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Article

A Closure Study of Secondary Organic Aerosol Estimation at an Urban Site of Yangtze River Delta, China

by
Zichao Wan
1,2,†,
Kai Song
1,2,†,
Wenfei Zhu
1,
Ying Yu
1,
Hui Wang
1,
Ruizhe Shen
1,
Rui Tan
1,
Daqi Lv
1,
Yuanzheng Gong
1,
Xuena Yu
1,
Shiyi Chen
1,
Limin Zeng
1,2,
Shengrong Lou
3,
Yijun Yu
4 and
Song Guo
1,2,*
1
State Key Joint Laboratory of Environmental Simulation and Pollution Control, International Joint Laboratory for Regional Pollution Control, Ministry of Education (IJRC), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
2
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai 200233, China
4
Changzhou Environmental Monitoring Center, Changzhou 213164, China
*
Author to whom correspondence should be addressed.
†
These authors contribute equally to this work.
Atmosphere 2022, 13(10), 1679; https://doi.org/10.3390/atmos13101679
Submission received: 23 September 2022 / Revised: 8 October 2022 / Accepted: 10 October 2022 / Published: 14 October 2022
(This article belongs to the Special Issue Chemical Composition and Sources of Particles in the Atmosphere)
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Versions Notes

Abstract

:
Secondary organic aerosols (SOA) are crucial components of ambient particulate matters. However, their composition and formation mechanisms remain uncertain. To investigate the SOA formation and evaluate various SOA estimation approaches, a comprehensive measurement was conducted at an urban site, Changzhou, in Yangtze River Delta (YRD) region. 98 kinds of volatile organic compounds (VOCs) were measured by an online gas chromatography-mass spectrometer/flame ionization detector (GC-MS/FID). Non-refractory submicron particulate matters (NR-PM1) were measured by an Aerodyne Aerosol Chemical Speciation Monitor (ACSM). Both bottom-up approaches, i.e., VOCs oxidation yield method, and top-down approaches, i.e., elemental carbon (EC) tracer method and ACSM, combined with positive matrix factorization (PMF) method, were utilized to estimate SOA. ACSM-PMF method estimated the highest SOA concentration, followed by EC tracer method. SOA from VOCs oxidation yield method accounted for 43.2 ± 41.9% of that from EC tracer method, suggesting the existence of missing SOA precursors, e.g., semivolatile organic compounds. The influencing factors of SOA formation were investigated and a good correlation of SOA with odd oxygen rather than aerosol liquid water content was found, suggesting the importance of photochemical formation of SOA.

1. Introduction

Atmospheric fine particulate matter (PM2.5) has been long studied due to its close relationship with human health, air quality, and climate change [1,2,3]. Organic aerosol (OA) consists of primary organic aerosol (POA) and secondary organic aerosol (SOA). Compared with POA, SOA has a percentage of more than 50% in some environments [4,5]. However, the formation mechanisms, sources, and estimations of SOA remain largely uncertain [6,7,8]. Therefore, more study on SOA is necessary.
Various approaches were used to estimate SOA, including top-down method, e.g., the tracer-yield method [9], the nonprimary organic carbon method (the receptor model) [10,11], the water-soluble organic carbon (WSOC) method [12,13], the elemental carbon (EC) tracer method [3,14], and bottom-up method, e.g., volatile organic compounds (VOCs) oxidation yield method [15,16,17]. A few studies have explored the discrepancies between different estimation approaches [18,19], but there are still many uncertainties. Previous studies reported the discrepancies between the EC tracer method and the VOCs oxidation method and found that the explained share varies among different climatic and environmental conditions [16,17].
In recent years, the Yangtze River Delta (YRD) region develops rapidly due to fast urbanization and industrialization. This fast development leads to severe and complex air pollution, which is featured as secondary pollution [20]. SOA contributed up to ~70% of OA in the YRD region [21], so it is important to understand the SOA formation mechanism to control PM2.5 in the YRD region. Changzhou is situated at the west of the YRD region. As the receptor region, the air quality of Changzhou was significantly influenced by the transport from the “Nanjing-Shanghai” riverside industrial belt, which emitted large amounts of VOCs. As a result, it is significant that we should comprehend the SOA formation in Changzhou to characterize the secondary aerosol formation in the YRD region.
In the present study, organic aerosols, non-refractory submicron particulate matter (NR-PM1), and ambient VOCs were measured from 2 November 2020 to 23 November 2020 simultaneously. Both bottom-up method, i.e., the VOCs oxidation method, and top-down method, i.e., EC tracer method, and Aerosol Chemical Speciation Monitor (ACSM)-Positive Matrix Factorization (PMF) method were utilized to estimate SOA. This work aims to: (1) provide basic information on particle concentrations and chemical compositions; (2) evaluate the closure of SOA formation through different approaches; (3) explore the impact factors for SOA characteristics in Changzhou.

2. Materials and Methods

2.1. Sampling Site

All the measurements were conducted at the Changzhou environmental monitoring center site (31.76° N, 119.96° E), which is an urban site situated in the northeast of Changzhou City, from 2 November to 23 November 2020. The sampling site was on the rooftop, ~20 m above the ground (see Figure S1 in Supplementary Materials). The Changzhou site is located in the downwind of the YRD region, which can be regarded as a regional receptor site. The air pollution at the Changzhou site reflects the regional pollution characteristics of the YRD region, which can also be affected by local emissions.

2.2. Sampling and Chemical Analysis

During the measurement, an online gas chromatography coupled with a mass spectrometer detector and a flame ionization detector (GC-MS/FID, Tianhong, China) was deployed to measure ambient VOCs, including 56 hydrocarbons (alkanes from C2 to C11, acetylene, alkenes, and aromatics), 14 oxygenated VOCs (OVOCs), and 28 halogenated hydrocarbons. Details are described in previous works [22]. All SOA species measured in this study are shown in Table S1.
The mass concentrations of NR-PM1 chemical composition were measured by an Aerosol Chemical Speciation Monitor (ACSM), with a time resolution of 15 min. The detailed operations of ACSM were described in previous work [23,24]. Briefly, the sampling flow rate, ionization efficiency (IE), and relative ionization efficiencies (RIE) were calibrated at the beginning and end of the campaign. The mass concentrations were analyzed with ACSM standard data analysis software (v 1.5.10.0). The composition-dependent collection efficiency (CE) used in this study was around 0.5, as recommended by Middlebrook, et al. [25]).
Hourly organic carbon (OC) and EC mass concentrations were measured by an in situ semicontinuous OC/EC analyzer (Sunset Laboratory Inc., Tigard, OR, USA), which was equipped with a PM2.5 cyclone. Improved ACE-Asia (NIOSH 5040) method for temperature protocol was used to collect ambient aerosol. The area of the quartz filter was 1.03 cm2, and the flow rate was 8 L/min. Details of the measurement were described in other works [26,27]. The Pearson correlation coefficient between OA measured by ACSM and OC measured by the OC/EC analyzer is 0.87, with a p-value of 2.5 × 10−146.
Gaseous species (O3, NO2, SO2, and CO) were measured by standard gas analyzers (Thermo Fischer Inc, Waltham, MA, USA). The PM2.5 mass was recorded by β-ray device (Thermo Scientific, Colorado Springs, CO, USA). The water-soluble inorganic ions (SO42−, Cl−, NO3−, Mg2+, Ca2+, NH4+, K+, and Na+) were measured by a Monitor for AeRosols and GAses (MARGA). Details of the measurement were described in a previous study [28].
In this study, Aerosol liquid water contents (ALWC) were estimated from the ISORROPIA-II model, based on the water-soluble inorganic ions measured by MARGA [29]. The simulation model was forward mode and metastable state. The forward mode assumes that the total concentrations of precursors are solved for this model, thus it is a relatively closed system. Metastable state means that the aerosols were composed only of a supersaturated aqueous phase. In our study, the concentrations of Na+, SO42−, NH4+, NO3−, Cl−, Ca2+, K+, Mg2+, relative humidity, and temperature were used to calculate the concentration of ALWC.

2.3. Estimation of Secondary Organic Aerosols

2.3.1. Bottom-Up Method to Estimate SOA

Bottom-up method, i.e., VOCs oxidation yield method, was used to estimate SOA. In Equation (1), it is assumed that OH is the only oxidant for the loss of ambient VOCs [15,22]. Besides, if two VOC species are emitted by similar emission sources and have discrepant atmospheric reactivity, the measured ratio of these two VOC species could describe the atmospheric photochemical age (Δt), such as ethylbenzene to m, p-xylene (Equation (2)) [22,30].
V O C i , c = V O C i , t   ×   ( exp ( k i [ O H ] Δ t )   −   1 )
where VOCi,c and VOCi,t are the consumption and measured concentration of VOC for species i, respectively. ki is the reaction rate constant of VOCi with OH radicals. [OH] is the concentration of OH radicals, which is not necessary for calculating VOCi considering that only the OH exposure (product of [OH] and Δt) is required in Equation (1).
( k E B −   k M P ) [ O H ] Δ t = ln ( [ E B ] [ M P ] | t = 0 ) − ln ( [ E B ] [ M P ] | t )
EB and MP represent ethylbenzene and m, p-xylene, respectively. kEB and kMP are the reaction rate constants of ethylbenzene and m, p-xylene with OH radical, with value of 7.0 × 10−12 cm3 molecule−1 s−1 and 18.7 × 10−12 cm3 molecule−1 s−1, respectively [31]. ([EB]/[MP])|t=0 and ([EB]/[MP])|t are the initial mixing ratios in the fresh emissions, and the measured ratio of EB and MP at time t, respectively.
In this study, ethylbenzene and m/p-xylene presented a better correlation (0.97) than benzene and toluene (0.26), for which we used the measured concentration ratios of EB to MP to estimate the photochemical age (Figure S2). The estimation of photochemical age by EB/MP ratio method has been proved to be suitable in the YRD region in previous studies [16,17]. We used the lowest 5% value of the measured ratio of EB to MP to represent the ([EB]/[MP])|t=0. As shown in Figure S3, the initial ratio of EB to MP is 0.41.
We estimated the photochemical consumption (Cc) of total VOCs by Equations (1) and (2), which depends on the measured concentration (Ct), reaction rate constant, and OH exposure for given VOCs. In Equation (3), the photochemical consumption of total VOCs was used to calculate the formation of SOAvoc considering that VOCs are significant precursors of SOA [11,16,32].
S O A V O C   = ∑ i V O C i , c   ×   Y i
where SOAVOC are the concentration of SOA measured from VOCs oxidation method; VOCi,c was mentioned above; Yi is the SOA yield of VOCi, which is determined from chamber studies. The SOA yield are nearly zero for short-chain alkanes as well as short-chain alkenes, while being relatively higher for aromatics. Previous studies pointed out that the concentration of NOx generally appears an opposite trend with SOA yields for most VOCs, especially hydrocarbons and aromatics [33]. In this work, SOA yields on high-NOx conditions were applied. The reaction rate constants and SOA yields for each VOC species in our previous work [17].

2.3.2. Top-Down Method to Estimate SOA

Two top-down methods were used to estimate SOA, i.e., EC tracer method, and ACSM-PMF method.
EC is widely used to estimate the concentration of primary organic carbon (POC), due to its chemical inertness [34], for which it assumes that both EC and POC are mostly emitted by combustion processes [14]. The most important hypothesizes of this method are that the ratio of primary OC to EC, i.e., (OC/EC)pri is assumed to be constant, and POC comes from the same combustion source with EC. SOC was estimated by Equations as follows:
POC = ( OC / EC ) pri   ×   EC + OC non
SOC = OC   −   POC
where the OCnon is the regional background carbon, which is usually interpreted as the POC concentration that didn’t involve the combustion process [35].
The only undetermined variable in Equations (4) and (5) is (OC/EC)pri. In previous studies, many methods were used to determine the (OC/EC)pri, aimed to lower the influence of photochemical activity i.e., SOA formation. These estimations include: focusing on the periods with high levels of CO and NOx that co-emitted with POA and EC [36], using the data only in the early morning to ensure a low level of solar radiation intensity [37,38], using minimum R squared method to determine the ratio when the R2 between SOC and EC was the lowest [14,39].
In our study, the (OC/EC)pri ratio was determined by the slope of a regression line between OC and EC. Only the lowest 0–10% percentile concentration of EC was considered to be from primary emission and thus was used in the regression. As shown in Figure S4, the (OC/EC)pri was 2.65 in this observation. The correlation was high with R2 = 0.94.
ACSM data combined with receptor model e.g., PMF is often used to measure and apportion OA into POA from various sources and SOA. Positive matrix factorization 2.exe (PMF2.exe) algorithm [40] and the PMF Evaluation Toolkit version 2.08D [41] were performed on OA mass spectral matrix to determine the potential sources. PMF factors were evaluated following the procedures detailed in Zhang, et al. [42]). Two OA factors including one primary OA, i.e., a hydrocarbon-like OA (HOA), and one secondary OA, i.e., an oxygenated OA (OOA), were identified during the whole observation period. The OOA is the so-called SOAACSM in this study. Details of the PMF approach for SOA estimation are described in other works [43].

3. Results

3.1. Overview of the Meteorology and Air Pollutants

The time series of the meteorological conditions, chemical compositions in PM1, measured VOCs, and gaseous pollutants are shown in Figure 1. During the measurement, atmospheric conditions were stagnant with a wind speed of 1.4 ± 0.6 m/s, and the wind was mainly from the south.
The concentrations of NOx and CO were 30 ± 18 ppbv and 0.68 ± 0.62 ppmv, respectively. The concentration of PM2.5 was 35 ± 20 μg/m3, varying from 2 μg/m3 to 122 μg/m3. The concentration of PM1 was 32 ± 17 μg/m3, accounting for 91% of the total PM2.5 mass, among which OA was the main contributor (46.1%), followed by nitrate (26.3%), sulfate (13.9%), ammonium (12.2%), and chloride (1.5%).
The concentration of measured VOCs was on average 44.2 ± 22.7 ppbv and alkanes were predominant (35.5%), followed by OVOCs (23.4%), halogenated hydrocarbons (19.3%), aromatics (12.2%), alkenes (6.0%), alkynes (3.5%), and biogenic volatile organic compounds (BVOCs, 0.1%). The concentrations of measured VOCs are listed in Table S1.
The concentrations of measured VOCs, EC, and OC are exhibited in Table 1. The pollution level of this site is comparable to that of Beijing (BNU site). Because of its location in the “Nanjing-Shanghai” riverside industrial belt and downwind zone, the Changzhou site is a regional receptor site in the YRD region. The Changzhou site was found to have a higher concentration of VOCs while comparable concentration of carbonaceous aerosols in the YRD region, indicating that Changzhou was influenced distinctly by industrial transport.

3.2. Diurnal Variation of Vocs and Particle Chemical Composition

Diurnal variations of carbonaceous aerosols and NR-PM1 are shown in Figure 2. The concentrations of EC and OC revealed the peak at dusk, varying on a large scale, which might be driven by the lower boundary layer and increment in emissions. Due to the high OH exposure in the noon, the OC/EC ratio showed a peak from 12:00 to 14:00, which might be explained by the formation of SOA. The peak of the OC/EC ratio is consistent with the peak of SOAVOC diurnal variation in Figure 3.
The diurnal variation of NR-PM1 also presented a bimodal pattern. However, the composition of NR-PM1 varied little in the morning while the OA component went up suddenly at dusk, which might be a piece of evidence that the high pollution was caused by POA and SOA in the morning and at dusk, respectively.
The diurnal variation of measured VOCs other than BVOCs, the ratio of [EB] to [MP], SOA estimated from ACSM data (SOAACSM), and SOA estimated by VOC oxidation (SOAVOC) are shown in Figure 3.
The diurnal patterns of measured VOCs showed higher patterns from noon to evening, while the diurnal patterns of SOAACSM showed a bimodal distribution. Vehicle emissions are the important source of VOCs emission in Changzhou according to source apportionment. Traffic emissions always showed higher peaks during the morning and evening rush hours. The peak of the bimodal distribution of SOAACSM trailed behind the peak of vehicle emissions in the morning and evening, which may mean that VOCs oxidized into SOA after a time of photochemical reaction. The diurnal trend of vehicle emissions may be one of the reasons for the bimodal distribution of SOAACSM. The similar SOA diurnal patterns also occurred in other regions [51,52]. It should be noted that some major SOA precursors, e.g., styrene and isoprene, revealed distinct patterns. Industrial emissions are regarded as the major source of styrene in the urban atmosphere [53], while industrial emissions and vehicle emissions are also significant sources of isoprene besides biogenic emissions [54,55,56]. The concentration of VOCs, which might be derived from industrial emissions in Changzhou, increased significantly at night due to the lower boundary layer and an increment in emissions. As a result, the formation of SOA reached its peak in the evening. Source apportionment using positive matrix factorization (PMF) was performed to further comprehend the sources of the predominant VOC species in Figure S5. In this study, 73 VOC species were utilized as inputs to the PMF model. Six source factors, including industrial, secondary formation, vehicle emissions, gasoline volatilization, biomass burning, and solvent volatilization, were identified by tracers in this observation. Details of this model were described in other works [57,58]. For styrene, the main contributor was from solvent usage sources (47%), followed by industrial (27%). For isoprene, the main contribution was industrial (29%), followed by secondary formation (28%), and vehicle emissions (20%). This indicated that industrial sources might be the crucial factor of primary VOC emissions.
Besides, though the relative diurnal trend of E/X varied significantly due to the variation of solar radiation, the absolute diurnal variation amount of E/X varied finitely (0.5 to 0.6 on average). The hydroxyl radical (OH) exposure in the observation varied finitely, so that the relative SOA formation potential at noon was not so high compared with other places. This might be another reason for the bimodal pattern of SOA formation.

4. Discussion

4.1. Closure Study on SOA Estimation from Different Approaches

4.1.1. Comparison of SOA Estimated from Different Approaches

The concentration of SOAVOC estimated by VOC oxidation was 1.3 ± 1.2 μg/m3 on average during the whole period. In this work, 1.8 was used as the specific value of SOA to secondary organic carbon (SOC) [16,32,59]. The average SOCVOC was only 0.72 ± 0.66 μg/m3, which was fairly lower than other YRD regions [16,17]. Styrene and isoprene contributed most to SOA, with contributions of 54.9% and 14.6%, respectively. However, their variations were contrary to those of the OH exposure (Figure S6). Generally, the SOA from anthropogenic VOCs oxidation, i.e., aromatics, was dominant, accounting for 73.6%. However, this percentage is lower than in other YRD regions [16,17].
The SOA concentration estimated by EC tracer method (SOAtracer) was 3.2 ± 2.1 μg/m3 on average during the whole period. The value of (OC/EC)pri in our study was comparable with some studies in YRD region cities [17,48], but a bit higher than some other studies [47]. The SOC concentration is lower than that calculated from other studies, and the SOC/OC ratio on average was only 28% ± 14%, while SOC accounted for approximately 50% of OC in other studies [17,60]. The concentration of SOA (SOAACSM) and POA estimated from ACSM data was 12.1 ± 6.6 μg/m3 and 2.6 ± 2.7 μg/m3 on average. The ratio of SOAACSM to OA was on average 83% ± 8%, which was comparable with other studies [5].
Here we compare the SOA concentration estimated from VOCs oxidation yield method (SOAVOC), EC tracer method (SOAtracer), and ACSM-PMF (SOAACSM) (shown in Figure 4). The results showed that SOAVOC and SOAACSM, SOAtracer and SOAACSM, correlated significantly (p < 0.001). The SOAVOC also correlated with SOAtracer, but with lower correlation coefficient.
The low values of correlation coefficients mean that the linear correlations between the various methods are not obvious. Multiple reasons could cause this discrepancy. For instance, the compositions of PM1 were measured by ACSM while the SOAtracer and SOAVOC were not size-resolved. The selection of yields for SOAVOC utilized in the yield method could also introduce uncertainties. However, the p-values of the Pearson correlation analysis were fairly low, which means that there is still a positive consistency but not a linear consistency of the SOA time series.
To explore the contributions of gas-phase VOCs oxidation to SOA, a closure study was conducted by comparing SOAVOC from bottom-up VOCs oxidation yield method with SOA estimated from top-down method. SOAVOC derived from VOCs oxidation could only explain 10.9% ± 8.6% of SOAACSM and 43.2% ± 41.9% of SOAtracer. Missing precursors are the main reason for SOAVOC underestimation.
The ACSM-PMF method estimated the highest concentration of SOA. It is interesting to note that the SOAACSM concentration was much higher than SOAVOC and SOAtracer at noon and in the early evening. This discrepancy may be due to the fact that part of the COA was apportioned to the SOA factor.
Underestimation of SOA was found in VOCs oxidation yield method. Previous studies found similar results, that the SOAVOC accounted for only 25–40% of top-down method SOA [17,32]. In this work, the diurnal pattern of SOA implied that SOAVOC differs most from SOAACSM 1–2 h after the morning and evening peak-hour on vehicles. It may be a piece of evidence that SOA formation is highly related to the oxidation of IVOCs from urban cooking and vehicular sources.

4.1.2. Influencing Factors of SOA Formation

The photochemical oxidation of VOCs is vital in SOA formation. Odd oxygen (Ox, O3 + NO2) has been widely used to represent the photochemical oxidation capacity [4]. Recent studies have found that the aqueous-phase reactions are also important pathways for SOA formation [61]. ALWC provides reaction interfaces and sites for liquid-phase reactions, for which the capability of aqueous-phase reactions could be represented by ALWC [9].
SOA was measured by three approaches, i.e., VOC oxidation, EC tracer method, and receptor model. Considering that there was distinct underestimation and overestimation from VOCs oxidation method and receptor model respectively, SOAtracer was applied as a function of Ox and ALWC to investigate the impacts of photochemical oxidation reactions and aqueous-phase processing on SOA formation, as shown in Figure 5.
SOAtracer appeared an obvious same trend with Ox when Ox was less than 100 ppbv, indicating that photochemical oxidation reactions might contribute much to SOA formation. Figure 5a shows an obviously positive correlation between Ox (O3 + NO2) and SOA with a p-value of 1.6 × 10−208, indicating that O3 was statistically correlated with SOA. This could be due to the high oxidation capacity of OH produced by O3, which has an indirect effect on SOA, primarily through the generation of OH radicals, increasing the oxidation of POA.
During the observation, no significant same trend was found between SOAtracer and ALWC in Figure 5b. The correlation coefficients between ALWC, SOAACSM, SOAtracer, and SOAVOC are 0.37, −0.30, and 0.04, respectively. Previous studies pointed out that a high concentration of ALWC would enhance the secondary formation [62,63]. This discrepancy might be caused by the negative correlation between Ox and ALWC and the relatively low concentration of ALWC compared to other regions. In our observation, the concentration of ALWC was mainly 0–30 μg/m3. In other regions where the aqueous-phase processing dominates, the concentrations of ALWC were always from 200–500 μg/m3 [64,65]. In Figure 5c, high SOAtracer concentrations were observed in higher concentration levels of either ALWC or Ox, indicating that both photochemical oxidation reactions and aqueous-phase processing would enhance the formation of SOA. However, high concentrations of SOAtracer occurred more frequently with the high level of Ox than with the high level of ALWC. In general, photochemical processing was a more critical factor in SOA formation compared with aqueous-phase reactions in Changzhou, so the positive correlation could be observed only between Ox and SOAtracer.

4.2. Uncertainty Analysis of Each Method

In our study, three approaches were used for SOA formation, and all of those methods presented various estimation biases, interfering with the closure of SOA formation. An uncertainty analysis of each method was performed accordingly.
The underestimation of SOAVOC is mainly caused by the missing precursors. Only the measured VOCs, not all precursors, were used to estimate SOA due to the restriction on measurements and estimation methods. Intermediate volatility organic compounds (IVOCs), especially polycyclic aromatic hydrocarbons (PAHs), have been confirmed to be potentially large sources of urban SOA [66]. Previous studies revealed that semi-volatile organic compounds (SVOCs) oxidation could also account for a high percentage of SOA formation [67]. For instance, a study added two SVOCs, i.e., naphthalene and methylnaphthalene, and explained 10.2% more of the SOA formation [17]. Another reason for the uncertainty of SOA estimation is that the yields for VOCs to SOA which we assume are constant, are, in fact, changing. Recent studies also found that yields could be reduced by the mixture of VOCs [68,69]. The intermediate products reacting with each other to generate high volatility products may be one of the reasons. Another reason for the underestimation of SOAVOC is that the aqueous-phase reactions were proved to be a significant process for SOA formation [63,70], which is not included in the VOCs oxidation yield method. Besides, the process of VOCs oxidation with other oxidants is also not included in the yield approach.
The EC tracer method is restricted by the assumption that POC is nonvolatile and nonreactive, which has been proved improperly [67]. From the bimodal pattern of SOA diurnal variation and the low SOC/OC ratio from the EC tracer method, we predicted that there was a considerable amount of primary emissions with photochemical reactivity and semi-/intermediate volatility during the whole observation. It might be the main reason for the underestimation of SOAtracer. Besides, the estimation of SOAtracer highly depends on the primary OC/EC ratio. The estimated SOA varied by about ±6% when the (OC/EC)pri changed from 2.65 ± 0.1. Previous studies have found that biomass burning produces particulate matter with high OC/EC ratios [71]. This could result in an underestimation of (OC/EC)pri and, as a result, an overestimation of SOC. However, in our study, biomass burning contributed 6% of the VOCs according to source apportionment from the PMF method. Due to the insignificant contribution of biomass burning, the influence of primary biomass burning on SOA estimation is inappreciable.
The concentration of SOAACSM was higher than SOAVOC and SOAtracer, the same as it is in other research [72,73]. Polidori found that POA represented 60–70% of the OA in Pittsburgh from June 2001 to November 2001, while Zhang estimated that only 33% of the OA was HOA in the same city in September 2002. The overestimation of SOA may be caused by the substitution of HOA for POA. POA included not only HOA but also cooking OA (COA), biomass burning OA (BBOA), and coal combustion OA (CCOA) [5]. The missing sources for POA calculation, especially COA, increased the estimation of SOA.

5. Conclusions

In this study, we presented a field measurement to estimate the formation of SOA through different approaches by using the state-of-the-art online instruments at Changzhou during November 2020. A bimodal pattern of SOA diurnal variation was observed during the period. From the perspective of VOCs oxidation, the major VOC precursors of SOA, i.e., styrene and isoprene, revealed distinct diurnal bimodal patterns compared with OH exposure. Emissions from vehicles and industries might be the main reason for the bimodal pattern of the diurnal variation of SOA. SOA estimated from VOCs oxidation yield method accounted for about only 10.7% of the SOA apportioned from the PMF receptor model and 40.2% of the SOA estimated from EC tracer method. This illustrated that there might be an underestimation of VOCs oxidation yield method. The underestimation of the VOCs oxidation yield method might be caused by the missing precursors, e.g., S/IVOCs. The inappropriate assumption that POC is nonvolatile and nonreactive might lead to the underestimation of the EC tracer method. Besides, the overestimation of the receptor model may come from the fact that part of COA was apportioned to SOA. A good correlation was found between SOA and Ox, suggesting the importance of photochemical processing in SOA formation. On the contrary, no clear relationship was found between SOA and ALWC. Further studies should be conducted to explore the impact of aqueous phase reactions on SOA formation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos13101679/s1. Figure S1: Location of observation sites in Changzhou from 2 November 2020 to 30 November 2020; Figure S2: Scatterplot of (a) toluene and benzene and (b) ethylbenzene and m/p-xylene; Figure S3: The distribution of measured ethylbenzene to m/p-xylene ratios during the observation; Figure S4: The regression line between OC and EC of the dataset with the lowest 0–10% percentile OC/EC ratios; Figure S5: Source profiles of measured VOCs in the observation; Figure S6: The diurnal variation of (a) styrene and (b) isoprene. The pink area represents the 95%confidence interval; Figure S7: The time series of SOA estimated by different approaches; Table S1: Summary of the concentrations of VOCs during the observation (Unit: ppbv).

Author Contributions

Conceptualization, S.G.; methodology, Z.W., K.S. and Y.Y. (Ying Yu); software, Z.W. and K.S.; validation, W.Z., H.W. and Y.G.; formal analysis, Z.W.; investigation, Z.W., K.S., W.Z., Y.Y. (Ying Yu), D.L., H.W., R.T., X.Y., L.Z. and R.S.; resources, X.Y., S.C. and L.Z.; data curation, Z.W. and K.S.; writing—original draft preparation, Z.W.; writing—review and editing, K.S., S.G., W.Z. and Y.G.; visualization, Z.W. and K.S.; supervision, S.G., S.L. and Y.Y. (Yijun Yu). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 91844301, 22221004, 41977179), the special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control (Nos. 22Y01SSPCP).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Atmosphere 13 01679 g001 550
Figure 1. Time series of (a) wind direction (WD) and wind speed (WS); (b) relate humidity (RH) and temperature; (c) PM1 compositions; (d) O3, NOx, and CO; (e) measured VOCs.
Figure 1. Time series of (a) wind direction (WD) and wind speed (WS); (b) relate humidity (RH) and temperature; (c) PM1 compositions; (d) O3, NOx, and CO; (e) measured VOCs.
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Figure 2. The diurnal variation of (a) non-refractory submicron particulate matter (NR-PM1); (b) the ratio of OC to EC; (c) OC; (d) EC. The pink area represents the 95% confidence interval.
Figure 2. The diurnal variation of (a) non-refractory submicron particulate matter (NR-PM1); (b) the ratio of OC to EC; (c) OC; (d) EC. The pink area represents the 95% confidence interval.
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Figure 3. The diurnal variation of (a) Measured VOCs other than BVOCs; (b) the ratio of [EB] to [MP]; (c) SOA estimated from ACSM data; (d) SOA estimated by VOC oxidation. The pink area represents the 95% confidence interval.
Figure 3. The diurnal variation of (a) Measured VOCs other than BVOCs; (b) the ratio of [EB] to [MP]; (c) SOA estimated from ACSM data; (d) SOA estimated by VOC oxidation. The pink area represents the 95% confidence interval.
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Figure 4. The correlation patterns of (a) SOAVOC and SOAACSM; (b) SOAtracer,SOAACSM, and the frequency distribution histogram of each method.
Figure 4. The correlation patterns of (a) SOAVOC and SOAACSM; (b) SOAtracer,SOAACSM, and the frequency distribution histogram of each method.
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Figure 5. The SOAtracer as a function of (a) Odd oxygen (Ox, bins of 20 ppbv) and (b) aerosol liquid water contents (ALWC, bins of 10 μg/m3). Box: 25% and 75% percentile. Whisker: 10% and 90% percentile. (c) Scatterplot of ALWC and Odd oxygen.
Figure 5. The SOAtracer as a function of (a) Odd oxygen (Ox, bins of 20 ppbv) and (b) aerosol liquid water contents (ALWC, bins of 10 μg/m3). Box: 25% and 75% percentile. Whisker: 10% and 90% percentile. (c) Scatterplot of ALWC and Odd oxygen.
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Table
Table 1. Comparisons of measured VOCs and carbonaceous aerosols with other sites in the YRD region.
Table 1. Comparisons of measured VOCs and carbonaceous aerosols with other sites in the YRD region.
SiteStation TypesDateOC (μg/m3)EC (μg/m3)VOCs (ppbv)References
ChangzhouU YRD2020.117.42.244.2This Study
PudongU, YRD2017.7n.a.n.a.22.7[44]
Jiangsu AESU, YRD2016n.a.n.a.25.7[45]
Shanghai AESU, YRD2013.8111.960[16]
PudongU, YRD2019.12–2020.25.171.0214.6[46]
Fudan UniversityU, YRD2013–20148.43.1n.a.[47]
Nanjing UniversityU, YRD2013–2015105.2n.a.[48]
ShanghaiU, YRD2012–201410.72n.a.[49]
TaizhouR, YRD2018.5–67.51.416[17]
Nanjing UniversityR, YRD2018.6–8n.a.n.a.33.9[21]
Beijing Normal UniversityU, BTH2016n.a.n.a.44.0[50]
n.a.: not available. AES: Academy of Environmental Science; YRD: Yangtze River Delta; BTH: Beijing-Tianjin-Hebei region; U: Urban site; R: Rural site.
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Wan, Z.; Song, K.; Zhu, W.; Yu, Y.; Wang, H.; Shen, R.; Tan, R.; Lv, D.; Gong, Y.; Yu, X.; et al. A Closure Study of Secondary Organic Aerosol Estimation at an Urban Site of Yangtze River Delta, China. Atmosphere 2022, 13, 1679. https://doi.org/10.3390/atmos13101679

AMA Style

Wan Z, Song K, Zhu W, Yu Y, Wang H, Shen R, Tan R, Lv D, Gong Y, Yu X, et al. A Closure Study of Secondary Organic Aerosol Estimation at an Urban Site of Yangtze River Delta, China. Atmosphere. 2022; 13(10):1679. https://doi.org/10.3390/atmos13101679

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Wan, Zichao, Kai Song, Wenfei Zhu, Ying Yu, Hui Wang, Ruizhe Shen, Rui Tan, Daqi Lv, Yuanzheng Gong, Xuena Yu, and et al. 2022. "A Closure Study of Secondary Organic Aerosol Estimation at an Urban Site of Yangtze River Delta, China" Atmosphere 13, no. 10: 1679. https://doi.org/10.3390/atmos13101679

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Wan, Z., Song, K., Zhu, W., Yu, Y., Wang, H., Shen, R., Tan, R., Lv, D., Gong, Y., Yu, X., Chen, S., Zeng, L., Lou, S., Yu, Y., & Guo, S. (2022). A Closure Study of Secondary Organic Aerosol Estimation at an Urban Site of Yangtze River Delta, China. Atmosphere, 13(10), 1679. https://doi.org/10.3390/atmos13101679

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