A Detailed Analysis of Air Pollution Monitoring System and Prediction Using Machine Learning Methods

Article Sidebar

Hardware Interfacing of sensors for monitoring and predicting the air pollution
PDF
Published: Jan 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2s.6028
Keywords:
Air Quality, Index, Pollution, Air Quality Index, Prediction

Main Article Content

Vinoth Thanagaraju
Assistant Professor,Department of Mechatronics Engineering Chennai Institute of Technology,Kundrathur, Chennai, Tamil Nadu 600069
Kothalam Krishnan Nagarajan
Associate Professor, Department of Electrical and ElectronicsEngineering SSN College of Engineering, Kalavakkam,,Tamil Nadu 603110

Abstract

Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in the time and space of pollutants and particulates.Due to the presence of governing factors, varying land uses, and many sources for the elaboration of pollution, the forecast and analysis of air pollution is a difficult procedure. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, various air pollution monitoring and prediction models with respect to hardware interfacing modules and various classification approaches. The Air Quality Index (AQI) parameter is used in this paper to monitor the quality of air pollution in various regions of the world. The drawbacks of the conventional air pollution monitoring and prediction models have been stated in this paper with the methodologies used for air pollution prediction.

Article Details

How to Cite
Thanagaraju, V. ., & Nagarajan, K. K. . (2023). A Detailed Analysis of Air Pollution Monitoring System and Prediction Using Machine Learning Methods. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 51–58. https://doi.org/10.17762/ijritcc.v11i2s.6028
Issue
Vol. 11 No. 2s (2023)
Section
Articles

References

Abu El-Magd, S., Soliman, G., Morsy, M. Environmental hazard assessment and monitoring for air pollution using machine learning and remote sensing. Int. J. Environ. Sci. Technol. (2022).

Tamas W, Notton G, Paoli C, Nivet ML, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: An original approach to detect pollutant peaks. Aerosol AirQual Res 16(2):405–416.

Ruiyun Y, Yang Y, Yang L, Guangjie H, Oguti AM (2016) RAQ–a random forest approach for predicting air quality in urban sensing systems. Sensors 16:86.

Park S, Kim M, Kim M, Namgung HG, Kim KT, Cho KH, Kwon SB (2018) Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN). J Hazard Mater 341:75–82.

Athanasiadis, Ioannis N. "Applying machine learning techniques on air quality data for real-time decision support." First international NAISO symposium on information technologies in environmental engineering (ITEE`2003), Gdansk, Poland. 2003.

Ioannis N. Athanasiadis, Kostas D. Karatzas and Pericles A. Mitkas. "Classification techniques for air quality forecasting." Fifth ECAI Workshop on Binding Environmental Sciences and Artificial Intelligence, 17th European Conference on Artificial Intelligence, Riva del Garda, Italy, August 2006.

M. Caselli & L. Trizio & G. de Gennaro & P. Ielpo. "A Simple Feedforward Neural Network for the PM10 Forecasting: Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model." Water Air Soil Pollut (2009) 201:365–377.

Pandey, Gaurav, Bin Zhang, and Le Jian. " Predicting sub-micron air pollution indicators: a machine learning approach.&quot ; Environmental Science: Processes & amp; Impacts 15.5 (2013): 996-1005.

Barré, J., Petetin, H., Guevara, M., Pérez García-Pando, C., Bowdalo, D., and JorbaCasellas, O. (2021). Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models. Atmos. Chem. Phys. 21, 7373–7394. doi: 10.5194/acp-21-7373-2021.

Betancourt, C., Stomberg, T., Stadtler, S., Roscher, R., and Schultz, M. G. (2021). AQ-Bench: a benchmark dataset for machine learning on global air quality metrics. Earth Syst. Sci. Data Discuss. 13, 3013–3033.

Athira, V., Geetha, P., Vinayakumar, R., and Soman, K. (2018). Deepairnet: applying recurrent networks for air quality prediction. Proc. Comput. Sci. 132, 1394–1403.

Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., and Lin, S. (2017). A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 4, 15. doi: 10.5194/isprs-annals-IV-4-W2-15-2017.

Gidhagen, L., Krecl, P., Targino, A. C., Polezer, G., Godoi, R. H. M., Felix, E., et al. (2021). An integrated assessment of the impacts of PM2.5 and black carbon particles on the air quality of a large Brazilian city. Air Qual. Atmos. Health. 14, 1455–1473.

Grange, S. K., Carslaw, D. C., Lewis, A. C., Boleti, E., and Hueglin, C. (2018). Random forest meteorological normalisation models for Swiss PM 10 trend analysis. Atmos. Chem. Phys. 18, 6223–6239.

Krishan, M., Jha, S., Das, J., Singh, A., Goyal, M. K., and Sekar, C. (2019). Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India. Air Qual. Atmos. Health 12, 899–908.

Kristiani, E., Lee, C.-F., Yang, C.-T., Huang, C.-Y., Tsan, Y.-T., and Chan, W.-C. (2020). Air quality monitoring and analysis with dynamic training using deep learning. J. Supercomput. 77, 5586–5605.

Navares, R., and Aznarte, J. L. (2020). Predicting air quality with deep learning LSTM: towards comprehensive models. Ecol. Inform. 55, 101019.

Qin, D., Yu, J., Zou, G., Yong, R., Zhao, Q., and Zhang, B. (2019). A novel combined prediction scheme based on CNN and LSTM for urban PM 2.5 concentration. IEEE Access 7, 20050–20059.

Mauro Castelli, Fabiana Martins Clemente, AlešPopovi?, Sara Silva, Leonardo Vanneschi, "A Machine Learning Approach to Predict Air Quality in California", Complexity, vol. 2020, Article ID 8049504, 23 pages, 2020.

W.-C. Wang, K.-W. Chau, C.-T. Cheng, and L. Qiu, “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series,” Journal of Hydrology, vol. 374, no. 3-4, pp. 294–306, 2009.

W.-Z. Lu and W.-J. Wang, “Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends,” Chemosphere, vol. 59, no. 5, pp. 693–701, 2005.

C. M. Vong, W. F. Ip, P. K. Wong, and J. Y. Yang, “Short-term prediction of air pollution in Macau using support vector machines,” Journal of Control Science and Engineering, vol. 2012, Article ID 518032, 11 pages, 2012.

Sotomayor-Olmedo, M. A. Aceves-Fernández, E. Gorrostieta-Hurtado, C. Pedraza-Ortega, J. M. Ramos-Arreguín, and J. E. Vargas-Soto, “Forecast urban air pollution in Mexico City by using support vector machines: a kernel performance approach,” International Journal of Intelligence Science, vol. 3, no. 3, pp. 126–135, 2013.

S. B. Kotsiantis, P. E. Pintelas, and D. Kanellopoulos, “Data preprocessing for supervised leaning,” International Journal of Computer Science, vol. 1, no. 1, 2006.

S. G. Gocheva-Ilieva, A. V. Ivanov, D. S. Voynikova, and D. T. Boyadzhiev, “Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach,” Stochastic Environmental Research and Risk Assessment, vol. 28, no. 4, pp. 1045–1060, 2014.

L. Cagliero, T. Cerquitelli, S. Chiusano, P. Garza, G. Ricupero, and X. Xiao, “Modeling correlations among air pollution-related data through generalized association rules,” in Proceedings of the 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016, St. Louis, MO, USA, May 2016.

P. Hajek and V. Olej, “Predicting common air quality index - the case of Czech microregions,” Aerosol and Air Quality Research, vol. 15, no. 2, pp. 544–555, 2015.

Ma, Q., et al.: Understanding the knowledge gaps between air pollution controls and health impacts including pathogen epidemic. Environ. Res. 189, 109949 (2020).

Sharma, R., et al.: Inferring air pollution from air quality index by different geographical areas: case study in India. Air Quality, Atmosphere & Health. 12(11), 1347– 1357 (2019).

Kyrkilis, G., Chaloulakou, A., Kassomenos, P.A.: Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: relation to potential health effects. Environ. Int. 33(5), 670– 676 (2007).

Sharma, V., Kumar, R., Kaur, R.: UAV-assisted content-based sensor search in IoTs. Electron. Lett. 53(11), 724– 726 (2017).

Idrees, Z., Zou, Z., Zheng, L.: Edge computing based IoT architecture for low cost air pollution monitoring systems: a comprehensive system analysis, design considerations & development. Sensors. 18(9), 3021 (2018).