Assessing disposable masks consumption and littering in the post COVID-19 pandemic in China

Leaching potentials of microplastic fibers and UV stabilizers from coastal-littered face masks

Synthetic fibrous textiles are ubiquitous plastic commodities in everyday existence. Nevertheless, there exists a dearth of understanding regarding their environmental occurrence and the releasing capacities of associated additives. In this study, ten additives were determined in twenty-eight kinds of daily used plastic products including face masks, synthetic clothing, and food containers. Our results revealed that a typical kind of fibrous plastic, face masks, contained a greater variety of additives with UV stabilizers in particular, when compared to other plastic commodities. The above phenomena triggered our field investigation for the occurrence and release potentials of face mask fibers and the co-existing UV stabilizers into the environment. We further collected 114 disposed masks from coastal areas and analyzed their UV stabilizer concentrations. Results showed that the abundance of littered face masks ranged from 40-1846 items/km2 along the Yangtze Estuary, China; and UV stabilizers were of 0.3 ± 0.7 ng/g and 0.7 ± 1.7 ng/g in main bodies and ear ropes, respectively. The UV stabilizer concentrations in the field collected masks were only ∼7 % of their new counterparts, implying their potential leaching after disposal. By simulating the weathering scenario, we predict that a substantial amount of microplastics, with 1.1 × 1010 polypropylene fibers and 3.7 × 1010 polyester fibers, are probably be released daily into the coastal environment after face masks disposal; whereas the accompanied leaching amount of UV stabilizers was relatively modest under the current scenario.

Microplastics and volatile organic compounds released from face masks after disinfection: Layers and materials differences

Effective disinfection methods are critical for ensuring the reusability of masks, yet these methods may inadvertently introduce health concerns associated with microplastics (MPs) and volatile organic compounds (VOCs). This study investigated the impact of ultraviolet germicidal irradiation (UVGI) and sodium hypochlorite (NaClO) bleaching on mask filter layers composed of four distinct materials. Our results revealed that UVGI induced more pronounced damage compared to bleaching, leading to the significant release of both MPs and VOCs. After UVGI treatment at conventional disinfection doses, meltblown (MB) fabrics released MPs reaching 864 ± 182 μg/g (92 ± 19 particles/g). For all filter layers, the quantity of released MPs followed the order: MB > HDPE>PU ≈ NW. These MPs were identified as degraded debris from the mask filter layers. The specific VOCs generated varied depending on the material composition. Non-woven (NW) and MB fabrics, both comprised of polypropylene, predominantly produced various branched aliphatic hydrocarbons and their derivative oxides. The cotton-like fabric, composed of high-density polyethylene, primarily emitted different linear aliphatic hydrocarbons and oxygenates. In contrast, the polyurethane filter layer of reusable masks released aromatic compounds, nitrogenous compounds, and their oxidation products. The formation of VOCs was primarily attributed to bond breakage and oxidative damage to the filter structure resulting from the disinfection process. In summary, as UVGI induced higher yields of MPs and VOCs compared to bleaching, the latter would be a safer option for mask disinfection.

Revealing the characteristics of biofilms on different polypropylene plastic products: Comparison between disposable masks and takeaway boxes

The increasingly severe plastic pollution issue was intensified by the enormous plastic emissions into ecosystems during the Covid-19 pandemic. Plastic wastes entering the environment were swiftly exposed to microorganisms and colonized by biofilms, and the plastic-biofilm combined effects further influenced the ecosystem. However, the non-woven structure of disposable masks discarded carelessly during the COVID-19 pandemic was different from those of plastics with flat surface. To reveal the potential effects of plastic structure on colonized biofilms, white disposable surgical masks (DM) and transparent takeaway boxes (TB), both made of polyethylene, were selected for the incubation of organic conditioning films and biofilms. The results indicated that the non-woven structure of disposable mask was destroyed by the influence of water infiltration and biofilm colonization. The influence of surface structure on conditioning films led to a relatively higher proportion of tryptophan-like substances on DM than those on TB samples. Therefore, biofilms with significantly higher microbial biomass and carbon metabolic capacity were formed on DM than those on TB samples owing to the combined effects of their differences in surface structure and conditioning films. Moreover, abundant functional microorganisms associated with stress tolerance, carbon metabolism and biofilm formation were observed in biofilms on disposable mask. Combining with the results of partial least squares regression analysis, the selective colonization of functional microorganisms on disposable masks with uneven surface longitudinal fluctuation was revealed. Although the predicted functions of biofilms on disposable masks and takeaway boxes showed more similarity to each other than to those of free-living aquatic microorganisms owing to the existence of the plastisphere, biofilms on disposable masks may potentially trigger environmental risks different from those of takeaway boxes by unique carbon metabolism and abundant biomass.

Identification of Additives in Disposable Face Masks and Evaluation of Their Toxicity Using Marine Medaka (Oryzias melastigma)

The COVID-19 pandemic has resulted in huge amounts of face masks worldwide. However, there is a lack of awareness on the additives and their potential risk to aquatic ecosystems of face masks. To address this issue, the additives and their toxicity in 13 face masks (e.g., polypropylene, polyethylene, and polylactic acid) were determined using nontarget analysis and bioassays. A total of 826 organic additives including intermediates (14.8%), surfactants (9.3%), plasticizers (8.2%), and antioxidants (6.1%) were tentatively identified, with 213 compounds being assigned confidence levels of 1 and 2. Interestingly, polylactic acid masks contained more additives than most polypropylene or polyethylene masks. Among these additives, the concentration of tris(2-ethylhexyl) phosphate in masks was 9.4-978.2 ng/g with a 100% detection frequency. Furthermore, 13 metals such as zinc (up to 202.0 μg/g), copper (32.5 μg/g), and chromium (up to 5.7 μg/g) were detected in the face masks. The methanol extracts of the masks showed the developmental toxicity, swimming behavior, and/or endocrine disruption in embryos/larvae of Oryzias melastigma. The findings demonstrate that face masks contain various toxic additives to marine medaka, which deserves close attention to pollution by face masks.

Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach

BACKGROUND

COVID-19 pandemic emerged worldwide at the end of 2019, causing a severe global public health threat, and smoking is closely related to COVID-19. Previous studies have reported changes in smoking behavior and influencing factors during the COVID-19 period, but none of them explored the main influencing factor and high-risk populations for smoking behavior during this period.

METHODS

We conducted a nationwide survey and obtained 21,916 valid data. Logistic regression was used to examine the relationships between each potential influencing factor (sociodemographic characteristics, perceived social support, depression, anxiety, and self-efficacy) and smoking outcomes. Then, variables related to smoking behavior were included based on the results of the multiple logistic regression, and the classification and regression tree (CART) method was used to determine the high-risk population for increased smoking behavior during COVID-19 and the most profound influencing factors on smoking increase. Finally, we used accuracy to evaluated the performance of the tree.

RESULTS

The strongest predictor of smoking behavior during the COVID-19 period is acceptance degree of passive smoking. The subgroup with a high acceptation degree of passive smoking, have no smokers smoked around, and a length of smoking of ≥ 30 years is identified as the highest smoking risk (34%). The accuracy of classification and regression tree is 87%.

CONCLUSION

The main influencing factor is acceptance degree of passive smoking. More knowledge about the harm of secondhand smoke should be promoted. For high-risk population who smoke, the "mask protection" effect during the COVID-19 pandemic should be fully utilized to encourage smoking cessation.