Evaluation on production trend, compositions, and impact of plastic waste in Chengdu, southwestern China
Creators: name: Zhao, Xue
name: Yong, Yi
name: Du, Cheng-Song
name: Guo, Wei-Guang
name: Luo, Da-Peng
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Space Science
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: Science Policy

description: Based on statistical data from 2005 to 2019, we used the back propagation (BP) neural network model to predict the production amount of plastic waste in Chengdu. In addition to the amount of waste produced we wanted to achieve an understanding of its composition and environmental impacts. Compositions of plastic waste were analyzed by sampling. Particulate matter in the air and greenhouse gas emissions (GHGs) from plastic waste incineration, bisphenol A (BPA) from plastic waste landfills, were also evaluated. Results indicated that (a) economic development, urban construction level, and residents’ consumption were pusitively correlated to different degrees to plastic waste production; (b) the production of plastic waste in Chengdu in 2025 and 2030 will reach 865.3 and 931 kilotons (Kt), respectively; (c) high density polyethylene (HDPE) and polypropylene (PP) are the two main components of plastic waste in Chengdu and accounted for 40.17% and 24.96%, respectively; (d) different degrees of environmental impacts occurred during plastic waste incineration and landfill (taking 2019 as an example, the incineration of plastic waste in Chengdu produced between 2874.82 and 4711.73 tons of inhalable particulate matter (PM) and emitted between 725.4 and 867.4 Kt of CO<sub>2</sub>, and between 65.02 and 910.27 kg of bisphenol A (BPA) leached from sanitary landfills); (e) positive policies and measures from the beginning to the end-of-life of plastics should be carried out in the future, which would improve the level of plastic waste management in Chengdu and mitigate the side-impacts from plastic waste treatment and disposal. <i>Implications</i>: The implications of this article are Generation trends of plastic waste were revealed by a BP neural network model, which provided essential data for authorities to make decisions on waste management.Influencing factors affecting plastic waste generation were analyzed, which will strongly support policy considerations regarding plastic waste control.This investigation first explored and reported the compositions of plastic waste mixed with municipal solid waste (MSW), which yielded valuable information concerning plastic waste and details concerning the impacts of plastic waste disposal processes. Generation trends of plastic waste were revealed by a BP neural network model, which provided essential data for authorities to make decisions on waste management. Influencing factors affecting plastic waste generation were analyzed, which will strongly support policy considerations regarding plastic waste control. This investigation first explored and reported the compositions of plastic waste mixed with municipal solid waste (MSW), which yielded valuable information concerning plastic waste and details concerning the impacts of plastic waste disposal processes. Those results of this investigation, being published here for the first time, will guide plastic waste management in Chengdu and could also provide useful information to other cities regarding that issue.