During the COVID-19 pandemic large numbers of single-use, surgical style face masks were lost or discarded in public spaces, primarily in on public streets and car parking settings. Many of these masks were blown onto the road surfaces where they were subjected to degradation through the tire impact of passing vehicle traffic. As series of field observations as well as experimental simulations show that the three-ply polypropylene mask fabric is subjected to shear forces when compressed between the tire and the road surface. The mechanical action breaks the bonds between the fibers (both spunbonded and meltblown) leading to a continual shedding of microfibers. Wind disperses these into the environment along road sides, while surface water action moves them into stormwater drains and from there into the waterways. As the decay is rapid, municipal agencies only have a short window of time to remove stray face masks from the urban environment if micro-fiber pollution is to be reduced.

Systematic seafloor surveys are a highly desirable method of marine litter monitoring, but the high costs involved in seafloor sampling are not a trivial handicap. In the present work, we explore the opportunity provided by the artisanal trawling fisheries to obtain systematic data on marine litter in the Gulf of Cadiz between 2019 and 2021. We find that plastic was the most frequent material, with a prevalence of single-use and fishing-related items. Litter densities decreased with increasing distance to shore with a seasonal migration of the main litter hotspots. During pre-lockdown and post-lockdown stages derived from COVID-19, marine litter density decreased by 65 %, likely related to the decline in tourism and outdoor recreational activities. A continuous collaboration of 33 % of the local fleet would imply a removal of hundreds of thousands of items each year. The artisanal trawl fishing sector can play a unique role of monitoring marine litter on the seabed

peer reviewed | In the global COVID-19 epidemic, humans are faced with a new challenge. The concept of quarantine as a preventive measure has changed human activities in all aspects of life. This challenge has led to changes in the environment as well. The air quality index is one of the immediate concrete parameters. In this study, the actual potential of quarantine effects on the air quality index and related variables in Tehran, the capital of Iran, is assessed, where, first, the data on the pollutant reference concentration for all measuring stations in Tehran, from February 19 to April 19, from 2017 to 2020, are monitored and evaluated. This study investigated the hourly concentrations of six particulate matters (PM), including PM2.5, PM10, and air contaminants such as nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO). Changes in pollution rate during the study period can be due to reduced urban traffic, small industrial activities, and dust mites of urban and industrial origins. Although pollution has declined in most regions during the COVID-19 quarantine period, the PM2.5 rate has not decreased significantly, which might be of natural origins such as dust. Next, the air quality index for the stations is calculated, and then, the interpolation is made by evaluating the root mean square (RMS) of different models. The local and global Moran index indicates that the changes and the air quality index in the study area are clustered and have a high spatial autocorrelation. The results indicate that although the bad air quality is reduced due to quarantine, major changes are needed in urban management to provide favorable conditions. Contaminants can play a role in transmitting COVID-19 as a carrier of the virus. It is suggested that due to the rise in COVID-19 and temperature in Iran, in future studies, the effect of increased temperature on COVID-19 can be assessed.

The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by ∼5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by ∼21% on average). Regarding the LCA endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus.

This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot–coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = −0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March–December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006–2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM₂.₅) and nitrogen dioxide (NO₂) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM₁₀ were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM₂.₅ and NO₂ was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.

Billions of disposable face masks are consumed daily due to the COVID-19 pandemic. The role of these masks as a source of nanoplastics (NPs) and microplastics (MPs) in the environment has not been studied in previous studies. We quantified and characterized face mask released particles and evaluated their potential for accumulation in humans and marine organisms. More than one billion of NPs and MPs were released from each surgical or N95 face mask. These irregularly-shaped particles sized from c. 5 nm to c. 600 μm. But most of them were nano scale sized <1 μm. The middle layers of the masks had released more particles than the outer and inner layers. That MPs were detected in the nasal mucus of mask wearers suggests they can be inhaled while wearing a mask. Mask released particles also adsorbed onto diatom surfaces and were ingested by marine organisms of different trophic levels. This data is useful for assessing the health and environmental risks of face masks.

The UK implemented a lockdown in Spring (2020) to curtail the person-to-person transmission of the SARS-CoV-2 virus. Measures restricted movements to one outing per day for exercise and shopping, otherwise most people were restricted to their dwelling except for key workers (e.g. medical, supermarkets, and transport). In this study, we quantified changes to air quality across the United Kingdom from 30/03/2020 to 03/05/2020 (weeks 14–18), the period of most stringent travel restrictions. Daily pollutant measurements of NO₂, O₃ and PM₂.₅ from the national network of monitoring sites during this period were compared with measurements over the same period during 2017–19. Comparisons were also made with predicted concentrations for the 2020 period from business-as-usual (BAU) modelling, where the contributions of normal anthropogenic activities were estimated under the observed meteorological conditions. During the lockdown study period there was a 69% reduction in traffic overall (74% reduction in light and 35% in heavy vehicles). Measurements from 129 monitoring stations, identified mean reductions in NO₂ of 38.3% (−8.8 μg/m³) and PM₂.₅ of 16.5% (−2.2 μg/m³). Improvements in NO₂ and PM₂.₅ were largest at urban traffic sites and more modest at background locations where a large proportion of the population live. In contrast, O₃ concentrations on average increased by 7.6% (+4.8 μg/m³) with the largest increases at roadside sites due to reductions in local emissions of NO. A lack of VOC monitoring limited our capacity to interpret changes in O₃ at urban background locations. BAU models predicted comparable NO₂ reductions and O₃ gains, although PM₂.₅ episodes would have been more prominent without lockdown. Results demonstrate the relatively modest contribution of traffic to air quality, suggesting that sustained improvements in air quality require actions across various sectors, including working with international and European initiatives on long-range transport air pollutants, especially PM₂.₅ and O₃.

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) is the first programme to tackle carbon dioxide (CO₂) emissions from a single industry at the global level, to realize the carbon-neutral growth of international flights from 2020 onwards. However, the COVID-19 pandemic has caused a drastic decline in the global aviation industry. The International Civil Aviation Organization (ICAO) has adjusted the CORSIA by removing 2020 emissions from the baseline, which now will only be based on 2019 emissions. We estimate that the total carbon dioxide (CO₂) emissions from global international flights decreased by 70 % from February to July 2020 compared to those in 2019. Our analysis suggests that the annual CO₂ emissions from international flights during the pilot stage of CORSIA (2021–2023) will be far below the revised baseline even if the global aviation industry could embrace an optimistic recovery. The major airline companies will have very limited motivations due to the CORSIA scheme to implement mitigation actions proactively. Therefore, more progressive actions are needed to align the industry recovery of global aviation and climate change mitigation during the post-COVID-19 period.