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Manuscript ID : JEST-2106-5428 (R1) Visit : 184 Page: 31 - 45

10.30495/jest.2022.61573.5428

Article Type: Original Research

Study the effects of Traffic Conditions on the PM2.5 emission Geographically Weighted Regression model (case study: Isfahan city)

Subject Areas : Air Pollution

Sharareh Mahmoudi 1 , Mozhgan Ahmadi Nadoushan 2

1 - Department of environmental sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
2 - Asisstant Professor, Department of environmental sciences, Waste and Wastewater Research Center, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. *(Corresponding Author)

Received: 2021-06-22 Accepted : 2022-04-06 Published : 2022-06-22

Keywords: PM2.5, Traffic, IDW model, Isfahan, Geographically weighted regression (GWR),

Abstract :

Background & Objectives: PM2.5 concentration has become a public concern in cities due to its harmful effects on human health. In this study, due to the importance of air pollution, the effect of urban traffic conditions on the emission of urban air pollutants (PM2.5) was studied using geographic weight regression model (GWR) and IDW interpolation method.Material and Methodology: For this purpose, concentration of PM2.5 in 2019 was collected from 9 air pollution monitoring stations of Isfahan Municipality and population data and traffic in the city were collected and entered into the model. Interpolation IDW method was used for preparing seasonal air pollutants dispersion maps. After performing geographical weight regression on the model parameters and in order to evaluate the validity of the model, the RMSE parameter was used, which is obtained from the difference between the actual value of the concentration and the predicted value and indicates the predictive power of the model. Finally, R2 values ​​were calculated and Moran's index was used to examine the spatial autocorrelation test.Findings: After performing geographical weight regression on the model parameters, in order to evaluate the validity of the model, the value of R2 was calculated and the Moran index was used to examine the spatial autocorrelation test.Discussion and Conclusion: According to the amount of R2=0.75 for PM2.5, a direct correlation has been shown between this pollutant and independent variables, especially in the summer. The Moran index results showed that the GWR model was a good model for investigating the spatial temporal pattern of suspended particles.

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  1. Cao, Q., Rui. G., Liang, Y., 2018. Study on PM2. 5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model. BMC public health. Vol. 18., pp. 1-10.
    2.
  2. Soleimani, M., Amini, N., Sadeghian, B., Wang, D., Fang, L., 2018. Heavy metals and their source identification in particulate matter (PM2.5) in Isfahan City, Iran. Journal of environmental sciences. Vol. 72, pp. 166-75. (In Persian)
    3.
  3. Wei, Q., Zhang, L., Duan, W., Zhen, Z., 2019. Global and geographically and temporally weighted regression models for modeling PM2. 5 in Heilongjiang, China from 2015 to 2018. International journal of environmental research and public health. Vol. 16., pp.
    4.
  4. Zallaghi, E., Geravandi, S., Nourzadeh Haddad, M., Goudarzi, G., Valipour, L., Salmanzadeh, S., 2015. Estimation of health effects attributed to nitrogen dioxide exposure using the airq model in Tabriz City, Iran. Health Scope. 4. pp. 31-37.
    5.
  5. Liu, Q., Wu, R., Zhang, W., Li, W., Wang, S., 2020. The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model. Environment International. Vol. 145. pp. 1-10.
    6.
  6. Zhai, L., Li, S., Zou, B., Sang, H., Fang, X., Xu, S., 2018. An improved geographically weighted regression model for PM2. 5 concentration estimation in large areas. Atmospheric Environment. Vol. 181. pp. 145-54.
    7.
  7. Mohseni Bandpi, A., Eslami, A., Shahsavani, A., Khodagholi, F., Alinejad, A., 2017. Physicochemical characterization of ambient PM2. 5 in Tehran air and its potential cytotoxicity in human lung epithelial cells (A549). Science of the Total Environment. Vol. 593. pp. 182-90. (In Persian)
    8.
  8. Rezaei Mofrad, M., HosseinDoost, G., Rangraz Jedi, F., Gilasi, H., Gharlipour, Z., Vafaei, R., 2014. Assessing the awareness of the people of Kashan about the sources, effects and methods of air pollution control. Scientific Journal of Ilam University of Medical Sciences. Vol. 22. pp. 52-58. (In Persian)
    9.
  9. Guo, B., Wang, X., Pei, L., Su, Y., Zhang, D., Wang, Y., 2021. Identifying the spatiotemporal dynamic of PM2. 5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015–2018. Science of The Total Environment. Vol. 751. DOI: 1016/j.scitotenv.2020.141765
    10.
  10. Chu, H-J., Huang, B., Lin, C-Y., 2015. Modeling the spatio-temporal heterogeneity in the PM10-PM2. 5 relationship. Atmospheric Environment. Vol. 102. pp. 176-82.
    11.
  11. O'Leary, B., Reiners Jr, JJ., Xu, X., Lemke, LD., 2016. Identification and influence of spatio-temporal outliers in urban air quality measurements. Science of the Total Environment. Vol. 573. pp. 55-65.
    12.
  12. An, X., Hou, Q., Li, N., Zhai, S., 2013. Assessment of human exposure level to PM10 in China. Atmospheric Environment. Vol. 70. pp. 376-86.
    13.
  13. Kaufman, YJ., Tanré, D., Boucher, O., 2002. A satellite view of aerosols in the climate system. Nature. Vol. 419. pp. 215-23.
    14.
  14. Gao, J., Li, S., 2011. Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression. Applied Geography. Vol. 31. pp. 292-302.
    15.
  15. Cardoso, D., Painho, M., Roquette, R., 2019. A geographically weighted regression approach to investigate air pollution effect on lung cancer: A case study in Portugal. Geospatial health. 14. pp. 35-45.
    16.
  16. Luo, J., Du, P., Samat, A., Xia, J., Che, M., Xue, Z., 2017. Spatiotemporal pattern of PM 2.5 concentrations in mainland China and analysis of its influencing factors using geographically weighted regression. Scientific reports. Vol. 7. pp. 1-14.
    17.
  17. Wang, J., Wang, S., Li, S., 2019. Examining the spatially varying effects of factors on PM2. 5 concentrations in Chinese cities using geographically weighted regression modeling. Environmental pollution. Vol. 248. pp. 792-803.
    18.
  18. Lin, G, Fu, J., Jiang, D., Hu, W., Dong, D., Huang, Y., 2014. Spatio-temporal variation of PM2. 5 concentrations and their relationship with geographic and socioeconomic factors in China. International journal of environmental research and public health. Vol. 11. pp. 173-86.
    19.
  19. Wang, Z-b., Fang, C-l., 2016. Spatial-temporal characteristics and determinants of PM2. 5 in the Bohai Rim Urban Agglomeration. Chemosphere. Vol. 148. pp. 148-62.
    20.
  20. Bahari, R., Abaspour, R., Pahlavani, P., 2016. Zoning of Particulate Matters (PM) pollution using local statistical models in GIS (Case Study: tehran Metropolisies). Journal of Geomatics Science and Technology. Vol. 5. pp. 165-74. (In Persian)
    21.
  21. Shirvani, E., Sadeghi, M., Hosseini, SM., Khosravi, A., Rabiei, K., Rahimi, M., 2020. Fine Particle Air Pollution (PM 2.5) and Cardiovascular Hospitalization in Isfahan in 2012: CAPACITY Study. Iranian Heart Journal. Vol. 21. pp.75-81. (In Persian)
    22.
  22. Meng, Y., Cave, M., Zhang, C., 2018. Spatial distribution patterns of phosphorus in top-soils of Greater London Authority area and their natural and anthropogenic factors. Applied Geochemistry. Vol. 88. pp. 213-20.
    23.
  23. Soltani, A., Ahmadian, A., Esmaeli Ayuki, Y., 2010. Application of Spatial Weight Regression Model in Investigating Relationships between Space Variables in Urban Area. Armanshahr Architecture & Urban Planning Journal. Vol. 4. pp. 99-110. (In Persian)
    24.
  24. Liu, L., Duan, Y., Li, L., Xu, L., Yang, Y., Cu, X., 2020. Spatiotemporal trends of PM2. 5 concentrations and typical regional pollutant transport during 2015–2018 in China. Urban Climate. Vol. 34. pp. 100710.
    25.
  25. Ranjan, AK., Patra, AK., Gorai, A., 2020. A Review on Estimation of Particulate Matter from Satellite-Based Aerosol Optical Depth: Data, Methods, and Challenges. Asia-Pacific Journal of Atmospheric Sciences. pp. 1-21. DOI:1007/s13143-020-00215-0
    26.
  26. Yoo, D., 2019. Geographically Weighted Regression: A Method for Spatial Analysis in Socio-Historical Research. Arch Iran Med. 2019;22(3): 155-160.
    27.
  27. Wang, Q., Ni, J., Tenhunen, J., 2005. Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global ecology and biogeography. Vol. 14. pp. 379-93.
    28.
  28. Dogru, AO., David, RM., Ulugtekin, N., Goksel, C., Seker, DZ., Sözen, S., 2017. GIS based spatial pattern analysis: Children with Hepatitis A in Turkey. Environmental research. Vol. 156. pp. 349-57.
    29.
  29. Peeters, A., Zude, M., Käthner, J., Ünlü, M., Kanber, R., Hetzroni, A., 2015. Getis–Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Computers and Electronics in Agriculture. Vol. 111. pp. 140-50.
    30.
  30. Zhang, H., Tripathi, NK., 2018. Geospatial hot spot analysis of lung cancer patients correlated to fine particulate matter (PM2. 5) and industrial wind in Eastern Thailand. Journal of Cleaner Production. Vol. 170. pp. 407-24.
    31.
  31. Hashemi Foumani, M., Motieian, H., 2020. Modeling the prevalence of super-acute avian influenza in Guilan province with data mining models and spatial information system in 2016: An ecological study. Scientific Journal of Rafsanjan University of Medical Sciences. Vol. 9. pp. 677-92. (In Persian)
    32.
  32. Zhao, R., Zhan, L., Yao, M., Yang, L., 2020. A geographically weighted regression model augmented by Geodetector analysis and principal component analysis for the spatial distribution of PM2. 5. Sustainable Cities and Society. Vol. 56. pp. 102106.
    33.
  33. Cheng, Y-H., Chang, H-P., Yan, J-W., 2012. Temporal variations in airborne particulate matter levels at an indoor bus terminal and exposure implications for terminal workers. Aerosol and Air Quality Research. Vol. 12. pp. 30-38.
    34.
  34. Soltani, A., Ahmadian, A., Esmaily IY., 2010. GWR Model Application In Investigation of Spatial Variables In An Urban District: Case Study Of Region 7, Municipality Of Tehran. (In Persian)
    35.
  35. Azadi Mubaraky, M., Ahmadi, M., 2020. Long-term variability of particulate matter (PM2.5) in Tabriz using remote sensing data. Physical Geography Research Quarterly. Vol. 52. pp. 467-80. (In Persian)
    36.
  36. Zhou, Q., Wang, C., Fang, S., 2019. Application of geographically weighted regression (GWR) in the analysis of the cause of haze pollution in China. Atmospheric Pollution Research. Vol. 10. pp. 835-46.
    37.
  37. Sarvar, H., Esmailpour, M., Kirizadeh, M., Amraei, M., 2020. Spatial analysis of factors affecting air pollution in Tabriz city. Journal of Natural Environmental Hazards. Vol. 9. pp. 151-72.
    38.

 

 

 

 

 

 

 

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