass concentration of 54 μg m−3 predicted by ISORROPIA-II model. The simulations are based on the average values for pre-lockdown period, with proportional changes from NHx and TNO3.

Full size image

Since the secondary transformation has not been considered in the thermodynamic model, how to reduce TNO3 and NHx to certain concentrations by controlling their corresponding precursors (NOx and NH3) needs more in-depth studies. Cutting down the TNO3 only via abating NOx emissions should be treated with cautions as decreasing NOx emissions may increase ozone and hydroxyl radical concentrations, which can enhance the conversion efficiency of NOx to HNO3 and then subdue the response of TNO3 to NOx emission reductions in the volatile organic compound (VOC)-limited ozone formation regime18,93. The increased photochemical oxidants were the major drivers for persistent heavy nitrate pollution in winter in North China Plain93. It should be noted that Wuhan is also in a VOC-limited ozone formation regime94.

Implications

To tackle the haze pollution, the Chinese government has implemented toughest ever emission control measures since 201313. Consequently, the anthropogenic emissions of NH3, NOx, PM2.5, and SO2 in Hubei province decreased by 7.8–70.0% from 2013 to 2017 (Supplementary Fig. 3a). The SO2 and NO2 concentrations in Wuhan reduced by 84.0 and 27.9% from 2014 to 2019, respectively (Supplementary Fig. 3b). The PM2.5 mass concentrations continually dropped by half from 141.2 to 73.6 μg m−3 during the 2014–2019 wintertime (Fig. 7a). However, the air quality improvement might not be sensed by the public since the average ambient visibility was less improved and still remained < 10 km (Fig. 7a), which obscured the efforts government paid to alleviate the air pollution. Though RH could also diminish the sky16,19, it was not the main reason curbing ambient visibility elevation as it displayed small fluctuations and no obvious variation was observed during 2014–2018 wintertime (Fig. 7a). The sharp decrease of ambient visibility in 2019 wintertime could be partly explained by the moderate increase in RH. The non-linear responses of visibility to PM2.5 could also explain its unsatisfactory improvement. In Fig. 2a, the ambient visibility showed decreasing sensitive to PM2.5 decrement with the aggravation of air pollution especially when the PM2.5 concentrations were higher than the PTV10. Thus, the large abatements in PM2.5 mass concentrations during 2014–2019 wintertime did not bring about the huge improvement in ambient visibility as they were still > 54 μg m−3, which pointed out the importance of establishing a strict hourly PM2.5 standard. The great ambient visibility improvement appearing in LP can be expected in the future with the decrease of PM2.5 when it is below the standard.

Fig. 7: Unsatisfactory visibility improvement along with steady increases in nitrate fractions.
figure 7

a Interannual trends in PM2.5 concentration, relative humidity (RH), and ambient visibility (VIS) in Wuhan during 2014–2019 wintertime (January and February). b Interannual trends in NH4+, NO3−, and SO42− concentrations and their contributions to SNA for PM2.5 during 2015–2019 wintertime (January and February). The error bar represents one standard deviation.

Full size image

Such frustrating visibility improvement in Wuhan is not a particular case, which has been also found in Eastern China16 and Southern China19. Even worse, it is likely to be widespread in China as Liu et al.16 has demonstrated that nearly 73.2% stations across the country exhibited increasing slopes of AOD/PM2.5 from 2013 to 2018. That is to say, though the PM2.5 mass concentration has been substantially reduced, the increase of AOD per unit PM2.5 indicated the less improved ambient visibility. Emission controls successfully brought down the loads of primary PM2.515 and inevitably reduced its bext19, while most of the visibility improvement benefits raised by PM2.5 reduction were balanced out by the elevation in aerosol optical hygroscopicity16,19. This increment was associated with the elevated proportions of NH4NO3 in PM2.5 mass and bext16. Indeed, (NH4)2SO4 typically dominated bext (~40%) in the past decade36, while the distinct emission controls of SO2 and NOx resulted in a larger reduction in sulfate than in nitrate in China from 2013 to 201717,93,95. Instead, nitrate is more important than sulfate as a driver for ambient visibility impairment96,97. Similarly, the nitrate mass concentration and its contribution to SNA have gradually increased during 2015–2019 wintertime in Wuhan despite the tremendous mitigation of PM2.5 pollution (Fig. 7b). The evidently increased nitrate proportions would directly trigger the decrease in PM2.5 deliquescence humidity16. It meant that a lower ambient RH was required for aerosol hygroscopic growth, which hindered the visibility improvement in Wuhan during wintertime.

Based on a positive example induced by the unexpected COVID-19 pandemic, this study reveals the co-benefits of reducing PM2.5 and improving ambient visibility by cutting down NH4NO3. Reducing NH4NO3 will increase deliquescence humidity and decrease optical hygroscopicity, which can maximize the efficiency of decreasing PM2.5 on improving ambient visibility under current air pollution condition. The recommendations for reducing PM2.5 and improving visibility in a short term by reducing more TNO3 than NHx are proposed. To resolve haze once and for all, the joint control of the two pollutants will gain other more welfares. It must be noted that wiping out the haze is not the terminus. The average PM2.5 concentrations during LP still remained four times higher than the World Health Organization recommendations. Secondary inorganic aerosol (45.6%) and biomass burning (26.8%) were still the largest contributors to PM2.5 (Supplementary Fig. 4) though the masses they contributed both decreased during LP, which needed further reductions. As shown in Supplementary Fig. 5, these contributions were both enhanced by the air masses transported from Eastern China56, suggesting the necessity of regional-joint control.

Methods

Observation

The sampling site (114.28°E, 30.6°N, Supplementary Fig. 6) is in a mixed residential and commercial area with no obvious industrial emissions. Hourly PM10 and PM2.5 dry mass concentrations were monitored by the oscillating balance method (TH, model: 2000Z, China)63 during PLP (23 December 2019–22 January 2020) and LP (23 January 2020–22 February 2020). CO, NOx (NO + NO2), O3, and SO2 (Supplementary Fig. 7) were hourly measured with a correlation infrared absorption analyzer (TAPI, model: 300E, USA), a chemiluminescence trace level NO–NO2–NOx analyzer (Casella, model: ML9841B, UK), an ultraviolet (UV) photometric O3 analyzer (TEI, model: 49i, USA), and a pulsed UV fluorescence SO2 analyzer (Casella, model: ML9850B, UK), respectively56.

Water-soluble ions, including NH4+, Na+, Mg2+, K+, Ca2+, Cl−, NO3−, and SO42−, and gaseous HNO3, HCl, and NH3 (Supplementary Fig. 7) were hourly detected using an online ion chromatograph (MARGA-1S, Switzerland). Hourly organic carbon and EC were monitored by a sunset OC/EC online analyzer (Model RT-4, Sunset Laboratory Inc., Tigard, OR, USA)98. Hourly trace elements were measured by a Xact multi-metal monitor (Model XactTM 625, Cooper Environmental Services, USA)99.

Meteorological parameters, including atmospheric pressure, ambient temperature, RH, wind speed, and wind direction, were obtained by an automatic meteorological observation instrument (WS6000-UMB, Luff, Germany) with 1-h resolution. Hourly precipitation was provided by local meteorological administration. Ambient visibility was measured with a visibility monitor (Belfort Model 6000, USA) with ±10% of uncertainty. The mixing layer height was derived from the HYSPLIT model56. The time series of meteorological parameters are displayed in Supplementary Fig. 8.

The dry bsp at 525 nm was measured using an integrating Nephelometer (Aurora-1000, Ecotech, Australia) with RH of inflow air heated to < 40%. The bap at 532 nm was converted by the concentration of black carbon measured at 880 nm with an Aethalometer (Magee Scientific Company, Berkeley, CA, USA, Model AE-31). The converted coefficient was 8.28 m2 g−1 100. To match the bsp at 525 nm, the bap at 532 nm was converted as referred to Nessler et al.101. The verification and calibration of the Nephelometer and Aethalometer can be found in Cao et al22. and Tao et al65. The single scattering albedo was calculated as the ratio of bsp to bext.

Data analysis

Fog, rain, and dust can reduce the atmospheric visibility. The datasets with RH > 97% was excluded to eliminate the effects of fog19. Data collected in the occurrence of precipitation were removed63. When the PM2.5/PM10 ratio was < 30%, the data were excluded as they might be impacted by long-range transport dust63,102,103. The hourly data were deleted if PM2.5 concentrations were higher the PM10 concentrations. Totally, the eliminated data accounted for 20% of the whole data.

The measured PM2.5 was reconstructed by the sum of (NH4)2SO4, NH4NO3, OM, EC, and fine soil104. The minimum R-Squared method105 and a constant converting factor were used to divide OM into POA and SOA24,59. The MSEs for above chemical components expect for EC were estimated by MLR41,66,106,107. The MAEs for EC were estimated based on the scatter plots of EC against bap24,41. Statistics of MSEs and MAEs are presented in Supplementary Table 1. The bulk f(RH) for PM2.5 was the ratio of estimated ambient bsp to corresponding measurements19,35,108. The thermodynamic model ISOROPPIA-II109,110 was run with “forward mode” to calculate the AWC and to conduct sensitivity tests86,87,111. Positive matrix factorization (PMF 5.0) was employed to identify the sources of PM2.524,38,56. More details about the data processing are listed in Supplementary Methods. The glossaries of abbreviations are provided in Supplementary Table 3.