Application of Machine Learning to Air Pollution Studies: A Systematic Review

Marvelous Ukachukwu *

Department of Geological Sciences, Nnamdi Azikiwe University, Awka, Nigeria.

Nnemeka Uzoamaka

Department of Geological Sciences, Nnamdi Azikiwe University, Awka, Nigeria.

Nnama Elochukwu

Department of Geological Sciences, Nnamdi Azikiwe University, Awka, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

 

Air pollution is a serious global issue that threatens human life and health, as well as the environment. Machine learning algorithms can be used to predict air pollution level data from both natural and anthropogenic activities. Environmental and government agencies can use these speculations to issue air pollution alerts. This review work is an attempt at the recent status and development of scientific studies on the use of machine learning algorithms to model air pollution challenges. This study uses the scientific web as a primary search engine and covers over 100 highly peer-reviewed articles from 2000-2022. Therefore, this review paper aims to highlight the various application methods of machine learning, notably data mining, in air pollution control and monitoring. It also comprehensively analyses published works by renowned scholars and authors worldwide, discussing how machine learning has been used in mitigating air pollution. By examining the chronological trends of machine learning in air pollution, this review paper provides an up-to-date account of the successes achieved in regulating air pollution using machine learning techniques. Additionally, it identifies areas that require further research, critically analyzing the current state of knowledge and potential research directions.

Keywords: Machine learning, environmental pollution, air pollution, algorithm, anthropogenic


How to Cite

Ukachukwu , Marvelous, Nnemeka Uzoamaka, and Nnama Elochukwu. 2023. “Application of Machine Learning to Air Pollution Studies: A Systematic Review”. Journal of Energy Research and Reviews 15 (2):1-11. https://doi.org/10.9734/jenrr/2023/v15i2302.

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References

Remoundou, Phoebe KoundouriInt. Environmental effects on public health: An economic perspective. International Journal of Environmental Research on Public Health. 2009;6(8):2160–2178.

Mengyuan Zhu, Jiawei Wang, Xiao Yang, Yu Zhang, Linyu Zhang, Hongqiang Ren, Bing Wu, Lin Ye. A review of the application of machine learning in water quality evaluation, Eco-Environment &Health. 2022;1(2).

Aniwetalu, et al. Spectral analysis of Rayleigh waves in the Southeastern part of Niger Delta, Nigeria. Int J Adv Geosci. 2018;6:51-6. Available:http://dx.doi.org/10.14419/ijag.v6i1.8776

Nwaezeapu VC, Tom IU, David ETA, Vivian OO. Hydrocarbon reservoir evaluation: A case study of Tymot field at Southwestern Offshore Niger Delta Oil Province, Nigeria. Nanosci Nanotechnol. 2018;2(2).

Ibekwe KN, Oguadinma VO, Okoro VK, Aniwetalu E, Lanisa A, Ahaneku CV. Reservoir characterization review in sedimentary basins. Journal of Energy Research and Reviews. 2023;13(2):20-28.

Hannah, Max Roser. Our world in data” contributes to raising awareness and combating climate change. Regional and Business Studies. 2017;11(2):87-92. DOI: 10.33568/rbs.2411

Yasin Akin Ayturan, Zeynep Cansu Ayturan, Hüseyin Oktay Altun. Air pollution modelling with deep learning. International Journal of Environmental Pollution and Environmental Modelling. 2018;1:58-62.

Jenkin ME, Clemitshaw KC. Ozone and other secondary photochemical pollutants: Chemical processes governing their formation in the planetary boundary layer. Atmospheric Environment. 2000;34(16):2499-2527.

Available:https://doi.org/10.1016/S1352-2310(99)00478-1

Alireza Zhalehdoost, Mohammed Taleai. A review of the application of machine learning and geospatial analysis method in air pollution analysis. International Journal of Pollution and Environmental Sciences; 2022.

Available:https://doi.org/10.22059/poll.2022.336044.1300

Ghadi ME, Qaderi F, Babanezhad E. Prediction of mortality resulted from NO 2 concentration in Tehran by Air Q+ software and artificial neural network. International Journal of Environmental Science and Technology. 2019;16(3): 1351-1368. Available:https://doi.org/10.1007/s13762-018-1818.

Oguadinma et al. Lithofacies and textural attributes of the nanka sandstone (eocene): Proxies for evaluating the depositional environment and reservoir quality. J Earth Sci Geotech Eng. 2014;4(4):1-16.

ISSN: 1792-9040 (print)

Oguadinma et al. An integrated approach to hydrocarbon prospect evaluation of the Vin field, Nova Scotia Basin. S.E.G. technical program expanded abstracts. International Exposition and Annual Meeting, Dallas, Texas. 2016:99-110. DOI: 10.1190/segam2016- 13843545.1

Oguadinma VO, Aniwetalu EU, Ezenwaka KC, Ilechukwu JN, Amaechi PO, Ejezie EO. Advanced study of seismic and well logs in the hydrocarbon prospectivity of Siram field, Niger Delta basin. Geol Soc Am Admin Programs. 2017;49. DOI: 10.1130/abs/2017AM-296312

Oguadinma et al. Study of the Pleistocene submarine canyons of the Southeastern Niger Delta basin: Tectonostratigraphic evolution and infilling Conference/Reunion des sciences de la Terre, Lyon, France; 2021.

Smith AB, et al. Air pollution emissions from onshore oil and gas exploration and production. Atmospheric Environment. 2018;193:1-10.

Ibekwe KN, Arukwe C, Ahaneku C, et al. Enhanced hydrocarbon recovery using the application of seismic attributes in fault detection and direct hydrocarbon indicator in Tomboy field, western-Offshore Niger Delta Basin. Authorea; 2023.

Joshua Pwavodi, Ibekwe N. Kelechi, Perekebina Angalabiri, Sharon Chioma Emeremgini, Vivian O Oguadinma. Pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality. Energy Geoscience. 2023;4(3).

Sonno Jaismal. Machine learning, java point; 2023, Available:https://www.javatpoint.com

Access on March 31, 2023

Zhang Q, Zheng Y, Tong D, Shao M, Wang S, et al. Drivers of improved PM2. 5 air quality in China from 2013 to 2017. Proceedings of the National Academy of Sciences. 2019;116(49):24463-24469.

Jin XB, Yang NX, Wang XY, Bai YT, Su TL, Kong JL. Deep hybrid model based on EMD with classification by frequency characteristics for long-term air quality prediction. Mathematics. 2020;8(2): 214.

Zeng B, Tan Y, Xu H, Quan J, Wang L, Zhou X. Forecasting the electricity consumption of commercial sector in hong kong using a novel grey dynamic prediction model. Journal of Grey System. 2018;30(1).

Huang X, Wang K, Fan B, Yang Q, Li G, Xie D, Crow ML. Robust current control of grid-tied inverters for renewable energy integration under non-ideal grid conditions. IEEE Transactions on Sustainable Energy. 2019;11(1):477-488.

Wang K, Li H, Maharjan S, Zhang Y, Guo S. Green energy scheduling for demand side management in the smart grid. IEEE Transactions on Green Communications and Networking. 2018;2 (2):596-611.

Zhang Y, Wang W, Liang L, Wang D, Cui X, Wei W. Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy. Science of the Total Environment. 2020;739:140197.

Oecd I. Energy and Air pollution: world energy outlook special report 2016; 2016.

Bai L, Wang J, Ma X, Lu H. Air pollution forecasts: An overview. International Journal of Environmental Research and Public Health. 2018;15(4):780. Available:https://doi.org/10.3390/ijerph15040780

David L Banks, Stephen E Fienberg. Data mining, statistics. Encyclopedia of Physical Science and Technology (Third Edition). 2003:247-261. Available:https://doi.org/10.1016/B0-12-22741-2

Gicela Lupera Calahorrana, Ahmed Shokry Abdelaleem, Sergio Medina Gonzalez, Antonio Espuna. Ordinary kriging: A machine learning tool applied to mix-integer multiparametric approach. 28th European Symposium on Computer Aided Chemical Engineering. 2018;43:531-536. Available:https://doi.org/10.1016/B978-0-444-64235-6.50094-2

Kyriaki Remoundou, Phoebe Kaundori. Environmental effects on public health: An economic perspective. International Journal of Environmental Research and Public Health. 2009;6(8):2160-2178.

Available:https://doi.org/10.3390/ijerph6082160

Oguadinma, et al. The art of integration: A basic tool in effective hydrocarbon field appraisal, Med-GU Conference, Istanbul. Turkey; 2021.

Oguadinma V, Okoro A, Reynaud J, Evangeline O, Ahaneku C, Emmanuel A, et al. The art of integration: A basic tool in effective hydrocarbon field appraisal. Mediterranean Geosciences Union Annual Meeting; 2021.

Vivian OO, Kelechi IN, Ademola L, et al. Reservoir and sequence stratigraphic analysis using subsurface data. ESS Open Arch; 2023.

Vivian OO, Kelechi IN, Ademola L, et al. Submarine canyon: A brief review. ESS Open Arch; 2023.