Quantification of different microplastic fibres discharged from textiles in machine wash and tumble drying

Microplastic fibres released in synthetic cloth washing have been shown to be a source of microplastics into the environment. The annual emission of polyester fibres from household washing machines has earlier been estimated to be 150,000 kg in a country with a population of 5.5 × 106 (Finland). The objectives of this study were (1) to quantify the emissions of synthetic textile fibres discharged from five sequential machine washes (fibre number and length) and tumble dryings (fibre mass) and (2) to determine the collection efficiency of two commercial fibre traps. The synthetic fabrics were five types of polyester textiles, one polyamide and one polyacryl. The number of fibres released from the test fabrics in the first wash varied in the range from 1.0 × 105 to 6.3 × 106 kg−1. The fibre lengths showed that the fleece fabrics released, on average, longer fibres than the technical sports t-shirts. The mass of fibres ranged from 10 to 1700 mg/kg w/w in the first drying. Fibre emissions showed a decreasing trend both in sequential washes and dryings. The ratio of the fibre emissions in machine wash to tumble drying varied between the fabrics: the ratio was larger than one to polyester and polyamide technical t-shirts whereas it was much lower to the other tested textiles. GuppyFriend washing bag and Cora Ball trapped 39% and 10% of the polyester fibres discharged in washings, respectively.

Interpretation of Urban Power Consumers Behaviors to Predict Power Loss in Summer

High Temperature In The Summer Of India. Interpretation Of Electricity Consumption Is Crucial In Summer For Urban Consumers. We Are Focus Here For Only Indian Summer Urban Customers Energy Consumption To Analysis And Predict Behavior Of Electricity Theft. Data Mining Techniques Are Employing To Analyses Indian Summer Urban Customers. Online Sequential Machine And Support Vector Machine Is Used For This Behaviors Classification And Prediction.Mainly we focus Support vector machine to classified consumers and online sequential machine is used to detect and predict consumers behaviors.