Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.This research had free and open access to all data sources. The work described in this paper has received funding from European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf the European Union through commercial contract Ref. CAMS_95p. Several CAMS Regional Models of the CAMS_50 Service contributed to the present work (CHIMERE, LOTOS-EUROS, MINNI, MOCAGE, MONARCH, SILAM) under CAMS_71 coordination. CAMS_COP066 service provided the lockdown emissions information. O.J. and M.G. thankfully acknowledge the computer resources at Marenostrum and the technical support provided by Barcelona Supercomputing Center (RES-AECT-2020-1-0007). SILAM model runs was also funded by Finnish Academy GLORIA project (No310372). The study was supported by the European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655).Peer Reviewed"Article signat per 18 autors/es: Rochelle Schneider, Pierre Masselot, Ana M. Vicedo-Cabrera, Francesco Sera, Marta Blangiardo, Chiara Forlani, John Douros, Oriol Jorba, Mario Adani, Rostislav Kouznetsov, Florian Couvidat, Joaquim Arteta, Blandine Raux, Marc Guevara, Augustin Colette, Jérôme Barré, Vincent-Henri Peuch & Antonio Gasparrini "Postprint (published version

A satellite-based spatio-temporal machine learning model to reconstruct daily PM2.5 concentrations across Great Britain

Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk

Regulating working families in the European Union: a history of disjointed strategies

Families in market economies worldwide have long been confronted with the demands of participating in paid work and providing care for their dependent members. The social, economic and political contexts within which families do so differ from country to country but an increasing number of governments are being asked to engage, or better engage, with this important area of public policy. What seems like a relatively simple goal – to enable families to better balance care-giving and paid employment – has raised several difficulties and dilemmas for policy makers which have been approached in different ways. This paper aims to identify and critique the nature and development of the means by which legal engagement with work-family reconciliation has, historically, been framed in the European Union. In doing so, and with reference to specific cohorts of workers, we demonstrate how disjointed the strategies are in relation to working carers and argue that the EU is unlikely to provide the legal framework necessary to bring about effective change in this fundamentally important area of social policy

Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803

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Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers

BACKGROUND: To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. RESULTS: In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. CONCLUSION: Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets

Transcriptional regulation of Saccharomyces cerevisiaeCYS3 encoding cystathionine γ-lyase

In studying the regulation of GSH11, the structural gene of the high-affinity glutathione transporter (GSH-P1) in Saccharomyces cerevisiae, a cis-acting cysteine responsive element, CCGCCACAC (CCG motif), was detected. Like GSH-P1, the cystathionine γ-lyase encoded by CYS3 is induced by sulfur starvation and repressed by addition of cysteine to the growth medium. We detected a CCG motif (−311 to −303) and a CGC motif (CGCCACAC; −193 to −186), which is one base shorter than the CCG motif, in the 5′-upstream region of CYS3. One copy of the centromere determining element 1, CDE1 (TCACGTGA; −217 to −210), being responsible for regulation of the sulfate assimilation pathway genes, was also detected. We tested the roles of these three elements in the regulation of CYS3. Using a lacZ-reporter assay system, we found that the CCG/CGC motif is required for activation of CYS3, as well as for its repression by cysteine. In contrast, the CDE1 motif was responsible for only activation of CYS3. We also found that two transcription factors, Met4 and VDE, are responsible for activation of CYS3 through the CCG/CGC and CDE1 motifs. These observations suggest a dual regulation of CYS3 by factors that interact with the CDE1 motif and the CCG/CGC motifs

Interactive effects of ambient fine particulate matter and ozone on daily mortality in 372 cities: two stage time series analysis

Objective To investigate potential interactive effects of fine particulate matter (PM2.5) and ozone (O3) on daily mortality at global level. Design Two stage time series analysis. Setting 372 cities across 19 countries and regions. Population Daily counts of deaths from all causes, cardiovascular disease, and respiratory disease. Main outcome measure Daily mortality data during 1994-2020. Stratified analyses by co-pollutant exposures and synergy index (>1 denotes the combined effect of pollutants is greater than individual effects) were applied to explore the interaction between PM2.5 and O3 in association with mortality. Results During the study period across the 372 cities, 19.3 million deaths were attributable to all causes, 5.3 million to cardiovascular disease, and 1.9 million to respiratory disease. The risk of total mortality for a 10 μg/m3 increment in PM2.5 (lag 0-1 days) ranged from 0.47% (95% confidence interval 0.26% to 0.67%) to 1.25% (1.02% to 1.48%) from the lowest to highest fourths of O3 concentration; and for a 10 μg/m3 increase in O3 ranged from 0.04% (−0.09% to 0.16%) to 0.29% (0.18% to 0.39%) from the lowest to highest fourths of PM2.5 concentration, with significant differences between strata (P for interaction <0.001). A significant synergistic interaction was also identified between PM2.5 and O3 for total mortality, with a synergy index of 1.93 (95% confidence interval 1.47 to 3.34). Subgroup analyses showed that interactions between PM2.5 and O3 on all three mortality endpoints were more prominent in high latitude regions and during cold seasons. Conclusion The findings of this study suggest a synergistic effect of PM2.5 and O3 on total, cardiovascular, and respiratory mortality, indicating the benefit of coordinated control strategies for both pollutants

Heat-related cardiorespiratory mortality: effect modification by air pollution across 482 cities from 24 countries

Background Evidence on the potential interactive effects of heat and ambient air pollution on cause-specific mortality is inconclusive and limited to selected locations. Objectives We investigated the effects of heat on cardiovascular and respiratory mortality and its modification by air pollution during summer months (six consecutive hottest months) in 482 locations across 24 countries. Methods Location-specific daily death counts and exposure data (e.g., particulate matter with diameters ≤ 2.5 µm [PM2.5]) were obtained from 2000 to 2018. We used location-specific confounder-adjusted Quasi-Poisson regression with a tensor product between air temperature and the air pollutant. We extracted heat effects at low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th percentile of the location-specific pollutant concentrations. Country-specific and overall estimates were derived using a random-effects multilevel meta-analytical model. Results Heat was associated with increased cardiorespiratory mortality. Moreover, the heat effects were modified by elevated levels of all air pollutants in most locations, with stronger effects for respiratory than cardiovascular mortality. For example, the percent increase in respiratory mortality per increase in the 2-day average summer temperature from the 75th to the 99th percentile was 7.7% (95% Confidence Interval [CI] 7.6-7.7), 11.3% (95%CI 11.2-11.3), and 14.3% (95% CI 14.1-14.5) at low, medium, and high levels of PM2.5, respectively. Similarly, cardiovascular mortality increased by 1.6 (95%CI 1.5-1.6), 5.1 (95%CI 5.1-5.2), and 8.7 (95%CI 8.7-8.8) at low, medium, and high levels of O3, respectively. Discussion We observed considerable modification of the heat effects on cardiovascular and respiratory mortality by elevated levels of air pollutants. Therefore, mitigation measures following the new WHO Air Quality Guidelines are crucial to enhance better health and promote sustainable development