|
Investigating air pollutant trends based on temporal air quality indexes in Karaj, Iran, during 2012 − 2018
Mohammad Noorisepehr1, Mehdi Vosoughi2, Afsane Chavoshani3, Zahra Eskandari4
1 Research Center for Health, Safety and Environment (RCHSE), Alborz University of Medical Sciences; Department of Environmental Health Engineering, School of Public Health, Alborz University of Medical Sciences, Karaj, Iran
2 Social Determinants of Health Research Center, Ardabil University of Medical Sciences; Department of Environmental Health Engineering, School of Public Health, Ardabil University of Medical Sciences, Ardabil, Iran
3 Department of Environmental Health Engineering, School of Health, Iranshahr University of Medical Sciences, Iranshahr, Iran
4 Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| Date of Submission | 24-Nov-2021 |
| Date of Decision | 01-Sep-2022 |
| Date of Acceptance | 01-Oct-2022 |
| Date of Web Publication | 17-Jul-2023 |
Correspondence Address:
Mrs. Zahra Eskandari
Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijehe.ijehe_40_21
Aim: Due to the importance of the relationship between air pollutants and the incidence of many diseases in polluted cities, in this study, we collected the data related to yearly, seasonally, monthly, daily, and hourly concentrations of particulate matter (PM) 2.5, PM10, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) recorded at four monitoring stations across Karaj city, Iran, to investigate the air pollutant trends based on air quality indexes (AQIs) in the city during 2012–2018. Materials and Methods: The correlations between PMs and gaseous pollutants were analyzed using the Pearson correlation coefficient. The concentrations of air pollutants indexes including O3, NO2, SO2, CO, PM10, and PM2.5 were recorded in four air pollution monitoring stations in Karaj obtained from the monitoring system of the environment department. Then, the data were analyzed using SPSS and Graph pad softwares. Results: The findings showed that in 20%–40% and 1%–5% of days during 2012–2018, higher concentrations of PM2.5 and PM10 were experienced than the national standard (NS) concentration, respectively. Furthermore, during this time, 0.3%–0.9% of days indicated the higher concentrations of CO and SO2 than the NS, respectively. Although the daily concentration of NO2 was lower than NS, 0.5%–5% of days were exposed to the higher concentration of O3 than NS. SO2 concentration showed a negative and positive correlation with PM10 (r = −0.69, P = 0.013) and O3(r = 0.58, P = 0.03), respectively. Conclusion: These results indicated that Karaj AQI was moderate and the most problem with air quality in Karaj city was attributed to the PM2.5 concentrations. To reduce health disorders related to this pollutant, it is necessary to control PM2.5 sources and sensitive groups should reduce outdoor activities.
Keywords: Air pollution, air quality index, Karaj, temporal changes
How to cite this article:
Noorisepehr M, Vosoughi M, Chavoshani A, Eskandari Z. Investigating air pollutant trends based on temporal air quality indexes in Karaj, Iran, during 2012 − 2018. Int J Env Health Eng 2023;12:14 |
How to cite this URL:
Noorisepehr M, Vosoughi M, Chavoshani A, Eskandari Z. Investigating air pollutant trends based on temporal air quality indexes in Karaj, Iran, during 2012 − 2018. Int J Env Health Eng [serial online] 2023 [cited 2023 Sep 24];12:14. Available from: https://www.ijehe.org/text.asp?2023/12/1/14/381730 |
| Introduction | |  |
Industrialization and urbanization over the last decades, along with rapid global economic growth, have resulted in an increase in ambient air pollution, which is a serious threat to human health.[1] Ambient air pollutants include complex mixtures of particles and gases such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and particulate matters (PMs).[2],[3],[4],[5] Exposure to PM2.5 and PM10 can aggravate chronic respiratory and cardiovascular diseases, alter host defenses, and damage lung tissue.[4],[6] It can also lead to premature death and cancer. Ground-level O3 can exacerbate chronic respiratory diseases and cause short-term reductions in lung function. The health effects of exposure to CO, SO2, and NO2 gases can include reduced work capacity, aggravation of existing cardiovascular diseases, negative effects on pulmonary function, respiratory illnesses, lung irritation, and alteration to lung defense systems.[7],[8] Estimations of the magnitude of risk have been based on the factors related to the air pollutant mixture, personal exposure, study methods, evaluation models, and monitoring network characteristics.[9],[10]
Atmospheric aerosol particles are known as PMs, which is a mixture of extremely small solid and liquid droplets, including acids, organic chemicals, metals, soil particles, dust, and some biological elements such as pollen and fungal spores.[10] The size of this suspended PM varies from a few nanometers to 10 μm, which has the ability to penetrate unfiltered deep into the lungs and bloodstream, potentially causing asthma, DNA mutations, and heart disease with other respiratory diseases.[11],[12] PM10 and PM2.5 originate primarily from motor vehicle-related emissions, biomass burning, combustion-derived carbon with ultrafine nitrates, sulfates and air dust, and endotoxins associated with many biological particles.[13] The World Health Organization established the standard levels for PM2.5 and PM10 as: (i) maximum 24-h average PM2.5 exposures: 25 μg/m3; maximum annual average exposure: 10 μg/m3 and (ii) maximum 24-h average PM10 exposure: 50 μg/m3; maximum annual average PM10 exposure: 20 μg/m3.[12]
Currently, tropospheric O3 is one of the most important atmospheric pollutants in China, the US, and Europe, and it is primarily generated from auto emissions. Breathing O3 can trigger a variety of health problems, such as chest pain, coughing, throat irritation, eye irritation, reduced lung function, and damaged lung tissue. O3 also harms ecosystems and damages sensitive vegetation during the growing season.[12],[14] The standard value of O3 is 100 μg/m3 for an 8-h averaging time.[15] CO is another toxic gas in the atmosphere that is primarily produced from the incomplete combustion of carbon-containing fuels such as gasoline, natural gas, oil, coal, and wood. Breathing high concentrations of CO can reduce O2 transport in hemoglobin and cause health effects, including headaches, chest pain, heart disease, etc.[16] To minimize health effects, an appropriate level of CO must be below 10.5 μg/m3 for an 8-h averaging time, and possibly as low as 4.6–5.8 μg/m3. The primary source for the anthropogenic emission of NO2 and SO2 into the atmosphere is a combination of the burning of fossil fuels, biomass, and emissions from cars, trucks, buses, power plants, and off-road equipment. The standard value of NO2 is 40 μg/m3 annual averaging time and 200 μg/m3 1 h averaging time, and for SO2 the standard values are 20 μg/m3 24-h averaging time and 500 μg/m310 min averaging time. Most of the atmospheric NO2 is emitted by NO, which is then rapidly oxidized by O3 to become NO2. Both NO2 and SO2 greatly impact the environment and human health because atmospheric NO2 and SO2 easily mix with rainwater, creating acid rain, which is very harmful to animals and plant life. Breathing with a high concentration of NO2 and SO2 can cause some respiratory illnesses such as asthma, coughing, and wheezing.[17],[18]
Up to now, the study of air pollutions has been done based on air quality index (AQI) in many cities of Iran. For example, Kermani et al. found that AQI in Tehran, Tabriz, Mashhad, Urmia, Ahvaz, and Irak in 341, 139, 347, 28, 162, and 81 days of the year was over the Environment Protection Agency of Iran's standard, respectively.[19] Furthermore, in all of the cities, PM was the main responsible pollutant. In another study, Ashtari et al. showed that an increase in AQI levels in Isfahan city was associated with a higher expanded disability status scale among multiple sclerosis patients.[20] With respect to air pollution studied conducted in Karaj city, it was cleared that several studies.[21],[22],[23],[24],[25],[26] have been investigated the importance of the relationship between air pollutants and the incidence of many diseases in Karaj city. However, there is no study that has surveyed the importance of the temporal variations and profiles of AQI in this city during a long time. Therefore, for air quality monitoring in this city, we decided to study the pollutants trends in yearly, seasonally, monthly, daily, and hourly concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 recorded from 2012 to 2018 at four monitoring stations across Karaj city, Iran. Furthermore, the correlations between PM and gaseous pollutants were analyzed using the Pearson correlation coefficient.
| Materials and Methods | | |
Study area
Karaj, a bustling metropolis in the center of Alborz Province with a population of nearly 3 million people is located 48 km northwest of Tehran (The capital of Iran). The city has 16 km in length and is 1300 m above the sea level, with a total area of 175.5 km2. It is located at the latitude of 35.4845 and longitude of 51.030 in the northern hemisphere. Karaj population in 2018 has been estimated 1,585,000. In general, its climate is similar to other parts of Alborz Province so that, in cold seasons, the weather is influenced by the north, northwest, and west, especially southwest climates, with atmospheric rainfall from November and August continuing until May. It experiences a huge volume of public and personal transportation daily due to its communication path with more than 15 provinces of Iran and suffers from severe air pollution where the emission by cars contributes to almost 75% of the pollution.[25]
Data collection and analysis
Concentrations of standard air pollutants (O3, NO2, SO2, CO, PM10, and PM2.5) in four air pollution monitoring stations in Karaj city (Farhangsara, Metro, Faculty of Environment, and District 6 Municipality) have been online recorded in the monitoring system of the Environment Department) [Figure 1]. In 2018, after referring to Alborz Environment Organization, it was obtained as an Excel file from 2012 to 2018. Then, data were analyzed by SPSS software. First, the normality of the data was measured using the Kolmogorov–Smirnov test, then descriptive data were expressed as average, standard deviation, maximum, and minimum. The analysis of variance test was also used to compare the concentrations of pollutants at different times (year, season, month, day, and hour). AQI, as an indicator for daily air quality, is divided into six categories and alerts people to air quality (clean or polluted) and provides related health effects. | Figure 1: Location of the study area and sampling stations in Karaj city (Yellow circles are monitoring air stations)
Click here to view |
| Results | | |
Particulate matter10 and particulate matter2.5 concentration
In this study, in [Table 1], the results of yearly average concentrations of PM2.5 and PM10 have been shown during 2012–2018. The highest yearly average PM10 and PM2.5 concentrations were obtained in 2014 (94.55 ± 46.6 μgm−3) and 2012 (34.48 ± 23.2 μg/m3), respectively. PM10 and PM2.5 concentrations during these 7 years had a significant difference (P < 0.001). In regard to seasonal distribution, the highest PM10 and PM2.5 values showed in summer (76.26 ± 88.7 μg/m3) and winter (31.56 ± 12.2 μg/m3), respectively [Table 2]. The seasonal average distribution of PM10 and PM2.5 concentrations during 2012–2018 showed a statistically significant difference (P < 0.001). The trend analysis of the monthly average of PM10 and PM2.5 at this time could be observed in [Table 3]. The highest monthly average concentrations of PM10 and PM2.5 (88.98 and 35.14 μg/m3) obtained in June and January, respectively. Furthermore, the daily maximum averages of both PM10 and PM2.5 have been reported on Monday in the range of 72.69 ± 12.3 and 30.02 ± 24.3 μg/m3, respectively [Table 4]. Monthly and daily trends in PM10 and PM2.5 concentrations showed a significant difference (P < 0.001). During this time, remarkable changes were not observed at hourly tends of PM10 and PM2.5 (Data not shown). [Figure 2] indicates day's percentage with a higher concentration than national standards (NSs) of PM10 and PM2.5 during 2012–2018. Based on these results, the worst days with the higher PM2.5 and PM10 concentrations were found in 2012 and 2014, respectively. | Figure 2: Days percentages with pollutants concentration higher than national standard
Click here to view |
Carbon monoxide concentration
[Table 1] indicates the results of CO concentration during 2012–2018. Based on these results, the maximum and minimum yearly average concentration of CO occurred in 2012 (2.35 ± 1.2 ppm) and 2014 (1.37 ± 0.8 ppm), respectively [Table 1]. Furthermore, the maximum and minimum average seasonally concentration of CO was obtained from autumn (1.98 ± 1.3 ppm) and summer (1.43 ± 1.01 ppm), respectively [Table 2]. The monthly average concentration of CO showed that the worst and best CO concentrations could be attributed to Noveber (2.21 ± 1.5 ppm) and September (0.001 ± 0.001 ppm) [Table 3], respectively. Furthermore, Tuesday (1.78 ± 1.1 ppm) and Friday (1.67 ± 1.09 ppm) showed the highest and lowest daily concentration of CO, respectively [Table 4]. However in hour 22 (2.33 ± 1.4 ppm) and hours 13 and 14 (1.39 ± 0.9 ppm) was reported the maximum and minimum hourly average concentration of CO, respectively (Data not shown). During 2012–2018, 0.3% of days had CO concentration higher than standard (9 ppm) [Figure 2].
Nitrogen dioxide concentration
Annually, seasonally, monthly, daily, and hourly averages of NO2 concentration during 7 years are observable in the following tables. These results show that the highest and lowest yearly average concentration of NO2 occurred in 2012 (0.031 ± 0.01 ppm) and 2016 (0.006 ± 0.005 ppm), respectively [Table 1]. Furthermore, the highest and lowest average seasonally concentration of NO2 was reported in winter (0.022 ± 0.01 ppm) and autumn (0.019 ± 0.01 ppm), respectively [Table 2]. January (0.028 ± 0.01 ppm) and أNovember (0.01 ± 0.01 ppm) indicated the highest and lowest monthly average concentrations of NO2, respectively [Table 3]. However, there is not any remarkable difference between daily and hourly NO2 concentration [Table 4]. Based on obtained results, during 2012-2018, all daily averages (%) of NO2 were lower than the national NO2 standard (100 ppb) [Figure 2].
Sulfur dioxide concentration
The change of average SO2 during 2012-2018 is observed in [Table 1]. The maximum and minimum yearly average concentrations of SO2 occurred in 2018 (0.029 ± 0.02 ppm) and 2013 (0.01 ± 0.003 ppm), respectively [Table 1]. Furthermore, the maximum and minimum average seasonally concentration of SO2 was obtained from spring and summer (0.017 ± 0.009 ppm) and winter (0.014 ± 0.01 ppm), respectively [Table 2]. September (0.02 ± 0.009 ppm) and October (0.01 ± 0.008 ppm) have been related to the highest and lowest monthly average concentration of SO2, respectively [Table 3]. It was clear that there was not any remarkable difference between daily and hourly SO2 concentrations [Table 4]. However in 2018, SO2 concentration of 0.8% days was higher than the NS [Figure 2].
Ozone concentration
The worst and best yearly average concentrations of O3 occurred in 2014 (0.029 ± 0.01 ppm) and 2017 (0.017 ± 0.1 ppm), respectively [Table 1]. Furthermore, seasonally concentration of O3 was observed in the summer (0.026 ± 0.019 ppm) and autumn (0.019 ± 0.01 ppm), respectively [Table 2]. In July (0.028 ± 0.02 ppm) and August (0.014 ± 0.0009 ppm), we observed the worst and best monthly average concentrations of O3, respectively [Table 3]. The highest and the lowest daily concentration of O3 was showed on Friday (0.023 ± 0.01 ppm) [Table 4]. The highest and lowest hourly average concentration of O3 was reported in hours 15 and 16 (0.034 ± 0.02 ppm) and hours 7 (0.015 ± 0.012 ppm), respectively (Data not shown). During 2012–2018, days percentage with O3 higher than the national O3 standard (0.007 ppb) are shown in [Figure 2].
Correlation between particulate matters and gaseous pollutants
Pearson correlation coefficient was used to examine the correlation between the average concentrations of pollutants during the 7 years of the study [Table 5]. Pearson correlation coefficient showed that there is a significant negative relationship between PM10 and SO2 (r = −0.69, P = 0.013). This averages that with the increase of PM10 pollutants, the amount of SO2 has decreased and vice versa. Also, a significant positive relationship was observed between PM2.5 and O3 (r = 0.57, P = 0.03). This averages that as the amount of PM2.5 pollutants increases, so does the amount of O3 pollutants. There was a significant positive relationship between SO2 and O3 pollutants (r = 0.58, P = 0.03). | Table 5: Pearson correlation coefficients between particulate matter and gaseous pollutants
Click here to view |
Air quality index trends in Karaj city during 2012-2018
During the study from 2012 to 2018, a one-way analysis of the variance test showed a significant difference between the average concentrations of pollutants (P < 0.001). Results obtained from this study showed that long-term trend of AQI during this time at four monitoring stations is directed to clean. At this time, only 2012 indicated an unhealthy AQI (102.47) [Figure 3]. It was observed that Karaj's AQI was moderate. | Figure 3: AQI trends in Karaj city during 2012–2018. AQI: Air quality index
Click here to view |
| Discussion | | |
Air pollution trends based on hourly, monthly, seasonal, and annual changes are important. The pattern of the temporal distribution of air pollutants is a suitable tool to assess air quality in comparison with national and international air standards. Overview of data indicated that the highest concentrations of PM10 (88.89 ± 0.1 μg/m3) in June, PM2.5 (35.14 ± 23.80 μg/m3) in January, SO2 (0.02 ± 0.009 ppm) in September, and O3 (0.028 ± 0.020 ppm) in July. However, the highest concentrations of CO (2.21 ± 1.5 ppm) in November and NO2 (0.028 ± 0.01 ppm) in January were reported. In this study, a quick look at Tables shows that some types of pollutants are worse in the summer, while others are worse in cold winter, it can be due to changes in air humidity and wind direction. Totally, due to nonrainy days in summer and combustion of more fuel in winter, PMs are the most part of air pollution, due to heat and sunlight, O3, NOx, and Volatile organic carbons (VOCs) are higher than other pollutants. Because of the falling and decay of leaves, VOCs can pose the highest threat to human health. In winter, smog and inversion cause that CO, NOx, PM10, PM2.5, and VOCs are trapped in the ground until the temperature change.[27]
The increasing temporal concentration of PM10 and PM2.5 can be due to agricultural activities during spring. In winter, smog and inversion cause that CO, NOx, PM10, PM2.5, and VOCs are trapped in the ground until the temperature change.[27] According to the results of Baltaci et al.,[28] three highest monthly average PM10 concentrations are found during March, February, and April months with average values of 55.8, 53.5, and 53.3 μg/m3, respectively. PM10 concentration exceeded the European (EU) threshold limit (50 μg/m3) in a total of 54% of days in March. In regard to seasonal distribution, the highest PM10 values in spring (53.8 μg/m3) are followed by winter, fall, and summer seasons with average values of 49.7, 43.2, and 42.8 μg/m3, respectively. Based on the results of Zhang et al.(2017),[29] extreme PM2.5 days usually occurred when the speed of wind is under 2 m/s. It is cleared this difference in PM concentration obtained from different studies is due to differences in geographical and atmospheric conditions, and distance from city centers. Furthermore, Goudarzi et al. found the maximum concentration of PM10 in Ahvaz in the cold season than in the hot season in Ahvaz city in Iran.[30] In our neighboring country, Ozel et al. showed that the average of daily total PM10 concentration in Turkey is 148 μg/m3, the average of monthly total PM10 concentration is 4 437 μg/m3, and the average of yearly total PM10 concentrations is 53 984 μg/m3.[25] In another study conducted by Kermani et al., it was observed that maximum and minimum annual concentrations of PM2.5 have happened in the autumn and spring seasons with a value of 67.48 and 19.85 μg/m3, respectively.[19] Furthermore, the citizens of Karaj are exposed to PM2.5 pollutants four times more than the US-EPA standard (10 μg/m3).[23] Moreover, Barzeghar et al. result in Tabriz showed the highest monthly average concentrations of PM10 and PM2.5 were observed in May (80.4 μg/m3) and December (42.5 μg/m3), respectively.[31] In this study, although the PMs trend has decreased and the air quality of Karaj improved, almost 20% of days in 2018 involved PM2.5 with a higher concentration than NS [Figure 2].
Despite our results that showed the low concentrations for CO and NO2, in the study of Xiao et al.(2018) in the Basin City of Chengdu, China, CO had concentration between 95.1 and 99.7 ppm. Also, in this study, it was cleared that in the city of Chengdu in China, reported the annually concentration of NO2 between 20 and 69 μg/m3 (37.6–129.77 ppb).[32] Furthermore, Hewitt[33] has investigated the concentrations of NO2 in a city and their results showed that the concentration of NO2 decreased with increasing the distance from the main road. Both low temperature and heating cause an increase in NOx concentrations. The low temperatures in winter are not conducive to the conversion of NO2, resulting in the accumulation of NOx. Heating increases the amount of coal used and increases the concentration of NO2. The results of Wang et al.(2020) showed that the maximum monthly average concentration of NOx and NO was reached in October, and the maximum monthly average concentration of NO2 was reached in March. The spatial distribution characteristics and annual average spatial distribution were the same. They found that the average daily variation in NOx concentration occurred at 07:00–08:00 in the morning, and the second peak occurred between 20:00 and 22:00 at night.[34]
In the present study, during 2012–2018, pollution during the cold season was higher than warm season. Another reason for these results can be due to the difference in topography, and meteorology of locations.[5] The highest days' percentage with SO2 higher than the national SO2 standard was 0.8%. The study of Krochmal and Kalina showed that the average of SO2 concentration.[35] In Tabriz, Barzeghar et al.(2020) found the highest monthly average concentration of O3 in June (78.4 μg/m3 or 156.8 × 10−3 ppm).[30]
The generation and distribution of O3 depend on ground air temperature, wind speed and direction, relative humidity, and precipitation associated with climate change have the potential to affect, and deposition of O3.[36] In the present study, in 2016, 5% of days had O3 higher than standards. According to the results of Faridi et al., O3 seasonal behavior shows higher concentrations during summer months, certainly due to the more intensive photochemical reactions, than in other months.[37] The amount of photochemical reactions conducted during time can affect O3 production in a region.[27] Surface O3 concentrations may be affected by many factors such as UV radiation, cloud cover, temperature, wind direction and speed, precipitation, and position of fronts, but in general, temporal O3 concentration (annually, seasonally, monthly, daily, and hourly) in the air all of them are highly positively correlated with the temperature.[38] In the present study, the correlation between PM10 and SO2 can be due to the adsorption of SO2 on dust particles. Ghaderi et al.(2018) in Ahvaz city showed that SO2 and PM10 variables are correlated with each other (P < 0.05) due to the absorption of PMs on SO2. The correlation between O3 and PM2.5 could be because O3 is related to secondary aerosols and not primary aerosols.[38] Wang et al. (2020) reported that the NOx concentration in Changchun was positively correlated with NO2, NO, PM2.5, PM10, and CO, and it had a significant negative correlation with O3. The NOx concentration had a significant positive correlation with the PM2.5 concentration because the secondary conversion of NOx had a significant effect on PM2.5. There was a significant negative correlation between NOx and O3 because the precursors were consumed and produced by photochemical reactions.[34]
| Conclusion | | |
In the present study, temporal profiles of air quality in Karaj were characterized by AQI during 2012–2018. Totally, AQI in Karaj during this time had a moderate range. Among six pollutants, PM2.5 was the major contributor to the AQI, and the annual average PM2.5 concentrations at all stations exceeded the NS. These results show that, for Karaj, PM2.5 is a major issue to be considered in improving air quality. In Karaj, the AQI profile and its pattern showed an unhealthy for sensitive groups who should reduce their activities in the outdoor air. Consequently, to better understand the long-term trend of air quality variation in Karaj, PM2.5 concentrations must be taken into account, and its resources should be considered and controlled. Furthermore, air pollution research with respect to epidemiological studies is recommended.
Financial support and sponsorship
The authors would like to thank the Alborz Environmental Organization for providing the Air Quality Data. This research was supported by the Student Research Committee of Alborz University of Medical Sciences in 2018, which has been implemented with the financial support of Alborz University of Medical Sciences, Alborz, Iran.The authors appreciate their support. Ethics Code: IR.ABZUMS.REC.1397.151.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Yang D, Ye C, Wang X, Lu D, Xu J, Yang H. Global distribution and evolvement of urbanization and PM2. 5 (1998–2015). Atmos Environ 2018;182:171-8.  |
| 2. | Langrish JP, Li X, Wang S, Lee MM, Barnes GD, Miller MR, et al. Reducing personal exposure to particulate air pollution improves cardiovascular health in patients with coronary heart disease. Environ Health Perspect 2012;120:367-72. |
| 3. | Vahedian M, Khanjani N, Mirzaee M, Koolivand A. Ambient air pollution and daily hospital admissions for cardiovascular diseases in Arak, Iran. ARYA Atheroscler 2017;13:117-34. |
| 4. | Guarnieri M, Balmes JR. Outdoor air pollution and asthma. Lancet 2014;383:1581-92. |
| 5. | Neisi A, Goudarzi G, Babaei AA, Vosoughi M, Hashemzadeh H, et al. Concentration of air pollutants as toxic matter in urban and rural areas of Ahvaz. Toxin Rev 2018;37:243-50. |
| 6. | Chen X, Zhang LW, Huang JJ, Song FJ, Zhang LP, Qian ZM, et al. Long-term exposure to urban air pollution and lung cancer mortality: A 12-year cohort study in Northern China. Sci Total Environ 2016;571:855-61. |
| 7. | Jiang XQ, Mei XD, Feng D. Air pollution and chronic airway diseases: What should people know and do? J Thorac Dis 2016;8:E31-40. |
| 8. | Liu J, Mauzerall DL, Chen Q, Zhang Q, Song Y, Peng W, et al. Air pollutant emissions from Chinese households: A major and underappreciated ambient pollution source. Proc Natl Acad Sci U.S.A 2016;113:7756-61. |
| 9. | Hoek G. Methods for assessing long-term exposures to outdoor air pollutants. Curr Environ Health Rep 2017;4:450-62. |
| 10. | Dong X, Zhao X, Peng F, Wang D. Population based air pollution exposure and its influence factors by integrating air dispersion modeling with GIS spatial analysis. Sci Rep 2020;10:479. |
| 11. | Xie W, Li G, Zhao D, Xie X, Wei Z, Wang W, et al. Relationship between fine particulate air pollution and ischaemic heart disease morbidity and mortality. Heart 2015;101:257-63. |
| 12. | Rahman MM, Mahamud S, Thurston GD. Recent spatial gradients and time trends in Dhaka, Bangladesh, air pollution and their human health implications. J Air Waste Manag Assoc 2019;69:478-501. |
| 13. | Li T, Hu R, Chen Z, Li Q, Huang S, Zhu Z, et al. Fine particulate matter (PM2.5): The culprit for chronic lung diseases in China. Chronic Dis Transl Med 2018;4:176-86. |
| 14. | Brunekreef B, Holgate ST. Air pollution and health. Lancet 2002;360:1233-42. |
| 15. | de Leeuw F, Benesova N, Horálek J. Evaluation of international air quality standards. ETC/ACM Technical Paper. 2016;10:1-31. |
| 16. | Sharma VK. Development of air quality indices for Mumbai, India. Int J Environ Pollut 1999;11:141-6. |
| 17. | Rahman A, Luo C, Khan MH, Ke J, Thilakanayaka V, Kumar S. Influence of atmospheric PM2. 5, PM10, O3, CO, NO2, SO2, and meteorological factors on the concentration of airborne pollen in Guangzhou, China. Atmos Environ 2019;212:290-304. |
| 18. | Zheng C, Zhao C, Li Y, Wu X, Zhang K, Gao J, et al. Spatial and temporal distribution of NO2 and SO2 in inner mongolia urban agglomeration obtained from satellite remote sensing and ground observations. Atmos Environ 2018;188:50-9. |
| 19. | Kermani M, Bahrami Asl F, Aghaei M, Arfaeinia H, Karimzadeh S, Shahsavani A. Comparative investigation of air quality index (AQI) for six industrial cities of Iran. Studies Med Sci 2014;25:810-19. |
| 20. | Ashtari F, Esmaeil N, Mansourian M, Poursafa P, Mirmosayyeb O, Barzegar M, et al. An 8-year study of people with multiple sclerosis in Isfahan, Iran: Association between environmental air pollutants and severity of disease. J Neuroimmunol 2018;319:106-11. |
| 21. | Mirsanjari MM, Zarandian A, Mohammadyari F, Visockiene JS. Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran. Environ Monit Assess 2020;192:1-23. |
| 22. | Aghasariatmadari Z, Kamali S. Evaluation of the effects of atmospheric pollutants on the performance of angestrom-prescott equation in estimating solar radiation (Case Study: Karaj). Iran J Soil Water Res 2018;48:1053-61. |
| 23. | Kermani M, Jonidi Jafari A, Gholami M, Fanaei F, Arfaeinia H. Association between meteorological parameter and PM2. 5 concentration in Karaj, Iran. Int J Environ Health Eng 2020;2020:1-6. |
| 24. | Amiri H, Hassanvand S, Mohammadi F, Nourmoradi H, Amarloei A, Mazloomi S. Quantification of health impacts related to PM10 and O3 pollutants in Karaj city. Iran J Health Saf Environ 2018;5:1115-21. |
| 25. | Ozel G, Cakmakyapan S. A new approach to the prediction of PM10 concentrations in Central Anatolia Region, Turkey. Atmos Pollut Res 2015;6:735-41. |
| 26. | Kermani M, Jonidi Jafari A, Gholami M, Arfaeinia H, Shahsavani A, Fanaei F. Characterization, possible sources and health risk assessment of PM2.5-bound heavy metals in the most industrial city of Iran. J Environ Health Sci Eng 2021;19:151-63. |
| 27. | Bodor Z, Bodor K, Keresztesi Á, Szép R. Major air pollutants seasonal variation analysis and long-range transport of PM 10 in an urban environment with specific climate condition in Transylvania (Romania). Environ Sci Pollut Res Int 2020;27:38181-99. |
| 28. | Baltaci H, Alemdar CS, Akkoyunlu BO. Background atmospheric conditions of high PM10 concentrations in Istanbul, Turkey. Atmos Pollut Res 2020;11:1524-34. |
| 29. | Zhang H, Wang Y, Park TW, Deng Y. Quantifying the relationship between extreme air pollution events and extreme weather events. Atmospheric Res 2017;188:64-79. |
| 30. | Goudarzi G, Alavi N, Geravandi S, Yari AR, Aslanpour Alamdari F, Dobaradaran S, et al. Ambient particulate matter concentration levels of Ahvaz, Iran, in 2017. Environ Geochem Health 2019;41:841-9. |
| 31. | Barzeghar V, Sarbakhsh P, Hassanvand MS, Faridi S. Long-term trend of ambient air PM10, PM2. 5, and O3 and their health effects in Tabriz city, Iran, during 2006–2017. Sustain Cities Soc 2020;54:101988. |
| 32. | Xiao K, Wang Y, Wu G, Fu B, Zhu Y, Spatiotemporal characteristics of air pollutants (PM10, PM2. 5, SO2, NO2, O3, and CO) in the inland basin city of Chengdu, southwest China. Atmosphere 2018;9:74. |
| 33. | Hewitt CN. Spatial variations in nitrogen dioxide concentrations in an urban area. Atmos Environ Part B Urban Atmos 1991;25:429-34. |
| 34. | Wang L, Wang J, Tan X, Fang C. Analysis of NOx pollution characteristics in the atmospheric environment in Changchun city. Atmosphere 2020;11:30. |
| 35. | Krochmal D, Kalina A. Measurements of nitrogen dioxide and sulphur dioxide concentrations in urban and rural areas of poland using a passive sampling method. Environ Pollut 1997;96:401-7. |
| 36. | Zoran MA, Savastru RS, Savastru DM, Tautan MN. Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy. Sci Total Environ 2020;740:140005. |
| 37. | Faridi S, Shamsipour M, Krzyzanowski M, Künzli N, Amini H, Azimi F, et al. Long-term trends and health impact of PM2.5 O3 in Tehran, Iran, 2006-2015. Environ Int 2018;114:37-49. |
| 38. | Ghaderi F, Karami M, Shekari P, Jafari A. Atmospheric dust deposition trend and its relation with selected climatic and spatial factors in Javanrood township. J Soil Water Conserv 2018;24:123-40. |
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
|
Search Pubmed for