Shinta Rahayu
Department of Civil Engineering, Faculty of Engineering, Universitas Negeri Padang, Indonesia

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Spatial modeling of water pollution parameters using inverse distance weighting in the Batanghari River, Indonesia Adella Masya Putri; Yaumal Arbi; Suci Handayani; Shinta Rahayu
Jurnal Pendidikan Teknologi Kejuruan Vol 9 No 2 (2026): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v9i2.50223

Abstract

The Batanghari River, one of the largest rivers in Sumatra, Indonesia, serves as a vital transportation corridor and supports domestic, agricultural, and socio-economic activities. Water quality in the middle reaches of the river is associated with increasing anthropogenic pressures, including settlements, agriculture, sand and gravel mining, and domestic waste disposal. This study models the spatial distribution of water pollution parameters in the Batanghari River, Indonesia, using the Inverse Distance Weighting (IDW) method. The river, which serves as the study site, is 2.5 km long, and sampling was conducted at 16 points in Terusan Village, Maro Sebo Ilir District, Batang Hari Regency. The parameters analysed were pH, Total Suspended Solids (TSS), and Chemical Oxygen Demand (COD). pH measurements were conducted in situ, while TSS and COD were analysed in the laboratory. The measured values were evaluated using the Indonesian Class II river water quality specifications in accordance with Government Regulation No. 22/2021. Spatial distribution modelling was performed using the Inverse Distance Weighting (IDW) interpolation method in ArcGIS 10.8, with model performance evaluated via cross-validation using Root Mean Squared Error (RMSE). The results showed that pH values ranged from 6.76 to 6.91 and were within the class II standard limits. Total Suspended Solids (TSS) ranged between 230.12 and 313.93 mg/L, and Chemical Oxygen Demand (COD) ranged between 35.44 and 93.92 mg/L, exceeding the permitted limits. Areas with higher pollution concentrations were associated with settlements and mining activities, as indicated by the spatial distribution map. These findings suggest that IDW can be applied to the spatial representation of water quality and its improvement as an indicator for pollution control and river management.
System dynamics modelling of integrated urban clean water management: A case study in Padang City, Indonesia Yaumal Arbi; Rumia Ramadhianty; Shinta Rahayu; Widia Putri
Journal of Engineering Researcher and Lecturer Vol. 4 No. 2 (2025): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/jerel.v4i2.184

Abstract

Access to clean water is essential for human life and a key target of Sustainable Development Goal (SDG) 6: Clean Water and Sanitation. However, cities in developing countries, including Padang City, Indonesia, face significant challenges in meeting the growing demand due to population growth and limited water infrastructure. This study used system dynamics modelling approach with Powersim Studio 10 to develop an integrated clean water management system for Padang City. The model simulates the dynamics of population growth, water consumption, production, and distribution efficiency over a 20-year period (2022–2042). Several policy scenarios—optimistic, moderate, and pessimistic—were tested to evaluate their impact on water availability. The baseline scenario predicts a continuous decline in clean water supply due to increasing population, high leakage rates (11.99%), and water wastage (2%), which surpass the water production growth rate (5.01%). As a result, a water deficit is expected. However, under the optimistic scenario, with increased production (10%), reduced leakage (8%), and reduced wastage (3%), Padang City could achieve a clean water surplus by 2042. The moderate and pessimistic scenarios still result in a deficit. This research highlights the value of the system dynamics modelling in forecasting urban water demand and assessing policy impacts. The findings emphasize the need for integrated planning, combining technical solutions and behavioural change, to ensure sustainable water management and support the achievement of SDG 6.