生物降解
环境科学
微塑料
废物管理
生物可分解塑胶
塑料污染
海水
远洋带
污染
环境工程
制浆造纸工业
工程类
环境化学
生态学
化学
生物
作者
D. Briassoulis,Anastasia Pikasi,Nikoleta-Georgia Papardaki,Α. Mistriotis
标识
DOI:10.1016/j.scitotenv.2023.168889
摘要
The increasing quantities of plastic litter accumulated in the oceans, including microplastics, represent a serious environmental threat. Despite the recent legislative actions, the plastic littering problem will not disappear in a short time. It may, however be ameliorated by replacing conventional non-degradable plastics with bio-based materials biodegradable in marine environment (targeting the non-recycled or mismanaged plastic waste). Although priority is set to prevention of plastic litter by means of the circular economy principles, biodegradability is a means of controlling unintentional plastic pollution. In this effort, the development of reliable test methods that would be used along with standard specifications for determining the biodegradability of novel polymeric materials or plastics in marine environments, is a necessary complementary component of the whole strategy to control the marine plastic litter and micro-, nano-plastics threat. The present work focuses on identifying gaps and improving available laboratory test methods for measuring the aerobic biodegradation of plastics in the seawater column within the coastal zone (pelagic environment). The research work followed a methodology that is based on recommendations of ASTM D6691:2017 concerning biodegradation of plastics in the seawater and the similar ISO 23977-1:2020. Three different implementation schemes of the test method were applied using different experimental setups and measuring techniques for monitoring the evolved CO2. The effect of critical parameters affecting nutrient adequacy (concentration in inoculum) and oxygen adequacy (bioreactor size, sample size, frequency of aeration) on the biodegradation of four tested materials was explored, and optimal values are proposed. The results allowed for the refinement of the proposed test method to improve reliability and reproducibility.
科研通智能强力驱动
Strongly Powered by AbleSci AI