An improved Bayesian inversion to estimate daily NOx emissions of Paris from TROPOMI NO2 observations between 2018–2023

Mols, Alba; Boersma, Klaas Folkert; Denier van der Gon, Hugo; Krol, Maarten

doi:10.5194/acp-26-1497-2026

Articles | Volume 26, issue 2

https://doi.org/10.5194/acp-26-1497-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-26-1497-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 2

Research article

|

29 Jan 2026

Research article |

| 29 Jan 2026

An improved Bayesian inversion to estimate daily NO_x emissions of Paris from TROPOMI NO₂ observations between 2018–2023

Alba Mols, Klaas Folkert Boersma, Hugo Denier van der Gon, and Maarten Krol

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Total article views: 5,452 (including HTML, PDF, and XML)

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Views and downloads (calculated since 21 Jan 2025)

Month	HTML	PDF	XML	Total
Jan 2025	170	26	10	206
Feb 2025	88	20	6	114
Mar 2025	28	8	0	36
Apr 2025	54	12	2	68
May 2025	56	18	4	78
Jun 2025	98	30	16	144
Jul 2025	58	18	2	78
Aug 2025	250	20	4	274
Sep 2025	1,246	34	12	1,292
Oct 2025	80	26	4	110
Nov 2025	108	66	8	182
Dec 2025	62	98	6	166
Jan 2026	244	91	15	350
Feb 2026	317	119	18	454
Mar 2026	219	151	11	381
Apr 2026	188	94	26	308
May 2026	87	78	22	187
Jun 2026	28	16	5	49
Jul 2026	113	47	20	180
Aug 2026	73	62	34	169
Sep 2026	209	169	122	500
Oct 2026	45	50	31	126

Cumulative views and downloads (calculated since 21 Jan 2025)

Month	HTML	PDF	XML	Total
Jan 2025	170	26	10	206
Feb 2025	88	20	6	114
Mar 2025	28	8	0	36
Apr 2025	54	12	2	68
May 2025	56	18	4	78
Jun 2025	98	30	16	144
Jul 2025	58	18	2	78
Aug 2025	250	20	4	274
Sep 2025	1,246	34	12	1,292
Oct 2025	80	26	4	110
Nov 2025	108	66	8	182
Dec 2025	62	98	6	166
Jan 2026	244	91	15	350
Feb 2026	317	119	18	454
Mar 2026	219	151	11	381
Apr 2026	188	94	26	308
May 2026	87	78	22	187
Jun 2026	28	16	5	49
Jul 2026	113	47	20	180
Aug 2026	73	62	34	169
Sep 2026	209	169	122	500
Oct 2026	45	50	31	126

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Total article views: 5,452 (including HTML, PDF, and XML) Thereof 5,309 with geography defined and 143 with unknown origin.

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Short summary

We created a new method to estimate city air pollution (NO_x emissions) using satellite data. Testing showed our approach works well to track how pollution spreads in urban areas. By combining observations with prior knowledge, we improved the accuracy of emission estimates. Applying this method in Paris, we found emissions were 9 % lower than expected and dropped significantly during COVID-19 lockdowns. Our method offers a reliable way to monitor pollution and support environmental policies.