Volume 7, Issue 2 (June 2022)
J Environ Health Sustain Dev 2022, 7(2): 1602-1613 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Safari Z, Fouladi-Fard R, Hosseini M R, Vahidmoghadam R, Ferrante M, Rezaali M, et al . Impact of the COVID-19 Outbreak on Air Pollutants, in 2020 Compared to the Same Period in 2019 in Qom, Iran. J Environ Health Sustain Dev 2022; 7 (2) :1602-1613
URL: http://jehsd.ssu.ac.ir/article-1-385-en.html
URL: http://jehsd.ssu.ac.ir/article-1-385-en.html
Zahra Safari 
, Reza Fouladi-Fard * , Mohammad Raza Hosseini , Razieh Vahidmoghadam , Margherita Ferrante , Mostafa Rezaali , Alireza Omidi Oskouei , Maria Fiore
, Reza Fouladi-Fard * , Mohammad Raza Hosseini , Razieh Vahidmoghadam , Margherita Ferrante , Mostafa Rezaali , Alireza Omidi Oskouei , Maria Fiore
Student Research Committee, Qom University of Medical Sciences, Qom, Iran.
Full-Text [PDF 1313 kb]
(321 Downloads)
| Abstract (HTML) (1285 Views)
.PNG)
Figure 1: Study area
Table 1: Comparison of scenarios of 2020 with the same period in 2019
.PNG)
Figure 2: Scenario 1. One month before the COVID-19 outbreak (a) in comparison to the same period in 2019 (b)
.PNG)
Figure 3: Scenario 2. The first month of the COVID-19 outbreak (a) in comparison to the same period in 2019 (b)
.PNG)
Figure 4: Scenario 3. Iranian New Year Eve holidays (a) in comparison to the same period in 2019 (b)
.PNG)
.PNG)
Figure 6: Scenario 5. The whole study period (a) in comparison to the same period in 2019 (b)
.PNG)
Figure 7: The difference percentage of SO2, NO2, CO, O3, PM10, and PM2.5 in 5 scenarios
Full-Text: (239 Views)
Impact of the COVID-19 Outbreak on Air Pollutants, in 2020 Compared to the Same Period in 2019 in Qom, Iran
Zahra Safari 1, 2, Reza Fouladi-Fard 1, 2*, Mohammad Raza Hosseini 2, Razieh Vahidmoghadam 1, Margherita Ferrante 3, Mostafa Rezaali 4, Alireza Omidi Oskouei 2, Maria Fiore 3
1 Research Center for Environmental Pollutants, Department of Environmental Health Engineering, Faculty of Health, Qom University of Medical Sciences, Qom, Iran.
2 Student Research Committee, Qom University of Medical Sciences, Qom, Iran.
3 Department of Medical, Surgical and Advanced Technologies, University of Catania, Catania, Italy.
4 Department of Civil and Environmental Engineering, Qom University of Technology, Qom, Iran.
Zahra Safari 1, 2, Reza Fouladi-Fard 1, 2*, Mohammad Raza Hosseini 2, Razieh Vahidmoghadam 1, Margherita Ferrante 3, Mostafa Rezaali 4, Alireza Omidi Oskouei 2, Maria Fiore 3
1 Research Center for Environmental Pollutants, Department of Environmental Health Engineering, Faculty of Health, Qom University of Medical Sciences, Qom, Iran.
2 Student Research Committee, Qom University of Medical Sciences, Qom, Iran.
3 Department of Medical, Surgical and Advanced Technologies, University of Catania, Catania, Italy.
4 Department of Civil and Environmental Engineering, Qom University of Technology, Qom, Iran.
| A R T I C L E I N F O | ABSTRACT | |
| ORIGINAL ARTICLE | Introduction: The present study aimed to investigate the change of air pollutants in 2020 in Qom compared to the same period in 2019 in five scenarios. Materials and Methods: The hourly air quality data was obtained from air quality monitoring stations of Qom Environmental Protection Organization (EPO). The meteorological parameters were obtained from Iranian Meteorological Organization website. The data were analyzed using Excel, SPSS, and WRPLOT view. Results: In the first month of the COVID-19 crisis, NO2, SO2, and CO decreased by 26.4, 39, and 0.2 µg/m3 compared to same period in 2019, respectively; however, PM2.5 and O3 increased by 7.1 and 2.3 µg/m3, respectively. In Iranian Nowruz holidays, an increase of 2.9 µg/m3 in O3 mean concentration and a decrease of 8.1, 23.8, 22.8, and 0.2 µg/m3 in mean concentration of PM10, NO2, SO2, and CO were experienced. The prevailing wind direction during the 2020 in each scenario was from the west of Qom city. Conclusion: Gaseous pollutants decreased during the crisis, but particulate pollutants increased slightly compared to the same period in 2019. The lockdown may have had the most impact in decreasing pollutants. A slight increase in wind speed from the west could be a factor in increasing particles. This crisis provided an opportunity to assess the role of policies, such as traffic reduction plans or discarding worn-out cars or urban management to improve air quality. |
|
Article History: Received: 25 February 2022 Accepted: 20 April 2022 |
||
*Corresponding Author: Reza Fouladi-Fard Email: rfouladi@muq.ac.ir Tel: +982537833361 |
||
Keywords: COVID-19, Air Pollution, Quarantine, Qom City. |
Citation: Safari Z, Fouladi-Fard R, Hosseini MR, et al. Impact of the COVID-19 Outbreak on Air Pollutants, in 2020 Compared to the Same Period in 2019 in Qom, Iran. J Environ Health Sustain Dev. 2022; 7(2): 1602-13.
Introduction
The COVID-19 pandemic has had a huge impact on human health on a global scale 1. Since the identification of SARS-CoV-2, there has been a swift enhancement in COVID-19 confirmed cases 2. The first death report in Iran due to the COVID-19 was reported on 19 February 2020 3. To control the spread of COVID-19, diverse studies have been conducted to probe significant factors affecting the transmission of SARS-CoV-2 4 and health policies have been adopted against SARS-CoV-2 among countries worldwide 5. Given that aggregation has a substantial impact on the COVID-19 pandemic 4, lockdown restrictions have been affirmed to be one of the helpful response measures in plenty of countries 6. This lockdown applies restrictions which lessen emissions from transportation and industries 7, limitations like dispensable travels in and out of cities, interruption of all transports, and fasten of factories. Due to these traffic and industrial restrictions, air pollution has decreased in many cities and countries 6; however, following orders and lockdown interventions may cause inconveniences, including the sick building syndrome 5,8.
Air pollution has become a striking problem all over the world, especially in developing countries 7. Extensive research studies around the world have shown that air pollution threatens young people due to cardiovascular 9 and respiratory diseases 10. Studies have shown that with the increase in particles as a result of increasing lakes drying around cities, mortality due to air pollution has also increased 11 and in some cities, people are exposed to PM2.5 higher than the WHO daily guideline in 58% to 96% of the days of a year 12. There is inevitable evidence that air pollution is associated with premature mortality 13 and harmful health effects 14. A study 15 estimated the relationship between higher concentrations of air pollutants and a higher risk of COVID-19. A review study in Spain found that chronic exposure to certain air pollutants complicates the condition of COVID-19 patients and makes recovery more difficult 16. Scientific evidence highlighted the important contribution of chronic exposure to air pollution on the COVID-19 spread and mortality 17. The results of a study in Italy, that examined the association between NO2 levels and COVID-19 cases, showed that there was no definite relationship between NO2 increase and COVID-19 18.
There have been remarkable environmental evolutions due to actions taken during the COVID-19 pandemic like reduction in air pollutants 19. The European Space Agency (ESA) satellite imagery demonstrated a notable deterioration in NO2 emissions in northern Italy 6. The Institute of Environmental Science and Meteorology (IESM) estimated a reduction in PM2.5 and PM10, as a result of a decline in utilizing machines that crush and grind 20. Studies have shown that lower temperature may increase the risk of transmitting both MERS and SARS 21,22 and MERS-CoV is more likely to arise in dry conditions 22. On the other hand, a study in Italy showed that the effectiveness of restrictions appears after about two weeks, and if the restrictions are applied more severely, this interval can be reduced 23.
The changes in this lockdown period may provide a vision into achievement of air quality enhancement 7; therefore, the changes of four gaseous and two particulate air pollutants in Qom, Iran were investigated and was compared with the same time of 2019. Most COVID-19-based studies have discussed health aspects. Few studies have examined the environmental aspects of the virus. The present study investigated the environmental effects of pandemic, which is the first study in Qom.
Materials and Methods
Study area
This study was conducted in Qom city, which is located at 130 km southwest of Tehran with 218.14 km2 area. It has a dry and semi-dry climate in the central region of Iran with an annual rainfall of 161 mm 24-28 (Figure 1).
The COVID-19 pandemic has had a huge impact on human health on a global scale 1. Since the identification of SARS-CoV-2, there has been a swift enhancement in COVID-19 confirmed cases 2. The first death report in Iran due to the COVID-19 was reported on 19 February 2020 3. To control the spread of COVID-19, diverse studies have been conducted to probe significant factors affecting the transmission of SARS-CoV-2 4 and health policies have been adopted against SARS-CoV-2 among countries worldwide 5. Given that aggregation has a substantial impact on the COVID-19 pandemic 4, lockdown restrictions have been affirmed to be one of the helpful response measures in plenty of countries 6. This lockdown applies restrictions which lessen emissions from transportation and industries 7, limitations like dispensable travels in and out of cities, interruption of all transports, and fasten of factories. Due to these traffic and industrial restrictions, air pollution has decreased in many cities and countries 6; however, following orders and lockdown interventions may cause inconveniences, including the sick building syndrome 5,8.
Air pollution has become a striking problem all over the world, especially in developing countries 7. Extensive research studies around the world have shown that air pollution threatens young people due to cardiovascular 9 and respiratory diseases 10. Studies have shown that with the increase in particles as a result of increasing lakes drying around cities, mortality due to air pollution has also increased 11 and in some cities, people are exposed to PM2.5 higher than the WHO daily guideline in 58% to 96% of the days of a year 12. There is inevitable evidence that air pollution is associated with premature mortality 13 and harmful health effects 14. A study 15 estimated the relationship between higher concentrations of air pollutants and a higher risk of COVID-19. A review study in Spain found that chronic exposure to certain air pollutants complicates the condition of COVID-19 patients and makes recovery more difficult 16. Scientific evidence highlighted the important contribution of chronic exposure to air pollution on the COVID-19 spread and mortality 17. The results of a study in Italy, that examined the association between NO2 levels and COVID-19 cases, showed that there was no definite relationship between NO2 increase and COVID-19 18.
There have been remarkable environmental evolutions due to actions taken during the COVID-19 pandemic like reduction in air pollutants 19. The European Space Agency (ESA) satellite imagery demonstrated a notable deterioration in NO2 emissions in northern Italy 6. The Institute of Environmental Science and Meteorology (IESM) estimated a reduction in PM2.5 and PM10, as a result of a decline in utilizing machines that crush and grind 20. Studies have shown that lower temperature may increase the risk of transmitting both MERS and SARS 21,22 and MERS-CoV is more likely to arise in dry conditions 22. On the other hand, a study in Italy showed that the effectiveness of restrictions appears after about two weeks, and if the restrictions are applied more severely, this interval can be reduced 23.
The changes in this lockdown period may provide a vision into achievement of air quality enhancement 7; therefore, the changes of four gaseous and two particulate air pollutants in Qom, Iran were investigated and was compared with the same time of 2019. Most COVID-19-based studies have discussed health aspects. Few studies have examined the environmental aspects of the virus. The present study investigated the environmental effects of pandemic, which is the first study in Qom.
Materials and Methods
Study area
This study was conducted in Qom city, which is located at 130 km southwest of Tehran with 218.14 km2 area. It has a dry and semi-dry climate in the central region of Iran with an annual rainfall of 161 mm 24-28 (Figure 1).
Figure 1: Study area
Data collection and analysis
The hourly air quality data was obtained from air quality monitoring stations of Qom Environmental Protection Organization (EPO) 29. Qom city has three stations to measure air pollution. Due to the lack of complete information in the past few years, only the information of Emam station was included in the study.
The meteorological parameters were obtained from Iranian Meteorological Organization website 30. The study period was from 21 January to 20 May 2020 and the same period in 2019.
After air quality processing, such as outlier data cleaning, sheet classification, and time standardization, the data was used to analyze 6 air pollutants during the study. The results were compared in 5 scenarios:
1) From 21 January to 19 February 2020 (one month before the COVID-19 outbreak) compared to the same period in 2019.
2) From 20 February to 19 March 2020 (the first month of the COVID-19 outbreak in Iran) compared to the same period in 2019.
3) From 20 March to 19 April 2020 (Iranian Nowruz holidays) compared to the same period in 2019.
4) From 20 April to 20 May 2020 (one month after Nowruz holidays) compared to the same period in 2019.
5) From 20 February to 20 May 2020 (the whole study period) compared to the same period in 2019.
To validate the results and ensure the impact of the COVID-19 outbreak on the ambient air, the most important meteorological parameters, such as precipitation, wind direction, wind speed, temperature, and number of rainy days in the five mentioned scenarios were investigated to compare with the same period in 2019. The purpose of this comparison was to ensure that adverse weather conditions did not indirectly affect the concentration of pollutants during the outbreak in Qom 31,32. The data were analyzed using Microsoft Excel 2016, IBM SPSS (version 23) and WRPLOTview (version 8.0.2). WRPLOT View is a fully operational wind rose program for your meteorological data. A wind rose demonstrates the frequency of occurrence of winds in each of the specified wind direction sectors and wind speed classes for a specific location and time 33. The data comparison between 2020 and 2019 was carried out using the Wilcoxon test and paired t-test.
Ethical issue
The present study was approved by Ethics Committee of Qom University of Medical Sciences (ID: IR.MUQ.REC.1400.051).
Results
Table 1 shows detailed information of air pollutants changes in the 5 defined scenarios. According to Table 1, the mean concentration of PM2.5 and NO2 were significantly different (p-value <0.05) from the same period in previous year. In fact, an increase of 5.6 µg/m3 (46.3%) in mean concentration of PM2.5 and a decrease of 30.9 µg/m3 (80.4%) in mean concentration of NO2 were experienced from 21 January to 19 February 2020 (Ozone data for the same period of 2019 was not recorded). In the first month of the COVID-19 crisis, all pollutants except PM10 were significantly different from 2019. NO2, SO2, and CO decreased by 26.4, 39, and 0.2 µg/m3 (79.3%, 94.7%, 14.3%) compared to the same period in 2019, respectively. On the other hand, PM2.5 and O3 increased by 7.1 and 2.3 µg/m3 (70.3%, 22.8%), respectively. From 20 March to 19 April 2020 (Iranian Nowruzholidays), an increase of 2.9 µg/m3 (27.3%) in mean concentration of O3 and a decrease of 8.1, 23.8, 22.8, and 0.2 µg/m3 (20.8%, 83.8%, 66.9%, 20%) in mean concentration of PM10, NO2, SO2, and CO were experienced in comparison to the same period in 2019, respectively. The mean concentration of PM2.5 had no significant difference from the same period in previous year. NO2, SO2, and CO was significantly different from 20 April to 20 May 2020 (scenario 4). The mean concentration of NO2 and CO decreased by 22.2 and 0.1 µg/m3 (75.5%, 10%), respectively and the mean concentration of SO2 increased by 9.6 µg/m3 (114.2%). In scenario 5, from 20 February to 20 May 2020 (the whole study period), the mean concentration of all pollutants except NO2 and SO2 had no significant difference in comparison to the same period in 2019. According to the table, a decrease of 24.1 µg/m3 (79.5%) and 16.9 µg/m3 (65.4%) in mean concentration of NO2 and SO2 was experienced, respectively. Figures 2 to 6 demonstrate the wind direction in 5 defined scenarios. The prevailing wind direction during the 2020 in each scenario was from the west of Qom city. Detailed information on the meteorological parameters, including temperature (average, maximum, and minimum), wind speed, sum of precipitation and number of rainy days is demonstrated in Table 2. The average temperature during the 5 scenarios of the COVID-19 crisis was 6.2°C, 12.5°C, 14.1°C, 21.9°C, and 13.7°C (for scenarios 1, 2, 3, 4 and 5, respectively), whereas it was 4.1°C, 6.7°C, 11.4°C, 15.3°C and 8.9°C (for scenarios 1, 2, 3, 4, and 5, respectively) during the same period in 2019. The average wind speed during the 5 scenarios of the COVID-19 crisis (3.19, 2.60, 2.87, 3.14, and 2.95 m/s for scenarios 1, 2, 3, 4, and 5, respectively) was almost the same as 2019 (2.21, 2.64, 2.42, 2.38, and 2.41 for scenarios 1, 2, 3, 4, and 5, respectively). According to this table, the percentage changes of average temperature in scenarios 2, 3, and 5 was 85.82, 23.59, and 45.29%, respectively. This indicates that the average temperature in March 2020 increased by 85.82%. The difference in average wind speed in scenarios 2, 3, and 5 was observed as 1.5, 18.6, and 22.4%, respectively. Finally, Figure 7 summarizes the percentage changes of 4 gaseous pollutants (SO2, NO2, CO, and O3) and 2 particulate pollutants (PM10, PM2.5) for 5 scenarios.
The hourly air quality data was obtained from air quality monitoring stations of Qom Environmental Protection Organization (EPO) 29. Qom city has three stations to measure air pollution. Due to the lack of complete information in the past few years, only the information of Emam station was included in the study.
The meteorological parameters were obtained from Iranian Meteorological Organization website 30. The study period was from 21 January to 20 May 2020 and the same period in 2019.
After air quality processing, such as outlier data cleaning, sheet classification, and time standardization, the data was used to analyze 6 air pollutants during the study. The results were compared in 5 scenarios:
1) From 21 January to 19 February 2020 (one month before the COVID-19 outbreak) compared to the same period in 2019.
2) From 20 February to 19 March 2020 (the first month of the COVID-19 outbreak in Iran) compared to the same period in 2019.
3) From 20 March to 19 April 2020 (Iranian Nowruz holidays) compared to the same period in 2019.
4) From 20 April to 20 May 2020 (one month after Nowruz holidays) compared to the same period in 2019.
5) From 20 February to 20 May 2020 (the whole study period) compared to the same period in 2019.
To validate the results and ensure the impact of the COVID-19 outbreak on the ambient air, the most important meteorological parameters, such as precipitation, wind direction, wind speed, temperature, and number of rainy days in the five mentioned scenarios were investigated to compare with the same period in 2019. The purpose of this comparison was to ensure that adverse weather conditions did not indirectly affect the concentration of pollutants during the outbreak in Qom 31,32. The data were analyzed using Microsoft Excel 2016, IBM SPSS (version 23) and WRPLOTview (version 8.0.2). WRPLOT View is a fully operational wind rose program for your meteorological data. A wind rose demonstrates the frequency of occurrence of winds in each of the specified wind direction sectors and wind speed classes for a specific location and time 33. The data comparison between 2020 and 2019 was carried out using the Wilcoxon test and paired t-test.
Ethical issue
The present study was approved by Ethics Committee of Qom University of Medical Sciences (ID: IR.MUQ.REC.1400.051).
Results
Table 1 shows detailed information of air pollutants changes in the 5 defined scenarios. According to Table 1, the mean concentration of PM2.5 and NO2 were significantly different (p-value <0.05) from the same period in previous year. In fact, an increase of 5.6 µg/m3 (46.3%) in mean concentration of PM2.5 and a decrease of 30.9 µg/m3 (80.4%) in mean concentration of NO2 were experienced from 21 January to 19 February 2020 (Ozone data for the same period of 2019 was not recorded). In the first month of the COVID-19 crisis, all pollutants except PM10 were significantly different from 2019. NO2, SO2, and CO decreased by 26.4, 39, and 0.2 µg/m3 (79.3%, 94.7%, 14.3%) compared to the same period in 2019, respectively. On the other hand, PM2.5 and O3 increased by 7.1 and 2.3 µg/m3 (70.3%, 22.8%), respectively. From 20 March to 19 April 2020 (Iranian Nowruzholidays), an increase of 2.9 µg/m3 (27.3%) in mean concentration of O3 and a decrease of 8.1, 23.8, 22.8, and 0.2 µg/m3 (20.8%, 83.8%, 66.9%, 20%) in mean concentration of PM10, NO2, SO2, and CO were experienced in comparison to the same period in 2019, respectively. The mean concentration of PM2.5 had no significant difference from the same period in previous year. NO2, SO2, and CO was significantly different from 20 April to 20 May 2020 (scenario 4). The mean concentration of NO2 and CO decreased by 22.2 and 0.1 µg/m3 (75.5%, 10%), respectively and the mean concentration of SO2 increased by 9.6 µg/m3 (114.2%). In scenario 5, from 20 February to 20 May 2020 (the whole study period), the mean concentration of all pollutants except NO2 and SO2 had no significant difference in comparison to the same period in 2019. According to the table, a decrease of 24.1 µg/m3 (79.5%) and 16.9 µg/m3 (65.4%) in mean concentration of NO2 and SO2 was experienced, respectively. Figures 2 to 6 demonstrate the wind direction in 5 defined scenarios. The prevailing wind direction during the 2020 in each scenario was from the west of Qom city. Detailed information on the meteorological parameters, including temperature (average, maximum, and minimum), wind speed, sum of precipitation and number of rainy days is demonstrated in Table 2. The average temperature during the 5 scenarios of the COVID-19 crisis was 6.2°C, 12.5°C, 14.1°C, 21.9°C, and 13.7°C (for scenarios 1, 2, 3, 4 and 5, respectively), whereas it was 4.1°C, 6.7°C, 11.4°C, 15.3°C and 8.9°C (for scenarios 1, 2, 3, 4, and 5, respectively) during the same period in 2019. The average wind speed during the 5 scenarios of the COVID-19 crisis (3.19, 2.60, 2.87, 3.14, and 2.95 m/s for scenarios 1, 2, 3, 4, and 5, respectively) was almost the same as 2019 (2.21, 2.64, 2.42, 2.38, and 2.41 for scenarios 1, 2, 3, 4, and 5, respectively). According to this table, the percentage changes of average temperature in scenarios 2, 3, and 5 was 85.82, 23.59, and 45.29%, respectively. This indicates that the average temperature in March 2020 increased by 85.82%. The difference in average wind speed in scenarios 2, 3, and 5 was observed as 1.5, 18.6, and 22.4%, respectively. Finally, Figure 7 summarizes the percentage changes of 4 gaseous pollutants (SO2, NO2, CO, and O3) and 2 particulate pollutants (PM10, PM2.5) for 5 scenarios.
Table 1: Comparison of scenarios of 2020 with the same period in 2019
| Variable | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | |||||
| 2020 | 2019 | 2020 | 2019 | 2020 | 2019 | 2020 | 2019 | 2020 | 2019 | |
| Median (24th-75th) |
Median (24th-75th) |
Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | |
| PM2.5 (µg/m3) | 11.7 (10.3-23.9) |
10.5* (7.1-17.8) |
15.3 (10.4-20.6) |
10.2* (10.2-10.2) |
8.7 (6.9-9.9) |
8.2 (4.8-10.4) |
13.9 (10.2-17.5) |
11.2 (8.3-21.5) |
11.7 (7.9-16.7) |
10.2 (8.0-11.2) |
| PM10 (µg/m3) | 45.3 (32.6-51.4) |
48.3 (38.8-64.1) |
46.9 (23.9-56.4) |
38.6 (29.4-45.5) |
28.2 (21.9-32.9) |
39.3* (26.6-48.5) |
51.0 (35.3-69.8) |
42.7 (35.3-61.1) |
37.2 (25.9-55.0) |
40.1 (29.8-49.5) |
| NO2 (ppb) | 7.8 (5.9-9.1) |
40.7* (31.0-43.7) |
7.3 (6.1-8.0) |
34.7* (28.8-38.2) |
4.5 (3.7-5.2) |
27.7* (25.3-30.9) |
6.9 (5.5-9.5) |
29.2* (26.7-31.9) |
6.1 (4.6-7.4) |
29.4 (26.8-33.1) |
| SO2 (ppb) | 29.6 (4.7-60.6) |
35.6* (10.5-43.4) |
2.2 (2.0-2.4) |
41.5* (39.2-42.6) |
13.6 (8.1-13.9) |
42.8* (29.6-44.0) |
20.9 (8.1-23.8) |
8.1* (7.9-8.7) |
8.1 (2.4-14.1) |
38.2 (8.4-42.8) |
| CO (ppm) | 1.5 (1.4-1.6) |
1.3 (1.1-1.8) |
1.2 (0.0-1.6) |
0.9* (0.7-0.9) |
0.7 (0.7-0.9) |
1.1* (0.9-1.1) |
0.9 (0.8-1.0) |
1.0* (1.0-1.1) |
0.9 (0.8-1.1) |
1.0 (0.9-1.1) |
| O3 (ppb) | 12.3 (11.4-117.3) |
- | 12.4 (11.8-13.1) |
10.1* (10.0-10.1) |
13.5 (13.1-14.0) |
10.1* (10.0-10.1) |
22.0 (13.4-47.3) |
10.1 (10.0-23.0) |
13.4 (12.6-14.3) |
10.1 (10.0-10.1) |
*p-value < 0.05
Table 2: Distribution of meteorological parameters by scenario and time period
Table 2: Distribution of meteorological parameters by scenario and time period
| Variable | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 | |||||
| 2020 | 2019 | 2020 | 2019 | 2020 | 2019 | 2020 | 2019 | 2020 | 2019 | |
| Median (24th-75th) |
Median (24th-75th) |
Median (24th-75th) |
Median (24th-75th) |
Median (24th-75th) | Median (24th-75th) | Median (24th-75th) | Median (24th-75th) |
Median (24th-75th) | Median (24th-75th) | |
| T (0c) | 5.6 (4.3-8.1) |
4.5 (2.8-5.7) |
12.3 (10.4-15.9) |
6.3 (5.1-8.9) |
14.6 (13.2-16.5) |
11.8 (10.1-12.6) |
22.1 (20.3-23.9) |
16.8 (11.8-19.4) |
13.6 (9.0-17.9) |
9.0 (5.5-12.5) |
| Tmax | 12.0 (10.3-17.0) |
13.4 (12.4-15.4) |
20.0 (17-22) |
16.2 (14.7-19.0) |
20.0 (19.0-24.0) |
22.0 (18.5-24.5) |
29.7 (27.2-30.9) |
32.0 (25.5-33.0) |
20.0 (15.0-25.0) |
18.4 (14.7-25.0) |
| Tmin | 0.0 (-2.0-1.8) |
1.1 (-1.4-3.3) |
5.0 (3.0-8.0) |
3.2 (1.0-4.2) |
9.0 (7.0-10.0) |
9.0 (5.6-11.0) |
14 (11.9-15.9) |
14 (10.0-17.3) |
7.3 (2.0-11.0) |
5.0 (2.0-11.0) |
| Wind speed (m/s) | 2.9 (1.9-3.9) |
2.0 (1.7-2.5) |
2.1 (1.9-2.8) |
2.5 (1.7-3.3) |
2.5 (1.9-3.5) |
2.4 (1.8-2.9) |
2.8 (2.4-3.9) |
2.0 (1.8-2.9) |
2.6 (1.9-3.6) |
2.3 (1.7-3.0) |
| Precipitation (mm) | 0 (0-0) |
0 (0-0) |
0 (0-0) |
0 (0-0) |
0 (0.-1.1) |
0 (0-2.3) |
0 (0-0) |
0 (0-0) |
0 (0-0) |
0 (0-0) |
| Rainy days (d) | 2 | 5 | 7 | 6 | 12 | 12 | 5 | 4 | 26 | 27 |
Figure 2: Scenario 1. One month before the COVID-19 outbreak (a) in comparison to the same period in 2019 (b)
Figure 3: Scenario 2. The first month of the COVID-19 outbreak (a) in comparison to the same period in 2019 (b)
|
b
|
|
a
|
Figure 4: Scenario 3. Iranian New Year Eve holidays (a) in comparison to the same period in 2019 (b)
|
a
|
|
b
|
Figure 5: Scenario 4. One month after New Year holidays (a) in comparison to the same period in 2019 (b)
|
b
|
|
a
|
Figure 6: Scenario 5. The whole study period (a) in comparison to the same period in 2019 (b)
Figure 7: The difference percentage of SO2, NO2, CO, O3, PM10, and PM2.5 in 5 scenarios
Discussion
This study was conducted to investigate the changes in concentration of air pollutants in Qom city during the COVID-19 outbreak and to compare them with the same period of last year. To better evaluate, five scenarios were defined, including (1) one month before the COVID-19 outbreak, (2) the first month of the COVID-19 outbreak in Iran, (3) Iranian Nowruz holidays, (4) one month after Nowruz holidays, and (5) the whole study period.
This study was conducted to investigate the changes in concentration of air pollutants in Qom city during the COVID-19 outbreak and to compare them with the same period of last year. To better evaluate, five scenarios were defined, including (1) one month before the COVID-19 outbreak, (2) the first month of the COVID-19 outbreak in Iran, (3) Iranian Nowruz holidays, (4) one month after Nowruz holidays, and (5) the whole study period.
Based on the authors’ ecological study, the mean concentration of particles, especially PM2.5 has increased compared to the last year. Based on a comparison of PM2.5 in February 2020, when there was no quarantine and restriction, an increase in PM2.5 was observed in March 2020. An increase in PM10 was also observed. The amount of particles in these two months also increased compared to last year. Based on the analysis of meteorological parameters, the average temperature, average wind speed, and sum of precipitation in February increased compared to March 2020 and last year. Therefore, meteorological parameters did not affect the increase of particulate pollutants. Given that southern cities, such as Ahvaz are affected by Mediterranean dust emitted from the west of the country, a slight increase in wind speed at this time from the west of the country may have affected the increase in particles. Therefore, considering that the application of quarantines did not reduce the particles, natural factors, such as wind from the desert areas of the country can be considered the cause of this increase. Moreover, due to the dry climate of Qom and several dust centers around Qom, particles may increase if the traffic decreases 34. It should be noted that particulate matter has increased compared to last year and particulate pollutants from natural origin are still increasing, solving this problem requires proper management programs. A similar study conducted by Faridi et al. in Tehran, showed a noticeable increase in particles during the COVID-19 outbreak 3. Another study conducted in Milan found that lockdown due to the COVID-19 outbreak led to a decrease in concentration of PM10, PM2.5, BC (Black carbon), CO, Benzene, and NOX. When the concentration of NO2 reduced, O3 concentration increased due to the minor NO concentration 35. The analysis showed a decrease in concentration of NO2 and SO2 during the whole study period in comparison to the same period of last year. This is similar to the results of a study in Spain, which demonstrated that air pollutants reduced due to lockdown of the COVID-19 outbreak 36. Another study in China, observed a decrease in emission of NO2 and an increase in emission of O3 during the lockdown, especially when the restrictions were removed, more emission of O3 was experienced 37. However, the results did not show a significant different between concentration of O3 during the COVID-19 outbreak (the whole study period) and previous year.
According to concentration changes in lockdown time (scenario 2 and 3) in Qom, which movement was restricted in the city, there was an increase in concentration of PM2.5 and O3, a decrease in concentration of CO, NO2, and SO2 (scenario 2), a decrease in concentration of PM10, NO2, SO2, and CO, and an increase in concentration of O3 (scenario 3). The reduction of gaseous pollutants can be attributed to the reduction of urban traffic, closure of shops, restaurants and closure of most factories and industries around the city. But restrictions on urban movements have played an important role in reducing emissions. Despite decreasing in concentration of NO2, the concentration of O3 increased slightly in both scenarios. It may be concluded due to increasing the average temperature in these scenarios compared to the same period of last year. In these 2 scenarios, similar to the whole study period, the average temperature, average wind speed, and sum of precipitations during the COVID-19 outbreak were higher than the same period of last year.
After removing lockdown, in scenario 4, an increase in concentration of SO2 was experienced; this may be due to opening of factories and industries and removing lockdown. It should be noted that in Scenario 4, the restriction of intra-city traffic and the closure of jobs were removed, but the closure of schools and universities still reduced the amount of urban traffic.
A study in industrial area in India demonstrated a reduction in air pollutants and Air Quality Index (AQI). NO2, which is mostly caused by industries and traffic jams, had the highest reduction among pollutants 38. The results of a study in Western Europe showed a decrease in NO2 and an increase in fine particles. The authors believe that residential heating emissions and fertilizer spreading in early springtime have increased the fine particles 39. Lack of recording meteorological data and air quality data in some hours, lack of recording ozone data in one of the scenarios were the limitations of this study.
Conclusions
According to the findings, gaseous pollutants decreased during the COVID-19 outbreak, but particulate pollutants increased slightly compared to the same period in 2019 and the quarantine time. This increase was experienced before applying lockdown, which a slight increase in wind speed compared to the same period in 2019 from the west, could be a factor in increasing particles. The average temperature, wind speed, and sum of precipitation were higher than the same period in 2019. These parameters may have affected air pollutants reduction, but the lockdown and closure of shops, restaurants, most of factories and industries, and decreasing traffic jam may have had the most impact. This crisis provided an opportunity to assess the role of policies, such as traffic reduction plans or discarding worn-out cars or urban management to improve air quality. Furthermore, the role of natural factors and their proper management on reducing or increasing pollutants was identified.
Limitations
One of the limitations of the study is the lack of data for more than one station. Due to the size of the city and approximate uniformity of population in the city, the concentrations of one station can be attributed to the whole city.
Acknowledgments
The authors thank Qom EPO, Meteorological Organization, and Deputy of Research and Technology of Qom University of Medical Sciences for their collaboration.
Funding
This study was supported by the Deputy of Research and Technology of Qom University of Medical Sciences (grant number: 003211).
Conflicts of interest
The authors declare that there is no conflict interest.
This is an Open-Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work for commercial use.
References
1. Rahimi NR, Fouladi-Fard R, Aali R, et al. Bidirectional Association Between COVID-19 and the Environment: a Systematic Review. Environ Res. 2021;194:110692.
2. Xu H, Yan C, Fu Q, et al. Possible environmental effects on the spread of COVID-19 in China. Sci Total Environ. 2020;731: 139211.
3. Faridi S, Yousefian F, Niazi S, et al. Impact of SARS-CoV-2 on ambient air particulate matter in Tehran. Aerosol Air Qual Res. 2020;8(20):1805-11.
4. Yongjian Z, Jingu X, Fengming H, et al. Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Sci Total Environ. 2020;727:138704.
5. Hosseini MR, Fouladi-Fard R, Aali R. COVID-19 pandemic and sick building syndrome. Indoor Built Environ. 2020;29(8):1181-3.
6. Bao R, Zhang A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci Total Environ. 2020;731:139052.
7. Sharma S, Zhang M, Anshika, et al. Effect of restricted emissions during COVID-19 on air quality in India. Sci Total Environ. 2020;728: 138878.
8. Rezaali M, Fouladi-Fard R. Aerosolized SARS-CoV-2 exposure assessment: dispersion modeling with AERMOD. J Environ Health Sci Eng.2021;19(1):285-93.
9. Peng RD, Bell ML, Geyh AS, et al. Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environ Health Perspect. 2009;117(6):957-63.
10. Nakao M, Ishihara Y, Kim CH, et al. The impact of air pollution, including Asian sand dust, on respiratory symptoms and health-related quality of life in outpatients with chronic respiratory disease in Korea: a panel study. J Prev Med Public Health. 2018;51(3):130.
11. Mohammadi A, Faraji M, Conti GO, et al. Mortality and morbidity due to exposure to particulate matter related to drying Urmia Lake in the NW Iran. Eur J Intern Med. 2019;60:e14-e5.
12. Hajizadeh Y, Jafari N, Mohammadi A, et al. Concentrations and mortality due to short-and long-term exposure to PM 2.5 in a megacity of Iran (2014–2019). Environ Sci Pollut Res. 2020;27(30):38004-14.
13. Lelieveld J, Evans JS, Fnais M, et al.The contribution of outdoor air pollutionsources to premature mortality on a globalscale. Nature. 2015;525(7569):367-71.
14. Hirabayashi S, Nowak DJ. Comprehensive national database of tree effects on air quality and human health in the United States. Environ Pollut. 2016;215:48-57.
15. Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ. 2020;724:138201.
16. Domingo JL, Marquès M, Rovira J. Influence of airborne transmission of SARS-CoV-2 on COVID-19 pandemic. A review. Environ Res. 2020;188:109861.
17. Copat C, Cristaldi A, Fiore M, et al. The role of air pollution (PM and NO2) in COVID-19 spread and lethality: a systematic review. Environ Res. 2020;191:110129.
18. Filippini T, Rothman KJ, Cocchio S, et al. Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide. Sci Total Environ. 2021;760:143355.
19. Aigner K, Bruckner S, Durmaz A, et al. The Covid-19 pandemic has clear links to nature exploitation and destruction. Department of Biosciences, University of Salzburg: Salzburg, Austria. 2020.
20. Wang P, Chen K, Zhu S, et al. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour Conserv Recycl. 2020;158:104814.
21. Lin K, Fong DYT, Zhu B, et al. Environmental factors on the SARS epidemic: air temperature, passage of time and multiplicative effect of hospital infection. Epidemiol Infect. 2006;134(2):223-30.
22. Gardner EG, Kelton D, Poljak Z, et al. A case-crossover analysis of the impact of weather on primary cases of Middle East respiratory syndrome. BMC Infect Dis. 2019;19(1):1-10.
23. Vinceti M, Filippini T, Rothman KJ, et al. Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking. EClinicalMedicine. 2020;25:100457.
24. Khazaei M, Mahvi AH, Fard RF, et al. Dental caries prevalence among Schoolchildren in Urban and Rural areas of Qom Province, Central part of Iran. Middle East J Sci Res. 2013;18(5): 584-91.
25. Fard RF, Naddafi K, Yunesian M, et al. The assessment of health impacts and external costs of natural gas-fired power plant of Qom. Environ Sci Pollut Res. 2016;23(20):20922-36.
26. Fard RF, Naddafi K, Hassanvand MS, et al. Trends of metals enrichment in deposited particulate matter at semi-arid area of Iran. Environ Sci Pollut Res. 2018;25(19):18737-51.
27. Fahiminia M, Fard RF, Ardani R, et al. Indoor radon measurements in residential dwellings in Qom, Iran. International Journal of Radiation Research. 2016;14(4):331.
28. Mojarrad H, Fouladi Fard R, Rezaali M, et al. Spatial trends, health risk assessment and ozone formation potential linked to BTEX. Hum Ecol Risk Assess. 2020;26(10):2836-57.
29. Qom Environmental Protection Organization. Available from: https://qom.doe.ir/portal/home/. [Cited 12 July 2020]
30. Iranian Meteorological Organization website. Available from: https://data.irimo.ir/login/ login. aspx. [Cited 12 July 2020]
31. Dantas G, Siciliano B, França BB, et al. The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil. Sci Total Environ. 2020;729:139085.
32. Kerimray A, Baimatova N, Ibragimova OP, et al. Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan. Sci Total Environ. 2020;730:139179.
33. Lakes Environmental Software. Available from: https://www.weblakes.com/ products/ wrplot/index.html. [Cited 24 May 2020]
34. Raoufi-fard M, Ramouz M. Statistical and descriptive study of the occurrence of dust phenomenon in Qom province. The first conference on the salt lake crisis and the dust phenomenon in the central basin of Iran; Qom, 2017.
35. Collivignarelli MC, Abbà A, Bertanza G, et al. Lockdown for CoViD-2019 in Milan: What are the effects on air quality?. Sci Total Environ. 2020;732:139280.
36. Tobías A, Carnerero C, Reche C, et al. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Sci Total Environ. 2020;726:138540.
37. Suresh A, Chauhan D, Othmani A, et al. Diagnostic comparison of changes in air quality over China before and during the COVID-19 pandemic. Res Sq. [Preprint]. 2020. Available from:https://www.researchsquare.com/article/rs30482/v1. [Cited 12 July 2020]: [19p].
38. Selvam S, Muthukumar P, Venkatramanan S, et al. SARS-CoV-2 pandemic lockdown: Effects on air quality in the industrialized Gujarat state of India. Sci Total Environ. 2020;737:140391.
39. Menut L, Bessagnet B, Siour G, et al. Impact of lockdown measures to combat Covid-19 on air quality over western Europe. Sci Total Environ. 2020;741:140426.
After removing lockdown, in scenario 4, an increase in concentration of SO2 was experienced; this may be due to opening of factories and industries and removing lockdown. It should be noted that in Scenario 4, the restriction of intra-city traffic and the closure of jobs were removed, but the closure of schools and universities still reduced the amount of urban traffic.
A study in industrial area in India demonstrated a reduction in air pollutants and Air Quality Index (AQI). NO2, which is mostly caused by industries and traffic jams, had the highest reduction among pollutants 38. The results of a study in Western Europe showed a decrease in NO2 and an increase in fine particles. The authors believe that residential heating emissions and fertilizer spreading in early springtime have increased the fine particles 39. Lack of recording meteorological data and air quality data in some hours, lack of recording ozone data in one of the scenarios were the limitations of this study.
Conclusions
According to the findings, gaseous pollutants decreased during the COVID-19 outbreak, but particulate pollutants increased slightly compared to the same period in 2019 and the quarantine time. This increase was experienced before applying lockdown, which a slight increase in wind speed compared to the same period in 2019 from the west, could be a factor in increasing particles. The average temperature, wind speed, and sum of precipitation were higher than the same period in 2019. These parameters may have affected air pollutants reduction, but the lockdown and closure of shops, restaurants, most of factories and industries, and decreasing traffic jam may have had the most impact. This crisis provided an opportunity to assess the role of policies, such as traffic reduction plans or discarding worn-out cars or urban management to improve air quality. Furthermore, the role of natural factors and their proper management on reducing or increasing pollutants was identified.
Limitations
One of the limitations of the study is the lack of data for more than one station. Due to the size of the city and approximate uniformity of population in the city, the concentrations of one station can be attributed to the whole city.
Acknowledgments
The authors thank Qom EPO, Meteorological Organization, and Deputy of Research and Technology of Qom University of Medical Sciences for their collaboration.
Funding
This study was supported by the Deputy of Research and Technology of Qom University of Medical Sciences (grant number: 003211).
Conflicts of interest
The authors declare that there is no conflict interest.
This is an Open-Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt, and build upon this work for commercial use.
References
1. Rahimi NR, Fouladi-Fard R, Aali R, et al. Bidirectional Association Between COVID-19 and the Environment: a Systematic Review. Environ Res. 2021;194:110692.
2. Xu H, Yan C, Fu Q, et al. Possible environmental effects on the spread of COVID-19 in China. Sci Total Environ. 2020;731: 139211.
3. Faridi S, Yousefian F, Niazi S, et al. Impact of SARS-CoV-2 on ambient air particulate matter in Tehran. Aerosol Air Qual Res. 2020;8(20):1805-11.
4. Yongjian Z, Jingu X, Fengming H, et al. Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Sci Total Environ. 2020;727:138704.
5. Hosseini MR, Fouladi-Fard R, Aali R. COVID-19 pandemic and sick building syndrome. Indoor Built Environ. 2020;29(8):1181-3.
6. Bao R, Zhang A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci Total Environ. 2020;731:139052.
7. Sharma S, Zhang M, Anshika, et al. Effect of restricted emissions during COVID-19 on air quality in India. Sci Total Environ. 2020;728: 138878.
8. Rezaali M, Fouladi-Fard R. Aerosolized SARS-CoV-2 exposure assessment: dispersion modeling with AERMOD. J Environ Health Sci Eng.2021;19(1):285-93.
9. Peng RD, Bell ML, Geyh AS, et al. Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environ Health Perspect. 2009;117(6):957-63.
10. Nakao M, Ishihara Y, Kim CH, et al. The impact of air pollution, including Asian sand dust, on respiratory symptoms and health-related quality of life in outpatients with chronic respiratory disease in Korea: a panel study. J Prev Med Public Health. 2018;51(3):130.
11. Mohammadi A, Faraji M, Conti GO, et al. Mortality and morbidity due to exposure to particulate matter related to drying Urmia Lake in the NW Iran. Eur J Intern Med. 2019;60:e14-e5.
12. Hajizadeh Y, Jafari N, Mohammadi A, et al. Concentrations and mortality due to short-and long-term exposure to PM 2.5 in a megacity of Iran (2014–2019). Environ Sci Pollut Res. 2020;27(30):38004-14.
13. Lelieveld J, Evans JS, Fnais M, et al.The contribution of outdoor air pollutionsources to premature mortality on a globalscale. Nature. 2015;525(7569):367-71.
14. Hirabayashi S, Nowak DJ. Comprehensive national database of tree effects on air quality and human health in the United States. Environ Pollut. 2016;215:48-57.
15. Xie J, Zhu Y. Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ. 2020;724:138201.
16. Domingo JL, Marquès M, Rovira J. Influence of airborne transmission of SARS-CoV-2 on COVID-19 pandemic. A review. Environ Res. 2020;188:109861.
17. Copat C, Cristaldi A, Fiore M, et al. The role of air pollution (PM and NO2) in COVID-19 spread and lethality: a systematic review. Environ Res. 2020;191:110129.
18. Filippini T, Rothman KJ, Cocchio S, et al. Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide. Sci Total Environ. 2021;760:143355.
19. Aigner K, Bruckner S, Durmaz A, et al. The Covid-19 pandemic has clear links to nature exploitation and destruction. Department of Biosciences, University of Salzburg: Salzburg, Austria. 2020.
20. Wang P, Chen K, Zhu S, et al. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour Conserv Recycl. 2020;158:104814.
21. Lin K, Fong DYT, Zhu B, et al. Environmental factors on the SARS epidemic: air temperature, passage of time and multiplicative effect of hospital infection. Epidemiol Infect. 2006;134(2):223-30.
22. Gardner EG, Kelton D, Poljak Z, et al. A case-crossover analysis of the impact of weather on primary cases of Middle East respiratory syndrome. BMC Infect Dis. 2019;19(1):1-10.
23. Vinceti M, Filippini T, Rothman KJ, et al. Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking. EClinicalMedicine. 2020;25:100457.
24. Khazaei M, Mahvi AH, Fard RF, et al. Dental caries prevalence among Schoolchildren in Urban and Rural areas of Qom Province, Central part of Iran. Middle East J Sci Res. 2013;18(5): 584-91.
25. Fard RF, Naddafi K, Yunesian M, et al. The assessment of health impacts and external costs of natural gas-fired power plant of Qom. Environ Sci Pollut Res. 2016;23(20):20922-36.
26. Fard RF, Naddafi K, Hassanvand MS, et al. Trends of metals enrichment in deposited particulate matter at semi-arid area of Iran. Environ Sci Pollut Res. 2018;25(19):18737-51.
27. Fahiminia M, Fard RF, Ardani R, et al. Indoor radon measurements in residential dwellings in Qom, Iran. International Journal of Radiation Research. 2016;14(4):331.
28. Mojarrad H, Fouladi Fard R, Rezaali M, et al. Spatial trends, health risk assessment and ozone formation potential linked to BTEX. Hum Ecol Risk Assess. 2020;26(10):2836-57.
29. Qom Environmental Protection Organization. Available from: https://qom.doe.ir/portal/home/. [Cited 12 July 2020]
30. Iranian Meteorological Organization website. Available from: https://data.irimo.ir/login/ login. aspx. [Cited 12 July 2020]
31. Dantas G, Siciliano B, França BB, et al. The impact of COVID-19 partial lockdown on the air quality of the city of Rio de Janeiro, Brazil. Sci Total Environ. 2020;729:139085.
32. Kerimray A, Baimatova N, Ibragimova OP, et al. Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan. Sci Total Environ. 2020;730:139179.
33. Lakes Environmental Software. Available from: https://www.weblakes.com/ products/ wrplot/index.html. [Cited 24 May 2020]
34. Raoufi-fard M, Ramouz M. Statistical and descriptive study of the occurrence of dust phenomenon in Qom province. The first conference on the salt lake crisis and the dust phenomenon in the central basin of Iran; Qom, 2017.
35. Collivignarelli MC, Abbà A, Bertanza G, et al. Lockdown for CoViD-2019 in Milan: What are the effects on air quality?. Sci Total Environ. 2020;732:139280.
36. Tobías A, Carnerero C, Reche C, et al. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Sci Total Environ. 2020;726:138540.
37. Suresh A, Chauhan D, Othmani A, et al. Diagnostic comparison of changes in air quality over China before and during the COVID-19 pandemic. Res Sq. [Preprint]. 2020. Available from:https://www.researchsquare.com/article/rs30482/v1. [Cited 12 July 2020]: [19p].
38. Selvam S, Muthukumar P, Venkatramanan S, et al. SARS-CoV-2 pandemic lockdown: Effects on air quality in the industrialized Gujarat state of India. Sci Total Environ. 2020;737:140391.
39. Menut L, Bessagnet B, Siour G, et al. Impact of lockdown measures to combat Covid-19 on air quality over western Europe. Sci Total Environ. 2020;741:140426.
Type of Study: Original articles |
Subject:
Environmental pollution
Received: 2022/02/25 | Accepted: 2022/04/20 | Published: 2022/06/20
Received: 2022/02/25 | Accepted: 2022/04/20 | Published: 2022/06/20
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
