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

Abstract

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.