key: cord-1002070-tv2cwl0h
authors: Wang, Yichen; Yuan, Yuan; Wang, Qiyuan; Liu, ChenGuang; Zhi, Qiang; Cao, Junji
title: Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions
date: 2020-05-06
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.139133
sha: f89e08a6cf6e218e640ae8781a69f2e06fe813ff
doc_id: 1002070
cord_uid: tv2cwl0h

Abstract 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.

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 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 J o u r n a l P r e -p r o o f 4 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).

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 one hour (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, J o u r n a l P r e -p r o o f 5 Xinjiang, and Qinghai) where the holiday was not extended to February 10, were not used in this study (Table S1 ).

2.1. Air quality during the control of Covid-19

Signifcant differences (p < 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 ( 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 (Fig. 3, Fig. 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 Journal Pre-proof J o u r n a l P r e -p r o o f 7 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 (Song et al., 2017) . 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 (Song et al., 2017; Zhao et al., 2018) .

Significantly positive relationships were found between the numbers of motor vehicles and the reduced AQIs (R 2 = 0.11, p < 0.1; Fig. 5 ) and between the percentages of secondary industries and the change in AQIs (R 2 = 0.25, p < 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 < 0.1; Fig. 6 ) and percentages of secondary industries (R 2 = 0.28, p < J o u r n a l P r e -p r o o f 8 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 < 0.001; Fig. 6 ) and industrial NO x emissions (R 2 = 0.36, p < 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 < 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 < 0.05; Fig. 6 ) and percentages of secondary industries (R 2 = 0.29, P < 0.01; Fig. 6 ), but the stronger correlation with the latter (Fig. 6) suggests a reduction in industrial emissions was more responsible for the decrease. Although the air quality improved, the average AQIs in 84 of the 169 cities in northern China were greater than 100 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 (Wu et al., 2017; 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 (Wu et al., 2017) . In fact, those measures have prevented increases in the vehicular emissions of air pollutants (except for NO x ) since 2010 (Wu et al., 2017) . 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 der A 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 (Huang et al., 2017) . These critical industries must operate continuously (Huang et al., 2017; 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 et al., 2017) , but the emissions from this source decreased from 2013 to 2017 (Zhang et al., 2019) , which caused the relative importance of the other pollution sources to increase proportionately. Indeed, the residential sector became the major contributor (>50%) to PM 2.5 in representative cities of northwestern China during the winter of 2016-2017 J o u r n a l P r e -p r o o f 13 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. 

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