Can TROPOMI-NO <sub>2</sub> satellite data be used to track the drop and resurgence of NO <sub>x</sub> emissions between 2019–2021 using the multi-source plume method (MSPM)?
Bibliographic record
Abstract
Abstract. Nitrogen dioxide (NOx) is an important primary air pollutant, dominantly produced by anthropogenic, mostly combustion based, activities from sectors such as industry, traffic and transport. NOx is directly linked to negative health and environmental impacts. Currently, the construction of emission inventories to keep track of NOx emissions is based on official national reported emissions and proxies such as activity data as well as direct measurements. The effort to properly construct an accurate inventory is significant and time consuming which causes a reporting offset between one and five years with respect to the current date. Next to this temporal lag difficulties in composed inventories can arise from legislative and protocol differences between countries and over time in reporting of emissions. Satellite based atmospheric composition measurements provide a unique opportunity to fill this gap and independently estimate emissions on a large scale in a consistent, transparent and comprehensible way. They give the possibility to check for compliance with emission reduction targets in a timely manner as well as to observe rapid emission reductions such as experienced during the COVID-19 lock-downs. In this study we apply a consistent methodology to derive NOx emissions over Germany for the years of 2019–2021. For the years where reporting is available differences between satellite estimates and inventory totals were within 100 kt. The large reduction of NOx emissions related to the COVID-19 lock-downs were observed in both the inventory and satellite derived emissions. The recent projections for the inventory emissions pointed to a recovery of the emissions towards pre-COVID19 levels this increase was not observed. While emissions from the larger power-plants did rebound to earlier levels, others sectors such as road transport and shipping did not and could be linked to a reduction in the number of heavier transport trucks. This again illustrates the value of having a consistent satellite based methodology for faster projections to guide and check the conventional emission inventory reporting. The method described in this manuscript also meet the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues. Transparency and comparability, two key values for emission reporting, are thus bolstered by this technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".