Rezwanul Hasan Rana1, Syed Afroz Keramat1, Jeff Gow This email address is being protected from spambots. You need JavaScript enabled to view it.1,2 

1 School of Commerce, University of Southern Queensland, Toowoomba, Queensland, Australia
2 School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban, South Africa


Received: November 9, 2020
Revised: March 24, 2021
Accepted: April 4, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.200614  


Cite this article:

Rana, R.H., Keramat, S.A., Gow, J. (2021). A Systematic Literature Review of the Impact of COVID-19 Lockdowns on Air Quality in China. Aerosol Air Qual. Res. 21, 200614. https://doi.org/10.4209/aaqr.200614


HIGHLIGHTS

  • A systematic review of the COVID-19 lockdowns and air quality was conducted.
  • Restricting human activities had an immediate impact on air pollution.
  • Urban, industrial, and inland areas experienced larger drop in pollutants.
  • Pollutants (NO2) arising from the road traffic movement reduced the most.
  • Reported drop in air pollution varied significantly based on the time compared.
 

ABSTRACT


This literature review systematically examines the effect of COVID-19 lockdowns on pollutant concentrations in China by synthesising the reported evidence. Following PRISMA guidelines, we used predefined eligibility criteria to search the databases of PubMed, Scopus, Web of Science and EBSCO Host for peer-reviewed published literature that investigated the nexus between COVID-19 and air quality in China. After screening the titles, abstracts and full texts of the retrieved results, two reviewers independently evaluated the relevant data. 35 of 508 studies met our criteria. The majority of the eligible studies reported data from central China (e.g., Wuhan and Hubei Province), and the most frequently measured air pollutant was nitrogen dioxide (NO2; 51 values in 28 studies), followed by fine particulate matter (PM2.5; 49 values in 26 studies). We found evidence of a substantial reduction in air pollution immediately after lockdown measures were implemented, with traffic-related NO2 exhibiting the largest decrease. The reported reductions in air pollution varied by region and period. Specifically, urban, industrial and highly populated areas of China experienced greater improvements in air quality than rural, residential and less populated areas. Additionally, owing to meteorological factors, the effects differed between inland and coastal regions. However, despite the changes, the pollutant concentrations in many regions (e.g., Beijing, where PM2.5 and PM10 levels remained above 100 µg m−3) still exceeded the World Health Organization (WHO)’s 24-hour mean guidelines (e.g., 25 µg m−3 and 50 µg m−3 for PM2.5 and PM10, respectively). Without the support of adaptive environmental strategies, the recent gains in air quality will be unsustainable.


Keywords: Air pollution, Air contamination, Atmospheric environment, Coronavirus, 2019-nCov


1 INTRODUCTION


The first novel coronavirus (COVID-19) outbreak was reported in Wuhan, China, in December 2019 (Filonchyk et al., 2020; He et al., 2020; Le et al., 2020; Ghahremanloo et al., 2021). Subsequently, COVID-19 has become a serious public health threat worldwide as it transmitted rapidly and caused millions of infections and deaths, especially among the elderly. Therefore, it has been declared as a global pandemic by the World Health Organization (WHO; Gautam, 2020). As of 23 March 2021, COVID-19 had affected 124 million people in 192 countries and territories with 2.7 million deaths around the world (Johns Hopkins University, 2021).

COVID-19 is an infectious disease that transmits from human to human through direct contact, droplet and aerosol transmission (Fernandez-Montero et al., 2020; Wang and Du, 2020). To prevent the spread of this infectious disease, the Chinese government took a nationwide contingency plan (followed by other nations) to restrict human activities. More specifically, the Chinese government implemented widespread restricted road traffic and human activities in late January to early February 2020 (Chen et al., 2020c). Similar measures have been taken by most of the countries of the world in the form of restricted transportation, and closing of business, economic, social, educational, cultural and recreational activities to control the transmission of the virus (Dantas et al., 2020). During the lockdown, economic activities decreased dramatically, and people were isolated in their homes. Within a short period of time, environmental researchers noticed the positive side effect of the lack of human economic activity. Lockdown measures resulted in the improvement in air quality, as air pollutants such as particulate matter with a diameter of 10 µm or less (PM10), particulate matter with a diameter of 2.5 µm or less (PM2.5), sulphur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2) decreased significantly (Fan et al., 2020a; Filonchyk et al., 2020; Lian et al., 2020; Nichol et al., 2020; Pei et al., 2020).

Air pollution is a significant environmental health threat to humans. According to the WHO (2016), ambient air pollution caused 4.2 million deaths worldwide and 81 deaths per 100,000 population in China in 2016 (WHO, 2016). Air pollution is a serious concern in China (Dong et al., 2019). In 2016, the country contributed 33%, 24%, and 31% of the world’s total emissions of SO2, NO2, and CO, respectively. Nationwide social lockdowns imposed by the national and provincial Chinese governments created an opportunity to evaluate changes in air quality. It is assumed that a decrease in human activities reduce pollutant levels in the atmosphere significantly.

The impact of lockdown on China’s air quality, which has a significant effect on global air quality, cannot be ignored. This positive impact of lockdown in terms of improvement in air quality in China (ranked 11th based on the average PM2.5 exposure) has not yet been identified adequately in the existing literature. In addition, there are significant heterogeneities in reported changes in the concentration of air pollutants in China during COVID-19 lockdowns. This calls for a comprehensive synthesis of the existing research. Several recent studies called for further research on this context (Chen et al., 2020c; Ming et al., 2020). There are several reasons for selecting China as the study setting. Firstly, with almost 1.4 billion people, China is the most populous country in the world (World Bank, 2017). Secondly, 48 Chinese cities featured among the 100 most polluted cities globally in 2019 (IQAir, 2020). Thirdly, China has an advanced nationwide air pollution monitoring system ensuring the availability of meticulous data (IQAir, 2020). Lastly, due to the COVID-19 outbreak, China imposed very strict lockdown measures in many cities and regions. Therefore, this study attempts to analyse evidence from scientific research articles on the extent of the improvement in China’s air quality due to COVID-19-related lockdowns.

The objective is to provide a quantitative as well as a narrative synthesis of the recent evidence from the published literature that reported on changes in air quality in China during COVID-19 lockdowns. Given the differences in the impacts of partial or full lockdown on China’s air quality at the national, provincial and regional level, the current study presented a systematic literature review based on a comprehensive analysis of 35 research articles published since February 2020.

This study considered two key issues: Did partial or full COVID-19-related lockdowns improve China’s air quality significantly? And what is the level of improvement in air quality measured in terms of the reduction of PM2.5, PM10, SO2, CO, ozone (O3) and NO2 and does it differ across China? The findings of this study may serve as reference for improvement in air quality due to lockdown measures and thus be helpful to policymakers for post-pandemic air quality management.

 
2 METHODS


 
2.1 Literature Search

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were used to conduct this systematic literature review (McInnes et al., 2018). The PRISMA approach provides strategies for a detailed database search using selected search terms and a set of predetermined inclusion and exclusion criteria (Shaffril et al., 2018). The authors conducted a systematic review of the articles that focused on and reported changes in air quality during COVID-19 lockdown in Chinese cities and provinces. The online databases of EBSCO Host, PubMed, Web of Science, and Scopus were searched from inception to 10 September 2020. The following pollutants were considered as a measure of air quality: NO2, PM2.5, PM10, SO2, CO, O3 and air quality index (AQI) (Xiong et al., 2020).

 
2.2 Eligibility Criteria

This study included journal articles that estimated variations in air quality in China using the following criteria: 1) the peer-reviewed published article was original; 2) the study included at least one Chinese city or province; and 3) the study included quantitative measures or results of at least one of the air pollutants. Studies estimating only the outdoor air quality were included in the review, and the study did not apply any limitations regarding study design or time. Finally, studies that did not quantitatively estimate and report the change in air quality were also excluded from this study. Table 1 lists the predetermined exclusion and inclusion criteria used in this study.

Table 1. Inclusion and exclusion criteria.

 
2.3 Search Terms and Database

Table 2 includes the complete search strategy of this literature review. Search terms included “COVID-19”, “air pollution” and “China”. Search terms related to these specific keywords were also included. A research librarian assisted in developing the search strategy. Based on the predetermined search strategy and the inclusion and exclusion criteria, two reviewers autonomously conducted the database search. This study identified additional literature by scanning the references (backward search) of the selected articles. Detailed search terms for specific database have been listed in Appendix A. This study used EndNote (X9) software to organise and manage the references.

Table 2. Characteristics of the included studies.

 
2.4 Study Selection and Data Extraction

Two authors independently evaluated studies identified from the database search to assess their eligibility for inclusion. They reviewed the title and abstract and screened full text of the article (if required). Full-text screening was conducted for articles that met the inclusion criteria (after an initial screening of the abstract). In case there were any differences of opinion, the two authors attempted to resolve the disagreement through discussion. If no agreement could be reached a third author was involved who resolved the conflict. Two authors also screened full-text versions of the included articles to provide independent judgment regarding their quality. Lastly, to locate potential additional studies, the reference lists of the included articles were also searched by the two authors independently.

The authors extracted the following data from the selected studies: author, year of publication, study design, study setting, time comparison, and key findings related to air pollutant measures. One author conducted the data extraction using the PRISMA guidelines (McInnes et al., 2018) and others verified the extraction of data from the selected studies.

 
2.5 Assessment of Study Quality

To evaluate the quality of the included studies, this study used the Strengthening the Reporting of Observational Studies in Epidemiology (Cardona et al., 2013) statement checklist (von Elm et al., 2007). 15 tools from the STROBE checklist were used: background, objective, setting, participants, data source, study size, quantitative variables, statistical method, sensitivity analysis, descriptive data adjusted and unadjusted results, limitations, interpretation of findings and funding sources of the study (Items 2, 3, 5–8, 10–14, 16, 18–20, 22) (Appendix B and Appendix C). Other items from the STROBE checklist were not relevant for assessing the quality of the papers.

Two authors independently evaluated the quality appraisal, which was further verified by another author. Each item was coded Y = present, N = not present, P = partially present or N/A = not applicable. Lastly, the positive judgement percentage was calculated to demonstrate the quality of the included studies.

 
2.6 Data Synthesis

There were considerable differences in the study setting, methods, measures of outcomes, time period comparison and significant results reported in the selected studies. Hence, this study conducted a narrative and qualitative synthesis of the key findings. The data are plotted in graphs to report the percentage change in air quality at different time periods, and box plots used to show median and interquartile ranges and correlation tests were conducted to understand the relationships between NO2 and PM2.5, PM10 and PM2.5, and SO2 and PM2.5. The main objective was to categorise and report both qualitative summary and quantitative estimates demonstrating changes in air quality during COVID-19 lockdowns in China.

 
3 RESULTS


 
3.1 Identification and Characteristics of Studies

This study identified 500 studies through the literature search, and another eight studies were included through the backward search of the included studies. A total of 396 studies remained after duplicates (same articles from two different database searches) were removed. The authors reviewed the title and abstract of 396 articles, and screened full texts of 141 articles, amongst which 35 studies met the predetermined inclusion criteria (Fig. 1). Several articles were excluded, because their main focus was to examine the impact of air quality on COVID-19-related infections. Other key reasons for exclusion of articles are illustrated in Fig. 1. Nine of these reported aggregate data on China and the rest estimated air quality changes for various cities and provinces of China (Fig. 2). A majority of the studies (n=24) included Wuhan (located in Hubei Province) as the primary study location.

Fig. 1. Framework of the systematic literature review process.
Fig. 1.
 Framework of the systematic literature review process.

Fig. 2. Study setting of the included literature that met the inclusion criteria. Note: Several studies reported data on multiple regions.Fig. 2. Study setting of the included literature that met the inclusion criteria. Note: Several studies reported data on multiple regions.

All the studies were published in the year 2020 and quantitative in their study design (Table 2). The included studies provided air quality data in China from satellite (n = 12) and ground-level (n = 23) stations. Further analysis revealed that studies conducting global analysis commonly used data from satellite and studies focusing on specific Chinese cities and provinces commonly used data from ground-level stations. A majority of the studies compared the lockdown period of 2020 in China with identical periods of 2019 (n = 15). Other studies compared pre- and post-lockdown periods of 2020 (n = 8), post-lockdown period of 2020 with a mean of identical periods of 2017–2019 (n = 7) or 2015–2019 (n = 5). 26 (74%) studies focused on measuring the change in air quality in specific cities, provinces and regions of China. Finally, we categorised the included studies based on types of pollutants measured. 28 (80%) provided 51 values for NO2 and 26 (75%) recorded 49 values for PM2.5.

 
3.2 Reduction in Air Pollution

Fig. 3 presents the dispersion of changes in the air quality measures of the included studies for all China, and the city of Wuhan and Hubei Province. The estimated median reduction for NO2, PM2.5, PM10, SO2, CO, and AQI during COVID-19 lockdown with data from all the included studies (irrespective of the time period compared) is 45.1%, 26.6%, 31.4%, 31.3%, 20.7% and 21.7%, respectively. All data box plots are comparatively short, which indicates fewer variations in the reported changes in air quality measures in China. All the means and medians are negative, which signifies improvement in air quality across China, irrespective of the time periods compared. The average reduction in all air quality measures (except for CO) were higher in Wuhan and Hubei Province (NO2 = 56.7%, PM2.5 = 31.4%, PM10 = 39.0%, SO2 = 31.9%, CO = 16.5%, and AQI = 40.7%) than the average reduction in China overall. Noticeably, for Wuhan and Hubei Province, SO2 showed the highest spread among the data followed by NO2. The highest reported decrease in SO2 was 105.6% (Zhang et al., 2020a), and the lowest was 3.9% (Lian et al., 2020).

Fig. 3. Percentage change (median) in NO2, PM2.5, PM10, SO2, CO, and AQI during COVID-19 lockdown. Note: x indicates mean value, line in the middle is median value, top line of the box indicates upper quartile, bottom line of the box represents lower quartile and thus, the middle box represents the middle 50% of scores for the group. Lower whisker shows the bottom 25% (quartile group 1) and upper whisker demonstrates the top 25% (quartile group 4) values.Fig. 3. Percentage change (median) in NO2, PM2.5, PM10, SO2, CO, and AQI during COVID-19 lockdown. Note: x indicates mean value, line in the middle is median value, top line of the box indicates upper quartile, bottom line of the box represents lower quartile and thus, the middle box represents the middle 50% of scores for the group. Lower whisker shows the bottom 25% (quartile group 1) and upper whisker demonstrates the top 25% (quartile group 4) values.

Table 3 depicts the percentage change in NO2, PM2.5, PM10, SO2, CO, O3, and AQI for Wuhan and other major regions and cities of China. These figures also present a comparison between different time periods.

Table 3. Quantitative summary of the key findings.

Table 3. (continued).

Table 3. (continued)

Results from aggregate outcomes (China) indicated that average NO2 and SO2 60 days after lockdown were 80% and 50% lower, respectively, than 30 days before lockdown (Fan et al., 2020a). Reduction in PM2.5 was higher (37–39%) immediately after the lockdown (30 days following 23 January 2020); nonetheless, as the comparison time increased (60–90 days) the reported drop was 10.5% (Silver et al., 2020) to 14.8% (Wang et al., 2020b) when compared to identical dates of 2019.

In Wuhan, studies that compared post-lockdown period (23 January 2020 onwards) with pre-lockdown periods or identical times in 2019 or average of 2015–2019 (Table 3) reported a reduction in NO2 (45–93%), PM2.5 (30–44%) and PM10 (35–48.7%). One study compared January–March of 2020 with 2019 and found only a 25% fall in NO2 because it included pre-lockdown period (before 23 January 2020).

For Beijing, the concentration of NO2, PM2.5, SO2, and CO was 25.6–38.8%, 6.4–33.2%, 37.1–48.1% and 11–40% lower, respectively, in 2020 compared to the same months of 2019 (Table 3). In the Yangtze River Delta (YRD) region the reduction in NO2 (27.2–45.1%) was similar to Beijing but the drop in SO2 (7.6–20.4%) was significantly smaller in 2020 compared to the same time in 2019. One study reported the changes in air quality in urban and rural areas of Hangzhou (Wang et al., 2020a). Post-lockdown, concentrations of NO2 (58.4% vs. 48%), PM2.5 (42.7% vs. 18.5%), and PM10 (47.9% vs. 39.6%) shrank considerably more in urban areas than rural (Table 3). Reduction in NO2 was 31.1–32.3% in Guangdong, 48.6–49.2% in Hubei, 30.1–46% in Guangzhou and 43.7% in Shanghai. In contrast, the drop in PM2.5 was 9.6–19.8%, 11.3–26.3%, 23–31% and 26.6–54.5% in Guangdong, Hubei, Guangzhou and Shanghai, respectively. The summary of the findings illustrates the percentage change in air quality in other key cities in China. All these studies reported significant reductions in NO2 (20.2–70%), PM2.5 (7.6–49.2%), PM10 (13.9–47.4%), SO2 (18.9–105.6%) and CO (29.3–66.8%). In contrast, Zhang et al. (2020a) reported an 11% increase in CO in Luzhou, and Wan et al. (2020) and Shakoor et al. (2020) found that SO2 concentration increased by 6.3% and 10.3% in Shenzhen, respectively.

The findings also indicated the rapidness of the change in air quality across China after the lockdown. For example, in the south-western region, NO2 dropped by 49% within two weeks (Chen et al., 2020d), by 63% within 12 days in Wuhan (Cole et al., 2020) and by 31.1% in 10 days in Guangdong (Chen et al., 2021) compared to the period before lockdown. Similarly, by the end of February 2020, NO2 decreased by 64.3% in Jingmen, 65.2% in Enshi (Xu et al., 2020a), and by 83% in Wuhan (Ghahremanloo et al., 2021) compared to February 2019.

Eight out of 10 included articles reported an increase in O3 during lockdown. The median increase was 11.4%, with the highest reported increases in south-western China (110%) and in Wuhan (116%) (Lian et al., 2020) comparing periods before and after lockdown (23 January 2020) (Table 3). Two studies matched O3 concentration in Wuhan between February 2020 with February 2019 and recorded 50% (Ghahremanloo et al., 2021) and 27.1% (Xu et al., 2020a) increases. Zhang et al. (2020b) also showed 10.7% and 18.1% drops in O3 concentration in Beijing during March and April 2020 when compared with the average of March and April over the period 2015–2019. The presence of ultraviolet radiations from sunlight or a lack of sunshine in Beijing during the period was the likely cause of this reduction (Zhang et al., 2020b).

In Table 4, the correlation between various measures of air pollutants was analysed. As expected, these pollutants demonstrated a positive relationship when measured against the reported data from the included studies. 10 studies reported changes in AQI, and the percentage changes in AQIs were significantly correlated with the changes in PM2.5, NO2, CO and PM10. As expected, changes in the level of PM2.5 in the air is highly correlated with PM10 and NO2 with SO2.

Table 4. Correlation analysis.

Table 5 summarises the findings of eight included studies that reported data comparing changes in the air quality for a single geographical area based on different levels of lockdowns or different time periods. Chen et al. (2020d) concluded that air quality improved significantly during Level I (24 January–15 March) compared to Level II (16 March–1 April). Identical findings were reported by Li et al. (2020), who found that reductions in concentration of PM2.5 were 10% lower in Level II than in Level I. Compared with 2019, the reductions of NO2 were 45.1% in Level I and 27.2% in Level II. He et al. (2020) showed that AQI in locked-down cities (19.8) reduced at a higher rate compared to cities that did not have formal lockdowns (6.3). Metya et al. (2020) concluded that China experienced larger reductions in NO2 in February (33%) than in March (15%).

Table 5. Effect of lockdown on air quality at different times and lockdown levels (summary of the key findings). 

Table 5. (continued).

Furthermore, Wang et al. (2020a) indicated that after 15 February (during resumption of work and production activities) both PM2.5 and PM10 increased in Hangzhou compared to the lockdown periods of 24 January–15 February. The findings from all of these studies demonstrate the immediate impact of COVID-19-related lockdowns on air quality in different parts of China. However, as restrictions were eased in subsequent months the rapid pace of improvement in air quality also receded. On the other hand, Liu et al. (2020b) and Wan et al. (2020) identified lower levels of AQI nationwide and in Foshan, respectively, in March (54 and 34, respectively) compared to January (82 and 83, respectively).


3.3 Key Findings in the Included Literature

The included studies made several important arguments regarding the fluctuations of criteria pollutants in China during COVID-19 lockdown. These studies unanimously concluded that lack of traffic movement due to travel restrictions played an important role in reducing air pollution in China (Agarwal et al., 2020; Bao and Zhang, 2020; Cole et al., 2020; Fan et al., 2020a; Le et al., 2020). Others found curbing industrial production, household consumption, and engineering construction (along with traffic movement) also contributed to improvement in air quality (Fan et al., 2020b; Le et al., 2020; Liu et al., 2020b; Ming et al., 2020; Wan et al., 2020; Zhang et al., 2020a; Zhang et al., 2020b).

Restriction on traffic mobility and industrial activity had a variable impact on different criteria pollutants across China. According to Fan et al. (2020b), lower traffic movement caused more reduction in PM2.5 in east China and PM10 in central China. In contrast, reduced industrial activities contributed to a higher drop in PM2.5 and PM10 in south-western and north-eastern China, respectively. The literature also suggested that urban and densely populated areas had experienced larger reductions in air pollution compared to rural areas (Fan et al., 2020a; Filonchyk et al., 2020; Wang et al., 2020a). In addition, Chen et al. (2020c) found that improvements in air quality was more prominent in north-eastern and inland provinces than in south-eastern coastal and western provinces. Similarly, Pei et al. (2020) concluded that concentration of PM2.5 decreased more in Wuhan (inland) compared to Guangzhou and Beijing and Wang et al. (2020a) found a sharp decrease in the NO2 concentration in urban than in rural areas of Hangzhou. In another study, Liu et al. (2020b) showed higher levels of improvement in air quality in commercial areas compared to residential areas.

Several studies argued the importance of accounting for weather conditions when observing pollution levels (Bao and Zhang, 2020; Chen et al., 2020c; Cole et al., 2020; Fan et al., 2020a; He et al., 2020; Pei et al., 2020; Xu et al., 2020b; Zhang et al., 2020b). For example, Cole et al. (2020) used a two-stage random forests machine learning approach and Bao and Zhang (2020) used a least-square dummy variable method to control for the confounding effects of meteorological conditions from pollution patterns. Noticeably, some included studies did not control for this key factor while measuring air quality. However, all the studies that took weather condition into consideration overwhelmingly concluded that COVID-19 lockdown significantly reduced air pollution in China (Bao and Zhang, 2020; Chen et al., 2020c; Fan et al., 2020a; He et al., 2020; Pei et al., 2020; Xu et al., 2020b; Zhang et al., 2020b).

There was an important distinction between the sources of data among the included studies. One group of studies collected data from satellite sources (Metya et al., 2020; Nichol et al., 2020; Shi and Brasseur, 2020; Sicard et al., 2020; Silver et al., 2020; Wang and Su, 2020; Wang et al., 2020a; Zhang et al., 2020b) and others from ground monitoring stations (Agarwal et al., 2020; Chauhan and Singh, 2020; Chen et al., 2020c; Cole et al., 2020). Marlier et al. (2020) concluded that satellite-based results were in general similar to air quality data from ground monitoring stations.

Lastly, numerous studies have concluded that the reduction in air pollution during COVID-19 whilst welcome is unsustainable for China (Bao and Zhang, 2020; Lian et al., 2020; Liu et al., 2020b; Nichol et al., 2020; Shi and Brasseur, 2020). Furthermore, Nichol et al. (2020) and Sicard et al. (2020) commented that despite the improvements, air quality in some regions in China during lockdown were still below the WHO and EU recommended standards.

 
4 DISCUSSION


The outbreak of COVID-19 pandemic has caused more than a million fatalities globally, which has prompted governments around the world (including China) to take unprecedented actions such as lockdowns of affected cities and regions. Restricting human mobility has assisted in lowering COVID-19 infection, morbidity and mortality. Although lockdown disrupted people’s lives and their livelihoods, one of the silver linings was the improvement in air quality globally. In particular, countries such as China, with some of the most polluted cities in the world, experienced significant improvements in air quality immediately following the lockdown. In addition, areas with stringent lockdown responses (Level I or formal lockdowns) experienced greater impact on air quality in China, compared to areas that had partial lockdowns. Through this novel systematic review, we attempted to provide a quantitative and narrative synthesis of the recent studies that examined the influence of COVID-19 lockdown on improvement in outdoor air quality in China.

The included studies demonstrated that the measures of air pollutants improved significantly during COVID-9 lockdown. Pollutants such as PM2.5, PM10, NO2, SO2 and CO dropped, whilst on the contrary, the O3 level increased. The increase in O3 concentration is due to NO2 emissions, and formaldehyde (HCHO) concentrations remaining steady due to the volatile organic compound (VOC) limitations during lockdown (Chen et al., 2020d; Kanniah et al., 2020; Pei et al., 2020; Sicard et al., 2020; Wan et al., 2020; Wang et al., 2020a; Wang et al., 2020c; Xu et al., 2020b). The overall reduction in all air pollutants was attributable to the limited movement of people (Agarwal et al., 2020; Bao and Zhang, 2020; Chauhan and Singh, 2020; Chen et al., 2020d; Cole et al., 2020). 50% of air pollutant PM (Li et al., 2017), 80% of CO and 40% of NO2 (Wang et al., 2008; Xue et al., 2010) in major urban cities of China originate from vehicular exhaustion. Previously, Wang et al. (2017) also found that fossil fuel consumption and transport were the primary elements of air pollution in urban areas of China. Hence, the dramatic reduction in road traffic (e.g., 77% and 39% fewer trucks and cars in the Beijing-Tianjin-Hebei region, respectively) played a major role in improving the air quality in China. Temporary suspension of other human activities such as industrial production (Bao and Zhang, 2020; Cole et al., 2020; Fan et al., 2020b) and construction (Li et al., 2020; Lian et al., 2020) were also responsible for the reduction in air pollution.

The findings further demonstrated that urban, industrial and densely populated areas of China experienced major improvements in air quality compared to rural, residential and less populated areas (Chen et al., 2020c; Filonchyk et al., 2020; Wang et al., 2020a). One probable explanation is that metropolitan and industrialised regions with many inhabitants are most likely to have initial poorer air quality (Chen et al., 2020a; Griffith et al., 2020; He et al., 2020; Ghahremanloo et al., 2021). Hence, lockdown measures had a greater effect. Noticeably, as traffic density is high in urban areas, it is highly correlated with NO2 concentration than in rural and less populated areas (Wang et al., 2020a). Since lockdown commenced, the flow of traffic reduced more in urban areas; it contributed to a greater improvement in air quality. Further analysis of the data also indicated that regions that were subject to stricter lockdown (e.g., Hubei Province) had more significant benefits. For example, Wuhan was under lockdown for 76 days, and its improvement in air quality was much higher than other regions (e.g., Beijing and Shanghai). Moreover, inland cities such as Wuhan had a greater reduction in air pollution compared to coastal cities, such as Guangdong. Due to meteorological factors such as high precipitation and wind flow, air pollution in coastal cities is comparatively low compared to inland cities (Chen et al., 2020c; Wan et al., 2020; Chen et al., 2021). Agarwal et al. (2020) show identical findings for coastal and inland states of India.

Three studies compared the variances in the improvement in air quality based on lockdown levels in China (Chen et al., 2020d; He et al., 2020; Li et al., 2020). All of the studies concluded that during the strictest control measures, all components of air quality (PM2.5, PM10, NO2, SO2, CO) improved significantly compared to areas that had lower levels of restrictions or periods when restrictions were lifted. Others concluded that the reduction in CO, NO2, PM2.5, SO2 were highest immediately after the lockdowns (February 2020) compared with other months (March or April 2020) (Liu et al., 2020a; Metya et al., 2020; Wan et al., 2020; Wang et al., 2020a). This is understandable as stricter lockdowns were associated with extremely low levels of traffic movements, industrial production and other human and economic activities.

It is well documented that weather conditions (e.g., temperature, rain and snowfall, daily maximum and minimum wind speed) play a pivotal role in the concentration of air pollutants (Demuzere et al., 2009; Grange and Carslaw, 2019; Fan et al., 2020a). Several of the included studies accounted for and recorded meteorological factors when indicating the percentage change in concentrations of air pollutants, and the evidence could be regarded as more reliable and complete. Nonetheless, the current study found sufficient evidence of improvement in air quality in China during COVID-19 lockdown irrespective of the changes in weather conditions.

It is important to mention that the level of air pollutants’ concentration in China was largely dependent on the time period compared. For example, Sicard et al. (2020) reported that in Wuhan, NO2 reduced by 57.2%, PM2.5 by 36.3%, PM10 by 48.7% and O3 increased by 37.7% between 23 January to 8 April in 2020 compared to the same period of the average of 2017–2019. In contrast, Xu et al. (2020a) concluded that NO2 reduced by 54.7%, PM2.5 by 44%, PM10 by 47.9% and O3 increased by 27.1% in February 2020 compared to February 2019. Therefore, policymakers and researchers interpreting the changes in air quality data need to pay consideration to this to avoid any misrepresentation of the actual impact of COVID-19 lockdown on air quality. Irrespective of this variability, the findings indicate a strong relationship between human economic activity and air quality. A significant improvement in the air quality immediately after the lockdown is an indication that with appropriate policies, efficient use of technology, and by reducing avoidable traffic movement, it is possible to reverse air pollution.

Several key policy implications could be drawn from the findings of our study. First, all the included studies concluded that reducing human activities (road traffic, industrial production, large scale construction etc.) can significantly improve air quality in a short period of time. However, these activities are essential for continuing economic growth. Hence, one area the Chinese government should focus on (to control air pollution) is reducing the consumption of fossil fuel through private vehicle restrictions (Chen et al., 2021; Liu et al., 2020b). The government should invest in improving public transport networks and encourage the use of vehicles with low carbon emission through tax incentives (Wu et al., 2017). Second, during this pandemic, many workers used internet-based virtual technology to conduct meetings and worked from home, which reduced traffic emissions (Han et al., 2020). Further studies are required on how to use digital technology to curb avoidable road traffic movements without compromising human economic activities. A long-term structural change in economic activities could be initiated (e.g., promote working from home and holding teleconferences) that emits less carbon. Third, China has already implemented many environmental policies that were effective in reducing pollution (Chen et al., 2020b; Ming et al., 2020; Venter et al., 2020). Since 2013 one of the key policies was to establish an air quality monitoring system across the country. This has ensured quality and real-time data on the concentrations of air pollutants throughout China. Accurate information is the key to making successful environmental policies. Other developing countries battling with air pollution could learn from the experience of China to generate accurate air quality information which will assist in developing policies related to air quality management. Fourth, for a large and geographically diverse country like China, it is important to be flexible in implementing environmental policies in different regions. Due to the level of industrialisation, population density and variation in meteorological factors, the concentration of air pollutants differs across China. The policy that is fit for a coastal region might not be appropriate or effective in an inland region. Lastly, future studies should use long-term data from specific regions to understand the exact long-run impact of COVID-19 lockdown in different regions in China. It is important to understand whether length or measures (strict to liberal) of lockdown or weather conditions (e.g., temperature, rainfall) played an important role in reducing air pollution during the lockdown. This will assist in implementing an effective air quality management plan in the future.

Some limitations of this systematic literature review are as follows. First, due to significant differences in the study design, statistical estimation and categories of treatment measured in the selected studies, it was not feasible to conduct a meta-analysis. Second, this study excluded all grey literature, report or non-English language articles. One downside of including published articles only is that studies with null findings has a limited probability of being accepted. Therefore, similar to past systematic reviews, this study could not avoid the likelihood of publication bias. Third, this study did not conduct any forward searching; hence, it difficult to judge whether all potential studies have been included in the review despite all the systematic effort. Lastly, the final search was conducted on the 20 September 2020. Any publication after that that was not included in this study.

 
5 CONCLUSIONS


This qualitative systematic review provides a narrative synthesis of the reported changes in air quality across China due to COVID-19-related lockdowns. Owing to the restrictions imposed on human activities, air pollution, led by traffic-related NO2, decreased significantly within a short period. However, the improvement in air quality varied by location: urban, industrial and densely populated areas experienced the largest gains, but inland regions also showed higher reductions in pollution than costal ones. Additionally, the percentage of decrease in the air pollution depended strongly on the periods chosen for comparison.

Compared to less stringent measures, full lockdowns produced considerably greater effects on the environment. Furthermore, meteorological factors, such as rainfall and temperature, strongly influenced the concentrations of pollutants in an area, although several of the eligible studies failed to address the role of weather conditions in their measurement results. The air pollutant data appeared to be consistent between the satellites and the ground-level monitoring stations, but the lack of identical studies precluded us from statistically verifying this agreement. Lastly, the lockdown-driven improvements in air quality will be insufficient as well as unsustainable unless strict, region-specific environmental policies are implemented.

Despite the limitations of the eligible studies, our review elucidates the relationship between economic activity and air pollution. Future research should continue investigating this link by focusing on specific activities and areas as well as incorporating meteorological factors (e.g., sunlight or rainfall) into estimates of pollutant concentrations. Finally, additional qualitative and quantitative studies should be conducted to assess the role of ground-level monitoring stations, which may enable other severely polluted regions to replicate China’s progress in air quality management.

 
ADDITIONAL INFORMATION


The authors of this study declare the following:

 
Ethics Approval and Consent to Participate

Not applicable as the study is a systematic literature review.

 
Consent for Publication

Not applicable.

 
Availability of Data and Materials

Not applicable.

 
Competing Interests or Conflict Of Interest

The authors of this study declare that they have no competing interest. The authors have no affiliations with or involvement in any organisation or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Rezwanul Hasan Rana declares no conflict of interest.
Syed Afroz Keramat declares no conflict of interest.
Jeff Gow declares no conflict of interest.

 
Funding

No funding was received by the authors of this study for this research study.

 
Acknowledgements

The research librarians of the University of Southern Queensland for assisting in developing the search terms.

 
Authorship Contribution Statement

RHR: worked on conceptualisation, developing methodology, and writing—original draft. SAK: conducted formal analysis. JG: writing—review and editing.


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