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. 2021 Oct 27;12(12):101247. doi: 10.1016/j.apr.2021.101247

Characteristics of air quality in different climatic zones of China during the COVID-19 lockdown

Honglei Wang a,b,∗, Yue Tan a, Lianxia Zhang c, Lijuan Shen a, Tianliang Zhao a, Qihang Dai d, Tianyi Guan d, Yue Ke a, Xia Li a,b
PMCID: PMC8548732  PMID: 34720609

Abstract

The diverse climate types and the complex anthropogenic source emissions in China lead to the great regional differences of air pollution mechanisms. The COVID-19 lockdown has given us a precious opportunity to understand the effect of weather conditions and anthropogenic sources on the distribution of air pollutants in different climate zones. In this study, to understand the impact of meteorological and socio-economic factors on air pollution during COVID-19 lockdown, we divided 358 Chinese cities into eight climate regions. Temporal, spatial and diurnal variations of six major air pollutants from January 1 to April 18, 2020 were analyzed. The differences in the characteristics of air pollutants in different climate zones were obvious. PM2.5 reduced by 59.0%–64.2% in cold regions (North-East China (NEC) and North-Western (NW)), while O3 surged by 99.0%–99.9% in warm regions (Central South (CS) and Southern Coast (SC)). Diurnal variations of atmospheric pollutants were also more prominent in cold regions. Moreover, PM2.5, PM10, CO and SO2 showed more prominent reductions (20.5%–64.2%) in heating regions (NEC, NW, NCP and MG) than no-heating regions (0.8%–48%). Climate has less influence on NO2, which dropped by 41.2%–57.1% countrywide during the lockdown. The influences of weather conditions on the atmospheric pollutants in different climate zones were different. The wind speed was not the primary reason for the differences in air pollutants in different climate zones. Temperature, precipitation, and air pollution emissions led to prominent regional differences in air pollutants throughout the eight climates. The effect of temperature on PM, SO2, CO, and NO2 varied obviously with the latitude, at which condition temperature was negatively correlated to PM, SO2, CO, and NO2 in the north but positively in the south. The temperature was positively correlated to ozone in different climate zones, and the correlation was the highest in NEC and the lowest in SC. The rainfall has a strong removal effect on atmospheric pollutants in the climate regions with more precipitation, but it increases the pollutant concentrations in the climate regions with less precipitation. In regions with more emission sources, air pollutants experienced more significant variations and returned to pre-lockdown levels earlier.

Keywords: COVID-19, Climate, Regional differences, Air pollutants

1. Introduction

With the strengthening of the convergence effect of resources, population, and education in big cities, the problem of atmospheric pollution in urban areas has become increasingly prominent in recent years (Awasthi et al., 2017; Li et al., 2017; Nielsen et al., 1996; Roorda-Knape et al., 1998; Shi et al., 2020; Xie et al., 2021). Having experienced decades of rapid development, China has seen rapid urbanization, followed by the increasing problem of air pollution (Guo et al., 2014; Han et al., 2016; Xu et al., 2013). A regional severe haze pollution incident broke out in central and eastern China in January 2013. Then China issued the “Air Pollution Prevention and Control Action Plan”, which formulated a series of detailed air pollutant emission reduction targets, and since then China has embarked on an arduous task of air pollution prevention and control (Andersson et al., 2015; Feng et al., 2019; Tao et al., 2014; Yang et al., 2015; Zheng et al., 2017). By 2017, China had completed the first phase of the air pollution prevention and control task for PM2.5 pollution, and the annual PM2.5 concentration in China had decreased significantly from 72.3 ± 37.4 to 47.4 ± 20.6 μg m−3 (Wang et al., 2020b). Although PM2.5 concentration in China has been continuously declining, O3 concentration has shown a significant increase, and O3 pollution has become increasingly serious (Li et al., 2020a; Lu et al., 2020). Since 2018, the second phase of the “Clean Air Action” has been focused on tackling O3 pollution, but the O3 concentration during 2018–2019 was still higher than that in the previous years and the average near-surface MDA8-O3 (maximum 8-h average concentration per day) in summer was increasing at a rate of 1.9 ppb∙a−1 (Li et al., 2020a).

The concentrations of air pollutants are mainly affected by unfavorable meteorological conditions (external factors) and emissions (internal factors). For a specific region, the emission of pollutants is relatively stable in the short term, so the impact of meteorological conditions on the concentration of air pollutants is relatively important (He et al., 2017; Li et al., 2019b; Shen et al., 2021a; Zhang et al., 2020b). During COVID-19, strict home quarantine measures were implemented nationwide, and anthropogenic emission sources were drastically reduced. However, air pollution still occurred in central and eastern China (Filonchyk and Peterson, 2020a; 2020b; Le et al., 2020; Zhu et al., 2020), indicating the significant impact of meteorological conditions on the formation and dissipation processes of air pollution. Filonchyk and Peterson (2020) found that daily concentrations of PM2.5, PM10, SO2, NO2, and CO in the Yangtze River Delta region during the lockdown period were reduced by 9%, 77%, 31.3%, 60.4%, and 3%, respectively, compared to the same period in 2019. Zhao et al. (2020) found out that meteorological variables helped decrease PM2.5 and O3 in some cities but helped increase PM2.5 and O3 in others, closely related to terrain.

Although there have been studies of improving Chinese air quality that resulted from short-term intervention (Chen et al., 2013; Liu et al., 2017), air quality's reaction with a long-term and large-scale lockdown could be different. The past short-term air quality control mainly was concentrated in a specific area, and the control of the anthropogenic pollution sources primarily was focused on a few areas, so inter-regional transmission and the influence of local anthropogenic emission sources cannot be ignored (Gao et al., 2017; Ji et al., 2018; Shen et al., 2017). On a global scale, daily confirmed Coivd-19 cases increased from discovering the virus until August 2020. Awasthi et al. (2020) found out the trend of COVID-19 cases with temperature, and it is estimated that with an increase in temperature by 1 °C, 30 new COVID-19 cases on daily basis will be expected to observe. In January 2020, the Chinese government adopted the emergency health response plan to inhibit the spread of COVID-19, including closing factories and restricting transportation (Tian et al., 2020; Wang et al., 2020a, 2021a). In addition, during COVID-19, the management and control of anthropogenic emission sources, such as traffic sources, were comprehensive, so it is beneficial for us to explore the impact mechanism of different pollution sources on air pollutants (Chapin and Roy, 2021; Le et al., 2020; Li et al., 2020b; Shen et al., 2021a, 2021b). Due to the sharp reduction of emission sources, major air pollutants like PM2.5 and SO2 decreased significantly during the lockdown (Chu et al., 2020; Li et al., 2020b). However, ozone concentration kept increasing, while the variations of SO2 concentration were not noticeable in warm climate regions (Almond et al., 2020; Li et al., 2020b; Wang et al., 2020c).

China has a vast territory and complex and diverse climate types. There are arid and rainless arid climates in the northwest and humid and rainy subtropical monsoon climates. The meteorological conditions in different climate zones are quite different, and their impacts on air pollutants are also different (Kan et al., 2012; Peng et al., 2020; Wang et al., 2021b). It is worth noting that China's population distribution and economic structure also show apparent differences in different climatic zones (http://www.stats.gov.cn/). For example, the Yangtze River Delta, located in central and eastern China, covers an area of about 3.7% of China, and its total permanent population and total gross domestic product (GDP) account for 16% and 23.5% of the country. By contrast, the Xinjiang Uygur Autonomous Region, located in northwestern China, covers an area of approximately 16.7% of China, but the total permanent population and total GDP only account for 1.8% and 1.38% of the country. The considerable differences in climate, economic structure, and population have determined the significant differences in the characteristics of air pollution in different regions of our country. The lockdown measures implemented during COVID-19 provided us with a previous opportunity to understand the influence mechanism of meteorological conditions and anthropogenic emission sources on the distribution of atmospheric pollutants in different climate zones. There have been a large number of studies discussing changes in air pollutants during COVID-19 (Bao and Zhang, 2020; Chu et al., 2020; Li et al., 2020b; Le et al., 2020; Shen et al., 2021a, 2021b). Shen et al. (2021b) found out that the primary pollutants reductions in large cities were larger than those in small-medium cities during lockdown. Wang et al. (2021a) found that PM2.5, aerosol chemical compositions, NO2 and CO decreased by 9.9%–64.0% during the Chinese New Year (CNY). Griffith et al. (2020) used the Community Multiscale Air Quality model (CMAQ) simulation with 50% reduced emission in China to simulate PM2.5 in Taiwan. Wang et al. (2021c), utilizing the dynamic-chemistry simulations, estimated that emission reduction during the lockdown weakened the aerosol-PBL interaction and thus caused a decrease of 25 μg m−3 (∼50%) in PM2.5 enhancement. However, most of these studies are conducted in a specific area, and there has never been a systematic study of the impact of meteorological conditions and anthropogenic sources on air pollutants in different climatic zones during COVID-19. Since China possesses a large land area with complex changes in topography and geomorphology, regional climate and socio-economic characteristics significantly impact air pollutants concentrations. Although the lockdown brought about considerable emission reduction, meteorological conditions such as static wind and temperature inversion could significantly increase PM2.5 (Sharma et al., 2020; Wang et al., 2020c). Local economic conditions and industrial presence also influence regional air pollutant concentration (Zhang et al., 2007). Furthermore, severe air pollution can increase the risk of COVID-19 infection (Chauhan and Johnston, 2003; Hendryx and Luo, 2020). Hence, there is a need to investigate the role of the climatic and socio-economic factors that affected air quality change during the lockdown. In this study, we utilized the daily observation data of 609 stations from 1971 to 2000 to divide China into 8 climate zones and combined them with the nationwide air pollution data (PM2.5, PM10, SO2, CO, NO2, and O3), data of meteorology (temperature, wind speed, and precipitation), GDP and car ownership to analyze the temporal and spatial distribution characteristics of air pollutants in different climate zones and their influencing factors. Moreover, the influence of meteorological elements and anthropogenic sources on air pollutants in different climate zones was analyzed in detail. This study could deepen our understanding of the formation mechanism of atmospheric compound pollution in different climate zones and provide a theoretical basis for formulating precise air pollution control measures in different climate zones.

2. Materials and methods

2.1. Study period and area

China's newest official climate classification scheme was based on daily observation data of 609 stations from 1971 to 2000 (Zheng et al., 2010). Based on this scheme, we divided China into eight regions (Fig. 1 , Table 1 ): North-East China (NEC) consists of the cold temperate climates and the humid/semi-humid mid-temperate climates, including 31 cities; Inner Mongolia (MG) consists of the semi-arid mid-temperate climate, including 8 cities; North China Plain (NCP) consists of the semi-humid warm temperate climate, including 91 cities; North-Western (NW) consists of the arid and semi-arid mid-temperate climate, including 30 cities; Yangtze River basin (YR) consists of the humid north subtropical climate, including 60 cities; Central South (CS) mainly consists of the humid mid-subtropical climate, including 73 cities; Southern Coast (SC) consists of the south subtropical climate, including 46 cities; Tibet Plateau (TP) consists of the plateau climate, including 19 cities. The climate classification was then validated by the NOAA meteorological data from January 1 to April 18 (Table 1).

Fig. 1.

Fig. 1

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Map of air pollution monitoring stations distribution and climate classification.

Table 1.

The average of meteorological and socioeconomic variables in the eight regions.

Region Number of cities Temperature （°C） Wind Speed (m s–1) Precipitation (mm) GDP (109CNY) Private vehicle population (106)
NEC 31 −15.3 2.8 42.8 151.3 0.57
NW 30 −12.9 2.4 26.4 119.1 0.46
NCP 91 −11.6 3.0 66.1 318.8 1.19
MG 8 −14.1 2.8 22.9 185.0 Missing
TP 19 −17.4 2.5 39.8 28.4 Missing
YR 60 3.4 2.2 379.4 515.3 0.98
CS 73 6.1 2.1 654.5 347.2 0.69
SC 46 14.0 2.7 385.6 314.4 0.97
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The period from January 1 to April 18, 2020 was divided into four phases: pre-lockdown (Pre-lock), Alert Level 1, Alert Level 2, and Alert Level 3, corresponding to a different level of restriction in the public health emergency plan (Fig. 2 ). According to the China State Council, industrial activities and transportation were forcibly suspended during Alert Level 1 (Jan 24-Feb 25) because of the dramatic growth of confirmed cases. Some industrial production parts resumed in Alert Level 2 (Feb 26-Mar 31), and traffic began increasing. During Alert Level 3 (Apr 1–18), the spread of COVID-19 in China was well controlled. Most production and living activities were restored to their pre-lockdown status, which resulted in increased air pollutant emissions (Li et al., 2020b).

Fig. 2.

Fig. 2

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Lockdown timeline from January 1 to April 18.

2.2. Data source

Air pollution data from 358 cities (Table 1) including 1663 stations were collected from the National Urban Air Quality Real-time Release Platform of the Ministry of Environmental Protection of China (https://air.cnemc.cn:18007/), with a time resolution of 1 h. We averaged the station data in each prefecture city to obtain the mean concentration of a city. Data from cities in each climate zone were then averaged to obtain the regional mean concentration. The micro oscillating balance method and the β absorption method are both used to measure PM2.5 and PM10. NO2, SO2, and O3 are measured using the chemiluminescence method, ultraviolet fluorescence, and UV-spectrophotometry, respectively. CO is measured using the non-dispersive infrared absorption method and the gas filter correlation infrared absorption method. The continuous monitoring systems for atmospheric pollutants consist of the sampling unit, the calibration device, the analytical instrument, and the data collection and transport unit. The meteorological data of 406 Chinese surface stations were collected from the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface Data (ISD) (ftp://ftp.ncdc.noaa.gov/pub/data/noaa/isd-lite/).

The quality assurance and data controls were implemented based on HJ 630–2011 and GB/T 37301-2019 specifications before releasing air pollutants data and meteorological data, respectively. The quality assurance/quality control procedure was also provided to all the operators in all cities, and all the gas analyzers were calibrated with standard gases once a week. The valid air pollutants and meteorological data for all climate zones exceed 99.5%. A detailed description of the instruments and data quality could be seen elsewhere (Zhao et al., 2016, 2020).

Regional air pollution conditions are tightly correlated with socio-economic factors (Han et al., 2014; Wang et al., 2017; Zhao et al., 2019). Hence, obtaining regional socio-economic data is essential for studying the regional differences in air pollution variations during the lockdown. Studies have shown that PM2.5 is significantly correlated with the gross domestic product (GDP) and vehicle population (Han et al., 2014; Wang et al., 2017; Zhao et al., 2019). Therefore, we chose the GDP and vehicle population to evaluate the regional emission potential. Since the official 2019 statistical yearbook has not yet been published and vehicle population data was absent in some cities, we did a web scraping of the annual GDP and private vehicle population from the 2019 Statistical bulletin of 367 cities in China Statistical Information Network (http://www.tjcn.org/tjgbsy/nd/36163.html).

3. Results

3.1. Temporal variations of air pollutants

As shown in Fig. 3 , temporal air pollutant fluctuations varied markedly between regions, highlighting the differences between the eight climate zones. Since the lockdown started, the concentrations of PM2.5, PM10, SO2, CO2, and NO2 dropped more significantly in NEC, NW, NCP, MG than in TP, CS, and SC. For example, in NCP, PM2.5 dropped by 88.9% from 137.3 μg m−3 on Jan 22 to 15.2 μg m−3 on Feb 15, while in SC PM2.5 merely dropped by 43.0% from 28.4 μg m−3 on Jan 22 to 16.2 μg m−3 on Feb 15. As shown in Table 1, NEC, NW, MG, and NCP had lower temperatures (from −11.6 to −15.3 °C) and less precipitation (from 22.9 to 66.1 mm) than southern China. More frequent temperature inversions and fewer removal processes in cold climates led to increased accumulation of air pollutants. Therefore, once the lockdown started and the number of emission sources sharply reduced, the concentration of air pollutants in NEC, NW, MG, and NCP dropped significantly to the same levels as other regions.

Fig. 3.

Fig. 3

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The variations of the daily average concentration of six major air pollutants from January 1 to April 18, 2020.

Despite the emission reductions, severe pollution events still occurred in northern China due to an abnormally shallow boundary layer (Su et al., 2020). More frequent high concentration pollutant peaks were observed in several regions, namely NEC, NW, MG, and NCP in Fig. 3. Moreover, the heating demands of NEC, NW, MG, NCP, and TP gradually decreased as the temperature increased, resulting in significant air pollutant reductions in cold regions. For example, from Jan 15 to Apr 15 in MG, CO dropped by 73.7% from 1.9 mg m−3 to 0.5 mg m−3. On Apr. 15, the CO concentration of MG was even lower than that of SC.

As the lockdown measurements mainly reduced anthropogenic sources of air pollution, the lockdown effect on reducing air pollution was most effective in more developed regions (He et al., 2020). As shown in Table 1, YR has a much higher GDP and vehicle population than other regions. Similar to what happened in cold regions, air pollution in YR was also more significantly reduced than in underdeveloped regions like TP. For example, from Jan 19 to Feb 15, the concentration of NO2 in YR dropped by 77.7% from 44.6 μg m−3 to 10.4 μg m−3, while the concentration of NO2 in TP dropped by a mere 43.6% from 15.6 μg m−3 to 8.8 μg m−3. In arid regions like NW, PM10 concentrations could still have peaks over 300 μg/m3. O3 continued to increase and fluctuate as the temperature increased (Fig. S1), with no remarkable difference between the eight climates in Fig. 3. Air pollutants in TP, CS, and SC remained stable with low-level concentrations. Compared to other regions, TP has the lowest GDP and vehicle population, indicating fewer anthropogenic sources, and that TP is the region least influenced by lockdown measures. As for CS and SC, high temperature and ample rainfall provided favorable dispersion and removal conditions for air pollutants.

3.2. Spatial distributions of air pollutants

To quantify regional differences in air pollutant variations, average concentrations at pre-lockdown, Alert Level 1, Alert Level 2, and Alert Level 3, the percentage changes of the concentrations of air pollutants of each Alert Level in relative to that of pre-lockdown were calculated, and the maximum percentage changes of different climates were compared.

Fig. 4 and Table 2 show that PM2.5 dropped significantly by 59.0%–64.2% in NEC, NW, NCP, and MG. But in CS and SC, which possess favorable dispersion and removal conditions for air pollutants, PM2.5 merely dropped by 17.0%–26.0%, not to mention that the average concentrations of PM2.5 at pre-lockdown in CS and SC were less than half of that in NCP. Similarly, the reduction of PM10 reached 40.3% and 47.3% in NEC and MG, respectively, while it only reached 21.3% and 27.5% in CS and SC, respectively. In arid climates like NW and TP, PM10 increased by 48.9% and 21.7%, respectively. In the underdeveloped TP, the primary source of SO2 and CO was residential heating.

Fig. 4.

Fig. 4

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The average concentrations of pre-lockdown, Level 1, Level 2, and Level 3.

Table 2.

Percentage changes of the average concentrations of air pollutants at Alert Level 1, 2 and 3 in relative to the pre-lockdown concentrations during COVID-19 lockdown in China.

Period Area PM2.5 PM10 SO2 CO NO2 O3
Alert Level 1 NEC −33.0% −31.4% −23.0% −27.6% −49.1% 47.8%
NW −28.7% −1.8% −5.7% −20.5% −44.0% 55.2%
NCP −34.5% −32.0% −28.2% −31.7% −53.2% 77.7%
MG −28.9% −19.2% −20.3% −34.3% −48.7% 52.8%
TP −23.8% −8.7% −9.4% −15.6% −41.2% 12.7%
YR −34.4% −33.7% −7.5% −21.5% −53.1% 62.0%
CS −17.7% −21.3% −17.4% −23.3% −53.2% 48.4%
SC −17.0% −27.5% −23.2% −20.6% −49.2% 11.9%
Alert Level 2 NEC −59.0% −47.3% −46.9% −47.4% −54.6% 68.3%
NW −43.2% 21.5% −31.9% −52.3% −50.0% 62.4%
NCP −58.3% −37.2% −42.2% −51.1% −38.6% 87.4%
MG −64.2% −40.3% −40.9% −59.8% −57.1% 69.0%
TP −32.2% −3.3% −19.5% −29.1% −36.9% 24.7%
YR −48.0% −28.7% 3.1% −24.8% −25.5% 69.0%
CS −26.1% −12.8% −0.8% −25.7% −19.7% 63.1%
SC −16.0% −14.9% −11.1% −15.0% −19.0% 7.3%
Alert Level 3 NEC −19.3% −6.7% −43.6% −37.9% −34.9% 86.2%
NW −36.3% 48.9% −41.1% −61.5% −46.1% 79.2%
NCP −59.8% −34.6% −39.7% −52.6% −30.5% 115.5%
MG −63.3% −29.6% −48.1% −63.6% −50.3% 91.6%
TP −20.7% 21.7% −24.1% −33.8% −26.2% 25.5%
YR −43.4% −16.0% 20.9% −31.6% −3.4% 99.0%
CS −24.6% −6.1% 13.4% −29.1% −6.1% 99.9%
SC −5.4% −2.5% 5.2% −13.0% −3.1% 23.2%
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As shown in Fig. 3, concentrations of SO2 and CO in TP kept decreasing until Level 3, reflecting the decreasing heating demand during the lockdown. Similarly, SO2 and CO dropped significantly by 41.1%–46.9% and 47.4%–63.6%, respectively, in cold regions like NEC, NW, MG, and NCP. In comparison, in warm regions like YR, CS, and SC (with no heating demands in winter), SO2 and CO moderately dropped by 7.5%–23.2% and 20.6%–31.6%, respectively. At Level 3, when lockdown measures were relaxed, SO2 even increased by 5.2%–20.9% in YR, CS, and SC due to increasing traffic and industrial activities.

Variations of NO2 and O3 exhibit no evident regional difference over the eight regions. NO2 dropped by 41.2%–57.1% in all regions. Similarly, large declines of NO2 were consistent in different regions of India during the lockdown (Singh et al., 2020). Therefore, NO2 reductions were less noticeably influenced by changing climatic conditions. But in developed regions like NCP and YR, reductions of NO2 were more significant at Level 1. NO2 reductions in developed regions rebounded to pre-lockdown levels earlier than in developing regions, like NEC and MG. As light intensity and temperature increased through lockdown, O3 surged by 79.2%–115.5% in almost all regions. However, in SC and TP, O3 merely increased by 23.2% and 25.5%, respectively. We calculated the Kendall correlation coefficients of 5% significance between O3 and other air pollutants. We observed that generally, O3 was negatively correlated with other air pollutants. For example, O3 was negatively correlated with PM2.5 (r = 0.19) and NO2 (r = 0.39) in TP. Hence, massive reductions in other air pollutants may have resulted in a drastic increase in tropospheric O3 concentration, attesting to the finding that a decrease in PM2.5 would speed up O3 production (Li et al., 2019a). Le et al. (2020) concluded that the NOx titration effect was the main cause of the O3 increase during the lockdown. However, we revealed that the spatial difference of NO2 variations was not as prominent as other pollutants. Therefore, this study opines that reducing other air pollutants like PM2.5 may have more significantly influenced surface O3 increase during the COVID-19 lockdown period.

The Kendall correlation tests were conducted to assess the correlations between meteorological factors and air pollutants in the eight regions. Overall precipitation intensity, temperature, and wind speed were negatively correlated with the concentrations of air pollutants. Temperate climates like NEC and NW had the lower average temperature and greater temperature increase through the lockdown than in other climates (Fig. S1). As shown in Table 3 , air pollutants in temperate climates generally show higher coefficients than those in subtropical climates and plateau climates. In arid regions like NW, MG, and TP, the correlations between precipitation and air pollutants generally failed to satisfy a 5% level of significance, suggesting a weak precipitation impact on air pollutants' concentrations in arid climates. SO2 and NO2 were negatively correlated with temperature in cold regions like NEC, NW, NCP, and TP with heating demands. Still, they were positively correlated with warm areas like YR, CS, and SC. CO was negatively correlated with meteorological variables in all regions, showing an especially significant correlation with wind speed. Different from other pollutants, O3 was significantly positively correlated with temperature and wind speed. In the cold areas with more incredible temperature growth from January to April, O3 was more significantly associated with temperature than in warm regions.

Table 3.

Correlation coefficients between air pollutants and meteorological factors.

Region Variable PM2.5 PM10 SO2 CO NO2 O3
NEC Precipitation −0.208* −0.192* −0.226* −0.179* −0.196*
Temp −0.177* −0.386* −0.297* 0.625*
WS −0.393* −0.229* −0.476* −0.445* −0.372* 0.378*
NW Precipitation 0.163**
Temp −0.417* 0.23* −0.518* −0.673* −0.345* 0.541*
WS −0.363* 0.201* −0.469* −0.582* −0.419* 0.425*
NCP Precipitation −0.252* −0.144** 0.136**
Temp −0.329* −0.185* −0.405* 0.491*
WS −0.434* −0.212* −0.417* −0.454* −0.172* 0.279*
MG Precipitation 0.156** 0.166** −0.144**
Temp −0.31* −0.151** −0.353* −0.405* −0.219* 0.537*
WS −0.271* −0.373* −0.385* −0.348* 0.382*
TP Precipitation −0.215* −0.209* 0.289*
Temp −0.139** 0.286* −0.394* −0.504* 0.252*
WS 0.257* −0.231* −0.321* 0.242*
YR Precipitation −0.176* −0.222* −0.283* −0.191*
Temp 0.169* 0.377* −0.196* 0.292* 0.415*
WS −0.172* 0.207*
CS Precipitation −0.274* −0.304* −0.211* −0.138**
Temp 0.332* 0.438* 0.317* 0.429*
WS −0.131**
SC Precipitation −0.324* −0.289* −0.193* −0.169* −0.194*
Temp 0.157** 0.31* 0.303* 0.335* 0.154**
WS −0.323* −0.326* −0.237* −0.318*
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*p < 0.01.

**p < 0.05.

3.3. Diurnal variations of air pollutants

The bimodal distributions of PM2.5 and PM10 were especially evident in NEC and TP (Fig. 5 , Fig. S4). Once the lockdown started, the diurnal variation flattened out. The decrease of the diurnal range was most pronounced in NEC, where the diurnal range for PM2.5 dropped by 58.3% from 30.7 μg m−3 (Pre-lock) to 12.8 μg m−3 (Level 2), and for PM10 decreased by 59.3% from 35.9 μg m−3 (Pre-lock) to 14.6 μg m−3 (Level 2). Then in Level 3, when diurnal traffic and manufacturing activity restarted, the diurnal range of NEC for PM2.5 surged by 308.6% to 52.3 μg m−3 and the daily range for PM10 surged by 288.4% to 56.7 μg m−3. As shown in Fig. S5 and Fig. S6, diurnal variations of SO2 and CO were prominent in cold regions with heating demands (NEC, NW, MG, TP). But the diurnal ranges remained stable among different Alert levels. Nevertheless, in NEC, the coldest region, SO2, and CO still show significant variations of diurnal ranges among different levels. From Alert Level 2 to Alert Level 3 in NEC, SO2 increased by 47.8% from 6.7 μg m−3 to 9.9 μg m−3, and CO increased by 85.7% from 0.21 mg m−3 to 0.39 mg m−3. While in other regions, there are no remarkable variations of diurnal ranges during Alert Level 2 to Alert Level 3.

Fig. 5.

Fig. 5

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The diurnal variation of PM2.5 of pre-lockdown, Level 1, Level 2 and Level 3.

NO2 showed a double peak distribution. Since NO2 is easily photolyzed, the troughs became more noticeable with time. The growth of diurnal ranges of NO2 was evident in NEC, NW, and TP (Fig. S7). For instance, in NW, the diurnal range grew by 78% from 17.0 μg m−3 (Pre-lock) to 30.2 μg m−3 (Level 3). Along with the increase in the average concentration of O3, the diurnal ranges of O3 also grew significantly. In NEC, NCP, YR, SC, and CS, the diurnal ranges of O3 rose from 20 to 30 μg m−3 (Level 2) to 40–60 μg m−3 (Level 3) as shown in Fig. S8. In SC, different from the stable trend of O3 in sections 3.1, 3.2, the diurnal ranges of O3 changed significantly. The diurnal range of O3 was 46.0 μg m−3 in Pre-lock and then be significantly suppressed to about 28.0 μg m−3 in Level 2.

4. Discussion

Air pollutants have different impact ranges due to the difference in their residence time in the atmosphere (An et al., 2019; Seinfeld and Pandis, 2016). Take the secondary pollutants, PM2.5 and O3, for example, PM2.5 stays in the boundary layer for a long time, so it has the characteristics of regional pollution; O3 has a shorter stay in the boundary layer, so the pollution range is relatively small (Seinfeld and Pandis, 2016). Therefore, there are significant differences in the response of different pollutants to meteorological conditions. And even with the same meteorological conditions, different regions have different effects on air pollutants. For example, the cold air in winter in the North China Plain tends to rapidly reduce the concentration of pollutants, while the cold air in the Yangtze River Delta region may bring a large quantity of pollutants and enhance the local pollution (Huang et al., 2020; Kang et al., 2019; Zheng et al., 2015). Therefore, at the current stage in China, where the control of PM2.5 has been below the standard, but O3 pollution is prominent, and the characteristics of air pollution have gradually evolved into the composite pollution situation in which O3 and aerosols coexist. It became essential to sort out the relationship between meteorological conditions and air pollutants and their differences in different climate zones.

In China, the distribution characteristics of topography, climate, population, and economy have consistency in space. China is divided into two distinct regions based on the “Heihe-Tengchong Line” (Chen et al., 2016; Zhang et al., 2020a). The terrain in the northwest of the “Heihe-Tengchong Line” is dominated by plateaus, mountains, grasslands, and deserts, accounting for 56.2% of the whole land area, but only 4% of the population is inhabited here, where the precipitation is sparse, the land is large, the population is sparse, and arid and semi-arid climates dominate the climate. However, the southeast of the “Heihe-Tengchong Line” terrain is dominated by the plain, accounting for 43.8% of the land area, but up to 96% of the population is inhabited there. Even in the southeast, there is a vast difference in climate between north and south. Heilongjiang province in northern China belongs to the cold temperate climate and temperate continental monsoon climate, while Hainan province in southern China belongs to the marine tropical monsoon climate. In addition, the urbanization process of different regions varies greatly. The North China Plain, the Yangtze River Delta, the Sichuan Basin, and the Pearl River Delta have formed dense large urban agglomerations with severe anthropogenetic pollution (An et al., 2019; Guo et al., 2014; Han et al., 2016; Shen et al., 2021a, Shen et al., 2021b; Shi et al., 2020; Xu et al., 2013). So, due to the differences in climate, topography, and emission sources in different regions, the evolution characteristics of air pollutants are different. A comprehensive review of the distribution characteristics of air pollutants, meteorological conditions, and the mechanism of the impact of emission sources on air pollutants in different climatic regions is important for the coordinated regional control of complex air pollution.

To prevent the spread of COVID-19, strict lockdown measures are implemented nationwide. Implementing these measures provides us with a precious opportunity to understand the mechanism of the influence of meteorological conditions and anthropogenic sources on the distribution of atmospheric pollutants in different climate zones. For example, the strict nationwide home quarantine measures during lockdown made the traffic emission sources drastically reduced. And except for the necessary transportation, all other means of transportation have been intermitted (Li et al., 2020b; Le et al., 2020). That has made the difference in traffic sources due to different city scales and urban-rural differences disappear, and it provides us a valuable opportunity to assess the degree and mechanism of the impact of traffic sources on air pollutants in different regions. In this study, we utilized the daily observation data of 609 stations from 1971 to 2000 to divide China into 8 climate zones and combined them with the nationwide air pollution data (PM2.5, PM10, SO2, CO, NO2, and O3), data of meteorology (temperature, wind speed, and precipitation), GDP and car ownership to analyze the temporal and spatial distribution characteristics of air pollutants in different climate zones and their influencing factors. Moreover, the influence of meteorological elements and anthropogenic sources on air pollutants in different climate zones was analyzed in detail.

PM2.5 reduced by 59.0%–64.2% in cold regions (NEC) and NW), while O3 surged by 99.0%–99.9% in warm regions (CS and SC). Diurnal variations of atmospheric pollutants were also more prominent in cold regions. The influences of weather conditions on the atmospheric pollutants in different climate zones were different. As the region with the least emissions, TP experienced a moderate decrease of air pollutants as heating demands decreased and dispersion conditions improved. Meanwhile, the reducing demands for house heating in cold regions (NEC, NW, NCP, and MG) resulted in more significant reductions in PM2.5, SO2, and CO than in warm regions (YR, CS, and SC). There were less prominent spatial variations of NO2 and O3 during the lockdown. NO2 dropped by 41.2%-57.1% countrywide, showing no distinct regional difference among the eight climates. Although O3 increased by around 24% in SC and TP, O3 surged by 79.2%-115.5% in all other regions.

5. Conclusions

Lockdown measurements in response to COVID-19 have indirectly improved air quality. Since different regions in China is different in climate and economics, the air pollution variation in the lockdown also has regional characteristics. Additionally, air pollution may increase the possibility of respiratory infections. Hence, studying the regional differences in air pollution changes is necessary for better control of air pollution and the epidemic. In this study, regional characteristics of spatial and temporal variation were analyzed based on the data of major air pollutants (PM2.5, PM10, SO2, CO, NO2, O3) from January 1st to April 18th, 2020. Together with meteorological and socio-economic data, the impacts of climate and socio-economic factors were discussed.

The differences in the characteristics of air pollutants in different climate zones were noticeable. PM2.5 reduced significantly by 59.0% and 64.2% in cold NEC and NW, respectively, but dropped by only 17.0% and 26.0%, respectively, in warm CS and SC. O3 increased by 99.0% and 99.9%, respectively, in warm YR and CS, around 20% larger than NW (79.2%). Diurnal variations of atmospheric pollutants were also more prominent in cold regions. The influences of weather conditions on the atmospheric pollutants in different climate zones were different. The wind speed was not the primary reason for the differences in air pollutants in different climate zones. The wind speed was directly proportional to ozone but was negatively correlated to most other pollutants for a specific climate region. Temperature, precipitation, and air pollution emissions led to prominent regional differences in air pollutants throughout the eight climates. The temperature was positively correlated to ozone in different climate zones, and the correlation was the highest in NEC and the lowest in SC. The rainfall has a strong removal effect on atmospheric pollutants in climate regions with more precipitation, but it increases the pollutant concentrations in climate regions with less precipitation.

The composition of pollution sources of a region resulted from the regional characteristics of climate and economy. The reducing demands for house heating in cold regions (NEC, NW, NCP, and MG) resulted in more significant PM2.5, SO2, and CO reductions than warm regions (YR, CS, and SC). In regions with more emission sources like NCP and YR, air pollutants exhibited greater variations and rebounded to pre-lockdown levels earlier than their surrounding regions. There were less prominent spatial variations of NO2 and O3 during the lockdown. NO2 dropped by 41.2%–57.1% countrywide, showing no distinct regional difference among the eight climates. Although O3 increased by around 24% in SC and TP, O3 surged by 79.2%–115.5% in all other regions. The sharp reductions of air pollutants like PM2.5 may have caused the drastic increase in O3 production rather than the titration effect of NOx.

Author contributions

Honglei Wang: Conceptualization, Methodology, Visualization, Investigation, Writing - Original Draft, Writing - Review & Editing, Funding acquisition; Tan Yue: Writing - Original Draft, Investigation, Software, Writing - Review & Editing; Lianxia Zhang: Writing - Original Draft, Investigation, Software; Lijuan Shen: Conceptualization, Methodology, Visualization, Investigation, Writing - Original Draft; Tianliang Zhao and Xia Li: Writing - Review & Editing; Qihang Dai and Tianyi Guan: Conceptualization, Methodology, Visualization, Writing - Original Draft; Yue Ke: Conceptualization, Investigation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (41830965 and 41805096), the China Postdoctoral Science Foundation (2018M640169), and the Open Grants of the State Key Laboratory of Severe Weather (2020LASW-B08).

Footnotes

Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control.

Appendix A

Supplementary data related to this article can be found at https://doi.org/10.1016/j.apr.2021.101247.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

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