Biodiversity, Urban Quality Life and Air Quality Indices for Hotspot

Detection of Transformation Opportunities in Cities: A Case Study in

Barcelona

Danielly Garc

ıa

1 a

, Mariona Ferrandiz-Rovira

2,3,4 b

, Oriol Serra

and M. Eul

alia Par

1 c

Centre Tecnol

ogic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Carl Friedrich Gauss 7, Castelldefels, Spain

Replantegem. Carrer Creueta 119, B-E, Sabadell, Catalonia, Spain

CREAF, Cerdanyola del Vall

es, Catalonia, Spain

BABVE, Universitat Aut

onoma de Barcelona, Cerdanyola del Vall

es, Catalonia, Spain

Keywords:

Biodiversity, Urban Life, Air Quality, Indicators.

Abstract:

Half of the world’s population lives in cities, where usually there are few little green space and there are also

high levels of air pollution. Moreover, the traditional urbanization of cities contributes to climate change,

promotes the loss of global biodiversity and induces serious health problems for citizens. Both climate change

and the loss of biodiversity affect negatively to the ecosystems and therefore human health, as they are respon-

sible for providing clean air, food, fresh water, medicines, renewable resources. . . This deterioration increases

signiﬁcantly the risk of human-borne infectious diseases such as coronavirus or HIV. The ability we have to

re-naturalize anthropogenic spaces and learn to generate spaces for coexistence will be key for the future of

our society. The research presented in this paper aims to do a step forward to achieve that ability by working in

three schools of the city of Barcelona and their surroundings. Among other actions, in this project, a diagnosis

of neighborhood has been carried out. The diagnosis includes the identiﬁcation and quantiﬁcation of relevant

indicators regarding neighborhood’s biodiversity and also the quality of daily life and the analysis of pollutants

(NO

and PM

) near the schools during the 2021-2022 school year. All these information has been merged

in a single geographic data base and relevant hotspots where to act have been identiﬁed. The information has

been shared with city council and citizens.

1 INTRODUCTION

In 2020, and as a way to provide response to the crisis

situation caused by covid-19, Barcelona City Council

promoted the Scientiﬁc Research Awards for Urban

Challenges in the City of Barcelona 2020. This paper

presents the main scientiﬁc outcomes from one of the

projects awarded on that call: the project ”Rethinking

school environments”. The proposal aroused from the

need to provide solutions to the current environmental

and urban planning situation in which Barcelona ﬁnds

itself. This is a multidisciplinary project joining biol-

ogy, urban planning, and air quality, among others, to

perform a deep review of the city to make it healthier,

more pleasant, sustainable, and a space for all urban

biodiversity to live together. As a speciﬁc objective,

https://orcid.org/0000-0002-8191-3308

https://orcid.org/0000-0001-8548-2851

https://orcid.org/0000-0003-2459-1768

the idea was to learn about school environments in or-

der to renaturalize human spaces and learn to generate

spaces for coexistence, at all levels and for all existing

species. The aim was to provide tools that allow to re-

design cities with a mixture of uses in which a change

in mobility is possible, improve air quality, improve

urban biodiversity and ﬁght the climate emergency.

This is how we will achieve healthier, more sustain-

able, and more pleasant cities. Studies carried out by

(Benedict W Wheeler et al., 2021; Ojala and Camp-

bell, 2020) have shown that the aspects of the city an-

alyzed in the development of this work directly affect

the life and health of the population, especially the

most vulnerable groups.

Our proposal relies on the creation of new indices,

and its join analysis to detect hotspots where to act

ﬁrst in order to improve citizens quality of life from

a multidisciplinary perspective. To do that, the use

of GIS tools is of foremost importance. In this paper

208

García, D., Ferrandiz-Rovira, M., Serra, O. and Parés, M.

Biodiversity, Urban Quality Life and Air Quality Indices for Hotspot Detection of Transformation Opportunities in Cities: A Case Study in Barcelona.

DOI: 10.5220/0011974100003473

In Proceedings of the 9th International Conference on Geographical Information Systems Theor y, Applications and Management (GISTAM 2023), pages 208-215

ISBN: 978-989-758-649-1; ISSN: 2184-500X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Figure 1: Graphical representation of the items analysed

for the biodiversity squares, at the top, and for the people’s

quality of daily life squares, at the bottom.

we present the methodology proposed, as well as the

analysis done in three neighborhoods of Barcelona.

2 METHODOLOGY

In this project we have deﬁned several indices that al-

low showing citizenship the quality of life in the sur-

roundings of schools. The indices have been designed

to be easy to understand. Separate but analogous in-

dices are created for biodiversity, urban quality life

and air pollution. The joint analysis of them is what

provide information on the areas where actions should

take place.

2.1 Biodiversity

The study of biodiversity in the city distinguish be-

tween two types of infrastructure: (1) Squares; and

(2) Streets. They are considered separately since

the squares usually have an area large enough for

the species to stay there for a certain period of time

(Beninde et al., 2015; Ian MacGregor-Fors, 2011).

Streets, on the other hand, are usually passage areas

for species, although sometimes they can also be stay-

ing areas, especially for vegetation. We evaluate each

neighborhood using relevant indicators for biodiver-

sity: 11 indicators in squares and 9 in streets, see Fig-

ures 1 and 2. All indicators are then used to create an

index ranging from 0 (poor quality) to 100 (optimal

quality) obtained by properly weighting all indicators.

Once the indices are computed, and in order to test

for differences between the studied districts, two lin-

ear models are used, one for the biodiversity squares

index and one for the streets index, with districts (i.e.

different urban designs) as the independent variable.

Figure 2: Graphical representation of the items analysed for

the biodiversity of the streets, at the top, and for the people’s

quality of daily life streets, at the bottom.

2.2 Urban Life

Analogously to the study of biodiversity, we propose

two types of indices for the quality of urban life:

(1) Squares and (2) Streets. We evaluate the peo-

ple’s quality of daily life (i.e. spaces with equity for

all individuals and groups) by using 15 indicators in

squares and 11 in streets (Figures 1 and 2). Again, all

indicators are used to create an index ranging from 0

to 100 obtained by weighting all indicators, and two

linear models are used to test for differences between

the three studied districts.

2.3 Air Quality

To estimate the amount of pollutants in the air two

types of technologies are proposed: (1) Ofﬁcial air

quality measurement stations; (2) Sentinel-5 Precur-

sor satellite data.

2.3.1 Ofﬁcial Air Quality Measurement Stations

To generate the ﬁrst air quality indicator, data from an

entire academic year are needed. Two pollutants are

considered here: nitrogen dioxide (NO

) and particu-

late matter 10 micrometers or less in diameter (PM

The ofﬁcial air quality reference stations has been se-

lected according to two criteria: (1) the closest to the

schools; (2) with similar environments.

Based on the reference values deﬁned by the

WHO global air quality guidelines of 2021 (WHO,

2021), the air quality indices rely on the 24-hour av-

erage calculated for each of the pollutants and their

annual mean. Table 1 shows the WHO reference val-

ues for these contaminants.

On a ﬁrst step, the annual average concentration

of pollutants in each station should be calculated to

compare the resulting value with the reference values

deﬁned in the WHO guideline. Later on, graphs has to

Biodiversity, Urban Quality Life and Air Quality Indices for Hotspot Detection of Transformation Opportunities in Cities: A Case Study in

Barcelona

209

Table 1: Reference values deﬁned in the 2021 WHO guide

for NO

and PM

pollutants.

Pollutant Averaging AQG Threshold

time level

Annual 15 70

µg/m

24-hour 45

Annual 10 40

µg/m

24-hour 25

be generated with the calculated averages of the con-

centration of the pollutants during 24 hours. This in-

formation allows to compare with the maximum limit

deﬁned by the WHO during 24 hours. The indices

ranges from 0 to 100 according to: optimal air quality

for values below the limit value deﬁned by the WHO

guidelines (100) to poor air quality for higher values

that represent more health risk (0).

2.3.2 Sentinel-5 Precursor Satellite

Sentinel-5 Precursor (S5p) is a satellite system that

provides information and services on air quality, cli-

mate and the ozone layer of our atmosphere. The

Sentinel-5p satellite, from the Copernicus Sentinel-5

Percursor mission, offers a spatial resolution of 7x7

and in Spain we have a new image every day at

approximately 12 noon. Through the Sentinel Hub

plugin installed in the QGIS program, an analysis of

the NO

concentration on each day of the academic

year was carried out in a simple way. The ﬁrst step

is to conﬁgure Sentinel Hub according to the data

objective to be displayed in QGIS. The plugin trans-

forms any layer deﬁned in Sentinel Hub conﬁguration

into a QGIS layer, (Sentinel Hub, 2019). Once the

processed satellite images are directly accessed and

with the color representation previously deﬁned in the

conﬁguration, the plugin enables a quick exploration,

customization and image download.

Initially, a ﬁrst ﬁltering should be carried out on

the data in order to discard the images that do not

have data in the study area. Next, the resulting data

set should be analyzed by week (Monday to Sunday),

with the aim of identifying patterns in the data. The

ﬁrst pattern we need to identify is whether all the

schools had the same levels of contamination or if the

one located furthest from the urban center had differ-

ent levels of contamination. The second pattern to

identify is if the days with the highest concentration

of NO

occur on workdays and the days with the low-

est concentrations are on weekends or holidays.

2.4 Joint Analysis

Once all the indicators are computed, for every street

or square we can analyze separately each indicator

and also we can do it jointly. The joint analysis of

the three indicators will allow us to have a better un-

derstanding of the situation (comfort, biodiversity and

air quality). The analysis of all streets and squares of

a neighborhood will allow us to detect hotspots to im-

prove inside of a neighborhood. And the comparison

of the neighborhoods as a whole, will allow us to de-

termine which neighborhood of the city need more

attention.

3 STUDY AREA AND DATA

3.1 Study Area

The project focus on three neighborhoods of

Barcelona. The selection of the three public schools

was carried out through the Consorci Escoles +

Sostenibles, which is part of a network made up

of organizations committed to environmental, social

and economic sustainability that collectively build a

responsible city with people and the environment,

(Ajuntament de Barcelona, 2022). With the aim of

analyzing the school environment taking into account

different types of urbanization and areas with differ-

ent urban fabrics. The selected schools were, Ferrer i

ardia (Nou Barris district - close to the Collserola

wood), Diputaci

o (Eixample district - dense with

wide streets and lot of cars) and Patronat Dom

enech

(Gracia district - pedestrian area with narrow streets).

The cartographic data base used in the delimita-

tion of the territory of Barcelona was downloaded

from the Open Data BCN portal.

3.2 Biodiversity and Quality of Urban

Life Data

The data for these indices has been obtained from the

Open Data BCN portal. Fieldwork to check the qual-

ity of the data base was performed in July 2021. The

Barcelona territory layer has been our base layer for

the insertion of the data to be analyzed. In the QGIS

program, an initial edition was made to the territory

layer, with the objective of sectioning each street sec-

tion into a polygon, see Figures 4 and 5, information

treated as urban connectors in the analyzes. Each

street section of the study area was associated with

data such as tree cover, presence of businesses, and el-

ements of interest for wildlife, among others, see Fig-

ures 1 and 2. An example of the analysis performed in

the GIS program to generate all the indexes is the cal-

culation of the percentage of trees per street section

generated from the tree cover data. It is important to

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

210

note that the processing and analysis of the data in

the QGIS program has been carried out jointly with

the WMS resources of the geoservices offered in the

GeoportalBCN.

3.3 Air Quality Data

The air quality analysis has been carried out for the

2021-2022 Barcelona’s school year. From Septem-

ber/2021 to June/2022 and in this study every day of

this period was analysed, both for the data obtained

from the surface reference stations and for the satel-

lite data.

3.3.1 Surface Reference Station

The Generalitat de Catalunya monitors the air qual-

ity of the territory based on the data collected by

the Air Pollution Monitoring and Forecasting Net-

work (XVPCA). The data collected by the stations are

available on the Open Data Portal of Catalonia, with

hourly frequency, (Generalitat de Catalunya, 2014).

To deﬁne the air quality around the Diputaci

o, Pa-

tronat Dom

enech and Ferrer i Gu

ardia schools, daily

data on NO

and PM

pollutants have been used

for the 2021-2022 academic year from the Eixample,

Gracia and Parc Vall Hebron stations, respectively.

See Figures 3b, 3a, 3c. The Parc Vall Hebron sta-

tion is not close to Nou Barris district, however, this

is the closest station that meets the second require-

ment of having an environment similar to the school

zones studied.

3.3.2 Satellite Data

In this study, an analysis of 302 Sentinel-5p images

was performed, all corresponding to the 2021-2022

academic year. The ﬁrst data ﬁltering was done to dis-

card the images that had no information in the study

area, 206 images were discarded. The objective of

deﬁning patterns in the data was carried out based on

the analysis of 96 images of the study area, which

would allow us to compare the results obtained with

the air quality data measured on the surface with the

patterns found based on satellite data. It is very im-

portant to note that the data from the surface stations

are measured at a height close to the pollutant emis-

sion source. Whereas, the satellite data is from the

entire air column, from the surface to the troposphere

and there is a dispersion time of the air in the column.

Thus, the values to create the indices from one sensor

and the other will never be the same.

(a) Gracia Air Quality Station.

(b) Eixample Air Quality Station.

Figure 3: Air Quality Stations.

4 RESULTS

This section presents the calculated indices of bio-

diversity and quality of urban life. Based on these

values, in the QGIS program, the edited layer of the

Barcelona territory was used to generate a graphical

representation of these indices, (Figures 4 and 5). For

the air quality index, the calculated averages are pre-

sented, the values measured at each station by means

of a graph (Figures 6, 7 and 8), and ﬁnally, a map of

the NO

concentration measured by the Sentinel-5p

satellite (Figure 9).

Biodiversity, Urban Quality Life and Air Quality Indices for Hotspot Detection of Transformation Opportunities in Cities: A Case Study in

Barcelona

211

(a) Gracia.

(b) Eixample.

Figure 4: Biodiversity quality of squares and streets in three

districts of Barcelona.

(a) Gracia.

(b) Eixample.

Figure 5: Urban Life quality of squares and streets in three

districts of Barcelona.

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

212

4.1 Biodiversity

Regarding the biodiversity square index, a mean of

38.16 for the three neighborhoods was obtained (SD:

11.45, range: 13-72) (see Table 2 for extended details

and Figure 4). Using the analysis of variance (Girden,

1992), surprisingly, no signiﬁcant differences were

found between the three districts studied (Gracia’s Es-

timate ± SE, p: -6.03 ± 2.96, 0.045; Nou Barris -4.18

± 3.62, 0.69; ANOVA p= 0.10; N = 78). In the case of

streets, a mean of 38.52 for the three neighborhoods

was obtained (SD: 12.09, range: 6-69) (see Table 3

and Figure 4). As expected, signiﬁcant differences

were found between districts (Gracia’s Estimate ± SE,

p: 9.65 ± 4.98, 0.053; Nou Barris 64.03 ± 7.51, <

0.0001; ANOVA p < 0.0001; N= 1,196).

Table 2: Biodiversity indices for squares.

Gracia Eixample Nou Barris

Items analysed 19 60 13

Min index val 13 13 29

Max index val 63 72 47

Mean index val 35.26 39.17 37.6

Table 3: Biodiversity indices for streets.

Gracia Eixample Nou Barris

Items analysed 590 589 75

Min index val 6 7 26

Max index val 63 67 69

Mean index val 30.46 45.50 45.50

4.2 Quality of Urban Life

Regarding the quality of people’s daily life indices,

a mean of 78.7 for the three neighborhoods was ob-

tained (SD: 11.83, range: 51-99) (see Table 4 for ex-

tended details and Figure 5) in the case of squares and

a mean of 75.08 (SD: 9.91, range: 36-94) (see Ta-

ble 5 for extended details and Figure 5) in the case of

streets. The obtained results were higher in both cases

than the results obtained for the biodiversity squares

and streets indices. Surprisingly, no signiﬁcant dif-

ferences were found for the quality of people’s daily

life squares (Gracia’s Estimate ± SE, p: 3.40 ± 3.41,

0.32; Nou Barris -0.40 ± 4.20, 0.92; ANOVA p= 0.58;

N= 82), while signiﬁcant differences were found for

the streets (Gracia’s Estimate ± SE, p: -6.57 ± 0.50, <

Table 4: Urban life indices for squares.

Gracia Eixample Nou Barris

Items analysed 17 52 13

Min index val 57 55 60

Max index val 91 97 97

Mean index val 78.47 75.73 75.70

Table 5: Urban life indices for streets.

Gracia Eixample Nou Barris

Items analysed 590 589 75

Min index val 49 47 40

Max index val 100 98 92

Mean index val 78.53 84.13 62.05

0.0001; Nou Barris -19.98 ± 1.04, < 0.0001; ANOVA

p < 0.0001; N= 1254).

4.3 Air Quality

According to the analyzes carried out, the environ-

ments of the three schools have poor air quality. This

indicator was deﬁned based on surface and satellite

air quality data.

4.3.1 Ofﬁcial Air Quality Measurement Stations

Firstly, it has been veriﬁed that during the week, the

day with the highest concentration of polluting gases

is predominantly a workdays, while the days with the

lowest concentrations take place on weekends or holi-

days. Second, the time series for the three study areas

have been generated from the calculation of the daily

average of pollution and compared with the maximum

daily proposed levels deﬁned by the WHO in the 2021

guide, presented in Table 1. Annual mean is also com-

puted (Table 6) and can be compared against refer-

ence values (Table 1). The values are clearly above

the ideal ones.

In the three neighborhoods during the 2021-2022

scholar course (Eixample and Gracia: almost all

days; Nou Barris: half of the days) the NO

Daily

thresholds (25 µg/m

) have been exceed (see Fig-

ures 6a, 7a and 8a). The NO

annual means are also

above WHO recommendations. Special attention to

the Eixample station, which exceeds not only the

recommended value but also the maximum threshold.

Regarding PM

, daily thresholds (45 µg/m

) have

been exceed in two neighborhoods during the 2021-

2022 scholar course (Eixample: 10 days; Gracia: 1

day; Nou Barris: 0 days)(see Figures 6b, 7b and 8b).

Table 6: Average air quality in the 2021-2022 academic

year.

Annual Gracia Eixample Nou Barris

(µg/m

)

36.35 45.15 24.32

21.78 30.80 17.05

4.3.2 Sentinel-5 Percursor Satellite Data

Analyzing the Sentinel-5p satellite images of the en-

tire study period, it has been possible to verify that the

Biodiversity, Urban Quality Life and Air Quality Indices for Hotspot Detection of Transformation Opportunities in Cities: A Case Study in

Barcelona

213

(a) NO

. (b) PM

Figure 6: Graphs of NO

and PM

at Gracia station during the period from September/2021 to June/2022.

(a) NO

. (b) PM

Figure 7: Graphs of NO

and PM

at Eixample station during the period from September/2021 to June/2022.

(a) NO

. (b) PM

Figure 8: Graphs of NO

and PM

at Parc Vall Hebron station during the period from September/2021 to June/2022.

(a) Workday - Thursday. (b) Weekend - Sunday.

Figure 9: Comparison between two images from the S5p satellite, from a weekday and from a weekend of the same week.

GISTAM 2023 - 9th International Conference on Geographical Information Systems Theory, Applications and Management

214

same pattern is seen as with the data from the ofﬁcial

surface stations. Separating the data as weekly sets

(Monday to Sunday), predominantly, the day with

the highest NO

concentration occurs on workdays

and the days with the lowest concentrations occur on

weekends or holidays. Figure 9 shows a comparison

between two images from the S5p satellite, Figure 9a

from a workday and Figure 9b from a weekend day of

the same week. In the comparison presented in Fig-

ure 9, it is easy to see that, in the same week, the con-

centration of NO

changes signiﬁcantly according to

the activities carried out by the majority of the city’s

inhabitants.

4.4 Joint Discussion

This study highlights the poor quality of green infras-

tructure and air quality in three districts of Barcelona.

This may be because squares and streets are designed

for humans and vehicles rather than for biodiversity

and health. In fact, considerably better results have

been obtained for human daily quality of life than for

biodiversity and health. Regarding street usages, the

ones with more trafﬁc (Eixample) are the ones pol-

luted and with less biodiversity.

5 CONCLUSIONS

The proposed joint approach enable us to detect

hotspots where transformations to enhance biodiver-

sity, people’s quality of daily life as a whole and

health are needed. Regarding the biodiversity and

urban life methodologies the indices generated are a

promising tool, specially for urban planning in this

sustainability context. Next steps on this area will be

to compare the results with citizens perceptions and to

improve indices calculations taking into account this

issue. Regarding the air quality proposed methodol-

ogy, it is important to note that the way we use Sen-

tinel data is not the most suitable for a complete anal-

ysis of air quality data from satellites. However, when

considering the processing time and the volume that

these data can reach, it is a recommendable methodol-

ogy for initial analysis, for more visual than quantita-

tive representations and to easily and accurately ﬁlter

the images that can be used in the study. Concerning

the location of air measurement stations, this model

does not allow to evaluate the air quality in different

streets of a neighborhood, a useful assessment to de-

termine, for example, how the implementation of a

pedestrian street reduces the emission of pollutants in

this Street.

ACKNOWLEDGEMENTS

This study was co-funded by the Barcelona City

Council’s “Fons COVID” scientiﬁc research awards

given to Mariona Ferrandiz-Rovira and AGAUR,

Generalitat de Catalunya, through the Consolidated

Research Group “Geomatics” (Ref: 2021-SGR-

00536). We warmly thank Jordi Noya and Dani

Rodr

ıguez for their ﬁeldwork and processing data as

well as Laia Llonch, Marc Deu and Cristina Terraza

for their helpful discussions about the project.

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