Nguyen-Quoc Dat1, Bich-Thuy Ly This email address is being protected from spambots. You need JavaScript enabled to view it.1, Trung-Dung Nghiem  1, Thu-Thi Hien Nguyen1, Kazuhiko Sekiguchi2, Truong-Thi Huyen2, Thai-Ha Vinh3, Le-Quang Tien4 

1 School of Chemistry and Life Sciences, Hanoi University of Science and Technology, Hai Ba Trung District, Hanoi, Vietnam
2 Graduate School of Science and Engineering, Saitama University, Sakura, Saitama 338-8570, Japan
3 Vietnam National Institute of Occupational Safety and Health, Hoan Kiem District, Hanoi, Vietnam
4 North Center of Environmental Monitoring, Long Bien District, Hanoi, Vietnam

Received: December 8, 2022
Revised: February 1, 2024
Accepted: February 1, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Dat, N.Q., Ly, B.T., Nghiem, T.D., Nguyen, H.T.T., Sekiguchi, K., Huyen, T.T., Vinh, T.H., Tien, L.Q. (2024). Influence of Secondary Inorganic Aerosol on the Concentrations of PM2.5 and PM0.1 during Air Pollution Episodes in Hanoi, Vietnam. Aerosol Air Qual. Res. 24, 220446.


  • SIA contributed 29.0%, and 14.1% to PM2.5 and PM0.1 in episodes.
  • Percentage of PM2.5-SIA in episode was slightly higher than normal.
  • PM2.5 were more regional and PM0.1 were more local.
  • Wind speed largely affected PM2.5 and its SIA.


The high concentration of PM2.5 in Hanoi has been an issue of great concern. There were several periods during the winter when PM2.5 concentrations were higher than the Vietnamese ambient air quality standards (NAAQS) and WHO guidelines for daily PM2.5. In this study, the periods when daily PM2.5 concentrations exceeded the NAAQS of 50 µg m–3 for a minimum of two consecutive days were determined as episode periods. The study focuses on the impact of secondary inorganic aerosol (SIA) on PM2.5 episodes in the dry winter period in Hanoi. To calculate SIA, water-soluble ions of daily PM2.5 and PM0.1 samples which were collected on the rooftop of a three-storeyed building at an urban site in Hanoi, Vietnam (HUST site) from October 14 to December 31, 2020, were analyzed. Levels and SIA of PM2.5 and PM0.1 at a transportation site (CEM site) in an episode period from December 20 to December 28, 2020 were also measured. The contribution of SIA on PM2.5 and PM0.1 during those episodes, the effects of meteorological conditions, and long-range transport were investigated. The results showed that SIA contributed on average 29.0% and 14.1%, respectively, to PM2.5 and PM0.1 concentrations during air pollution episodes at HUST. Those were higher than the average contribution percentages of total SIA to PM concentrations in non-episode periods for PM2.5 (25.6%) and PM0.1 (10.6%) at HUST. Among meteorological factors, wind speed largely affected PM2.5 concentration and SIA of PM2.5. Relative humidity, pressure, temperature, and radiation had a good correlation with SIA of PM0.1 and a moderate correlation with PM0.1. Concentration-weighted trajectory analysis demonstrated that PM2.5 and SIA levels were also influenced by long-range transportation from the upper areas. This study highlighted the importance and served as pioneered research on SIA contribution to PM2.5 episodes in the country.

Keywords: SIA, PM2.5, PM0.1, Air pollution episodes, Hanoi


Particulate matter (PM) pollution is a common phenomenon of great concern in recent decades in Southeast Asia and in Hanoi specifically. PM can be classified by aerodynamic diameter. PM2.5 is particulate matter with an aerodynamic diameter of less than 2.5 µm, and PM0.1 is particulate matter with an aerodynamic diameter of less than 0.1 µm. PM2.5 is the most concerning air pollutant in Hanoi because of its high annual levels exceeding the national ambient standards (NAAQS) QCVN 05/2013:BTNMT of 25 µg m–3 and WHO guidelines of 5 µg m–3. For example, the current annual monitoring data at an urban and a transportation site from August 2019 to June 2020 were 46 and 49 µg m–3, respectively (Makkonen et al., 2023). Daily PM2.5 often increases to levels higher than the NAAQS of 50 µg m–3 in the winter and spring. PM0.1 is also of concern in Hanoi because of its high levels (Huyen et al., 2023; Dung et al., 2020). Additionally, small particles within the PM0.1 range can later grow into bigger particles, increasing PM2.5 levels significantly.

Air pollution episodes are periods with high pollutant levels. Because higher levels of pollutants pose greater health effects, air pollution episodes significantly affect public health. Luong et al. (2017) showed that in Hanoi from 2010 to 2011, hospitalization rates of children under five years old increased by 1.4%, 2.2%, and 2.5%, respectively, for every 10 µg m–3 increase in PM10, PM2.5, and PM1.0. There is no official definition of a PM episode in Vietnam. In this study, the term pollution episode is defined as the period when daily average PM2.5 concentrations exceed the NAAQS of 50 µg m–3 for at least two consecutive days.

Because of the serious impact of air pollution episodes, there were many studies about air pollution episodes in the world as well as in the Southeast Asia (SEA) region. For instance, a study of PM2.5 episodes in 100 cities across the world from 2013 to 2017 (Morawska et al., 2021) showed that the frequency, magnitude, and duration of severe air pollution episodes have been increasing in 46 out of 100 cities. In SEA, several studies on the periods of high PM levels have been conducted (Sulong et al., 2017; Jaafar et al., 2018; Pinto et al., 1998; See et al., 2007; Huang et al., 2016; Kim Oanh and Leelasakultum, 2011; Chomanee et al., 2020). Based on chemical compositions, different causes of high PM levels in SEA have been identified (Van et al., 2022 and references therein). Besides SEA, several studies about air pollution episodes were investigated in China, showing deep insights into the episode formation mechanisms (Tao et al., 2017; Wang et al., 2021; Yang et al., 2015; Shen et al., 2017). The chemical compositions of particles are important information to identify the emission sources and understand the transformation processes of particles. This is the scientific underpinnings for countries to propose effective management strategies to reduce the negative consequences of PM. Lying in SEA, next to China, with reports of several episodes in winter (Ly et al., 2018; Van et al., 2022), Vietnam has no studies that have been conducted digging into the chemical compositions of PM in episode periods. This study aims to fill this information gap, concentrating on secondary inorganic aerosol (SIA) components.

SIA are the main components of PM2.5 in episode periods in SEA as well as in normal periods (Van et al., 2022 and references therein). They are denoted ‘secondary’ since they are the products of physical and chemical processes after emission into the atmosphere. The main SIA contains NH4+, SO42 and NO3 (occurring as NH4NO3, NH4HSO4, and (NH4)2SO4). These SIA are produced when SO2, NOx, and NH3 undergo complex chemical reactions in the air, on droplets, and particles (Weijers et al., 2010; An et al., 2019). SO42, NO3, and NH4+ are mainly derived from anthropogenic origin, as their precursor gases are largely emitted from transportation, energy production, and agricultural sources. Few studies on PM’s chemical constituents, including SIA, were conducted in Hanoi, such as Dung et al. (2020), Makkonen et al. (2023), and Huyen et al. (2021, 2023). However, they mainly concentrated on seasonal variations of SIA rather than focusing on SIA contributions during high-level periods.

Many studies in China, South Korea, Malaysia, and Europe demonstrated that the level of SIA increased in air pollution episodes, and this increment contributed to heavy pollution (Tao et al., 2017; Tao et al., 2014; Li et al., 2016a; Shen et al., 2017; Wang et al., 2021; Yang et al., 2015; Geun et al., 2016; Sulong et al., 2017; Weijers et al., 2010; Allen et al., 2019). SIA’s main gaseous precursors are SO2, NOx, and NH3 (Weijers et al., 2010). SO2 is released by coal-fired power plants and industrial sources such as cement, smelters, and industrial boilers. NOx is mainly emitted from fuel combustions in transportation, coal-fired plants, and other industrial sources (Weijers et al., 2010) and partly from bacterial processes, wildfires, and lightning (Weijers et al., 2010; Thurston, 2008). Meanwhile, agricultural activities are the main source of emissions of NH3 (Weijers et al., 2010; Behera et al., 2013). Thus, the reduction of gaseous precursors has been linked to downward trends in PM levels. Numerous studies have shown that reducing agricultural NH3 emissions can result in a reduction in PM2.5 (Wang et al., 2018; Cheng et al., 2021; Pozzer et al., 2017; An et al., 2019; Mar et al., 2016; Huang et al., 2012). A study in China also reported that the reduction of NH3 may even be more effective in reducing PM2.5 than decreasing SO2 and NOx emissions (Li et al., 2016b). The absolute impact on PM2.5 reduction was found strongest in East Asia and China, even for small source reductions (Pozzer et al., 2017). Simulations conducted in Europe and North America have not shown immediate changes when limiting agriculture NH3 sources, however, a significant downtrend can still be seen clearly when NH3 emissions are systematically reduced (Pozzer et al., 2017).

This work aims to investigate the contribution of SIA to PM2.5 concentrations during air pollution episodes in Vietnam. Water-soluble ions in daily PM2.5 and PM0.1 samples were collected in a dry winter in Hanoi and analyzed. The percentage of SIA of PM2.5 and PM0.1 in episode and normal periods data were calculated to see the contribution of SIA to those PM levels. Pearson correlation and polar plots were applied to investigate the effects of meteorological factors on PM2.5 and PM0.1. The HYSPLIT, especially the concentration-weighted trajectory (CWT) model was applied to determine the effects of long-range transport on PMs and SIA.


2.1 Sampling Site

The location of the monitoring site is shown in Fig. 1. The monitoring site was selected in Hanoi, the capital of Vietnam. More information about Hanoi’s characteristics and meteorological conditions can be found in the studies by Hien et al. (2002), Hai and Kim Oanh (2013), Ly et al. (2018), and Huyen et al. (2021). In short, Hanoi is an actively developed city with high pressure of rapid urbanization and a high increasing percentage of vehicles. Hanoi meteorology is divided into the northeast monsoon in winter and the southeast monsoon in summer. From October to December, northerly to northeasterly flow coming from the inland of China brings mainly dry and cold air (Hien et al., 2002). From January to March/April, air masses have to travel a long way over the Pacific Ocean before reaching North Vietnam via the Gulf of Tonkin bringing humic air (Hien et al., 2002). The meteorological conditions have been demonstrated to largely affect the air quality of the city (Hien et al., 2002; Hai and Kim Oanh, 2013; Ly et al., 2018; Huyen et al., 2021).

 Fig. 1. Sampling site at Hanoi University of Science and Technology (HUST), Hanoi, Vietnam (21°00′18′′N, 105°50′38′′E), the Northern Monitoring Center (CEM) (21°2′56′′N, 105°52′57′′E) and additional data site (BAM) at American Club Hanoi, No. 21 Hai Ba Trung Street, Hoan Kiem district (21°1′25′′N, 105°51′14′′E).Fig. 1. Sampling site at Hanoi University of Science and Technology (HUST), Hanoi, Vietnam (21°00′18′′N, 105°50′38′′E), the Northern Monitoring Center (CEM) (21°2′56′′N, 105°52′57′′E) and additional data site (BAM) at American Club Hanoi, No. 21 Hai Ba Trung Street, Hoan Kiem district (21°1′25′′N, 105°51′14′′E).

The first sampling site was at the rooftop of a three-storeyed building at Hanoi University of Science and Technology (HUST) (No. 1 Dai Co Viet, Hai Ba Trung district, Hanoi, Vietnam (21°00′18′′N, 105°50′38′′E)). A detailed description of this site has been presented in the study of Huyen et al. (2021). In short, this site is surrounded by residential houses and is more than 100 m away from roads with heavy traffic. The PM level and composition at this sampling site can be influenced by different emission sources in this urban area, such as transportation, construction, and cooking activities. It can therefore be considered both a mixed site and an urban site. The second sampling site (CEM) was on the rooftop of a one-storeyed building at the North Center of Monitoring, Vietnam Administration of Environment. The sampling site is next to the six-lane Nguyen Van Cu Street. It can be considered as a transportation site.

2.2 PM2.5 and PM0.1 Monitoring and Additional Data

2.2.1 Sampling method

Our sampling methodology has been previously reported in our article on PM concentrations (Ha et al., 2023) as well as in the paper of Huyen et al. (2023) investigating the seasonable variation of PM components. A total of 72 daily PM2.5 and 76 daily PM0.1 samples were collected during the dry winter (October 14–December 31, 2020) at HUST for 23.5 hours from 10:00 to 9:30 of the next day (Ha et al., 2023). Nine PM2.5 and PM0.1 samples were collected from December 20 to December 28, 2020, at CEM with the same sampling time frame. PM2.5 and PM0.1 samples were collected on 47 mm quartz fiber (2500 QAT-UP, Pall Corp., USA) using a cyclone device (URG-2000-30EH University Research Glassware Corp., Chapel Hill, NC, USA) with a flow rate of 16.7 L min1 and on 55 mm quartz fiber (2500 QAT-UP, Pall Corp., USA) using Nanosampler II (Model 3182, KANOMAX, Suita, Japan) at a flow rate of 40 L min1, respectively. Before sampling, filters were baked at 350°C for 2 hours. After sampling, each filter was placed into a petri dish and kept separately in an aluminum bag. The samples were stored in a freezer at –20°C to avoid volatilization and additional reactions, before undergoing further analysis.

2.2.2 Analysis

a. Mass analysis

To determine the mass of PM, filters were weighted on a microbalance (readability 1 µg) in a humidity and temperature-controlled room. A detailed description of sample filter weighing was presented in the study by Ha et al. (2023).

b. Ion analysis

The water-soluble ion components in the analysis target included cations: K+, Na+, Ca2+, Mg2+, and NH4+ and anions: Cl, SO42, NO3 (WMO/GAW, 2003) by the ion chromatograph device ICS-1600, Dionex Corp., Sunnyvale, CA, USA. The process of determining the ionic composition was made in 2 main steps: extraction and sample analysis. The method has been presented in the study of Huyen et al. (2023).

2.2.3 Additional data

Hourly data of PM2.5 from a Beta Attenuation Monitor (BAM), 2.1 km from the monitoring site (HUST) were obtained to compare with the study’s results. The observation site is presented in Fig. 1.

Hanoi meteorological data were collected through R software version 4.1.3. Among those data, wind speed (Ws), wind direction (Wd), relative humidity (RH), and temperature (Temp) were observed at Noi Bai International Airport (approximately 30 km away from the HUST sampling site). Other meteorological factors (surface pressure (Press), radiation (Rad), and rainfall data (Pr)) were measured at Hanoi DONRE sites, which were presented on the website (

2.3 Data Analysis

R software version 4.1.3 with various support packages including BMA, psych, openair, worldmet, etc. was used to perform HYSPLIT, concentration-weighted trajectory, Pearson correlation, and polar plots.

HYSPLIT was used to determine the air mass trajectory. Concentration-weighted trajectory (CWT) analysis was then used to investigate the effects of the long-range transport on PM2.5 and SIA levels in Hanoi. HYSPLIT data inputs were downloaded from the National Oceanic and Atmospheric Administration (NOAA) websites: More details of the HYSPLIT model and CWT were presented in NOAA (2018). According to Cohen et al. (2010), mixing heights in Hanoi during 206 days of sampling time between 2005 and 2006 ranged from 270 m (early morning) and 920 m (late afternoon). In this study, 500 m in height was selected to simulate air trajectories to Hanoi.


3.1 Characteristics of PM2.5 and PM0.1

3.1.1 PM2.5 and PM0.1 variations

The temperature during the research period ranged from 12°C–26°C, and relative humidity ranged from 38%–95% (Fig. S1). Those two parameters were well presented for dry winter periods.

The detailed results of PM2.5 and PM0.1 at HUST are presented by Ha et al. (2023) and depicted in Fig. 2(a). PM2.5 measured by BAM at a site 2.1 km away from the HUST site and PM2.5 measured at CEM are presented in Fig. 2(b). PM2.5 concentrations at HUST varied from 19 to 147 µg m–3 (with an average of 59.5 µg m–3) (Ha et al., 2023). For BAM data, PM2.5 varied from 9 to 120 µg m–3 with an average of 53 µg m–3. The concentration trends of PM2.5 in HUST and BAM sites are quite similar. The concentrations of PM2.5 at CEM were in the same range (57–117 µg m–3) as those of two other sites during the same period.

Fig. 2. Water-soluble ions and PM2.5 concentration: (a) at HUST site (from October 14 to December 31, 2020); (b) at CEM site (from December 20 to December 28, 2020). Note: The blue shades showed the episode period. The orange shades showed the non-episode period.Fig. 2. Water-soluble ions and PM2.5 concentration: (a) at HUST site (from October 14 to December 31, 2020); (b) at CEM site (from December 20 to December 28, 2020). Note: The blue shades showed the episode period. The orange shades showed the non-episode period.

Seven air pollution episodes (as defined as two consecutive days with PM2.5 higher than NAAQS of 50 µg m–3) were identified during the sampling periods, lasting from two to eight days (Ha et al., 2023). Those determined episodes also fit well with data measured by BAM during the same sampling period (Fig. 1(a)). For further analysis, data from HUST, BAM, and CEM in the period of 20–28 December, which is an episode based on data at HUST, were compared. The averages of PM2.5 in this period in these three sites were comparable at 82.1, 75.4, and 88.2 µg m–3 at HUST, BAM, and CEM, respectively. The intensives of the episode (period) at HUST and CEM were nine days and at BAM was eight days. Furthermore, given that similar concentration trends of PM2.5 have been demonstrated in four sites within 60 km in the Red River delta during previous winter periods (Ly et al., 2021), we can refer that PM2.5 episodes often cover a large area and air pollution episodes from our sampling site can be indicative of high pollution conditions in the broader area.

PM0.1 concentrations at HUST varied from 2 to 13 µg m–3, with an average concentration of 6 µg m–3 (Ha et al., 2023). The fractions of PM0.1 to PM2.5 in episode and non-episode periods were 9.2% and 15.5%, respectively. PM0.1 in the investigation period at CEM varied from 9 to 16 µg m–3 with an average of 13.1 µg m–3, more than two times higher than the value at HUST of 7.4 µg m–3 at the same time (Fig. S2). The much higher levels of PM0.1 in CEM, which is the road site, demonstrated the local effect of traffic on PM0.1. The effect of traffic is less significant on PM2.5. There is no regulated limit for PM0.1 in Vietnam, thus, PM0.1 concentrations in several cities in the world were used for comparison with our data (Table 1). The average concentration of PM0.1 at HUST in this study was within the same range as those measured in the same/nearby sites of other studies (Dung et al., 2020; Huyen et al., 2021, 2023). Those levels were close to those in European cities. The PM0.1 concentration in CEM was approximately two times higher than it was at HUST and almost equal to the value in Beirut.

Table 1. PM0.1 concentration compared to other studies (µg m–3).

3.1.2 Ion components in PM2.5 and PM0.1

a. Ion components and SIA percentage in PM2.5 and PM0.1 at HUST and CEM sites

Fig. 2(a) shows that the concentration trend of ion components in PM2.5 at HUST followed quite well those of PM2.5 concentrations. All PM2.5 peaks observed during the sampling period, such as from November 4 to November 7, coincided with a high SIA concentration. The Pearson correlation factors of SIA (SO42, NO3, NH4+) and PM2.5 were 0.77–0.84. The contribution of water-soluble ions to PM2.5 and PM0.1 concentration in air pollution episodes and non-episode periods are presented in Fig. 3. Total water-soluble ions contributed, on average, approximately 32.7% to the PM2.5 mass concentration during the sampling time, while SIA (SO42, NO3, NH4+) contributed an average of 27.5% (6.2%–51.6%). Among the analyzed ions during sampling time, SO42 contributed the highest fraction of PM2.5 concentration of 12.7%, followed by NO3 (8.2%) and NH4+ (6.6%). These SIA component ratios were also within the same range as those reported in Hanoi (Huyen et al., 2021; Hai and Kim Oanh, 2013) and other SEA countries (Van et al., 2022 and references therein). The ion concentrations at CEM (Fig. 2(b)) did not follow the trend of PM2.5 as at HUST. The SIA contributed 29.0% of PM2.5 at CEM during the investigation period, lower than it was at HUST in that period of 38.1% (Fig. 2(b)).

Fig. 3. Contribution of water-soluble ions in PM2.5 and PM0.1: (a) PM2.5 in air pollution episode periods; (b) PM2.5 in non-episode periods; (c) PM0.1 in air pollution episode periods; (d) PM0.1 in non-episode periods.Fig. 3. Contribution of water-soluble ions in PM2.5 and PM0.1: (a) PM2.5 in air pollution episode periods; (b) PM2.5 in non-episode periods; (c) PM0.1 in air pollution episode periods; (d) PM0.1 in non-episode periods.

The trend of SIA ions in PM0.1 also followed quite well with PM0.1 variation (Fig. S2). The Pearson correlation factors of PM0.1 and its SO42, NO3, and NH4+ were 0.64, 0.39, and 0.56, respectively. The SIA contribution to PM0.1 at HUST was 12.5% (3.0%–24.6%) during the whole sampling time. The SIA contribution to PM0.1 at CEM was 7.9% (Fig. S3), lower than it was at HUST in the investigated period of 13.9%. These values are within the same range as the SIA contribution of 16.28 ± 2.67% at a nearby site presented in the study by Dung et al. (2020). The higher SIA contribution percentage in PM2.5 than in PM0.1 (approximated two times) can be explained by the fact that PM2.5 was largely affected by long-range transportation, the PM2.5 source was regional (Ly et al., 2021; Thuy et al., 2018) whereas PM0.1 was mainly local (Dung et al., 2020). Several previous studies demonstrated a high contribution percentage of SIA in PM2.5 in the region. For example, the percentage of SIA in PM2.5 in Guangdong was high (Wen et al., 2022; Huang et al., 2014).

b. SIA percentages in PM2.5 and PM0.1 at HUST in haze and non-haze periods

There were no significant differences in average contribution percentages of total SIA to PM2.5 concentration between episode (29.0%) and non-episode periods (25.6%) at HUST. This similar contribution percentage is different from the increment trend of SIA percentages when PM2.5 increases higher than 50 µg m–3 in Guangdong and higher than 100 µg m–3 in Beijing (Huang et al., 2014) but not different from the unclear trend in Xi’an and Shanghai (Huang et al., 2014). The contribution of SO42/NO3/NH4+ to the PM2.5 concentration in non-episode and episode periods at HUST were 12.7%/6.5%/6.4% and 12.6%/9.7%/6.7%, respectively.

For PM0.1, the average contribution percentage of total SIA to PM0.1 concentration increased from 10.6% in non-episode periods to 14.1% in episode periods. The contribution of SO42/NO3/NH4+ to the PM0.1 concentration in non-episode and episode periods at HUST were 3.8%/3.4%/3.4% and 5.2%/4.2%/4.7%, respectively. All ions had increasing contribution percentages in episodes compared with non-episode periods.

As described in Section 1, SO2 and NOx (the precursors of sulfate and nitrate ions) are mainly emitted from transportation, power plants, and industrial sources, but NH3 (precursor of NH4+) is mainly emitted from the agriculture field. The reductions of emissions from those sources, especially NH3 from agriculture are anticipated to reduce the SIA levels, thus reducing PM2.5 levels in long-term periods as well as during episodes.

The high concentration of Ca2+, which contributed approximately 1.7% to PM2.5 might be due to construction activities that took place in the vicinity of the sampling. K+ contributed approximately 1.2% to the PM2.5 concentration, which might be due to biomass and coal burning (Yu et al., 2018). However, no noticeable variation during the sampling time was observed for Na+, Mg2+, and Cl, which mainly originate from natural resources.

To further analyze the differences of each component of PM between episode and non-episode periods, the differential percentages were calculated and presented in Fig. 4. For PM2.5, whereas SO42 almost remained constant between non-episode periods and episode periods (decreased by 0.4%), components excluding SO42 have increased. NO3 increased by 49.4% in its contribution percentage. NH4+ percentage slightly increased by 4.3% compared to normal periods. For PM0.1, the contribution of NO3 increased by approximately 23.5% during the air pollution episodes. Meanwhile, the percentage of SO42 and NH4+ in PM0.1 increased by approximately 37.6% and 35.0%, respectively.

Fig. 4. Changing the percentage of SIA ion contribution and meteorological parameters from normal to episodes.Fig. 4. Changing the percentage of SIA ion contribution and meteorological parameters from normal to episodes.

Fig. 4 also presents differential percentages of meteorological parameters between episode and non-episode periods. The data shows a large change between episodes and non-episode periods. The windspeed and solar radiation had a remarkable change with pollution levels, whereas temperature and relative humidity remained approximately stable (the temperature increased by 2.3% and RH decreased by 10.3%). Specifically, wind speed during air pollution episodes decreased by 31.0% compared to non-episode periods. In contrast, solar radiation increased by 46.2%. This movement was consistent with the theory that increasing solar radiation promotes photos curated reactions leading to secondary particle formation.

3.1.3 Correlation of ion concentrations at the HUST site

The concentration of NH4+ was strongly correlated with that of SO42 and NO3, with correlation coefficients (R2) of 0.90 and 0.89, respectively (Fig. 5). From the result shown in Fig. 5, it could be inferred that NH4+ combined with SO42 to form both (NH4)2SO4 and NH4HSO4. Besides, the R2 of NH4+ and Cl was 0.60, thus NH4+ neutralized SO42 to form ammonium sulfate and ammonium nitrate first, then the remaining NH4+ combined with Cl to generate NH4Cl. The high correlation coefficiencies of NH4+ with SO42 and NO3 were well in line with the research at the same site of Huyen et al. (2021), which also demonstrated that cations and anions in PM2.5 and PM0.1 had a high Spearman correlation coefficiency.

Fig. 5. Correlation between cation and anion during sampling time: (a) NH4+ versus SO42–; (b) NH4+ versus NO3–; (c) NH4+ versus Cl–.Fig. 5. Correlation between cation and anion during sampling time: (a) NH4+ versus SO42; (b) NH4+ versus NO3; (c) NH4+ versus Cl.

3.2 Effects of Meteorological Factors, Long-range Transport on PM2.5 and PM0.1 and SIA Levels

3.2.1 Effects of long-range transport

To investigate the influence of long-range transport on PM2.5 levels, 72 h backward air trajectories to Hanoi over the study period were examined. Fig. 6 shows two main clusters of air trajectory to Hanoi (C1: northeast-originated air mass; C2: east-originated air mass) during the sampling periods. The winter of Vietnam was affected by the northeast monsoon, so the northeast and east were the two predominant directions (Dung et al., 2020; Ly et al., 2018; Hien et al., 2002). The frequency of air mass No. 1 which started inside China and then arrived in Hanoi in the northeast direction was 79.7%. In Vietnam, they passed Quang Ninh, Bac Ninh, and Hai Duong provinces. Another came from the South China Sea passing through Hai Phong, Hai Duong, and Hung Yen provinces in Vietnam with 20.3% of frequency. Hai Phong and Quang Ninh are two neighboring provinces with a high density of coal-fired power plants. It was found that they had various sources, including urban, traffic, industry, coal, and oil combustion (Chifflet et al., 2018; Hang and Kim Oanh, 2014). Hai Duong and Bac Ninh provinces were affected by local sources such as coal combustion (from the Pha Lai coal-fired power plant), biomass burning, traffic, and long-range transportation.

Fig. 6. Trajectories and trajectory clusters in the research period.Fig. 6. Trajectories and trajectory clusters in the research period.

Fig. 7(a) shows the PM2.5 CWT result. The CWT covered an area ranging from Gansu province (China) in the north to Nghe An province (located in north central Vietnam) in the south. The range of color from blue to red indicated increasing concentrations. The darker color indicated higher potential sources of pollutants. The darkest area of PM2.5 was found around the Taklamakan and Gobi deserts, approximately 3000 kilometers from the sampling site. This was also the longest trajectory before entering the sampling site. The long-range transport related to the Gobi desert corresponded with the study by Cohen et al. (2010) which proved that the air mass from the Taklamakan and Gobi deserts contributed to air pollution episodes in Hanoi. High levels of PM2.5 were also found in the southside of mainland China.

Fig. 7. (a) Concentration weight trajectory of PM2.5; (b) Concentration weight trajectory of SIA ion in PM2.5 in the sampling time.Fig. 7. (a) Concentration weight trajectory of PM2.5; (b) Concentration weight trajectory of SIA ion in PM2.5 in the sampling time.

To our knowledge, no study has used CWT analysis for SIA in Vietnam up to date. Due to their lifetimes lasting days to weeks, SIA could be transported far away from the source areas (Allen et al., 2019). In this paper, CWT of SO42, NO3, NH4+, and Cl were simulated, which is shown in Fig. 7(b). The CWT maps of all ions are relatively similar, with the regions of the highest CWT heat map of SIA around South China including Guanxi and the southern Guangdong provinces, nearby and along the ocean, similar to those of PM2.5 CWT. However, the strongest CWT of PM2.5 around the Taklamakan and Gobi deserts does not appear similarly in the map of SIA. It is anticipated because of the different PM compositions in the Taklamakan and Gobi desert regions (rich in dust) and those in south China (high SIA percentage). For example, the percentage of SIA in PM2.5 in Guangdong was demonstrated to be high (Wen et al., 2022; Huang et al., 2014).

3.2.2 Effects of meteorological factors on PM2.5, PM0.1, and their SIA at HUST

The Pearson correlation analysis between meteorological parameters and PM2.5, PM0.1, and their SIA (Table S1) shows that wind speed had a quite high correlation with PM2.5 (r = –0.68), and its SIA ions (r from –0.59 to –0.56). However, wind speed had a low correlation with PM0.1 (r = –0.35) and its SIA ions (r from –0.35 to –0.24). The reason for the different effects of wind speed on the SIA of PM2.5 and PM0.1 can be explained by the effect of wind speed on PM2.5 and PM0.1 levels. As the SIA of PM2.5 had a strong correlation with PM2.5 concentration (r from 0.77 to 0.84), the diluted effect of high wind speed on PM2.5 also reduced the levels of its SIA. However, as wind speed did not affect PM0.1 levels, wind speed did not significantly affect its SIA. All other meteorological factors had no significant effects on PM2.5, PM0.1, and SIA of PM2.5 (|r| < 0.5). It is interesting to note that the SIA of PM0.1 was significantly affected by temperature, RH, and atmospheric pressure, radiation. RH had correlation factors of –0.74, –0.62, and –0.73 with SO42–, NO3, and NH4+, respectively. The temperature had correlation factors of –0.55 with NO3. Atmospheric pressure had correlation factors of –0.58, and –0.53 with NO3, NH4+. Radiation had correlation factors of 0.64, and 0.54 with SO42–, and NH4+, respectively. The effects of those meteorological factors on the SIA of PM0.1 demonstrate the importance of those meteorological factors for the forming and depleting mechanism of those SIA ions in PM0.1.

The polar plots of PM2.5 (Fig. S4) show that low wind speed (< 3 m s–1) in all directions is related to high PM2.5 concentration. When wind speed is low, the air is relatively stagnant, and the wind direction does not significantly affect the PM2.5 concentration. The highly effective effect of wind speed is well in line with the Pearson correlation analysis above. The polar plot pointed out the importance of the build-up of local emissions for high PM2.5 episodes. For PM0.1, wind speed up to 4 m s–1 from the southwest is related to high PM0.1 concentration. Low wind speeds from other directions did not link with comparable high PM0.1 concentration as those with wind direction from the southwest. The results implied that there were local sources of PM0.1 from the southwest. It is predicted that the source is mainly transportation as the southwest area of the site has a high density of transportation. The effect of transportation on PM0.1 also agrees with the research of Dung et al. (2020). Results of polar plots confirm the previous discussion that PM2.5 sources were more regional and PM0.1 sources were more local. However, at low wind speeds, the build-up of local PM2.5 significantly contributes to the PM2.5 episode.

Further analysis of SIA compositions at low wind speed sampling periods (wind speed < 2 m s–1) and high wind speed periods (wind speed ≥ 2 m s–1) at HUST showed a difference in average SIA percentages of 31.9% and 26.4%, respectively. Average SO42– percentages remain at 12.6% (6.7%–18.0%) in two periods, while the NO3 proportion average in low wind speed periods of 11.9% (3.8%–24.6) was significantly higher than it in high wind speed periods of 7.4% (0.7%–18.4%). SIA percentage in low and high wind speeds is comparable to those in episode and non-episode because of the significant affection of wind speed to PM2.5 concentrations as discussed above. Additionally, SO42–/NO3/NH4+ percentage distribution at high wind speed is quite similar to the regions of the highest CWT heat map such as Nanning (23.5%/5.7%/9.4% during February–March 2016 as claimed by Mao et al. (2021)), Pearl River Delta (15.2%/12.7%/11.1% in 2019 as stated by Yan et al. (2020)), indicating long-term transportation from these areas during strong wind periods.


This study investigated water-soluble ion components of PM2.5, and PM0.1 in air pollution episodes (daily average PM2.5 concentrations exceeded the NAAQS of 50 µg m–3 for a minimum of two consecutive days) and non-episode periods at an urban site (HUST) and an episode period at a transportation site (CEM) in dry winter periods in Hanoi, Vietnam. PM2.5 concentration in a nearby urban site was also examined.

The PM2.5 periods are similarly found at three sites within 6.2 km in Hanoi. This similarity confirms the large covering area of PM2.5 episodes in the area. The average concentrations of PM2.5 and PM0.1 in episode periods were 74.6 and 6.8 µg m–3, respectively. The average concentrations of PM2.5 and PM0.1 in non-episode periods were 31.5 and 4.9 µg m–3, respectively. The fractions of PM0.1 to PM2.5 in episode and non-episode periods were 9.2% and 15.5%, respectively.

SIA contributed 29.0% and 14.1% to PM2.5 and PM0.1 composition during air pollution episodes, respectively at HUST. The SIA contribution in episodes was slightly higher than the contribution in non-episode periods for PM2.5 (increasing 13%). The investigated episode period in a nearby transportation site, CEM has a similar SIA contribution to those at HUST. The CWT of PM2.5 and its SIA demonstrated that PM2.5 and SIA were affected by long-range transportation. The contribution of SIA in episodes was significantly higher than the contribution in non-episode periods for PM0.1 of 10.6% at HUST (increasing 32%).

PM2.5 were more regional as demonstrated by CWT and the fact that their concentrations and variations at nearby sites were quite similar. However, the results of the polar plot that the low wind speed correlated to high PM2.5 also demonstrated that the build-up of local PM2.5 significantly contributed to PM2.5 episodes. PM0.1 sources, on the other hand, were more local as their concentrations at the transportation site were double those at the urban site. Polar plot analysis with significantly higher PM2.5 concentration when the wind direction was southwest suggested for a local source from the southwest.

Pearson correlations showed that wind speed affected PM2.5 and its SIA concentrations but did not affect PM0.1 and its SIA levels. RH, pressure, temperature, and radiation have high correlations with the SIA of PM0.1 but no significant correlation with the SIA of PM2.5.


We would like to thank Ich-Hung Ngo for his technical support. We would like to thank Divya Gudur for her English support. This work is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.99–2019.322.


  1. Allen, S.A.A., Ree, A.G., Ayodeji, S.A.M., Deborah, S.A.E., Ejike, O.M. (2019). Secondary inorganic aerosols: impacts on the global climate system and human health. Biodivers. Int. J. 3, 249–259.

  2. An, Z., Huang, R.J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z., Han, Y., Gu, Z., Ji, Y. (2019). Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes. PNAS 116, 8657–8666.

  3. Behera, S.N., Sharma, M., Aneja, V.P., Balasubramanian, R. (2013). Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and deposition on terrestrial bodies. Environ. Sci. Pollut. Res. Int. 20, 8092–8131.

  4. Borgie, M., Dagher, Z., Ledoux, F., Verdin, A., Cazier, F., Martin, P., Hachimi, A., Shirali, P., Greige-Gerges, H., Courcot, D. (2015). Comparison between ultrafine and fine particulate matter collected in Lebanon: Chemical characterization, in vitro cytotoxic effects and metabolizing enzymes gene expression in human bronchial epithelial cells. Environ. Pollut. 205, 250–260.

  5. Cheng, L., Ye, Z., Cheng, S., Guo, X. (2021). Agricultural ammonia emissions and its impact on PM2.5 concentrations in the Beijing-Tianjin-Hebei region from 2000 to 2018. Environ. Pollut. 291, 118162.

  6. Chifflet, S., Amouroux, D., Bérail, S., Barre, J., Van, T.C., Baltrons, O., Brune, J., Dufour, A., Guinot, B., Mari, X. (2018). Origins and discrimination between local and regional atmospheric pollution in Haiphong (Vietnam), based on metal(loid) concentrations and lead isotopic ratios in PM10. Environ. Sci. Pollut. Res. Int. 25, 26653–26668.

  7. Chomanee, J., Thongboon, K., Tekasakul, S., Furuuchi, M., Dejchanchaiwong, R., Tekasakul, P. (2020). Physicochemical and toxicological characteristics of nanoparticles in aerosols in southern Thailand during recent haze episodes in lower southeast Asia. J. Environ. Sci. 94, 72–80.

  8. Cohen, D.D., Crawford, J., Steller, E., Bac, V.T. (2010). Long range transport of fine particle windblown soils and coal-fired power station emissions into Hanoi between 2001 to 2008. Atmos. Environ. 44, 3761–3769.

  9. Dung, N.T., Thuy, N.T.T., Hien, N.T.T., Ly, B.T., Sekiguchi, K., Yamaguchi, R., Thuy, P.C. (2020). Chemical characterization and source apportionment of ambient nanoparticles: A case study in Hanoi, Vietnam. Environ. Sci. Pollut. Res. Int. 27, 30661–30672.​s11356-020-09417-5

  10. Geun, H.Y., Yan, Z., Sung, Y.C., Seungshik, P. (2016). Influence of haze pollution on water-soluble chemical species in PM2.5 and size-resolved particles at an urban site during fall. J Environ Sci. 57, 370–382.

  11. Ha, V.T.L., Van, D.A., Hien, N.T.T., Nam, D.D., Dung, N.T., Ly, B.T. (2023). Concentrations of PM0.1 and PM2.5 at high polluting event days in Ha Noi and the effects of meteorological conditions. Vietnam J. Sci. Technol. 61, 471–479.

  12. Hai, C.D., Kim Oanh, N.T. (2013). Effects of local, regional meteorology and emission sources on mass and compositions of particulate matter in Hanoi. Atmos. Environ. 78, 105–112.

  13. Hang, N.T., Kim Oanh, N.T. (2014). Chemical characterization and sources apportionment of fine particulate pollution in a mining town of Vietnam. Atmos. Res. 145–146, 214–225.

  14. Hien, P.D., Bac, V.T., Tham, H.C., Nhan, D.D., Vinh, L.D. (2002). Influence of meteorological conditions on PM2.5 and PM2.5−10 concentrations during the monsoon season in Hanoi, Vietnam. Atmos. Environ. 36, 3473–3484.

  15. Huang, R.J., Zhang, Y., Bozzetti, C., Ho, K.F., Cao, J.J., Han, Y., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., et al. (2014). High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222.

  16. Huang, X., Li, M., Li, J., Song, Y. (2012). A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products. Atmos. Environ. 50, 9–15.

  17. Huang, X., Betha, R., Tan, L.Y., Balasubramanian, R. (2016). Risk assessment of bioaccessible trace elements in smoke haze aerosols versus urban aerosols using simulated lung fluids. Atmos. Environ. 125, 505–511.

  18. Huyen, T.T., Yamaguchi, R., Kurotsuchi, Y., Sekiguchi, K., Dung, N.T., Thuy, N.T.T., Thuy, L.B. (2021). Characteristics of chemical components in fine particles (PM2.5) and ultrafine particles (PM0.1) in Hanoi, Vietnam: a case study in two seasons with different humidity. Water Air Soil Pollut. 232, 183.

  19. Huyen, T.T., Sekiguchi, K., Ly, B.T., Nghiem, T.D. (2023). Assessment of traffic-related chemical components in ultrafine and fine particles in urban areas in Vietnam. Sci. Total Environ. 858, 159869.

  20. Jaafar, S.A., Latif, M.T., Razak, I.S., Wahid, N.B.A., Khan, M.F., Srithawirat, T. (2018). Composition of carbohydrates, surfactants, major elements and anions in PM2.5 during the 2013 Southeast Asia high pollution episode in Malaysia. Particuology 37, 119–126.​partic.2017.04.012

  21. Kim Oanh, N.T., Leelasakultum, K. (2011). Analysis of meteorology and emission in haze episode prevalence over mountain-bounded region for early warning. Sci. Total Environ. 409, 2261–2271.

  22. Li, L.J., Wang, Z.S., Zhang, D.W., Chen, T., Jiang, L., Li, Y.T. (2016a). Analysis of heavy air pollution episodes in Beijing during 2013-2014. China Environ. Sci. 36, 27–35. (in Chinese)

  23. Li, Y., Tao, J., Zhang, L., Jia, X., Wu, Y. (2016b). High contributions of secondary inorganic aerosols to PM2.5 under polluted levels at a regional station in northern China. Int. J. Environ. Res. Public Health 13, 1202.

  24. Luong, L.M.T., Phung, D., Sly, P.D., Morawska, L., Thai, P.K. (2017). The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam. Sci. Total Environ. 578, 249–255.

  25. Ly, B.T., Yutaka, M., Tomoki, N., Sakamoto, Y., Kajii, Y., Dung, N.T. (2018). Characterizing PM2.5 in Hanoi with new high temporal resolution sensor. Aerosol Air Qual. Res. 18, 2487–2497.

  26. Ly, B.T., Yukata, M., Tuan, V.V., Sekiguchi, K., Thu, T.N., Chau, T.P., Dung, N.T., Hung, N.I., Yuta, K., Hien, N.T., Nakayama, T. (2021). The effects of meteorological conditions and long-range transport on PM2.5 levels in Hanoi revealed from multi-site measurement using compact sensors and machine learning approach. J. Aerosol Sci. 152, 105716.  ttps://​jaerosci.2020.105716

  27. Makkonen, U., Vestenius, M., Huy, L.N., Anh, N.T.N., Linh, P.T.V., Thuy, P.T., Phuong, H.T.M., Nguyen, H., Thuy, L.T., Aurela, M., Hellén, H., Loven, K., Kouznetsov, R., Kyllönen, K., Teinilä, K., Kim Oanh, N.T. (2023). Chemical composition and potential sources of PM2.5 in Hanoi. Atmos. Environ. 299, 119650.

  28. Mao, J., Yang, L., Mo, Z., Jiang, Z., Krishnan, P., Sarkar, S., Zhang, Q., Chen, W., Zhong, B., Yang, Y., Jia, S., Wang, X. (2021). Comparative study of chemical characterization and source apportionment of PM2.5 in South China by filter-based and single particle analysis. Elem. Sci. Anth. 9, 00046.

  29. Mar, V., Xavier, Q., Andrés, A., Augustin, C., Florian, C., Jérôme, D., Bertrand, B., Anke, L. (2016). Secondary inorganic aerosols from agriculture in Europe. ETC/ACM Technical Paper, European Topic Centre on Air Pollution and Climate Change Mitigation, Netherlands.

  30. Morawska, L., Zhu, T., Liu, N., Amouei Torkmahalleh, M., De Fatima Andrade, M., Barratt, B., Broomandi, P., Buonanno, G., Carlos Belalcazar Ceron, L., Chen, J., Cheng, Y., Evans, G., Gavidia, M., Guo, H., Hanigan, I., Hu, M., Jeong, C.H., Kelly, F., Gallardo, L., Kumar, P., et al. (2021). The state of science on severe air pollution episodes: Quantitative and qualitative analysis. Environ. Int. 156, 106732.

  31. National Oceanic and Atmospheric Administration (NOAA) (2018). HYSPLIT Basic Tutorial Contents. 

  32. Pennanen, A.S., Sillanpää, M., Hillamo, R., Quass, U., John, A.C., Branis, M., Hůnová, I., Meliefste, K., Janssen, N.A.H., Koskentalo, T., Castaño-Vinyals, G., Bouso, L., Chalbot, M.C., Kavouras, I.G., Salonen, R.O. (2007). Performance of a high-volume cascade impactor in six European urban environments: Mass measurement and chemical characterization of size-segregated particulate samples. Sci. Total Environ. 374, 297–310.

  33. Pinto, J.P., Grant L.D., Hartlage T.A. (1998). Report on U.S. EPA Air Monitoring of Haze from S.E. Asia Biomass Fires. National Center for Environmental Assessment, Office of Research and Development, US Environmental Protection Agency.

  34. Pozzer, A., Tsimpidi, A.P., Karydis, V.A., De Meij, A., Lelieveld, J. (2017). Impact of agricultural emission reductions on fine-particulate matter and public health. Atmos. Chem. Phys. 17, 12813–12826.

  35. See, S.W., Balasubramanian, R., Rianawati, E., Karthikeyan, S., Streets, D.G. (2007). Characterization and source apportionment of particulate matter ≤ 2.5 µm in Sumatra, Indonesia, during a recent peat fire episode. Environ. Sci. Technol. 41, 3488–3494.

  36. Shen, R., Schäfer, K., Shao, L., Schnelle-Kreis, J., Wang, Y., Li, F., Liu, Z., Emeis, S., Schmid, H.P. (2017). Chemical characteristics of PM2.5 during haze episodes in spring 2013 in Beijing. Urban Clim. 22, 51–63.

  37. Sulong, N.A., Latif, M.T., Khan, M.F., Amil, N., Ashfold, M.J., Wahab, M.I.A., Chan, K.M., Sahani, M. (2017). Source apportionment and health risk assessment among specific age groups during haze and non-haze episodes in Kuala Lumpur, Malaysia. Sci. Total Environ. 601–602, 556–570.

  38. Tao, J., Zhang, L., Cao, J., Zhang, R. (2017). A review of current knowledge concerning PM2.5 chemical composition, aerosol optical properties and their relationships across China. Atmos. Chem. Phys. 17, 9485–9518.

  39. Tao, Y., Yin, Z., Ye, X., Ma, Z., Chen, J. (2014). Size distribution of water-soluble inorganic ions in urban aerosols in Shanghai. Atmos. Pollut. Res. 5, 639–647.​073

  40. Thurston, G.D. (2008). Outdoor Air Pollution: Sources, Atmospheric Transport, and Human Health Effects, in: International Encyclopedia of Public Health, Elsevier, pp. 700–712.​10.1016/B978-012373960-5.00275-6

  41. Thuy, N.T.T., Dung, N.T., Sekiguchi, K., Thuy, L.B., Hien, N.T.T., Yamaguchi, R. (2018). Mass concentrations and carbonaceous compositions of PM0.1, PM2.5, and PM10 at urban locations of Hanoi, Vietnam. Aerosol Air Qual. Res. 18, 1591–1605.​11.0502

  42. Van, D.A., Vu, T.V., Nguyen, T.H.T., Vo, L.H.T., Le, N.H., Nguyen, P.H.T., Pongkiatkul, P., Ly, B.T. (2022). A Review of characteristics, causes, and formation mechanisms of haze in Southeast Asia. Curr. Pollut. Rep. 8, 201–220.

  43. Wang, C., Yin, S., Bai, L., Zhang, X., Gu, X., Zhang, H., Lu, Q., Zhang, R. (2018). High-resolution ammonia emission inventories with comprehensive analysis and evaluation in Henan, China, 2006–2016. Atmos. Environ. 193, 11–23.

  44. Wang, S., Wang, L., Wang, N., Ma, S., Su, F., Zhang, R. (2021). Formation of droplet-mode secondary inorganic aerosol dominated the increased PM2.5 during both local and transport haze episodes in Zhengzhou, China. Chemosphere 269, 128744.​chemosphere.2020.128744

  45. Weijers, E.P., Sahan, E., ten Brink, H.M., Schaap, M., Matthijsen, J., Otjes, R.P., van Arkel, F. (2010). Contribution of secondary inorganic aerosols to PM10 and PM2.5 in the Netherlands; measurements and modelling results. Netherlands Environmental Assessment Agency, The Netherlands. 

  46. Wen, L., Yang, C., Liao, X., Zhang, Y., Chai, X., Gao, W., Guo, S., Bi, Y., Tsang, S.Y., Chen, Z.F., Qi, Z., Cai, Z. (2022). Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China. J. Environ. Sci. 115, 443–452.

  47. World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) (2003). Aerosol measurement procedures, guidelines and recommendations. WMO/TD-No. 1178, GAW Report No. 153, World Meteorological Organization, Geneva.

  48. Yan, F., Chen, W., Jia, S., Zhong, B., Yang, L., Mao, J., Chang, M., Shao, M., Yuan, B., Situ, S., Wang, X., Chen, D., Wang, X. (2020). Stabilization for the secondary species contribution to PM2.5 in the Pearl River Delta (PRD) over the past decade, China: A meta-analysis. Atmos. Environ. 242, 117817.

  49. Yang, Y.R., Liu, X.G., Qu, Y., An, J.L., Jiang, R., Zhang, Y.H., Sun, Y.L., Wu, Z.J., Zhang, F., Xu, W.Q., Ma, Q.X. (2015). Characteristics and formation mechanism of continuous hazes in China: A case study during the autumn of 2014 in the North China Plain. Atmos. Chem. Phys. 15, 8165–8178.

  50. Yu, J., Yan, C., Liu, Y., Li, X., Zhou, T., Zheng, M. (2018). Potassium: A tracer for biomass burning in Beijing? Aerosol Air Qual. Res. 18, 2447–2459. 

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