1. Kazemi Z, Kazemi Z, Jafari AJ, Farzadkia M, Hosseini J, Amini P, et al. Estimating the health impacts of exposure to Air pollutants and the evaluation of changes in their concentration using a linear model in Iran. Toxicology Reports 2024;12:56-64.## [ DOI:10.1016/j.toxrep.2023.12.006] [ PMID] [ ] 2. Jaafarzadeh N, Nouhjah S, Shahbazian H, Shenavar B. The relationship between hot spots of air pollution and the incidence of gestational diabetes based on spatial analysis: A study on one of the most air-polluted metropolis of Iran. Environmental Health Engineering And Management Journal 2024: 11(1); 83-92.## [ DOI:10.34172/EHEM.2024.10] 3. Salmabadi H, Saeedi M, Roy A, Kaskaoutis DG. Quantifying the contribution of Middle Eastern dust sources to PM10 levels in Ahvaz, Southwest Iran. Atmospheric Research 2023; 295: 106993.## [ DOI:10.1016/j.atmosres.2023.106993] 4. Taghavi F, Asadi A. The Persian Gulf 12th April 2007 dust storm: observation and model analysis. EUMETSAT Meteorological Satellite Conference, Darmstadt; Germany; 2008.## 5. Rahimi M, Yazdani MR, Asadi M, Haydari MT. Investigating the Air Pollution of Sanandaj whit emphasis on temporal variation of PM10 concentration. Journal of Urban Ecology Researches 2015; 6(11): 99-116.## 6. Kalantari E, Gholami H, Malakooti H, Eftekhari M, Saneei P, Esfandiarpour D, et al. Evaluating traditional versus ensemble machine learning methods for predicting missing data of daily PM10 concentration. Atmospheric Pollution Research 2024;15(5):102063.## [ DOI:10.1016/j.apr.2024.102063] 7. Fadaei A, Ahmadi H, Fatahpoor E, Jalilpour Y, Ariyanfar M, Jalili Naghan D. Journal of School of Public Health and Institute of Public Health Research 2024; 21(4): 487-502.## 8. Neisi A, Goudarzi G, Ahmadi Angali K, Nejat SH. Evaluation of suspended particles concentration (pm2.5 and pm10) and its relationship with death rate due to cardiovascular and respiratory diseases 2014-2017, in susangard city, khuzestan province, Iran. Journal of Environmental Health Engineering 2022; 9(2): 145-56.## [ DOI:10.61186/jehe.9.2.145] 9. Hadian A, Moradizadeh M. Modeling the concentration distribution of NO2 and O3 pollutants with an appropriate spatial resolution by combining ground and satellite data. Iranian Journal of Remote Sensing & GIS 2024;16(2): 85-104.## 10. Ebrahimi -Khusfi Z, Ebrahimi -Khusfi M, Mirakbari M. Analysis of dust pollution in arid regions using an index based on local and extra-local events. Journal of Arid Regions Geographic Studies 2024; 15(55): 131-16.## 11. bakhshizadeh F, Neysani Samany N, toomanian A. Developing GA-ANFIS model to predict long-term (PM)_10 concentration The Case study of Tehran city. Physical Geography Research .2023; 55(1): 57-87.## 12. Said S, Salah Z, Wahab MMA, Alfaro SC. Retrieving PM10 surface concentration from aeronet aerosol optical depth: the cairo and delhi megacities case studies. Journal of the Indian Society of Remote Sensing 2023; 51(8): 1797-807.## [ DOI:10.1007/s12524-023-01736-7] 13. Yang Y, Cermak J, Chen X, Chen Y, Hou X. High-Resolution PM10 estimation using satellite data and model-agnostic meta-learning. Remote Sensing 2024;16(13): 2498.## [ DOI:10.3390/rs16132498] 14. Sehat Kashani S, Rahnama M, Karami S, RanjbarSaadatabadi A, Khoddam N. Evaluation of environmental parameters influencing dust sources activation over ilam province. Physical Geography Research 2022; 54(3): 403-27.## 15. Tuna Tuygun G, Elbir T. Estimation of particulate matter concentrations in Türkiye using a random forest model based on satellite AOD retrievals. Stochastic Environmental Research and Risk Assessment 2023; 37(9): 3469-91.## [ DOI:10.1007/s00477-023-02459-4] 16. Harati H, Kiadaliri M, Tavana A, Rahnavard A, Amirnezhad R. Relationship between changes in water body and vegetation in the eastern of lake urmia with the phenomenon of dust storms. Journal of Civil and Environmental Engineering University of Tabriz 2023; 53(1): 44-54.## 17. Sheikh ghaderi SH, Alizadeh T, Ziaeian Firoozabadi P, Sharifi R. Temporal and spatial analysis of dust storms in Kermanshah. Journal of Spatial Analysis Environmental Hazards 2023;10(1): 71-90.## [ DOI:10.61186/jsaeh.10.1.71] 18. Mirakbari M, Khusfi ZE. Investigation of spatial and temporal changes in atmospheric aerosol using aerosol optical depth in Southeastern Iran. Journal of RS and GIS for Natural Resources 2020; 3(11): 87-105.## 19. Shirgholami M. Identifying trajectories and sources of dust events in Yazd province using HYSPLIT model and remote sensing data. Journal of Arid Biome 2023; 13(2): 35-52.## 20. Ridwana R, Ihsan HM, Syaripah G, Raihanah R, Rabbi FA, Maulana I. Optical aerosol depth comparison using multi sensor satellite image for air quality control in West Java Province, Indonesia. AIP Conference Proceedings; 2024, 3082.## [ DOI:10.1063/5.0202134] 21. Velayatzadeh M. Introducing the causes, origins and effects of dust in Iran. Journal of Air Pollution and Health 2020; 5(1): 63-70.## [ DOI:10.18502/japh.v5i1.2860] 22. Arami SA, Ownegh M, MohammadianBehbahani A, Akbari M, Zarasvandi A. The analysis of dust hazard studies in southwest region of Iran in 22 years (1996-2017). Journal of Spatial Analysis Environmental Hazarts 2018; 5(1): 39-66.## [ DOI:10.29252/jsaeh.5.1.39] 23. Karami M, Sarvestan R, Mansourzadeh AM. Examining The Evolution Of The Khuzestan Urban Population Using The Urban Primacy Indexes, 2017.## 24. Safaeepoor M, Sayahi Z, Zargar shooshtary M, Daripoor N. Study the endowment effect on economic development of Ahvaz city 2014; 5(16): 89-104.## 25. Gharibi S, Shayesteh K. Application of Sentinel 5 satellite imagery in identifying air pollutants Hotspots in Iran. Journal of Spatial Analysis Environmental Hazards 2021; 8(3): 123-38.## 26. Saeidi O, Peyvand N. Investigating and analyzing the components of the creative city in the neighborhoods of the 4th municipality of Ahvaz. Geographical Engineering of Territory 2019; 3(5): 111-25.## 27. Chu DA, Kaufman Y, Zibordi G, Chern J, Mao J, Li C, et al. Global monitoring of air pollution over land from the Earth Observing System‐Terra Moderate Resolution Imaging Spectroradiometer (MODIS). Journal of Geophysical Research: Atmospheres 2003;108(D21).## [ DOI:10.1029/2002JD003179] 28. Najim AO, Meteab MA, Jasim AT, Ajaj QM, Jumaah HJ, Sulyman MHA. Spatial analysis of particulate matter (PM10) using MODIS aerosol optical thickness observations and GIS over East Malaysia. The Egyptian Journal of Remote Sensing and Space Science 2023; 26(2): 265-71.## [ DOI:10.1016/j.ejrs.2023.03.001] 29. Wang J, Christopher SA. Intercomparison between satellite‐derived aerosol optical thickness and PM2. 5 mass: Implications for air quality studies. Geophysical research letters 2003; 30(21): 2095.## [ DOI:10.1029/2003GL018174] 30. Park Y, Kwon B, Heo J, Hu X, Liu Y, Moon T. Estimating PM2. 5 concentration of the conterminous United States via interpretable convolutional neural networks. Environmental Pollution 2020; 256: 113395.## [ DOI:10.1016/j.envpol.2019.113395] [ PMID] 31. Téllez-Rojo MM, Rothenberg SJ, Texcalac-Sangrador JL, Just AC, Kloog I, Rojas-Saunero LP, et al. Children's acute respiratory symptoms associated with PM2. 5 estimates in two sequential representative surveys from the Mexico City Metropolitan Area. Environmental Research 2020; 180: 108868.## [ DOI:10.1016/j.envres.2019.108868] [ PMID] 32. Phan VH, Pham DPH, Pham TV, Qureshi KN, Pham-Quoc C. An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta. Future Internet 2023; 15(7): 245.## [ DOI:10.3390/fi15070245] 33. Tian Z, Wei J, Li Z. How important is satellite-retrieved aerosol optical depth in deriving surface PM2. 5 using machine learning? Remote Sensing 2023; 15(15): 3780.## [ DOI:10.3390/rs15153780] 34. Li J, Ge X, He Q, Abbas A. Aerosol optical depth (AOD): spatial and temporal variations and association with meteorological covariates in Taklimakan desert, China. PeerJ 2021; 9: e10542.## [ DOI:10.7717/peerj.10542] [ PMID] [ ] 35. Ghotbi S, Sotoudeheian S, Arhami M. Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model. Atmospheric Environment. 2016; 141: 333-46.## [ DOI:10.1016/j.atmosenv.2016.06.057] 36. Wang Y, Chen L, Xin J, Wang X. Impact of the dust aerosol model on the VIIRS aerosol optical depth (AOD) product across China. Remote Sensing 2020; 12(6): 991.## [ DOI:10.3390/rs12060991] 37. Salmabadi H, Khalidy R, Saeedi M. Transport routes and potential source regions of the Middle Eastern dust over Ahvaz during 2005-2017. Atmospheric Research 2020; 241: 104947.## [ DOI:10.1016/j.atmosres.2020.104947] 38. Guo B, Wang Z, Pei L, Zhu X, Chen Q, Wu H, et al. Reconstructing MODIS aerosol optical depth and exploring dynamic and influential factors of AOD via random forest at the global scale. Atmospheric Environment 2023; 315: 120159.## [ DOI:10.1016/j.atmosenv.2023.120159] 39. Chedin A, Capelle V, Scott N. Detection of IASI dust AOD trends over Sahara: How many years of data required? Atmospheric Research 2018; 212: 120-9.## [ DOI:10.1016/j.atmosres.2018.05.004] 40. Lyapustin A, Martonchik J, Wang Y, Laszlo I, Korkin S. Multiangle implementation of atmospheric correction (MAIAC): 1. Radiative transfer basis and look‐up tables. Journal of Geophysical Research: Atmospheres 2011; 116; D03210.## [ DOI:10.1029/2010JD014985] 41. Lyapustin A, Wang Y, Korkin S, Huang D. MODIS collection 6 MAIAC algorithm. Atmospheric Measurement Techniques 2018; 11(10): 5741-65.## [ DOI:10.5194/amt-11-5741-2018] 42. Stirnberg R, Cermak J, Andersen H. An analysis of factors influencing the relationship between satellite-derived AOD and ground-level PM10. Remote Sensing 2018; 10(9): 1353.## [ DOI:10.3390/rs10091353] 43. Amanollahi Jamil, Kaboodvandpour, Shahram. Assessment of MODIS images and data validation to measure PM10 in related to ground station data. Case study: Sanandaj, Iran. Geographic Space 2014; 14(46): 129-49.## 44. Khafaie MA, Saeidi M, Mohammadi S, Marioryad H, Jamshidi A. Modeling of PM10 Particulate Matter in Ahvaz City Using Remote Sensing and Meteorological Parameters. Journal of Environmental Health and Sustainable Development 2024; 9(3): 2304-17.## [ DOI:10.18502/jehsd.v9i3.16579] 45. Rashki Alireza, Kaskaoutis, Dimitris G, Francois P, Kosmopoulos PG, Legrand MJAR. Dust-storm dynamics over Sistan region., Iran: Seasonality, Transport Characteristics and Affected Areas 2015; 16: 35-48.## [ DOI:10.1016/j.aeolia.2014.10.003] 46. Goudarzi Gh, Geravandi S, Saeidimehr S, Mohammadi, MJ, Vosoughi Niri M, Salmanzadeh SH. Estimation of health effects for PM10 exposure using of Air Q model in Ahvaz City during 2009. 2015;8(1):117-126 .## 47. Ranjan AK, Patra AK, Gorai A. A review on estimation of particulate matter from satellite-based aerosol optical depth: Data, methods, and challenges. Asia-Pacific Journal of Atmospheric Sciences 2021; 57: 679-99.## [ DOI:10.1007/s13143-020-00215-0] 48. Rangzan K, Kabolizadeh M, Mohammadi S. Assessment of Spatiotemporal Changes of NO2 Using TROPOMI Sensor in Khuzestan Province, Iran. Journal of Health System Research 2021; 17(2): 87-96.## 49. Gholizadeh MH, Amanollahi J, Rahimi F. Assessment of correlation between pm10 data measured at ground station of sanandaj and AOD data of MODIS sensor. Iranian Journal of Remote Sensing & GIS 2022; 14(2): 51-60.## [ DOI:10.52547/gisj.14.2.51] 50. Chen Y, Li D, Karimian H, Wang S, Fang S. The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta. Chemosphere 2022; 308: 136301.## [ DOI:10.1016/j.chemosphere.2022.136301] [ PMID] 51. Rafati S, Rafati Alashti M. Application of modis optical depth data in estimation of PM10 (case study: Tehran). The Journal of Spatial Planning and Geomatics 2020; 24(4): 1-28.## 52. Guo Y, Tang Q, Gong DY, Zhang Z. Estimating ground-level PM2. 5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment 2017; 198: 140-9.## [ DOI:10.1016/j.rse.2017.06.001] |
