Global fine-scale changes in NO2 during COVID-19 lockdowns.
Matthew J. Cooper orcid.org
Randall V. Martin is at orcid.org.
Melanie S. Hammer can be found at orcid.org.
F. Levelt 4,6,
The name of the person is Pepijn Veefkind.
Lok N.
The orcid.org has a biography of Nickolay A.
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Jeffrey R.
Chris A. McLinden can be found at orcid.org.
This article wasCited by Nature.
Nitrogen dioxide is an important contributor to air pollution and can adversely affect human health. There has been a decrease in NO2 concentrations as a result of measures to reduce the spread of COVID-1910,11,12,13,14,15,16,17,18,19,20. There are questions regarding the relationship of satellite-derived atmospheric column NO2 data with health-relevant ambient ground-level concentrations and the representativeness of limited ground-based monitoring data for global assessment. Here we derive global ground-level NO2 concentrations from NO2 column densities observed by the TROPOMI satellite instrument at sufficiently fine resolution to allow assessment of individual cities during COVID-19 lockdowns in 2020 compared to 2019. We apply these estimates to quantify NO2 changes in more than 200 cities. Mean country-level NO2 concentrations are 29% lower in countries with strict lockdown conditions than in those without. During COVID-19 lockdowns, NO2 decreases exceed recent Ozone Monitoring Instrument (OMI)-derived year-to-year decreases from emission controls, comparable to 15 4 years of reductions globally. The sensitivity of NO2 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.
Nitrogen dioxide is a pollutant that contributes to air pollution as a primary pollutant and as a precursor to ozone and fine particulate matter production. Human exposure to elevated NO2 concentrations is associated with a range of adverse outcomes. The NO2 observations show air quality relationships with pollution. Initial investigations found a decrease in the atmospheric NO2 column from satellite observations and from ground-based monitoring. There are still questions about the relationship of atmospheric columns with health and policy-relevant ambient ground-level concentrations. Satellite observations of NO2 columns need to be compared to ground-level concentrations. It is important to consider the effect of meteorology on recent NO2 changes and to quantify NO2 changes due to COVID-19 interventions in the context of longer-term trends. The preferential location of air quality monitoring sites in higher-income regions raises questions about how NO2 changed in lower-income regions. Estimates of changes in ground-level NO2 concentrations derived from satellite remote sensing would fill gaps between ground-based monitors, offer valuable information in regions with sparse monitoring, and more clearly connect satellite observations with ground-level ambient air quality.
Satellite NO2 column densities were compared to ground-level concentrations using information on the vertical distribution of NO2 from a chemical transport model. Recent work improved upon this technique by allowing the satellite column densities to constrain the vertical profile shape, allowing for more accurate representation of sub-model-grid variability, reducing the sensitivity to model resolution and simulation profile shape errors, and improving agreement between the satellite-derived ground-level concentrations and in Applying this technique to examine changes in NO2 during lockdowns bridges the gap between previous studies focusing on ground monitors or satellite column densities, thus providing a more complete and reliable picture of the changes in exposure.
The Ozone Monitoring Instrument (OMI) on board NASA's Earth Observation System Aura satellite28,29 has been the gold standard for NO2 observations. The European Space Agency has been providing NO2 observations with higher instrument sensitivity since the launch of the TROPOMI30 on the Copernicus Sentinel 5p satellite. The generation of TROPOMI NO2 maps is 100 times better than the spatial and temporal average needed for accurate OMI maps. The excellent stability of the OMI instrument over the last 15 years makes it an ideal dataset for long-term trend analysis.
Lockdown restrictions are an experiment about the effectiveness of activity reductions on reducing air pollution. The Oxford COVID-19 Government Response Tracker has been monitoring government imposed restrictions. There is not much information on how various emission sectors respond to lockdowns. An observation-based metric for lockdown intensity could provide useful information for examining lockdowns on city-level scales or for examining the effects on air quality that are associated with lockdowns in different emission sectors.
We use the high spatial resolution of TROPOMI to infer global ground-level NO2 estimates at, to our knowledge, an unprecedented spatial resolution sufficient to assess individual cities worldwide, and to examine changes in ground-level NO2 occurring during COVID-19 lockdowns. Satellite-based estimates provide information on important spatial variability in NO2 changes and the NO2 response to lockdowns in various emissions sectors. The long-term record of OMI observations provides an opportunity to examine trends in ground-level NO2 over the last 15 years to provide context for the recent changes.
An initial baseline is provided by the global annual mean TROPOMI-derived ground-level NO2 concentrations. Heterogeneity is revealed by the excellent resolution of ground-level NO2 concentrations. 1–10). Over urban and industrial regions, NO2 enhancements are apparent. TROPOMI-derived ground-level concentrations are consistent with in situ observations. The consistency of ground monitors is degraded by neglecting the spatial and temporal variability in the NO2 column-to-surface relationship.
Satellite-derived NO2 concentrations are shown in the first figure.
TROPOMI-derived annual mean ground-level NO2 concentrations from 2005 to 2019. The colour intensity shows the significance of the trend. The black and red values give trends for the period when ground-monitor data is available. Only monitors that have data available over the entire time period are included. Satellite-inferred ground-level NO2 concentrations in South America, Africa and the Middle East are included in the population-weighted mean. The trends are given at the top. The time periods were chosen to reflect the most recent years. The error bars represent uncertainties in population-weighted means.
Context for changes during COVID-19 lockdowns is offered by the examination of long-term changes in air pollution. 1–10). Satellite-derived NO2 concentrations decreased in urban areas across most of the USA and Europe, eastern China, Japan, and near Johannesburg, South Africa, largely reflecting emission controls on vehicles and power generation. In Mexico, the oil sands region in northern Canada, and throughout the Balkan peninsula, central and northern China, India and the Middle East, NO2 increases are observed. The ground-level concentrations in China increased from 2005 to 2010 and then decreased from 2010 to 2019. The change is consistent with observed changes in NO2 columns. Concentrations increased in urban and industrial areas of South America from 2005 to 2010 and in South Africa and the Middle East from 2005 to 2015, but decreased in recent years. There are maps of trends in these regions. Increasing coal-powered electricity demands and growing industrial emissions led to an increase in Concentrations in India. Satellite observations in North America, Europe and China were used to calculate the trends in population-weighted NO2 concentrations. Satellite-derived concentrations are decreasing in Europe, North America, and China.
The April 2020 to April 2020 difference between ground-level NO2 concentrations is shown in Figure 2. The global population-weighted mean concentrations of NO2 are decreasing in 2020 relative to 2019. The map shows the month with the largest change in population-weighted regional mean concentration for each region, with an additional period included for China, because of the earlier restrictions in other countries. Mean concentrations decreased by savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay savesay The largest decreases occur in China in February with concentration decreases greater than 10 parts per billion by volume. Over the last two decades, the decreasing trends across North America, Europe, and China have been aided by technological advances in vehicles and power generation, while temporarily buffering changes from increasing energy demands in India and the Middle East. In April 2020 there will be NO2 increases in central China.
The differences in April mean ground-level NO2 from 2020 to 2019.
Concentrations were derived using TROPOMI observations.
The changes in ground-level NO2 are shown in the third figure.
The monthly mean NO2 differences from 2020–2019 are left in each pair of images. The NO2 trends are derived from OMI+TROPOMI. Seasonal variation is corrected for annual mean long-term trends. The time periods for trend calculations in each region were chosen to reflect the most recent years where a consistent trend is observed. The mean difference for the given region is represented by the value under each panel.
Maps of long-term NO2 trends are shown in Figure 3. The long-term trends in most regions show that the observed changes exceed the expected year-to-year differences. The population-weighted mean concentration changes are lower than long-term trends by factors of 17 7 in North America, 19 2 Europe, and 3.6 0.6 in Asia.
The table has TROPOMI-derived ground-level NO2 data.
The meteorological differences are calculated using emission inventories that do not include changes that occurred due to COVID-19 policies. The changes at 2.0 2.5 resolution are derived from TROPOMI. Population-weighted NO2 concentration changes due to meteorology in Asia, Europe, South America, Africa and the Middle East are a factor of 2–6 smaller than observed. Concentration increases in the central USA do not seem to be meteorologically driven and may be due to changes in biogenic NOx sources.
The ratio of population-weighted January–June monthly mean NO2 concentrations is shown in supplementary fig. Most regions have the largest decrease in NO2 in April when the global mean COVID restriction stringency index reached a maximum of 0.79 on 18 April, apart from China. In most regions, 2020 NO2 concentrations return towards pre-lockdown values in May or June due to relaxing travel restrictions as well as increasing soil, lightning and biomass-burning emissions that diminish the sensitivity of ambient NO2 to anthropogenic emissions.
The fine resolution of our satellite-derived ground-level NO2 dataset allows the assessment of larger changes in NO2 concentrations at the city level. The mean ground-level NO2 for the month is calculated by taking the ten most populous cities in each country with a population greater than 1 million. Most cities have TROPOMI-derived NO2 decreases that can't be explained by meteorology alone. Satellite-derived NO2 concentrations in Beijing decreased by 45% in March despite the favorable meteorological conditions. Despite unfavorable meteorological conditions, Jakarta, Manila, Istanbul, Los Angeles and Buenos Aires had decreased NO2. Moscow, Tokyo, London, New York, Toronto and Delhi had meteorological conditions that would have led to NO2 decreases regardless of emission changes, but observed concentration changes exceeded the expected meteorological change.
The analysis shows a decrease of 32 2% for 215 cities. The mean expected meteorologically driven change was 1% and the mean expected change due to long-term trends was 0.4%. The reductions are consistent with those found in 381 ground-monitor values. 65 of the 215 cities are in countries that did not have ground-monitoring data available for previous studies. The majority of the 65 cities without monitors are in Africa and southeast Asia. The average gross national income per capita for unmonitored countries is US$7,100, compared to US$25,000 for monitored countries, illustrating the potential of satellite-derived ground-level concentrations for providing information about lower-income regions. In summary, the observed decreases in NO2 across more than 200 cities worldwide were mostly driven by COVID-19 lockdowns, with local variations by meteorology and business-as-usual changes.
Table 1 shows monthly mean country-level population-weighted NO2 concentrations, changes during COVID-19 restrictions, meteorological effects and long-term trends for the month. At the national and regional level, the meteorological effects were not very significant. Context for the scale of the changes observed during COVID-19 lockdowns is provided by multi-year trends. Four years of long-term NO2 reductions were equivalent to the decrease in March NO2 concentrations in the USA. Changes in NO2 were equivalent to three years of reductions in China and up to 23 years in Germany. The April 2020 population-weighted NO2 concentration was less than in April 2019: equivalent to 15 years of global NO2 reductions.
Evidence for the impact of travel restrictions can be found in the relationship between this satellite-derived ground-level NO2 dataset and lockdown stringency. Each month from January to June, the population-weighted mean NO2 was calculated for each country. The NO2 ratio in countries with the strictest lockdown was 29 3% lower than in countries with the weakest. The maximum and median ratios were lower in countries with strict lockdowns. Both distributions have the same variability, which is due to meteorology. When focusing on only the month with the strictest lockdown for each country, changes in NO2 are correlated with the intensity of the lockdown, with changes in countries with strict lockdowns more than three times as large as those with weaker lockdowns.
The resolution of TROPOMI observations went from 7 km to 5.5 km in August. Interannual comparisons may be affected by this change, particularly with respect to the sub-grid downscaling of process which relies on the spatial structure observed by the satellite. To test the influence of the resolution change, we use two different sub-grid scaling factors to calculate the annual mean surface concentrations over Asia. The mean relative difference between the two tests was 9% for grid boxes with annual mean concentrations greater than 1 ppbv, with a change in regional population-weighted NO2 concentrations of 3%. Greater sensitivity to observation resolution can be seen in regions with larger NO2 enhancements, although relative differences greater than 25% can be found in fewer than 5% of grid boxes. The impact on population exposure estimates is small, according to the tests.
Uncertainty values presented above represent uncertainty in the conversion of satellite-observed slant columns into surface concentrations and do not represent systematic errors in the retrieval of slant columns from satellite-observed radiances. Errors related to air mass factor calculations can be reduced by using higher-resolution inputs in air mass factor calculations and partially mitigated by converting column densities to surface concentrations.
If the sampling rate is low, biases in monthly mean calculations may persist, even if we apply a scaling factor to correct them. Most of the cities examined in Supplementary Table 1 had sufficient sampling to allow for a robust monthly mean calculation, except for two cities which had less than 5 days of observations per month. Despite the lower sampling frequencies, the results from these cities were consistent with nearby cities.
The updated NO2 estimate is less sensitive to model resolution and uses higher-resolution satellite observations than previous estimates. However, there are still limitations. The 1 1 km2 resolution used here can't be captured by the satellite observations, so there can be considerable fine-scale variability. In times when ground-monitor data is unavailable, absolute concentration values are less accurate than they would be if observed column densities were converted to ground-level concentrations. The data is still useful for analyzing relative interannual variability. We assume that the spatial gradients observed by TROPOMI in 2020 can be applied to the entire time series of OMI. Past evaluations of similar assumptions have not found it to be a substantial error source. In areas with a lot of free NO2 sources, there may be additional errors in the column to ground-level conversion.
The annual mean ground-level NO2 concentrations developed here are available. TROPOMI-derived concentrations are available at the following websites. Satellite-derived ground-level NO2 concentrations are available for trend analysis. Satellite column data is available from the NASA Goddard Earth Sciences Data and Information Services Center. The model version of the GEOS-Chem is available. The US Environmental Protection Agency Air Quality System has hourly NO2 readings from ground monitors in the USA. The Oxford COVID-19 Government Response Tracker provides policy information. Population distribution data is available from the Center for International Earth Science Information Network. The NO2 changes from previous studies used for comparison here were compiled by Gkatzelis et al.34 and can be found at https://covid-aqs.fz-juelich.de. The data was provided by the World Bank.
The code used to calculate NO2 concentrations from satellite columns can be requested. The Climate Data Toolbox was used to produce some features in the maps.
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The Canadian Urban Environmental Health Research Consortium supported the research. R.V.M. acknowledges support from NASA. The NO2 data was made public by the OMI and TROPOMI teams.
The authors do not have competing interests.
Nature thanks the anonymous reviewers for their work.
Supplementary Methods, Supplementary Table 1, Supplementary Figures 1–20, and additional references are contained in this file.
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The country or region is named.
The month with the greatest change.
The population-weighted mean NO2 concentration is for the month of January.
The monthly population-weighted mean is 2020.
The change from meteorology is expected from 2020 to 2019.
Population-weighted NO2a has a long-term trend.
There is a ratio of 2020–2019 difference to long-term trend.
Chinab.
January.
0.2
2.5
0.057 0.03
0.8
3.4 0.6
Indiab.
June.
0.96 0.06
0.29
0.062
0.017 0.005
It's na.
USA.
March.
3.0 0.1
0.08
0.12
0.119
3.4 0.7
Indonesiab
June.
1.24 0.04
0.2
0.031
0.016
20 20
Brazilc
April.
1.01 0.04
0.2
0.15
0.064
5 4
Bangladeshb.
April.
0.82 0.05
0.24
0.18
0.026
It's na.
Mexico.
May.
2.75 0.06
0.06
0.02 0.02
0.095 0.006
It's na.
Russia.
April.
4.18 0.07
0.2
0.29
0.074
19 3
Japanb.
April.
3.0 0.2
1.9
0.20
0.24
8 2
Egyptd.
May.
3.1 0.1
0.2
0.03
0.25
1.3
Irand.
April.
2.66 0.07
0.7
0.080
0.12
4 6
Turkeyd.
April.
4.23 0.08
1.5
0.17 0.03
0.135 0.007.
It's na.
Germany.
March.
7.45 0.2
0.7
0.77
0.12
23 4
Thailandb.
March.
1.34 0.08
0.25
0.052
0.003
100 200.
France.
April.
4.56 0.03
3.1 0.1
0.117
0.168
19 1
United Kingdom.
April.
6.42 0.03
2.8 0.1
0.20
0.43
6.7
Italy.
February.
10.9
2.8
2.84 0.05
0.10
8 1
South Africa.
May.
7.7 0.1
2.5
0.06
0.2
7 3
Spain.
April.
3.16 0.04
2.1 0.1
0.103
0.169
12.6
Argentinac
April.
1.63 0.07
0.8
0.03
0.08
11 10
Africad.
May.
0.66 0.02
0.05
0.012
0.051
2.6 0.6
Asiab.
March.
3.0 0.1
0.70
0.002
0.10
3.6 0.6
East Asiab.
February.
6.4 0.1
1.86
0.068
0.05
3.4 0.4
South Asiab.
June.
0.98 0.06
0.28
0.044
0.05 0.06
It's na.
Europe.
April.
3.87 0.02
1.67
0.096
0.090
19 2
West Europe.
April.
4.52 0.02
2.08 0.07
0.115
0.163
12.8
Central Europe.
April.
2.86 0.05
1.0
0.013
0.053 0.005
It's na.
East Europe.
April.
3.43 0.03
1.10 0.06
0.167
0.049
29 2
North America.
April.
2.41 0.07
0.1
0.105
0.029
17 7
There is an area called the Oceania.
May.
1.59 0.09
0.2
0.024
0.086
2 2
South Americac.
April.
1.10 0.05
0.4
0.022
0.056
8 7
The country level is global.
April.
1.5 0.2
0.05
0.050
0.04
15 4
Population-weighted.
April.
2.2
0.08
0.04
0.10
5 3
The countries with the largest populations and annual mean population-weighted NO2 concentrations greater than 1 ppbv are shown for months. There is also global and regional data shown.
Satellite-inferred annual mean ground-level NO2 trends are scaled by the ratio of the monthly mean to the annual mean.
The long-term country-level trends are not calculated for countries.
B Asia: from 2013 to 2019.
South America from 2011 to 2019.
The Middle East and Africa are covered in this book.
When one value is positive and one is negative, the ratio is not calculated.
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