We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Application of GIS in forecasting ocean plastic waste in the South Central Coast, Vietnam (Case study in Da Nang City)
Summary
Researchers applied GIS-based modeling to forecast the transport and distribution of plastic waste entering the ocean along Vietnam's South Central Coast, demonstrating how spatial data can support policymakers in targeting plastic waste reduction interventions.
Plastic waste in the marine environment has become a major concern due to its persistence in the oceans, and its detrimental consequences for marine life and human health. To propose strategic policies to reduce plastic waste, policymakers must be updated with quantitative information on ocean plastic waste sources. Garbage arising from activities in each individual's life, if not collected and managed, will be leaked and carried by wind, rain, to slope areas, and canals, then drifted along rivers and discharged into the sea. This study aims to present a method using a geographic information system (GIS) to analyze secondary data of plastic waste from the discharge sources to the ocean based on socio-demographic data, secondary statistics, the weights of plastic waste density, and topographical characteristics such as rivers and hydrology, including spatial and temporal variation. The simulated results of the research provide a comparison and correction to the survey data reported by the World Bank (WB), the United Nations Environment Program (UNEP), and local government environmental offices regarding the distribution of plastic waste in Da Nang’s rivers. The simulation estimates that an average density of 304,5 kg/month/1km2 (in 2021) is currently flowing into the ocean from rivers in the study area. The results of the model are further evaluated by interpreting high-resolution satellite images at 10 hotspots. The findings of this study improve the existing model with more detail and localization for estimating ocean plastic waste volumes and provide data for future strategic policies to monitor and reduce plastic debris.
Sign in to start a discussion.
More Papers Like This
Integrating Field Data and Numerical Modeling to Assess Microplastic Distribution in the Aquatic Environment of Ha Long Bay, Vietnam
Researchers combined field sampling with computer modeling to map microplastic distribution in Ha Long Bay, Vietnam, finding the highest concentrations in coastal zones near cities and industries, while islands naturally blocked plastic from spreading offshore. The study identified cruise tourism, coal mining, and coastal development as key pollution sources, and recommends plastic capture at river mouths to protect the bay.
Assessment and Sustainable Management Strategies for Plastic Waste in Can Tho City, Vietnam: A Circular Economy Approach
Researchers conducted a comprehensive analysis of plastic waste accumulation in both land and water environments in Can Tho City, Vietnam, applying a circular economy framework. The study proposes sustainable management strategies for reducing plastic pollution in a country with high per capita plastic consumption and significant waste discharge.
Time-series of shoreline marine debris highlights impact of urbanization on coastal pollution pathways, Nha Trang, Vietnam.
A time-series analysis of shoreline marine debris in Nha Trang, Vietnam, revealed that urbanization drives increased coastal plastic pollution by altering land-based discharge pathways. The study documented how shoreline debris levels fluctuated seasonally and with rainfall events, providing insights for pollution management in developing coastal cities.
Current Plastic Waste Status and Its Leakage at Tam Giang–Cau Hai Lagoon System in Central Vietnam
Researchers quantified the annual leakage of plastic waste into the Tam Giang-Cau Hai lagoon system in central Vietnam by surveying surrounding urban and rural areas and applying a waste flow diagram model, estimating substantial annual plastic input to this coastal lagoon. The findings highlight the link between inadequate solid waste management and plastic contamination of aquatic ecosystems.
Mapping the Plastic Legacy: Geospatial Predictions of a Microplastic Inventory in a Complex Estuarine System Using Machine Learning
Researchers applied machine learning geospatial modelling to predict microplastic distribution across a complex estuarine system, using sediment samples as a training dataset to generate spatial inventory maps of microplastic accumulation. The model leveraged the estuary's role as a land-sea interface and plastic accumulation bottleneck to produce high-resolution predictions of microplastic hotspots for monitoring and management purposes.