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Article

Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China

by
Xinlei Zhao
1,2,
Xian-Xiang Li
1,2,3,*,
Rui Xin
1,2,
Yuejuan Zhang
1,2 and
Chun-Ho Liu
4
1
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
3
Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-sen University, Zhuhai 519082, China
4
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(9), 1144; https://doi.org/10.3390/atmos15091144
Submission received: 25 July 2024 / Revised: 18 September 2024 / Accepted: 20 September 2024 / Published: 23 September 2024
(This article belongs to the Section Air Quality)
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Abstract

:
The photochemical mechanisms of ozone (O3) formation are complex, and simply reducing nitrogen oxide (NOx) emissions is insufficient to reduce O3 concentrations. The lockdown due to the Coronavirus Disease 2019 (COVID-19) pandemic provided a rare opportunity to explore the mechanisms of O3 formation and evaluate the performance of NOx emission control strategies through practical observations. This study integrates data from ground stations with observations from the TROPOMI sensor on the Sentinel-5P satellite to analyze air quality changes during the two one-month lockdown periods in Guangzhou, China, in March and November 2022. Our analysis particularly focuses on the impact of these lockdowns on O3 and NO2 concentrations, along with shifts in the sensitivity of ozone formation. Furthermore, we have assessed concentration changes of four major pollutants: PM2.5, PM10, SO2, and CO. The results show that the average O3 concentration in Guangzhou decreased during the March lockdown, while the average O3 concentration at three stations in the western part of Guangzhou increased during the November lockdown. The western part of Guangzhou is a VOCs (volatile organic compounds)-limited zone, and the NO2 emission reduction from the lockdown reduced the titration effect on O3, which led to the increase in O3 concentration. Overall, the impact of COVID-19 lockdowns on O3 concentrations depended on the local O3 producing sensitive system, and emissions of other major pollutants were reduced substantially, as reported in many other cities around the world.

Graphical Abstract

1. Introduction

The global concern over air pollution has escalated in recent years. According to the World Health Organization (WHO), about 99% of the world’s population lives in places where air pollution levels exceed WHO guideline limits, which results in about 6.7 million deaths per year [1]. Air pollution profoundly affects the respiratory and cardiovascular systems [2], increasing the risk of diseases such as stroke, chronic obstructive pulmonary disease, pneumonia, heart disease, and cancer [1,3,4,5,6]. Especially in urban areas, where economic activities are developed and industrialization is rapid, air pollution significantly impacts human health.
Ozone (O3) is an allotrope of oxygen, forming Earth’s protective layer in the stratosphere, while it acts as a pollutant in the troposphere. Tropospheric (or surface) O3, a secondary pollutant, has garnered substantial attention due to its detrimental effects on human health, vegetation growth, and global climate [7,8,9,10,11]. The first appearance of photochemical smog pollution in Los Angeles in the 1950s prompted European and American countries to strengthen the observation and control of O3 and its precursor pollutants. Although O3 concentrations in Europe increased year by year in the early years, with efforts such as reducing O3 precursor emissions and implementing environmental policies, European countries, especially suburban sites, have seen a recent decline in O3 levels [12]. The successful experience of European countries in the management of O3 pollution shows that O3 pollution can be reduced with scientific guidance and sustainable management [13,14].
During the past few decades, China has experienced rapid economic growth and urbanization, which has changed the nature of air pollution. From smoke and dust in the 1970s and acid rain in the 1980s, the 1990s saw a shift to complex pollution, characterized by nitrogen oxides (NOx) from vehicle emissions, sulfur dioxide (SO2) from coal combustion, particulate matter (PM), and photochemical smog [15,16]. Following the implementation of the ‘Air Pollution Prevention and Control Action Plan’ in 2013, PM2.5 concentrations significantly decreased, but summer ground-level O3 pollution has worsened, becoming a major air pollutant in China [16,17,18,19,20,21,22]. Notably, O3 pollution in China now exceeds that of other industrialized nations [17].
Surface O3 is generated through photochemical reactions involving nitrogen monoxide (NO) and volatile organic compounds (VOCs) under sunlight and is influenced by meteorological factors such as temperature, humidity, and wind speed, which control the occurrence of photochemical reactions and the transport and deposition of O3 [23,24,25,26,27,28,29,30]. Conditions such as high temperatures, low humidity, and low wind speeds are conducive to the formation of O3 pollution [31]. The relationship between O3 and its precursors is non-linear, with formation efficiency varying based on photochemical conditions, which can be NOx-limited, VOC-limited, or mixed control [32,33]. Strategies for controlling O3 pollution must consider these complex interactions.
To analyze responses to changes in O3 concentrations, it is necessary to know the concentrations of VOCs and NOx emissions in the atmosphere. Although many ground-level air quality monitoring stations regularly measure near-surface nitrogen dioxide (NO2) concentrations, they do not measure VOC concentrations. Due to the challenge of determining the exact composition of VOC emissions and considering that formaldehyde (HCHO) is a secondary product in the oxidation pathways of many VOCs, it is usually found around the emission sources. Additionally, HCHO is also directly emitted into the atmosphere through processes such as combustion, and this direct emission should not be overlooked. Despite direct emissions, VOC emissions can be inferred from HCHO concentrations, making HCHO an effective indicator of VOC emissions. Therefore, Sillman [34,35] proposed using HCHO as a proxy for VOC emissions and the HCHO/NOx ratio as a measure of O3 sensitivity. Subsequently, Tonnesen and Dennis [36] proposed using the HCHO/NO2 ratio as a more reliable indicator of O3 sensitivity than the HCHO/NOx ratio. This is because HCHO and NO2 have similar lifetimes and better reflect the response to OH radicals between VOCs and NO2 when NO2 is used instead of NOx. Monitoring atmospheric levels of NO2 and formaldehyde has been the primary goal of many observational satellites in recent decades, prompting researchers to study the ratio of tropospheric columns of formaldehyde to nitrogen dioxide (FNR) to understand the sensitivity of O3 formation [37,38,39,40,41].
The Coronavirus Disease 2019 (COVID-19) pandemic broke out in December 2019. Many countries have taken preventive measures, of which lockdown is one of the most important, to cope with the epidemic and prevent its spread [42]. During lockdowns, human and industrial activities, such as vehicle use, industrial activities, construction operations, and restaurant management, were drastically reduced. Human activities, the largest source of air pollution, were severely restricted, and these restrictions had a direct impact on air quality [43]. Many studies show that there has been an improvement in air quality in different countries during the 2020 lockdown. These studies primarily analyze the impact of COVID-19 lockdowns on air quality through changes in pollutants such as PM2.5 and PM10 (PM with aerodynamic diameters less than 2.5 and 10 μm, respectively), NO2, SO2, carbon monoxide (CO), and O3. In Spain, the concentration of NO2 (−51%) and black carbon (BC, −41%) decreased, but the concentration of O3 (+33–57%) increased [44]. During the Indian lockdown in the spring of 2020, NO2 (−20–40%) and O3 (−5–10%) decreased significantly [45]. In Brazil [46], the concentrations of NO2 (−54.3%) and CO (−64.8%) decreased, while the concentration of O3 (+30%) increased, during the partial lockdown. In Malaysia, concentrations of most air pollutants were found to be decreasing, with NO2 (−40%) decreasing the most, but O3 still did not show much difference [47]. In Europe, compared to the simulated non-lockdown scenario, the observed NO and NO2 emissions during the lockdown period decreased by about 55%, while the net O3 formation rate was not affected by the emission reduction [48].
Since late January 2020, the central government of China has implemented lockdown measures nationwide, and Chinese society almost came to a standstill [43]. During this period, Pei et al. found that the NO2 concentration in three big cities in China (Beijing, Wuhan, and Guangzhou) decreased significantly, while the O3 concentration increased notably in Beijing and Wuhan. The O3 concentration in Guangzhou was still high despite a slight decrease [49]. Shi et al. [50] observed that average PM2.5 and NO2 concentrations decreased by about 29 ± 22% and 53 ± 10%, respectively. In addition, the O3 concentration increased by 2.0 ± 0.7 times at the northern China site. In Wuhan, they observed a decrease in PM2.5 and NO2 concentrations of 31 ± 6% and 54 ± 7%, respectively, and an increase in O3 concentration by 2.2 ± 0.2 times, along with a decrease in SO2 and NOx levels [50]. Overall, during the lockdown period, NO2, PMs, and CO all decreased obviously, while O3 showed different variation patterns in different areas.
To further study the impact of urban lockdown measures on air pollution, particularly O3, the present research selected two one-month lockdown periods in Guangzhou during March and November 2022 as case studies. During these periods, lockdown measures were implemented to curb the spread of COVID-19, leading to a significant reduction in human activities. This study assesses the changes in O3 and NO2 concentrations during these two lockdown periods, the sensitivity of the ozone generation system during the lockdown, and the changes in concentrations of four other pollutants (PM2.5, PM10, SO2, CO). Changes in air quality during the COVID-19 pandemic can provide an important reference for regional air quality control. Combining the situation of air pollution when economic activities were reduced under the COVID-19 lockdown scenario in 2022 with the lockdown in 2020, which has been extensively studied before, can foster a more comprehensive understanding of the contribution of local emission sources to air pollutants and thus provide scientific recommendations for the mitigation and control of air pollution.

2. Data and Methods

2.1. Study Area and Period

Guangzhou is situated in the central and northern part of the Pearl River Delta (PRD), where the West River, North River, and East River converge. It is also the core city of the Guangdong-Hong Kong-Macao Greater Bay Area and the PRD urban agglomeration, with a high population density and a well-developed industry. As of the end of 2022, the total population of Guangzhou reached 10.35 million, with a population density of 2520 people/km2. The total regional GDP of Guangzhou reached CNY 2883.9 billion, and the total industrial output value was CNY 2557.4 billion, with a per capita regional GDP of CNY 153,625 [51]. Since the implementation of the National Environmental Air Quality Standards (GB3095-2012) in 2013, the air quality of Guangzhou has continued to improve. Although PM2.5, PM10, NO2, SO2, and CO in Guangzhou all met the standards in 2022, the O3 concentration still exceeded the standard and has become the primary factor affecting the air quality standard [52]. Due to the highly developed industry in Guangzhou, industrial emissions of VOCs and O3 precursors such as NO2 are highly likely to cause the accumulation of O3 and other pollutants in Guangzhou [53]. In addition, the seasonal characteristics of pollution in Guangzhou are obvious, with PM and NO2 dominating in the winter and spring, with O3 dominating in the summer and autumn [54].
In response to recurrent COVID-19 outbreaks and to mitigate virus transmission, the Guangzhou Municipal Government implemented two one-month lockdown measures restricting social contact from 9–30 March and 6–30 November, 2022, respectively. To evaluate the emission reduction effects and exclude the impacts of increased pollutant emissions due to economic growth and industrialization, climate change, and pandemic factors post-2020 on pollutant concentrations, the study period was selected as March and November of 2017–2019 and 2022.

2.2. Data Source

2.2.1. Ground Data

This study used hourly (or three-hourly) concentrations of NO2, O3, PM10, PM2.5, SO2, and CO from the National Urban Air Quality Real-Time Dissemination Platform (NUAQRDP) of the China General Environmental Monitoring Station (CGEMS) (https://air.cnemc.cn:18007/, accessed on 10 November 2023) network for the periods of 2017–2019 and March and November 2022. The 2-m temperature, precipitation, wind speed, and wind direction were obtained from the China Meteorological Administration (http://data.cma.cn/, accessed on 19 November 2023). The meteorological data from the nearest meteorological station were used to describe the meteorological conditions of each air quality station, and air quality stations more than 15 km away from the nearest meteorological station were removed from the analysis. In this study, ground-level pollutant data from 11 air quality stations and data from 5 meteorological stations in Guangzhou were used, with the locations of these stations depicted in Figure 1b.
To minimize the influence of factors other than lockdown on the change of pollutant concentrations, dates with all hourly wind speeds ≤5 m/s and all six-hourly cumulative precipitation ≤6 mm within one day were first selected. Then, the average temperature and relative humidity for the dates during the BASE period were calculated. Finally, dates during the LOCK period were screened to ensure that the temperature and relative humidity were within ±10 °C and ±25% of the corresponding average values from the BASE period. These meteorological factors are based on Chinese standards (GB/T28591-2012 and GB/T28592-2012), which define wind scale and precipitation grade, as well as previous research by Lin et al. [55]. The pollution data used were derived on the selected dates from the air quality station nearest to each meteorological station. The specific research dates are divided into six phases: BASE1, BASE2, LOCK1, LOCK2, S5P1, and S5P2. Here, BASE refers to the years 2017–2019, LOCK refers to the year 2022, and S5P refers to the year 2019. The number following the letters, 1 or 2, denotes March or November, respectively. The exact dates within each phase are shown in Table 1.

2.2.2. Satellite Data

Data for this study were obtained from the Tropospheric Monitoring Instrument (TROPOMI), a component of the Sentinel-5 Precursor (S5P) satellite [56,57]. Launched by the European Space Agency in October 2017, S5P’s mission encompasses the monitoring of UV radiation, air quality indicators, ozone layer status, and climate variables [56,58]. S5P operates in a near-polar, sun-synchronous orbit at an altitude of 824 km. It crosses the equator at approximately 13:30 local solar time and completes 14 orbits each day, delivering a comprehensive global view of atmospheric composition daily [56], with a transit time through Guangzhou at approximately 12:30 local time (BJT). The onboard technology, TROPOMI, measures reflected solar radiation and retrieves the concentrations of various atmospheric gases, including CO, SO2, NO2, O3, HCHO, and methane (CH4). With a spatial resolution of 3.5 × 7 km2 and a swath width of 2600 km, TROPOMI/S5P provides global coverage [56,58,59].
All S5P datasets are available in three versions: near real-time (NRTI), offline (OFFL), and reprocessed (RPRO). NRTI data are available within 3 h of data collection, OFFL data are available within a few days of collection, and there is no time limit on RPRO data (implemented when major product upgrades are required) [60]. Data on NO2 and HCHO were available on 31 April 2018. Data processed with version 02 processors were uniformly selected using the RPRO version for 2019 data and the OFFL version for 2022 data to avoid the impact of processor version updates on data quality. The data were transformed and filtered through the HARP toolbox, which is designed to process atmospheric data, merging and pre-processing satellite data in time and space so that the final data have the same spatial, temporal, and grid format for comparison and manipulation [61]. The tool filters and removes pixels that fail to satisfy the quality assurance (qa_value), thereby discarding cloud cover and other substandard retrievals. Specifically, for tropospheric HCHO columns, pixels with a qa_value above 0.5 are removed, and for tropospheric NO2 columns, pixels with a qa_value exceeding 0.75 are filtered out. The code used to process the data is adapted from Nirdesh [62].

2.2.3. Local Climate Zones (LCZ) Data

The Local Climate Zone (LCZ) data used in this study was obtained from Xin et al. with a spatial resolution of 120 m [63]. LCZ is a classification method that divides the urban subsurface into 17 types based on 10 indicators, including the sky visibility factor, aspect ratio, percentage of built-up surfaces, percentage of impervious surfaces, and surface albedo. The method considers differences in surface materials, land cover types, surface structures, and human activities. LCZ includes 10 building types from LCZ 1 to LCZ 10 and 7 natural types from LCZ A to LCZ G [64]. Due to a resolution mismatch between the LCZ map and the S5P product processed by the HARP toolbox, the LCZ map is resampled to 0.02° using the mode algorithm. After resampling, the LCZ type is checked where the filtered air quality stations are located. In this study, LCZ1-10 represents urban land use types, and LCZ A-G represents suburbs. Based on this, the air quality stations are classified into urban stations and suburban stations. Figure 2b shows the resampled LCZ map and air quality station classifications.

3. Results and Discussion

3.1. Changes in Ozone and Nitrogen Dioxide

In this study, the concentrations of O3 and NO2 in Guangzhou were analyzed during the lockdown (LOCK) and the non-lockdown (BASE) periods under similar meteorological conditions. Figure 3 and Figure 4 display the station distribution and variation of mean O3 and NO2 concentrations during BASE and LOCK in Guangzhou. The average concentrations of NO2 and O3 in different parts of Guangzhou are shown in Table 2. O3 concentrations were generally high in Guangzhou, while NO2 concentrations were most prominent in the western center of the city.
During the BASE period, the average daily maximum 8-h average O3 (MDA8O3) concentration in Guangzhou exceeded 90 μg/m3, while the average NO2 concentration at the four high concentration sites in the western part of the city exceeded 50 μg/m3. However, during the LOCK period, all the sites’ NO2 emissions decreased (March: −40.17 ± 12.19%, November: −12.29 ± 0.65%), resulting in regional average NO2 concentrations dropping from 63.87 μg/m3 during BASE1 to 30.20 μg/m3 during LOCK1 in March, and from 54.68 μg/m3 during BASE2 to 37.89 μg/m3 during LOCK2 in November. NO2 is a reddish-brown gas commonly used as an indicator of urban air pollution and industrial activities and is primarily generated during high-temperature combustion of fossil fuels, industrial emissions, and vehicle exhausts [44,65,66]; therefore, the reduction in traffic emissions and industrial operations during the lockdown could significantly reduce the airborne concentrations of NO2 [67,68,69,70,71]. Similar reductions in NO2 concentrations were observed in other areas during the lockdown [45,72,73,74]. In contrast, O3 showed a significant decrease (55.63 ± 0.52%) at three stations in the central part of Guangzhou in March, where the mean MDA8O3 decreased from 96.94 μg/m3 to 43.00 μg/m3. However, during November, a significant increase (28.13 ± 7.89%) was observed at three stations in the western part of Guangzhou, where the mean MDA8O3 increased from 94.28 μg/m3 to 120.89 μg/m3.
The differences in the spatial patterns of NO2 and O3 can be attributed to the complex reaction mechanisms that control ground-level O3 production. On one hand, the decrease in NO2 concentration is associated with the NOx titration effect, where the decrease in NOx, especially the decrease in NO from road traffic, leads to a decrease in O3 depletion, resulting in an increased O3 concentration [75,76,77,78,79]; that is, according to the following reactions:
NO2 + hγ → NO + O
O + O2 + M → O3 + M
NO + O3 → NO2 + O
where Reaction (3) represents the O3 depletion of NOx, so as the NOx concentration decreases, the O3 concentration increases.
On the other hand, the generation of O3 also relies on the content of VOCs. Hydroxyl radicals (OH) oxidize VOCs and promote the creation of organic radicals (RO2·and RO·) and O3 formation through photolysis of NO2, as shown in the following reactions:
VOCs + OH → RO2 + H2O
RO2 + NO → RO + NO2
At the end of Reaction (5), the resultant NO2 undergoes photolysis, yielding atomic oxygen. This atomic oxygen then merges with oxygen to form O3 (Reaction (1)), thereby initiating a new cycle of Reactions (1)–(3) [80]. Thus, when the VOC concentration remains constant or does not decrease significantly, and the NOx concentration decreases, the NO generated in Reaction (1) also decreases, so the amount of ozone consumed in (3) decreases and the ozone concentration increases [49,77]. Other studies have also observed a significant increase in O3 pollution in urban areas [81], especially in megacities [82], during lockdowns. As ground-level O3 production is driven by non-linear, complex photochemical reaction processes controlled by VOC and NOx emissions [83,84,85], the reduction in precursor emissions associated with the lockdown may also lead to a reduction in O3 concentrations in some areas [86,87].

3.2. Sensitivity Analysis of Ozone Generation

In this study, we used the FNR as an indicator to distinguish between different levels of O3 formation sensitivity. Since the in situ observation of HCHO is generally not available, the FNR was calculated as the ratio of the column of HCHO to NO2 from TROPOMI satellite data. When the FNR is lower than 1, it is considered to be in the VOC-limited zone (NOx saturated), where the O3 concentration increases with the reduction of NOx. When the FNR is higher than 2, it suggests a NOx-limited zone, where the reduction of NOx reduces the O3 formation. When the FNR is between 1 and 2, it is in the mixed control zone [32,37,88].
Based on the FNR of the four periods in Guangzhou, as shown in Figure 5, it can be observed that the eastern suburbs of Guangzhou are located between the NOx-limited zone and the mixing control zone, while the western and central urban areas are situated between the VOC-limited zone and the mixing control zone. As previously stated, HCHO is a secondary product in the oxidation pathway of many VOCs. Therefore, changes in HCHO can be used to represent changes in VOCs. During the LOCK1 period, the O3 concentration in the VOC-limited zone (Figure 3c) did not increase or even decrease significantly. This is caused by the combined effects of two factors: the reduction in VOC emissions during this period, as shown in Figure 6a, led to a reduction in O3; and the reduction in NOx emissions, particularly from traffic and other sources, led to a weakened NO titration effect on O3, since in a VOC-limited zone a decrease in NOx leads to less NO available to react with O3, slowing down the O3 depletion reaction and allowing for an accumulation of O3. The effect of VOC reduction offset the effect of weakened titration, resulting in the reduced O3 concentration. In contrast, the O3 concentration in the western part of Guangzhou increased during LOCK2 (Figure 3f). In addition to the weakened titration effect caused by reduced NO, the increase in HCHO in western Guangzhou during LOCK2, i.e., the increase in VOC emissions, as shown in Figure 6b, is also a contributing factor to the rise in O3 concentrations in the VOC-limited zone [89,90]. During LOCK2, three school sites in western Guangzhou had elevated O3 concentrations (Figure 3f). To maintain normal teaching and learning during lockdowns, schools were operated in a closed environment, which increased VOCs from daily sources (e.g., VOCs emitted from coal, gas, and biomass combustion in activities such as cooking and heating in residential household life and commercial activities such as lodging and catering).

3.3. Changes in Other Pollutants

The changes in concentrations of PM2.5, PM10, SO2, and CO during BASE and LOCK periods under similar meteorological conditions were analyzed and are presented in Figure 7. Table 2 presents the average concentrations of these four pollutants in various regions of Guangzhou. The lockdown measures implemented during the LOCK periods led to a decrease in the number of people and vehicles in public places, as well as the closure of most industrial enterprises, construction sites, entertainment, and catering companies These lockdowns led to an unprecedented reduction in ambient air pollution in many areas.
Overall, significant decreases were observed for all four pollutants at most air quality stations in Guangzhou. Compared with BASE, the average concentration of PM2.5 in Guangzhou decreased by 19.82 μg/m3 in March and 4.59 μg/m3 in November. Similarly, the average concentration of PM10 decreased by 29.45 μg/m3 in March and 4.39 μg/m3 in November. PMs originate from various sources, such as emissions from the food industry and biomass combustion, as well as road dust [91,92]. Other pollution control measures, in addition to epidemic lockdowns, can also result in a reduction of PM concentrations. Li et al. [93] documented a significant decrease in PM10 and PM2.5 concentrations by the pollution control measures implemented during the Asia-Pacific Economic Cooperation (APEC) meeting in Beijing, China. The SO2 concentration in Guangzhou decreased by 4.83 μg/m3 in March and 2.49 μg/m3 in November. SO2 emissions mainly originate from stationary pollutants such as factories, so a reduction in industrial activities due to the lockdown would reduce SO2 emissions from industrial sources [94,95]. CO is a toxic gas that mainly comes from the incomplete combustion of carbon-containing substances. In comparison to BASE, the average concentration of CO in Guangzhou decreased by 20.7 mg/m3 in March and 11.2 mg/m3 in November. During the lockdown, with people spending more time at home, domestic activities, including cooking and water heating using gas, became more frequent, which may have contributed to increased CO emissions in some areas [96].

3.4. Monthly Variation of Pollutants in Urban and Suburban Areas

Pollutants are primarily emitted by transportation, industrial production, and power plants and are often concentrated in downtown areas. Therefore, the effects of emission reduction in cities and suburbs may differ. Figure 8 and Figure 9 analyze the monthly fluctuations of six ground-level pollutants at urban and suburban sites in Guangzhou. During the lockdowns, several measures were implemented to control environmental pollution, such as limiting traffic and reducing industrial production, which had a positive effect on improving the city’s air quality [97,98]. The epidemic led to a decrease in economic activity and traffic, resulting in reduced carbon emissions and air pollution levels in China. This has helped to alleviate ecological and environmental pollution [99].
However, changes in pollutant concentrations varied between urban and suburban areas. Based on the data presented in Figure 8 and Figure 9, it can be observed that the implementation of lockdown measures had a significant impact on improving air quality in Guangzhou. The concentrations of the five ground pollutants besides O3 in the city are higher than those in the suburbs, with NO2 showing the largest difference (March: 60.13%, November: 132.89%). This difference may be due to emissions from urban traffic, industry, and other activities that result in more intensive emissions. On the other hand, the urban O3 concentration is lower than that in the suburbs (March: −30.43%, November: −16.12%). This is caused by the titration effect of NOx on O3, as mentioned in Section 3.1. However, in 2022, the gap in pollutant concentration between urban and suburban areas has narrowed. Even the PM2.5 concentration in March and the SO2 concentrations in March and November are higher in suburban areas compared to urban areas. The impact of measures such as travel restrictions and factory shutdowns during the lockdowns on air pollution is more noticeable in urban areas. This is because pollution sources and emissions in urban areas were effectively controlled during the lockdown, while pollution sources and emissions in suburban areas may not have been controlled to the same degree.
It is worth mentioning that throughout March, the O3 concentration increased in urban areas, while in the suburbs in March and in the entire Guangzhou area in November, the O3 concentration decreased. This is the opposite of the situation described in Section 3.1, when only the selected dates were used for comparison. Meteorological conditions, such as temperature, wind speed, and wind direction, are related to changes in O3 concentration. The opposite situation in March indicates that conditions are conducive to O3 generation and not diffusion, thus increasing O3 concentration. In contrast, the conditions in November are not conducive to increasing O3 concentration.
At the same time, it can be noted that the urban-rural differences for different pollutants vary differently in March and November. This variation may be attributed to factors such as the characteristics of different emission sources, the influence of meteorological conditions, and differences in the degree of implementation of lockdown measures.
The decrease in the number of people and vehicles in public areas during the lockdowns, as well as the closure of numerous industrial establishments, construction sites, and entertainment and catering facilities, may have resulted in unexpected changes in the concentrations of certain pollutants. Furthermore, the concentrations of NO2, PM2.5, and PM10 were higher on the grey background dates than on the other dates. This may be caused by rainfall washing pollutants from the air to the ground (wet scavenging), thereby reducing the airborne pollutant concentration. Additionally, windy conditions disperse pollutants, leading to a reduced concentration [100,101].

3.5. Comparison with Lockdowns in 2020

To contextualize the findings from the 2022 lockdown periods within the broader scope of air quality trends, a comparative analysis with data from the literature focusing on the January–March 2020 lockdown in Guangzhou is warranted. The average O3 concentration in 2022 was 84 ± 19 µg/m3, whereas Lin et al. reported an average O3 concentration of 48 ± 10.4 µg/m3 during the 2020 lockdown, indicating an increase in ozone levels in 2022 [55]. Similarly, the average NO2 concentration in 2022 was 36 ± 12 µg/m3, while Pei et al. reported an average NO2 concentration of about 30 µg/m3 during the 2020 lockdown [49]. Our results in Section 3.1 note that, compared to the BASE period, although NO2 levels decreased due to reduced human activities, O3 levels increased in some areas, highlighting the complex balance between precursor emissions and photochemical reactions, as also mentioned in Lin et al. [55].
The concentration of PM2.5 in 2022 were 32 ± 4 µg/m3, while Lin et al. reported that the concentration of PM2.5 was approximately 25 µg/m3 during the 2020 lockdown [55]. This suggests that, despite similarities in lockdown measures between the two years, changes in pollutant concentrations could be influenced by meteorological conditions, variations in economic activity, and the intensity of the lockdown, among other factors.
The comparison of air pollutant concentrations between 2020 and 2022 may also be influenced by seasonal and meteorological differences. The 2020 Guangzhou lockdown occurred in the first few months of the year, and the weather patterns during this period may be different compared to the data from March and November 2022. The weather conditions, particularly temperature, sunlight hours, and precipitation, can significantly affect the formation and dispersion of pollutants, which could partially explain the observed differences in pollutant concentrations. The comparison between the two years’ datasets highlights the importance of considering seasonal variations and meteorological conditions when assessing the effectiveness of air quality management strategies.

4. Conclusions

This study evaluated the concentrations of O3 and NO2 in Guangzhou during the lockdown and non-lockdown periods. This study found that during the two lockdown periods in March and November 2022, the NO2 emissions from all stations in Guangzhou decreased (March: −40.17 ± 12.19%, November: −12.29 ± 0.65%), while the change in O3 concentration was more complex, showing different trends in different regions and periods. During LOCK1, the O3 concentration at the three central stations decreased significantly (55.63 ± 0.52%), while during LOCK2, the O3 concentration at the three western stations in Guangzhou increased significantly (28.13 ± 7.89%). These changes are closely related to the reduction in traffic emissions, industrial activities, and anthropogenic activities induced by the lockdown measures.
In the process of analyzing the sensitivity of O3 formation, the ratio of HCHO to NO2 (FNR) was used as an indicator to distinguish the sensitivity of O3 formation. The results showed that the suburbs of Guangzhou were between the NOx-limited zone and the mixed control zone, so the reduction of NO2 emissions in the suburbs inhibited the formation of O3 during the day, leading to a decrease in O3 concentration in March. Conversely, the urban area was between the VOC-limited zone and the mixed control zone, resulting in an increase in O3 concentration in the western part of Guangzhou in November due to the reduction of NOx emitted by traffic.
We also evaluated the variations in other pollutants like PM2.5, PM10, SO2, and CO. It was found that during the lockdown periods, most air quality stations showed a significant decrease in air pollution levels, and measures such as the closure of public places and traffic restrictions during the lockdown period alleviated air pollution levels. In addition, we also found that during the lockdown period, the changes in pollutant concentrations in urban and suburban areas were different. The impact of travel restrictions and factory shutdowns on air pollution was more pronounced in urban areas during the lockdown period.
In summary, the results of this study reveal the impact of the lockdown on air quality in Guangzhou and provide an in-depth analysis of the monthly changes in different pollutants in urban and suburban areas, providing important reference information on how to develop effective environmental policies for future public health crises. These findings are of great value for formulating environmental protection and emission reduction policies. However, our study also shows that some short-term environmental improvements brought about by the lockdown cannot replace long-term environmental protection policies and measures. Therefore, it is necessary to find more effective ways to improve our environment to achieve sustainable development [102].

Author Contributions

Conceptualization, X.Z., X.-X.L. and C.-H.L.; methodology, X.Z. and X.-X.L.; software, X.Z. and Y.Z.; validation, X.Z.; formal analysis, X.Z.; investigation, X.Z. and X.-X.L.; resources, X.-X.L.; data curation, X.-X.L., R.X. and Y.Z.; writing—original draft preparation, X.Z.; writing—review and editing, X.-X.L. and C.-H.L.; visualization, X.Z.; supervision, X.-X.L.; project administration, X.-X.L.; funding acquisition, X.-X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (No. SML2023SP216), Guangdong Basic and Applied Basic Research Foundation (No. 2021B0301030007 and 2023A1515240065), and National Natural Science Foundation of China (No. 42088101).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The air pollution data used in this study can be downloaded from National Urban Air Quality Real-Time Dissemination Platform (NUAQRDP) of the China General Environmental Monitoring Station (CGEMS) (https://air.cnemc.cn:18007/, accessed on 10 November 2023). The meteorological data can be downloaded from China Meteorological Administration (http://data.cma.cn/, accessed on 19 November 2023). The Sentinel-5 Precursor (S5P) satellite data can be downloaded from Sentinel Data Hub (https://www.sentinel-hub.com/, accessed on 28 April 2024).

Acknowledgments

We thank the two anonymous reviewers and editor for their constructive comments, which helped to greatly improve this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Guangzhou city-level administrative planning. (a) Guangzhou ground observation station. (b) Air quality stations more than 15 km away from the nearest meteorological station were deleted. Blue triangles denote meteorological station locations, red circles denote air quality monitoring station locations, and pink circle shadows denote the range within 15 km from the meteorological station.
Figure 1. Guangzhou city-level administrative planning. (a) Guangzhou ground observation station. (b) Air quality stations more than 15 km away from the nearest meteorological station were deleted. Blue triangles denote meteorological station locations, red circles denote air quality monitoring station locations, and pink circle shadows denote the range within 15 km from the meteorological station.
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Figure 2. Local Climate Zone (LCZ) maps at the resolution of (a) 120 m and (b) 0.02°. Cyan triangles represent urban air quality stations, and pink triangles represent suburban air quality stations.
Figure 2. Local Climate Zone (LCZ) maps at the resolution of (a) 120 m and (b) 0.02°. Cyan triangles represent urban air quality stations, and pink triangles represent suburban air quality stations.
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Figure 3. Average ground-level MDA8O3 concentration (μg/m3) for (a) LOCK1, (b) BASE1, (d) LOCK2, and (e) BASE2 periods; and MDA8O3 concentration difference (μg/m3) between (c) LOCK1 and BASE1 and (f) LOCK2 and BASE2 periods. Circles represent sites with data, and x represents sites with missing data.
Figure 3. Average ground-level MDA8O3 concentration (μg/m3) for (a) LOCK1, (b) BASE1, (d) LOCK2, and (e) BASE2 periods; and MDA8O3 concentration difference (μg/m3) between (c) LOCK1 and BASE1 and (f) LOCK2 and BASE2 periods. Circles represent sites with data, and x represents sites with missing data.
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Figure 4. Average ground-level NO2 concentration (μg/m3) for (a) LOCK1, (b) BASE1, (d) LOCK2, and (e) BASE2 periods; NO2 concentration difference (μg/m3) between (c) LOCK1 and BASE1 and (f) LOCK2 and BASE2 periods. Circles represent sites with data, and x represents sites with missing data.
Figure 4. Average ground-level NO2 concentration (μg/m3) for (a) LOCK1, (b) BASE1, (d) LOCK2, and (e) BASE2 periods; NO2 concentration difference (μg/m3) between (c) LOCK1 and BASE1 and (f) LOCK2 and BASE2 periods. Circles represent sites with data, and x represents sites with missing data.
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Figure 5. FNR of Guangzhou as calculated from satellite data during (a) S5P1, (b) LOCK1, (c) S5P2, and (d) LOCK2 periods.
Figure 5. FNR of Guangzhou as calculated from satellite data during (a) S5P1, (b) LOCK1, (c) S5P2, and (d) LOCK2 periods.
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Figure 6. Difference in satellite-derived HCHO tropospheric column concentration between lockdown and corresponding baseline periods in Guangzhou in 2022. (a) LOCK1-S5P1 and (b) LOCK2-S5P2.
Figure 6. Difference in satellite-derived HCHO tropospheric column concentration between lockdown and corresponding baseline periods in Guangzhou in 2022. (a) LOCK1-S5P1 and (b) LOCK2-S5P2.
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Figure 7. Differences in ground-level concentrations of PM2.5, PM10, SO2, and CO between LOCK and corresponding BASE periods. (a,b) PM2.5, (c,d) PM2.5, (e,f) SO2, and (g,h) CO. Circles represent sites with data, and x represents sites with missing data.
Figure 7. Differences in ground-level concentrations of PM2.5, PM10, SO2, and CO between LOCK and corresponding BASE periods. (a,b) PM2.5, (c,d) PM2.5, (e,f) SO2, and (g,h) CO. Circles represent sites with data, and x represents sites with missing data.
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Figure 8. Monthly changes in ground-level concentrations of six pollutants in cities and suburbs for March 2022 and 2017–2019. Ozone is the daily maximum 8-h moving average, and the other pollutants are the 24-h averages. The unit for CO is mg/m3, and the units for the other five pollutants are μg/m3. The yellow line represents the average concentration of pollutants during the BASE period, the purple line represents the average concentration of pollutants in 2022, the thin line represents concentration of the pollutant, the yellow shading is the standard deviation, and the grey shading indicates the selected dates after the meteorological condition filtering process (i.e., LOCK1 and BASE1 periods).
Figure 8. Monthly changes in ground-level concentrations of six pollutants in cities and suburbs for March 2022 and 2017–2019. Ozone is the daily maximum 8-h moving average, and the other pollutants are the 24-h averages. The unit for CO is mg/m3, and the units for the other five pollutants are μg/m3. The yellow line represents the average concentration of pollutants during the BASE period, the purple line represents the average concentration of pollutants in 2022, the thin line represents concentration of the pollutant, the yellow shading is the standard deviation, and the grey shading indicates the selected dates after the meteorological condition filtering process (i.e., LOCK1 and BASE1 periods).
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Figure 9. Monthly changes in ground-level concentrations of six pollutants in cities and suburbs for November 2022 and 2017–2019. Ozone is the daily maximum 8-h moving average, and the other pollutants are the 24-h averages. The unit for CO is mg/m3, and the units for the other five pollutants are μg/m3. The yellow line represents the average concentration of pollutants during the BASE period, the purple line represents the average concentration of pollutants in 2022, the thin line represents concentration of the pollutant, the yellow shading is the standard deviation, and the grey shading indicates the selected dates after the meteorological condition filtering process (i.e., LOCK2 and BASE2 periods).
Figure 9. Monthly changes in ground-level concentrations of six pollutants in cities and suburbs for November 2022 and 2017–2019. Ozone is the daily maximum 8-h moving average, and the other pollutants are the 24-h averages. The unit for CO is mg/m3, and the units for the other five pollutants are μg/m3. The yellow line represents the average concentration of pollutants during the BASE period, the purple line represents the average concentration of pollutants in 2022, the thin line represents concentration of the pollutant, the yellow shading is the standard deviation, and the grey shading indicates the selected dates after the meteorological condition filtering process (i.e., LOCK2 and BASE2 periods).
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Table 1. The selected date corresponding to each station in each research period. BASE refers to the years 2017–2019, LOCK refers to the year 2022, and S5P refers to the year 2019. The number following the letters, 1 or 2, denotes March or November, respectively.
Table 1. The selected date corresponding to each station in each research period. BASE refers to the years 2017–2019, LOCK refers to the year 2022, and S5P refers to the year 2019. The number following the letters, 1 or 2, denotes March or November, respectively.
PeriodMeteorological StationYearSelected Dates
BASE159284201710, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23, 24, 25, 26, 27, 28
20189, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29
201910, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
59285201712, 13, 14, 15, 16, 17, 20, 21, 24, 25, 26, 27, 28, 29
20189, 10, 11, 12, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 29
201911, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
5928720179, 10, 11, 12, 13, 15, 16, 17, 20, 21, 23, 24, 25, 26, 27, 28, 29
20189, 10, 11, 12, 13, 15, 16, 22, 23, 24, 25, 26, 27, 28, 29
201910, 11, 12, 13, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28
5929420179, 10, 11, 12, 13, 16, 17, 20, 21, 23, 24, 25, 26, 27, 29
201810, 11, 12, 13, 14, 15, 16, 17, 18, 22, 23, 24, 25, 26, 27, 28, 29
201911, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
5948120179, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23, 24, 25, 26, 27, 28, 29
20189, 10, 11, 12, 13, 14, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29
201910, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
BASE25928420176, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
20186, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29
20196, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
5928520176, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 28, 29
20186, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 27, 28, 29
20196, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29
5928720176, 8, 9, 10, 11, 12, 15, 16, 17, 19, 25, 27, 28, 29
20186, 7, 9, 10, 11, 13, 14, 15, 17, 20, 23, 24, 27, 28, 29
20196, 9, 10, 11, 12, 15, 16, 17, 19, 20, 21, 22, 23, 24, 26, 29
5929420177, 9, 10, 15, 16, 17, 20, 21, 24, 26, 27, 28, 29
20186, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 24, 27, 28, 29
20196, 9, 10, 11, 12, 13, 15, 16, 17, 21, 22, 23, 24, 27
5948120176, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
20186, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29
20196, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
LOCK159284202213, 14, 17, 18, 19, 20, 21, 22, 28, 29
59285202212, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 29
59287202216, 21, 22, 28, 29
59294202216, 17, 18, 19, 21, 22, 26, 28, 29
59481202213, 14, 16, 17, 18, 19, 20, 21, 22, 28, 29
LOCK25928420226, 7, 8, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21
5928520226, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29
5928720226, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 27, 28, 29
5929420226, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 27, 28, 29
59481202213, 15, 16, 17, 19, 20, 21, 27
S5P159284201910, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
59285201911, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
59287201910, 11, 12, 13, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28
59294201911, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28
59481201910, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
S5P25928420196, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
5928520196, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29
5928720196, 9, 10, 11, 12, 15, 16, 17, 19, 20, 21, 22, 23, 24, 26, 29
5929420196, 9, 10, 11, 12, 13, 15, 16, 17, 21, 22, 23, 24, 27
5948120196, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29
Table 2. Average concentrations of ground-level pollutants for each study period.
Table 2. Average concentrations of ground-level pollutants for each study period.
PollutantPeriodWestern GuangzhouCentral GuangzhouAll
O3 (µg/m3)LOCK188.01 ± 2.8743.00 ± 4.0165.50 ± 24.85
LOCK2120.89 ± 10.8287.07 ± 3.23103.98 ± 19.85
BASE188.79 ± 2.9096.94 ± 9.0892.86 ± 7.50
BASE294.28 ± 1.6597.71 ± 1.1995.99 ± 2.28
NO2 (µg/m3)LOCK134.71 ± 4.9229.53 ± 8.4432.12 ± 6.80
LOCK243.35 ± 8.5736.83 ± 16.2740.09 ± 12.16
BASE160.01 ± 3.8150.51 ± 21.6855.26 ± 14.87
BASE250.88 ± 4.7642.20 ± 21.7646.54 ± 15.31
PM2.5 (µg/m3)LOCK122.24 ± 1.6315.93 ± 1.8919.08 ± 3.80
LOCK235.63 ± 4.1032.13 ± 5.2333.88 ± 4.62
BASE140.98 ± 0.2236.83 ± 6.7738.91 ± 4.85
BASE238.09 ± 3.6338.84 ± 4.3938.47 ± 3.62
PM10 (µg/m3)LOCK140.20 ± 3.1631.4 ± 6.1035.80 ± 6.49
LOCK259.41 ± 3.3760.09 ± 8.2659.75 ± 5.66
BASE167.30 ± 3.6463.20 ± 14.9265.25 ± 9.97
BASE263.56 ± 3.3464.71 ± 16.6764.14 ± 10.77
SO2 (µg/m3)LOCK15.43 ± 1.937.07 ± 1.536.25 ± 1.80
LOCK27.24 ± 0.566.06 ± 0.886.65 ± 0.92
BASE110.85 ± 2.2911.32 ± 3.6311.08 ± 2.72
BASE29.29 ± 1.028.99 ± 0.119.14 ± 0.67
CO (mg/m3)LOCK10.74 ± 0.020.82 ± 0.130.78 ± 0.09
LOCK20.78 ± 0.100.80 ± 0.130.79 ± 0.11
BASE10.90 ± 0.020.78 ± 0.070.84 ± 0.08
BASE20.89 ± 0.030.76 ± 0.090.82 ± 0.09
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Zhao, X.; Li, X.-X.; Xin, R.; Zhang, Y.; Liu, C.-H. Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China. Atmosphere 2024, 15, 1144. https://doi.org/10.3390/atmos15091144

AMA Style

Zhao X, Li X-X, Xin R, Zhang Y, Liu C-H. Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China. Atmosphere. 2024; 15(9):1144. https://doi.org/10.3390/atmos15091144

Chicago/Turabian Style

Zhao, Xinlei, Xian-Xiang Li, Rui Xin, Yuejuan Zhang, and Chun-Ho Liu. 2024. "Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China" Atmosphere 15, no. 9: 1144. https://doi.org/10.3390/atmos15091144

APA Style

Zhao, X., Li, X. -X., Xin, R., Zhang, Y., & Liu, C. -H. (2024). Impact of Lockdowns on Air Pollution: Case Studies of Two Periods in 2022 in Guangzhou, China. Atmosphere, 15(9), 1144. https://doi.org/10.3390/atmos15091144

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