Abstract
Precise and efficient air quality monitoring is a pivotal step in combating the harmful effects of pollution. Our research addresses the challenges in obtaining accurate air quality data and identifying pollution sources. It introduces an algorithm to optimize the deployment of air quality monitoring devices, enhancing distribution efficiency. The algorithm considers spatial distribution to minimize capital and operational costs. Utilizing an extensive 300 days dataset covering Durgapur (80 km\(^2\)), it achieves an above 90% accuracy rate in predicting air quality. By strategically selecting monitoring locations, it maximizes data coverage while minimizing costs. This advancement supports cost-effective pollution control and resource allocation decisions in affected regions.
Similar content being viewed by others
Availability of data and materials
The dataset will be provided upon reasonable request.
References
Alsaber A, Alsahli R, Al-Sultan A et al (2023) Evaluation of various machine learning prediction methods for particulate matter \(pm_{10}\) in Kuwait. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01521-2
Baume O, Gebhardt A, Gebhardt C et al (2011) Network optimization algorithms and scenarios in the context of automatic mapping. Comput Geosci 37:289–294. https://doi.org/10.1016/j.cageo.2010.04.014
Chang H, Yu Z, Yu Z et al (2021) Location selection for air quality monitoring with consideration of limited budget and estimation error. IEEE Trans Mobile Comput. https://doi.org/10.1109/TMC.2021.3065656
Hsieh HP, Lin SD, Zheng Y (2015) Inferring air quality for station location recommendation based on urban big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, KDD ’15, pp 437–446. https://doi.org/10.1145/2783258.2783344
Li J, Chen J, He S, et al (2011) On energy-efficient trap coverage in wireless sensor networks. In: 2011 IEEE 32nd Real-Time Systems Symposium, pp 139–148. https://doi.org/10.1109/RTSS.2011.20
Liu (2020) Exploring the relationship between air pollution and meteorological conditions in china under environmental governance. Sci Reports 10:14518. https://doi.org/10.1038/s41598-020-71338-7
Loukili H, Abdelkader A, Jioui I et al (2022) Combining multiple regression and principal component analysis to evaluate the effects of ambient air pollution on children’s respiratory diseases. Int J Inf Technol 14:1305–1310. https://doi.org/10.1007/s41870-022-00906-z
Ngo V, Duong Thi Thuy V, Nguyen-Tat BT et al (2023) A big data smart agricultural system: recommending optimum fertilisers for crops. Int J Inf Technol. https://doi.org/10.1007/s41870-022-01150-1
Rahi P, Sood S, Bajaj R et al (2021) Air quality monitoring for smart ehealth system using firefly optimization and support vector machine. Int J Inf Technol. https://doi.org/10.1007/s41870-021-00778-9
Robinson C, Franklin R, Roberts J (2021) Optimising for equity: sensor coverage, networks and the responsive city. https://doi.org/10.21203/rs.3.rs-902765/v1
Shaban KB, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16(8):2598–2606
Sun C, Li VOK, Lam JCK et al (2019) Optimal citizen-centric sensor placement for air quality monitoring: a case study of city of Cambridge, the United Kingdom. IEEE Access 7:47390–47400. https://doi.org/10.1109/ACCESS.2019.2909111
Thakur N, Karmakar S, Shrivastava R (2023) Hybrid deep learning algorithms for forecasting air quality index using dimension reduction technique in search of precise results. Int J Inf Technol. https://doi.org/10.1007/s41870-023-01350-3
Wu L, Bocquet M (2011) Optimal redistribution of the background ozone monitoring stations over France. Atmos Environ 45:772–783. https://doi.org/10.1016/j.atmosenv.2010.08.038
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors affirm that there are no identifiable conflicting financial interests or personal affiliations that could have potentially influenced the findings presented in this paper.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sarkar, P., Saha, M. Optimizing air quality monitoring device deployment: a strategy to enhance distribution efficiency. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01893-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41870-024-01893-z