Appraisal of microplastic pollution and its related risks for urban indoor environment in Bangladesh using machine learning and diverse risk evolution indices.

The widespread presence of Microplastics (MPs) is increasing in the indoor environment due to increasing annual plastic usage, which is becoming a global threat to human health. Therefore, this is the first research in Bangladesh to identify, and characterize, MP pollution and its allied threats to human health in the indoor urban environment, where 80 household dust samples were collected from the whole study area. The presence of MPs in household dust of the urban indoor environment was 25.8±6.43 particles/g with a significant variety, whereas the fiber shape (73%), 0.5-1.00 mm ranged MPs size (58%), blue color (21%), and polystyrene polymer (34%) was the most ubiquitous MPs category. The pollution load index (1.61-2.96) indicated significant pollution due to the high abundance of MPs. Besides, other risks evaluating indices including contamination factor (1.00-3.51), and Nemerow pollution index (1.60-3.51) represent moderate to high MP-induced pollution. The polymer hazard index (119.54±70.34) indicated significant risks for the selected polymers to the indoor environment living inhabitants. Machine learning approaches, especially random forest and support random vector machine were effective in predicting the number of MPs, where EC, salinity, pH, OC, and texture classes acted as controlling factors. Children and adults might be ingesting 4.12±1.01 and 2.27±0.57 particles/day through the ingestion exposure route, which has significant health effects. Polymer-associated lifetime cancer risk assessment results show that there are moderate risks for both adults and children, but children tend to be more susceptible to MP risks. The overall study found that Dhaka was the most severely MPs induced risky division among the others. This study reveals that high quantities of MPs in indoor environments could pose a serious health hazard' to different exposure groups.

Application of machine learning and multivariate approaches for assessing microplastic pollution and its associated risks in the urban outdoor environment of Bangladesh.

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.