• No results found

Environmental injustice in Cape Town

N/A
N/A
Protected

Academic year: 2021

Share "Environmental injustice in Cape Town"

Copied!
50
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

1 Cedric Steijn

10117997

Environmental injustice in Cape Town

Bachelor thesis project Urban Poverty and Inequality Supervisor: Marit de Vries

(2)

2 Source of front page image: http://www.isid.org/icid/

(3)

3

Contents

1. Introduction………..……….4.

2. Theoretical Framework………6.

2.1 Historical Factors……….6.

2.2 Socio-economic status and environmentally induced health risks……….……8.

2.3 Environmental injustice………..10.

2.4 Environmental injustice in Cape Town……….13.

2.5 Environmental health risks caused by pollution………14.

3. Methodology………16.

3.1 Research and sub questions……….16.

3.2 Causal Model ………17.

3.3 The case: Cape Town……….18.

3.4 Type of research..………18.

3.5 Spatial analysis in ArcGIS………22.

3.6 The analyses………...24.

3.7 Operationalization………..………26.

4. Results………..27.

4.1 Spatial analysis in ArcGIS: socio-economic status……….27.

4.2 Spatial analysis in ArcGIS: air pollution……….29.

4.3 Analysis of the 2008 episodes……….31.

4.4 Conclusion of the analyses………33.

5. Conclusion and final remarks………35.

5.1 Conclusions and discussion………..35.

5.2 Recommendations for further research……….37.

6. References………....38.

(4)

4

1. Introduction

When I visited Cape Town in January 2014 I had heard a lot of stories about the infamous townships on the Cape Flats. I had seen poverty before during visits to Cuba and Latin America so I thought I was well prepared for the displays of poverty in South Africa. This thought, however, proved to be untrue. When taking the bus from Cape Town to Knysna, the bus driver, for unknown reasons, took a detour through the Delft township. What I witnessed there was extremely poor housing, dirty streets, people walking around in rags, people lying passed out on the street (not knowing whether they were still alive), and extreme poverty in general. One of the things that struck me the most was the large amount of open fires everywhere. It was around dinner time so women were outside cooking food on burning wood, coal, and even car tires, producing a huge amount of suffocating smoke I was able to smell even inside the bus. I had never witnessed this degree of poverty before and seeing the massive columns of black smoke rising from a burning container filled with tires made me think of the neighbourhood I was staying in, near Table Mountain. Here the people were wealthy, houses were well protected, and there were no open fires. These differences within the city show an explicit inequality, not only in terms of income, but also in the degree of air pollution. Poverty forces townships residents to burn whatever they can find to provide heat and build cooking fires, but it must also have a big impact on health(Bruce et al., 2000). In this research I will explore the link between socio-economic status and environmentally induced health risks.

Poverty and inequality remain major issues in South Africa. A large part of the population lives in poverty and often lives in housing of poor quality, making poverty and inequality a policy issue of growing importance for the South African government (Rogerson, 1999). This is underlined by the president of South Africa, Jacob Zuma, who said the following about the post-apartheid era: “Our people still have to daily confront the impact. Many still live in areas once designated for black people away from economic opportunities and civic services”(Turok, 2010).On the other hand, other people are wealthy and have food security, a stable income, and live in formal housing. This

inequality is very visible, especially in urban areas where poor and rich areas often lay in close proximity (Wratten, 1995). The inequalities are exacerbated by, but are not simply a legacy of apartheid history. Apartheid policy during the 20th century has caused a spatial distribution of people based on ethnicity, forcing Black Africans and Coloureds to live in the least favourable locations (Wilkinson, 2001). The historical factors have a great influence in the contemporary lives of the residents of Cape Town as well. Apartheid led to, and still enforces, segregation based on income. Because of this lack of income people are less able to choose a proper location to live and often end up in low income areas in Cape Town. These low income areas often lack formal housing, in the Khayelitsha township, for example, over half of the population lives in informal housing (City of Cape Town census, 2011). Other socio-economic characteristics, such as employment, income, and education are all also relatively low in in townships on the Cape Flats compared to Cape Town as a whole. These income inequalities sorts people across space according to their ability to buy into different quality neighbourhoods. Leading to poor people remaining in poor quality areas; the Cape

Flats townships (Turok, 2001). Scientific literature has often linked socio-economic status to a higher degree of exposure to

air pollution (Burnett et al. 2001: Forastiere et al. 2007: Dixon & Ramutsindela, 2006). Data from the City of Cape Town also suggests that people living in townships have a higher degree of exposure to air pollution (City of Cape Town, 2005). Because of this higher exposure to air pollution there is an increased environmental risk. In the context of this research environmental risk is understood as a health risk as air pollution exposure has been linked to various respiratory, and cardiovascular diseases, cancers, and increased mortality rates (WHO, 2011: Brunekreef & Holgate, 2002). Since its inequalities in socio-economic status(e.g. income, employment, education, housing) that appear to be the cause for differences in air pollution exposure there is a form environmental injustice: marginalized communities suffer a disproportionate environmental risk (Pellow, 2000). When the amount of air pollution in a certain area exceeds the guideline drawn up by the SANS Air Quality Standards for human health, and used by the City of Cape Town, then the risk for the human health

(5)

5

is higher than when the amount is lower than the guideline. In other words: the higher the amount of air pollution the higher the environmental risk (WHO, 2011). Meaning that people living areas with a lot of air pollution have an increased environmental health risk.

This research will focus on answering the following question: do differences in socio-economic characteristics between suburbs in Cape Town lead to differences in environmentally induced health risks for their residents? In order to answer this question I will examine how the earlier mentioned historical factors have influenced the socio-economic status of the Cape Town neighbourhoods, how these factors still lead to growing poverty and inequality, how poverty and inequality can lead to a spatial distribution of incomes and how this distribution can lead to an increased environmental risk. This leads me to formulating the following hypothesis: in suburbs where the socio-economic status is relatively low (e.g. high unemployment, low income) people are exposed to higher amounts of air pollution and thus have a higher environmental health risk. Burnett et al. (2001), for instance, have found a strong correlation between characteristics such as housing, income, employment, and education and a higher degree of exposure to air pollution for Hamiltion,

Canada (Burnett et al., 2001). The research paper will be organized as follows. The next chapter will explain how the

different concepts, historical background, socio-economic status, environmentally induced health risks, and environmental injustice are explained in scientific literature and how this manifests itself in Cape Town. The different links between the different concepts will be explained through existing scientific literature on the subject supported by census- and other data from the City of Cape Town. This is the theoretical framework, providing background information on the variables and the research context. The following chapter will introduce the data used in the analyses, provide the different methods used for the analyses , the arguments for the choice of the methods, and how the analyses will be carried out in practice. In section 4 I will present the results of the analyses and link the results from the different methods. Section 5 will conclude by answering the research question and the sub questions, linking together the results from the analyses and the literature from the theoretical framework. I will end with recommendations for future research on this subject in Cape Town.

(6)

6

2. Theoretical framework

This chapter will review the existing scientific literature about the main concepts used in this research. The main concepts of this research are the historical background of Cape Town, socio-economic characteristics of neighbourhoods in Cape Town, environmentally induced health risks, and environmental injustice. I will define the concepts and explain why they are relevant. The chapter will be organized in chronological order. I will start by providing an historical background of Cape Town, explaining the time of apartheid. I focus on how apartheid has led to, and still enforces segregation and how this legacy of apartheid influences the socio-economic status of Cape Town

neighbourhoods. In the next section I will explain what will be understood as socio-economic status and how this is linked to environmentally induced health risks in existing literature. The next section will elaborate on what is understood as environmental injustice, providing definitions of

environmental racism, inequality and environmental risk from existing literature. This section will also provide examples of environmental injustice in Hamilton, Canada and Rome, Italy which also provides an example of a previous GIS based research linking socio-economic status with

environmentally induced health risks. The research conducted in Hamilton, however, is based on individuals as research units. In this research neighbourhoods are the research units, therefore I will also provide an example of environmental injustice research on the neighbourhood level. The next section will provide an explanation on how environmental injustice takes place in Cape Town. The last sections of this chapter will be about the relationship between exposure to air pollution and

health problems and which pollutants lead to which health problems.

2.1 Historical factors

Historical factors have played a role in both creating the spatial distribution of people across the city and the composition of the population of Cape Town. Population distribution and composition will be explained in this section along with its importance for shaping inequalities in the socio-economic characteristics of the different Cape Town neighbourhoods. Apartheid has had a large role in shaping

socio-economic characteristics and will be explained thoroughly, focusing on its legacy (Turok, 2001). 2.1.1 Apartheid

The composition of the population during the 20th century and the fact that the White group was a minority, led to the (White dominated) South African government adopting the apartheid policy (Wilkinson, 2001). Racial segregation started during the period of British colonial control. During a period of rapid industrialization in the 20th century Black Africans were brought to Cape Town as a

cheap labour source. Their barracks were built in segregated locations ,on the periphery of the city. In 1923 a partly independent South African government was regulating urban segregation with help of the Pass Law, which restricted Black internal migration to authorized work-seeking at specific urban areas. During the Second World War South Africa made an economic spurt causing Blacks and Coloureds to flock to the city looking for jobs. The term coloured refers to any person of mixed blood and includes any descendant of Black-White, Black-Asian, White-Asian, and Black-Coloured unions. Black African refers to those who originate from Africa (Brown, 2000). A political movement among the white Afrikaners had been proposing a segregative policy of apartness since the 1930s, later called apartheid. The virtually all white electorate (the right to vote was for a large part restricted to whites) felt more and more the appeal of apartheid. In 1948 the National Party won the elections,

(7)

7

clearing the way for the implementation of apartheid laws as described below (Western, 2002). The population registration act and the group areas act, both passed in 1950 by the

governing National Party, had as primary goal “total apartheid”: the segregation of people based on race (Wilkinson, 2001). In Cape Town this led to the displacement of thousands of people to new public housing estates or “townships” built on the Cape Flats, in the east of Cape Town. In the early 1970s, however, the building of public housing could no longer keep pace with the increasing demands. This led to emergence of squatting around the city’s periphery. By the 1980’s many coloured and African households were living in overcrowded conditions in existing townships (Wilkinson,2001). Different ethnic groups have a distinct spatial pattern across the city. The Cape Flats on the eastside of town is mostly home to black Africans and Coloureds whereas the more affluent northern suburbs are populated mostly by whites (Rowntree et al., 2009).

2.1.2 The legacy of apartheid and its consequences for socio-economic inequalities

Urban planning during apartheid produced distorted settlement patterns characterized by social segregation and physical fragmentation. The peripheral townships were denied industrial, commercial and retail development. This limited access to jobs and shops. Laws made it impossible for blacks to start a business and generate income. The townships lacked essential services and infrastructure was not maintained (Turok, 2001). The townships, located in the eastern part of Cape Town, are excluded politically, socially, economically, and spatially from the City’s opportunities and resources. In today’s Cape Town the highest population densities are still in Cape Flats’ townships, where, due to informal housing, the populations are 4 to 5 times as high compared to wealthy suburbs (Wilkinson, 2001). This spatial inequality finds its roots in the segregation that was created during apartheid and still leads to growing social polarization. Income inequalities sort people across space according to their ability to buy into different quality neighbourhoods (Turok, 2001). This means that apartheid is not the only cause for inequalities, but has provided the initial conditions for growing inequalities. The most important elements of cities are employment, housing and the transport between them. They are important factors in people’s lives, so access to them has a big effect on their living standards. Differences in access to jobs and housing and the mobility of

residents is an important cause for inequality. Spatial differences in access to elements causes spatial inequalities. This inequality is growing as formal job growth only occurs in and around already affluent urban nodes. The spatial mismatch in access to resources and opportunities is reinforced by the disproportionate population increase in townships and informal settlements (Turok, 2010). Apartheid has led to segregation by race that still leads to poverty, unemployment and bad housing conditions (informal settlements) in the townships and big income inequalities between

neighbourhoods caused by differences in acess to resources (Wilkinson, 2001: Turok, 2001: Turok, 2010).

Another, less direct link between apartheid and the current spatial and social polarization is provided by Lemanski (2007). Lemanski sees Cape Town as the prime Sub Saharan African city to become a global city. The problem, however, is that striving for Global City status potentially increases inequality and segregation. This is especially problematic for Cape Town since apartheid already has left the city with great inequalities both socially and spatially (Lemanski, 2007). The government attempts to reduce this legacy of urban fragmentation in favour of a city integration within a pro-poor environment. There are concerns that Cape Town’s growing potential global recognition and competitiveness undermine these goals. The question is whether Cape Town can reduce poverty and inequalities while simultaneously aspire to become a Global City. In the years

(8)

8

following the demise of the apartheid regime investments in Cape Town’s economy have been huge, driving Cape Town towards a good position in global competiveness. This pro-growth, rather than redistribution policy, has negative implications for the spatial and social polarization. Increasing spatial polarization is caused by overinvestment in one central urban area at the expense of other areas in the city. A form of social polarization found in Cape Town is the growing inequality between wealthy professionals and the urban poor. Both these forms of polarization increase the pre-existing segregation caused by apartheid (Lemanski, 2007). Despite Cape Town’s growing potential to become a global city, and the economic growth that went along with this growth, poverty and inequality remain dominant. It is clear that the benefits of international trade has not alleviated poverty, nor reduced segregation. In other words: the spoils of globalization are not equally shared (Lemanski, 2007). In sum: the growing polarization caused by growing global competitiveness is not a direct consequence of apartheid, but it reinforces the inequalities that have been created by

apartheid. The inequalities in socio-economic status caused by apartheid have led to a distinct spatial distribution of ethnicities, but also of segregation based on income (poor residents are less able to choose where they want to live and often end up in the townships) (Turok, 2001). Apartheid is not the only cause for segregation but has provided the initial conditions. Socio-economic inequalities are caused by the differences in access to employment, housing, and transportations which leads to certain groups having less opportunities leading to differences in income and other factors (Turok, 2001: Turok, 2010).

2.2 Socio-economic status and environmentally induced health risks

The previous section explained how historical and current factors have affected economic status and its spatial pattern in Cape Town. The assumption in this research is that socio-economic status (income, employment status, housing type, and education level) leads to an

increased environmental health risk and is based on a variety of different researches which all found correlations between socio-economic characteristics and an increased health risk caused by pollution (Burnett et al., 2001: Forastiere et al., 2004: Bruce et al., 2000: Smith, 2002: Evans & Krantowitz, 2002: Martins et al., 2004). The research conducted by Burnett et al. (2001) in Hamilton, Canada examines the link between socio-economic status of individuals and the amount of air pollution exposure, while the article by Forastiere et al. (2007) links socio-economic status of census blocks (areas in Rome) directly to an increased mortality rate caused by air pollution in Rome, Italy (Burnett et al., 2001: Forastiere et al. 2004). The research focusing on Rome concluded that socio-economic status is an important determinant for increased mortalities caused by PM10 exposure (PM10 is a form

air pollution with particles with a diameter smaller than 10 micrometers (μm)) . However, the results suggest that people living in more affluent areas in Rome experience a higher exposure to ambient PM10 pollution, which is contradictory to other research conducted in Hamilton (Burnett et al., 2001).

The research in Rome nevertheless still concludes that people living in poorer areas still have a higher mortality rate caused by air pollution, which can be explained by differences in susceptibility. The differences in susceptibility are caused by differences in diet, smoking and drinking habits,

psychosocial stress, and genetic differences (Forastiere et al. 2004). This means that, even though, the exposure to PM10 pollution is lower, the mortality is still higher. The article concludes that this is

not the case for other places and that for Europe the outcomes of different researches are mixed. In the UK, for instance, areas with low air quality do correlate with areas with a lower socio-economic status, same as in the United States and Canada (Forastiere et al. 2004). In the case of Hamilton,

(9)

9

Canada a strong correlation has been found between socio-economic status and high exposures to air pollution (Burnett et al. 2001). The indicators of socio-economic status used by Burnett et al. (2001) will be the same used in this research regarding Cape Town. These indicators are: employment, income, education level, and housing. There seems to be a direct link between increased mortality rates and air pollution. Yet this does not mean that a low socio-economic status does always leads to higher exposure. This depends on specific characteristics of a city, for example the proximity of roads and highways to residential areas (Forastiere et al., 2007). The question is in which category does Cape Town fit? Do areas in Cape Town with a low socio-economic status have a higher or lower exposure to air pollution?

Research focusing on developing countries seems to confirm the latter question: lower socio economic status does lead to higher environmentally induced health risks caused by a

disproportionate exposure to air pollution (Bruce et al., 2001: Smith, 2002). About half of the world’s population, most of which in developing countries, still rely on unprocessed biomass fuels in the form of wood, dung, and crop residues for heating and cooking. These are typically burnt indoors in open fires or poorly functioning stoves. As a result there are high levels of air pollution, to which women and young children are most heavily exposed. The indoor air pollution caused by the burning of biomass fuels is not limited to the household where the burning takes place. When vented to the outdoors, unprocessed solid fuels produce enough pollution to significantly affect local,

neighbourhood pollution levels with implication for total exposures. Because cook stoves are

essentially used every day at times when people are present, their exposure effectiveness (or intake fraction) is high, i.e. the percentage of their emissions that reach people’s breathing zones is much higher than for outdoor sources (Smith, 2002). Many of the substances in biomass smoke can damage human health. Especially PM10 can penetrate deeply into the lungs and appear to have the

greatest potential for damaging health. The consequence of exposure to indoor air pollution is an increased risk of acute respiratory infections in childhood, chronic obstructive pulmonary disease and lung cancer (Bruce et al.,2001).

Bruce et al. (2001) identify poverty as the most important driver for the use of bio-mass fuels, whereas higher income areas often use petroleum products and electricity, which are relatively cleaner. In general the types of fuel used become cleaner and more convenient, efficient and costly as people move up the energy ladder, where animal dung is the lowest rung on the ladder and electricity the highest. People tend to move up the energy ladder when socio-economic conditions improve (Bruce et al. 2001). Meaning that wealthier people rely less on biomass fuels and therefore have less exposure to indoor air pollution, implying a direct relation between socio-economic status and air pollution exposure/ environmental health risk. In sum the relation between socio-economic status and environmental health risks has often been established in scientific literature, but the outcomes aren’t always the same. The outcome depends on, for example, the proximity of roads, highways or industry (Forastiere et al., 2004). For developing countries indoor air pollution and the burning of biomass fuels are the biggest risks for human health. This risk is less in developed country as higher incomes provide less polluting fuel sources (Bruce et al., 2001). All the research mentioned in this paragraph agree on the fact that poor people have an increased health risk compared to wealthier counterparts (Bruce et al., 2001: Forastiere et al., 2004: Burnett et al., 2001: Smith, 2002).

(10)

10 2.3 Environmental injustice

Environmental injustice is a discourse within environmental science that explores whether certain groups (e.g. minorities) have an increased environmental risk. This means, for instance, that when an area has low socio-economic, as explained in the previous section, and an increased health risk caused by air pollution will lead to environmental injustice. Environmental injustice has various definitions which will be discussed and the definition best suited for this research will be identified. First I will identify what problems are seen as environmental problems and what forms they take in society in order to see what effects they may have on human health. Risk is an important component of environmental injustice and will be explained in the next section.

2.3.1 Environmental risk

An intervention in the physical environment, road construction, the building of industrials plants or the enlargement of an airfield. This could lead to an increase in traffic, industrial activity and more airplanes landing and taking off causes environmental change. Environmental changes can in their turn be evaluated differently both positively and negatively. Tellegen & Wolsink (2004) identify two main negative outcomes of environmental change: annoyance and risk. Annoyance is seen as disturbances affecting normal day-to-day life. An environmental risk is the probability that a certain objectively defined negative effect caused by other parties will occur, due to environmental factors (Tellegen & Wolsink, 2004). It is important to note that environmental risks are not taken voluntarily: they are generaly inevitable. There are two types of environmental risks: events that may occur with a probability of less than 100% and effects on humans that may result (also with a

probability less than 100%) from given environmental damage (Tellegen & Wolsink, 2004). The risk of undesirable events belongs to the first type. Many kinds of environmental impact do not have frequently returning character. In fact many of them do not actually occur at all, there is only the possibility that it may occur. Nevertheless, incidents may happen and so they represent

environmental hazards that should be taken into consideration. The second type involves a number of already existing examples of environmental damage, sometimes as a result of catastrophes in the past. The environmental damage seldom affects humans directly. For example pollution represents a health risk that mostly only affects a small part of the population, but at the same time nobody knows for sure whether he will be affected. So although the probability is small, the risk is present for all. An important form of environmental risk is the negative effect on human health. Health risks can be caused by noise, noise can lead to psycho-social stress after prolonged exposure, this can

influence sleeping behaviour and health in general (Tellegen & Wolsink, 2004). Air pollution can lead to illness and disease and an increase in mortality (Brunekeef & Holhate,2002).

2.3.2 Environmental injustice discourse

Environmental risks are present for all, but it may be possible that people living in certain areas are more prone to these risks. The risks may not be equally distributed across space. For instance the degree of air pollution can vary across space which may affect certain areas

disproportionately. Traditional environmental justice literature provides a comprehensive analysis of the disproportionate environmental risks suffered by residents of low-income and minority

communities in regards to toxic exposure, resource extraction, waste exports, climate change, and of their causes and health impacts (Anguelovski, 2013). Most of this literature concludes that people in these low-income and minority (marginalized) communities, indeed, suffer from a higher degree of

(11)

11

negative consequences (Pellow, 2000). Dixon & Ramutsindela (2006) identify two main perspectives from which environmental injustice can be defined. The perspective from natural sciences on the one hand, and the social scientific perspective on the other. The natural science perspective states that environmental justice is limited to protecting the natural environment. Ecologists who have embraced this perspective, accuse human of violating the rights of non-humans. The natural scientific perspective thus focuses on preserving the physical environment. This means that in the environmental injustice discourse plants, animals, rivers, and forests are seen as the marginalized groups which suffer from unequal distributions of environmental risks. The second perspective from social sciences emphasizes an anthropocentric view of the environment. From the initial

preoccupation with the hardships suffered by local populations from nature preservation projects, through government planning and the need for better services, social scientists sought to bring the plight of poor and marginalized to the center of environmental and development planning. The emphasis of social scientists is on the living and working conditions of the poor, and the environment is redefined to include the totality of life in communities, the air and water, safe jobs for all at decent

wages, housing, education, and health care (Dixon & Ramutsindela, 2006). Most of the literature on environmental injustice related to socio-economic inequalities

adopts the social scientific perspective. However, the definition of what is exactly understood by environmental injustice differs among the different scholars. Pellow (2000) argues that most scholars that use the term environmental injustice do so with little attention on how to define this concept, and often interchange it with the term environmental racism. Environmental racism refers to institutional rules, regulations and policies of government or corporate decisions that deliberately target certain communities for least desirable land uses, resulting in the disproportionate exposure of toxic and hazardous waste on communities based on prescribed biological characteristics. Environmental racism also refers to the systematic exclusion of people of colour from decisions affecting their communities (Pellow, 2000). Environmental racism could apply to the situation in Cape Town where Black Africans and Coloureds were systematically excluded from decision making processes during apartheid, forcing them to live in less desirable locations (Wilkinson, 2001). Pellow quotes Bryant (1995) when defining environmental injustice:

“Environment Justice (EJ) refers to those cultural norms and values, rules, regulations, behaviours, policies, and decisions to support sustainable communities where people can interact with confidence that the environment is safe, nurturing and productive. Environmental justice is served when people can realize their highest potential. EJ is supported by decent paying safe jobs, quality schools and recreation, decent housing and adequate healthcare, democratic decision making and personal empowerment, and communities free of violence, drugs, and poverty. These are communities where both cultural and biological diversity are respected and highly revered and where distributed justice prevails.” (Pellow, 2000).

The definition given by Bryant is still very broad. The concept of “Environmental inequality” can be used to narrow the definition down . This concept focuses on broader dimensions of the intersection between environmental qualities and social hierarchies and therefore focuses more on social

inequality (as in the unequal distribution of power and resources in society) and environmental burdens. Environmental inequalities, unlike environmental racism, include any form of

environmental hazard that burdens a particular social group. Environmental inequality thus has a

(12)

12

Anguelovski (2013) provides examples of what EJ-activists articulate as their demands. These demands provide a good insight of the different forms EJ can take. One of the demands of EJ-activists is that “every person of all races, incomes, and culture to a decent and safe quality of life”. Another demand of EJ-activism strives for “the right to well-connected, affordable and clean transit systems in cities and the right to health and affordable food and to community food security”. EJ

organizations have also started advocating for green, affordable health housing along with recycling practices and spaces for gardens inside housing complexes and for the provision of economic opportunities for disenfranchised communities around the green economy. Their demands include jobs and training for energy efficiency project and funding or redistribution of revenues from utility companies for weatherizing house structures (Angueloviski, 2013). The demands of EJ-activists have

a wide range, but none of the demands fit well in the context of this research. Pellow (2000) states that most of the scientific literature on environmental injustice agrees

that communities that are poor and populated by people of colour bear a disproportionate burden of environmental risks and externalities. In other words: environmental injustice occurs when

marginalized communities suffer a disproportionate environmental risk. In the context of Cape Town this would mean that people living in the Cape Flats townships suffer a higher degree of air pollution than their richer counterparts and therefore have a higher environmental health risk. This what will be understood as environmental injustice in the context of this research as it best fits the hypothesis (in areas where the socio-economic characteristics are relatively bad (e.g. high unemployment) people are exposed to higher amounts of air pollution).

2.3.3 Environmental injustice research in practice

A problem that arises when researching environmental injustice is exposure assessment. Problems with exposure assessment lead to uncertainty in the results that are derived from statistical data. In order to reduce the effect of misclassifying exposure, Burnett et al. (2001) use a combination of geographic information systems (GIS) and fixed-site air-pollution monitors to improve exposure estimates for environmental justice and health analysis in. The research of Burnett et al. focuses on the link between particulate air pollution and socioeconomic status in Hamilton, Canada (Burnett et al., 2001). Like this research, its hypothesis is that people with a low socio-economic status are exposed to higher amounts of air pollution. As shown in Figure 1 (see appendix), a mix of the spatial segregation of socioeconomic groups and the geographical siting of pollution facilities leads to spatial inequality in pollution exposure. Figure 1 also shows that environmental justice influences the geographical location of polluting facilities and the segregation of racial and socioeconomic groups over space. Burnett et al. (2001) identify three factors that determine the spatial distribution of pollution and other hazards in relation to specific socioeconomic groups. These three factors are: political power, market failures, and land-use institutions (Burnett et al., 2001). These three factors can be linked to the situation in Cape Town where political power had a great impact on the cities spatial distribution of socio-economic status and race a like as it determined the housing locations for the better part of the 20th century (Wilkinson, 2001: Western, 2002). Market failures in Cape Town consist of the gap between formal housing and the demand for formal housing. The large influx of migrant workers during the Second World War, and the continuing growth of the township population force a good part of these migrants to live in informal settlements as formal housing projects remain largely insufficient to support the population growth. These informal settlements are mostly located in the townships (Western, 2002). Land use institutions designate areas for a certain purpose. Turok (2001) argues that economic growth mostly occurs in certain

(13)

13

economic nodes near affluent suburbs. The land use institutions in Cape Town thus designate areas outside the township for economic activity, further exacerbating the inequalities (Turok, 2001).

Figure 1 shows that spatial inequality in pollution exposure can indirectly lead to health effects of pollution (through other health factors). The research for Hamiltion, Canada concludes that groups with lower socioeconomic status are exposed to higher levels of particulate air pollution than other groups with a higher socioeconomic status. Another conclusion is that people living in low-cost housing have a higher potential exposure to air pollution. The same goes for people with a low income and areas with high unemployment rates (Burnett et al., 2001). This conclusion is important as it is the same as the hypothesis for the research in Cape Town: people living in neighbourhoods

with low socio-economic status have a higher environmentally induced health risk. An example of area-based air pollution health risk research is the research conducted in

Rome by Forastiere et al. (2007). The data used for the Rome research came from census data per census block (5,736 census blocks in Rome with about 480 residents each). For each census block a median per capita index was derived for each census block to estimate income. All of the data (education level, income, family size, working-age employment rates, crowding, and percentage of dwellings owned/rented) was aggregated to the census block level. All the census blocks were then divided into four categories (low, mid-low, mid-high, high)these categories were then compared to each other based on vehicle air pollution and mortality rates (Forastiere et al., 2004). The conclusion of this research was that high-income areas have more air pollution, but a lower mortality rate. The difference in mortality are caused by differences in susceptibility to air pollution. Socio-economic status influences causes individuals to have psychosocial stress, a poor diet, and smoking and excessive drinking habits, which leads to a higher susceptibility. This means that a higher degree of air pollution exposure does not necessarily lead to a higher health risk, susceptibility may play an important role (Forastiere et al., 2007).

2.4 Environmental injustice in Cape Town

In the context of South Africa Dixon and Ramutsindela(2006) describe environmental justice as “giving citizens the right to an environment that is not harmful to their health or wellbeing” (Dixon & Ramutsindela, 2006). This definition comes from the South African constitution. The definition is rather narrow compared to the definitions of environmental (in)justice provided in section 2.3.2. If the definition provided Dixon & Ramutsindela would be used then, by the same token,

environmental injustice would occur when the environment is harmful to an individual’s health or wellbeing. This definition lacks the component environmental risk, because even if citizens have an environment that is not harmful, environmental risk may still be present since risk does not affect everyone (Tellegen & Wolsink, 2004).Since the air pollution in Khayelitsha exceeds the guidelines and causes health issues one could say that there is no environmental justice in Khayelitsha (City of Cape Town, 2005). This environmental injustice is emphasized in the Cape Town sustainability report from 2005. This report states that low-income areas such as Khayelitsha, emissions pose a

particularly serious health risk to residents. For these people medical care is often expensive, and access limited. The reliance on polluting energy sources by poor residents of Cape Town is a critical health issue (City of Cape Town,2005). This highlights another problem, namely the source of the pollution. PM10 is the most common found polluter in in low income areas such as Khayelitsha, where the PM10 guideline of 50 μg/m3 is exceeded very often (City of Cape Town, 2004). The World Health Organization (WHO) identifies the combustion of solid fuels on open fires as the principal source of PM10 (WHO, 2011), but also diesel trucks and power plants emit PM10 (EPA, 1995). The

(14)

14

cause of the fact that PM10 guideline in Khayelitsha is often exceeded is because of the burning of wood and paraffin by its residents that often use open fires to heat their informal houses. Other sources are the use of older vehicles (especially taxis). Similar levels of PM10 pollution are likely to be seen in other areas with densely populated informal settlements (City of Cape Town, 2005). Even though industrial activity still emits a lot of PM10 it does so higher into the atmosphere, allowing it to be relatively diluted before it is inhaled by humans. Domestic burning, however, is at the level where people are breathing, which exposes them to high levels of PM10 pollution. Goudie (2006) underlines this and states that in many cities in poorer countries air pollution is increasing and that in some countries this is caused by a heavy reliance on coal, oil, and wood for cooking and heating leading to a high level of sulfur dioxide (SO2) and PM10 emissions (Goudie, 2006).

2.5 Environmental health risk caused by air pollution

High amounts of exposure to air pollution can have a negative impact on the human health. This means that high exposure to air pollution leads to an increased health risk. But what are the sources of the pollutions ,what are the consequences for the human health caused by the different pollutants, and what concentrations should be regarded as dangerous for the people exposed?

2.5.1 Consequences of air pollution for the human health

Scientific literature in the last two decades strongly relate air pollution to the occurrence of disease and illness. The interest in the health effects of air pollution increased after a cohort research conducted in a few cities in the United States suggested that exposure to fine particulate matter in the air was associated with life shortening (Brunekreef & Holgate,2002) . This research established that exposure to air pollution can shorten life expectancy by 1 to 2 years, which is a rather big influence compared to other environmental risk factors related to mortality (Brunekreef &

Holgate,2002). Both scientific literature and the World Health Organization (WHO) confirm that air pollution has diverse health risks for humans (e.g. cancer or cardiovascular disease) (Brunekreef & Holgate, 2002: WHO, 2011: Burnett et al. 2001: Forastiere et al., 2007: Bruce et al., 2000). Other researchers link higher air pollution levels to socio-economic status (see paragraph 2.2). Scholars have thus often linked an increased environmentally induced health risks to socio-economic status (Forastiere et al., 2007: Burnett et al., 2001: Bruce et al., 2000: Martins et al., 2004: Evans &

Kantrowitz, 2002).This paragraph explains what the effects of different forms of air pollution are for

humans and what the effects are on mortality statistics. The WHO recognizes two major dimensions in which air pollution affects the human health:

in- and outdoor pollution. Indoor pollution mostly occurs in low-income countries where coal and other biomass fuels are used to cook food and heat houses. The WHO estimates that in 2012

approximately 4.2 million premature were attributable to household air pollution. The biggest part of these deaths also occurred in low income countries (WHO, 2011). The other dimension, outdoor air pollution, is a major environmental health problem affecting everyone. The WHO estimates that some 80% of outdoor air pollution-related deaths were due to heart disease and strokes, while 14% of deaths were due to chronic obstructive pulmonary disease or acute lower respiratory infections. The other 6% of the deaths are attributable to lung cancer. Other factors, however, may influence health as well, for example smoking can also cause lung cancer. A 2013 assessment by the

(15)

15

carcinogenic (any substance directly causing cancer) to humans, with PM10 being most closely related associated with an increased occurrence of (mostly lung) cancer. Ambient (outdoor air pollution) in both urban and rural areas have caused an estimated 3.7 milliion premature deaths worldwide per year in 2012; this mortality is due to exposure to PM10 which causes cardiovascular and respiratory disease and cancers. Some 88% of the 3.7 million premature deaths occurred in low- and middle income countries, which are thus disproportionately affected (WHO, 2011).

Both scientific literature and the WHO agree that air pollution increases mortality and the occurrence of diseases and illnesses, but what diseases and illnesses does it actually cause (WHO, 2011: Brunekreef & Holgate, 2002: Agius & Oberdörster, 1995: Seaton et al., 1995: Bruce et al., 2000)? The WHO describes the effects of air pollution on the human health for each of the pollutants mentioned above. PM10 is the most widespread pollutant and there is a close relationship between

exposure to high concentrations of small particulates and increased mortality, both daily and over time. When concentrations of PM10 are lower, related mortality will also be lower. This will only be

the case when other factors remain the same. Indoor air pollution from solid fuel use pose a major risk factor for cardiovascular disease, chronic obstructive pulmonary disease and lung cancer among adults (WHO, 2011).

Studies have shown that symptoms of bronchitis in asthmatic children increase in association with long-term exposure to nitrogen dioxide. Reduced lung function growth is also linked to NO2

concentrations that are currently measured or observed in cities in Europe and North America. At short term concentrations that exceed 200 μg/m3 it is a toxic gas that causes significant inflammation of the airways (WHO, 2011).

Sulfur dioxide can affect the respiratory system and the functions of the lungs, and causes irritation of the eyes. Inflammation of the respiratory tract causes coughing, mucus secretion, aggravation of asthma and chronic bronchitis and makes people more prone to infections of the respiratory tract. Hospital admissions for cardiac disease and mortality increase on days with higher Sulfur dioxide levels. When Sulfur dioxide combines with water, it forms sulfuric acid; this is the main component of acid rain which is a cause of deforestation (WHO, 2011).

(16)

16

3. Methodology

In this chapter I will introduce the research- and sub questions, followed by the conceptual model which explains the assumed causality in this research. The following paragraph will explain the various methods used in this research. The arguments behind the choices will be presented for each of the methods. The final paragraph of this chapter will introduce the data and how I process the data before the analyses. This includes the re-categorizations of the different variables that constitute “socio-economic status” and the practical reasons for the new categories. The air-pollution data will be introduced along with the limitations of the datasets available and an explanation of how the limitations are dealt with in order to still be able to analyze the data and come up with relevant conclusions.

3.1 Research and sub questions

The question this research will be trying to answer is:

“Do differences in socio-economic characteristics between neighbourhood in Cape Town lead to differences in environmentally induced health risks on a neighbourhood level?”

The research units for this research are the neighbourhoods in Cape Town. These neighbourhoods are called ‘suburbs’ in the census from which the data for this research is derived. Because of limited data from fixed air pollution monitoring sites, only suburbs where air pollution data has been

collected will be included in the research (see map 1 for the locations of the monitoring sites). To answer the research question the following sub questions were formulated in order to

split the research question into researchable fragments. Sub questions:

1) Did historical factors influence socio-economic status in modern day Cape Town? 2) What is the spatial distribution of socio-economic status among the Cape Town

neighbourhoods?

3) What are the (environmentally induced) health risks caused by pollution in Cape Town? And what is the spatial distribution of these risks in the city?

4) Is there a relationship between socio-economic status and the spatial distribution of health risks?

(17)

17 3.2 Causal model

The causal model designed for this research shows the hypothetical relations between the different concepts discussed in the theoretical framework. The conclusion of the research will determine whether the relationships are there or that the causal relations should be adapted or proof to be non-existent and should therefore be rejected altogether.

The hypothesis of this research is that historical factors have led to a distinct spatial distribution of socio-economic status across the city. Part of this influence can be explained by market failures, land-use institutions, and political power (Burnett et al., 2001). Market failures have a great impact on the formal housing supply, especially in townships where formal housing is often lacking (Lemanski, 2009: Wilksinson, 2001). Political power during the apartheid is the cause of the racial segregation in Cape Town and the economic and political exclusion of the townships where the marginalized coloured and black Africans were forced to live (Wilksinson, 2001: Turok, 2001). Today, land-use institutions still bypass the townships by designating specific areas for economic growth. These areas often lay in close proximity to already affluent neighbourhoods, far from the poverty stricken townships (Turok, 2001). These three factors also influence the location choices for

environmental hazards, such as factories, roads, power plants, and airports (Burnett et al., 2001) The assumption in this research is that socio-economic status influences the susceptibility of individuals to air pollution. People living in areas with low socio-economic status often have a higher

susceptibility to negative effects of air pollution due to differences in diet, drinking and smoking habits, social status, lifestyle, occupation, and psychosocial stress (Forastiere et al., 2007: Burnett et al., 2001). In turn, air pollution has been identified as a direct cause for negative health effects such as illnesses and increased mortality rates, i.e. air pollution leads to an increased health risk

(Brunekreef & Holgate, 2002: WHO, 2011: Burnett et al. 2001: Forastiere et al., 2007: Bruce et al., 2000: Seaton et al, 1995). Finally, if indeed areas with low socio-economic status have an increased environmentally induced health risk due to a disproportionate degree of air pollution exposure this could be seen as a form environmental injustice according to the definition provided by Pellow (2000): “environmental injustice occurs when marginalized communities suffer a disproportionate environmental risk” (Pellow, 2000). A problem in this research for this conceptual model is that not all variables can be researched. Statistics on health data are lacking, as well as data on the factors influencing susceptibility. This leaves me unable to proof that these variables do indeed play a role in Historical factors Socio-economic status of neighbourhoods Environmentally induced health risks Environmental injustice Susceptibility to air pollution Degree of air pollution exposure Diet, drinking and

smoking habits, psychosocial stress, social position, occupation

Market failures, land-use institutions, political power

(18)

18

Cape Town. As I cannot proof that susceptibility may be higher in some areas, the conclusion for environmentally induced health risk will be based on the degree of air pollution exposure, though it will be noted that this variable is not fully responsible for health risks (Forastiere et al., 2007: Burnett et al., 2001). To fully understand how environmental risks are distributed across Cape Town, future research must include statistics on the factors influencing the susceptibility of individuals and how many of these individuals’ health are actually affected by air pollution.

3.3 The case: Cape Town

Cape Town is located on the south-western tip of Africa and is the second city of South Africa in terms of the size of its local economy and population size. With a population of 2.6 million and a rich colonial history, Cape Town is one of the most culturally and racially diverse cities of Africa. Whereas Black African is the dominant ethnic group in South Africa, Cape Town has a relatively high percentage of Coloureds and Whites. The racial segregation brought about by apartheid has had a great impact on the urban structure, displacing thousands and initiating the rise of the infamous townships (Wilkinson, 2001). My principal reason for choosing Cape Town as the case for my research is my visit to the city with the University of Amsterdam in January 2013. The displays of poverty and the huge inequalities between neighbourhoods sparked my interest in the city.

Map of South Africa with Cape Town in the South-west. Source:

http://www.adventure-travel.org.uk/AFRICA/cape_town.php

3.4 Type of research

The research design used will be a cross-sectional design. This design entails the collection of data on more than one case at a single point in time. This done in order to collect a body of quantitative data in connection with two or more variables which are then examined to detect patterns of association . Employing a cross-sectional design is mostly done when researchers are interested in variation. Variation can only be detected when multiple cases are examined (Bryman, 2008). The cases in this research will be the suburbs of Cape Town. The data supplied by the City of Cape Town is

quantitative as it expresses the values of the indicators, employment, income, education, and housing type in numbers. Through data analysis in ArcGIS patterns of association between the indicators will, if present, be detected.

(19)

19

3.4.1 Operationalization of the data: census data (socio-economic status)

Cape Town is divided into three different administrative levels: subcouncils, suburbs, and wards. There are 24 subcouncils in Cape Town, these are the largest administrative areas. Each of

subcouncils is divided into wards, of which there are 111. Wards are small communities of 13.000 to 15.000 voters which can elect their own ward councilor, and is thus a political demarcation. Suburbs are about the same size as wards and function as an area demarcation system for census research . The census data is provided the City of Cape Town in Excel format. The data, published in 2012, was collected in 2011 for each suburb in Cape Town on a variety of variables of which adult education, dwelling type, household income, and employment status are used in this research as they are the components that make up socio-economic status. The datasets provided by the City of Cape Town make a distinction based on ethnicity for each of the suburbs, but since ethnicity plays no role in this research only look at the total amounts (all ethnicities combined) and the total percentages for each variable.

Variable Indicators Census categories Re-categorization

Dwelling type Relative amount of housing type shown as a percentage of the total amount of houses for each suburb.

Formal dwelling, Informal dwelling/ shack in backyard, Informal dwelling. Shack not in backyard, Other

Formal housing, Informal housing, Other

The distinction between informal dwelling/ shack in backyard and shack NOT in backyard is not relevant for my research (where the informal dwelling is located does not matter as long as it is counted as informal).

Variable Indicators Census categories Re-categorization

Employment status Relative amount of residents that are in any of the categories shown as a percentage of the total number of residents for each suburb. Employed, Unemployed, Discouraged work-seeker, Other not economically active Employed, Unemployed, Other not economically active

In this research only the distinction between employed and unemployed is of importance. Since the City of Cape Town does not count Discouraged work-seeker or other not economically active as unemployed, neither will I. The OECD defines “discouraged work-seeker” as: persons who, while willing and able to engage in a job, are not seeking or have ceased to work because they believe there are no suitable available jobs (United Nations, 1984). The government of South Africa defines “Not economically active” as: a person who is not working and not seeking work or not available for work. This group includes full time students, housewives, the disabled who cannot work, retired people and other who cannot work. The term is only officially applied to those of working age (15-65) (statssa.gov.za, 2005).

For the category income, the City of Cape Town identifies ten categories . After checking the income data from the suburbs included in this research I noticed that the category “Unspecified” was

(20)

20

zero for all the suburbs, making this category obsolete, resulting in 9 categories. For a better

overview I will re-categorize income to fewer categories. The re-categorization of this variable will be done based on a document from a bureau of market research called UNISA. UNISA identifies 7 income classes in South Africa ranging from poor (R0-R54,344 per annum) to affluent (R1,329,845+ per annum)(see figure 3) (UNISA, 2011). A problem arises, however, when translating the categories from the census data to the new categories: not every category fits right in. One option would be to interpolate to estimate the amount of people that would fall into one category. Since this method is unreliable (people may be placed in the wrong category) I will therefore still use income brackets used in the census data.

Variable Indicators Census categories Re-categorization

Household income Relative amount of households in any of the categories shown as percentage of the total households for each of the suburbs

No Income, R1-4.800, R4.801-9.600, R9.601-19.200, R19.201-38.400 R38.401-76.800, R76.801-153.600, R153.601-307.200, R307.201-614.400, R614.401-1.228.800 R1.228.801-2.457.600 R2.457.601 or more Unspecified Poor: R0-38.400, Lower middle class: R38.401-307.200 Upper middle class: R307.201-1.228.800, Affluent: R1.228.801 or more

Figure 3: Income groups for South Africa for households per annum (UNISA,2011)

The last variable that constitutes socio-economic status is Education. This categorization is not based on categorization found elsewhere in documents or literature, but still has a certain logic. The category low education is made up by people who have either no education or have no secondary education (have not attended high school). Medium education constitutes of people who either only

(21)

21

partly attended high school or finished it. High education is when people have engaged in higher education (such as universities) after high school.

Variable Indicators Census categories Re-categorization

Adult education Relative amount of residents in any of the categories shown as a percentage of the total residents for each suburb No schooling, Some primary, Completed primary, Some secondary, Grade 12, Higher, Other

Low level education, Medium level education,

High level education

3.4.2 Presentation of the data: air pollution

The air pollution data used in in this research comes from an annual report from the City of Cape Town’s Air Quality Monitoring Network from 2008. This annual report shows only a part of the total data collected by the Monitoring Network, but the rest of the data could not be acquired for this research because it is classified and will only be released to local researchers or researchers with ties with local universities. I was unable to get access to more air pollution so this research is based on the limited data provided by the 2008 annual report. The data is limited because it shows only the annual means of the different pollutants measured at the different sites located as shown in map 1 and not every pollutant is measured at every site, not every site has been included in the table of results, and not every measuring site has a reported operating radius (Athlone), making it impossible to use the values of that measuring site outside the suburb in which it is located (City of Cape Town, 2008). The data used from the annual report are the annual means of the pollutants Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), Particulate Matter 10 (PM10), and Ozone (O3) and the reported

radiuses of the different measuring sites. How this data is used will be explained in the next paragraph.

3.4.3 Presentation of the data: the pollutants

In this paragraph the various sources of air pollution will be discussed along with the guidelines for the pollutants used by the City of Cape.The World Health Organization (WHO) has established worldwide guidelines based on expert evaluation of current scientific evidence for four pollutants. These four pollutants are: Particualte Matter (PM10), Ozone (O3), Nitrogen Dioxide (NO2), and Sulfur

Dioxide (SO2). These pollutants all come from different sources and have different health effects

(WHO, 2011). The health effects will be discussed in the next section.

Ozone is a strong oxidizing agent that is formed in the troposphere through a complex series of chemical reactions involving the action of sunlight on nitrogen dioxide and hydrocarbons. Concentrations of ozone in city centers tend to be lower compared to suburbs mainly because of scavenging of ozone by nitric oxide originating from traffic (Brunekreef & Holgate,2002). Since sunlight plays a major role in the formation of ozone, its concentrations are higher during sunny weather. The sources of the pollutants that can lead to the formation of ozone are industrial and vehicle emissions (WHO, 2011).

The biggest sources of anthropogenic emissions of nitrogen oxides are stationary sources, such as heating and power generation, and combustion engines from vehicles. In ambient conditions, nitric oxide is rapidly transformed into nitrogen dioxide by atmospheric oxidants such as ozone

(22)

22

(Brunekreef & Holgate,2002). As mentioned earlier PM10 is one of the biggest polluters and affects more people than any

other pollutant (WHO, 2011). Particulate air pollution is a combination of solid, liquid, or solid and liquid particles suspended in the air. The size of these particles varies, from a few nanometers (nm) to tens of micrometers (μm). The largest particles are mechanically produced by attrition of largest particles. Small particles are largely formed from gasses, while the smallest are formed by nucleation resulting from condensation or chemical reactions that form new particles. In practical terms a distinction is made between PM10, which are particles smaller than 10 μm in diameter can penetrate

into the lower respiratory system, and PM2.5, which are particles smaller than 2,5 μm in diameter

which can penetrate into the gas exchange region of the lungs (Brunekreef & Holgate, 2002). The major components of PM10 are sulfate, nitrates, ammonia, sodium chloride, black carbon mineral dust, and water (WHO, 2011). The source of these various substances is combustion are diesel engines, and the burning of coal and biomass fuels (such as wood) (Seaton et al., 1995: City of Cape Town, 2005).

Sulfur dioxide is a colourless gas with a sharp odour. It is produced from the burning of fossil fuels (coal and oil) and the smelting of mineral ores that contain sulfur. The main anthropogenic source of sulfur dioxide is the burning of sulfur-containing fossil fuels for domestic heating, power generation, and motor vehicles (WHO, 2011).

3.5 Operationalization

In order to make it possible for the sub questions to be researched they need to be measurable. In this paragraph I will explain the concepts from the theoretical framework

measurable. Each concept is explained in tables stating the dimensions, indicators and methods.

Concept Dimensions Indicators Questions / methods

Air pollution Ozone (O3)

Sulfur Dioxide (SO2)

Nitrogen Dioxide (NO2)

PM 10

18 μg/m3 annual mean 30 μg/m3 annual mean 30 μg/m3 annual mean 50 μg/m3 annual mean

Causes for these pollutants will be found through the analysis of documents of the WHO, the City of Cape Town, and by analyzing literature on air pollution. The spatial distribution of air pollution will be spatially analyzed through ArcGIS. The data is derived from the Cape Town annual report of 2008. Further analysis will be done through the use of the 2008 episodes which show the amount of exceedances for measuring sites.

(23)

23

The indicators are based on the UK/CT guidelines used in City of Cape Town’s annual air pollution report from 2008 and shows the maximum annual mean for each pollutant (City of Cape Town, 2008).

Concept Dimensions Indicators Question/ methods

Socio economic status Employment status

Household income

Dwelling type

Adult education

Unemployment rate in % of the total suburb population.

Annual household income will categorized into 4 categories based on the categories used by UNISA (see figure 3). The data used will be on suburb level. Formal dwelling, informal dwelling/ shack in backyard, and informal dwelling shack NOT in backyard. Measured as percentages of total households per suburb. Four categories of education measured as percentages of total population for each suburb.

Answers question 2. The dimensions as found in the 2011 census data from Cape Town have been re-categorized and will be analyzed through ArcGIS, showing the spatial distribution of each the dimensions.

Concept Dimensions Indicators Questions / methods

Environmentally induced health risks

Breathing problems, asthma, reduced lung functions, lung diseases, irritation of eyes, bronchitis, cardiac disease, mucus secretion,

inflammation of respiratory tract, and increased mortality (WHO, 2011).

Presence of exceeding amounts of Ozone, Sulfur Dioxide, and Nitrogen Dioxide. Maximum hourly amounts are

mentioned above and the presence of PM10 exceeding the 24 hour guideline mentioned above.

Answers sub question 4.

The air pollution data on which the answer for this question is based is measured by City of Cape Town’s Air Quality Monitoring Network. This data will be analysed through ArcGIS in order to show its spatial distribution and the amounts present in the air.

(24)

24 3.6 Spatial analysis in ArcGIS

In order to see what the socio-economic characteristics are of the different suburbs in Cape Town I will use census data provided by city of Cape Town. The data that will be used for analysis are income, employment, education level, dwelling type (formal/informal), and the amount of particles in the air of the substances O3, NO2, PM10 and SO2. For the environmental health risk I will use spatial

GIS data provided by the GIS department of Cape Town. I will use this data to see which suburbs are most affected by pollution and would therefore have a higher environmentally induced health risk. The program ArcGIS will be used to do a spatial analyses of both the social status and the pollution. This will be done by connecting the data in excel tables to the spatial data in ArcGIS. Then I will show the spatial distribution of each of the indicators mentioned earlier. The maps will offer insight of where the highest amounts of pollution overlap with the different components of socio-economic status. When comparing the maps I will be able to conclude whether the components that make up socio-economic status lead to a higher degree of air pollution for each of the components

individually. So when a high degree of poverty is concentrated in the same suburb as a high amount of pollution of any of the pollutants then that would mean that there is a spatial correlation between poverty and a particular (or various) pollutant(s).

3.6.1 Preparing the data before the analyses

For the spatial analysis on air pollution data from the Air Quality Monitoring Network of Cape Town, as presented earlier, is used. The air pollution monitoring sites are fixed and located only in several suburbs. This makes it possible to only do research for the suburbs that are located within the radius of one or more measuring sites. The radius of each measuring site can be found in the table below.

Site location Radius

City Hall Foreshore Atlantis Killarney Potsdam Bellville South Bothasig Khayelitsha Table View Wallacedene Molteno Goodwood Athlone 100m 100m 500m 500m 500m 500m 4 km 4 km 4 km 4 km 10 km 10 km Missing

It is possible that a measuring is not 100% in the right place, as they are placed in the map by hand. The next step is to assign buffers to the various measuring sites. These buffers correspond to the radiuses of the various measuring sites mentioned earlier (for a map with the buffers and the measuring sites see Map 1 in the appendix). The third step is to select all suburbs that are completely or partly within range of any of the buffers. However, not all suburbs that are partly within a buffer have been selected for this research. This is because only a small part of a suburb could be within

(25)

25

range of a measuring site. The suburbs that are for more than 50% inside the radius of any measuring site will be included in the analyses. When all these suburbs are selected I scan the socio-economic status data for these suburbs before transferring the data to the excel datasheet. When a suburb has a population of 0 or missing data the suburb will be excluded from the research. For the suburbs that are within the radius of multiple sites, data from both sites is used after determining which site is closer. The site that is closest will provide the initial data, when the closest site lacks data for a certain pollutant then the data from the second site is used. The final step is to connect the data in the excel sheet to the spatial data of the researched suburbs. Once the data has been joined the data from the excel sheet can be used to make maps which are the main component of the analyses. The methods for the analyses will be discussed in paragraph 3.4.3.

Besides the spatial analysis through ArcGIS I will also look at the “episodes” provided by the City of Cape Town (see figure 4 for an example of an episode). These episodes show the location where on a certain date the guideline for a pollutant was exceeded, showing which pollutant has exceeded the guideline and by how much. Using all the episodes from 2008 I will make a bar graph showing how many times a measuring site has measured an exceeding pollutant. This will provide an overview of which location measures the most guideline exceeding air pollution. There is a total of episodes from 2013. Each episode will count as one guideline exceeding incidence for each location. Location where not exceeding pollutants have been measured will not be included in the bar graph in order to keep a clear picture. All the guideline exceeding instances in the year 2013 are from the pollutant PM10.

Figure 4: Example of an episode for Khayelitsha on 20th april 2013. Source: City of Cape Town Air Quality Monitoring Network (2013).

3.6.2 Practical issues for the analyses

The the Molteno and Goodwood sites have an unspecified radius that can measure pollution “many kilometers from the source” (City of Cape Town, 2008). The reason to set the radius to 10 kilometers is that this way the radiuses of Goodwood and Molteno do not overlap too much and still provide data for a lot of suburbs near the Cape Town City Centre. Another reason to set the radius at only 10 kilometers is to make sure that suburbs that are nearer to another measuring site but not within its radius (because of a smaller radius) do not get the data from a measuring site that is far away. This would lead to inaccuracies as it is improbable that the data from the measuring site with a large radius is more accurate than local smaller measuring sites. Rather than risking this inaccuracy by setting the radius higher than 10 kilometer to include these suburbs I choose to exclude the

suburbs. The Athlone measuring site has no reported radius and, therefore, the data from this site will only be used for the Athlone suburb in which it is located. The measuring sites at Atlantis and

Somerset West do have a certain radius but the data from these sites is missing from the datasheet provided in the 2008 annual report. Therefore, the suburbs that are located within the radiuses of

Referenties

GERELATEERDE DOCUMENTEN

Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:.. • A submitted manuscript is

bestuurders en voorpassagiers van personenauto's gestabiliseerd is; het draagpercentage van de gordel buiten de bebouwde kom bij bestuurders en voorpassagiers van

Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of

(Expertisenetwerk) Hieronder lichten we deze onderdelen en medewerking die we daarbij vragen toe. Onderzoek naar indicatoren voor goede kwaliteit van ondersteuning bij Levensvragen

Strikwerda argues that they nonetheless do not thereby change the balance needed between infor- mation privacy and the right to receive information as a member of the

In practice, however, more than one spontaneous decay is necessary to cool a molecule: When the fields are kept constant, a molecule will generally end up in state 兩s典 in the

Als we deze re- sultaten combïneren met cle daling van het totaal aantal korte ritten op natte dagen kunnen we daarom concluderen dat op natte dagen 12% meer korte

i) Technical inefficiency in the conventional technology T 1 differs substantially on average between Murty et al.’s models (8) and (25), and Dakpo et al.’s models (20) and (29), but