We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Shaping a dynamic open platform for the holistic assessment of micro- and nano-plastic emissions from plastic products
Summary
A proposed open platform integrates big data analytics, neural networks, and material flow analysis to build a comprehensive global emission inventory of micro- and nanoplastic releases from plastic products across their full life cycle to soil, water, and air. This framework would enable evidence-based policy by quantifying how much MNP pollution originates from specific product types and disposal pathways.
The widespread use of plastics and their improper disposal have released a large number of micro- and nano-plastics (MNPs) into various environmental media. Although the release of MNPs from individual plastic products has been widely reported, there is a lack of a holistic assessment framework to determine the overall release of plastic products to soil, water, and air during their life cycle. Therefore, based on big data, neural network algorithms, and material flows, a new open platform for the comprehensive assessment of the release of MNPs from plastic products will be developed. The proposed emission inventory platform consists of three main modules: a global polymer product production dataset, an assessment of the emission processes, influencing factors, and emission factors of MNPs, and an emission inventory of MNP releases to the environment. The global data on polymer production, use, and waste disposal, and collate data on the degradation behavior of different plastic types under various environmental conditions will be collected. Next, big data analysis will be applied to train the patterns of MNP production and emissions, and algorithms such as neural networks will be used to simulate the complex processes and mechanisms of MNP emissions. Finally, a comprehensive emission inventory model will be established. The proposed dynamic MNPs emission assessment platform integrates material flow analysis and experimentally validated release kinetics. Utilizing machine learning techniques and laboratory and field datasets, the platform can derive dynamic, environment-specific emission factors to support specific emission estimates, source prioritization, and targeted emission reduction strategies.