Addressing the Urban Heat Island and Researching the Effects of Extensive Green Roofs and Solar Sedum Green Roofs on the Urban Heat Island in Amsterdam

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Bachelor Thesis

Addressing the Urban Heat Island and Researching the Effects of Extensive

Green Roofs and Solar Sedum Green Roofs on the Urban Heat Island in

Amsterdam

Name: Nicholas Schilder

Student number: 11898747

Date: 28-05-2021

Institute: University of Amsterdam

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Abstract

The Urban Heat Island (UHI) effect has globally become a more significant complication due to increasing urbanization. Temperatures within cities have been increasing substantially compared to their surrounding rural areas. The increasing temperatures within cities are problematic especially since climate change also causes temperature rises. However, in cities in the Netherlands the UHI has not been seen as threatening as in other cities, because it is located in a temperate climate. It was assumed that cities based in warmer climates would be more affected by the phenomenon. However, recent studies that analyzed the UHI effect in Dutch cities have addressed that the phenomenon causes a threat for the Netherlands. One way to mitigate this effect is to construct more green areas and vegetation in the cities. In this study the effect of extensive green roofs and solar sedum roofs on the UHI is analyzed in the city of Amsterdam. Temperatures have been manually measured with a professional thermometer at six locations where these kinds of roofs are excessive over a period of 3 weeks. Other temperatures have been obtained from five amateur weather stations within Amsterdam and one weather station managed by the KNMI located at Schiphol Airport. Hereafter, the data has been analyzed and visualized with ArcGIS. Findings suggest a mean UHI of 0.271 °C. However, differences between the urban and rural areas were not significant. Moreover, both extensive green roofs and the solar sedum roofs do not significantly mitigate the UHI.

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Table of Figures

Figure 1: Temperature profile in a city (EPA, 2012) ... 5

Figure 2: UHI air temperature at night, 02-07-2006 (van der Hoeven & Wandl, 2015) ... 8

Figure 3: Conceptual model of the variables relating to the nocturnal Urban Heat Island effect ... 11

Figure 4: Cross-section of a green roof (Kosareo & Ries, 2007) ... 11

Figure 5: Conceptual model of operationalized variables ... 13

Figure 7: Solar sedum roofs in Amsterdam (Gemeente Amsterdam, 2021) ... 14

Figure 6: Extensive green roofs in Amsterdam (Gemeente Amsterdam, 2021) ... 14

Figure 8: Temperatures measured by the six weather stations ... 18

Figure 9: Urban Heat Island in Amsterdam ... 20

Figure 10: Rural and urban temperatures ... 20

Figure 11: Manually measured temperatures at six green roof locations ... 21

Figure 13: Address density per postcode area in Amsterdam ... 23

Figure 12: Vegetation in Ha per postcode area in Amsterdam ... 23

Table 1: Suggested causative factors of the UHI. The main factors are demoted by F1, F2 and F3, and the subfactors are denoted by G1, G2 and G3. (Fan and Sailor, 2005; Ryu & Baik, 2012) ... 10

Table 2: Major types of green roofs and their characteristics (Hui, 2006) ... 12

Table 3: Operationalization table ... 17

Table 4: Descriptive statistics weather stations ... 18

Table 5: Test of normality (Kolmogorov-Smirnov test) ... 19

Table 6: Test of normality (Shapiro-Wilk test) ... 19

Table 7: Mann-Whitney U test of the amateur weather stations compared to the KNMI weather station ... 19

Table 8: Descriptive statistics of the UHI effect in Amsterdam ... 20

Table 9: Descriptive statistics of the temperatures measured at the various green roofs and the urban temperature ... 21

Table 10: Test of normality (Kolmogorov-Smirnov test) ... 22

Table 11: Test of normality (Shapiro-Wilk test) ... 22

Table 12: Mann-Whitney U test of the amateur weather stations compared to the KNMI weather station ... 23

Table 13: The six green roofs with the amount of vegetation and address density of their postcode area. ... 24

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1. Introduction

As temperatures increase due to climate change globally, the effects of the urban heat island effect (UHI) have become more severe over the years (Gallo et al., 2002). Studies have shown that due to an increase of human pursuits and urbanization, temperatures within cities have been significantly higher than in rural areas (EPA, 2012; Mohajerani et al., 2017). It seems that these differences in temperatures are the strongest at night (Voogt & Oke, 2003) The Environmental Protection Agency (EPA) of the United States argues that these temperatures in urban areas can increase up to 4 degrees Celsius during daytime and up to 2.5 degrees Celsius during nighttime compared to outlying areas (Figure 1). However, in the Netherlands issues associated with climate change are mostly focused on flooding and increased precipitation (van der Hoeven & Wandl, 2015). The UHI effect has been seen as relatively unimportant since the Netherlands is located in a moderate climate (Steeneveld et al., 2011). Water has been the main concern for the Netherlands and increasing temperatures within cities were until recently only linked to climate mitigation, while the UHI effect has stayed relatively unknown. More recent studies have indeed shown that the UHI effect does affect Dutch cities. The studies used satellite images, meteorological data and mapping data to indicate the UHI in the Netherlands. Furthermore, van der Hoeven and Wandl (2015) argue that there is strong evidence that Amsterdam is challenged with this phenomenon since their study shows that nocturnal air temperatures have been 7 °C to 9 °C higher in the city compared to surrounding areas during a heatwave in 2006. Unfortunately, no conclusion could be drawn from the heatwave in 2003, since no usable satellite images are available from this time period. Steeneveld et al. (2011) also argue that the UHI effect in Dutch cities is substantial, since they measured a mean daily maximum UHI of 2.3 K.

The increase of temperatures within city has many negative effects. First of all, the rising temperatures will cause an increase in energy consumption (Akbari, 2005). The EPA states that for several countries the electricity demands have been increasing approximately 1-9% per degree Celsius. Especially in the United States the demand has been increasing, because many buildings require air conditioning. Another negative effect that occurs because of the UHI is an elevation in greenhouse gas emissions (EPA, 2019). Companies that supply electricity needed for cooling mostly rely on fossil fuels. As the demand for electricity increases the production of electricity also increases, which causes an increase in greenhouse gas emissions and other

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air pollutants (Voogt & Oke, 2003). These pollutants contribute to air quality issues such as smog and acid rain. Furthermore, they also cause harm to the human health. A third negative effect is caused directly by the increase of temperatures in cities. Higher day- and nighttime temperatures will cause an increase in heat-related illnesses and heat-related deaths (Steeneveld et al., 2011). Especially more vulnerable population groups, such as elderly, children and those with already existing health issues, will be more at risk due to increasing temperatures (Tan et al., 2010). Moreover, heatwaves will be more extreme and dangerous. In the Netherlands 1700 people died as a result of extreme temperatures caused by a heatwave in 2003 (van der Hoeven & Wandl, 2015). At last, water quality will decrease. Higher temperatures will heat up rooftops, pavements and other surfaces. Water runoff heats up on those surfaces which causes water temperatures in nearby ponds, rivers and lakes to rise in temperature when the runoff water drains from the sewers into the various water resources (EPA, 2019).

A good way to mitigate the UHI effect is the usage of vegetation. Vegetation reduces temperatures by providing more shade and causing a reduction in air temperature through evapotranspiration (Mohajerani et al., 2017; Solecki, 2005). Wong and Yu (2005) perceived a maximum of 4 °C difference between a vegetated area and the business district of Singapore during a summer night. Moreover, the reduction of the air temperature is not only bound to a certain green area but would also reduce temperatures in adjacent areas (Yu & Hien, 2006). It also seems that according to Rafiee, Dias & Koomen (2016) the UHI is not only mitigated by parks and forestry. Street vegetation and different kinds of green roofs also play a crucial role on the UHI. This shows that not only clustered vegetation mitigates the UHI, but that also scattered smaller bundles of vegetation play an important role in the dynamics of the city’s temperature. The usage of green roofs could be an ideal solution to this problem, since cities are dense and space is limited. Studies have shown that the use of green roofs with minimal vegetation can already restrain the UHI effect (Kleerekoper et al., 2012; Rosenzweig et al., 2006; Susca et al., 2011; Wong & Yu, 2005). Amsterdam has been stimulating the usage of green roofs and even offers grants to citizens and organizations for the realization of green roofs. Rafiee et al. (2016) have already conducted a study on how local trees mitigate the nocturnal UHI in Amsterdam, but it is yet unknown how green roofs might mitigate UHI effect in Amsterdam. In this research the effect of two kinds of green roofs on the UHI are examined. The first kind of green roofs are extensive green roofs (sedum roofs). These roofs only consist of grasses mosses and are cheaper to realize and easier to maintain than intensive green roofs which consist of also trees and plants (Nardini, Andri & Crasso, 2012). The second kind of green roofs are the solar sedum roofs. These roofs are essentially extensive green roofs (sedum roofs), but also consist out of solar panels (Ciriminna et al., 2019).

The aim of this study is to explore how extensive green roofs and solar sedum roofs might mitigate the UHI effect in Amsterdam. The Research question for this thesis is as follows: To what extent do extensive green roofs and solar sedum roofs mitigate the urban heat island effect in Amsterdam? Temperatures around certain hotspots of sedum and solar sedum roofs in Amsterdam have been manually measured and compared with temperatures measured by five amateur weather stations located in Amsterdam and a weather station of the Royal Netherlands Meteorological Institute (KNMI) located at Schiphol Airport. This way differences in the measured temperatures may give an indication on how green roofs effect the UHI effect in Amsterdam. Results are statistically analyzed in SPSS and mapped in ArcGIS to give a clear indication and display on how and to what extent the extensive green roofs lower temperatures in different areas in the city.

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2. Theoretical Framework

2.1. Urban Heat Island effect

Higher temperatures are measured in urbanized areas compared to outlying and rural areas. This occurrence is called the urban heat island (UHI) effect (Perini et al., 2017). The UHI occurs as a result of urbanization in a few ways. Solar radiation is emitted onto cities and reflects multiple times on buildings and roads before emitted back into space. Hence, grasses and crops generally have a higher albedo than cities. Furthermore, thermal emissions to space are limited, because buildings restrain the sky view of surfaces. In addition, the urban fabric (asphalt, concrete, etc.) has a higher heat capacity than rural areas causing the surface to cool down less in the evening. Also, buildings prevent wind-flow and affect wind speed. Finally, anthropogenic activities cause more thermal emissions in cities (Mohajerani et al., 2017; Steeneveld et al., 2011; Voogt & Oke, 2003). According to Peng et al. (2012) the UHI effect has an influence on cities all across the globe. The study also shows that developed countries have a higher annual UHI than developing countries and that the UHI does vary seasonally. Mid-to-high latitude cities have shown to differ more in daytime UHI temperatures during summer and winter, while lower latitude cities vary less in UHI daytime temperatures during summer and winter. This could partially be explained because of the different growing seasons between the lower latitude and the mid-to-high latitude cities.

2.1.1. UHI in Amsterdam

A recent project called “Amsterwarm” has been mapping the implications of the UHI in Amsterdam (van der Hoeven & Wandl, 2015). The objectives of the research were to see how severe the UHI is in Amsterdam, how the land-use of Amsterdam influences the UHI, how spatial distributions of populations groups may become vulnerable to the UHI and where cooling within the city is necessary. Various methods have been used to measure the land surface temperature (LST) and the urban canopy layer (UCL) to show differences in temperatures between Amsterdam and its outlying rural areas. The LST represents the average temperature of a certain land surface area and the UCL represents the temperatures of the air layer below the average height of tops of buildings and/or trees. The project mapped out the diurnal, nocturnal and semi-diurnal UHI using remote sensing data from the month July 2006. As stated in the introduction, nocturnal air temperatures showed differences of 7 °C to 9 °C compared to Amsterdam’s outlying areas (Figure 2). Semi-diurnal UHI temperatures showed the differences between temperatures during the night and day in Amsterdam and its surrounding areas. It was concluded that day-night differences in temperatures were around 6 °C to 8 °C, but rural areas seemed to cool faster. The Diurnal LST UHI effect showed temperature differences of the surface of Amsterdam to be between 10 °C and 20 °C higher than rural areas northeast of the city. Furthermore, the Amsterwarm project combined maps of the LST, vulnerable individuals (aged 0-1 or 75+ and pregnant inhabitants), energy labels of buildings, size of the work force and the quality of life to see what groups and areas are vulnerable to the UHI. It was argued that in southeastern, southwestern, northwestern and eastern areas of Amsterdam infants and elderly are more vulnerable to the UHI, since these areas consisted of buildings with substandard energy efficiency and an average to negative quality of life. Furthermore, the project identified buildings that are vulnerable to the UHI by combining data of the energy label of the buildings, the LST and the workplaces per ha. Most energy-inefficient buildings were located in the center, southeastern and southern parts of Amsterdam. At last, the characteristics of various land-uses were analyzed within a multivariate

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regression to see how they may aggravate the LST. The analysis revealed that impervious surface coverage has the most effect on the LST. More factors also used in the analysis were albedo, surface water, vegetation, building envelope, sky-view factor, shadow and traffic space. Results showed that the land-use does influence the LST. Areas with no impervious surface coverage differed on average 11.6 °C per ha from areas with the most impervious surface coverage. Areas with the most and least vegetations showed differences of 5.3 °C per ha and areas with the most and least water coverage showed differences of 3.3 °C per ha.

2.1.2. UHI in West-Europe:

The UHI is also present in other cities in West-Europe with around the same latitude and the same growing season as Amsterdam. A study by Ward et al. (2019) researched the UHI in 70 European cities during the heatwave in 2006. It was found that the northern West-European cities are more vulnerable to the UHI, while the southern West-West-European cities are better adapted. During the heatwave in 2006 the northern cities showed on average higher differences in temperatures during heatwaves than the more southern cities with a warmer climate. Another study by Sarkar and De Ridder (2011) examined the UHI in Paris. This study used a simulation to determine the UHI in Paris during the heatwave in 2006. It was found that differences in temperatures within Paris were up to 6 °C higher than in its outlying area. Amsterdam seems to be more vulnerable for the UHI, because the temperature differences are higher in Amsterdam than Paris. At last, a study by Lauwaet et al. (2016) attempted to assess the current and future UHI in Brussels. They found that the nighttime UHI over all summers from 2000 to 2009 is 3.15 °C. These studies clearly show the presence of the UHI and all stress the hazards it brings with it.

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9 2.1.3. Variables of the UHI

According to Ryu and Baik (2012) the UHI is dependent on three main factors: impervious surfaces, anthropogenic heat, and dimensional urban geometry. Moreover, the three-dimensional urban geometry can be subdivided into three components: Wind speed reduction, heat stored in vertical walls, and radiation trapping (Table 1). These factors were established through a mesoscale atmospheric model used on a midlatitude city with summertime conditions.

The importance of the factors varies between day- and nighttime. During daytime it seemed that impervious surfaces have the most impact on the UHI. This is due to the fact that the impervious surfaces have a low evapotranspiration rate (Taha, 1997). The anthropogenic heat contributes the second most to the UHI during daytime. Human activities cause more thermal emissions (Steeneveld et al., 2011). However, the three-dimensional urban geometry seems to decrease the UHI intensity. This is mainly because walls of buildings are able to store a considerable amount of heat. Furthermore, a study by Dupont and Mestayer (2006) showed that the UHI increases when the effects of walls on temperature are not accounted for. Also, various studies showed that during daytime less blockage of the sky, thus having a greater sky-view factor leads to an increase in temperature (Giridharan et al., 2007; Kruger et al., 2011).

During night time it seems that Anthropogenic heat contributes the most to the UHI intensity. A study by Fan and Sailor (2005) modeled the impacts of anthropogenic activities on the urban climate in Philadelphia and concluded that anthropogenic heat emissions had a great impact on nighttime UHI, while it had little impact on the daytime UHI. The impervious surfaces have the second biggest impact on the UHI during nighttime, causing a slight increase in temperature. The three-dimensional urban geometry also contributes positively to the UHI intensity. A general trend found by multiple studies shows that generally the nighttime UHI intensity increases with increasing building density and building height (Oke 1987; Giridharan et al. 2007). Heat stored in walls of buildings contributes the most to this factor. Furthermore, these factors might differ depending on the location of the city (e.g. latitude), season (e.g. summer- and wintertime conditions), meteorological conditions (e.g. windspeed) and surrounding environment (e.g. land-use, landcover and soil properties). The relations of these factors are outlined in the conceptual model below (Figure 3). Pent et al. (2012) argues that mid-to-high latitude cities have shown to differ more in daytime UHI temperatures during summer and winter, while lower latitude cities vary less in UHI daytime temperatures during summer and winter. All factors and their relation to the UHI are shown in Table 1.

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Table 1: Suggested causative factors of the UHI. The main factors are demoted by F1, F2 and F3, and

the subfactors are denoted by G1, G2 and G3. (Fan and Sailor, 2005; Ryu & Baik, 2012)

On the basis of the research done by Fan and soilar (2005), Ryu and Baik (2012) and van der Hoeven and Wandl (2015) the relation of various land-use parameters and causative factors to the UHI can be integrated in the conceptual model (Figure 3). The albedo entails the reflection of radiation by a surface. Typically higher albedos result in lower temperatures, because more radiation is reflected towards the sky. According to Hoeven and Wandl (2015) the albedos of grasses and vegetation is not that high. The cooling effect of these surfaces is generally caused by evapotranspiration. However, according to Mohajerani et al. (2017) the albedos of these surfaces are higher than those of urban surfaces causing for a positive effect on the albedo as seen in Figure 3. Vegetation including green roofs tend to reduce temperatures through evapotranspiration and shades (Kleerekoper et al., 2012; Mohajerani et al., 2017; Solecki, 2005; Wong & Yu, 2005). Water surfaces contribute to lower temperatures in a city, since they are able to store a considerable amount of heat. However, at night these water bodies are relatively warm. Deeper and larger water bodies have a more stable temperature (Hoeven & Wandl, 2015; Mohajerani et al., 2017). Another variable influencing the UHI is the sky-view factor. As stated above a lower sky-view factor results in more radiation trapping during the day-time and thus resulting in a temperature decrease. However, this trapped heat will cause for an increase in temperature in the night-time as seen in Figure 3 (Giridharan et al., 2007; Hoeven & Wandl, 2015; Kruger et al., 2011). Moreover, the building envelope, as discussed by Hoeven & Wandl (2015), are the exterior properties of buildings. It is again stated that in the night-time heat emits that was stored in buildings during day-time solar exposure. This causes an increase in

Causative Factors Description UHI daytime UHI nighttime

Anthropogenic Heat F1 Additional heat released

by human activities. Contributes second most to the UHI Contributes most to the UHI

Impervious surfaces F2 Reduction in surface

moisture availability and increase in thermal inertia of urban surface materials. Contributes most to the UHI Contributes second most to the UHI Three-dimensional urban geometry F3. Consists out of G1, G2 and G3. Decreases UHI because of G1 and G2 Contributes third most to the UHI

Additional heat stored in vertical walls G1

Additional surfaces in the vertical (walls) that are able to absorb and store heat.

Decreases UHI Increases UHI

Radiation trapping G2 Increase in absorption

of shortwave radiation and decrease in loss of longwave radiation within an urban canyon.

Decreases UHI Increases UHI

Wind speed reduction G3 Wind speed reduction

above and within an urban canopy layer due to the existence of buildings.

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temperature during the night, but a decrease in temperature during the day. The last variable included in the Amsterwarm project is traffic space. Traffic space was thought to act the same as the impervious surfaces. However, an observed cooling effect was found since most of the traffic space is covered with trees providing shades (Hoeven & Wandl, 2015).

2.2. ConceptualModel

2.3. Green Roofs

Since buildings have a high rate of resource and energy consumption many energy efficient and more sustainable strategies have been proposed to decrease urbanization (Vijayaraghavan, 2016). Green roofs provide various benefits that contribute to making buildings and cities more sustainable (Berardi et al., 2014). They improve water quality, provide noise reduction, reduce energy consumption of the building, improve air quality and provide ecological preservation (Berardi et al., 2014; Francis & Jensen, 2017; Vijayaraghavan, 2016). In Figure 4 a typical cross-section of a green roof is displayed. Ordinarily, the layers of material are the same for all types of green roofs. Only the growing medium varies in size (Kosareo & Ries, 2007).

Figure 3: Conceptual model of the variables relating to the nocturnal Urban Heat Island effect

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12 2.3.1. Intensive Green Roofs

Green roofs can be divided into “Intensive” and “Extensive” roofs (Getter & Rowe, 2006; Kosareo & Ries, 2007; Maclvro & Lundholm, 2011). Intensive roofs contain more trees and bushes than extensive roofs. Both roofs are designed to represent natural ground landscaping. The usage of large varieties of trees and shrubs on intensive green roofs stimulate ecological processes and attract more wildlife. Some intensive roofs are open for the general public and can be compared to parks (Getter & Rowe, 2006). The growing medium of an intensive green roof varies between 150 to 1200 mm, allowing larger plants to prosper (Kosareo & Ries, 2007). The large plants provide more shade and insulation, but require more maintenance (Nardini, Andri & Crasso, 2012).

2.3.2. Extensive Green Roofs

The extensive green roofs have shallower substrate layers than the intensive green roofs. The growing medium varies between 50 and 150 mm (Kosareo & Ries, 2007). Because of this no trees and larger shrubs can grow on these roofs. The roofs only consist of grasses and herbs causing less ecological processes to be stimulated and less insulation (Getter & Rowe, 2006; Nardini, Andri & Crasso, 2012). The extensive green roofs are also called “sedum roofs”. However, the extensive green roof still mitigates the UHI effect as it causes for a reduction in air temperature (Mohajerani et al., 2017).

2.3.3. Semi-intensive Green roofs

At last, according to Hui (2006) and Vacek, Struhala and Matějka (2017) semi-intensive green roofs are a mix of both intensive and extensive green roofs. The semi-intensive green roofs have deeper soil layers than extensive green roofs but not deep enough for trees to grow. The semi-intensive green roofs are mostly vegetated by mosses, grasses and smaller shrubs (Vacek et al., 2017). These roofs also reduce air temperatures. They are more productive in mitigating the UHI effect than extensive green roofs since they provide more shade and since more evapotranspiration occurs (Kleerekoper et al., 2012). The characteristics of the three types of green roofs vary slightly in different literature, but in Table 2 the characteristics of the major types of green roofs are displayed according to Hui (2006).

Characteristics Extensive Semi-intensive Intensive

Depth of material 150 mm or less Above and below 150 mm

More than 150 mm

Accessibility Often inaccessible May be partially accessible Usually accessible Fully saturated weight Low (70-170 kg/m2) Varies (170-290 kg/m2₎ High (290-970 kg/m2₎

Plant diversity Low Greater Greatest

Plant communities Moss-sedum-herbs and grasses

Grass-herbs and shrubs

Lawn or perennials, shrubs and trees

Use Ecological

protection layer

Designed green roof Park like garden

Cost Low Varies Highest

Maintenance Minimal Varies Highest

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13 2.4. Solar Sedum Roofs

Another sustainable method proposed to decrease urbanization is the realization of more solar sedum roofs. Urban rooftops are not only used for the construction of green roofs, but are also outstanding locations for photovoltaic (PV) modules (Nash et al., 2016). These modules are necessary to mitigate greenhouse gas emissions and enhance cities towards low-carbon or zero-carbon cities. According to Ciriminna et al. (2019) solar green roofs, also called “biosolar roofs”, are the perfect mix between green roofs and solar roofs. They benefit from all perks an extensive green roofs provides while also producing onsite renewable energy. The roofs consist of mostly extensive vegetation while they are also functionalized with PV modules. Biodiversity is still enhanced on these types of roofs, because PV modules provide, while lying on their back, an superb microhabitat for birds and insects. Moreover, Connop et al. (2016) argue that the shadows provided by the PV modules cause more diverse and increased living conditions for organisms as a result of less evaporation and thus more moisture retaining. Multiple studies have showed the significance of these biosolar roofs, because they indicated that a combinations of green and PV modules on a roof can actually increase the performance of the solar panels (Kohler et al., 2007; Perez et al., 2012; Chemisana & Lamnatou, 2014). The study done by Kohler et al. (2007) also showed an increase in plant variation and species richness on the biosolar roofs.

2.5 Conceptual Model 2

Unfortunately, it is not possible to operationalize every concept seen in Figure 3. As will be discussed in the methodology, data is available for only a small amount of concepts. In Figure 5 a simplified conceptual model is shown with the concepts and relations that are possible to operationalize. Data from these concepts will be used to determine the effect of extensive (sedum) and solar sedum roofs on the UHI in Amsterdam.

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3. Methodology

The overall methodology has a quantitative comparable research design, because numerical data is needed to assess the UHI and find the influence of green roofs on the UHI. Furthermore, the research is comparable, because data from both kinds of green roofs are compared with each other as well as temperatures measured by weather stations in rural and urban areas. Data needed for the methodology and results is based on manually measured temperatures, temperatures measured by weather stations and numerical data of municipality of Amsterdam and the Central Bureau for Statistics (CBS, 2020). This data was statistically analyzed with SPSS and mapped with ArcGIS to try to find correlations between the UHI effect in Amsterdam and the two kinds of green roofs.

3.1. Data Collection

3.1.1. KNMI Weather Station (Rural Temperature)

To determine how severe the UHI at this moment is temperature measurements are necessary in rural and urban areas. Unfortunately, detailed temperature observations measured by weather station of official authorities are scarce in the Netherlands. Only the KNMI (2021) observes temperatures according to the World Meteorological Organization guidelines. The KNMI has 34 automatic weather stations that measure temperatures on land and 14 on sea. The closest weather station to Amsterdam is located at Schiphol Airport. Data collected by this weather station is used as an indicator of temperatures in the rural area.

3.1.2 Amateur Weather Stations (Urban Temperature)

However, there is data available from amateur weather stations located in Amsterdam. The observations can be obtained via http://hetweeractueel.nl. This site collects data from over 200 amateur weather stations every hour. Five of those stations are located in Amsterdam and are used to determine the effect of green roofs on the UHI effect. The weather stations that are located in Nieuwendam, Amsterdam-Noord and Osdorp are part of the Davis Vantage Pros Series by Davis Instruments Corp. Weather stations from this series have also been used in the study by Steeneveld et al. (2011). The weather station used in Amsterdam Zuid-Oost is the Alecto ws-4000 and the station used in Holendrecht is the TFA Nexus PRO. All the weather stations are by means of active or passive ventilation shielded against direct sun radiation except for the station in Amsterdam Zuid-Oost. This station is not ventilated but still shielded. Data collected by these weather stations are used as an indicator for temperatures in the urban area.

Figure 7: Extensive green roofs

in Amsterdam (Gemeente Amsterdam, 2021)

Figure 6: Solar sedum roofs

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15 3.1.3 Temperatures of the Green Roofs

Furthermore, temperatures around green roofs also need to be measured to find any correlation between the kinds of green roofs and the UHI. Temperatures have been manually measured with a professional thermometer at various hotspots of green roofs over a 3-week period from the 29th of March 2021 till the 18th of April 2021 between 20:00 and 21:00. The reason for this specific time is because the UHI effects seems to be strongest at night (Voogt & Oke, 2003; Wolters & Brandsma, 2012). It was unfortunately not possible to measure temperatures at later times since the Netherlands was in lock-down with an appointed curfew, which did not allow anyone to be outside later than 21:00. In Figure 6 all extensive green roofs of Amsterdam are shown and in Figure 7 all solar sedum roofs of Amsterdam are shown. Both maps are created on the basis of municipality GIS database (Gemeente Amsterdam, 2021). At the six locations highlighted with a red circle, temperatures will be measured four times a week in this 3-week period.

3.1.4 Other Concepts Influencing the UHI

After the temperatures are acquired other data needs to be obtained which also influences the UHI as seen in Figure 3. The municipality GIS database offers a few maps that are useful. Unfortunately, as stated in the theoretical framework not all concepts in Figure 3 can be operationalized, since no data is available. Data only of address densities, vegetation and wind speeds are available. This results in a operationalization of the concepts seen in Figure 5. Maps of vegetation can be found in the GIS database of the municipality of Amsterdam (Gemeente Amsterdam, 2011). Windspeeds are available from the KNMI weather station at Schiphol (KNMI, 2021) and the five amateur weather stations located in Amsterdam. Data of address densities can be found on the CBS database (2020). The thermometer was not able to also manually measure wind speeds at the locations of the extensive green roofs so an assumption is made that the windspeeds at these locations are the same as the average windspeed measured by the five amateur weather station through Amsterdam.

3.2. Operationalization

3.2.1. Assessment of the Current UHI in Amsterdam

The research question is divided into three sub questions. The first sub question is: How severe

is the UHI effect currently? The temperatures from these five amateur stations are averaged to

determine the actual temperature in the city. Subsequently, the UHI has been determined according to the following formula: UHI = Turban – Trural. This formula is derived from a study

by Wolters and Brandsma (2012) who also used observations by amateur weather stations to estimate the UHI in residential areas is the Netherlands. Turban in this formula is the average

temperature observed by the weather stations located in Amsterdam. Trural is the temperature

measured by the KNMI weather station at Schiphol Airport (KNMI, 2021). These weather stations are comparable since they both offer live measurements of temperatures on different locations. Data of temperatures over the same 3-week period from the 29th_{of March 2021 till}

the 18th of April 2021 between 20:00 and 21:00 are used. After the collection a mean analysis is performed in SPSS to determine any differences between the means. Moreover, the Kolmogorov-Smirnov test and the Shapiro-Wilk test are used to check if the data is normally distributed. If that is the case the t-test is used for the mean analysis. Otherwise, the Mann-Whitney U test is used to determine any significant differences.

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3.2.2. Effect of Extensive Green Roofs on the UHI in Amsterdam

To further examine how green roofs may mitigate the effects of the UHI the following second sub question is composed: To what extent do the extensive green roofs mitigate the UHI? In SPSS a mean analysis has been executed to determine the differences in mean temperatures of the extensive green roofs and the mean urban temperature. This way any significant differences in the mean temperatures can be indicated. The same statistical steps in SPSS as for the assessment of the current UHI in Amsterdam are used.

3.2.3. Effect of Solar Sedum Roofs on the UHI in Amsterdam

The last sub question composed is the following: To what extent do solar sedum roofs mitigate

the UHI? This sub question will indicate any significant differences in temperatures between

the solar sedum roofs and urban temperature in Amsterdam. The same methods apply for this sub question as the previous one. Only now manually measured temperatures of solar sedum roofs have been used.

3.2.4. Other Concepts Influencing the UHI

The other variables operationalized are windspeeds, vegetation and address density. Windspeeds are operationalized by executing a simple linear regression in SPSS. The dependent variable is the UHI as determined by the formula from Wolters and Brandsma (2012). The independent variable are the windspeeds determined by the KNMI (2021) and the amateur weather stations. The vegetation data (Gemeente Amsterdam, 2011) has been operationalized in ArcGIS. Vegetation data from the municipality of Amsterdam has been spatially joined with a PC4 map of Amsterdam. The PC4 can also be found the GIS database of the municipality of Amsterdam (Gemeente Amsterdam, 2021). This results in a map of the amount of vegetation in Ha per postcode area. The address density has also been operationalized in ArcGIS. Address density obtained from the CBS (2020) has been joined based on attributes with the PC4 map of Amsterdam. This generated a map of addresses per km2 per postcode area. Thereafter, the six locations of the green roofs were added to both maps so that it became visible what vegetation levels and address density levels were present.

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17 3.3. Operationalization Table

Concept Variable Definition Measurement

Urban Heat Island Effect

UHI Heat islands are urbanized areas that experience higher temperatures than outlying areas. In this research it is the difference in temperature between the urbanized area and outlying area.

UHI = Turban – Trural

Turban Urban temperature Temperature of KNMI weather

station at Schiphol Airport

Trural. Rural temperature Average temperature of five

amateur weather stations in Amsterdam

Extensive (Sedum) Green Roofs

TSedum Temperature of hotspot of

extensive green roofs

Manually measured with professional thermometer Solar Sedum Roofs T Solar-Sedum Temperature of hotspot of solar sedum roofs

Manually measured with professional thermometer Wind Speed v Rate at which air is moving

in an area (km/h)

Average wind speed of five amateur weather stations in Amsterdam

Address Density ρaddress Average number of

addresses per km2

Acquired by joining CBS data with PC4 map of Amsterdam Vegetation V Assemblage of all plant

species in a certain area in Ha

Acquired by joining data from the municipality of Amsterdam with PC4 map of Amsterdam

Table 3: Operationalization table

3.4. Ethical Considerations

This research complies with the ethical guidelines of the Graduate School of Social Sciences, University of Amsterdam (Ethical Guidelines for Student Research, 2019). No participants, interviewees or informants were used to complete this study, meaning that there is no need for anonymity. Most of the data used in this research has been gathered from public authorities and is open to the public. Empirical data gathered through own independent measurements are safely stored and can be obtained by contacting the author. The author is in no case biased to emphasize the study’s concerns more. Furthermore, no principles of the Netherlands Code of Conduct for Research Integrity (2018) have been violated. Under no circumstances has scientific fraud and/or plagiarism been carried out.

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4. Results

4.1. Mean Analysis Weather Stations

In the figure below the various temperatures measured by the five weather stations over a 3-week period are shown. The means of these five stations are used to determine the urban temperature (Turban). Statistical analysis in SPSS is used to determine whether the mean

temperatures of these amateur weather stations are actually higher than temperatures measured in rural areas by the KNMI. Table 4 shows all descriptive statistics of the weather stations. At first glance the mean temperatures measured by the amateur weather stations do look higher than the temperatures measured by the KNMI.

Weather Station N Mean Temperature (°C) Std. Deviation

KNMI (Trural) 21 6.029 3.5802 Nieuwendam 21 6.214 3.6944 Noord 21 6.152 3.5059 Osdorp 21 6.633 3.7703 Zuid-Oost 21 6.205 3.6411 Holendrecht 21 6.295 3.6019

Table 4: Descriptive statistics weather stations

The Kolmogorov-Smirnov test and Shapiro-Wilk test are used to determine whether the variables are normally distributed since the sample size is below 30 (N = 21). The tests suggests that all six variables do not follow a normal distribution since p < 0.05 for all variables (Table 5 and Table 6).

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Weather Station Statistic df Sig.

KNMI (Trural) 0.213 21 0.014 Nieuwendam 0.215 21 0.012 Noord 0.192 21 0.042 Osdorp 0.188 21 0.051 Zuid-Oost 0.219 21 0.010 Holendrecht 0.210 21 0.016

Table 5: Test of normality (Kolmogorov-Smirnov test)

Weather Station Statistic df Sig.

KNMI (Trural) 0.889 21 0.021 Nieuwendam 0.871 21 0.010 Noord 0.877 21 0.013 Osdorp 0.885 21 0.018 Zuid-Oost 0.875 21 0.012 Holendrecht 0.885 21 0.018

Table 6: Test of normality (Shapiro-Wilk test)

Only Osdorp shows a p > 0.05 when the Kolmogorov-Smirnov test is used. But since the p value is still below 0.05 when using the Shapiro-Wilk test an assumption is made that this variable does not follow a normal distribution pattern. The Mann-Whitney U test is now used to determine if the distribution of means of the amateur weather stations differ from that of the KNMI weather stations. The independent sample t-test cannot be used, because the variables are not normally distributed. The Mann-Whitney U test is executed five times since each mean of the amateur stations is compared individually to that of the KNMI weather station. So, H0:

There is no significant difference in temperatures measured by amateur weather stations within Amsterdam and temperatures perceived in the outlying area.

Weather Station Mann-Whitney U Z Sig. (2-tailed)

Nieuwendam 209.500 -0.277 0.782

Noord 215.500 -0.126 0.900

Osdorp 190.000 -0.767 0.443

Zuid-Oost 209.500 -0.277 0.782

Holendrecht 201.000 -0.491 0.624

Table 7: Mann-Whitney U test of the amateur weather stations compared to the KNMI weather station

As Table 7 shows, there are insignificant differences between the amateur weather stations and the KNMI weather station, because p > 0.05 for all variables. This results in the retaining of H0,

meaning that there is no significant difference in temperatures measured by weather stations within Amsterdam and outside Amsterdam.

Furthermore, the UHI effect in Amsterdam is still determined on the basis of the formula constructed by Wolters and Brandsma (2012): UHI = Turban – Trural. In Figure 9 and 10 these

results are shown. It does clearly show that urban temperatures are on average higher than the rural temperatures. A statistical descriptive analysis supports this observation as can be seen in Table 8.

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Table 8: Descriptive statistics of the UHI effect in Amsterdam

Variables N Mean Temperature (°C) Std. Deviation

KNMI (Trural) 21 6.029 3.5802

Urban Temperature (Turban) 21 6.300 3.5802

UHI (Turban – Trural) 21 0.271 0.2656

Figure 10: Rural and urban temperatures Figure 9: Urban Heat Island in Amsterdam

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4.2. Mean Analysis Temperatures Solar and Solar Sedum Roofs

The figure below shows the temperatures measured on the hotspots of green roofs. Sedum 1, 2 and 3 represents the temperatures measured at the three sedum roofs and solar sedum 1, 2 and 3 represent the temperatures measured at the three solar sedum roofs as discussed in the methods. Statistical analysis in SPSS is used to determine whether these manually measured mean temperatures are actually lower than the mean temperatures measured by the amateur weather stations.

Table 9 shows the descriptive statistics of the temperatures measured at the various green roofs and the urban temperature that is computed by the temperatures measured by the amateur weather stations. Again, at first glance the mean urban temperature seems to be lower than the mean temperatures measured at the green roofs. The same statistical analysis as with the means of the amateur weather stations has been used for the mean temperatures of the green roofs to determine the differences.

Variables N Mean Temperature

(°C) Std. Deviation Urban Temperature (Turban) 21 6.300 3.6373 Sedum 1 12 7.375 4.0127 Sedum 2 12 7.100 3.9024 Sedum 3 12 7.108 3.9892 Solar Sedum 1 12 7.167 3.9218 Solar Sedum 2 12 7.225 3.8627 Solar Sedum 3 12 7.442 4.0554

Table 9: Descriptive statistics of the temperatures measured at the various green roofs and the urban temperature

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The Kolmogorov-Smirnov test and Shapiro-Wilk test are used again to determine whether the variables are normally distributed since the sample size is below 30 (N = 21 and N = 12). The tests suggests that all seven variables do not follow a normal distribution for the Kolmogorov-Smirnov test since p < 0.05 for all variables (Table 10).

Table 10: Test of normality (Kolmogorov-Smirnov test)

Variables Statistic df Sig.

Urban Temperature (Turban) 0.878 21 0.013

Sedum 1 0.865 12 0.057 Sedum 2 0.865 12 0.057 Sedum 3 0.854 12 0.041 Solar Sedum 1 0.861 12 0.050 Solar Sedum 2 0.850 12 0.037 Solar Sedum 3 0.856 12 0.044

Table 11: Test of normality (Shapiro-Wilk test)

Table 11 shows more interesting results. The Shapiro Wilk test of the temperatures of Sedum 1, Sedum 2 and Solar sedum 1 show that these temperatures are somewhat normally distributed. However, an assumption is made that these variables do not follow a normal distribution pattern, since the Kolmogorov-Smirnov test was used as an indicator for the various weather stations and in this case also show that the variables are not normally distributed.

The Mann-Whitney U test is now used to determine if the distribution of means of the green roofs differ from that of the urban temperature. The Mann-Whitney U test is executed six times since each mean of the manually measured temperatures at the green roofs is compared individually to that of the mean urban temperature. So, H0:There is no significant difference in

temperatures between the manually measured temperatures at the sedum green roofs and the urban temperature of Amsterdam. And, H0:There is no significant difference in temperatures

between the manually measured temperatures at the solar sedum green roofs and the urban temperature of Amsterdam.

As Table 12 shows, there are insignificant differences between the mean temperatures of the green roofs and the mean temperature of Amsterdam, because p > 0.05 for all variables. This results in the retaining of both H0 hypotheses, which means that there is no significant difference

in mean temperatures measured at the six green roof locations and the mean temperature of Amsterdam.

Variables Statistic df Sig.

Urban Temperature (Turban) 0.222 21 0.008

Sedum 1 0.248 12 0.041 Sedum 2 0.250 12 0.037 Sedum 3 0.271 12 0.015 Solar Sedum 1 0.257 12 0.028 Solar Sedum 2 0.284 12 0.021 Solar Sedum 3 0.264 12 0.021

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Table 12: Mann-Whitney U test of the amateur weather stations compared to the KNMI weather station

4.3. Addressing Variables Influencing the Urban Temperature

As stated in the methodology, limited data is available concerning factors influencing the UHI. Variables that are available are windspeeds, address density and vegetation quantities. To analyze the effects of windspeeds on the UHI a simple linear regression is executed within SPSS. The measured temperatures in Amsterdam (Turban) is the dependent variable and the

measured windspeeds is the independent variable. The results of the regression indicated that the model explained 23.0% (R2_{= 0.230) of the model and that the model was significant,}

F(1,19) = 5.68, p < 0.05. It was found that windspeeds significantly influence the urban temperature (β1 = -0.193, p < 0.05). Finally, the predictive model looked as follows: Urban

temperature = 9.707 + (-0.193 * km/h wind).

Green Roofs Mann-Whitney U Z Sig. (2-tailed)

Sedum 1 101.500 -0.917 0.359 Sedum 2 113.00 -0.487 0.626 Sedum 3 116.500 -0.356 0.722 Solar Sedum 1 112.500 -0.506 0.613 Solar Sedum 2 111.00 -0.562 0.574 Solar Sedum 3 98.500 -1.030 0.303

Figure 13: Vegetation in Ha per postcode area in Amsterdam

Figure 12: Address density per postcode area in Amsterdam

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The variables address density and vegetation quantities are analyzed using ArcGIS. The six locations of the green roofs, the address densities and vegetation quantities are shown in the figures above. In the Table 13 mean temperatures from the green roofs and data from the postcode area wherein these roofs are located are combined. What strikes is that for sedum roof 1 and solar sedum roof 3 the amount of vegetation low is, while the mean temperatures are high compared to the other roofs. However, solar sedum roof 2 shows an average mean temperature while the vegetation levels are high. This could be because of the high address density in this postcode area. On the other hand sedum roof 2 has the lowest average temperature while the vegetation levels are low and the address density is high for that postcode area.

Table 13: The six green roofs with the amount of vegetation and address density of their postcode area.

Variables Mean Temperature (°C)

Vegetation in Ha Address density in km2 Sedum 1 7.375 > 5 6000 – 8000 Sedum 2 7.100 > 5 8000 – 10000 Sedum 3 7.108 5 - 20 6000 – 8000 Solar Sedum 1 7.167 5 - 20 4000 - 6000 Solar Sedum 2 7.225 20 - 40 8000 - 10000 Solar Sedum 3 7.442 > 5 6000 - 8000

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5. Conclusions

Based on the result, a number of conclusions can be drawn to answer the research question: To what extent do extensive green roofs and solar sedum roofs mitigate the urban heat island effect in Amsterdam? First, the current UHI in Amsterdam was addressed. Using the formula from Wolters and Brandsma (2012) a mean UHI of 0.271 °C was found. This means that over the period from the 29th of March 2021 till the 18th of April 2021 on average the temperature within Amsterdam 0.271 °C higher was than the temperature measured in the outlying area (Schiphol). However, statistical analysis within SPSS using the Mann-Whitney U test showed no significant differences in temperatures between the amateur weather stations in Amsterdam and the weather station of KNMI. So, H0:There is no significant difference in temperatures

measured by amateur weather stations within Amsterdam and temperatures perceived in the outlying area, is retained. Second, the influence of the extensive (sedum) and solar sedum on the UHI was examined. The same statistical analysis as for the weather stations was conducted. Unfortunately, no significant results were found when conducting the Mann-Whitney U test. So, H0: There is no significant difference in temperatures between the manually measured

temperatures at the sedum green roofs and the urban temperature of Amsterdam, and, H0:There

is no significant difference in temperatures between the manually measured temperatures at the solar sedum green roofs and the urban temperature of Amsterdam, are both retained. Finally, three other variables influencing the measured temperatures were assessed. It was found that windspeeds significantly influence the urban temperature (β1 = -0.193, p < 0.05). The predictive

model looked as follows: Urban temperature = 9.707 + (-0.193 * km/h wind). The address densities and vegetations levels were mapped in ArcGIS, but no direct link with the mean temperatures of the green roofs was found.

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6. Discussion

This research has tried to point out the influence of two kinds of green roofs on the UHI in Amsterdam. However, during this research a few uncertainties have come to light that may have influenced the outcomes of the statistical analysis. Theoretically, this study has assumed that measuring temperatures allowed to see the cohesion between the green roofs and the UHI. Other methods have been omitted. Other implications are that the formula by Wolters and Brandsma (2012) does correctly compute the UHI by using data of amateur weather stations.

The methodology also has some uncertainties. First of all, the data availability was limited. The first conceptual model drawn from the literature (Figure 3) is very complicated and shows how many factors influence the temperature in a city. Many of those concepts could not be operationalized, because no online data was available and/or because the variables could not be manually measured. Figure 5 shows a simplified conceptual model, but many influential concepts are not included causing outcomes to be less reliable. Second, the manually measured temperatures could be inaccurate. The research has been conducted by measuring temperatures at certain locations in Amsterdam where the two types of roofs are abundant. It would have been more ideal to measure temperatures on top of the roofs, but no roofs were open to the public. Furthermore, it was not possible to visit any residents and ask for access to their green roof during the corona lockdown. Third, the temperatures measured by the amateur weather stations could also be inaccurate. As stated in the methodology, the weather stations in Amsterdam Zuid-Oost and Holendrecht differ from each other. This could result in different measured temperatures. Furthermore, the weather stations are not maintained and controlled by official authorities like the KNMI. They do not observe temperatures according to the World Meteorological Organization guidelines making their findings less reliable. Fourth, temperatures had to be measured between 20:00 and 21:00, because of the appointed curfew. Measurements at later times would be more ideal, since the UHI would be more noticeable during later hours. Fifth, measurements were executed at the end of March and beginning of April. Measurements executed in summer months would have been more ideal, since the UHI is more noticeable on hotter days. Sixth, the professional thermometer was not able to measure any windspeeds at the locations of the green roofs. At last, data of only a three week period was used, because some temperatures had to be manually measured. This is most definitely the reason why the majority of the results are insignificant.

It is recommended that the municipality of Amsterdam upscales this research so that the influence of extensive and solar sedum roofs on the UHI is better understanded. The municipality of Amsterdam has a bigger budget, more technological possibilities and more man power, which will be key to a better understanding of this problem. An idea for an improved research on the green roofs is to leave various thermometers on multiple green roofs around the city for a year. Now temperatures can be measured every hour throughout the day for a longer period of time. Moreover, the municipality is also able to operationalize other core concepts seen in Figure 3. Both of these actions will make the data analysis more reliable.

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8. Appendices

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33 8.2. Locations of the green roofs

Extensive green roofs in Amsterdam

Addressing the Urban Heat Island and Researching the Effects of Extensive Green Roofs and Solar Sedum Green Roofs on the Urban Heat Island in Amsterdam

Bachelor Thesis