Quantifying urban, industrial, and background changes in NO <sub>2</sub> during the COVID-19 lockdown period based on TROPOMI satellite observations
Bibliographic record
Abstract
Abstract. The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO2). While the overall NO2 decline over some large cities is well-established, its quantification remains a challenge because of a variety of sources of NO2. In this study, a new method of isolation of three components: background NO2, NO2 from urban sources, and from industrial point sources is applied to estimate the COVID-19 lockdown impact on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by the observed winds. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The NO2 vertical column density (VCD) values measured by Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel‐5 Precursor over 263 urban areas for the period from March 16 to June 15, 2020, were compared with the average VCD values for the same period in 2018 and 2019. While background NO2 component remained almost unchanged, the urban NO2 component declined by 18–28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a 40–50 % urban emissions decline. In contrast, decline over urban area in China, where the lockdown was over during the analyzed period, was only 3 % except for Wuhan, where more than 60 % decline was observed. Emissions from large industrial sources in the analyzed urban areas varies largely from region to region from +5 % for China to −40 % for India. Changes in urban emissions are correlated with changes in Google mobility data (the correlation coefficient is 0.66) confirming that changes in traffic was one of the key elements in decline of urban NO2 emissions. No correlation was found between changes in background NO2 and Google mobility data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".