Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods
-
Kazhal Masroor
,
Farzad Fanaei
,
Somayeh Yousefi
,
Mohammad Raeesi
,
Hossein Abbaslou
,
Abbas Shahsavani
,
Mostafa Hadei
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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Dust storms impacts on air pollution and public health
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R, Espinosa SI, et al. Kriging method application and
traffic behavior profiles from local radar network database:
A proposal to support traffic solutions and air pollution
control strategies. Sustainable Cities and Society.
2020:102062.
23. Yu H, Russell A, Mulholland J, Odman T, Hu Y, Chang
HH, et al. Cross-comparison and evaluation of air pollution
field estimation methods. Atmospheric environment.
2018;179:49-60.
24. Qiao P, Lei M, Yang S, Yang J, Guo G, Zhou X. Comparing
ordinary kriging and inverse distance weighting
for soil as pollution in Beijing. Environmental Science
and Pollution Research. 2018;25(16):15597-608.
25. Tsui H-C, Chen C-H, Wu Y-H, Chiang H-C, Chen
B-Y, Guo YL. Lifetime exposure to particulate air
pollutants is negatively associated with lung function
in non-asthmatic children. Environmental Pollution.
2018;236:953-61.
al. Harmful impact of air pollution on severe acute exacerbation
of chronic obstructive pulmonary disease:
particulate matter is hazardous. International journal of chronic obstructive pulmonary disease. 2018;13:1053.
2. Nabizadeh R, Yousefian F, Moghadam VK, Hadei M.
Characteristics of cohort studies of long-term exposure
to PM2.5: a systematic review. Environmental Science
and Pollution Research. 2019:1-7.
3. Hadei M, Naddafi K. Cardiovascular effects of airborne
particulate matter: A review of rodent model studies.
Chemosphere. 2020;242:125204.
4. Landrigan PJ, Fuller R, Acosta NJ, Adeyi O, Arnold R,
Baldé AB, et al. The Lancet Commission on pollution
and health. The lancet. 2018;391(10119):462-512.
5. Landrigan PJ, Fuller R, Horton R. Environmental pollution,
health, and development: a Lancet–Global
Alliance on Health and Pollution–Icahn School of
Medicine at Mount Sinai Commission. The Lancet.
2015;386(10002):1429-31.
6. Shahsavani A, Tobías A, Querol X, Stafoggia M, Abdolshahnejad
M, Mayvaneh F, et al. Short-term effects of
particulate matter during desert and non-desert dust days
on mortality in Iran. Environ Int. 2020;134:105299.
7. Hadei M, Shahsavani A, Krzyzanowski M, Querol X,
Stafoggia M, Nazari SSH, et al. Burden of mortality attributed
to PM2.5 exposure in cities of Iran; contribution
of short-term pollution peaks. Atmospheric Environment.
2020;224:117365.
8. Borge R, Narros A, Artíñano B, Yagüe C, Gómez-Moreno
FJ, de la Paz D, et al. Assessment of microscale spatiotemporal
variation of air pollution at an urban hotspot
in Madrid (Spain) through an extensive field campaign.
Atmospheric environment. 2016;140:432-45.
9. Gunarathna M, Kumari M, Nirmanee K. Evaluation of
interpolation methods for mapping pH of groundwater.
International journal of latest technology in engineering,
management & applied science. 2016;3:1-5.
10. Deligiorgi D, Philippopoulos K. Spatial interpolation
methodologies in urban air pollution modeling: application
for the greater area of metropolitan Athens, Greece.
Advanced Air Pollution. 2011:341-62.
11. Eslami A, Ghasemi SM. Determination of the best interpolation
method in estimating the concen-tration of
environmental air pollutants in Tehran city in 2015.
Journal of Air Pollution and Health. 2018.
12. Hoshdar Tehrani sh ER, and G.g. S. Study of Spatial
Distribution Pattern of Carbon Monoxide in Mashhad
City Using Interpolation Methods During 2017-218.
Journal of Research in Environmental Health, journal
of Research in Environmental Health,. 2019;5(1): p:53-
64.
13. Montero J-M, Fernández-Avilés G. Functional kriging
prediction of atmospheric particulate matter concentrations
in Madrid, Spain: Is the new monitoring system
masking potential public health problems? Journal of
Cleaner Production. 2018;175:283-93.
14. Khan J, Kakosimos K, Raaschou-Nielsen O, Brandt J,
Jensen SS, Ellermann T, et al. Development and performance
evaluation of new AirGIS–a GIS based air
pollution and human exposure modelling system. AtAtmospheric
environment. 2019;198:102-21.
15.Shi X, Li M, Hunter O, Guetti B, Andrew A, Stommel E,
et al. Estimation of environmental exposure: interpolation,
kernel density estimation or snapshotting. Annals
of GIS. 2019;25(1):1-8.
16. Tuna F, Buluc M. Analysis of PM10 pollutant in Istanbul
by using Kriging and IDW methods: between 2003
and 2012. Int J Comput Inf Technol. 2015;4(1):170-5.
17. Sun S, Stewart JD, Eliot MN, Yanosky JD, Liao D, Tinker
LF, et al. Short-term exposure to air pollution and
incidence of stroke in the Women’s Health Initiative.
Environment international. 2019;132:105065.
18. Xu H, Bechle MJ, Wang M, Szpiro AA, Vedal S, Bai
Y, et al. National PM2.5 and NO2 exposure models for
China based on land use regression, satellite measurements,
and universal kriging. Science of the Total Environment.
2019;655:423-33.
19. Dastoorpoor M, Idani E, Goudarzi G, Khanjani N.
Acute effects of air pollution on spontaneous abortion,
premature delivery, and stillbirth in Ahvaz, Iran: a timeseries
study. Environmental Science and Pollution Research.
2018;25(6):5447-58.
20. 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. Environmental research. 2018;161:114-21.
21. 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(1):101-5.
22. Pinto JA, Kumar P, Alonso MF, Andreão WL, Pedruzzi
R, Espinosa SI, et al. Kriging method application and
traffic behavior profiles from local radar network database:
A proposal to support traffic solutions and air pollution
control strategies. Sustainable Cities and Society.
2020:102062.
23. Yu H, Russell A, Mulholland J, Odman T, Hu Y, Chang
HH, et al. Cross-comparison and evaluation of air pollution
field estimation methods. Atmospheric environment.
2018;179:49-60.
24. Qiao P, Lei M, Yang S, Yang J, Guo G, Zhou X. Comparing
ordinary kriging and inverse distance weighting
for soil as pollution in Beijing. Environmental Science
and Pollution Research. 2018;25(16):15597-608.
25. Tsui H-C, Chen C-H, Wu Y-H, Chiang H-C, Chen
B-Y, Guo YL. Lifetime exposure to particulate air
pollutants is negatively associated with lung function
in non-asthmatic children. Environmental Pollution.
2018;236:953-61.
| Files | ||
| Issue | Vol 5 No 2 (2020): Spring 2020 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v5i2.4237 | |
| Keywords | ||
| Exposure; Interpolation; Particulate matter; Ambient air pollution; Geographic information system (GIS) | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Masroor K, Fanaei F, Yousefi S, Raeesi M, Abbaslou H, Shahsavani A, Hadei M. Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods. JAPH. 2020;5(2):89-96.
