Abstract
The objective of designing and installation air quality monitoring network (AQMN) is to reduce network density with a view to acquire maximum information on air quality with minimum expenses. In spite of the best research efforts, there has been no general acceptance of any method for deciding the number of stations. Majority of the cities have, therefore, installed monitoring stations with their own guidelines. The present paper presents a useful formulation for classification of the existing air quality monitoring stations (AQMS) using fuzzy similarity measures. The case study has been demonstrated by applying the methodology to the already-installed AQMS in Delhi, India.
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Acknowledgements
The authors are grateful to the CPCB authorities in India for the permission to use of air quality parametric data in the case study and thankful to the reviewer for reviewing the manuscript and providing useful comments.
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Maji, K.J., Dikshit, A.K., Deshpande, A. (2016). Classification of Air Quality Monitoring Stations Using Fuzzy Similarity Measures: A Case Study. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_34
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DOI: https://doi.org/10.1007/978-3-319-32229-2_34
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