- Arsalani, F., & Alijani, B. (2021). Identification of effective factors concentration of heavy metals in the dust existing in the air of Tehran metropolis. Environmental Management Hazards, 8(4), 321-335. (in Persian)
- Balram, D., Lian, K.-Y., & Sebastian, N. (2019). Air quality warning system based on a localized PM2. 5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection. Ecotoxicology and environmental safety, 182, 109386.
- Birjandi, N., Ghobadi, M., & Ahmadi, M. (2019). Analysis and zoning of air pollution in urban landscape using different models of spatial analysis (Case study: Tehran). Advances in Environmental Technology, 5(3), 185-191.
- Chandra, K., Meijer, E., Andow, S., Arroyo-Fang, E., Dea, I., George, J., . . . Tempest, A. (2019). Gradient descent: The ultimate optimizer. arXiv preprint arXiv:1909.13371.
- Dehnavi eelagh, M., & Ali Abbaspour, R. (2023). Estimation of Missing Values in Time Series of Air Pollution Data in Tehran City. Journal of Environmental Studies, 48(4), 439-459. (in Persian)
- Faraji, M., & Nadi, S. (2020). Hazards caused by the concentration of pollutants PM_2.5 by using Regression Methods and Spatial-temporal Similarity in Order to Impute the Missing Values in their Time Series (Case Study of Tehran). Environmental Management Hazards, 7(3), 299-312. (in Persian)
- Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., & Wang, J. (2015). Artificial neural networks forecasting of PM2. 5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, 118-128.
- Gholizadeh, A., Neshat, A. A., Conti, G. O., Ghaffari, H. R., Aval, H. E., Almodarresi, S. A., . . . Mohammadi, A. (2019). PM2. 5 concentration modeling and mapping in the urban areas. Modeling Earth Systems and Environment, 5(3), 897-906.
- Hamed, H. H., Jumaah, H. J., Kalantar, B., Ueda, N., Saeidi, V., Mansor, S., & Khalaf, Z. A. (2021). Predicting PM2. 5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques. Geomatics, Natural Hazards and Risk, 12(1), 1778-1796.
- Khorshiddoust, A.M., Mohammadi, G. H., Aghlmand, F., & Hosseini Sadr, A. (2018). Descriptive-statistical Analysis of the Relationship between Atmospheric Conditions and Urban Pollution in Tabriz. Environmental Management Hazards, 5(2), 217-230. (in Persian)
- Kong, L., & Tian, G. (2020). Assessment of the spatio-temporal pattern of PM2. 5 and its driving factors using a land use regression model in Beijing, China. Environmental monitoring and assessment, 192(2), 1-19.
- Li, R., Ma, T., Xu, Q., & Song, X. (2018). Using MAIAC AOD to verify the PM2. 5 spatial patterns of a land use regression model. Environmental Pollution, 243, 501-509.
- Lin, G., Fu, J., Jiang, D., Hu, W., Dong, D., Huang, Y., & Zhao, M. (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, 11(1), 173-186.
- Mahmoudi, S., & Ahmadi Nadoushan, M. (2022). Study the effects of Traffic Conditions on the PM2.5 emission Geographically Weighted Regression model (case study: Isfahan city). Journal of Environmental Science and Technology, 24(4), 31-45. doi:10.30495/jest.2022.61573.5428. (in Persian)
- Pope III, C. A. (2000). Epidemiological basis for particulate air pollution health standards. Aerosol Science & Technology, 32(1), 4-14.
- Pourmohammadi, S., Lotfi, A., & Alranaee, M. (2022). Investigating the Effects of Land Changes on some Pollutants in the Mahshahr Industrial Zone using Remote Sensing and Analysis of Variance (ANOVA) Images. Geography and Environmental Planning, 33(4), 79-96. doi:10.22108/gep.2022.133195.1510. (in Persian)
- Querol, X., Alastuey, A., Ruiz, C., Artiñano, B., Hansson, H., Harrison, R., . . . Bruckmann, P. (2004). Speciation and origin of PM10 and PM2. 5 in selected European cities. Atmospheric Environment, 38(38), 6547-6555.
- Razavi-Termeh, S. V., Sadeghi-Niaraki, A., & Choi, S.-M. (2022). Spatio-temporal modelling of asthma-prone areas using a machine learning optimized with metaheuristic algorithms. Geocarto International, 1-26.
- 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, 289, 117859.
- Wu, J., Wang, Y., Liang, J., & Yao, F. (2021). Exploring common factors influencing PM2. 5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies. Environmental Pollution, 285, 117138.
- Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics, 18(1), 183-190.
- Yousefian, F., Mahvi, A. H., Yunesian, M., Hassanvand, M. S., Kashani, H., & Amini, H. (2018). Long-term exposure to ambient air pollution and autism spectrum disorder in children: a case-control study in Tehran, Iran. Science of the total environment, 643, 1216-1222.
- Yu, W., Guo, Y., Shi, L., & Li, S. (2020). The association between long-term exposure to low-level PM2. 5 and mortality in the state of Queensland, Australia: a modelling study with the difference-in-differences approach. PLoS medicine, 17(6), e1003141.
- Zarandi, S. M., Shahsavani, A., Nasiri, R., & Pradhan, B. (2021). A hybrid model of environmental impact assessment of PM2. 5 concentration using multi-criteria decision-making (MCDM) and geographical information system (GIS)—a case study. Arabian Journal of Geosciences, 14(3), 1-20.
- 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, 56, 102106.
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