Microplastics reach the aquatic environment through wastewater. Larger debris is removed in sewage treatment plants, but filters are not explicitly designed to retain sewage sludge’s microplastic or terrestrial soils. Therefore, the effective quantification of filtration system to mitigate microplastics is needed. To mitigate microplastics, various devices have been designed, and the removal efficiency of devices was compared. However, this study focused on identifying different fabrics that shed fewer microplastics. Therefore, in this study, fabric-specific analyses of microplastics of three different fabrics during washing and drying processes were studied. Also, the change in the generation of microplastics for each washing process of standard washing was investigated. The amount of microplastics released according to the washing process was analyzed, and the collected microplastics’ weight, length, and diameter were measured and recorded. According to the different types of yarn, the amount of microplastic fibers produced during washing and drying varied. As the washing processes proceed, the amount of microplastics gradually decreased. The minimum length (>40 µm) of micro-plastics generated were in plain-woven fabric. These results will be helpful to mitigate microplastics in the production of textiles and in selecting built-in filters, and focusing on the strict control of other parameters will be useful for the development of textile-based filters, such as washing bags.

With the increasing production of synthetic materials, more microplastic fibers are being generated while washing clothes. Consequently, these particles are increasingly detected in the aquatic environment. Synthetic fibers produced via washing have a relatively high contribution to microplastic pollution. Hence, recent research on reducing the release of microplastic fibers is attracting considerable attention. In this study, fabric-specific analysis was performed by strictly controlling various factors, and each washing and drying process was improved by focusing on the mechanical factors affecting microplastic release. Furthermore, the mass of the collected microplastic fibers and their length distribution were measured. Fabric construction, including chemical composition and yarn type, impacted the microplastics released during washing and drying. Differences in the mechanical factors during washing helped to identify the physical factors affecting microplastic release. These results on the release of microplastics may provide a basis for developing a filter system that can minimize the unintended environmental consequences.

This study facilitates an effective design and development of various high stretch compression products by analyzing fabric size, stretch properties, and clothing pressure for various knit structures. Four types of fabrics were knitted with polyester SCY (Single Covered Yarn). Then fabric size including weight and thickness, stretch properties (stretch, elastic recovery), and clothing pressure were then measured, to analyze their interrelation. A comparison of fabric size indicated that yarn floating caused reduction in both course and wale-wise specimens; in addition, yarn overlapping caused a release in course-wise and shrinkage in wale–wise due to tuck. The high density caused by shrinkage in the course-wise due to yarn floating rather than overlapping influenced the weight and thickness of knitted fabrics. Yarn floating reduced course-wise elasticity and increased wale-wise elasticity in the fabric stretch test; however, yarn overlapping reduced elasticity in both directions. The elastic recovery analysis indicated that the recovery value gap among four specimens decreased over time. In comparison clothing pressure, ‘plain-float’ fabric showed higher clothing pressure than ‘plain’, while the pressure value of ‘plain-tuck’ was similar to ‘plain’. Dimensional change in course-wise had a greater effect on clothing pressure than in wale-wise in the correlation among fabric size, stretch properties and clothing pressure. Weight and thickness change exerted a strong influence on clothing pressure which vertically presses down the body. The clothing pressure value of knitted specimen having a lower stretch ratio was higher in course-wise.

Research was conducted in this study to design data-based size recommendation and size coding systems specifically for online shopping malls, expecting to lighten the burden of holding excessive inventories often caused by the high return rate in these online malls. The recommendation system has been implemented focusing mainly on size extraction and recommendation functions along with a UI (user interface). For the former function, data are necessary to extract customers’ sizes and, for instance, the system to be used in China adopts their Chinese standard body size GB/T (Chinese national standard) considering that there are a variety of body types in their substantial population. The system shows the most similar size dataset among the body size GB/T dataset to the customer once he/she inputs his/her height and weight. Each GB/T data was entered after categorizing it according to the proportion between height and weight. For the latter function, size recommendation, size coding was performed first for all the clothes by the shop owner by entering individual size data. The clothes providing the most suitable fit for the customer are recommended by the selection of that which has the smallest deviation between coded clothes size and the customer body data after performing a series of comparative calculations. To validate the effectiveness of the extraction, a method that checks whether the difference between extracted size and the body size that has been measured remains within the error range of 4cm was used. The result showed there to be an approximate 88% matching rate for women and a slightly lower accuracy of 80% for men. Moreover, the error rate was relatively smaller for the upper half clothing such as shirts, jackets, and blouses or one-piece dresses. Such a result may have been generated since the GB/T data were actually the average data entered 10 years prior without categorizing nationalities, ages, and body types in detail. This research emphasized the necessity of a database containing a more segmented human body size data, which can be effective for extracting and recommending sizes more accurately as the latest ones continue to accumulate.