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

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

Why we should have a universal air quality index?

Air pollution is known to be one of the major risk factors for premature morbidity and mortality globally, which are preventable. Therefore, the public is informed about air pollution through the Air Quality Index (AQI). The AQI represents integrated data of selected pollutants and produces a combined overall index for specific locations and time. The AQI algorithm generates a range of numbers and categories of colors that indicate likely health risks from air pollutants and the public's actions to minimize the risks. However, it lacks emerging evidence on chemical toxicity or composition. Hence, policymakers may also consider addinga toxicity matrix of fine particles to refine the algorithm, such as oxidative potential. Further, the risk is commonly communicated in numbers and not in color as dictated by the AQI. The AQI values and categories vary significantly between countries for the same pollution concentration, leading to confusion. Hence, we recommend developing a universal AQI (UAQI) with a consistent relationship between colors, concentrations, and toxicity to communicate air pollution risks to the public. Further, communication media should be encouraged to use universal color-coding rather than AQI values, i.e., numbers. Therefore, a global policy framework for regulatory authorities and policymakers is needed to communicate air pollution risk information consistently and to minimize public health exposure