A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.World Meteorological Organization Global Atmospheric Watch programme is gratefully acknowledged for initiating and coordinating this study and for supporting this publication. We acknowledge the following projects for supporting the analysis contained in this article: Air Pollution and Human Health for an Indian Megacity project PROMOTE funded by UK NERC and the Indian MOES, Grant reference number NE/P016391/1; Regarding project funding from the European Commission, the sole responsibility of this publication lies with the authors. The European Commission is not responsible for any use that may be made of the information contained therein. This project has received funding from the European Commission’s Horizon 2020 research and innovation program under grant agreement No 874990 (EMERGE project). European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Estonian Research Council (project PRG714); Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (KKOBS, project 2014-2020.4.01.20-0281). European network for observing our changing planet project (ERAPLANET, grant agreement no. 689443) under the European Union’s Horizon 2020 research and innovation program, Estonian Ministry of Sciences projects (grant nos. P180021, P180274), and the Estonian Research Infrastructures Roadmap project Estonian Environmental Observatory (3.2.0304.11-0395). Eastern Mediterranean and Middle East—Climate and Atmosphere Research (EMME-CARE) project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 856612) and the Government of Cyprus. INAR acknowledges support by the Russian government (grant number 14.W03.31.0002), the Ministry of Science and Higher Education of the Russian Federation (agreement 14.W0331.0006), and the Russian Ministry of Education and Science (14.W03.31.0008). We are grateful to to the following agencies for providing access to data used in our analysis: A.M. Obukhov Institute of Atmospheric Physics Russian Academy of Sciences; Agenzia Regionale per la Protezione dell’Ambiente della Campania (ARPAC); Air Quality and Climate Change, Parks and Environment (MetroVancouver, Government of British Columbia); Air Quality Monitoring & Reporting, Nova Scotia Environment (Government of Nova Scotia); Air Quality Monitoring Network (SIMAT) and Emission Inventory, Mexico City Environment Secretariat (SEDEMA); Airparif (owner & provider of the Paris air pollution data); ARPA Lazio, Italy; ARPA Lombardia, Italy; Association Agr´e´ee de Surveillance de la Qualit´e de l’Air en ˆIle-de- France AIRPARIF / Atmo-France; Bavarian Environment Agency, Germany; Berlin Senatsverwaltung für Umwelt, Verkehr und Klimaschutz, Germany; California Air Resources Board; Central Pollution Control Board (CPCB), India; CETESB: Companhia Ambiental do Estado de S˜ao Paulo, Brazil. China National Environmental Monitoring Centre; Chandigarh Pollution Control Committee (CPCC), India. DCMR Rijnmond Environmental Service, the Netherlands. Department of Labour Inspection, Cyprus; Department of Natural Resources Management and Environmental Protection of Moscow. Environment and Climate Change Canada; Environmental Monitoring and Science Division Alberta Environment and Parks (Government of Alberta); Environmental Protection Authority Victoria (Melbourne, Victoria, Australia); Estonian Environmental Research Centre (EERC); Estonian University of Life Sciences, SMEAR Estonia; European Regional Development Fund (project MOBTT42) under the Mobilitas Pluss programme; Finnish Meteorological Institute; Helsinki Region Environmental Services Authority; Haryana Pollution Control Board (HSPCB), IndiaLondon Air Quality Network (LAQN) and the Automatic Urban and Rural Network (AURN) supported by the Department of Environment, Food and Rural Affairs, UK Government; Madrid Municipality; Met Office Integrated Data Archive System (MIDAS); Meteorological Service of Canada; Minist`ere de l’Environnement et de la Lutte contre les changements climatiques (Gouvernement du Qu´ebec); Ministry of Environment and Energy, Greece; Ministry of the Environment (Chile) and National Weather Service (DMC); Moscow State Budgetary Environmental Institution MOSECOMONITORING. Municipal Department of the Environment SMAC, Brazil; Municipality of Madrid public open data service; National institute of environmental research, Korea; National Meteorology and Hydrology Service (SENAMHI), Peru; New York State Department of Environmental Conservation; NSW Department of Planning, Industry and Environment; Ontario Ministry of the Environment, Conservation and Parks, Canada; Public Health Service of Amsterdam (GGD), the Netherlands. Punjab Pollution Control Board (PPCB), India. R´eseau de surveillance de la qualit´e de l’air (RSQA) (Montr´eal); Rosgydromet. Mosecomonitoring, Institute of Atmospheric Physics, Russia; Russian Foundation for Basic Research (project 20–05–00254) SAFAR-IITM-MoES, India; S˜ao Paulo State Environmental Protection Agency, CETESB; Secretaria de Ambiente, DMQ, Ecuador; Secretaría Distrital de Ambiente, Bogot´a, Colombia. Secretaria Municipal de Meio Ambiente Rio de Janeiro; Mexico City Atmospheric Monitoring System (SIMAT); Mexico City Secretariat of Environment, Secretaría del Medio Ambiente (SEDEMA); SLB-analys, Sweden; SMEAR Estonia station and Estonian University of Life Sciences (EULS); SMEAR stations data and Finnish Center of Excellence; South African Weather Service and Department of Environment, Forestry and Fisheries through SAAQIS; Spanish Ministry for the Ecological Transition and the Demographic Challenge (MITECO); University of Helsinki, Finland; University of Tartu, Tahkuse air monitoring station; Weather Station of the Institute of Astronomy, Geophysics and Atmospheric Science of the University of S˜ao Paulo; West Bengal Pollution Control Board (WBPCB).http://www.elsevier.com/locate/envintam2023Geography, Geoinformatics and Meteorolog

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Evidence for magnetic interactions among magnetite nanoparticles dispersed in photoreticulated PEGDA-600 matrix

Magnetite nanoparticles having mean diameter of about 8 nm have been prepared by a thermo-chemical route. Different amounts (5 and 10% wt) of a stable dispersion of magnetite nanoparticles in n-hexane were added to polyethylene glycol diacrylate (PEGDA-600) oligomer containing 2% wt of radicalic photoinitiator. The homogenized mixture was poured on a silica glass substrate and the resulting film was photoreticulated in N2 atmosphere using a UV lamp. As a result, a polymer-based magnetic nanocomposite was obtained, where the magnetic nanoparticles are dispersed in the diamagnetic matrix, as checked by SEM. Morphology, composition, and size of as-prepared nanoparticles were checked by SEM and X-ray diffraction. The magnetic properties of magnetite nanoparticles prior to and after inclusion in the polymeric matrix have been studied by means of an alternating-gradient magnetometer (T interval: 10–300 K, HMAX: 18 kOe). FC-ZFC curves were obtained in the same temperature interval. The results show that the nanocomposites cannot be simply described as containing superparamagnetic particles undergoing an anisotropy-driven blocking and that collective magnetic interactions play a non-negligible role. Lowtemperature hysteretic properties indicate that the polymeric matrix affects the effective anisotropy of magnetite nanoparticles. Dispersion of magnetite NPs in PEGDA has non-trivial consequences on their magnetic properties

Impact of weather and pollution on the rate of cerebrovascular events in a large metropolitan area

BACKGROUND: Despite mounting evidence, there is uncertainty on the impact of the interplay between weather and pollution features on the risk of acute cerebrovascular events (CVE). We aimed at appraising role of weather and pollution on the daily risk of CVE.METHODS: Anonymized data from a hub CVE center in a large metropolitan area were collected and analyzed according to weather (temperature. pressure. humidity, and rainfall) and pollution (carbon monoxide [CO]. nitrogen dioxide [NO2], nitrogen oxides [NOX], ozone [O-3], and particulate matter [PM]) on the same and the preceding days. Poisson regression and time series analyses were used to appraise the association between environmental features and daily CVE, distinguishing also several subtypes of events.RESULTS: We included a total of 2534 days, with 1363 days having ?1 CVE, from 2012 to 2017. Average daily rate was 1.56 (95% confidence interval: 1.49; 1.63) for CVE, with other event rates ranging between 1.42 for stroke and 0.01 for ruptured intracranial aneurysm. Significant associations were found between CVE and temperature, pressure, CO, NO2,NOX, O-3, and PM &lt;10 mu m (all P&lt;0.05), whereas less stringent associations were found for humidity, rainfall, and PM &lt;2.5 mu m. Time series analysis exploring lag suggested that associations were stronger at same-day analysis (lag 0), but even environmental features predating several days or weeks were significantly associated with events. Multivariable analysis suggested that CO (point estimate 1.362 [1.011; 1.836], P=0.042) and NO2 (1.011 [1.005; 1.016], P&lt;0.001) were the strongest independent predictors of CVE.CONCLUSIONS: Environmental features are significantly associated with CVE, even several days before the actual event. Levels of CO and NO2 can be potentially leveraged for population-level interventions to reduce the burden of CVE

Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events

Background: Despite mounting evidence, the impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. Methods: Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from 3 tertiary care center from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for enviromental protection, and from the Metereological Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rate of acute cardiac and cerebrovascular events with Poisson models. Results: As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated 4 separate clusters: mostly winter days with low temperatures and high ozone concentrations (cluster 1, n=60, 5.1%), days with moderately high temperatures and low pollutants concentrations (cluster 2, n=419, 35.8%), mostly summer and spring days with high temperatures and high ozone concentrations (cluster 3, n=673, 57.6%), and mostly winter days with low temperatures and low ozone concentrations (cluster 4, n=17, 1.5%). Overall cluster-wise comparisons showed significant overall differences in adverse cardiac and cerebrovascular events (p&lt;0.001), as well as in cerebrovascular events (p&lt;0.001) and strokes (p=0.001). Between-cluster comparisons showed that Cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to Cluster 2, Cluster 3 and Cluster 4 (all p&lt;0.05), as well as AMI in comparison to Cluster 3 (p=0.047). In addition, Cluster 2 was associated with a higher risk of strokes in comparison to Cluster 4 (p=0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events and strokes for Cluster 1 and Cluster 2. Conclusions: Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increases risk of acute cardiovascular events, especially cerebrovascular events. These findings may improve collective and individual risk prediction and prevention

Impact of temporary traffic bans on the risk of acute coronary syndromes in a large metropolitan area

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Genomic characterization of hepatoid tumors: context matters

Hepatoid tumors (HT) are rare neoplasms, morphologically resembling hepatocellular carcinoma, which arise in several organs other than the liver. A comprehensive molecular profile of this group of neoplasms is still lacking. Genomic characterization of 19 HT from different organs (3 colon, 4 esophagogastric, 4 biliary, 6 genitourinary, 2 lung) was performed using a multigene next-generation sequencing panel. NGS unraveled a composite molecular profile of HT. Their genetic alterations were clearly clustered by tumor site: i) colorectal HT displayed microsatellite instability, high tumor mutational burden, mutations in ARID1A/B genes and NCOA4-RET gene fusion (2/3 cases); ii) gastric HT had TP53 mutations (2/4); iii) biliary HT displayed loss of CDKN2A (3/4) and loss of chromosome 18 (2/4); iv) genital HT showed gain of chromosome 12 (3/6); v) lung HT had STK11 somatic mutations (2/2). The only commonly mutated gene occurring in HT of different sites was TP53 (8/19 cases: 2 colon, 2 esophagogastric, 2 biliary, 1 genital, 1 lung). This study shows that most genetic alterations of HT were clustered by site, indicating that context matters. The novel potential targets for HT precision oncology are also clustered based on the anatomic origin. This study shed light into the biology of these rare cancers, and may have important consequences for treatment decision and clinical trial selection for HT patients

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts