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.

International audience | The presence of SARS-CoV-2 RNA has been extensively reported at the influent of wastewater treatment plants (WWTPs) worldwide, and its monitoring has been proposed as a potential surveillance tool to early alert of epidemic outbreaks. However, the fate of the SARS-CoV-2 RNA in the treatment process of WWTP has not been widely studied yet; therefore, in this study, we aimed to evaluate the efficiency of treatment processes in reducing SARS-CoV-2 RNA levels in wastewater. The treatment process of three WWTPs of the Parisian area in France was monitored on six different weeks over a period of 2 months (from April 14 to June 9, 2021). SARS-CoV-2 RNA copies were detected using digital polymerase chain reaction (dPCR). Investigation on the presence of variants of concern (Del69-70, E484K, and L452R) was also performed. Additionally, SARS-CoV-2 RNA loads in the WWTPs influents were expressed as the viral concentration in per population equivalent (PE) and showed a good correlation with French public health indicators (incidence rate). SARS-CoV-2 RNA loads were notably reduced along the water treatment lines of the three WWTPs studied (2.5-3.4 log reduction). Finally, very low SARS-CoV-2 RNA loads were detected in effluents (non-detected in over half of the samples) which indicated that the potential risk of the release of wastewater effluents to the environment is probably insignificant, in the case of WWTPs enabling an efficient biological removal of nitrogen.

International audience | The presence of SARS-CoV-2 RNA has been extensively reported at the influent of wastewater treatment plants (WWTPs) worldwide, and its monitoring has been proposed as a potential surveillance tool to early alert of epidemic outbreaks. However, the fate of the SARS-CoV-2 RNA in the treatment process of WWTP has not been widely studied yet; therefore, in this study, we aimed to evaluate the efficiency of treatment processes in reducing SARS-CoV-2 RNA levels in wastewater. The treatment process of three WWTPs of the Parisian area in France was monitored on six different weeks over a period of 2 months (from April 14 to June 9, 2021). SARS-CoV-2 RNA copies were detected using digital polymerase chain reaction (dPCR). Investigation on the presence of variants of concern (Del69-70, E484K, and L452R) was also performed. Additionally, SARS-CoV-2 RNA loads in the WWTPs influents were expressed as the viral concentration in per population equivalent (PE) and showed a good correlation with French public health indicators (incidence rate). SARS-CoV-2 RNA loads were notably reduced along the water treatment lines of the three WWTPs studied (2.5-3.4 log reduction). Finally, very low SARS-CoV-2 RNA loads were detected in effluents (non-detected in over half of the samples) which indicated that the potential risk of the release of wastewater effluents to the environment is probably insignificant, in the case of WWTPs enabling an efficient biological removal of nitrogen.

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.

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.

People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%–52.0%), 32.3% (95% CI: 22.5%–42.4%), and 14.2% (7.9%–20.5%) in the number of COVID-19 cases for every 10-μg/m³ increase in long-term exposure to NO₂, PM₂.₅, and PM₁₀, respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.

Nitrogen dioxide (NO₂) is an important air pollutant that causes direct harms to the environment and human health. Ground NO₂ mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO₂ concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R² values of 0.84 and 0.79. The annual mean and standard deviation of ground NO₂ concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m³, with that in 0.6% of China’s area (10% of the population) exceeding the annual air quality standard (40 μg/m³). The ground NO₂ concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO₂ was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO₂ concentrations across all of China. This was also an early application to use the satellite-estimated ground NO₂ data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO₂ data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia’s most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series’ of nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide CO and particulate matter with diameter < 2.5 μm (PM₂.₅), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM₂.₅ (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM₂.₅ 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6–343.) excess deaths and 3490.0 (95% CI 1325.9–5653.5) additional hospitalisations were likely as a result of PM₂.₅ and O₃ exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO₂ decreased by 21.5 and 8% in Victoria and NSW respectively. O₃ and PM₂.₅ remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.

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.