A large role of missing volatile organic compound reactivity from anthropogenic emissions in ozone pollution regulation

Abstract

There are thousands of volatile organic compound (VOC) species in ambient air, while existing techniques can only detect a small part of them (approximately several hundred). The large number of unmeasured VOCs prevents us from understanding the photochemistry of ozone and aerosols in the atmosphere. The major sources and photochemical effects of these unmeasured VOCs in urban areas remain unclear. The missing VOC reactivity, which is defined as the total OH reactivity of the unmeasured VOCs, is a good indicator for constraining the photochemical effect of unmeasured VOCs. Here, we identified the dominant role of anthropogenic emission sources in the missing VOC reactivity (accounting for up to 70 %) by measuring missing VOC reactivity and tracer-based source analysis in a typical megacity in China. Omitting the missing VOC reactivity from anthropogenic emissions in model simulations will remarkably affect the diagnosis of sensitivity regimes for ozone formation, overestimating the degree of VOC-limited regimes by up to 46 %. Therefore, a thorough quantification of missing VOC reactivity from various anthropogenic emission sources is urgently needed for constraints of air quality models and the development of effective ozone control strategies.

1 Introduction

Volatile organic compounds (VOCs) are key precursors of major photochemical pollutants, including ozone (O${}_{\mathrm{3}}$) and secondary organic aerosols (Atkinson, 2000; Atkinson and Arey, 2003). Severe O${}_{\mathrm{3}}$ and particle pollution is frequently related to high emissions of VOCs (Atkinson and Arey, 2003; Monks et al., 2015). There exist thousands of VOC species in ambient air that are emitted from either natural processes or anthropogenic activities (Goldstein and Galbally, 2007). No one instrument can capture all VOCs out there, and even when they can be measured, there is information missing on identification and properties (Yuan et al., 2017; Wang et al., 2014). A gas chromatograph–mass spectrometer/flame ionization detector (GC–MS/FID) can measure C${}_{\mathrm{2}}$–C${}_{\mathrm{12}}$ non-methane hydrocarbons (NMHCs) and C${}_{\mathrm{2}}$–C${}_{\mathrm{6}}$ oxygenated VOCs (OVOCs) but cannot measure NMHCs and OVOCs with larger carbon numbers (Wang et al., 2014). A proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS) is able to measure a huge number of OVOCs, aromatics and several alkanes but cannot measure most alkanes and alkenes and cannot distinguish isomers (Yuan et al., 2017). The 2,4-dinitrophenylhydrazine (DNPH) or high-performance liquid chromatography (HPLC) method can measure several carbonyls but cannot measure non-polar organic species (Wang et al., 2009). The two-dimensional GC is able to measure some intermediate-volatile and semivolatile non-polar organics (Song et al., 2022). A lack of standard gases prevents these technologies from accurate quantification even if these technologies can identify more VOC species. In general, many branched alkenes, OVOCs with complex functional groups, intermediate-volatile and semivolatile organics and complex biogenic VOCs cannot currently be well quantified even if they can be identified by instruments. As a result, the total amount of VOCs in ambient air has generally been underestimated. Currently, emission inventories used in air quality models such as the Community Emissions Data System (CEDS) emission inventory and the Multi-resolution Emission Inventory for China (MEIC) only include the VOC species that can be measured, such as some C${}_{\mathrm{1}}$–C${}_{\mathrm{9}}$ hydrocarbons and simple-structure OVOCs with a small carbon number ($\mathit{<}$ C${}_{\mathrm{6}}$). This will lead to an underestimation of the photochemical effect of the total VOCs and thus causes uncertainties in predicting secondary pollution. The quantification of the unmeasured VOCs is crucial for assessing secondary pollution precisely.

The total OH reactivity ($R_{\mathrm{OH}}$), which can be directly measured, is an index for evaluating the amount of reductive pollutants in terms of ambient OH loss. The total OH reactivity is defined as

1$$R_{\mathrm{OH}}={\sum }_i{k}_{\mathrm{OH}+X_i}\left[X_i\right],$$ where $X$ represents a reactive species including carbon monoxide (CO), nitrogen oxides (NO${}_X$) and VOCs, and $k_{\mathrm{OH}+X_i}$ is the reaction rate constant for the oxidation of species $X$ by OH. The measured $R_{\mathrm{OH}}$ is higher than that calculated based solely on the measured reactive species, and the difference between them is mostly from unmeasured VOCs (Yang et al., 2017). Missing VOC reactivity (missing VOC${}_{\mathrm{R}}$), defined as the VOC reactivity (VOC${}_{\mathrm{R}}$) of all unmeasured VOCs, can be obtained by subtracting the calculated $R_{\mathrm{OH}}$ from the measured $R_{\mathrm{OH}}$: $$\begin{array}{}\text{2}& {\displaystyle }\mathrm{missing}{\mathrm{VOC}}_{\mathrm{R}}=\mathrm{measured}{R}_{\mathrm{OH}}\text{-calculated}{R}_{\mathrm{OH}},\text{3}& {\displaystyle }\mathrm{calculated}{R}_{\mathrm{OH}}={\sum }_i{k}_{\mathrm{OH}+\mathrm{reactive}{\mathrm{species}}_i}\left[\mathrm{reactive}{\mathrm{species}}_i\right],\end{array}$$ where “reactive species” represents measured VOCs and “reactive inorganic species” includes carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO${}_{\mathrm{2}}$), O${}_{\mathrm{3}}$, sulfur dioxide (SO${}_{\mathrm{2}}$), nitrous acid (HONO), and so on. The missing VOC${}_{\mathrm{R}}$ provides a constraint for evaluating the photochemical roles of unmeasured VOCs in the atmosphere (Sadanaga et al., 2005; Yang et al., 2016b). The inclusion of the missing VOC${}_{\mathrm{R}}$ can help to improve the performance of the box model and air quality models in simulating photochemistry processes. A relatively high missing VOC${}_{\mathrm{R}}$ has been found in forests (Di Carlo et al., 2004; Hansen et al., 2014; Nakashima et al., 2014; Nölscher et al., 2016; Praplan et al., 2019), urban areas (Shirley et al., 2006; Yoshino et al., 2006; Dolgorouky et al., 2012; Yang et al., 2017), and suburban areas (Kovacs et al., 2003; Yang et al., 2017; Fuchs et al., 2017; Lou et al., 2010), accounting for 10 %–75 % of the total $R_{\mathrm{OH}}$. Given that the total VOC${}_{\mathrm{R}}$ is one part of the total $R_{\mathrm{OH}}$, missing VOC${}_{\mathrm{R}}$ would account for a larger percentage of the total VOC${}_{\mathrm{R}}$ ($\mathit{>}$ 10 %–75 %).

The potential sources of missing VOC${}_{\mathrm{R}}$ include anthropogenic emissions, biogenic emissions, soil emissions, and photochemical production (Yang et al., 2016b). Previous studies have reported that the missing VOC${}_{\mathrm{R}}$ in forest areas was mainly from direct emissions or photochemical oxidation of biogenic VOCs (Di Carlo et al., 2004; Hansen et al., 2014; Nakashima et al., 2014; Nölscher et al., 2016; Praplan et al., 2019). Nevertheless, the dominant source of the missing VOC${}_{\mathrm{R}}$ in urban and suburban areas remains unclear or under debate.

Surface O${}_{\mathrm{3}}$ pollution has become a major public health concern in cities worldwide (Paoletti et al., 2014; Lefohn et al., 2018). A critical issue in determining an emission control strategy for ozone pollution is to understand the relative benefits of NO${}_x$ and VOC emission controls. This is generally framed in terms of ozone precursor sensitivity, i.e., whether ozone production is NO${}_X$-limited or VOC-limited (Kleinman, 1994; Sillman et al., 1990). Nevertheless, the effect of missing VOCs on ozone precursor sensitivity has not been well understood yet. Given that the missing VOC${}_{\mathrm{R}}$ could potentially account for a large part of the total VOC${}_{\mathrm{R}}$, clearly clarifying the role of missing VOC${}_{\mathrm{R}}$ in determining ozone precursor sensitivity is an urgent need for the diagnosis of ozone sensitivity regimes and the formulation of an effective emission reduction roadmap.

China has become a global hotspot of ground-level ozone pollution in recent years (Lu et al., 2018; Wang et al., 2022). The Pearl River Delta (PRD) remains one of the most O${}_{\mathrm{3}}$-polluted regions in China (Li et al., 2022), although many control measures have been attempted. Here, we measured $R_{\mathrm{OH}}$ in Guangzhou, a megacity in the PRD, and quantified the missing VOC${}_{\mathrm{R}}$. The dominant source of the missing VOC${}_{\mathrm{R}}$ and its impact on ozone precursor sensitivity were comprehensively investigated.

2 Method

2.1 Overview of the measurement

The field campaign was conducted from 25 September to 30 October 2018 continuously at an urban site in downtown Guangzhou (113.2° E, 23° N). The sampling site is located on the ninth floor of a building on the campus of the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, 25 m above ground level. This site is primarily influenced by industrial and vehicular emissions. ROH, VOCs, NO${}_X$, O${}_{\mathrm{3}}$, HONO, SO${}_{\mathrm{2}}$, CO, photolysis frequencies, and meteorological factors were simultaneously measured during the measurement period.

2.2

$R_{\mathrm{OH}}$ measurement

The total $R_{\mathrm{OH}}$ was measured by the comparative reactivity method (CRM) (Sinha et al., 2008). The CRM system consists of three major components, i.e., an inlet and calibration system, a reactor, and a measuring system. Here, pyrrole (C${}_{\mathrm{4}}$H${}_{\mathrm{5}}$N) was used as the reference substance in the CRM, and its concentration was quantified by a quadrupole proton-transfer-reaction mass spectrometer (PTR-MS) (Ionicon Analytik GmbH, Innsbruck, Austria). The CRM system was calibrated by propane, propene, toluene standards, and 16 VOC mixed standards (acetaldehyde, methanol, ethanol, isoprene, acetone, acetonitrile, methyl vinyl ketone, methyl ethyl ketone, benzene, toluene, o-xylene, $\mathit{\alpha }$-pinene, 1,2,4-trimethylbenzene, phenol, m-cresol, and naphthalene). Measured and calculated $R_{\mathrm{OH}}$ agreed well within 15 % for all the calibrations. The $R_{\mathrm{OH}}$ measurement by the CRM method is interfered by ambient nitric oxide (NO), which produces additional OH radicals via the reaction of HO${}_{\mathrm{2}}$ radicals with NO (Sinha et al., 2008). To correct this interference, a series of experiments were conducted by introducing different levels of NO (0–160 ppb) and given amounts of VOC into the CRM reactor. A correction curve was acquired from these NO interference experiments, which can be used to correct the $R_{\mathrm{OH}}$ thanks to the simultaneous measurement of ambient NO concentrations (Supplement S1; Fig. S1). The detection limits of the CRM method were around 2.5 s${}^{-\mathrm{1}}$, and the total uncertainty was estimated to be about 15 %. The CRM method has been successfully applied to measure OH reactivity in urban areas with high NO${}_X$ levels in previous studies (Dolgorouky et al., 2012; Yang et al., 2017; Hansen et al., 2015). The intercomparison between the CRM method and the pump–probe technique indicates that the CRM method can be used under high-NO${}_X$ conditions (NO${}_X\mathit{>}\mathrm{10}$ ppb) if a NO${}_X$-dependent correction is applied (Hansen et al., 2015).

2.3 VOC measurements

NMHCs were measured using a GC–MS/FID system coupled with a cryogen-free preconcentration device (Wang et al., 2014). The system contains two-channel sampling and GC column separation, which are able to measure C${}_{\mathrm{2}}$–C${}_{\mathrm{5}}$ hydrocarbons with the FID in one channel and measure C${}_{\mathrm{5}}$–C${}_{\mathrm{12}}$ hydrocarbons using an MS detector in the other channel. After removal of water vapor, VOCs were trapped at $-$155° in a deactivated quartz capillary column (15 cm $\times$ 0.53 mm ID) and a porous-layer open tubular (PLOT) capillary column (15 cm $\times$ 0.53 mm ID) for the MS channel and the FID channel, respectively. The system was calibrated weekly by TO-15 (Air Environmental Inc., USA) and PAMS gas standards (Spectra Gases Inc., USA). Detection limits for various compounds were in the range of 0.002–0.070 ppbv. A total of 56 NMHC species were measured (Table S1). The time resolution of the measurement was 1 h. The uncertainties of VOC measurements by GC–MS/FID are in the range of 15 %–20 %. More details of this method can be found in previous studies (Wang et al., 2014; Yuan et al., 2012).

An online PTR-ToF-MS (Ionicon Analytic GmbH, Innsbruck, Austria) with H${}_{\mathrm{3}}$O${}^{+}$ and NO${}^{+}$ ion sources was also used to measure VOCs. During the campaign, the PTR-ToF-MS automatically switched between H${}_{\mathrm{3}}$O${}^{+}$ and NO${}^{+}$ chemistry every 10–20 min. The H${}_{\mathrm{3}}$O${}^{+}$ mode was used to measure OVOCs and aromatics, while the NO${}^{+}$ model was used to measure alkanes with more carbons (C${}_{\mathrm{8}}$–C${}_{\mathrm{20}}$). When running in the H${}_{\mathrm{3}}$O${}^{+}$ ionization mode, the drift tube was at a temperature of 50°, a pressure of 3.8 mbar, and a voltage of 920 V, leading to an operating $E/N$ ($E$ is the electric field, and $N$ is the number density of the gas in the drift tube) ratio of 120 Td. When running in the NO${}^{+}$ ionization mode, the drift tube was at a temperature of 50°, a pressure of 3.8 mbar, and a voltage of 470 V, leading to an operating $E/N$ ratio of 60 Td. The PTR-ToF-MS technique is capable of measuring OVOCs and higher alkanes that a GC–MS/FID cannot measure (Wu et al., 2020; C. Wang et al., 2020). The time resolution of PTR-ToF-MS measurements was 10 s. A total of 31 VOCs were calibrated using either gas or liquid standards (Table S2). For other measured VOCs, we used the method proposed by Sekimoto et al. (2017) to determine the relationship between VOC sensitivity and kinetic rate constants for proton-transfer reactions of H${}_{\mathrm{3}}$O${}^{+}$ with VOCs. The fitted line was used to determine the concentrations of those uncalibrated species. The uncertainties of the concentrations for uncalibrated species were about 50 % (Sekimoto et al., 2017). With this method, PTR-ToF-MS can additionally measure 128 VOCs, which were included in the analysis of this study. The detailed information for this method can be found in Wu et al. (2020), and all VOC species measured by a PTR-ToF-MS were provided in Table S4 of that article. The PTR-ToF-MS is capable of measuring additional VOC species that GC–MS/FID cannot measure, including alkanes with more carbons (C${}_{\mathrm{12}}$–C${}_{\mathrm{20}}$) and OVOCs, including aldehydes, ketones, carboxylic acids, alcohols, and nitrophenols. Formaldehyde (HCHO) was measured by a custom-built instrument based on the Hantzsch reaction and absorption photometry (Xu et al., 2022).

2.4 Other measurements

Nitrous acid (HONO) was measured by a custom-built LOPAP (long-path-absorption photometer) based on wet chemical sampling and photometric detection (Yu et al., 2022). The uncertainty of the measurement was 8 %. NO${}_X$, O${}_{\mathrm{3}}$, SO${}_{\mathrm{2}}$, and CO were measured by an NO${}_X$ analyzer (Thermo Scientific, Model 42i), an O${}_{\mathrm{3}}$ analyzer (Thermo Scientific, Model 49i), an SO${}_{\mathrm{2}}$ analyzer (Thermo Scientific, Model 43i), and a CO analyzer (Thermo Scientific, Model 48i), respectively. The meteorological data, including temperature ($T$), relative humidity (RH), and wind speed and direction (WS, WD), were recorded by Vantage Pro2 Weather Station (Davis Instruments Inc., Vantage Pro2) with a time resolution of 1 min. Photolysis frequencies of O${}_{\mathrm{3}}$, NO${}_{\mathrm{2}}$, HONO, H${}_{\mathrm{2}}$O${}_{\mathrm{2}}$, HCHO, and NO${}_{\mathrm{3}}$ were measured by a spectrometer (Focused Photonics Inc., PFS-100) (Shetter and Müller, 1999; Wang et al., 2019).

2.5 Multiple linear regression

Multiple linear regression (MLR) has been successfully applied to quantify the sources of air pollutants (Li et al., 2019; Yang et al., 2016a). In this study, a tracer-based MLR analysis was used to decouple the individual contributions of anthropogenic emissions, secondary production, biogenic emissions, and background level to missing VOC${}_{\mathrm{R}}$, as shown in Eq. (4).

4$$\begin{array}{rl}\mathrm{Missing}{\mathrm{VOC}}_{\mathrm{R}}& =a\mathrm{\Delta }\mathrm{CO}+b\left[{\mathrm{O}}_X\right]+c\left[{\mathrm{isoprene}}_{\mathrm{initial}}\right]\\ & +C_{\mathrm{background}}\end{array}$$ O${}_X$ is defined as O${}_{\mathrm{3}}+$ NO${}_{\mathrm{2}}$. $\mathrm{\Delta }\mathrm{CO}$, [O${}_X$], and [${\mathrm{isoprene}}_{\mathrm{initial}}$] are concentrations of tracers for anthropogenic emissions, secondary production, and biogenic emissions, respectively. $\mathrm{\Delta }\mathrm{CO}$ is the relative change between the ambient CO and background CO of 150 ppb (C. Wang et al., 2020). [${\mathrm{isoprene}}_{\mathrm{initial}}$] represents the initial concentration of isoprene from biogenic emissions that has not undergone any photochemical reactions, which is calculated from the observed isoprene and its photochemical products methyl vinyl ketone (MVK) and methacrolein (MACR) (Xie et al., 2008). $C_{\mathrm{background}}$ indicates the background level of missing VOC${}_{\mathrm{R}}$. $a$, $b$, $c$, and $C_{\mathrm{backgound}}$ are coefficients fitted by the multiple linear regression.

2.6 Observation-based box model

A zero-dimensional box model coupled with the Master Chemical Mechanism (MCM) v3.3.1 (Jenkin et al., 2003) was used to simulate the photochemical production of RO${}_X$ (RO${}_X=$ OH $+$ HO${}_{\mathrm{2}}+$ RO${}_{\mathrm{2}}$) radicals and O${}_{\mathrm{3}}$ during the field campaign. The model was constrained by the observations of meteorological parameters, photolysis frequencies, VOCs, NO, NO${}_{\mathrm{2}}$, O${}_{\mathrm{3}}$, CO, SO${}_{\mathrm{2}}$, and HONO. The model runs were performed in a time-dependent mode with a time resolution of 1 h and a spin-up of 4 d. A 24 h lifetime was introduced for all simulated species, including secondary species and radicals, to approximately simulate dry deposition and other losses of these species (Lu et al., 2013; W. Wang et al., 2020). Sensitivity tests show that this assumed physical loss lifetime has a relatively small influence on RO${}_X$ radicals and ozone production rates.

Measured OVOCs such as HCHO, acetaldehyde, and acetone were constrained in the model, and unmeasured OVOCs were simulated according to the photochemical oxidation of NMHCs by OH radicals. RO${}_{\mathrm{2}}$, HO${}_{\mathrm{2}}$, and OH radicals were simulated by the box model to calculate the net O${}_{\mathrm{3}}$ production rate ($P$(O${}_{\mathrm{3}}$)) and O${}_{\mathrm{3}}$ loss rate ($L$(O${}_{\mathrm{3}}$)) as shown in Eqs. (5) and (6) as derived by Mihelcic et al. (2003). $$\begin{array}{}\text{5}& {\displaystyle }\begin{array}{rl}P\left({\mathrm{O}}_{\mathrm{3}}\right)& =k_{{\mathrm{HO}}_{\mathrm{2}}+\mathrm{NO}}\left[{\mathrm{HO}}_{\mathrm{2}}\right]\left[\mathrm{NO}\right]\\ & +{\sum }_i\left(k_{{\mathrm{RO}}_{\mathrm{2}}+\mathrm{NO}}^i\left[{\mathrm{RO}}_{\mathrm{2}}^i\right]\left[\mathrm{NO}\right]\right)\\ & -k_{\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}}\left[\mathrm{OH}\right]\left[{\mathrm{NO}}_{\mathrm{2}}\right]-L\left({\mathrm{O}}_{\mathrm{3}}\right)\end{array}\text{6}& {\displaystyle }\begin{array}{rl}L\left({\mathrm{O}}_{\mathrm{3}}\right)& =(\mathit{\theta }j\left({\mathrm{O}}^{\mathrm{1}}D\right)+k_{\mathrm{OH}+{\mathrm{O}}_{\mathrm{3}}}\left[\mathrm{OH}\right]+k_{{\mathrm{HO}}_{\mathrm{2}}+{\mathrm{O}}_{\mathrm{3}}}\left[{\mathrm{HO}}_{\mathrm{2}}\right]\\ & +{\sum }_j\left(k_{\mathrm{alkene}+{\mathrm{O}}_{\mathrm{3}}}^j\left[{\mathrm{alkene}}^j\right]\right)\left[{\mathrm{O}}_{\mathrm{3}}\right]\end{array}\end{array}$$ $\mathit{\theta }$ is the fraction of O${}^{\mathrm{1}}$D from ozone photolysis that reacts with water vapor, and $i$ and $j$ represent the number of species of RO${}_{\mathrm{2}}$ and alkenes, respectively.

The box model was used to evaluate the impact of missing VOC${}_{\mathrm{R}}$ on the O${}_{\mathrm{3}}$ production rate. In the base scenario, the box model was constrained by all measured inorganic and organic gases, but the missing VOC${}_{\mathrm{R}}$ was not considered. To consider the missing VOC${}_{\mathrm{R}}$ in the box model, we additionally increased the concentration of NMHCs to exactly compensate for the missing VOC${}_{\mathrm{R}}$ by multiplying a factor on the basis of measured NMHC concentrations. We simulated four scenarios by increasing the concentration of (1) $n$-pentane, (2) ethylene, (3) toluene, and (4) all measured 56 NMHCs. For the scenario of increasing all 56 NMHCs, concentrations of the 56 NMHC species were increased by multiplying the same factor. Given that the VOC${}_{\mathrm{R}}$ of unconstrained secondary products increases with the increase in the concentration of NMHCs, several attempts for different values are needed to determine the increasing factor.

3 Results and discussion

3.1

Quantification of missing VOC${}_{\mathrm{R}}$ during the campaign

Figure 1 shows the time series of measured $R_{\mathrm{OH}}$, calculated $R_{\mathrm{OH}}$ according to all measured reactive gases, and missing VOC${}_{\mathrm{R}}$ (the gap between the measured and calculated $R_{\mathrm{OH}}$) in Guangzhou. By using a GC-MS/FID, we measured 56 NMHCs. By using a PTR-ToF-MS, we measured 159 VOCs, and 128 of them were difficult to measure before. In addition to the alkanes with carbons less than 12, a PTR-ToF-MS can measure alkanes with more carbons (C${}_{\mathrm{12}}$–C${}_{\mathrm{20}}$). With regard to OVOCs, not only common OVOC species including formaldehyde and C${}_{\mathrm{2}}$–C${}_{\mathrm{4}}$ carbonyls, but also carbonyls with more carbons (C${}_{\mathrm{5}}$–C${}_{\mathrm{10}}$) and some $N$-containing OVOC species such as nitrophenol and methyl nitrophenol were measured by a PTR-ToF-MS. Thanks to these additional measured VOCs, the measured $R_{\mathrm{OH}}$ was close to the calculated $R_{\mathrm{OH}}$ within 20 % in most periods. In some periods the missing VOC${}_{\mathrm{R}}$ was negative, which is probably due to the uncertainty in the measurements of $R_{\mathrm{OH}}$ and reactive gases. The negative missing VOC${}_{\mathrm{R}}$ primarily occurred in the afternoon (12:00–17:00) when the photochemistry was most active. Nevertheless, there were still some days exhibiting remarkable missing VOC${}_{\mathrm{R}}$. Days with a missing VOC${}_{\mathrm{R}}$ of more than 25 % of the total $R_{\mathrm{OH}}$, i.e., high missing-VOC${}_{\mathrm{R}}$ days, are indicated by yellow background in Fig. 1a. The largest missing VOC${}_{\mathrm{R}}$ occurred on 15, 16, 25, and 26 October, with average values of 16 s${}^{-\mathrm{1}}$. During the period of 24 to 26 October, the total $R_{\mathrm{OH}}$ was highest and the missing VOC${}_{\mathrm{R}}$ was also relatively high among all the days. Figure 1b shows the contribution of different species classifications to the total $R_{\mathrm{OH}}$ during high missing-VOC${}_{\mathrm{R}}$ days. Inorganic species, NMHCs, and OVOCs account for 34 %, 13 %, and 14 % of the total $R_{\mathrm{OH}}$, respectively, with missing VOC${}_{\mathrm{R}}$ accounting for 39 %. The fraction of missing VOC${}_{\mathrm{R}}$ (39 %) during the high missing-VOC${}_{\mathrm{R}}$ days is comparable to measurements in Los Angeles in 2010 (Griffith et al., 2016) and in Seoul in 2016 (Sanchez et al., 2021).

Figure 1

The level of missing VOC${}_{\mathrm{R}}$ during the measurements in Guangzhou. (a) Time series of measured $R_{\mathrm{OH}}$ and calculated $R_{\mathrm{OH}}$ from all measured reactive gases in Guangzhou. Yellow background represents the high missing-VOC${}_{\mathrm{R}}$ days, with missing VOC${}_{\mathrm{R}}$ accounting for more than 30 % of the total $R_{\mathrm{OH}}$. (b) Contributions of different compositions to $R_{\mathrm{OH}}$ on high missing-VOC${}_{\mathrm{R}}$ days. The error bar represents the standard deviation of missing VOC${}_{\mathrm{R}}$.

[Figure omitted. See PDF]

We evaluated the uncertainty of the missing VOC${}_{\mathrm{R}}$. The uncertainty of the $R_{\mathrm{OH}}$ measurement was 15 %. In addition, according to reports by the Jet Propulsion Laboratory (Burkholder et al., 2020), reaction rate constants used for the calculation of $R_{\mathrm{OH}}$ in Eq. (3) have uncertainties of 5 %–30 %, depending on different species. We took the uncertainties in the reaction rate constants and the measurements of all the reactive gases into account when calculating $R_{\mathrm{OH}}$, according to error propagation. As a result, the uncertainties in the missing VOC${}_{\mathrm{R}}$ are 3.8 and 5.2 s${}^{-\mathrm{1}}$ for the whole measurement period and the high missing-VOC${}_{\mathrm{R}}$ days, respectively. The average missing VOC${}_{\mathrm{R}}$ during the high missing-VOC${}_{\mathrm{R}}$ days is 13 s${}^{-\mathrm{1}}$, which is significantly higher than the uncertainty of 5.2 s${}^{-\mathrm{1}}$, suggesting that the missing VOC${}_{\mathrm{R}}$ really exists during the high missing-VOC${}_{\mathrm{R}}$ days.

3.2

The sources of missing VOC${}_{\mathrm{R}}$

To explore the sources of missing VOC${}_{\mathrm{R}}$ during the whole measurement period, we investigated the correlation between missing VOC${}_{\mathrm{R}}$ and tracers characterizing primary emissions (CO, NO${}_X$, and NMHCs) and secondary production (O${}_X\equiv$ O${}_{\mathrm{3}}+$ NO${}_{\mathrm{2}}$ and formic acid). The correlation of missing VOC${}_{\mathrm{R}}$ with CO, reactivity of NMHCs (NMHC${}_{\mathrm{R}}$), and NO${}_X$ is moderate, with a correlation coefficient ($R$) in the range of 0.47–0.56 (Figs. 2a and b, S2) while there is no significant correlation of missing VOC${}_{\mathrm{R}}$ with O${}_X$ and formic acid (Figs. 2c and S2). Furthermore, there is no significant correlation between missing VOC${}_{\mathrm{R}}$ and acetonitrile, which is a tracer of biomass burning (de Gouw et al., 2003; Wang et al., 2007) (Fig. S2), indicating that biomass burning was not a major contributor to missing VOC${}_{\mathrm{R}}$ during this campaign. In terms of the diurnal variation, the missing VOC${}_{\mathrm{R}}$ was higher in the morning (07:00–10:00) and evening (18:00–22:00), when the anthropogenic emissions, especially vehicle exhaust, were intensive, and was lower in the afternoon, when the photochemistry was most active (Fig. 2d). The diurnal profile of missing VOC${}_{\mathrm{R}}$ was similar to those of CO, NO${}_X$, and NMHC${}_{\mathrm{R}}$. In contrast, the diurnal profiles of secondary species, including O${}_X$, formic acid, and acetic acid, which peaked in the afternoon, evidently differ from the diurnal profile of missing VOC${}_{\mathrm{R}}$ (Fig. S3). Further, we investigated the influence of air mass aging on missing VOC${}_{\mathrm{R}}$. The ratio of ethylbenzene to $m$,$p$-xylene was used to characterize the degree of air mass aging (de Gouw et al., 2005; Yuan et al., 2013). A higher ratio of ethylbenzene to $m$,$p$-xylene corresponds to a higher degree of air mass aging as the $m$,$p$-xylene has a larger reaction rate constant (18.9 $\times$ 10${}^{-\mathrm{12}}$ cm${}^{\mathrm{3}}$ molecule${}^{-\mathrm{1}}$ s${}^{-\mathrm{1}}$) than ethylbenzene (7.0 $\times$ 10${}^{-\mathrm{12}}$ cm${}^{\mathrm{3}}$ molecule${}^{-\mathrm{1}}$ s${}^{-\mathrm{1}}$) when reacting with the major oxidant – OH radicals. As shown in Fig. 2e, missing VOC${}_{\mathrm{R}}$ decreases with the ratio of ethylbenzene to $m$,$p$-xylene. Given that secondary production generally increased with air mass aging, this result further demonstrates that missing VOC${}_{\mathrm{R}}$ was not caused by enhanced secondary production.

Figure 2

Correlation of missing VOC${}_{\mathrm{R}}$ with major tracers during the whole measurement period. (a–c) Correlation of missing VOC${}_{\mathrm{R}}$ with CO, OH reactivity of NMHCs (NMHC${}_{\mathrm{R}}$), and O${}_X$. Each point represents hourly data. (d) Diurnal variations in missing VOC${}_{\mathrm{R}}$, CO, NO${}_X$, and NMHCs. (e) The dependence of missing VOC${}_{\mathrm{R}}$ on the ethylbenzene $/$ $m$,$p$-xylene ratio. The red squares indicate the mean values of missing VOC${}_{\mathrm{R}}$ in different ranges of the ratio of ethylbenzene to $m$,$p$-xylene with a classification width of 0.1, and the error bars represent the standard deviation.

[Figure omitted. See PDF]

During the high missing-VOC${}_{\mathrm{R}}$ days, the correlation coefficient for missing VOC${}_{\mathrm{R}}$ versus CO is 0.76 (Fig. 3a), which is higher than that in the whole measurement period (0.56) shown in Fig. 2a. We then quantify the sources of missing VOC${}_{\mathrm{R}}$ during the high missing-VOC${}_{\mathrm{R}}$ days by applying MLR. The fitted coefficients are as follows: $a$ is 0.031 s${}^{-\mathrm{1}}$ ppb${}^{-\mathrm{1}}$, $b$ is 0.012 s${}^{-\mathrm{1}}$ ppb${}^{-\mathrm{1}}$, $c$ is 1.8 s${}^{-\mathrm{1}}$ ppb${}^{-\mathrm{1}}$, and $C_{\mathrm{background}}$ is 1.3 s${}^{-\mathrm{1}}$. The coefficient of determination ($R^{\mathrm{2}}$) for the MLR is 0.68. As shown in Fig. 3b, anthropogenic emissions were the largest contributor to missing VOC${}_{\mathrm{R}}$, accounting for 70 % of missing VOC${}_{\mathrm{R}}$. Secondary production, biogenic emissions, and background contribution played a minor role in missing VOC${}_{\mathrm{R}}$ (13 %, 7 %, and 10 %, respectively). The parametric relationship between missing VOC${}_{\mathrm{R}}$ and relevant tracers established by MLR provides a valid approach to estimate the missing VOC${}_{\mathrm{R}}$ according to readily available gases, including CO, O${}_X$, and isoprene.

Although anthropogenic emissions are identified as the major source of missing VOC${}_{\mathrm{R}}$, which species dominantly contributes to the missing VOC${}_{\mathrm{R}}$ remains unclear. A potential source is the unmeasured branched alkenes because of their high reactivity previously observed from vehicle exhaust (Nakashima et al., 2010) and gasoline evaporation emissions (Wu et al., 2015). Another possible source is emitted OVOCs with a more complex functional group that cannot be accurately measured. In addition, directly emitted semivolatile and intermediate-volatility organic compounds are also possible sources of missing VOC${}_{\mathrm{R}}$ (Stewart et al., 2021).

Figure 3

The source apportionment of missing VOC${}_{\mathrm{R}}$ on high missing-VOC${}_{\mathrm{R}}$ days. (a) Correlation of missing VOC${}_{\mathrm{R}}$ with CO. Each point represents hourly data. (b) Contributions of different sources to missing VOC${}_{\mathrm{R}}$ according to the MLR.

[Figure omitted. See PDF]

3.3

The impact of missing VOC${}_{\mathrm{R}}$ on O${}_{\mathrm{3}}$ sensitivity regimes

The reaction of OH with VOCs is key to the propagation and amplification of OH radicals, thus determining the ozone production rate (Tonnesen and Dennis, 2000). The box model was used to evaluate the impact of missing VOC${}_{\mathrm{R}}$ on the O${}_{\mathrm{3}}$ production rate during high missing-VOC${}_{\mathrm{R}}$ days. The settings of model simulations for different scenarios are depicted in Sect. 2.6. In the base scenario, on average, the measured VOC${}_{\mathrm{R}}$s of $n$-pentane, ethylene, toluene, and all 56 NMHCs are 0.14, 0.53, 0.60, and 4.6 s${}^{-\mathrm{1}}$, respectively. To consider the missing VOC${}_{\mathrm{R}}$ (an average of 13 s${}^{-\mathrm{1}}$) in the model, four scenarios were simulated by additionally increasing $n$-pentane, ethylene, toluene, and 56 NMHCs by factors of 70, 16, 13.3, and 1.9, respectively. These increasing factors led to an additional increase in VOC${}_{\mathrm{R}}$ of both NMHCs and unconstrained secondary products, which exactly compensated for the missing VOC${}_{\mathrm{R}}$. Figure 4 shows the simulated $P$(O${}_{\mathrm{3}}$) for the base scenario and the scenarios considering missing VOC${}_{\mathrm{R}}$. The daytime average $P$(O${}_{\mathrm{3}}$) in the scenarios considering missing VOC${}_{\mathrm{R}}$ is a factor of 1.5–4.5 for the results in the base scenario. The difference in the added species has a large effect on $P$(O${}_{\mathrm{3}}$). Adding toluene causes a larger increase in $P$(O${}_{\mathrm{3}}$) than adding $n$-pentane or ethene, as toluene has a stronger ability to amplify the production of radicals.

Figure 4

Simulated daytime mean $P$(O${}_{\mathrm{3}}$) for the base scenario (without missing VOC${}_{\mathrm{R}}$) and the scenario considering missing VOC${}_{\mathrm{R}}$, respectively, on high missing-VOC${}_{\mathrm{R}}$ days. The missing VOC${}_{\mathrm{R}}$ is considered by adding individual species ($n$-pentane, ethene, or toluene) or increasing all measured NMHCs to compensate for the missing VOC${}_{\mathrm{R}}$. The error bar represents the standard deviation of $P$(O${}_{\mathrm{3}}$) induced by the uncertainty of the missing VOC${}_{\mathrm{R}}$.

[Figure omitted. See PDF]

O${}_{\mathrm{3}}$ precursor sensitivity depends on the dominant loss pathways of RO${}_X$ radicals (RO${}_X=$ OH $+$ HO${}_{\mathrm{2}}+$ RO${}_{\mathrm{2}}$). O${}_{\mathrm{3}}$ production is NO${}_X$-limited if the self-reaction of peroxy radicals (HO${}_{\mathrm{2}}$ and RO${}_{\mathrm{2}}$) dominates the RO${}_X$ sink and VOC-limited if the reaction of NO${}_{\mathrm{2}}$ with OH dominates (Kleinman et al., 1997, 2001). Accordingly, the ratio of the RO${}_X$ sink induced by OH $+$ NO${}_{\mathrm{2}}$ reaction to the total rate of the two RO${}_X$ sinks, i.e., $L_N/Q$, is used to identify O${}_{\mathrm{3}}$ sensitivity regimes. O${}_{\mathrm{3}}$ production is NO${}_X$-limited if $L_N/Q$ is lower than 0.5; otherwise, it is VOC-limited (Kleinman et al., 1997). 7$$L_N/Q={\displaystyle \frac{k_{\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}}\left[\mathrm{OH}\right]\left[{\mathrm{NO}}_{\mathrm{2}}\right]}{\begin{array}{c}{k}_{{\mathrm{HO}}_{\mathrm{2}}+{\mathrm{RO}}_{\mathrm{2}}}\left[{\mathrm{HO}}_{\mathrm{2}}\right]\left[{\mathrm{RO}}_{\mathrm{2}}\right]+k_{{\mathrm{HO}}_{\mathrm{2}}+{\mathrm{HO}}_{\mathrm{2}}}\left[{\mathrm{HO}}_{\mathrm{2}}\right]\left[{\mathrm{HO}}_{\mathrm{2}}\right]\\ +k_{\mathrm{OH}+{\mathrm{HO}}_{\mathrm{2}}}\left[\mathrm{OH}\right]\left[{\mathrm{HO}}_{\mathrm{2}}\right]+k_{\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}}\left[\mathrm{OH}\right]\left[{\mathrm{NO}}_{\mathrm{2}}\right]\end{array}}}$$ As shown in Fig. 5a, in the base scenario, $L_N/Q$ remained at a stable and high level ($\mathit{>}$ 0.9) during the daytime, when photochemical production of ozone occurs, indicating that O${}_{\mathrm{3}}$ production was VOC-limited. In the scenarios considering missing VOC${}_{\mathrm{R}}$, $L_N/Q$ decreased significantly regardless of which VOC species was added, compared to the base scenario. Adding toluene caused the largest decrease in $L_N/Q$, followed by adding all measured NMHC species, adding the alkane, and adding the alkene. It is worth noting that adding toluene and all measured NMHC species caused the $L_N/Q$ to be close to 0.5 in the afternoon, indicating that the O${}_{\mathrm{3}}$ production shifted to transitional or NO${}_X$-limited regimes in these scenarios. Figure 5b shows the changes in radical sinks before and after considering missing VOC${}_{\mathrm{R}}$. All radical sinks including self-reactions of peroxy radicals and OH $+$ NO${}_{\mathrm{2}}$ reaction increased after considering missing VOC${}_{\mathrm{R}}$. Nevertheless, the increased proportion of the self-reactions of peroxy radicals was larger than that of OH $+$ NO${}_{\mathrm{2}}$ reaction, leading to a decrease in $L_N/Q$ and thus a shift toward an NO${}_X$-limited regime.

Figure 5

The impact of missing VOC${}_{\mathrm{R}}$ on O${}_{\mathrm{3}}$ sensitivity for the high-missing VOC${}_{\mathrm{R}}$ days. (a) Diurnal variations in $L_N/Q$ for the base scenario and the scenarios considering missing VOC${}_{\mathrm{R}}$. The missing VOC${}_{\mathrm{R}}$ is considered by adding individual species ($n$-pentane, ethene, or toluene) or increasing all measured NMHCs to fill the missing VOC${}_{\mathrm{R}}$. The dashed line represents the threshold value of $L_N/Q$ that distinguishes VOC-limited and NO${}_X$-limited regimes. (b) The averages of radical sinks in the afternoon (12:00–18:00) for the base scenario (red bar) and the scenario considering missing VOC${}_{\mathrm{R}}$ (blue bar) by increasing all measured NMHCs to fill the missing VOC${}_{\mathrm{R}}$. (c) Model-simulated dependence of daily peak O${}_{\mathrm{3}}$ concentrations on daily mean NO${}_X$ concentrations for the base scenario (red curve) and the scenario considering missing VOC${}_{\mathrm{R}}$ (blue curve) by increasing all measured NMHCs to fill the missing VOC${}_{\mathrm{R}}$. The dashed lines parallel to the $y$ axis represent the threshold of NO${}_X$ levels for distinguishing between VOC-limited and NO${}_X$-limited regimes. The shaded area represents the standard deviation induced by the uncertainty in the missing VOC${}_{\mathrm{R}}$.

[Figure omitted. See PDF]

Figure 5c shows the dependence of daily peak O${}_{\mathrm{3}}$ concentrations on NO${}_X$ concentrations, which was calculated by the box model for the base scenario and the scenario considering missing VOC${}_{\mathrm{R}}$. The NO${}_X$ concentration level corresponding to the maximum of O${}_{\mathrm{3}}$ concentrations was determined. This NO${}_X$ concentration level reflects the threshold for distinguishing between VOC-limited and NO${}_X$-limited regimes. The larger threshold of NO${}_X$ represents a higher possibility of ozone production in an NO${}_X$-limited regime. The threshold of NO${}_X$ for the scenario considering missing VOC${}_{\mathrm{R}}$ is 46 % higher than for the base scenario. Note that the uncertainty in missing VOC${}_{\mathrm{R}}$ leads to 17 % uncertainty in the threshold of NO${}_X$ for the scenario considering missing VOC${}_{\mathrm{R}}$. Overall, Fig. 5 suggests that omitting the missing VOC${}_{\mathrm{R}}$ will overestimate the degree of the VOC-limited regime and thus overestimate the effect of VOC abatement in reducing ozone pollution, which in turn may mislead ozone control strategy.

3.4 Atmospheric implications

Although many previous studies have reported that photochemical production processes and biogenic emissions are important sources of missing VOC${}_{\mathrm{R}}$ (Lou et al., 2010; Dolgorouky et al., 2012; Yang et al., 2017; Sanchez et al., 2021; Di Carlo et al., 2004), we find that anthropogenic emissions may dominate the missing VOC${}_{\mathrm{R}}$ in urban regions. In zero-dimensional box models and three-dimensional chemistry-transport models, the input of VOC emission information mainly contains well-studied simple-structure alkanes, alkenes, and aromatics, while those unmeasured or unknown VOC species have been neglected. This will lead to biases in quantifying ozone production and diagnosing ozone sensitivity regimes. Our study demonstrates that the ambient measurement of $R_{\mathrm{OH}}$ at urban sites can provide quantification of missing VOC${}_{\mathrm{R}}$, which can be used in models to account for the missing VOC${}_{\mathrm{R}}$ from anthropogenic emissions. In addition, the parametric equation of missing VOC${}_{\mathrm{R}}$ derived from the MLR method (Eq. 4) can be used here to estimate missing VOC${}_{\mathrm{R}}$ according to measurements of CO, O${}_X$, and isoprene. Further study should try to parse the specific sources of the missing VOC${}_{\mathrm{R}}$, e.g., whether the missing VOC${}_{\mathrm{R}}$ is from intermediate-volatility and semivolatile organic compounds emitted from vehicles or whether it is from some other sources. Furthermore, future studies can focus on direct measurements of missing VOC${}_{\mathrm{R}}$ for various emission sources to develop a comprehensive emission inventory of missing VOC${}_{\mathrm{R}}$, which will help to improve O${}_{\mathrm{3}}$ pollution mitigation strategies.

Data availability

The observational data and the model code used in this study are available from the corresponding authors upon request ([email protected]).

The supplement related to this article is available online at: https://doi.org/10.5194/acp-24-4017-2024-supplement.

Author contributions

BY and WW designed the research. WW and BY prepared the manuscript with contributions from the other authors. WW performed the data analysis with contributions from HS, YC, FB, CX, and CM. JQ, SW, WS, XW, HW, SL, and MS collected data.

Competing interests

At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 42121004, 42275103, 42230701, and 42175135). This work was also supported by the Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (grant no. 2019B121205004).

Financial support

This research has been supported by the National Natural Science Foundation of China (grant nos. 42121004, 42275103, 42230701, and 42175135).The article processing charges for this open-access publication were covered by the Max Planck Society.

Review statement

This paper was edited by Rob MacKenzie and reviewed by two anonymous referees.