Korean scientists claim “paradigm shift” development in machine learning waste management
08 Jan 2024 --- Recent research revealed that various factors of microplastics (MP), such as type, size, shape and dosage, significantly alter soil properties. Scientists made the discoveries using machine learning (ML) to assess the impact of MPs on soil properties in a stride toward understanding environmental sustainability.
The international research team, led by professor Yong Sik Ok from Korea University, uses ML algorithms to assess and predict the influence of MPs on soil properties.
The study, which is said to have high relevance in the context of environmental, social and governance (ESG) goals, sheds light on the intricate ways microplastics affect the ecosystem.
Microplastics, tiny fragments resulting from the degradation of plastic products, have become ubiquitous in nature, with alarming concentrations found in oceans and soil worldwide. Their presence in soil, leading to absorption by plants, poses concern as they could enter the human food chain, causing potential health complications.
Professor Ok explains that ML represents a dynamic and transformative domain within AI, employing algorithms and models to acquire knowledge and formulate predictions from extensive datasets. Leveraging ML to comprehend the involvement of MPs in soil systems is time- and resource-efficient and provides a foundation for future research on this subject.
It is still unknown whether microplastic exposure results in adverse health effects at low levels.Decoding microplastics in soil
The research demonstrates that the size of microplastics emerged as a key factor affecting soil health, providing more understanding of the implications of microplastic contamination, extending beyond marine ecosystems to terrestrial ones.
The study highlights the role of innovative research in guiding corporate sustainability efforts. Professor Ok affirms that this study contributes data to support informed decision-making on plastic waste management, aligning with the global focus on sustainability and ESG principles.
The scientists say their research represents a paradigm shift in comprehending and mitigating the plastic waste dilemma. Traditional methods of studying MPs’ impact on soil have been complex and resource-intensive. However, the ML-based approach offers a more efficient and accurate way to address these challenges.
Professor Ok addresses that their study on plastic pollution underscores the importance of preserving soil ecosystems as these endeavors have the potential to exert influence on industry standards.
The research follows the prior endeavors by scientists who have employed ML to study MP. Last year, researchers from the Korea Institute of Materials Science successfully developed an AI-based kit designed to detect microplastics, known for their potential to induce human and genetic toxicity through environmental pollution and the food chain.
Public health challenge
MPs, once considered primarily an oceanic pollutant, are now understood to be a pervasive terrestrial contaminant.
Incorporating machine learning insights to examine the influence of MPs within the framework of ESG principles is in alignment with social responsibility. This approach promotes sustainable practices that yield positive effects on communities.
Their presence in soils worldwide presents an environmental concern and a public health issue. Scientists have already found microplastics in human blood, indicating that these particles can travel within the body and potentially lodge in organs.
The potential health risks are profound. Laboratory tests have shown that microplastics can cause damage to human cells, including allergic reactions and cell death, with the entire human population likely exposed to microplastics through various means, including ingestion, inhalation and dermal contact.
By Sichong Wang
