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.

Pharmaceutical contaminants in surface water have raised significant concerns because of their potential ecological risks. In particular, coronavirus disease 2019 (COVID-19)-related pharmaceuticals can be released to surface water and reduce environmental water quality. Therefore, reliable and robust sampling tools are required for monitoring pharmaceuticals. In this study, passive sampling devices of diffusive gradients in thin films (DGTs) were developed for sampling 35 pharmaceuticals in surface waters. The results demonstrated that hydrophilic–lipophilic balance (HLB) was more suitable for DGT-based devices compared with XAD18 and XDA1 resins. For most pharmaceuticals, the performance of the HLB-DGT devices were independent of pH (5.0–9.0), ionic strength (0.001–0.5 M), and flow velocity (0–400 rpm). The HLB-DGT devices exhibited linear pharmaceutical accumulation for 7 days, and time-weighted average concentrations provided by the HLB-DGT were comparable to those measured by conventional grab sampling. Compared to previous studies, we extended DGT monitoring to include three antiviral drugs used for COVID-19 treatment, which may inspire further exploration on identifying the effects of COVID-19 on ecological and human health.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM₁₀, PM₂.₅, and O₃). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

With the implementation of COVID-19 restrictions and consequent improvement in air quality due to the nationwide lockdown, ozone (O₃) pollution was generally amplified in China. However, the O₃ levels throughout the Guangxi region of South China showed a clear downward trend during the lockdown. To better understand this unusual phenomenon, we investigated the characteristics of conventional pollutants, the influence of meteorological and anthropogenic factors quantified by a multiple linear regression (MLR) model, and the impact of local sources and long-range transport based on a continuous emission monitoring system (CEMS) and the HYSPLIT model. Results show that in Guangxi, the conventional pollutants generally declined during the COVID-19 lockdown period (January 24 to February 9, 2020) compared with their concentrations during 2016–2019, while O₃ gradually increased during the resumption (10 February to April 2020) and full operation periods (May and June 2020). Focusing on Beihai, a typical Guangxi region city, the correlations between the daily O₃ concentrations and six meteorological parameters (wind speed, visibility, temperature, humidity, precipitation, and atmospheric pressure) and their corresponding regression coefficients indicate that meteorological conditions were generally conducive to O₃ pollution mitigation during the lockdown. A 7.84 μg/m³ drop in O₃ concentration was driven by meteorology, with other decreases (4.11 μg/m³) explained by reduced anthropogenic emissions of O₃ precursors. Taken together, the lower NO₂/SO₂ ratios (1.25–2.33) and consistencies between real-time monitored primary emissions and ambient concentrations suggest that, with the closure of small-scale industries, residual industrial emissions have become dominant contributors to local primary pollutants. Backward trajectory cluster analyses show that the slump of O₃ concentrations in Southern Guangxi could be partly attributed to clean air mass transfer (24–58%) from the South China Sea. Overall, the synergistic effects of the COVID-19 lockdown and meteorological factors intensified O₃ reduction in the Guangxi region of South China.

The spread of SARS-CoV-2, the beta coronavirus responsible for the current pneumonia pandemic outbreak, has been speculated to be linked to short-term and long-term atmospheric pollutants exposure. The present work has been aimed at analyzing the atmospheric pollutants concentrations (PM₁₀, PM₂.₅, NO₂) and spatio-temporal distribution of cases and deaths (specifically incidence, mortality and lethality rates) across the whole Italian national territory, down to the level of each individual territorial area, with the goal of checking any potential short-term correlation between these two phenomena. The data analysis has been limited to the first quarter of 2020 to reduce the lockdown-dependent biased effects on the atmospheric pollutant levels as much as possible. The analysis looked at non-linear, monotonic correlations using the Spearman non-parametric correlation index. The statistical significance of the Spearman correlations has also been evaluated. The results of the statistical analysis suggest the hypothesis of a moderate-to-strong correlation between the number of days exceeding the annual regulatory limits of PM₁₀, PM₂.₅ and NO₂ atmospheric pollutants and COVID-19 incidence, mortality and lethality rates for all the 107 territorial areas in Italy. A weak-to-moderate correlation seems to exist when considering the 36 territorial areas in four of the most affected regions (Lombardy, Piedmont, Emilia-Romagna and Veneto). Overall, PM₁₀ and PM₂.₅ showed a higher non-linear correlation than NO₂ with incidence, mortality and lethality rates. As to particulate matters, PM₁₀ profile has been compared with the incidence rate variation that occurred in three of the most affected territorial areas in Northern Italy (i.e., Milan, Brescia, and Bergamo). All areas showed a similar PM₁₀ time trend but a different incidence rate variation, that was less severe in Milan compared with Brescia and Bergamo.

The consumption of disposable face masks increases greatly because of the outbreak of the COVID-19 pandemic. Inappropriate disposal of wasted face masks has already caused the pollution of the environment. As made from plastic nonwoven fabrics, disposable face masks could be a potential source of microplastics for the environment. In this study, we evaluated the ability of new and used disposable face masks of different types to release microplastics into the water. The microplastic release capacity of the used masks increased significantly from 183.00 ± 78.42 particles/piece for the new masks to 1246.62 ± 403.50 particles/piece. Most microplastics released from the face masks were medium size transparent polypropylene fibers originated from the nonwoven fabrics. The abrasion and aging during the using of face masks enhanced the releasing of microplastics since the increasing of medium size and blue microplastics. The face masks could also accumulate airborne microplastics during use. Our results indicated that used disposable masks without effective disposal could be a critical source of microplastics in the environment. The efficient allocation of mask resources and the proper disposal of wasted masks are not only beneficial to pandemic control but also to environmental safety.