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Assessing Urban Microplastic Pollution: A Study Utilizing Integrated Spectroscopic Analysis and Predictive Modeling Approaches

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dc.contributor.author Minaa, Sohail
dc.date.accessioned 2024-12-20T06:11:10Z
dc.date.available 2024-12-20T06:11:10Z
dc.date.issued 2024
dc.identifier.other Reg. 400493
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/48432
dc.description Supervisor: Dr. Nasir M. Ahmed en_US
dc.description.abstract In this thesis, we investigate, characterize, and transport mechanisms of MPs in the soil, water, and atmosphere by examining the prevalence of MPs in the soil, water, and atmosphere in an urban setting within Islamabad, Pakistan. A combination of advanced analytical techniques such as Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Thermogravimetric Analysis (TGA) was employed to isolate, identify, and quantify microplastics from samples collected at two specific locations: At the National University of Sciences and Technology (NUST) campus, Islamabad, on a lake bank and on lakebed. A Predictive Airborne Microplastic Dispersion Model was also developed and applied in order to assess the role of the atmosphere as an agent of microplastic transport and deposition across urban environments. Density separation methods were used to process soil samples, and microplastic particles were found in concentrations far higher in the lakebed than other areas, which was attributed to sewage inflows boosting contamination levels in the area. Microplastics that were detected were polyethylene (PE), polypropylene (PP) and polyester fibers that are polymers commonly used to produce synthetic textiles, packaging, and urban waste. These materials' presence in the soil indicates strong correlation between human activities and environmental plastic pollution. Concentration of microplastic in both surface water and groundwater was analyzed in water samples. The study found high loads of microplastics from the sewage affected lake water (W2) with PE and PS being dominant polymers and indicating that the main sources of aquatic pollution are urban runoff, improper waste disposal and effluent discharge. These findings, as microplastics were detected in groundwater samples (W1), became seriously critical regarding the leakage of surface contaminants into subsurface water systems and represented the broader environmental impact of microplastic pollution. Analysis of airborne microplastics in air samples was informed using the Predictive Airborne Microplastic Dispersion Model and confirmed that microplastics are actually being transported in the atmosphere with fibers and fragments dominant in air samples. The dissemination of microplastics from terrestrial to aquatic systems revealed in these xx findings illustrates the interconnected nature of environmental compartments, including the wind driven role in mediating transfer of microplastics between compartments. Field observations were characterized by the model predictions, confirming atmospheric deposition as a major contribution to microplastic distribution to urban environments. Through integration of environmental sampling with advanced spectroscopic analysis and predictive modeling this research extends the growing literature on microplastic contamination in the environment. New results highlight the importance of standardized standards for detecting microplastics across environmental matrices to quantify microplastic concentrations and understand their wider ecological — and public health — implications. This finding stresses the need for policy interventions to reduce the microplastic pollution at its sources and improve waste management practices in growing urban areas. en_US
dc.language.iso en en_US
dc.publisher School of Chemical and Material Engineering SCME, NUST en_US
dc.subject Microplastics, Environmental contamination, Urban pollution, Polymer analysis, Predictive modelling, Characterization. en_US
dc.title Assessing Urban Microplastic Pollution: A Study Utilizing Integrated Spectroscopic Analysis and Predictive Modeling Approaches en_US
dc.type Thesis en_US


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