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Article

Volatile Organic Compounds in the North China Plain: Characteristics, Sources, and Effects on Ozone Formation

by
Xue Yang
1,2,*,
Luhong Gao
1,
Shiyang Zhao
2,
Guang Pan
1,
Guolan Fan
1,
Zhiyong Xia
1,
Xiaoyan Sun
1,
Hongyu Xu
1,
Yanjun Chen
1 and
Xiaolong Jin
2
1
Shandong Jinan Ecological Environment Monitoring Center, Jinan 250101, China
2
College of Municipal and Environmental Engineering, Shandong Jianzhu University, Jinan 250101, China
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(2), 318; https://doi.org/10.3390/atmos14020318
Submission received: 3 January 2023 / Revised: 24 January 2023 / Accepted: 1 February 2023 / Published: 5 February 2023
(This article belongs to the Special Issue Source Apportionment of Regional Ozone Pollution)
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Abstract

:
Enhanced volatile organic compounds (VOCs) observations were made on ozone-exceeding days in June 2020 in Linyi, China. A total of 69 VOCs were collected (1 alkyne, 29 alkanes, 10 alkenes, 14 aromatic hydrocarbons, and 15 oxygenated volatile organic compounds (OVOCs)). The average concentration of total VOCs (TVOCs) was 36.0 ± 0.66 ppb, and the top three VOCs components were alkanes, OVOCs, and aromatic hydrocarbons, which accounted for 40.75%, 27.02%, and 11.30%, respectively. Based on the positive matrix factorization (PMF) model, the main sources of VOCs in Linyi City were divided into vehicle exhaust sources (39.11%), biomass combustion sources (21.82%), oil and gas volatilization sources (21.46%), and solvent use sources (17.61%). The ozone formation potential (OFP) contribution rate was dominated by alkenes, OVOCs, and aromatics, with contribution rates of 26.37%, 25.30%, and 23.65%, respectively. The top six VOCs that contributed the most to the OFP were formaldehyde, acetaldehyde, 1-butene, butadiene, trans-2-butene, and propylene. The empirical kinetic modelling approach (EKMA) curve indicated that the in situ ozone (O3) production was limited by VOCs, and reducing the concentration of O3 precursors in accordance with the VOCs/NOx concentration ratio of 1.15 can control O3 pollution more effectively.

1. Introduction

In recent years, China’s strict regulations on air pollution have led to significant drops in PM2.5 concentrations. However, O3 pollution has become worse, with near-ground O3 concentrations exceeding the national ambient air quality standards of China frequently [1]. As a crucial precursor of both O3 and PM2.5, volatile organic compounds (VOCs) play a crucial role in air pollution, and mitigation of VOCs pollution has been a top concern for environmental preservation in China [2].
VOCs are organic chemicals with a vapor pressure greater than or equal to 0.01 KPa at 20 °C [3]. VOCs have many different natural and man-made sources. For instance, there are many biological sources such as vegetative emissions and a number of anthropogenic sources, such as solvent use, industrial pollutants, and traffic emissions, mostly resulting from production and living processes [4]. VOCs play a key role in atmospheric photochemical processes. For example, most VOCs are reactive and can react with oxidants such as hydroxyl radicals (●OH) and O3 in the environment with lifetimes varying from a few hours to several tens of days [5,6]. OVOCs, O3, peroxyacetyl nitrates (PANs), and other components of photochemical smog can be produced through these complex reactions [7,8]. VOCs have a significant negative impact on ecosystems and human health due to their low concentration, high activity, and high hazard characteristics [9,10]. Accordingly, understanding the characteristics and sources of VOCs is key to the control of air pollution in China.
Many studies focusing on VOCs’ pollution characteristics and their impact on O3 have been performed in China. Liu et al. (2021) underlined that the aromatics, alkenes, and alkanes together contributed to most of the O3 production in Shenyang of northeast China, with TVOCs’ concentrations varying from high to low during the winter, fall, spring, and summer [11]. Zhu et al. (2018) employed intensive ship measurement to examine VOC concentrations in the middle and lower reaches of the Yangtze River (MLYR) and compared them to other cities in China. They discovered that the MLYR has lower levels of aromatic hydrocarbons than Shanghai and Wuhan [12]. Mo et al. (2021) measured the vertical distribution of VOCs in Guangzhou and found that with an increase in height, the content of VOCs decreased, while the content of oxygenated VOCs (OVOCs) such as formaldehyde and acetaldehyde increased. This is mainly due to the dilution and chemical loss of VOCs and secondary formation of OVOCs during vertical transport [13]. Zhou et al. (2021) constructed VOC source profiles based on all of the product processes (e.g., wood-based panel manufacturing and pharmaceuticals) and collected VOC source profiles of light and medium-sized automobile exhaust based on actual road conditions at different speeds in Chengdu, a megacity in southwest China. The results showed that OVOCs accounted for 27–84% of the total VOC emissions [14]. On the whole, these investigations were mainly conducted in or around the megacities of China; however, less attention has been paid to small- to mid-sized cities, where the atmosphere can be influenced by different human and industrial activities.
Numerous studies have focused on the influence of VOCs on O3. However, the majority of these studies have focused exclusively on the contribution of non-methane total hydrocarbons (NMHCs) to O3 and have not considered the significance of OVOCs. Actually, in recent years, the contribution of OVOCs to O3 has been increasingly confirmed. Zhang et al. (2021) found that the relative contribution of VOCs to O3 in summer in Beijing is alkenes > OVOCs > aromatics > alkanes > alkyne > halocarbons. The contribution rates of alkenes, OVOCs, and aromatic hydrocarbons to OFP were 33.72%, 29.28%, and 21.49%, respectively, which were much higher than those of halocarbons, alkanes, and alkynes [15]. Tan et al. (2021) also found that the top five contributors to O3 formation in Hong Kong were isoprene, MEK, xylene, acetaldehyde, and acrolein in order, with isoprene and OVOCs being the dominant species in the process of O3 formation [16]. Hence, the simultaneous observation of both VOCs and OVOCs in small- to mid-sized cities of China is urgently needed.
The observation of VOCs and their pollution characteristics in Shandong, China, has been the subject of several pieces of research [17,18]. However, to our knowledge, the co-observation of OVOCs and VOCs and their effects on O3 pollution have rarely been reported. To better dissect the characteristics and sources of VOCs during high ozone episodes in the North China Plain, a comprehensive field measurement of VOCs together with OVOCs was conducted in the Linyi area of Shandong Province in the summer of 2020. The study site was carefully chosen and characterized with rapid industrial development and high intensity of VOCs and related pollutant emissions, which can be represented as a typical small- and medium-sized city in NCP. In the following discussion, we first provide an overview of the qualitative and quantitative characteristics of VOCs; we then analyze the contribution of sources of VOCs; we then assess the photochemical reactivates of VOCs; and finally, we then apply an observation-based model (OBM) to examine the ozone formation regimes.

2. Materials and Methods

2.1. Data Collection and Analysis

The observation site for this study is situated in the Linyi Hedong state control station (35.09° N, 118.40° E), which is situated in the south of Shandong, China (Figure 1). The observation site is situated approximately 15 m above the ground and surrounded by arterial roads, commercial neighborhoods, and government agencies. The Hedong Industry Park is located approximately 4 km away, northeast of the sampling site. Detailed descriptions of the study site have been provided elsewhere [19].
Continuous data for the VOCs, OVOCs, and other parameters were measured from 15 to 29 June 2020. Ambient VOCs were sampled using 3 L stainless steel SilcoCan canisters. The VOCs’ sample period was between 8:00 and 20:00 local time (LT) during the enhanced observation period, and the sampling duration was 2 h with sampling intervals of 2–3 h. Normally, a total of 6 VOC samples were obtained per day. No sampling was conducted if it was cloudy or raining outside. Note that there was a lot of rain during our observation period; thus, a total of 7 days worth of samples were collected for this enhanced observation of VOCs, which were on the 15th, 19~21th, and 24~26th of June 2020, and a total of 41 groups of VOC samples were collected. After sampling, a five-column multiple gas chromatograph (GC) system was used. The VOC analysis was used for the detection of VOC species based on the USEPA-promoted TO-11 and TO-15 methodologies [20,21,22].
OVOC samples were sampled by collecting ambient air through a 2,4-dinitrophenylhydrazine (DNPH)-coated sorbent cartridge [23]. When sampling, an O3 filter tube (O3 Scrubber, coated with potassium iodide) was added in front of the sampling tube to prevent O3 from reacting with OVOCs [24]. Twelve samples were sampled from 00:00 to 23:00 LT at a flow rate of 1 L min−1 and a duration of 2 h each day. Note that during the sampling time, one field blank and one laboratory blank was also collected. In total, 170 OVOC samples were obtained. Finally, these OVOC samples were analyzed by high-performance liquid chromatography (HPLC) with 15 liquid standard OVOC solutions, e.g., formaldehyde, acetaldehyde, acetone, butyraldehyde, propionaldehyde, crotonaldehyde, hexaldehyde, benzaldehyde, iso-valeraldehyde, valeraldehyde, o-tolualdehyde, m-tolualdehyde, p-tolualdehyde, acrolein, and dimethyl-benzaldehyde. Details of the OVOCs’ collection and detection can be referred tto o in Yang et al. (2017, 2018) [25,26] and Shen et al. [27]. In addition, the relevant concentrations of O3, CO, and NOx were simultaneously monitored by a series of commercial instruments. Meteorological parameters (such as temperature, relative humidity, wind speed, direction, pressure, etc.) were provided by the local weather station.

2.2. Data Processing Methods

2.2.1. Empirical Kinetic Modelling Approach (EKMA)

In order to qualitatively provide the precursor reduction scheme to improve O3 reduction strategies, an empirical kinetic modelling approach (EKMA) was employed in this study [4]. EKMA is an important tool to identify sensitive precursors of O3 and can develop a synergistic control strategy for VOCs and NOx [28]. In this study, observation-based modeling (OBM) based on the latest version of Master Chemical Mechanism (MCM) (version 3.3; http://mcm.leeds.ac.uk/MCM/; accessed on 12 December 2020) was employed to derive EKMA [29,30].
Generally speaking, a number of different scenarios for O3 precursors were used, i.e., six initial volume fraction scenarios for NOx (with the NOx volume fraction being 0.1, 0.2, 0.4, 0.6, 0.8, and 0.95 of the observed volume fractions of NOx) and six initial volume fraction scenarios for VOCs (with the VOCs volume fraction being 0.1, 0.2, 0.4, 0.6, 0.8, and 0.95 of the observed volume fraction of VOCs). Note that there are in total 36 emission scenarios in our stimulation; the other parameters are consistent with the actual observed data, except the VOCs and NOx. These data were used as the OBM model input data to simulate O3 formation under different concentrations of precursors. Finally, the maximum daily (7:00 to 18:00 LT) O3 can be obtained using various volume fractions of NOx and VOCs. The O3 maximum concentration was plotted against various volume fractions of VOCs and NOx to generate a contour plot. Equation (1) can be used to express the nonlinear VOCs–NOx–O3 relationship:
p ( O 3 ) = f ( NO ± Δ NO X , VOCs ± Δ VOCs )

2.2.2. Positive Matrix Factorization (PMF)

Positive matrix factorization (PMF) is a conceptual model that can be used to explore potential source categories and contributions. In this study, the positive matrix factorization model (PMF 5.0) recommended by the US Environmental Protection Agency (EPA) was used to analyze the pollution sources of VOC species [31]. As shown in Equation (2), the PMF model could be expressed as a chemical mass balance equation in terms of contributions from p-independent sources to the n chemical species measured.
X i j = ∑ k = 1 p G i k · F k j + E i j ;   i = 1 , 2 , 3 , … , n
where Xij is the jth chemical species concentration determined in the ith sample; Gik is the species contribution of the kth source to the ith sample; Fkj is the loading of the jth species on the kth factor; and Eij is the residual resulting from bias in the measurement of Gik and Fkj and represents the total number of independent sources. In this study, 41 × 54 matrix (sample number × 54 species) data sets were introduced to PMF 5.0 to identify VOC sources. The model’s settings and physical parameterizations can be obtained in the PMF 5.0 user guide and have been described elsewhere [32,33].

3. Results and Discussion

3.1. General Characteristics of VOCs

VOCs’ Concentration and Chemical Composition

A total of 69 VOC components were observed during the summer observation period in Linyi City. For comparison, we divided the 69 VOCs into five groups based on their chemical structures (see Table 1), namely alkanes, alkenes, alkyne, OVOCs, and aromatics. A comparison of VOCs’ component concentrations in different regions can be seen in Table 2. During the observation period, the total VOC concentration (TVOCs) in Linyi City was 36.55 ± 10.82 ppb, which is extremely higher than that in Wuhan [34], Chongqing [35] and Taixi [36] but comparable to heavily polluted cities such as Shanghai [37] and Guangzhou [38]. Of the five categories, alkanes were the most abundant VOCs, accounting for 40.75% of the total VOCs, with an average concentration of 15.41 ± 9.09 ppb, in which the average isopentane concentration was 2.47 ± 2.10 ppb, which was higher than that of Wuhan [34] and Taixi [36]. The average concentration of propane was 2.42 ± 2.79 ppb, which was higher than that of Handan [39] and Taixi [36]. The concentration of OVOCs was 9.62 ± 5.499 ppb, accounting for 27.02%. Acetone and acetaldehyde with average concentrations of 2.03 ± 0.84 ppb and 1.66 ± 0.99 ppb, respectively, were higher than in Wuhan [34]. The proportion of aromatics was 11.30%, with the average concentration of 4.02 ± 0.18 ppb. The average concentration of benzene was 0.51 ± 0.21, which was higher than that of the Hong Kong administrative region [16] and close to the average concentration of benzene in Taixi [36] and Guangzhou [38]. The proportion of alkenes was 9.37%, with an average concentration of 3.34 ± 0.14 ppb. The average concentration of isoprene was 0.37 ± 0.12 ppb, accounting for 1.03%, which was close to the isoprene concentration in Chongqing [35] and Hong Kong [16]. The average concentration of ethylene was 0.35 ± 0.18 ppb, which was close to the concentration of the ethylene component in Handan [39]. Overall, this comparison highlights the severity of VOC pollution in small- to mid-sized cities, i.e., Linyi in the NCP region.
The top 10 VOCs in Linyi City during the observation period were formaldehyde, isopentane, propane, acetone, n-butane, acetaldehyde, isobutane, 2-methylhexane, ethane, and 2-methylpentane, which account for 51.37% of the total VOCs. It is clearly seen from Figure 2 that, of the top 10 VOCs species, alkanes contributed mostly, with a high proportion of isopentane and propane. Formaldehyde exhibited high levels of 3.90 ± 3.52 ppbv and this indicated the possible source of solvent usage considering the abundance of plate processing industries in Linyi. The sources of isopentane and propane were associated with motor vehicle exhaust [40,41] indicating that atmospheric VOCs’ concentrations in Linyi may be influenced by mobile source emissions, which will be discussed in the following section.
The source of VOCs pollution and its effects on other pollutants can be initially investigated based on the variations of VOCs. Figure 3 shows the daily average changes of VOCs and related gas and meteorological parameters during the enhanced VOC observation period. The trends for the primary pollutants CO, NO, and TVOCs are generally consistent, with morning peak values. To be specific, the trend for VOCs shows a clear single-peak variation at 10:00 LT, with hourly VOC concentrations gradually increasing between 8:00 and 10:00 LT. This variation is mainly influenced by human activities, such as the nearby industrial emission process, photolysis loss intensity, and diurnal variation in the atmospheric boundary layer. In the early morning, human activities such as traffic and industrial production are frequent, and the photolysis rate is low, which increases the accumulation of VOCs, allowing the concentration of VOCs to increase. There is a gradual decrease at midday and after noon (10:00–14:00 LT) due to the effect of the increase in temperature, which enhances the photolysis of VOCs. After 16:00 LT, the photolysis rate gradually decreases, and the concentration of VOCs stabilizes at this stage. Overall, the diurnal variation characteristics of VOCs are basically consistent with those in Wuhan, Haerbin, and Hangzhou in China [42,43,44]. The diurnal variation of O3 showed a typical afternoon peak at 16:00 LT, which was 73.38 ppbv. The peak value of O3 appeared after the precursors NOx and TVOCs, indicating the influence of photochemical interaction on the observed O3 [45].

3.2. Sources of VOCs

In order to understand the source characteristics of atmospheric VOCs in Linyi and to provide scientific support, in this study, we then used the PMF method to analyze the source of atmospheric VOCs in this area [46]. Only the periods with measured data points from both VOCs and OVOCs were included in the PMF analysis. From the primary species that are distinctive of VOCs, 54 species were chosen and entered into the model for calculation, considering their ambient concentrations, source indications, and signal versus noise values (S/N). Four significant sources of atmospheric VOCs in Linyi City were finally identified by PMF source analysis. The sources of VOCs can be identified based on the high and low loadings of representative chemicals in the source composition spectra of VOCs as shown in Figure 4.
High loadings of ethylbenzene and xylene were found in factor 1. Benzene in urban atmospheres is mainly related to the use of solvents such as spray materials and adhesives [35,47]. Many plate processing workshops, auto sales and service businesses, paint rooms, and other spraying process production and emission enterprises can be found in Linyi City [19]. Therefore, factor 1 was identified as a solvent source, which contributes 17.61% of the VOCs to the urban air environment in Linyi City.
The analysis showed that acetone, formaldehyde, and acetaldehyde contributed as much to factor 2 as factor 1 with 10.42%, 9.85%, and 9.09%, respectively. In addition, the contribution of n-butyraldehyde was relatively large (6.74%). Compared to Factor 1, however, the contribution of n-dodecane, n-pentane, n-butane, and other high-carbon chain n-alkanes was also greater in factor 2. N-alkanes are important in emissions from coal and straw combustion [48]. There were no large coal-fired enterprises in the surrounding area according to the monitoring sites [19]. Therefore, factor 2 was defined as a biomass combustion source. The contribution of biomass combustion sources to VOCs in Linyi’s urban air environment was 21.82%.
The third factor has a higher contribution from alkane VOCs such as propane, n-butane, isobutane, isopentane, and C4–C10 benzene. Isopentane, propane, and butane are considered to be typical tracers of gasoline volatilization [49], and gasoline is mainly composed of hydrocarbons of the C4–C10 group [40]. Therefore, we defined factor 3 as a vehicle exhaust source. In the urban air environment of Linyi City, the contribution of vehicle emissions to VOCs was the largest, with a value of 39.11%.
High loadings of isopentane and alkenes such as trans-2-butene and 1,3-butadiene were found in factor 4. Isopentane is an important component of liquefied petroleum gas (LPG), and some studies have found that a by-product of using liquefied petroleum gas is trans-2-butene [49,50]. According to the monitoring site inquiry, Sinopec Linyi Petroleum Branch is located at the monitoring site in Linyi [19]. Therefore, factor 4 was defined as the source of oil and gas volatilization, and the contribution of oil and gas volatilization sources to VOCs in Linyi’s urban air environment was 21.46%.

3.3. Photochemical Reactivates

3.3.1. Reactivity Studies on VOCs

The photochemical reactivity of VOCs is influenced by both the concentration and individual reactivity of the VOCs. To estimate the reactivity of individual VOCs and their contribution to O3 formation, both OH reactivities and ozone formation potential for individual VOCs in Linyi were calculated. The OH reactivity (LOH) of a given VOC species can be calculated by multiplying its volume fraction with its ●OH reaction rate constant (KOH) [51]. The OFP of each VOC species was determined by OFPi = [VOCi] × MIRi, a product of VOCs in μg m−3 and the corresponding maximum incremental reactivity (MIR) taken from the literatures [52]. Table 3 presents the descriptive statistics of the concentration of VOCs together with their OFP and LOH. Moreover, Figure 5 also depicts their reactivity and distribution obtained in Linyi. The total OFP was 338.94 μg/m3. The OFP contribution of the TVOCs was in the order of alkenes > OVOCs > aromatics > alkanes > biogenic sources (isoprene), with the contribution reaching 26.37% (89.38 μg/m3), 25.30% (85.76 μg/m3), 23.65% (80.14 μg/m3), 21.23% (71.96 μg/m3), and 3.45% (11.70 μg/m3), respectively. The results highlight the relative importance of OVOCs in O3 formation and were consistent with previous studies which proved the high OVOCs contribution (33%) in the summer in the Pearl River Delta region, (Louie et al. 2013) [8]. However, the OFP of the VOC groups in Linyi is slightly different from the OFP contribution of the VOC groups in Zhengzhou in the summer, where the contribution rate of TVOCs to OFP in Zhengzhou was in the order of aromatics > alkenes > alkanes > OVOCs > biological sources (isoprene), and the contribution of aromatic hydrocarbons to OFP reached 42.7% [53]. The top six VOCs that contributed most to the OFP were formaldehyde (37.60 μg/m3, 11.09%), acetaldehyde (17.88 μg/m3, 5.28%), p-m-xylene (17.54 μg/m3, 5.18%), 1-Butene (16.75 μg/m3, 4.94%), o-Xylene (14.82 μg/m3, 4.37%), and butadiene (13.68 μg/m3, 4.03%), accounting for 34.89% of the total OFP. The results from LOH demonstrated again that alkenes were the VOC species that contributed the most to O3 (41.48%, 4.5 s−1), followed by OVOCs (28.42%, 3.09 s−1) and aromatics (13.05%, 1.42 s−1). In particular, the top six hydroxyl radical reactivity contributors were n-butyraldehyde (11.04%, 1.20 s−1), isoprene (8.31%, 0.90 s−1), formaldehyde (8.28%, 0.90 s−1), 1, 3-butadiene (6.79%, 0.74 s−1), trans-2-pentene (6.00%, 0.65 s−1), and acetaldehyde (5.62%, 0.61 s−1), reflecting that ozone formation was mainly due to a small number of VOCs. It is worth noting the formaldehyde made a great contribution to OFP as well as LOH. The important contribution of formaldehyde has also been observed in Beijing, which concluded its secondary sources [26].

3.3.2. EKMA

In order to further study the nonlinear relationship between O3 and its precursors, in this study, the EKMA curve to estimate the sensitivity of both NOX and VOCs to ozone pollution was simulated by the OBM model [54]. The EKMA curves were plotted by the constraints of the actual observed data for H2O, temperature, pressure, NOx, and VOCs under different scenario simulations during our entire observation period. Figure 6 depicted the O3-VOCs-NOx sensitivity study for Linyi. The maximum O3 generation value simulated from 07:00 to 18:00 LT is marked with points of different colors on the corresponding Z-axis. The ridgeline, also known as the line linking the highest O3 production fraction turning point, is represented by the dark line. The ridgeline divides the contour plot into two parts roughly: NOx-limited and VOC-limited (see Figure 6). O3 formation is controlled by VOCs above the ridge line and NOx below the ridge line, and a transition zone close to the ridgeline. The corresponding VOCs/NOx concentration ratio k of the ridgeline is 20:17.4 (~1.15), which reveals that cutting VOCs and NOx emissions according to this ratio would be more effective for controlling O3 pollution [55]. Furthermore, the calculated VOCs/NOx ratio during our experiment is about 1.96 > k, indicating that the ozone concentration in Linyi City is significantly affected by VOCs. In other words, a reduction in VOCs can effectively control local ozone pollution, which is consistent with the previous research in Nanjing [54]. Note that this conclusion was drawn for the short observation period, and future long time series of observations of O3 and associated pollutants in the area should be conducted.

4. Conclusions

Concurrent measurements of VOCs, OVOCs, O3, and other related parameters were made to dissect the characteristics and sources of VOCs during high ozone episodes in June 2020 of Linyi, China. The average mass concentration of VOCs was 36.0 ± 0.66 ppb. The top three VOC components in terms of concentration were alkanes, OVOCs, and aromatic hydrocarbons, with concentrations of 40.75%, 27.02%, and 11.30%, respectively. The concentration of the top ten VOCs species in Linyi accounted for 51.37% of the total VOC concentration, with formaldehyde, isopentane, propane, and acetone accounting for a relatively high concentration. The PMF source analysis results showed that the main sources of atmospheric VOCs in Linyi were divided into vehicle exhaust sources, biomass combustion sources, oil and gas volatilization sources, and solvent use sources, which contributed 39.11%, 21.82%, 21.46%, and 17.61% to the total atmospheric VOCs, respectively. Alkenes which account for 10.74% of VOCs had the highest reactivity among the components of the VOCs and contributed up to 44.28% to the LOH. The contribution of OFP was dominated by alkenes, OVOCs, and aromatics, with 26.37%, 25.30%, and 23.65%, respectively. The top six VOCs that contributed the most to the O3 generation potential were: formaldehyde, acetaldehyde, 1-butene, butadiene, trans-2-butene, and propylene. The EKMA result based on the OBM mode demonstrated that the in situ O3 production in the observed area is more sensitive to VOCs, thus suggesting a reduction in O3 precursor concentrations according to the VOCs/NOx concentration ratio of 1.15 can achieve the optimal O3 concentration control results.

Author Contributions

Conceptualization, X.J.; data curation, Z.X.; formal analysis, X.S. and H.X.; methodology, S.Z. and G.P.; supervision, G.F.; validation, Y.C.; writing—original draft, X.Y.; writing—review and editing, L.G. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (No. 42005092), the Natural Science Foundation of Shandong Province (No. ZR2020QD058), and the Doctoral Research Fund of Shandong Jianzhu University (No. XNBS1936).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data available on request from the authors.

Acknowledgments

We are grateful for financial support from the National Natural Science Foundation of China (No. 42005092), the Natural Science Foundation of Shandong Province (No. ZR2020QD058), and the Doctoral Research Fund of Shandong Jianzhu University (No. XNBS1936).

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationship that could have appeared to have influenced the work reported in this paper.

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Figure 1. (a) Map showing the location of Linyi, China. (b) Shandong province color-coded by the daily mean O3 values in June 2020 (https://quotsoft.net/air/; accessed on 2 February 2021).
Figure 1. (a) Map showing the location of Linyi, China. (b) Shandong province color-coded by the daily mean O3 values in June 2020 (https://quotsoft.net/air/; accessed on 2 February 2021).
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Figure 2. Top 10 VOCs in Linyi.
Figure 2. Top 10 VOCs in Linyi.
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Figure 3. Daily average changes in TVOCs and related gases and meteorological parameters during the enhanced observation period.
Figure 3. Daily average changes in TVOCs and related gases and meteorological parameters during the enhanced observation period.
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Figure 4. Analysis of atmospheric VOC sources in Linyi city in the summer based on the PMF model.
Figure 4. Analysis of atmospheric VOC sources in Linyi city in the summer based on the PMF model.
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Figure 5. Average concentration, OH reactivities, and ozone formation potential for individual VOCs in Linyi.
Figure 5. Average concentration, OH reactivities, and ozone formation potential for individual VOCs in Linyi.
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Figure 6. The isopleth diagram of modeled maximum O3 on NOx and VOCs using EKMA. The X-axis represents the concentration of VOCs; the Y-axis represents the concentration of NOx.
Figure 6. The isopleth diagram of modeled maximum O3 on NOx and VOCs using EKMA. The X-axis represents the concentration of VOCs; the Y-axis represents the concentration of NOx.
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Table
Table 1. Levels of VOCs and other relevant pollutants during the observation period.
Table 1. Levels of VOCs and other relevant pollutants during the observation period.
Species appb-Average (Mean ± SD)Range
O348 ± 291–145
CO0.61 ± 0.320.08–2.32
NO2.22 ± 3.460.75–60.48
NO214.05 ± 8.574.38–60.38
SO22.21 ± 1.540.35–10.15
TEMP (°C)24.5 ± 4.416.7–37.6
RH (%)67 ± 1815–89
Alkanes15.41 ± 9.094.27–35.27
Alkenes3.70 ± 0.902.22–7.09
Alkyne0.43 ± 0.180.14–0.85
Aromatics3.32 ± 0.882.00–7.90
OVOCs9.62 ± 5.492.94–53.48
a: The unit is ppb except CO (in ppm).
Table
Table 2. Comparison of VOC concentrations in Linyi and the other different regions (ppb).
Table 2. Comparison of VOC concentrations in Linyi and the other different regions (ppb).
ReferenceSampling PeriodSampling
Site		Type of SiteAverage of VOCs Species
TVOCsEthyleneIsopentanePropaneEthyneBenzeneIsopreneAcetoneAcetaldehyde
This studyJune 2020LinyiUrban36.55 ± 10.820.35 ± 0.182.47 ± 2.102.42 ± 2.790.43 ± 0.190.51 ± 0.210.36 ± 0.202.03 ± 0.841.66 ± 0.99
[16]August–October 2018HongkongUrban-----0.29 ± 0.200.47 ± 0.47--
[34]August 2016–2017WuhanUrban11.2 ± 5.72.621.085.402.350.73-2.27-
[35]August–September 2015ChongqingRural13.0 ± 9.63.0 ± 1.8-3.3 ± 4.0-1.0 ± 0.50.4 ± 0.40.8 ± 0.32.2 ± 2.4
[36]2016Taixi
(Taiwan)		Urban11.200.930.511.420.830.25---
[37]May 2017ShanghaiUrban42.7 ± 23.0---1.2 ± 0.9----
[38]June–August 2011GuangzhouRural40.552.99-4.34-0.621.14--
[39]May–September 2019HandanUrban-0.41-1.601.021.10---
Table
Table 3. Descriptive statistics of the concentration of VOCs and their O3 generation potential (OFP) and hydroxyl radical reactivity (LOH).
Table 3. Descriptive statistics of the concentration of VOCs and their O3 generation potential (OFP) and hydroxyl radical reactivity (LOH).
VOCs SpeciesVOCs Concentration (ppbv)KOH × 10−12LOHMIROFP (μg/m3)
Acetylene0.43 0.90.010.950.48
Alkanes
Ethane0.87 0.260.010.280.33
Propane2.42 1.150.070.492.33
n-Butane1.82 2.540.111.155.42
Isobutane1.61 2.120.081.235.12
Cyclopentane0.445.160.042.392.46
n-Pentane0.50 3.940.051.312.12
Isopentane2.48 3.60.221.4511.55
Cyclohexane0.47 7.490.051.461.61
2, 2-Dimethylbutane0.36 2.320.021.171.60
2, 3-Dimethylbutane0.73 2.320.040.972.71
Methylcyclopentane0.53 5.160.052.193.46
2-Methylpentane0.86 5.60.121.54.95
3-Methylpentane0.53 5.70.071.83.64
n-Hexane0.32 7.490.061.071.31
Methylcyclohexane0.41 10.40.041.71.25
2, 3-Dimethylpentane0.43 5.70.041.341.81
2, 4-Dimethylpentane0.40 4.770.031.552.02
2-Methylhexane1.10 7.490.201.195.83
3-Methylhexane0.25 7.150.041.611.76
n-Heptane0.26 7.150.051.071.25
2,2,4-trimethylpentane0.25 3.340.011.261.15
2,3,4-trimethylpentane0.31 6.60.031.030.82
2-Methylheptane0.38 7.150.041.071.19
3-Methylheptane0.33 7.150.031.241.04
Octane0.16 8.680.040.90.75
Nonane0.15 9.70.040.780.68
Decane0.14 11.60.040.680.61
n-undecane0.27 12.30.080.611.14
n-Dodecane0.38 13.90.130.551.57
Alkenes
Ethylene0.35 8.520.0793.96
Propylene0.36 26.30.2311.667.89
1, 3-Butadiene0.45 66.60.7412.6113.68
1-butene0.69 31.40.539.7316.75
trans-2-butene0.34 640.5415.1612.99
cis-2-butene0.25 56.40.3414.248.83
1-pentene0.34 31.40.267.217.56
trans-2-pentene0.40 670.6510.5613.04
cis-2-pentene0.14 650.2310.384.67
Isoprene0.36 1010.9010.6111.70
Aromatics
Benzene0.51 1.230.020.721.28
Toluene0.53 5.960.0848.63
Styrene0.29 580.411.732.31
Ethylbenzene0.31 7.10.053.044.50
p-Ethyltoluene0.21 12.10.064.444.88
p/m-xylene0.48 190.227.817.54
o-xylene0.41 13.70.147.6414.82
o-Ethyltoluene0.04 12.30.015.591.29
Isopropylbenzene0.04 6.50.012.520.60
n-propylbenzene0.06 60.012..030.69
m-Ethyltoluene0.06 19.20.037.392.38
1,3,5-Trimethylbenzene0.1157.50.1611.767.08
1,2,4-Trimethylbenzene0.2132.50.178.8710.17
1,2,3-Trimethylbenzene0.0632.70.0511.973.96
OVOCs
Formaldehyde3.89 9.370. 907.237.60
Acetaldehyde1.66 150.615.517.88
Acetone2.03 0.170.00030.361.89
n-Butyraldehyde0.68 241.205.9713.03
Propionaldehyde0.34 200.177.086.27
Valeraldehyde0.23 280.165.084.48
n-Hexanal0.18 300.034.353.45
Acrolein0.0613.60.027.451.15
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Yang, X.; Gao, L.; Zhao, S.; Pan, G.; Fan, G.; Xia, Z.; Sun, X.; Xu, H.; Chen, Y.; Jin, X. Volatile Organic Compounds in the North China Plain: Characteristics, Sources, and Effects on Ozone Formation. Atmosphere 2023, 14, 318. https://doi.org/10.3390/atmos14020318

AMA Style

Yang X, Gao L, Zhao S, Pan G, Fan G, Xia Z, Sun X, Xu H, Chen Y, Jin X. Volatile Organic Compounds in the North China Plain: Characteristics, Sources, and Effects on Ozone Formation. Atmosphere. 2023; 14(2):318. https://doi.org/10.3390/atmos14020318

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Yang, Xue, Luhong Gao, Shiyang Zhao, Guang Pan, Guolan Fan, Zhiyong Xia, Xiaoyan Sun, Hongyu Xu, Yanjun Chen, and Xiaolong Jin. 2023. "Volatile Organic Compounds in the North China Plain: Characteristics, Sources, and Effects on Ozone Formation" Atmosphere 14, no. 2: 318. https://doi.org/10.3390/atmos14020318

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