TY - JOUR
T1 - Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh
AU - Chakraborty, Tapos Kumar
AU - Rahman, Md Sozibur
AU - Nice, Md Simoon
AU - Netema, Baytune Nahar
AU - Islam, Khandakar Rashedul
AU - Debnath, Partha Chandra
AU - Chowdhury, Pragga
AU - Halder, Monishanker
AU - Zaman, Samina
AU - Ghosh, Gopal Chandra
AU - Rayhan, Md Abu
AU - Asif, Sk Mahmudul Hasan
AU - Biswas, Aditi
AU - Sarker, Sarajit
AU - Hasan, MD Jahid
AU - Ahmmed, Mahfuz
AU - Munna, Asadullah
N1 - Copyright © 2024 Elsevier B.V. All rights reserved.
PY - 2024/7/1
Y1 - 2024/7/1
N2 - Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in the outdoor urban environment using machine learning and multivariate approaches. The occurrences of MPs in the urban road dust were 52.76 ± 20.24 particles/g with high diversity, where fiber shape (77%), 0.1–0.5 mm size MPs (75%), blue color (26%), and low-density polyethylene (24%) polymer was the dominating MPs category. Pollution load index value (1.28–4.42), showed severe pollution by MPs. Additionally, the contamination factor (1.00–5.02), and Nemerow pollution index (1.38–5.02), indicate moderate to severe MP pollution. The identified polymers based on calculated potential ecological risk (2248.52 ± 1792.79) and polymer hazard index (814.04 ± 346.15) showed very high and high risks, respectively. The occurrences of MPs could effectively be predicted by random forest, and support random vector machine, where EC, salinity, pH, OC, and texture classes were the influencing parameters. Considering the human health aspect, children and adults could be acutely exposed to 19259.68 and 5777.90 MP particles/ year via oral ingestion. Monte-Carlo-based polymers associated cancer risk assessment results indicate moderate risk and high risk for adults and children, respectively, where children were more vulnerable than adults for MP pollution risks. Overall assessment mentioned that Dhaka was the most polluted division among the other divisions.
AB - Microplastics (MPs) are an emerging global concern due to severe toxicological risks for ecosystems and public health. Therefore, this is the first study in Bangladesh to assess MP pollution and its associated risks for ecosystems and human health in the outdoor urban environment using machine learning and multivariate approaches. The occurrences of MPs in the urban road dust were 52.76 ± 20.24 particles/g with high diversity, where fiber shape (77%), 0.1–0.5 mm size MPs (75%), blue color (26%), and low-density polyethylene (24%) polymer was the dominating MPs category. Pollution load index value (1.28–4.42), showed severe pollution by MPs. Additionally, the contamination factor (1.00–5.02), and Nemerow pollution index (1.38–5.02), indicate moderate to severe MP pollution. The identified polymers based on calculated potential ecological risk (2248.52 ± 1792.79) and polymer hazard index (814.04 ± 346.15) showed very high and high risks, respectively. The occurrences of MPs could effectively be predicted by random forest, and support random vector machine, where EC, salinity, pH, OC, and texture classes were the influencing parameters. Considering the human health aspect, children and adults could be acutely exposed to 19259.68 and 5777.90 MP particles/ year via oral ingestion. Monte-Carlo-based polymers associated cancer risk assessment results indicate moderate risk and high risk for adults and children, respectively, where children were more vulnerable than adults for MP pollution risks. Overall assessment mentioned that Dhaka was the most polluted division among the other divisions.
KW - Ecological risks
KW - Machine learning
KW - Microplastics
KW - Pollution indices
KW - Public health
KW - Road dust
KW - Urban environment
UR - http://www.scopus.com/inward/record.url?scp=85191655907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191655907&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/1048afc2-0105-3e7b-8400-6bc8cd86658f/
U2 - 10.1016/j.jhazmat.2024.134359
DO - 10.1016/j.jhazmat.2024.134359
M3 - Article
C2 - 38691990
AN - SCOPUS:85191655907
SN - 0304-3894
VL - 472
JO - Journal of Hazardous Materials
JF - Journal of Hazardous Materials
M1 - 134359
ER -