Elucidate the formation mechanism of particulate nitrate based on direct radical observations in the Yangtze River Delta summer 2019

Abstract

Particulate nitrate (NO${}_{\mathrm{3}}^{-})$ is one of the dominant components of fine particles in China, especially during pollution episodes, and has a significant impact on human health, air quality, and climate. Here a comprehensive field campaign that focuses on the atmospheric oxidation capacity and aerosol formation and their effects in the Yangtze River Delta (YRD) was conducted from May to June 2019 at a regional site in Changzhou, Jiangsu Province in China. The concentrations of NO${}_{\mathrm{3}}^{-}$, OH radicals, N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$, NO${}_{\mathrm{2}}$, O${}_{\mathrm{3}}$, and relevant parameters were measured simultaneously. We showed a high NO${}_{\mathrm{3}}^{-}$ mass concentration with 10.6 $\pm$ 8.9 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ on average, which accounted for 38.3 % of total water-soluble particulate components and 32.0 % of total PM${}_{\mathrm{2.5}}$, followed by the proportion of sulfate, ammonium, and chloride by 26.0 %, 18.0 %, and 2.0 %, respectively. This result confirmed that the heavy nitrate pollution in eastern China happened not only in winter, but also in summer. This study's high nitrate oxidation ratio (NOR) emphasized the solid atmospheric oxidation and fast nitrate formation capacity in the YRD. It was found that OH $+$ NO${}_{\mathrm{2}}$ during daytime dominated nitrate formation on clean days, while N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ hydrolysis vastly enhanced and became comparable with that of OH $+$ NO${}_{\mathrm{2}}$ during polluted days (67.2 % and 30.2 %, respectively). The updated observed-constraint Empirical Kinetic Modeling Approach (EKMA) was used to assess the kinetic controlling factors of both local O${}_{\mathrm{3}}$ and NO${}_{\mathrm{3}}^{-}$ productions, which indicated that the O${}_{\mathrm{3}}$-targeted scheme (VOCs $:$ NO${}_x$ $=$ $\mathrm{2}:\mathrm{1}$) is adequate for mitigating the O${}_{\mathrm{3}}$ and nitrate pollution coordinately during summertime in this region. Our results promote the understanding of nitrate pollution mechanisms and mitigation based on field observation and model simulation and call for more attention to nitrate pollution in the summertime.

1 Introduction

Chemical compositions of fine particles have been measured in China during the past 20 years, and secondary inorganic aerosol is regarded as one of the dominant species in aerosol (Cao et al., 2012; Hagler et al., 2006; Zhao et al., 2013; Andreae et al., 2008). Since the Air Pollution Prevention and Control Action Plan, there has been a significant decrease in SO${}_{\mathrm{2}}$, NO${}_{\mathrm{2}}$, and PM${}_{\mathrm{2.5}}$ concentrations in China, while the inorganic nitrate ratio in PM${}_{\mathrm{2.5}}$ increased and became the considerable component in PM${}_{\mathrm{2.5}}$ (Shang et al., 2021; Zhang et al., 2022). Therefore, a comprehensive understanding of the particulate nitrate formation mechanism is essential and critical for mitigating haze pollution in China.

Massive research has been done in China to investigate nitrate formation mechanisms, and a basic framework has been established (Sun et al., 2006; Chang et al., 2018; Wu et al., 2019). In the daytime, NO${}_{\mathrm{2}}$ $+$ OH radical oxidation (Reaction 1) is the major particulate nitrate formation pathway. The product (HNO${}_{\mathrm{3}})$ reacts with alkaline substances in aerosol, generating particulate nitrate. This pathway is mainly controlled by precursor concentrations as well as the gas-particle partition of gaseous nitric acid, and particulate nitrate depends on temperature, relative humidity (RH), NH${}_{\mathrm{3}}$ concentration, and aerosol acidity (Wang et al., 2009; Song and Carmichael, 2001; Meng et al., 2020; Zhang et al., 2021). At night, N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake is a vital nitrate formation pathway (Reaction 4) (Chen et al., 2020; Wang et al., 2022). N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ is formed through NO${}_{\mathrm{2}}$ $+$ NO${}_{\mathrm{3}}$ (Reaction 3), and there exists a quick thermal equilibrium balance ($K_{\text{eq}}=\mathrm{5.5}\times {\mathrm{10}}^{-\mathrm{17}}$ cm${}^{-\mathrm{3}}$ molec.${}^{-\mathrm{1}}$ s${}^{-\mathrm{1}}$, 298 K). However, two problems remain ambiguous in quantifying the contribution of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake to nitrate formation. The first is that the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake coefficient ($\mathit{\gamma })$ on ambient aerosol is highly varied, with a range from 10${}^{-\mathrm{4}}$ to 10${}^{-\mathrm{1}}$ based on previous lab and field measurements (Bertram and Thornton, 2009; Brown et al., 2009; Z. Wang et al., 2017; Wang and Lu, 2016). The other one is the ClNO${}_{\mathrm{2}}$ production yield which influences nitrate contribution due to the extensive variation range (Phillips et al., 2016; Staudt et al., 2019; Tham et al., 2018). Both parameters are complex to well-predicted by current schemes. No${}_{\mathrm{2}}$ heterogeneous uptake has been found to be non-negligible for nitrate formation, which can be a vital pathway during heavy haze events according to recent studies (Qiu et al., 2019; Chan et al., 2021). The uptake coefficient and nitrate yield remain uncertain, as with the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous reaction. In addition, N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ homogeneous hydrolysis and NO${}_{\mathrm{3}}$ radical oxidation have a minor contribution to particulate nitrate under ambient conditions (Brown et al., 2009; Seinfeld and Pandis, 2016).

$$\begin{array}{}\text{R1}& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+\mathrm{OH}\to {\mathrm{HNO}}_{\mathrm{3}}\text{R2}& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{O}}_{\mathrm{3}}\to {\mathrm{NO}}_{\mathrm{3}}+{\mathrm{O}}_{\mathrm{2}}\text{R3.1}& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{NO}}_{\mathrm{3}}+M\to {\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+M\text{R3.2}& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+M\to {\mathrm{NO}}_{\mathrm{2}}+{\mathrm{NO}}_{\mathrm{3}}+M\text{R4}& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+\left({\mathrm{H}}_{\mathrm{2}}\mathrm{O}\text{or}{\mathrm{Cl}}^{-}\right)\to (\mathrm{2}-\mathit{\phi }){\mathrm{NO}}_{\mathrm{3}}^{-}+\mathit{\phi }{\mathrm{ClNO}}_{\mathrm{2}}\end{array}$$

As a critical area of China's economy and industry, the Yangtze River Delta (YRD) has suffered severe air pollution during past decades, and fine particle pollution in the YRD has raised widespread concern (Guo et al., 2014; Zhang et al., 2015, 2017; Ming et al., 2017; Xue et al., 2019). However, most research focuses on wintertime PM${}_{\mathrm{2.5}}$ pollution and lacks measurements of critical intermediate species and radicals to assess the importance of each nitrate formation pathway. In this study, with the direct measurements of hydroxyl radicals and the reactive nitrogen compounds and chemical box model analyses, we explore the characteristics of nitrate and precursors in the YRD in the summer of 2019, the importance of particulate nitrate formation pathways is quantified, and the controlling factors are explored. A further suggestion for summer pollution prevention and control in the local area is proposed.

2 Site description and methods

2.1 The campaign site

This campaign took place at a suburban sanatorium from 30 May to 18 June 2019 in Changzhou, China. Changzhou (119.95${}^{\circ }$ E, 31.79${}^{\circ }$ N) is located in Jiangsu Province and about 150 km northwest of Shanghai. The sanatorium, located 420 m east of Lake Ge (one of the largest lakes in Jiangsu Province, 164 km${}^{\mathrm{2}}$), is surrounded by farmland and fishponds. With the closest arterial traffic 1 km away, several industry zones are 4 km to the east. The prevailing wind was from the southern and southeastern sectors (about 30 % of the time) compared to 20 % from the western sector, of which only 15 % came from the east. The wind speed was usually lower than 5 m s${}^{-\mathrm{1}}$, with faster speed from the west. This site was influenced by anthropogenic and biological sources with occasional biomass burning.

Figure 1

The location of the campaign site (red star), Changzhou, is 150 km on the northwestern side of Shanghai.

[Figure omitted. See PDF]

2.2 The instrumentation

Multiple gaseous and particulate parameters were measured simultaneously during the campaign to comprehensively interpret the nocturnal atmospheric capacity and aerosol formation. The related instruments are listed in Table 1. N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ and particle number and size distribution (PNSD) were measured on the fourth floor of the sanatorium, which is the top of the building. Other instruments were placed in containers on the ground 170 m northeast of the building and sampling inlets at circa 5 m above the ground through the containers' roof.

N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ was measured by a cavity-enhanced absorption spectrometer (CEAS) based on the Lambert–Beer law which was developed by Wang et al. (2017b). Briefly, air samples were drawn through the window and reached out of the wall 30 cm to prevent influence from surface deposition. The aerosol membrane filter was deployed before the perfluoroalkoxy alkane (PFA) sampling tube and changed every 2 h at night to avoid a decrease in N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ transmission efficiency due to the increased loss of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ from the accumulated aerosols on the filter. N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ was decomposed into NO${}_{\mathrm{3}}$ and NO${}_{\mathrm{2}}$ by preheating tube heat at 130${}^{\circ }$ and detected within a PFA-coated resonator cavity heated at 110${}^{\circ }$ to prevent the formation of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ by reversible reaction subsequently. At the end of each sampling cycle (5 min), a 30 s injection of high-concentration NO (10 ppm, 20 mL min${}^{-\mathrm{1}})$ mixed with sample air was set to eliminate NO${}_{\mathrm{3}}$–N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ from the system. The NO titration spectra were adopted as the dynamic background spectrum by assuming no H${}_{\mathrm{2}}$O concentration variation in a single sampling cycle. The loss of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ in the sampling system and filter was also considered during data correction. The limit of detection (LOD) was estimated to be 2.7 pptv (1$\mathit{\sigma })$ with an uncertainty of 19 %.

OH radical measurement was conducted by fluorescence assay by gas expansion laser-induced fluorescence techniques (FAGE-LIF). Ambient air was expanded through a 0.4 mm nozzle to low pressure in a detection chamber, where the 308 nm laser pulse irradiated OH radicals at a repetition rate of 8.5 kHz (Chen et al., 2018). NO${}_x$ and O${}_{\mathrm{3}}$ were monitored by commercial monitors (Thermo-Fisher 42i and 49i). Volatile organic compounds (VOCs) were measured by an automated gas chromatograph equipped with a mass spectrometer and flame ionization detector (GC-MS) with a time resolution of 60 min. The photolysis frequencies were determined from the spectral actinic photon flux density measured by a spectroradiometer (Bohn et al., 2008).

PM${}_{\mathrm{2.5}}$ concentration was obtained by a Tapered Element Oscillating Microbalance (TEOM 1405, Thermo Scientific Inc). Aerosol surface concentration ($S_{\text{a}})$ was converted from the particle number and size distribution, which was measured by a Scanning Mobility Particle Sizer (SMPS, TSI 3936) and an Aerosol Particle Sizer (APS, TSI 3321) and modified to the wet particle-state $S_{\text{a}}$ with a hygroscopic growth factor (Liu et al., 2013). The uncertainty of the wet $S_{\text{a}}$ was $\sim \mathrm{30}$ %. Meanwhile, water-soluble particulate components and their gaseous precursors were analyzed through the Monitor for AeRosols and GAses in ambient air (MARGA, Chen et al., 2017). Meteorological data were also available, including the temperature, RH, pressure, wind speed, and wind direction.

Table 1

The observed gas and particle parameters during the campaign.

Parameters	Detection of limit	Method	Accuracy
N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$	2.7 pptv (1$\mathit{\sigma }$, 1 min)	CEAS	$\pm \mathrm{19}$ %
OH	1.6 $\times$ 10${}^{\mathrm{5}}$ cm${}^{-\mathrm{3}}$ (1$\mathit{\sigma }$, 60 s)	LIF${}^{\text{a}}$	$\pm \mathrm{21}$ %
NO	60 pptv (2$\mathit{\sigma }$, 1 min)	PC${}^{\text{c}}$	$\pm \mathrm{10}$ %
NO${}_{\mathrm{2}}$	0.3 ppbv (2$\mathit{\sigma }$, 1 min)	PC${}^{\text{c}}$	$\pm \mathrm{10}$ %
O${}_{\mathrm{3}}$	0.5 ppbv (2$\mathit{\sigma }$, 1 min)	UV photometry	$\pm \mathrm{5}$ %
VOCs	20–300 pptv (60 min)	GC-MS	$\pm \mathrm{15}$ %
PM${}_{\mathrm{2.5}}$	0.1 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ (1 min)	TEOM${}^{\text{d}}$	$\pm \mathrm{5}$ %
Photolysis frequencies	$\mathrm{5}\times {\mathrm{10}}^{-\mathrm{5}}$ s${}^{-\mathrm{1}}$ (1 min)	SR${}^{\text{e}}$	$\pm \mathrm{10}$ %
PNSD	14–700 nm (4 min)	SMPS, APS	$\pm \mathrm{10}$ %
HNO${}_{\mathrm{3}}$, NO${}_{\mathrm{3}}$, HCl	0.06 ppbv (30 min)	MARGA${}^{\text{f}}$	$\pm \mathrm{20}$ %
NH${}_{\mathrm{4}}^{+}$, NO${}_{\mathrm{3}}^{-}$, Cl${}^{-}$, SO${}_{\mathrm{4}}^{\mathrm{2}-}$	0.05 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ (30 min)	MARGA${}^{\text{f}}$	$\pm \mathrm{20}$ %

${}^{\text{a}}$ Laser-induced fluorescence. ${}^{\text{b}}$ Chemiluminescence. ${}^{\text{c}}$ Photolytic converter. ${}^{\text{d}}$ Tapered Element Oscillating Microbalance. ${}^{\text{e}}$ Spectroradiometer. ${}^{\text{f}}$ The Monitor for AeRosols and GAses in ambient air.

2.3 The Empirical Kinetic Modeling Approach

A box model coupled with the Regional Atmospheric Chemical Mechanism version 2 (RACM2, Goliff, Stockwell and Lawson, 2013) is used to conduct the mitigation strategy studies. The model is operated at 1 h time resolution with measurement results of temperature, relative humidity, pressure, CO, NO${}_{\mathrm{2}}$, H${}_{\mathrm{2}}$O, photolysis frequencies, and aggregated VOC input to constrain the model. It should be noted that HONO concentration is calculated by NO${}_{\mathrm{2}}$ $\times$ 0.02, as suggested by Elshorbany et al. (2012), and has been used in the box model before (Lou et al., 2022). Long-lived species such as H${}_{\mathrm{2}}$ and CH${}_{\mathrm{4}}$ are assumed to be constants (550 and 1900 ppbv, respectively). Moreover, a 13 h constant loss rate of unconstrained intermediate and secondary products, the result of synthetic evaluating secondary simulation of secondary species, is set for representing the multiple effects of deposition, transformation, and transportation.

The approaches to the chemical production of O${}_{\mathrm{3}}$ (P(O${}_{\mathrm{3}}\left)\right)$ and inorganic nitrate (P(NO${}_{\mathrm{3}}^{-}\left)\right)$ are described in previous articles (Tan et al., 2021, 2018) and expressed as Eqs. (1) and (4).

$$\begin{array}{}\text{1}& {\displaystyle }\mathrm{P}\left({\mathrm{O}}_{\mathrm{3}}\right)=\mathrm{F}\left({\mathrm{O}}_{\mathrm{3}}\right)-\mathrm{D}\left({\mathrm{O}}_{\mathrm{3}}\right)\text{2}& {\displaystyle }\mathrm{F}\left({\mathrm{O}}_{\mathrm{3}}\right)=k_{{\mathrm{HO}}_{\mathrm{2}}+\mathrm{NO}}\left[\mathrm{NO}\right]\left[{\mathrm{HO}}_{\mathrm{2}}\right]+k_{({\mathrm{RO}}_{\mathrm{2}}+\mathrm{NO})\mathrm{eff}}\left[\mathrm{NO}\right]\left[{\mathrm{RO}}_{\mathrm{2}}\right]\text{3}& {\displaystyle }\begin{array}{rl}\mathrm{D}\left({\mathrm{O}}_{\mathrm{3}}\right)=& k_{\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}}\left[\mathrm{OH}\right]\left[{\mathrm{NO}}_{\mathrm{2}}\right]+(k_{\mathrm{OH}+{\mathrm{O}}_{\mathrm{3}}}\left[\mathrm{OH}\right]\\ +& k_{{\mathrm{HO}}_{\mathrm{2}}+{\mathrm{O}}_{\mathrm{3}}}\left[{\mathrm{HO}}_{\mathrm{2}}\right]+k_{\mathrm{alkenes}+{\mathrm{O}}_{\mathrm{3}}}\left[\mathrm{alkenes}\right])\left[{\mathrm{O}}_{\mathrm{3}}\right]\end{array}\text{4}& {\displaystyle }\mathrm{P}\left({\mathrm{NO}}_{\mathrm{3}}^{-}\right)=\mathrm{P}\left({\mathrm{HNO}}_{\mathrm{3}}\right)+\mathrm{P}(p{\mathrm{NO}}_{\mathrm{3}}^{-})\text{5}& {\displaystyle }\mathrm{P}\left({\mathrm{HNO}}_{\mathrm{3}}\right)=k_{\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}}\left[\mathrm{OH}\right]\left[{\mathrm{NO}}_{\mathrm{2}}\right]\text{6}& {\displaystyle }\mathrm{P}\left(p{\mathrm{NO}}_{\mathrm{3}}^{-}\right)=\mathrm{0.25}(\mathrm{2}-\mathit{\phi })C{\mathit{\gamma }}_{S_{\text{a}}}\left[{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\right]\end{array}$$

Briefly, P(O${}_{\mathrm{3}})$ is net ozone production, which is calculated by peroxyl radial $+$ NO oxidation (Eq. 2) minus the chemical loss of O${}_{\mathrm{3}}$ and NO${}_{\mathrm{2}}$ (Eq. 3). P(NO${}_{\mathrm{3}}^{-})$ is constituted by reaction OH $+$ NO${}_{\mathrm{2}}$ (Eq. 5) and N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake (Eq. 6). Here, rate constants of reactions are obtained from the NASA JPL publication or RACM2 (Goliff et al., 2013). $\mathit{\gamma }$ is the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient calculated from parameterization (${\mathit{\gamma }}_{\text{P}}$; more details in Sect. 3.3). $\mathit{\phi }$ represents ClNO${}_{\mathrm{2}}$ production yield through N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ hydrolysis, and the mean value reported by Xia et al. (2020) is used in this work.

The Empirical Kinetic Modeling Approach (EKMA) was invented to study the effects of precursor (VOC and NO${}_x)$ reactivity on the region's ozone pollution by Kanaya et al. (2008) and helps recognize the region's susceptibility to precursors by weight and became a prevalent tool to study the process of ozone formation (Tan et al., 2018; D. Yu et al., 2020; Kanaya et al., 2008). The prevention and control problem of pollutant generation can be transformed through the EKMA curve to reduce its precursors' emissions. Furthermore, the precursor reduction scheme needed for total pollutant control is given qualitatively. P(NO${}_{\mathrm{3}}^{-})$ can also be analyzed through EKMA for the nonlinear secondary formation relationship with precursor reactivity. Here, an isopleth diagram of the net ozone production rate as functions of the reactivities of NO${}_x$ and VOCs can be derived from EKMA. In detail, 0.01 to 1.2 emission reduction strategy assumptions are exponentially interpolated into 20 kinds of emission situations of NO${}_x$ and VOCs, respectively, which counts 400 scenarios.

2.4 The calculation of aerosol liquid water content

Aerosol liquid water content (ALWC) is calculated through ISORROPIA II (Fountoukis and Nenes, 2007). Forward mode is applied in this study. Furthermore, water-soluble particulate components in PM${}_{\mathrm{2.5}}$ and gaseous species (NH${}_{\mathrm{3}}$ $+$ HNO${}_{\mathrm{3}}$ $+$ HCl) obtained from MARGA, along with RH and $T$, are input as initial input. In addition, a metastable aerosol state is chosen due to high RH during this campaign.

3 Result and discussion

3.1 Overview of measurements

The time used in this study is China Standard Time (UTC $+$ 8), and the local sunrise and sunset times during the campaign were around 17:00 and 19:00, respectively. The whole campaign period is divided into four PM${}_{\mathrm{2.5}}$ clean periods and four PM${}_{\mathrm{2.5}}$ polluted periods (9 out of 14 d; the latter polluted period days refer to PM${}_{\mathrm{2.5}}$ pollution except for a specified description) according to the Chinese National Air Quality Standard (CNAAQS) Grade I of daily PM${}_{\mathrm{2.5}}$ concentrations ($<\mathrm{35.0}$ $\mathrm{\mu }$g m${}^{-\mathrm{3}})$. Figure 2 shows the meteorological parameters and gas-phase and particulate species time series during the observation. During the campaign, the temperature was high; the maximum reached 34.5${}^{\circ }$, with an average of 25.1 $\pm$ 3.7${}^{\circ }$. RH changed drastically from 21 % to 88 %, with a mean value of 58.9 $\pm$ 14.0 %. The mean NO${}_{\mathrm{2}}$ concentration was 14.8 $\pm$ 9.5 ppbv. Meanwhile, the O${}_{\mathrm{3}}$ average was 54.6 $\pm$ 28.8 ppbv, exceeding CNAAQS Grade II for a maximum daily average of 8 h ozone (160 $\mathrm{\mu }$g m${}^{-\mathrm{3}})$ on 14 out of 19 d and exceeding 200 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ on 6 d.

Figure 2

Time series of NO${}_{\mathrm{2}}$, O${}_{\mathrm{3}}$, N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$, OH radicals, PM${}_{\mathrm{2.5}}$, and water-soluble particulate components, temperature, and RH. The vertical dotted line represents the zero clock. The black horizontal solid line in the O${}_{\mathrm{3}}$ and PM${}_{\mathrm{2.5}}$ panels represents Chinese national air quality standards for O${}_{\mathrm{3}}$ and PM${}_{\mathrm{2.5}}$, respectively. The top panel color blocks represent the PM${}_{\mathrm{2.5}}$ clean day (light green) and PM${}_{\mathrm{2.5}}$ polluted day (salmon).

[Figure omitted. See PDF]

Daytime OH radicals ranged from 2 $\times$ 10${}^{\mathrm{6}}$ to 8 $\times$ 10${}^{\mathrm{6}}$ molec. cm${}^{-\mathrm{3}}$ with a daily peak over 3 $\times$ 10${}^{\mathrm{6}}$ molec. cm${}^{-\mathrm{3}}$. Maximum OH radicals reached 8.18 $\times$ 10${}^{\mathrm{6}}$ molec. cm${}^{-\mathrm{3}}$ in this campaign. Compared with other summertime OH radicals observed in China, the OH radical concentration at this site is relatively low but still on the same order of magnitude (Lu et al., 2012, 2013; Ma et al., 2022; Tan et al., 2017; Woodward-Massey et al., 2020; Yang et al., 2021). N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ mean concentration was 21.9 $\pm$ 39.8 pptv with a nocturnal average of 61.0 $\pm$ 63.1 pptv and a daily maximum of over 200 pptv on eight nights. The maximum concentration of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ (477.2 pptv, 5 min resolution) appeared at 20:47 on 8 June. The average NO${}_{\mathrm{3}}$ radical production rate P(NO${}_{\mathrm{3}})$ is 2.1 $\pm$ 1.4 ppbv h${}^{-\mathrm{1}}$, with nocturnal average P(NO${}_{\mathrm{3}})$ 2.8 $\pm$ 1.6 ppbv h${}^{-\mathrm{1}}$ and daytime P(NO${}_{\mathrm{3}})$ 2.2 $\pm$ 1.4 ppbv h${}^{-\mathrm{1}}$. P(NO${}_{\mathrm{3}})$ is about twice the documented value in Taizhou and the North China Plain (Wang et al., 2017a, 2018b, 2020a) but close to another result in the YRD before (Chen et al., 2019). The average PM${}_{\mathrm{2.5}}$ was 34.6 $\pm$ 17.8 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, with a maximum reach of 163.0 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. The water-soluble particulate components of PM${}_{\mathrm{2.5}}$ are displayed as well. The average NO${}_{\mathrm{3}}^{-}$ concentration was 10.6 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, which accounts for the 38.3 % mass concentration of water-soluble particulate components and 32.0 % total PM${}_{\mathrm{2.5}}$, while the proportions of sulfate, ammonium, and chloride are 26.0 %, 18 %, and 2.0 %, respectively. To sum up, during the campaign period, the pollution of PM${}_{\mathrm{2.5}}$ would be generally exacerbated on high O${}_{\mathrm{3}}$ and NO${}_{\mathrm{2}}$ days. Precipitation occurred during four clean processes and receded pollutant concentration; otherwise, the pollution condition remained severe.

The mean diurnal variations (MDCs) of temperature, RH, NO${}_{\mathrm{2}}$, O${}_{\mathrm{3}}$, P(NO${}_{\mathrm{3}})$, N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$, OH radicals, and PM${}_{\mathrm{2.5}}$ in different air quality are shown in Fig. 3. The temperature, RH, and OH radical MDCs show indistinct differences between clean days (CDs) and polluted days (PDs). The MDC of NO${}_{\mathrm{2}}$ has two concentration peaks that appear at 06:00 and 21:00 on CDs, while on PDs, its peak appears at 20:00 and maintains a high level during the whole night. The O${}_{\mathrm{3}}$ diurnal pattern reflects a typical urban-influenced character with a maximum O${}_{\mathrm{3}}$ peak that lasts 3 h from 14:00 to 17:00, while the PD O${}_{\mathrm{3}}$ peak concentration is 1.2 higher than the CD O${}_{\mathrm{3}}$ peak concentration. P(NO${}_{\mathrm{3}})$ grows after the O${}_{\mathrm{3}}$ peak, and the maximum P(NO${}_{\mathrm{3}})$ shows at 19:00 with an average value of 1.7 ppbv h${}^{-\mathrm{1}}$ on CDs. By contrast, the mean PD P(NO3) is 2.6 ppbv h${}^{-\mathrm{1}}$, and the maximum value reaches 4.7 ppbv h${}^{-\mathrm{1}}$. In contrast, the CD N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ has a higher average and maximum concentration than PDs, which suggests a faster removal process during PDs. PM${}_{\mathrm{2.5}}$ has a similar trend to P(NO${}_{\mathrm{3}})$ and has a higher concentration during nighttime.

Figure 3

The mean diurnal variations of temperature, RH, NO${}_{\mathrm{2}}$, O${}_{\mathrm{3}}$, PM${}_{\mathrm{2.5}}$, OH radicals (orange), N${}_{\mathrm{2}}$O${}_{\mathrm{5},}$ and P(NO${}_{\mathrm{3}})$ of clean days and polluted days.

[Figure omitted. See PDF]

3.2 The evolution of nitrate pollution

Figure 4a shows the relationship between nitrate and sulfate with water-soluble particulate components. Nitrate positively correlates with total water-soluble particulate components, while the sulfate ratio has an inverse correlation. With PM${}_{\mathrm{2.5}}$ concentration increasing, nitrate proportion increases rapidly and keeps high weight at a heavy PM${}_{\mathrm{2.5}}$ period, while the sulfate ratio shows the opposite phenomenon. Once the mass concentration of the total water-soluble particulate component is over 30 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, the mass fraction of nitrate in total water-soluble particulate components is up to 50 % on average. This result illustrates that particulate nitrate is one of the vital sources of explosive growth particulate matter.

Figure 4

(a) Particulate ion mass concentration ratio of nitrate and sulfate to water-soluble ion. (b) NOR against RH, colored with temperature. (c) SOR against RH, colored with temperature.

[Figure omitted. See PDF]

To further assess the conversion capacity of nitrate and sulfate at this site, the sulfur oxidation ratio (SOR) and the nitrogen oxidation ratio (NOR) are used to indicate the secondary transformation ratio of SO${}_{\mathrm{2}}$ and NO${}_{\mathrm{2}}$, respectively (Sun et al., 2006). SOR and NOR are estimated using the formulae below:

$$\begin{array}{}\text{7}& {\displaystyle }\text{SOR}={\displaystyle \frac{n{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}}{n{\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}+n{\mathrm{SO}}_{\mathrm{2}}}},\text{8}& {\displaystyle }\text{NOR}={\displaystyle \frac{n{\mathrm{NO}}_{\mathrm{3}}^{-}}{n{\mathrm{NO}}_{\mathrm{3}}^{-}+n{\mathrm{NO}}_{\mathrm{2}}}}.\end{array}$$

$n$ refers to the molar concentration, and the higher SOR and NOR represent more oxidation of gaseous species into a secondary aerosol. As depicted in Fig. 4b–c, NOR rapidly increases at RH $<$ 45 %, remains constant at 45 % $<$ RH $<$ 75 %, and ends with a sharp increase at RH $>$ 75 %. During the study period, not only is the average concentration of NO${}_{\mathrm{2}}$ higher among PDs, but there is also a significant difference between PD and CD NOR. The average values of NOR are 0.32 on PDs and 0.25 on CDs, respectively, which manifests in the more secondary transformation and pollution potential on PDs. In contrast, the SOR stays constant at a high value ($\sim \mathrm{0.5}$) during the whole RH scale, which shows a different pattern from previous research (Li et al., 2017; Zheng et al., 2015). One possible explanation is that SO${}_{\mathrm{2}}$ concentration stays low during the whole campaign (4.4 $\pm$ 2.4 ppbv on average), and SO${}_{\mathrm{2}}$ oxidation depends on the limit of SO${}_{\mathrm{2}}$ instead of oxidation capability. Meanwhile, the mean SOR in both situations is over 0.5 (0.52 on CDs and 0.56 on PDs), further supporting the SO${}_{\mathrm{2}}$-limited hypothesis. In addition, Table 2 summarizes NOR and SOR values in the YRD. NOR and SOR in this study are similar to values reported in other YRD research (Shu et al., 2019; Y. Zhang et al., 2020; Qin et al., 2021; Zhao et al., 2022), except for values in 2013, but higher than the North China study which emphasizes the solid atmospheric oxidation capacity in the YRD region.

Table 2

Statistical result of NOR and SOR in the YRD.

Location and year	SOR				NOR				References
	Max	Min	Mean	SD	Max	Min	Mean	SD
Nanjing 2013 winter	0.42	0.10	0.28	0.11	0.29	0.15	0.21	0.05	Wang et al. (2016)
Suzhou 2013 winter	0.41	0.15	0.27	0.11	0.30	0.06	0.16	0.08
Lin'an 2013 winter	0.50	0.19	0.35	0.11	0.24	0.12	0.18	0.05
Hangzhou 2013 winter	0.30	0.14	0.21	0.06	0.11	0.06	0.09	0.02
Ningbo 2013 winter	0.35	0.09	0.21	0.11	0.23	0.03	0.11	0.07
YRD 2016 summer	–	–	0.347	–	–	–	0.11	–	Shu et al. (2019)
YRD 2016 winter	–	–	0.247	–	–	–	0.15	–
Nanjing 2019 spring	0.48	0.38	–	–	0.31	0.29	–	–	Qin et al. (2021)
Changzhou 2019 spring	0.35	0.3	–	–	0.27	0.23	–	–
Changzhou 2019 winter	0.68	0.24	0.35	0.12	0.44	0.13	0.2	0.1	Zhang et al. (2020)
Changzhou 2019 summer	0.16	0.76	0.54	0.1	0.08	0.63	0.28	0.14	This work

3.3

The derivation of the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient

Statistical analysis of the observation above highlights the rapid formation of particulate nitrate. To assess the contribution of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ hydrolysis to particulate nitrate formation, two methods are applied to calculate the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient. The first method is a stationary-state approximation (Brown et al., 2003). By assuming that the rates of production and loss of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ are approximately in balance, the total loss rate of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ ($k_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$) can be calculated through Eq. (9). $k_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$ is mainly dominated by N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake, since homogeneous hydrolysis of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ contributes little (Brown and Stutz, 2012). The N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient through the steady state (denoted as $\mathit{\gamma }\mathit{\_}\text{S})$ is derived as Eq. (10). Here $C$ is the mean molecular speed of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$, and $S_{\text{a}}$ is the aerosol surface concentration.

$$\begin{array}{}\text{9}& {\displaystyle }{\mathit{\tau }}_{\mathrm{ss}}\left({\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\right)={\displaystyle \frac{\left[{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\right]}{k_{\mathrm{R}\mathrm{3.1}}\left[{\mathrm{NO}}_{\mathrm{2}}\right]\left[{\mathrm{O}}_{\mathrm{3}}\right]}}={\left(k_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}+{\displaystyle \frac{k_{{\mathrm{NO}}_{\mathrm{3}}}}{K_{\mathrm{eq}}\left[{\mathrm{NO}}_{\mathrm{2}}\right]}}\right)}^{-\mathrm{1}}\text{10}& {\displaystyle }{k}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}=\mathrm{0.25}C\mathit{\gamma }\mathit{\_}\text{S}{S}_{\text{a}}\end{array}$$

Due to the fast variety of NO${}_{\mathrm{3}}$ loss rates from VOCs, the steady-state method has been unattainable under conditions affected by emission interferences. During the whole campaign, we only retrieve three valid fitting results. As shown in Fig. 5, the fitted $\mathit{\gamma }\mathit{\_}\text{S}$ ranged from 0.057 to 0.123, which is comparable with Taizhou (0.041, Wang et al., 2020a) and much higher than other results in China (C. Yu et al., 2020; Wang et al., 2018a, 2020b, 2017a). The calculated $k_{{\mathrm{NO}}_{\mathrm{3}}}$ ranged from 0.002 to 0.16 s${}^{-\mathrm{1}}$, representing drastic VOC change during this campaign.

Figure 5

Derived N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficients from N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ steady lifetime ($\mathit{\gamma }\mathit{\_}\text{S}$) with NO${}_{\mathrm{2}}$ and $S_{\text{a}}$; plots (a–c) represent the linear fitting results on the nights of 30 May, 10 and 11 June, respectively.

[Figure omitted. See PDF]

The other approach is the parameterization by C. Yu et al. (2020), which is depicted as follows. 11$$\begin{array}{rl}& \mathit{\gamma }\mathit{\_}\text{P}={\displaystyle \frac{\mathrm{4}}{c}}{\displaystyle \frac{V_{\mathrm{a}}}{{\mathrm{S}}_{\mathrm{a}}}}{K}_{\mathrm{H}}\times \mathrm{3.0}\times {\mathrm{10}}^{\mathrm{4}}\times \left[{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\right]\\ & \left(\mathrm{1}-{\displaystyle \frac{\mathrm{1}}{\left(\mathrm{0.033}\times \frac{\left[{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\right]}{\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]}\right)+\mathrm{1}+\left(\mathrm{3.4}\times \frac{\left[{\mathrm{Cl}}^{-}\right]}{\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]}\right)}}\right)\end{array}$$ $V_{\text{a}}/S_{\text{a}}$ is the measured aerosol volume–surface area ratio by the SMPS, $K_{\text{H}}$ is Henry's law coefficient, which is set to 51 as recommended, [NO${}_{\mathrm{3}}^{-}$] and [Cl${}^{-}$] are aerosol inorganic concentrations measured by MARGA, and [H${}_{\mathrm{2}}$O] is aerosol water content calculated by ISORROPIA II. The valid parameterization-calculated N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient (denoted as $\mathit{\gamma }\mathit{\_}\text{P}$) from 30 May to 8 June 2019 shows in Fig. 6 a good consistency between the trends of $\mathit{\gamma }\mathit{\_}\text{P}$ and aerosol water content. Nighttime $\mathit{\gamma }\mathit{\_}\text{P}$ varies from 0.001 to 0.024 with an average of 0.069 $\pm$ 0.0050 under polluted conditions and 0.0036 $\pm$ 0.0026 under clean conditions. The N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient shows a good correlation between RH and aerosol water content. For the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient, although particulate nitrate mass concentration increased during the pollution event, an antagonistic effect on the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient was not obvious for the nitrate molarity decrease.

Furthermore, we compare the difference between $\mathit{\gamma }\mathit{\_}\text{S}$ and $\mathit{\gamma }\mathit{\_}\text{P}$. Taking the night of 30 May as an example, $\mathit{\gamma }\mathit{\_}\text{S}$ is 0.089, while $\mathit{\gamma }\mathit{\_}\text{P}$ ranges from 0.024 to 0.057 with an average value of 0.013 $\pm$ 0.0051. The difference between steady state and parameterization is significant; one possible explanation is uncertainty for stationary-state approximation caused by local NO or VOC emission (Brown et al., 2009; Chen et al., 2022). Another reason is that the parameterization by C. Yu et al. (2020) ignores the impact of organic matter on the fine particles. The difference in aerosol composition between this work and C. Yu et al. (2020) may also bring uncertainty. Overall, $\mathit{\gamma }\mathit{\_}\text{P}$ will be chosen for the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake coefficient in later analysis and discussion.

Figure 6

Results of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficients through parameterization ($\mathit{\gamma }\mathit{\_}\text{P}$). Panel (a) shows time series of $\mathit{\gamma }\mathit{\_}\text{P}$ and ISORROPIA II results of aerosol water content (AWC). Panel (b) is the box plot of the polluted days and clean days, the hollow square represents the mean value, and the solid line across the box shows the median score for the data set, while the top and bottom whiskers represent the 90 % and 10 % values of $\mathit{\gamma }\mathit{\_}\text{P}$, respectively.

[Figure omitted. See PDF]

3.4 Quantifying the contribution of nitrate formation pathways

After the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient is counted, nitrate production potential P(NO${}_{\mathrm{3}}^{-})$ can be calculated. Here the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient is set to 0.036 on clean days and 0.069 on polluted days, respectively, which are the average values derived from parameterization. The production ratio of NO${}_{\mathrm{3}}^{-}$ (by considering the ClNO${}_{\mathrm{2}}$ yield of 0.54) is set to 1.46 in the former study (Xia et al., 2020). Gas-particle distribution is considered by the result of the particulate nitrate and gas-phase nitrate by MARGA (input ${\mathrm{HNO}}_{\mathrm{3}}/{\mathrm{NO}}_{\mathrm{3}}^{-}$ ratio to the model as the OH $+$ NO${}_{\mathrm{2}}$ nitrate production rate). The NO${}_{\mathrm{2}}$ heterogeneous uptake coefficient is set to 5.8 $\times$ 10${}^{-\mathrm{6}}$ depending on the report by Yu et al. (2021), which is the result of 70 % RH on urban grime.

The mean diurnal variations of the nitrate production potential of clean and polluted days are depicted in Fig. 7. The OH $+$ NO${}_{\mathrm{2}}$ pathway shows no significate difference between clean and polluted days and dominates CD nitrate formation potential, since the levels of OH and NO${}_{\mathrm{2}}$ are less affected by the fine particle level. However, the rapid increase in the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake pathway on polluted days is fatal, and its peak formation rate at night over the OH $+$ NO${}_{\mathrm{2}}$ pathway can be used to explain nighttime nitrate explosive growth.

As shown in Fig. 7c, OH $+$ NO${}_{\mathrm{2}}$ dominates nitrate production on clean days, while the N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake pathway only contributes 13.6 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$. On polluted days, the ability of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake grows quickly, reaching 50.1 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$, while the OH pathway does not change much. There is no distinct difference in the daytime pathway (OH $+$ NO${}_{\mathrm{2}}$) between clean days and polluted days, while the nighttime pathway ratio rises from 38.1 % on clean days to 67.2 % on polluted days. NO${}_{\mathrm{2}}$ heterogeneous uptake increases from 0.93 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ on clean days to 2.0 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ on polluted days, but the contribution proportion does not change obviously. Both the higher N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ uptake coefficient and the higher $S_{\text{a}}$ on polluted days increase the contribution of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ hydrolysis to particulate nitrate under pollution conditions.

Figure 7

The mean diurnal variations of the nitrate production potential of clean days (a) and polluted days (b) and the P(NO${}_{\mathrm{3}}^{-})$ distribution of clean days and polluted days (c).

[Figure omitted. See PDF]

3.5 Mitigation strategies of particulate nitrate and ozone productions

We selected two pollution episodes (Episode I, 30 May 2019 00:00–2 June 2019 00:00, and IV, 14 June 2019 17:30–17 June 2019 12:00) to explore the mitigation way of ozone and nitrate pollution. Figure 8 shows the EKMA of P(O${}_{\mathrm{3}})$ and P(NO${}_{\mathrm{3}}^{-})$ of these two periods, O${}_{\mathrm{3}}$ located in the VOC-controlling area in the two pollution episodes, which is consistent with a previous YRD urban ozone sensitivity study (Jiang et al., 2018; K. Zhang et al., 2020; Xu et al., 2021). The best precursor reduction for O${}_{\mathrm{3}}$ is VOC $:$ NO${}_x$ $=$ $\mathrm{2}:\mathrm{1}$, while nitrate is located in the transition area, which means either of the precursors' reductions will mitigate nitrate pollution. For the regional and complex air pollution characteristics in this region, a fine particle-targeting reduction scheme will aggravate O${}_{\mathrm{3}}$ pollution. In contrast, the O${}_{\mathrm{3}}$-targeting scheme can mitigate O${}_{\mathrm{3}}$ and fine particles simultaneously.

Figure 8

Isogram of P(O${}_{\mathrm{3}})$ and P(NO${}_{\mathrm{3}}^{-}$) of polluted episodes IV (30 May 2019 00:00–6 June 2019 00:00) and I (14 June 2019 17:30–17 June 2019 12:00) with different NO${}_x$ and VOC reduction degrees. The grey dashed line represents the ridge line.

[Figure omitted. See PDF]

4 Conclusions

A comprehensive campaign was conducted to interpret the atmospheric oxidation capacity and aerosol formation from 30 May to 18 June 2019 in Changzhou, China. The high O${}_{\mathrm{3}}$ and PM${}_{\mathrm{2.5}}$ concentrations confirm complex air pollution characteristics in Changzhou, and nitrate accounts for 38.3 % mass concentration of total water-soluble particulate components and 32.0 % of total PM${}_{\mathrm{2.5}}$. In addition, the average values of NOR are 0.32 on PDs and 0.25 on CDs. The positive correlation between NOR and RH and the inverse correlation refer to the contribution of N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake to nitrate formation.

Based on field observations of OH and related parameters, we show that OH oxidation of the NO${}_{\mathrm{2}}$ pathway steadily contributes to nitrate formation no matter the clean or polluted period and dominates CD nitrate production (about 22 $\mathrm{\mu }$g m${}^{-\mathrm{3}})$. N${}_{\mathrm{2}}$O${}_{\mathrm{5}}$ heterogeneous uptake contribution proliferated on polluted days, from 13.6 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ (38.1 %) on clean days to 50.1 $\mathrm{\mu }$g m${}^{-\mathrm{3}}$ (67.2 %) on polluted days. NO${}_{\mathrm{2}}$ heterogeneous uptake contributes little to nitrate formation (2.6 %).

The precursor reduction simulation suggests the reduction ratio of VOC $:$ NO${}_x$ equaling $\mathrm{2}:\mathrm{1}$ can simultaneously and effectively mitigate O${}_{\mathrm{3}}$ and fine particle pollution during the summertime complex pollution period in Changzhou. To more precisely and delicately establish a cooperative control scheme for regional O${}_{\mathrm{3}}$ and nitrate, the regional and long-time field campaigns are needed in the future to analyze the seasonal and interannual variation of O${}_{\mathrm{3}}$ and nitrate and relevant parameters.

Code and data availability

The data sets used in this study are available from the corresponding author upon request ([email protected]).

Author contributions

KL and YZ designed the study. TZ analyzed the data and wrote the paper with input from all the authors.

Competing interests

The contact author has declared that neither of the authors has any competing interests.

Disclaimer

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgements

Thanks for the data contributed by the field campaign team.

Financial support

This project is supported by the National Natural Science Foundation of China (grant nos. 21976006 and 42175111), the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (JQ19031), and the National Research Program for Key Issues in Air Pollution Control (DQGG0103-01, 2019YFC0214800).

Review statement

This paper was edited by Guangjie Zheng and reviewed by two anonymous referees.