key: cord-0822473-2ym6ci2i authors: Chu, Biwu; Zhang, Shuping; Liu, Jun; Ma, Qingxin; He, Hong title: Significant concurrent decrease in PM(2.5) and NO(2) concentrations in China during COVID-19 epidemic date: 2020-07-01 journal: J Environ Sci (China) DOI: 10.1016/j.jes.2020.06.031 sha: 9a50d28807e43c781ad0fdad2ccf279d244dbaea doc_id: 822473 cord_uid: 2ym6ci2i The strict control measures and social lockdowns initiated to combat COVID-19 epidemic have had a notable impact on air pollutant concentrations. According to observation data obtained from the China National Environmental Monitoring Center, compared to levels in 2019, the average concentration of NO(2) in early 2020 during COVID-19 epidemic has decreased by 53%, 50%, and 30% in Wuhan city, Hubei Province (Wuhan excluded), and China (Hubei excluded), respectively. Simultaneously, PM(2.5) concentration has decreased by 35%, 29%, and 19% in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. Less significant declines have also been found for SO(2) and CO concentrations. We also analyzed the temporal variation and spatial distribution of air pollutant concentrations in China during COVID-19 epidemic. The decreases in PM(2.5) and NO(2) concentrations showed relatively consistent temporal variation and spatial distribution. These results support control of NO(x) to further reduce PM(2.5) pollution in China. The concurrent decrease in NO(x) and PM(2.5) concentrations resulted in an increase of O(3) concentrations across China during COVID-19 epidemic, indicating that coordinated control of other pollutants is needed. Coronavirus disease 2019 has spread rapidly all over the world. As of 12 May 2020, the total accumulative cases of COVID-19 epidemic exceeded four million. In China, this novel pneumonia-like disease of unknown origin was first reported in December 2019 in Wuhan city, Hubei Province. Soon afterwards, due to dramatical increase of COVID-19 case numbers, the government officially closed urban transportation system in Wuhan on 23 January 2020. Subsequently, all 31 provincial regions in Chinese mainland, including Hubei (with its capital city Wuhan), began initiating their first-level response to a major public health emergency. By 25 January 2020, which was the start of the Chinese spring festival (i.e., Chinese New Year, CNY), an increasing number of control measures were introduced by local governments to reduce gatherings and travel. The strictest control measures lasted until 23 February 2020, after the peak in COVID-19 case numbers in China. Then, based on the sustained downward trend in COVID-19 case numbers, the Chinese government allowed the resumption of livelihoods and industries by stages and districts. In late March of 2020, the transmission of COVID-19 experienced a substantial decline in China, while cases imported from foreign countries became the dominate source. By 2 May 2020, the 31 provincial regions in Chinese mainland, including Hubei, terminated their public health emergency first-level response. The measures and lockdowns introduced to stop the spread of COVID-19 in China have had a remarkable impact on industrial production and social life, and therefore have likely had an effect on pollutant emissions and environmental air quality. Unlike stringent emission controls, the declines in pollutant emissions during COVID-19 epidemic have primarily come from changes in social life and light industry, with power generators and heavy industry expected to be less affected. Stringent emission controls have been shown to be effective for temporary improvement of air quality during specific events, such as the 2008 Beijing Olympic Games Xing et al., 2011) and 2014 Asia-Pacific Economy Cooperation (APEC) China Summit (Sun et al., 2016; Xu et al., 2019) . The COVID-19 epidemic has provided the opportunity to investigate the influence of changes in societal production and life on air quality. In this study, based on analysis of concentrations of nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), ozone (O 3 ), and fine particulate matter (PM 2.5 hourly data from 365 cities with measurements from 2015 to early 2020 were used. Similar data quality control methods as employed in previous studies (Shi et al., 2018; Wu et al., 2018) were used to reduce errors in the CNEMC monitoring network dataset. First, zero or negative concentrations were set as missing. Second, extreme outlier concentrations were deleted (i.e., SO 2 > 1 428 µg m -3 , NO 2 > 1 026 µg m -3 , CO > 62.5 mg m -3 , PM 2.5 > 10 000 µg m -3 , PM 10 > 10 000 µg m -3 , O 3 > 1 071 µg m -3 ). Third, identical data repeated three or more times, which were likely duplicated by the monitoring network reporting system due to communication error (Rohde and Muller, 2015) , were also set as missing, except for the first value. Fourth, temporally inconsistent outliers were removed following Wu et al. (2018) . Our epidemic . Coinciding with the decrease in NO 2 , PM 2.5 concentrations also showed an obvious decrease compared to levels in 2019, with reductions of 35%, 29%, and 19% in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. The PM 10 concentrations also decreased, with similar percentage reductions as that of PM 2.5 . This might indicate that the secondary aerosol and primary aerosol decreased with similar percentages during COVID-19 epidemic since higher percentage of primary aerosol contributed to PM 10 than that to PM 2.5 (Seinfeld and Pandis, 2006) . The percentage changes in NO 2 and PM 2.5 concentrations were similar in background area with those in urban area. Compared to levels in 2019, the concentrations of SO 2 and CO also decreased during COVID-19 epidemic in 2020, but at much lower rates than that observed for NO 2 , PM 2.5 and PM 10 . The decreases in SO 2 and CO were not significant in background area of Wuhan or Hubei (Table S1 ). In general, NO 2 and PM 2.5 showed marked decreases and SO 2 and CO exhibited much smaller decreases. These phenomena indicated that the emission from stationary sources, such as coal-fired power plants, iron and steel production, didn't decreased as much as traffic. In contrast, due to less NO to react with O 3 (Seinfeld and Pandis, 2006) , as well as less heterogeneous HO 2 radical loss and higher actinic flux with lower concentration of particle (Li et al., 2019b) , O 3 demonstrated a notable increase, with a higher rate observed in Wuhan (58%) than Hubei or China. March in 2019 and 2020, and changes in percentage of concentrations. To Table S2 ). As shown in Figure 3 and Figures respectively. P5 is the period when the livelihoods and industries were basically restored to order. Detail information of P1 to P5 in each year is listed in Table S2 . Error bar is one standard variation of day-average concentrations in each period. Period 1 to 5 are the same as P1 to P5 in Figure 3 . Period 1 to 4 are the early stage before the outbreak, the rapid growth period, the fastigium, and the decline period of COVID-19 epidemic, respectively. Period 5 is the period when the livelihoods and industries were basically restored to order. Detail information of period 1 to 5 in each year is listed in Table S2 . The declines in concentrations differed for different air pollutants, as shown in Figure 4 and Tables S3-S5. NO 2 concentrations showed the most significant decrease. The changes in air pollutant concentrations due to the introduction of COVID-19 epidemic control measures indicated that the resulting changes in societal production and life had a notable impact on pollutant emissions and air quality. Following social lockdown, traffic intensity decreased markedly , leading to a significant decrease in NO 2 concentrations, given that power generators and heavy industry were less affected. NO x plays key roles in the formation of atmospheric secondary aerosol (Cheng et al., 2016; Chu et al., 2019; He et al., 2014; Russell et al., 1988) , which is the main contributor of PM 2.5 mass in China Huang et al., 2014; Liu et al., 2015) . Due to the more rapid decrease in SO 2 emissions than NO x in recent years, aerosol pollution has shifted from sulfate-dominated to nitrate-dominated in many eastern Chinese cities (Li et al., 2019a; Wen et al., 2018) . NO x is also highly active in the formation of oxidants in the gas phase, such as O 3 and OH and NO 3 radicals (Seinfeld and Pandis, 2006) , and contributes to oxidation capacity in heterogeneous and aqueous reactions (Chen et al., 2019; Cheng et al., 2016; He et al., 2014; Xue et al., 2016) . The results of this study corroborated the above mechanisms. The relatively consistent temporal variation and spatial distributions of changes in concentration of PM 2.5 and NO 2 following the introduction of strict COVID-19 epidemic control measures indicated that PM 2.5 is highly related to NO 2 . SO 2 , NO x , NH 3 , and volatile organic compounds (VOCs) are the key precursors of secondary PM 2.5 Kerminen, 1999; Liu et al., 2013; Meng et al., 2011) . At present, the economic costs for further substantial decreases in SO 2 emissions will be huge after the universal desulphurization in coal-fired power plants in China (Zheng et al., 2018) . VOCs and NH 3 are derived from a wealth of sources, with a lack of effective control methods relative to NO x (Fu et al., 2017; Liu et al., 2015; Zheng et al., 2018) . These situations, plus the finding of coincidental decreases in PM 2.5 and NO 2 concentrations in this study, support the control of NO x to further reduce PM 2.5 pollution in China. The decrease in NO x and PM 2.5 concentrations may cause an increase in O 3 concentration, indicating the need for coordinated control of VOCs. However, in some areas where control of VOCs is difficult, a high reduction in NO x may overcome the increase of O 3 concentration due to the decrease in both NO x and PM 2.5 , thus progressing toward coordinated control of PM 2.5 and O 3 . 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Supplementary data associated with this article can be found in the online version.