Measuring the Urban Heat Island Effect

By Riccardo William Monfardini

Student ID: 7484334(1)

The University of Manchester

in partnership with Georgios Nikou

Supervised by Dr Ann Webb

MPhys Project Academic Year 2012-2013 Semester 1

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Abstract

The urban heat island of Manchester in the year 2012 was measured by comparing air temperatures measured by 12 iButtons placed in 3 locations: the North Campus of the University of Manchester, Whitworth park and Platt Fields park. It was found an average annual temperature difference of 0.59 ± 0.51º C between North campus and Whitworth park, 0.78 ± 0.67º C between North campus and Platt Fields park and 0.19 ± 0.33º C between Whitworth park and Platt Fields. The maximum difference was 4.0 ± 0.5º C at 6:53am on the 5th of November between North campus and Platt Fields park.Solar radiation, wind speed and cloud cover data acquired from the Whitworth Observatory was compared with the air temperature differences; considering nights when the temperature difference between North campus and Platt Fields park was above 3° C and 2.5° C a relation is found: respectively for the two cases, wind speeds were 1.3 ±0.5 and 1.3 ±0.5 m/s, cloud cover was 0.6 ±1.1 and 1.1 ±2.0 oktas and time of day 01:51am ±4:15 00:55am ±3:54. The difference with Whitworth park was measured to be 1.0 ±0.4 °C and 1.1 ±0.6 °C less. Finally, it was not possible to conclude the influence of the length of the night on the urban heat island of Manchester.

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Introduction

It is commonly known that urban temperatures are generally warmer than the nearby countryside. This effect was first studied by Luke Howard in the first half of the 19th century [1] and is called Urban Heat Island (UHI) because, as illustrated in figure 1, isotherms over the city resemble a hot island in the cool “sea” of the countryside. Maintaining the island analogy, it can be seen that the island morphology is affected by many factors: the wind direction creates a steeper ‘cliff’ on the windward side shown by the closer isotherms and a smoother slope on the leeward side; city centres create high “mountains” while parks create “valleys”; the overall heat island has a close correlation with the city's built up area.This effect has been confirmed and measured in numerous cities such as New York [2] or Barcelona [3].

It has been determined that the UHI contribution to global warming and climate change is not significant [5] and this is evident if we consider that cities occupy less than 1% of global land surface [6]. However, studying the UHI effect becomes important because approximately 50% of the world's population is living in cities [5] and thus experiencing the local urban climate. The summer of 2003 is tragically remembered for the heat wave that affected Europe which claimed more than 35000 lives [7], mainly individuals with fragile health living within a city; these events are predicted to become more frequent due to climate change. It is therefore necessary to understand UHI; in a study by Clarke [8] the 4-7°C higher temperature in a city caused an increased stress on its inhabitants, which could not benefit from cool night relief experienced by people living in the countryside.

Apart from health related issues understanding the UHI is important from an energy efficiency point of view. Energy can be saved on heating thanks to UHI but as well be wasted on air conditioning. In Athens there can be a 10°C difference with respect to the surrounding rural area; it was found that the electricity consumed for cooling can be tripled especially at peak times, and the efficiency of air conditioning units was reduced by 25%; on the other hand in winter it was measured that the heating

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Figure 1: Hypothetical representation of an Urban Heat Island in ideal conditions of calm wind and clear night. (a) shows a transect, a measurement of the air temperature along the line from A to B; (b) illustrates the overlap between built up area (grey shade) and isotherms [4].

load needed for urban buildings was 30-50% less than their counterparts located in the rural surroundings [9].

Previous studies on this experiment have measured the temperature and influence a pond has on air temperatures of a park and how surface temperature of different materials change during a day; this study will concentrate, however, on the correlation between solar radiation, wind speeds and cloud cover with the intensity of the UHI.

Background theory

The UHI happens due to the different characteristics between the natural environment of the rural area and the urban environment. The maximum difference is mainly found during the night, and it is due to the different cooling rates of the 2 environments [4], as is shown in figure 2.

From the figure it is noticed that rural air temperature is mainly influenced by the Sun's radiation and as the Sun goes down it starts to cool rapidly; on the other hand, the urban air temperature, while it is driven by the Sun, has a lower cooling rate thus creating a temperature difference with the rural surroundings. The UHI maximum is found soon after sunset and continues until it disappears soon after sunrise where both tend to reach similar air temperatures [4].

Therefore the UHI is due to a difference in the surface energy balance of rural and urban areas [4]. There are four main differences between urban and rural areas which cause

UHI: reduced vegetation, urban materials, urban geometry and anthropogenic heat.

Urbanisation produces a change in the surface types, using materials like concrete which are impervious to moisture. Figure 3 shows where rain water will go in the 2 environments. The reduced amount of moisture in the cities decreases evapotranspiration. This is the process of evaporation of

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Figure 2. Graph describing an hypothetical measurement of air temperature in urban and rural areas over a clear calm day. Notice how the rural air temperature (green line) cooling rate is correlated with the Sun while the urban (red line) cooling rate tends to be much slower. Their difference (black line) creates the heat island in the city. [10][4]

water from the ground and transpiration from the leaves and plants; it cools the environment in a similar way sweat, evaporating, cools down the body of a sportsman. Thus less water means less cooling, and more heat will be stored in the materials (pavements, buildings) and the air.

Urban materials are varied, but overall have a higher heat capacity than rural materials like soil; therefore they can store more heat that will then be released during the night. In addition the typical urban geometry, especially in the city centres, is characterised by urban canyons; narrow streets boarded by high buildings. These canyons can shade some areas but the overall effect is they will trap more heat; sunlight that enters the canyon is reflected and absorbed more efficiently by the buildings, and at night the urban canyon will slow down cooling as the buildings will reabsorb the heat as longwave radiation[10]. Wind, as well, is reduced thus preventing the mixing of air that could level out the heat island.Anthropogenic heat influence is complicated to determine, since it is difficult to measure directly but estimates can be made [4]. Values can change widely between cities and seasons with the maximum value found generally in winter (in summer 20-40 Wm-2 and in winter 70-210 Wm-2 in some major US cities); however the effect is relatively small, noticeable only in city centres and disappears quickly in commercial and residential areas[11]

All of these factors contribute to the UHI if, a priori, the right weather conditions occur. In fact, as this study shows in detail, Manchester presents a UHI only with clear skies and calm winds. These factors are the major influencers of the magnitude of temperature difference recorded, and depending on them Manchester can have a strong UHI or none. Strong wind, in fact, would increase air mixing dissipating the extra heat out of the city; while cloud cover will reduce solar radiation, thus the heat entering the city during the day and act as a blanket during the night retaining the escaping energy; therefore both will reduce the maximum cooling rate greatly reducing the possible difference between urban and rural.

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Figure 3: Urban (left) and rural (right) distribution of water after rainfall. Notice how most of the water leaves the city as runoff and little infiltrates the ground, while in the rural there is little runoff and most of the water infiltrates the ground. Plants and trees will pull moisture from the ground and fuel the evapotranspiration, but little vegetation is in the city. [10]

Method

In this study the UHI effect of the city of Manchester was measured by comparing the air temperature in 3 locations, Platt Fields Park (PP), Whitworth Park (WP) and the North Campus (NC) of the University of Manchester (UoM), which are shown in figure 4. Previous studies have found that the North Campus temperatures can be considered typical of Manchester city centre [12]. The parks air temperatures cannot be considered those of the surrounding countryside but were still hypothesised to evidence the UHI effect.

The sensors used were iButtons DS1922L [13], shown in figure 5. They recorded the air temperature average every 10 minutes with a

resolution of 0.5 ºC. The iButtons were inside a white open box (see figure 6) installed on poles ,4 in each location, 12 in total. The maximum data stored in the iButtons was 8192 bits before overwriting, thus it was downloaded about every 50 days onto a computer. The experiment was set up before this study was conducted, in December 2010; therefore calibration of the iButtons was needed to correct any relative differences in the measurements. The 12 iButtons were placed in a Stevenson screen on the 2nd floor of the Simon building in UoM, as depicted in figure 7, together with a 13th

iButton which was considered the standard for comparison. Left there for 5 days, the measured temperature was compared and relative differences were accounted for in the analysis of the data. The clocks of the iButtons were also corrected, since a large drift was noticed in all, ranging from +85 to +104 minutes on the 27/11/2012. Having recorded the time drift during 2 separate downloads, and assuming a linear shift in the error, the times of the data were corrected.

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Figure 4: Map of Manchester showing the 3 location where temperature measurements were taken. From Google Maps, Jan 2013.

Figure 5: The iButtons used to take air temperature measurement averages every 10 minutes; resolution 0.5ºC.

Figure 6: White open box where iButtons were placed in order to protect them from direct sunlight but allowing free movement of air.

The specific location of the iButtons in the 3 environments are shown in figure 9. Notice the different land use: tall buildings with little vegetation in North Campus, and a lot of vegetation in the two parks. As shown in figure 4, Whitworth park is closer to the city centre and smaller than Platt Fields park; this fact, together with the pond, influences the air temperature which gives some difference between the parks.

Time correction, calibration and analysis of the data were all done with MATLAB script written by R. W. Monfardini. This

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Figure 7: The temporarily open Stevenson screen with the 13 iButtons within it during the calibration, on the 2nd floor of the Simon Building of UoM.

Figure 8: Photos showing the positions of the iButtons in North Campus (left), Whitworth park (right) and Platt Fields park (bottom). Maps are not on the same scale. Each iButton is referred to by a number, but since the start of the experiment in 2008 most have been lost, thus they are not sequential. Modified from Google Maps, January 2013.

study has taken into consideration data collected across the year 2012. IButton 19, as can be seen in the photos of figure 9, is relatively close to the Lakeside building and surrounded by a concrete pavement. Platt Fields is considered the rural counterpart to the North Campus. It was decided to neglect the reading from this iButton because the building and pavement influence raised the nearby air temperature. Notice in fact how, compared to the other iButtons in Platt Fields, number 19 is in a more urban environment in its 5m radius. The abnormal temperatures were measured in studies by previous students working on this experiment [12] as well in this study. Figure 8 illustrates this showing the iButtons temperatures of Platt Fields park plotted against time and evidencing the iButton data which was neglected.

The temperature measurements were averaged together to have a single value for each location at a given time. To reduce noise and fluctuations the data is averaged over 1 hour period and the temperature value is assigned to the median time. Then the period is shifted forwards by 20 minutes and the process repeated. Weather data from the Whitworth Observatory is used in this study in order to compare air temperature measurements with solar radiation (thus sunset, sunrise and the general Sun cycle), wind speed and cloud cover. Cloud cover is expressed in oktas, a common unit of measurement for weather forecasts; oktas are the number of eighths of the sky covered by clouds: 8 oktas is a completely overcast sky while 0 oktas is a clear sky.

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Figure 9: The 4 Platt Fields iButtons plotted against an arbitrary chosen period. The uncertainty in the measurements is of ±0.5ºC. Readings from iButton 19 are highlighted by the thick dark green line. Notice how on certain occasions its temperature readings are anomalous compared with the readings of the other 3 Platt Fields iButtons.

Results and Discussion

The hypothesis that Manchester had a measurable UHI effect was found to be true. In fact on many occasions, especially with clear skies and little wind it was possible to detect different cooling rates for the 3 locations. For example figure 10 shows the temperature averages for the night of 27th of May. Notice the resemblance to figure 2 in the background theory section. It can be noted that cooling starts before sunset but, while WP and PP temperatures are approximately the same during the day, NC rises 1º C higher due to the release of stored heat from urban materials used in nearby buildings.

Figure 11 shows the temperature difference between locations compared to solar radiation, wind and cloud cover for the year 2012 of analysis. The fluctuations of the air temperature differences can be seen, and detailed analysis relates them to the solar radiation, wind speed and cloud cover. Some example periods are discussed below.

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Figure 10: Graphs showing location average temperatures (top), solar radiation both global and indirect (centre) and temperature differences (bottom). The uncertainty in the temperature is of ±0.3ºC. Notice how NC in red has a slower cooling rate than the parks. Further cooling starts later in NC due to the release of heat stored in the urban materials of the nearby buildings

It was confirmed that Manchester exhibited a UHI; as shown in Table 1, average annual temperature of NC is higher than the parks. Even between the parks a temperature difference is measured which is explained by the greater distance from the city centre and by the bigger dimension of the park itself. In fact, as figure 1 suggests, the highest intensity of the UHI is near the city centre, becoming less intense as distance increases.

Location Temperature difference

North campus – Whitworth park

North campus – Platt Fields park

Whitworth park – Platt Fields park

Average 0.59 ± 0.51º C 0.78 ± 0.67º C 0.19 ± 0.33º C

Table 1: Table showing the average temperature difference between each location with the standard deviation. The values are conservative since calibration periods were included in the calculation.

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Figure 11: Temperature differences between location averages (top), solar radiation (centre), and weather conditions (bottom). The two periods with nearly 0 difference centred around the 5th of February and 6th of October are calibration periods . Notice the good weather week around the 25th of March, with little wind and no clouds; it corresponds to several nights of significant UHI.

The maximum difference recorded was found on the early morning of the 5th of November, shown in figure 12, with a difference of 4.0 ± 0.5º C at 6:53. The case is rather curious; in fact it is theorised that maximum difference is found soon after sunset [4], but for the most part of the night a weak UHI of less than 2° is seen, until a few hours before sunrise. This is understood by examining the wind speed; at approximately 2:30 wind speeds are measured to drop from above 4m/s to below 1.5 m/s; thus the reduction in air mixing allows Whitworth and Platt Fields park to cool quicker than NC and go, in 3 hours, from about 2.5º C to 3º and 4º C respectively.

The week of good weather between the 21st and the 28th of March, on the other hand, gave the highest sequential occasion of significant UHI; in fact the stable weather conditions of light winds and clear skies are ideal for maximum UHI. Figure 13 shows the measurements for this period. It may appear strange that at some times the temperature difference is measured to be negative. This is known as the Urban Cool Island effect. Because of the stable conditions the solar radiation influence on air temperature is dominant and, in particular, more immediate. As the sun rises above the horizon, light

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Figure 12: Case study, 5th of November 2012. Uncertainty of the temperatures is ±0.1ºC.

will penetrate and hit the ground easily in the parks compared to NC; photos in figure 9 show that the only obstacle sun light finds in the parks are trees, that compared to the high compact buildings in NC provided little shade, especially in the early hours when the Sun is still at a low elevation angle. This fact permits the air temperature in the parks to rise rapidly, quicker that NC until the Sun radiation manages to enter in the urban canyons of NC.

On the other hand opposite weather was found on 23rd and the 26th of October as is shown in figure 14. The sky is completely covered, with oktas rarely being lower than 8, and wind speeds were constantly above 4m/s. Air temperature differences rarely rose above 1º C, due to the “blanket” effect of clouds as explained in the background theory and especially due to the higher wind speed; in fact when it was measured to be nearly 5m/s, the temperature differences were less than half a degree.

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Figure 13: Case study from the 21st to the 28th of March 2012. Uncertainty of the temperatures is ±0.1ºC.

All the nights when the temperature difference between NC and PP was found to exceed a threshold of 3° and 2.5° C were considered; the difference with WP was nearly always found to be less than PP. For each event the maximum temperature difference was recorded between NC and both parks, together with the wind speed, oktas and time. Since the data was analysed over an hour period it was considered appropriate to take a single value to express the UHI characteristics for the night. However, some details are not considered, in particular the length of the UHI; some nights could have several hours of significant temperature difference and others with a short sharp peak. Nevertheless, 26 and 60 nights were recorded with a 3° and 2.5° threshold respectively, and table 2 shows the mean values of each.

Threshold NC-PP NC-WP Wind speed Oktas Time of day

ΔT =3 °C (26) 3.4 ±0.2 °C 2.4 ±0.3 °C 1.3 ±0.5 m/s 0.6 ±1.1 01:51am ±4:15

ΔT =2.5 °C (60) 3.0 ±0.4 °C 2.1 ±0.4 °C 1.4 ±0.7 m/s 1.1 ±2.0 00:55am ±3:54

Table 2: Mean maximum night-time temperature differences, wind speed, oktas and time between NC and the parks. Considered are only nights when NC-PP air temperature went above the 3° and 2.5° thresholds..

The results show that the UHI is 1.0 ±0.4 °C more intense in WP than in PP for the first case, and 1.1 ±0.6 °C in the second case, agreeing with the assumption that the maximum intensity is found towards the city centre. Significant UHI is found when wind speeds are as low as 1.4 ±0.7 m/s, confirming the discussion about the case study of the 5th of November in figure 12: air temperature difference cannot increase until wind speeds are sufficiently light. Cloud cover is somewhat less important for the

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Figure 14: Case study from the 23rd to the 26th of October 2012. Uncertainty of the temperatures is ±0.1ºC.

creation of the UHI but it is a significant driver of the maximum intensity. Contrary to what Oke theorised [4], the maximum UHI is not necessarily found soon after sunset but instead, as suggested by the 4:15 and 3:54 hours standard deviations, it can happen at any time throughout the night. It should be noted that Oke assumed ideal conditions for the whole night while these results do not discriminate nights with non-ideal weather.

Finally, the influence the length of the day could have on the UHI is considered. The hypothesis between the partners of this study were different: longer nights could allow more time to reach a bigger difference,but longer days will increase the solar radiation stored thus further slowing down cooling in NC. Figure 15 shows the air temperature differences between locations where longer periods of a day and a week are used; the average is calculated for the period and assigned to the median time, then the period is shifted by 1/3 forwards in time and calculated again. No conclusion could be extracted from the graph; no influence by the night's length was noted, or possibly the fluctuations are bigger than its influence.

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Figure 15: Data analysis using the average of a longer time period. Location average temperature difference with period of a day (top) and a week (centre) compared to the solar radiation (bottom), which is roughly proportional to the length of the day but influenced by the weather conditions. Small differences centred around the 4th of February and the 7th of October are due to calibration periods. Gaps in the solar radiation are due to down periods of the Whitworth Observatory equipment.

Conclusions

Manchester was found to exhibit an urban heat island over the year 2012. The urban heat island was measured comparing air temperature between the North campus of the University of Manchester and 2 parks, Whitworth and Platt Fields. The average annual air temperature difference was measured to be 0.59 ± 0.51º C between North campus and Whitworth park, and 0.78 ± 0.67º C between North campus and Platt Fields park. The maximum temperature difference averaged over an hour was found to be 4.0 ± 0.5º C at 6:53 on the 5th of November between North campus and Platt Fields park. Between the 21st and 29th of March, the highest number of consecutive nights with significant UHI (at least 2° difference) were found, caused by the long stable weather conditions.By considering the 26 nights with at least 3° C difference between North Campus and Platt Fields it was found that Whitworth park UHI, which is closer to the city centre, was 1.0 ±0.4 °C less intense than in Platt Fields; as expected wind speeds and cloud cover were found to be low, with means 1.3 ±0.5 m/s and 0.6 ±1.1 oktas; the mean time of maximum intensity was found to spread all through the night, with mean 01:51am ±4:15. Similarly for the 60 nights with a difference of 2.5° C, Whitworth park temperature was 1.1 ±0.6 °C warmer than Platt Fields park, wind speed was 1.4 ±0.7 m/s, cloud cover was 1.1 ±2.0 oktas and mean time of day of 00:55am ±3:54. Finally it was considered whether the length of the night would influence the UHI but no conclusions could be drawn. This study of the UHI of Manchester was limited to the comparison of only 3 locations with 4 sensors each; better cover of the city would give details on the morphological shape of the UHI and what other weather, such as wind direction or building height would have. This study considered the wind speed and cloud cover influence but no study was done on factors such as land use, anthropogenic heat or evapotranspiration. Finally, the influence of the length of the night could be determined by analysis of more past years.

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References

[1] Howard, L. "The climate of London, vols. I–III." London: Harvey and Dorton (1833).

[2] Bornstein, Robert D. ‘Observations of the Urban Heat Island Effect in New York City’. Journal of Applied Meteorology 7, no. 4 (August 1968): 575–582. doi:10.1175/1520-0450(1968)007<0575:OOTUHI>2.0.CO;2.

[3] Moreno-garcia, M. Carmen. ‘Intensity and Form of the Urban Heat Island in Barcelona’. International Journal of Climatology 14, no. 6 (1994): 705–710. doi:10.1002/joc.3370140609.

[4] Oke, T. R. ‘The Energetic Basis of the Urban Heat Island’. Quarterly Journal of the Royal Meteorological Society 108, no. 455 (1982): 1–24. doi:10.1002/qj.49710845502.

[5] Peterson, Thomas C., Kevin P. Gallo, Jay Lawrimore, Timothy W. Owen, Alex Huang, and David A. McKittrick. ‘Global rural temperature trends’. Geophysical Research Letters 26, no. 3 (1999): 329–332. doi:10.1029/1998GL900322.

[6] McCarthy, M. P., M. J. Best, and R. A. Betts. ‘Cities Under a Changing Climate’. In Extended Abstract for the Seventh International Conference on Urban Climate (ICUC-7), Yokohama, Japan. Vol. 29, 2009. .

[7] Larsen, J. ‘Record Heat Wave in Europe Takes 35,000 Lives: Far Greater Losses May Lie Ahead’. Earth Policy Institute (2003). https://www.earth-policy.org/plan_b_updates/2003/update29.

[8] Clarke, John F. ‘Some Effects of the Urban Structure on Heat Mortality’. Environmental Research 5, no. 1 (March 1972): 93–104. doi:10.1016/0013-9351(72)90023-0.

[9] Santamouris, M, N Papanikolaou, I Livada, I Koronakis, C Georgakis, A Argiriou, and D.N Assimakopoulos. ‘On the Impact of Urban Climate on the Energy Consumption of Buildings’. Solar Energy 70, no. 3 (2001): 201–216. doi:10.1016/S0038-092X(00)00095-5.

[10] Wong, E. ‘Urban Heat Island Basic’. Reducing Urban Heat Islands: Compendium of Strategies (2005). http://www.epa.gov/hiri/resources/pdf/BasicsCompendium.pdf.

[11] Taha, Haider. ‘Urban Climates and Heat Islands: Albedo, Evapotranspiration, and Anthropogenic Heat’. Energy and Buildings 25, no. 2 (1997): 99–103. doi:10.1016/S0378-7788(96)00999-1.

[12] Dr. Ann Webb, The University of Manchester, October 2012. Supervisor of the project

[13] ‘DS1922L/DS1922T Datasheet. Temperature Logger iButton with 8KB Data-Log Memory’. Maxim Integrated, 2011. http://datasheets.maximintegrated.com/en/ds/DS1922L-DS1922T.pdf.

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Acknowledgements

I would like to thank first of all my partner Yorgos (Georgios) Nikou for helping me in the research of this project; and another big thanks goes to our supervisor Dr. Ann Webb for the guidance that she gave us. Dr. Michael Flynn of the Whitworth Observatory also has my thanks for providing us with the weather data. And a final thanks to my friend Finn Box, my sister Sophia Monfardini and my mother Lucille Watters for proof reading this report.

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MPhys Project Risk AssessmentMeasuring the Urban Heat Island Effect

Yorgos (Georgios) Nikou& Riccardo William Monfardini

9th October 2012

Our project consists of measuring air temperatures of 13 I-button thermometers previously installed in North Campus, Whitworth Park and Platt Fields Park. The I-buttons were placed in boxes high on poles and buildings. Thus, a ladder was used in order to reach them and download their data. The data is (were) analysed on a computer with MATLAB (using the MATLAB software). An infrared camera (and a normal camera) is (were) used to take pictures of each location.

ActivityHazards &

RisksWho is affected

Actions Risk Ratin

gUse of ladder to reach I-buttons in the boxes.

Falling, ladder slipping or not securely placed.

Person collecting data

Taking care to securely place the ladder before climbing.Other person holding in place the ladder.

Low

Wind, rain, ice and adverse weather condition.

Persons collecting data

Postpone the activity until better weather conditions.Dressing appropriately for weather conditions.

Low

Carrying the ladder on the street.

Pedestrians, cars

Carrying the ladder very carefully. Wearing fluorescent jackets.

Low

Data analysis Eye fatigue due to prologue use of a display.

Persons analysing data

Taking frequent breaks away from display.

Low

Taking measure-ments with infrared thermometer.

Taking pictures with infrared camera.

Carrying heavy equipment for long distances.

Persons carrying equipment

Use of a rucksack for easy carrying.

Low

Wind, rain, ice and adverse weather condition.

Persons collecting data

Postpone the activity until better weather conditions.Dressing appropriately for weather conditions.

Low

Use of expensive equipment.

Equipment Use of a rucksack for safe carrying. Low

Passing by “dangerous” parks during the night.

Persons carrying equipment, equipment

Being very careful while taking measurements.

Low

The risk assessment was conducted by the students involved in the project, Yorgos (Georgios) Nikou and Riccardo William Monfardini.

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