HYBRID MODEL AND FRAMEWORK FOR PREDICTING AIR POLLUTANTS IN SMART CITIES
DOI:
https://doi.org/10.31272/jeasd.28.3.9Keywords:
Air Pollution, Air Quality Index, Machine Learning, Smart CitiesAbstract
The pollution index of any urban area is indicated by its air quality. It also shows a fine balance is maintained between the needs of the populace and the industrial ecosystem. To mitigate such pollution in real-time, smart cities have a significant role to play. It's common knowledge that air pollution in a city severely affects the health of its dependents. More alarmingly, human health damage and disease burden are caused by phenomena like acid rain, and global warming. More precisely, lung ailments, CPOD, heart problems and skin cancer are caused by polluted air in congested urban places. Amongst the worst air pollutants, CO, C6H6, SO2, NO2, O3, RSPM/PM10, and PM2.5 cause maximum havoc. The climatic variables like atmospheric wind velocity, direction, relative humidity, and temperature control air contaminants in the air. Lately, numerous techniques have been applied by researchers and environmentalists to determine the Air Quality Index over a place. However, not a single technique has found acceptance from all quarters as being effective in every situation or scenario. Here, the main aspect relates to achieving authentic prediction in AQI levels by applying Machine Learning algorithms so worst situations can be averted by timely action. To enhance the performance of Machine Learning methods study adopted imputation and feature selection methods. When feature selection is applied, the experimental outcomes indicate a more accurate prediction over other techniques, showing promise for the application of the model in smart cities by syncing data from different monitoring stations.
References
Wark, K.;Warner, C.F. Air Pollution: Its Origin and Control; Harper and Row: New York, NY, USA, 1981, Book:ISBN:9780700225347
Seinfeld, J.H.; Pandis, S.N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change; JohnWiley& Sons: New York, NY, USA, 1998. https://doi.org/10.1063/1.882420
Mlakar, P.; Boznar, M. Perceptron neural network-based model predicts air pollution. In Proceedings of the Intelligent Information Systems, Grand Bahama Island, Bahamas, 8–10 December 1997; pp. 345–349. https://doi.org/10.1109/IIS.1997.645288
Murtadah, I., Al-Sharify, Z. T., & Hasan, M. B. (2020, June). Atmospheric concentration saturated and aromatic hydrocarbons around Dura refinery. In IOP Conference Series: Materials Science and Engineering (Vol. 870, No. 1, p. 012033). IOP Publishing. https:// https://doi.org/10.1088/1757-899X/870/1/012033
Abdulhussein, Z. A., Al-Sharify, Z. T., Alzuraiji, M., & Onyeaka, H. (2023). Environmental significance of fouling on the crude oil flow. A comprehensive review. Journal of Engineering and Sustainable Development (JEASD), 27(3). https://doi.org/10.31272/jeasd.27.3.3
Aalhashem, N. A., Naser, Z. A., Al-Sharify, T. A., Al-Sharify, Z. T., Al-Sharify, M. T., Al-Hamd, R. K. S., & Onyeaka, H. (2022, November). Environmental impact of using geothermal clean energy (heating and cooling systems) in economic sustainable modern buildings architecture design in Iraq: A review. In AIP Conference Proceedings (Vol. 2660, No. 1). AIP Publishing. https://doi.org/10.1063/5.0109553
Muhaisn, L. F., Naser, Z. A., Nayel, D. H., Al-Sharify, Z. T., & Muhaisn, F. F. (2023, July). Investigate the environmental impact of aircraft on the Earth’s atmosphere and analyzing its effect on air and water pollution. In AIP Conference Proceedings (Vol. 2787, No. 1). AIP Publishing. https://doi.org/10.1063/5.0150150
AL-Bakri, N. F., & Hashim, S. H. (2019). A study on the accuracy of prediction in recommendation system based on similarity measures. Baghdad Science Journal, 16(1 Supplement), 263-269. http://dx.doi.org/10.21123/bsj.2019.16.1(Suppl.).0263
Salih, N. Z., & Khalaf, W. (2021). Improving students performance prediction using machine learning and synthetic minority oversampling technique. Journal of Engineering and Sustainable Development, 25(6), 56-64. https://doi.org/10.31272/jeasd.25.6.6
Brunelli, U.; Piazza, V.; Pignato, L.; Sorbello, F.; Vitabile, S. Three hours ahead prevision of SO2 pollutant concentration using an Elman neural based forecaster. Build. Environ. 2008, 43, 304–314. https://doi.org/10.1016/j.buildenv.2006.05.011
Anad, A. M., Hassoon, A. F., & Al-Jiboori, M. H. (2022). Assessment of air pollution around Durra refinery (Baghdad) from emission NO2 gas at April Month. Baghdad Science Journal, 19(3), 0515-0515. http://dx.doi.org/10.21123/bsj.2022.19.3.0515
Singh, K.P.; Gupta, S.; Rai, P. Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos. Environ. 2013, 80, 426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023
Fernando, H.J.; Mammarella, M.C. Grandoni, G.; Fedele, P.; Di Marco, R.; Dimitrova, R.; Hyde, P. Forecasting PM10 in metropolitan areas: Efficacy of neural networks. Environ. Pollut. 2012, 163, 62–67. https://doi.org/10.1016/j.envpol.2011.12.018
Talib, A. H., & Zainab, A. (2021). Measurement of some Air Pollutantsin Printing Units and Copy Centers Within Baghdad City. Baghdad Science Journal, 18(1 (Suppl.)), 0687-0687. http://dx.doi.org/10.21123/bsj.2021.18.1(Suppl.).0687
Ali, S. M. (2017). A study of Land Zoning using ArcGIS. Al-Khwarizmi Engineering Journal, 13(4), 137-151. https://doi.org/10.22153/kej.2017.06.002
Air Quality Control Company. Tehran Air Pollution Forecasting System; MF96/05/01 (U/01); Air Quality Control Company: Tehran, Iran, 2018. (In Persian). https://doi.org/10.3390/ijgi8020099
Kelly, F.J.; Fuller, G.W.; Walton, H.A.; Fussell, J.C. Monitoring air pollution: Use of early warning systems for public health. Respirology 2012, 17, 7–19. https://doi.org/10.1111/j.1440-1843.2011.02065.x
مريم حسن احمد سليمان. (2016). practical and theoretical study of air pollution of the north gas company in the city of kirkuk. Journal of Engineering and Sustainable Development, 20(3). https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/606/480
Pope, C.A., III; Dockery, D.W. Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manag. Assoc. 2006, 56, 709–742. https://doi.org/10.1080/10473289.2006.10464485
Wang, P.; Liu, Y.; Qin, Z.; Zhang, G. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Total Environ. 2015, 505, 1202–1212. https://doi.org/10.1016/j.scitotenv.2014.10.078
Venegas, L.E.; Mazzeo, N.A.; Dezzutti, M.C. A simple model for calculating air pollution within street canyons. Atmos. Environ. 2014, 87, 77–86. https://doi.org/10.1016/j.atmosenv.2014.01.005
Gopalakrishnan, V. (2021). Hyperlocal air quality prediction using machine learning. Towards data science.
https://towardsdatascience.com/hyperlocal-air-quality-prediction-using-machine-learning-ed3a661b9a71
Sanjeev, D. (2021). Implementation of machine learning algorithms for analysis and prediction of air quality. International Journal of Engineering Research & Technology (IJERT), 10(3), 533-538. https://doi.org/10.17577/IJERTV10IS030323
Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A machine learning approach to predict air quality in California. Complexity, 2020. https://doi.org/10.1155/2020/8049504
Kumar, K., & Pande, B. P. (2023). Air pollution prediction with machine learning: a case study of Indian cities. International Journal of Environmental Science and Technology, 20(5), 5333-5348. https://doi.org/10.1007/s13762-022-04241-5
Harishkumar, K. S., Yogesh, K. M., & Gad, I. (2020). Forecasting air pollution particulate matter (PM2. 5) using machine learning regression models. Procedia Computer Science, 171, 2057-2066. https://doi.org/10.1016/j.procs.2020.04.221
Liang, Y. C., Maimury, Y., Chen, A. H. L., & Juarez, J. R. C. (2020). Machine learning-based prediction of air quality. applied sciences, 10(24), 9151. https://doi.org/10.3390/app10249151
Bibri, S. E., & Bibri, S. E. (2018). Data science for urban sustainability: Data mining and data-analytic thinking in the next wave of city analytics. Smart Sustainable Cities of the Future: The Untapped Potential of Big Data Analytics and Context–Aware Computing for Advancing Sustainability,189-246. https://doi.org/10.1007/978-3-319-73981-6_4
Cabrera, B. A Geostatistical Method for the Analysis and Prediction of Air Quality Time Series: Application to the Aburrá Valley Region. Master’s Thesis, Technische Universität München (TUM), München, Germany, 2016.
Mohammed, Q. H. & Reddy, E. S. (2019, February). Exploring Missing Data using Adaptive LASSO Regression Imputation in Relation to Parkinson’s disease. International Journal of Innovative Technology and Exploring Engineering, 8(4S), 413-421.
Mahdi, G. J., & Salih, O. M. (2022). Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset. Baghdad Science Journal, 19(3), 0551-0551. http://dx.doi.org/10.21123/bsj.2022.19.3.0551
Abbas, H. K., Al-Zuky, A. A., & Mahdy, A. H. (2014). Multifocus Images Fusion Based on Homogeneity and Edges Measures. Baghdad Science Journal, 11(2), 660-672. https://doi.org/10.21123/bsj.2014.11.2
Jabbar, R. R., & Alkhafaji, A. A. A. (2023). Analysis of Traditional and Fuzzy Quality Control Charts to Improve Short-Run Production in the Manufacturing Industry. Journal of Engineering, 29(6), 159-176. https://doi.org/10.31026/j.eng.2023.06.12
Awad, J. H., & Majeed, B. D. (2020). Moving Objects Detection Based on Frequency Domain. Baghdad Science Journal, 17(2). http://dx.doi.org/10.21123/bsj.2020.17.2.0556
Yusro, M. M., Ali, R., & Hitam, M. S. (2023). Comparison of Faster R-CNN and YOLOv5 for Overlapping Objects Recognition. Baghdad Science Journal, 20(3), 0893-0893. https://doi.org/10.21123/bsj.2022.7243
Zaki, S. M., Jaber, M. M., & Kashmoola, M. A. (2022). Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network. Baghdad Science Journal, 19(6), 1356-1356. https://dx.doi.org/10.21123/bsj.2022.5965
Downloads
Key Dates
Received
Revised
Accepted
Published Online First
Published
Issue
Section
License
Copyright (c) 2024 Qutaiba Humadi Mohammed, Anupama Namburu (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
