In the 21st century, modernization and industrialization are at the highest point globally, and the risk of air pollution is increasing. Air pollution is one of the biggest threats to public health globally. The low-quality air is causing severe health issues through respiratory diseases, cardiovascular, cancer, intelligence quotient loss, premature mortality, asthma, heart attacks, chronic bronchitis, lung diseases, and shortness of breath (
Alqasemi et al., 2021
;
Rahman et al., 2021
). Besides, it has been estimated that 90% of the world’s population lives in areas where air pollution levels are above the safe threshold level for human health (
WHO, 2016
) and that these outcomes are causing about seven million deaths each year and shortening the average life expectancy by about two years (
Wyche et al., 2021
).
Due to rapid industrialization and uncontrolled population, automobile traffic pressure and the cities’ unscrupulous development are increasing daily, resulting in massive amounts of environmental air pollution (
Rahman et al., 2021
). There are five major air pollutants such as ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter i.e. PM2.5, and PM10, which help in the determination of air quality (
Kumari & Toshniwal, 2020
). Air quality is severely affected by anthropogenic emissions, and the primary sources of anthropogenic particulate matter emissions are various industries, the transportation sector, and households (
Khorsandi et al., 2021
). In addition, weather factors are the main determinants of air quality (
Kayes et al., 2019
;
Manju et al., 2018
) and are closely interconnected through atmospheric dynamic processes and chemical reactions (
Radaideh, 2017
). Therefore, air pollution also adversely affects the economy, climate, environment, and vegetation.
The COVID-19 outbreak has had a tremendous influence on society and caused unexpected changes in air quality. Governments all over the world have implemented various policies to promote social distancing, and it is the most efficient strategy to stop the virus’s spread (
Chinazzi et al., 2020
;
Kraemer et al., 2020
;
Sun & Zhai, 2020
;
Tang et al., 2020
;
Tian et al., 2020
). The Xi’an Municipal Government has also enacted an extreme lockdown from January 25, 2020, which includes the closure of all public transit, restaurants, enterprises, and construction sites. This unexpected quarantine significantly reduced pollutant emissions, offering vital information on the variables that could affect air quality (
Pomponi et al., 2020
;
Tanzer-Gruener et al., 2020
).
Recent research indicates that lockdown situations related to the COVID-19 outbreak significantly impact air quality in many cities and nations worldwide. The COVID-19 lockdown affected the world’s air quality, and the concentrations of PM2.5, NO2, PM10, CO, and SO2 decreased while those of O3 increased during the COVID-19 pandemic (
Liu et al., 2021
). Many infectious diseases’ epidemiological dynamics depend on environmental factors (
Shi et al., 2020
). Generally, weather indicators can affect virus transmission (
Raza et al., 2021
). Firstly,
Jahangiri et al. (2020)
pointed out that temperature is a critical element in the spread of COVID-19. According to
Chen et al. (2021)
, the spread of COVID-19 was also considerably impacted by temperature and humidity. Similarly,
Tosepu et al. (2020)
analyzed the relationship between weather indicators (rainfall, temperature) and the COVID-19 outbreak in Indonesia and found a significant positive association between COVID-19 and temperature. Furthermore, relative humidity and temperature significantly impact the dissemination of the COVID-19 outbreak in China (
Wang & Su, 2020
) and affected COVID-19 deaths in Wuhan (
Ma et al., 2020
;
Yao et al., 2021
). Few studies have investigated air pollutants’ spatial and temporal distribution during the COVID-19 pandemic. For example,
Liu et al. (2021)
investigated spatiotemporal distribution patterns and variations in air pollution before, during, and after the lockdown. Furthermore,
Mehmood et al. (2021)
pointed out the spatiotemporal variability of the COVID-19 pandemic concerning socioeconomic factors, climate, and air pollution in Pakistan. They found a significant relationship between humidity and COVID-19 cases.
Some scholars went much more profoundly to analyze the resources of pollutants. They discovered that while transportation and industrial emissions decreased, ozone (O3) concentrations increased, which implies that emissions from urban activities should be prioritized (
Abdullah et al., 2020
;
Borhani et al., 2021
;
Dhahad et al., 2021
;
Islam et al., 2021
;
Mor et al., 2021
;
Latif et al., 2021
;
Wang et al., 2020b
;
Yang et al., 2020
). Eliminating the lockdown of the COVID-19 pandemic at the national level has caused significant changes in air pollution worldwide (
Singh et al., 2020
). For example, during the COVID-19 pandemic, most of the emissions in Almaty, Kazakhstan, were from residential heating systems and coal power plants rather than vehicles (
Kerimray et al., 2020
). However, these studies have not examined the complex interplay between the decline in pollutant concentrations and the reduction in emissions during the COVID-19 lockdown. Therefore, this research investigated the connection between climatic variables, air quality, and the COVID-19 pandemic.
Xi’an is located west of the FenWei Plain graben. The Fenhe-Weihe Plain is one of China’s most polluted regions due to the high cost of coal, which represents 90% of local energy consumption and is 30% more expensive than the national average. In 2019, the overall air quality in Xi’an was “Unhealthy” with an average US AQI number of 56.6 (
WHO, 2021
). This area’s primary particulate matter (PM) sources include coal consumption, dust emission, industrial activities, lighting, and motor vehicles. Compared to the emission source analysis results of previous years, the proportion of industrial operations declined from 58% to 11.3% in 2020, coal burning decreased from 52% to 18.8%, and motor vehicles increased from 2% to 27.4% (
Dai et al., 2018
;
Wang et al., 2014
;
Wang et al., 2019
;
Wang et al., 2020a
). Other research focused on biological and human factors that contribute to O3 levels. The primary anthropogenic sources of O3 in Xi’an are industrial emissions, which account for most of the city’s O3 emissions; motor vehicles, which account for most of the city’s volatile organic compounds (VOC) emissions (64.4%); organic solvents, and biomass combustion. Various creatures and plants release numerous VOCs in the Qinling Mountain region near Xi’an, which can combine with NOx to produce O3 in the vicinity of Xi’an (
Feng et al., 2016
;
Li et al., 2018
;
Sun & Zhai, 2020
). Understanding the impact of different human and environmental factors on persistent outbreaks is critical for infection control policymaking, particularly in places where transmission risk is perhaps underestimated, such as wet and warm places. Effective mapping and analysis of city air quality are essential to identify the air quality situation and formulate effective policies (
Mahato et al., 2020
). These investigations demonstrated the complexities of Xi’an’s pollution contributions, but additional research is needed from the viewpoints of geospatial data and urban planning issues.
This research aims to discover the relationships between air quality, the COVID-19 pandemic, and meteorological variables in Xi’an. First, we noticed changes in the COVID-19 distribution from January 2020 to July 2022. Second, we show the meteorological and air quality parameter concentration distribution from 2019 to 2020. We also illustrated how the pollutants were distributed seasonally before and during COVID-19. We used concentrations from the previous full year for a more accurate comparison. We divided them into three seasons (spring, summer, and winter) to correspond to the 12 months of the COVID-19 year. Third, we use R software to conduct a correlation to determine the relationships between air pollutants, COVID-19, and meteorological parameters before and after the COVID-19 period.
2. Data Collection and Methodology2.1. Stduy Area
Xi’an is the largest city in the central part of Northwest China, and it is the capital of Shaanxi Province. It is located south of the Qinling Mountains, north of the Weihe River, and in the middle of the Guangzhong plain of the Yellow River (
Mokoena et al., 2019
). The study area is located approximately between geo-coordinate 34˚40'0''N, 107˚40'00''E, and 33˚40'0''N, 109˚40'00''E (
Figure 1
). The land extent of Xi’an is 10,097 km2, with over 8.7 million inhabitants (
Mokoena et al., 2019
). The climate of the study area is sub-mixed and temperate continental monsoon, and it has a unique model with four seasons each year. The terrain of the study area is primarily flat, and the mountains in the northeast and east are about 1800 meters above sea level. Xi’an, known as Changan in ancient times, is China’s most renowned cultural and historical city. It has a severe problem of air pollution and ranks among the top 10 worst cities polluted with air pollutants in China (
Cao et al., 2018
). The geography and meteorology surrounding Xi’an are major factors in the poor air quality because of anticyclonic airflows stopping before mountains and leeward slopes, which cause pollutants to collect, and northeasterly winds blowing at low or even zero speeds, which prevent pollutants from dispersing.
Figure 1. Geographic location of Xi’an city in the capital of Shaanxi Province in the central part of Northwest China.
2.2. Data Collection
In this study, two years (2019 and 2020) of daily meteorological parameters (humidity, precipitation, temperature, and wind speed) data from NASA POWER (
https://power.larc.nasa.gov/
) were used. Data on the daily concentration of air pollutants such as O3, NO2, PM10, PM2.5, SO2, CO, and the Air Quality Index, as well as mean values of NO2, PM2.5, PM10, CO, and SO2, and the 90th percentile concentration of monthly O3 data were collected from August 2019 to August 2020 using the National Meteorological Information Center (
http://www.nmc.cn/
). COVID-19 case data was obtained from Xi’an Center for Disease Control and Prevention (
http://www.xiancdc.com/
). The complete methodology is shown in
Figure 2
.
Figure 2. The workflow of data collection, processing, and output in this study.
2.3. Spatial Variability of Air Quality
A geographic information system (GIS) is a robust tool for managing, storing, analyzing, and presenting spatial or geographic data. Under the expert judgment of GIS users or analysts, it can generate remedies to spatial difficulties. Researchers have used GIS technology to investigate pollutants’ temporal and spatial distribution (
Jensen et al., 2001
;
Kumar et al., 2016
). This study used spatial analysis to detect the spatial distribution patterns of air pollutants in the study area. The data regarding the monthly mean concentration of contaminants, such as NO2, O3, PM10, and PM2.5 from January to August 2019-20, were analyzed and visualized as a color-coded map regarding their spatial distribution in the study area.
2.4. Temporal Variability and Daily Trend of Air Pollutants
Time series data are ubiquitous, and time series analysis aims to deeply understand the phenomenon, discover repetitive patterns and trends, and predict future trends (
van Wijk & van Selow, 1999
). This study emphasized determining the temporal characteristics of Air quality index values. For this purpose, daily air quality index data from January 01 to December 31 during 2019-20 were collected. As well as visualized the temporal variation of the air quality index as the data calendar.
2.5. Mann-Kendall Trend Test
This test was used to understand the trend of air pollutants in over 13 districts of Xi’an. Mann-Kendall analysis can be applied to data that are not normally distributed (
Meals et al., 2011
). The Mann-Kendall analysis assumes that one value can always be declared lower than, greater than, or equal to another: the data are independent; data distribution in the original or converted units remains unchanged. Since Mann-Kendall analysis statistics are not changed to log transformation (test statistics have the same value as the original and log transformation data), it could be applied to many cases. To achieve a Mann-Kendall test, compute the difference between the later-measured value and all earlier-measured values (Yj − Yi), where j > i, and assign the integer value of 1, 0, or −1 to positive differences, no differences, and negative differences, respectively. The test statistic (S) is then computed as the sum of the integers using the following equation.
(1)
When S is a large positive number, the value measured in the later stage is often larger than that in the early stage and indicates an upward trend (
Meals et al., 2011
). When S is a large negative number, the later value is often less than the earlier value and indicates a downward trend. No trend will be displayed when the absolute value of S is small. Test statistics τ can be calculated as follows:
(2)
The values of τ range from −1 to +1, similar to the correlation coefficient in regression analysis. When S and τ significantly differ from zero, refuse to deny the null hypothesis of any trend (
Meals et al., 2011
). If a significant trend is established, the rate of change can be calculated using the Sen Slope estimator (
Helsel & Hirsch, 1992
;
Meals et al., 2011
) as given below.
(3)
For all, i < j and i = 1, 2, …, n − 1 and j = 2, 3, …, n; in other words, computing the slope for all pairs of data that were used to calculate S. The median of those slopes is the Sen Slope estimator (
Meals et al., 2011
).
2.6. Relationship between Weather Parameters and Air Pollutants
A scatter diagram is a tool to analyze the relationship between two variables. One variable is drawn on the vertical axis, and the other is on the horizontal axis. Their interesting point patterns can be displayed graphically concerning patterns (
Chow & Yeung, 2006
). Using a scatter diagram, this study analyzed the O3, NO2, CO, SO2, PM2.5, and PM10 with weather indicators such as wind speed, air temperature, precipitation, and relative humidity between January to December 2019 and 2020.
3. Results and Discussions3.1. Spatio-Temporal Analysis of Air Quality
To distinguish the spatial distribution pattern of pollutants may include the seasonal component. In this study, the spatial distribution pattern of the pollutants before and during the COVID-19 period was compared during winter (from January to February), spring (from March to May), and summer (from June to August).
Figure 3
illustrates the spatial distribution pattern of air pollutants in the winter season with mean and 90th percentile values of air pollutant concentrations of districts before and during COVID-19. During the winter season (before the COVID-19 period), the highest PM10 value i.e. 200.0 to 250.0 µg∙m−3 was reported in Zhoushi District. However, during the COVID-19 winter season, the concentration of PM10 was significantly decreased over the study area, and its concentration ranged from 50.0 to 200.0 µg∙m−3.
Similarly, the concentration of NO2 and PM2.5 was significantly decreased during the COVID-19 winter season compared to that recorded before the COVID-19 winter season (
Figure 3
). The reported values during the COVID-19 winter season ranged from 50.0 to 150.0 µg∙m−3 (
Figure 3
). Before COVID-19 in January, the concentration of PM2.5 had the highest value ranging from 150.0 to 200.0 µg∙m−3 (
Figure 3
). Earlier, the studies conducted in China revealed a reduction of 29% - 34% and 26% - 48% in the concentration of PM10, and PM2.5, respectively, during the COVID-19 period from January to March 2020 compared to that recorded in the same period before COVID-19 (
Li et al., 2020
).
The concentration of O3 in Xi’an was distributed evenly both before and during the COVID-19 period in January, with values between 50.0 to 150.0 µg∙m−3. An upward distribution pattern only in February was reported, which has a higher value than before the pandemic, and it varied between 100.0 to 150.0 µg∙m−3 than before the COVID-19 period. In February (before COVID-19), a variation of the distribution of O3 among all districts of the study area can be identified (
Figure 4
). According to
Figure 4
, Zhouzhi, Lantian, Linton, Yanta, and Xincheng districts reported a concentration of O3 between 50.0 and 100.0 µg∙m−3. However, the concentration of O3 was higher during the COVID-19 period than in the previous period i.e. the concentration of O3 ranged between 100.0 to 150.0 µg∙m−3 for the whole city of Xi’an during the COVID-19 period.
Air pollutants in winter season before COVID-19 period
Air pollutants in winter season during COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId22.jpeg?20240819103519" /></p>Figure 3. Spatial distribution pattern of air pollutants in winter season before and during COVID-19 period.
Air pollutants in winter season before COVID-19 period
Air pollutants in winter season during COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId24.jpeg?20240819103520" /></p>Figure 4. Mean values for PM<sub>10</sub>, PM<sub>2.5</sub>, and NO<sub>2</sub> concentrations and 90th percentile value of ozone (O<sub>3</sub>) concentrations in winter before and during the COVID-19 period.
Figure 5
illustrates the spatial distribution pattern of air pollutants in the district before and during the COVID-19 period in the spring season with their mean and 90th percentile concentration values. In the spring season, the contaminants of PM10 fluctuated among all the districts of Xi’an, and values ranged between 50.0 - 150.0 µg∙m−3. During the spring season of 2019, the concentration of PM10 ranged from 50.0 to 150.0 µg∙m−3, while lower concentrations were noted before and during the COVID-19 period in the winter season. NO2 and PM2.5 values ranged from 0.001 to 100.0 µg∙m−3, varying from district to district monthly. During May, the lowest NO2 levels were recorded in all districts, which ranged between 0.001 to 50.0 µg∙m−3. However, the O3 level showed a steady increase during this season. Accordingly, the highest value of O3 concentration was recorded in the Huyi district in May, with a value range of 200.0 to 250.0 µg∙m−3. Overall, the distribution of air pollutants decreased in the spring season (before the COVID-19 period) compared to the winter season (before the CO- VID-19 period).
In the spring season of 2020, the PM10 values were evenly distributed over three months, with values ranging from 50.0 to 100.0 µg∙m−3 in all the districts of Xi’an (
Figure 5
). A significant decrease in PM10 concentrations was noted during the COVID-19 spring season. In addition, PM2.5 and NO2 showed a steady decline. It showed the lowest values in districts of Xi’an from the beginning of spring (March) to the end of spring (May). During the last month of the spring
Air pollutants in spring season before COVID-19 period
Air pollutants in spring season during COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId26.jpeg?20240819103520" /></p>Figure 5. Spatial variation of air pollutants in spring season before and during COVID-19 period.
season i.e. May, the reported values ranged from 0.0 to 50.0 µg∙m−3 in all districts. However, their levels steadily increased over the months of this season. The highest value of O3 concentration was recorded during May in Xi’an, with values ranging from 200.0 to 250.0 µg∙m−3. The reported values of O3 concentration were higher than other air pollutants (PM2.5 and PM10). Hence, the distribution pattern of air pollutants (NO2, PM10, and PM2.5) showed a decreasing trend during the spring (during the COVID-19 period) compared to that observed during the spring before the COVID-19 period.
Figure 6
shows this variability.
Air pollutants in spring season before COVID-19 period
Air pollutants in spring season during COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId28.jpeg?20240819103519" /></p>Figure 6. Mean values for PM<sub>10</sub>, PM<sub>2.5</sub>, and NO<sub>2</sub> concentrations and 90th percentile value of ozone (O<sub>3</sub>) concentrations in spring before and during the COVID-19 period.
In the summer, the concentration of studied air pollutants was low compared to other seasons (
Figure 7
). In the summer of 2019, PM10 concentration varied from district to district, ranging from 0.0 to 100.0 µg∙m−3. During June, July, and August, PM10 spread equally in Zhouzhi, Huyi, and Changan districts with ranges between 0.0 to 50.0 µg∙m−3. The highest PM10 value was reported from the Yanliang district throughout the summer season before the COVID-19 period. During the summer season (before the COVID-19 period), PM2.5 and NO2 spread throughout all districts in Xi’an without fluctuation, and their concentrations ranged from 0.000 to 50.000 µg∙m−3 for the whole city of Xi’an (
Figure 7
). The concentration of O3 was higher than other air pollutants (PM10, NO2, and PM2.5) during the summer season before the COVID-19 period, and its values ranged from 150.0 to 250.0 µg∙m−3 (
Figure 7
). The amount of O3 also varied from district
Air pollutants in summer season before COVID-19 period
Air pollutants in summer season during COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId30.jpeg?20240819103518" /></p>Figure 7. Spatial distribution pattern of air pollutants in summer season before and during COVID-19 period.
to district during this season. In August, a downward distribution pattern in the concentration of O3 was recorded in the Zhouzhi district, and it ranged from 100.0 to 150.0 µg∙m−3.
Figure 7
illustrates that the PM10 level declined from the beginning to the end of the summer season during COVID-19. The concentration of PM10 ranged from 50.0 to 100.0 µg∙m−3 during the June 11 districts of the study area. Nevertheless, it was not reported in any district in August, and its concentration ranged from 0.0 to 50.0 µg∙m−3. During the COVID-19 period in the summer season, a similar concentration range of PM2.5 and NO2 was recorded. Its spread was uniform throughout all the districts of the study area. The concentration of PM2.5 and NO2 ranged from 0.0 to 50.0 µg∙m−3 for the whole city of Xi’an during the COVID-19 period. Not only that, but also this distribution pattern was also the same as before the COVID-19 period.
However, the concentration of O3 in all the districts of Xi’an was higher in the summer than in other seasons, and its values ranged from 200.0 to 250.0 µg∙m−3 during the COVID-19 period. June and August showed the same distribution pattern of O3, while the highest O3 value was recorded in the Huyi and Changan districts during the three months of the summer season. The reported PM10, PM2.5, and NO2 values were lower in summer than in winter and spring seasons. In a similar study,
Singh et al. (2020)
reported that a decreasing trend in PM2.5 can be observed across all regions because of the winter-spring-summer transition. However, the concentration of O3 was reported to be higher than in other seasons. The bar charts included in
Figure 8
showed this variability very clearly.
Air pollutants in summer season before COVID-19 period
Air pollutants in summer season during COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId32.jpeg?20240819103519" /></p>Figure 8. Mean values for PM<sub>10</sub>, PM<sub>2.5</sub>, and NO<sub>2</sub> concentrations and 90th percentile value of ozone (O<sub>3</sub>) concentrations in summer before and during the COVID-19 period.
Air pollutants fluctuate annually, seasonally, monthly, and diurnally in different environments (
Singh et al., 2020
). Meteorology and local emissions control changes in a specific geographic location. Due to changes in the weather conditions, the primary pollutants emitted during winter to spring and summer may decrease, respectively, while O3 concentrations tend to increase during the transition from winter, spring, and summer due to photochemical production (
Singh et al., 2020
). However, some pollutants were highest in other months, possibly due to anthropogenic activities such as transportation, industries, thermal power plants, and residential biomass burning in that area. During the COVID-19 period, the concentration of air pollutants in all the districts showed a significant decrease compared to before the COVID-19 period, which might be due to the reduction of industrial production, strict traffic, and people’s lack of willingness to travel, thermal power plants, and residential biomass burning due to the pandemic in the study area. In a similar study,
Kumar (2020)
applied air quality in India during this COVID-19 period. His conclusion showed that due to the strict lockdown in India, all public transport, industries, and individual activities were shut down, reflected in air quality and aerosols all over India. The aerosols have decreased sharply in India, in contrast to the average value of AOD over the last three years. As well as it’s very clear from the plot that the concentration of NO2 is reduced after the lockdown compared to the three-year average value of NO2. Finally, they found an apparent reduction in all the pollutants for all cities during the lockdown period.
According to this study, the difference in atmospheric pollutant concentrations during the COVID-19 period and before the COVID-19 period in Xi’an indicates that the emergency response mechanism caused by the COVID-19 pandemic had a significant effect on changing the air pollutant concentration in Xi’an.
The Air Quality Index (AQI) before and during COVID-19 has been visualized through two data calendar maps; one calendar map in 2019 with month and day variation, and the other for 2020 with month and day variation. Daily air quality values have been presented as a calendar map, which is helpful for an intuitive inspection of the severity of pollutants (
Figure 9
and
Figure 10
).
In 2019, before COVID-19, the highest air quality values were recorded compared to those observed in 2020 (
Figure 9
). The pollutant concentrations were extremely high during January, May, and December. These three months had severely polluted days with AQI values of 301 - 500. The lowest AQI was reported during September 2019, with an air quality index of 0 - 50, and it had 11 excellent days. During 2019, around 225 days were excellent/good days (air quality value less than 100) in selected months, and 140 days were polluted with AQI values greater than 101.
Figure 9. Xi’an’s air quality index and air quality level before the COVID-19 period (January to December 2019).
Figure 10. Xi’an’s air quality index and air quality level of Xian, during the COVID-19 period (January to December 2020).
Figure 10
illustrates that pollutant concentrations were extremely high during January and December 2020, but it did not include severely polluted days as the AQI values were not greater than 300. The lowest air quality index was reported for August, with an air quality value of 0 - 50, and it has 11 excellent days. Not only that but no day in 2020 that was severely polluted was also reported in all the months. In 2020, 250 days can be identified as excellent/good days with air quality values less than 100 in the selected months, and 116 days were polluted with AQI values greater than 101.
According to
Figure 9
and
Figure 10
, sixteen more excellent days were reported during COVID-19 in 2020 compared to before COVID-19 in 2019, which was 41% more than in 2019. Ten more good days were reported during COVID-19 in 2020 than in 2019. Air quality levels of heavy pollution days decreased in 2020 and 2019 by 31.8%, and eleven lightly polluted days decreased in 2020 compared to 2019. Hence, no severely polluted days were reported in 2020, and air quality was 100% decreased, which was a good sign for the environment during COVID-19. In a similar study,
Mahato et al. (2020)
documented that stated earlier the lockdown started on the 24th of March, and just after 1 day of the beginning of the lockdown (i.e. 25th of March), there was a significant upgrade in air quality in comparison to that of the pre-lockdown phase in New Delhi, India. There are several reasons behind the variations in air quality indices of the study area during the pre-and-post COVID-19 period. Xi’an is the largest city and the provincial capital in Central China. It is one of the most attractive places for tourists in China and has a market and trade center. Hence, it has rapid urbanization, a high population growth rate, and increasing traffic volume daily. The above factors led to increasing the pollution indices before the COVID-19 period. During the COVID-19 period, Xi’an restricted most of its economic and human activities, except for the utilities of primary products and facilities such as health, food, medicine, human and animal well-being, banks, families, public transport, construction, and emergency services, and energy (electricity and fuel). As a result, the primary source of emissions changed to a low level. Not only that, but the metrological conditions lead to a change in air pollution indices. For example, heavy and severe pollution days increased more in the winter (December, January, and February) than in other seasons before and during COVID-19. The researchers observed similar changes regarding air quality in different countries (
Gouda et al., 2021
;
Khorsandi et al., 2021
). Overall, the COVID-19 period leads to comparatively healthier air quality than before the COVID-19 period.
To examine the trends (decreasing or increasing) of air pollutants’ variation over time, Mann-Kendall’s (M. K.) trend analysis was performed in this study.
Figure 11
represents the Mann-Kendall test results for the daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) in Xi’an from January 01 to December 31, 2019 (before the COVID-19 period) and January 01 to December 31, 2020 (during COVID-19 period).
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
Figure 11. Daily PM2.5, PM10, NO2, SO2, CO, O3-8h (µg∙m−3) before COVID-19 and during the COVID-19 period.
In the Mann-Kendall analysis of air pollution data, the overall results of the M. K. trend analysis are presented in Table 1. If the p-value was less than the significance level i.e. α = 0.05, H0 was rejected. Rejecting H0 indicates a trend in the time series while accepting H0 indicates no trend was detected. When rejecting the null hypothesis, the results have been said to be statistically significant.
<xref ref-type="bibr" rid="scirp.135305-"></xref>Table 1. Summary of results in Mann Kendall Test-before COVID-19 period.
Airpollutants
Mann-Kendall Statistic (S)
Kendall’sTau
Var(S)
p-value atα = 0.05 (two tailed test)
Test Clarification
Mann Kendall Test Results-2019
PM2.5
−9391.0
−0.142
5423253.67
<0.0001
H0 = Reject
PM10
−12512.0
−0.189
5424592.00
<0.0001
H0 = Reject
NO2
−2134.0
−0.032
5422440.67
0.360
H0 = Accept
SO2
−1747.0
−0.028
5353320.33
0.450
H0 = Accept
CO
−5922.0
−0.094
5345195.33
0.010
H0 = Reject
O3-8h
−3536.0
−0.053
5424514.67
0.129
H0 = Accept
Mann Kendall Test Results-2020
PM2.5
−7943.0
−0.142
5423253.67
<0.0001
H0 = Reject
PM10
−12512.0
−0.189
5424592.00
<0.0001
H0 = Reject
NO2
−2134.0
0.098
5421663.33
0.006
H0 = Reject
SO2
−7477.0
−0.118
5360057.00
0.001
H0 = Reject
CO
643.0
0.010
5314985.00
0.781
H0 = Accept
O3-8h
−11663.0
−0.176
5424219.00
<0.0001
H0 = Reject
H0 = There is no trend in the study period.
H1 = There is a trend in the study period.
The M. K. test gave fascinating insights into air pollutants data before and during COVID-19. The M. K. test Statistic (S) indicated a decreased trend for the five air pollutants such as PM2.5, PM10, NO2, SO2, CO, and O3-8h before COVID-19 (Table 1). However, an increasing trend in O3-8h and NO2 air pollutants was recorded during COVID-19. The null hypothesis (H0) was rejected for PM2.5, PM10, and CO air pollutants before the COVID-19 period, while it was accepted for the pollutants i.e. O3-8h, NO2, and SO2 before the COVID-19 period. This trend suggests that a significant decreasing trend can be identified for PM2.5, PM10, and CO, and no trend was identified for O3-8h, NO2, and SO2 before COVID-19. During the COVID-19 period 2020, the null hypothesis (H0) was rejected for PM2.5, PM10, NO2, SO2, and O3-8h while it was accepted for CO only. This suggests that a significant decreasing trend can be identified for PM2.5, PM10, NO2, SO2, and O3-8h, with no significant trend for CO during the COVID-19 period. Among the pollutants, the concentrations of five major pollutants showed a decreasing trend. In contrast, CO had no significant trend during the COVID-19 period.
3.2. Relationship between Weather Parameters and Air Pollutants in the Study Area before and during the COVID-19 Pandemic
The scatter plot method was applied to study the relationship between the weather and air pollutants data from January to December 2019 and 2020 (
Figures 12-15
). The details about each relationship are given below:
Before the COVID-19 period
<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId49.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId50.jpeg?20240819103528" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId51.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId52.jpeg?20240819103527" /></p>During the COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId47.jpeg?20240819103527" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId53.jpeg?20240819103526" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId54.jpeg?20240819103527" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId55.jpeg?20240819103526" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId56.jpeg?20240819103528" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId57.jpeg?20240819103526" /></p>Figure 12. The relationship between air pollutant and temperature before and during the COVID-19 period.
Before the COVID-19 period
<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId60.jpeg?20240819103527" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId61.jpeg?20240819103526" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId62.jpeg?20240819103527" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId63.jpeg?20240819103525" /></p>During the COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId64.jpeg?20240819103527" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId65.jpeg?20240819103525" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId66.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId67.jpeg?20240819103526" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId68.jpeg?20240819103527" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId69.jpeg?20240819103525" /></p>Figure 13. The relationship between air pollutants and precipitation before and during the COVID-19 period.
Before the COVID-19 period
<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId72.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId73.jpeg?20240819103528" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId74.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId75.jpeg?20240819103527" /></p>During the COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId76.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId77.jpeg?20240819103527" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId78.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId79.jpeg?20240819103528" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId80.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId81.jpeg?20240819103527" /></p>Figure 14. The relationship between air pollutants and relative humidity before and during the COVID-19 period.
Before the COVID-19 period
<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId84.jpeg?20240819103526" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId85.jpeg?20240819103527" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId86.jpeg?20240819103525" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId87.jpeg?20240819103526" /></p>During the COVID-19 period<p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId88.jpeg?20240819103525" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId89.jpeg?20240819103527" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId90.jpeg?20240819103528" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId91.jpeg?20240819103526" /></p><p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId92.jpeg?20240819103528" /></p> <p class="imgGroupCss_v"><img class=" imgMarkCss lazy" data-original="https://html.scirp.org/file/2172953-rId93.jpeg?20240819103526" /></p>Figure 15. The relationship between air pollutants and wind speed before and during the COVID-19 period.
A negative correlation was recorded between different air pollutants (PM2.5, NO2, PM10, CO, and SO2) and the temperature in 2019 with correlation coefficient (R2) values i.e. 0.4592, 0.234, 0.2853, 0.4867, 0.4342, respectively (
Figure 12
). These results reveal that air pollutants such as PM2.5, NO2, PM10, CO, and SO2 reduce when the temperature increases. However, a moderately positive correlation was recorded between O3-8h and temperature during 2019. Its correlation coefficient value was R2 = 0.6344, which showed that O3-8h in the air increases with an increase in temperature.
During the COVID-19 period in 2020, a negative correlation between different air pollutants (PM2.5, NO2, PM10, CO, and SO2) and temperature was recorded with R2 = 0.4269, 0.0264, 0.2251, 0.3359, 0.3815, respectively (
Figure 12
). These results display that the concentration of air pollutants such as PM2.5, NO2, PM10, CO, and SO2 decreased as the temperature increased. However, a moderately positive correlation (R2 = 0.4411) between O3-8h and temperature was observed during 2020, which showed that the O3-8h in ambient air escalates with increasing temperature.
Comparing the correlation results between air pollutants and temperature before and during COVID-19, the R2 values were lower than during COVID-19. It has been found that the unpropitious impact of environmental ozone pollution on humans’ well-being increases with increasing temperature (
Jacob & Winner, 2009
). The natural cover of an urban environment composed of wetlands, vegetation, waterbody, bare soil, and open spaces is usually substituted by the characteristics of diverse land-use types, such as roads, sidewalks, buildings, and other artificial structures made of different materials (such as asphalt), metals and bricks, which themselves can absorb heat during the day and then release heat at night; All these together lead to the rise of surface temperature in an urban environment, which in turn contributes to the development of Urban Heat Island (UHI) phenomenon (
Ayanlade, 2016
;
Oke, 1973
;
Sampson et al., 2021
) and it supports the increase in air pollution.
Jiang et al. (2020)
conducted a similar study in Fuzhou, China, and recorded similar results i.e. the temperature was negatively correlated with PM10, PM2.5, and NO2 and positively correlated with O3-8h.
Figure 13
shows a weak negative correlation between air pollutants i.e. PM2.5, NO2, PM10, CO, SO2, and O3-8h, and the precipitation before and during COVID-19. These results show that air pollutants such as PM2.5, NO2, PM10, CO, SO2, and O3-8h increase as the precipitation decreases. However, this reduction is the same in magnitude as that detected during a similar time during the period of COVID-19 in this study. Moreover, the values of R2 during the COVID-19 period were lower than before the COVID-19 period. Earlier, it has been found that precipitation can have a variable effect on air pollutants by removing gaseous pollution and particulate matter deposition through atmospheric chemical processes (
Shukla et al., 2008
). Similarly,
Liu et al. (2020)
pointed out that the exclusion impact of precipitation on aerosol particles was associated with raindrop diameter, aerosol particle size, and precipitation intensity. They suggested that air pollutants may not be correlated with precipitation.
Air pollutants such as PM10, NO2, SO2, and O3-8h were negatively correlated with relative humidity before and during COVID-19 (
Figure 14
). These results show that the concentration of air pollutants such as PM10, NO2, SO2, and O3-8h decreases with increasing relative humidity.
Kayes et al., (2019)
found that the concentration of most air pollutants had a negative relationship with relative humidity. Relative humidity affects the movement of particles and may deposit particles on the ground surface. Consequently, air pollutants decrease with the rise in relative humidity (
Giri et al., 2008
;
Kumar, 2020
). A weak but positive correlation between PM2.5, CO, and relative humidity was recorded before and during COVID-19.
A weak and negative correlation between air pollutants (PM2.5, NO2, SO2, CO, and O3-8h) and wind speed was recorded before and during the COVID-19 period in the study area (
Figure 15
). These results showed that air pollutants such as PM2.5, NO2, SO2, CO, and O3-8h increase as the wind speed decreases. The wind significantly impacts pollution (
Liu et al., 2020
).
Dincer et al., (2010)
mentioned that wind speed negatively correlates with the concentration of air pollutants data. However, in this study, PM10 and wind speed have shown a weak but positive correlation during COVID-19. The relationship between air pollutants and wind speed can supply significant details about air pollution, as wind speeds can move air pollutants from distant sources (
Dincer et al., 2010
).
The densely populated capital of the Shanxi province of Xi’an had the highest air pollutant emissions before the COVID-19 period. However, activities of air pollutant emission sources, such as houses, coal-based thermal power plants, residential biomass burning, industries (sulfur-contained fuel used in different industries), and construction activities, were reduced during COVID-19. As a result, the correlation coefficient values regarding various parameters studies decreased during the COVID-19 period compared to before the COVID-19 period.
4. Conclusions and Recommendations
The COVID-19 pandemic (SARS-COV-2) has led to an improvement in global air quality. This study investigated the spatial distribution pattern of air pollutants, the temporal distribution of air quality indices, and the trends in air pollutant patterns. Finally, it analyzed the relationship between meteorological indicators (temperature, precipitation, humidity, and wind speed) and air pollutants (PM10, NO2, PM2.5, CO, SO2, and O3-8h), in Xi’an, China, before and during the COVID-19 period. Based on the experimental analyses, this study concluded that the concentration of air pollutants such as PM2.5, NO2, PM10, CO, and SO2 decreases in winter, spring, and summer. In contrast, the concentration of O3-8h increases from winter to summer due to the variation in weather conditions during COVID-19. No severely polluted days were reported during COVID-19, and the air quality index decreased by 100 percent. A significant trend was identified in air pollutants (PM2.5, PM10, and CO), while no significant trend was observed in the case of O3-8h, NO2, and SO2 before COVID-19.
The results recorded in this study will help recognize Xi’an’s air pollution during and before COVID-19. The results may also provide helpful evidence of the benefits of different air pollution control approaches before and during COVID-19. In addition, these analyses are an addition to identifying the literature before and during the impact of COVID-19 on air pollution and, more generally, identifying air quality changes related to specific causes. In this study, outcomes provide information for further research to categorize the status of exposure performance and level of air pollution, particularly from the studies with the robust procedure usual for essential confounders. The advantages of air quality measures during the COVID-19 lockdown were performed to be an inimitable chance for pollution control strategies. Finally, this study would aid in various clean air programs and air pollution modeling in the future globally.
Acknowledgements
This research was funded by the Postdoctoral Startup Research Fund of Henan University, number (CJ3050A0671293).
List of Abbreviations
Particulate Matter (PM), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Sulfur Dioxide SO2, Ozone O3, Air Quality Index (AQI), Volatile Organic Compounds (VOC), Nitrogen Oxide (NOx), particulate matter (PM), National Aeronautics and Space Administration (NASA), Prediction of Worldwide Energy Resources (POWER), Standard Deviation (SD), Geographic Information System (GIS).
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