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Spatiotemporal variability analysis of air pollution data from IoT based participatory sensing


Air pollution has become a major environmental risk of the new civilized world due to its severe influence on public health and the environment. Eventually, understanding the spatiotemporal variability of air pollution at high granularity is necessary to make relevant public policies. To explore spatiotemporal variability of air pollution at high granularity we have utilized the power of IoT based participatory sensing and data science. In this paper, we propose a predictive model for spatiotemporal air pollution estimation technique called Multiview data Fusion model (MVDF) that can consider spatial as well as temporal dependencies of air pollutants. The proposed technique is evaluated based on real-world air pollution dataset collected by participants over a period of 1 year in an urban area of city Kolkata. The results show that MVDF dominates over some baselines like Simple Kriging (SK), Modified Shepard’s Method (MSM) and Nearest Neighbor (NN). Besides, in this paper, we attempt to perform visual analysis that consists of state-of-the-art visualization techniques to explore spatiotemporal variability at different granularities on the estimated pollution levels of MVDF.

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Manuscript has associated data in a private repository. Data will be made available on reasonable request.


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The research work of Asif Iqbal Middya is supported by UGC-NET Junior Research Fellowship (UGC-Ref. No.: 3684/(NET-JULY 2018)) provided by the University Grants Commission, Government of India. This research work is supported by the project entitled- “Participatory and Realtime Pollution Monitoring System For Smart City”, funded by Higher Education, Science and Technology and Biotechnology, Department of Science and Technology, Government of West Bengal, India.

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Correspondence to Sarbani Roy.

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Middya, A.I., Roy, S. & Das, R. Spatiotemporal variability analysis of air pollution data from IoT based participatory sensing. J Ambient Intell Human Comput (2021).

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  • Spatial interpolation
  • Data fusion
  • Participatory sensing