Supplementary material to "Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric chemistry modeling"

Abstract. Social-distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business as usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5756 observation sites in 46 countries from January through June 2020. Reductions in NO2 correlate with timing and intensity of COVID-19 restrictions, ranging from 60 % in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were 18 % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5 % to the business as usual estimate. NO2 reductions in Europe and the US have been more gradual with a halting recovery starting in late March. We estimate that the global NOx (NO + NO2) emission reduction during the first 6 months of 2020 amounted to 2.9 TgN, equivalent to 5.1 % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of non-linear atmospheric chemistry. While surface O3 increased by up to 50 % in some locations, we find the overall net impact on daily average O3 between February–June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in night time ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production. The O3 response is dependent on season, time scale, and environment, with declines in surface O3 forecasted if NOx emission reductions continue.

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Supplementary material to "A high-resolution inventory of air pollutant emissions from crop residue burning in China"

10.5194/acp-2017-1113-supplement ◽

2018 ◽

Author(s):

Xiaohui Zhang ◽

Yan Lu ◽

Qin'geng Wang ◽

Xin Qian

Keyword(s):

High Resolution ◽

Crop Residue ◽

Air Pollutant ◽

Pollutant Emissions ◽

Crop Residue Burning ◽

Supplementary Material

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Supplementary material to "Long-term historical trends in air pollutant emissions in Asia: Regional Emission inventory in ASia (REAS) version 3.1"

10.5194/acp-2019-1122-supplement ◽

2019 ◽

Cited By ~ 1

Author(s):

Junichi Kurokawa ◽

Toshimasa Ohara

Keyword(s):

Emission Inventory ◽

Air Pollutant ◽

Pollutant Emissions ◽

Historical Trends ◽

Supplementary Material

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Global impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-3555-2021 ◽

2021 ◽

Vol 21 (5) ◽

pp. 3555-3592

Author(s):

Christoph A. Keller ◽

Mathew J. Evans ◽

K. Emma Knowland ◽

Christa A. Hasenkopf ◽

Sruti Modekurty ◽

...

Keyword(s):

Nitrogen Dioxide ◽

Atmospheric Chemistry ◽

Air Pollutants ◽

Learning Algorithm ◽

Air Pollutant ◽

Pollutant Emissions ◽

Global Impact ◽

The Us ◽

Business As Usual ◽

Surface O3

Abstract. Social distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5756 observation sites in 46 countries from January through June 2020. Reductions in NO2 coincide with the timing and intensity of COVID-19 restrictions, ranging from 60 % in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were 18 (13–23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5 % of the business-as-usual estimate. NO2 reductions in Europe and the US have been more gradual, with a halting recovery starting in late March. We estimate that the global NOx (NO + NO2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6–3.6) TgN, equivalent to 5.5 (4.7–6.4) % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of nonlinear atmospheric chemistry. While surface O3 increased by up to 50 % in some locations, we find the overall net impact on daily average O3 between February–June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production. The O3 response is dependent on season, timescale, and environment, with declines in surface O3 forecasted if NOx emission reductions continue.

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Changes in global air pollutant emissions during the COVID-19 pandemic: a dataset for atmospheric chemistry modeling

10.5194/essd-2020-348 ◽

2021 ◽

Author(s):

Thierno Doumbia ◽

Claire Granier ◽

Nellie Elguindi ◽

Idir Bouarar ◽

Sabine Darras ◽

...

Keyword(s):

South America ◽

Atmospheric Chemistry ◽

Transport Model ◽

Eastern China ◽

Air Pollutant ◽

Control Measures ◽

Atmospheric Pollutants ◽

Pollutant Emissions ◽

The World ◽

The Impact

Abstract. In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1 × 0.1 latitude/longitude degree resolution, on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs is provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC are estimated. As discussed in the paper, reductions vary highly across regions and sectors, due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).

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