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Developing data approaches for accumulation of plastic waste modelling using environment and socio-economic data product
Rinasti A.N.a, Sakti A.D.a, Agustina E.a, Wikantika K.a
a Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.Today the world faces the fact that 10 million tonnes of waste, primarily plastic waste, pas through the river. This global issue has become a serious problem that can be resolved with location-based that utilizes remote sensing technology, following recent developments of technology. This study aims to estimate the weight of potential plastic waste at the estuary before it enters the ocean and becomes marine debris. Several parameters are developed based on three main aspects, which are environment as graded by LULC, social aspect as provided by population density using building data, and economic aspect as provided by nightlight from NOAA’s VIIRS. The estimation used raster-based digital numbers processing, by using estimated data and providing weight. As a result, it shows that most of the metropolitan cities such as Jakarta contribute almost 507 tonne of plastic waste per day. For other cities, its generation is directly proportional to the increase in population. This study could become a consideration to synthesize policies, with the fact that massive population density impacts the increasing of the plastic waste generation.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Digital numbers,Economic aspects,Location based,Metropolitan cities,Plastic wastes,Population densities,Remote sensing technology,Socio-economic data[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/592/1/012013[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]