Association between meteorological parameter and PM2.5 concentration in Karaj, Iran
Majid Kermani, Ahmad Jonidi Jafari, Mitra Gholami, Farzad Fanaei, Hossein Arfaeinia
Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
|Date of Submission||21-Feb-2020|
|Date of Acceptance||06-Apr-2020|
|Date of Web Publication||31-Jul-2020|
Iran University of Medical Sciences, Tehran
Source of Support: None, Conflict of Interest: None
Aim: The present study aimed to investigate the effect of meteorological parameters on particulate matter concentration (PM2.5) in ambient air of Karaj metropolitan. Materials and Methods: In this cross-sectional descriptive study, 17 sampling points in Karaj city were chosen using GIS software. Sampling of PM2.5 was carried out for 24 h using the sampling pilot during the four seasons in the air of Karaj city. Sampling of PM2.5 was performed using PTFE filters, peripheral pump, and personal modular impactor. The concentration of particles collected on the filter was calculated by gravimetric method. Meteorological parameters including temperature, humidity, pressure, cloud coverage rate, ultraviolet (UV), and dew point were recorded during sampling through portable devices. Finally, the relationship between PM2.5 concentration and meteorological parameters was assessed by SPSS24 and Excel software. Results: Seasonal variation trends of PM2.5 particles showed that maximum and minimum annual concentrations have happened in autumn and spring seasons with a value of 67.48 and 19.85 μg/m3, respectively. Furthermore, the citizens of Karaj are exposed to PM2.5 pollutants four times more than the Environmental Protection Agency-recommended standard (10 μg/m3). The findings of this study also show that there is a positive correlation between PM2.5 particle concentration and temperature, relative humidity, and pressure (P < 0.05). Moreover, a weak and negative correlation was found between UV radiation, cloud cover, and dew point with PM2.5 particle concentration (P > 0.05). Conclusion: The highest concentrations of particles were observed in autumn season. Furthermore, the particle concentration scatter map shows that the central and eastern regions and north of Karaj are more polluted than other areas. Therefore, to protect the health of citizens of Karaj, appropriate policies and strategies should be adopted to reduce the concentration of particulate matter and other pollutants in the ambient air of this city.
Keywords: Air pollution, Karaj city, meteorological parameters, PM2.5
|How to cite this article:|
Kermani M, Jafari AJ, Gholami M, Fanaei F, Arfaeinia H. Association between meteorological parameter and PM2.5 concentration in Karaj, Iran. Int J Env Health Eng 2020;9:4
|How to cite this URL:|
Kermani M, Jafari AJ, Gholami M, Fanaei F, Arfaeinia H. Association between meteorological parameter and PM2.5 concentration in Karaj, Iran. Int J Env Health Eng [serial online] 2020 [cited 2021 Jan 15];9:4. Available from: https://www.ijehe.org/text.asp?2020/9/1/4/291245
| Introduction|| |
Air pollution has long been clear to the public since the early twentieth century, with the London Smoke (1952) and Denora (1948) events. Research by the World Health Organization (WHO) has shown that half the people of the world live somewhere that the concentrations of air pollutants are higher than the standard level. The mother womb is believed to be the safest place in the world, but studies at the University of Belgium have shown that air pollution even can affect the fetus in the womb. Hence, air pollution is one of the global problems that wherever we live on the earth, air pollution is existing high or low. Hence, it can be said that there is no clean air., In addition to the heavy costs that air pollution on governments imposes, those include a variety of diseases ranging from asthma to stroke and eventually death., According to the report by the World Bank in 2005 on the mortality rate and the resulting costs published, total death from air pollution in Iran is approximately 0.57% of GDP. There are also about 45,000 mortalities annually from air pollution diseases in Iran. Major air pollutants include CO, HCHO, NOX, SO2, O3, and suspended particles. However, particles with aerodynamic diameter smaller than 2.5 microns (PM2.5) are executed the most role in air pollution. PM2.5 particles with their strength and influence go deep into the lungs and cause respiratory disorders. Particles cause an effect on meteorological parameters including impact on the amount of solarization and ultimately change the earth's surface temperatures and reduce visibility. The concentration of these particles depending on the geographical location, weather conditions and other factors can be changed. This city faces a large volume of daily pollutant production due to the communication route of 15 provinces of the country, the existence of small and large industries, and some other sources. Population of this city because of the working positioners and occupation every year is growing. According to the report of the Karaj Environmental Protection Organization, the main pollutants in recent years were PM2.5 particles.
Numerous studies have been performed on the influence of particle concentration on the meteorological parameters. Ansari et al. (2017–2018) have reviewed the influence of changing meteorological parameters on the ambient fine particulate matter. It was found that there is a weak correlation between PM2.5 particles and mean monthly temperature (r = 0.42, P < 0.05) and mean relative humidity (r = 0.37, P < 0.05). The results of Jamshidi et al. on the concentration of PM2.5 particles in Gachsaran showed that concentrations of these particles were increased in month of May, July, and August. Furthermore, Tecer et al. find that there is an adverse correlation between meteorological parameters (include: temperature, precipitation, and pressure) and PM2.5 particle concentration. In another study which had been conducted in Malaysia, the concentration of particles has shown a negative effect on wind speed and relative humidity. The study by Sammaritano et al. in Austria was showed that the highest concentration of particles was in winter season. In the other hand, Zhang et al. in their article showed that the relative humidity and precipitation are negative correlation and temperature and wind speed are positive correlation to the concentration of particles in the air. In the study conducted by Yet et al., the weak positive relationship was found between temperature and PM2.5 particle concentration. In the study of Ye et al., a negative relationship between PM2.5 concentration and wind speed has observation (r = −0.28, P < 0.05).
Due to the above-mentioned reasons, this study aimed to: (i) investigate the concentration and seasonal variation of PM2.5 in ambient air of Karaj and (ii) investigate the effect of meteorological parameters including temperature, relative humidity, pressure, ultraviolet (UV) rate, cloud coverage rate, and dew point on the concentration of PM2.5 in the ambient air of Karaj, Iran.
| Materials and Methods|| |
Geographical location of the study area
Karaj is one of the major metropolises with a population of about 2.5 million located 48 km northwest of Tehran, capital of Iran. The length of this city is about 16 km and 1300 m above sea level and its surface area is approximately 175.5 km 2. Furthermore, the latitude and longitude of the city are 35°48´N and 51°00´E, respectively. Because of the connection between this city and 15 provinces of the country, it faces a large volume of pollutants daily. This reason causes an intensification of air pollution from the most important source of its formation, namely moving vehicles. On the other hand, various small and large industries i.e. petrochemical industries, gas and oil refineries, welding workshops, aluminum industries, cement factories and etc. are also active near the Karaj city and release high amount of PM into the ambient air of this city. Therefore, Karaj is one of the most polluted megacity in Iran because of above-mentioned reasons. Although there are sporadic studies on the concentration of particulate matter in this city, there is not exist integrated data that can be based on the organizational policies in order to advancement control of air pollution. This study is a descriptive-analytical that was done to intentional measurement of PM2.5 particles and influence of meteorological parameters such as temperature, relative humidity, pressure, UV rate, cloud coverage, and dew point seasonally in seasons spring, summer, autumn, and winter in 2018–2019 in Karaj city. In this study, 17 points from Karaj city with cooperation of the Health Department of Alborz Province were selected [Figure 1]. On the other hand, to ensure the security of installation of air pollution devices, all selected sites are health-care centers undercovered by Alborz University of Medical Sciences. Furthermore, these sites were selected based on the standards of US Environmental Protection Agency (EPA). It is necessary to mention that in order to achieve a desirable result, sampling was not performed on holidays and days associated with snow and rain.
PM2.5 sampling and meteorological parameters
For the intention of sampling particles, PM2.5 from a peripheral pump (Leland Legacy [SKC]) and a Personal Modular Impactor (PMI) holder was used. The PTFE filter (Zephon, USA) with a diameter of 2 mm was selected according to the type of holder. Forasmuch as purpose is to measurement PM2.5 particles; therefore, the filter diameter used was selected one micron. Sampling time was selected based on guideline EPA-TO/13 A 24 h and flow rate samples according to the type of filter used and guideline of maker company on the 3 l/min adjustment. Because if the flow rate above this value enters the holder filter, due to Create turbulence in PMI(Personal Modular Impactor), PM2.5 particles do not fit properly on the filter. Accordingly, before each time sampling, the pump used was calibrated by a rotameter.
During the period of PM2.5 particle sampling, meteorological factors including temperature (°C), relative humidity (%), pressure (Pa), UV, cloud cover (%), and dew point were also recorded using a portable PHB-318 device. Information as to cloud coverage, UV and dew points obtained it from Karaj Environmental Protection Agency (affiliated to Karaj Municipality). At the end of the sampling (24 h), again, these parameters were measured, and afterward, the obtained data were imported into Excel and average of them was calculated.
After calculating the PM2.5 concentrations by gravimetric method and calculating the average meteorological parameters in four seasons, Statistical Package for the Social Sciences (SPSS) software was used to obtain the correlation coefficient and correlation between meteorological data. At this step, the normality of data was identified by Kolmogorov–Smirnov, and to the reason of normality of data, Pearson's statistical test was used for correlation analysis. Whatever the correlation® Closter to −1 and +1, the intensity of the correlation will be more here. Boxplot diagram of PM2.5 concentrations was drawn by R software. On the other hand, the GIS software for zoning PM2.5 concentrations was used.
| Results|| |
Relationship between meteorological parameters and concentrations of PM2.5
In [Figure 2], a variation of average temperature, relative humidity, dew point, UV radiation, pressure, and cloud cover in four seasons during 2018–2019 is brought. The average of annual temperature (°c), humidity (%), pressure (Pa), UV radiation, cloud cover (%), and dew point is 16.19, 33.04, 0.89, 4.69, 30.17, and −1.30, respectively. On the other hand, [Figure 3] shows a boxplot diagram concentration of PM2.5 seasonally sampled points. As shown in this figure, the highest concentration of PM2.5 was found in winter (110.22 μg/m 3) and the lowest is in spring (10.06 μg/m 3). Moreover, the annual average concentration of PM2.5 is 40.64 μg/m 3 that is four times higher than the standard announced by the WHO (10 μg/m 3). Therefore, the citizens of Karaj are exposed to high concentrations of PM2.5.
|Figure 2: Average of temperature, dew point, cloud coverage, ultraviolet rate, relative humidity, and pressure seasonally in Karaj at 2018–2019|
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[Figure 4] shows the distribution of PM2.5 concentrations in Karaj during the period of study. This figure shows that the sampling points 11, 12, 16, and 17 have the highest annual mean concentration. Due to highways and factories in these areas, their concentration is much higher than the standard. Furthermore, points 1, 2, 4, 7, and 9 had lower annual mean concentrations than other points.
[Table 1] shows the relationship between the mean annual of PM2.5 concentrations with the meteorological parameters. As shown in [Table 1], a positive relationship between mean annual PM2.5 concentrations with temperature (r = 0.497, P < 0.042), humidity (r = 0.206, P < 0.05), and pressure (r = 0.236, P < 0.05) in the period of study at Karaj was observed. In addition, a negative relationship between UV radiation (r = −0.271, P < 0.369), cloud cover rate (r = −0.276, P < 0.428), and dew point (r = −0.524, P < 0.19) was observed in Karaj at 2018–2019.
|Table 1: Correlation of between PM2.5 particles and meteorological parameter in Karaj at 2018-2019|
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| Discussion|| |
Industrialization of the Asian continent in recent decades has created many environmental problems including the production of hazardous pollutants in air. On the other hand, meteorological parameters can be effects on amount of air pollutant. Factors such as traffic and burning fuels increase the concentration of pollutants in the air such as PM, CO, and NO. Furthermore, one of the influencing factors is inversion. There have been studies about the concentrations of particulate matter and correlation between them in air. In the study conducted by Asrari and Paydar et al., the maximum PM2.5 concentration occurred in October. Furthermore, in Miri et al.'s study, the maximum concentration for Tehran in winter was estimated. On the other hands. Bahrami Asl and et al., in study at Tehran. In another study in Tehran, the maximum particle concentration in summer was reported. Erener et al. reported that a peak of PM2.5 concentration in the autumn is 44.45 μg/m 3 that was consistent with the results of our study. In below, the relationship between parameters will be surveyed.
According to a study which was done on more than 74 million people (74,225,200) in 13 countries at 1958, it turned out that extreme temperature accounts for <1% of all deaths, whereas low temperature ideally about 8% of all deaths includes. The highest deaths (66.6% of all) were happened at moderate and cold weather. In the present study, there was a positive correlation between PM2.5 concentration and temperature in Karaj at 2017–2018 (r = 0.497, P < 0.042). The results of this study are consistent with the study of Kioumourtzoglou. Furthermore, Ansari and Ehrampoush found a positive relationship between temperature and PM2.5 particle concentration (r = 0.42, P < 0.018). On the other hand, a Santi study showed that air temperature had a positive effect on air particle concentrations, and available concentration causes health effects, including deaths of people over 60 years of age. Another study by Wang et al. showed that there was a positive relationship between temperature and PM2.5 concentration. Study results of Yao et al. confirm this. Furthermore, Liu et al. observed that PM2.5 concentration was positively correlated with air temperature in four seasons.
Based on the results of this study, a positive relationship was observed between relative humidity and PM2.5 concentrations (r = 0.206, P < 0.05) in the period of study at Karaj. The results of this study were corresponded with the study of Gao et al., Bai et al., Lin et al., Ryu et al., Guo et al., Huang et al. and Hou et al., However, Munir et al. in Mecca showed the negative correlation between moisture and PM2.5 concentration. Furthermore, Chence et al. found in their study a negative relationship between relative humidity and PM2.5 concentration (r = 0.03 P < −0.79).
Cloud coverage rate
The increased aerosols or pollutants in urban air caused by industrial or human activities affect air pollution rates. Based on the results of this study, a negative relationship between PM2.5 concentrations and the amount of cloud cover in Karaj at 2017–2018 was obtained (r = −0.276, P < 0.428). Whiteman et al. about the relationship of these two parameters as well reached to this conclusion. Scottn Williams et al. state that cloud cover decreases in winter when the PM2.5 concentration reaches its maximum which indicates a weak or negative correlation between these two parameters. It should not be overlooked that the rate of cloud cover also affects the trend of precipitation changes, so that on rainy days, the particles are washed by the rain; as a result, the concentration of particles reaches its minimum value. Faridi et al. reached to this conclusion that PM2.5 concentrations were negatively correlated with precipitation (r = −0.19, P < 0.5). Following that, Ansari and Ehrampoush in 2017–2018 at Tehran Province found a negative relationship between PM2.5 and precipitation (r = −0.179, P < 0.127).
UV index in most countries of the world causes the most common cancers, including skin cancer. In Iran too, according to the cancer registry system most common during the years 2003–2004, skin cancer malignancy has been in the total population., Based on contents, a negative relationship between PM2.5 concentration and UV radiation was observed in Karaj during 2017–2018 (r = −0.271, P < 0.362). Barnard et al. in their study showed that air pollution can diminish the increase of UV levels. In addition, Hoseinzadeh et al. reported a negative correlation between PM2.5 and UV radiation. It was also expressed that the highest amount of UV radiation occurred in summer.
Correlation analysis in the present study showed that there was a positive relationship between air pressure (Pa) and PM2.5 concentrations during the period of study (r = 0.236, P < 0.05). On cold days of the year, air pressure has increased; as a result, it increases the concentration of the particles. Panahi, Yang et al., Lin and Zhu, and Wu et al. also came to this conclusion.
The results of the present study have shown a negative correlation between PM2.5 and dew point (r = −0.524 P < 0.19). Tai et al. showed a negative relationship between these two parameters in their study. Furthermore, in another study by Li et al., this proved to be the concentration of PM2.5, which was negatively correlated with dew point.
| Conclusions|| |
The results of the current work indicated that Karaj citizens are exposed to PM2.5 concentration at four times higher than the standard recommended by EPA. Therefore, citizens of Karaj are likely to be exposed to a variety of diseases caused by air pollution and their pollutants. Subsequently, it was found that temperature, relative humidity, and pressure increased the concentration of PM2.5, and the rate of cloud cover, dew point, and UV reduces the concentration of them. According to the results of this study and high concentration of PM2.5 in Karaj city and the harmful effects of these pollutants on the health of citizens, the need for better and accurate planning and follow-up of those officials for controlling or reducing this environmental dilemma is becoming more obvious.
This article is the result of MSc approved thesis, research project no. 33150. Thus, the authors are thankful for the funding provided by the Department of Environmental Health Engineering Research Center of Environmental Health Technology, Iran University of Medical Sciences.
Financial support and sponsorship
Iran University of Medical Sciences, Tehran, Iran.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Chu DA, Kaufman Y, Zibordi G, Chern J, Mao J, Li C, et al
. Global monitoring of air pollution over land from the Earth observing system-terra moderate resolution imaging spectroradiometer (MODIS). J Geophysical Res Atmospheres 2003;108(D21).
Colls J. Air pollution. 2nd
ed. London, New York: Spon Press; 2002.
Gouveia N, Mascolli MA. Air pollution: An important threat to infant health. BJOG 2018;125:1279.
Li X, Jin L, Kan H. Air pollution: A global problem needs local fixes. Nature 2019;570:437-9.
Mukherjee A, Agrawal M. World air particulate matter: Sources, distribution and health effects. Environ Chem Lett 2017;15:283-309.
Martinez GS, Spadaro JV, Chapizanis D, Kendrovski V, Kochubovski M, Mudu P. Health Impacts and economic costs of air pollution in the metropolitan area of skopje. Int J Environ Res Public Health 2018;15:626.
Li X, Tang K, Jin XR, Xiang Y, Xu J, Yang LL, et al
. Short-term air pollution exposure is associated with hospital length of stay and hospitalization costs among inpatients with type 2 diabetes: A hospital-based study. J Toxicol Environ Health A 2018;81:819-29.
Tavakoly M. Estimation of Acute Air Pollution Conditions in Tehran Due to the Concentration of Ozone and Particulate Matter Using Artificial Neural Network: Master's Thesis, Ministry of Science, Research, Technology Tarbiat Modares. 2018.
Taghavi H. Investigation of Time and Space Distribution of Air Pollutants Pollutants in Mashhad city and Its: Masters Thesis. Iran: Faculty of Engineering of Ferdowsi University of Mashhad; 2012.
Heydari G, Ranjbar Vakilabadi D, Kermani M, Rayani M, Poureshgh Y, Behroozi M, et al
. Load characteristics and inhalation risk assessment of benzene series (BTEX) pollutant in indoor air of Ghalyan and/or cigarette cafes compared to smoking-free cafes. Environ Pollut Bioavailab 2020;32:26-35.
Shi Y, Zhao T, Yang X, Sun B, Li Y, Duan J, et al
. PM2.5-induced alteration of DNA methylation and RNA-transcription are associated with inflammatory response and lung injury. Sci Total Environ 2019;650:908-21.
Mirhosseini SH, Birjandi M, Zare MR, Fatehizadeh A. Analysis of particulate matter (PM 10 and PM 2.5) concentration in Khorramabad city. Int J Environ Health eng 2013;2:3.
Hosseini V, Shahbazi H. Urban air pollution in Iran. Iran Stud 2016;49:1029-46.
Ansari M, Ehrampoush MH. Meteorological correlates and AirQ+ health risk assessment of ambient fine particulate matter in Tehran, Iran. Environ Res 2019;170:141-50.
Jamshidi A, Karimzadeh Shirazi K, Raygan Shirazi A. Particulate air pollution concentration in the city of Gachsaran, 2005-2006. Armaghane Danesh 2007;12:89-97.
Tecer LH, Süren P, Alagha O, Karaca F, Tuncel G. Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal-mining area in Zonguldak, Turkey. J Air Waste Manag Assoc 2008;58:543-52.
Dominick D, Latif MT, Juahir H, Aris AZ, Zain SM. An assessment of influence of meteorological factors on PM10 and NO2 at selected stations in Malaysia. Sustain Environ Rese 2012;22:305-15.
Sammaritano MA, Bustos DG, Poblete AG, Wannaz ED. Elemental composition of PM 2.5 in the urban environment of San Juan, Argentina. Environ Sci Pollut Res 2018;25:4197-203.
Zhang H, Wang Y, Hu J, Ying Q, Hu XM. Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ Res 2015;140:242-54.
Ye WF, Ma ZY, Ha XZ. Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities. Sci Total Environ 2018;631-632:524-33.
Halek F. Vertical profile of particulate matter concentrations in indoor air (case study: Karaj, Iran). Partic Sci Technol 2015;33:617-20.
Asrari E, Paydar M. Investigation of the airborne particulate matter concentration trend changes in Mashhad by using meteorological data during 2010-2015. ??? 2018;2:258-67.
Miri A, Ahmadi H, Ghanbari A, Moghaddamnia A. Dust storms impacts on air pollution and public health under hot and dry climate. Int J Energy Environ 2007;2:101-5.
Asl FB, Leili M, Vaziri Y, Arian SS, Cristaldi A, Conti GO, et al
. Health impacts quantification of ambient air pollutants using AirQ model approach in Hamadan, Iran. Environ Res 2018;161:114-21.
Erener A, Sarp G, Yıldırım Ö. Seasonal air pollution investigation and relation analysis of air pollution parameters to meteorological data (Kocaeli/Turkey). Advances in Remote Sensing and Geo Informatics Applications. Earth and Environmental Science. Springer; 2019. p. 355-8.
Ebenstein A, Fan M, Greenstone M, He G, Zhou M. New evidence on the impact of sustained exposure to air pollution on life expectancy from China's Huai River Policy. Proc National Acad Sci 2017;114:10384-9.
Kioumourtzoglou MA, Schwartz JD, Weisskopf MG, Melly SJ, Wang Y, Dominici F, et al
. Long-term PM2.5 exposure and neurological hospital admissions in the Northeastern United States. Environ Health Perspect 2016;124:23-9.
Santi D, Magnani E, Michelangeli M, Grassi R, Vecchi B, Pedroni G, et al
. Seasonal variation of semen parameters correlates with environmental temperature and air pollution: A big data analysis over 6 years. Environ Pollut 2018;235:806-13.
Wang H, Li J, Peng Y, Zhang M, Che H, Zhang X. The impacts of the meteorology features on PM2. 5 levels during a severe haze episode in central-east China. Atmospheric Environ 2019;197:177-89.
Yao Y, Zhou J, Tian Y, Diao X, Chen H, Zhang J, et al
. The Effect of Air Pollution and Temperature on Pulmonary Function in Healthy People in Xi'an, China. Research Square 2020;44:310-25.
Liu M, Huang Y, Ma Z, Jin Z, Liu X, Wang H, et al
. Spatial and temporal trends in the mortality burden of air pollution in China: 2004-2012. Environ Int 2017;98:75-81.
Gao X, Colicino E, Shen J, Kioumourtzoglou MA, Just AC, Nwanaji-Enwerem JC, et al
. Impacts of air pollution, temperature, and relative humidity on leukocyte distribution: An epigenetic perspective. Environ Int 2019;126:395-405.
Bai Y, Qi H, Liu L, Chen C, Lin C, Li W. Study on the nonlinear relationship among the visibility, PM2. 5 concentration and relative humidity in Wuhan and the visibility prediction. Acta Meteorologica Sinica 2016;74:189-99.
Lin G, Fu J, Jiang D, Wang J, Wang Q, Dong D. Spatial variation of the relationship between PM2. 5 concentrations and meteorological parameters in China. BioMed Res Int 2015;2015:2314-6133.
Ryu J, Kim JJ, Byeon H, Go T, Lee SJ. Removal of fine particulate matter (PM2.5) via atmospheric humidity caused by evapotranspiration. Environ Pollut 2019;245:253-9.
Guo H, Wang Y, Zhang H. Characterization of criteria air pollutants in Beijing during 2014-2015. Environ Res 2017;154:334-44.
Hou X, Fei D, Kang H, Zhang Y, Gao J. Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013-2017. Natural Hazards 2018;93:677-98.
Huang F, Li X, Wang C, Xu Q, Wang W, Luo Y, et al
. PM2.5 Spatiotemporal variations and the relationship with meteorological factors during 2013-2014 in Beijing, China. PLoS One 2015;10:e0141642.
Munir S, Habeebullah TM, Mohammed AM, Morsy EA, Rehan M, Ali K. Analysing PM2. 5 and its association with PM10 and meteorology in the arid climate of Makkah, Saudi Arabia. Aerosol Air Qual Res 2017;17:453-64.
Chen C, Xia Z, Wu M, Zhang Q, Wang T, Wang L, et al
. Concentrations, source identification, and lung cancer risk associated with springtime PM 2.5-bound polycyclic aromatic hydrocarbons (PAHs) in nanjing, China. Arch Environ Contamin Toxicol 2017;73:391-400.
Liu Y, Sun XH. CaL: Extending data locality to consider concurrency for performance optimization. IEEE Trans Big Data 2017;4:273-88.
Whiteman CD, Hoch SW, Horel JD, Charland A. Relationship between particulate air pollution and meteorological variables in Utah's Salt Lake Valley. Atmosp Environ 2014;94:742-53.
Williamson SN, Hik DS, Gamon JA, Jarosch AH, Anslow FS, Clarke GK, et al
. Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains. Remote Sensing Environ 2017;189:14-24.
Faridi S, Shamsipour M, Krzyzanowski M, Künzli N, Amini H, Azimi F, et al
. Long-term trends and health impact of PM 2.5 and O in Tehran, Iran, 2006-2015. Environ Int 2018;114:37-49.
MacKie RM. Long-term health risk to the skin of ultraviolet radiation. Prog Biophys Mol Biol 2006;92:92-6.
Nabizadeh R, Salehi SS, Younesian M, Nadafi K. Evaluation of the relationship between global ultraviolet index in different regions of Iran and skin cancer in 1383. Iranian Journal of Health and Environment 2010;4:258-67.
Barnard WF, Saxena VK, Wenny BN, DeLuisi JJ. Daily surface UV exposure and its relationship to surface pollutant measurements. J Air Waste Manag Assoc 2003;53:237-45.
Hoseinzadeh E, Taha P, Wei C, Godini H, Ashraf GM, Taghavi M, et al
. The impact of air pollutants, UV exposure and geographic location on vitamin D deficiency. Food Chem Toxicol 2018;113:241-54.
Panahi A. Investigating the relationship between AQI pollutants and meteorological parameters in severe inversions of Tabriz, Iran. Geograp Space 2018;18:63-76.
Yang BY, Qian Z, Howard SW, Vaughn MG, Fan SJ, Liu KK, et al
. Global association between ambient air pollution and blood pressure: A systematic review and meta-analysis. Environ Pollut 2018;235:576-88.
Lin B, Zhu J. Changes in urban air quality during urbanization in China. J Cleaner Produc 2018;188:312-21.
Wu J, Zheng H, Zhe F, Xie W, Song J. Study on the relationship between urbanization and fine particulate matter (PM2. 5) concentration and its implication in China. J Cleaner Product 2018;182:872-82.
Tai AP, Mickley LJ, Jacob DJ. Correlations between fine particulate matter (PM2. 5) and meteorological variables in the United States: Implications for the sensitivity of PM2. 5 to climate change. Atmosp Environ 2010;44:3976-84.
Li L, Qian J, Ou CQ, Zhou YX, Guo C, Guo Y. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011. Environ Pollut 2014;190:75-81.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]