Investigation of relationship between particulate matter (PM2.5) and meteorological parameters in Isfahan, Iran
-
Majid Kermani
,
Ahmad Jonidi Jafari
,
Mitra Gholami
,
Mahdi Farzadkia
,
Hossein Arfaeinia
,
Abbas Shahsavani
,
Abbas Norouzian
,
Mohsen Dowlati
,
Farzad Fanaei
Abstract
Introduction: Estimating air pollution levels in areas with no measurements is a major concern in health-related studies. Therefore, the aim of this study was to investigate the amount of exposure to particulate matter below 2.5 µ (PM2.5) in the metropolis of Tehran.
Materials and methods: The hourly concentrations of PM2.5 during 2017- 2018 period were acquired from the Department of Environment (DOE) and Air Quality Control Company of Tehran (AQCC). The hourly concentrations were validated and 24-h concentrations were calculated. Inverse distance weighting (IDW), Universal Kriging, and Ordinary Kriging were used to spatially model the PM2.5 over Tehran metropolis area. Root Mean Square Error (RMSE) and Mean Error (ME) were used to measure and control for the accuracy of the methods.
Results: The results of this study showed that RMSE and MENA values in Kriging method was less than the IDW, which indicates that the Kriging was the best method to estimate PM2.5 concentrations. According to the final map, the highest annual concentrations of PM2.5 were observed in the southern and southwestern areas of Tehran (districts 10, 15, 16, 17, and 18). The lowest exposure to PM2.5 was found to be in districts 1, 2, 3, 6, and 8.
Conclusion: It can be concluded that Kriging method can predict spatial variations of PM2.5 more accurately than IDW method.
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27. Hou, X., et al., Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013–2017. Natural Hazards, 2018. 93(2): p. 677-698.
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30. Santi, D., et al., Seasonal variation of semen parameters correlates with environmental temperature and air pollution: A big data analysis over 6 years. Environmental Pollution, 2018. 235: p. 806-813.
31. Liu, M., et al., Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012. Environment international, 2017. 98: p. 75-81.
32. Alvarez, H.A.O., et al., The value of using seasonality and meteorological variables to model intra-urban PM2. 5 variation. Atmospheric Environment, 2018. 182: p. 1-8.
33. Gao, X., et al., Impacts of air pollution, temperature, and relative humidity on leukocyte distribution: an epigenetic perspective. Environment international, 2019. 126: p. 395-405.
34. Bai, Y., et al., Study on the nonlinear relationship among the visibility, PM2. 5 concentration and relative humidity in Wuhan and the visibility prediction. Acta Meteorological sinica, 2016. 74(2): p. 189-199.
35. Lin, G., et al., Spatial variation of the relationship between PM2. 5 concentrations and meteorological parameters in China. BioMed research international, 2015. 2015.
36. Guo, H., Y. Wang, and H. Zhang, Characterization of criteria air pollutants in Beijing during 2014–2015. Environmental research, 2017. 154: p. 334-344.
37. Feng, J., et al., The pollution characteristics of PM 2.5 and correlation analysis with meteorological parameters in Xinxiang during the Shanghai Cooperation Organization Prime Ministers’ Meeting. Environmental geochemistry and health, 2018. 40(3): p. 1067-1076.
38. Williamson, S.N., et al., Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains. Remote Sensing of Environment, 2017. 189: p. 14-24.
39. Faridi, S., et al., Long-term trends and health impact of PM2. 5 and O3 in Tehran, Iran, 2006–2015. Environment international, 2018. 114: p. 37-49.
40. Barnard, W.F., et al., Daily surface UV exposure and its relationship to surface pollutant measurements. Journal of the Air & Waste Management Association, 2003. 53(2): p. 237-245.
41. Hoseinzadeh, E., et al., The impact of air pollutants, UV exposure and geographic location on vitamin D deficiency. Food and Chemical Toxicology, 2018. 113: p. 241-254.
42. panahi, A., Investigating the Relationship between AQI Pollutants and Meteorological Parameters in Severe Inversions of Tabriz, Iran. 2018.
43. Yang, B.-Y., et al., Global association between ambient air pollution and blood pressure: a systematic review and meta-analysis. Environmental pollution, 2018. 235: p. 576-588.
44. Lin, B. and J. Zhu, Changes in urban air quality during urbanization in China. Journal of cleaner production, 2018. 188: p. 312-321.
45. Wu, J., et al., Study on the relationship between urbanization and fine particulate matter (PM2. 5) concentration and its implication in China. Journal of Cleaner Production, 2018. 182: p. 872-882.
46. Wang, H., et al., The impacts of the meteorology features on PM2. 5 levels during a severe haze episode in central-east China. Atmospheric Environment, 2019. 197: p. 177-189.
2. Rovira, J., J.L. Domingo, and M. Schuhmacher, Air quality, health impacts and burden of disease due to air pollution (PM10, PM2. 5, NO2 and O3): Application of AirQ+ model to the Camp de Tarragona County (Catalonia, Spain). Science of The Total Environment, 2020. 703: p. 135538.
3. Schraufnagel, D.E., et al., Air pollution and noncommunicable diseases: A review by the Forum of International Respiratory Societies’ Environmental Committee, Part 2: Air pollution and organ systems. Chest, 2019. 155(2): p. 417-426.
4. Loomis, D., W. Huang, and G. Chen, The International Agency for Research on Cancer (IARC) evaluation of the carcinogenicity of outdoor air pollution: focus on China. Chinese journal of cancer, 2014. 33(4): p. 189.
5. Heydari, G., 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. Environmental Pollutants and Bioavailability, 2020. 32(1): p. 26-35.
6. Kermani, M., et al., Quantification of Health Effects Attributed to Ozone in Five Metropolises of Iran Using AirQ Model. Journal of Health, 2015. 6(3): p. 266-280.
7. Isen, A., M. Rossin-Slater, and W.R. Walker, Every breath you take—every dollar you’ll make: The long-term consequences of the clean air act of 1970. Journal of Political Economy, 2017. 125(3): p. 848-902.
8. Deryugina, T., et al., The mortality and medical costs of air pollution: Evidence from changes in wind direction. American Economic Review, 2019. 109(12): p. 4178-4219.
9. Tavakoly, M., Estimation of acute air pollution conditions in Tehran due to the concentration of ozone and particulate matter using artificial neural network. Masters Thesis], Ministry of Science, Research, Technology Tarbiat Modares ….
10. Dehghani, M.H., et al., The data on the dispersion modeling of traffic-related PM10 and CO emissions using CALINE3; A case study in Tehran, Iran. Data in brief, 2018. 19: p. 2284-2290.
11. Nabizadeh, R., M. Yousefi, and F. Azimi, Study of particle number size distributions at Azadi terminal in Tehran, comparing high-traffic and no traffic area. MethodsX, 2018. 5: p. 1549-1555.
12. Matus, P. and M. Oyarzún, Impact of Particulate Matter (PM2, 5) and children’s hospitalizations for respiratory diseases. A case cross-over study. Revista chilena de pediatria, 2019. 90(04): p. 166-174.
13. Huang, F., et al., PM2. 5 spatiotemporal variations and the relationship with meteorological factors during 2013-2014 in Beijing, China. PloS one, 2015. 10(11).
14. Soleimani, M., et al., Heavy metals and their source identification in particulate matter (PM2. 5) in Isfahan City, Iran. Journal of Environmental Sciences, 2018. 72: p. 166-175.
15. Ilten, N. and A.T. Selici, Investigating the impacts of some meteorological parameters on air pollution in Balikesir, Turkey. Environmental monitoring and assessment, 2008. 140(1-3): p. 267-277.
16. Ansari, M. and M.H. Ehrampoush, Meteorological correlates and AirQ+ health risk assessment of ambient fine particulate matter in Tehran, Iran. Environmental research, 2019. 170: p. 141-150.
17. Tecer, L.H., et al., Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal-mining area in Zonguldak, Turkey. Journal of the Air & Waste Management Association, 2008. 58(4): p. 543-552.
18. Dominick, D., et al., An assessment of influence of meteorological factors on PM10 and NO2 at selected stations in Malaysia. Sustainable Environment Research, 2012. 22(5): p. 305-315.
19. Sammaritano, M.A., et al., Elemental composition of PM 2.5 in the urban environment of San Juan, Argentina. Environmental Science and Pollution Research, 2018. 25(5): p. 4197-4203.
20. Zhang, H., et al., Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environmental research, 2015. 140: p. 242-254.
21. Abdolahnejad, A., et al., Mortality and Morbidity Due to Exposure to Ambient NO2, SO2, and O3 in Isfahan in 2013–2014. International journal of preventive medicine, 2018. 9.
22. Al-Hemoud, A., et al., Exposure levels of air pollution (PM2. 5) and associated health risk in Kuwait. Environmental research, 2019. 179: p. 108730.
23. Conti, G.O., et al., A review of AirQ Models and their applications for forecasting the air pollution health outcomes. Environmental Science and Pollution Research, 2017. 24(7): p. 6426-6445.
24. Asrari, E., Investigation of the airborne particulate matter concentration trend changes in Mashhad by using meteorological data during 2010-2015. 2018.
25. Miri, M., et al., Mortality and morbidity due to exposure to outdoor air pollution in Mashhad metropolis, Iran. The AirQ model approach. Environmental research, 2016. 151: p. 451-457.
26. Asl, F.B., et al., Health impacts quantification of ambient air pollutants using AirQ model approach in Hamadan, Iran. Environmental research, 2018. 161: p. 114-121.
27. Hou, X., et al., Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013–2017. Natural Hazards, 2018. 93(2): p. 677-698.
28. Asimakopoulos, D., et al., The role of meteorology on different sized aerosol fractions (PM10, PM2. 5, PM2. 5–10). Science of the Total Environment, 2012. 419: p. 124-135.
29. Wang, J. and S. Ogawa, Effects of meteorological conditions on PM2. 5 concentrations in Nagasaki, Japan. International journal of environmental research and public health, 2015. 12(8): p. 9089-9101.
30. Santi, D., et al., Seasonal variation of semen parameters correlates with environmental temperature and air pollution: A big data analysis over 6 years. Environmental Pollution, 2018. 235: p. 806-813.
31. Liu, M., et al., Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012. Environment international, 2017. 98: p. 75-81.
32. Alvarez, H.A.O., et al., The value of using seasonality and meteorological variables to model intra-urban PM2. 5 variation. Atmospheric Environment, 2018. 182: p. 1-8.
33. Gao, X., et al., Impacts of air pollution, temperature, and relative humidity on leukocyte distribution: an epigenetic perspective. Environment international, 2019. 126: p. 395-405.
34. Bai, Y., et al., Study on the nonlinear relationship among the visibility, PM2. 5 concentration and relative humidity in Wuhan and the visibility prediction. Acta Meteorological sinica, 2016. 74(2): p. 189-199.
35. Lin, G., et al., Spatial variation of the relationship between PM2. 5 concentrations and meteorological parameters in China. BioMed research international, 2015. 2015.
36. Guo, H., Y. Wang, and H. Zhang, Characterization of criteria air pollutants in Beijing during 2014–2015. Environmental research, 2017. 154: p. 334-344.
37. Feng, J., et al., The pollution characteristics of PM 2.5 and correlation analysis with meteorological parameters in Xinxiang during the Shanghai Cooperation Organization Prime Ministers’ Meeting. Environmental geochemistry and health, 2018. 40(3): p. 1067-1076.
38. Williamson, S.N., et al., Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains. Remote Sensing of Environment, 2017. 189: p. 14-24.
39. Faridi, S., et al., Long-term trends and health impact of PM2. 5 and O3 in Tehran, Iran, 2006–2015. Environment international, 2018. 114: p. 37-49.
40. Barnard, W.F., et al., Daily surface UV exposure and its relationship to surface pollutant measurements. Journal of the Air & Waste Management Association, 2003. 53(2): p. 237-245.
41. Hoseinzadeh, E., et al., The impact of air pollutants, UV exposure and geographic location on vitamin D deficiency. Food and Chemical Toxicology, 2018. 113: p. 241-254.
42. panahi, A., Investigating the Relationship between AQI Pollutants and Meteorological Parameters in Severe Inversions of Tabriz, Iran. 2018.
43. Yang, B.-Y., et al., Global association between ambient air pollution and blood pressure: a systematic review and meta-analysis. Environmental pollution, 2018. 235: p. 576-588.
44. Lin, B. and J. Zhu, Changes in urban air quality during urbanization in China. Journal of cleaner production, 2018. 188: p. 312-321.
45. Wu, J., et al., Study on the relationship between urbanization and fine particulate matter (PM2. 5) concentration and its implication in China. Journal of Cleaner Production, 2018. 182: p. 872-882.
46. Wang, H., et al., The impacts of the meteorology features on PM2. 5 levels during a severe haze episode in central-east China. Atmospheric Environment, 2019. 197: p. 177-189.
| Files | ||
| Issue | Vol 5 No 2 (2020): Spring 2020 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v5i2.4238 | |
| Keywords | ||
| Air pollution Isfahan city meteorological parameters PM2.5 | ||
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How to Cite
1.
Kermani M, Jonidi Jafari A, Gholami M, Farzadkia M, Arfaeinia H, Shahsavani A, Norouzian A, Dowlati M, Fanaei F. Investigation of relationship between particulate matter (PM2.5) and meteorological parameters in Isfahan, Iran. JAPH. 2020;5(2):97-106.
