Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction

Abstract

The increase in the global population is directly responsible for the acceleration in the production as well as the consumption of textile products. The use of textiles and garment materials is one of the primary reasons for the microfibers generation and it is anticipated to grow increasingly. Textile microfibers have been found in marine sediments and organisms, posing a real threat to the environment as it is invisible pollution caused by the textile industry. To protect against the damaging effects that microplastics can have, the formulation of mitigation strategies is urgently required. Therefore, the primary focus of this review manuscript is on finding an environmentally friendly long-term solution to the problem of microfiber emissions caused by the domestic washing process, as well as gaining an understanding of the various properties of textiles and how they influence this problem. In addition, it discussed the effect that mechanical and chemical finishes have on microfiber emissions and identified research gaps in order to direct future research objectives in the area of chemical finishing processes. In addition to that, it included a variety of preventative and minimizing strategies for reduction. Last but not least, an emphasis was placed on the potential and foreseeable applications of machine vision (i.e., quantification, data storage, and data sharing) to reduce the amount of microfibers emitted by residential washing machines.

Keywords: chemical finishing; data sharing; machine-vision; mechanical finishing; microfibers; microplastics; supply chain; sustainability; textile.

Figure 1

Textile fiber production worldwide from 1975 to 2020, with forecasts for 2025–2030 (in million metric tons); inside shows the different fiber productions [5].

Figure 2

Principal sources of fibrous microplastics and their routes to the human body (i.e., Inside the cartoons are copied from pixabay.com under CC0 license).

Figure 3

A depiction in the form of a schematic illustrating the process by which microfibers are generated in textiles of cellulosic origin and microfibers are generated in polyester fabrics (Modified and reused from [21] under C.C 4.0 license).

Figure 4

Various textile characteristics are desirable for reducing the number of microfibers and microfibers that are released throughout the washing and drying processes.

Figure 5

Factors to consider in order to minimize the microfibers/microplastics throughout the laundering and drying processes.

Figure 6

Builders and their impacts on stain removal under hard water (a), action on microfiber reduction (b).

Figure 7

Various mechanical finishing and their impact on microfibers emission during domestic washing.

Figure 8

The mechanism for the microfiber generations from denim and their possible finishing techniques to reduce the microfiber generations (Reused from [90], with permission from ACS Creative Commons Permission).

Figure 9

Positive actions toward avoidance and reduction of microfibers and microfiber generations.

Figure 10

Schematic representation of extra filtering system for washing machine.

Figure 11

Scheme for the implementation of machine vision in the textile manufacturing chain.

Figure 12

Proposed application of a NN to quantify an object directly from the scattering pattern. (a); Diagram of the NN used for the microspheres and real-time experiments (b). (Reused from [148], with Creative Commons 4.0 Permission).

Figure 13

Classification of microplastics in µIR images using a machine learning approach: (a) optical image, (b) µIR image, (c) pixel-wise classification output of the machine learning model for the different polymer types (including PE, PP, PMMA, PAN, PS, and non-polymer particles), and (d) classification result overlaid on the optical image of the sample. (Reused from [158] with permission from The Royal Society of Chemistry Creative Commons Attribution 3.0).

Figure 14

Classification results based on the pixel test set of cotton, viscose, and lyocell samples of known compositions (a) (Reused from [159], with permission from The Royal Society of Chemistry Creative Commons Attribution 3.0); Image predictions, their polyester contents based on image predictions (b) (Reused from [160], with permission from Elsevier Creative Commons Attribution license).