COVID-19 lockdown induced changes in NO2 levels across India observed by multi-satellite and surface observations

© Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.We have estimated the spatial changes in NO 2levels over different regions of India during the COVID-19 lockdown (25 March-3 May 2020) using the satellite-based tropospheric column NO 2observed by the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), as well as surface NO 2concentrations obtained from the Central Pollution Control Board (CPCB) monitoring network. A substantial reduction in NO 2levels was observed across India during the lockdown compared to the same period during previous business-as-usual years, except for some regions that were influenced by anomalous fires in 2020. The reduction (negative change) over the urban agglomerations was substantial (~20 %-40 %) and directly proportional to the urban size and population density. Rural regions across India also experienced lower NO 2values by ~15 %-25 %. Localised enhancements in NO 2associated with isolated emission increase scattered across India were also detected. Observed percentage changes in satellite and surface observations were consistent across most regions and cities, but the surface observations were subject to larger variability depending on their proximity to the local emission sources. Observations also indicate NO 2enhancements of up to~25%during the lockdown associated with fire emissions over the north-east of India and some parts of the central regions. In addition, the cities located near the large fire emission sources show much smaller NO 2reduction than other urban areas as the decrease at the surface was masked by enhancement in NO 2due to the transport of the fire emissions.Peer reviewedFinal Published versio

Mass and ionic composition of atmospheric fine particles over Belgium and their relation with gaseous air pollutants

Original article can be found at: http://www.rsc.org/publishing/journals/EM/Index.asp Copyright Royal Society of Chemistry. DOI: 10.1039/b805157gMass, major ionic components (MICs) of PM2.5, and related gaseous pollutants (SO2, NOx, NH3, HNO2, and HNO3) were monitored over six locations of different anthropogenic influence (industrial, urban, suburban, and rural) in Belgium. SO42-, NO3- NH4+, and Na+ were the primary ions of PM2.5 with averages diurnal concentrations ranging from 0.4-4.5, 0.3-7.6, 0.9-4.9, and 0.4-1.2 g/m3, respectively. MICs formed 39% of PM2.5 on an average, but it could reach up to 80-98 %. The SO2, NO, NO2, HNO2, and HNO3 levels showed high seasonal and site-specific fluctuations. The NH3 levels were similar over all the sites (2-6 g/m3), indicating its relation to the evenly distributed animal husbandry activities. The sulfur and nitrogen oxidation ratios for PM2.5 point towards a low-to-moderate formation of secondary sulfate and nitrate aerosols over five cities/towns, but their fairly intensive formation at the rural Wingene. Cluster analysis revealed the association of three groups of compounds in PM2.5; (i) NH4NO3, KNO3; (ii) Na2SO4; and (iii) MgCl2, CaCl2, MgF2, CaF2, corresponding to anthropogenic, sea-salt, and mixed (sea-salt + anthropogenic) aerosols, respectively. The neutralization and cation-to-anion ratios indicate that MICs of PM2.5 appeared mostly as (NH4)2SO4 and NH4NO3 salts. Sea-salt input was maximal during winter reaching up to 12 % of PM2.5. The overall average Cl-loss for sea-salt particles of PM2.5 at the six sites varied between 69 and 96 % with an average of 87 %. Principal component analysis revealed vehicular emission, coal/wood burning and animal farming as the dominating sources for the ionic components of PM2.5.Peer reviewe

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

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.Peer reviewedFinal Published versio

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

Predicting the natural yeast phenotypic landscape with machine learning

L'étude des caractères complexes des organismes joue un rôle important dans divers domaines, notamment en biologie évolutive, en médecine, ou encore en agriculture. La compréhension des facteurs génétiques impliqués dans le contrôle de ces caractéristiques peut ainsi être d'une grande importance. Notamment, la plupart des caractéristiques liées aux maladies sont complexes et l'identification de nouvelles cibles médicamenteuses peut conduire à des méthodes de traitement nouvelles et améliorées. De même, en agriculture, l'identification de loci génétiques associés à des caractéristiques intéressantes, telles que le rendement, l'adaptabilité et la résistance, peut contribuer à améliorer la productivité et la qualité des cultures. De plus, la variation génétique présente au niveau de la population peut grandement contribuer à la variance des caractères phénotypiques qui eux aussi peuvent être de grande importance. Dans cette thèse, nous étudions la variation au niveau de la population de plus de 200 caractères complexes dans une collection naturelle de Saccharomyces cerevisiae comprenant 1011 souches. L'étude peut être divisée en trois parties principales. Dans la première partie, nous décrivons les modèles de corrélation globale entre les 223 phénotypes, en mettant en évidence certaines corrélations inattendues entre des phénotypes non apparentés. En outre, nous avons quantifié la corrélation entre les distances génétiques et phénotypiques des souches et ses variations entre les différents clades. Dans la deuxième partie, nous identifions les marqueurs génétiques associés aux 223 phénotypes à l'aide d'études d'association à l'échelle du génome (GWAS). Nous avons pu ainsi confirmer que les modèles observés au niveau du phénome de la population se reflétaient au niveau génomique, un plus grand nombre de variants génétiques significativement associés étant partagés entre les phénotypes les plus corrélés et vice versa. Enfin, la dernière partie est consacrée à la prédiction du phénome à partir de diverses données génomiques et phénomiques. Nous avons développé une ``pipeline" d'apprentissage automatique (GenPhen) qui met en œuvre l'automatisation du processus d'optimisation que ça soit des paramètres ou des hyperparamètres du modèle afin d'obtenir le modèle le plus proche des phénotypes individuels. En outre, la pipeline intègre quatre méthodes d'apprentissage automatique linéaires et non linéaires. Nous fournissons une comparaison de la capacité des différents modèles pour la prédiction des phénotypes avec différents types de prédicteurs en entrée, y compris le pangénome, les polymorphismes de nucléotides simples (SNP), la transcriptomique, la protéomique, etc.Enfin, nous avons mis en œuvre des modèles d'apprentissage automatique multicibles capables de prédire l'ensemble du phénome avec une précision globale comparable à celle des prédictions de phénotypes individuels. Dans l'ensemble, nous avons montré que les prédictions varient fortement en fonction du phénotype et que la plupart des caractères sont fortement polygéniques, c'est-à-dire qu'ils sont contrôlés par un grand nombre de facteurs génétiques ayant des effets très faibles. De manière générale, notre étude donne un aperçu de l'utilité des différentes méthodes d'apprentissage automatique pour la prédiction des phénotypes complexes, elle permet aussi la comparaison de différents types de prédicteurs pour la hiérarchisation des données expérimentales requises pour les prédictions. De plus, elle permet l'interprétation des modèles d'apprentissage automatique pour comprendre les mécanismes biologiques sous-jacents qui contrôlent les caractères.The study of complex traits is of central importance in various fields, including evolutionary biology, medicine, agriculture, etc. Understanding the genetic factors involved in controlling these traits can be of paramount significance. For example, most diseases-related traits are complex, and unraveling novel drug targets can lead to new and improved treatment methods. Similarly, in agriculture, the identification of genetic loci associated with traits of interest, such as yield, adaptability, and resistance, can help improve crop productivity and quality. The genetic variation present at the population level can greatly contribute to the variance in phenotypic traits. In this thesis, we study the population-level variation in more than 200 complex traits in a natural Saccharomyces cerevisiae collection comprising of 1,011 strains. The study can be divided into three main parts. In the first part, we describe the global correlation patterns among all 223 phenotypes, highlighting some unexpected correlations between unrelated phenotypes. Furthermore, we quantified the correlation between the genetic and phenotypic distances of the strains and its variations between the different clades. In the second part, we identify genetic markers associated with the 223 phenotypes using genome-wide association studies (GWAS). Moreover, we confirmed that the patterns observed at the phenome level of the population were reflected at the genomic level, with a higher number of significantly associated genetic variants being shared between the more correlated phenotypes and vice versa. Finally, the last part is focused on predicting the phenome from various genomic and phenomic data. We developed a machine learning pipeline (GenPhen) that implements the automatization of the hyperparameters optimization process during model learning to obtain the most optimized model for individual phenotypes. In addition, the pipeline can be used to implement four ML methods capable of learning linear to highly non-linear models. We provide a comparison of the ability of the different ML models to predict phenotypes and also different kinds of input predictors including the pangenome, Single Nucleotide Polymorphisms (SNPs), transcriptomic, proteomics, etc. Finally, we implemented multitarget machine learning models that can predict the entire phenome with overall accuracy comparable to that of individual phenotype predictions. Overall, we showed that predictions vary highly depending on the phenotype and that most of the traits were highly polygenic, i.e., they are controlled by a large number of genetic factors with very small effects. In general, our study provides insight into the usefulness of different machine learning methods for predicting complex phenotypes, comparison of different types of predictors for the prioritization of the experimental data required for predictions, and interpretation of ML models to understand the underlying biological mechanisms controlling a trait

Quantification of vehicle fleet PM10 particulate matter emission factors from exhaust and non-exhaust sources using tunnel measurement techniques

Road tunnels act like large laboratories; they provide an excellent environment to quantify atmospheric particles emission factors from exhaust and non-exhaust sources due to their known boundary conditions. Current work compares the High Volume, Dichotomous Stacked Filter Unit and Partisol Air Sampler for coarse, PM10 and PM2.5 particle concentration measurement and found that they do not differ significantly (p ¼ 95%). PM2.5 fraction contributes 66% of PM10 proportions and significantly influenced by traffic (turbulence) and meteorological conditions. Mass emission factors for PM10 varies from 21.3 ± 1.9 to 28.8 ± 3.4 mg/vkm and composed of Motorcycle (0.0003e0.001 mg/vkm), Cars (26.1 e33.4 mg/vkm), LDVs (2.4e3.0 mg/vkm), HDVs (2.2e2.8 mg/vkm) and Buses (0.1 mg/vkm). Based on Lawrence et al. (2013), source apportionment modelling, the PM10 emission of brake wear (3.8e4.4 mg/ vkm), petrol exhaust (3.9e4.5 mg/vkm), diesel exhaust (7.2e8.3 mg/vkm), re-suspension (9e10.4 mg/vkm), road surface wear (3.9e4.5 mg/vkm), and unexplained (7.2 mg/vkm) were also calculated. The current study determined that the combined non-exhaust fleet PM10 emission factor ( (16.7e19.3 mg/ vkm) are higher than the combined exhaust emission factor (11.1e12.8 mg/vkm). Thus, highlight the significance of non-exhaust emissions and the need for legislation and abatement strategies to reduce their contributions to ambient PM concentrations.Peer reviewe