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Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions

Measures taken to control the disease (Covid-19) caused by the novel coronavirus dramatically reduced the number of vehicles on the road and diminished factory production. For this study, changes in the air quality index (AQI) and the concentrations of six air pollutants (PM2.5, PM10, CO, SO2, NO2, and O3) were evaluated during the Covid-19 control period in northern China. Overall, the air quality improved, most likely due to reduced emissions from the transportation and secondary industrial sectors. Specifically, the transportation sector was linked to the NO2 emission reductions, while lower emissions from secondary industries were the major cause for the reductions of PM2.5 and CO. The reduction in SO2 concentrations was only linked to the industrial sector. However, the reductions in emissions did not fully eliminate air pollution, and O3 actually increased, possibly because lower fine particle loadings led to less scavenging of HO2 and as a result greater O3 production. These results also highlight need to control emissions from the residential sector.


Introduction
In December 2019, a disease that was eventually linked to a beta coronavirus and named Covid-19 was reported in Wuhan, China (Zhu et al., 2020). In the weeks that followed, measures were taken to reduce large gatherings to control the spread of the disease (China State Science of the Total Environment 731 (2020) 139133 Council, 2020). For example, the Spring Festival holiday was extended beyond February 10, and during that time only essential enterprises involving people's immediate needs (such as health care, or providing food), were allowed to operate. In addition, the opening of schools after the holiday was postponed.
These measures led to a dramatically reduced number of vehicles on the road and a near total reduction in factory production (MEP, 2020;Wang et al., 2020). Pollution emissions from the transportation and industrial sectors were expected to decrease, and the following questions can be asked: Did the air quality improve during the control of Covid-19? If so, how was the improvement related to the reduced emissions from these two sectors? Also, what was the major cause for the improvements in air quality and which atmospheric species were most affected? Answering these questions is a way to evaluate the influences of traffic and industrial emissions on air quality during winter in China. The results of the evaluation can then be used to make recommendations regarding air pollution control during winter in China.
In this study, changes in the air quality index (AQI) and the concentrations of six air pollutants (PM 2.5 , PM 10 , CO, SO 2 , NO 2 , and O 3 ) were evaluated, and the causes for these changes during the control period for Covid-19 were investigated. Correlation analyses were then used to analyze the relationships between socioeconomic factors (i.e., motor vehicle usage, percentage of secondary industries, and industrial emissions) and air quality. Finally, suggestions are given for air pollution control in China during winter.

Study area and period
A total of 366 urban areas in China's mainland were selected for study. These cities are widely distributed over China with the greatest coverage in eastern-southeastern parts of the country (Fig. 1). Taken together, the sites can be considered representative of the overall air quality in China (Kuerban et al., 2019;Zhao et al., 2019).
The overall strategy for the study was to compare the concentrations of selected pollutants before and after the Covid-19 control measures were put in place. The period prior to the controls was from 1 to the 23 January 2020, while the Covid-19 control period was from 24 January to 9 February 2020. During the control period, a series of measures was undertaken to reduce gatherings of people (China State Council, 2020). Specifically, all non-essential factories were shut down, and schools were closed as were all entertainment venues and restaurants. A dramatic reduction in road traffic was observed during the control period. For example, the flow of commercial trucks and buses in the Beijing-Tianjin-Hebei region and its surrounding areas decreased by 77% and 39%, respectively, during the control period (MEP, 2020).

Data sources
The real-time monitoring data for AQI, PM 2.5 , PM 10 , SO 2 , NO 2 , O 3, and CO in the 366 urban areas were obtained from China's National Environmental Monitoring Center. The data had a time resolution of 1 h (http:// www.cnemc.cn). Previous studies have shown that data from China's National Environmental Monitoring Center are statistically reliable (Kuerban et al., 2019;Zhao et al., 2019). The AQI monitoring values calculated from the six priority pollutants (Bai et al., 2019) and the data for the individual pollutants themselves were averaged for the periods prior to and during the Covid-19 controls.
The geographical distributions of secondary industries (which convert raw materials produced by primary industries into goods and products) and industrial SO 2 and NO x emissions were obtained from the 2018 China City Statistical Yearbook. The numbers of motor vehicles in operation were obtained from the China Statistic Yearbook. Notably, the socioeconomic data from seven provinces (Tianjin, Sichuan, Jilin, Heilongjiang, Gansu, Xinjiang, and Qinghai) where the holiday was not extended to February 10, were not used in this study (Table S1).

Results and discussion
3.1. Air quality during the control of Covid-19 Significant differences (p b 0.01) were found in the AQI and the concentrations of six air pollutants (PM 2.5 , PM 10 , CO, SO 2 , NO 2 , and O 3 ) of 366 urban areas before and during the control of Covid-19, suggesting the air quality changed during the control period. The AQI averaged over all stations decreased by 20%, from 89.6 before the control period for Covid-19 to 71.6 during the controls (Fig. 2), demonstrating an overall improvement in air quality from the control measures. A total of 322 of the 366 cities studied experienced a decline in the AQI. The highest AQI reductions occurred in the Ningxia, Shandong, and Henan Provinces (Fig. S1), which normally have high numbers of vehicles in use (Shandong and Henan) (Fig. S2a) and many secondary industries in operation (Ningxia, Shandong, and Henan) (Fig. S2b). The lowest AQI reductions occurred in the Yunnan, Guizhou, and Hainan Provinces (Fig. S1), where motor vehicle usage and secondary industries are relatively low (Fig. S2). Therefore, the improvement of air quality apparently was related to the number of motor vehicles in use and the percentages of secondary industries in each province. The AQI in 366 urban areas had the highest correlation coefficient (r 2 of 0.99) with the PM 2.5 before and during the control of Covid-19, which reflects the fact that fine particles were the major air pollutant throughout the study.
The concentrations of the PM 2.5 , PM 10 , SO 2 , NO 2 , and CO, but not O 3 , decreased during the Covid-19 control period ( Fig. S3) compared with those before the controls were put in place. The concentration of PM 2.5 , decreased from 65.0 μg m −3 to 51.4 μg m −3 (Fig. S3), and 315 of the 366 cities experienced a decrease in PM 2.5 . The regions with the highest and lowest reductions in PM 2.5 concentrations were the same as those for the AQI (Figs. S1, S4), again, because fine particles were most often the major air pollutant.
After the controls were implemented, the concentrations of CO, SO 2 , and NO 2 decreased by 0.23 mg m −3 , 2.2 μg m −3 , and 19.4 μg m −3 , respectively ( Fig. S3). A total of 331 (309, 366) of the 366 cities studied experienced reductions in CO (SO 2 , NO 2 ). The highest reductions of CO and NO 2 occurred in the Shandong and Henan Provinces (Figs. 3,S5) where there are large numbers of motor vehicles and many secondary industries (Fig. S2). This suggests that the reduced emissions from the transportation and industrial sectors were what caused the concentrations of these two gases to decrease. The largest decreases in SO 2 were found in Shanxi and Jiangxi Provinces (Fig. 4), which have low numbers of vehicles but many secondary industries (Fig. S2). This indicates that the reduced SO 2 concentration was probably caused by the lower emissions from secondary industries during the control period.
The amplitude of the concentration variation (ACV) was calculated using the equation, ACV = (y − x) / x × 100%, where x and y are the mass concentrations of a substance of interest before and during the control period for Covid-19, respectively. The air pollutant that showed the largest decrease with the Covid-19 controls was NO 2 (ACV = −54%) ( Fig. S3) while SO 2 , showed the smallest decline (ACV = −16%). The SO 2 /NO 2 ratio is an indicator of the relative contributions of air pollutants from stationary versus mobile sources (Aneja et al., 2001), and higher values occur when there are greater influences from stationary sources. The SO 2 /NO 2 ratio averaged over the two sets of samples increased from 0.39 to 0.70 after the controls were in place (Fig. S6), suggesting an increase in the relative importance of stationary sources . The AVCs for PM 2.5 and PM 10 were −21% and −27%, respectively (Fig. S3), and the PM 2.5 /PM 10 ratio increased from 0.76 to 0.82 (Fig. S6); these are signs of either decreased impacts from dust sources or enhanced secondary aerosol formation during the Covid-19 control period Zhao et al., 2018).

Factors driving the improvements in air quality
Significantly positive relationships were found between the numbers of motor vehicles and the reduced AQIs (R 2 = 0.11, p b 0.1; Fig. 5) and between the percentages of secondary industries and the change in AQIs (R 2 = 0.25, p b 0.05; Fig. 5). With the people largely confined to their homes, the provinces with higher numbers of vehicles should have had greater reductions in vehicle emissions during the control period, and the same should have been true for the secondary industries. The decreases in AQIs were more strongly correlated with the percentages of secondary industries than with motor vehicle numbers (Fig. 5), suggesting that the changes in industrial emissions were more responsible for the improvements air quality, especially fine particles, than motor vehicle usage.
The decreased PM 2.5 concentrations were positively correlated with motor vehicle numbers (R 2 = 0.11, p b 0.1; Fig. 6) and percentages of secondary industries (R 2 = 0.28, p b 0.05; Fig. 6). Therefore, the reduction in the PM 2.5 is best explained by lower emissions from the transportation and industrial sector. The reduced NO 2 concentrations were positively correlated with both vehicle numbers (R 2 = 0.44, p b 0.001; Fig. 6) and industrial NO x emissions (R 2 = 0.36, p b 0.01; Fig. 6), indicating that decreased emissions from both the transportation and industrial sectors led to improvements in NO 2 . As the reduced NO 2 concentrations were more strongly correlated with vehicle population than with the industrial NO x emissions, transportation probably was more responsible for the decrease in NO 2 concentrations. The SO 2 concentrations showed a significant positive relationship with industrial SO 2 emissions (R 2 = 0.16, p b 0.1; Fig. 6) but not with motor vehicle numbers. Therefore, the reduced SO 2 concentrations were only linked to the industrial sector. The CO concentrations showed a significant positive correlation with both the vehicle numbers (R 2 = 0.17, p b 0.05; Fig. 6) and percentages of secondary industries (R 2 = 0.29, p b 0.01; Fig. 2. Comparison of air quality indices (AQI) before and during the control of Covid-19. Fig. 6), but the stronger correlation with the latter (Fig. 6) suggests a reduction in industrial emissions was more responsible for the decrease.

Reducing air pollution
The air quality improved during the control period for Covid-19, and that apparently was mainly caused by reductions in emissions from the transportation and industrial sectors. The transportation sector was the major factor for the reduction of NO 2 mass concentrations, indicating the control measures greatly reduced the pollution emissions caused by the movement of people. As a major pollutant emitted from the coal heating in winter (Kuerban et al., 2019), the mass concentration of SO 2 decreased the least, suggesting the emissions from coal heating activities were probably little affected by the control measures.
Although the air quality improved, the average AQIs in 84 of the 169 cities in northern China were N100 after the controls were implemented, suggesting that the air pollutants in many cities were still at harmful levels. This means that even though the reduced emissions from the transportation and industrial sectors did lead to improvements in air quality, the concentrations of some pollutants were still at unhealthy levels.
The transportation sector is not generally thought to be the major source for PM 2.5 during winters in northern China (Huang et al., 2014;Elser et al., 2016). Rather, this source has been shown to contribute to 6%-22% of the PM 2.5 mass concentration (Tao et al., 2017) and 5%-21% of the organic aerosol mass . The total number of motor vehicles in China increased from 5.5 million in 1990 to 327 million in 2019 MPSC, 2019), which is a 60-fold increase over 30 years. However, increasingly stringent emission standards, electric vehicle subsidies, and the promotion and development of the public transportation have limited the impacts from mobile emissions . In fact, those measures have prevented increases in the vehicular emissions of air pollutants (except for NO x ) since 2010 . As a result, the contribution of the transportation sector to air pollution has not increased in parallel with the rising numbers of vehicles on the roads ( Van et al., 2017).
Industrial emissions are the major contributor to PM 2.5 pollution in China (Shi et al., 2017), but the reduced emissions from that sector did not prevent air pollution during the control period. In fact, essential industries, some of which emit large amounts of pollutants, did not curtail operations during the control period for Covid-19 (MEP, 2020). For perspective, under normal circumstances, thermal power generation contributes 20.1% of the total SO 2 emissions and 32.6% of the total NO x in China . These critical industries must operate continuously MEP, 2020), and therefore, reducing their impacts on air quality obviously should be a central element of pollution control efforts.
The residential sector contributed 39% of the total PM 2.5 emissions in China in 2010 , and emissions from residences were the most likely cause for air pollution during the Covid-19 control period. The industrial sector was largest contributor to fine PM in 2013 (Shi Fig. 3. Change in NO 2 mass concentrations (ΔNO 2 ) between the pre-control and Covid-19 control period. ΔNO 2 refers to the average NO 2 mass concentrations before the control of Covid-19 in each province minus the average NO 2 mass concentrations during the control period. et al., 2017), but the emissions from this source decreased from 2013 to 2017 , which caused the relative importance of the other pollution sources to increase proportionately. Indeed, the residential sector became the major contributor (N50%) to PM 2.5 in representative cities of northwestern China during the winter of 2016-2017 (Yang et al., 2020). Therefore, plans for controlling emissions from residences should be developed and implemented to combat air pollution during the winter in northern China.

Ozone pollution control
Although the reduced emissions from transportation and secondary industrial sectors did not eliminate all air pollution, NO 2 , a precursor for Fig. 4. Changes in the concentrations of SO 2 (ΔSO 2 ) during the control period for Covid-19. ΔSO 2 refers to the average SO 2 mass concentrations before the control of Covid-19 in each province minus the average SO 2 mass concentrations during the control period. Fig. 5. Scatterplot of the change in air quality index (ΔAQI) and socioeconomic factors (number of motor vehicles, secondary industry share) in provinces with air quality improvements. ΔAQI on the Y-axis refers to the average AQI before the control of Covid-19 in each province minus the average AQI during the control period. O 3 , did decrease dramatically during the control period (Figs. 3, S3). The emission factors for NO x from the transportation and industrial sectors are much higher than those for other sectors , making these the major contributors to NO x in China ( Van et al., 2017). In fact, the Multi-resolution Emission Inventory for China (MEIC) (http:// www.meicmodel.org/) indicated that 64% of the NO 2 emissions originated from these two sectors.
In the troposphere, NO x reacts with volatile organic compounds (VOCs) to form O 3 , and therefore, one would expect that O 3 would have decreased along with NO x during the control period for Covid-19; however, this was not the case. In fact, O 3 averaged over all stations increased by 20.1 μg m −3 from 39.0 μg m −3 to 59.1 μg m −3 (Fig. S3), and the AVC for the O 3 concentration was +51% (Fig. S3). For perspective, NO x concentrations have been declining in China since 2011 (De Smedt et al., 2015), while O 3 has increased over the same time period .
The observed increases in O 3 during the Covid-19 controls may be related to the observed decreases in fine particles. That is, the lower PM 2.5 would be a less effective sink for hydroperoxy radicals (HO 2 ), which would increase peroxy radical-mediated O 3 production . In addition, decreased NO x would have little or no effect on O 3 in any urban areas where O 3 production is VOC limited (Jin et al., 2015;Wang et al., 2017;He et al., 2019;Lyu et al., 2019). Simultaneously reducing both NO x and VOC s would be the most effective way to reduce surface ozone in China , but the sources for VOC s during winters in China are complex (H. Zheng et al., 2018;B. Zheng et al., 2018;Song et al., 2019;Huang et al., 2020). Indeed, controlling the NO x emissions from transportation and secondary industries may not reduce VOC concentrations to the extent that O 3 is greatly affected.

Conclusions
The conclusions for the study can be summarized as answers to the questions posed in the introduction: Did the air quality improve during the control period for Covid-19? Yes, the overall air quality improved: the AQI decreased from 89.6 prior to the control period to 71.6 during the control period. In fact, a large majority (322 of 366) of the cities studied experienced a decline in the AQI. The concentrations of five air pollutants (PM 2.5 , PM 10 , CO, SO 2 , NO 2 ) decreased during the control period for Covid-19, but O 3 did not decrease.
Which emission sources were responsible for the reduced concentrations of atmospheric species during the Covid-19 control period? Lower emissions from motor vehicles and secondary industries most likely were responsible for the observed decreases in PM 2.5 , NO 2, and CO concentrations during the control period. Lower emissions from the transportation sector were the main cause for NO 2 reduction, while the industrial sector was responsible for the PM 2.5 and CO reductions. The reduction in SO 2 was only related to reduced emissions from the industrial sector.
What are the policy implications? Despite restrictions placed on motor vehicles and secondary industries during the control period, air pollution continued to be problematic in large parts of northern China. The concentrations of O 3 actually increased during the control period, possibly because lower fine particle loadings led to less scavenging of HO 2 and as a result greater O 3 production. These results illustrate the importance of reactions that can occur between gaseous and particulate pollutants, but clearly, lowering the emissions of both NO x and VOC s will be needed to control O 3 . Results of our study highlight the importance of emissions from the residential sector for wintertime pollution in northern China, and they show that this source must be taken into account in developing pollution mitigation plans.