Example of usage

Monitoring air pollution using Sentinel-5P data

The COViD-19 pandemic put air pollution observation from space in the spotlight. International media, national administration, research institutions, and many others, published plenty of images and maps which reported a significant decrease in air pollution due to the lockdown. The vast majority of those publications have been prepared based on Sentinel-5P data. That proves how essential data derived from Sentinel-5P is.

Research on air pollution based on space-borne data has been conducted since 1972. Then, aerosol concentration measurements were carried out based on the ERTS-1 satellite (Landsat 1) with appropriate algorithms. The years 1999-2004 were important for the modern history of using remote sensing for pollution measurements when the Terra (1999), Aqua (2002), and Aura (2004) satellites were launched into space. Aura carries an OMI sensor for monitoring concentrations of O3, NO2, SO2, BrO, formaldehyde, and aerosols.

The newest sensor - TROPOMI, installed on the Sentinel-5P, measures the concentration of O3, SO2, NO2, CO, formaldehyde, CH4, and aerosols. The spatial resolution of TROPOMI is 5,5 km x 3,5 km. In the case of OMI, it is 13 km x 24 km, and it can be improved to 13 km x 13 km in urban areas. Regarding measurements' capabilities and spatial and temporal resolutions of S5P, it is noticed that Sentinel-5P is the most advanced satellite contributing to air quality measurements.

Air pollution data on the CREODIAS platform

Sentinel-5P data are provided as products. Thanks to CREODIAS, it is possible to obtain data at two processing levels: LEVEL 1B, which is radiance in each band of TROPOMI (UV, UVIS, NIR, SWIR), and LEVEL 2, which provides a concentration of gases (NO2, SO2, O3, HCHO, and CH4), two clouds products (CLOUD and NP) and two aerosols products (Aerosol Layer Height and Aerosol Index). The products are distinguished concerning timeliness: near-real-time (NRTI), offline (OFFL), and reprocessing (RPRO).

According to the Royal Netherlands Meteorological Institute, which was responsible for the creation of retrieval algorithms for TROPOMI, it is recommended to use the offline data (OFFL), available within a few days after acquisition, or the latest version of reprocessed data. NRTI data is usually available much faster, even within a few hours after acquisition. However, it may be incomplete and has a slightly lower data quality as compared to the OFFL and RPRO. What is more, each product is divided into numerous layers. For instance, the NO2 product consists of data on tropospheric NO2, stratospheric NO2, averaging kernels, air mass factor, and many more.

An example of search criteria for NO2 pollution data: offline product processed at LEVEL 2 captured from 11th of November 2022 to 21st of November 2022 over Italy. As can be seen, there are a lot of images captured within this time range. This is because of the high 1-day temporal resolution (in fact, there is more than one image per day for a specific area).


Use cases

NO2 Spatio-temporal distribution over Poland

In the presented case study, the tropospheric column number density of NO2 (NO2 TVCD) was used. It provides data on NO2 concentration in the troposphere – the surface layer of the atmosphere nearest to the Earth.

An average of NO2 TVCD over Europe in 2019 and 2020 was calculated. It was found that the most polluted areas in Europe are located in Belgium, Netherlands, western Germany, and northern Italy as well as urban areas of cities such as Madrid, London, Barcelona, Istanbul, Paris, Katowice, and several more. Further, averaging NO2 TVCD for each country revealed that the five most polluted countries were Belgium, Netherlands, Germany, Luxembourg, and the Czech Republic, both in 2019 and 2020. The least NO2-polluted countries were Iceland, Norway, Sweden, Finland, and Ireland, both in 2019 and 2020.

Let’s look at air pollution in local conditions. Combining NO2 raster data, administrative borders vector data, and Jupyter Notebook tools provides us with results on average NO2 pollution for any area of interest: country, voivodeship district, or even a single city. This information could be very useful for public administration. 

An analogous analysis can be carried out by adding a time variable, which will enable variation to be tracked over time and space. Seasonal, monthly, and daily averages of NO2 TVCD over Poland in 2019 are presented below. What can be observed? Air pollution is highest in winter and lowest in summer. In autumn, it can be almost as high as in winter. As NO2 is closely related to human activity, it is lower at weekends compared to weekdays.

It is worth noting, however, that the results of an analysis conducted for areas over the southern hemisphere would be the opposite! It is sufficient to look at NO2 TVCD over South Africa in January and July.

How Sentinel-5P data can be used in other ways?


The NO2 TVCD map over Europe shows a sort of linear area of higher pollution in the Bay of Biscay region. A similar pattern is observed next to Gibraltar. What are these lines? They are sea lanes. Marine traffic is one of the biggest sources of NO2 pollution. Its impact is particularly observed over the seas, which are areas of clean air.

Sentinel-5P is not just about NO2. CO column density (CO VCD) products can be used for wildfire detection and monitoring. The negative impact on air quality could be monitored, as well. Below, there is an example of using CO VCD during a huge wildfire in Biebrza National Park in Poland in 2020 (left), and after the event (right). It can be seen that during the fire, the pollution plume extended southwards. However, after a few days, the air condition got back to normal.


To sum up, there are a lot of possibilities to use Sentinel-5P data: a long-term and wide area analysis, spatial statistics, monitoring air quality, monitoring natural hazards, and even distinguishing and tracing marine routes. All current and archived Sentinel-5P data for the entire Earth, among all other Copernicus satellite data, can be acquired and processed on the CREODIAS platform.

Author: Patryk Grzybowski, Institute of Geophysics, Faculty of Physics, The University of Warsaw