Carbon footprint assessment of face masks in the context of the COVID-19 pandemic: Based on different protective performance and applicable scenarios.

Widespread concerns have been raised about the huge environmental burden caused by massive consumption of face masks in the context of the COVID-19 pandemic. However, most of the existing studies only focus on the environmental impact associated with the product itself regardless of the actual usage scenarios and protective performance of products, resulting in unrealistic conclusions and poor applicability. In this context, this study integrated the product performance into the existing carbon footprint assessment methodology, with focus on the current global concerns regarding climate change. Computational case studies were conducted for different mask products applicable to the scenarios of low-, medium- and high-risk levels. The results showed that reusable cotton masks and disposable medical masks suitable for low-risk settings have a total carbon footprint of 285.484 kgCO2-eq/FU and 128.926 kgCO2-eq/FU respectively, with a break-even point of environmental performance between them of 16.886, which implies that cotton masks will reverse the trend and become more environmentally friendly after 17 washes, emphasizing the importance of improving the washability of cotton masks. Additionally, the total carbon footprints of disposable surgical masks and KN95 respirators were 154.328 kg CO2-eq/FU and 641.249 kg CO2-eq/FU respectively, while disposable medical masks and disposable surgical masks were identified as alternatives with better environmental performance in terms of medium- and high-risk environments respectively. The whole-life-cycle oriented carbon footprint evaluation further indicated that the four masks have greater potential for carbon emission reduction in the raw material processing and production processes. The results obtained in this study can provide scientific guidance for manufacturers and consumers on the production and use of protective masks. Moreover, the proposed model can be applied to other personal protective equipment with similar properties, such as protective clothing, in the future.

Analysis of the Causes of Solitary Pulmonary Nodule Misdiagnosed as Lung Cancer by Using Artificial Intelligence: A Retrospective Study at a Single Center.

Artificial intelligence (AI) adopting deep learning technology has been widely used in the med-ical imaging domain in recent years. It realized the automatic judgment of benign and malig-nant solitary pulmonary nodules (SPNs) and even replaced the work of doctors to some extent. However, misdiagnoses can occur in certain cases. Only by determining the causes can AI play a larger role. A total of 21 Coronavirus disease 2019 (COVID-19) patients were diagnosed with SPN by CT imaging. Their Clinical data, including general condition, imaging features, AI re-ports, and outcomes were included in this retrospective study. Although they were confirmed COVID-19 by testing reverse transcription-polymerase chain reaction (RT-PCR) with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), their CT imaging data were misjudged by AI to be high-risk nodules for lung cancer. Imaging characteristics included burr sign (76.2%), lobulated sign (61.9%), pleural indentation (42.9%), smooth edges (23.8%), and cavity (14.3%). The accuracy of AI was different from that of radiologists in judging the nature of be-nign SPNs (p < 0.001, κ = 0.036 < 0.4, means the two diagnosis methods poor fit). COVID-19 patients with SPN might have been misdiagnosed using the AI system, suggesting that the AI system needs to be further optimized, especially in the event of a new disease outbreak.

Should patients stop their radiotherapy or chemotherapy during the COVID-19 pandemic.

In the previous stage, there were too many patients with Corona virus disease 2019 (COVID-19) in Wuhan. Ordinary people, patients, even doctors, had a great sense of desperate. On the one hand, almost all doctors participated in the treatment of patients of COVID-19. On the other hand, the government restricted residents to go out, and the sick people were also afraid of being infected with COVID-19 when seeking medical treatment. Whether cancer patients seek medical treatment or not has become a contradiction for a long time. Our Viewpoint paper is to provide a positive signal to doctors and patients that patients with in the middle or advanced stage of cancer can receive radiotherapy and/or chemotherapy normally under protective measures.

A novel method for textile odor removal using engineered water nanostructures.

The malodor attached to textiles not only causes indoor environmental pollution but also endangers people's health even at low concentrations. Existing technologies cannot effectively eliminate the odor. Herein, an effective and environmentally friendly technology was proposed to address this challenging issue. This technology utilizes electrospraying process to produce Engineered Water Nanostructures (EWNS) in a controllable manner. Upon application of a high voltage to the Taylor cone, EWNS can be generated from the condensed vapor water through a Peltier element. Smoking, cooking and perspiration, considered the typical indoor malodorous gases emitted from human activities, were studied in this paper. A headspace SPME method in conjunction with GC-MS was employed for the extraction, detection and quantification of any odor residues. Results indicated that EWNS played a significant role in the deodorization process with removal efficiencies for the three odors were 95.3 ± 0.1%, 100.0 ± 0.0% and 43.7 ± 2.3%, respectively. The Reactive Oxygen Species (ROS) contained in the EWNS, mainly hydroxyl (OH˙) and superoxide radicals are the possible mechanisms for the odor removal. These ROS are strong oxidative and highly reactive and have the ability to convert odorous compounds to non-odorous compounds through various chemical reaction mechanisms. This study showed clearly the potential of the proposed method in the field of odor removal and can be applied in the battle against indoor air pollution.

Fiber-Content Measurement of Wool-Cashmere Blends Using Near-Infrared Spectroscopy.

Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool-cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool-cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere-wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool-cashmere blend and have potential to be used for tackling the cashmere adulteration problem.

Blue and grey water footprint of textile industry in China.

Water footprint (WF) is a newly developed idea that indicates impacts of freshwater appropriation and wastewater discharge. The textile industry is one of the oldest, longest and most complicated industrial chains in the world's manufacturing industries. However, the textile industry is also water intensive. In this paper, we applied a bottom-up approach to estimate the direct blue water footprint (WFdir,blue) and direct grey water footprint (WFdir,grey) of China's textile industry at sector level based on WF methodology. The results showed that WFdir,blue of China's textile industry had an increasing trend from 2001 to 2010. The annual WFdir,blue surpassed 0.92 Gm(3)/yr (giga cubic meter a year) since 2004 and rose to peak value of 1.09 Gm(3)/yr in 2007. The original and residuary WFdir,grey (both were calculated based on the concentration of chemical oxygen demand (CODCr)) of China's textile industry had a similar variation trend with that of WFdir,blue. Among the three sub-sectors of China's textile industry, the manufacture of textiles sector's annual WFdir,blue and WFdir,grey were much larger than those of the manufacture of textile wearing apparel, footware and caps sector and the manufacture of chemical fibers sector. The intensities of WFdir,blue and WF(res)dir,grey of China's textile industry were year by year decreasing through the efforts of issuing restriction policies on freshwater use and wastewater generation and discharge, and popularization of water saving and wastewater treatment technologies.