Determining the optimal route with GIS, based on the lowest amount of PM2.5 pollutant
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیایی
Seyed Hossein Jalali
1
,
Hossein Aghamohammadi
2
,
Mohammad H. Vahidnia
3
,
Alireza Mirzahosseini
4
1 - Department of Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Remote Sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
4 - Department of Environmental Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
کلید واژه: Optimal route, GIS, air pollution, IDW, PM2.5,
چکیده مقاله :
Air pollution in Tehran is one of the most important problems of this metropolis. One of the most important pollutants in the air of Tehran is the PM2.5 pollutant, which has various effects on people, especially the elderly and children, and can cause all kinds of diseases, including cardiac, respiratory, and even death. In this study, the first goal is to prepare a distribution map of air pollutant p pollution in Tehran city with the help of data related to 15 pollution measurement stations and interpolate the data using the IDW method, and secondly, optimal routing based on the cost layer of PM2.5 pollution. This work has been done for two different days that had different levels of pollution. In order to prepare the desired map, ARC software has been used, as well as NET tool for routing. The routing output from the origin to the destination, for the two days of December 4 and December 26, shows the different routes obtained, which mainly passed through the blue and yellow areas of the pollution map, which had less pollution. This means that with the help of routing based on the pollution cost layer, the optimal route with the least amount of pollution can be determined, which can be very effective in determining the route for sick people, the elderly, and children who are most affected by pollution.
Air pollution in Tehran is one of the most important problems of this metropolis. One of the most important pollutants in the air of Tehran is the PM2.5 pollutant, which has various effects on people, especially the elderly and children, and can cause all kinds of diseases, including cardiac, respiratory, and even death. In this study, the first goal is to prepare a distribution map of air pollutant p pollution in Tehran city with the help of data related to 15 pollution measurement stations and interpolate the data using the IDW method, and secondly, optimal routing based on the cost layer of PM2.5 pollution. This work has been done for two different days that had different levels of pollution. In order to prepare the desired map, ARC software has been used, as well as NET tool for routing. The routing output from the origin to the destination, for the two days of December 4 and December 26, shows the different routes obtained, which mainly passed through the blue and yellow areas of the pollution map, which had less pollution. This means that with the help of routing based on the pollution cost layer, the optimal route with the least amount of pollution can be determined, which can be very effective in determining the route for sick people, the elderly, and children who are most affected by pollution.
Boulanger, G., Bayeux, T., Mandin, C., Kirchner, S., Vergriette, B., Pernelet-Joly, V., Kopp, P. (2017). Socio-economic costs of indoor air pollution: A tentative estimation for some pollutants of health interest in France. Environment International, 104, 14-24.
Bahari, R., Abbaspour, R., Pahlavani, P. (2016). Zoning of suspended particulate pollution using local statistical models in GIS (case study, Tehran). Journal of Geomatics Science and Technology, 5(3), (in Persian).
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frosted, J., Estep, K. … Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet, 389, 1907-1918.
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., Schwartz, J. (2016). Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environmental Science and Technology, 50(9), 4712-4721.
Dons, E., Poppel, M. V., Panis, L. I., Prins, S. D., Berghmans, P., Koppen, G., & Matheeussen, C. (2014). Land Use Regression Models as a Tool for Short, Medium- and Long-Term Exposure to Traffic Related Air Pollution. Science of the Total Environment, 476, 378-386.
Davarpanah, A., Vahidnia, M. H. (2022). Optimal Route Finding of Water Transmission Lines Using Different MCDM Methods and the Least-cost Path Algorithm in a Raster GIS (Case Study: from Ardak to Mashhad). Water Resources Engineering Journal, 14(51).
Guarnieri, M., Balmes, J. R. (2014). Outdoor air pollution and asthma. The Lancet, 383(9928), 1581–1592.
Gamble, J. F. (1998). PM2.5 and mortality in long-term prospective cohort studies: cause-effect or statistical associations?. Environ Health Perspect, 106(9), 535-549. doi: 10.1289/ehp.98106535.
Gariazzo, C., Carlino, G., Silibello, C., Gianni, T., Renzi, M., Finardi, S., Pepe, N. … Stafoggia, M. (2021). Impact of different exposure models and spatial resolution on the long-term effects of air pollution. Environmental Research, 192, 110351. https://doi.org/10.1016/j.envres.2020.110351.
Honda, T., Pun, V. C., Manjourides, J., Suh, H. (2017). Anemia prevalence and hemoglobin levels are associated with long-term exposure to air pollution in an older population. Environment International, 101, 125-132.
Hamra, G. B., Guha, N., Cohen, A., Laden, F., Raaschou-Nielsen, O., Samet, J. M. … Loomis, D. (2014). Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environmental Health Perspectives, 122(9), 906–911.
Ismain, S. H. A., Salleh, S. A., Mohammad Sham, N., Wan Azmi, W. N. F., Zulkiflee, A., Ab Rahman, A. Z. (2023). Spatial Distribution of Particulate Matter (PM2.5) in Klang Valley using Inverse Distance Weighting Interpolation Model. Earth and Environmental Science, 1217. doi:10.1088/1755-1315/1217/1/012033.
Jalali, S. H. ,Vafaeinejad, A. R. , Aghamohammadi, H. , Esmaeili Bidhendi, M. (2019). The Study of CO Symptoms' Impacts on Individuals, Using GIS and Agent-based Modeling (ABM). Pollution, 5(3), 463-471.
Kukkonen, J., Karl, M., Keuken, M. P., van der Gon, H. A. C. D., Denby, B. R., Singh, V., Douros, J. … Sokhi, R. S. (2016). Modelling the dispersion of particle numbers in five European cities. Geoscientific Model Development, 9, 451-478.
Kermani, M., Aghaei, M., Gholami, M., Bahramiasl, F., Karimzadeh, S., Fallah, S., Dolati, M. (2011). Estimation of Mortality Attributed to PM2.5 and CO Exposure in eight industrialized cities of Iran during 2011. Iran Occupational Health, 13(4), 49-61, (in Persian).
Karimi, M., Khosnavaz, S., Shamsipour, A., Moghbel, M. (2020). Modeling the Effect of Street Orientation on the Air Pollution Dispersion (District Six of Tehran Municipality). Motaleate Shahri, 9(34), 77-90, (in Persian).
Lee, K. H., Bae, M. S. (2021). Integration of air quality model with GIS for the monitoring of PM2.5 from local primary emission at a rural site. Environmental Monitoring Assessment, 193(10), 682. doi: 10.1007/s10661-021-09461-9. PMID: 34595610.
Liu, Z., Xie, M., Tian, K., Gao, P. (2017). GIS-based analysis of population exposure to PM2.5 air pollution—A case study of Beijing. Journal of Environmental Sciences, 59, 48-53.
Li, T., Hu, R., Chen, Z., Li, Q., Huang, S., Zhu, Z., Zhou, L. F. (2018). Fine particulate matter (PM2.5): The culprit for chronic lung diseases in China. Chronic Diseases and Translational Medicine, 4(3), 176-186.
Liu, L., He, H., Zhu, Y., Liu, J., Wu, J., Tan, Z., Xie, H. (2023). Spatiotemporal Distribution Characteristics and Multi-Factor Analysis of Near-Surface PM2.5 Concentration in Local-Scale Urban Areas. Atmosphere , 14(10), 1583.
Mamic, L., Gasparovic, M., Kaplan, G. (2023). Developing PM2.5 and PM10 prediction models on a national and regional scale using open source remote sensing data. Environmental Monitoring Assessment, 195, 644. https://doi.org/10.1007/s10661-023-11212-x.
Mohammadi, F., Teiri, H., Hajizadeh, Y., Abdolahnejad, A., Ebrahimi, A. (2024). Prediction of atmospheric PM2.5 level by machine learning techniques in Isfahan, Iran. Scientific Reports, 14(1), 2109. doi: 10.1038/s41598-024-52617-z.
Masroor, K., Fanaei, F., Yousefi, S., Raeesi, M., Abbaslou, H., Shahsavani, A., Hadei, M. (2020). Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods. Journal of Air Pollution and Health, 5(2), 89-96.
Ostro, B., Lipsett, M., Reynolds, P., Goldberg, D., Hertz, A., Garcia, C., Henderson, K. D., Bernstein, L. (2010). Long-term exposure to constituents of fine particulate air pollution and mortality: results from the California Teachers Study. Environmental Health Perspectives, 118(3), 363-369.
Ouyang, Wei., Guo, B., Cai, G., Li, Q., Han, S., Liu, B., Liu, X. (2015). The washing effect of precipitation on particulate matter and the pollution dynamics of rainwater in downtown Beijing. Science of The Total Environment, 505, 306-314.
Ouyang, W., Gao, B., Cheng, H., Hao, Z., Wu, N. (2018). Exposure inequality assessment for PM2.5 and the potential association with environmental health in Beijing. Science of The Total Environment, 635, 769-778.
Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., Thurston, G. D. (2002). Lung cancer, cardiopulmonary mortality, and ling-term exposure to fine particulate air pollution. JAMA, 287(9), 1132-1141.
Rahmani, H., Sadeghi, S., Roshani, M., Amani, D., Ghazanfari, T., Nariman, M. (2019). Particulate matter effects on peritoneal macrophages inflammatory function in C57BL/6 mice. Research on Medicine, 43(1), 1-7, (in Persian).
Rafieetoroghi, H., Hosseinali, F., Sheikhmohammadzadeh, A. (2017). Analysis and modeling of effective environmental effects on PM2.5 pollutant using LUR method in Tehran city. The 2nd National Conference on Geospatial Information Technology (NCGIT), K.N.Toosi University of Technology, Faculty of Geomatics Engineering, Tehran, Iran, (in Persian).
Safavi, S. Y., Alijani, B. (2007). Investigation of geographical factors in air pollution in Tehran. Geographical Research Quarterly, 38(58), 99-112, (in Persian).
Shogrkhodaei, S. Z., Razavi-Termeh, S. V., Fathnia, A. (2021). Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms. Environmental Pollution, 15, 117859.
Susymary, J., Deepalakshmi, P. (2020). Innovative Methods of Air Pollution Exposure Assessment for Environmental Safety. Procedia Computer Science, 171, 689-698.
Sayed, A. S., Romani, I., Hashem, A. H. (2017). GIS-Based Network Analysis for the Roads Network of the Greater Cairo Area. Conference: International Conference on Applied Research in Computer Science and Engineering (ICAR 2017), Beirut, Lebanon.
Stedman, J. R., Grice, S., Kent, A., Cooke, S. (2009). GIS-based models for ambient PM exposure and health impact assessment for the UK. Journal of Physics: Conference Series, 151. doi:10.1088/1742-6596/151/1/012002.
Vahidnia, M. H., Vafaeinejad, A. R., Shafiei, M. (2019). Heuristic game-theoretic equilibrium establishment with application to task distribution among agents in spatial networks. Journal of Spatial Science, 64(1), 131-152.
Vallejo, M., Ruiz, S., Hermosillo, A. G., Borja-aburto, V. H., Rdenas, M. CA. (2006). Ambient fine particles modify heart rate variability in young healthy adults. Journal of Exposure Science and Environmental Epidemiology, 16, 125-130.
World Health Organization. (2005). WHO air quality guidelines for particulatematter, ozone, nitrogen dioxide and sulfur dioxide. global update, 1–21.
Xu, D., Lin, W., Gao, J., Jiang, Y., Li, L., Gao, F. (2022). PM2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS. International Journal of Environmental Research Public Health, 19(10), 6154. https://doi.org/10.3390/ijerph19106154.
Zanobetti, A., Schwartz, J. (2009). The effect of fine and coarse particulate air pollution on mortality: a national analysis. Environ Health Perspect, 117(6), 898-903. doi: 10.1289/ehp.0800108.
Zhang, Z., Chai, P., Wang, J., Ye, Z., Shen, P., Lu, H., Jin, M. … Chen, K. (2019). Association of particulate matter air pollution and hospital visits for respiratory diseases : a time-series study from China. Environmental Science and Pollution Research International, 26(12), 12280-12287.
Zhang, Y., Li, Z. (2015). Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160, 252-262.
