MITIGATION OF URBAN HEAT ISLANDS WITH CRYPTOGAMIC COVERS

Abhinav Vetcha

AP Research

Mrs. Katie Upton

May 27, 2018

Abstract

The urban heat island effect occurs when temperatures in urban areas are higher than the temperatures of the surrounding environment. This effect can be caused by decreased vegetation levels, heat-trapping building arrangements, anthropogenic heat gain (due to heat produced by the high concentration of buildings, cars, and people), and the increased heat absorption caused by dark-colored (low albedo) materials used in building cities.The increase in temperature can have many harmful effects to the health of residents while also posing a significant economic burden to the city. The purpose of this research is to investigate the potential use of cryptogamic covers, autotrophic organisms that reproduce without seeds such as moss and lichens, in order to reduce the magnitude of the urban heat island effect. The experiment was conducted on plastic buildings of varying sizes and thermometers were used to measure the temperatures inside and outside the buildings. A heat lamp was turned on to mimic the effects of the sun, and the city received one of three treatments: moss on all the buildings, moss on half of the buildings, or moss on none of the buildings.Though analysis of the original data comparing all three categories showed no significant difference, the partial moss trials suggested that the presence of moss affected temperatures inside individual buildings. There was a statistically significant difference of more than 0.5 degrees celsius between these buildings. Although more research is needed, the results found by the experiment suggest that cryptogamic covers can have a significant impact in reducing the intensity of the urban heat island effect.

Introduction

The urban heat island effect (UHI) is a phenomenon in which surface temperatures in urban areas are higher than those of the surrounding areas^[1]. Many cities experience a temperature that exceeds the rural areas surrounding them by about 1 to 3 degrees Celsius, and under certain conditions this temperature difference can reach 12 degrees Celsius^[2]. According to the Environmental Protection Agency (EPA), this effect arises due to the differences between rural environments and urban areas, such as building materials, less vegetation, and urban geometry^[2]. This temperature increase is not without consequence: as shown by a study in Oklahoma City in 2010, the effects of UHI combined with a heat wave in the city may have caused exacerbation of many serious medical conditions of urban residents^[3]. As stated in a study by Zhao et al., there are many solutions proven to decrease the intensity of the Urban Heat Island effect such as street vegetation, high-albedo pavement, cool roofs, green roofs, and solar roofs^[1]. One potential solution that has not been thoroughly investigated is the use of cryptogamic covers, organisms such as mosses and lichens, and the efficiency of these plants in reducing urban temperatures will be investigated within this study.

Purpose of Literature Review

The purpose of this literature review will be to delineate major developments in the understanding of the urban heat island effect, explain of UHI’s negative impacts on cities, overview the current methods utilized in urban areas to mitigate UHI, and introduce cryptogamic covers as a feasible and cost-effective solution to counter the effects of UHI in urban areas.

History

This Urban Heat Island effect was first discovered by British chemist Luke Howard^[4]. Through observation of temperature in London and its surrounding areas during the 25 year period from 1806 to 1830, Howard found that the average temperature difference between the city of London and various sites surrounding the city was 1.579 degrees Celsius^[4]. By comparing city and rural temperatures by month, Howard discovered that the temperature retaining capacity of urban areas is higher than that of rural areas; therefore, urban areas gain and lose heat more gradually than rural areas^[4]. Howard’s analysis of London’s climate conditions was expanded on by T. J. Chandler. In his publication, “The Climate of London”, Chandler observes that air pollution reduces the solar radiation in London compared to its rural surroundings, identifying a key contributor to UHI^[5]. Using data collected at the Kew Observatory in London, Chandler was able to find that there is a difference between central and outer areas of London, and this difference is even higher at night^[5]. Outside of those discussed, there have been relatively few studies of the urban heat island until the last couple decades.

Negative Impacts of Urban Heat Island

The Urban Heat Island Effect can have many detrimental impacts to the residents of a city. One of the biggest impacts of the increased temperature brought about by UHI is the impact on human physiology. A study by the World Health Organization found that excessive heat in the environment can override the body’s natural mechanisms of heat loss and the body can gain heat^[6]. The WHO also observed that high temperatures of 40.5 degrees celsius and above can cause heat stroke, which can lead to “adult respiratory distress syndrome, kidney failure, liver failure and disseminated intravascular coagulation”^[6]. The excessive sweating that is present in individuals exposed to high heat levels can cause dehydration, and the elderly are especially susceptible^[6]. The wide host of health issues caused by excessive heat are especially exacerbated by Urban Heat Islands, which can increase urban temperatures by 1 to 2 degrees Celsius, as shown in a study on the effects of a heat wave in Oklahoma City^[3]. In addition to impacts on health, Urban Heat Islands can have a negative economic impact on residents of cities due to increased cooling costs. As stated by Santamouris, an increase of just one degree fahrenheit (0.56 degrees Celsius) in cities with more than 100,000 people can cause a 1.5 to 2% increase in peak electricity load^[7]. Even more alarmingly, by 2050, energy use for heating and cooling is predicted to increase by 7 to 40 percent from today’s levels, even considering the implementation of more effective green buildings^[8]. There are, however, measures that are currently being utilized in cities to mitigate some of the negative impacts of UHI.

Causes of Urban Heat Islands

There are a variety of elements of city design and structure that contribute to the urban heat island effect. One of these is albedo, which is defined by the EPA as “the percentage of solar energy reflected by a surface”^[2]. Exposed surfaces can already be over 27 degrees Celsius hotter than the air temperature, but this effect is even more pronounced in dark surfaces, which have a low albedo and consequently absorb a lot of heat^[2]. Mohajerani observes that asphalt surfaces can be up to 20 degrees Celsius warmer than the surrounding grass surfaces^[9]. In cities, this is a problem as more than 60% of the land surface can be covered with heat-absorbing man-made materials, much of which has a low albedo^[9]. Another source of heat in an urban area is anthropogenic heat gain which is the leaking of heat from buildings, cars, and other manmade structures into the surrounding environment^[10]. According to Valsson and Bharat, “industry, transportation, exhaust heat, and air conditioning” can also contribute to this effect^[11]. Rosenfeld et al. find that this anthropogenic heat gain accounts for one percent of excess heat in the summer but ten percent of excess heat in the winter^[10]. In an article published in The Geographical Journal, Grimmond states that, in cities, evapotranspiration, an effect of plants that cools the air through water loss, decreases due to loss of vegetation^[12]. Another factor that Grimmond mentions is the sky-view factor, which quantifies “the openness of a site within an urban setting”^[12]. Narrower streets have a lower sky-view factor and less radiative loss, while wider streets have a higher sky-view factor and are cooler than the narrow streets^[12]. Dixon and Mote add that buildings in urban areas have “less of their surface area exposed to open air and have more area facing other warm building surfaces,” thus decreasing the loss of heat in urban areas and contributing to relatively high nighttime temperatures^[13].

Current Solutions

There are currently many solutions to mitigate UHI and cool urban areas. One of these is to increase trees and greenspace within a city. The WHO states that trees in urban areas can have numerous benefits including “shading, cooling by evapotranspiration, dust control, runoff control, consumption of carbon dioxide, and water conservation”^[6]. In Sacramento, a 25% increase in tree cover has the potential to decrease cooling energy costs by about 40%^[6]. Using a simulation of UHI, Zhao, Lee, and Schultz determined that street vegetation can cause cooling effects due to “changes in albedo, convection efficiency, evaporation, and storage”^[1]. Accordingly, a study conducted in Beijing showed that the loss of green space contributed to an net increase in the average land temperature of the city^[14]. Another way of mitigating the urban heat island effect is to increase the albedo level of surfaces in the city to decrease heat absorption. Cool roofs, which are special reflective roofs with high albedos, can remain more than 28 degrees Celsius cooler than traditional roofs in peak summertime^[2]. According to chapter 4 of the EPA Heat Island Compendium, normal roofs only reflect about 5 to 15 percent of solar energy and absorb the rest, while cool roofs can reflect more than 65 percent of the solar energy, cooling the air around them and the inside of the building^[2]. In New York, a simulation found that just covering 50% of roofs with cool roofing would result in a 0.2 degrees Celsius reduction in citywide temperature^[2]. These are just a few of the solutions in place to mitigate the effects of UHI.

Potential use of Cryptogamic covers

Cryptogamic covers are defined by Elbert et al. as “photoautotrophic communities, capable of synthesizing their own food from inorganic substances using sunlight as an energy source”^[15]. These include organisms such as moss and lichens, and they serve an important niche in the ecosystem by stabilizing soil and promoting plant growth through the recycling of nutrients^[15]. Although moss is usually removed from urban areas, it has the potential to confer many benefits to an urban heat island. A study by Elbert et al. estimates that cryptogamic covers contribute to 7 percent of global oxygen production by vegetation and almost half of global nitrogen fixation^[15]. This oxygen producing capacity can serve an important function in an urban area. Rosner observes that mosses, common cryptogamic covers, have many uses in urban areas including absorption of CO₂, buffering of rainwater, producing oxygen, and absorbing fine particulate matter^[16]. Mosses are also very adaptable to different environments as they do not require soil and thrive in shaded areas which makes them well-suited to urban environments^[16]. Despite this, there has been no research on the use of mosses in mitigating the Urban Heat Island effect and researchers have tended to rather focus on more common solutions such as cool roofs, vegetation, and green roofs. Cryptogamic covers could potentially make a large contribution towards mitigating the effects of Urban Heat Island, and this is what will be explored through the experimental research that follows. Through this study, the effectiveness of mosses in reducing the temperature of model buildings will be determined.

Methods

To determine the validity of this hypothesis, an experiment will be conducted testing the effects of moss on the temperature of a model city. By carefully controlling every part of the experiment and allowing the only variable to be the presence of mosses, this type of research will allow for the determination of causation. This is important to the value of the research; by assuring causation rather than correlation, researchers can be sure that moss is what changes the surrounding temperature rather than anything else in the experiment. Most of the research papers in this field of science use experimental research studies and to create a study that generates similarly accurate results, it will be the best to use a similar method. Another benefit of conducting a true experiment is the collection of comprehensive quantitative data detailing the exact effects of moss, which can be extrapolated to larger scale uses of cryptogamic covers.
The experiment depends on the construction of model buildings of different sizes grouped together to form a model city (Fig. 1). This model has been created to accurately represent real buildings for the purposes of this experiment while using a cost-efficient approach. To fabricate the buildings, transparent, black-tinted polycarbonate sheeting was used, mainly because it is very durable and easy to cut into shapes. A benefit of this material is that it allows light to pass through it, simulating the windows that cover most of the walls of urban buildings. Specifically, there were a total of 11 buildings built: five of them were one storey high, four of them were two storeys high, and two of them were three storeys high. In the building with multiple storeys, the levels were separated by polycarbonate sheets. Each building was four inches wide and four inches long, and each storey measured four inches tall. The polycarbonate pieces were cut out of a larger sheet and glued together with sealant covering the gaps to form an airtight seal. These buildings also had holes drilled into one side to allow for placement of partial-immersion thermometers. Buildings with multiple storeys had one hole in each storey to measure temperature differences in different levels. Two buildings of each height were picked randomly to have thermometers placed in them for temperature measurement. Each of the buildings were approximately four inches apart from each other to simulate the presence of roads, and each row of buildings were offset due to create space for the thermometers. The buildings were placed in a random assortment to vary the types of buildings in each row. Four full immersion thermometers were elevated with wood blocks and set in various locations throughout the model city to record air temperatures (Fig. 2). The heat source for the experiment was a heat lamp, which was placed above the plants with a ladder and situated at an angle so that the middle of the light fell exactly upon the middle of the “city” (Fig. 3). All these parts of the experiment were kept constant throughout all the trials to minimize any uncertainties in the data.

The independent variable in the experiment was the level of moss covering the tops of the buildings. There were three levels of moss coverage that were explored: full coverage, partial coverage, and no coverage. Each of these were repeated two times to reduce variability in the data. For the partial coverage experiment, the second trial allowed for moss coverage on buildings that were not covered in trial one and vice versa. This was done to specifically compare the temperature inside the same building with moss or without moss. For each trial, the moss was sprayed with water until it was moist, and it was also sprayed with water after every hour to keep it from drying out due to the heat. During trials with no moss, water was sprayed on the tops of buildings to keep water as a constant between trials and make sure that any temperature difference must be caused by the presence and level of moss. Before turning on the heat lamp, initial temperatures were recorded from each thermometer and for the next four hours, temperatures were recorded each hour.

Data

Figures 4-6 show the data collected by the thermometers over all 12 trials, with the four trials in each group (full moss, partial moss, no moss) averaged by to produce one graph per group. Each thermometer measured temperature in degrees Celsius and was labeled with what building it corresponded with and what level of the building it was located in. For example, thermometer 3A-2 was measuring the temperature of the second floor of one of the 3-floor buildings. The raw temperature values for each trial can be found in the appendices at the end of this paper.

Analysis

The general trend in all of the types of trials seems to be similar, with a large increase in temperature during the first hour and a slowing down of temperature gain every hour. In all three of the graphs the temperature of building 1A was the greatest at four hours, most likely because the building was the closest to the heat source. However there does not seem to be much of an impact made by the presence of moss. In fact, the average temperature change for all thermometers for each category are as follows: Full Moss – 5.8171875, Partial Moss – 5.5984375, No Moss – 5.9828125. This data does not support the hypothesis that the full moss trials would have the lowest temperature change. Although the no moss trials had the highest temperature change, as predicted, the partial moss trials had the lowest temperature change. The results of this data, therefore, could support that an increase in the level of moss coverage is directly related to a decrease in temperature. However, an interesting trend emerges when solely considering the partial moss trials. As shown in part B of the appendix, each partial moss trial alternated the buildings with moss and without moss, so as to have two distinct subtypes of partial moss trials, with two of each. The average temperature change of thermometers in buildings with no moss in all the partial moss trials was 5.708 degrees Celsius, while that of the thermometers in buildings with moss in all the partial moss trials was 5.121 degrees Celsius. This is a greater difference than those between the averages of full moss, partial moss, and no moss trials, and to test if it is statistically significant, a t-test was used. A t-test analyses different populations to determine if the difference between them is statistically significant, which means that it did not occur by chance. The specific test that was used was the independent 2-sample t-test, which can be used for two different populations (thermometers in buildings covered with moss vs thermometers in buildings not covered with moss). The formula for finding the t-statistic is as follows:

Where x₁ and x₂ are the sample means of each data set, s₁and s₂ are the variances of each data set, and n₁ and n₂are the sample sizes. When the data points from the moss and no moss samples were plugged into this equation, the resulting t-value was 2.86249. The null hypothesis of the experiment is that the presence of moss does not affect temperature, while the alternative hypothesis is that the presence of moss does affect the temperature. To determine which hypothesis is correct, a p-value needs to be determined. The p-value in statistics measures the probability of finding the results that were observed if the null hypothesis is true. This means that a low p-value signifies that the null hypothesis is probably wrong, while a high p-value signifies that the null hypothesis is probably right. The exact cutoff point is called the alpha value, and it is usually 0.05 for most experiments, because any p-value below 0.05 has a low chance of occuring. Using an online calculator, the p-value for t-statistic that was calculated before turned out to be 0.04675 which is less than the alpha value of 0.05. Therefore the data that was collected by comparing thermometers in moss covered buildings and uncovered buildings in the partial trials is statistically significant and most likely not due to chance.

Limitations

This research study was created with the intent to be as accurate as possible but it was limited in a couple of ways. There were limited types of materials to choose from to create the model buildings, and only one translucent plastic was used which was not an accurate approximation of the urban landscape which the experiment is trying to emulate. There were also some possible confounding variables that may have influenced the temperature readings. These include non-constants such as outside temperature changes, minute air currents, level of humidity in the room, volume of water sprayed each trial, and many others. Since this experiment was very small-scale, these factors may have influenced the temperatures of the buildings. However, because these trials were all completed in two days, the confounding variables were held relatively constant.

Discussions

There needs to be further research on this topic before it is thoroughly implemented in urban areas. Most importantly, this experiment should be replicated on a larger scale, with many more trials to reduce variability of the results. Conducting the experiment in a lab with a controlled climate and no air circulation would also help to eliminate some of the confounding variables that could not be controlled in this study. The experiment could also be carried out with materials similar to those which are used to make buildings, such as glass and cement. Although this type of experimentation would take a lot more time and resources to complete, the results would be much more accurate and applicable to a real city. Another way this study could be expanded upon is comparing different types of moss, such as sphagnum moss, peat moss, and sheet moss. Along those lines, the study could also have been built upon by testing out different kinds of cryptogamic covers such as lichens, and algae. Utilizing the various types of plants available in the cryptogamic cover category would help researchers determine which one is the most effective to use in cities for the reduction of the Urban Heat Island Effect. However, there are already solutions in place to reduce UHI, so the efficiency of cryptogamic covers should be compared to solutions such as vegetation, green roofs, and cool roofs in other studies. Another way to improve this study is to more precisely control the volume of water sprayed on each building. Furthermore, future studies could investigate the economic impacts associated with using cryptogamic covers and perform a cost-benefit analysis. Other than lower temperature, there are many other benefits that moss and other covers can offer including carbon dioxide absorption, pollution reduction, and water runoff control.

Conclusions

Although this research did not support my hypothesis that higher coverages of moss correspond to a smaller temperature increase, the partial moss trials did show that moss can decrease temperature in individual buildings. There may not have been a perfect linear correlation due to uncontrollable factors such as changes in room temperature and slightly inaccurate readings of the thermometers. However, this research gives insight into the application of a novel technology for reducing the Urban Heat Island Effect. Although the experiment is not a completely accurate model, the results imply that moss can be used on a larger scale as well to reduce temperatures in buildings. There are other solutions for reducing the temperature of buildings, but moss can replace or supplement these solutions for a couple reasons. Firstly, moss is easier to maintain and consumes less water than grass and other vascular plants. The use of moss rather than other plants can allow the building to divert water to other places where it is needed, or simply conserve more water. Moss can be more beneficial than cool roofs as well because it can also decrease pollution by absorbing fine particulate matter and control water runoff. The most effective solution for the reduction of the Urban Heat Island Effect would be a combination of many different strategies, and mosses could be central to these approaches as they can be placed almost anywhere, especially shady areas where other plants would not grow well. The reduction of UHI can confer many benefits to residents of cities, such as improved health and lower air conditioning costs. Although more research is necessary, this preliminary study shows the potential of moss in reducing the temperatures in model buildings, and in the potential future, whole cities.

Appendix A

Full Moss Trials

These appendices consist of the raw data of the trials in the urban heat island experiment. The labeling of the thermometers is explained in the data section above. The green color of certain thermometers signify that these thermometers were in buildings covered with moss; alternatively, the white coloration of the box shows that these thermometers were located in buildings without moss.

Thermometers		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
	1A-1	22.1	27.1	28.9	29	29	6.9
	1E–1	21.2	25.7	26.5	26.6	27.1	5.9
	2A-1	21.9	25.3	25.9	26.1	26.9	5
	2A-2	22.8	25.6	26.3	26.3	27.2	4.4
	2B-1	21.9	26.8	27.7	27.3	28.1	6.2
	2B-2	20.8	25.7	26.4	26.2	27	6.2
	3A-1	22.8	26.9	27.8	27.8	28	5.2
	3A-2	21.2	25.4	25.9	26.1	26.7	5.5
	3A-3	21.6	25.5	26	27	26.8	5.2
	3B-1	21.1	24.8	25.7	25.8	26.5	5.4
	3B-2	21.7	25.2	25.8	25.9	26.7	5
	3B-3	21.8	24.9	25.6	25.6	26.3	4.5
	Air-1	22	27.1	27.6	27.3	27.8	5.8
	Air-2	21.5	26.5	27	27.1	28	6.5
	Air-3	21.1	26.6	27.1	27.5	27.8	6.7
	Air-4	20.3	24.7	25.5	25.7	25.7	5.4

A1 – Full Moss Trial 1 (degrees Celsius)

		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	21.8	26.5	27	27.2	27.7	5.9
	1E–1	20.7	24.4	25.2	25.5	25.8	5.1
	2A-1	21.2	24.1	24.6	24.8	25.1	3.9
	2A-2	22	24.6	24.9	25.2	25.6	3.6
	2B-1	21.1	26.1	26.8	26.9	27.2	6.1
	2B-2	20.5	24.8	25.7	25.8	26.1	5.6
	3A-1	22	26.7	27	27.3	27.7	5.7
	3A-2	21.6	24.9	25.1	25.5	25.8	4.2
	3A-3	21.3	24.8	25	25.5	25.8	4.5
	3B-1	20.5	23.5	24.1	24.4	24.7	4.2
	3B-2	21.1	23.6	24.1	24.5	24.7	3.6
	3B-3	21.1	23.8	24.2	24.5	25	3.9
	Air-1	22	27	27.1	27.4	28	6
	Air-2	21.4	25.4	25.5	25.6	26	4.6
	Air-3	20.5	25.8	26.1	26.3	26.6	6.1
	Air-4	20.5	23.4	23.9	24.1	24.6	4.1

A2 – Full Moss Trial 2 (degrees Celsius)

		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	21	27.1	28.8	29.3	29.5	8.5
	1E–1	20	26.1	27.1	27.8	28.2	8.2
	2A-1	21.7	25.6	26.5	26.9	27.2	5.5
	2A-2	22.2	26.1	26.6	27.2	27.6	5.4
	2B-1	21	26.9	27.5	28.6	29	8
	2B-2	20.2	26	27.8	27.5	27.7	7.5
	3A-1	22.2	27.5	28.2	28.8	29.1	6.9
	3A-2	20.7	25.8	26.5	26.8	27.1	6.4
	3A-3	21.1	25.9	26.5	26.9	27.2	6.1
	3B-1	21	24.7	26.5	26.8	27.4	6.4
	3B-2	21.8	25.2	26.4	26.9	27.3	5.5
	3B-3	21.8	25.5	26.5	26.9	27.3	5.5
	Air-1	20	27	28	28	28.1	8.1
	Air-2	20.5	26.9	27.2	27.7	27.9	7.4
	Air-3	21	26.3	27.5	27.6	28	7
	Air-4	19.5	24.4	25	26.1	25.9	6.4

A3 – Full Moss Trial 3 (degrees Celsius)

		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	23.1	27.5	28.4	29.5	30.4	7.3
	1E–1	22.8	25.6	26.8	27.7	28	5.2
	2A-1	22.5	25.7	26.8	27.8	28.6	6.1
	2A-2	23.1	26.2	27.1	27.8	28.7	5.6
	2B-1	23	27.2	27.9	29	29.7	6.7
	2B-2	22.3	26.1	26.9	27.8	28.4	6.1
	3A-1	23.6	28.1	29	29.8	30.5	6.9
	3A-2	22.2	26.3	26.9	27.8	28.2	6
	3A-3	23.1	26.2	26.9	27.7	28.1	5
	3B-1	22.2	25.5	26.5	27.4	28.2	6
	3B-2	22.9	25.8	26.5	27.5	28.1	5.2
	3B-3	23.2	25.9	26.6	27.5	28.2	5
	Air-1	21	28	29	30	30.2	9.2
	Air-2	22	26.8	27.6	28	29	7
	Air-3	24	27.1	28	29	29.2	5.2
	Air-4	21.7	24.9	24.8	25.5	25.8	4.1

A4 – Full Moss Trial 4 (degrees Celsius)

Appendix B

Partial Moss Trials

		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	21.2	26	27	26.7	26.8	5.6
	1E–1	20.1	23.5	24.3	25.7	26.6	6.5
	2A-1	20.7	24	24.4	24.8	25.1	4.4
	2A-2	21.8	24.2	24.7	24.8	25.5	3.7
	2B-1	20.9	25.1	25.8	26	27.1	6.2
	2B-2	20.2	24.6	25.1	25.7	26.2	6
	3A-1	21.6	26	26.4	26.3	27.1	5.5
	3A-2	20.1	24.2	24.7	24.8	25.3	5.2
	3A-3	20.8	24.2	24.7	24.8	25.2	4.4
	3B-1	19.5	22.9	23.9	24.5	25.2	5.7
	3B-2	20.3	23.4	24.2	24.7	25.5	5.2
	3B-3	20.8	24.1	25.5	25.9	26.6	5.8
	Air-1	20.5	26.3	26.8	26.8	27.1	6.6
	Air-2	18.8	25.4	25.5	25.8	26	7.2
	Air-3	18.9	24.5	25.4	25.7	26.6	7.7
	Air-4	19.3	23	23.2	23.8	24.5	5.2

B1 – Partial Moss Trial 1 (degrees Celsius)

		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	24	28.1	28.9	31	30.7	6.7
	1E–1	23.2	26.7	27.5	28	28.3	5.1
	2A-1	23.9	26.1	26.9	27.8	27.7	3.8
	2A-2	24.8	26.9	27.2	28.8	28.8	4
	2B-1	23	27.8	28.6	29.1	29.2	6.2
	2B-2	22.9	26.9	27.7	28.1	28.4	5.5
	3A-1	24.5	27.9	28.5	29.2	30	5.5
	3A-2	23	26.9	27.4	27.8	28	5
	3A-3	23.8	26.9	27.3	27.9	28.2	4.4
	3B-1	22.8	26.2	26.8	27.5	28.1	5.3
	3B-2	23.5	26.5	26.9	27.3	27.6	4.1
	3B-3	23.5	26.8	27	27.6	27.6	4.1
	Air-1	23	28.3	29	30.5	31	8
	Air-2	23.1	27.5	27.6	28.6	28.2	5.1
	Air-3	22	27.6	28.2	29.1	29.3	7.3
	Air-4	21	25.5	26.7	27.4	27.5	6.5

B2 – Partial Moss Trial 2 (degrees Celsius)


		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	22.2	28	28	28.5	28.9	6.7
	1E–1	21.5	27.2	27.8	28	28.5	7
	2A-1	21.9	25.2	25.9	26	26.2	4.3
	2A-2	22.8	25.8	26.2	26.6	26.8	4
	2B-1	22.1	27.1	27.6	27.8	28	5.9
	2B-2	22.7	26.8	27.2	27.7	27.8	5.1
	3A-1	22.8	27	27.1	27.9	28.3	5.5
	3A-2	21.2	25.3	25.8	26	26.7	5.5
	3A-3	22	25.8	26	26.3	26.6	4.6
	3B-1	21.3	24.8	25.6	25.6	26.2	4.9
	3B-2	21.9	25.3	25.9	26.1	26.6	4.7
	3B-3	22.2	27	27.8	27.6	28.1	5.9
	Air-1	21.3	27	27.5	27.4	27.6	6.3
	Air-2	21.5	24.5	27.5	27	27	5.5
	Air-3	21	27	26.9	27.2	28.1	7.1
	Air-4	21.3	25.6	25	25.1	25.7	4.4

B3 – Partial Moss Trial 3 (degrees Celsius)


		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	22	28.1	29.3	30	30.5	8.5
	1E–1	21.1	25.4	26.2	26.7	27.1	6
	2A-1	21.2	24.9	25.4	26	26.3	5.1
	2A-2	22.3	26.1	27.3	27.8	27.7	5.4
	2B-1	21.3	26.1	27.1	27.8	28	6.7
	2B-2	20.9	25.1	26.2	26.7	26.9	6
	3A-1	20.6	26	27	27.8	28.2	7.6
	3A-2	21.2	24.8	25.6	27	26.5	5.3
	3A-3	20.3	25.5	26.2	26.9	27.1	6.8
	3B-1	22.1	24.3	25.1	25.9	26.4	4.3
	3B-2	20.8	24.4	25.2	26	26.2	5.4
	3B-3	21.5	24.7	25.4	26.1	26.3	4.8
	Air-1	22.1	25.4	27	27	28	5.9
	Air-2	22.1	26	26.1	27	27	4.9
	Air-3	21.2	26.1	26.5	27.5	27.8	6.6
	Air-4	21	23.6	24.5	25.1	25.1	4.1

B4 – Partial Moss Trial 4 (degrees Celsius)
Appendix C
Control (No Moss) Trials


		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	22	27	28.2	29	29.5	7.5
	1E–1	21.5	25.5	26.4	27	27.4	5.9
	2A-1	21.7	24.8	25.5	25.9	26.2	4.5
	2A-2	22.9	25.3	26.8	27.3	27.6	4.7
	2B-1	21.8	26.3	27	27.8	27.9	6.1
	2B-2	21.2	25.6	26.1	26.7	26.8	5.6
	3A-1	22.2	26.9	27.5	27.8	28.2	6
	3A-2	21	25	25.7	26	26.3	5.3
	3A-3	21.8	25.2	25.7	26	26.9	5.1
	3B-1	21	24.1	24.4	25.3	25.6	4.6
	3B-2	20.8	24.5	25.2	26.1	26.2	5.4
	3B-3	22.1	25.1	26.6	27.3	27.4	5.3
	Air-1	22.8	27	27.1	27.8	28	5.2
	Air-2	22.3	25.7	25.2	26.8	26.8	4.5
	Air-3	21.7	26.1	27	27.2	27.7	6
	Air-4	21.6	24.5	24.8	25.3	25.5	3.9

C1 – Control Trial 1 (degrees Celsius)


		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	20	26.9	28	28.5	28.5	8.5
	1E–1	19.5	23.5	24.8	25.9	26.2	6.7
	2A-1	20.3	23.8	24.2	24.9	24.9	4.6
	2A-2	20.9	24.5	25.3	26.1	26.3	5.4
	2B-1	19.5	24.9	25.5	26.2	26.3	6.8
	2B-2	18.9	24.8	25	25.8	25.8	6.9
	3A-1	21	25.9	26	26.3	26.8	5.8
	3A-2	19.3	24	24.2	24.8	25	5.7
	3A-3	19.9	23.9	24.3	25.1	25.2	5.3
	3B-1	18.9	22.5	23.4	24.2	24.5	5.6
	3B-2	19.8	23.2	23.9	24.8	25.1	5.3
	3B-3	20.4	24	24.7	26.2	26.2	5.8
	Air-1	17.5	26	26.5	26.8	27	9.5
	Air-2	20	25	25.2	25.1	25.8	5.8
	Air-3	18.8	24.6	25.4	25.9	26.2	7.4
	Air-4	18.5	22.8	23.1	24.3	24.6	6.1

C2 – Control Trial 2 (degrees Celsius)


		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	20.9	25	26	27.2	28.1	7.2
	1E–1	20.3	23	24.4	25.5	26.4	6.1
	2A-1	20.5	22.9	23.6	24.1	24.8	4.3
	2A-2	21.3	23.8	24.2	25	26.1	4.8
	2B-1	20.7	24.3	25.1	25.6	26.7	6
	2B-2	19.9	23.8	25.2	25.9	26.3	6.4
	3A-1	21.1	25.1	25.4	25.9	26.9	5.8
	3A-2	19.7	23.9	24.3	24.9	25.5	5.8
	3A-3	20.4	23.9	24.2	24.9	26.1	5.7
	3B-1	19.5	21.4	22.4	23.5	24.2	4.7
	3B-2	20.3	22.3	23.3	24	24.8	4.5
	3B-3	20.6	23.1	23.8	24.6	26.1	5.5
	Air-1	21.2	26	26.1	27	27.2	6
	Air-2	21	23.5	24.5	25	25.5	4.5
	Air-3	20.5	24.5	25.1	25.7	26.7	6.2
	Air-4	19.4	23.2	23.8	24.2	24.8	5.4

C3 – Control Trial 3 (degrees Celsius)

		Initial	1 hour	2 hours	3 hours	4 hours	Change in Temperature
Thermometers	1A-1	22	27.3	28.8	29.9	30.5	8.5
	1E–1	21.4	25.4	26.7	28.1	28.9	7.5
	2A-1	22.2	25.5	26.6	27.2	27.3	5.1
	2A-2	22.8	26.1	26.8	27.2	27.7	4.9
	2B-1	21.2	26.2	27.8	28.6	29	7.8
	2B-2	22.2	25.7	26.9	28	28.6	6.4
	3A-1	23	26.9	28	28.6	28.9	5.9
	3A-2	21.7	25.9	26.8	27.2	27.6	5.9
	3A-3	22.2	26	26.8	27.2	27.7	5.5
	3B-1	20.5	24.4	25.5	26.5	27	6.5
	3B-2	21.5	25.1	26.1	27	27.4	5.9
	3B-3	22.2	25.7	26.6	28	28.5	6.3
	Air-1	21	28	29	29.1	29.3	8.3
	Air-2	20	26	27.1	28	28.1	8.1
	Air-3	20.5	27	27.9	28.5	28.2	7.7
	Air-4	19.1	23.7	25.3	26	26	6.9

C4 – Control Trial 4 (degrees Celsius)

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About the Author

Abhinav Vetcha will be studying biomedical engineering at UT Austin starting in the fall of 2018.. He is interested in reading about and researching various science topics such as global warming, robotic technology, and experimental cancer treatments.

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