Analysis of the Sentinel-5P NO2 Data Download
Process during 2023 in Chile
Joaquı́n Fernández Verdugo
June 8, 2025
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Contents
1 Download and Conversion Methodology
1.1 Initial Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Main Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Analysis of Obtained Data
2.1 Technical Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Method Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Detected Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Latitudinal Data Loss . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Spatial Variability Induced by GeoJSON Sources . . . . . . . . .
2.3.3 Source Comparison: Sentinel-5P vs. SINCA . . . . . . . . . . . .
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3 Technical Recommendations
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4 Conclusions
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Abstract
This work describes an automated system for downloading and processing satellite data of nitrogen dioxide (NO2 ) obtained from the TROPOMI instrument on
the Sentinel-5P satellite, using Python scripts. The extracted data correspond to
vertical column densities of NO2 , expressed in mol/m2 , allowing for a continuous
spatial representation of the total pollutant load in the atmosphere. Unlike ground
stations, which provide surface concentrations (ppb or µg/m3 ), this metric offers
a comprehensive view of the atmospheric behavior of the gas. The methodology,
operational advantages, and limitations of the system are documented, along with
comparisons with ground sources and recommendations for future improvements.
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1
Download and Conversion Methodology
The following procedure was developed to extract, convert, and store satellite nitrogen dioxide (NO2 ) data from the Sentinel Hub platform, with the goal of obtaining
estimated concentrations in units comparable to those reported by ground monitoring
stations (ppb).
1.1
Initial Configuration
• Authentication with Sentinel Hub using personal credentials (Client ID and Client
Secret).
• Selection of the SENTINEL5P collection corresponding to the TROPOMI instrument.
• Definition of a custom evalscript to extract the tropospheric NO2 band and filter
observations with minimum quality (qa value ≥ 0.75).
1.2
Main Workflow
The complete process can be divided into seven functional stages:
1. Initialization: Input parameters are defined (download dates, label language, path
to GeoJSON file).
2. Reading area of interest (AOI): Coordinates of the region of interest are extracted from the GeoJSON file and transformed to the appropriate reference system.
3. Generating request to Sentinel Hub: A SentinelHubRequest object is constructed including temporal, spatial, and processing metadata (resolution, resampling method, etc.).
4. Data download: The query is executed and files in GeoTIFF format are obtained
with NO2 column values (in mol/m2 ).
5. Optional conversion to volumetric concentration (µg/m3 ): Assuming a
homogeneous tropospheric column height of 10 km, the formula is applied:
Cµg/m3 =
Ccol
· M · 106
H
where Ccol is the concentration in mol/m2 , H = 10 000 m and M = 46.01 g/mol.
6. Optional conversion to parts per billion (ppb): Estimation is performed with:
Cppb =
24.45 · Cµg/m3
M
where 24.45 is the molar volume of an ideal gas at standard conditions (STP).
7. Obtaining statistical values of interest: Indicators such as mean, maximum,
and standard deviation are automatically calculated for each region and month.
8. Geographic visualization of obtained concentration: The system generates
regional maps showing the spatial distribution of NO2 clearly and georeferenced.
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9. Result storage: Converted data is stored, and the request object is returned for
traceability and eventual auditing.
The general process, from reading input parameters to the final unit conversion, is
represented in the following diagram:
Figure 1: Complete workflow of the NO2 data download, conversion, and storage system
from Sentinel Hub. Includes reading the area of interest, query execution, physicochemical processing, and generation of outputs in standardized atmospheric units.
Optionally, a spatial interpolation module is incorporated using geostatistical methods
(e.g., Kriging), to obtain a continuous representation from the discrete values provided
by the sensor.
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Analysis of Obtained Data
2.1
Technical Specifications
Parameter
Value
Units
Spatial resolution
Temporal resolution
Coverage
Output format
mol/m2
7 × 3.5 km
Daily
Global
GeoTIFF
Table 1: NO2 data specifications
2.2
Method Advantages
The implemented system has characteristics that make it especially suitable for systematic
and reproducible processing of large-scale satellite NO2 data. Its main advantages are
listed below:
• Full automation: Allows massive downloads without manual intervention, covering multiple defined regions and periods. Facilitates periodic database updates.
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• Built-in quality validation: Integrates a quality filter (minQa) that excludes
low-reliability observations, reducing the influence of clouds, atmospheric noise, or
other artifacts.
• Integrated post-processing: Includes an optional spatial interpolation stage
(Kriging) that converts sparse data into continuous, comparable, and mappable
surfaces.
• Structured file organization: Storage follows a hierarchy by year, region, and
month, facilitating traceability, exploration, and auditing of results.
• GIS interoperability: Outputs are generated in standard formats (GeoTIFF,
CSV), compatible with tools like QGIS, ArcGIS, and Python libraries, easing integration with other spatial analyses.
• Obtaining statistical values of interest: Metrics such as mean, maximum, and
standard deviation are automatically calculated for each region and period.
• Geographic visualization of obtained concentration: The system generates
regional NO2 distribution maps, ready for visual or communication analyses.
2.3
Detected Limitations
2.3.1
Latitudinal Data Loss
Throughout this study, monthly 2023 nitrogen dioxide (NO2 ) data were downloaded for
Chile’s 16 regions (2015 administrative division). Although this division is prior, it fully
covers the national territory, so it does not limit the validity of the analysis.
Number
XV
I
II
III
IV
V
XIII
VI
VII
VIII
IX
XIV
X
XI
XII
Region
Arica and Parinacota Region
Tarapacá Region
Antofagasta Region
Atacama Region
Coquimbo Region
Valparaı́so Region
Santiago Metropolitan Region
Libertador General Bernardo O’Higgins Region
Maule Region
Biobı́o Region
La Araucanı́a Region
Los Rı́os Region
Los Lagos Region
Aysén del General Carlos Ibáñez del Campo Region
Magallanes and Chilean Antarctica Region
Table 2: Chile’s regional division in 2015
The first diagnosis, shown in Figure 2 and Figure 3, revealed the regional sets most
affected by losses. Figure 2 shows the monthly average of obtainable valid data under the
established download conditions. It shows that the upper and lower limits of this average
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were Magallanes and Chilean Antarctica with 17853.6 and Arica and Parinacota with
1241.9 data respectively. On the other hand, Los Rı́os and Arica and Parinacota were
the regions most affected by percentage loss compared to the Metropolitan Region, with
up to 25% fewer valid pixels than the capital. On the other side, northern regions like
Tarapacá and central regions like Valparaı́so exceeded the metropolitan reference.
Figure 2: Regional average of valid data for 2023
Figure 3: Percentage loss of valid data in regions relative to the Metropolitan Region
A finer inspection of valid pixel density by latitude confirmed a systematic pattern:
the correlation coefficient between latitude and density is ρ = −0.82, implying that
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at higher southern latitudes, satellite coverage degrades. In numbers, southern regions
(Aysén, Magallanes) concentrate up to 85% less data than northern regions; the most
extreme case was Magallanes, with only 5 valid pixels in June 2023.
This variability in the amount of validated data becomes noticeable when studying
its behavior throughout the year. The following figure shows the monthly regional count
with a diagram for each region.
Figure 4: Count of validated samples by month in 2023 for each Region of Chile
When analyzing each case individually, an increasing trend of sample numbers towards
the end of the year is noticeable; however, a deeper look reveals significant differences
in these quantities among the regions studied. For example, Magallanes and Chilean
Antarctica shows a volume of 30667 data points in December, corresponding to its fourth
highest value recorded that year. On the other hand, Coquimbo peaks in that month but
with 4627 data, nearly 85% less. This can be seen in depth from the following images:
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Figure 5: Dispersion of validated data (Qa = 0.75) for Coquimbo and Magallanes and
Chilean Antarctica regions in 2023
The images show the spatial distribution of raw data collected for December, represented in a color scale according to the measured value (in mol/m²). Although the
Magallanes and Chilean Antarctica region has a total of 30,667 observations, visual
coverage is interrupted by large areas with no apparent information. In contrast, the
Coquimbo region presents a visually more continuous and complete map, despite having
85% fewer data than the former.
This contrast is particularly striking, as it suggests that not only the total amount of
data determines the spatial quality or completeness of the visualization, but also factors
such as the distribution and spatial density of valid pixels. Additionally, geographical differences between both regions, with Magallanes located at much more southern latitudes
and with a more extensive territory in longitude, could influence effective satellite coverage due to factors such as viewing angle, higher cloud or ice presence, and the satellite’s
orbital geometry.
This disparity is explained by the observation geometry of the Sentinel-5 Precursor
satellite: its orbit is near-polar and sun-synchronous, inclined 98.7° relative to the equator,
so that zenith angles increase at latitudinal extremes and reduce effective resolution
according to:
RESeffective =
RESnominal
cos(θ)
(1)
These findings confirm that the coverage of the TROPOMI instrument in Chile
has a significant latitudinal bias, particularly in the far south, which must be critically
considered in national-scale atmospheric assessments.
2.3.2
Spatial Variability Induced by GeoJSON Sources
During satellite data processing for the Aysén region in May 2023, the impact of using
different vector files (GeoJSON) as Area of Interest (AOI) delimiters was evaluated.
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Although both sources officially represent regional boundaries, the obtained results reveal
relevant spatial inconsistencies in the analysis of NO2 derived from TROPOMI.
Figure 6: NO2 values for Aysén in May 2023 using the first GeoJSON source
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Figure 7: NO2 values for Aysén in May 2023 using the second GeoJSON source
It can be observed that although both figures exhibit comparable spatial distributions concerning magnitude and concentration, the regional delineation notably displays
displacements and gaps. Close examination reveals that specific borders contain either
missing or overrepresented pixels, especially on the eastern boundary. This indicates a
lack of complete coincidence in the projection or geographic definition between the two
sources.
This difference is also reflected in the statistical metrics of the collected data, as seen
in the following distributions:
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Figure 8: Frequency distributions of NO2 values for Aysén in May 2023 with two GeoJSON sources
Both histograms approximate a normal distribution; however, small variations in
skewness and dispersion are observed. To quantitatively characterize these differences, the
files response statistics jlhonora/geo.csv and response statistics caracena/chile-geojson.
were used, whose values are summarized below:
Statistic
Mean
Standard deviation
Maximum
Minimum
N° of Data
jlhonora/geo
−2.26 × 10−6
6.56 × 10−6
1.9940513 × 10−5
−2.9138968 × 10−5
6053
caracena/chile-geojson
−2.24 × 10−6
6.55 × 10−6
1.9940513 × 10−5
−2.9138968 × 10−5
6011
Table 3: Comparative statistics of NO2 for Aysén in May 2023 by GeoJSON source
Despite small absolute differences in statistical values, a systematic variation is evident, originating exclusively from the geometric definition of the region of interest. This
effect proves particularly sensitive in areas with low valid pixel density or complex geometry, as occurs in southern zones.
The Aysén region, located at a high latitude, shows greater sensitivity to variations
in spatial definition due to the viewing geometry of the TROPOMI sensor. Variability
in borders generates changes in the total number of pixels selected for analysis and can
introduce unintentional biases in longitudinal or comparative studies.
To visually highlight the geometric differences between both vector sources used,
the boundaries of the Aysén region according to definitions available in the repositories
jlhonora/geo and caracena/chile-geojson are presented below:
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Figure 9: Regional polygons from jlhonora/geo
Figure 10: Regional polygons from caracena/chile-geojson
Superficially, no major differences are apparent between both images. However, this
superficial similarity contrasts with the measurable effects observed in previous analyses.
Therefore, it is concluded that data derived from remote sensors can be susceptible to
noticeable variations even when geometric differences in the area of interest definition
seem minimal.
This finding highlights the need to adopt a single standardized vector source, with
validated projection and explicit compatibility with the reference system used in satellite
products. The lack of standardization between AOI files—even for the same administrative unit—can lead to systematic spatial selection errors and thus compromise the
comparability and reproducibility of atmospheric analyses.
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2.3.3
Source Comparison: Sentinel-5P vs. SINCA
The Sistema de Información Nacional de Calidad de Aire (SINCA) or National Air Quality Information System in english is an official platform of Chile’s Ministry of the Environment that compiles, validates, and publishes atmospheric pollutant measurements
from a network of ground stations distributed throughout the country. These stations
operate in real time and report data with high temporal resolution (hourly or daily), under international quality and calibration standards. In particular, the Parque O’Higgins
station in Santiago represents one of the most used reference points for evaluating urban
NO2 concentration.
During 2023, SINCA reported a monthly average NO2 concentration at this station
with an annual mean of 22.65 ppb, a monthly maximum of 35.47 ppb, and a minimum
of 12.64 ppb (Figure 12). In contrast, the satellite estimate processed from products of
the TROPOMI instrument on board the Sentinel-5P satellite provides a considerably
lower monthly average for the Metropolitan Region, with a maximum value in May of
only 0.76 ppb and an annual mean near 0.23 ppb (Figure 11).
Figure 11: Monthly average NO2 concentration in 2023 according to Sentinel-5P for the
Metropolitan Region
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NO2 - registro diario - promedio mensual
x1:Parque O’Higgins, 0003, RAT[M], Value
Tipo deGrafico: Grafico de Barras
Serie Tiempos seleccionada:
x2:Parque O’Higgins, 0003, VAL[M], Value
01/01/2023 00 - 31/12/2023 00 Acumulacion hasa Valores Mensuales
x3:Parque O’Higgins, 0003, LIN[M], Value
40
36
32
28
24
20
16
12
8
4
0
19/01/2023
16/03/2023
Registros validados, Mensual media (ppb)
m=22.65 s=7.772 min=12.64 max=35.47 n=12
11/05/2023
06/07/2023
31/08/2023
26/10/2023
21/12/2023
Registros preliminares, Mensual media (ppb)
m=--- s=-0 min=--- max=--- n=0
Registros no validados, Mensual media (ppb)
m=--- s=-0 min=--- max=--- n=0
Figure 12: Monthly average NO2 concentration in 2023 according to SINCA Parque
O’Higgins station (RM)
The discrepancy between both sources is notable: satellite values represent between
1% and 4% of those reported by the ground station. This difference can be explained
by multiple technical factors:
• Measurement height: SINCA ground stations measure surface concentrations,
while satellites observe total tropospheric NO2 columns, diluting values throughout
the atmospheric thickness.
• Spatial resolution: Sentinel-5P has a nominal resolution of 3.5 × 5.5 km in
nominal mode, limiting its ability to capture intense point sources like dense urban
hotspots.
• Atmospheric conditions: Cloud cover, surface reflectance, and aerosol presence
affect the validation of satellite data, reducing sensitivity in dense urban environments.
• Comparison units: Both instruments measure NO2 but under different methodologies; the satellite provides data in molecular columns (mol/m2 ) that must be
converted and weighted to compare with parts per billion (ppb) units used by
SINCA.
To perform this comparison more coherently, additional processing was applied to the
raw satellite data. From the column values obtained via the SentinelHub API, concentrations were estimated in units equivalent to SINCA (ppb), assuming a homogeneous
tropospheric column 10 km thick. The conversion procedure included two stages:
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1. Conversion from column (mol/m2 ) to volumetric concentration (µg/m3 )
using the formula:
Ccol
· M · 106
Cµg/m3 =
H
where:
• Ccol is the tropospheric column
• H the estimated height (10 km)
• M the molar mass of NO2 (46.01 g/mol).
2. Conversion to ppb using the stoichiometric factor for gases at standard conditions:
24.45 · Cµg/m3
Cppb =
M
Despite these adjustments, differences between both sources remain evident. This
underscores that, although it is possible to approximate measurement units, there are
inherent limits to the comparability between remote platforms and ground stations. Consequently, satellite estimates should be interpreted as regional representations of atmospheric load, not as direct substitutes for human exposure measured at surface level.
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Technical Recommendations
Given the exploratory nature of the system and the observed limitations, the following
improvements are proposed for future versions:
• Incorporate adaptive geometric correction according to extreme viewing angles,
especially in southern zones.
• Implement latitudinal adjustment factors based on historical series to compensate
for systematic coverage losses.
• Complement with additional satellite missions (OMI, future post-TROPOMI sensors) in low-coverage regions.
• Apply specific spatial reconstruction models for winter months with higher cloud
incidence.
• Strengthen validation with ground stations in sectors with high discrepancy between
sources.
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Conclusions
The analysis shows that the orbital geometry of Sentinel-5P introduces systematic data
losses in southern Chile, limiting its applicability for homogeneous national studies. However, the developed system allows automated and efficient extraction in central and northern regions, integrating standard formats and basic validations.
As a prototype, there is room for improvement. Nevertheless, its modular and replicable design provides a solid foundation for future iterations incorporating geometric
corrections, multi-sensor integration, and more robust validation. Its evolution into a
useful tool for national-scale satellite environmental monitoring is projected.
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References
• Copernicus Data Space Ecosystem. OpenEO API Collections. Available at: https:
//documentation.dataspace.copernicus.eu/APIs/openEO/Collections.html
• Copernicus Sentinel-5P. S5P Processing and Products. Available at: https://
sentiwiki.copernicus.eu/web/s5p-processing
• Caracena, M. (2021). Official GeoJSON of Chile by administrative division (2015).
GitHub repository: https://github.com/caracena/chile-geojson
• Honora, J.L. (2019). Chilean regions GeoJSONs (alternative version). GitHub
repository: https://github.com/jlhonora/geo/tree/master
• SINCA - National Air Quality Information System. Chilean Ministry of the Environment. Available at: https://sinca.mma.gob.cl/
• Cressie, N. (1993). Statistics for Spatial Data. Wiley-Interscience, New York.
• Savenets, M. (2021). Air pollution in Ukraine: the view from Sentinel-5P. Időjárás
– Quarterly Journal of the Hungarian Meteorological Service, 125(2), 271–289.
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