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An integrated approach towards marble waste management: GIS, SFA, and recycling options
Muhammad Fawad1, Farid Ullah2, Wisal Shah3
1Department of Environmental Sciences, The University of Haripur, Khyber Pakhtunkhwa, Pakistan. mfawad@uoh.edu.pk.
Abstract:
Marble units generate an enormous amount of non-biodegradable waste during the processing operations and are considered one of the environmentally unfriendly industrial sectors. This sector has become a global nuisance due to its multi-dimensional damaging nature. Therefore, a multidimensional approach is needed to geographically describe the pollution sources, their waste load, collection mechanism, and their proper disposal or reuse. This article highlights an integrated approach to sorting out the multidimensional issues associated with the marble sector. More than 150 marble processing units (MPUs) are scattered in the study area pouring waste into the environment in the form of slurry. The produced waste roots environmental issues both for fauna and flora of the terrestrial and aquatic segments of the environment. A geospatial-based attempt has been made through geographic information system (GIS) for the identification and description of the pollution sources, MPUs, in the study area. The quantitative assessment has been made through substance flow analysis (SFA) by taking raw marble as the input source and marble product as output. Furthermore, material characterization has been carried out to confirm the chemical composition of the slurry waste for its potential use. Results confirmed that a major part (> 90%) of marble powder is calcium carbonate (CaCO3) which has so many potential uses as raw material. The integrated approach of GIS, SFA, and chemical characterization set forth a model that satisfies multi-dimensional queries regarding pollution sources, pollution load, and sustainable solutions to the problem. The output integrated model provides a digital environmental baseline for the monitoring of MPUs, the amount of waste generated by these MPUs, and its potential reuse options. The proposed model can be utilized worldwide as a decision support tool due to its optimum results.
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