37 research outputs found
Initial and Repeated Point Prevalence Surveys to Inform SARS-CoV-2 Infection Prevention in 26 Skilled Nursing Facilities - Detroit, Michigan, March-May 2020
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 2015â2019. 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 2015â2019 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 peoplesâ 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 2015â2019. 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 2015â2019 (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 2015â2019.
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 2015â2019 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 peoplesâ 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 2015â2019. 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 2015â2019 (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 Commissionâs
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 Unionâs
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 EastâClimate and Atmosphere Research (EMME-CARE) project, which has received funding from the
European Unionâs 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 dellâAmbiente 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 AgrÂŽeÂŽee de Surveillance de la QualitÂŽe de lâAir en ËIle-de-
France AIRPARIF / Atmo-France;
Bavarian Environment Agency, Germany;
Berlin Senatsverwaltung fĂŒr Umwelt, Verkehr und Klimaschutz,
Germany;
California Air Resources Board;
Central Pollution Control Board (CPCB), India;
CETESB: Companhia Ambiental do Estado de SËao 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 lâEnvironnement et de la Lutte contre les changements
climatiques (Gouvernement du QuÂŽebec);
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.
RÂŽeseau de surveillance de la qualitÂŽe de lâair (RSQA) (MontrÂŽeal);
Rosgydromet. Mosecomonitoring, Institute of Atmospheric Physics,
Russia;
Russian Foundation for Basic Research (project 20â05â00254)
SAFAR-IITM-MoES, India;
SËao Paulo State Environmental Protection Agency, CETESB;
Secretaria de Ambiente, DMQ, Ecuador;
SecretarĂa Distrital de Ambiente, BogotÂŽa, Colombia.
Secretaria Municipal de Meio Ambiente Rio de Janeiro;
Mexico City Atmospheric Monitoring System (SIMAT); Mexico City
Secretariat of Environment, SecretarĂa 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 SËao Paulo;
West Bengal Pollution Control Board (WBPCB).http://www.elsevier.com/locate/envintam2023Geography, Geoinformatics and Meteorolog
Assessing Electronic Death Registration and American Red Cross Systems for Mortality Surveillance During Hurricane Sandy, October 29âNovember 10, 2012, New York City
Spatial Spectroscopic Models for Remote Exploration
Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional "science traceability matrices" into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite
Spatial Spectroscopic Models for Remote Exploration
Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional "science traceability matrices" into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite