Effect of changing NO_x lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO₂ columns over China

Satellite observations of tropospheric NO₂ columns are extensively used to infer trends in anthropogenic emissions of nitrogen oxides (${\mathrm{NO}}_x\equiv \mathrm{NO}+{\mathrm{NO}}_{\mathrm{2}}$), but this may be complicated by trends in NO_x lifetime. Here we use 2004–2018 observations from the Ozone Monitoring Instrument (OMI) satellite-based instrument (QA4ECV and POMINO v2 retrievals) to examine the seasonality and trends of tropospheric NO₂ columns over central–eastern China, and we interpret the results with the GEOS-Chem chemical transport model. The observations show a factor of 3 increase in NO₂ columns from summer to winter, which we explain in GEOS-Chem as reflecting a longer NO_x lifetime in winter than in summer (21 h versus 5.9 h in 2017). The 2005–2018 summer trends of OMI NO₂ closely follow the trends in the Multi-resolution Emission Inventory for China (MEIC), with a rise over the 2005–2011 period and a 25 % decrease since. We find in GEOS-Chem no significant trend of the NO_x lifetime in summer, supporting the emission trend reported by the MEIC. The winter trend of OMI NO₂ is steeper than in summer over the entire period, which we attribute to a decrease in NO_x lifetime at lower NO_x emissions. Half of the NO_x sink in winter is from N₂O₅ hydrolysis, which counterintuitively becomes more efficient as NO_x emissions decrease due to less titration of ozone at night. The formation of organic nitrates also becomes an increasing sink of NO_x as NO_x emissions decrease but emissions of volatile organic compounds (VOCs) do not.

1 Introduction

Emissions of nitrogen oxides (${\mathrm{NO}}_x\equiv \mathrm{NO}+{\mathrm{NO}}_{\mathrm{2}}$) from fossil fuel combustion in China have been changing fast in the past few decades due to rapid economic expansion on the one hand and strengthening environmental regulations on the other hand. Growing fossil fuel use with weak pollution controls resulted in an almost threefold increase in China's NO_x emissions between 1990 and 2010, according to bottom-up inventories based on activity data and emission factors (Granier et al., 2011; Q. Zhang et al., 2012). Since then, China has adopted strong measures to decrease air pollution by setting stringent emissions standards, capping coal use, increasing vehicle fuel efficiency, closing outdated facilities, and growing renewable energy use (Liu et al., 2016; Zheng et al., 2018). Bottom-up inventories estimate that China's NO_x emissions decreased by 20 % between 2011 and 2017, despite continuing economic expansion (Sun et al., 2018; Zheng et al., 2018). There is a strong need to evaluate these emission inventories and their trends for air quality management.

Satellite-based observations of tropospheric NO₂ columns by solar backscatter have been used extensively as a proxy for NO_x emissions and their trends (Martin, 2008; Streets et al., 2013). These observations have been qualitatively consistent with the trends in Chinese NO_x emission inventories, showing an increasing trend of NO₂ columns over China between 1994 and 2011, with a sharp reversal in eastern China since 2011 (Richter et al., 2005; van der A et al., 2006, 2008; Lin et al., 2010; Krotkov et al., 2016; Schneider et al., 2015; Duncan et al., 2016; Cui et al., 2016; Georgoulias et al., 2019; Wang et al., 2019). However, the trends in the NO₂ columns are steeper than in the emission inventories (Zhang et al., 2007; Q. Zhang et al., 2012; Stavrakou et al., 2008; Hilboll et al., 2013; Liu et al., 2017; Zheng et al., 2018). For example, Zhang et al. (2007) found that NO₂ columns over China increased twofold from 1996 to 2004, while their emission inventory reported a 60 % increase. For 2010–2015, the Multi-resolution Emission Inventory for China (MEIC) estimates a 14 % decrease in NO_x emissions, but NO₂ columns from the Ozone Monitoring Instrument (OMI) satellite instrument indicate a 22 % decrease (Zheng et al., 2018).

Differences in the trends between NO₂ columns and NO_x emission inventories could reflect errors in the inventories (Saikawa et al., 2017) and satellite retrievals (Lin et al., 2014; Lorente et al., 2017) but also trends in the lifetime of NO_x against atmospheric oxidation. This lifetime is of the order of hours and may change with the chemical environment, including the NO_x concentration itself (Stavrakou et al., 2008; Lamsal et al., 2011; Valin et al., 2011; Lu and Streets, 2012; Duncan et al., 2013; Gu et al., 2016; Cooper et al., 2017; Laughner and Cohen, 2019). NO_x is oxidized to nitric acid (HNO₃) and organic nitrates (RONO₂), including peroxyacyl nitrates (RC(O)OONO₂). There is also a minor sink from NO₂ dry deposition (L. Zhang et al., 2012). Oxidation in the daytime is driven by photochemically produced hydrogen oxide (${\mathrm{HO}}_x\equiv \mathrm{OH}+{\mathrm{HO}}_{\mathrm{2}}+{\mathrm{RO}}_{\mathrm{2}}$) radicals:

$$\begin{array}{}\text{(R1)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+\mathrm{OH}\stackrel{M}{⟶}{\mathrm{HNO}}_{\mathrm{3}},\text{(R2)}& {\displaystyle }& {\displaystyle }\mathrm{NO}+{\mathrm{RO}}_{\mathrm{2}}\stackrel{\mathit{\alpha }M}{⟶}\left(\mathrm{1}-\mathit{\alpha }\right){\mathrm{NO}}_{\mathrm{2}}+\left(\mathrm{1}-\mathit{\alpha }\right)\mathrm{RO}+\mathit{\alpha }{\mathrm{RONO}}_{\mathrm{2}},\text{(R3)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+\mathrm{RC}\left(\mathrm{O}\right)\mathrm{OO}\stackrel{M}{⟷}\mathrm{RC}\left(\mathrm{O}\right){\mathrm{OONO}}_{\mathrm{2}},\end{array}$$

where α in Reaction (R2) is the branching ratio for organic nitrate formation. NO_x and HO_x concentrations are tightly interlinked (Kleinman, 1994; Laughner and Cohen, 2019). When NO_x levels are sufficiently low (so-called NO_x-limited conditions), an increase in NO_x drives an increase in HO_x, particularly OH through the $\mathrm{NO}+{\mathrm{HO}}_{\mathrm{2}}⟶{\mathrm{NO}}_{\mathrm{2}}+\mathrm{OH}$ reaction. When NO_x levels are high (so-called NO_x-saturated conditions), Reaction (R1) becomes the dominant HO_x sink, and an increase in NO_x causes a decrease in HO_x.

At night, the chemical loss of NO_x proceeds through a series of steps in Reactions (R4)–(R10) beginning with the oxidation of NO and NO₂ by ozone to form the nitrate radical (NO₃) and dinitrogen pentoxide (N₂O₅). N₂O₅ and NO₃ react in aerosols to produce HNO₃, and NO₃ additionally reacts with volatile organic compounds (VOCs) to form either HNO₃ or RONO₂.

$$\begin{array}{}\text{(R4)}& {\displaystyle }& {\displaystyle }\mathrm{NO}+{\mathrm{O}}_{\mathrm{3}}⟶{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{O}}_{\mathrm{2}}\text{(R5)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{O}}_{\mathrm{3}}⟶{\mathrm{NO}}_{\mathrm{3}}+{\mathrm{O}}_{\mathrm{2}}\text{(R6)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{3}}+{\mathrm{NO}}_{\mathrm{2}}\stackrel{M}{⟷}{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\text{(R7)}& {\displaystyle }& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right)\stackrel{\text{aerosols}}{⟶}\mathrm{2}{\mathrm{HNO}}_{\mathrm{3}}\text{(R8)}& {\displaystyle }& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+{\mathrm{Cl}}^{-}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right)\stackrel{\text{aerosols}}{⟶}{\mathrm{HNO}}_{\mathrm{3}}+{\mathrm{ClNO}}_{\mathrm{2}}+{\mathrm{OH}}^{-}\text{(R9)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{3}}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right)\stackrel{\text{aerosols}}{⟶}{\mathrm{HNO}}_{\mathrm{3}}+\mathrm{OH}\text{(R10)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{3}}+\mathrm{VOC}⟶{\mathrm{HNO}}_{\mathrm{3}},{\mathrm{RONO}}_{\mathrm{2}},\text{other products}\end{array}$$

A change in NO_x emissions can change the nighttime levels of ozone, and Reaction (R6) is quadratic in NO_x concentrations, leading to nonlinearity between NO_x emissions and NO₂ concentrations. In addition, aerosol concentrations in China have decreased by about 30 % since 2013 (Lin et al., 2018; Zheng et al., 2018; Zhai et al., 2019; Ma et al., 2019), which would decrease the rate of nighttime NO_x loss (Reactions R7–R9). A decrease in aerosol concentrations will also slow the NO_x loss by the hydrolysis of NO₂ in aerosols.

$$\begin{array}{}\text{(R11)}& {\mathrm{NO}}_{\mathrm{2}}\stackrel{{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right),\text{aerosols}}{⟶}\mathrm{0.5}\mathrm{HONO}+\mathrm{0.5}{\mathrm{HNO}}_{\mathrm{3}}\end{array}$$

Here we present trends in tropospheric NO₂ columns over China from 2004 to 2018 observed by the OMI satellite instrument, showing a peak in 2011. We use the GEOS-Chem chemical transport model applied to the MEIC (Zheng et al., 2018) to investigate how changes in NO_x lifetimes have affected the relationship between tropospheric NO₂ columns and NO_x emissions and whether this can reconcile the differences in trends between the two quantities. The results have important implications for the use of satellite NO₂ retrievals to infer trends in NO_x emissions.

2 Observations and model

2.1 OMI NO₂ column retrievals

We use 2004–2018 tropospheric NO₂ column data retrieved from the OMI instrument aboard the NASA Aura satellite. Aura is in sun-synchronous polar orbit with a daytime Equator crossing at 13:45 local solar time (LST). OMI measures backscattered solar radiation from the Earth in the ultraviolet and visible wavelength range (270–504 nm). It has a swath width of 2600 km and a ground pixel size of 13 km × 24 km at nadir (Levelt et al., 2006, 2018). Several OMI pixels are affected by the so-called row anomaly, possibly from an obstruction in their field of view (Dobber et al., 2008). Pixels not affected by the row anomaly have no significant calibration drift over the length of the record (Boersma et al., 2018).

We use tropospheric NO₂ columns from two retrievals: the European Quality Assurance for Essential Climate Variables (QA4ECV) project's NO₂ ECV precursor product (Boersma et al., 2018) and the Peking University POMINO product version 2 (Lin et al., 2015; Liu et al., 2019). NO₂ tropospheric column retrieval in the ECV product involves (1) a spectral fit of the backscattered solar radiation in the 405–465 nm window to obtain the total NO₂ slant column (SC), (2) the removal of the stratospheric component by data assimilation with the TM5-MP chemical transport model, and (3) the conversion of the tropospheric SC to a tropospheric vertical column (VC) with an air mass factor (AMF = SC ∕ VC) that depends on viewing geometry, surface albedo, retrieved cloud properties, and the NO₂ vertical profile (taken from the TM5-MP model).

The POMINO v2 product starts with the tropospheric NO₂ slant columns from the ECV retrieval but uses different methods and data sources for the AMF calculation. The main difference is the treatment of aerosols. ECV assumes that aerosol effects are implicitly accounted for in the independent retrieval of cloud pressure and cloud fraction, which are prerequisites for NO₂ retrievals. POMINO explicitly accounts for aerosols in the radiative transfer calculations, with aerosol optical properties and vertical profiles from the GEOS-Chem model corrected with satellite observations from the MODIS and CALIOP instruments. In polluted areas (aerosol optical depth greater than 0.5), the choice of aerosol correction method can affect the AMF by 45 % (Lorente et al., 2017), and the sign of the correction depends on the vertical distribution of aerosols relative to NO₂ (Palmer et al., 2001; Martin et al., 2003; Liu et al., 2019). POMINO also includes the angular dependence of surface reflectance, an online radiative transfer calculation, and consistency in retrievals of cloud properties and NO₂ (Lin et al., 2015; Liu et al., 2019).

We create monthly mean gridded (0.5^∘ latitude × 0.625^∘ longitude) datasets of ECV and POMINO NO₂ columns over China for June–July–August (JJA) 2005–2018 and December–January–February (DJF) 2004–2018. We exclude pixels with snow-covered surfaces and a cloud fraction greater than 30 %. We use cloud fraction data from the corresponding retrievals. We include only cross-track viewing positions of 5 through 22 to exclude data affected by the row anomaly (Boersma et al., 2018) and swath edges. For comparison with GEOS-Chem, the POMINO and ECV NO₂ columns are recalculated with modified air mass factors using the pixel-specific GEOS-Chem NO₂ vertical profiles (Palmer et al., 2001).

2.2 Ground-based observations

We use hourly measurements of NO₂ and O₃ concentrations from the network of ∼ 1000 sites operated by the China Ministry of Ecology and Environment (MEE) since 2013 (http://beijingair.sinaapp.com, last access: 10 March 2019). We correct the known interference of organic nitrates and HNO₃ in the NO₂ measurements by using the GEOS-Chem-simulated concentrations for those species following Lamsal et al. (2008) and then grid ($\mathrm{0.5}{}^{\circ }\times \mathrm{0.625}{}^{\circ }$ grid) and seasonally average the data, discarding sites with less than 50 % coverage in a season. The correction for HNO₃ and organic nitrates decreases the reported seasonal mean NO₂ concentrations over eastern China by 25 % in summer and 6 % in winter.

2.3 GEOS-Chem model

We use the GEOS-Chem chemical transport model version 12.1.0 (https://doi.org/10.5281/zenodo.1553349) driven by assimilated meteorological fields from the NASA Global Modeling and Assimilation Office's Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2; Gelaro et al., 2017). GEOS-Chem simulates the chemistry of major gas- and aerosol-phase species in the troposphere (Pye et al., 2009; Kim et al., 2015; Travis et al., 2016; Fisher et al., 2016; Sherwen et al., 2016). We use the GEOS-Chem Classic nested-grid configuration over East Asia (11^∘ S–55^∘ N, 60–150^∘ E) at $\mathrm{0.5}{}^{\circ }\times \mathrm{0.625}{}^{\circ }$ resolution (Wang et al., 2004; Chen et al., 2009), with lateral chemical boundary conditions from a $\mathrm{4}{}^{\circ }\times \mathrm{5}{}^{\circ }$ global simulation. Anthropogenic emissions over China are from the MEIC updated annually for 2000–2017 (http://www.meicmodel.org/, last access: 30 June 2018; Zheng et al., 2018). The MEIC includes monthly emission profiles for each sector (Li et al., 2017a) and hourly profiles developed at Tsinghua University. We vertically resolve emissions from point sources (power plants and industries) following profiles used in the LOTOS-EUROS model (Manders et al., 2017) and speciate anthropogenic NO_x emissions as NO (90 %), NO₂ (9.2 %), and HONO (0.8 %) following Menut et al. (2013). GEOS-Chem includes additional NO_x emissions from soil and fertilizer use (Hudman et al., 2012), lightning (Murray et al., 2012), shipping (Vinken et al., 2011; Holmes et al., 2014), and aircraft (Stettler et al., 2011). Vertical mixing in the planetary boundary layer is simulated using a nonlocal mixing scheme (Lin and McElroy, 2010).

Figure 1 Tropospheric NO₂ columns over China. Values are 3-month means for June–July–August (JJA) 2017 and December–January–February (DJF) 2016/17 on the $\mathrm{0.5}{}^{\circ }\times \mathrm{0.625}{}^{\circ }$ GEOS-Chem grid. OMI retrievals from POMINO (Liu et al., 2019) and ECV (Boersma et al., 2018) are compared with the GEOS-Chem model. The JJA and DJF panels have different color scales. Locations where none of the OMI data met our selection criteria are in grey. The mean NO₂ columns over central–eastern China (blue rectangle) are given in the inset (10¹⁵ molecules cm⁻², e.g., 4.8×10¹⁵ molecules cm⁻² for the top left panel). The scatterplots show the spatial correlations between the OMI retrievals and GEOS-Chem on the $\mathrm{0.5}{}^{\circ }\times \mathrm{0.625}{}^{\circ }$ grid for central–eastern China, along with the Pearson's correlation coefficient (r), reduced-major-axis linear regressions, and regression slopes. Error standard deviations on the slopes were derived by the bootstrap method.

We modified the standard GEOS-Chem version 12.1.0 chemistry to update the reactive uptake coefficients (γ) of N₂O₅, NO₃, and NO₂ on aerosols (Jacob, 2000) based on recent comparison of GEOS-Chem to observations from the Wintertime Investigation of Transport, Emissions, and Reactivity (WINTER) aircraft campaign over the eastern United States (Jaeglé et al., 2018; Shah et al., 2018). ${\mathit{\gamma }}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$ is computed following Bertram and Thornton (2009) for sulfate–nitrate–ammonium aerosols and is taken to be $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{4}}$ (RH < 50 %) or $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{3}}$ (RH ≥ 50 %) for organic aerosols. N₂O₅ hydrolysis produces HNO₃ and ClNO₂ on sea salt aerosols with a 1 : 1 branching ratio (Reaction R8) and only HNO₃ on other aerosol types (Reaction R7). Uniform values of ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{3}}}$ and ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{2}}}$ are used for all aerosol types and all RH conditions. ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{3}}}$ is taken to be $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{3}}$ following Jacob (2000). ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{2}}}$ for the hydrolysis Reaction (R11) is decreased from $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{4}}$ (Jacob, 2000) to $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{5}}$ on the basis of laboratory measurements (Bröske et al., 2003; Stemmler et al., 2007; Tan et al., 2016). This decrease in ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{2}}}$ yields a 24 h mean wintertime HONO∕NO₂ molar concentration ratio of 0.035 over eastern China in GEOS-Chem, consistent with the observed range of 0.015–0.071 (Hendrick et al., 2014; Spataro et al., 2013; Wang et al., 2017, 2013). The GEOS-Chem HONO∕NO₂ ratio is likely low because of unknown sources of HONO, particularly during the day (Kleffmann, 2007; Spataro and Ianniello, 2014). For the WINTER campaign, Jaeglé et al. (2018) used a ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{2}}}$ of $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{4}}$ but assumed that the reaction produces only HONO. Using this ${\mathit{\gamma }}_{{\mathrm{NO}}_{\mathrm{2}}}$ for eastern China would lead to a significant overestimate of the observed HONO∕NO₂ ratio.

We evaluate the model with the spatial and seasonal distributions of OMI NO₂ observations for 2016/17 DJF and 2017 JJA (the latest year with MEIC data) and use these two periods to analyze the seasonality of NO_x lifetime and loss pathways in the model. To calculate the emission-driven changes in NO_x lifetimes, we conduct a sensitivity simulation in which we set anthropogenic emissions over China to 2012 levels but use the 2016/17 meteorology and NO_x emissions from soils, lightning, ships, and aircraft. Chinese NO_x emissions decreased by 25 % from 2012 to 2017 according to the MEIC (Fig. S1 in the Supplement). For comparison with OMI observations, we sample the model at 13:00–14:00 LST and exclude model columns with surface snow cover or with a model cloud fraction greater than 30 %. We focus on the large polluted region of central–eastern China (30–41^∘ N, 112–122^∘ E; rectangles in Fig. 1), where we may expect tropospheric NO₂ columns to be most sensitive to Chinese NO_x emissions and where the relatively narrow latitude range leads to consistent seasonal variations. This region accounted for 50 % of anthropogenic Chinese NO_x emissions in 2017 according to the MEIC.

3 Results and discussion

3.1 Seasonal variation of NO₂ columns and NO_x lifetimes

Figure 1 shows the NO₂ columns from the POMINO and ECV retrievals, as well as from the GEOS-Chem model, for JJA 2017 and DJF 2016/17. In central–eastern China, we find that in both seasons over 70 % of the GEOS-Chem tropospheric NO₂ column as would be observed by OMI is in the boundary layer below 2 km of altitude. Thus, we expect the NO₂ column to reflect mostly the local NO_x emissions rather than the free tropospheric background (Silvern et al., 2019). In summer, average GEOS-Chem NO₂ columns over central–eastern China are within 10 % of the POMINO and ECV NO₂ columns. There is scatter in the spatial relationship (r≈ 0.5) that could be due to a combination of model and retrieval errors. In winter, however, POMINO is 42 % higher than GEOS-Chem, while ECV is 16 % lower. The difference in aerosol correction between POMINO and ECV is largest in winter due to high aerosol concentrations and high solar zenith angles. In ECV, polluted scenes with high aerosol optical depths (and likely high NO₂) can be misclassified as cloudy and excluded from the seasonal mean, which leads to a negative sampling bias (Lin et al., 2014, 2015; Liu et al., 2019). On the other hand, retrieving NO₂ columns under high aerosol conditions can be uncertain because of the strong sensitivity to the vertical distribution of NO₂ relative to that of aerosols, although this is less of a problem in POMINO as it uses the CALIOP-observed aerosol vertical profiles (Lin et al., 2014, 2015; Liu et al., 2019).

Liu et al. (2019) compared the ECV and POMINO retrievals to ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) NO₂ column observations on 49 d in three Chinese cities. POMINO was on average closer to the MAX-DOAS NO₂ than ECV (−3 % vs. −22 % bias) and on hazy days (+4 % versus −26 % bias), but on clear (cloud fraction = 0) days ECV performed better (−6 % bias) than POMINO (+30 % bias). These biases were slightly larger in fall and winter, although sampling in individual seasons was sparse. There is uncertainty in the comparison as the column observed by MAX-DOAS may not be representative of the satellite pixel, and the aerosol vertical profiles used in the satellite and MAX-DOAS retrievals may be inconsistent (Lin et al., 2014).

Figure 2 Mean winter–summer ratios of NO₂ concentrations over central–eastern China. The values are ratios of the seasonal mean NO₂ surface concentrations observed at the ensemble of MEE sites in central–eastern China and of the mean POMINO and ECV tropospheric NO₂ columns (recalculated using the GEOS-Chem NO₂ profiles) at the MEE station locations. Central–eastern China is as defined by the rectangle in Fig. 1. GEOS-Chem values sampled at the measurement locations are also shown. Observations and model results are for JJA 2017 (summer) and DJF 2016/17 (winter).

Figure 2 compares the mean winter–summer ratios of NO₂ columns from the ECV and POMINO retrievals over central–eastern China with GEOS-Chem and with the ratios of 24 h mean surface NO₂ concentrations at the MEE sites. GEOS-Chem shows similar ratios for the afternoon NO₂ columns and 24 h mean surface NO₂, despite different averaging times. We find that the seasonal amplitude in the surface NO₂ data is most consistent with the ECV NO₂ columns, whereas the seasonal amplitude of POMINO NO₂ is larger. The winter–summer ratios of NO₂ columns are 2.6 in the ECV retrieval, 3.5 in POMINO, and 2.7 in GEOS-Chem. The winter–summer ratios of surface NO₂ concentrations are 2.3 in the MEE data and 2.5 in GEOS-Chem. Anthropogenic NO_x emissions in the MEIC show little seasonality, with a winter–summer ratio of only 1.15 (Li et al., 2017b). We find in GEOS-Chem that the seasonal variation in NO₂ columns is mainly driven by the NO_x lifetime in the boundary layer, which increases from 5.9 h in summer to 21 h in winter (24 h mean lifetimes for 2017; Fig. 3). We find little difference between the boundary layer and the tropospheric column NO_x lifetimes (Fig. S2) since most of the column is in the boundary layer.

Figure 3 NO_x lifetime (τ) and loss pathways in the boundary layer over central–eastern China. The lifetimes are the GEOS-Chem averages for the bottom 0–2 km of the atmosphere over the domain delineated in Fig. 1, and the pie charts show the relative contributions of the different NO_x sinks. For the lifetime calculation we define NO_x as $\mathrm{NO}+{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{NO}}_{\mathrm{3}}+\mathrm{2}{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+\mathrm{HONO}+{\mathrm{HNO}}_{\mathrm{4}}+{\mathrm{ClNO}}_{\mathrm{2}}$. Values are given for summer (JJA) and winter (DJF) 2012 and 2017. The sink from N₂O₅+H₂O excludes the fraction that forms ClNO₂. The sink from $\mathrm{NO}/{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{RO}}_{\mathrm{2}}$ is the net flux, accounting for the partial recycling of the organic nitrates, and includes the contributions from peroxyacyl nitrates (PANs). The “other” sinks include NO₃ + VOC reactions, NO₂ and NO₃ hydrolysis in aerosols, and NO_x deposition.

In summer, NO_x is lost mostly through oxidation by OH in daytime (43 %) and through N₂O₅ hydrolysis forming HNO₃ at night (33 %). The daytime oxidation processes have a larger effect on the afternoon NO₂ columns because of the short NO_x lifetime, particularly in summer. In winter, the NO_x lifetime is much longer because of the lower concentrations of OH and RO₂ radicals. N₂O₅ hydrolysis accounts for 51 % of NO_x loss in winter. Remarkably, the loss of NO_x from N₂O₅ hydrolysis is a factor of 2 slower in winter than in summer, despite the longer nights and higher aerosol concentrations (Zhai et al., 2019), because of the low nighttime ozone concentrations. The overall NO_x lifetime in winter and the contribution from N₂O₅ hydrolysis are similar to values inferred over the eastern US during the WINTER campaign (Jaeglé et al., 2018), despite a factor of ∼ 5 difference in aerosol concentrations between the two regions. Loss of NO_x by N₂O₅ hydrolysis in China in winter is limited by the supply of ozone, not the supply of aerosol.

The modeled NO_x lifetimes can be affected by uncertainties in the modeled aerosol surface area, ${\mathit{\gamma }}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$, and the yield of ClNO₂. We find that the GEOS-Chem aerosol concentrations at the surface are about 30 % higher than observations from the MEE network. On the other hand, GEOS-Chem's ${\mathit{\gamma }}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$ values are 25 % lower on average than the observation-based estimates from the WINTER campaign (Jaeglé et al., 2018; McDuffie et al., 2018b). We tested the sensitivity of the modeled NO_x lifetime to the aerosol surface area and ${\mathit{\gamma }}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$ with a separate simulation and find that a 30 % change in either quantity changes the NO_x loss by N₂O₅ hydrolysis by only 8 % in summer and less than 2 % in winter. We assume that the yield of ClNO₂ from N₂O₅ hydrolysis for all aerosols other than sea salt is 0, which is lower than the average value of ∼ 0.3 estimated by field studies in China (McDuffie et al., 2018a; Tham et al., 2018). If we had assumed a higher ClNO₂ yield in the model, the loss of NO_x by N₂O₅ hydrolysis would be slower, particularly in winter when it is limited by ozone and the HNO₃ and ClNO₂ branches compete for the limited N₂O₅, and the daytime NO_x loss would increase because of the additional daytime NO₂ from ClNO₂ photolysis. Sub-grid-scale variability in NO_x and oxidant concentrations can also affect the modeled NO_x lifetimes because of the nonlinearity of NO_x chemistry. For example, NO_x lifetime within a concentrated NO_x plume at night can be long because of oxidant depletion, and assuming that the plume is instantaneously diluted within the model grid cell can result in a shorter NO_x lifetime (Brown et al., 2012). However, studies that explicitly treat sub-grid-scale plumes suggest that this effect can be important locally near large sources but is small at the regional scale (Karamchandani et al., 2011).

Leue et al. (2001) previously estimated the NO_x lifetime in the eastern US by using the offshore gradient of satellite-observed NO₂ columns. We tried this approach and found that the offshore gradients of NO₂ columns perpendicular to the east coast of China are consistent between the model and observations (Fig. S3). However, there is little seasonal difference in the gradients, suggesting that their magnitudes are determined by dilution more than chemical decay.

Figure 4 Trends in NO_x emissions and NO₂ concentrations over central–eastern China. The figure shows the JJA and DJF relative trends in the OMI (ECV) tropospheric NO₂ columns averaged over central–eastern China (domain delineated in Fig. 1) for the 2004–2018 duration of the OMI record and the corresponding trends in annual NO_x emissions estimated by the MEIC. GEOS-Chem 2012–2017 trends in NO₂ columns are also shown. All quantities are normalized to a value of 1 in 2012.

3.2 2004–2018 trends in NO₂ columns and lifetimes

Figure 4 shows the trends in the summer and winter NO₂ columns from the ECV retrieval, as well as in anthropogenic NO_x emissions from the MEIC, over central–eastern China for the 2004–2018 extent of the OMI record. According to the MEIC, NO_x emissions increased by 5 %–6 % a⁻¹ in 2005–2011 and decreased at the same pace after 2011. OMI NO₂ columns mirror the trajectory of the MEIC NO_x emissions: rising between 2005 and 2011, reversing direction in 2011/12, and falling back to around 2005 levels by 2018. Zheng et al. (2018) also showed consistency in the trends of OMI NO₂ columns and the MEIC NO_x emissions but found that the NO₂ trends at the MEE surface sites are flatter because the sites are urban and more sensitive to vehicular NO_x emissions. The summer trends in OMI NO₂ closely track the MEIC emissions, but the winter trends are steeper. The same summer–winter differences in NO₂ column trends over China are seen in the POMINO retrieval (Fig. S4) and in retrievals from the Ozone Mapping Profiler Suite (OMPS) instrument (Lin et al., 2019). Previous studies also reported such a seasonal difference between summer and winter NO₂ column trends (Uno et al., 2007; Zhang et al., 2007; Stavrakou et al., 2008; Gu et al., 2013).

The steeper slopes in winter could reflect a trend in NO_x lifetime as NO_x and other emissions change. A few modeling studies have previously explored this dependence for China. Uno et al. (2007) found no significant change in the annual mean NO_x lifetime over 1996–2004. Stavrakou et al. (2008) found that the increase in NO_x emissions over 1997–2006 drove a 25 % decrease in summer NO_x lifetime due to higher OH from faster NO+HO₂ reaction and a 10 % increase in winter NO_x lifetime due to lower OH from faster NO₂+OH reaction.

We examined the effect of 2012–2017 changes in MEIC emissions for NO_x and other species on the lifetime of NO_x simulated by GEOS-Chem (Fig. 3). During that period, NO_x emissions in central–eastern China decreased by 25 %, and boundary layer aerosol concentrations in GEOS-Chem decreased by 20 %. Observed aerosol concentrations from the MEE network decreased by 30 % over the 2013–2017 period (Zhai et al., 2019). We find no significant change in the summer NO_x lifetime between 2012 and 2017. The NO_x lifetime during the day shortened slightly, as summertime OH concentrations increased by 6 % and RO₂ concentrations increased by 13 %. However, the NO_x lifetime during the night increased as aerosol concentrations dropped, canceling the overall effect.

In contrast, the winter NO_x lifetime decreased by 22 % (from 27 to 21 h) between 2012 and 2017 (Fig. 3), driven mostly by faster loss by N₂O₅ hydrolysis in aerosols and also by faster loss from reactions with OH and RO₂. The loss rate from ${\mathrm{RO}}_{\mathrm{2}}+\mathrm{NO}/{\mathrm{NO}}_{\mathrm{2}}$ is largely determined by the emission rate of VOCs, which changed by less than 3 % over 2012–2017 (Zheng et al., 2018), effectively decreasing the NO_x lifetime as NO_x emissions dropped. At lower NO_x emissions, NO_x loss by NO₃+VOC reactions also increased relative to NO_x loss by N₂O₅ hydrolysis because of an increase in the ratio of NO₃ to N₂O₅ concentrations. However, the contribution of NO₃+VOC reactions to the total NO_x loss remained less than 5 % because of high NO₂. The faster loss by N₂O₅ hydrolysis in 2017 relative to 2012 might seem counterintuitive since both aerosols and NO_x dropped over the period. However, winter aerosol levels for converting N₂O₅ to HNO₃ remain in excess even in 2017. Instead, we find that the driving factor behind the increase in N₂O₅ hydrolysis is a rise in nighttime ozone concentrations. At night, fast titration by NO (Reaction R4) is an important sink of ozone close to NO_x sources. As NO_x emissions decrease, less ozone is titrated, which then enables the formation of NO₃ by Reaction (R5) and the subsequent formation of N₂O₅. The simulated NO_x lifetime against loss by N₂O₅ hydrolysis decreases by 26 % from 54 h in the winter 2011/12 to 40 h in the winter 2016/17. Ground-based observations at the MEE sites show an increase in winter nighttime ozone, consistent with the model (Fig. 5).

Figure 5 Trend in nighttime surface ozone in China in winter. Values are DJF means over 21:00–05:00 local time for the network of sites in central–eastern China operated since 2013 by the China Ministry of Ecology and Environment (MEE). The sites are gridded on the $\mathrm{0.5}{}^{\circ }\times \mathrm{0.625}{}^{\circ }$ GEOS-Chem grid. The symbols are averages for all grid cells containing sites, and the vertical bars are the standard deviation of the spatial distribution. Model values are similarly sampled and gridded over the ensemble of MEE sites operating since 2013. Trends are from an ordinary least-squares regression.

Figure 4 shows the implications of these changes in seasonal NO_x lifetime for the 2012–2017 NO₂ column trends simulated by GEOS-Chem. Neither the 24 h mean nor the daytime NO_x lifetime changes significantly in summer, and therefore the NO₂ columns track the MEIC emission trends, consistent with observations. In winter, the shortening of the NO_x lifetime steepens the trends of NO₂ columns relative to NO_x emissions. Wintertime GEOS-Chem NO₂ columns decrease by 35 % between 2011/12 and 2016/17, faster than the 25 % decrease in the MEIC NO_x emissions. The NO_x lifetime in winter is about 1 d, long enough for faster NO_x loss at night to affect NO₂ columns in the afternoon when the OMI makes its observations. Comparison to the observed wintertime trend suggests that the GEOS-Chem decrease in NO_x lifetime over 2012–2017 may not be steep enough. There is substantial uncertainty in the factors controlling wintertime OH concentrations (Tan et al., 2018; Miao et al., 2018) and these might also affect the model trend. Meteorological variability can also cause interannual changes in wintertime NO₂ of ∼ 20 % (Lin and McElroy, 2011), but the effect on longer-term trends will be smaller. GEOS-Chem trends for 2005–2012 should be symmetrical with those for 2012–2017, since NO_x emissions in 2005 are similar to those in 2017 (Fig. 4).

The use of satellite-based NO₂ column observations to evaluate trends in NO_x emission inventories in China can be compared to similar work previously done for the US. Jiang et al. (2018) found that OMI NO₂ columns over the US during 2009–2015 decreased slower than NO_x emissions in the US Environmental Protection Agency (EPA) National Emissions Inventory (NEI), suggesting that NO_x emission controls were not as effective as reported by the NEI. However, Silvern et al. (2019) explained this discrepancy with a large relative contribution of the free tropospheric background to the NO₂ column over the US, weakening the relationship between NO₂ columns and US anthropogenic NO_x emissions. This is not a major concern over central–eastern China, where the contribution of the free troposphere above 2 km to the tropospheric NO₂ column as sensed by the OMI is less than 30 %. Laughner and Cohen (2019) find an increase in summer NO_x lifetime over 2010–2013 in OMI observations of isolated urban plumes over the US, reflecting NO_x-limited conditions in which OH concentrations decrease as NO_x decreases. This would dampen the response of NO₂ columns to emission reductions. Such an effect is not apparent in central–eastern China, which is prevailingly in the so-called transition regime between NO_x-saturated and NO_x-limited conditions (Jin and Holloway, 2015; Li et al., 2019).

4 Conclusions

We examined the seasonality and trends of satellite-derived tropospheric NO₂ columns over China and their relation to NO_x emissions. Observations from the satellite-based OMI instrument show a factor of 3 increase in tropospheric NO₂ columns from summer to winter, and we show that this can be explained by the seasonal variation in NO_x lifetime against oxidation. NO₂ columns for the 2004–2018 duration of the OMI record peak in 2011 and subsequently decrease, consistent with the Multi-resolution Emission Inventory for China (MEIC). The summer trends in OMI NO₂ columns closely match the MEIC emission trends, but the winter trends are steeper than the MEIC. We attribute the steeper winter trends to a decrease in the NO_x lifetime, mostly by faster N₂O₅ hydrolysis in aerosols, as NO_x emissions decrease. Lower NO_x emissions lead to an increase in nighttime ozone in winter, promoting N₂O₅ formation. Our analysis of the OMI NO₂ column observations thus supports the magnitude and trends of NO_x emissions in the MEIC, while emphasizing the need to account for changes in NO_x lifetime when interpreting trends in satellite NO₂ columns in terms of trends in NO_x emissions.