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2023
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Is a dense city a healthy city? A preliminary study on the interplay between urban density and air quality in Oran, Algeria

Une ville dense est-elle une ville saine ? Une étude préliminaire sur l’interaction entre la densité urbaine et la qualité de l’air à Oran, en Algérie
¿Es una ciudad densa una ciudad saludable? Estudio preliminar sobre la interacción entre la densidad urbana y la calidad del aire en Orán (Argelia)
Chahrazede Boudalia, Amine M. Kasmi et Abdessamad Alili

Résumés

Il est communément admis est que les villes denses sont plus durables. Cependant, une densité urbaine élevée ou une forme urbaine compacte peuvent affecter la santé des citadins, plus particulièrement lorsque la compacité n’est pas associée à un système de transport en commun. Cet article analyse la corrélation entre les indicateurs de densité urbaine et la pollution atmosphérique à Oran (Algérie), une ville qui souffre encore d’un manque de transports en commun. Il évalue la densité des espaces verts nécessaires pour réduire les polluants atmosphériques générés dans les villes et examine les impacts de l’exposition aux valeurs limites de la pollution atmosphérique sur la mortalité respiratoire, en utilisant une méthodologie d’évaluation quantitative de l’impact sur la santé. Les résultats montrent que la densité de la population et du cadre bâties fortement corrélée à la pollution atmosphérique en raison du transport motorisé et d’autres activités humaines comme les industries, le chauffage résidentiel et le manque d’espaces verts. Les résultats indiquent que pour une densité de population supérieure à 12 100 habitants/ha et des valeurs excédant 100 pour la densité du bâti et des espaces verts, les niveaux de pollution de l’air deviennent supérieurs à la limite d’exposition recommandée au niveau international par l’OMS. En outre, 588 décès prématurés annuels, soit 0,2 % de la population totale des dix-huit districts et 3,7 % du nombre total de décès, étaient directement ou indirectement liés à la concentration de NOx. Cet article conclut qu’en dépit du consensus conventionnel selon lequel les villes plus denses sont plus durables et plus saines, les zones urbaines à forte densité tendent à être associées à une mauvaise qualité de l’air lorsqu’elles ne bénéficient pas d’un système de transport en commun.

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1. Introduction

1By 2050, the world’s population living in urban areas is expected to increase to 70% (UN, 2015). A large part of literature has deeply reviewed the consequences of urbanization and provides robust evidence that the Sustainable urbanization is key to successful urban development (Ochoa et al., 2018). In 2016, the World Health Organization declared that “health is one of the most effective markers of any city’s successful sustainable development” (OMS, 2017). There is now a growing consensus that the urban environment exacerbates or mitigates health and well-being outcomes and it is an important determinant of health (Barton, 2009).

2The general consensus is that more compact and dense cities are more sustainable and thus healthier (Bibri et al., 2020; Burton et al., 2003; Conticelli, 2020). Indeed, in contrast to urban sprawl, urban compactness improve sustainability through a number of factors, including reducing soil and energy consumption, reducing motorized traffic and vehicle kilometers traveled by promoting proximity and accessibility through the provision of mass transit systems and therefore reducing greenhouse gas emissions, improving mixed land-use and thus social diversity, increasing physical activity by promoting active travel and thus less car dependency (Coorey, Lau, 1970; Tan, Lu, 2018). However, high urban density or compact urban form may also affect the health of city dwellers through overcrowding, lack of green space which can reduce opportunities for health-promoting activities such as outdoor physical activity and stress-reducing recreation, high exposure to noise and air pollution, the heat island effect, high urban congestion and therefore a high stress level (Beenackers et al., 2018; Shirazi, Falahat, 2012; Xu et al., 2019).

3Air quality is unquestionably related to the conditions of urban life. Exposure to air pollution is associated with a myriad of severe health problems. Particularly ischemic heart disease, chronic obstructive pulmonary disease, stroke, respiratory infections and lung cancer (Jiang et al., 2016). According to the World Health Organization, every year, 4.2 million premature deaths worldwide are directly related to outdoor air pollution (WHO, 2016).

4Nitrogen oxide (NOx), used in this study as an indicator for air pollution, is considered one of the most important gases causing atmospheric pollution (César et al., 2015). NOx may form smog or yellow cloud that covers dense cities and provides poor air quality (Mohammadi et al., 2012). It also leads to acid rain and contribute to global warming (Boningari, Smirniotis, 2016). NOx can cause serious damage to human health, including asthma and respiratory infections (Griffith et al., 2009). NOx includes two gases-nitric oxide (NO) and nitrogen dioxide (NO2). NO reacts with oxygen or ozone in the air to form nitrogen dioxide. NO2 is the most prevalent form of NOx in the atmosphere, So NOx levels are similar to standard values for NO2 (Fonseca Hernandez, 2013). Moreover, NO2 generates other pollutants including ozone which has several adverse health effects particularly breathing problems, asthma, reduced lung function and respiratory diseases (Goodman et al., 2015).

5Several studies have explored the relation between urban density and air pollution with conflicting results. Some have shown that denser areas are associated with lower air pollutant concentrations (Glaeser, Kahn, 2010). Conversely, increased density has been shown, by others, to lead to greater air pollution exposure (Castells-Quintana et al., 2021). Most of these studies examined population-emissions per capita. For their part, Carozzi and Roth (2020) examined the effect of city size or population density on air pollution in US cities and found that denser cities lead to higher pollutant concentration. Previous work by Borck and Pflüger (2019) argued how air pollution may increase or decrease according to population size. Another series of studies have analyzed the relationship between urbanization rate and air pollution. For example, in US cities, it has been shown that a 1% increase in urban rate correlates with a 0.95% increase in total air pollutants (Ponce de Leon Barido, Marshall, 2014). Recent literature has also addressed the effect of urban green space on air pollution and mortality of respiratory and cardiovascular diseases (Bauwelinck et al., 2021; Bloemsma et al., 2019; Jaafari et al., 2020; Liu, Shen, 2014; Warren, 1973).

6However, comprehensive analysis of the joint effects of urban density, green areas, urban air pollution and related respiratory mortality is still missing in the literature. Our study aims to address this gap by providing a comprehensive analysis. Indeed, Ahlfeldt & Pietrostefani’s survey (2019) argued that the effect of density on pollution is an area where more empirical evidence is needed. From this perspective, the main objectives of this paper are: (1) to determine urban density indicators that have direct impact on air pollution; (2) to examine the impact of urban density on pollutant concentration; (3) to determine thresholds from which green areas serve as a filter for NOx pollution caused by urban density, (4) to estimate air pollution attributable deaths, and (5) to indicate that despite the conventional claim that denser cities tend to be greener, environmentally friendly and healthier, air pollution exposure and attributable deaths are important in higher dense regions.

7The paper is structured as follows. The next section of the paper presents the study area. Section 3 presents the data and estimation methods. Our results are shown in section 4. The paper ends with a discussion and conclusions of the research results.

2. Study area

8Oran, Algeria’s second-largest city, is located in the north-west of the country (Figure 1). It is located at latitude and longitude of 35° 42′ 10″ N and 0° 38′ 57″ E, with an area of about 64 km² and with a population of about 609 940 inhabitants and a density of about 9 530 in hab/Km²(ONS, 2008), making it the second most populated city in the country. It benefits from a Mediterranean climate, with annual precipitation of 420 mm/year and annual average temperature of 17°C. The city is experiencing urban and population growth as well as increasing number of carburetor vehicles, which exacerbates air pollution conditions in Oran. Built-up areas and road traffic space share of the total study area is 61% and 32%, respectively, while green space, which can mitigate the air pollutant concentration (Diener, Mudu, 2021), has a share of only 2%. City of Oran is endowed mainly with two modes of public transport, namely the tram and bus network but which still suffers from shortcomings due to the dilapidated state of the bus fleet and the insufficiency which does not meet the travel needs of the inhabitants. which in turn pushes the inhabitants to resort to private vehicles (Hamza, Nassima; Rebouha, Pochet, 2009). Respiratory diseases due to air pollution in Oran is in an alarming state (Snouber, 2021). Therefore, studying this issue is of great significance for adopting preventive measures. A total of 18 districts as defined by the Urban Development Master Plan (PDAU), were analyzed (Figure 1).

Figure 1: Geographical location of Oran and the study districts

Figure 1: Geographical location of Oran and the study districts

3. Data and methods

3.1 Data from the PDAU

9In order to evaluate urban density, the following indicators are selected based on existing research: in their urban density assessment, Abdullahi et al (2017) used three indicators to estimate urban density, including population density, residential density and built-up area density.

10Population density is measured by the number of inhabitants divided by the total area of region of interest with a unit of people per hectare (Lin, Yang, 2006). The available data for population were for the year 2000 with population of 555,935 (Table 1). Thus, the population density for each district was calculated according to the number of people per hectare as shown in Equation 1.

11Residential density is calculated by the number of dwellings divided by the total area of land they occupy (Kubota et al., 2008). The available land use maps collected from Oran planning authority included information on the number of residential units (see Table 1). Residential density was calculated through the following equation:

12Built-up area density is expressed by the ratio between the built-up area and the total surface of the land on which buildings are located (Sim, 2019). Data of built-up area of each district were collected from the Urban Development Master Plan (PDAU) (PDAU, 2000) (see Table 1). This quantity has no unit. Built-up area density was calculated through the following equation:

13Floor area ratio (FAR): for a comprehensive analysis of urban density, we added another indicator to the three previous ones, which strongly expresses urban density level (Dovey, Pafka, 2014). The FAR is a crucial urban planning rule as it allows influencing the density of an area, i.e., a high FAR value allows more built surface per square meters of land, and thus densifying and verticalizing the urban zone. Conversely, a low FAR value prevents high rise construction and thus lower density. Data of FAR values of each district were collected from the Oran PDAU (PDAU, 2000) (see Figure 2).

Figure 2: Floor area ratio (FAR) districts retrieved from the Urban Development Masterplan (PDAU) (PDAU, 2000)

Figure 2: Floor area ratio (FAR) districts retrieved from the Urban Development Masterplan (PDAU) (PDAU, 2000)

Figure 3: Green space surface in percent

Figure 3: Green space surface in percent

14Green space density: in order to offset the effect of urban density on air pollution, green space density was used. The latter is considered as a filter to urban air pollution. Data on the area of green space for each of the 18 study areas were retrieved from the PDAU (see Figure 3). According to Ståhle (2010), green space density is calculated through the following formula:

Table 1: data of built-up area, residential units, population and green space retrieved from the Urban Development Master Plan (PDAU)

Table 1: data of built-up area, residential units, population and green space retrieved from the Urban Development Master Plan (PDAU)

3.2 Air pollution data

15The data collection of Oran’s air pollution was based on the work of Rahal et al. (2018). Annual mean NOx levels were calculated using a simulation with the EMISENS model. EMISENS (EMIssionSENSitivity) is a model for the calculation of road traffic emission inventories. This model combines the top-down and bottom-up approaches in order to achieve greater coherence for urban emissions estimation (Nadège et al., 2014). First, vehicles are classified into categories. Average emission factors should then be specified for each vehicle category. This can be done from measurements (Belalcazar et al., 2010) or from the COPERT European methodology. This grouping into categories is used to calculate emissions more quickly from data on the vehicle fleet: number of vehicles in each category, vehicle flows, vehicle speed, average emission factor, etc. This model also calculates the uncertainties related to the input and identification parameters, which need to be better, estimated to significantly improve the result. The calculation of emissions by the EMISENS model is based on a computer program to calculate road traffic emissions by the COPERT methodology (Ntziachristos et al., 2009) The visualization and analysis of the results are done on a geographic information system.

16The result of this simulation is represented in the form of a grid covering the city of Oran, for each grid cell, a NOx value has been assigned (see Figure 4). Then, based on this data, a NOx value was attributed to each FAR district and to each of the 18 Land Occupancy Plan (POS) districts (see Table 4).

Figure 4: air pollution for Oran (Rahal et al., 2018)

Figure 4: air pollution for Oran (Rahal et al., 2018)

NOx pollution source

3.2.1 Road traffic

17In Algeria, NOx is mainly emitted by the transport sector. In fact, 69% of nitrogen oxide emissions are caused by road traffic (Rahal et al., 2014). Based on the data on the road network of Oran, as well as on the estimation of vehicle flows established by Rahal et al. (2018), we calculated the vehicle flow for each district and for each vehicle category; PV (Particular Vehicle), LCV (Light Commercial Vehicle), HV (Heavy vehicle) and BUS (see Table 2).

3.2.2 NOx emissions from households

18Rahal et al‘s paper (2014) shows that the NOx emission density per capita per year in Oran is 6.3Kg/capita/year. As shown in Table 3, the NOx emissions from households were calculated for each district.

Table 2: estimation of vehicle flows in veh.Km.h-1

District

PV

LCV

HV

BUS

Nox value (µg/m3)

1

61 505

20 703

6 664

2 422

60 -70

2

2 121

714

230

84

10 -15

3

4 242

1 428

460

167

10 -15

4

6 363

2 142

689

251

15 -20

5

12 725

4 283

1 379

501

20 -30

6

10 604

3 569

1 149

418

20 -30

7

8 483

2 856

919

334

20 -30

8

19 088

6 425

2 068

752

30 -40

9

23 329

7 853

2 528

919

40 -50

10

25 450

8 567

2 758

1 002

40 -50

11

16 967

5 711

1 838

668

30 -40

12

50 901

17 133

5 515

2 004

50 -60

13

46 659

15 705

5 056

1 837

50 -60

14

42 417

14 278

4 596

1 670

40 -50

15

29 692

9 994

3 217

1 169

40 -50

16

21 209

7 139

2 298

835

30 -40

17

27 571

9 280

2 987

1 086

40 -50

18

14 846

4 997

1 609

585

30 -40

Table 3: NOx emission density per capita per year in Oran

District

Total population

NOx emission density per capita per year
(Kg/capita/year)

NOx value (µg/m3)

1

66 674

10 583

60 -70

2

20 725

3 290

10 -15

3

58 873

9 345

10 -15

4

48 194

7 650

15 -20

5

29 910

4 748

20 -30

6

25 874

4 107

20 -30

7

40 080

6 362

20 -30

8

22 477

3 568

30 -40

9

12 735

2 021

40 -50

10

7 646

1 214

40 -50

11

14 000

2 222

30 -40

12

63 700

10 111

50 -60

13

38 324

6 083

50 -60

14

42 807

6 795

40 -50

15

16 991

2 697

40 -50

16

27 630

4 386

30 -40

17

7 818

1 241

40 -50

18

11 476

1 822

30 -40

4. Health impact assessment framework

4.1 HIA methodology

19A quantitative health impact assessment (HIA) methodology (Mueller et al., 2017) was carried out for Oran at the Land Occupancy Plan (POS) level (n=18), employing population and mortality data (ORS, 2017). The analysis evaluated the effect of non-compliance with WHO’s international exposure level recommendation for the air pollution related exposure (NOx) on respiratory mortality for Oran residents (n=555 935). We used the standard HIA framework (Figure 5) which is based on the comparative risk assessment approach according to the following steps: (1) we identified current exposure levels (see Figure 4), (2) we determined recommended exposure level based on WHO guidelines. NO reacts with oxygen or ozone in the air to form nitrogen dioxide. NO2 is the most prevalent form of NOx in the atmosphere, So NOx levels are similar to standard values for NO2(Fonseca Hernandez, 2013). The WHO recommends that annual mean NO2, and thus NOx, exposure concentration should not exceed 40µg/m³ (WHO, 2006) (3). For the 18 districts, we calculated the exposure level difference between current and recommended exposures (40 µg/m³) (see Table 9), the current exposures were retrieved from the previous work by (Rahal et al., 2018) as shown in Figure 4. (4) we retrieved exposure response function ( ERF) from the literature (Table 5); the relative risk associated with NOx exposure is 1.02 (95% CI: 1.018-1.022) per 10 µg/ m³ (Atkinson et al., 2018), quantifying the association between exposure and mortality, (5) we calculated the relative risk (RR) as shown in the Equation 5 (Gowers, Environmental, 2014), the relative risk equals the risk estimate -retrieved from the literature 1.02 (95% CI: 1.018-1.022) due to NOx exposure for every 10 µg/ m³ above 40µg/ m³ - to the power of the ratio between exposure difference and 10. (6) We scaled the population attributable fraction (PAF) following Equation 7 (Gowers, Environmental, 2014).The PAF is the proportion of cases for an outcome of interest that can be attributed to a given risk factor among the entire population. Therefore, to obtain number of deaths due to NOx high level exposure, total number of deaths for each district is multiplied by the PAF value, divided by 100.Thus, the attributable number of deaths is the product of PAF and the total number of deaths. Analyses were conducted using QGIS (v 3.24.3).

20Where RR’ is the mortality effects of NOx modeled employing a linear exposure-response function and x is the exposure difference.

21Where PAF is the population attributable fraction.

Figure 5: Conceptual framework of the Health Impact Assessment (HIA) tool.

Figure 5: Conceptual framework of the Health Impact Assessment (HIA) tool.

(1) Recommended exposure level; (2) current exposure level; (3) exposure difference between recommended and current exposure level; (4) exposure response function (ERF) quantifying association between exposure and mortality; (5) relative risk (RR) corresponding to exposure difference; (6) population attributable fraction (PAF) corresponding to exposure difference.

4.2. Analysis of the relation of NOx pollution, urban density and green space

22As mentioned above in the method section and based on the air pollution data source, a NOx value has been assigned to each FAR district (see Table 4). Based on FAR values, we have grouped the study areas into four categories (FAR:2-3; FAR: 1.4-2; FAR: 0.7-1.4; FAR <0.7). Generally, high NOx levels were observed in relatively high values of FAR (FAR>0.7). Areas with a FAR<0.7, were expected to have low NOx level. However, districts including El Othmania, Es Salem, Noussair, Cavaignac, Dar El Beida, Ibn Rochd II and Es Seddikia show a high level of NOx concentrations.

Table 4: FAR districts and their related NOx values

FAR value

districts

Designation

Nox (µg/m3)

area covered by Nox value
(ha)

Area %

Nox value specific to the FAR category

2-3

1

El Amir

60 -70

69,2

82%

60 -70 : 82%

4

Karama

15 -20

15,6

18%

1,4-2

2

Nasr et El Marsa

15 -20

15

15%

50 -60 : 85%

13

Ibn Sina

50 -60

84,3

85%

0,7-1,4

2

Kheddim Mustapha

10 -15

4,9

1%

60 -70 : 0%
50 -60: 6%
40- 50: 30%
30 -40: 49%
20 -30: 0%
15 -20: 15%
10 -15: 1%
5 -10: 0%
0 -5: 0%

3

les planteurs

30 -40

68,6

12%

4

rest of the district Sidi El Bachir

15 -20

85,8

15%

5

Mahéddine and Terrade

30 -40

48,2

8%

7

El Othmani and Oussama districts

40 -50

61,1

11%

9

El Hamri

30 -40

57,9

10%

12

Delmonte

50 -60

32,3

6%

13

Ibn Sina

30 -40

39,8

7%

14

El Makari

40 -50

110,2

19%

16

Ibn Rochd I

30 -40

71,8

12%

<0,7

2

rest of the district El Houari

10 -15

19,7

2%

60 -70 : 0%
50 -60: 2%
40- 50: 21%
30 -40: 38%
20 -30: 23%
15 -20: 0%
10 -15: 15%
5 -10: 0%
0 -5: 0%

3

les planteurs

10 -15

133,5

13%

5

rest of the district Maheddine

20 -30

77

7%

6

El Badr

20 -30

134,8

13%

7

El Mokrani and Abd EL Moumen districts

20 -30

35,7

3%

8

El Othmania

30 -40

133,8

13%

9

rest of the district El Hamri

40 -50

33,4

3%

10

Es Salem

40 -50

91,4

9%

11

Noussair

30 -40

54,2

5%

12

Cavaignac

50 -60

24,3

2%

15

Dar El Beida

30 -40

94,7

9%

17

Ibn Rochd II

40 -50

90,25

9%

18

Es Seddikia

30 -40

111,8

11%

23After calculating all the urban density indicators, the next step was to evaluate the correlation between all of these indicators and their relationship to air pollution. Finally, in order to estimate thresholds at which a higher pollution value than that recommended by WHO is reported, the ratio between each urban density indicator and green space density was calculated (see Table 8). In other words, these thresholds express the density of green space needed to absorb airborne pollutants generated by urban density.

24As shown in Table 6, densities of the four indicators were calculated and NOx values were identified for the eighteen POS districts. The NOx concentration between different sites changes greatly. The spatial variation of NOx levels in Oran may be for significant importance. The maximum value of annual average concentration was between 60 and 70 µg/m3 (district1), while the minimum value ranges between 10 and 15 µg/m3 (districts 2 and 3). The air pollution level in eight districts (districts 1, 9, 10, 12, 13, 14, 15 and 17), was higher than 40µg/m3, which is not in compliance with WHO Air quality guidelines.

Table 5: Risk estimate for mortality and data source for NOx exposure

Exposure domain

Risk estimate

Exposure

Study design

reference

NOx

1,02(95%CI: 1,018-1,022)

per 10µg/m³ increase in NOx exposure

Meta-analysis

 Mueller et al., 2017

Notes: CI, confidence interval; NOx, nitrogen oxide

a Mortality effect of NOx modeled employing a linear exposure-response function

Table 6: Densities of the four indicators and their related NOx exposures

districts

Population density

(Inhab./ha)

built-up area density

Residential density

(residential units/ha)

Green space density

NOx value (µg/m3)

1

427,4

0,7

72,2

0,001

60 -70

2

265,7

0,4

43,3

0,065

10 -15

3

232,7

0,5

40

0,056

10 -15

4

280,2

0,7

46,6

0,067

15 -20

5

137,2

0,6

19,2

0,023

20 -30

6

148,7

0,5

21,7

0,025

20 -30

7

200,4

0,6

26,8

0,034

20 -30

8

123,5

0,5

32,1

0,011

30 -40

9

84,9

0,7

33,4

0,007

40 -50

10

48,7

0,4

6,6

0,004

40 -50

11

140

0,6

4

0,015

30 -40

12

700

0,5

24,5

0,002

50 -60

13

228,8

0,5

29,9

0,002

50 -60

14

221,8

0,5

31,6

0,003

40 -50

15

107,2

0,7

16,7

0,006

40 -50

16

153,5

0,7

23,2

0,013

30 -40

17

67,4

0,5

11,2

0,005

40 -50

18

61,7

0,4

10

0,01

30 -40

25The primary focus of this study is to determine whether there is a statistical correlation between concentration levels of NOx and urban density indicators, with the aim of identifying the most relevant indicators. Results are presented in a matrix correlation as shown in Table 7. Pearson coefficients indicate the extent of the correlation. Results show that population density followed by built-up area density, with ǀrǀ=0.28 and ǀrǀ=0.1 respectively, strongly correlate with NOx pollution. This suggests that the latter indicators significantly increase the NOx level. However, residential density slightly correlates with NOx pollution. This means that this latter does not generate as much pollution as the other two indicators mentioned above. On the other hand, green space clearly negatively correlates with the NOx value. This indicates that increase of vegetation area could decrease NOx concentration and thus serving as urban air pollution reducer.

Table 7: correlation matrix between concentration levels of NOx and urban density indicators

 

Population density

built-up area density

Residential density

Green space density

NOx pollution

Population density

1.0000

built-up area density

0.0657

1.0000

Residential density

0.4997

0.3255

1.0000

Green space density

0.0546

-0.0316

0.3279

1.0000

NOx pollution

0.2895

0.1010

0.0166

-0.8673

1.0000

26Table 8 reports the results of the ratio between each urban density indicator and green space density. After a descending sorting of NOx values, it could be noticed that from a value equal to 12 100 inhabitants/ha for the population density and green space density ratio, and a value equal to 100 for the built-up area density and green space density ratio, we start to have a NOx concentration higher than 40µg/m3, and therefore higher than the international limit value.

Table 8: ratio between urban density indicators and green space and their related NOx values

districts

population density/ green space density

(Inhab./ha)

built-up area density/ green space density

residential density/ green space density

(Residential units/ha)

NOx value

1

427400

700

72200

60 -70

12

350000

250

12250

50 -60

13

114400

250

14950

50 -60

14

73933

167

10533

40 -50

15

17867

117

2783

40 -50

17

13480

100

2240

40 -50

10

12175

100

1650

40 -50

9

12100

100

4771

40 -50

16

11808

53,8

1785

30 -40

8

11227

45,5

2918

30 -40

11

9333

40,0

267

30 -40

18

6170

40,0

1000

30 -40

5

5965

26,1

835

20 -30

6

5948

20,0

868

20 -30

7

5894

17,6

788

20 -30

4

4182

10,4

696

15 -20

3

4155

8,9

714

10 -15

2

4088

6,2

666

10 -15

Attributable deaths

27Exposure response function (ERF) was retrieved from the literature (Table 5); the relative risk associated with NOx exposure is 1.02 (95% CI: 1.018-1.022) per 10µg/ m³ (Atkinson et al., 2018), quantifying the association between exposure and mortality, we calculated attributable deaths for ERF=1.02, ERF=1.018 and ERF=1.022. On the other words, we calculated the mean value of attributable deaths with 95% of confidence interval, whereby the mean value lies between an upper and lower interval. Findings in Table 8 show that NOx values exceed recommended annual mean level in eight districts (i.e. with + 10 µg/m3 in districts 14, 15, 17, 10 and 9, +20µg/m3 in districts 12 and 13 and + 30µg/m3 in district 1). 588 annual premature deaths (95% CI: 529-643), i.e. 0,2% of the total population of the eighteen districts and 3,7% of the total number of deaths, were estimated to be preventable if there would be compliance with international exposure recommendation of NOx level.

Table 9: Estimated respiratory mortality attributable to non-compliance with NOx exposure guideline in Oran

Table 9: Estimated respiratory mortality attributable to non-compliance with NOx exposure guideline in Oran

RR: Relative Risk
AD: Attributable Deaths

5. Discussion and conclusions

28This paper assesses effects of urban density on air pollution and shows that, despite the conventional consensus that more dense cities are more sustainable and healthier, denser urban areas tend to be associated with poor air quality and high level of pollutant concentration and therefore, more negative effects on public health are observed if the latter are not equipped with an adequate and sufficient public transport network. In this section, we discuss and explain the possible underlying channels behind findings of this research, which contribute to the literature in several ways.

29First, among the four selected urban density indicators, only two correlate strongly with NOx levels, namely population density and built-up area density. However, regarding the FAR indicator, the hypothesis, according to which FAR values correlate positively with air pollution, is not verified in both directions. Results show that among areas with FAR<0.7, there are those covered by a high level of NOx; this means that there are other factors of air pollution caused by urban density. The strong correlation between population density and air pollution indicates that high concentration of urban dwellers generate pollution as shown in Table 3, this is clearly due to a high use of private cars by city dwellers who often do not use public transport. In fact, the more the number of population grows, the more the number of vehicles increases and therefore the more emissions of greenhouse gases increase too. In Table 2, it is clear that areas with a high vehicle flow are associated with high exposure to air pollution. On the other hand, built-up area density, which indicates the rate of constructions concentration in the area, correlates strongly with air pollution. It can be concluded that a high concentration of buildings causes poor air circulation and stagnation of pollution. So, the conclusion that can be drawn from this work is that there is a relation between NOx levels and population density, linked to the number of vehicles, and also a relation between high concentration of buildings and stagnation of pollution. It can be argued that high urban density requires a strong presence of green areas to mitigate and absorb pollution caused by dense cities. It is necessary to strike the right balance between them.

30Our paper predicts that denser cities could have higher pollution concentration. Urban density is often related to high building or population density with the hypothesis that if either parameter has a high value, the presence of green spaces is very low. Urban density could have adverse effects on the environment and human health if it is not balanced by a sufficient availability of green spaces and the development of more sustainable transport modes (metro network, bicycle lanes...). For sustainability and live ability, cities have to balance the natural environment with human settlements. Results in this paper show that for an urban air exposure that does not exceed international recommendation (40µg/m3), the ratio between population density and green space density should not exceed 12 100 inhabitants/ha and 100 for the ratio between built-up area density and green space density. These findings support the importance of the availability of green space in urban areas. Our study has shown that each additional 10 µg/m3 for NOx, correspond to 2.6% increase in respiratory mortality (95% CI: -0.2% to 5.6%). Higher amounts of green space indicate not only lower respiratory mortality rates but also other potential beneficial health effects such as: reduced exposure to noise and excess heat, restorative effects by reducing stress, stimulating social contacts and improving physical activities.

31Finally, this paper calls for further research. First, while we have taken NOx as a surrogate for air pollution exposure, other pollutants could be taken into account for a comprehensive analysis of the impact of air pollution on public health (Particulate matter PM10 and PM2.5, Ozone (O3), Carbon monoxide (CO) and Sulphur dioxide (SO2)). Second, there are other factors that affect air pollution, but our research only focused on urban density. In addition, more influencing factors could be considered such as industrial areas, meteorological, geographical and contextual conditions.

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Table des illustrations

Titre Figure 1: Geographical location of Oran and the study districts
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Titre Figure 2: Floor area ratio (FAR) districts retrieved from the Urban Development Masterplan (PDAU) (PDAU, 2000)
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Titre Figure 3: Green space surface in percent
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Titre Table 1: data of built-up area, residential units, population and green space retrieved from the Urban Development Master Plan (PDAU)
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Titre Figure 4: air pollution for Oran (Rahal et al., 2018)
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Titre Figure 5: Conceptual framework of the Health Impact Assessment (HIA) tool.
Légende (1) Recommended exposure level; (2) current exposure level; (3) exposure difference between recommended and current exposure level; (4) exposure response function (ERF) quantifying association between exposure and mortality; (5) relative risk (RR) corresponding to exposure difference; (6) population attributable fraction (PAF) corresponding to exposure difference.
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Titre Table 9: Estimated respiratory mortality attributable to non-compliance with NOx exposure guideline in Oran
Légende RR: Relative Risk AD: Attributable Deaths
URL http://journals.openedition.org/cybergeo/docannexe/image/40585/img-13.png
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Référence électronique

Chahrazede Boudalia, Amine M. Kasmi et Abdessamad Alili, « Is a dense city a healthy city? A preliminary study on the interplay between urban density and air quality in Oran, Algeria », Cybergeo: European Journal of Geography [En ligne], Aménagement, Urbanisme, document 1054, mis en ligne le 17 août 2023, consulté le 16 février 2025. URL : http://journals.openedition.org/cybergeo/40585 ; DOI : https://doi.org/10.4000/cybergeo.40585

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Auteurs

Chahrazede Boudalia

EOLE laboratory, Faculty of Technology, University of Abou Bekr Belkaid, Tlemcen, Algeria, chahrazede.boudalia@univ-tlemcen.dz

Amine M. Kasmi

Associate Professor at the Faculty of Technology, University of Abou Bekr Belkaid, Tlemcen, Algeria, mohammedelamine.kasmi@univ-tlemcen.dz

Abdessamad Alili

Professor at the Faculty of Technology, University of Abou Bekr Belkaid, Tlemcen, Algeria, MECAS laboratory, abdessamad.alili@univ-tlemcen.dz

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