Spatial and temporal changes of the ozone sensitivity in China based on satellite and ground-based observations

Ground-level ozone (O₃) pollution has been steadily getting worse in most parts of eastern China during the past 5 years. The non-linearity of O₃ formation with its precursors like nitrogen oxides (NO_x= NO + NO₂) and volatile organic compounds (VOCs) are complicating effective O₃ abatement plans. The diagnosis from space-based observations, i.e. the ratio of formaldehyde (HCHO) columns to tropospheric NO₂ columns (HCHO $/$ NO₂), has previously been proved to be highly consistent with our current understanding of surface O₃ chemistry. HCHO $/$ NO₂ ratio thresholds distinguishing O₃ formation sensitivity depend on regions and O₃ chemistry interactions with aerosol. To shed more light on the current O₃ formation sensitivity over China, we have derived HCHO $/$ NO₂ ratio thresholds by directly connecting satellite-based HCHO $/$ NO₂ observations and ground-based O₃ measurements over the major Chinese cities in this study. We find that a VOC-limited regime occurs for HCHO $/$ NO₂ < 2.3, and a NO_x-limited regime occurs for HCHO $/$ NO₂ > 4.2. The HCHO $/$ NO₂ between 2.3 and 4.2 reflects the transition between the two regimes. Our method shows that the O₃ formation sensitivity tends to be VOC-limited over urban areas and NO_x-limited over rural and remote areas in China. We find that there is a shift in some cities from the VOC-limited regime to the transitional regime that is associated with a rapid drop in anthropogenic NO_x emissions, owing to the widely applied rigorous emission control strategies between 2016 and 2019. This detected spatial expansion of the transitional regime is supported by rising surface O₃ concentrations. The enhanced O₃ concentrations in urban areas during the COVID-19 lockdown in China indicate that a protocol with simultaneous anthropogenic NO_x emissions and VOC emissions controls is essential for O₃ abatement plans.

1 Introduction

Ground-level ozone (O₃) is one of the major air pollutants that has negative impacts on human health and can result in eye and nose irritation, respiratory disease, and lung function impairment (Jerrett et al., 2009; Khaniabadi et al., 2017; Huang et al., 2018). Y. Tian et al. (2020) observed increased admissions for pneumonia associated with O₃ exposure, especially for elderly people. In addition, it also has important impacts on climate as a greenhouse gas by absorbing thermal radiation (Fishman et al., 1979; IPCC, 2014). Photochemical tropospheric O₃ is formed in a non-linear manner from O₃ precursors such as volatile organic compounds (VOCs) and nitrogen oxides (NO_x= NO + NO₂) in the presence of sunlight (Crutzen, 1974; Jacob, 2000).

In 2008, China was found to be the largest contributor to Asian emissions of carbon monoxide (CO), NO_x, non-methane volatile organic carbon (NMVOC), and methane (CH₄) (Kurokawa et al., 2013). Because of these large emissions of anthropogenic air pollutants, the Chinese State Council released the “Air Pollution Prevention and Action Plan” (APPAP) on September 2013, which has as a key task to prevent and control air pollution in China (Cai et al., 2017). Since then, critical emission control strategies have been carried out that are designed to reduce the concentrations of six environmental pollutants: sulfur dioxide (SO₂), nitrogen dioxide (NO₂), CO, O₃, and particulate matter (PM_2.5 and PM₁₀) (Zhang et al., 2016; Feng and Liao, 2016). During the past decade, the concentrations of many pollutants including SO₂, NO₂, CO, PM_2.5, and PM₁₀ have declined in most cities; however, O₃ concentrations showed an increasing trend (W. N. Wang et al., 2017; Z. Wang et al., 2019; Zeng et al., 2019). Therefore, reducing O₃ concentrations has become the focus of China's next air quality control strategy (Cheng et al., 2018).

In terms of O₃ concentrations, the effectiveness of emissions control strategy depends on whether the photochemical regime of O₃ formation is a VOC-limited or NO_x-limited regime (Jin et al., 2020). In the VOC-limited (or NO_x-saturated) regime, VOC emission reductions reduce the chemical production of organic radicals (RO₂), which in turn lead to decreased cycling with NO_x and consequently lower concentration of O₃ (Milford et al., 1989). In the NO_x-limited (or VOC-saturated) regime, NO_x emission reductions reduce NO₂ photolysis, which is the primary source of free oxygen atoms. Therefore, in a NO_x-limited regime, NO_x reductions reduce ambient O₃. In contrast, in a VOC-limited regime, NO_x acts to reduce O₃, so a NO_x decrease in emissions promotes O₃ production (Kleinman, 1994).

The observed photochemical indicators and observation-based models (OBMs) are the most commonly used tools to diagnose the O₃ formation sensitivity. O₃ production efficiency (OPE =ΔO₃ $/$ ΔNO_z) and the H₂O₂ $/$ NO_z (or H₂O₂ $/$ HNO₃) ratio are two widely used indicators to infer the O₃ formation regimes (Chou et al., 2011; Ding et al., 2013). T. Wang et al. (2017) concluded that lower OPE values (e.g. < 4) indicate a VOC-limited regime. In contrast, higher OPE values (e.g. > 7) indicate a NO_x-limited regime. OPE values in the medium range (e.g. 4 < OPE < 7) mark the transition between the two regimes. Another indicator of the O₃ formation sensitivity regime is the H₂O₂ $/$ NO_z ratio. Hammer et al. (2002) defined that, in the VOC-limited regime, lower H₂O₂ $/$ NO_z ratios would be expected and higher H₂O₂ $/$ NO_z ratios indicate the NO_x-limited regime. In the past decade, the observed photochemical indicators have been applied to identify the O₃ formation sensitivity in different periods and regions of China.

The OBM combines in situ field observations and chemical box modelling. It is built on widely used chemistry mechanisms (e.g. Master Chemical Mechanism (MCM), Carbon Bond, Regional Atmospheric Chemical Mechanism (RACM), Statewide Air Pollution Research Center mechanism (SAPRC)) and applied to the observed atmospheric conditions to simulate various atmospheric chemical processes, including the in situ O₃ production rate. However, ground-based measurements are often limited in time period and spatial extent. The OBM analysis requires measuring nitric oxide (NO) at sub-ppb levels and more than 50 different types of VOCs with high accuracy, which is difficult to achieve (T. Wang et al., 2017).

Satellite remote sensing provides an alternative way to investigate long time periods of O₃ formation sensitivity on large spatial scales. For over 2 decades, satellite-based spectrometers have provided continuous global observations on a daily basis for two species indicative of O₃ precursors, i.e. NO₂ for NO_x (Martin et al., 2004; Lamsal et al., 2014) and formaldehyde (HCHO) for VOCs (Palmer et al., 2003; Fu et al., 2007). NO_x can be approximated from satellite observation of NO₂ column because of the short lifetime of NO_x and high ratio of NO₂ $/$ NO_x in the boundary layer (Duncan et al., 2010; Jin and Holloway, 2015). HCHO is an intermediate of the oxidation reaction of various VOCs in the atmosphere. The production of HCHO is approximately proportional to the summed rate of reactions of VOC with OH radicals (Sillman, 1995). Therefore, HCHO can be used as a tracer for VOCs in the absence of other VOC observations (Martin et al., 2004; Duncan et al., 2010). The O₃ formation sensitivity is defined by the ratio of HCHO to NO₂ (referred to as FNR) (Martin et al., 2004). Duncan et al. (2010) combined models and Ozone Monitoring Instrument (OMI) HCHO and NO₂ data to show certain ranges of FNR that can be useful for classifying a region into VOC-limited or NO_x-limited regime. An FNR smaller than 1 indicates the VOC-limited conditions, and an FNR higher than 2 indicates the NO_x-limited conditions. An FNR in the range of 1–2 should generally be considered indicative of the transitional regime. These FNR thresholds defined by Duncan et al. (2010) have been widely used for various regions (Choi and Souri, 2015; Jin and Holloway, 2015; Souri et al., 2017; Jeon et al., 2018) and with different satellite instruments (Choi et al., 2012).

However, these prior studies linked FNR with surface O₃ sensitivity in models (Martin et al., 2004; Duncan et al., 2010). Modelled and observed HCHO columns, NO₂ columns, and surface O₃ often disagree. Jin et al. (2017) found that the spatial and temporal correlations between the modelled and satellite-derived FNR vary over the used satellite instruments. Brown-Steiner et al. (2015) found persistent O₃ biases under all configurations of a global climate–chemistry model (GCCM) with detailed tropospheric chemistry. Although FNR thresholds defined by Duncan et al. (2010) have been used previously to investigate O₃-NO_x-VOC sensitivity in China (Witte et al., 2011; Tang et al., 2012; Jin and Holloway, 2015), their conclusions were based on the atmospheric situations in the United States and may not be suitable for the more complicated air pollution in China, concerning the different emission factors, sources, pollution levels, and climatology. For example, compared with the United States, most cities in China have higher aerosol levels (van Donkelaar et al., 2010; X. Li et al., 2019). Secondary aerosol production may become a large sink of radicals, which could shift O₃ production toward a VOC-limited regime under these FNR thresholds suited to the United States (Liu et al., 2012; K. Li et al., 2019). It is therefore useful to describe surface O₃ sensitivity using FNR thresholds derived entirely from satellite-observed FNR and ground-based measurements of O₃. In addition, Schroeder et al. (2017) using airborne measurements suggested that the range and span of FNR marking the transitional regime varies regionally.

In this study, we assess whether space-based HCHO $/$ NO₂ ratios capture the non-linearity of O₃ chemistry by matching satellite observations with ground-based O₃ measurements over major Chinese cities. Thresholds suited for China between space-based HCHO $/$ NO₂ and the ground-based O₃ response patterns are derived from observations instead of model results. We focus on the spatial and temporal variability of O₃ formation sensitivity using our FNR thresholds on a nationwide scale and in typical cities from 2016 to 2019.

More recently, a new unique situation has occurred with the outbreak of the COVID-19 pandemic, which provided a unique opportunity to demonstrate our predicted effects on O₃ pollution in China. Efforts to halt the spread of COVID-19 have drastically reduced human activities worldwide (Siciliano et al., 2020; H. Tian et al., 2020). As a result of these restrictions, a significant reduction in primary air pollutant emissions, especially in the concentration of NO₂, has been noticed in China and several European and American countries (Tobías et al., 2020; Wang and Su, 2020; Bauwens et al., 2020; Ding et al., 2020). By contrast, increasing O₃ concentrations during the same period were observed in densely populated metropolitan areas throughout the world (Siciliano et al., 2020; Zoran et al., 2020; Huang et al., 2020).

Section 2 describes the data and methods used in this study. Section 3 presents our derived FNR thresholds method and variations of O₃ formation sensitivity in China. In addition, impacts of the COVID-19 outbreak on O₃ levels are discussed. Finally, Sect. 4 gives a brief summary.

2 Data

2.1 Satellite data

We use the NO₂ and HCHO observations from the Ozone Monitoring Instrument (OMI) aboard the National Aeronautics and Space Administration (NASA) satellite Aura, which was launched in July 2004 (Levelt et al., 2006). In an ascending sun-synchronous polar orbit, OMI passes the Equator at about 13:40 LT (local time), providing global measurements of aerosol parameters, cloud, and various trace gases (NO₂ and HCHO among them) (Levelt et al., 2006). The high spatial resolution (13 km × 24 km at nadir) allows for observing fine details of atmospheric parameters (Jin and Holloway, 2015). OMI data are considered to be reliable and of good quality for the full mission thus far (Zara et al., 2018). In addition, the OMI overpass time is well suited to detect the O₃ formation sensitivity during the afternoon, when O₃ photochemical production peaks and when the boundary layer is high and the solar zenith angle is small, maximizing instrument sensitivity to HCHO and NO₂ in the lower troposphere (Jin et al., 2017).

We use the OMI tropospheric NO₂ and HCHO data products from the European Quality Assurance for Essential Climate Variables project (QA4ECV, http://www.qa4ecv.eu/, last access: 6 May 2021). NO₂ data are compiled by the Royal Netherlands Meteorological Institute (KNMI). The tropospheric NO₂ column density is defined as the vertically integrated number of NO₂ molecules between the Earth's surface and the tropopause per unit area. We select QA4ECV NO₂ daily observations following the recommendations given in the product specification document (Boersma et al., 2011) for this data product: (1) no processing error, (2) less than 10 % snow or ice coverage, (3) solar zenith angle less than 80 ^∘, and (4) cloud radiance fraction less than 50 %. The QA4ECV NO₂ monthly datasets are processed with a spatial resolution of 0.125^∘ × 0.125^∘. Boersma et al. (2018) reported the single-pixel uncertainties for the QA4ECV NO₂ columns are 35 %–45 % in the polluted regions; the monthly mean NO₂ columns are estimated to have an uncertainty of ±10 %.

The OMI tropospheric HCHO data are retrieved by the Belgian Institute for Space Aeronomy (BIRA-IASB) (Smedt et al., 2017a). We select processing_quality_flags = 0 or > 255, providing a selection of observations that is considered optimal. Zara et al. (2018) found that the QA4ECV HCHO slant column densities (SCDs) have uncertainties of 8–12×10¹⁵ molecule/cm² and a remarkably stable trend (increase < 1 %/yr). The QA4ECV HCHO monthly datasets are available with a spatial resolution of 0.05^∘ × 0.05^∘. Temporal averaging has been shown to reduce the HCHO measurements uncertainty and noise (Millet et al., 2008). We regrid the monthly OMI HCHO data (0.05^∘ × 0.05^∘) to the same grid as for the monthly OMI NO₂ data (0.125^∘ × 0.125^∘).

2.2 NO_x emission

Emission inventories of air pollutants are important sources of information for policy makers and form essential input for air quality models. Bottom-up inventories are usually compiled from statistics on emitting activities and their typical emission factors but are sporadically updated (Li et al., 2017). Satellite-derived emission inventories have important advantages over bottom-up emission inventories: they are spatially consistent, have high temporal resolution, and provide up-to-date emission information (Mijling and van der A, 2012). In this study, we use monthly mean NO_x surface emission estimates derived from OMI observations of tropospheric NO₂ columns (the QA4ECV product discussed in Sect. 2) by the Daily Emission estimation Constrained by Satellite Observations (DECSO) algorithm. Mijling and van der A (2012) for the first time developed DECSO (version 1) by calculating the sensitivity of concentration to emission based on a chemical transport model and using trajectory analysis to account for transport away from the source. Ding et al. (2015) improved DECSO (version 3) and demonstrated that it is able to detect the monthly change of NO_x emissions due to air quality regulations on a city level. The NO_x emissions derived by the improved DECSO version 5 are in good agreement with other bottom-up anthropogenic emission inventories. In addition, the improved algorithm is able to better capture the seasonality of NO_x emissions. The precision of monthly NO_x emissions derived by DECSO version 5 for each grid cell is about 20 % (Ding et al., 2017). Here, we use NO_x emissions derived by the latest DECSO version 5.1qa which provides monthly emissions for the last decade (2007–2020) (Ding et al., 2018). These datasets are available from https://www.temis.nl/emissions/region_asia/datapage.php (last access: 6 May 2021).

2.3 Ground-based observations

Since 2012, the Chinese government at various levels began to establish a national air quality monitoring network, which released real-time ground-level O₃ monitoring data to the public. By 2016, the establishment of more than 1000 sites was completed, covering more than 300 cities across the country. At each monitoring site, the concentration of O₃ is measured using the ultraviolet absorption spectrometry method and differential optical absorption spectroscopy; NO₂ is measured using the chemiluminescence method by a set of commercial instruments. The instrumental operation, maintenance, data assurance, and quality control were conducted based on the most recent revisions of China environmental protection standards (CMEE, 2013).

We use hourly O₃ and NO₂ concentrations (in standard conditions: 273 K, 101.325 kPa) from the network of ∼1000 sites operated by the China Ministry of Ecology and Environment (CMEE) since 2016. CMEE revised the monitoring of pollutants to a new reference conditions (298 K, 101.325 kPa) since 1 September 2018 (CMEE, 2018). Daily ground-based O₃ and NO₂ observations are calculated from hourly observations at OMI overpass time (average of 13:00 and 14:00 LT). In this study, we convert the gas concentrations before 1 September 2018 from the standard conditions to the reference conditions. The temperature dependence is according to Charles's law (Eq. 1),

$$\begin{array}{}\text{(1)}& {\displaystyle \frac{V_{\mathrm{std}}}{T_{\mathrm{std}}}}={\displaystyle \frac{V_{\mathrm{ref}}}{T_{\mathrm{ref}}}},\end{array}$$

where V_std is the volume of a gas under standard conditions, V_ref is the volume of a gas under reference conditions, T_std (unit: K) is the thermodynamic temperature of standard conditions, and T_ref (unit: K) is the thermodynamic temperature of reference conditions. The gas concentration conversion follows

$$\begin{array}{}\text{(2)}& {\displaystyle \frac{C_{\mathrm{std}}}{C_{\mathrm{ref}}}}={\displaystyle \frac{M/V_{\mathrm{std}}}{M/V_{\mathrm{ref}}}}={\displaystyle \frac{V_{\mathrm{ref}}}{V_{\mathrm{std}}}},\end{array}$$

where C_std is the gas concentration under standard conditions, and C_ref is the gas concentration under reference conditions.

Because the Chinese national air quality monitoring network stations are mostly located in the centre of cities or densely populated areas, which are usually the most polluted regions, we select the Naha station, located on the small island of Okinawa in Japan, as a location with a clean atmosphere. The hourly O₃ and NO₂ observations of Naha station are provided by the Japanese Atmospheric Environmental Regional Observation System (AEROS; http://soramame.taiki.go.jp/Index.php, last access: 6 May 2021).

2.4 CLASS model

We simulate the non-linear relationship among O₃, NO₂, and HCHO using the Chemistry Land-surface Atmosphere Soil Slab model (CLASS). We performed a series of numerical experiments with the same dynamic and chemistry conditions listed in Table 1, but we modified only the concentrations of NO₂ and HCHO. The initial mixing ratios of chemical species are shown in Table S1 in the Supplement. The initial mixing ratio data are from van Stratum et al. (2012). All other species (except for molecular oxygen and nitrogen) are initialized at zero, and we modified only the concentrations of NO₂ and HCHO.

The CLASS model solves the diurnal evolution of dynamical variables (temperature, specific humidity, and wind) and chemical species over time in a well-mixed convective atmospheric boundary layer (ABL) in which entrainment and boundary layer growth are considered (Vilà-Guerau de Arellano et al., 2015; van Heerwaarden et al., 2010). All these variables are assumed to be constant with height due to intense turbulent mixing driven by convection (van Heerwaarden et al., 2010). The surface is assumed to be homogeneous in this box model. Chemistry is represented by a chemical scheme based on 27 reactions that control O₃ formation described by van Stratum et al. (2012), with O₃, NO_x, and isoprene as the most important species. This simplified chemical scheme is able to represent the evolution of chemical species in semirural areas (Janssen et al., 2012; van Stratum et al., 2012). This chemical scheme is able to represent the evolution of the O₃–NO_x–VOC–HO_x cycle in semirural areas (Vilà-Guerau de Arellano et al., 2011; Janssen et al., 2012; van Stratum et al., 2012). The model has been validated under various dynamical conditions (Barbaro et al., 2014; Janssen et al., 2012; van Heerwaarden et al., 2010).

Table 1 Configuration and settings of the CLASS modelling system.

3 Results

3.1 O₃ formation sensitivity regime classification

In Fig. 1a, the CLASS model is applied to generate O₃ isopleths, which illustrate O₃ as a function of NO₂ and HCHO values. The isopleths show that O₃ formation is a highly non-linear process in relation to NO₂ and HCHO. When NO₂ is low, the O₃ increases with increasing NO₂. As NO₂ increases, the O₃ eventually reaches a local maximum. At higher NO₂ concentrations, the O₃ would decrease with increasing NO₂.

Figure 1 (a) The simulated O₃ isopleths versus NO₂ and HCHO using the CLASS model. (b) The 360 cities' monthly mean in situ O₃ concentrations versus in situ NO₂ concentrations and HCHO columns from OMI observations in the summer during 2016–2019. Note that daily ground-based O₃ and NO₂ observations are calculated from hourly observations at OMI overpass time (averaged at 13:00 and 14:00 LT). The O₃ numeric value of the grid cells is average of all points falling in each bin. (c) Same as (b) but with NO₂ columns from OMI observations. (d) The top 10 % monthly O₃ values and corresponding FNRs of each city. FNR thresholds are defined as the ±30 % range from the median of monthly O₃ exceeding 160 µg/m³ in the top 10 % dataset.

We first evaluate if satellite-based HCHO and NO₂ columns can capture the non-linear O₃–NO₂–HCHO chemistry shown by the CLASS model. In order to obtain a representative observation sample, we create monthly mean ground-based O₃ and NO₂ observations of 360 cities across China from the Chinese national air quality monitoring network from 2016 to 2019 and the background station observations from Naha, Japan, for comparison. Temperature is also a major factor in O₃ chemistry. O₃ pollution is rare when the ambient temperature is below 20 ^∘C (Sillman, 2003). The seasonality of ground-level O₃ concentrations also exhibited monthly variability peaking in summer and reaching the lowest levels in winter over China (W. N. Wang et al., 2017). In addition, long NO_x lifetime and low concentrations of OH and RO₂ radicals would lead most regions of China to a VOC-limited regime in winter (Shah et al., 2020). Therefore, we focus in this study on May–October as the summer period when meteorology is favourable for O₃ formation (Jin et al., 2017).

By directly connecting HCHO columns from OMI observations with ground-based measurements of NO₂ and O₃ from 360 cities across China during May–October from 2016 to 2019 in Fig. 1b, we find that the satellite-based HCHO columns and ground-based NO₂ concentrations can capture non-linear O₃ chemistry consistent with the CLASS model results. It indicates that tropospheric HCHO columns from OMI can represent the near-surface HCHO environment as revealed by previous studies (Martin et al., 2004; Duncan et al., 2010; Jin et al., 2017). The overall O₃–NO₂–HCHO chemistry is also captured by satellite-based HCHO and NO₂ columns in Fig. 1c, where we construct the O₃ isopleth using only observations.

Having established this relationship between satellite-based HCHO $/$ NO₂ columns and surface O₃ concentrations, we subsequently derive the FNR thresholds marking the O₃ transitional regime. The local O₃ maximum can be thought of as a dividing line separating two different photochemical regimes (Sillman, 1999). According to the Chinese national ambient air quality standards released in 2012, 1 h average O₃ concentration should below 160 µg/m³ in rural regions and below 200 µg/m³ in urban regions (Li et al., 2018). We assume that the monthly O₃ concentration (daily O₃ data are averaged at 13:00 and 14:00 LT) exceeding 160 µg/m³ has a large component that is due to local photochemical production not meteorology or regional transport. We calculated for each city the monthly mean surface O₃ as a function of the monthly column densities of NO₂ and HCHO for all months during May–October from 2016 to 2019. The results are shown in Fig. 1c. We only consider observations of monthly HCHO columns higher than 2 × 10¹⁵ molecule/cm² (detection limitation), NO₂ columns more than 1.5 × 10¹⁵ molecule/cm² (which are defined as polluted regions), and O₃ columns above 160 µg/m³ (minimizing the effect of background ozone). We then plot in Fig. 1d the surface O₃ concentrations as a function of the FNR to determine the range of FNRs, which includes the O₃ maximum for most (> 60 %) cities. We define this range as the transition between the NO_x-limited and VOC-limited regimes.

It should be noted that the actual split between NO_x-limited and VOC-limited regimes includes a broad transitional region rather than a sharp dividing line (Sillman, 1999). Although we reduce the noise by gridding, there is a blurry transition between NO_x-limited and VOC-limited regimes. The lack of sharp and clear transitions between two O₃ sensitivity regimes is likely influenced by factors such as meteorology, chemical and depositional loss of O₃, and noisy satellite data. We find a relationship between FNR and the O₃ response patterns that is qualitatively similar but quantitatively distinct across cities. Taking into account the range of transitional regime, the FNR thresholds [2.3, 4.2], marking the transitional regime, are defined as the ±30 % range from the median (3.28), covering the O₃ maximum in most (60 %) studied cities.

To minimize the effect of background O₃ by transport or meteorological variability, we use monthly mean O₃ concentrations above 160 µg/m³ in summertime when the O₃ chemistry is strongest. We assume that the results are applicable for the whole of China. To check this assumption, we investigate the FNR thresholds in different latitude zones (18–28^∘ N, 28–38^∘ N, and 38–53^∘ N) in Fig. S1 in the Supplement. Generally, we conclude that the derived FNR thresholds range of [2.3, 4.2] for the whole domain is a good representation for all latitude zones in China.

Figure S2a in the Supplement shows monthly O₃ concentration in winter (December–January–February), which rarely exceed 160 µg/m³, including the FNR thresholds derived using summertime data. Based on Fig. S2b, we assume that our FNR thresholds [2.3, 4.2] derived using summertime data will be valid for all seasons. Three regimes can be roughly identified from the FNR thresholds we adopted: a VOC-limited regime should occur when the FNR < 2.3, and a NO_x-limited regime should occur when the FNR > 4.2. The FNR between 2.3 and 4.2 reflects the transition between the two regimes.

3.2 Variations in O₃ formation sensitivity in China

Figure 2a and b show the photochemical regime classification over China in summer of 2016 and 2019 using our FNR thresholds. Combined with the China provincial administrative division in Fig. S3 in the Supplement, we see the VOC-limited regimes mainly appear in the North China Plain (NCP), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), and the NO_x-limited regimes dominate the remaining areas, which are consistent with results from N. Wang et al. (2019) and Jin and Holloway (2015). In the NCP, the VOC-limited regimes are found in Beijing and some big cities in Hebei province, central regions in Shandong province, and Henan province. Transitional regimes control the remaining regions of Shandong province and Henan province and most regions of Hefei province. In the YRD, the VOC-limited regimes are found in Shanghai and southern Jiangsu province. In the PRD, the VOC-limited regimes are found in Guangzhou. Outside the NCP, YRD and PRD, the VOC-limited regimes concentrate in city centres of Shenyang, Chengdu, Chongqing, Xi'an, and Wuhan, which are surrounded by transitional regimes in the suburban areas. It has been acknowledged that the urban O₃ formations are generally VOC-limited due to the large amount of NO_x emissions from diverse sectors, like transportation, industry, residential sector, and power plants (Shao et al., 2009; Wang et al., 2009; Sun et al., 2011). The NO_x-limited or transitional regimes dominated O₃ formation in the suburban and rural areas of eastern China (Xing et al., 2011; Jin et al., 2017).

Figure 2 (a) Photochemical regime classification over China in the summer of 2016. (b) Same as (a) but for 2019. Note that no data grids in (a) and (b) corresponds to monthly HCHO columns below the detection limit (2 × 10¹⁵ molecule/cm²) or NO₂ columns lower than 1.5 × 10¹⁵ molecule/cm². (c) Mean HCHO columns from OMI over China in the summer of 2016. (d) Same as (c) but for 2019. (e) Mean NO₂ columns from OMI over China in the summer of 2016. (f) Same as (e) but for 2019.

Comparison of O₃ sensitivities between 2016 and 2019 shows noticeable changes from VOC-limited regime to transitional regime in the NCP, YRD, and PRD. In the NCP, the continuous area of VOC-limited regimes that occurred in 2016 change to transitional regimes in 2019. The VOC-limited regimes remain in central Beijing, Tianjin, Shijiazhuang, Jinan, and Zhengzhou. In the YRD, Shanghai and Nanjing remain in the VOC-limited regime, and other cities mostly change to the transitional regime. In the PRD, the VOC-limited regime still controls Guangzhou, while the transitional regimes control its surrounding cities.

Figure 2c and d show mean HCHO columns over China in the summer of 2016 and 2019. The columns exceed 15 × 10¹⁵ molecule/cm² in megacity clusters, such as in the NCP, YRD, and PRD, as well as the Sichuan Basin. Shen et al. (2019) found large increases of HCHO columns during May–September over 2005–2016 in the NCP and the YRD, consistent with the trend of anthropogenic VOC emissions. Our results show that the satellite HCHO columns increase in the NCP and the YRD and decrease in the PRD and in the Sichuan Basin during May–October of the 2016–2019 period. Figure 2e shows mean NO₂ columns over China in the summer of 2016. The NCP, YRD, PRD, Sichuan Basin, and Urumqi have high levels (80 × 10¹⁵ molecule/cm²) of NO₂ columns. Figure 2f shows the satellite NO₂ columns have a strong decline in the NCP, the PRD, Hunan, Hubei, and Jiangxi provinces in summer from 2016 to 2019. However, the YRD shows increasing NO₂ columns in 2019.

We select typical cities (Beijing, Shanghai, Guangzhou, Neijiang, Lhasa, and Naha) to analyse in more detail the O₃ formation sensitivity in the summers of 2016 to 2019 in Fig. 3. These cities are selected based on their different chemical regimes in 2016. The locations of the six cities are shown in Fig. S4 in the Supplement. Economically developed megacities or provincial capital cities such as Beijing, Shanghai, and Guangzhou, with high levels of tropospheric NO₂ and HCHO, remain in the VOC-limited regime over 2016–2019. The reduction of tropospheric NO₂ results in a shift in the O₃ formation sensitivity in cities such as Neijiang over 2016–2019. Lhasa as a city with low NO₂ and the background station in Naha with even lower HCHO and NO₂ columns remain in the NO_x-limited regime over 2016–2019.

Figure 3 The change of O₃ formation sensitivity of six cities (Beijing, Shanghai, Guangzhou, Neijiang, Lhasa and Naha) in summer from 2016 to 2019. The arrows represent time step from 2016 to 2019.

As we know, O₃ increases with increasing NO_x in the NO_x-limited regime and decreases with increasing NO_x in the VOC-limited regime. The contrast between NO_x-limited and VOC-limited regimes illustrates the difficulties involved in developing policies to reduce O₃ in NO_x polluted regions. Reductions in VOCs will only be effective in reducing O₃ if VOC-limited chemistry predominates. Reductions in NO_x will be effective only if NO_x-limited chemistry predominates and may actually increase O₃ in VOC-sensitive regions. If cities belonging to the VOC-limited regime like Beijing only focus on the reduction of NO_x while ignore the control of VOC emissions, they will experience a process of rising O₃ concentrations, the more NO_x decrease, the greater the increase in O₃ will be.

3.3 Observed response of ground-level O₃ to chemical formation sensitivity

To validate the regimes derived from satellite observations, we also analyse the surface NO₂ observations from ground-based measurements. Figure 4a and b show the mean ground-based NO₂ concentrations in summer of 2016 and 2019. According to the NO_x surface emission estimates derived with DECSO from OMI observations, the NO_x emissions in eastern China (18^∘ N, 104^∘ E, 41.5^∘ N, 124^∘ E) decrease from 5.93 Tg/yr in 2016 to 4.21 Tg/yr in 2019. Such a strong decline in NO_x emissions led to decreasing ambient NO₂ concentrations at NCP (Beijing, Shijiazhuang, Zhengzhou, Jinan) and YRD (Hefei and other cities in Anhui province). In Fig. 4c, the national average NO₂ concentration decrease by 14.4 % in summer from 2016 to 2019.

Figure 4 (a) Mean ground-based NO₂ concentration at each city in the summer of 2016. (b) Same as (a) but for 2019. (c) The bars indicate the number of cities (left axis) in a certain NO₂ range in summer from 2016 to 2019. The black line indicates the average NO₂ concentration (right axis) of all cities. (d) Mean ground-based O₃ concentration at each city in summer of 2016. (e) Same as (d) but for 2019. (f) Same as (c) but for O₃. Note that daily in situ NO₂ and O₃ data are the average of 13:00–14:00 LT of the sites in each city.

Figure 4d and e show the mean ground-based O₃ concentration of about 360 cities across China in summer of 2016 and 2019. Generally, the O₃ levels in western China are lower than in eastern China. In 2016, few cities have an average O₃ concentration above 140 µg/m³. In 2019, cities with a mean O₃ concentration exceeding 140 µg/m³ occurred at the NCP (Tianjin, Shijiazhuang, some cities in Shandong and Henan province), the YRD (Nanjing), and the PRD (Guangzhou). In Fig. 4f, we see the number of cities with average O₃ values above 140 µg/m³ increases rapidly from 2.20 % in 2016 to 31.37 % in 2019. The cities with an average O₃ value below 80 µg/m³ decrease from 11.02 % in 2016 to 2.24 % in 2019. In addition, the nationwide O₃ average in summer increases year by year from 2016 (104.86 µg/m³) to 2019 (125.14 µg/m³). K. Li et al. (2019) reported the increasing O₃ trends in summer in megacity clusters of eastern China and the highest O₃ concentrations are in the NCP, which are consistent with our results.

A complex coupling of primary emissions, chemical transformation, and dynamic transport at different scales determine the O₃ pollution (Jacob, 1999). NO_x and VOCs play important roles in O₃ formation. Emissions of NO_x and VOCs to the environment are the starting point of O₃ pollution problems. During the past decade in China, ambitious steps have been taken to control NO_x emissions. In 2013, the Chinese State Council issued the APPAP. Stringent control measures were carried out since then, including phasing out highly emitting industries, closing outdated factories, tightening industrial emission standard, improving fuel quality (N. Wang et al., 2019). However, to the other important O₃ precursors, VOCs, less attention has been given in emission control strategy. M. Li et al. (2019) concluded that anthropogenic NMVOC emissions in China during 1990–2017 have been increasing continuously due to the dramatic growth in activity rates and absence of effective control measures. Following China's past control strategy on VOCs, we can regard VOC emissions as rising or in steady state.

Figure 5 (a) Differences in total NO_x emissions derived from OMI observations in summer in east China between 2019 and 2016. (b) Variations in total NO_x emissions in five cities (Beijing, Shanghai, Guangzhou, Neijiang, and Naha) in summer from 2016 to 2019. (c) Variations in mean ground-based O₃ concentrations in five cities in summer from 2016 to 2019.

The reduction of the NO_x emissions for cities in the VOC-limited regime is one of the main reason for the increasing of O₃. Figure 5a shows the difference of total NO_x emissions derived from OMI observations in summer in east China between 2019 and 2016. A decline in NO_x emissions centres at the NCP, YRD and PRD, where most areas belong to the VOC-limited regime. In order to provide further insight into the impact of NO_x emission variations on O₃ concentrations, five selected typical cities (Beijing, Shanghai, Guangzhou, Neijiang and Naha) are shown in more detail (see Fig. 5b and c). For cities under the control of VOC-limited chemistry (Beijing, Shanghai and Guangzhou), accompanied with decreasing NO_x emissions, O₃ concentrations generally show an opposite behaviour to NO_x emissions. The O₃ formation sensitivity in Neijiang shows a shift from the transitional to the NO_x-limited regime over 2016–2019. The reduction of NO_x emissions in the transitional regime is accompanied by decreasing O₃ in Neijiang. Although the O₃ data in Naha for 2016–2018 are unavailable, we see that O₃ concentrations in Naha are low in 2019, and NO_x emissions are stable during 2016–2019. Note that we find a qualitative relationship between NO_x emission and the O₃ response patterns, confirming the non-linear O₃–NO₂–VOC chemistry but not in a quantitative sense. For example, the changes of NO_x emissions in Beijing (−2.17 Gg N/cell), Shanghai (−1.18 Gg N/cell), Guangzhou (−0.28 Gg N/cell), and Neijiang (−0.15 Gg N/cell) during 2016–2019 lead to different levels of O₃ changes in Beijing (10.43 µg/m³), Shanghai (7.81 µg/m³), Guangzhou (25.54 µg/m³), and Neijiang (−22.66 µg/m³). Because of the VOC-limited chemistry conditions, O₃ increases with decreasing NO_x emissions in Beijing, Shanghai, and Guangzhou. The NO_x-limited conditions lead to decreasing O₃ with decreasing NO_x emissions in Neijiang. Compared with Beijing, NO_x emissions in Guangzhou remained basically constant in 2016 and 2019. But O₃ concentrations in Guangzhou increased more than in Beijing. The local O₃ formation sensitivity is helpful to present the way that O₃ responds to NO_x emission, but VOC emission are needed when discussing their relationship in a quantitative way.

3.4 Enhanced O₃ levels during the COVID-19 lockdown in China

The measures in response to the outbreak of the COVID-19 lead to sudden changes of NO_x emissions and anthropogenic HCHO emissions in China in the beginning of 2020 (Wang et al., 2020; Hui et al., 2020). We analyse the change of O₃ concentrations during the lockdown period to validate our method. To look into COVID-19 lockdown impacts on short-term O₃ level, we choose two time periods covering 357 cities across China: period I (3–23 January 2020) and period II (9–29 February 2020), to avoid the coincidence of Chinese New Year holidays (24 January to 8 February 2020).

Figure 6 (a) Differences in mean ground-based O₃ concentrations in east China between period I and period II. (b) Differences in mean NO_x emissions in east China between period I and period II. (c) O₃ formation sensitivity in east China during period I. (d) Same as (c), but for period II. Note that period I (3–23 January 2020) is before the lockdown, and period II (9–29 February 2020) is during the lockdown.

Figure 6a shows enhanced O₃ levels in most cities of eastern China during the COVID-19 lockdown, except for some cities in PRD and Fujian province. The cities with O₃ concentration increases of more than 40 µg/m³ are located in the NCP and the YRD, i.e. the populous regions of China, indicating a potential negative health effect from O₃ exposure in these regions. Figure 6b shows strong reductions in NO_x emissions in eastern China, especially in Henan, Hubei, and Jiangsu provinces, where as a consequence of the lockdown, transportation, construction, and light industry activities have been dramatically decreased.

Assuming that our observation-based FNR thresholds derived using summertime data also apply during winter, we see that most regions of eastern China belong to the VOC-limited regime during periods I and II in Fig. 6c and d. Previous studies also reported that the O₃ chemistry in the urban areas in China in wintertime is in a VOC-limited regime due to the relative lack of HO_x radicals (Seinfeld and Pandis, 2016). During winter (VOC-limited conditions), when the concentration of NO_x is high and the level of UV radiation is low, the O₃ production varies inversely with the NO_x concentration (Sillman et al., 1990). During the lockdown period, both the anthropogenic emissions of NO_x and VOCs were reduced. The NO_x reduction during the lockdown is higher than the VOC reduction according to Sicard et al. (2020). The reductions of VOC emissions are generally effective in reducing O₃ concentrations. However, such air quality improvements are largely offset by reductions in NO_x emissions leading to increases in O₃ concentrations due to the strongly VOC-limited conditions in the NCP in winter (Xing et al., 2020). The NO_x reduction during the lockdown is higher than the VOC reduction (Sicard et al., 2020). Thus, a reduction in NO_x leads to an increase in the O₃ concentrations in most regions of eastern China during period II. Besides, reduction of freshly emitted NO in particular from road traffic alleviates O₃ titration locally (Seinfeld and Pandis, 2016; Levy et al., 2014). The O₃ titration occurs particularly in winter (less photolysis reactions of NO₂) under high NO_x levels (Sillman, 1999). However, the lockdown measures result primarily in a lower titration of O₃ by NO due to the reduction in local NO_x emissions by road transport, which also enhances O₃ levels in urban areas. On the other hand, some cities, mainly located in southeastern China, showed decreasing O₃ levels. Zhao et al. (2020) concluded that the cause of O₃ decline in these cities is the emission changes of NO_x and VOC. In Fig. 6c we see that some cities in Fujian and Guangdong provinces belong to the transitional regime. Theoretically, the transitional regime should correspond to the conditions at which O₃ formation is most efficient, indicating that reductions or increases in NO_x and VOCs will reduce the O₃ concentration.

4 Conclusion

Satellite-based HCHO $/$ NO₂ ratios and ground-based O₃ measurements were directly connected to capture the non-linearity of surface O₃ chemistry over major Chinese cities in this study. Evaluating the FNR thresholds marking the O₃ transitional regime in which O₃ formation is less sensitive to the precursors, we found a broad transitional region, which reflects differences in factors among 360 cities, such as emissions, meteorology, and regional transport. The national FNR thresholds are defined as follows: a VOC-limited regime should occur for FNR < 2.3 and a NO_x-limited regime should occur for FNR > 4.2. The FNR between 2.3 and 4.2 reflects the transition between the two regimes. Our FNR thresholds derived from satellite and ground-based observations are higher than previously reported model-based values. The non-linear chemistry of O₃ depends on its precursors NO₂ and VOCs with contributions from both local and regional sources (Xue et al., 2014). Modelling studies are good at simulating the response of surface O₃ to an overall reduction in NO_x or VOC emissions. The FNR thresholds derived with in situ O₃ observations will be more indicative of the local O₃ chemistry than the model, including the effect of NO_x titration over urban areas (Jin et al., 2020).

We analysed the spatial and temporal variability of O₃ formation sensitivity using our FNR thresholds over China from 2016 to 2019. Our results showed that O₃ formation sensitivity tends to be VOC-limited over urban areas and NO_x-limited over rural and remote areas in China. In 2016, the VOC-limited regimes mainly appear in the NCP, the YRD, and the PRD. In 2019, there was a shift in most NCP regions from the VOC-limited to the transitional regime. The area with a VOC-limited regime in the YRD and PRD also shrank. We found that O₃ formation sensitivity changes in these regions were associated with a strong decline in tropospheric NO₂ columns in the NCP and the PRD. For megacities such as Beijing and Guangzhou, although they remained in the VOC-limited regime over 2016–2019, there was still a decrease in NO₂ columns. Consistent with decreasing tropospheric NO₂ columns, the national average surface NO₂ concentration decreased by 14.4 % in summer from 2016 to 2019 and the NO_x emissions in eastern China decreased from 5.93 Tg/yr in 2016 to 4.21 Tg/yr in 2019. This detected spatial expansion of the transitional regime and NO_x emission reduction in the VOC-limited regime has contributed to rising surface O₃ concentrations. The nationwide averaged O₃ concentration in summer increased year by year from 2016 (104.86 µg/m³) to 2019 (125.14 µg/m³). The cities with average O₃ values above 140 µg/m³ increased rapidly from 2.20 % in 2016 to 31.37 % in 2019.

Satellite instruments measure the vertically integrated column density, which we use as a proxy of the actual surface concentrations. To reduce the effect of short-term variability in vertical distributions caused by meteorological changes, we use monthly mean averages. Therefore, our satellite-based HCHO $/$ NO₂ method is limited to identification of long-term evolution in O₃ sensitivity, focusing on understanding the average air quality.

We presented the level of O₃ formed from photo-oxidation of total measured HCHO only not differentiating the contributions from different sources (directly emitted or photochemically formed). Due to the higher temperature and stronger solar radiation in summer, the higher concentration level of HCHO mainly results from the intense photo-oxidation of VOCs. Emission sources of HCHO, as a tracer of VOCs, can be anthropogenic and biogenic. Shen et al. (2019) found that the OMI HCHO distribution follows their anthropogenic inventory in megacity clusters over China, while it does not follow the biogenic emissions inventory. Despite the fact that local sources of anthropogenic VOCs are difficult to identify, our FNR thresholds derived from satellite-based information have the potential to provide important information to air quality planners. Compared with stringent control measures for NO_x emissions, VOC emissions got less attention as the other O₃ precursor in China. The case study of O₃ level changes during the COVID-19 lockdown in China demonstrated that the strong reductions in anthropogenic NO_x emissions resulted in significant O₃ enhancement due to the VOC-limited regime in winter. It indicates that a protocol with strict measures to control NO_x emissions,without simultaneous VOC emissions controls for power plants and heavy industry, such as petrochemical facilities, achieves only limited effects on O₃ pollution.