Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 May 23;303:135013. doi: 10.1016/j.chemosphere.2022.135013

Impact of reduced anthropogenic emissions on chemical characteristics of urban aerosol by individual particle analysis

Li Li a,d, Qiyuan Wang a,b,c,∗, Yong Zhang a, Suixin Liu a, Ting Zhang a, Shuang Wang a, Jie Tian a, Yang Chen e, Steven Sai Hang Ho f, Yongming Han a,b,c, Junji Cao g,∗∗
PMCID: PMC9701139  PMID: 35618050

Abstract

A single particle aerosol mass spectrometer was deployed in a heavily polluted area of China during a coronavirus lockdown to explore the impact of reduced anthropogenic emissions on the chemical composition, size distributions, mixing state, and secondary formation of urban aerosols. Ten particle groups were identified using an adaptive resonance network algorithm. Increased atmospheric oxidation during the lockdown period (LP) resulted in a 42.2%–54% increase in the major NaK-SN particle fraction relative to the normal period (NP). In contrast, EC-aged particles decreased from 31.5% (NP) to 23.7% (LP), possibly due to lower emissions from motor vehicles and coal combustion. The peak particle size diameter increased from 440 nm during the NP to 500 nm during LP due to secondary particle formation. High proportions of mixed 62NO3− indicate extensive particle aging. Correlations between secondary organic (43C2H3O+, oxalate) and secondary inorganic species (62NO3−, 97HSO4− and 18NH4+) versus oxidants (Ox = O3 + NO2) and relative humidity (RH) indicate that increased atmospheric oxidation promoted the generation of secondary species, while the effects of RH were more complex. Differences between the NP and LP show that reductions in primary emissions had a remarkable impact on the aerosol particles. This study provides new insights into the effects of pollution emissions on atmospheric reactions and the specific aerosol types in urban regions.

Keywords: COVID-19, Secondary formation, Chemical characteristics, Mixing state, Individual particles

Graphical abstract

Image 1

Open in a new tab

1. Introduction

Increased attention recently has focused on aerosol particles because of their importance for air quality (An et al., 2019), human health (West et al., 2016), and global climate change (Twohy et al., 2021). To address severe air pollution problems and protect public health in China, a series of clean air policies has been established, such as the “Air Pollution Prevention and Control Action Plan” promulgated in 2013. Despite large reductions in primary emissions after the implementation of these controls (Feng et al., 2019; Zheng et al., 2018), the formation of secondary aerosols has continued to be a serious problem (He et al., 2017; Ma et al., 2017). On the other hand, strict short-term, local anthropogenic emission controls (such as those placed on traffic, catering, and industries) that were implemented for several international events (Li et al., 2016; Sun et al., 2016; Wang et al., 2010, Wang et al., 2010, 2016a; Zhao et al., 2017) have led improvements in air quality. For example, Chen et al. (2015) reported decreases (40–80%) in all target aerosol species during the 2014 Asia-Pacific Economic Cooperation (APEC) period. Meanwhile, Xu et al. (2015) reported that the contributions of secondary inorganic aerosol and organics were reduced by 62–69% and 35%, respectively, during that period, and the particle size distribution also showed a shift in size from 650 nm to 400 nm. Shen et al. (2017) found that emission controls mainly influenced potassium-rich, organic and elemental carbon, and metal particles with diameters of 0.4–1.4 μm during the Second World Internet Conference held in Jiaxing, China.

These and other studies have shown that the effects of transport on the physicochemical characteristics of particles, source contributions, and spatial and temporal variations can become more apparent due to regional pollution controls, even for a short period. The evolution and formation mechanisms of air pollutants need to be further investigated to better understand how control measures can affect aerosol pollution.

In response to the rapid spread of the coronavirus 2019 disease (COVID-19), large-scale restrictions were placed on many outdoor human activities in China (including motor vehicle usage, catering, and industries) (Wang et al., 2020a). Due to large reductions of emissions during the COVID-19 lockdown period in China, both gaseous pollutants [e.g., nitrogen oxides (NOx) and sulfur dioxide (SO2)] and particulate matter (PM, including PM2.5 and PM10) showed clear decreases (Li et al., 2020a; Wang et al., 2020b; Zhao et al., 2020; Zheng et al., 2020). Nonetheless, haze events still occurred frequently in China during the COVID-19 lockdown.

Previous studies have related the occurrence of haze events to increases in the atmospheric oxidization capacity, which promotes the formation of secondary aerosols (Huang et al., 2021; Le et al., 2020; Sun et al., 2020). Unfavorable meteorological conditions have long been recognized as another factor that often contributes to air pollution events (Wang et al., 2020b). Indeed, complex interactions between chemistry, transport, and meteorology have made it difficult to quantify the contributions of primary emissions versus secondary particle formation to air quality. Highly time-resolved data for the characteristics of individual aerosol particles can provide important information relative to the effects and importance of the atmosphere aging processes.

The reactions and processes that drive the evolution of individual particles have been extensively investigated in urban areas. Some notable studies have shown that carbonaceous and the potassium-rich particles were the major particle types, each accounting for more than 30% of the total sources, whereas biomass burning, heavy metal, dust, and other particle types accounted for <15% (Li et al., 2014; Zhang et al., 2015). Other studies have shown that biomass burning, heavy metals, and dust particles have played important roles in specific pollution events (Chen et al., 2017; Ma et al., 2016; Dall'Osto et al., 2014). Fewer in-depth studies, however, have focused on secondary organic components (e.g., oxalic acid), metallic particles (e.g., Pb, Fe, etc.), and organic nitrogen compounds (e.g., imidazole, amine, etc.). Most of these components usually are in relatively low content, but they can have substantial effects on atmospheric reactions and human health (Zhou et al., 2020; Chen et al., 2018; Zhang et al., 2020). Generally, the formation of major secondary components (e.g., nitrate, sulfate and organics) suggests rapid condensation on existing particles during atmospheric aging (Khalizov et al., 2009; Zhang et al., 2013; Huang et al., 2019). Carbonaceous particles are more inclined to be internally mixed with sulfate than nitrate, while the opposite is true for inorganic particles (Zhang et al., 2015; Dall'Osto and Harrison, 2012). Aerosol mixing states can differ greatly with location in response to variations in chemical composition and secondary species (e.g., HSO4 −, NO3 −). These results highlight the differences in the behavior between carbonaceous versus inorganic particles in the atmosphere.

Relatively few studies of individual particles have been conducted during the large-scale COVID-19 lockdowns to date, but in one study, Li et al. (2020a) found that the numbers of carbonaceous particles decreased by 20.2% due to restrictions on industrial production and motor vehicle usage. Significant signal intensities of secondary inorganic ion fragments (46NO2 −, 62NO3 −, and 97HSO4 −) were observed in all classes of particles at Liaocheng, an urban site in the North China Plain. Meng et al. (2021) investigated the impact of the COVID-19 lockdown on the formation of secondary organic aerosols (i.e., oxalic acid-containing particles) using a single-particle aerosol mass spectrometer (SPAMS), and these authors found that relative humidity (RH) promoted the conversion of precursors (e.g., glyoxal and methylglyoxal) to oxalic acid particles during the normal period. The ozone (O3) driven photochemical pathway also played an important role in the formation of oxalic acid particles under low RH, and the O3 concentrations increased remarkably during the COVID-19 lockdown period.

The Fenwei Plain was designated as a major pollution control region in China in 2018. As one of the eleven largest city clusters in the Fenwei Plain, Xianyang often suffers from severe air pollution, especially in winter. Indeed, over 57% of the days have been classified as polluted in fall and winter (i.e., October 2018 to March 2019); 43 days had extremely heavy pollution, and only 76 days had fine air-quality (http://www.scio.gov.cn/index.htm). The air quality of Xianyang has ranked low among the cities in China (http://www.mee.gov.cn, in Chinese). Source apportionments for Xianyang reported by Zhang (2018) showed that secondary species (26.5%) and vehicle exhaust (24.3%) were the two major contributors to PM2.5 pollution while those from coal combustions and biomass burning were 21% and 12% respectively in winter. Therefore, emissions from solid biofuels are of concern in the cold season.

Short-term air pollution control measures typically include restrictions on motor vehicle usage, closures of industries, etc. Studies of the composition, characteristics, and formation mechanisms of PM during restricted periods can provide insights into the causes of air pollution that would be helpful for developing efficient long-term control measures. In this study, the chemical composition and secondary formation of aerosols in Xianyang were investigated using an on-line SPAMS during the COVID-19 lockdown period. This study provides insights into the variations of chemical composition, size distribution, mixing state, and formation mechanism of aerosols in relation to changes in anthropogenic emissions at a heavily polluted city in China.

2. Methodology

2.1. Observation site

On-line measurements of aerosol size distributions and chemical composition were made from the rooftop of a 33-floor height building (distance to ground ∼100 m) that was to the south of downtown Xianyang (34°18′ N, 108°38′ E, Fig. S1) from 1 January to February 18, 2020. The sampling site was in a new development zone and surrounded by residential and commercial areas; it was close to two expressways (G3023 and G344) and adjacent to the Liangsidu Park, which is to the southeast at a distance of ∼2 km. There are some small industries around the site. Based on the run-up time of the Public Health Emergency Response Level I in Shaanxi Province, the sampling campaign was divided into two periods, the normal period (NP) from 1 to January 23, 2020 and the COVID-19 lockdown period (LP) from 24 January to February 18, 2020.

2.2. Measurements

A single-particle aerosol mass spectrometer (SPAMS, Model 0515, Hexin Analytical Instrument Co, Ltd, China) was used to continuously measure individual particles with aerodynamic sizes ranging from 0.2 to 2.0 μm. The flow rate for the SPAMS was 75 mL min−1, and its operating principles have been discussed in detail elsewhere (Li et al., 2011, 2019; Wang et al., 2016b). In brief, after dried particles enter the SPAMS, they are immediately focused and accelerated to different velocities with the use of an aerodynamic lens. The aerodynamic diameters of the particles are then obtained through the use of two fixed distance (6 cm) continuous neodymium/yttrium aluminum garnet (Nd:YAG) diode lasers (532 nm) in the sizing region. The particles’ chemical composition was determined using a desorption/ionization process with a pulsed 266 nm Nd:YAG laser. Counts for the generated positive and negative ion fragments were obtained along with the vacuum aerodynamic diameters.

A diagram of the SPAMS and the principles of the particle size calculations are presented in Fig. S2. Before the SPAMS analyses, the aerodynamic diameters and the mass-to-charge ratio (m/z) of positive and negative ion fragments were calibrated and optimized, respectively (Li et al., 2011). Contamination in the critical orifice was cleaned during the sampling periods, and the inlet pressure was controlled to within 2.40 ± 0.05 Torr. Further information on the SPAMS is provided in Supplemental Information Section 1.1.

2.3. Data acquisition

The YAADA 2.1 software (Allen, 2005), which is a MATLAB-based software toolkit (Version, 2014b, www.mathworks.com), was used to analyze the SPAMS achieved data. The mass spectra imported into YAADA were further processed using an adaptive resonance network (ART-2a) algorithm (Song and Hopke, 1999). For this study, the parameters for the ART-2a were set to a vigilance factor of 0.8, a learning rate of 0.05, and an iteration number of 20 for the data processing. A total of 1,839,083 mass spectra for 40, 306, 899 sized particles were acquired by SPAMS during the sampling campaign, and 9858 clusters were classified using the ART-2a algorithm.

The clusters were further merged into ten major groups, which accounted for 95% of total particles. Information on the composition of the ten groups is presented in Supplemental Information (Section 1.2). Information on the mixing states of the particles was obtained by evaluating the contributions of selected ion fragments to each particle type (Li et al., 2020b; Chen et al., 2016). The criteria used for searching the primary and secondary species in the SPAMS dataset are summarized in Table S2. The mixing states of the particle groups (not including “other” particles) that were classified as primary (based on levoglucosan and 35,37Cl－) and secondary (43C2H3O+, 89HC2O4 −, 97HSO4－, 62NO3－, and 18NH4 +) were then compared between the NP and LP periods.

The mass concentrations of PM2.5 and gaseous species [i.e., nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3)], relative humidity (RH), temperature (T), wind direction (WD) and wind speed (WS) were obtained from the official data source of the Department of Ecology and Environment of Shaanxi Province (http://sthjt.shaanxi.gov.cn). The sampling site where those measurements were made was the national monitoring station at Liangsidu (Fig. S1), which is about 2 km from our SPAMS site. The average values for PM2.5, gaseous species, T and RH are shown in Table S1. Time-series plots of PM2.5, gaseous species and meteorological parameters are shown in Fig. S3, and the variations in these species and parameters are presented in Supplemental Information (Section 1.3).

3. Results and discussion

3.1. Variations of individual particles

The average particle number concentrations and the relative fractions of the particle groups are summarized in Table 1 . The classification criteria for individual particles and the characteristic ion fragments for each particle type may be found in Supplemental Information Section 1.2. The ratios of the number of particles of a particular type to the total number of particles are presented as percentages.

Table 1.

Summary of the number concentrations, relative fractions, and tracer ions for each particle group during the normal and COVID-19 lockdown periods.

Type Number count
 Fraction in total (%)
 Tracer ions
Normal period Lockdown period Normal period Lockdown period
NaK-SN 243,131 202,101 42.2 54.0 23Na+,39K+, 97HSO4−, 62NO3−
EC-aged 181,434 88,883 31.5 23.7 Cn± (n = 1～6), 39K+, 97HSO4−, 62NO3−
EC-fresh 2944 1770 0.5 0.5 Cn± (n = 1～9), 39K+
BB 69,932 47,051 12.1 12.6 39K+, levoglucosan (45CHO2−, 59C2H3O2−, 73C3HO3−), 26CN−, 35,37Cl−, 42CNO−
OC 30,741 6909 5.3 1.8 27C2H3+, 37C3H+, 38C3H2+, 39C3H3+, 43C2H3O+, 51C4H3+, 63C5H3+
Dust 13,370 4020 2.3 1.1 40Ca+, 76SiO3−, 79PO3−
PAH 12,455 15,553 2.2 4.2 178C14H10+, 202C16H10+, 227C18H11+
OCEC 10,738 2375 1.9 0.6 OC and EC ion peaks were coexisted
Metal 5574 1924 1.0 0.5 51V+, 55Mn+, 56Fe+, 64,66,68Zn+, 63,65Cu+, 206,207,208Pb+
Others 5375 3978 0.9 1.1 No obvious characteristic peaks
Open in a new tab

Abbreviations for particle types: NaK-SN: sodium, potassium, sulfate and nitrate; EC: elemental carbon; BB: biomass burning; OC: organic carbon; PAH: polycyclic aromatic hydrocarbon.

During the NP, the largest hourly-averaged number fraction was for the NaK-SN group (42.4%), followed by EC-aged (31.5%), BB (12.1%), OC (5.3%), dust (2.3%), PAH-containing (2.2%), and OCEC (1.9%). The metal containing and EC-fresh particles have no further analysis—their relative percentages were low (≤1%). The NaK-SN fraction was higher by 54.0% during the LP compared with the NP, and that can be attributed to the stronger oxidation during the LP because higher levels of oxidants favor the formation of secondary species. Reductions in traffic emissions during the LP can explain the decrease in the EC-aged fraction (23.7%). The BB fraction (12.6%), in contrast, was relatively consistent between the two periods. Dilution effects associated with changes in air masses between 14 and 18 February (Fig. S4c) are a likely reason why the mass concentration of PM2.5 decreased rapidly after 22:00 on 13 February. After that time, the number concentrations of NaK-SN and EC-aged particles decreased sharply (Fig. S5), while the number concentration of BB particles showed only minor changes. The PAH-containing particles showed an upward trend after the air masses changed, and that resulted in a higher fraction of PAH-containing particles (4.2%) during the LP compared with the NP.

Plots of diurnal variations show that increases in solar radiation were coincident with the formation of secondary species, and this can be seen as regular increases in the NaK-SN fraction from 08:00 to 15:00 local time (LT) during the NP (Fig. 1 ). The NaK-SN group showed minor changes after 16:00 LT, likely because aqueous-phase reactions contributed to the formation of secondary species when the radiative intensity decreased (Fig. S6). Different from the NP, a higher fraction of NaK-SN was seen at nighttime during the LP, suggesting that aqueous-phase reactions promoted secondary particle formation under high RH and O3 (Fig. S6). In contrast, decreases in the concentrations of the precursor NO2 may have diminished the formation of secondary NaK-SN at 17:00 LT during the NP, even though concentrations of O3 were relatively high (Fig. S6). The EC-aged fraction showed similar temporal variations for both periods. That is EC-aged showed increases from 05:00 to 17:00 LT, and that can be ascribed to increases of traffic and motor vehicle emissions during the daytime despite the restrictions on motor vehicles during the LP.

Fig. 1.

Fig. 1

Open in a new tab

Diurnal patterns of six major particle fractions during the normal (left, black axis) and lockdown periods (right, blue axis). The box and whisker plots show the hourly-mean relative percentages of the six main particle groups; the lower, middle, and upper lines of the boxes denote the 25th, 50th, and 75th percentiles. Average values are shown as connected white dots. The lower and upper whiskers show the 10th and 90th percentiles, respectively. Particle type abbreviations as in Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

The diurnal trends in the counts and relative fractions of BB, OC, PAH-containing, and OCEC particle were similar during the NP and LP, except for a peak in BB counts (Fig. S7) at 12:00 LT during the LP—this is a time when household cooking takes place and heating is active (Niu et al., 2016). As most people were confined at home during the LP, and there were no restrictions on biomass burning, the relative percentage of the BB fraction showed no obvious diurnal trends. The percent increases in the BB fraction at 16:00 LT was because there were lower concentrations of other anthropogenic pollutants (such as NaK-SN particles). Clearly, the day-to-day changes in human activities and the increased atmospheric oxidation capacity during the lockdown period had a great influence on both primary emissions and the chemical composition of PM.

The temporal trends in the number concentrations (Fig. 2 a) and the relative percentages (Fig. 2b) in each particle group were similar. Variations in the EC-aged particle fraction were generally consistent with those of the PM2.5 mass concentration, while the NaK-SN fraction showed minor differences (Fig. 2b). For example, the PM2.5 concentration was elevated on 22 and 23 January and increased further on 24 to 26 January. Meanwhile, the relative fraction of EC-aged particles increased significantly (from 26.8% to 50.7%), but the NaK-SN showed only small fluctuations (54.3 ± 5.4%). The mass concentration of PM2.5 decreased sharply from 14 to 18 February, and the EC-aged fraction decreased simultaneously 10.4 ± 3.3%, while the NaK-SN fraction showed a smaller decreased and was the dominant particle group at 43.1 ± 12.1% of the particles in the period. As shown in Fig. 2, NaK-SN particle was the most abundant aerosol component in Xianyang overall, and these particles are discussed in detail below.

Fig. 2.

Fig. 2

Open in a new tab

Time-series plots of (a) the number concentrations and (b) the relative percentages of ten particle groups and PM2.5 mass concentrations during the study. The normal and lockdown periods are separated by yellow dashed lines. The solid, light blue line shows the PM2.5 mass concentrations. Particle type abbreviations as in Table 1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

An important finding of our study is that reductions in primary emissions, including those from coal combustion and traffic, due to the restrictions on anthropogenic activities, did not eliminate air pollution. In fact, the formation of secondary species increased as the atmospheric oxidation capacity increased (Sun et al., 2020). This finding concerning secondary particles formation should be considered in the development of pollution control plans.

3.2. Size distributions and mixing state

The number concentrations and number fractions of the size-resolved, particle groups during the two periods are illustrated in Fig. 3 . The aerodynamic equivalent particle diameters were mainly in the range of 0.3–1.0 μm. In fact, aerosols in this size range accounted for >90% of the total detected particles. Our results are consistent with those from previous urban studies using the same instrument (Xu et al., 2018; G.H. Zhang et al., 2017). The maximum total particle number during the LP was for a particle diameter of 500 nm, while the corresponding value during the NP was 440 nm. This result shows that particle size generally increased during the LP.

Fig. 3.

Fig. 3

Open in a new tab

(a and b) Size distributions of the total number particle counts of nine particle types (excluding the “other” group) during the sampling campaign. Solid lines represent the normal period (NP) and the dash lines represent the lockdown period (LP); the relative percentages (%) of the total particles for nine groups during the (c) NP and (d) LP. Particle type abbreviations as above.

Most of the nine particle types, that is the ten groups excluding “other” particle, showed maxima in their distributions at larger sizes during the LP compared with NP (Fig. 3a and b). In particular, the peaks in the distribution for the NaK-SN and dust particles shifted from 440 to 520 nm, and 460–540 nm, respectively, while the BB, OCEC, and PAH-containing particles increased from 440 to 500 nm. The size-resolved mixing states of secondary species and the above-mentioned particle types (dust, EC-aged, NaK-SN, BB, OC, PAH-containing particles) (Fig. S8) indicate that as the particle sizes increased, the proportions of HSO4－, NO3－ and NH4 + in the dust, EC-aged, NaK-SN, and BB particles also increased. Moreover, the proportions of C2H3O+ and oxalate in the NaK-SN, BB, OC, and PAH-containing particles increased as well. This is compelling evidence that the observed increases in particles size were associated with the enhanced formation of secondary species during the lockdown, and it is worth reiterating that during the LP, the atmospheric oxidizing capacity was higher than in the NP (Tian et al., 2021).

The size-resolved number fractions for the EC-aged and NaK-SN particles exhibited bimodal distributions during both the NP and LP (Fig. 3c and d). The smaller of the two particle size peaks presumably reflects the influences of direct emissions on the condensed particle mode (<300 nm), while the larger-sized peak was likely associated with secondary processes that affected the droplet mode (>400 nm). The number fractions of the size-resolved BB and OC particles showed similar distributions during the NP and LP, with peak values at ∼480 and ∼380 nm, respectively. Furthermore, the number percentage of the size-resolved PAH-containing particles with diameters >400 nm was higher during the LP than in the NP.

The mixing states (Fig. 4 ) of the particles were investigated by calculating the contributions of selected ion fragments (levoglucosan, 35,37Cl－, 43C2H3O+, 89HC2O4 −, 97HSO4－, 62NO3－, and 18NH4 +) to each particle type. Previous studies have shown that levoglucosan fragments (i.e., 45CHO2 −, 59C2H3O2 −, 71C3H3O2 −) are useful indicators of particles from biomass burning (Silva et al., 1999; Sullivan et al., 2007). During the NP, 69% of BB particles, 63% of PAH-containing particles, and 48% of OC particles contained levoglucosan fragments, and their proportions increased to 78%, 65%, and 64%, respectively, during the LP. The increases in the levoglucosan fractions of these particle groups may be explained by the fact that biomass-burning was not restricted during the LP. However, the increases also may have caused by the oxidation of organics during atmospheric aging (Zauscher et al., 2013; Zhang et al., 2019). Furthermore, it is noteworthy that the 35,37Cl− fragment, a possible primary indicator, which was mainly mixed with BB, OC and PAH-containing particles (19–29%). The other particle groups during both periods showed lower number fraction of 35,37Cl− (4–17%).

Fig. 4.

Fig. 4

Open in a new tab

Mixing states of nine particle groups with primary (35,37Cl－, levoglucosan (Lev)) and secondary (97HSO4－, 62NO3－, 18NH4+, oxalate (HC2O4－)) ion fragments during the normal (NP) and lockdown periods (LP). Particle type abbreviations as above.

The 43C2H3O+ and 89HC2O4 − ion fragments have been used as indicators of secondarily formed organic species (Moffet et al., 2008). As shown in Figs. 4, 43C2H3O+ had similar proportions in each particle group for both periods. Among all particle groups, the 43C2H3O+ ion fragment was mainly associated with BB particles, and it had contributions of 31% and 26% during NP and LP, respectively. The OC particles were next most abundant group that had this ion fragment (14% and 18%), and that group was followed by OCEC particles (8% and 14%). The percent contributions of 43C2H3O+ for the remaining particle groups were <9%.

Oxalate is another secondary species indicator, and the fractions of the 89HC2O4 − ion fragments for each particle group were relatively low in the NP (3–15%) but increased by roughly two-fold (11–25%) during the LP. In terms of the particle groups, 89HC2O4 − mainly was associated with the BB and PAH-containing particles during both periods. Lin et al. (2019) have shown that the main precursors of oxalate (89HC2O4 −) include the following ion fragments: acetate (59C2H3O2 −), methylglyoxal (71C3H3O2 −), glyoxylate (73C2HO3 −), pyruvate (87C3H3O3 −), malonate (103C3H3O4 −), and succinate (117C4H5O4 −). Due to the substantial amounts of these precursors emitted from biomass burning, oxalate probably formed on BB particles heterogeneously (Ervens et al., 2011). As discussed above, the PAH-containing particles also were influenced by the biomass burning, and that can explain why the oxalate ion fragment also was associated with this particle group.

Nitrate ion fragments (62NO3 −) were more widely mixed with most of the particle types than the sulfate fragments (97HSO4 −) (Fig. 4), presumably due to the relative concentrations or reactivities of their precursors; that is, NO2 versus SO2 (Table S1). Except for the EC-fresh particles (<20%), 62NO3 − had a fraction of >70% for all particle groups in both periods. In contrast, the ammonium ion fragment (18NH4 +) was a minor fraction of all particle groups (<4%), and the fraction of 97HSO4 − was particularly high (>80%) for the organic particle groups (i.e., OCEC and OC particles). The particle mass spectra obtained from source emissions (see Section 1.4 of Supplementary Information) indicate that the strong signals for 62NO3 − were caused by atmosphere aging. Therefore, these results are compelling evidence that most of the particle groups, except for the EC-fresh particles, had experienced a considerable degree of aging.

3.3. Secondary species formation

The 43C2H3O+, 89HC2O4 −, 62NO3 −, 97HSO4 −, and 18NH4 + ion fragments were used in our study to investigate the formation mechanism of secondary species (Chen et al., 2016). To characterize the impacts of the atmospheric oxidation and aqueous-phase reactions during the NP and LP, correlations were calculated for the number fractions of each secondary ion fragment versus the oxidant concentrations (Ox = O3 + NO2) (Fig. 5 ), and RH (Fig. 6 ).

Fig. 5.

Fig. 5

Open in a new tab

Correlations of Ox (NO2 + O3) with the relative percentages (%) of secondary ion fragments (43C2H3O+, oxalate, 62NO3－, 97HSO4－ and 18NH4+) during the normal (upper, a, c, e, g, i) and lockdown periods (lower, b, d, f, h, j). Grey error bars represent the standard deviations. Points for Ox >120 μg m−3 were omitted in (b) and (d) because C2H3O+ and oxalate showed insignificant correlations during both the NP and LP. For the same reason, in (f), the points were out of linear range when Ox < 50 μg m−3, thus they were not included in the analyses. To compare the correlations of the secondary products at same level of Ox, all data for Ox < 70 μg m−3 were omitted in (i) and (j).

Fig. 6.

Fig. 6

Open in a new tab

Relationships between relative humidity (RH) and the relative percentages (%) of secondary ion fragments (43C2H3O+, oxalate, 62NO3－, 97HSO4－ and 18NH4+) during the normal (a, c, e, g, i) and lockdown periods (b, d, f, h, j), respectively. Grey error bars represent the standard deviations. The data shown by grey dots were omitted in (e) and (f) due to the large differences compared with the two nearest values.

As shown in Fig. 5a-d, when Ox was <120 μg m−3, the number fractions of 43C2H3O+ (R2 = 0.11) and 89HC2O4 − (R2 = 0.44) showed weak to modest correlations with Ox during the NP but much higher correlations of 0.72 and 0.84, respectively, during the LP. Furthermore, the slopes of the linear regressions, with relative fraction as the dependent variable, were also higher during the LP, indicating stronger oxidant effects on the formation of secondary organic species during the lockdown. We note that when Ox was >120 μg m−3 during the LP, the number fractions of 43C2H3O+ and 89HC2O4 − decreased when Ox increased. This inverse relationship can be attributed to the photolysis of 43C2H3O+ and 89HC2O4 − under high O3 (>97 μg m−3). A similar explanation has been proposed relative to the photodegradation of Fe-oxalate complexes in other studies (Huang et al., 2019; Zhou et al., 2020). Furthermore, the high correlations between the precursor species (e.g., 45[HCO2]−, 59[CH3CO2]−, 71[CH3CO2]−, 73[C2HO3]−, 87[C3H3O3]−) of oxalate and Ox (Fig. S9) suggest that the decreased concentration of these precursors may limit oxalate formation.

The number fractions of 62NO3 − were positively correlated with Ox during the NP (R2 = 0.84, Fig. 5e–f), which implies that oxidation played an important role in aerosol nitrate formation. Interestingly, the correlation between the number fraction of 62NO3 − and Ox was lower during the LP (R2 = 0.74). Moreover, when Ox was >70 μg m−3, a higher slope was observed during the LP (0.097 m3 μg−1) compared with the NP (0.08 m3 μg−1), and this is further evidence that more oxidization occurred during the covid lockdown.

The percentages of 62NO3 − ion fragments decreased sharply (from 49% to 13%) from 16:00 13 February to 03:00 14 February, and during that time, the air mass trajectories to the site were from the north-northwest (Fig. S4c). Thus, the average value for the percentages of 62NO3 − ion fragments over this Ox range (72.5 μg m−3) reached 38%. When conditions were unfavorable for diffusion, 62NO3 − increased rapidly with Ox. For 97HSO4 −, the correlations with Ox were not significant for either the NP or LP dataset [Fig. 5g-h]. On the other hand, when Ox was >70 μg m−3, the correlations between the number fractions of 18NH4 + and Ox were strong during both the NP (R2 = 0.91) and LP (R2 = 0.81) [Fig. 5i-j]. However, the number fraction of 18NH4 + remained stable when the Ox was <70 μg m−3 during both periods. The relatively high average value for 18NH4 + at Ox < 70 μg m−3 was due to the high fractions (2.5%) observed during the NP from 11:00 3 January to 13:00 8 January. The high 18NH4 + may have been a consequence of high NO2 and SO2 because ammonium salts could produce through reactions initiated by these species.

For the secondary organic ion fragments [Fig. 6a-d], 43C2H3O+ and 89HC2O4 − showed strong negative correlations with RH during the NP (R2 = 0.72 and R2 = 0.88, respectively) and LP (R2 = 0.87). Similar decreasing slopes for 43C2H3O+ versus RH were found during the NP (−0.033) and LP (−0.038). In comparison, the slopes for 89HC2O4 − obviously were much more negative when the RH increased (NP = −0.038; LP = −0.10). Previous studies have shown that 89HC2O4 − mainly forms through heterogeneous reactions of precursor volatile organic compounds (VOCs) (Turekian et al., 2003; Kerminen et al., 2000), especially aqueous processes (Chen et al., 2015; Furukawa and Takahashi, 2011; J.K. Zhang et al., 2017). In addition, the organic precursors can polymerize to form more complex high molecular-weight products in humid environments (Carlton et al., 2007; Cheng et al., 2017; Tan et al., 2009).

As shown in Fig. 6e-j, the number fractions of 62NO3 − and 18NH4 + increased with the RH to some extent during both periods, indicating a critical role for aqueous-phase reactions in the formation of these species. Specifically, a higher correlation between the number fraction of 62NO3 − and RH was seen during LP (R2 = 0.76) than NP (R2 = 0.54) when RH was <60%, and the slopes of the regressions were both positive but quite different (0.080 and 0.57 during the NP and LP respectively). These results suggest that increases in humidity led to the production of 62NO3 −. It also is worth noting that the average O3 concentration was ∼70% higher during the LP (77.0 μg m−3) compared with the NP (45.1 μg m−3). However, when RH was >60% during the LP, the number fractions of 62NO3 − tended to be stable, probably because the formation pathways were limited by low concentrations of precursor NO2 (25.7 μg m−3).

Strong positive correlations were found between the number fractions of 18NH4 + and RH, with R2 values of 0.67 and 0.94 during NP and LP, respectively. In addition, similar slopes indicate comparable effects of aqueous-phase reactions leading to the formation of 18NH4 + during both periods. In contrast, 97HSO4 − showed inverse relationships with RH during NP (R2 = 0.29) and LP (R2 = 0.50). These findings are unlike those in other studies (Huang et al., 2021; Tian et al., 2019; Yue et al., 2019), and the disparate results could be related to the comparatively lower mass concentration of SO2 (9.8 ± 4.2 μg m−3) during the campaign at our site. In fact, several other field studies have not shown positive correlations between 97HSO4 − and RH under the low concentrations of SO2 (<5 ppb) (Drewnick et al., 2006; Twohy and Anderson, 2008; Schneider et al., 2017).

4. Conclusions

We compared and contrasted the chemical components, size distributions, mixing states of individual particles, and secondary particle formation for a normal period and the COVID-19 lockdown period using data obtained with a single particle aerosol mass spectrometer (SPAMS). Ten particle groups were identified, including NaK-SN, EC-aged, EC-fresh, BB, OC, dust, PAH-containing, OCEC, metal, and other particles during the study. The two largest particle number fractions showed remarkable differences between the NP and LP: NaK-SN (42.4%–54.0% in the NP and LP, respectively) and EC-aged particles (31.5%–23.7%), which we attribute to reduced anthropogenic emissions and increased atmospheric oxidation during the lockdown. Compared with the NP, particles sampled during the LP were larger in size due to an increased abundance of secondary species. The strong mixing of 62NO3 − and 97HSO4 − species in the total particle groups (except for EC-fresh) suggests that they had undergone atmospheric aging.

Oxidation reactions promoted the formation of secondary organic species (43C2H3O+ and 89HC2O4 −), but RH was inversely correlated with these species. Positive correlations between the number fractions of 62NO3 − and 18NH4 + versus Ox and RH during both periods also indicate that there were impacts from photooxidation and aqueous-phase chemistry on nitrate and ammonium formation. However, there was no obvious correlation between Ox or RH versus 97HSO4 −, which may have been due to the low concentration of SO2, the precursor species. As Xianyang is one of the cities in the Fenwei Plain that would most benefit from pollution controls, the results of our study provide scientific data that provide insights into the causes of air pollution, especially reactions that lead to secondary pollutants. The results also have strong implications for effective controls on haze formation through reductions in traffic and coal emissions.

Credit author statement

Li Li: Data acquisition and processing, Writing – original draft, Writing – review & editing. Qiyuan Wang: Funding acquisition, Writing – review & editing, Supervision. Junji Cao: Conceptualization, Review, Supervision. Yong Zhang, Suixin Liu, Ting Zhang, Shuang Wang, Jie Tian, Yang Chen, Steven Sai Hang Ho, Yongming Han contributed to the paper with useful scientific discussions or comments.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was financially supported by the Key Research and Development Program of Shaanxi Province (2018-ZDXM3-01), the Strategic Priority Research Program of Chinese Academy of Sciences (XDB40000000), and the Youth Innovation Promotion Association of the Chinese Academy of Sciences (2019402).

Handling Editor: Volker Matthias

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.chemosphere.2022.135013.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (3.4MB, docx)

References

  1. Allen J.O. YAADA: Software toolkit to analyze single-particle mass spectral data. Arizona State University. 2005:3–57. [Google Scholar]
  2. An Z.S., Huang R.J., Zhang R.Y., Tie X.X., Li G.H., Cao J.J., Zhou W.J., Shi Z.G., Han Y.M., Gu Z.L., Ji Y.M. Severe haze in northern China: a synergy of anthropogenic emissions and atmospheric processes. Proc. Natl. Acad. Sci. U.S.A. 2019;116:8657–8666. doi: 10.1073/pnas.1900125116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Carlton A.G., Turpin B.J., Altieri K.E., Seitzinger S., Reff A., Lim H.J., Ervens B. Atmospheric oxalic acid and SOA production from glyoxal: results of aqueous photooxidation experiments. Atmos. Environ. 2007;41:7588–7602. [Google Scholar]
  4. Chen C., Sun Y.L., Xu W.Q., Du W., Zhou L.B., Han T.T., Wang Q.Q., Fu P.Q., Wang Z.F., Gao Z.Q., Zhang Q., Worsnop D.R. Characteristics and sources of submicron aerosols above the urban canopy (260 m) in Beijing, China, during the 2014 APEC summit. Atmos. Chem. Phys. 2015;15:12879–12895. [Google Scholar]
  5. Chen Y., Cao J.J., Huang R.J., Yang F.M., Wang Q.Y., Wang Y.C. Characterization, mixing state, and evolution of urban single particles in Xi'an (China) during wintertime haze days. Sci. Total Environ. 2016;573:937–945. doi: 10.1016/j.scitotenv.2016.08.151. [DOI] [PubMed] [Google Scholar]
  6. Chen Y., Wenger J.C., Yang F.M., Cao J.J., Huang R.J., Shi G.M., Zhang S.M., Tian M., Wang H.B. Source characterization of urban particles from meat smoking activities in Chongqing, China using single particle aerosol mass spectrometry. Environ. Pollut. 2017;228:92–101. doi: 10.1016/j.envpol.2017.05.022. [DOI] [PubMed] [Google Scholar]
  7. Chen Y., Tian M., Huang R.J., Shi g., Wang H.B., Peng C., Cao J.J., Wang Q.Y., Zhang S.M., Guo D.M., Zhang L.M., Yang F.M. Characterization of urban amine-containing particles in Southwestern China: seasonal variation, source, and processing. Atmos. Chem. Phys. 2018;19:3245–3255. [Google Scholar]
  8. Cheng C.L., Li M., Chan C.K., Tong H.J., Chen C.H., Chen D.H., Wu D., Li L., Wu C., Cheng P., Gao W., Huang Z.X., Li X., Zhang X.J., Fu Z., Bi Y.R., Zhou Z. Mixing state of oxalic acid containing particles in the rural area of Pearl River Delta, China: implications for the formation mechanism of oxalic acid. Atmos. Chem. Phys. 2017;17:9519–9533. [Google Scholar]
  9. Dall'Osto M., Harrison R.M. Urban organic aerosols measured by single particle mass spectrometry in the megacity of London. Atmos. Chem. Phys. 2012;12:4127–4142. [Google Scholar]
  10. Dall'Osto M., Beddows D.C.S., Gietl J.K., Olatunbosun O.A., Yang X.G., Harrison R.M. Characteristics of tyre dust in polluted air: studies by single particle mass spectrometry (ATOFMS) Atmos. Environ. 2014;94:224–230. [Google Scholar]
  11. Drewnick F., Schneider J., Hings S.S., Hock N., Noone K., Targino A., Weimer S., Borrmann S. Measurement of ambient, interstitial, and residual aerosol particles on a mountaintop site in central Sweden using an aerosol mass spectrometer and a CVI. J. Atmos. Chem. 2006;56:1–20. [Google Scholar]
  12. Ervens B., Turpin B.J., Weber R.J. Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies. Atmos. Chem. Phys. 2011;11:11069–11102. [Google Scholar]
  13. Feng Y.Y., Ning M., Lei Y., Sun Y.M., Liu W., Wang J.N. Defending blue sky in China: effectiveness of the “air pollution prevention and control action plan” on air quality improvements from 2013 to 2017. J. Environ. Manag. 2019;252:1095–8603. doi: 10.1016/j.jenvman.2019.109603. [DOI] [PubMed] [Google Scholar]
  14. Furukawa T., Takahashi Y. Oxalate metal complexes in aerosol particles: implications for the hygroscopicity of oxalate-containing particles. Atmos. Chem. Phys. 2011;11:4289–4301. [Google Scholar]
  15. He J.J., Gong S.L., Yu Y., Yu L.J., Wu L., Mao H.J., Song C.B., Zhao S.P., Liu H.L., Li X.Y., Li R.P. Air pollution characteristics and their relation to meteorological conditions during 2014-2015 in major Chinese cities. Environ. Pollut. 2017;223:484–496. doi: 10.1016/j.envpol.2017.01.050. [DOI] [PubMed] [Google Scholar]
  16. Huang X.J., Zhang J.K., Luo B., Luo J.Q., Zhang W., Rao Z.H. Characterization of oxalic acid-containing particles in summer and winter seasons in Chengdu, China. Atmos. Environ. 2019;198:133–141. [Google Scholar]
  17. Huang X., Ding A.J., Gao J., Zheng B., Zhou D.R., Qi X.M., Tang R., Wang J.P., Ren C.H., Nie W., Chi X.G., Xu Z., Chen L.D., Li Y.Y., Che F., Pang N.N., Wang H.K., Tong D., Qin W., Cheng W., Liu W.J., Fu Q.Y., Liu B.X., Chai F.H., Davis S.J., Zhang Q., He K. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl. Sci. Rev. 2021;8(2):nwaa137–n139. doi: 10.1093/nsr/nwaa137. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kerminen V.M., Ojanen C., Pakkanen T., Hillamo R., Aurela M., Meriläіnen J. Low-molecular-weight dicarboxylic acids in an urban and rural atmosphere. J. Aerosol Sci. 2000;31(3):349–362. [Google Scholar]
  19. Khalizov A.F., Zhang R.Y., Zhang D., Xue H.X., Pagels J., McMurry P.H. Formation of highly hygroscopic soot aerosols upon internal mixing with sulfuric acid vapor. J. Geophys. Res. Atmos. 2009;114(D5) 16 March 2009. [Google Scholar]
  20. Le T.H., Wang Y., Liu L., Yang J.N., Yung Y.L., Li G.H., Seinfeld J.H. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science. 2020;369:702–706. doi: 10.1126/science.abb7431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li L., Huang Z.X., Dong J.G., Li M., Gao W., Nian H.Q., Fu Z., Zhang G.H., Bi X.H., Cheng P., Zhou Z. Real time bipolar time-of-flight mass spectrometer for analyzing single aerosol particles. Int. J. Mass Spectrom. 2011;303:118–124. [Google Scholar]
  22. Li L., Li M., Huang Z.X., Gao W., Nian H.Q., Fu Z., Gao J., Chai F.H., Zhou Z. Ambient particle characterization by single particle aerosol mass spectrometry in an urban area of Beijing. Atmos. Environ. 2014;94:323–331. [Google Scholar]
  23. Li L., Li Q., Huang L., Wang Q., Zhu A.S., Xu J., Liu Z.Y., Li H.L., Shi L.S., Li R., Azari M., Wang Y.J., Zhang X.J., Liu Z.Q., Zhu Y.H., Zhang K., Xue S.H., Gee Ooi M.C., Zhang D.P., Chan A. Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: an insight into the impact of human activity pattern changes on air pollution variation. Sci. Total Environ. 2020;732 doi: 10.1016/j.scitotenv.2020.139282. 139282-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Li L., Wang Q.Y., Zhang X., She Y.Y., Zhou J.M., Chen Y., Wang P., Liu S.X., Zhang T., Dai W.T., Han Y.M., Cao J.J. Characteristics of single atmospheric particles in a heavily polluted urban area of China: size distributions and mixing states. Environ. Sci. Pollut. Res. 2019;26:11730–11742. doi: 10.1007/s11356-019-04579-3. [DOI] [PubMed] [Google Scholar]
  25. Li S.W., Li H.B., Luo J., Li H.M., Qian X., Liu M.M., Bi J., Cui X.Y., Ma L.Q. Influence of pollution control on lead inhalation bio accessibility in PM2.5: a case study of 2014 Youth Olympic Games in Nanjing. Environ. Int. 2016;94:69–75. doi: 10.1016/j.envint.2016.05.010. [DOI] [PubMed] [Google Scholar]
  26. Li Z., Meng J.J., Zhou L., Zhou R.W., Fu M.X., Wang Y.C., Yi Y.N., Song A.J., Guo Q.C., Hou Z.F., Yan L. Impact of the COVID-19 Event on the characteristics of atmospheric single particle in the northern China. Aerosol Air Qual. Res. 2020;20:1716–1726. [Google Scholar]
  27. Lin Q.H., Bi X.H., Zhang G.H., Yang Y.X., Peng L., Lian X.F., Fu Y.Z., Li M., Chen D.H., Miller M., Ou J., Tang M.J., Wang X.M., Peng P.A., Sheng G.Y., Zhou Z. In-cloud formation of secondary species in iron-containing particles. Atmos. Chem. Phys. 2019;19:1195–1206. [Google Scholar]
  28. Ma L., Li M., Huang Z., Li L., Gao W., Nian H., Zou L., Fu Z., Gao J., Chai F., Zhou Z. Real time analysis of lead-containing atmospheric particles in Beijing during springtime by single particle aerosol mass spectrometry. Chemosphere. 2016;154:454–462. doi: 10.1016/j.chemosphere.2016.04.001. [DOI] [PubMed] [Google Scholar]
  29. Ma Q.X., Wu Y.F., Tao J., Xia Y.J., Liu X.Y., Zhang D.Z., Han Z.W., Zhang X.L., Zhang R.J. Variations of chemical composition and source apportionment of PM2.5 during winter haze episodes in Beijing. Aerosol Air Qual. Res. 2017;17:2791–2803. [Google Scholar]
  30. Meng J.J., Li Z., Zhou R.W., Chen M., Li Y.Y., Yi Y.N., Ding Z.J., Li H.J., Yan L., Hou Z.F., Wang G.H. Enhanced photochemical formation of secondary organic aerosols during the COVID-19 lockdown in Northern China. Sci. Total Environ. 2021;758:143709–143719. doi: 10.1016/j.scitotenv.2020.143709. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Moffet R.C., Foy B.D., Molina L.T., Molina M.J., Prather K.A. Measurement of ambient aerosols in northern Mexico City by single particle mass spectrometry. Atmos. Chem. Phys. 2008;8:4499–4516. [Google Scholar]
  32. Niu X.Y., Cao J.J., Shen Z.X., Ho S.S.H., Tie X.X., Zhao S.Y., Xu H.M., Zhang T., Huang R.J. vol. 147. 2016. pp. 458–469. (PM2.5 from the Guanzhong Plain: Chemical Composition and Implications for Emission Reductions). [Google Scholar]
  33. Schneider J., Mertes S., van Pinxteren D., Herrmann H., Borrmann S. Uptake of nitric acid, ammonia, and organics in orographic clouds: mass spectrometric analyses of droplet residual and interstitial aerosol particles. Atmos. Chem. Phys. 2017;17:1571–1593. [Google Scholar]
  34. Shen L.J., Wang H.L., Lü S., Zhang X.H., Yuan J., Tao S.K., Zhang G.J., Wang F., Li L. Influence of pollution control on air pollutants and the mixing state of aerosol particles during the 2nd World Internet Conference in Jiaxing, China. J. Clean. Prod. 2017;149:436–447. [Google Scholar]
  35. Silva P.J., Liu D.Y., Noble C.A., Prather K.A. Size and chemical characterization of individual particles resulting from biomass burning of local Southern California species. Environ. Sci. Technol. 1999;33:3068–3076. [Google Scholar]
  36. Song X.H., Hopke P.K. Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A. Anal. Chem. 1999;71:860–865. [Google Scholar]
  37. Sullivan R.C., Guazzotti S.A., Sodeman D.A., Prather K.A. Direct observations of the atmospheric processing of Asian mineral dust. Atmos. Chem. Phys. 2007;7:1213–1236. [Google Scholar]
  38. Sun Y.L., Lu L., Wei Z., Chen C., He Y., Sun J.X., Li Z.J., Xu W.Q., Wang Q.Q., Ji D.S., Fu P.Q., Wang Z.F., Worsnop D.R. A chemical cocktail during the COVID-19 outbreak in Beijing, China: insights from six-year aerosol particle composition measurements during the Chinese New Year holiday. Sci. Total Environ. 2020;742 doi: 10.1016/j.scitotenv.2020.140739. 140739-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sun Y.L., Wang Z.F., Wild O., Xu W.Q., Chen C., Fu P.Q., Du W., Zhou L.B., Zhang Q., Han T.T., Wang Q.Q., Pan X.L., Zheng H.T., Li J., Guo X.F., Liu J.G., Worsnop D.R. APEC blue”: secondary aerosol reductions from emission controls in beijing. Sci. Rep. 2016;6(1):20668–20669. doi: 10.1038/srep20668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Tan Y., Perri M.J., Seitzinger S.P., Turpin B.J. Effects of precursor concentration and acidic sulfate in aqueous glyoxal-OH radical oxidation and implications for secondary organic aerosol. Environ. Sci. Technol. 2009;43:8105–8112. doi: 10.1021/es901742f. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Tian M., Liu Y., Yang F.m., Zhang L.m., Peng C., Chen Y., Shi G.m., Wang H.b., Luo B., Jiang C.t., Li B., Takeda N., Koizumi K. Increasing importance of nitrate formation for heavy aerosol pollution in two megacities in Sichuan Basin, southwest China. Environ. Pollut. 2019;250:898–905. doi: 10.1016/j.envpol.2019.04.098. [DOI] [PubMed] [Google Scholar]
  42. Tian J., Wang Q.Y., Zhang Y., Yan M.Y., Liu H.K., Zhang N.N., Ran W.K., Cao J.J. Impacts of primary emissions and secondary aerosol formation on air pollution in an urban area of China during the COVID-19 lockdown. Environ. Int. 2021;150 doi: 10.1016/j.envint.2021.106426. 106426-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Turekian V.C., Macko S.A., Keene W.C. Concentrations, isotopic compositions, and sources of size-resolved, particulate organic carbon and oxalate in near-surface marine air at Bermuda during spring. J. Geophys. Res. 2003;108(D5):4157–4158. [Google Scholar]
  44. Twohy C.H., Anderson J.R. Droplet nuclei in non-precipitating clouds: composition and size matter. Environ. Res. Lett. 2008;3(4):45002–45009. [Google Scholar]
  45. Twohy C.H., DeMott P.J., Russell L.M., Toohey D.W., Rainwater B., Geiss R., Sanchez K.J., Lewis S., Roberts G.C., Humphries R.S., McCluskey C.S., Moore K.A., Selleck P.W., Keywood M.D., Ward J.P., McRobert I.M. Cloud-nucleating particles over the Southern Ocean in a changing climate. Earth's Future. 2021;9(3):1–21. [Google Scholar]
  46. Wang Y.Q., Zhang Y., Schauer J.J., Foy B.D., Guo B., Zhang Y.X. Relative impact of emissions controls and meteorology on air pollution mitigation associated with the Asia-Pacific Economic Cooperation (APEC) conference in Beijing, China. Sci. Total Environ. 2016;571:1467–1476. doi: 10.1016/j.scitotenv.2016.06.215. [DOI] [PubMed] [Google Scholar]
  47. Wang H.L., An J.L., Shen L.J., Zhu B., Li X., Duan Q., Zou J.N. Mixing state of ambient aerosols in Nanjing city by single particle mass spectrometry. Atmos. Environ. 2016;132:123–132. [Google Scholar]
  48. Wang Y.C., Yuan Y., Wang Q.Y., Liu C.G., Zhi Q., Cao J.J. Changes in air quality related to the control of coronavirus in China: implications for traffic and industrial emissions. Sci. Total Environ. 2020;731(14):139133–139138. doi: 10.1016/j.scitotenv.2020.139133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Wang P.F., Chen K.Y., Zhu S.Q., Wang P., Zhang H.L. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour. Conserv. Recycl. 2020;158(4):104814–104819. doi: 10.1016/j.resconrec.2020.104814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Wang S.X., Zhao M., Xing J., Wu Y., Zhou Y., Lei Y., He K., Fu L.X., Hao J.M. Quantifying the air pollutants emission reduction during the 2008 olympic games in beijing. Environ. Sci. Technol. 2010;44:2490–2496. doi: 10.1021/es9028167. [DOI] [PubMed] [Google Scholar]
  51. Wang T., Nie W., Gao J.J., Xue L.K., Gao X.M., Wang X.F., Qiu J., Poon C.N., Meinardi S., Blake D., Wang S.L., Ding A.J., Chai F.H., Zhang Q.Z., Wang W.X. Air quality during the 2008 Beijing Olympics: secondary pollutants and regional impact. Atmos. Chem. Phys. 2010;10:7603–7615. [Google Scholar]
  52. West J.J., Cohen A., Dentener F., Brunekreef B., Zhu T., Armstrong B., Bell M.L., Brauer M., Carmichael G., Costa D.L., Dockery D.W., Kleeman M., Krzyzanowski M., Künzli N., Liousse C., Lung S.-C.C., Martin R.V., Pöschl U., Pope C.A., III, Roberts J.M., Russell A.G., Wiedinmyer C. What we breathe impacts our health: improving understanding of the link between air pollution and health. Environ. Sci. Technol. 2016;50:4895–4904. doi: 10.1021/acs.est.5b03827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Xu W.Q., Sun Y.L., Chen C., Du W., Han T.T., Wang Q.Q., Fu P.Q., Wang Z.F., Zhao X.J., Zhou L.B., Ji D.S., Wang P.C., Worsnop D.R. Aerosol composition, oxidation properties, and sources in Beijing: results from the 2014 Asia-Pacific Economic Cooperation summit study. Atmos. Chem. Phys. 2015;15:13681–13698. [Google Scholar]
  54. Xu L.L., Wu X., Hong Z.Y., Zhang Y.R., Deng J.J., Hong Y.W., Chen J.S. Composition, mixing state, and size distribution of single submicron particles during pollution episodes in a coastal city in southeast China. Environ. Sci. Pollut. Res. 2018;26:1464–1473. doi: 10.1007/s11356-018-3469-x. [DOI] [PubMed] [Google Scholar]
  55. Yue F.G., Xie Z.Q., Zhang P.F., Song S.J., He P.Z., Liu C., Wang L.Q., Yu X.W., Kang H. The role of sulfate and its corresponding S(IV)+NO2 formation pathway during the evolution of haze in Beijing. Sci. Total Environ. 2019;687:741–751. doi: 10.1016/j.scitotenv.2019.06.096. [DOI] [PubMed] [Google Scholar]
  56. Zauscher M.D., Wang Y., Moore M.J., Gaston C.J., Prather K.A. Air quality impact and physicochemical aging of biomass burning aerosols during the 2007 San Diego wildfires. Environ. Sci. Technol. 2013;47:7633–7643. doi: 10.1021/es4004137. [DOI] [PubMed] [Google Scholar]
  57. Zhang G.H., Bi X.H., Li L., Chan L.Y., Li M., Wang X.M., Sheng G.Y., Fu J.M., Zhou Z. Mixing state of individual submicron carbon-containing particles during spring and fall seasons in urban Guangzhou, China: a case study. Atmos. Chem. Phys. 2013;13(9):4723–4735. [Google Scholar]
  58. Zhang G.H., Lin Q.H., Peng L., Yang Y.X., Fu Y.Z., Bi X.H., Li M., Chen D.H., Chen J.X., Cai Z., Wang X.M., Peng P.A., Sheng G.Y., Zhou Z. Insight into the in-cloud formation of oxalate based on in situ measurement by single particle mass spectrometry. Atmos. Chem. Phys. 2017;17:13891–13901. [Google Scholar]
  59. Zhang G.H., Han B.X., Bi X.H., Dai S.X., Huang W., Chen D.H., Wang X.M., Sheng G.Y., Fu J.M., Zhou Z. Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry. Atmos. Res. 2015;153:286–295. [Google Scholar]
  60. Zhang G.H., Lin Q.H., Peng L., Yang Y.X., Jiang F., Cai Z., Bi X.H., Miller M., Tang M.J., Huang W.L., Wang X.M., Peng P.A., Sheng G.Y. Oxalate formation enhanced by Fe-containing particles and environmental implications. Environ. Sci. Technol. 2019;53:1269–1277. doi: 10.1021/acs.est.8b05280. 2019. [DOI] [PubMed] [Google Scholar]
  61. Zhang G.H., Liang X.F., Fu Y.Z., Lin Q.H., Li L., Song W., Wang Z.Y., Tang M.J., Chen D.H., Bi X.H., Wang X.M., Sheng G.Y. High secondary formation of nitrogen-containing organics (NOCs) and its possible link to oxidized organics and ammonium. Atmos. Chem. Phys. 2020;20(3):1469–1481. [Google Scholar]
  62. Zhang J.K., Luo B., Zhang J.Q., Ouyang F., Song H.Y., Liu P.C., Cao P., Schäfer K., Wang S.G., Huang X.J., Lin Y.F. Analysis of the characteristics of single atmospheric particles in Chengdu using single particle mass spectrometry. Atmos. Environ. 2017;157:91–100. [Google Scholar]
  63. Zhang Y. 2018. Sources Apportionment and Controls of Particulate Matter in Xianyang City, Northwest China. (A thesis submitted to University of Chinese Academy of Sciences in partial fulfillment of the requirement for the degree of Master of Engineering in Environment Engineering) [Google Scholar]
  64. Zhao J., Du W., Zhang Y.J., Wang Q.Q., Chen C., Xu W.Q., Han T.T., Wang Y.Y., Fu P.Q., Wang Z.F., Li Z.Q., Sun Y.L. Insights into aerosol chemistry during the 2015 China Victory Day parade: results from simultaneous measurements at ground level and 260 m in Beijing. Atmos. Chem. Phys. 2017;17:3215–3232. [Google Scholar]
  65. Zhao Y.B., Zhang K., Xu X.T., Shen H.Z., Zhu X., Zhang Y.X., Hu Y.T., Shen G.F. Substantial Changes in nitrogen dioxide and ozone after excluding meteorological impacts during the COVID-19 outbreak in mainland China. Environ. Sci. Technol. Lett. 2020;7:402–408. doi: 10.1021/acs.estlett.0c00304. [DOI] [PubMed] [Google Scholar]
  66. Zheng B., Tong D., Li M., Liu F., Hong C.P., Geng G.N., Li H.Y., Li X., Peng L.Q., Qi J., Yan L., Zhang Y.X., Zhao H.Y., Zheng Y.X., He K.B., Zhang Q. Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 2018;18:14095–14111. [Google Scholar]
  67. Zheng H., Kong S., Chen N., Yan Y., Liu D., Zhu B., Xu K., Cao W., Ding Q., Lan B., Zhang Z., Zheng M., Fan Z., Cheng Y., Zheng S., Yao L., Bai Y., Zhao T., Qi S. Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19. Sci. Total Environ. 2020;739:140000–140010. doi: 10.1016/j.scitotenv.2020.140000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zhou Y., Zhang Y.J., Griffith S.M., Wu G., Li L., Zhao Y.H., Li M., Zhou Z., Yu J.Z. Field evidence of fe-mediated photochemical degradation of oxalate and subsequent sulfate formation observed by single particle mass spectrometry. Environ. Sci. Technol. 2020;54:6562–6574. doi: 10.1021/acs.est.0c00443. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.docx (3.4MB, docx)

Articles from Chemosphere are provided here courtesy of Elsevier

RESOURCES