Microplastic Contamination in Yogyakarta's Rivers: Spatial Analysis and Factor Assessment to Identify Key Pollutants

Eka Sulistyaningsih, Rokhana Dwi Bekti, Kris Suryowati, Erma Susanti, Gupita Cahyaning Mutiara, Maria Oktafiana Dedu

Abstract


River water quality monitoring aims to determine the state of river water quality and to ensure its safety for human health and the sustainability of its use. Some important parameters that are often used to measure river water quality include chemical oxygen demand (COD), biological oxygen demand (BOD), total suspended solids (TSS), pH, Temperature, and microplastic content. This study uses multiple linear regression to determine which factors contribute significantly to river water quality. Samples were collected from the Winongo, Gadjah Wong, Bulus, Oyo, Belik, Tambakbayan, Opak, and Kuning rivers in Daerah Istimewa Yogyakarta (DIY) and distributed in 20 points. The results of the correlation matrix show the relationships between the variables in the data. The DO variable has the most substantial relationship with microplastics, suggesting that water quality, measured by oxygen levels, may be related to microplastic pollution. The relationship between pH and Temperature is also moderate. However, other relationships tend to be weak, suggesting that other factors may be more influential in determining these variables' relationships. The multiple linear regression model shows that an increase in pH, a decrease in Temperature, an increase in DO, and a decrease in TSS will increase the amount of microplastics. Furthermore, through spatial analysis and geographically Weighted Regression (GWR) modelling, DO significantly affects 12 observation points and does not affect eight. The spatial approach shows that the causes of river water pollution are different in each location. Therefore, each site's treatment is also different according to its characteristics.


Keywords


microplastics; pollutants; spatial analysis; Geographically Weighted Regression (GWR); multiple linear regression

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DOI: http://dx.doi.org/10.12962/j25481479.v10i1.22265

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