Title: GEOSS: an intelligent methodology for identifying site suitability of air sample collection

Authors: Kamonasish Mistry; Biplab Biswas; Siwen Zhang; Tao Wu; Liang Zhou; Abdelfettah Benchrif; Srimanta Gupta

Addresses: Department of Geography, Sammilani Mahavidyalaya, Baghajatin, Kolkata, India ' Department of Geography, The University of Burdwan, Burdwan, India ' School of Economics & Management, Tong Ji University, Shanghai, China ' Shanghai Jiao Tong University School of Medicine, Shanghai, China ' Shanghai Jiao Tong University School of Medicine, Shanghai, China ' National Centre for Nuclear Energy, Sciences and Technology (CNESTEN), Morocco ' Department of Environmental Science, The University of Burdwan, Burdwan, India

Abstract: Air pollution (AP) types and levels change with changes in land use land cover (LULC) types. However, there is no such attempt to develop any common methodology or model for optimum sampling which can be correlated between LULC types and changes with the AP level types and changes. A pre-planned, well-calculated geospatial method is needed to evaluate the ambient AP level, type and its variation over different LULC types. 'GEOSS' (geospatial estimation of optimum sample site) has been innovated to identify the optimum AP sampling sites so that it can represent the wide spatial coverage over varied LULC types. Classified satellite images and statistical tools are used to optimize sampling locations. Validation approach based on nearest neighbour analysis (NNA) has justified that GEOSS employed sampling points are systematically distributed and fulfilled all the basic assumptions of the present sampling procedure.

Keywords: geospatial estimation of optimum sample site; GEOSS; geospatial modelling; optimum location; land use land cover; LULC: Kolkata Metropolitan Area; KMA; air pollution level and change; sampling techniques.

DOI: 10.1504/IJISTA.2020.111062

International Journal of Intelligent Systems Technologies and Applications, 2020 Vol.19 No.5, pp.421 - 443

Received: 26 Mar 2019
Accepted: 24 Aug 2019
Published online: 09 Nov 2020 *

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