Evolution of toxicological research on e-waste: A bibliometric and text mining analysis (2006–2024)
Electronic waste (e-waste) has become one of the fastest-growing sources of environmental contamination, generating complex risks for human health. Although research on e-waste toxicity has expanded over the past two decades, the overall structure and evolution of this literature remain insufficiently synthesized. This study examines how toxicological research on e-waste has evolved between 2006 and 2024 using an integrated bibliometric and text-mining approach. A total of 525 peer-reviewed articles indexed in the Web of Science Core Collection were analyzed through a combined bibliometric and text-mining framework. Latent Dirichlet allocation was applied to identify dominant research themes, while the Louvain and Leiden algorithms were used to uncover conceptual groupings within the literature. The results reveal a clear progression from early studies focusing on heavy metals and persistent organic pollutants to increased attention to brominated flame retardants and polychlorinated biphenyls in the 2010s. Since 2020, research has shifted toward cadmium-related environmental and epidemiological studies, alongside growing interest in oxidative stress, indoor dust, and plasticizers. These trends reflect a broader emphasis on indirect exposure pathways and multisystem health effects. Overall, this study provides a broad overview of evolving toxicological research trends in e-waste studies and highlights recent thematic developments related to environmental health and exposure pathways.
- Kaya, M. Recovery of metals and nonmetals from electronic waste by physical and chemical recycling processes. Waste Manag. 2016;57:64-90. doi: 10.1016/j.wasman.2016.08.004
- Lin S, Ali MU, Zheng C, et al. Toxic chemicals from uncontrolled e-waste recycling: Exposure, body burden, health impact. J Hazard Mater. 2022;426:127792. doi: 10.1016/j.jhazmat.2021.127792
- Cucchiella F, D’Adamo I, Lenny Koh SC, et al. Recycling of WEEEs: An economic assessment of present and future e-waste streams. Renew Sustain Energy Rev. 2015;51:263-272. doi: 10.1016/j.rser.2015.06.010
- UNITAR Sustainable Cycles Programme, International Telecommunication Union, Fondation Carmignac. The Global E-waste Monitor 2024. Global E-waste Monitor. 2024. Accessed 15 May 2026.. https://ewastemonitor.info/ the-global-e-waste-monitor-2024/
- Hicks C, Dietmar R, Eugster M. The recycling and disposal of electrical and electronic waste in China—legislative and market responses. Environ Impact Assess Rev. 2005;25(5):459-471. doi: 10.1016/j.eiar.2005.04.007
- Lin C, Paengsri P, Yang Y. Impact of China’s National Sword Policy on waste import: A difference-in-differences approach. Econ Anal Policy. 2023;78:887-903. doi: 10.1016/j.eap.2023.04.033
- Cui J, Zhang L. Metallurgical recovery of metals from electronic waste: A review. J Hazard Mater. 2008;158(2- 3):228-256. doi: 10.1016/j.jhazmat.2008.02.001
- Naik S, Satya Eswari J. Electrical waste management: Recent advances challenges and future outlook. Total Environ Res Themes. 2022;1-2:100002. doi: 10.1016/j.totert.2022.100002
- Mmereki D, Li B, Baldwin A, et al. The Generation, Composition, Collection, Treatment and Disposal System, and Impact of E-Waste. In: E-Waste in Transition - From Pollution to Resource. Rigeka Croatia: InTech; 2016. doi: 10.5772/61332
- Robinson BH. E-waste: An assessment of global production and environmental impacts. Sci Total Environ. 2009;408(2):183-191. doi: 10.1016/j.scitotenv.2009.09.044
- Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372. doi: 10.1136/bmj.n71
- Bukar UA, Sayeed MS, Razak SFA, et al. A method for analyzing text using VOSviewer. MethodsX. 2023;11:102339. doi: 10.1016/j.mex.2023.102339
- van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2009;84(2):523-538. doi: 10.1007/s11192-009-0146-3
- Xu Z, Ge Z, Wang X, et al. Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change. 2021;170:120896. doi: 10.1016/j.techfore.2021.120896
- Hollenbeck JR, Jamieson BB. Human Capital, Social Capital, and Social Network Analysis: Implications for Strategic Human Resource Management. AMP. 2015;29(3):370-385. doi: 10.5465/amp.2014.0140
- Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res. 2003;3:993-1022.
- Blondel VD, Guillaume JL, Lambiotte R, et al. Fast unfolding of communities in large networks. J Stat Mech. 2008;2008(10):P10008. doi: 10.1088/1742-5468/2008/10/p10008
- Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9(1). doi: 10.1038/s41598-019-41695-z
- Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. doi: 10.1080/00031305.2017.1380080
- United Nations Environment Programme. New report: The Impact of the COVID-19 Pandemic on E-waste in the First Three Quarters of 2020. Accessed 15 May 2026. https:// www.unep.org/ietc/news/story/new-report-impact-covid-19-pandemic-e-waste-first-three-quarters-2020
- Faradillah P, Sultan AAM, Samat KF, et al. Strengthening e-waste governance: A decision framework for sustainable transboundary movements under the Swiss-Ghana Amendments. Chin J Popul Resour Environ. 2025;23(3):310- 323. doi: 10.1016/j.cjpre.2025.07.003
