Articles | Volume 23, issue 7
Research article
12 Apr 2023
Research article |  | 12 Apr 2023

Coarse particulate matter air quality in East Asia: implications for fine particulate nitrate

Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao

Air quality network data in China and South Korea show very high year-round mass concentrations of coarse particulate matter (PM), as inferred by the difference between PM10 and PM2.5. Coarse PM concentrations in 2015 averaged 52 µg m−3 in the North China Plain (NCP) and 23 µg m−3 in the Seoul Metropolitan Area (SMA), contributing nearly half of PM10. Strong daily correlations between coarse PM and carbon monoxide imply a dominant source from anthropogenic fugitive dust. Coarse PM concentrations in the NCP and the SMA decreased by 21 % from 2015 to 2019 and further dropped abruptly in 2020 due to COVID-19 reductions in construction and vehicle traffic. Anthropogenic coarse PM is generally not included in air quality models but scavenges nitric acid to suppress the formation of fine particulate nitrate, a major contributor to PM2.5 pollution. GEOS-Chem model simulation of surface and aircraft observations from the Korea–United States Air Quality (KORUS-AQ) campaign over the SMA in May–June 2016 shows that consideration of anthropogenic coarse PM largely resolves the previous model overestimate of fine particulate nitrate. The effect is smaller in the NCP which has a larger excess of ammonia. Model sensitivity simulations for 2015–2019 show that decreasing anthropogenic coarse PM directly increases PM2.5 nitrate in summer, offsetting 80 % the effect of nitrogen oxide and ammonia emission controls, while in winter the presence of coarse PM increases the sensitivity of PM2.5 nitrate to ammonia and sulfur dioxide emissions. Decreasing coarse PM helps to explain the lack of decrease in wintertime PM2.5 nitrate observed in the NCP and the SMA over the 2015–2021 period despite decreases in nitrogen oxide and ammonia emissions. Continuing decrease of fugitive dust pollution means that more stringent nitrogen oxide and ammonia emission controls will be required to successfully decrease PM2.5 nitrate.

1 Introduction

Coarse particulate matter (coarse PM; particulate matter between 2.5 and 10 µm aerodynamic diameter) is a severe air pollution problem in East Asia, contributing a particle mass comparable to fine particulate matter (PM2.5) and thus about half of PM10 (Chen et al., 2019; Lee et al., 2015; Qiu et al., 2014; X. Wang et al., 2018). It is mainly fugitive mineral dust, with contributions from both natural desert dust and anthropogenic sources including on-road traffic, construction, and agriculture (Wu et al., 2016; Zhao et al., 2017; Liu et al., 2021; Katra, 2020). Atmospheric chemistry models used in air quality applications generally do not include anthropogenic fugitive dust, due to the lack of available emission inventories except for a few urban areas (Li et al., 2021a, b, c). Aside from its direct interest as an air pollutant, coarse PM can suppress PM2.5 by heterogeneously taking up acids (HNO3, SO2, and H2SO4) that would otherwise lead to PM2.5 formation. This uptake has been observed for natural dust events (Wang et al., 2017; Heim et al., 2020; Z. Wang et al., 2018; Park et al., 2004; Stone et al., 2011), but the more ubiquitous effect from anthropogenic dust has received little study (Kakavas and Pandis, 2021; Hodzic et al., 2006). With increasingly stringent control measures to decrease fugitive dust air pollution in East Asia (Chinese State Council, 2019; Noh et al., 2018; Wu et al., 2016; Xing et al., 2018), it is important to better understand the impact on PM2.5 air quality.

A specific issue is the effect of anthropogenic dust on PM2.5 nitrate. Nitrate is a major component of PM2.5 in urban regions of East Asia including the North China Plain (NCP) (Li et al., 2019; Zhai et al., 2021a) and the Seoul Metropolitan Area (SMA) (Jeong et al., 2022; Kim et al., 2020), and it can dominate haze pollution events in both regions (Fu et al., 2020; Li et al., 2018; Xu et al., 2019; Kim et al., 2017, 2020). PM2.5 nitrate over North China in winter has not decreased in recent years despite reductions in emissions of the precursor nitrogen oxides (NOxNO+NO2) (Zhai et al., 2021a; Fu et al., 2020) from fossil fuel combustion. This has been attributed to limitation by ammonia (NH3) emissions, since PM2.5 nitrate is mainly present as ammonium nitrate (Zhai et al., 2021a). Decreasing coarse PM emissions is another possible explanation, as it would allow more HNO3 to be available for PM2.5 nitrate formation, and it could also shift PM2.5 nitrate formation to be more NH3-limited. Better understanding this sensitivity of PM2.5 nitrate to coarse PM is of crucial importance because of recent efforts by the Chinese government to decrease NH3 emissions (Liao et al., 2022), which are mainly from agriculture with additional urban contributions from vehicle, industrial, and waste disposal sources (Mgelwa et al., 2022).

In this work, we show that coarse PM over the NCP and the SMA is mainly anthropogenic and decreased by 21 % during the 2015–2019 period. We find that accounting for this anthropogenic coarse PM in the GEOS-Chem atmospheric chemistry model greatly improves the ability of the model to simulate PM2.5 nitrate during the Korea–United States Air Quality (KORUS-AQ) aircraft campaign over Korea where previous GEOS-Chem simulations found a large overestimate (Travis et al., 2022; Zhai et al., 2021b). From there we examine the implications for the effects of emission controls on long-term trends of PM2.5 nitrate in China and South Korea.

2 Coarse PM in China and South Korea

Figure 1 shows the annual mean concentrations of coarse PM in 2015, 2019, and 2020 measured at air quality networks in China and South Korea as the PM10–PM2.5 difference. Data for China are from the Ministry of Ecology and Environment (MEE) network (, last access: 18 December 2022), and data for South Korea are from the AirKorea network (, last access: 18 December 2022). We remove spurious data when PM2.5 is higher than PM10, which account for 1.7 % and 0.2 % of the dataset, respectively, in China and South Korea.

Figure 1Distributions and trends of coarse PM concentrations over China and South Korea during 2015–2020. Here and elsewhere, coarse particulate matter (PM) is defined as particles between 2.5 and 10 µm aerodynamic diameter, and its concentration is determined by subtracting PM2.5 from PM10 in the air quality network data. Panels (a)(c) show the annual mean concentrations in 2015, 2019, and 2020 over China and panels (d)(f) show the same for South Korea. The rectangles in (a) and (d) delineate the North China Plain or NCP (113–122.5 E, 34.5–41.5 N) and the Seoul Metropolitan area or SMA (126.7–127.3 E, 37.3–37.8 N). Panel (g) shows annual trends relative to 2015 in the NCP (197 sites) and the SMA (33 sites) averaged over sites with at least 70 % data coverage each year from 2015 to 2020. Panel (h) shows the mean 2015–2019 seasonality over the NCP and SMA.

Figure 2Response of coarse PM to COVID-19 lockdown in China. (a) Coarse PM averaged for the 3 weeks before the China national lockdown (1–23 January 2020). (b) Coarse PM averaged during the 3-week lockdown (24 January–15 February 2020). (c) Percent changes of coarse PM between lockdown and pre-lockdown periods.

We see from Fig. 1 that coarse PM concentrations in China and South Korea are highest in the NCP and the SMA, respectively, indicating a dominant urban anthropogenic origin. Coarse PM in year 2015 averaged 52 µg m−3 in the NCP and 23 µg m−3 in the SMA, contributing nearly half of total PM10 (120 µg m−3 in the NCP and 50 µg m−3 in the SMA). National air quality standards for annual mean PM10 are 70 µg m−3 in China (urban) and 50 µg m−3 in South Korea, well above the World Health Organization (WHO) recommended annual standard of 15 µg m−3. Coarse PM decreased by 21 % in both the NCP and the SMA from 2015 to 2019, reflecting emission controls on fugitive dust (Chinese State Council, 2013, 2018; Noh et al., 2018; Wu et al., 2016) and further decreased strongly in 2020 because of COVID-19 restrictions on traffic and construction. The COVID-19 impact is evident in China by comparing concentrations before and after the sharp 24 January 2020 lockdown (Fig. 2).

Figure 3 shows further evidence of the dominant anthropogenic contribution to coarse PM as the daily correlation with carbon monoxide (CO) in 2015. CO is emitted by incomplete combustion and is a tracer of urban influence. We find strong correlations between coarse PM and CO with consistent slopes, except in spring, which features high coarse PM outliers attributable to desert dust events (Heim et al., 2020; Shao and Dong, 2006). Similar correlations to 2015 are found in other years (Fig. S1 in the Supplement). The desert dust events drive the seasonal maximum of coarse PM in Fig. 1h.

Figure 3Daily correlations of coarse PM and CO concentrations over the North China Plain (NCP) and Seoul Metropolitan Area (SMA) in 2015. Coarse PM and CO concentrations are 24 h averages of air quality network observations spatially averaged over the two regions. Also shown are the correlation coefficients and reduced-major-axis regression lines, except in spring when the correlation is not significant (p value > 0.05). We include February in spring to cover the season of natural dust events (Tang and Han, 2017).


3 Effect of anthropogenic coarse PM on fine particulate nitrate during KORUS-AQ

We simulated the effect of anthropogenic coarse PM on PM2.5 nitrate using the GEOS-Chem model and evaluated the model with observations from the KORUS-AQ aircraft campaign over South Korea in May–June 2016 (Crawford et al., 2021). KORUS-AQ offers a unique dataset of detailed aerosol and gas-phase composition over East Asia. Previous GEOS-Chem simulations showed a large overestimate of fine particulate nitrate and a large underestimate of coarse PM (Travis et al., 2022; Zhai et al., 2021b). Particulate nitrate concentrations were measured during KORUS-AQ at the Korea Institute of Science and Technology (KIST) surface site and on the aircraft by aerosol mass spectrometers (AMS) with size cut of 1 µm diameter (PM1 nitrate) (Kim et al., 2017, 2018). The AMS only detects non-refractory nitrate, taken here to be ammonium nitrate (Fig. S2). Total particulate nitrate with size cut of 4 µm diameter (PM4 nitrate) was also sampled on the aircraft by the soluble acidic gases and aerosol (SAGA) instrument (Dibb et al., 2003; McNaughton et al., 2007). Additional measurements on the aircraft included HNO3 concentrations with a chemical ionization time of flight mass spectrometer (CIT-ToF-CIMS), and aerosol size distributions including coarse PM with a DMT CPSPD (Droplet Measurement Technologies cloud particle spectrometer with polarization detection) probe. We focus on the observations over the SMA and exclude observations from two process-directed flights (RF7 and RF8) and the Daesan power plant plume following Park et al. (2021).

We use GEOS-Chem version 13.0.2 (, last access: 18 December 2022) in a nested-grid simulation over East Asia (100–150 E, 20–50 N) with a horizontal resolution of 0.5×0.625. The model simulates detailed oxidant-aerosol chemistry relevant to PM2.5 nitrate formation (Zhai et al., 2021a) and is driven by meteorological data from the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Formation of semi-volatile ammonium nitrate aerosol is governed by ISORROPIA version 2.2 thermodynamics (Fountoukis and Nenes, 2007). Dry deposition of gases and particles follows a standard resistance-in-series scheme (Wesely, 1989). Wet deposition of gases and particles includes contributions from rainout, washout, and scavenging in convective updrafts (Liu et al., 2001; Luo et al., 2019). The model includes reactive uptake of HNO3 on dust limited by dust alkalinity and mass transfer (Fairlie et al., 2010), assuming 7.1 % Ca2+ and 1.1 % Mg2+ as carbonates per mass in emitted dust (Shah et al., 2020a; Tang and Han, 2017; Zhang et al., 2014). The relative humidity (RH)-dependent reactive uptake coefficient (γ) of HNO3 is based on laboratory studies (Liu et al., 2008; Huynh and McNeill, 2020) and observations during natural dust events in Beijing (Tian et al., 2021; Wang et al., 2017), and increases from 0.06 to 0.21 as RH increases from 40 % to 80 %. Monthly anthropogenic emissions for China are from the Multi-resolution Emission Inventory for China (MEIC) (Zheng et al., 2018, Zheng et al., 2021a, b), and emissions for other Asian countries including South Korea are from the KORUSv5 inventory (Woo et al., 2020). Fine anthropogenic mineral dust emissions from combustion and industrial sources (ash) are derived from the MEIC and KORUSv5 inventories as the residual of anthropogenic primary PM2.5 emissions after excluding primary organic aerosol, black carbon, and primary sulfate (Philip et al., 2017).

We compare the results from the standard model as described above to a simulation where we add anthropogenic coarse PM by using 24 h average observed coarse PM concentrations from the air quality networks (Fig. 1) as boundary conditions at the lowest model level. For this purpose, we linearly interpolate the daily mean coarse PM data from the network to the GEOS-Chem model horizontal grid and apply them to the coarse dust GEOS-Chem model component with an effective diameter of 4.8 µm. This concentration boundary condition in the lowest model level serves as an implicit source and defines the vertical concentration profile. The resulting vertical profiles of coarse PM in GEOS-Chem over South Korea are consistent with KORUS-AQ aircraft observations (Fig. S3). Anthropogenic coarse PM is assumed to be mainly fugitive dust with the same alkalinity properties as natural dust (Zhang et al., 2014; Tang and Han, 2017).

Figure 4Effect of anthropogenic coarse PM on nitrate concentrations over the Seoul Metropolitan Area (SMA) during the KORUS-AQ campaign (May–June 2016). GEOS-Chem model results without (standard) and with anthropogenic coarse PM are compared to surface and aircraft observations. (a) Median diurnal variation (error bars are 25th and 75th percentiles) of non-refractory PM1 nitrate (taken to be ammonium nitrate) at the Korea Institute of Science and Technology (KIST) site. (b–e) Median vertical profiles of non-refractory PM1 nitrate, PM1–4 nitrate, coarse PM (PM2.5–10), and HNO3 concentrations for the ensemble of flights over the SMA. Horizontal bars for the observations indicate 25th–75th percentiles.


Figure 4 compares GEOS-Chem to the KORUS-AQ observations including median diurnal PM1 nitrate at the KIST site and median aircraft vertical profiles over the SMA. The model is sampled along the aircraft flight tracks at the times of the observations, all in daytime. PM1–4 nitrate is derived as the difference between SAGA PM4 nitrate and AMS PM1 nitrate. Here we take ammonium nitrate in the model for comparison to PM1 observations and size-resolved dust nitrate for comparison to PM1–4 observations. In this way, any dust-associated refractory PM1 nitrate is included in the PM1–4 profiles, for both observations and the GEOS-Chem model. Such classification does not allow for supermicron ammonium nitrate, but KORUS-AQ observations found ammonium nitrate to be mainly submicron (Kim et al., 2018). GEOS-Chem results are shown both for the standard model (not including anthropogenic coarse PM) and with the addition of anthropogenic coarse PM. In both simulations, we adjusted the diurnal variation of NH3 emission to match the NH3 observations made at the Olympic Park site, 7 km southeast of KIST (Fig. S4).

The standard GEOS-Chem simulation without anthropogenic fugitive dust overestimates daytime PM1 nitrate (aircraft and surface) by about a factor of 2, while underestimating PM1–4 nitrate by about a factor of 2 (Fig. 4a–c). Coarse PM in the standard simulation (from natural dust and sea salt) is near zero, in contrast to observations (Fig. 4d). Adding anthropogenic coarse PM to the model corrects this bias and further corrects the PM1 and PM1–4 nitrate biases by providing an added sink for HNO3. We find that anthropogenic coarse PM takes up HNO3 3 times faster than dry deposition and that this uptake is limited by mass-transfer rather than alkalinity (only 60 %–70 % of the coarse dust alkalinity in surface air is neutralized on average). The shift from PM1 to PM1–4 nitrate is consistent with the uptake of HNO3 by coarse PM, with some of this uptake in the model taking place on dust coarser than 4 µm and so not observed by PM1–4 nitrate. Half of the model overestimate of HNO3 is corrected (Fig. 4e), with the remainder possibly due to an underestimate of HNO3 deposition velocity (Travis et al., 2022). The model overestimates nighttime nitrate in surface air at the KIST site, even with anthropogenic coarse PM. This nighttime nitrate in the model is driven by heterogeneous NO2 and N2O5 chemistry under stratified conditions, which could be subject to large local errors (Travis et al., 2022).

Figure 5Long-term trend of fine particulate nitrate concentrations in Beijing and Seoul over the 2015–2021 period. Mean PM1 or PM2.5 concentrations in winter and summer are compiled from individual field campaigns in Beijing at the Institute of Atmospheric Physics (IAP), Tsinghua University (Tsinghua), and Peking University (PKU) sites and in Seoul at the National Institute of Environmental Research (NIER) and Korea Institute of Science and Technology (KIST) sites (Table S1). Note the differences in scales between panels.


We also examined the effect of anthropogenic coarse PM on PM2.5 nitrate concentrations in the NCP. PM2.5 nitrate observations in NCP are mostly filter-collected bulk PM2.5 nitrate, which could be biased low in summer due to volatilization (Chow et al., 2005). Previous evaluation of GEOS-Chem with 2013 and 2015 PM2.5 nitrate observations across China in summer and winter found no significant bias in 2015 or winter 2013 but an overestimate in summer 2013 (Zhai et al., 2021a). That simulation did not include HNO3 uptake by dust (natural or anthropogenic). We find here that including HNO3 uptake by fine (PM2.5) dust has little effect on total PM2.5 nitrate but partitions 10 % of ammonium nitrate mass to fine dust nitrate in winter and 23 % in summer (Fig. S5). Adding anthropogenic coarse PM in GEOS-Chem decreases modeled ammonium nitrate in the NCP by 10 %–20 % in winter and by 25 %–30 % in summer, a relatively more modest effect than over the SMA because of larger excess of NH3. The comparison with PM2.5 nitrate observations here indicates that fine dust associated nitrate should be considered when comparing modeled particle nitrate to bulk PM2.5 nitrate data.

4 Implications for long-term trends of PM2.5 nitrate and responses to emission controls

There are to our knowledge no readily accessible continuous long-term records of PM2.5 nitrate concentrations in China or South Korea. Figure 5 shows a multi-year compilation of winter and summer mean PM1 and PM2.5 nitrate observations from individual field campaigns in Beijing and Seoul over 2015–2021 (Table S1 in the Supplement). We find no significant trends in winter, consistent with previous studies in the NCP that examined earlier periods (Fu et al., 2020). In summer, observations tend to show a decrease over the period but with large interannual variations driven by meteorology (Li et al., 2018; Zhai et al., 2021a).

Changes in anthropogenic emissions of NOx, SO2, NH3, PM2.5, and coarse PM could all affect PM2.5 nitrate, and we used GEOS-Chem to investigate these effects for the 2015–2019 period. The Multi-resolution Emission Inventory for China (MEIC) reports that NOx emissions in the NCP decreased by 11 % from 2015 to 2019, SO2 emissions decreased by 54 %, and primary PM2.5 from combustion decreased by 35 % (Zheng et al., 2021a). This primary PM2.5 includes a 40 % contribution from mineral ash that we treat as anthropogenic fine dust, and it decreased by 27 % from 2015 to 2019. The MEIC also reports a 15 % decrease of NH3 emissions over China from 2015 to 2019 (19 % for the NCP), while the PKU-NH3 emission inventory reports a 6 % decrease over China from 2015 to 2018 (Liao et al., 2022). Observations of surface NO2 and SO2 over the SMA imply a 22 % decrease of NOx emissions and a 40 % decrease of SO2 emissions from 2015 to 2019 (Bae et al., 2021; Colombi et al., 2023). Coarse PM decreased by 33 % over the NCP and by 31 % over SMA during the same period (considering winter and summer data only).

Figure 6 shows the resulting emission-driven changes of PM2.5 nitrate over the NCP and SMA from 2015 to 2019 as simulated by GEOS-Chem in sensitivity simulations, applying either 2015 or 2019 emissions to the same meteorological year (2019), and with or without anthropogenic coarse PM. The sum of changes driven by individual emission changes amounts to the total emission-driven net change. Sensitivities to NH3 and primary PM2.5 emissions in the SMA are solely driven by emission trends in China, since we assume no emission trends for these species in South Korea.

Figure 6Emission-driven changes in mean PM2.5 nitrate from 2015 to 2019 over the NCP and SMA. Results are from GEOS-Chem sensitivity simulations including total and individual emission changes over the period, all for the same meteorological year (2019) and applied both to China and South Korea (so the effects of NH3 and primary PM2.5 over the SMA are due to long-range transport from China). Values are seasonal means for winter and summer. The blue lines superimposed on the NOx, SO2, and NH3 sensitivity bars show the effects from simulations not accounting for the effect of HNO3 uptake by anthropogenic coarse PM.


The model reproduces the lack of trend in winter and the decreasing trend in summer seen in the observations for both the NCP and SMA. The lack of trend in winter reflects offsetting influences from decreasing NOx, NH3, and primary PM2.5 emissions on the one hand, and decreasing SO2 and coarse PM emissions on the other hand. Decreasing SO2 increases the availability of NH3 for nitrate formation (Fu et al., 2020; Zhai et al., 2021a). Decreasing primary PM2.5 reduces the aerosol volume available for heterogeneous conversion of NOx to HNO3 (Shah et al., 2020b). Decreasing coarse PM has relatively little direct effect on PM2.5 nitrate in winter in the NCP because abundant atmospheric NH3 combined with low temperatures drives HNO3 near-quantitively to ammonium-nitrate particles, and subsequent mass transfer of HNO3 from ammonium nitrate to coarse PM is very slow because of the weak HNO3 partial pressure (Wexler and Seinfeld, 1992). The decrease of coarse PM still quantitatively offsets the benefit from NOx emission controls, which has been the main vehicle for controlling PM2.5 nitrate. Consideration of coarse PM in the model further increases the sensitivity of PM2.5 nitrate to NH3 and SO2 emissions, respectively, by 30 % and 46 %. This is because coarse PM provides an additional sink for the small fraction of HNO3 that remains in the gas phase, which increases the sensitivity of the atmospheric lifetime of total nitrate (ammonium nitrate +HNO3) to changes in NH3 or SO2 emissions (Zhai et al., 2021a).

In summer, we find that the decrease in coarse PM over the 2015–2019 period directly cancels half of the benefit from decreasing NOx, SO2, NH3, and primary PM2.5 emissions in the NCP, with less effect in the SMA. Over the NCP, the decrease of coarse PM offsets 80 % of the benefits from NOx and NH3 emission controls. Unlike in winter, decreasing SO2 suppresses nitrate formation by decreasing the aerosol liquid water content (Stelson and Seinfeld, 1982). The effect of decreasing coarse PM emissions in summer is larger than in winter because warmer temperatures allow more HNO3 to remain in the gas phase under NH3HNO3H2SO4 thermodynamics and can thus be scavenged by coarse PM.

5 Conclusions

Coarse PM (PM10–PM2.5) in urban areas of China and South Korea is very high year-round and is mainly of anthropogenic origin as fugitive dust except for natural desert dust events in spring. Annual mean coarse PM concentrations decreased by 21 % from 2015 to 2019 in both the North China Plain (NCP) and the Seoul Metropolitan Area (SMA), with steeper decreases in 2020 because of COVID-19 restrictions on traffic and construction. Considering only winter and summer when the influence of natural dust is small, we find that anthropogenic fugitive dust emissions decreased by about 30 % from 2015 to 2019 in both the NCP and the SMA.

Anthropogenic coarse PM is of direct air quality concern because it accounts for about half of total PM10 in the NCP and the SMA, but it also takes up HNO3 effectively and can thus suppress formation of fine particulate nitrate which is a major component of PM2.5 pollution. Comparison of GEOS-Chem model simulations to surface and aircraft observations from the KORUS-AQ campaign over the SMA in May–June 2016 shows that accounting for anthropogenic coarse PM largely corrects previous model overestimates of fine particulate nitrate.

Decrease in anthropogenic coarse PM emissions to improve PM10 air quality could have the unintended consequence of increasing PM2.5 nitrate, offsetting the gains from decreases in NOx and NH3 emissions. Compilation of 2015–2021 observations of fine particulate nitrate in Beijing and Seoul suggests little trend in winter and a decrease in summer, consistent with GEOS-Chem. Decreasing coarse PM in the model in winter offsets the benefit of decreasing NOx emissions, and coarse PM further increases the sensitivity of PM2.5 nitrate to changes in NH3 and SO2 emissions by affecting the lifetime of total inorganic nitrate (ammonium nitrate +HNO3). In summer, decreasing coarse PM in the NCP offsets 80 % of the PM2.5 nitrate benefit of decreasing NOx and NH3 emissions. As coarse PM continues to decrease in response to fugitive dust pollution control, there is a greater need to reduce NH3 and NOx emissions in order to decrease fine particulate nitrate air pollution in East Asia.

Data availability

PM2.5, PM10, and CO data over China are from (MEE, 2022), and over South Korea are from (KEC, 2022). Surface and aircraft data during KORUS-AQ are from (NASA, 2022). Multi-year compilation of winter and summer mean PM1 and PM2.5 nitrate are provided in Table S1.


The supplement related to this article is available online at:

Author contributions

SZ and DJJ designed the research. SZ performed the research. DCP, NKC, VS, LHY, and HL helped with data analysis and results interpretation. QZ provided the MEIC emission inventory. SW, HK, YS, JSC, JSP, JED, TL, JSH, and BEA provided observation data. JHW and YK provided the KORUSv5 emission inventory. GL, FY, and KL helped with model simulations. SZ and DJJ wrote the paper with input from all other authors.

Competing interests

The contact author has declared that none of the authors has any competing interests.


Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


We thank Bo Zheng (Tsinghua Shenzhen International Graduate School, Tsinghua University) for processing the MEIC emission inventory. We thank Paul O. Wennberg, Michelle J. Kim, Alexander P. Teng, and John D. Crounse from the California Institute of Technology for their contributions to HNO3 measurements during KORUS-AQ.

Financial support

This work was funded by the Harvard–NUIST Joint Laboratory for Air Quality and Climate (JLAQC) and the Samsung Advanced Institute of Technology.

Review statement

This paper was edited by Yuan Wang and reviewed by three anonymous referees.


Bae, M., Kim, B.-U., Kim, H. C., Kim, J., and Kim, S.: Role of emissions and meteorology in the recent PM2.5 changes in China and South Korea from 2015 to 2018, Environ. Pollut., 270, 116233,, 2021. 

Chen, R., Yin, P., Meng, X., Wang, L., Liu, C., Niu, Y., Liu, Y., Liu, J., Qi, J., You, J., Kan, H., and Zhou, M.: Associations between Coarse Particulate Matter Air Pollution and Cause-Specific Mortality: A Nationwide Analysis in 272 Chinese Cities, Environ. Health Perspect., 127, 017008,, 2019. 

Chow, J. C., Watson, J. G., Lowenthal, D. H., and Magliano, K. L.: Loss of PM2.5 nitrate from filter samples in central California, J. Air Waste Manage. Assoc., 55, 1158–1168,, 2005. 

Chinese State Council: Action Plan on Prevention and Control of Air Pollution, (last access: 18 December 2022), 2013. 

Chinese State Council: Three-year Action Plan for Protecting Blue Sky, (last access: 18 December 2022), 2018. 

Chinese State Council: Beijing has set up more than 1,000 sites to monitor dust, (last access: 18 December 2022), 2019. 

Colombi, N. K., Jacob, D. J., Yang, L. H., Zhai, S., Shah, V., Grange, S. K., Yantosca, R. M., Kim, S., and Liao, H.: Why is ozone in South Korea and the Seoul metropolitan area so high and increasing?, Atmos. Chem. Phys., 23, 4031–4044,, 2023. 

Crawford, J. H., Ahn, J.-Y., Al-Saadi, J., Chang, L., Emmons, L. K., Kim, J., Lee, G., Park, J.-H., Park, R. J., Woo, J. H., Song, C.-K., Hong, J.-H., Hong, Y.-D., Lefer, B. L., Lee, M., Lee, T., Kim, S., Min, K.-E., Yum, S. S., Shin, H. J., Kim, Y.-W., Choi, J.-S., Park, J.-S., Szykman, J. J., Long, R. W., Jordan, C. E., Simpson, I. J., Fried, A., Dibb, J. E., Cho, S., and Kim, Y. P.: The Korea–United States Air Quality (KORUS-AQ) field study, Elementa, 9, 1–27,, 2021. 

Dibb, J. E., Talbot, R. W., Scheuer, E. M., Seid, G., Avery, M. A., and Singh, H. B.: Aerosol chemical composition in Asian continental outflow during the TRACE-P campaign: Comparison with PEM-West B, J. Geophys. Res.-Atmos., 108, 8815,, 2003. 

Fairlie, T. D., Jacob, D. J., Dibb, J. E., Alexander, B., Avery, M. A., van Donkelaar, A., and Zhang, L.: Impact of mineral dust on nitrate, sulfate, and ozone in transpacific Asian pollution plumes, Atmos. Chem. Phys., 10, 3999–4012,, 2010. 

Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+Ca2+Mg2+NH4+Na+SO42-NO3-ClH2O aerosols, Atmos. Chem. Phys., 7, 4639–4659,, 2007. 

Fu, X., Wang, T., Gao, J., Wang, P., Liu, Y., Wang, S., Zhao, B., and Xue, L.: Persistent Heavy Winter Nitrate Pollution Driven by Increased Photochemical Oxidants in Northern China, Environ. Sci. Technol., 54, 3881–3889,, 2020. 

Heim, E. W., Dibb, J., Scheuer, E., Jost, P. C., Nault, B. A., Jimenez, J. L., Peterson, D., Knote, C., Fenn, M., Hair, J., Beyersdorf, A. J., Corr, C., and Anderson, B. E.: Asian dust observed during KORUS-AQ facilitates the uptake and incorporation of soluble pollutants during transport to South Korea, Atmos. Environ., 224, 117305,, 2020. 

Hodzic, A., Bessagnet, B., and Vautard, R.: A model evaluation of coarse-mode nitrate heterogeneous formation on dust particles, Atmos. Environ., 40, 4158–4171,, 2006. 

Huynh, H. N. and McNeill, V. F.: Heterogeneous Chemistry of CaCO3 Aerosols with HNO3 and HCl, J. Phys. Chem., 124, 3886–3895,, 2020. 

Jeong, J. I., Seo, J., and Park, R. J.: Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea, Remote Sens., 14, 5310,, 2022. 

Kakavas, S. and Pandis, S. N.: Effects of urban dust emissions on fine and coarse PM levels and composition, Atmos. Environ., 246, 118006,, 2021. 

Katra, I.: Soil Erosion by Wind and Dust Emission in Semi-Arid Soils Due to Agricultural Activities, Agronomy, 10, 89,, 2020. 

KEC – Korea Environment Corporation: PM10 and PM2.5 dataset in South Korea, KEC [data set],, last access: 18 December 2022. 

Kim, H., Zhang, Q., Bae, G. N., Kim, J. Y., and Lee, S. B.: Sources and atmospheric processing of winter aerosols in Seoul, Korea: insights from real-time measurements using a high-resolution aerosol mass spectrometer, Atmos. Chem. Phys., 17, 2009–2033,, 2017. 

Kim, H., Zhang, Q., and Heo, J.: Influence of intense secondary aerosol formation and long-range transport on aerosol chemistry and properties in the Seoul Metropolitan Area during spring time: results from KORUS-AQ, Atmos. Chem. Phys., 18, 7149–7168,, 2018. 

Kim, H., Zhang, Q., and Sun, Y.: Measurement report: Characterization of severe spring haze episodes and influences of long-range transport in the Seoul metropolitan area in March 2019, Atmos. Chem. Phys., 20, 11527–11550,, 2020. 

Lee, H., Honda, Y., Hashizume, M., Guo, Y. L., Wu, C.-F., Kan, H., Jung, K., Lim, Y.-H., Yi, S., and Kim, H.: Short-term exposure to fine and coarse particles and mortality: A multicity time-series study in East Asia, Environ. Pollut., 207, 43–51,, 2015. 

Li, H., Zhang, Q., Zheng, B., Chen, C., Wu, N., Guo, H., Zhang, Y., Zheng, Y., Li, X., and He, K.: Nitrate-driven urban haze pollution during summertime over the North China Plain, Atmos. Chem. Phys., 18, 5293–5306,, 2018. 

Li, H., Cheng, J., Zhang, Q., Zheng, B., Zhang, Y., Zheng, G., and He, K.: Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions, Atmos. Chem. Phys., 19, 11485–11499,, 2019. 

Li, T., Bi, X., Dai, Q., Wu, J., Zhang, Y., and Feng, Y.: Optimized approach for developing soil fugitive dust emission inventory in “2+26” Chinese cities, Environ. Pollut., 285, 117521,, 2021a. 

Li, T., Dong, W., Dai, Q., Feng, Y., Bi, X., Zhang, Y., and Wu, J.: Application and validation of the fugitive dust source emission inventory compilation method in Xiong'an New Area, China, Sci. Total Environ., 798, 149114,, 2021b. 

Li, T., Ma, S., Liang, W., Li, L., Dai, Q., Bi, X., Wu, J., Zhang, Y., and Feng, Y.: Application of the high spatiotemporal resolution soil fugitive dust emission inventory compilation method based on CAMx model, Atmos. Res., 262, 105770,, 2021c. 

Liao, W., Liu, M., Huang, X., Wang, T., Xu, Z., Shang, F., Song, Y., Cai, X., Zhang, H., Kang, L., and Zhu, T.: Estimation for ammonia emissions at county level in China from 2013 to 2018, Sci. China Earth Sci., 65, 1116–1127,, 2022. 

Liu, S., Xing, J., Sahu, S. K., Liu, X., Liu, S., Jiang, Y., Zhang, H., Li, S., Ding, D., Chang, X., and Wang, S.: Wind-blown dust and its impacts on particulate matter pollution in Northern China: current and future scenarios, Environ. Res. Lett., 16, 114041,, 2021. 

Liu, H., Jacob, D. J., Bey, I., and Yantosca, R. M.: Constraints from 210Pb and 7Be on wet deposition and transport in a global three-dimensional chemical tracer model driven by assimilated meteorological fields, J. Geophys. Res.-Atmos., 106, 12109–12128,, 2001. 

Liu, Y., Gibson, Cain, Wang, H., Grassian, and Laskin, A.: Kinetics of Heterogeneous Reaction of CaCO3 articles with Gaseous HNO3 over a Wide Range of Humidity, J. Phys. Chem. A, 112, 1561–1571,, 2008. 

Luo, G., Yu, F., and Schwab, J.: Revised treatment of wet scavenging processes dramatically improves GEOS-Chem 12.0.0 simulations of nitric acid, nitrate, and ammonium over the United States, Geosci. Model Dev., 12, 3439–3447,, 2019. 

McNaughton, C. S., Clarke, A. D., Howell, S. G., Pinkerton, M., Anderson, B., Thornhill, L., Hudgins, C., Winstead, E., Dibb, J. E., Scheuer, E., and Maring, H.: Results from the DC-8 Inlet Characterization Experiment (DICE): Airborne Versus Surface Sampling of Mineral Dust and Sea Salt Aerosols, Aerosol Sci. Tech., 41, 136–159,, 2007. 

MEE – Ministry of Ecology and Environment: PM10 and PM2.5 dataset in China, MEE [data set],, last access: 18 December 2022. 

Mgelwa, A. S., Song, L., Fan, M., Li, Z., Zhang, Y., Chang, Y., Pan, Y., Gurmesa, G. A., Liu, D., Huang, S., Qiu, Q., and Fang, Y.: Isotopic imprints of aerosol ammonium over the north China plain, Environ. Pollut., 315, 120376,, 2022. 

NASA – National Aeronautics and Space Administration: Airborne Science Data for Atmospheric Composition, NASA [data set],, last access: 18 December 2022. 

Noh, H.-J., Lee, S.-K., and Yu, J.-H.: Identifying Effective Fugitive Dust Control Measures for Construction Projects in Korea, Sustainability, 10, 1206,, 2018. 

Park, R. J., Oak, Y. J., Emmons, L. K., Kim, C.-H., Pfister, G. G., Carmichael, G. R., Saide, P. E., Cho, S.-Y., Kim, S., Woo, J.-H., Crawford, J. H., Gaubert, B., Lee, H.-J., Park, S.-Y., Jo, Y.-J., Gao, M., Tang, B., Stanier, C. O., Shin, S. S., Park, H. Y., Bae, C., and Kim, E.: Multi-model intercomparisons of air quality simulations for the KORUS-AQ campaign, Elementa, 9, 00139,, 2021. 

Park, S. H., Song, C. B., Kim, M. C., Kwon, S. B., and Lee, K. W.: Study on Size Distribution of Total Aerosol and Water-Soluble Ions During an Asian Dust Storm Event at Jeju Island, Korea, Environ. Monit. Assess., 93, 157–183,, 2004. 

Philip, S., Martin, R. V., Snider, G., Weagle, C. L., van Donkelaar, A., Brauer, M., Henze, D. K., Klimont, Z., Venkataraman, C., and Guttikunda, S. K.: Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models, Environ. Res. Lett., 12, 044018,, 2017. 

Qiu, H., Tian, L. W., Pun, V. C., Ho, K.-f., Wong, T. W., and Yu, I. T. S.: Coarse particulate matter associated with increased risk of emergency hospital admissions for pneumonia in Hong Kong, Respirat. Epidemiol., 69, 1027,, 2014. 

Shah, V., Jacob, D. J., Moch, J. M., Wang, X., and Zhai, S.: Global modeling of cloud water acidity, precipitation acidity, and acid inputs to ecosystems, Atmos. Chem. Phys., 20, 12223–12245,, 2020a. 

Shah, V., Jacob, D. J., Li, K., Silvern, R. F., Zhai, S., Liu, M., Lin, J., and Zhang, Q.: Effect of changing NOx lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO2 columns over China, Atmos. Chem. Phys., 20, 1483–1495,, 2020b. 

Shao, Y. and Dong, C. H.: A review on East Asian dust storm climate, modelling and monitoring, Global Planet. Change, 52, 1–22,, 2006. 

Stelson, A. W. and Seinfeld, J. H.: Thermodynamic prediction of the water activity, NH4NO3 dissociation constant, density and refractive index for the NH4NO3–(NH4) 2SO4H2O system at 25 C, Atmos. Environ., 16, 2507–2514,, 1982. 

Stone, E. A., Yoon, S.-C., and Schauer, J. J.: Chemical Characterization of Fine and Coarse Particles in Gosan, Korea during Springtime Dust Events, Aerosol Air Qual. Res., 11, 31–43,, 2011. 

Tang, Y. and Han, G.: Characteristics of major elements and heavy metals in atmospheric dust in Beijing, China, J. Geochem. Explor., 176, 114–119,, 2017. 

Tian, R., Ma, X., Sha, T., Pan, X., and Wang, Z.: Exploring dust heterogeneous chemistry over China: Insights from field observation and GEOS-Chem simulation, Sci. Total Environ., 798, 149307,, 2021. 

Travis, K. R., Crawford, J. H., Chen, G., Jordan, C. E., Nault, B. A., Kim, H., Jimenez, J. L., Campuzano-Jost, P., Dibb, J. E., Woo, J. H., Kim, Y., Zhai, S., Wang, X., McDuffie, E. E., Luo, G., Yu, F., Kim, S., Simpson, I. J., Blake, D. R., Chang, L., and Kim, M. J.: Limitations in representation of physical processes prevent successful simulation of PM2.5 during KORUS-AQ, Atmos. Chem. Phys., 22, 7933–7958,, 2022. 

Wang, X., Zhang, L., Yao, Z., Ai, S., Qian, Z., Wang, H., BeLue, R., Liu, T., Xiao, J., Li, X., Zeng, W., Ma, W., and Lin, H.: Ambient coarse particulate pollution and mortality in three Chinese cities: Association and attributable mortality burden, Sci. Total Environ., 628–629, 1037–1042,, 2018. 

Wang, Z., Pan, X., Uno, I., Li, J., Wang, Z., Chen, X., Fu, P., Yang, T., Kobayashi, H., Shimizu, A., Sugimoto, N., and Yamamoto, S.: Significant impacts of heterogeneous reactions on the chemical composition and mixing state of dust particles: A case study during dust events over northern China, Atmos. Environ., 159, 83–91,, 2017. 

Wang, Z., Pan, X., Uno, I., Chen, X., Yamamoto, S., Zheng, H., Li, J., and Wang, Z.: Importance of mineral dust and anthropogenic pollutants mixing during a long-lasting high PM event over East Asia, Environ. Pollut., 234, 368–378,, 2018. 

Wesely, M. L.: Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models, Atmos. Environ., 23, 1293–1304,, 1989. 

Wexler, A. S. and Seinfeld, J. H.: Analysis of aerosol ammonium nitrate: Departures from equilibrium during SCAQS, Atmos. Environ. A, 26, 579–591,, 1992. 

Woo, J.-H., Kim, Y., Kim, H.-K., Choi, K.-C., Eum, J.-H., Lee, J.-B., Lim, J.-H., Kim, J., and Seong, M.: Development of the CREATE Inventory in Support of Integrated Climate and Air Quality Modeling for Asia, Sustainability, 12, 7930,, 2020. 

Wu, Z., Zhang, X., and Wu, M.: Mitigating construction dust pollution: state of the art and the way forward, J. Clean. Prod., 112, 1658–1666,, 2016. 

Xing, J., Ye, K., Zuo, J., and Jiang, W.: Control Dust Pollution on Construction Sites: What Governments Do in China?, Sustainability, 10, 2945,, 2018. 

Xu, Q., Wang, S., Jiang, J., Bhattarai, N., Li, X., Chang, X., Qiu, X., Zheng, M., Hua, Y., and Hao, J.: Nitrate dominates the chemical composition of PM2.5 during haze event in Beijing, China, Sci. Total Environ., 689, 1293–1303,, 2019. 

Zhai, S., Jacob, D. J., Wang, X., Liu, Z., Wen, T., Shah, V., Li, K., Moch, J. M., Bates, K. H., Song, S., Shen, L., Zhang, Y., Luo, G., Yu, F., Sun, Y., Wang, L., Qi, M., Tao, J., Gui, K., Xu, H., Zhang, Q., Zhao, T., Wang, Y., Lee, H. C., Choi, H., and Liao, H.: Control of particulate nitrate air pollution in China, Nat. Geosci., 14, 389–395,, 2021a.  

Zhai, S., Jacob, D. J., Brewer, J. F., Li, K., Moch, J. M., Kim, J., Lee, S., Lim, H., Lee, H. C., Kuk, S. K., Park, R. J., Jeong, J. I., Wang, X., Liu, P., Luo, G., Yu, F., Meng, J., Martin, R. V., Travis, K. R., Hair, J. W., Anderson, B. E., Dibb, J. E., Jimenez, J. L., Campuzano-Jost, P., Nault, B. A., Woo, J. H., Kim, Y., Zhang, Q., and Liao, H.: Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM2.5): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations, Atmos. Chem. Phys., 21, 16775–16791,, 2021b. 

Zhang, Q., Shen, Z., Cao, J., Ho, K., Zhang, R., Bie, Z., Chang, H., and Liu, S.: Chemical profiles of urban fugitive dust over Xi'an in the south margin of the Loess Plateau, China, Atmos. Pollut. Res., 5, 421–430,, 2014. 

Zhao, G., Chen, Y., Hopke, P. K., Holsen, T. M., and Dhaniyala, S.: Characteristics of traffic-induced fugitive dust from unpaved roads, Aerosol Sci. Tech., 51, 1324–1331,, 2017.  

Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111,, 2018. 

Zheng, B., Zhang, Q., Geng, G., Chen, C., Shi, Q., Cui, M., Lei, Y., and He, K.: Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020, Earth Syst. Sci. Data, 13, 2895–2907,, 2021a. 

Zheng, B., Cheng, J., Geng, G., Wang, X., Li, M., Shi, Q., Qi, J., Lei, Y., Zhang, Q., and He, K.: Mapping anthropogenic emissions in China at 1 km spatial resolution and its application in air quality modeling, Sci. Bull., 66, 612–620,, 2021b. 

Short summary
Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Final-revised paper