Global fine-scale changes in ambient NO <sub>2</sub> during COVID-19 lockdowns

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Abstract

Nitrogen dioxide (NO ₂) is an important contributor to air pollution and can adversely affect human health ^{1–
9}. A decrease in NO ₂ concentrations has been reported as a result of lockdown measures to reduce the spread of COVID-19 ^{10–
20}. Questions remain, however, regarding the relationship of satellite-derived atmospheric column NO ₂ data with health-relevant ambient ground-level concentrations, and the representativeness of limited ground-based monitoring data for global assessment. Here we derive spatially resolved, global ground-level NO ₂ concentrations from NO ₂ column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution (approximately one kilometre) to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO ₂ changes in more than 200 cities, including 65 cities without available ground monitoring, largely in lower-income regions. Mean country-level population-weighted NO ₂ concentrations are 29% ± 3% lower in countries with strict lockdown conditions than in those without. Relative to long-term trends, NO ₂ decreases during COVID-19 lockdowns exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 ± 4 years of reductions globally. Our case studies indicate that the sensitivity of NO ₂ to lockdowns varies by country and emissions sector, demonstrating the critical need for spatially resolved observational information provided by these satellite-derived surface concentration estimates.

Abstract

The satellite instrument TROPOMI is used to assess ambient NO ₂ levels at approximately one-kilometre resolution across 215 cities worldwide during COVID-19 lockdowns, finding about 30% lower NO ₂ concentrations in countries with strict lockdowns.

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Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Christopher J L Murray, Aleksandr Y Aravkin, Peng Zheng … (2022)

Summary Background Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding Bill & Melinda Gates Foundation.

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Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions

Bo Zheng, Dan Tong, Meng Li … (2018)

Abstract. To tackle the problem of severe air pollution, China has implemented active clean air policies in recent years. As a consequence, the emissions of major air pollutants have decreased and the air quality has substantially improved. Here, we quantified China's anthropogenic emission trends from 2010 to 2017 and identified the major driving forces of these trends by using a combination of bottom-up emission inventory and index decomposition analysis (IDA) approaches. The relative change rates of China's anthropogenic emissions during 2010–2017 are estimated as follows: −62 % for SO 2 , −17 % for NO x , +11 % for nonmethane volatile organic compounds (NMVOCs), +1 % for NH 3 , −27 % for CO, −38 % for PM 10 , −35 % for PM 2.5 , −27 % for BC, −35 % for OC, and +16 % for CO 2 . The IDA results suggest that emission control measures are the main drivers of this reduction, in which the pollution controls on power plants and industries are the most effective mitigation measures. The emission reduction rates markedly accelerated after the year 2013, confirming the effectiveness of China's Clean Air Action that was implemented since 2013. We estimated that during 2013–2017, China's anthropogenic emissions decreased by 59 % for SO 2 , 21 % for NO x , 23 % for CO, 36 % for PM 10 , 33 % for PM 2.5 , 28 % for BC, and 32 % for OC. NMVOC emissions increased and NH 3 emissions remained stable during 2010–2017, representing the absence of effective mitigation measures for NMVOCs and NH 3 in current policies. The relative contributions of different sectors to emissions have significantly changed after several years' implementation of clean air policies, indicating that it is paramount to introduce new policies to enable further emission reductions in the future.

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The ozone monitoring instrument

Johan de Vries, H. Saari, J.O.V. Lundell … (2006)

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Author and article information

Contributors

Matthew J. Cooper:

ORCID: http://orcid.org/0000-0002-4145-3458

cooperm2@dal.ca

Journal

Journal ID (nlm-ta): Nature

Journal ID (iso-abbrev): Nature

Title: Nature

Publisher: Nature Publishing Group UK (London )

ISSN (Print): 0028-0836

ISSN (Electronic): 1476-4687

Publication date (Electronic): 19 January 2022

Publication date PMC-release: 19 January 2022

Publication date (Print): 2022

Volume: 601

Issue: 7893

Pages: 380-387

Affiliations

[1 ]GRID grid.55602.34, ISNI 0000 0004 1936 8200, Department of Physics and Atmospheric Science, , Dalhousie University, ; Halifax, Nova Scotia Canada

[2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Energy, Environmental & Chemical Engineering, , Washington University in St. Louis, ; St. Louis, MO USA

[3 ]GRID grid.455754.2, ISNI 0000 0001 1781 4754, Harvard-Smithsonian Center for Astrophysics, ; Cambridge, MA USA

[4 ]GRID grid.8653.8, ISNI 0000000122851082, Royal Netherlands Meteorological Institute (KNMI), ; De Bilt, Netherlands

[5 ]GRID grid.5292.c, ISNI 0000 0001 2097 4740, University of Technology Delft, ; Delft, Netherlands

[6 ]GRID grid.57828.30, ISNI 0000 0004 0637 9680, National Center for Atmospheric Research, ; Boulder, CO USA

[7 ]GRID grid.5292.c, ISNI 0000 0001 2097 4740, Department of Geoscience and Remote Sensing, , Delft University of Technology, ; Delft, Netherlands

[8 ]GRID grid.133275.1, ISNI 0000 0004 0637 6666, NASA Goddard Space Flight Center, ; Greenbelt, MD USA

[9 ]GRID grid.410493.b, ISNI 0000 0000 8634 1877, Universities Space Research Association, ; Columbia, MD USA

[10 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Dalla Lana School of Public Health, , University of Toronto, ; Toronto, Ontario Canada

[11 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Chemical Engineering and Applied Chemistry, , University of Toronto, ; Toronto, Ontario Canada

[12 ]GRID grid.410334.1, ISNI 0000 0001 2184 7612, Environment and Climate Change Canada, ; Toronto, Ontario Canada

Author information

Matthew J. Cooper http://orcid.org/0000-0002-4145-3458

Randall V. Martin http://orcid.org/0000-0003-2632-8402

Melanie S. Hammer http://orcid.org/0000-0001-8443-7979

Nickolay A. Krotkov http://orcid.org/0000-0001-6170-6750

Chris A. McLinden http://orcid.org/0000-0001-5054-1380

Article

Publisher ID: 4229

DOI: 10.1038/s41586-021-04229-0

PMC ID: 8770130

PubMed ID: 35046607

SO-VID: 9b11153d-a963-475a-99b5-77dddf5431c3

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 9 February 2021

Date accepted : 11 November 2021

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ScienceOpen disciplines: Uncategorized

Keywords: atmospheric chemistry,environmental monitoring

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ScienceOpen disciplines: Uncategorized

Keywords: atmospheric chemistry, environmental monitoring

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