On Granularity Variation of Air Quality Index Vizualization from

Sentinel-5

Jordan Salas Cuno

, Arthur Andrade Bezerra

, Aura Conci

and Luiz M. G. Gonc¸alves

Computer Institute, Universidade Federal Fluminense, Niter

oi, Brazil

Grad. Prog. in Elec. and Comp. Eng., Univ. Fed. do Rio Grande do Norte, Natal, Brazil

Keywords:

Air Quality Index, Sentinel-5 Dataset, Pandemic Dynamics, Data-Driven Prediction.

Abstract:

Air quality has been a hot research topic not only because it is directly related to climate change and the

greenhouse effect, but most because it has been strongly associated to the transmission of respiratory diseases.

Considering that different pollutants affect air quality, a methodology based on satellite data processing is

proposed. The objective is to obtain images and measure the main atmospheric pollutants in Brazil. Using

satellite systems with spectrometers is an alternative technology that has been recently developed for dealing

with such a problem. Sentinel-5 is one of these satellites that works contantly monitoring the earth surface

generating a vast amount of data mainly for climate monitoring, and that is used in this research. The main

contribution of this research is a computational workﬂow that uses Sentinel-5 data to generate images of Brazil

and its states, in addition to calculating the average value of the main atmospheric pollutants, data that can be

used in the prediction of pollution as well as the identiﬁcation of most polluted regions.

1 INTRODUCTION

The quality of the air that is breathed by living beings

has been highly affected in recent years. Changes in

the composition of the atmosphere were studied dur-

ing the COVID-19 pandemic, when restrictive mobil-

ity measures were adopted around the world, causing

the sudden change on society’s behavior, and having

an impact on the generation of different atmospheric

pollutants. Given the chronological nature of air pol-

lutants over the years, techniques based on data or ar-

tiﬁcial intelligence (AI) have been developed to pre-

dict air quality dynamics (Pereira et al., 2020; Arag

et al., 2022). Particularly, the techniques based on

Long Short Term Memory (LSTM) and Mean Abso-

lute Error (MAE) are known to have general appli-

cability in time series data prediction (Pereira et al.,

2020)

To determine the composition of the air, which

may be suitable or harmful for living beings, the air

quality index (AQI) is used. Each country or environ-

mental regulatory entity has parameters to determine

the AQI level. Different indices of air pollutants are

used to generate a single value that represents the air

quality in a given region. At the same time, it is im-

portant to identify regions with high pollution levels

so that environmental authorities can adopt measures

to protect the population’s health.

In the present work, our focus of study is the gen-

eration of pictures that represent the levels of each at-

mospheric pollutant in Brazil and, average value for

each pollutant. High pollution levels or an imbal-

ance in atmospheric composition may be related to

spreading diseases that can cause respiratory prob-

lems and their worsening. Respiratory infections can

be more widespread with a low AQI (Fermo et al.,

2021; Piscitelli et al., 2022).

It is well known that pollution and other kind of

(even natural) phenomena (as combustion or burning)

provoque particle dispersion having substantially air

quality effect. The consumption of fossil fuels and

the huge amount of ﬁres (natural or provoqued) are

undoubtedly the worst ones. Mainly because of them,

air quality has been substantially degradaded in the

last decades (for instance in USA, with fog almost

going to Manhathan). This lost of quality due to fos-

sil fuels became more evident when the COVID-19

pandemic was declared by World Health Organization

(WHO) in the beginning of 2020. The social isolation

forced a stop of vehicles and fabrics, promoting im-

mediat change in pollutant levels, and improving the

air quality. The decrease of emissions from vehicles,

fabrics, and other activities, increased the air quality.

Thus, showing that the quality of the air presents an

Cuno, J., Bezerra, A., Conci, A. and Gonçalves, L.

On Granularity Variation of Air Quality Index Vizualization from Sentinel-5.

DOI: 10.5220/0012459000003660

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 3: VISAPP, pages

711-720

ISBN: 978-989-758-679-8; ISSN: 2184-4321

711

important role, not only in desease countermeasures

but also in other situations such as giving an overview

of its impacts in the climatic global changing, for ex-

ample.

The determination of AQI uses a set of atmo-

spheric parameters that are usually measured in some

way and serve as input for its calculation. Here we

aim to use data coming from the Sentinel-5 satel-

lite. Thanks to this satellite that is contantly tak-

ing all kinds of raster data from the earth’s surface,

this process can be done for different regions of the

planet. We use in the study the Brazilian geographical

area, in order to make the experiment feasible. This

means analysing different atmospheric pollutant from

the satellite data in the geodesic positions of the states

of Brazil. Although, there is a traditional approach to

compute de AQI, a question that arise is how precisely

calculate it from satellite data. Also, other issues ap-

pear such as what is the proper granularity of data that

should be useful and efﬁcient for a country observa-

tion.

This study contributes to a larger project, for

predicting the dynamics of viral epidemics and

infectious contagious diseases with clustered data

analysis from the perspective of artiﬁcial intelli-

gence. The project goal is to predict the dy-

namics of the advancing behavior of the COVID-

19 pandemic, using AI methods. Normally, there

are parameters or behaviors do not used (or that

cannot be) in traditional epidemiological mod-

els such as the Susceptible, Infectious, Recov-

ered (SIR), Autoregressive Integrated Moving Aver-

age (ARIMA), and Susceptible-Exposed-Infectious-

Recovered-Deceased (SEIRD), among other predic-

tion models (Pereira et al., 2020). Hence, the advan-

tage of this approach is the incorporation of new as-

pects that inﬂuence the behavior of the pandemic for

predictions (as mobility indexes, climatic factors, and

air pollution, being the latter the topic of research of

the current work).

Thus, our main contribution here is the develop-

ment of a technique that can be used to calculate air

pollutant rates within a period of time, with a possibly

ﬁner granularity. As said, the focus is a geographic

area inside Brazil, initially. So, valid characteristics

for the Sentinel-5 satellite and data set are used in-

side this region. As aforementioned, the predictions

given by the data-driven approaches use AQI, and

other variables, and here we have contributed with

the use of Sentinel-5 data for calculating AQI at some

desired level of granularity that is recquired by these

data-driven tools.

2 METHODOLOGY

Calculating the air pollutant indices for a given re-

gion helps evaluate air quality. As mentioned previ-

ously, to estimate the AQI for a given area, it is nec-

essary ﬁrst to obtain the air pollutant indices; based

on the analysis of the various index, it is possible to

check whether the air in a given location is dangerous

for humans and animals (Arag

ao et al., 2022; Fermo

et al., 2021; Piscitelli et al., 2022). The general ﬁnd-

ing is that by improving air quality respiratory prob-

lems will be minimized, as well other chronic diseases

that are ssociated with the deaths from Covid-19.

Therefore, next subsections discuss the levels of

atmospheric pollutants, the way in which the data pro-

vided by Sentinel5 is acquired, and the structure of

how all this information is made available.

2.1 Acquiring Pollutant Data

One of the most important atmosferic parameters

from which the Air Quality Index (AQI) can be

most of time straight calculated is particulate matter

(PM), more speciﬁcally PM2.5 and PM10. Moreover,

other pollutants as Ozone (O3), Nitrogen Dioxide

(NO

), Sulfur Dioxide (SO2), and Carbon Monox-

ide (CO) emissions can also be used in its determi-

nation. Nowadays, there exist ground monitoring sta-

tions for acquiring both PM2.5 and PM10 data, with

a few exceptions where only the PM10 data is avail-

able (Scale, 2022).

As said, these pollutant can be acquired in two dif-

ferrent ways. The ﬁrst one is by using an in-loco mon-

itoring station, which has the several sensors types

installed inside it. In this case, they can be used to-

gether in a system that captures their speciﬁc data, on

the several variables above (Ozone, PM, and so on).

Figure 1 shows some of the existing stations around

the world (Scale, 2022). Notice that a few of them are

located in Brazil, where they are mainly in the cities

of the southern states.

On a second way, it is possible to have these data

acquired and calculated by using data provided by

satellites. They normally capture the radiation of light

from regions on the earth’s surface, from which it is

possible to extract information that can be used to es-

timate the values for the pollutant. This can be done

thanks to air quality scientists that have discovered

that each pollutant has a speciﬁc radiation distribu-

tion, which can be used to separate them. Thanks to

this, satellite data is often used nowadays in order to

measure pollution itens, such as the World’s Air Pol-

lution: Real-time Air Quality Index (Scale, 2022), Air

quality index (AQI) and PM2.5 air pollution (Project,

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712

Figure 1: World map of existing AQI stations.

2022b), and Air Pollution: Real-time Air Quality In-

dex (AQI) (Project, 2022a).

Determination of the AQI for a particular places

where it is generally worst is also possible, as for

example the Hanoi AQI and the Vietnam Air Pollu-

tion (Hanoi, 2022). There are web sites as the Health

and Air Quality Data Pathﬁnder from NASA (NASA,

2022) that provide open access to their data for re-

search scientists. It is said that air pollution is one of

the largest global environmental and health threats.

Actually, their satellites and airborne platforms have

been equiped with instruments that continuouslly ac-

quire new data about these pollutants.

The data Sentinel collects is available through

Copernicus data Space Ecosystem, an open platform

that provides free access to a wide range of data

from the Copernicus Sentinel missions. In January of

2023 Copernicus Data Space Ecosystem, old Coper-

nicus Open Access Hub initial service, allowing the

analysis and exploration of pollutant data freely, al-

lowing the use of data relating to air quality, ozone

layer, and many other data. Here, we use such kind

of data provided by Sentinel-5 (The-European-Space-

Agency, 2022) in order to calculate the index of atmo-

spheric pollutants, which is known as having high-

quality. Besides in the current work we focus only

on data provided by Sentinel-5, we notice that the use

of in-loco (PM, humidity, and temperature) sensors is

also an option.

2.2 Data Preprocessing

The Copernicus Data Space Ecosystem provides vast

information about different atmospheric pollutants, so

it is necessary to select the types of pollutants that will

be used in this work. Sentinel-5 makes data available

in the form of bands around the earth, providing broad

research coverage and collecting diverse information

for each pollutant. All this information downloaded

from the Copernicus Space Ecosystem is of consider-

able size.

As this work aims to measure energy, it is nec-

essary to pre-process the data to work with only the

essential information. The geographic delimitation is

part of the pre-processing since our geographic area

of interest is Brazil. This pre-processing results in

ﬁles that are lighter and simpler to manipulate, but

containing the original information that is essential

for our work.

2.3 Pollutant Measurements

The data collected by Sentinel-5 follows a chronolog-

ical order, as explained in greater detail in the next

session. Sentinel-5 orbits around the earth and gen-

erates .NC ﬁles that contain all the information. The

geographic extension is expanded in this work by con-

sidering Brazil’s area of interest 2. To obtain atmo-

spheric information that covers the entire Brazilian

territory, 2 to 3 .NC ﬁles are required. The generated

On Granularity Variation of Air Quality Index Vizualization from Sentinel-5

713

images use all available .NC ﬁles that follow a time-

line. The result is daily images that capture the levels

of atmospheric pollutants across the entire geographic

area of Brazil.

Figure 2: Sentinel-5 scanning orbit.

3 SENTINEL-5 POLLUTANTS

Sentinel-5 is a low-orbit satellite designed to pro-

vide information on air quality and climate composi-

tion, in addition to monitoring the ozone layer (The-

European-Space-Agency, 2022). It is part of the Eu-

ropean Earth Obs.ervation Program (Copernicus) di-

rected by the European Commission (EC). The main

objective is to carry out atmospheric measurements

with high Spatio-temporal resolution, related to air

quality, components of the climate system, ozone and

UV radiation (Veefkind et al., 2012).

3.1 Considered Features

The Sentinel-5 payload consists of a high-resolution

spectrometer system operating in the ultraviolet to

shortwave infrared range, consisting of 7 differ-

ent spectral bands UV-1 (270-300nm), UV-2 (300-

370nm), VIS ( 370-500nm), NIR-1 (685-710nm),

NIR-2 (745-773nm), SWIR-1 (1590-1675nm) and

SWIR-3 (2305-2385nm). The spectral resolution

varies from 1nm in UV1 to 0.25nm in SWIR chan-

nels, with the main climatic components being O

, SO

, HCHO, CO, CH

. The sweep angle of the

sensor is 108°, considering the height of the satellite

orbit (817km) the corresponding distance of the track

on the ground is 2670km. Figure 4 illustrates how this

is performed, based on the TROPOMI measurement

principle (Veefkind et al., 2012).

3.2 Data Structure

Information provided by the Sentinel-5 is organized

into a three-tiered structure:

• Level-0: Contains information about the satel-

lite’s orientation, this information is saved but not

available to users.

• Level-1B: Contains geolocated and corrected ter-

restrial radiation information.

• Level-2: Contains geophysical information de-

rived from the processing of measured data pro-

vided by Level-1B.

All data used here are obtained from Level-2. At

this level, there are three types of ﬂows to work with

this data. The ﬁrst is NRT, which is the near real-time

stream available 3 hours after scanning. The other is

the ofﬂine stream, where information is available after

a few days. And, the last is the reprocessing stream,

where possible missing information is corrected. Ta-

ble 1 shows an example of the Level 2 products, with

identiﬁer and institution.

Table 1: Level 2 Products.

Product type Parameter

L2 AER AI UV Aerosol Index

L2 CH4 Methane (CH

)

L2 CLOUD Cloud fraction

L2 CO Carbon Monoxide (CO)

L2 HCHO Formaldehyde (HCHO)

L2 NO2 Nitrogen Dioxide (NO

)

L2 O3 Ozone (O

)

L2 SO Sulfur Dioxide (SO

)

3.3 Dataset

All this information is available through the Coper-

nicus Open Access Hub website. The data range re-

ferring to a speciﬁc date can be downloaded directly

from this website or a script can be used to automate

this process, depending on the amount of data to be

used in the experiments. It is necessary to deﬁne pa-

rameters in the request for the data ranges of our in-

terest. Time interval that can be set as days, weeks,

or months. The type of product, in the case of pol-

lutant analysis, must be Level-2. And the geographic

area of interest, it is deﬁned as a georeferenced poly-

gon. The deﬁned period considers the ﬁrst six days of

January 2019 and the ﬁrst six days of January 2020,

the selected product of Level-2 is Nitrogen Dioxide

(L2 NO

), and the territory of Brazil is the geographic

area of study. The result is a set of ﬁles (.NC), each of

which represents a satellite scan range. Information

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714

Figure 3: Images ordered chronologically, time interval of 31 days in the month of January, pollutant selected for the graphic

example: NO2.

Figure 4: TROPOMI measurement principle. The dark-

gray ground pixel is imaged on the two-dimensional detec-

tor as a spectrum. All ground pixels in the 2600km wide

swath are simultaneously measured (Veefkind et al., 2012).

within the ﬁles has a structure of groups and layers to

represent the data needed in the experiments. Figure

5 shows a ”.NC” generic ﬁle description for a Level-2

ﬁle.

3.4 Sentinel-5 Resolution

The main features of Sentinel-5 are a swath width of

2600km and a spatial Sampling of 7x7km, in addi-

tion to the multispectral capability that has already

been mentioned. Considering Brazil with an area of

8.510000 million km

as a geographical area of inter-

est, we obtain a two-dimensional matrix that repre-

sents the pollution information over the Brazilian ter-

ritory. This area can be represented visually as an im-

Figure 5: Description of the generic structure of Level-2

ﬁles (.nc) (The-European-Space-Agency, 2022).

On Granularity Variation of Air Quality Index Vizualization from Sentinel-5

715

age with an approximate resolution of 810x810 pix-

els, as shown in Figure 6. The resolution obtained

is more than sufﬁcient to determine the indicators of

air pollutants in Brazil as a country as described in

the experiments section. There is the possibility of

carrying out a more detailed analysis of atmospheric

pollutants, that is, no longer considering Brazil as a

single region, if not carrying out a study on smaller

scales, considering each of the states as independent

regions as shown in Figure 7, in this research an anal-

ysis is also carried out using an even smaller scale,

considering all cities in Brazil. At this point in the re-

search, a new question arises: when the interest was

to analyze the pollutant indexes over a given city, the

quality of the indexes could be compromised by the

resolution provided in Figure 8, a graphic example

of the Sentinel-5 resolution considering the cities in

Brazil.

Figure 6: Sentinel-5 Data Resolution.

Figure 7: State of S

ao Paulo.

Figure 8: Sao Paulo City.

4 IMPLEMENTATIONS

Hence, data provided by Sentinel-5 are two-

dimensional matrices. To determine the pollution

levels with high precision, the information from

Sentinel-5 must be geographically delimited and seg-

mented on the cartographic dataset where queries and

geographic information retrieval are essential. When

deﬁning two levels of granularity (country, states), it

is necessary to deﬁne a step within the workﬂow that

can generate polygonal masks for all levels of gran-

ularity. So our basic architecture consists of a three-

step workﬂow: dataset processing, polygonal mask

generation, and region segmentation as shown in Fig-

ure 9.

4.1 Chronological Database Processing

Each ﬁle downloaded from Sentinel-5 has a size be-

tween 300MB and 500MB. Considering that each of

these ﬁles represents a range of readings for one day

of Sentinel-5, depending on the geographic area of

interest and the deﬁned time interval, it is essential to

use more than one range to cover the entire area of

interest for one day. As said, our area of interest is

Brazil as a country. Thus, on some speciﬁc days it

is necessary to use up to three ﬁles to get all the es-

sential data. Then, it is expected that ﬁle sizes reach

gigabytes (GB) or terabytes (TB) quickly. An infor-

mation extraction process is carried out together with

temporal processing. The result of this ﬁrst step is a

set of smaller ﬁles, simple to use and with informa-

tion on the selected pollutant in chronological order

as show in Figure 3.

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Figure 9: Workﬂow of our proposed methodology.

4.2 Polygonal Mask Generation

It is necessary to use a cartographic dataset, which

must contain geometric information for all previously

deﬁned granularity levels. It is possible to generate

polygonal masks that will be used in the workﬂow’s

next step. The polygonal masks have a vital function

in achieving an accurate measurement of the pollutant

indicators. The two-dimensional data matrix is seg-

mented using the polygonal masks, depending on the

level of granularity the size of the data matrix is es-

sential to achieve an accurate delimitation. As an ex-

ample, Brazil’s mask as a country and the mask of the

state of S

ao Paulo is shown in Figure 10. When the fo-

cus is to carry out a more detailed analysis in smaller

geographic areas, it is necessary to deﬁne masks with

a lower level of granularity, as an example is shown

in the masks of the city of Sao Paulo in Figure 11.

Figure 10: Granularity levels: states, masks to delimit the

geographic area of interest.

Figure 11: Granularity levels: cities, masks to delimit the

geographic area of interest.

4.3 Region Segmentation

The last step consists of using the results of the two

previous ones as input. The two-dimensional ma-

trix with the pollutant indicators is segmented using

polygonal masks. Each mask will be used depending

on the level of granularity. As an example, the seg-

mentation result is shown in Figure 12 for the country,

in Figure 7 for S

ao Paulo state, and ﬁgure 8 for Sao

Paulo city.

Figure 12: Granularity levels: country, masks to delimit the

geographic area of interest.

5 EXPERIMENTS AND RESULTS

We have done a series of experiments in order to test

our proposal. In all of them, the levels of granular-

ity should be ﬁrst deﬁned. The used cartographic

database provides information for two levels of gran-

ularity. The ﬁrst level is Brazil as a country and the

second level is the 27 states that compose it. The time

interval is set to 31 days, with intervals between Jan-

uary 1 and 31, of two years: 2019 and 2020. The se-

lected pollutant are AER, CH

, CO, NO

, O

. Where

any pollutant provided by Sentinel-5 in Table 1 can

be used in the workﬂow deﬁned in Figure 9. As a re-

sult of the ﬂow, we obtain the average pollutant index

On Granularity Variation of Air Quality Index Vizualization from Sentinel-5

717

Figure 13: Aerosol index Absorsion in January 2019 and January 2020.

Figure 14: CH

column volume mixing ratio dry air comparation in January 2019 and January 2020.

Table 2: Mean value of air pollutant indexes in Brazil.

Brazil

Pollutant Unit 2019 2020

AER no unit -0.6575 -0.6358

ppbv 1773.2306 1793.5013

CO mol/m

0.0281 0.0300

mol/m

1.0700e-05 8.9800e-06

mol/m

0.117998 0.115422

for two-time intervals, January of 2019 and January

of 2020.

Based on the time intervals deﬁned for the years

2019 and 2020, Table 2 and Figures 13 to 17 show

the average of AER, CH

, CO, NO

, O

indicators

obtained for the ﬁrst level of granularity, that is, the

Brazilian country.

6 CONCLUSION

Sentinel-5 undoubtedly provided a signiﬁcant ad-

vance on Earth data acquisition, making available to

researchers a large amount of information related to

the quality of the atmosphere. All these data can be

used for the analysis of pollutants individually, as it

was done in this research. As well it can be used for

an AQI analysis, by using all pollutant indicators pro-

vided by the Copernicus Data Space Ecosystem. An-

alyzing the results obtained, we can observe that the

resolution of the data is quite enough to determine av-

erage pollutant indicators for countries and states due

to their larger territory. Nevertheless, if an analysis is

necessary considering ﬁner levels of granularity, such

as small cities, the quality of the measurements may

not be enough, and this has not been assessed yet.

Hence, to this end, we have shown how data from

the Sentinel-5 can help to increase reliability in the

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Figure 15: CO column number density in January 2019 and January 2020.

Figure 16: Tropospheric NO

column number density in January 2019 and January 2020.

Figure 17: O

column numberdensity in January 2019 and January 2020.

data quality, completing it where there is a lack of

more accurate sensor devices. Actually, this parame-

ter has been used by our data scientists for predicting

the dynamics prediction of Covid-19, increasing con-

ﬁdence in their results.

In future work, we intend to study how interpola-

tion and extrapolation techniques can be used in order

to devise a more reliable value when a ﬁner granular-

ity is necessary for these data. We believe that using

splines or other mathematical surfaces could play an

important role in this process. Indeed, the use of deep

learning is another option that will be studied for this.

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