References
- Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature. 2015;525:367-71. DOI: 10.1038/nature15371.
- Viana M, de Leeuw F, Bartonova A, Castell N, Ozturk E, Ortiz AG. Air quality mitigation in European cities: Status and challenges ahead. Environ Int. 2020;143:105907. DOI: 10.1016/j.envint.2020.105907.
- Sanjay C, Harshita K, Nand K, Virendra KS. Examining the locational approach towards optimal siting of air quality monitoring stations in India. Environ Eng Manage J. 2024;23(6):1139-50. DOI: 10.21203/rs.3.rs-2079414/v1.
- Mao W, Jiao L, Wang W, Wang J, Tong X, Zhao S. A hybrid integrated deep learning model for predicting various air pollutants, GIScience Remote Sensing. 2021;58(8):1395-412. DOI: 10.1080/15481603.2021.1988429.
- Bekkar A, Hssina B, Douzi S, Douzi K. Air-pollution prediction in smart city, deep learning approach. J Big Data. 2021;8:161. DOI: 10.1186/s40537-021-00548-1.
- Du S, Li T, Yang Y, Horng S-J. Deep air quality forecasting using hybrid deep learning framework. IEEE. 2021;33(6):2412-24. DOI: 10.1109/TKDE.2019.2954510.
- Chang W, Fang J, Zhao H, Zhang H. Attention-based inductive graph neural networks for spatiotemporal kriging. Proc 2024 Int Conf Artificial Intelligence Autonomous Transportation. 2025;1390:256-63. DOI: 10.1007/978-981-96-3961-8_25.
- Ben-Nun T, Hoefler T. Demystifying parallel and distributed deep learning: An in-depth concurrency analysis. ACM Computing Surveys (CSUR). 2019; 52(4):1-43. DOI: 10.1145/332006.
- Gharaibeh A, Salahuddin MA, Hussini SJ, Khreishah A, Khalil I, Guizani M, et al. Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys Tutorials. 2017;19(4):2456-501. DOI: 10.1109/COMST.2017.2736886.
- Zeinalnezhad M, Gholamzadeh A, Kleme J. Air pollution pre-diction using semi-experimental regression model and adaptive neuro-fuzzy inference system. J Cleaner Prod. 2020;261:121218. DOI: 10.1016/j.jclepro.2020.121218.
- Zhang Z, Zhang S, Zhao X, Chen L, Yao J. Temporal difference-based graph transformer networks for air quality PM2.5 prediction: A case study in China. Frontiers Environ Sci. 2022;10:924986. DOI: 10.21203/rs.3.rs-1168251/v1.
- Pirani M, Gulliver J, Fuller GW, Blangiardo M. Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas. J Exposure Sci Environ Epidemiology. 2013;24:319-27. DOI: 0.1038/jes.2013.85.
- Calculli C, Fassò A, Finazzi F, Pollice A, Turnone A. Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy. Environmetrics. 2015;26(6):406-17. DOI: 10.1002/env.2345.
- Li X, Peng L, Hu Y, Shao J, Chi TH. Deep learning architecture for air quality predictions. Environ Sci Pollut Res. 2016;23:22408-17. DOI: 10.1007/s11356-016-7812-9.
- Maheshwar T, Jaisharma K. Air prediction analysis based on accuracy for air quality index using modified random forest novel technique in comparison with logistic regression. AIP Conf Proc. 2023;2822(1):020153. DOI: 10.1063/5.0172915.
- Clifford S, Low-Choy S, Mazaheri M, Salimi F. A Bayesian spatiotemporal model of panel design data: airborne particle number concentration in Brisbane, Australia. Environmetrics. 2019;30(7):e2597. DOI: 10.1002/env.2597.
- Wan Y, Xu M, Huang H, Chen SX. A spatio-temporal model for the analysis and prediction of fine particulate matter concentration in Beijing. Environmetrics. 2021;32(1):E2648. DOI: 10.1002/env.2648.
- Lu YJ, Li CT. AGSTN: Learning attention-adjusted graph spatio-temporal networks for short-term urban sensor value forecasting. 2020 IEEE Int Conf Data Mining (ICDM). 2020;1148-53. DOI: 10.1109/ICDM50108.2020.00140.
- Ouyang XC, Yang Y, Zhang YL, Zhou W. Spatial-temporal dynamic graph convolution neural network for air quality prediction. 2021 Int Joint Conf Neural Networks (IJCNN), Shenzhen, China. 2021;1-8. DOI: 10.1109/IJCNN52387.2021.9534167.
- Calo S, Bistaffa F, Jonsson A, Gómez V, Viana M. Spatial air quality prediction in urban areas via message passing. Eng Appl Artificial Intelligence. 2024;133:108191. DOI: 10.1016/j.engappai.2024.108191.
- Saez M, Barceló MA. Spatial prediction of air pollution levels using a hierarchical Bayesian spatiotemporal model in Catalonia. Spain. Environ Modelling Software. 2022;151:105369. DOI: 10.1016/j.envsoft.2022;105369.
- Han JD, Liu H, Xiong HY, Yang J. Semi-supervised air quality forecasting via self-supervised hierarchical graph neural network. IEEE Trans Knowledge Data Eng. 2023;35(5):5230-43. DOI: 10.1109/TKDE.2022.3149815.
- Shaddick G, Yan H, Vienneau D. A Bayesian hierarchical model for assessing the impact of human activity on nitrogen dioxide concentrations in Europe. Environ Ecol Stat. 2013;20:553-70. DOI: 10.1007/s10651-012-0234-z.
- Nicolis O, Díaz M, Sahu SK, Marín JC. Bayesian spatiotemporal modeling for estimating short-term exposure to air pollution in Santiago de Chile. Environmetrics. 2019;30(7):e2574. DOI: 10.1002/env.2574.
- Fiovaranti G, Martino S, Cameletti M, Cattani G. Spatio-temporal modelling of PM10 daily concentrations in ltaly using the SPDE approach. Atmos Environ. 2021;248118192. DOI: 10.1016/j.atmosenv.2021.118192.
- Manoj H, Suhas Suresh A, Nayanita B. Deep learning based detection and management of scrap materials. Environ Eng Manage J. 2024;23(7):1495-505. DOI: 10.30638/eemj.2024.122.
- Mukhopadhyay S, Sahu SK. A Bayesian spatiotemporal model to estimate long-term exposure to outdoor air pollution at coarser administrative geographies in England and Wales. J R Stat Soc Ser A Stat Soc. 2018; 181(2):465-86. DOI: 10.1002/env.2574.
- Rafsyam Y, Wibowo EP, Candra DS, Talita AS, Rinaldi A. Prediction of cumulonimbus clouds in airport vicinity using NOAA satellite imagery and random forest models. J Logistics Informatics Service Sci. 2024; 11(6):34-54. DOI: 10.33168/JLISS.2024.0603.
- Gariazzo C, Carlino G, Silibello C, Renzi M, Finardi S, Pepe N, et al. A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data. Sci Total Environ. 2022;724:138102. DOI: 10.1016/j.scitotenv.2020;138102.
- Alzu’Bi F, Al-Rawabdeh A, Almagbile A. Predicting air quality using random forest: A case study in Amman-Zarqa. Egyptian J Remote Sensing Space Sci. 2024;27(3):604-13. DOI: 10.1016/j.ejrs.2024.07.004.
- Wei J, Huang W, Li Z, Xue W, Peng Y, Sun L, et al. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sens Environ. 2019;231:111221. DOI: 10.1016/j.rse.2019.111221.
- Yang F. Analysing economic growth and environmental quality: A classical and Bayesian approach. Ecol Chem Eng S. 2024;31(3):425-32. DOI: 10.2478/eces-2024-0029.
- Rajasegarar S, Zhang P, Zhou Y, Karuasekera S, Leckie C, Palaniswami M. High resolution spatio-temporal monitoring of air pollutants using wireless sensor networks. Proc 2014 IEEE Ninth Int Conf Intelligent Sensors. Sensor Networks and Information Processing (ISSNIP). Singapore. 2014;1-6. DOI: 10.1109/ISSNIP.2014.6827607.
- Wang TT, Wang XP, Ma R, Li XY, Hu XP, Chan FTS, et al. Random forest-Bayesian optimisation for product quality prediction with large-scale dimensions in process industrial cyber-physical systems. IEEE Internet Things J. 2020;7(9):8641-53. DOI: 10.1109/JIOT.2020.2992811.
- Huang WZ, He WY, Knibbs LD, Jalaludin B, Guo YM, Morawska L, et al. Improved morbidity-based air quality health index development using Bayesian multi-pollutant weighted model. Environ Res. 2022;204:112397. DOI: 10.1016/j.envres.2021.112397.
- Wu XF. Enhanced green logistics: sustainable distribution and warehousing with IMU positioning. Ecol Chem Eng S. 2024;31(2):225-41. DOI: 10.2478/eces-2024-0016.
- Feng JS. Implementation of decision support system for ecological environment planning of urban green space. Ecol Chem Eng S. 2024;31(2):177-92. DOI: 10.2478/eces-2024-0012.
- Wang Y, Peng H, Wang G, Tang X, Wang X, Liu C. Monitoring industrial control systems viaspatio-temporal graph neural networks. Eng Appl Artificial Intelligence. 2023;122. DOI: 10.1016/j.engappai.2023.106144.
- Zheng Z, Su Y, Wang X, Zhou Z. Developing a construction waste management performance calculator for highway construction. Sci Reports. 2024;14(1):27679. DOI: 10.1038/s41598-024-79522-9.
- Liu T. Time-varying influence of policy risk on carbon emissions analysis. J Service Innovation Sust Development. 2024;5(2):95-115. DOI: 10.33168/SISD.2024.0206.