Distinct Regimes of O₃ Response to COVID-19 Lockdown in China

Abstract

Restrictions on human activities remarkably reduced emissions of air pollutants in China during the COVID-19 lockdown periods. However, distinct responses of O₃ concentrations were observed across China. In the Beijing–Tianjin–Hebei (BTH) and Yangtze River Delta (YRD) regions, O₃ concentrations were enhanced by 90.21 and 71.79% from pre-lockdown to lockdown periods in 2020, significantly greater than the equivalent concentrations for the same periods over 2015–2019 (69.99 and 43.62%, p < 0.001). In contrast, a decline was detected (−1.1%) in the Pearl River Delta (PRD) region. To better understand the underlying causes for these inconsistent responses across China, we adopted the least absolute shrinkage and selection operator (Lasso) and ordinary linear squares (OLS) methods in this study. Statistical analysis indicated that a sharp decline in nitrogen dioxide (NO₂) was the major driver of enhanced O₃ in the BTH region as it is a NO_x-saturated region. In the YRD region, season-shift induced changes in the temperature/shortwave radiative flux, while lockdown induced declines in NO₂, attributable to the rise in O₃. In the PRD region, the slight drop in O₃ is attributed to the decreased intensity of radiation. The distinct regimes of the O₃ response to the COVID-19 lockdown in China offer important insights into different O₃ control strategies across China.

1. Introduction

Tropospheric O₃ is formed through complex reactions involving volatile organic compounds (VOCs) and nitrogen oxides (NOx), along with influences of meteorological conditions (such as radiation, temperature, wind, relative humidity, daily precipitation amount, and surface pressure). O₃ concentrations usually exhibit a nonlinear response to source emissions of precursors, in such a way that the O₃ response to emission control of one precursor (e.g., NO_x) also depends on emissions of other precursors (e.g., VOCs) due to their complex interactions [1]. Accordingly, it is challenging to identify the driving precursors and meteorological processes. There have been considerable attempts made to examine relationships between O₃ and influencing factors, such as VOCs, NO₂, sunshine hours, temperature, wind speed, relative humidity, daily precipitation amount, surface pressure and geopotential height [1,2,3,4,5,6]. It was demonstrated that the O₃ sensitivity regimes and leading influencing meteorological factors vary across regions and time periods [1,2,3,4,5,6].

In December 2019, the coronavirus disease, named later as COVID-19 by the World Health Organization (WHO) [7], emerged in Wuhan and it has spread worldwide quickly since then. The entire metro network along with all other public transport in Wuhan were shut down on January 23, 2020 to prevent the spread of infection. Subsequently, public gatherings, planned events, etc. were canceled, and education was suspended in the rest of China. These restrictions remarkably reduced emissions of air pollutants in China [8]. TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI) revealed the unprecedented declines in NO₂ column density over China [9]. Evident declines in particulate matter and CO were also identified, whereas observations suggested an unexpected uptrend in O₃ [10,11,12]. Liu et al. [13], Le et al. [14] and Zhang et al. [15] further declared that reductions in NO_x emissions resulted in the enhancement of O₃, which would have increased atmospheric oxidation and promoted the formation of secondary aerosols. Due to the uncertain sensitivity of O₃ to precursors in air quality models and large uncertainties in emission inventories [16], these findings need more careful investigation along with observations. Additionally, the roles of meteorological conditions in the enhancement of O₃ levels during the COVID-19 lockdown period are not well understood. The COVID-19 lockdown provides a terrific evaluation testbed of emission control policy in mitigating O₃ pollution in China.

In this study, we used statistical methods, e.g., Lasso (the least absolute shrinkage and selection operator) and ordinary linear squares (OLS), to select the driving factors for the enhancement of O₃ in China during the epidemic lockdown period. The selected factors are compared against those for previous years to represent the unusual situation of 2020. The results will advance our understanding of the relative importance of meteorological conditions and O₃ precursors in the formation of O₃ in China under a low-emission scenario.

2. Materials and Methods

2.1. Meteorological Data and Observations of Air Pollutants

In our study, gridded meteorological variables with a spatial resolution of $0.1°\times 0.1°$ were derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (https://cds.climate.copernicus.eu/cdsapp#!/home) [17]. We considered near surface temperature (T2), wind speeds (WS10), relative humidity (RH2), and mean surface net shortwave radiation flux (SWR) in this study. Hourly surface concentrations of O₃, PM_2.5, and NO₂ were obtained from the China National Environmental Monitoring Center (CNEMC) network (http://106.37.208.233:20035/, last access: 9 August 2020) [18]. As VOCs are not monitored by the CNEMC network, we used formaldehyde (HCHO) tropospheric vertical column concentrations (VCDs) retrieved from the Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM) [19], and surface HCHO concentrations from Ground based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in this study [20]. OMPS-NM measures UV radiation at wavelengths ranging from 300 to 380 nm using a single grating and a charge-coupled device (CCD) detector. It has a 110°cross-track field of view (FOV), similarly to OMI (Ozone Monitoring Instrument). OMPS-NM products are provided at 50 km × 50 km spatial grids by combining measurements into 35 cross-track bins in OMPS-NM standard Earth science mode. Ground based MAX-DOAS observes tropospheric aerosols and trace gases based on scattered sunlight under different viewing angles [21]. Additionally, tropospheric vertical column concentrations of HCHO and NO₂ retrieved from TROPOspheric Monitoring Instrument (TROPOMI) [22,23,24], a satellite instrument on board of the Copernicus Sentinel-5 Precursor satellite, were used to distinguish O₃ sensitivity regimes.

Previously, satellite HCHO measurements were used to constrain VOCs emissions in Asia, Fu et al. [25] and Su et al. [19] also demonstrated that tropospheric HCHO is mostly concentrated below 1 km. To examine the reliability of representing surface concentrations of HCHO with satellite observed HCHO column, we compared OMPS HCHO VCDs against surface HCHO observations from MAX-DOAS at multiple stations across China, and found that they are significantly correlated (Figure 1).

2.2. Statistical Analysis

To investigate the influence of the COVID-19 lockdown on the association between the features (O₃ precursors and meteorological conditions) and O₃, we first applied the least absolute shrinkage and selection operator (Lasso) for relevant feature selection and then used ordinary least square (OLS) regression to obtain the statistical significance of the selected features. Lasso, proposed by Tibshirani [26], is featured both in feature selection and model interpretability. Lasso excels over other feature selection methods, such as stepwise selection, with consideration of a penalty term, which can shrink the regression coefficients towards zero to prevent overfitting. We can regard it as a variant of OLS by imposing sparse constraint to the regression model as follows:

$${\stackrel{\wedge }{\beta }}_{\mathrm{lasso}}=\underset{\beta }{\arg \min }\left\{\frac{1}{2}{\displaystyle \sum _{i=1}^N{\left(y_i-{\beta }_0-{\displaystyle \sum _{j=1}^p{x}_{ij}{\beta }_j}\right)}^2+\lambda {\displaystyle \sum _{j=1}^p\left|{\beta }_j\right|}}\right\}$$

(1)

where $y_i$ and $x_i{}_j$ are the regression target and the value of $j_{th}$ feature for the $i_{th}$ sample, respectively. ${\left\{{\beta }_j\right\}}_{j=1}^p$ are feature weights to be estimated from the model, and $\lambda$ is a hyperparameter controlling the strength of the sparse constraint. Here, we choose the optimal value of $\lambda$ via cross validation. The optimization algorithm to iteratively infer ${\left\{{\beta }_j\right\}}_{j=1}^p$ is least angle regression (LARs) [27]. Considering the features are of different scales, instead of directly feeding them into the Lasso model, they are first normalized to have a zero mean and standard deviation of one. The output of Lasso is a set of features with non-zero weight values. In order to statistically assess their importance (e.g., sensitivity of O3 concentration to gaseous precursors and meteorological parameters), we adopted OLS to generate p values of t-tests for input features.

3. Results

3.1. Evolution of O₃ from Pre-Lockdown to Lockdown Periods over 2015–2020

Since 23 January 2020 when Wuhan announced the lockdown, most of the economic activities and public transportation in China were restricted [28]. We define pre-lockdown and lockdown periods in this study as the five weeks before and after 23 January, respectively. Although lockdown occurred only in 2020, we use pre-lockdown and lockdown periods in this study to denote the same time periods in previous years as well. Human activities during the spring festival holidays are usually distinctively different from those during working days, including widespread usage of fireworks, reduction in traffic flow, shutdown of factories, etc. [29,30]. To avoid such influence, we exclude data over the spring festival holidays of 2015–2020. As emission levels and meteorological conditions vary greatly across seasons and years, we use the percentage changes of O₃ concentrations from pre-lockdown to lockdown periods to investigate the driving factors. As listed in

Table 1. Mean O₃ concentrations (ppbv) during pre-lockdown and lockdown periods and the percentage changes over 2015–2020 in China.

China	2015	2016	2017	2018	2019	2015–2019	2020
pre-lockdown	23.28	22.56	25.54	27.59	21.69	24.13	23.93
lockdown	29.31	33.07	39.14	36.42	32.96	34.18	39.31
percentage	25.90	46.59	53.25	32.00	51.96	41.63	64.27
significance	***	***	***	***	***	***	-
BTH
pre-lockdown	17.82	16.21	17.69	22.48	18.92	18.62	19.30
lockdown	25.84	30.34	33.53	35.88	31.75	31.47	36.71
percentage	45.01	87.17	89.54	59.61	67.81	69.99	90.21
significance	***	-	-	***	***	***	-
YRD
pre-lockdown	25.24	26.36	28.07	27.07	21.42	25.63	24.03
lockdown	32.23	37.06	42.74	36.99	35.05	36.81	41.28
percentage	27.69	40.59	52.26	36.65	63.63	43.62	71.79
significance	***	***	***	***	*	***	-
PRD
pre-lockdown	38.45	23.92	37.78	44.75	23.25	33.63	39.09
lockdown	40.72	27.71	49.79	38.07	36.80	38.62	38.66
percentage	5.90	15.84	31.79	−14.93	58.28	14.83	−1.10
significance	*	***	***	***	***	***	-

*** indicates that the differences between previous years and 2020 are significant at 0.001 level, * indicates that the previous years and 2020 are significant at 0.1 levels, and - means no significant difference (p > 0.1). BTH: Beijing–Tianjin–Hebei; YRD: Yangtze River Delta; PRD: Pearl River Delta.

, compared to the pre-lockdown period, O₃ concentrations in China increased by 64.27% in 2020, significantly higher than any previous year over 2015–2019 and the mean percentage change of 41.63% over 2015–2019 (p < 0.001).

However, the changes of O₃ from before and after January 23 exhibit a heterogeneous spatial distribution (Figure 2). Pronounced growth of O₃ is observed in the Beijing–Tianjin–Hebei (BTH) region, especially in 2016, 2017 and 2020 (Figure 2b,c,f). In 2020, O₃ concentrations in the BTH region increased by 90.21% from pre-lockdown to lockdown periods, higher than the percentage changes for other years (Table 1). Similar enhancements of O₃ concentrations are also observed in the Yangtze River Delta (YRD) region (Figure 2) over 2015–2020. In 2020, O₃ concentrations in the YRD region rose by 71.79% from pre-lockdown to lockdown periods, significantly higher than that for any other year and the mean of previous years (Table 1). In contrast, an opposite trend of O₃ from pre-lockdown to lockdown (−1.1%) periods in 2020 was identified for the Pearl River Delta (PRD) region (Table 1), which is significantly different from those for previous years. Over 2015–2019, the mean O₃ concentrations increased by 14.83% from five weeks before January 23 to five weeks after January 23 in the PRD (Table 1). These inconsistent responses of O₃ to pandemic lockdown across China indicate distinct regimes of O₃ control.

3.2. Potential Driving Factors for Different Responses of O₃ to Pandemic Lockdown in China

The concentration of O₃ is affected by multiple factors, including the abundance of gaseous precursors (i.e., VOCs and NO_x), intensity of radiation, winds, etc. We consider, in this study, meteorological parameters including temperature (T2), wind speed (WS10), relative humidity (RH2), and mean surface net shortwave radiation flux (SWR). Stronger temperature and solar radiation enhance emissions of O₃ precursors and accelerate photochemical O₃ production under high precursor concentrations [31]. Given the spatial distribution of natural emissions of VOCs across China, the effects of accelerated photochemical O₃ production would be more important in north China, while both effects could be important in south China [31]. For the influence of related chemical species, we include concentrations of HCHO, PM_2.5 and NO₂. PM_2.5 concentrations are considered based on the recent reports on the interactions between O₃ formation and particles in China [32]. Similarly to the analysis of O₃, we calculated the percentage changes of these parameters for each observation site. We then used Lasso and OLS methods to explore the potential triggering factors for the changes in O₃ during the COVID-19 lockdown period in different regions in China, as the relationship between O₃ formation and influencing factors would vary across regions [33,34]. The results for the unusual year of 2020 are also compared against those over 2015–2019.

Figure 3 illustrates the dominant factors for percentage changes of O₃ over 2015–2019 and in 2020 for the BTH, YRD and PRD regions. Coefficients of some variables listed in Figure 3 are zero, suggesting the negligible roles of these variables. The importance of each selected parameters is statistically evaluated and summarized in Table S1. In the BTH region, intensified T and SWR from pre-lockdown to lockdown periods are important factors (positive effects) for enhanced O₃ over previous years (2015–2019), due to enhanced emissions of biogenic VOCs and photochemical O₃ production [35,36]. Additionally, declines in PM_2.5 might have also played a role (Figure 3a), due to the effects of higher actinic flux and reduced sink of hydroperoxyl [32]. A negative correlation between O₃ and PM_2.5 was also revealed by Chu et al. [37] in the winter in north China when the PM_2.5 concentration was above 50 ug/m³. In 2020, the enormous increase in O₃ was provoked mainly by the sharp declines in NO₂, while reduced concentrations of PM_2.5 might also have played a role (Figure 3b). Different from previous years, changes in meteorological conditions exhibit negligible contributions in 2020 in the BTH region (Figure 3b). Previous model sensitivity analysis by Gao et al. [18] suggests that the BTH region is VOC-limited in winter and the same sensitivity is observed during the pre-lockdown period (Figure 4) in the BTH region, where declines in NO_x would be likely to enhance O₃ concentrations [38,39,40]. Accordingly, enhanced O₃ concentrations in the BTH region during the 2020 pandemic lockdown was mainly driven by sharp declines in NO₂ (Table S1).

In the YRD region, for both previous years and 2020, O₃ is enhanced by augmented T and SWR, while being negatively affected by changes in NO₂ (Figure 3c). In 2020, the influences of these factors are stronger than those over 2015–2019. The response of O₃ to declines in NO₂ in the YRD region is in line with that in the BTH region. This is also consistent with the findings of sensitivity of O₃ to emission sectors in the YRD by Gao et al. [18] and the sensitivity indicated by satellites (Figure 4). In addition, a positive relationship between changes in HCHO and response of O₃ is identified in 2020 in the YRD. Although HCHO levels declined in the YRD region in 2020 (−18.64%, Table S3), its suppression of O₃ formation might have been downplayed by stronger declines in NO₂ (−56.63%, Table S3) and intensified T/SWR (Table S3). Thus, meteorological conditions (i.e., T and SWR) and changes in gaseous precursors (mainly declines in NO₂) work together to augment O₃ in the YRD.

In the PRD region, the enhancement of O₃ from pre-lockdown to lockdown periods over 2015–2019 are well explained by the intensified SWR due to shift of season (Figure 3e). Additionally, increases in NO₂ would also slightly promote the formation of O₃ (Figure 3e). As indicated in Gao et al. [18] and Figure 4, the PRD region exhibits a different sensitivity regime from the BTH and YRD regions, and the increase in NOx emissions is likely to promote the formation of O₃ in the PRD. From pre-lockdown to lockdown periods in 2020, both T and SWR declined (Table S4). Lower T and SWR would suppress the photochemical reaction rates and reduce emissions of precursors to lower levels of O₃. As indicated in Figure 3f, the dominant non-meteorological factor for the response of O₃ in the PRD in 2020 is HCHO. As observed by satellite and surface monitors, HCHO abundance in the PRD increased from 9.83 to 9.97 × 10¹⁵ molec/cm², while NO₂ concentrations declined by 55.47% (Table S4). In the PRD region, changes of O₃ positively correlate with both changes in HCHO and NO₂ (Figure 5b), suggesting that O₃ formation in the PRD region is likely in the transition regime [5]. The promotion of O₃ by enhanced HCHO might have been downplayed by reduced intensity of SWR. Thus, decreased SWR from pre-lockdown to lockdown periods in 2020 served as the major driver of slightly declined O₃ in the PRD region.

4. Discussion

Due to the uncertainties in chemical transport modeling, we use statistical methods in this study, i.e., Lasso and OLS, to understand the driving factors for diverse responses of O₃ concentration in China during the COVID-19 lockdown. We report that O₃ exhibits distinct responses across China. In the BTH region, enhanced O₃ concentrations during lockdown were mainly driven by sharp reductions in NOx emissions, which is related to the restrictions on transportation and economic activities. In the YRD region, both meteorological conditions (i.e., T and SWR) and changes in gaseous precursors (mainly declines in NO₂) work together to augment O₃ in the YRD. However, O₃ declined in the PRD region during the pandemic lockdown, mainly due to decreased intensity of SWR. These results implicate that controlling sources of VOCs would be more efficient to reduce wintertime O₃ levels in both BTH and YRD regions, while controlling either NO_x or VOCs would work for the PRD region.

These results highly depend on the quality of the used observation datasets. The accuracy of CNEMC data has been validated and demonstrated extensively with independent surface observations in previous studies [41]. However, uncertainties in the adopted HCHO satellite column and its representation of near surface HCHO would bring about uncertainties in the conclusion. As O₃ concentrations in the BTH region are mainly affected by NO₂, and meteorological factors played a more important role in the other two regions, the adverse effects of the uncertainties in HCHO observations are limited in this study. Yet, this further emphasizes the need for densely distributed ground-based observations of HCHO and other VOC species. Our results also emphasize that O₃ control strategies should be carefully designed with consideration of differences among regions.