Citation: Qianyu Li, Juan Li, Binghua Jing, Didi Li, Zhimin Ao. Environmental risk analysis and sustainable modification of polylactic acid (PLA): Integrated bibliometric and machine learning insights[J]. Chinese Chemical Letters, ;2026, 37(11): 112563. doi: 10.1016/j.cclet.2026.112563 shu

Environmental risk analysis and sustainable modification of polylactic acid (PLA): Integrated bibliometric and machine learning insights

Qianyu Li ^a ,
Juan Li ^a ,
Binghua Jing ^b ,
Didi Li ^c ,
Zhimin Ao ^{a, *,}

a.
Guangdong Provincial Key Laboratory of Wastewater Information Analysis and Early Warning, Advanced Interdisciplinary Institute of Environment and Ecology, School of Technology for Sustainability, Beijing Normal University, Zhuhai 519087, China
b.
School of Resources and Environment, State Key Laboratory of New Textile Materials and Advanced Processing, Wuhan Textile University, Wuhan 430200, China
c.
School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China

Zhimin.ao@bnu.edu.cn

Received Date: 21 November 2025
Revised Date: 26 February 2026
Accepted Date: 28 February 2026
Available Online: 1 March 2026

Figures(8)

Polylactic acid (PLA), a bio-based biodegradable plastic, is often misperceived as environmentally harmless, obscuring critical environmental risk gaps. Previous PLA reviews focus primarily on performance optimization or isolated degradation pathways, while discussions on environmental risks and targeted modification strategies are scattered, creating a gap between risk identification and mitigation design. To address this gap, this review systematically integrates PLA’s lifecycle environmental risks and sustainable modification strategies via a bibliometric framework. Drawing on 1810 Web of Science Core Collection publications (2004–2025), we employed CiteSpace and VOSviewer for knowledge mapping, integrated with degradation and modification mechanism analysis. Three core findings emerged. First, incomplete PLA degradation (driven by photoaging, hydrolysis and thermal oxidation) produces persistent microplastics (MPs) and nanoplastics (NPs), which accumulate in diverse ecosystems, act as pollutant vectors and induce ecological toxicity. Second, sustainable modification has evolved into three targeted pathways: chemical, physical and biological modification. Third, machine learning (ML) is a key integrated tool, linking PLA’s lifecycle environmental risk assessment with sustainable modification optimization to enhance research systematicity and efficiency. This review bridges PLA degradation mechanism understanding with ecological risks, links sustainable modification strategies to targeted risk mitigation, and provides a theoretical foundation for low-risk PLA material development.
- Polylactic acid (PLA),
- Environmental risk,
- Modification,
- Machine learning,
- Microplastics
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