Taghizadeh, F., Mokhtarani, B. & Rahmanian, N. Air pollution in iran: the current status and potential solutions. Environ. Monit. Assess. 195 (6), 737 (2023).
Sohrabi, Z. & Maleki, J. Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach. Sci. Rep. 15 (1), 21449 (2025).
Wen, C. et al. A novel Spatiotemporal convolutional long short-term neural network for air pollution prediction. Sci. Total Environ. 654, 1091–1099 (2019).
Wang, J. & Ogawa, S. Effects of meteorological conditions on PM2. 5 concentrations in Nagasaki, Japan. Int. J. Environ. Res. Public Health. 12 (8), 9089–9101 (2015).
Nguyen, T. N. T., Trinh, T. D. & Vu, P. C. L. T. Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2. 5) in Vietnam. J. Environ. Eng. Landsc. Manage. 32 (4), 292–304 (2024).
Li, N. et al. Predicting personal exposure to PM2. 5 using different determinants and machine learning algorithms in two megacities, China. Indoor Air. 2024 (1), 5589891 (2024).
Bai, L. et al. Air pollution forecasts: an overview. Int. J. Environ. Res. Public Health. 15 (4), 780 (2018).
Saiohai, J. et al. Statistical PM2. 5 prediction in an urban area using vertical meteorological factors. Atmosphere 14 (3), 589 (2023).
Zhang, K. et al. Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: a case study of Huaihai economic zone. J. Clean. Prod. 277, 123231 (2020).
Damkliang, K. & Chumnaul, J. Deep learning and statistical approaches for area-based PM2. 5 forecasting in hat Yai, Thailand. J. Big Data. 12 (1), 36 (2025).
Ma, J. et al. Improving air quality prediction accuracy at larger Temporal resolutions using deep learning and transfer learning techniques. Atmos. Environ. 214, 116885 (2019).
Adong, P., Bainomugisha, E. & Dev, S. Evaluating machine learning methods for PM2.5 estimation using satellite AOD, low-cost and reference-grade monitors in Kampala. Int. J. Environ. Sci. Technol. 22, 15747–15756 (2025).
Vovk, T., Kryza, M. & Werner, M. Using random forest to improve EMEP4PL model estimates of daily PM2. 5 in Poland. Atmos. Environ. 332, 120615 (2024).
Qiu, M., Zigler, C. & Selin, N. E. Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions. Atmos. Chem. Phys. 22 (16), 10551–10566 (2022).
Lauret, P. et al. Atmospheric dispersion modeling using artificial neural network based cellular automata. Environ. Model. Softw. 85, 56–69 (2016).
Tang, P. et al. Pixel-Level projection of PM 2.5 using Landsat images and cellular automata models in the Yangtze river Delta, China. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16, 6656–6670 (2023).
Sonnenschein, T. S. et al. Hybrid Cellular automata-based Air Pollution Model for Traffic Scenario Microsimulations p. 106356 (Environmental Modelling & Software, 2025).
Marın, M. et al. Cellular automata simulation of dispersion of pollutants. Comput. Mater. Sci. 18 (2), 132–140 (2000).
Gounaridis, D. et al. A random Forest-Cellular automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. Sci. Total Environ. 646, 320–335 (2019).
Antonioni, A., Sanchez, A. & Tomassini, M. Global information and mobility support coordination among humans. Sci. Rep. 4 (1), 6458 (2014).
Barkwith, A. et al. Simulating the influences of groundwater on regional geomorphology using a distributed, dynamic, landscape evolution modelling platform Vol. 74, 1–20 (Environmental Modelling & Software, 2015).
Chowdhury, S. et al. Tracking ambient PM2. 5 build-up in Delhi National capital region during the dry season over 15 years using a high-resolution (1 km) satellite aerosol dataset. Atmos. Environ. 204, 142–150 (2019).
Yan, X. et al. Satellite-based PM2. 5 Estimation using fine-mode aerosol optical thickness over China. Atmos. Environ. 170, 290–302 (2017).
Zang, L. et al. Estimation of Spatiotemporal PM1. 0 distributions in China by combining PM2. 5 observations with satellite aerosol optical depth. Sci. Total Environ. 658, 1256–1264 (2019).
Van Donkelaar, A. et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors Vol. 50, 3762–3772 (Environmental science & technology, 2016).
Tripathy, P. & Kumar, A. Monitoring and modelling spatio-temporal urban growth of Delhi using cellular automata and geoinformatics. Cities 90, 52–63 (2019).
Deng, F. et al. The MR-CA models for analysis of pollution sources and prediction of PM 2.5. IEEE Trans. Syst. Man. Cybernetics: Syst. 49 (4), 814–820 (2017).
Jiang, W. et al. Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model Vol. 135, 104895 (Environmental Modelling & Software, 2021).
Rui, X. et al. Forest fire spread simulation algorithm based on cellular automata. Nat. Hazards. 91, 309–319 (2018).
Enviroment, D. O. Air Pollution Monitoring System. ; (2025). Available from: https://aqms.doe.ir/Home/Index
Van Donkelaar, A. et al. Monthly global estimates of fine particulate matter and their uncertainty Vol. 55, 15287–15300 (Environmental Science & Technology, 2021).
Loeser, C. et al. Famine early warning systems network (FEWS NET) land data assimilation system (LDAS) and other assimilated hydrological data at NASA GES DISC. in American Meteorological Society Annual Meeting. (2020).
McNally, A. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. Data. 4 (1), 1–19 (2017).
McNally, A. et al. A central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan. Earth Syst. Sci. Data. 14 (7), 3115–3135 (2022).
Arsenault, K. R. et al. The NASA hydrological forecast system for food and water security applications. Bull. Am. Meteorol. Soc. 101 (7), E1007–E1025 (2020).
Hazra, A. et al. NASA’s NMME-based S2S hydrologic forecast system for food insecurity early warning in Southern Africa. J. Hydrol. 617, 129005 (2023).
Meng, X. et al. Estimating PM2. 5 concentrations in Northeastern China with full Spatiotemporal coverage, 2005–2016. Remote Sens. Environ. 253, 112203 (2021).
